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PubMed2026

Seed shattering habit in millets and the secrets of the abscission layer - a comprehensive review.

Though seed shattering continues to be a significant barrier affecting yield stability and harvesting efficiency in millets and other grasses, millets are increasingly acknowledged as climate-resilient, nutrient-rich 2007cereal crops with the potential to strengthen global nutritional and food security under the combined pressures of climate change, population growth, and limited natural resources. Since strong artificial selection favoured non-shattering phenotypes during domestication, seed shattering, an adaptive trait in wild species that promotes seed dispersal through the formation and activation of specialised abscission layers, became a distinguishing feature of cultivated cereals. With a focus on the morphological, physiological, hormonal, and genetic modulation of the abscission zone, this article summarizes the state of the art regarding seed shattering in millets. Abscission layer morphology, location, and lignification vary greatly among grasses, from well-defined lignified zones in rice and sorghum to non-lignified and anatomically subtle zones in Setaria and Panicum species. Cell wall-modifying enzymes like polygalacturonases, cellulases, expansins, and pectin methylesterases that mediate middle lamella degradation are modulated by coordinated hormonal signalling involving auxin, ethylene, and abscisic acid, which controls the timing and progression of cell separation at the physiological level. Domestication-related genes, including SH1, qSH1, SH4, and LES1, demonstrate convergent evolutionary mechanisms controlling abscission layer development in a variety of grass lineages at the molecular level. Understanding these regulatory networks has been greatly enhanced by recent developments in transcriptomics, functional genomics, and genome sequencing in both model species and underused millets. The role of millets as climate-smart cereals for sustainable future agriculture is reinforced by the integration of anatomical, physiological, and genetic insights, which offer a solid basis for targeted breeding and genome-editing strategies intended to improve seed retention, enhance yield stability, and increase harvest efficiency.

Abscission Layer↗

PubMed2026

Balancing growth and immunity of potato by humidity-dependent expression of a late blight resistance gene.

Inducible expression of resistance genes is an effective approach to balance plant growth and immunity, thus facilitating the development of disease-resistant crop cultivars. While pathogen-responsive and immunity-related promoters have been adopted for this purpose, alternative design strategies remain to be explored. High relative humidity (RH) has been recognized as a crucial permissive environmental condition for the occurrence of devastating plant diseases including tomato and potato late blight. Here, we identified humidity-activated cis-regulatory elements (HAEs) in Solanum lycopersicum through an integrative analysis of transcriptomics and chromatin accessibility data. Sequence homology-inferred HAEs in S. tuberosum can predict humidity-elicited changes in downstream gene expression. Transgenic S. tuberosum lines expressing a late blight resistance gene driven by an artificial humidity-inducible promoter containing a natural S. tuberosum HAE were generated. These transgenic lines exhibited comparable late blight resistance levels to the lines overexpressing the same resistance gene in controlled zoospore inoculation bioassays, while avoiding growth suppression and tuber yield penalties in common garden experiments. Our findings highlight the importance of plant cis-regulatory elements in the transcriptional responses to high RH and provide a proof-of-concept for a humidity-inducible environment-responsive resistance gene deployment strategy to engineer disease-resistant crop cultivars without compromising growth and yield.

Phytophthora infestans↗

PubMed2026

The current and future perspective of ChickenGTEx project and its applications in precision breeding.

