SEARCH · PubMed Health

Results for “Transcriptome Dynamics”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 73 records · Page 4Linked to original sources

PubMed2025

Spatiotemporal profile of an optimal host response to virus infection in the primate central nervous system.

Viral infections of the central nervous system (CNS) are a major cause of morbidity largely due to lack of prevention and inadequate treatments. While mortality from viral CNS infections is significant, nearly two thirds of the patients survive. Thus, it is important to understand how the human CNS can successfully control virus infection and recover. Since it is not possible to study the human CNS throughout the course of viral infection at the cellular level, here we analyzed a non-lethal viral infection in the CNS of nonhuman primates (NHPs). We inoculated NHPs intracerebrally with a high dose of La Crosse virus (LACV), a bunyavirus that can infect neurons and cause encephalitis primarily in children, but with a very low (≤ 1%) mortality rate. To profile the CNS response to LACV infection, we used an integrative approach that was based on comprehensive analyses of (i) spatiotemporal dynamics of virus replication, (ii) identification of types of infected neurons, (iii) spatiotemporal transcriptomics, and (iv) morphological and functional changes in CNS intrinsic and extrinsic cells. We identified the location, timing, and functional repertoire of optimal transcriptional and translational regulation of the primate CNS in response to virus infection of neurons. These CNS responses involved a well-coordinated spatiotemporal interplay between astrocytes, lymphocytes, microglia, and CNS-border macrophages. Our findings suggest a multifaceted program governing an optimal CNS response to virus infection with specific events coordinated in space and time. This allowed the CNS to successfully control the infection by rapidly clearing the virus from infected neurons, mitigate damage to neurophysiology, activate and terminate immune responses in a timely manner, resolve inflammation, restore homeostasis, and initiate tissue repair. An increased understanding of these processes may provide new therapeutic opportunities to improve outcomes of viral CNS diseases in humans.

Animals↗

PubMed2026

Integrated dual transcriptome sequencing and experimental validation reveal potential mechanisms of baicalin against pneumocystis pneumonia in immunosuppressed rats.

BACKGROUND: Pneumocystis pneumonia (PCP) remains a major cause of morbidity and mortality in immunocompromised individuals. Although baicalin (Ba), a natural bioactive flavonoid, has demonstrated protective and therapeutic effects against PCP, its molecular mechanisms remain undefined. We employed dual RNA sequencing (dual RNA-seq) to characterize host and pathogen transcriptional responses to Ba treatment in an immunosuppressed rat model of PCP. METHODS: Comparative transcriptomic analyses identified differentially expressed genes in both the host and Pneumocystis, followed by Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and gene set enrichment analyses. Candidate targets were further investigated using network pharmacology, protein-protein interaction analysis, molecular docking, and molecular dynamics simulations. Key findings were validated by immunohistochemistry, enzyme-linked immunosorbent assay, and quantitative PCR. RESULTS: Ba markedly remodeled host and pathogen transcriptomes. Host transcriptomic analyses showed that Ba attenuated inflammatory and oxidative stress responses by modulating immune-related pathways, including Toll-like receptor, NF-κB, cytokine-cytokine receptor interaction, chemokine signaling, Th17 cell differentiation, and antigen processing and presentation. Experimental validation demonstrated that Ba reduced pulmonary expression of indoleamine 2,3-dioxygenase 1 (IDO1), Toll-like receptor 2 (TLR2), and TLR4 while increasing nuclear factor erythroid 2-related factor 2 (Nrf2) and its downstream antioxidant enzyme heme oxygenase-1 (HO-1). Pathogen transcriptomic analysis identified Pneumocystis Rtt109 (PcRtt109), a fungal histone acetyltransferase, as a potential pathogen-specific target that was significantly downregulated after Ba treatment. Molecular docking and molecular dynamics simulations supported stable interactions between Ba and IDO1, Nrf2, TLR2, TLR4, and PcRtt109, with the strongest predicted binding observed for PcRtt109. CONCLUSION: Dual RNA-seq revealed that Ba exerts anti-PCP activity through coordinated modulation of host and pathogen molecular networks. Its therapeutic effects are associated with suppression of inflammatory signaling, enhancement of antioxidant defenses, and inhibition of a fungal virulence-associated target. These findings provide mechanistic insights into host-pathogen interactions during PCP and support Ba as a potential therapeutic candidate for PCP.

