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PubMed2027

Teratoma Formation and Genomic Profiling Using Multi-Omics Approaches.

Teratoma formation is the gold standard assay for evaluating the developmental pluripotency of human and mouse embryonic stem cells (ESCs) and induced pluripotent stem cells (iPSCs). Following subcutaneous injection into immunodeficient mice, pluripotent stem cells spontaneously differentiate into derivatives representing all three embryonic germ layers-ectoderm, mesoderm, and endoderm. Beyond serving as a functional assay for pluripotency, teratomas provide a unique three-dimensional model system for studying early human development and lineage specification in vivo. This chapter describes comprehensive protocols for teratoma formation in immunodeficient mice, tissue processing for multiple downstream genomic applications, and multi-omics profiling approaches. We detail methods for embryonic stem cell culture, teratoma generation via subcutaneous injection, tissue dissection and processing for chromatin immunoprecipitation followed by sequencing (ChIP-Seq), RNA sequencing (RNA-Seq), single-cell multiome profiling combining chromatin accessibility (ATAC-Seq) and gene expression (scRNA-Seq), and histological analysis using hematoxylin and eosin (H&E) staining. Additionally, we provide bioinformatics workflows for analyzing the resulting genomic datasets to characterize the epigenetic and transcriptional landscapes of teratoma-derived tissues. These methods enable comprehensive molecular characterization of developmental processes and provide valuable resources for stem cell biologists studying pluripotency, differentiation, and early embryonic development.

Teratoma↗

PubMed2026

PCSK9 as a Key Gene of Metastasis in Lung Adenocarcinoma: A Multi-omics and Experimental Validation Study.

BACKGROUND: Lung adenocarcinoma (LUAD) is the most common form of lung cancer. Proprotein convertase subtilisin/kexin type 9 (PCSK9) is abnormally expressed in various tumor tissues and is associated with malignant phenotypes. However, the clinical significance, function, and mechanism of LUAD invasion and metastasis remain unclear. METHODS: We retrospectively enrolled 100 patients with LUAD in this study. Initially, qRT-PCR was performed to detect PCSK9 levels in clinical tissues. Subsequently, bioinformatics analysis of scRNA-seq and The Cancer Genome Atlas Program (TCGA) datasets was performed to predict the role of PCSK9 in tumor cell malignancy and its potential downstream pathways. These predictions were validated experimentally using the CCK-8 assay, TUNEL staining, wound healing, transwell invasion assay, and an in vivo lung metastasis model. Finally, Western blotting and an AKT inhibitor (MK2206) were used to verify the underlying mechanism. RESULTS: PCSK9 was significantly upregulated in LUAD tissues compared to paracancerous tissues and was associated with poorer OS and DFS. Bioinformatics analysis of scRNA-seq data and TCGA analysis predicted that PCSK9 is highly enriched in tumor cells and is involved in EMT, and that the PI3K/AKT pathway plays a significant role in LUAD development. Experiments confirmed that PCSK9 markedly promoted LUAD cell proliferation, migration, and invasion in vitro and lung metastasis in vivo. PCSK9 overexpression significantly upregulated p-AKT, p-PI3K, and p-mTOR levels. Furthermore, the AKT inhibitor, MK2206, reversed the promoting effects of PCSK9. CONCLUSIONS: PCSK9 expression is associated with the prognosis and diagnosis of LUAD. This molecule activates the PI3K/AKT signaling pathway, thereby driving invasion, metastasis, and proliferation in LUAD.

Humans↗

PubMed2026

The mechanism by which long-term exposure to TDCIPP promotes cognitive impairment in 3 ×Tg-AD mice: Insights from multi-omics studies.

