Biology subjects

Agrawal, A. A.

Publications and source records attributed to Agrawal, A. A..

6 recordsLinked to original sources

bioRxiv2026

Toxin resistance in the monarch butterfly expanded its milkweed host range

A central problem in ecology and evolution is to understand the extent to which herbivorous insect host plant specialization has been shaped by bottom-up natural selection from host-plant chemical defenses or top-down natural selection from natural enemies. Here, we show that the stepwise evolution of ATP target-site insensitivity (TSI) in the monarch butterfly lineage consistently tracked a dietary association with cardenolide-defended Asclepias host plants. We further show that Drosophila melanogaster flies edited with CRISPR-Cas9 genome engineering to carry the monarch butterflys more derived ATP genotype maintain survival across Asclepias host-plant diets that span a broad cardenolide defense gradient, whereas flies carrying early-diverging ATP genotypes in the monarch lineage show lower survival. We conclude that a resistance trait linked to sequestration and enemy protection also expanded dietary resistance to highly defended host plants, revealing how a single molecular adaptation may have been driven by both top-down and bottom-up selective pressures during herbivore specialization.

evolutionary biology↗

bioRxiv2026

Unveiling the taxonomic diversity and unprecedented biosynthetic treasure of the phylum Myxococcota

Natural products remain vital sources of therapeutics, particularly anti-infectives, and members of the phylum Myxococcota constitute an especially rich reservoir for their discovery. Based on decades of microbiological efforts, we present 154 new Myxococcota genomes and propose a revised taxonomy expanding the number of described families from 11 to 28 and genera from 32 to 90. Comparison with an equivalent set from the prime source Actinomycetota shows that Myxococcota possess a comparable biosynthetic diversity, underscoring their promise for large-scale isolation and sequencing efforts. The vast untapped potential reflected in 2,387 uncharacterized gene cluster families is highlighted by genome mining efforts, yielding four validated compounds exhibiting novel chemistry, including myxolutamids and myxopentacins. We show that many Myxococcota-derived natural products, such as myxolutamid A and two new sorangicin derivatives, are conserved within taxonomic lineages. New described families thus bear high biosynthetic potential underpinning the importance of precise taxonomic classification guiding targeted drug discovery.

microbiology↗

bioRxiv2026

LoReMINE: Long Read-based Microbial genome mining pipeline

Microbial natural products represent a chemically diverse repertoire of small molecules with major pharmaceutical potential. Despite the increasing availability of microbial genome sequences, large-scale natural product discovery remains challenging because the existing genome mining approaches lack integrated workflows for rapid dereplication of known compounds and prioritization of novel candidates, forcing researchers to rely on multiple tools that requires extensive manual curation and expert intervention at each step. To address these limitations, we introduce LoReMINE (Long Read-based Microbial genome mining pipeline), a fully automated end-to-end pipeline that generates high-quality assemblies, performs taxonomic classification, predicts biosynthetic gene clusters (BGCs) responsible for biosynthesis of natural products, and clusters them into gene cluster families (GCFs) directly from long-read sequencing data. By integrating state-of-the-art tools into a seamless pipeline, LoReMINE enables scalable, reproducible, and comprehensive genome mining across diverse microbial taxa. The pipeline is openly available at https://github.com/kalininalab/LoReMINE and can be installed via Conda (https://anaconda.org/kalininalab/loremine), facilitating broad adoption by the natural product research community. Author summaryFor decades, microbial natural products have been a major source of medicines, with most of the clinically used antibiotics being their derivatives. Recent advances in DNA sequencing technologies now allow the reconstruction of more complete and continuous microbial genomes, revealing a vast and largely untapped diversity of biosynthetic gene clusters responsible for natural product biosynthesis. Despite these advances, large-scale natural product discovery remains difficult because current genome mining approaches rely on many separate tools and lack an integrated workflow to dereplicate known compounds and prioritize novel biosynthetic pathways. To address these limitations, we introduce LoReMINE, an automated pipeline designed to simplify microbial genome mining directly from long-read sequencing data. LoReMINE integrates genome assembly, taxonomic classification, identification of biosynthetic gene clusters, and their clustering into gene cluster families within a single, reproducible workflow. This streamlined approach enables scalable analysis across diverse microbial taxa and facilitates comprehensive exploration of microbial biosynthetic potential. The pipeline is designed for both experimental and computational researchers, helping to advance natural product research and contribute towards the discovery of new therapeutic drugs.