Nrf2↗

PubMed2026

Transcriptome analysis of the pectoral fin degeneration in half-smooth tongue sole (Cynoglossus semilaevis).

Appendage degeneration is a notable morphological feature of some teleosts with specialized benthic lifestyles. The half-smooth tongue sole (Cynoglossus semilaevis) undergoes severe pectoral fin regression during metamorphosis. However, the molecular basis underlying rapid pectoral fin degeneration remains unclear. Here, we performed time-series transcriptome sequencing on pectoral fins at pre-metamorphosis, metamorphosis peak and post-metamorphosis to characterize the molecular changes associated with pectoral fin degeneration. Transcriptional dynamics and functional enrichment showed that no significant enrichment of classical apoptosis-related transcriptional pathways was detected during pectoral fin degeneration. Instead, sustained downregulation of twist1b, identified as a transcriptomic candidate, together with significant upregulation of ssh1, coupled with enrichment of lysosome and ubiquitin-proteasome system (UPS) pathways, suggested enhanced tissue remodeling during pectoral fin degeneration. Temporal expression clustering revealed heterochronic misalignment in the developmental gene expression: upstream initiator tbx5 was upregulated at early metamorphosis, while downstream maintenance signal fgf10 decreased synchronously. Distal patterning gene hoxd12a exhibited premature expression and rapid decay, losing sustained late-phase expression. Moreover, transient elevation of gli3 during metamorphosis may contribute to restricted distal fin growth. We conclude that pectoral fin degeneration in C. semilaevis is associated with heterochronic disruption of developmental signaling and extensive tissue remodeling. This study provides transcriptomic insights into pectoral fin degeneration in tongue soles and establishes a basis for future functional studies of appendage reduction in teleosts.

Animals↗

PubMed2025

Genomics-informed drug-repurposing strategy identifies two therapeutic targets for preventing liver disease associated with metabolic dysfunction.

Identification of drug-repurposing targets with genetic and biological support is an economically and temporally efficient strategy for improving the treatment of diseases. We employed a cross-disciplinary approach to identify potential therapeutics for the prevention of metabolic-dysfunction-associated steatotic liver disease (MASLD) in at-risk individuals by using humans as a model organism. We identified 212 putative candidate genes associated with MASLD by using data from a large multi-ancestry genetic association study, of which 158 (74.5%) were previously unreported. From this set, we identified 57 genes that encode for druggable protein targets and for which the effects of increasing genetically predicted gene expression on MASLD risk align with the function of that drug on the protein target. We then used We then evaluated these potential targets for evidence of efficacy by using Mendelian randomization, pathway analysis, and protein structural modeling. Through these approaches, we present compelling evidence to suggest that the activation of FADS1 by icosapent ethyl, as well as S1PR2 by fingolimod, could be a promising therapeutic strategy for MASLD prevention.

Humans↗

PubMed2026

Differential regulation of CYP46A1 in ischemic core and peri-infarct regions of male mouse brain after permanent middle cerebral artery occlusion.