The Chicken Genotype-Tissue Expression (ChickenGTEx) project was established to systematically characterize the regulatory landscape of the chicken genome and to accelerate the translation of functional genomics into precision breeding. By integrating whole-genome sequencing with multi-tissue transcriptomic profiling, ChickenGTEx provides a comprehensive atlas of gene expression regulation across diverse tissues and physiological systems. Current findings demonstrate that complex production traits are governed by coordinated regulatory networks rather than isolated loci, with substantial contributions from tissue-specific gene expression, structural variation, and genotype-by-sex interactions. Sex-dependent regulatory effects further refine the genetic architecture of metabolic, immune, and reproductive traits, highlighting the importance of incorporating sex as a biological variable in genomic analyses. Application of integrative omics frameworks within elite layer populations has revealed multilayer regulatory mechanisms underlying extended laying performance, feed efficiency, metabolic health, and eggshell quality. By partitioning phenotypic variance into genetic, regulatory, and host-microbiome components, these approaches move beyond association-based mapping toward causal inference and biological interpretation. Importantly, validated regulatory loci identified through ChickenGTEx and related analyses provide actionable markers for genomic selection and rational targets for precision genome modification. Looking forward, continued expansion of regulatory atlases, incorporation of single-cell and longitudinal data in diverse environmental conditions, and integration of functional annotation into breeding pipelines will further enhance prediction accuracy and sustainable genetic improvement. The ChickenGTEx project thus represents a foundational platform bridging functional genomics and practical poultry breeding.

Animals↗

PubMed2025

Advances in tumor subclone formation and mechanisms of growth and invasion.

Tumor subclones refer to distinct cell populations within the same tumor that possess different genetic characteristics. They play a crucial role in understanding tumor heterogeneity, evolution, and therapeutic resistance. The formation of tumor subclones is driven by several key mechanisms, including the inherent genetic instability of tumor cells, which facilitates the accumulation of novel mutations; selective pressures from the tumor microenvironment and therapeutic interventions, which promote the expansion of certain subclones; and epigenetic modifications, such as DNA methylation and histone modifications, which alter gene expression patterns. Major methodologies for studying tumor subclones include single-cell sequencing, liquid biopsy, and spatial transcriptomics, which provide insights into clonal architecture and dynamic evolution. Beyond their direct involvement in tumor growth and invasion, subclones significantly contribute to tumor heterogeneity, immune evasion, and treatment resistance. Thus, an in-depth investigation of tumor subclones not only aids in guiding personalized precision therapy, overcoming drug resistance, and identifying novel therapeutic targets, but also enhances our ability to predict recurrence and metastasis risks while elucidating the mechanisms underlying tumor heterogeneity. The integration of artificial intelligence, big data analytics, and multi-omics technologies is expected to further advance research in tumor subclones, paving the way for novel strategies in cancer diagnosis and treatment. This review aims to provide a comprehensive overview of tumor subclone formation mechanisms, evolutionary models, analytical methods, and clinical implications, offering insights into precision oncology and future translational research.

Humans↗

PubMed2025

Tissue tropism, pathology, and pathogenesis of West Nile virus infection in saltwater crocodile (Crocodylus porosus).

West Nile virus (WNV) is one of the leading causes of economic losses to the saltwater crocodile farming industry due to skin lesions, known as "pix", induced by the infection. Our previous study suggested a possible immunopathological pathway causing these lesions. We therefore resolved to investigate the kinetics of WNV-infection and the elicited immune responses in experimentally challenged saltwater crocodile hatchlings. Employing virus isolation, quantitation of viral genome loads in tissues by RT-qPCR and immunohistochemistry, we demonstrated that upon infection, the virus replicates in the spleen, liver and later in the pancreas. Transcriptomic analysis, based on RNA sequencing and RT-qPCR of kidney and liver tissues, revealed that the early host response is primarily via alteration of cellular structure and metabolism. As the infection progresses, the response becomes predominantly inflammatory and antiviral. The results suggest that the kidney and gastrointestinal tract are primary nidi of viral replication leading to cloacal shedding, but a link to skin lesion development remains to be fully clarified.

Animals↗

PubMed2025

Spatial mutual nearest neighbors for spatial transcriptomics data.

