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Normozoospermic infertile men possess subpopulations of sperm varying in DNA accessibility, relating to differing reproductive outcomes

STUDY QUESTION

Can a reliable assay be developed to quantify DNA accessibility in human sperm to help with the assessment of pre-implantation development affected by dense packaging of mammalian sperm's genetic material?

SUMMARY ANSWER

We adapted NicE-view, an assay that directly labels accessible DNA, for use in human sperm and applied it to examine spermatozoa from infertile individuals with distinct reproductive outcomes.

WHAT IS KNOWN ALREADY

Existing data suggest a connection between sperm chromatin compaction and reproductive outcomes. The assays used to generate this data, however, measure chromatin compaction indirectly and thus understanding their meaning is challenging.

STUDY DESIGN, SIZE, DURATION

Between April 2020 to December 2023, 60 normozoospermic infertile men were invited to participate in an experimental study and asked to provide a semen sample.

PARTICIPANTS/MATERIALS, SETTING, METHODS

Among the 60 individuals forty had undergone at least one treatment with ART. Among these ART-treated participants, 20 were included in the study because after fertilization only one or no embryos developed during embryo culture (low blastocyst growth rate, LBGR). The other 20 men were included as at least 50% of cultured embryos developed to the blastocyst stage (high blastocyst growth rate). Additionally, 20 previously infertile individuals obtained a pregnancy naturally (NATP) and were included as well. Washed spermatozoa obtained from seminal plasma or prepared by swim-up procedure were processed for NicE-view to determine DNA accessibility as a marker of chromatin condensation using confocal microscopy. Images of more than 3 million spermatozoa were acquired. Computer-assisted image segmentation was used to identify individual sperm heads and DNA accessibility levels were then quantified in each. We also compared NicE-view to chromomycin A3 (CMA3), a conventional marker of chromatin de-condensation, and ATAC-see, an alternative assay for measuring DNA accessibility.

MAIN RESULTS AND THE ROLE OF CHANCE

Both semen and swim-up samples of participants contained two well-delineated subpopulations of spermatozoa with distinct DNA accessibility levels, the frequencies of which varied among individuals. Interestingly, individuals with high frequencies of highly accessible sperm DNA, as measured in semen, possessed decreased sperm concentrations. Moreover, participants with high frequency of highly accessible sperm DNA were more common in the LBGR sub-group. Surprisingly, selection of motile sperm by swim-up enriched for sperm with high DNA accessibility in participants from all three sub-groups. Chromatin accessibility measurements by Nice-view were distinct from DNA staining with the fluorescent CMA3 dye, and NicE-view allowed much clearer separation of sperm subpopulations than ATAC-see.

LIMITATIONS, REASONS FOR CAUTION

This was a single-centre study with a cohort of 60 individuals. Sperm samples containing very high frequencies of sperm with increased DNA accessibility were more common in the LBGR sub-group. The number of individuals with this pattern was, however, limited, even within this category.

WIDER IMPLICATIONS OF THE FINDINGS

High DNA accessibility is associated with poor pre-implantation embryonic development in vitro and NicE-view may be used for the prediction of abnormal embryonic development in ART. Further studies examining samples from larger cohorts of participants and the localization of accessible regions within the sperm genome are needed to fully evaluate the utility of this method.

STUDY FUNDING/COMPETING INTEREST(S)

Swiss National Science Foundation (Grant No. 189264), Swiss Center for Applied Human Toxicology (SCAHT) research (Grant No. 1 'male reproductive toxicity') (both to C.D.G.), and the Novartis Research Foundation (to A.H.F.M.P.). M.E.G., C.D.G., and A.H.F.M.P. are authors on a patent application (EP23210754.0) on the use of NicE-view for the assessment of sperm.

TRIAL REGISTRATION NUMBER

ClinicalTrials.gov ID NCT04256668.

Identification of chromatin remodeling-related gene signature to predict the prognosis in breast cancer

Growing evidence highlights the critical role of chromatin remodeling in tumor development and progression. This study explores the relationship between chromatin remodeling-related genes (CRRGs) and breast cancer (BRCA). Public databases were used to retrieve the TCGA-BRCA and GSE20685 datasets. CRRGs were sourced from prior studies. Prognosis-associated CRRGs were identified using univariate Cox regression analysis. TCGA-BRCA BRCA samples were grouped into CRRG-related subtypes through consensus clustering analysis. Differential expression analysis was conducted in TCGA-BRCA (BRAC vs. control) and among subtypes to identify differentially expressed genes (DEGs). Candidate genes were obtained through the intersection of these DEGs. Prognostic genes were selected using univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analyses. Independent prognostic factors were identified, and a nomogram model was developed. Functional enrichment, immune infiltration, clinical relevance, and drug sensitivity analyses were subsequently performed. TCGA-BRCA BRCA samples were classified into two CRRG-related subtypes (clusters 1 and 2) based on prognosis-associated CRRGs. A total of 141 candidate genes were identified by intersecting 250 DEGs (cluster 1 vs. cluster 2) with 3,145 DEGs (BRCA vs. control). Five prognostic genes-LHX5, ZP2, GABRQ, APOA2, and CLCNKB-were selected, and a prognostic risk model was developed. In clinical samples, APOA2 (P = 0.0290) and GABRQ (P = 0.0132) expression were significantly up-regulated, CLCNKB (P < 0.0001) and ZP2 (P = 0.0445) expression were significantly down-regulated, while LHX5 (P = 0.1508) expression did not present a significant difference. A nomogram was created, and calibration and Receiver Operating Characteristic (ROC) curves demonstrated its superior predictive ability for BRCA. Gene Set Variation Analysis (GSVA) revealed 16 pathways, such as "mTORC1 signaling" and "E2F targets," were enriched in the high-risk group, while 9 pathways, including "estrogen response early" and "myogenesis," were enriched in the low-risk group. Additionally, significant differences in immune cell types, including CD8+ T cells and follicular helper T cells, were observed between the two risk groups. The risk score was also significantly associated with six drugs, including AZD1332 1463 and SB505124 1194. This study presents a prognostic model based on five genes (LHX5, ZP2, GABRQ, APOA2, and CLCNKB) for BRCA, offering a novel perspective on the link between CRRGs and BRCA.

Integration of Bioinformatics and Machine Learning Strategies Identifies Ferroptosis and Immune Infiltration Signatures in Peri-Implantitis

Peri-implantitis (PI) is a chronic inflammatory disease that ultimately leads to the dysfunction and loss of implants with established osseointegration. Ferroptosis has been implicated in the progression of PI, but its precise mechanisms remain unclear. This study explores the molecular mechanisms of ferroptosis in the pathology of PI through bioinformatics, offering new insights into its diagnosis and treatment. The microarray datasets for PI (GSE33774 and GSE106090) were retrieved from the GEO database. The differentially expressed genes (DEGs) and ferroptosis-related genes (FRGs) were intersected to obtain PI-Ferr-DEGs. Using three machine learning algorithms, the Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine-Recursive Feature Elimination (SVM-RFE), and Boruta, we successfully identified the most crucial biomarkers. Additionally, these key biomarkers were validated using a verification dataset (GSE223924). Gene set enrichment analysis (GSEA) was also utilized to analyze the associated gene enrichment pathways. Moreover, immune cell infiltration analysis compared the differential immune cell profiles between PI and control samples. Also, we targeted biomarkers for drug prediction and conducted molecular docking analysis on drugs with potential development value. A total of 13 PI-Ferr-DEGs were recognized. Machine learning and validation confirmed toll-like receptor-4 (TLR4) and FMS-like tyrosine kinase 3 (FLT3) as ferroptosis biomarkers in PI. In addition, GSEA was significantly enriched by the biomarkers in the cytokine-cytokine receptor interaction and chemokine signaling pathway. Immune infiltration analysis revealed that the levels of B cells, M1 macrophages, and natural killer cells differed significantly in PI. Ibudilast and fedratinib were predicted as potential drugs for PI that target TLR4 and FLT3, respectively. Finally, the occurrence of ferroptosis and the expression of the identified key markers in gingival fibroblasts under inflammatory conditions were validated by RT-qPCR and immunofluorescence analysis. This study identified TLR4 and FLT3 as ferroptosis and immune cell infiltration signatures in PI, unraveling potential novel targets to treat PI.

Uncovering potential biomarkers of endometriosis: transcriptomic and single-cell analysis

Background

The link between programmed cell death (PCD) and mitochondria has been documented in various diseases. However, its role in endometriosis (EMS) remains unexplored. This study aims to identify potential biomarkers in EMS associated with both PCD and mitochondrial functions.

Methods

This analysis incorporates datasets related to EMS, PCD-related genes (PCD-RGs), and mitochondria-related genes (MRGs) sourced from public repositories. To uncover potential biomarkers, differential expression analysis, weighted gene co-expression network analysis (WGCNA), Boruta feature selection, expression validation, and diagnostic assessments were conducted. Functional analyses, immune infiltration profiling, and the construction of regulatory networks further elucidated the mechanisms through which these biomarkers may influence EMS. Finally, single-cell data were leveraged to examine the expression and functionality of these biomarkers at a granular level.

Results

Apoptosis-inducing factor mitochondria-associated 1 (AIFM1) and pyruvate dehydrogenase kinase 4 (PDK4) were identified as potential biomarkers, with PDK4 upregulated and AIFM1 downregulated in EMS. Both genes demonstrated strong diagnostic potential. Enrichment analyses indicated their involvement in pathways associated with the cell cycle. Immune infiltration analyses revealed that AIFM1 had a significant positive correlation with resting dendritic cells and a negative correlation with M2 macrophages, whereas PDK4 was positively associated with M2 macrophages and inversely related to follicular helper T cells. Moreover, AIFM1 and PDK4 were regulated by 16 miRNAs (e.g., hsa-mir-16-5p) and 18 lncRNAs (e.g., LINC00294). Single-cell analysis further revealed dynamic expression trends of these potential biomarkers across cell differentiation stages, including gametocytes, monocytes, mesenchymal stem cells, and neutrophils.

Conclusion

In this study, potential biomarkers (AIFM1 and PDK4) related to PCD and mitochondria were identified in EMS, offering valuable insights for the diagnosis and therapeutic strategies for the disease.

Discovery of core genes and intercellular communication role in osteosarcoma

Osteosarcoma is a primary malignant bone tumor that affects children and young adults. Understanding the molecular mechanisms underlying osteosarcoma is critical to develop effective treatments. This study aimed to identify core genes and explore the role of intercellular communication in osteosarcoma. We used GSE87437 and GSE152048 dataset to conduct a weighted correlation network analysis (WGCNA) and identify co-expression modules. The enriched biological processes and cellular components of the genes in the steelblue module were analyzed. Next, we explored the expression, diagnostic value, correlation, and association with immune infiltrate of CCSER1 and LOC101929154. Finally, we utilized CIBERSORT algorithm to predict the infiltrated immune cells in osteosarcoma tissues. Our results identified 44 co-expression modules, and the steelblue module was mainly associated with axon development, axonogenesis, and innervation. CCSER1 and LOC101929154 were significantly upregulated in osteosarcoma tissues with poor response to preoperative chemotherapy. Moreover, the expressions of CCSER1 and LOC101929154 were positively correlated. The area under the receiver operating characteristic curve of CCSER1 and LOC101929154 was 0.800 and 0.773, respectively. The expression of CCSER1 was negatively correlated with follicular helper T cells and positively correlated with M0 macrophages, while LOC101929154 was negatively correlated with activated mast cells. Besides, CD4 memory-activated T cells were observed at lower levels in patients who responded well to chemotherapy. Our study identified core genes CCSER1 and LOC101929154 and provided insight into the intercellular communication profile in osteosarcoma. Our results suggested that targeting CCSER1, LOC101929154, and CD4 memory-activated T cells may be a promising strategy for the treatment of osteosarcoma.

