Biology subjects

Kumar, R.

Publications and source records attributed to Kumar, R..

At least 19 recordsLinked to original sources

bioRxiv2026

Fatty Acids Synergize with Bacteriophages Against Multidrug-Resistant Pathogens

In the face of rising antimicrobial resistance (AMR), phage therapy presents a promising alternative to antibiotics, but efficacy is limited by biofilms, narrow host range, and hosts rapidly developing phage resistance. Here we present a new concept for phage-based antimicrobials: using small-molecules to help phages infect and kill bacteria. Using checkerboard synergy assays, growth curves, and biofilm assays, we evaluate short- and medium-chain fatty acids (SCFAs/MCFAs) with diverse lytic phages against 18 strains of Stenotrophomonas maltophilia and extend our analysis to include more than 120 clinical isolates of Staphylococcus aureus, Enterococcus spp., and Pseudomonas aeruginosa. pH-neutral MCFAs (C6-C10) are bactericidal, do not diminish phage viability, and synergized with phages to delay or prevent phage-resistant bacterial rebound over 48 h. MCFAs lowered both attached and free bacterial biomass and were synergistic with envelope-active antibiotics (polymyxin B, ceftazidime, meropenem), supporting a mechanism involving bacterial envelope disruption. Phage-MCFA cooperativity was observed across both Gram-negative and Gram-positive bacteria: across 18 S. maltophilia strains and four lytic phages, phage-MCFA combinations were predominantly additive-to- synergistic; in 91 strains of Enterococcus spp., pH-neutral C8 produced widespread synergy with phage CAR, and enabled killing of strains recalcitrant to phage alone; in 18 strains of S. aureus, effects were stoichiometry-dependent, showing both synergy and antagonism depending on the ratio of phage-to- MCFA; in 14 strains of P. aeruginosa, interactions were largely additive. Together, this work elevates MCFAs as potential therapeutic accompaniments that stabilize and broaden phage efficacy, and highlights the general potential of small molecules as phage adjuvants.

microbiology↗

bioRxiv2026

PlasmoRhythm: A comprehensive database for malaria-associated rhythmic transcripts, proteins, and metabolites

Abstract: Motivation: The malaria transmission cycle is temporally organized across three interacting biological systems: the parasite, vertebrate host, and mosquito vector. Rhythms in parasite development, host physiology, and vector behavior govern infection dynamics, transmission, and the efficacy of stage-specific antimalarial interventions. However, the molecular architecture connecting these rhythms remains difficult to interrogate because time-resolved omics datasets are distributed across species, experimental paradigms, and analytical frameworks, with no unified resource for comparative analysis. Results: We developed PlasmoRhythm, a curated database integrating 67 transcriptomic, proteomic, and metabolomic datasets from 15 studies, encompassing 1,126 sampling events across Plasmodium species, mammalian hosts, and Anopheles vectors. Harmonized metadata and rhythmicity outputs from JTK_CYCLE, ARSER, Lomb-Scargle, and the meta2d function support interactive cross-study interrogation of molecular profiles. Dedicated modules enable the exploration of rhythmic virulence factors and antimalarial drug interactors, providing a unified framework for hypothesis generation across the malaria transmission cycle. Availability: PlasmoRhythm is freely accessible at https://project.iith.ac.in/cgntlab/PlasmoRhythm/.

bioinformatics↗

bioRxiv2026

Thioesterase modulates antibacterial Type VI secretion system via alteration in oxidative stress responses in Burkholderia

Bacteria utilize specialized protein secretion systems, particularly the type VI secretion system (T6SS), to dominate interbacterial competitions and kill the prey bacteria in a contact-dependent manner. Here, we report that disruption of the thioesterase gene encoded in the gramibactin (a bacterial siderophore) biosynthetic gene cluster activates the T6SS in the Burkholderia gladioli strain NGJ1, even in the absence of prey. The thioesterase mutant exhibits enhanced antibacterial activity, and ectopic expression of the gene complements the phenotype. Notably, thio mutant as well as thioesterase-complement strains show enhanced sensitivity to oxidative stress. The qRT-PCR analysis showed that the thio mutant had upregulated expression of RpoN1, a sigma factor, which positively regulates T6SS functions. We demonstrate that rpoN1 mutant is hypersensitive to oxidative stress and defective in T6SS activation, whereas overexpression of RpoN1 enhances oxidative stress tolerance in NGJ1 and activates T6SS. We propose that, during interbacterial conflicts encountered during plant colonization, NGJ1 faces an oxidative-stress-enriched environment that modulates thioesterase expression, induces RpoN1, and activates T6SS. Overall, our study exemplifies the broader physiological role of Thioesterase in maintaining intracellular ROS homeostasis in bacteria and stimulating antibacterial activity.

