Transposon Insertion Sequencing, a Global Measure of Gene Function.

The goal of genomics and systems biology is to understand how complex systems of factors assemble into pathways and structures that combine to form living organisms. Great advances in understanding biological processes result from determining the function of individual genes, a process that has classically relied on characterizing single mutations. Advances in DNA sequencing has made available the complete set of genetic instructions for an astonishing and growing number of species. To understand the function of this ever-increasing number of genes, a high-throughput method was developed that in a single experiment can measure the function of genes across the genome of an organism. This occurred approximately 10 years ago, when high-throughput DNA sequencing was combined with advances in transposon-mediated mutagenesis in a method termed transposon insertion sequencing (TIS). In the subsequent years, TIS succeeded in addressing fundamental questions regarding the genes of bacteria, many of which have been shown to play central roles in bacterial infections that result in major human diseases. The field of TIS has matured and resulted in studies of hundreds of species that include significant innovations with a number of transposons. Here, we summarize a number of TIS experiments to provide an understanding of the method and explanation of approaches that are instructive when designing a study. Importantly, we emphasize critical aspects of a TIS experiment and highlight the extension and applicability of TIS into nonbacterial species such as yeast.

RNA transcripts stimulate homologous recombination by forming DR-loops.

Homologous recombination (HR) repairs DNA double-strand breaks (DSBs) in the S and G2 phases of the cell cycle1-3. Several HR proteins are preferentially recruited to DSBs at transcriptionally active loci4-10, but how transcription promotes HR is poorly understood. Here we develop an assay to assess the effect of local transcription on HR. Using this assay, we find that transcription stimulates HR to a substantial extent. Tethering RNA transcripts to the vicinity of DSBs recapitulates the effects of local transcription, which suggests that transcription enhances HR through RNA transcripts. Tethered RNA transcripts stimulate HR in a sequence- and orientation-dependent manner, indicating that they function by forming DNA-RNA hybrids. In contrast to most HR proteins, RAD51-associated protein 1 (RAD51AP1) only promotes HR when local transcription is active. RAD51AP1 drives the formation of R-loops in vitro and is required for tethered RNAs to stimulate HR in cells. Notably, RAD51AP1 is necessary for the DSB-induced formation of DNA-RNA hybrids in donor DNA, linking R-loops to D-loops. In vitro, RAD51AP1-generated R-loops enhance the RAD51-mediated formation of D-loops locally and give rise to intermediates that we term 'DR-loops', which contain both DNA-DNA and DNA-RNA hybrids and favour RAD51 function. Thus, at DSBs in transcribed regions, RAD51AP1 promotes the invasion of RNA transcripts into donor DNA, and stimulates HR through the formation of DR-loops.

Index and biological spectrum of human DNase I hypersensitive sites.

DNase I hypersensitive sites (DHSs) are generic markers of regulatory DNA1-5 and contain genetic variations associated with diseases and phenotypic traits6-8. We created high-resolution maps of DHSs from 733 human biosamples encompassing 438 cell and tissue types and states, and integrated these to delineate and numerically index approximately 3.6 million DHSs within the human genome sequence, providing a common coordinate system for regulatory DNA. Here we show that these maps highly resolve the cis-regulatory compartment of the human genome, which encodes unexpectedly diverse cell- and tissue-selective regulatory programs at very high density. These programs can be captured comprehensively by a simple vocabulary that enables the assignment to each DHS of a regulatory barcode that encapsulates its tissue manifestations, and global annotation of protein-coding and non-coding RNA genes in a manner orthogonal to gene expression. Finally, we show that sharply resolved DHSs markedly enhance the genetic association and heritability signals of diseases and traits. Rather than being confined to a small number of distal elements or promoters, we find that genetic signals converge on congruently regulated sets of DHSs that decorate entire gene bodies. Together, our results create a universal, extensible coordinate system and vocabulary for human regulatory DNA marked by DHSs, and provide a new global perspective on the architecture of human gene regulation.

The Monarch Initiative in 2024: an analytic platform integrating phenotypes, genes and diseases across species.

