Bioinformatics Aided Elucidation of Functional and Structural Attributes of Bubalus Arnee Bubalis (BAB) Prochymosin

Chymosin, an aspartic protease present in the stomachs of young ruminants like cows (bovine), causes milk coagulation and cheese production through the breakdown of κ-casein peptide bonds at the Met105-Phe106 site. Bovine chymosin is first synthesized as a pre-prochymosin that is cleaved to produce the mature chymosin protein. Despite significant strides in research, our understanding of this crucial enzyme remains incomplete. The purpose of this work was to perform in silico evolutionary and functional analysis and to gain unique insights into the structure of this protein. For this, the sequence of Bos taurus chymosin from UniProt database was subjected to various bioinformatics analyses. We found that bovine chymosin is a low molecular weight and hydrophilic protein that has homologs in other Bovidae species. Two active sites of aspartic peptidases, along with a functional domain, were identified. Gene Ontology analysis further confirmed chymosin's involvement in proteolysis and aspartic endopeptidase activity. Potential disordered residues and post-translational modification sites were also uncovered. It was revealed that the secondary structure of bovine chymosin is comprised of beta strands (44.27%), coils (43.65%), and alpha helices (12.07%). A highly optimized 3D structure was also obtained. Moreover, crucial protein–protein interactions were unveiled. Altogether, these findings provide valuable insights that could guide future research on bovine chymosin and its biological roles.

Previous studies have shown that asthma is a risk factor for lung cancer, while the mechanisms involved remain unclear. We attempted to further explore the association between asthma and non-small cell lung cancer (NSCLC) via bioinformatics analysis. We obtained GSE143303 and GSE18842 from the GEO database. Lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) groups were downloaded from the TCGA database. Based on the results of differentially expressed genes (DEGs) between asthma and NSCLC, we determined common DEGs by constructing a Venn diagram. Enrichment analysis was used to explore the common pathways of asthma and NSCLC. A protein-protein interaction (PPI) network was constructed to screen hub genes. KM survival analysis was performed to screen prognostic genes in the LUAD and LUSC groups. A Cox model was constructed based on hub genes and validated internally and externally. Tumor Immune Estimation Resource (TIMER) was used to evaluate the association of prognostic gene models with the tumor microenvironment (TME) and immune cell infiltration. Nomogram model was constructed by combining prognostic genes and clinical features. 114 common DEGs were obtained based on asthma and NSCLC data, and enrichment analysis showed that significant enrichment pathways mainly focused on inflammatory pathways. Screening of 5 hub genes as a key prognostic gene model for asthma progression to LUAD, and internal and external validation led to consistent conclusions. In addition, the risk score of the 5 hub genes could be used as a tool to assess the TME and immune cell infiltration. The nomogram model constructed by combining the 5 hub genes with clinical features was accurate for LUAD. Five-hub genes enrich our understanding of the potential mechanisms by which asthma contributes to the increased risk of lung cancer.

Surface accessibility of different types of the same elements of secondary structure has been studied in 10 non-redundant sets of proteins (total number of three-dimensional structures is 1730) with a help of DSSP (Dictionary of Secondary Structure of Proteins). Random coils (C), beta strands (B), and alpha helices (H) have been classified according to their flanking elements of secondary structure in a polypeptide chain. Thanks to this kind of classification, for the first time it has been shown that random coils situated between a beta strand and an alpha helix (BCH) contain significantly lower fraction of exposed residues compared to other types of random coils; the least accessible beta strands are situated between two alpha helices (HBH), and the least accessible alpha helices are situated between a beta strand and an alpha helix (BHH). Discovered trends are explained as consequences of the natural selection that had been stabilizing the secondary structure of proteins on early steps of their evolution. Acquired differences in amino acid content of different types of random coils, alpha helices, and beta strands led to the formation of partially buried but hydrophilic BCH random coils because of their enrichment by Ser, Thr, and Asp residues. As a result, BCH random coils became prone to bind cations because of their lower hydration and decreased usage of positively charged amino acid residues. The mechanism described above led to the formation of active centers in ancient metalloenzymes. Nowadays one can observe decreased surface accessibility of amino acid residues in BCH random coils, in HBH beta strands, and in BHH alpha helices in proteins possessing hydrophobic cores.

