GeNets: a unified web platform for network-based genomic analyses

Wekemo Bioincloud: A user-friendly platform for meta-omics data analyses

The increasing application of meta-omics approaches to investigate the structure, function, and intercellular interactions of microbial communities has led to a surge in available data. However, this abundance of human and environmental microbiome data has exposed new scalability challenges for existing bioinformatics tools. In response, we introduce Wekemo Bioincloud-a specialized platform for -omics studies. This platform offers a comprehensive analysis solution, specifically designed to alleviate the challenges of tool selection for users in the face of expanding data sets. As of now, Wekemo Bioincloud has been regularly equipped with 22 workflows and 65 visualization tools, establishing itself as a user-friendly and widely embraced platform for studying diverse data sets. Additionally, the platform enables the online modification of vector outputs, and the registration-independent personalized dashboard system ensures privacy and traceability. Wekemo Bioincloud is freely available at https://www.bioincloud.tech/.

Context-defined cancer co-dependency mapping identifies a functional interplay between PRC2 and MLL-MEN1 complex in lymphoma

Interplay between chromatin-associated complexes and modifications critically contribute to the partitioning of epigenome into stable and functionally distinct domains. Yet there is a lack of systematic identification of chromatin crosstalk mechanisms, limiting our understanding of the dynamic transition between chromatin states during development and disease. Here we perform co-dependency mapping of genes using CRISPR-Cas9-mediated fitness screens in pan-cancer cell lines to quantify gene-gene functional relationships. We identify 145 co-dependency modules and further define the molecular context underlying the essentiality of these modules by incorporating mutational, epigenome, gene expression and drug sensitivity profiles of cell lines. These analyses assign new protein complex composition and function, and predict new functional interactions, including an unexpected co-dependency between two transcriptionally counteracting chromatin complexes - polycomb repressive complex 2 (PRC2) and MLL-MEN1 complex. We show that PRC2-mediated H3K27 tri-methylation regulates the genome-wide distribution of MLL1 and MEN1. In lymphoma cells with EZH2 gain-of-function mutations, the re-localization of MLL-MEN1 complex drives oncogenic gene expression and results in a hypersensitivity to pharmacologic inhibition of MEN1. Together, our findings provide a resource for discovery of trans-regulatory interactions as mechanisms of chromatin regulation and potential targets of synthetic lethality.

A Rare but Aggressive Malignancy: A Case Report of a Gastrointestinal Neuroectodermal Tumor (GNET)

Gastrointestinal neuroectodermal tumors (GNETs) are extremely rare and intriguing malignancies originating from neural crest cells in the digestive tract. The digestive tract's neural crest cells can give rise to incredibly unusual and interesting gastrointestinal neuroectodermal tumors (GNETs). GNETs present considerable hurdles in diagnosis and management because of their rarity and varied expression. In this case report, a 45-year-old male patient is described who had signs of GNET, such as exhaustion, weight loss, and abdominal pain. A 7-cm jejunum tumor and related thickening of the gut wall were discovered using imaging investigations. The diagnosis of malignant GNET was confirmed by surgical resection, and adjuvant treatment was given. A recurring tumor required a second surgical procedure despite an initial disease-free period. The report emphasizes the difficulties involved in the diagnosis, treatment, and long-term effects of GNETs. The rarity of GNETs necessitates the development of standardized treatment protocols as well as additional research to enhance diagnostic precision and explore novel therapeutic approaches for this aggressive malignancy.

Update on genetics of attention deficit/hyperactivity disorder: current status 2023

PURPOSE OF REVIEW

Attention deficit/hyperactivity disorder (ADHD) shows consistently high heritability in genetic research. In this review article, we give an overview of the analysis of common and rare variants and some insight into current genetic methodology and their link to clinical practice.

RECENT FINDINGS

The heritability of about 80% is also high in comparison to other psychiatric diseases. However, recent studies estimate the proportion of heritability based on single nucleotide variants at 22%. The hidden heritability is an ongoing question in ADHD genetics. Common variants derived from mega genome-wide association analyses (GWAS) and subsequent meta-analyses usually display small effect sizes and explain only a small fraction of phenotypic variance. Rare variants, on the contrary, not only display large effect sizes but also rather explain, due to their rareness, a small fraction on phenotypic variance. Applying polygenic risk score (PRS) analysis is an improved approach of combining effect sizes of many common variants with clinically relevant measures in ADHD.

SUMMARY

We provide a concise overview on how genetic analysis, with a focus on GWAS and PRS, can help explain different behavioural phenotypes in ADHD and how they can be used for diagnosis and therapy prediction. Increased sample sizes of GWAS, meta-analyses and use of PRS is increasingly informative and sets the course for a new era in genetics of ADHD.

Artificial Intelligence: A Promising Tool in Exploring the Phytomicrobiome in Managing Disease and Promoting Plant Health

There is increasing interest in harnessing the microbiome to improve cropping systems. With the availability of high-throughput and low-cost sequencing technologies, gathering microbiome data is becoming more routine. However, the analysis of microbiome data is challenged by the size and complexity of the data, and the incomplete nature of many microbiome databases. Further, to bring microbiome data value, it often needs to be analyzed in conjunction with other complex data that impact on crop health and disease management, such as plant genotype and environmental factors. Artificial intelligence (AI), boosted through deep learning (DL), has achieved significant breakthroughs and is a powerful tool for managing large complex datasets such as the interplay between the microbiome, crop plants, and their environment. In this review, we aim to provide readers with a brief introduction to AI techniques, and we introduce how AI has been applied to areas of microbiome sequencing taxonomy, the functional annotation for microbiome sequences, associating the microbiome community with host traits, designing synthetic communities, genomic selection, field phenotyping, and disease forecasting. At the end of this review, we proposed further efforts that are required to fully exploit the power of AI in studying phytomicrobiomes.

