Biology subjects

Mathew, B.

Publications and source records attributed to Mathew, B..

At least 19 recordsLinked to original sources

bioRxiv2026

aaRSID, an engineered pyrrolysyl-tRNA synthetase platform for multi-probe proximity proteomics

Proximity labeling (PL) methods utilize spatially targeted chemical or enzymatic generation of a diffusible, reactive intermediate to covalently tag neighboring proteins in living systems. Unlike other tools for studying molecular interactions, PL can detect transient protein relationships with high spatial and temporal sensitivity, allowing for insight into their roles in biological processes. However, current enzymatic PL tools, such as TurboID and APEX2, are limited by their substrate structure and chemistry, which can generate significant background and/or perturb cellular physiology. To address these limitations, we have developed aminoacyl-tRNA synthetase ID (aaRSID), a PL tool that leverages an engineered pyrrolysyl tRNA synthetase (PylRS) for proximity labeling of proteins. We chose PylRS because it can catalyze promiscuous lysine labeling in the absence of its cognate tRNA and utilize a variety of non-canonical amino acids (ncAAs) as substrates. Here, we demonstrate aaRSID's intrinsic proximity labeling activity, use directed evolution to improve this activity, and apply the improved mutant (aaRSID-Ma1.3) for subcellular proteomics and multiplexed imaging. Our work establishes aminoacyl-tRNA synthetases as a new PL enzyme class and introduces a versatile chemical platform for developing ncAA-derived probes to map cellular microenvironments, greatly expanding the applications possible of PL technology.

biochemistry↗

bioRxiv2026

A multitask single-cell analysis framework with knowledge graph as a prior

Single-cell RNA sequencing has reshaped cancer biology by revealing the cellular and molecular diversity of the tumor microenvironment, yet most analysis pipelines still treat clustering, annotation, pathway scoring, metabolic inference, and cell-cell communication as separate tasks. Here, we present scKNIFE (Knowledge graph-based NMF for Inference of Functional Entities), a unified graph-regularized non-negative matrix factorization framework that jointly reconstructs single-cell expression profiles and infers activities of diverse biological entities, including pathways, cell-type markers, metabolic reactions and tasks, ligand-receptor interactions, and cell-state programs. By embedding a heterogeneous knowledge graph directly into the factorization objective, scKNIFE propagates information between biologically related entities through Laplacian regularization while sparsity constraints preserve interpretability. The integrated prior graph spans 47,274 nodes and 506,620 edges, assembled from complementary resources including Gene Ontology, Reactome, Hallmark gene sets, Human-GEM, CellChat, PanglaoDB, Cytopus, and Tabula Muris-derived annotations. Across multiple cancer-focused single-cell datasets, scKNIFE yields competitive to leading performance for clustering and cell-type annotation, while also recovering biologically coherent metabolic programs that agree with dedicated metabolism-focused methods and capture therapy-response-associated states. In addition, the framework supports downstream inference of cell-type-specific biological activities from a single latent representation. Together, these results establish scKNIFE as a modular and extensible framework for end-to-end biological interpretation of single-cell cancer transcriptomics

