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PubMed2026

Freely available genomic datasets for atrial fibrillation research: current resources and analytical pipeline.

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia, characterized by clinical and genetic heterogeneity. Increasing use of genomics and other omics approaches has driven reliance on publicly available AF datasets to advance biological discovery. Thus, this systematic review aimed to identify freely available genomic AF datasets through Mendeley Data and its interconnected repositories, and to characterize the most common analyses performed on these data. The search was conducted in adherence to the PRISMA 2020 guideline. Nineteen freely available genomic AF datasets were identified: Summary statistics for 'Biobank-driven genomic discovery yields new insight into atrial fibrillation biology', hum0014.v8.58qt.v1, AF GWAS in UK Biobank, UK Biobank (Publication 9659), GWAS summary statistics from a 2025 multi-ancestry AF meta-analysis, GSE115574, GSE128188, GSE14975, GSE2240, GSE238242, GSE254133, GSE261170, GSE271748, GSE271839, GSE293813, GSE294456, GSE31821, GSE41177, and GSE79768. The GEO datasets were further examined using differential gene expression, functional enrichment, protein-protein interaction networks, hub gene analysis, microRNA target prediction, and gene clustering, as well as, for the more recently deposited datasets, eQTL colocalization, single-cell/single-nucleus clustering, cell-cell communication analysis, and gene-dosage-dependent transcriptional and electrophysiological profiling. These analyses show some consistency but also considerable heterogeneity in initial conditions, data normalization, and analytical methodological settings. In conclusion, only a limited number of datasets are freely available, so additional, well-characterized and standardized datasets are needed to provide a complete picture of the AF pathology.

Mendeley Data↗
PubMed2025

Multi-omics analyses reveal DjTcf4 critical for proper timing of differentiation in planarian regeneration.

The blastema is key to forming complete tissues in regenerating Dugesia japonica (D. japonica). However, the dynamic changes in cellular compositions and transcription landscapes in blastema during regeneration are understudied. Here, through genome reannotation, 3D spatial transcriptome construction, single-cell RNA sequencing (scRNA-seq), and single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq) analyses of changes in gene expression and chromatin structures, we delineate key transcription factors regulating the developmental trajectories of major cell clusters in the regenerating head. Importantly, we find that the T cell factor 4 (DjTcf4)-positive cells highly accumulate at wound areas, and its gene network is critical for the proper timing of development during regeneration in multiple progenitor cells. Depletion of DjTcf4 and its target genes leads to singular eye and/or dull tail phenotypes and delays regeneration. Taken together, we build multi-omics atlases in D. japonica and reveal the noncanonical function of the DjTcf4 network in developmental pattern formation, laying a foundation for studies of regeneration in D. japonica.

Animals↗
PubMed2026

Big data analytics for CLEC5A dynamics based on single cell genomics and proteomics reveal its diverse functions in human diseases.

BACKGROUND: CLEC5A (C-type lectin domain family 5 member A) is an innate immune receptor implicated in inflammatory signaling, contributing to hyperinflammatory responses in infections and sterile inflammation. However, CLEC5A dynamics in human diseases remain to be identified. Here, we systematically characterized CLEC5A dynamics in humans across cells, tissues, and disease states, and to explore the functional significance of CLEC5A in macrophage activation based on single-cell genomics. METHODS: With multi-omics (scRNA-seq, proteomics and big data analytics), we analyzed extensive human transcriptomic datasets (>42,000 samples) to profile CLEC5A expression by cell type, tissue, and disease. Single-nucleus RNA-seq (snRNA-seq) from pediatric congenital heart disease and a virtual CLEC5A gene knockout were also performed to characterize CLEC5A dynamics in humans. RESULTS: CLEC5A is highly enriched in innate immune cells, particularly in macrophages and neutrophils. Baseline CLEC5A in most tissues is low, but it is markedly upregulated in inflammatory and infectious diseases. CLEC5A expression has sex-specific differences in certain organs. Single-cell analysis showed that CLEC5A can be considered novel marker of proinflammatory macrophages with elevated cytokine production, antigen presentation, and impaired phagocytosis. Virtual CLEC5A knockout analysis identified coordinated perturbation of immune-regulatory pathways and overlapping genes linking CLEC5A to macrophage activation networks. CONCLUSION: CLEC5A is predominantly expressed in myeloid cells and acts as a key amplifier of inflammation in human diseases. Our findings highlight CLEC5A as a potential biomarker and therapeutic target in myeloid-driven hyperinflammatory conditions, warranting further experimental and translational validation.