microbiology↗

bioRxiv2026

TxCyto: A machine learning framework for estimating cytokine activity from whole transcriptome

Cytokines are critical mediators of intercellular communication, and a comprehensive characterization of their activity is essential for understanding health and disease. Existing tools to infer cytokine activity rely on experimental measurements. However, such measurements are available only for a small minority (43) of cytokines, and moreover, cytokine activity and response are highly context-specific, making a comprehensive experimental profiling across tissues, disease states, and biological contexts impractical. To address this gap, we developed TxCyto - a deep learning-based framework that infers the activity of cytokines, and more broadly of the tumor secretome, directly from the whole transcriptome profile of a sample. Trained on pan-cancer TCGA tumor transcriptomes, TxCyto was extensively validated in multiple independent datasets, including cytokine perturbation experiments. Across multiple cancer immunotherapy cohorts, TxCyto identified cytokines whose predicted activity was associated with therapeutic response. Furthermore, in spatial transcriptomic data for Liver cancer, TxCyto discovered spatial niches associated with response to immunotherapy. Overall, we develop a machine learning tool -TxCyto, for predicting the activity of 645 cytokines and tumor secretome from readily available whole transcriptomes. The TxCyto framework is generally applicable to other classes of regulatory molecules and TxCyto code base, and the tools are provided at https://github.com/Rahulncbs/TxCyto.

systems biology↗

bioRxiv2026

ARCHER-LD: Rapid Long-Range Linkage Disequilibrium Calculations at Biobank Scale using GPU Acceleration

Linkage disequilibrium (LD) information from one's own dataset is considered optimal for downstream analyses such as statistical fine-mapping. However, the computational complexity ({approx}N2 / 2 computations for N variants) lead most studies to use external reference panels, such as from 1000 Genomes. To capture LD across all variant pairs in biobank-scale whole-genome sequencing (WGS) datasets with hundreds of millions of variants, new computational strategies are essential. We present a novel approach that uses multi-GPU distributed computing to compute R2 for every variant pair in a dataset. On chromosome 22 (1.8 million variants) of the 30x WGS 1000 Genomes dataset, our method using eight consumer-level 12GB GPUs (NVIDIA RTX 2080TIs) takes <10 minutes, while the same calculation with PLINK using a high-end 64-threaded CPU (Intel Xeon Gold 6338) takes >80 minutes, corresponding to a {approx}8x speedup. We further demonstrate true biobank-scale performance in the Penn Medicine Biobank (PMBB; 57,170 samples), computing chromosome 1 LD ({approx}1.38 million variants) in 49 minutes versus 22.7 hours for PLINK, a {approx}28x speedup. With this method, we successfully computed on the aforementioned 30x WGS 1000 Genomes dataset ({approx}120 million variants and {approx}2500 samples) the entire LD matrix (>1e16, or 10 quadrillion elements) in under 6 hours using 512 NVIDIA 40GB A100 GPUs on the Department of Energy Argonne Leadership Computing Facility Polaris Supercomputer. We make this tool, coded in Python using CuPy, publicly available. Using this tool, researchers can leverage the full extent of their genomic data without relying on external LD reference panels and acquire more accurate, population-specific findings, particularly for groups underrepresented in existing databases.

bioinformatics↗

bioRxiv2026

Targeting Histamine H3 Receptors to Suppress NGF-Driven Hyperexcitability in Neuropathic Pain