cancer biology↗

bioRxiv2026

Deterministic retrieval recovers biomedical associations lost by language models

Biomedical research increasingly relies on expert-curated databases to connect diseases, genes, variants, phenotypes, pathways and therapeutics. Incomplete or irreproducible retrieval can distort interpretation, mechanistic inference, variant assessment or therapeutic prioritization despite an unchanged evidence base. Agentic natural-language-to-SQL (NL2SQL) systems and Model Context Protocol-enabled agents can autonomously retrieve evidence from biomedical databases, including Open Targets and the Highly Confident Drug-Target Database, in response to natural-language questions. However, they may truncate large result sets, miss records when terminology differs from database vocabulary and return different outputs across runs. We systematically quantify these failures and introduce BioChirp, a goal-directed autonomous system for reliable retrieval from structured biomedical databases, built on interpretation-execution separation. A coordinated language-model layer interprets intent and resolves database-field assignments, after which entity resolution maps user terms to database terminology. A deterministic Steiner-tree planner and executor then retrieve matching records without further language-model involvement. Across curated biomedical databases spanning more than one million associations, BioChirp retrieved thousands of records for exhaustive queries, whereas baseline agents returned at most a few hundred or failed. Across 70 queries, five runs and three BioChirp database backends, BioChirp achieved a median cross-run Jaccard similarity of 1.0. Across 910 database-question evaluations derived from expert-written BioASQ questions and spanning ten databases, BioChirp retrieved at least one database record in 85.4% of cases and produced a fully correct answer in 51.1% of cases, compared with 44.6% and 17.4% for agentic NL2SQL. BioChirp is publicly available at https://biochirp.iiitd.edu.in.

bioinformatics↗

bioRxiv2026

Cell type-specific proximity labeling of organ secretomes reveals energy balance-dependent proteomic remodeling.

Intercellular communication is critical for maintaining organismal metabolic homeostasis. Here, we present a new method enabling temporally controlled, cell type-specific labeling of secreted and membrane proteins in key metabolic tissues. The method employs a genetically encoded proximity-labeling strategy by targeting a Cre-dependent TurboID ligase to the endoplasmic reticulum (ER) in ES cell-derived mice. Expression of TurboID in liver, adipose tissue, and spleen enabled the characterization of organ-specific ER proteomes at baseline and in response to fasting, inflammation, and dietary obesity, revealing tissue-and perturbation-specific changes and augmenting our understanding of how the proteomes of individual tissues change to regulate systemic energy balance. This comprehensive resource represents an important advance toward understanding both how cell-to-cell communication changes in response to energy homeostasis and how it contributes to these alterations. This method is broadly applicable and provides a means for identifying biomarkers and therapeutic targets across a wide range of tissues.

physiology↗

bioRxiv2025

Mechanism-aware inference of response to targeted cancer therapies

Targeted therapies like small-molecule inhibitors often work by blocking proteins that cancer cells rely on for survival. Omics based modeling of drug sensitivity alone lack mechanistic grounding. We propose FORGE (Factorization Of Response and Gene Essentiality) a simple yet powerful joint matrix factorization framework that co-models drug response and target gene essentiality, enabling the stratification of promising treatment groups for targeted therapy consideration. FORGE also provides Benefit Score -- a predictive score that estimates treatment efficacy from basal gene expression profiles. We validated the predictive performance of FORGE across multiple targeted therapies, including Erlotinib (EGFR inhibitor) and Daporinad (NAMPT inhibitor). Our meta-analysis of large scale in-vitro studies underscores FORGEs ability to identify common determinants of drug vulnerabilities and target gene essentiality. Such convergences were not observed when treatment vulnerabilities and gene essentialities were modeled independently. We also demonstrated the universality of Erlotinib Benefit Scores by transferring transformations learned from high-throughput drug response studies across other published datasets, including the TAHOE-100M single-cell perturbation atlas and patient-derived xenograft studies. FORGE successfully identified key regulators within the molecular pathways targeted by these therapies, reinforcing its potential for mechanistically grounded treatment stratification.

cancer biology↗

bioRxiv2025

Microbial Scl1 Activates TGFBR1 receptor kinase signalling to Drive Fibrosis-Inflammation Axis in Alcohol-Associated Liver Disease

