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Results for “Transcriptome Dynamics”

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PubMed2026

Spatiotemporal single-cell profiling reveals T cell clonal dynamics and phenotypic plasticity in human graft-versus-host disease.

Allogeneic hematopoietic cell transplantation cures hematologic diseases but is limited by acute graft‑versus‑host disease. How human T cell clones drive epithelial injury remains poorly mapped. We studied 31 transplant recipients, integrating longitudinal T cell antigen receptor (TCR) profiling with single-cell RNA sequencing/TCR sequencing and spatial transcriptomics to track T cell clonal dynamics. We developed DecompTCR to resolve temporal dynamics and adapted computational tools to map clone phenotypes and niches in tissue. Our analyses revealed that cyclophosphamide selectively depletes alloreactive clones, although insufficient early expansion leads to incomplete depletion and severe disease. Severe graft‑versus‑host disease is marked by persistent expansion of alloreactive clones, rewiring of homeostatic cell types and diversification of donor-derived CD8+ clonotypes that acquire Hobit (ZNF683)+ tissue‑resident memory T (TRM) cell programs during migration to epithelium. Spatial deconvolution identified CD8+ effector/Hobit+ TRM hubs near intestinal stem‑cell-rich crypt bases and crypt‑loss regions. This clonotype‑resolved framework links tissue‑instructed TRM cell remodeling to localized epithelial injury, nominating early-repertoire dynamics and spatial hub burden as biomarkers.

Journal Article↗

PubMed2005

A global regulatory role of gluconeogenic genes in Escherichia coli revealed by transcriptome network analysis.

In bacterial adaptation to the dynamic environment, metabolic genes are typically thought to be the executors, whereas global transcription regulators are regarded as the decision makers. Although the feedback from metabolic consequence is believed to be important, much less is understood. This work demonstrates that the gluconeogenic genes in Escherichia coli, ppsA, sfcA, and maeB, provide a feedback loop to the global regulator, cAMP receptor protein (CRP), in carbon source transition. Disruption of one of the gluconeogenic pathways has no phenotype in balanced growth, but causes a significant delay in the diauxic transition from glucose to acetate. To investigate the underlying mechanism, we measured the transcriptome profiles during the transition using DNA microarray, and network component analysis was employed to obtain the transcription factor activities. Results showed that one of the global regulators, CRP, was insufficiently activated during the transition in the ppsA deletion mutant. Indeed, addition of cAMP partially rescued the delay in transition. These results suggest that the gluconeogenic flux to phosphoenolpyruvate is important for full activation of adenylate cyclase through the phosphorylated enzyme IIA(glu) of the phosphotransferase system. Reduction of this flux causes insufficient activation of CRP and a global metabolic deficiency, which exemplifies a significant feedback interaction from metabolism to the a global regulatory system.

Acetates↗

PubMed2006

Toxicogenomic response of Staphylococcus aureus to peracetic acid.

Staphylococcus aureus is responsible for many incidents of hospital-acquired infection, which causes 90,000 deaths and dollars 4.5 billion loss a year in the United States. Despite a wide use of disinfectants such as peracetic acid in health care environments, we certainly need better understanding of the effects of antimicrobial application on target pathogens to avert infection outbreaks. Consequently, herein, we explored for the first time the toxicogenomic response of S. aureus to a sublethal concentration of peracetic acid (1 mM) by using microarray-based transcriptome analysis. In particular, we investigated the dynamics of global gene expression profiles during its cellular response, which involved initial growth inhibition (10 min) and subsequent partial recovery (20 min). Further, we compared transcriptome responses to peracetic acid between S. aureus and Pseudomonas aeruginosa. Our findings show that (i) the regulation of membrane transport genes was significantly altered, (ii) DNA repair and replication genes were selectively induced, and (iii) primary metabolism-related genes were differently repressed between the two growth states. Most intriguingly, we revealed that many virulence factor genes were induced upon the exposure, which proposes a possibilitythatthe pathogenesis of S. aureus may be stimulated in response to peracetic acid.

Anti-Infective Agents↗

PubMed2025

Revealing cancer driver genes through integrative transcriptomic and epigenomic analyses with Moonlight.

