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Venäläinen MS, Biehl A, Holstila M, Kuusalo L, Elo LL. Deep Learning Enables Automatic Detection of Joint Damage Progression in Rheumatoid Arthritis-Model Development and External Validation. Rheumatology (Oxford) 2024:keae215. [PMID: 38597875 DOI: 10.1093/rheumatology/keae215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Revised: 02/21/2024] [Accepted: 03/26/2024] [Indexed: 04/11/2024] Open
Abstract
OBJECTIVES Although deep learning has demonstrated substantial potential in automatic quantification of joint damage in rheumatoid arthritis (RA), evidence for detecting longitudinal changes at an individual patient level is lacking. Here, we introduce and externally validate our automated RA scoring algorithm (AuRA), and demonstrate its utility for monitoring radiographic progression in a real-world setting. METHODS The algorithm, originally developed during the Rheumatoid Arthritis 2-Dialogue for Reverse Engineering Assessment and Methods (RA2-DREAM) challenge, was trained to predict expert-curated Sharp-van der Heijde total scores in hand and foot radiographs from two previous clinical studies (n = 367). We externally validated AuRA against data (n = 205) from Turku University Hospital and compared the performance against two top-performing RA2-DREAM solutions. Finally, for 54 patients, we extracted additional radiograph sets from another control visit to the clinic (average time interval of 4.6 years). RESULTS In the external validation cohort, with a root-mean-square-error (RMSE) of 23.6, AuRA outperformed both top-performing RA2-DREAM algorithms (RMSEs 35.0 and 35.6). The improved performance was explained mostly by lower errors at higher expert-assessed scores. The longitudinal changes predicted by our algorithm were significantly correlated with changes in expert-assessed scores (Pearson's R = 0.74, p< 0.001). CONCLUSION AuRA had the best external validation performance and demonstrated potential for detecting longitudinal changes in joint damage. Available in https://hub.docker.com/r/elolab/aura, our algorithm can easily be applied for automatic detection of radiographic progression in the future, reducing the need for laborious manual scoring.
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Affiliation(s)
- Mikko S Venäläinen
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- Department of Medical Physics, Turku University Hospital, Turku, Finland
| | - Alexander Biehl
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Milja Holstila
- Department of Radiology, University of Turku and Turku University Hospital, Turku, Finland
| | - Laura Kuusalo
- Centre for Rheumatology and Clinical Immunology, Division of Medicine, University of Turku and Turku University Hospital, Turku, Finland
| | - Laura L Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- Institute of Biomedicine, University of Turku, Turku, Finland
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Kalinen S, Kallonen T, Gunell M, Ettala O, Jambor I, Knaapila J, Syvänen KT, Taimen P, Poutanen M, Aronen HJ, Ollila H, Pietilä S, Elo LL, Lamminen T, Hakanen AJ, Munukka E, Boström PJ. Differences in Gut Microbiota Profiles and Microbiota Steroid Hormone Biosynthesis in Men with and Without Prostate Cancer. EUR UROL SUPPL 2024; 62:140-150. [PMID: 38500636 PMCID: PMC10946286 DOI: 10.1016/j.euros.2024.02.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/14/2024] [Indexed: 03/20/2024] Open
Abstract
Background Although prostate cancer (PCa) is the most common cancer in men in Western countries, there is significant variability in geographical incidence. This might result from genetic factors, discrepancies in screening policies, or differences in lifestyle. Gut microbiota has recently been associated with cancer progression, but its role in PCa is unclear. Objective Characterization of the gut microbiota and its functions associated with PCa. Design setting and participants In a prospective multicenter clinical trial (NCT02241122), the gut microbiota profiles of 181 men with a clinical suspicion of PCa were assessed utilizing 16S rRNA sequencing. Outcome measurements and statistical analysis Sequences were assigned to operational taxonomic units, differential abundance analysis, and α- and β-diversities, and predictive functional analyses were performed. Plasma steroid hormone levels corresponding to the predicted microbiota steroid hormone biosynthesis profiles were investigated. Results and limitations Of 364 patients, 181 were analyzed, 60% of whom were diagnosed with PCa. Microbiota composition and diversity were significantly different in PCa, partially affected by Prevotella 9, the most abundant genus of the cohort, and significantly higher in PCa patients. Predictive functional analyses revealed higher 5-α-reductase, copper absorption, and retinol metabolism in the PCa-associated microbiome. Plasma testosterone was associated negatively with the predicted microbial 5-α-reductase level. Conclusions Gut microbiota of the PCa patients differed significantly compared with benign individuals. Microbial 5-α-reductase, copper absorption, and retinol metabolism are potential mechanisms of action. These findings support the observed association of lifestyle, geography, and PCa incidence. Patient summary In this report, we found that several microbes and potential functions of the gut microbiota are altered in prostate cancer compared with benign cases. These findings suggest that gut microbiota could be the link between environmental factors and prostate cancer.
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Affiliation(s)
- Sofia Kalinen
- Research Center for Infections and Immunity, Institute of Biomedicine, University of Turku, Turku, Finland
- Department of Clinical Microbiology, Turku University Hospital, Turku, Finland
| | - Teemu Kallonen
- Department of Clinical Microbiology, Turku University Hospital, Turku, Finland
- Clinical Microbiome Bank, Microbe Center, Turku University Hospital and University of Turku, Turku, Finland
| | - Marianne Gunell
- Department of Clinical Microbiology, Turku University Hospital, Turku, Finland
- Clinical Microbiome Bank, Microbe Center, Turku University Hospital and University of Turku, Turku, Finland
| | - Otto Ettala
- Department of Urology, Turku University Hospital and University of Turku, Turku, Finland
| | - Ivan Jambor
- Department of Diagnostic Radiology, Turku University Hospital and University of Turku, Turku, Finland
- Enterprise Service Group - Radiology, Mass General Brigham, Boston, MA
| | - Juha Knaapila
- Department of Urology, Turku University Hospital and University of Turku, Turku, Finland
| | - Kari T. Syvänen
- Department of Urology, Turku University Hospital and University of Turku, Turku, Finland
| | - Pekka Taimen
- Institute of Biomedicine, University of Turku, Turku, Finland
- Department of Pathology, Turku University Hospital, Turku, Finland
| | - Matti Poutanen
- Institute of Biomedicine, University of Turku, Turku, Finland
- Centre for Integrative Physiology and Pharmacology, University of Turku, Turku, Finland
- Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Hannu J. Aronen
- Department of Diagnostic Radiology, Turku University Hospital and University of Turku, Turku, Finland
| | - Helena Ollila
- Turku Clinical Research Centre, Turku University Hospital, Turku, Finland
| | - Sami Pietilä
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Laura L. Elo
- Institute of Biomedicine, University of Turku, Turku, Finland
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Tarja Lamminen
- Department of Urology, Turku University Hospital and University of Turku, Turku, Finland
| | - Antti J. Hakanen
- Department of Clinical Microbiology, Turku University Hospital, Turku, Finland
- Clinical Microbiome Bank, Microbe Center, Turku University Hospital and University of Turku, Turku, Finland
| | - Eveliina Munukka
- Clinical Microbiome Bank, Microbe Center, Turku University Hospital and University of Turku, Turku, Finland
- Biocodex: Biocodex Nordics, Espoo, Finland
| | - Peter J. Boström
- Department of Urology, Turku University Hospital and University of Turku, Turku, Finland
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Buchacher T, Shetty A, Koskela SA, Smolander J, Kaukonen R, Sousa AGG, Junttila S, Laiho A, Rundquist O, Lönnberg T, Marson A, Rasool O, Elo LL, Lahesmaa R. PIM kinases regulate early human Th17 cell differentiation. Cell Rep 2023; 42:113469. [PMID: 38039135 PMCID: PMC10765319 DOI: 10.1016/j.celrep.2023.113469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 09/23/2023] [Accepted: 11/03/2023] [Indexed: 12/03/2023] Open
Abstract
The serine/threonine-specific Moloney murine leukemia virus (PIM) kinase family (i.e., PIM1, PIM2, and PIM3) has been extensively studied in tumorigenesis. PIM kinases are downstream of several cytokine signaling pathways that drive immune-mediated diseases. Uncontrolled T helper 17 (Th17) cell activation has been associated with the pathogenesis of autoimmunity. However, the detailed molecular function of PIMs in human Th17 cell regulation has yet to be studied. In the present study, we comprehensively investigated how the three PIMs simultaneously alter transcriptional gene regulation during early human Th17 cell differentiation. By combining PIM triple knockdown with bulk and scRNA-seq approaches, we found that PIM deficiency promotes the early expression of key Th17-related genes while suppressing Th1-lineage genes. Further, PIMs modulate Th cell signaling, potentially via STAT1 and STAT3. Overall, our study highlights the inhibitory role of PIMs in human Th17 cell differentiation, thereby suggesting their association with autoimmune phenotypes.
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Affiliation(s)
- Tanja Buchacher
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland; InFLAMES Research Flagship Center, University of Turku, 20520 Turku, Finland.
| | - Ankitha Shetty
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland; InFLAMES Research Flagship Center, University of Turku, 20520 Turku, Finland; Department of Microbiology and Immunology, University of California San Francisco, San Francisco, CA 94143, USA
| | - Saara A Koskela
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland; InFLAMES Research Flagship Center, University of Turku, 20520 Turku, Finland; Institute of Biomedicine, University of Turku, 20520 Turku, Finland
| | - Johannes Smolander
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland; InFLAMES Research Flagship Center, University of Turku, 20520 Turku, Finland
| | - Riina Kaukonen
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland; InFLAMES Research Flagship Center, University of Turku, 20520 Turku, Finland
| | - António G G Sousa
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland; InFLAMES Research Flagship Center, University of Turku, 20520 Turku, Finland
| | - Sini Junttila
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland; InFLAMES Research Flagship Center, University of Turku, 20520 Turku, Finland
| | - Asta Laiho
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland; InFLAMES Research Flagship Center, University of Turku, 20520 Turku, Finland
| | - Olof Rundquist
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland; InFLAMES Research Flagship Center, University of Turku, 20520 Turku, Finland
| | - Tapio Lönnberg
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland; InFLAMES Research Flagship Center, University of Turku, 20520 Turku, Finland
| | - Alexander Marson
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA 94158, USA; Department of Medicine, University of California San Francisco, San Francisco, CA 94143, USA
| | - Omid Rasool
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland; InFLAMES Research Flagship Center, University of Turku, 20520 Turku, Finland
| | - Laura L Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland; InFLAMES Research Flagship Center, University of Turku, 20520 Turku, Finland; Institute of Biomedicine, University of Turku, 20520 Turku, Finland
| | - Riitta Lahesmaa
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland; InFLAMES Research Flagship Center, University of Turku, 20520 Turku, Finland; Institute of Biomedicine, University of Turku, 20520 Turku, Finland.
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Biehl A, Venäläinen MS, Suojanen LU, Kupila S, Ahola AJ, Pietiläinen KH, Elo LL. Development and validation of a weight-loss predictor to assist weight loss management. Sci Rep 2023; 13:20661. [PMID: 38001145 PMCID: PMC10673897 DOI: 10.1038/s41598-023-47930-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 11/20/2023] [Indexed: 11/26/2023] Open
Abstract
This study aims to develop and validate a modeling framework to predict long-term weight change on the basis of self-reported weight data. The aim is to enable focusing resources of health systems on individuals that are at risk of not achieving their goals in weight loss interventions, which would help both health professionals and the individuals in weight loss management. The weight loss prediction models were built on 327 participants, aged 21-78, from a Finnish weight coaching cohort, with at least 9 months of self-reported follow-up weight data during weight loss intervention. With these data, we used six machine learning methods to predict weight loss after 9 months and selected the best performing models for implementation as modeling framework. We trained the models to predict either three classes of weight change (weight loss, insufficient weight loss, weight gain) or five classes (high/moderate/insufficient weight loss, high/low weight gain). Finally, the prediction accuracy was validated with an independent cohort of overweight UK adults (n = 184). Of the six tested modeling approaches, logistic regression performed the best. Most three-class prediction models achieved prediction accuracy of > 50% already with half a month of data and up to 97% with 8 months. The five-class prediction models achieved accuracies from 39% (0.5 months) to 89% (8 months). Our approach provides an accurate prediction method for long-term weight loss, with potential for easier and more efficient management of weight loss interventions in the future. A web application is available: https://elolab.shinyapps.io/WeightChangePredictor/ .The trial is registered at clinicaltrials.gov/ct2/show/NCT04019249 (Clinical Trials Identifier NCT04019249), first posted on 15/07/2019.
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Affiliation(s)
- Alexander Biehl
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Tykistökatu 6 A, 20520, Turku, Finland
| | - Mikko S Venäläinen
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Tykistökatu 6 A, 20520, Turku, Finland
| | - Laura U Suojanen
- Obesity Research Unit, Research Program for Clinical and Molecular Metabolism, University of Helsinki, Helsinki, Finland
| | - Sakris Kupila
- Obesity Research Unit, Research Program for Clinical and Molecular Metabolism, University of Helsinki, Helsinki, Finland
| | - Aila J Ahola
- Obesity Research Unit, Research Program for Clinical and Molecular Metabolism, University of Helsinki, Helsinki, Finland
- Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland
- Department of Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Kirsi H Pietiläinen
- Obesity Research Unit, Research Program for Clinical and Molecular Metabolism, University of Helsinki, Helsinki, Finland
- Obesity Center, Endocrinology, Abdominal Center, University of Helsinki and Helsinki University Central Hospital, Helsinki, Finland
| | - Laura L Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Tykistökatu 6 A, 20520, Turku, Finland.
- Institute of Biomedicine, University of Turku, Turku, Finland.
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Moulder R, Välikangas T, Hirvonen MK, Suomi T, Brorsson CA, Lietzén N, Bruggraber SFA, Overbergh L, Dunger DB, Peakman M, Chmura PJ, Brunak S, Schulte AM, Mathieu C, Knip M, Elo LL, Lahesmaa R. Targeted serum proteomics of longitudinal samples from newly diagnosed youth with type 1 diabetes distinguishes markers of disease and C-peptide trajectory. Diabetologia 2023; 66:1983-1996. [PMID: 37537394 PMCID: PMC10542287 DOI: 10.1007/s00125-023-05974-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 06/06/2023] [Indexed: 08/05/2023]
Abstract
AIMS/HYPOTHESIS There is a growing need for markers that could help indicate the decline in beta cell function and recognise the need and efficacy of intervention in type 1 diabetes. Measurements of suitably selected serum markers could potentially provide a non-invasive and easily applicable solution to this challenge. Accordingly, we evaluated a broad panel of proteins previously associated with type 1 diabetes in serum from newly diagnosed individuals during the first year from diagnosis. To uncover associations with beta cell function, comparisons were made between these targeted proteomics measurements and changes in fasting C-peptide levels. To further distinguish proteins linked with the disease status, comparisons were made with measurements of the protein targets in age- and sex-matched autoantibody-negative unaffected family members (UFMs). METHODS Selected reaction monitoring (SRM) mass spectrometry analyses of serum, targeting 85 type 1 diabetes-associated proteins, were made. Sera from individuals diagnosed under 18 years (n=86) were drawn within 6 weeks of diagnosis and at 3, 6 and 12 months afterwards (288 samples in total). The SRM data were compared with fasting C-peptide/glucose data, which was interpreted as a measure of beta cell function. The protein data were further compared with cross-sectional SRM measurements from UFMs (n=194). RESULTS Eleven proteins had statistically significant associations with fasting C-peptide/glucose. Of these, apolipoprotein L1 and glutathione peroxidase 3 (GPX3) displayed the strongest positive and inverse associations, respectively. Changes in GPX3 levels during the first year after diagnosis indicated future fasting C-peptide/glucose levels. In addition, differences in the levels of 13 proteins were observed between the individuals with type 1 diabetes and the matched UFMs. These included GPX3, transthyretin, prothrombin, apolipoprotein C1 and members of the IGF family. CONCLUSIONS/INTERPRETATION The association of several targeted proteins with fasting C-peptide/glucose levels in the first year after diagnosis suggests their connection with the underlying changes accompanying alterations in beta cell function in type 1 diabetes. Moreover, the direction of change in GPX3 during the first year was indicative of subsequent fasting C-peptide/glucose levels, and supports further investigation of this and other serum protein measurements in future studies of beta cell function in type 1 diabetes.
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Affiliation(s)
- Robert Moulder
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - Tommi Välikangas
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - M Karoliina Hirvonen
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - Tomi Suomi
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - Caroline A Brorsson
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Niina Lietzén
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | | | - Lut Overbergh
- Katholieke Universiteit Leuven/Universitaire Ziekenhuizen, Leuven, Belgium
| | - David B Dunger
- Department of Paediatrics, University of Cambridge, Cambridge, UK
| | - Mark Peakman
- Immunology & Inflammation Research Therapeutic Area, Sanofi, Boston, MA, USA
| | - Piotr J Chmura
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Soren Brunak
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | | | - Chantal Mathieu
- Katholieke Universiteit Leuven/Universitaire Ziekenhuizen, Leuven, Belgium
| | - Mikael Knip
- Pediatric Research Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Tampere Center for Child Health Research, Tampere University Hospital, Tampere, Finland
| | - Laura L Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.
- InFLAMES Research Flagship Center, University of Turku, Turku, Finland.
- Institute of Biomedicine, University of Turku, Turku, Finland.
| | - Riitta Lahesmaa
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.
- InFLAMES Research Flagship Center, University of Turku, Turku, Finland.
- Institute of Biomedicine, University of Turku, Turku, Finland.
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Smolander J, Junttila S, Elo LL. Cell-connectivity-guided trajectory inference from single-cell data. Bioinformatics 2023; 39:btad515. [PMID: 37624916 PMCID: PMC10474950 DOI: 10.1093/bioinformatics/btad515] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 07/24/2023] [Accepted: 08/23/2023] [Indexed: 08/27/2023] Open
Abstract
MOTIVATION Single-cell RNA-sequencing enables cell-level investigation of cell differentiation, which can be modelled using trajectory inference methods. While tremendous effort has been put into designing these methods, inferring accurate trajectories automatically remains difficult. Therefore, the standard approach involves testing different trajectory inference methods and picking the trajectory giving the most biologically sensible model. As the default parameters are often suboptimal, their tuning requires methodological expertise. RESULTS We introduce Totem, an open-source, easy-to-use R package designed to facilitate inference of tree-shaped trajectories from single-cell data. Totem generates a large number of clustering results, estimates their topologies as minimum spanning trees, and uses them to measure the connectivity of the cells. Besides automatic selection of an appropriate trajectory, cell connectivity enables to visually pinpoint branching points and milestones relevant to the trajectory. Furthermore, testing different trajectories with Totem is fast, easy, and does not require in-depth methodological knowledge. AVAILABILITY AND IMPLEMENTATION Totem is available as an R package at https://github.com/elolab/Totem.
