Non-canonical transcriptional regulation of the poor prognostic factor UGT2B17 in chronic lymphocytic leukemic and normal B cells

BACKGROUND

High expression of the glycosyltransferase UGT2B17 represents an independent adverse prognostic marker in chronic lymphocytic leukemia (CLL). It also constitutes a predictive marker for therapeutic response and a drug resistance mechanism. The key determinants driving expression of the UGT2B17 gene in normal and leukemic B-cells remain undefined. The UGT2B17 transcriptome is complex and is comprised of at least 10 alternative transcripts, identified by previous RNA-sequencing of liver and intestine. We hypothesized that the transcriptional program regulating UGT2B17 in B-lymphocytes is distinct from the canonical expression previously characterized in the liver.

RESULTS

RNA-sequencing and genomics data revealed a specific genomic landscape at the UGT2B17 locus in normal and leukemic B-cells. RNA-sequencing and quantitative PCR data indicated that the UGT2B17 enzyme is solely encoded by alternative transcripts expressed in CLL patient cells and not by the canonical transcript widely expressed in the liver and intestine. Chromatin accessible regions (ATAC-Seq) in CLL cells mapped with alternative promoters and non-coding exons, which may be derived from endogenous retrotransposon elements. By luciferase reporter assays, we identified key cis-regulatory STAT3, RELA and interferon regulatory factor (IRF) binding sequences driving the expression of UGT2B17 in lymphoblastoid and leukemic B-cells. Electrophoretic mobility shift assays and pharmacological inhibition demonstrated key roles for the CLL prosurvival transcription factors STAT3 and NF-κB in the leukemic expression of UGT2B17.

CONCLUSIONS

UGT2B17 expression in B-CLL is driven by key regulators of CLL progression. Our data suggest that a NF-κB/STAT3/IRF/UGT2B17 axis may represent a novel B-cell pathway promoting disease progression and drug resistance.

SOS genes are rapidly induced while translesion synthesis polymerase activity is temporally regulated

The DNA damage inducible SOS response in bacteria serves to increase survival of the species at the cost of mutagenesis. The SOS response first initiates error-free repair followed by error-prone repair. Here, we have employed a multi-omics approach to elucidate the temporal coordination of the SOS response. Escherichia coli was grown in batch cultivation in bioreactors to ensure highly controlled conditions, and a low dose of the antibiotic ciprofloxacin was used to activate the SOS response while avoiding extensive cell death. Our results show that expression of genes involved in error-free and error-prone repair were both induced shortly after DNA damage, thus, challenging the established perception that the expression of error-prone repair genes is delayed. By combining transcriptomics and a sub-proteomics approach termed signalomics, we found that the temporal segregation of error-free and error-prone repair is primarily regulated after transcription, supporting the current literature. Furthermore, the heterology index (i.e., the binding affinity of LexA to the SOS box) was correlated to the maximum increase in gene expression and not to the time of induction of SOS genes. Finally, quantification of metabolites revealed increasing pyrimidine pools as a late feature of the SOS response. Our results elucidate how the SOS response is coordinated, showing a rapid transcriptional response and temporal regulation of mutagenesis on the protein and metabolite levels.

SARS-CoV-2 infection modifies the transcriptome of the megakaryocytes in the bone marrow

Megakaryocytes, integral to platelet production, predominantly reside in the bone marrow and undergo regulated fragmentation within sinusoid vessels to release platelets into the bloodstream. Inflammatory states and infections influence megakaryocyte transcription, potentially affecting platelet functionality. Notably, COVID-19 has been associated with altered platelet transcriptomes. In this study, we investigated the hypothesis that SARS-CoV-2 infection could impact the transcriptome of bone marrow megakaryocytes. Utilizing spatial transcriptomics to discriminate subpopulations of megakaryocytes based on proximity to bone marrow sinusoids, we identified approximately 19,000 genes in megakaryocytes. Machine learning techniques revealed that the transcriptome of healthy murine bone marrow megakaryocytes exhibited minimal differences based on proximity to sinusoid vessels. Further, at peak SARS-CoV-2 viremia, when the disease primarily affected the lungs, megakaryocytes were not significantly different from those from healthy mice. Conversely, a significant divergence in the megakaryocyte transcriptome was observed during systemic inflammation, although SARS-CoV-2 RNA was never detected in bone marrow and it was no longer detectable in the lungs. Under these conditions, the megakaryocyte transcriptional landscape was enriched in pathways associated with histone modifications, megakaryocyte differentiation, NETosis, and autoimmunity, which could not be explained by cell proximity to sinusoid vessels. Notably, the type-I interferon signature and calprotectin (S100A8/A9) were not induced in megakaryocytes under any condition. However, inflammatory cytokines induced in the blood and lungs of COVID-19 mice were different from those found in the bone marrow, suggesting a discriminating impact of inflammation on this specific subset of cells. Collectively, our data indicate that a new population of bone marrow megakaryocytes may emerge through COVID-19-related pathogenesis.

Loss of testosterone induces postprandial insulin resistance and increases the expression of the hepatic antioxidant flavin-containing monooxygenases in mice exposed to intermittent hypoxia

AIM

We tested the hypothesis that low testosterone alters the effects of intermittent hypoxia (IH) on glucose homeostasis, hepatic oxidative stress, and transcriptomic profile in male mice.

METHODS

We used sham-operated or orchiectomized (ORX) mice exposed to normoxia (Nx) or IH for 2 weeks. We performed fasting insulin and glucose tolerance tests and assessed fasting and postprandial insulin resistance with the HOMA-IR. The activity of hepatic prooxidant (NADPH oxidase-NOX), antioxidant enzymes (superoxide dismutase, catalase, and glutathione peroxidase-SOD, Cat, GPx), lipid peroxidation (MDA concentration), and the total concentration of glutathione (GSH) were measured under postprandial conditions. mRNA sequencing and pathway enrichment analyses were used to identify hepatic genes underlying the interactions between IH and testosterone.

RESULTS

In Sham mice, IH improves fasting insulin sensitivity and glucose tolerance, while there are no effects of IH in ORX mice. In ORX mice, IH induces postprandial hyperinsulinemia, insulin resistance, and a prooxidant profile of enzyme activity (low SOD activity) without altering hepatic MDA and GSH content. ORX and IH altered the expression of genes involved in oxidoreductase activities, cytochromes-dependent pathways, and glutathione metabolism. Among the genes upregulated in ORX-IH mice, the flavin-containing monooxygenases (FMO) are particularly relevant since these are potent hepatic antioxidants that could help prevent overt oxidative stress in ORX-IH mice.

CONCLUSION

Low levels of testosterone in male mice exposed to IH induce post-prandial hyperinsulinemia and insulin resistance and determine the mechanisms by which the liver handles IH-induced oxidative stress.

Predictive model for vitamin C levels in hyperinsulinemic individuals based on age, sex, waist circumference, low-density lipoprotein, and immune-associated serum proteins

Vitamin C (ascorbic acid) is an important water-soluble antioxidant associated with decreased oxidative stress in type 2 diabetes (T2D) patients. A previous targeted plasma proteomic study has indicated that ascorbic acid is associated with markers of the immune system in healthy subjects. However, the association between the levels of ascorbic acid and blood biomarkers in subjects at risk of developing T2D is still unknown. Serum ascorbic acid was measured by ultra-performance liquid chromatography and serum proteins were quantified by untargeted liquid-chromatography mass spectrometry in 25 hyperinsulinemia subjects that were randomly assigned a high dairy intake diet or an adequate dairy intake diet for 6 weeks, then crossed-over after a 6-week washout period. Spearman correlation followed by gene ontology analyses were performed to identify biological pathways associated with ascorbic acid. Finally, machine learning analysis was performed to obtain a specific serum protein signature that could predict ascorbic acid levels. After adjustments for waist circumference, LDL, HDL, fasting insulin, fasting blood glucose, age, gender, and dairy intake; serum ascorbic acid correlated positively with different aspects of the immune system. Machine learning analysis indicated that a signature composed of 21 features that included 17 proteins (mainly from the immune system), age, sex, waist circumference, and LDL could predict serum ascorbic acid levels in hyperinsulinemia subjects. In conclusion, the result reveals a correlation as well as modulation between serum ascorbic acid levels and proteins that play vital roles in regulating different aspects of the immune response in individuals at risk of T2D. The development of a predictive signature for ascorbic acid will further help the assessment of ascorbic acid status in clinical settings.

