Bacterial vaginosis (BV) is perceived as a condition of disrupted vaginal microbiota, but remains of unknown aetiology. In this study, vaginal microbiota composition was determined in twenty-one... Show moreBacterial vaginosis (BV) is perceived as a condition of disrupted vaginal microbiota, but remains of unknown aetiology. In this study, vaginal microbiota composition was determined in twenty-one women with BV, before and after treatment with metronidazole or clindamycin. Microbiota composition varied greatly between women and defining a (un)healthy vaginal microbiota state remains elusive, challenging BV diagnosis and treatment. While relative abundance ofLactobacillusincreased after antibiotic treatment in two-third of women, its abundance was not associated with treatment outcome. Instead, remaining complaints of abnormal vaginal discharge were more common after metronidazole treatment and associated with increased relative abundance ofUreaplasma. Show less
Gut microbiota composition in patients with Clostridioides difficile colonization is not well investigated. We aimed to identify bacterial signatures associated with resistance and susceptibility... Show moreGut microbiota composition in patients with Clostridioides difficile colonization is not well investigated. We aimed to identify bacterial signatures associated with resistance and susceptibility to C. difficile colonization (CDC) and infection (CDI). Therefore, gut microbiota composition from patients with CDC (n = 41), with CDI (n = 41), and without CDC (controls, n = 43) was determined through 16S rRNA gene amplicon sequencing. Bacterial diversity was decreased in CDC and CDI patients (p < 0.01). Overall microbiota composition was significantly different between control, CDC, and CDI patients (p = 0.001). Relative abundance of Clostridioides (most likely C. difficile) increased stepwise from controls to CDC and CDI patients. In addition, differential abundance analysis revealed that CDI patients' gut microbiota was characterized by significantly higher relative abundance of Bacteroides and Veillonella than CDC patients and controls. Control patients had significantly higher Eubacterium hallii and Fusicatenibacter abundance than colonized patients. Network analysis indicated that Fusicatenibacter was negatively associated with Clostridioides in CDI patients, while Veillonella was positively associated with Clostridioides in CDC patients. Bacterial microbiota diversity decreased in both CDC and CDI patients, but harbored a distinct microbiota. Eubacterium hallii and Fusicatenibacter may indicate resistance against C. difficile colonization and subsequent infection, while Veillonella may indicate susceptibility to colonization and infection by C. difficile. Show less
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... Show moreWhen 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. Show less
