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Resistance training is used to combat skeletal muscle function decline in older adults. Few studies have been designed specific for females, resulting in very limited treatment options for skeletal muscle atrophy in aging women. Here, we analyzed the gene expression profiles of skeletal muscle samples from sedentary young women, sedentary older women, and resistance-trained older women, using microarray data from public database. A total of 45 genes that were differentially expressed during female muscle aging and reversed by resistance training were identified. Functional and pathway enrichment analysis, protein–protein interaction network analysis, and receiver operating characteristic analysis were performed to reveal the key genes and pathways involved in the effects of resistance training on female muscle aging. The collagen genes COL1A1, COL3A1, and COL4A1 were identified important regulators of female muscle aging and resistance training, by modulating multiple signaling pathways, such as PI3 kinase-Akt signaling, focal adhesions, extracellular matrix-receptor interactions, and relaxin signaling. Interestingly, the expression of CDKN1A and TP63 were increased during aging, and further upregulated by resistance training in older women, suggesting they may negatively affect resistance training outcomes. Our findings provide novel insights into the molecular mechanisms of resistance training on female muscle aging and identify potential biomarkers and targets for clinical intervention. [ABSTRACT FROM AUTHOR]

This document is an erratum for an article titled "The quaternary question: Determining allostery in spastin through dynamics classification learning and bioinformatics." The authors found errors in the network and community analysis section of the methods, specifically in the calculation of DCCM maps. As a result, figures 3-5, which report results from the network analysis, have been updated. The correction also led to changes in the analysis of network centrality and optimal and sub-optimal paths between allosterically important positions in the results and discussion section. The updated tables and figures can be found in the supplementary material. [Extracted from the article]

In this narrative review, we explore developments in the field of computational musculoskeletal model personalization using the Physiome and Musculoskeletal Atlas Projects. Model geometry personalization; statistical shape modeling; and its impact on segmentation, classification, and model creation are explored. Examples include the trapeziometacarpal and tibiofemoral joints, Achilles tendon, gastrocnemius muscle, and pediatric lower limb bones. Finally, a more general approach to model personalization is discussed based on the idea of multiscale personalization called scaffolds. [ABSTRACT FROM AUTHOR]

Circadian rhythms are driven by an internal molecular clock which controls physiological and behavioral processes. Disruptions in these rhythms have been associated with health issues. Therefore, studying circadian rhythms is crucial for understanding physiology, behavior, and pathophysiology. However, it is challenging to study circadian rhythms over gene expression data, due to a scarcity of time labels. In this paper, we propose a novel approach to predict the phases of un-timed samples based on a deep neural network (DNN) architecture. This approach addresses two challenges: (1) prediction of sample phases and reliable identification of cyclic genes from high-dimensional expression data without relying on conserved circadian genes and (2) handling small sample-sized datasets. Our algorithm begins with initial gene screening to select candidate cyclic genes using a Minimum Distortion Embedding framework. This stage is then followed by greedy layer-wise pre-training of our DNN. Pre-training accomplishes two critical objectives: First, it initializes the hidden layers of our DNN model, enabling them to effectively capture features from the gene profiles with limited samples. Second, it provides suitable initial values for essential aspects of gene periodic oscillations. Subsequently, we fine-tune the pre-trained network to achieve precise sample phase predictions. Extensive experiments on both animal and human datasets show accurate and robust prediction of both sample phases and cyclic genes. Moreover, based on an Alzheimer's disease (AD) dataset, we identify a set of hub genes that show significant oscillations in cognitively normal subjects but had disruptions in AD, as well as their potential therapeutic targets. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Tissue Engineering Research / Zhongguo Zuzhi Gongcheng Yanjiu is the property of Chinese Journal of Tissue Engineering Research and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Sesquiterpene lactones (STLs) are a prominent group of plant secondary metabolites predominantly found in the Asteraceae family and have multiple ecological roles and medicinal applications. This review describes the evolutionary and ecological significance of STLs, highlighting their roles in plant defence mechanisms against herbivory and as phytotoxins, alongside their function as environmental signalling molecules. We also cover the substantial role of STLs in medicine and their mode of action in health and disease. We discuss the biosynthetic pathways and the various modifications that make STLs one of the most diverse groups of metabolites. Finally, we discuss methods for identifying and predicting STL biosynthesis pathways. This article is part of the theme issue 'The evolution of plant metabolism'. [ABSTRACT FROM AUTHOR]

Background: Metabarcoding targeting the internal transcribed spacer (ITS) region is commonly used to characterize fungal communities of various environments. Given their size and complexity, raw ITS sequences are necessarily processed and quality-filtered with bioinformatic pipelines. However, such pipelines are not yet standardized, especially for fungal communities, and those available may produce contrasting results. While some pipelines cluster sequences based on a specified percentage of base pair similarity into operational taxonomic units (OTUs), others utilize denoising techniques to infer amplicon sequencing variants (ASVs). While ASVs are now considered a more accurate representation of taxonomic diversity for prokaryote communities based on 16S rRNA amplicon sequencing, the applicability of this method for fungal ITS sequences is still debated. Results: Here we compared the performance of two commonly used pipelines DADA2 (inferring ASVs) and mothur (clustering OTUs) on fungal metabarcoding sequences originating from two different environmental sample types (fresh bovine feces and pasture soil). At a 99% OTU similarity threshold, mothur consistently identified a higher fungal richness compared to DADA2. In addition, mothur generated homogenous relative abundances across multiple technical replicates (n = 18), while DADA2 results for the same replicates were highly heterogeneous. Conclusions: Our study highlights a potential pipeline-associated bias in fungal metabarcoding data analysis of environmental samples. Based on the homogeneity of relative abundances across replicates and the capacity to detect OTUs/ASVs, we suggest using OTU clustering with a similarity of 97% as the most appropriate option for processing fungal metabarcoding data. [ABSTRACT FROM AUTHOR]

Background: The targeted application of cutting-edge high-throughput molecular data technologies provides an enormous opportunity to address key health, economic and environmental issues in the tropics within the One Health framework. The Earth's tropical regions are projected to contain > 50% of the world's population by 2050 coupled with 80% of its biodiversity however these regions are relatively less developed economically, with agricultural productivity substantially lower than temperate zones, a large percentage of its population having limited health care options and much of its biodiversity understudied and undescribed. The generation of high-throughput molecular data and bespoke bioinformatics capability to address these unique challenges offers an enormous opportunity for people living in the tropics. Main: In this review we discuss in depth solutions to challenges to populations living in tropical zones across three critical One Health areas: human health, biodiversity and food production. This review will examine how some of the challenges in the tropics can be addressed through the targeted application of advanced omics and bioinformatics and will discuss how local populations can embrace these technologies through strategic outreach and education ensuring the benefits of the One Health approach is fully realised through local engagement. Conclusion: Within the context of the One Health framework, we will demonstrate how genomic technologies can be utilised to improve the overall quality of life for half the world's population. [ABSTRACT FROM AUTHOR]

The aim of this study was to investigate the key targets and signaling pathways of paeoniflorin (PF) for the treatment of gastric cancer (GC). First, the differentially expressed genes (DEGs) of gastric cancer were obtained by analyzing GSE118916 Gene Chip, and then the active components of paeoniflorin and their targets of action were found. And the intersection genes of the two were analyzed for target and pathway analysis. In addition, cell viability after PF intervention was detected by CCK-8. Clone formation assay, wound scratch assay, transwell assay were used to detect cell migration and invasion. The qRT-PCR and Western blot methods were used to verify the mechanism of action. The results showed that a total of 286 paeoniflorin targets and 1799 DEGs were obtained. Secondly, we found that PF could treat gastric cancer through RAS/MAPK signaling pathway. In addition, through in vitro cellular experiments, we also found that PF had a significant therapeutic effect on gastric cancer. Therefore, we believe that PF inhibits the proliferation and metastasis of gastric cancer, and its effect may be exerted by regulating the RAS/MAPK signaling pathway. PF is a promising drug for the treatment of gastric cancer. Combined with the in vitro experiments, we found that the therapeutic effect of PF is related to the regulation of the RAS/MAPK signaling pathway, and the results of the present study preliminarily revealed its complex mechanism, which will lay the foundation for future clinical treatment. [ABSTRACT FROM AUTHOR]

Background: Preeclampsia (PE) poses significant diagnostic and therapeutic challenges. This study aims to identify novel genes for potential diagnostic and therapeutic targets, illuminating the immune mechanisms involved. Methods: Three GEO datasets were analyzed, merging two for training set, and using the third for external validation. Intersection analysis of differentially expressed genes (DEGs) and WGCNA highlighted candidate genes. These were further refined through LASSO, SVM-RFE, and RF algorithms to identify diagnostic hub genes. Diagnostic efficacy was assessed using ROC curves. A predictive nomogram and fully Connected Neural Network (FCNN) were developed for PE prediction. ssGSEA and correlation analysis were employed to investigate the immune landscape. Further validation was provided by qRT-PCR on human placental samples. Result: Five biomarkers were identified with validation AUCs: CGB5 (0.663, 95% CI: 0.577-0.750), LEP (0.850, 95% CI: 0.792-0.908), LRRC1 (0.797, 95% CI: 0.728-0.867), PAPPA2 (0.839, 95% CI: 0.775-0.902), and SLC20A1 (0.811, 95% CI: 0.742-0.880), all of which are involved in key biological processes. The nomogram showed strong predictive power (C-index 0.873), while FCNN achieved an optimal AUC of 0.911 (95% CI: 0.732-1.000) in five-fold cross-validation. Immune infiltration analysis revealed the importance of T cell subsets, neutrophils, and NK cells in PE, linking these genes to immune mechanisms underlying PE pathogenesis. Conclusion: CGB5 , LEP , LRRC1 , PAPPA2 , and SLC20A1 are validated as key diagnostic biomarkers for PE. Nomogram and FCNN could credibly predict PE. Their association with immune infiltration underscores the crucial role of immune responses in PE pathogenesis. [ABSTRACT FROM AUTHOR]

