Functionally enigmatic genes: a case study of the brain ignorome

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

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

Microenzymes: Is There Anybody Out There?

Biological macromolecules are found in different shapes and sizes. Among these, enzymes catalyze biochemical reactions and are essential in all organisms, but is there a limit size for them to function properly? Large enzymes such as catalases have hundreds of kDa and are formed by multiple subunits, whereas most enzymes are smaller, with molecular weights of 20-60 kDa. Enzymes smaller than 10 kDa could be called microenzymes and the present literature review brings together evidence of their occurrence in nature. Additionally, bioactive peptides could be a natural source for novel microenzymes hidden in larger peptides and molecular downsizing could be useful to engineer artificial enzymes with low molecular weight improving their stability and heterologous expression. An integrative approach is crucial to discover and determine the amino acid sequences of novel microenzymes, together with their genomic identification and their biochemical biological and evolutionary functions.

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

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

Gene of the month: VSTM2A

The V-set and transmembrane domain containing 2A (VSTM2A) gene is located on chromosome 7. In the physiological state, VSTM2A regulates preadipocyte cell differentiation. VSTM2A is highly expressed in normal human brain tissue and minimally expressed in other normal tissues. Mucinous tubular and spindle cell carcinoma (MTSCC) of the kidney is a distinct renal tumour subtype with signature chromosomal copy number alterations and an indolent outcome in the majority of cases. VSTM2A overexpression is highly enriched in this renal cancer subtype and has been shown to have potential diagnostic value in distinguishing MTSCC from renal tumours with overlapping histological appearances.

Isoflurane Rapidly Modifies Synaptic and Cytoskeletal Phosphoproteomes of the Supraoptic Nucleus of the Hypothalamus and the Cortex

INTRODUCTION

Despite the widespread use of general anaesthetics, the mechanisms mediating their effects are still not understood. Although suppressed in most parts of the brain, neuronal activity, as measured by FOS activation, is increased in the hypothalamic supraoptic nucleus (SON) by numerous general anaesthetics, and evidence points to this brain region being involved in the induction of general anaesthesia (GA) and natural sleep. Posttranslational modifications of proteins, including changes in phosphorylation, enable fast modulation of protein function which could be underlying the rapid effects of GA. In order to identify potential phosphorylation events in the brain-mediating GA effects, we have explored the phosphoproteome responses in the rat SON and compared these to cingulate cortex (CC) which displays no FOS activation in response to general anaesthetics.

METHODS

Adult Sprague-Dawley rats were treated with isoflurane for 15 min. Proteins from the CC and SON were extracted and processed for nano-LC mass spectrometry (LC-MS/MS). Phosphoproteomic determinations were performed by LC-MS/MS.

RESULTS

We found many changes in the phosphoproteomes of both the CC and SON in response to 15 min of isoflurane exposure. Pathway analysis indicated that proteins undergoing phosphorylation adaptations are involved in cytoskeleton remodelling and synaptic signalling events. Importantly, changes in protein phosphorylation appeared to be brain region specific suggesting that differential phosphorylation adaptations might underlie the different neuronal activity responses to GA between the CC and SON.

CONCLUSION

In summary, these data suggest that rapid posttranslational modifications in proteins involved in cytoskeleton remodelling and synaptic signalling events might mediate the central mechanisms mediating GA.

Phylogenomic analyses provide insights into primate evolution

Comparative analysis of primate genomes within a phylogenetic context is essential for understanding the evolution of human genetic architecture and primate diversity. We present such a study of 50 primate species spanning 38 genera and 14 families, including 27 genomes first reported here, with many from previously less well represented groups, the New World monkeys and the Strepsirrhini. Our analyses reveal heterogeneous rates of genomic rearrangement and gene evolution across primate lineages. Thousands of genes under positive selection in different lineages play roles in the nervous, skeletal, and digestive systems and may have contributed to primate innovations and adaptations. Our study reveals that many key genomic innovations occurred in the Simiiformes ancestral node and may have had an impact on the adaptive radiation of the Simiiformes and human evolution.

Ageing restructures the transcriptome of the hypothalamic supraoptic nucleus and alters the response to dehydration

Ageing is associated with altered neuroendocrine function. In the context of the hypothalamic supraoptic nucleus, which makes the antidiuretic hormone vasopressin, ageing alters acute responses to hyperosmotic cues, rendering the elderly more susceptible to dehydration. Chronically, vasopressin has been associated with numerous diseases of old age, including type 2 diabetes and metabolic syndrome. Bulk RNAseq transcriptome analysis has been used to catalogue the polyadenylated supraoptic nucleus transcriptomes of adult (3 months) and aged (18 months) rats in basal euhydrated and stimulated dehydrated conditions. Gene ontology and Weighted Correlation Network Analysis revealed that ageing is associated with alterations in the expression of extracellular matrix genes. Interestingly, whilst the transcriptomic response to dehydration is overall blunted in aged animals compared to adults, there is a specific enrichment of differentially expressed genes related to neurodegenerative processes in the aged cohort, suggesting that dehydration itself may provoke degenerative consequences in aged rats.

Knowledge-Driven Mechanistic Enrichment of the Preeclampsia Ignorome

Preeclampsia is a leading cause of maternal and fetal morbidity and mortality. Currently, the only definitive treatment of preeclampsia is delivery of the placenta, which is central to the pathogenesis of the disease. Transcriptional profiling of human placenta from pregnancies complicated by preeclampsia has been extensively performed to identify differentially expressed genes (DEGs). The decisions to investigate DEGs experimentally are biased by many factors, causing many DEGs to remain uninvestigated. A set of DEGs which are associated with a disease experimentally, but which have no known association to the disease in the literature are known as the ignorome. Preeclampsia has an extensive body of scientific literature, a large pool of DEG data, and only one definitive treatment. Tools facilitating knowledge-based analyses, which are capable of combining disparate data from many sources in order to suggest underlying mechanisms of action, may be a valuable resource to support discovery and improve our understanding of this disease. In this work we demonstrate how a biomedical knowledge graph (KG) can be used to identify novel preeclampsia molecular mechanisms. Existing open source biomedical resources and publicly available high-throughput transcriptional profiling data were used to identify and annotate the function of currently uninvestigated preeclampsia-associated DEGs. Experimentally investigated genes associated with preeclampsia were identified from PubMed abstracts using text-mining methodologies. The relative complement of the text-mined- and meta-analysis-derived lists were identified as the uninvestigated preeclampsia-associated DEGs (n=445), i.e., the preeclampsia ignorome. Using the KG to investigate relevant DEGs revealed 53 novel clinically relevant and biologically actionable mechanistic associations.

Transcriptomics of the Carotid Body

The carotid body (CB) has emerged as a potential therapeutic target for treating sympathetically mediated cardiovascular, respiratory, and metabolic diseases. In adjunct to its classical role as an arterial O₂ sensor, the CB is a multimodal sensor activated by a range of stimuli in the circulation. However, consensus on how CB multimodality is achieved is lacking; even the best studied O₂-sensing appears to involve multiple convergent mechanisms. A strategy to understand multimodal sensing is to adopt a hypothesis-free, high-throughput transcriptomic approach. This has proven instrumental for understanding fundamental mechanisms of CB response to hypoxia and other stimulants, its developmental niche, cellular heterogeneity, laterality, and pathophysiological remodeling in disease states. Herein, we review this published work that reveals novel molecular mechanisms underpinning multimodal sensing and reveals numerous gaps in knowledge that require experimental testing.

