Domain Database Research Articles

Database resources of the National Center for Biotechnology Information in 2025.

The National Center for Biotechnology Information (NCBI) provides online information resources for biology, including the GenBank® nucleic acid sequence repository and the PubMed® repository of citations and abstracts published in life science journals. NCBI provides search and retrieval operations for most of these data from 31 distinct repositories and knowledgebases. The E-utilities serve as the programming interface for most of these. Resources receiving significant updates in the past year include PubMed, PubMed Central, Bookshelf, the NIH Comparative Genomics Resource, BLAST, SequenceReadArchive, Taxonomy, iCn3D, Conserved Domain Database, Pathogen Detection, antimicrobial resistance resourcesand PubChem. These resources can be accessed through the NCBI home page at https://www.ncbi.nlm.nih.gov.

ProCogGraph: a graph-based mapping of cognate ligand domain interactions.

Mappings of domain-cognate ligand interactions can enhance our understanding of the core concepts of evolution and be used to aid docking and protein design. Since the last available cognate-ligand domain database was released, the PDB has grown significantly and new tools are available for measuring similarity and determining contacts. We present ProCogGraph, a graph database of cognate-ligand domain mappings in PDB structures. Building upon the work of the predecessor database, PROCOGNATE, we use data-driven approaches to develop thresholds and interaction modes. We explore new aspects of domain-cognate ligand interactions, including the chemical similarity of bound cognate ligands and how domain combinations influence cognate ligand binding. Finally, we use the graph to add specificity to partial EC IDs, showing that ProCogGraph can complete partial annotations systematically through assigned cognate ligands. The ProCogGraph pipeline, database and flat files are available at https://github.com/bashton-lab/ProCogGraph and https://doi.org/10.5281/zenodo.13165851.

Structural and functional insights from the sequences and complex domain architecture of adhesin-like proteins from Methanobrevibacter smithii and Methanosphaera stadtmanae.

Methanogenic archaea, or methanogens, are crucial in guts and rumens, consuming hydrogen, carbon dioxide, and other fermentation products. While their molecular interactions with other microorganisms are not fully understood, genomic sequences provide information. The first genome sequences of human gut methanogens, Methanosphaera stadtmanae and Methanobrevibacter smithii, revealed genes encoding adhesin-like proteins (ALPs). These proteins were also found in other gut and rumen methanogens, but their characteristics and functions remain largely unknown. This study analyzes the ALP repertoire of M. stadtmanae and M. smithii using AI-guided protein structure predictions of unique ALP domains. Both genomes encode more than 40 ALPs each, comprising over 10% of their genomes. ALPs contain repetitive sequences, many of which are unmatched in protein domain databases. We present unique sequence signatures of conserved ABD repeats in ALPs and propose a classification based on domain architecture. Our study offers insights into ALP features and how methanogens may interact with other microorganisms.

Identification and characterization of sulphotransferase (SOT) genes for tolerance against drought and heat in wheat and six related species.

Sulphotransferase (SOT) enzyme (encoded by a conserved family of SOT genes) is involved in sulphonation of a variety of compounds, through transfer of a sulphuryl moiety from 3'phosphoadenosine- 5'phosphosulphate (PAPS) to a variety of secondary metabolites. The PAPS itself is derived from 3'adenosine-5'phosphosulphate (APS) that is formed after uptake of sulphate ions from the soil. The process provides tolerance against abiotic stresses like drought and heat in plants. Therefore, a knowledge of SOT genes in any crop may help in designing molecular breeding methods for improvement of tolerance for drought and heat. Sequences of rice SOT genes and SOT domain (PF00685) of corresponding proteins were both used for identification of SOT genes in wheat and six related species (T. urartu, Ae. tauschii, T. turgidum, Z. mays, B. distachyon and Hordeum vulgare), although detailed analysis was conducted only in wheat. The wheat genes were mapped on individual chromosomes and also subjected to synteny and collinearity analysis. The proteins encoded by these genes were examined for the presence of a complete SOT domain using 'Conserved Domain Database' (CDD) search tool at NCBI. In wheat, 107 TaSOT genes, ranging in length from 969bp to 7636bp, were identified and mapped onto individual chromosomes. SSRs (simple sequence repeats), microRNAs, long non-coding RNAs (lncRNAs) and their target sites were also identified in wheat SOT genes. SOT proteins were also studied in detail. An expression assay of TaSOT genes via wheat RNA-seq data suggested engagement of these genes in growth, development and responses to various hormones and biotic/abiotic stresses. The results of the present study should help in further functional characterization of SOT genes in wheat and other related crops.

