High-throughput Datasets Research Articles

Neutrophil Extracellular Traps: Potential Thrombotic Markers and Therapeutic Targets in Colorectal Cancer.

Neutrophil extracellular traps (NETs) are promising promoters in venous thromboembolism (VTE). In the present study, we have investigated the potential thrombogenic role of NETs in colorectal cancer (CRC). A total of 583 patients with gastrointestinal malignancies who were diagnosed with or without VTE by extremities arteriovenous ultrasound and computed tomography were enrolled. The incidence of VTE in CRC was as high as 17.53%. In serological ELISA experiments, Cit-H3, MPO and cfDNA were significantly overexpressed in CRC patients with VTE compared to CRC patients without VTE and healthy individuals. Neutrophils from CRC patients with VTE produced appreciable amounts of NETs after stimulation with phorbol-12-myristate-13-acetate, which were lacking in CRC patients without VTE and healthy individuals. CfDNA was positively correlated with plasmin-α2-antiplasmin complex and tissue plasmin activator inhibitor-1 complex, and Cit-H3 was positively correlated with plasmin-α2-antiplasmin complex, suggesting that NETs are associated with increased fibrinolytic activity. We screened some NETs-related genes by analysing several high-throughput sequencing datasets of VTE and NETs. FCGR1A was identified as the optimal target gene by pan-cancer expression analysis and survival analysis. FCGR1A was significantly overexpressed in the peripheral blood of CRC patients without VTE compared to healthy individuals and showed a positive correlation with cfDNA. Neutrophil-derived NETs were significantly reduced by FCGR1A inhibitor exposure. These findings indicate that NETs are actively involved in VTE in CRC. NETs are promising thrombotic marker and therapeutic target in CRC to prevent the thrombotic consequences of cancer.

Global transcription and metabolic profiles of five tissues in pepper fruits

Studying the regulatory mechanisms in different tissues of pepper is crucial for understanding organ formation, growth, and development. However, relevant studies are far from sufficient. In the current study, the stipe, calyx, pericarp, placenta, and seed of ripe pepper were sampled, and metabolites were determined by the untargeted metabolomics method. Transcriptome sequencing was performed by Illumina NovaSeq 6000, and then a high-throughput data set was built. The results showed that a total of 4879 annotated metabolites were detected in 15 samples of the five tissues under positive and negative ion mode. A total of 110.66 Gb of clean data was obtained by transcriptome sequencing, the clean data of each sample reached 6.21 Gb, and a total of 35 336 annotated expression genes were obtained. Furthermore, validate the accuracy of the data by combining principal component analysis and other methods. In summary, this study provides valuable information for the genetic improvement and breeding of peppers, and it holds potential application value, particularly in enhancing the quality and nutritional value of pepper fruits.

Archaeal community from the Northern Hangzhou Bay to the East China Sea: biogeography, ecological processes, and functional potential

IntroductionArchaeal communities play a crucial role in marine ecosystems, yet our understanding of their ecological and functional traits remains incomplete. This study focuses on northern Hangzhou Bay to fill gaps in knowledge regarding the biogeography and functionality of archaeal groups.MethodsWe utilized a high-throughput sequencing dataset based on the 16S rRNA gene to characterize the archaeal community, aiming to identify biogeographic patterns and assess the influence of environmental factors on community structure.ResultsThe predominant phyla identified were Woesearchaeota, Thaumarchaeota, Euryarchaeota, and Crenarchaeota. Archaeal community structure in sediments showed a geographical pattern along the environmental gradient, influenced by factors such as salinity, ammonium, total phosphorus, pH, and total nitrogen content. Network analysis revealed nonrandom co-occurrence patterns, with associations changing along the salinity gradient. Additionally, this study directly proved the existence of dispersal limitation in this strongly connected marine ecological system through null model analyses. Variation in the archaeal community was attributed to both environmental constraints and stochastic processes due to dispersal limitation. Furthermore, our results revealed that the key biogeochemical functions of the archaeal community also exhibited a clear salinity gradient, the functional differences appear to be influenced by salinity, and the critical roles of archaeal diversity were highlighted.DiscussionAll these findings enhance our understanding of microbial ecology and element transformation in estuarine environments. The highlighted roles of archaeal diversity and the influence of environmental factors on community structure and function underscore the complexity of marine microbial ecosystems.

