Transcriptomic Datasets Research Articles

Decreased miR-486-5p is involved in LPS-induced HTR-8/SVneo cell dysfunction by promoting Smad2 expression.

Placenta-associated pathologies, including early pregnancy loss (EPL) and preeclampsia (PE), share a common phenomenon of insufficient extravillous trophoblasts (EVTs) invasion. It was previously observed that down-regulated miR-486-5p expression inhibited the invasion of EVTs, and a decreased peripheral miR-486-5p was associated with EPL. However, the exact roles of miR-486-5p played in pathogenesis of EPL, as well as the molecular pathway underlying roles of miR-486-5p in EVTs invasion, remains poorly understood. In this study, a decreased miR-486-5p expression in uterine embryo implantation site at gestation day (GD) 8.5, and an increased expression of Smad2, a target of miR-486-5p, were observed in the lipopolysaccharide (LPS)-induced EPL mouse model. The invasion and viability of immortalized human EVTs line, HTR-8/SVneo, were inhibited by LPS, accompanied with a reduced miR-486-5p expression. LPS showed a promoting effect on the Smad2 expression, of which could be attenuated by miR-486-5p mimics. And the down-regulated Smad2 could effectively restore the impaired invasion and viability of HTR-8/SVneo cells caused by LPS or miR-486-5p inhibitor. Furthermore, LPS could promote the TNFα production in HTR-8/SVneo cells, whereas both of siSmad and miR-486-5p mimics could reverse such an effect. By analyzing the human decidua single-cell RNA sequencing and transcriptome datasets derived from the Gene Expression Omnibus, it was found that, compared to control early pregnant women, the Smad2 expression was significantly increased in recurrent miscarriages (RM) patients. Collectively, these data suggested that, decreased miR-486-5p expression might lead to EPL at least partially by inhibiting invasion and/or promoting TNFα production of EVTs via targeting Smad2.

Comprehensive evaluation of immunological attributes and immunotherapy responses of positive T cell function regulators in colorectal cancer

BackgroundPositive regulators of T-cell function (PTFRs), integral to T-cell proliferation and activation, have been identified as potential prognostic markers in colorectal cancer (CRC). Despite this, their role within the tumor microenvironment (TME) and their response to immunotherapy are not yet fully understood.MethodsThis study delved into PTFR-related CRC subtypes by analyzing four independent transcriptome datasets, emphasizing the most significant prognostic PTFRs. We identified differentially expressed genes (DEGs) between two subtypes and developed a PTFR risk model using LASSO and Cox regression methods. The model’s associations with survival time, clinical features, TME characteristics, tumor mutation profiles, microsatellite instability (MSI), cancer stem cell (CSC) index, and responses to chemotherapy, targeted therapy, and immunotherapy were subsequently explored.ResultsThe PTFR risk model demonstrated a strong predictive capacity for CRC. It facilitated the estimation of immune cell composition, HLA expression levels, immune checkpoint expression, mutation burden, CSC index features, and the effectiveness of immunotherapy.ConclusionsThis study enhances our understanding of the role of PTFRs in CRC progression and introduces an innovative assessment framework for CRC immunotherapy. This framework improves the prediction of treatment outcomes and aids in the customization of therapeutic strategies.

Pairwise analysis of gene expression for oral squamous cell carcinoma via a large-scale transcriptome integration.

Among all cancers occurring in the head and neck region, oral squamous cell carcinoma (OSCC) is the most common oral malignant tumours characterized by its aggressiveness and metastasis. The development of transcriptomics technology has greatly facilitated the diagnosis of various cancers. However, identifying genetic biomarkers is limited by data from a single batch of OSCC samples, and integrating analysis across different platforms remains a great challenge. In this study, we integrated five OSCC transcriptome datasets using an innovative strategy capable of mitigating batch effect, and extracting information from different datasets based on changes in the relative expression of gene pairs. By leveraging a machine learning method, we developed a prediction model including 27 differential gene pairs (DGPs) to discriminate OSCC from control samples, achieving an area under the receiver operating characteristic curve (AUC) of 0.8987 for the training set. Moreover, the model demonstrated commendable performance in four external validation sets, with AUCs of 0.9926, 0.9688, 0.8052 and 0.8565, respectively. Subsequently, a prognostic model was constructed based on six key gene pairs through univariate and multivariate Cox regression analysis. The AUCs of the model at 1-year and 3-year overall survival time prediction were 0.717 and 0.779 in an independent dataset. Our result demonstrates the effectiveness of this new method of integrating data and identifying DGPs. Using DGPs can significantly improve the performance of both diagnostic and prognostic models.