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Affiliation(s)
- Johannes Smolander
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland
| | - Sini Junttila
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland
| | - Laura L Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland
- Institute of Biomedicine, University of Turku, 20520 Turku, Finland
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Kuusalo L, Venäläinen MS, Kirjala H, Saranpää S, Elo LL, Pirilä L. Development of prediction model for alanine transaminase elevations during the first 6 months of conventional synthetic DMARD treatment. Sci Rep 2023; 13:12943. [PMID: 37558753 PMCID: PMC10412531 DOI: 10.1038/s41598-023-39694-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Accepted: 07/29/2023] [Indexed: 08/11/2023] Open
Abstract
Frequent laboratory monitoring is recommended for early identification of toxicity when initiating conventional synthetic disease-modifying antirheumatic drugs (csDMARDs). We aimed at developing a risk prediction model to individualize laboratory testing at csDMARD initiation. We identified inflammatory joint disease patients (N = 1196) initiating a csDMARD in Turku University Hospital 2013-2019. Baseline and follow-up safety monitoring results were drawn from electronic health records. For rheumatoid arthritis patients, diagnoses and csDMARD initiation/cessation dates were manually confirmed. Primary endpoint was alanine transaminase (ALT) elevation of more than twice the upper limit of normal (ULN) within 6 months after treatment initiation. Computational models for predicting incident ALT elevations were developed using Lasso Cox proportional hazards regression with stable iterative variable selection (SIVS) and were internally validated against a randomly selected test cohort (1/3 of the data) that was not used for training the models. Primary endpoint was reached in 82 patients (6.9%). Among baseline variables, Lasso model with SIVS predicted subsequent ALT elevations of > 2 × ULN using higher ALT, csDMARD other than methotrexate or sulfasalazine and psoriatic arthritis diagnosis as important predictors, with a concordance index of 0.71 in the test cohort. Respectively, at first follow-up, in addition to baseline ALT and psoriatic arthritis diagnosis, also ALT change from baseline was identified as an important predictor resulting in a test concordance index of 0.72. Our computational model predicts ALT elevations after the first follow-up test with good accuracy and can help in optimizing individual testing frequency.
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Affiliation(s)
- Laura Kuusalo
- Division of Medicine, Centre for Rheumatology and Clinical Immunology, University of Turku and Turku University Hospital, Kiinamyllynkatu 4-6, P.O. Box 52, 20521, Turku, Finland.
| | - Mikko S Venäläinen
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Tykistökatu 6, 20520, Turku, Finland
- Department of Medical Physics, Turku University Hospital, Turku, Finland
| | - Heidi Kirjala
- Division of Medicine, Centre for Rheumatology and Clinical Immunology, University of Turku and Turku University Hospital, Kiinamyllynkatu 4-6, P.O. Box 52, 20521, Turku, Finland
| | - Sofia Saranpää
- Division of Medicine, Centre for Rheumatology and Clinical Immunology, University of Turku and Turku University Hospital, Kiinamyllynkatu 4-6, P.O. Box 52, 20521, Turku, Finland
| | - Laura L Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Tykistökatu 6, 20520, Turku, Finland
- Institute of Biomedicine, University of Turku, Turku, Finland
- InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - Laura Pirilä
- Division of Medicine, Centre for Rheumatology and Clinical Immunology, University of Turku and Turku University Hospital, Kiinamyllynkatu 4-6, P.O. Box 52, 20521, Turku, Finland
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Wang N, Khan S, Elo LL. VarSCAT: A computational tool for sequence context annotations of genomic variants. PLoS Comput Biol 2023; 19:e1010727. [PMID: 37566612 PMCID: PMC10446208 DOI: 10.1371/journal.pcbi.1010727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 08/23/2023] [Accepted: 07/20/2023] [Indexed: 08/13/2023] Open
Abstract
The sequence contexts of genomic variants play important roles in understanding biological significances of variants and potential sequencing related variant calling issues. However, methods for assessing the diverse sequence contexts of genomic variants such as tandem repeats and unambiguous annotations have been limited. Herein, we describe the Variant Sequence Context Annotation Tool (VarSCAT) for annotating the sequence contexts of genomic variants, including breakpoint ambiguities, flanking bases of variants, wildtype/mutated DNA sequences, variant nomenclatures, distances between adjacent variants, tandem repeat regions, and custom annotation with user customizable options. Our analyses demonstrate that VarSCAT is more versatile and customizable than the currently available methods or strategies for annotating variants in short tandem repeat (STR) regions or insertions and deletions (indels) with breakpoint ambiguity. Variant sequence context annotations of high-confidence human variant sets with VarSCAT revealed that more than 75% of all human individual germline and clinically relevant indels have breakpoint ambiguities. Moreover, we illustrate that more than 80% of human individual germline small variants in STR regions are indels and that the sizes of these indels correlated with STR motif sizes. VarSCAT is available from https://github.com/elolab/VarSCAT.
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Affiliation(s)
- Ning Wang
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - Sofia Khan
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Laura L. Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- InFLAMES Research Flagship Center, University of Turku, Turku, Finland
- Institute of Biomedicine, University of Turku, Turku, Finland
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9
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Suomi T, Starskaia I, Kalim UU, Rasool O, Jaakkola MK, Grönroos T, Välikangas T, Brorsson C, Mazzoni G, Bruggraber S, Overbergh L, Dunger D, Peakman M, Chmura P, Brunak S, Schulte AM, Mathieu C, Knip M, Lahesmaa R, Elo LL. Gene expression signature predicts rate of type 1 diabetes progression. EBioMedicine 2023; 92:104625. [PMID: 37224769 DOI: 10.1016/j.ebiom.2023.104625] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 04/06/2023] [Accepted: 05/09/2023] [Indexed: 05/26/2023] Open
Abstract
BACKGROUND Type 1 diabetes is a complex heterogenous autoimmune disease without therapeutic interventions available to prevent or reverse the disease. This study aimed to identify transcriptional changes associated with the disease progression in patients with recent-onset type 1 diabetes. METHODS Whole-blood samples were collected as part of the INNODIA study at baseline and 12 months after diagnosis of type 1 diabetes. We used linear mixed-effects modelling on RNA-seq data to identify genes associated with age, sex, or disease progression. Cell-type proportions were estimated from the RNA-seq data using computational deconvolution. Associations to clinical variables were estimated using Pearson's or point-biserial correlation for continuous and dichotomous variables, respectively, using only complete pairs of observations. FINDINGS We found that genes and pathways related to innate immunity were downregulated during the first year after diagnosis. Significant associations of the gene expression changes were found with ZnT8A autoantibody positivity. Rate of change in the expression of 16 genes between baseline and 12 months was found to predict the decline in C-peptide at 24 months. Interestingly and consistent with earlier reports, increased B cell levels and decreased neutrophil levels were associated with the rapid progression. INTERPRETATION There is considerable individual variation in the rate of progression from appearance of type 1 diabetes-specific autoantibodies to clinical disease. Patient stratification and prediction of disease progression can help in developing more personalised therapeutic strategies for different disease endotypes. FUNDING A full list of funding bodies can be found under Acknowledgments.
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Affiliation(s)
- Tomi Suomi
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520, Turku, Finland; InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - Inna Starskaia
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520, Turku, Finland; InFLAMES Research Flagship Center, University of Turku, Turku, Finland; Turku Doctoral Programme of Molecular Medicine, University of Turku, Turku, Finland
| | - Ubaid Ullah Kalim
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520, Turku, Finland; InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - Omid Rasool
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520, Turku, Finland; InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - Maria K Jaakkola
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520, Turku, Finland; InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - Toni Grönroos
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520, Turku, Finland; InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - Tommi Välikangas
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520, Turku, Finland; InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - Caroline Brorsson
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Gianluca Mazzoni
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | | | - Lut Overbergh
- Katholieke Universiteit Leuven/Universitaire Ziekenhuizen, Leuven, Belgium
| | - David Dunger
- Department of Paediatrics, University of Cambridge, Cambridge, England, UK
| | - Mark Peakman
- Immunology & Inflammation Research Therapeutic Area, Sanofi, MA, USA
| | - Piotr Chmura
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Søren Brunak
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | | | - Chantal Mathieu
- Katholieke Universiteit Leuven/Universitaire Ziekenhuizen, Leuven, Belgium
| | - Mikael Knip
- Paediatric Research Centre, University of Helsinki and Helsinki University Hospital, Helsinki, Finland; Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland; Tampere Centre for Child Health Research, Tampere University Hospital, Tampere, Finland
| | - Riitta Lahesmaa
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520, Turku, Finland; InFLAMES Research Flagship Center, University of Turku, Turku, Finland; Institute of Biomedicine, University of Turku, FI-20520, Turku, Finland.
| | - Laura L Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520, Turku, Finland; InFLAMES Research Flagship Center, University of Turku, Turku, Finland; Institute of Biomedicine, University of Turku, FI-20520, Turku, Finland.
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10
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Keemu H, Alakylä KJ, Klén R, Panula VJ, Venäläinen MS, Haapakoski JJ, Eskelinen AP, Pamilo K, Kettunen JS, Puhto AP, Vasara AI, Elo LL, Mäkelä KT. Risk factors for revision due to prosthetic joint infection following total knee arthroplasty based on 62,087 knees in the Finnish Arthroplasty Register from 2014 to 2020. Acta Orthop 2023; 94:215-223. [PMID: 37140202 PMCID: PMC10158790 DOI: 10.2340/17453674.2023.12307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Indexed: 05/05/2023] Open
Abstract
BACKGROUND AND PURPOSE Periprosthetic joint infection (PJI) is the commonest reason for revision after total knee arthroplasty (TKA). We assessed the risk factors for revision due to PJI following TKA based on the Finnish Arthroplasty Register (FAR). PATIENTS AND METHODS We analyzed 62,087 primary condylar TKAs registered between June 2014 and February 2020 with revision for PJI as the endpoint. Cox proportional hazards regression was used to estimate hazard ratios (HR) with 95% confidence intervals (CI) for the first PJI revision using 25 potential patient- and surgical-related risk factors as covariates. RESULTS 484 knees were revised for the first time during the first postoperative year because of PJI. The HRs for revision due to PJI in unadjusted analysis were 0.5 (0.4-0.6) for female sex, 0.7 (0.6-1.0) for BMI 25-29, and 1.6 (1.1-2.5) for BMI > 40 compared with BMI < 25, 4.0 (1.3-12) for preoperative fracture diagnosis compared with osteoarthritis, and 0.7 (0.5-0.9) for use of an antimicrobial incise drape. In adjusted analysis the HRs were 2.2 (1.4-3.5) for ASA class III-IV compared with class I, 1.7 (1.4-2.1) for intraoperative bleeding ≥ 100 mL, 1.4 (1.2-1.8) for use of a drain, 0.7 (0.5-1.0) for short duration of operation of 45-59 minutes, and 1.7 (1.3-2.3) for long operation duration > 120 min compared with 60-89 minutes, and 1.3 (1.0-1.8) for use of general anesthesia. CONCLUSION We found increased risk for revision due to PJI when no incise drape was used. The use of drainage also increased the risk. Specializing in performing TKA reduces operative time and thereby also the PJI rate.
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Affiliation(s)
- Hannes Keemu
- Department of Orthopaedics and Traumatology, Turku University Hospital and University of Turku, Turku.
| | - Kasperi J Alakylä
- Department of Orthopaedics and Traumatology, Turku University Hospital and University of Turku, Turku
| | - Riku Klén
- Turku PET Centre, University of Turku and Turku University Hospital, Turku
| | - Valtteri J Panula
- Department of Orthopaedics and Traumatology, Turku University Hospital and University of Turku, Turku
| | - Mikko S Venäläinen
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku; Department of Medical Physics, Turku University Hospital, Turku
| | | | - Antti P Eskelinen
- Coxa Hospital for Joint Replacement and Faculty of Medicine and Health Technologies, University of Tampere, Tampere
| | - Konsta Pamilo
- Coxa Hospital for Joint Replacement and Faculty of Medicine and Health Technologies, University of Tampere, Tampere
| | - Jukka S Kettunen
- Department of Orthopaedics and Traumatology, Kuopio University Hospital, Kuopio
| | - Ari-Pekka Puhto
- OYS Centre for Musculoskeletal Surgery, Oulu University Hospital, Oulu
| | - Anna I Vasara
- Department of Orthopaedics and Traumatology, Helsinki University Hospital and University of Helsinki, Finland
| | - Laura L Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku
| | - Keijo T Mäkelä
- Department of Orthopaedics and Traumatology, Turku University Hospital and University of Turku, Turku
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11
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Jalkanen J, Khan S, Elima K, Huttunen T, Wang N, Hollmén M, Elo LL, Jalkanen S. Polymorphism in interferon alpha/beta receptor contributes to glucocorticoid response and outcome of ARDS and COVID-19. Crit Care 2023; 27:112. [PMID: 36927455 PMCID: PMC10018638 DOI: 10.1186/s13054-023-04388-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 03/01/2023] [Indexed: 03/18/2023] Open
Abstract
BACKGROUND The use of glucocorticoids has given contradictory results for treating acute respiratory distress syndrome (ARDS). The use of intravenous Interferon beta (IFN β) for the treatment of ARDS was recently tested in a phase III ARDS trial (INTEREST), in which more than half of the patients simultaneously received glucocorticoids. Trial results showed deleterious effects of glucocorticoids when administered together with IFN β, and therefore, we aimed at finding the reason behind this. METHODS We first sequenced the genes encoding the IFN α/β receptor of the patients, who participated in the INTEREST study (ClinicalTrials.gov Identifier: NCT02622724 , November 24, 2015) in which the patients were randomized to receive an intravenous injection of IFN β-1a (144 patients) or placebo (152 patients). Genetic background was analyzed against clinical outcome, concomitant medication, and pro-inflammatory cytokine levels. Thereafter, we tested the influence of the genetic background on IFN α/β receptor expression in lung organ cultures and whether, it has any effect on transcription factors STAT1 and STAT2 involved in IFN signaling. RESULTS We found a novel disease association of a SNP rs9984273, which is situated in the interferon α/β receptor subunit 2 (IFNAR2) gene in an area corresponding to a binding motif of the glucocorticoid receptor (GR). The minor allele of SNP rs9984273 associates with higher IFNAR expression, more rapid decrease of IFN γ and interleukin-6 (IL-6) levels and better outcome in IFN β treated patients with ARDS, while the major allele associates with a poor outcome especially under concomitant IFN β and glucocorticoid treatment. Moreover, the minor allele of rs9984273 associates with a less severe form of coronavirus diseases (COVID-19) according to the COVID-19 Host Genetics Initiative database. CONCLUSIONS The distribution of this SNP within clinical study arms may explain the contradictory results of multiple ARDS studies and outcomes in COVID-19 concerning type I IFN signaling and glucocorticoids.
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Affiliation(s)
| | - Sofia Khan
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- InFLAMES Flagship, University of Turku and Åbo Akademi University, Turku, Finland
| | - Kati Elima
- InFLAMES Flagship, University of Turku and Åbo Akademi University, Turku, Finland
- Institute of Biomedicine, University of Turku, Turku, Finland
| | | | - Ning Wang
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- InFLAMES Flagship, University of Turku and Åbo Akademi University, Turku, Finland
| | - Maija Hollmén
- InFLAMES Flagship, University of Turku and Åbo Akademi University, Turku, Finland
- MediCity Research Laboratory, University of Turku, Tykistökatu 6, 20520, Turku, Finland
| | - Laura L Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- InFLAMES Flagship, University of Turku and Åbo Akademi University, Turku, Finland
- Institute of Biomedicine, University of Turku, Turku, Finland
| | - Sirpa Jalkanen
- InFLAMES Flagship, University of Turku and Åbo Akademi University, Turku, Finland.
- Institute of Biomedicine, University of Turku, Turku, Finland.
- MediCity Research Laboratory, University of Turku, Tykistökatu 6, 20520, Turku, Finland.
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12
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Oras A, Kallionpää H, Suomi T, Koskinen S, Laiho A, Elo LL, Knip M, Lahesmaa R, Aints A, Uibo R. Profiling of peripheral blood B-cell transcriptome in children who developed coeliac disease in a prospective study. Heliyon 2023; 9:e13147. [PMID: 36718152 PMCID: PMC9883278 DOI: 10.1016/j.heliyon.2023.e13147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 12/20/2022] [Accepted: 01/17/2023] [Indexed: 01/22/2023] Open
Abstract
Background In coeliac disease (CoD), the role of B-cells has mainly been considered to be production of antibodies. The functional role of B-cells has not been analysed extensively in CoD. Methods We conducted a study to characterize gene expression in B-cells from children developing CoD early in life using samples collected before and at the diagnosis of the disease. Blood samples were collected from children at risk at 12, 18, 24 and 36 months of age. RNA from peripheral blood CD19+ cells was sequenced and differential gene expression was analysed using R package Limma. Findings Overall, we found one gene, HNRNPL, modestly downregulated in all patients (logFC -0·7; q = 0·09), and several others downregulated in those diagnosed with CoD already by the age of 2 years. Interpretation The data highlight the role of B-cells in CoD development. The role of HNRPL in suppressing enteroviral replication suggests that the predisposing factor for both CoD and enteroviral infections is the low level of HNRNPL expression. Funding EU FP7 grant no. 202063, EU Regional Developmental Fund and research grant PRG712, The Academy of Finland Centre of Excellence in Molecular Systems Immunology and Physiology Research (SyMMyS) 2012-2017, grant no. 250114) and, AoF Personalized Medicine Program (grant no. 292482), AoF grants 292335, 294337, 319280, 31444, 319280, 329277, 331790) and grants from the Sigrid Jusélius Foundation (SJF).
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Affiliation(s)
- Astrid Oras
- Department of Immunology, Institute of Biomedicine and Translational Medicine, University of Tartu, Estonia
| | - Henna Kallionpää
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Finland
| | - Tomi Suomi
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Finland,InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - Satu Koskinen
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Finland
| | - Asta Laiho
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Finland,InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - Laura L. Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Finland,InFLAMES Research Flagship Center, University of Turku, Turku, Finland,Institute of Biomedicine, University of Turku, Finland
| | - Mikael Knip
- Pediatric Research Center, Children's Hospital, University of Helsinki and Helsinki University Hospital, Helsinki, Finland,Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland,Center for Child Health Research, Tampere University Hospital, Tampere, Finland
| | - Riitta Lahesmaa
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Finland,InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - Alar Aints
- Department of Immunology, Institute of Biomedicine and Translational Medicine, University of Tartu, Estonia,Corresponding author. Department of Immunology, Institute of Biomedicine and Translational Medicine, University of Tartu, Ravila 19, EE50411, Tartu, Estonia.
| | - Raivo Uibo
- Department of Immunology, Institute of Biomedicine and Translational Medicine, University of Tartu, Estonia
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13
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Rytkönen KT, Adossa N, Mahmoudian M, Lönnberg T, Poutanen M, Elo LL. Cell type markers indicate distinct contributions of decidual stromal cells and natural killer cells in preeclampsia. Reproduction 2022; 164:V9-V13. [PMID: 36111648 DOI: 10.1530/rep-22-0079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 09/15/2022] [Indexed: 11/08/2022]
Abstract
In brief Preeclampsia is a common serious disorder that can occur during pregnancy. This study uses integrative analysis of preeclampsia transcriptomes and single-cell transcriptomes to predict cell type-specific contributions to preeclampsia. Abstract Preeclampsia is a devastating pregnancy disorder and a major cause of maternal and perinatal mortality. By combining previous transcriptomic results on preeclampsia with single-cell sequencing data, we here predict distinct and partly unanticipated contributions of decidual stromal cells and uterine natural killer cells in early- and late-onset preeclampsia.