Identification and experimental validation of biomarkers associated with mitochondrial and programmed cell death in major depressive disorder

Background

Major depressive disorder (MDD) is associated with mitochondrial dysfunction and programmed cell death (PCD), though the underlying mechanisms remain unclear. This study aimed to investigate the molecular pathways involved in MDD using a transcriptomic analysis approach.

Methods

Transcriptomic data related to MDD were obtained from public databases. Differentially expressed genes (DEGs), PCD-related genes (PCDs), and mitochondrial-related genes (MitoGs) were analyzed to identify key gene sets: PCD-DEGs and MitoG-DEGs. Correlation analysis (|correlation coefficient| > 0.9, p < 0.05) was performed to select candidate genes. Protein-protein interaction (PPI) network analysis and intersection of four algorithms were used to identify key candidate genes. Machine learning and gene expression validation were employed, followed by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) for further validation. A nomogram was developed to predict MDD probability based on biomarkers. Additional analyses included immune infiltration, regulatory networks, and drug predictions.

Results

CD63, IL17RA, and IL1R1 were identified as potential biomarkers, with significantly higher expression levels in the MDD cohort. These findings were validated by RT-qPCR. A nomogram based on these biomarkers demonstrated predictive capacity for MDD. Differential immune cell infiltration was observed, with significant differences in nine immune cell types, including activated T cells and eosinophils, between the MDD and control groups. ATF1 was identified as a common transcription factor for CD63, IL17RA, and IL1R1. Shared miRNAs for CD63 and IL1R1 included hsa-miR-490-3p and hsa-miR-125a-3p. Drug prediction analysis identified 50 potential drugs, including verteporfin, etynodiol, and histamine, targeting these biomarkers.

Conclusion

CD63, IL17RA, and IL1R1 are key biomarkers for MDD, providing insights for diagnostic development and targeted therapies. The predictive nomogram and drug predictions offer valuable tools for MDD management.

Unraveling the role of histone acetylation in sepsis biomarker discovery

Introduction

Sepsis is a life-threatening condition caused by a dysregulated immune response to infection. Despite advances in clinical care, effective biomarkers for early diagnosis and prognosis remain lacking. Emerging evidence suggests that histone acetylation plays a crucial role in the pathophysiology of sepsis.

Methods

Transcriptomic and single-cell RNA sequencing data were used to identify histone acetylation-related genes. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were performed, followed by machine learning algorithms (LASSO, SVM-RFE, and Boruta) to screen for potential biomarkers. Mendelian randomization (MR), RT-qPCR, and functional assays were conducted for validation.

Results

BLOC1S1, NDUFA1, and SFT2D1 were identified as key biomarkers. A predictive nomogram demonstrated strong diagnostic potential. Immune infiltration and single-cell analyses linked the biomarkers to macrophage activity. MR analysis confirmed SFT2D1 as a causal factor in sepsis. Functional assays showed that knockdown of SFT2D1 suppressed CXCL10 and IL-6 expression, indicating its pro-inflammatory role.

Discussion

This study identifies novel biomarkers associated with histone acetylation and immune dysregulation in sepsis. These findings deepen our understanding of sepsis pathogenesis and may facilitate the development of improved diagnostic and therapeutic strategies.

Unveiling Salt Tolerance Mechanisms in Plants: Integrating the KANMB Machine Learning Model With Metabolomic and Transcriptomic Analysis

Salt stress presents a substantial threat to cereal crop productivity, especially in coastal agricultural regions where salinity levels are high. Addressing this challenge requires innovative approaches to uncover genetic resources that support molecular breeding of salt-tolerant crops. In this study, a novel machine learning model, KANMB is introduced, designed to analyze integrated multi-omics data from the natural halophyte Spartina alterniflora under various NaCl concentrations. Using KANMB, 226 metabolic biomarkers significantly linked to salt stress responses, grounded in metabolomic and transcriptomic profiles are identified. These biomarkers correlate with metabolic pathways associated with salt tolerance, providing insight into the underlying biochemical mechanisms. A co-expression analysis further highlights the MYB gene SaMYB35 as a pivotal regulator in the flavonoid biosynthesis pathway under salt stress. When overexpressed SaMYB35 in rice (ZH11) grown under high salinity, it triggers the upregulation of key flavonoid biosynthetic genes, elevates flavonoid content, and enhances salt tolerance compared to wild-type plants. The findings from this study offer a valuable genetic toolkit for breeding salt-tolerant cereal varieties and demonstrate the power of machine learning in accelerating biomarker discovery for stress resilience in non-model plant species.

Identification of prognostic and therapeutic biomarkers associated with macrophage and lipid metabolism in pancreatic cancer

Although macrophages and lipid metabolism significantly influence the progression of various cancers, their precise roles in pancreatic cancer (PC) remain unclear. This study focuses on identifying and validating biomarkers associated with macrophage-related genes (MRGs) and lipid metabolism-related genes (LMRGs), providing new targets and strategies for therapeutic intervention. This research utilized datasets from TCGA-PAAD, GSE62452, and GSE57495. Candidate genes were identified by overlapping differentially expressed genes with MRGs from WGCNA and LMRGs. Regression analyses were performed to pinpoint potential biomarkers and construct a risk model, which underwent evaluation. A nomogram was subsequently developed and validated. Additional analyses, including functional enrichment, somatic mutation profiling, immune landscape assessment, and RT-qPCR, were performed to investigate the underlying biological mechanisms in PC. The study identified ADH1A, ACACB, CD36, CERS4, PDE3B, ALOX5, and CRAT as biomarkers for PC. RT-qPCR results revealed reduced expression of ADH1A, ACACB, CD36, CERS4, PDE3B, and CRAT in tumor samples compared to adjacent tissues, whereas ALOX5 expression was significantly elevated in tumor samples. A risk model utilizing these biomarkers classified PC patients into high- and low-risk cohorts, with high-risk patients showing lower survival probabilities. Subsequently, risk score and N stage were identified as independent prognostic factors, leading to the development of a nomogram. Notably, both risk cohorts showed significant enrichment in the "cell cycle" pathway. Furthermore, TP53 mutations were prevalent in both high-risk (76%) and low-risk (50%) cohorts. Correlation analysis indicated that PVRL2 (an immunosuppressive factor), CD276 (an immunoactivator), and CCL20 (a chemotactic factor) had the highest positive correlation with the risk score. In this study, ADH1A, ACACB, CD36, CERS4, PDE3B, ALOX5, and CRAT were identified as biomarkers for PC, with their expression levels validated in clinical samples. These findings offered a potential theoretical foundation for developing targeted treatments for PC.

LRRK2 mediates haloperidol-induced changes in indirect pathway striatal projection neurons

Haloperidol is used to manage psychotic symptoms in several neurological disorders through mechanisms that involve antagonism of dopamine D2 receptors that are highly expressed in the striatum. Significant side effects of haloperidol, known as extrapyramidal symptoms, lead to motor deficits similar to those seen in Parkinson's disease and present a major challenge in clinical settings. The underlying molecular mechanisms responsible for these side effects remain poorly understood. Parkinson's disease-associated leucine-rich repeat kinase 2 (LRRK2) has an essential role in striatal physiology and a known link to dopamine D2 receptor signaling. Here, we systematically explore convergent signaling of haloperidol and LRRK2 through pharmacological or genetic inhibition of LRRK2 kinase, as well as knock-in mouse models expressing pathogenic mutant LRRK2 with increased kinase activity. Behavioral assays show that LRRK2 kinase inhibition ameliorates haloperidol-induced motor changes in mice. A combination of electrophysiological and anatomical approaches reveals that LRRK2 kinase inhibition interferes with haloperidol-induced changes, specifically in striatal neurons of the indirect pathway. Proteomic studies and targeted intracellular pathway analyses demonstrate that haloperidol induces a similar pattern of intracellular signaling as increased LRRK2 kinase activity. Our study suggests that LRRK2 kinase plays a key role in striatal dopamine D2 receptor signaling underlying the undesirable motor side effects of haloperidol. This work opens up new therapeutic avenues for dopamine-related disorders, such as psychosis, also furthering our understanding of Parkinson's disease pathophysiology.

Reshaping the gut microbiota: A novel oppinion of Eucommiae cortex polysaccharide alleviate learning and memory impairments in Alzheimer's disease

BACKGROUND

Alzheimer's disease (AD), which is a chronic neurodegenerative disorder, is marked by the progressive deteriorations in learning and memory capabilities. The microbiota-gut-brain axis has come to be regarded as a crucial element in relation to the pathogenesis as well as the treatment of AD. Eucommiae cortex polysaccharides (EPs), being among the most plentiful substances present in the Eucommiae cortex, show the potential to exert immunomodulatory and neuroprotective function. However, whether EPs are protective against AD and their mechanism of action remain to be investigated OBJECTIVES: We hypothesize that EPs can regulate brain glutamine metabolism through gut microbiota and the butyric acid metabolized by them, improve oxidative stress and autophagy in the brain, and thus alleviate AD.

METHODS

In the present study, we used EPs (0.25 % w/w in food) and fecal microbiota transplantation, as well as butyrate supplementation (0.1 M in water), to intervene in AD mice. Multi-omics were used to determine the mechanism by which EPs improve AD-related learning and memory impairments.

RESULTS

Our results suggest that EPs, functioning as a prebiotic, alleviated learning and memory impairments in AD mice. Mechanistically, EPs are able to reshape the gut microbiota, promote the growth of gut microbiota involved in short-chain fatty acid metabolism, particularly butyrate-producing microbes. The butyrate produced by these microbes improves the brain microenvironment by modulating oxidative stress and autophagy mediated by brain glutamate metabolism, improving learning and memory impairments in AD mice, and inhibiting the formation and deposition of beta-amyloid proteins. Fecal microbiota transplantation (FMT) and butyrate supplementation further confirm this conclusion.

CONCLUSIONS

Our results highlighted that EPs can alleviate learning and memory impairments in AD with a gut microbiota-dependent manner and that butyric acid metabolized by butyric acid-metabolizing bacteria in the gut plays a central role in regulating brain glutamine metabolism to improve brain microenvironmental homeostasis. Meanwhile, the present study provides new insights into the treatment of AD with natural products.

Bioinformatic Analysis of Apoptosis-Related Genes in Preeclampsia Using Public Transcriptomic and Single-Cell RNA Sequencing Datasets

Purpose

Apoptosis, which is crucial in preeclampsia (PE), affects trophoblast survival and placental function. We used transcriptomics and single-cell RNA sequencing (scRNA-seq) to explore apoptosis-related genes (ARGs) and their cellular mechanisms as potential PE biomarkers.

