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Konwar, K.

Publications and source records attributed to Konwar, K..

3 recordsLinked to original sources

Magnetic field-induced ER stress reprograms the tumor microenvironment to improve triple-negative breast cancer survival

BackgroundNon-invasive electromagnetic field (EMF)-based therapies offer a potential route to modulate local tumor-immune interactions but their mechanistic basis remains poorly defined. MethodsWe evaluated Asha therapy, a proprietary low-intensity (50khz, 2 mT, 25% duty cycle) alternating magnetic-field treatment in preclinical breast cancer models. Cellular responses in human triple negative breast cancer cell lines (MDA-MB-231 and MDA-MB-468) were evaluated using bulk RNA sequencing, quantitative proteomics, flow cytometry, and cytokine analysis and proteomics analysis. Tumor microenvironment responses in mouse 4T1 breast cancer model was characterized using single-cell CITE-seq analysis. Functional efficacy was assessed in vivo using the murine 4T1 triple-negative breast cancer model, both as monotherapy and in combination with anti-PD1 checkpoint blockade. Clinical relevance was assessed by deriving a 19-gene neutrophil activation signature from Asha-induced transcriptional changes and projecting it onto two independent TNBC patient cohorts (METABRIC n=338, SCAN-B n=874) for survival analysis. ResultsAsha therapy induced endoplasmic reticulum (ER) stress and activated an adaptive unfolded-protein response in tumor cells, triggering robust NF-{kappa}B and interferon signaling and time-dependent secretion of inflammatory cytokines. In vivo, these tumor-intrinsic changes propagated to the tumor microenvironment (TME), reprogramming fibroblasts from contractile states to immune-recruiting, interferon-responsive phenotypes and enriching for interferon-stimulated, metabolically active neutrophils and macrophages. These coordinated innate immune changes occurred without overt cytotoxicity and were associated with significant reductions in metastasis and improved survival. Combination with anti-PD1 therapy markedly enhanced efficacy, reducing lung metastasis and mortality by 88% compared with control. The neutrophil activation signature derived from Asha-treated tumors was associated with improved overall survival in both METABRIC (log-rank p=0.036) and SCAN-B (p=0.048) TNBC cohorts by Kaplan-Meier analysis, with pooled multivariable Cox regression confirming significant survival benefit (HR=0.75, 95% CI 0.59-0.94, p=0.01). ConclusionsAsha therapy triggers a controlled ER stress response in tumor cells that drives interferon-mediated cytokine release and immune reprogramming of the TME, resulting in anti-metastatic and survival benefits. These findings identify electromagnetic-field exposure as a potential non-pharmacologic strategy to activate innate immunity and sensitize tumors to checkpoint blockade, supporting further clinical development of EMF-based immunotherapy.

cancer biology↗

MetaPathways v3.5: Modularity and Scalability Improvements for Pathway Inference from Environmental Genomes

Over the past decade MO_SCPLOWETAC_SCPLOWPO_SCPLOWATHWAYSC_SCPLOW has advanced as a modular pipeline for constructing environmental pathway genome databases (ePGDBs), increasing our understanding of microbial metabolism at the individual, population and community levels of biological organization. With this release, we have addressed several user experience issues related to installation, module integration, and database management. With a refactored code base, MO_SCPLOWETAC_SCPLOWPO_SCPLOWATHWAYSC_SCPLOW v3.5 enhances the user experience through streamlined installation via package indexes or containers, refined modules, and interface upgrades. It boasts updated algorithm support for sequence feature prediction, annotation, metabolic inference, and coverage metrics including genome resolved metagenomes. Tested and refined on synthetic datasets, MO_SCPLOWETAC_SCPLOWPO_SCPLOWATHWAYSC_SCPLOW v3.5 demonstrates improved performance and usability; facilitating more in-depth exploration of microbial interactions and metabolic functions in environmental genomes that scales with con-temporary sequencing throughput. Availability and ImplementationMO_SCPLOWETAC_SCPLOWPO_SCPLOWATHWAYSC_SCPLOW v3.5 is available via AO_SCPLOWNACONDAC_SCPLOW, DO_SCPLOWOCKERC_SCPLOW, and AO_SCPLOWPPTAINERC_SCPLOW. The source code is available on BO_SCPLOWITC_SCPLOWBO_SCPLOWUCKETC_SCPLOW:https://bitbucket.org/BCB2/metapathways/ The documentation is available via RO_SCPLOWEADC_SCPLOWTO_SCPLOWHEC_SCPLOWDO_SCPLOWOCSC_SCPLOW:https://metapathways.readthedocs.io Contactshallam@mail.ubc.ca

bioinformatics↗

MetaPredict: A machine learning-based tool for predicting metabolic modules in incomplete bacterial genomes

The reconstruction of complete microbial metabolic pathways using omics data from environmental samples remains challenging. Computational pipelines for pathway reconstruction that utilize machine learning methods to predict the presence or absence of KEGG modules in incomplete genomes are lacking. Here, we present MetaPathPredict, a software tool that incorporates machine learning models to predict the presence of complete KEGG modules within bacterial genomic datasets. Using gene annotation data and information from KEGG module databases, MetaPathPredict employs neural network and XGBoost stacked ensemble models to reconstruct and predict the presence of KEGG modules in a genome. MetaPathPredict can be used as a command line tool or as an R package, and both options are designed to be run locally or on a compute cluster. In our benchmarks, MetaPathPredict makes robust predictions of KEGG module presence within highly incomplete genomes.

bioinformatics↗