Co-expression networks reveal the tissue-specific regulation of transcription and splicing

Dynamic convergence of autism disorder risk genes across neurodevelopment

Over a hundred risk genes underlie risk for autism spectrum disorder (ASD) but the extent to which they converge on shared downstream targets to increase ASD risk is unknown. To test the hypothesis that cellular context impacts the nature of convergence, here we apply a pooled CRISPR approach to target 29 ASD loss-of-function genes in human induced pluripotent stem cell (hiPSC)-derived neural progenitor cells, glutamatergic neurons, and GABAergic neurons. Two distinct approaches (gene-level and network-level analyses) demonstrate that convergence is greatest in mature glutamatergic neurons. Convergent effects are dynamic, varying in strength, composition, and biological role between cell types, increasing with functional similarity of the ASD genes examined, and driven by cell-type-specific gene co-expression patterns. Stratification of ASD genes yield targeted drug predictions capable of reversing gene-specific convergent signatures in human cells and ASD-related behaviors in zebrafish. Altogether, convergent networks downstream of ASD risk genes represent novel points of individualized therapeutic intervention.

Computational strategies for cross-species knowledge transfer and translational biomedicine

Research organisms provide invaluable insights into human biology and diseases, serving as essential tools for functional experiments, disease modeling, and drug testing. However, evolutionary divergence between humans and research organisms hinders effective knowledge transfer across species. Here, we review state-of-the-art methods for computationally transferring knowledge across species, primarily focusing on methods that utilize transcriptome data and/or molecular networks. We introduce the term "agnology" to describe the functional equivalence of molecular components regardless of evolutionary origin, as this concept is becoming pervasive in integrative data-driven models where the role of evolutionary origin can become unclear. Our review addresses four key areas of information and knowledge transfer across species: (1) transferring disease and gene annotation knowledge, (2) identifying agnologous molecular components, (3) inferring equivalent perturbed genes or gene sets, and (4) identifying agnologous cell types. We conclude with an outlook on future directions and several key challenges that remain in cross-species knowledge transfer.

Biallelic variants in SNUPN cause a limb girdle muscular dystrophy with myofibrillar-like features

Alterations in RNA-splicing are a molecular hallmark of several neurological diseases, including muscular dystrophies, where mutations in genes involved in RNA metabolism or characterized by alterations in RNA splicing have been described. Here, we present five patients from two unrelated families with a limb-girdle muscular dystrophy (LGMD) phenotype carrying a biallelic variant in SNUPN gene. Snurportin-1, the protein encoded by SNUPN, plays an important role in the nuclear transport of small nuclear ribonucleoproteins (snRNPs), essential components of the spliceosome. We combine deep phenotyping, including clinical features, histopathology and muscle MRI, with functional studies in patient-derived cells and muscle biopsies to demonstrate that variants in SNUPN are the cause of a new type of LGMD according to current definition. Moreover, an in vivo model in Drosophila melanogaster further supports the relevance of Snurportin-1 in muscle. SNUPN patients show a similar phenotype characterized by proximal weakness starting in childhood, restrictive respiratory dysfunction and prominent contractures, although inter-individual variability in terms of severity even in individuals from the same family was found. Muscle biopsy showed myofibrillar-like features consisting of myotilin deposits and Z-disc disorganization. MRI showed predominant impairment of paravertebral, vasti, sartorius, gracilis, peroneal and medial gastrocnemius muscles. Conservation and structural analyses of Snurportin-1 p.Ile309Ser variant suggest an effect in nuclear-cytosol snRNP trafficking. In patient-derived fibroblasts and muscle, cytoplasmic accumulation of snRNP components is observed, while total expression of Snurportin-1 and snRNPs remains unchanged, which demonstrates a functional impact of SNUPN variant in snRNP metabolism. Furthermore, RNA-splicing analysis in patients' muscle showed widespread splicing deregulation, in particular in genes relevant for muscle development and splicing factors that participate in the early steps of spliceosome assembly. In conclusion, we report that SNUPN variants are a new cause of limb girdle muscular dystrophy with specific clinical, histopathological and imaging features, supporting SNUPN as a new gene to be included in genetic testing of myopathies. These results further support the relevance of splicing-related proteins in muscle disorders.

Critical reasoning on the co-expression module QTL in the dorsolateral prefrontal cortex

Expression quantitative trait locus (eQTL) analysis is a popular method of gaining insight into the function of regulatory variation. While cis-eQTL resources have been instrumental in linking genome-wide association study variants to gene function, complex trait heritability may be additionally mediated by other forms of gene regulation. Toward this end, novel eQTL methods leverage gene co-expression (module-QTL) to investigate joint regulation of gene modules by single genetic variants. Here we broadly define a "module-QTL" as the association of a genetic variant with a summary measure of gene co-expression. This approach aims to reduce the multiple testing burden of a trans-eQTL search through the consolidation of gene-based testing and provide biological context to eQTLs shared between genes. In this article we provide an in-depth examination of the co-expression module eQTL (module-QTL) through literature review, theoretical investigation, and real-data application of the module-QTL to three large prefrontal cortex genotype-RNA sequencing datasets. We find module-QTLs in our study that are disease associated and reproducible are not additionally informative beyond cis- or trans-eQTLs for module genes. Through comparison to prior studies, we highlight promises and limitations of the module-QTL across study designs and provide recommendations for further investigation of the module-QTL framework.

All exons are not created equal-exon vulnerability determines the effect of exonic mutations on splicing

It is now widely accepted that aberrant splicing of constitutive exons is often caused by mutations affecting cis-acting splicing regulatory elements (SREs), but there is a misconception that all exons have an equal dependency on SREs and thus a similar vulnerability to aberrant splicing. We demonstrate that some exons are more likely to be affected by exonic splicing mutations (ESMs) due to an inherent vulnerability, which is context dependent and influenced by the strength of exon definition. We have developed VulExMap, a tool which is based on empirical data that can designate whether a constitutive exon is vulnerable. Using VulExMap, we find that only 25% of all exons can be categorized as vulnerable, whereas two-thirds of 359 previously reported ESMs in 75 disease genes are located in vulnerable exons. Because VulExMap analysis is based on empirical data on splicing of exons in their endogenous context, it includes all features important in determining the vulnerability. We believe that VulExMap will be an important tool when assessing the effect of exonic mutations by pinpointing whether they are located in exons vulnerable to ESMs.

Genome-wide expression quantitative trait locus analysis reveals silk-preferential gene regulatory network in maize

Silk of maize (Zea mays L.) contains diverse metabolites with complicated structures and functions, making it a great challenge to explore the mechanisms of metabolic regulation. Genome-wide identification of silk-preferential genes and investigation of their expression regulation provide an opportunity to reveal the regulatory networks of metabolism. Here, we applied the expression quantitative trait locus (eQTL) mapping on a maize natural population to explore the regulation of gene expression in unpollinated silk of maize. We obtained 3,985 silk-preferential genes that were specifically or preferentially expressed in silk using our population. Silk-preferential genes showed more obvious expression variations compared with broadly expressed genes that were ubiquitously expressed in most tissues. We found that trans-eQTL regulation played a more important role for silk-preferential genes compared to the broadly expressed genes. The relationship between 38 transcription factors and 85 target genes, including silk-preferential genes, were detected. Finally, we constructed a transcriptional regulatory network around the silk-preferential gene Bx10, which was proposed to be associated with response to abiotic stress and biotic stress. Taken together, this study deepened our understanding of transcriptome variation in maize silk and the expression regulation of silk-preferential genes, enhancing the investigation of regulatory networks on metabolic pathways.