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Affiliation(s)
- Kalle T Rytkönen
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.,Institute of Biomedicine, Research Centre for Integrative Physiology and Pharmacology, University of Turku, Turku, Finland
| | - Nigatu Adossa
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Mehrad Mahmoudian
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.,Institute of Biomedicine, Research Centre for Integrative Physiology and Pharmacology, University of Turku, Turku, Finland
| | - Tapio Lönnberg
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.,InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - Matti Poutanen
- Institute of Biomedicine, Research Centre for Integrative Physiology and Pharmacology, University of Turku, Turku, Finland
| | - Laura L Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.,InFLAMES Research Flagship Center, University of Turku, Turku, Finland.,Institute of Biomedicine, University of Turku, Turku, Finland
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14
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Peuhu E, Jacquemet G, Scheele CL, Isomursu A, Laisne MC, Koskinen LM, Paatero I, Thol K, Georgiadou M, Guzmán C, Koskinen S, Laiho A, Elo LL, Boström P, Hartiala P, van Rheenen J, Ivaska J. MYO10-filopodia support basement membranes at pre-invasive tumor boundaries. Dev Cell 2022; 57:2350-2364.e7. [DOI: 10.1016/j.devcel.2022.09.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 08/26/2022] [Accepted: 09/28/2022] [Indexed: 11/03/2022]
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15
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Junttila S, Smolander J, Elo LL. Benchmarking methods for detecting differential states between conditions from multi-subject single-cell RNA-seq data. Brief Bioinform 2022; 23:6649780. [PMID: 35880426 PMCID: PMC9487674 DOI: 10.1093/bib/bbac286] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 06/07/2022] [Accepted: 06/23/2022] [Indexed: 12/13/2022] Open
Abstract
Single-cell RNA-sequencing (scRNA-seq) enables researchers to quantify transcriptomes of thousands of cells simultaneously and study transcriptomic changes between cells. scRNA-seq datasets increasingly include multisubject, multicondition experiments to investigate cell-type-specific differential states (DS) between conditions. This can be performed by first identifying the cell types in all the subjects and then by performing a DS analysis between the conditions within each cell type. Naïve single-cell DS analysis methods that treat cells statistically independent are subject to false positives in the presence of variation between biological replicates, an issue known as the pseudoreplicate bias. While several methods have already been introduced to carry out the statistical testing in multisubject scRNA-seq analysis, comparisons that include all these methods are currently lacking. Here, we performed a comprehensive comparison of 18 methods for the identification of DS changes between conditions from multisubject scRNA-seq data. Our results suggest that the pseudobulk methods performed generally best. Both pseudobulks and mixed models that model the subjects as a random effect were superior compared with the naïve single-cell methods that do not model the subjects in any way. While the naïve models achieved higher sensitivity than the pseudobulk methods and the mixed models, they were subject to a high number of false positives. In addition, accounting for subjects through latent variable modeling did not improve the performance of the naïve methods.
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Affiliation(s)
| | | | - Laura L Elo
- Corresponding author: Laura L. Elo, Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland. Tel.: +358504680795; E-mail:
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16
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Laajala E, Kalim UU, Grönroos T, Rasool O, Halla-Aho V, Konki M, Kattelus R, Mykkänen J, Nurmio M, Vähä-Mäkilä M, Kallionpää H, Lietzén N, Ghimire BR, Laiho A, Hyöty H, Elo LL, Ilonen J, Knip M, Lund RJ, Orešič M, Veijola R, Lähdesmäki H, Toppari J, Lahesmaa R. Umbilical cord blood DNA methylation in children who later develop type 1 diabetes. Diabetologia 2022; 65:1534-1540. [PMID: 35716175 PMCID: PMC9345803 DOI: 10.1007/s00125-022-05726-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 03/09/2022] [Indexed: 11/30/2022]
Abstract
AIMS/HYPOTHESIS Distinct DNA methylation patterns have recently been observed to precede type 1 diabetes in whole blood collected from young children. Our aim was to determine whether perinatal DNA methylation is associated with later progression to type 1 diabetes. METHODS Reduced representation bisulphite sequencing (RRBS) analysis was performed on umbilical cord blood samples collected within the Finnish Type 1 Diabetes Prediction and Prevention (DIPP) Study. Children later diagnosed with type 1 diabetes and/or who tested positive for multiple islet autoantibodies (n = 43) were compared with control individuals (n = 79) who remained autoantibody-negative throughout the DIPP follow-up until 15 years of age. Potential confounding factors related to the pregnancy and the mother were included in the analysis. RESULTS No differences in the umbilical cord blood methylation patterns were observed between the cases and controls at a false discovery rate <0.05. CONCLUSIONS/INTERPRETATION Based on our results, differences between children who progress to type 1 diabetes and those who remain healthy throughout childhood are not yet present in the perinatal DNA methylome. However, we cannot exclude the possibility that such differences would be found in a larger dataset.
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Affiliation(s)
- Essi Laajala
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- InFLAMES Research Flagship Center, University of Turku, Turku, Finland
- Turku Doctoral Programme of Molecular Medicine, University of Turku, Turku, Finland
- Department of Computer Science, Aalto University, Espoo, Finland
| | - Ubaid Ullah Kalim
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - Toni Grönroos
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - Omid Rasool
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - Viivi Halla-Aho
- Department of Computer Science, Aalto University, Espoo, Finland
| | - Mikko Konki
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Roosa Kattelus
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - Juha Mykkänen
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
- Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland
| | - Mirja Nurmio
- Research Centre for Integrative Physiology and Pharmacology, Institute of Biomedicine, University of Turku, Turku, Finland
| | - Mari Vähä-Mäkilä
- Research Centre for Integrative Physiology and Pharmacology, Institute of Biomedicine, University of Turku, Turku, Finland
| | - Henna Kallionpää
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Niina Lietzén
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Bishwa R Ghimire
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Asta Laiho
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - Heikki Hyöty
- Department of Virology, Faculty of Medicine and Biosciences, University of Tampere, Tampere, Finland
| | - Laura L Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- InFLAMES Research Flagship Center, University of Turku, Turku, Finland
- Institute of Biomedicine, University of Turku, Turku, Finland
| | - Jorma Ilonen
- Immunogenetics Laboratory, Institute of Biomedicine, University of Turku, Turku, Finland
| | - Mikael Knip
- Pediatric Research Center, Children's Hospital, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Center for Child Health Research, Tampere University Hospital, Tampere, Finland
| | - Riikka J Lund
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Matej Orešič
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- InFLAMES Research Flagship Center, University of Turku, Turku, Finland
- School of Medical Sciences, Örebro University, Örebro, Sweden
| | - Riitta Veijola
- Department of Pediatrics, PEDEGO Research Unit, Medical Research Center, Oulu University Hospital and University of Oulu, Oulu, Finland
| | - Harri Lähdesmäki
- Department of Computer Science, Aalto University, Espoo, Finland
| | - Jorma Toppari
- Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland
- Research Centre for Integrative Physiology and Pharmacology, Institute of Biomedicine, University of Turku, Turku, Finland
- Department of Pediatrics, Turku University Hospital, Turku, Finland
| | - Riitta Lahesmaa
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.
- InFLAMES Research Flagship Center, University of Turku, Turku, Finland.
- Institute of Biomedicine, University of Turku, Turku, Finland.
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17
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Kleino I, Frolovaitė P, Suomi T, Elo LL. Computational solutions for spatial transcriptomics. Comput Struct Biotechnol J 2022; 20:4870-4884. [PMID: 36147664 PMCID: PMC9464853 DOI: 10.1016/j.csbj.2022.08.043] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 08/18/2022] [Accepted: 08/18/2022] [Indexed: 11/18/2022] Open
Abstract
Transcriptome level expression data connected to the spatial organization of the cells and molecules would allow a comprehensive understanding of how gene expression is connected to the structure and function in the biological systems. The spatial transcriptomics platforms may soon provide such information. However, the current platforms still lack spatial resolution, capture only a fraction of the transcriptome heterogeneity, or lack the throughput for large scale studies. The strengths and weaknesses in current ST platforms and computational solutions need to be taken into account when planning spatial transcriptomics studies. The basis of the computational ST analysis is the solutions developed for single-cell RNA-sequencing data, with advancements taking into account the spatial connectedness of the transcriptomes. The scRNA-seq tools are modified for spatial transcriptomics or new solutions like deep learning-based joint analysis of expression, spatial, and image data are developed to extract biological information in the spatially resolved transcriptomes. The computational ST analysis can reveal remarkable biological insights into spatial patterns of gene expression, cell signaling, and cell type variations in connection with cell type-specific signaling and organization in complex tissues. This review covers the topics that help choosing the platform and computational solutions for spatial transcriptomics research. We focus on the currently available ST methods and platforms and their strengths and limitations. Of the computational solutions, we provide an overview of the analysis steps and tools used in the ST data analysis. The compatibility with the data types and the tools provided by the current ST analysis frameworks are summarized.
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Key Words
- AOI, area of illumination
- BICCN, Brain Initiative Cell Census Network
- BOLORAMIS, barcoded oligonucleotides ligated on RNA amplified for multiplexed and parallel in situ analyses
- Baysor, Bayesian Segmentation of Spatial Transcriptomics Data
- BinSpect, Binary Spatial Extraction
- CCC, cell–cell communication
- CCI, cell–cell interactions
- CNV, copy-number variation
- Computational biology
- DSP, digital spatial profiling
- DbiT-Seq, Deterministic Barcoding in Tissue for spatial omics sequencing
- FA, factor analysis
- FFPE, formalin-fixed, paraffin-embedded
- FISH, fluorescence in situ hybridization
- FISSEQ, fluorescence in situ sequencing of RNA
- FOV, Field of view
- GRNs, gene regulation networks
- GSEA, gene set enrichment analysis
- GSVA, gene set variation analysis
- HDST, high definition spatial transcriptomics
- HMRF, hidden Markov random field
- ICG, interaction changed genes
- ISH, in situ hybridization
- ISS, in situ sequencing
- JSTA, Joint cell segmentation and cell type annotation
- KNN, k-nearest neighbor
- LCM, Laser Capture Microdissection
- LCM-seq, laser capture microdissection coupled with RNA sequencing
- LOH, loss of heterozygosity analysis
- MC, Molecular Cartography
- MERFISH, multiplexed error-robust FISH
- NMF (NNMF), Non-negative matrix factorization
- PCA, Principal Component Analysis
- PIXEL-seq, Polony (or DNA cluster)-indexed library-sequencing
- PL-lig, padlock ligation
- QC, quality control
- RNAseq, RNA sequencing
- ROI, region of interest
- SCENIC, Single-Cell rEgulatory Network Inference and Clustering
- SME, Spatial Morphological gene Expression normalization
- SPATA, SPAtial Transcriptomic Analysis
- ST Pipeline, Spatial Transcriptomics Pipeline
- ST, Spatial transcriptomics
- STARmap, spatially-resolved transcript amplicon readout mapping
- Single-cell analysis
- Spatial data analysis frameworks
- Spatial deconvolution
- Spatial transcriptomics
- TIVA, Transcriptome in Vivo Analysis
- TMA, tissue microarray
- TME, tumor micro environment
- UMAP, Uniform Manifold Approximation and Projection for Dimension Reduction
- UMI, unique molecular identifier
- ZipSeq, zipcoded sequencing.
- scRNA-seq, single-cell RNA sequencing
- scvi-tools, single-cell variational inference tools
- seqFISH, sequential fluorescence in situ hybridization
- sequ-smFISH, sequential single-molecule fluorescent in situ hybridization
- smFISH, single molecule FISH
- t-SNE, t-distributed stochastic neighbor embedding
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Affiliation(s)
- Iivari Kleino
- Turku Bioscience Centre, University of Turku and Åbo Akademi University Turku, Turku, Finland
| | - Paulina Frolovaitė
- Turku Bioscience Centre, University of Turku and Åbo Akademi University Turku, Turku, Finland
| | - Tomi Suomi
- Turku Bioscience Centre, University of Turku and Åbo Akademi University Turku, Turku, Finland
| | - Laura L. Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University Turku, Turku, Finland
- Institute of Biomedicine, University of Turku, Turku, Finland
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18
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Välikangas T, Junttila S, Rytkönen KT, Kukkonen-Macchi A, Suomi T, Elo LL. COVID-19-specific transcriptomic signature detectable in blood across multiple cohorts. Front Genet 2022; 13:929887. [PMID: 35991542 PMCID: PMC9388772 DOI: 10.3389/fgene.2022.929887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 06/27/2022] [Indexed: 01/08/2023] Open
Abstract
The coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is spreading across the world despite vast global vaccination efforts. Consequently, many studies have looked for potential human host factors and immune mechanisms associated with the disease. However, most studies have focused on comparing COVID-19 patients to healthy controls, while fewer have elucidated the specific host factors distinguishing COVID-19 from other infections. To discover genes specifically related to COVID-19, we reanalyzed transcriptome data from nine independent cohort studies, covering multiple infections, including COVID-19, influenza, seasonal coronaviruses, and bacterial pneumonia. The identified COVID-19-specific signature consisted of 149 genes, involving many signals previously associated with the disease, such as induction of a strong immunoglobulin response and hemostasis, as well as dysregulation of cell cycle-related processes. Additionally, potential new gene candidates related to COVID-19 were discovered. To facilitate exploration of the signature with respect to disease severity, disease progression, and different cell types, we also offer an online tool for easy visualization of the selected genes across multiple datasets at both bulk and single-cell levels.
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Affiliation(s)
- Tommi Välikangas
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Sini Junttila
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Kalle T. Rytkönen
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- Institute of Biomedicine, University of Turku, Turku, Finland
| | - Anu Kukkonen-Macchi
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Tomi Suomi
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- *Correspondence: Tomi Suomi, ; Laura L. Elo,
| | - Laura L. Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- Institute of Biomedicine, University of Turku, Turku, Finland
- *Correspondence: Tomi Suomi, ; Laura L. Elo,
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19
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Khan MM, Khan MH, Kalim UU, Khan S, Junttila S, Paulin N, Kong L, Rasool O, Elo LL, Lahesmaa R. Long Intergenic Noncoding RNA MIAT as a Regulator of Human Th17 Cell Differentiation. Front Immunol 2022; 13:856762. [PMID: 35784351 PMCID: PMC9242727 DOI: 10.3389/fimmu.2022.856762] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Accepted: 05/10/2022] [Indexed: 11/13/2022] Open
Abstract
T helper 17 (Th17) cells protect against fungal and bacterial infections and are implicated in autoimmunity. Several long intergenic noncoding RNAs (lincRNA) are induced during Th17 differentiation, however, their contribution to Th17 differentiation is poorly understood. We aimed to characterize the function of the lincRNA Myocardial Infarction Associated Transcript (MIAT) during early human Th17 cell differentiation. We found MIAT to be upregulated early after induction of human Th17 cell differentiation along with an increase in the chromatin accessibility at the gene locus. STAT3, a key regulator of Th17 differentiation, directly bound to the MIAT promoter and induced its expression during the early stages of Th17 cell differentiation. MIAT resides in the nucleus and regulates the expression of several key Th17 genes, including IL17A, IL17F, CCR6 and CXCL13, possibly by altering the chromatin accessibility of key loci, including IL17A locus. Further, MIAT regulates the expression of protein kinase C alpha (PKCα), an upstream regulator of IL17A. A reanalysis of published single-cell RNA-seq data showed that MIAT was expressed in T cells from the synovium of RA patients. Our results demonstrate that MIAT contributes to human Th17 differentiation by upregulating several genes implicated in Th17 differentiation. High MIAT expression in T cells of RA patient synovia suggests a possible role of MIAT in Th17 mediated autoimmune pathologies.
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Affiliation(s)
- Mohd Moin Khan
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.,InFLAMES Research Flagship Center , University of Turku, Turku, Finland.,Turku Doctoral Programme of Molecular Medicine, University of Turku, Turku, Finland
| | - Meraj Hasan Khan
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.,InFLAMES Research Flagship Center , University of Turku, Turku, Finland
| | - Ubaid Ullah Kalim
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.,InFLAMES Research Flagship Center , University of Turku, Turku, Finland
| | - Sofia Khan
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.,InFLAMES Research Flagship Center , University of Turku, Turku, Finland
| | - Sini Junttila
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.,InFLAMES Research Flagship Center , University of Turku, Turku, Finland
| | - Niklas Paulin
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.,InFLAMES Research Flagship Center , University of Turku, Turku, Finland
| | - Lingjia Kong
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.,The Broad Institute of Massachusetts Institute of Technology (MIT) and Harvard, Cambridge, MA, United States.,Center for Computational and Integrative Biology, Massachusetts General Hospital, Boston, MA, United States
| | - Omid Rasool
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.,InFLAMES Research Flagship Center , University of Turku, Turku, Finland
| | - Laura L Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.,InFLAMES Research Flagship Center , University of Turku, Turku, Finland.,Institute of Biomedicine, University of Turku, Turku, Finland
| | - Riitta Lahesmaa
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.,InFLAMES Research Flagship Center , University of Turku, Turku, Finland.,Institute of Biomedicine, University of Turku, Turku, Finland
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20
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Pietilä S, Suomi T, Elo LL. Introducing untargeted data-independent acquisition for metaproteomics of complex microbial samples. ISME Commun 2022; 2:51. [PMID: 37938742 PMCID: PMC9723653 DOI: 10.1038/s43705-022-00137-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 05/27/2022] [Accepted: 06/14/2022] [Indexed: 05/17/2023]
Abstract
Mass spectrometry-based metaproteomics is a relatively new field of research that enables the characterization of the functionality of microbiota. Recently, we demonstrated the applicability of data-independent acquisition (DIA) mass spectrometry to the analysis of complex metaproteomic samples. This allowed us to circumvent many of the drawbacks of the previously used data-dependent acquisition (DDA) mass spectrometry, mainly the limited reproducibility when analyzing samples with complex microbial composition. However, the DDA-assisted DIA approach still required additional DDA data on the samples to assist the analysis. Here, we introduce, for the first time, an untargeted DIA metaproteomics tool that does not require any DDA data, but instead generates a pseudospectral library directly from the DIA data. This reduces the amount of required mass spectrometry data to a single DIA run per sample. The new DIA-only metaproteomics approach is implemented as a new open-source software package named glaDIAtor, including a modern web-based graphical user interface to facilitate wide use of the tool by the community.
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Affiliation(s)
- Sami Pietilä
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520, Turku, Finland
| | - Tomi Suomi
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520, Turku, Finland
| | - Laura L Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520, Turku, Finland.
- Institute of Biomedicine, University of Turku, FI-20520, Turku, Finland.
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21
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Keskitalo A, Munukka E, Aatsinki A, Saleem W, Kartiosuo N, Lahti L, Huovinen P, Elo LL, Pietilä S, Rovio SP, Niinikoski H, Viikari J, Rönnemaa T, Lagström H, Jula A, Raitakari O, Pahkala K. An Infancy-Onset 20-Year Dietary Counselling Intervention and Gut Microbiota Composition in Adulthood. Nutrients 2022; 14:nu14132667. [PMID: 35807848 PMCID: PMC9268486 DOI: 10.3390/nu14132667] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 06/16/2022] [Accepted: 06/21/2022] [Indexed: 12/10/2022] Open
Abstract
The randomized controlled Special Turku Coronary Risk Factor Intervention Project (STRIP) has completed a 20-year infancy-onset dietary counselling intervention to reduce exposure to atherosclerotic cardiovascular disease risk factors via promotion of a heart-healthy diet. The counselling on, e.g., low intake of saturated fat and cholesterol and promotion of fruit, vegetable, and whole-grain consumption has affected the dietary characteristics of the intervention participants. By leveraging this unique cohort, we further investigated whether this long-term dietary intervention affected the gut microbiota bacterial profile six years after the intervention ceased. Our sub-study comprised 357 individuals aged 26 years (intervention n = 174, control n = 183), whose gut microbiota were profiled using 16S rRNA amplicon sequencing. We observed no differences in microbiota profiles between the intervention and control groups. However, out of the 77 detected microbial genera, the Veillonella genus was more abundant in the intervention group compared to the controls (log2 fold-change 1.58, p < 0.001) after adjusting for multiple comparison. In addition, an association between the study group and overall gut microbiota profile was found only in males. The subtle differences in gut microbiota abundances observed in this unique intervention setting suggest that long-term dietary counselling reflecting dietary guidelines may be associated with alterations in gut microbiota.