Patients and Methods

All the data included in this study were sourced from public databases. We used scRNA-seq and differential expression analysis, combined with five algorithms from the CytoHubba plugin, to identify ARGs as PE biomarkers. These were integrated into diagnostic nomograms. Mechanistic studies involved enrichment analysis and immune profiling. Biomarker expression was examined at the single-cell level, and verified in clinical samples by RT-qPCR.

Results

Cluster of Differentiation 44 (CD44), Macrophage migration inhibitory factor (MIF), PIK3R1, and Toll-like receptor 4 (TLR4) were identified as PE biomarkers. CD44 and TLR4 were down-regulated, while MIF and PIK3R1 were up-regulated. When integrated into the diagnostic nomogram, they showed clinical utility and affected cell functions. In the immune profile of PE, monocytes decreased, resting NK cells increased, and the activities of APC, checkpoint, T-cell co-stimulation, and MHC class I pathways reduced. ScRNA-seq identified 11 cell types, 10 of which were significantly different. Endothelial cell communication with other cell types decreased, while the interaction between common myeloid progenitors (CMP) and villous cytotrophoblasts (VCT) enhanced. The expression levels of CD44, MIF, and PIK3R1 in VCT were significantly different and key to PE. Their decrease in early PE and increase in late PE reflected the placenta's adaptation to adverse pregnancy conditions.

Conclusion

Four ARGs, CD44, MIF, PIK3R1, and TLR4, identified through comprehensive analyses, served as significant biomarkers for PE and offered insights into PE's cellular mechanisms of PE, providing valuable references for further research.

Identification and experimental verification of biomarkers related to butyrate metabolism in osteoarthritis

Butyrate plays a crucial role in osteoarthritis (OA) development. However, the relationship between butyrate metabolism-related genes (BMRGs) and OA remains unclear. This study investigates the potential correlation between BMRGs and OA using OA-related datasets (GSE55235, GSE12021 and GSE143514). Differential expression analysis identified 38 differentially expressed butyrate metabolism-related genes (DE-BMRGs) from the overlap of 782 OA-related differentially expressed genes (DEGs) and 385 BMRGs in GSE55235. Enrichment analysis indicated that these DE-BMRGs were tightly associated with cell proliferation, differentiation, and apoptosis, which are key processes in OA pathogenesis. Six candidate biomarkers (IL1B, IGF1, CXCL8, PTGS2, SERPINE1, MMP9) were identified through two machine-learning algorithms. IL1B, CXCL8, and PTGS2 were upregulated in controls, exhibiting consistent patterns across validation datasets. Gene set enrichment analysis (GSEA) revealed that dysregulated expression of these biomarkers lead to abnormal cell proliferation and differentiation, contributing to OA progression. Furthermore, significant differences in immune cell infiltration-particularly activated and resting mast cells-along with correlations to immune regulatory factors (CD86, CXCL12, TNFSF9, IL6), highlighted potential therapeutic targets. Quantitative RT-PCR further confirmed elevated expression of IL1B, CXCL8 and PTGS2 in control group. This study identifies IL1B, CXCL8 and PTGS2 as OA-related biomarkers linked to butyrate metabolism, offering a theoretical foundation and potential therapeutic strategies.

Pan-cancer analysis of CHRDL1 expression and its mechanistic role in inhibiting EMT via the TGF-β pathway in lung adenocarcinoma

Background

The primary objective of this study is to conduct a pan-cancer analysis of CHRDL1 expression, to determine its correlation with patient survival rates, immune cell infiltration, and drug sensitivity. Additionally, the study aimed to further validate the mechanistic role of CHRDL1 in lung adenocarcinoma (LUAD), clarifying its contribution to tumorigenesis and evaluating its potential as a therapeutic target for LUAD.

Methods

We employed bioinformatics strategies to analyze CHRDL1 expression using data from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression Project (GTEx). Survival analysis was executed with GEPIA2, while drug sensitivity to chemotherapeutic agents was evaluated via the CellMiner database. Mutational profiles were examined using cBioPortal, and the immune microenvironment was assessed through the TIMER database. To substantiate our findings, we conducted in vitro cellular assays and in vivo animal models to validate the mechanistic actions of CHRDL1 in LUAD.

Results

CHRDL1 expression levels showed significant variation across different cancer types, with tumor tissues typically demonstrating lower expression compared to their normal counterparts. In certain cancers, elevated CHRDL1 expression was linked to poorer survival outcomes, whereas in LUAD, it was associated with improved survival. Furthermore, CHRDL1 expression correlated with the IC50 values of multiple chemotherapeutic drugs and played a role in modulating the immune microenvironment. We discovered that CHRDL1 inhibits the epithelial-mesenchymal transition (EMT) in LUAD through the TGF-β pathway.

Conclusion

CHRDL1 exerts a complex influence on cancer development and progression, particularly in LUAD, by impacting tumor progression, immune regulation, chemosensitivity, and EMT regulation. This research offers valuable insights into the overarching mechanisms of cancer progression and aids in the discovery of innovative therapeutic strategies for LUAD treatment.

Identification and characterization of mitochondrial autophagy-related genes in osteosarcoma and predicting clinical prognosis

Osteosarcoma (OS), the most prevalent primary malignant bone tumor, is characterized by a poor prognosis and high metastatic potential. Mitochondrial autophagy has been implicated in cancer suppression. This study aimed to identify prognostic genes associated with mitochondrial autophagy in OS. Public datasets, including TARGET-OS, GSE99671, and GSE21257, were retrieved for analysis. Differentially expressed genes (DEGs1) between OS and normal samples were identified from GSE99671. Single-sample Gene Set Enrichment Analysis (ssGSEA) was applied to quantify the enrichment scores of 29 mitochondrial autophagy-related genes (MARGs) in OS samples from TARGET-OS, categorizing them into high- and low-score groups to extract DEGs2. The intersection of DEGs1 and DEGs2 yielded mitochondrial autophagy-associated differentially expressed genes (MDGs). Prognostic genes were subsequently screened through a multi-step regression analysis, and a risk score was computed. TARGET-OS samples were stratified into high- and low-risk groups based on the optimal cutoff value of the risk score. GSEA was conducted between the two risk groups. Additionally, associations between prognostic genes and the immune microenvironment were explored. A total of 31 MDGs were identified from the overlap of 3,207 DEGs1 and 622 DEGs2. Five prognostic genes-KLK2, NRXN1, HES5, OR2W3, and HS3ST4-were further selected. Kaplan-Meier survival analysis indicated significantly reduced survival in the high-risk group. GSEA revealed enrichment in ABC transporter activity and glycolysis/gluconeogenesis pathways. Immunoanalysis demonstrated significant differences in 11 immune cell populations and three immune functions between risk groups, notably myeloid-derived suppressor cells (MDSCs) and Type 1 T helper cells. HS3ST4 exhibited the strongest positive correlation with macrophages, whereas NRXN1 showed the most pronounced negative correlation with memory B cells. Expressions of HAVCR2 and PDCD1LG2 were elevated in the low-risk group. Functional analysis indicated significant differences in dysfunction patterns between risk groups. This study identified five mitochondrial autophagy-related prognostic genes and constructed a risk model, offering novel insights into OS diagnosis and therapeutic strategies.

Identification and Validation of Prognostic Genes Related to Histone Lactylation Modification in Glioblastoma: An Integrated Analysis of Transcriptome and Single-cell RNA Sequencing

Background: The impact of histone lactylation modification (HLM) on glioblastoma (GBM) progression is not well understood. This study aimed to identify HLM-associated prognostic genes in GBM and explore their mechanisms of action. Methods: The presence and role of lactylation in glioma clinical tissue samples and its correlation with GBM progression were analysed through immunohistochemical staining and Western blotting. Sequencing data for GBM were obtained from publicly available databases. An initial correlation analysis was performed between global HLM levels and GBM. Differentially expressed HLM-related genes (HLMRGs) in GBM were identified by intersecting differentially expressed genes (DEGs) from the TCGA-GBM dataset, key module genes derived from weighted gene coexpression network analysis (WGCNA), and previously reported HLMRGs. Prognostic genes were subsequently identified through univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses, which formed the basis for constructing a risk prediction model. Associations between HLMRGs and GBM were further evaluated via single-cell RNA sequencing (scRNA-seq) datasets. Complementary analyses, including functional enrichment, immune infiltration, somatic mutation, and nomogram-based survival prediction, were conducted alongside in vitro experiments. Additionally, drug sensitivity and Chinese medicine prediction analyses were performed to identify potential therapeutic agents for GBM. Results: We detected a significant increase in global lactylation levels in GBM, which correlated with patient survival. We identified 227 differentially expressed HLMRGs from the intersection of 3,343 differentially expressed genes and 948 key module genes, indicating strong prognostic potential. Notably, genes such as SNCAIP, TMEM100, NLRP11, HOXC11, and HOXD10 were highly expressed in GBM. Functional analysis suggested that HLMRGs are involved primarily in pathways related to cytokine‒cytokine receptor interactions, cell cycle regulation, and cellular interactions, including microglial differentiation states. Further connections were established between HLMRGs and infiltrating immune cells, particularly type 1 T helper (Th1) cells, as well as mutations in genes such as PTEN. The potential therapeutic agents identified included ATRA and Can Sha. Conclusion: The HLM-related gene risk prediction model shows substantial promise for improving patient management in GBM, providing crucial insights for clinical prognostic evaluations and immunotherapeutic approaches in GBM.

Mechanism of action and experimental validation of key genes common to diabetic retinopathy and coronary heart disease based on multiple bioinformatics investigations

Introduction

The relationship between diabetic retinopathy (DR) and coronary artery disease (CHD) has been established as a reliable predictor. However, the underlying mechanisms linking these two conditions remain poorly understood. Identifying common key genes could provide new therapeutic targets for both diseases.

Methods

Public databases were used to compile training and validation datasets for DR and CHD. Machine learning algorithms and expression validation were employed to identify these key genes. To investigate immune cell differences, single-sample gene set enrichment analysis (ssGSEA) and the Wilcoxon test were applied. Spearman correlation analysis further explored the relationship between key genes and immune cell variations. Additionally, potential therapeutic drugs targeting these key genes were identified and a key gene-drug network was constructed. The role of the key genes in the pathogenesis of DR and CHD was further examined through reverse transcription-quantitative polymerase chain reaction (RT-qPCR).

Results

Consistent expression trends observed across datasets (GSE221521, GSE113079, GSE189005, GSE42148) led to the identification of HIRIP3 and ZNF416 as key genes. In GSE221521, HIRIP3 was positively correlated with CD56 bright natural killer cells (cor = 0.329, P < 0.001) and type 1T helper cells (cor = 0.327, P < 0.001), while ZNF416 showed significant correlations with CD4 T cell activation (cor = 0.340, P < 0.001) and type 1T helper cells (cor = 0.273, P < 0.05). Moreover, 82 transcription factors (TFs) were predicted, including SP3. Binding free energy calculations for key genes and potential drugs suggested stable binding conformations. RT-qPCR results revealed elevated expression of both HIRIP3 and ZNF416 in the control group compared to the DR with CHD (DRwCHD) group, with only ZNF416 showing significant differences between the groups (p < 0.05).

Discussion

These findings highlight HIRIP3 and ZNF416 as crucial genes in DR and CHD detection, providing a foundation for identifying novel therapeutic targets for both diseases.

