Boesendaley0845

Detecting protein complexes is one of the keys to understanding cellular organization and processes principles. With high-throughput experiments and computing science development, it has become possible to detect protein complexes by computational methods. However, most computational methods are based on either unsupervised learning or supervised learning. Unsupervised learning-based methods do not need training datasets, but they can only detect one or several topological protein complexes. Supervised learning-based methods can detect protein complexes with different topological structures. However, they are usually based on a type of training model, and the generalization of a single model is poor. Therefore, we propose an Ensemble Learning Framework for Detecting Protein Complexes (ELF-DPC) within protein-protein interaction (PPI) networks to address these challenges. The ELF-DPC first constructs the weighted PPI network by combining topological and biological information. Second, it mines protein complex cores using the protein complex core mining strategy we designed. Third, it obtains an ensemble learning model by integrating structural modularity and a trained voting regressor model. Finally, it extends the protein complex cores and forms protein complexes by a graph heuristic search strategy. The experimental results demonstrate that ELF-DPC performs better than the twelve state-of-the-art approaches. Moreover, functional enrichment analysis illustrated that ELF-DPC could detect biologically meaningful protein complexes. The code/dataset is available for free download from https//github.com/RongquanWang/ELF-DPC.Pituitary tumor-transforming gene 1 (PTTG1) encodes a multifunctional protein that is involved in many cellular processes. However, the potential role of PTTG1 in tumor formation and its prognostic function in human pan-cancer is still unknown. The analysis of gene alteration, PTTG1 expression, prognostic function, and PTTG1-related immune analysis in 33 types of tumors was performed based on various databases such as The Cancer Genome Atlas database, the Genotype-Tissue Expression database, and the Human Protein Atlas database. Additionally, PTTG1-related gene enrichment analysis was performed to investigate the potential relationship and possible molecular mechanisms between PTTG1 and tumors. Overexpression of PTTG1 may lead to tumor formation and poor prognosis in various tumors. Consequently, PTTG1 acts as a potential oncogene in most tumors. Additionally, PTTG1 is related to immune infiltration, immune checkpoints, tumor mutational burden, and microsatellite instability. Thus, PTTG1 could be potential biomarker for both prognosis and outcomes of tumor treatment and it could also be a promising target in tumor therapy.Dynamic changes of microbiome communities may play important roles in human health and diseases. The recent rise in longitudinal microbiome studies calls for statistical methods that can model the temporal dynamic patterns and simultaneously quantify the microbial interactions and community stability. Here, we propose a novel autoregressive zero-inflated mixed-effects model (ARZIMM) to capture the sparse microbial interactions and estimate the community stability. ARZIMM employs a zero-inflated Poisson autoregressive model to model the excessive zero abundances and the non-zero abundances separately, a random effect to investigate the underlining dynamic pattern shared within the group, and a Lasso-type penalty to capture and estimate the sparse microbial interactions. Based on the estimated microbial interaction matrix, we further derive the estimate of community stability, and identify the core dynamic patterns through network inference. Through extensive simulation studies and real data analyses we evaluate ARZIMM in comparison with the other methods.Pesticides are a group of environmental pollutants widely used in agriculture to protect crops, and their indiscriminate use has led to a growing public awareness about the health hazards associated with exposure to these substances. In fact, exposure to pesticides has been associated with an increased risk of developing diseases, including cancer. In a study previously published by us, we observed the induction of specific chromosomal alterations and, in general, the deleterious effect of pesticides on the chromosomes of five individuals exposed to pesticides. Considering the importance of our previous findings and their implications in the identification of cytogenetic biomarkers for the monitoring of exposed populations, we decided to conduct a new study with a greater number of individuals exposed to pesticides. Considering the above, the aim of this study was to evaluate the type and frequency of chromosomal alterations, chromosomal variants, the level of chromosomal instability and the clonal heterogenenstability and, therefore, to an increased risk of developing diseases.Background Claudins (CLDNs) are a family of closely related transmembrane proteins that have been linked to oncogenic transformation and metastasis across a range of cancers, suggesting that they may be valuable diagnostic and/or prognostic biomarkers that can be used to evaluate patient outcomes. However, CLDN expression patterns associated with colorectal cancer (CRC) remain to be defined. Methods The mRNA levels of 21 different CLDN family genes were assessed across 20 tumor types using the Oncomine database. Protokylol price Correlations between these genes and patient clinical outcomes, immune cell infiltration, clinicopathological staging, lymph node metastasis, and mutational status were analyzed using the GEPIA, UALCAN, Human Protein Atlas, Tumor Immune Estimation