Bridging the gap between genetic variations, environmental determinants, and phenotypic outcomes is critical for supporting clinical diagnosis and understanding mechanisms of diseases. It requires integrating open data at a global scale. The Monarch Initiative advances these goals by developing open ontologies, semantic data models, and knowledge graphs for translational research. The Monarch App is an integrated platform combining data about genes, phenotypes, and diseases across species. Monarch's APIs enable access to carefully curated datasets and advanced analysis tools that support the understanding and diagnosis of disease for diverse applications such as variant prioritization, deep phenotyping, and patient profile-matching. We have migrated our system into a scalable, cloud-based infrastructure; simplified Monarch's data ingestion and knowledge graph integration systems; enhanced data mapping and integration standards; and developed a new user interface with novel search and graph navigation features. Furthermore, we advanced Monarch's analytic tools by developing a customized plugin for OpenAI's ChatGPT to increase the reliability of its responses about phenotypic data, allowing us to interrogate the knowledge in the Monarch graph using state-of-the-art Large Language Models. The resources of the Monarch Initiative can be found at monarchinitiative.org and its corresponding code repository at github.com/monarch-initiative/monarch-app.

Genomic encoding of transcriptional burst kinetics.

Mammalian gene expression is inherently stochastic1,2, and results in discrete bursts of RNA molecules that are synthesized from each allele3-7. Although transcription is known to be regulated by promoters and enhancers, it is unclear how cis-regulatory sequences encode transcriptional burst kinetics. Characterization of transcriptional bursting, including the burst size and frequency, has mainly relied on live-cell4,6,8 or single-molecule RNA fluorescence in situ hybridization3,5,8,9 recordings of selected loci. Here we determine transcriptome-wide burst frequencies and sizes for endogenous mouse and human genes using allele-sensitive single-cell RNA sequencing. We show that core promoter elements affect burst size and uncover synergistic effects between TATA and initiator elements, which were masked at mean expression levels. Notably, we provide transcriptome-wide evidence that enhancers control burst frequencies, and demonstrate that cell-type-specific gene expression is primarily shaped by changes in burst frequencies. Together, our data show that burst frequency is primarily encoded in enhancers and burst size in core promoters, and that allelic single-cell RNA sequencing is a powerful model for investigating transcriptional kinetics.

g:Profiler-interoperable web service for functional enrichment analysis and gene identifier mapping (2023 update).

g:Profiler is a reliable and up-to-date functional enrichment analysis tool that supports various evidence types, identifier types and organisms. The toolset integrates many databases, including Gene Ontology, KEGG and TRANSFAC, to provide a comprehensive and in-depth analysis of gene lists. It also provides interactive and intuitive user interfaces and supports ordered queries and custom statistical backgrounds, among other settings. g:Profiler provides multiple programmatic interfaces to access its functionality. These can be easily integrated into custom workflows and external tools, making them valuable resources for researchers who want to develop their own solutions. g:Profiler has been available since 2007 and is used to analyse millions of queries. Research reproducibility and transparency are achieved by maintaining working versions of all past database releases since 2015. g:Profiler supports 849 species, including vertebrates, plants, fungi, insects and parasites, and can analyse any organism through user-uploaded custom annotation files. In this update article, we introduce a novel filtering method highlighting Gene Ontology driver terms, accompanied by new graph visualizations providing a broader context for significant Gene Ontology terms. As a leading enrichment analysis and gene list interoperability service, g:Profiler offers a valuable resource for genetics, biology and medical researchers. It is freely accessible at https://biit.cs.ut.ee/gprofiler.

The risks of using unapproved gene symbols.

The use of approved nomenclature in publications is vital to enable effective scientific communication and is particularly crucial when discussing genes of clinical relevance. Here, we discuss several examples of cases where the failure of researchers to use a HUGO Gene Nomenclature Committee (HGNC)-approved symbol in publications has led to confusion between unrelated human genes in the literature. We also inform authors of the steps they can take to ensure that they use approved nomenclature in their manuscripts and discuss how referencing HGNC IDs can remove ambiguity when referring to genes that have previously been published with confusing alias symbols.

Gene action, genetic variation, and GWAS: A user-friendly web tool.

Fisher's partitioning of genotypic values and genetic variance is highly relevant in the current era of genome-wide association studies (GWASs). However, despite being more than a century old, a number of persistent misconceptions related to nonadditive genetic effects remain. We developed a user-friendly web tool, the Falconer ShinyApp, to show how the combination of gene action and allele frequencies at causal loci translate to genetic variance and genetic variance components for a complex trait. The app can be used to demonstrate the relationship between a SNP effect size estimated from GWAS and the variation the SNP generates in the population, i.e., how locus-specific effects lead to individual differences in traits. In addition, it can also be used to demonstrate how within and between locus interactions (dominance and epistasis, respectively) usually do not lead to a large amount of nonadditive variance relative to additive variance, and therefore, that these interactions usually do not explain individual differences in a population.

ERF mutations reveal a balance of ETS factors controlling prostate oncogenesis.