MOTIVATION: Mutual nearest neighbors (MNN) is a widely used computational tool to perform batch correction for single-cell RNA-sequencing data. However, in applications such as spatial transcriptomics, it fails to take into account the 2D spatial information. RESULTS: Here, we present spatialMNN, an algorithm that integrates multiple spatial transcriptomic samples and identifies spatial domains. Our approach begins by building a k-nearest neighbors (kNN) graph based on the spatial coordinates, prunes noisy edges, and identifies niches to act as anchor points for each sample. Next, we construct a MNN graph across the samples to identify similar niches. Finally, the spatialMNN graph can be partitioned using existing algorithms, such as the Louvain algorithm to predict spatial domains across the tissue samples. We demonstrate the performance of spatialMNN using large datasets, including one with N = 31 10x Genomics Visium samples. We also evaluate the computing performance of spatialMNN to other popular spatial clustering methods. AVAILABILITY AND IMPLEMENTATION: Our software package is available on GitHub (https://github.com/Pixel-Dream/spatialMNN). The code is available on Zenodo (https://doi.org/10.5281/zenodo.15073963).

Algorithms↗

PubMed2025

De novo transcriptome assembly and gene expression analysis of Cnidium officinale under high-temperature conditions.

BACKGROUND: The medicinal plant Cnidium officinale (CO) is widespread in Northeast Asia and vulnerable to heat stress. The naturally occurring composition of pharmacological ingredients of CO results in overall physiological consequences; therefore, it is crucial to have a comprehensive understanding of metabolic response to ambient heat in terms of acclimation to estimate how much CO is exposed to threatening environmental conditions. RESULTS: Transcriptome analysis is critical for understanding the consequences of long-term physiological adaptation of CO to abiotic stress. However, transcriptome analysis on this species, particularly under prolonged stress conditions, has remained limited. We employed a temperature gradient tunnel (TGT) to subject CO to high-temperature exposure for four months, enabling us to observe the cumulative effects of heat and assess its acclimation mechanisms. In the absence of genome sequencing data, we performed de novo transcriptome assembly and compared DEGs from temperature treatment plots of a TGT and a growth chamber (GC). Since interpreting transcriptomic data can be complex, we employed a sequential analytical approach, including DEG clustering, GO enrichment, KEGG pathway mapping, miRNA-target gene analysis, and multiple rounds of RNA sequencing validation. DEGs were classified into two categories: genes exhibiting significant fold changes and genes showing significant count changes rather than fold changes. Then, we analyzed the functional roles of DEGs to determine which pathways respond to ambient and stressful high temperatures and validated the findings through cross-comparison with GC. Additionally, we conducted miRNA analysis to investigate post-transcriptional regulation under high temperatures. CO grown under higher ambient temperatures exhibited slight upregulation of pathways related to protein stability and turnover, ABA biosynthesis, and energy production, such as photosynthesis and oxidative phosphorylation. However, under extreme heat stress, most metabolic pathways were downregulated except for those involved in transcription, translation, oxidative phosphorylation and the biosynthesis of cutin, suberin, and wax. CONCLUSION: This study demonstrated that proper clustering of genes based on expression levels and fold changes in two different experimental conditions, along with pathway mapping, may provide a comprehensive understanding of CO's response to heat stress. These insights could contribute to future research on heat tolerance and crop improvement.

Gene Expression Profiling↗

PubMed2024

Pterostilbene Targets Hallmarks of Aging in the Gene Expression Landscape in Blood of Healthy Rats.

