CD38-RyR2 axis-mediated signaling impedes CD8+ T cell response to anti-PD1 therapy in cancer.

PD1 blockade therapy, harnessing the cytotoxic potential of CD8+ T cells, has yielded clinical success in treating malignancies. However, its efficacy is often limited due to the progressive differentiation of intratumoral CD8+ T cells into a hypofunctional state known as terminal exhaustion. Despite identifying CD8+ T cell subsets associated with immunotherapy resistance, the molecular pathway triggering the resistance remains elusive. Given the clear association of CD38 with CD8+ T cell subsets resistant to anti-PD1 therapy, we investigated its role in inducing resistance. Phenotypic and functional characterization, along with single-cell RNA sequencing analysis of both in vitro chronically stimulated and intratumoral CD8+ T cells, revealed that CD38-expressing CD8+ T cells are terminally exhausted. Exploring the molecular mechanism, we found that CD38 expression was crucial in promoting terminal differentiation of CD8+ T cells by suppressing TCF1 expression, thereby rendering them unresponsive to anti-PD1 therapy. Genetic ablation of CD38 in tumor-reactive CD8+ T cells restored TCF1 levels and improved the responsiveness to anti-PD1 therapy in mice. Mechanistically, CD38 expression on exhausted CD8+ T cells elevated intracellular Ca2+ levels through RyR2 calcium channel activation. This, in turn, promoted chronic AKT activation, leading to TCF1 loss. Knockdown of RyR2 or inhibition of AKT in CD8+ T cells maintained TCF1 levels, induced a sustained anti-tumor response, and enhanced responsiveness to anti-PD1 therapy. Thus, targeting CD38 represents a potential strategy to improve the efficacy of anti-PD1 treatment in cancer.

A mechanistic study on the tolerance of PAM distal end mismatch by SpCas9.

The therapeutic application of CRISPR-Cas9 is limited due to its off-target activity. To have a better understanding of this off-target effect, we focused on its mismatch-prone PAM distal end. The off-target activity of SpCas9 depends directly on the nature of mismatches, which in turn results in deviation of the active site of SpCas9 due to structural instability in the RNA-DNA duplex strand. In order to test the hypothesis, we designed an array of mismatched target sites at the PAM distal end and performed in vitro and cell line-based experiments, which showed a strong correlation for Cas9 activity. We found that target sites having multiple mismatches in the 18th to 15th position upstream of the PAM showed no to little activity. For further mechanistic validation, Molecular Dynamics simulations were performed, which revealed that certain mismatches showed elevated root mean square deviation values that can be attributed to conformational instability within the RNA-DNA duplex. Therefore, for successful prediction of the off-target effect of SpCas9, along with complementation-derived energy, the RNA-DNA duplex stability should be taken into account.

Microbiome Metabolome Integration Platform (MMIP): a web-based platform for microbiome and metabolome data integration and feature identification.

A microbial community maintains its ecological dynamics via metabolite crosstalk. Hence, knowledge of the metabolome, alongside its populace, would help us understand the functionality of a community and also predict how it will change in atypical conditions. Methods that employ low-cost metagenomic sequencing data can predict the metabolic potential of a community, that is, its ability to produce or utilize specific metabolites. These, in turn, can potentially serve as markers of biochemical pathways that are associated with different communities. We developed MMIP (Microbiome Metabolome Integration Platform), a web-based analytical and predictive tool that can be used to compare the taxonomic content, diversity variation and the metabolic potential between two sets of microbial communities from targeted amplicon sequencing data. MMIP is capable of highlighting statistically significant taxonomic, enzymatic and metabolic attributes as well as learning-based features associated with one group in comparison with another. Furthermore, MMIP can predict linkages among species or groups of microbes in the community, specific enzyme profiles, compounds or metabolites associated with such a group of organisms. With MMIP, we aim to provide a user-friendly, online web server for performing key microbiome-associated analyses of targeted amplicon sequencing data, predicting metabolite signature, and using learning-based linkage analysis, without the need for initial metabolomic analysis, and thereby helping in hypothesis generation.

MeganServer: facilitating interactive access to metagenomic data on a server.

