Experimental Framework for Assessing Mouse Retinal Regeneration Through Single-Cell RNA-Sequencing.

Retinal degenerative diseases including age-related macular degeneration and glaucoma are estimated to currently affect more than 14 million people in the United States, with an increased prevalence of retinal degenerations in aged individuals. An expanding aged population who are living longer forecasts an increased prevalence and economic burden of visual impairments. Improvements to visual health and treatment paradigms for progressive retinal degenerations slow vision loss. However, current treatments fail to remedy the root cause of visual impairments caused by retinal degenerations-loss of retinal neurons. Stimulation of retinal regeneration from endogenous cellular sources presents an exciting treatment avenue for replacement of lost retinal cells. In multiple species including zebrafish and Xenopus, Müller glial cells maintain a highly efficient regenerative ability to reconstitute lost cells throughout the organism's lifespan, highlighting potential therapeutic avenues for stimulation of retinal regeneration in humans. Here, we describe how the application of single-cell RNA-sequencing (scRNA-seq) has enhanced our understanding of Müller glial cell-derived retinal regeneration, including the characterization of gene regulatory networks that facilitate/inhibit regenerative responses. Additionally, we provide a validated experimental framework for cellular preparation of mouse retinal cells as input into scRNA-seq experiments, including insights into experimental design and analyses of resulting data.

Oxidation-reduction process of Arabidopsis thaliana roots induced by bisphenol compounds based on RNA-seq analysis.

Bisphenol compounds (BPs) have various industrial uses and can enter the environment through various sources. To evaluate the ecotoxicity of BPs and identify potential gene candidates involved in the plant toxicity, Arabidopsis thaliana was exposed to bisphenol A (BPA), BPB, BPE, BPF, and BPS at 1, 3, 10 mg/L for a duration of 14 days, and their growth status were monitored. At day 14, roots and leaves were collected for internal BPs exposure concentration detection, RNA-seq (only roots), and morphological observations. As shown in the results, exposure to BPs significantly disturbed root elongation, exhibiting a trend of stimulation at low concentration and inhibition at high concentration. Additionally, BPs exhibited pronounced generation of reactive oxygen species, while none of the pollutants caused significant changes in root morphology. Internal exposure concentration analysis indicated that BPs tended to accumulate in the roots, with BPS exhibiting the highest level of accumulation. The results of RNA-seq indicated that the shared 211 differently expressed genes (DEGs) of these 5 exposure groups were enriched in defense response, generation of precursor metabolites, response to organic substance, response to oxygen-containing, response to hormone, oxidation-reduction process and so on. Regarding unique DEGs in each group, BPS was mainly associated with the redox pathway, BPB primarily influenced seed germination, and BPA, BPE and BPF were primarily involved in metabolic signaling pathways. Our results provide new insights for BPs induced adverse effects on Arabidopsis thaliana and suggest that the ecological risks associated with BPA alternatives cannot be ignored.

mRNA-seq-based analysis predicts: AEG-1 is a therapeutic target and immunotherapy biomarker for pan-cancer, including OSCC.