SCOPE: Polyphenols from the phytoestrogen group, including pterostilbene (PTS), are known for their antioxidant, anti-inflammatory, and anti-cancer effects. In recent reports, phytoestrogens attenuate age-related diseases; however, their pro-longevity effects in healthy models in mammals remain unknown. As longevity research demonstrates age-related transcriptomic signatures in human blood, the current study hypothesizes that phytoestrogen-supplemented diet may induce changes in gene expression that ultimately confer pro-longevity benefits. METHODS AND RESULTS: In the present study, RNA sequencing is conducted to determine transcriptome-wide changes in gene expression in whole blood of healthy rats consuming diets supplemented with phytoestrogens. Ortholog cell deconvolution is applied to analyze the omics data. The study discovered that PTS leads to changes in the gene expression landscape and PTS-target genes are associated with functions counteracting hallmarks of aging, including genomic instability, epigenetic alterations, compromised autophagy, mitochondrial dysfunction, deregulated nutrient sensing, altered intercellular interaction, and loss of proteostasis. These functions bridge together under anti-inflammatory effects through multiple pathways, including immunometabolism, where changes in cellular metabolism (e.g., ribosome biogenesis) impact the immune system. CONCLUSION: The findings provide a rationale for pre-clinical and clinical longevity studies and encourage investigations on PTS in maintaining cellular homeostasis, decelerating the process of aging, and improving conditions with chronic inflammation.

Animals↗

PubMed2026

Amaranth: enhanced single-cell transcript assembly via discriminative modelling of UMI reads and internal reads.

MOTIVATION: Single-cell RNA sequencing (scRNA-seq) has transformed transcriptome profiling at cellular resolution, yet accurate reconstruction of full-length transcripts for individual cells remains a central challenge. Emerging scRNA-seq protocols can produce reads that span entire transcripts, enabling isoform-level expression analysis. For example, Smart-seq protocols combine unique molecular identifier (UMI)-linked reads that index and stitch together multiple reads from the same molecule, with internal reads filling coverage gaps. We demonstrate that these read types exhibit markedly different biological and statistical properties in strandness, 5'/3' coverage bias, and genomic locality. Existing assemblers fail to leverage these distinctions, yielding suboptimal assembly. RESULTS: We developed Amaranth, a novel single-cell assembler that discriminatively models UMI and internal reads. Amaranth implements heuristics specifically designed to address the distinct biases of UMI-linked and internal reads, enabling accurate strandness assignment for internal reads, reliable splicing graph refinement, and precise transcript start site determination. We also developed Amaranth-meta, which integrates information across cells to enhance individual cell assemblies. Benchmarked on Smart-seq3 datasets from human HEK293T and mouse fibroblast cells, Amaranth outperformed other state-of-the-art assemblers in assembling individual cells and in meta-assembly. Amaranth advances isoform-level analysis in single-cell transcriptomics, facilitating detailed studies at cellular resolution. AVAILABILITY AND IMPLEMENTATION: Amaranth is implemented in C++ and is freely available at https://github.com/Shao-Group/amaranth under the BSD-3-Clause license. Scripts, documentation, and data for reproducing experiments in this manuscript are available at https://github.com/Shao-Group/amaranth-test.

Single-Cell Gene Expression Analysis↗

PubMed2025

SpaceBar enables clone tracing in spatial transcriptomic data.

We report a cellular barcoding strategy, SpaceBar, that enables simultaneous clone tracing and spatial transcriptomics profiling. Our approach uses a library of 96 synthetic barcode sequences that can be robustly detected by imaging based spatial transcriptomics (seqFISH), delivered such that each cell is labeled with a combination of barcodes. We used these barcodes to label melanoma cells in a tumor xenograft model and profiled both clone identity and spatial gene expression in situ. We developed a gene scoring metric that quantifies how strongly gene expression is driven by intrinsic cellular cues or extrinsic environmental signals. Our framework distinguishes between clonal dynamics and environmentally-driven transcriptional regulation in complex tissue contexts.

Journal Article↗

PubMed2026

Functional characterization of SHC-like triterpene cyclase genes in azole response and virulence-related traits of Aspergillus fumigatus.