Tri(1,3-dichloro-2-propyl) phosphate (TDCIPP) is a commonly used organophosphate ester that has the potential to adversely affect human health. Although previous studies have closely associated TDCIPP with cognitive impairment, the underlying mechanisms remain unclear. To elucidate the neurotoxic effects of TDCIPP and its mechanistic contribution to cognitive impairment in 3 ×Tg-AD mice, a multi-omics approach incorporating proteomics, untargeted metabolomics, and 16S ribosomal RNA (rRNA) gene sequencing was employed to evaluate the impact of TDCIPP exposure on neurobehavioral function. TDCIPP exposure promoted cognitive impairment in 3 ×Tg-AD mice. Proteomic analyses revealed that this promotion is associated with disturbances in the hippocampal mitochondrial autophagy pathway. Furthermore, TDCIPP may interfere with the PINK1/Parkin-mediated mitophagy pathway at the functional level, without altering PINK1 protein abundance. Untargeted metabolomic analysis of urine samples demonstrated that TDCIPP exposure altered the metabolic profile of 3 ×Tg-AD mice, with 58 metabolites upregulated and 11 downregulated. Additionally, 16S rRNA sequencing revealed substantial modifications in gut microbiome composition following exposure to TDCIPP. Notably, significant correlations were identified between the perturbed bacterial genera and the differential metabolites. In conclusion, exposure to TDCIPP promotes cognitive impairment in 3 ×Tg-AD mice, which is associated with the interference with the PINK1/Parkin-mediated mitophagy pathway, as well as alterations in the urinary metabolome and gut microbiota. These findings suggest the potential to mitigate such cognitive impairment by targeting the microbiota-gut-brain axis.

Animals↗

PubMed2025

Single-cell glycome and transcriptome profiling enabled by a library of anti-glycan antibodies.

Glycans play critical roles in cellular processes and clinical applications, but they remain difficult to study due to a shortage of well-characterized anti-glycan reagents and high-throughput technologies for glycome profiling, especially ones capable of single-cell resolution. To meet these needs, we generated a database of 650 anti-glycan antibody sequences, recombinantly expressed a library of 154 antibodies, and extensively characterized their binding properties using glycan microarrays. In addition to providing valuable information and resources for the field, the sequence database and microarray data also enabled development of "Glycomic-seq" (Glycome profiling via multiplexed immunoglobulins combined with sequencing), a DNA-barcoded anti-glycan antibody platform that enables high-throughput, single-cell profiling of both RNA and cell-surface glycan expression. Using Glycomic-seq, we profiled two isogenic colorectal cancer cell lines. The results revealed various glycans associated with cancer stem cells and metastasis, demonstrating the power of integrating glycomic information with multi-omic efforts to discover biomarkers and therapeutic targets.

Polysaccharides↗

PubMed2006

Reflections on biomedical informatics: from cybernetics to genomic medicine and nanomedicine.

Expanding on our previous analysis of Biomedical Informatics (BMI), the present perspective ranges from cybernetics to nanomedicine, based on its scientific, historical, philosophical, theoretical, experimental, and technological aspects as they affect systems developments, simulation and modelling, education, and the impact on healthcare. We then suggest that BMI is still searching for strong basic scientific principles around which it can crystallize. As -omic biological knowledge increasingly impacts the future of medicine, ubiquitous computing and informatics become even more essential, not only for the technological infrastructure, but as a part of the scientific enterprise itself. The Virtual Physiological Human and investigations into nanomedicine will surely produce yet more unpredictable opportunities, leading to significant changes in biomedical research and practice. As a discipline involved in making such advances possible, BMI is likely to need to re-define itself and extend its research horizons to meet the new challenges.

Cybernetics↗

PubMed2026

Rumen microbiota-associated stress alleviation by creatine pyruvate in newly received cattle: a multi-omics study.

BACKGROUND: Stress experienced by newly received cattle is a significant challenge in the beef industry, frequently resulting in weakened immune responses and impaired growth. The rumen microbiota is essential to host health, and its imbalance can exacerbate stress. This study investigates the mechanisms by which creatine pyruvate (CrPyr) mitigates stress in newly received cattle through multi-omics approaches, including metagenomics, metabolomics, in vitro and in vivo experiments, and rumen microbiota transplantation (RMT) in mice. RESULTS: Our results revealed that CrPyr significantly reduces stress-related hormones (cortisol and adrenocorticotropic hormone) and inflammatory markers (IL-6, IL-1β, and TNF-α), and enhanced antioxidant capacity (SOD: 57.38 versus 46.93 U/mL, P < 0.05; GSH-Px: 305.87 versus 217.07 U/mL, P < 0.05; T-AOC: 9.62 versus 7.66 U/mL, P < 0.05). Metagenomic analysis demonstrated that CrPyr increased Prevotella abundance, a key rumen bacterium involved in volatile fatty acid (VFA) production, and enriches metabolic pathways associated with energy metabolism (ATP synthesis, and pyruvate metabolism) and antioxidant defense (glutathione metabolism, FC = 1.08, P < 0.05). In vitro and in vivo experiments, as well as RMT studies in mice, further validate these findings, demonstrating that CrPyr promote VFA synthesis and increased ATP production through the electron transport phosphorylation pathway. CONCLUSIONS: CrPyr modulates the abundance of ruminal Prevotella in transport-stressed cattle to enhance glutathione and VFA metabolism and to accelerate ATP and nucleotide synthesis, thereby alleviating stress in newly received cattle. This multimodal approach established CrPyr as an effective nutritional intervention that improves rumen function and increases livestock productivity. Video Abstract.

