Transcriptomic Datasets Research Articles

UBAP2 contributes to radioresistance by enhancing homologous recombination through SLC27A5 ubiquitination in hepatocellular carcinoma

Radiotherapy stands as an effective method in the clinical treatment of hepatocellular carcinoma (HCC) patients. However, both primary and acquired radioresistance limit its clinical application in HCC. Therefore, investigating the mechanism of radioresistance may provide other options for treating HCC. Based on single-cell RNA sequencing (scRNA-seq) and HCC transcriptome datasets, 227 feature genes with prognostic value were selected to establish the tSNE score. The tSNE score emerged as an independent prognostic factor for HCC and correlated with cell proliferation and radioresistance-related biological functions. UBAP2 was identified as the most relevant gene with the tSNE score, consistently elevated in human HCC samples, and positively associated with patient prognosis. Functionally, UBAP2 knockdown impeded HCC development and reduced radiation resistance in vitro and in vivo. The ectopic expression of SLC27A5 reversed the effects of UBAP2. Mechanically, we uncovered that UBAP2, through the ubiquitin-proteasome system, decreased the homologous recombination-related gene RAD51, not the non-homologous end-joining (NHEJ)-related gene CTIP, by degrading the antioncogene SLC27A5, thereby generating radioresistance in HCC. The findings recapitulated that UBAP2 promoted HCC progression and radioresistance via SLC27A5 stability mediated by the ubiquitin-proteasome pathway. It was also suggested that targeting the UBAP2/SLC27A5 axis could be a valuable radiosensitization strategy in HCC.

Deciphering the role of oxidative stress genes in idiopathic pulmonary fibrosis: a multi-omics mendelian randomization approach.

Oxidative stress (OS) is crucial in idiopathic pulmonary fibrosis (IPF) pathogenesis, with its genes potentially acting as both causes and consequences of the disease. We identified OS-related genes from GeneCards and performed a meta-analysis on pulmonary transcriptome datasets to discover differentially expressed genes (DEGs) related to OS in IPF. We integrated this data with the largest available IPF GWAS summaries, expression quantitative trait loci (eQTLs), and DNA methylation QTLs (mQTLs) from blood. This approach aimed to identify blood OS genes and regulatory elements linked to IPF risk, incorporating the latest pulmonary eQTLs and bronchoalveolar lavage fluid microbial QTLs (bmQTLs) for a comprehensive view of gene-lung microbiota interactions through SMR and colocalization analyses. Sensitivity analyses were conducted using two additional mendelian randomization (MR) methods. Meta-analysis revealed 1090 differentially expressed OS genes between IPF patients and controls. Integration with IPF GWAS, eQTL, and mQTL data identified key genes and regulatory elements involved in IPF pathogenesis, highlighting the role of specific genes such as KCNMA1 and SLC22A5 in modulating IPF risk through epigenetic mechanisms. Colocalization analysis further identified potential interactions between gene expression and lung microbiota. Our findings elucidate the complex interplay between OS genes and IPF, suggesting potential therapeutic targets and highlighting the importance of considering epigenetic and microbial interactions in the disease's etiology and progression.

G0 arrest gene patterns to predict the prognosis and drug sensitivity of patients with lung adenocarcinoma.

G0 arrest (G0A) is widely recognized as a crucial factor contributing to tumor relapse. The role of genes related to G0A in lung adenocarcinoma (LUAD) was unclear. This study aimed to develop a gene signature based on for LUAD patients and investigate its relationship with prognosis, tumor immune microenvironment, and therapeutic response in LUAD. We use the TCGA-LUAD database as the discovery cohort, focusing specifically on genes associated with the G0A pathway. We used various statistical methods, including Cox and lasso regression, to develop the model. We validated the model using bulk transcriptome and single-cell transcriptome datasets (GSE50081, GSE72094, GSE127465, GSE131907 and EMTAB6149). We used GSEA enrichment and the CIBERSORT algorithm to gain insight into the annotation of the signaling pathway and the characterization of the tumor microenvironment. We evaluated the response to immunotherapy, chemotherapy, and targeted therapy in these patients. The expression of six genes was validated in cell lines by quantitative real-time PCR (qRT-PCR). Our study successfully established a six-gene signature (CHCHD4, DUT, LARP1, PTTG1IP, RBM14, and WBP11) that demonstrated significant predictive power for overall survival in patients with LUAD. It demonstrated independent prognostic value in LUAD. To enhance clinical applicability, we developed a nomogram based on this gene signature, which showed high reliability in predicting patient outcomes. Furthermore, we observed a significant association between G0A-related risk and tumor microenvironment as well as drug susceptibility, highlighting the potential of the gene signature to guide personalized treatment strategies. The expression of six genes were significantly upregulated in the LUAD cell lines. This signature holds the potential to contribute to improved prognostic prediction and new personalized therapies specifically for LUAD patients.

