Search Results (42)

Growth and carcass traits are key economic traits in beef cattle production, and identifying their associated genetic markers is crucial for improving breeding efficiency. Charolais cattle, as a superior beef breed, exhibit excellent performance in growth rate and meat production. The aim of this study was to utilize the preferred high-coverage whole-genome resequencing (hcWGS) as a replacement for single nucleotide polymorphism (SNP) chips to identify significant SNPs and candidate genes associated with growth (body weight, body height, cross height, body length, and chest measurement across different growth stages) and carcass traits (live backfat thickness and eye muscle area at 18 months) in 240 Charolais cattle, thereby providing guidance for beef cattle breeding. Through hcWGS (approximately 13× coverage) and quality control, 4,088,633 SNPs were identified and subsequently used for genetic analyses. Through FarmCPU-based genome-wide association studies, 196 potentially significant SNPs associated with growth traits and 29 SNPs with carcass traits were identified. Annotation analyses revealed 353 candidate genes (such as RBM33, KCTD17, PTHLH, RAC2, CHD6, TRDN, WBP1L, TLL2, CH25H, and ST13) linked to growth traits and 26 candidate genes linked to carcass traits (such as CHST11, LRRK2, RIOK2, and INTS10). Additionally, three SNPs (g.8674692C>G, g.54418624G>T, and g.71085551G>A) were validated via polymerase chain reaction–restriction fragment length polymorphism (PCR-RFLP), enabling efficient marker-assisted selection. Furthermore, eight SNPs in the Acyl-CoA oxidase 1 (ACOX1) gene were found to be associated with growth and backfat thickness traits. These findings provide valuable preliminary insights into the genetic mechanisms underlying growth and carcass traits in Charolais cattle, facilitating genome-assisted breeding. Full article

Background: The reproductive ability of equine species is a critical component of equine breeding programs, with sperm quality serving as a primary determinant of reproductive success. In this study, we perform an integrative analysis of proteomics and metabolomics in seminal plasma to identify proteins and metabolites associated with sperm quality and reproductive ability in equine species. Methods: We utilized the CEROS instrument to assess the morphology and motility of sperm samples from three horses and three donkeys. Additionally, we statistically analyzed the mating frequency and pregnancy rates in both species. Meanwhile, the 4D-DIA high-throughput proteomic and metabolomic profiling of seminal plasma samples from horses and donkeys revealed a complex landscape of proteins and metabolites. Results: Our findings reveal a certain degree of correlation between seminal plasma proteins and metabolites and sperm quality, as well as overall fertility. Notably, we found that the proteins B3GAT3, XYLT2, CHST14, HS2ST1, GLCE, and HSPG2 in the glycosaminoglycan biosynthesis signaling pathway; the metabolites D-glucose, 4-phosphopantetheine, and 4-hydroxyphenylpyruvic acid in the tyrosine metabolism, starch, and source metabolisms; and pantothenate CoA biosynthesis metabolism present unique characteristics in the seminal plasma of equine species. Conclusions: This comprehensive approach provides new insights into the molecular mechanisms underlying sperm quality and has identified potential proteins and metabolites that could be used to indicate reproduction ability. The findings from this study could be instrumental in developing novel strategies to enhance equine breeding practices and reproductive management. Future research will focus on exploring their potential for clinical application in the equine industry. Full article

