Predictability of human differential gene expression

Resource: A curated database of brain-related functional gene sets (Brain.GMT)

Transcriptional profiling has become a common tool for investigating the nervous system. During analysis, differential expression results are often compared to functional ontology databases, which contain curated gene sets representing well-studied pathways. This dependence can cause neuroscience studies to be interpreted in terms of functional pathways documented in better studied tissues (e.g., liver) and topics (e.g., cancer), and systematically emphasizes well-studied genes, leaving other findings in the obscurity of the brain "ignorome". To address this issue, we compiled a curated database of 918 gene sets related to nervous system function, tissue, and cell types ("Brain.GMT") that can be used within common analysis pipelines (GSEA, limma, edgeR) to interpret results from three species (rat, mouse, human). Brain.GMT includes brain-related gene sets curated from the Molecular Signatures Database (MSigDB) and extracted from public databases (GeneWeaver, Gemma, DropViz, BrainInABlender, HippoSeq) and published studies containing differential expression results. Although Brain.GMT is still undergoing development and currently only represents a fraction of available brain gene sets, "brain ignorome" genes are already better represented than in traditional Gene Ontology databases. Moreover, Brain.GMT substantially improves the quantity and quality of gene sets identified as enriched with differential expression in neuroscience studies, enhancing interpretation. •We compiled a curated database of 918 gene sets related to nervous system function, tissue, and cell types ("Brain.GMT").•Brain.GMT can be used within common analysis pipelines (GSEA, limma, edgeR) to interpret neuroscience transcriptional profiling results from three species (rat, mouse, human).•Although Brain.GMT is still undergoing development, it substantially improved the interpretation of differential expression results within our initial use cases.

Novel Insights into Post-Myocardial Infarction Cardiac Remodeling through Algorithmic Detection of Cell-Type Composition Shifts

Background

Recent advances in single cell sequencing have led to an increased focus on the role of cell-type composition in phenotypic presentation and disease progression. Cell-type composition research in the heart is challenging due to large, frequently multinucleated cardiomyocytes that preclude most single cell approaches from obtaining accurate measurements of cell composition. Our in silico studies reveal that ignoring cell type composition when calculating differentially expressed genes (DEGs) can have significant consequences. For example, a relatively small change in cell abundance of only 10% can result in over 25% of DEGs being false positives.

Methods

We have implemented an algorithmic approach that uses snRNAseq datasets as a reference to accurately calculate cell type compositions from bulk RNAseq datasets through robust data cleaning, gene selection, and multi-sample cross-subject and cross-cell-type deconvolution. We applied our approach to cardiomyocyte-specific α1A adrenergic receptor (CM-α1A-AR) knockout mice. 8-12 week-old mice (either WT or CM-α1A-KO) were subjected to permanent left coronary artery (LCA) ligation or sham surgery (n=4 per group). Transcriptomes from the infarct border zones were collected 3 days later and analyzed using our algorithm to determine cell-type abundances, corrected differential expression calculations using DESeq2, and validated these findings using RNAscope.

Results

Uncorrected DEGs for the CM-α1A-KO X LCA interaction term featured many cell-type specific genes such as Timp4 (fibroblasts) and Aplnr (cardiomyocytes) and overall GO enrichment for terms pertaining to cardiomyocyte differentiation (P=3.1E-4). Using our algorithm, we observe a striking loss of cardiomyocytes and gain in fibroblasts in the α1A-KO + LCA mice that was not recapitulated in WT + LCA animals, although we did observe a similar increase in macrophage abundance in both conditions. This recapitulates prior results that showed a much more severe heart failure phenotype in CM-α1A-KO + LCA mice. Following correction for cell-type, our DEGs now highlight a novel set of genes enriched for GO terms such as cardiac contraction (P=3.7E-5) and actin filament organization (P=6.3E-5).

Conclusions

Our algorithm identifies and corrects for cell-type abundance in bulk RNAseq datasets opening new avenues for research on novel genes and pathways as well as an improved understanding of the role of cardiac cell types in cardiovascular disease.

Transcriptome-wide characterization of genetic perturbations

Single cell CRISPR screens such as Perturb-seq enable transcriptomic profiling of genetic perturbations at scale. However, the data produced by these screens are often noisy due to cost and technical constraints, limiting power to detect true effects with conventional differential expression analyses. Here, we introduce TRanscriptome-wide Analysis of Differential Expression (TRADE), a statistical framework which estimates the transcriptome-wide distribution of true differential expression effects from noisy gene-level measurements. Within TRADE, we derive multiple novel, interpretable statistical metrics, including the "transcriptome-wide impact", an estimator of the overall transcriptional effect of a perturbation which is stable across sampling depths. We analyze new and published large-scale Perturb-seq datasets to show that many true transcriptional effects are not statistically significant, but detectable in aggregate with TRADE. In a genome-scale Perturb-seq screen, we find that a typical gene perturbation affects an estimated 45 genes, whereas a typical essential gene perturbation affects over 500 genes. An advantage of our approach is its ability to compare the transcriptomic effects of genetic perturbations across contexts and dosages despite differences in power. We use this ability to identify perturbations with cell-type dependent effects and to find examples of perturbations where transcriptional responses are not only larger in magnitude, but also qualitatively different, as a function of dosage. Lastly, we expand our analysis to case/control comparison of gene expression for neuropsychiatric conditions, finding that transcriptomic effect correlations are greater than genetic correlations for these diagnoses. TRADE lays an analytic foundation for the systematic comparison of genetic perturbation atlases, as well as differential expression experiments more broadly.

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.

Exploring gene network and protein interaction analysis of neurotrophin signaling pathway in ameloblastoma

Ameloblastoma is a non-cancerous but aggressive oral tumor emerging from odontogenic epithelial tissue involved during odontogenesis. Since there is lack in unravelling the complete molecular pathogenesis of ameloblastoma, chemotherapy is less attempted and a lot of disagreement over the optimal treatment option. Hence, till date, wide surgical resection is considered to be the reliable treatment for ameloblastoma. The Neurotrophin Signaling pathway plays an important role in neuron signaling and it is closely related with the MAPK pathway, which on the other hand regulated cell differentiation, apoptosis, proliferation, plasticity and survival. Protein- Protein Interaction analysis was analysed with STRING tool using WNL value, identified that CTNNB1, HRAS, NGFR, NGFR, and SORT1 having high interacting with BDNF, NT4, p75NTR, NGF, and NT3. The results of ontology analysis revealed that Neurotrophin signaling pathway is associated with Cell surface receptor signaling pathway, regulation of cell differentiation, regulation of development process, EGFR tyrosine kinase inhibitor resistance, MAPK signaling pathway, PI3K-Akt signaling pathway and Ras signaling pathway leading to pathogenesis involving genes. Further, clustering coefficient values of proteins BDNF, NT4, p75NTR, NGF & NT3 were identified as 0.627, 0.708, 0.367, 0.644 & 0.415. The results of molecular docking studies revealed among the selected ligands Methyl-ɣ-oresellinate, N-(4-Hydroxy-phenyl)-2-phenyl-N-phenylacetyl-acetamide, Atranorin and Oresellinate exhibited high binding affinity with selected protein. The key genes involved in Neurotrophin signaling pathway leading to ameloblastoma pathogenesis is revealed, which are closely associated with cell differentiation, cell proliferation, pro-apoptosis, and pro-survival regulations. Further it can be concluded that Neurotrophin signaling pathway could be one of the promising pathway to tailor the targeted drug therapy for Ameloblastoma treatment.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-024-00223-2.

