Comparing methods for metabolic network analysis and an application to metabolic engineering

Bioinformatics tools have facilitated the reconstruction and analysis of cellular metabolism of various organisms based on information encoded in their genomes. Characterization of cellular metabolism is useful to understand the phenotypic capabilities of these organisms. It has been done quantitatively through the analysis of pathway operations. There are several in silico approaches for analyzing metabolic networks, including structural and stoichiometric analysis, metabolic flux analysis, metabolic control analysis, and several kinetic modeling based analyses. They can serve as a virtual laboratory to give insights into basic principles of cellular functions. This article summarizes the progress and advances in software and algorithm development for metabolic network analysis, along with their applications relevant to cellular physiology, and metabolic engineering with an emphasis on microbial strain optimization. Moreover, it provides a detailed comparative analysis of existing approaches under different categories.     

Functional annotation of putative hypothetical proteins from Candida dubliniensis

An extensive analysis of C. dubliniensis proteomics data showed that ~ 22% protein are conserved hypothetical proteins (HPs) whose function is still not determined precisely. Analysis of gene sequence of HPs provides a platform to establish sequence–function relationships to a more profound understanding of the molecular machinery of organisms at systems level. Here we have combined the latest versions of bioinformatics tools including, protein family, motifs, intrinsic features from the amino acid sequence, sequence–function relationship, pathway analysis, etc. to assign a precise function to HPs for which no any experimental information is available. Our results show that 27 HPs have well defined functions and we categorized them as enzyme, nucleic acid binding, transport protein, etc. Five HPs showed adhesin character that is likely to be essential for the survival of yeast and pathogenesis. We also addressed issues related to the sub-cellular localization and signal peptide identification which provides an idea about its colocalization and function. The outcome of the present study may facilitate better understanding of mechanism of virulence, drug resistance, pathogenesis, adaptability to host, tolerance for host immune response, and drug discovery for treatment of C. dubliniensis infections.     

Protein repertoire impact of Ubiquitin-Proteasome System impairment: insight into the protective role of beta-estradiol

The Ubiquitin-Proteasome System (UPS) and the Autophagy-Lysosome Pathways (ALP) are key mechanisms for cellular homeostasis sustenance and protein clearance. A wide number of Neurodegenerative Diseases (NDs) are tied with UPS impairment and have been also described as proteinopathies caused by aggregate-prone proteins, not efficiently removed by proteasome. Despite the large knowledge on proteasome biological role, molecular mechanisms associated with its impairment are still blur. We have pursued a comprehensive proteomic investigation to evaluate the phenotypic rearrangements in protein repertoires associated with a UPS blockage. Different functional proteomic approaches have been employed to tackle UPS impairment impact on human NeuroBlastoma (NB) cell lines responsive to proteasome inhibition by Epoxomicin. 2-Dimensional Electrophoresis (2-DE) separation combined with Mass Spectrometry and Shotgun Proteomics experiments have been employed to design a thorough picture of protein profile. Unsupervised meta-analysis of the collected proteomic data revealed that all the identified proteins relate each other in a functional network centered on beta-estradiol. Moreover we showed that treatment of cells with beta-estradiol resulted in aggregate removal and increased cell survival due to activation of the autophagic pathway. Our data may provide the molecular basis for the use of beta-estradiol in neurodegenerative disorders by induction of protein aggregate removal.     

Evaluation of computational techniques for predicting non-synonymous single nucleotide variants pathogenicity

The human genetic diseases associated with many factors, one of these factors is the non-synonymous Single Nucleotide Variants (nsSNVs) cause single amino acid change with another resulting in protein function change leading to disease. Many computational techniques have been released to expect the impacts of amino acid alteration on protein function and classify mutations as pathogenic or neutral. Here in this article, we assessed the performance of eight techniques; FATHMM, SIFT, Provean, iFish, Mutation Assessor, PANTHER, SNAP2, and PON- P2 using a VaribenchSelectedPure dataset of 2144 pathogenic variants and 3777 neutral variants extracted from the free standard database “Varibench.” The first five techniques achieve (45.60–83.75) % specificity, (52.64–94.13) % sensitivity, (51.00–88.90) % AUC, and (49.76–88.24) % ACC on whole dataset, while all eight techniques achieve (36.54–77.88) % specificity, (50.00–75.00) % sensitivity, (51.00–76.40) % AUC, and (25.00–77.78) % ACC on random sample dataset. We also created a Meta classifier (CSTJ48) that combines FATHMM, iFish, and Mutation Assessor. It registers 96.33% specificity, 86.07% sensitivity, 91.20% AUC, and 91.89 ACC. By comparing the results, it's clear that FATHMM gives the highest performance over the seven individual techniques, where it achieves 83.75% and 77.88% specificity, 94.13%, and 75.00% sensitivity, 88.90% and 76.40% AUC, and 88.24% and 77.78% ACC on whole and random sample dataset, respectively. Also, the launched Meta classifier (CSTJ48) is outperforming over all the eight individual tools that compared here.     

