A dive into the unknome.

We may never understand the function of all genes, findings by Freeman, Munro and colleagues suggest, unless we rethink our approaches. They make a thorough attempt at quantifying the unknownness of protein-coding genes and experimentally prove that many neglected genes hold the seed of important discoveries.

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.

The status of the human gene catalogue.

Scientists have been trying to identify every gene in the human genome since the initial draft was published in 2001. In the years since, much progress has been made in identifying protein-coding genes, currently estimated to number fewer than 20,000, with an ever-expanding number of distinct protein-coding isoforms. Here we review the status of the human gene catalogue and the efforts to complete it in recent years. Beside the ongoing annotation of protein-coding genes, their isoforms and pseudogenes, the invention of high-throughput RNA sequencing and other technological breakthroughs have led to a rapid growth in the number of reported non-coding RNA genes. For most of these non-coding RNAs, the functional relevance is currently unclear; we look at recent advances that offer paths forward to identifying their functions and towards eventually completing the human gene catalogue. Finally, we examine the need for a universal annotation standard that includes all medically significant genes and maintains their relationships with different reference genomes for the use of the human gene catalogue in clinical settings.

On the Decoupling of Evolutionary Changes in mRNA and Protein Levels.

Variation in gene expression across lineages is thought to explain much of the observed phenotypic variation and adaptation. The protein is closer to the target of natural selection but gene expression is typically measured as the amount of mRNA. The broad assumption that mRNA levels are good proxies for protein levels has been undermined by a number of studies reporting moderate or weak correlations between the two measures across species. One biological explanation for this discrepancy is that there has been compensatory evolution between the mRNA level and regulation of translation. However, we do not understand the evolutionary conditions necessary for this to occur nor the expected strength of the correlation between mRNA and protein levels. Here, we develop a theoretical model for the coevolution of mRNA and protein levels and investigate the dynamics of the model over time. We find that compensatory evolution is widespread when there is stabilizing selection on the protein level; this observation held true across a variety of regulatory pathways. When the protein level is under directional selection, the mRNA level of a gene and the translation rate of the same gene were negatively correlated across lineages but positively correlated across genes. These findings help explain results from comparative studies of gene expression and potentially enable researchers to disentangle biological and statistical hypotheses for the mismatch between transcriptomic and proteomic data.

RNA polymerase II associates with active genes during DNA replication.

The transcriptional machinery is thought to dissociate from DNA during replication. Certain proteins, termed epigenetic marks, must be transferred from parent to daughter DNA strands in order to maintain the memory of transcriptional states1,2. These proteins are believed to re-initiate rebuilding of chromatin structure, which ultimately recruits RNA polymerase II (Pol II) to the newly replicated daughter strands. It is believed that Pol II is recruited back to active genes only after chromatin is rebuilt3,4. However, there is little experimental evidence addressing the central questions of when and how Pol II is recruited back to the daughter strands and resumes transcription. Here we show that immediately after passage of the replication fork, Pol II in complex with other general transcription proteins and immature RNA re-associates with active genes on both leading and lagging strands of nascent DNA, and rapidly resumes transcription. This suggests that the transcriptionally active Pol II complex is retained in close proximity to DNA, with a Pol II-PCNA interaction potentially underlying this retention. These findings indicate that the Pol II machinery may not require epigenetic marks to be recruited to the newly synthesized DNA during the transition from DNA replication to resumption of transcription.

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.

Genome-wide mapping of signatures of selection using a high-density array identified candidate genes for growth traits and local adaptation in chickens.

BACKGROUND: Availability of single nucleotide polymorphism (SNP) genotyping arrays and progress in statistical analyses have allowed the identification of genomic regions and genes under selection in chicken. In this study, SNP data from the 600 K Affymetrix chicken array were used to detect signatures of selection in 23 local Italian chicken populations. The populations were categorized into four groups for comparative analysis based on live weight (heavy vs light) and geographical area (Northern vs Southern Italy). Putative signatures of selection were investigated by combining three extended haplotype homozygosity (EHH) statistical approaches to quantify excess of haplotype homozygosity within (iHS) and between (Rsb and XP-EHH) groups. Presence of runs of homozygosity (ROH) islands was also analysed for each group. RESULTS: After editing, 541 animals and 313,508 SNPs were available for statistical analyses. In total, 15 candidate genomic regions that are potentially under selection were detected among the four groups: eight within a group by iHS and seven by combining the results of Rsb and XP-EHH, which revealed divergent selection between the groups. The largest overlap between genomic regions identified to be under selection by the three approaches was on chicken chromosome 8. Twenty-one genomic regions were identified with the ROH approach but none of these overlapped with regions identified with the three EHH-derived statistics. Some of the identified regions under selection contained candidate genes with biological functions related to environmental stress, immune responses, and disease resistance, which indicate local adaptation of these chicken populations. CONCLUSIONS: Compared to commercial lines, local populations are predominantly reared as backyard chickens, and thus, may have developed stronger resistance to environmental challenges. Our results indicate that selection can play an important role in shaping signatures of selection in local chicken populations and can be a starting point to identify gene mutations that could have a useful role with respect to climate change.