Clover: An unbiased method for prioritizing differentially expressed genes using a data-driven approach

Identifying key genes from a list of differentially expressed genes (DEGs) is a critical step in transcriptome analysis. However, current methods, including Gene Ontology analysis and manual annotation, essentially rely on existing knowledge, which is highly biased depending on the extent of the literature. As a result, understudied genes, some of which may be associated with important molecular mechanisms, are often ignored or remain obscure. To address this problem, we propose Clover, a data-driven scoring method to specifically highlight understudied genes. Clover aims to prioritize genes associated with important molecular mechanisms by integrating three metrics: the likelihood of appearing in the DEG list, tissue specificity, and number of publications. We applied Clover to Alzheimer's disease data and confirmed that it successfully detected known associated genes. Moreover, Clover effectively prioritized understudied but potentially druggable genes. Overall, our method offers a novel approach to gene characterization and has the potential to expand our understanding of gene functions. Clover is an open-source software written in Python3 and available on GitHub at https://github.com/G708/Clover.

A protein sequence-based deep transfer learning framework for identifying human proteome-wide deubiquitinase-substrate interactions

Protein ubiquitination regulates a wide range of cellular processes. The degree of protein ubiquitination is determined by the delicate balance between ubiquitin ligase (E3)-mediated ubiquitination and deubiquitinase (DUB)-mediated deubiquitination. In comparison to the E3-substrate interactions, the DUB-substrate interactions (DSIs) remain insufficiently investigated. To address this challenge, we introduce a protein sequence-based ab initio method, TransDSI, which transfers proteome-scale evolutionary information to predict unknown DSIs despite inadequate training datasets. An explainable module is integrated to suggest the critical protein regions for DSIs while predicting DSIs. TransDSI outperforms multiple machine learning strategies against both cross-validation and independent test. Two predicted DUBs (USP11 and USP20) for FOXP3 are validated by "wet lab" experiments, along with two predicted substrates (AR and p53) for USP22. TransDSI provides new functional perspective on proteins by identifying regulatory DSIs, and offers clues for potential tumor drug target discovery and precision drug application.

Network-based approach elucidates critical genes in BRCA subtypes and chemotherapy response in triple negative breast cancer

Breast cancers (BRCA) exhibit substantial transcriptional heterogeneity, posing a significant clinical challenge. The global transcriptional changes in a disease context, however, are likely mediated by few key genes which reflect disease etiology better than the differentially expressed genes (DEGs). We apply our network-based tool PathExt to 1,059 BRCA tumors across 4 subtypes to identify key mediator genes in each subtype. Compared to conventional differential expression analysis, PathExt-identified genes exhibit greater concordance across tumors, revealing shared and subtype-specific biological processes; better recapitulate BRCA-associated genes in multiple benchmarks, and are more essential in BRCA subtype-specific cell lines. Single-cell transcriptomic analysis reveals a subtype-specific distribution of PathExt-identified genes in multiple cell types from the tumor microenvironment. Application of PathExt to a TNBC chemotherapy response dataset identified subtype-specific key genes and biological processes associated with resistance. We described putative drugs that target key genes potentially mediating drug resistance.

Resource: A Curated Database of Brain-Related Functional Gene Sets (Brain.GMT)

Transcriptional profiling has become a common tool for investigating the nervous system. During analysis, differential expression results are often compared to functional ontology databases, which contain curated gene sets representing well-studied pathways. This dependence can cause neuroscience studies to be interpreted in terms of functional pathways documented in better studied tissues (e.g., liver) and topics (e.g., cancer), and systematically emphasizes well-studied genes, leaving other findings in the obscurity of the brain "ignorome". To address this issue, we compiled a curated database of 918 gene sets related to nervous system function, tissue, and cell types ("Brain.GMT") that can be used within common analysis pipelines (GSEA, limma, edgeR) to interpret results from three species (rat, mouse, human). Brain.GMT includes brain-related gene sets curated from the Molecular Signatures Database (MSigDB) and extracted from public databases (GeneWeaver, Gemma, DropViz, BrainInABlender, HippoSeq) and published studies containing differential expression results. Although Brain.GMT is still undergoing development and currently only represents a fraction of available brain gene sets, "brain ignorome" genes are already better represented than in traditional Gene Ontology databases. Moreover, Brain.GMT substantially improves the quantity and quality of gene sets identified as enriched with differential expression in neuroscience studies, enhancing interpretation.

Feature engineering from meta-data for prediction of differentially expressed genes: An investigation of Mus musculus exposed to space-conditions

Transcription profiling is a key process that can reveal those biological mechanisms driving the response to various exposure conditions or gene perturbations. In this work, we investigate the prediction of differentially expressed genes (DEGs) when exposed to conditions in space from a set of diverse engineered features. To do this, we collected DEGs and non-differentially expressed genes (NDEGs) of Mus musculus-based experiments on the GeneLab database. We engineered a diverse set of features from factors reported in the literature to affect gene expression. An extreme gradient boosting (XGBoost) model was trained to predict if a given gene would be differentially expressed at various levels of differential expression. The test results on a separate holdout dataset showed an area under the receiver operating characteristics curves (AUCs) of 0.90±0.07, averaged across the five selected percentages of the most and least differentially expressed genes. Subsequently, we investigated the impact of selection of features, both individually with a correlation-based feature-selection procedure and in groups with a combination procedure, on the prediction performance. The feature selection confirmed some known drivers of adaptation to radiation and highlighted some new transcription factors and micro RNAs (miRNAs). Finally, gene ontology (GO) analysis revealed biological processes that tend to have expression patterns most suitable for this approach. This work highlights the potential of detection of differentially expressed genes using a machine learning (ML) approach, and provides some evidence of gene expression changes being captured by a diverse feature set not related to the condition under study.

Meta-Research: Understudied genes are lost in a leaky pipeline between genome-wide assays and reporting of results

Present-day publications on human genes primarily feature genes that already appeared in many publications prior to completion of the Human Genome Project in 2003. These patterns persist despite the subsequent adoption of high-throughput technologies, which routinely identify novel genes associated with biological processes and disease. Although several hypotheses for bias in the selection of genes as research targets have been proposed, their explanatory powers have not yet been compared. Our analysis suggests that understudied genes are systematically abandoned in favor of better-studied genes between the completion of -omics experiments and the reporting of results. Understudied genes remain abandoned by studies that cite these -omics experiments. Conversely, we find that publications on understudied genes may even accrue a greater number of citations. Among 45 biological and experimental factors previously proposed to affect which genes are being studied, we find that 33 are significantly associated with the choice of hit genes presented in titles and abstracts of -omics studies. To promote the investigation of understudied genes, we condense our insights into a tool, find my understudied genes (FMUG), that allows scientists to engage with potential bias during the selection of hits. We demonstrate the utility of FMUG through the identification of genes that remain understudied in vertebrate aging. FMUG is developed in Flutter and is available for download at fmug.amaral.northwestern.edu as a MacOS/Windows app.

Meta-Research: understudied genes are lost in a leaky pipeline between genome-wide assays and reporting of results

Present-day publications on human genes primarily feature genes that already appeared in many publications prior to completion of the Human Genome Project in 2003. These patterns persist despite the subsequent adoption of high-throughput technologies, which routinely identify novel genes associated with biological processes and disease. Although several hypotheses for bias in the selection of genes as research targets have been proposed, their explanatory powers have not yet been compared. Our analysis suggests that understudied genes are systematically abandoned in favor of better-studied genes between the completion of -omics experiments and the reporting of results. Understudied genes remain abandoned by studies that cite these -omics experiments. Conversely, we find that publications on understudied genes may even accrue a greater number of citations. Among 45 biological and experimental factors previously proposed to affect which genes are being studied, we find that 33 are significantly associated with the choice of hit genes presented in titles and abstracts of - omics studies. To promote the investigation of understudied genes we condense our insights into a tool, find my understudied genes (FMUG), that allows scientists to engage with potential bias during the selection of hits. We demonstrate the utility of FMUG through the identification of genes that remain understudied in vertebrate aging. FMUG is developed in Flutter and is available for download at fmug.amaral.northwestern.edu as a MacOS/Windows app.

