Analyzing the impact of T7L variants overexpression on the metabolic profile of Escherichia coli.

INTRODUCTION: Exploring metabolic changes within host E. coli through an untargeted metabolomic study of T7L variants overexpression to optimize engineered endolysins for clinical/therapeutic use. AIM AND OBJECTIVE: This study aims to assess the impact of overexpressing T7L variants on the metabolic profiles of E. coli. The two variants considered include T7L-H37A, which has enhanced lytic activity compared to its wild-type protein, and T7L-H48K, a dead mutant with no significant activity. METHODS: 1H NMR-based metabolomics was employed to compare the metabolic profiles of E. coli cells overexpressing T7L wild-type protein and its variants. RESULTS: Overexpression of the T7L wild-type (T7L-WT) protein and its variants (T7L-H48K and T7L-H37A) was compared to RNAP overexpression in E. coli cells using 1H NMR-based metabolomics, analyzing a total of 75 annotated metabolites, including organic acids, amino acids, sugars, and nucleic acids. The results showed distinct clustering patterns for the two T7L variant groups compared with the WT, in which the dead mutant (H48K) group showed clustering close to that of RNAP. Pathway impact analysis revealed different effects of T7L variants on E. coli metabolic profiles, with T7L-H48K showing minimal alterations in energy and amino acid pathways linked to osmotic stress compared to noticeable alterations in these pathways for both T7L-H37A and T7L-WT. CONCLUSIONS: This study uncovered distinct metabolic fingerprints when comparing the overexpression of active and inactive mutants of T7L lytic enzymes in E. coli cells. These findings could contribute to the optimization and enhancement of suitable endolysins as potential alternatives to antibiotics.

Serum Metabolomics of Retinoblastoma: Assessing the Differential Serum Metabolic Signatures of Unilateral and Bilateral Patients.

Retinoblastoma (Rb) is the most common pediatric eye cancer. To identify the biomarkers for early diagnosis and monitoring the progression of Rb in patients, mapping of the alterations in their metabolic profiles is essential. The present study aims at exploring the metabolic disparity in serum from Rb patients and controls using NMR-based metabolomics. A total of 72 metabolites, including carbohydrates, amino acids, and organic acids, were quantified in serum samples from 24 Rb patients and 26 controls. Distinct clusters of Rb patients and controls were obtained using the partial least-squares discriminant analysis (PLS-DA) model. Further, univariate and multivariate analyses of unilateral and bilateral Rb patients with respect to their age-matched controls depicted their distinct metabolic fingerprints. Metabolites including 2-phosphoglycerate, 4-aminobutyrate, proline, O-phosphocholine, O-phosphoethanolamine, and Sn-glycero-3-phosphocholine (Sn-GPC) showed significant perturbation in both unilateral and bilateral Rb patients. However, metabolic differences among the bilateral Rb cases were more pronounced than those in unilateral Rb cases with respect to controls. In addition to major discriminatory metabolites for Rb, unilateral and bilateral Rb cases showed specific metabolic changes, which might be the result of their differential genetic/somatic mutational backgrounds. This further suggests that the aberrant metabolic perturbation in bilateral patients signifies the severity of the disease in Rb patients. The present study demonstrated that identified serum metabolites have potential to serve as a noninvasive method for detection of Rb, discriminate bilateral from unilateral Rb patients, and aid in better understanding of the RB tumor biology.

Engineering of a T7 Bacteriophage Endolysin Variant with Enhanced Amidase Activity.

The therapeutic use of bacteriophage-encoded endolysins as enzybiotics has increased significantly in recent years due to the emergence of antibiotic resistant bacteria. Phage endolysins lyse the bacteria by targeting their cell wall. Various engineering strategies are commonly used to modulate or enhance the utility of therapeutic enzymes. This study employed a structure-guided mutagenesis approach to engineer a T7 bacteriophage endolysin (T7L) with enhanced amidase activity and lysis potency via replacement of a noncatalytic gating residue (His 37). Two H37 variants (H37A and H37K) were designed and characterized comprehensively using integrated biophysical and biochemical techniques to provide mechanistic insights into their structure-stability-dynamics-activity paradigms. Among the studied proteins, cell lysis data suggested that the obtained H37A variant exhibits amidase activity (∼35%) enhanced compared to that of wild-type T7 endolysin (T7L-WT). In contrast to this, the H37K variant is highly unstable, prone to aggregation, and less active. Comparison of the structure and dynamics of the H37A variant to those of T7L-WT evidenced that the alteration at the site of H37 resulted in long-range structural perturbations, attenuated the conformational heterogeneity, and quenched the microsecond to millisecond time scale motions. Stability analysis confirmed the altered stability of H37A compared to that of its WT counterpart. All of the obtained results established that the H37A variant enhances the lysis activity by regulating the stability-activity trade-off. This study provided deeper atomic level insights into the structure-function relationships of endolysin proteins, thus aiding researchers in the rational design of engineered endolysins with enhanced therapeutic properties.

Overexpression of bacteriophage T4 and T7 endolysins differentially regulate the metabolic fingerprint of host Escherichia coli.