The progression of colorectal cancer (CRC) is driven by dynamic interactions between tumor cells and their microenvironment, particularly the extracellular matrix (ECM). Ion channels, critical regulators of cellular signaling, have emerged as mediators of ECM remodeling and tumor aggressiveness. In this study, we integrate transcriptomic data from 185 CRC tumors and 157 adjacent normal tissues with network modeling to dissect the interplay between ion channels and the ECM. We identified 4036 differentially expressed genes (DEGs), including 188 ion channel-associated DEGs (IC-DEGs) enriched in ECM-related pathways, such as collagen assembly, matrix metalloproteinase regulation, and mechanotransduction. Structural equation modeling revealed an active CRC−ion channel module (CRC-IC) comprising 482 nodes and 422 edges, highlighting dysregulated interactions between ECM components (e.g., COL1A1, COL5A2, VCAN, LAMA4, LA-MA5, LAMC1), ion channels (e.g., TRPM5 and SLC16A1), and cytoskeletal regulators. Key nodes, including CHST11 and VCAN, were associated with ECM sulfation, tumor invasiveness, and immune evasion. Notably, survival was associated with MAPK1, SLC16A1, and ABCB4 in relation to patient prognosis. Our findings underscore the pivotal role of ion channels as co-factors in ECM dynamics in CRC, offering mechanistic insights into tumor-stroma crosstalk and identifying potential therapeutic targets to disrupt microenvironment-driven progression. Full article

Mesothelioma is characterized by the inactivation of tumor suppressor genes, with frequent mutations in neurofibromin 2 (NF2), BRCA1-associated protein 1 (BAP1), and cyclin-dependent kinase inhibitor 2A (CDKN2A). These mutations lead to disruptions in the Hippo signaling pathway and histone methylation, thereby promoting tumor growth. NF2 mutations result in Merlin deficiency, leading to uncontrolled cell proliferation, whereas BAP1 mutations impair chromatin remodeling and hinder DNA damage repair. Emerging molecular targets in mesothelioma include mesothelin (MSLN), oxytocin receptor (OXTR), protein arginine methyltransferase (PRMT5), and carbohydrate sulfotransferase 4 (CHST4). MSLN-based therapies, such as antibody–drug conjugates and immunotoxins, have shown efficacy in clinical trials. OXTR, upregulated in mesothelioma, is correlated with poor prognosis and represents a novel therapeutic target. PRMT5 inhibition is being explored in tumors with MTAP deletions, commonly co-occurring with CDKN2A loss. CHST4 expression is associated with improved prognosis, potentially influencing tumor immunity. Immune checkpoint inhibitors targeting PD-1/PD-L1 have shown promise in some cases; however, resistance mechanisms remain a challenge. Advances in multi-omics approaches have improved our understanding of mesothelioma pathogenesis. Future research will aim to identify novel therapeutic targets and personalized treatment strategies, particularly in the context of epigenetic therapy and combination immunotherapy. Full article

Previously, we reported that mating induces an early transcriptional response in the oviductal mucosa of rats. The functional category ‘cell-to-cell signaling and interaction’ was overrepresented in this gene list. Therefore, in the present study, we describe the role of one of these genes, carbohydrate sulfotransferase 10 (Chst10), in the oviductal mucosa. CHST10 participates in the synthesis of the carbohydrate moiety human natural killer-1 (HNK-1), which mediates cell-to-cell interactions. When using one-dimensional Western blot and sulfotransferase analyses, we found that mating increased the protein level and activity of CHST10 in the oviductal mucosa at 3 h after stimulation. A two-dimensional Western blot analysis and mass spectrometry were used to identify the novel HNK-1 glycoproteins aldehyde dehydrogenase 9 family, member A1 (ALDH9A1), fructose bisphosphate aldolase A (ALDOA), and four and a half LIM domains protein 1 (FHL1) in the oviductal mucosa, and we found that mating induces the synthesis of their acidic variants. Interestingly, in the utero-tubal junction (UTJ), acrosome-reacted sperm apparently were interacting with regions in which ALDH9A1 and HNK-1 signals overlap. Furthermore, vaginocervical stimulation applied to unmated rats increased the mRNA level of Chst10 in the oviductal mucosa. In conclusion, mating increases the activity of CHST10 in the oviductal mucosa, which in turn induces the synthesis of acidic variants of ALDH9A1 and FHL1 via HNK-1 glycosylation. ALDH9A1, HNK-1-ALDH9A1, and/or other HNK-1 glycoproteins could participate in the negative selection of sperm in the UTJ, since we detected acrosome-reacted sperm apparently interacting with regions where these proteins are located. Finally, the sensorial component of mating could regulate early events (e.g., sperm transport and selection) occurring in the oviductal mucosa after mating. Full article