Cancer involves dynamic changes caused by (epi)genetic alterations such as mutations or abnormal DNA methylation patterns which occur in cancer driver genes. These driver genes are divided into oncogenes and tumor suppressors depending on their function and mechanism of action. Discovering driver genes in different cancer (sub)types is important not only for increasing current understanding of carcinogenesis but also from prognostic and therapeutic perspectives. We have previously developed a framework called Moonlight which uses a systems biology multi-omics approach for prediction of driver genes. Here, we present an important development in Moonlight2 by incorporating a DNA methylation layer which provides epigenetic evidence for deregulated expression profiles of driver genes. To this end, we present a novel functionality called Gene Methylation Analysis (GMA) which investigates abnormal DNA methylation patterns to predict driver genes. This is achieved by integrating the tool EpiMix which is designed to detect such aberrant DNA methylation patterns in a cohort of patients and further couples these patterns with gene expression changes. To showcase GMA, we applied it to three cancer (sub)types (basal-like breast cancer, lung adenocarcinoma, and thyroid carcinoma) where we discovered 33, 190, and 263 epigenetically driven genes, respectively. A subset of these driver genes had prognostic effects with expression levels significantly affecting survival of the patients. Moreover, a subset of the driver genes demonstrated therapeutic potential as drug targets. This study provides a framework for exploring the driving forces behind cancer and provides novel insights into the landscape of three cancer sub(types) by integrating gene expression and methylation data.

Humans↗

PubMed2005

Metabolomic networks in plants: Transitions from pattern recognition to biological interpretation.

Nowadays techniques for non-targeted metabolite profiling allow for the generation of huge amounts of relevant data essential for the construction of dynamic metabolomic networks. Thus, metabolomics, besides transcriptomics or proteomics, provides a major tool for the characterization of postgenomic processes. In this work, we introduce comparative correlation analysis as a complementary approach to characterize the physiological states of various organs of diverse plant species with focus on specific participation of metabolites in different reaction networks. The correlations observed are induced by diminutive fluctuations in environmental conditions, which propagate through the system and induce specific patterns depending on the genomic background. In order to examine this hypothesis, numeric examples of such fluctuations are computed and compared with experimentally obtained metabolite data.

Algorithms↗

PubMed2004

Soluble factors from human endometrium promote angiogenesis and regulate the endothelial cell transcriptome.

BACKGROUND: Angiogenesis and vascular remodeling play critical roles in the cyclical growth and regression of endometrium. They also appear to play roles in the pathogenesis of endometriosis. METHODS AND RESULTS: Supernatants were collected from cultured endometrium isolated from women with and without endometriosis. These supernatants induced endothelial cell proliferation and angiogenesis in vitro. They contained vascular endothelial growth factor (VEGF)-A, and their proliferative effects on endothelial cells were partially abrogated by a blocking anti-VEGF-A antibody. Gene array analysis showed that culture supernatants from proliferative phase endometrium, and to a lesser extent secretory phase endometrium, induced significant changes in the transcriptome of endothelial cells. We could not detect any association between endometriosis and the ability of endometrial-derived soluble factors to promote angiogenesis or to regulate the endothelial transcriptome. In addition, we could not detect any association between endometriosis and the concentration of VEGF-A in supernatants from cultured endometrium or in menstrual effluent. CONCLUSIONS: We have shown that endometrium cultured in vitro produced soluble factors, including VEGF-A, that promoted angiogenesis. Proliferative phase endometrium promoted significant endothelial cell transcriptome changes that appear overall to be pro-angiogenic. These transcriptome changes provide insight into the dynamic control of vessel structure on which both eutopic endometrium and endometriotic lesions depend.

Case-Control Studies↗

PubMed2025

MitoScribe single-cell molecular recorder logs graded signaling dynamics into mitochondrial DNA.

Genetically encoded DNA recorders convert transient biological events into stable genomic mutations, offering a means to reconstruct past cellular states. However, current approaches to log historical events by modifying genomic DNA have limited capacity to record the magnitude of biological signals within individual cells. Here, we introduce MitoScribe, a mitochondrial DNA (mtDNA)-based recording platform that uses mtDNA base editors (DdCBEs) to write graded biological signals into mtDNA as neutral, single-nucleotide substitutions at a defined site. Taking advantage of the hundreds to thousands of mitochondrial genome copies per cell, we demonstrate MitoScribe enables reproducible, highly sensitive, non-destructive, durable, and high-throughput measurements of molecular signals, including hypoxia, NF-κB activity, BMP and Wnt signaling. We show multiple modes of operation, including multiplexed recordings of two independent signals, and coincidence detection of temporally overlapping signals. Coupling MitoScribe with single-cell RNA sequencing and mitochondrial transcript enrichment, we further reconstruct signaling dynamics at the single-cell transcriptome level. Applying this approach during the directed differentiation of human induced pluripotent stem cells (iPSCs) toward mesoderm, we show that early heterogeneity in response to a differentiation cue predicts the later cell state. Together, MitoScribe provides a scalable platform for high-resolution molecular recording in complex cellular contexts.