Analyzing high-throughput assay data to advance the rapid screening of environmental chemicals for human reproductive toxicity.

While high-throughput (HTP) assays have been proposed as platforms to rapidly assess reproductive toxicity, there is currently a lack of established assays that specifically address germline development/function and fertility. We assessed the applicability domains of yeast (S. cerevisiae) and nematode (C. elegans) HTP assays in toxicity screening of 124 environmental chemicals, determining their agreement in identifying toxicants and their concordance with reproductive toxicity in vivo. We integrated data generated in the two models and compared results using a streamlined, semi-automated benchmark dose (BMD) modeling approach. We then extracted and modeled relevant mammalian in vivo data available for the matching chemicals included in the Toxicological Reference Database (ToxRefDB). We ranked potencies of common compounds using the BMD and evaluated correlation between the datasets using Pearson and Spearman correlation coefficients. We found moderate to good correlation across the three data sets, with r = 0.48 (95 % CI: 0.28-1.00, p<0.001) and rs = 0.40 (p=0.002) for the parametric and rank order correlations between the HTP BMDs; r = 0.95 (95 % CI: 0.76-1.00, p=0.0005) and rs = 0.89 (p=0.006) between the yeast assay and ToxRefDB BMDs; and r = 0.81 (95 % CI: 0.28-1.00, p=0.014) and rs = 0.75 (p=0.033) between the worm assay and ToxRefDB BMDs. Our findings underscore the potential of these HTP assays to identify environmental chemicals that exhibit reproductive toxicity. Integrating these HTP datasets into mammalian in vivo prediction models using machine learning methods could further enhance their predictive value in future rapid screening efforts.

Exploring the interaction between immune cells in the prostate cancer microenvironment combining weighted correlation gene network analysis and single-cell sequencing: An integrated bioinformatics analysis.

The rise of treatment resistance and variability across malignant profiles has made precision oncology an imperative in today's medical landscape. Prostate cancer is a prevalent form of cancer in males, characterized by significant diversity in both genomic and clinical characteristics. The tumor microenvironment consists of stroma, tumor cells, and various immune cells. The stromal components and tumor cells engage in mutual communication and facilitate the development of a low-oxygen and pro-cancer milieu by producing cytokines and activating pro-inflammatory signaling pathways. In order to discover new genes associated with tumor cells that interact and facilitate a hypoxic environment in prostate cancer, we conducted a cutting-edge bioinformatics investigation. This included analyzing high-throughput genomic datasets obtained from the cancer genome atlas (TCGA). A combination of weighted gene co-expression network analysis and single-cell sequencing has identified nine dysregulated immune hub genes (AMACR, KCNN3, MME, EGFR, FLT1, GDF15, KDR, IGF1, and KRT7) that are believed to have significant involvement in the biological pathways involved with the advancement of prostate cancer enviriment. In the prostate cancer environment, we observed the overexpression of GDF15 and KRT7 genes, as well as the downregulation of other genes. Additionally, the cBioPortal platform was used to investigate the frequency of alterations in the genes and their effects on the survival of the patients. The Kaplan-Meier survival analysis indicated that the changes in the candidate genes were associated with a reduction in the overall survival of the patients. In summary, the findings indicate that studying the genes and their genomic changes may be used to develop precise treatments for prostate cancer. This approach involves early detection and targeted therapy.

High-throughput dataset of impurity adsorption on common catalysts in biomass upgrading applications

An extensive dataset consisting of adsorption energies of pernicious impurities present in biomass upgrading processes on common catalysts and support materials has been generated. This work aims to inform catalyst and process development for the conversion of biomass-derived feedstocks to fuels and chemicals. A high-throughput workflow was developed to execute density functional theory calculations for a diverse set of atomic (Al, B, Ca, Cl, Fe, K, Mg, Mn, N, Na, P, S, Si, Zn) and molecular (COS, H2S, HCl, HCN, K2O, KCl, NH3) species on 35 unique surfaces for transition-metal (Ag, Au, Co, Cu, Fe, Ir, Ni, Pd, Pt, Re, Rh, Ru) and metal-oxide (Al2O3, MgO, anatase-TiO2, rutile-TiO2, ZnO, ZrO2) catalysts and supports. Approximately 3,000 unique adsorption geometries and corresponding adsorption energies were obtained.