Resource, STRING, Genenetwork, cBioportal, and DAVID databases in an effort to clarify the potential functional roles of different CLDN protein in CRC. Molecular docking analyses were used to probe potential interactions between CLDN4 and TGFβ1. Levels of CLrved between such infiltration and the expression of CLDN3 and CLDN15. A positive correlation between CLDN1, CLDN16, and neutrophil infiltration was additionally detected, whereas neutrophil levels were negatively correlated with the expression of CLDN3 and CLDN15. Molecular docking suggested that CLDN4 was able to directly bind via hydrogen bond with TGFβ1. Relative to paracancerous tissues, clinical CRC tumor tissue samples exhibited CLDN4 and CLDN11 upregulation and downregulation, respectively. LY364947 was able to suppress the expression of CLDN4 in both the HT29 and HCT116 cell lines. Conclusion Together, these results suggest that the expression of different CLDN family genes is closely associated with CRC tumor clinicopathological staging and immune cell infiltration. Moreover, CLDN4 expression is closely associated with TGFβ1 in CRC, suggesting that it and other CLDN family members may represent viable targets for antitumor therapeutic intervention.Keratoconus (KTCN), characterized by the steeper curvature of the cornea and the thinner central corneal thickness, was a degenerative corneal ectasia with ambiguous etiology and mechanism. We aim to reveal underlying pathological mechanisms of KTCN by multi-level transcriptomic, integrative omics analyses. We performed RNA-sequencing on corneal epithelial samples from seven patients and seven control donors, as well as peripheral matched blood samples from three of the patients and three control donors. After RNA extraction, library construction, and sequencing, differentially expressed genes and splicing events were identified, followed by over-representation analysis. In total, 547 differential expressed genes were identified in KTCN corneal epithelial samples, which were mainly enriched in immune responses and inflammatory processes. WGCNA module analysis, the transcriptomic analysis of peripheral blood samples, multiple omics data, and the meta-analysis of GEO samples confirmed the involvement of immune and inflammatory factors. Besides, 190 and 1,163 aberrant splicing events were identified by rMATS combined with CASH methods in corneal epithelial and blood samples with KCTN. In conclusion, this comprehensive transcriptome analysis of KTCN patients based on patients' tissue and blood samples uncovered a significant association between immune-inflammatory genes and pathways with KCTN, highlighting the contribution of the perturbed immune signaling to the pathogenesis of KCTN. Our study suggested the importance of measures to control inflammation in the treatment of KCTN.Hepatic inflammation is always accompanied with abnormal lipid metabolism. Whether N6-methyladenosine (m6A) mRNA methylation affects irregular inflammatory lipid level is unclear. Here, the m6A modification patterns in chicken liver at the acute stage of LPS-stimulated inflammation and at the normal state were explored via m6A and RNA sequencing and bioinformatics analysis. A total of 7,815 m6A peaks distributed in 5,066 genes were identified in the normal chicken liver and were mostly located in the CDS, 3'UTR region, and around the stop codon. At 2 h after the LPS intraperitoneal injection, the m6A modification pattern changed and showed 1,200 different m6A peaks. The hyper- and hypo-m6A peaks were differentially located, with the former mostly located in the CDS region and the latter in the 3'UTR and in the region near the stop codon. The hyper- or hypo-methylated genes were enriched in different GO ontology and pathways. Co-analysis revealed a significantly positive relationship between the fold change of m6A methylation level and the relative fold change of mRNA expression. Moreover, computational prediction of protein-protein interaction (PPI) showed that genes with altered m6A methylation and mRNA expression levels were clustered in processes involved in lipid metabolism, immune response, DNA replication, and protein ubiquitination. CD18 and SREBP-1 were the two hub genes clustered in the immune process and lipid metabolism, respectively. Hub gene AGPAT2 was suggested to link the immune response and lipid metabolism clusters in the PPI network. This study presented the first m6A map of broiler chicken liver at the acute stage of LPS induced inflammation. The findings may shed lights on the possible mechanisms of m6A-mediated lipid metabolism disorder in inflammation.In cultivated plants, shoot morphology is an important factor that influences crop economic value. However, the effects of gene expression patterns on shoot morphology are not clearly understood. In this study, the molecular mechanism behind shoot morphology (including leaf, stem, and node) was analyzed using RNA sequencing to compare weedy (creeper) and cultivar (stand) growth types obtained in F7 derived from a cross of wild and cultivated soybeans. A total of 12,513 (in leaves), 14,255 (in stems), and 11,850 (in nodes) differentially expressed genes were identified among weedy and cultivar soybeans. Comparative transcriptome and expression analyses revealed 22 phytohormone-responsive genes. We found that GIBBERELLIN 2-OXIDASE 8 (GA2ox), SPINDLY (SPY), FERONIA (FER), AUXIN RESPONSE FACTOR 8 (ARF8), CYTOKININ DEHYDROGENASE-1 (CKX1), and ARABIDOPSIS HISTIDINE KINASE-3 (AHK3), which are crucial phytohormone response genes, were mainly regulated in the shoot of weedy and cultivar types. These results indicate that interactions between phytohormone signaling genes regulate shoot morphology in weedy and cultivar growth type plants.