Biology subjects

Goswami, A.

Publications and source records attributed to Goswami, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Adaptive siderophore repression and redox-metabolic remodeling limit E. coli Fenton-mediated cytotoxicity after H2O2 exposure

Hydrogen peroxide imposes linked redox and metal stress on bacteria by promoting ferrous iron-dependent Fenton chemistry, generating reactive species that damage DNA, proteins, iron-sulfur cofactors, and membranes. Because siderophores expand iron acquisition, their continued production during peroxide exposure may increase cytotoxicity by enlarging the labile iron pool. Here, we identify an adaptive iron-restriction response that connects siderophore repression, envelope integrity, and metabolic remodeling during bacterial peroxide stress. Using Acinetobacter baumannii, Escherichia coli, Pseudomonas aeruginosa, and Staphylococcus aureus, we evaluated cellular oxidation kinetics, growth-normalized extracellular siderophore release, membrane permeabilization, and viability following sub-inhibitory H2O2 exposure. Acute peroxide treatment significantly elevated general fluorophore oxidation across all species (p < 0.0001), while prolonged exposure reduced biomass-normalized siderophore output, most strongly in E. coli and S. aureus. Membrane permeability accounted for a substantial fraction of extracellular siderophore variation, indicating that envelope alterations link peroxide-induced damage to iron-acquisition dynamics. We further integrated these physical phenotypes with differential transcriptomics, oxidation gene expression regression, E-Flux-constrained genome-scale metabolic modeling, flux variability analysis, shadow price analysis, and in silico gene-essentiality mapping in acute and 500-generation H2O2-adapted E. coli. Acute stress transiently co-activated iron uptake, efflux, NADPH regeneration, and stress-defense genes. In contrast, long-term adaptation systematically repressed enterobactin biosynthesis and uptake components (fepA, entE, fes, ryhB) while inducing persistent peroxide- and acid-resistance modules (ahpC, gadA, gadB). Genome-scale metabolic modeling independently verified that this evolutionary adaptation contracted flux variability, relieved envelope bottlenecks, and eliminated acute stress-specific single-gene vulnerabilities at the cost of reduced maximal growth capacity. Together, these results define siderophore repression as a system-level adaptive response that limits Fenton-reactive iron influx while reallocating metabolic resources toward redox buffering, membrane stabilization, and genetic robustness.

microbiology↗

bioRxiv2026

Multi-Omics Integration Predicts Cell-Specific Gene Regulatory Response and Rhizosphere Dynamics in Maize Root Fertilizer Treatment

Improving nitrogen use efficiency in maize (Zea mays) requires understanding how distinct root cell types and regulatory networks process fertilizer inputs. Given the current limited understanding of fertilizer-induced, cell-type-resolved maize roots and regulatory networks, computational biology frameworks are needed to model and predict how nutrient inputs are translated into transcriptional responses. Here, we integrated fertilizer-induced maize root bulk RNA-seq with reference atlases of single-cell RNA-seq and scATAC-seq to construct and predict a cell-specific regulome of the maize root under inorganic and mixed amendments. We demonstrate that inorganic fertilization induced stress associated and management pathways. Regulome analysis identified transcription factors (TF) from the AP2/ERF, NAC, HSF, and WRKY superfamilies that were preferentially active across root tissues. Deconvolution of the regulome onto single-cell atlases predicted core TF activity to the vascular cylinder and pith across both regimes, while mature cortex regulatory programs diverged. Construction of a gene regulatory network revealed that shared TF:target edges maintained the same regulatory orientation across fertilizer regimes. However, a small number of stress related TFs, including WRKY24, DREB1A, and NAC61, underwent a directional change between fertilization treatments. In silico knockout analysis predicted the activation targets for six of the seven regulators in their resident vascular/pith tissues, indicating the network behaves as a coherent, perturbable system. Additionally, soil metagenomic analysis showed that host soil microbial functions overlap with differentially expressed genes (DEGs) in shared functional categories, linking host regulome dynamics to rhizosphere processes. These findings and predictions suggest that the maize root regulome is spatially organized and dynamically reprogrammed by master regulators, predicting high-priority candidate nodes for engineering improved nutrient use efficiency.

plant biology↗

bioRxiv2026

Insulin-degrading enzyme activity is modulated by interaction with pyrroline-5-carboxylate reductase 1