Protein interaction studies in human induced neurons indicate convergent biology underlying autism spectrum disorders

Autism spectrum disorders (ASDs) have been linked to genes with enriched expression in the brain, but it is unclear how these genes converge into cell-type-specific networks. We built a protein-protein interaction network for 13 ASD-associated genes in human excitatory neurons derived from induced pluripotent stem cells (iPSCs). The network contains newly reported interactions and is enriched for genetic and transcriptional perturbations observed in individuals with ASDs. We leveraged the network data to show that the ASD-linked brain-specific isoform of ANK2 is important for its interactions with synaptic proteins and to characterize a PTEN-AKAP8L interaction that influences neuronal growth. The IGF2BP1-3 complex emerged as a convergent point in the network that may regulate a transcriptional circuit of ASD-associated genes. Our findings showcase cell-type-specific interactomes as a framework to complement genetic and transcriptomic data and illustrate how both individual and convergent interactions can lead to biological insights into ASDs.

Islet Gene View-a tool to facilitate islet research

Characterization of gene expression in pancreatic islets and its alteration in type 2 diabetes (T2D) are vital in understanding islet function and T2D pathogenesis. We leveraged RNA sequencing and genome-wide genotyping in islets from 188 donors to create the Islet Gene View (IGW) platform to make this information easily accessible to the scientific community. Expression data were related to islet phenotypes, diabetes status, other islet-expressed genes, islet hormone-encoding genes and for expression in insulin target tissues. The IGW web application produces output graphs for a particular gene of interest. In IGW, 284 differentially expressed genes (DEGs) were identified in T2D donor islets compared with controls. Forty percent of DEGs showed cell-type enrichment and a large proportion significantly co-expressed with islet hormone-encoding genes; glucagon (<i>GCG</i>, 56%), amylin (<i>IAPP</i>, 52%), insulin (<i>INS</i>, 44%), and somatostatin (<i>SST</i>, 24%). Inhibition of two DEGs, <i>UNC5D</i> and <i>SERPINE2</i>, impaired glucose-stimulated insulin secretion and impacted cell survival in a human β-cell model. The exploratory use of IGW could help designing more comprehensive functional follow-up studies and serve to identify therapeutic targets in T2D.

A Novel Sparse Graph-Regularized Singular Value Decomposition Model and Its Application to Genomic Data Analysis

Learning the gene coexpression pattern is a central challenge for high-dimensional gene expression analysis. Recently, sparse singular value decomposition (SVD) has been used to achieve this goal. However, this model ignores the structural information between variables (e.g., a gene network). The typical graph-regularized penalty can be used to incorporate such prior graph information to achieve more accurate discovery and better interpretability. However, the existing approach fails to consider the opposite effect of variables with negative correlations. In this article, we propose a novel sparse graph-regularized SVD model with absolute operator (AGSVD) for high-dimensional gene expression pattern discovery. The key of AGSVD is to impose a novel graph-regularized penalty ( | u|^T L| u| ). However, such a penalty is a nonconvex and nonsmooth function, so it brings new challenges to model solving. We show that the nonconvex problem can be efficiently handled in a convex fashion by adopting an alternating optimization strategy. The simulation results on synthetic data show that our method is more effective than the existing SVD-based ones. In addition, the results on several real gene expression data sets show that the proposed methods can discover more biologically interpretable expression patterns by incorporating the prior gene network.

Integrating human brain proteomes with genome-wide association data implicates novel proteins in post-traumatic stress disorder

Genome-wide association studies (GWAS) have identified several risk loci for post-traumatic stress disorder (PTSD); however, how they confer PTSD risk remains unclear. We aimed to identify genes that confer PTSD risk through their effects on brain protein abundance to provide new insights into PTSD pathogenesis. To that end, we integrated human brain proteomes with PTSD GWAS results to perform a proteome-wide association study (PWAS) of PTSD, followed by Mendelian randomization, using a discovery and confirmatory study design. Brain proteomes (N = 525) were profiled from the dorsolateral prefrontal cortex using mass spectrometry. The Million Veteran Program (MVP) PTSD GWAS (n = 186,689) was used for the discovery PWAS, and the Psychiatric Genomics Consortium PTSD GWAS (n = 174,659) was used for the confirmatory PWAS. To understand whether genes identified at the protein-level were also evident at the transcript-level, we performed a transcriptome-wide association study (TWAS) using human brain transcriptomes (N = 888) and the MVP PTSD GWAS results. We identified 11 genes that contribute to PTSD pathogenesis via their respective cis-regulated brain protein abundance. Seven of 11 genes (64%) replicated in the confirmatory PWAS and 4 of 11 also had their cis-regulated brain mRNA levels associated with PTSD. High confidence level was assigned to 9 of 11 genes after considering evidence from the confirmatory PWAS and TWAS. Most of the identified genes are expressed in other PTSD-relevant brain regions and several are preferentially expressed in excitatory neurons, astrocytes, and oligodendrocyte precursor cells. These genes are novel, promising targets for mechanistic and therapeutic studies to find new treatments for PTSD.

Dissection of multiple sclerosis genetics identifies B and CD4+ T cells as driver cell subsets

Background

Multiple sclerosis (MS) is an autoimmune condition of the central nervous system with a well-characterized genetic background. Prior analyses of MS genetics have identified broad enrichments across peripheral immune cells, yet the driver immune subsets are unclear.

Results

We utilize chromatin accessibility data across hematopoietic cells to identify cell type-specific enrichments of MS genetic signals. We find that CD4 T and B cells are independently enriched for MS genetics and further refine the driver subsets to T_h17 and memory B cells, respectively. We replicate our findings in data from untreated and treated MS patients and find that immunomodulatory treatments suppress chromatin accessibility at driver cell types. Integration of statistical fine-mapping and chromatin interactions nominate numerous putative causal genes, illustrating complex interplay between shared and cell-specific genes.

Conclusions

Overall, our study finds that open chromatin regions in CD4 T cells and B cells independently drive MS genetic signals. Our study highlights how careful integration of genetics and epigenetics can provide fine-scale insights into causal cell types and nominate new genes and pathways for disease.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13059-022-02694-y.