Background: Acute myocardial infarction (AMI) is a leading cause of death and morbidity worldwide. Ferroptosis, a form of regulated cell death, plays a critical role in modulating immune functions during AMI. This study aimed to identify ferroptosis-related hub genes that could serve as potential therapeutic targets in the progression of AMI. Methods: Bioinformatics was used to identify overlapping genes associated with ferroptosis and the infiltration of 22 immune cells by Cell-type Identification by Estimating Relative Subsets of RNA Transcript (CIBERSORT) analysis. The expression of ferroptosis-related genes in AMI was validated across independent datasets, clinical samples, and in vitro cellular experiments. The predictive value for heart failure was evaluated in the first dimension of principal component analysis (PCA) using receiver operating characteristic (ROC) analysis. Results: The study identified 11 key ferroptosis-related genes significantly correlated with immune cell abundance. CIBERSORT analysis highlighted immune dysregulation in AMI. JDP2, DUSP1, TLR4, NFS1, and SLC1A5 were identified as potential biomarkers for AMI progression. Additionally, JDP2, DUSP1, and DDIT4 demonstrated strong predictive value for long-term heart failure. Conclusion: This study highlights the potential association of ferroptosis-related genes with the pathogenesis of AMI, suggesting a role in the molecular mechanisms that may underlie acute coronary events. [ABSTRACT FROM AUTHOR]

Background: Esophageal squamous carcinoma (ESCC) is the most prevalent pathological subtype of esophageal cancer (EC). It has the characteristics of significant local invasion, quick disease progression, high recurrence rates, and a dismal prognosis for survival. Phosphatidylinositol 3-kinase/serine-threonine kinase (PI3K/AKT) is a signaling system whose aberrant activation regulates downstream factors, leading to the promotion of cancer development. This study looks at a protein called Glycerol-3-phosphate dehydrogenase 1-like (GPD1L), which strongly affects the development of several cancers. However, its association with ESCC development and its underlying mechanisms are not clear. Methods: In this paper, we analyzed six ESCC transcriptome data obtained from the GEO database. We utilized bioinformatics technology and immunohistochemistry to differentially analyze GPD1L levels of mRNA and protein expression in ESCC and normal adjacent tissues. Furthermore, we conducted survival, co-expression, enrichment, immune infiltration and drug sensitivity analysis. Moreover, we further investigated the role and mechanism of GPD1L by Western Blot (WB), Cell Counting Kit-8 (CCK8), wound healing assay, Transwell assay, and flow cytometry. Finally, the addition of IGF-1, the activator of PI3K/AKT, could rescue the inhibitory effect of GPD1L on ESCC. Results: The findings manifest that the expression of GPD1L was low in ESCC, and functional experiments showed that GPD1L promoted apoptosis in vitro while blocking cell migration, invasion, and proliferation. Based on mechanism research, GPD1L's impact on ESCC could be explained by its suppression of the PI3K/AKT signaling pathway's activation. Conclusion: To sum up, our findings imply that GPD1L may impede the initiation and advancement of ESCC via modulating the PI3K/AKT signaling pathway. GPD1L is considered to be a promising therapeutic target and biomarker to diagnose and treat ESCC. Key points: 1. We demonstrate the tumor suppressive role of GPD1L in ESCC. 2. We reveal a novel mechanism by which GPD1L functions, potentially by modulating the PI3K/AKT signaling pathway. 3. Bioinformatics analysis was conducted to investigate the potential correlation between GPD1L and ESCC, including related genes, immune cell and immunoinfiltration analysis, drug sensitivity, etc. [ABSTRACT FROM AUTHOR]

Background: Recurrent gliomas rapidly progress and have high mortality and poor prognosis in the central nervous system. Therefore, further investigation of prognostic and therapeutic markers is needed. Methods: The mRNA expression, clinical data, and coagulation-related genes (CRGs) associated with recurrent glioma were obtained and calculated from the Chinese Glioma Genome Atlas and Kyoto Encyclopedia of Genes and Genomes databases. The significant CRGs were calculated by Weighted gene co-expression network analysis and PPI network. A prediction model was constructed using the least absolute shrinkage and selection operator regression analysis. Recurrent gliomas were stratified into high and low-risk groups based on the median risk score (RS). The Kaplan–Meier curve was used to analyze the difference in overall survival (OS) between these groups, while the receiver operating characteristic (ROC) curve was used to evaluate the accuracy of the gene model at 1-, 3-, and 5-years. Clinical factors, including age, gender, MGMT methylation status, radiotherapy, chemotherapy, and RS, were included in the univariate and multivariate regression analysis. A prognostic nomogram and calibration curve were established based on these factors. Results: Overall, seven CRGs associated with the prognosis were identified, including BTK, ITGB1, GNAI3, CFH, LYN, CFI, and F3. OS and survival rates were lower in the high-risk group compared with the low-risk group. The ROC curve demonstrated the area under the curve values >0.65 at 1-, 3-, and 5-years, confirming the reliability of the prognostic model. The univariate regression analysis indicated that tumor grade (grades 2, 3, and 4), histopathology (GBM or not), chemotherapy, IDH mutation, and 1p19q co-deletion status were independent prognostic indicators. Univariate and multivariate regression analyses indicated that RS was an independent prognostic factor for patients with recurrent glioma. Immune analysis revealed low infiltration of resting dendritic cells and high expression of programmed death receptor 1 in the high-risk group. Conclusion: This study comprehensively investigated the correlation between CRGs and recurrent glioma prognosis, offering crucial insights for further research into glioma recurrence mechanisms and treatment strategies. [ABSTRACT FROM AUTHOR]

Background: Peritumoral brain edema (PTBE) represents a characteristic phenotype of intracranial gliomas. However, there is a lack of consensus regarding the prognosis and mechanism of PTBE. In this study, clinical imaging data, along with publicly available imaging data, were utilized to assess the prognosis of PTBE in glioblastoma (GBM) patients, and the associated mechanisms were preliminarily analyzed. Methods: We investigated relevant imaging features, including edema, in GBM patients using ITK-SNAP imaging segmentation software. Risk factors affecting progression-free survival (PFS) and overall survival (OS) were assessed using a Cox proportional hazard regression model. In addition, the impact of PTBE on PFS and OS was analyzed in clinical GBM patients using the Kaplan–Meier survival analysis method, and the results further validated by combining data from The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA). Finally, functional enrichment analysis based on TCIA and TCGA datasets identified several pathways potentially involved in the mechanism of edema formation. Results: This study included a total of 32 clinical GBM patients and 132 GBM patients from public databases. Univariate and multivariate analyses indicated that age and edema index (EI) are independent risk factors for PFS, but not for OS. Kaplan–Meier curves revealed consistent survival analysis results between IE groups among both clinical patients and TCIA and TCGA patients, suggesting a significant effect of PTBE on PFS but not on OS. Furthermore, functional enrichment analysis predicted the involvement of several pathways related mainly to cellular bioenergetics and vasculogenic processes in the mechanism of PTBE formation. While these novel results warrant confirmation in a larger patient cohort, they support good prognostic value for PTBE assessment in GBM. Conclusions: Our results indicate that a low EI positively impacts disease control in GBM patients, but this does not entirely translate into an improvement in OS. Multiple genes, signaling pathways, and biological processes may contribute to the formation of peritumoral edema in GBM through cytotoxic and vascular mechanisms. [ABSTRACT FROM AUTHOR]

Background: Preeclampsia (PE) poses significant diagnostic and therapeutic challenges. This study aims to identify novel genes for potential diagnostic and therapeutic targets, illuminating the immune mechanisms involved. Methods: Three GEO datasets were analyzed, merging two for training set, and using the third for external validation. Intersection analysis of differentially expressed genes (DEGs) and WGCNA highlighted candidate genes. These were further refined through LASSO, SVM-RFE, and RF algorithms to identify diagnostic hub genes. Diagnostic efficacy was assessed using ROC curves. A predictive nomogram and fully Connected Neural Network (FCNN) were developed for PE prediction. ssGSEA and correlation analysis were employed to investigate the immune landscape. Further validation was provided by qRT-PCR on human placental samples. Result: Five biomarkers were identified with validation AUCs: CGB5 (0.663, 95% CI: 0.577-0.750), LEP (0.850, 95% CI: 0.792-0.908), LRRC1 (0.797, 95% CI: 0.728-0.867), PAPPA2 (0.839, 95% CI: 0.775-0.902), and SLC20A1 (0.811, 95% CI: 0.742-0.880), all of which are involved in key biological processes. The nomogram showed strong predictive power (C-index 0.873), while FCNN achieved an optimal AUC of 0.911 (95% CI: 0.732-1.000) in five-fold cross-validation. Immune infiltration analysis revealed the importance of T cell subsets, neutrophils, and NK cells in PE, linking these genes to immune mechanisms underlying PE pathogenesis. Conclusion: CGB5 , LEP , LRRC1 , PAPPA2 , and SLC20A1 are validated as key diagnostic biomarkers for PE. Nomogram and FCNN could credibly predict PE. Their association with immune infiltration underscores the crucial role of immune responses in PE pathogenesis. [ABSTRACT FROM AUTHOR]