Identification and validation of biomarkers associated with mitochondrial dysfunction and ferroptosis in rat spinal cord injury

Introduction

Mitochondrial dysfunction and ferroptosis have been implicated in the pathophysiological processes following spinal cord injury (SCI), with evidence suggesting their interplay influences neuronal cell survival and repair mechanisms. This study seeks to identify mitochondria- and ferroptosis-related biomarkers through comprehensive bioinformatics analysis.

Methods

Mitochondria- and ferroptosis-associated differentially expressed genes (DEGs) were identified through the integration of differential expression analysis and weighted gene co-expression network analysis. Two machine learning algorithms, least absolute shrinkage and selection operator (LASSO) and Boruta, were employed to isolate SCI-associated feature genes. Biomarkers were subsequently identified by analyzing their expression levels. An artificial neural network (ANN) diagnostic model was constructed to predict SCI likelihood based on these biomarkers. Further evaluations were performed using enrichment analysis, immune infiltration profiling, molecular modulation assessment, and drug prediction. The biomarkers' expression levels were validated using RT-qPCR.

Results

In this study, two biomarkers, Hcrt and Cdca2, linked to mitochondrial function and ferroptosis in SCI, were found to be highly expressed in SCI samples. Tissue-specific analysis from the GTEx database revealed expression of these biomarkers in brain and spinal cord tissues. The ANN model, constructed using these biomarkers, accurately discriminated between SCI and control samples. Enrichment analysis highlighted several co-enriched pathways for Hcrt and Cdca2, including "ubiquitin-mediated proteolysis," "endocytosis," and the "neurotrophin signaling pathway." Immune infiltration analysis, based on the Wilcoxon test, demonstrated significant differences in T follicular helper cell levels, which were lower in SCI samples compared to controls. Notably, T follicular helper cells exhibited a positive correlation with Hcrt and a negative correlation with Cdca2. Furthermore, seven transcription factors, including CEBPB, FOXC1, and GATA2, were identified as potential co-regulators of Hcrt and Cdca2. Drug prediction analysis revealed stable interactions of Cdca2 with pinosylvin, zinc acetate dihydrate, hydroquinone, lucanthone, and dasatinib. RT-qPCR validation confirmed the expression patterns of Hcrt and Cdca2 in alignment with the dataset, showing statistically significant differences.

Discussion

This study identifies Hcrt and Cdca2 as biomarkers related to mitochondrial function and ferroptosis in SCI, providing new insights for the diagnosis and mechanistic understanding of SCI.

Phenotypic and genotypic characterization of antimicrobial resistance and virulence profiles of Salmonella enterica serotypes isolated from necropsied horses in Kentucky

Salmonella is a foodborne pathogen that poses a significant threat to global public health. It affects several animal species, including horses. Salmonella infections in horses can be either asymptomatic or cause severe clinical illness. Infections caused by Salmonella are presently controlled with antibiotics. Due to the formation of biofilms and the emergence of antimicrobial resistance, the treatment has become more complicated. Our study focused on investigating the prevalence of Salmonella enterica in necropsied horses, assessing the capability for biofilm formation, and motility, determining the phenotypic and genotypic profiles of antibiotic resistance, and detecting virulence genes. A total of 2,182 necropsied horses were tested for the presence of Salmonella. Intestinal samples were enriched in selenite broth and cultured on hektoen and eosin methylene blue agar plates, whereas other samples were directly cultured on aforementioned plates. Confirmation of the serotypes was performed according to the Kauffmann-White-Le Minor Scheme followed by biofilm formation screening using crystal violet assay. The resistance profile of the isolates was determined by broth microdilution assay using the Sensititre️ Vet (Equine EQUIN2F). The genotypic antimicrobial resistance (AMR) and virulence profiles were detected using polymerase chain reaction (PCR). The overall prevalence of Salmonella was 1.19% (26/2182), with 11 different serotypes identified. Salmonella Typhimurium was the most prevalent serotype with 19.2% prevalence. All of the isolates were identified as biofilm producers and motile. Virulence genes related to invasion (invA, hilA, mgtC, and spiA), biofilm formation (csgA and csgB), and motility (filA, motA, flgG, figG, flgH, fimC, fimD, and fimH) of Salmonella were detected among 100% of the isolates. An overall 11.4% of the isolates were identified as multidrug-resistant (MDR), with resistance to gentamicin, amikacin, ampicillin, ceftazidime, ceftiofur, chloramphenicol, and trimethoprim/sulfamethoxazole. We found that beta-lactamase-producing genes blaTEM, blaCTXM, and blaSHV2 were identified in 11.5% of the isolates, while only 3.8% carried the blaOXA-9 gene. The presence of MDR pathogenic Salmonella in horses is alarming for human and animal health, especially when they have a high affinity for forming biofilm. Our study found horses as potential sources of pathogenic Salmonella transmission to humans. Thus, it is important to perform continuous monitoring and surveillance studies to track the source of infection and develop preventive measures.

IMPORTANCE

This study focuses on understanding how Salmonella, specifically isolated from horses, can resist antibiotics and cause disease. Salmonella is a well-known foodborne pathogen that can pose risks not only to animals but also to humans. By studying the bacteria from necropsied horses, the research aims to uncover how certain Salmonella strains develop resistance to antibiotics and which genetic factors make them more dangerous. In addition to antibiotic resistance, the research explores the biofilm-forming ability of these strains, which enhances their survival in harsh environments. The study also investigates their motility, a factor that contributes to the spread of infection. The findings can improve treatment strategies for horses and help prevent the transmission of resistant bacteria to other animals as well as humans. Ultimately, the research could contribute to better management of antibiotic resistance in both veterinary and public health contexts, helping to safeguard animal welfare and public health.

Transcriptome Data Combined With Mendelian Randomization Analysis Identifies Key Genes Associated With Mitochondria and Programmed Cell Death in Intervertebral Disc Degeneration

Background

Intervertebral disc degeneration (IDD) is a major cause of cervical and lumbar diseases, significantly impacting patients' quality of life. Mitochondria and cell death have been implicated in IDD, but the key related genes remain unknown.

Methods

Differentially expressed genes (DEGs) between IDD and control samples were identified using GSE70362. Mitochondria-related genes (MRGs) and programmed cell death-related genes (PCDRGs) were intersected with DEGs to find DE-MRGs and DE-PCDRGs. Weighted gene co-expression network analysis (WGCNA) identified key module genes, and the overlap with DEGs revealed candidate genes. Mendelian randomization (MR) analysis was used to determine genes causally linked to IDD. Machine learning and expression validation further refined key genes, which were then used to build a nomogram to predict IDD risk. Additionally, gene set enrichment analysis (GSEA), immune infiltration, and single-cell analysis were performed.

Results

A total of 515 DEGs were intersected with 224 key module genes, yielding 31 candidate genes. Six genes-BCKDHB, BID, TNFAIP6, VRK1, CAB39L, and TMTC1-showed a causal relationship with IDD. BID, TNFAIP6, and TMTC1 were further identified as key genes through machine learning and validation. A nomogram was developed based on these genes. GSEA revealed BID and TMTC1 were enriched in N-glycan biosynthesis, TNFAIP6 and TMTC1 in aminoacyl tRNA biosynthesis, and BID and TMTC1 in ribosomal pathways. Activated dendritic cells, CD56dim natural killer cells, monocytes, and other immune cells were elevated in IDD, with TNFAIP6 strongly correlating with activated dendritic cells. Key genes were expressed at higher levels in degraded samples.

Conclusion

BID, TMTC1, and TNFAIP6 were identified as key genes linked to mitochondria and cell death in IDD, offering new insights for diagnosis and treatment.

Anoikis-related PLCB4 is linked to immunotherapy response in osteosarcoma

Osteosarcoma, the most common primary bone malignancy, poses significant management challenges due to its aggressiveness and metastatic potential. This study investigates the role of anoikis-related genes, particularly phospholipase C beta 4 (PLCB4), as a prognostic biomarker in osteosarcoma. We analyzed transcriptome data from the TARGET and GSE21257 cohorts using bioinformatics tools, identifying 15 significant genes, with PLCB4 as a key marker linked to decreased survival. Our findings indicate a negative correlation between PLCB4 and immune microenvironment scores and checkpoint molecules, suggesting its impact on immunotherapy responses. Drug sensitivity analyses revealed that high PLCB4 expression correlates with lower IC50 values for several chemotherapeutic agents. In vitro experiments showed that silencing PLCB4 inhibited cell proliferation and reduced PD-L1 expression. This study underscores the critical role of PLCB4 in osteosarcoma progression and its potential as a therapeutic target, offering insights into the molecular mechanisms of osteosarcoma biology and improving prognostic accuracy and treatment strategies.

Identification of Prognostic Genes Related to Cell Senescence and Lipid Metabolism in Glioblastoma Based on Transcriptome and Single-Cell RNA-Seq Data

Glioblastoma (GBM) is the most aggressive primary brain cancer, with poor prognosis due to its aggressive behavior and high heterogeneity. This study aimed to identify cellular senescence (CS) and lipid metabolism (LM)-related prognostic genes to improve GBM prognosis and treatment. Transcriptome and scRNA-seq data, CS-associated genes (CSAGs), and LM-related genes (LMRGs) were acquired from public databases. Prognostic genes were identified by intersecting CSAGs, LMRGs, and differentially expressed genes (DEGs), followed by WGCNA and univariate Cox regression. A risk model and nomogram were constructed. Analyses covered clinicopathological features, immune microenvironment, somatic mutations, and drug sensitivity. GBM scRNA-seq data identified key cells and prognostic gene expression. SOCS1 and PHB2 were identified as prognostic markers, contributing to the construction of a robust risk model with excellent predictive ability. High-risk group (HRG) patients had poorer survival, higher immune and stromal scores, and distinct somatic mutation profiles. Drug sensitivity analysis revealed significant differences in IC50 values. In microglia differentiation, SOCS1 and PHB2 showed dynamic expression patterns. These findings provide new strategies for GBM prognosis and treatment.

Polycystic ovary syndrome and epithelial-mesenchymal transition: Mendelian randomization and single-cell analysis insights

BACKGROUND

The process of epithelial-mesenchymal transition (EMT) may promote fibrosis in ovarian tissue related to polycystic ovary syndrome (PCOS), thus affecting ovarian function and hormonal balance.

OBJECTIVE

This study aimed to explore key genes associated with EMT in PCOS and their potential molecular regulatory mechanisms, exclusively from the perspective of transcriptomics and single-cell RNA sequencing (scRNA-seq), combined with Mendelian Randomization (MR) analysis.

METHODS

The dataset for PCOS and EMT-related genes (EMT-RGs) were sourced from public databases. The key genes in this study were identified via differential expression analysis, MR, and evaluation of expression levels. Enrichment analysis and a series of functional analyses were conducted on these genes to further elucidate their potential mechanisms. Subsequently, using scRNA-seq data and validation of the expression of key genes, key cell group in PCOS were identified, followed by pseudo-time and cell communication analyses to provide deeper insights.