Humans↗
PubMed2006

A survey of laboratory and statistical issues related to farmworker exposure studies.

Developing internally valid, and perhaps generalizable, farmworker exposure studies is a complex process that involves many statistical and laboratory considerations. Statistics are an integral component of each study beginning with the design stage and continuing to the final data analysis and interpretation. Similarly, data quality plays a significant role in the overall value of the study. Data quality can be derived from several experimental parameters including statistical design of the study and quality of environmental and biological analytical measurements. We discuss statistical and analytic issues that should be addressed in every farmworker study. These issues include study design and sample size determination, analytical methods and quality control and assurance, treatment of missing data or data below the method's limits of detection, and post-hoc analyses of data from multiple studies. Key words: analytical methodology, biomarkers, laboratory, limit of detection, omics, quality control, sample size, statistics.

Agriculture↗
PubMed2026

CCNA2 orchestrates the PI3K/AKT signaling axis to propel prostate cancer metastasis.

BACKGROUND: Prostate cancer (PCa) remains one of the most common malignancies in men, posing a persistent global burden in terms of both public health and socioeconomic costs. Although early detection is essential for improving patient outcomes, existing clinical tools, including prostate-specific antigen (PSA) screening, digital rectal examination, and transrectal ultrasound-guided biopsy, are hampered by suboptimal specificity and positive predictive value, resulting in frequent overdiagnosis and overtreatment of indolent lesions while missing a subset of aggressive tumors at an early stage. In this context, the rapid advancement of high-throughput omics technologies, coupled with sophisticated machine learning (ML) algorithms, provides a powerful computational framework to dissect high-dimensional genomic data, uncover latent gene expression signatures, and identify candidate biomarkers with superior discriminative performance over conventional clinicopathological parameters. Therefore, in this study, we sought to screen for crucial ML-based biomarkers associated with PCa, with a particular focus on systematically assessing the diagnostic and prognostic value of CCNA2. Leveraging large-scale transcriptomic cohorts from public repositories, we employed an ensemble of ML approaches to prioritize candidate genes and subsequently evaluated the diagnostic performance of CCNA2 through receiver operating characteristic curve analysis, as well as its prognostic utility via Kaplan-Meier survival estimation and multivariate Cox proportional hazards modeling. Our findings are anticipated to elucidate the molecular landscape of PCa and offer a promising biomarker candidate for early detection and risk stratification. METHODS: This study integrated single-cell RNA sequencing, bulk transcriptomic data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) repositories, immunofluorescence, and multiple ML algorithms with in vitro functional assays to evaluate CCNA2 expression, clinical relevance, and biological behavior in PCa. RESULTS: CCNA2 was linked to metastasis and poor prognosis. High CCNA2 expression significantly correlated with adverse survival outcomes, and knockdown of CCNA2 suppressed proliferation, migration, and invasion in PCa cell lines. Mechanistically, CCNA2 modulated the PI3K/AKT signaling pathway. An ML-based diagnostic model incorporating CCNA2 demonstrated high predictive accuracy across multiple validation cohorts. CONCLUSIONS: CCNA2 serves as a promising prognostic biomarker and therapeutic target in prostate adenocarcinoma, driving tumor progression potentially via the PI3K/AKT axis.

CCNA2↗
PubMed2025

Multi-Omics Platforms Reveal Synergistic Intestinal Toxicity in Tilapia from Acute Co-Exposure to Polystyrene Microplastics, Sulfamethoxazole, and BDE153.

Polystyrene microplastic (MP) and its co-existing contaminants may exert different toxic effects on its surrounding aquatic organisms. In order to detect the intestinal harmful responses, tilapia were subjected to exposure with 75 nm of MPs, 100 ng·L-1 of sulfamethoxazole (SMZ), 5 ng·L-1 of BDE153, and combinations thereof over periods of 2, 4, and 8 days. Enzymatic assays, transcriptomics, proteomics, and metabolomics were employed to evaluate intestinal histopathological effects. Results showed that significant reductions were observed in ATP, ROS, SOD, EROD, lipid metabolism-related enzymes, pro-inflammatory cytokines (TNFα and IL-1β), and apoptosis marker caspase 3 across all groups at day 8. Histological evaluation revealed diminished goblet cell density, with distinct vacuole formation in the BDE153+MPs group. KEGG pathway analysis highlighted disruptions in endocytosis, MAPK signaling, phagosome formation, and actin cytoskeleton regulation. Proteomic findings indicated notable enrichment in endocytosis (decreased sorting nexin-2; increased Si:dkey-13a21.4), MAPK/PPAR signaling, protein processing in the endoplasmic reticulum (Sec61 subunit gamma), and cytoskeletal modulation (reduced fibronectin; elevated activation peptide fragment 1), with or without SMZ and BDE153. Metabolomic profiling showed significant alterations in ABC transporters, aminoacyl-tRNA biosynthesis, protein digestion and absorption, and linoleic acid metabolism. In summary, these findings suggest that BDE153 and MPs synergistically exacerbate intestinal damage and gene/protein expression over time, while SMZ appears to exert an antagonistic, mitigating effect.