A DNA Microarray-based Analysis of the Host Response to a Nonviral Gene Carrier: A Strategy for Improving the Immune Response

Objective: To research the network mechanism of Rhizoma Gastrodiae for Parkinson’s disease (PD) based on network pharmacology. Methods: “Rhizoma Gastrodiae” and “Parkinson’s disease” were searched as keywords in the Genecards database and Encyclopedia of Traditional Chinese Medicine (ETCM) database to obtain related gene targets, followed by the Venny intersection analysis. Subsequently, Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and protein-protein interaction (PPI) analysis were conducted through bioinformatic methods. Finally, literature review was utilized to evaluate the role of core genes in PD. Results: 248 gene targets of Rhizoma Gastrodiae and 8184 PD related genes were downloaded, discovering 163 intersected gene targets through Venny intersection analysis. GO and KEGG analysis revealed that Rhizoma Gastrodiae treatment primarily influences biological processes such as excitatory postsynaptic potential and sodium ion transport, with cellular components mainly involving extracellular exosome and mitochondria. Molecular functions include voltage-gated ion channel activity and transmitter-gated ion channel activity. KEGG pathways impacted include metabolic pathways and neuroactive ligand-receptor interaction. A PPI network identified 10 hub genes, with ALB, INS, and TNF being the top three, potentially serving as core treatment targets. Analysis of the relationship between PPI, GO analysis, and KEGG pathways highlighted SRC, PPARG, and PTGS2 as potential targets for treatment regulation. Literature comparison via Pubmed revealed extensive reporting on CASP3, suggesting its potential translational application as a reference. The remaining nine hub genes, lacking literature documentation, representing innovative candidates for further exploration. Conclusions: This study discovered the fundamental network mechanism underlying Rhizoma Gastrodiae’s efficacy in treating PD, assessing its innovative potential and translational applications. These findings serve as a significant reference for elucidating the central network mechanism of Rhizoma Gastrodiae in PD treatment, offering a scientific foundation and valuable insights for future clinical trials.

Random coil structures in bacterial proteins. Relationships of their amino acid compositions to flanking structures and corresponding genic base compositions

Background: Pituitary senescence constitutes a multifaceted process characterized by numerous morphological alterations, functional disruptions, and metabolic impairments within the pituitary tissue. It stands as a pivotal risk factor contributing to the heightened prevalence of neurodegenerative diseases. But the underlying molecular network mechanism remains to be known. Objective: This study analyzed the gene targets of pituitary with aging by bioinformatics, hoping to screen out promising targets for the diagnosis and treatment of aging pituitary. Methods: The GeneCards database (https://www.genecards.org) was utilized to retrieve targets associated with aging and the pituitary. The dataset was filtered using a score threshold of "Relevance score ≥10". Intersection genes were obtained through Venny intersection analysis. Protein-protein interaction (PPI) network analysis, Gene Ontology (GO) analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of the intersection genes were conducted on these intersection genes. The association between the obtained findings and the aging process of the pituitary gland was explored and compared with the existing literature. Results: Through bioinformatics analysis, we obtained 17 common genes between aging and pituitary-related genes. GO enrichment, KEGG pathway analysis, and PPI interaction showed that the genes IGF1, AKT1, RET, and POMC manifested down-regulation in aging process, whereas, LEP exhibited a marked up-regulation. Moreover, GO analysis reported activation of protein kinase activity, regulation of multicellular organism growth, and glucose metabolic processes within the realm of GO Biological Process (BP) enrichment. Likewise, the GO Cellular Component (CC) enrichment implicated the Wnt signalosome and catenin complex. In terms of Molecular Function (MF), results pointed to receptor ligand activity, insulin receptor binding, and estrogen receptor binding. Moreover, KEGG pathway enrichment analysis highlighted significant pathways associated with aging, such as Growth hormone synthesis, secretion, and action, Breast cancer, Rap1 signaling pathway, and JAK-STAT signaling pathway. Conclusions: We delved into the intricate link between aging and the pituitary gland, and identify several gene targets through the GeneCards database. By analyzing protein interactions, GO, and KEGG pathways, we found the 17 intersecting genes, which could be used to explain the molecular-level connections in the process of pituitary aging.