Bioactive proteins are often overexpressed in different host systems for biotechnological/biomedical applications. Endolysins are natural bactericidal proteins that cleave the bacterial peptidoglycan membrane, and have the potential to be the next-generation enzybiotics. Therefore, the present study aims to elucidate the impact of two endolysins (T4L, T7L) overexpression on metabolic fingerprint of E. coli using NMR spectroscopy. The 1H NMR-based metabolomics analysis revealed global metabolite profiles of E. coli in response to endolysins. The study has identified nearly 75 metabolites, including organic acids, amino acids, sugars and nucleic acids. RNA Polymerase (RNAP) has been considered as reference protein for marking the specific alterations in metabolic pathways. The data suggested downregulation of central carbon metabolic pathway in both endolysins overexpression, but to a different extent. Also, the endolysin overexpression have highlighted the enhanced metabolic load and stress generation in the host cells, thus leading to the activation of osmoregulatory pathways. The overall changes in metabolic fingerprint of E. coli highlights the enhanced perturbations during the overexpression of T4L as compared to T7L. These untargeted metabolic studies shed light on the regulation of molecular pathways during the heterologous overexpression of these lytic enzymes that are lethal to the host.

Elucidating the lactic acid tolerance mechanism in vaginal clinical isolates of Candida glabrata.

Incidence of vulvovaginal candidiasis are strikingly high and treatment options are limited with nearly 50% Candida glabrata cases left untreated or experience treatment failures. The vaginal microenvironment is rich in lactic acid, and the adaptation of C. glabrata to lactic acid (LA) is the main reason for clinical treatment failure. In the present study, C. glabrata and its vaginal clinical isolates were comprehensively investigated for their growth response, metabolic adaptation and altered cellular pathway to LA using different biochemical techniques, metabolic profiling and transcriptional studies. C. glabrata shown considerable variations in its topological and biochemical features without compromising growth in LA media. Chemical profiling data highlighted involvement of cell wall/membrane, ergosterol and oxidative stress related pathways in mediating adaptative response of C. glabrata towards LA. Further, one dimensional proton (1H) NMR spectroscopy based metabolic profiling revealed significant modulation in 19 metabolites of C. glabrata cells upon growth in LA. Interestingly myo-inositol, xylose, putrescine and betaine which are key metabolites for cell growth and viability were found to be differentially expressed by clinical isolates. These observations were supported by the transcriptional expression study of selected genes evidencing cell wall/membrane re-organisation, altered oxidative stress, and reprogramming of carbon metabolic pathways. Collectively, the study advances our understanding on adaptative response of C. glabrata in vaginal microenvironment to lactic acid for survival and virulence.

In vaginal tract, lactic acid present as a natural carbon source is a potentiating factor for vulvovaginal candidiasis caused by C. glabrata is highest. The present article delineates the lactic acid adaptation in vaginal clinical isolates of C. glabrata using a comprehensive approach of biochemical, metabolic and transcriptional studies.

Molecular investigations on Candida glabrata clinical isolates for pharmacological targeting.

Prevalence of drug resistant C. glabrata strains in hospitalized immune-compromised patients with invasive fungal infections has increased at an unexpected pace. This has greatly pushed researchers in identification of mutations/variations in clinical isolates for better assessment of the prevailing drug resistance trends and also for updating of antifungal therapy regime. In the present investigation, the clinical isolates of C. glabrata were comprehensively characterized at a molecular level using metabolic profiling and transcriptional expression analysis approaches in combination with biochemical, morphological and chemical profiling methods. Biochemically, significant variations in azole susceptibility, surface hydrophobicity, and oxidative stress generation were observed among the isolates as compared to wild-type. The 1H NMR profiling identified 18 differential metabolites in clinical strains compared to wild-type and were classified into five categories, that include: sugars (7), amino acids and their derivatives (7), nitrogen bases (3) and coenzymes (1). Transcriptional analysis of selective metabolic and regulatory enzymes established that the major differences were found in cell membrane stress, carbohydrate metabolism, amino acid biosynthesis, ergosterol pathway and turnover of nitrogen bases. This detailed molecular level/metabolic fingerprint study is a useful approach for differentiating pathogenic/clinical isolates to that of wild-type. This study comprehensively delineated the differential cellular pathways at a molecular level that have been re-wired by the pathogenic clinical isolates for enhanced pathogenicity and virulence traits.

Elucidating Protein-protein Interactions Through Computational Approaches and Designing Small Molecule Inhibitors Against them for Various Diseases.

BACKGROUND: To carry out wide range of cellular functionalities, proteins often associate with one or more proteins in a phenomenon known as Protein-Protein Interaction (PPI). Experimental and computational approaches were applied on PPIs in order to determine the interacting partners, and also to understand how an abnormality in such interactions can become the principle cause of a disease. OBJECTIVE: This review aims to elucidate the case studies where PPIs involved in various human diseases have been proven or validated with computational techniques, and also to elucidate how small molecule inhibitors of PPIs have been designed computationally to act as effective therapeutic measures against certain diseases. RESULTS: Computational techniques to predict PPIs are emerging rapidly in the modern day. They not only help in predicting new PPIs, but also generate outputs that substantiate the experimentally determined results. Moreover, computation has aided in the designing of novel inhibitor molecules disrupting the PPIs. Some of them are already being tested in the clinical trials. CONCLUSION: This review delineated the classification of computational tools that are essential to investigate PPIs. Furthermore, the review shed light on how indispensable computational tools have become in the field of medicine to analyze the interaction networks and to design novel inhibitors efficiently against dreadful diseases in a shorter time span.