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

Concentration of Danish research funding on individual researchers and research topics: Patterns and potential drivers

The degree of concentration in research funding has long been a principal matter of contention in science policy. Strong concentration has been seen as a tool for optimizing and focusing research investments but also as a damaging path towards hypercompetition, diminished diversity, and conservative topic selection. While several studies have documented funding concentration linked to individual funding organizations, few have looked at funding concentration from a systemic perspective. In this article, we examine nearly 20,000 competitive grants allocated by 15 major Danish research funders. Our results show a strongly skewed allocation of funding towards a small elite of individual researchers, and towards a select group of research areas and topics. We discuss potential drivers and highlight that funding concentration likely results from a complex interplay between funders’ overlapping priorities, excellence-dominated evaluation criteria, and lack of coordination between both public and private research funding bodies.

Embracing human genetics: a primer for developmental biologists

Understanding the etiology of congenital disorders requires interdisciplinary research and close collaborations between clinicians, geneticists and developmental biologists. The pace of gene discovery has quickened due to advances in sequencing technology, resulting in a wealth of publicly available sequence data but also a gap between gene discovery and crucial mechanistic insights provided by studies in model systems. In this Spotlight, I highlight the opportunities for developmental biologists to engage with human geneticists and genetic resources to advance the study of congenital disorders.

Hybrid analysis of gene dynamics predicts context-specific expression and offers regulatory insights

Motivation

To understand the regulatory pathways underlying diseases, studies often investigate the differential gene expression between genetically or chemically differing cell populations. Differential expression analysis identifies global changes in transcription and enables the inference of functional roles of applied perturbations. This approach has transformed the discovery of genetic drivers of disease and possible therapies. However, differential expression analysis does not provide quantitative predictions of gene expression in untested conditions. We present a hybrid approach, termed Differential Expression in Python (DiffExPy), that uniquely combines discrete, differential expression analysis with in silico differential equation simulations to yield accurate, quantitative predictions of gene expression from time-series data.

Results

To demonstrate the distinct insight provided by DiffExpy, we applied it to published, in vitro, time-series RNA-seq data from several genetic PI3K/PTEN variants of MCF10a cells stimulated with epidermal growth factor. DiffExPy proposed ensembles of several minimal differential equation systems for each differentially expressed gene. These systems provide quantitative models of expression for several previously uncharacterized genes and uncover new regulation by the PI3K/PTEN pathways. We validated model predictions on expression data from conditions that were not used for model training. Our discrete, differential expression analysis also identified SUZ12 and FOXA1 as possible regulators of specific groups of genes that exhibit late changes in expression. Our work reveals how DiffExPy generates quantitatively predictive models with testable, biological hypotheses from time-series expression data.

Availability and implementation

DiffExPy is available on GitHub (https://github.com/bagherilab/diffexpy).

Supplementary information

Supplementary data are available at Bioinformatics online.

Non-homology-based prediction of gene functions in maize (Zea mays ssp. mays)

Advances in genome sequencing and annotation have eased the difficulty of identifying new gene sequences. Predicting the functions of these newly identified genes remains challenging. Genes descended from a common ancestral sequence are likely to have common functions. As a result, homology is widely used for gene function prediction. This means functional annotation errors also propagate from one species to another. Several approaches based on machine learning classification algorithms were evaluated for their ability to accurately predict gene function from non-homology gene features. Among the eight supervised classification algorithms evaluated, random-forest-based prediction consistently provided the most accurate gene function prediction. Non-homology-based functional annotation provides complementary strengths to homology-based annotation, with higher average performance in Biological Process GO terms, the domain where homology-based functional annotation performs the worst, and weaker performance in Molecular Function GO terms, the domain where the accuracy of homology-based functional annotation is highest. GO prediction models trained with homology-based annotations were able to successfully predict annotations from a manually curated "gold standard" GO annotation set. Non-homology-based functional annotation based on machine learning may ultimately prove useful both as a method to assign predicted functions to orphan genes which lack functionally characterized homologs, and to identify and correct functional annotation errors which were propagated through homology-based functional annotations.

Structure and expression of the long noncoding RNA gene MIR503 in humans and non-human primates

Recent technical and other advances in genomics provide unique opportunities to improve our understanding of human physiology and disease predisposition through a detailed analysis of gene structure and expression by examining data in public genome and gene-expression repositories. Yet, the vast majority of human genes remain understudied. This is particularly true of genes for long noncoding RNAs (lncRNAs). Here, we describe the detailed characterization of MIR503HG, a lncRNA gene found on the X chromosome in humans. Using information extracted from public databases, we show that human MIR503HG is a 5-exon gene, and that it is highly conserved among 5 non-human primates spanning over 85 million years ago of evolutionary diversification. MIR503HG is transcribed and processed into multiple distinct RNAs in each of these species through differential exon use and alternative RNA splicing, with a higher abundance of transcripts being found in reproductive tissues, especially during the early stages of ovary and testis development, indicating a possible role in reproductive biology. Furthermore, in select reproductive system cancers, MIR503HG transcripts are downregulated, with higher levels of RNA expression being associated with clinical outcomes. Collectively, these investigations show how the use of genomic, gene expression, and other genetic resources can lead to new insights about human biology and disease, and argue that MIR503HG is worthy of additional study.

