The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements

NFATc4 Knockout Promotes Neuroprotection and Retinal Ganglion Cell Regeneration After Optic Nerve Injury

Retinal ganglion cells (RGCs), neurons transmitting visual information via the optic nerve, fail to regenerate their axons after injury. The progressive loss of RGC function underlies the pathophysiology of glaucoma and other optic neuropathies, often leading to irreversible blindness. Therefore, there is an urgent need to identify the regulators of RGC survival and the regenerative program. In this study, we investigated the role of the family of transcription factors known as nuclear factor of activated T cells (NFAT), which are expressed in the retina; however, their role in RGC survival after injury is unknown. Using the optic nerve crush (ONC) model, widely employed to study optic neuropathies and central nervous system axon injury, we found that NFATc4 is specifically but transiently up-regulated in response to mechanical injury. In the injured retina, NFATc4 immunolocalized primarily to the ganglionic cell layer. Utilizing NFATc4-/- and NFATc3-/- mice, we demonstrated that NFATc4, but not NFATc3, knockout increased RGC survival, improved retina function, and delayed axonal degeneration. Microarray screening data, along with decreased immunostaining of cleaved caspase-3, revealed that NFATc4 knockout was protective against ONC-induced degeneration by suppressing pro-apoptotic signaling. Finally, we used lentiviral-mediated NFATc4 delivery to the retina of NFATc4-/- mice and reversed the pro-survival effect of NFATc4 knockout, conclusively linking the enhanced survival of injured RGCs to NFATc4-dependent mechanisms. In summary, this study is the first to demonstrate that NFATc4 knockout may confer transient RGC neuroprotection and decelerate axonal degeneration after injury, providing a potent therapeutic strategy for optic neuropathies.

Precision and Accuracy of Single-Cell/Nuclei RNA Sequencing Data

Single-cell/nuclei RNA sequencing (sc/snRNA-Seq) is widely used for profiling cell-type gene expressions in biomedical research. An important but underappreciated issue is the quality of sc/snRNA-Seq data that would impact the reliability of downstream analyses. Here we evaluated the precision and accuracy in 18 sc/snRNA-Seq datasets. The precision was assessed on data from human brain studies with a total of 3,483,905 cells from 297 individuals, by utilizing technical replicates. The accuracy was evaluated with sample-matched scRNA-Seq and pooled-cell RNA-Seq data of cultured mononuclear phagocytes from four species. The results revealed low precision and accuracy at the single-cell level across all evaluated data. Cell number and RNA quality were highlighted as two key factors determining the expression precision, accuracy, and reproducibility of differential expression analysis in sc/snRNA-Seq. This study underscores the necessity of sequencing enough high-quality cells per cell type per individual, preferably in the hundreds, to mitigate noise in expression quantification.

Reliable biological and multi-omics research through biometrology

Metrology is the science of measurement and its applications, whereas biometrology is the science of biological measurement and its applications. Biometrology aims to achieve accuracy and consistency of biological measurements by focusing on the development of metrological traceability, biological reference measurement procedures, and reference materials. Irreproducibility of biological and multi-omics research results from different laboratories, platforms, and analysis methods is hampering the translation of research into clinical uses and can often be attributed to the lack of biologists' attention to the general principles of metrology. In this paper, the progresses of biometrology including metrology on nucleic acid, protein, and cell measurements and its impacts on the improvement of reliability and comparability in biological research are reviewed. Challenges in obtaining more reliable biological and multi-omics measurements due to the lack of primary reference measurement procedures and new standards for biological reference materials faced by biometrology are discussed. In the future, in addition to establishing reliable reference measurement procedures, developing reference materials from single or multiple parameters to multi-omics scale should be emphasized. Thinking in way of biometrology is warranted for facilitating the translation of high-throughput omics research into clinical practices.

Molecular underpinnings of programming by early-life stress and the protective effects of early dietary ω6/ω3 ratio, basally and in response to LPS: Integrated mRNA-miRNAs approach

Early-life stress (ELS) exposure increases the risk for mental disorders, including cognitive impairments later in life. We have previously demonstrated that an early diet with low ω6/ω3 polyunsaturated fatty acid (PUFA) ratio protects against ELS-induced cognitive impairments. Several studies have implicated the neuroimmune system in the ELS and diet mediated effects, but currently the molecular pathways via which ELS and early diet exert their long-term impact are not yet fully understood. Here we study the effects of ELS and dietary PUFA ratio on hippocampal mRNA and miRNA expression in adulthood, both under basal as well as inflammatory conditions. Male mice were exposed to chronic ELS by the limiting bedding and nesting material paradigm from postnatal day(P)2 to P9, and provided with a diet containing a standard (high (15:1.1)) or protective (low (1.1:1)) ω6 linoleic acid to ω3 alpha-linolenic acid ratio from P2 to P42. At P120, memory was assessed using the object location task. Subsequently, a single lipopolysaccharide (LPS) injection was given and 24 h later hippocampal genome-wide mRNA and microRNA (miRNA) expression was measured using microarray. Spatial learning deficits induced by ELS in mice fed the standard (high ω6/ω3) diet were reversed by the early-life protective (low ω6/ω3) diet. An integrated miRNA - mRNA analysis revealed that ELS and early diet induced miRNA driven mRNA expression changes into adulthood. Under basal conditions both ELS and the diet affected molecular pathways related to hippocampal plasticity, with the protective (low ω6/ω3 ratio) diet leading to activation of molecular pathways associated with improved hippocampal plasticity and learning and memory in mice previously exposed to ELS (e.g., CREB signaling and endocannabinoid neuronal synapse pathway). LPS induced miRNA and mRNA expression was strongly dependent on both ELS and early diet. In mice fed the standard (high ω6/ω3) diet, LPS increased miRNA expression leading to activation of inflammatory pathways. In contrast, in mice fed the protective diet, LPS reduced miRNA expression and altered target mRNA expression inhibiting inflammatory signaling pathways and pathways associated with hippocampal plasticity, which was especially apparent in mice previously exposed to ELS. This data provides molecular insights into how the protective (low ω6/ω3) diet during development could exert its long-lasting beneficial effects on hippocampal plasticity and learning and memory especially in a vulnerable population exposed to stress early in life, providing the basis for the development of intervention strategies.

Standardizing digital biobanks: integrating imaging, genomic, and clinical data for precision medicine

Advancements in data acquisition and computational methods are generating a large amount of heterogeneous biomedical data from diagnostic domains such as clinical imaging, pathology, and next-generation sequencing (NGS), which help characterize individual differences in patients. However, this information needs to be available and suitable to promote and support scientific research and technological development, supporting the effective adoption of the precision medicine approach in clinical practice. Digital biobanks can catalyze this process, facilitating the sharing of curated and standardized imaging data, clinical, pathological and molecular data, crucial to enable the development of a comprehensive and personalized data-driven diagnostic approach in disease management and fostering the development of computational predictive models. This work aims to frame this perspective, first by evaluating the state of standardization of individual diagnostic domains and then by identifying challenges and proposing a possible solution towards an integrative approach that can guarantee the suitability of information that can be shared through a digital biobank. Our analysis of the state of the art shows the presence and use of reference standards in biobanks and, generally, digital repositories for each specific domain. Despite this, standardization to guarantee the integration and reproducibility of the numerical descriptors generated by each domain, e.g. radiomic, pathomic and -omic features, is still an open challenge. Based on specific use cases and scenarios, an integration model, based on the JSON format, is proposed that can help address this problem. Ultimately, this work shows how, with specific standardization and promotion efforts, the digital biobank model can become an enabling technology for the comprehensive study of diseases and the effective development of data-driven technologies at the service of precision medicine.

