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PubMed2025

How to analyze and understand the human immune system.

To enhance our understanding of the pathogenesis of diseases, including rheumatic diseases, and to improve disease control, it is essential to attain a thorough understanding of the human immune system, alongside mouse immunology. Historically, the investigation of the human immune system has posed significant challenges due to methodological limitations. Nonetheless, recent advancements in genomic studies of multifactorial diseases have elucidated that numerous risk-associated genetic variants affecting quantitative differences in cell-specific gene expression. In light of these findings, we are currently examining individual genetic variations in both healthy individuals and patients, as well as categorizing cells into distinct subsets in order to construct a comprehensive dataset concerning the human immune system. This is accomplished by combining data on gene expression, factors influencing the expression mechanisms, protein expression, metabolomics, and environmental variables pertinent to immune functionality-such as gut microbiota. These datasets will facilitate the comprehensive characterization of the human immune system. Using these datasets and through the integrative analyses of data related to risk genetic variations and gene expression profiles of each disease and individual, we anticipate uncovering novel insights into the human immune system, the heterogeneity of diseases, immune function mechanisms, and their regulatory strategies that may not be achievable through murine models.

Humans↗

PubMed2026

Integrated metagenomic and metabolomic analysis identifies severity-specific inflammatory and metabolic signatures in post-stroke depression.

Post-stroke depression (PSD) is a common complication that significantly impacts patient prognosis. This study aimed to systematically characterize the associations among gut microbial ecology, metabolic profiles, and inflammatory responses across different severities of PSD. We conducted metagenomic sequencing, non-targeted metabolomics, and serum cytokine analysis (IL-1β, IL-6, IL-10, IL-18, TNF-α, IFN-γ, and CRP) in 91 patients with varying degrees of PSD and non-PSD controls. Bioinformatics analyzes were employed to construct multi-omics association networks and machine learning models. Results indicated that PSD patients exhibited significantly increased gut microbiota alpha-diversity, suggesting dysbiosis. Mild depression was characterized by compensatory neural signaling activation, whereas the moderate depression group exhibited abnormalities in tryptophan/indole metabolism, oxidative stress-related metabolic imbalances, and functional decompensation. Further analyzes suggested that Alistipes, Blautia_A, Evtepia gabavorous, and Lachnospira were associated with inflammatory features, GABA-related metabolic alterations, aromatic amino acid/indole metabolism, and lipid-amino acid metabolism, respectively. Under a more rigorous 10-fold cross-validation framework, the performance of different multi-omics combination models showed heterogeneity; however, some combinations still demonstrated superior discriminatory ability compared to single-omics approaches. This study provides multi-omics clues suggesting associations between different PSD severity levels and features such as increased Alistipes abundance, reduced antioxidant capacity, and altered tryptophan metabolism. It provides candidate biomarker combinations that may be useful for PSD stratification and suggests that the gut microbiome may represent a potential target for future PSD intervention. In summary, PSD may be associated with dynamic alterations along the "gut-brain-inflammation-metabolism" axis. These findings provide integrated evidence for microbial, metabolic, and inflammatory abnormalities across different PSD severity levels, but still require validation in larger samples, longitudinal cohorts, and mechanistic studies.

Humans↗

PubMed2026

Imaging and genomics in stroke.

Imaging after ischemic and hemorrhagic stroke may allow measurement of key phenotypes of injury and recovery for which targeted therapies are still lacking. Such imaging endophenotypes provide quantifiable and heritable biomarkers that can represent mechanistic aspects of disease processes better than clinical measures. Artificial intelligence is allowing extraction of these imaging biomarkers in large cohorts, which can be paired with genomic and other omics data. This will allow the evaluation of what genetic and other biologic variations impact stroke injury and recovery. Integration of these analyses with bioinformatics tools (such as Mendelian randomization and multi-trait analysis) could further dissect how stroke complications overlap with other biologic processes and how they may be causally linked to risk factors. Further work is required to confirm the translational impact of these methods in elucidating mechanisms and drug targets for stroke. However, global collaborations are accelerating analyses on large multi-ethnic stroke cohorts, with availability of imaging data facilitated by federally-funded repositories such as the Imaging Repository for the Cerebrovascular Disease Knowledge Portal (iCDKP).