Transcriptomic Analysis of Mineralized Adipose-Derived Stem Cell Tissues for Calcific Valve Disease Modelling

Calcific aortic valve disease (CAVD) is characterized by the fibrosis and mineralization of the aortic valve, which leads to aortic stenosis and heart failure. At the cellular level, this is due to the osteoblastic-like differentiation of valve interstitial cells (VICs), resulting in the calcification of the tissue. Unfortunately, human VICs are not readily available to study CAVD pathogenesis and the implicated mechanisms in vitro; however, adipose-derived stromal/stem cells (ASCs), carrying the patient's specific genomic features, have emerged as a promising cell source to model cardiovascular diseases due to their multipotent nature, availability, and patient-specific characteristics. In this study, we describe a comprehensive transcriptomic analysis of tissue-engineered, scaffold-free, ASC-embedded mineralized tissue sheets using bulk RNA sequencing. Bioinformatic and gene set enrichment analyses revealed the up-regulation of genes associated with the organization of the extracellular matrix (ECM), suggesting that the ECM could play a vital role in the enhanced mineralization observed in these tissue-engineered ASC-embedded sheets. Upon comparison with publicly available gene expression datasets from CAVD patients, striking similarities emerged regarding cardiovascular diseases and ECM functions, suggesting a potential link between ECM gene expression and CAVDs pathogenesis. A matrisome-related sub-analysis revealed the ECM microenvironment promotes the transcriptional activation of the master gene runt-related transcription factor 2 (RUNX2), which is essential in CAVD development. Tissue-engineered ASC-embedded sheets with enhanced mineralization could be a valuable tool for research and a promising avenue for the identification of more effective aortic valve replacement therapies.

Behavioral as well as hippocampal transcriptomic and microglial responses differ across sexes in adult mouse offspring exposed to a dual genetic and environmental challenge

INTRODUCTION

A wide range of positive, negative, and cognitive symptoms compose the clinical presentation of schizophrenia. Schizophrenia is a multifactorial disorder in which genetic and environmental risk factors interact for a full emergence of the disorder. Infectious challenges during pregnancy are a well-known environmental risk factor for schizophrenia. Also, genetic variants affecting the function of fractalkine signaling between neurons and microglia were linked to schizophrenia. Translational animal models recapitulating these complex gene-environment associations have a great potential to untangle schizophrenia neurobiology and propose new therapeutic strategies.

METHODS

Given that genetic variants affecting the function of fractalkine signaling between neurons and microglia were linked to schizophrenia, we compared the outcomes of a well-characterized model of maternal immune activation induced using the viral mimetic polyinosinic:polycytidylic acid (Poly I:C) in wild-type versus fractalkine receptor knockout mice. Possible behavioral and immune alterations were assessed in male and female offspring during adulthood. Considering the role of the hippocampus in schizophrenia, microglial analyses and bulk RNA sequencing were performed within this region to assess the neuroimmune dynamics at play. Males and females were examined separately.

RESULTS

Offspring exposed to the dual challenge paradigm exhibited symptoms relevant to schizophrenia and unpredictably to mood disorders. Males displayed social and cognitive deficits related to schizophrenia, while females mainly presented anxiety-like behaviors related to mood disorders. Hippocampal microglia in females exposed to the dual challenge were hypertrophic, indicative of an increased surveillance, whereas those in males showed on the other end of the spectrum blunted morphologies with a reduced phagocytosis. Hippocampal bulk-RNA sequencing further revealed a downregulation in females of genes related to GABAergic transmission, which represents one of the main proposed causes of mood disorders.

CONCLUSIONS

Building on previous results, we identified in the current study distinctive behavioral phenotypes in female mice exposed to a dual genetic and environmental challenge, thus proposing a new model of neurodevelopmentally-associated mood and affective symptoms. This paves the way to future sex-specific investigations into the susceptibility to developmental challenges using animal models based on genetic and immune vulnerability as presented here.

Immune-related transcriptomic and epigenetic reconfiguration in BV2 cells after lipopolysaccharide exposure: an in vitro omics integrative study

BACKGROUND

Molecular alterations affecting microglia have been consistently associated with the inflammatory response. These cells can have pro- or anti-inflammatory activity, phenotypes thought to be regulated by epigenetic mechanisms. Still, little is known about the details on how epigenetic marks regulate the expression of genes in the context of an inflammatory response.

METHODS

Through CUT&RUN, we profiled four genome-wide histone marks (HM) (H3K4me1, H3K4me3, H3K27ac, and H3K27me3) in lipopolysaccharide-exposed cells and compared their distributions to control cells. Transcriptomic profiles were determined through RNA-seq and differentially expressed genes were identified and contrasted with the epigenetic landscapes. Other downstream analyses were also included in this study.

RESULTS

Our results illustrate an effectively induced M1 phenotype in microglial cells derived from LPS exposure. We observed differential bound regions associated with the genes classically involved in the inflammatory response in the expected direction according to each histone modification. Consistently, our transcriptomic analysis yielded a conspicuous illustration of the LPS-induced immune activity showing the up-regulation of Nf-κB-induced mRNAs (TNF-α, nfκbiz, nfκbia) and other important genes (Marco, Il-6, etc.). Furthermore, we integrated both omics profiles and identified an important reconfiguration of the genome induced by LPS. The latter was depicted by 8 different chromatin states that changed between conditions and that associated with unique clusters of differentially expressed genes, which not only represented regulatory elements, but also underlined distinct biological functions (inhibition of morphogenesis; changes in metabolism, homeostasis, and cytokine regulation; activation of the inflammatory response).

CONCLUSION

This study exhibits important differences in the distribution of histone modifications in treated and control BV2 cells, constituting an epigenetic reconfiguration that leads to the inflammatory response. Also, it highlights the importance of these marks' regulatory role in gene expression and provides possible targets for further studies in the context of inflammation.

Unveiling the crucial neuronal role of the proteasomal ATPase subunit gene PSMC5 in neurodevelopmental proteasomopathies

Neurodevelopmental proteasomopathies represent a distinctive category of neurodevelopmental disorders (NDD) characterized by genetic variations within the 26S proteasome, a protein complex governing eukaryotic cellular protein homeostasis. In our comprehensive study, we identified 23 unique variants in PSMC5 , which encodes the AAA-ATPase proteasome subunit PSMC5/Rpt6, causing syndromic NDD in 38 unrelated individuals. Overexpression of PSMC5 variants altered human hippocampal neuron morphology, while PSMC5 knockdown led to impaired reversal learning in flies and loss of excitatory synapses in rat hippocampal neurons. PSMC5 loss-of-function resulted in abnormal protein aggregation, profoundly impacting innate immune signaling, mitophagy rates, and lipid metabolism in affected individuals. Importantly, targeting key components of the integrated stress response, such as PKR and GCN2 kinases, ameliorated immune dysregulations in cells from affected individuals. These findings significantly advance our understanding of the molecular mechanisms underlying neurodevelopmental proteasomopathies, provide links to research in neurodegenerative diseases, and open up potential therapeutic avenues.

Mass spectrometry in cerebrospinal fluid uncovers association of glycolysis biomarkers with Alzheimer's disease in a large clinical sample

Alzheimer's disease (AD) is a complex and heterogeneous neurodegenerative disorder with contributions from multiple pathophysiological pathways. One of the long-recognized and important features of AD is disrupted cerebral glucose metabolism, but the underlying molecular basis remains unclear. In this study, unbiased mass spectrometry was used to survey CSF from a large clinical cohort, comparing patients who are either cognitively unimpaired (CU; n = 68), suffering from mild-cognitive impairment or dementia from AD (MCI-AD, n = 95; DEM-AD, n = 72), or other causes (MCI-other, n = 77; DEM-other, n = 23), or Normal Pressure Hydrocephalus (NPH, n = 57). The results revealed changes related to altered glucose metabolism. In particular, two glycolytic enzymes, pyruvate kinase (PKM) and aldolase A (ALDOA), were found to be upregulated in CSF from patients with AD compared to those with other neurological conditions. Increases in full-length PKM and ALDOA levels in CSF were confirmed with immunoblotting. Levels of these enzymes furthermore correlated negatively with CSF glucose in matching CSF samples. PKM levels were also found to be increased in AD in publicly available brain-tissue data. These results indicate that ALDOA and PKM may act as technically-robust potential biomarkers of glucose metabolism dysregulation in AD.