Phylogenomics supports a single origin of terrestriality in isopods.

Terrestriality, the adaptation to life on land, is one of the key evolutionary transitions, occurring numerous times across the tree of life. Within Arthropoda, there have been several independent transitions: in hexapods, myriapods, arachnids and isopods. Isopoda is a morphologically diverse order within Crustacea, with species adapted to almost every environment on Earth. The order is divided into 11 suborders with the most speciose, Oniscidea, including terrestrial isopods such as woodlice and sea-slaters. Recent molecular phylogenetic studies have challenged traditional isopod morphological taxonomy, suggesting that several well-accepted suborders, including Oniscidea, may be non-monophyletic. This implies that terrestriality may have evolved multiple times. Current molecular hypotheses, however, are based on limited sequence data. Here, I collate available genome and transcriptome datasets for 36 isopods and four peracarid crustaceans from public sources, generate assemblies and use 970 single-copy orthologues to estimate isopod relationships and divergence times with molecular dating. The resulting phylogenetic analyses support monophyly of terrestrial isopods and suggest conflicting relationships based on nuclear ribosomal RNA sequences may be caused by long-branch attraction. Dating analyses suggest a Permo-Carboniferous origin of isopod terrestriality, much more recently than other terrestrial arthropods.

Dissecting the lung transcriptome of pulmonary fibrosis-associated pulmonary hypertension.

Integrative multiomics can help elucidate the pathophysiology of pulmonary fibrosis (PF)-associated pulmonary hypertension (PH) (PF-PH). Weighted gene coexpression network analysis (WGCNA) was performed on a transcriptomic dataset of explanted lung tissue from 116 patients with PF. Patients were stratified by pulmonary vascular resistance (PVR), and differential gene expression analysis was conducted. Gene modules were correlated with hemodynamics at the time of transplantation and tested for enrichment in the lung transcriptomics signature of an independent pulmonary arterial hypertension (PAH) cohort. We found 1,250 differentially expressed genes between high and low PVR groups. WGCNA identified that black and yellowgreen modules negatively correlated with PVR, whereas the tan and darkgrey modules are positively correlated with PVR in PF-PH. In addition, the tan module showed the strongest enrichment for an independent PAH gene signature, suggesting shared gene expression patterns between PAH and PF-PH. Pharmacotranscriptomic analysis using the Connectivity Map implicated the tan and darkgrey modules as potentially pathogenic in PF-PH, given their combined module signature demonstrated a high negative connectivity score for treprostinil, a medication used in the treatment of PF-PH, and a high positive connectivity score for bone morphogenetic protein (BMP) loss of function. Pathway enrichment analysis revealed that inflammatory pathways and oxidative phosphorylation were downregulated, whereas epithelial-mesenchymal transition was upregulated in modules associated with increased PVR. Our integrative systems biology approach to the lung transcriptome of PF with and without PH identified several PH-associated coexpression modules and gene targets with shared molecular features with PAH warranting further investigation to uncover potential new therapies for PF-PH.NEW & NOTEWORTHY An integrative systems biology approach that included transcriptomic analysis of explanted lung tissue from patients with pulmonary fibrosis (PF) with and without pulmonary hypertension (PH) undergoing lung transplantation, combined with hemodynamic correlation and pharmacotranscriptomics, identified modules of genes associated with pulmonary vascular disease severity. Comparison with an independent pulmonary arterial hypertension (PAH) dataset identified shared gene expression patterns between PAH and PF-PH.