Identification of gene biomarkers for brain diseases via multi-network topological semantics extraction and graph convolutional network

BACKGROUND

Brain diseases pose a significant threat to human health, and various network-based methods have been proposed for identifying gene biomarkers associated with these diseases. However, the brain is a complex system, and extracting topological semantics from different brain networks is necessary yet challenging to identify pathogenic genes for brain diseases.

RESULTS

In this study, we present a multi-network representation learning framework called M-GBBD for the identification of gene biomarker in brain diseases. Specifically, we collected multi-omics data to construct eleven networks from different perspectives. M-GBBD extracts the spatial distributions of features from these networks and iteratively optimizes them using Kullback-Leibler divergence to fuse the networks into a common semantic space that represents the gene network for the brain. Subsequently, a graph consisting of both gene and large-scale disease proximity networks learns representations through graph convolution techniques and predicts whether a gene is associated which brain diseases while providing associated scores. Experimental results demonstrate that M-GBBD outperforms several baseline methods. Furthermore, our analysis supported by bioinformatics revealed CAMP as a significantly associated gene with Alzheimer's disease identified by M-GBBD.

CONCLUSION

Collectively, M-GBBD provides valuable insights into identifying gene biomarkers for brain diseases and serves as a promising framework for brain networks representation learning.

Auxin response factors fine-tune lignin biosynthesis in response to mechanical bending in bamboo

Lignin contributes to plant mechanical properties during bending loads. Meanwhile, phytohormone auxin controls various plant biological processes. However, the mechanism of auxin's role in bending-induced lignin biosynthesis was unclear, especially in bamboo, celebrated for its excellent deformation stability. Here, we reported that auxin response factors (ARF) 3 and ARF6 from Moso bamboo (Phyllostachys edulis (Carrière) J. Houz) directly regulate lignin biosynthesis pathway genes, and affect lignin biosynthesis in bamboo. Auxin and lignin exhibited asymmetric distribution patterns, and auxin promoted lignin biosynthesis in response to bending loads in bamboo. Employing transcriptomic and weighted gene co-expression network analysis approach, we discovered that expression patterns of ARF3 and ARF6 strongly correlated with lignin biosynthesis genes. ARF3 and ARF6 directly bind to the promoter regions of 4-coumarate: coenzyme A ligase (4CL3, 4CL7, and 4CL9) or caffeoyl-CoA O-methyltransferase (CCoAOMT2) genes, pivotal to lignin biosynthesis, and activate their expressions. Notably, the efficacy of this binding hinges on auxin levels. Alternation of the expressions of ARF3 and ARF6 substantially altered lignin accumulation in transgenic bamboo. Collectively, our study shed light on bamboo lignification genetics. Auxin signaling could directly modulate lignin biosynthesis genes to impact plant lignin content.

Systematic differences in discovery of genetic effects on gene expression and complex traits

Most signals in genome-wide association studies (GWAS) of complex traits implicate noncoding genetic variants with putative gene regulatory effects. However, currently identified regulatory variants, notably expression quantitative trait loci (eQTLs), explain only a small fraction of GWAS signals. Here, we show that GWAS and cis-eQTL hits are systematically different: eQTLs cluster strongly near transcription start sites, whereas GWAS hits do not. Genes near GWAS hits are enriched in key functional annotations, are under strong selective constraint and have complex regulatory landscapes across different tissue/cell types, whereas genes near eQTLs are depleted of most functional annotations, show relaxed constraint, and have simpler regulatory landscapes. We describe a model to understand these observations, including how natural selection on complex traits hinders discovery of functionally relevant eQTLs. Our results imply that GWAS and eQTL studies are systematically biased toward different types of variant, and support the use of complementary functional approaches alongside the next generation of eQTL studies.

Adjustment of spurious correlations in co-expression measurements from RNA-Sequencing data

MOTIVATION

Gene co-expression measurements are widely used in computational biology to identify coordinated expression patterns across a group of samples. Coordinated expression of genes may indicate that they are controlled by the same transcriptional regulatory program, or involved in common biological processes. Gene co-expression is generally estimated from RNA-Sequencing data, which are commonly normalized to remove technical variability. Here, we demonstrate that certain normalization methods, in particular quantile-based methods, can introduce false-positive associations between genes. These false-positive associations can consequently hamper downstream co-expression network analysis. Quantile-based normalization can, however, be extremely powerful. In particular, when preprocessing large-scale heterogeneous data, quantile-based normalization methods such as smooth quantile normalization can be applied to remove technical variability while maintaining global differences in expression for samples with different biological attributes.

RESULTS

We developed SNAIL (Smooth-quantile Normalization Adaptation for the Inference of co-expression Links), a normalization method based on smooth quantile normalization specifically designed for modeling of co-expression measurements. We show that SNAIL avoids formation of false-positive associations in co-expression as well as in downstream network analyses. Using SNAIL, one can avoid arbitrary gene filtering and retain associations to genes that only express in small subgroups of samples. This highlights the method's potential future impact on network modeling and other association-based approaches in large-scale heterogeneous data.

AVAILABILITY AND IMPLEMENTATION

The implementation of the SNAIL algorithm and code to reproduce the analyses described in this work can be found in the GitHub repository https://github.com/kuijjerlab/PySNAIL.

AutoCore: A network-based definition of the core module of human autoimmunity and autoinflammation

Although research on rare autoimmune and autoinflammatory diseases has enabled definition of nonredundant regulators of homeostasis in human immunity, because of the single gene-single disease nature of many of these diseases, contributing factors were mostly unveiled in sequential and noncoordinated individual studies. We used a network-based approach for integrating a set of 186 inborn errors of immunity with predominant autoimmunity/autoinflammation into a comprehensive map of human immune dysregulation, which we termed "AutoCore." The AutoCore is located centrally within the interactome of all protein-protein interactions, connecting and pinpointing multidisease markers for a range of common, polygenic autoimmune/autoinflammatory diseases. The AutoCore can be subdivided into 19 endotypes that correspond to molecularly and phenotypically cohesive disease subgroups, providing a molecular mechanism-based disease classification and rationale toward systematic targeting for therapeutic purposes. Our study provides a proof of concept for using network-based methods to systematically investigate the molecular relationships between individual rare diseases and address a range of conceptual, diagnostic, and therapeutic challenges.

Proteogenomic analysis reveals RNA as a source for tumor-agnostic neoantigen identification

Systemic pan-tumor analyses may reveal the significance of common features implicated in cancer immunogenicity and patient survival. Here, we provide a comprehensive multi-omics data set for 32 patients across 25 tumor types for proteogenomic-based discovery of neoantigens. By using an optimized computational approach, we discover a large number of tumor-specific and tumor-associated antigens. To create a pipeline for the identification of neoantigens in our cohort, we combine DNA and RNA sequencing with MS-based immunopeptidomics of tumor specimens, followed by the assessment of their immunogenicity and an in-depth validation process. We detect a broad variety of non-canonical HLA-binding peptides in the majority of patients demonstrating partially immunogenicity. Our validation process allows for the selection of 32 potential neoantigen candidates. The majority of neoantigen candidates originates from variants identified in the RNA data set, illustrating the relevance of RNA as a still understudied source of cancer antigens. This study underlines the importance of RNA-centered variant detection for the identification of shared biomarkers and potentially relevant neoantigen candidates.

