Biology subjects

Yennamalli, R. M.

Publications and source records attributed to Yennamalli, R. M..

11 recordsLinked to original sources

bioRxiv2026

Integrative Whole-Genome Analysis reveals Genomic Signatures of Innate Immunity in Indicine Cattle.

Indicine cattle (Bos indicus) are known for resilience to infectious diseases and environmental stress. However, the genomic basis underlying this advantage remains poorly understood. To characterize variations in immune-related genetic elements in four indicine breeds (Kangayam, Gir, Tharparkar, and Sahiwal), a taurine breed (Holstein Friesian), and a taurine-indicine crossbreed (Karan Fries), we performed integrative whole-genome analysis. Using whole-genome data representing 108 animals, we identified structural variants, copy number variants, single-nucleotide variations, and insertions/deletions. High-impact single-nucleotide variations in the key innate immune genes, CARD9 and NLRP8, shared across all indicine breeds, were absent in the other breeds. Genetic differentiation analysis identified several innate immune genes showing strong divergence between the indicine breeds and the taurine breed. Selective sweep detection analysis highlighted multiple breed-specific immune-related sweep regions. Functional enrichment analysis showed significant enrichment of immune pathways in indicine breeds. A comparison of the candidate genes with basal gene expression profiles of unchallenged peripheral blood mononuclear cells indicated that genomic variation influences the differential expression of several genes in indicine breeds. We synthesized the data from population genome structure analysis, nucleotide diversity, genetic differentiation, selective sweep analysis, and correlations with gene expression profiles. Indicine breeds exhibited a higher number of immune-related variants and stronger signals of selection in immune pathways. These findings provide a curated set of innate immune gene candidates in indicine breeds for future functional studies and breeding programs.

bioinformatics↗

bioRxiv2025

Structural and Boolean Network Modeling of the Levan Biosynthetic Pathway in Bacillus subtilis.

MotivationLevan is a fructose polymer with applications in the creation of hydrogels for drug delivery and wound healing. In industrial biotechnology. Bacillus subtilis is the key organism for producing levan. However, the metabolic models of Bacillus subtilis available do not include the biosynthesis of levan. To understand levan biosynthesis in B. subtilis, we employed structural systems biology integrating known structural details of proteins in the B. subtilis metabolic pathway to create a structure-annotated genome-scale metabolic model (GEM). To fill gaps in structural information about the enzymes, AlphaFold2 was used. Thus, this study enhances the metabolic model of B. subtilis by incorporating the biosynthesis of levan and including structural information about the proteins involved. ResultsThe manually curated model links proteins and reactions to protein data bank (PDB) entries, providing structural perspectives previously overlooked in GEMs. We mapped 508 PDB structures to 168 UniProt IDs to unravel 331 out of 1250 reactions (26.5%) in B. subtilis with focused coverage of sacB, sacC, sacX/Y, levD/E/F/G, and sacP. The structural layer does not alter stoichiometry or constraints unless explicitly parameterized. This structure-annotated resource enables the systematic testing of phenotype predictions and design strategies. Our structure-based metabolic model advances the understanding of levan production and microbial metabolism, facilitating sustainable and efficient biotechnological processes for industrial applications. Availability and implementationData available at the github page (https://github.com/raghuyennamalli/levan_ssbio)

systems biology↗

bioRxiv2025

Meta-Analysis of Transcriptomic Datasets Reveals Key Immune Gene Profiles and Signaling Pathways in Bos taurus

Understanding immune mechanisms in cattle is vital for improving disease resistance through informed breeding and vaccine development. Meta-analysis enables the integration of multiple transcriptomic studies to identify consistent gene expression patterns and enhance statistical power. In this study, we performed a meta-analysis of four bovine transcriptomic datasets (GSE45439, GSE62048, GSE125964, and GSE247921) to identify immune-related differentially expressed genes (DEGs) in Bos taurus. These datasets covered infections with Mycobacterium bovis and Mycobacterium avium subsp. paratuberculosis, comparing diseased and healthy cattle. Our pipeline included FastQC, Trimmomatic, Bowtie2, SAMtools, FeatureCounts, DESeq2, and MetaRNASeq, leading to the discovery of 28 significant DEGs (12 upregulated and 16 downregulated). Comparison with an innate immune gene database identified five key immune-related genes--IL1A, RGS2, RCAN1, and ZBP1--with known regulatory roles in immunity. KEGG enrichment analysis revealed involvement in Necroptosis, Osteoclast Differentiation, Oxytocin Signaling, and cGMP-PKG Signaling pathways, associated with inflammatory cell death, cytokine signaling, and immune cell differentiation. This meta-analysis enhances understanding of conserved immune signaling mechanisms in cattle and highlights genes that may serve as biomarkers for immune competence, disease susceptibility, and vaccine responsiveness, offering valuable insights for future bovine immunogenomics research.