Background: The aberrant expression of AEG-1 is significantly correlated with tumorigenesis, development, neurodegeneration and inflammation. However, the relationship between AEG-1 expression and immune infiltration in OSCC, as well as other tumor types, has yet to be comprehensively analyzed. Methods: The expression levels, prognostic and clinicopathological characteristics, mutation patterns and methylation landscapes of AEG-1 in various tumors were obtained from multiple databases, including TIMER, GEPIA, HPA, TCGA, UALCAN, cBioPortal, SMART and TISIDB, in addition to single-cell RNA-seq data. The integration of these datasets facilitated the elucidation of the relationships among pan-cancer cellular heterogeneity, immune infiltration and AEG-1 expression levels. In vitro experiments created AEG-1 overexpressing cell lines, and mRNA-seq analyzed AEG-1-related differential genes in OSCC. RT-PCR validated these findings in vivo using xenograft tumors. Tumor cell lines were developed to study AEG-1's effects through H&E, Masson, and PAS staining. Immunohistochemistry examined AEG-1-related gene expression patterns. Results: Our analysis demonstrated that AEG-1 is highly expressed across various cancer types and is associated with tumor grade and patient prognosis. Additionally, AEG-1 amplification was observed in multiple cancers. Notably, we identified a significant elevation of AEG-1 expression in OSCC, which strongly correlated with patient prognosis and immune infiltration. Through mRNA-seq analysis of differentially expressed genes and immune-related gene sets, we identified a strong correlation between AEG-1 and immune infiltration markers such as LCP2, CD247, HLA-DPA1, HLA-DRA, HLA-DRB1, CIITA and CD74 in OSCC. Additionally, AEG-1 was found to regulate Th1/Th2 immune homeostasis, promote glycogen accumulation, and contribute to tumor fibrosis. Conclusion: In conclusion, AEG-1 significantly correlates with prognosis and immune infiltration across various cancer types and holds potential as a novel prognostic immune biomarker for OSCC. This finding may facilitate the identification of patients who are most likely to benefit from adjuvant immunotherapy.

RNA-Seq-Based Transcriptome Analysis of Chinese Cordyceps Aqueous Extracts Protective Effect against Adriamycin-Induced mpc5 Cell Injury.

Pharmacogenomic analysis based on drug transcriptome characteristics is widely used to identify mechanisms of action. The purpose of this study was to elucidate the molecular mechanism of protective effect against adriamycin (ADM)-induced mpc5 cell injury of Chinese cordyceps aqueous extracts (WCCs) by a systematic transcriptomic analysis. The phytochemicals of WCCs were analyzed via the "phenol-sulfuric acid method", high-performance liquid chromatography (HPLC), and HPLC-mass spectrometry (MS). We analyzed the drug-reaction transcriptome profiles of mpc5 cell after treating them with WCCs. RNA-seq analysis revealed that WCCs alleviated ADM-induced mpc5 cell injury via restoring the expression of certain genes to normal level mainly in the one-carbon pool by the folate pathway, followed by the relaxin, apelin, PI3K-Akt, and nucleotide-binding, oligomerization domain (NOD)-like receptor signaling pathway, enhancing DNA synthesis and repair, cell proliferation, fibrosis reduction, and immune regulation. Otherwise, WCCs also modulated the proliferation and survival of the mpc5 cell by regulating metabolic pathways, and partially restores the expression of genes related to human disease pathways. These findings provide an innovative understanding of the molecular mechanism of the protective effect of WCCs on ADM-induced mpc5 cell injury at the molecular transcription level, and Mthfd2, Dhfr, Atf4, Creb5, Apln, and Serpine1, etc., may be potential novel targets for treating nephrotic syndrome.

Enhancing RNA-seq analysis by addressing all co-existing biases using a self-benchmarking approach with 2D structural insights.

We introduce a groundbreaking approach: the minimum free energy-based Gaussian Self-Benchmarking (MFE-GSB) framework, designed to combat the myriad of biases inherent in RNA-seq data. Central to our methodology is the MFE concept, facilitating the adoption of a Gaussian distribution model tailored to effectively mitigate all co-existing biases within a k-mer counting scheme. The MFE-GSB framework operates on a sophisticated dual-model system, juxtaposing modeling data of uniform k-mer distribution against the real, observed sequencing data characterized by nonuniform k-mer distributions. The framework applies a Gaussian function, guided by the predetermined parameters-mean and SD-derived from modeling data, to fit unknown sequencing data. This dual comparison allows for the accurate prediction of k-mer abundances across MFE categories, enabling simultaneous correction of biases at the single k-mer level. Through validation with both engineered RNA constructs and human tissue RNA samples, its wide-ranging efficacy and applicability are demonstrated.

