ABSTRACT
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.
ABSTRACT
The gut microbiome is critical in providing resistance against colonization by exogenous microorganisms. The mechanisms via which the gut microbiota provide colonization resistance (CR) have not been fully elucidated, but they include secretion of antimicrobial products, nutrient competition, support of gut barrier integrity, and bacteriophage deployment. However, bacterial enteric infections are an important cause of disease globally, indicating that microbiota-mediated CR can be disturbed and become ineffective. Changes in microbiota composition, and potential subsequent disruption of CR, can be caused by various drugs, such as antibiotics, proton pump inhibitors, antidiabetics, and antipsychotics, thereby providing opportunities for exogenous pathogens to colonize the gut and ultimately cause infection. In addition, the most prevalent bacterial enteropathogens, including Clostridioides difficile, Salmonella enterica serovar Typhimurium, enterohemorrhagic Escherichia coli, Shigella flexneri, Campylobacter jejuni, Vibrio cholerae, Yersinia enterocolitica, and Listeria monocytogenes, can employ a wide array of mechanisms to overcome colonization resistance. This review aims to summarize current knowledge on how the gut microbiota can mediate colonization resistance against bacterial enteric infection and on how bacterial enteropathogens can overcome this resistance.
Subject(s)
Anti-Infective Agents/pharmacology , Bacteria/metabolism , Bacterial Infections/microbiology , Gastrointestinal Microbiome , Animals , Bacteria/pathogenicity , Fatty Acids, Volatile/biosynthesis , Humans , Mice , MucusABSTRACT
OBJECTIVES: Clostridioides difficile infection (CDI) has become the main cause of nosocomial infective diarrhoea. To survey and control the spread of different C. difficile strains, there is a need for suitable rapid tests. The aim of this study was to identify peptide/protein markers for the rapid recognition of C. difficile strains by matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS). METHODS: We analysed 44 well-characterized strains, belonging to eight different multi-locus sequence types (MLST), using ultrahigh-resolution Fourier transform ion cyclotron resonance (FTICR) MS. The amino acid sequence of two peptide markers specific for MLST-1 and MLST-11 strains was elucidated by MALDI-TOF-MS/MS. The investigation of 2689 C. difficile genomes allowed the determination of the sensitivity and specificity of these markers. C18-solid-phased extraction was used to enrich the MLST-1 marker. RESULTS: Two peptide markers (m/z 4927.81 and m/z 5001.84) were identified and characterized for MLST-1 and MLST-11 strains, respectively. The MLST-1 marker was found in 786 genomes of which three did not belong to MLST-1. The MLST-11 marker was found in 319 genomes, of which 14 did not belong to MLST-11. Importantly, all MLST-1 and MLST-11 genomes were positive for their respective marker. Furthermore, a peptide marker (m/z 5015.86) specific for MLST-15 was found in 59 genomes. We translated our findings into a fast and simple method that allowed the unambiguous identification of the MLST-1 marker on a MALDI-TOF-MS platform. CONCLUSIONS: MALDI-FTICR MS-based peptide profiling resulted in the identification of peptide markers for C. difficile MLST-1 and MLST-11.