bioinformatics↗

bioRxiv2023

Machine learning and phylogenetic analysis allow for predicting antibiotic resistance in M. tuberculosis

Antimicrobial resistance (AMR) poses a significant global health threat, and an accurate prediction of bacterial resistance patterns is critical for effective treatment and control strategies. In recent years, machine learning (ML) approaches have emerged as powerful tools for analyzing large-scale bacterial AMR data. However, ML methods often ignore evolutionary relationships among bacterial strains, which can greatly impact performance of the ML methods, especially if resistance-associated features are attempted to be detected. Genomewide association studies (GWAS) methods like linear mixed models accounts for the evolutionary relationships in bacteria, but they uncover only highly significant variants which have already been reported in literature. In this work, we introduce a novel phylogeny-related parallelism score (PRPS), which measures whether a certain feature is correlated with the population structure of a set of samples. We demonstrate that PRPS can be used, in combination with SVM- and random forest-based models, to reduce the number of features in the analysis, while simultaneously increasing models performance. We applied our pipeline to publicly available AMR data from PATRIC database for Mycobacterium tuberculosis against six common antibiotics. Using our pipeline, we re-discovered known resistance-associated mutations as well as new candidate mutations which can be related to resistance and not previously reported in the literature.

bioinformatics↗

bioRxiv2021

Genes identified in rodent studies of alcohol intake are enriched for heritability of human substance use

BackgroundRodent paradigms and human genome-wide association studies (GWASs) on drug use have the potential to provide biological insight into the pathophysiology of addiction. MethodsUsing GeneWeaver, we created rodent alcohol and nicotine gene-sets derived from 19 gene expression studies on alcohol and nicotine outcomes. We partitioned the SNP-heritability of these gene-sets using four large human GWASs: 1) alcoholic drinks per week, 2) problematic alcohol use, 3) cigarettes per day and 4) smoking cessation. We benchmarked our findings with curated human alcoholism and nicotine addiction gene-sets and performed specificity analyses using other rodent gene-sets (e.g., locomotor behavior) and other human GWASs (e.g., height). ResultsThe rodent alcohol gene-set was enriched for heritability of drinks per week, cigarettes per day, and smoking cessation, but not problematic alcohol use. However, the rodent nicotine gene-set was not significantly associated with any of these traits. Both rodent gene-sets showed enrichment for several non-substance use GWASs, and the extent of this relationship tended to increase as a function of trait heritability. In general, larger gene-sets demonstrated more significant enrichment. Finally, when evaluating human traits with similar heritabilities, both rodent gene-sets showed greater enrichment for substance use traits. ConclusionOur results suggest that rodent gene expression studies can help to identify genes that capture heritability of substance use traits in humans, yet the specificity to human substance use was less than expected due to various factors such as the genetic architecture of a trait. We outline various limitations, interpretations and considerations for future research.

genetics↗

bioRxiv2019

Genetic variation in parental effects contribute to the evolutionary potential of prey responses to predation risk

Despite the ubiquity of parental effects and their potential impact on evolutionary dynamics, their contribution to the evolution of ecologically relevant adaptations remains poorly understood. Using quantitative genetics, here we demonstrate that parental effects contribute substantially to the evolutionary potential of larval antipredator responses in a leaf beetle (Leptinotarsa decemlineata). Previous research showed that larger L. decemlineata larvae elicit stronger antipredator responses, and mothers perceiving predators improved offspring responses by increasing intraclutch cannibalism -an extreme form of offspring provisioning. We now report substantial additive genetic variation underlying maternal ability to induce intraclutch cannibalism, indicating the potential of this adaptive maternal effect to evolve by natural selection. We also show that paternal size, a heritable trait, impacted larval responses to predation risk, but that larval responses themselves had little additive genetic variation. Together, these results demonstrate how larval responses to predation risk can evolve via two types of parental effects, both of which provide indirect sources of genetic variation for offspring traits.

evolutionary biology↗