Inhibition of SUMOylation Induces Adaptive Antitumor Immunity against Pancreatic Cancer through Multiple Effects on the Tumor Microenvironment.

Improvement of outcome in patients with pancreatic ductal adenocarcinoma (PDAC) requires exploration of novel therapeutic targets. Thus far, most studies of PDAC therapies, including those inhibiting small ubiquitin-like modifications (SUMOylation), have focused on PDAC epithelial cell biology, yet SUMOylation occurs in a variety of cell types. The mechanisms by which SUMOylation impacts PDAC in the context of its tumor microenvironment are poorly understood. We used clinically relevant orthotopic PDAC mouse models to investigate the effect of SUMOylation inhibition using a specific, clinical-stage compound, TAK-981. In contrast to its inhibition of PDAC cell proliferation in vitro, the survival benefit conferred by TAK-981 in vivo is dependent on the presence of T cells, suggesting that induction of adaptive antitumor immunity is an important antitumor effect of SUMOylation inhibition in vivo. To understand how this adaptive antitumor immunity is promoted, we investigated how SUMOylation inhibition in vivo alters major cell types/subtypes and their communications in the PDAC tumor microenvironment by performing transcriptomic analyses at single-cell resolution, which allowed mapping of cells in our orthotopic mouse model to cells in human PDAC tumors based on gene expression profiles. Findings are further validated by flow cytometry, immunofluorescence, IHC, western blots, and qPCR. The single-cell transcriptome dataset provided here suggests several combination strategies to augment adaptive immune responses that are necessary for durable disease control in patients with PDAC.

Transcriptomic Insights into the Physiological Aspects of the Saprotrophic Fungus Penicillium citrinum During the Spoilage of Tobacco Leaves

Penicillium citrinum is one of the most prevalent tobacco spoilage fungi. However, the mechanisms underlying fungal growth on tobacco leaves remain largely unknown. In this study, transcriptomic analyses were performed to reveal the genome-wide expression profiles of P. citrinum growing on tobacco leaves. First, a comparative analysis was conducted between two sets of transcriptomic data from P. citrinum growing on chemically defined media and tobacco leaves. Enrichment analyses showed that differentially regulated genes were mainly associated with carbohydrate degradation (e.g., cellulose, pectin, and xylan) and the catabolism of fatty acids and aromatic compounds. Comparative transcriptomic analyses between different time points indicated that the fungal transcriptome varied dynamically during the spoilage process, and the enriched terms were associated with small-molecule degradation and fungal development. Enrichment analyses indicated that more up-regulated genes appeared in all enriched Gene Ontology terms. Notably, more organelles significantly contributed to further fungal growth on tobacco leaves. In conclusion, P. citrinum activates a comprehensive transcriptome that changes dynamically when causing tobacco mildew.

Pre-receptor regulation of 11-oxyandrogens differs between normal and cancerous endometrium and across endometrial cancer grades and molecular subtypes.

Endometrial cancer (EC) is a prevalent gynecological malignancy globally, with a rising incidence trend. While classic androgens have been implicated with EC risk, the role of their 11-oxygenated metabolites is poorly understood. Here, we studied 11-oxyandrogen formation from steroid precursors in EC for the first time. We performed in vitro studies on a panel of four EC cell lines of varying differentiation degree and molecular subtype and a control cell line of normal endometrium to assess 11-oxyandrogen formation from steroid precursors. We also characterized the transcriptomic effects of dihydrotestosterone (DHT) and 11-keto-DHT on Ishikawa and RL95-2. Key molecular players in 11-oxyandrogen metabolism and action were explored in endometrial tumors using public transcriptomic datasets. We discovered that within endometrial tumors, the formation of 11-oxyandrogens does not occur from classic androgen precursors. However, we observed distinct regulatory mechanisms at a pre-receptor level in normal endometrium compared to cancerous tissue, and between low- and high-grade tumors. Specifically, in vitro models of low-grade EC formed higher levels of bioactive 11-keto-testosterone from 11-oxyandrogen precursors compared to models of noncancerous endometrium and high-grade, TP53-mutated EC. Moreover, the potent androgen, DHT and its 11-keto homologue induced mild transcriptomic effects on androgen receptor (AR)-expressing EC model, Ishikawa. Finally, using public transcriptomic datasets, we found HSD11B2 and SRD5A2, coding for key enzymes in steroid metabolism, to be associated with better disease-specific survival, whereas higher intra-tumoral AR expression correlated with lower recurrence in TP53-wt tumors. The intra-tumoral metabolism of 11-oxyandrogen precursors is characteristic for low-grade EC of non-TP53-alt molecular subtypes. Our findings support further exploration of circulating 11-oxyandrogens as prognostic biomarkers in EC.