A human-based multi-gene signature enables quantitative drug repurposing for metabolic disease

Insulin resistance (IR) contributes to the pathophysiology of diabetes, dementia, viral infection, and cardiovascular disease. Drug repurposing (DR) may identify treatments for IR; however, barriers include uncertainty whether in vitro transcriptomic assays yield quantitative pharmacological data, or how to optimise assay design to best reflect in vivo human disease. We developed a clinical-based human tissue IR signature by combining lifestyle-mediated treatment responses (>500 human adipose and muscle biopsies) with biomarkers of disease status (fasting IR from >1200 biopsies). The assay identified a chemically diverse set of >130 positively acting compounds, highly enriched in true positives, that targeted 73 proteins regulating IR pathways. Our multi-gene RNA assay score reflected the quantitative pharmacological properties of a set of epidermal growth factor receptor-related tyrosine kinase inhibitors, providing insight into drug target specificity; an observation supported by deep learning-based genome-wide predicted pharmacology. Several drugs identified are suitable for evaluation in patients, particularly those with either acute or severe chronic IR.

TIGA: target illumination GWAS analytics

MOTIVATION

Genome-wide association studies can reveal important genotype-phenotype associations; however, data quality and interpretability issues must be addressed. For drug discovery scientists seeking to prioritize targets based on the available evidence, these issues go beyond the single study.

RESULTS

Here, we describe rational ranking, filtering and interpretation of inferred gene-trait associations and data aggregation across studies by leveraging existing curation and harmonization efforts. Each gene-trait association is evaluated for confidence, with scores derived solely from aggregated statistics, linking a protein-coding gene and phenotype. We propose a method for assessing confidence in gene-trait associations from evidence aggregated across studies, including a bibliometric assessment of scientific consensus based on the iCite relative citation ratio, and meanRank scores, to aggregate multivariate evidence.This method, intended for drug target hypothesis generation, scoring and ranking, has been implemented as an analytical pipeline, available as open source, with public datasets of results, and a web application designed for usability by drug discovery scientists.

AVAILABILITY AND IMPLEMENTATION

Web application, datasets and source code via https://unmtid-shinyapps.net/tiga/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Application of Deep Learning in Plant-Microbiota Association Analysis

Unraveling the association between microbiome and plant phenotype can illustrate the effect of microbiome on host and then guide the agriculture management. Adequate identification of species and appropriate choice of models are two challenges in microbiome data analysis. Computational models of microbiome data could help in association analysis between the microbiome and plant host. The deep learning methods have been widely used to learn the microbiome data due to their powerful strength of handling the complex, sparse, noisy, and high-dimensional data. Here, we review the analytic strategies in the microbiome data analysis and describe the applications of deep learning models for plant–microbiome correlation studies. We also introduce the application cases of different models in plant–microbiome correlation analysis and discuss how to adapt the models on the critical steps in data processing. From the aspect of data processing manner, model structure, and operating principle, most deep learning models are suitable for the plant microbiome data analysis. The ability of feature representation and pattern recognition is the advantage of deep learning methods in modeling and interpretation for association analysis. Based on published computational experiments, the convolutional neural network and graph neural networks could be recommended for plant microbiome analysis.

DECO: a framework for jointly analyzing de novo and rare case/control variants, and biological pathways

MOTIVATION

Rare variant-based analyses are beginning to identify risk genes for neuropsychiatric disorders and other diseases. However, the identified genes only account for a fraction of predicted causal genes. Recent studies have shown that rare damaging variants are significantly enriched in specific gene-sets. Methods which are able to jointly model rare variants and gene-sets to identify enriched gene-sets and use these enriched gene-sets to prioritize additional risk genes could improve understanding of the genetic architecture of diseases.

RESULTS

We propose DECO (Integrated analysis of de novo mutations, rare case/control variants and omics information via gene-sets), an integrated method for rare-variant and gene-set analysis. The method can (i) test the enrichment of gene-sets directly within the statistical model, and (ii) use enriched gene-sets to rank existing genes and prioritize additional risk genes for tested disorders. In simulations, DECO performs better than a homologous method that uses only variant data. To demonstrate the application of the proposed protocol, we have applied this approach to rare-variant datasets of schizophrenia. Compared with a method which only uses variant information, DECO is able to prioritize additional risk genes.

AVAILABILITY

DECO can be used to analyze rare-variants and biological pathways or cell types for any disease. The package is available on Github https://github.com/hoangtn/DECO.

From DNA human sequence to the chromatin higher order organisation and its biological meaning: Using biomolecular interaction networks to understand the influence of structural variation on spatial genome organisation and its functional effect

The three-dimensional structure of the human genome has been proven to have a significant functional impact on gene expression. The high-order spatial chromatin is organised first by looping mediated by multiple protein factors, and then it is further formed into larger structures of topologically associated domains (TADs) or chromatin contact domains (CCDs), followed by A/B compartments and finally the chromosomal territories (CTs). The genetic variation observed in human population influences the multi-scale structures, posing a question regarding the functional impact of structural variants reflected by the variability of the genes expression patterns. The current methods of evaluating the functional effect include eQTLs analysis which uses statistical testing of influence of variants on spatially close genes. Rarely, non-coding DNA sequence changes are evaluated by their impact on the biomolecular interaction network (BIN) reflecting the cellular interactome that can be analysed by the classical graph-theoretic algorithms. Therefore, in the second part of the review, we introduce the concept of BIN, i.e. a meta-network model of the complete molecular interactome developed by integrating various biological networks. The BIN meta-network model includes DNA-protein binding by the plethora of protein factors as well as chromatin interactions, therefore allowing connection of genomics with the downstream biomolecular processes present in a cell. As an illustration, we scrutinise the chromatin interactions mediated by the CTCF protein detected in a ChIA-PET experiment in the human lymphoblastoid cell line GM12878. In the corresponding BIN meta-network the DNA spatial proximity is represented as a graph model, combined with the Proteins-Interaction Network (PIN) of human proteome using the Gene Association Network (GAN). Furthermore, we enriched the BIN with the signalling and metabolic pathways and Gene Ontology (GO) terms to assert its functional context. Finally, we mapped the Single Nucleotide Polymorphisms (SNPs) from the GWAS studies and identified the chromatin mutational hot-spots associated with a significant enrichment of SNPs related to autoimmune diseases. Afterwards, we mapped Structural Variants (SVs) from healthy individuals of 1000 Genomes Project and identified an interesting example of the missing protein complex associated with protein Q6GYQ0 due to a deletion on chromosome 14. Such an analysis using the meta-network BIN model is therefore helpful in evaluating the influence of genetic variation on spatial organisation of the genome and its functional effect in a cell.