Suggesting disease associations for overlooked metabolites using literature from metabolic neighbors

In human health research, metabolic signatures extracted from metabolomics data have a strong added value for stratifying patients and identifying biomarkers. Nevertheless, one of the main challenges is to interpret and relate these lists of discriminant metabolites to pathological mechanisms. This task requires experts to combine their knowledge with information extracted from databases and the scientific literature. However, we show that most compounds (>99%) in the PubChem database lack annotated literature. This dearth of available information can have a direct impact on the interpretation of metabolic signatures, which is often restricted to a subset of significant metabolites. To suggest potential pathological phenotypes related to overlooked metabolites that lack annotated literature, we extend the "guilt-by-association" principle to literature information by using a Bayesian framework. The underlying assumption is that the literature associated with the metabolic neighbors of a compound can provide valuable insights, or an a priori, into its biomedical context. The metabolic neighborhood of a compound can be defined from a metabolic network and correspond to metabolites to which it is connected through biochemical reactions. With the proposed approach, we suggest more than 35,000 associations between 1,047 overlooked metabolites and 3,288 diseases (or disease families). All these newly inferred associations are freely available on the FORUM ftp server (see information at https://github.com/eMetaboHUB/Forum-LiteraturePropagation).

Hippocampal ceRNA networks from chronic intermittent ethanol vapor-exposed male mice and functional analysis of top-ranked lncRNA genes for ethanol drinking phenotypes

The molecular mechanisms regulating the development and progression of alcohol use disorder (AUD) are largely unknown. While noncoding RNAs have previously been implicated as playing key roles in AUD, long-noncoding RNA (lncRNA) remains understudied in relation to AUD. In this study, we first identified ethanol-responsive lncRNAs in the mouse hippocampus that are transcriptional network hub genes. Microarray analysis of lncRNA, miRNA, circular RNA, and protein coding gene expression in the hippocampus from chronic intermittent ethanol vapor- or air- (control) exposed mice was used to identify ethanol-responsive competing endogenous RNA (ceRNA) networks. Highly interconnected lncRNAs (genes that had the strongest overall correlation to all other dysregulated genes identified) were ranked. The top four lncRNAs were novel, previously uncharacterized genes named Gm42575, 4930413E15Rik, Gm15767, and Gm33447, hereafter referred to as Pitt1, Pitt2, Pitt3, and Pitt4, respectively. We subsequently tested the hypothesis that CRISPR/Cas9 mutagenesis of the putative promoter and first exon of these lncRNAs in C57BL/6J mice would alter ethanol drinking behavior. The Drinking in the Dark (DID) assay was used to examine binge-like drinking behavior, and the Every-Other-Day Two-Bottle Choice (EOD-2BC) assay was used to examine intermittent ethanol consumption and preference. No significant differences between control and mutant mice were observed in the DID assay. Female-specific reductions in ethanol consumption were observed in the EOD-2BC assay for Pitt1, Pitt3, and Pitt4 mutant mice compared to controls. Male-specific alterations in ethanol preference were observed for Pitt1 and Pitt2. Female-specific increases in ethanol preference were observed for Pitt3 and Pitt4. Total fluid consumption was reduced in Pitt1 and Pitt2 mutants at 15% v/v ethanol and in Pitt3 and Pitt4 at 20% v/v ethanol in females only. We conclude that all lncRNAs targeted altered ethanol drinking behavior, and that lncRNAs Pitt1, Pitt3, and Pitt4 influenced ethanol consumption in a sex-specific manner. Further research is necessary to elucidate the biological mechanisms for these effects. These findings add to the literature implicating noncoding RNAs in AUD and suggest lncRNAs also play an important regulatory role in the disease.

Multi-omics analysis reveals expression complexity and functional diversity of mouse kinome

Protein kinases are a crucial component of signaling pathways involved in a wide range of cellular responses, including growth, proliferation, differentiation, and migration. Systematic investigation of protein kinases is critical to better understand phosphorylation-mediated signaling pathways and may provide insights into the development of potential therapeutic drug targets. Here we perform a systems-level analysis of the mouse kinome by analyzing multi-omics data. We used bulk and single-cell transcriptomic data from the C57BL/6J mouse strain to define tissue- and cell-type-specific expression of protein kinases, followed by investigating variations in sequence and expression between C57BL/6J and DBA/2J strains. We then profiled a deep brain phosphoproteome from C57BL/6J and DBA/2J strains as well as their reciprocal hybrids to infer the activity of the mouse kinome. Finally, we performed phenome-wide association analysis using the BXD recombinant inbred (RI) mice (a cross between C57BL/6J and DBA/2J strains) to identify any associations between variants in protein kinases and phenotypes. Collectively, our study provides a comprehensive analysis of the mouse kinome by investigating genetic sequence variation, tissue-specific expression patterns, and associations with downstream phenotypes.

A roadmap for the functional annotation of protein families: a community perspective

Over the last 25 years, biology has entered the genomic era and is becoming a science of ‘big data’. Most interpretations of genomic analyses rely on accurate functional annotations of the proteins encoded by more than 500 000 genomes sequenced to date. By different estimates, only half the predicted sequenced proteins carry an accurate functional annotation, and this percentage varies drastically between different organismal lineages. Such a large gap in knowledge hampers all aspects of biological enterprise and, thereby, is standing in the way of genomic biology reaching its full potential. A brainstorming meeting to address this issue funded by the National Science Foundation was held during 3–4 February 2022. Bringing together data scientists, biocurators, computational biologists and experimentalists within the same venue allowed for a comprehensive assessment of the current state of functional annotations of protein families. Further, major issues that were obstructing the field were identified and discussed, which ultimately allowed for the proposal of solutions on how to move forward.

Differential expression of the Tmem132 family genes in the developing mouse nervous system

The family of novel transmembrane proteins (TMEM) 132 have been associated with multiple neurological disorders and cancers in humans, but have hardly been studied in vivo. Here we report the expression patterns of the five Tmem132 genes (a, b, c, d and e) in developing mouse nervous system with RNA in situ hybridization in wholemount embryos and tissue sections. Our results reveal differential and partially overlapping expression of multiple Tmem132 family members in both the central and peripheral nervous system, suggesting potential partial redundancy among them.