The power of effective study design in animal Experimentation: Exploring the statistical and ethical implications of asking multiple questions of a data set

One of the chief advantages of using highly standardised biological models including model organisms is that multiple variables can be precisely controlled so that the variable of interest is more easily studied. However, such an approach often obscures effects in sub-populations resulting from natural population heterogeneity. Efforts to expand our fundamental understanding of multiple sub-populations are in progress. However, such stratified or personalised approaches require fundamental modifications of our usual study designs that should be implemented in Brain, Behavior and Immunity (BBI) research going forward. Here we explore the statistical feasibility of asking multiple questions (including incorporating sex) within the same experimental cohort using statistical simulations of real data. We illustrate and discuss the large explosion in sample numbers necessary to detect effects with appropriate power for every additional question posed using the same data set. This exploration highlights the strong likelihood of type II errors (false negatives) for standard data and type I errors when dealing with complex genomic data, where studies are too under-powered to appropriately test these interactions. We show this power may differ for males and females in high throughput data sets such as RNA sequencing. We offer a rationale for the use of alternative experimental and statistical strategies based on interdisciplinary insights and discuss the real-world implications of increasing the complexities of our experimental designs, and the implications of not attempting to alter our experimental designs going forward.

Characterizing the targets of transcription regulators by aggregating ChIP-seq and perturbation expression data sets

Mapping the gene targets of chromatin-associated transcription regulators (TRs) is a major goal of genomics research. ChIP-seq of TRs and experiments that perturb a TR and measure the differential abundance of gene transcripts are a primary means by which direct relationships are tested on a genomic scale. It has been reported that there is a poor overlap in the evidence across gene regulation strategies, emphasizing the need for integrating results from multiple experiments. Although research consortia interested in gene regulation have produced a valuable trove of high-quality data, there is an even greater volume of TR-specific data throughout the literature. In this study, we show a workflow for the identification, uniform processing, and aggregation of ChIP-seq and TR perturbation experiments for the ultimate purpose of ranking human and mouse TR-target interactions. Focusing on an initial set of eight regulators (ASCL1, HES1, MECP2, MEF2C, NEUROD1, PAX6, RUNX1, and TCF4), we identified 497 experiments suitable for analysis. We used this corpus to examine data concordance, to identify systematic patterns of the two data types, and to identify putative orthologous interactions between human and mouse. We build upon commonly used strategies to forward a procedure for aggregating and combining these two genomic methodologies, assessing these rankings against independent literature-curated evidence. Beyond a framework extensible to other TRs, our work also provides empirically ranked TR-target listings, as well as transparent experiment-level gene summaries for community use.

Risk factors and actionable molecular signatures in COVID-19-associated lung adenocarcinoma and lung squamous cell carcinoma patients

The molecular mechanism of COVID-19's pathogenic effect on lung cancer patients is yet unknown. In this study, we used differential gene expression pattern analysis to try to figure out the possible disease mechanism of COVID-19 and its associated risk factors in patients with the two most common types of non-small-cell lung cancer, lung adenocarcinoma and lung squamous cell carcinoma. We also used network-based approaches to identify potential diagnostic and molecular targets for COVID-19-infected lung cancer patients. Our study showed that lung cancer and COVID-19 patients share 36 genes that are expressed differently and in common. Most of these genes are expressed in lung tissues and are mostly involved in the pathogenesis of different respiratory tract diseases. Additionally, we also found that COVID-19 may affect the expression of several cancer-associated genes in lung cancer patients, such as the oncogenes JUN, TNC, and POU2AF1. Moreover, we also reported that COVID-19 may predispose lung cancer patients to other diseases like acute liver failure and respiratory distress syndrome. Also, our findings in concert with published literature suggest that molecular signatures like hsa-mir-93-5p, CCNB2, IRF1, CD163, and different immune cell-based approaches could help both diagnose and treat this group of patients. Overall, the scientific results of this research will aid in the formulation of suitable management strategies as well as the development of diagnostic and therapeutic methods for COVID-19-infected lung cancer patients.

RASSF1 is identified by transcriptome coordination analysis as a target of ATF4

Evaluation of gene co-regulation is a powerful approach for revealing regulatory associations between genes and predicting biological function, especially in genetically diverse samples. Here, we applied this strategy to identify transcripts that are co-regulated with unfolded protein response (UPR) genes in cultured fibroblasts from outbred deer mice. Our analyses showed that the transcriptome associated with RASSF1, a tumor suppressor involved in cell cycle regulation and not previously linked to UPR, is highly correlated with the transcriptome of several UPR-related genes, such as BiP/GRP78, DNAJB9, GRP94, ATF4, DNAJC3, and CHOP/DDIT3. Conversely, gene ontology analyses for genes co-regulated with RASSF1 predicted a previously unreported involvement in UPR-associated apoptosis. Bioinformatic analyses indicated the presence of ATF4-binding sites in the RASSF1 promoter, which were shown to be operational using chromatin immunoprecipitation. Reporter assays revealed that the RASSF1 promoter is responsive to ATF4, while ablation of RASSF1 mitigated the expression of the ATF4 effector BBC3 and abrogated tunicamycin-induced apoptosis. Collectively, these results implicate RASSF1 in the regulation of endoplasmic reticulum stress-associated apoptosis downstream of ATF4. They also illustrate the power of gene coordination analysis in predicting biological functions and revealing regulatory associations between genes.

PTPN18 Serves as a Potential Oncogene for Glioblastoma by Enhancing Immune Suppression

Glioblastoma is characterized as one of the deadliest cancers in humans. The survival time is not improved by standard treatment. Although immunotherapy has revolutionized cancer treatment, the current therapy targets for glioblastoma patients are not satisfied. We systematically analyzed the expression patterns, predictive values, and immunological characteristics of PTPN18 in glioblastoma. The independent datasets and functional experiments were employed to validate our findings. Our data showed that PTPN18 is potentially cancerogenic in glioblastoma with advanced grades and poor prognosis. High expression of PTPN18 correlated with CD8⁺ T cell exhaustion and immune suppression in glioblastoma. In addition, PTPN18 facilitates glioblastoma progression by accelerating glioma cell prefiltration, colony formation, and tumor growth in mice. PTPN18 also promotes cell cycle progression and inhibits apoptosis. Our results illustrate the characterization of PTPN18 in glioblastoma and highlight the potential value as an immunotherapeutic target for glioblastoma treatment.

Differential Expression Enrichment Tool (DEET): an interactive atlas of human differential gene expression

Differential gene expression analysis using RNA sequencing (RNA-seq) data is a standard approach for making biological discoveries. Ongoing large-scale efforts to process and normalize publicly available gene expression data enable rapid and systematic reanalysis. While several powerful tools systematically process RNA-seq data, enabling their reanalysis, few resources systematically recompute differentially expressed genes (DEGs) generated from individual studies. We developed a robust differential expression analysis pipeline to recompute 3162 human DEG lists from The Cancer Genome Atlas, Genotype-Tissue Expression Consortium, and 142 studies within the Sequence Read Archive. After measuring the accuracy of the recomputed DEG lists, we built the Differential Expression Enrichment Tool (DEET), which enables users to interact with the recomputed DEG lists. DEET, available through CRAN and RShiny, systematically queries which of the recomputed DEG lists share similar genes, pathways, and TF targets to their own gene lists. DEET identifies relevant studies based on shared results with the user's gene lists, aiding in hypothesis generation and data-driven literature review.