Leveraging transcriptome sequence read archives for virus detection in wild and colony populations of triatomines (Hemiptera: Reduviidae: Triatominae)

Triatomines are infamous as vectors of the parasite Trypanosoma cruzi, the causative agent of Chagas disease. However, climate-driven range expansion and urbanization adaptation of triatomine populations, coupled with their highly diverse feeding strategies (vertebrate haematophagy, kleptohaematophagy, and coprophagy), and has elevated interest in triatomines as potential arboviral vectors. Information on the triatomine virome is scant, with prior records including only eight insect-specific viruses: Triatoma virus (TrV) and Rhodnius prolixus viruses 1–7. Here, we leverage publicly available transcriptome datasets to assess viral diversity in 122 wild and colony kissing bugs representing eight species from six countries. In total, six viruses were detected (including Rhodnius prolixus viruses 4–6), and TrV was detected in almost half of all screened triatomines. This is the first report of TrV in Triatoma brasiliensis and in members of the genus Mepraia (M. gajardoi, M. spinolai, and M. parapatrica), and this effort has vastly expanded the publicly available genomic resources of TrV, adding 39 genome sequences to the single genome sequence currently available in the GenBank database. Furthermore, two additional viruses—Meccus longipennis virus 1 and Drosophila melanogaster Nora virus—are herein reported for the first time from kissing bugs. Meccus longipennis virus 1 was detected in Triatoma infestans from Argentina, Brazil, Chile, and Peru, and Drosophila melanogaster Nora virus was found in T. infestans from Argentina. Our results illustrate the advantage and utility of low-cost transcriptome data mining for the discovery of known and novel arboviruses in triatomines and other potential insect vectors.

Modulation of diabetes-related retinal pathophysiology by PTX3

Diabetic retinopathy (DR) is a common complication of diabetes characterized by vascular pathology and neuroinflammation. Pentraxin 3 (PTX3) is a soluble pattern recognition molecule that functions at the crossroads between innate immunity, inflammation, and tissue remodeling. DR is known to involve inflammatory pathways, although the potential relevance of PTX3 has not been explored. We found that PTX3 protein levels increased in the retina of diabetic mice. Similarly, evaluation of a publicly available transcriptomic human dataset revealed increased PTX3 expression in DR with diabetic macular edema and proliferative retinopathy, when compared to nondiabetic retinas or diabetic retinas without complications. To further understand the role of PTX3 within DR, we employed the streptozotocin-induced diabetes model in PTX3 knockout mice (PTX3KO), which were followed up for 9 mo to evaluate hallmarks of disease progression. In diabetic PTX3KO mice, we observed decreased reactive gliosis, diminished microglia activation, and reduced vasodegeneration, when compared to diabetic PTX3 wild-type littermates. The decrease in DR-associated pathological features in PTX3KO retinas translated into preserved visual function, as evidenced by improved optokinetic response, restored b-wave amplitude in electroretinograms, and attenuated neurodegeneration. We showed that PTX3 induced an inflammatory phenotype in human retinal macroglia, characterized by GFAP upregulation and increased secretion of IL6 and PAI-1. We confirmed that PTX3 was required for TNF-α-induced reactive gliosis, as PTX3KO retinal explants did not up-regulate GFAP in response to TNF-α. This study reveals a unique role for PTX3 as an enhancer of sterile inflammation in DR, which drives pathogenesis and ultimately visual impairment.

A Comprehensive Analysis of the Genomic and Expressed Repertoire of the T-Cell Receptor Beta Chain in Equus caballus

In this paper, we report a comprehensive and consistent annotation of the locus encoding the β-chain of the equine T-cell receptor (TRB), as inferred from recent genome assembly using bioinformatics tools. The horse TRB locus spans approximately 1 Mb, making it the largest locus among the mammalian species studied to date, with a significantly higher number of genes related to extensive duplicative events. In the region, 136 TRBV (belonging to 29 subgroups), 2 TRBD, 13 TRBJ, and 2 TRBC genes, were identified. The general genomic organization resembles that of other mammals, with a V cluster of 135 TRBV genes located upstream of two in-tandem aligned TRBD-J-C clusters and an inverted TRBV gene at the 3′ end of the last TRBC gene. However, the horse b-chain repertoire would be affected by a high number of non-functional TRBV genes. Thus, we queried a transcriptomic dataset derived from splenic tissue of a healthy adult horse, using each TRBJ gene as a probe to analyze clonotypes encompassing the V(D)J junction. This analysis provided insights into the usage of the TRBV, TRBD, and TRBJ genes and the variability of the non-germline-encoded CDR3. Our results clearly demonstrated that the horse β-chain constitutes a complex level of variability, broadly like that described in other mammalian species.