Objective To investigate the mechanism of development and rupture of intracranial aneurysm (IA) on the protein level and to search for target protein. Methods Specimens were collected from 3 cases of IA undergoing microsurgical clipping and STA (superficial temporal artery) in 3 cases as controls undergoing craniotomy through pterional approach from October 2015 to May 2016 at Department of Neurosurgery, Huashan Hospital Affiliated to Fudan University. All those samples were analyzed by bioinformatics and proteomics. The GO (gene ontology) analysis was performed for study of biological process, cell component and molecular function. PPI (protein-protein interaction) and KEGG (Kyoto Encyclopedia of Genes and Genomes) were used to analyze pathway and differentially expressed proteins. Results Compared between the IA and STA groups, there were 106 differentially expressed proteins identified. Among them, 20 were significantly dysregulated including 6 down-regulated proteins and 14 up-regulated ones. The GO analysis showed that differentially expressed proteins in IA group were mainly related to cellular biosynthetic process, and membrane-bounded organelles represented the most significant cellular structural difference between the IA and STA groups. Further analysis through PPI and KEGG pathway showed that the most significantly up-regulated protein was THBS4, which played a key role in extracellular matrix interaction; and the most significantly down-regulated protein was PTGS1, which was important for platelet activation. Conclusions Label-free quantitative mass spectrometry and bioinformatics could be effective ways to analyze the proteins in IA tissues. THBS could be a key protein in IA progression and a potential target for intervention. Key words: Intracranial aneurysm; Label-free; Proteomics; Computational biology

Magnesium and manganese binding sites on proteins have the same predominant motif of secondary structure

Objective: This study employed bioinformatic analysis to investigate the changes of gene expression in aging temporal lobes, aiming to identify promising targets for the diagnosis and treatment of temporal lobes aging. Methods: The GeneCards database (https:www.genecards.org) was accessed to obtain aging and temporal lobe-related targets. The data were conditioned based on a correlation score threshold of "Relevance score ≥10". The intersection genes between aging-related genes and temporal lobe-related genes were identified using Venny intersection analysis. Subsequently, protein interaction analysis, Gene Ontology (GO) analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted on the identified intersection genes. Meanwhile, the literature was reviewed to investigate the association of these analytical results with the aging process in the temporal lobe, so as to integrate and analyze data completely. Results: We identified 23 intersected genes associated with aging in the temporal lobe through Venny analysis. Genes correlated with protein interaction relationships were identified through the Protein-Protein Interaction (PPI) network analysis. Subsequently, the top 10 terms pertaining to biological processes (BP), cellular components (CC), molecular functions (MF), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, were derived from comprehensive analyses of GO enrichment and KEGG pathways, respectively. Through a literature review on identified key targets, we found that GFAP increases, Brain-derived neurotrophic factor (BDNF) and proopiomelanocortin (POMC) declined with aging, and potassium voltage-gated channel subfamily Q member 2 (KCNQ2) and Apolipoprotein E (APOE) ε4 allele link to cognitive decline. Whereas, APOE ε4 carriers accelerated atrophy in medial temporal lobe (MTL) with altered cerebral blood flow (CBF). Enrichment analysis reveals aging-related terms: "neuron death," "astrocyte activation," "growth cone," etc. KEGG highlights pathways like "Neurodegeneration" and "Alzheimer's disease," therefore enriching our understanding of aging complexities. Conclusions: Using the the GeneCards database (https://www.genecards.org), this study reported the correlation between the temporal lobe and aging by identifying shared gene targets between them. The study delved into the molecular-level relationship between the temporal lobe and aging through GO analysis, KEGG pathway analysis, and Protein-Protein Interaction (PPI) network analysis.