Blood transcriptome of Rasa Aragonesa rams with different sexual behavior phenotype reveals CRYL1 and SORCS2 as genes associated with this trait

Reproductive fitness of rams is seasonal, showing the highest libido during short days coinciding with the ovarian cyclicity resumption in the ewe. However, the remarkable variation in sexual behavior between rams impair farm efficiency and profitability. Intending to identify in vivo sexual behavior biomarkers that may aid farmers to select active rams, transcriptome profiling of blood was carried out by analyzing samples from 6 sexually active (A) and 6 nonactive (NA) Rasa Aragonesa rams using RNA-Seq technique. A total of 14,078 genes were expressed in blood but only four genes were differentially expressed (FDR < 0.10) in the A vs. NA rams comparison. The genes, acrosin inhibitor 1 (ENSOARG00020023278) and SORCS2, were upregulated (log2FC > 1) in active rams, whereas the CRYL1 and immunoglobulin lambda-1 light chain isoform X47 (ENSOARG00020025518) genes were downregulated (log2FC < -1) in this same group. Gene set Enrichment Analysis (GSEA) identified 428 signaling pathways, predominantly related to biological processes. The lysosome pathway (GO:0005764) was the most enriched, and may affect fertility and sexual behavior, given the crucial role played by lysosomes in steroidogenesis, being the SORCS2 gene related to this signaling pathway. Furthermore, the enriched positive regulation of ERK1 and ERK2 cascade (GO:0070374) pathway is associated with reproductive phenotypes such as fertility via modulation of hypothalamic regulation and GnRH-mediated production of pituitary gonadotropins. Furthermore, external side of plasma membrane (GO:0009897), fibrillar center (GO:0001650), focal adhesion (GO:0005925), and lamellipodium (GO:0030027) pathways were also enriched, suggesting that some molecules of these pathways might also be involved in rams' sexual behavior. These results provide new clues for understanding the molecular regulation of sexual behavior in rams. Further investigations will be needed to confirm the functions of SORCS2 and CRYL1 in relation to sexual behavior.

Translational research into frailty from bench to bedside: Salivary biomarkers for inflammaging

Frailty is a complex physiological syndrome associated with adverse ageing and decreased physiological reserves. Frailty leads to cognitive and physical disability and is a significant cause of morbidity, mortality and economic costs. The underlying cause of frailty is multifaceted, including immunosenescence and inflammaging, changes in microbiota and metabolic dysfunction. Currently, salivary biomarkers are used as early predictors for some clinical diseases, contributing to the effective prevention and treatment of diseases, including frailty. Sample collection for salivary analysis is non-invasive and simple, which are paramount factors for testing in the vulnerable frail population. The aim of this review is to describe the current knowledge on the association between frailty and the inflammatory process and discuss methods to identify putative biomarkers in salivary fluids to predict this syndrome. This study describes the relationship between i.-inflammatory process and frailty; ii.-infectious, chronic, skeletal, metabolic and cognitive diseases with inflammation and frailty; iii.-inflammatory biomarkers and salivary fluids. There is a limited number of previous studies focusing on the analysis of inflammatory salivary biomarkers and frailty syndrome; hence, the study of salivary fluids as a source for biomarkers is an open area of research with the potential to address the increasing demands for frailty-associated biomarkers.

Defining the extent of gene function using ROC curvature

MOTIVATION

Interactions between proteins help us understand how genes are functionally related and how they contribute to phenotypes. Experiments provide imperfect 'ground truth' information about a small subset of potential interactions in a specific biological context, which can then be extended to the whole genome across different contexts, such as conditions, tissues or species, through machine learning methods. However, evaluating the performance of these methods remains a critical challenge. Here, we propose to evaluate the generalizability of gene characterizations through the shape of performance curves.

RESULTS

We identify Functional Equivalence Classes (FECs), subsets of annotated and unannotated genes that jointly drive performance, by assessing the presence of straight lines in ROC curves built from gene-centric prediction tasks, such as function or interaction predictions. FECs are widespread across data types and methods, they can be used to evaluate the extent and context-specificity of functional annotations in a data-driven manner. For example, FECs suggest that B cell markers can be decomposed into shared primary markers (10-50 genes), and tissue-specific secondary markers (100-500 genes). In addition, FECs suggest the existence of functional modules that span a wide range of the genome, with marker sets spanning at most 5% of the genome and data-driven extensions of Gene Ontology sets spanning up to 40% of the genome. Simple to assess visually and statistically, the identification of FECs in performance curves paves the way for novel functional characterization and increased robustness in the definition of functional gene sets.

AVAILABILITY AND IMPLEMENTATION

Code for analyses and figures is available at https://github.com/yexilein/pyroc.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Phenology of the transcriptome coincides with the physiology of double-crested cormorant embryonic development

The rigorous timing of the dynamic transcriptome within the embryo has to be well orchestrated for normal development. Identifying the phenology of the transcriptome along with the physiology of embryonic development in birds may suggest periods of increased sensitivity to contaminant exposure depending on the contaminant's mechanism of action. Double-crested cormorants (Nannopterum auritum, formerly Phalacrocorax auritus) are commonly used in ecotoxicological studies, but relatively little is known about their functional transcriptome profile in early development. In this study, we tracked the phenology of the transcriptome during N. auritum embryogenesis. Fresh eggs were collected from a reference site and artificially incubated from collection until four days prior to hatching. Embryos were periodically sampled throughout incubation for a total of seven time points. A custom microarray was designed for cormorants (over 14,000 probes) and used for transcriptome analysis in whole body (days 5, 8) and liver tissue (days 12, 14, 16, 20, 24). Three main developmental periods (early, mid, and late incubation) were identified with differentially expressed genes, gene sets, and pathways within and between each developmental transition. Overall, the timing of differentially expressed genes and enriched pathways corresponded to previously documented changes in morphology, neurology, or physiology during avian embryonic development. Targeted investigation of a subset of genes involved in endogenous and xenobiotic metabolism (e.g., cytochrome P450 cyp1a, cyp1b1, superoxide dismutase 1 sod1) were expressed in a pattern similar to reported endogenous compound levels. These data can provide insights on normal embryonic development in an ecologically relevant species without any environmental contaminant exposure.

RNA biomarkers for alcohol use disorder

Alcohol use disorder (AUD) is highly prevalent and one of the leading causes of disability in the US and around the world. There are some molecular biomarkers of heavy alcohol use and liver damage which can suggest AUD, but these are lacking in sensitivity and specificity. AUD treatment involves psychosocial interventions and medications for managing alcohol withdrawal, assisting in abstinence and reduced drinking (naltrexone, acamprosate, disulfiram, and some off-label medications), and treating comorbid psychiatric conditions (e.g., depression and anxiety). It has been suggested that various patient groups within the heterogeneous AUD population would respond more favorably to specific treatment approaches. For example, there is some evidence that so-called reward-drinkers respond better to naltrexone than acamprosate. However, there are currently no objective molecular markers to separate patients into optimal treatment groups or any markers of treatment response. Objective molecular biomarkers could aid in AUD diagnosis and patient stratification, which could personalize treatment and improve outcomes through more targeted interventions. Biomarkers of treatment response could also improve AUD management and treatment development. Systems biology considers complex diseases and emergent behaviors as the outcome of interactions and crosstalk between biomolecular networks. A systems approach that uses transcriptomic (or other -omic data, e.g., methylome, proteome, metabolome) can capture genetic and environmental factors associated with AUD and potentially provide sensitive, specific, and objective biomarkers to guide patient stratification, prognosis of treatment response or relapse, and predict optimal treatments. This Review describes and highlights state-of-the-art research on employing transcriptomic data and artificial intelligence (AI) methods to serve as molecular biomarkers with the goal of improving the clinical management of AUD. Considerations about future directions are also discussed.

