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Dual RNA isolation from blood: an optimized protocol for host and bacterial RNA purification for dual RNA-sequencing analysis in whole blood sepsis samples.

Dual RNA-sequencing (dual RNA-seq) holds significant promise for deciphering bacterial virulence mechanisms during systemic infections. However, its application in sepsis research is hindered by technical challenges, including a low bacterial burden in blood and limited sample volumes and RNA yield from vulnerable populations, such as neonates. We developed an optimized protocol [dual RNA isolation from blood (DRIB)] for simultaneous stabilization, isolation and purification of high-quality host leukocyte and bacterial RNA from low-volume whole blood samples (0.5 ml). This protocol is compatible with clinical sample collection workflows and high-throughput RNA sequencing. The feasibility of DRIB for dual RNA-seq was validated using a pilot cohort of clinical adult sepsis samples, enabling the investigation of host-bacterial gene expression during sepsis. The DRIB protocol yielded 2.10-6.91 µg of total RNA per clinical sample in our pilot cohort. Dual-species ribosomal RNA (rRNA) depletion and RNA-seq generated 16.6-24.8 million filtered reads per sample, with 63±7% of reads uniquely mapped to host or bacterial sequences. Host genes accounted for 51-68% (8.4-10.9 million) reads, while 0.5-6.7% (79,496-789,808 reads) mapped to bacterial genomes. Bioinformatic analysis revealed that both shared and individual transcriptional patterns were identified in host and bacterial responses, including pathways related to immune metabolism and metal-ion binding. Our optimized DRIB protocol and RNA-seq pipeline effectively captured both host and bacterial RNA transcription in clinical sepsis samples. Expanding this approach to larger cohorts and varying disease timepoints will provide crucial new insights into host-bacterial gene co-expression dynamics in sepsis progression and outcomes.

Humans↗

Alevin-fry-atac enables rapid and memory frugal mapping of single-cell ATAC-seq data using virtual colors for accurate genomic pseudoalignment.

Ultrafast mapping of short reads via lightweight mapping techniques such as pseudoalignment has significantly accelerated transcriptomic and metagenomic analyses, often with minimal accuracy loss compared to alignment-based methods. However, applying pseudoalignment to large genomic references, like chromosomes, is challenging due to their size and repetitive sequences. We introduce a new and modified pseudoalignment scheme that partitions each reference into "virtual colors…. These are essentially overlapping bins of fixed maximal extent on the reference sequences that are treated as distinct "colors" from the perspective of the pseudoalignment algorithm. We apply this modified pseudoalignment procedure to process and map single-cell ATAC-seq data in our new tool alevin-fry-atac . We compare alevin-fry-atac to both Chromap and Cell Ranger ATAC . Alevin-fry-atac is highly scalable and, when using 32 threads, is approximately 2.8 times faster than Chromap (the second fastest approach) while using approximately one third of the memory and mapping slightly more reads. The resulting peaks and clusters generated from alevin-fry-atac show high concordance with those obtained from both Chromap and the Cell Ranger ATAC pipeline, demonstrating that virtual colorenhanced pseudoalignment directly to the genome provides a fast, memory-frugal, and accurate alternative to existing approaches for single-cell ATAC-seq processing. The development of alevin-fry-atac brings single-cell ATAC-seq processing into a unified ecosystem with single-cell RNA-seq processing (via alevin-fry ) to work toward providing a truly open alternative to many of the varied capabilities of CellRanger . Furthermore, our modified pseudoalignment approach should be easily applicable and extendable to other genome-centric mapping-based tasks and modalities such as standard DNA-seq, DNase-seq, Chip-seq and Hi-C.

Journal Article↗

Altered histone modifications in Aedes aegypti following Rift Valley fever virus exposure.