Dual RNA-seq reveals distinct families of co-regulated and structurally conserved effectors in Botrytis cinerea infection of Arabidopsis thaliana.

BACKGROUND: Botrytis cinerea is a broad-host-range pathogen causing gray mold disease and significant yield losses of numerous crops. However, the mechanisms underlying its rapid invasion and efficient killing of plant cells remain unclear. RESULTS: In this study, we elucidated the dynamics of B. cinerea infection in Arabidopsis thaliana by live cell imaging and dual RNA sequencing. We found extensive transcriptional reprogramming events in both the pathogen and the host, which involved metabolic pathways, signaling cascades, and transcriptional regulation. For the pathogen, we identified 591 candidate effector proteins (CEPs) and comprehensively analyzed their co-expression, sequence similarity, and structural conservation. The results revealed temporal co-regulation patterns of these CEPs, indicating coordinated deployment of effectors during B. cinerea infection. Through functional screening of 48 selected CEPs in Nicotiana benthamiana, we identified 11 cell death-inducing proteins (CDIPs) in B. cinerea. CONCLUSIONS: The findings provide important insights into the transcriptional dynamics and effector biology driving B. cinerea pathogenesis. The rapid infection of this pathogen involves the temporal co-regulation of CEPs and the prominent role of CDIPs in host cell death. This work highlights significant changes in gene expression associated with gray mold disease, underscoring the importance of a diverse repertoire of effectors crucial for successful infection.

scDFN: enhancing single-cell RNA-seq clustering with deep fusion networks.

Single-cell ribonucleic acid sequencing (scRNA-seq) technology can be used to perform high-resolution analysis of the transcriptomes of individual cells. Therefore, its application has gained popularity for accurately analyzing the ever-increasing content of heterogeneous single-cell datasets. Central to interpreting scRNA-seq data is the clustering of cells to decipher transcriptomic diversity and infer cell behavior patterns. However, its complexity necessitates the application of advanced methodologies capable of resolving the inherent heterogeneity and limited gene expression characteristics of single-cell data. Herein, we introduce a novel deep learning-based algorithm for single-cell clustering, designated scDFN, which can significantly enhance the clustering of scRNA-seq data through a fusion network strategy. The scDFN algorithm applies a dual mechanism involving an autoencoder to extract attribute information and an improved graph autoencoder to capture topological nuances, integrated via a cross-network information fusion mechanism complemented by a triple self-supervision strategy. This fusion is optimized through a holistic consideration of four distinct loss functions. A comparative analysis with five leading scRNA-seq clustering methodologies across multiple datasets revealed the superiority of scDFN, as determined by better the Normalized Mutual Information (NMI) and the Adjusted Rand Index (ARI) metrics. Additionally, scDFN demonstrated robust multi-cluster dataset performance and exceptional resilience to batch effects. Ablation studies highlighted the key roles of the autoencoder and the improved graph autoencoder components, along with the critical contribution of the four joint loss functions to the overall efficacy of the algorithm. Through these advancements, scDFN set a new benchmark in single-cell clustering and can be used as an effective tool for the nuanced analysis of single-cell transcriptomics.

Exploring common pathogenic association between Epstein Barr virus infection and long-COVID by integrating RNA-Seq and molecular dynamics simulations.