A high-throughput gene expression analysis software tool for developmental time series and gene signature analysis of human cardiomyocyte differentiation

Publicly available high-throughput gene expression data enable the investigation of biological processes by the scientific community. Although several bioinformatics tools offer methodologies for basic differential gene expression analysis, difficulties arise in the analysis of multiple sample groups comprising a developmental time series, especially when the identification and classification of unique gene expression patterns is the primary goal of the study. Data analysis using these tools requires programming experience, which limits the accessibility of these tools to the broader community. To streamline developmental time-series investigations, we created the Developmental Gene Expression Analysis (devGEA) tool. This environment can be implemented locally or via web browsers to expedite differential gene expression analysis. This tool provides gene signature determination methods that can classify differentially expressed genes based on their correlation with gene expression patterns. devGEA was used to characterize cardiac development gene expression signatures from high-throughput RNA-seq datasets profiling small-molecule directed cardiomyocyte differentiation of human pluripotent stem cell lines (hiPSCs). After pre-processing, discrete gene expression criteria-based expected changes were used to classify the genes into developmental signatures. Several cardiomyocyte differentiation markers and candidate cardiac genes representing different developmental signatures were experimentally validated using the GIBCOTM hiPSC line. This method was then compared to a gene signature correlation approach that classified expressed genes based on their degree of similarity with key cardiac developmental signatures representing the stages of cardiomyocyte differentiation. Therefore, devGEA provides a robust workflow for investigator-driven analysis of developmental time series, allowing for the identification of differentially expressed genes and gene signatures for extensive experimental investigation. We also introduced a method for classifying genes by their correlation with genes or developmental patterns of interest. Our correlation-based method takes advantage of a priori knowledge of an experiment and is conceptually simpler than unsupervised clustering approaches.

Prediction and design of transcriptional repressor domains with large-scale mutational scans and deep learning.

Regulatory proteins have evolved diverse repressor domains (RDs) to enable precise context-specific repression of transcription. However, our understanding of how sequence variation impacts the functional activity of RDs is limited. To address this gap, we generated a high-throughput mutational scanning dataset measuring the repressor activity of 115,000 variant sequences spanning more than 50 RDs in human cells. We identified thousands of clinical variants with loss or gain of repressor function, including TWIST1 HLH variants associated with Saethre-Chotzen syndrome and MECP2 domain variants associated with Rett syndrome. We also leveraged these data to annotate short linear interacting motifs (SLiMs) that are critical for repression in disordered RDs. Then, we designed a deep learning model called TENet ( T ranscriptional E ffector Net work) that integrates sequence, structure and biochemical representations of sequence variants to accurately predict repressor activity. We systematically tested generalization within and across domains with varying homology using the mutational scanning dataset. Finally, we employed TENet within a directed evolution sequence editing framework to tune the activity of both structured and disordered RDs and experimentally test thousands of designs. Our work highlights critical considerations for future dataset design and model training strategies to improve functional variant prioritization and precision design of synthetic regulatory proteins.

Chemical Graph-Based Transformer Models for Yield Prediction of High-Throughput Cross-Coupling Reaction Datasets.

The chemical reaction yield is an important factor to determine the reaction conditions. Recently, many data-driven models for yield prediction using high-throughput experimentation datasets have been reported. In this study, we propose a neural network architecture based on the chemical graphs of the reaction components to predict the reaction yield. The proposed model is the sequential combination of a message-passing neural network and a transformer encoder (MPNN-Transformer). The reaction components are converted to molecular matrices by the first network, followed by the interplay of the reaction components in the second network after adding the embeddings of the compound roles in the chemical reaction. The predictive ability of the proposed models was compared with state-of-the-art yield prediction models using two high-throughput experimental datasets: the Buchwald-Hartwig cross-coupling (BHC) and Suzuki-Miyaura cross-coupling (SMC) reaction datasets. Overall, the MPNN-Transformer models showed high prediction accuracy for the BHC reaction datasets and some of the extrapolation-oriented SMC reaction datasets. These models also performed well when the training dataset size was relatively large. Furthermore, analyzing the poorly predicted reactions for the BHC reaction dataset revealed a limitation of the data-driven yield prediction approach based on the chemical structural similarity.

