Bacterial Operational Taxonomic Units Replace the Interactive Roles of Other Operational Taxonomic Units Under Strong Environmental Changes.

BACKGROUND: Microorganisms are an important component of an aquatic ecosystem and play a critical role in the biogeochemical cycle which influences the circulation of the materials and maintains the balance in aquatic ecosystems. OBJECTIVE: The seasonal variation along with the impact of anthropogenic activities, water quality, bacterial community composition and dynamics in the Loktak Lake, the largest freshwater lake of North East India, located in the Indo-Burma hotspot region was assessed during post-monsoon and winter season through metagenome analysis. METHODS: Five soil samples were collected during Post-monsoon and winter season from the Loktak Lake that had undergone different anthropogenic impacts. The metagenomic DNA of the soil samples was extracted using commercial metagenomic DNA extraction kits following the manufacturer's instruction. The extracted DNA was used to prepare the NGS library and sequenced in the Illumina MiSeq platform. RESULTS: Metagenomics analysis reveals Proteobacteria as the predominant community followed by Acidobacteria and Actinobacteria. The presence of these groups of bacteria indicates nitrogen fixation, oxidation of iron, sulfur, methane, and source of novel antibiotic candidates. The bacterial members belonging to different groups were involved in various biogeochemical processes, including fixation of carbon and nitrogen, producing streptomycin, gramicidin and perform oxidation of sulfur, sulfide, ammonia, and methane. CONCLUSION: The outcome of this study provides a valuable dataset representing a seasonal profile across various land use and analysis, targeting at establishing an understanding of how the microbial communities vary across the land use and the role of keystone taxa. The findings may contribute to searches for microbial bio-indicators as biodiversity markers for improving the aquatic ecosystem of the Loktak Lake.

Hydrolysis of homocysteine thiolactone results in the formation of Protein-Cys-S-S-homocysteinylation.

An increased level of homocysteine, a reactive thiol amino acid, is associated with several complex disorders and is an independent risk factor for cardiovascular disease. A majority (>80%) of circulating homocysteine is protein bound. Homocysteine exclusively binds to protein cysteine residues via thiol disulfide exchange reaction, the mechanism of which has been reported. In contrast, homocysteine thiolactone, the cyclic thioester of homocysteine, is believed to exclusively bind to the primary amine group of lysine residue leading to N-homocysteinylation of proteins and hence studies on binding of homocysteine thiolactone to proteins thus far have only focused on N-homocysteinylation. Although it is known that homocysteine thiolactone can hydrolyze to homocysteine at physiological pH, surprisingly the extent of S-homocysteinylation during the exposure of homocysteine thiolactone with proteins has never been looked into. In this study, we clearly show that the hydrolysis of homocysteine thiolactone is pH dependent, and at physiological pH, 1 mM homocysteine thiolactone is hydrolysed to ~0.71 mM homocysteine within 24 h. Using albumin, we also show that incubation of HTL with albumin leads to a greater proportion of S-homocysteinylation (0.41 mol/mol of albumin) than N-homocysteinylation (0.14 mol/mol of albumin). S-homocysteinylation at Cys34 of HSA on treatment with homocysteine thiolactone was confirmed using LC-MS. Further, contrary to earlier reports, our results indicate that there is no cross talk between the cysteine attached to Cys34 of albumin and homocysteine attached to lysine residues.

Genomic convergence and network analysis approach to identify candidate genes in Alzheimer's disease.