Humans↗

PubMed2006

OMICS-driven biomarker discovery in nutrition and health.

While traditional nutrition research has dealt with providing nutrients to nourish populations, it nowadays focuses on improving health of individuals through diet. Modern nutritional research is aiming at health promotion and disease prevention and on performance improvement. As a consequence of these ambitious objectives, the disciplines "nutrigenetics" and "nutrigenomics" have evolved. Nutrigenetics asks the question how individual genetic disposition, manifesting as single nucleotide polymorphisms, copy-number polymorphisms and epigenetic phenomena, affects susceptibility to diet. Nutrigenomics addresses the inverse relationship, that is how diet influences gene transcription, protein expression and metabolism. A major methodological challenge and first pre-requisite of nutrigenomics is integrating genomics (gene analysis), transcriptomics (gene expression analysis), proteomics (protein expression analysis) and metabonomics (metabolite profiling) to define a "healthy" phenotype. The long-term deliverable of nutrigenomics is personalised nutrition for maintenance of individual health and prevention of disease. Transcriptomics serves to put proteomic and metabolomic markers into a larger biological perspective and is suitable for a first "round of discovery" in regulatory networks. Metabonomics is a diagnostic tool for metabolic classification of individuals. The great asset of this platform is the quantitative, non-invasive analysis of easily accessible human body fluids like urine, blood and saliva. This feature also holds true to some extent for proteomics, with the constraint that proteomics is more complex in terms of absolute number, chemical properties and dynamic range of compounds present. Apart from addressing the most complex "-ome", proteomics represents the only platform that delivers not only markers for disposition and efficacy but also targets of intervention. The Omics disciplines applied in the context of nutrition and health have the potential to deliver biomarkers for health and comfort, reveal early indicators for disease disposition, assist in differentiating dietary responders from non-responders, and, last but not least, discover bioactive, beneficial food components. This paper reviews the state-of-the-art of the three Omics platforms, discusses their implication in nutrigenomics and elaborates on applications in nutrition and health such as digestive health, allergy, diabetes and obesity, nutritional intervention and nutrient bioavailability. Proteomic developments, applications and potential in the field of nutrition have been specifically addressed in another review issued by our group.

Animals↗

PubMed2025

APNet, an explainable sparse deep learning model to discover differentially active drivers of severe COVID-19.

MOTIVATION: Computational analyses of bulk and single-cell omics provide translational insights into complex diseases, such as COVID-19, by revealing molecules, cellular phenotypes, and signalling patterns that contribute to unfavourable clinical outcomes. Current in silico approaches dovetail differential abundance, biostatistics, and machine learning, but often overlook nonlinear proteomic dynamics, like post-translational modifications, and provide limited biological interpretability beyond feature ranking. RESULTS: We introduce APNet, a novel computational pipeline that combines differential activity analysis based on SJARACNe co-expression networks with PASNet, a biologically informed sparse deep learning model, to perform explainable predictions for COVID-19 severity. The APNet driver-pathway network ingests SJARACNe co-regulation and classification weights to aid result interpretation and hypothesis generation. APNet outperforms alternative models in patient classification across three COVID-19 proteomic datasets, identifying predictive drivers and pathways, including some confirmed in single-cell omics and highlighting under-explored biomarker circuitries in COVID-19. AVAILABILITY AND IMPLEMENTATION: APNet's R, Python scripts, and Cytoscape methodologies are available at https://github.com/BiodataAnalysisGroup/APNet.

COVID-19↗

PubMed2025

Permutation tests to assess sex differences in omics data.