Animals↗
PubMed2026

Genomic characterization of aggressiveness in pituitary neuroendocrine tumors.

BACKGROUND: Aggressive evolution of PitNETs is rare; metastatic spread is even more. Defining aggressiveness and malignancy is challenging, subsequently hard to predict, and to understand. The aim was to provide a molecular definition of aggressiveness using genomic approaches. METHODS: PitNETs from 206 patients were included. Associations between 9 clinicopathological features of aggressiveness and PitNETs' omics were explored. Omics included transcriptome, DNA methylation, chromosomal alterations, and mutations. Clonal tumor evolution was monitored in 7 patients. RESULTS: Among the 9 clinicopathological features of aggressiveness, only rapid progression, progression after radiotherapy, Ki67/MIB1 proliferation index ≥10%, temozolomide treatment, metastases, and specific death were associated with specific omics signatures, while tumour maximal diameter ≥40 mm, cavernous, and sphenoid invasion were not. The omic signatures associated with these features of aggressiveness overlapped but remained distinct between corticotroph and mammo-somato-thyrotroph lineages. For each lineage, a common signature of aggressiveness was identified, associating a proliferative transcriptome signature and DNA hypermethylation. Alterations in specific genes were associated with aggressive features, including a novel PitNET gene, LRP1B, and known cancer genes (TP53, CDKN2A), while USP8 and GNAS alterations were not. Integration of gene alterations with methylome and transcriptome signatures isolated a subset of molecularly aggressive PitNETs. Molecular signatures were stable during the course of the disease, despite evolution toward aggressiveness and potential clonal divergence. CONCLUSION: This systematic analysis of clinicopathological features of aggressiveness using an integrated multiomic approach establishes a histomolecular definition of aggressiveness in PitNETs. Prospective cohort studies are needed to validate these molecular signatures and establish their prognostic value.

Humans↗
PubMed2006

Population and systems genetics analyses of cortisol in pigs divergently selected for stress.

This study presents a systems genetic analysis on the physiology of cortisol in mice and pigs with an aim to show the potential of a comprehensive computational approach to quickly identify candidate genes and avoid a costly whole-genome quantitative trait locus (QTL) mapping. Population genetics analyses were performed on measurements of cortisol from a pig selection experiment. Expression QTL were mapped and gene networks were built using gene expressions for Crhr1 (corticotrophin-releasing hormone receptor) gene and single nucleotide polymorphisms from public mouse data. Results from mouse data were used to infer potential candidate regulatory genes involved in pig cortisol regulation, using a comparative or translational systems genetics approach. The pig data used were from a 10-yr divergent genetic selection experiment, providing data on 417 individuals. Population genetics analysis showed that cortisol is highly genetically determined with heritabilities of 0.40-0.70. Furthermore, a major gene with an additive effect of 86 ng/ml is segregating. Genetical-genomics investigations revealed two trans-acting eQTL for Crhr1 gene expression on chromosomes 2 and 13. Candidate gene search under trans-eQTL peaks yielded 63 genes for Crhr1 expression phenotypes. Functional links for Crhr1 genes with other genes/proteins in the gene network using mouse data were shown for the first 10 statistically significant genes involved. Results show translational or comparative systems genetics approaches reduce costs and time in large-scale genetics and "-omics" investigations. This is the first study to report a strong genetic basis for cortisol physiology using a systems approach.

Animals↗
PubMed2006

The promise of a virtual lab in drug discovery.

To date, the life sciences 'omics' revolution has not lived up to the expectation of boosting the drug discovery process. The major obstacle is dealing with the volume and diversity of data generated. An enhanced-science (e-science) approach based on remote collaboration, reuse of data and methods, and supported by a virtual laboratory (VL) environment promises to get the drug discovery process afloat. The creation, use and preservation of information in formalized knowledge spaces is essential to the e-science approach. VLs include Grid computation and data communication as well as generic and domain-specific tools and methods for information management, knowledge extraction and data analysis. Problem-solving environments (PSEs) are the domain-specific experimental environments of VLs. Thus, VL-PSEs can support virtual organizations, based on the changing partnerships characteristic of successful drug discovery enterprises.