Systems Genetics for Mechanistic Discovery in Heart Diseases

Cardiovascular diseases are the leading cause of death worldwide. Complex diseases with highly heterogenous disease progression among patient populations, cardiovascular diseases feature multifactorial contributions from both genetic and environmental stressors. Despite significant effort utilizing multiple approaches from molecular biology to genome-wide association studies, the genetic landscape of cardiovascular diseases, particularly for the nonfamilial forms of heart failure, is still poorly understood. In the past decade, systems-level approaches based on omics technologies have become an important approach for the study of complex traits in large populations. These advances create opportunities to integrate genetic variation with other biological layers to identify and prioritize candidate genes, understand pathogenic pathways, and elucidate gene-gene and gene-environment interactions. In this review, we will highlight some of the recent progress made using systems genetics approaches to uncover novel mechanisms and molecular bases of cardiovascular pathophysiological manifestations. The key technology and data analysis platforms necessary to implement systems genetics will be described, and the current major challenges and future directions will also be discussed. For complex cardiovascular diseases, such as heart failure, systems genetics represents a powerful strategy to obtain mechanistic insights and to develop individualized diagnostic and therapeutic regiments, paving the way for precision cardiovascular medicine.

A Simple and Scalable Strategy for Analysis of Endogenous Protein Dynamics

The ability to analyze protein function in a native context is central to understanding cellular physiology. This study explores whether tagging endogenous proteins with a reporter is a scalable strategy for generating cell models that accurately quantitate protein dynamics. Specifically, it investigates whether CRISPR-mediated integration of the HiBiT luminescent peptide tag can easily be accomplished on a large-scale and whether integrated reporter faithfully represents target biology. For this purpose, a large set of proteins representing diverse structures and functions, some of which are known or potential drug targets, were targeted for tagging with HiBiT in multiple cell lines. Successful insertion was detected for 86% of the targets, as determined by luminescence-based plate assays, blotting, and imaging. In order to determine whether endogenously tagged proteins yield more representative models, cells expressing HiBiT protein fusions either from endogenous loci or plasmids were directly compared in functional assays. In the tested cases, only the edited lines were capable of accurately reproducing the anticipated biology. This study provides evidence that cell lines expressing HiBiT fusions from endogenous loci can be rapidly generated for many different proteins and that these cellular models provide insight into protein function that may be unobtainable using overexpression-based approaches.

The Zmat2 gene in non-mammalian vertebrates: Organizational simplicity within a divergent locus in fish

ZMAT2 is among the least-studied of mammalian proteins and genes, even though it is the ortholog of Snu23, a protein involved in pre-mRNA splicing in yeast. Here we have used data from genomic and gene expression repositories to examine the Zmat2 gene and locus in 8 terrestrial vertebrates, 10 ray-finned fish, and 1 lobe-finned fish representing > 500 million years of evolutionary diversification. The analyses revealed that vertebrate Zmat2 genes are similar to their mammalian counterparts, as in 16/19 species studied they contain 6 exons, and in 18/19 encode a single conserved protein. However, unlike in mammals, no Zmat2 pseudogenes were identified in these vertebrates, although an expressed Zmat2 paralog was characterized in flycatcher that resembled a DNA copy of a processed and retro-transposed mRNA, and thus could be a proto-pseudogene captured during its evolutionary journey from active to inert. The Zmat2 locus in terrestrial vertebrates, and in spotted gar and coelacanth, also shares additional genes with its mammalian counterparts, including Histidyl-tRNA synthetase (Hars), Hars2, and others, but these are absent from the Zmat2 locus in teleost fish, in which Stem-loop-binding protein 2 (Slbp2) and Lymphocyte cytosolic protein 2a (Lcp2a) are present instead. Taken together, these observations argue that a recognizable Zmat2 was present in the earliest vertebrate ancestors, and postulate that during chromosomal tetraploidization and subsequent re-diploidization during modern teleost evolution, the duplicated Zmat2 gene was retained and the original lost. This study also highlights how information from genomic resources can be leveraged to reveal new biologically significant insights.