BACKGROUND: Alzheimer's disease (AD) is one of the leading genetically complex and heterogeneous disorder that is influenced by both genetic and environmental factors. The underlying risk factors remain largely unclear for this heterogeneous disorder. In recent years, high throughput methodologies, such as genome-wide linkage analysis (GWL), genome-wide association (GWA) studies, and genome-wide expression profiling (GWE), have led to the identification of several candidate genes associated with AD. However, due to lack of consistency within their findings, an integrative approach is warranted. Here, we have designed a rank based gene prioritization approach involving convergent analysis of multi-dimensional data and protein-protein interaction (PPI) network modelling. RESULTS: Our approach employs integration of three different AD datasets- GWL,GWA and GWE to identify overlapping candidate genes ranked using a novel cumulative rank score (SR) based method followed by prioritization using clusters derived from PPI network. SR for each gene is calculated by addition of rank assigned to individual gene based on either p value or score in three datasets. This analysis yielded 108 plausible AD genes. Network modelling by creating PPI using proteins encoded by these genes and their direct interactors resulted in a layered network of 640 proteins. Clustering of these proteins further helped us in identifying 6 significant clusters with 7 proteins (EGFR, ACTB, CDC2, IRAK1, APOE, ABCA1 and AMPH) forming the central hub nodes. Functional annotation of 108 genes revealed their role in several biological activities such as neurogenesis, regulation of MAP kinase activity, response to calcium ion, endocytosis paralleling the AD specific attributes. Finally, 3 potential biochemical biomarkers were found from the overlap of 108 AD proteins with proteins from CSF and plasma proteome. EGFR and ACTB were found to be the two most significant AD risk genes. CONCLUSIONS: With the assumption that common genetic signals obtained from different methodological platforms might serve as robust AD risk markers than candidates identified using single dimension approach, here we demonstrated an integrated genomic convergence approach for disease candidate gene prioritization from heterogeneous data sources linked to AD.

S-linked protein homocysteinylation: identifying targets based on structural, physicochemical and protein-protein interactions of homocysteinylated proteins.

An elevated level of homocysteine, a thiol-containing amino acid is associated with a wide spectrum of disease conditions. A majority (>80 %) of the circulating homocysteine exist in protein-bound form. Homocysteine can bind to free cysteine residues in the protein or could cleave accessible cysteine disulfide bonds via thiol disulfide exchange reaction. Binding of homocysteine to proteins could potentially alter the structure and/or function of the protein. To date only 21 proteins have been experimentally shown to bind homocysteine. In this study we attempted to identify other proteins that could potentially bind to homocysteine based on the criteria that such proteins will have significant 3D structural homology with the proteins that have been experimentally validated and have solvent accessible cysteine residues either with high dihedral strain energy (for cysteine-cysteine disulfide bonds) or low pKa (for free cysteine residues). This analysis led us to the identification of 78 such proteins of which 68 proteins had 154 solvent accessible disulfide cysteine pairs with high dihedral strain energy and 10 proteins had free cysteine residues with low pKa that could potentially bind to homocysteine. Further, protein-protein interaction network was built to identify the interacting partners of these putative homocysteine binding proteins. We found that the 21 experimentally validated proteins had 174 interacting partners while the 78 proteins identified in our analysis had 445 first interacting partners. These proteins are mainly involved in biological activities such as complement and coagulation pathway, focal adhesion, ECM-receptor, ErbB signalling and cancer pathways, etc. paralleling the disease-specific attributes associated with hyperhomocysteinemia.

A single nucleotide polymorphism in transcobalamin II (I5V) induces structural changes in the protein as revealed by molecular modeling studies.

Cobalamin is an essential micronutrient in mammals. Deficiencies of this micronutrient have been implicated as risk factors for various complex diseases. Cobalamin is transported to the cells by the transport protein transcobalamin II (TCII), and hence genetic variations (like single nucleotide polymorphisms) in TCII could be perceived to affect the binding of cobalamin to TCII, thereby modulating the intracellular concentrations of cobalamin. To understand whether three nonsynonymous mutations in TCII (I5V, P241R, and R381Q) alter the structure of the protein which could potentially affect cobalamin binding, we performed molecular dynamics simulation in silico. Superimposition of active sites of the four simulated models (wild type and three variants) with the human TCII crystal structure revealed that the distance between the Nε nitrogen atom of His-173 and the cobalt ion of cobalamin deviated considerably in the I5V model as compared to wild type and other variants. His-173 directly coordinates with the cobalt ion of cobalamin. Further, from our dynamic cross-correlation and principal component analysis it appears that in the I5V model the ß-domain moves apart from the α-domain creating a wide gap between the two domains. This might facilitate the initial binding of cobalamin in the I5V model as cobalamin enters the binding site through the gap between the two domains. These observations were not found in the other variants. We thus speculate that binding of cobalamin will be more facile in the I5V variant.

Next-generation computational tools and resources for coronavirus research: From detection to vaccine discovery.