Journal Article↗

PubMed2025

Through the lens of bioenergy crops: advances, bottlenecks, and promises of plant engineering.

Advances in engineering of bioenergy crops were driven over the past years by adapting technological breakthroughs and accelerating conventional applications but also exposed intriguing challenges. New tools revealed rich interconnectivity in the exponentially growing and dynamic 'big' omics data' of metabolomes, transcriptomes, and genomes at previously inaccessible magnitude (global, cross-species, meta-) and resolution (single cell). Insights enabled fresh hypotheses and stimulated disciplines such as functional genomics with discovery of broad regulatory networks and their determinants, that is, DNA parts, including promoters, regulatory elements, and transcription factors. Their rational design, assembly into increasingly complex blueprints, and installation into diverse chassis is an existing frontier that may benefit from emerging technologies to address bottlenecks. Interweaving nature-inspired to fully synthetic parts has already allowed building of fine-tuned regulatory circuits, or new-to-nature metabolic routes insulated from the biological context of the chassis species. Similarly, developments and the evolving need for unifying principles in plant transformation and species-agnostic technologies highlight future opportunities for engineering the next generation of bioenergy plants.

Crops, Agricultural↗

PubMed2026

Uncovering hub genes and key pathways responsive to drought stress in rice via meta-analysis of transcriptomic data.

Drought stress presents a formidable threat to global rice cultivation, triggering complex molecular responses that impact plant growth and productivity. To decipher the underlying gene expression dynamics, we performed a comprehensive meta-analysis of transcriptomic datasets derived from drought-tolerant rice genotypes. Via microarray data from three independent studies, we identified a set of consistently expressed differentially expressed genes (DEGs) under drought conditions. Integration of functional annotation tools, including GO and KEGG pathway enrichment, revealed key biological processes and signaling cascades involved in stress mitigation, such as ABA signaling, protein folding, and photosynthesis suppression. Protein-protein interaction (PPI) network construction, followed by hub gene identification via maximal clique centrality (MCC), highlighted pivotal regulators including LEA proteins, dehydrins, HSP70, and several transcription factors. Machine learning approaches further prioritize potential biomarkers, with Random Forest models achieving high classification accuracy and pinpointing key predictive genes. Chromosomal localization analysis provided spatial insights into the distribution of these hub genes, whose expression patterns were further compared against qRT-PCR data from previously published studies. This integrative approach identifies candidate genomic markers and mechanistic insights that may support future breeding strategies for drought-tolerant rice, pending experimental validation.

Cytoscape↗

PubMed2005

A microarray data-based semi-kinetic method for predicting quantitative dynamics of genetic networks.

BACKGROUND: Elucidating the dynamic behaviour of genetic regulatory networks is one of the most significant challenges in systems biology. However, conventional quantitative predictions have been limited to small networks because publicly available transcriptome data has not been extensively applied to dynamic simulation. RESULTS: We present a microarray data-based semi-kinetic (MASK) method which facilitates the prediction of regulatory dynamics of genetic networks composed of recurrently appearing network motifs with reasonable accuracy. The MASK method allows the determination of model parameters representing the contribution of regulators to transcription rate from time-series microarray data. Using a virtual regulatory network and a Saccharomyces cerevisiae ribosomal protein gene module, we confirmed that a MASK model can predict expression profiles for various conditions as accurately as a conventional kinetic model. CONCLUSION: We have demonstrated the MASK method for the construction of dynamic simulation models of genetic networks from time-series microarray data, initial mRNA copy number and first-order degradation constants of mRNA. The quantitative accuracy of the MASK models has been confirmed, and the results indicated that this method enables the prediction of quantitative dynamics in genetic networks composed of commonly used network motifs, which cover considerable fraction of the whole network.

Amino Acid Motifs↗

PubMed2002

Analysis of altered genomic expression profiles in the senescent and diseased myocardium using cDNA microarrays.

Cardiac function deteriorates with aging or disease. Short term, any changes in heart function may be beneficial, but long term the alterations are often detrimental. At a molecular level, functional adaptations involve quantitative and qualitative changes in gene expression. Analysis of all the RNA transcripts present in a cell's population (transcriptome) offers unprecedented opportunities to map these transitions. Microarrays (chips), capable of evaluating thousands of transcripts in one assay, are ideal for transcriptome analyses. Gene expression profiling provides information about the dynamics of total genome expression in response to environmental changes and may point to candidate genes responsible for the cascade of events that result in disease or are a consequence of aging. The aim of this review is to describe how comparisons of cellular transcriptomes by cDNA array based techniques provide information about the dynamics of total gene expression, and how the results can be applied to the study of cardiovascular disease and aging.