MOTIVATION: Metagenomic projects often involve large numbers of large sequencing datasets (totaling hundreds of gigabytes of data). Thus, computational preprocessing and analysis are usually performed on a server. The results of such analyses are then usually explored interactively. One approach is to use MEGAN, an interactive program that allows analysis and comparison of metagenomic datasets. Previous releases have required that the user first download the computed data from the server, an increasingly time-consuming process. Here, we present MeganServer, a stand-alone program that serves MEGAN files to the web, using a RESTful API, facilitating interactive analysis in MEGAN, without requiring prior download of the data. We describe a number of different application scenarios. AVAILABILITY AND IMPLEMENTATION: MeganServer is provided as a stand-alone program tools/megan-server in the MEGAN software suite, available at https://software-ab.cs.uni-tuebingen.de/download/megan6. Source is available at: https://github.com/husonlab/megan-ce/tree/master/src/megan/ms. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

DeepToA: an ensemble deep-learning approach to predicting the theater of activity of a microbiome.

MOTIVATION: Metagenomics is the study of microbiomes using DNA sequencing. A microbiome consists of an assemblage of microbes that is associated with a 'theater of activity' (ToA). An important question is, to what degree does the taxonomic and functional content of the former depend on the (details of the) latter? Here, we investigate a related technical question: Given a taxonomic and/or functional profile estimated from metagenomic sequencing data, how to predict the associated ToA? We present a deep-learning approach to this question. We use both taxonomic and functional profiles as input. We apply node2vec to embed hierarchical taxonomic profiles into numerical vectors. We then perform dimension reduction using clustering, to address the sparseness of the taxonomic data and thus make the problem more amenable to deep-learning algorithms. Functional features are combined with textual descriptions of protein families or domains. We present an ensemble deep-learning framework DeepToA for predicting the ToA of amicrobial community, based on taxonomic and functional profiles. We use SHAP (SHapley Additive exPlanations) values to determine which taxonomic and functional features are important for the prediction. RESULTS: Based on 7560 metagenomic profiles downloaded from MGnify, classified into 10 different theaters of activity, we demonstrate that DeepToA has an accuracy of 98.30%. We show that adding textual information to functional features increases the accuracy. AVAILABILITY AND IMPLEMENTATION: Our approach is available at http://ab.inf.uni-tuebingen.de/software/deeptoa. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Glucogallin Attenuates the LPS-Induced Signaling in Macrophages and Protects Mice against Sepsis.

The anti-oxidant and anti-inflammatory effect of beta-glucogallin (BGG), a plant-derived natural product, was evaluated in both in vitro and in vivo studies. For the in vitro study, the ability of BGG pre-treatment to quench LPS-induced effects compared to LPS alone in macrophages was investigated. It was found that BGG pre-treatment showed a significant decrease in ROS, NO, superoxide, and pro-inflammatory cytokines (TNF-alpha, IL-4, IL-17, IL-1ß, and IL-6) and increased reduced glutathione coupled with the restoration of mitochondrial membrane potential. Gene profiling and further validation by qPCR showed that BGG pre-treatment downregulated the LPS-induced expression of c-Fos, Fas, MMP-9, iNOS, COX-2, MyD88, TRIF, TRAF6, TRAM, c-JUN, and NF-κB. We observed that BGG pre-treatment reduced nuclear translocation of LPS-activated NF-κB and thus reduced the subsequent expressions of NLRP3 and IL-1ß, indicating the ability of BGG to inhibit inflammasome formation. Molecular docking studies showed that BGG could bind at the active site of TLR4. Finally, in the LPS-driven sepsis mouse model, we showed that pre-treatment with BGG sustained toxic shock, as evident from their 100% survival. Our study clearly showed the therapeutic potential of BGG in toxic shock syndrome.

Protease Inhibitory Effect of Natural Polyphenolic Compounds on SARS-CoV-2: An In Silico Study.

The current pandemic, caused by SARS-CoV-2 virus, is a severe challenge for human health and the world economy. There is an urgent need for development of drugs that can manage this pandemic, as it has already infected 19 million people and led to the death of around 711,277 people worldwide. At this time, in-silico studies are providing lots of preliminary data about potential drugs, which can be a great help in further in-vitro and in-vivo studies. Here, we have selected three polyphenolic compounds, mangiferin, glucogallin, and phlorizin. These compounds are isolated from different natural sources but share structural similarities and have been reported for their antiviral activity. The objective of this study is to analyze and predict the anti-protease activity of these compounds on SARS-CoV-2main protease (Mpro) and TMPRSS2 protein. Both the viral protein and the host protein play an important role in the viral life cycle, such as post-translational modification and viral spike protein priming. This study has been performed by molecular docking of the compounds using PyRx with AutoDock Vina on the two aforementioned targets chosen for this study, i.e., SARS-CoV-2 Mpro and TMPRSS2. The compounds showed good binding affinity and are further analyzed by (Molecular dynamic) MD and Molecular Mechanics Poisson-Boltzmann Surface Area MM-PBSA study. The MD-simulation study has predicted that these natural compounds will have a great impact on the stabilization of the binding cavity of the Mpro of SARS-CoV-2. The predicted pharmacokinetic parameters also show that these compounds are expected to have good solubility and absorption properties. Further predictions for these compounds also showed no involvement in drug-drug interaction and no toxicity.