The dark genome and pleiotropy: challenges for precision medicine

Surprisingly we remain ignorant of the function of the majority of genes in the human and mouse genomes. The dark genome is a major obstacle to the interpretation of the function of human genetic variation and its impact on disease. At the same time, pleiotropy, how individual variants influence multiple phenotypes, is key to understanding gene function and the role of genes and genetic networks in disease systems. Both understanding the genetics of disease and developing new therapeutic approaches and advances in precision medicine are all compromised by our limited knowledge of gene function and pleiotropic effects. Illuminating the dark genome and revealing pleiotropy across the genome requires a highly coordinated and international effort to acquire and analyse high-dimensional phenotype data from model organisms. We describe briefly how the International Mouse Phenotyping Consortium is addressing these challenges and the novel features of the pleiotropic landscape that are revealed by functional genomics programmes at genome-wide scale.

Systems Biochemistry Approaches to Defining Mitochondrial Protein Function

Defining functions for the full complement of proteins is a grand challenge in the post-genomic era and is essential for our understanding of basic biology and disease pathogenesis. In recent times, this endeavor has benefitted from a combination of modern large-scale and classical reductionist approaches-a process we refer to as "systems biochemistry"-that helps surmount traditional barriers to the characterization of poorly understood proteins. This strategy is proving to be particularly effective for mitochondria, whose well-defined proteome has enabled comprehensive analyses of the full mitochondrial system that can position understudied proteins for fruitful mechanistic investigations. Recent systems biochemistry approaches have accelerated the identification of new disease-related mitochondrial proteins and of long-sought "missing" proteins that fulfill key functions. Collectively, these studies are moving us toward a more complete understanding of mitochondrial activities and providing a molecular framework for the investigation of mitochondrial pathogenesis.

We aren't good at picking candidate genes, and it's slowing us down

In order to gain a molecular understanding of the genetic basis for plant traits, we need to be able to identify the underlying gene and the causal allele for genetic loci. This process usually involves a step where a researcher selects likely candidate genes from a list. The process of picking candidate genes is inherently prone to distortion due to human bias, and this is slowing down our research enterprise.

A census of pathway maps in cancer systems biology

A key goal of cancer systems biology is to use big data to elucidate the molecular networks by which cancer develops. However, to date there has been no systematic evaluation of how far these efforts have progressed. In this Analysis, we survey six major systems biology approaches for mapping and modelling cancer pathways with attention to how well their resulting network maps cover and enhance current knowledge. Our sample of 2,070 systems biology maps captures all literature-curated cancer pathways with significant enrichment, although the strong tendency is for these maps to recover isolated mechanisms rather than entire integrated processes. Systems biology maps also identify previously underappreciated functions, such as a potential role for human papillomavirus-induced chromosomal alterations in ovarian tumorigenesis, and they add new genes to known cancer pathways, such as those related to metabolism, Hippo signalling and immunity. Notably, we find that many cancer networks have been provided only in journal figures and not for programmatic access, underscoring the need to deposit network maps in community databases to ensure they can be readily accessed. Finally, few of these findings have yet been clinically translated, leaving ample opportunity for future translational studies. Periodic surveys of cancer pathway maps, such as the one reported here, are critical to assess progress in the field and identify underserved areas of methodology and cancer biology.

Novel phylogenetic methods are needed for understanding gene function in the era of mega-scale genome sequencing

Ongoing large-scale genome sequencing projects are forecasting a data deluge that will almost certainly overwhelm current analytical capabilities of evolutionary genomics. In contrast to population genomics, there are no standardized methods in evolutionary genomics for extracting evolutionary and functional (e.g. gene-trait association) signal from genomic data. Here, we examine how current practices of multi-species comparative genomics perform in this aspect and point out that many genomic datasets are under-utilized due to the lack of powerful methodologies. As a result, many current analyses emphasize gene families for which some functional data is already available, resulting in a growing gap between functionally well-characterized genes/organisms and the universe of unknowns. This leaves unknown genes on the 'dark side' of genomes, a problem that will not be mitigated by sequencing more and more genomes, unless we develop tools to infer functional hypotheses for unknown genes in a systematic manner. We provide an inventory of recently developed methods capable of predicting gene-gene and gene-trait associations based on comparative data, then argue that realizing the full potential of whole genome datasets requires the integration of phylogenetic comparative methods into genomics, a rich but underutilized toolbox for looking into the past.

Soft windowing application to improve analysis of high-throughput phenotyping data

Motivation

High-throughput phenomic projects generate complex data from small treatment and large control groups that increase the power of the analyses but introduce variation over time. A method is needed to utlize a set of temporally local controls that maximizes analytic power while minimizing noise from unspecified environmental factors.

Results

Here we introduce ‘soft windowing’, a methodological approach that selects a window of time that includes the most appropriate controls for analysis. Using phenotype data from the International Mouse Phenotyping Consortium (IMPC), adaptive windows were applied such that control data collected proximally to mutants were assigned the maximal weight, while data collected earlier or later had less weight. We applied this method to IMPC data and compared the results with those obtained from a standard non-windowed approach. Validation was performed using a resampling approach in which we demonstrate a 10% reduction of false positives from 2.5 million analyses. We applied the method to our production analysis pipeline that establishes genotype–phenotype associations by comparing mutant versus control data. We report an increase of 30% in significant P-values, as well as linkage to 106 versus 99 disease models via phenotype overlap with the soft-windowed and non-windowed approaches, respectively, from a set of 2082 mutant mouse lines. Our method is generalizable and can benefit large-scale human phenomic projects such as the UK Biobank and the All of Us resources.