ACTOR: a latent Dirichlet model to compare expressed isoform proportions to a reference panel

The relative proportion of RNA isoforms expressed for a given gene has been associated with disease states in cancer, retinal diseases, and neurological disorders. Examination of relative isoform proportions can help determine biological mechanisms, but such analyses often require a per-gene investigation of splicing patterns. Leveraging large public data sets produced by genomic consortia as a reference, one can compare splicing patterns in a data set of interest with those of a reference panel in which samples are divided into distinct groups, such as tissue of origin, or disease status. We propose A latent Dirichlet model to Compare expressed isoform proportions TO a Reference panel (ACTOR), a latent Dirichlet model with Dirichlet Multinomial observations to compare expressed isoform proportions in a data set to an independent reference panel. We use a variational Bayes procedure to estimate posterior distributions for the group membership of one or more samples. Using the Genotype-Tissue Expression project as a reference data set, we evaluate ACTOR on simulated and real RNA-seq data sets to determine tissue-type classifications of genes. ACTOR is publicly available as an R package at https://github.com/mccabes292/actor.

Non-additive effects of schizophrenia risk genes reflect convergent downstream function

Genetic studies of schizophrenia (SCZ) reveal a complex polygenic risk architecture comprised of hundreds of risk variants, the majority of which are common in the population at-large and confer only modest increases in disorder risk. Precisely how genetic variants with individually small predicted effects on gene expression combine to yield substantial clinical impacts in aggregate is unclear. Towards this, we previously reported that the combinatorial perturbation of four SCZ risk genes ("eGenes", whose expression is regulated by common variants) resulted in gene expression changes that were not predicted by individual perturbations, being most non-additive among genes associated with synaptic function and SCZ risk. Now, across fifteen SCZ eGenes, we demonstrate that non-additive effects are greatest within groups of functionally similar eGenes. Individual eGene perturbations reveal common downstream transcriptomic effects ("convergence"), while combinatorial eGene perturbations result in changes that are smaller than predicted by summing individual eGene effects ("sub-additive effects"). Unexpectedly, these convergent and sub-additive downstream transcriptomic effects overlap and constitute a large proportion of the genome-wide polygenic risk score, suggesting that functional redundancy of eGenes may be a major mechanism underlying non-additivity. Single eGene perturbations likewise fail to predict the magnitude or directionality of cellular phenotypes resulting from combinatorial perturbations. Overall, our results indicate that polygenic risk cannot be extrapolated from experiments testing one risk gene at a time and must instead be empirically measured. By unravelling the interactions between complex risk variants, it may be possible to improve the clinical utility of polygenic risk scores through more powerful prediction of symptom onset, clinical trajectory, and treatment response, or to identify novel targets for therapeutic intervention.

A Cartographic Tool to Predict Disease Risk-associated Pseudo-Dynamic Networks from Tissue-specific Gene Expression

Understanding how genes are differentially expressed across tissues is key to reveal the etiology of human diseases. Genes are never expressed in isolation, but rather co-expressed in a community; thus, they co-act through intricate but well-orchestrated networks. However, existing approaches cannot coalesce the full properties of gene-gene communication and interactions into networks. In particular, the unavailability of dynamic gene expression data might impair the application of existing network models to unleash the complexity of human diseases. To address this limitation, we developed a statistical pipeline named DRDNetPro to visualize and trace how genes dynamically interact with each other across diverse tissues, to ascertain health risk from static expression data. This protocol contains detailed tutorials designed to learn a series of networks, with the illustration example from the Genotype-Tissue Expression (GTEx) project. The proposed toolbox relies on the method developed in our published paper ( Chen et al., 2022 ), coding all genes into bidirectional, signed, weighted, and feedback looped networks, which will provide profound genomic information enabling medical doctors to design precise medicine. Graphical abstract Flowchart illustrating the use of DRDNetPro. The left panel contains the summarized pipeline of DRDNetPro and the right panel contains one pseudo-illustrative example. See the Equipment and Procedure sections for detailed explanations.

Co-expression of fibrotic genes in inflammatory bowel disease; A localized event?

Introduction

Extracellular matrix turnover, a ubiquitous dynamic biological process, can be diverted to fibrosis. The latter can affect the intestine as a serious complication of Inflammatory Bowel Diseases (IBD) and is resistant to current pharmacological interventions. It embosses the need for out-of-the-box approaches to identify and target molecular mechanisms of fibrosis.

Methods and results

In this study, a novel mRNA sequencing dataset of 22 pairs of intestinal biopsies from the terminal ileum (TI) and the sigmoid of 7 patients with Crohn's disease, 6 with ulcerative colitis and 9 control individuals (CI) served as a validation cohort of a core fibrotic transcriptomic signature (FIBSig), This signature, which was identified in publicly available data (839 samples from patients and healthy individuals) of 5 fibrotic disorders affecting different organs (GI tract, lung, skin, liver, kidney), encompasses 241 genes and the functional pathways which derive from their interactome. These genes were used in further bioinformatics co-expression analyses to elucidate the site-specific molecular background of intestinal fibrosis highlighting their involvement, particularly in the terminal ileum. We also confirmed different transcriptomic profiles of the sigmoid and terminal ileum in our validation cohort. Combining the results of these analyses we highlight 21 core hub genes within a larger single co-expression module, highly enriched in the terminal ileum of CD patients. Further pathway analysis revealed known and novel inflammation-regulated, fibrogenic pathways operating in the TI, such as IL-13 signaling and pyroptosis, respectively.

Discussion

These findings provide a rationale for the increased incidence of fibrosis at the terminal ileum of CD patients and highlight operating pathways in intestinal fibrosis for future evaluation with mechanistic and translational studies.

Bridging the splicing gap in human genetics with long-read RNA sequencing: finding the protein isoform drivers of disease

Aberrant splicing underlies many human diseases, including cancer, cardiovascular diseases and neurological disorders. Genome-wide mapping of splicing quantitative trait loci (sQTLs) has shown that genetic regulation of alternative splicing is widespread. However, identification of the corresponding isoform or protein products associated with disease-associated sQTLs is challenging with short-read RNA-seq, which cannot precisely characterize full-length transcript isoforms. Furthermore, contemporary sQTL interpretation often relies on reference transcript annotations, which are incomplete. Solutions to these issues may be found through integration of newly emerging long-read sequencing technologies. Long-read sequencing offers the capability to sequence full-length mRNA transcripts and, in some cases, to link sQTLs to transcript isoforms containing disease-relevant protein alterations. Here, we provide an overview of sQTL mapping approaches, the use of long-read sequencing to characterize sQTL effects on isoforms, the linkage of RNA isoforms to protein-level functions and comment on future directions in the field. Based on recent progress, long-read RNA sequencing promises to be part of the human disease genetics toolkit to discover and treat protein isoforms causing rare and complex diseases.

