Harmonizing model organism data in the Alliance of Genome Resources

Computational strategies for cross-species knowledge transfer and translational biomedicine

Research organisms provide invaluable insights into human biology and diseases, serving as essential tools for functional experiments, disease modeling, and drug testing. However, evolutionary divergence between humans and research organisms hinders effective knowledge transfer across species. Here, we review state-of-the-art methods for computationally transferring knowledge across species, primarily focusing on methods that utilize transcriptome data and/or molecular networks. We introduce the term "agnology" to describe the functional equivalence of molecular components regardless of evolutionary origin, as this concept is becoming pervasive in integrative data-driven models where the role of evolutionary origin can become unclear. Our review addresses four key areas of information and knowledge transfer across species: (1) transferring disease and gene annotation knowledge, (2) identifying agnologous molecular components, (3) inferring equivalent perturbed genes or gene sets, and (4) identifying agnologous cell types. We conclude with an outlook on future directions and several key challenges that remain in cross-species knowledge transfer.

A Cost-effective, High-throughput, Highly Accurate Genotyping Method for Outbred Populations

Affordable sequencing and genotyping methods are essential for large scale genome-wide association studies. While genotyping microarrays and reference panels for imputation are available for human subjects, non-human model systems often lack such options. Our lab previously demonstrated an efficient and cost-effective method to genotype heterogeneous stock rats using double-digest genotyping-by-sequencing. However, low-coverage whole-genome sequencing offers an alternative method that has several advantages. Here, we describe a cost-effective, high-throughput, high-accuracy genotyping method for N/NIH heterogeneous stock rats that can use a combination of sequencing data previously generated by double-digest genotyping-by-sequencing and more recently generated by low-coverage whole-genome-sequencing data. Using double-digest genotyping-by-sequencing data from 5,745 heterogeneous stock rats (mean 0.21x coverage) and low-coverage whole-genome-sequencing data from 8,760 heterogeneous stock rats (mean 0.27x coverage), we can impute 7.32 million bi-allelic single-nucleotide polymorphisms with a concordance rate >99.76% compared to high-coverage (mean 33.26x coverage) whole-genome sequencing data for a subset of the same individuals. Our results demonstrate the feasibility of using sequencing data from double-digest genotyping-by-sequencing or low-coverage whole-genome-sequencing for accurate genotyping, and demonstrate techniques that may also be useful for other genetic studies in non-human subjects.

Sex-specific survival gene mutations are discovered as clinical predictors of clear cell renal cell carcinoma

Although sex differences have been reported in patients with clear cell renal cell carcinoma (ccRCC), biological sex has not received clinical attention and genetic differences between sexes are poorly understood. This study aims to identify sex-specific gene mutations and explore their clinical significance in ccRCC. We used data from The Cancer Genome Atlas-Kidney Renal Clear Cell Carcinoma (TCGA-KIRC), The Renal Cell Cancer-European Union (RECA-EU) and Korean-KIRC. A total of 68 sex-related genes were selected from TCGA-KIRC through machine learning, and 23 sex-specific genes were identified through verification using the three databases. Survival differences according to sex were identified in nine genes (ACSS3, ALG13, ASXL3, BAP1, JADE3, KDM5C, KDM6A, NCOR1P1, and ZNF449). Female-specific survival differences were found in BAP1 in overall survival (OS) (TCGA-KIRC, p = 0.004; RECA-EU, p = 0.002; and Korean-KIRC, p = 0.003) and disease-free survival (DFS) (TCGA-KIRC, p = 0.001 and Korean-KIRC, p = 0.000004), and NCOR1P1 in DFS (TCGA-KIRC, p = 0.046 and RECA-EU, p = 0.00003). Male-specific survival differences were found in ASXL3 (OS, p = 0.017 in TCGA-KIRC; and OS, p = 0.005 in RECA-EU) and KDM5C (OS, p = 0.009 in RECA-EU; and DFS, p = 0.016 in Korean-KIRC). These results suggest that biological sex may be an important predictor and sex-specific tailored treatment may improve patient care in ccRCC.

Improving laboratory animal genetic reporting: LAG-R guidelines

The biomedical research community addresses reproducibility challenges in animal studies through standardized nomenclature, improved experimental design, transparent reporting, data sharing, and centralized repositories. The ARRIVE guidelines outline documentation standards for laboratory animals in experiments, but genetic information is often incomplete. To remedy this, we propose the Laboratory Animal Genetic Reporting (LAG-R) framework. LAG-R aims to document animals' genetic makeup in scientific publications, providing essential details for replication and appropriate model use. While verifying complete genetic compositions may be impractical, better reporting and validation efforts enhance reliability of research. LAG-R standardization will bolster reproducibility, peer review, and overall scientific rigor.

Cellular and transcriptional profiles of peripheral blood mononuclear cells pre-vaccination predict immune response to preventative MUC1 vaccine

A single arm trial (NCT007773097) and a double-blind, placebo controlled randomized trial ( NCT02134925 ) were conducted in individuals with a history of advanced colonic adenoma to test the safety and immunogenicity of the MUC1 tumor antigen vaccine and its potential to prevent new adenomas. These were the first two trials of a non-viral cancer vaccine administered in the absence of cancer. The vaccine was safe and strongly immunogenic in 43% (NCT007773097) and 25% ( NCT02134925 ) of participants. The lack of response in a significant number of participants suggested, for the first time, that even in a premalignant setting, the immune system may have already been exposed to some level of suppression previously reported only in cancer. Single-cell RNA-sequencing (scRNA-seq) on banked pre-vaccination peripheral blood mononuclear cells (PBMCs) from 16 immune responders and 16 non-responders identified specific cell types, genes, and pathways of a productive vaccine response. Responders had a significantly higher percentage of CD4+ naive T cells pre-vaccination, but a significantly lower percentage of CD8+ T effector memory (TEM) cells and CD16+ monocytes. Differential gene expression (DGE) and transcription factor inference analysis showed a higher level of expression of T cell activation genes, such as Fos and Jun, in CD4+ naive T cells, and pathway analysis showed enriched signaling activity in responders. Furthermore, Bayesian network analysis suggested that these genes were mechanistically connected to response. Our analyses identified several immune mechanisms and candidate biomarkers to be further validated as predictors of immune responses to a preventative cancer vaccine that could facilitate selection of individuals likely to benefit from a vaccine or be used to improve vaccine responses.

Functional characterization of archaic-specific variants in mitonuclear genes: insights from comparative analysis in S. cerevisiae

Neanderthal and Denisovan hybridisation with modern humans has generated a non-random genomic distribution of introgressed regions, the result of drift and selection dynamics. Cross-species genomic incompatibility and more efficient removal of slightly deleterious archaic variants have been proposed as selection-based processes involved in the post-hybridisation purge of archaic introgressed regions. Both scenarios require the presence of functionally different alleles across Homo species onto which selection operated differently according to which populations hosted them, but only a few of these variants have been pinpointed so far. In order to identify functionally divergent archaic variants removed in humans, we focused on mitonuclear genes, which are underrepresented in the genomic landscape of archaic humans. We searched for non-synonymous, fixed, archaic-derived variants present in mitonuclear genes, rare or absent in human populations. We then compared the functional impact of archaic and human variants in the model organism Saccharomyces cerevisiae. Notably, a variant within the mitochondrial tyrosyl-tRNA synthetase 2 (YARS2) gene exhibited a significant decrease in respiratory activity and a substantial reduction of Cox2 levels, a proxy for mitochondrial protein biosynthesis, coupled with the accumulation of the YARS2 protein precursor and a lower amount of mature enzyme. Our work suggests that this variant is associated with mitochondrial functionality impairment, thus contributing to the purging of archaic introgression in YARS2. While different molecular mechanisms may have impacted other mitonuclear genes, our approach can be extended to the functional screening of mitonuclear genetic variants present across species and populations.

