Biology subjects

Jenq, R.

Publications and source records attributed to Jenq, R..

3 recordsLinked to original sources

bioRxiv2024

Intratumoral Microbiome of Adenoid Cystic Carcinomas and Comparison with other Head and Neck Cancers

BackgroundAdenoid cystic carcinoma (ACC) is a rare, slow growing yet aggressive head and neck malignancy. Despite its clinical significance, our understanding of the cellular evolution and microenvironment in ACC remains limited. MethodsWe investigated the intratumoral microbiome of 50 ACC tumors and 33 adjacent normal tissues using 16S rRNA gene sequencing. This allowed us to characterize the bacterial communities within ACC and explore potential associations between the bacterial community structure, patients clinical characteristics, and tumor molecular features obtained through RNA sequencing. ResultsBacterial composition in ACC displayed significant differences compared to adjacent normal salivary tissue and exhibited diverse levels of species richness. We identified two main microbial subtypes within ACC: oral-like and gut-like. Oral-like microbiomes, characterized by higher diversity and abundance of genera like Neisseria, Leptotrichia, Actinomyces, Streptococcus, Rothia, and Veillonella (commonly found in healthy oral cavities), were associated with the less aggressive ACC-II molecular subtype and improved patient outcomes. Notably, we identified the same oral genera in oral cancer and in head and neck squamous cell carcinomas. In both cancers, they were part of shared oral communities associated with more diverse microbiome, less aggressive tumor phenotype, and better survival. Conversely, gut-like microbiomes in ACC, featuring low diversity and colonization by gut mucus layer-degrading species like Bacteroides, Akkermansia, Blautia, Bifidobacterium, and Enterococcus, were associated with poorer outcomes. Elevated levels of Bacteroides thetaiotaomicron were independently associated with significantly worse survival, regardless of other clinical and molecular factors. Furthermore, this association positively correlated with tumor cell biosynthesis of glycan-based cell membrane components. ConclusionsOur study uncovers specific intratumoral oral genera as potential pan-cancer biomarkers for favorable microbiomes in ACC and other head and neck cancers. These findings highlight the pivotal role of the intratumoral microbiome in influencing ACC prognosis and disease biology.

microbiology↗

bioRxiv2023

Functional Genomics of Gastrointestinal Escherichia coli Isolated from Patients with Cancer and Diarrhea

We describe the epidemiology and clinical characteristics of 29 patients with cancer and diarrhea in whom Enteroaggregative Escherichia coli (EAEC) was initially identified by GI BioFire panel multiplex. E. coli strains were successfully isolated from fecal cultures in 14 of 29 patients. Six of the 14 strains were identified as EAEC and 8 belonged to other diverse E. coli groups of unknown pathogenesis. We investigated these strains by their adherence to human intestinal organoids, cytotoxic responses, antibiotic resistance profile, full sequencing of their genomes, and annotation of their functional virulome. Interestingly, we discovered novel and enhanced adherence and aggregative patterns for several diarrheagenic pathotypes that were not previously seen when co-cultured with immortalized cell lines. EAEC isolates displayed exceptional adherence and aggregation to human colonoids compared not only to diverse GI E. coli, but also compared to prototype strains of other diarrheagenic E. coli. Some of the diverse E. coli strains that could not be classified as a conventional pathotype also showed an enhanced aggregative and cytotoxic response. Notably, we found a high carriage rate of antibiotic resistance genes in both EAEC strains and diverse GI E. coli isolates and observed a positive correlation between adherence to colonoids and the number of metal acquisition genes carried in both EAEC and the diverse E. coli strains. This work indicates that E. coli from cancer patients constitute strains of remarkable pathotypic and genomic divergence, including strains of unknown disease etiology with unique virulomes. Future studies will allow for the opportunity to re-define E. coli pathotypes with greater diagnostic accuracy and into more clinically relevant groupings.

microbiology↗

bioRxiv2021

Performance Determinants of Unsupervised Clustering Methods for Microbiome Data

BackgroundIn microbiome data analysis, unsupervised clustering is often used to identify naturally occurring clusters, which can then be assessed for associations with characteristics of interest. In this work, we systematically compared beta diversity and clustering methods commonly used in microbiome analyses. We applied these to four published datasets where highly distinct microbiome profiles could be seen between sample groups. ResultsAlthough no single method outperformed the others consistently, we did identify key scenarios where certain methods can underperform. Specifically, the Bray Curtis metric resulted in poor clustering in a dataset where high-abundance OTUs were relatively rare. In contrast, the unweighted UniFrac metric clustered poorly when used on a dataset with a high prevalence of low-abundance OTUs. To test our proposition, we systematically modified properties of the poorly performing datasets and found that this approach resulted in improved Bray Curtis and unweighted UniFrac performance. Based on these observations, we rationally combined the Bray Curtis metric and the unweighted UniFrac metrics and found that this new beta diversity metric showed high performance across all datasets. We also evaluated our findings by examining a clinical dataset where clusters are less separated. ConclusionsOur systematic evaluation of clustering performance in these five datasets demonstrates that there is no existing clustering method that universally performs best across all datasets. We propose a combined metric of Bray Curtis and unweighted UniFrac that capitalizes on the complementary strengths of the two metrics.

microbiology↗