Cholesterol 24-hydroxylase (CYP46A1) regulates brain cholesterol homeostasis and synaptic plasticity, playing a crucial role in ischemic stroke. Although previous studies have reported post-ischemic CYP46A1 upregulation, its spatiotemporal dynamics remain poorly defined. To elucidate these dynamics, we investigated the expression of CYP46A1 and other essential cholesterol homeostasis-related genes from 6 h to 3 days after permanent middle cerebral artery occlusion (pMCAO) in CB-17 mice. We utilized single-cell and single-nucleus transcriptomics, regional quantitative PCR, and high-resolution immunohistochemistry. CYP46A1 is predominantly expressed in neurons. Following ischemia, the cholesterol network exhibited a dynamic spatiotemporal divergence. Acutely (6 h post-ischemia), surviving regions transiently upregulated cell-autonomous cholesterol synthesis genes and CYP46A1. Subacutely (3 days), this response shifted toward a widespread upregulation of glia-dependent cholesterol transport genes and general CYP46A1 downregulation. At 24 h, CYP46A1 protein was substantially reduced in the necrotic core and superficial layer II/III of the peri-infarct cortex, but upregulated in deeper layer V, hippocampus, and lateral striatum. Notably, this localized upregulation spatially coincided with reactive microglial hypertrophy. These findings indicate that CYP46A1 is dynamically modulated in viable tissues following ischemic stress. This spatial divergence likely reflects a synergistic interaction between inflammatory propagation and neural circuit-mediated oxidative stress. Resolving these spatiotemporal profiles provides a rigorous foundation for evaluating CYP46A1 functionality and developing stage-specific therapeutic interventions.

Cholesterol 24-hydroxylase↗

PubMed2026

A Computational Workflow for Prioritizing Microbial Metabolite-Associated Host Genes in Constipation-Predominant Irritable Bowel Syndrome.

No standardized computational pipeline exists for systematically prioritizing microbial metabolite-associated host genes and protein-ligand complexes from publicly available chemical, genomic, and structural databases. This article describes an eight-stage workflow that accepts a user-defined set of gut microbiota-derived metabolites and produces a ranked shortlist of candidate metabolite-associated host genes, enriched biological pathways, and structurally prioritized protein-ligand complexes for experimental follow-up. The pipeline integrates (i) chemoinformatic metabolite profiling; (ii) multi-database candidate target prediction using protein-chemical interaction and ligand-based target-prediction tool and a molecular docking program; (iii) differential gene expression analysis of publicly available transcriptomic data; (iv) target-differentially expressed gene overlap; (v) protein-protein interaction network construction and pathway enrichment; (vi) molecular docking with a molecular docking program; (vii) 200 ns molecular dynamics simulation using a molecular dynamics engine with a protein force field used for molecular dynamics simulations; and (viii) MM-PBSA binding free-energy estimation. As a worked example, nine gut microbiota-derived or microbiota-modified metabolites representing short-chain fatty acids, bile acids, tryptophan-derived metabolites, and urolithin A were processed using the public IBS-C rectal mucosal transcriptomic dataset GSE36701. The workflow ranked 17 unique predicted metabolite-associated genes that were differentially expressed in this dataset. Docking, molecular dynamics simulation, and MM-PBSA analyses structurally prioritized five metabolite-protein complexes: lithocholic acid-VDR, lithocholic acid-NR1H4/FXR, ursodeoxycholic acid-NR1H4/FXR, tryptamine-HTR2A (simulated in an explicit 1-Palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) lipid bilayer), and urolithin A-CASP3. The protocol is designed to be adaptable to other metabolite sets, disease transcriptomic datasets, and target classes; all outputs are hypothesis-generating computational predictions that require independent transcriptomic replication, protein-level validation, and functional ligand-response assays before causal or therapeutic conclusions can be drawn.

Irritable Bowel Syndrome↗

PubMed2026

Single-cell transcriptome revealed the aberrant keratinocytes activation in antigen presentation in atopic dermatitis.