SMNDC1 links chromatin remodeling and splicing to regulate pancreatic hormone expression

Insulin expression is primarily restricted to the pancreatic β cells, which are physically or functionally depleted in diabetes. Identifying targetable pathways repressing insulin in non-β cells, particularly in the developmentally related glucagon-secreting α cells, is an important aim of regenerative medicine. Here, we perform an RNA interference screen in a murine α cell line to identify silencers of insulin expression. We discover that knockdown of the splicing factor Smndc1 triggers a global repression of α cell gene-expression programs in favor of increased β cell markers. Mechanistically, Smndc1 knockdown upregulates the β cell transcription factor Pdx1 by modulating the activities of the BAF and Atrx chromatin remodeling complexes. SMNDC1's repressive role is conserved in human pancreatic islets, its loss triggering enhanced insulin secretion and PDX1 expression. Our study identifies Smndc1 as a key factor connecting splicing and chromatin remodeling to the control of insulin expression in human and mouse islet cells.

Network Embedding Across Multiple Tissues and Data Modalities Elucidates the Context of Host Factors Important for COVID-19 Infection

COVID-19 is a heterogeneous disease caused by SARS-CoV-2. Aside from infections of the lungs, the disease can spread throughout the body and damage many other tissues, leading to multiorgan failure in severe cases. The highly variable symptom severity is influenced by genetic predispositions and preexisting diseases which have not been investigated in a large-scale multimodal manner. We present a holistic analysis framework, setting previously reported COVID-19 genes in context with prepandemic data, such as gene expression patterns across multiple tissues, polygenetic predispositions, and patient diseases, which are putative comorbidities of COVID-19. First, we generate a multimodal network using the prior-based network inference method KiMONo. We then embed the network to generate a meaningful lower-dimensional representation of the data. The input data are obtained via the Genotype-Tissue Expression project (GTEx), containing expression data from a range of tissues with genomic and phenotypic information of over 900 patients and 50 tissues. The generated network consists of nodes, that is, genes and polygenic risk scores (PRS) for several diseases/phenotypes, as well as for COVID-19 severity and hospitalization, and links between them if they are statistically associated in a regularized linear model by feature selection. Applying network embedding on the generated multimodal network allows us to perform efficient network analysis by identifying nodes close by in a lower-dimensional space that correspond to entities which are statistically linked. By determining the similarity between COVID-19 genes and other nodes through embedding, we identify disease associations to tissues, like the brain and gut. We also find strong associations between COVID-19 genes and various diseases such as ischemic heart disease, cerebrovascular disease, and hypertension. Moreover, we find evidence linking PTPN6 to a range of comorbidities along with the genetic predisposition of COVID-19, suggesting that this kinase is a central player in severe cases of COVID-19. In conclusion, our holistic network inference coupled with network embedding of multimodal data enables the contextualization of COVID-19-associated genes with respect to tissues, disease states, and genetic risk factors. Such contextualization can be exploited to further elucidate the biological importance of known and novel genes for severity of the disease in patients.

ASpediaFI: Functional Interaction Analysis of Alternative Splicing Events

Alternative splicing (AS) regulates biological processes governing phenotypes and diseases. Differential AS (DAS) gene test methods have been developed to investigate important exonic expression from high-throughput datasets. However, the DAS events extracted using statistical tests are insufficient to delineate relevant biological processes. In this study, we developed a novel application, Alternative Splicing Encyclopedia: Functional Interaction (ASpediaFI), to systemically identify DAS events and co-regulated genes and pathways. ASpediaFI establishes a heterogeneous interaction network of genes and their feature nodes (i.e., AS events and pathways) connected by co-expression or pathway gene set knowledge. Next, ASpediaFI explores the interaction network using the random walk with restart algorithm and interrogates the proximity from a query gene set. Finally, ASpediaFI extracts significant AS events, genes, and pathways. To evaluate the performance of our method, we simulated RNA sequencing (RNA-seq) datasets to consider various conditions of sequencing depth and sample size. The performance was compared with that of other methods. Additionally, we analyzed three public datasets of cancer patients or cell lines to evaluate how well ASpediaFI detects biologically relevant candidates. ASpediaFI exhibits strong performance in both simulated and public datasets. Our integrative approach reveals that DAS events that recognize a global co-expression network and relevant pathways determine the functional importance of spliced genes in the subnetwork. ASpediaFI is publicly available at https://bioconductor.org/packages/ASpediaFI.

SRTdb: an omnibus for human tissue and cancer-specific RNA transcripts

The production of functional mature RNA transcripts from genes undergoes various pre-transcriptional regulation and post-transcriptional modifications. Accumulating studies demonstrated that gene transcription carries out in tissue and cancer type-dependent ways. However, RNA transcript-level specificity analysis in large-scale transcriptomics data across different normal tissue and cancer types is lacking. We applied reference-based de novo transcript assembly and quantification of 27,741 samples across 33 cancer types, 29 tissue types, and 25 cancer cell line types. We totally identified 231,836 specific RNA transcripts (SRTs) across various tissue and cancer types, most of which are found independent of specific genes. Almost half of tumor SRTs are also tissue-specific but in different tissues. Furthermore, we found that 10 ~ 20% of tumor SRTs in most tumor types were testis-specific. The SRT database (SRTdb) was constructed based on these resources. Taking liver cancer as an example, we showed how SRTdb resource is utilized to optimize the identification of RNA transcripts for more precision diagnosis of particular cancers. Our results provide a useful resource for exploring transcript specificity across various cancer and tissue types, and boost the precision medicine for tumor patients.

SETMAR, a case of primate co-opted genes: towards new perspectives

BACKGROUND

We carry out a review of the history and biological activities of one domesticated gene in higher primates, SETMAR, by discussing current controversies. Our purpose is to open a new outlook that will serve as a framework for future work about SETMAR, possibly in the field of cognition development.

MAIN BODY

What is newly important about SETMAR can be summarized as follows: (1) the whole protein sequence is under strong purifying pressure; (2) its role is to strengthen existing biological functions rather than to provide new ones; (3) it displays a tissue-specific pattern of expression, at least for the alternative-splicing it undergoes. Studies reported here demonstrate that SETMAR protein(s) may be involved in essential networks regulating replication, transcription and translation. Moreover, during embryogenesis, SETMAR appears to contribute to brain development.

SHORT CONCLUSION

Our review underlines for the first time that SETMAR directly interacts with genes involved in brain functions related to vocalization and vocal learning. These findings pave the way for future works regarding SETMAR and the development of cognitive abilities in higher primates.

acorde unravels functionally interpretable networks of isoform co-usage from single cell data

Alternative splicing (AS) is a highly-regulated post-transcriptional mechanism known to modulate isoform expression within genes and contribute to cell-type identity. However, the extent to which alternative isoforms establish co-expression networks that may be relevant in cellular function has not been explored yet. Here, we present acorde, a pipeline that successfully leverages bulk long reads and single-cell data to confidently detect alternative isoform co-expression relationships. To achieve this, we develop and validate percentile correlations, an innovative approach that overcomes data sparsity and yields accurate co-expression estimates from single-cell data. Next, acorde uses correlations to cluster co-expressed isoforms into a network, unraveling cell type-specific alternative isoform usage patterns. By selecting same-gene isoforms between these clusters, we subsequently detect and characterize genes with co-differential isoform usage (coDIU) across cell types. Finally, we predict functional elements from long read-defined isoforms and provide insight into biological processes, motifs, and domains potentially controlled by the coordination of post-transcriptional regulation. The code for acorde is available at https://github.com/ConesaLab/acorde .

