Luisa Delgado-Serrano

BACKGROUND: Changes in respiratory tract microbiota have been associated with diseases such as tuberculosis, a global public health problem that affects millions of people each year. This pilot study was carried out using sputum, oropharynx, and nasal respiratory tract samples collected from patients with pulmonary tuberculosis and healthy control individuals, in order to compare sample types and their usefulness in assessing changes in bacterial and fungal communities. FINDINGS: Most V1-V2 16S rRNA gene sequences belonged to the phyla Firmicutes, Bacteroidetes, Proteobacteria, Actinobacteria, and Fusobacteria, with differences in relative abundances and in specific taxa associated with each sample type. Most fungal ITS1 sequences were classified as Ascomycota and Basidiomycota, but abundances differed for the different samples. Bacterial and fungal community structures in oropharynx and sputum samples were similar to one another, as indicated by several beta diversity analyses, and both differed from nasal samples. The only difference between patient and control microbiota was found in oropharynx samples for both bacteria and fungi. Bacterial diversity was greater in sputum samples, while fungal diversity was greater in nasal samples. CONCLUSIONS: Respiratory tract microbial communities were similar in terms of the major phyla identified, yet they varied in terms of relative abundances and diversity indexes. Oropharynx communities varied with respect to health status and resembled those in sputum samples, which are collected from tuberculosis patients only due to the difficulty in obtaining sputum from healthy individuals, suggesting that oropharynx samples can be used to analyze community structure alterations associated with tuberculosis.

Extracellular vesicles (EVs) secreted by tumors are abundant in plasma, but their potential for interrogating the molecular features of tumors through multi-omic profiling remains widely unexplored. Genomic and transcriptomic profiling of circulating EV-DNA and EV-RNA isolated from in vitro and in vivo models of metastatic prostate cancer (mPC) reveal a high contribution of tumor material to EV-loaded DNA/RNA, validating the findings in two cohorts of longitudinal plasma samples collected from patients during androgen receptor signaling inhibitor (ARSI) or taxane-based therapy. EV-DNA genomic features recapitulate matched-patient biopsies and circulating tumor DNA (ctDNA) and associate with clinical progression. We develop a novel approach to enable transcriptomic profiling of EV-RNA (RExCuE). We report how the transcriptome of circulating EVs is enriched for tumor-associated transcripts, captures certain patient and tumor features, and reflects on-therapy tumor adaptation changes. Altogether, we show that EV profiling enables longitudinal transcriptomic and genomic profiling of mPC in liquid biopsy.

BACKGROUND: The taxonomic and phylogenetic classification based on sequence analysis of the ITS1 genomic region has become a crucial component of fungal ecology and diversity studies. Nowadays, there is no accurate alignment-free classification tool for fungal ITS1 sequences for large environmental surveys. This study describes the development of a machine learning-based classifier for the taxonomical assignment of fungal ITS1 sequences at the genus level. RESULTS: A fungal ITS1 sequence database was built using curated data. Training and test sets were generated from it. A Naïve Bayesian classifier was built using features from the primary sequence with an accuracy of 87 % in the classification at the genus level. CONCLUSIONS: The final model was based on a Naïve Bayes algorithm using ITS1 sequences from 510 fungal genera. This classifier, denoted as Mycofier, provides similar classification accuracy compared to BLASTN, but the database used for the classification contains curated data and the tool, independent of alignment, is more efficient and contributes to the field, given the lack of an accurate classification tool for large data from fungal ITS1 sequences. The software and source code for Mycofier are freely available at https://github.com/ldelgado-serrano/mycofier.git .

Microbial explorations of hot springs have led to remarkable discoveries and improved our understanding of life under extreme conditions. The Andean Mountains harbor diverse habitats, including an extensive chain of geothermal heated water sources. In this study, we describe and compare the planktonic microbial communities present in five high-mountain hot springs with distinct geochemical characteristics, at varying altitudes and geographical locations in the Colombian Andes. The diversity and structure of the microbial communities were assessed by pyrosequencing the V5 - V6 region of the 16S rRNA gene. The planktonic communities varied in terms of diversity indexes and were dominated by the bacterial phyla Proteobacteria, Aquificae, Chloroflexi, Cyanobacteria, Firmicutes, Nitrospirae, and Thermotogae, with site-specific bacterial taxa also observed in some cases. Statistical analyses showed that these microbial communities were distinct from one another and that they clustered in a manner consistent with physicochemical parameters of the environment sampled. Multivariate analysis suggested that pH and sulfate were among the main variables influencing population structure and diversity. The results show that despite their geographical proximity and some shared geochemical characteristics, there were few shared operational taxonomic units (OTUs) and that community structure was influenced mainly by environmental factors that have resulted in different microbial populations.