It is common to sex-stratify analyses of omics data and to report effects as 'sex-specific' when they are significant in only one sex. However, when analysing hundreds or thousands of molecules, this approach will yield many spurious 'sex-specific' effects if not supported by significant interactions. I illustrate this problem using an RNA sequencing dataset showing almost no significant sex by treatment interactions, but where sex-stratified analyses yield hundreds of 'sex-specific' effects of treatment. These 'sex-specific' effects could be spurious or could be real but not show interactions due to low statistical power. To distinguish these possibilities, I describe permutation tests, which provide an intuitive way to determine if a pattern of observations differs from what would be expected due to chance. For this dataset, assigning sex at random often generates more 'sex-specific' effects than the real data, demonstrating that there is little evidence of sex differences. Next, I simulate an RNA sequencing dataset that includes genes modelled to have sex-specific effects of a condition. As expected, analysis of this simulated dataset yields both significant interactions and sex-specific effects in sex-stratified analyses. While stratified analyses detect a higher number of sex-specific effects than the analysis of interactions, they erroneously identify genes not modelled to show sex-specific effects more often than interactions. A permutation test confirms that the number of sex-specific effects observed in the simulated dataset is greater than expected due to chance. Permutation tests can be applied to omics studies of sex differences, simultaneously providing (i) a clear and simple demonstration of the problems of sex-stratified analyses, and (ii) additional evidence of sex-specific effects where these are present. R code is provided for permutations, simulations, and plots to visualize potential sex-specific effects, which can be adapted to other types of data.

Female↗

PubMed2005

A systems biology approach to genetic studies of complex diseases.

Revealing mechanisms underlying complex diseases poses great challenges to biologists. The traditional linkage and linkage disequilibrium analysis that have been successful in the identification of genes responsible for Mendelian traits, however, have not led to similar success in discovering genes influencing the development of complex diseases. Emerging functional genomic and proteomic ('omic') resources and technologies provide great opportunities to develop new methods for systematic identification of genes underlying complex diseases. In this report, we propose a systems biology approach, which integrates omic data, to find genes responsible for complex diseases. This approach consists of five steps: (1) generate a set of candidate genes using gene-gene interaction data sets; (2) reconstruct a genetic network with the set of candidate genes from gene expression data; (3) identify differentially regulated genes between normal and abnormal samples in the network; (4) validate regulatory relationship between the genes in the network by perturbing the network using RNAi and monitoring the response using RT-PCR; and (5) genotype the differentially regulated genes and test their association with the diseases by direct association studies. To prove the concept in principle, the proposed approach is applied to genetic studies of the autoimmune disease scleroderma or systemic sclerosis.

Genomics↗

PubMed2026

Multi-omics reveal molecular changes during suspension adaptation of HEK293 cells.

Human embryonic kidney 293 (HEK293) cells have been successfully adapted from adherent to suspension culture and widely applied in both scientific research and the pharmaceutical industry. Although some studies investigated the variances between established adherent and suspension HEK293 cells of different strains, specific alterations in the cells during this consecutive process of suspension adaptation and possible factors driving this process have not been well described. Here, we adapted adherent HEK293 to suspension with desirable cell growth and high productivity for recombinant adenoviral vectors, and cells at several stages throughout the process were characterized. Slower cell growth, lower glucose uptake, increased lactate production, and weaker cell-surface adhesion were observed in suspension cells compared to their adherent counterparts. We further performed transcriptomics, proteomics, and metabolomics analysis to identify key cellular switches. A total of 2476 differentially expressed genes were found, including 1218 upregulated and 1258 downregulated genes in suspension cells. A similar and correlated pattern was observed in the proteomic study, and 702 differentially expressed metabolites were identified by untargeted metabolomics. In light of enrichment analysis, we summarized that HEK293 adherent cells survived and adapted to suspension culture via structural remodeling, metabolic shift and stress resistance. Our results provide a molecular enlightenment for suspension adaptation and potential directions for rational modification of HEK293 cell lines for future use. KEY POINTS: • Suspension adaptation reduced adhesion and reshaped the HEK293 cytoskeleton. • Multi-omics revealed metabolic rewiring and enhanced stress resistance. • An optimized suspension line outperformed an internal HEK293 suspension reference.