Defining characteristics and conservation of poorly annotated genes in Caenorhabditis elegans using WormCat 2.0

Omics tools provide broad datasets for biological discovery. However, the computational tools for identifying important genes or pathways in RNA-seq, proteomics, or GWAS (Genome-Wide Association Study) data depend on Gene Ontogeny annotations and are biased toward well-described pathways. This limits their utility as poorly annotated genes, which could have novel functions, are often passed over. Recently, we developed an annotation and category enrichment tool for Caenorhabditis elegans genomic data, WormCat, which provides an intuitive visualization output. Unlike Gene Ontogeny-based enrichment tools, which exclude genes with no annotation information, WormCat 2.0 retains these genes as a special UNASSIGNED category. Here, we show that the UNASSIGNED gene category enrichment exhibits tissue-specific expression patterns and can include genes with biological functions identified in published datasets. Poorly annotated genes are often considered to be potentially species-specific and thus, of reduced interest to the biomedical community. Instead, we find that around 3% of the UNASSIGNED genes have human orthologs, including some linked to human diseases. These human orthologs themselves have little annotation information. A recently developed method that incorporates lineage relationships (abSENSE) indicates that the failure of BLAST to detect homology explains the apparent lineage specificity for many UNASSIGNED genes. This suggests that a larger subset could be related to human genes. WormCat provides an annotation strategy that allows the association of UNASSIGNED genes with specific phenotypes and known pathways. Building these associations in C. elegans, with its robust genetic tools, provides a path to further functional study and insight into these understudied genes.

Gag-like proteins: Novel mediators of prenatal alcohol exposure in neural development

Background

We previously showed that ethanol did not kill fetal neural stem cells (NSCs), but that their numbers nevertheless are decreased due to aberrant maturation and loss of self‐renewal. To identify mechanisms that mediate this loss of NSCs, we focused on a family of Gag‐like proteins (GLPs), derived from retroviral gene remnants within mammalian genomes. GLPs are important for fetal development, though their role in brain development is virtually unexplored. Moreover, GLPs may be transferred between cells in extracellular vesicles (EVs) and thereby transfer environmental adaptations between cells. We hypothesized that GLPs may mediate some effects of ethanol in NSCs.

Methods

Sex‐segregated male and female fetal murine cortical NSCs, cultured ex vivo as nonadherent neurospheres, were exposed to a dose range of ethanol and to mitogen‐withdrawal‐induced differentiation. We used siRNAs to assess the effects of NSC‐expressed GLP knockdown on growth, survival, and maturation and in silico GLP knockout, in an in vivo single‐cell RNA‐sequencing dataset, to identify GLP‐mediated developmental pathways that were also ethanol‐sensitive.

Results

PEG10 isoform‐1, isoform‐2, and PNMA2 were identified as dominant GLP species in both NSCs and their EVs. Ethanol‐exposed NSCs exhibited significantly elevated PEG10 isoform‐2 and PNMA2 protein during differentiation. Both PEG10 and PNMA2 were mediated apoptosis resistance and additionally, PEG10 promoted neuronal and astrocyte lineage maturation. Neither GLP influenced metabolism nor cell cycle in NSCs. Virtual PEG10 and PNMA2 knockout identified gene transcription regulation and ubiquitin‐ligation processes as candidate mediators of GLP‐linked prenatal alcohol effects.

Conclusions

Collectively, GLPs present in NSCs and their EVs may confer apoptosis resistance within the NSC niche and contribute to the abnormal maturation induced by ethanol.

NeuroSCORE is a genome-wide omics-based model that identifies candidate disease genes of the central nervous system

To identify candidate disease genes of central nervous system (CNS) phenotypes, we created the Neurogenetic Systematic Correlation of Omics-Related Evidence (NeuroSCORE). We identified five genome-wide metrics highly associated with CNS phenotypes to score 19,601 protein-coding genes. Genes scored one point per metric (range: 0-5), identifying 8298 scored genes (scores ≥ 1) and 1601 "high scoring" genes (scores ≥ 3). Using logistic regression, we determined the odds ratio that genes with a NeuroSCORE from 1 to 5 would be associated with known CNS-related phenotypes compared to genes that scored zero. We tested NeuroSCORE using microarray copy number variants (CNVs) in case-control cohorts and aggregate mouse model data. High scoring genes are associated with CNS phenotypes (OR = 5.5, p < 2E-16), enriched in case CNVs, and mouse ortholog genes that cause behavioral and nervous system abnormalities. We identified 1058 high scoring genes with no disease association in OMIM. Transforming the logistic regression results indicates high scoring genes have an 84-92% chance of being associated with a CNS phenotype. Top scoring genes include GRIA1, MAP4K4, SF1, TNPO2, and ZSWIM8. Finally, we interrogated CNVs in the Clinical Genome Resource, finding the majority of clinically significant CNVs contain high scoring genes. These findings can direct future research and improve molecular diagnostics.

The characteristics of early-stage research into human genes are substantially different from subsequent research

Throughout the last 2 decades, several scholars observed that present day research into human genes rarely turns toward genes that had not already been extensively investigated in the past. Guided by hypotheses derived from studies of science and innovation, we present here a literature-wide data-driven meta-analysis to identify the specific scientific and organizational contexts that coincided with early-stage research into human genes throughout the past half century. We demonstrate that early-stage research into human genes differs in team size, citation impact, funding mechanisms, and publication outlet, but that generalized insights derived from studies of science and innovation only partially apply to early-stage research into human genes. Further, we demonstrate that, presently, genome biology accounts for most of the initial early-stage research, while subsequent early-stage research can engage other life sciences fields. We therefore anticipate that the specificity of our findings will enable scientists and policymakers to better promote early-stage research into human genes and increase overall innovation within the life sciences.

Co-evolution based machine-learning for predicting functional interactions between human genes

Over the next decade, more than a million eukaryotic species are expected to be fully sequenced. This has the potential to improve our understanding of genotype and phenotype crosstalk, gene function and interactions, and answer evolutionary questions. Here, we develop a machine-learning approach for utilizing phylogenetic profiles across 1154 eukaryotic species. This method integrates co-evolution across eukaryotic clades to predict functional interactions between human genes and the context for these interactions. We benchmark our approach showing a 14% performance increase (auROC) compared to previous methods. Using this approach, we predict functional annotations for less studied genes. We focus on DNA repair and verify that 9 of the top 50 predicted genes have been identified elsewhere, with others previously prioritized by high-throughput screens. Overall, our approach enables better annotation of function and functional interactions and facilitates the understanding of evolutionary processes underlying co-evolution. The manuscript is accompanied by a webserver available at: https://mlpp.cs.huji.ac.il.

With the rise in number of eukaryotic species being fully sequenced, large scale phylogenetic profiling can give insights on gene function, Here, the authors describe a machine-learning approach that integrates co-evolution across eukaryotic clades to predict gene function and functional interactions among human genes.

Identifying genetic risk variants associated with brain volumetric phenotypes via K-sample Ball Divergence method

Regional human brain volumes including total area, average thickness, and total volume are heritable and associated with neurological disorders. However, the genetic architecture of brain structure and function is still largely unknown and worthy of exploring. The Pediatric Imaging, Neurocognition, and Genetics (PING) data set provides an excellent resource with genome-wide genetic data and related neuroimaging data. In this study, we perform genome-wide association studies (GWAS) of 315 brain volumetric phenotypes from the PING data set including 1036 samples with 539,865 single-nucleotide polymorphisms (SNPs). We introduce a nonparametric test based on K-sample Ball Divergence (KBD) to identify genetic risk variants that influence regional brain volumes. We carry out simulations to demonstrate that KBD is a powerful test for identifying significant SNPs associated with multivariate phenotypes although controlling the type I error rate. We successfully identify nine SNPs below a significance level of 5 × 10^-5 for the PING data. Among the nine identified genetic variants, two SNPs rs486179 and rs562110 are located in the ADRA1A gene that is a well-known risk factor of mental illness, such as schizophrenia and attention deficit hyperactivity disorder. Our study suggests that the nonparametric test KBD is an effective method for identifying genetic variants associated with complex diseases in large-scale GWAS of multiple phenotypes.

