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Biology subjects

Ortega, M. R.

Publications and source records attributed to Ortega, M. R..

2 recordsLinked to original sources

Accurate quantitation of 16S gene copies in low biomass samples post-antibiotic treatment through deep sequencing with a balanced nucleotide synthetic spike-in approach

The microbiota significantly impacts health and treatment outcomes. While 16S rRNA gene sequencing reveals relative bacterial abundances, it does not provide absolute quantification. We developed a cost-effective solution incorporating synthetic DNA standards designed to ensure balanced nucleotide representation at each position. These standards are spiked into samples before DNA extraction, enabling simultaneous quantification of both relative and absolute bacterial abundances. We applied this method to samples collected from mice and patients, both before and after antibiotic treatment. Our approach showed a reduction in total bacterial density in mice and patients post-antibiotic treatment. This spike-in standard method can be adapted to samples with varying bacterial densities, allowing quantification of absolute taxonomical abundances without the need for an additional quantitative PCR assessment. Our approach also improves sequencing quality scores for low biomass samples.

microbiology↗

Learning predictive signatures of HLA type from T-cell repertoires

T cells recognize a wide range of pathogens using surface receptors that interact directly with pep-tides presented on major histocompatibility complexes (MHC) encoded by the HLA loci in humans. Understanding the association between T cell receptors (TCR) and HLA alleles is an important step towards predicting TCR-antigen specificity from sequences. Here we analyze the TCR alpha and beta repertoires of large cohorts of HLA-typed donors to systematically infer such associations, by looking for overrepresentation of TCRs in individuals with a common allele.TCRs, associated with a specific HLA allele, exhibit sequence similarities that suggest prior antigen exposure. Immune repertoire sequencing has produced large numbers of datasets, however the HLA type of the corresponding donors is rarely available. Using our TCR-HLA associations, we trained a computational model to predict the HLA type of individuals from their TCR repertoire alone. We propose an iterative procedure to refine this model by using data from large cohorts of untyped individuals, by recursively typing them using the model itself. The resulting model shows good predictive performance, even for relatively rare HLA alleles.

immunology↗