Peripheral nerve injury induces long-lasting changes in sensory neurons that contribute to the development of neuropathic pain. Although the histamine H3 receptor (H3R) is best known for regulating neurotransmitter release in the central nervous system, its expression and role in dorsal root ganglia (DRG) remain poorly understood. In this study, we examined the expression, cellular distribution, and functional role of H3 R in DRG and implications in neuropathic pain. Sciatic nerve injury increased membrane-associated H3R protein expression in the DRG, suggesting enhanced receptor trafficking or stabilization at the neuronal membrane. Single-cell transcriptomic analysis revealed that H3R mRNA expression in DRG is predominantly restricted in peptidergic sensory neurons and C-LTMRs neurons. Fluorescence imaging and transcriptomic analysis also suggest that the majority of H3R co-express with TrkA (a NGF receptor) sensory neuron populations. These findings identify a subset of NGF-responsive nociceptors in which H3R may directly influence injury-induced sensitization. Pain behavior assessment demonstrated a paradoxical role of an H3R inverse agonist (GSK334429), which reduces mechanical hypersensitivity caused by chronic constriction injury (CCI) with no change in thermal pain. Patch-clamp recordings show that the GSK334429 attenuates NGF-induced hyperexcitability in DRG neuronal cultures. Overall, our work suggests a contributing role of H3R in modality-specific effects on sensory processing through its enrichment in specific neuronal populations in DRG.

neuroscience↗

bioRxiv2026

Loss of the high-affinity vacuolar Ca2+ pump Pmc1p confers echinocandin tolerance in Candida albicans through enhancing calcineurin-based responses.

Signaling through the calcium-activated calcineurin phosphatase promotes fungal survival of stressful conditions including those imposed by antifungal medications. Calcium is an essential secondary messenger that regulates diverse cellular processes in eukaryotes; however, it is also profoundly toxic and cytoplasmic concentrations must be tightly controlled. In fungi, the vacuole serves as a major calcium reservoir with the H+-exchanger Vcx1p and P-type ATPase Pmc1p sequestering intracellular calcium. Upon stimulation, these stores can be released through the Yvc1p ion channel to create transient cytoplasmic pulses that activate calcium-dependent responses. Despite the importance of calcineurin signaling in sustaining fungal viability upon antifungal insult, the contribution of many other proteins responsible for intracellular calcium homeostasis during antifungal exposure remains poorly understood. Here, we investigated whether Yvc1p, Vcx1p, and Pmc1p influence Candida albicans capacity to endure exposure to the first-line echinocandin antifungals. Our results demonstrate that loss of Pmc1p function confers high-levels of tolerance, with pmc1?/? mutant cells sustaining less damage, surviving and capable of proliferation in the presence of supra-MIC concentrations of the echinocandins. Moreover, this phenotype is dependent upon elevated signaling through the calcineurin pathway. Finally, we demonstrate that at least a subset of drugs previously identified as echinocandin antagonists activate calcineurin signaling in a Pmc1-dependent manner to drive echinocandin tolerance. These findings reveal how genetic or pharmacological modulation of calcium homeostasis can have profound consequences on the outcome of C. albicans - echinocandin interaction.

microbiology↗

bioRxiv2026

Extracellular protein catabolism drives regulated nitrogen handling and ammonia buffering in acute myeloid leukemia

Acute myeloid leukemia (AML) cells exhibit pronounced metabolic plasticity, yet how amino acid supply is coordinated to sustain leukemic metabolism remains poorly understood. Here, we show that AML cells catabolize extracellular proteins as a major source of amino acids through lysosomal degradation of albumin. This proteocatabolic activity supports anabolic processes and mitochondrial energy production and establishes a regulated, high-throughput regime of primary nitrogen-containing metabolites (nitrogen regimen). Sustained proteocatabolism inevitably generates ammonia, and we find elevated ammonia concentrations in bone marrow plasma from newly diagnosed AML patients that decline with effective induction therapy. Using metabolomics, isotope tracing and targeted genetic and pharmacological manipulations, we identify glutamate-ammonia ligase (GS/GLUL) as a central enzyme that buffers proteocatabolism-derived ammonia by stabilizing intracellular nitrogen homeostasis. Loss of GS function limits sustainable nitrogen handling capacity, thereby impairing leukemic proliferation and delaying disease progression in vivo. Together, our findings define extracellular protein catabolism as a regulating nitrogen management strategy in AML and reveal GS as a capacity-defining vulnerability of proteocatabolic growth.