Quantitative Aspect of Bacillus subtilis σB Regulatory Network on a Proteome Level-A Computational Simulation.

Bacillus subtilis is a model organism used to study molecular processes in Gram-positive bacteria. Sigma factor B, which associates with RNA polymerase, is one of the transcriptional regulators involved in the cell's response to environmental stress. Experiments have proven that the amounts of free σB (SigB) are controlled by a system of anti- (RsbW) and anti-anti-sigma (RsbV) factors expressed from the same operon as SigB. Moreover, the phosphorylation state of RsbV is controlled by phosphatases RsbP and RsbU, which directly dephosphorylate RsbV. A set of chemical equations describing the network controlling the levels of free SigB was converted to a set of differential equations quantifying the dynamics of the network. The solution of these equations allowed the simulation of the kinetic behavior of the network and its components under real conditions reflected in the time series of protein expression. In this study, the time series of protein expression measured by mass spectrometry were utilized to investigate the role of phosphatases RsbU/RsbP in transmitting the environmental signal. Additionally, the influence of kinetic constants and the amounts of other network components on the functioning of the network was investigated. A comparison with the same simulation performed using a transcriptomic dataset showed that while the time series between the proteomic and transcriptomic datasets are not correlated, the results are the same. This indicates that when modeling is performed within one dataset, it does not matter whether the data come from the mRNA or protein level. In summary, the computational results based on experimental data provide a quantitative insight into the functioning of the SigB-dependent circuit and offer a template for the quantitative study of similar systems.

The Mitogenome of the Haecon-5 Strain of Haemonchus contortus and a Comparative Analysis of Its Nucleotide Variation with Other Laboratory Strains.

Haemonchus contortus (the barber's pole worm)-a highly pathogenic gastric nematode of ruminants-causes significant economic losses in the livestock industry worldwide. H. contortus has become a valuable model organism for both fundamental and applied research (e.g., drug and vaccine discovery) because of the availability of well-defined laboratory strains (e.g., MHco3(ISE).N1 in the UK and Haecon-5 in Australia) and genomic, transcriptomic and proteomic data sets. Many recent investigations have relied heavily on the use of the chromosome-contiguous genome of MHco3(ISE).N1 in the absence of a genome for Haecon-5. However, there has been no genetic comparison of these and other strains to date. Here, we assembled and characterised the mitochondrial genome (14.1 kb) of Haecon-5 and compared it with that of MHco3(ISE).N1 and two other strains (i.e., McMaster and NZ_Hco_NP) from Australasia. We detected 276 synonymous and 25 non-synonymous single nucleotide polymorphisms (SNPs) within Haecon-5. Between the Haecon-5 and MHco3(ISE).N1 strains, we recorded 345 SNPs, 31 of which were non-synonymous and linked to fixed amino acid differences in seven protein-coding genes (nad5, nad6, nad1, atp6, nad2, cytb and nad4) between these strains. Pronounced variation (344 and 435 SNPs) was seen between Haecon-5 and each of the other two strains from Australasia. The question remains as to what impact these mitogenomic mutations might have on the biology and physiology of H. contortus, which warrants exploration. The high degree of mitogenomic variability recorded here among these strains suggests that further work should be undertaken to assess the nature and extent of the nuclear genomic variation within H. contortus.

