Cultivation and Genomics Prove Long-Term Colonization of Donor's Bifidobacteria in RecurrentClostridioides difficilePatients Treated With Fecal Microbiota Transplantation

Fecal microbiota transplantation (FMT) is an effective treatment for recurrentClostridioides difficileinfection (rCDI) and it's also considered for treating other indications. Metagenomic studies have indicated that commensal donor bacteria may colonize FMT recipients, but cultivation has not been employed to verify strain-level colonization. We combined molecular profiling ofBifidobacteriumpopulations with cultivation, molecular typing, and whole genome sequencing (WGS) to isolate and identify strains that were transferred from donors to recipients. SeveralBifidobacteriumstrains from two donors were recovered from 13 recipients during the 1-year follow-up period after FMT. The strain identities were confirmed by WGS and comparative genomics. Our results show that specific donor-derived bifidobacteria can colonize rCDI patients for at least 1 year, and thus FMT may have long-term consequences for the recipient's microbiota and health. Conceptually, we demonstrate that FMT trials combined with microbial profiling can be used as a platform for discovering and isolating commensal strains with proven colonization capacity for potential therapeutic use.Peer reviewe

Cultivation and Genomics Prove Long-Term Colonization of Donor's Bifidobacteria in Recurrent Clostridioides difficile Patients Treated With Fecal Microbiota Transplantation

Fecal microbiota transplantation (FMT) is an effective treatment for recurrentClostridioides difficileinfection (rCDI) and it's also considered for treating other indications. Metagenomic studies have indicated that commensal donor bacteria may colonize FMT recipients, but cultivation has not been employed to verify strain-level colonization. We combined molecular profiling ofBifidobacteriumpopulations with cultivation, molecular typing, and whole genome sequencing (WGS) to isolate and identify strains that were transferred from donors to recipients. SeveralBifidobacteriumstrains from two donors were recovered from 13 recipients during the 1-year follow-up period after FMT. The strain identities were confirmed by WGS and comparative genomics. Our results show that specific donor-derived bifidobacteria can colonize rCDI patients for at least 1 year, and thus FMT may have long-term consequences for the recipient's microbiota and health. Conceptually, we demonstrate that FMT trials combined with microbial profiling can be used as a platform for discovering and isolating commensal strains with proven colonization capacity for potential therapeutic use

Tool evaluation for the detection of variably sized indels from next generation whole genome and targeted sequencing data

Insertions and deletions (indels) in human genomes are associated with a wide range of phenotypes, including various clinical disorders. High-throughput, next generation sequencing (NGS) technologies enable the detection of short genetic variants, such as single nucleotide variants (SNVs) and indels. However, the variant calling accuracy for indels remains considerably lower than for SNVs. Here we present a comparative study of the performance of variant calling tools for indel calling, evaluated with a wide repertoire of NGS datasets. While there is no single optimal tool to suit all circumstances, our results demonstrate that the choice of variant calling tool greatly impacts the precision and recall of indel calling. Furthermore, to reliably detect indels, it is essential to choose NGS technologies that offer a long read length and high coverage coupled with specific variant calling tools.Author summaryThe development of next generation sequencing (NGS) technologies and computational algorithms enabled the large scale, simultaneous detection of a wide range of genetic variants, such as single nucleotide variants as well as insertions and deletions (indels), which may confer potential clinical significance. Recently, many studies have been conducted to evaluate variant calling tools for indel calling. However, the optimal indel size range for different variant calling tools remains unclear. A good benchmarking dataset for indel calling evaluation should contain biologically representative high-confident indels with a wide size range and preferably come from various sequencing settings. In this article, we created a semi-simulated whole genome sequencing dataset where the sequencing data were computationally generated. The indels in the semi-simulated genome were incorporated from a real human sample to represent biologically realistic indels and to avoid the inclusion of variants due to potential technical sequencing errors. Furthermore, we used three real-world NGS datasets generated by whole genome or targeted sequencing to further evaluate our candidate tools. Our results demonstrated that variant calling tools vary greatly in calling different sizes of indels. Deletion calling and insertion calling also showed differences among the tools. The sequencing settings in coverage and read length also had a great impact on indel calling. Our results suggest that the accuracy of indel calling was dependent on the combination of a variant calling tool, indel size range, and sequencing settings.</p