The term "Long-COVID" (LC) is characterized by the aftereffects of COVID-19 infection. Various studies have suggested that Epstein-Barr virus (EBV) reactivation is among the significant reported causes of LC. However, there is a lack of in-depth research that could largely explore the pathogenic mechanism and pinpoint the key genes in the EBV and LC context. This study mainly aimed to predict the potential disease-associated common genes between EBV reactivation and LC condition using next-generation sequencing (NGS) data and reported naturally occurring biomolecules as inhibitors. We applied the bulk RNA-Seq from LC and EBV-infected peripheral blood mononuclear cells (PBMCs), identified the differentially expressed genes (DEGs) and the Protein-Protein interaction (PPI) network using the STRING database, identified hub genes using the cytoscape plugins CytoHubba and MCODE, and performed enrichment analysis using ClueGO. The interaction analysis of a hub gene was performed against naturally occurring bioflavonoid molecules using molecular docking and the molecular dynamics (MD) simulation method. Out of 357 common genes, 22 genes (CCL2, CCL20, CDCA2, CEP55, CHI3L1, CKAP2L, DEPDC1, DIAPH3, DLGAP5, E2F8, FGF1, NEK2, PBK, TOP2A, CCL3, CXCL8, DEPDC1, IL6, RETN, MMP2, LCN2, and OLR1) were classified as hub genes, and the remaining ones were classified as neighboring genes. Enrichment analysis showed the role of hub genes in various pathways such as immune-signaling pathways, including JAK-STAT signaling, interleukin signaling, protein kinase signaling, and toll-like receptor pathways associated with the symptoms reported in the LC condition. ZNF and MYBL TF-family were predicted as abundant TFs controlling hub genes' transcriptional machinery. Furthermore, OLR1 (PDB: 7XMP) showed stable interactions with the five shortlisted refined naturally occurring bioflavonoids, i.e., apigenin, amentoflavone, ilexgenin A, myricetin, and orientin compounds. The total binding energy pattern was observed, with amentoflavone being the top docked molecule (with a binding affinity of -8.3 kcal/mol) with the lowest total binding energy of -18.48 kcal/mol. In conclusion, our research has predicted the hub genes, their molecular pathways, and the potential inhibitors between EBV and LC potential pathogenic association. The in vivo or in vitro experimental methods could be utilized to functionally validate our findings, which would be helpful to cure LC or to prevent EBV reactivation.

Key platelet genes play important roles in predicting the prognosis of sepsis.

Sepsis is a life-threatening organ malfunction induced by an imbalanced immunological reaction to infection in the host. Many studies have utilized traditional RNA sequencing (RNA-seq) data to identify important biological targets to predict sepsis prognosis. However, alterations in core cells and functional status cannot be effectively detected in sepsis patients. The goal of this study was to identify key cells through single-cell RNA-seq (scRNA-seq), and combine bulk RNA-seq data and multiple algorithm analysis to construct a stable prognostic model for sepsis. The scRNA-seq and bulk RNA-seq data from sepsis patients were collected from the Gene Expression Omnibus (GEO) database. The R package "Seurat" was used to process the scRNA-seq data. Cell communication was investigated using the R package "CellChat". The pseudo-time of the cells was calculated using the R package "monocle". The R package "limma" was used to identify differentially expressed genes (DEGs) between the sepsis group and the control group. Weighted gene correlation network analysis (WGCNA) was used to identify critical modules. Eight kinds of machine learning and 90 algorithm combinations were used to construct the prognostic model for sepsis. Quantitative real-time PCR (qRT‒PCR) was performed to determine the expression of key genes in the cecal ligation and puncture (CLP)-induced sepsis mouse model. The immunological status and related properties of DEGs were then investigated in the high- and low-risk groups delineated by the model. By combining the scRNA-seq data from nine samples, 13 clusters and 9 cell types were identified. CellChat analysis revealed that the number and strength of interactions between platelets and a variety of cells increased. We identified key platelet genes from the scRNA-seq data and combined these genes and the results of differential analysis and WGCNA of the bulk RNA-seq data. After univariate Cox regression analysis, we calculated the Cindex of the model constructed by the combination of 90 algorithms, and we finally determined the "CoxBoost + Lasso" combination. Multivariate Cox regression was used to construct the final prognostic model. The qRT-PCR results revealed significant differences in five key prognostic genes between the CLP and sham groups. The data was classified into high- and low-risk groups based on the model score. The high-risk group had a poorer survival rate and less immune infiltration. We identified the importance of platelets in sepsis patients through scRNA-seq, and established prognostic models with key genes that were identified via scRNA-seq combined with bulk RNA-seq analysis. The results of this model were closely associated with patient survival rates and immunological status and this model is useful for the prognostic management of sepsis.