When arthropod-borne viruses (arboviruses) are delivered to vector mosquitoes in an infectious bloodmeal, viral components interact with host proteins to hijack cells and initiate replication. The extent to which arbovirus infection alters mosquito host transcriptional and genomic regulatory processes is currently unknown. We hypothesized that histone modifications would be altered in mosquitoes exposed to Rift Valley fever virus (RVFV MP12, Phlebovirus riftense, family Phleboviridae). We interrogated transcriptome and chromatin landscapes in Aedes aegypti midguts by performing Cleavage Under Targets and Release Using Nuclease (CUT&RUN), using H3K27ac and H3K9me3 marks. Altered H3K27ac marks were identified following RVFV MP12 exposure, as well as upon bloodfeeding alone. It took several days for differential H3K27ac marks to be associated with differentially expressed genes (DEGs) in RVFV-exposed midguts. H3K27ac peaks showed progressive depletion as infection progressed. Gene set enrichment analysis revealed that immune response transcripts were enriched at 1 and 3 dpf (days post-feeding) but depleted by 7 dpf. Hedgehog/Gli (glioma-associated oncogene homolog) signaling pathway transcripts were depleted, indicating possible viral manipulation of cellular polarization. Moreover, at 7 dpf, 7 of 102 DEGs were proximal to differentially acetylated sites in a pattern expected to favor viral propagation. However, one transcript coding for an antiviral effector (LysM-TLDc domain protein) showed significant depletion of both H3K9me3 and H3K27ac marks. Analysis of midguts after a non-infectious bloodmeal versus sugar-fed controls revealed global changes to H3K27ac and H3K9me3 marks during and following the period of bloodmeal digestion. Differential H3K27ac marks were proximal to one quarter of all DEGs at 1 dpf, consistent with an important role of H3K27ac in bloodmeal digestion. These results demonstrate that H3K27ac and H3K9me3 patterns are altered upon virus exposure in a complex interplay that favors viral replication but is also countered by host responses to limit replication.

ChIP-Seq↗

Trichoderma reesei Nsd3 transcription factor: pleiotropic roles in development, stress response, secondary metabolism, and cellulase production.

Trichoderma reesei is known for its ability to secrete high amounts of cellulases, enzymes of fundamental importance in generating products from lignocellulosic biomass. Diverse signaling pathways and transcription factors (TFs) control the cellulolytic repertoire in T. reesei to ensure correct adaptation to the environment. Here, we analyzed RNA-Seq data and identified a new potential regulator of cellulase production in T. reesei: a novel TF named Nsd3, a homolog of NsdC from Aspergilli. Deletion of nsd3 reduced vegetative growth and conidiation on solid medium. Phenotypic characterization of the Δnsd3 strain showed that it is more sensitive to osmotic stress, but more resistant to cell wall and oxidative stresses. Our results showed that Nsd3 is a repressor of cellulase expression by directly regulating key genes in the cellulolytic pathway, an unreported role for this TF in fungi. Loss of nsd3 leads to a faster and more robust induction of cellulolytic genes, and higher cellulase and hemicellulase activities. Transcriptional profiling by RNA-Seq, chromatin accessibility profiling by ATAC-Seq, and protein-DNA interaction assays showed that sugar transporters are important targets of Nsd3 during cellulase expression regulation. Combined with microscopy and gene expression analyses, the ATAC-Seq data also highlighted Nsd3 as a central regulator of cell wall remodeling and organization. Furthermore, the transcriptomics also showed that Nsd3 regulates genes involved in secondary metabolism. These results showed that Nsd3 regulates several physiological processes and provide novel insights into the regulatory system of cellulases in T. reesei that can be used in the design of high-performance strains for biorefinery.IMPORTANCETrichoderma reesei is a key player in the production of hydrolytic enzymes for the degradation of lignocellulose biomass, and transcription factors are important targets for genetic engineering to construct cellulase-hyperproducing strains. Here, we identified the transcription factor Nsd3 and characterized its role as a regulator of cellulase production in T. reesei. We applied two powerful genomics methods (transcriptome sequencing and chromatin accessibility sequencing) to unravel the global role of Nsd3 and its regulatory mechanism. Nsd3 participates in various biological processes in T. reesei, including cell wall remodeling, calcium metabolism, and secondary metabolism, in addition to regulating the expression of sugar transporters. Protein-DNA interaction assays demonstrate that Nsd3 acts through important genes to regulate cellulase expression, including ace4, crt1, stp1, and cel1b. Our study provides mechanistic insights about how Nsd3 regulates diverse physiological processes in T. reesei. This work also applied ATAC-Seq for the first time to study chromatin accessibility in T. reesei.