TCF12 haploinsufficiency causes autosomal dominant Kallmann syndrome and reveals network-level interactions between causal loci

Dysfunction of the gonadotropin-releasing hormone (GnRH) axis causes a range of reproductive phenotypes resulting from defects in the specification, migration and/or function of GnRH neurons. To identify additional molecular components of this system, we initiated a systematic genetic interrogation of families with isolated GnRH deficiency (IGD). Here, we report 13 families (12 autosomal dominant and one autosomal recessive) with an anosmic form of IGD (Kallmann syndrome) with loss-of-function mutations in TCF12, a locus also known to cause syndromic and non-syndromic craniosynostosis. We show that loss of tcf12 in zebrafish larvae perturbs GnRH neuronal patterning with concomitant attenuation of the orthologous expression of tcf3a/b, encoding a binding partner of TCF12, and stub1, a gene that is both mutated in other syndromic forms of IGD and maps to a TCF12 affinity network. Finally, we report that restored STUB1 mRNA rescues loss of tcf12 in vivo. Our data extend the mutational landscape of IGD, highlight the genetic links between craniofacial patterning and GnRH dysfunction and begin to assemble the functional network that regulates the development of the GnRH axis.

De novo variation in bipolar disorder

Bipolar disorder (BD) is a common, highly heritable disorder that affects 1-2% of the world's population. To date, most genetic studies of BD have focused on common gene variation, and while robustly associated loci have been identified, a substantial proportion of the heritability remains missing and could be partially attributable to rare variation. In this study, we apply a de novo paradigm in BD to identify newly arisen variants that have yet to undergo natural selection and may represent highly pathogenic variants. We performed whole genome sequencing of 97 trios of Ashkenazi Jewish descent, selecting "simplex" families with no family history of BD and an early age of onset. We found a total of 6882 de novo variants (an average of 70.9 ± 12.9 S.D. variants per trio), including 107 variants within protein-coding genes. We combined our exonic variations with the results of 79 previously published BD trios, identifying 20 loss-of-function (LoF) and 77 missense damaging de novo variants in BD. These variants showed significant enrichment for constrained genes and for genes located to the postsynaptic density (PSD) (all Bonferroni corrected p < 0.05). Pathway analyses showed enrichment in several pathways, including "Phosphoinositides (PI) and their downstream targets" (Bonferroni p = 4.2 × 10-6), a pathway prominently featured in lithium's hypothesized mechanism of action. In addition, while we found overall evidence for transmission of common variant polygenic risk of BD in our full sample (pTDT p = 2.21 × 10-4), specific trios with LoF variants showed no evidence of polygenic transmission. In sum, our findings support the de novo paradigm as a contributor to the genetic architecture of BD and provide evidence that constrained genes, as well as genes within the PSD and PI pathway harbor rare variation associated with BD.

A machine learning approach to brain epigenetic analysis reveals kinases associated with Alzheimer's disease

Alzheimer's disease (AD) is influenced by both genetic and environmental factors; thus, brain epigenomic alterations may provide insights into AD pathogenesis. Multiple array-based Epigenome-Wide Association Studies (EWASs) have identified robust brain methylation changes in AD; however, array-based assays only test about 2% of all CpG sites in the genome. Here, we develop EWASplus, a computational method that uses a supervised machine learning strategy to extend EWAS coverage to the entire genome. Application to six AD-related traits predicts hundreds of new significant brain CpGs associated with AD, some of which are further validated experimentally. EWASplus also performs well on data collected from independent cohorts and different brain regions. Genes found near top EWASplus loci are enriched for kinases and for genes with evidence for physical interactions with known AD genes. In this work, we show that EWASplus implicates additional epigenetic loci for AD that are not found using array-based AD EWASs.

Integrative analysis of genomic variants reveals new associations of candidate haploinsufficient genes with congenital heart disease

Numerous genetic studies have established a role for rare genomic variants in Congenital Heart Disease (CHD) at the copy number variation (CNV) and de novo variant (DNV) level. To identify novel haploinsufficient CHD disease genes, we performed an integrative analysis of CNVs and DNVs identified in probands with CHD including cases with sporadic thoracic aortic aneurysm. We assembled CNV data from 7,958 cases and 14,082 controls and performed a gene-wise analysis of the burden of rare genomic deletions in cases versus controls. In addition, we performed variation rate testing for DNVs identified in 2,489 parent-offspring trios. Our analysis revealed 21 genes which were significantly affected by rare CNVs and/or DNVs in probands. Fourteen of these genes have previously been associated with CHD while the remaining genes (FEZ1, MYO16, ARID1B, NALCN, WAC, KDM5B and WHSC1) have only been associated in small cases series or show new associations with CHD. In addition, a systems level analysis revealed affected protein-protein interaction networks involved in Notch signaling pathway, heart morphogenesis, DNA repair and cilia/centrosome function. Taken together, this approach highlights the importance of re-analyzing existing datasets to strengthen disease association and identify novel disease genes and pathways.

Bioinformatics-based analysis of the association between the A1-chimaerin (CHN1) gene and gastric cancer

Gastric cancer (GC) is one of the most common causes of cancer-related deaths worldwide and the identification of additional therapeutic targets and biomarkers has become vital. The A1-chimaerin (CHN1) gene encodes a ras-related protein that can be activated or inactivated by binding to GTP or GDP. The present study aimed to assess the expression of CHN1 in GC tissue and cells, to explore its relationship with GC progression, and to discover the potential mechanisms underlying these associations. The ONCOMINE database and The Cancer Genome Atlas (TCGA) were used to determine the transcriptional levels of CHN1 in GC. Western blot and immunohistochemistry were used for detecting protein expression. Correlations between CHN1 levels and the clinical outcomes of GC patients were examined using Kaplan–Meier and Cox regression analyses. Moreover, the CIBERSORT algorithm was used to estimate immune cell infiltration. In GC patients, CHN1 transcription and CHN1 protein expression were upregulated, and a high expression of CHN1 was remarkably linked to poor survival in GC patients. CHN1 expression was associated with immune infiltrates and this gene showed potential involvement in multiple cancer-related pathways. Furthermore, the expression of CHN1 was correlated with the immunotherapeutic response. Finally, our results indicated that the pro-carcinogenic role of CHN1 may involve DNA methylation. To our knowledge, this is the first report characterizing CHN1 expression in GC. Our results show that high CHN1 levels could be used as a clinical biomarker for poor prognosis and that CHN1 inhibitors may have potential as anti-cancer drugs.