Background and AimsAlcohol consumption alters gut microbiota, which can affect metabolism, immune regulation, and signalling pathways and lead to alcohol-associated liver disease (ALD). We investigated how alcohol-associated gut microbiota (AGMs) modulate liver kinome signalling to drive inflammation and fibrosis in ALD. MethodLiver kinome and metaproteome changes were studied in rats (n=6/group) colonized with stool of severe alcohol-related hepatitis (SAH) patients (SAH[->]healthy-rats) or healthy human donors (HD[->]ALD-rats). AGM-associated liver kinome changes were cross-correlated with bacterial genera. A bacterial protein; Streptococcal collagen-like protein 1 (Scl1) was identified with affinity for TGF{beta}R1. This was validated by molecular docking and immunoprecipitation-LCMS assay. Expression of Streptococcus pneumonia and pyogenes was done in stool of SAH patients (n=10). Also, Scl1 was quantified in patient stool (n=24) and liver tissue (n=19) samples. Validation was performed by assessing Scl1 level in plasma and Streptococcus level in stool samples before and after fecal microbiota transplantation (FMT) in SAH patients. ResultsStool metaproteomics showed significant increase in 10 bacterial genera (Streptococcus, Staphylococcus, and Clostridium) in SAH[->]Healthy-rats, mirroring changes seen in ALD-rats (FC>1.5, p<0.05). Clusters of Orthologous Groups analysis indicated increased post-translational modifications (PTMs) and decreased lipid metabolism in ALD-rats and SAH[->]Healthy-rats. Liver kinome profiling showed 85 upregulated kinases in SAH[->]Healthy-rats, with 34 overlapping with ALD-rats, associated with inflammation (Mapk14, Map3k10 and others), fibrosis (Tgfbr1, Igf1r, and others), lipid metabolism (Cdk14, Cdk18) and regeneration (Met, Bmpr1a and others). FMT from healthy human-donors to ALD-rats reversed the expression of Streptococcus (10-fold), Staphylococcus (3-fold), and Clostridium (5-fold), along with 18 kinases associated with inflammation (Btk, Camk4, Nuak1) and fibrosis (Tgfbr1, Col4a1, Fgfr2 and others). Strong association (r{superscript 2}>0.9, p<0.05) was seen between Streptococcus abundance and fibrosis and inflammation-related kinases. Level of Scl1 was significantly high in SAH and ALD-rat stool samples (FC>2, p<0.05). It showed strong affinity for TGF{beta}R1, as validated by molecular docking (>86%confidence) and immunoprecipitation assays (>100FC,p<0.05), indicating it as a potential ligand for TGF{beta}R1. Concordantly, expression of Scl1 was highest amongst all the known ligands for TGF{beta}R1 (p<0.05) suggesting Scl1 is a major contributor for TGF{beta}R1 activation and its downstream signalling in these patients. Following fecal microbiota transplantation, expression of Scl1 in plasma and Streptococcus levels in stool were significantly reduced by 2.1-folds and 1.9 folds respectively in SAH patients. Further, Scl1 levels SAH plasma is capable of predicting poor therapeutic response and 30-day mortality with high accuracy (AUC=0.92, cutoff >70 normalized abundance). ConclusionAlcohol induced gut dysbiosis alters the liver kinome, promoting inflammation, fibrosis, and lipid dysregulation. Streptococcal Scl1 is identified as a bacterial protein mimicking human collagen and capable of activating fibrotic signalling pathway through TGF{beta}R1in liver and is capable of predicting poor response in SAH. FMT from healthy donor restores microbial imbalance and harmful microbial-host interactions.

microbiology↗

bioRxiv2025

Liver Kinome Profiling Identifies PS1145 as a Potential Therapeutic molecule for amelioration of Systemic Inflammation in Alcohol-related Liver Disease