RNA-Seq analysis for breast cancer detection: a study on paired tissue samples using hybrid optimization and deep learning techniques.

PROBLEM: Breast cancer is a leading global health issue, contributing to high mortality rates among women. The challenge of early detection is exacerbated by the high dimensionality and complexity of gene expression data, which complicates the classification process. AIM: This study aims to develop an advanced deep learning model that can accurately detect breast cancer using RNA-Seq gene expression data, while effectively addressing the challenges posed by the data's high dimensionality and complexity. METHODS: We introduce a novel hybrid gene selection approach that combines the Harris Hawk Optimization (HHO) and Whale Optimization (WO) algorithms with deep learning to improve feature selection and classification accuracy. The model's performance was compared to five conventional optimization algorithms integrated with deep learning: Genetic Algorithm (GA), Artificial Bee Colony (ABC), Cuckoo Search (CS), and Particle Swarm Optimization (PSO). RNA-Seq data was collected from 66 paired samples of normal and cancerous tissues from breast cancer patients at the Jawaharlal Nehru Cancer Hospital & Research Centre, Bhopal, India. Sequencing was performed by Biokart Genomics Lab, Bengaluru, India. RESULTS: The proposed model achieved a mean classification accuracy of 99.0%, consistently outperforming the GA, ABC, CS, and PSO methods. The dataset comprised 55 female breast cancer patients, including both early and advanced stages, along with age-matched healthy controls. CONCLUSION: Our findings demonstrate that the hybrid gene selection approach using HHO and WO, combined with deep learning, is a powerful and accurate tool for breast cancer detection. This approach shows promise for early detection and could facilitate personalized treatment strategies, ultimately improving patient outcomes.

Transipedia.org: k-mer-based exploration of large RNA sequencing datasets and application to cancer data.

Indexing techniques relying on k-mers have proven effective in searching for RNA sequences across thousands of RNA-seq libraries, but without enabling direct RNA quantification. We show here that arbitrary RNA sequences can be quantified in seconds through their decomposition into k-mers, with a precision akin to that of conventional RNA quantification methods. Using an index of the Cancer Cell Line Encyclopedia (CCLE) collection consisting of 1019 RNA-seq samples, we show that k-mer indexing offers a powerful means to reveal non-reference sequences, and variant RNAs induced by specific gene alterations, for instance in splicing factors.

Polyamine impact on physiology of early stages of reef-building corals-insights from rearing experiments and RNA-Seq analysis.

Polyamines are involved in various functions related to the cellular-level responses. To assess effects of polyamines on marine organisms, rearing experiments and comprehensive gene expression analyses were conducted on Acropora digitifera and Acropora sp.1, representative reef-building corals along the west-central coast of Okinawa, Japan, to evaluate effects of putrescine. Concentrations of putrescine ≥ 1 mM dissolved tissues of juvenile polyps and increased mortality of planula larvae. RNA-Seq analysis of juvenile polyps exposed to putrescine at the stage before effects became visible revealed dynamic fluctuations in gene expression in the putrescine-treated samples, with increased expression of stress-responsive genes (e.g. NAD-dependent protein deacylase sirtuin-6) and the polyamine transporter Slc18b1-like protein. These results also suggest that putrescine affects expression of genes related to ribosomes and translation. This study provides important insights into roles of polyamines and future directions regarding physiological responses of corals.

Visualizing scRNA-Seq data at population scale with GloScope.

Increasingly, scRNA-Seq studies explore cell populations across different samples and the effect of sample heterogeneity on organism's phenotype. However, relatively few bioinformatic methods have been developed which adequately address the variation between samples for such population-level analyses. We propose a framework for representing the entire single-cell profile of a sample, which we call a GloScope representation. We implement GloScope on scRNA-Seq datasets from study designs ranging from 12 to over 300 samples and demonstrate how GloScope allows researchers to perform essential bioinformatic tasks at the sample-level, in particular visualization and quality control assessment.