Brain proteome-wide association study implicates novel proteins in depression pathogenesis

Depression is a common condition, but current treatments are only effective in a subset of individuals. To identify new treatment targets, we integrated depression genome-wide association study (GWAS) results (N = 500,199) with human brain proteomes (N = 376) to perform a proteome-wide association study of depression followed by Mendelian randomization. We identified 19 genes that were consistent with being causal in depression, acting via their respective cis-regulated brain protein abundance. We replicated nine of these genes using an independent depression GWAS (N = 307,353) and another human brain proteomic dataset (N = 152). Eleven of the 19 genes also had cis-regulated mRNA levels that were associated with depression, based on integration of the depression GWAS with human brain transcriptomes (N = 888). Meta-analysis of the discovery and replication proteome-wide association study analyses identified 25 brain proteins consistent with being causal in depression, 20 of which were not previously implicated in depression by GWAS. Together, these findings provide promising brain protein targets for further mechanistic and therapeutic studies.

GLIDE: combining local methods and diffusion state embeddings to predict missing interactions in biological networks

Motivation

One of the core problems in the analysis of biological networks is the link prediction problem. In particular, existing interactions networks are noisy and incomplete snapshots of the true network, with many true links missing because those interactions have not yet been experimentally observed. Methods to predict missing links have been more extensively studied for social than for biological networks; it was recently argued that there is some special structure in protein–protein interaction (PPI) network data that might mean that alternate methods may outperform the best methods for social networks. Based on a generalization of the diffusion state distance, we design a new embedding-based link prediction method called global and local integrated diffusion embedding (GLIDE). GLIDE is designed to effectively capture global network structure, combined with alternative network type-specific customized measures that capture local network structure. We test GLIDE on a collection of three recently curated human biological networks derived from the 2016 DREAM disease module identification challenge as well as a classical version of the yeast PPI network in rigorous cross validation experiments.

Results

We indeed find that different local network structure is dominant in different types of biological networks. We find that the simple local network measures are dominant in the highly connected network core between hub genes, but that GLIDE’s global embedding measure adds value in the rest of the network. For example, we make GLIDE-based link predictions from genes known to be involved in Crohn’s disease, to genes that are not known to have an association, and make some new predictions, finding support in other network data and the literature.

Availability and implementation

GLIDE can be downloaded at https://bitbucket.org/kap_devkota/glide.

Supplementary information

Supplementary data are available at Bioinformatics online.

Cutting-edge genetics in obsessive-compulsive disorder

This article reviews recent advances in the genetics of obsessive-compulsive disorder (OCD). We cover work on the following: genome-wide association studies, whole-exome sequencing studies, copy number variation studies, gene expression, polygenic risk scores, gene–environment interaction, experimental animal systems, human cell models, imaging genetics, pharmacogenetics, and studies of endophenotypes. Findings from this work underscore the notion that the genetic architecture of OCD is highly complex and shared with other neuropsychiatric disorders. Also, the latest evidence points to the participation of gene networks involved in synaptic transmission, neurodevelopment, and the immune and inflammatory systems in this disorder. We conclude by highlighting that further study of the genetic architecture of OCD, a great part of which remains to be elucidated, could benefit the development of diagnostic and therapeutic approaches based on the biological basis of the disorder. Studies to date revealed that OCD is not a simple homogeneous entity, but rather that the underlying biological pathways are variable and heterogenous. We can expect that translation from bench to bedside, through continuous effort and collaborative work, will ultimately transform our understanding of what causes OCD and thus how best to treat it.

Summary-data-based Mendelian randomization prioritizes potential druggable targets for multiple sclerosis

Multiple sclerosis is a complex autoimmune disease caused by a combination of genetic and environmental factors. Translation of Genome-Wide Association Study findings into therapeutics and effective preventive strategies has been limited to date. We used summary-data-based Mendelian randomization to synthesize findings from public expression quantitative trait locus, methylation quantitative trait locus and Multiple Sclerosis Genome-Wide Association Study datasets. By correlating the effects of methylation on multiple sclerosis, methylation on expression and expression on multiple sclerosis susceptibility, we prioritize genetic loci with evidence of influencing multiple sclerosis susceptibility. We overlay these findings onto a list of 'druggable' genes, i.e. genes which are currently, or could theoretically, be targeted by therapeutic compounds. We use GeNets and search tool for the retrieval of interacting genes/proteins to identify protein-protein interactions and druggable pathways enriched in our results. We extend these findings to a model of Epstein-Barr virus-infected B cells, lymphoblastoid cell lines. We conducted a systematic review of prioritized genes using the Open Targets platform to identify completed and planned trials targeting prioritized genes in multiple sclerosis and related disease areas. Expression of 45 genes in peripheral blood was strongly associated with multiple sclerosis susceptibility (False discovery rate 0.05). Of these 45 genes, 20 encode a protein which is currently targeted by an existing therapeutic compound. These genes were enriched for Gene Ontology terms pertaining to immune system function and leucocyte signalling. We refined this prioritized gene list by restricting to loci where CpG site methylation was associated with multiple sclerosis susceptibility, with gene expression and where expression was associated with multiple sclerosis susceptibility. This approach yielded a list of 15 prioritized druggable target genes for which there was evidence of a pathway linking methylation, expression and multiple sclerosis. Five of these 15 genes are targeted by existing drugs and three were replicated in a smaller expression Quantitative Trait Loci dataset (CD40, MERTK and PARP1). In lymphoblastoid cell lines, this approach prioritized 7 druggable gene targets, of which only one was prioritized by the multi-omic approach in peripheral blood (FCRL3). Systematic review of Open Targets revealed multiple early-phase trials targeting 13/20 prioritized genes in disorders related to multiple sclerosis. We use public datasets and summary-data-based Mendelian randomization to identify a list of prioritized druggable genetic targets in multiple sclerosis. We hope our findings could be translated into a platform for developing targeted preventive therapies.