BACKGROUND: Atopic dermatitis (AD), a common chronic inflammatory skin disease, has been extensively studied using single-cell genomics. However, keratinocytes, as key effector cells in AD, have underlying mechanisms remain incompletely understood and require further investigation. METHODS: We integrated single-cell transcriptomic data from skin tissues of healthy controls, chronic active AD patients, spontaneously healed AD (SHAD) patients, and an ovalbumin-induced AD mouse model. The study particularly emphasized the gene expression and cellular dynamics of keratinocytes across the different groups, as well as their interactions with immune cells. RESULTS: Compared to healthy controls, we observed significant changes in the keratinocyte transcriptome, cellular state, and keratinocyte-immune cell ligand-receptor interactions in AD skin, particularly the marked activation of genes involved in antigen processing and presentation. Interestingly, such gene activation was not observed in keratinocytes from the ovalbumin-induced AD mouse model, despite its phenotype closely resembling human AD. Furthermore, in SHAD, we identified a recovery of both the ligand-receptor interaction patterns and antigen processing and presentation genes, accompanied by a notable shift in the transcriptome. This involved a significant downregulation of genes related to cytoplasmic transcription and oxidative phosphorylation. Notably, this pattern was not observed in the self-healing mouse model following the removal of ovalbumin stimulation. CONCLUSION: Our results suggest that the persistent activation of antigen processing and presentation pathways in keratinocytes may be a key driver of chronic inflammation in AD. Therefore, redirecting anti-allergic therapeutic strategies from solely targeting immune cells to targeting of keratinocyte-mediated antigen presentation may offer a more effective approach. Furthermore, we raise concerns about the use of ovalbumin-induced mouse models to recapitulate human chronic AD, as the underlying mechanisms may differ significantly.

Dermatitis, Atopic↗

PubMed2026

Stage-specific ROMO1 in rheumatoid arthritis: predictive immune insights into the MIF pathway and HLA-DR/IL2RA axis via integrated GWAS, transcriptomic, single-cell, and spatial profiling.

Emerging evidence links reactive oxygen species modulator 1 (ROMO1), a key mitochondrial ROS regulator, to rheumatoid arthritis (RA) pathogenesis. However, its exact mechanism remains elusive given the conflicting evidence about its specific function. We used a four-level integrative framework combining multi-omics data and literature‑supported mechanistic inference. At the genetic level, Mendelian randomization (MR) was performed to explore potential causal relationships between ROMO1, IL2RA, HLA-DR, MIF, and RA risk, followed by differential expression analysis and machine learning-based feature selection to identify key mROS genes. The temporal expression dynamics of ROMO1 were assessed in RA progression. At the cellular and tissue levels, we integrated single-cell RNA sequencing and spatial transcriptomics to map cell-type-specific expression and synovial localization of ROMO1-related immune cells and pathways. Finally, our multi-omics findings were contextualized with literature-supported mechanistic inference. (1) MR results were consistent with a potential protective effect of ROMO1 on RA (OR = 0.52) and its potential regulation of risk factors IL2RA (OR = 0.46) and HLA-DR (OR = 0.40). Conversely, IL2RA (OR = 1.42), HLA-DR (OR = 1.88), and MIF (OR = 1.17) were positively associated with RA risk. Additionally, ROMO1 was identified as a top candidate diagnostic predictor with stage-specific dynamics: downregulated in the early but upregulated in the late/remission stages. (2) Single-cell RNA sequencing showed ROMO1's cell-specific expression in CD14+ HLA-DR+ CD74+ monocytes and CD4+ IL2RA+ T cells. Cell communication analysis further suggested that these cells may participate in MIF pathway regulation. Spatial transcriptomics subsequently identified that ROMO1-related cells localized to synovial pathological regions, with MIF pathway changes correlated with RA progression. (3) Finally, literature-supported mechanistic inference suggests that ROMO1 may modulate mROS levels to promote anti-inflammatory M2 macrophage polarization, which could theoretically contribute to reduced systemic inflammation and the alleviation of multi-organ decline in RA. This integrated multi-omics investigation, supported by literature-based mechanistic inference, suggests ROMO1 as a stage-dependent biomarker candidate and potential immune regulator in RA.

Humans↗

PubMed2026

Deconstructing the Alternative Lengthening of Telomeres: Integromics Prioritizes Five Master Hubs Dictating Clinical Survival and Therapeutic Vulnerabilities.