cancer biology↗

bioRxiv2026

Coarse composition suffices: tabular in-context learning for multi-activity antimicrobial peptide profiling

Antimicrobial peptides (AMPs) often act against multiple pathogen classes, making multi-label activity prediction a more realistic screening target than binary antimicrobial classification. The ESCAPE benchmark formalizes this setting, but leading approaches typically rely on multimodal, structure-conditioned deep models that are costly to train and tune. We show that a simple, sequence-only pipeline can match and surpass these methods by combining 330 interpretable sequence descriptors with TabPFN, a tabular foundation model that performs in-context prediction in a single forward pass without gradient-based training or hyperparameter search. On ESCAPE (82,359 peptides; five labels), a label-powerset TabPFN model achieves mAP-5=77.8%, improving on the previously best reported 72.1%. A probabilistic classifier chain is the first method to match or exceed the best published average precision on each of the five labels simultaneously. The gains persist under the prior state-of-the-art single-fold training protocol, indicating they are not a training-set-size artefact, and are largest for remote homologues (+11.2 points below 30% sequence identity). Ablations further show that predicted structure is unnecessary at inference and that performance is not driven by any single descriptor family: ten global physicochemical scalars recover 91% of full-feature performance. Finally, explicitly modelling label dependence yields targeted benefits for scarce activities and supports ranking which activity to assay next from partial positive evidence.

bioinformatics↗

bioRxiv2026

Phenotypic plasticity, stalk geometry, and noncoding variation underpin stalk lodging resistance in maize

Stalk lodging causes severe yield losses in maize (Zea mays L.) worldwide, worsening food and feed security. Stalk lodging resistance is influenced by multiple traits at various levels of biological organization, collectively referred to as intermediate traits, but their identities, genetic bases, and interrelationships remain poorly resolved. Here, evaluation of multiple geometric and structural intermediate traits in a maize diversity panel across four environments showed that macroenvironmental variation is the predominant driver of phenotype plasticity and that plasticity varies with internode position along the stalk, consistent with height-dependent mechanosensing. Major and minor diameters, moment of inertia, and rind penetration resistance, were genetically tractable and showed strong genetic correlations with stalk flexural stiffness. Multivariate analyses revealed two distinct but complementary mechanistic pathways, represented by cross-sectional geometry and rind architecture, that contribute to stalk mechanical performance. Association analyses using whole-genome resequencing data identified 705 SNPs associated with intermediate traits, fewer than 20% of which overlapped genic regions, indicating that most associated variation resides outside annotated genes. Interestingly, about 22% of SNPs were shared between at least two traits, indicating substantial shared genetic control among intermediate traits. Candidate gene analyses highlighted novel promising candidate loci associated with intermediate traits while recovering genes previously implicated in stalk lodging resistance. The predominance of noncoding associations further suggests that regulatory variation may contribute substantially to natural variation in intermediate traits underlying stalk lodging resistance.

genetics↗

bioRxiv2026

UPLC-ESI-MS based lipidomics revealed novel biomarkers in insulin receptor knockdown induced type 2 diabetes model of Drosophila