From CFTR to a CF signalling network: a systems biology approach to study Cystic Fibrosis

BackgroundCystic Fibrosis (CF) is a monogenic disease caused by mutations in the gene coding the Cystic Fibrosis Transmembrane Regulator (CFTR) protein, but its overall physio-pathology cannot be solely explained by the loss of the CFTR chloride channel function. Indeed, CFTR belongs to a yet not fully deciphered network of proteins participating in various signalling pathways.MethodsWe propose a systems biology approach to study how the absence of the CFTR protein at the membrane leads to perturbation of these pathways, resulting in a panel of deleterious CF cellular phenotypes.ResultsBased on publicly available transcriptomic datasets, we built and analyzed a CF network that recapitulates signalling dysregulations. The CF network topology and its resulting phenotypes were found to be consistent with CF pathology.ConclusionAnalysis of the network topology highlighted a few proteins that may initiate the propagation of dysregulations, those that trigger CF cellular phenotypes, and suggested several candidate therapeutic targets. Although our research is focused on CF, the global approach proposed in the present paper could also be followed to study other rare monogenic diseases.

RNA-Binding Proteins as Novel Effectors in Osteoblasts and Osteoclasts: A Systems Biology Approach to Dissect the Transcriptional Landscape.

Bone health is ensured by the coordinated action of two types of cells-the osteoblasts that build up bone structure and the osteoclasts that resorb the bone. The loss of balance in their action results in pathological conditions such as osteoporosis. Central to this study is a class of RNA-binding proteins (RBPs) that regulates the biogenesis of miRNAs. In turn, miRNAs represent a critical level of regulation of gene expression and thus control multiple cellular and biological processes. The impact of miRNAs on the pathobiology of various multifactorial diseases, including osteoporosis, has been demonstrated. However, the role of RBPs in bone remodeling is yet to be elucidated. The aim of this study is to dissect the transcriptional landscape of genes encoding the compendium of 180 RBPs in bone cells. We developed and applied a multi-modular integrative analysis algorithm. The core methodology is gene expression analysis using the GENEVESTIGATOR platform, which is a database and analysis tool for manually curated and publicly available transcriptomic data sets, and gene network reconstruction using the Ingenuity Pathway Analysis platform. In this work, comparative insights into gene expression patterns of RBPs in osteoblasts and osteoclasts were obtained, resulting in the identification of 24 differentially expressed genes. Furthermore, the regulation patterns upon different treatment conditions revealed 20 genes as being significantly up- or down-regulated. Next, novel gene-gene associations were dissected and gene networks were reconstructed. Additively, a set of osteoblast- and osteoclast-specific gene signatures were identified. The consolidation of data and information gained from each individual analytical module allowed nominating novel promising candidate genes encoding RBPs in osteoblasts and osteoclasts and will significantly enhance the understanding of potential regulatory mechanisms directing intracellular processes in the course of (patho)physiological bone turnover.

Using flux theory in dynamic omics data sets to identify differentially changing signals using DPoP