RESULTS

Three key genes, NUCB2 [odds ratio (OR) = 0.8634, 95% confidence interval (CI): 0.8145-0.9152, P < 0.0001], PGF (OR = 0.8393, 95% CI: 0.7185-0.9805, P < 0.05), and CRIM1 (OR = 0.7539, 95% CI: 0.6556-0.670, P < 0.0001), were identified as having a unidirectional causal association with PCOS and were associated with a reduced risk of PCOS. In public datasets, NUCB2 exhibited significantly increased expression in PCOS samples, while PGF and CRIM1 showed the opposite trends. These three genes were enriched in pathways related to cellular functions, metabolic processes, and the operation of the nervous system, and they were co-expressed in smooth muscle. Additionally, five cell clusters were annotated, among which fibroblasts were identified as key cells due to their highest expression of all three key genes. Further analysis revealed a bifurcation event occurring during the mid-development stage of fibroblasts, with PCOS samples displaying a higher abundance of fibroblasts. In PCOS samples, fibroblasts exhibited more extensive communication with secretory epithelial cells, indicating a more complex intercellular interaction within this condition.

CONCLUSION

This study identified three EMT-RGs: NUCB2, PGF, and CRIM1, which were associated with a reduced risk of PCOS, with fibroblast identified as a key cell group in the disease's pathology. This provides new insights for PCOS research.

Deciphering Planktonic Bacterial Community Assembly in the Storage Reservoir of the Long-Distance Water Diversion Project

Storage reservoirs are crucial components of long-distance water diversion projects, where water diversion may lead to changes in microbial diversity and community structure. Seasonal variations also drive alterations in microbial communities. However, the way that microbes assemble under the combined effects of water diversion and seasonal variations in the storage reservoir has not been extensively studied. Jihongtan Reservoir is the terminal storage reservoir of the Yellow River to Qingdao Water Diversion Project (YQWD), which had an average annual water diversion period exceeding 290 days in recent years. In this study, 16S rDNA amplicon sequencing was used to investigate the seasonal dynamics and assembly of planktonic bacterial communities during the water diversion period in Jihongtan Reservoir. The results indicate that planktonic bacteria were able to maintain stable diversity across all four seasons, while the community structure underwent significant seasonal succession. Water temperature (WT) was found to be the primary driving environmental factor influencing the seasonal dynamic of planktonic bacterial communities. Co-occurrence network patterns of planktonic bacterial communities varied across different seasons, particularly in spring and winter. The spring network displayed the most complexity, showcasing the highest connectivity and greater stability. In contrast, the winter network was simpler, exhibiting lower local connectivity but higher global connectivity and lower stability. The analysis of the neutral community model and null model revealed that the relative importance of deterministic and stochastic processes in governing planktonic bacterial community assembly varies seasonally. Stochastic processes (dispersal limitation) are more prominent in spring, summer, and autumn, while deterministic processes (heterogeneous selection) play a greater role in winter. This study is essential for gaining a comprehensive understanding of the effects of water diversion projects and offers valuable references for the assessment of other similar projects.

Comprehensive analysis of aging-related gene expression patterns and identification of potential intervention targets

PURPOSE

This study aims to understand the molecular mechanisms underlying the aging process and identify potential interventions to mitigate age-related decline and diseases.

METHODS

This study utilized the GSE168753 dataset to conduct comprehensive differential gene expression analysis and co-expression module analysis. Machine learning and Mendelian randomization analyses were employed to identify core aging-associated genes and potential drug targets. Molecular docking simulations and mediation analysis were also performed to explore potential compounds and mediators involved in the aging process.

RESULTS

The analysis identified 4164 differentially expressed genes, with 1893 upregulated and 2271 downregulated genes. Co-expression analysis revealed 21 modules, including both positively and negatively correlated modules between older age and younger age groups. Further exploration identified 509 aging-related genes with distinct biological functions. Machine learning and Mendelian randomization analyses identified eight core genes associated with aging, including DPP9, GNAZ, and RELL2. Molecular docking simulations suggested resveratrol, folic acid, and ethinyl estradiol as potential compounds capable of attenuating aging through modulation of RELL2 expression. Mediation analysis indicated that eosinophil counts and neutrophil count might act as mediators in the causal relationship between genes and aging-related indicators.

CONCLUSION

This comprehensive study provides valuable insights into the molecular mechanisms of aging and offers important implications for the development of anti-aging therapeutics. Key Messages What is already known on this topic - Prior research outlines aging's complexity, necessitating precise molecular targets for intervention. What this study adds - This study identifies novel aging-related genes, potential drug targets, and therapeutic compounds, advancing our understanding of aging mechanisms. How this study might affect research, practice, or policy - Findings may inform targeted therapies for age-related conditions, influencing future research and clinical practices.

Unveiling Drought Tolerant Cotton Genotypes: Insights from Morpho-Physiological and Biochemical Markers at Flowering

Drought stress substantially restricts cotton growth, decreasing cotton production potential worldwide. This study evaluated cotton genotypes at the flowering stage to identify drought-resilient genotypes under moderate and severe drought conditions using physio-morphic and biochemical markers. Five genotypes were examined in a completely randomized design with three replicates across three treatments. Growth and biochemical traits were measured after 14 days of drought stress. The Multi-trait Genotype-Ideotype Distance Index (MGIDI) identified the most drought-tolerant genotypes. Severe drought had a pronounced negative effect on growth and biochemical traits, followed by moderate drought. Among the genotypes, FH-912 exhibited the strongest resilience, with significant increases in proline, peroxidase, catalase, and total chlorophyll. In contrast, chlorophyll a and transpiration rates were largely unaffected. Genotypes VH-351, VH-281, and GH-99 showed moderate drought tolerance, while FH-556 was highly sensitive to water stress. Statistical analyses, including ANOVA, PCA, and heatmaps, confirmed FH-912's superior performance under drought stress. The drought-resilient genotype, FH-912, holds promise for breeding drought-tolerant cotton varieties to sustain cotton productivity in water-limited environments, especially in drought-prone regions.

Neuronal hyperactivity in neurons derived from individuals with gray matter heterotopia

Periventricular heterotopia (PH), a common form of gray matter heterotopia associated with developmental delay and drug-resistant seizures, poses a challenge in understanding its neurophysiological basis. Human cerebral organoids (hCOs) derived from patients with causative mutations in FAT4 or DCHS1 mimic PH features. However, neuronal activity in these 3D models has not yet been investigated. Here we show that silicon probe recordings reveal exaggerated spontaneous spike activity in FAT4 and DCHS1 hCOs, suggesting functional changes in neuronal networks. Transcriptome and proteome analyses identify changes in neuronal morphology and synaptic function. Furthermore, patch-clamp recordings reveal a decreased spike threshold specifically in DCHS1 neurons, likely due to increased somatic voltage-gated sodium channels. Additional analyses reveal increased morphological complexity of PH neurons and synaptic alterations contributing to hyperactivity, with rescue observed in DCHS1 neurons by wild-type DCHS1 expression. Overall, we provide new comprehensive insights into the cellular changes underlying symptoms of gray matter heterotopia.

Transcriptome and single-cell profiling of the mechanism of diabetic kidney disease

BACKGROUND

The NOD-like receptor thermal protein domain associated protein 3 (NLRP3) inflammasome may play an important role in diabetic kidney disease (DKD). However, the exact link remains unclear.

AIM

To investigate the role of the NLRP3 inflammasome in DKD.

METHODS

Using datasets from the Gene Expression Omnibus database, 30 NLRP3 inflammasome-related genes were identified. Differentially expressed genes were selected using differential expression analysis, whereas intersecting genes were selected based on overlapping differentially expressed genes and NLRP3 inflammasome-related genes. Subsequently, three machine learning algorithms were used to screen genes, and biomarkers were identified by overlapping the genes from the three algorithms. Potential biomarkers were validated by western blotting in a db/db mouse model of diabetes.

RESULTS

Two biomarkers, sirtuin 2 (SIRT2) and caspase 1 (CASP1), involved in the Leishmania infection pathway were identified. Both biomarkers were expressed in endothelial cells. Pseudo-temporal analysis based on endothelial cells showed that DKD mostly occurs during the mid-differentiation stage. Western blotting results showed that CASP1 expression was higher in the DKD group than in the control group (P < 0.05), and SIRT2 content decreased (P < 0.05).

CONCLUSION

SIRT2 and CASP1 provide a potential theoretical basis for DKD treatment.

Identification of Key Genes in Esketamine's Therapeutic Effects on Perioperative Neurocognitive Disorders via Transcriptome Sequencing

Background

Esketamine ameliorates propofol-induced brain damage and cognitive impairment in mice. However, the precise role and underlying mechanism of esketamine in perioperative neurocognitive disorders (PND) remain unclear. Therefore, this study aimed to investigate the key genes associated with the role of esketamine in PND through animal modeling and transcriptome sequencing.

Methods

The present study established a mice model of PND and administered esketamine intervention to the model, and mice were divided into control, surgical group, and surgical group with esketamine. Behavioral assessments were conducted using the Morris water maze and Y maze paradigms, while transcriptome sequencing was performed on hippocampal samples obtained from 3 groups. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were performed on sequencing data to identify candidate genes related to esketamine treating PND. Thereafter, protein-protein interaction (PPI) network analysis was implemented to select key genes. The genes obtained from each step were subjected to enrichment analysis, and a regulatory network for key genes was constructed.

Results

The Morris water maze and Y maze findings demonstrated the successful construction of our PND model, and indicated that esketamine exhibits a certain therapeutic efficacy for PND. Ank1, Cbln4, L1cam, Gap43, and Shh were designated as key genes for subsequent analysis. The 5 key genes were significantly enriched in cholesterol biosynthesis, nonsense mediated decay (NMD), formation of a pool of free 40s subunits, major pathway of rRNA processing in the nucleolus and cytosol, among others. Notably, the miRNAs, mmu-mir-155-5p and mmu-mir-1a-3p, functionally co-regulated the expression of Ank1, Gap43, and L1cam.

Conclusion

We uncovered the therapeutic efficacy of esketamine in treating PND and identified 5 key genes (Ank1, Cbln4, L1cam, Gap43, and Shh) that contribute to its therapeutic effects, providing a valuable reference for further mechanistic studies on esketamine's treatment of PND.

Disruption of boreal lake circulation in response to mid-Holocene warmth; evidence from the varved sediments of Lake Nautajärvi, southern Finland

Future climate projections are expected to have a substantial impact on boreal lake circulation regimes. Understanding lake sensitivity to warmer climates is therefore critical for mitigating potential ecological and societal impacts. The Holocene Thermal Maximum (HTM; ca 7-5 ka BP) provides a valuable analogue to investigate lake responses to warmer climates devoid of major anthropogenic influences. Here, we analyse the micro-X-ray core scanning profiles (μ-XRF) of the annually laminated (varved) sediments from Lake Nautajärvi (NAU-23) in southern Finland to elucidate changes in lake circulation and sedimentation patterns. Principal component analysis (PCA) identifies two key components in the geochemical data associated with the nature of the sediments, i.e. detrital vs organic sedimentation (PC1), and hypolimnetic oxidation (PC2). Our findings reveal that during the HTM, the lake became more sensitive to changes in oxygenation and mixing intensity. These changes were triggered by a warmer climate, which increased organic matter and redox sensitive metal solute concentrations in the water column, strengthening lake stratification and weakening dimictic circulation patterns. Superimposed on HTM weakened circulation are distinct phases of increased oxidation and iron-rich varve formation that do not happen when the background conditions are cooler (i.e. the early and late Holocene). This is driven by temporary strengthening of the mixing regime in response to climatic variability and storminess cycles across southern Scandinavia. These findings demonstrate that whilst warmer conditions weaken boreal lake circulation regimes, they can also make them increasingly vulnerable to short term oscillations in prevalent climatic conditions and weather patterns, which could have significant impacts on lake water quality and aquatic ecosystems. These findings underscore the non-stationary nature of lake sensitivity to short-term climatic variability and emphasize the potential for similar shifts to occur under future warming scenarios.