A Novel Gene Fusion YLPM1::PRKD1 Identified in a Cribriform Subtype of Polymorphous Adenocarcinoma

Cribriform adenocarcinoma of the salivary gland (CASG) is an entity that is currently classified under polymorphous adenocarcinoma (PAC), cribriform subtype per the 2022 WHO classification of head and neck tumours. There is debate about whether CASG should be considered a separate diagnostic entity, as CASG differs from conventional PAC in anatomic site, clinical behaviors, and molecular patterns. Herein we describe a challenging and unique case which shares histologic and behavioral features between CASG and conventional PAC with a YLPM1::PRKD1 rearrangement not previously reported in the literature.

WormBase 2024: status and transitioning to Alliance infrastructure

WormBase has been the major repository and knowledgebase of information about the genome and genetics of Caenorhabditis elegans and other nematodes of experimental interest for over 2 decades. We have 3 goals: to keep current with the fast-paced C. elegans research, to provide better integration with other resources, and to be sustainable. Here, we discuss the current state of WormBase as well as progress and plans for moving core WormBase infrastructure to the Alliance of Genome Resources (the Alliance). As an Alliance member, WormBase will continue to interact with the C. elegans community, develop new features as needed, and curate key information from the literature and large-scale projects.

Mouse Genome Informatics: an integrated knowledgebase system for the laboratory mouse

Mouse Genome Informatics (MGI) is a federation of expertly curated information resources designed to support experimental and computational investigations into genetic and genomic aspects of human biology and disease using the laboratory mouse as a model system. The Mouse Genome Database (MGD) and the Gene Expression Database (GXD) are core MGI databases that share data and system architecture. MGI serves as the central community resource of integrated information about mouse genome features, variation, expression, gene function, phenotype, and human disease models acquired from peer-reviewed publications, author submissions, and major bioinformatics resources. To facilitate integration and standardization of data, biocuration scientists annotate using terms from controlled metadata vocabularies and biological ontologies (e.g. Mammalian Phenotype Ontology, Mouse Developmental Anatomy, Disease Ontology, Gene Ontology, etc.), and by applying international community standards for gene, allele, and mouse strain nomenclature. MGI serves basic scientists, translational researchers, and data scientists by providing access to FAIR-compliant data in both human-readable and compute-ready formats. The MGI resource is accessible at https://informatics.jax.org. Here, we present an overview of the core data types represented in MGI and highlight recent enhancements to the resource with a focus on new data and functionality for MGD and GXD.

FlyBase: updates to the Drosophila genes and genomes database

FlyBase (flybase.org) is a model organism database and knowledge base about Drosophila melanogaster, commonly known as the fruit fly. Researchers from around the world rely on the genetic, genomic, and functional information available in FlyBase, as well as its tools to view and interrogate these data. In this article, we describe the latest developments and updates to FlyBase. These include the introduction of single-cell RNA sequencing data, improved content and display of functional information, updated orthology pipelines, new chemical reports, and enhancements to our outreach resources.

Updates to the Alliance of Genome Resources central infrastructure

The Alliance of Genome Resources (Alliance) is an extensible coalition of knowledgebases focused on the genetics and genomics of intensively studied model organisms. The Alliance is organized as individual knowledge centers with strong connections to their research communities and a centralized software infrastructure, discussed here. Model organisms currently represented in the Alliance are budding yeast, Caenorhabditis elegans, Drosophila, zebrafish, frog, laboratory mouse, laboratory rat, and the Gene Ontology Consortium. The project is in a rapid development phase to harmonize knowledge, store it, analyze it, and present it to the community through a web portal, direct downloads, and application programming interfaces (APIs). Here, we focus on developments over the last 2 years. Specifically, we added and enhanced tools for browsing the genome (JBrowse), downloading sequences, mining complex data (AllianceMine), visualizing pathways, full-text searching of the literature (Textpresso), and sequence similarity searching (SequenceServer). We enhanced existing interactive data tables and added an interactive table of paralogs to complement our representation of orthology. To support individual model organism communities, we implemented species-specific "landing pages" and will add disease-specific portals soon; in addition, we support a common community forum implemented in Discourse software. We describe our progress toward a central persistent database to support curation, the data modeling that underpins harmonization, and progress toward a state-of-the-art literature curation system with integrated artificial intelligence and machine learning (AI/ML).

Massively parallel reporter assay confirms regulatory potential of hQTLs and reveals important variants in lupus and other autoimmune diseases

We designed a massively parallel reporter assay (MPRA) in an Epstein-Barr virus transformed B cell line to directly characterize the potential for histone post-translational modifications, i.e., histone quantitative trait loci (hQTLs), expression QTLs (eQTLs), and variants on systemic lupus erythematosus (SLE) and autoimmune (AI) disease risk haplotypes to modulate regulatory activity in an allele-dependent manner. Our study demonstrates that hQTLs, as a group, are more likely to modulate regulatory activity in an MPRA compared with other variant classes tested, including a set of eQTLs previously shown to interact with hQTLs and tested AI risk variants. In addition, we nominate 17 variants (including 11 previously unreported) as putative causal variants for SLE and another 14 for various other AI diseases, prioritizing these variants for future functional studies in primary and immortalized B cells. Thus, we uncover important insights into the mechanistic relationships among genotype, epigenetics, and gene expression in SLE and AI disease phenotypes.

Potential of Viruses as Environmental Etiological Factors for Non-Syndromic Orofacial Clefts

In this study, we analyzed the potential of viral infections in the species Homo sapiens as environmental causes of orofacial clefts (OFCs). A scoring system was adapted for qualitatively assessing the potential of viruses to cause cleft lip and/or palate (CL/P). This assessment considered factors such as information from the literature, nucleotide and amino acid similarities, and the presence of Endogenous Viral Elements (EVEs). The analysis involved various algorithm packages within Basic Local Alignment Search Tool 2.13.0 software and databases from the National Center for Biotechnology Information and the International Committee on Taxonomy of Viruses. Twenty significant viral species using different biosynthesis strategies were identified: Human coronavirus NL63, Rio Negro virus, Alphatorquevirus homin9, Brisavirus, Cosavirus B, Torque teno mini virus 4, Bocaparvovirus primate2, Human coronavirus HKU1, Monkeypox virus, Mammarenavirus machupoense, Volepox virus, Souris mammarenavirus, Gammapapillomavirus 7, Betainfluenzavirus influenzae, Lymphocytic choriomeningitis mammarenavirus, Ledantevirus kern, Gammainfluenzavirus influenzae, Betapolyomavirus hominis, Vesiculovirus perinet, and Cytomegalovirus humanbeta5. The evident viral etiological potential in relation to CL/P varies depending on the Baltimore class to which the viral species belongs. Given the multifactorial nature of CL/P, this relationship appears to be dynamic.

