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Misztak, A.

Publications and source records attributed to Misztak, A..

2 recordsLinked to original sources

Spatial Transcriptomics of TNBC tumours and corresponding lymph node metastasis reveals immune hubs driven by TNF/NF-κB signalling of TMSB4X and CD74 expressing cells.

PurposeLymph node metastasis is a critical prognostic factor in triple-negative breast cancer (TNBC), but the spatial organization of signalling networks driving metastatic colonization and persistence remains poorly defined. Understanding these networks may reveal therapeutic vulnerabilities in metastatic TNBC. MethodsWe applied Visium spatial transcriptomics to paired primary tumours and lymph node metastases from four TNBC patients. Spatial gene expression profiles were analysed using trajectory inference, cell-type deconvolution, and cell-cell communication mapping. Results were validated in independent single-cell RNA sequencing datasets from TNBC tumours and lymph node metastases. ResultsTMSB4X and CD74 emerged as key drivers of metastasis-associated transcriptional programs, defining spatially distinct immune communication hubs enriched in myeloid, stromal, and endothelial cells. These hubs preferentially activated NF-{kappa}B/TNF signalling alongside PI3K-Akt and Rap1 pathways. In primary tumours, NF-{kappa}B/TNF signalling was confined to localized immune hubs, whereas lymph node metastases exhibited widespread signalling across cellular compartments with additional IL-1 pathway activation. This spatial rewiring coincided with endothelial cells assuming central coordinating roles in metastatic lesions. Independent validation confirmed myeloid-endothelial crosstalk as a conserved feature of TNBC, with TMSB4X-CD74 programs enriched in clinically relevant immune and vascular subpopulations. ConclusionTNBC lymph node metastases display spatially expanded inflammatory networks centred on TMSB4X-CD74 immune hubs. Metastatic progression involves coordinated myeloid-endothelial signalling, suggesting these pathways as potential biomarkers and therapeutic targets. Disrupting inflammatory and vascular communication networks may offer a strategy to prevent metastatic persistence and overcome therapy resistance in TNBC.

molecular biology↗

Consensus assessment of the contamination level of publicly available cyanobacterial genomes

BACKGROUNDPublicly available genomes are crucial for phylogenetic and metagenomic studies, in which contaminating sequences can be the cause of major problems. This issue is expected to be especially important for Cyanobacteria because axenic strains are notoriously difficult to obtain and keep in culture. Yet, despite their great scientific interest, no data are currently available concerning the quality of publicly available cyanobacterial genomes.\n\nRESULTSAs reliably detecting contaminants is a complex task, we designed a pipeline combining six methods in a consensus strategy to assess the contamination level of 440 genome assemblies of Cyanobacteria. Two methods are based on published reference databases of ribosomal genes (SSU rRNA 16S and ribosomal proteins), one is indirectly based on a reference database of marker genes (CheckM), and three are based on complete genome analysis. Among those genome-wide methods, Kraken and DIAMOND blastx share the same reference database that we derived from Ensembl Bacteria, whereas CONCOCT does not require any reference database, instead relying on differences in DNA tetramer frequencies. Given that all the six methods appear to have their own strengths and limitations, we used the consensus of their rankings to infer that >5% of cyanobacterial genome assemblies are highly contaminated by foreign DNA (i.e., contaminants were detected by 5 or 6 methods).\n\nCONCLUSIONSOur results will help researchers to check the quality of publicly available genomic data before use in their own analyses. Moreover, we argue that journals should make mandatory the submission of raw read data along with genome assemblies in order to facilitate the detection of contaminants in sequence databases.

genomics↗