Transcriptomic Datasets Research Articles

Mechanistic insights into PROS1 inhibition of bladder cancer progression and angiogenesis via the AKT/GSK3β/β-catenin pathway

Bladder cancer (BLCA) is one of the ten most common cancers worldwide. However, the deregulation of PROS1 and its specific function in BLCA is not well understood. By combining proteomic and transcriptomic datasets, we discovered PROS1 expression was significantly reduced in BLCA tissues and revealed the clinical relevance of PROS1 with BLCA. Analysis of multiple BLCA datasets consistently showed the group with reduced PROS1 expression was linked to cancer-promoting pathways, more aggressive characteristics, and a greater chance of responding positively to immunotherapy. Next, various functional experiments were performed and the results revealed PROS1 overexpression inhibited the proliferation, cell cycle progression, migration, invasion, and angiogenesis of BLCA. In recovery trials, the AKT activator SC79 offered additional proof that PROS1 may influence BLCA cells via the AKT/GSK3β/β-catenin pathway. In conclusion, as an angiogenesis-related gene, PROS1 may play an inhibitory role in the biological functions of bladder cancer.

Blood molecular subtypes to guide precision treatment strategies in systemic juvenile idiopathic arthritis

BackgroundSystemic juvenile idiopathic arthritis (sJIA) is the most severe subtype of JIA, with a combination of diverse clinical manifestations and a variable clinical course. A comprehensive understanding of molecular signatures at the systems level and the discovery of molecular subtypes are the initial steps toward personalized medicine in sJIA.MethodsA blood transcriptomic dataset was collected from patients with systemic JIA (sJIA) (n = 168), polyarticular JIA (n = 254), oligoarticular JIA (n = 96), enthesitis-related arthritis (n = 40), and healthy controls (n = 220). Gene expression profiles were filtered for differentially expressed genes and unsupervised clustering, gene set enrichment, and network-based centrality analyses. The molecular signatures of three novel sJIA subgroups (designated as C1, C2, and C3) were investigated, focusing on their distinct features and treatment responses.ResultsNeutrophil degranulation and the IL-1 signaling pathway were the shared key processes for the three subgroups. Proinflammatory signals, including TNF, IL-6, TLR, and G-CSF signaling pathways, were identified with variation across the subgroups. C1 was the most inflammatory subset with a high-risk profile for macrophage activation syndrome. The C2 subset had the most activated IL-1 and IL-18 signaling pathways. C2 and C3 have higher levels of interferon-stimulated signatures. In a canakinumab-treated dataset, treatment response was correlated with IL1B expression and NF-κB signaling pathway, and neutrophil activation-associated processes were effectively suppressed in a good responder group. GSK3B and p38 MAPK inhibitors showed a significant counteracting effect on the perturbed gene expression of sJIA.ConclusionsNeutrophil activation was the key feature in active sJIA. The three molecular subtype scheme enables the formulation of precision medicine strategies in sJIA.

Sideroflexin family genes were dysregulated and associated with tumor progression in prostate cancers

Sideroflexin (SFXN) family genes encode for a group of mitochondrial proteins involved in cellular processes such as iron homeostasis, amino acid metabolism, and energy production. Recent studies showed that they were aberrantly expressed in certain human cancers. However, there is a paucity of information about their expression in prostate cancer. In this study, we took a comprehensive approach to investigate their expression profiles in benign prostate tissue, prostate-derived cell lines, and prostate cancer tissues using multiple transcriptome datasets. Our results showed that SFXN1/3/4 genes were predominantly expressed in prostate tissue and cell lines. SFXN2/4 genes were significantly upregulated while the SFXN3 expression was significantly downregulated in malignant tissues compared to benign tissues. SFXN4 expression was identified as a diagnostic biomarker and prognostic factor for unfavorite survival outcomes. In advanced prostate cancers, SFXN2/4 expressions were positively correlated with the androgen receptor signaling activity but negatively correlated with the neuroendocrinal features. Further analysis discovered that SFXN5 expression was significantly elevated in neuroendocrinal prostate cancers. In conclusion, SFXN2/4 expressions are novel biomarkers in prostate cancer diagnosis and prognosis.