Prevalence of and gene regulatory constraints on transcriptional adaptation in single cells

BackgroundCells and tissues have a remarkable ability to adapt to genetic perturbations via a variety of molecular mechanisms. Nonsense-induced transcriptional compensation, a form of transcriptional adaptation, has recently emerged as one such mechanism, in which nonsense mutations in a gene trigger upregulation of related genes, possibly conferring robustness at cellular and organismal levels. However, beyond a handful of developmental contexts and curated sets of genes, no comprehensive genome-wide investigation of this behavior has been undertaken for mammalian cell types and conditions. How the regulatory-level effects of inherently stochastic compensatory gene networks contribute to phenotypic penetrance in single cells remains unclear.ResultsWe analyze existing bulk and single-cell transcriptomic datasets to uncover the prevalence of transcriptional adaptation in mammalian systems across diverse contexts and cell types. We perform regulon gene expression analyses of transcription factor target sets in both bulk and pooled single-cell genetic perturbation datasets. Our results reveal greater robustness in expression of regulons of transcription factors exhibiting transcriptional adaptation compared to those of transcription factors that do not. Stochastic mathematical modeling of minimal compensatory gene networks qualitatively recapitulates several aspects of transcriptional adaptation, including paralog upregulation and robustness to mutation. Combined with machine learning analysis of network features of interest, our framework offers potential explanations for which regulatory steps are most important for transcriptional adaptation.ConclusionsOur integrative approach identifies several putative hits—genes demonstrating possible transcriptional adaptation—to follow-up on experimentally and provides a formal quantitative framework to test and refine models of transcriptional adaptation.

ExpOmics: a comprehensive web platform empowering biologists with robust multi-omics data analysis capabilities.

High-throughput technologies yield a broad spectrum of multi-omics datasets, which offer unparalleled insights into complex biological systems. However, effectively analyzing this diverse array of data presents challenges, considering factors such as species diversity, data types, costs, and limitations of the available tools. Herein, we present ExpOmics, a comprehensive web platform featuring 7 applications and 4 toolkits, with 28 customizable analysis functions spanning various analyses of differential expression, co-expression, Weighted Gene Co-expression Network Analysis (WGCNA), feature selection, and functional enrichment. ExpOmics allows users to upload and explore multi-omics data without organism restrictions, supporting various expression data, including genes, mRNAs, lncRNAs, miRNAs, circRNAs, piRNAs, and proteins and is compatible with diverse gene nomenclatures and expression values. Moreover, ExpOmics enables users to analyze 22427 transcriptomic datasets of 196 cancer subtypes sourced from 63 projects of The Cancer Genome Atlas Program (TCGA) to identify cancer biomarkers. The analysis results from ExpOmics are presented in high-quality graphical formats suitable for publication and are available for free download. A case study using ExpOmics identified two potential oncogenes, SERPINE1 and SLC43A1, that may regulate colorectal cancer through distinct biological processes. In summary, ExpOmics can serves as a robust platform for global researchers to explore multi-omics data, gain biological insights, and formulate testable hypotheses. ExpOmics is available at http://www.biomedical-web.com/expomics.

Immune landscape of isocitrate dehydrogenase-stratified primary and recurrent human gliomas.

Human gliomas are classified using isocitrate dehydrogenase (IDH) status as a prognosticator; however, the influence of genetic differences and treatment effects on ensuing immunity remains unclear. In this study, we used sequential single-cell transcriptomics on 144,678 and spectral cytometry on over two million immune cells encompassing 48 human gliomas to decipher their immune landscape. We identified 22 distinct immune cell types that contribute to glioma immunity. Specifically, brain-resident microglia (MG) were reduced with a concomitant increase in CD8+ T lymphocytes during glioma recurrence independent of IDH status. In contrast, IDH-wild-type-associated patterns, such as an abundance of antigen-presenting cell-like MG and cytotoxic CD8+ T cells, were observed. Beyond elucidating the differences in IDH, relapse, and treatment-associated immunity, we discovered novel inflammatory MG subpopulations expressing granulysin, a cytotoxic peptide, which is otherwise expressed in lymphocytes only. Furthermore, we provide a robust genomic framework for defining macrophage polarization beyond M1/M2 paradigm and reference signatures of glioma-specific tumor immune microenvironment (termed Glio-TIME-36) for deconvoluting transcriptomic datasets. This study provides advanced optics of the human pan-glioma immune contexture as a valuable guide for translational and clinical applications.

Integrative Analysis of Multi-Omics Data to Identify Deregulated Molecular Pathways and Druggable Targets in Chronic Lymphocytic Leukemia.