The Alternative Lengthening of Telomeres (ALT) pathway drives replicative immortality in aggressive malignancies, particularly sarcomas and gliomas. Clinical ALT stratification has relied on screening for structural ATRX and DAXX mutations. However, this genotypic approach fails to capture the dynamic macro-reprogramming required to sustain ALT. Here, we established and validated a 28-gene transcriptomic signature that captures the ALT-associated transcriptomic phenotype of the ALT phenotype. Using multivariate Cox proportional hazards models and time-dependent ROC analyses, we demonstrate that this signature is a robust, independent predictor of poor overall survival in Sarcoma (SARC) and Lower Grade Glioma (LGG) cohorts, outperforming the prognostic value of traditional ATRX/DAXX mutational status. Genomic mapping revealed this transcriptional synchrony is structurally facilitated by non-random focal clustering on Chromosome 8. To deconstruct the machinery driving this lethal phenotype, we employed an integromic approach, synthesizing protein-protein and metabolic flux networks. Topological algorithms prioritized five indispensable hubs: TP53, ATM, ATR, PCNA, and UBE2I. Gene-metabolite profiling identified PCNA as a bottleneck funneling extreme deoxyribonucleotide (dNTP) demand to sustain break-induced telomeric recombination. To translate these vulnerabilities into actionable treatments, we mapped these hubs to a precision pharmacological network. We propose a multi-targeted strategy combining FDA-approved PARP inhibitors to exploit ATR-mediated synthetic lethality, alongside antimetabolites to induce nucleotide starvation. This study redefines ALT risk stratification and provides a data-driven framework to target and treat resistant ALT-positive tumors.

Alternative Lengthening of Telomeres↗

PubMed2025

Transcriptome and metabolome profiling of the medicinal plant Dictamnus dasycarpus reveal key genes involved in quinoline alkaloids biosynthesis and limonoids biosynthesis.

BACKGROUND: As a member of Rutaceae family, Dictamnus dasycarpus Turcz. represents a prominent medicinal plant and economically valuable crop in traditional Chinese medicine, and is renowned for its therapeutic efficacy in treating dermatological conditions. The pharmacological activity of this species primarily stems from quinoline alkaloids and limonoids, which predominantly accumulate in the taproots. These bioactive compounds serve as critical determinants of both medicinal quality and crop yield. Nevertheless, the molecular mechanisms governing their dynamic accumulation patterns in D. dasycarpus taproots remain uncertain, and the fundamental biochemical basis underlying this process has yet to be elucidated. RESULTS: Metabolomic and transcriptomic analyses were carried out to investigate metabolites and gene expression during the development of D. dasycarpus taproots. The differentially accumulated secondary metabolites (DAMs) mainly included quinoline alkaloids and limonoids, and the accumulation of total alkaloids and total limonoids primarily occurred during 2- and 4-year-old. The differentially expressed genes (DEGs) are related to Glycolysis/Gluconeogenesis, Phenylalanine, tyrosine and tryptophan biosynthesis, Tryptophan metabolism, Terpenoid backbone biosynthesis, Sesquiterpenoid and triterpenoid biosynthesis, which had a close relationship with the accumulation of quinoline alkaloids and limonoids. Furthermore, we identified that some CYP450s, acetyltransferase, isomerase, 2-ODDs and others may play an important role in the process of producing quinoline alkaloids and limonoids. CONCLUSION: These results elucidated the molecular mechanisms and metabolic changes underlying the dynamic accumulation process occurring in the taproots of D. dasycarpus. These findings provide a theoretical basis for the planting and harvesting of D. dasycarpus.

Limonins↗

PubMed2025

Variability in intrinsic promoter strength underlies the temporal hierarchy of the Caulobacter SOS response induction.