Humans↗

PubMed2005

Reconstructing the metabolic network of a bacterium from its genome.

The prospect of understanding the relationship between the genome and the physiology of an organism is an important incentive to reconstruct metabolic networks. The first steps in the process can be automated and it does not take much effort to obtain an initial metabolic reconstruction from a genome sequence. However, such a reconstruction is certainly not flawless and correction of the many imperfections is laborious. It requires the combined analysis of the available information on protein sequence, phylogeny, gene-context and co-occurrence but is also aided by high-throughput experimental data. Simultaneously, the reconstructed network provides the opportunity to visualize the "omics" data within a relevant biological functional context and thus aids the interpretation of those data.

Bacteria↗

PubMed2025

scMGCL: accurate and efficient integration representation of single-cell multi-omics data.

MOTIVATION: Single-cell multi-omics data integration is essential for understanding cellular states and disease mechanisms, yet integrating heterogeneous data modalities remains a challenge. We present scMGCL, a graph contrastive learning framework for robust integration of single-cell ATAC-seq and RNA-seq data. Our approach leverages self-supervised learning on cell-cell similarity graphs, in which each modality's graph structure serves as an augmentation for the other. This cross-modality contrastive paradigm enables the learning of biologically meaningful, shared representations while preserving modality-specific features. RESULTS: Benchmarking against state-of-the-art methods demonstrates that scMGCL outperforms others in cell-type clustering, label transfer accuracy, and preservation of marker-gene correlations. Additionally, scMGCL significantly improves computational efficiency, reducing runtime and memory usage. The method's effectiveness is further validated through extensive analyses of cell-type similarity and functional consistency, providing a powerful tool for multi-omics data exploration. AVAILABILITY AND IMPLEMENTATION: Code and datasets are released at https://github.com/zlCreator/scMGCL.

Single-Cell Analysis↗

PubMed2025

Multi-omics Mendelian randomization integrating RNA-seq, eQTL and pQTL data revealed CPXM1 as a potential drug target for osteoporosis.

Osteoporosis, a prevalent skeletal disorder characterized by decreased bone mineral density and increased fracture risk, continues to be a major global health concern. Traditional treatments for osteoporosis have limited efficacy and safety profiles, highlighting the need for novel therapeutic targets. This study integrates multi-omics data, including RNA-seq, expression quantitative trait loci (eQTL), and protein quantitative trait loci (pQTL) data, through Mendelian randomization (MR) to identify potential drug targets for osteoporosis. By leveraging bidirectional two-sample MR analysis, we identified CPXM1 (Carboxypeptidase X, M14 family member 1) as a novel gene that is causally linked to osteoporosis risk. Through transcriptomic and proteomic validation, we demonstrate that CPXM1 was upregulated in aged bone tissues and osteoporotic conditions in both human and murine models. Gene set enrichment analysis (GSEA) revealed significant dysregulation of bone homeostasis pathways, including increased extracellular matrix degradation and suppression of osteoblast differentiation in aged mice. Furthermore, phenome-wide association studies (PheWAS) confirmed minimal off-target effects of CPXM1, reinforcing its potential as a therapeutic target. Finally, computational drug repurposing predicted several promising drug candidates, including Doxorubicin, 5-Fluorouracil, and 2-Methylcholine, which may target CPXM1 pathways for osteoporosis treatment. These findings highlight CPXM1 as a potential biomarker and therapeutic target, offering new avenues for osteoporosis therapy.

Osteoporosis↗

PubMed2004

Geometric trajectory analysis of metabolic responses to toxicity can define treatment specific profiles.