Insulin-degrading enzyme (IDE, insulysin, insulinase) is a peptidase that hydrolyzes a number of bioactive peptides including insulin and the amyloid beta peptide, making it a promising therapeutic target for diabetes and Alzheimers disease. Aspects of its physiological role are still controversial, however. In an attempt to further define IDEs role in cells, we used co-immunoprecipitation experiments to identify potential IDE interacting proteins. The enzyme pyrroline-5-carboxylate reductase 1 (PYCR1) was found associated with IDE in three different cell lines, and the two proteins colocalize in HeLa cells. Purified PYCR1 activates IDE toward small peptide substrates, suggesting a modulatory role for the interaction in vivo. Modeling suggests that the unstructured N-terminal region of PYCR1 inserts into allosteric sites of IDE, contributing to the observed activation. Deleting this sequence alters, but does eliminate, the interaction between PYCR1 and IDE. Since pyrroline-5-carboxylate reductase 1 is a mitochondrial protein, we posit that their interaction could regulate a previously described mitochondrial pool of IDE, which may serve to degrade mitochondrial targeting sequences or amyloid beta peptide that localizes to that organelle.

biochemistry↗

bioRxiv2026

Chronic Inorganic Fertilization Shifts Evolutionary Trajectories and Induces a Regulatory Shield in Maize Rhizosphere Bacteria

Long-term agricultural fertilization is a major driver of structural shifts in the soil microbiome, yet traditional metagenomic analyses often fail to capture the population-level evolutionary dynamics and heritable genetic adaptations that dictate species survival in the rhizosphere. Here, we built and utilize a high-resolution, gene-centric population-genomic framework to resolve the microdiversity and selective landscapes shaping maize rhizosphere bacteria across a chronic management gradient including Inorganic NPK fertilization, Organic compost amendment, and an Unfertilized Control. We observed a distinct decoupling between total mutational volume and adaptive fixation rates. Inorganic fertilization acts as a massive genotoxic hotspot generating 5,544 variants but maintaining a baseline intra-population variants consensus rate of 37.3% due to intense purifying selection. Whereas organic management restricts global mutational volume but drives relatively high rate of 49.6%, signaling a highly coordinated, directional selection where transposable elements function as bridges for structural adaptations. Crucially, we uncover that regulatory shield mechanism that protects the core genome from deleterious mutations while core metabolic and structural genes exhibit absolute sequence stasis. Additionally, we report that mutations were precisely accumulated within the immediate 0-200 bp promoter region of key stress-defense and metabolic genes. This targeted regulatory remodeling is chemically fingerprinted by a depressed transition-to-transversion ratio and structurally supported by Mobile Fortress model that significantly expands the repository of Highly Conserved Mobile Loci sequestered on cryptic circular plasmids. Together, these evolutionary trajectories indicate that long-term inorganic fertilization forces rhizosphere bacteria to prioritize cellular repair and resource hoarding over host interactions. By demonstrating how high-input management shifts selection from cooperative pathways toward internal defense, these findings provide a population-genomic explanation for the suppression of biological nutrient-use efficiency observed in intensive agroecosystems.

ecology↗

bioRxiv2026

A probabilistic and phylogenetic principal component analysis for modelling high-dimensional trait evolution

Given the ever-increasing availability of highly detailed phenotypes, modelling trait evolution in a multivariate framework is becoming a challenging task. Current phylogenetic comparative methods often struggle with high-dimensional datasets because they suffer computational limitations and interpretability. Here, we propose a maximum likelihood-based approach called Probabilistic and Phylogenetic Principal Components Analysis (P3CA) to circumvent current limitations. This approach is based on a continuous latent variable model, whereby observed traits are explained by a smaller number of unobserved variables that evolve according to a given evolutionary model. We implement the approach under Pagels lambda model using an Expectation-Maximisation algorithm that makes it computationally efficient and allows missing values. Using simulations, we demonstrate that evolutionary parameters are accurately estimated, regardless of phylogenetic signal, the number of traits or the proportion of missing values. The reconstruction of the reduced space is more accurate than the one obtained using other dimensionality reduction approaches, such as phylogenetic and conventional PCA. Likewise, the estimated values for missing data are more accurate than those obtained using current phylogenetic data imputation approaches. We illustrate the approach on a 3D geometric morphometric dataset describing Crocodyliformes skull shapes and containing around 4% of missing data. Our P3CA method unlocks the possibility to analyse and more easily interpret the large-scale multivariate datasets generated in recent decades within a phylogenetic comparative framework.

evolutionary biology↗

bioRxiv2026

Extreme disparity in the appendicular skeleton of domestic dogs (Canis familiaris)