Kernel-bounded clustering for spatial transcriptomics enables scalable discovery of complex spatial domains.

Spatial transcriptomics are a collection of technologies that have enabled characterization of gene expression profiles and spatial information in tissue samples. Existing methods for clustering spatial transcriptomics data have primarily focused on data transformation techniques to represent the data suitably for subsequent clustering analysis, often using an existing clustering algorithm. These methods have limitations in handling complex data characteristics with varying densities, sizes, and shapes (in the transformed space on which clustering is performed), and they have high computational complexity, resulting in unsatisfactory clustering outcomes and slow execution time even with GPUs. Rather than focusing on data transformation techniques, we propose a new clustering algorithm called kernel-bounded clustering (KBC). It has two unique features: (1) It is the first clustering algorithm that employs a distributional kernel to recruit members of a cluster, enabling clusters of varying densities, sizes, and shapes to be discovered, and (2) it is a linear-time clustering algorithm that significantly enhances the speed of clustering analysis, enabling researchers to effectively handle large-scale spatial transcriptomics data sets. We show that (1) KBC works well with a simple data transformation technique called the Weisfeiler-Lehman scheme, and (2) a combination of KBC and the Weisfeiler-Lehman scheme produces good clustering outcomes, and it is faster and easier-to-use than many methods that employ existing clustering algorithms and data transformation techniques.

Potential biomarkers and immune infiltration linking endometriosis with recurrent pregnancy loss based on bioinformatics and machine learning

ObjectiveEndometriosis (EMs) is a chronic inflammatory disease characterized by the presence of endometrial tissue in the non-uterine cavity, resulting in dysmenorrhea, pelvic pain, and infertility. Epidemiologic data have suggested the correlation between EMs and recurrent pregnancy loss (RPL), but the pathological mechanism is unclear. This study aims to investigate the potential biomarkers and immune infiltration in EMs and RPL, providing a basis for early detection and treatment of the two diseases.MethodsTwo RPL and six EMs transcriptomic datasets from the Gene Expression Omnibus (GEO) database were used for differential analysis via limma package, followed by weighted gene co-expression network analysis (WGCNA) for key modules screening. Protein-protein interaction (PPI) network and two machine learning algorithms were applied to identify the common core genes in both diseases. The diagnostic capabilities of the core genes were assessed by receiver operating characteristic (ROC) curves. Moreover, immune cell infiltration was estimated using CIBERSORTx, and the Cancer Genome Atlas (TCGA) database was employed to elucidate the role of key genes in endometrial carcinoma (EC).Results26 common differentially expressed genes (DEGs) were screened in both diseases, three of which were identified as common core genes (MAN2A1, PAPSS1, RIBC2) through the combination of WGCNA, PPI network, and machine learning-based feature selection. The area under the curve (AUC) values generated by the ROC indicates excellent diagnostic powers in both EMs and RPL. The key genes were found to be significantly associated with the infiltration of several immune cells. Interestingly, MAN2A1 and RIBC2 may play a predominant role in the development and prognostic stratification of EC.ConclusionWe identified three key genes linking EMs and RPL, emphasizing the heterogeneity of immune infiltration in the occurrence of both diseases. These findings may provide new mechanistic insights or therapeutic targets for further research of EMs and RPL.