Molecular evidence of hepatozoonosis in tigers of Vidarbha region of Maharashtra State of India

BackgroundHepatozoonosis has been reported in many species around the world. Few incidences have been reported in various species of wild felids. Tigers are endangered large cats and are protected under the Wildlife Protection Act, 1972 under Schedule I. The study was carried out to estimate the positivity rate of hepatozoonosis in tigers of the Vidarbha region of Maharashtra, India.MethodsBlood (n = 21) or tissue samples (n = 5) were collected from 26 wild captured / zoo-born or dead tigers during the quarantine period/post-mortem examination. Blood smear examination along with Polymerase Chain Reaction (PCR) studies were conducted for the detection of hepatozoonosis. All the amplicons from the positive samples were purified and sequenced, and the sequences were subjected to nBLAST analysis to detect the species of Hepatozoon. The sequences were deposited into public domain database of National Center for Biotechnology Information (NCBI) and accession numbers were allotted. A phylogenetic study was undertaken to understand the evolutionary lineage of the pathogen. Tissue distribution studies were carried out on tissue samples received during post mortem. A clinical case in a tiger cub was managed and sub-clinical cases were monitored for relapse. Age-wise, sex-wise, region-wise and captive time-wise positivity rate was estimated. The data was analyzed using statistical tools.ResultsA total of 12 tigers were found positive for H. felis during the screening. A clinical case was diagnosed and successfully treated. The age group of 0–3 years reported a positivity rate of 66.66%, and all the cases found positive were reported between the age group of 0–7 years. Males reported a positivity rate of 58.33 per cent, while females reported 35.71%. Taboba and Andhari Tiger Reserve of the state had a positivity rate of 52.94 per cent. However, the statistical analysis for blood parameters and positivity rate by ‘t’ test and Chi-squared test were found to be non-significant.ConclusionsAn overall positivity rate of 46.15% indicates the wide distribution of hepatozoonosis among wild tigers of the Vidarbha region of Maharashtra, India, which is strategically important considering the gene flow and migration of tigers. Hepatozoonosis can progress to clinical outcomes in young animals and require veterinary intervention. Molecular tools and phylogenetic studies can supplement important data on circulating species of Hepatozoon in the field. Further studies on the clinical management and epidemiology of the infection in wild felids will comprehend the cause of wildlife conservation.

DepoScope: Accurate phage depolymerase annotation and domain delineation using large language models.

Bacteriophages (phages) are viruses that infect bacteria. Many of them produce specific enzymes called depolymerases to break down external polysaccharide structures. Accurate annotation and domain identification of these depolymerases are challenging due to their inherent sequence diversity. Hence, we present DepoScope, a machine learning tool that combines a fine-tuned ESM-2 model with a convolutional neural network to identify depolymerase sequences and their enzymatic domains precisely. To accomplish this, we curated a dataset from the INPHARED phage genome database, created a polysaccharide-degrading domain database, and applied sequential filters to construct a high-quality dataset, which is subsequently used to train DepoScope. Our work is the first approach that combines sequence-level predictions with amino-acid-level predictions for accurate depolymerase detection and functional domain identification. In that way, we believe that DepoScope can greatly enhance our understanding of phage-host interactions at the level of depolymerases.