Background: Sepsis is a serious condition that occurs when the body's response to infection becomes uncontrolled, resulting in a high risk of death. Despite improvements in healthcare, identifying sepsis early is difficult because of its diverse nature and the absence of distinct biomarkers. Recent studies suggest that 5-methylcytosine (m5C)-related genes play a significant role in immune responses, yet their diagnostic potential in sepsis remains unexplored. Methods: This research combined and examined four sepsis-related datasets (GSE95233, GSE57065, GSE100159, and GSE65682) sourced from the Gene Expression Omnibus (GEO)database to discover m5C-related genes with differential expression. Various machine learning methods, such as decision tree, random forest, and XGBoost, were utilized in identifying crucial hub genes. Receiver Operating Characteristic (ROC) curve analysis was used to assess the diagnostic accuracy of these genetic markers. Additionally, single-gene enrichment and immune infiltration analyses were conducted to investigate the underlying mechanisms involving these hub genes in sepsis. Results: Three hub genes, DNA Methyltransferase 1 (DNMT1), tumor protein P53 (TP53), and toll-like receptor 8 (TLR8), were identified and validated for their diagnostic efficacy, showing area under the curve (AUC) values above 0.7 in both test and validation sets. Enrichment analyses revealed that these genes are involved in key pathways such as p53 signaling and Toll-like receptor signaling. Immune infiltration analysis indicated significant correlations between hub genes and various immune cell types, suggesting their roles in modulating immune responses during sepsis. Conclusion: The study highlights the diagnostic potential of m5C-related genes in sepsis and their involvement in immune regulation. These findings offer new insights into sepsis pathogenesis and suggest that DNMT1 , TP53 , and TLR8 could serve as valuable biomarkers for early diagnosis. Further studies should prioritize validating these biomarkers in clinical settings and investigating their potential for therapy. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Anhui Agricultural Sciences is the property of Journal of Anhui Agricultural Sciences Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Atherosclerosis (AS) is the leading cause of coronary heart disease, which is the primary cause of death worldwide. Recent studies have identified disulfidptosis as a new type of cell death that may be involved in onset and development of many diseases. However, the role of disulfidptosis in AS is not clear. In this study, bioinformatics analysis and experiments in vivo and in vitro were performed to evaluate the potential relationship between disulfidptosis and AS. AS-related sequencing data were obtained from the Gene Expression Omnibus (GEO). Bioinformatics techniques were used to evaluate differentially expressed genes (DEGs) associated with disulfidptosis-related AS. Hub genes were screened using least absolute shrinkage and selection operator (LASSO) and random forests (RF) methods. In addition, we established a foam cell model in vitro and an AS mouse model in vivo to verify the expressions of hub genes. In addition, we constructed a diagnostic nomogram with hub genes to predict progression of AS. Finally, the consensus clustering method was used to establish two different subtypes, and associations between subtypes and immunity were explored. As the results, 9 disulfidptosis-related AS DEGs were identified from GSE28829 and GSE43292 datasets. Evaluation of DEGs using LASSO and RF methods resulted in identification of 4 hub genes (CAPZB, DSTN, MYL6, PDLIM1), which were analyzed for diagnostic value using ROC curve analysis and verified in vitro and in vivo. Furthermore, a nomogram including hub genes was established that accurately predicted the occurrence of AS. The consensus clustering algorithm was used to separate patients with early atherosclerotic plaques and patients with advanced atherosclerotic plaques into two disulfidptosis subtypes. Cluster B displayed higher levels of infiltrating immune cells, which indicated that patients in cluster B may have a positive immune response for progression of AS. In summary, disulfidptosis-related genes including CAPZB, DSTN, MYL6, and PDLIM1 may be diagnostic markers and therapeutic targets for AS. In addition, these genes are closely related to immune cells, which may inform immunotherapy for AS. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Tissue Engineering Research / Zhongguo Zuzhi Gongcheng Yanjiu is the property of Chinese Journal of Tissue Engineering Research and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: The biological significance of cancer-associated fibroblasts (CAFs) in bladder urothelial carcinoma (BUC) warrants further investigation. There is an urgent need to explore the predictive utility of CAF-related genes for prognosis in BUC. Methods: The transcriptome and clinical data of 407 BUC patients in The Cancer Genome Atlas (TCGA) database were analyzed and a prognostic model was established. A total of 476 BUC cases from the E-MTAB-4321 database were used for validation. A risk model was constructed utilizing CAF-related genes through LASSO Cox regression, investigating its association with prognosis, gene mutations, immune cell infiltration, and drug sensitivity in BUC. Results: We identified five CAF-related genes (EGFL6, NRSN2, SEMA3D, TM4SF1 and TPST1) in both the TCGA and E-MTAB-4321 datasets, and established a prognostic model using LASSO Cox regression. The high-risk group showed a significant correlation with poor survival. Furthermore, the low-risk group exhibited higher tumor mutational burden and lower levels of immune cell infiltration, and this model holds promise for guiding drug selection in BUC patients. Conclusions: These findings underscore the pivotal role of CAF-related genes in prognostic prediction for BUC patients. Clinical decision-making and tailored therapeutics stand to benefit from these results, providing a valuable reference for future research endeavors. [ABSTRACT FROM AUTHOR]

In plants, the BAX inhibitor-1 (BI-1) gene plays a crucial part in controlling cell death under stress conditions. This mechanism of Programmed Cell Death (PCD) is genetically regulated and is crucial for the elimination of unwanted or damaged cells in a controlled manner, which is essential for normal development and tissue maintenance. A study on cucumber identified and characterized five BI-1 genes: CsBI1, CsBI2, CsBI3, CsBI4, and CsBI5. These genes share conserved domains, indicating common evolutionary history and function. Physicochemical analysis revealed their molecular weights and isoelectric points, while subcellular localization showed their presence in different cellular compartments. The phylogenetic analysis highlighted evolutionary relationships with related crops. Chromosomal distribution and synteny analysis suggested segmental or tandem duplications within the gene family. Protein-protein interaction analysis revealed extensive interactions with other cucumber proteins. Cis-regulatory elements in the promoter regions provided insights into potential functions and transcriptional regulation. miRNAs showed diverse regulatory mechanisms, including mRNA cleavage and translational inhibition. The CsBI3, CsBI4 and CsBI5 genes exhibit elevated expression levels during cold stress, suggesting their vital involvement in cucumber plant defense mechanisms. The application of chitosan oligosaccharides externally confirms their distinct expression patterns. The qRT-PCR confirms the upregulation of CsBI genes in ToLCNDV-infected plants, indicating their potential to mitigate biotic and abiotic stresses. The comprehensive genome-wide exploration provides opportunities for the development of cold-tolerant and virus-resistant cucumber variants by traditional breeding or gene. [ABSTRACT FROM AUTHOR]

Introduction: This study aimed to investigate the virome diversity of the SLE disease and the association between viral infections and the disease. Methods: SLE-related RNA-Seq data were retrieved from public databases. A rigorous computational workflow was employed to identify the human viruses. Differential expression analysis and functional enrichment analysis were conducted in R. Results: We identified ten human virus species from 826 RNA-Seq samples of human blood, comprising 688 SLE patients and 138 healthy controls. Eight of the ten virus species exhibited higher positive rates in SLE patients compared to healthy controls, with Human betaherpesvirus 5 (HHV5) having the highest positive rate (4.1%) and being exclusively detected in SLE samples. The virus abundances were low and comparable in both SLE patients and healthy controls. Analysis of the antiviral interferon-stimulated genes (ISGs) in samples showed higher ISG expression levels in HHV4 and HHV5-positive samples compared to virus-negative samples. Several genes that were up-regulated in SLE patients were further up-regulated after HHV5 infection, and they were mainly enriched in immune response-related biological processes. Additionally, the expression levels of several marker genes of SLE severity were compared between HHV5-positive and virus-negative SLE patients, suggesting that HHV5 infection may be associated with aggravated SLE disease. Discussion: We found that SLE patients are more susceptible to viral infections than healthy individuals. Viral infections, such as HHV5, may be associated with aggravated SLE disease. This study deepens our understanding of the association between viruses and SLE and provides new insights into prevention and control of the disease. [ABSTRACT FROM AUTHOR]