ATAC-Seq↗

Quantitative trait loci mapping of gene expression and chromatin accessibility in primary fibroblasts reveals shared allelic effects between Latin American and European ancestries.

BACKGROUND: Quantitative Trait Locus (QTL) analysis of molecular data has identified genetic variants associated with traits such as gene expression, and colocalization of these functional QTL with GWAS risk loci has offered insights into the genetic basis of human disease. We employed gene expression (RNA-seq) and chromatin accessibility (ATAC-seq) obtained from human primary fibroblasts to investigate quantitative trait loci (QTLs) in cohorts ascertained for bipolar disorder of European (n = 150) and Latin American (n = 96) ancestries. RESULTS: Leveraging data from three countries of origin (The Netherlands, Colombia, Costa Rica) within our cohort, we characterized differences among individuals at the SNP, gene, and accessible-chromatin levels to compute ancestry-specific expression (e)QTLs and chromatin-accessibility (ca)QTLs. Across ancestries, we observed R2 ≥ 0.93 for eQTL effect sizes and R2 ≥ 0.95 for caQTLs, indicating a high degree of concordance. Integrating chromatin data with expression and genotype information enabled precise fine-mapping of eQTLs, yielding 203 genes with high-confidence (posterior probability > 90%) candidate regulatory pathways. In downstream analyses, transcriptome-wide (TWAS) and chromatin-wide (CWAS) association studies with brain- and skin-related GWAS identified 36 TWAS-significant genes and 77 CWAS-significant open chromatin regions. CONCLUSIONS: These findings underscore the shared genetic regulatory mechanisms across European and Latin American ancestries, while demonstrating that ancestry-specific reference panels enhance the accuracy of TWAS and CWAS in diverse populations. More broadly, this study highlights the value of paired multi-omic datasets from diverse cohorts for interpreting disease-associated genetic variation.

Humans↗

FZD5 drives macrophage-mediated immunomodulation and predicts prognosis in glioma: evidence from single-cell sequencing.

BACKGROUND: Gliomas are highly malignant brain tumors characterized by an immunosuppressive microenvironment, which limits therapeutic efficacy and contributes to poor clinical outcomes. The WNT/β-catenin signaling pathway is critically involved in tumor progression, and FZD5, a key receptor within this pathway, may participate in immune regulation. However, its specific role and underlying mechanisms in glioma remain unclear. METHODS: RNA-seq and microarray datasets from the Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA), together with single-cell RNA sequencing (scRNA-seq) datasets from GEO, were comprehensively analyzed. The Seurat package was used to identify macrophage-related clusters and mitophagy-associated pathways. Cox and LASSO regression analyses, along with a prognostic nomogram, were applied to evaluate the prognostic significance of FZD5. Immune infiltration, functional enrichment, and immunotherapy response analyses were conducted, followed by validation using spatial transcriptomics, immunohistochemistry, and in vitro assays. RESULTS: In bulk glioma transcriptomes, FZD5 emerged as an independent predictor of poor prognosis. Crucially, single-cell and spatial analyses revealed that the biologically significant FZD5 signal originated predominantly within tumor-associated macrophages (TAMs), where it colocalized with the M2 marker CD163. Consistently, elevated FZD5 levels correlated with increased myeloid infiltration and an immunosuppressive tumor microenvironment. Functionally, macrophage-expressed FZD5 was associated with mitophagy-related programs and promoted an M2-skewed phenotype, thereby enhancing glioma cell proliferation, migration, and invasion via macrophage-glioma crosstalk. CONCLUSION: FZD5 is a TAM-enriched marker in glioma tissues and a potential regulator of macrophage-associated immunosuppressive programs, supporting its utility as a prognostic biomarker and a candidate target for microenvironment-oriented interventions in glioma.