Addressing the mean-correlation relationship in co-expression analysis

Estimates of correlation between pairs of genes in co-expression analysis are commonly used to construct networks among genes using gene expression data. As previously noted, the distribution of such correlations depends on the observed expression level of the involved genes, which we refer to this as a mean-correlation relationship in RNA-seq data, both bulk and single-cell. This dependence introduces an unwanted technical bias in co-expression analysis whereby highly expressed genes are more likely to be highly correlated. Such a relationship is not observed in protein-protein interaction data, suggesting that it is not reflecting biology. Ignoring this bias can lead to missing potentially biologically relevant pairs of genes that are lowly expressed, such as transcription factors. To address this problem, we introduce spatial quantile normalization (SpQN), a method for normalizing local distributions in a correlation matrix. We show that spatial quantile normalization removes the mean-correlation relationship and corrects the expression bias in network reconstruction.

A Pan-Cancer Analysis of the Oncogenic Role of Nuclear Transport Factor 2 in Human Cancers

Nuclear transport factor 2 (NUTF2) is a GDP-binding protein that participates in the nucleocytoplasmic transport process. The role of NUTF2 in cancer development is largely unknown and lacks systemic assessment across human cancers. In this study, we performed a pan-cancer analysis of NUTF2 in human cancers. Out of 33 types of cancers, 19 types had significantly different expression of NUTF2 between tumor and normal tissues. Meanwhile, survival analysis showed that NUTF2 could be an independent prognostic factor in several tumor types. Further analysis suggested that the expression of NUTF2 expression was correlated with the infiltration of immune cells, such as CD8⁺ T cells, effector memory CD4⁺ T cells, and cancer-associated fibroblasts in kidney renal clear cell carcinoma. Moreover, co-expression analysis showed the positive association between NUTF2 and cell proliferation biomarkers (MKI67and PCNA) and epithelial–mesenchymal transition markers (VIM, TWIST1, SNAI1, SNAI2, FN1, and CDH2), suggesting that NUTF2 plays important roles in regulating cancer proliferation and metastasis. This pan-cancer analysis of NUTF2 provides a systemic understanding of its oncogenic role across different types of cancers.

Interpreting Functional Impact of Genetic Variations by Network QTL for Genotype–Phenotype Association Study

An enormous challenge in the post-genome era is to annotate and resolve the consequences of genetic variation on diverse phenotypes. The genome-wide association study (GWAS) is a well-known method to identify potential genetic loci for complex traits from huge genetic variations, following which it is crucial to identify expression quantitative trait loci (eQTL). However, the conventional eQTL methods usually disregard the systematical role of single-nucleotide polymorphisms (SNPs) or genes, thereby overlooking many network-associated phenotypic determinates. Such a problem motivates us to recognize the network-based quantitative trait loci (QTL), i.e., network QTL (nQTL), which is to detect the cascade association as genotype → network → phenotype rather than conventional genotype → expression → phenotype in eQTL. Specifically, we develop the nQTL framework on the theory and approach of single-sample networks, which can identify not only network traits (e.g., the gene subnetwork associated with genotype) for analyzing complex biological processes but also network signatures (e.g., the interactive gene biomarker candidates screened from network traits) for characterizing targeted phenotype and corresponding subtypes. Our results show that the nQTL framework can efficiently capture associations between SNPs and network traits (i.e., edge traits) in various simulated data scenarios, compared with traditional eQTL methods. Furthermore, we have carried out nQTL analysis on diverse biological and biomedical datasets. Our analysis is effective in detecting network traits for various biological problems and can discover many network signatures for discriminating phenotypes, which can help interpret the influence of nQTL on disease subtyping, disease prognosis, drug response, and pathogen factor association. Particularly, in contrast to the conventional approaches, the nQTL framework could also identify many network traits from human bulk expression data, validated by matched single-cell RNA-seq data in an independent or unsupervised manner. All these results strongly support that nQTL and its detection framework can simultaneously explore the global genotype–network–phenotype associations and the underlying network traits or network signatures with functional impact and importance.

Multi-Omic Approaches to Identify Genetic Factors in Metabolic Syndrome

Metabolic syndrome (MetS) is a highly heritable disease and a major public health burden worldwide. MetS diagnosis criteria are met by the simultaneous presence of any three of the following: high triglycerides, low HDL/high LDL cholesterol, insulin resistance, hypertension, and central obesity. These diseases act synergistically in people suffering from MetS and dramatically increase risk of morbidity and mortality due to stroke and cardiovascular disease, as well as certain cancers. Each of these component features is itself a complex disease, as is MetS. As a genetically complex disease, genetic risk factors for MetS are numerous, but not very powerful individually, often requiring specific environmental stressors for the disease to manifest. When taken together, all sequence variants that contribute to MetS disease risk explain only a fraction of the heritable variance, suggesting additional, novel loci have yet to be discovered. In this article, we will give a brief overview on the genetic concepts needed to interpret genome-wide association studies (GWAS) and quantitative trait locus (QTL) data, summarize the state of the field of MetS physiological genomics, and to introduce tools and resources that can be used by the physiologist to integrate genomics into their own research on MetS and any of its component features. There is a wealth of phenotypic and molecular data in animal models and humans that can be leveraged as outlined in this article. Integrating these multi-omic QTL data for complex diseases such as MetS provides a means to unravel the pathways and mechanisms leading to complex disease and promise for novel treatments. © 2022 American Physiological Society. Compr Physiol 12:1-40, 2022.

The role of alternative splicing in adaptation and evolution

Regulation of gene expression plays a central role in adaptive divergence and evolution. Although the role of gene regulation in microevolutionary processes is gaining wide acceptance, most studies have only investigated the evolution of transcript levels, ignoring the potentially significant role of transcript structures. We argue that variation in alternative splicing plays an important and widely unexplored role in adaptation (e.g., by increasing transcriptome and/or proteome diversity, or buffering potentially deleterious genetic variation). New studies increasingly highlight the potential for independent evolution in alternative splicing and transcript level, providing alternative paths for selection to act upon. We propose that alternative splicing and transcript levels can provide contrasting, nonredundant mechanisms of equal importance for adaptive diversification of gene function and regulation.

Alternative Splicing in Myeloid Malignancies

Alternative RNA splicing (AS) is an essential physiologic function that diversifies the human proteome. AS also has a crucial role during cellular development. In fact, perturbations in RNA-splicing have been implicated in the development of several cancers, including myeloid malignancies. Splicing dysfunction can be independent of genetic lesions or appear as a direct consequence of mutations in components of the RNA-splicing machinery, such as in the case of mutations occurring in splicing factor genes (i.e., SF3B1, SRSF2, U2AF1) and their regulators. In addition, cancer cells exhibit marked gene expression alterations, including different usage of AS isoforms, possibly causing tissue-specific effects and perturbations of downstream pathways. This review summarizes several modalities leading to splicing diversity in myeloid malignancies.

Network analysis reveals rare disease signatures across multiple levels of biological organization

Rare genetic diseases are typically caused by a single gene defect. Despite this clear causal relationship between genotype and phenotype, identifying the pathobiological mechanisms at various levels of biological organization remains a practical and conceptual challenge. Here, we introduce a network approach for evaluating the impact of rare gene defects across biological scales. We construct a multiplex network consisting of over 20 million gene relationships that are organized into 46 network layers spanning six major biological scales between genotype and phenotype. A comprehensive analysis of 3,771 rare diseases reveals distinct phenotypic modules within individual layers. These modules can be exploited to mechanistically dissect the impact of gene defects and accurately predict rare disease gene candidates. Our results show that the disease module formalism can be applied to rare diseases and generalized beyond physical interaction networks. These findings open up new venues to apply network-based tools for cross-scale data integration.