Bacteria encode for gene regulatory networks crucial for sensing and repairing DNA damage. Upon exposure to genotoxic stress, these transcriptional networks are induced in a temporally structured manner. A case in point is of the highly conserved SOS response that is regulated by the LexA repressor. Studies have proposed that affinity of LexA towards promoters of SOS response genes is the primary determinant of its expression dynamics. Here, we describe an additional level of regulation beyond LexA box properties that modulates the SOS response gene expression pattern. Using transcriptomic analyses, we reveal a distinct temporal hierarchy in the induction of SOS-regulated genes in Caulobacter crescentus. We observe that LexA box properties are insufficient in predicting the temporal hierarchy of these genes. Instead, we find that intrinsic promoter strength underlies the order of gene activation, with differential sigma factor association as one of the factors modulating gene expression timing. Our findings highlight a novel regulatory layer in SOS dynamics and underscore the importance of promoter properties in shaping bacterial stress responses.

Promoter Regions, Genetic↗

PubMed2026

Identification and characterization of non-canonical azole antifungal resistance pathways in Aspergillus fumigatus.

UNLABELLED: Human fungal infections, especially those caused by Aspergillus fumigatus, pose a significant global health threat, particularly in immunocompromised individuals. Azole antifungals are the primary treatment for this pathogen; however, the prevalence of azole-resistant A. fumigatus strains is steadily increasing. Mutations in cyp51A, which encodes an enzyme involved in ergosterol biosynthesis and the molecular target of the azoles, are well established to confer resistance in this fungal species. However, additional mechanisms governing resistance to this antifungal class remain understudied and poorly characterized, despite growing recognition of their importance in clinical resistance. In this study, we investigated the genetic basis of azole resistance in A. fumigatus isolates from clinical settings worldwide, with a particular focus on mechanisms independent of cyp51A (non-canonical). Using a combination of genomic and functional approaches, including whole-genome sequencing and transcriptomic analysis, we identified novel genetic variants and characterized population structure, advancing our understanding of the genetic diversity and evolutionary dynamics of resistance in A. fumigatus. By expanding our understanding of the complex genetic and molecular factors underlying azole resistance in this important human fungal pathogen, this research is poised to inform the development of novel antifungal strategies and contribute to global efforts to combat fungal infections. IMPORTANCE: Azole antifungals are the frontline therapy for infections caused by the opportunistic mold Aspergillus fumigatus, yet resistance to these drugs is rapidly increasing worldwide. Most studies have focused on mutations in cyp51A, the canonical target of azoles; however, a growing proportion of resistant clinical isolates lack these mutations, indicating that alternative resistance mechanisms are emerging. Here, we integrate population genomics, transcriptomics, and functional analyses across a global collection of isolates to define the architecture of cyp51-independent (non-canonical) azole resistance. We show that this resistance phenotype is strongly associated with a distinct population lineage and is driven by a highly polygenic network of metabolic, mitochondrial, and regulatory adaptations rather than single target site mutations. These isolates exhibit extensive transcriptional rewiring and metabolic remodeling under azole stress, suggesting distinct survival strategies beyond canonical resistance. Our findings reveal that azole resistance in A. fumigatus can evolve through diverse evolutionary routes and emphasize the need to monitor and therapeutically target non-canonical pathways that may increasingly contribute to antifungal treatment failure.

Aspergillus fumigatus↗

PubMed2026

Accurately Deciphering Tissue Heterogeneity From Spatial Multi-Modal and Multi-Omics With STransformer.