bioinformatics↗

bioRxiv2025

Mapping the Metalloproteome of Deinococcus indicus DR1 through Integrative Structure and Function Annotation

Deinococcus indicus DR1 is a rod-shaped bacterium isolated from the Dadri wetlands (Uttar Pradesh, India) that tolerates ionizing radiation and arsenic. The molecular basis of its wider heavy-metal resilience, particularly among the 1017 out of 4128 proteins still annotated as hypothetical, remains unclear. We performed a proteome-wide structural and functional survey to address this gap. All the 4128 proteins were modeled with AlphaFold2, yielding very-high-confidence structures (pLDDT [≥] 90) for 2145 sequences. CATH/InterPro analysis assigned domains to 2735 proteins. Functions were predicted by combining DeepFRI (graph neural-network), MorphologFinder (Foldseek plus EggNOG-Mapper), and existing GenBank annotations. The integrated workflow suggests that more than 100 previously uncharacterized proteins may bind or transport arsenic, chromium, cobalt, copper, iron, manganese, molybdenum, nickel, or zinc, indicating a metal-handling capacity that extends beyond the known ars operon. Recurrent domain architectures that include P-loop NTPases, Rossmann folds, GNAT acetyl-transferases, and sensor modules (CHASE, PAS, GAF) point to coordinated redox regulation and efflux pathways. Twenty high-confidence metal-binding candidates have been prioritized for experimental validation through expression, mutagenesis, and knockout studies. All structural models, domain assignments, and query tools are available at https://deinococcus.in, providing a resource for future investigations of heavy-metal tolerance in this organism.

bioinformatics↗

bioRxiv2025

Machine learning-based structural classification of lytic polysaccharide monooxygenases

Lytic polysaccharide monooxygenase (LPMO) is a copper-dependent redox enzyme and according to CAZy is classified either as cellulolytic or chitinolytic. According to CAZy, there are eight families of LPMO namely AA9, AA10, AA11, AA13, AA14, AA15, AA16, and AA17, where AA stands for Auxiliary Activity. Previously, using the sequence information machine learning-based functional annotation was successfully completed using neural network and LSTM models. This was done for AA9 and AA10 as the number of sequences was large enough to train a model with high-performance indices. Here, the goal is to use existing 3D structures and AI-based models of the remaining LPMO sequences and train a machine learning model to identify and classify LPMO with the help of structural features by performing either a neural network algorithm or other suitable methods. Using the features of LPMO such as surface depth, accessible area, electrostatic charge distribution, and geometric features (independent features that define the shape and are not based on enzyme reaction mechanism) we will identify the features with high signal-to-noise ratio/significance using ensemble feature selection method. The features were extracted using Pymol, MdTraj, Biopython, Open3d, and Bio3D tools from OBJ and STL format. The model thus trained using structural features will enable identifying and annotating newer LPMO sequences belonging to one of the eight AA families.

bioinformatics↗

bioRxiv2025

Calcium Dependent Conformational Changes in Human Transglutaminase 2 and its Implications in Celiac Disease

Transglutaminase 2 (TG2) serves as a modifiable transamidating acyltransferase that precipitates calcium-induced protein alterations. The enzyme plays a crucial role in the cell and disease states, such as tissue repair, calcium signal transduction, celiac disease, and cancer. It is implicated in protein crosslinking and has been found in high concentrations in the small intestines of those with celiac disease. The function of TG2 hinges upon calcium ions binding to particular sites on the enzyme. In this study, we delve into the contribution of calcium-responsive transglutaminase 2 (TG2) in celiac disease, utilizing both molecular dynamics simulations and coarse-grained models, and investigate the impact of non-synonymous single nucleotide polymorphisms (nsSNPs) on TG2. Molecular dynamics reveal prominent conformational differences between the open and closed conformations. In the coarse-grained model, key residues are found adjacent to the active site in the open conformation, while in the closed conformation, key residues are distant from the active site. We further explore the functional impact of non-synonymous single nucleotide polymorphisms (nsSNPs) in TG2 using both sequence-based and structure-based computational tools. Through a consensus approach, we identify ten nsSNPs that are predicted to destabilize TG2 or alter its structural flexibility, with mutations such as R48H, E186Q, C277S, and E549G likely to influence active site accessibility and calcium coordination. The findings from this research enhances our understanding of the molecular processes underpinning celiac disease and helps facilitate innovative treatment approaches that target calcium-responsive TG2.

bioinformatics↗

bioRxiv2025

PreDSLpmo V2.0: A deep learning-based prediction tool for functional annotation of lytic polysaccharide monooxygenases