Causal genetic regulation of DNA replication on immune microenvironment in colorectal tumorigenesis: Evidenced by an integrated approach of trans-omics and GWAS

The interplay between DNA replication stress and immune microenvironment alterations is known to play a crucial role in colorectal tumorigenesis, but a comprehensive understanding of their association with and relevant biomarkers involved in colorectal tumorigenesis is lacking. To address this gap, we conducted a study aiming to investigate this association and identify relevant biomarkers. We analyzed transcriptomic and proteomic profiles of 904 colorectal tumor tissues and 342 normal tissues to examine pathway enrichment, biological activity, and the immune microenvironment. Additionally, we evaluated genetic effects of single variants and genes on colorectal cancer susceptibility using data from genome-wide association studies (GWASs) involving both East Asian (7062 cases and 195745 controls) and European (24476 cases and 23073 controls) populations. We employed mediation analysis to infer the causal pathway, and applied multiplex immunofluorescence to visualize colocalized biomarkers in colorectal tumors and immune cells. Our findings revealed that both DNA replication activity and the flap structure-specific endonuclease 1 ( FEN1) gene were significantly enriched in colorectal tumor tissues, compared with normal tissues. Moreover, a genetic variant rs4246215 G>T in FEN1 was associated with a decreased risk of colorectal cancer (odds ratio = 0.94, 95% confidence interval: 0.90-0.97, P meta = 4.70 × 10 -9). Importantly, we identified basophils and eosinophils that both exhibited a significantly decreased infiltration in colorectal tumors, and were regulated by rs4246215 through causal pathways involving both FEN1 and DNA replication. In conclusion, this trans-omics incorporating GWAS data provides insights into a plausible pathway connecting DNA replication and immunity, expanding biological knowledge of colorectal tumorigenesis and therapeutic targets.

Genetic Programs Between Steroid-Sensitive and Steroid-Insensitive Interstitial Lung Disease

The effectiveness of corticosteroids (GCs) varies greatly in interstitial lung diseases (ILDs). In this study, we aimed to compare the gene expression profiles of patients with cryptogenic organizing pneumonia (COP), idiopathic pulmonary fibrosis (IPF), and non-specific interstitial pneumonia (NSIP) and identify the molecules and pathways responsible for GCs sensitivity in ILDs. Three datasets (GSE21411, GSE47460, and GSE32537) were selected. Differentially expressed genes (DEGs) among COP, IPF, NSIP, and healthy control (CTRL) groups were identified. Functional enrichment analysis and protein-protein interaction network analysis were performed to examine the potential functions of DEGs. There were 128 DEGs when COP versus CTRL, 257 DEGs when IPF versus CTRL, 205 DEGs when NSIP versus CTRL, and 270 DEGs when COP versus IPF. The DEGs in different ILDs groups were mainly enriched in the inflammatory response. Further pathway analysis showed that "interleukin (IL)-17 signaling pathway" (hsa04657) and "tumor necrosis factor (TNF) signaling pathway" were associated with different types of ILDs. A total of 10 genes associated with inflammatory response were identified as hub genes and their expression levels in the IPF group were higher than those in the COP group. Finally, we identified two GCs' response-related differently expressed genes (FOSL1 and DDIT4). Our bioinformatics analysis demonstrated that the inflammatory response played a pathogenic role in the progression of ILDs. We also illustrated that the inflammatory reaction was more severe in the IPF group compared to the COP group and identified two GCs' response-related differently expressed genes (FOSL1 and DDIT4) in ILDs.

The Sympathetic Nervous System Promotes Hepatic Lymphangiogenesis, which Is Protective Against Liver Fibrosis

Liver is the largest lymph-producing organ. In cirrhotic patients, lymph production significantly increases concomitant with lymphangiogenesis. The aim of this study was to determine the mechanism of lymphangiogenesis in liver and its implication in liver fibrosis. Liver biopsies from portal hypertensive patients with portal-sinusoidal vascular disease (n = 22) and liver cirrhosis (n = 5) were evaluated for lymphangiogenesis and compared with controls (n = 9 and n = 6, respectively). For mechanistic studies, rats with partial portal vein ligation (PPVL) and bile duct ligation (BDL) were used. A gene profile data set (GSE77627), including 14 histologically normal liver, 18 idiopathic noncirrhotic portal hypertension, and 22 cirrhotic patients, was analyzed. Lymphangiogenesis was significantly increased in livers from patients with portal-sinusoidal vascular disease, cirrhotic patients, as well as PPVL and BDL rats. Importantly, Schwann cells of sympathetic nerves highly expressed vascular endothelial growth factor-C in PPVL rats. Vascular endothelial growth factor-C neutralizing antibody or sympathetic denervation significantly decreased lymphangiogenesis in livers of PPVL and BDL rats, which resulted in progression of liver fibrosis. Liver specimens from cirrhotic patients showed a positive correlation between sympathetic nerve/Schwann cell-positive areas and lymphatic vessel numbers, which was supported by gene set analysis from patients with noncirrhotic portal hypertension and cirrhotic patients. Sympathetic nerves promote hepatic lymphangiogenesis in noncirrhotic and cirrhotic livers. Increased hepatic lymphangiogenesis can be protective against liver fibrosis.

Transcriptional Profiling of SARS-CoV-2-Infected Calu-3 Cells Reveals Immune-Related Signaling Pathways

The COVID-19 disease, caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), emerged in late 2019 and rapidly spread worldwide, becoming a pandemic that infected millions of people and caused significant deaths. COVID-19 continues to be a major threat, and there is a need to deepen our understanding of the virus and its mechanisms of infection. To study the cellular responses to SARS-CoV-2 infection, we performed an RNA sequencing of infected vs. uninfected Calu-3 cells. Total RNA was extracted from infected (0.5 MOI) and control Calu-3 cells and converted to cDNA. Sequencing was performed, and the obtained reads were quality-analyzed and pre-processed. Differential expression was assessed with the EdgeR package, and functional enrichment was performed in EnrichR for Gene Ontology, KEGG pathways, and WikiPathways. A total of 1040 differentially expressed genes were found in infected vs. uninfected Calu-3 cells, of which 695 were up-regulated and 345 were down-regulated. Functional enrichment analyses revealed the predominant up-regulation of genes related to innate immune response, response to virus, inflammation, cell proliferation, and apoptosis. These transcriptional changes following SARS-CoV-2 infection may reflect a cellular response to the infection and help to elucidate COVID-19 pathogenesis, in addition to revealing potential biomarkers and drug targets.

The Quartet Data Portal: integration of community-wide resources for multiomics quality control

The Quartet Data Portal facilitates community access to well-characterized reference materials, reference datasets, and related resources established based on a family of four individuals with identical twins from the Quartet Project. Users can request DNA, RNA, protein, and metabolite reference materials, as well as datasets generated across omics, platforms, labs, protocols, and batches. Reproducible analysis tools allow for objective performance assessment of user-submitted data, while interactive visualization tools support rapid exploration of reference datasets. A closed-loop "distribution-collection-evaluation-integration" workflow enables updates and integration of community-contributed multiomics data. Ultimately, this portal helps promote the advancement of reference datasets and multiomics quality control.

Quartet RNA reference materials improve the quality of transcriptomic data through ratio-based profiling

Certified RNA reference materials are indispensable for assessing the reliability of RNA sequencing to detect intrinsically small biological differences in clinical settings, such as molecular subtyping of diseases. As part of the Quartet Project for quality control and data integration of multi-omics profiling, we established four RNA reference materials derived from immortalized B-lymphoblastoid cell lines from four members of a monozygotic twin family. Additionally, we constructed ratio-based transcriptome-wide reference datasets between two samples, providing cross-platform and cross-laboratory 'ground truth'. Investigation of the intrinsically subtle biological differences among the Quartet samples enables sensitive assessment of cross-batch integration of transcriptomic measurements at the ratio level. The Quartet RNA reference materials, combined with the ratio-based reference datasets, can serve as unique resources for assessing and improving the quality of transcriptomic data in clinical and biological settings.

Quartet protein reference materials and datasets for multi-platform assessment of label-free proteomics

BACKGROUND

Quantitative proteomics is an indispensable tool in life science research. However, there is a lack of reference materials for evaluating the reproducibility of label-free liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based measurements among different instruments and laboratories.

RESULTS

Here, we develop the Quartet standard as a proteome reference material with built-in truths, and distribute the same aliquots to 15 laboratories with nine conventional LC-MS/MS platforms across six cities in China. Relative abundance of over 12,000 proteins on 816 mass spectrometry files are obtained and compared for reproducibility among the instruments and laboratories to ultimately generate proteomics benchmark datasets. There is a wide dynamic range of proteomes spanning about 7 orders of magnitude, and the injection order has marked effects on quantitative instead of qualitative characteristics.

CONCLUSION

Overall, the Quartet offers valuable standard materials and data resources for improving the quality control of proteomic analyses as well as the reproducibility and reliability of research findings.