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Affiliation(s)
- Anniina Keskitalo
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, 20520 Turku, Finland; (A.K.); (N.K.); (S.P.R.); (H.N.); (O.R.)
- Centre for Population Health Research, University of Turku and Turku University Hospital, 20520 Turku, Finland; (A.A.); (H.L.)
- Department of Clinical Microbiology, Turku University Hospital, 20520 Turku, Finland;
| | - Eveliina Munukka
- Microbiome Biobank, Institute of Biomedicine, University of Turku, 20520 Turku, Finland;
| | - Anna Aatsinki
- Centre for Population Health Research, University of Turku and Turku University Hospital, 20520 Turku, Finland; (A.A.); (H.L.)
| | - Wisam Saleem
- Department of Computing, Faculty of Technology, University of Turku, 20520 Turku, Finland; (W.S.); (L.L.)
| | - Noora Kartiosuo
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, 20520 Turku, Finland; (A.K.); (N.K.); (S.P.R.); (H.N.); (O.R.)
- Centre for Population Health Research, University of Turku and Turku University Hospital, 20520 Turku, Finland; (A.A.); (H.L.)
- Department of Mathematics and Statistics, University of Turku, 20520 Turku, Finland
| | - Leo Lahti
- Department of Computing, Faculty of Technology, University of Turku, 20520 Turku, Finland; (W.S.); (L.L.)
| | - Pentti Huovinen
- Department of Clinical Microbiology, Turku University Hospital, 20520 Turku, Finland;
- Institute of Biomedicine, University of Turku, 20520 Turku, Finland;
| | - Laura L. Elo
- Institute of Biomedicine, University of Turku, 20520 Turku, Finland;
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland;
| | - Sami Pietilä
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland;
| | - Suvi P. Rovio
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, 20520 Turku, Finland; (A.K.); (N.K.); (S.P.R.); (H.N.); (O.R.)
- Centre for Population Health Research, University of Turku and Turku University Hospital, 20520 Turku, Finland; (A.A.); (H.L.)
| | - Harri Niinikoski
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, 20520 Turku, Finland; (A.K.); (N.K.); (S.P.R.); (H.N.); (O.R.)
- Centre for Population Health Research, University of Turku and Turku University Hospital, 20520 Turku, Finland; (A.A.); (H.L.)
- Department of Physiology/Department of Pediatrics, University of Turku, 20520 Turku, Finland
| | - Jorma Viikari
- Department of Medicine, University of Turku, 20520 Turku, Finland; (J.V.); (T.R.)
- Division of Medicine, Turku University Hospital, 20520 Turku, Finland
| | - Tapani Rönnemaa
- Department of Medicine, University of Turku, 20520 Turku, Finland; (J.V.); (T.R.)
- Division of Medicine, Turku University Hospital, 20520 Turku, Finland
| | - Hanna Lagström
- Centre for Population Health Research, University of Turku and Turku University Hospital, 20520 Turku, Finland; (A.A.); (H.L.)
- Department of Public Health, University of Turku and Turku University Hospital, 20520 Turku, Finland
| | - Antti Jula
- Department of Public Health Solutions, Institute for Health and Welfare, 20520 Turku, Finland;
| | - Olli Raitakari
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, 20520 Turku, Finland; (A.K.); (N.K.); (S.P.R.); (H.N.); (O.R.)
- Centre for Population Health Research, University of Turku and Turku University Hospital, 20520 Turku, Finland; (A.A.); (H.L.)
- Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, University of Turku, 20520 Turku, Finland
| | - Katja Pahkala
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, 20520 Turku, Finland; (A.K.); (N.K.); (S.P.R.); (H.N.); (O.R.)
- Centre for Population Health Research, University of Turku and Turku University Hospital, 20520 Turku, Finland; (A.A.); (H.L.)
- Paavo Nurmi Centre & Unit for Health and Physical Activity, University of Turku, 20520 Turku, Finland
- Correspondence: ; Tel.: +358-40-578-6122
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22
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Ammunét T, Wang N, Khan S, Elo LL. Deep learning tools are top performers in long non-coding RNA prediction. Brief Funct Genomics 2022; 21:230-241. [PMID: 35136929 PMCID: PMC9123429 DOI: 10.1093/bfgp/elab045] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 11/08/2021] [Accepted: 12/02/2021] [Indexed: 11/23/2022] Open
Abstract
The increasing amount of transcriptomic data has brought to light vast numbers of potential novel RNA transcripts. Accurately distinguishing novel long non-coding RNAs (lncRNAs) from protein-coding messenger RNAs (mRNAs) has challenged bioinformatic tool developers. Most recently, tools implementing deep learning architectures have been developed for this task, with the potential of discovering sequence features and their interactions still not surfaced in current knowledge. We compared the performance of deep learning tools with other predictive tools that are currently used in lncRNA coding potential prediction. A total of 15 tools representing the variety of available methods were investigated. In addition to known annotated transcripts, we also evaluated the use of the tools in actual studies with real-life data. The robustness and scalability of the tools' performance was tested with varying sized test sets and test sets with different proportions of lncRNAs and mRNAs. In addition, the ease-of-use for each tested tool was scored. Deep learning tools were top performers in most metrics and labelled transcripts similarly with each other in the real-life dataset. However, the proportion of lncRNAs and mRNAs in the test sets affected the performance of all tools. Computational resources were utilized differently between the top-ranking tools, thus the nature of the study may affect the decision of choosing one well-performing tool over another. Nonetheless, the results suggest favouring the novel deep learning tools over other tools currently in broad use.
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Affiliation(s)
- Tea Ammunét
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Ning Wang
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Sofia Khan
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Laura L Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- Institute of Biomedicine, University of Turku, Turku, Finland
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23
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Starskaia I, Laajala E, Grönroos T, Härkönen T, Junttila S, Kattelus R, Kallionpää H, Laiho A, Suni V, Tillmann V, Lund R, Elo LL, Lähdesmäki H, Knip M, Kalim UU, Lahesmaa R. Early DNA methylation changes in children developing beta cell autoimmunity at a young age. Diabetologia 2022; 65:844-860. [PMID: 35142878 PMCID: PMC8960578 DOI: 10.1007/s00125-022-05657-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2021] [Accepted: 11/10/2021] [Indexed: 12/14/2022]
Abstract
AIMS/HYPOTHESIS Type 1 diabetes is a chronic autoimmune disease of complex aetiology, including a potential role for epigenetic regulation. Previous epigenomic studies focused mainly on clinically diagnosed individuals. The aim of the study was to assess early DNA methylation changes associated with type 1 diabetes already before the diagnosis or even before the appearance of autoantibodies. METHODS Reduced representation bisulphite sequencing (RRBS) was applied to study DNA methylation in purified CD4+ T cell, CD8+ T cell and CD4-CD8- cell fractions of 226 peripheral blood mononuclear cell samples longitudinally collected from seven type 1 diabetes-specific autoantibody-positive individuals and control individuals matched for age, sex, HLA risk and place of birth. We also explored correlations between DNA methylation and gene expression using RNA sequencing data from the same samples. Technical validation of RRBS results was performed using pyrosequencing. RESULTS We identified 79, 56 and 45 differentially methylated regions in CD4+ T cells, CD8+ T cells and CD4-CD8- cell fractions, respectively, between type 1 diabetes-specific autoantibody-positive individuals and control participants. The analysis of pre-seroconversion samples identified DNA methylation signatures at the very early stage of disease, including differential methylation at the promoter of IRF5 in CD4+ T cells. Further, we validated RRBS results using pyrosequencing at the following CpG sites: chr19:18118304 in the promoter of ARRDC2; chr21:47307815 in the intron of PCBP3; and chr14:81128398 in the intergenic region near TRAF3 in CD4+ T cells. CONCLUSIONS/INTERPRETATION These preliminary results provide novel insights into cell type-specific differential epigenetic regulation of genes, which may contribute to type 1 diabetes pathogenesis at the very early stage of disease development. Should these findings be validated, they may serve as a potential signature useful for disease prediction and management.
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Affiliation(s)
- Inna Starskaia
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- InFLAMES Research Flagship Center, University of Turku, Turku, Finland
- Turku Doctoral Programme of Molecular Medicine, University of Turku, Turku, Finland
| | - Essi Laajala
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- InFLAMES Research Flagship Center, University of Turku, Turku, Finland
- Turku Doctoral Programme of Molecular Medicine, University of Turku, Turku, Finland
- Department of Computer Science, Aalto University, Espoo, Finland
| | - Toni Grönroos
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - Taina Härkönen
- Pediatric Research Center, Children's Hospital, University of Helsinki, and Helsinki University Hospital, Helsinki, Finland
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Sini Junttila
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - Roosa Kattelus
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - Henna Kallionpää
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Asta Laiho
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - Veronika Suni
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Vallo Tillmann
- Children's Clinic of Tartu University Hospital, Tartu, Estonia
- Institute of Clinical Medicine, University of Tartu, Tartu, Estonia
| | - Riikka Lund
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Laura L Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- InFLAMES Research Flagship Center, University of Turku, Turku, Finland
- Institute of Biomedicine, University of Turku, Turku, Finland
| | - Harri Lähdesmäki
- Department of Computer Science, Aalto University, Espoo, Finland
| | - Mikael Knip
- Pediatric Research Center, Children's Hospital, University of Helsinki, and Helsinki University Hospital, Helsinki, Finland
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Tampere Center for Child Health Research, Tampere University Hospital, Tampere, Finland
| | - Ubaid Ullah Kalim
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.
- InFLAMES Research Flagship Center, University of Turku, Turku, Finland.
| | - Riitta Lahesmaa
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.
- InFLAMES Research Flagship Center, University of Turku, Turku, Finland.
- Institute of Biomedicine, University of Turku, Turku, Finland.
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24
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Rytkönen KT, Faux T, Mahmoudian M, Heinosalo T, Nnamani MC, Perheentupa A, Poutanen M, Elo LL, Wagner GP. Histone H3K4me3 breadth in hypoxia reveals endometrial core functions and stress adaptation linked to endometriosis. iScience 2022; 25:104235. [PMID: 35494227 PMCID: PMC9051620 DOI: 10.1016/j.isci.2022.104235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 02/11/2022] [Accepted: 04/07/2022] [Indexed: 11/21/2022] Open
Abstract
Trimethylation of histone H3 at lysine 4 (H3K4me3) is a marker of active promoters. Broad H3K4me3 promoter domains have been associated with cell type identity, but H3K4me3 dynamics upon cellular stress have not been well characterized. We assessed this by exposing endometrial stromal cells to hypoxia, which is a major cellular stress condition. We observed that hypoxia modifies the existing H3K4me3 marks and that promoter H3K4me3 breadth rather than height correlates with transcription. Broad H3K4me3 domains mark genes for endometrial core functions and are maintained or selectively extended upon hypoxia. Hypoxic extension of H3K4me3 breadth associates with stress adaptation genes relevant for the survival of endometrial cells including transcription factor KLF4, for which we found increased protein expression in the stroma of endometriosis lesions. These results substantiate the view on broad H3K4me3 as a marker of cell identity genes and reveal participation of H3K4me3 extension in cellular stress adaptation.
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Affiliation(s)
- Kalle T. Rytkönen
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Tykistökatu 6, 20520 Turku, Finland
- Yale Systems Biology Institute, West Haven, CT 06516, USA
- Institute of Biomedicine, Research Centre for Integrative Physiology and Pharmacology, University of Turku, Kiinamyllynkatu 10, 20014 Turku, Finland
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06511, USA
- Corresponding author
| | - Thomas Faux
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Tykistökatu 6, 20520 Turku, Finland
| | - Mehrad Mahmoudian
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Tykistökatu 6, 20520 Turku, Finland
- Institute of Biomedicine, Research Centre for Integrative Physiology and Pharmacology, University of Turku, Kiinamyllynkatu 10, 20014 Turku, Finland
| | - Taija Heinosalo
- Institute of Biomedicine, Research Centre for Integrative Physiology and Pharmacology, University of Turku, Kiinamyllynkatu 10, 20014 Turku, Finland
| | - Mauris C. Nnamani
- Yale Systems Biology Institute, West Haven, CT 06516, USA
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06511, USA
| | - Antti Perheentupa
- Institute of Biomedicine, Research Centre for Integrative Physiology and Pharmacology, University of Turku, Kiinamyllynkatu 10, 20014 Turku, Finland
- Department of Obstetrics and Gynecology, Turku University Hospital, Kiinamyllynkatu 4-8, 20521 Turku, Finland
| | - Matti Poutanen
- Institute of Biomedicine, Research Centre for Integrative Physiology and Pharmacology, University of Turku, Kiinamyllynkatu 10, 20014 Turku, Finland
| | - Laura L. Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Tykistökatu 6, 20520 Turku, Finland
- Institute of Biomedicine, University of Turku, Kiinamyllynkatu 10, 20014 Turku, Finland
| | - Günter P. Wagner
- Yale Systems Biology Institute, West Haven, CT 06516, USA
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06511, USA
- Department of Obstetrics, Gynecology and Reproductive Sciences, Yale Medical School, New Haven, CT 06510, USA
- Department of Obstetrics and Gynecology, Wayne State University, Detroit, MI 48201, USA
- Corresponding author
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25
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Hong Y, Flinkman D, Suomi T, Pietilä S, James P, Coffey E, Elo LL. Correction to: PhosPiR: an automated phosphoproteomic pipeline in R. Brief Bioinform 2022; 23:6565175. [PMID: 35393608 PMCID: PMC9116209 DOI: 10.1093/bib/bbac153] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Ye Hong
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Dani Flinkman
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.,Lund University, Lund, Sweden
| | - Tomi Suomi
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Sami Pietilä
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Peter James
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.,Lund University, Lund, Sweden
| | - Eleanor Coffey
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Laura L Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.,Institute of Biomedicine, University of Turku, Turku, Finland
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26
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Suomi T, Kalim UU, Rasool O, Laiho A, Kallionpää H, Vähä-Mäkilä M, Nurmio M, Mykkänen J, Härkönen T, Hyöty H, Ilonen J, Veijola R, Toppari J, Knip M, Elo LL, Lahesmaa R. Type 1 Diabetes in Children With Genetic Risk May Be Predicted Very Early With a Blood miRNA. Diabetes Care 2022; 45:e77-e79. [PMID: 35134118 PMCID: PMC9016735 DOI: 10.2337/dc21-2120] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 01/20/2022] [Indexed: 02/03/2023]
Affiliation(s)
- Tomi Suomi
- Turku Bioscience Centre, University of Turku and bo Akademi University, Turku, Finland.,InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - Ubaid Ullah Kalim
- Turku Bioscience Centre, University of Turku and bo Akademi University, Turku, Finland.,InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - Omid Rasool
- Turku Bioscience Centre, University of Turku and bo Akademi University, Turku, Finland.,InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - Asta Laiho
- Turku Bioscience Centre, University of Turku and bo Akademi University, Turku, Finland.,InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - Henna Kallionpää
- Turku Bioscience Centre, University of Turku and bo Akademi University, Turku, Finland
| | - Mari Vähä-Mäkilä
- Research Centre for Integrative Physiology and Pharmacology, Institute of Biomedicine, University of Turku, Turku, Finland.,Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland
| | - Mirja Nurmio
- Research Centre for Integrative Physiology and Pharmacology, Institute of Biomedicine, University of Turku, Turku, Finland.,Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland
| | - Juha Mykkänen
- Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland.,Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
| | - Taina Härkönen
- Pediatric Research Center, Children's Hospital, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.,Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Heikki Hyöty
- Department of Virology, Faculty of Medicine and Biosciences, University of Tampere, Tampere, Finland.,Fimlab Laboratories, Pirkanmaa Hospital District, Tampere, Finland
| | - Jorma Ilonen
- Immunogenetics Laboratory, Institute of Biomedicine, University of Turku, Turku, Finland
| | - Riitta Veijola
- Department of Pediatrics, PEDEGO Research Unit, Medical Research Centre, University of Oulu, Oulu, Finland.,Department of Children and Adolescents, Oulu University Hospital, Oulu, Finland
| | - Jorma Toppari
- InFLAMES Research Flagship Center, University of Turku, Turku, Finland.,Research Centre for Integrative Physiology and Pharmacology, Institute of Biomedicine, University of Turku, Turku, Finland.,Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland.,Department of Pediatrics, Turku University Hospital, Turku, Finland
| | - Mikael Knip
- Pediatric Research Center, Children's Hospital, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.,Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland.,Centre for Child Health Research, Tampere University Hospital, Tampere, Finland
| | - Laura L Elo
- Turku Bioscience Centre, University of Turku and bo Akademi University, Turku, Finland.,InFLAMES Research Flagship Center, University of Turku, Turku, Finland.,Institute of Biomedicine, University of Turku, Turku, Finland
| | - Riitta Lahesmaa
- Turku Bioscience Centre, University of Turku and bo Akademi University, Turku, Finland.,InFLAMES Research Flagship Center, University of Turku, Turku, Finland.,Institute of Biomedicine, University of Turku, Turku, Finland
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27
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Suomi T, Elo LL. Statistical and machine learning methods to study human CD4+ T cell proteome profiles. Immunol Lett 2022; 245:8-17. [DOI: 10.1016/j.imlet.2022.03.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 03/11/2022] [Accepted: 03/15/2022] [Indexed: 11/05/2022]
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28
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Kupila SKE, Venäläinen MS, Suojanen LU, Rosengård-Bärlund M, Ahola AJ, Elo LL, Pietiläinen KH. Weight Loss Trajectories in Healthy Weight Coaching: Cohort Study. JMIR Form Res 2022; 6:e26374. [PMID: 35262494 PMCID: PMC8943569 DOI: 10.2196/26374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 05/12/2021] [Accepted: 01/07/2022] [Indexed: 11/26/2022] Open
Abstract
Background As global obesity prevalence continues to increase, there is a need for accessible and affordable weight management interventions, such as web-based programs. Objective This paper aims to assess the outcomes of healthy weight coaching (HWC), a web-based obesity management program integrated into standard Finnish clinical care. Methods HWC is an ongoing, structured digital 12-month program based on acceptance and commitment therapy. It includes weekly training sessions focused on lifestyle, general health, and psychological factors. Participants received remote one-on-one support from a personal coach. In this real-life, single-arm, prospective cohort study, we examined the total weight loss, weight loss profiles, and variables associated with weight loss success and program retention in 1189 adults (963 women) with a BMI >25 kg/m² among participants of the program between October 2016 and March 2019. Absolute (kg) and relative (%) weight loss from the baseline were the primary outcomes. We also examined the weight loss profiles, clustered based on the dynamic time-warping distance, and the possible variables associated with greater weight loss success and program retention. We compared different groups using the Mann-Whitney test or Kruskal-Wallis test for continuous variables and the chi-squared test for categorical variables. We analyzed changes in medication using the McNemar test. Results Among those having reached the 12-month time point (n=173), the mean weight loss was 4.6% (SE 0.5%), with 43% (n=75) achieving clinically relevant weight loss (≥5%). Baseline BMI ≥40 kg/m² was associated with a greater weight loss than a lower BMI (mean 6.6%, SE 0.9%, vs mean 3.2%, SE 0.6%; P=.02). In addition, more frequent weight reporting was associated with greater weight loss. No significant differences in weight loss were observed according to sex, age, baseline disease, or medication use. The total dropout rate was 29.1%. Dropouts were slightly younger than continuers (47.2, SE 0.6 years vs 49.2, SE 0.4 years; P=.01) and reported their weight less frequently (3.0, SE 0.1 entries per month vs 3.3, SE 0.1 entries per month; P<.001). Conclusions A comprehensive web-based program such as HWC is a potential addition to the repertoire of obesity management in a clinical setting. Heavier patients lost more weight, but weight loss success was otherwise independent of baseline characteristics.