Aspergillus fumigatus is a major opportunistic fungal pathogen, and increasing azole resistance poses a challenge for aspergillosis treatment. Squalene is an upstream precursor of ergosterol biosynthesis and may also be utilized by SHC-like triterpene cyclases, suggesting a potential link between squalene-associated metabolism, membrane adaptation, and azole response. However, the roles of SHC-like triterpene cyclase genes in A. fumigatus remain unclear. Here, we characterized three candidates, shc1, shc2, and shc3, using comparative bioinformatic analysis, gene deletion, phenotypic assays, azole susceptibility testing, transcriptomics, and host-interaction models. Sequence, genomic-context, phylogenetic, and structural analyses suggested divergence among the three candidates. Individual shc deletion caused limited effects on vegetative growth, whereas loss of shc1 mildly reduced susceptibility to voriconazole and posaconazole, as reflected by twofold MIC increases and lower inhibition rates. Transcriptomic analysis revealed distinct remodeling patterns, with Δshc3 showing the broadest transcriptional changes despite no detectable MIC shift. Targeted metabolite profiling and PI uptake analysis further supported an association between shc deletion, sterol/hopane-type triterpenoid balance, and membrane-associated properties. shc deletion also altered epithelial cell interaction phenotypes, while Δshc1 showed reduced lethality in Galleria mellonella. In clinical isolates, elevated shc transcription was associated with azole-resistant backgrounds. These findings suggest functional diversification among SHC-like triterpene cyclase genes and indicate that shc1 may contribute to azole-associated adaptation and virulence-related traits in A. fumigatus.

Aspergillus fumigatus↗

PubMed2026

Cellular transcriptomic signatures underpinning the heterogeneity of depression in Alzheimer's disease.

INTRODUCTION: Late-onset Alzheimer's disease (LOAD) and major depressive disorder (MDD) share genetic etiologies. Here, we investigated brain transcriptomic landscapes to gain insights into shared and divergent molecular and biological etiologies across LOAD and MDD. METHODS: Brain single-nucleus RNA sequencing (snRNA-seq) datasets from cognitively normal older and young individuals and LOAD patients stratified by comorbid MDD were analyzed to identify differential expressed genes (DEGs). Using cell type-specific DEGs we performed biological pathway and intercellular-communication networks analyses. We investigated shared DEGs across MDD and LOAD cohorts and sex-specific DEGs. Results were validated by comparison with four transcriptomic and proteomic studies of MDD and depression. RESULTS: MDD-associated dysregulated genes and pathways were shared between LOAD and cognitive-normal individuals, including JUNB and DUSP1 in glutamatergic neurons, and PRAM1 and SNX9 in microglia. DEGs shared between the MDD and LOAD cohorts included HSPA1A and NDUFB7 in glutamatergic neurons. Sex interaction analysis identified numerous new DEGs in the MDD cohorts, whereas there were ≈5 to 10 times more DEGs in female than in male individuals. LOAD and MDD common microglial pathways included neuronal injury, stress, peroxisome proliferator-activated receptor (PPAR) signaling and interferon alpha/beta signaling. DISCUSSION: LOAD and MDD exhibited common molecular profiles, dysregulated pathways, and cellular communication changes. MDD develops earlier in life, thus, our findings provide a window into early molecular and biological processes preceding LOAD-onset.

Humans↗

PubMed2026

A contextual activity score (CAS) for inferring ADAR-associated transcriptional activity across RNA-seq, single-cell, and spatial transcriptomics.