Despite the increasing morbidity and mortality associated with fungal diseases in low-income and sub-Saharan Africa countries, it is apparent that most of the fungal pathogens involved have been omitted from the dilated list of neglected and emerging tropical diseases (NETDs). Because most of these pathogens are inextricably linked with other underlying diseases such as tuberculosis and AIDS, the main fungal pathogens causing diseases viz. Pneumocyctosis and meningitis are categorized as deadly AIDS-associated opportunistic infections, and they most often coexist with tuberculosis. In addition, other infections like candidiasis, keratitis, histoplasmosis, and mycetoma often cause deforming and debilitating illnesses that affect the working class in rural communities, largely due to underlying comorbidities. Inadequate diagnostic tests prevent easy identification of the burden associated with these diseases making treatment very difficult. Likewise, the rise of resistance to most antifungal medications currently accessible in Africa is still extremely concerning, as observed in other locations. To expatiate on the destructive impact of fungal infections on the socioeconomic and health status of sub-Saharan Africa's skint populace and to improve the menace posed by these pathogens, there is a need to stop trivializing and underestimating fungal pathogens of health importance. This viewpoint is intended to revisit threats associated with neglected, emerging, and re-emerging fungal pathogens (NEFPs) in sub-Saharan Africa. As a result, information needed to prioritize strategies for the diagnosis, prevention, and control of neglected fungal pathogens and emerging superbugs will be presented. Revisiting threats and possible diagnoses associated with NEFPs could serve as a better leverage toward curbing the effects of opportunistic pathogens on individuals living with either an underlying health condition or impaired immunological status in sub-Saharan Africa/ low-income countries. Article Highlights: Structural presentation of deforming and debilitating illnesses associated with NEFPs. Reassessment of important therapeutic antibodies against NEFPs. Improved diagnostic methods for the diagnosis, prevention, and control of neglected fungal pathogens and emerging superbugs. [ABSTRACT FROM AUTHOR]

Background: Alzheimer's disease (AD) represents a progressive neurodegenerative disorder characterized by the accumulation of misfolded amyloid beta protein, leading to the formation of amyloid plaques and the aggregation of tau protein into neurofibrillary tangles within the cerebral cortex. The role of carbohydrates, particularly apolipoprotein E (ApoE), is pivotal in AD pathogenesis due to its involvement in lipid and cholesterol metabolism, and its status as a genetic predisposition factor for the disease. Despite its significance, the mechanistic contributions of Lipid Metabolism-related Genes (LMGs) to AD remain inadequately elucidated. This research endeavor seeks to bridge this gap by pinpointing biomarkers indicative of early-stage AD, with an emphasis on those linked to immune cell infiltration. To this end, advanced machine-learning algorithms and data derived from the Gene Expression Omnibus (GEO) database have been employed to facilitate the identification of these biomarkers. Methods: Differentially expressed genes (DEGs) were identified by comparing gene expression profiles between healthy individuals and Alzheimer's disease (AD) patients, using data from two Gene Expression Omnibus (GEO) datasets: GSE5281 and GSE138260. Functional enrichment analysis was conducted to elucidate the biological relevance of the DEGs. To ensure the reliability of the results, samples were randomly divided into training and validation sets. The analysis focused on lipid metabolism-related DEGs (LMDEGs) to explore potential biomarkers for AD. Machine learning algorithms, including Support Vector Machine-Recursive Feature Elimination (SVM-RFE) and the Least Absolute Shrinkage and Selection Operator (LASSO) regression model, were applied to identify a key gene biomarker. Additionally, immune cell infiltration and its relationship with the gene biomarker were assessed using the CIBERSORT algorithm. The Integrated Traditional Chinese Medicine (ITCM) database was also referenced to identify Chinese medicines related to lipid metabolism and their possible connection to AD. This comprehensive strategy aims to integrate modern computational methods with traditional medicine to deepen our understanding of AD and its underlying mechanisms. Results: The study identified 137 genes from a pool of 751 lipid metabolism-related genes (LMGs) significantly associated with autophagy and immune response mechanisms. Through the application of LASSO and SVM-RFE machine-learning techniques, four genes—choline acetyl transferase (CHAT), member RAS oncogene family (RAB4A), acyl-CoA binding domain-containing protein 6 (ACBD6), and alpha-galactosidase A (GLA)—emerged as potential biomarkers for Alzheimer's disease (AD). These genes demonstrated strong therapeutic potential due to their involvement in critical biological pathways. Notably, nine Chinese medicine compounds were identified to target these marker genes, offering a novel treatment approach for AD. Further, ceRNA network analysis revealed complex regulatory interactions involving these genes, underscoring their importance in AD pathology. CIBERSORT analysis highlighted a potential link between changes in the immune microenvironment and CHAT expression levels in AD patients, providing new insights into the immunological dimensions of the disease. Conclusion: The discovery of these gene markers offers substantial promise for the diagnosis and understanding of Alzheimer's disease (AD). However, further investigation is necessary to validate their clinical utility. This study illuminates the role of Lipid Metabolism-related Genes (LMGs) in AD pathogenesis, offering potential targets for therapeutic intervention. It enhances our grasp of AD's complex mechanisms and paves the way for future research aimed at refining diagnostic and treatment strategies. [ABSTRACT FROM AUTHOR]

In plants, the BAX inhibitor-1 (BI-1) gene plays a crucial part in controlling cell death under stress conditions. This mechanism of Programmed Cell Death (PCD) is genetically regulated and is crucial for the elimination of unwanted or damaged cells in a controlled manner, which is essential for normal development and tissue maintenance. A study on cucumber identified and characterized five BI-1 genes: CsBI1, CsBI2, CsBI3, CsBI4, and CsBI5. These genes share conserved domains, indicating common evolutionary history and function. Physicochemical analysis revealed their molecular weights and isoelectric points, while subcellular localization showed their presence in different cellular compartments. The phylogenetic analysis highlighted evolutionary relationships with related crops. Chromosomal distribution and synteny analysis suggested segmental or tandem duplications within the gene family. Protein-protein interaction analysis revealed extensive interactions with other cucumber proteins. Cis-regulatory elements in the promoter regions provided insights into potential functions and transcriptional regulation. miRNAs showed diverse regulatory mechanisms, including mRNA cleavage and translational inhibition. The CsBI3, CsBI4 and CsBI5 genes exhibit elevated expression levels during cold stress, suggesting their vital involvement in cucumber plant defense mechanisms. The application of chitosan oligosaccharides externally confirms their distinct expression patterns. The qRT-PCR confirms the upregulation of CsBI genes in ToLCNDV-infected plants, indicating their potential to mitigate biotic and abiotic stresses. The comprehensive genome-wide exploration provides opportunities for the development of cold-tolerant and virus-resistant cucumber variants by traditional breeding or gene. [ABSTRACT FROM AUTHOR]

Background: Metagenomics, the whole genome sequencing of microbial communities, has provided insight into complex ecosystems. It has facilitated the discovery of novel microorganisms, explained community interactions and found applications in various fields. Advances in high-throughput and third-generation sequencing technologies have further fuelled its popularity. Nevertheless, managing the vast data produced and addressing variable dataset quality remain ongoing challenges. Another challenge arises from the number of assembly and binning strategies used across studies. Comparing datasets and analysis tools is complex as it requires the quantitative assessment of metagenome quality. The inherent limitations of metagenomic sequencing, which often involves sequencing complex communities, mean community members are challenging to interrogate with traditional culturing methods leading to many lacking reference sequences. MIMAG standards aim to provide a method to assess metagenome quality for comparison but have not been widely adopted. Results: To address the need for simple and quick metagenome quality assignation, here we introduce the pipeline MAGqual (Metagenome-Assembled Genome qualifier) and demonstrate its effectiveness at determining metagenomic dataset quality in the context of the MIMAG standards. Conclusions: The MAGqual pipeline offers an accessible way to evaluate metagenome quality and generate metadata on a large scale. MAGqual is built in Snakemake to ensure readability and scalability, and its open-source nature promotes accessibility, community development, and ease of updates. MAGqual is built in Snakemake, R, and Python and is available under the MIT license on GitHub at https://github.com/ac1513/MAGqual. EB_9RbKd3CYdEq3zCTHKaq Video Abstract [ABSTRACT FROM AUTHOR]

Background: Human cancers, including head and neck squamous cell carcinoma (HNSCC), are complex and heterogeneous diseases driven by uncontrolled cell growth and proliferation. Post-translational modifications (PTMs) of proteins play a crucial role in cancer progression, making them a promising target for pharmacological intervention. This study aims to identify key exercise-related genes with prognostic value in HNSCC through comprehensive bioinformatics analysis, with a particular focus on the therapeutic potential of placental growth factor (PIGF). Methods: Transcriptome data for HNSCC were obtained from The Cancer Genome Atlas (TCGA) database. Differently expressed genes (DEGs) were identified and analyzed for their prognostic significance. Exercise-related gene sets were retrieved from the Gene Set Enrichment Analysis (GSEA) database. Functional enrichment analyses, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and GSEA, were conducted. The biological functions and clinical implications of key genes were further explored through single-gene expression analysis, immune infiltration analysis, and in vitro cellular experiments. Results: The study identified exercise-related genes associated with survival prognosis in HNSCC. GO and KEGG pathway analyses highlighted the biological functions of these genes, and Kaplan-Meier survival curves confirmed their prognostic value. PIGF expression analysis using TCGA data showed its diagnostic potential, with higher expression linked to advanced tumor stages. Single-cell sequencing revealed PIGF's role in the tumor microenvironment. In vitro experiments demonstrated that PIGF plays a pivotal role in enhancing cell proliferation and colony formation in HNSCC, with PIGF knockdown significantly impairing these functions, highlighting its importance in tumor growth regulation. Additionally, PIGF's predictive performance in drug sensitivity across cancer datasets suggests its potential as a pharmacological target, offering opportunities to modulate the immune microenvironment and improve therapeutic outcomes in cancer treatment. Conclusion: This study provides new insights into the molecular mechanisms underlying HNSCC and identifies exercise-related genes, particularly PIGF, as promising biomarkers for clinical treatment and personalized medicine. By focusing on PTMs and their role in cancer progression, our findings suggest that targeting PIGF may offer innovative therapeutic strategies. [ABSTRACT FROM AUTHOR]