Domestic dogs (Canis familiaris) display more morphological variation than any other mammal. Cranial morphology has been extensively studied, as have the relationships with function, development, genetics, veterinary medicine, and breed welfare. Postcrania remain comparatively understudied, despite well-documented breed-specific predispositions to musculoskeletal disease. Here, we apply three-dimensional landmark-free morphometrics to quantify the shape of 743 elements from 213 dogs, including the scapula, humerus, radius, ulna, pelvic girdle, femur, tibia, and fibula. We assess integration among limb elements and investigate drivers of shape variation within and between breeds. Across most breeds, limb bone shape is strikingly similar. Dachshunds, however, exhibit distinct morphology across all elements and one to two orders of magnitude greater variation than any other breed. Despite this disparity, integration remains high between all element pairs. Remarkably, we find no significant relationship between bone shape and body mass, age, or pathology, but comparison with historic specimens reveals marked changes in dachshund long bone shape over the past [~]150 years. These extreme differences are not shared by other sampled chondrodysplastic breeds, underscoring the need to understand morphological diversity beyond simple categorisation. These findings provide a quantitative framework for linking postcranial morphology with function, disease risk, and evidence-based improvements to canine welfare.

zoology↗

bioRxiv2026

An Empirical Bayes approach for the study of phenotypic evolution from high-dimensional data

Multivariate phylogenetic comparative methods for modelling high-dimensional traits such as 3D shapes or gene expression proBiles have been recently developed. However, these approaches are impractical and almost impossible to use when the number of traits exceeds a few thousands, as they become computationally prohibitive. We overcome these limitations by proposing a new maximum likelihood approach based on the Empirical Bayes framework. This approach takes into account the information of the complete covariances (among species and traits) to infer parameters and compare models of trait evolution for high-dimensional datasets. Through simulations, we demonstrate that the proposed approach can accurately estimate parameters of various trait evolution models, even when the number of traits is ten times larger than the number of lineages; it requires less memory and is at least 10 times faster than currently available approaches. This fast, efBicient framework enabled us to extend the high-dimensional multivariate phylogenetic comparative toolkit by including an Ornstein-Uhlenbeck process with multiple optima to study adaptation to various selective regimes. Applying our approach to the evolution of jaw morphology in relation to dietary adaptation in mammals, we demonstrate morphological convergence in carnivorous and herbivorous lineages. The proposed Empirical Bayes framework, implemented in the R package mvMORPH, enables phylogenetic comparative methods to efBiciently handle high-dimensional datasets and complex models of trait evolution.

evolutionary biology↗

bioRxiv2026

Genomic-island cassette architecture drives pathogenic Enterococcus cecorum lineages: Cassette2Vec-EC, a structural genomics and machine-learning framework

Mobile genetic elements and genomic islands (GIs) frequently encode antibiotic resistance and host-adaptation cargo, yet routine genome comparison pipelines often miss the higher-order organization of how genes co-occur as transferable, GI-anchored modules. We present Cassette2Vec-EC, a structural genomics framework that converts annotated genomes into cassette units (local gene neighborhoods with GI context), encodes each cassette as a fixed-length feature vector, and applies genome-grouped machine learning to predict pathogenic lineages while preventing within-genome leakage. Using a curated Enterococcus cecorum cohort from poultry production systems, we integrate pangenome context, GI calls, mobility markers, and AMR/virulence annotations into cassette-level features and evaluate models strictly under GroupKFold-by-genome. Cassette2Vec-EC achieves strong genome-level generalization (AUROC 0.975 {+/-} 0.030, average precision 0.938 {+/-} 0.077, Brier score 0.056 {+/-} 0.058). When evaluated at the cassette unit level under the same genome-grouped protocol, performance remains high (AUROC 0.974 {+/-} 0.029, AP 0.919 {+/-} 0.093, Brier 0.057 {+/-} 0.057), supporting that cassette representations capture transferable signal rather than genome identity. Baselines show that GI burden alone can partially rank genomes but yields poorer calibration and limited interpretability. By combining comparative genomics with cassette-aware features and providing locus-level explanations (SHAP) that map predictions to specific GI-associated modules, Cassette2Vec-EC provides a practical blueprint for genomic-island-aware pathogen surveillance, junction-based diagnostics, and targeted monitoring of high-risk lineages. ImportancePathogenic Enterococcus cecorum lineages threaten poultry health and production, yet many genomic surveillance workflows reduce genomes to gene presence-absence lists and overlook how mobile elements are organized into transferable modules. Cassette2Vec-EC addresses this gap by representing genomic islands as GI-anchored cassette neighborhoods and encoding each cassette as a fixed-length feature vector linking cassette architecture to pathogenic risk. Under a strict genome-grouped evaluation protocol, the framework predicts pathogenic lineages while preventing within-genome leakage and supports calibrated probability estimates in the full model. Cassette level explanations (SHAP) localize predictive signal to specific GI-anchored modules, yielding tractable targets for surveillance prioritization and junction-based diagnostics. Although developed for poultry-associated E. cecorum, this cassette and GI-aware representation is transferable to other bacterial pathogens to strengthen genomic surveillance and help partners identify high-risk isolates before clinical disease emerges.