HGCA2.0: An RNA-Seq Based Webtool for Gene Coexpression Analysis in Homo sapiens.

Genes with similar expression patterns in a set of diverse samples may be considered coexpressed. Human Gene Coexpression Analysis 2.0 (HGCA2.0) is a webtool which studies the global coexpression landscape of human genes. The website is based on the hierarchical clustering of 55,431 Homo sapiens genes based on a large-scale coexpression analysis of 3500 GTEx bulk RNA-Seq samples of healthy individuals, which were selected as the best representative samples of each tissue type. HGCA2.0 presents subclades of coexpressed genes to a gene of interest, and performs various built-in gene term enrichment analyses on the coexpressed genes, including gene ontologies, biological pathways, protein families, and diseases, while also being unique in revealing enriched transcription factors driving coexpression. HGCA2.0 has been successful in identifying not only genes with ubiquitous expression patterns, but also tissue-specific genes. Benchmarking showed that HGCA2.0 belongs to the top performing coexpression webtools, as shown by STRING analysis. HGCA2.0 creates working hypotheses for the discovery of gene partners or common biological processes that can be experimentally validated. It offers a simple and intuitive website design and user interface, as well as an API endpoint.

Identification of spatially variable genes with graph cuts.

Single-cell gene expression data with positional information is critical to dissect mechanisms and architectures of multicellular organisms, but the potential is limited by the scalability of current data analysis strategies. Here, we present scGCO, a method based on fast optimization of hidden Markov Random Fields with graph cuts to identify spatially variable genes. Comparing to existing methods, scGCO delivers a superior performance with lower false positive rate and improved specificity, while demonstrates a more robust performance in the presence of noises. Critically, scGCO scales near linearly with inputs and demonstrates orders of magnitude better running time and memory requirement than existing methods, and could represent a valuable solution when spatial transcriptomics data grows into millions of data points and beyond.

A mechanism for oxidative damage repair at gene regulatory elements.

Oxidative genome damage is an unavoidable consequence of cellular metabolism. It arises at gene regulatory elements by epigenetic demethylation during transcriptional activation1,2. Here we show that promoters are protected from oxidative damage via a process mediated by the nuclear mitotic apparatus protein NuMA (also known as NUMA1). NuMA exhibits genomic occupancy approximately 100 bp around transcription start sites. It binds the initiating form of RNA polymerase II, pause-release factors and single-strand break repair (SSBR) components such as TDP1. The binding is increased on chromatin following oxidative damage, and TDP1 enrichment at damaged chromatin is facilitated by NuMA. Depletion of NuMA increases oxidative damage at promoters. NuMA promotes transcription by limiting the polyADP-ribosylation of RNA polymerase II, increasing its availability and release from pausing at promoters. Metabolic labelling of nascent RNA identifies genes that depend on NuMA for transcription including immediate-early response genes. Complementation of NuMA-deficient cells with a mutant that mediates binding to SSBR, or a mitotic separation-of-function mutant, restores SSBR defects. These findings underscore the importance of oxidative DNA damage repair at gene regulatory elements and describe a process that fulfils this function.

Scientists isolate the genes that shape the human pelvis.

Embryonic tissue samples reveal how pelvis shape-primed for bipedalism-comes to life.

Divergent genomic trajectories predate the origin of animals and fungi.

Animals and fungi have radically distinct morphologies, yet both evolved within the same eukaryotic supergroup: Opisthokonta1,2. Here we reconstructed the trajectory of genetic changes that accompanied the origin of Metazoa and Fungi since the divergence of Opisthokonta with a dataset that includes four novel genomes from crucial positions in the Opisthokonta phylogeny. We show that animals arose only after the accumulation of genes functionally important for their multicellularity, a tendency that began in the pre-metazoan ancestors and later accelerated in the metazoan root. By contrast, the pre-fungal ancestors experienced net losses of most functional categories, including those gained in the path to Metazoa. On a broad-scale functional level, fungal genomes contain a higher proportion of metabolic genes and diverged less from the last common ancestor of Opisthokonta than did the gene repertoires of Metazoa. Metazoa and Fungi also show differences regarding gene gain mechanisms. Gene fusions are more prevalent in Metazoa, whereas a larger fraction of gene gains were detected as horizontal gene transfers in Fungi and protists, in agreement with the long-standing idea that transfers would be less relevant in Metazoa due to germline isolation3-5. Together, our results indicate that animals and fungi evolved under two contrasting trajectories of genetic change that predated the origin of both groups. The gradual establishment of two clearly differentiated genomic contexts thus set the stage for the emergence of Metazoa and Fungi.