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Affiliation(s)
- Sakris K E Kupila
- Obesity Research Unit, Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Mikko S Venäläinen
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Laura-Unnukka Suojanen
- Abdominal Center, Obesity Center, Endocrinology, University of Helsinki and Helsinki University Central Hospital, Helsinki, Finland
| | - Milla Rosengård-Bärlund
- Abdominal Center, Obesity Center, Endocrinology, University of Helsinki and Helsinki University Central Hospital, Helsinki, Finland
| | - Aila J Ahola
- Obesity Research Unit, Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland.,Abdominal Center, Obesity Center, Endocrinology, University of Helsinki and Helsinki University Central Hospital, Helsinki, Finland.,Abdominal Center, Nephrology, University of Helsinki and Helsinki University Central Hospital, Helsinki, Finland.,Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland
| | - Laura L Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Kirsi H Pietiläinen
- Obesity Research Unit, Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland.,Abdominal Center, Obesity Center, Endocrinology, University of Helsinki and Helsinki University Central Hospital, Helsinki, Finland
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29
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Abstract
Clustering of cells based on gene expression is one of the major steps in single-cell RNA-sequencing (scRNA-seq) data analysis. One key challenge in cluster analysis is the unknown number of clusters and, for this issue, there is still no comprehensive solution. To enhance the process of defining meaningful cluster resolution, we compare Bayesian latent Dirichlet allocation (LDA) method to its non-parametric counterpart, hierarchical Dirichlet process (HDP) in the context of clustering scRNA-seq data. A potential main advantage of HDP is that it does not require the number of clusters as an input parameter from the user. While LDA has been used in single-cell data analysis, it has not been compared in detail with HDP. Here, we compare the cell clustering performance of LDA and HDP using four scRNA-seq datasets (immune cells, kidney, pancreas and decidua/placenta), with a specific focus on cluster numbers. Using both intrinsic (DB-index) and extrinsic (ARI) cluster quality measures, we show that the performance of LDA and HDP is dataset dependent. We describe a case where HDP produced a more appropriate clustering compared to the best performer from a series of LDA clusterings with different numbers of clusters. However, we also observed cases where the best performing LDA cluster numbers appropriately capture the main biological features while HDP tended to inflate the number of clusters. Overall, our study highlights the importance of carefully assessing the number of clusters when analyzing scRNA-seq data. Summary: Dirichlet mixture models (LDA and HDP) are applied for clustering cells in scRNA-Seq data. Here we made a comprehensive comparison of LDA and HDP model-based clustering for scRNA-seq data.
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Affiliation(s)
- Nigatu A Adossa
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520, Turku, Finland
| | - Kalle T Rytkönen
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520, Turku, Finland.,Institute of Biomedicine, Research Centre for Integrative Physiology and Pharmacology, University of Turku, FI-20014, Finland
| | - Laura L Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520, Turku, Finland.,Institute of Biomedicine, University of Turku, FI-20014, Finland
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30
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Wang N, Lysenkov V, Orte K, Kairisto V, Aakko J, Khan S, Elo LL. Tool evaluation for the detection of variably sized indels from next generation whole genome and targeted sequencing data. PLoS Comput Biol 2022; 18:e1009269. [PMID: 35176018 PMCID: PMC8916674 DOI: 10.1371/journal.pcbi.1009269] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 03/11/2022] [Accepted: 01/30/2022] [Indexed: 11/18/2022] Open
Abstract
Insertions and deletions (indels) in human genomes are associated with a wide range of phenotypes, including various clinical disorders. High-throughput, next generation sequencing (NGS) technologies enable the detection of short genetic variants, such as single nucleotide variants (SNVs) and indels. However, the variant calling accuracy for indels remains considerably lower than for SNVs. Here we present a comparative study of the performance of variant calling tools for indel calling, evaluated with a wide repertoire of NGS datasets. While there is no single optimal tool to suit all circumstances, our results demonstrate that the choice of variant calling tool greatly impacts the precision and recall of indel calling. Furthermore, to reliably detect indels, it is essential to choose NGS technologies that offer a long read length and high coverage coupled with specific variant calling tools.
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Affiliation(s)
- Ning Wang
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Vladislav Lysenkov
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Katri Orte
- Department of Pathology, Laboratory Division, Turku University Hospital, Turku, Finland
- Department of Genomics, Laboratory Division, Turku University Hospital, Turku, Finland
| | - Veli Kairisto
- Department of Genomics, Laboratory Division, Turku University Hospital, Turku, Finland
| | - Juhani Aakko
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Sofia Khan
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- * E-mail: (SK); (LLE)
| | - Laura L. Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- Institute of Biomedicine, University of Turku, Finland
- * E-mail: (SK); (LLE)
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31
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Ruohonen ST, Gaytan F, Usseglio Gaudi A, Velasco I, Kukoricza K, Perdices-Lopez C, Franssen D, Guler I, Mehmood A, Elo LL, Ohlsson C, Poutanen M, Tena-Sempere M. Selective loss of kisspeptin signaling in oocytes causes progressive premature ovulatory failure. Hum Reprod 2022; 37:806-821. [PMID: 35037941 PMCID: PMC8971646 DOI: 10.1093/humrep/deab287] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 12/08/2021] [Indexed: 12/12/2022] Open
Abstract
STUDY QUESTION Does direct kisspeptin signaling in the oocyte have a role in the control of follicular dynamics and ovulation? SUMMARY ANSWER Kisspeptin signaling in the oocyte plays a relevant physiological role in the direct control of ovulation; oocyte-specific ablation of kisspeptin receptor, Gpr54, induces a state of premature ovulatory failure in mice that recapitulates some features of premature ovarian insufficiency (POI). WHAT IS KNOWN ALREADY Kisspeptins, encoded by the Kiss1 gene, are essential for the control of ovulation and fertility, acting primarily on hypothalamic GnRH neurons to stimulate gonadotropin secretion. However, kisspeptins and their receptor, Gpr54, are also expressed in the ovary of different mammalian species, including humans, where their physiological roles remain contentious and poorly characterized. STUDY DESIGN, SIZE, DURATION A novel mouse line with conditional ablation of Gpr54 in oocytes, named OoGpr54−/−, was generated and studied in terms of follicular and ovulatory dynamics at different age-points of postnatal maturation. A total of 59 OoGpr54−/− mice and 47 corresponding controls were analyzed. In addition, direct RNA sequencing was applied to ovarian samples from 8 OoGpr54−/− and 7 control mice at 6 months of age, and gonadotropin priming for ovulatory induction was conducted in mice (N = 7) from both genotypes. PARTICIPANTS/MATERIALS, SETTING, METHODS Oocyte-selective ablation of Gpr54 in the oocyte was achieved in vivo by crossing a Gdf9-driven Cre-expressing transgenic mouse line with a Gpr54 LoxP mouse line. The resulting OoGpr54−/− mouse line was subjected to phenotypic, histological, hormonal and molecular analyses at different age-points of postnatal maturation (Day 45, and 2, 4, 6 and 10–11 months of age), in order to characterize the timing of puberty, ovarian follicular dynamics and ovulation, with particular attention to identification of features reminiscent of POI. The molecular signature of ovaries from OoGpr54−/− mice was defined by direct RNA sequencing. Ovulatory responses to gonadotropin priming were also assessed in OoGpr54−/− mice. MAIN RESULTS AND THE ROLE OF CHANCE Oocyte-specific ablation of Gpr54 caused premature ovulatory failure, with some POI-like features. OoGpr54−/− mice had preserved puberty onset, without signs of hypogonadism. However, already at 2 months of age, 40% of OoGpr54−/− females showed histological features reminiscent of ovarian failure and anovulation. Penetrance of the phenotype progressed with age, with >80% and 100% of OoGpr54−/− females displaying complete ovulatory failure by 6- and 10 months, respectively. This occurred despite unaltered hypothalamic Gpr54 expression and gonadotropin levels. Yet, OoGpr54−/− mice had decreased sex steroid levels. While the RNA signature of OoGpr54−/− ovaries was dominated by the anovulatory state, oocyte-specific ablation of Gpr54 significantly up- or downregulated of a set of 21 genes, including those encoding pituitary adenylate cyclase-activating polypeptide, Wnt-10B, matrix-metalloprotease-12, vitamin A-related factors and calcium-activated chloride channel-2, which might contribute to the POI-like state. Notably, the anovulatory state of young OoGpr54−/− mice could be rescued by gonadotropin priming. LARGE SCALE DATA N/A. LIMITATIONS, REASONS FOR CAUTION Conditional ablation of Gpr54 in oocytes unambiguously caused premature ovulatory failure in mice; yet, the ultimate molecular mechanisms for such state of POI can be only inferred on the basis of RNAseq data and need further elucidation, since some of the molecular changes observed in OoGpr54−/− ovaries were secondary to the anovulatory state. Direct translation of mouse findings to human disease should be made with caution since, despite the conserved expression of Kiss1/kisspeptin and Gpr54 in rodents and humans, our mouse model does not recapitulate all features of common forms of POI. WIDER IMPLICATIONS OF THE FINDINGS Deregulation of kisspeptin signaling in the oocyte might be an underlying, and previously unnoticed, cause for some forms of POI in women. STUDY FUNDING/COMPETING INTEREST(S) This work was primarily supported by a grant to M.P. and M.T.-S. from the FiDiPro (Finnish Distinguished Professor) Program of the Academy of Finland. Additional financial support came from grant BFU2017-83934-P (M.T.-S.; Ministerio de Economía y Competitividad, Spain; co-funded with EU funds/FEDER Program), research funds from the IVIRMA International Award in Reproductive Medicine (M.T.-S.), and EFSD Albert Renold Fellowship Programme (S.T.R.). The authors have no conflicts of interest to declare in relation to the contents of this work. TRIAL REGISTRATION NUMBER N/A.
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Affiliation(s)
- Suvi T Ruohonen
- Research Centre for Integrative Physiology and Pharmacology, Institute of Biomedicine, University of Turku, Turku, Finland.,Turku Center for Disease Modeling, Turku, Finland
| | - Francisco Gaytan
- Department of Cell Biology, Physiology and Immunology, University of Córdoba, Córdoba, Spain.,Instituto Maimónides de Investigación Biomédica de Córdoba and Hospital Universitario Reina Sofia, Córdoba, Spain
| | - Andrea Usseglio Gaudi
- Research Centre for Integrative Physiology and Pharmacology, Institute of Biomedicine, University of Turku, Turku, Finland
| | - Inmaculada Velasco
- Department of Cell Biology, Physiology and Immunology, University of Córdoba, Córdoba, Spain.,Instituto Maimónides de Investigación Biomédica de Córdoba and Hospital Universitario Reina Sofia, Córdoba, Spain
| | - Krisztina Kukoricza
- Research Centre for Integrative Physiology and Pharmacology, Institute of Biomedicine, University of Turku, Turku, Finland.,Turku Center for Disease Modeling, Turku, Finland.,Drug Research Doctoral Program, University of Turku, Turku, Finland
| | - Cecilia Perdices-Lopez
- Department of Cell Biology, Physiology and Immunology, University of Córdoba, Córdoba, Spain.,Instituto Maimónides de Investigación Biomédica de Córdoba and Hospital Universitario Reina Sofia, Córdoba, Spain
| | - Delphine Franssen
- Department of Cell Biology, Physiology and Immunology, University of Córdoba, Córdoba, Spain.,Instituto Maimónides de Investigación Biomédica de Córdoba and Hospital Universitario Reina Sofia, Córdoba, Spain
| | - Ipek Guler
- Instituto Maimónides de Investigación Biomédica de Córdoba and Hospital Universitario Reina Sofia, Córdoba, Spain
| | - Arfa Mehmood
- Research Centre for Integrative Physiology and Pharmacology, Institute of Biomedicine, University of Turku, Turku, Finland.,Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Laura L Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Claes Ohlsson
- Centre for Bone and Arthritis Research, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Matti Poutanen
- Research Centre for Integrative Physiology and Pharmacology, Institute of Biomedicine, University of Turku, Turku, Finland.,Turku Center for Disease Modeling, Turku, Finland.,Centre for Bone and Arthritis Research, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Manuel Tena-Sempere
- Research Centre for Integrative Physiology and Pharmacology, Institute of Biomedicine, University of Turku, Turku, Finland.,Turku Center for Disease Modeling, Turku, Finland.,Department of Cell Biology, Physiology and Immunology, University of Córdoba, Córdoba, Spain.,Instituto Maimónides de Investigación Biomédica de Córdoba and Hospital Universitario Reina Sofia, Córdoba, Spain.,CIBER Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Córdoba, Spain
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32
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Chakroborty D, Ojala VK, Knittle AM, Drexler J, Tamirat MZ, Ruzicka R, Bosch K, Woertl J, Schmittner S, Elo LL, Johnson MS, Kurppa KJ, Solca F, Elenius K. An Unbiased Functional Genetics Screen Identifies Rare Activating ERBB4 Mutations. Cancer Res Commun 2022; 2:10-27. [PMID: 36860695 PMCID: PMC9973412 DOI: 10.1158/2767-9764.crc-21-0021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 10/04/2021] [Accepted: 12/21/2021] [Indexed: 06/18/2023]
Abstract
UNLABELLED Despite the relatively high frequency of somatic ERBB4 mutations in various cancer types, only a few activating ERBB4 mutations have been characterized, primarily due to lack of mutational hotspots in the ERBB4 gene. Here, we utilized our previously published pipeline, an in vitro screen for activating mutations, to perform an unbiased functional screen to identify potential activating ERBB4 mutations from a randomly mutated ERBB4 expression library. Ten potentially activating ERBB4 mutations were identified and subjected to validation by functional and structural analyses. Two of the 10 ERBB4 mutants, E715K and R687K, demonstrated hyperactivity in all tested cell models and promoted cellular growth under two-dimensional and three-dimensional culture conditions. ERBB4 E715K also promoted tumor growth in in vivo Ba/F3 cell mouse allografts. Importantly, all tested ERBB4 mutants were sensitive to the pan-ERBB tyrosine kinase inhibitors afatinib, neratinib, and dacomitinib. Our data indicate that rare ERBB4 mutations are potential candidates for ERBB4-targeted therapy with pan-ERBB inhibitors. STATEMENT OF SIGNIFICANCE ERBB4 is a member of the ERBB family of oncogenes that is frequently mutated in different cancer types but the functional impact of its somatic mutations remains unknown. Here, we have analyzed the function of over 8,000 randomly mutated ERBB4 variants in an unbiased functional genetics screen. The data indicate the presence of rare activating ERBB4 mutations in cancer, with potential to be targeted with clinically approved pan-ERBB inhibitors.
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Affiliation(s)
- Deepankar Chakroborty
- Institute of Biomedicine, University of Turku, Turku, Finland
- Medicity Research Laboratories, University of Turku, Turku, Finland
- Turku Doctoral Programme of Molecular Medicine, Turku, Finland
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Veera K. Ojala
- Institute of Biomedicine, University of Turku, Turku, Finland
- Medicity Research Laboratories, University of Turku, Turku, Finland
- Turku Doctoral Programme of Molecular Medicine, Turku, Finland
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Anna M. Knittle
- Institute of Biomedicine, University of Turku, Turku, Finland
| | | | - Mahlet Z. Tamirat
- Structural Bioinformatics Laboratory, Biochemistry, Faculty of Science and Engineering, Åbo Akademi University, Turku, Finland
- InFLAMES Research Flagship Center, Åbo Akademi University, Turku, Finland
- Graduate School of Åbo Akademi University (Informational and Structural Biology Doctoral Network), Turku, Finland
| | | | - Karin Bosch
- Boehringer Ingelheim RCV GmbH & Co KG, Vienna, Austria
| | | | | | - Laura L. Elo
- Institute of Biomedicine, University of Turku, Turku, Finland
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Mark S. Johnson
- Structural Bioinformatics Laboratory, Biochemistry, Faculty of Science and Engineering, Åbo Akademi University, Turku, Finland
- InFLAMES Research Flagship Center, Åbo Akademi University, Turku, Finland
| | - Kari J. Kurppa
- Institute of Biomedicine, University of Turku, Turku, Finland
- Medicity Research Laboratories, University of Turku, Turku, Finland
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Flavio Solca
- Boehringer Ingelheim RCV GmbH & Co KG, Vienna, Austria
| | - Klaus Elenius
- Institute of Biomedicine, University of Turku, Turku, Finland
- Medicity Research Laboratories, University of Turku, Turku, Finland
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- Department of Oncology, Turku University Hospital, Turku, Finland
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Välikangas T, Lietzén N, Jaakkola MK, Krogvold L, Eike MC, Kallionpää H, Tuomela S, Mathews C, Gerling IC, Oikarinen S, Hyöty H, Dahl-Jorgensen K, Elo LL, Lahesmaa R. Pancreas Whole Tissue Transcriptomics Highlights the Role of the Exocrine Pancreas in Patients With Recently Diagnosed Type 1 Diabetes. Front Endocrinol (Lausanne) 2022; 13:861985. [PMID: 35498413 PMCID: PMC9044038 DOI: 10.3389/fendo.2022.861985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Accepted: 03/09/2022] [Indexed: 11/16/2022] Open
Abstract
Although type 1 diabetes (T1D) is primarily a disease of the pancreatic beta-cells, understanding of the disease-associated alterations in the whole pancreas could be important for the improved treatment or the prevention of the disease. We have characterized the whole-pancreas gene expression of patients with recently diagnosed T1D from the Diabetes Virus Detection (DiViD) study and non-diabetic controls. Furthermore, another parallel dataset of the whole pancreas and an additional dataset from the laser-captured pancreatic islets of the DiViD patients and non-diabetic organ donors were analyzed together with the original dataset to confirm the results and to get further insights into the potential disease-associated differences between the exocrine and the endocrine pancreas. First, higher expression of the core acinar cell genes, encoding for digestive enzymes, was detected in the whole pancreas of the DiViD patients when compared to non-diabetic controls. Second, In the pancreatic islets, upregulation of immune and inflammation related genes was observed in the DiViD patients when compared to non-diabetic controls, in line with earlier publications, while an opposite trend was observed for several immune and inflammation related genes at the whole pancreas tissue level. Third, strong downregulation of the regenerating gene family (REG) genes, linked to pancreatic islet growth and regeneration, was observed in the exocrine acinar cell dominated whole-pancreas data of the DiViD patients when compared with the non-diabetic controls. Fourth, analysis of unique features in the transcriptomes of each DiViD patient compared with the other DiViD patients, revealed elevated expression of central antiviral immune response genes in the whole-pancreas samples, but not in the pancreatic islets, of one DiViD patient. This difference in the extent of antiviral gene expression suggests different statuses of infection in the pancreas at the time of sampling between the DiViD patients, who were all enterovirus VP1+ in the islets by immunohistochemistry based on earlier studies. The observed features, indicating differences in the function, status and interplay between the exocrine and the endocrine pancreas of recent onset T1D patients, highlight the importance of studying both compartments for better understanding of the molecular mechanisms of T1D.