Integrative bioinformatic approach reveals novel melatonin-related biomarkers for Alzheimer's disease

BACKGROUND

Melatonin (MLT) can improve mitophagy, thereby ameliorating cognitive deficits in Alzheimer's disease (AD) patients. Hence, our research focused on the potential value of MLT-related genes (MRGs) in AD through bioinformatic analysis.

METHODS

First, the key cells in the single-cell dataset GSE138852 were screened out based on the proportion of annotated cells and Fisher's test between the AD and control groups. The differentially expressed genes (DEGs) in the key cell and GSE5281 datasets were identified, and the MRGs in GSE5281 were selected via weighted gene coexpression network analysis. After intersecting two sets of DEGs and MRGs, we performed Mendelian randomization analysis to identify the MRGs causally related to AD. Biomarkers were further ascertained through receiver operating characteristic curve (ROC) and expression analysis in GSE5281 and GSE48350. Furthermore, gene set enrichment analysis, immune infiltration analysis and correlation analysis with metabolic pathways were conducted, as well as construction of a regulator network and molecular docking.

RESULTS

According to the Fisher test, oligodendrocytes were regarded as key cells due to their excellent abundance in the GSE138852 dataset, in which there were 281 DEGs between the AD and control groups. After overlapping with 3,490 DEGs and 550 MRGs in GSE5281, four genes were found to be causally related to AD, namely, G protein-coupled receptor, family C, group 5, member B (GPRC5B), Methyltransferase-like protein 7 A (METTL7A), NF-κB inhibitor alpha (NFKBIA) and RAS association domain family 4(RASSF4). Moreover, GPRC5B, NFKBIA and RASSF4 were deemed biomarkers, except for METTL7A, because of their indistinctive expression between the AD and control groups. Biomarkers might be involved in oxidative phosphorylation, adipogenesis and heme metabolism. Moreover, T helper type 17 cells, natural killer cells and CD56dim natural killer cells were significantly correlated with biomarkers. Transcription factors (GATA2, POU2F2, NFKB1, etc.) can regulate the expression of biomarkers. Finally, we discovered that all biomarkers could bind to MLT with a strong binding energy.

CONCLUSION

Our study identified three novel biomarkers related to MLT for AD, namely, GPRC5B, NFKBIA and RASSF4, providing a novel approach for the investigation and treatment of AD patients.

Quaternary Ammonium Compounds: A New Driver and Hidden Threat for mcr-1 Prevalence in Hospital Wastewater and Human Feces

The emergence of mobile colistin resistance gene mcr-1 has attracted global attention. The prevalence of mcr-1-positive Escherichia coli (MCRPEC) in humans largely decreased following the ban of colistin as an animal growth promoter in China. However, the prevalence of MCRPEC in the hospital environment and the relationship between disinfectants and mcr-1 remain unclear. We found that MCRPEC prevalence was low in the feces of healthy humans attending physical examinations in six hospitals (4.6%, 71/1532) but high in hospital wastewater (50.0%, 27/54). mcr-1 was mainly located on IncI2 (63.0% in wastewater and 62.0% in feces) and IncHI2 plasmids (18.5% in wastewater and 21.1% in feces). High similarity of the mcr-1 context and its carrying plasmids was observed in human and wastewater MCRPEC, with several isolates clustering together. The coexistence of the ESBL gene blaCTX-M with mcr-1 occurred in 19.7% of IncI2 plasmids. Notably, 60.0% of IncHI2 plasmids exhibited co-occurrence of mcr-1 with the disinfectant resistance gene (DRG) qacEΔ1, conferring resistance to quaternary ammonium compounds (QACs). We revealed that QACs, rather than the other two types of disinfectants─ortho-phthalaldehyde (OPA) and povidone-iodine (PVP-I)─select for plasmids carrying both qacEΔ1 and mcr-1 and elevate their conjugative transfer frequency. Monitoring of DRGs in MCRPEC and managing disinfectant use are urgently needed in healthcare settings to mitigate the spread of colistin resistance from hospital environments to inpatients.

Fibroblast growth factor-inducible 14 regulates satellite cell self-renewal and expansion during skeletal muscle repair

Skeletal muscle regeneration in adults is predominantly driven by satellite cells. Loss of satellite cell pool and function leads to skeletal muscle wasting in many conditions and disease states. Here, we demonstrate that the levels of fibroblast growth factor-inducible 14 (Fn14) were increased in satellite cells after muscle injury. Conditional ablation of Fn14 in Pax7-expressing satellite cells drastically reduced their expansion and skeletal muscle regeneration following injury. Fn14 was required for satellite cell self-renewal and proliferation as well as to prevent precocious differentiation. Targeted deletion of Fn14 inhibited Notch signaling but led to the spurious activation of STAT3 signaling in regenerating skeletal muscle and in cultured muscle progenitor cells. Silencing of STAT3 improved proliferation and inhibited premature differentiation of Fn14-deficient satellite cells. Furthermore, conditional ablation of Fn14 in satellite cells exacerbated myopathy in the mdx mouse model of Duchenne muscular dystrophy (DMD), whereas its overexpression improved the engraftment of exogenous muscle progenitor cells into the dystrophic muscle of mdx mice. Altogether, our study highlights the crucial role of Fn14 in the regulation of satellite cell fate and function and suggests that Fn14 can be a potential molecular target to improve muscle regeneration in muscular disorders.

Microplastics mediates the spread of antimicrobial resistance plasmids via modulating conjugal gene expression

Antimicrobial resistance (AMR) and environmental degradation are existential global public health threats. Linking microplastics (MPs) and AMR is particularly concerning as MPs pollution would have significant ramifications on controlling of AMR; however, the effects of MPs on the spread and genetic mechanisms of AMR bacteria remain unclear. Herein, we performed Simonsen end-point conjugation to investigate the impact of four commonly used MPs on transfer of clinically relevant plasmids. The transfer breadth of a representative pA/C_MCR-8 plasmid across bacterial communities was confirmed by the cell sorting and 16S rRNA gene amplicon sequencing. Our study shows that exposure to four commonly found MPs promotes the conjugation rates of four clinically relevant AMR plasmids by up to 200-fold, when compared to the non-exposed group and that the transfer rates are MP concentrations demonstrate a positive correlation with higher transfer rates. Furthermore, we show that MPs induce the expression of plasmid-borne conjugal genes and SOS-linked genes such as recA, lexA, dinB and dinD. High-throughput sequencing of the broad transmission of plasmid pA/C_MCR-8, shows distribution over two main phyla, Pseudomonadota (50.0 %-95.0 %) and Bacillota (0.4 %-2.0 %). These findings definitively link two global health emergencies - AMR and environmental degradation via MPs, and to tackle global AMR, we must also now consider plastic utilisation and waste management.

Identification and Validation of a Novel Prognostic Signature of Gastric Cancer Based on Seven Complement System-Related Genes: An Integrated Analysis

The complement system (CS) is linked to the progression of gastric cancer (GC), which has a high mortality rate, though its mechanisms in GC remain unclear. This study aims to identify CS-related prognostic genes with causal links to GC, and to investigate their mechanisms. The intersection between differentially expressed genes (DEGs) obtained from the TCGA-STAD dataset and CS-related genes (CRGs) was defined as differentially expressed CRGs (DCRGs). Prognostic genes with a causal association with GC (pCDCRGs) were sequentially identified via Mendelian randomization (MR) analysis and Cox and least absolute shrinkage and selection operator (LASSO) regression analyses, followed by expression analysis. A gene signature and a nomogram were then established based on pCDCRGs and independent prognostic factors. Subsequent analyses focused on functional enrichment, immune relevance, drug sensitivity, gene interactions, and molecular regulatory networks. Eventually, reverse transcription-quantitative PCR (RT-qPCR) was employed to validate expression of pCDCRGs. DCRGs were obtained from the intersection of 8,418 DEGs and 241 CRGs. Among 12 DCRGs with causal association (CDCRGs) with GC, 7 genes were identified as pCDCRGs, including FANCG, FANCF, F2R, C4BPA, SERPINF2, PROC, and CD59. Notably, CD59 was markedly highly expressed in the normal group, whereas the other genes were markedly highly expressed in the GC group. Afterward, an accurate pCDCRG signature was developed. Risk score, age, and stage were recognized as independent risk factors, and the constructed nomogram demonstrated strong predictive accuracy. Additionally, analyses indicated that these 7 pCDCRGs may influence GC by affecting pathways such as complement and coagulation cascades, immune cell infiltration, immune characteristics, immunotherapy responses, and drug sensitivity. These effects may be linked to gene interactions and the regulatory roles of lncRNAs like RMRP and miRNAs such as hsa-mir-613. RT-qPCR showed C4BPA, PROC, F2R, and SERPINF2 were markedly up-regulated, whereas CD59 was markedly down-regulated in GC tissues. This study identified seven complement system-related prognostic genes with causal links to GC, based on which we developed a highly predictive 7-pCDCRG signature, providing valuable insights for clinical prognostic prediction and immunotherapy in GC patients.

TPD52 as a Therapeutic Target Identified by Machine Learning Shapes the Immune Microenvironment in Breast Cancer

Breast cancer (BRCA) is one of the most common malignancies and a leading cause of cancer-related mortality among women globally. Despite advances in diagnosis and treatment, the heterogeneity of BRCA presents significant challenges for effective management and prognosis. Recent studies emphasise the critical role of the tumour microenvironment, particularly immune cells, in influencing tumour behaviour and patient outcomes. This study uses machine learning-based methodologies to investigate the role of tumour protein D52 (TPD52) as a pivotal immune regulator in BRCA. We employed single-cell RNA sequencing (scRNA-seq) to characterise the immune landscape of breast tumours and identify differentially expressed genes (DEGs) associated with TPD52. Our findings indicate that TPD52 may modulate immune cell infiltration and the tumour immune landscape, impacting tumour aggression and patient survival. Furthermore, we performed in vitro validation to elucidate the functional implications of TPD52. By integrating computational analysis with experimental validation, this research highlights TPD52's potential as a biomarker for therapeutic intervention and provides insights into its role in immune regulation within the BRCA microenvironment.

Loss of SIL1 Affects Actin Dynamics and Leads to Abnormal Neural Migration

SIL1 is a nucleotide exchange factor for the molecular chaperone protein Bip in the endoplasmic reticulum that plays a crucial role in protein folding. The Sil1 gene is currently the only known causative gene of Marinesco-Sjögren syndrome (MSS). Intellectual developmental disability is the main symptom of MSS, and its mechanism has not been fully elucidated. Studies have shown that mutations in the Sil1 gene can delay neuronal migration during cortical development, but the underlying molecular mechanisms remain unclear. To further identify potential molecules involved in the regulation of central nervous system development by SIL1, we established a cortical neuron model with SIL1 protein deficiency and used proteomic analysis to screen for differentially expressed proteins after Sil1 silencing, followed by GO functional enrichment and protein‒protein interaction (PPI) network analysis. We identified 68 upregulated and 137 downregulated proteins in total, and among them, 10 upregulated and 3 downregulated proteins were mainly related to actin cytoskeleton dynamics. We further validated the differential changes in actin-related molecules using qRT‒PCR and Western blotting of a Sil1 gene knockout (Sil1-/-) mouse model. The results showed that the protein levels of ACTN1 and VIM decreased, while their mRNA levels increased as a compensatory response to protein deficiency. The mRNA and protein levels of IQGAP1 both showed a secondary increase. In conclusion, we identified ACTN1 and VIM as the key molecules regulated by SIL1 that are involved in neuronal migration during cortical development.