Multi-omics data integration using ratio-based quantitative profiling with Quartet reference materials

Characterization and integration of the genome, epigenome, transcriptome, proteome and metabolome of different datasets is difficult owing to a lack of ground truth. Here we develop and characterize suites of publicly available multi-omics reference materials of matched DNA, RNA, protein and metabolites derived from immortalized cell lines from a family quartet of parents and monozygotic twin daughters. These references provide built-in truth defined by relationships among the family members and the information flow from DNA to RNA to protein. We demonstrate how using a ratio-based profiling approach that scales the absolute feature values of a study sample relative to those of a concurrently measured common reference sample produces reproducible and comparable data suitable for integration across batches, labs, platforms and omics types. Our study identifies reference-free 'absolute' feature quantification as the root cause of irreproducibility in multi-omics measurement and data integration and establishes the advantages of ratio-based multi-omics profiling with common reference materials.

Correcting batch effects in large-scale multiomics studies using a reference-material-based ratio method

BACKGROUND

Batch effects are notoriously common technical variations in multiomics data and may result in misleading outcomes if uncorrected or over-corrected. A plethora of batch-effect correction algorithms are proposed to facilitate data integration. However, their respective advantages and limitations are not adequately assessed in terms of omics types, the performance metrics, and the application scenarios.

RESULTS

As part of the Quartet Project for quality control and data integration of multiomics profiling, we comprehensively assess the performance of seven batch effect correction algorithms based on different performance metrics of clinical relevance, i.e., the accuracy of identifying differentially expressed features, the robustness of predictive models, and the ability of accurately clustering cross-batch samples into their own donors. The ratio-based method, i.e., by scaling absolute feature values of study samples relative to those of concurrently profiled reference material(s), is found to be much more effective and broadly applicable than others, especially when batch effects are completely confounded with biological factors of study interests. We further provide practical guidelines for implementing the ratio based approach in increasingly large-scale multiomics studies.

CONCLUSIONS

Multiomics measurements are prone to batch effects, which can be effectively corrected using ratio-based scaling of the multiomics data. Our study lays the foundation for eliminating batch effects at a ratio scale.

Uniformly shaped harmonization combines human transcriptomic data from different platforms while retaining their biological properties and differential gene expression patterns

Introduction: Co-normalization of RNA profiles obtained using different experimental platforms and protocols opens avenue for comprehensive comparison of relevant features like differentially expressed genes associated with disease. Currently, most of bioinformatic tools enable normalization in a flexible format that depends on the individual datasets under analysis. Thus, the output data of such normalizations will be poorly compatible with each other. Recently we proposed a new approach to gene expression data normalization termed Shambhala which returns harmonized data in a uniform shape, where every expression profile is transformed into a pre-defined universal format. We previously showed that following shambhalization of human RNA profiles, overall tissue-specific clustering features are strongly retained while platform-specific clustering is dramatically reduced. Methods: Here, we tested Shambhala performance in retention of fold-change gene expression features and other functional characteristics of gene clusters such as pathway activation levels and predicted cancer drug activity scores. Results: Using 6,793 cancer and 11,135 normal tissue gene expression profiles from the literature and experimental datasets, we applied twelve performance criteria for different versions of Shambhala and other methods of transcriptomic harmonization with flexible output data format. Such criteria dealt with the biological type classifiers, hierarchical clustering, correlation/regression properties, stability of drug efficiency scores, and data quality for using machine learning classifiers. Discussion: Shambhala-2 harmonizer demonstrated the best results with the close to 1 correlation and linear regression coefficients for the comparison of training vs validation datasets and more than two times lesser instability for calculation of drug efficiency scores compared to other methods.

Differences of molecular events driving pathological and radiological progression of lung adenocarcinoma

BACKGROUND

Ground-glass opacity (GGO)-like lung adenocarcinoma (LUAD) has been detected increasingly in the clinic and its inert property and superior survival indicate unique biological characteristics. However, we do not know much about them, which hampers identification of key reasons for the inert property of GGO-like LUAD.

METHODS

Using whole-exome sequencing and RNA sequencing, taking into account both radiological and pathological classifications of the same 197 patients concomitantly, we systematically interrogate genes driving the progression from GGO to solid nodule and potential reasons for the inertia of GGO. Using flow cytometry and IHC, we validated the abundance of immune cells and activity of cell proliferation.

FINDINGS

Identifying the differences between GGO and solid nodule, we found adenocarcinoma in situ/minimally invasive adenocarcinoma (AIS/MIA) and GGO-like LUAD exhibited lower TP53 mutation frequency and less active cell proliferation-related pathways than solid nodule in LUAD. Identifying the differences in GGO between AIS/MIA and LUAD, we noticed that EGFR mutation frequency and CNV load were significantly higher in LUAD than in AIS/MIA. Regulatory T cell was also higher in LUAD, while CD8+ T cell decreased from AIS/MIA to LUAD. Finally, we constructed a transcriptomic signature to quantify the development from GGO to solid nodule, which was an independent predictor of patients' prognosis in 11 external LUAD datasets.

INTERPRETATION

Our results provide deeper insights into the indolent nature of GGO and provide a molecular basis for the treatment of GGO-like LUAD.

FUNDING

This study was supported in part by the National Natural Science Foundation of China (32170657), the National Natural Science Foundation of China (82203037), and Shanghai Sailing Program (22YF1408900).

BatMan: Mitigating Batch Effects Via Stratification for Survival Outcome Prediction

Reproducible translation of transcriptomics data has been hampered by the ubiquitous presence of batch effects. Statistical methods for managing batch effects were initially developed in the setting of sample group comparison and later borrowed for other settings such as survival outcome prediction. The most notable such method is ComBat, which adjusts for batches by including it as a covariate alongside sample groups in a linear regression. In survival prediction, however, ComBat is used without definable groups for survival outcome and is done sequentially with survival regression for a potentially batch-confounded outcome. To address these issues, we propose a new method called BATch MitigAtion via stratificatioN (BatMan). It adjusts batches as strata in survival regression and uses variable selection methods such as the regularized regression to handle high dimensionality. We assess the performance of BatMan in comparison with ComBat, each used either alone or in conjunction with data normalization, in a resampling-based simulation study under various levels of predictive signal strength and patterns of batch-outcome association. Our simulations show that (1) BatMan outperforms ComBat in nearly all scenarios when there are batch effects in the data and (2) their performance can be worsened by the addition of data normalization. We further evaluate them using microRNA data for ovarian cancer from the Cancer Genome Atlas and find that BatMan outforms ComBat while the addition of data normalization worsens the prediction. Our study thus shows the advantage of BatMan and raises caution about the use of data normalization in the context of developing survival prediction models. The BatMan method and the simulation tool for performance assessment are implemented in R and publicly available at LXQin/PRECISION.survival-GitHub.

Early Gestational Blood Markers to Predict Preeclampsia Complicating Gestational Diabetes Mellitus

Objective

Gestational diabetes mellitus (GDM) and preeclampsia (PE) are common pregnancy complications that share some common risk factors. GDM patients are also at high risk for PE. There are no sensitive markers for prediction, especially for the occurrence of PE in GDM patients. This study investigated plasma proteins for the prediction of PE in GDM patients.

Methods

A total of 10 PE, 10 GDM, and 5 PE complicated with GDM cases, as well as 10 pregnant controls without obvious complications, were included in the nested cohort. The proteomics in the plasma collected at 12-20 weeks of gestational age (GA) were analyzed by liquid chromatography‒mass spectrometry/mass spectrometry. Some potential markers, such as soluble transferrin receptor (sTfR), ceruloplasmin (CP), apolipoprotein E (ApoE) and inositol 1,4,5-trisphosphate receptor 1 (ITPR1), were validated using enzyme-linked immunosorbent assays.

Results

Functional analysis of the plasma showed that proteasome activation, pancreatic secretion, and fatty acid degradation were activated in the GDM group, and renin secretion-, lysosome-, and proteasome pathways involving iron transport and lipid metabolism were enriched in the PE group, distinguishing PE complicating GDM.

Conclusion

Through proteomics analysis of plasma in early pregnancy, PE complicating GDM may have a unique mechanism from that of PE alone. Plasma sTfR, CP and ApoE levels have potential clinical applications in early screening.