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Affiliation(s)
- Tommi Välikangas
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - Niina Lietzén
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Maria K. Jaakkola
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- InFLAMES Research Flagship Center, University of Turku, Turku, Finland
- Department of Mathematics and Statistics, University of Turku, Turku, Finland
| | - Lars Krogvold
- Pediatric Department, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Dentistry, Faculty of Dentistry, University of Oslo, Oslo, Norway
| | - Morten C. Eike
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
| | - Henna Kallionpää
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Soile Tuomela
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Clayton Mathews
- Department of Pathology, University of Florida, Gainesville, FL, United States
| | - Ivan C. Gerling
- Department of Medicine, University of Tennessee Health Science Center, Memphis, TN, United States
| | - Sami Oikarinen
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Heikki Hyöty
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Fimlab Laboratories, Pirkanmaa Hospital District, Tampere, Finland
| | - Knut Dahl-Jorgensen
- Pediatric Department, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Laura L. Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- InFLAMES Research Flagship Center, University of Turku, Turku, Finland
- Institute of Biomedicine, University of Turku, Turku, Finland
- *Correspondence: Riitta Lahesmaa, ; Laura L. Elo,
| | - Riitta Lahesmaa
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- InFLAMES Research Flagship Center, University of Turku, Turku, Finland
- Institute of Biomedicine, University of Turku, Turku, Finland
- *Correspondence: Riitta Lahesmaa, ; Laura L. Elo,
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Smolander J, Junttila S, Venäläinen MS, Elo LL. scShaper: an ensemble method for fast and accurate linear trajectory inference from single-cell RNA-seq data. Bioinformatics 2021; 38:1328-1335. [PMID: 34888622 PMCID: PMC8825760 DOI: 10.1093/bioinformatics/btab831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 11/30/2021] [Accepted: 12/03/2021] [Indexed: 01/05/2023] Open
Abstract
MOTIVATION Computational models are needed to infer a representation of the cells, i.e. a trajectory, from single-cell RNA-sequencing data that model cell differentiation during a dynamic process. Although many trajectory inference methods exist, their performance varies greatly depending on the dataset and hence there is a need to establish more accurate, better generalizable methods. RESULTS We introduce scShaper, a new trajectory inference method that enables accurate linear trajectory inference. The ensemble approach of scShaper generates a continuous smooth pseudotime based on a set of discrete pseudotimes. We demonstrate that scShaper is able to infer accurate trajectories for a variety of trigonometric trajectories, including many for which the commonly used principal curves method fails. A comprehensive benchmarking with state-of-the-art methods revealed that scShaper achieved superior accuracy of the cell ordering and, in particular, the differentially expressed genes. Moreover, scShaper is a fast method with few hyperparameters, making it a promising alternative to the principal curves method for linear pseudotemporal ordering. AVAILABILITY AND IMPLEMENTATION scShaper is available as an R package at https://github.com/elolab/scshaper. The test data are available at https://doi.org/10.5281/zenodo.5734488. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Johannes Smolander
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Tykistökatu 6, 20520 Turku, Finland
| | - Sini Junttila
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Tykistökatu 6, 20520 Turku, Finland
| | - Mikko S Venäläinen
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Tykistökatu 6, 20520 Turku, Finland
| | - Laura L Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Tykistökatu 6, 20520 Turku, Finland,Institute of Biomedicine, University of Turku, 20520 Turku, Finland,To whom correspondence should be addressed.
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35
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Hong Y, Flinkman D, Suomi T, Pietilä S, James P, Coffey E, Elo LL. PhosPiR: an automated phosphoproteomic pipeline in R. Brief Bioinform 2021; 23:6456296. [PMID: 34882763 PMCID: PMC8787428 DOI: 10.1093/bib/bbab510] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 10/25/2021] [Accepted: 11/04/2021] [Indexed: 01/01/2023] Open
Abstract
Large-scale phosphoproteome profiling using mass spectrometry (MS) provides functional insight that is crucial for disease biology and drug discovery. However, extracting biological understanding from these data is an arduous task requiring multiple analysis platforms that are not adapted for automated high-dimensional data analysis. Here, we introduce an integrated pipeline that combines several R packages to extract high-level biological understanding from large-scale phosphoproteomic data by seamless integration with existing databases and knowledge resources. In a single run, PhosPiR provides data clean-up, fast data overview, multiple statistical testing, differential expression analysis, phosphosite annotation and translation across species, multilevel enrichment analyses, proteome-wide kinase activity and substrate mapping and network hub analysis. Data output includes graphical formats such as heatmap, box-, volcano- and circos-plots. This resource is designed to assist proteome-wide data mining of pathophysiological mechanism without a need for programming knowledge.
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Affiliation(s)
- Ye Hong
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Dani Flinkman
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.,Lund University, Lund, Sweden
| | - Tomi Suomi
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Sami Pietilä
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Peter James
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.,Lund University, Lund, Sweden
| | - Eleanor Coffey
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Laura L Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.,Institute of Biomedicine, University of Turku, Turku, Finland
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36
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Venäläinen MS, Heervä E, Hirvonen O, Saraei S, Suomi T, Mikkola T, Bärlund M, Jyrkkiö S, Laitinen T, Elo LL. Improved risk prediction of chemotherapy-induced neutropenia-model development and validation with real-world data. Cancer Med 2021; 11:654-663. [PMID: 34859963 PMCID: PMC8817096 DOI: 10.1002/cam4.4465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Revised: 11/07/2021] [Accepted: 11/16/2021] [Indexed: 11/08/2022] Open
Abstract
BACKGROUND The existing risk prediction models for chemotherapy-induced febrile neutropenia (FN) do not necessarily apply to real-life patients in different healthcare systems and the external validation of these models are often lacking. Our study evaluates whether a machine learning-based risk prediction model could outperform the previously introduced models, especially when validated against real-world patient data from another institution not used for model training. METHODS Using Turku University Hospital electronic medical records, we identified all patients who received chemotherapy for non-hematological cancer between the years 2010 and 2017 (N = 5879). An experimental surrogate endpoint was first-cycle neutropenic infection (NI), defined as grade IV neutropenia with serum C-reactive protein >10 mg/l. For predicting the risk of NI, a penalized regression model (Lasso) was developed. The model was externally validated in an independent dataset (N = 4594) from Tampere University Hospital. RESULTS Lasso model accurately predicted NI risk with good accuracy (AUROC 0.84). In the validation cohort, the Lasso model outperformed two previously introduced, widely approved models, with AUROC 0.75. The variables selected by Lasso included granulocyte colony-stimulating factor (G-CSF) use, cancer type, pre-treatment neutrophil and thrombocyte count, intravenous treatment regimen, and the planned dose intensity. The same model predicted also FN, with AUROC 0.77, supporting the validity of NI as an endpoint. CONCLUSIONS Our study demonstrates that real-world NI risk prediction can be improved with machine learning and that every difference in patient or treatment characteristics can have a significant impact on model performance. Here we outline a novel, externally validated approach which may hold potential to facilitate more targeted use of G-CSFs in the future.
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Affiliation(s)
- Mikko S Venäläinen
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Eetu Heervä
- Department of Oncology, Turku University Hospital and FICAN West, Turku, Finland.,University of Turku, Turku, Finland
| | - Outi Hirvonen
- Department of Oncology, Turku University Hospital and FICAN West, Turku, Finland.,Department of Clinical Oncology, University of Turku, Turku, Finland.,Palliative Center, Turku University Hospital, Turku, Finland
| | - Sohrab Saraei
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Tomi Suomi
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Toni Mikkola
- Tays Research Services, Clinical Informatics Team, Tampere University Hospital and University of Tampere, Tampere, Finland
| | - Maarit Bärlund
- Department of Oncology, Tays Cancer Centre, Tampere University Hospital, Tampere, Finland.,Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Sirkku Jyrkkiö
- Department of Oncology, Turku University Hospital and FICAN West, Turku, Finland
| | - Tarja Laitinen
- Department of Pulmonary Medicine, University of Turku and Turku University Hospital, Turku, Finland.,Administration Center, Tampere University Hospital and University of Tampere, Tampere, Finland
| | - Laura L Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.,Institute of Biomedicine, University of Turku, Turku, Finland
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37
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Panula VJ, Alakylä KJ, Venäläinen MS, Haapakoski JJ, Eskelinen AP, Manninen MJ, Kettunen JS, Puhto AP, Vasara AI, Elo LL, Mäkelä KT. Risk factors for prosthetic joint infections following total hip arthroplasty based on 33,337 hips in the Finnish Arthroplasty Register from 2014 to 2018. Acta Orthop 2021; 92:665-672. [PMID: 34196592 PMCID: PMC8635657 DOI: 10.1080/17453674.2021.1944529] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/08/2023] Open
Abstract
Background and purpose - Periprosthetic joint infection (PJI) is a devastating complication and more information on risk factors for PJI is required to find measures to prevent infections. Therefore, we assessed risk factors for PJI after primary total hip arthroplasty (THA) in a large patient cohort.Patients and methods - We analyzed 33,337 primary THAs performed between May 2014 and January 2018 based on the Finnish Arthroplasty Register (FAR). Cox proportional hazards regression was used to estimate hazard ratios with 95% confidence intervals (CI) for first PJI revision operation using 25 potential patient- and surgical-related risk factors as covariates.Results - 350 primary THAs were revised for the first time due to PJI during the study period. The hazard ratios for PJI revision in multivariable analysis were 2.0 (CI 1.3-3.2) for ASA class II and 3.2 (2.0-5.1) for ASA class III-IV compared with ASA class I, 1.4 (1.1-1.7) for bleeding > 500 mL compared with < 500 mL, 0.4 (0.2-0.7) for ceramic-on-ceramic bearing couple compared with metal-on-polyethylene and for the first 3 postoperative weeks, 3.0 (1.6-5.6) for operation time of > 120 minutes compared with 45-59 minutes, and 2.6 (1.4-4.9) for simultaneous bilateral operation. In the univariable analysis, hazard ratios for PJI revision were 2.3 (1.7-3.3) for BMI of 31-35 and 5.0 (3.5-7.1) for BMI of > 35 compared with patients with BMI of 21-25.Interpretation - We found several modifiable risk factors associated with increased PJI revision risk after THA to which special attention should be paid preoperatively. In particular, high BMI may be an even more prominent risk factor for PJI than previously assessed.
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Affiliation(s)
- Valtteri J Panula
- Department of Orthopaedics and Traumatology, Turku University Hospital, and University of Turku, Turku
| | - Kasperi J Alakylä
- Department of Orthopaedics and Traumatology, Turku University Hospital, and University of Turku, Turku;,CONTACT Kasperi J ALAKYLÄ
| | - Mikko S Venäläinen
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku
| | | | | | | | - Jukka S Kettunen
- Department of Orthopaedics and Traumatology, Kuopio University Hospital, Kuopio
| | - Ari-Pekka Puhto
- Division of Operative Care, Department of Orthopaedic and Trauma Surgery, Oulu University Hospital, Oulu
| | | | - Laura L Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku
| | - Keijo T Mäkelä
- Department of Orthopaedics and Traumatology, Turku University Hospital, and University of Turku, Turku
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38
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Munne PM, Martikainen L, Räty I, Bertula K, Nonappa, Ruuska J, Ala-Hongisto H, Peura A, Hollmann B, Euro L, Yavuz K, Patrikainen L, Salmela M, Pokki J, Kivento M, Väänänen J, Suomi T, Nevalaita L, Mutka M, Kovanen P, Leidenius M, Meretoja T, Hukkinen K, Monni O, Pouwels J, Sahu B, Mattson J, Joensuu H, Heikkilä P, Elo LL, Metcalfe C, Junttila MR, Ikkala O, Klefström J. Compressive stress-mediated p38 activation required for ERα + phenotype in breast cancer. Nat Commun 2021; 12:6967. [PMID: 34845227 PMCID: PMC8630031 DOI: 10.1038/s41467-021-27220-9] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Accepted: 11/04/2021] [Indexed: 01/01/2023] Open
Abstract
Breast cancer is now globally the most frequent cancer and leading cause of women's death. Two thirds of breast cancers express the luminal estrogen receptor-positive (ERα + ) phenotype that is initially responsive to antihormonal therapies, but drug resistance emerges. A major barrier to the understanding of the ERα-pathway biology and therapeutic discoveries is the restricted repertoire of luminal ERα + breast cancer models. The ERα + phenotype is not stable in cultured cells for reasons not fully understood. We examine 400 patient-derived breast epithelial and breast cancer explant cultures (PDECs) grown in various three-dimensional matrix scaffolds, finding that ERα is primarily regulated by the matrix stiffness. Matrix stiffness upregulates the ERα signaling via stress-mediated p38 activation and H3K27me3-mediated epigenetic regulation. The finding that the matrix stiffness is a central cue to the ERα phenotype reveals a mechanobiological component in breast tissue hormonal signaling and enables the development of novel therapeutic interventions. Subject terms: ER-positive (ER + ), breast cancer, ex vivo model, preclinical model, PDEC, stiffness, p38 SAPK.
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Affiliation(s)
- Pauliina M Munne
- Finnish Cancer Institute, FICAN South Helsinki University Hospital & Translational Cancer Medicine, Medical Faculty, University of Helsinki. Cancer Cell Circuitry Laboratory, PO Box 63 Haartmaninkatu 8, 00014 University of Helsinki, Helsinki, Finland
| | - Lahja Martikainen
- Department of Applied Physics, Molecular Materials Group, Aalto University School of Science, PO Box, 15100, FI-00076, Espoo, Finland
| | - Iiris Räty
- Finnish Cancer Institute, FICAN South Helsinki University Hospital & Translational Cancer Medicine, Medical Faculty, University of Helsinki. Cancer Cell Circuitry Laboratory, PO Box 63 Haartmaninkatu 8, 00014 University of Helsinki, Helsinki, Finland
| | - Kia Bertula
- Department of Applied Physics, Molecular Materials Group, Aalto University School of Science, PO Box, 15100, FI-00076, Espoo, Finland
| | - Nonappa
- Department of Applied Physics, Molecular Materials Group, Aalto University School of Science, PO Box, 15100, FI-00076, Espoo, Finland
- Department of Bioproducts and Biosystems, Aalto University School of Chemical Engineering, Espoo, Finland
| | - Janika Ruuska
- Finnish Cancer Institute, FICAN South Helsinki University Hospital & Translational Cancer Medicine, Medical Faculty, University of Helsinki. Cancer Cell Circuitry Laboratory, PO Box 63 Haartmaninkatu 8, 00014 University of Helsinki, Helsinki, Finland
| | - Hanna Ala-Hongisto
- Finnish Cancer Institute, FICAN South Helsinki University Hospital & Translational Cancer Medicine, Medical Faculty, University of Helsinki. Cancer Cell Circuitry Laboratory, PO Box 63 Haartmaninkatu 8, 00014 University of Helsinki, Helsinki, Finland
| | - Aino Peura
- Finnish Cancer Institute, FICAN South Helsinki University Hospital & Translational Cancer Medicine, Medical Faculty, University of Helsinki. Cancer Cell Circuitry Laboratory, PO Box 63 Haartmaninkatu 8, 00014 University of Helsinki, Helsinki, Finland
| | - Babette Hollmann
- Finnish Cancer Institute, FICAN South Helsinki University Hospital & Translational Cancer Medicine, Medical Faculty, University of Helsinki. Cancer Cell Circuitry Laboratory, PO Box 63 Haartmaninkatu 8, 00014 University of Helsinki, Helsinki, Finland
| | - Lilya Euro
- Research Program of Stem Cells and Metabolism, Biomedicum Helsinki, University of Helsinki, 00290, Helsinki, Finland
| | - Kerim Yavuz
- Applied Tumor Genomics Research Program, Enhancer Biology Laboratory, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Linda Patrikainen
- Finnish Cancer Institute, FICAN South Helsinki University Hospital & Translational Cancer Medicine, Medical Faculty, University of Helsinki. Cancer Cell Circuitry Laboratory, PO Box 63 Haartmaninkatu 8, 00014 University of Helsinki, Helsinki, Finland
| | - Maria Salmela
- Finnish Cancer Institute, FICAN South Helsinki University Hospital & Translational Cancer Medicine, Medical Faculty, University of Helsinki. Cancer Cell Circuitry Laboratory, PO Box 63 Haartmaninkatu 8, 00014 University of Helsinki, Helsinki, Finland
| | - Juho Pokki
- Department of Electrical Engineering and Automation, Aalto University, Espoo, Finland
| | - Mikko Kivento
- Applied Tumor Genomics Research Program, Faculty of Medicine, Oncogenomics Laboratory, University of Helsinki, Helsinki, Finland
| | - Juho Väänänen
- Applied Tumor Genomics Research Program, Faculty of Medicine, Oncogenomics Laboratory, University of Helsinki, Helsinki, Finland
| | - Tomi Suomi
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520, Turku, Finland
| | - Liina Nevalaita
- Finnish Cancer Institute, FICAN South Helsinki University Hospital & Translational Cancer Medicine, Medical Faculty, University of Helsinki. Cancer Cell Circuitry Laboratory, PO Box 63 Haartmaninkatu 8, 00014 University of Helsinki, Helsinki, Finland
| | - Minna Mutka
- Department of Pathology, HUSLAB and Haartman Institute, Helsinki University Central Hospital and University of Helsinki, Helsinki, Finland
| | - Panu Kovanen
- Department of Pathology, HUSLAB and Haartman Institute, Helsinki University Central Hospital and University of Helsinki, Helsinki, Finland
| | - Marjut Leidenius
- Breast Surgery Unit, Helsinki University Central Hospital, Helsinki, Finland
| | - Tuomo Meretoja
- Breast Surgery Unit, Helsinki University Central Hospital, Helsinki, Finland
| | - Katja Hukkinen
- Department of Mammography, Helsinki University Central Hospital, Helsinki, Finland
| | - Outi Monni
- Applied Tumor Genomics Research Program, Faculty of Medicine, Oncogenomics Laboratory, University of Helsinki, Helsinki, Finland
| | - Jeroen Pouwels
- Finnish Cancer Institute, FICAN South Helsinki University Hospital & Translational Cancer Medicine, Medical Faculty, University of Helsinki. Cancer Cell Circuitry Laboratory, PO Box 63 Haartmaninkatu 8, 00014 University of Helsinki, Helsinki, Finland
| | - Biswajyoti Sahu
- Applied Tumor Genomics Research Program, Enhancer Biology Laboratory, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Johanna Mattson
- Department of Oncology, University of Helsinki & Helsinki University Hospital, Helsinki, Finland
| | - Heikki Joensuu
- Department of Oncology, University of Helsinki & Helsinki University Hospital, Helsinki, Finland
| | - Päivi Heikkilä
- Department of Pathology, HUSLAB and Haartman Institute, Helsinki University Central Hospital and University of Helsinki, Helsinki, Finland
| | - Laura L Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520, Turku, Finland
| | - Ciara Metcalfe
- Genentech Inc., 1 DNA Way, South San Francisco, CA, 94080, USA
| | | | - Olli Ikkala
- Department of Applied Physics, Molecular Materials Group, Aalto University School of Science, PO Box, 15100, FI-00076, Espoo, Finland
- Department of Bioproducts and Biosystems, Aalto University School of Chemical Engineering, Espoo, Finland
| | - Juha Klefström
- Finnish Cancer Institute, FICAN South Helsinki University Hospital & Translational Cancer Medicine, Medical Faculty, University of Helsinki. Cancer Cell Circuitry Laboratory, PO Box 63 Haartmaninkatu 8, 00014 University of Helsinki, Helsinki, Finland.