Background: Clostridium botulinum and Clostridium perfringens, 2 major foodborne pathogenic fusobacteria, have a variety of virulent protein types with nervous and enterotoxic pathogenic potential, respectively. Objective: The relationship between the molecular evolution of the 2 Clostridium genomes and virulence proteins was studied via a bioinformatics prediction method. The genetic stability, main features of gene coding and structural characteristics of virulence proteins were compared and analyzed to reveal the phylogenetic characteristics, diversity, and distribution of virulence factors of foodborne Clostridium strains. Methods: The phylogenetic analysis was performed via composition vector and average nucleotide identity based methods. Evolutionary distances of virulence genes relative to those of housekeeping genes were calculated via multilocus sequence analysis. Bioinformatics software and tools were used to predict and compare the main functional features of genes encoding virulence proteins, and the structures of virulence proteins were predicted and analyzed through homology modeling and a deep learning algorithm. Results: According to the diversity of toxins, genome evolution tended to cluster based on the protein-coding virulence genes. The evolutionary transfer distances of virulence genes relative to those of housekeeping genes in C. botulinum strains were greater than those in C. perfringens strains, and BoNTs and alpha toxin proteins were located extracellularly. The BoNTs have highly similar structures, but BoNT/A/B and BoNT/E/F have significantly different conformations. The beta2 toxin monomer structure is similar to but simpler than the alpha toxin monomer structure, which has 2 mobile loops in the N-terminal domain. The C-terminal domain of the CPE trimer forms a "claudin-binding pocket" shape, which suggests biological relevance, such as in pore formation. Conclusions: According to the genotype of protein-coding virulence genes, the evolution of Clostridium showed a clustering trend. The genetic stability, functional and structural characteristics of foodborne Clostridium virulence proteins reveal the taxonomy and diverse distribution of virulence factors. [ABSTRACT FROM AUTHOR]

This comprehensive review provides insights and suggested strategies for the analysis of germline variants using second- and third-generation sequencing technologies (SGS and TGS). It addresses the critical stages of data processing, starting from alignment and preprocessing to quality control, variant calling, and the removal of artifacts. The document emphasized the importance of meticulous data handling, highlighting advanced methodologies for annotating variants and identifying structural variations and methylated DNA sites. Special attention is given to the inspection of problematic variants, a step that is crucial for ensuring the accuracy of the analysis, particularly in clinical settings where genetic diagnostics can inform patient care. Additionally, the document covers the use of various bioinformatics tools and software that enhance the precision and reliability of these analyses. It outlines best practices for the annotation of variants, including considerations for problematic genetic alterations such as those in the human leukocyte antigen region, runs of homozygosity, and mitochondrial DNA alterations. The document also explores the complexities associated with identifying structural variants and copy number variations, underscoring the challenges posed by these large-scale genomic alterations. The objective is to offer a comprehensive framework for researchers and clinicians, ensuring that genetic analyses conducted with SGS and TGS are both accurate and reproducible. By following these best practices, the document aims to increase the diagnostic accuracy for hereditary diseases, facilitating early diagnosis, prevention, and personalized treatment strategies. This review serves as a valuable resource for both novices and experts in the field, providing insights into the latest advancements and methodologies in genetic analysis. It also aims to encourage the adoption of these practices in diverse research and clinical contexts, promoting consistency and reliability across studies. [ABSTRACT FROM AUTHOR]

Background: This study seeks to elucidate the expressions of lncRNA TSIX in Osteoarthritis (OA) and to explore its mechanisms in regulating OA progression. Methods: RT-qPCR was employed to analyze the expression of TSIX in OA patients classified by Kellgren-Lawrence (K-L) grades. Receiver operator characteristic (ROC) was conducted to evaluate the diagnostic value of TSIX. Correlation between TSIX levels and clinical scores such as Lysholm and visual analogue scale (VAS) score was evaluated using Pearson method. IL-1β-induced SW1353 cells served as an in vitro model. The cell function were assessed by flow cytometry and cell counting kit-8 (CCK-8) assay. The relationship between TSIX and miR-320a was verified by luciferase reporting system, while bioinformatics approaches were utilized to predict the downstream target genes of miR-320a. Results: The findings revealed that TSIX level in OA patients was elevated compared to that of the control group, with a notable progressive increase in TSIX expression correlated with higher K-L grades. In OA patients, the Lysholm score showed a negative correlation with TSIX expression, while the VAS score displayed a positive correlation with TSIX levels. Cell studies demonstrated that inhibition of TSIX enhanced cell viability and mitigated IL-1β-induced apoptosis by targeting miR-320a, in addition to promoting Aggrecan and Collagen II secretion. Luciferase reporter assay further validated the targeting interaction among TSIX, miR-320a, and PTEN. Conclusions: This study demonstrated an increased expression of TSIX in OA patients. It suggests that TSIX may play a role in chondrocyte dysfunction during OA by modulating the miR-320a/PTEN axis. [ABSTRACT FROM AUTHOR]

Objective: This study elucidated the mechanistic role of Cyathulae Radix (CR) in type 2 diabetes mellitus (T2DM) through bioinformatics analysis and experimental validation. Methods: Components and targets of CR were retrieved from the traditional Chinese medical systems pharmacology, while potential T2DM targets were obtained from GeneCards and Online Mendelian Inheritance in Man databases. Intersecting these datasets yielded target genes between CR and T2DM. Differential genes were used for constructing a protein–protein interaction network, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Molecular docking and dynamics simulations were performed using AutoDock and GROMACS, respectively, and in vitro experiments validated the results. Experiments evaluated the effect of CR on T2DM pancreatic β‐cells. Results: Bioinformatics analysis identified four active compounds of CR, 157 related genes, and 5431 T2DM target genes, with 141 shared targets. Key targets such as JUN, MAPK1, and MAPK14 were identified through topological analysis of the PPI network. GO analysis presented 2663 entries, while KEGG analysis identified 161 pathways. The molecular docking results demonstrated favorable binding energy between the core components and the core proteins. Among them, JUN‐rubrosterone, MAPK1‐rubrosterone, and MAPK14‐rubrosterone deserved further investigation. Molecular dynamics results indicated that all of them can form stable binding interactions. CR could inhibit the expression of JUN, MAPK1, and MAPK14, promote insulin secretion, alleviate apoptosis, and regulate autophagy in INS‐1 cells. Conclusion: This study suggests CR approach to T2DM management by multitarget and multipathway provides a scientific basis for further research on the hypoglycemic effect of CR. [ABSTRACT FROM AUTHOR]

Current interventions for oral/dental diseases heavily rely on operative/surgical procedures, while the discovery of novel drug targets may enable access to noninvasive pharmacotherapy. Therefore, this study aims to leverage large-scale data and Mendelian randomization (MR) techniques, utilizing genetic variants as instruments, to identify potential therapeutic targets for oral and dental diseases supported by genetic evidence. By intersecting 4,302 druggable genes with expression quantitative trait loci from 31,684 blood samples, we identified 2,580 druggable targets as exposures. Single nucleotide polymorphisms associated with dental disease/symptom traits were collected from FinnGen R9, the Gene–Lifestyle Interactions in Dental Endpoints consortium, and the UK Biobank to serve as outcomes for both discovery and replication purposes. Through MR analysis, we identified 43 druggable targets for various dental disease/symptom traits. To evaluate the viability of these targets, we replicated the analysis using circulating protein quantitative trait loci as exposures. Additionally, we conducted sensitivity, colocalization, Gene Ontology/Kyoto Encyclopedia of Genes and Genomes annotation, protein–protein interaction analyses, and validated dental trait–associated druggable gene expression in animal models. Among these targets, IL12RB1 (odds ratio [OR], 1.01; 95% confidence interval [CI], 1.01–1.01) and TNF (OR, 0.98; 95% CI, 0.97–0.99) exhibited therapeutic promise for oral ulcers, whereas CXCL10 (OR, 0.84; 95% CI, 0.76–0.91) was for periodontitis. Through a rigorous quality control and validation pipeline, our study yields compelling evidence for these druggable targets, which may enhance the clinical prognosis by developing novel drugs or repurposing existing ones. [ABSTRACT FROM AUTHOR]