Lytic polysaccharide monooxygenases (LPMOs) are crucial enzymes that enhance the breakdown of polysaccharides, especially for biofuel production. Current computational tools for LPMO annotation are limited to a few families, leaving many unexplored. Here, we introduce PreDSLpmo v2.0, a deep learning-based tool that classifies and annotates the fast growing numbers of LPMOs across eight families using a curated dataset comprising over 30,000 LPMO sequences. To capture the compositional, physicochemical, and structural properties of these sequences, we extracted features using Python (iFeature) as well as R-based pipelines, generating over 13500 descriptors per sequence. Ensemble feature selection was used to identify significant features for binary and multiclass classification. To address data imbalance, we maintained a 1:1 ratio for all positive and negative sets during training and validation. A range of machine learning models were systematically trained and evaluated. An independent dataset was used to estimate the performance of the trained models. The multiclass Bi-LSTM model demonstrated the highest accuracy, robustness, and generalizability, outperforming feature-based approaches. We compared the performance of the model with Pfam, dbCAN3, and BlastP searches against the UniProtKB/swissprot database. The F1-score shows that the models predicted LPMO sequences are accurate. The reliability of predictions by Bi-LSTM model is comparable to that of dbCAN3 and often better, as confirmed by Pfam domain annotation and SignalP. The model, now deployed as a web server (https://predlpmo.in) for high-throughput, sequence-based functional annotation of LPMOs, provides a scalable and reliable solution for enzyme discovery in bioenergy and industrial biotechnology.

bioinformatics↗

bioRxiv2024

Genetic basis of immunity in Indian cattle as revealed by comparative analysis of Bos genome

Indicine cattle (Bos indicus) show notable resilience and disease resistance compared with taurine breeds, but the genomic basis of these traits remains largely unexplored. Identification of genomic elements for immunity will enable future controlled crossbreeding programs using molecular breeding methods. Therefore, we performed a genome-wide comparison among Nelore, Gir, and Hereford breeds using their whole-genome sequences, majorly focusing on immune-related structural and sequence variation. Our aims were to catalog insertions, deletions, and single nucleotide variants (SNVs) that intersect immune loci and known quantitative trait loci (QTLs), identify runs of homozygosity and selective-sweep signals, and prioritize candidate genes for follow-up functional studies. We retrieved whole-genome sequencing data for Nelore breed (n=14) and Gir breed (n=20) from NCBI using the SRA toolkit. Reads were checked with FastQC, filtered with fastp to remove low-quality bases and adaptors, and retained high-quality reads based on Q20 and Q30. The reads were mapped to the Bos taurus reference (ARS-UCD2.0) with BWA-MEM; alignments were processed with SAMtools for sorting, duplicate marking, and MAPQ [≥] 20 filtering. Variants (insertions, deletions, SNVs) were called with GATK HaplotypeCaller, hard-filtered, normalized with bcftools, and annotated with SnpEff and SnpSift. Common variants were identified via in-house Python scripts; immune loci were detected from InnateDB and keyword searches; QTL overlaps were identified using Animal QTLdb; DAVID was used for GO and KEGG enrichment (P < 0.05). ROH islands were defined in PLINK as regions shared by >50% of individuals or samples, and selective sweeps were detected with RAiSD; genes overlapping ROH islands and RAiSD peaks were prioritized as candidate selection signatures. GATK identified 1,884,058 indels and 13,997,533 SNVs in Nelore breed, and 1,457,337 indels and 11,627,881 SNVs in Gir breed, with Ti/Tv ratios of [~]2.26 and [~]2.25, respectively. Nelore breed has more number of variants than Gir. We observed frameshift insertions in TLR3 and LOC508441 (CD33) in both the breeds and frameshift deletions in JAM3 in Nelore breed and PAX5 in Gir breed. The variants are also identified in the regulatory regions of both breeds. The high-impact SNVs were in CD46 and IL26 genes in Nelore breed, and PLG gene in Gir breed. Genome-wide scans using RAiSD identified selective sweeps in 707 candidate genes in Nelore breed and 165 in Gir breed. Comparing the ROH and RAiSD results, we prioritized the genes ANKRD11, MAGI2, LOC132345096, FOXP2, TCF12, and ATP5PO in Nelore breed, and the genes MEFV and ORIF1 in Gir breed. These genes are found in QTLs linked to milk and health traits. Functional enrichment showed that the genes exhibiting all the three variants belong to immune pathways such as, NF-kappaB signaling, T-cell receptor signaling, and MAPK signaling in both breeds. These results reveal breed-specific genomic variation locating immune loci and its associated QTLs and provide a list of candidate genes and regions for experimental validation and marker development to improve disease resistance and productivity in Indicine cattle. Keywords: Hereford, Nelore, Gir, immune-related genes, QTLs, Whole genome sequence

bioinformatics↗

bioRxiv2022

Targeting the Search: An Upgraded Structural and Functional Repository of Antimicrobial Peptides for Biofilm Studies (B-AMP v2.0) with a Focus on Biofilm Protein Targets