Mitochondrial miRNAs (mitomiRs), which are miRNAs that located within mitochondria, have emerged as crucial regulators in a variety of human diseases, including multiple types of cancers. However, the specific role of mitomiRs in clear cell renal cell carcinoma (ccRCC) remains elusive. In this study, we employed a combination of experimental and bioinformatic approaches to uncover the diverse and abundant subcellular distribution of miRNAs within mitochondria in ccRCC. Notably, RNA sequencing after mitochondrial fractionation identified miR-134-5p as a miRNA predominantly detected in the mitochondria of 786O cells, and its expression is significantly upregulated compared to that in 293T cells. Differential expression and survival analyses from TCGA reveal that the upregulation of miR-134-5p is prevalent and closely associated with poor survival outcomes in ccRCC patients. Functionally, exogenous overexpression of miR-134-5p mimics promotes migration in both 786O and Caki-1 cells. Mechanistically, overexpressing the miR-134-5p mimic dramatically downregulates the mRNA levels of CHST6, SFXN2, and GRIK3, whereas the miR-134-5p inhibitor markedly upregulates their expression. Notably, these target mRNAs also predominantly detected in the mitochondria of 786O cells. The downregulated expression signatures of CHST6, SFXN2, and GRIK3 are also closely correlated with poor survival outcomes in ccRCC patients. Taken together, our work identifies a novel mitomiR, miR-134-5p, in ccRCC, provides potential targets that could serve as effective biomarkers for ccRCC diagnosis and prognosis, and opens new avenues for understanding the mitomiR-directed regulatory network in ccRCC progression. Full article

Background: Ulcerative colitis is a chronic inflammatory disease affecting the colon. During chronic inflammation of epithelial cells, lipid metabolism via pro-inflammatory eicosanoids is known to modify the immune response. Methods: Starting from the Mammalian Metabolic Database, the expression of metabolic enzymes was investigated in two independent cohorts from transcriptome datasets GSE38713 and GSE11223, which analyzed ulcerative colitis tissue samples from the digestive tract. Results: In the first cohort, 145 differentially expressed enzymes were identified as significantly regulated between ulcerative colitis tissues and normal controls. Overexpressed enzymes were selected to tune an Elastic Net model in the second cohort. Using the best parameters, the model achieved a prediction accuracy for ulcerative colitis with an area under the curve (AUC) of 0.79. Twenty-two metabolic enzymes were found to be commonly overexpressed in both independent cohorts, with decreasing Elastic Net predictive coefficients as follows: LIPG (3.98), PSAT1 (3.69), PGM3 (2.74), CD38 (2.28), BLVRA (1.99), CBR3 (1.94), NT5DC2 (1.76), PHGDH (1.71), GPX7 (1.58), CASP1 (1.56), ASRGL1 (1.4), SOD3 (1.25), CHST2 (0.965), CHST11 (0.95), KYNU (0.94), PLAG2G7 (0.92), SRM (0.87), PTGS2 (0.80), LPIN1 (0.47), ME1 (0.31), PTGDS (0.14), and ADA (0.13). Functional enrichment analysis using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database highlighted the main implications of these enzymes in cysteine and methionine metabolism (adjusted p-value = 0.01), arachidonic acid and prostaglandin metabolism (adjusted p-value = 0.01), and carbon metabolism (adjusted p-value = 0.04). A metabolic score based on the transcriptional activation of the validated twenty-two enzymes was found to be significantly greater in Ulcerative colitis samples compared to healthy donor samples (p-value = 1.52 × 10⁻⁸). Conclusions: A metabolic expression score was established and reflects the implications of heterogeneous metabolic pathway deregulations in the digestive tract of patients with ulcerative colitis. Full article