Humans↗

Transcriptome analysis of the diseased intervertebral disc tissue in patients with spinal tuberculosis.

OBJECTIVE: To investigate the differential expression genes (DEGs) in spinal tuberculosis using transcriptomics, with the aim of identifying novel therapeutic targets and prognostic indicators for the clinical management of spinal tuberculosis. METHODS: Patients who visited the Department of Orthopedics at the Second Hospital, Lanzhou University from January 2021 to May 2023 were enrolled. Based on the inclusion and exclusion criteria, there were 5 patients in the test group and 5 patients in the control group. Total RNA was extracted and paired-end sequencing was conducted on the sequencing platform. After processing the sequencing data with clean reads and annotating the reference genome, FPKM normalization and differential expression analysis were performed. The DEGs and long non-coding RNAs (LncRNAs) were analyzed for Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment. The cis-regulation of differentially expressed mRNAs (DE mRNAs) by LncRNAs was predicted and analyzed to establish a co-expression network. RESULTS: This study identified 2366 DEGs, with 974 genes significantly upregulated and 1392 genes significantly downregulated. The upregulated genes are associated with cytokine-cytokine receptor interactions, tuberculosis, and TNF-α signaling pathways, primarily enriched in biological processes such as immunity and inflammation. The downregulated genes are related to muscle development, contraction, fungal defense response, and collagen metabolism processes. Analysis of LncRNAs from bone tuberculosis RNA-seq data detected a total of 3652 LncRNAs, with 356 significantly upregulated and 184 significantly downregulated. Further analysis identified 311 significantly different LncRNAs that could cis-regulate 777 target genes, enriched in pathways such as muscle contraction, inflammatory response, and immune response, closely related to bone tuberculosis. There are 51 genes enriched in the immune response pathway regulated by cis-acting LncRNAs. LncRNAs that regulate immune response-related genes, such as upregulated RP11-451G4.2, RP11-701P16.5, AC079767.4, AC017002.1, LINC01094, CTA-384D8.35, and AC092484.1, as well as downregulated RP11-2C24.7, may serve as potential prognostic and therapeutic targets. CONCLUSION: The DE mRNAs and LncRNAs in spinal tuberculosis are both associated with immune regulatory pathways. These pathways promote or inhibit the tuberculosis infection and development at the mechanistic level and play an important role in the process of tuberculosis transferring to bone tissue.

Humans↗

Multi-omics characterization of a GPRC5A+ epithelial subpopulation associated with malignant features in colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) exhibits marked cellular heterogeneity, and the cellular context of malignancy-associated epithelial programs remains incompletely defined. METHODS: We integrated 2,993 CRC samples spanning bulk RNA-seq (n = 2,568; two OS/RFS cohorts), scRNA-seq (281,961 cells/152 specimens), spatial transcriptomics (n = 6), and proteomics (n = 267). Analyses included single-cell integration/annotation, GSVA/HALLMARK, interactome, pseudotime, and ligand-receptor mapping; functional CRISPR assays, EMT immunoblotting, and xenografts; TF profiling (SCENIC/JASPAR/ChIP-qPCR); and exploratory drug-response prediction (OncoPredict), cell-sensitivity assays, and docking/MD modeling. RESULTS: We constructed a stage-stratified single-cell atlas and resolved eleven malignant epithelial subsets, characterizing Epi_4 as late-stage-enriched with EMT, hypoxia, and inflammatory programs and adverse OS/RFS. GPRC5A marked this subset, which we define as GPRC5A+Epi; its expression rose from stage I→IV and was associated with poor outcomes across cohorts, with concordant spatial/proteomic observations. GPRC5A perturbation affected CRC proliferation, migration/invasion, EMT, and xenograft tumorigenicity, supporting a functionally important role in the tested models. SCENIC and ChIP-qPCR supported FOSL1 as an upstream regulator that occupies the GPRC5A promoter. Spatial and ligand-receptor analyses predicted close association and potentially reciprocal signaling between GPRC5A+Epi and POSTN+fibroblasts (COL1A1-SDC4, COL1A1/1A2-ITGA2/ITGB1, PPIA-BSG); concurrent high GPRC5A+Epi/POSTN+Fib signatures were associated with inferior OS/RFS. Drug-response analyses identified an association between GPRC5A status and trametinib sensitivity. Docking/MD produced a computational model of a possible trametinib-GPRC5A interaction, which remains experimentally unvalidated. CONCLUSIONS: GPRC5A⁺Epi is a malignancy-associated epithelial state in CRC, and GPRC5A is functionally important for malignant phenotypes in the tested models. Its inferred relationships with POSTN⁺ fibroblasts and the trametinib findings should be regarded as hypothesis-generating pending functional crosstalk, direct-binding, and therapeutic validation.