Background and aimsAlcohol-related liver disease (ALD) has high mortality due to systemic inflammation. We analysed liver and monocyte kinome profiles in a chronic ethanol-fed pre-clinical rat model to identify therapeutic targets that could mitigate inflammation in ALD. MethodKinome profile was performed in liver and circulating monocytes at baseline, and after 8,12,16,20 and 24-weeks of 40% ethanol administration. Pathway-specific inhibitors, including PS1145 (IKK-phosphorylation inhibitor), PH-797804 (MAPK14 inhibitor), resveratrol and prednisolone were tested for their anti-inflammatory effects in ALD-rats, PBMC from patients and NIAAA mouse model. ResultsKinome profiling identified 497 liver and 345 monocyte kinases in ALD rats (FDR<0.01). A time-dependent increase in MAPK14-associated kinases was observed in both tissues (FC>1.5, p<0.05). By 24 weeks, 172 liver and 48 monocyte kinases were significantly upregulated, particularly those linked to MyD88-TLR4, PI3K-Akt, TNF, TGF{beta}, and cellular senescence pathways. Key contributors included TGF{beta}R1, ROS-generating kinases, and IL-1-driven MAPK14 and IKK phosphorylation. Targeting this axis, PS1145 (an IKK inhibitor) suppressed NF{kappa}B activation and inflammation in THP1 and HepG2 cells, as well as PBMCs from healthy and SAH patients, outperforming PH797804, resveratrol, and prednisolone (p<0.05, FC>1.5). PS1145 significantly reduced IL-6, TNF, and NF{kappa}B, while increasing IL-10. In vivo, PS1145 treatment in the NIAAA mouse model markedly reduced hepatic steatosis, cellular stress and inflammatory pathways (cytokine signalling, IL-36 signalling and others) more effectively than standard therapies, Notably, it also downregulated IL36R, impairing TLR receptor dimerization, suggesting a dual mechanism of action (p<0.05) highlighting its therapeutic potential in ameliorating systemic inflammation in ALD. ConclusionOur study highlights the key role of MAPK14 and MYD88-TLR4 pathway kinases in driving systemic inflammation in SAH. The IKK inhibitor PS1145, by blocking both IKK phosphorylation and TLR dimerization, effectively suppresses inflammatory signalling and improves liver pathology, positioning it as a promising targeted therapy for ALD.

pharmacology and toxicology↗

bioRxiv2025

Metabolomic Profiling of Ashwagandha (Withania somnifera): Linking Bioactive Compounds to Functional Therapeutic Roles

IntroductionWithania somnifera (Ashwagandha) is a widely used medicinal plant with established roles in traditional systems of medicine. Known for diverse pharmacological effects, including neuroprotection, hepatoprotection, enhancement of male fertility, and anti-inflammatory activity. Despite its longstanding use, a deeper understanding of its phytochemical composition and bioactive mechanisms is required to bridge traditional knowledge with modern therapeutic applications. MethodsA comprehensive metabolomics profiling of Withania somnifera was performed to identify and quantify its bioactive secondary metabolites. Focus was placed on major classes such as alkaloids, phenolic compounds, and terpenoids. Known compounds were annotated and categorized based on their abundance, chemical class, and reported biological functions. Particular attention was given to compounds implicated in neuroprotection, liver health, reproductive support, and inflammation-modulation. ResultsThe analysis revealed a rich but low-abundance of known metabolites. Alkaloids such as Gentianaine (8.44%) and Sauroxine (2.94%), and phenolics like Mulberrofuran A (14.58%) and Sulphuretin (9.61%), were among the most prominent. These compounds are associated with antioxidative, anti-inflammatory, and neuroregenerative activities. Terpenoids including Pinocarvone and Ergosterol contributed to hepatoprotective and anti-inflammatory properties. Compounds such as Gomphrenin-I, Sitosterol, Pfaffic acid, beta-Carotene and others (>3%) supported spermatogenesis, while Embelin and Kievitone enhanced oxidative stress response. Although most identified compounds were individually present in low percentages (<0.30%), their collective activity suggests synergistic effects. ConclusionMetabolomic analysis of Withania somnifera confirms high abundance of bioactive secondary metabolites that have hepatoprotective, neuroprotective, anti-oxidant and spermatogenesis-enhancing effects. These findings justify protective and beneficial effect of Withania in promoting stress relief, energy, and overall wellness. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=104 SRC="FIGDIR/small/659832v1_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@1ae8e5forg.highwire.dtl.DTLVardef@105300corg.highwire.dtl.DTLVardef@1958320org.highwire.dtl.DTLVardef@d3ba13_HPS_FORMAT_FIGEXP M_FIG C_FIG

pharmacology and toxicology↗

bioRxiv2025

Chenodeoxycholic Acid Modulation via Bacteroides intestinalis AM1 underscores a Novel Approach in Acute Liver Failure