BackgroundDerivative profiling is a novel approach to identify differential signals from dynamic omics data sets. This approach applies variable step-size differentiation to time dynamic omics data. This work assumes that there is a general omics derivative that is a useful and descriptive feature of dynamic omics experiments. We assert that this omics derivative, or omics flux, is a valuable descriptor that can be used instead of, or with, fold change calculations.ResultsThe results of derivative profiling are compared to established methods such as Multivariate Adaptive Regression Splines, significance versus fold change analysis (Volcano), and an adjusted ratio over intensity (M/A) analysis to find that there is a statistically significant similarity between the results. This comparison is repeated for transcriptomic and phosphoproteomic expression profiles previously characterized in Aspergillus nidulans. This method has been packaged in an open-source, GUI-based MATLAB app, the Derivative Profiling omics Package (DPoP). Gene Ontology (GO) term enrichment has been included in the app so that a user can automatically/programmatically describe the over/under-represented GO terms in the derivative profiling results using domain specific knowledge found in their organism’s specific GO database file. The advantage of the DPoP analysis is that it is computationally inexpensive, it does not require fold change calculations, it describes both instantaneous as well as overall behavior, and it achieves statistical confidence with signal trajectories of a single bio-replicate over four or more points.ConclusionsWhile we apply this method to time dynamic transcriptomic and phosphoproteomic datasets, it is a numerically generalizable technique that can be applied to any organism and any field interested in time series data analysis. The app described in this work enables omics researchers with no computer science background to apply derivative profiling to their data sets, while also allowing multidisciplined users to build on the nascent idea of profiling derivatives in omics.

Tetraspanin32 (TSPAN32) is downregulated in rheumatoid arthritis: Evidence from animal models and patients.

This study aimed to investigate the role of TSPAN32, a member of the tetraspanin family, in rheumatoid arthritis (RA). The objective was to assess the expression levels of TSPAN32 in experimental RA models and in RA patient immune cells, exploring its potential as a regulatory factor in RA pathogenesis. The study employed adjuvant-induced arthritis in rats and collagen-induced arthritis (CIA) in mice as experimental models. Exvivo analyses included evaluating TSPAN32 expression in immune cells at different stages of the disease. In silico data analysis involved examining transcriptomic datasets from drug-naïve and treated RA patients to correlate TSPAN32 expression with clinical parameters. TSPAN32 overexpression experiments in splenocytes from CIA mice aimed to demonstrate its functional impact on antigen-specific immune responses. The animal models revealed a significant downregulation of TSPAN32, particularly in synovial-infiltrating T cells. Also, TSPAN32 overexpression inhibited pro-inflammatory cytokine production in splenocytes. In RA patients, TSPAN32 was consistently downregulated in circulating and synovial-infiltrating T cells, as well as in CD8+ T cells, B cells and NK cells. Drug treatment did not significantly alter TSPAN32 levels. Negative correlations were observed between TSPAN32 expression and inflammatory markers (CRP, ESR) and clinical scores (SDAI) in RA patients. This study suggests that reduced TSPAN32 expression characterizes pathogenic T-cell populations in RA, highlighting its potential as biomarker for inflammation and disease activity. TSPAN32 may play a crucial role in shaping adaptive immune responses in RA, opening avenues for novel therapeutic strategies targeting this tetraspanin family member.

Single-cell multi-omics map of human fetal blood in Down syndrome

Down syndrome predisposes individuals to haematological abnormalities, such as increased number of erythrocytes and leukaemia in a process that is initiated before birth and is not entirely understood1–3. Here, to understand dysregulated haematopoiesis in Down syndrome, we integrated single-cell transcriptomics of over 1.1 million cells with chromatin accessibility and spatial transcriptomics datasets using human fetal liver and bone marrow samples from 3 fetuses with disomy and 15 fetuses with trisomy. We found that differences in gene expression in Down syndrome were dependent on both cell type and environment. Furthermore, we found multiple lines of evidence that haematopoietic stem cells (HSCs) in Down syndrome are ‘primed’ to differentiate. We subsequently established a Down syndrome-specific map linking non-coding elements to genes in disomic and trisomic HSCs using 10X multiome data. By integrating this map with genetic variants associated with blood cell counts, we discovered that trisomy restructured regulatory interactions to dysregulate enhancer activity and gene expression critical to erythroid lineage differentiation. Furthermore, as mutations in Down syndrome display a signature of oxidative stress4,5, we validated both increased mitochondrial mass and oxidative stress in Down syndrome, and observed that these mutations preferentially fell into regulatory regions of expressed genes in HSCs. Together, our single-cell, multi-omic resource provides a high-resolution molecular map of fetal haematopoiesis in Down syndrome and indicates significant regulatory restructuring giving rise to co-occurring haematological conditions.