Identification of Gene Signatures for Diagnosis and Prognosis of Hepatocellular Carcinomas Patients at Early Stage

The onset of liver cancer is insidious. Currently, there is no effective method for the early detection of hepatocellular carcinoma (HCC). Transcriptomic profiles of 826 tissue samples from the Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), Genotype tissue expression (GTEx), and International Cancer Genome Consortium (ICGC) databases were utilized to establish models for early detection and surveillance of HCC. The overlapping differentially expressed genes (DEGs) were screened by elastic net and robust rank aggregation (RRA) analyses to construct the diagnostic prediction model for early HCC (DP.eHCC). Prognostic prediction genes were screened by univariate cox regression and lasso cox regression analyses to construct the survival risk prediction model for early HCC (SP.eHCC). The relationship between the variation of transcriptome profile and the oncogenic risk-score of early HCC was analyzed by combining Weighted Correlation Network Analysis (WGCNA), Gene Set Enrichment Analysis (GSEA), and genome networks (GeNets). The results showed that the AUC of DP.eHCC model for the diagnosis of early HCC was 0.956 (95% CI: 0.941–0.972; p < 0.001) with a sensitivity of 90.91%, a specificity of 92.97%. The SP.eHCC model performed well for predicting the overall survival risk of HCC patients (HR = 10.79; 95% CI: 6.16–18.89; p < 0.001). The oncogenesis of early HCC was revealed mainly involving in pathways associated with cell proliferation and tumor microenvironment. And the transcription factors including EZH2, EGR1, and SOX17 were screened in the genome networks as the promising targets used for precise treatment in patients with HCC. Our findings provide robust models for the early diagnosis and prognosis of HCC, and are crucial for the development of novel targets applied in the precision therapy of HCC.

Proteomic and Metabolomic Characterization of COVID-19 Patient Sera

Early detection and effective treatment of severe COVID-19 patients remain major challenges. Here, we performed proteomic and metabolomic profiling of sera from 46 COVID-19 and 53 control individuals. We then trained a machine learning model using proteomic and metabolomic measurements from a training cohort of 18 non-severe and 13 severe patients. The model was validated using 10 independent patients, 7 of which were correctly classified. Targeted proteomics and metabolomics assays were employed to further validate this molecular classifier in a second test cohort of 19 COVID-19 patients, leading to 16 correct assignments. We identified molecular changes in the sera of COVID-19 patients compared to other groups implicating dysregulation of macrophage, platelet degranulation, complement system pathways, and massive metabolic suppression. This study revealed characteristic protein and metabolite changes in the sera of severe COVID-19 patients, which might be used in selection of potential blood biomarkers for severity evaluation.

Association study based on topological constraints of protein-protein interaction networks

The non-random interaction pattern of a protein–protein interaction network (PIN) is biologically informative, but its potentials have not been fully utilized in omics studies. Here, we propose a network-permutation-based association study (NetPAS) method that gauges the observed interactions between two sets of genes based on the comparison between permutation null models and the empirical networks. This enables NetPAS to evaluate relationships, constrained by network topology, between gene sets related to different phenotypes. We demonstrated the utility of NetPAS in 50 well-curated gene sets and comparison of association studies using Z-scores, modified Zʹ-scores, p-values and Jaccard indices. Using NetPAS, a weighted human disease network was generated from the association scores of 19 gene sets from OMIM. We also applied NetPAS in gene sets derived from gene ontology and pathway annotations and showed that NetPAS uncovered functional terms missed by DAVID and WebGestalt. Overall, we show that NetPAS can take topological constraints of molecular networks into account and offer new perspectives than existing methods.

Prediction of cancer driver genes through network-based moment propagation of mutation scores

MOTIVATION

Gaining a comprehensive understanding of the genetics underlying cancer development and progression is a central goal of biomedical research. Its accomplishment promises key mechanistic, diagnostic and therapeutic insights. One major step in this direction is the identification of genes that drive the emergence of tumors upon mutation. Recent advances in the field of computational biology have shown the potential of combining genetic summary statistics that represent the mutational burden in genes with biological networks, such as protein-protein interaction networks, to identify cancer driver genes. Those approaches superimpose the summary statistics on the nodes in the network, followed by an unsupervised propagation of the node scores through the network. However, this unsupervised setting does not leverage any knowledge on well-established cancer genes, a potentially valuable resource to improve the identification of novel cancer drivers.

RESULTS

We develop a novel node embedding that enables classification of cancer driver genes in a supervised setting. The embedding combines a representation of the mutation score distribution in a node's local neighborhood with network propagation. We leverage the knowledge of well-established cancer driver genes to define a positive class, resulting in a partially labeled dataset, and develop a cross-validation scheme to enable supervised prediction. The proposed node embedding followed by a supervised classification improves the predictive performance compared with baseline methods and yields a set of promising genes that constitute candidates for further biological validation.

AVAILABILITY AND IMPLEMENTATION

Code available at https://github.com/BorgwardtLab/MoProEmbeddings.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Design and analysis of CRISPR-Cas experiments

A large and ever-expanding set of CRISPR-Cas systems now enables the rapid and flexible manipulation of genomes in both targeted and large-scale experiments. Numerous software tools and analytical methods have been developed for the design and analysis of CRISPR-Cas experiments, including resources to design optimal guide RNAs for various modes of manipulation and to analyze the results of such experiments. A major recent focus has been the development of comprehensive tools for use on data from large-scale CRISPR-based genetic screens. As this field continues to progress, a clear ongoing challenge is not only to innovate, but to actively maintain and improve existing tools so that researchers across disciplines can rely on a stable set of excellent computational resources for CRISPR-Cas experiments.