A pan-cancer single-cell RNA-seq atlas of intratumoral B cells.

Tumor-infiltrating B cells play a significant role in tumor development, progression, and prognosis, yet a comprehensive classification system is lacking. To address this gap, we present a pan-cancer single-cell RNA sequencing (scRNA-seq) atlas of tumor-infiltrating B and plasma cells across a large sample cohort. We identify key B cell subset signatures, revealing distinct subpopulations and highlighting the heterogeneity and functional diversity of these cells in the tumor microenvironment. We explore associations between B cell subsets and checkpoint inhibitor therapy responses, finding subset-specific effects on overall response. Additionally, we examine B and T cell crosstalk, identifying unique ligand-receptor pairs for specific B cell subsets, spatially validated. This comprehensive dataset serves as a valuable resource, providing a detailed atlas that enhances the understanding of B cell complexity in tumors and opens new avenues for research and therapeutic strategies.

scRNA+TCR-seq reveals the pivotal role of dual receptor T lymphocytes in the pathogenesis of Kawasaki disease and during IVIG treatment.

Introduction: Kawasaki disease (KD), a common cause of acquired heart disease in children in developed countries, is primarily treated with intravenous immunoglobulin (IVIG), but some children demonstrate IVIG resistance with increased coronary artery injury risk. T cells have been demonstrated to be involved in the pathogenesis of KD and its treatment with IVIG. However, the role and mechanism of dual TCR T lymphocytes in the occurrence of KD and IVIG therapy remain unclear. Methods: This study, based on scRNA-seq combined with TCR-seq technology, clustered the peripheral blood mononuclear cells of 3 healthy controls and 6 KD patients before and after IVIG treatment. Comparative analysis was conducted to investigate the differences in the proportion of single/dual receptor T cells, the characteristics of CDR3 repertoires, cell types, and the expression of transcription factors among the three groups. The study aimed to explore the correlation between dual TCR T cells and KD as well as IVIG treatment. Results: In our experimental results, we observed the presence of dual TCR T cells in all three groups. However, compared to the healthy control group and the IVIG-treated group, the KD patients before IVIG treatment exhibited a lower proportion of dual TCR T cells, with variability between samples, ranging from 4% to 15%. Notably, after IVIG treatment, the proportion of dual TCR T cells significantly increased, stabilizing above 12%, and these T cells also exhibited clonal expansion and a preference for V gene usage. In addition we found differences in dual TCR T cell subsets among the three groups, for example, IVIG treatment increases the proportion of dual TCR Treg cells, but it still remains below that of healthy control groups, significantly higher proportions of both dual TCR CD8 central and effector memory T cells in IVIG-treated KD patients, and differences in the expression of transcription factors between single and dual TCR T cells. These results suggest dual TCR T cells correlate with KD and IVIG treatment. Conclusion: Dual TCR T lymphocytes, especially dual TCR CD8 T cells and Treg cells, play crucial roles in the pathogenesis of KD and during IVIG treatment, providing strong support for further elucidating KD pathogenesis and optimizing treatment strategies.

Establishment of a tumor-associated fibroblast associated gene score based on scRNA-seq to predict prognosis in patients with triple-negative breast cancer.