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39
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Jaakkola MK, Elo LL. Estimating cell type-specific differential expression using deconvolution. Brief Bioinform 2021; 23:6396788. [PMID: 34651640 PMCID: PMC8769698 DOI: 10.1093/bib/bbab433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 09/17/2021] [Accepted: 09/23/2021] [Indexed: 12/02/2022] Open
Affiliation(s)
- Maria K Jaakkola
- Department of Mathematics and Statistics, University of Turku, Yliopistonmäki, 20014, Turku, Finland
| | - Laura L Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Tykistökatu 6, FI-20520, Turku, Finland.,Institute of Biomedicine, University of Turku, Kiinamyllynkatu 10, FI-20520, Turku, Finland
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40
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Faux T, Rytkönen KT, Mahmoudian M, Paulin N, Junttila S, Laiho A, Elo LL. Differential ATAC-seq and ChIP-seq peak detection using ROTS. NAR Genom Bioinform 2021; 3:lqab059. [PMID: 34235431 PMCID: PMC8253552 DOI: 10.1093/nargab/lqab059] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Revised: 05/12/2021] [Accepted: 06/11/2021] [Indexed: 12/30/2022] Open
Abstract
Changes in cellular chromatin states fine-tune transcriptional output and ultimately lead to phenotypic changes. Here we propose a novel application of our reproducibility-optimized test statistics (ROTS) to detect differential chromatin states (ATAC-seq) or differential chromatin modification states (ChIP-seq) between conditions. We compare the performance of ROTS to existing and widely used methods for ATAC-seq and ChIP-seq data using both synthetic and real datasets. Our results show that ROTS outperformed other commonly used methods when analyzing ATAC-seq data. ROTS also displayed the most accurate detection of small differences when modeling with synthetic data. We observed that two-step methods that require the use of a separate peak caller often more accurately called enrichment borders, whereas one-step methods without a separate peak calling step were more versatile in calling sub-peaks. The top ranked differential regions detected by the methods had marked correlation with transcriptional differences of the closest genes. Overall, our study provides evidence that ROTS is a useful addition to the available differential peak detection methods to study chromatin and performs especially well when applied to study differential chromatin states in ATAC-seq data.
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Affiliation(s)
- Thomas Faux
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Tykistökatu 6, 20520, Turku, Finland
| | - Kalle T Rytkönen
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Tykistökatu 6, 20520, Turku, Finland
- Institute of Biomedicine, University of Turku, Kiinamyllynkatu 10, 20014, Finland
| | - Mehrad Mahmoudian
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Tykistökatu 6, 20520, Turku, Finland
- Department of Future Technologies, University of Turku, FI-20014 Turku, Finland
| | - Niklas Paulin
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Tykistökatu 6, 20520, Turku, Finland
| | - Sini Junttila
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Tykistökatu 6, 20520, Turku, Finland
| | - Asta Laiho
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Tykistökatu 6, 20520, Turku, Finland
| | - Laura L Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Tykistökatu 6, 20520, Turku, Finland
- Institute of Biomedicine, University of Turku, Kiinamyllynkatu 10, 20014, Finland
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41
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Mahmoudian M, Venäläinen MS, Klén R, Elo LL. Stable Iterative Variable Selection. Bioinformatics 2021; 37:4810-4817. [PMID: 34270690 PMCID: PMC8665768 DOI: 10.1093/bioinformatics/btab501] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Revised: 05/20/2021] [Accepted: 07/14/2021] [Indexed: 11/13/2022] Open
Abstract
Motivation The emergence of datasets with tens of thousands of features, such as high-throughput omics biomedical data, highlights the importance of reducing the feature space into a distilled subset that can truly capture the signal for research and industry by aiding in finding more effective biomarkers for the question in hand. A good feature set also facilitates building robust predictive models with improved interpretability and convergence of the applied method due to the smaller feature space. Results Here, we present a robust feature selection method named Stable Iterative Variable Selection (SIVS) and assess its performance over both omics and clinical data types. As a performance assessment metric, we compared the number and goodness of the selected feature using SIVS to those selected by Least Absolute Shrinkage and Selection Operator regression. The results suggested that the feature space selected by SIVS was, on average, 41% smaller, without having a negative effect on the model performance. A similar result was observed for comparison with Boruta and caret RFE. Availability and implementation The method is implemented as an R package under GNU General Public License v3.0 and is accessible via Comprehensive R Archive Network (CRAN) via https://cran.r-project.org/package=sivs. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Mehrad Mahmoudian
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.,Department of Future Technologies, University of Turku, Turku, Finland
| | - Mikko S Venäläinen
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Riku Klén
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Laura L Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.,Institute of Biomedicine, University of Turku, Turku, Finland
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42
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Rytkönen KT, Heinosalo T, Mahmoudian M, Ma X, Perheentupa A, Elo LL, Poutanen M, Wagner GP. Transcriptomic responses to hypoxia in endometrial and decidual stromal cells. Reproduction 2021; 160:39-51. [PMID: 32272449 DOI: 10.1530/rep-19-0615] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Accepted: 04/09/2020] [Indexed: 11/08/2022]
Abstract
Human reproductive success depends on a properly decidualized uterine endometrium that allows implantation and the formation of the placenta. At the core of the decidualization process are endometrial stromal fibroblasts (ESF) that differentiate to decidual stromal cells (DSC). As variations in oxygen levels are functionally relevant in endometrium both upon menstruation and during placentation, we assessed the transcriptomic responses to hypoxia in ESF and DSC. In both cell types, hypoxia-upregulated genes in classical hypoxia pathways such as glycolysis and the epithelial mesenchymal transition. In DSC, hypoxia restored an ESF-like transcriptional state for a subset of transcription factors that are known targets of the progesterone receptor, suggesting that hypoxia partially interferes with progesterone signaling. In both cell types, hypoxia modified transcription of several inflammatory transcription factors that are known regulators of decidualization, including decreased transcription of STATs and increased transcription of CEBPs. We observed that hypoxia-upregulated genes in ESF and DSC had a significant overlap with genes previously detected to be upregulated in endometriotic stromal cells. Promoter analysis of the genes in this overlap suggested the hypoxia-upregulated Jun/Fos and CEBP transcription factors as potential drivers of endometriosis-associated transcription. Using immunohistochemistry, we observed increased expression of JUND and CEBPD in endometriosis lesions compared to healthy endometria. Overall, the findings suggest that hypoxic stress establishes distinct transcriptional states in ESF and DSC and that hypoxia influences the expression of genes that contribute to the core gene regulation of endometriotic stromal cells.
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Affiliation(s)
- Kalle T Rytkönen
- Institute of Biomedicine, Research Centre for Integrative Physiology and Pharmacology, University of Turku, Turku, Finland.,Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.,Yale Systems Biology Institute, West Haven, Connecticut, USA.,Department of Ecology and Evolutionary Biology, Yale University, New Haven, Connecticut, USA
| | - Taija Heinosalo
- Institute of Biomedicine, Research Centre for Integrative Physiology and Pharmacology, University of Turku, Turku, Finland
| | - Mehrad Mahmoudian
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.,Department of Future Technologies, University of Turku, Turku, Finland
| | - Xinghong Ma
- Yale Systems Biology Institute, West Haven, Connecticut, USA.,Department of Ecology and Evolutionary Biology, Yale University, New Haven, Connecticut, USA.,College of Life Sciences, Northeast Agricultural University, Harbin, China
| | - Antti Perheentupa
- Institute of Biomedicine, Research Centre for Integrative Physiology and Pharmacology, University of Turku, Turku, Finland.,Department of Obstetrics and Gynecology, Turku University Hospital, Turku, Finland
| | - Laura L Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Matti Poutanen
- Institute of Biomedicine, Research Centre for Integrative Physiology and Pharmacology, University of Turku, Turku, Finland
| | - Günter P Wagner
- Yale Systems Biology Institute, West Haven, Connecticut, USA.,Department of Ecology and Evolutionary Biology, Yale University, New Haven, Connecticut, USA.,Department of Obstetrics, Gynecology and Reproductive Sciences, Yale Medical School, New Haven, Connecticut, USA.,Department of Obstetrics and Gynecology, Wayne State University, Detroit, Michigan, USA
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43
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Laine A, Nagelli SG, Farrington C, Butt U, Cvrljevic AN, Vainonen JP, Feringa FM, Grönroos TJ, Gautam P, Khan S, Sihto H, Qiao X, Pavic K, Connolly DC, Kronqvist P, Elo LL, Maurer J, Wennerberg K, Medema RH, Joensuu H, Peuhu E, de Visser K, Narla G, Westermarck J. CIP2A Interacts with TopBP1 and Drives Basal-Like Breast Cancer Tumorigenesis. Cancer Res 2021; 81:4319-4331. [PMID: 34145035 DOI: 10.1158/0008-5472.can-20-3651] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Revised: 03/02/2021] [Accepted: 06/16/2021] [Indexed: 12/14/2022]
Abstract
Basal-like breast cancers (BLBC) are characterized by defects in homologous recombination (HR), deficient mitotic checkpoint, and high-proliferation activity. Here, we discover CIP2A as a candidate driver of BLBC. CIP2A was essential for DNA damage-induced initiation of mouse BLBC-like mammary tumors and for survival of HR-defective BLBC cells. CIP2A was dispensable for normal mammary gland development and for unperturbed mitosis, but selectively essential for mitotic progression of DNA damaged cells. A direct interaction between CIP2A and a DNA repair scaffold protein TopBP1 was identified, and CIP2A inhibition resulted in enhanced DNA damage-induced TopBP1 and RAD51 recruitment to chromatin in mammary epithelial cells. In addition to its role in tumor initiation, and survival of BRCA-deficient cells, CIP2A also drove proliferative MYC and E2F1 signaling in basal-like triple-negative breast cancer (BL-TNBC) cells. Clinically, high CIP2A expression was associated with poor patient prognosis in BL-TNBCs but not in other breast cancer subtypes. Small-molecule reactivators of PP2A (SMAP) inhibited CIP2A transcription, phenocopied the CIP2A-deficient DNA damage response (DDR), and inhibited growth of patient-derived BLBC xenograft. In summary, these results demonstrate that CIP2A directly interacts with TopBP1 and coordinates DNA damage-induced mitotic checkpoint and proliferation, thereby driving BLBC initiation and progression. SMAPs could serve as a surrogate therapeutic strategy to inhibit the oncogenic activity of CIP2A in BLBCs. SIGNIFICANCE: These results identify CIP2A as a nongenetic driver and therapeutic target in basal-like breast cancer that regulates DNA damage-induced G2-M checkpoint and proliferative signaling.
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Affiliation(s)
- Anni Laine
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.,Division of Tumor Biology and Immunology, Oncode Institute, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Srikar G Nagelli
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.,Institute of Biomedicine, University of Turku, Turku, Finland
| | - Caroline Farrington
- Division of Genetic Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan.,Rogel Cancer Center, University of Michigan, Ann Arbor, Michigan
| | - Umar Butt
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.,Institute of Biomedicine, University of Turku, Turku, Finland
| | - Anna N Cvrljevic
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Julia P Vainonen
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Femke M Feringa
- Division of Cell Biology, Oncode Institute, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Tove J Grönroos
- Turku PET Center, University of Turku, Turku, Finland.,Department of Oncology and Radiotherapy, Turku University Hospital, Turku, Finland
| | - Prson Gautam
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Sofia Khan
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Harri Sihto
- Department of Pathology, University of Helsinki, Helsinki University Hospital, Helsinki, Finland
| | - Xi Qiao
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Karolina Pavic
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Denise C Connolly
- Molecular Therapeutics Program, Fox Chase Cancer Center, Philadelphia, Pennsylvania
| | | | - Laura L Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.,Institute of Biomedicine, University of Turku, Turku, Finland
| | - Jochen Maurer
- Department of Obstetrics and Gynecology, University Hospital Aachen (UKA), Aachen, Germany
| | - Krister Wennerberg
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Rene H Medema
- Division of Cell Biology, Oncode Institute, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Heikki Joensuu
- Department of Pathology, University of Helsinki, Helsinki University Hospital, Helsinki, Finland
| | - Emilia Peuhu
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.,Institute of Biomedicine, University of Turku, Turku, Finland
| | - Karin de Visser
- Division of Tumor Biology and Immunology, Oncode Institute, The Netherlands Cancer Institute, Amsterdam, The Netherlands.,Department of Immunohematology and Blood Transfusion, Leiden University Medical Center, Leiden, The Netherlands
| | - Goutham Narla
- Division of Genetic Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan.,Rogel Cancer Center, University of Michigan, Ann Arbor, Michigan
| | - Jukka Westermarck
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland. .,Institute of Biomedicine, University of Turku, Turku, Finland
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44
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Smolander J, Junttila S, Venäläinen MS, Elo LL. ILoReg: a tool for high-resolution cell population identification from single-cell RNA-seq data. Bioinformatics 2021; 37:1107-1114. [PMID: 33151294 PMCID: PMC8150131 DOI: 10.1093/bioinformatics/btaa919] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Revised: 10/13/2020] [Accepted: 10/19/2020] [Indexed: 12/13/2022] Open
Abstract
MOTIVATION Single-cell RNA-seq allows researchers to identify cell populations based on unsupervised clustering of the transcriptome. However, subpopulations can have only subtle transcriptomic differences and the high dimensionality of the data makes their identification challenging. RESULTS We introduce ILoReg, an R package implementing a new cell population identification method that improves identification of cell populations with subtle differences through a probabilistic feature extraction step that is applied before clustering and visualization. The feature extraction is performed using a novel machine learning algorithm, called iterative clustering projection (ICP), that uses logistic regression and clustering similarity comparison to iteratively cluster data. Remarkably, ICP also manages to integrate feature selection with the clustering through L1-regularization, enabling the identification of genes that are differentially expressed between cell populations. By combining solutions of multiple ICP runs into a single consensus solution, ILoReg creates a representation that enables investigating cell populations with a high resolution. In particular, we show that the visualization of ILoReg allows segregation of immune and pancreatic cell populations in a more pronounced manner compared with current state-of-the-art methods. AVAILABILITY AND IMPLEMENTATION ILoReg is available as an R package at https://bioconductor.org/packages/ILoReg. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Johannes Smolander
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku 20520, Finland
| | - Sini Junttila
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku 20520, Finland
| | - Mikko S Venäläinen
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku 20520, Finland
| | - Laura L Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku 20520, Finland.,Institute of Biomedicine, University of Turku, Turku, Finland
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45
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Laurila S, Sun L, Lahesmaa M, Schnabl K, Laitinen K, Klén R, Li Y, Balaz M, Wolfrum C, Steiger K, Niemi T, Taittonen M, U-Din M, Välikangas T, Elo LL, Eskola O, Kirjavainen AK, Nummenmaa L, Virtanen KA, Klingenspor M, Nuutila P. Secretin activates brown fat and induces satiation. Nat Metab 2021; 3:798-809. [PMID: 34158656 DOI: 10.1038/s42255-021-00409-4] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Accepted: 05/07/2021] [Indexed: 02/06/2023]
Abstract
Brown adipose tissue (BAT) thermogenesis is activated by feeding. Recently, we revealed a secretin-mediated gut-BAT-brain axis, which stimulates satiation in mice, but the purpose of meal-induced BAT activation in humans has been unclear. In this placebo-controlled, randomized crossover study, we investigated the effects of intravenous secretin on BAT metabolism (measured with [18F]FDG and [15O]H2O positron emission tomography) and appetite (measured with functional magnetic resonance imaging) in healthy, normal weight men (GUTBAT trial no. NCT03290846). Participants were blinded to the intervention. Secretin increased BAT glucose uptake (primary endpoint) compared to placebo by 57% (median (interquartile range, IQR), 0.82 (0.77) versus 0.59 (0.53) μmol per 100 g per min, 95% confidence interval (CI) (0.09, 0.89), P = 0.002, effect size r = 0.570), while BAT perfusion remained unchanged (mean (s.d.) 4.73 (1.82) versus 6.14 (3.05) ml per 100 g per min, 95%CI (-2.91, 0.07), P = 0.063, effect size d = -0.549) (n = 15). Whole body energy expenditure increased by 2% (P = 0.011) (n = 15). Secretin attenuated blood-oxygen level-dependent activity (primary endpoint) in brain reward circuits during food cue tasks (significance level false discovery rate corrected at P = 0.05) (n = 14). Caloric intake did not significantly change, but motivation to refeed after a meal was delayed by 39 min (P = 0.039) (n = 14). No adverse effects were detected. Here we show in humans that secretin activates BAT, reduces central responses to appetizing food and delays the motivation to refeed after a meal. This suggests that meal-induced, secretin-mediated BAT activation is relevant in the control of food intake in humans. As obesity is increasing worldwide, this appetite regulating axis offers new possibilities for clinical research in treating obesity.
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Affiliation(s)
- Sanna Laurila
- Turku PET Centre, University of Turku, Turku, Finland
- Heart Center, Turku University Hospital, Turku, Finland
- Satakunta Central Hospital, Pori, Finland
| | - Lihua Sun
- Turku PET Centre, University of Turku, Turku, Finland
| | - Minna Lahesmaa
- Turku PET Centre, University of Turku, Turku, Finland
- Department of Internal Medicine, Jorvi Hospital, Helsinki University Hospital, Helsinki, Finland
| | - Katharina Schnabl
- Chair for Molecular Nutritional Medicine, Technical University of Munich, TUM School of Life Sciences, Freising, Germany
- EKFZ - Else Kröner Fresenius Center for Nutritional Medicine, Technical University of Munich, Freising, Germany
- ZIEL - Institute for Food & Health, Technical University of Munich, Freising, Germany
| | - Kirsi Laitinen
- Institute of Biomedicine, University of Turku, Turku, Finland
| | - Riku Klén
- Turku PET Centre, University of Turku, Turku, Finland
| | - Yongguo Li
- Chair for Molecular Nutritional Medicine, Technical University of Munich, TUM School of Life Sciences, Freising, Germany
- EKFZ - Else Kröner Fresenius Center for Nutritional Medicine, Technical University of Munich, Freising, Germany
- ZIEL - Institute for Food & Health, Technical University of Munich, Freising, Germany
| | - Miroslav Balaz
- Institute of Food, Nutrition and Health, ETH Zürich, Schwerzenbach, Switzerland
| | - Christian Wolfrum
- Institute of Food, Nutrition and Health, ETH Zürich, Schwerzenbach, Switzerland
| | - Katja Steiger
- Institue of Pathology, School of Medicine, Technical University of Munich, Munich, Germany
| | - Tarja Niemi
- Department of Plastic and General Surgery, Turku University Hospital, Turku, Finland
| | - Markku Taittonen
- Department of Anesthesiology, Turku University Hospital, Turku, Finland
| | - Mueez U-Din
- Turku PET Centre, University of Turku, Turku, Finland
- Turku PET Centre, Turku University Hospital, Turku, Finland
| | | | - Laura L Elo
- Institute of Biomedicine, University of Turku, Turku, Finland
- Turku Bioscience Centre, University of Turku, Turku, Finland
- Turku Bioscience Centre, Åbo Akademi University, Turku, Finland
| | - Olli Eskola
- Turku PET Centre, University of Turku, Turku, Finland
| | | | - Lauri Nummenmaa
- Turku PET Centre, University of Turku, Turku, Finland
- Department of Psychology, University of Turku, Turku, Finland
| | - Kirsi A Virtanen
- Turku PET Centre, Turku University Hospital, Turku, Finland
- Institute of Public Health and Clinical Nutrition - University of Eastern Finland (UEF), Kuopio, Finland
- Department of Endocrinology and Clinical Nutrition, Kuopio University Hospital, Kuopio, Finland
| | - Martin Klingenspor
- Chair for Molecular Nutritional Medicine, Technical University of Munich, TUM School of Life Sciences, Freising, Germany
- EKFZ - Else Kröner Fresenius Center for Nutritional Medicine, Technical University of Munich, Freising, Germany
- ZIEL - Institute for Food & Health, Technical University of Munich, Freising, Germany
| | - Pirjo Nuutila
- Turku PET Centre, University of Turku, Turku, Finland.