Metabonomics can be viewed as the process of defining multivariate metabolic trajectories that describe the systemic response of organisms to physiological perturbations through time. We have explored the hypothesis that the homothetic geometry of a metabolic trajectory, i.e., the metabolic response irrespective of baseline values and overall magnitude, defines the mode of response of the organism to treatment and is hence the key property when considering the similarity between two sets of measurements. A modeling strategy to test for homothetic geometry, called scaled-to-maximum, aligned, and reduced trajectories (SMART) analysis, is presented that together with principal components analysis (PCA) facilitates the visualization of multivariate response similarity and hence the interpretation of metabonomic data. Several examples of the utility of this approach from toxicological studies are presented as follows: interlaboratory variation in hydrazine response, CCl(4) dose-response relationships, and interspecies comparison of bromobenzene toxicity. In each case, the homothetic trajectories hypothesis is shown to be an important concept for the successful multivariate modeling and interpretation of systemic metabolic change. Overall, geometric trajectory analysis based on a homothetic modeling strategy like SMART facilitates the amalgamation and comparison of metabonomic data sets and can improve the accuracy and precision of classification models based on metabolic profile data. Because interlaboratory variation, normal physiological variation, dose-response relationships, and interspecies differences are also key areas of concern in genomic and proteomic as well as metabonomic studies, the methods presented here may also have an impact on many other multilaboratory efforts to produce screenable "-omics" databases useful for gauging toxicity in safety assessment and drug discovery.

Animals↗

PubMed2026

Genetic evidence that advanced COVID-19 accelerates longitudinal brain atrophy: A Mendelian randomization study.

Coronavirus disease 2019 (COVID-19) was reported to persist long-term in the brain and leave several long-term neurologic sequelae. However, the causal relationship between COVID-19 and brain aging is still unknown. The genome-wide association study (GWAS) data on COVID-19 phenotypes (susceptibility, hospitalization, and severity), involving a total of 5,779,391 participants, were collected from the COVID-19 Host Genetics Initiative. In addition, GWAS data on longitudinal changes in 15 brain structures, assessed via magnetic resonance imaging across the lifespan, were sourced from the ENIGMA Consortium and involved 15,640 participants. Two-sample Mendelian randomization was conducted to infer the causal relationship between COVID-19 and longitudinal brain changes. Multi-trait GWAS meta-analysis, colocalization, and fine-mapping analyses were performed to identify shared genetic etiologies. H3K27me3 ChIP-seq was used to evaluate the regulatory effect of colocalized loci. Two-step Mendelian randomization was applied to explore potential mediating mechanisms across multi-omics layers, including proteomics, metabolomics, and immunomics. Our results showed that COVID-19 hospitalization (β = -262.405, P = .041) and severity (β = -177.676, P = .049) were genetically associated with atrophied volume of total brain during longitudinal change. This suggests that individuals with advanced COVID-19 may be more susceptible to accelerated global brain aging. Caudate was genetically affected by all COVID-19 phenotypes. Seven variants were shared between advanced COVID-19 and global brain aging. rs117169628 was colocalized between advanced COVID-19 and global brain aging, and exerted an inhibitory effect on CDH15 expression, further strengthening the causality. Six metabolites, 1 protein, and 1 immune trait were identified as potential mediators. Our study indicates that advanced COVID-19 might be genetically associated with accelerated brain aging. Brain health should be paid more attention in long COVID-19.

Humans↗

PubMed2026

Multi-omics characterization of flavor profile differences in the Longissimus thoracis between Angus and Hereford cattle.