FAIR Header Reference genome: a TRUSTworthy standard

The lack of interoperable data standards among reference genome data-sharing platforms inhibits cross-platform analysis while increasing the risk of data provenance loss. Here, we describe the FAIR bioHeaders Reference genome (FHR), a metadata standard guided by the principles of Findability, Accessibility, Interoperability and Reuse (FAIR) in addition to the principles of Transparency, Responsibility, User focus, Sustainability and Technology. The objective of FHR is to provide an extensive set of data serialisation methods and minimum data field requirements while still maintaining extensibility, flexibility and expressivity in an increasingly decentralised genomic data ecosystem. The effort needed to implement FHR is low; FHR's design philosophy ensures easy implementation while retaining the benefits gained from recording both machine and human-readable provenance.

The protein tyrosine phosphatase PPH-7 is required for fertility and embryonic development in C. elegans at elevated temperatures

Post-translational modifications are key in the regulation of activity, structure, localization, and stability of most proteins in eukaryotes. Phosphorylation is potentially the most studied post-translational modification, also due to its reversibility and thereby the regulatory role this modification often plays. While most research attention was focused on kinases in the past, phosphatases remain understudied, most probably because the addition and presence of the modification is more easily studied than its removal and absence. Here, we report the identification of an uncharacterized protein tyrosine phosphatase PPH-7 in C. elegans, a member of the evolutionary conserved PTPN family of phosphatases. Lack of PPH-7 function led to reduction of fertility and embryonic lethality at elevated temperatures. Proteomics revealed changes in the regulation of targets of the von Hippel-Lindau (VHL) E3 ligase, suggesting a potential role for PPH-7 in the regulation of VHL.

Comparative genomic analysis of uropathogenic Escherichia coli strains from women with recurrent urinary tract infection

Introduction

Recurrent urinary tract infections (RUTIs) caused by uropathogenic Escherichia coli are costly public health problems impacting patients' quality of life.

Aim

In this work, a comparative genomics analysis of three clinical RUTI strains isolated from bladder biopsy specimens was performed.

Materials and methods

One hundred seventy-two whole genomes of urinary tract E. coli strains were selected from the NCBI database. The search for virulence factors, fitness genes, regions of interest, and genetic elements associated with resistance was manually carried out. The phenotypic characterization of antibiotic resistance, haemolysis, motility, and biofilm formation was performed. Moreover, adherence and invasion assays with human bladder HTB-5 cells, and transmission electron microscopy (TEM) were performed.

Results

The UTI-1_774U and UTI-3_455U/ST1193 strains were associated with the extraintestinal pathotypes, and the UTI-2_245U/ST295 strain was associated with the intestinal pathotype, according to a phylogenetic analysis of 172 E. coli urinary strains. The three RUTI strains were of clinical, epidemiological, and zoonotic relevance. Several resistance genes were found within the plasmids of these strains, and a multidrug resistance phenotype was revealed. Other virulence genes associated with CFT073 were not identified in the three RUTI strains (genes for type 1 and P fimbriae, haemolysin hlyA, and sat toxin). Quantitative adherence analysis showed that UTI-1_774U was significantly (p < 0.0001) more adherent to human bladder HTB-5 cells. Quantitative invasion analysis showed that UTI-2_245U was significantly more invasive than the control strains. No haemolysis or biofilm activity was detected in the three RUTI strains. The TEM micrographs showed the presence of short and thin fimbriae only in the UTI-2_245U strain.

Conclusion

The high variability and genetic diversity of the RUTI strains indicate that are a mosaic of virulence, resistance, and fitness genes that could promote recurrence in susceptible patients.

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.

Integrating non-mammalian model organisms in the diagnosis of rare genetic diseases in humans

Next-generation sequencing technology has rapidly accelerated the discovery of genetic variants of interest in individuals with rare diseases. However, showing that these variants are causative of the disease in question is complex and may require functional studies. Use of non-mammalian model organisms - mainly fruitflies (Drosophila melanogaster), nematode worms (Caenorhabditis elegans) and zebrafish (Danio rerio) - enables the rapid and cost-effective assessment of the effects of gene variants, which can then be validated in mammalian model organisms such as mice and in human cells. By probing mechanisms of gene action and identifying interacting genes and proteins in vivo, recent studies in these non-mammalian model organisms have facilitated the diagnosis of numerous genetic diseases and have enabled the screening and identification of therapeutic options for patients. Studies in non-mammalian model organisms have also shown that the biological processes underlying rare diseases can provide insight into more common mechanisms of disease and the biological functions of genes. Here, we discuss the opportunities afforded by non-mammalian model organisms, focusing on flies, worms and fish, and provide examples of their use in the diagnosis of rare genetic diseases.

Exploring the Regulation and Function of Rpl3l in the Development of Early-Onset Dilated Cardiomyopathy and Congestive Heart Failure Using Systems Genetics Approach

BACKGROUND

Cardiomyopathies, diseases affecting the myocardium, are common causes of congestive heart failure (CHF) and sudden cardiac death. Recently, biallelic variants in ribosomal protein L3-like (RPL3L) have been reported to be associated with severe neonatal dilated cardiomyopathy (DCM) and CHF. This study employs a systems genetics approach to gain understanding of the regulatory mechanisms underlying the role of RPL3L in DCM.

METHODS

Genetic correlation, expression quantitative trait loci (eQTL) mapping, differential expression analysis and comparative functional analysis were performed using cardiac gene expression data from the patients and murine genetic reference populations (GRPs) of BXD mice (recombinant inbred strains from a cross of C57BL/6J and DBA/2J mice). Additionally, immune infiltration analysis was performed to understand the relationship between DCM, immune cells and RPL3L expression.

RESULTS

Systems genetics analysis identified high expression of Rpl3l mRNA, which ranged from 11.31 to 12.16 across murine GRPs of BXD mice, with an ~1.8-fold difference. Pathways such as "diabetic cardiomyopathy", "focal adhesion", "oxidative phosphorylation" and "DCM" were significantly associated with Rpl3l. eQTL mapping suggested Myl4 (Chr 11) and Sdha (Chr 13) as the upstream regulators of Rpl3l. The mRNA expression of Rpl3l, Myl4 and Sdha was significantly correlated with multiple echocardiography traits in BXD mice. Immune infiltration analysis revealed a significant association of RPL3L and SDHA with seven immune cells (CD4, CD8-naive T cell, CD8 T cell, macrophages, cytotoxic T cell, gamma delta T cell and exhausted T cell) that were also differentially infiltrated between heart samples obtained from DCM patients and normal individuals.

CONCLUSIONS

RPL3L is highly expressed in the heart tissue of humans and mice. Expression of Rpl3l and its upstream regulators, Myl4 and Sdha, correlate with multiple cardiac function traits in murine GRPs of BXD mice, while RPL3L and SDHA correlate with immune cell infiltration in DCM patient hearts, suggesting important roles for RPL3L in DCM and CHF pathogenesis via immune inflammation, necessitating experimental validations of Myl4 and Sdha in Rpl3l regulation.