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by deficits in social communication, restricted interests, and repetitive behaviors. Despite considerable research efforts, the genetic complexity of ASD remains poorly understood, complicating diagnosis and treatment, especially in the Arab population, with its genetic diversity linked to migration, tribal structures, and high consanguinity. To address the scarcity of ASD genetic data in the Middle East, we conducted genome sequencing (GS) on 50 ASD subjects and their unaffected parents. Our analysis revealed 37 single-nucleotide variants from 36 candidate genes and over 200 CGG repeats in the FMR1 gene in one subject. The identified variants were classified as uncertain, likely pathogenic, or pathogenic based on in-silico algorithms and ACMG criteria. Notably, 52% of the identified variants were homozygous, indicating a recessive genetic architecture to ASD in this population. This finding underscores the significant impact of high consanguinity within the Qatari population, which could be utilized in genetic counseling/screening program in Qatar. We also discovered single nucleotide variants in 13 novel genes not previously associated with ASD: ARSF, BAHD1, CHST7, CUL2, FRMPD3, KCNC4, LFNG, RGS4, RNF133, SCRN2, SLC12A8, USP24, and ZNF746. Our investigation categorized the candidate genes into seven groups, highlighting their roles in cognitive development, including the ubiquitin pathway, transcription factors, solute carriers, kinases, glutamate receptors, chromatin remodelers, and ion channels. Full article

Introduction: Non-invasive assays are needed to better discriminate patients with prostate cancer (PCa) to avoid over-treatment of indolent disease. We analyzed 14 methylated DNA markers (MDMs) from urine samples of patients with biopsy-proven PCa relative to healthy controls and further studied discrimination of clinically significant PCa (csPCa) from healthy controls and Gleason 6 cancers. Methods: To evaluate the panel, urine from 24 healthy male volunteers with no clinical suspicion for PCa and 24 men with biopsy-confirmed disease across all Gleason scores was collected. Blinded to clinical status, DNA from the supernatant was analyzed for methylation signal within specific DNA sequences across 14 genes (HES5, ZNF655, ITPRIPL1, MAX.chr3.6187, SLCO3A1, CHST11, SERPINB9, WNT3A, KCNB2, GAS6, AKR1B1, MAX.chr3.8028, GRASP, ST6GALNAC2) by target enrichment long-probe quantitative-amplified signal assays. Results: Utilizing an overall specificity cut-off of 100% for discriminating normal controls from PCa cases across the MDM panel resulted in 71% sensitivity (95% CI: 49–87%) for PCa detection (4/7 Gleason 6, 8/12 Gleason 7, 5/5 Gleason 8+) and 76% (50–92%) for csPCa (Gleason ≥ 7). At 100% specificity for controls and Gleason 6 patients combined, MDM panel sensitivity was 59% (33–81%) for csPCa (5/12 Gleason 7, 5/5 Gleason 8+). Conclusions: MDMs assayed in urine offer high sensitivity and specificity for detection of clinically significant prostate cancer. Prospective evaluation is necessary to estimate discrimination of patients as first-line screening and as an adjunct to prostate-specific antigen (PSA) testing. Full article

Respiratory syncytial virus (RSV) is a common respiratory pathogen that infects the human lungs and respiratory tract, often causing symptoms similar to the common cold. Vaccination is the most effective strategy for managing viral outbreaks. Currently, extensive efforts are focused on developing a vaccine for RSV. Traditional vaccine design typically involves using an attenuated form of the pathogen to elicit an immune response. In contrast, peptide-based vaccines (PBVs) aim to identify and chemically synthesize specific immunodominant peptides (IPs), known as T-cell epitopes (TCEs), to induce a targeted immune response. Despite their potential for enhancing vaccine safety and immunogenicity, PBVs have received comparatively less attention. Identifying IPs for PBV design through conventional wet-lab experiments is challenging, costly, and time-consuming. Machine learning (ML) techniques offer a promising alternative, accurately predicting TCEs and significantly reducing the time and cost of vaccine development. This study proposes the development and evaluation of eight hybrid ML predictive models created through the permutations and combinations of two classification methods, two feature weighting techniques, and two feature selection algorithms, all aimed at predicting the TCEs of RSV. The models were trained using the experimentally determined TCEs and non-TCE sequences acquired from the Bacterial and Viral Bioinformatics Resource Center (BV-BRC) repository. The hybrid model composed of the XGBoost (XGB) classifier, chi-squared (ChST) weighting technique, and backward search (BST) as the optimal feature selection algorithm (ChST−BST–XGB) was identified as the best model, achieving an accuracy, sensitivity, specificity, F1 score, AUC, precision, and MCC of 97.10%, 0.98, 0.97, 0.98, 0.99, 0.99, and 0.96, respectively. Additionally, K-fold cross-validation (KFCV) was performed to ensure the model’s reliability and an average accuracy of 97.21% was recorded for the ChST−BST–XGB model. The results indicate that the hybrid XGBoost model consistently outperforms other hybrid approaches. The epitopes predicted by the proposed model may serve as promising vaccine candidates for RSV, subject to in vitro and in vivo scientific assessments. This model can assist the scientific community in expediting the screening of active TCE candidates for RSV, ultimately saving time and resources in vaccine development. Full article