Animals↗

PubMed2026

scSNViz: visualization and analysis of cell-specific expressed SNVs.

MOTIVATION: Accurately characterizing expressed genetic variation at the single-cell level is essential for understanding transcriptional heterogeneity, allelic regulation, and mutational dynamics within complex tissues. However, few tools enable comprehensive visualization and quantitative analysis of expressed variants across individual cells. RESULTS: scSNViz is an R package for the exploration, quantification, and visualization of expressed single-nucleotide variants (SNVs) from cell-barcoded single-cell RNA sequencing (scRNA-seq) data. The software supports estimation of variant allele fractions, clustering of SNV expression profiles, and 2D and 3D visualization of individual SNVs or user-defined SNV groups. Beyond visualization, scSNViz facilitates investigation of cell-, cluster-, or lineage-specific variant expression patterns, as well as allelic dynamics including imprinting, random allele inactivation, and transcriptional bursting. It interoperates seamlessly with established single-cell frameworks-Seurat for clustering, Slingshot for trajectory inference, scType for cell-type annotation, and CopyKat for copy-number profiling-enabling integrative multi-omic analyses of expressed variation. AVAILABILITY AND IMPLEMENTATION: scSNViz is implemented in R and freely available at https://github.com/HorvathLab/scSNViz (DOI: 10.5281/zenodo.17307516). The package includes comprehensive documentation and example workflows designed for users with limited bioinformatics experience.

Software↗

PubMed2026

The Brazilian contribution to ant toxinology: challenges and perspectives.

Ant toxinology in Brazil is a small but growing field that has revealed wide biochemical diversity and clear potential for bioprospecting therapeutic molecules. This review compiles the Brazilian contribution, focusing on advances in the characterization of venoms from medically and ecologically important species of Dinoponera, Solenopsis, Paraponera, Pachycondyla, Neoponera, and Ectatomma. Brazilian groups applied omics approaches, including transcriptomics and proteomics, to resolve the composition of these venoms and identified peptide-rich arsenals with antimicrobial, antiparasitic, antitumor, and neuroactive activity. Studies of venom phenotypic plasticity showed ecological factors such as diet and seasonality shape venom composition. Two challenges persist: assigning function to still unidentified components and obtaining venom in the quantities that broad analysis requires. The near-term prospects are the rational design of peptide analogues with improved activity and continued bioprospecting of new species, which together position Brazil as a central contributor to ant toxinology.

Animals↗

PubMed2025

Droplet-Based Single-Cell 3' mRNA Sequencing of Marburg Virus-Infected Samples.

Single-cell technologies are continually evolving with emerging methods that are gradually uncovering the central DNA-RNA-protein dogma. Single-cell RNA sequencing is one arm of a multi-omic approach that achieves an astounding level of granularity to reveal the complexity of virus-host interactions at the transcriptomic level. Cell tropism, virus replication, pathogenesis, and gene expression changes mediated by the virus and the host's immune response to infection are just some areas of study that are gaining better clarity due to the high-resolution analysis afforded by the technology.We describe a single-cell sequencing protocol for Marburg virus infection in vivo using nonhuman primate blood and the 10× Chromium Next GEM single-cell genomics methodology. Working with pathogens of high consequence is logistically complicated, requiring containment in biosafety level (BSL)-4 laboratories and harsh inactivation procedures before samples can safely be removed to lower biosafety conditions. We provide procedural insight into sample isolation and processing conducted in BSL-4 and describe the requirements for safe sample removal without jeopardizing quality for down-stream sequencing and analysis in BSL-2 conditions. Characterization of complicated biological processes mediated by high-containment pathogens, typically restricted to analogous model systems, e.g., minigenome, can be achieved using live virus.