Cardiovascular Diseases↗

PubMed2006

Interpreter of maladies: redescription mining applied to biomedical data analysis.

Comprehensive, systematic and integrated data-centric statistical approaches to disease modeling can provide powerful frameworks for understanding disease etiology. Here, one such computational framework based on redescription mining in both its incarnations, static and dynamic, is discussed. The static framework provides bioinformatic tools applicable to multifaceted datasets, containing genetic, transcriptomic, proteomic, and clinical data for diseased patients and normal subjects. The dynamic redescription framework provides systems biology tools to model complex sets of regulatory, metabolic and signaling pathways in the initiation and progression of a disease. As an example, the case of chronic fatigue syndrome (CFS) is considered, which has so far remained intractable and unpredictable in its etiology and nosology. The redescription mining approaches can be applied to the Centers for Disease Control and Prevention's Wichita (KS, USA) dataset, integrating transcriptomic, epidemiological and clinical data, and can also be used to study how pathways in the hypothalamic-pituitary-adrenal axis affect CFS patients.

Algorithms↗

PubMed2026

Uncovering hidden complexity in the Apis mellifera mitotranscriptome: a polyadenylation-centered perspective.

Mitochondrial transcription is gaining increasing attention as researchers seek to better understand the full coding potential of mitochondrial DNA (mtDNA). Emerging evidence suggests that mtDNA may encode additional elements beyond classical oxidative phosphorylation genes, pointing to a more complex transcriptional architecture than previously recognized. In this study, we explored the mitochondrial transcriptome of Apis mellifera (Insecta: Hymenoptera), with a particular focus on polyadenylation-associated features. Our analysis revealed that both sense and antisense transcripts undergo polyadenylation, although transcript abundance and poly(A) tail lengths varied markedly across mitochondrial genes. Several transcripts exhibited alternative isoforms, either extended or truncated, frequently including intergenic regions. These regions may represent functional non-coding elements or structural variants rather than conventional untranslated regions (UTRs). Interestingly, some transcripts also contained non-templated nucleotide additions particularly cytosine residues immediately upstream of the poly(A) tails. Monocistronic units that included portions of downstream intergenic regions were among the most abundantly represented, suggesting a possible regulatory role for these sequences. To experimentally validate our in silico findings, we performed RT-qPCR to assess relative gene expression and applied 3' RACE-PCR to define transcript boundaries. These approaches confirmed the presence of multiple transcript isoforms and supported the involvement of polyadenylation in shaping mitochondrial RNA diversity. Together, our findings reveal a previously underappreciated level of complexity in the A. mellifera mitochondrial transcriptome and highlight the potential regulatory significance of polyadenylation dynamics and intergenic region transcription.

Animals↗

PubMed2026

Bridging Organ-on-a-Chip and Omics: A Multi-Dimensional Frontier in Biomedical Research.

Organ-on-a-Chip (OOC) technology offers a powerful platform for replicating human tissue-specific microenvironments, thereby narrowing the translational gap between conventional biomedical models and actual human physiology. Concurrently, omics technologies deliver comprehensive molecular-level insights into biological systems. This review highlights the transformative potential of integrating OOC platforms with high-throughput omics methodologies. We systematically examine the classification, structural configurations, and engineering principles underlying OOC systems, alongside the defining attributes of key omics domains-genomics, transcriptomics, proteomics, and metabolomics. The convergence of dynamic OOC models with advanced omics technologies enables high-resolution, multi-dimensional analyses across numerous biomedical applications, including drug metabolism, disease mechanisms, environmental toxicity assessments, and host-microbiome interactions. This interdisciplinary integration is driving a paradigm shift in precision and translational medicine. However, several challenges remain to be addressed, such as the development of whole-organ mimetics, adaptation of sample collection techniques, and real-time artificial intelligence-based integration of biosensor data with multi-omics datasets. Addressing these hurdles will be vital for unlocking the full potential of this technological synergy in biomedical science.

Multiomics↗

PubMed2026

Comparative genomics reveals genotype-phenotype concordance and cryptic resistomes in clinical Pseudomonas aeruginosa.