Differential expression and regulation of MS4A family members in myeloid cells in physiological and pathological conditions

The MS4A gene family encodes 18 tetraspanin-like proteins, most of which with unknown function. MS4A1 (CD20), MS4A2 (FcεRIβ), MS4A3 (HTm4), and MS4A4A play important roles in immunity, whereas expression and function of other members of the family are unknown. The present investigation was designed to obtain an expression fingerprint of MS4A family members, using bioinformatics analysis of public databases, RT-PCR, and protein analysis when possible. MS4A3, MS4A4A, MS4A4E, MS4A6A, MS4A7, and MS4A14 were expressed by myeloid cells. MS4A6A and MS4A14 were expressed in circulating monocytes and decreased during monocyte-to-Mϕ differentiation in parallel with an increase in MS4A4A expression. Analysis of gene expression regulation revealed a strong induction of MS4A4A, MS4A6A, MS4A7, and MS4A4E by glucocorticoid hormones. Consistently with in vitro findings, MS4A4A and MS4A7 were expressed in tissue Mϕs from COVID-19 and rheumatoid arthritis patients. Interestingly, MS4A3, selectively expressed in myeloid precursors, was found to be a marker of immature circulating neutrophils, a cellular population associated to COVID-19 severe disease. The results reported here show that members of the MS4A family are differentially expressed and regulated during myelomonocytic differentiation, and call for assessment of their functional role and value as therapeutic targets.

Identification of pathological transcription in autosomal dominant polycystic kidney disease epithelia

Autosomal dominant polycystic kidney disease (ADPKD) affects more than 12 million people worldwide. Mutations in PKD1 and PKD2 cause cyst formation through unknown mechanisms. To unravel the pathogenic mechanisms in ADPKD, multiple studies have investigated transcriptional mis-regulation in cystic kidneys from patients and mouse models, and numerous dysregulated genes and pathways have been described. Yet, the concordance between studies has been rather limited. Furthermore, the cellular and genetic diversity in cystic kidneys has hampered the identification of mis-expressed genes in kidney epithelial cells with homozygous PKD mutations, which are critical to identify polycystin-dependent pathways. Here we performed transcriptomic analyses of Pkd1- and Pkd2-deficient mIMCD3 kidney epithelial cells followed by a meta-analysis to integrate all published ADPKD transcriptomic data sets. Based on the hypothesis that Pkd1 and Pkd2 operate in a common pathway, we first determined transcripts that are differentially regulated by both genes. RNA sequencing of genome-edited ADPKD kidney epithelial cells identified 178 genes that are concordantly regulated by Pkd1 and Pkd2. Subsequent integration of existing transcriptomic studies confirmed 31 previously described genes and identified 61 novel genes regulated by Pkd1 and Pkd2. Cluster analyses then linked Pkd1 and Pkd2 to mRNA splicing, specific factors of epithelial mesenchymal transition, post-translational protein modification and epithelial cell differentiation, including CD34, CDH2, CSF2RA, DLX5, HOXC9, PIK3R1, PLCB1 and TLR6. Taken together, this model-based integrative analysis of transcriptomic alterations in ADPKD annotated a conserved core transcriptomic profile and identified novel candidate genes for further experimental studies.

Osmoregulation of the transcriptome of the hypothalamic supraoptic nucleus: A resource for the community

The hypothalamic supraoptic nucleus (SON) is a core osmoregulatory control centre that deciphers information about the metabolic state of the organism and orchestrates appropriate homeostatic (endocrine) and allostatic (behavioural) responses. We have used RNA sequencing to describe the polyadenylated transcriptome of the SON of the male Wistar Han rat. These data have been mined to generate comprehensive catalogues of functional classes of genes (enzymes, transcription factors, endogenous peptides, G protein coupled receptors, transporters, catalytic receptors, channels and other pharmacological targets) expressed in this nucleus in the euhydrated state, and that together form the basal substrate for its physiological interactions. We have gone on to show that fluid deprivation for 3 days (dehydration) results in changes in the expression levels of 2247 RNA transcripts, which have similarly been functionally catalogued, and further mined to describe enriched gene categories and putative regulatory networks (Regulons) that may have physiological importance in SON function related plasticity. We hope that the revelation of these genes, pathways and networks, most of which have no characterised roles in the SON, will encourage the neuroendocrine community to pursue new investigations into the new 'known-unknowns' reported in the present study.

The C. elegans homolog of human panic-disorder risk gene TMEM132D orchestrates neuronal morphogenesis through the WAVE-regulatory complex

TMEM132D is a human gene identified with multiple risk alleles for panic disorders, anxiety and major depressive disorders. Defining a conserved family of transmembrane proteins, TMEM132D and its homologs are still of unknown molecular functions. By generating loss-of-function mutants of the sole TMEM132 ortholog in C. elegans, we identify abnormal morphologic phenotypes in the dopaminergic PDE neurons. Using a yeast two-hybrid screen, we find that NAP1 directly interacts with the cytoplasmic domain of human TMEM132D, and mutations in C. elegans tmem-132 that disrupt interaction with NAP1 cause similar morphologic defects in the PDE neurons. NAP1 is a component of the WAVE regulatory complex (WRC) that controls F-actin cytoskeletal dynamics. Decreasing activity of WRC rescues the PDE defects in tmem-132 mutants, whereas gain-of-function of TMEM132D in mammalian cells inhibits WRC, leading to decreased abundance of select WRC components, impaired actin nucleation and cell motility. We propose that metazoan TMEM132 family proteins play evolutionarily conserved roles in regulating NAP1 protein homologs to restrict inappropriate WRC activity, cytoskeletal and morphologic changes in the cell.

Open Agile text mining for bioinformatics: the PubAnnotation ecosystem

MOTIVATION

Most currently available text mining tools share two characteristics that make them less than optimal for use by biomedical researchers: they require extensive specialist skills in natural language processing and they were built on the assumption that they should optimize global performance metrics on representative datasets. This is a problem because most end-users are not natural language processing specialists and because biomedical researchers often care less about global metrics like F-measure or representative datasets than they do about more granular metrics such as precision and recall on their own specialized datasets. Thus, there are fundamental mismatches between the assumptions of much text mining work and the preferences of potential end-users.

RESULTS

This article introduces the concept of Agile text mining, and presents the PubAnnotation ecosystem as an example implementation. The system approaches the problems from two perspectives: it allows the reformulation of text mining by biomedical researchers from the task of assembling a complete system to the task of retrieving warehoused annotations, and it makes it possible to do very targeted customization of the pre-existing system to address specific end-user requirements. Two use cases are presented: assisted curation of the GlycoEpitope database, and assessing coverage in the literature of pre-eclampsia-associated genes.