Animals↗

PubMed2026

Identification and evaluation of glutamine-related gene characteristics based on multi-omics to predict the prognosis of patients with colorectal cancer.

BACKGROUND: Colorectal cancer (CRC), a prevalent malignancy of the gastrointestinal tract, ranks among the leading causes of cancer-related morbidity and mortality. Its clinical course is marked by high fatality and poor prognosis. Elucidating the mechanisms underlying CRC initiation and recurrence is therefore critical for identifying novel therapeutic targets. METHODS: This study incorporated two datasets, TCGA-CRC and GSE17537. A total of 84 glutamine metabolism-related genes (GMRGs) were identified, and differential expression analysis was conducted using the TCGA-CRC dataset. Weighted Gene Co-expression Network Analysis (WGCNA) was applied to determine gene modules most strongly associated with GMRG scores. Single-cell RNA sequencing (scRNA-seq) was utilized to characterize key cellular clusters and to identify differentially expressed genes (DEGs) between high and low glutamine metabolism (GM) groups. Overlapping GMRGs were visualized using the ggVennDiagram package in R. A CRC risk prediction model was developed through Cox proportional hazards and LASSO regression analyses, with performance evaluated by ROC curves. Cell type enrichment across 64 immune and stromal populations was assessed via xCell, and intergroup differences were tested using the Wilcoxon rank-sum test. TIDE scores were used to estimate immunotherapy responsiveness, while oncoPredict facilitated drug sensitivity profiling. PCOLCE2 expression in CRC was validated by RT-qPCR and Western blotting. Its functional role was examined through CCK-8 assays, invasion and migration tests, flow cytometry, and glutamate quantification. RESULTS: ScRNA-seq analysis identified two key cell populations and 437 DEGs associated with GM status. WGCNA pinpointed the MEgreen module as most significantly correlated with GMRG scores, encompassing 1075 genes. Integration of DEGs, module genes, and GM-related DEGs yielded 60 candidate genes for downstream analysis. A GMRG-based prognostic model comprising six genes (SRPX, CXCL1, GPX3, PCOLCE2, CLU, SEMA3E) demonstrated strong predictive performance. Prognostic gene expression correlated with immune and stromal infiltration patterns, as indicated by Spearman correlation analysis. The high-risk group exhibited diminished predicted response to immunotherapy (TIDE scores). Drug sensitivity analysis identified four compounds—Dasatinib-51, WH-4-023-56, TWS-119-366, and LDN-193189-478—with elevated efficacy in high-risk CRC cases. PCOLCE2 expression was significantly reduced in CRC tissues. Functional assays revealed that PCOLCE2 knockdown did not substantially affect cell proliferation but significantly impaired invasion and migration in CRC cells, increased apoptosis, and suppressed both glutamine uptake and glutamate production—highlighting its oncogenic role. CONCLUSION: Six GMRGs—SRPX, CXCL1, GPX3, PCOLCE2, CLU, and SEMA3E—were identified as key components of a robust prognostic model for CRC. These findings offer valuable insights into CRC pathogenesis and potential therapeutic strategies. Notably, this study provides the first evidence implicating PCOLCE2 as a tumor-promoting factor in CRC.

Glutamine↗

PubMed2003

From transcriptomics to bibliomics.

BACKGROUND: Current biological investigations tend to operate with genomes, instead of genes as during the last century. It is possible to compare entire genomes, transcriptomes or proteomes, using alphanumeric data corresponding to the differential expression levels of thousands of genes. What remains difficult is to link array results to factual or bibliographical data and retrieve information that is highly structured and - in Shannon's sense - rare. MATERIAL/METHODS: We have developed a tool, Documentation and Information LIBrary (DILIB), that enables us to retrieve, organize and analyze huge amounts of data available on the Internet and related to microarray experiments. DILIB can link hundreds of differentially expressed genes - through their Single Identifier or GenBank accession number - to hundreds of Medline records, and can retrieve, analyze, and compare automatically thousands of non-trivial descriptors related to gene clusters. RESULTS: As exemplified with frequency comparison of MEdical Subject Headings and Registry Number descriptors, we reanalyzed the involvement of 'integrin', 'interleukin' and 'CD Antigens' in mesotheliomas. Thus, DILIB allowed us to: (i). associate literature to expressed genes, (ii). link functional transcriptomes in various experiments, (iii). associate specific descriptors to experiments, (iv). define new research areas, and eventually (v). find new functions for co-expressed genes. CONCLUSIONS: We propose a new concept, 'bibliomics', representing a subset of high quality and rare information, retrieved and organized by systematic literature-searching tools from existing databases, and related to a subset of genes functioning together in '-omic' sciences.