Glioblastoma (GBM), a highly malignant tumour of the central nervous system, presents with a dire prognosis and low survival rates. The heterogeneous and recurrent nature of GBM renders current treatments relatively ineffective. In our study, we utilized an integrative systems biology approach to uncover the molecular mechanisms driving GBM progression and identify viable therapeutic drug targets for developing more effective GBM treatment strategies. Our integrative analysis revealed an elevated expression of CHST2 in GBM tumours, designating it as an unfavourable prognostic gene in GBM, as supported by data from two independent GBM cohorts. Further, we pinpointed WZ-4002 as a potential drug candidate to modulate CHST2 through computational drug repositioning. WZ-4002 directly targeted EGFR (ERBB1) and ERBB2, affecting their dimerization and influencing the activity of adjacent genes, including CHST2. We validated our findings by treating U-138 MG cells with WZ-4002, observing a decrease in CHST2 protein levels and a reduction in cell viability. In summary, our research suggests that the WZ-4002 drug candidate may effectively modulate CHST2 and adjacent genes, offering a promising avenue for developing efficient treatment strategies for GBM patients. Full article

Osteoarthritis is a chronic progressive joint disease that clinically debuts at the stage of pronounced morphologic changes, which makes treatment difficult. In this regard, an important task is the study of genetic markers of the disease, which have not been definitively established, due to the clinical and ethnic heterogeneity of the studied populations. To find the genetic markers for the development of knee osteoarthritis (OA) in women from the Volga-Ural region of Russia, we conducted research in two stages using different genotyping methods, such as the restriction fragment length polymorphism (RFLP) measurement, TaqMan technology and competitive allele-specific PCR—KASP^TM. In the first stage, we studied polymorphic variants of candidate genes (ACAN, ADAMTS5, CHST11, SOX9, COL1A1) for OA development. The association of the *27 allele of the VNTR locus of the ACAN gene was identified (OR = 1.6). In the second stage, we replicated the GWAS results (ASTN2, ALDH1A2, DVWA, CHST11, GNL3, NCOA3, FILIP/SENP1, MCF2L, GLT8D, DOT1L) for knee OA studies. The association of the *T allele of the rs7639618 locus of the DVWA gene was detected (OR = 1.54). Thus, the VNTR locus of ACAN and the rs7639618 locus of DVWA are risk factors for knee OA in women from the Volga-Ural region of Russia. Full article

Malignant pleural mesothelioma (MPM) develops primarily from asbestos exposures and has a poor prognosis. In this study, The Cancer Genome Atlas was used to perform a comprehensive survival analysis, which identified the CHST4 gene as a potential predictor of favorable overall survival for patients with MPM. An enrichment analysis of favorable prognostic genes, including CHST4, showed immune-related ontological terms, whereas an analysis of unfavorable prognostic genes indicated cell-cycle-related terms. CHST4 mRNA expression in MPM was significantly correlated with Bindea immune-gene signatures. To validate the relationship between CHST4 expression and prognosis, we performed an immunohistochemical analysis of CHST4 protein expression in 23 surgical specimens from surgically treated patients with MPM who achieved macroscopic complete resection. The score calculated from the proportion and intensity staining was used to compare the intensity of CHST4 gene expression, which showed that CHST4 expression was stronger in patients with a better postoperative prognosis. The median overall postoperative survival was 107.8 months in the high-expression-score group and 38.0 months in the low-score group (p = 0.044, log-rank test). Survival after recurrence was also significantly improved by CHST4 expression. These results suggest that CHST4 is useful as a prognostic biomarker in MPM. Full article