A recent update on development, synthesis methods, properties and application of natural products derived carbon dots.

Natural resources are practically infinitely abundant in nature, which stimulates scientists to create new materials with inventive uses and minimal environmental impact. Due to the various benefits of natural carbon dots (NCDs) from them has received a lot of attention recently. Natural products-derived carbon dots have recently emerged as a highly promising class of nanomaterials, showcasing exceptional properties and eco-friendly nature, which make them appealing for diverse applications in various fields such as biomedical, environmental sensing and monitoring, energy storage and conversion, optoelectronics and photonics, agriculture, quantum computing, nanomedicine and cancer therapy. Characterization techniques such as Photoinduced electron transfer, Aggregation-Induced-Emission (AIE), Absorbance, Fluorescence in UV-Vis and NIR Regions play crucial roles in understanding the structural and optical properties of Carbon dots (CDs). The exceptional photoluminescence properties exhibited by CDs derived from natural products have paved the way for applications in tissue engineering, cancer treatment, bioimaging, sensing, drug delivery, photocatalysis, and promising remarkable advancements in these fields. In this review, we summarized the various synthesis methods, physical and optical properties, applications, challenges, future prospects of natural products-derived carbon dots etc. In this expanding sector, the difficulties and prospects for NCD-based materials research will also be explored.

DIAMOND + MEGAN Microbiome Analysis.

Metagenomics is the study of microbiomes using DNA sequencing technologies. Basic computational tasks are to determine the taxonomic composition (who is out there?), the functional composition (what can they do?), and also to correlate changes of composition to changes in external parameters (how do they compare?). One approach to address these issues is to first align all sequences against a protein reference database such as NCBI-nr and to then perform taxonomic and functional binning of all sequences based on their alignments. The resulting classifications can then be interactively analyzed and compared. Here we illustrate how to pursue this approach using the DIAMOND+MEGAN pipeline, on two different publicly available datasets, one containing short-read samples and other containing long-read samples.

Prenatal arsenic exposure stymies gut butyrate production and enhances gut permeability in post natal life even in absence of arsenic deftly through miR122-Occludin pathway.

This discourse attempts to capture a few important dimensions of gut physiology like microbial homeostasis, short chain fatty acid (SCFA) production, occludin expression, and gut permeability in post-natal life of mice those received arsenic only during pre-natal life. Adult Balb/c mice were fed with 4 ppm arsenic trioxide in drinking water during breeding and gestation. After the birth of the pups, the arsenic water was withdrawn and replaced with clean drinking water. The pups were allowed to grow for 28 days (pAs-mice) and age matched Balb/c mice which were never exposed to arsenic served as control The pAs-mice showed a striking reduction in Firmicutes to Bacteroidetes (F/B) ratio coupled with a decrease in tight junction protein, occludin resulting in an increase in gut permeability, increased infiltration of inflammatory cells in the colon and decrease in common SCFAs in which butyrate reduction was quite prominent in fecal samples as compared to normal control. The above phenotypes of pAs-mice were mostly reversed by supplementing 5% sodium butyrate (w/w) with food from 21st to 28th day. The ability of butyrate in enhancing occludin expression, in particular, was dissected further. As miR122 causes degradation of Occludin mRNA, we transiently overexpressed miR122 by injecting appropriate plasmids and showed reversal of butyrate effects in pAs-mice. Thus, pre-natal arsenic exposure orchestrates variety of effects by decreasing butyrate in pAs-mice leading to increased permeability due to reduced occludin expression. Our research adds a new dimension to our understanding that pre-natal arsenic exposure imprints in post-natal life while there was no further arsenic exposure.

A comprehension on structure guided alignment dependent 3D-QSAR modelling, and molecular dynamics simulation on 2,4-thiazolidinediones as aldose reductase inhibitors for the management of diabetic complications.