BioDiscViz: A visualization support and consensus signature selector for BioDiscML results

Machine learning (ML) algorithms are powerful tools to find complex patterns and biomarker signatures when conventional statistical methods fail to identify them. While the ML field made significant progress, state of the art methodologies to build efficient and non-overfitting models are not always applied in the literature. To this purpose, automatic programs, such as BioDiscML, were designed to identify biomarker signatures and correlated features while escaping overfitting using multiple evaluation strategies, such as cross validation, bootstrapping and repeated holdout. To further improve BioDiscML and reach a broader audience, better visualization support and flexibility in choosing the best models and signatures are needed. Thus, to provide researchers with an easily accessible and usable tool for in depth investigation of the results from BioDiscML outputs, we developed a visual interaction tool called BioDiscViz. This tool provides summaries, tables and graphics, in the form of Principal Component Analysis (PCA) plots, UMAP, t-SNE, heatmaps and boxplots for the best model and the correlated features. Furthermore, this tool also provides visual support to extract a consensus signature from BioDiscML models using a combination of filters. BioDiscViz will be a great visual support for research using ML, hence new opportunities in this field by opening it to a broader community.

ASVmaker: A New Tool to Improve Taxonomic Identifications for Amplicon Sequencing Data

The taxonomic assignment of sequences obtained by high throughput amplicon sequencing poses a limitation for various applications in the biomedical, environmental, and agricultural fields. Identifications are constrained by the length of the obtained sequences and the computational processes employed to efficiently assign taxonomy. Arriving at a consensus is often preferable to uncertain identification for ecological purposes. To address this issue, a new tool called "ASVmaker" has been developed to facilitate the creation of custom databases, thereby enhancing the precision of specific identifications. ASVmaker is specifically designed to generate reference databases for allocating amplicon sequencing data. It uses publicly available reference data and generates specific sequences derived from the primers used to create amplicon sequencing libraries. This versatile tool can complete taxonomic assignments performed with pre-trained classifiers from the SILVA and UNITE databases. Moreover, it enables the generation of comprehensive reference databases for specific genes in cases where no directly applicable database exists for taxonomic classification tools.

Design of experiment and machine learning inform on the 3D printing of hydrogels for biomedical applications

In the biomedical field, 3D printing has the potential to deliver on some of the promises of personalized therapy, notably by enabling point-of-care fabrication of medical devices, dosage forms and bioimplants. To achieve this full potential, a better understanding of the 3D printing processes is necessary, and non-destructive characterization methods must be developed. This study proposes methodologies to optimize the 3D printing parameters for soft material extrusion. We hypothesize that combining image processing with design of experiment (DoE) analyses and machine learning could help obtaining useful information from a quality-by-design perspective. Herein, we investigated the impact of three critical process parameters (printing speed, printing pressure and infill percentage) on three critical quality attributes (gel weight, total surface area and heterogeneity) monitored with a non-destructive methodology. DoE and machine learning were combined to obtain information on the process. This work paves the way for a rational approach to optimize 3D printing parameters in the biomedical field.

Use of Elasticsearch-based business intelligence tools for integration and visualization of biological data

The emergence of massive datasets exploring the multiple levels of molecular biology has made their analysis and knowledge transfer more complex. Flexible tools to manage big biological datasets could be of great help for standardizing the usage of developed data visualizations and integration methods. Business intelligence (BI) tools have been used in many fields as exploratory tools. They have numerous connectors to link numerous data repositories with a unified graphic interface, offering an overview of data and facilitating interpretation for decision makers. BI tools could be a flexible and user-friendly way of handling molecular biological data with interactive visualizations. However, it is rather uncommon to see such tools used for the exploration of massive and complex datasets in biological fields. We believe that two main obstacles could be the reason. Firstly, we posit that the way to import data into BI tools are not compatible with biological databases. Secondly, BI tools may not be adapted to certain particularities of complex biological data, namely, the size, the variability of datasets and the availability of specialized visualizations. This paper highlights the use of five BI tools (Elastic Kibana, Siren Investigate, Microsoft Power BI, Salesforce Tableau and Apache Superset) onto which the massive data management repository engine called Elasticsearch is compatible. Four case studies will be discussed in which these BI tools were applied on biological datasets with different characteristics. We conclude that the performance of the tools depends on the complexity of the biological questions and the size of the datasets.

Developing a cluster-based approach for deciphering complexity in individuals with neurodevelopmental differences

Objective

Individuals with neurodevelopmental disorders such as global developmental delay (GDD) present both genotypic and phenotypic heterogeneity. This diversity has hampered developing of targeted interventions given the relative rarity of each individual genetic etiology. Novel approaches to clinical trials where distinct, but related diseases can be treated by a common drug, known as basket trials, which have shown benefits in oncology but have yet to be used in GDD. Nonetheless, it remains unclear how individuals with GDD could be clustered. Here, we assess two different approaches: agglomerative and divisive clustering.

Methods

Using the largest cohort of individuals with GDD, which is the Deciphering Developmental Disorders (DDD), characterized using a systematic approach, we extracted genotypic and phenotypic information from 6,588 individuals with GDD. We then used a k-means clustering (divisive) and hierarchical agglomerative clustering (HAC) to identify subgroups of individuals. Next, we extracted gene network and molecular function information with regard to the clusters identified by each approach.

Results

HAC based on phenotypes identified in individuals with GDD revealed 16 clusters, each presenting with one dominant phenotype displayed by most individuals in the cluster, along with other minor phenotypes. Among the most common phenotypes reported were delayed speech, absent speech, and seizure. Interestingly, each phenotypic cluster molecularly included several (3-12) gene sub-networks of more closely related genes with diverse molecular function. k-means clustering also segregated individuals harboring those phenotypes, but the genetic pathways identified were different from the ones identified from HAC.

Conclusion

Our study illustrates how divisive (k-means) and agglomerative clustering can be used in order to group individuals with GDD for future basket trials. Moreover, the result of our analysis suggests that phenotypic clusters should be subdivided into molecular sub-networks for an increased likelihood of successful treatment. Finally, a combination of both agglomerative and divisive clustering may be required for developing of a comprehensive treatment.

Early Diagnosis: End-to-End CNN-LSTM Models for Mass Spectrometry Data Classification

Liquid chromatography-mass spectrometry (LC-MS) is a powerful method for cell profiling. The use of LC-MS technology is a tool of choice for cancer research since it provides molecular fingerprints of analyzed tissues. However, the ubiquitous presence of noise, the peaks shift between acquisitions, and the huge amount of information owing to the high dimensionality of the data make rapid and accurate cancer diagnosis a challenging task. Deep learning (DL) models are not only effective classifiers but are also well suited to jointly learn feature representation and classification tasks. This is particularly relevant when applied to raw LC-MS data and hence avoid the need for costly preprocessing and complicated feature selection. In this study, we propose a new end-to-end DL methodology that addresses all of the above challenges at once, while preserving the high potential of LC-MS data. Our DL model is designed to early discriminate between tumoral and normal tissues. It is a combination of a convolutional neural network (CNN) and a long short-term memory (LSTM) Network. The CNN network allows for significantly reducing the high dimensionality of the data while learning spatially relevant features. The LSTM network enables our model to capture temporal patterns. We show that our model outperforms not only benchmark models but also state-of-the-art models developed on the same data. Our framework is a promising strategy for improving early cancer detection during a diagnostic process.

Resolving the Temporal Splenic Proteome during Fungal Infection for Discovery of Putative Dual Perspective Biomarker Signatures

Fungal pathogens are emerging threats to global health with the rise of incidence associated with climate change and increased geographical distribution; factors also influencing host susceptibility to infection. Accurate detection and diagnosis of fungal infections is paramount to offer rapid and effective therapeutic options. For improved diagnostics, the discovery and development of protein biomarkers presents a promising avenue; however, this approach requires a priori knowledge of infection hallmarks. To uncover putative novel biomarkers of disease, profiling of the host immune response and pathogen virulence factor production is indispensable. In this study, we use mass-spectrometry-based proteomics to resolve the temporal proteome of Cryptococcus neoformans infection of the spleen following a murine model of infection. Dual perspective proteome profiling defines global remodeling of the host over a time course of infection, confirming activation of immune associated proteins in response to fungal invasion. Conversely, pathogen proteomes detect well-characterized C. neoformans virulence determinants, along with novel mapped patterns of pathogenesis during the progression of disease. Together, our innovative systematic approach confirms immune protection against fungal pathogens and explores the discovery of putative biomarker signatures from complementary biological systems to monitor the presence and progression of cryptococcal disease.