Half of all prostate cancers are caused by the TMPRSS2-ERG gene-fusion, which enables androgens to drive expression of the normally silent E26 transformation-specific (ETS) transcription factor ERG in prostate cells. Recent genomic landscape studies of such cancers have reported recurrent point mutations and focal deletions of another ETS member, the ETS2 repressor factor ERF. Here we show these ERF mutations cause decreased protein stability and mostly occur in tumours without ERG upregulation. ERF loss recapitulates the morphological and phenotypic features of ERG gain in normal mouse prostate cells, including expansion of the androgen receptor transcriptional repertoire, and ERF has tumour suppressor activity in the same genetic background of Pten loss that yields oncogenic activity by ERG. In the more common scenario of ERG upregulation, chromatin immunoprecipitation followed by sequencing indicates that ERG inhibits the ability of ERF to bind DNA at consensus ETS sites both in normal and in cancerous prostate cells. Consistent with a competition model, ERF overexpression blocks ERG-dependent tumour growth, and ERF loss rescues TMPRSS2-ERG-positive prostate cancer cells from ERG dependency. Collectively, these data provide evidence that the oncogenicity of ERG is mediated, in part, by competition with ERF and they raise the larger question of whether other gain-of-function oncogenic transcription factors might also inactivate endogenous tumour suppressors.

Human knockouts and phenotypic analysis in a cohort with a high rate of consanguinity.

A major goal of biomedicine is to understand the function of every gene in the human genome. Loss-of-function mutations can disrupt both copies of a given gene in humans and phenotypic analysis of such 'human knockouts' can provide insight into gene function. Consanguineous unions are more likely to result in offspring carrying homozygous loss-of-function mutations. In Pakistan, consanguinity rates are notably high. Here we sequence the protein-coding regions of 10,503 adult participants in the Pakistan Risk of Myocardial Infarction Study (PROMIS), designed to understand the determinants of cardiometabolic diseases in individuals from South Asia. We identified individuals carrying homozygous predicted loss-of-function (pLoF) mutations, and performed phenotypic analysis involving more than 200 biochemical and disease traits. We enumerated 49,138 rare (<1% minor allele frequency) pLoF mutations. These pLoF mutations are estimated to knock out 1,317 genes, each in at least one participant. Homozygosity for pLoF mutations at PLA2G7 was associated with absent enzymatic activity of soluble lipoprotein-associated phospholipase A2; at CYP2F1, with higher plasma interleukin-8 concentrations; at TREH, with lower concentrations of apoB-containing lipoprotein subfractions; at either A3GALT2 or NRG4, with markedly reduced plasma insulin C-peptide concentrations; and at SLC9A3R1, with mediators of calcium and phosphate signalling. Heterozygous deficiency of APOC3 has been shown to protect against coronary heart disease; we identified APOC3 homozygous pLoF carriers in our cohort. We recruited these human knockouts and challenged them with an oral fat load. Compared with family members lacking the mutation, individuals with APOC3 knocked out displayed marked blunting of the usual post-prandial rise in plasma triglycerides. Overall, these observations provide a roadmap for a 'human knockout project', a systematic effort to understand the phenotypic consequences of complete disruption of genes in humans.

Intragenic DNA methylation prevents spurious transcription initiation.

In mammals, DNA methylation occurs mainly at CpG dinucleotides. Methylation of the promoter suppresses gene expression, but the functional role of gene-body DNA methylation in highly expressed genes has yet to be clarified. Here we show that, in mouse embryonic stem cells, Dnmt3b-dependent intragenic DNA methylation protects the gene body from spurious RNA polymerase II entry and cryptic transcription initiation. Using different genome-wide approaches, we demonstrate that this Dnmt3b function is dependent on its enzymatic activity and recruitment to the gene body by H3K36me3. Furthermore, the spurious transcripts can either be degraded by the RNA exosome complex or capped, polyadenylated, and delivered to the ribosome to produce aberrant proteins. Elongating RNA polymerase II therefore triggers an epigenetic crosstalk mechanism that involves SetD2, H3K36me3, Dnmt3b and DNA methylation to ensure the fidelity of gene transcription initiation, with implications for intragenic hypomethylation in cancer.

Genenames.org: the HGNC and VGNC resources in 2021.