Availability and implementation

The method is freely available in the R package SmoothWin, available on CRAN http://CRAN.R-project.org/package=SmoothWin.

Supplementary information

Supplementary data are available at Bioinformatics online.

Digital magic, or the dark arts of the 21st century-how can journals and peer reviewers detect manuscripts and publications from paper mills?

In recent years, it has been proposed that unrealistic requirements for academics and medical doctors to publish in scientific journals, combined with monetary publication rewards, have led to forms of contract cheating offered by organizations known as paper mills. Paper mills are alleged to offer products ranging from research data through to ghostwritten fraudulent or fabricated manuscripts and submission services. While paper mill operations remain poorly understood, it seems likely that paper mills need to balance product quantity and quality, such that they produce or contribute to large numbers of manuscripts that will be accepted for publication. Producing manuscripts at scale may be facilitated by the use of manuscript templates, which could give rise to shared features such as textual and organizational similarities, the description of highly generic study hypotheses and experimental approaches, digital images that show evidence of manipulation and/or reuse, and/or errors affecting verifiable experimental reagents. Based on these features, we propose practical steps that editors, journal staff, and peer reviewers can take to recognize and respond to research manuscripts and publications that may have been produced with undeclared assistance from paper mills.

The Canadian Rare Diseases Models and Mechanisms (RDMM) Network: Connecting Understudied Genes to Model Organisms

Advances in genomics have transformed our ability to identify the genetic causes of rare diseases (RDs), yet we have a limited understanding of the mechanistic roles of most genes in health and disease. When a novel RD gene is first discovered, there is minimal insight into its biological function, the pathogenic mechanisms of disease-causing variants, and how therapy might be approached. To address this gap, the Canadian Rare Diseases Models and Mechanisms (RDMM) Network was established to connect clinicians discovering new disease genes with Canadian scientists able to study equivalent genes and pathways in model organisms (MOs). The Network is built around a registry of more than 500 Canadian MO scientists, representing expertise for over 7,500 human genes. RDMM uses a committee process to identify and evaluate clinician-MO scientist collaborations and approve 25,000 Canadian dollars in catalyst funding. To date, we have made 85 clinician-MO scientist connections and funded 105 projects. These collaborations help confirm variant pathogenicity and unravel the molecular mechanisms of RD, and also test novel therapies and lead to long-term collaborations. To expand the impact and reach of this model, we made the RDMM Registry open-source, portable, and customizable, and we freely share our committee structures and processes. We are currently working with emerging networks in Europe, Australia, and Japan to link international RDMM networks and registries and enable matches across borders. We will continue to create meaningful collaborations, generate knowledge, and advance RD research locally and globally for the benefit of patients and families living with RD.

Zmat2 in mammals: conservation and diversification among genes and Pseudogenes

BACKGROUND

Recent advances in genetics and genomics present unique opportunities for enhancing our understanding of mammalian biology and evolution through detailed multi-species comparative analysis of gene organization and expression. Yet, of the more than 20,000 protein coding genes found in mammalian genomes, fewer than 10% have been examined in any detail. Here we elucidate the power of data available in publicly-accessible genomic and genetic resources by querying them to evaluate Zmat2, a minimally studied gene whose human ortholog has been implicated in spliceosome function and in keratinocyte differentiation.

RESULTS

We find extensive conservation in coding regions and overall structure of Zmat2 in 18 mammals representing 13 orders and spanning ~ 165 million years of evolutionary development, and in their encoded proteins. We identify a tandem duplication in the Zmat2 gene and locus in opossum, but not in other monotremes, marsupials, or other mammals, indicating that this event occurred subsequent to the divergence of these species from one another. We also define a collection of Zmat2 pseudogenes in half of the mammals studied, and suggest based on phylogenetic analysis that they each arose independently in the recent evolutionary past.

CONCLUSIONS

Mammalian Zmat2 genes and ZMAT2 proteins illustrate conservation of structure and sequence, along with the development and diversification of pseudogenes in a large fraction of species. Collectively, these observations also illustrate how the focused identification and interpretation of data found in public genomic and gene expression resources can be leveraged to reveal new insights of potentially high biological significance.