Computer Simulation↗
PubMed2007

Expression profiling in granulomatous lung disease.

Granulomatous lung diseases, such as sarcoidosis, hypersensitivity pneumonitis, Wegener's granulomatosis, and chronic beryllium disease, along with granulomatous diseases of known infectious etiologies, such as tuberculosis, are major causes of morbidity and mortality throughout the world. Clinical manifestations of these diseases are highly heterogeneous, and the determinants of disease susceptibility and clinical course (e.g., resolution vs. chronic, progressive fibrosis) are largely unknown. The underlying pathogenic mechanisms of these diseases also remain poorly understood. Within this context, these diseases have been approached using genomic and proteomic technologies to allow us to identify patterns of gene/protein expression that track with clinical disease or to identify new pathways involved in disease pathogenesis. The results from these initial studies highlight the potential for these "-omics" approaches to reveal novel insights into the pathogenesis of granulomatous lung disease and provide new tools to improve diagnosis, clinical classification, course prediction, and response to therapy. Realizing this potential will require collaboration among multidisciplinary groups with expertise in the respective technologies, bioinformatics, and clinical medicine for these complex diseases.

Gene Expression Profiling↗
PubMed2026

A nucleolar stress gene signature enables quantitative scoring across multi-omics contexts.

The nucleolus is essential for ribosome biogenesis and cellular homeostasis, and its dysfunction can induce nucleolar stress, a process implicated in cancer and other diseases. However, nucleolar stress is commonly inferred from morphological changes or a limited set of functional assays, and quantitative approaches based on gene expression profiles remain lacking. Here, we integrate literature curation with multi-dataset screening to define a nucleolar stress gene signature and develop a nucleolar stress score (NuS) applicable to bulk transcriptomics, single-cell transcriptomics, proteomics, and spatial transcriptomics. Using this framework, we show in colorectal cancer models that oxaliplatin induces nucleolar stress, suppresses nascent rRNA synthesis, and activates p53 signaling, whereas these responses are attenuated in oxaliplatin-resistant cells. Combined with a ribosome biogenesis activity score (RiboSis), NuS captures related but distinct dimensions of nucleolar function and stratifies tumors into functional states associated with clinical outcomes. NuS-based analysis of perturbational transcriptomes further prioritizes compounds with putative nucleolar stress-inducing activity. Collectively, this study provides a quantitative framework for evaluating nucleolar stress and illustrates its applications in disease stratification and drug mechanism discovery.

Cell Nucleolus↗
PubMed2025

Study research protocol for Phenome India-CSIR Health Cohort Knowledgebase: A prospective multi-modal follow-up study on a nationwide employee cohort.

Predicting individual health trajectories based on risk scores can help formulate effective preventive strategies for diseases and their complications. Currently, most risk prediction algorithms rely on epidemiological data from the Caucasian population, which often do not translate well to the Indian population due to ethnic diversity, differing dietary and lifestyle habits, and unique risk profiles. In this multi-center prospective longitudinal study conducted across India, we aim to address these challenges by developing clinically relevant risk prediction scores for cardio-metabolic diseases specifically tailored to the Indian population. India, which accounts for nearly 18% of the global population, also has a significant diaspora worldwide. This program targets longitudinal collection and bio-banking of samples from over 10 000 employees both working and retirees of the Council of Scientific and Industrial Research and their spouses, with baseline sample collection already completed. During the baseline collection, we gathered multi-parametric data including clinical questionnaires, lifestyle and dietary habits, anthropometric parameters, lung function assessments, liver elastography by Fibroscan, electrocardiogram readings, biochemical data, and molecular assays, including but not limited to genomics, plasma proteomics, metabolomics, and fecal microbiome analysis. In addition to exploring associations between these parameters and their cardio-metabolic outcomes, we plan to employ artificial intelligence algorithms to develop predictive models for phenotypic conditions. This study could pave the way for precision medicine tailored to the Indian population, particularly for the middle-income strata, and help refine the normative values for health and disease indicators in India.

cardio-metabolic↗
PubMed2025

Integrative genomics elucidates the evolutionary, temporal, and developmental origins of a hydrocephalus risk gene.