Expression and biological significance of topoisomerase II α (TOP2A) in oral squamous cell carcinoma

BackgroundTopoisomerase II α(TOP2A) is usually highly expressed in rapidly proliferating cells, and its expression is regulated by cell cycle. The relationship between TOP2A and oral squamous cell carcinoma (OSCC) needs further study.MethodsTOP2A immunoreactivity was analyzed using immunohistochemical (IHC) staining analysis in specimens from 20 OSCC patients. Based on the high-throughput sequencing and gene microarray database, the expression of TOP2A mRNA in OSCC was calculated and its ability to identify OSCC tissues was evaluated by diagnostic analysis. CRISPR screen was used to select the genes necessary for OSCC cell growth, and the gene set was analyzed for function enrichment. Single-cell RNA sequencing analysis was conducted to evaluate the expression level of TOP2A mRNA in OSCC cells.ResultsCompared with normal oral tissues, the expression of TOP2A protein was up-regulated in OSCC tissues. A total of 1240 OSCC and 428 non-OSCC oral tissue samples were included based on high-throughput dataset retrieval, and it was confirmed that TOP2A mRNA was highly expressed in OSCC tissues [SMD = 1.51 (95% CI 0.94–2.07), sROC AUC = 0.96 (95% CI 0.94–0.98)]. As an essential gene for OSCC cell growth, single-cell RNA sequencing data also confirmed that TOP2A mRNA expression was up-regulated in OSCC cells.ConclusionThe up-regulation of TOP2A may play a pivotal role in OSCC.

Differential Gene Expression and Immune Cell Infiltration in Patients with Steroid-induced Necrosis of the Femoral Head.

The study aimed to study the differential gene expression and immune cell infiltration in patients with steroid-induced necrosis of the femoral head (SANFH), identify the key genes and immune cells of SANFH, and explore the relationship between immune cells and SANFH. The high-throughput gene chip dataset GSE123568 was downloaded from the GEO database, and the differential gene expression was analyzed with the R language. The STRING database and Cytoscape software were used to analyze the protein interaction network and screen key genes, and enrichment analysis was carried out on key genes. The infiltration of immune cells in SANFH patients was analyzed and verified by immunohistochemistry. EP300, TRAF6, STAT1, JAK1, CASP8, and JAK2 are key genes in the pathogenesis of SANFH, which mainly involve myeloid cell differentiation, cytokine-mediated signaling pathway, tumor necrosis factor-mediated signaling pathway, and cellular response to tumor necrosis factor through JAK-STAT, NOD-like receptor, toll-like receptor, and other signaling pathways, leading to the occurrence of diseases; immune infiltration and immunohistochemical results have shown the expression of memory B cells and activated dendritic cells as reduced in SANFH patients, while in the same SANFH samples, M1 macrophages have been positively correlated with monocytes, and neutrophils have been negatively correlated with monocytes expression. EP300, TRAF6, STAT1, JAK1, CASP8, and JAK2 have exhibited significant differences in SANFH (spontaneous osteonecrosis of the femoral head). Memory B cells, activated dendritic cells, M1 macrophages, monocytes, and neutrophils have shown abnormal expression in SANFH.

Putting error bars on density functional theory

Predicting the error in density functional theory (DFT) calculations due to the choice of exchange–correlation (XC) functional is crucial to the success of DFT, but currently, there are limited options to estimate this a priori. This is particularly important for high-throughput screening of new materials. In this work, the structure and elastic properties of binary and ternary oxides are computed using four XC functionals: LDA, PBE-GGA, PBEsol, and vdW-DF with C09 exchange. To analyze the systemic errors inherent to each XC functional, we employed materials informatics methods to predict the expected errors. The predicted errors were also used to better the DFT-predicted lattice parameters. Our results emphasize the link between the computed errors and the electron density and hybridization errors of a functional. In essence, these results provide “error bars” for choosing a functional for the creation of high-accuracy, high-throughput datasets as well as avenues for the development of XC functionals with enhanced performance, thereby enabling the accelerated discovery and design of new materials.

Integrating ATAC-Seq and RNA-Seq Reveals the Signal Regulation Involved in the Artemia Embryonic Reactivation Process.