BACKGROUND: Angus and Hereford cattle are premier breeds widely used in genetic improvement and crossbreeding programs to enhance meat quality, yet the flavor differences between them remain poorly understood. RESULTS: In this study, we performed an integrated analysis using headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry (HS-SPME-GC-MS)-based volatile metabolomics, lipidomics, and untargeted metabolomics to characterize the flavor profiles of the Longissimus thoracis (LT) muscle from both breeds and to identify potential precursor substances underlying flavor formation. In total, we identified 76 differential volatile organic compounds (VOCs) among the 493 candidate VOCs. By combing relative odor activity value (ROAV) and sensory attribute annotation, 2,3-butanedione which may contribute to the creamy aroma was revealed as the core differential VOC between the two breeds. This finding was robustly validated across SHAP (Shapley additive explanations) analysis, KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment, and flavor annotation. Lipidomic analysis revealed 689 differential lipids primarily belonging to classes such as phosphatidylcholine, triglycerides, and phosphatidylethanolamine. Correlation analysis further linked these lipid profiles to flavor, showing that fatty acids (FAs) including FA(19:0), FA(18:2 + O), FA(14:1), FA(16:1), and FA(14:0) were significantly correlated with 2,3-butanedione. Notably, the unsaturated fatty acids (UFAs) in the Longissimus thoracis (LT) of Hereford cattle exhibited higher double bond content compared to Angus cattle, suggesting a greater potential for rich flavor development. Untargeted metabolomics revealed that nine of the 9474 metabolites were significantly correlated with both 2,3-butanedione and FAs, including norepinephrine, l-beta-aspartyl-l-leucine, and artemetin. CONCLUSIONS: Overall, our research has identified differential flavor compounds and potential precursor substances between Angus cattle and Hereford cattle, providing targeted guidance for breed improvement. © 2026 Society of Chemical Industry.

2,3‐butanedione↗

PubMed2026

Pan-cancer analysis identifies KANSL2 as a cell-cycle-associated regulator of tumor progression and immunity in liver hepatocellular carcinoma.

KANSL2, a core component of the NSL histone acetyltransferase complex, has been implicated in tumorigenesis. However, its pan-cancer relevance and functional role in liver hepatocellular carcinoma (LIHC) remain unclear. Multi-omics data from TCGA, GEO, and HPA were integrated to systematically evaluate KANSL2 expression, clinical significance, genomic alterations, and immune associations across cancers. Functional enrichment, immune infiltration analyses, and single-cell transcriptomics were performed. In vitro assays were conducted to validate the biological effects of KANSL2 in LIHC cells. KANSL2 is broadly upregulated across cancers and exhibits strong diagnostic performance. Elevated KANSL2 expression correlates with unfavorable prognosis, particularly in LIHC. Mechanistically, KANSL2 and its co-expressed genes are enriched in cell-cycle progression. KANSL2 expression is also closely associated with immune infiltration and immunoregulatory signaling within the tumor microenvironment, with single-cell data indicating preferential expression in proliferative T-cell subsets. Functional experiments demonstrate that KANSL2 silencing suppresses proliferation, migration, and invasion, and induces G2/M phase arrest in LIHC cells. Notably, its effects on apoptosis are limited, suggesting that KANSL2 primarily drives tumor progression through cell-cycle-dependent mechanisms. This study identifies KANSL2 as a key regulator of tumor progression and immune remodeling in LIHC. By promoting malignancy predominantly via cell-cycle control, KANSL2 represents a promising biomarker for diagnosis and prognosis, and a potential therapeutic target.

Carcinoma, Hepatocellular↗

PubMed2025

MaxComp: Predicting single-cell chromatin compartments from 3D chromosome structures.