Type 2 diabetes (T2D) is a prevalent metabolic disorder affecting millions worldwide, characterized by insulin resistance and impaired glucose homeostasis. While mammalian models are widely used, Drosophila melanogaster provides a powerful alternative due to its conserved insulin signaling pathways, genetic tractability, and suitability for high throughput studies. In addition to glucose dysregulation, lipid metabolism plays a crucial role in T2D pathophysiology, as alterations in lipid composition contribute to insulin resistance and metabolic dysfunction. Lipidomic studies have emerged as an essential approach to identify metabolic signatures and potential biomarkers for disease progression and therapeutic targeting. In this study, T2D like model was established by inducing insulin resistance through knockdown of the insulin receptor in brain insulin-producing cells using the dilp2-Gal4>UAS-InRRNAi system. This genetic manipulation resulted in significant metabolic dysregulation, including elevated glucose, trehalose, and triacylglyceride levels, along with increased oxidative stress indicators. Additionally, mRNA expression analysis of key insulin signaling components, including insulin receptor substrate 1, dilp2, dilp3, dilp5, and phosphorylated Akt, further validated the model. To further investigate metabolic alterations, Lipid profiling was performed using ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) in non targeted LC-MS-based metabolomics approach to identify lipid biomarkers associated with T2D. Multivariate statistical analyses, including PCA and PLS-DA, revealed distinct lipid signatures between wild-type and T2D flies. Notably, specific phosphatidylglycerol species PG 34:0, PG 34:4, PA 38:3, PIP 38:1, PIP2 38:6, and LPS 24:0 demonstrated an area under the curve (AUC) of 1, indicating their strong reliability as lipid biomarkers for T2D diagnosis.

biochemistry↗

bioRxiv2026

Hydrogen sulfide dynamically upregulates copper uptake and localization

The reactivity of copper, an essential micronutrient that undergoes facile cycling between Cu1+ and Cu2+ redox states, is carefully controlled within the confines of protein binding sites, and by sequestration in storage vesicles, or harnessed to kill pathogens by active pumping of Cu1+ into phagosomes. We have discovered that hydrogen sulfide, a signaling metabolite generated in copious quantities at the host-microbiome interface, upregulates Cu accumulation in diffusely dispersed puncta across the cell, as visualized by X-ray fluorescence microscopy. The Cu is predominantly in the Cu2+ state with oxygen/nitrogen ligands. Cu import occurs via the non- canonical ZNT1 transporter, while export, following sulfide withdrawal, is ATP7A-dependent. Cu accumulates at the apices of colon crypts in a mouse model of elevated sulfide exposure due to SQOR deficiency in the intestinal epithelium, establishing in vivo relevance. Our study reveals that sulfide is a dynamic regulator of the Cu pool, stimulating Cu2+ influx into highly concentrated puncta.

cell biology↗

bioRxiv2026

Spindle Assembly Checkpoint Competency Determines Sensitivity to KIF18A Inhibition in Small-Cell Lung Cancer

BackgroundSmall-cell lung cancer (SCLC) is characterized by pervasive chromosomal instability (CIN) and remains largely refractory to targeted therapies. KIF18A, a motor protein that regulates chromosome alignment during mitosis, has emerged as a selective dependency in CIN-high tumors. Whether this dependency extends to SCLC, a prototypical CIN-high cancer, has not been established, and biomarkers predicting response to KIF18A inhibition, currently in clinical trials, are lacking. MethodsWe integrated analyses of patient tumor datasets, neuroendocrine (NE) and non- NE SCLC cell lines, and functional perturbation models to define the determinants of response to KIF18A inhibition. Chromosomal instability metrics, transcriptional programs, mitotic dynamics, and spindle assembly checkpoint (SAC) function were assessed using genomic profiling, live-cell imaging, genetic perturbation, and pharmacologic inhibition. ResultsKIF18A expression was elevated in SCLC tumors and correlated with CIN-associated transcriptional programs, proliferative markers, and NE status; however, these features did not predict sensitivity to KIF18A inhibition. Instead, response was determined by the functional integrity of the SAC. SAC-proficient SCLC cells underwent sustained mitotic arrest followed by apoptotic cell death upon KIF18A inhibition, whereas SAC-defective cells failed to maintain checkpoint activation and survived. Mechanistically, resistant cells exhibited impaired kinetochore recruitment of core SAC components, including MAD1 and BUBR1. Importantly, transient induction of acute CIN through MPS1 inhibition partially restored sensitivity to KIF18A inhibition in resistant models. ConclusionsThis study provides the first mechanistic characterization of KIF18A dependency in SCLC, identifying SAC competency as the primary determinant of response. These findings establish a biologically informed framework for patient stratification and rational combination strategies. Translational RelevanceSmall-cell lung cancer (SCLC) is an aggressive malignancy with few effective targeted therapies and marked chromosomal instability. KIF18A has emerged as a potential therapeutic target in genomically unstable cancers, but biomarkers predicting response to KIF18A inhibition are lacking. We demonstrate that sensitivity to KIF18A inhibition in SCLC is determined not by KIF18A expression, neuroendocrine subtype, or baseline chromosomal instability, but by the functional integrity of the spindle assembly checkpoint (SAC). SCLC cells with intact SAC signaling undergo sustained mitotic arrest and apoptosis upon KIF18A inhibition, whereas SAC-defective cells bypass checkpoint activation and survive aberrant mitosis. Notably, transient induction of acute chromosomal instability through MPS1 inhibition partially restores sensitivity in resistant models. Together, these findings identify mitotic checkpoint competency as a mechanistic determinant and candidate predictive biomarker for KIF18A-targeted therapies, providing a biologically informed framework for patient stratification and rational combination strategies relevant to ongoing KIF18A inhibitor clinical trials.