Trends in biological data integration for the selection of enzymes and transcription factors related to cellulose and hemicellulose degradation in fungi

Fungi are key players in biotechnological applications. Although several studies focusing on fungal diversity and genetics have been performed, many details of fungal biology remain unknown, including how cellulolytic enzymes are modulated within these organisms to allow changes in main plant cell wall compounds, cellulose and hemicellulose, and subsequent biomass conversion. With the advent and consolidation of DNA/RNA sequencing technology, different types of information can be generated at the genomic, structural and functional levels, including the gene expression profiles and regulatory mechanisms of these organisms, during degradation-induced conditions. This increase in data generation made rapid computational development necessary to deal with the large amounts of data generated. In this context, the origination of bioinformatics, a hybrid science integrating biological data with various techniques for information storage, distribution and analysis, was a fundamental step toward the current state-of-the-art in the postgenomic era. The possibility of integrating biological big data has facilitated exciting discoveries, including identifying novel mechanisms and more efficient enzymes, increasing yields, reducing costs and expanding opportunities in the bioprocess field. In this review, we summarize the current status and trends of the integration of different types of biological data through bioinformatics approaches for biological data analysis and enzyme selection.

Alternative splicing and gene expression play contrasting roles in the parallel phenotypic evolution of a salmonid fish

Understanding the contribution of different molecular processes to evolution and development is crucial for identifying the mechanisms of adaptation. Here, we used RNA-sequencing data to test the importance of alternative splicing and differential gene expression in a case of parallel adaptive evolution, the replicated postglacial divergence of the salmonid fish Arctic charr (Salvelinus alpinus) into sympatric benthic and pelagic ecotypes across multiple independent lakes. We found that genes differentially spliced between ecotypes were mostly not differentially expressed (<6% overlap) and were involved in different biological processes. Differentially spliced genes were primarily enriched for muscle development and functioning, while differentially expressed genes were involved in metabolism, immunity and growth. Furthermore, alternative splicing and gene expression were mostly controlled by independent cis-regulatory quantitative trait loci (<3.4% overlap). Cis-regulatory regions were associated with the parallel divergence in splicing (16.5% of intron clusters) and expression (6.7%-10.1% of differentially expressed genes), indicating shared regulatory variation across ecotype pairs. Contrary to theoretical expectation, we found that differentially spliced genes tended to be highly central in regulatory networks ("hub genes") and were annotated to significantly more gene ontology terms compared to nondifferentially spliced genes, consistent with a higher level of pleiotropy. Together, our results suggest that the concerted regulation of alternative splicing and differential gene expression through different regulatory regions leads to the divergence of complementary processes important for local adaptation. This provides novel insights into the importance of contrasting but putatively complementary molecular processes in rapid parallel adaptive evolution.

Computational biology approaches for mapping transcriptional regulatory networks

Transcriptional Regulatory Networks (TRNs) are mainly responsible for the cell-type- or cell-state-specific expression of gene sets from the same DNA sequence. However, so far there are no precise maps of TRNs available for each cell-type or cell-state, and no ideal tool to map those networks clearly and in full from biological samples. In this review, major approaches and tools to map TRNs from high-throughput data are presented, depending on the type of methods or data used to infer them, and their advantages and limitations are discussed. After summarizing the main principles defining the topology and structure–function relationships in TRNs, an overview of the extensive work done to map TRNs from bulk transcriptomic data will be presented by type of methodological approach. Most recent modellings of TRNs using other types of molecular data or integrating different data types, including single-cell RNA-sequencing and chromatin information, will then be discussed, before briefly concluding with improvements expected to come in the field.

CAFU: a Galaxy framework for exploring unmapped RNA-Seq data

A widely used approach in transcriptome analysis is the alignment of short reads to a reference genome. However, owing to the deficiencies of specially designed analytical systems, short reads unmapped to the genome sequence are usually ignored, resulting in the loss of significant biological information and insights. To fill this gap, we present Comprehensive Assembly and Functional annotation of Unmapped RNA-Seq data (CAFU), a Galaxy-based framework that can facilitate the large-scale analysis of unmapped RNA sequencing (RNA-Seq) reads from single- and mixed-species samples. By taking advantage of machine learning techniques, CAFU addresses the issue of accurately identifying the species origin of transcripts assembled using unmapped reads from mixed-species samples. CAFU also represents an innovation in that it provides a comprehensive collection of functions required for transcript confidence evaluation, coding potential calculation, sequence and expression characterization and function annotation. These functions and their dependencies have been integrated into a Galaxy framework that provides access to CAFU via a user-friendly interface, dramatically simplifying complex exploration tasks involving unmapped RNA-Seq reads. CAFU has been validated with RNA-Seq data sets from wheat and Zea mays (maize) samples. CAFU is freely available via GitHub: https://github.com/cma2015/CAFU.

Comparative Analyses of Gene Co-expression Networks: Implementations and Applications in the Study of Evolution

Similarities and differences in the associations of biological entities among species can provide us with a better understanding of evolutionary relationships. Often the evolution of new phenotypes results from changes to interactions in pre-existing biological networks and comparing networks across species can identify evidence of conservation or adaptation. Gene co-expression networks (GCNs), constructed from high-throughput gene expression data, can be used to understand evolution and the rise of new phenotypes. The increasing abundance of gene expression data makes GCNs a valuable tool for the study of evolution in non-model organisms. In this paper, we cover motivations for why comparing these networks across species can be valuable for the study of evolution. We also review techniques for comparing GCNs in the context of evolution, including local and global methods of graph alignment. While some protein-protein interaction (PPI) bioinformatic methods can be used to compare co-expression networks, they often disregard highly relevant properties, including the existence of continuous and negative values for edge weights. Also, the lack of comparative datasets in non-model organisms has hindered the study of evolution using PPI networks. We also discuss limitations and challenges associated with cross-species comparison using GCNs, and provide suggestions for utilizing co-expression network alignments as an indispensable tool for evolutionary studies going forward.

An atlas of protein-protein interactions across mouse tissues

Cellular processes arise from the dynamic organization of proteins in networks of physical interactions. Mapping the interactome has therefore been a central objective of high-throughput biology. However, the dynamics of protein interactions across physiological contexts remain poorly understood. Here, we develop a quantitative proteomic approach combining protein correlation profiling with stable isotope labeling of mammals (PCP-SILAM) to map the interactomes of seven mouse tissues. The resulting maps provide a proteome-scale survey of interactome rewiring across mammalian tissues, revealing more than 125,000 unique interactions at a quality comparable to the highest-quality human screens. We identify systematic suppression of cross-talk between the evolutionarily ancient housekeeping interactome and younger, tissue-specific modules. Rewired proteins are tightly regulated by multiple cellular mechanisms and are implicated in disease. Our study opens up new avenues to uncover regulatory mechanisms that shape in vivo interactome responses to physiological and pathophysiological stimuli in mammalian systems.