Aldose reductase is an oxo-reductase enzyme belonging to the aldo-keto reductase class. Compounds having thiazolidine-2,4-dione scaffold are reported as potential aldose reductase inhibitors for diabetic complications. The present work uses structure-guided alignment-dependent Gaussian field- and atom-based 3D-QSAR on a dataset of 84 molecules. 3D-QSAR studies on two sets of dataset alignment have been carried out to understand the favourable and unfavourable structural features influencing the affinity of these inhibitors towards the enzyme. Using common pharmacophore hypotheses, the five-point pharmacophores for aldose reductase favourable features were generated. The molecular dynamics simulations (up to 100 ns) were performed for the potent molecule from each alignment set (compounds 24 and 65) compared to reference standard tolrestat and epalrestat to study target-ligand complexes' binding energy and stability. Compound 65 was most stable with better interactions in the aldose reductase binding pocket than tolrestat. The MM-PBSA study suggests compound 65 possessed better binding energy than reference standard tolrestat, i.e. -87.437 ± 19.728 and -73.424 ± 12.502 kJ/mol, respectively. The generated 3D-QSAR models provide information about structure-activity relationships and ligand-target binding energy. Target-specific stability data from MD simulation would be helpful for rational compound design with better aldose reductase activity.Communicated by Ramaswamy H. Sarma.

Integrating Multi-Omics Data to Construct Reliable Interconnected Models of Signaling, Gene Regulatory, and Metabolic Pathways.

Alteration of the status of the metabolic enzymes could be a probable way to regulate metabolic reprogramming, which is a critical cellular adaptation mechanism especially for cancer cells. Coordination among biological pathways, such as gene-regulatory, signaling, and metabolic pathways is crucial for regulating metabolic adaptation. Also, incorporation of resident microbial metabolic potential in human body can influence the interplay between the microbiome and the systemic or tissue metabolic environments. Systemic framework for model-based integration of multi-omics data can ultimately improve our understanding of metabolic reprogramming at holistic level. However, the interconnectivity and novel meta-pathway regulatory mechanisms are relatively lesser explored and understood. Hence, we propose a computational protocol that utilizes multi-omics data to identify probable cross-pathway regulatory and protein-protein interaction (PPI) links connecting signaling proteins or transcription factors or miRNAs to metabolic enzymes and their metabolites using network analysis and mathematical modeling. These cross-pathway links were shown to play important roles in metabolic reprogramming in cancer scenarios.

Investigation of the anti-cancer potential of epoxyazadiradione in neuroblastoma: experimental assays and molecular analysis.

Neuroblastoma, the most common childhood solid tumor, originates from primitive sympathetic nervous system cells. Epoxyazadiradione (EAD) is a limonoid derived from Azadirachta indica, belonging to the family Meliaceae. In this study, we isolated the EAD from Azadirachta indica seed and studied the anti-cancer potential against neuroblastoma. Herein, EAD demonstrated significant efficacy against neuroblastoma by suppressing cell proliferation, enhancing the rate of apoptosis and cycle arrest at the SubG0 and G2/M phases. EAD enhanced the pro-apoptotic Caspase 3 and Caspase 9 and inhibited the NF-kß translocation in a dose-dependent manner. In order to identify the specific EAD target, a gel-free quantitative proteomics study on SH-SY5Y cells using Liquid Chromatography with tandem mass spectrometry was done in a dose-dependent manner, followed by detailed bioinformatics analysis to identify effects on protein. Proteomics data identified that Enolase1 and HSP90 were up-regulated in neuroblastoma. EAD inhibited the expression of Enolase1 and HSP90, validated by mRNA expression, immunoblotting, Enolase1 and HSP90 kit and flow-cytometry based bioassay. Molecular docking study, Molecular dynamic simulation, and along with molecular mechanics/Poisson-Boltzmann surface area analysis also suggested that EAD binds at the active site of the proteins and were stable throughout the 100 ns Molecular dynamic simulation study. Overall, this study suggested EAD exhibited anti-cancer activity against neuroblastoma by targeting Enolase1 and HSP90 pathways.Communicated by Ramaswamy H. Sarma.

MuLan-Methyl-multiple transformer-based language models for accurate DNA methylation prediction.