Proteomic and Metabolomic Characterization of COVID-19 Patient Sera

Early detection and effective treatment of severe COVID-19 patients remain major challenges. Here, we performed proteomic and metabolomic profiling of sera from 46 COVID-19 and 53 control individuals. We then trained a machine learning model using proteomic and metabolomic measurements from a training cohort of 18 non-severe and 13 severe patients. The model was validated using 10 independent patients, 7 of which were correctly classified. Targeted proteomics and metabolomics assays were employed to further validate this molecular classifier in a second test cohort of 19 COVID-19 patients, leading to 16 correct assignments. We identified molecular changes in the sera of COVID-19 patients compared to other groups implicating dysregulation of macrophage, platelet degranulation, complement system pathways, and massive metabolic suppression. This study revealed characteristic protein and metabolite changes in the sera of severe COVID-19 patients, which might be used in selection of potential blood biomarkers for severity evaluation.

Systems Analysis Implicates WAVE2 Complex in the Pathogenesis of Developmental Left-Sided Obstructive Heart Defects

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Combining CHD phenotype–driven gene set enrichment and CRISPR knockdown screening in zebrafish is an effective approach to identifying novel CHD genes.

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Mutations affecting genes coding for the WAVE2 protein complex and small GTPase-mediated signaling are associated with LVOTO lesions.

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WAVE2 complex genes brk1, nckap1, and wasf2 and regulators of small GTPase signaling cul3a and racgap1 are critical to zebrafish heart development.

Genetic variants are the primary driver of congenital heart disease (CHD) pathogenesis. However, our ability to identify causative variants is limited. To identify causal CHD genes that are associated with specific molecular functions, the study used prior knowledge to filter de novo variants from 2,881 probands with sporadic severe CHD. This approach enabled the authors to identify an association between left ventricular outflow tract obstruction lesions and genes associated with the WAVE2 complex and regulation of small GTPase-mediated signal transduction. Using CRISPR zebrafish knockdowns, the study confirmed that WAVE2 complex proteins brk1, nckap1, and wasf2 and the regulators of small GTPase signaling cul3a and racgap1 are critical to cardiac development.

A network view of microRNA and gene interactions in different pathological stages of colon cancer

BACKGROUND

Colon cancer is one of the common cancers in human. Although the number of annual cases has decreased drastically, prognostic screening and translational methods can be improved. Hence, it is critical to understand the molecular mechanisms of disease progression and prognosis.

RESULTS

In this study, we develop a new strategy for integrating microRNA and gene expression profiles together with clinical information toward understanding the regulation of colon cancer. Particularly, we use this approach to identify microRNA and gene expression networks that are specific to certain pathological stages. To demonstrate the application of our method, we apply this approach to identify microRNA and gene interactions that are specific to pathological stages of colon cancer in The Cancer Genome Atlas (TCGA) datasets.

CONCLUSIONS

Our results show that there are significant differences in network connections between miRNAs and genes in different pathological stages of colon cancer. These findings point to a hypothesis that these networks signify different roles of microRNA and gene regulation in the pathogenesis and tumorigenesis of colon cancer.

Affected Sib-Pair Analyses Identify Signaling Networks Associated With Social Behavioral Deficits in Autism

Autism spectrum disorders (ASDs) are characterized by deficits in three core behavioral domains: reciprocal social interactions, communication, and restricted interests and/or repetitive behaviors. Several hundreds of risk genes for autism have been identified, however, it remains a challenge to associate these genes with specific core behavioral deficits. In multiplex autism families, affected sibs often show significant differences in severity of individual core phenotypes. We hypothesize that a higher mutation burden contributes to a larger difference in the severity of specific core phenotypes between affected sibs. We tested this hypothesis on social behavioral deficits in autism. We sequenced synaptome genes (n = 1,886) in affected male sib-pairs (n = 274) in families from the Autism Genetics Research Exchange (AGRE) and identified rare (MAF ≤ 1%) and predicted functional variants. We selected affected sib-pairs with a large (≥10; n = 92 pairs) or a small (≤4; n = 108 pairs) difference in total cumulative Autism Diagnostic Interview-Revised (ADI-R) social scores (SOCT_CS). We compared burdens of unshared variants present only in sibs with severe social deficits and found a higher burden in SOCT_CS≥10 compared to SOCT_CS ≤ 4 (SOCT_CS≥10: 705.1 ± 16.2; SOCT_CS ≤ 4, 668.3 ± 9.0; p = 0.025). Unshared SOCT_CS≥10 genes only in sibs with severe social deficits are significantly enriched in the SFARI gene set. Network analyses of these genes using InWeb_IM, molecular signatures database (MSigDB), and GeNetMeta identified enrichment for phosphoinositide 3-kinase (PI3K)-AKT-mammalian target of rapamycin (mTOR) (Enrichment Score [eScore] p value = 3.36E−07; n = 8 genes) and Nerve growth factor (NGF) (eScore p value = 8.94E−07; n = 9 genes) networks. These studies support a key role for these signaling networks in social behavioral deficits and present a novel approach to associate risk genes and signaling networks with core behavioral domains in autism.

The impact of proinflammatory cytokines on the β-cell regulatory landscape provides insights into the genetics of type 1 diabetes

Early stages of type 1 diabetes (T1D) are characterized by local autoimmune inflammation and progressive loss of insulin-producing pancreatic β cells. We show here that exposure to pro-inflammatory cytokines unmasks a marked plasticity of the β-cell regulatory landscape. We expand the repertoire of human islet regulatory elements by mapping stimulus-responsive enhancers linked to changes in the β-cell transcriptome, proteome and 3D chromatin structure. Our data indicate that the β cell response to cytokines is mediated by the induction of new regulatory regions as well as the activation of primed regulatory elements prebound by islet-specific transcription factors. We find that T1D-associated loci are enriched of the newly mapped cis-regulatory regions and identify T1D-associated variants disrupting cytokine-responsive enhancer activity in human β cells. Our study illustrates how β cells respond to a pro-inflammatory environment and implicate a role for stimulus-response islet enhancers in T1D.