Chronic Lymphocytic Leukemia (CLL) is the most common B-cell malignancy in the Western world, characterized by frequent relapses despite temporary remissions. Our study integrated publicly available proteomic, transcriptomic, and patient survival datasets to identify key differences between healthy and CLL samples. We exposed approximately 1000 proteins that differentiate healthy from cancerous cells, with 608 upregulated and 415 downregulated in CLL cases. Notable upregulated proteins include YEATS2 (an epigenetic regulator), PIGR (Polymeric immunoglobulin receptor), and SNRPA (a splicing factor), which may serve as prognostic biomarkers for this disease. Key pathways implicated in CLL progression involve RNA processing, stress resistance, and immune response deficits. Furthermore, we identified three existing drugs-Bosutinib, Vorinostat, and Panobinostat-for potential further investigation in drug repurposing in CLL. We also found limited correlation between transcriptomic and proteomic data, emphasizing the importance of proteomics in understanding gene expression regulation mechanisms. This generally known disparity highlights once again that mRNA levels do not accurately predict protein abundance due to many regulatory factors, such as protein degradation, post-transcriptional modifications, and differing rates of translation. These results demonstrate the value of integrating omics data to uncover deregulated proteins and pathways in cancer and suggest new therapeutic avenues for CLL.

SR-TWAS: leveraging multiple reference panels to improve transcriptome-wide association study power by ensemble machine learning

Multiple reference panels of a given tissue or multiple tissues often exist, and multiple regression methods could be used for training gene expression imputation models for transcriptome-wide association studies (TWAS). To leverage expression imputation models (i.e., base models) trained with multiple reference panels, regression methods, and tissues, we develop a Stacked Regression based TWAS (SR-TWAS) tool which can obtain optimal linear combinations of base models for a given validation transcriptomic dataset. Both simulation and real studies show that SR-TWAS improves power, due to increased training sample sizes and borrowed strength across multiple regression methods and tissues. Leveraging base models across multiple reference panels, tissues, and regression methods, our real studies identify 6 independent significant risk genes for Alzheimer’s disease (AD) dementia for supplementary motor area tissue and 9 independent significant risk genes for Parkinson’s disease (PD) for substantia nigra tissue. Relevant biological interpretations are found for these significant risk genes.

14 Natural killer cells have impaired cytotoxicity in advanced renal cell carcinoma

Abstract Background Natural killer (NK) cells are key mediators of anti-tumor activity in multiple cancers, including clear cell renal cell carcinoma (ccRCC). However, the phenotype and function of NK cells across different disease stages of ccRCC is incompletely characterized. Methods Single-cell RNA (scRNA)-sequencing (10x Genomics) data from tumor and adjacent normal kidney from ccRCC was analyzed at multiple clinical stages (localized – stage I/II/III; and advanced – stage IV). Graph-based clustering and lineage markers were used to identify distinct NK cell populations. Differential gene expression analysis was performed to further investigate NK cell phenotype and to derive a gene expression signature (GES). Gene signatures from NK cell subclusters of interest were used to interrogate bulk transcriptomic datasets and expression with clinical outcomes. 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 (IFNgamma) and cytotoxicity (CD107a) markers by flow cytometry. Results Single-cell transcriptomic data were analyzed from 13 patients with ccRCC (tumor and normal kidney), resulting in 21,139 high-quality NK cells. Patients with advanced/metastatic RCC had a lower proportion of NK cells versus total immune cells in the tumor microenvironment compared to normal kidney (p=0.036) and localized ccRCC (p=0.088). Clustering analysis revealed 6 distinct NK cell subsets. The C0.Bright-like NK cell cluster was significantly enriched in advanced ccRCC compared to localized ccRCC (p=0.048) and normal kidney (p=0.0059), expressed markers of tissue residency (ZNF683, ITGA1, CD9, ITGAE), and had decreased expression of cytotoxicity genes (GZMB, PRF1). A GES signature composed of genes upregulated in the dysfunctional C0.Bright-like NK cell cluster was used to interrogate bulk RNA-sequencing data from The Cancer Genome Atlas clear cell cohort, providing validation in a large, independent cohort, of an increase in this dysfunctional NK subset in advanced ccRCC compared to localized ccRCC (p=0.00039) and normal tissue (p=5.3e-05). To functionally confirm the decreased cytotoxicity of this dysfunctional NK population, CD45+CD56+CD3- NK cells were isolated from advanced ccRCC, and co-cultured with K562 target cells. Consistent with the transcriptomic phenotype, tumor-resident CD49a+CD9+ NK cells had cytokine production (IFNgamma) but decreased markers of cytotoxicity (CD107a) compared to CD49a-CD9- NK cells. By contrast, CD49a+CD9+ NK cells isolated from peripheral blood or normal kidney tissue maintained cytotoxic activity. Conclusions Among patients with ccRCC, a single-cell transcriptomic analysis revealed heterogeneous NK cell populations. A dysfunctional tumor resident NK cell phenotype was enriched among patients with advanced disease in ccRCC, and functionally confirmed to have diminished cytotoxicity. This study therefore provides insight into a new axis of immune dysfunction – decreased NK-mediated cytotoxicity – in advanced ccRCC. An improved understanding of NK cell dysfunction within ccRCC may provide a foundation for therapeutic restoration of NK-mediated anti-tumor activity in ccRCC. DOD CDMRP Funding: yes