Transformer-based language models are successfully used to address massive text-related tasks. DNA methylation is an important epigenetic mechanism, and its analysis provides valuable insights into gene regulation and biomarker identification. Several deep learning-based methods have been proposed to identify DNA methylation, and each seeks to strike a balance between computational effort and accuracy. Here, we introduce MuLan-Methyl, a deep learning framework for predicting DNA methylation sites, which is based on 5 popular transformer-based language models. The framework identifies methylation sites for 3 different types of DNA methylation: N6-adenine, N4-cytosine, and 5-hydroxymethylcytosine. Each of the employed language models is adapted to the task using the "pretrain and fine-tune" paradigm. Pretraining is performed on a custom corpus of DNA fragments and taxonomy lineages using self-supervised learning. Fine-tuning aims at predicting the DNA methylation status of each type. The 5 models are used to collectively predict the DNA methylation status. We report excellent performance of MuLan-Methyl on a benchmark dataset. Moreover, we argue that the model captures characteristic differences between different species that are relevant for methylation. This work demonstrates that language models can be successfully adapted to applications in biological sequence analysis and that joint utilization of different language models improves model performance. Mulan-Methyl is open source, and we provide a web server that implements the approach.

Screening of Azadirachta indica phytoconstituents as GSK-3ß inhibitor and its implication in neuroblastoma: molecular docking, molecular dynamics, MM-PBSA binding energy, and in-vitro study.

Glycogen synthase kinase-3 (GSK-3), a constitutively active serine/threonine kinase, primary regulator of various cellular activities varying from glycogen metabolism to cell proliferation and regulation. GSK-3ß is associated with the pathogenesis of numerous human diseases, including cancer, metabolic disorder, and Alzheimer's disease. In this study, Azadirachta indica compounds were selected and further screened on the BOILED-Egg model. The compounds showing good GIT absorption were docked with the crystal structure of GSK-3ß. The compounds with high docking score were submitted for the molecular dynamic simulation (MDS) and Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA). Based upon the MDS and MM-PBSA study, gedunin showed the highest binding energy throughout the MDS process. Gedunin was isolated from the Azadirachta indica, and its efficacy on GSK-3ß inhibition was studied in the human neuroblastoma (SH-SY5Y) cells. Gedunin induced apoptosis and anti-proliferative activity by arresting G2/M phase, as evident by cell-cycle analysis. From immunoblot study, gedunin significantly enhanced the expression of an inhibitory form of GSK-3ß (p-GSK-3ß Ser9) in concentration-dependent manner. Our findings demonstrate that gedunin may act as an effective GSK-3ß inhibitor suggesting that this compound may be used for the management of neuroblastoma. Further preclinical and clinical investigation is desirable.Communicated by Ramaswamy H. Sarma.

Using AnnoTree to Get More Assignments, Faster, in DIAMOND+MEGAN Microbiome Analysis.

In microbiome analysis, one main approach is to align metagenomic sequencing reads against a protein reference database, such as NCBI-nr, and then to perform taxonomic and functional binning based on the alignments. This approach is embodied, for example, in the standard DIAMOND+MEGAN analysis pipeline, which first aligns reads against NCBI-nr using DIAMOND and then performs taxonomic and functional binning using MEGAN. Here, we propose the use of the AnnoTree protein database, rather than NCBI-nr, in such alignment-based analyses to determine the prokaryotic content of metagenomic samples. We demonstrate a 2-fold speedup over the usage of the prokaryotic part of NCBI-nr and increased assignment rates, in particular assigning twice as many reads to KEGG. In addition to binning to the NCBI taxonomy, MEGAN now also bins to the GTDB taxonomy. IMPORTANCE The NCBI-nr database is not explicitly designed for the purpose of microbiome analysis, and its increasing size makes its unwieldy and computationally expensive for this purpose. The AnnoTree protein database is only one-quarter the size of the full NCBI-nr database and is explicitly designed for metagenomic analysis, so it should be supported by alignment-based pipelines.

Easy efficient HDR-based targeted knock-in in Saccharomyces cerevisiae genome using CRISPR-Cas9 system.

During the last two decades, yeast has been used as a biological tool to produce various small molecules, biofuels, etc., using an inexpensive bioprocess. The application of Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-CRISPR-associated protein (Cas) techniques in yeast genetic and metabolic engineering has made a paradigm shift, particularly with a significant improvement in targeted chromosomal integration using synthetic donor constructs, which was previously a challenge. This study reports the CRISPR-Cas9-based highly efficient strategy for targeted chromosomal integration and in-frame expression of a foreign gene in the genome of Saccharomyces cerevisiae (S. cerevisiae) by homology-dependent recombination (HDR); our optimized methods show that CRISPR-Cas9-based chromosomal targeted integration of small constructs at multiple target sites of the yeast genome can be achieved with an efficiency of 74%. Our study also suggests that 15 bp microhomology flanked arms are sufficient for 50% targeted knock-in at minimal knock-in construct concentration. Whole-genome sequencing confirmed that there is no off-target effect. This study provides a comprehensive and streamlined protocol that will support the targeted integration of essential genes into the yeast genome for synthetic biology and other industrial purposes.Highlights• CRISPR-Cas9 based in-frame expression of foreign protein in Saccharomyces cerevisiae using Homology arm without a promoter.• As low as 15 base pairs of microhomology (HDR) are sufficient for targeted integration in Saccharomyces cerevisiae.• The methodology is highly efficient and very specific as no off-targeted effects were shown by the whole-genome sequence.

