Current Genomics - Volume 24, Issue 2, 2023

Biological sequence analysis is the most fundamental work in bioinformatics. Many research methods have been developed in the development of biological sequence analysis. These methods include sequence alignment-based methods and alignment-free methods. In addition, there are also some sequence analysis methods based on the feature definition and quantification of the sequence itself. This editorial introduces the methods of biological sequence analysis and explores the significance of defining features and quantitative research of biological sequences.

Circular RNAs (circRNAs) are a class of endogenous functional RNA generated by backsplicing. Recently, circRNAs have been found to have certain coding potential. Proteins/peptides translated from circRNAs play essential roles in various diseases. Here, we briefly summarize the basic knowledge and technologies that are usually applied to study circRNA translation. Then, we focus on the research progress of circRNA translation in cardiovascular diseases and discuss the perspective and future direction of translatable circRNA study in cardiovascular diseases.

Plants are a vital source of bioactive molecules for various drug development processes. Tetrastigma hemsleyanum is one of the endangered medicinal plant species well known to the world due to its wide range of therapeutic effects. Many bioactive molecules have been identified from this plant, including many classes of secondary metabolites such as flavonoids, phenols, terpenoids, steroids, alkaloids, etc. Due to its slow growth, it usually takes 3-5 years to meet commercial medicinal materials for this plant. Also, T. hemsleyanum contains low amounts of specific bioactive compounds, which are challenging to isolate easily. Currently, scientists are attempting to increase bioactive molecules' production from medicinal plants in different ways or to synthesize them chemically. The genomic tools helped to understand medicinal plants' genome organization and led to manipulating genes responsible for various biosynthesis pathways. Metabolic engineering has made it possible to enhance the production of secondary metabolites by introducing manipulated biosynthetic pathways to attain high levels of desirable bioactive molecules. Metabolic engineering is a promising approach for improving the production of secondary metabolites over a short time period. In this review, we have highlighted the scope of various biotechnological approaches for metabolic engineering to enhance the production of secondary metabolites for pharmaceutical applications in T. hemsleyanum. Also, we summarized the progress made in metabolic engineering for bioactive molecule enhancement in T. hemsleyanum. It may lead to reducing the destruction of the natural habitat of T. hemsleyanum and conserving them through the cost-effective production of bioactive molecules in the future.

Background: Crohn's disease (CD) is a chronic idiopathic inflammatory bowel disease affecting the entire gastrointestinal tract from the mouth to the anus. These patients often experience a period of symptomatic relapse and remission. A 20 - 30% symptomatic recurrence rate is reported in the first year after surgery, with a 10% increase each subsequent year. Thus, surgery is done only to relieve symptoms and not for the complete cure of the disease. The determinants and the genetic factors of this disease recurrence are also not well-defined. Therefore, enhanced diagnostic efficiency and prognostic outcome are critical for confronting CD recurrence. Methods: We analysed ileal mucosa samples collected from neo-terminal ileum six months after surgery (M6=121 samples) from Crohn's disease dataset (GSE186582). The primary aim of this study is to identify the potential genes and critical pathways in post-operative recurrence of Crohn128;™s disease. We combined the differential gene expression analysis with Recursive feature elimination (RFE), a machine learning approach to get five critical genes for the postoperative recurrence of Crohn's disease. The features (genes) selected by different methods were validated using five binary classifiers for recurrence and remission samples: Logistic Regression (LR), Decision tree classifier (DT), Support Vector Machine (SVM), Random Forest classifier (RF), and K-nearest neighbor (KNN) with 10-fold cross-validation. We also performed weighted gene co-expression network analysis (WGCNA) to select specific modules and feature genes associated with Crohn's disease postoperative recurrence, smoking, and biological sex. Combined with other biological interpretations, including Gene Ontology (GO) analysis, pathway enrichment, and protein-protein interaction (PPI) network analysis, our current study sheds light on the indepth research of CD diagnosis and prognosis in postoperative recurrence. Results: PLOD2, ZNF165, BOK, CX3CR1, and ARMCX4, are the important genes identified from the machine learning approach. These genes are reported to be involved in the viral protein interaction with cytokine and cytokine receptors, lysine degradation, and apoptosis. They are also linked with various cellular and molecular functions such as Peptidyl-lysine hydroxylation, Central nervous system maturation, G protein-coupled chemoattractant receptor activity, BCL-2 homology (BH) domain binding, Gliogenesis and negative regulation of mitochondrial depolarization. WGCNA identified a gene co-expression module that was primarily involved in mitochondrial translational elongation, mitochondrial translational termination, mitochondrial translation, mitochondrial respiratory chain complex, mRNA splicing via spliceosome pathways, etc.; Both the analysis result emphasizes that the mitochondrial depolarization pathway is linked with CD recurrence leading to oxidative stress in promoting inflammation in CD patients. Conclusion: These key genes serve as the novel diagnostic biomarker for the postoperative recurrence of Crohn128;™s disease. Thus, among other treatment options present until now, these biomarkers would provide success in both diagnosis and prognosis, aiming for a long-lasting remission to prevent further complications in CD.