BackgroundAcute liver failure (ALF) is associated with rapid and progressive hepatocellular injury, and severe metabolic-microbial derangements. We investigated early metabolic markers of non-survival, and a potential microbial intervention using Bacteroides intestinalis-AM1, to improve outcomes in ALF. MethodPlasma metabolomics and meta-proteomics were performed in 40 ALF patients and 5 healthy controls (training cohort). A non-survival marker panel was identified and validated in 270 ALF patients (test cohort) using high resolution mass spectrometry and machine learning. It was functionally validated in acetaminophen-induced ALF mouse model. B. intestinalis-AM1 was used to study alteration of gut bacteria and amelioration of liver injury. ResultsALF non-survivors showed a distinct metabolomic signature with elevated primary bile acids {chenodeoxycholic acid (CDCA), cholic acid (CA)}, tryptophan, tyrosine, and enrichment of pathways linked to inflammation, cell death, and stress response (p<0.01, FDR<0.01, FC>1.5). Non-survivors had higher alpha/beta diversity (p<0.05) with increase in Proteobacteria, Firmicutes, Actinobacteria (p<0.05); functionally associated with energy, amino acid and xenobiotic metabolism (p<0.05). A gut microbiota derangement in converting primary to secondary bile acids was evident as CDCA and cytotoxic metabolites (4-(2-Amino phenyl)-2,4-dioxobutanoate, L-Tyrosine) were higher. Elevated CDCA (logFC>10) levels correlated with mortality in ALF patients as well as in mouse model. In the later, administration of B. intestinalis-AM1 bacteria, (10^9) reduced CDCA and CA levels by enhancing FXR, FGF15, SLC10A1 gene expression, attenuating inflammation (IL-1beta, TLR4-signalling), necroptosis, and modulating glutathione(oxidative-repair), tryptophan(inflammation), and histidine (tissue repair) metabolism. ConclusionHigh levels of chenodeoxycholic acid (CDCA) represent a poor prognostic indicator in ALF patients. B. intestinalis-AM1, a primary-to-secondary bile acid converter, effectively reduce CDCA levels, activated FXR, reduced inflammation and protected hepatocytes, highlighting its therapeutic potential in ALF.

microbiology↗

bioRxiv2024

Selective promotion of oligonucleotides in the course of evolution

Evolution shapes the structure and content of genomes, yet the contribution of local sequence composition to variant selection remains poorly understood. While traditional models emphasize protein function or cross-species conservation, we propose that intra-genomic patterns of oligonucleotide (k-mer) frequencies also reflect selective forces. To explore this, we developed kGain score, a metric that quantifies the frequency shift of a k-mer upon single-nucleotide substitution, using the surrounding genomic context as a baseline. We hypothesize that variants arising in high-kGain contexts are more likely to persist due to evolutionary favorability. We validated this hypothesis across multiple systems. In E. coli and S. cerevisiae long-term evolution experiments, we found that fixed, essential, and parallel mutations consistently show elevated kGain scores. This trend held in SARS-CoV-2 variants of concern and in an in-house antibiotic adaptation experiment, where a high-kGain fusA Y515N mutation conferred resistance and maintained fitness when overexpressed, demonstrating a causal link between kGain and adaptive potential. To enable cross-species generalization, we trained a transformer-based neural network regressor on LTEE-derived mutations to predict kGain from sequence alone. The model achieved high correlation in held-out in-domain data (Pearson r = 0.81) and accurately predicted kGain trends in out-of-domain data (Pearson r = 0.82), demonstrating that k-mer-based sequence constraints learned from one genome can be effectively transferred to others. Together, our results establish kGain as a biologically meaningful, scalable metric for probing within-genome sequence selection, offering a complementary lens to existing conservation-based frameworks for understanding evolutionary fitness and variant persistence.