Exome sequencing identifies breast cancer susceptibility genes and defines the contribution of coding variants to breast cancer risk

Linkage and candidate gene studies have identified several breast cancer susceptibility genes, but the overall contribution of coding variation to breast cancer is unclear. To evaluate the role of rare coding variants more comprehensively, we performed a meta-analysis across three large whole-exome sequencing datasets, containing 26,368 female cases and 217,673 female controls. Burden tests were performed for protein-truncating and rare missense variants in 15,616 and 18,601 genes, respectively. Associations between protein-truncating variants and breast cancer were identified for the following six genes at exome-wide significance (P < 2.5 × 10-6): the five known susceptibility genes ATM, BRCA1, BRCA2, CHEK2 and PALB2, together with MAP3K1. Associations were also observed for LZTR1, ATR and BARD1 with P < 1 × 10-4. Associations between predicted deleterious rare missense or protein-truncating variants and breast cancer were additionally identified for CDKN2A at exome-wide significance. The overall contribution of coding variants in genes beyond the previously known genes is estimated to be small.

Lipid metabolic reprogramming of hepatic CD4+ T cells during SIV infection

While liver inflammation is associated with AIDS, little is known so far about hepatic CD4+ T cells. By using the simian immunodeficiency virus (SIV)-infected rhesus macaque (RM) model, we aimed to characterize CD4+ T cells. The phenotype of CD4+ T cells was assessed by flow cytometry from uninfected (n = 3) and infected RMs, with either SIVmac251 (n = 6) or SHIVSF162p3 (n = 6). After cell sorting of hepatic CD4+ T cells, viral DNA quantification and RNA sequencing were performed.Thus, we demonstrated that liver CD4+ T cells strongly expressed the SIV coreceptor, CCR5. We showed that viremia was negatively correlated with the percentage of hepatic effector memory CD4+ T cells. Consistent with viral sensing, inflammatory and interferon gene transcripts were increased. We also highlighted the presence of harmful CD4+ T cells expressing GZMA and members of TGFB that could contribute to fuel inflammation and fibrosis. Whereas RNA sequencing demonstrated activated CD4+ T cells displaying higher levels of mitoribosome and membrane lipid synthesis transcripts, few genes were related to glycolysis and oxidative phosphorylation, which are essential to sustain activated T cells. Furthermore, we observed lower levels of mitochondrial DNA and higher levels of genes associated with damaged organelles (reticulophagy and mitophagy). Altogether, our data revealed that activated hepatic CD4+ T cells are reprogrammed to lipid metabolism. Thus, strategies aiming to reprogram T cell metabolism with effector function could be of interest for controlling viral infection and preventing liver disorders.IMPORTANCEHuman immunodeficiency virus (HIV) infection may cause liver diseases, associated with inflammation and tissue injury, contributing to comorbidity in people living with HIV. Paradoxically, the contribution of hepatic CD4+ T cells remains largely underestimated. Herein, we used the model of simian immunodeficiency virus (SIV)-infected rhesus macaques to access liver tissue. Our work demonstrates that hepatic CD4+ T cells express CCR5, the main viral coreceptor, and are infected. Viral infection is associated with the presence of inflamed and activated hepatic CD4+ T cells expressing cytotoxic molecules. Furthermore, hepatic CD4+ T cells are reprogrammed toward lipid metabolism after SIV infection. Altogether, our findings shed new light on hepatic CD4+ T cell profile that could contribute to liver injury following viral infection.

Pearl shape classification using deep convolutional neural networks from Tahitian pearl rotation in Pinctada margaritifera

Tahitian pearls, artificially cultivated from the black-lipped pearl oyster Pinctada margaritifera, are renowned for their unique color and large size, making the pearl industry vital for the French Polynesian economy. Understanding the mechanisms of pearl formation is essential for enabling quality and sustainable production. In this paper, we explore the process of pearl formation by studying pearl rotation. Here we show, using a deep convolutional neural network, a direct link between the rotation of the pearl during its formation in the oyster and its final shape. We propose a new method for non-invasive pearl monitoring and a model for predicting the final shape of the pearl from rotation data with 81.9% accuracy. These novel resources provide a fresh perspective to study and enhance our comprehension of the overall mechanism of pearl formation, with potential long-term applications for improving pearl production and quality control in the industry.

Sex difference contributes to phenotypic diversity in individuals with neurodevelopmental disorders

Objective

Gain a better understanding of sex-specific differences in individuals with global developmental delay (GDD), with a focus on phenotypes and genotypes.

Methods

Using the Deciphering Developmental Disorders (DDD) dataset, we extracted phenotypic information from 6,588 individuals with GDD and then identified statistically significant variations in phenotypes and genotypes based on sex. We compared genes with pathogenic variants between sex and then performed gene network and molecular function enrichment analysis and gene expression profiling between sex. Finally, we contrasted individuals with autism as an associated condition.

Results

We identified significantly differentially expressed phenotypes in males vs. females individuals with GDD. Autism and macrocephaly were significantly more common in males whereas microcephaly and stereotypies were more common in females. Importantly, 66% of GDD genes with pathogenic variants overlapped between both sexes. In the cohort, males presented with only slightly increased X-linked genes (9% vs. 8%, respectively). Individuals from both sexes harbored a similar number of pathogenic variants overall (3) but females presented with a significantly higher load for GDD genes with high intolerance to loss of function. Sex difference in gene expression correlated with genes identified in a sex specific manner. While we identified sex-specific GDD gene mutations, their pathways overlapped. Interestingly, individuals with GDD but also co-morbid autism phenotypes, we observed distinct mutation load, pathways and phenotypic presentation.

Conclusion

Our study shows for the first time that males and females with GDD present with significantly different phenotypes. Moreover, while most GDD genes overlapped, some genes were found uniquely in each sex. Surprisingly they shared similar molecular functions. Sorting genes by predicted tolerance to loss of function (pLI) led to identifying an increased mutation load in females with GDD, suggesting potentially a tolerance to GDD genes of higher pLI compared to overall GDD genes. Finally, we show that considering associated conditions (for instance autism) may influence the genomic underpinning found in individuals with GDD and highlight the importance of comprehensive phenotyping.

Rapid and Technically Simple Detection of SARS-CoV-2 Variants Using CRISPR Cas12 and Cas13

The worldwide proliferation of the SARS-CoV-2 virus in the past 3 years has allowed the virus to accumulate numerous mutations. Dangerous lineages have emerged one after another, each leading to a new wave of the pandemic. In this study, we have developed the THRASOS pipeline to rapidly discover lineage-specific mutation signatures and thus advise the development of Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-based diagnostic tests. We also optimized a strategy to modify loop-mediated isothermal amplification amplicons for downstream use with Cas12 and Cas13 for future multiplexing. The close ancestry of the BA.1 and BA.2 variants of SARS-CoV-2 (Omicron) made these excellent candidates for the development of a first test using this workflow. With a quick turnaround time and low requirements for laboratory equipment, the test we have created is ideally suited for settings such as mobile clinics lacking equipment such as Next-Generation Sequencers or Sanger Sequencers and the personnel to run these devices.

Glucocorticoid stimulation induces regionalized gene responses within topologically associating domains

Transcription-factor binding to cis-regulatory regions regulates the gene expression program of a cell, but occupancy is often a poor predictor of the gene response. Here, we show that glucocorticoid stimulation led to the reorganization of transcriptional coregulators MED1 and BRD4 within topologically associating domains (TADs), resulting in active or repressive gene environments. Indeed, we observed a bias toward the activation or repression of a TAD when their activities were defined by the number of regions gaining and losing MED1 and BRD4 following dexamethasone (Dex) stimulation. Variations in Dex-responsive genes at the RNA levels were consistent with the redistribution of MED1 and BRD4 at the associated cis-regulatory regions. Interestingly, Dex-responsive genes without the differential recruitment of MED1 and BRD4 or binding by the glucocorticoid receptor were found within TADs, which gained or lost MED1 and BRD4, suggesting a role of the surrounding environment in gene regulation. However, the amplitude of the response of Dex-regulated genes was higher when the differential recruitment of the glucocorticoid receptor and transcriptional coregulators was observed, reaffirming the role of transcription factor-driven gene regulation and attributing a lesser role to the TAD environment. These results support a model where a signal-induced transcription factor induces a regionalized effect throughout the TAD, redefining the notion of direct and indirect effects of transcription factors on target genes.

