Thermus thermophilus DNA can be used as internal control for process monitoring of clinical metagenomic next-generation sequencing of urine samples

Pathogen quantitative efficacy of different spike-in internal controls and clinical application in central nervous system infection with metagenomic sequencing

IMPORTANCE

Metagenomic next-generation sequencing (mNGS) has been used broadly for pathogens detection of infectious diseases. However, there is a lack of method for the absolute quantitation of pathogens by mNGS. We compared the quantitative efficiency of three mNGS internal controls (ICs) Thermus thermophilus, T1 phages, and artificial DNA sequence and developed the most applicable strategies for pathogen quantitation via mNGS in central nervous system infection. The IC application strategy we developed will enable mNGS analysis to assess the pathogen load simultaneously with the detection of pathogens, which should provide critical information for quick decision-making of treatment as well as clinical prognosis.

Targeting the 16S rRNA Gene by Reverse Complement PCR Next-Generation Sequencing: Specific and Sensitive Detection and Identification of Microbes Directly in Clinical Samples

The detection and accurate identification of bacterial species in clinical samples are crucial for diagnosis and appropriate antibiotic treatment. To date, sequencing of the 16S rRNA gene has been widely used as a complementary molecular approach when identification by culture fails. The accuracy and sensitivity of this method are highly affected by the selection of the 16S rRNA gene region targeted. In this study, we assessed the clinical utility of 16S rRNA reverse complement PCR (16S RC-PCR), a novel method based on next-generation sequencing (NGS), for the identification of bacterial species. We investigated the performance of 16S RC-PCR on 11 bacterial isolates, 2 polymicrobial community samples, and 59 clinical samples from patients suspected of having a bacterial infection. The results were compared to culture results, if available, and to the results of Sanger sequencing of the 16S rRNA gene (16S Sanger sequencing). By 16S RC-PCR, all bacterial isolates were accurately identified to the species level. Furthermore, in culture-negative clinical samples, the rate of identification increased from 17.1% (7/41) to 46.3% (19/41) when comparing 16S Sanger sequencing to 16S RC-PCR. We conclude that the use of 16S RC-PCR in the clinical setting leads to an increased sensitivity of detection of bacterial pathogens, resulting in a higher number of diagnosed bacterial infections, and thereby can improve patient care. IMPORTANCE The identification of the causative infectious pathogen in patients suspected of having a bacterial infection is essential for diagnosis and the start of appropriate treatment. Over the past 2 decades, molecular diagnostics have improved the ability to detect and identify bacteria. However, novel techniques that can accurately detect and identify bacteria in clinical samples and that can be implemented in clinical diagnostics are needed. Here, we demonstrate the clinical utility of bacterial identification in clinical samples by a novel method called 16S RC-PCR. Using 16S RC-PCR, we reveal a significant increase in the number of clinical samples in which a potentially clinically relevant pathogen is identified compared to the commonly used 16S Sanger method. Moreover, RC-PCR allows automation and is well suited for implementation in a diagnostic laboratory. In conclusion, the implementation of this method as a diagnostic tool is expected to result in an increased number of diagnosed bacterial infections, and in combination with adequate treatment, this could improve clinical outcomes for patients.

Effect of bacterial DNA enrichment on detection and quantification of bacteria in an infected tissue model by metagenomic next-generation sequencing

Before implementing metagenomic next-generation sequencing (mNGS) in the routine diagnostic laboratory, several challenges need to be resolved. To address strengths and limitations of mNGS in bacterial detection and quantification in samples with overwhelming host DNA abundance, we used the pig muscle tissue spiked with a home-made bacterial mock community, consisting of four species from different phyla. From the spiked tissue, we extracted DNA using: (i) a procedure based on mechanical/chemical lysis (no bacterial DNA enrichment); (ii) the Ultra-Deep Microbiome Prep (Molzym) kit for bacterial DNA enrichment; and (iii) the same enrichment kit but replacing the original proteinase K treatment for tissue solubilization by a collagenases/thermolysin digestion and cell filtration. Following mNGS, we determined bacterial: 'host' read ratios and taxonomic abundance profiles. We calculated the load of each mock-community member by combining its read counts with read counts and microscopically-determined cell counts of other co-spiked bacteria. In unenriched samples, bacterial quantification and taxonomic profiling were fairly accurate but at the expense of the sensitivity of detection. The removal of 'host' DNA by the modified enrichment protocol substantially improved bacterial detection in comparison to the other two extraction procedures and generated less distorted taxonomic profiles as compared to the original enrichment protocol.