Transcriptome Analysis of Duck and Chicken Brains Infected with Aquatic Bird Bornavirus-1 (ABBV-1)

Aquatic bird bornavirus 1 (ABBV-1) is a neurotropic virus that infects waterfowls, resulting in persistent infection. Experimental infection showed that both Muscovy ducks and chickens support persistent ABBV-1 infection in the central nervous system (CNS), up to 12 weeks post-infection (wpi), without the development of clinical disease. The aim of the present study was to describe the transcriptomic profiles in the brains of experimentally infected Muscovy ducks and chickens infected with ABBV-1 at 4 and 12 wpi. Transcribed RNA was sequenced by next-generation sequencing and analyzed by principal component analysis (PCA) and differential gene expression. The functional annotation of differentially expressed genes was evaluated by gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. The PCA showed that the infected ducks sampled at both 4 and 12 wpi clustered separately from the controls, while only the samples from the chickens at 12 wpi, but not at 4 wpi, formed a separate cluster. In the ducks, more genes were differentially expressed at 4 wpi than 12 wpi, and the majority of the highly differentially expressed genes (DEG) were upregulated. On the other hand, the infected chickens had fewer DEGs at 4 wpi than at 12 wpi, and the majority of those with high numbers of DEGs were downregulated at 4 wpi and upregulated at 12 wpi. The functional annotation showed that the most enriched GO terms were immune-associated in both species; however, the terms associated with the innate immune response were predominantly enriched in the ducks, whereas the chickens had enrichment of both the innate and adaptive immune response. Immune-associated pathways were also enriched according to the KEGG pathway analysis in both species. Overall, the transcriptomic analysis of the duck and chicken brains showed that the main biological responses to ABBV-1 infection were immune-associated and corresponded with the levels of inflammation in the CNS.

SOPHIE: Generative Neural Networks Separate Common and Specific Transcriptional Responses

Genome-wide transcriptome profiling identifies genes that are prone to differential expression (DE) across contexts, as well as genes with changes specific to the experimental manipulation. Distinguishing genes that are specifically changed in a context of interest from common differentially expressed genes (DEGs) allows more efficient prediction of which genes are specific to a given biological process under scrutiny. Currently, common DEGs or pathways can only be identified through the laborious manual curation of experiments, an inordinately time-consuming endeavor. Here we pioneer an approach, Specific cOntext Pattern Highlighting In Expression data (SOPHIE), for distinguishing between common and specific transcriptional patterns using a generative neural network to create a background set of experiments from which a null distribution of gene and pathway changes can be generated. We apply SOPHIE to diverse datasets including those from human, human cancer, and bacterial pathogen Pseudomonas aeruginosa. SOPHIE identifies common DEGs in concordance with previously described, manually and systematically determined common DEGs. Further molecular validation indicates that SOPHIE detects highly specific but low-magnitude biologically relevant transcriptional changes. SOPHIE's measure of specificity can complement log2 fold change values generated from traditional DE analyses. For example, by filtering the set of DEGs, one can identify genes that are specifically relevant to the experimental condition of interest. Consequently, these results can inform future research directions. All scripts used in these analyses are available at https://github.com/greenelab/generic-expression-patterns. Users can access https://github.com/greenelab/sophie to run SOPHIE on their own data.

Allostery: Allosteric Cancer Drivers and Innovative Allosteric Drugs

Here, we discuss the principles of allosteric activating mutations, propagation downstream of the signals that they prompt, and allosteric drugs, with examples from the Ras signaling network. We focus on Abl kinase where mutations shift the landscape toward the active, imatinib binding-incompetent conformation, likely resulting in the high affinity ATP outcompeting drug binding. Recent pharmacological innovation extends to allosteric inhibitor (GNF-5)-linked PROTAC, targeting Bcr-Abl1 myristoylation site, and broadly, allosteric heterobifunctional degraders that destroy targets, rather than inhibiting them. Designed chemical linkers in bifunctional degraders can connect the allosteric ligand that binds the target protein and the E3 ubiquitin ligase warhead anchor. The physical properties and favored conformational state of the engineered linker can precisely coordinate the distance and orientation between the target and the recruited E3. Allosteric PROTACs, noncompetitive molecular glues, and bitopic ligands, with covalent links of allosteric ligands and orthosteric warheads, increase the effective local concentration of productively oriented and placed ligands. Through covalent chemical or peptide linkers, allosteric drugs can collaborate with competitive drugs, degrader anchors, or other molecules of choice, driving innovative drug discovery.

Role of PCK2 in the proliferation of vascular smooth muscle cells in neointimal hyperplasia

Vascular smooth muscle cell (VSMC) proliferation is a hallmark of neointimal hyperplasia (NIH) in atherosclerosis and restenosis post-balloon angioplasty and stent insertion. Although numerous cytotoxic and cytostatic therapeutics have been developed to reduce NIH, it is improbable that a multifactorial disease can be successfully treated by focusing on a preconceived hypothesis. We, therefore, aimed to identify key molecules involved in NIH via a hypothesis-free approach. We analyzed four datasets (GSE28829, GSE43292, GSE100927, and GSE120521), evaluated differentially expressed genes (DEGs) in wire-injured femoral arteries of mice, and determined their association with VSMC proliferation in vitro. Moreover, we performed RNA sequencing on platelet-derived growth factor (PDGF)-stimulated human VSMCs (hVSMCs) post-phosphoenolpyruvate carboxykinase 2 (PCK2) knockdown and investigated pathways associated with PCK2. Finally, we assessed NIH formation in Pck2 knockout (KO) mice by wire injury and identified PCK2 expression in human femoral artery atheroma. Among six DEGs, only PCK2 and RGS1 showed identical expression patterns between wire-injured femoral arteries of mice and gene expression datasets. PDGF-induced VSMC proliferation was attenuated when hVSMCs were transfected with PCK2 siRNA. RNA sequencing of PCK2 siRNA-treated hVSMCs revealed the involvement of the Akt-FoxO-PCK2 pathway in VSMC proliferation via Akt2, Akt3, FoxO1, and FoxO3. Additionally, NIH was attenuated in the wire-injured femoral artery of Pck2-KO mice and PCK2 was expressed in human femoral atheroma. PCK2 regulates VSMC proliferation in response to vascular injury via the Akt-FoxO-PCK2 pathway. Targeting PCK2, a downstream signaling mediator of VSMC proliferation, may be a novel therapeutic approach to modulate VSMC proliferation in atherosclerosis.

Predicting which genes will respond to transcription factor perturbations

The ability to predict which genes will respond to the perturbation of a transcription factor serves as a benchmark for our systems-level understanding of transcriptional regulatory networks. In previous work, machine learning models have been trained to predict static gene expression levels in a biological sample by using data from the same or similar samples, including data on their transcription factor binding locations, histone marks, or DNA sequence. We report on a different challenge-training machine learning models to predict which genes will respond to the perturbation of a transcription factor without using any data from the perturbed cells. We find that existing transcription factor location data (ChIP-seq) from human cells have very little detectable utility for predicting which genes will respond to perturbation of a transcription factor. Features of genes, including their preperturbation expression level and expression variation, are very useful for predicting responses to perturbation of any transcription factor. This shows that some genes are poised to respond to transcription factor perturbations and others are resistant, shedding light on why it has been so difficult to predict responses from binding locations. Certain histone marks, including H3K4me1 and H3K4me3, have some predictive power when located downstream of the transcription start site. However, the predictive power of histone marks is much less than that of gene expression level and expression variation. Sequence-based or epigenetic properties of genes strongly influence their tendency to respond to direct transcription factor perturbations, partially explaining the oft-noted difficulty of predicting responsiveness from transcription factor binding location data. These molecular features are largely reflected in and summarized by the gene's expression level and expression variation. Code is available at https://github.com/BrentLab/TFPertRespExplainer.