Type 2 Low Biomarker Stability and Exacerbations in Severe Uncontrolled Asthma

We investigated the stability of T2 low status, based on low levels of T2 biomarkers, and exacerbation rates in T2 low and non-T2 low asthma from clinical retrospective data of severe uncontrolled asthma patients. Knowledge of the T2 low biomarker profile is sparse and biomarker stability is uncharted. Secondary care patients with severe uncontrolled asthma and at least two blood eosinophil counts (BEC) and fractional exhaled nitric oxide (FeNO) measured for determination of type 2 inflammation status were evaluated from a follow-up period of 4 years. Patients were stratified into four groups: T2 low150 (n = 31; BEC 150 cells/µL and/or FeNO > 25 ppb), T2 low300 (n = 66; BEC 300 cells/µL and/or FeNO > 25 ppb). Exacerbation rates requiring hospital care, stability of biomarker status, and cumulative OCS and ICS doses were assessed during follow-up. Among patients with severe uncontrolled asthma, 18% (n = 31) were identified as T2 low150, and 39% (n = 66) as T2 low300. In these groups, the low biomarker profile was stable in 55% (n = 11) and 72% (n = 33) of patients with follow-up measures. Exacerbation rates were different between the T2 low and non-T2 low groups: 19.7 [95% CI: 4.3–45.6] in T2 low150 vs. 8.4 [4.7–13.0] in non-T2 low150 per 100 patient-years. BEC and FeNO are useful biomarkers in identifying T2 low severe uncontrolled asthma, showing a stable follow-up biomarker profile in up to 72% of patients. Repeated monitoring of these biomarkers is essential in identifying and treating patients with T2 low asthma.Peer reviewe

Smoking is a predictor of complications in all types of surgery : a machine learning-based big data study

Background: Machine learning algorithms are promising tools for smoking status classification in big patient data sets. Smoking is a risk factor for postoperative complications in major surgery. Whether this applies to all surgery is unknown. The aims of this retrospective cohort study were to develop a machine learning algorithm for clinical record-based smoking status classification and to determine whether smoking and former smoking predict complications in all surgery types. Methods: All surgeries performed in a Finnish hospital district from 1 January 2015 to 31 December 2019 were analysed. Exclusion criteria were age below 16 years, unknown smoking status, and unknown ASA class. A machine learning algorithm was developed for smoking status classification. The primary outcome was 90-day overall postoperative complications in all surgeries. Secondary outcomes were 90-day overall complications in specialties with over 10 000 surgeries and critical complications in all surgeries. Results: The machine learning algorithm had precisions of 0.958 for current smokers, 0.974 for ex-smokers, and 0.95 for never-smokers. The sample included 158 638 surgeries. In adjusted logistic regression analyses, smokers had increased odds of overall complications (odds ratio 1.17; 95 per cent c.i. 1.14 to 1.20) and critical complications (odds ratio 1.21; 95 per cent c.i. 1.14 to 1.29). Corresponding odds ratios of ex-smokers were 1.09 (95 per cent c.i. 1.06 to 1.13) and 1.09 (95 per cent c.i. 1.02 to 1.17). Smokers had increased odds of overall complications in all specialties with over 10 000 surgeries. ASA class was the most important complication predictor. Conclusion: Machine learning algorithms are feasible for smoking status classification in big surgical data sets. Current and former smoking predict complications in all surgery types.Peer reviewe

Data-Independent Acquisition Mass Spectrometry in Metaproteomics of Gut Microbiota—Implementation and Computational Analysis

Metagenomic approaches focus on taxonomy or gene annotation but lack power in defining functionality of gut microbiota. Therefore, metaproteomics approaches have been introduced to overcome this limitation. However, the common metaproteomics approach uses data-dependent acquisition mass spectrometry, which is known to have limited reproducibility when analyzing samples with complex microbial composition. In this work, we provide a proof-of-concept for data-independent acquisition (DIA) metaproteomics. To this end, we analyze metaproteomes using DIA mass spectrometry and introduce an open-source data analysis software package diatools, which enables accurate and consistent quantification of DIA metaproteomics data. We demonstrate the feasibility of our approach in gut microbiota metaproteomics using laboratory assembled microbial mixtures as well as human fecal samples. </p

Distinct Patterns in Human Milk Microbiota and Fatty Acid Profiles Across Specific Geographic Locations

Breast feeding results in long term health benefits in the prevention of communicable and non-communicable diseases at both individual and population levels. Geographical location directly impacts the composition of breast milk including microbiota and lipids. The aim of this study was to investigate the influence of geographical location, i.e., Europe (Spain and Finland), Africa (South Africa), and Asia (China), on breast milk microbiota and lipid composition in samples obtained from healthy mothers after the 1 month of lactation. Altogether, 80 women (20 from each country) participated in the study, with equal number of women who delivered by vaginal or cesarean section from each country. Lipid composition particularly that of polyunsaturated fatty acids differed between the countries, with the highest amount of n-6 PUFA (25.6%) observed in the milk of Chinese women. Milk microbiota composition also differed significantly between the countries (p = 0.002). Among vaginally delivered women. Spanish women had highest amount of Bacteroidetes (mean relative abundance of 3.75) whereas Chinese women had highest amount of Actinobacteria (mean relative abundance 5.7). Women who had had a cesarean section had higher amount of Proteobacteria as observed in the milk of the Spanish and South African women. Interestingly, the Spanish and South African women had significantly higher bacterial genes mapped to lipid, amino acid and carbohydrate metabolism (p < 0.05). Association of the lipid profile with the microbiota revealed that monounsaturated fatty acids (MUFA) were negatively associated with Proteobacteria (r = 0.43, p < 0.05), while Lactobacillus genus was associated with MUFA (r = 0.23, p = 0.04). These findings reveal that the milk microbiota and lipid composition exhibit differences based on geographical locations in addition to the differences observed due to the mode of delivery