Structure classification of the proteins from Salmonella enterica pangenome revealed novel potential pathogenicity islands

Salmonella enterica is a pathogenic bacterium known for causing severe typhoid fever in humans, making it important to study due to its potential health risks and significant impact on public health. This study provides evolutionary classification of proteins from Salmonella enterica pangenome. We classified 17,238 domains from 13,147 proteins from 79,758 Salmonella enterica strains and studied in detail domains of 272 proteins from 14 characterized Salmonella pathogenicity islands (SPIs). Among SPIs-related proteins, 90 proteins function in the secretion machinery. 41% domains of SPI proteins have no previous sequence annotation. By comparing clinical and environmental isolates, we identified 3682 proteins that are overrepresented in clinical group that we consider as potentially pathogenic. Among domains of potentially pathogenic proteins only 50% domains were annotated by sequence methods previously. Moreover, 36% (1330 out of 3682) of potentially pathogenic proteins cannot be classified into Evolutionary Classification of Protein Domains database (ECOD). Among classified domains of potentially pathogenic proteins the most populated homology groups include helix-turn-helix (HTH), Immunoglobulin-related, and P-loop domains-related. Functional analysis revealed overrepresentation of these protein in biological processes related to viral entry into host cell, antibiotic biosynthesis, DNA metabolism and conformation change, and underrepresentation in translational processes. Analysis of the potentially pathogenic proteins indicates that they form 119 clusters or novel potential pathogenicity islands (NPPIs) within the Salmonella genome, suggesting their potential contribution to the bacterium’s virulence. One of the NPPIs revealed significant overrepresentation of potentially pathogenic proteins. Overall, our analysis revealed that identified potentially pathogenic proteins are poorly studied.

In Silico Screening of Some Active Phytochemicals to Identify Promising Inhibitors Against SARS-CoV-2 Targets.

There are very few small-molecule drug candidates developed against SARS-CoV-2 that have been revealed since the epidemic began in November 2019. The typical medicinal chemistry discovery approach requires more than a decade of the year of painstaking research and development and a significant financial guarantee, which is not feasible in the challenge of the current epidemic. This current study proposes to find and identify the most effective and promising phytomolecules against SARS-CoV-2 in six essential proteins (3CL protease, Main protease, Papain- Like protease, N-protein RNA binding domain, RNA-dependent RNA polymerase, and Spike receptor binding domain target through in silico screening of 63 phytomolecules from six different Ayurveda medicinal plants. The phytomolecules and SARS-CoV-2 proteins were taken from public domain databases such as PubChem and RCSB Protein Data Bank. For in silico screening, the molecular interactions, binding energy, and ADMET properties were investigated. The structure-based molecular docking reveals some molecules' greater affinity towards the target than the co-crystal ligand. Our results show that tannic acid, cyanidin-3-rutinoside, zeaxanthin, and carbolactone are phytomolecules capable of inhibiting SARS-CoV-2 target proteins in the least energy conformations. Tannic acid had the least binding energy of -8.8 kcal/mol, which is better than the binding energy of its corresponding co-crystal ligand (-7.5 kcal/mol) against 3 CL protease. Also, it has shown the least binding energy of -9.9 kcal/mol with a more significant number of conventional hydrogen bond interactions against the RdRp target. Cyanidin-3-rutinoside showed binding energy values of -8.8 and -7.6 kcal/mol against Main protease and Papain-like protease, respectively. Zeaxanthin was the top candidate in the N protein RBD with a binding score of - 8.4 kcal/mol, which is slightly better when compared to a co-crystal ligand (-8.2 kcal/mol). In the spike, carbolactone was the suitable candidate with the binding energy of -7.2 kcal/mol and formed a conventional hydrogen bond and two hydrophobic interactions. The best binding affinity-scored phytomolecules were selected for the MD simulations studies. The present in silico screening study suggested that active phytomolecules from medicinal plants could inhibit SARS-CoV-2 targets. The elite docked compounds with drug-like properties have a harmless ADMET profile, which may help to develop promising COVID-19 inhibitors.

Bio-Chemoinformatics-Driven Analysis of nsp7 and nsp8 Mutations and Their Effects on Viral Replication Protein Complex Stability.