evolutionary biology↗

bioRxiv2024

Elucidating the molecular mechanism of phytochemicals against Parkinson's disease through an integrated systems biology and molecular modeling approach

Withdrawal StatementThe authors have withdrawn their manuscript owing to the inability to complete the revisions or follow up on the manuscript at this time, due to personal circumstances. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author.

bioinformatics↗

bioRxiv2024

Mechanistic modeling of Mycobacterium tuberculosis β-carbonic anhydrase inhibitors using integrated systems biology and the QSAR approach

bioinformatics↗

bioRxiv2024

Elucidating Molecular Mechanism and Chemical Space of Chalcones through Knowledge Graph and Machine Learning: A Combined Bio-Cheminformatic Pipeline

We developed a bio-cheminformatics method, exploring disease inhibition mechanisms using machine learning-enhanced quantitative structure-activity relationship (ML-QSAR) models and knowledge-driven neural networks. ML-QSAR models were developed using molecular fingerprint descriptors and the Random Forest algorithm to explore the chemical spaces of Chalcones inhibitors against diverse disease properties, including antifungal, anti-inflammatory, anticancer, antimicrobial, and antiviral effects. We generated and validated robust machine learning-based bioactivity prediction models ((https://ashspred.streamlit.app/) for the top genes. These models underwent ROC and applicability domain analysis, followed by molecular docking studies to elucidate the molecular mechanisms of the molecules. Through comprehensive neural network analysis, crucial genes such as AKT1, HSP90A1, SRC, and STAT3 were identified. The PubChem fingerprint-based model revealed key descriptors: PubchemFP521 for AKT1, PubchemFP180 for SRC, PubchemFP633 for HSP90, and PubchemFP145 and PubchemFP338 for STAT3, consistently contributing to bioactivity across targets. Notably, chalcone derivatives demonstrated significant bioactivity against target genes, with compound RA1 displaying a predictive pIC50 value of 5.76 against HSP90A and strong binding affinities across other targets. Compounds RA5 to RA7 also exhibited high binding affinity scores comparable to or exceeding existing drugs. These findings emphasize the importance of knowledge-based neural network-based research for developing effective drugs against diverse disease properties. These interactions warrant further in vitro and in vivo investigations to elucidate their potential in rational drug design. The presented models provide valuable insights for inhibitor design and hold promise for drug development. Future research will prioritize investigating these molecules for mycobacterium tuberculosis, enhancing the comprehension of effectiveness in addressing infectious diseases.

bioinformatics↗

bioRxiv2024

Multimolecular feature-based machine learning: system biology enhanced RF-QSAR modeling for the efficient prediction of the inhibitory potential of diverse SARS CoV-2 3CL Protease inhibitors

bioinformatics↗

bioRxiv2024

Improved Python Package for DNA Sequence Encoding using Frequency Chaos Game Representation

SummaryFrequency Chaos Game Representation (FCGR), an extended version of Chaos Game Representation (CGR), emerges as a robust strategy for DNA sequence encoding. The core principle of the CGR algorithm involves mapping a onedimensional sequence representation into a higher-dimensional space, typically in the two-dimensional spatial domain. This paper introduces a use case wherein FCGR serves as a kmer frequency-based encoding method for motif classification using a publicly available dataset. Availability and implementationThe FCGR python package, use case, along with additional functionalities, is available in the GitHub. Our FCGR package demonstrates superior accuracy and computational efficiency compared to a leading R-based FCGR library [1], which is designed for versatile tasks, including proteins, letters, and amino acids with user-defined resolution. Nevertheless, it is important to note that our Python package is specifically designed for DNA sequence encoding, where the resolution is predetermined based on the kmer length. It is a drawback of our current package compared to the state-of-the-art R-based kaos package [1].

bioinformatics↗

bioRxiv2023

A mitochondrial quality control mechanism reverses the phagosome maturation arrest caused by Mycobacterium tuberculosis