Single nucleotide polymorphisms in genes that are common targets of luteotropin and luteolysin in primate corpus luteum: Computational exploration

Luteal insufficiency affects fertility and hence study of mechanisms that regulate corpus luteum (CL) function is of prime importance to overcome infertility problems. Exploration of human genome sequence has helped to study the frequency of single nucleotide polymorphisms (SNPs). Clinical benefits of screening SNPs in infertility are being recognized well in recent times. Examining SNPs in genes associated with maintenance and regression of CL may help to understand unexplained luteal insufficiency and related infertility. Publicly available microarray gene expression databases reveal the global gene expression patterns in primate CL during the different functional state. We intend to explore computationally the deleterious SNPs of human genes reported to be common targets of luteolysin and luteotropin in primate CL. Different computational algorithms were used to dissect out the functional significance of SNPs in the luteinizing hormone sensitive genes. The results raise the possibility that screening for SNPs might be integrated to evaluate luteal insufficiency associated with human female infertility for future studies.     

Genetic association of KCNJ10 rs1130183 with seizure susceptibility and computational analysis of deleterious non-synonymous SNPs of KCNJ10 gene

Establishing genetic basis of Idiopathic generalized epilepsies (IGE) is challenging because of their complex inheritance pattern and genetic heterogeneity. Kir4.1 inwardly rectifying channel (KCNJ10) is one of the independent genes reported to be associated with seizure susceptibility. In the current study we have performed a comprehensive in silico analysis of genetic variants in KCNJ10gene at functional and structural level along with a case–control analysis for the association ofrs1130183 (R271C) polymorphism in Indian patients with IGE. Age and sex matched 108epileptic patients and normal healthy controls were examined. Genotyping of KCNJ10rs1130183variation was performed using PCR-RFLP method. The risk association was determined by using odds ratio and 95% confidence interval. Functional effects of non-synonymous SNPs (nsSNPs) in KCNJ10 gene were analyzed using SIFT PolyPhen-2, I-Mutant 2.0, PANTHER and FASTSNP. Subsequently, homology modeling of protein three dimensional (3D) structures was performed using Modeller tool (9.10v) and compared the native protein with mutant for assessment of structure and stability. SIFT, PolyPhen-2, I-Mutant 2.0 and PANTHER collectively showed rs1130183, rs1130182 and rs137853073 SNPs inKCNJ10 gene affect protein structure and function. There was a considerable variation in the Root Mean Square Deviation (RMSD) value between the native and mutant structure (1.17?). Association analysis indicate KCNJ10rs1130183 did not contribute to risk of seizure susceptibility in Indian patients with IGE (OR- 0.38; 95%CI, 0.07–2.05) and T allele frequency (0.02%) was in concordance with dbSNP reports. This study identifies potential SNPs that may contribute to seizure susceptibility and further studies with the selected SNPs in larger number of samples and their functional analysis is required for understanding the variants of KCNJ10with seizure susceptibility.     

Hippocampal RNA sequencing in mice selectively bred for high and low activity

High and Low Activity strains of mice were bidirectionally selected for differences in open-field activity (DeFries et al., 1978, Behavior Genetics, 8: 3–13) and subsequently inbred to use as a genetic model for studying anxiety-like behaviors (Booher et al., 2021, Genes, Brain and Behavior, 20: e12730). Hippocampal RNA-sequencing of the High and Low Activity mice identified 3901 differentially expressed protein-coding genes, with both sex-dependent and sex-independent effects. Functional enrichment analysis (PANTHER) highlighted 15 gene ontology terms, which allowed us to create a narrow list of 264 top candidate genes. Of the top candidate genes, 46 encoded four Complexes (I, II, IV and V) and two electron carriers (cytochrome c and ubiquinone) of the mitochondrial oxidative phosphorylation process. The most striking results were in the female high anxiety, Low Activity mice, where 39/46 genes relating to oxidative phosphorylation were upregulated. In addition, comparison of our top candidate genes with two previously curated High and Low Activity gene lists highlight 24 overlapping genes, where Ndufa13, which encodes the supernumerary subunit A13 of complex I, was the only gene to be included in all three lists. Mitochondrial dysfunction has recently been implicated as both a cause and effect of anxiety-related disorders and thus should be further explored as a possible novel pharmaceutical treatment for anxiety disorders.