Systematic phenomics analysis of autism-associated genes reveals parallel networks underlying reversible impairments in habituation

A major challenge facing the genetics of autism spectrum disorders (ASDs) is the large and growing number of candidate risk genes and gene variants of unknown functional significance. Here, we used Caenorhabditis elegans to systematically functionally characterize ASD-associated genes in vivo. Using our custom machine vision system, we quantified 26 phenotypes spanning morphology, locomotion, tactile sensitivity, and habituation learning in 135 strains each carrying a mutation in an ortholog of an ASD-associated gene. We identified hundreds of genotype-phenotype relationships ranging from severe developmental delays and uncoordinated movement to subtle deficits in sensory and learning behaviors. We clustered genes by similarity in phenomic profiles and used epistasis analysis to discover parallel networks centered on CHD8•chd-7 and NLGN3•nlg-1 that underlie mechanosensory hyperresponsivity and impaired habituation learning. We then leveraged our data for in vivo functional assays to gauge missense variant effect. Expression of wild-type NLG-1 in nlg-1 mutant C. elegans rescued their sensory and learning impairments. Testing the rescuing ability of conserved ASD-associated neuroligin variants revealed varied partial loss of function despite proper subcellular localization. Finally, we used CRISPR-Cas9 auxin-inducible degradation to determine that phenotypic abnormalities caused by developmental loss of NLG-1 can be reversed by adult expression. This work charts the phenotypic landscape of ASD-associated genes, offers in vivo variant functional assays, and potential therapeutic targets for ASD.

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.

An omics perspective on drug target discovery platforms

The drug discovery process starts with identification of a disease-modifying target. This critical step traditionally begins with manual investigation of scientific literature and biomedical databases to gather evidence linking molecular target to disease, and to evaluate the efficacy, safety and commercial potential of the target. The high-throughput and affordability of current omics technologies, allowing quantitative measurements of many putative targets (e.g. DNA, RNA, protein, metabolite), has exponentially increased the volume of scientific data available for this arduous task. Therefore, computational platforms identifying and ranking disease-relevant targets from existing biomedical data sources, including omics databases, are needed. To date, more than 30 drug target discovery (DTD) platforms exist. They provide information-rich databases and graphical user interfaces to help scientists identify putative targets and pre-evaluate their therapeutic efficacy and potential side effects. Here we survey and compare a set of popular DTD platforms that utilize multiple data sources and omics-driven knowledge bases (either directly or indirectly) for identifying drug targets. We also provide a description of omics technologies and related data repositories which are important for DTD tasks.

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.

Systems genetics applications in metabolism research

The common forms of metabolic diseases are highly complex, involving hundreds of genes, environmental and lifestyle factors, age-related changes, sex differences and gut-microbiome interactions. Systems genetics is a population-based approach to address this complexity. In contrast to commonly used 'reductionist' approaches, such as gain or loss of function, that examine one element at a time, systems genetics uses high-throughput 'omics' technologies to quantitatively assess the many molecular differences among individuals in a population and then to relate these to physiologic functions or disease states. Unlike genome-wide association studies, systems genetics seeks to go beyond the identification of disease-causing genes to understand higher-order interactions at the molecular level. The purpose of this review is to introduce the systems genetics applications in the areas of metabolic and cardiovascular disease. Here, we explain how large clinical and omics-level data and databases from both human and animal populations are available to help researchers place genes in the context of pathways and networks and formulate hypotheses that can then be experimentally examined. We provide lists of such databases and examples of the integration of reductionist and systems genetics data. Among the important applications emerging is the development of improved nutritional and pharmacological strategies to address the rise of metabolic diseases.

Tracking human genes along the translational continuum

Understanding the drivers of research on human genes is a critical component to success of translation efforts of genomics into medicine and public health. Using publicly available curated online databases we sought to identify specific genes that are featured in translational genetic research in comparison to all genomics research publications. Articles in the CDC’s Public Health Genomics and Precision Health Knowledge Base were stratified into studies that have moved beyond basic research to population and clinical epidemiologic studies (T1: clinical and population human genome epidemiology research), and studies that evaluate, implement, and assess impact of genes in clinical and public health areas (T2+: beyond bench to bedside). We examined gene counts and numbers of publications within these phases of translation in comparison to all genes from Medline. We are able to highlight those genes that are moving from basic research to clinical and public health translational research, namely in cancer and a few genetic diseases with high penetrance and clinical actionability. Identifying human genes of translational value is an important step towards determining an evidence-based trajectory of the human genome in clinical and public health practice over time.

The NIH Open Citation Collection: A public access, broad coverage resource

Citation data have remained hidden behind proprietary, restrictive licensing agreements, which raises barriers to entry for analysts wishing to use the data, increases the expense of performing large-scale analyses, and reduces the robustness and reproducibility of the conclusions. For the past several years, the National Institutes of Health (NIH) Office of Portfolio Analysis (OPA) has been aggregating and enhancing citation data that can be shared publicly. Here, we describe the NIH Open Citation Collection (NIH-OCC), a public access database for biomedical research that is made freely available to the community. This dataset, which has been carefully generated from unrestricted data sources such as MedLine, PubMed Central (PMC), and CrossRef, now underlies the citation statistics delivered in the NIH iCite analytic platform. We have also included data from a machine learning pipeline that identifies, extracts, resolves, and disambiguates references from full-text articles available on the internet. Open citation links are available to the public in a major update of iCite (https://icite.od.nih.gov).