Embryonic diapause is a common evolutionary adaptation observed across a wide range of organisms. Artemia is one of the classic animal models for diapause research. The current studies of Artemia diapause mainly focus on the induction and maintenance of the embryonic diapause, with little research on the molecular regulatory mechanism of Artemia embryonic reactivation. The first 5 h after embryonic diapause breaking has been proved to be most important for embryonic reactivation in Artemia. In this work, two high-throughput sequencing methods, ATAC-seq and RNA-seq, were integrated to study the signal regulation process in embryonic reactivation of Artemia at 5 h after diapause breaking. Through the GO and KEGG enrichment analysis of the high-throughput datasets, it was showed that after 5 h of diapause breaking, the metabolism and regulation of Artemia cyst were quite active. Several signal transduction pathways were identified in the embryonic reactivation process, such as G-protein-coupled receptor (GPCR) signaling pathway, cell surface receptor signaling pathway, hormone-mediated signaling pathway, Wnt, Notch, mTOR signaling pathways, etc. It indicates that embryonic reactivation is a complex process regulated by multiple signaling pathways. With the further protein structure analysis and RT-qPCR verification, 11 GPCR genes were identified, in which 5 genes function in the embryonic reactivation stage and the other 6 genes contribute to the diapause stage. The results of this work reveal the signal transduction pathways and GPCRs involved in the embryonic reactivation process of Artemia cysts. These findings offer significant clues for in-depth research on the signal regulatory mechanisms of the embryonic reactivation process and valuable insights into the mechanism of animal embryonic diapause.

Transcriptional and epigenetic characterization of a new in vitro platform to model the formation of human pharyngeal endoderm

BackgroundThe Pharyngeal Endoderm (PE) is an extremely relevant developmental tissue, serving as the progenitor for the esophagus, parathyroids, thyroids, lungs, and thymus. While several studies have highlighted the importance of PE cells, a detailed transcriptional and epigenetic characterization of this important developmental stage is still missing, especially in humans, due to technical and ethical constraints pertaining to its early formation.ResultsHere we fill this knowledge gap by developing an in vitro protocol for the derivation of PE-like cells from human Embryonic Stem Cells (hESCs) and by providing an integrated multi-omics characterization. Our PE-like cells robustly express PE markers and are transcriptionally homogenous and similar to in vivo mouse PE cells. In addition, we define their epigenetic landscape and dynamic changes in response to Retinoic Acid by combining ATAC-Seq and ChIP-Seq of histone modifications. The integration of multiple high-throughput datasets leads to the identification of new putative regulatory regions and to the inference of a Retinoic Acid-centered transcription factor network orchestrating the development of PE-like cells.ConclusionsBy combining hESCs differentiation with computational genomics, our work reveals the epigenetic dynamics that occur during human PE differentiation, providing a solid resource and foundation for research focused on the development of PE derivatives and the modeling of their developmental defects in genetic syndromes.

Significance and Possible Biological Mechanism for CLDN8 Downregulation in Kidney Renal Clear Cell Carcinoma Tissues.

The clinical role of claudin 8 (CLDN8) in kidney renal clear cell carcinoma (KIRC) remains unclarified. Herein, the expression level and potential molecular mechanisms of CLDN8 underlying KIRC were determined. High-throughput datasets of KIRC were collected from GEO, ArrayExpress, SRA, and TCGA databases to determine the mRNA expression level of the CLDN8. In-house tissue microarrays and immunochemistry were performed to examine CLDN8 protein expression. A summary receiver operating characteristic curve (SROC) and standardized mean difference (SMD) forest plot were generated using Stata v16.0. Single-cell analysis was conducted to further prove the expression level of CLDN8. A clustered regularly interspaced short palindromic repeats knockout screen analysis was executed to assess the growth impact of CLDN8. Functional enrichment analysis was conducted using the Metascape database. Additionally, single-sample gene set enrichment analysis was implied to explore immune cell infiltration in KIRC. A total of 17 mRNA datasets comprising 1,060 KIRC samples and 452 non-cancerous control samples were included in this study. Additionally, 105 KIRC and 16 non-KIRC tissues were analyzed using in-house immunohistochemistry. The combined SMD was -5.25 (95% confidence interval (CI): -6.13 to -4.37), and CLDN8 downregulation yielded an SROC area under the curve (AUC) close to 1.00 (95% CI: 0.99 - 1.00). CLDN8 downregulation was also confirmed at the single-cell level. Knocking out CLDN8 stimulated KIRC cell proliferation. Lower CLDN8 expression was correlated with worse overall survival of KIRC patients (hazard ratio of CLDN8 downregulation = 1.69, 95% CI: 1.2 - 2.4). Functional pathways associated with CLDN8 co-expressed genes were centered on carbon metabolism obstruction, with key hub genes ACADM, ACO2, NDUFS1, PDHB, SDHD, SUCLA2, SUCLG1, and SUCLG2. CLDN8 is downregulated in KIRC and is considered a potential tumor suppressor. CLDN8 deficiency may promote the initiation and progression of KIRC, potentially in conjunction with metabolic dysfunction.