Early ART reduces viral seeding and innate immunity in liver and lungs of SIV-infected macaques

Identifying immune cells and anatomical tissues that contribute to the establishment of viral reservoirs is of central importance in HIV-1 cure research. Herein, we used rhesus macaques (RMs) infected with SIVmac251 to analyze viral seeding in the liver and lungs of either untreated or early antiretroviral therapy-treated (ART-treated) RMs. Consistent with viral replication and sensing, transcriptomic analyses showed higher levels of inflammation, pyroptosis, and chemokine genes as well as of interferon-stimulating gene (ISG) transcripts, in the absence of ART. Our results highlighted the infiltration of monocyte-derived macrophages (HLA-DR+CD11b+CD14+CD16+) in inflamed liver and lung tissues associated with the expression of CD183 and CX3CR1 but also with markers of tissue-resident macrophages (CD206+ and LYVE+). Sorting of myeloid cell subsets demonstrated that CD14+CD206-, CD14+CD206+, and CD14-CD206+ cell populations were infected, in the liver and lungs, in SIVmac251-infected RMs. Of importance, early ART drastically reduced viral seeding consistent with the absence of ISG detection but also of genes related to inflammation and tissue damage. Viral DNA was only detected in CD206+HLA-DR+CD11b+ cells in ART-treated RMs. The observation of pulmonary and hepatic viral rebound after ART interruption reinforces the importance of early ART implementation to limit viral seeding and inflammatory reactions.

Enhancing Classification of liquid chromatography mass spectrometry data with Batch Effect Removal Neural Networks (BERNN)

Liquid Chromatography Mass Spectrometry (LC-MS) is a powerful method for profiling complex biological samples. However, batch effects typically arise from differences in sample processing protocols, experimental conditions and data acquisition techniques, significantlyimpacting the interpretability of results. Correcting batch effects is crucial for the reproducibility of proteomics research, but current methods are not optimal for removal of batch effects without compressing the genuine biological variation under study. We propose a suite of Batch Effect Removal Neural Networks (BERNN) to remove batch effects in large LC-MS experiments, with the goal of maximizing sample classification performance between conditions. More importantly, these models must efficiently generalize in batches not seen during training. Comparison of batch effect correction methods across three diverse datasets demonstrated that BERNN models consistently showed the strongest sample classification performance. However, the model producing the greatest classification improvements did not always perform best in terms of batch effect removal. Finally, we show that overcorrection of batch effects resulted in the loss of some essential biological variability. These findings highlight the importance of balancing batch effect removal while preserving valuable biological diversity in large-scale LC-MS experiments.

The contribution of inflammatory astrocytes to BBB impairments in a brain-chip model of Parkinson's disease

Astrocyte dysfunction has previously been linked to multiple neurodegenerative disorders including Parkinson's disease (PD). Among their many roles, astrocytes are mediators of the brain immune response, and astrocyte reactivity is a pathological feature of PD. They are also involved in the formation and maintenance of the blood-brain barrier (BBB), but barrier integrity is compromised in people with PD. This study focuses on an unexplored area of PD pathogenesis by characterizing the interplay between astrocytes, inflammation and BBB integrity, and by combining patient-derived induced pluripotent stem cells with microfluidic technologies to generate a 3D human BBB chip. Here we report that astrocytes derived from female donors harboring the PD-related LRRK2 G2019S mutation are pro-inflammatory and fail to support the formation of a functional capillary in vitro. We show that inhibition of MEK1/2 signaling attenuates the inflammatory profile of mutant astrocytes and rescues BBB formation, providing insights into mechanisms regulating barrier integrity in PD. Lastly, we confirm that vascular changes are also observed in the human postmortem substantia nigra of both males and females with PD.

PSMC3 proteasome subunit variants are associated with neurodevelopmental delay and type I interferon production

A critical step in preserving protein homeostasis is the recognition, binding, unfolding, and translocation of protein substrates by six AAA-ATPase proteasome subunits (ATPase-associated with various cellular activities) termed PSMC1-6, which are required for degradation of proteins by 26S proteasomes. Here, we identified 15 de novo missense variants in the PSMC3 gene encoding the AAA-ATPase proteasome subunit PSMC3/Rpt5 in 23 unrelated heterozygous patients with an autosomal dominant form of neurodevelopmental delay and intellectual disability. Expression of PSMC3 variants in mouse neuronal cultures led to altered dendrite development, and deletion of the PSMC3 fly ortholog Rpt5 impaired reversal learning capabilities in fruit flies. Structural modeling as well as proteomic and transcriptomic analyses of T cells derived from patients with PSMC3 variants implicated the PSMC3 variants in proteasome dysfunction through disruption of substrate translocation, induction of proteotoxic stress, and alterations in proteins controlling developmental and innate immune programs. The proteostatic perturbations in T cells from patients with PSMC3 variants correlated with a dysregulation in type I interferon (IFN) signaling in these T cells, which could be blocked by inhibition of the intracellular stress sensor protein kinase R (PKR). These results suggest that proteotoxic stress activated PKR in patient-derived T cells, resulting in a type I IFN response. The potential relationship among proteosome dysfunction, type I IFN production, and neurodevelopment suggests new directions in our understanding of pathogenesis in some neurodevelopmental disorders.

Multi-Omics Data Integration Reveals Key Variables Contributing to Subgingival Microbiome Dysbiosis-Induced Inflammatory Response in a Hyperglycemic Microenvironment

Subgingival microbiome dysbiosis promotes the development of periodontitis, an irreversible chronic inflammatory disease associated with metabolic diseases. However, studies regarding the effects of a hyperglycemic microenvironment on host-microbiome interactions and host inflammatory response during periodontitis are still scarce. Here, we investigated the impacts of a hyperglycemic microenvironment on the inflammatory response and transcriptome of a gingival coculture model stimulated with dysbiotic subgingival microbiomes. HGF-1 cells overlaid with U937 macrophage-like cells were stimulated with subgingival microbiomes collected from four healthy donors and four patients with periodontitis. Pro-inflammatory cytokines and matrix metalloproteinases were measured while the coculture RNA was submitted to a microarray analysis. Subgingival microbiomes were submitted to 16s rRNA gene sequencing. Data were analyzed using an advanced multi-omics bioinformatic data integration model. Our results show that the genes krt76, krt27, pnma5, mansc4, rab41, thoc6, tm6sf2, and znf506 as well as the pro-inflammatory cytokines IL-1β, GM-CSF, FGF2, IL-10, the metalloproteinases MMP3 and MMP8, and bacteria from the ASV 105, ASV 211, ASV 299, Prevotella, Campylobacter and Fretibacterium genera are key intercorrelated variables contributing to periodontitis-induced inflammatory response in a hyperglycemic microenvironment. In conclusion, our multi-omics integration analysis unveiled the complex interrelationships involved in the regulation of periodontal inflammation in response to a hyperglycemic microenvironment.

Chromosome-level assembly of the Rangifer tarandus genome and validation of cervid and bovid evolution insights

BACKGROUND

Genome assembly into chromosomes facilitates several analyses including cytogenetics, genomics and phylogenetics. Despite rapid development in bioinformatics, however, assembly beyond scaffolds remains challenging, especially in species without closely related well-assembled and available reference genomes. So far, four draft genomes of Rangifer tarandus (caribou or reindeer, a circumpolar distributed cervid species) have been published, but none with chromosome-level assembly. This emblematic northern species is of high interest in ecological studies and conservation since most populations are declining.

RESULTS

We have designed specific probes based on Oligopaint FISH technology to upgrade the latest published reindeer and caribou chromosome-level genomes. Using this oligonucleotide-based method, we found six mis-assembled scaffolds and physically mapped 68 of the largest scaffolds representing 78% of the most recent R. tarandus genome assembly. Combining physical mapping and comparative genomics, it was possible to document chromosomal evolution among Cervidae and closely related bovids.

CONCLUSIONS

Our results provide validation for the current chromosome-level genome assembly as well as resources to use chromosome banding in studies of Rangifer tarandus.

Integrative analysis of green ash phloem transcripts and proteins during an emerald ash borer infestation

BACKGROUND

Emerald ash borer (Agrilus planipennis; EAB) is an Asian insect species that has been invasive to North America for 20 years. During this time, the emerald ash borer has killed tens of millions of American ash (Fraxinus spp) trees. Understanding the inherent defenses of susceptible American ash trees will provide information to breed new resistant varieties of ash trees.