Humans↗

BHLHE40 and ChREBP associate with hepatic enhancer clusters containing PPARα, RXRα, and HNF4 nuclear receptors.

BHLHE40/DEC1 is a basic helix-loop-helix transcription factor (TF) that regulates circadian rhythm and T-cell responses. In hepatocytes, its function and interplay with other TFs are poorly understood. Employing a genome-wide approach, we show that its genomic binding strongly overlapped with that of carbohydrate response-element binding protein, a sugar-sensing TF and known inducer of BHLHE40 expression. Transcriptomic analysis of primary mouse hepatocytes revealed reduced expression of genes involved in genomic stability on Bhlhe40 knockdown by siRNA. Bhlhe40 depletion potentiated fructose responsiveness of genes involved in cell-cycle regulation. Strikingly, genomic binding of BHLHE40 extensively overlapped with enhancers occupied by PPARα, RXRα, and HNF4 nuclear receptors and BHLHE40 fine-tuned the expression of PPARα target genes. Using HEK293 cells, we further observed that BHLHE40 physically interacted with RXRα and PPARα cofactors. Collectively, our data suggest that through cooperation with carbohydrate response-element binding protein and nuclear receptors, BHLHE40 is a central regulator of hepatic gene expression with potential to integrate inputs from nutrient signals contributing to the metabolic flexibility of the liver.

Animals↗

OmnibusX: A unified platform for accessible multi-omics analysis.

OmnibusX is an integrated, privacy-centric platform that enables code-free multi-omics data analysis by bridging computational methodologies with user-friendly interfaces. Designed to overcome challenges posed by fragmented analytical tools and high computational barriers, OmnibusX consolidates workflows for diverse technologies - including bulk RNA-seq, single-cell RNA-seq, single-cell ATAC-seq, and spatial transcriptomics - into a single, cohesive application. The application integrates established open-source tools such as Scanpy, DESeq2, SciPy, and scikit-learn into transparent, reproducible pipelines, offering users control over analytical parameters. Additionally, OmnibusX features proprietary modules, including a highly accurate cell-type prediction engine and an interactive plotting editor for generating publication-quality visualizations. Available as a standalone desktop application and an enterprise edition for centralized server deployment, OmnibusX ensures all data processing is conducted locally, eliminating external data transfer and usage tracking. By lowering technical barriers and enhancing reproducibility, OmnibusX aims to accelerate biological discovery and foster robust, data-driven collaborations. A fully documented trial version is accessible at: https://omnibusx.com/apps.

Computational Biology↗

The role of stem cells in pituitary tumour formation.

Pituitary tumours are intracranial neoplasms that pose significant clinical challenges due to their potential for recurrence, therapeutic resistance and resultant endocrine dysfunction and mass effects. In the normal anterior pituitary, resident pituitary stem cells (PSCs) contribute to tissue homeostasis and cellular turnover. The extent to which PSCs contribute to tumourigenesis is not known, but an increasing number of studies have been aiming to address this. In this review, we summarise current evidence implicating PSCs and tumour stem-like populations in pituitary tumour biology, including potential roles in tumour initiation, maintenance and progression. We outline practical criteria for defining tumour stem cells and evaluate findings from functional studies of human tumours, emerging single-cell and spatial transcriptomic datasets and murine lineage-tracing models. We also provide a curated overview of published single-cell RNA sequencing studies of pituitary tumours, highlighting reported stem/progenitor populations and transcriptional signatures across tumour subtypes and propose a framework for future genomic analyses. Finally, we discuss the translational implications of these findings, including the potential for targeting stem-like populations and their associated signalling pathways.