John N, Zaidi F, Doyle F, Fasullo M. High-throughput screening of the Saccharomyces cerevisiae genome for 2-amino-3-methylimidazo [4,5-f] quinoline resistance identifies colon cancer-associated genes

Heterocyclic aromatic amines (HAAs) are potent carcinogenic agents found in charred meats and cigarette smoke. However, few eukaryotic resistance genes have been identified. We used Saccharomyces cerevisiae (budding yeast) to identify genes that confer resistance to 2-amino-3-methylimidazo[4,5-f] quinoline (IQ). CYP1A2 and NAT2 activate IQ to become a mutagenic nitrenium compound. Deletion libraries expressing human CYP1A2 and NAT2 or no human genes were exposed to either 400 or 800 µM IQ for 5 or 10 generations. DNA barcodes were sequenced using the Illumina HiSeq 2500 platform and statistical significance was determined for exactly matched barcodes. We identified 424 ORFs, including 337 genes of known function, in duplicate screens of the "humanized" collection for IQ resistance; resistance was further validated for a select group of 51 genes by growth curves, competitive growth, or trypan blue assays. Screens of the library not expressing human genes identified 143 ORFs conferring resistance to IQ per se. Ribosomal protein and protein modification genes were identified as IQ resistance genes in both the original and "humanized" libraries, while nitrogen metabolism, DNA repair, and growth control genes were also prominent in the "humanized" library. Protein complexes identified included the casein kinase 2 (CK2) and histone chaperone (HIR) complex. Among DNA Repair and checkpoint genes, we identified those that function in postreplication repair (RAD18, UBC13, REV7), base excision repair (NTG1), and checkpoint signaling (CHK1, PSY2). These studies underscore the role of ribosomal protein genes in conferring IQ resistance, and illuminate DNA repair pathways for conferring resistance to activated IQ.

A scan of all coding region variants of the human genome, identifies 13q12.2-rs9579139 and 15q24.1-rs2277598 as novel risk loci for pancreatic ductal adenocarcinoma

Coding sequence variants comprise a small fraction of the germline genetic variability of the human genome. However, they often cause deleterious change in protein function and are therefore associated with pathogenic phenotypes. To identify novel pancreatic ductal adenocarcinoma (PDAC) risk loci, we carried out a complete scan of all common missense and synonymous SNPs and analysed them in a case-control study comprising four different populations, for a total of 14 538 PDAC cases and 190 657 controls. We observed a statistically significant association between 13q12.2-rs9581957-T and PDAC risk (P = 2.46 × 10-9), that is in linkage disequilibrium (LD) with a deleterious missense variant (rs9579139) of the URAD gene. Recent findings suggest that this gene is active in peroxisomes. Considering that peroxisomes have a key role as molecular scavengers, especially in eliminating reactive oxygen species, a malfunctioning URAD protein might expose the cell to a higher load of potentially DNA damaging molecules and therefore increase PDAC risk. The association was observed in individuals of European and Asian ethnicity. We also observed the association of the missense variant 15q24.1-rs2277598-T, that belongs to BBS4 gene, with increased PDAC risk (P = 1.53 × 10-6). rs2277598 is associated with body mass index and is in LD with diabetes susceptibility loci. In conclusion, we identified two missense variants associated with the risk of developing PDAC independently from the ethnicity highlighting the importance of conducting reanalysis of genome-wide association studies (GWASs) in light of functional data.

Integrating spatial and single-cell transcriptomics data using deep generative models with SpatialScope

The rapid emergence of spatial transcriptomics (ST) technologies is revolutionizing our understanding of tissue spatial architecture and biology. Although current ST methods, whether based on next-generation sequencing (seq-based approaches) or fluorescence in situ hybridization (image-based approaches), offer valuable insights, they face limitations either in cellular resolution or transcriptome-wide profiling. To address these limitations, we present SpatialScope, a unified approach integrating scRNA-seq reference data and ST data using deep generative models. With innovation in model and algorithm designs, SpatialScope not only enhances seq-based ST data to achieve single-cell resolution, but also accurately infers transcriptome-wide expression levels for image-based ST data. We demonstrate SpatialScope's utility through simulation studies and real data analysis from both seq-based and image-based ST approaches. SpatialScope provides spatial characterization of tissue structures at transcriptome-wide single-cell resolution, facilitating downstream analysis, including detecting cellular communication through ligand-receptor interactions, localizing cellular subtypes, and identifying spatially differentially expressed genes.

Systems genetics approaches for understanding complex traits with relevance for human disease

Quantitative traits are often complex because of the contribution of many loci, with further complexity added by environmental factors. In medical research, systems genetics is a powerful approach for the study of complex traits, as it integrates intermediate phenotypes, such as RNA, protein, and metabolite levels, to understand molecular and physiological phenotypes linking discrete DNA sequence variation to complex clinical and physiological traits. The primary purpose of this review is to describe some of the resources and tools of systems genetics in humans and rodent models, so that researchers in many areas of biology and medicine can make use of the data.

angptl4 gene expression as a marker of adaptive homeostatic response to social isolation across the lifespan in zebrafish

Social isolation has detrimental health effects, but the underlying mechanisms are unclear. Here, we investigated the impact of 2 weeks of isolation on behavior and gene expression in the central nervous system at different life stages of zebrafish. Results showed that socially deprived young adult zebrafish experienced increased anxiety, accompanied by changes in gene expression. Most gene expression patterns returned to normal within 24 hours of reintroduction to a social environment, except angptl4, which was upregulated after reintroduction, suggesting an adaptive mechanism. Similarly, aging zebrafish displayed heightened anxiety and increased central nervous system expression of angptl4 during isolation, but effects were reversed upon reintroduction to a social group. The findings imply that angptl4 plays a homeostatic role in response to social isolation, which varies across the lifespan. The study emphasizes the importance of social interactions for psychological well-being and highlights the negative consequences of isolation, especially in older individuals. Further research may unravel how social isolation affects angptl4 expression and its developmental and aging effects.

The NIH Comparative Genomics Resource: addressing the promises and challenges of comparative genomics on human health

Comparative genomics is the comparison of genetic information within and across organisms to understand the evolution, structure, and function of genes, proteins, and non-coding regions (Sivashankari and Shanmughavel, Bioinformation 1:376-8, 2007). Advances in sequencing technology and assembly algorithms have resulted in the ability to sequence large genomes and provided a wealth of data that are being used in comparative genomic analyses. Comparative analysis can be leveraged to systematically explore and evaluate the biological relationships and evolution between species, aid in understanding the structure and function of genes, and gain a better understanding of disease and potential drug targets. As our knowledge of genetics expands, comparative genomics can help identify emerging model organisms among a broader span of the tree of life, positively impacting human health. This impact includes, but is not limited to, zoonotic disease research, therapeutics development, microbiome research, xenotransplantation, oncology, and toxicology. Despite advancements in comparative genomics, new challenges have arisen around the quantity, quality assurance, annotation, and interoperability of genomic data and metadata. New tools and approaches are required to meet these challenges and fulfill the needs of researchers. This paper focuses on how the National Institutes of Health (NIH) Comparative Genomics Resource (CGR) can address both the opportunities for comparative genomics to further impact human health and confront an increasingly complex set of challenges facing researchers.