Genome-wide detection of somatic mosaicism at short tandem repeats.

Somatic mosaicism has been implicated in several developmental disorders, cancers, and other diseases. Short tandem repeats (STRs) consist of repeated sequences of 1-6bp and comprise >1 million loci in the human genome. Somatic mosaicism at STRs is known to play a key role in the pathogenicity of loci implicated in repeat expansion disorders and is highly prevalent in cancers exhibiting microsatellite instability. While a variety of tools have been developed to genotype germline variation at STRs, a method for systematically identifying mosaic STRs is lacking. We introduce prancSTR, a novel method for detecting mosaic STRs from individual high-throughput sequencing datasets. prancSTR is designed to detect loci characterized by a single high-frequency mosaic allele, but can also detect loci with multiple mosaic alleles. Unlike many existing mosaicism detection methods for other variant types, prancSTR does not require a matched control sample as input. We show that prancSTR accurately identifies mosaic STRs in simulated data, demonstrate its feasibility by identifying candidate mosaic STRs in Illumina whole genome sequencing data derived from lymphoblastoid cell lines for individuals sequenced by the 1000 Genomes Project, and evaluate the use of prancSTR on Element and PacBio data. In addition to prancSTR, we present simTR, a novel simulation framework which simulates raw sequencing reads with realistic error profiles at STRs. prancSTR and simTR are freely available at https://github.com/gymrek-lab/trtools. Detailed documentation is available at https://trtools.readthedocs.io/.

DeepCRISTL: deep transfer learning to predict CRISPR/Cas9 on-target editing efficiency in specific cellular contexts.

CRISPR/Cas9 technology has been revolutionizing the field of gene editing. Guide RNAs (gRNAs) enable Cas9 proteins to target specific genomic loci for editing. However, editing efficiency varies between gRNAs and so computational methods were developed to predict editing efficiency for any gRNA of interest. High-throughput datasets of Cas9 editing efficiencies were produced to train machine-learning models to predict editing efficiency. However, these high-throughput datasets have a low correlation with functional and endogenous datasets, which are too small to train accurate machine-learning models on. We developed DeepCRISTL, a deep-learning model to predict the editing efficiency in a specific cellular context. DeepCRISTL takes advantage of high-throughput datasets to learn general patterns of gRNA editing efficiency and then fine-tunes the model on functional or endogenous data to fit a specific cellular context. We tested two state-of-the-art models trained on high-throughput datasets for editing efficiency prediction, our newly improved DeepHF and CRISPRon, combined with various transfer-learning approaches. The combination of CRISPRon and fine-tuning all model weights was the overall best performer. DeepCRISTL outperformed state-of-the-art methods in predicting editing efficiency in a specific cellular context on functional and endogenous datasets. Using saliency maps, we identified and compared the important features learned by DeepCRISTL across cellular contexts. We believe DeepCRISTL will improve prediction performance in many other CRISPR/Cas9 editing contexts by leveraging transfer learning to utilize both high-throughput datasets and smaller and more biologically relevant datasets. DeepCRISTL is available via https://github.com/OrensteinLab/DeepCRISTL.

Shared genetics and causal association between plasma levels of SARS-CoV-2 entry receptor ACE2 and Alzheimer's disease.