The aim of the present study was to identify the differentially expressed genes between cervical intraepithelial neoplasias (CIN) and adjacent normal tissue, and to construct a protein-protein interaction (PPI) network. A CIN dataset was obtained from Gene Expression Omnibus, and data of gene expression in CIN and adjacent normal tissue were extracted from GSE64217. The differentially expressed genes were selected using software package and heat map was drawn using the ‘pheatmap’ package. The selected differentially expressed genes were subjected to PPI, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis using Cytoscape, Database for Annotation, Visualization and Integrated Discovery, STRING and KOBAS. In the present study, 287 genes were differentially expressed between CIN and adjacent normal tissue, of which 170 were significantly upregulated and 118 genes were significantly downregulated (P<0.00001, fold-change >6). A differential gene expression network map was constructed to show the interactions of 30 protein products encoded by differentially expressed genes using STRING software. In particular, the key gene, EGR1, was identified using Cytoscape software. The KEGG pathway analysis revealed that the differential genes were mainly involved in several pathways, including ‘glutathione metabolism’, ‘arachidonic acid metabolism’, and ‘pentose phosphate pathway’. Results of the GO analysis showed that differential genes were enriched in different subsets. Specifically, small proline-rich protein 2E and 3, distal-less homeobox 5, epithelial membrane protein 1, cornifelin, periplakin, homeobox protein Hox-A13, estrogen receptor α, transglutaminase 1, small proline-rich protein 2A, Rh C glycoprotein, tumor protein p63, TGM3, homeobox B5 and small proline-rich protein 2D were enriched in ‘epithelial cell differentiation’, which affected the differentiation of epithelial cells. In conclusion, 287 differentially expressed genes were identified successfully. The key gene was identified based on the results of PPI, GO and KEGG analyses, and functional annotation and pathway analysis were also performed. Our study provides the basis for further studies on the interaction among differentially expressed genes.

Background:Tenascin-C (TNC) is a large glycoprotein of the extracellular matrix which associated with poor clinical outcomes in several malignancies. TNC over-expression is repeatedly observed in several cancer tissues and promotes several processes in tumor progression. Until quite recently, more needs to be known about the potential mechanisms of TNC as a key player in cancer progression and metastasis.Materials and Methods:In the present study, we performed a bioinformatics analysis of breast and colorectal cancer expression microarray data to survey TNC role and function with holistic view. Gene expression profiles were analyzed to identify differentially expressed genes (DEGs) between normal samples and cancer biopsy samples. The protein-protein interaction (PPI) networks of the DEGs with CluePedia plugin of Cytoscape software were constructed. Furthermore, after PPI network construction, gene-regulatory networks analysis was performed to predict long noncoding RNAs and microRNAs associated with TNC and cluster analysis was performed. Using the Clue gene ontology (GO) plugin of Cytoscape software, the GO and pathway enrichment analysis were performed.Results:PPI and DEGs-miRNA-lncRNA regulatory networks showed TNC is a significant node in a huge network, and one of the main gene with high centrality parameters. Furthermore, from the regulatory level perspective, TNC could be significantly impressed by miR-335-5p. GO analysis results showed that TNC was significantly enriched in cancer-related biological processes.Conclusions:It is important to identify the TNC underlying molecular mechanisms in cancer progression, which may be clinically useful for tumor-targeting strategies. Bioinformatics analysis provides an insight into the significant roles that TNC plays in cancer progression scenarios.