A Review on CRISPR-mediated Epigenome Editing: A Future Directive for Therapeutic Management of Cancer.

Recent studies have shed light on the role of epigenetic marks in certain diseases like cancer, type II diabetes mellitus (T2DM), obesity, and cardiovascular dysfunction, to name a few. Epigenetic marks like DNA methylation and histone acetylation are randomly altered in the disease state. It has been seen that methylation of DNA and histones can result in down-regulation of gene expression, whereas histone acetylation, ubiquitination, and phosphorylation are linked to enhanced expression of genes. How can we precisely target such epigenetic aberrations to prevent the advent of diseases? The answer lies in the amalgamation of the efficient genome editing technique, CRISPR, with certain effector molecules that can alter the status of epigenetic marks as well as employ certain transcriptional activators or repressors. In this review, we have discussed the rationale of epigenetic editing as a therapeutic strategy and how CRISPR-Cas9 technology coupled with epigenetic effector tags can efficiently edit epigenetic targets. In the later part, we have discussed how certain epigenetic effectors are tagged with dCas9 to elicit epigenetic changes in cancer. Increased interest in exploring the epigenetic background of cancer and non-communicable diseases like type II diabetes mellitus and obesity accompanied with technological breakthroughs has made it possible to perform large-scale epigenome studies.

Interactive analysis of biosurfactants in fruit-waste fermentation samples using BioSurfDB and MEGAN.

Agroindustrial waste, such as fruit residues, are a renewable, abundant, low-cost, commonly-used carbon source. Biosurfactants are molecules of increasing interest due to their multifunctional properties, biodegradable nature and low toxicity, in comparison to synthetic surfactants. A better understanding of the associated microbial communities will aid prospecting for biosurfactant-producing microorganisms. In this study, six samples of fruit waste, from oranges, mangoes and mixed fruits, were subjected to autochthonous fermentation, so as to promote the growth of their associated microbiota, followed by short-read metagenomic sequencing. Using the DIAMOND+MEGAN analysis pipeline, taxonomic analysis shows that all six samples are dominated by Proteobacteria, in particular, a common core consisting of the genera Klebsiella, Enterobacter, Stenotrophomonas, Acinetobacter and Escherichia. Functional analysis indicates high similarity among samples and a significant number of reads map to genes that are involved in the biosynthesis of lipopeptide-class biosurfactants. Gene-centric analysis reveals Klebsiella as the main assignment for genes related to putisolvins biosynthesis. To simplify the interactive visualization and exploration of the surfactant-related genes in such samples, we have integrated the BiosurfDB classification into MEGAN and make this available. These results indicate that microbiota obtained from autochthonous fermentation have the genetic potential for biosynthesis of biosurfactants, suggesting that fruit wastes may provide a source of biosurfactant-producing microorganisms, with applications in the agricultural, chemical, food and pharmaceutical industries.

An insight into SARS-CoV-2 structure, pathogenesis, target hunting for drug development and vaccine initiatives.

SARS-CoV-2, the virus responsible for the COVID-19 pandemic, has been confirmed to be a new coronavirus having 79% and 50% similarity with SARS-CoV and MERS-CoV, respectively. For a better understanding of the features of the new virus SARS-CoV-2, we have discussed a possible correlation between some unique features of the genome of SARS-CoV-2 in relation to pathogenesis. We have also reviewed structural druggable viral and host targets for possible clinical application if any, as cases of reinfection and compromised protection have been noticed due to the emergence of new variants with increased infectivity even after vaccination. We have also discussed the types of vaccines that are being developed against SARS-CoV-2. In this review, we have tried to give a brief overview of the fundamental factors of COVID-19 research like basic virology, virus variants and the newly emerging techniques that can be applied to develop advanced treatment strategies for the management of COVID-19 disease.