Genes and Diseases: Insights from Transcriptomics Studies

Results of expression studies can be useful to clarify the genotype-phenotype relationship. However, according to data from recent literature, there is a large group of genes that are revealed as differentially expressed (DE) in many studies, regardless of the biological context. Additional analyses could shed more light on the relationships between genes, their differential expression, and diseases. We generated a set of 9972 disease genes from five gene-phenotype databases (OMIM, ORPHANET, DDG2P, DisGeNet and MalaCards) and a report of the International Union of Immunological Societies. To study transcriptomics of disease and non-disease genes in healthy tissues, we obtained data from the Human Protein Atlas (HPA) website. We analyzed the dependency between expression in healthy tissues and gene occurrence in Gene Expression Omnibus series using tools within the Enrichr libraries. The results of expression studies were annotated with Gene Ontology (GO) and Human Phenotype Ontology (HPO) terms. Using transcriptomics analysis of healthy tissues, we validated the previous findings of higher expression levels of disease genes in pathologically linked tissues compared to other tissues. Preferentially DE genes were generally highly expressed in one or multiple tissues and were enriched for disease genes. According to the results of GO enrichment analyses, both down- and up-regulated DE genes most often took part in immune response, translation and tissue-specific processes. A connection between DE-related pathology and the diversity of HPO terms was found. Investigating a link between expression and phenotype contributes to understanding the mode of development and progression of human diseases.

Elucidating gene expression patterns across multiple biological contexts through a large-scale investigation of transcriptomic datasets

Distinct gene expression patterns within cells are foundational for the diversity of functions and unique characteristics observed in specific contexts, such as human tissues and cell types. Though some biological processes commonly occur across contexts, by harnessing the vast amounts of available gene expression data, we can decipher the processes that are unique to a specific context. Therefore, with the goal of developing a portrait of context-specific patterns to better elucidate how they govern distinct biological processes, this work presents a large-scale exploration of transcriptomic signatures across three different contexts (i.e., tissues, cell types, and cell lines) by leveraging over 600 gene expression datasets categorized into 98 subcontexts. The strongest pairwise correlations between genes from these subcontexts are used for the construction of co-expression networks. Using a network-based approach, we then pinpoint patterns that are unique and common across these subcontexts. First, we focused on patterns at the level of individual nodes and evaluated their functional roles using a human protein-protein interactome as a referential network. Next, within each context, we systematically overlaid the co-expression networks to identify specific and shared correlations as well as relations already described in scientific literature. Additionally, in a pathway-level analysis, we overlaid node and edge sets from co-expression networks against pathway knowledge to identify biological processes that are related to specific subcontexts or groups of them. Finally, we have released our data and scripts at https://zenodo.org/record/5831786 and https://github.com/ContNeXt/ , respectively and developed ContNeXt ( https://contnext.scai.fraunhofer.de/ ), a web application to explore the networks generated in this work.

A multivariate statistical test for differential expression analysis

Statistical tests of differential expression usually suffer from two problems. Firstly, their statistical power is often limited when applied to small and skewed data sets. Secondly, gene expression data are usually discretized by applying arbitrary criteria to limit the number of false positives. In this work, a new statistical test obtained from a convolution of multivariate hypergeometric distributions, the Hy-test, is proposed to address these issues. Hy-test has been carried out on transcriptomic data from breast and kidney cancer tissues, and it has been compared with other differential expression analysis methods. Hy-test allows implicit discretization of the expression profiles and is more selective in retrieving both differential expressed genes and terms of Gene Ontology. Hy-test can be adopted together with other tests to retrieve information that would remain hidden otherwise, e.g., terms of (1) cell cycle deregulation for breast cancer and (2) "programmed cell death" for kidney cancer.

Allostery, and how to define and measure signal transduction

Here we ask: What is productive signaling? How to define it, how to measure it, and most of all, what are the parameters that determine it? Further, what determines the strength of signaling from an upstream to a downstream node in a specific cell? These questions have either not been considered or not entirely resolved. The requirements for the signal to propagate downstream to activate (repress) transcription have not been considered either. Yet, the questions are pivotal to clarify, especially in diseases such as cancer where determination of signal propagation can point to cell proliferation and to emerging drug resistance, and to neurodevelopmental disorders, such as RASopathy, autism, attention-deficit/hyperactivity disorder (ADHD), and cerebral palsy. Here we propose a framework for signal transduction from an upstream to a downstream node addressing these questions. Defining cellular processes, experimentally measuring them, and devising powerful computational AI-powered algorithms that exploit the measurements, are essential for quantitative science.

Deep learning-based brain transcriptomic signatures associated with the neuropathological and clinical severity of Alzheimer's disease

Brain tissue gene expression from donors with and without Alzheimer's disease has been used to help inform the molecular changes associated with the development and potential treatment of this disorder. Here, we use a deep learning method to analyse RNA-seq data from 1114 brain donors from the Accelerating Medicines Project for Alzheimer's Disease consortium to characterize post-mortem brain transcriptome signatures associated with amyloid-β plaque, tau neurofibrillary tangles and clinical severity in multiple Alzheimer's disease dementia populations. Starting from the cross-sectional data in the Religious Orders Study and Memory and Aging Project cohort (n = 634), a deep learning framework was built to obtain a trajectory that mirrors Alzheimer's disease progression. A severity index was defined to quantitatively measure the progression based on the trajectory. Network analysis was then carried out to identify key gene (index gene) modules present in the model underlying the progression. Within this data set, severity indexes were found to be very closely correlated with all Alzheimer's disease neuropathology biomarkers (R ∼ 0.5, P < 1e-11) and global cognitive function (R = -0.68, P < 2.2e-16). We then applied the model to additional transcriptomic data sets from different brain regions (MAYO, n = 266; Mount Sinai Brain Bank, n = 214), and observed that the model remained significantly predictive (P < 1e-3) of neuropathology and clinical severity. The index genes that significantly contributed to the model were integrated with Alzheimer's disease co-expression regulatory networks, resolving four discrete gene modules that are implicated in vascular and metabolic dysfunction in different cell types, respectively. Our work demonstrates the generalizability of this signature to frontal and temporal cortex measurements and additional brain donors with Alzheimer's disease, other age-related neurological disorders and controls, and revealed that the transcriptomic network modules contribute to neuropathological and clinical disease severity. This study illustrates the promise of using deep learning methods to analyse heterogeneous omics data and discover potentially targetable molecular networks that can inform the development, treatment and prevention of neurodegenerative diseases like Alzheimer's disease.

Canine transmissible venereal tumour established in immunodeficient mice reprograms the gene expression profiles associated with a favourable tumour microenvironment to enable cancer malignancy

Background

Canine transmissible venereal tumours (CTVTs) can cross the major histocompatibility complex barrier to spread among dogs. In addition to the transmissibility within canids, CTVTs are also known as a suitable model for investigating the tumour–host immunity interaction because dogs live with humans and experience the same environmental risk factors for tumourigenesis. Moreover, outbred dogs are more appropriate than inbred mice models for simulating the diversity of human cancer development. This study built a new model of CTVTs, known as MCTVTs, to further probe the shaping effects of immune stress on tumour development. For xenotransplantation, CTVTs were first injected and developed in immunodeficient mice (NOD.CB17-Prkdc^scid/NcrCrl), defined as XCTVTs. The XCTVTs harvested from NOD/SCID mice were then inoculated and grown in beagles and named mouse xenotransplantation of CTVTs (MCTVTs).

Results

After the inoculation of CTVTs and MCTVTs into immune-competent beagle dogs separately, MCTVTs grew faster and metastasized more frequently than CTVTs did. Gene expression profiles in CTVTs and MCTVTs were analysed by cDNA microarray to reveal that MCTVTs expressed many tumour-promoting genes involved in chronic inflammation, chemotaxis, extracellular space modification, NF-kappa B pathways, and focal adhesion. Furthermore, several well-known tumour-associated biomarkers which could predict tumour progression were overexpressed in MCTVTs.

