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PubMed2003

The path from molecular indicators of exposure to describing dynamic biological systems in an aquatic organism: microarrays and the fathead minnow.

The extent to which humans and wildlife are exposed to toxicants is an important focus of environmental research. This work has been directed toward the development of molecular indicators diagnostic for exposure to various stressors in freshwater fish. Research includes the discovery of genes, indicative of environmental exposure, in the Agency's long-established aquatic toxicological organism, the fathead minnow (Pimephales promelas). Novel cDNAs and coding sequences will be used in DNA microarray analyses for pattern identification of stressor-specific, differentially up- and down-regulated genes. The methods currently used to discover genes in this organism, for which few annotated nucleic acid sequences exist, are cDNA subtraction libraries, differential display, exploiting PCR primers for known genes of other members of the family Cyprinidae and use of degenerate PCR primers designed from regions of moderate protein homology. Single or multiple genes noted as being differentially expressed in microarray analyses will then be used in separate studies to measure bioavailable stressors in the laboratory and field. These analyses will be accomplished by quantitative RT-PCR. Moving from analysis of single gene exposures to the global state of the transcriptome offers possibilities that those genes identified by DNA microarray analyses might be critical components of dynamic biological systems and networks, wherein chemical stressors exert toxic effects through various modes of action. Additionally, the ability to discriminate bioavailability of stressors in complex environmental mixtures, and correlation with adverse effects downstream from these early molecular events, presents challenging new ground to be broken in the area of risk assessment.

Animals↗

PubMed2005

Metabolic and transcriptomic adaptation of Lactococcus lactis subsp. lactis Biovar diacetylactis in response to autoacidification and temperature downshift in skim milk.

For the first time, a combined genome-wide transcriptome and metabolic analysis was performed with a dairy Lactococcus lactis subsp. lactis biovar diacetylactis strain under dynamic conditions similar to the conditions encountered during the cheese-making process. A culture was grown in skim milk in an anaerobic environment without pH regulation and with a controlled temperature downshift. Fermentation kinetics, as well as central metabolism enzyme activities, were determined throughout the culture. Based on the enzymatic analysis, a type of glycolytic control was postulated, which was shared by most of the enzymes during the growth phase; in particular, the phosphofructokinase and some enzymes of the phosphoglycerate pathway during the postacidification phase were implicated. These conclusions were reinforced by whole-genome transcriptomic data. First, limited enzyme activities relative to the carbon flux were measured for most of the glycolytic enzymes; second, transcripts and enzyme activities exhibited similar changes during the culture; and third, genes involved in alternative metabolic pathways derived from some glycolytic metabolites were induced just upstream of the postulated glycolytic bottlenecks, as a consequence of accumulation of these metabolites. Other transcriptional responses to autoacidification and a decrease in temperature were induced at the end of the growth phase and were partially maintained during the stationary phase. If specific responses to acid and cold stresses were identified, this exhaustive analysis also enabled induction of unexpected pathways to be shown.

Acclimatization↗

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↗

PubMed2005

Uncovering evolutionary patterns of gene expression using microarrays.

The advent of microarray technology is providing new insights into fundamental questions in evolutionary biology. Here, we review the recent literature on the use of microarrays to study the evolution of genome-wide patterns of gene expression within and between species. Large levels of variation in gene expression patterns have been observed at the intra and interspecific level, and a substantial fraction of transcriptional variation has a genetic component that is contributed by changes in both cis-acting and trans-acting regulatory elements. We argue that there is solid evidence to show that the temporal dynamics of transcriptional variation is largely determined by natural selection, with the fraction of the transcriptome more closely related to sex and reproduction evolving more rapidly.

Animals↗

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↗

PubMed2006

Proteome analysis of salt stress response in the cyanobacterium Synechocystis sp. strain PCC 6803.

In the present study, changes in protein synthesis patterns after salt shock visualized by 35S-methionine labeling and the changed protein composition in salt-acclimated cells of the cyanobacterium Synechocystis sp. strain PCC 6803 were analyzed by a combination of 2-DE for protein separation and PMF for protein identification. As a basis for the differential analysis, a proteome map with 500 identified protein spots comprising 337 different protein species was established. Fifty-five proteins were found, which are induced by salt shock or accumulated after long-term salt acclimation. Some of the proteins are salt stress-specific, such as enzymes involved in the synthesis of the compatible solute glucosylglycerol, while most of them are involved in general stress acclimation. Particularly, heat-shock proteins and proteins acting against lesions by reactive oxygen species were found. Moreover, changes in enzymes involved in basic carbohydrate metabolism were detected. The dynamic of the proteome of salt-stressed Synechocystis cells was compared to previous data concerning transcriptome analysis revealing that 89% of the proteins induced shortly after salt shock were also found to be induced at the RNA level. However, 42% of the stably up-regulated proteins in salt-acclimated cells were not detected previously using DNA microarrays. The comparison of transcriptomic and proteomic analyses shows the significance of post-transcriptional regulatory mechanisms in acclimation of Synechocystis to high salt concentrations.