Optimal Selection of IFN-α-Inducible Genes to Determine Type I Interferon Signature Improves the Diagnosis of Systemic Lupus Erythematosus

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease characterized by the production of autoantibodies specific to self-molecules in the nucleus, cytoplasm, and cell surface. The diversity of serologic and clinical manifestations observed in SLE patients challenges the development of diagnostics and tools for monitoring disease activity. Elevated type I interferon signature (IFN- I) in SLE leads to dysregulation of innate and adaptive immune function, resulting in autoantibodies production. The most common method to determine IFN-I signature is measuring the gene expression of several IFN-α-inducible genes (IFIGs) in blood samples and calculating a score. Optimal selection of IFIGs improves the sensitivity, specificity, and accuracy of the diagnosis of SLE. We describe the mechanisms of the immunopathogenesis of IFN-I signature (IFNα production) and its clinical consequences in SLE. In addition, we explore the association between IFN-I signature, the presence of autoantibodies, disease activity, medical therapy, and ethnicity. We discuss the presence of IFN-I signature in some patients with other autoimmune diseases, including rheumatoid arthritis, systemic and multiple sclerosis, Sjogren’s syndrome, and dermatomyositis. Prospective studies are required to assess the role of IFIG and the best combination of IFIGs to monitor SLE disease activity and drug treatments.

Leveraging transcriptomics for precision diagnosis: Lessons learned from cancer and sepsis

Diagnostics require precision and predictive ability to be clinically useful. Integration of multi-omic with clinical data is crucial to our understanding of disease pathogenesis and diagnosis. However, interpretation of overwhelming amounts of information at the individual level requires sophisticated computational tools for extraction of clinically meaningful outputs. Moreover, evolution of technical and analytical methods often outpaces standardisation strategies. RNA is the most dynamic component of all -omics technologies carrying an abundance of regulatory information that is least harnessed for use in clinical diagnostics. Gene expression-based tests capture genetic and non-genetic heterogeneity and have been implemented in certain diseases. For example patients with early breast cancer are spared toxic unnecessary treatments with scores based on the expression of a set of genes (e.g., Oncotype DX). The ability of transcriptomics to portray the transcriptional status at a moment in time has also been used in diagnosis of dynamic diseases such as sepsis. Gene expression profiles identify endotypes in sepsis patients with prognostic value and a potential to discriminate between viral and bacterial infection. The application of transcriptomics for patient stratification in clinical environments and clinical trials thus holds promise. In this review, we discuss the current clinical application in the fields of cancer and infection. We use these paradigms to highlight the impediments in identifying useful diagnostic and prognostic biomarkers and propose approaches to overcome them and aid efforts towards clinical implementation.

Data-driven approach to integrating genomic and behavioral preclinical traumatic brain injury research

Understanding recovery from TBI is complex, involving multiple systems and modalities. The current study applied modern data science tools to manage this complexity and harmonize large-scale data to understand relationships between gene expression and behavioral outcomes in a preclinical model of chronic TBI (cTBI). Data collected by the Moody Project for Translational TBI Research included rats with no injury (naïve animals with similar amounts of anesthetic exposure to TBI and sham-injured animals), sham injury, or lateral fluid percussion TBI, followed by recovery periods up to 12 months. Behavioral measures included locomotor coordination (beam balance neuroscore) and memory and cognition assessments (Morris water maze: MWM) at multiple timepoints. Gene arrays were performed using hippocampal and cortical samples to probe 45,610 genes. To reduce the high dimensionality of molecular and behavioral domains and uncover gene-behavior associations, we performed non-linear principal components analyses (NL-PCA), which de-noised the data. Genomic NL-PCA unveiled three interpretable eigengene components (PC2, PC3, and PC4). Ingenuity pathway analysis (IPA) identified the PCs as an integrated stress response (PC2; EIF2-mTOR, corticotropin signaling, etc.), inflammatory factor translation (PC3; PI3K-p70S6K signaling), and neurite growth inhibition (PC4; Rho pathways). Behavioral PCA revealed three principal components reflecting the contribution of MWM overall speed and distance, neuroscore/beam walk, and MWM platform measures. Integrating the genomic and behavioral domains, we then performed a 'meta-PCA' on individual PC scores for each rat from genomic and behavioral PCAs. This meta-PCA uncovered three unique multimodal PCs, characterized by robust associations between inflammatory/stress response and neuroscore/beam walk performance (meta-PC1), stress response and MWM performance (meta-PC2), and stress response and neuroscore/beam walk performance (meta-PC3). Multivariate analysis of variance (MANOVA) on genomic-behavioral meta-PC scores tested separately on cortex and hippocampal samples revealed the main effects of TBI and recovery time. These findings are a proof of concept for the integration of disparate data domains for translational knowledge discovery, harnessing the full syndromic space of TBI.

msmsEDA & msmsTests: Label-Free Differential Expression by Spectral Counts

msmsTests is an R/Bioconductor package providing functions for statistical tests in label-free LC-MS/MS data by spectral counts. These functions aim at discovering differentially expressed proteins between two biological conditions. Three tests are available: Poisson GLM regression, quasi-likelihood GLM regression, and the negative binomial of the edgeR package. The three models admit blocking factors to control for nuisance variables. To assure a good level of reproducibility a post-test filter is available, where (1) a minimum effect size considered biologically relevant, and (2) a minimum expression of the most abundant condition, may be set. A companion package, msmsEDA, proposes functions to explore datasets based on msms spectral counts. The provided graphics help in identifying outliers, the presence of eventual batch factors, and check the effects of different normalizing strategies. This protocol illustrates the use of both packages on two examples: A purely spike-in experiment of 48 human proteins in a standard yeast cell lysate; and a cancer cell-line secretome dataset requiring a biological normalization.

Navigating Transcriptomic Connectivity Mapping Workflows to Link Chemicals with Bioactivities

Screening new compounds for potential bioactivities against cellular targets is vital for drug discovery and chemical safety. Transcriptomics offers an efficient approach for assessing global gene expression changes, but interpreting chemical mechanisms from these data is often challenging. Connectivity mapping is a potential data-driven avenue for linking chemicals to mechanisms based on the observation that many biological processes are associated with unique gene expression signatures (gene signatures). However, mining the effects of a chemical on gene signatures for biological mechanisms is challenging because transcriptomic data contain thousands of noisy genes. New connectivity mapping approaches seeking to distinguish signal from noise continue to be developed, spurred by the promise of discovering chemical mechanisms, new drugs, and disease targets from burgeoning transcriptomic data. Here, we analyze these approaches in terms of diverse transcriptomic technologies, public databases, gene signatures, pattern-matching algorithms, and statistical evaluation criteria. To navigate the complexity of connectivity mapping, we propose a harmonized scheme to coherently organize and compare published workflows. We first standardize concepts underlying transcriptomic profiles and gene signatures based on various transcriptomic technologies such as microarrays, RNA-Seq, and L1000 and discuss the widely used data sources such as Gene Expression Omnibus, ArrayExpress, and MSigDB. Next, we generalize connectivity mapping as a pattern-matching task for finding similarity between a query (e.g., transcriptomic profile for new chemical) and a reference (e.g., gene signature of known target). Published pattern-matching approaches fall into two main categories: vector-based use metrics like correlation, Jaccard index, etc., and aggregation-based use parametric and nonparametric statistics (e.g., gene set enrichment analysis). The statistical methods for evaluating the performance of different approaches are described, along with comparisons reported in the literature on benchmark transcriptomic data sets. Lastly, we review connectivity mapping applications in toxicology and offer guidance on evaluating chemical-induced toxicity with concentration-response transcriptomic data. In addition to serving as a high-level guide and tutorial for understanding and implementing connectivity mapping workflows, we hope this review will stimulate new algorithms for evaluating chemical safety and drug discovery using transcriptomic data.

bestDEG: a web-based application automatically combines various tools to precisely predict differentially expressed genes (DEGs) from RNA-Seq data

Background

Differential gene expression analysis using RNA sequencing technology (RNA-Seq) has become the most popular technique in transcriptome research. Although many R packages have been developed to analyze differentially expressed genes (DEGs), several evaluations have shown that no single DEG analysis method outperforms all others. The validity of DEG identification could be increased by using multiple methods and producing the consensus results. However, DEG analysis methods are complex and most of them require prior knowledge of a programming language or command-line shell. Users who do not have this knowledge need to invest time and effort to acquire it.