OXidative Stress PREDictor: A Supervised Learning Approach for Annotating Cellular Oxidative Stress States in Inflammatory Cells

Oxidative stress, characterized by an imbalance between reactive oxygen species (ROS) and antioxidants, plays a pivotal role in inflammatory responses associated with both chronic diseases and acute injuries. In this study, OXidative Stress PREDictor (OxSpred), a supervised learning model tailored to accurately annotate the oxidative stress state of innate immune cells at the single‐cell level, is introduced. Compared to the traditional gene‐set‐variation‐analysis‐based enrichment method, OxSpred demonstrates superior accuracy with an area under the receiver operating characteristic curve of 0.89 and offers interpretable embeddings with significant biological relevance. Using the predicted ROS states, precise elucidation and interpretation of the roles of novel innate immune cell subtypes can be achieved. Overall, OxSpred enhances the utility of single‐cell transcriptomic datasets by providing a robust in silico method for determining intracellular oxidative stress states, thereby enriching the understanding of innate immune cell functions during inflammation.

In silico prediction of effector proteins in oil palm pest, Metisa plana

Metisa plana is one of the main oil palm leaf defoliators in Malaysia. Infestation of M. plana at the oil palm plantation caused severe yield loss in the past. Many control methods have been introduced to combat the infestation, but the problem persists till today. More information regarding the bagworm is needed to understand the pest-host interaction so that an effective control method can be properly designed. This study was carried out to predict the repertoire of effector proteins secreted by M. plana that could potentially suppress the host defence mechanisms. In silico screening of the secretory proteins from predicted genes was done by using several tools namely SignalP, TMHMM, TargetP and WOLFPSORT. Then, the acquired dataset was subjected to functional annotation through a similarity search against UniProt-Trembl database, gene ontology (GO) using BLAST2GO, InterPro protein domain classification, cluster of orthologous groups (COG), and KEGG pathway prediction. To determine the expression of the predicted genes, TPM value was calculated using the available transcriptomic dataset of M. plana. We identified a total of 948 secretory proteins from the initial screening of the 28,738 proteins predicted from BRAKER2 pipeline. Several proteins were classified as specific protein classes associated with effector functions in insect pests, namely detoxifying enzymes, lipases, peroxidases, Ca2+ binding proteins, and chemosensory and odorant binding proteins. This predicted effector data can be used as a reference for future works in validating the protein’s interaction and mechanisms with the oil palm defence responses.

Abstract We080: Mapping the effect of Hedgehog signaling on cardiac differentiation and migration in space and time

Background: Organ morphogenesis requires precise control of organ-specific progenitor cell migration and differentiation. Hedgehog signaling is a major signaling pathway during animal development and is essential for proper cardiac development. Previous work demonstrated that Hedgehog signaling can regulate migration and the timing of differentiation of cardiac progenitors. However, how Hedgehog signaling integrates control of differentiation timing and migration in space to enable proper organ formation during development remains unclear. Hypothesis: We hypothesize that Hedgehog signaling explicitly controls cardiac morphogenesis by coordinating cellular migration and differentiation of cardiac progenitors to determining where and when they turn into cardiomyocytes. Methods: We combine mouse genetics, in-vivo lineage tracing, whole mouse embryo light-sheet imaging, and metanalysis of publicly available spatial transcriptomics datasets to address the hypothesis. Results: We identified the sites of active Hedgehog signaling by studying a reporter for Hedgehog signaling (GBS-GFP). Next, we determined the necessity of Hedgehog signaling for differentiation of cardiomyocytes at the right place and time by tracing the cardiac progenitors over developmental time in mutants in controls compared to Hedgehog signaling mutants. Conclusion and future directions: This work will establish the dual role of Hedgehog signaling in differentiation timing and migration control as a novel paradigm that may be generalizable to other organ systems dependent on Hedgehog signaling (e.g., neural cord and limb patterning).