cancer biology↗

bioRxiv2026

Deep Brain Stimulation Microelectrodes as a Source of Human Subcortical RNA: Validation of a Low-Input Transcriptomic Protocol

BackgroundUnderstanding the molecular basis of Parkinsons disease (PD) phenotypic heterogeneity maybe improved by in vivo access to deep brain tissue. Deep brain stimulation (DBS) surgery offers a unique opportunity: microelectrodes traversing the subthalamic nucleus (STN) carry adherent brain tissue upon withdrawal, providing a source of RNA from subcortical regions in living patients without additional invasive procedures. AimTo develop and validate a low-input RNA extraction and transcriptomic profiling protocol using DBS microelectrodes in post-mortem human brain, to confirm that recovered RNA is of brain rather than blood origin, and to characterise its sub-regional and cellular identity. MethodologyDBS microelectrodes were inserted without image guidance or guide tube into three unfixed post-mortem human brains targeting the STN trajectory. In clinical practice, a guide tube shields the electrode from cortical tissue; its absence here means tissue from the full insertion trajectory may contribute to recovered RNA. Thirty-eight microelectrodes were evaluated across single and pooled strategies. RNA was extracted using a modified RNeasy Micro low-input protocol; libraries prepared using NEBNext Single Cell/Low Input RNA Library Kit and sequenced on Illumina NovaSeq 6000 (paired-end, 2x150 bp). Tissue identity was validated against GTEx v10 (54 tissues) and Allen Human Brain Atlas (ABA, 19 subcortical regions including STN) using Pearson correlation with permutation testing (1,000 permutations) and BH-FDR correction. Transcriptional overlap between subcortical and cortical reference regions was quantified and subcortical-enriched gene filtering performed. ResultsTwenty-five RNA isolates were obtained from 38 microelectrodes; 54.5% of Bioanalyzer-assessed samples achieved RIN [≥]5 (median 7.1; range: 5.9-7.8). Fifteen libraries passed sequencing QC (mean depth 43.0 {+/-} 14.1 million read pairs; mean Q30 83.2 {+/-} 5.6 %). PCA of rlog transformed expression data resolved samples by donor identity. All samples confirmed brain tissue origin by GTEx {tau}-index tissue specificity correlation (n=996 brain and blood specific markers). Brain correlations were highest for frontal cortex (mean r= 0.683 {+/-} 0.072), anterior cingulate cortex (mean r= 0.658 {+/-} 0.069), amygdala (mean r= 0.615 {+/-} 0.068), and basal ganglia including caudate (mean r= 0.553 {+/-} 0.059), putamen (mean r= 0.546 {+/-} 0.059), and nucleus accumbens (mean r= 0.544 {+/-} 0.057); all BH-FDR < 0.05. Cerebellum showed the lowest brain correlations (cerebellar hemisphere: mean r = 0.327 {+/-} 0.039; cerebellum: mean r = 0.314 {+/-} 0.037). Whole blood correlation was strongly negative (mean r= -0.250 {+/-} 0.079), confirming non-haematological origin. ABA genome-wide analysis confirmed positive STN correlation (r=0.54-0.62; permutation p<0.001). MuSiC cell-type deconvolution against the Allen Brain Atlas HMBA-BG snRNA-seq reference identified oligodendrocytes (33.5 {+/-} 17.2%), frontal cortical neurons (9.1 {+/-} 7.0%), STR D1 MSNs (6.0 {+/-} 4.9%), and dopaminergic neurons (1.1{+/-} 2.7%) as the principal cell types, independently corroborating bulk transcriptomic findings at single-cell resolution. ConclusionThis study validates a low-input RNA extraction protocol for DBS microelectrodes, confirming brain-specific transcriptomic profiles consistent with STN-adjacent subcortical sampling. The 96% transcriptional overlap between cortical and subcortical regions, combined with absence of a guide tube in this post-mortem model, limits sub-regional specificity; clinical application with a guide tube would enrich the subcortical signal. These findings provide the methodological framework for in vivo molecular profiling of the human basal ganglia during DBS surgery in PD patients.