Methods

We developed a novel web application called "bestDEG" to automatically analyze DEGs with different tools and compare the results. A differential expression (DE) analysis pipeline was created combining the edgeR, DESeq2, NOISeq, and EBSeq packages; selected because they use different statistical methods to identify DEGs. bestDEG was evaluated on human datasets from the MicroArray Quality Control (MAQC) project.

Results

The performance of the bestDEG web application with the human datasets showed excellent results, and the consensus method outperformed the other DE analysis methods in terms of precision (94.71%) and specificity (97.01%). bestDEG is a rapid and efficient tool to analyze DEGs. With bestDEG, users can select DE analysis methods and parameters in the user-friendly web interface. bestDEG also provides a Venn diagram and a table of results. Moreover, the consensus method of this tool can maximize the precision or minimize the false discovery rate (FDR), which reduces the cost of gene expression validation by minimizing wet-lab experiments.

Identification of a Novel Long Non-coding RNA, lnc-ATMIN-4:2, and its Clinicopathological and Prognostic Significance in Advanced Gastric Cancer

BACKGROUND/AIM

Long non-coding RNAs (lncRNAs) are emerging as significant regulators of gene expression and a novel promising biomarker for cancer diagnosis and prognosis. This study identified a novel, differentially expressed lncRNA in advanced gastric cancer (AGC), Inc-ATMIN-4:2, and evaluated its clinicopathological and prognostic significance.

PATIENTS AND METHODS

Whole transcriptome sequencing was performed to identify differentially expressed lncRNAs in AGC tissue samples. We also analyzed lnc-ATMIN-4:2 expression in 317 patients with AGC using RNA in situ hybridization.

RESULTS

High (>30 dots) lnc-ATMIN-4:2 expression significantly correlated with younger age, poorly differentiated histology, diffuse type, deeper invasion depth, perineural invasion, lymph node metastasis, and higher stage group. In addition, high lnc-ATMIN-4:2 expression was significantly associated with worse overall survival in patients with AGC.

CONCLUSION

This study elucidated the significance of lncRNAs in AGC and indicated the value of lnc-ATMIN-4:2 expression as a predictive biomarker for the overall survival of patients with AGC.

Evidence that a working memory cognitive phenotype within schizophrenia has a unique underlying biology

It is suggested studying phenotypes within the syndrome of schizophrenia will accelerate understanding the complex molecular pathology of the disorder. Supporting this hypothesis, we have identified a sub-group within schizophrenia with impaired working memory (WM) and have used Affymetrix™ Human Exon 1.0 ST Arrays to compare their blood RNA levels (n=16) to a group of with intact WM (n=18). Levels of 72 RNAs were higher in blood from patients with impaired WM, 11 of which have proven links to the maintenance of different aspects of working memory (cognition). Overall, changed gene expression in those with impaired WM could be linked to cognition through glutamatergic activity, olfaction, immunity, inflammation as well as energy and metabolism. Our data gives preliminary support to the hypotheses that there is a working memory deficit phenotype within the syndrome of schizophrenia with has a biological underpinning. In addition, our data raises the possibility that a larger study could show that the specific changes in gene expression we have identified could prove to be the biomarkers needed to develop a blood test to identify those with impaired WM; a significant step toward allowing the use of personalised medicine directed toward improving their impaired working memory.

Comprehensive analysis of transcriptomics and metabolomics to understand chronic ethanol induced murine cardiotoxicity

Alcohol abuse has attracted public attention and long-term alcohol exposure can lead to alcohol-featured non-ischemic dilated cardiomyopathy. However, the precise underlying mechanisms of alcoholic cardiomyopathy remain to be elucidated. This study aimed to comprehensively characterize alcohol abuse-mediated effects on downstream metabolites and genes transcription using a multi-omics strategy. We established chronic ethanol intoxication model in adult male C57BL/6 mice through 8 weeks of 95% alcohol vapor administration and performed metabolomics analysis, mRNA-seq and microRNA-seq analysis with myocardial tissues. Firstly, ethanol markedly induced ejection fraction reductions, cardiomyocyte hypertrophy, and myocardial fibrosis in mice with myocardial oxidative injury. In addition, the omics analysis identified a total of 166 differentially expressed metabolites (DEMs), 241 differentially expressed genes (DEGs) and 19 differentially expressed microRNAs (DEmiRNAs), respectively. The results highlighted that alcohol abuse mainly interfered with endogenous lipids, amino acids and nucleotides production and the relevant genes transcription in mice hearts. Based on KEGG database, the affected signaling pathways are primarily mapped to the antigen processing and presentation, regulation of actin cytoskeleton, AMPK signaling pathway, tyrosine metabolism and PPAR signaling pathway, etc. Furthermore, 9 hub genes related to oxidative stress from DEGs were selected based on function annotation, and potential alcoholic cardiotoxic oxidative stress biomarkers were determined through establishing PPI network and DEmiRNAs-DEGs cross-talk. Altogether, our data strongly supported the conclusion that ethanol abuse characteristically affected amino acid and energy metabolism, nucleotide metabolism and especially lipids metabolism in mice hearts, and underlined the values of lipids signaling and oxidative stress in the treatment strategies.

Adapted tensor decomposition and PCA based unsupervised feature extraction select more biologically reasonable differentially expressed genes than conventional methods

Tensor decomposition- and principal component analysis-based unsupervised feature extraction were proposed almost 5 and 10 years ago, respectively; although these methods have been successfully applied to a wide range of genome analyses, including drug repositioning, biomarker identification, and disease-causing genes' identification, some fundamental problems have been identified: the number of genes identified was too small to assume that there were no false negatives, and the histogram of P values derived was not fully coincident with the null hypothesis that principal component and singular value vectors follow the Gaussian distribution. Optimizing the standard deviation such that the histogram of P values is as much as possible coincident with the null hypothesis results in an increase in the number and biological reliability of the selected genes. Our contribution was that we improved these methods so as to be able to select biologically more reasonable differentially expressed genes than the state of art methods that must empirically assume negative binomial distributions and dispersion relation, which is required for the selecting more expressed genes than less expressed ones, which can be achieved by the proposed methods that do not have to assume these.

Molecular pathways enhance drug response prediction using transfer learning from cell lines to tumors and patient-derived xenografts

Computational models have been successful in predicting drug sensitivity in cancer cell line data, creating an opportunity to guide precision medicine. However, translating these models to tumors remains challenging. We propose a new transfer learning workflow that transfers drug sensitivity predicting models from large-scale cancer cell lines to both tumors and patient derived xenografts based on molecular pathways derived from genomic features. We further compute feature importance to identify pathways most important to drug response prediction. We obtained good performance on tumors (AUROC = 0.77) and patient derived xenografts from triple negative breast cancers (RMSE = 0.11). Using feature importance, we highlight the association between ER-Golgi trafficking pathway in everolimus sensitivity within breast cancer patients and the role of class II histone deacetylases and interlukine-12 in response to drugs for triple-negative breast cancer. Pathway information support transfer of drug response prediction models from cell lines to tumors and can provide biological interpretation underlying the predictions, serving as a steppingstone towards usage in clinical setting.

Transcriptomic Harmonization as the Way for Suppressing Cross-Platform Bias and Batch Effect

(1) Background: Emergence of methods interrogating gene expression at high throughput gave birth to quantitative transcriptomics, but also posed a question of inter-comparison of expression profiles obtained using different equipment and protocols and/or in different series of experiments. Addressing this issue is challenging, because all of the above variables can dramatically influence gene expression signals and, therefore, cause a plethora of peculiar features in the transcriptomic profiles. Millions of transcriptomic profiles were obtained and deposited in public databases of which the usefulness is however strongly limited due to the inter-comparison issues; (2) Methods: Dozens of methods and software packages that can be generally classified as either flexible or predefined format harmonizers have been proposed, but none has become to the date the gold standard for unification of this type of Big Data; (3) Results: However, recent developments evidence that platform/protocol/batch bias can be efficiently reduced not only for the comparisons of limited transcriptomic datasets. Instead, instruments were proposed for transforming gene expression profiles into the universal, uniformly shaped format that can support multiple inter-comparisons for reasonable calculation costs. This forms a basement for universal indexing of all or most of all types of RNA sequencing and microarray hybridization profiles; (4) Conclusions: In this paper, we attempted to overview the landscape of modern approaches and methods in transcriptomic harmonization and focused on the practical aspects of their application.