Transcriptional Networks of Microglia in Alzheimer's Disease and Insights into Pathogenesis

Microglia, the main immune cells of the central nervous system, are increasingly implicated in Alzheimer's disease (AD). Manifold transcriptomic studies in the brain have not only highlighted microglia's role in AD pathogenesis, but also mapped crucial pathological processes and identified new therapeutic targets. An important component of many of these transcriptomic studies is the investigation of gene expression networks in AD brain, which has provided important new insights into how coordinated gene regulatory programs in microglia (and other cell types) underlie AD pathogenesis. Given the rapid technological advancements in transcriptional profiling, spanning from microarrays to single-cell RNA sequencing (scRNA-seq), tools used for mapping gene expression networks have evolved to keep pace with the unique features of each transcriptomic platform. In this article, we review the trajectory of transcriptomic network analyses in AD from brain to microglia, highlighting the corresponding methodological developments. Lastly, we discuss examples of how transcriptional network analysis provides new insights into AD mechanisms and pathogenesis.

Assessment of network module identification across complex diseases

Many bioinformatics methods have been proposed for reducing the complexity of large gene or protein networks into relevant subnetworks or modules. Yet, how such methods compare to each other in terms of their ability to identify disease-relevant modules in different types of network remains poorly understood. We launched the 'Disease Module Identification DREAM Challenge', an open competition to comprehensively assess module identification methods across diverse protein-protein interaction, signaling, gene co-expression, homology and cancer-gene networks. Predicted network modules were tested for association with complex traits and diseases using a unique collection of 180 genome-wide association studies. Our robust assessment of 75 module identification methods reveals top-performing algorithms, which recover complementary trait-associated modules. We find that most of these modules correspond to core disease-relevant pathways, which often comprise therapeutic targets. This community challenge establishes biologically interpretable benchmarks, tools and guidelines for molecular network analysis to study human disease biology.

Genes with High Network Connectivity Are Enriched for Disease Heritability

Recent studies have highlighted the role of gene networks in disease biology. To formally assess this, we constructed a broad set of pathway, network, and pathway+network annotations and applied stratified LD score regression to 42 diseases and complex traits (average N = 323K) to identify enriched annotations. First, we analyzed 18,119 biological pathways. We identified 156 pathway-trait pairs whose disease enrichment was statistically significant (FDR < 5%) after conditioning on all genes and 75 known functional annotations (from the baseline-LD model), a stringent step that greatly reduced the number of pathways detected; most significant pathway-trait pairs were previously unreported. Next, for each of four published gene networks, we constructed probabilistic annotations based on network connectivity. For each gene network, the network connectivity annotation was strongly significantly enriched. Surprisingly, the enrichments were fully explained by excess overlap between network annotations and regulatory annotations from the baseline-LD model, validating the informativeness of the baseline-LD model and emphasizing the importance of accounting for regulatory annotations in gene network analyses. Finally, for each of the 156 enriched pathway-trait pairs, for each of the four gene networks, we constructed pathway+network annotations by annotating genes with high network connectivity to the input pathway. For each gene network, these pathway+network annotations were strongly significantly enriched for the corresponding traits. Once again, the enrichments were largely explained by the baseline-LD model. In conclusion, gene network connectivity is highly informative for disease architectures, but the information in gene networks may be subsumed by regulatory annotations, emphasizing the importance of accounting for known annotations.

Dephosphorylation of the transcriptional cofactor NACA by the PP1A phosphatase enhances cJUN transcriptional activity and osteoblast differentiation

The transcriptional cofactor nascent polypeptide-associated complex and co-regulator α (NACA) regulates osteoblast maturation and activity. NACA functions, at least in part, by binding to Jun proto-oncogene, AP-1 transcription factor subunit (cJUN) and potentiating the transactivation of AP-1 targets such as osteocalcin (Bglap) and matrix metallopeptidase 9 (Mmp9). NACA activity is modulated by phosphorylation carried out by several kinases, but a phosphatase regulating NACA's activity remains to be identified. Here, we used affinity purification with MS in HEK293T cells to isolate NACA complexes and identified protein phosphatase 1 catalytic subunit α (PP1A) as a NACA-associated Ser/Thr phosphatase. NACA interacted with multiple components of the PP1A holoenzyme complex: the PPP1CA catalytic subunit and the regulatory subunits PPP1R9B, PPP1R12A and PPP1R18. MS analysis revealed that NACA co-expression with PPP1CA causes dephosphorylation of NACA at Thr-89, Ser-151, and Thr-174. NACA Ser/Thr-to-alanine variants displayed increased nuclear localization, and NACA dephosphorylation was associated with specific recruitment of novel NACA interactants, such as basic transcription factor 3 (BTF3) and its homolog BTF3L4. NACA and PP1A cooperatively potentiated cJUN transcriptional activity of the AP-1-responsive MMP9-luciferase reporter, which was abolished when Thr-89, Ser-151, or Thr-174 were substituted with phosphomimetic aspartate residues. We confirmed the NACA-PP1A interaction in MC3T3-E1 osteoblastic cells and observed that NACA phosphorylation status at PP1A-sensitive sites is important for the regulation of AP-1 pathway genes and for osteogenic differentiation and matrix mineralization. These results suggest that PP1A dephosphorylates NACA at specific residues, impacting cJUN transcriptional activity and osteoblast differentiation and function.

Integrative transcriptome analyses of the aging brain implicate altered splicing in Alzheimer's disease susceptibility

Here we use deep sequencing to identify sources of variation in mRNA splicing in the dorsolateral prefrontal cortex (DLPFC) of 450 subjects from two aging cohorts. Hundreds of aberrant pre-mRNA splicing events are reproducibly associated with Alzheimer's disease. We also generate a catalog of splicing quantitative trait loci (sQTL) effects: splicing of 3,006 genes is influenced by genetic variation. We report that altered splicing is the mechanism for the effects of the PICALM, CLU and PTK2B susceptibility alleles. Furthermore, we performed a transcriptome-wide association study and identified 21 genes with significant associations with Alzheimer's disease, many of which are found in known loci, whereas 8 are in novel loci. These results highlight the convergence of old and new genes associated with Alzheimer's disease in autophagy-lysosomal-related pathways. Overall, this study of the transcriptome of the aging brain provides evidence that dysregulation of mRNA splicing is a feature of Alzheimer's disease and is, in some cases, genetically driven.