A Young Boy with 21q21.1 Microdeletion Showing Speech Delay, Spastic Diplegia, and MRI Abnormalities: Original Case Report

Chromosome 21q deletion syndrome is a rare disorder affecting the long arm of chromosome 21 and manifesting with wide phenotypic features depending on the size and position of the deleted region. In the syndrome, three distinct deleted regions have been distinguished: region 1, from the centromere to approximately 31.2 Mb (21q11.2-q22.11); region 2, from 31.2 to 36 Mb (21q22.11-q22.12); and region 3, from 36 to 37.5 Mb to the telomere (21q22.12-q22.3). The clinical features are highly variable manifesting with mild, poorly recognizable signs or with severe symptoms including craniofacial dysmorphism, growth failure, developmental delay, behavioral/affective abnormalities, and systemic malformations. We report here the case of a young boy with speech delay, mild spastic diplegia, and brain anomalies on magnetic resonance imaging (MRI). The genetic analysis displayed a microdeletion of the long arm of chromosome 21 approximately extending up to 1.08 Mb. Clinical presentation of the patient and cases of 21q21 deletion reported by the literature are discussed.

Prognostic value of GLCE and infiltrating immune cells in Ewing sarcoma

Background

The prognostic value of D-glucuronyl C5-epimerase (GLCE) and mast cell infiltration in Ewing sarcoma (ES) has not been well specified and highlighted, which may facilitate survival prediction and treatment.

Methods

Several qualified datasets were downloaded from the GEO website. Common differentially expressed genes between normal subjects and ES patients in GSE17679, GSE45544, and GSE68776 were identified and screened by multiple algorithms to find hub genes with prognostic value. The prognostic value of 64 infiltrating cells was also explored. A prognostic model was established and then validated with GSE63155 and GSE63156. Finally, functional analysis was performed.

Results

GLCE and mast cell infiltration were screened as two indicators for a prognostic model. The Kaplan‒Meier analysis showed that patients in the low GLCE expression, mast cell infiltration and risk score groups had poorer outcomes than patients in the high GLCE expression, mast cell infiltration and risk score groups, both in the training and validation sets. Scatter plots and heatmaps also indicated the same results. The concordance indices and calibration analyses indicated a high prediction accuracy of the model in the training and validation sets. The time-dependent receiver operating characteristic analyses suggested high sensitivity and specificity of the model, with area under the curve values between 0.76 and 0.98. The decision curve analyses suggested a significantly higher net benefit by the model than the treat-all and treat-none strategies. Functional analyses suggested that glycosaminoglycan biosynthesis-heparan sulfate/heparin, the cell cycle and microRNAs in cancer were upregulated in ES patients.

Conclusions

GLCE and mast cell infiltration are potential prognostic indicators in ES. GLCE may affect the proliferation, angiogenesis and metastasis of ES by affecting the biosynthesis of heparan sulfate and heparin.

The Ontology of Biological Attributes (OBA)-computational traits for the life sciences

Existing phenotype ontologies were originally developed to represent phenotypes that manifest as a character state in relation to a wild-type or other reference. However, these do not include the phenotypic trait or attribute categories required for the annotation of genome-wide association studies (GWAS), Quantitative Trait Loci (QTL) mappings or any population-focussed measurable trait data. The integration of trait and biological attribute information with an ever increasing body of chemical, environmental and biological data greatly facilitates computational analyses and it is also highly relevant to biomedical and clinical applications. The Ontology of Biological Attributes (OBA) is a formalised, species-independent collection of interoperable phenotypic trait categories that is intended to fulfil a data integration role. OBA is a standardised representational framework for observable attributes that are characteristics of biological entities, organisms, or parts of organisms. OBA has a modular design which provides several benefits for users and data integrators, including an automated and meaningful classification of trait terms computed on the basis of logical inferences drawn from domain-specific ontologies for cells, anatomical and other relevant entities. The logical axioms in OBA also provide a previously missing bridge that can computationally link Mendelian phenotypes with GWAS and quantitative traits. The term components in OBA provide semantic links and enable knowledge and data integration across specialised research community boundaries, thereby breaking silos.

Revisiting Schistosoma mansoni Micro-Exon Gene (MEG) Protein Family: A Tour into Conserved Motifs and Annotation

Genome sequencing of the human parasite Schistosoma mansoni revealed an interesting gene superfamily, called micro-exon gene (meg), that encodes secreted MEG proteins. The genes are composed of short exons (3-81 base pairs) regularly interspersed with long introns (up to 5 kbp). This article recollects 35 S. mansoni specific meg genes that are distributed over 7 autosomes and one pair of sex chromosomes and that code for at least 87 verified MEG proteins. We used various bioinformatics tools to produce an optimal alignment and propose a phylogenetic analysis. This work highlighted intriguing conserved patterns/motifs in the sequences of the highly variable MEG proteins. Based on the analyses, we were able to classify the verified MEG proteins into two subfamilies and to hypothesize their duplication and colonization of all the chromosomes. Together with motif identification, we also proposed to revisit MEGs' common names and annotation in order to avoid duplication, to help the reproducibility of research results and to avoid possible misunderstandings.

A ligand-receptor interactome atlas of the zebrafish

Studies in zebrafish can unravel the functions of cellular communication and thus identify novel bench-to-bedside drugs targeting cellular communication signaling molecules. Due to the incomplete annotation of zebrafish proteome, the knowledge of zebrafish receptors, ligands, and tools to explore their interactome is limited. To address this gap, we de novo predicted the cellular localization of zebrafish reference proteome using deep learning algorithm. We combined the predicted and existing annotations on cellular localization of zebrafish proteins and created repositories of zebrafish ligands, membrane receptome, and interactome as well as associated diseases and targeting drugs. Unlike other tools, our interactome atlas is based on both the physical interaction data of zebrafish proteome and existing human ligand-receptor pair databases. The resources are available as R and Python scripts. DanioTalk provides a novel resource for researchers interested in targeting cellular communication in zebrafish, as we demonstrate in applications studying synapse and axo-glial interactome. DanioTalk methodology can be applied to build and explore the ligand-receptor atlas of other non-mammalian model organisms.

LINKIN-associated proteins necessary for tissue integrity during collective cell migration

Cell adhesion plays essential roles in almost every aspect of metazoan biology. LINKIN (Human: ITFG1, Caenorhabditis elegans: lnkn-1) is a conserved transmembrane protein that has been identified to be necessary for tissue integrity during migration. In C. elegans, loss of lnkn-1 results in the detachment of the lead migratory cell from the rest of the developing male gonad. Previously, three interactors of ITFG1/lnkn-1 - RUVBL1/ruvb-1, RUVBL2/ruvb-2, and alpha-tubulin - were identified by immunoprecipitation-mass spectrometry (IP-MS) analysis using human HEK293T cells and then validated in the nematode male gonad. The ITFG1-RUVBL1 interaction has since been independently validated in a breast cancer cell line model that also implicates the involvement of the pair in metastasis. Here, we showed that epitope-tagged ITFG1 localized to the cell surface of MDA-MB-231 breast cancer cells. Using IP-MS analysis, we identified a new list of potential interactors of ITFG1. Loss-of-function analysis of their C. elegans orthologs found that three of the interactors - ATP9A/tat-5, NME1/ndk-1, and ANAPC2/apc-2 - displayed migratory detachment phenotypes similar to that of lnkn-1. Taken together with the other genes whose reduction-of-function phenotype is similar to that of lnkn-1 (notably cohesion and condensin), suggests the involvement of membrane remodeling and chromosome biology in LINKIN-dependent cell adhesion and supports the hypothesis for a structural role of chromosomes in post-mitotic cells.