We recently showed that 6-sulfo sialyl N-acetyllactosamine (LacNAc) in O-linked glycans recognized by the CL40 antibody is abundant in the pleural mesothelium under physiological conditions and that these glycans undergo complementary synthesis by GlcNAc6ST2 (encoded by Chst4) and GlcNAc6ST3 (encoded by Chst5) in mice. GlcNAc6ST3 is essential for the synthesis of R-10G-positive keratan sulfate (KS) in the brain. The predicted minimum epitope of the R-10G antibody is a dimeric asialo 6-sulfo LacNAc. Whether R-10G-reactive KS/sulfated LacNAc oligosaccharides are also present in the pleural mesothelium was unknown. The question of which GlcNAc6STs are responsible for R-10G-reactive glycans was an additional issue to be clarified. Here, we show that R-10G-reactive glycans are as abundant in the pulmonary pleura as CL40-reactive glycans and that GlcNAc6ST3 is only partially involved in the synthesis of these pleural R-10G glycans, unlike in the adult brain. Unexpectedly, GlcNAc6ST2 is essential for the synthesis of R-10G-positive KS/sulfated LacNAc oligosaccharides in the lung pleura. The type of GlcNAc6ST and the magnitude of its contribution to KS glycan synthesis varied among tissues in vivo. We show that GlcNAc6ST2 is required and sufficient for R-10G-reactive KS synthesis in the lung pleura. Interestingly, R-10G immunoreactivity in KSGal6ST (encoded by Chst1) and C6ST1 (encoded by Chst3) double-deficient mouse lungs was markedly increased. MUC16, a mucin molecule, was shown to be a candidate carrier protein for pleural R-10G-reactive glycans. These results suggest that R-10G-reactive KS/sulfated LacNAc oligosaccharides may play a role in mesothelial cell proliferation and differentiation. Further elucidation of the functions of sulfated glycans synthesized by GlcNAc6ST2 and GlcNAc6ST3, such as R-10G and CL40 glycans, in pathological conditions may lead to a better understanding of the underlying mechanisms of the physiopathology of the lung mesothelium. Full article

Recent breakthroughs are making a significant contribution to big data in biomedicine which are anticipated to assist in disease diagnosis and patient care management. To obtain relevant information from this data, effective administration and analysis are required. One of the major challenges associated with biomedical data analysis is the so-called “curse of dimensionality”. For this issue, a new version of Binary Sand Cat Swarm Optimization (called PILC-BSCSO), incorporating a pinhole-imaging-based learning strategy and crossover operator, is presented for selecting the most informative features. First, the crossover operator is used to strengthen the search capability of BSCSO. Second, the pinhole-imaging learning strategy is utilized to effectively increase exploration capacity while avoiding premature convergence. The Support Vector Machine (SVM) classifier with a linear kernel is used to assess classification accuracy. The experimental results show that the PILC-BSCSO algorithm beats 11 cutting-edge techniques in terms of classification accuracy and the number of selected features using three public medical datasets. Moreover, PILC-BSCSO achieves a classification accuracy of 100% for colon cancer, which is difficult to classify accurately, based on just 10 genes. A real Liver Hepatocellular Carcinoma (TCGA-HCC) data set was also used to further evaluate the effectiveness of the PILC-BSCSO approach. PILC-BSCSO identifies a subset of five marker genes, including prognostic biomarkers HMMR, CHST4, and COL15A1, that have excellent predictive potential for liver cancer using TCGA data. Full article