AVAILABILITY AND IMPLEMENTATION

The three tools that make up the ecosystem, PubAnnotation, PubDictionaries and TextAE are publicly available as web services, and also as open source projects. The dictionaries and the annotation datasets associated with the use cases are all publicly available through PubDictionaries and PubAnnotation, respectively.

Precision medicine - networks to the rescue

Genetic variants are often not predictive of the phenotypic outcome. Individuals carrying the same pathogenic variant, associated with Mendelian or complex disease, can manifest to different extents, from severe-to-mild to no disease. Improving the accuracy of predicted clinical manifestations of genetic variants has emerged as one of the biggest challenges in precision medicine, which can only be addressed by understanding the mechanisms underlying genotype-phenotype relationships. Efforts to understand the molecular basis of these relationships have identified complex systems of interacting biomolecules that underlie cellular function. Here, we review recent advances in how modeling cellular systems as networks of interacting proteins has fueled identification of disease-associated processes, delineation of underlying molecular mechanisms, and prediction of the pathogenicity of variants. This review is intended to be inspiring for clinicians, geneticists, and network biologists alike who aim to jointly advance our understanding of human disease and accelerate progress toward precision medicine.

Metabolic coessentiality mapping identifies C12orf49 as a regulator of SREBP processing and cholesterol metabolism

Coessentiality mapping has been useful to systematically cluster genes into biological pathways and identify gene functions1-3. Here, using the debiased sparse partial correlation (DSPC) method3, we construct a functional coessentiality map for cellular metabolic processes across human cancer cell lines. This analysis reveals 35 modules associated with known metabolic pathways and further assigns metabolic functions to unknown genes. In particular, we identify C12orf49 as an essential regulator of cholesterol and fatty acid metabolism in mammalian cells. Mechanistically, C12orf49 localizes to the Golgi, binds membrane-bound transcription factor peptidase, site 1 (MBTPS1, site 1 protease) and is necessary for the cleavage of its substrates, including sterol regulatory element binding protein (SREBP) transcription factors. This function depends on the evolutionarily conserved uncharacterized domain (DUF2054) and promotes cell proliferation under cholesterol depletion. Notably, c12orf49 depletion in zebrafish blocks dietary lipid clearance in vivo, mimicking the phenotype of mbtps1 mutants. Finally, in an electronic health record (EHR)-linked DNA biobank, C12orf49 is associated with hyperlipidaemia through phenome analysis. Altogether, our findings reveal a conserved role for C12orf49 in cholesterol and lipid homeostasis and provide a platform to identify unknown components of other metabolic pathways.

Functionally Enigmatic Genes in Cancer: Using TCGA Data to Map the Limitations of Annotations

Cancer is a comparatively well-studied disease, yet despite decades of intense focus, we demonstrate here using data from The Cancer Genome Atlas that a substantial number of genes implicated in cancer are relatively poorly studied. Those genes will likely be missed by any data analysis pipeline, such as enrichment analysis, that depends exclusively on annotations for understanding biological function. There is no indication that the amount of research - indicated by number of publications - is correlated with any objective metric of gene significance. Moreover, these genes are not missing at random but reflect that our information about genes is gathered in a biased manner: poorly studied genes are more likely to be primate-specific and less likely to have a Mendelian inheritance pattern, and they tend to cluster in some biological processes and not others. While this likely reflects both technological limitations as well as the fact that well-known genes tend to gather more interest from the research community, in the absence of a concerted effort to study genes in an unbiased way, many genes (and biological processes) will remain opaque.

New Targets and Emergent Therapies for Osteoporosis

The 11 existing FDA-approved osteoporosis drug treatments include hormone replacement therapy, 2 SERMs (raloxifene and bazedoxifene), 5 inhibitors of bone-resorbing osteoclasts (4 bisphosphonates and anti-RANKL denosumab), 2 parathyroid hormone analogues (teriparatide and abaloparatide), and 1 WNT signaling enhancer (romosozumab). These therapies are effective and provide multiple options for patients and physicians. As the genomic revolution continues, potential novel targets for future drug development are identified. This review takes a wide perspective to describe potentially rewarding topics to explore, including knowledge of genes and pathways involved in bone cell metabolism, the utility of animal models, targeting drugs to bone, and ongoing advances in drug design and delivery.

Genetic Dissection of Femoral and Tibial Microarchitecture

Our understanding of the genetic control of bone strength has relied mainly on estimates of bone mineral density. Here we have mapped genetic factors that influence femoral and tibial microarchitecture using high‐resolution x‐ray computed tomography (8‐μm isotropic voxels) across a family of 61 BXD strains of mice, roughly 10 isogenic cases per strain and balanced by sex. We computed heritabilities for 25 cortical and trabecular traits. Males and females have well‐matched heritabilities, ranging from 0.25 to 0.75. We mapped 16 genetic loci most of which were detected only in females. There is also a bias in favor of loci that control cortical rather than trabecular bone. To evaluate candidate genes, we combined well‐established gene ontologies with bone transcriptome data to compute bone‐enrichment scores for all protein‐coding genes. We aligned candidates with those of human genome‐wide association studies. A subset of 50 strong candidates fell into three categories: (1) experimentally validated genes already known to modulate bone function (Adamts4, Ddr2, Darc, Adam12, Fkbp10, E2f6, Adam17, Grem2, Ifi204); (2) candidates without any experimentally validated function in bone (eg, Greb1, Ifi202b), but linked to skeletal phenotypes in human cohorts; and (3) candidates that have high bone‐enrichment scores, but for which there is not yet any functional link to bone biology or skeletal system disease (including Ifi202b, Ly9, Ifi205, Mgmt, F2rl1, Iqgap2). Our results highlight contrasting genetic architecture between sexes and among major bone compartments. The alignment of murine and human data facilitates function analysis and should prove of value for preclinical testing of molecular control of bone structure. © 2019 The Authors. JBMR Plus published by Wiley Periodicals, Inc. on behalf of American Society for Bone and Mineral Research.

Identifying gene function and module connections by the integration of multispecies expression compendia

The functions of many eukaryotic genes are still poorly understood. Here, we developed and validated a new method, termed GeneBridge, which is based on two linked approaches to impute gene function and bridge genes with biological processes. First, Gene-Module Association Determination (G-MAD) allows the annotation of gene function. Second, Module-Module Association Determination (M-MAD) allows predicting connectivity among modules. We applied the GeneBridge tools to large-scale multispecies expression compendia—1700 data sets with over 300,000 samples from human, mouse, rat, fly, worm, and yeast—collected in this study. G-MAD identifies novel functions of genes—for example, DDT in mitochondrial respiration and WDFY4 in T cell activation—and also suggests novel components for modules, such as for cholesterol biosynthesis. By applying G-MAD on data sets from respective tissues, tissue-specific functions of genes were identified—for instance, the roles of EHHADH in liver and kidney, as well as SLC6A1 in brain and liver. Using M-MAD, we identified a list of module-module associations, such as those between mitochondria and proteasome, mitochondria and histone demethylation, as well as ribosomes and lipid biosynthesis. The GeneBridge tools together with the expression compendia are available as an open resource, which will facilitate the identification of connections linking genes, modules, phenotypes, and diseases.