The genome is organized into distinct chromatin compartments with at least two main classes, a transcriptionally active A and an inactive B compartment, broadly corresponding to euchromatin and heterochromatin. Chromatin regions within the same compartment preferentially interact with each other over regions in the opposite compartment. A/B compartments are traditionally identified from ensemble Hi-C contact frequency matrices using principal component analysis of their covariance matrices. However, defining compartments at the single-cell level from sparse single-cell Hi-C data is challenging, especially since homologous copies are often not resolved. To address this, we present MaxComp, an unsupervised method, for inferring single-cell A/B compartments based on 3D geometric considerations in single-cell chromosome structures-derived either from multiplexed FISH-omics imaging or 3D structure models derived from Hi-C data. By representing each 3D chromosome structure as an undirected graph with edge-weights encoding structural information, MaxComp reformulates compartment prediction as a variant of the Max-cut problem, solved using semidefinite graph programming (SPD) to optimally partition the graph into two structural compartments. Our results show that the population average of MaxComp single-cell compartment annotations closely matches those derived from ensemble Hi-C principal component analysis, demonstrating that compartmentalization can be recovered from geometric principles alone, using only the 3D coordinates and nuclear microenvironment of chromatin regions. Our approach reveals widespread cell-to-cell variability in compartment organization, with substantial heterogeneity across genomic loci. When applied to multiplexed FISH imaging data, MaxComp also uncovers relationships between compartment annotations and transcriptional activity at the single-cell level. In summary, MaxComp offers a new framework for understanding chromatin compartmentalization in single cells, connecting 3D genome architecture, and transcriptional activity with the cell-to-cell variations of chromatin compartments.

Chromatin↗

PubMed2026

Impact of Genomic Mutations on the Transcriptional Pathways and Tumor Microenvironment Landscape of Localized Early Prostate Cancer.

BACKGROUND: The management of intermediate-risk early prostate cancer (PCa) is challenging due to the difficulty in distinguishing indolent from aggressive tumors. This study explores the association between genomic alterations and the tumor and its microenvironment (TME) and implications for disease progression. METHODS: We performed multi-omic profiling in a cohort of 53 localized PCa using targeted sequencing, transcriptional, and proteomic spatial profiling. RESULTS: Somatic mutations and copy number alterations in RB1 (21%), PTEN (18%), and TP53 (9%) were identified. Kaplan-Meier analysis revealed that alterations in the RB and Cell Cycle pathways, particularly aberrations in PTEN, TP53, or RB1, were associated with shorter biochemical recurrence-free survival (p < 0.001). Spatial proteomic analysis demonstrated a complex immune landscape in patients with mutations. The tumor compartment demonstrated higher expression of immune checkpoint markers, T-cell activation proteins, and proliferation markers; and a TME that is enriched with CD8 + T cells and antigen-presenting cells, but also with immunosuppressive M2 macrophages, suggesting adaptive immune resistance. CONCLUSIONS: Our analysis demonstrates that genomic alterations in PTEN, TP53, or RB1 are not only prognostic for poor outcomes but are also associated with a unique, immunologically complex TME in this Brazilian cohort.

Humans↗

PubMed2026

De Novo Genome Sequence Assembly of the Algal Endosymbiont Micractinium conductrix Derived From Its Host Paramecium bursaria 186b.

Endosymbiosis is a major driver of evolutionary innovation and underpins the function of diverse ecosystems. The origins and evolution of endosymbiosis are challenging to study experimentally due to the short-lived culturability of many microbial strains derived from endosymbiotic interactions. The facultative endosymbiosis between the ciliate, Paramecium bursaria, and the green alga, Micractinium conductrix (Chlorellaceae, Trebouxiophyceae), is ecologically widespread and has emerged as a powerful lab-tractable model system. This endosymbiosis is founded upon a reciprocal nutrient exchange, but each of the species can be cultured independently enabling quantification of symbiotic fitness effects, new partnerships to be generated in the lab, and co-associations to be subject to experimental evolution. To date, evolve-and-resequence approaches have been limited due to a lack of high-quality genome assemblies enabling gene variants to be identified. Here, we report a near telomere-to-telomere genome assembly for M. conductrix 186b, using a range of sequencing technologies. Comparative analysis shows that this is one of the most complete Chlorellaceae algal genome assemblies available to date. To aid accurate gene calling and annotation, we conducted both RNAseq and Iso-Seq transcriptome sequencing experiments. Collectively, these 'omics datasets will facilitate: (i) comparative genomics studies of endosymbiont evolution, (ii) evolve-and-resequence experiments, (iii) genome-scale metabolic modeling studies, and (iv) identification of targets for genetic modification experiments and biotechnological applications.

Symbiosis↗