ELLA: Modeling Subcellular Spatial Variation of Gene Expression within Cells in High-Resolution Spatial Transcriptomics.

Spatial transcriptomic technologies are becoming increasingly high-resolution, enabling precise measurement of gene expression at the subcellular level. Here, we introduce a computational method called subcellular expression localization analysis (ELLA), for modeling the subcellular localization of mRNAs and detecting genes that display spatial variation within cells in high-resolution spatial transcriptomics. ELLA creates a unified cellular coordinate system to anchor diverse cell shapes and morphologies, utilizes a nonhomogeneous Poisson process to model spatial count data, leverages an expression gradient function to characterize subcellular expression patterns, and produces effective control of type I error and high statistical power. We illustrate the benefits of ELLA through comprehensive simulations and applications to four spatial transcriptomics datasets from distinct technologies, where ELLA not only identifies genes with distinct subcellular localization patterns but also associates these patterns with unique mRNA characteristics. Specifically, ELLA shows that genes enriched in the nucleus exhibit an abundance of long noncoding RNAs or protein-coding mRNAs, often characterized by longer gene lengths. Conversely, genes containing signal recognition peptides, encoding ribosomal proteins, or involved in membrane related activities tend to enrich in the cytoplasm or near the cellular membrane. Furthermore, ELLA reveals dynamic subcellular localization patterns during the cell cycle, with certain genes showing decreased nuclear enrichment in the G1 phase while others maintain their enrichment patterns throughout the cell cycle. Overall, ELLA represents a calibrated, powerful, robust, scalable, and versatile tool for modeling subcellular spatial expression variation across diverse high-resolution spatial transcriptomic platforms.

Unmasking Neuroendocrine Prostate Cancer with a Machine Learning-Driven 7-Gene Stemness Signature that Predicts Progression.

Prostate cancer (PCa) poses a significant global health challenge, particularly due to its progression into aggressive forms like neuroendocrine prostate cancer (NEPC). This study developed and validated a stemness-associated gene signature using advanced machine learning techniques, including Random Forest and Lasso regression, applied to large-scale transcriptomic datasets. The resulting 7-gene signature (KMT5C, MEN1, TYMS, IRF5, DNMT3B, CDC25B and DPP4) was validated across independent cohorts and patient-derived xenograft (PDX) models. The signature demonstrated strong prognostic value for progression-free, disease-free, relapse-free, metastasis-free, and overall survival. Importantly, the signature not only identified specific NEPC subtypes, such as large-cell neuroendocrine carcinoma, which is associated with very poor outcomes, but also predicted a poor prognosis for PCa cases that exhibit this molecular signature, even when they were not histopathologically classified as NEPC. This dual prognostic and classifier capability makes the 7-gene signature a robust tool for personalized medicine, providing a valuable resource for predicting disease progression and guiding treatment strategies in PCa management.

INSPIRE: interpretable, flexible and spatially-aware integration of multiple spatial transcriptomics datasets from diverse sources.

Recent advances in spatial transcriptomics technologies have led to a growing number of diverse datasets, offering unprecedented opportunities to explore tissue organizations and functions within spatial contexts. However, it remains a significant challenge to effectively integrate and interpret these data, often originating from different samples, technologies, and developmental stages. In this paper, we present INSPIRE, a deep learning method for integrative analyses of multiple spatial transcriptomics datasets to address this challenge. With designs of graph neural networks and an adversarial learning mechanism, INSPIRE enables spatially informed and adaptable integration of data from varying sources. By incorporating non-negative matrix factorization, INSPIRE uncovers interpretable spatial factors with corresponding gene programs, revealing tissue architectures, cell type distributions and biological processes. We demonstrate the capabilities of INSPIRE by applying it to human cortex slices from different samples, mouse brain slices with complementary views, mouse hippocampus and embryo slices generated through different technologies, and spatiotemporal organogenesis atlases containing half a million spatial spots. INSPIRE shows superior performance in identifying detailed biological signals, effectively borrowing information across distinct profiling technologies, and elucidating dynamical changes during embryonic development. Furthermore, we utilize INSPIRE to build 3D models of tissues and whole organisms from multiple slices, demonstrating its power and versatility.