Genes expressed at low levels raise false discovery rates in RNA samples contaminated with genomic DNA

Background

RNA preparations contaminated with genomic DNA (gDNA) are frequently disregarded by RNA-seq studies. Such contamination may generate false results; however, their effect on the outcomes of RNA-seq analyses is unknown. To address this gap in our knowledge, here we added different concentrations of gDNA to total RNA preparations and subjected them to RNA-seq analysis.

Results

We found that the contaminating gDNA altered the quantification of transcripts at relatively high concentrations. Differentially expressed genes (DEGs) resulting from gDNA contamination may therefore contribute to higher rates of false enrichment of pathways compared with analogous samples lacking numerous DEGs. A strategy was developed to correct gene expression levels in gDNA-contaminated RNA samples, which assessed the magnitude of contamination to improve the reliability of the results.

Conclusions

Our study indicates that caution must be exercised when interpreting results associated with low-abundance transcripts. The data provided here will likely serve as a valuable resource to evaluate the influence of gDNA contamination on RNA-seq analysis, particularly related to the detection of putative novel gene elements.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08785-1.

Analyzing biomarker discovery: Estimating the reproducibility of biomarker sets

Many researchers try to understand a biological condition by identifying biomarkers. This is typically done using univariate hypothesis testing over a labeled dataset, declaring a feature to be a biomarker if there is a significant statistical difference between its values for the subjects with different outcomes. However, such sets of proposed biomarkers are often not reproducible – subsequent studies often fail to identify the same sets. Indeed, there is often only a very small overlap between the biomarkers proposed in pairs of related studies that explore the same phenotypes over the same distribution of subjects. This paper first defines the Reproducibility Score for a labeled dataset as a measure (taking values between 0 and 1) of the reproducibility of the results produced by a specified fixed biomarker discovery process for a given distribution of subjects. We then provide ways to reliably estimate this score by defining algorithms that produce an over-bound and an under-bound for this score for a given dataset and biomarker discovery process, for the case of univariate hypothesis testing on dichotomous groups. We confirm that these approximations are meaningful by providing empirical results on a large number of datasets and show that these predictions match known reproducibility results. To encourage others to apply this technique to analyze their biomarker sets, we have also created a publicly available website, https://biomarker.shinyapps.io/BiomarkerReprod/, that produces these Reproducibility Score approximations for any given dataset (with continuous or discrete features and binary class labels).

Polyadenylation ligation-mediated sequencing (PALM-Seq) characterizes cell-free coding and non-coding RNAs in human biofluids

Background

Cell‐free messenger RNA (cf‐mRNA) and long non‐coding RNA (cf‐lncRNA) are becoming increasingly important in liquid biopsy by providing biomarkers for disease prediction, diagnosis and prognosis, but the simultaneous characterization of coding and non‐coding RNAs in human biofluids remains challenging.

Methods

Here, we developed polyadenylation ligation‐mediated sequencing (PALM‐Seq), an RNA sequencing strategy employing treatment of RNA with T4 polynucleotide kinase to generate cell‐free RNA (cfRNA) fragments with 5′ phosphate and 3′ hydroxyl and RNase H to deplete abundant RNAs, achieving simultaneous quantification and characterization of cfRNAs.

Results

Using PALM‐Seq, we successfully identified well‐known differentially abundant mRNA, lncRNA and microRNA in the blood plasma of pregnant women. We further characterized cfRNAs in blood plasma, saliva, urine, seminal plasma and amniotic fluid and found that the detected numbers of different RNA biotypes varied with body fluids. The profiles of cf‐mRNA reflected the function of originated tissues, and immune cells significantly contributed RNA to blood plasma and saliva. Short fragments (<50 nt) of mRNA and lncRNA were major in biofluids, whereas seminal plasma and amniotic fluid tended to retain long RNA. Body fluids showed distinct preferences of pyrimidine at the 3′ end and adenine at the 5′ end of cf‐mRNA and cf‐lncRNA, which were correlated with the proportions of short fragments.

Conclusion

Together, PALM‐Seq enables a simultaneous characterization of cf‐mRNA and cf‐lncRNA, contributing to elucidating the biology and promoting the application of cfRNAs.

Shambhala-2: A Protocol for Uniformly Shaped Harmonization of Gene Expression Profiles of Various Formats

Uniformly shaped harmonization of gene expression profiles is central for the simultaneous comparison of multiple gene expression datasets. It is expected to operate with the gene expression data obtained using various experimental methods and equipment, and to return harmonized profiles in a uniform shape. Such uniformly shaped expression profiles from different initial datasets can be further compared directly. However, current harmonization techniques have strong limitations that prevent their broad use for bioinformatic applications. They can either operate with only up to two datasets/platforms or return data in a dynamic format that will be different for every comparison under analysis. This also does not allow for adding new data to the previously harmonized dataset(s), which complicates the analysis and increases calculation costs. We propose here a new method termed Shambhala-2 that can transform multi-platform expression data into a universal format that is identical for all harmonizations made using this technique. Shambhala-2 is based on sample-by-sample cubic conversion of the initial expression dataset into a preselected shape of the reference definitive dataset. Using 8390 samples of 12 healthy human tissue types and 4086 samples of colorectal, kidney, and lung cancer tissues, we verified Shambhala-2's capacity in restoring tissue-specific expression patterns for seven microarray and three RNA sequencing platforms. Shambhala-2 performed well for all tested combinations of RNAseq and microarray profiles, and retained gene-expression ranks, as evidenced by high correlations between different single- or aggregated gene expression metrics in pre- and post-Shambhalized samples, including preserving cancer-specific gene expression and pathway activation features. © 2022 Wiley Periodicals LLC. Basic Protocol: Shambhala-2 harmonizer Alternate Protocol 1: Linear Shambhala/Shambhala-1 Alternate Protocol 2: Alternative (flexible-format and uniformly shaped) normalization methods Support Protocol 1: Watermelon multisection (WM) Support Protocol 2: Calculation of cancer-to-normal log-fold-change (LFC) and pathway activation level (PAL).

Perturbation of Wnt/β-catenin signaling and sexual dimorphism in non-alcoholic fatty liver disease

AIMS

The prevalence of non-alcoholic fatty liver disease (NAFLD) and its progression to non-alcoholic steatohepatitis (NASH) is higher in postmenopausal women than men. The aim of this study was to determine the molecular mechanisms underlying this sexual dimorphism in NAFLD.

METHODS

A total of 24 frozen liver samples of both sexes (normal and NAFLD/NASH) were used in this study. Total RNAseq was first used to identify differentially expressed genes (DEGs) between samples. Enrichment analysis of Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Reactome were used to analyze biological pathways. RT2 profiler polymerase chain reaction (PCR) arrays were used to identify genes associated with the biological pathways. Immunoblotting was used to validate protein expression of certain genes.

RESULTS

We identified 4362 genes that are differentially expressed between NAFLD/NASH and normal samples; of those 745 genes were characterized as sex specific in NAFLD/NASH. Multiple pathway analysis platforms showed that Wnt-signaling is a candidate shared for a common biological pathway-associated with NAFLD/NASH. Using Wnt pathway focused PCR array we identified many genes involved in canonical pathway (Wnt/β-catenin activation) such as CTNNB1, c-Myc and CCND2 are overexpressed in female cases, whereas these genes are either not detected or downregulated in male cases. Immunoblot analysis validated the expression of CTNNB1 in female cases but not in male protein samples.

CONCLUSIONS

Our study suggests, for the first time, that the activation of canonical Wnt signaling could be one of the main pathways associated with sexual dimorphism in NAFLD and NASH.

Impact of gene annotation choice on the quantification of RNA-seq data

Background

RNA sequencing is currently the method of choice for genome-wide profiling of gene expression. A popular approach to quantify expression levels of genes from RNA-seq data is to map reads to a reference genome and then count mapped reads to each gene. Gene annotation data, which include chromosomal coordinates of exons for tens of thousands of genes, are required for this quantification process. There are several major sources of gene annotations that can be used for quantification, such as Ensembl and RefSeq databases. However, there is very little understanding of the effect that the choice of annotation has on the accuracy of gene expression quantification in an RNA-seq analysis.