INTRODUCTION: A prior integrative, multi-omics human genetics and functional genomics study identified maelstrom (MAEL), a gene involved in regulation of DNA transposon activity and genome structure, as a transcriptome-wide predictor of hydrocephalus (HC) in the brain cortex. Here we expand on this discovery and further characterize the evolutionary origin and expression of MAEL across developmental timescales and cell-lineages in the neonatal human brain towards a mechanistic understanding how variation in MAEL expression may cause HC. OBJECTIVE: To characterize the evolutionary, temporal, developmental, and lineages of MAEL expression in HC and the developing human brain. METHODS: Ensembl was used to delineate the evolution and taxonomy of MAEL across species. Analysis of single-cell RNA sequencing (scRNA-seq) of 49 brain regions across pre- and post-natal timescales from the Developing Human Brain Atlas (Allen Institute) identified temporal and spatial MAEL expression patterns. We quantified MAEL expression in primary cortical brain tissue obtained during the surgical treatment of HC. RESULTS: We performed taxonomic gene-mapping to define the evolutionary origin of MAEL to assess suitability for mechanistic characterization in vitro and in vivo across species. We find that MAEL is among the top 0.01% human-specific genes and < 50% sequence homology among commonly used model organisms with highly divergent functions, necessitating mechanistic validation in human tissue. scRNA-seq of the non-disease prenatal human brain identified MAEL expression enriched in cortical excitatory neurons, which was recapitulated in primary HC brain tissue obtained during surgery. Finally, using scRNA-seq of primary HC brain tissue, we functionally validated reduced MAEL expression, consistent with a prior human TWAS analysis. CONCLUSIONS: We identify the evolutionary, temporal, and developmental expression pattern of MAEL in the neonatal human brain. We also provide direct evidence for reduced MAEL expression in human HC brain tissue. These data, at least in part, implicate reduced MAEL expression underlying human HC across etiologies.

Journal Article↗
PubMed2026

Deciphering CD8+ T cell exhaustion in human cancers through single-cell and spatial transcriptomics.

Exhausted CD8+ T cells (Tex) within the tumor microenvironment (TME) represents a critical barrier limiting anti-tumor immune responses. Tex cells are characterized by upregulated inhibitory immune checkpoint receptors, reduced cytotoxicity, and functional heterogeneity. Their genomic features and regulatory networks remain poorly defined, and only a minority of patients respond to immune checkpoint blockade (ICB) therapy. Single-cell RNA sequencing (scRNA-seq), through high-resolution transcriptomic profiling, has revealed diverse Tex subpopulations, identified subpopulation-specific marker genes and regulatory pathways. Spatial transcriptomics has further mapped the spatial distribution of Tex and their interaction networks with immune cells, tumor cells, and stromal cells, elucidating the impact of spatial heterogeneity on Tex functionality. Current studies indicate that the exhausted state of Tex is dynamic and modifiable, with functional differences among subpopulations closely associated with tumor progression and therapeutic response. However, the genomic characteristics, epigenetic regulation, and spatial interaction mechanisms of Tex require further exploration. This review summarizes recent advances in high-resolution omics technologies for precisely dissecting Tex heterogeneity, functional features, and interactions with other cells. It emphasizes the central value of optimizing Tex-targeted tumor immunotherapy strategies, providing theoretical foundations and directional guidance for developing more effective anti-tumor immunotherapies.

Humans↗
PubMed2026

Integrative analysis of rumen microbiota activity and host metabolism following methanogenesis inhibition in dairy cattle.

Enteric methane emission from dairy cattle is an environmental challenge. The most efficient mitigation strategies nowadays include the use of methanogenesis inhibitors that specifically target the rumen methanogens. Specific inhibitors, such as 3-nitrooxypropanol (3-NOP), reduce methane emissions without negative effects on the products of fermentation that serve as energy metabolites for the host. However, the concomitant effects of methanogenesis inhibition on rumen microbiota and host metabolism are poorly characterized. Thus, the objective of this study was to explore the association between rumen microbiota and host metabolism when methanogenesis is inhibited. Thirteen dairy cows were used as controls, and 12 were supplemented with 3-NOP for 6 weeks. Rumen microbiota composition and activity were characterized using metagenomics and metatranscriptomics. The host metabolism was assessed in a previous publication by a metabolomic analysis of the plasma. Microbiota data were used as explanatory variables of the metabolome data in a multiblock sparse partial least squares analysis. Overall, the association between rumen microbiota and host metabolism was moderate. Notwithstanding this, a few downregulated transcripts related to glycolysis, hydrogen transfer, and protein synthesis, together with a decrease in the proportion of taxa of the Oscillospirales order, showed a correlation with host one-carbon metabolites (|r| > 0.6). These associations raised novel hypotheses that remain to be elucidated, especially with regard to the effects of dihydrogen on the accumulation of microbial glycolysis and methanogenesis metabolite intermediates.IMPORTANCEDairy cattle produce a substantial amount of methane, a potent greenhouse gas. Several strategies have been designed to reduce methane production by targeting the rumen microbiota. One such strategy specifically inhibits methanogens with a molecule called 3-nitrooxypropanol. This study uses an integrative data analysis approach, combining rumen microbiota and host metabolome information, to explore the consequences of inhibiting methanogenesis on the holobiont. This provides additional holistic insight into the effect of methane mitigation strategies on dairy cattle.

