Proof of Concept Study: Comparability of Microbiome Diversity in Self- and Physician-Collected HPV-Positive and HPV-Negative Cervicovaginal Samples.

Recent studies have revealed the impact of human papillomavirus (HPV) infections on the cervicovaginal microbiome; however, few have explored the utility of self-collected specimens (SCS) for microbiome detection, obtained using standardised methods for HPV testing. Here, we present a proof-of-concept analysis utilising Oxford Nanopore sequencing of the 16S rRNA gene in paired samples collected either by the patient using an Evalyn Brush or collected by a physician using liquid-based cytology (LBC). We found no significant differences in the α-diversity estimates between the SCS and LBC samples. Similarly, when analysing ß-diversity, we observed a close grouping of paired samples, indicating that both collection methods detected the same microbiome features. The identification of genera and Lactobacillus species in each sample allowed for their classification into community state types (CSTs). Notably, paired samples had the same CST, while HPV-positive and -negative samples belonged to distinct CSTs. As previously described in other studies, HPV-positive samples exhibited heightened bacterial diversity, reduced Lactobacillus abundance, and an increase in genera like Sneathia or Dialister. Altogether, this study showed comparable results between the SCS and LBC samples, underscoring the potential of self-sampling for analysing the microbiome composition in cervicovaginal samples initially collected for HPV testing in the context of cervical cancer screening.

Performance of a Shotgun Prediction Model for Colorectal Cancer When Using 16S rRNA Sequencing Data.

Colorectal cancer (CRC), the third most common cancer globally, has shown links to disturbed gut microbiota. While significant efforts have been made to establish a microbial signature indicative of CRC using shotgun metagenomic sequencing, the challenge lies in validating this signature with 16S ribosomal RNA (16S) gene sequencing. The primary obstacle is reconciling the differing outputs of these two methodologies, which often lead to divergent statistical models and conclusions. In this study, we introduce an algorithm designed to bridge this gap by mapping shotgun-derived taxa to their 16S counterparts. This mapping enables us to assess the predictive performance of a shotgun-based microbiome signature using 16S data. Our results demonstrate a reduction in performance when applying the 16S-mapped taxa in the shotgun prediction model, though it retains statistical significance. This suggests that while an exact match between shotgun and 16S data may not yet be feasible, our approach provides a viable method for comparative analysis and validation in the context of CRC-associated microbiome research.

Microbiome Profiling from Fecal Immunochemical Test Reveals Microbial Signatures with Potential for Colorectal Cancer Screening.

Colorectal cancer (CRC) is the third most common cancer and the second leading cause of cancer deaths worldwide. Early diagnosis of CRC, which saves lives and enables better outcomes, is generally implemented through a two-step population screening approach based on the use of Fecal Immunochemical Test (FIT) followed by colonoscopy if the test is positive. However, the FIT step has a high false positive rate, and there is a need for new predictive biomarkers to better prioritize cases for colonoscopy. Here we used 16S rRNA metabarcoding from FIT positive samples to uncover microbial taxa, taxon co-occurrence and metabolic features significantly associated with different colonoscopy outcomes, underscoring a predictive potential and revealing changes along the path from healthy tissue to carcinoma. Finally, we used machine learning to develop a two-phase classifier which reduces the current false positive rate while maximizing the inclusion of CRC and clinically relevant samples.

A Mouse Model Suggests That Heart Failure and Its Common Comorbidity Sleep Fragmentation Have No Synergistic Impacts on the Gut Microbiome.

Heart failure (HF) is a common condition associated with a high rate of hospitalizations and adverse outcomes. HF is characterized by impairments of either the cardiac ventricular filling, ejection of blood capacity or both. Sleep fragmentation (SF) involves a series of short sleep interruptions that lead to fatigue and contribute to cognitive impairments and dementia. Both conditions are known to be associated with increased inflammation and dysbiosis of the gut microbiota. In the present study, mice were distributed into four groups, and subjected for four weeks to either HF, SF, both HF and SF, or left unperturbed as controls. We used 16S metabarcoding to assess fecal microbiome composition before and after the experiments. Evidence for distinct alterations in several bacterial groups and an overall decrease in alpha diversity emerged in HF and SF treatment groups. Combined HF and SF conditions, however, showed no synergism, and observed changes were not always additive, suggesting preliminarily that some of the individual effects of either HF or SF cancel each other out when applied concomitantly.