The nonstructural proteins 7 and 8 (nsp7 and nsp8) of SARS-CoV-2 are highly important proteins involved in the RNA-dependent polymerase (RdRp) protein replication complex. In this study, we analyzed the global mutation of nsp7 and nsp8 in 2022 and 2023 and analyzed the effects of mutation on the viral replication protein complex using bio-chemoinformatics. Frequently occurring variants are found to be single amino acid mutations for both nsp7 and nsp8. The most frequently occurring mutations for nsp7 which include L56F, L71F, S25L, M3I, D77N, V33I and T83I are predicted to cause destabilizing effects, whereas those in nsp8 are predicted to cause stabilizing effects, with the threonine to isoleucine mutation (T89I, T145I, T123I, T148I, T187I) being a frequent mutation. A conserved domain database analysis generated critical interaction residues for nsp7 (Lys-7, His-36 and Asn-37) and nsp8 (Lys-58, Pro-183 and Arg-190), which, according to thermodynamic calculations, are prone to destabilization. Trp-29, Phe-49 of nsp7 and Trp-154, Tyr-135 and Phe-15 of nsp8 cause greater destabilizing effects to the protein complex based on a computational alanine scan suggesting them as possible new target sites. This study provides an intensive analysis of the mutations of nsp7 and nsp8 and their possible implications for viral complex stability.

E2F1 promotes cell migration in hepatocellular carcinoma via FNDC3B.

FNDC3B (fibronectin type III domain containing 3B) is highly expressed in hepatocellular carcinoma (HCC) and other cancer types, and fusion genes involving FNDC3B have been identified in HCC and leukemia. Growing evidence suggests the significance of FNDC3B in tumorigenesis, particularly in cell migration and tumor metastasis. However, its regulatory mechanisms remain elusive. In this study, we employed bioinformatic, gene regulation, and protein-DNA interaction screening to investigate the transcription factors (TFs) involved in regulating FNDC3B. Initially, 338 candidate TFs were selected based on previous chromatin immunoprecipitation (ChIP)-seq experiments available in public domain databases. Through TF knockdown screening and ChIP coupled with Droplet Digital PCR assays, we identified that E2F1 (E2F transcription factor 1) is crucial for the activation of FNDC3B. Overexpression or knockdown of E2F1 significantly impacts the expression of FNDC3B. In conclusion, our study elucidated the mechanistic link between FNDC3B and E2F1. These findings contribute to a better understanding of FNDC3B in tumorigenesis and provide insights into potential therapeutic targets for cancer treatment.

THE CONTRIBUTION OF TRANSGENIC RICE TO ENHANCE GRAIN YIELD

Breeders' main goal is to increase the proportion of high-quality rice produced overall. To create effective rice breeding strategies, possible yield-related loci have been mined. many researchers are using transgenic strategies as cutting-edge methods to increase rice productivity. Quantitative trait loci (QTLs) play a pivotal role in governing grain yield in Oryza sativa L., commonly known as rice. The genes contributing to QTLs that determine grain size, length, and weight have been successfully identified. Numerous genes are upregulated to enhance the overall yield of rice. Recent advancements have led to the discovery of genes and QTLs specifically associated with rice yield. Through an in-depth analysis of various yield characteristics, including grain weight, thousand grain weight, grain length, grain width, grain yield per plant, grain number per panicle, and panicles per plant, we conducted a comprehensive review using extensive literature research and public domain databases. Additionally, we explored the progress made in transgenic technology and advanced genomic techniques. The compiled information on genes and QTLs related to yield enhancement aims to provide a valuable resource. The integrated analysis of existing data on genes and/or QTLs provide evidence on potential combinations for creating superior genotypes that combine high yield across multiple traits. Integration of molecular markers, transgenic techniques and conventional breeding as discussed in this extensive review opens up the prospect of developing high yielding rice varieties