bioinformatics↗

bioRxiv2026

Decoupled Representation Learning Improves Generalization in CRISPR Off-Target Prediction

BackgroundComputational prediction of CRISPR-Cas9 off-target activity is essential for safe guide-RNA design, yet models trained on large proxy datasets often fail to generalize to experimentally validated sites. MethodsWe present a modular two-stage deep learning framework that separates sequence representation learning from off-target classification. In Stage 1, guide RNA sequences are encoded using frozen, pretrained DNABERT embeddings learned from large genomic corpora. In Stage 2, these embeddings are integrated with mismatch-level and pairwise sequence features within a hybrid CNN-Transformer classifier trained exclusively on a high-throughput proxy dataset. ResultsOn the external TrueOT benchmark, a curated collection of low-throughput, experimentally confirmed off-target sites, the full model achieved a mean ROC-AUC of 0.70 {+/-} 0.03 and a PR-AUC of 0.30 {+/-} 0.03, markedly surpassing the proxy-only baseline (ROC-AUC=0.64, PR-AUC=0.22). Ablation studies confirmed that the performance gain arises from the pretrained sequence representations rather than architectural complexity. ConclusionsDecoupling representation learning from downstream classification and leveraging frozen transformer-based embeddings substantially improves generalization to biologically relevant off-target predictions. The proposed framework provides a reproducible baseline for the assessment of CRISPR-Cas9 risk and underscores the importance of transfer learning in the integration of proxy test data and experimental results in the real-world.

bioinformatics↗

bioRxiv2025

Oxidative stress facilitates a diverse adaptive response in bacteria

The adaptive oxidative stress response allows bacteria to adapt to hostile environmental conditions. We explored how the evolution of cellular ROS in oxidative stress triggers physiological and genetic changes in multiple pathogenic species. Our study posits that growth-survival tradeoffs under oxidative stress enact a diverse array of genetic and physiological changes resulting in cross-resistance to acid, antimicrobial resistance, and metabolic reprogramming in bacteria.

microbiology↗

bioRxiv2025

A Mathematical Genomics Perspective on the Moonlighting Role of Glyceraldehyde-3-Phosphate Dehydrogenase (GAPDH)

Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) is a well-conserved enzyme across Archaea, Bacteria, and Eukarya, known not only for its canonical role in glycolysis, but also for diverse moonlighting functions including transcription regulation, host-pathogen interactions, and immune modulation. Studying GAPDH quantitatively is crucial for understanding how subtle variations at the sequence and structural levels drive such functional diversity across evolutionary lineages. In this study, 165 GAPDH protein sequences from 158 organisms were analyzed to uncover conserved and divergent features underlying multifunctionality. While core catalytic residues were strongly preserved, selective enrichment of small non-polar residues such as valine and alanine suggested a structural basis for flexibility and adaptive potential. The balanced distribution of order- and disorder-promoting residues and the avoidance of long homopolymeric stretches indicated evolutionary selection for both structural coherence and local flexibility. Spatial distribution of amino acids in GAPDH sequences revealed low fractal variance across sequences, with moderate differences in residue clustering patterns pointing to localized adaptations without compromising overall organization. These findings demonstrate that GAPDH multifunctionality is encoded through compositional signatures and conserved spatial architecture, allowing the coexistence of metabolic stability and regulatory plasticity. The results have broad implications for understanding protein evolution, structural adaptability in extreme environments, and functional versatility in pathogenic contexts. This study establishes GAPDH as a model for exploring principles of protein moonlighting and highlights the potential of quantitative compositional analysis in uncovering hidden functional layers.