RotaRod and acoustic startle reflex performance of two potential mouse models for Meniere's disease

Meniere's disease (MD) is a disorder of the inner ear characterized by chronic episodes of vertigo, tinnitus, increased aural pressure, and sensorineural hearing loss. Causes of MD are unknown, but endolymphatic hydrops is a hallmark. In addition, 5%-15% of MD cases have been identified as familial. Whole-genome sequencing studies of individuals with familial MD identified DTNA and FAM136A as candidate genes for autosomal dominant inheritance of MD. Although the exact roles of these genes in MD are unknown, FAM136A encodes a mitochondrial protein, and DTNA encodes a cytoskeletal protein involved in synapse formation and maintenance, important for maintaining the blood-brain barrier. It is also associated with a particular aquaporin. We tested vestibular and auditory function in dtna and fam136a knockout (KO) mice, using RotaRod and startle reflex-based clicker tests, respectively. Three-factor analysis of variance (ANOVA) results indicated that sex, age, and genotype were significantly correlated with reduced mean latencies to fall ("latencies") for male dtna KO mice, while only age was a significant factor for fam136a KO mice. Fam136a KO mice lost their hearing months before WTs (9-11 months vs. 15-20 months). In male dtna KO mice, divergence in mean latencies compared with other genotypes was first evident at 4 months of age, with older males having an even greater decrease. Our results indicate that fam136a gene mutations generate hearing problems, while dtna gene mutations produce balance deficits. Both mouse models should help to elucidate hearing loss and balance-related symptoms associated with MD.

A deficiency screen of the 3rd chromosome for dominant modifiers of the Drosophila ER integral membrane protein, Jagunal

The mechanism surrounding chromosome inheritance during cell division has been well documented, however, organelle inheritance during mitosis is less understood. Recently, the endoplasmic reticulum (ER) has been shown to reorganize during mitosis, dividing asymmetrically in proneuronal cells prior to cell fate selection, indicating a programmed mechanism of inheritance. ER asymmetric partitioning in proneural cells relies on the highly conserved ER integral membrane protein, Jagunal (Jagn). Knockdown of Jagn in the compound Drosophila eye displays a pleotropic rough eye phenotype in 48% of the progeny. To identify genes involved in Jagn dependent ER partitioning pathway, we performed a dominant modifier screen of the 3rd chromosome for enhancers and suppressors of this Jagn-RNAi-induced rough eye phenotype. We screened through 181 deficiency lines covering the 3L and 3R chromosomes and identified 12 suppressors and 10 enhancers of the Jagn-RNAi phenotype. Based on the functions of the genes covered by the deficiencies, we identified genes that displayed a suppression or enhancement of the Jagn-RNAi phenotype. These include Division Abnormally Delayed (Dally), a heparan sulfate proteoglycan, the γ-secretase subunit Presenilin, and the ER resident protein Sec63. Based on our understanding of the function of these targets, there is a connection between Jagn and the Notch signaling pathway. Further studies will elucidate the role of Jagn and identified interactors within the mechanisms of ER partitioning during mitosis.

PANGEA: a new gene set enrichment tool for Drosophila and common research organisms

Gene set enrichment analysis (GSEA) plays an important role in large-scale data analysis, helping scientists discover the underlying biological patterns over-represented in a gene list resulting from, for example, an 'omics' study. Gene Ontology (GO) annotation is the most frequently used classification mechanism for gene set definition. Here we present a new GSEA tool, PANGEA (PAthway, Network and Gene-set Enrichment Analysis; https://www.flyrnai.org/tools/pangea/), developed to allow a more flexible and configurable approach to data analysis using a variety of classification sets. PANGEA allows GO analysis to be performed on different sets of GO annotations, for example excluding high-throughput studies. Beyond GO, gene sets for pathway annotation and protein complex data from various resources as well as expression and disease annotation from the Alliance of Genome Resources (Alliance). In addition, visualizations of results are enhanced by providing an option to view network of gene set to gene relationships. The tool also allows comparison of multiple input gene lists and accompanying visualisation tools for quick and easy comparison. This new tool will facilitate GSEA for Drosophila and other major model organisms based on high-quality annotated information available for these species.

A new gene-scoring method for uncovering novel glaucoma-related genes using non-negative matrix factorization based on RNA-seq data

Early diagnosis and treatment of glaucoma are challenging. The discovery of glaucoma biomarkers based on gene expression data could potentially provide new insights for early diagnosis, monitoring, and treatment options of glaucoma. Non-negative Matrix Factorization (NMF) has been widely used in numerous transcriptome data analyses in order to identify subtypes and biomarkers of different diseases; however, its application in glaucoma biomarker discovery has not been previously reported. Our study applied NMF to extract latent representations of RNA-seq data from BXD mouse strains and sorted the genes based on a novel gene scoring method. The enrichment ratio of the glaucoma-reference genes, extracted from multiple relevant resources, was compared using both the classical differentially expressed gene (DEG) analysis and NMF methods. The complete pipeline was validated using an independent RNA-seq dataset. Findings showed our NMF method significantly improved the enrichment detection of glaucoma genes. The application of NMF with the scoring method showed great promise in the identification of marker genes for glaucoma.

Global analysis of the yeast knockout phenome

Genome-wide phenotypic screens in the budding yeast Saccharomyces cerevisiae, enabled by its knockout collection, have produced the largest, richest, and most systematic phenotypic description of any organism. However, integrative analyses of this rich data source have been virtually impossible because of the lack of a central data repository and consistent metadata annotations. Here, we describe the aggregation, harmonization, and analysis of ~14,500 yeast knockout screens, which we call Yeast Phenome. Using this unique dataset, we characterized two unknown genes (YHR045W and YGL117W) and showed that tryptophan starvation is a by-product of many chemical treatments. Furthermore, we uncovered an exponential relationship between phenotypic similarity and intergenic distance, which suggests that gene positions in both yeast and human genomes are optimized for function.

Recessive aminoacyl-tRNA synthetase disorders: lessons learned from in vivo disease models

Protein synthesis is a fundamental process that underpins almost every aspect of cellular functioning. Intriguingly, despite their common function, recessive mutations in aminoacyl-tRNA synthetases (ARSs), the family of enzymes that pair tRNA molecules with amino acids prior to translation on the ribosome, cause a diverse range of multi-system disorders that affect specific groups of tissues. Neurological development is impaired in most ARS-associated disorders. In addition to central nervous system defects, diseases caused by recessive mutations in cytosolic ARSs commonly affect the liver and lungs. Patients with biallelic mutations in mitochondrial ARSs often present with encephalopathies, with variable involvement of peripheral systems. Many of these disorders cause severe disability, and as understanding of their pathogenesis is currently limited, there are no effective treatments available. To address this, accurate in vivo models for most of the recessive ARS diseases are urgently needed. Here, we discuss approaches that have been taken to model recessive ARS diseases in vivo, highlighting some of the challenges that have arisen in this process, as well as key results obtained from these models. Further development and refinement of animal models is essential to facilitate a better understanding of the pathophysiology underlying recessive ARS diseases, and ultimately to enable development and testing of effective therapies.