Background: Salmonella typhi biofilm confers a serious public health issue for lengthy periods and the rise in antibiotic resistance and death rate. Biofilm generation has rendered even the most potent antibiotics ineffective in controlling the illness, and the S. typhi outbreak has turned into a fatal disease typhoid. S. typhi infection has also been connected to other deadly illnesses, such as a gall bladder cancer. The virulence of this disease is due to the interaction of numerous genes and proteins of S. typhi. Objective: The study aimed to identify a cascade of target proteins in S. typhi biofilm condition with the help of genomic data mining and protein-protein interaction analysis. Methods: The goal of this study was to notice some important pharmacological targets in S. typhi. using genomic data mining, and protein-protein interaction approaches were used so that new drugs could be developed to combat the disease. Results: In this study, we identified 15 potential target proteins that are critical for S. typhi biofilm growth and maturation. Three proteins, CsgD, AdrA, and BcsA, were deciphered with their significant role in the synthesis of cellulose, a critical component of biofilm's extracellular matrix. The CsgD protein was also shown to have high interconnectedness and strong interactions with other important target proteins of S. typhi. As a result, it has been concluded that CsgD is involved in a range of activities, including cellulose synthesis, bacterial pathogenicity, quorum sensing, and bacterial virulence. Conclusion: All identified targets in this study possess hydrophobic properties, and their cellular localization offered proof of a potent therapeutic target. Overall results of this study, drug target shortage in S. typhi is also spotlighted, and we believe that obtained result could be useful for the design and development of some potent anti-salmonella agents for typhoid fever in the future.

Background: Due to the heterogeneity of Hepatocellular carcinoma (HCC), there is an urgent need for reliable diagnosis and prognosis. Mitochondria-mediated abnormal lipid metabolism affects the occurrence and progression of HCC. Objective: This study aims to investigate the potential of mitochondrial lipid metabolism (MTLM) genes as diagnostic and independent prognostic biomarkers for HCC. Methods: MTLM genes were screened from the Gene Expression Omnibus (GEO) and Gene Set Enrichment Analysis (GSEA) databases, followed by an evaluation of their diagnostic values in both The Cancer Genome Atlas Program (TCGA) and the Affiliated Cancer Hospital of Guangxi Medical University (GXMU) cohort. The TCGA dataset was utilized to construct a gene signature and investigate the prognostic significance, immune infiltration, and copy number alterations. The validity of the prognostic signature was confirmed through GEO, International Cancer Genome Consortium (ICGC), and GXMU cohorts. Results: The diagnostic receiver operating characteristic (ROC) curve revealed that eight MTLM genes have excellent diagnostic of HCC. A prognostic signature comprising 5 MTLM genes with robust predictive value was constructed using the lasso regression algorithm based on TCGA data. The results of the Stepwise regression model showed that the combination of signature and routine clinical parameters had a higher area under the curve (AUC) compared to a single risk score. Further, a nomogram was constructed to predict the survival probability of HCC, and the calibration curves demonstrated a perfect predictive ability. Finally, the risk score also unveiled the different immune and mutation statuses between the two different risk groups. Conclusion: MTLT-related genes may serve as diagnostic and prognostic biomarkers for HCC as well as novel therapeutic targets, which may be beneficial for facilitating further understanding the molecular pathogenesis and providing potential therapeutic strategies for HCC.