genetics↗

bioRxiv2026

Apple scar skin viroid disease induced physicochemical and metabolic alterations in Apples

Apple scar skin disease (ASSD), caused by Apple scar skin viroid (ASSVd), is characterized by peel scarring, cracking, dappling, and fruit deformation, resulting in reduced fruit quality and marketability. Despite its economic importance, the physicochemical and metabolic alterations underlying disease progression remain poorly understood. To address this knowledge gap, apple fruits representing four stages of ASSD (healthy, lightly infected, moderately infected, and highly infected) were comprehensively characterized. ASSVd infection was confirmed by RT-PCR, amplicon sequencing, and phylogenetic analysis. Fruit morphology and quality attributes, including firmness, total soluble solids (TSS), pH, titratable acidity (TA), and total phenolic content (TPC), were evaluated. ASSVd infection significantly reduced fruit weight and firmness and altered TSS and TA, indicating progressive deterioration of fruit quality. To investigate the underlying metabolic changes, peel and pulp tissues were analysed separately using gas chromatography-mass spectrometry (GC-MS), while major soluble sugars were quantified by 1H nuclear magnetic resonance (1H NMR) spectroscopy. Integrated metabolomic analyses revealed distinct tissue-specific metabolic reprogramming during disease progression. Major soluble sugars declined significantly during early infection, followed by tissue-dependent recovery at later stages, whereas organic acids, amino acids, phenolics, lipids, polyols, and pentacyclic triterpenoids exhibited dynamic stage-dependent changes. Notably, lupeol accumulated progressively, whereas ursolic acid and oleanolic acid declined, indicating disease-associated alterations in host triterpenoid metabolism. Multivariate analyses demonstrated clear metabolic separation among disease stages. Lupeol, ursolic acid, and chlorogenic acid were identified as candidate discriminatory metabolites in the peel, whereas myo-inositol, chlorogenic acid, and aspartic acid were identified in the pulp. Collectively, these findings demonstrate that ASSD induces coordinated, tissue-specific physicochemical and metabolic reprogramming that compromises postharvest fruit quality and reshapes defence-associated metabolism. This study provides the first integrated metabolomic characterization of ASSD progression and identifies potential metabolic biomarkers for disease diagnosis and severity assessment. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=142 SRC="FIGDIR/small/741181v1_ufig1.gif" ALT="Figure 1"> View larger version (56K): org.highwire.dtl.DTLVardef@50763eorg.highwire.dtl.DTLVardef@82eaddorg.highwire.dtl.DTLVardef@1096b06org.highwire.dtl.DTLVardef@15161b4_HPS_FORMAT_FIGEXP M_FIG C_FIG

plant biology↗

bioRxiv2026

PMBB Geno-Pheno Toolkit: A suite of scalable, reproducible pipelines for cross-biobank association analyses