Regulatory SNPs: Altered Transcription Factor Binding Sites Implicated in Complex Traits and Diseases

The vast majority of the genetic variants (mainly SNPs) associated with various human traits and diseases map to a noncoding part of the genome and are enriched in its regulatory compartment, suggesting that many causal variants may affect gene expression. The leading mechanism of action of these SNPs consists in the alterations in the transcription factor binding via creation or disruption of transcription factor binding sites (TFBSs) or some change in the affinity of these regulatory proteins to their cognate sites. In this review, we first focus on the history of the discovery of regulatory SNPs (rSNPs) and systematized description of the existing methodical approaches to their study. Then, we brief the recent comprehensive examples of rSNPs studied from the discovery of the changes in the TFBS sequence as a result of a nucleotide substitution to identification of its effect on the target gene expression and, eventually, to phenotype. We also describe state-of-the-art genome-wide approaches to identification of regulatory variants, including both making molecular sense of genome-wide association studies (GWAS) and the alternative approaches the primary goal of which is to determine the functionality of genetic variants. Among these approaches, special attention is paid to expression quantitative trait loci (eQTLs) analysis and the search for allele-specific events in RNA-seq (ASE events) as well as in ChIP-seq, DNase-seq, and ATAC-seq (ASB events) data.

Alternative splicing associated with cancer stemness in kidney renal clear cell carcinoma

Backgroud

Cancer stemness is associated with metastases in kidney renal clear cell carcinoma (KIRC) and negatively correlates with immune infiltrates. Recent stemness evaluation methods based on the absolute expression have been proposed to reveal the relationship between stemness and cancer. However, we found that existing methods do not perform well in assessing the stemness of KIRC patients, and they overlooked the impact of alternative splicing. Alternative splicing not only progresses during the differentiation of stem cells, but also changes during the acquisition of the stemness features of cancer stem cells. There is an urgent need for a new method to predict KIRC-specific stemness more accurately, so as to provide help in selecting treatment options.

Methods

The corresponding RNA-Seq data were obtained from the The Cancer Genome Atlas (TCGA) data portal. We also downloaded stem cell RNA sequence data from the Progenitor Cell Biology Consortium (PCBC) Synapse Portal. Independent validation sets with large sample size and common clinic pathological characteristics were obtained from the Gene Expression Omnibus (GEO) database. we constructed a KIRC-specific stemness prediction model using an algorithm called one-class logistic regression based on the expression and alternative splicing data to predict stemness indices of KIRC patients, and the model was externally validated. We identify stemness-associated alternative splicing events (SASEs) by analyzing different alternative splicing event between high- and low- stemness groups. Univariate Cox and multivariable logistic regression analysisw as carried out to detect the prognosis-related SASEs respectively. The area under curve (AUC) of receiver operating characteristic (ROC) was performed to evaluate the predictive values of our model.

Results

Here, we constructed a KIRC-specific stemness prediction model with an AUC of 0.968,and to provide a user-friendly interface of our model for KIRC stemness analysis, we have developed KIRC Stemness Calculator and Visualization (KSCV), hosted on the Shiny server, can most easily be accessed via web browser and the url https://jiang-lab.shinyapps.io/kscv/. When applied to 605 KIRC patients, our stemness indices had a higher correlation with the gender, smoking history and metastasis of the patients than the previous stemness indices, and revealed intratumor heterogeneity at the stemness level. We identified 77 novel SASEs by dividing patients into high- and low- stemness groups with significantly different outcome and they had significant correlations with expression of 17 experimentally validated splicing factors. Both univariate and multivariate survival analysis demonstrated that SASEs closely correlated with the overall survival of patients.

Conclusions

Basing on the stemness indices, we found that not only immune infiltration but also alternative splicing events showed significant different at the stemness level. More importantly, we highlight the critical role of these differential alternative splicing events in poor prognosis, and we believe in the potential for their further translation into targets for immunotherapy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08470-8.

DataRemix: a universal data transformation for optimal inference from gene expression datasets

MOTIVATION

RNA-seq technology provides unprecedented power in the assessment of the transcription abundance and can be used to perform a variety of downstream tasks such as inference of gene-correlation network and eQTL discovery. However, raw gene expression values have to be normalized for nuisance biological variation and technical covariates, and different normalization strategies can lead to dramatically different results in the downstream study.

RESULTS

We describe a generalization of singular value decomposition-based reconstruction for which the common techniques of whitening, rank-k approximation and removing the top k principal components are special cases. Our simple three-parameter transformation, DataRemix, can be tuned to reweigh the contribution of hidden factors and reveal otherwise hidden biological signals. In particular, we demonstrate that the method can effectively prioritize biological signals over noise without leveraging external dataset-specific knowledge, and can outperform normalization methods that make explicit use of known technical factors. We also show that DataRemix can be efficiently optimized via Thompson sampling approach, which makes it feasible for computationally expensive objectives such as eQTL analysis. Finally, we apply our method to the Religious Orders Study and Memory and Aging Project dataset, and we report what to our knowledge is the first replicable trans-eQTL effect in human brain.

AVAILABILITYAND IMPLEMENTATION

DataRemix is an R package which is freely available at GitHub (https://github.com/wgmao/DataRemix).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Polee: RNA-Seq analysis using approximate likelihood

The analysis of mRNA transcript abundance with RNA-Seq is a central tool in molecular biology research, but often analyses fail to account for the uncertainty in these estimates, which can be significant, especially when trying to disentangle isoforms or duplicated genes. Preserving uncertainty necessitates a full probabilistic model of the all the sequencing reads, which quickly becomes intractable, as experiments can consist of billions of reads. To overcome these limitations, we propose a new method of approximating the likelihood function of a sparse mixture model, using a technique we call the Pólya tree transformation. We demonstrate that substituting this approximation for the real thing achieves most of the benefits with a fraction of the computational costs, leading to more accurate detection of differential transcript expression and transcript coexpression.

Multilayer modelling of the human transcriptome and biological mechanisms of complex diseases and traits

Here, we performed a comprehensive intra-tissue and inter-tissue multilayer network analysis of the human transcriptome. We generated an atlas of communities in gene co-expression networks in 49 tissues (GTEx v8), evaluated their tissue specificity, and investigated their methodological implications. UMAP embeddings of gene expression from the communities (representing nearly 18% of all genes) robustly identified biologically-meaningful clusters. Notably, new gene expression data can be embedded into our algorithmically derived models to accelerate discoveries in high-dimensional molecular datasets and downstream diagnostic or prognostic applications. We demonstrate the generalisability of our approach through systematic testing in external genomic and transcriptomic datasets. Methodologically, prioritisation of the communities in a transcriptome-wide association study of the biomarker C-reactive protein (CRP) in 361,194 individuals in the UK Biobank identified genetically-determined expression changes associated with CRP and led to considerably improved performance. Furthermore, a deep learning framework applied to the communities in nearly 11,000 tumors profiled by The Cancer Genome Atlas across 33 different cancer types learned biologically-meaningful latent spaces, representing metastasis (p < 2.2 × 10⁻¹⁶) and stemness (p < 2.2 × 10⁻¹⁶). Our study provides a rich genomic resource to catalyse research into inter-tissue regulatory mechanisms, and their downstream consequences on human disease.

Uncovering genetic mechanisms of hypertension through multi-omic analysis of the kidney

The kidney is an organ of key relevance to blood pressure (BP) regulation, hypertension and antihypertensive treatment. However, genetically mediated renal mechanisms underlying susceptibility to hypertension remain poorly understood. We integrated genotype, gene expression, alternative splicing and DNA methylation profiles of up to 430 human kidneys to characterize the effects of BP index variants from genome-wide association studies (GWASs) on renal transcriptome and epigenome. We uncovered kidney targets for 479 (58.3%) BP-GWAS variants and paired 49 BP-GWAS kidney genes with 210 licensed drugs. Our colocalization and Mendelian randomization analyses identified 179 unique kidney genes with evidence of putatively causal effects on BP. Through Mendelian randomization, we also uncovered effects of BP on renal outcomes commonly affecting patients with hypertension. Collectively, our studies identified genetic variants, kidney genes, molecular mechanisms and biological pathways of key relevance to the genetic regulation of BP and inherited susceptibility to hypertension.