Background: Because germline microRNA-based variants predict toxicity risk to anti-PD-1/PDL-1 treatment, and CTLA4 inhibitors are often used in combination with them, we wanted to see if we could also predict toxicity and/or response to anti-CTLA4 therapy. Methods: The study included 69 patients from 3 institutions treated with anti-CTLA4 therapy alone. Patients were 23-89 years old with median age of 62. “Toxicity” was defined as grade 3 or higher toxicity and “Response” as a complete response, partial response, or stable disease. A panel of previously tested variants (n=139), age, sex and cycles of anti-CTLA4 were used to predict endpoints of interest. To balance model dimensionality we pre-screened variants and included them if they were marginally associated with our outcomes of interest via Fisher’s Exact Test or Jonckheere-Terpstra Test at a p-value threshold of &amp;lt; 0.2. We fit Elastic Net (EN), Random Forest (RF), and Boosted Tree (BT) models with up-sampling to predict outcomes of interest. Leave-one-out cross validation (LOOCV) was used to evaluate model performance, and the model with the highest LOOCV AUC (between all EN, RF, and BT) was chosen as the final model and re-fit using all observations. We also performed a stratified gene ontology (GO) analysis to assess biological differences between variants involved in the signatures. Our GO analysis compared gene clusters across groupings with an adjusted p-value cutoff of 0.05 against a universal genomic background. Results: 15 patients had toxicity and 21 patients had a response, with 27/139 and 25/139 variants marginally associated with these endpoints, respectively. Our toxicity model had a LOOCV AUC of 0.811 with the top variables being age, TNNT2_rs3729843, REV3L_rs465646, miR34b_c_rs4938723, and XRCC3_rs861539. Our response model had an LOOCV AUC of 0.716 with top variants being IL2RA_rs2476491, REV3L_rs465646, and IL10RB_rs2834167. Including anti-CTLA4 cycles improved the prediction accuracy for Response with an AUC 0.764. GO analysis revealed 117 pathways enriched in both the Toxicity and Response signatures, which were predominantly involved in pri-miRNA transcriptional regulation. Top GO terms in the Response signature (333 unique) were related to apoptosis and cell death. Top GO terms in the Toxicity signature (425 unique) were related to DNA replication and cellular signaling and response, including the interleukin-35-mediated signaling pathway. Conclusion: We identified germline signatures predicting toxicity and response following anti-CTLA4 therapy. These signatures differed from each other as well as from a previously identified signature of toxicity to anti-PD1/PDL-1 therapy, and GO analysis identified plausible underlying signaling pathways. Our findings continue to validate the potential of these variants to predict meaningful endpoints in cancer therapy, and thus further research in larger and more diverse cohorts is warranted. Citation Format: Joanne B. Weidhaas, Kristen McGreevy, Nicholas Marco, Nora Sundahl, Christopher R. Cabanski, Christine Spencer, Piet Ost, Donatello Telesca. Germline signatures predicting toxicity and response to CTLA4 inhibitors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2986.

BackgroundCompeting endogenous RNA (ceRNA) networks play important roles in the mechanism and development of a variety of diseases. This study aimed to construct a ceRNA network of hypertrophic cardiomyopathy (HCM).MethodsWe searched the Gene Expression Omnibus (GEO) database and then analyzed the RNAs of 353 samples to explore differentially expressed lncRNAs (DELs), microRNAs (miRNAs; DEMs), and messenger RNAs (DEmRNAs) during the progression of HCM. Weighted gene co-expression network analysis (WGCNA), Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and transcription factor (TF) prediction of miRNAs were also performed, and the GO terms, KEGG pathway terms, protein-protein interaction (PPI) network, and Pearson correlation network of the DEGs were visualized with the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database and through Pearson analysis. In addition, a ceRNA network related to HCM was constructed on the basis of the DELs, DEMs, and DEs. Finally, the function of the ceRNA network was explored via GO and KEGG enrichment analyses.ResultsThrough our analysis, 93 DELs (77 upregulated and 16 downregulated), 163 DEMs (91 upregulated and 72 downregulated), and 432 DEGs (238 upregulated and 194 downregulated) were screened. The functional enrichment analysis results for miRNAs showed that the miRNAs were mainly related to the VEGFR signaling network and the INFr pathway and were mainly regulated by TFs such as SOX1, TEAD1, and POU2F1. Gene set enrichment analysis (GSEA), GO analysis, and KEGG enrichment analysis showed that the DEGs were enriched in the Hedgehog signaling pathway, IL-17 signaling pathway, and TNF signaling pathway. In addition, a ceRNA network including 8 lncRNAs (e.g., LINC00324, SNHG12, and ALMS1-IT1), 7 miRNAs (e.g., hsa-miR-217, hsa-miR-184, and hsa-miR-140-5p), and 52 mRNAs (e.g., IGFBP5, TMED5, and MAGT1) was constructed. The results revealed that SNHG12, hsa-miR-140-5p, hsa-miR-217, TFRC, HDAC4, TJP1, IGFBP5, and CREB5 may form an important network involved in the pathology of HCM.ConclusionsThe novel ceRNA network that we have demonstrated will provide new research points about molecular mechanisms of HCM.

Identification of ST3GAL6-AS1 Interactive Protein in Multiple Myeloma