BACKGROUND AND OBJECTIVE: Adenosine-to-inosine RNA editing, catalyzed by Adenosine Deaminases Acting on RNA (ADARs), is a widespread modification involved in neural function, immune regulation, and cancer. The Alu Editing Index (AEI) is the standard metric to estimate ADAR activity but requires raw sequencing reads and is poorly suited for single-cell and spatial transcriptomic data. This study aimed to develop an alternative framework for inferring ADAR-associated transcriptional activity from gene expression data across diverse transcriptomic technologies. METHODS: We developed the Contextual Activity Score (CAS), a framework based on transcriptional signatures from ADAR perturbation experiments. Context-specific signatures were generated for human neurons, mouse neurons, and cancer models to infer ADAR1 and ADAR2 activity. CAS was computed from normalized gene expression matrices using regulon-based enrichment analysis. Performance was evaluated by comparing with the Alu Editing Index across bulk RNA sequencing datasets, simulated sequencing depths, and library preparation protocols. RESULTS: CAS showed strong concordance with the Alu Editing Index across multiple datasets, while remaining robust to reduced sequencing depth and different library protocols. Unlike the Alu Editing Index, CAS can be applied to single-cell and spatial transcriptomic data and enables the independent assessment of ADAR2 activity. In cancer and neuronal contexts, CAS captured biologically meaningful variations in ADAR-associated transcriptional activity at sample, cell-type, and spatial levels. CONCLUSION: CAS provides a scalable approach applicable across multiple RNA-seq protocols for estimating ADAR-associated transcriptional activity using gene expression data. This method, implemented in an open-source R package for broad adoption, expands the ability to study ADAR-associated transcriptional activity across transcriptomic modalities where direct editing quantification is challenging, such as single-cell and spatial transcriptomics.

Adenosine Deaminase↗

PubMed2026

Predicting cellular responses to perturbation across diverse contexts with State.

While machine learning models offer potential for predicting transcriptomic effects of perturbation, they currently struggle to generalize across cellular contexts. Here, we introduce State, a machine learning model that predicts perturbation effects while accounting for cellular heterogeneity within and across experiments. State is trained using single-cell gene expression data to predict perturbation effects across sets of cells. State improved discrimination of effects on large datasets by more than 30% and identified differentially expressed genes across genetic, signaling, and chemical perturbations with significantly improved accuracy compared with baselines. Its cell embeddings trained on observational data from 167 million cells enable the identification of strong perturbations in cellular contexts where no perturbations were observed during training. We further introduce Cell-Eval, a comprehensive evaluation framework that can be used to evaluate future models. Overall, the performance and flexibility of State set the stage for scaling the development of AI models of cell state.

Machine Learning↗

PubMed2026

Transcriptome mining and comparative genomics reveal 36 putative novel marafivirus species and conserved evolution of the marafibox regulatory element.

BACKGROUND: Marafiviruses are plant-infecting RNA viruses associated with several economically important crops, but their genomic diversity remains incompletely characterized. OBJECTIVE: This study aimed to identify previously unrecognized marafivirus genomes and investigate their genomic features and evolutionary relationships. METHODS: Publicly available plant transcriptome datasets were systematically mined to detect marafivirus-like sequences. Recovered genomes were analyzed using comparative sequence analysis, phylogenetic reconstruction, and genome organization characterization. RESULTS: A total of 62 marafivirus-like genomes were recovered from 33 independent sources representing diverse plant hosts. Polyprotein-based comparative and phylogenetic analyses grouped these genomes into 36 lineages likely representing novel species. All newly identified viruses clustered within the Marafivirus clade. Genome organization analysis revealed conserved polyprotein architecture and widespread presence of the marafibox promoter element. Conservation of additional open reading frames among closely related isolates aided identification of potentially functional genes. CONCLUSION: These findings substantially expand the known diversity of marafiviruses and demonstrate the effectiveness of transcriptome mining for discovering previously unrecognized plant viruses.

Phylogeny↗

PubMed2025

Paired Single-Cell Transcriptome and DNA Barcode Detection in Zebrafish Using ScarTrace.

ScarTrace is a CRISPR/Cas9-based genetic lineage tracing method that allows for uniquely barcoding the DNA of single cells at a target GFP sequence during developing zebrafish embryos. Single cells from barcoded adult zebrafish can be isolated from various tissues (e.g., marrow, brain, eyes, fins), and their transcriptome and barcode sequences are captured by single-cell cDNA amplification and genomic DNA nested PCR, respectively. Computationally, cell type and barcode identification permit clone tracing and lineage tree reconstruction of tissues to unravel fate decisions during embryogenesis.

Animals↗

PubMed2026

Multi-omics integrative analysis provides insight into potential molecular responses to sustained high water flow in common carp (Cyprinus carpio) cultured in recirculating aquaculture.