Humans↗

Construction and validation of a β-hydroxybutyrylation-related molecular model for predicting prognosis of papillary thyroid carcinoma.

BACKGROUND: Papillary thyroid carcinoma (PTC) usually has a favorable prognosis, yet a subset of patients develops persistent, recurrent, or biologically aggressive disease. The clinical relevance of lysine β-hydroxybutyrylation (Kbhb)-related transcriptional programs in PTC remains unclear. Accordingly, this study aimed to characterize Kbhb-related molecular heterogeneity in PTC, construct a prognostic signature, and explore its association with the tumor microenvironment (TME). METHODS: Transcriptomic and clinical data from PTC samples within The Cancer Genome Atlas Thyroid Carcinoma (TCGA-THCA) cohort were analyzed to identify Kbhb-related differentially expressed genes (DEGs), define molecular subtypes, construct a prognostic signature, and characterize tumor microenvironmental features. Single-cell RNA-sequencing data from PTC were further used to explore the cellular distribution of representative genes. RESULTS: We identified 51 Kbhb-related DEGs in PTC and defined two Kbhb molecular subtypes. The Kbhb_C2 subtype showed shorter progression-free interval (PFI) and a more immune- and stroma-enriched microenvironment. A six-gene prognostic signature comprising TARID, CDSN, PIMREG, KLRC1, SYT13, and NPR3 was then established. High-risk patients had significantly worse PFI in the full, training, and testing cohorts, with 1-, 3-, and 5-year areas under the curve (AUCs) of 0.715, 0.793, and 0.771, respectively, in the full cohort. High-risk tumors also exhibited higher stromal, immune, and ESTIMATE scores, altered immune infiltration, and increased expression of multiple immune checkpoint molecules. Single-cell analysis confirmed distinct cell-type-specific expression patterns of representative genes. CONCLUSIONS: Kbhb-related transcriptional programs define clinically relevant molecular heterogeneity in PTC and are closely associated with prognosis and TME remodeling. The identified six-gene signature provides a biologically interpretable framework for risk stratification in PTC.

Papillary thyroid carcinoma (PTC)↗

Scalable single-cell total RNA-seq reveals non-coding programs in immunity, infection, and brain development.

Non-coding RNAs represent a widespread and diverse layer of post-transcriptional regulation across cell types and states, yet much of their diversity remains uncharted at single-cell resolution. This gap stems from the limitations of widely used single-cell RNA-sequencing protocols, which focus on polyadenylated transcripts and miss many short or non-polyadenylated RNAs. Here, we adapted single-cell RNA-sequencing on the 10x Genomics platform to capture a broad complement of coding and non-coding RNAs-including miRNAs, tRNAs, lncRNAs, histone RNAs, and non-adenylated viral transcripts. This approach enabled the discovery of rich, dynamic non-coding RNA programs across immune cells, virally infected hepatocytes, and the developing human brain. In dengue virus-infected hepatocytes, we detect non-adenylated viral transcripts and distinguish active from transcriptionally quiescent infected states, each with distinct host regulatory signatures. In brain tissue, we identify biotype-specific, cell-type-restricted non-coding RNAs, including miRNAs whose expression anticorrelates with predicted targets, consistent with post-transcriptional regulatory relationships. We show that MIR137, one of the strongest GWAS loci associated with schizophrenia and intellectual disability, is expressed specifically in Cajal-Retzius cells, an early-born but transient population that guides subsequent cortical neuron migration. These findings demonstrate the importance of non-coding RNAs in defining cell identity and state, and show how expanded transcriptome coverage can reveal additional layers of gene control-now accessible through practical and scalable single-cell profiling.