Advances in spatially resolved technologies enable the simultaneous acquisition of diverse data modalities within a tissue slice while preserving critical spatial context, which presents unprecedented opportunities to decipher intricate tissue heterogeneity. However, existing computational approaches lack the intrinsic flexibility to universally process both spatial multi-modal and multi-omics data. Here, we introduce STransformer, a unified deep learning framework designed to seamlessly accommodate a comprehensive landscape of spatial data. By simultaneously capturing short-range cellular interactions and tissue-wide semantic patterns, it extracts robust representations to accurately dissect complex tissue heterogeneity. Systematic evaluations across diverse species, tissue types, and data modalities highlight its profound versatility. For spatial multi-modal data, STransformer delineates intricate anatomical structures in the human cortex, uncovers pathological mechanisms in Alzheimer's disease, and characterizes dynamic spatiotemporal developmental trajectories during chicken cardiogenesis. Scaling to spatial multi-omics data, STransformer synergizes spatial transcriptomic and proteomic profiles to decipher intricate immune microenvironments within the human tonsil, and jointly analyzes spatial epigenomic and transcriptomic data to infer regulatory mechanisms in the mouse embryonic brain. Consequently, STransformer serves as a highly versatile and robust analytical framework for advancing our understanding of tissue heterogeneity and disease pathogenesis.

Multiomics↗

PubMed2026

Chemo-selective proteomics in microbial systems.

SUMMARYOver the past two decades, the field of bioorthogonal chemistry has transitioned from emerging to an established cornerstone of scientific inquiry. In parallel, advances in microbial and host-microbe research have highlighted the need for functional approaches that extend beyond genomic and transcriptomic analyses to directly interrogate protein-level activity. Despite this need, proteomic strategies capable of resolving dynamic, heterogeneous, and low-abundance protein populations remain underdeveloped in microbial systems. This review highlights the convergence of chemo-selective proteomic technologies with microbial biology, focusing on bioorthogonal non-canonical amino acid tagging (BONCAT), activity- or affinity-based protein profiling, and bioorthogonal post-translational modifications, and comments on possibilities for novel applications for the use of click chemistry-based tools in the functional interrogation of microbial systems. Together, these strategies enable spatiotemporal resolution of protein synthesis, selective profiling of microbial subpopulations, and direct characterization of protein activity and regulation in complex biological contexts, including single-species cultures, host-associated environments, and polymicrobial communities. Continued development and utilization of these technologies will enable deeper mechanistic insight into how microbial systems function and respond to environmental and host-derived cues.

bioorthogonal chemistry↗

PubMed2025

Inferring cell trajectories of spatial transcriptomics via optimal transport analysis.

The integration of cell transcriptomics and spatial position to organize differentiation trajectories remains a challenge. Here, we introduce SpaTrack, which leverages optimal transport to reconcile both gene expression and spatial position from spatial transcriptomics into the transition costs, thereby reconstructing cell differentiation. SpaTrack can construct detailed spatial trajectories that reflect the differentiation topology and trace cell dynamics across multiple samples over temporal intervals. To capture the dynamic drivers of differentiation, SpaTrack models cell fate as a function of expression profiles influenced by transcription factors over time. By applying SpaTrack, we successfully disentangle spatiotemporal trajectories of axolotl telencephalon regeneration and mouse midbrain development. Diverse malignant lineages expanding within a primary tumor are uncovered. One lineage, characterized by upregulated epithelial mesenchymal transition, implants at the metastatic site and subsequently colonizes to form a secondary tumor. Overall, SpaTrack efficiently advances trajectory inference from spatial transcriptomics, providing valuable insights into differentiation processes.

Animals↗

PubMed2026

Decoding the spatiotemporal patterns of food spoilage microbial communities: Integrating multi-omics and artificial intelligence to enable precision preservation.