A Transcriptomic Dataset of Liver Tissues from Global and Liver-Specific Bmal1 Knockout Mice

The circadian clock regulates various physiological processes in mammals. The core circadian clock gene Bmal1 is crucial for maintaining the oscillations of the circadian clock system by controlling the rhythmic expression of numerous circadian clock-controlled genes. To explore the transcriptional changes associated with Bmal1 deletion in liver tissues, we collected liver tissues from global and liver-specific Bmal1 knockout mice, along with their respective control groups, at two circadian time points (CT2 and CT14) and used them for transcriptome sequencing analysis. Genotyping, locomotor activity analysis, and comprehensive quality control analyses, including base quality scores, GC content, and mapping rates, confirmed the high quality of sequencing data. Differential expression analysis and RT-qPCR validation confirmed the reliability and validity of the dataset. These data offer a valuable resource for researchers investigating the role of BMAL1 in liver physiology, pathology, and the broader field of circadian biology.

Local Inflammation Precedes Diaphragm Wasting and Fibrotic Remodelling in a Mouse Model of Pancreatic Cancer.

Cancer cachexia represents a debilitating muscle wasting condition that is highly prevalent in gastrointestinal cancers, including pancreatic ductal adenocarcinoma (PDAC). Cachexia is estimated to contribute to ~30% of cancer-related deaths, with deterioration of respiratory muscles suspected to be a key contributor to cachexia-associated morbidity and mortality. In recent studies, we identified fibrotic remodelling of respiratory accessory muscles as a key feature of human PDAC cachexia. To gain insight into mechanisms driving respiratory muscle wasting and fibrotic remodelling in response to PDAC, we conducted temporal histological and transcriptomic analyses on diaphragm muscles harvested from mice-bearing orthotopic murine pancreatic (KPC) tumours at time points reflective of precachexia (D8 and D10), mild-moderate cachexia (D12 and D14) and advanced cachexia (endpoint). During the precachexia phase, diaphragms showed significant leukocyte infiltration (+3-fold to +13-fold; D8-endpoint vs. Sham, p < 0.05) and transcriptomic enrichment of inflammatory processes associated with tissue injury that remained increased through endpoint. Diaphragm inflammation was followed by increases in PDGFR-ɑ+ fibroadipogenic progenitors (+2.5 to +3.8-fold; D10-endpoint vs. Sham, p < 0.05), fibre atrophy (-16% to -24%, D12 to endpoint vs. Sham, p < 0.05), ECM expansion (+1.5 to +1.8-fold; D14-endpoint vs. Sham, p < 0.05), collagen accumulation (+3.8-fold; endpoint vs. Sham, p = 0.0013) and reductions in breathing frequency (-55%, p = 0.0074) and diaphragm excursion (-43%, p = 0.0006). These biological processes were supported by changes in the diaphragm transcriptome. Ingenuity pathway analysis predicted factors involved in inflammatory responses to tissue injury, including TGF-β1, angiotensin and PDGF BB, as top upstream regulators activated in diaphragms prior to and throughout cachexia progression, while PGC-1α and the insulin receptor were among the top upstream regulators predicted to be suppressed. The transcriptomic dataset further revealed progressive disturbances to networks involved in lipid, glucose and oxidative metabolism, activation of the unfolded protein response and neuromuscular junction remodelling associated with denervation. In summary, our data support leukocyte infiltration and expansion of PDGFRα mesenchymal progenitors as early events that precede wasting and fibrotic remodelling of the diaphragm in response to PDAC that may also underlie metabolic disturbances, weakness and respiratory complications.

Single-cell delineation of the microbiota-gut-brain axis: Probiotic intervention in Chd8 haploinsufficient mice.

Emerging research underscores the gut microbiome's impact on the nervous system via the microbiota-gut-brain axis, yet comprehensive insights remain limited. Using a CHD8-haploinsufficient model for autism spectrum disorder (ASD), we explored host-gut microbiota interactions by constructing a single-cell transcriptome atlas of brain and intestinal tissues in wild-type and mutant mice across three developmental stages. CHD8 haploinsufficiency caused delayed development of radial glial precursors and excitatory neural progenitors in the E14.5 brain, inflammation in the adult brain, immunodeficiency, and abnormal intestinal development. Selective CHD8 knockdown in intestinal epithelial cells generated Chd8ΔIEC mice, which exhibited normal sociability but impaired social novelty recognition. Probiotic intervention with Lactobacillus murinus selectively rescued social deficits in Chd8ΔIEC mice, with single-cell transcriptome analysis revealing underlying mechanisms. This study provides a detailed single-cell transcriptomic dataset of ASD-related neural and intestinal changes, advancing our understanding of the gut-brain axis and offering potential therapeutic strategies for ASD.