Animals↗
PubMed2026

A single-cell study of transcription and RNA splicing in MDD and ALC.

Major depressive disorder (MDD) and problematic alcohol use (ALC) commonly co-occur, yet the extent, genomic distribution, and biological context of their shared genetic architecture remain incompletely understood. Here, we integrated genome-wide and local genetic architecture analyses with tissue, spatial, single-cell, and multi-omics analyses to characterize the shared genetic basis of MDD and ALC. Across methods, the two phenotypes showed a consistent positive genetic correlation (rg = 0.380-0.582). MiXeR estimated that they shared approximately 5479 variants with non-zero additive genetic effects, with the shared component accounting for a larger proportion of the polygenic architecture of ALC than of MDD. Local analyses further indicated that shared genetic covariance was concentrated in a limited number of genomic segments. At the tissue and cellular levels, genetic signals were primarily associated with central nervous system tissues and neuronal lineages, with additional support for oligodendrocyte-related populations; the two phenotypes also differed in the distribution and within-cell-type heterogeneity of disease-relevance scores. Multi-omics integration prioritized MED19 and ACO2 as candidate genes and highlighted processes related to mitochondrial energy metabolism and synaptic function. These findings refine the genomic, tissue, and cellular context of the shared genetic architecture of MDD and ALC and provide prioritized genomic regions, cell types, and candidate genes for validation in independent populations and functional studies.

Major Depressive Disorder↗
PubMed2025

Multidimensional OMICs reveal ARID1A orchestrated control of DNA damage, splicing, and cell cycle in normal-like and malignant urothelial cells.

Epigenetic regulators, such as the SWI/SNF complex, with important roles in tissue development and homeostasis, are frequently mutated in cancer. ARID1A, a subunit of the SWI/SNF complex, is mutated in approximately 20% of all bladder tumors; however, the consequences of this remain poorly understood. Finding truncations to be the most common mutation, we generated loss- and gain-of-function models to conduct RNA-Seq, interactome analyses, Omni-ATAC-Seq, and functional studies to characterize ARID1A-affected pathways potentially suitable for the treatment of ARID1A-deficient bladder cancers. We observed decreased cell proliferation and deregulation of stress-regulated pathways, including DNA repair, in ARID1A-deficient cells. Furthermore, ARID1A was linked to alternative splicing and translational regulation on RNA and interactome levels. ARID1A deficiency drastically reduced the accessibility of chromatin, especially around introns and distal enhancers, in a functional enrichment analysis. Less accessible chromatin areas were mapped to pathways such as cell proliferation and DNA damage response. Indeed, the G2/M checkpoint appeared impaired after DNA damage in ARID1A-deficient cells. Together, our data highlight the broad impact of ARID1A loss and the possibility of targeting proliferative and DNA repair pathways for treatment.

Transcription Factors↗
PubMed2004

Genome data mining of lactic acid bacteria: the impact of bioinformatics.

Lactic acid bacteria (LAB) have been widely used in food fermentations and, more recently, as probiotics in health-promoting food products. Genome sequencing and functional genomics studies of a variety of LAB are now rapidly providing insights into their diversity and evolution and revealing the molecular basis for important traits such as flavor formation, sugar metabolism, stress response, adaptation and interactions. Bioinformatics plays a key role in handling, integrating and analyzing the flood of 'omics' data being generated. Reconstruction of metabolic potential using bioinformatics tools and databases, followed by targeted experimental verification and exploration of the metabolic and regulatory network properties, are the present challenges that should lead to improved exploitation of these versatile food bacteria.

Adaptation, Biological↗