Identifying novel aging-related diagnostic and prognostic models and aging-targeted drugs for sepsis patients

Sepsis is defined as a dysfunctional, life-threatening response to infection leading to multiorgan dysfunction and failure. During the past decade, studies have highlighted the relationship between sepsis and aging. However, the role of aging-related mechanisms in the progression and prognosis of sepsis remains unclear. In the present study, we divided sepsis patients into High- and Low-aging groups based on the gene set variation analysis (GSVA) scores of GOBP-AGING gene set. Sepsis patients in the high-aging group exhibited higher levels of infiltration of innate immune cells, lower levels of infiltration of adaptive immune cells, and a worse prognosis than those in the Low-aging group. Additionally, the MPO to MME ratio (MPO/MME) appears to be an effective biomarker for predicting the prognosis of sepsis patients. Moreover, ARG1/SEC63 and ARG1/CDKN1C appear to be effective and robust biomarkers for the early diagnosis of sepsis patients. Finally, we found that thalidomide (TAL) significantly ameliorated LPS induced inflammation and organ injury and attenuated LPS induced cellular senescence in lung and kidney. Overall, this study provides new insights into the heterogeneity of sepsis, reveals the vital role of aging-related markers in the prognosis and diagnosis of sepsis and demonstrates that TAL is a novel aging-targeted drug for sepsis patients by attenuating LPS induced cellular senescence.

Cathepsin L in Lung Adenocarcinoma: Prognostic Significance and Immunotherapy Response Through a Multi Omics Perspective

Objectives

Lung adenocarcinoma (LUAD), a predominant form of lung cancer, is characterized by a high rate of metastasis and recurrence, leading to a poor prognosis for LUAD patients. This study aimed to identify and rigorously validate a highly precise biomarker, Cathepsin L (CTSL), for the prognostic prediction of lung adenocarcinoma.

Methods

We employed a multicenter and omics-based approach, analyzing RNA sequencing data and mutation information from public databases such as The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The DepMap portal with Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR/Cas9) technology was used to assess the functional impact of CTSL. Immunohistochemistry (IHC) was conducted on a local cohort to validate the prognostic significance of CTSL at the protein expression level.

Results

Our findings revealed a significant correlation between elevated CTSL expression and advanced disease stage in LUAD patients. Kaplan-Meier survival analysis and Cox regression modeling revealed that high CTSL expression is associated with poor overall survival. The in vitro studies corroborated these findings, revealing notable suppression of tumor proliferation following CTSL knockout in cell lines, particularly in LUAD. Functional enrichment revealed that CTSL activated pathways associated with tumor progression, such as angiogenesis and Transforming growth factor beta (TGF-beta) signaling, and inhibited pathways such as apoptosis and DNA repair. Mutation analysis revealed distinct variations in the CTSL expression groups.

Conclusion

This study highlights the crucial role of CTSL as a prognostic biomarker in LUAD. This combined multicenter and omics-based analysis provides comprehensive insights into the biological role of CTSL, supporting its potential as a target for therapeutic intervention and a marker for prognosis in patients with LUAD.

Mitochondria-associated programmed cell death: elucidating prognostic biomarkers, immune checkpoints, and therapeutic avenues in multiple myeloma

Background

Multiple myeloma (MM) is a hematological malignancy characterized by the abnormal proliferation of plasma cells. Mitochondrial dysfunction and dysregulated programmed cell death (PCD) pathways have been implicated in MM pathogenesis. However, the precise roles of mitochondria-related genes (MRGs) and PCD-related genes (PCDRGs) in MM prognosis remain unclear.

Methods

Transcriptomic data from MM patients and healthy controls were analyzed to identify differentially expressed genes (DEGs). Candidate genes were selected by intersecting DEGs with curated lists of MRGs and PCDRGs. Univariate Cox, least absolute shrinkage and selection operator (LASSO), multivariate Cox, and stepwise regression analyses identified prognostic genes among the candidates. A risk model was constructed from these genes, and patients were stratified into high- and low-risk groups for survival analysis. Independent prognostic factors were incorporated into a nomogram to predict MM patient outcomes. Model performance was evaluated using calibration curves, receiver operating characteristic (ROC) analysis, and decision curve analysis (DCA). Finally, associations between prognostic genes and immune cell infiltration/drug responses were explored.

Results

2,192 DEGs were detected between MM and control samples. 30 candidate genes were identified at the intersection of DEGs, 1,136 MRGs, and 1,548 PCDRGs. TRIAP1, TOMM7, PINK1, CHCHD10, PPIF, BCL2L1, and NDUFA13 were selected as prognostic genes. The risk model stratified patients into high- and low-risk groups with significantly different survival probabilities. Age, gender, ISS stage, and risk score were independent prognostic factors. The nomogram displayed good calibration and discriminative ability (AUC) in predicting survival, with clinical utility demonstrated by DCA. 9 immune cell types showed differential infiltration between MM and controls, with significant associations to risk scores and specific prognostic genes. 57 drugs, including nelarabine and vorinostat, were predicted to interact with the prognostic genes. Ultimately, qPCR in clinical samples from MM patients and healthy donors validated the expression levels of the seven key prognostic genes, corroborating the bioinformatic findings.

Conclusion

Seven genes (TRIAP1, TOMM7, PINK1, CHCHD10, PPIF, BCL2L1, NDUFA13) involved in mitochondrial function and PCD pathways were identified as prognostic markers in MM. These findings provide insights into MM biology and prognosis, highlighting potential therapeutic targets.

Efficient derivation of functional astrocytes from human induced pluripotent stem cells (hiPSCs)

Astrocytes are specialized glial cell types of the central nervous system (CNS) with remarkably high abundance, morphological and functional diversity. Astrocytes maintain neural metabolic support, synapse regulation, blood-brain barrier integrity and immunological homeostasis through intricate interactions with other cells, including neurons, microglia, pericytes and lymphocytes. Due to their extensive intercellular crosstalks, astrocytes are also implicated in the pathogenesis of CNS disorders, such as ALS (amyotrophic lateral sclerosis), Parkinson's disease and Alzheimer's disease. Despite the critical importance of astrocytes in neurodegeneration and neuroinflammation are recognized, the lack of suitable in vitro systems limits their availability for modeling human brain pathologies. Here, we report the time-efficient, reproducible generation of astrocytes from human induced pluripotent stem cells (hiPSCs). Our hiPSC-derived astrocytes expressed characteristic astrocyte markers, such as GFAP, S100b, ALDH1L1 and AQP4. Furthermore, hiPSC-derived astrocytes displayed spontaneous calcium transients and responded to inflammatory stimuli by the secretion of type A1 and type A2 astrocyte-related cytokines.

Pheno-Ranker: a toolkit for comparison of phenotypic data stored in GA4GH standards and beyond

BACKGROUND

Phenotypic data comparison is essential for disease association studies, patient stratification, and genotype-phenotype correlation analysis. To support these efforts, the Global Alliance for Genomics and Health (GA4GH) established Phenopackets v2 and Beacon v2 standards for storing, sharing, and discovering genomic and phenotypic data. These standards provide a consistent framework for organizing biological data, simplifying their transformation into computer-friendly formats. However, matching participants using GA4GH-based formats remains challenging, as current methods are not fully compatible, limiting their effectiveness.

RESULTS

Here, we introduce Pheno-Ranker, an open-source software toolkit for individual-level comparison of phenotypic data. As input, it accepts JSON/YAML data exchange formats from Beacon v2 and Phenopackets v2 data models, as well as any data structure encoded in JSON, YAML, or CSV formats. Internally, the hierarchical data structure is flattened to one dimension and then transformed through one-hot encoding. This allows for efficient pairwise (all-to-all) comparisons within cohorts or for matching of a patient's profile in cohorts. Users have the flexibility to refine their comparisons by including or excluding terms, applying weights to variables, and obtaining statistical significance through Z-scores and p-values. The output consists of text files, which can be further analyzed using unsupervised learning techniques, such as clustering or multidimensional scaling (MDS), and with graph analytics. Pheno-Ranker's performance has been validated with simulated and synthetic data, showing its accuracy, robustness, and efficiency across various health data scenarios. A real data use case from the PRECISESADS study highlights its practical utility in clinical research.

CONCLUSIONS

Pheno-Ranker is a user-friendly, lightweight software for semantic similarity analysis of phenotypic data in Beacon v2 and Phenopackets v2 formats, extendable to other data types. It enables the comparison of a wide range of variables beyond HPO or OMIM terms while preserving full context. The software is designed as a command-line tool with additional utilities for CSV import, data simulation, summary statistics plotting, and QR code generation. For interactive analysis, it also includes a web-based user interface built with R Shiny. Links to the online documentation, including a Google Colab tutorial, and the tool's source code are available on the project home page: https://github.com/CNAG-Biomedical-Informatics/pheno-ranker .

Endothelial Pim3 kinase protects the vascular barrier during lung metastasis

Endothelial cells (ECs) form a tissue-specific barrier for disseminating cancer cells in distant organs. However, the molecular regulation of the ECs in the metastatic niche remains unclear. Here, we analyze using scRNA-Seq, the transcriptional reprogramming of lung ECs six hours after the arrival of melanoma cells in mouse lungs. We discover a reactive capillary EC cluster (rCap) that increases from general capillary ECs in response to infiltrating cancer cells. rCap is enriched for angiogenic and inflammatory pathways and is also found in human lung datasets. The JAK-STAT activated oncogenic Pim3 kinase is a marker of rCap, being upregulated in spontaneous metastasis models. Notably, PIM inhibition increases vascular leakage and metastatic colonization and impairs the EC barrier by decreasing the junctional cadherin-5 and catenins α, β and δ. These results highlight the pulmonary endothelium's plasticity and its protection by PIM3, which may impair the efficacy of PIM inhibitors in cancer therapies.

Transcription factors ASCL1 and OLIG2 drive glioblastoma initiation and co-regulate tumor cell types and migration

Glioblastomas (GBMs) are highly aggressive, infiltrative, and heterogeneous brain tumors driven by complex genetic alterations. The basic-helix-loop-helix (bHLH) transcription factors ASCL1 and OLIG2 are dynamically co-expressed in GBMs; however, their combinatorial roles in regulating the plasticity and heterogeneity of GBM cells are unclear. Here, we show that induction of somatic mutations in subventricular zone (SVZ) progenitor cells leads to the dysregulation of ASCL1 and OLIG2, which then function redundantly and are required for brain tumor formation in a mouse model of GBM. Subsequently, the binding of ASCL1 and OLIG2 to each other's loci and to downstream target genes then determines the cell types and degree of migration of tumor cells. Single-cell RNA sequencing (scRNA-seq) reveals that a high level of ASCL1 is key in specifying highly migratory neural stem cell (NSC)/astrocyte-like tumor cell types, which are marked by upregulation of ribosomal protein, oxidative phosphorylation, cancer metastasis, and therapeutic resistance genes.