The HUGO Gene Nomenclature Committee (HGNC) based at EMBL's European Bioinformatics Institute (EMBL-EBI) assigns unique symbols and names to human genes. There are over 42,000 approved gene symbols in our current database of which over 19 000 are for protein-coding genes. While we still update placeholder and problematic symbols, we are working towards stabilizing symbols where possible; over 2000 symbols for disease associated genes are now marked as stable in our symbol reports. All of our data is available at the HGNC website https://www.genenames.org. The Vertebrate Gene Nomenclature Committee (VGNC) was established to assign standardized nomenclature in line with human for vertebrate species lacking their own nomenclature committee. In addition to the previous VGNC core species of chimpanzee, cow, horse and dog, we now name genes in cat, macaque and pig. Gene groups have been added to VGNC and currently include two complex families: olfactory receptors (ORs) and cytochrome P450s (CYPs). In collaboration with specialists we have also named CYPs in species beyond our core set. All VGNC data is available at https://vertebrate.genenames.org/. This article provides an overview of our online data and resources, focusing on updates over the last two years.

Genes with High Network Connectivity Are Enriched for Disease Heritability.

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

Family Clustering of Autoimmune Vitiligo Results Principally from Polygenic Inheritance of Common Risk Alleles.

Vitiligo is an autoimmune disease that results in patches of depigmented skin and hair. Previous genome-wide association studies (GWASs) of vitiligo have identified 50 susceptibility loci. Variants at the associated loci are generally common and have individually small effects on risk. Most vitiligo cases are "simplex," where there is no family history of vitiligo, though occasional family clustering of vitiligo occurs, and some "multiplex" families report numerous close affected relatives. Here, we investigate whether simplex and multiplex vitiligo comprise different disease subtypes with different underlying genetic etiologies. We developed and compared the performance of several different vitiligo polygenic risk scores derived from GWAS data. By using the best-performing risk score, we find increased polygenic burden of risk alleles identified by GWAS in multiplex vitiligo cases relative to simplex cases. We additionally find evidence of polygenic transmission of common, low-effect-size risk alleles within multiplex-vitiligo-affected families. Our findings strongly suggest that family clustering of vitiligo involves a high burden of the same common, low-effect-size variants that are relevant in simplex cases. We furthermore find that a variant within the major histocompatibility complex (MHC) class II region contributes disproportionately more to risk in multiplex vitiligo cases than in simplex cases, supporting a special role for adaptive immune triggering in the etiology of multiplex cases. We suggest that genetic risk scores can be a useful tool in analyzing the genetic architecture of clinical disease subtypes and identifying subjects with unusual etiologies for further investigation.

Comparing Within- and Between-Family Polygenic Score Prediction.

Polygenic scores are a popular tool for prediction of complex traits. However, prediction estimates in samples of unrelated participants can include effects of population stratification, assortative mating, and environmentally mediated parental genetic effects, a form of genotype-environment correlation (rGE). Comparing genome-wide polygenic score (GPS) predictions in unrelated individuals with predictions between siblings in a within-family design is a powerful approach to identify these different sources of prediction. Here, we compared within- to between-family GPS predictions of eight outcomes (anthropometric, cognitive, personality, and health) for eight corresponding GPSs. The outcomes were assessed in up to 2,366 dizygotic (DZ) twin pairs from the Twins Early Development Study from age 12 to age 21. To account for family clustering, we used mixed-effects modeling, simultaneously estimating within- and between-family effects for target- and cross-trait GPS prediction of the outcomes. There were three main findings: (1) DZ twin GPS differences predicted DZ differences in height, BMI, intelligence, educational achievement, and ADHD symptoms; (2) target and cross-trait analyses indicated that GPS prediction estimates for cognitive traits (intelligence and educational achievement) were on average 60% greater between families than within families, but this was not the case for non-cognitive traits; and (3) much of this within- and between-family difference for cognitive traits disappeared after controlling for family socio-economic status (SES), suggesting that SES is a major source of between-family prediction through rGE mechanisms. These results provide insights into the patterns by which rGE contributes to GPS prediction, while ruling out confounding due to population stratification and assortative mating.

Genes for Good: Engaging the Public in Genetics Research via Social Media.

The Genes for Good study uses social media to engage a large, diverse participant pool in genetics research and education. Health history and daily tracking surveys are administered through a Facebook application, and participants who complete a minimum number of surveys are mailed a saliva sample kit ("spit kit") to collect DNA for genotyping. As of March 2019, we engaged >80,000 individuals, sent spit kits to >32,000 individuals who met minimum participation requirements, and collected >27,000 spit kits. Participants come from all 50 states and include a diversity of ancestral backgrounds. Rates of important chronic health indicators are consistent with those estimated for the general U.S. population using more traditional study designs. However, our sample is younger and contains a greater percentage of females than the general population. As one means of verifying data quality, we have replicated genome-wide association studies (GWASs) for exemplar traits, such as asthma, diabetes, body mass index (BMI), and pigmentation. The flexible framework of the web application makes it relatively simple to add new questionnaires and for other researchers to collaborate. We anticipate that the study sample will continue to grow and that future analyses may further capitalize on the strengths of the longitudinal data in combination with genetic information.