Functional annotation of lncRNA in high-throughput screening

Recent efforts on the characterization of long non-coding RNAs (lncRNAs) revealed their functional roles in modulating diverse cellular processes. These include pluripotency maintenance, lineage commitment, carcinogenesis, and pathogenesis of various diseases. By interacting with DNA, RNA and protein, lncRNAs mediate multifaceted mechanisms to regulate transcription, RNA processing, RNA interference and translation. Of more than 173000 discovered lncRNAs, the majority remain functionally unknown. The cell type-specific expression and localization of the lncRNA also suggest potential distinct functions of lncRNAs across different cell types. This highlights the niche of identifying functional lncRNAs in different biological processes and diseases through high-throughput (HTP) screening. This review summarizes the current work performed and perspectives on HTP screening of functional lncRNAs where different technologies, platforms, cellular responses and the downstream analyses are discussed. We hope to provide a better picture in applying different technologies to facilitate functional annotation of lncRNA efficiently.

Transcriptional changes through menstrual cycle reveal a global transcriptional derepression underlying the molecular mechanism involved in the window of implantation

The human endometrium is a dynamic tissue that only is receptive to host the embryo during a brief time in the middle secretory phase, called the window of implantation (WOI). Despite its importance, regulation of the menstrual cycle remains incompletely understood. The aim of this study was to characterize the gene cooperation and regulation of menstrual cycle progression, to dissect the molecular complexity underlying acquisition of endometrial receptivity for a successful pregnancy, and to provide the scientific community with detailed gene co-expression information throughout the menstrual cycle on a user-friendly web-tool database. A retrospective gene co-expression analysis was performed based on the endometrial receptivity array (ERarray) gene signature from 523 human endometrial samples collected across the menstrual cycle, including during the WOI. Gene co-expression analysis revealed the WOI as having the significantly smallest proportion of negative correlations for transcriptional profiles associated with successful pregnancies compared to other cycle stages, pointing to a global transcriptional derepression being involved in acquisition of endometrial receptivity. Regulation was greatest during the transition between proliferative and secretory endometrial phases. Further, we prioritized nuclear hormone receptors as major regulators of this derepression and proved that some genes and transcription factors involved in this process were dysregulated in patients with recurrent implantation failure. We also compiled the wealth of gene co-expression data to stimulate hypothesis-driven single-molecule endometrial studies in a user-friendly database: Menstrual Cycle Gene Co-expression Network (www.menstrualcyclegcn.com). This study revealed a global transcriptional repression across the menstrual cycle, which relaxes when the WOI opens for transcriptional profiles associated with successful pregnancies. These findings suggest that a global transcriptional derepression is needed for embryo implantation and early development.

Conserved co-functional network between maize and Arabidopsis aid in the identification of seed defective genes in maize

BACKGROUND

The biological pathways related to Arabidopsis seed development have been well studied and functional genes involved in it have been discovered. However, functional studies about maize seed development were more limited compared to Arabidopsis.

OBJECTIVE

Therefore, transferring knowledge from Arabidopsis into maize would facilitate functional studies about maize seed development.

METHOD

In this study, public transcriptome data of the two species related to seed development were obtained. Co-expression network in each species was compared by integrating orthology information.

RESULTS

This conserved co-functional network contained 4510 maize and 4808 Arabidopsis genes, respectively. Most of these genes were expressed in throughout embryo, early or later endosperm/seed. These conserved co-functional genes were significantly enriched for members of PPR protein family, which was consistent with that PPR proteins play an important role in maize seed development. Spatial-temporally co-functional genes were discovered in the seed coat and embryo. Furthermore, 66 well-studied genes involved in Arabidopsis seed development were co-functional with 319 maize genes and one maize gene (GRMZM2G036050) was further confirmed using an EMS-induced seed defective mutant by bulked segregating RNA sequencing (BSR) analysis.

CONCLUSIONS

Altogether, these results showed the potential of this approach to support functional studies in maize seed development by transferring knowledge from Arabidopsis.

Enhanced Understanding of Molecular Interactions and Function Underlying Pain Processes Through Networks of Transcript Isoforms, Genes, and Gene Families

Introduction

Molecular networks based on the abundance of mRNA at the gene level and pathway networks that relate families or groups of paralog genes have supported the understanding of interactions between molecules. However, multiple molecular mechanisms underlying health and behavior, such as pain signal processing, are modulated by the abundances of the transcript isoforms that originate from alternative splicing, in addition to gene abundances. Alternative splice variants of growth factors, ion channels, and G-protein-coupled receptors can code for proteoforms that can have different effects on pain and nociception. Therefore, networks inferred using abundance from more agglomerative molecular units (eg, gene family, or gene) have limitations in capturing interactions at a more granular level (eg, gene, or transcript isoform, respectively) do not account for changes in the abundance at the transcript isoform level.

Objective

The objective of this study was to evaluate the relative benefits of network inference using abundance patterns at various aggregate levels.

Methods

Sparse networks were inferred using Gaussian Markov random fields and a novel aggregation criterion was used to aggregate network edges. The relative advantages of network aggregation were evaluated on two molecular systems that have different dimensions and connectivity, circadian rhythm and Toll-like receptor pathways, using RNA-sequencing data from mice representing two pain level groups, opioid-induced hyperalgesia and control, and two central nervous system regions, the nucleus accumbens and the trigeminal ganglia.

Results

The inferred networks were benchmarked against the Kyoto Encyclopedia of Genes and Genomes reference pathways using multiple criteria. Networks inferred using more granular information performed better than networks inferred using more aggregate information. The advantage of granular inference varied with the pathway and data set used.

Discussion

The differences in inferred network structure between data sets highlight the differences in OIH effect between central nervous system regions. Our findings suggest that inference of networks using alternative splicing variants can offer complementary insights into the relationship between genes and gene paralog groups.

Inference of phenotype-relevant transcriptional regulatory networks elucidates cancer type-specific regulatory mechanisms in a pan-cancer study

Reconstruction of transcriptional regulatory networks (TRNs) is a powerful approach to unravel the gene expression programs involved in healthy and disease states of a cell. However, these networks are usually reconstructed independent of the phenotypic (or clinical) properties of the samples. Therefore, they may confound regulatory mechanisms that are specifically related to a phenotypic property with more general mechanisms underlying the full complement of the analyzed samples. In this study, we develop a method called InPheRNo to identify "phenotype-relevant" TRNs. This method is based on a probabilistic graphical model that models the simultaneous effects of multiple transcription factors (TFs) on their target genes and the statistical relationship between the target genes' expression and the phenotype. Extensive comparison of InPheRNo with related approaches using primary tumor samples of 18 cancer types from The Cancer Genome Atlas reveals that InPheRNo can accurately reconstruct cancer type-relevant TRNs and identify cancer driver TFs. In addition, survival analysis reveals that the activity level of TFs with many target genes could distinguish patients with poor prognosis from those with better prognosis.