FAM136A promotes the progression and metastasis of various tumors. However, there are few studies on the role of FAM136A in esophageal cancer (ESCA). The TCGA, GTEx, and GEO databases are employed to analyze the expression of FAM136A in ESCA, and qPCR and TMA experiments are performed for validation. Enrichment analyzes are performed to investigate the association of FAM136A expression with immune features, m6A modification, alternative splicing, cuproptosis, and the ceRNA network via bioinformatics analysis. FAM136A is highly expressed in ESCA and correlated with lymph node metastasis and overall survival (OS). Bioinformatics analysis suggested that FAM136A may participate in the following processes to promote ESCA development and progression: 1) Promotion of mast cells infiltration to influence the ESCA immune microenvironment, 2) HNRNPC upregulation to regulate m6A modification, 3) ALYREF upregulation to increase the occurrence of retained intron (RI) events, 4) CDK5RAP1 upregulation to achieve inhibition of tumor cell apoptosis, and 5) promotion of ESCA progression through the lncRNA SNHG15/hsa‐miR‐29c‐3p/FAM136A ceRNA network. FAM136A is a potential biomarker for ESCA diagnosis and treatment response evaluation, and the underlying mechanisms may be associated with immune infiltration, m6A modification, alternative splicing, cuproptosis, and the ceRNA regulatory network. [ABSTRACT FROM AUTHOR]

Background: Pulmonary arterial hypertension (PAH) is a severe disease with poor prognosis and high mortality, lacking simple and sensitive diagnostic biomarkers in clinical practice. This study aims to identify novel diagnostic biomarkers for PAH using genomics research. Methods: We conducted a comprehensive analysis of a large transcriptome dataset, including PAH and inflammatory response genes (IRGs), integrated with 113 machine learning models to assess diagnostic potential. We developed a clinical diagnostic model based on hub genes, evaluating their effectiveness through calibration curves, clinical decision curves, and ROC curves. An animal model of PAH was also established to validate hub gene expression patterns. Results: Among the 113 machine learning algorithms, the Lasso + LDA model achieved the highest AUC of 0.741. Differential expression profiles of hub genes CTGF, DDR2, FGFR2, MYH10, and YAP1 were observed between the PAH and normal control groups. A diagnostic model utilizing these hub genes was developed, showing high accuracy with an AUC of 0.87. MYH10 demonstrated the most favorable diagnostic performance with an AUC of 0.8. Animal experiments confirmed the differential expression of CTGF, DDR2, FGFR2, MYH10, and YAP1 between the PAH and control groups (p < 0.05); Conclusions: We successfully established a diagnostic model for PAH using IRGs, demonstrating excellent diagnostic performance. CTGF, DDR2, FGFR2, MYH10, and YAP1 may serve as novel molecular diagnostic markers for PAH. [ABSTRACT FROM AUTHOR]

Objective: Osteoporosis is a severe bone disease with a complex pathogenesis involving various immune processes. With the in‐depth understanding of bone immune mechanisms, discovering new therapeutic targets is crucial for the prevention and treatment of osteoporosis. This study aims to explore novel bone immune markers related to osteoporosis based on single‐cell and transcriptome data, utilizing bioinformatics and machine learning methods, in order to provide novel strategies for the diagnosis and treatment of the disease. Methods: Single cell and transcriptome data sets were acquired from Gene Expression Omnibus (GEO). The data was then subjected to cell communication analysis, pseudotime analysis, and high dimensional WGCNA (hdWGCNA) analysis to identify key immune cell subpopulations and module genes. Subsequently, ConsensusClusterPlus analysis was performed on the key module genes to identify different diseased subgroups in the osteoporosis (OP) training set samples. The immune characteristics between subgroups were evaluated using Cibersort, EPIC, and MCP counter algorithms. OP's hub genes were screened using 10 machine learning algorithms and 113 algorithm combinations. The relationship between hub genes and immunity and pathways was established by evaluating the immune and pathway scores of the training set samples through the ESTIMATE, MCP‐counter, and ssGSEA algorithms. Real‐time fluorescence quantitative PCR (RT‐qPCR) testing was conducted on serum samples collected from osteoporosis patients and healthy adults. Results: In OP samples, the proportions of bone marrow‐derived mesenchymal stem cells (BM‐MSCs) and neutrophils increased significantly by 6.73% (from 24.01% to 30.74%) and 6.36% (from 26.82% to 33.18%), respectively. We found 16 intersection genes and four hub genes (DND1, HIRA, SH3GLB2, and F7). RT‐qPCR results showed reduced expression levels of DND1, HIRA, and SH3GLB2 in clinical blood samples of OP patients. Moreover, the four hub genes showed positive correlations with neutrophils (0.65–0.90), immature B cells (0.76–0.92), and endothelial cells (0.79–0.87), while showing negative correlations with myeloid‐derived suppressor cells (negative 0.54–0.73), T follicular helper cells (negative 0.71–0.86), and natural killer T cells (negative 0.75–0.85). Conclusion: Neutrophils play a crucial role in the occurrence and development of osteoporosis. The four hub genes potentially inhibit metabolic activities and trigger inflammation by interacting with other immune cells, thereby significantly contributing to the onset and diagnosis of OP. [ABSTRACT FROM AUTHOR]

Background: The complex and expensive processes involved in the production of current clostridial toxoid vaccines require the design of new methods. The aim of this study was to introduce single and combined nontoxic mutants of Clostridium perfringens epsilon toxin (ETX) into the vaccine production. Methods: Antigenic properties, amino acid sequence, physicochemical characteristics, stability, and protein structure of individual and combined ETX mutants were investigated with bioinformatics software. Spatial structure prediction was done by using a homologous modeling method based on the structure of wild type ETX (1UYJ1A.pdb) and the validity of the drawn spatial model was evaluated by estimating the quality and accuracy of the spatial chemistry of the modeled mutant proteins by Ramachandran plot. Results: The highest instability index was observed in H106P/N137A mutant. The results of the homology modeling did not show a clear structural change in any of the mutants compared to the wild ETX. The percentages of amino acids in the favored regions, allowed regions, and outlier regions were 92.81%, 5.03%, and 2.16%, respectively, which indicate the desirability of the proposed model for the three-dimensional structure of the H106P/N137A hybrid mutant. Conclusions: Recombinant H106P/N137A mutant of ETX can be considered a suitable candidate for the production of epsilon genetic toxoid, and this requires extensive laboratory evaluations. [ABSTRACT FROM AUTHOR]

The in vitro generation of spermatogonial stem cells (SSCs) from embryonic stem cells (ESCs) offers a viable approach for addressing male infertility. A multitude of molecules participate in this intricate process, which requires additional elucidation. Despite the decline in SSCs in aged testes, SSCs are deemed immortal since they can multiply for three years with repeated transplantation. Nonetheless, the examination of aging is challenging due to the limited quantity and absence of precise indicators. Using a microarray, we assessed genome-wide transcripts (about 55,000 transcripts) of fibroblasts and SSCs. The WGCNA approach was then used to look for SSC-specific transcription factors (TFs) and hub SSC-specific genes based on ATAC-seq, DNase-seq, RNA-seq, and microarray data from the GEO databases as well as gene expression data (RNA-seq and microarray data). The microarray analysis of three human cases with different SSCs revealed that 6 genes were upregulated, and the expression of 23 genes was downregulated compared to the normal case in relation to aging genes. To reach these results, online assessments of Enrich Shiny GO, STRING, and Cytoscape were used to forecast the molecular and functional connections of proteins before identifying the master routes. The biological process and molecular function keywords of cell–matrix adhesion, telomerase activity, and telomere cap complex were shown to be significantly altered in upregulated differentially expressed genes (DEGs) by the functional enrichment analysis. According to our preliminary research, cell-specific TFs and TF-mediated GRNs are involved in the creation of SSCs. In order to maximize the induction efficiency of ESC differentiation into SSCs in vitro, hub SSC-specific genes and important SSC-specific TFs were identified, and sophisticated network regulation was proposed. According to our research, these genes and the hub proteins that they interact with may be able to shine a light on the pathophysiologies of infertility and aberrant germ cells. [ABSTRACT FROM AUTHOR]

Acute myeloid leukemia (AML) emerges as one of the most common and fatal leukemias. Treatment of the disease remains highly challenging owing to profound metabolic rewiring mechanisms that confer plasticity to AML cells, ultimately resulting in therapy resistance. Autophagy, a highly conserved lysosomal-driven catabolic process devoted to macromolecular turnover, displays a dichotomous role in AML by suppressing or promoting disease development and progression. Glycolytic metabolism represents a pivotal strategy for AML cells to sustain increasing energy needs related to uncontrolled growth during disease progression. In this study, we tested the hypothesis that a high glycolytic rate and low autophagy flux could represent an advantage for AML cell proliferation and thus be detrimental for patient's prognosis, and vice versa. TCGA in silico analysis of the AML cohort shows that the high expression of MAP1LC3B (along with that of BECN1 and with low expression of p62/SQSTM1) and the high expression of BNIP3 (along with that of PRKN and of MAP1LC3B), which together are indicative of increased autophagy and mitophagy, correlate with better prognosis. On the other hand, the high expression of glycolytic markers HK2, PFKM, and PKM correlates with poor prognosis. Most importantly, the association of a low expression of glycolytic markers with a high expression of autophagy–mitophagy markers conferred the longest overall survival for AML patients. Transcriptomic analysis showed that this combined signature correlates with the downregulation of a subset of genes required for the differentiation of myeloid cells, lactate/pyruvate transporters, and cell cycle progression, in parallel with the upregulation of genes involved in autophagy/lysosomal trafficking and proteolysis, anti-tumor responses like beta-interferon production, and positive regulation of programmed cell death. Taken together, our data support the view that enhanced autophagy-mitophagy flux together with low glycolytic rate predisposes AML patients to a better clinical outcome, suggesting that autophagy inducers and glucose restrictors may hold potential as adjuvant therapeutics for improving AML management. [ABSTRACT FROM AUTHOR]