Leveraging the Genetics of Psychiatric Disorders to Prioritize Potential Drug Targets and Compounds.

Genetics has the potential to inform biologically relevant drug treatment and repurposing which may ultimately improve patient care. In this study, we combine methods which leverage the genetics of psychiatric disorders to prioritize potential drug targets and compounds. We used the largest available genome-wide association studies, in European ancestry, of four psychiatric disorders [i.e., attention deficit hyperactivity disorder (ADHD), bipolar disorder, depression, and schizophrenia] along with genes encoding drug targets. With this data, we conducted drug enrichment analyses incorporating the novel and biologically specific GSA-MiXeR tool. We then conducted a series of molecular trait analyses using large-scale transcriptomic and proteomic datasets sampled from brain and blood tissue. This included the novel use of the UK Biobank proteomic data for a proteome-wide association study of psychiatric disorders. With the accumulated evidence, we prioritize potential drug targets and compounds for each disorder. We reveal candidate drug targets shared across multiple disorders as well as disorder-specific targets. Drug prioritization indicated genetic support for several currently used psychotropic medications including the antipsychotic paliperidone as the top ranked drug for schizophrenia. We also observed genetic support for other commonly used psychotropics (e.g., clozapine, risperidone, duloxetine, lithium, and valproic acid). Opportunities for drug repurposing were revealed such as cholinergic drugs for ADHD, estrogens for depression, and gabapentin enacarbil for schizophrenia. Our findings also indicate the genetic liability to schizophrenia is associated with reduced brain and blood expression of CYP2D6, a gene encoding a metabolizer of drugs and neurotransmitters, suggesting a genetic risk for poor drug response and altered neurotransmission. Here we present a series of complimentary and comprehensive analyses that highlight the utility of genetics for informing drug development and repurposing for psychiatric disorders. Our findings present novel opportunities for refining psychiatric treatment.

Combining Transcriptomics and Proteomics to Screen Candidate Genes Related to Bovine Birth Weight.

We established transcriptome and proteome databases for low-birth-weight (LB) and high-birth-weight (HB) calf placentae, identifying key genes and proteins associated with birth weight through bioinformatics analyses that included functional enrichment and protein-protein interactions (PPIs). Both mRNA and protein levels were validated. A total of 1494 differentially expressed genes (DEGs) and 294 differentially expressed proteins (DEPs) were identified when comparing the LB group to the HB group. Furthermore, we identified 53 genes and proteins exhibiting significant co-expression across both transcriptomic and proteomic datasets; among these, 40 were co-upregulated, 8 co-downregulated, while 5 displayed upregulation at the protein level despite downregulation at the mRNA level. Functional enrichment analyses (GO and KEGG) and protein-protein interaction (PPI) analysis indicate that, at the transcriptional level, the primary factor contributing to differences in calf birth weight is that the placenta of the high-birth-weight (HB) group provides more nutrients to the fetus, characterized by enhanced nutrient transport (SLC2A1 and SLC2A11), energy metabolism (ACSL1, MICALL2, PAG2, COL14A1, and ELOVL5), and lipid synthesis (ELOVL5 and ELOVL7). In contrast, the placenta of the low-birth-weight (LB) group prioritizes cell proliferation (PAK1 and ITGA3) and angiogenesis. At the protein level, while the placentae from the HB group exhibit efficient energy production and lipid synthesis, they also demonstrate reduced immunity to various diseases such as systemic lupus erythematosus and bacterial dysentery. Conversely, the LB group placentae excel in regulating critical biological processes, including cell migration, proliferation, differentiation, apoptosis, and signal transduction; they also display higher disease immunity markers (COL6A1, TNC CD36, CD81, Igh-1a, and IGHG) compared to those of the HB group placentae. Co-expression analysis further suggests that increases in calf birth weight can be attributed to both high-efficiency energy production and lipid synthesis within the HB group placentae (ELOVL5, ELOVL7, and ACSL1), alongside cholesterol biosynthesis and metabolic pathways involving CYP11A1 and CYP17A1. We propose that ELOVL5, ELOVL7, ACSL1, CYP11A1, and CYP17A1 serve as potential protein biomarkers for regulating calf birth weight through the modulation of the fatty acid metabolism, lipid synthesis, and cholesterol levels.