Understanding allergic multimorbidity within the non-eosinophilic interactome

BACKGROUND

The mechanisms explaining multimorbidity between asthma, dermatitis and rhinitis (allergic multimorbidity) are not well known. We investigated these mechanisms and their specificity in distinct cell types by means of an interactome-based analysis of expression data.

METHODS

Genes associated to the diseases were identified using data mining approaches, and their multimorbidity mechanisms in distinct cell types were characterized by means of an in silico analysis of the topology of the human interactome.

RESULTS

We characterized specific pathomechanisms for multimorbidities between asthma, dermatitis and rhinitis for distinct emergent non-eosinophilic cell types. We observed differential roles for cytokine signaling, TLR-mediated signaling and metabolic pathways for multimorbidities across distinct cell types. Furthermore, we also identified individual genes potentially associated to multimorbidity mechanisms.

CONCLUSIONS

Our results support the existence of differentiated multimorbidity mechanisms between asthma, dermatitis and rhinitis at cell type level, as well as mechanisms common to distinct cell types. These results will help understanding the biology underlying allergic multimorbidity, assisting in the design of new clinical studies.

Exploring the dark genome: implications for precision medicine

The increase in the number of both patients and healthcare practitioners who grew up using the Internet and computers (so-called "digital natives") is likely to impact the practice of precision medicine, and requires novel platforms for data integration and mining, as well as contextualized information retrieval. The "Illuminating the Druggable Genome Knowledge Management Center" (IDG KMC) quantifies data availability from a wide range of chemical, biological, and clinical resources, and has developed platforms that can be used to navigate understudied proteins (the "dark genome"), and their potential contribution to specific pathologies. Using the "Target Importance and Novelty Explorer" (TIN-X) highlights the role of LRRC10 (a dark gene) in dilated cardiomyopathy. Combining mouse and human phenotype data leads to increased strength of evidence, which is discussed for four additional dark genes: SLX4IP and its role in glucose metabolism, the role of HSF2BP in coronary artery disease, the involvement of ELFN1 in attention-deficit hyperactivity disorder and the role of VPS13D in mouse neural tube development and its confirmed role in childhood onset movement disorders. The workflow and tools described here are aimed at guiding further experimental research, particularly within the context of precision medicine.

Predicting human disease mutations and identifying drug targets from mouse gene knockout phenotyping campaigns

Two large-scale mouse gene knockout phenotyping campaigns have provided extensive data on the functions of thousands of mammalian genes. The ongoing International Mouse Phenotyping Consortium (IMPC), with the goal of examining all ∼20,000 mouse genes, has examined 5115 genes since 2011, and phenotypic data from several analyses are available on the IMPC website (www.mousephenotype.org). Mutant mice having at least one human genetic disease-associated phenotype are available for 185 IMPC genes. Lexicon Pharmaceuticals' Genome5000™ campaign performed similar analyses between 2000 and the end of 2008 focusing on the druggable genome, including enzymes, receptors, transporters, channels and secreted proteins. Mutants (4654 genes, with 3762 viable adult homozygous lines) with therapeutically interesting phenotypes were studied extensively. Importantly, phenotypes for 29 Lexicon mouse gene knockouts were published prior to observations of similar phenotypes resulting from homologous mutations in human genetic disorders. Knockout mouse phenotypes for an additional 30 genes mimicked previously published human genetic disorders. Several of these models have helped develop effective treatments for human diseases. For example, studying Tph1 knockout mice (lacking peripheral serotonin) aided the development of telotristat ethyl, an approved treatment for carcinoid syndrome. Sglt1 (also known as Slc5a1) and Sglt2 (also known as Slc5a2) knockout mice were employed to develop sotagliflozin, a dual SGLT1/SGLT2 inhibitor having success in clinical trials for diabetes. Clinical trials evaluating inhibitors of AAK1 (neuropathic pain) and SGLT1 (diabetes) are underway. The research community can take advantage of these unbiased analyses of gene function in mice, including the minimally studied 'ignorome' genes.

Traumatic brain injury induces long-lasting changes in immune and regenerative signaling

There are no existing treatments for the long-term degenerative effects of traumatic brain injury (TBI). This is due, in part, to our limited understanding of chronic TBI and uncertainty about which proposed mechanisms for long-term neurodegeneration are amenable to treatment with existing or novel drugs. Here, we used microarray and pathway analyses to interrogate TBI-induced gene expression in the rat hippocampus and cortex at several acute, subchronic and chronic intervals (24 hours, 2 weeks, 1, 2, 3, 6 and 12 months) after parasagittal fluid percussion injury. We used Ingenuity pathway analysis (IPA) and Gene Ontology enrichment analysis to identify significantly expressed genes and prominent cell signaling pathways that are dysregulated weeks to months after TBI and potentially amenable to therapeutic modulation. We noted long-term, coordinated changes in expression of genes belonging to canonical pathways associated with the innate immune response (i.e., NF-κB signaling, NFAT signaling, Complement System, Acute Phase Response, Toll-like receptor signaling, and Neuroinflammatory signaling). Bioinformatic analysis suggested that dysregulation of these immune mediators—many are key hub genes—would compromise multiple cell signaling pathways essential for homeostatic brain function, particularly those involved in cell survival and neuroplasticity. Importantly, the temporal profile of beneficial and maladaptive immunoregulatory genes in the weeks to months after the initial TBI suggests wider therapeutic windows than previously indicated.

Predictability of human differential gene expression

The identification of genes that are differentially expressed provides a molecular foothold onto biological questions of interest. Whether some genes are more likely to be differentially expressed than others, and to what degree, has never been assessed on a global scale. Here, we reanalyze more than 600 studies and find that knowledge of a gene’s prior probability of differential expression (DE) allows for accurate prediction of DE hit lists, regardless of the biological question. This result suggests redundancy in transcriptomics experiments that both informs gene set interpretation and highlights room for growth within the field.

Differential expression (DE) is commonly used to explore molecular mechanisms of biological conditions. While many studies report significant results between their groups of interest, the degree to which results are specific to the question at hand is not generally assessed, potentially leading to inaccurate interpretation. This could be particularly problematic for metaanalysis where replicability across datasets is taken as strong evidence for the existence of a specific, biologically relevant signal, but which instead may arise from recurrence of generic processes. To address this, we developed an approach to predict DE based on an analysis of over 600 studies. A predictor based on empirical prior probability of DE performs very well at this task (mean area under the receiver operating characteristic curve, ∼0.8), indicating that a large fraction of DE hit lists are nonspecific. In contrast, predictors based on attributes such as gene function, mutation rates, or network features perform poorly. Genes associated with sex, the extracellular matrix, the immune system, and stress responses are prominent within the “DE prior.” In a series of control studies, we show that these patterns reflect shared biology rather than technical artifacts or ascertainment biases. Finally, we demonstrate the application of the DE prior to data interpretation in three use cases: (i) breast cancer subtyping, (ii) single-cell genomics of pancreatic islet cells, and (iii) metaanalysis of lung adenocarcinoma and renal transplant rejection transcriptomics. In all cases, we find hallmarks of generic DE, highlighting the need for nuanced interpretation of gene phenotypic associations.