KINtaro: protein kinase-like database

ObjectiveThe superfamily of protein kinases features a common Protein Kinase-like (PKL) three-dimensional fold. Proteins with PKL structure can also possess enzymatic activities other than protein phosphorylation, such as AMPylation or glutamylation. PKL proteins play a vital role in the world of living organisms, contributing to the survival of pathogenic bacteria inside host cells, as well as being involved in carcinogenesis and neurological diseases in humans. The superfamily of PKL proteins is constantly growing. Therefore, it is crucial to gather new information about PKL families.ResultsTo this end, the KINtaro database (http://bioinfo.sggw.edu.pl/kintaro/) has been created as a resource for collecting and sharing such information. KINtaro combines protein sequence information and additional annotations for more than 70 PKL families, including 32 families not associated with PKL superfamily in established protein domain databases. KINtaro is searchable by keywords and by protein sequence and provides family descriptions, sequences, sequence alignments, HMM models, 3D structure models, experimental structures with PKL domain annotations and sequence logos with catalytic residue annotations.

Additive manufacturing systems integration

This research explores real-time database systems’ evolution, focusing on unique features and the addressed challenges. It examines the role of multi-material additive manufacturing quality domain databases in innovation and maintaining standards. It also looks at the challenges of implementing quality manufacturing systems from a technology, organization, and people of European Manufacturing research perspective. The study offers a new perspective on reconfigurable intelligent surfaces with multi-material additive manufacturing with system integration, discussing its applications and digital products’ transformative potential. Enhancing multi-material additive manufacturing capabilities redefines the industries, creating a strong communication culture by adopting quality integration of quality and robotics while exploring the future of mathematics complexity in optimizing manufacturing education. The paper also explains the statistical classification of historical technology in manufacturing engineering education by flagging the platform‘s role of opportunities in secure research collaboration. The manufacturing horizontal is essential for effectively managing the multi-material additive manufacturing system through advanced technology to quality management integration. Certified advanced training and competency development econophysics show the multi-material additive manufacturing systems development influence on the production theories and mechanics of complex electronics. To enable, for example, communication links and cellural multi-material additive manufacturing integrations research for new technologies.

Delivering a net zero National Health Service: where does otorhinolaryngology - head and neck surgery stand?

The National Health Service (NHS) recognised the risk to public health brought by climate change by launching the Greener NHS National Programme in 2020. These organisational changes aim to attain net zero direct carbon emissions. This article reviews the literature on initiatives aimed at mitigating the environmental impact of ENT practice. Systematic review of the literature using scientific, healthcare and general interest (public domain) databases. The initiatives reviewed can be broken down into strategies for mitigating the carbon footprint of long patient stay, use of operative theatres and healthcare travel. The carbon footprint of in-patient stay can be mitigated by a shift towards day-case surgery. The ENT community is currently focused on the reduction of theatre waste and the use of disposable instruments. Furthermore, supply chains and healthcare delivery models are being redesigned to reduce travel. Future areas of development include designing waterless theatre scrubs, waste-trapping technologies for anaesthetic gases and a continuing investment in virtual healthcare.

CAU-Net: An Improved Attention U-Net for CT Head and Neck Organs at Risk Contouring

Segmentation of head and neck (H&N) organs at risk (OARs) is an intricate process, we propose Contest- extractor Attention U-Net (CAU-Net) to improve the segmentation accuracy of OARs contours by addressing the problem of limited accuracy of 2D segmentation algorithms in current radiotherapy techniques. A total of 60 patients from CSTRO2019's H&N dataset containing 22 organs, were available to train and evaluate a prototype deep learning-based normal tissue 2D auto-segmentation algorithm. Our CAU-Net is based on U-Net, by using the edge attention module to enhance the boundary representation, the null convolution block in the context extraction module to encode high-level semantic feature information, and the convolution of the sensory field to assign to different targets. A Dice loss function combine contour loss function was used in training the models. The contour loss function was improved to segment the target regions by the weights of different organ occurrences and the region assignment of false positives and false negatives to accurately predict the boundary structure. The OARs were delineated by a single experienced physician. A subset of 10 cases was withheld from training and used for validation. On those, we set three different deep-learning networks trained with CSTRO and compared them to the gold data: A) CAU-Net, B) nnUNet, and C) UNet++. To test its applicability, we used another public H&N dataset Public Domain Database for Computational Anatomy (PDDCA) containing 8 organs with 47 patients, among which 10 cases were used for validation: D) CAU-Net with the PDDCA, E) UNet2022 with the PDDCA. The Dice similarity coefficient (DSC) was used to measure the overlap between the results of the gold data and the automated segmentations. The average DSC scores for method A, B, and C across all OARs in the 10 evaluation cases were 0.67±0.08, 0.58±0.11 and 0.62±0.12, respectively. The difference in mean DSC scores was significant (p<0.05). The A/B difference was significant in Lens-L, Lens-R and Pituitary. Method A scored the highest DSC in all OARs except for the Spinal Cord, Mandible-L and Mandible-R. 16 OARs showed DSC≥0.6 on CSTRO. Method D, and E achieved 0.84±0.10 and 0.83±0.09 average DSC respectively. All OARs showed DSC≥0.7 on PDDCA. The CAU-Net proposed by us achieved better results than the baseline network for H&N OAR segmentation. This new development will provide the possibility of H&N organ segmentation and rapid diagnosis of radiotherapy. All the networks trained with PDDCA scored higher than CSTRO. Auto segmentation results can differ significantly when the same algorithm is trained on data from different institutions.