The COVID-19 pandemic has affected 215 countries and territories around the world with 60,187,347 coronavirus cases and 17,125,719 currently infected patients confirmed as of the November 25, 2020. Currently, many countries are working on developing new vaccines and therapeutic drugs for this novel virus strain, and a few of them are in different phases of clinical trials. The advancement in high-throughput sequence technologies, along with the application of bioinformatics, offers invaluable knowledge on genomic characterization and molecular pathogenesis of coronaviruses. Recent multi-disciplinary studies using bioinformatics methods like sequence-similarity, phylogenomic, and computational structural biology have provided an in-depth understanding of the molecular and biochemical basis of infection, atomic-level recognition of the viral-host receptor interaction, functional annotation of important viral proteins, and evolutionary divergence across different strains. Additionally, various modern immunoinformatic approaches are also being used to target the most promiscuous antigenic epitopes from the SARS-CoV-2 proteome for accelerating the vaccine development process. In this review, we summarize various important computational tools and databases available for systematic sequence-structural study on coronaviruses. The features of these public resources have been comprehensively discussed, which may help experimental biologists with predictive insights useful for ongoing research efforts to find therapeutics against the infectious COVID-19 disease.

Repurposing of FDA-approved drugs to target MurB and MurE enzymes in Mycobacterium tuberculosis.

Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb) is one amongst the top 10 causes of death worldwide. The growing rise in antibiotic resistance compounded with slow and expensive drug discovery has further aggravated the situation. 'Drug repurposing' is a promising approach where known drugs are examined for a new indication. In the present study, we have attempted to identify drugs that could target MurB and MurE enzymes involved in the muramic acid synthesis pathway (Mur Pathway) in Mtb. FDA-approved drugs from two repositories i.e. Drug Bank (1932 drugs) and e-LEA3D (1852 drugs) were screened against these proteins. Several criteria were applied to study the protein-drug interactions and the consensus drugs were further studied by molecular dynamics (MD) simulation. Our study found Sulfadoxine (-7.3 kcal/mol) and Pyrimethamine (-7.8 kcal/mol) to show stable interaction with MurB while Lifitegrast (-10.5 kcal/mol) and Sildenafil (-9.1 kcal/mol) showed most reliable interaction with MurE. Furthermore, binding free energy (ΔGbind), RMSD and RMSF data and the number of hydrogen bonds corroborated the stability of interactions and hence these drugs for repurposing should be explored further.Communicated by Ramaswamy H. Sarma.

Structural bioinformatics-based identification of putative plant based lead compounds for Alzheimer Disease Therapy.

Plant based lead compounds have been historically incredible as a source of therapeutic agents for various complex disorders including Alzheimer's disease (AD). AD is one of the leading neurodegenerative disorder in which the underlying risk factors remain largely unclear and presently, there is no disease modifying treatment available. Despite its potential, to date only few compounds have entered for clinical trials. Herein, we described the identification of plant based lead compounds for treatment of AD through an integrative approach of pharmacokinetics and structure bioinformatics approach. In particular we performed screening of lead compounds from 3 traditional medicinal plants namely Withania somnifera, Bacopa monnieri and Morus alba, which are known to have potential for treatment of neurodegenerative disease. We retrieved a total of 210 plant based compounds of which 21 compounds were screened based on their pharmacokinetic properties. Further, Docking study against 7 known AD associated targets were carried out to identify the binding sites and direct interacting residues. In addition we investigate the stable and reliable binding mechanism of top such plant compounds against 3 targets through molecular docking followed by Molecular Dynamic(MD) simulation. The results obtained in the study revealed that 3 drug compounds namely Morusin (MRSN), Withanone (WTHN) and 27-Hydroxywithanolide B (HWTHN) were identified as putative lead compounds against mono amine oxidase (MAOB), Beta-secretase 1(BACE1) and phosphodiesterase 4D.

Synthesis of D-Ring Annulated Pyridosteroids from ß-Formyl Enamides and Their Biological Evaluations.