Btbd3 expression regulates compulsive-like and exploratory behaviors in mice

BTB/POZ domain-containing 3 (BTBD3) was identified as a potential risk gene in the first genome-wide association study of obsessive-compulsive disorder (OCD). BTBD3 is a putative transcription factor implicated in dendritic pruning in developing primary sensory cortices. We assessed whether BTBD3 also regulates neural circuit formation within limbic cortico-striato-thalamo-cortical circuits and behaviors related to OCD in mice. Behavioral phenotypes associated with OCD that are measurable in animals include compulsive-like behaviors and reduced exploration. We tested Btbd3 wild-type, heterozygous, and knockout mice for compulsive-like behaviors including cage-mate barbering, excessive wheel-running, repetitive locomotor patterns, and reduced goal-directed behavior in the probabilistic learning task (PLT), and for exploratory behavior in the open field, digging, and marble-burying tests. Btbd3 heterozygous and knockout mice showed excessive barbering, wheel-running, impaired goal-directed behavior in the PLT, and reduced exploration. Further, chronic treatment with fluoxetine, but not desipramine, reduced barbering in Btbd3 wild-type and heterozygous, but not knockout mice. In contrast, Btbd3 expression did not alter anxiety-like, depression-like, or sensorimotor behaviors. We also quantified dendritic morphology within anterior cingulate cortex, mediodorsal thalamus, and hippocampus, regions of high Btbd3 expression. Surprisingly, Btbd3 knockout mice only showed modest increases in spine density in the anterior cingulate, while dendritic morphology was unaltered elsewhere. Finally, we virally knocked down Btbd3 expression in whole, or just dorsal, hippocampus during neonatal development and assessed behavior during adulthood. Whole, but not dorsal, hippocampal Btbd3 knockdown recapitulated Btbd3 knockout phenotypes. Our findings reveal that hippocampal Btbd3 expression selectively modulates compulsive-like and exploratory behavior.

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.

Target 2035: probing the human proteome

Biomedical scientists tend to focus on only a small fraction of the proteins encoded by the human genome despite overwhelming genetic evidence that many understudied proteins are important for human disease. One of the best ways to interrogate the function of a protein and to determine its relevance as a drug target is by using a pharmacological modulator, such as a chemical probe or an antibody. If these tools were available for most human proteins, it should be possible to translate the tremendous advances in genomics into a greater understanding of human health and disease, and catalyze the creation of innovative new medicines. Target 2035 is a global federation for developing and applying new technologies with the goal of creating chemogenomic libraries, chemical probes, and/or functional antibodies for the entire proteome.

An Ontology to Standardize Research Output of Nutritional Epidemiology: From Paper-Based Standards to Linked Content

BACKGROUND

The use of linked data in the Semantic Web is a promising approach to add value to nutrition research. An ontology, which defines the logical relationships between well-defined taxonomic terms, enables linking and harmonizing research output. To enable the description of domain-specific output in nutritional epidemiology, we propose the Ontology for Nutritional Epidemiology (ONE) according to authoritative guidance for nutritional epidemiology.

METHODS

Firstly, a scoping review was conducted to identify existing ontology terms for reuse in ONE. Secondly, existing data standards and reporting guidelines for nutritional epidemiology were converted into an ontology. The terms used in the standards were summarized and listed separately in a taxonomic hierarchy. Thirdly, the ontologies of the nutritional epidemiologic standards, reporting guidelines, and the core concepts were gathered in ONE. Three case studies were included to illustrate potential applications: (i) annotation of existing manuscripts and data, (ii) ontology-based inference, and (iii) estimation of reporting completeness in a sample of nine manuscripts.

RESULTS

Ontologies for "food and nutrition" (n = 37), "disease and specific population" (n = 100), "data description" (n = 21), "research description" (n = 35), and "supplementary (meta) data description" (n = 44) were reviewed and listed. ONE consists of 339 classes: 79 new classes to describe data and 24 new classes to describe the content of manuscripts.

CONCLUSION

ONE is a resource to automate data integration, searching, and browsing, and can be used to assess reporting completeness in nutritional epidemiology.

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.

A Perspective on Extreme Open Science: Companies Sharing Compounds without Restriction

Although the human genome provides the blueprint for life, most of the proteins it encodes remain poorly studied. This perspective describes how one group of scientists, in seeking new targets for drug discovery, used open science through unrestricted sharing of small molecules to shed light on dark matter of the genome. Starting initially with a single pharmaceutical company before expanding to multiple companies, a precedent was established for sharing published kinase inhibitors as chemical tools. The integration of open science and kinase chemogenomics has supported the study of many new potential drug targets by the scientific community.