Conclusions

This study demonstrated that defective host immunity can result in gene instability and enable transcriptome reprogramming within tumour cells. Fast tumour growth in beagle dogs and overexpression of tumour-associated biomarkers were found in a CTVT strain previously established in immunodeficient mice. In addition, dysregulated interaction of chronic inflammation, chemotaxis, and extracellular space modification were revealed to imply the possibly exacerbating mechanisms in the microenvironments of these tumours. In summary, this study offers a potential method to facilitate tumour progression and provide a niche for discovering tumour-associated biomarkers in cancer research.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12917-021-03093-4.

A permissive epigenetic landscape facilitates distinct transcriptional signatures of activating transcription factor 6 in the liver

Restoring homeostasis following proteostatic stress hinges on a stress-specific transcriptional signature. How these signatures are regulated is unknown. We use functional genomics to uncover how activating transcription factor 6 (ATF6), a central factor in the unfolded protein response, regulates its target genes in response to toxicant induced and physiological stress in the liver. We identified 652 conserved putative ATF6 targets (CPATs), which functioned in metabolism, development and proteostasis. Strikingly, Atf6 activation in the zebrafish liver by transgenic nAtf6 overexpression, ethanol and arsenic exposure resulted in a distinct CPAT signature for each; with only 34 CPATs differentially expressed in all conditions. In contrast, during liver regeneration in mice resulted in a dynamic differential expression pattern of 53% of CPATs. These CPATs were distinguished by residing in open chromatin, H3K4me3 occupancy and the absence of H3K27me3 on their promoters. This suggests that a permissive epigenetic landscape allows stress-specific Atf6 target gene expression.

Targeting the Transcriptome Through Globally Acting Components

Transcription is a step in gene expression that defines the identity of cells and its dysregulation is associated with diseases. With advancing technologies revealing molecular underpinnings of the cell with ever-higher precision, our ability to view the transcriptomes may have surpassed our knowledge of the principles behind their organization. The human RNA polymerase II (Pol II) machinery comprises thousands of components that, in conjunction with epigenetic and other mechanisms, drive specialized programs of development, differentiation, and responses to the environment. Parts of these programs are repurposed in oncogenic transformation. Targeting of cancers is commonly done by inhibiting general or broadly acting components of the cellular machinery. The critical unanswered question is how globally acting or general factors exert cell type specific effects on transcription. One solution, which is discussed here, may be among the events that take place at genes during early Pol II transcription elongation. This essay turns the spotlight on the well-known phenomenon of promoter-proximal Pol II pausing as a step that separates signals that establish pausing genome-wide from those that release the paused Pol II into the gene. Concepts generated in this rapidly developing field will enhance our understanding of basic principles behind transcriptome organization and hopefully translate into better therapies at the bedside.

Network-based transcriptomic analysis identifies the genetic effect of COVID-19 to chronic kidney disease patients: A bioinformatics approach

COVID-19 has emerged as global health threats. Chronic kidney disease (CKD) patients are immune-compromised and may have a high risk of infection by the SARS-CoV-2. We aimed to detect common transcriptomic signatures and pathways between COVID-19 and CKD by systems biology analysis. We analyzed transcriptomic data obtained from peripheral blood mononuclear cells (PBMC) infected with SARS-CoV-2 and PBMC of CKD patients. We identified 49 differentially expressed genes (DEGs) which were common between COVID-19 and CKD. The gene ontology and pathways analysis showed the DEGs were associated with "platelet degranulation", "regulation of wound healing", "platelet activation", "focal adhesion", "regulation of actin cytoskeleton" and "PI3K-Akt signalling pathway". The protein-protein interaction (PPI) network encoded by the common DEGs showed ten hub proteins (EPHB2, PRKAR2B, CAV1, ARHGEF12, HSP90B1, ITGA2B, BCL2L1, E2F1, TUBB1, and C3). Besides, we identified significant transcription factors and microRNAs that may regulate the common DEGs. We investigated protein-drug interaction analysis and identified potential drugs namely, aspirin, estradiol, rapamycin, and nebivolol. The identified common gene signature and pathways between COVID-19 and CKD may be therapeutic targets in COVID-19 patients with CKD comorbidity.

Responsiveness to perturbations is a hallmark of transcription factors that maintain cell identity in vitro

Identifying the particular transcription factors that maintain cell type in vitro is important for manipulating cell type. Identifying such transcription factors by their cell-type-specific expression or their involvement in developmental regulation has had limited success. We hypothesized that because cell type is often resilient to perturbations, the transcriptional response to perturbations would reveal identity-maintaining transcription factors. We developed perturbation panel profiling (P3) as a framework for perturbing cells across many conditions and measuring gene expression responsiveness transcriptome-wide. In human iPSC-derived cardiac myocytes, P3 showed that transcription factors important for cardiac myocyte differentiation and maintenance were among the most frequently upregulated (most responsive). We reasoned that one function of responsive genes may be to maintain cellular identity. We identified responsive transcription factors in fibroblasts using P3 and found that suppressing their expression led to enhanced reprogramming. We propose that responsiveness to perturbations is a property of transcription factors that help maintain cellular identity in vitro. A record of this paper's transparent peer review process is included in the supplemental information.

An information-theoretic approach for measuring the distance of organ tissue samples using their transcriptomic signatures

Motivation

Recapitulating aspects of human organ functions using in vitro (e.g. plates, transwells, etc.), in vivo (e.g. mouse, rat, etc.), or ex vivo (e.g. organ chips, 3D systems, etc.) organ models is of paramount importance for drug discovery and precision medicine. It will allow us to identify potential side effects and test the effectiveness of new therapeutic approaches early in their design phase, and will inform the development of better disease models. Developing mathematical methods to reliably compare the ‘distance/similarity’ of organ models from/to the real human organ they represent is an understudied problem with important applications in biomedicine and tissue engineering.

Results

We introduce the Transcriptomic Signature Distance (TSD), an information-theoretic distance for assessing the transcriptomic similarity of two tissue samples, or two groups of tissue samples. In developing TSD, we are leveraging next-generation sequencing data as well as information retrieved from well-curated databases providing signature gene sets characteristic for human organs. We present the justification and mathematical development of the new distance and demonstrate its effectiveness and advantages in different scenarios of practical importance using several publicly available RNA-seq datasets.

Availability and Implementation

The computation of both TSD versions (simple and weighted) has been implemented in R and can be downloaded from https://github.com/Cod3B3nd3R/Transcriptomic-Signature-Distance.

Contact

dimitris.manatakis@emulatebio.com

Supplementary information

Supplementary data are available at Bioinformatics online.

Network analysis identifies DAPK3 as a potential biomarker for lymphatic invasion and colon adenocarcinoma prognosis

Colon adenocarcinoma is a prevalent malignancy with significant mortality. Hence, the identification of molecular biomarkers with prognostic significance is important for improved treatment and patient outcomes. Clinical traits and RNA-Seq of 551 patient samples in the UCSC Toil Recompute Compendium of The Cancer Genome Atlas TARGET and Genotype Tissue Expression project datasets (primary_site = colon) were used for weighted gene co-expression network analysis to reveal the association between gene networks and cancer cell invasion. One module, containing 151 genes, was significantly correlated with lymphatic invasion, a histopathological feature of higher risk colon cancer. DAPK3 (death-associated protein kinase 3) was identified as the pseudohub of the module. Gene ontology identified gene enrichment related to cytoskeletal organization and apoptotic signaling processes, suggesting modular involvement in tumor cell survival, migration, and epithelial-mesenchymal transformation. Although DAPK3 expression was reduced in patients with colon cancer, high expression of DAPK3 was significantly correlated with greater lymphatic invasion and poor overall survival.