Herein, we report the synthesis of a novel class of substituted androst[17,16- b]pyridines (pyridosteroids) from the reaction of ß-formyl enamides with alkynes in high yields. The optimized reaction protocol was extended to acyclic and cyclic ß-formyl enamides to afford nonsteroidal pyridines. Cell survival assay of all compounds were carried against prostate cancer PC-3 cells wherein 3-hydroxy-5-en-2',3'-dicarbethoxy-androst[17,16- b]pyridine showed the highest cytotoxic activity. Phase contrast microscopy and flow cytometry studies exhibited marked morphological features characteristic of apoptosis in 3-hydroxy-5-en-2',3'-dicarbethoxy-androst[17,16- b]pyridine and abiraterone treated PC-3 cells. The treatment of 3-hydroxy-5-en-2',3'-dicarbethoxy-androst[17,16- b]pyridine induces G2/M phase cell cycle arrest in prostate cancer PC-3 cells. Enhancement of apoptotic inductions of PC-3 cells by 3-hydroxy-5-en-2',3'-dicarbethoxy-androst[17,16- b]pyridine and abiraterone through the activation of caspases-6, -7, and -8 pathways were supported by qRT-PCR. In silico study of the compound 3-hydroxy-5-en-2',3'-dicarbethoxy-androst[17,16- b]pyridine showed stable and promising interaction with the key caspase proteins. Our studies revealed that the pyridosteroid 3-hydroxy-5-en-2',3'-dicarbethoxy-androst[17,16- b]pyridine, bearing pyridine-2,3-dicarbethoxy pharmacophore, facilitated initiation of caspase-8 and activates downstream effectors caspase-6 and caspase-7 and thereby triggering apoptosis of PC-3 cancer cells.

Network pharmacology-based virtual screening of natural products from Clerodendrum species for identification of novel anti-cancer therapeutics.

Plant-derived natural products (NPs) play a vital role in the discovery of new drug molecules and these are used for development of novel therapeutic drugs for a specific disease target. Literature review suggests that natural products possess strong inhibitory efficacy against various types of cancer cells. Clerodendrum indicum and Clerodendrum serratum are reported to have anticancer activity; therefore a study was carried out to identify selective anticancer agents from these plants species. In this report, we employed a docking weighted network pharmacological approach to understand the multi-therapeutics potentiality of C. indicum and C. serratum against various types of cancer. A library of 53 natural products derived from these plants was compiled from the literature and three dimensional space analyses were performed in order to establish the drug-likeness of the NPs library. Further, an NPs-cancer network was built based on docking. We predicted five compounds, namely apigenin 7-glucoside, hispidulin, scutellarein-7-O-beta-d-glucuronate, acteoside and verbascoside, to be potential binding therapeutics for cancer target proteins. Apigenin 7-glucoside and hispidulin were found to have maximum binding interactions (relationship) with 17 cancer drug targets in terms of docking weighted network pharmacological analysis. Hence, we used an integrative approach obtained from network pharmacology for identifying combinatorial drug actions against the cancer targets. We believe that our present study may provide important clues for finding novel drug inhibitors for cancer.

Inhibition of Mycobacterium tuberculosis dihydrodipicolinate synthase by alpha-ketopimelic acid and its other structural analogues.

The Mycobacterium tuberculosis dihydrodipicolinate synthase (Mtb-dapA) is an essential gene. Mtb-DapA catalyzes the aldol condensation between pyruvate and L-aspartate-beta-semialdehyde (ASA) to yield dihydrodipicolinate. In this work we tested the inhibitory effects of structural analogues of pyruvate on recombinant Mtb-DapA (Mtb-rDapA) using a coupled assay with recombinant dihydrodipicolinate reductase (Mtb-rDapB). Alpha-ketopimelic acid (α-KPA) showed maximum inhibition of 88% and IC50 of 21 µM in the presence of pyruvate (500 µM) and ASA (400 µM). Competition experiments with pyruvate and ASA revealed competition of α-KPA with pyruvate. Liquid chromatography-mass spectrometry (LC-MS) data with multiple reaction monitoring (MRM) showed that the relative abundance peak of final product, 2,3,4,5-tetrahydrodipicolinate, was decreased by 50%. Thermal shift assays showed 1 °C Tm shift of Mtb-rDapA upon binding α-KPA. The 2.4 Å crystal structure of Mtb-rDapA-α-KPA complex showed the interaction of critical residues at the active site with α-KPA. Molecular dynamics simulations over 500 ns of pyruvate docked to Mtb-DapA and of α-KPA-bound Mtb-rDapA revealed formation of hydrogen bonds with pyruvate throughout in contrast to α-KPA. Molecular descriptors analysis showed that ligands with polar surface area of 91.7 Å(2) are likely inhibitors. In summary, α-hydroxypimelic acid and other analogues could be explored further as inhibitors of Mtb-DapA.