RESULTS

We have performed RNA-seq on naturally infested green ash (F. pennsylvanica) trees at low, medium and high levels of increasing EAB infestation and proteomics on low and high levels of EAB infestation. Most significant transcript changes we detected occurred between the comparison of medium and high levels of EAB infestation, indicating that the tree is not responding to EAB until it is highly infested. Our integrative analysis of the RNA-Seq and proteomics data identified 14 proteins and 4 transcripts that contribute most to the difference between highly infested and low infested trees.

CONCLUSIONS

The putative functions of these transcripts and proteins suggests roles of phenylpropanoid biosynthesis and oxidation, chitinase activity, pectinesterase activity, strigolactone signaling, and protein turnover.

Specific alterations in riboproteomes composition of isonicotinic acid treated arabidopsis seedlings

Plants have developed strategies to deal with the great variety of challenges they are exposed to. Among them, common targets are the regulation of transcription and translation to finely modulate protein levels during both biotic and abiotic stresses. Increasing evidence suggests that ribosomes are highly adaptable modular supramolecular structures which remodel to adapt to stresses. Each Arabidopsis thaliana ribosome consists of approximately 81 distinct ribosomal proteins (RPs), each of which is encoded by two to seven genes. To investigate the identity of ribosomal proteins of the small subunit (RPS) and of the large subunit (RPL) as well as ribosomes-associated proteins, we analysed by LC/MS/MS immunopurified ribosomes from A. thaliana leaves treated with isonicotinic acid (INA), an inducer of plant innate immunity. We quantified a total of 2084 proteins. 165 ribosome-associated proteins showed increased abundance while 52 were less abundant. Of the 52 identified RPS (from a possibility of 104 encoding genes), 15 were deregulated. Similarly, from the 148 possible RPL, 80 were detected and 9 were deregulated. Our results revealed potential candidates involved in innate immunity that could be interesting targets for functional genomic studies.

Hedgehog signaling regulates Wolffian duct development through the primary cilium†

Primary cilia play pivotal roles in embryonic patterning and organogenesis through transduction of the Hedgehog signaling pathway (Hh). Although mutations in Hh morphogens impair the development of the gonads and trigger male infertility, the contribution of Hh and primary cilia in the development of male reproductive ductules, including the epididymis, remains unknown. From a Pax2Cre; IFT88fl/fl knock-out mouse model, we found that primary cilia deletion is associated with imbalanced Hh signaling and morphometric changes in the Wolffian duct (WD), the embryonic precursor of the epididymis. Similar effects were observed following pharmacological blockade of primary cilia formation and Hh modulation on WD organotypic cultures. The expression of genes involved in extracellular matrix, mesenchymal-epithelial transition, canonical Hh and WD development was significantly altered after treatments. Altogether, we identified the primary cilia-dependent Hh signaling as a master regulator of genes involved in WD development. This provides new insights regarding the etiology of sexual differentiation and male infertility issues.

Transgenerational impact of grand-paternal lifetime exposures to both folic acid deficiency and supplementation on genome-wide DNA methylation in male germ cells

BACKGROUND

DNA methylation (DNAme) erasure and reacquisition occur during prenatal male germ cell development; some further remodeling takes place after birth during spermatogenesis. Environmental insults during germline epigenetic reprogramming may affect DNAme, presenting a potential mechanism for transmission of environmental exposures across multiple generations.

OBJECTIVES

We investigated how germ cell DNAme is impacted by lifetime exposures to diets containing either low or high, clinically relevant, levels of the methyl donor folic acid and whether resulting DNAme alterations were inherited in germ cells of male offspring of subsequent generations.

MATERIALS AND METHODS

Female mice were placed on a control (FCD), 7-fold folic acid deficient (7FD) or 10- to 20-fold supplemented (10FS and 20FS) diet before and during pregnancy. Resulting F1 litters were weaned on the respective diets. F2 and F3 males received control diets. Genome-wide DNAme at cytosines (within CpG sites) was assessed in F1 spermatogonia, and in F1, F2 and F3 sperm.

RESULTS

In F1 germ cells, a greater number of differentially methylated cytosines (DMCs) were observed in spermatogonia as compared with F1 sperm for all folic acid diets. DMCs were lower in number in F2 versus F1 sperm, while an unexpected increase was found in F3 sperm. DMCs were predominantly hypomethylated, with genes in neurodevelopmental pathways commonly affected in F1, F2 and F3 male germ cells. While no DMCs were found to be significantly inherited inter- or transgenerationally, we observed over-representation of repetitive elements, particularly young long interspersed nuclear elements (LINEs).

DISCUSSION AND CONCLUSION

These results suggest that the prenatal window is the time most susceptible to folate-induced alterations in sperm DNAme in male germ cells. Altered methylation of specific sites in F1 germ cells was not present in later generations. However, the presence of DNAme perturbations in the sperm of males of the F2 and F3 generations suggests that epigenetic inheritance mechanisms other than DNAme may have been impacted by the folate diet exposure of F1 germ cells.

Association between genome-wide epigenetic and genetic alterations in breast cancer tissue and response to HER2-targeted therapies in HER2-positive breast cancer patients: new findings and a systematic review

Recent evidence suggests that genetic and epigenetic mechanisms might be associated with acquired resistance to cancer therapies. The aim of this study was to assess the association of genome-wide genetic and epigenetic alterations with the response to anti-HER2 agents in HER2-positive breast cancer patients. PubMed was screened for articles published until March 2021 on observational studies investigating the association of genome-wide genetic and epigenetic alterations, measured in breast cancer tissues or blood, with the response to targeted treatment in HER2-positive breast cancer patients. Sixteen studies were included in the review along with ours, in which we compared the genome-wide DNA methylation pattern in breast tumor tissues of patients who acquired resistance to treatment (case group, n = 6) to that of patients who did not develop resistance (control group, n = 6). Among genes identified as differentially methylated between the breast cancer tissue of cases and controls, one of them, PRKACA, was also reported as differentially expressed in two studies included in the review. Although included studies were heterogeneous in terms of methodology and study population, our review suggests that genes of the PI3K pathway may play an important role in developing resistance to anti-HER2 agents in breast cancer patients. Genome-wide genetic and epigenetic alterations measured in breast cancer tissue or blood might be promising markers of resistance to anti-HER2 agents in HER2-positive breast cancer patients. Further studies are needed to confirm these data.

Vitamin C modulates the levels of several proteins of the mitochondrial complex III and its activity in the mouse liver

Ascorbate is a crucial antioxidant and essential cofactor of biosynthetic and regulatory enzymes. Unlike humans, mice can synthesize ascorbate thanks to the key enzyme gulonolactone oxidase (Gulo). In the present study, we used the Gulo-/- mouse model, which cannot synthesize their own ascorbate to determine the impact of this vitamin on the liver proteome of specific subcellular organelles. We performed label-free Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) global quantitative proteomic profiling to identify and quantify proteins in microsomal enriched liver extracts (MEE) from Gulo-/- mice treated with 0-0.4% (w/v) ascorbate in drinking water until the age of four months. Using a principal component analysis on normalized and imputed data of the label-free protein quantifications, a sex-based difference in MEE proteome profiles was observed for all the different ascorbate treated mice. Suboptimal hepatic ascorbate concentrations affected the levels of more proteins and hence biochemical processes in females than in males. Nevertheless, Pearson correlation analyses revealed that the MS intensities of various proteins involved in complement activation inversely correlated with liver ascorbate concentrations in both Gulo-/- males and females. Moreover, the correlation analyses also indicated that several proteins in the mitochondrial complex III of the electron transport chain positively correlated with liver ascorbate concentrations in both Gulo-/- females and males. Consequently, the mitochondrial complex III activity in Gulo-/- female and male mice treated with suboptimal hepatic concentrations of ascorbate was significantly lower than Gulo-/- mice treated with optimal ascorbate concentration. Finally, the whole liver of ascorbate-deficient Gulo-/- mice exhibited lower ATP levels and increased reactive oxygen species. These findings provide new information on how ascorbate deficiency potentially induces mitochondrial dysfunction in the liver of mice.