- Turku PET Centre, Turku University Hospital, Turku, Finland.
- Department of Endocrinology, Turku University Hospital, Turku, Finland.
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46
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Kelkka T, Savola P, Bhattacharya D, Huuhtanen J, Lönnberg T, Kankainen M, Paalanen K, Tyster M, Lepistö M, Ellonen P, Smolander J, Eldfors S, Yadav B, Khan S, Koivuniemi R, Sjöwall C, Elo LL, Lähdesmäki H, Maeda Y, Nishikawa H, Leirisalo-Repo M, Sokka-Isler T, Mustjoki S. Corrigendum: Adult-Onset Anti-Citrullinated Peptide Antibody-Negative Destructive Rheumatoid Arthritis Is Characterized by a Disease-Specific CD8+ T Lymphocyte Signature. Front Immunol 2021; 12:710831. [PMID: 34135915 PMCID: PMC8202119 DOI: 10.3389/fimmu.2021.710831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 05/19/2021] [Indexed: 11/13/2022] Open
Abstract
[This corrects the article DOI: 10.3389/fimmu.2020.578848.].
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Affiliation(s)
- Tiina Kelkka
- Hematology Research Unit Helsinki, University of Helsinki, Helsinki, Finland.,Department of Hematology, Helsinki University Hospital Comprehensive Cancer Center, Helsinki, Finland.,Department of Clinical Chemistry and Hematology, University of Helsinki, Helsinki, Finland.,Translational Immunology Research Program, University of Helsinki, Helsinki, Finland
| | - Paula Savola
- Hematology Research Unit Helsinki, University of Helsinki, Helsinki, Finland.,Department of Hematology, Helsinki University Hospital Comprehensive Cancer Center, Helsinki, Finland.,Department of Clinical Chemistry and Hematology, University of Helsinki, Helsinki, Finland.,Translational Immunology Research Program, University of Helsinki, Helsinki, Finland
| | - Dipabarna Bhattacharya
- Hematology Research Unit Helsinki, University of Helsinki, Helsinki, Finland.,Department of Hematology, Helsinki University Hospital Comprehensive Cancer Center, Helsinki, Finland.,Department of Clinical Chemistry and Hematology, University of Helsinki, Helsinki, Finland.,Translational Immunology Research Program, University of Helsinki, Helsinki, Finland
| | - Jani Huuhtanen
- Hematology Research Unit Helsinki, University of Helsinki, Helsinki, Finland.,Department of Hematology, Helsinki University Hospital Comprehensive Cancer Center, Helsinki, Finland.,Department of Clinical Chemistry and Hematology, University of Helsinki, Helsinki, Finland.,Translational Immunology Research Program, University of Helsinki, Helsinki, Finland
| | - Tapio Lönnberg
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Matti Kankainen
- Hematology Research Unit Helsinki, University of Helsinki, Helsinki, Finland.,Department of Hematology, Helsinki University Hospital Comprehensive Cancer Center, Helsinki, Finland.,Translational Immunology Research Program, University of Helsinki, Helsinki, Finland
| | - Kirsi Paalanen
- Rheumatology, Jyväskylä Central Hospital, Jyväskylä, Finland
| | - Mikko Tyster
- Hematology Research Unit Helsinki, University of Helsinki, Helsinki, Finland.,Department of Hematology, Helsinki University Hospital Comprehensive Cancer Center, Helsinki, Finland.,Department of Clinical Chemistry and Hematology, University of Helsinki, Helsinki, Finland.,Translational Immunology Research Program, University of Helsinki, Helsinki, Finland
| | - Maija Lepistö
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
| | - Pekka Ellonen
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
| | - Johannes Smolander
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Samuli Eldfors
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
| | - Bhagwan Yadav
- Hematology Research Unit Helsinki, University of Helsinki, Helsinki, Finland.,Department of Hematology, Helsinki University Hospital Comprehensive Cancer Center, Helsinki, Finland.,Department of Clinical Chemistry and Hematology, University of Helsinki, Helsinki, Finland.,Translational Immunology Research Program, University of Helsinki, Helsinki, Finland
| | - Sofia Khan
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Riitta Koivuniemi
- Rheumatology, University of Helsinki, Helsinki University Hospital, Helsinki, Finland
| | - Christopher Sjöwall
- Department of Biomedical and Clinical Sciences, Division of Inflammation and Infection, Linköping University, Linköping, Sweden
| | - Laura L Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.,Institute of Biomedicine, University of Turku, Turku, Finland
| | - Harri Lähdesmäki
- Department of Computer Science, Aalto University School of Science, Espoo, Finland
| | - Yuka Maeda
- Division of Cancer Immunology, Research Institute/Exploratory Oncology Research and Clinical Trial Center (EPOC), National Cancer Center, Tokyo, Japan
| | - Hiroyoshi Nishikawa
- Division of Cancer Immunology, Research Institute/Exploratory Oncology Research and Clinical Trial Center (EPOC), National Cancer Center, Tokyo, Japan
| | | | - Tuulikki Sokka-Isler
- Rheumatology, Jyväskylä Central Hospital, Jyväskylä, Finland.,University of Eastern Finland, Faculty of Health Sciences, Kuopio, Finland
| | - Satu Mustjoki
- Hematology Research Unit Helsinki, University of Helsinki, Helsinki, Finland.,Department of Hematology, Helsinki University Hospital Comprehensive Cancer Center, Helsinki, Finland.,Department of Clinical Chemistry and Hematology, University of Helsinki, Helsinki, Finland.,Translational Immunology Research Program, University of Helsinki, Helsinki, Finland
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47
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Smolander J, Khan S, Singaravelu K, Kauko L, Lund RJ, Laiho A, Elo LL. Evaluation of tools for identifying large copy number variations from ultra-low-coverage whole-genome sequencing data. BMC Genomics 2021; 22:357. [PMID: 34000988 PMCID: PMC8130438 DOI: 10.1186/s12864-021-07686-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Accepted: 05/07/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Detection of copy number variations (CNVs) from high-throughput next-generation whole-genome sequencing (WGS) data has become a widely used research method during the recent years. However, only a little is known about the applicability of the developed algorithms to ultra-low-coverage (0.0005-0.8×) data that is used in various research and clinical applications, such as digital karyotyping and single-cell CNV detection. RESULT Here, the performance of six popular read-depth based CNV detection algorithms (BIC-seq2, Canvas, CNVnator, FREEC, HMMcopy, and QDNAseq) was studied using ultra-low-coverage WGS data. Real-world array- and karyotyping kit-based validation were used as a benchmark in the evaluation. Additionally, ultra-low-coverage WGS data was simulated to investigate the ability of the algorithms to identify CNVs in the sex chromosomes and the theoretical minimum coverage at which these tools can accurately function. Our results suggest that while all the methods were able to detect large CNVs, many methods were susceptible to producing false positives when smaller CNVs (< 2 Mbp) were detected. There was also significant variability in their ability to identify CNVs in the sex chromosomes. Overall, BIC-seq2 was found to be the best method in terms of statistical performance. However, its significant drawback was by far the slowest runtime among the methods (> 3 h) compared with FREEC (~ 3 min), which we considered the second-best method. CONCLUSIONS Our comparative analysis demonstrates that CNV detection from ultra-low-coverage WGS data can be a highly accurate method for the detection of large copy number variations when their length is in millions of base pairs. These findings facilitate applications that utilize ultra-low-coverage CNV detection.
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Affiliation(s)
- Johannes Smolander
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520, Turku, Finland
| | - Sofia Khan
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520, Turku, Finland
| | - Kalaimathy Singaravelu
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520, Turku, Finland
| | - Leni Kauko
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520, Turku, Finland
| | - Riikka J Lund
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520, Turku, Finland
| | - Asta Laiho
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520, Turku, Finland
| | - Laura L Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520, Turku, Finland.
- Institute of Biomedicine, University of Turku, 20520, Turku, Finland.
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48
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Adossa N, Khan S, Rytkönen KT, Elo LL. Computational strategies for single-cell multi-omics integration. Comput Struct Biotechnol J 2021; 19:2588-2596. [PMID: 34025945 PMCID: PMC8114078 DOI: 10.1016/j.csbj.2021.04.060] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Revised: 04/23/2021] [Accepted: 04/24/2021] [Indexed: 02/06/2023] Open
Abstract
Single-cell omics technologies are currently solving biological and medical problems that earlier have remained elusive, such as discovery of new cell types, cellular differentiation trajectories and communication networks across cells and tissues. Current advances especially in single-cell multi-omics hold high potential for breakthroughs by integration of multiple different omics layers. To pair with the recent biotechnological developments, many computational approaches to process and analyze single-cell multi-omics data have been proposed. In this review, we first introduce recent developments in single-cell multi-omics in general and then focus on the available data integration strategies. The integration approaches are divided into three categories: early, intermediate, and late data integration. For each category, we describe the underlying conceptual principles and main characteristics, as well as provide examples of currently available tools and how they have been applied to analyze single-cell multi-omics data. Finally, we explore the challenges and prospective future directions of single-cell multi-omics data integration, including examples of adopting multi-view analysis approaches used in other disciplines to single-cell multi-omics.
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Affiliation(s)
- Nigatu Adossa
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland
| | - Sofia Khan
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland
| | - Kalle T. Rytkönen
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland
- Institute of Biomedicine, University of Turku, 20520 Turku, Finland
| | - Laura L. Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland
- Institute of Biomedicine, University of Turku, 20520 Turku, Finland
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49
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Sieberts SK, Schaff J, Duda M, Pataki BÁ, Sun M, Snyder P, Daneault JF, Parisi F, Costante G, Rubin U, Banda P, Chae Y, Chaibub Neto E, Dorsey ER, Aydın Z, Chen A, Elo LL, Espino C, Glaab E, Goan E, Golabchi FN, Görmez Y, Jaakkola MK, Jonnagaddala J, Klén R, Li D, McDaniel C, Perrin D, Perumal TM, Rad NM, Rainaldi E, Sapienza S, Schwab P, Shokhirev N, Venäläinen MS, Vergara-Diaz G, Zhang Y, Wang Y, Guan Y, Brunner D, Bonato P, Mangravite LM, Omberg L. Crowdsourcing digital health measures to predict Parkinson's disease severity: the Parkinson's Disease Digital Biomarker DREAM Challenge. NPJ Digit Med 2021; 4:53. [PMID: 33742069 PMCID: PMC7979931 DOI: 10.1038/s41746-021-00414-7] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Accepted: 02/08/2021] [Indexed: 12/16/2022] Open
Abstract
Consumer wearables and sensors are a rich source of data about patients' daily disease and symptom burden, particularly in the case of movement disorders like Parkinson's disease (PD). However, interpreting these complex data into so-called digital biomarkers requires complicated analytical approaches, and validating these biomarkers requires sufficient data and unbiased evaluation methods. Here we describe the use of crowdsourcing to specifically evaluate and benchmark features derived from accelerometer and gyroscope data in two different datasets to predict the presence of PD and severity of three PD symptoms: tremor, dyskinesia, and bradykinesia. Forty teams from around the world submitted features, and achieved drastically improved predictive performance for PD status (best AUROC = 0.87), as well as tremor- (best AUPR = 0.75), dyskinesia- (best AUPR = 0.48) and bradykinesia-severity (best AUPR = 0.95).
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Affiliation(s)
| | | | - Marlena Duda
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Bálint Ármin Pataki
- Department of Physics of Complex Systems, ELTE Eötvös Loránd University, Budapest, Hungary
| | | | | | - Jean-Francois Daneault
- Dept of PM&R, Harvard Medical School, Spaulding Rehabilitation Hospital, Charlestown, MA, USA
- Dept of Rehabilitation and Movement Sciences, Rutgers University, Newark, NJ, USA
| | - Federico Parisi
- Dept of PM&R, Harvard Medical School, Spaulding Rehabilitation Hospital, Charlestown, MA, USA
- Wyss Institute, Harvard University, Boston, MA, USA
| | - Gianluca Costante
- Dept of PM&R, Harvard Medical School, Spaulding Rehabilitation Hospital, Charlestown, MA, USA
- Wyss Institute, Harvard University, Boston, MA, USA
| | - Udi Rubin
- Early Signal Foundation, 311 W 43rd Street, New York, NY, USA
| | - Peter Banda
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | | | | | - E Ray Dorsey
- Center for Health + Technology, University of Rochester, Rochester, NY, USA
| | - Zafer Aydın
- Department of Electrical and Computer Engineering, Abdullah Gul University, Kayseri, Turkey
| | - Aipeng Chen
- Prince of Wales Clinical School, UNSW Sydney, Sydney, Australia
| | - Laura L Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Tykistökatu 6, Turku, Finland
| | - Carlos Espino
- Early Signal Foundation, 311 W 43rd Street, New York, NY, USA
| | - Enrico Glaab
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Ethan Goan
- School of Electrical Engineering and Robotics, Queensland University of Technology, Brisbane, QLD, Australia
| | | | - Yasin Görmez
- Department of Electrical and Computer Engineering, Abdullah Gul University, Kayseri, Turkey
| | - Maria K Jaakkola
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Tykistökatu 6, Turku, Finland
- Department of Mathematics and Statistics, University of Turku, Turku, Finland
| | - Jitendra Jonnagaddala
- School of Public Health and Community Medicine, UNSW Sydney, Sydney, Australia
- WHO Collaborating Centre for eHealth, UNSW Sydney, Sydney, Australia
| | - Riku Klén
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Tykistökatu 6, Turku, Finland
| | - Dongmei Li
- Clinical and Translational Science Institute, University of Rochester Medical Center, Rochester, NY, USA
| | - Christian McDaniel
- Artificial Intelligence, University of Georgia, Athens, GA, USA
- Computer Science, University of Georgia, Athens, GA, USA
| | - Dimitri Perrin
- School of Computer Science, Queensland University of Technology, Brisbane, QLD, Australia
| | | | - Nastaran Mohammadian Rad
- Institute for Computing and Information Sciences, Radboud University, Nijmegen, The Netherlands
- Fondazione Bruno Kessler (FBK), Via Sommarive 18, Povo, Trento, Italy
- University of Trento, Trento, Italy
| | - Erin Rainaldi
- Verily Life Sciences, 269 East Grand Ave, South San Francisco, CA, USA
| | - Stefano Sapienza
- Dept of PM&R, Harvard Medical School, Spaulding Rehabilitation Hospital, Charlestown, MA, USA
| | - Patrick Schwab
- Institute of Robotics and Intelligent Systems, ETH Zurich, Zurich, Switzerland
| | | | - Mikko S Venäläinen
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Tykistökatu 6, Turku, Finland
| | - Gloria Vergara-Diaz
- Dept of PM&R, Harvard Medical School, Spaulding Rehabilitation Hospital, Charlestown, MA, USA
| | - Yuqian Zhang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Yuanjia Wang
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Yuanfang Guan
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Daniela Brunner
- Early Signal Foundation, 311 W 43rd Street, New York, NY, USA
- Dept. of Psychiatry, Columbia University, New York, NY, USA
| | - Paolo Bonato
- Dept of PM&R, Harvard Medical School, Spaulding Rehabilitation Hospital, Charlestown, MA, USA
- Wyss Institute, Harvard University, Boston, MA, USA
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50
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Gülich AF, Rica R, Tizian C, Viczenczova C, Khamina K, Faux T, Hainberger D, Penz T, Bosselut R, Bock C, Laiho A, Elo LL, Bergthaler A, Ellmeier W, Sakaguchi S. Complex Interplay Between MAZR and Runx3 Regulates the Generation of Cytotoxic T Lymphocyte and Memory T Cells. Front Immunol 2021; 12:535039. [PMID: 33815354 PMCID: PMC8010151 DOI: 10.3389/fimmu.2021.535039] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Accepted: 03/03/2021] [Indexed: 12/12/2022] Open
Abstract
The BTB zinc finger transcription factor MAZR (also known as PATZ1) controls, partially in synergy with the transcription factor Runx3, the development of CD8 lineage T cells. Here we explored the role of MAZR as well as combined activities of MAZR/Runx3 during cytotoxic T lymphocyte (CTL) and memory CD8+ T cell differentiation. In contrast to the essential role of Runx3 for CTL effector function, the deletion of MAZR had a mild effect on the generation of CTLs in vitro. However, a transcriptome analysis demonstrated that the combined deletion of MAZR and Runx3 resulted in much more widespread downregulation of CTL signature genes compared to single Runx3 deletion, indicating that MAZR partially compensates for loss of Runx3 in CTLs. Moreover, in line with the findings made in vitro, the analysis of CTL responses to LCMV infection revealed that MAZR and Runx3 cooperatively regulate the expression of CD8α, Granzyme B and perforin in vivo. Interestingly, while memory T cell differentiation is severely impaired in Runx3-deficient mice, the deletion of MAZR leads to an enlargement of the long-lived memory subset and also partially restored the differentiation defect caused by loss of Runx3. This indicates distinct functions of MAZR and Runx3 in the generation of memory T cell subsets, which is in contrast to their cooperative roles in CTLs. Together, our study demonstrates complex interplay between MAZR and Runx3 during CTL and memory T cell differentiation, and provides further insight into the molecular mechanisms underlying the establishment of CTL and memory T cell pools.
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Affiliation(s)
- Alexandra Franziska Gülich
- Division of Immunobiology, Institute of Immunology, Center for Pathophysiology, Infectiology and Immunology, Medical University of Vienna, Vienna, Austria
| | - Ramona Rica
- Division of Immunobiology, Institute of Immunology, Center for Pathophysiology, Infectiology and Immunology, Medical University of Vienna, Vienna, Austria
| | - Caroline Tizian
- Division of Immunobiology, Institute of Immunology, Center for Pathophysiology, Infectiology and Immunology, Medical University of Vienna, Vienna, Austria
| | - Csilla Viczenczova
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
| | - Kseniya Khamina
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
| | - Thomas Faux
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Daniela Hainberger
- Division of Immunobiology, Institute of Immunology, Center for Pathophysiology, Infectiology and Immunology, Medical University of Vienna, Vienna, Austria
| | - Thomas Penz
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
| | - Remy Bosselut
- Laboratory of Immune Cell Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States
| | - Christoph Bock
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
- Institute of Artificial Intelligence and Decision Support, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Asta Laiho
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Laura L. Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Andreas Bergthaler
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
| | - Wilfried Ellmeier
- Division of Immunobiology, Institute of Immunology, Center for Pathophysiology, Infectiology and Immunology, Medical University of Vienna, Vienna, Austria
| | - Shinya Sakaguchi
- Division of Immunobiology, Institute of Immunology, Center for Pathophysiology, Infectiology and Immunology, Medical University of Vienna, Vienna, Austria
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