genomics↗

bioRxiv2025

Rapid canalisation of mandible structure in Tetrapoda

Evolution of the mandibular structure has played a key role in the taxonomic diversification and niche exploration of tetrapods, from the initial water-to-land transition to the origin and radiation of mammals. While jaw evolution through these key transition points have long attracted attention, no studies of jaw evolution to date capture the breadth of tetrapod diversity and evolutionary history. Here, we reconstruct the compositional evolution of the tetrapod mandible with a comprehensive dataset of 36 traits scored in 3047 species. This sampling spans the earliest eotetrapodiforms to extant amphibians, mammals, and reptiles and birds, capturing immense ecological, developmental, and taxonomic diversity among a near-even split of extant and fossil tetrapods. We find that lower jaw morphology rapidly canalises at the base of each major clade, including, Eotetrapodiformes, Amniota, Synapsida, Sauropsida, and Amphibia, as well as subclades within those groups, and that, as a result, jaw compositional variation is highly phylogenetically structured. We estimate an average of over 100 shifts in three key traits: number of elements (dermal and endochondral bones), number of tooth-bearing elements, and total teeth per hemimandible, with symmetrical rates of gains and losses estimated for the number of elements, and correlated evolution between these three characters. Although novel elements arise occasionally, disparity in jaw composition overwhelmingly decreases as clades canalise rapidly, successively, and pervasively through tetrapod evolutionary history.

evolutionary biology↗

bioRxiv2025

Divergent morphologies with convergent performance in the mandible of pelagiarian fishes

Mandibles represent a key evolutionary innovation that enabled jawed vertebrates to adapt and diversify in response to a range of food sources. Using a phylogenetic comparative approach, we explore the phenotypic disparity and mechanical properties of the lower jaw in Pelagiaria, a morphologically diverse but relatively small clade of open-ocean fishes which are hypothesized to have radiated near the Cretaceous/Paleogene (K/Pg) mass extinction event. We found that body elongation and diet are not significantly correlated with jaw shape, but that habitat depth and tooth type are. Mechanical advantage (MA) is significantly correlated with mandible shape, with jaw-closing MA being most strongly correlated. Pelagiarian jaw shapes fall broadly into six morphotypes, of which two show significantly higher closing MA than other groups, despite differing substantially in shape. The high morphological disparity of pelagiarian mandible shape was established very early in their evolutionary history, and high levels of disparity have been maintained over tens of millions of years; this is consistent with the hypothesis that Pelagiaria represents an ancient adaptive radiation. Our results demonstrate both the mechanical and morphological diversity of the pelagiarian mandible and highlight the crucial role that morphological diversification has played in the trophic radiation of this clade.

evolutionary biology↗

bioRxiv2024

Introducing SPROUT (Semi-automated Parcellation of Region Outputs Using Thresholding): an adaptable computer vision tool to generate 3D segmentations

The segmentation of fine-grained and complex structures from volumetric data, such as 3D biomedical images, is a manually intensive process, with performance hindered by limited training data and the difficulty of adapting AI models for specialised datasets. Here, we introduce SPROUT, a user-friendly and interpretable segmentation framework that leverages domain-specific priors, enabling experts to translate their knowledge into reproducible, high-quality segmentations across diverse imaging modalities without the need for training data. Its adaptive design facilitates parameter transferability and improves generalisation across similar datasets. Implemented as scripts and a napari plugin, it supports interactive editing and scalable batch processing, lowering the technical barrier for domain experts. We applied SPROUT to datasets spanning different imaging modalities, anatomical complexities, postures, and target structures, producing high-quality segmentations across 2D and 3D tasks. Quantitative comparisons with other methods on a representative dataset showed SPROUT achieved results comparable to expert-corrected interpolation while requiring substantially less manual input. In scenarios where SPROUT achieved high-quality results, supervised models often struggled to reach similar accuracy, highlighting the challenge of deep learning methods in complex domains. We also explored integration with foundation models to accelerate segmentation in high-contrast datasets, illustrating potential for hybrid workflows.