From Molecules to Mechanisms: Functional Proteomics and Its Application to Renal Tubule Physiology

Classical physiological studies using electrophysiological, biophysical, biochemical, and molecular techniques have created a detailed picture of molecular transport, bioenergetics, contractility and movement, and growth, as well as the regulation of these processes by external stimuli in cells and organisms. Newer systems biology approaches are beginning to provide deeper and broader understanding of these complex biological processes and their dynamic responses to a variety of environmental cues. In the past decade, advances in mass spectrometry-based proteomic technologies have provided invaluable tools to further elucidate these complex cellular processes, thereby confirming, complementing, and advancing common views of physiology. As one notable example, the application of proteomics to study the regulation of kidney function has yielded novel insights into the chemical and physical processes that tightly control body fluids, electrolytes, and metabolites to provide optimal microenvironments for various cellular and organ functions. Here, we systematically review, summarize, and discuss the most significant key findings from functional proteomic studies in renal epithelial physiology. We also identify further improvements in technological and bioinformatics methods that will be essential to advance precision medicine in nephrology.

High Fidelity of Mouse Models Mimicking Human Genetic Skeletal Disorders

The 2019 International Skeletal Dysplasia Society nosology update lists 441 genes for which mutations result in rare human skeletal disorders. These genes code for enzymes (33%), scaffolding proteins (18%), signal transduction proteins (16%), transcription factors (14%), cilia proteins (8%), extracellular matrix proteins (5%), and membrane transporters (4%). Skeletal disorders include aggrecanopathies, channelopathies, ciliopathies, cohesinopathies, laminopathies, linkeropathies, lysosomal storage diseases, protein-folding and RNA splicing defects, and ribosomopathies. With the goal of evaluating the ability of mouse models to mimic these human genetic skeletal disorders, a PubMed literature search identified 260 genes for which mutant mice were examined for skeletal phenotypes. These mouse models included spontaneous and ENU-induced mutants, global and conditional gene knockouts, and transgenic mice with gene over-expression or specific base-pair substitutions. The human X-linked gene ARSE and small nuclear RNA U4ATAC, a component of the minor spliceosome, do not have mouse homologs. Mouse skeletal phenotypes mimicking human skeletal disorders were observed in 249 of the 260 genes (96%) for which comparisons are possible. A supplemental table in spreadsheet format provides PubMed weblinks to representative publications of mutant mouse skeletal phenotypes. Mutations in 11 mouse genes (Ccn6, Cyp2r1, Flna, Galns, Gna13, Lemd3, Manba, Mnx1, Nsd1, Plod1, Smarcal1) do not result in similar skeletal phenotypes observed with mutations of the homologous human genes. These discrepancies can result from failure of mouse models to mimic the exact human gene mutations. There are no obvious commonalities among these 11 genes. Body BMD and/or radiologic dysmorphology phenotypes were successfully identified for 28 genes by the International Mouse Phenotyping Consortium (IMPC). Forward genetics using ENU mouse mutagenesis successfully identified 37 nosology gene phenotypes. Since many human genetic disorders involve hypomorphic, gain-of-function, dominant-negative and intronic mutations, future studies will undoubtedly utilize CRISPR/Cas9 technology to examine transgenic mice having genes modified to exactly mimic variant human sequences. Mutant mice will increasingly be employed for drug development studies designed to treat human genetic skeletal disorders.

SIGNIFICANCE

Great progress is being made identifying mutant genes responsible for human rare genetic skeletal disorders and mouse models for genes affecting bone mass, architecture, mineralization and strength. This review organizes data for 441 human genetic bone disorders with regard to heredity, gene function, molecular pathways, and fidelity of relevant mouse models to mimic the human skeletal disorders. PubMed weblinks to citations of 249 successful mouse models are provided.

Large-scale investigation of the reasons why potentially important genes are ignored

Biomedical research has been previously reported to primarily focus on a minority of all known genes. Here, we demonstrate that these differences in attention can be explained, to a large extent, exclusively from a small set of identifiable chemical, physical, and biological properties of genes. Together with knowledge about homologous genes from model organisms, these features allow us to accurately predict the number of publications on individual human genes, the year of their first report, the levels of funding awarded by the National Institutes of Health (NIH), and the development of drugs against disease-associated genes. By explicitly identifying the reasons for gene-specific bias and performing a meta-analysis of existing computational and experimental knowledge bases, we describe gene-specific strategies for the identification of important but hitherto ignored genes that can open novel directions for future investigation.

Unexplored therapeutic opportunities in the human genome

A large proportion of biomedical research and the development of therapeutics is focused on a small fraction of the human genome. In a strategic effort to map the knowledge gaps around proteins encoded by the human genome and to promote the exploration of currently understudied, but potentially druggable, proteins, the US National Institutes of Health launched the Illuminating the Druggable Genome (IDG) initiative in 2014. In this article, we discuss how the systematic collection and processing of a wide array of genomic, proteomic, chemical and disease-related resource data by the IDG Knowledge Management Center have enabled the development of evidence-based criteria for tracking the target development level (TDL) of human proteins, which indicates a substantial knowledge deficit for approximately one out of three proteins in the human proteome. We then present spotlights on the TDL categories as well as key drug target classes, including G protein-coupled receptors, protein kinases and ion channels, which illustrate the nature of the unexplored opportunities for biomedical research and therapeutic development.

C8orf46 homolog encodes a novel protein Vexin that is required for neurogenesis in Xenopus laevis

Neural basic helix-loop helix (bHLH) transcription factors promote progenitor cell differentiation by activation of downstream target genes that coordinate neuronal differentiation. Here we characterize a neural bHLH target gene in Xenopus laevis, vexin (vxn; previously sbt1), that is homologous to human c8orf46 and is conserved across vertebrate species. C8orf46 has been implicated in cancer progression, but its function is unknown. Vxn is transiently expressed in differentiating progenitors in the developing central nervous system (CNS), and is required for neurogenesis in the neural plate and retina. Its function is conserved, since overexpression of either Xenopus or mouse vxn expands primary neurogenesis and promotes early retinal cell differentiation in cooperation with neural bHLH factors. Vxn protein is localized to the cell membrane and the nucleus, but functions in the nucleus to promote neural differentiation. Vxn inhibits cell proliferation, and works with the cyclin-dependent kinase inhibitor p27Xic1 (cdkn1b) to enhance neurogenesis and increase levels of the proneural protein Neurog2. We propose that vxn provides a key link between neural bHLH activity and execution of the neurogenic program.

TMEM132: an ancient architecture of cohesin and immunoglobulin domains define a new family of neural adhesion molecules

Summary

The molecular functions of TMEM132 genes remain poorly understood and under-investigated despite their mutations associated with non-syndromic hearing loss, panic disorder and cancer. Here we show the full domain architecture of human TMEM132 family proteins solved using in-depth sequence and structural analysis. We reveal them to be five previously unappreciated cell adhesion molecules whose domain architecture has an early holozoan origin prior to the emergence of choanoflagellates and metazoa. The extra-cellular portions of TMEM132 proteins contain five conserved domains including three tandem immunoglobulin domains, and a cohesin domain homologue, the first such domain found in animals. These findings strongly predict a cellular adhesion function for TMEM132 family, connecting the extracellular medium with the intracellular actin cytoskeleton.