Progressive natural killer cell dysfunction in advanced-stage clear-cell renal cell carcinoma and association with clinical outcomes.

Natural killer (NK) cells are important contributors to antitumor immunity in clear-cell renal cell carcinoma (ccRCC). However, their phenotype, function, and association with clinical outcomes in ccRCC remain poorly understood. We analyzed single-cell RNA sequencing data from 13 primary tumors, 1 localized tumor extension, and 1 metastasis from ccRCC patients at different clinical stages. For each primary tumor specimen, paired normal kidneys were also analyzed. Differential gene expression analysis was carried out to investigate NK cell phenotypes and to derive a gene expression signature. Gene signatures from NK cell subclusters of interest were used to interrogate bulk transcriptomic datasets and expression with clinical outcomes. Finally, tumor-infiltrating NK cell function (cytokine production and cytotoxicity) was assessed by isolation of live NK cells from ccRCC tissue, co-culture with K562 target cells, and measurement of cytokine production (interferon-γ) and cytotoxicity (CD107a) markers by flow cytometry. Single-cell transcriptomic data were analyzed from 13 patients with ccRCC (tumor/normal kidney), resulting in 21 139 NK cells. Clustering analysis revealed six NK cell subsets. Bright-like NK cells were significantly enriched in advanced ccRCC compared with localized ccRCC and normal kidney, expressed markers of tissue residency (ZNF683/Hobit, ITGA1/CD49a, CD9, ITGAE/CD103), and had decreased expression of cytotoxicity genes (GZMB/Granzyme-B, PRF1/perforin). In independent cohorts (The Cancer Genome Atlas ccRCC cohort, CheckMate 025), a gene expression score representing this dysfunctional NK cell phenotype was enriched in advanced ccRCC and was associated with worse overall survival. Functional interrogation of tumor-infiltrating NK cells from ccRCC confirmed that tumor-resident CD49a+CD9+ NK cells had impaired cytotoxicity compared with CD49a-CD9- NK cells. A dysfunctional, tumor-resident NK cell phenotype was enriched among patients with metastatic disease and associated with worse survival in patients with advanced ccRCC across multiple patient cohorts. Restoration of NK cell function (via cytokine stimulation or NK cell engineering) could provide a novel avenue for therapeutic intervention against ccRCC.

Anticancer and therapeutic efficacy of XPO1 inhibition in pancreatic ductal adenocarcinoma through DNA damage and modulation of miR-193b/KRAS/LAMC2/ERK/AKT signaling cascade.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive and grave malignancies with confined and ineffective therapeutic options. XPO1 is a critical regulator of nuclear export and activation of tumor suppressor proteins. The present study evaluated the therapeutic potential and molecular mechanisms of XPO1 inhibition against PDAC. Firstly, we observed significant overexpression of XPO1 transcript in 179 PDAC patients than 171 normal pancreatic tissues in TCGA transcriptomic dataset. Higher XPO1 transcript levels displayed worse overall and disease-free survival. Further, we confirmed significant upregulation of XPO1 in a panel of PDAC cells. Eltanexor treatment resulted in significant inhibition of cell viability, clonogenic growth, migration, and epithelial-mesenchymal transition (EMT), along with the induction of cell cycle arrest. Mechanistically, eltanexor modulated the expression of key proteins including p21, p27, p53, cyclin B1, cyclin D1, c-Myc, N-cadherin, vimentin, E-cadherin associated with the cell viability, growth, cell cycle and EMT. Additionally, the eltanexor treatment resulted in marked increase in expression of γH2AX, and cleaved PARP, cleaved caspase-9 leading to induction of DNA damage and apoptosis of PDAC cells, respectively. Moreover, eltanexor treatment regulated the expression of key non-coding RNAs including miR193b, DINO, MALAT-1, H19, and SOX21-AS1 linked with tumorigenesis. Our results revealed a correlation among miR193b/KRAS/LAMC2, XPO1/KRAS, and LAMC2/KRAS. The findings also revealed that eltanexor treatment rescued the expression of miR193b which acts as a sponge for LAMC2 and KRAS resulting in the suppression of AKT/ERK downstream signaling cascade in PDAC. Interestingly, the combination of eltanexor with gemcitabine showed significant anticancer activity in PDAC cells. Altogether, our findings revealed the crucial role of XPO1 in modulating the expression of oncogenic proteins, ncRNAs, and DNA damage during PDAC progression as well as identified novel therapeutic miR-193b/KRAS/LAMC2/ERK/AKT axis.