Journal Article↗

The maternal-to-zygotic transition is a critical window for PFOA-induced disruption of developmental programming.

Early embryogenesis is governed by precisely timed gene regulatory programs that coordinate cell fate specification, tissue patterning, and morphogenesis. The maternal-to-zygotic transition (MZT) represents a pivotal developmental milestone during which regulatory control shifts from maternally deposited transcripts to activation of the zygotic genome. Disruption of this transition has the potential to alter developmental trajectories with lasting consequences. Per- and polyfluoroalkyl substances (PFAS), environmentally persistent contaminants, have been linked to developmental abnormalities, yet their impact on core embryonic gene regulatory networks especially with exposure during MZT is not well understood. Using zebrafish (Danio rerio), a tractable vertebrate model and New Approach Methodology (NAM), we investigated how PFAS exposure during the MZT alters early developmental programming. Embryos were exposed starting at different times before and within the MZT time window and collected at 24 h post-fertilization (hpf) for transcriptomic analysis. Targeted qRT-PCR revealed dysregulation of genes controlling transcriptional activation, lineage specification, proliferation, and differentiation. Whole-transcriptome RNA sequencing (RNA-seq) further identified widespread perturbations in gene networks governing transcriptional regulation, cell signaling, and embryonic morphogenesis. Temporal analysis revealed that exposure beginning at 3.5 hpf, followed by 8 hpf, corresponding to early zygotic genome activation and near completion of zygotic activation, respectively, resulted in the greatest differential gene expression changes at 24 hpf. Consistent with these early gene regulatory perturbations, larvae exposed starting at 8 hpf also exhibited altered behavior at 5 days post-fertilization. Together, these findings demonstrate that PFAS exposure during MZT disrupts the establishment of embryonic gene regulatory networks, linking environmental toxicant exposure to altered developmental patterning and organismal outcomes. This work underscores the vulnerability of early developmental transitions to environmental perturbation and positions MZT as a critical window of susceptibility during development.

NAMs (new approach methodologies)↗

Multi-Omics and Integrative Analytics in Natural Products Discovery.

Natural products (NPs) have long been an essential source of new bioactive compounds for drug discovery; however, traditional methods for screening and isolating these compounds can be slow and often yield diminishing returns. Fortunately, advanced multi-omics and computational approaches present powerful solutions to these challenges. This review highlights innovative methodologies that integrate metabolomics, genomics, transcriptomics, and proteomics with bioinformatics and analytical chemistry to accelerate NP discovery. For instance, untargeted metabolomics platforms like high-resolution liquid chromatography-tandem mass spectrometry (LC-MS/MS) and Global Natural Products Social (GNPS) molecular networking allow for comprehensive profiling of new compounds, while targeted isotope-labeling strategies enhance this process. Additionally, genome and metagenome mining tools such as antibiotics and secondary metabolite analysis shell (antiSMASH), Deep Biosynthetic Gene Cluster (DeepBGC), and Pipeline for Reconstructing Integrated Syntheses of Metabolites (PRISM) quickly identify biosynthetic gene clusters (BGCs) in both cultured and uncultured organisms, often using heterologous expression to validate products. Transcriptomic analyses, including RNA sequencing (RNA-seq), co-expression networks, and fluxomics, help clarify how pathways are regulated, while quantitative proteomics techniques like tandem mass tags/isobaric tags for relative and absolute quantitation (TMT/iTRAQ) and label-free methods, along with chemoproteomics approaches such as cellular thermal shift assay and thermal proteome profiling (TPP), uncover molecular targets and their mechanisms of action. This review also places significant emphasis on the role of artificial intelligence (AI) and machine learning (ML) in integrating multi-omics data, spanning activities from constructing gene-metabolite correlation networks to leveraging knowledge graphs and graph neural networks for data fusion and functional prediction. Finally, this review concludes by discussing the synergistic benefits of multi-omics for natural-product discovery, addressing current technical challenges, and exploring future directions toward high-throughput, intelligent data integration for next-generation NP research.

Biological Products↗