Contact

luis.sanchez-pulido@igmm.ed.ac.uk.

Supplementary information

Supplementary data are available at Bioinformatics online.

Improving Reproducibility and Candidate Selection in Transcriptomics Using Meta-analysis

Transcriptomic experiments are often used in neuroscience to identify candidate genes of interest for further study. However, the lists of genes identified from comparable transcriptomic studies often show limited overlap. One approach to addressing this issue of reproducibility is to combine data from multiple studies in the form of a meta-analysis. Here, we discuss recent work in the field of circadian biology, where transcriptomic meta-analyses have been used to improve candidate gene selection. With the increasing availability of microarray and RNA-Seq data due to deposition in public databases, combined with freely available tools and code, transcriptomic meta-analysis provides an ideal example of how open data can benefit neuroscience research.

Gene annotation bias impedes biomedical research

We found tremendous inequality across gene and protein annotation resources. We observed that this bias leads biomedical researchers to focus on richly annotated genes instead of those with the strongest molecular data. We advocate that researchers reduce these biases by pursuing data-driven hypotheses.

Identification of 22q13 genes most likely to contribute to Phelan McDermid syndrome

Chromosome 22q13.3 deletion (Phelan McDermid) syndrome (PMS) is a rare genetic neurodevelopmental disorder resulting from deletions or other genetic variants on distal 22q. Pathological variants of the SHANK3 gene have been identified, but terminal chromosomal deletions including SHANK3 are most common. Terminal deletions disrupt up to 108 protein-coding genes. The impact of these losses is highly variable and includes both significantly impairing neurodevelopmental and somatic manifestations. The current review combines two metrics, prevalence of gene loss and predicted loss pathogenicity, to identify likely contributors to phenotypic expression. These genes are grouped according to function as follows: molecular signaling at glutamate synapses, phenotypes involving neuropsychiatric disorders, involvement in multicellular organization, cerebellar development and functioning, and mitochondrial. The likely most impactful genes are reviewed to provide information for future clinical and translational investigations.

Identification of genetic elements in metabolism by high-throughput mouse phenotyping

Metabolic diseases are a worldwide problem but the underlying genetic factors and their relevance to metabolic disease remain incompletely understood. Genome-wide research is needed to characterize so-far unannotated mammalian metabolic genes. Here, we generate and analyze metabolic phenotypic data of 2016 knockout mouse strains under the aegis of the International Mouse Phenotyping Consortium (IMPC) and find 974 gene knockouts with strong metabolic phenotypes. 429 of those had no previous link to metabolism and 51 genes remain functionally completely unannotated. We compared human orthologues of these uncharacterized genes in five GWAS consortia and indeed 23 candidate genes are associated with metabolic disease. We further identify common regulatory elements in promoters of candidate genes. As each regulatory element is composed of several transcription factor binding sites, our data reveal an extensive metabolic phenotype-associated network of co-regulated genes. Our systematic mouse phenotype analysis thus paves the way for full functional annotation of the genome.

Amplification of Adipogenic Commitment by VSTM2A

Despite progress in our comprehension of the mechanisms regulating adipose tissue development, the nature of the factors that functionally characterize adipose precursors is still elusive. Defining the early steps regulating adipocyte development is needed for the generation of tools to control adipose tissue size and function. Here, we report the discovery of V-set and transmembrane domain containing 2A (VSTM2A) as a protein expressed and secreted by committed preadipocytes. VSTM2A expression is elevated in the early phases of adipogenesis in vitro and adipose tissue development in vivo. We show that VSTM2A-producing cells associate with the vasculature and express the common surface markers of adipocyte progenitors. Overexpression of VSTM2A induces adipogenesis, whereas its depletion impairs this process. VSTM2A controls preadipocyte determination at least in part by modulating BMP signaling and PPARγ2 activation. We propose a model in which VSTM2A is produced to preserve and amplify the adipogenic capability of adipose precursors.

Big knowledge from big data in functional genomics

With so much genomics data being produced, it might be wise to pause and consider what purpose this data can or should serve. Some improve annotations, others predict molecular interactions, but few add directly to existing knowledge. This is because sequence annotations do not always implicate function, and molecular interactions are often irrelevant to a cell's or organism's survival or propagation. Merely correlative relationships found in big data fail to provide answers to the Why questions of human biology. Instead, those answers are expected from methods that causally link DNA changes to downstream effects without being confounded by reverse causation. These approaches require the controlled measurement of the consequences of DNA variants, for example, either those introduced in single cells using CRISPR/Cas9 genome editing or that are already present across the human population. Inferred causal relationships between genetic variation and cellular phenotypes or disease show promise to rapidly grow and underpin our knowledge base.

Meta-analysis of transcriptomic datasets identifies genes enriched in the mammalian circadian pacemaker

The master circadian pacemaker in mammals is located in the suprachiasmatic nuclei (SCN) which regulate physiology and behaviour, as well as coordinating peripheral clocks throughout the body. Investigating the function of the SCN has often focused on the identification of rhythmically expressed genes. However, not all genes critical for SCN function are rhythmically expressed. An alternative strategy is to characterize those genes that are selectively enriched in the SCN. Here, we examined the transcriptome of the SCN and whole brain (WB) of mice using meta-analysis of publicly deposited data across a range of microarray platforms and RNA-Seq data. A total of 79 microarrays were used (24 SCN and 55 WB samples, 4 different microarray platforms), alongside 17 RNA-Seq data files (7 SCN and 10 WB). 31 684 MGI gene symbols had data for at least one platform. Meta-analysis using a random effects model for weighting individual effect sizes (derived from differential expression between relevant SCN and WB samples) reliably detected known SCN markers. SCN-enriched transcripts identified in this study provide novel insights into SCN function, including identifying genes which may play key roles in SCN physiology or provide SCN-specific drivers.

Disease model discovery from 3,328 gene knockouts by The International Mouse Phenotyping Consortium

Although next-generation sequencing has revolutionized the ability to associate variants with human diseases, diagnostic rates and development of new therapies are still limited by a lack of knowledge of the functions and pathobiological mechanisms of most genes. To address this challenge, the International Mouse Phenotyping Consortium is creating a genome- and phenome-wide catalog of gene function by characterizing new knockout-mouse strains across diverse biological systems through a broad set of standardized phenotyping tests. All mice will be readily available to the biomedical community. Analyzing the first 3,328 genes identified models for 360 diseases, including the first models, to our knowledge, for type C Bernard-Soulier, Bardet-Biedl-5 and Gordon Holmes syndromes. 90% of our phenotype annotations were novel, providing functional evidence for 1,092 genes and candidates in genetically uncharacterized diseases including arrhythmogenic right ventricular dysplasia 3. Finally, we describe our role in variant functional validation with The 100,000 Genomes Project and others.