Colibactin mutational signatures in NTHL1 tumor syndrome and MUTYH associated polyposis patients.

Polyketide synthase (pks) island harboring Escherichia coli are, under the right circumstances, able to produce the genotoxin colibactin. Colibactin is a risk factor for the development of colorectal cancer and associated with mutational signatures SBS88 and ID18. This study explores colibactin-associated mutational signatures in biallelic NTHL1 and MUTYH patients. Targeted Next Generation Sequencing (NGS) was performed on colorectal adenomas and carcinomas of one biallelic NTHL and 12 biallelic MUTYH patients. Additional fecal metagenomics and genome sequencing followed by mutational signature analysis was conducted for the NTHL1 patient. Targeted NGS of the NTHL1 patient showed somatic APC variants fitting SBS88 which was confirmed using WGS. Furthermore, fecal metagenomics revealed pks genes. Also, in 1 out of 11 MUTYH patient a somatic variant was detected fitting SBS88. This report shows that colibactin may influence development of colorectal neoplasms in predisposed patients.

Toward Standards in Clinical Microbiota Studies: Comparison of Three DNA Extraction Methods and Two Bioinformatic Pipelines.

When studying the microbiome using next-generation sequencing, the DNA extraction method, sequencing procedures, and bioinformatic processing are crucial to obtain reliable data. Method choice has been demonstrated to strongly affect the final biological interpretation. We assessed the performance of three DNA extraction methods and two bioinformatic pipelines for bacterial microbiota profiling through 16S rRNA gene amplicon sequencing, using positive and negative controls for DNA extraction and sequencing and eight different types of high- or low-biomass samples. Performance was evaluated based on quality control passing, DNA yield, richness, diversity, and compositional profiles. All DNA extraction methods retrieved the theoretical relative bacterial abundance with a maximum 3-fold change, although differences were seen between methods, and library preparation and sequencing induced little variation. Bioinformatic pipelines showed different results for observed richness, but diversity and compositional profiles were comparable. DNA extraction methods were successful for feces and oral swabs, and variation induced by DNA extraction methods was lower than intersubject (biological) variation. For low-biomass samples, a mixture of genera present in negative controls and sample-specific genera, possibly representing biological signal, were observed. We conclude that the tested bioinformatic pipelines perform equally, with pipeline-specific advantages and disadvantages. Two out of three extraction methods performed equally well, while one method was less accurate regarding retrieval of compositional profiles. Lastly, we again demonstrate the importance of including negative controls when analyzing low-bacterial-biomass samples.IMPORTANCE Method choice throughout the workflow of a microbiome study, from sample collection to DNA extraction and sequencing procedures, can greatly affect results. This study evaluated three different DNA extraction methods and two bioinformatic pipelines by including positive and negative controls and various biological specimens. By identifying an optimal combination of DNA extraction method and bioinformatic pipeline use, we hope to contribute to increased methodological consistency in microbiota studies. Our methods were applied not only to commonly studied samples for microbiota analysis, e.g., feces, but also to more rarely studied, low-biomass samples. Microbiota composition profiles of low-biomass samples (e.g., urine and tumor biopsy specimens) were not always distinguishable from negative controls, or showed partial overlap, confirming the importance of including negative controls in microbiota studies, especially when low bacterial biomass is expected.