Integrating network pharmacology and bioinformatics to explore the mechanism of Xiaojian Zhongtang in treating major depressive disorder: An observational study.

Major depressive disorder (MDD) is a common mental illness. The traditional Chinese medicine compound Xiaojian Zhongtang (XJZT) has a good therapeutic effect on MDD, but the specific mechanism is not clear. The aim of this study is to explore the molecular mechanism of XJZT in the treatment of MDD through network pharmacology and bioinformatics. The traditional Chinese medicine system pharmacology database was used to screen the chemical components and targets of XJZT, while the online Mendelian inheritance in man, DisGeNET, Genecards, and therapeutic target database databases were used to collect MDD targets and identify the intersection targets of XJZT and MDD. A "drugs-components-targets" network was constructed using the Cytoscape platform, and the STRING was used for protein-protein interaction analysis of intersecting targets. Gene Ontology and Kyoto encyclopedia of genes and genomes analysis of intersecting targets was performed using the DAVID database. Obtain serum and brain transcriptome datasets of MDD from the gene expression omnibus database, and perform differentially expressed genes, weighted gene co-expression network analysis, gene set enrichment analysis, and receiver operating characteristic analysis. A total of 127 chemical components and 767 targets were obtained from XJZT, among which quercetin, kaempferol, and maltose are the core chemical components, and 1728 MDD targets were screened out, with 77 intersecting targets between XJZT and MDD. These targets mainly involve AGE-RAGE signaling pathway in diabetic complexes, epidermal growth factor receptor tyrosine kinase inhibitor resistance, and HIF-1 signaling pathway, and these core targets have strong binding activity with core components. In addition, 1166 differentially expressed genes were identified in the MDD serum transcriptome dataset, and weighted gene co-expression network analysis identified the most relevant gene modules (1269 genes), among which RAC-alpha serine/threonine-protein kinase (AKT1), D(4) dopamine receptor (DRD4), and kynurenine 3-monooxygenase (KMO) were target genes for the treatment of MDD with XJZT, these 3 genes are mainly related to the ubiquitin-mediated proteolysis, arachidonic acid (AA) metabolism, and Huntington disease pathways, and the expression of AKT1, DRD4, and KMO was also found in the MDD brain transcriptome dataset, which is significantly correlated with the occurrence of MDD. We have identified 3 key targets for XJZT treatment of MDD, including AKT1, KMO, and DRD4, and they can be regulated by the key components of XJZT, including quercetin, maltose, and kaempferol. This provides valuable insights for the early clinical diagnosis and development of therapeutic drugs for MDD.

Linking transcriptome and morphology in bone cells at cellular resolution with generative AI

Abstract Recent advancements in deep learning (DL) have revolutionized the capability of artificial intelligence (AI) by enabling the analysis of large-scale, complex datasets that are difficult for humans to interpret. However, large amounts of high-quality data are required to train such generative AI models successfully. With the rapid commercialization of single-cell sequencing and spatial transcriptomics platforms, the field is increasingly producing large-scale datasets such as histological images, single-cell molecular data, and spatial transcriptomic data. These molecular and morphological datasets parallel the multimodal text and image data used to train highly successful generative AI models for natural language processing and computer vision. Thus, these emerging data types offer great potential to train generative AI models that uncover intricate biological processes of bone cells at a cellular level. In this Perspective, we summarize the progress and prospects of generative AI applied to these datasets and their potential applications to bone research. In particular, we highlight three AI applications: predicting cell differentiation dynamics, linking molecular and morphological features, and predicting cellular responses to perturbations. To make generative AI models beneficial for bone research, important issues, such as technical biases in bone single-cell datasets, lack of profiling of important bone cell types, and lack of spatial information, need to be addressed. Realizing the potential of generative AI for bone biology will also likely require generating large-scale, high-quality cellular-resolution spatial transcriptomics datasets, improving the sensitivity of current spatial transcriptomics datasets, and thorough experimental validation of model predictions.