The tumor microenvironment (TME) is emerging as a tool for the development of improved patient prognosis and the development of novel antitumor drugs. As the most important stromal cells in the tumor microenvironment, cancer-associated fibroblasts (CAFs) play an important role in the development of TNBC. The rise of single-cell sequencing technology has facilitated our study of the various cell types in TME. In this study, we interpreted the heterogeneity of TNBCs from the perspective of tumor-associated fibroblasts in the tumor microenvironment based on the TNBC single-cell sequencing dataset GSE118389, in the hope of providing help for individualised treatment. Combining the TCGA database and the GSE103091 dataset, four genes associated with CAFs in TNBC (CERCAM, KLF10, ECM1,HGF) were identified using the R package Seurat as well as correlation consensus clustering analysis. Meanwhile, qRT-PCR, WB and IHC experiments confirmed their expression in TNBC. Based on these genes, CAFs Score was established and validated to correlate with the prognosis of patients with TNBC, with patients in the high score group surviving significantly worse than those in the low score group (P<0.001). In addition, there were significant differences in immune cell infiltration and expression of immune checkpoints between the high and low scoring groups. Compared to Stage I & II, the CAFs Score was higher in Stage III & IV TNBC patients (P = 0.043) and higher in N1-3 TNBC patients than in N0 TNBC patients (P = 0.035). EMT scores were higher within the high CAFs Score group (P = 1.4e-11) and there was a positive correlation between Stemness Score and CAFs Score (R = 0.61, P = 3.6e-09). Drug sensitivity analysis combining the GSE128099 showed a higher sensitivity to Gemcitabine in the low CAFs Score group (P = 0.0048). We speculate that these four CAFs-related genes are likely to be involved in regulating gemcitabine resistance in TNBC patients.

Combined Integrative RNA-Seq and Serological sIgE Analysis Enhances Understanding of Fish Allergen Profiles and Diagnostic Strategy for Fish Allergy.

Fish allergy is a significant health concern, with diagnosis and management complicated by diverse fish species and allergens. We conducted a comprehensive RNA-seq analysis of eight fish species to identify allergen profiles, integrating ImmunoCAP sIgE data to explore associations with allergen expression and diagnostic performance. Over 30 putative fish allergens were identified, with varying sequence similarities and expression levels, roughly classifying fish into two groups based on parvalbumin (PV) expression. Higher similarities in allergen expression correlated with stronger sIgE data relationships among fish extracts. High PV expression and conserved PV sequences were linked to elevated sIgE measurements, potentially indicating higher allergenicity. For diagnosis, species-specific extract sIgE remained the best indicator of corresponding fish allergy diagnosis, while incorporating multiple sIgE data enhanced performance. In component-resolved diagnosis (CRD), the current panel with PV alone showed comparable performance to fish extract for PV-high fish allergy, while PV-low fish may require the inclusion of more minor allergens for improved CRD accuracy. This RNA-seq allergen analysis helps reveal fish allergen profiles, classify fish groups, and predict allergenicity, potentially improving CRD design and food management in fish allergy.

Key genes and immune pathways in T-cell mediated rejection post-liver transplantation identified via integrated RNA-seq and machine learning.

Liver transplantation is the definitive treatment for end-stage liver disease, yet T-cell mediated rejection (TCMR) remains a major challenge. This study aims to identify key genes associated with TCMR and their potential biological processes and mechanisms. The GSE145780 dataset was subjected to differential expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms to pinpoint key genes associated with TCMR. Gene Set Enrichment Analysis (GSEA), immune infiltration analysis, and regulatory networks were constructed to ascertain the biological relevance of these genes. Expression validation was performed using single-cell RNA-seq (scRNA-seq) data and liver biopsy tissues from patients. We identified 5 key genes (ITGB2, FCER1G, IL-18, GBP1, and CD53) that are associated with immunological functions, such as chemotactic activity, antigen processing, and T cell differentiation. GSEA highlighted enrichment in chemokine signaling and antigen presentation pathways. A lncRNA-miRNA-mRNA network was delineated, and drug target prediction yielded 26 potential drugs. Evaluation of expression levels in non-rejection (NR) and TCMR groups exhibited significant disparities in T cells and myeloid cells. Tissue analyses from patients corroborated the upregulation of GBP1, IL-18, CD53, and FCER1G in TCMR cases. Through comprehensive analysis, this research has identified 4 genes intimately connected with TCMR following liver transplantation, shedding light on the underlying immune activation pathways and suggesting putative targets for therapeutic intervention.