BACKGROUND: Pseudomonas aeruginosa (P. aeruginosa) is a major pathogen because of its adaptability. It shows rapid evolution of multidrug resistance (MDR). Phenotype-based diagnostics often fail to detect silent resistance determinants and early adaptive changes. This study integrates phenotypic profiling with whole-genome sequencing (WGS) to examine resistance architecture in clinical isolates from eastern India. METHODS: From 1295 culture-positive P. aeruginosa specimens collected at a tertiary care hospital in eastern India. Using predefined criteria, representative MDR and non-MDR isolates were selected, including distinct resistance phenotypes, specimen-source diversity, and hospital and community-acquired settings; multivariate analysis of resistance profiles illustrated phenotypic diversity. Antimicrobial susceptibility assessed using VITEK-2 and Kirby-Bauer disk diffusion, species identity confirmed by 16 S rRNA sequencing, and genomic analysis processed through a reference-guided workflow. Antimicrobial Resistance (AMR) determinants were identified through CARD, and phylogenetic tree constructed from 454 publicly available P. aeruginosa genomes. RESULTS: MDR exhibited greater sequence divergence relative to PA14 (~ 69,000 variants) than the non-MDR isolate (~ 58,700 variants), with > 92% coverage at ≥ 30X depth. Strong genotype-phenotype concordance observed in MDR isolates across five antibiotic classes, associated with β-lactamase variants (PDC-67, OXA-396) and regulatory adaptations (ArmR, cprS). The non-MDR isolate harboured gyrA (T83I) resistance-associated mutations, PDC-1, and OXA-847 without phenotypic expression, indicating silent resistome. Phylogenetically, MDR isolates clustered tightly within the phylogeny, while the non-MDR isolate formed a distinct lineage. CONCLUSION: Observed genomic differences align with adaptation under antimicrobial selection, though confirmation requires larger collections. The non-MDR isolate retained a silent resistome. Findings highlight limitations of phenotype-only diagnostics, support genomic data integration, and emphasize transcriptomics for hidden resistance expression and regulatory dynamics.

Pseudomonas aeruginosa↗

PubMed2004

Progress in the use of microarray technology to study the neurobiology of disease.

The diverse functions of the brain are mediated by neurons and glia whose phenotype is defined by a dynamically maintained set of gene transcripts, or 'transcriptome'. Large-scale analysis of gene expression in postmortem brain using microarray technology has the potential to elucidate molecular changes that occur in disease states. There are unique challenges associated with studies of postmortem brain, including limited sample sizes and variable clinical phenotypes that are typical of complex disorders. Nevertheless, recent microarray-based studies have implicated both individual dysregulated genes and abnormal patterns of gene expression in brain disorders.

Animals↗

PubMed2006

Systematic proteome and transcriptome analysis of stem cell populations.

Methods for relative assessment of the transcriptional activity of the cell are now routinely employed and obtain large amounts of information regarding process such as transformation or development. These approaches have great impact and are of significant value. Nonetheless, mRNA is an intermediate in the process of protein synthesis and changes in mRNA expression do not reflect absolute or relative changes in protein levels. The mechanisms which translate mRNA to protein are highly regulated, and it remains unclear how the transcriptome reflects the functional state of the cell, as defined by its protein output. Large scale analyses of the proteome are now becoming a reality due to technical advances in protein arrays and mass spectrometry. Thus for the first time data on large numbers of mRNA transcripts and the levels of expression of their associated proteins is available in dynamic systems. Analysis of one such comparison, the transcriptome and proteome of primary haematopoietic stem cells, reveals post-translational regulation of the proteome in stem cell populations. The factors which must be considered when comparing two systematically acquired 'omics' datasets are reviewed and the relative merits of transcriptome and proteome approaches are discussed.

Animals↗

PubMed2026

Four-dimensional molecular mapping from a spatial snapshot reveals the dynamics of hair follicle organogenesis.

Understanding organ formation requires capturing molecular information simultaneously in three-dimensional (3D) space and across developmental time. To this end, we developed 3D DNase-Enhanced Expression Profiling (3DEEP), a tissue-clearing approach that removes genomic DNA to extend spatial transcriptomic profiling hundreds of microns into intact tissues. We applied 3DEEP to neonatal mouse skin, capturing hundreds of developing hair follicles across their organogenesis trajectory. Ordering follicles by molecularly inferred developmental age transformed this single spatial snapshot into a four-dimensional (3D + time) molecular map of organogenesis. This map revealed developmental dynamics spanning stem cell compartment stratification, emergence of new cell subtypes within the follicle, and cascading structural transformations leading to hair canal formation. Comparative analysis of Foxn1-deficient nude mice, a hairlessness model, revealed organ-wide changes in developmental dynamics, including delayed molecular progression, reduced coordination, and increased developmental instability, preceding overt structural defects. This work demonstrates how deep-tissue spatial transcriptomics can uncover hidden dynamics of organ formation.

Animals↗