Databases, Genetic↗

PubMed2026

The Multi-Omics Landscape of Enzymatic Alterations in Systemic Lupus Erythematosus.

OBJECTIVE: Systemic lupus erythematosus (SLE) is an autoimmune disease closely associated with enzyme dysfunction, yet its underlying molecular mechanisms remain incompletely understood. This study aims to characterize enzyme-network alterations associated with SLE status and disease activity and to identify candidate molecules with potential clinical relevance. METHODS: We integrated proteomic and phosphoproteomic data from peripheral blood mononuclear cells (PBMCs) of 130 SLE patients and 90 healthy controls (HC), along with transcriptomic data from 1461 SLE patients. Through systematic analysis of key enzyme phosphorylation sites, upstream transcription factors (TFs), and computationally prioritized candidate compounds, we sought to characterize enzyme-centered regulatory associations. RESULTS: Integrated proteomic and phosphoproteomic analyses revealed significant metabolic and signaling pathway disturbances, along with distinct phosphorylation patterns in SLE immune cells. Multiple SLE-associated and disease-activity-associated candidate molecules were identified. Regulatory network analysis uncovered an upstream transcription factor cluster centered around STAT1. Computational drug screening identified computationally prioritized candidate compounds with multi-gene DSigDB associations, which require further clinical safety evaluation and experimental validation. CONCLUSIONS: This study constructs a molecular map of SLE, highlighting associations between enzyme-network alterations, catalytic dysregulation, and SLE-related immune molecular signatures, and identifies candidate molecules for future clinical and functional evaluation.

Humans↗

PubMed2025

Integrative multi-omics profiling deciphers tumor microenvironment heterogeneity and immunotherapy vulnerabilities in lung neuroendocrine carcinomas.

INTRODUCTION: Lung neuroendocrine carcinomas (Lu-NECs) are rare, highly aggressive lung tumors with poor prognosis and limited therapeutic options. Understanding the tumor immune microenvironment (TIME) is crucial towards personalized therapeutic strategies. OBJECTIVES: This study aims to systematically characterize the heterogeneity and complexity of the TIME in Lu-NECs by integrating proteomic, transcriptomic, and genomic data. METHODS: We performed comprehensive immune-proteomic profiling of 76 Lu-NECs across diverse histopathological subtypes to elucidate intra-tumoral TIME heterogeneity at the proteomic level. Validation was conducted in multiple independent cohorts, including 112 Lu-NECs using immunohistochemistry, 147 Lu-NECs, and 17 small cell lung carcinoma samples using transcriptomics. We integrated proteomic, transcriptomic, genomic, and clinical data to assess molecular, immunological, and clinical features, as well as therapeutic vulnerabilities across different immune subtypes. RESULTS: We delineated the immuno-proteomic landscape of Lu-NECs and identified two major immuno-proteomic clusters with distinct immunological, molecular, and clinical characteristics. IPC1 was characterized by high immune cell infiltration, while IPC2 exhibited sparse immune cell presence. Genomic analysis revealed distinct mutational patterns, with IPC1 showing a higher incidence of APOBEC-associated mutation signatures and IPC2 being enriched for mutations associated with defective DNA mismatch repair and tobacco-related mutagens. Functional analyses indicated that IPC1 was related to immune and oncogenic signaling activity, whereas IPC2 was associated with cancer stemness and proliferation-related features. Furthermore, IPC1 and IPC2 demonstrated histological subtype-specific clinical benefits from postoperative chemotherapy. Finally, we developed a machine learning model (iPROM) to predict Lu-NECs immune classification and improve risk stratification, which was validated across multiple independent cohorts. CONCLUSIONS: This study advances the understanding of the tumor immune microenvironment in Lu-NECs through multi-omics characterization and highlights potential personalized therapeutic vulnerabilities tailored to the specific immune landscapes of Lu-NECs.

Humans↗

PubMed2006

Nutrigenomics, proteomics, metabolomics, and the practice of dietetics.