Depression and associated factors among Brazilian adults: the 2019 national healthcare population-based study

BackgroundMental disorders represent a major public health challenge worldwide, affecting 80% of people living in low- and middle-income countries. Depression, a mental disorder, is a chronic disease of long duration that causes changes in the brain, resulting from a combination of genetic, physiologic, environmental, and behavioral factors. The aim of this study was to investigate possible factors associated with depression in Brazilian adults. MethodsA population-based, cross-sectional study was carried out using the public domain database of the 2019 National Health Survey, conducted in Brazil. Depression was considered the dependent variable, and through hierarchical analysis, predictor variables were investigated such as, at the distal level—socioeconomic variables, at the intermediate level—variables related to lifestyle behavior, health condition, and history, and at the proximal level—demographic variables. Logistic regression analysis was used to obtain the adjusted Odds Ratio and the respective 95% confidence interval to identify possible factors associated with depression.ResultsThe study included 88,531 participant records with 10.27% diagnosed with depression. The adjusted association measurements, after selecting the independent variables in the hierarchical analysis, showed the following factors associated with depression with differing magnitudes: age, brown and white race/skin color, female sex, poor, very poor, or regular self-reported health condition, diagnosis of cardiovascular disease, work-related musculoskeletal disorder, history of smoking habit, and macroeconomic region.ConclusionsAn effective strategy for preventing and managing depression in Brazilian adults must include the control of health status and lifestyle behavior factors, with actions and programs to reduce people's exposure to these factors, understanding that socioeconomic-demographic differences of each population can potentially reduce the disease burden.

Construction and application of knowledge graph for construction accidents based on deep learning

PurposeLearning from safety accidents and sharing safety knowledge has become an important part of accident prevention and improving construction safety management. Considering the difficulty of reusing unstructured data in the construction industry, the knowledge in it is difficult to be used directly for safety analysis. The purpose of this paper is to explore the construction of construction safety knowledge representation model and safety accident graph through deep learning methods, extract construction safety knowledge entities through BERT-BiLSTM-CRF model and propose a data management model of data–knowledge–services.Design/methodology/approachThe ontology model of knowledge representation of construction safety accidents is constructed by integrating entity relation and logic evolution. Then, the database of safety incidents in the architecture, engineering and construction (AEC) industry is established based on the collected construction safety incident reports and related dispute cases. The construction method of construction safety accident knowledge graph is studied, and the precision of BERT-BiLSTM-CRF algorithm in information extraction is verified through comparative experiments. Finally, a safety accident report is used as an example to construct the AEC domain construction safety accident knowledge graph (AEC-KG), which provides visual query knowledge service and verifies the operability of knowledge management.FindingsThe experimental results show that the combined BERT-BiLSTM-CRF algorithm has a precision of 84.52%, a recall of 92.35%, and an F1 value of 88.26% in named entity recognition from the AEC domain database. The construction safety knowledge representation model and safety incident knowledge graph realize knowledge visualization.Originality/valueThe proposed framework provides a new knowledge management approach to improve the safety management of practitioners and also enriches the application scenarios of knowledge graph. On the one hand, it innovatively proposes a data application method and knowledge management method of safety accident report that integrates entity relationship and matter evolution logic. On the other hand, the legal adjudication dimension is innovatively added to the knowledge graph in the construction safety field as the basis for the postincident disposal measures of safety accidents, which provides reference for safety managers' decision-making in all aspects.