Transcriptome datasets of salt-stressed tomato plants treated with zinc oxide nanoparticles.

Salinity diminishes agricultural productivity and quality, resulting in overall economic losses on a worldwide scale. Zinc oxide nanoparticles (ZnO-NPs) have been found to enhance plant physiological and metabolic processes, as well as increase overall resilience to abiotic stressors. A research study was undertaken to assess the effects of foliar application of chemically produced ZnO-NPs on tomato plants, both in the presence and absence of a NaCl stressor. The datasets were obtained through the utilization of the shallow mRNA sequencing technology. Six datasets from the SRA were uploaded to NCBI. The aforementioned datasets encompass the Transcriptome Shotgun Assembly (TSA), the contigs that underwent blasting, mapping, and annotation from the pre-processed datasets, and the count table derived from the quantification of RNA-seq reads. All the aforementioned data is encompassed under the Mendeley database. Moving forward, the utilization of databases will facilitate the examination of modifications in plant biochemical reactions at the level of gene expression.

Wnt/β-catenin signalling underpins juvenile Fasciola hepatica growth and development.

Infection by the liver fluke, Fasciola hepatica, places a substantial burden on the global agri-food industry and poses a significant threat to human health in endemic regions. Widespread resistance to a limited arsenal of chemotherapeutics, including the frontline flukicide triclabendazole (TCBZ), renders F. hepatica control unsustainable and accentuates the need for novel therapeutic target discovery. A key facet of F. hepatica biology is a population of specialised stem cells which drive growth and development - their dysregulation is hypothesised to represent an appealing avenue for control. The exploitation of this system as a therapeutic target is impeded by a lack of understanding of the molecular mechanisms underpinning F. hepatica growth and development. Wnt signalling pathways govern a myriad of stem cell processes during embryogenesis and drive tumorigenesis in adult tissues in animals. Here, we identify five putative Wnt ligands and five Frizzled receptors in liver fluke transcriptomic datasets and find that Wnt/β-catenin signalling is most active in juveniles, the most pathogenic life stage. FISH-mediated transcript localisation revealed partitioning of the five Wnt ligands, with each displaying a distinct expression pattern, consistent with each Wnt regulating the development of different cell/tissue types. The silencing of each individual Wnt or Frizzled gene yielded significant reductions in juvenile worm growth and, in select cases, blunted the proliferation of neoblast-like cells. Notably, silencing FhCTNNB1, the key effector of the Wnt/β-catenin signal cascade led to aberrant development of the neuromuscular system which ultimately proved lethal - the first report of a lethal RNAi-induced phenotype in F. hepatica. The absence of any discernible phenotypes following the silencing of the inhibitory Wnt/β-catenin destruction complex components is consistent with low destruction complex activity in rapidly developing juvenile worms, corroborates transcriptomic expression profiles and underscores the importance of Wnt signalling as a key molecular driver of growth and development in early-stage juvenile fluke. The putative pharmacological inhibition of Wnt/β-catenin signalling using commercially available inhibitors phenocopied RNAi results and provides impetus for drug repurposing. Taken together, these data functionally and chemically validate the targeting of Wnt signalling as a novel strategy to undermine the pathogenicity of juvenile F. hepatica.