Multi-omics analysis: Paving the path toward achieving precision medicine in cancer treatment and immuno-oncology

The acceleration of large-scale sequencing and the progress in high-throughput computational analyses, defined as omics, was a hallmark for the comprehension of the biological processes in human health and diseases. In cancerology, the omics approach, initiated by genomics and transcriptomics studies, has revealed an incredible complexity with unsuspected molecular diversity within a same tumor type as well as spatial and temporal heterogeneity of tumors. The integration of multiple biological layers of omics studies brought oncology to a new paradigm, from tumor site classification to pan-cancer molecular classification, offering new therapeutic opportunities for precision medicine. In this review, we will provide a comprehensive overview of the latest innovations for multi-omics integration in oncology and summarize the largest multi-omics dataset available for adult and pediatric cancers. We will present multi-omics techniques for characterizing cancer biology and show how multi-omics data can be combined with clinical data for the identification of prognostic and treatment-specific biomarkers, opening the way to personalized therapy. To conclude, we will detail the newest strategies for dissecting the tumor immune environment and host–tumor interaction. We will explore the advances in immunomics and microbiomics for biomarker identification to guide therapeutic decision in immuno-oncology.

Design and validation of a 63K genome-wide SNP-genotyping platform for caribou/reindeer (Rangifer tarandus)

BACKGROUND

Development of large single nucleotide polymorphism (SNP) arrays can make genomic data promptly available for conservation problematic. Medium and high-density panels can be designed with sufficient coverage to offer a genome-wide perspective and the generated genotypes can be used to assess different genetic metrics related to population structure, relatedness, or inbreeding. SNP genotyping could also permit sexing samples with unknown associated metadata as it is often the case when using non-invasive sampling methods favored for endangered species. Genome sequencing of wild species provides the necessary information to design such SNP arrays. We report here the development of a SNP-array for endangered Rangifer tarandus using a multi-platform sequencing approach from animals found in diverse populations representing the entire circumpolar distribution of the species.

RESULTS

From a very large comprehensive catalog of SNPs detected over the entire sample set (N = 894), a total of 63,336 SNPs were selected. SNP selection accounted for SNPs evenly distributed across the entire genome (~ every 50Kb) with known minor alleles across populations world-wide. In addition, a subset of SNPs was selected to represent rare and local alleles found in Eastern Canada which could be used for ecotype and population assignments - information urgently needed for conservation planning. In addition, heterozygosity from SNPs located in the X-chromosome and genotyping call-rate of SNPs located into the SRY gene of the Y-chromosome yielded an accurate and robust sexing assessment. All SNPs were validated using a high-throughput SNP-genotyping chip.

CONCLUSION

This design is now integrated into the first genome-wide commercially available genotyping platform for Rangifer tarandus. This platform would pave the way to future genomic investigation of populations for this endangered species, including estimation of genetic diversity parameters, population assignments, as well as animal sexing from genetic SNP data for non-invasive samples.

Overview of methods for characterization and visualization of a protein–protein interaction network in a multi-omics integration context

At the heart of the cellular machinery through the regulation of cellular functions, protein–protein interactions (PPIs) have a significant role. PPIs can be analyzed with network approaches. Construction of a PPI network requires prediction of the interactions. All PPIs form a network. Different biases such as lack of data, recurrence of information, and false interactions make the network unstable. Integrated strategies allow solving these different challenges. These approaches have shown encouraging results for the understanding of molecular mechanisms, drug action mechanisms, and identification of target genes. In order to give more importance to an interaction, it is evaluated by different confidence scores. These scores allow the filtration of the network and thus facilitate the representation of the network, essential steps to the identification and understanding of molecular mechanisms. In this review, we will discuss the main computational methods for predicting PPI, including ones confirming an interaction as well as the integration of PPIs into a network, and we will discuss visualization of these complex data.

Uncovering the Contribution of Moderate-Penetrance Susceptibility Genes to Breast Cancer by Whole-Exome Sequencing and Targeted Enrichment Sequencing of Candidate Genes in Women of European Ancestry

Rare variants in at least 10 genes, including BRCA1, BRCA2, PALB2, ATM, and CHEK2, are associated with increased risk of breast cancer; however, these variants, in combination with common variants identified through genome-wide association studies, explain only a fraction of the familial aggregation of the disease. To identify further susceptibility genes, we performed a two-stage whole-exome sequencing study. In the discovery stage, samples from 1528 breast cancer cases enriched for breast cancer susceptibility and 3733 geographically matched unaffected controls were sequenced. Using five different filtering and gene prioritization strategies, 198 genes were selected for further validation. These genes, and a panel of 32 known or suspected breast cancer susceptibility genes, were assessed in a validation set of 6211 cases and 6019 controls for their association with risk of breast cancer overall, and by estrogen receptor (ER) disease subtypes, using gene burden tests applied to loss-of-function and rare missense variants. Twenty genes showed nominal evidence of association (p-value < 0.05) with either overall or subtype-specific breast cancer. Our study had the statistical power to detect susceptibility genes with effect sizes similar to ATM, CHEK2, and PALB2, however, it was underpowered to identify genes in which susceptibility variants are rarer or confer smaller effect sizes. Larger sample sizes would be required in order to identify such genes.

Cytokines and Lipid Mediators of Inflammation in Lungs of SARS-CoV-2 Infected Mice

Coronavirus disease 19 (COVID-19) is the clinical manifestation of severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) infection. A hallmark of COVID-19 is a lung inflammation characterized by an abundant leukocyte infiltrate, elevated levels of cytokines/chemokines, lipid mediators of inflammation (LMI) and microthrombotic events. Animal models are useful for understanding the pathophysiological events leading to COVID-19. One such animal model is the K18-ACE2 transgenic mice. Despite their importance in inflammation, the study of LMI in lung of SARS-CoV-2 infected K18-ACE2 mice has yet to be studied to our knowledge. Using tandem mass spectrometry, the lung lipidome at different time points of infection was analyzed. Significantly increased LMI included N-oleoyl-serine, N-linoleoyl-glycine, N-oleoyl-alanine, 1/2-linoleoyl-glycerol, 1/2-docosahexaenoyl-glycerol and 12-hydroxy-eicosapenatenoic acid. The levels of prostaglandin (PG) E₁, PGF_2α, stearoyl-ethanolamide and linoleoyl-ethanolamide were found to be significantly reduced relative to mock-infected mice. Other LMI were present at similar levels (or undetected) in both uninfected and infected mouse lungs. In parallel to LMI measures, transcriptomic and cytokine/chemokine profiling were performed. Viral replication was robust with maximal lung viral loads detected on days 2-3 post-infection. Lung histology revealed leukocyte infiltration starting on day 3 post-infection, which correlated with the presence of high concentrations of several chemokines/cytokines. At early times post-infection, the plasma of infected mice contained highly elevated concentration of D-dimers suggestive of blood clot formation/dissolution. In support, the presence of blood clots in the lung vasculature was observed during infection. RNA-Seq analysis of lung tissues indicate that SARS-CoV-2 infection results in the progressive modulation of several hundred genes, including several inflammatory mediators and genes related to the interferons. Analysis of the lung lipidome indicated modest, yet significant modulation of a minority of lipids. In summary, our study suggests that SARS-CoV-2 infection in humans and mice share common features, such as elevated levels of chemokines in lungs, leukocyte infiltration and increased levels of circulating D-dimers. However, the K18-ACE2 mouse model highlight major differences in terms of LMI being produced in response to SARS-CoV-2 infection. The potential reasons and impact of these differences on the pathology and therapeutic strategies to be employed to treat severe COVID-19 are discussed.

Recurrent chromosomal translocations in sarcomas create a megacomplex that mislocalizes NuA4/TIP60 to Polycomb target loci

Chromosomal translocations frequently promote carcinogenesis by producing gain-of-function fusion proteins. Recent studies have identified highly recurrent chromosomal translocations in patients with endometrial stromal sarcomas (ESSs) and ossifying fibromyxoid tumors (OFMTs), leading to an in-frame fusion of PHF1 (PCL1) to six different subunits of the NuA4/TIP60 complex. While NuA4/TIP60 is a coactivator that acetylates chromatin and loads the H2A.Z histone variant, PHF1 is part of the Polycomb repressive complex 2 (PRC2) linked to transcriptional repression of key developmental genes through methylation of histone H3 on lysine 27. In this study, we characterize the fusion protein produced by the EPC1-PHF1 translocation. The chimeric protein assembles a megacomplex harboring both NuA4/TIP60 and PRC2 activities and leads to mislocalization of chromatin marks in the genome, in particular over an entire topologically associating domain including part of the HOXD cluster. This is linked to aberrant gene expression-most notably increased expression of PRC2 target genes. Furthermore, we show that JAZF1-implicated with a PRC2 component in the most frequent translocation in ESSs, JAZF1-SUZ12-is a potent transcription activator that physically associates with NuA4/TIP60, its fusion creating outcomes similar to those of EPC1-PHF1 Importantly, the specific increased expression of PRC2 targets/HOX genes was also confirmed with ESS patient samples. Altogether, these results indicate that most chromosomal translocations linked to these sarcomas use the same molecular oncogenic mechanism through a physical merge of NuA4/TIP60 and PRC2 complexes, leading to mislocalization of histone marks and aberrant Polycomb target gene expression.