Phenotypic Landscape of Schizophrenia-Associated Genes Defines Candidates and Their Shared Functions

Genomic studies have identified hundreds of candidate genes near loci associated with risk for schizophrenia. To define candidates and their functions, we mutated zebrafish orthologs of 132 human schizophrenia-associated genes. We created a phenotype atlas consisting of whole-brain activity maps, brain structural differences, and profiles of behavioral abnormalities. Phenotypes were diverse but specific, including altered forebrain development and decreased prepulse inhibition. Exploration of these datasets identified promising candidates in more than 10 gene-rich regions, including the magnesium transporter cnnm2 and the translational repressor gigyf2, and revealed shared anatomical sites of activity differences, including the pallium, hypothalamus, and tectum. Single-cell RNA sequencing uncovered an essential role for the understudied transcription factor znf536 in the development of forebrain neurons implicated in social behavior and stress. This phenotypic landscape of schizophrenia-associated genes prioritizes more than 30 candidates for further study and provides hypotheses to bridge the divide between genetic association and biological mechanism.

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.

Successive Paradigm Shifts in the Bacterial Cell Cycle and Related Subjects

A paradigm shift in one field can trigger paradigm shifts in other fields. This is illustrated by the paradigm shifts that have occurred in bacterial physiology following the discoveries that bacteria are not unstructured, that the bacterial cell cycle is not controlled by the dynamics of peptidoglycan, and that the growth rates of bacteria in the same steady-state population are not at all the same. These paradigm shifts are having an effect on longstanding hypotheses about the regulation of the bacterial cell cycle, which appear increasingly to be inadequate. I argue that, just as one earthquake can trigger others, an imminent paradigm shift in the regulation of the bacterial cell cycle will have repercussions or "paradigm quakes" on hypotheses about the origins of life and about the regulation of the eukaryotic cell cycle.

Data science for the scientific life cycle

Data science can be incorporated into every stage of a scientific study. Here we describe how data science can be used to generate hypotheses, to design experiments, to perform experiments, and to analyse data. We also present our vision for how data science techniques will be an integral part of the laboratory of the future.

The temporal profile of activity-dependent presynaptic phospho-signalling reveals long-lasting patterns of poststimulus regulation

Depolarization of presynaptic terminals stimulates calcium influx, which evokes neurotransmitter release and activates phosphorylation-based signalling. Here, we present the first global temporal profile of presynaptic activity-dependent phospho-signalling, which includes two KCl stimulation levels and analysis of the poststimulus period. We profiled 1,917 regulated phosphopeptides and bioinformatically identified six temporal patterns of co-regulated proteins. The presynaptic proteins with large changes in phospho-status were again prominently regulated in the analysis of 7,070 activity-dependent phosphopeptides from KCl-stimulated cultured hippocampal neurons. Active zone scaffold proteins showed a high level of activity-dependent phospho-regulation that far exceeded the response from postsynaptic density scaffold proteins. Accordingly, bassoon was identified as the major target of neuronal phospho-signalling. We developed a probabilistic computational method, KinSwing, which matched protein kinase substrate motifs to regulated phosphorylation sites to reveal underlying protein kinase activity. This approach allowed us to link protein kinases to profiles of co-regulated presynaptic protein networks. Ca²⁺- and calmodulin-dependent protein kinase IIα (CaMKIIα) responded rapidly, scaled with stimulus strength, and had long-lasting activity. Mitogen-activated protein kinase (MAPK)/extracellular signal–regulated kinase (ERK) was the main protein kinase predicted to control a distinct and significant pattern of poststimulus up-regulation of phosphorylation. This work provides a unique resource of activity-dependent phosphorylation sites of synaptosomes and neurons, the vast majority of which have not been investigated with regard to their functional impact. This resource will enable detailed characterization of the phospho-regulated mechanisms impacting the plasticity of neurotransmitter release.

Analysis of activity-dependent phosphorylation-based signalling in synaptosomes revealed six patterns of long-lasting presynaptic regulation from 1,917 phosphopeptides. The authors identified patterns most likely to be regulated by CamKII and MAPK/ERK and showed the active zone scaffold protein bassoon to be a major signalling target.

Neurobiological processes are altered by linking neuronal activity to regulated changes in protein phosphorylation levels that influence protein function. Although some of the major targets of activity-dependent phospho-signalling have been identified, a large number of substrates remain unknown. Here, we have screened systematically for these substrates and extended the list from hundreds to thousands of phosphorylation sites, thereby providing a new depth of understanding. We monitored phospho-signalling for 15 min after the stimulation, which to our knowledge had not been attempted at a large scale. We focused on presynaptic protein substrates of phospho-signalling by isolating the presynaptic terminal. We also stimulated hippocampal neurons but did not monitor the poststimulus. Although the phospho-signalling is immensely complex, the findings could be simplified through data exploration. We identified distinct patterns of presynaptic phospho-regulation across the time course that may constitute co-regulated protein networks. In addition, we found a subset of proteins that had many more phosphorylation sites than the average and high-magnitude responses, implying major signalling or functional roles for these proteins. We also determined the likely protein kinases with the strongest responses to the stimulus at different times using KinSwing, a computational tool that we developed. This resource reveals a new depth of activity-dependent phospho-signalling and identifies major signalling targets, major protein kinases, and co-regulated phosphoprotein networks.