Role of Actionable Genes in Pursuing a True Approach of Precision Medicine in Monogenic Diabetes.

Monogenic diabetes is a genetic disorder caused by one or more variations in a single gene. It encompasses a broad spectrum of heterogeneous conditions, including neonatal diabetes, maturity onset diabetes of the young (MODY) and syndromic diabetes, affecting 1-5% of patients with diabetes. Some of these variants are harbored by genes whose altered function can be tackled by specific actions ("actionable genes"). In suspected patients, molecular diagnosis allows the implementation of effective approaches of precision medicine so as to allow individual interventions aimed to prevent, mitigate or delay clinical outcomes. This review will almost exclusively concentrate on the clinical strategy that can be specifically pursued in carriers of mutations in "actionable genes", including ABCC8, KCNJ11, GCK, HNF1A, HNF4A, HNF1B, PPARG, GATA4 and GATA6. For each of them we will provide a short background on what is known about gene function and dysfunction. Then, we will discuss how the identification of their mutations in individuals with this form of diabetes, can be used in daily clinical practice to implement specific monitoring and treatments. We hope this article will help clinical diabetologists carefully consider who of their patients deserves timely genetic testing for monogenic diabetes.

Mutational Landscape Screening Through Comprehensive In Silico Analysis for Polycystic Ovarian Syndrome-Related Genes.

Polycystic ovary syndrome (PCOS) is a multifactorial endocrinopathy of indistinguishable etiopathogenesis that is liable to entail genetic and environmental machinery synergistically interacting with its phenotypic expression. It has been hypothesized that the environment secondarily interacts with genes to define the quantifiable phenotype in a primary, genetically determined, hyper-androgenic ovarian defect. The severity and prevalence of the disease are escalating due to uncontrolled diet and lifestyle, the influence of multiple environmental factors as well as genetic disorders. Many candidate genes have been identified to be one of the causes of PCOS. Different studies have been carried out to find the genetic correlation of PCOS. The mutational landscape analysis scans the entire genes for SNPs which usually occurs more frequently in patients and not in healthy individuals. In this study, an extensive computational analysis of all reported nsSNPs of the 27 selected PCOS-related genes was performed to infer the most pathogenic forms associated with PCOS. As a result, 28 genetic variants from 11 genes were predicted to be most harmful. Results of the present study can be useful for building an integrative genotype-phenotype database for further studies.

A Novel Noncanonical Splicing Mutation of ANOS1 Gene in Siblings with Kallmann Syndrome Identified by Whole-Exome Sequencing.

Kallmann syndrome (KS) is a rare genetic disorder that is characterized by idiopathic hypogonadotropic hypogonadism associated with anosmia. Genetic variants in ANOS1 gene are the most common mutations associated with X-linked recessive form of KS. Canonical ± 1 or 2 splice site variants in ANOS1 have been described to be responsible for KS. Here, we identified a novel noncanonical splice site variant (c.1062+4T>C) in ANOS1 gene in two siblings with KS by whole-exome sequencing (WES). Sanger sequencing showed this mutation was inherited from their mother, whose brother was a KS patient as well. Through the functional assay in vitro, we found that this mutation resulted in a 50-bp deletion of exon 7, which caused frameshift mutation leading to a premature termination of translation and a truncated anosmin-1 protein. Our results revealed that this noncanonical splice site variant is involved in KS. Thus, it is suggested that we should pay attention to the noncanonical splice site variants when using molecular genetic diagnostics of KS.

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.

Flimma: a federated and privacy-aware tool for differential gene expression analysis.

Aggregating transcriptomics data across hospitals can increase sensitivity and robustness of differential expression analyses, yielding deeper clinical insights. As data exchange is often restricted by privacy legislation, meta-analyses are frequently employed to pool local results. However, the accuracy might drop if class labels are inhomogeneously distributed among cohorts. Flimma ( https://exbio.wzw.tum.de/flimma/ ) addresses this issue by implementing the state-of-the-art workflow limma voom in a federated manner, i.e., patient data never leaves its source site. Flimma results are identical to those generated by limma voom on aggregated datasets even in imbalanced scenarios where meta-analysis approaches fail.

Identification of Feature Autophagy-Related Genes and DNA Methylation Profiles in Systemic Lupus Erythematosus Patients.