Saccharomyces genome database update: server architecture, pan-genome nomenclature, and external resources

As one of the first model organism knowledgebases, Saccharomyces Genome Database (SGD) has been supporting the scientific research community since 1993. As technologies and research evolve, so does SGD: from updates in software architecture, to curation of novel data types, to incorporation of data from, and collaboration with, other knowledgebases. We are continuing to make steps toward providing the community with an S. cerevisiae pan-genome. Here, we describe software upgrades, a new nomenclature system for genes not found in the reference strain, and additions to gene pages. With these improvements, we aim to remain a leading resource for students, researchers, and the broader scientific community.

From multiallele fish to nonstandard environments, how ZFIN assigns phenotypes, human disease models, and gene expression annotations to genes

Danio rerio is a model organism used to investigate vertebrate development. Manipulation of the zebrafish genome and resultant gene products by mutation or targeted knockdown has made the zebrafish a good system for investigating gene function, providing a resource to investigate genetic contributors to phenotype and human disease. Phenotypic outcomes can be the result of gene mutation, targeted knockdown of gene products, manipulation of experimental conditions, or any combination thereof. Zebrafish have been used in various genetic and chemical screens to identify genetic and environmental contributors to phenotype and disease outcomes. The Zebrafish Information Network (ZFIN, zfin.org) is the central repository for genetic, genomic, and phenotypic data that result from research using D. rerio. Here we describe how ZFIN annotates phenotype, expression, and disease model data across various experimental designs, how we computationally determine wild-type gene expression, the phenotypic gene, and how these results allow us to propagate gene expression, phenotype, and disease model data to the correct gene, or gene related entity.

The Gene Ontology knowledgebase in 2023

The Gene Ontology (GO) knowledgebase (http://geneontology.org) is a comprehensive resource concerning the functions of genes and gene products (proteins and noncoding RNAs). GO annotations cover genes from organisms across the tree of life as well as viruses, though most gene function knowledge currently derives from experiments carried out in a relatively small number of model organisms. Here, we provide an updated overview of the GO knowledgebase, as well as the efforts of the broad, international consortium of scientists that develops, maintains, and updates the GO knowledgebase. The GO knowledgebase consists of three components: (1) the GO-a computational knowledge structure describing the functional characteristics of genes; (2) GO annotations-evidence-supported statements asserting that a specific gene product has a particular functional characteristic; and (3) GO Causal Activity Models (GO-CAMs)-mechanistic models of molecular "pathways" (GO biological processes) created by linking multiple GO annotations using defined relations. Each of these components is continually expanded, revised, and updated in response to newly published discoveries and receives extensive QA checks, reviews, and user feedback. For each of these components, we provide a description of the current contents, recent developments to keep the knowledgebase up to date with new discoveries, and guidance on how users can best make use of the data that we provide. We conclude with future directions for the project.

2022 updates to the Rat Genome Database: a Findable, Accessible, Interoperable, and Reusable (FAIR) resource

The Rat Genome Database (RGD, https://rgd.mcw.edu) has evolved from simply a resource for rat genetic markers, maps, and genes, by adding multiple genomic data types and extensive disease and phenotype annotations and developing tools to effectively mine, analyze, and visualize the available data, to empower investigators in their hypothesis-driven research. Leveraging its robust and flexible infrastructure, RGD has added data for human and eight other model organisms (mouse, 13-lined ground squirrel, chinchilla, naked mole-rat, dog, pig, African green monkey/vervet, and bonobo) besides rat to enhance its translational aspect. This article presents an overview of the database with the most recent additions to RGD's genome, variant, and quantitative phenotype data. We also briefly introduce Virtual Comparative Map (VCMap), an updated tool that explores synteny between species as an improvement to RGD's suite of tools, followed by a discussion regarding the refinements to the existing PhenoMiner tool that assists researchers in finding and comparing quantitative data across rat strains. Collectively, RGD focuses on providing a continuously improving, consistent, and high-quality data resource for researchers while advancing data reproducibility and fulfilling Findable, Accessible, Interoperable, and Reusable (FAIR) data principles.

PANGEA: A New Gene Set Enrichment Tool for Drosophila and Common Research Organisms

Gene set enrichment analysis (GSEA) plays an important role in large-scale data analysis, helping scientists discover the underlying biological patterns over-represented in a gene list resulting from, for example, an 'omics' study. Gene Ontology (GO) annotation is the most frequently used classification mechanism for gene set definition. Here we present a new GSEA tool, PANGEA (PAthway, Network and Gene-set Enrichment Analysis; https://www.flyrnai.org/tools/pangea/ ), developed to allow a more flexible and configurable approach to data analysis using a variety of classification sets. PANGEA allows GO analysis to be performed on different sets of GO annotations, for example excluding high-throughput studies. Beyond GO, gene sets for pathway annotation and protein complex data from various resources as well as expression and disease annotation from the Alliance of Genome Resources (Alliance). In addition, visualisations of results are enhanced by providing an option to view network of gene set to gene relationships. The tool also allows comparison of multiple input gene lists and accompanying visualisation tools for quick and easy comparison. This new tool will facilitate GSEA for Drosophila and other major model organisms based on high-quality annotated information available for these species.

Structure-Guided Prediction of the Functional Impact of DCLK1 Mutations on Tumorigenesis

Doublecortin-like kinase 1 (DCLK1) is a functional serine/threonine (S/T)-kinase and a member of the doublecortin family of proteins which are characterized by their ability to bind to microtubules (MTs). DCLK1 is a proposed cancer driver gene, and its upregulation is associated with poor overall survival in several solid cancer types. However, how DCLK1 associates with MTs and how its kinase function contributes to pro-tumorigenic processes is poorly understood. This review builds on structural models to propose not only the specific functions of the domains but also attempts to predict the impact of individual somatic missense mutations on DCLK1 functions. Somatic missense mutations in DCLK1 are most frequently located within the N-terminal MT binding region and likely impact on the ability of DCLK1 to bind to αβ-tubulin and to polymerize and stabilize MTs. Moreover, the MT binding affinity of DCLK1 is negatively regulated by its auto-phosphorylation, and therefore mutations that affect kinase activity are predicted to indirectly alter MT dynamics. The emerging picture portrays DCLK1 as an MT-associated protein whose interactions with tubulin heterodimers and MTs are tightly controlled processes which, when disrupted, may confer pro-tumorigenic properties.

Bioinformatics analysis of prognosis and immune microenvironment of immunological cell death-related gemcitabine-resistance genes in bladder cancer

Background

Bladder cancer (BC) is the most common malignant tumor of the urinary system. Gemcitabine resistance partly accounts for treatment failure and recurrence in BC. Immunological cell death (ICD) is correlated with chemoresistance. The prognosis of patients with similar tumor stage still varies in response to chemotherapy, recurrence, and disease progression. Therefore, our study aimed to provide a prognostic model based on ICD-related and gemcitabine-resistance genes for BC.