Hepatic lipase deficiency is a rare genetic condition caused by biallelic loss-of-function variants in the LIPC gene encoding hepatic lipase. These variants reduce or abolish the protein's lipolytic activity, resulting in elevated plasma lipids. The condition is classified as autosomal recessive, since dyslipidemia is inconsistently observed in heterozygous patients with only one LIPC variant. However, this has been concluded from historical studies encompassing a few families and having very small sample sizes. Here, we conduct a retrospective chart review of 46 heterozygous subjects, each harboring one rare pathogenic LIPC variant. We compare plasma levels of total cholesterol, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), triglycerides, and apolipoprotein B to those of matched controls without LIPC variants. Variant pathogenicity is classified according to the guidelines of the American College of Medical Genetics and Genomics. We observe that levels of total cholesterol, LDL-C, and triglycerides are significantly elevated in the LIPC variant heterozygotes, but HDL-C and apolipoprotein B are not. When filtering solely with respect to pathogenic or likely pathogenic variants, all lipid variables emerge as significantly elevated compared to controls. Thus, hepatic lipase deficiency may not necessarily be a recessive condition, but perhaps semi-dominant since individuals with one variant are phenotypically distinct from the controls. These hypothesis-generating findings regarding LIPC genetic variations observed in a clinical cohort should be evaluated in larger populations and databases. [ABSTRACT FROM AUTHOR]

Background: The earliest manifestation of alcohol‐associated liver disease (ALD) is steatosis characterized by deposition of fat in specialized organelles called lipid droplets (LDs). While alcohol administration causes a rise in LD numbers in the hepatocytes, little is known regarding their characteristics that allow their accumulation and size to increase. The aim of the present study is to gain insights into underlying pathophysiological mechanisms by investigating the ethanol‐induced changes in hepatic LD proteome as a function of LD size. Methods: Adult male Wistar rats (180–200 g BW) were fed with ethanol liquid diet for 6 weeks. At sacrifice, large‐, medium‐, and small‐sized hepatic LD subpopulations (LD1, LD2, and LD3, respectively) were isolated and subjected to morphological and proteomic analyses. Results: Morphological analysis of LD1‐LD3 fractions of ethanol‐fed rats clearly demonstrated that LD1 contained larger LDs compared with LD2 and LD3 fractions. Our preliminary results from principal component analysis showed that the proteome of different‐sized hepatic LD fractions was distinctly different. Proteomic data analysis identified over 2000 proteins in each LD fraction with significant alterations in protein abundance among the three LD fractions. Among the altered proteins, several were related to fat metabolism, including synthesis, incorporation of fatty acid, and lipolysis. Ingenuity pathway analysis revealed increased fatty acid synthesis, fatty acid incorporation, LD fusion, and reduced lipolysis in LD1 compared to LD3. Overall, the proteomic findings indicate that the increased level of protein that facilitates fusion of LDs combined with an increased association of negative regulators of lipolysis dictates the generation of large‐sized LDs during the development of alcohol‐associated hepatic steatosis. Conclusion: Several significantly altered proteins were identified in different‐sized LDs isolated from livers of ethanol‐fed rats. Ethanol‐induced increases in specific proteins that hinder LD lipid metabolism led to the accumulation and persistence of large‐sized LDs in the liver. [ABSTRACT FROM AUTHOR]

Multiple epidermal growth factor-like domains protein 6 (MEGF6) is a high molecular weight protein with several EGF-like domains. Although it has been demonstrated to promote metastasis and chemoresistance in colon cancer cells, its specific oncogenic and immunologic functions remain largely unexplored. This study aims to comprehensively analyze publicly available datasets to define the role of MEGF6 in colon cancer development and immune response modulation. The expression of MEGF6 in colon adenocarcinoma (COAD) and normal tissues at both mRNA and protein levels was assessed using the GEPIA2 and HPA databases, respectively. The UALCAN database was employed to examine the association between MEGF6 expression and clinicopathological outcomes in COAD patients, and survival analysis was conducted using the KM plotter database. Functional enrichment analysis of MEGF6- correlated genes was performed using the ShinyGO online tool. Additionally, the correlation of MEGF6 with immune cell infiltration and immunosuppressive molecules was explored through the TIMER and TISIDB databases. Our analysis revealed significantly higher mRNA and protein expressions of MEGF6 in COAD tissues compared to normal tissues, with associations to cancer stage, nodal metastasis status, and histological subtype. High MEGF6 expression was correlated with poorer survival outcomes, and genes correlated with MEGF6 were enriched in biological processes related to cellular component organization, cell adhesion, and extracellular matrix interaction. Furthermore, MEGF6 expression demonstrated a significant correlation with immune cell infiltration and the expression of immunosuppressive molecules, particularly CTLA-4, PD-1, PD-L1, TGF-β, and IL-10. In conclusion, our findings suggest that MEGF6 may play an oncogenic role by influencing cell and extracellular matrix interactions. Its overexpression may indicate immunosuppressive properties within the cancer microenvironment. Therefore, MEGF6 holds promise as a potential biomarker for predicting both prognosis and therapeutic responses in colon cancer. [ABSTRACT FROM AUTHOR]

Approaches based on graph descriptors have been used in cheminformatics and bioinformatics for molecular property prediction. In this article, we apply partition technique and simple counting schemes to describe the structure of a two-dimensional (2D) kagome graphene and then derive the closed form of various degree-based topological indices (graph descriptor) of 2D kagome graphene, and its tubular and toroidal forms. [ABSTRACT FROM AUTHOR]

Xiao–Jian–Zhong–Tang (XJZT) has the effect of warming the middle and tonifying the deficiency, easing the urgency and relieving pain according to the theory of traditional Chinese medicine (TCM), and is able to treat spleen deficiency type chronic atrophic gastritis (CAG). Metabolites of TCM in cecum contents are common metabolites of intestinal bacteria and hosts, which can reflect the metabolic status in disease states. The present work was performed to study the effect of XJZT against CAG coupled with the cecal metabolites analysis and bioinformatics. A total of nine prototypical components and 144 metabolites were firstly identified in the cecum metabolites of XJZT using ultra‐high performance liquid chromatography added to the quadrupole‐time of flight mass spectrometry (UHPLC‐Q‐TOF/MS), which underwent the metabolism of oxidation, reduction, methylation, and glucuronic acid reaction Furthermore, different prototypical compounds might metabolize into identical metabolites in the presence of intestinal flora. Bioinformatics was further used to correlate these metabolites with the disease and intestinal flora. Components and targets were screened by Cytoscape, and molecular docking of key targets and core components showed good binding ability. This study provided important information for exploring the mechanism of TCM formulae. [ABSTRACT FROM AUTHOR]

Cancer is highly diverse and heterogeneous. Accurate and rapid analysis of the characteristics of individual cancer cells, using a complex array of big data that includes various clinicopathological features and molecular mechanisms, is crucial for advancing precision medicine. In recent years, experts in biomedical sciences and data sciences have explored the potential of artificial intelligence (AI) to analyze such extensive data sets. The next phase of AI-based medical research on cancer should focus on the practical applications of AI tools and how they can be effectively used in actual medical research settings. Recently, translational research that leverages AI and comprehensive genetic analysis data has emerged as a significant research focus. This field represents an opportunity for groundbreaking discoveries to be shared globally. To further precision medicine in clinical practice, it is vital to develop sophisticated AI tools for cancer research. These tools should not only identify potential therapeutic targets through comprehensive genetic analysis but also predict therapeutic outcomes in clinical settings. [ABSTRACT FROM AUTHOR]

Background: Huashi Baidu Formula (HBF) is a clinical formula known for its efficacy against coronavirus disease 2019 (COVID-19). HBF may reduce the number of patients with abnormal serum creatinine while improving respiratory symptoms, suggesting that this formula may have potential for treating acute kidney injury (AKI). However, the protective effect of HBF on AKI has not been definitively confirmed, and the mechanism remains unclear. Therefore, the present study explored the renoprotective effects and molecular mechanisms of HBF and screened for its active ingredients to identify new potential applications of renoprotection by HBF. Methods: The present study first assessed the protective effects of HBF on AKI in a DOX-induced mouse model. Then, RNA-seq and bioinformatics analyses were used to explore the related pathological processes and potential molecular mechanisms, which were subsequently validated using qRT-PCR and Western blotting. Furthermore, candidate compounds with potential binding affinity to two pivotal targets, sphingosine kinase 1 (SphK1) and plasminogen activator inhibitor-1 (PAI-1), were screened from the 29 constituents present in the blood using Microscale Thermophoresis (MST). Finally, to identify the active ingredients, the candidate components were re-screened using the SphK1 kinase activity detection system or the uPA/PAI-1 substrate colorimetric assay system. Results: In the DOX-induced AKI mouse model, therapeutic administration of HBF significantly reduced the levels of CRE, BUN, TNF-α, IL-1β, IL-6, and UA in plasma and the levels of MDA, T-CHO, and TG in kidney tissue. Additionally, the levels of TP and Alb in plasma and SOD and CAT in the kidney tissue were significantly increased. Histopathological assessment revealed that HBF reduced tubular vacuolation, renal interstitial inflammatory cell infiltration, tubular atrophy, and positive staining of renal interstitial collagen. RNA-seq and bioinformatics analyses showed that oxidative stress, the immune-inflammatory response, and extracellular matrix (ECM) formation could be the pathological processes that HBF targets to exerts its renoprotective effects. Furthermore, HBF regulated the APJ/SPHK1/NF-κB and APJ/PAI-1/TGFβ signaling axes and reduced the phosphorylation levels of NF-κB p65 and SMAD2 and the expression of cytokines and the ECM downstream of the axis. Finally, six SphK1 inhibitors (paeoniflorin, astragalin, emodin, glycyrrhisoflavone, quercetin, and liquiritigenin) and three PAI-1 inhibitors (glycyrrhisoflavone, licochalcone B, and isoliquiritigenin) were identified as potentially active ingredients in HBF. Conclusion: In brief, our investigation underscores the renoprotective effect of HBF in a DOX-induced AKI model mice, elucidating its mechanisms through distinct pathological processes and identifying key bioactive compounds. These findings offer new insights for broadening the clinical applications of HBF and unravelling its molecular mode of action. [ABSTRACT FROM AUTHOR]