Abstract 3897: Ribosomal translational regulation is a potential mechanism for leukemia-related thrombo-embolic event in childhood acute lymphoblastic leukemia

Introduction: Thrombo-embolic event (TE) is a frequent complication of childhood acute lymphoblastic leukemia (cALL) and is associated with reduced survival. Overexpression of podoplanin or coagulome genes and coagulation pathway activation have been identified in cancer-induced TE but the role of leukemia environment in TE occurrence has not been fully elucidated in ALL. We assessed whether leukemia gene expression (GE) signature at diagnosis was associated with TE development in cALL.

Methods: We included children aged 0-18 years old (y.o.), from two hospitals, with newly diagnosed ALL and available RNA sequencing data from bone marrow at diagnosis. The primary outcome was the occurrence of grade ≥2 TE during ALL therapy using the Ponte Di Legno Working Group classification. TEs were classified as early (ET) if they occurred within 6 weeks from treatment start, or late (LT) otherwise. We compared differential gene expression (DE) in children with and without TE, adjusted for age (&lt;10 or ≥10 y.o.) and ALL type (T or B-ALL). A secondary analysis stratified children between ET, LT and no TE. Gene set enrichment analysis (GSEA) was performed on KEGG and gene ontology (GO) databases. DE with absolute fold change ≥2 and p-values &lt;0.05 were considered significant.

Results: We included 80 patients (median age: 5 years [interquartile range, IQR: 3-11 years], 53% male, 83% precursor B-cell ALL) of whom 19 (23.8%) developed a TE (7 ET and 12 LT) at a median of 76 days (IQR: 31-133 days) following cancer diagnosis. Patients with TE were more likely to be ≥10 y.o., while other demographic and clinical characteristics were similar. No genes from the coagulome, podoplanin or Hallmark coagulation pathways were differentially expressed in children with and without TE. The KEGG Ribosome pathway was the most upregulated pathway in the group with TE (normalized enrichment score (NES) = 2.35, adjusted p-value= 0.012) and ET (NES = 2.63, adjusted p-value &lt; 0.001). LT was not significantly associated with ribosome pathway dysregulation. Interestingly, prior reports identified functional enrichment in ribosomal pathway as a biomarker for venous TE. A heatmap classification for ribosomal genes revealed 3 distinct signatures: ribosomal downregulation, moderate and high ribosomal activation. High ribosomal activation profile was seen in 6/7 ET and 9/12 LT, corresponding to a positive and negative predictive value of 0.55 and 0.92 for TE, respectively.

Conclusion: TEs in cALL were not associated with dysregulation in coagulation pathways or podoplanin gene at the RNA level. However, ribosomal translational pathway was highly upregulated in the group with thrombosis, particularly with ET. Dysregulation of posttranscriptional machinery might explain the pro-thrombotic effect of leukemia environment in cALL and warrants further investigation including proteomic exploration.

Citation Format: Marie-Claude Pelland-Marcotte, Meredith Michelle Rémy, Yan Ma, Jessica Liu, Camille Jimenez-Cortes, Thomas Sontag, Maxime Caron, Pascal Saint-Onge, Sylvie Langlois, Charles Joly-Beauparlant, Daniel Sinnett, Arnaud Droit, Thai Hoa Tran, Raoul Santiago. Ribosomal translational regulation is a potential mechanism for leukemia-related thrombo-embolic event in childhood acute lymphoblastic leukemia [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3897.

MRG Proteins Are Shared by Multiple Protein Complexes With Distinct Functions

MRG15/MORF4L1 is a highly conserved protein in eukaryotes that contains a chromodomain (CHD) recognizing methylation of lysine 36 on histone H3 (H3K36me3) in chromatin. Intriguingly, it has been reported in the literature to interact with several different factors involved in chromatin modifications, gene regulation, alternative mRNA splicing, and DNA repair by homologous recombination. To get a complete and reliable picture of associations in physiological conditions, we used genome editing and tandem affinity purification to analyze the stable native interactome of human MRG15, its paralog MRGX/MORF4L2 that lacks the CHD, and MRGBP (MRG-binding protein) in isogenic K562 cells. We found stable interchangeable association of MRG15 and MRGX with the NuA4/TIP60 histone acetyltransferase/chromatin remodeler, Sin3B histone deacetylase/demethylase, ASH1L histone methyltransferase, and PALB2-BRCA2 DNA repair protein complexes. These associations were further confirmed and analyzed by CRISPR tagging of endogenous proteins and comparison of expressed isoforms. Importantly, based on structural information, point mutations could be introduced that specifically disrupt MRG15 association with some complexes but not others. Most interestingly, we also identified a new abundant native complex formed by MRG15/X-MRGBP-BRD8-EP400NL (EP400 N-terminal like) that is functionally similar to the yeast TINTIN (Trimer Independent of NuA4 for Transcription Interactions with Nucleosomes) complex. Our results show that EP400NL, being homologous to the N-terminal region of NuA4/TIP60 subunit EP400, creates TINTIN by competing for BRD8 association. Functional genomics indicate that human TINTIN plays a role in transcription of specific genes. This is most likely linked to the H4ac-binding bromodomain of BRD8 along the H3K36me3-binding CHD of MRG15 on the coding region of transcribed genes. Taken together, our data provide a complete detailed picture of human MRG proteins-associated protein complexes, which are essential to understand and correlate their diverse biological functions in chromatin-based nuclear processes.

Mass spectrometry-based proteomics identify novel serum osteoarthritis biomarkers

Background

Osteoarthritis (OA) is a slowly developing and debilitating disease, and there are no validated specific biomarkers for its early detection. To improve therapeutic approaches, identification of specific molecules/biomarkers enabling early determination of this disease is needed. This study aimed at identifying, with the use of proteomics/mass spectrometry, novel OA-specific serum biomarkers. As obesity is a major risk factor for OA, we discriminated obesity-regulated proteins to target only OA-specific proteins as biomarkers.

Methods

Serum from the Osteoarthritis Initiative cohort was used and divided into 3 groups: controls (n=8), OA-obese (n=10) and OA-non-obese (n=10). Proteins were identified and quantified from the liquid chromatography–tandem mass spectrometry analyses using MaxQuant software. Statistical analysis used the Limma test followed by the Benjamini-Hochberg method. To compare the proteomic profiles, the multivariate unsupervised principal component analysis (PCA) followed by the pairwise comparison was used. To select the most predictive/discriminative features, the supervised linear classification model sparse partial least squares regression discriminant analysis (sPLS-DA) was employed. Validation of three differential proteins was performed with protein-specific assays using plasma from a cohort derived from the Newfoundland Osteoarthritis.

Results

In total, 509 proteins were identified, and 279 proteins were quantified. PCA-pairwise differential comparisons between the 3 groups revealed that 8 proteins were differentially regulated between the OA-obese and/or OA-non-obese with controls. Further experiments using the sPLS-DA revealed two components discriminating OA from controls (component 1, 9 proteins), and OA-obese from OA-non-obese (component 2, 23 proteins). Proteins from component 2 were considered related to obesity. In component 1, compared to controls, 7 proteins were significantly upregulated by both OA groups and 2 by the OA-obese. Among upregulated proteins from both OA groups, some of them alone would not be a suitable choice as specific OA biomarkers due to their rather non-specific role or their strong link to other pathological conditions. Altogether, data revealed that the protein CRTAC1 appears to be a strong OA biomarker candidate. Other potential new biomarker candidates are the proteins FBN1, VDBP, and possibly SERPINF1. Validation experiments revealed statistical differences between controls and OA for FBN1 (p=0.044) and VDPB (p=0.022), and a trend for SERPINF1 (p=0.064).

Conclusion

Our study suggests that 4 proteins, CRTAC1, FBN1, VDBP, and possibly SERPINF1, warrant further investigation as potential new biomarker candidates for the whole OA population.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13075-022-02801-1.