Yes-Associated Protein Is Crucial for Constitutive Androstane Receptor-Driven Hepatocyte Proliferation But Not for Induction of Drug Metabolism Genes in Mice.

BACKGROUND AND AIMS: Constitutive androstane receptor (CAR) agonists, such as 1,4-bis [2-(3,5-dichloropyridyloxy)] benzene (TCPOBOP), are known to cause robust hepatocyte proliferation and hepatomegaly in mice along with induction of drug metabolism genes without any associated liver injury. Yes-associated protein (Yap) is a key transcription regulator that tightly controls organ size, including that of liver. Our and other previous studies suggested increased nuclear localization and activation of Yap after TCPOBOP treatment in mice and the potential role of Yap in CAR-driven proliferative response. Here, we investigated a direct role of Yap in CAR-driven hepatomegaly and hepatocyte proliferation using hepatocyte-specific Yap-knockout (KO) mice. APPROACH AND RESULTS: Adeno-associated virus 8-thyroxine binding globulin promoter-Cre recombinase vector was injected to Yap-floxed mice for achieving hepatocyte-specific Yap deletion followed by TCPOBOP treatment. Yap deletion did not decrease protein expression of CAR or CAR-driven induction of drug metabolism genes (including cytochrome P450 [Cyp] 2b10, Cyp2c55, and UDP-glucuronosyltransferase 1a1 [Ugt1a1]). However, Yap deletion substantially reduced TCPOBOP-induced hepatocyte proliferation. TCPOBOP-driven cell cycle activation was disrupted in Yap-KO mice because of delayed (and decreased) induction of cyclin D1 and higher expression of p21, resulting in decreased phosphorylation of retinoblastoma protein. Furthermore, the induction of other cyclins, which are sequentially involved in progression through cell cycle (including cyclin E1, A2, and B1), and important mitotic regulators (such as Aurora B kinase and polo-like kinase 1) was remarkably reduced in Yap-KO mice. Microarray analysis revealed that 26% of TCPOBOP-responsive genes that were mainly related to proliferation, but not to drug metabolism, were altered by Yap deletion. Yap regulated these proliferation genes through alerting expression of Myc and forkhead box protein M1, two critical transcriptional regulators of CAR-mediated hepatocyte proliferation. CONCLUSIONS: Our study revealed an important role of Yap signaling in CAR-driven hepatocyte proliferation; however, CAR-driven induction of drug metabolism genes was independent of Yap.

Gene name errors: Lessons not learned.

Erroneous conversion of gene names into other dates and other data types has been a frustration for computational biologists for years. We hypothesized that such errors in supplementary files might diminish after a report in 2016 highlighting the extent of the problem. To assess this, we performed a scan of supplementary files published in PubMed Central from 2014 to 2020. Overall, gene name errors continued to accumulate unabated in the period after 2016. An improved scanning software we developed identified gene name errors in 30.9% (3,436/11,117) of articles with supplementary Excel gene lists; a figure significantly higher than previously estimated. This is due to gene names being converted not just to dates and floating-point numbers, but also to internal date format (five-digit numbers). These findings further reinforce that spreadsheets are ill-suited to use with large genomic data.

Extracting multi-way chromatin contacts from Hi-C data.

There is a growing realization that multi-way chromatin contacts formed in chromosome structures are fundamental units of gene regulation. However, due to the paucity and complexity of such contacts, it is challenging to detect and identify them using experiments. Based on an assumption that chromosome structures can be mapped onto a network of Gaussian polymer, here we derive analytic expressions for n-body contact probabilities (n > 2) among chromatin loci based on pairwise genomic contact frequencies available in Hi-C, and show that multi-way contact probability maps can in principle be extracted from Hi-C. The three-body (triplet) contact probabilities, calculated from our theory, are in good correlation with those from measurements including Tri-C, MC-4C and SPRITE. Maps of multi-way chromatin contacts calculated from our analytic expressions can not only complement experimental measurements, but also can offer better understanding of the related issues, such as cell-line dependent assemblies of multiple genes and enhancers to chromatin hubs, competition between long-range and short-range multi-way contacts, and condensates of multiple CTCF anchors.