Identification of oxidative stress-related biomarkers in uterine leiomyoma: a transcriptome-combined Mendelian randomization analysis

Background

Oxidative stress has been implicated in the pathogenesis of uterine leiomyoma (ULM) with an increasing incidence. This study aimed to identify potential oxidative stress-related biomarkers in ULM using transcriptome data integrated with Mendelian randomization (MR) analysis.

Methods

Data from GSE64763 and GSE31699 in the Gene Expression Omnibus (GEO) were included in the analysis. Oxidative stress-related genes (OSRGs) were identified, and the intersection of differentially expressed genes (DEGs), Weighted Gene Co-expression Network Analysis (WGCNA) genes, and OSRGs was used to derive differentially expressed oxidative stress-related genes (DE-OSRGs). Biomarkers were subsequently identified via MR analysis, followed by Gene Set Enrichment Analysis (GSEA) and immune infiltration analysis. Nomograms, regulatory networks, and gene-drug interaction networks were constructed based on the identified biomarkers.

Results

A total of 883 DEGs were identified between ULM and control samples, from which 42 DE-OSRGs were screened. MR analysis revealed four biomarkers: ANXA1, CD36, MICB, and PRDX6. Predictive nomograms were generated based on these biomarkers. ANXA1, CD36, and MICB were significantly enriched in chemokine signaling and other pathways. Notably, ANXA1 showed strong associations with follicular helper T cells, resting mast cells, and M0 macrophages. CD36 was positively correlated with resting mast cells, while MICB was negatively correlated with macrophages. Additionally, ANXA1 displayed strong binding energy with amcinonide, and MICB with ribavirin.

Conclusion

This study identified oxidative stress-related biomarkers (ANXA1, CD36, MICB, and PRDX6) in ULM through transcriptomic and MR analysis, providing valuable insights for ULM therapeutic research.

Transcriptome combined with Mendelian randomization to screen key genes associated with mitochondrial and programmed cell death causally associated with diabetic retinopathy

Background

Mitochondrial dysfunction in the retina can induce apoptosis of retinal capillary cells, leading to diabetic retinopathy (DR). This study aimed to explore key genes related to programmed cell death (PCD) and mitochondria in DR via bioinformatic analysis.

Methods

A differential analysis was performed to identify differentially expressed genes (DEGs) between DR and control samples using the GSE94019 dataset from the Gene Expression Omnibus (GEO) database. Pearson correlation analysis was then utilized to select genes linked to mitochondrial function and PCD (M-PCD). Candidate genes were identified by overlapping DR-DEGs and M-PCD genes, followed by functional annotation. Mendelian randomization (MR) analysis was employed to identify genes with causal relationships to DR. Key genes were identified through protein-protein interaction (PPI) analysis using six algorithms (DEgree, DMNC, EPC, MCC, Genes are BottleNeck, and MNC) within Cytoscape software. The expression patterns of these genes were validated using GSE94019 and GSE60436 datasets, as well as RT-qPCR. Enrichment analysis provided insights into the function and pathways of these key genes in DR. Differential immune cell profiles were determined via immune infiltration analysis, followed by exploring the relationships between immune cells, cytokines, and the identified genes. Correlations between key genes and apoptosis genes were also examined. In vivo experiments using RT-PCR, immunohistochemistry (IHC), and western blot analysis confirmed that MYC and SLC7A11 expression was significantly elevated in DR rat retinal tissues.

Results

From 658 candidate genes, 12 showed significant causal associations with DR. MYC and SLC7A11 were particularly notable, showing upregulated expression in DR samples and involvement in apoptosis and diabetes-related pathways. These genes were significantly associated with apoptotic genes and correlated positively with altered immune cell types and cytokines, suggesting a link between immune response and DR pathogenesis. In vivo findings confirmed that MYC and SLC7A11 expression was elevated in DR rat retinal tissues.

Conclusion

Key genes (MYC and SLC7A11) associated with mitochondrial function and PCD in DR were identified, offering insights into DR's pathological mechanisms and potential targets for diagnostic and therapeutic strategies.

In-depth analysis of lymph node metastasis-related sialylated protein profiling and their clinical and biological significance in colorectal cancer using mass spectrometry and multi-omics technologies

Colorectal cancer (CRC) lymph node metastasis (LNM) is a crucial factor affecting the prognosis and treatment outcomes of CRC patients. It has been confirmed that altered glycosylation is a key event during CRC lymphatic metastases. Sialylation is one of the most significant glycosylation alterations in tumors. However, the predictive role of sialylation and sialylated protein in CRC remains elusive, especially in CRC-LNM. In this study, we explored and identified 1102 sialylated glycoproteins in CRC-LNM using metabolic labeling strategy and proteomics analysis. Combined with comprehensive analysis with bioinformatics and machine learning algorithms, we screened 25 prognostic sialylation-related genes (SRGs) to construct a new molecular phenotype (LRSRGs-Phenotype) and a prognostic SRG signature (LRSRGs-related Gene Signature) in CRC. Then, we further confirmed that patients in different phenotypes had different prognosis, molecular biological characteristics, immune cell infiltration and could be closely linked to three previously reported immune phenotypes: immune-excluded (Phenotype A), immune-desert (Phenotype B), and immune-inflamed (Phenotype C). Besides, we evaluated and validated the LRSRGs-related gene (ACADM, EHD4, FLOT1, GPC1, GSR, LRRC8A, NGFR, SDHB, and SEC61G) signature and found the risk score was an independent risk factor for CRC prognosis. CRC patients in different risk groups had different somatic mutation, tumor microenvironment and immunotherapy response. Finally, we also identified the potential therapeutic agents for CRC patients in different risk groups. In conclusion, we explored the key sialylated glycoproteins, which may play a key role in tumor LNM and clinical outcomes. And constructed the LRSRGs-phenotype and signature with prognostic and therapeutic predictive value in CRC, hoping to provide reliable scientific basis for future treatments in CRC patients.

Therapeutic benefits of maintaining CDK4/6 inhibitors and incorporating CDK2 inhibitors beyond progression in breast cancer

The combination of CDK4/6 inhibitors (CDK4/6i) and endocrine therapy has revolutionized treatment for hormone receptor-positive (HR+) metastatic breast cancer. However, the emergence of resistance in most patients often leads to treatment discontinuation with no consensus on effective second-line therapies. The therapeutic benefits of maintaining CDK4/6i or incorporating CDK2 inhibitors (CDK2i) after disease progression remain unclear. Here, we demonstrate that sustained CDK4/6i therapy, either alone or combined with CDK2i, significantly suppresses the growth of drug-resistant HR+ breast cancer. Continued CDK4/6i treatment induces a non-canonical pathway for retinoblastoma protein (Rb) inactivation via post-translational degradation, resulting in diminished E2F activity and delayed G1 progression. Importantly, our data highlight that CDK2i should be combined with CDK4/6i to effectively suppress CDK2 activity and overcome resistance. We also identify cyclin E overexpression as a key driver of resistance to CDK4/6 and CDK2 inhibition. These findings provide crucial insights into overcoming resistance in HR+ breast cancer, supporting the continued use of CDK4/6i and the strategic incorporation of CDK2i to improve therapeutic outcomes.

Enhanced pathogenicity and synergistic effects of co-infection with bovine viral diarrhea virus 1 and HoBi-like virus in cattle and guinea pigs

Introduction

The Bovine Viral Diarrhea Virus 1 (BVDV1) and HoBi-like virus (BVDV3), both within the same genus, share genomic homology and exhibit low antigenic cross-reactivity despite presenting similar clinical manifestations. In 2021, a bovine respiratory disease complex (BRDC) outbreak on two cattle farms in the Inner Mongolia Autonomous Region of China resulted in ten fatalities.

Methods

Metagenomic and metatranscriptomic analyses were used to identify viral agents, including a co-infection case. A genetic evolution analysis assessed the relationships with related strains. Experimental infections in guinea pigs and calves evaluated the pathogenicity of the viruses.

Results

Phylogenetic analysis of the BVDV3 isolate IM2201 revealed close relatedness to Brazilian strains, with 97.06% nucleotide homology to the highly virulent strain SV478/07. Experimental co-infection in guinea pigs resulted in more severe clinical signs, including fever, cough, diarrhea, and significant pathological changes, and led to a higher mortality rate (40%) compared to no mortality from single-virus infections with BVDV1 or BVDV3. Similarly, co-infected cattle exhibited more severe clinical signs, including bloody diarrhea and rectal temperatures exceeding 40°C, along with persistent viremia and nasal viral shedding from 7 to 21 days post-infection. Blood analysis revealed significant reductions in white blood cell counts, particularly in co-infected cattle.

Discussion

This study highlights the enhanced pathogenicity and synergistic effects of BVDV1 and BVDV3 co-infection, exacerbating disease severity.

Construction of a Prognostic Model for Mitochondria and Macrophage Polarization Correlation in Glioma Based on Single-Cell and Transcriptome Sequencing

BACKGROUND

Numerous diseases are associated with the interplay of mitochondrial and macrophage polarization. However, the correlation of mitochondria-related genes (MRGs) and macrophage polarization-related genes (MPRGs) with the prognosis of glioma remains unclear. This study aimed to examine this relationship based on bioinformatic analysis.

METHODS

Glioma-related datasets (TCGA-GBMLGG, mRNA-seq-325, mRNA-seq-693, GSE16011, GSE4290, and GSE138794) were included in this study. The intersection genes were obtained by overlapping differentially expressed genes (DEGs) from differential expression analysis in GSE16011, key module genes from WGCNA, and MRGs. Subsequently, the intersection genes were further screened to obtain prognostic genes. Following this, a risk model was developed and verified. After that, independent prognostic factors were identified, followed by the construction of a nomogram and subsequent evaluation of its predictive ability. Furthermore, immune microenvironment analysis and expression validation were implemented. The GSE138794 dataset was utilized to evaluate the expression of prognostic genes at a cellular level, followed by conducting an analysis on cell-to-cell communication. Finally, the results were validated in different datasets and tissue samples from patients.

RESULTS

ECI2, MCCC2, OXCT1, SUCLG2, and CPT2 were identified as prognostic genes for glioma. The risk model constructed based on these genes in TCGA-GBMLGG demonstrated certain accuracy in predicting the occurrence of glioma. Additionally, the nomogram constructed based on risk score and grade exhibited strong performance in predicting patient survival. Significant differences were observed in the proportion of 27 immune cell types (e.g., activated B cells and macrophages) and the expression of 32 immune checkpoints (e.g., CD70, CD200, and CD48) between the two risk groups. Single-cell RNA sequencing showed that CPT2, ECI2, and SUCLG2 were highly expressed in oligodendrocytes, neural progenitor cells, and BMDMs, respectively. The results of cell-cell communication analysis revealed that both oligodendrocytes and BMDMs exhibited a substantial number of interactions with high strength.

CONCLUSION

This study revealed five genes associated with the prognosis of glioma (ECI2, MCCC2, OXCT1, SUCLG2, and CPT2), providing novel insights into individualized treatment and prognosis.