To investigate the potential molecular responses by which water flow intensity affects the growth of common carp (Cyprinus carpio) in a recirculating aquaculture system (RAS), a control group (CG, actual water velocity 0.3 cm/s) and three sustained flow treatment groups were established, including a low-flow group (LF, 1 body length per second, bl/s), a medium-flow group (MF, 2 bl/s), and a high-flow group (HF, 3 bl/s). After 12 weeks of culture in the RAS, growth performance was compared among groups under different flow intensities. The best-performing group and the control group were then selected for the determination of intestinal digestive enzyme activities, as well as transcriptomic and whole-genome bisulfite sequencing analyses of muscle tissue. The results showed that the specific growth rate and feed intake of the HF group were significantly higher than those of the other groups (P < 0.05), whereas no significant difference in feed conversion ratio was observed among groups. Compared with the CG group, lipase activity was significantly higher in the HF group (P < 0.05), while α-amylase and trypsin activities showed increasing trends without significant differences. RNA-seq identified a total of 273 differentially expressed genes, including 72 upregulated genes and 201 downregulated genes in the HF group relative to the CG group. These genes were mainly enriched in glycolysis, pyruvate metabolism, ATP metabolism, the pentose phosphate pathway, the insulin signaling pathway, the PPAR signaling pathway, and the adipocytokine signaling pathway, indicating that sustained high water flow induced a muscle transcriptional response characterized by remodeling of energy metabolism and substrate utilization. Whole-genome bisulfite sequencing analysis showed that DNA methylation in common carp muscle occurred predominantly in the CpG context. Differentially methylated regions between the HF and CG groups were mainly distributed in transcription-related regulatory regions, including promoters, CpG islands, and CpG island shores. In promoter regions, the number of hypermethylated regions in the HF group relative to the CG group was markedly higher than that of hypomethylated regions. Integrated analysis further identified two candidate genes showing both promoter differential methylation and differential expression, namely LOC109094644 and bcorl1, suggesting that adaptation to high water flow may involve IGF-related growth regulation and remodeling of upstream transcriptional programs. The qPCR results were consistent with the transcriptomic data. Taken together, within the tested range, a sustained water flow of 3 bl/s was more conducive to the growth of common carp in the RAS, which may be associated with enhanced lipid digestion and utilization, remodeling of the muscle energy metabolic network, changes in promoter methylation, and the coordinated regulation of key candidate genes. This study provides a theoretical basis for clarifying the exercise adaptation mechanism of common carp in recirculating aquaculture and for optimizing flow velocity parameters.

Animals↗

PubMed2025

Droplet-Based Single-Cell 3' mRNA Sequencing of Marburg Virus-Infected Samples.

Single-cell technologies are continually evolving with emerging methods that are gradually uncovering the central DNA-RNA-protein dogma. Single-cell RNA sequencing is one arm of a multi-omic approach that achieves an astounding level of granularity to reveal the complexity of virus-host interactions at the transcriptomic level. Cell tropism, virus replication, pathogenesis, and gene expression changes mediated by the virus and the host's immune response to infection are just some areas of study that are gaining better clarity due to the high-resolution analysis afforded by the technology.We describe a single-cell sequencing protocol for Marburg virus infection in vivo using nonhuman primate blood and the 10× Chromium Next GEM single-cell genomics methodology. Working with pathogens of high consequence is logistically complicated, requiring containment in biosafety level (BSL)-4 laboratories and harsh inactivation procedures before samples can safely be removed to lower biosafety conditions. We provide procedural insight into sample isolation and processing conducted in BSL-4 and describe the requirements for safe sample removal without jeopardizing quality for down-stream sequencing and analysis in BSL-2 conditions. Characterization of complicated biological processes mediated by high-containment pathogens, typically restricted to analogous model systems, e.g., minigenome, can be achieved using live virus.

Animals↗