evolutionary biology↗

bioRxiv2024

Landmark-based estimates of genomic disparity

Chromosomal architecture has played a key role in the evolution of biodiversity. Detecting structural variants (SVs) on chromosomes has informed the study of speciation, sex determination, adaptation, and some of the earliest divergences in the tree of life. Here we present a computationally non-intensive approach, based on geometric morphometrics, that uses conserved DNA sequences as landmarks to quantify structural disparities of focal chromosomes across multiple species, individuals, or cell types. Based on two approaches, we show that this geno-metric method can be applied at micro- and macroevolutionary scales to discover and diagnose SVs. Using human X-linked genes and ultraconserved elements as landmarks, we provide empirical demonstrations with amniote sex chromosomes, the Drosophila virilis group, and placental mammal genomes. Landmark-based structural disparity analysis effectively identifies chromosomal rearrangements and has parallels with traditional morphometrics regarding chromosome size, landmark orientation and landmark availability. Using simulations, we show that structural disparity inferred from ultraconserved elements is correlated with overall levels of chromosome evolution; an attribute which is consistent with observed disparity between and within mammalian orders. We found that the disparity patterns of SVs have significant phylogenetic signal, giving them broad importance for studying evolutionary biology. Structural disparity analyses are a valuable addition to the comparative genomic toolkit in that they offer an intuitive, rapid mechanism for detecting SVs associated with single copy genetic landmarks and the potential to reveal broader patterns of chromosome evolution related to expansions, contractions, rearrangements and phylogeny.

genomics↗

bioRxiv2024

AAV-mediated genome editing is influenced by the formation of R-loops

Recombinant adeno-associated viral vectors (rAAV) hold an intrinsic ability to stimulate homologous recombination (AAV-HR) and are the most used in clinical settings for in vivo gene therapy. However, rAAVs also integrate throughout the genome. Here, we describe DNA-RNA immunoprecipitation sequencing (DRIP-seq) in murine HEPA1-6 hepatoma cells and whole murine liver to establish the similarities and differences in genomic R-loop formation in a transformed cell line and intact tissue. We show enhanced AAV-HR in mice upon genetic and pharmacological upregulation of R-loops. Selecting the highly expressed Albumin gene as a model locus for genome editing in both in vitro and in vivo experiments showed that the R-loop prone, 3 end of Albumin was efficiently edited by AAV-HR, whereas the upstream R-loop- deficient region did not result in detectable vector integration. In addition, we found a positive correlation between previously reported off-target rAAV integration sites and R-loop enriched genomic regions. Thus, we conclude that high levels of R-loops, present in highly transcribed genes, promote rAAV vector genome integration. These findings may shed light on potential mechanisms for improving the safety and efficacy of genome editing by modulating R-loops and may enhance our ability to predict regions most susceptible to off-target insertional mutagenesis by rAAV vectors.

molecular biology↗

bioRxiv2024

Assessing the application of landmark-free morphometrics to macroevolutionary analyses

The study of phenotypic evolution has been transformed by methods allowing three-dimensional quantification of anatomical form. The present state-of-the-art 3D geometric morphometrics, which relies heavily on manual landmarking, is time-consuming, prone to operator bias, and cannot effectively compare disparate shapes. Emerging automated approaches, notably landmark-free techniques, offer promise but have only been applied to closely related species. Here, we compare landmark-free approaches with high-density geometric morphometric methods on 322 mammals across 180 families. Leveraging the benefits of Poisson meshes, which combine open and closed projections, we show how landmark-free methods have greater power to differentiate major taxonomic groups. Although there is coarse correspondence in shape variation between the two methods, the finer features of the landmark-free approach likely reflect its broader sampling of the surface structure. Our study underscores the robustness of landmark-free methods for large-scale comparative analysis and helps propel morphometrics into a new era of bigger data.

evolutionary biology↗

bioRxiv2024

Horizontally acquired HosA transcription factor bound with 4-hydroxy-benzoic acid exhibits unique tug-of-water dynamics

Pathogenic Escherichia coli (E. coli) causes serious illnesses in humans aided by several multiple antibiotic resistance regulator (MarR) transcription factors. Among these proteins, HosA provides support to these organisms by executing crucial roles in functions ranging from flagella motility to organic compound metabolism. The crystal structure of HosA from enteropathogenic E. coli is elucidated in this study along with conformational changes orchestrated by different amino acids due to para-hydroxy benzoic acid (PHB) binding in the hinge region. Structural analysis and extensive molecular dynamics simulation reveal role of a dynamic water molecule as a bridging entity in PHB bound structure which is not shown for any MarR structure yet. Also, it is shown that the HosA gene is transferred horizontally from Shigella to pathogenic E. coli, having 97.6% sequence similarity with an uncharacterized transcription factor from Shigella dysenteriae Sd197. This study may be promising to address several unanswered questions for the functioning of MarR transcription factors from infectious E. coli and design inhibitors to combat these pathogens.

biochemistry↗