Inferring multi-slice spatially resolved gene expression from H&E-stained histology images with STMCL.

Spatial transcriptomics has significantly advanced the measurement of spatial gene expression in the field of biology. However, the high cost of ST limits its application in large-scale studies. Using deep learning to predict spatial gene expression from H&E-stained histology images offers a more cost-effective alternative, but existing methods fail to fully leverage the multimodal information provided by Spatial transcriptomics and pathology images. In response, this paper proposes STMCL, a novel multimodal contrastive learning framework. STMCL integrates multimodal information, including histology images, gene expression features of spots, and their locations, to accurately infer spatial gene expression profiles. We tested four different types of multi-slice spatial transcriptomics datasets generated by the 10X Genomics platform. The results indicate that STMCL has advantages over baseline methods in predicting spatial gene expression profiles. Furthermore, STMCL is capable of capturing cancer-specific highly expressed genes and preserving gene expression patterns while maintaining the original spatial structure of gene expression. Our code is available at https://github.com/wenwenmin/STMCL.

Scatterbar: an R package for visualizing proportional data across spatially resolved coordinates.

Displaying proportional data across many spatially resolved coordinates is a challenging but important data visualization task, particularly for spatially resolved transcriptomics data. Scatter pie plots are one type of commonly used data visualization for such data but present perceptual challenges that may lead to difficulties in interpretation. Increasing the visual saliency of such data visualizations can help viewers more accurately identify proportional trends and compare proportional differences across spatial locations. We developed scatterbar, an open-source R package that extends ggplot2, to visualize proportional data across many spatially resolved coordinates using scatter stacked bar plots. We apply scatterbar to visualize deconvolved cell-type proportions from a spatial transcriptomics dataset of the adult mouse brain to demonstrate how scatter stacked bar plots can enhance the distinguishability of proportional distributions compared to scatter pie plots. scatterbar is available on CRAN https://cran.r-project.org/package=scatterbar with additional documentation and tutorials at https://jef.works/scatterbar/. Supplementary data are available at Bioinformatics online.

Interneuron loss and microglia activation by transcriptome analyses in the basal ganglia of Tourette disorder.

Tourette disorder is characterized by motor hyperactivity and tics that are believed to originate in basal ganglia. Postmortem immunocytochemical analyses previously revealed decreases in cholinergic, parvalbumin, and somatostatin interneurons (IN) within the caudate/putamen of individuals with TS. We obtained transcriptome and open chromatin datasets by snRNAseq and snATAC-seq, respectively, from caudate/putamen postmortem specimens of 6 adult TS and 6 matched normal control (NC). Differential gene expression and differential chromatin accessibility analyses were performed in identified cell types. The data reproduced the known cellular composition of the human striatum, including a majority of medium spiny neurons (MSN) and small populations of GABAergic and cholinergic IN. IN were decreased by ∼50% in TS brains, with no difference in other cell types. Differential gene expression analysis suggested that mitochondrial oxidative metabolism in MSN and synaptic adhesion and function in IN were both decreased in TS subjects, while there was activation of immune response in microglia. Gene expression changes correlated with changes in activity of cis-regulatory elements, suggesting a relationship of transcriptomic and regulatory abnormalities in MSN, OL and AST of TS brains. This initial analysis of the TS basal ganglia transcriptome at the single cell level confirms the loss and synaptic dysfunction of basal ganglia IN, consistent with in vivo basal ganglia hyperactivity. In parallel, oxidative metabolism was decreased in MSN and correlated with activation of microglia cells, attributable at least in part to dysregulated activity of putative enhancers, implicating altered epigenomic regulation in TS.