In the global food supply chain, food wastage caused by spoilage has resulted in significant economic losses, food shortages, and environmental pressure. This process is fundamentally driven by the spatiotemporal dynamics of microbial communities. However, traditional research methods struggle to elucidate the complex mechanisms of spatial heterogeneity, interspecies interactions, and functional succession. This limits the development of effective preservation strategies. This review systematically reviews the cutting-edge progress of integrating multi-omics technologies and artificial intelligence (AI) to study food spoilage microbial communities, breaking through this bottleneck. We propose an intelligent theoretical framework that could potentially analyze microbial metabolic activities and predict dynamic shelf life if implemented. The conceptual framework integrates multidimensional data, including spatial metabolomics, temporal metatranscriptomics, single-cell transcriptomics, and longitudinal metagenomics. It can also be combined with AI models, such as graph neural networks. The article elaborates on the principles and applications of spatio-temporal monitoring technologies, such as nano secondary ion mass spectrometry, hyperspectral imaging, and the Internet of Things sensing. Through illustrative cases of typical perishable foods, it also explores how such a multi-omics - AI system might be applied to spoilage warning and precise intervention. Additionally, the article addresses the current challenges in data coverage, model generalization, and federated learning implementation. Then the research further explores emerging areas such as engineered probiotics, edge AI, and microfluidic sensing. These areas are targeted at transforming food preservation from an empirical control approach to a data-driven, precise regulatory framework. This transformation provides theoretical support and technical approaches for developing a smart, sustainable food preservation system.

Multiomics↗

PubMed2026

Sexually dimorphic expression and hormonal responsiveness of steroidogenic Cyp genes during gonadal differentiation in mandarin fish.

Steroid hormones play a pivotal role in fish sex differentiation, yet the dynamic expression patterns of key steroidogenic enzymes during this process remain incompletely characterized. Here, we combined genome-wide identification, time series transcriptomes spanning gonadal development (5-360 days post-hatch), and multiple hormone treatment experiments (17α-methyltestosterone, estrone, and etonogestrel) to investigate the Cyp11, Cyp17, Cyp19, and Cyp21 subfamilies in mandarin fish (Siniperca chuatsi). Seven steroidogenic Cyp genes were identified, showing teleost-specific expansion, with one duplicated pair (cyp17a2 and cyp2u1) exhibiting strong purifying selection. Expression profiling revealed pronounced sexually dimorphic and stage-specific patterns: During female differentiation (20-30 days), cyp19a1a and associated genes were highly expressed, coinciding with ovarian differentiation; during male differentiation (30-60 days), cyp17a2 and related genes were upregulated, aligning with testicular development. Exogenous hormone treatments further demonstrated that these genes are dynamically responsive: cyp19a1a and cyp17a2 were highly responsive to androgenic and progestogenic treatments, and their expression changes correlated closely with gonadal sex reversal phenotypes observed histologically. Collectively, this study provides a comprehensive expression atlas of steroidogenic Cyp genes during gonadal differentiation and identifies key hormonally responsive candidates for sex control in aquaculture.

Animals↗

PubMed2025

De novo clustering of large long-read transcriptome datasets with isONclust3.

MOTIVATION: Long-read sequencing techniques can sequence transcripts from end to end, greatly improving our ability to study the transcription process. Although there are several well-established tools for long-read transcriptome analysis, most are reference-based. This limits the analysis of organisms without high-quality reference genomes and samples or genes with high variability (e.g. cancer samples or some gene families). In such settings, analysis using a reference-free method is favorable. The computational problem of clustering long reads by region of common origin is well-established for reference-free transcriptome analysis pipelines. Such clustering enables large datasets to be split roughly by gene family and, therefore, an independent analysis of each cluster. There exist tools for this. However, none of those tools can efficiently process the large amount of reads that are now generated by long-read sequencing technologies. RESULTS: We present isONclust3, an improved algorithm over isONclust and isONclust2, to cluster massive long-read transcriptome datasets into gene families. Like isONclust, isONclust3 represents each cluster with a set of minimizers. However, unlike other approaches, isONclust3 dynamically updates the cluster representation during clustering by adding high-confidence minimizers from new reads assigned to the cluster and employs an iterative cluster-merging step. We show that isONclust3 yields results with higher or comparable quality to state-of-the-art algorithms but is 10-100 times faster on large datasets. Also, using a 256 Gb computing node, isONclust3 was the only tool that could cluster 37 million PacBio reads, which is a typical throughput of the recent PacBio Revio sequencing machine. AVAILABILITY AND IMPLEMENTATION: https://github.com/aljpetri/isONclust3.

Algorithms↗