Methods

The data of BC patients were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs), and differentially expressed gemcitabine resistance-related genes (DEGRRGs) were identified using the edgeR package. The survival-associated DEGRRGs were identified by univariate Cox analysis. A prognostic model was established by univariate Cox regression analysis and validated by GEO dataset. The outcome of low-risk group and high-risk group was analyzed by the Kaplan-Meier curve. The relationship between risk score and immune cell infiltration was investigated using the TIMER online database.

Results

The prognosis of patients in the ICD-high group was significantly better than ICD-low group. A prognostic model containing 5 gemcitabine resistance-related ICD-associated genes, including PTPRR, HOXB3, SIGLEC15, UNC5CL, and CASQ1, was established. In both TCGA prognostic model and GEO validation model, patients in the low-risk group had better outcomes than high-risk group. According to the receiver operating characteristic (ROC) curves, the risk score area under ROC curve (AUC) of the TCGA prognostic model were calculated to be 0.705, while the risk score of the GEO validation model were calculated to be 0.716. Patients in the high-risk group had a significantly higher immune score, stromal score, and infiltration of M0 macrophages, M1 macrophages, M2 macrophages, and activated CD4⁺ T cells. Patients in the high-risk group had significantly lower infiltration of the regulatory T cells, resting dendritic cell (DCs), and activated DCs.

Conclusions

The present study highlighted the functional role of gemcitabine resistance-related ICD-associated genes, constructed a prognostic score for the outcome evaluation and searched for potential targets to overcome gemcitabine chemoresistance in BC.

Deciphering the endometrial immune landscape of RIF during the window of implantation from cellular senescence by integrated bioinformatics analysis and machine learning

Recurrent implantation failure (RIF) is an extremely thorny issue in in-vitro fertilization (IVF)-embryo transfer (ET). However, its intricate etiology and pathological mechanisms are still unclear. Nowadays, there has been extensive interest in cellular senescence in RIF, and its involvement in endometrial immune characteristics during the window of implantation (WOI) has captured scholars' growing concerns. Therefore, this study aims to probe into the pathological mechanism of RIF from cellular senescence and investigate the correlation between cellular senescence and endometrial immune characteristics during WOI based on bioinformatics combined with machine learning strategy, so as to elucidate the underlying pathological mechanisms of RIF and to explore novel treatment strategies for RIF. Firstly, the gene sets of GSE26787 and GSE111974 from the Gene Expression Omnibus (GEO) database were included for the weighted gene correlation network analysis (WGCNA), from which we concluded that the genes of the core module were closely related to cell fate decision and immune regulation. Subsequently, we identified 25 cellular senescence-associated differentially expressed genes (DEGs) in RIF by intersecting DEGs with cellular senescence-associated genes from the Cell Senescence (CellAge) database. Moreover, functional enrichment analysis was conducted to further reveal the specific molecular mechanisms by which these molecules regulate cellular senescence and immune pathways. Then, eight signature genes were determined by the machine learning method of support vector machine-recursive feature elimination (SVM-RFE), random forest (RF), and artificial neural network (ANN), comprising LATS1, EHF, DUSP16, ADCK5, PATZ1, DEK, MAP2K1, and ETS2, which were also validated in the testing gene set (GSE106602). Furthermore, distinct immune microenvironment abnormalities in the RIF endometrium during WOI were comprehensively explored and validated in GSE106602, including infiltrating immunocytes, immune function, and the expression profiling of human leukocyte antigen (HLA) genes and immune checkpoint genes. Moreover, the correlation between the eight signature genes with the endometrial immune landscape of RIF was also evaluated. After that, two distinct subtypes with significantly distinct immune infiltration characteristics were identified by consensus clustering analysis based on the eight signature genes. Finally, a "KEGG pathway-RIF signature genes-immune landscape" association network was constructed to intuitively uncover their connection. In conclusion, this study demonstrated that cellular senescence might play a pushing role in the pathological mechanism of RIF, which might be closely related to its impact on the immune microenvironment during the WOI phase. The exploration of the molecular mechanism of cellular senescence in RIF is expected to bring new breakthroughs for disease diagnosis and treatment strategies.

Promoting validation and cross-phylogenetic integration in model organism research

Model organism (MO) research provides a basic understanding of biology and disease due to the evolutionary conservation of the molecular and cellular language of life. MOs have been used to identify and understand the function of orthologous genes, proteins, cells and tissues involved in biological processes, to develop and evaluate techniques and methods, and to perform whole-organism-based chemical screens to test drug efficacy and toxicity. However, a growing richness of datasets and the rising power of computation raise an important question: How do we maximize the value of MOs? In-depth discussions in over 50 virtual presentations organized by the National Institutes of Health across more than 10 weeks yielded important suggestions for improving the rigor, validation, reproducibility and translatability of MO research. The effort clarified challenges and opportunities for developing and integrating tools and resources. Maintenance of critical existing infrastructure and the implementation of suggested improvements will play important roles in maintaining productivity and facilitating the validation of animal models of human biology and disease.

Biolink Model: A universal schema for knowledge graphs in clinical, biomedical, and translational science

Within clinical, biomedical, and translational science, an increasing number of projects are adopting graphs for knowledge representation. Graph-based data models elucidate the interconnectedness among core biomedical concepts, enable data structures to be easily updated, and support intuitive queries, visualizations, and inference algorithms. However, knowledge discovery across these "knowledge graphs" (KGs) has remained difficult. Data set heterogeneity and complexity; the proliferation of ad hoc data formats; poor compliance with guidelines on findability, accessibility, interoperability, and reusability; and, in particular, the lack of a universally accepted, open-access model for standardization across biomedical KGs has left the task of reconciling data sources to downstream consumers. Biolink Model is an open-source data model that can be used to formalize the relationships between data structures in translational science. It incorporates object-oriented classification and graph-oriented features. The core of the model is a set of hierarchical, interconnected classes (or categories) and relationships between them (or predicates) representing biomedical entities such as gene, disease, chemical, anatomic structure, and phenotype. The model provides class and edge attributes and associations that guide how entities should relate to one another. Here, we highlight the need for a standardized data model for KGs, describe Biolink Model, and compare it with other models. We demonstrate the utility of Biolink Model in various initiatives, including the Biomedical Data Translator Consortium and the Monarch Initiative, and show how it has supported easier integration and interoperability of biomedical KGs, bringing together knowledge from multiple sources and helping to realize the goals of translational science.

CDK activity sensors: genetically encoded ratiometric biosensors for live analysis of the cell cycle

Cyclin-dependent kinase (CDK) sensors have facilitated investigations of the cell cycle in living cells. These genetically encoded fluorescent biosensors change their subcellular location upon activation of CDKs. Activation is primarily regulated by their association with cyclins, which in turn trigger cell-cycle progression. In the absence of CDK activity, cells exit the cell cycle and become quiescent, a key step in stem cell maintenance and cancer cell dormancy. The evolutionary conservation of CDKs has allowed for the rapid development of CDK activity sensors for cell lines and several research organisms, including nematodes, fish, and flies. CDK activity sensors are utilized for their ability to visualize the exact moment of cell-cycle commitment. This has provided a breakthrough in understanding the proliferation-quiescence decision. Further adoption of these biosensors will usher in new discoveries focused on the cell-cycle regulation of development, ageing, and cancer.