Results

In this paper, we present results from our comparison of Ensembl and RefSeq human annotations on their impact on gene expression quantification using a benchmark RNA-seq dataset generated by the SEQC consortium. We show that the use of RefSeq gene annotation models led to better quantification accuracy, based on the correlation with ground truths including expression data from >800 real-time PCR validated genes, known titration ratios of gene expression and microarray expression data. We also found that the recent expansion of the RefSeq annotation has led to a decrease in its annotation accuracy. Finally, we demonstrated that the RNA-seq quantification differences observed between different annotations were not affected by the use of different normalization methods.

Conclusion

In conclusion, our study found that the use of the conservative RefSeq gene annotation yields better RNA-seq quantification results than the more comprehensive Ensembl annotation. We also found that, surprisingly, the recent expansion of the RefSeq database, which was primarily driven by the incorporation of sequencing data into the gene annotation process, resulted in a reduction in the accuracy of RNA-seq quantification.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04644-8.

Perplexity: evaluating transcript abundance estimation in the absence of ground truth

Background

There has been rapid development of probabilistic models and inference methods for transcript abundance estimation from RNA-seq data. These models aim to accurately estimate transcript-level abundances, to account for different biases in the measurement process, and even to assess uncertainty in resulting estimates that can be propagated to subsequent analyses. The assumed accuracy of the estimates inferred by such methods underpin gene expression based analysis routinely carried out in the lab. Although hyperparameter selection is known to affect the distributions of inferred abundances (e.g. producing smooth versus sparse estimates), strategies for performing model selection in experimental data have been addressed informally at best.

Results

We derive perplexity for evaluating abundance estimates on fragment sets directly. We adapt perplexity from the analogous metric used to evaluate language and topic models and extend the metric to carefully account for corner cases unique to RNA-seq. In experimental data, estimates with the best perplexity also best correlate with qPCR measurements. In simulated data, perplexity is well behaved and concordant with genome-wide measurements against ground truth and differential expression analysis. Furthermore, we demonstrate theoretically and experimentally that perplexity can be computed for arbitrary transcript abundance estimation models.

Conclusions

Alongside the derivation and implementation of perplexity for transcript abundance estimation, our study is the first to make possible model selection for transcript abundance estimation on experimental data in the absence of ground truth.

Augmenting Imaging Biomarker Performance with Blood-Based Gene Expression Levels for Predicting Alzheimer’s Disease Progression

Background: Structural brain imaging metrics and gene expression biomarkers have previously been used for Alzheimer’s disease (AD) diagnosis and prognosis, but none of these studies explored integration of imaging and gene expression biomarkers for predicting mild cognitive impairment (MCI)-to-AD conversion 1-2 years into the future. Objective: We investigated advantages of combining gene expression and structural brain imaging features for predicting MCI-to-AD conversion. Selection of the differentially expressed genes (DEGs) for classifying cognitively normal (CN) controls and AD patients was benchmarked against previously reported results. Methods: The current work proposes integrating brain imaging and blood gene expression data from two public datasets (ADNI and ANM) to predict MCI-to-AD conversion. A novel pipeline for combining gene expression data from multiple platforms is proposed and evaluated in the two independents patient cohorts. Results: Combining DEGs and imaging biomarkers for predicting MCI-to-AD conversion yielded 0.832-0.876 receiver operating characteristic (ROC) area under the curve (AUC), which exceeded the 0.808-0.840 AUC from using the imaging features alone. With using only three DEGs, the CN versus AD predictive model achieved 0.718, 0.858, and 0.873 cross-validation AUC for the ADNI, ANM1, and ANM2 datasets. Conclusion: For the first time we show that combining gene expression and imaging biomarkers yields better predictive performance than using imaging metrics alone. A novel pipeline for combining gene expression data from multiple platforms is proposed and evaluated to produce consistent results in the two independents patient cohorts. Using an improved feature selection, we show that predictive models with fewer gene expression probes can achieve competitive performance.

geoCancerPrognosticDatasetsRetriever: a bioinformatics tool to easily identify cancer prognostic datasets on Gene Expression Omnibus (GEO)

SUMMARY

Having multiple datasets is a key aspect of robust bioinformatics analyses, because it allows researchers to find possible confirmation of the discoveries made on multiple cohorts. For this purpose, Gene Expression Omnibus (GEO) can be a useful database, since it provides hundreds of thousands of microarray gene expression datasets freely available for download and usage. Despite this large availability, collecting prognostic datasets of a specific cancer type from GEO can be a long, time-consuming and energy-consuming activity for any bioinformatician, who needs to execute it manually by first performing a search on the GEO website and then by checking all the datasets found one by one. To solve this problem, we present here geoCancerPrognosticDatasetsRetriever, a Perl 5 application which reads a cancer type and a list of microarray platforms, searches for prognostic gene expression datasets of that cancer type and based on those platforms available on GEO, and returns the GEO accession codes of those datasets, if found. Our bioinformatics tool can easily generate in a few minutes a list of cancer prognostic datasets that otherwise would require numerous hours of manual work to any bioinformatician. geoCancerPrognosticDatasetsRetriever can handily retrieve multiple prognostic datasets of gene expression of any cancer type, laying the foundations for numerous bioinformatics studies and meta-analyses that can have a strong impact on oncology research.

AVAILABILITY AND IMPLEMENTATION

geoCancerPrognosticDatasetsRetriever is freely available under the GPLv2 license on the Comprehensive Perl Archive Network (CPAN) at https://metacpan.org/pod/App::geoCancerPrognosticDatasetsRetriever and on GitHub at https://github.com/AbbasAlameer/geoCancerPrognosticDatasetsRetriever.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

R-ODAF: Omics data analysis framework for regulatory application

Despite the widespread use of transcriptomics technologies in toxicology research, acceptance of the data by regulatory agencies to support the hazard assessment is still limited. Fundamental issues contributing to this are the lack of reproducibility in transcriptomics data analysis arising from variance in the methods used to generate data and differences in the data processing. While research applications are flexible in the way the data are generated and interpreted, this is not the case for regulatory applications where an unambiguous answer, possibly later subject to legal scrutiny, is required. A reference analysis framework would give greater credibility to the data and allow the practitioners to justify their use of an alternative bioinformatic process by referring to a standard. In this publication, we propose a method called omics data analysis framework for regulatory application (R-ODAF), which has been built as a user-friendly pipeline to analyze raw transcriptomics data from microarray and next-generation sequencing. In the R-ODAF, we also propose additional statistical steps to remove the number of false positives obtained from standard data analysis pipelines for RNA-sequencing. We illustrate the added value of R-ODAF, compared to a standard workflow, using a typical toxicogenomics dataset of hepatocytes exposed to paracetamol.

Surfing the Big Data Wave: Omics Data Challenges in Transplantation

In both research and care, patients, caregivers, and researchers are facing a leap forward in the quantity of data that are available for analysis and interpretation, marking the daunting "big data era." In the biomedical field, this quantitative shift refers mostly to the -omics that permit measuring and analyzing biological features of the same type as a whole. Omics studies have greatly impacted transplantation research and highlighted their potential to better understand transplant outcomes. Some studies have emphasized the contribution of omics in developing personalized therapies to avoid graft loss. However, integrating omics data remains challenging in terms of analytical processes. These data come from multiple sources. Consequently, they may contain biases and systematic errors that can be mistaken for relevant biological information. Normalization methods and batch effects have been developed to tackle issues related to data quality and homogeneity. In addition, imputation methods handle data missingness. Importantly, the transplantation field represents a unique analytical context as the biological statistical unit is the donor-recipient pair, which brings additional complexity to the omics analyses. Strategies such as combined risk scores between 2 genomes taking into account genetic ancestry are emerging to better understand graft mechanisms and refine biological interpretations. The future omics will be based on integrative biology, considering the analysis of the system as a whole and no longer the study of a single characteristic. In this review, we summarize omics studies advances in transplantation and address the most challenging analytical issues regarding these approaches.