Spatial single-cell maps reveal ST6GAL1 promoting ovarian cancer metastasis.

In this study, spatial and single-cell transcriptome techniques were used to investigate the role of beta-galactoside alpha-2,6-sialyltransferase 1 (ST6GAL1) in promoting peritoneal metastasis in ovarian cancer epithelial cells. We collected single-cell transcriptomic (GSE130000) and spatial transcriptomic datasets (GSE211956) from the Gene Expression Omnibus and RNA-sequencing data from The Cancer Genome Atlas. The Robust Cell Type Decomposition (RCTD) approach was implemented to integrate spatial and single-cell transcriptomic data. In addition, pseudo-time trajectory analysis, cell-cell communication networks, transcription factor activity profiling, spatial interaction mapping, and prognostic significance of gene expression were assessed. A significant enrichment of ST6GAL1 was observed in the epithelial cells of ovarian cancer, particularly in peritoneal metastases, which exhibited elevated metabolic activity compared to primary tumors. The levels of ST6GAL1 were significantly high in peritumoral and adjacent non-tumorous tissues, with increased metabolic activity, while the tumor core demonstrated ST6GAL1-negative epithelial cells. Extensive cell-cell communication and transcription factor networks were unraveled, potentially influencing vascular permeability and intracellular signaling. Clinically, high expression of ST6GAL1 in epithelial cells is associated with diminished progression-free survival, indicating its prognostic potential. In conclusion, ST6GAL1 is likely to significantly impact the progression and metastasis of ovarian cancer.

DSCT: A novel deep learning framework for rapid and accurate spatial transcriptomic cell typing

Abstract Unraveling the complex cell-type composition and gene expression patterns at the cellular spatial resolution is crucial for understanding intricate cell functions in the brain. In this study, we developed Deep Neural Network-based Spatial Cell Typing (DSCT), an innovative framework for spatial cell typing within spatial transcriptomic datasets. This approach utilizes a synergistic integration of an enhanced gene selection strategy and a lightweight deep neural network for data training, offering a more rapid and accurate solution for the analysis of spatial transcriptomic data. Based on comprehensive analysis, DSCT achieved exceptional accuracy in cell-type identification across various brain regions, species, and spatial transcriptomic platforms. It also performed well in mapping finer cell types, thereby showcasing its versatility and adaptability across diverse datasets. Strikingly, DSCT exhibited high efficiency and remarkable processing speed, with fewer computational resource demands. As such, this novel approach opens new avenues for exploring the spatial organization of cell types and gene expression patterns, advancing our understanding of biological functions and pathologies within the nervous system.

A quantitative prediction method utilizing whole omics data for biosensing

Omics data provide a plethora of quantifiable information that can potentially be used to identify biomarkers targeting the physiological processes and ecological phenomena of organisms. However, omics data have not been fully utilized because current prediction methods in biomarker construction are susceptible to data multidimensionality and noise. We developed OmicSense, a quantitative prediction method that uses a mixture of Gaussian distributions as the probability distribution, yielding the most likely objective variable predicted for each biomarker. Our benchmark test using a transcriptome dataset revealed that OmicSense achieves accurate and robust prediction against background noise without overfitting. Weighted gene co-expression network analysis revealed that OmicSense preferentially utilized hub nodes of the network, indicating the interpretability of the method. Application of OmicSense to single-cell transcriptome, metabolome, and microbiome datasets confirmed high prediction performance (r > 0.8), suggesting applicability to diverse scientific fields. Given the recent rapidly expanding availability of omics data, the developed prediction tool OmicSense, can accelerate the use of omics data as a “biosensor” based on an assemblage of potential biomarkers.