Measuring the absolute abundance of the microbiome by adding yeast containing 16S rRNA gene from a hyperthermophile

High-throughput sequencing (HTS) of 16S rRNA gene amplicons provides compositional information regarding the microbial community, but not the absolute abundance of the bacteria. We aimed to develop a standardized method for quantifying the absolute abundance of bacteria in microbiome studies. To demonstrate the utility of our approach, we quantified the number of bacteria from the compositional data of the fecal and cecal microbiomes. The 16S rRNA gene of a hyperthermophile, Thermus aquaticus, was cloned into Pichia pastoris (yeast) genome, and an equivalent amount of the yeast was added to the stool and cecal samples of mice before DNA extraction. 16S rRNA gene library construction and HTS were performed after DNA extraction. The absolute abundances of bacteria were calculated using T. aquaticus reads. The average relative abundances of T. aquaticus in the five stool and five cecal samples were 0.95% and 0.33%, respectively, indicating that the number of bacteria in a cecum sample is 2.9 times higher than that in a stool sample. The method proposed for quantifying the absolute abundance of the bacterial population in this study is expected to overcome the limitation of showing only compositional data in most microbiome studies.

Current ocular microbiome investigations limit reproducibility and reliability: Critical review and opportunities

Enthusiasm for research describing microbial communities using next-generation sequencing (NGS) has outpaced efforts to standardize methodology. Without consistency in the way research is carried out in this field, the comparison of data between studies is near impossible and the utility of results remains limited. This holds true for bacterial microbiome research of the ocular surface, and other sites, in both humans and animals. In addition, the ocular surface remains under-explored when compared to other mucosal sites. Low bacterial biomass samples from the ocular surface lead to further technical challenges. Taken together, two major problems were identified: (1) Normalization of the workflow in studies utilizing NGS to investigate the ocular surface bacteriome is necessary in order to propel the field forward and improve research impact through cross-study comparisons. (2) Current microbiome profiling technology was developed for high bacterial biomass samples (such as feces or soil), posing a challenge for analyses of samples with low bacterial load such as the ocular surface. This article reviews the challenges and limitations currently facing ocular microbiome research and provides recommendations for minimum reporting standards for veterinary ophthalmologists and clinician scientists to limit inter-study variation, improve reproducibility, and ultimately render results from these studies more impactful. The move toward normalization of methodology will expedite and maximize the potential for microbiome research to translate into meaningful discovery and tangible clinical applications.

Baseline gut microbiome composition predicts metformin therapy short-term efficacy in newly diagnosed type 2 diabetes patients

Background

The study was conducted to investigate the effects of metformin treatment on the human gut microbiome’s taxonomic and functional profile in the Latvian population, and to evaluate the correlation of these changes with therapeutic efficacy and tolerance.

Methods

In this longitudinal observational study, stool samples for shotgun metagenomic sequencing-based analysis were collected in two cohorts. The first cohort included 35 healthy nondiabetic individuals (metformin dose 2x850mg/day) at three time-points during metformin administration. The second cohort was composed of 50 newly-diagnosed type 2 diabetes patients (metformin dose–determined by an endocrinologist) at two concordant times. Patients were defined as Responders if their HbA1c levels during three months of metformin therapy had decreased by ≥12.6 mmol/mol (1%), while in Non-responders HbA1c were decreased by <12.6 mmol/mol (1%).

Results

Metformin reduced the alpha diversity of microbiota in healthy controls (p = 0.02) but not in T2D patients. At the species level, reduction in the abundance of Clostridium bartlettii and Barnesiella intestinihominis, as well as an increase in the abundance of Parabacteroides distasonis and Oscillibacter unclassified overlapped between both study groups. A large number of group-specific changes in taxonomic and functional profiles was observed. We identified an increased abundance of Prevotella copri (FDR = 0.01) in the Non-Responders subgroup, and enrichment of Enterococcus faecium, Lactococcus lactis, Odoribacter, and Dialister at baseline in the Responders group. Various taxonomic units were associated with the observed incidence of side effects in both cohorts.

Conclusions

Metformin effects are different in T2D patients and healthy individuals. Therapy induced changes in the composition of gut microbiome revealed possible mediators of observed short-term therapeutic effects. The baseline composition of the gut microbiome may influence metformin therapy efficacy and tolerance in T2D patients and could be used as a powerful prediction tool.