Bacterial biofilms, often as multispecies communities, are recalcitrant to conventional antibiotics, making the treatment of biofilm infections a challenge. There is a push towards developing novel anti-biofilm approaches, such as antimicrobial peptides (AMPs), with activity against specific biofilm targets. In previous work, we developed Biofilm-AMP, a structural and functional repository of AMPs for biofilm studies (B-AMP v1.0) with more than 5000 structural models of AMPs and a vast library of AMP annotations to existing biofilm literature. In this study, we present an upgraded version of B-AMP, with a focus on existing and novel bacterial biofilm targets. B-AMP v2.0 hosts a curated collection of 2502 biofilm protein targets across 473 bacterial species, with structural protein models and functional annotations from PDB, UniProt, and PubMed databases. The biofilm targets can be searched for using the name of the source organism, and function and type of protein, and results include designated Target IDs (unique to B-AMP v2.0), UniProt IDs, 3D predicted protein structures, PDBQT files, pre-defined protein function, and relevant scientific literature. To present an example of the combined applicability of both, the AMP and biofilm target libraries in the repository, we present two case studies. In the first case study, we expand an in silico pipeline to evaluate AMPs against a single biofilm target in the multidrug resistant, bacterial pathogen Corynebacterium striatum, using 3D protein-peptide docking models from previous work and Molecular Dynamics simulations ([~]1.18 {micro}s). In the second case study, we build an in silico pipeline to identify candidate AMPs (using AMPs with both anti-Gram positive and anti-Gram negative activity) against two biofilm targets with a common functional classification in Pseudomonas aeruginosa and Staphylococcus aureus, widely-encountered bacterial co-pathogens. With its enhanced structural and functional capabilities, B-AMP v2.0 serves as a comprehensive resource for AMP investigations related to biofilm studies. B-AMP v2.0 is freely available at https://b-amp.karishmakaushiklab.com and will be regularly updated with structural models of AMPs and biofilm targets, as well as 3D protein-peptide interaction models for key biofilm-forming pathogens.

bioinformatics↗

bioRxiv2021

Comparative analysis of Lysine and Arginine biosynthesis pathway in Deinococcus genomes

The members of the Deinococcaceae family have the ability to survive extreme environmental conditions. Deinococcus species have a complex cell envelope composed of L-ornithine containing peptidoglycan. Anabolism of L-ornithine is intrinsically linked to L-lysine and L-arginine biosynthetic pathways. To understand these two pathways, we analyzed the L-lysine and L-arginine pathways using 23 Deinococcus genomes, including D. indicus. We used BLAST-P based ortholog identification using D. radiodurans genes as the query. We identified some BLAST-P hits that shared the same functional annotation. We analyzed three (class I aminotransferase, acetyl-lysine deacetylase, and acetyl glutamate/acetyl aminoadipate kinase) from L-lysine biosynthesis pathway and three (bifunctional ornithine acetyltransferase or N-acetyl glutamate synthase protein, nitric oxide synthase-like protein, and Acetyl-lysine deacetylase) from L-arginine biosynthesis pathway. Two proteins showed certain structural variations. Specifically, [LysW]-lysine hydrolase proteins sequence and structure level changes indicated changes in oligomeric conformation, which could likely be a result of divergent evolution. And, bifunctional ornithine acetyltransferase or N-acetyl glutamate synthase had its active site pocket positions shifted at the structural level and we hypothesize that it may not perform at the optimal level. Thus, we were able to compare and contrast different Deinococcus species indicating some genes occurring because of divergent evolution.

bioinformatics↗

bioRxiv2017

Molecular mechanism of cell-cell adhesion mediated by cadherin-23

Adherin-junctions are traditionally described by the homophilic-interactions of classical cadherin-proteins at the extracellular region. However, the role of long-chain non-classical cadherins like cadherin-23(Cdh23) is not explored as yet even though it is implicated in tissue-morphogenesis, cancer, and force-sensing in neuronal tissues. Here, we identified a novel antiparallel-binding interface of Cdh23 homodimer in solution by combining biophysical and computational methods, in-vitro cell-binding, and mutational modifications. The dimer consists of two electrostatic-based interfaces extended up to two terminal domains, atypical to classical-cadherins known so far, and forms the strongest interactions in cadherin-family as measured using single-molecule force-spectroscopy. We further identified single point-mutation, E78K, that completely disrupts this binding. Interestingly, the mutation, S77L, found in skin cancers falls within the binding interface of the antiparallel-dimer. Overall, we provide the molecular architecture of Cdh23 at the cell-cell junctions which are likely to have far-reaching applications in the fields of mechanobiology and cancer.

biophysics↗