Bacterial Proteins↗

PubMed2007

Proteomics analysis of the interactome of N-myc downstream regulated gene 1 and its interactions with the androgen response program in prostate cancer cells.

NDRG1 is known to play important roles in both androgen-induced cell differentiation and inhibition of prostate cancer metastasis. However, the proteins associated with NDRG1 function are not fully enumerated. Using coimmunoprecipitation and mass spectrometry analysis, we identified 58 proteins that interact with NDRG1 in prostate cancer cells. These proteins include nuclear proteins, adhesion molecules, endoplasmic reticulum (ER) chaperons, proteasome subunits, and signaling proteins. Integration of our data with protein-protein interaction data from the Human Proteome Reference Database allowed us to build a comprehensive interactome map of NDRG1. This interactome map consists of several modules such as a nuclear module and a cell membrane module; these modules explain the reported versatile functions of NDRG1. We also determined that serine 330 and threonine 366 of NDRG1 were phosphorylated and demonstrated that the phosphorylation of NDRG1 was prominently mediated by protein kinase A (PKA). Further, we showed that NDRG1 directly binds to beta-catenin and E-cadherin. However, the phosphorylation of NDRG1 did not interrupt the binding of NDRG1 to E-cadherin and beta-catenin. Finally, we showed that the inhibition of NDRG1 expression by RNA interference decreased the ER inducible chaperon GRP94 expression, directly proving that NDRG1 is involved in the ER stress response. Intriguingly, we observed that many members of the NDRG1 interactome are androgen-regulated and that the NDRG1 interactome links to the androgen response network through common interactions with beta-catenin and heat shock protein 90. Therefore we overlaid the transcriptomic expression changes in the NDRG1 interactome in response to androgen treatment and built a dual dynamic picture of the NDRG1 interactome in response to androgen. This interactome map provides the first road map for understanding the functions of NDRG1 in cells and its roles in human diseases, such as prostate cancer, which can progress from androgen-dependent curable stages to androgen-independent incurable stages.

Androgens↗

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↗

PubMed2007

Comparison of sampling techniques for parallel analysis of transcript and metabolite levels in Saccharomyces cerevisiae.

Mathematical modelling of cellular processes is crucial for the understanding of the cell or organism as a whole. Genome-wide observations, at the levels of the transcriptome, proteome and metabolome, provide a high coverage of the molecular constituents of the system in study. Time-course experiments are important for gaining insight into a system's dynamics and are needed for mathematical modelling. In time-course experiments it is crucial to use efficient and fast sampling techniques. We evaluated several techniques to sample and process yeast cultures for parallel analysis of the transcriptome and metabolome. The evaluation was made by measuring the quality of the RNA obtained with UV-spectroscopy, capillary electrophoresis and microarray hybridization. The protocol developed involves rapid collection by spraying the sample into -40 degrees C tricine-buffered methanol (as previously described for yeast metabolome analysis), followed by the separation of cells from the culture medium in low-temperature rapid centrifugation. Removal of the residual methanol is carried out by freeze-drying the pellet at -35 degrees C. RNA and metabolites can then be extracted from the same freeze-dried sample obtained with this procedure.

Centrifugation↗

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↗

PubMed2007

Widespread mRNA polyadenylation events in introns indicate dynamic interplay between polyadenylation and splicing.

mRNA polyadenylation and pre-mRNA splicing are two essential steps for the maturation of most human mRNAs. Studies have shown that some genes generate mRNA variants involving both alternative polyadenylation and alternative splicing. Polyadenylation in introns can lead to conversion of an internal exon to a 3' terminal exon, which is termed composite terminal exon, or usage of a 3' terminal exon that is otherwise skipped, which is termed skipped terminal exon. Using cDNA/EST and genome sequences, we identified polyadenylation sites in introns for all currently known human genes. We found that approximately 20% human genes have at least one intronic polyadenylation event that can potentially lead to mRNA variants, most of which encode different protein products. The conservation of human intronic poly(A) sites in mouse and rat genomes is lower than that of poly(A) sites in 3'-most exons. Quantitative analysis of a number of mRNA variants generated by intronic poly(A) sites suggests that the intronic polyadenylation activity can vary under different cellular conditions for most genes. Furthermore, we found that weak 5' splice site and large intron size are the determining factors controlling the usage of composite terminal exon poly(A) sites, whereas skipped terminal exon poly(A) sites tend to be associated with strong polyadenylation signals. Thus, our data indicate that dynamic interplay between polyadenylation and splicing leads to widespread polyadenylation in introns and contributes to the complexity of transcriptome in the cell.

Animals↗