Comprehensive analysis of a necroptosis-associated diagnostic signature for myelodysplastic syndromes based on single-cell RNA-seq and bulk RNA-seq.

BACKGROUND: Myelodysplastic syndromes (MDS) are heterogeneous and clonal hematological disorders. The role and mechanism of necroptosis in MDS remain poorly understood. METHODS: mRNA expression profiles and single-cell RNA-sequencing (scRNA-seq) data were sourced from the GEO database. ScRNA-seq data were processed using the "Seurat" package. After cell annotation, necroptosis-related scores (NRscores) for each cell were calculated using the "UCell" package. Differentially expressed genes (DEGs) and their associated biological functions in NRscore-related cell populations were identified. Additionally, DEGs and necroptosis-related genes (DE-NRGs) between MDS patients and healthy controls were identified. Consensus clustering was employed to classify MDS patients into distinct subclusters based on DE-NRGs. The biological functions and immune characteristics of these classifications were analyzed. Prognostic gene signatures were determined using LASSO and SVM-RFE analyses, and a nomogram was constructed based on the prognostic gene signature. RESULTS: A total of 12 cell types were identified in MDS and healthy controls. NRscore was found to be elevated in monocytes and common lymphoid precursors (CLPs). Enrichment analysis revealed that monocytes and CLPs with high NRscore were associated with mitochondria-related and immune-related pathways. Eleven DEGs in monocytes and CLPs between MDS patients and healthy controls were identified. Additionally, 13 DE-NRGs were identified from 951 DEGs between MDS and healthy controls. MDS patients were classified into two distinct subclusters based on these 13 DE-NRGs, revealing several immune-related processes and signaling pathways. Differences in immune subpopulations between the two subclusters were observed. A necroptosis-related diagnostic gene signature (IRF9, PLA2G4A, MLKL, BAX, JAK2, and STAT3) was identified as predictive of MDS prevalence. CONCLUSION: Necroptosis plays a role in MDS progression by inducing inflammation. A novel necroptotic gene signature has been developed to distinguish and diagnose MDS at early stages of the disease.

A combined analysis of bulk RNA-seq and scRNA-seq was performed to investigate the molecular mechanisms associated with the occurrence of myocardial infarction.

BACKGROUND: Myocardial infarction (MI) induces complex transcriptional changes across diverse cardiac cell types. Single-cell RNA sequencing (scRNA-seq) provides an unparalleled ability to discern cellular diversity during infarction, yet the veracity of these discoveries necessitates confirmation. This investigation sought to elucidate MI mechanisms by integrating scRNA-seq and bulk RNA-seq data. METHODS: Publicly available scRNA-seq (GSE136088) and bulk RNA-seq (GSE153485) data from mice MI models were analyzed. Cell types were annotated, and differential expression analysis conducted. Bulk RNA-seq underwent quality control, principal component analysis, and differential expression analysis. RESULTS: In scRNA-seq data, the comparison between MI and sham groups unveiled a reduction in endothelial cell populations, but macrophages and monocytes increased. Within fibroblast subgroups, three distinct categories were discerned, with two exhibiting upregulation in MI. Notably, endothelial cells exhibited an elevated expression of genes associated with apoptosis and ferroptosis. In bulk RNA-seq analysis, distinct patterns emerged when comparing MI and sham groups. Specifically, six genes linked to endothelial ferroptosis exhibited heightened expression in MI group, thereby corroborating the scRNA-seq findings. Moreover, the examination of isolated cardiac macrophages from mice MI model revealed increased expression of Spp1, Col1a2, Col3a1, Ctsd, and Lgals3 compared to sham group, thus substantiating the dysregulation of macrophage apoptosis-related proteins following MI. CONCLUSION: MI altered the transcriptomic landscapes of cardiac cells with increased expression of apoptotic genes. Moreover, the upregulation of macrophage apoptosis marker was confirmed within MI models. The presence of endothelial cell depletion and ferroptosis in MI has been demonstrated.