Phagosome maturation arrest (PMA) imposed by Mycobacterium tuberculosis (Mtb) is a classic tool that helps Mtb evade macrophage anti-bacterial responses. The exclusion of RAB7, a small GTPase, from Mtb-phagosomes causes PMA. Here, we report an unexpected mechanism that triggers crosstalk between the mitochondrial quality control (MQC) and the phagosome maturation pathways that reverses the PMA. CRISPR-mediated p62/SQSTM1 depletion (p62KD) does not appear to impact mitochondrial quality. The p62KD cells are restrictive to Mtb growth, triggered by an increasingly oxidative environment and increased lysosomal targeting. The lysosomal targeting of Mtb is facilitated by enhanced TOM20+ mitochondria-derived vesicles (MDVs) biogenesis, a key MQC mechanism. In p62KD cells, TOM20+-MDVs biogenesis is MIRO1/MIRO2-dependent and gets delivered to lysosomes for degradation in a RAB7-dependent manner. Upon infection in p62KD cells, TOM20+-MDVs get extensively targeted to Mtb-phagosomes, inadvertently facilitating RAB7 recruitment, PMA reversal and lysosomal targeting of Mtb; the phenotype also replicated in p62/SQSTM1 knockout cells. Triggering MQC collapse in p62KDcells further diminishes Mtb survival, signifying cooperation between redox- and lysosome-mediated mechanisms. The MQC-anti-bacterial pathway crosstalk could be exploited for host-directed anti-tuberculosis therapies.

microbiology↗

bioRxiv2023

Antibacterial activity of Xenopsylla cheopis attacins against Yersinia pestis

Antimicrobial peptide resistance has been proposed to play a major role in the flea-borne transmission of Yersinia pestis. However, the antimicrobial peptide response in fleas and their interaction with Y. pestis is largely unknown. Attacins are one of the most abundantly expressed antimicrobial peptides within the first hours after Y. pestis infection of Xenopsylla cheopis, a major vector of plague. In this study, we report the cloning, expression, and purification of two X. cheopis attacin peptides and describe their interactions with and antimicrobial activities against Y. pestis. These flea attacins were shown to bind lipopolysaccharides and have potent activity against Y. pestis, however the mechanism of killing does not involve extensive membrane damage. Treatment with attacins rapidly inhibits Y. pestis colony formation and results in oxidative stress, yet live-cell imaging revealed that bacteria continue to grow and divide for several hours in the presence of attacins before undergoing morphological changes and subsequent lysis. This data provides insights into an early battle between vector and pathogen that may impact transmission of one of the most virulent diseases known to man.

microbiology↗

bioRxiv2023

Enterobacterial common antigen biosynthesis in Yersinia pestis is tied to antimicrobial peptide resistance

Resistance to antimicrobial peptides (AMPs) plays an important role in allowing Yersinia pestis to maintain a successful infection in the flea vector Xenopsylla cheopis. Mutants that are unable to modify lipid A in their outer membrane with aminoarabinose (Ara4N), showed increased sensitivity to AMPs such as polymyxin B (PB), as well as decreased survival in fleas. A deletion mutant of wecE, a gene involved in biosynthesis of enterobacterial common antigen (ECA), also displayed hypersusceptibility to PB in vitro. Additional mutants in the ECA biosynthetic pathway were generated, some designed to cause accumulation of intermediate products that sequester undecaprenyl phosphate (Und-P), a lipid carrier that is also used in numerous other pathways, including for peptidoglycan, O-antigen, and Ara4N biosynthesis. Mutants that accumulate Und-PP-linked intermediates (ECA-lipid II) showed increased susceptibility to PB, reduced Ara4N-modified lipid A, altered cell morphology, and decreased ability to maintain flea infections. These effects are consistent with a model where Y. pestis has a sufficiently limited free Und-P pool such that sequestration of Und-P as ECA-lipid II prevents adequate Ara4N biosynthesis, ultimately resulting in AMP hypersusceptibility.

microbiology↗