Introduction: Modern histocompatibility algorithms depend on the comparison and analysis of high-resolution HLA protein sequences and structures, especially when considering epitope-based algorithms, which aim to model the interactions involved in antibody or T cell binding. HLA genotype imputation can be performed in the cases where only low/intermediate-resolution HLA genotype is available or if specific loci are missing, and by providing an individuals' race/ethnicity/ancestry information, imputation results can be more accurate. This study assesses the effect of imputing high-resolution genotypes on molecular mismatch scores under a variety of ancestry assumptions. Methods: We compared molecular matching scores from "ground-truth" highresolution genotypes against scores from genotypes which are imputed from low-resolution genotypes. Analysis was focused on a simulated patient-donor dataset and confirmed using two real-world datasets, and deviations were aggregated based on various ancestry assumptions. Results: We observed that using multiple imputation generally results in lower error in molecular matching scores compared to single imputation, and that using the correct ancestry assumptions can reduce error introduced during imputation. Discussion: We conclude that for epitope analysis, imputation is a valuable and low-risk strategy, as long as care is taken regarding epitope analysis context, ancestry assumptions, and (multiple) imputation strategy. [ABSTRACT FROM AUTHOR]

Background: Roux-en-Y (RYGB) and one anastomosis gastric bypass (OAGB) represent two of the most used bariatric/metabolic surgery (BMS) procedures. Gut microbiota (GM) shift after bypass surgeries, currently understated, may be a possible key driver for the short- and long-term outcomes. Methods: Prospective, multicenter study enrolling patients with severe obesity, randomized between OAGB or RYGB. Fecal and blood samples were collected, pre- (T0) and 24 months postoperatively (T1). GM was determined by V3-V4 16S rRNA regions sequencing and home-made bioinformatic pipeline based on Qiime2 plugin and R packages. Objects: To compare OAGB vs RYGB microbiota profile at T1 and its impact on metabolic and nutritional status. Results: 54 patients completed the study, 27 for each procedure. An overall significant variation was detected in anthropometric and serum nutritional parameters at T1, with a significant, similar decrease in overall microbial alpha and beta diversity observed in both groups. An increase in relative abundances of Actinobacteria and Proteobacteria and a reduction of Bacteroidetes, no significant changes in Firmicutes and Verrucomicrobia, with an increase of the Firmicutes/Bacteroidetes ratio were observed. Conclusions: BMS promotes a dramatic change in GM composition. This is the first multicenter, RCT evaluating the impact of OAGB vs Roux-en-Y bypass on GM profile. The bypass technique per se did not impact differently on GM or other examined metabolic parameters. The emergence of slightly different GM profile postoperatively may be related to clinical conditions or may influence medium or long-term outcomes and as such GM profile may represent a biomarker for bariatric surgery's outcomes. [ABSTRACT FROM AUTHOR]

Background: Prostate cancer is one of the leading causes of cancer-related deaths in men. Its molecular pathogenesis is closely linked to various genetic and epigenetic alterations, including posttranslational modifications like SUMOylation. Identifying biomarkers that predict outcomes and specific therapeutic targets depends on a comprehensive understanding of these processes. With growing interest in SUMOylation as a mechanism affecting prostate cancer-related genes, this study aimed to investigate the central role of SUMOylation in prostate cancer prognostics, focusing on the significance of NOP58. Methods: We conducted a comprehensive bioinformatics analysis, integrating differential expression analysis, survival analysis, gene set enrichment analysis (GSEA), and single-cell transcriptomic analyses using data from The Cancer Genome Atlas (TCGA). Key genes were identified through intersections of Venn diagrams, Boralta algorithm signatures, and machine learning models. These signaling mechanisms were validated through experimental studies, including immunohistochemical staining and gene ontology analyses. Results: The dual-gene molecular subtype analysis with SUMO1, SUMO2, and XPO1 genes revealed significant differences in survival outcomes across molecular subtypes, further emphasizing the potential impact of NOP58 on SUMOylation, a key post-translational modification, in prostate cancer. NOP58 overexpression was strongly associated with shorter overall survival (OS), progression-free interval (PFI), and disease-specific death in prostate cancer patients. Immunohistochemical analysis confirmed that NOP58 was significantly overexpressed in prostate cancer tissues compared to normal tissues. ROC curve analysis demonstrated that NOP58 could distinguish prostate cancer from control samples with high diagnostic accuracy. Gene Ontology analysis, along with GSVA and GSEA, suggested that NOP58 may be involved in cell cycle regulation and DNA repair pathways. Moreover, NOP58 knockdown led to increased BCL2 expression and decreased Ki67 levels, promoting apoptosis and inhibiting cell proliferation. Colony formation assays further showed that NOP58 knockdown inhibited, while its overexpression promoted, colony formation, highlighting the critical role of NOP58 in prostate cancer cell growth and survival. Additionally, NOP58 was linked to drug responses, including Methotrexate, Rapamycin, Sorafenib, and Vorinostat. Conclusion: NOP58 is a key regulator of prostate cancer progression through its mediation of the SUMOylation pathway. Its expression level serves as a reliable prognostic biomarker and an actionable therapeutic target, advancing precision medicine for prostate cancer. Targeting NOP58 may enhance therapeutic efficacy and improve outcomes in oncology. [ABSTRACT FROM AUTHOR]

The democratisation of next‐generation sequencing has vastly increased the availability of sequencing data from metabarcoding. However, to effectively prepare these metabarcoding data for subsequent analysis, researchers must consistently apply several different bioinformatic tools—including those which denoise reads, cluster sequences and assign taxonomic identities. This often creates a bioinformatics bottleneck in workflows for non‐specialists due to obstacles around: (a) integrating different tools, (b) the inability to easily modify and rerun bioinformatic pipelines involving non‐scripted ('point‐and‐click') elements and (c) the multiple outputs that may be required of a single dataset (e.g. amplicon sequence variants [ASVs] and operational taxonomic units [OTUs]), which often results in users running pipelines multiple times.Here, we introduce SimpleMetaPipeline, an open‐source bioinformatics pipeline implemented in R, which addresses these obstacles. SimpleMetaPipeline integrates the most robust and commonly used existing bioinformatic tools in a single reproducible pipeline, with a streamlined choice of parameters, to generate a sequence data table containing alternative clustering and assignment options. SimpleMetaPipeline accepts demultiplexed paired‐end and single reads from multiple sequencing runs.We describe the pipeline and demonstrate how alternative annotations enable the easy implementation of multi‐algorithm agreement tests to strengthen inferences.SimpleMetaPipeline represents a valuable addition to the existing library of pipelines, providing easy and reproducible bioinformatics, including a range of commonly desired clustering and assignment options, such as OTUs and ASVs. [ABSTRACT FROM AUTHOR]

Antarctic krill (Euphausia superba Dana) is a keystone species in the Southern Ocean ecosystem, with ecological and commercial significance. However, its vulnerability to climate change requires an urgent investigation of its adaptive potential to future environmental conditions. Historical museum collections of krill from the early 20th century represent an ideal opportunity to investigate how krill have changed over time due to predation, fishing and climate change. However, there is currently no cost‐effective method for implementing population scale collection genomics for krill given its genome size (48 Gbp). Here, we assessed the utility of two inexpensive methods for population genetics using historical krill samples, specifically low‐coverage shotgun sequencing (i.e. 'genome‐skimming') and exome capture. Two full‐length transcriptomes were generated and used to identify 166 putative gene targets for exome capture bait design. A total of 20 historical krill samples were sequenced using shotgun and exome capture. Mitochondrial and nuclear ribosomal sequences were assembled from both low‐coverage shotgun and off‐target of exome capture data demonstrating that endogenous DNA sequences could be assembled from historical collections. Although, mitochondrial and ribosomal sequences are variable across individuals from different populations, phylogenetic analysis does not identify any population structure. We find exome capture provides approximately 4500‐fold enrichment of sequencing targeted genes, suggesting this approach can generate the sequencing depth required to call identify a significant number of variants. Unlocking historical collections for genomic analyses using exome capture, will provide valuable insights into past and present biodiversity, resilience and adaptability of krill populations to climate change. [ABSTRACT FROM AUTHOR]