BACKGROUND The aim of this study was to identify feature autophagy-related genes (ARGs) in systemic lupus erythematosus (SLE), evaluate their diagnostic value, and further explore DNA methylation and expression levels in the pathogenesis of SLE. MATERIAL AND METHODS WGCNA was used to construct network and selected hub genes based on gene expression dataset GSE81622. ARGS were overlapped with hub genes, and feature ARGs were identified. A diagnostic model was established by these feature ARGs using LASSO. GSE96879 was used to analyze the methylation levels of feature ARGs. The expression and methylation levels of feature ARGs were verified using RT-PCR and methylation-specific PCR. RESULTS We found that 55 hub genes were highly connected to the red module of WGCNA, and ARGs were extracted from the Human Autophagy Database and the GO_AUTOPHAGY gene set. Overlapping of 55 hub gene with ARGs resulted in 18 feature ARGs. S100A8, MyD88, and NCR3 from the 18 feature ARGs showed higher good diagnostic value for SLE. Five differentially methylated positions locating to S100A8, MyD88, and NCR3 genes were identified from GSE96879. After validation tests, RT-PCR showed that gene expressions of MyD88 and S100A8 were increased in the PBMCs samples of SLE patients compared with healthy controls, whereas NCR3 was the opposite. MSP found that cg24898863 (S100A8) was hypomethylated, while cg27490128 (NCR3) was hypermethylated in the SLE group, and S100A8 and NCR3 methylation were positively correlated with their expressions. CONCLUSIONS Our present study identified the potential roles of feature ARGs in SLE diagnosis, and shows correlation among DNA methylation and gene expressions of these feature ARGs in SLE.

Haplotype-aware variant calling with PEPPER-Margin-DeepVariant enables high accuracy in nanopore long-reads.

Long-read sequencing has the potential to transform variant detection by reaching currently difficult-to-map regions and routinely linking together adjacent variations to enable read-based phasing. Third-generation nanopore sequence data have demonstrated a long read length, but current interpretation methods for their novel pore-based signal have unique error profiles, making accurate analysis challenging. Here, we introduce a haplotype-aware variant calling pipeline, PEPPER-Margin-DeepVariant, that produces state-of-the-art variant calling results with nanopore data. We show that our nanopore-based method outperforms the short-read-based single-nucleotide-variant identification method at the whole-genome scale and produces high-quality single-nucleotide variants in segmental duplications and low-mappability regions where short-read-based genotyping fails. We show that our pipeline can provide highly contiguous phase blocks across the genome with nanopore reads, contiguously spanning between 85% and 92% of annotated genes across six samples. We also extend PEPPER-Margin-DeepVariant to PacBio HiFi data, providing an efficient solution with superior performance over the current WhatsHap-DeepVariant standard. Finally, we demonstrate de novo assembly polishing methods that use nanopore and PacBio HiFi reads to produce diploid assemblies with high accuracy (Q35+ nanopore-polished and Q40+ PacBio HiFi-polished).

Bulk Gene Expression Deconvolution Reveals Infiltration of M2 Macrophages in Retinal Neovascularization.

Purpose: This study interrogated the transcriptional features and immune cellular landscape of the retinae of rats subjected to oxygen-induced retinopathy (OIR). Methods: Bulk RNA sequencing was performed with retinal RNA isolated from control and OIR rats. Gene set enrichment analysis (GSEA) was undertaken to identify gene sets associated with immune responses in retinal neovascularization. Bulk gene expression deconvolution analysis by CIBERSORTx was performed to identify immune cell types involved in retinal neovascularization, followed by functional enrichment analysis of differentially expressed genes (DEGs). Protein-protein interaction analysis was performed to predict the hub genes relevant to identified immune cell types. CIBERSORTx was applied to profile immune cell types in the macula of patients with both proliferative diabetic retinopathy (PDR) and diabetic macular edema using a public RNA-seq dataset. Results: Transcriptome analysis by GSEA revealed that the retina of OIR rats and patients with PDR is characterized by increased immunoregulatory interactions and complement cascade. Deconvolution analysis demonstrated that M2 macrophages infiltrate the retinae of OIR rats and patients with PDR. Functional enrichment analysis of DEGs in OIR rats showed that the dysregulated genes are related to leukocyte-mediated immunity and myeloid leukocyte activation. Downstream protein-protein interaction analysis revealed that several potential hub genes, including Ccl2, Itgam, and Tlr2, contribute to M2 macrophage infiltration in the ischemic retina. Conclusions: This study highlights application of the gene expression deconvolution tool to identify immune cell types in inflammatory ocular diseases with transcriptomes, providing a new approach to assess changes in immune cell types in diseased ocular tissues.