The Mining for Flowering-Related Genes Based on De Novo Transcriptome Sequencing in the Endangered Plant Phoebe chekiangensis

Phoebe chekiangensis is an indigenous, endangered, and valuable timber and garden tree species in China, which is notable for having a short juvenile phase (early flowering), unique among the Phoebe genus. However, the molecular mechanisms regulating the flowering of P. chekiangensis remain unexplored, primarily due to the lack of transcriptomic or genomic data. In the present study, transcriptome sequencing yielded 53 million RNA reads, resulting in 111,250 unigenes after de novo assembly. Of these, 47,525 unigenes (42.72%) were successfully annotated in the non-redundant (Nr) database. Furthermore, 15,605 unigenes were assigned to Clusters of Orthologous Groups (KOGs), and 36,370 unigenes were classified into Gene Ontology (GO) categories. A total of 16,135 unigenes were mapped to the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, involving 298 pathways. Based on the expression levels, Gibberellin signaling pathway-related genes were the most predominant expression levels. Hormonal analysis showed that gibberellin (GA) levels varied across tissues and flowering stages, as GA20 levels in leaves were low during full bloom, while GA1 and GA5 levels peaked in flowers. Furthermore, several key genes involved in gibberellin biosynthesis, including CPS, GID1, GA20ox, GA3ox, and GA2ox, exhibited stage-specific expression patterns. Certain genes were highly expressed during the initial phases of flowering, while others, like GA3ox and GA2ox, reached peak expression at full bloom. These findings provide valuable insights into the molecular mechanisms underlying flowering in P. chekiangensis, laying the foundation for future breeding efforts. This transcriptome dataset will serve as an important public resource for molecular research on this species, facilitating the discovery of functional genes related to its growth, development, and flowering regulation.

P0172 In silico drug repurposing approach for the discovery of therapeutics for intestinal fibrosis in crohn’s disease

Abstract Background Intestinal fibrosis in Crohn’s disease (CD) can lead to stricture formation and other disease-related complications.1,2 Given the lack of anti-fibrotic drugs, current treatment strategies are limited to anti-inflammatory therapies, endoscopic balloon dilation and surgery.1 Our aim was to identify compounds and druggable targets that can reverse the gene expression signature in mucosal fibroblasts associated with intestinal fibrosis. Methods A signature associated with fibrotic CD was derived from three publicly available transcriptomic datasets of fibroblasts isolated from fibrostenotic strictures of CD patients. We conducted a meta-regression analysis across the transcriptomic datasets to identify significant differentially expressed genes (DEGs) associated with fibrostenotic CD. Three drug repurposing platforms (iLINCS, L1000 CDS2 and CLUE io), connected to the NIH LINCS database,3,4 were used to identify compounds that could reverse the fibrotic signature, ranked by their anti-fibrotic potential using the CoDReS (Composite Drug Reranking Scoring) tool. Transcription factors and miRNAs targeting the fibrostenotic signature were identified via the TRRUST and miRWalk databases. A gene interaction network was constructed, incorporating transcription factors (TRRUST TF), miRNAs and DEGs. Drugs targeting key network components were identified using the DGiDb database. Results Via the analysis of combined transcriptomic datasets from fibrostenotic- versus non-stenosis-associated mucosal fibroblasts, fibroblast activation protein (FAP), IL7 receptor and transcription factor AP-2 gamma, emerged as the most significantly upregulated genes in fibrostenotic CD (Figure 1). Out of 6,783 compounds, PI3K/Akt/mTOR inhibitors, Src kinase family inhibitors and histone deacetylase inhibitors were predicted as being most effective in reversing this pathologic gene signature. The gene interaction network revealed 15 hub genes as central components with the most interconnections (Table 1). Among these top 15 key gene regulators, IL6, PPARγ and angiotensin receptor type 1 (AGTR1) were the highest ranked druggable targets, based on the drug-gene interaction analysis. Conclusion Our in silico drug repurposing approach identified several potential anti-fibrotic compounds and druggable targets for the treatment of CD-associated intestinal fibrosis. These findings open novel avenues for the development of anti-fibrotic drugs and provide a foundation for future research on cell and pathway-specific mechanisms driving fibrosis in CD.