Alzheimer's disease (AD) is the highest risk of COVID-19 infection, hospitalization, and mortality. However, it remains largely unclear about the link between AD and COVID-19 outcomes. ACE2 is an entry receptor for SARS-CoV-2. Circulating ACE2 is a novel biomarker of death and associated with COVID-19 outcomes. Here, we explored the shared genetics and causal association between AD and plasma ACE2 levels using large-scale genome-wide association study, gene expression, expression quantitative trait loci, and high-throughput plasma proteomic profiling datasets. We found a significant causal effect of genetically increased circulating ACE2 on increased risk of AD. Cross-trait association analysis identified 19 shared genetic variants, and three variants rs3104412, rs2395166, and rs3135344 at chromosome 6p21.32 were associated with COVID-19 infection, hospitalization, and severity. We mapped 19 variants to 117 genes, which were significantly upregulated in lung, spleen, and small intestine, downregulated in brain tissues, and involved in immune system, immune disease, and infectious disease pathways. The plasma proteins corresponding to LST1, AGER, TNXB, and APOC1 were predominantly associated with COVID-19 infection, ventilation, and death. Together, our findings suggest the shared genetics and causal association between AD and plasma ACE2 levels, which may partially explain the link between AD and COVID-19.

Identification and bioinformatics analysis of genes associated with pyroptosis in spinal cord injury of rat and mouse

The mechanism of spinal cord injury (SCI) is highly complex, and an increasing number of studies have indicated the involvement of pyroptosis in the physiological and pathological processes of secondary SCI. However, there is limited bioinformatics research on pyroptosis-related genes (PRGs) in SCI. This study aims to identify and validate differentially expressed PRGs in the GEO database, perform bioinformatics analysis, and construct regulatory networks to explore potential regulatory mechanisms and therapeutic targets for SCI. We obtained high-throughput sequencing datasets of SCI in rats and mice from the GEO database. Differential analysis was conducted using the “limma” package in R to identify differentially expressed genes (DEGs). These genes were then intersected with previously reported PRGs, resulting in a set of PRGs in SCI. GO and KEGG enrichment analyses, as well as correlation analysis, were performed on the PRGs in both rat and mouse models of SCI. Additionally, a protein–protein interaction (PPI) network was constructed using the STRING website to examine the relationships between proteins. Hub genes were identified using Cytoscape software, and the intersection of the top 5 hub genes in rats and mice were selected for subsequent experimentally validated. Furthermore, a competing endogenous RNA (ceRNA) network was constructed to explore potential regulatory mechanisms. The gene expression profiles of GSE93249, GSE133093, GSE138637, GSE174549, GSE45376, GSE171441_3d and GSE171441_35d were selected in this study. We identified 10 and 12 PRGs in rats and mice datasets respectively. Six common DEGs were identified in the intersection of rats and mice PRGs. Enrichment analysis of these DEGs indicated that GO analysis was mainly focused on inflammation-related factors, while KEGG analysis showed that the most genes were enriched on the NOD-like receptor signaling pathway. We constructed a ceRNA regulatory network that consisted of five important PRGs, as well as 24 miRNAs and 34 lncRNAs. This network revealed potential regulatory mechanisms. Additionally, the three hub genes obtained from the intersection were validated in the rat model, showing high expression of PRGs in SCI. Pyroptosis is involved in secondary SCI and may play a significant role in its pathogenesis. The regulatory mechanisms associated with pyroptosis deserve further in-depth research.

OneSC: A computational platform for recapitulating cell state transitions.

Computational modelling of cell state transitions has been a great interest of many in the field of developmental biology, cancer biology and cell fate engineering because it enables performing perturbation experiments in silico more rapidly and cheaply than could be achieved in a wet lab. Recent advancements in single-cell RNA sequencing (scRNA-seq) allow the capture of high-resolution snapshots of cell states as they transition along temporal trajectories. Using these high-throughput datasets, we can train computational models to generate in silico 'synthetic' cells that faithfully mimic the temporal trajectories. Here we present OneSC, a platform that can simulate synthetic cells across developmental trajectories using systems of stochastic differential equations govern by a core transcription factors (TFs) regulatory network. Different from the current network inference methods, OneSC prioritizes on generating Boolean network that produces faithful cell state transitions and steady cell states that mimic real biological systems. Applying OneSC to real data, we inferred a core TF network using a mouse myeloid progenitor scRNA-seq dataset and showed that the dynamical simulations of that network generate synthetic single-cell expression profiles that faithfully recapitulate the four myeloid differentiation trajectories going into differentiated cell states (erythrocytes, megakaryocytes, granulocytes and monocytes). Finally, through the in-silico perturbations of the mouse myeloid progenitor core network, we showed that OneSC can accurately predict cell fate decision biases of TF perturbations that closely match with previous experimental observations.