Hidden in plain sight: what remains to be discovered in the eukaryotic proteome?

The first decade of genome sequencing stimulated an explosion in the characterization of unknown proteins. More recently, the pace of functional discovery has slowed, leaving around 20% of the proteins even in well-studied model organisms without informative descriptions of their biological roles. Remarkably, many uncharacterized proteins are conserved from yeasts to human, suggesting that they contribute to fundamental biological processes (BP). To fully understand biological systems in health and disease, we need to account for every part of the system. Unstudied proteins thus represent a collective blind spot that limits the progress of both basic and applied biosciences. We use a simple yet powerful metric based on Gene Ontology BP terms to define characterized and uncharacterized proteins for human, budding yeast and fission yeast. We then identify a set of conserved but unstudied proteins in S. pombe, and classify them based on a combination of orthogonal attributes determined by large-scale experimental and comparative methods. Finally, we explore possible reasons why these proteins remain neglected, and propose courses of action to raise their profile and thereby reap the benefits of completing the catalogue of proteins' biological roles.

The Possibility of Systematic Research Fraud Targeting Under-Studied Human Genes: Causes, Consequences, and Potential Solutions

A major reason for biomarker failure is the selection of candidate biomarkers based on inaccurate or incorrect published results. Incorrect research results leading to the selection of unproductive biomarker candidates are largely considered to stem from unintentional research errors. The additional possibility that biomarker research may be actively misdirected by research fraud has been given comparatively little consideration. This review discusses what we believe to be a new threat to biomarker research, namely, the possible systematic production of fraudulent gene knockdown studies that target under-studied human genes. We describe how fraudulent papers may be produced in series by paper mills using what we have described as a 'theme and variations' model, which could also be considered a form of salami slicing. We describe features of these single-gene knockdown publications that may allow them to evade detection by journal editors, peer reviewers, and readers. We then propose a number of approaches to facilitate their detection, including improved awareness of the features of publications constructed in series, broader requirements to post submitted manuscripts to preprint servers, and the use of semi-automated literature screening tools. These approaches may collectively improve the detection of fraudulent studies that might otherwise impede future biomarker research.

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.

Far away from the lamppost

This Formal Comment responds to a recent Meta-Research Article by identifying initiatives that are already in place for funding risky exploratory research that illuminate mysteries of the dark genome.

Identifying High-Priority Proteins Across the Human Diseasome Using Semantic Similarity

Identifying the genes and proteins associated with a biological process or disease is a central goal of the biomedical research enterprise. However, relatively few systematic approaches are available that provide objective evaluation of the genes or proteins known to be important to a research topic, and hence researchers often rely on subjective evaluation of domain experts and laborious manual literature review. Computational bibliometric analysis, in conjunction with text mining and data curation, attempts to automate this process and return prioritized proteins in any given research topic. We describe here a method to identify and rank protein-topic relationships by calculating the semantic similarity between a protein and a query term in the biomerical literature while adjusting for the impact and immediacy of associated research articles. We term the calculated metric the weighted copublication distance (WCD) and show that it compares well to related approaches in predicting benchmark protein lists in multiple biological processes. We used WCD to extract prioritized "popular proteins" across multiple cell types, subanatomical regions, and standardized vocabularies containing over 20 000 human disease terms. The collection of protein-disease associations across the resulting human "diseasome" supports data analytical workflows to perform reverse protein-to-disease queries and functional annotation of experimental protein lists. We envision that the described improvement to the popular proteins strategy will be useful for annotating protein lists and guiding method development efforts as well as generating new hypotheses on understudied disease proteins using bibliometric information.

Reply to "Far away from the lamppost"

In this Formal Comment, the authors of the recent publication "Large-scale investigation of the reasons why potentially important genes are ignored" maintain that it can be read as an opportunity to explore the unknown.

Human genes: Time to follow the roads less traveled?

Determining the functions of human genes is a key objective for understanding disease and enabling development of new therapeutic approaches. A number of recent studies have shown that the amount of attention the research community gives to each of the more than 20,000 human genes is dramatically skewed toward specific, well-known genes. In this issue, Stoeger and colleagues uncover the factors that explain this bias and offer a way ahead to move more genes into the research limelight.

Some genes get all the luck. This Primer explores a new analysis as to why the amount of research attention given to each of the more than 20,000 human genes is dramatically skewed towards specific, well known genes, and asks whether we need to take steps to change it.