A Novel Mapper Machine Learning Algorithm for Semantic Domain Mapping for Domain Database Updation

A Novel Mapper Machine Learning Algorithm for Semantic Domain Mapping for Domain Database Updation

Diverse hydrocarbon degradation genes, heavy metal resistome, and microbiome of a fluorene-enriched animal-charcoal polluted soil.

Environmental compartments polluted with animal charcoal from the skin and hide cottage industries are rich in toxic heavy metals and diverse hydrocarbon classes, some of which are carcinogenic, mutagenic, and genotoxic, and thus require a bio-based eco-benign decommission strategies. A shotgun metagenomic approach was used to decipher the microbiome, hydrocarbon degradation genes, and heavy metal resistome of a microbial consortium (FN8) from an animal-charcoal polluted site enriched with fluorene. Structurally, the FN8 microbial consortium consists of 26 phyla, 53 classes, 119 orders, 245 families, 620 genera, and 1021 species. The dominant phylum, class, order, family, genus, and species in the consortium are Proteobacteria (51.37%), Gammaproteobacteria (39.01%), Bacillales (18.09%), Microbulbiferaceae (11.65%), Microbulbifer (12.21%), and Microbulbifer sp. A4B17 (19.65%), respectively. The microbial consortium degraded 57.56% (28.78 mg/L) and 87.14% (43.57 mg/L) of the initial fluorene concentration in 14 and 21 days. Functional annotation of the protein sequences (ORFs) of the FN8 metagenome using the KEGG GhostKOALA, KofamKOALA, NCBI's conserved domain database, and BacMet revealed the detection of hydrocarbon degradation genes for benzoate, aminobenzoate, polycyclic aromatic hydrocarbons (PAHs), chlorocyclohexane/chlorobenzene, chloroalkane/chloroalkene, toluene, xylene, styrene, naphthalene, nitrotoluene, and several others. The annotation also revealed putative genes for the transport, uptake, efflux, and regulation of heavy metals such as arsenic, cadmium, chromium, mercury, nickel, copper, zinc, and several others. Findings from this study have established that members of the FN8 consortium are well-adapted and imbued with requisite gene sets and could be a potential bioresource for on-site depuration of animal charcoal polluted sites.

De Novo Mining and Validating Novel Microsatellite Markers to Assess Genetic Diversity in Maruca vitrata (F.), a Legume Pod Borer.

Maruca vitrata (Fabricius) is an invasive insect pest capable of causing enormous economic losses to a broad spectrum of leguminous crops. Microsatellites are valuable molecular markers for population genetic studies; however, an inadequate number of M. vitrata microsatellite loci are available to carry out population association studies. Thus, we utilized this insect's public domain databases for mining expressed sequence tags (EST)-derived microsatellite markers. In total, 234 microsatellite markers were identified from 10053 unigenes. We discovered that trinucleotide repeats were the most predominant microsatellite motifs (61.53%), followed by dinucleotide repeats (23.50%) and tetranucleotide repeats (14.95%). Based on the analysis, twenty-five markers were selected for validation in M. vitrata populations collected from various regions of India. The number of alleles (Na), observed heterozygosity (Ho), and expected heterozygosity (He) ranged from 2 to 5; 0.00 to 0.80; and 0.10 to 0.69, respectively. The polymorphic loci showed polymorphism information content (PIC), ranging from 0.09 to 0.72. Based on the genetic distance matrix, the unrooted neighbor-joining dendrogram differentiated the selected populations into two discrete groups. The SSR markers developed and validated in this study will be helpful in population-level investigations of M. vitrata to understand the gene flow, demography, dispersal patterns, biotype differentiation, and host dynamics.