•

WCGNA reveals a gene module linked to lymphatic invasion in colon adenocarcinoma

•

DAPK3 is a pseudohub gene with differential expression in colon cancer

•

Gene ontology identified relationships to cytoskeletal organization and apoptosis

•

DAPK3 was correlated with lymphatic invasion and poor overall survival

Optimization of a modeling platform to predict oncogenes from genome-scale metabolic networks of non-small-cell lung cancers

Cancer cell dysregulations result in the abnormal regulation of cellular metabolic pathways. By simulating this metabolic reprogramming using constraint-based modeling approaches, oncogenes can be predicted, and this knowledge can be used in prognosis and treatment. We introduced a trilevel optimization problem describing metabolic reprogramming for inferring oncogenes. First, this study used RNA-Seq expression data of lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) samples and their healthy counterparts to reconstruct tissue-specific genome-scale metabolic models and subsequently build the flux distribution pattern that provided a measure for the oncogene inference optimization problem for determining tumorigenesis. The platform detected 45 genes for LUAD and 84 genes for LUSC that lead to tumorigenesis. A high level of differentially expressed genes was not an essential factor for determining tumorigenesis. The platform indicated that pyruvate kinase (PKM), a well-known oncogene with a low level of differential gene expression in LUAD and LUSC, had the highest fitness among the predicted oncogenes based on computation. By contrast, pyruvate kinase L/R (PKLR), an isozyme of PKM, had a high level of differential gene expression in both cancers. Phosphatidylserine synthase 1 (PTDSS1), an oncogene in LUAD, was inferred to have a low level of differential gene expression, and overexpression could significantly reduce survival probability. According to the factor analysis, PTDSS1 characteristics were close to those of the template, but they were unobvious in LUSC. Angiotensin-converting enzyme 2 (ACE2) has recently garnered widespread interest as the SARS-CoV-2 virus receptor. Moreover, we determined that ACE2 is an oncogene of LUSC but not of LUAD. The platform developed in this study can identify oncogenes with low levels of differential expression and be used to identify potential therapeutic targets for cancer treatment.

Investigating Transcriptional Dynamics Changes and Time-Dependent Marker Gene Expression in the Early Period After Skeletal Muscle Injury in Rats

Following skeletal muscle injury (SMI), from post-injury reaction to repair consists of a complex series of dynamic changes. However, there is a paucity of research on detailed transcriptional dynamics and time-dependent marker gene expression in the early stages after SMI. In this study, skeletal muscle tissue in rats was taken at 4 to 48 h after injury for next-generation sequencing. We examined the transcriptional kinetics characteristics during above time periods after injury. STEM and maSigPro were used to screen time-correlated genes. Integrating 188 time-correlated genes with 161 genes in each time-related gene module by WGCNA, we finally identified 18 network-node regulatory genes after SMI. Histological staining analyses confirmed the mechanisms underlying changes in the tissue damage to repair process. Our research linked a variety of dynamic biological processes with specific time periods and provided insight into the characteristics of transcriptional dynamics, as well as screened time-related biological indicators with biological significance in the early stages after SMI.

Protein structure-based gene expression signatures

Gene expression signatures (GES) connect phenotypes to differential messenger RNA (mRNA) expression of genes, providing a powerful approach to define cellular identity, function, and the effects of perturbations. The use of GES has suffered from vague assessment criteria and limited reproducibility. Because the structure of proteins defines the functional capability of genes, we hypothesized that enrichment of structural features could be a generalizable representation of gene sets. We derive structural gene expression signatures (sGES) using features from multiple levels of protein structure (e.g., domain and fold) encoded by the mRNAs in GES. Comprehensive analyses of data from the Genotype-Tissue Expression Project (GTEx), the all RNA-seq and ChIP-seq sample and signature search (ARCHS4) database, and mRNA expression of drug effects on cardiomyocytes show that sGES are useful for characterizing biological phenomena. sGES enable phenotypic characterization across experimental platforms, facilitates interoperability of expression datasets, and describe drug action on cells.

Resilience, plasticity and robustness in gene expression during aging in the brain of outbred deer mice

BACKGROUND

Genes that belong to the same network are frequently co-expressed, but collectively, how the coordination of the whole transcriptome is perturbed during aging remains unclear. To explore this, we calculated the correlation of each gene in the transcriptome with every other, in the brain of young and older outbred deer mice (P. leucopus and P. maniculatus).

RESULTS

In about 25 % of the genes, coordination was inversed during aging. Gene Ontology analysis in both species, for the genes that exhibited inverse transcriptomic coordination during aging pointed to alterations in the perception of smell, a known impairment occurring during aging. In P. leucopus, alterations in genes related to cholesterol metabolism were also identified. Among the genes that exhibited the most pronounced inversion in their coordination profiles during aging was THBS4, that encodes for thrombospondin-4, a protein that was recently identified as rejuvenation factor in mice. Relatively to its breadth, abolishment of coordination was more prominent in the long-living P. leucopus than in P. maniculatus but in the latter, the intensity of de-coordination was higher.

CONCLUSIONS

There sults suggest that aging is associated with more stringent retention of expression profiles for some genes and more abrupt changes in others, while more subtle but widespread changes in gene expression appear protective. Our findings shed light in the mode of the transcriptional changes occurring in the brain during aging and suggest that strategies aiming to broader but more modest changes in gene expression may be preferrable to correct aging-associated deregulation in gene expression.

COVID-19 and the differential dilemma

The conundrums of choosing candidate genes, via differential expression between treated and mock specimens, are tackled by Ghandikota et al. in this issue of Patterns in their efforts to tease out genetic patterns that are characteristic of coronavirus disease 2019 (COVID-19) outcomes.

Experience and activity-dependent control of glucocorticoid receptors during the stress response in large-scale brain networks

The diversity of actions of the glucocorticoid stress hormones among individuals and within organs, tissues and cells is shaped by age, gender, genetics, metabolism, and the quantity of exposure. However, such factors cannot explain the heterogeneity of responses in the brain within cells of the same lineage, or similar tissue environment, or in the same individual. Here, we argue that the stress response is continuously updated by synchronized neural activity on large-scale brain networks. This occurs at the molecular, cellular and behavioral levels by crosstalk communication between activity-dependent and glucocorticoid signaling pathways, which updates the diversity of responses based on prior experience. Such a Bayesian process determines adaptation to the demands of the body and external world. We propose a framework for understanding how the diversity of glucocorticoid actions throughout brain networks is essential for supporting optimal health, while its disruption may contribute to the pathophysiology of stress-related disorders, such as major depression, and resistance to therapeutic treatments.

Curation of over 10 000 transcriptomic studies to enable data reuse

Vast amounts of transcriptomic data reside in public repositories, but effective reuse remains challenging. Issues include unstructured dataset metadata, inconsistent data processing and quality control, and inconsistent probe-gene mappings across microarray technologies. Thus, extensive curation and data reprocessing are necessary prior to any reuse. The Gemma bioinformatics system was created to help address these issues. Gemma consists of a database of curated transcriptomic datasets, analytical software, a web interface and web services. Here we present an update on Gemma's holdings, data processing and analysis pipelines, our curation guidelines, and software features. As of June 2020, Gemma contains 10 811 manually curated datasets (primarily human, mouse and rat), over 395 000 samples and hundreds of curated transcriptomic platforms (both microarray and RNA sequencing). Dataset topics were represented with 10 215 distinct terms from 12 ontologies, for a total of 54 316 topic annotations (mean topics/dataset = 5.2). While Gemma has broad coverage of conditions and tissues, it captures a large majority of available brain-related datasets, accounting for 34% of its holdings. Users can access the curated data and differential expression analyses through the Gemma website, RESTful service and an R package. Database URL: https://gemma.msl.ubc.ca/home.html.