Deep multiomic profiling reveals molecular signatures that underpin preschool wheeze and asthma

Wheezing in childhood is prevalent, with over one-half of all children experiencing at least 1 episode by age 6. The pathophysiology of wheeze, especially why some children develop asthma while others do not, remains unclear. This study addresses the knowledge gap by investigating the transition from preschool wheeze to asthma using multiomic profiling. Unsupervised, group-agnostic integrative multiomic factor analysis was performed using host/bacterial (meta)transcriptomic and bacterial shotgun metagenomic datasets from bronchial brush samples paired with metabolomic/lipidomic data from bronchoalveolar lavage samples acquired from children 1-17 years old. Two multiomic factors were identified: one characterizing preschool-aged recurrent wheeze and another capturing an inferred trajectory from health to wheeze and school-aged asthma. Recurrent wheeze was driven by type 1-immune signatures, coupled with upregulation of immune-related and neutrophil-associated lipids and metabolites. Comparatively, progression toward asthma from ages 1 to 18 was dominated by changes related to airway epithelial cell gene expression, type 2-immune responses, and constituents of the airway microbiome, such as increased Haemophilus influenzae. These factors highlighted distinctions between an inflammation-related phenotype in preschool wheeze, and the predominance of airway epithelial-related changes linked with the inferred trajectory toward asthma. These findings provide insights into the differential mechanisms driving the progression from wheeze to asthma and may inform targeted therapeutic strategies.

From molecular subgroups to molecular targeted therapy in rheumatoid arthritis: A bioinformatics approach

(1)BackgroundRheumatoid Arthritis (RA) is a heterogeneous autoimmune disease with multiple unidentified pathogenic factors. The inconsistency between molecular subgroups poses challenges for early diagnosis and personalized treatment strategies. In this study, we aimed to accurately distinguish RA patients at the transcriptome level using bioinformatics methods. (2)MethodsWe collected a total of 362 transcriptome datasets from RA patients in three independent samples from the GEO database. Consensus clustering was performed to identify molecular subgroups, and clinical features were assessed. Differential analysis was employed to annotate the biological functions of specifically upregulated genes between subgroups. (3)ResultsBased on consensus clustering of RA samples, we identified three robust molecular subgroups, with Subgroup III representing the high-risk subgroup and Subgroup II exhibiting a milder phenotype, possibly associated with relatively higher levels of autophagic ability. Subgroup I showed biological functions mainly related to viral infections, cellular metabolism, protein synthesis, and inflammatory responses. Subgroup II involved autophagy of mitochondria and organelles, protein localization, and organelle disassembly pathways, suggesting heterogeneity in the autophagy process of mitochondria that may play a protective role in inflammatory diseases. Subgroup III represented a high-risk subgroup with pathological processes including abnormal amyloid precursor protein activation, promotion of inflammatory response, and cell proliferation. (4)ConclusionThe classification of the RA dataset revealed pathological heterogeneity among different subgroups, providing new insights and a basis for understanding the molecular mechanisms of RA, identifying potential therapeutic targets, and developing personalized treatment approaches.

AKNNO: single-cell and spatial transcriptomics clustering with an optimized adaptive k-nearest neighbor graph

Typical clustering methods for single-cell and spatial transcriptomics struggle to identify rare cell types, while approaches tailored to detect rare cell types gain this ability at the cost of poorer performance for grouping abundant ones. Here, we develop aKNNO to simultaneously identify abundant and rare cell types based on an adaptive k-nearest neighbor graph with optimization. Benchmarking on 38 simulated and 20 single-cell and spatial transcriptomics datasets demonstrates that aKNNO identifies both abundant and rare cell types more accurately than general and specialized methods. Using only gene expression aKNNO maps abundant and rare cells more precisely compared to integrative approaches.