RNA Sequencing in COVID-19 patients identifies neutrophil activation biomarkers as a promising diagnostic platform for infections

Infection with the SARS-CoV2 virus can vary from asymptomatic, or flu-like with moderate disease, up to critically severe. Severe disease, termed COVID-19, involves acute respiratory deterioration that is frequently fatal. To understand the highly variable presentation, and identify biomarkers for disease severity, blood RNA from COVID-19 patient in an intensive care unit was analyzed by whole transcriptome RNA sequencing. Both SARS-CoV2 infection and the severity of COVID-19 syndrome were associated with up to 25-fold increased expression of neutrophil-related transcripts, such as neutrophil defensin 1 (DEFA1), and 3-5-fold reductions in T cell related transcripts such as the T cell receptor (TCR). The DEFA1 RNA level detected SARS-CoV2 viremia with 95.5% sensitivity, when viremia was measured by ddPCR of whole blood RNA. Purified CD15+ neutrophils from COVID-19 patients were increased in abundance and showed striking increases in nuclear DNA staining by DAPI. Concurrently, they showed >10-fold higher elastase activity than normal controls, and correcting for their increased abundance, still showed 5-fold higher elastase activity per cell. Despite higher CD15+ neutrophil elastase activity, elastase activity was extremely low in plasma from the same patients. Collectively, the data supports the model that increased neutrophil and decreased T cell activity is associated with increased COVID-19 severity, and suggests that blood DEFA1 RNA levels and neutrophil elastase activity, both involved in neutrophil extracellular traps (NETs), may be informative biomarkers of host immune activity after viral infection.

RNA-Seq Profiling to Investigate the Mechanism of Qishen Granules on Regulating Mitochondrial Energy Metabolism of Heart Failure in Rats

Background. Qishen granules (QSG) are a frequently prescribed formula with cardioprotective properties prescribed to HF for many years. RNA-seq profiling revealed that regulation on cardiac mitochondrial energy metabolism is the main therapeutic effect. However, the underlying mechanism is still unknown. In this study, we explored the effects of QSG on regulating mitochondrial energy metabolism and oxidative stress through the PGC-1α/NRF1/TFAM signaling pathway. RNA-seq technology revealed that QSG significantly changed the differential gene expression of mitochondrial dysfunction in myocardial ischemic tissue. The mechanism was verified through the left anterior descending artery- (LAD-) induced HF rat model and oxygen glucose deprivation/recovery- (OGD/R-) established H9C2 induction model both in vivo and in vitro. Echocardiography and HE staining showed that QSG could effectively improve the cardiac function of rats with myocardial infarction in functionality and structure. Furthermore, transcriptomics revealed QSG could significantly regulate mitochondrial dysfunction-related proteins at the transcriptome level. The results of electron microscopy and immunofluorescence proved that the mitochondrial morphology, mitochondrial membrane structural integrity, and myocardial oxidative stress damage can be effectively improved after QSG treatment. Mechanism studies showed that QSG increased the expression level of mitochondrial biogenesis factor PGC-1α/NRF1/TFAM protein and regulated the balance of mitochondrial fusion/fission protein expression. QSG could regulate mitochondrial dysfunction in ischemia heart tissue to protect cardiac function and structure in HF rats. The likely mechanism is the adjustment of PGC-1α/NRF1/TFAM pathway to alleviate oxidative stress in myocardial cells. Therefore, PGC-1α may be a potential therapeutic target for improving mitochondrial dysfunction in HF.

The promise of graphene-based transistors for democratizing multiomics studies

As biological research has synthesized genomics, proteomics, metabolomics, and transcriptomics into systems biology, a new multiomics approach to biological research has emerged. Today, multiomics studies are challenging and expensive. An experimental platform that could unify the multiple omics approaches to measurement could increase access to multiomics data by enabling more individual labs to successfully attempt multiomics studies. Field effect biosensing based on graphene transistors have gained significant attention as a potential unifying technology for such multiomics studies. This review article highlights the outstanding performance characteristics that makes graphene field effect transistor an attractive sensing platform for a wide variety of analytes important to system biology. In addition to many studies demonstrating the biosensing capabilities of graphene field effect transistors, they are uniquely suited to address the challenges of multiomics studies by providing an integrative multiplex platform for large scale manufacturing using the well-established processes of semiconductor industry. Furthermore, the resulting digital data is readily analyzable by machine learning to derive actionable biological insight to address the challenge of data compatibility for multiomics studies. A critical stage of systems biology will be democratizing multiomics study, and the graphene field effect transistor is uniquely positioned to serve as an accessible multiomics platform.

Imbalance of the intestinal virome and altered viral-bacterial interactions caused by a conditional deletion of the vitamin D receptor

Vitamin D receptor (VDR) deficiency is associated with cancer, infection, and chronic inflammation. Prior research has demonstrated VDR regulation of bacteria; however, little is known regarding VDR and viruses. We hypothesize that VDR deficiency impacts on the intestinal virome and viral-bacterial interactions. We specifically deleted VDR from intestinal epithelial cells (VDR^ΔIEC), Paneth cells (VDR^ΔPC), and myeloid cells (VDR^ΔLyz) in mice. Feces were collected for shotgun metagenomic sequencing and metabolite profiling. To test the functional changes, we evaluated pattern recognition receptors (PRRs) and analyzed microbial metabolites. Vibrio phages, Lactobacillus phages, and Escherichia coli typing phages were significantly enriched in all three conditional VDR-knockout mice. In the VDR^ΔLyz mice, the levels of eight more virus species (2 enriched, 6 depleted) were significantly changed. Altered virus species were primarily observed in female VDR^ΔLyz (2 enriched, 3 depleted) versus male VDR^ΔLyz (1 enriched, 1 depleted). Altered alpha and beta diversity (family to species) were found in VDR^ΔLyz. In VDR^ΔIEC mice, bovine viral diarrhea virus 1 was significantly enriched. A significant correlation between viral and bacterial alterations was found in conditional VDR knockout mice. There was a positive correlation between Vibrio phage JSF5 and Cutibacterium acnes in VDR^ΔPC and VDR^ΔLyz mice. Also, there were more altered viral species in female conditional VDR knockout mice. Notably, there were significant changes in PRRs: upregulated TLR3, TLR7, and NOD2 in VDR^ΔLyz mice and increased CLEC4L expression in VDR^ΔIEC and VDR^ΔPC mice. Furthermore, we identified metabolites related to virus infection: decreased glucose in VDR^ΔIEC mice, increased ribulose/xylulose and xylose in VDR^ΔLyz mice, and increased long-chain fatty acids in VDR^ΔIEC and VDR^ΔLyz female mice. Tissue-specific deletion of VDR changes the virome and functionally changes viral receptors, which leads to dysbiosis, metabolic dysfunction, and infection risk. This study helps to elucidate VDR regulating the virome in a tissue-specific and sex-specific manner.

Clinical drug response prediction from preclinical cancer cell lines by logistic matrix factorization approach

Predicting tumor drug response using cancer cell line drug response values for a large number of anti-cancer drugs is a significant challenge in personalized medicine. Predicting patient response to drugs from data obtained from preclinical models is made easier by the availability of different knowledge on cell lines and drugs. This paper proposes the TCLMF method, a predictive model for predicting drug response in tumor samples that was trained on preclinical samples and is based on the logistic matrix factorization approach. The TCLMF model is designed based on gene expression profiles, tissue type information, the chemical structure of drugs and drug sensitivity (IC 50) data from cancer cell lines. We use preclinical data from the Genomics of Drug Sensitivity in Cancer dataset (GDSC) to train the proposed drug response model, which we then use to predict drug sensitivity of samples from the Cancer Genome Atlas (TCGA) dataset. The TCLMF approach focuses on identifying successful features of cell lines and drugs in order to calculate the probability of the tumor samples being sensitive to drugs. The closest cell line neighbours for each tumor sample are calculated using a description of similarity between tumor samples and cell lines in this study. The drug response for a new tumor is then calculated by averaging the low-rank features obtained from its neighboring cell lines. We compare the results of the TCLMF model with the results of the previously proposed methods using two databases and two approaches to test the model's performance. In the first approach, 12 drugs with enough known clinical drug response, considered in previous methods, are studied. For 7 drugs out of 12, the TCLMF can significantly distinguish between patients that are resistance to these drugs and the patients that are sensitive to them. These approaches are converted to classification models using a threshold in the second approach, and the results are compared. The results demonstrate that the TCLMF method provides accurate predictions across the results of the other algorithms. Finally, we accurately classify tumor tissue type using the latent vectors obtained from TCLMF's logistic matrix factorization process. These findings demonstrate that the TCLMF approach produces effective latent vectors for tumor samples. The source code of the TCLMF method is available in https://github.com/emdadi/TCLMF.