The human genome is estimated to encode over 30,000 genes, and to be responsible for generating more than 100,000 functionally distinct proteins. Understanding the interrelationships among genes, gene products, and dietary habits is fundamental to identifying those who will benefit most from or be placed at risk by intervention strategies. Unraveling the multitude of nutrigenomic, proteomic, and metabolomic patterns that arise from the ingestion of foods or their bioactive food components will not be simple but is likely to provide insights into a tailored approach to diet and health. The use of new and innovative technologies, such as microarrays, RNA interference, and nanotechnologies, will provide needed insights into molecular targets for specific bioactive food components and how they harmonize to influence individual phenotypes. Undeniably, to understand the interaction of food components and gene products, there is a need for additional research in the "omics" of nutrition. It is incumbent upon dietetics professionals to recognize that an individual's response to dietary intervention will depend on his or her genetic background and that this information may be used to promote human health and disease prevention. The objectives of this review are to acquaint nutritional professionals with terms relating to "omics," to convey the state of the science to date, to envision the possibilities for future research and technology, and to recognize the implications for clinical practice.

Animals↗

PubMed2003

Medical malpractice predictors and risk factors for ophthalmologists performing LASIK and photorefractive keratectomy surgery.

PURPOSE: To identify physician predictors in LASIK and photorefractive keratectomy (PRK) surgery that correlate with a higher risk for malpractice liability claims and lawsuits. DESIGN: Retrospective, longitudinal, cohort study. PARTICIPANTS AND METHODS: A comparison of physician demographic and practice pattern data of 100 consecutive Ophthalmic Mutual Insurance Company (OMIC) LASIK and PRK claims and lawsuits with demographic and practice pattern data for all active refractive surgeons insured by OMIC between 1996 to 2002 was made. Background information and data were obtained from OMIC underwriting applications, a physician practice pattern survey, and claims file records. Using an outcome of whether or not a physician had a history of a claim or lawsuit, logistic regression analyses were used separately for each predictor as well as controlling for refractive surgery volume. MAIN OUTCOME MEASURE: Malpractice claim or lawsuit for performance of PRK or LASIK surgery. RESULTS: Logistic regression analysis demonstrated that the most important predictor of filing a claim was surgical volume, with those performing more surgery having a greater risk of incurring a claim (odds ratio [OR] = 31.4 for >1000 surgeries/year versus 0-20 surgeries/year, 95% confidence interval [CI] = 7.9-125, P = 0.0001). Having one or more prior claim was the only other predictor examined that remained statistically significant after controlling for patient volume (OR = 6.4, 95% CI = 2.5-16.4, P = 0.0001). Physician gender, advertising use, preoperative time spent with patient, and comanagement seemed to be strong predictors in multivariate analyses when surgical volume was greater than 100 cases per year. CONCLUSION: The chances for incurring a malpractice claim or lawsuit for PRK or LASIK correlate significantly with higher surgical volume and a history of a claim or lawsuit. Additional risk factors that increase in importance with higher surgical volume include physician gender, advertising use, preoperative time spent with the patient, and comanagement with optometrists. These findings may be used in the future to help improve the quality of care for patients undergoing refractive surgery and to provide data for underwriting criteria and risk management protocols to manage proactively and perhaps reduce the risk for claims and lawsuits against refractive surgeons.

Adult↗

PubMed2003

Genomics and its impact on parasitology and the potential for development of new parasite control methods.

Parasitic organisms remain the scourge of the developed and underdeveloped worlds. Malaria, schistosomiasis, leishmaniasis, and trypanosomiasis, for example, still result in a large number of human deaths each year worldwide, while drug resistance among nematodes still poses a major problem to the livestock industries. Genome projects involving parasitic organisms are now abundant, and technologies for the investigations of the parasite transcriptome and proteome are well established. There is no doubt the era of the "omics" is with parasitology, and current trends in the discipline are addressing fundamental biological questions that can make best use of the new technologies, as well as the vast amount of new data being generated. Will this become the "golden age of molecular parasitology," leading to the control of parasitic diseases that have plagued mankind for hundreds of years? The primary aim of this paper is to review advances in the general area of parasite genomics, and to outline where the application of "omics" technologies can and have impacted on the development of new control methods for parasitic organisms.

Animals↗

PubMed2026

PathMED: an R toolkit for single-sample molecular scoring and machine learning with omics data.