SummaryElectronic health record (EHR)-linked biobanks generate unprecedented genomic and phenotypic datasets, but their scientific utility is constrained by data fragmentation across institutional silos and incompatible computing infrastructures, forcing researchers to rewrite ad-hoc scripts for each new environment. We present the PMBB Geno-Pheno Toolkit, a suite of modular Nextflow pipelines for biobank-scale association analyses. This note focuses on the toolkits SAIGE family of pipelines -- supporting genome-wide (GWAS), exome-wide (ExWAS), and phenome-wide (PheWAS) association testing -- together with the companion GWAMA and ExWAS meta-analysis pipelines that enable cross-biobank replication. All components are containerized (Docker/Apptainer) and orchestrated with Nextflow, allowing the same workflows to run unmodified on local HPC clusters, cloud platforms, and the All of Us Research Workbench. Complementary toolkit pipelines for PLINK-based GWAS, polygenic scoring, LD-based clumping, and phenotype harmonization are also available and briefly noted. AvailabilityThe PMBB Geno-Pheno Toolkit is freely available at https://github.com/PMBB-Informatics-and-Genomics/pmbb-geno-pheno-toolkit under MIT open-source license.

bioinformatics↗

bioRxiv2026

Comparative Evaluation of Commercial Gradient Diffusion and Disk Diffusion Tests for Aztreonam-Avibactam Susceptibility Testing

BackgroundAztreonam-avibactam (AZA) is a recently approved agent active against metallo-{beta}-lactamase-producing Enterobacterales. However, independent evaluations of commercially available antimicrobial susceptibility testing (AST) products remain limited. MethodsWe evaluated 56 carbapenem-resistant Enterobacterales isolates using four newly available AZA AST products, including two gradient diffusion (bioMerieux Etest; Liofilchem MIC Test Strip) and two disk diffusion (Hardy, Liofilchem) tests across three Mueller-Hinton agar manufacturers at two independent clinical laboratories. A diverse isolate collection enriched for nonsusceptible and breakpoint-adjacent MICs was used to rigorously assess categorical agreement. Broth microdilution (BMD) was used as the reference method. Essential agreement (EA), categorical agreement (CA), error rates, and reproducibility were analyzed. ResultsAll four commercial AST products demonstrated acceptable analytical performance compared with reference BMD. Gradient diffusion demonstrated high EA (bioMerieux 94.3%, Liofilchem 90.5%), whereas CA ranged from 83.3% to 89.0% across all four products. Minor errors accounted for all categorical discrepancies, and occurred almost exclusively among breakpoint-adjacent isolates. Media-related variability was modest, and quality control performance was generally high, with strong within-site precision and consistent performance across laboratories. ConclusionsThis study provides the first comprehensive head-to-head comparison of commercial AZA susceptibility testing products and supports their implementation in clinical microbiology laboratories.

microbiology↗

bioRxiv2026

An Integrated Proteomics and Genomics Approach to Identify Essential Protein Kinases During Human Trophoblast Development

In the developing human placenta, three subtypes of trophoblast cells, cytotrophoblasts (CTBs), extravillous trophoblasts (EVTs), and syncytiotrophoblasts (STBs), mediate critical functions essential for a successful pregnancy. CTBs constitute the stem/progenitor compartment and differentiate into STBs and EVTs within the floating and anchoring villi, respectively. STBs establish the maternal-fetal exchange interface and secrets human chorionic gonadotropin (hCG), a hormone vital for the maintenance of early pregnancy. EVTs anchor the maternal endometrium and invade the uterine tissue to remodel maternal cells, supporting implantation and progression of pregnancy. In this study, we used human trophoblast stem cells (hTSCs) as a model system and performed quantitative, label-free liquid chromatography-tandem Mass Spectrometry (LC-MS/MS) to profile proteome and phosphoproteome in TSC stem state (analogous to undifferentiated CTBs) and following their differentiation to STBs and EVTs. Through a multiomics approach, we integrated our proteomics data with global gene expression profiles to correlate cell-type specific gene and protein expression during human trophoblast development. We also identified global phosphoproteome and analyzed kinases that are specifically active in hTSC stem state, as well as in differentiated STBs and EVTs. We experimentally validated specific kinases, such as BUB1B, PAK6, PKYMT1 and TNIK are essential for maintaining the hTSC stem-state. Additionally, atypical protein kinase C isoforms PKC{zeta} is essential for STB development, while PTK2B, SRC, TRIO and LYN are important for EVT development. Our findings highlight key kinases uniquely required for specific stages of trophoblast development during human placentation and suggest that pharmacological inhibition of these kinases could negatively impact the placentation process during pregnancy.

developmental biology↗