MOTIVATION: Molecular scoring is a popular approach for studying pathway-level functional alterations with omics data. Using molecular scores for tasks such as single-sample molecular characterisation, phenotype prediction or disease stratification has several advantages compared to using omics data directly. Molecular scores provide biological interpretability and are more generalisable across datasets, facilitating data integration and machine learning applications. However, numerous scoring methods are available through different software packages, and currently there is a lack of tools to easily use these scores for model training and prediction. RESULTS: We developed pathMED, an R/Bioconductor package that unifies various scoring methods in a simple framework. Furthermore, pathMED also contains a machine learning module to train and test models that use the calculated molecular scores to predict clinical outcomes. We demonstrate some of its potential applications in three use cases using public omics data. We showed the generalisability of machine learning models trained on transcriptomic scores in predicting clinical outcomes when deploying on proteomic scores. We also demonstrated the application of transcriptomics scores in predicting breast cancer treatment response and identifying pathways strongly associated to tumour biology and treatment response. Finally, we demonstrated the benefit of integrating a novel gene set dissection step into the analysis pipeline to resolve disease heterogeneity at the pathway level. AVAILABILITY: PathMED is freely available in the Bioconductor repository (https://bioconductor.org/packages/release/bioc/html/pathMED.html). Code to reproduce the analyses is publicly available at https://github.com/GENyO-BioInformatics/pathMED_article.

Software↗

PubMed2026

SpxA1 and SpxA2 function as a stoichiometry-dependent regulatory rheostat governing virulence gene expression in group A Streptococcus.

UNLABELLED: Group A Streptococcus (GAS) is a human-restricted pathogen whose global incidence has surged in the post-COVID era. The ability of GAS to shift from a colonizing to invasive phenotype depends on coordinated virulence gene regulation in response to host-derived signals. However, the mechanisms by which individual stress-sensing systems interact to reshape the virulence gene regulatory landscape remain incompletely understood. Here, we define the regulatory programs of two conserved transcriptional regulator paralogs, SpxA1 and SpxA2, using an integrated multi-omic approach combining RNA-seq, data-independent acquisition proteomics, NanoString-based transcriptional profiling across multiple host-relevant stress conditions, and chromatin immunoprecipitation with exonuclease treatment (ChIP-exo). RNA-seq revealed functionally distinct regulons with SpxA1 governing oxidative stress defense and SpxA2 coordinating virulence-associated gene expression linked to the CovRS two-component regulatory system. Proteomic analysis established SpxA2 as a ClpXP protease substrate in GAS and identified reciprocal paralog accumulation upon loss of either SpxA1 or SpxA2, consistent with compensatory transcriptional upregulation. NanoString profiling under bacitracin and human neutrophil peptide-1 challenge identified four gene modules with distinct stoichiometry-dependent and condition-dependent regulatory logic, revealing that the SpxA1/SpxA2 ratio rather than the activity of either paralog alone determines which transcriptional programs are engaged. ChIP-exo demonstrated that SpxA2 directly modulates CovR-DNA binding occupancy in a CovR-binding motif-dependent manner, simultaneously antagonizing CovR dimer binding at an extended (25 bp) CovR motif and facilitating CovR monomer binding at the canonical ATTARA motif. These findings establish the LiaFSR-SpxA2-CovRS axis as a cross-regulatory circuit through which GAS cell envelope stress sensing is directly transduced into coordinated virulence gene regulatory changes. IMPORTANCE: Group A Streptococcus (GAS) causes millions of infections annually, including a recent global surge in invasive disease. To survive in the human host, GAS must rapidly reprogram virulence gene expression in response to host-derived stresses. This study characterizes two conserved transcriptional regulators, SpxA1 and SpxA2, that govern this response through interaction with RNA polymerase to indirectly influence the DNA-binding activity of downstream transcription factors. We show that SpxA2, activated by a cell envelope stress-sensing system responding to human antimicrobial peptides, reshapes the binding of the master virulence regulator CovR in a promoter-specific manner, coupling cell envelope stress sensing to virulence gene regulation. The stoichiometric balance between SpxA1 and SpxA2 functions as a regulatory rheostat calibrating overall virulence gene regulatory tone, providing a framework for understanding how RNA polymerase-interacting regulators coordinate stress responses and virulence gene control across Gram-positive bacterial pathogens.

Streptococcus pyogenes↗