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.

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.

Lipid-based Nutrient Supplements Do Not Affect Gut Bifidobacterium Microbiota in Malawian Infants: A Randomized Trial.

OBJECTIVES: The aim of the study was to assess the effect of nutritional supplementation with lipid-based nutrient supplements (LNS) and corn-soy blend flour on Bifidobacterium and Staphylococcus aureus gut microbiota composition in Malawian infants. In addition, the microbiota changes over time were characterized in the study infants. METHODS: Healthy 6-month-old Malawian infants were randomly assigned to 1 of 4 intervention schemes for a 6-month period. Infants in the control group were not provided with any supplementary food. Infants in other 3 groups received either micronutrient-fortified corn-soy blend, micronutrient-fortified LNS with milk protein base, or micronutrient-fortified LNS with soy protein base between 6 and 12 months of age. Fecal bifidobacteria and S aureus gut microbiota at 6 and 12 months of age were analyzed by quantitative real-time polymerase chain reaction method. RESULTS: There was no difference in change in bacterial prevalence or counts between the intervention groups during the 6-month study period. When looking at the total study population, higher counts of total bacteria (P = 0.028), Bifidobacterium genus (P = 0.027), B catenulatum (P = 0.031), and lower counts of B infantis (P < 0.001), B lactis (P < 0.001), B longum (P < 0.001), and S aureus (P < 0.001) were detected in the children's stools at 12 months rather than at 6 months of age. CONCLUSIONS: The dietary supplementation did not have an effect on the Bifidobacterium and S aureus microbiota composition of the study infants. The fecal bifidobacterial diversity of the infants, however, changed toward a more adult-like microbiota profile within the observed time.

Distinctive Intestinal Lactobacillus Communities in 6-Month-Old Infants From Rural Malawi and Southwestern Finland.

OBJECTIVE: Our aim was to compare the composition and diversity of Lactobacillus microbiota in infants living in Malawi and Southwestern Finland. METHODS: The composition and diversity of the Lactobacillus group was analyzed in the feces of healthy 6-month-old infants living in rural Malawi (n = 44) and Southwestern Finland (n = 31), using the quantitative polymerase chain reaction method and PCR-denaturing gradient gel electrophoresis fingerprinting. RESULTS: Malawian infants had higher counts of lactobacilli than their Finnish counterparts (7.45 log cells/g vs 6.86 log cells/g, P < 0.001, respectively) and the Lactobacillus community was richer and more diverse in the Malawian infants. Leuconostoc citreum and Weissella confusa were the predominant species in both study groups, but Malawian infants were more often colonized by these species (100% vs 74.2%, P < 0.001; 95.5% vs 41.9%, P < 0.001, respectively). Moreover, Lactobacillus ruminis, Lactobacillus gasseri, Lactobacillus acidophilus, and Lactobacillus mucosae were detected more often in the Malawian infants (59.1% vs 0.0%, P < 0.001; 38.6% vs 9.7%, P = 0.004; 29.5% vs 0.0%, P < 0.001; 22.7% vs 3.2%, P = 0.017, respectively). Lactobacillus casei group species, however, were only detected in the Finnish infants. CONCLUSIONS: Malawian infants have a more abundant Lactobacillus microbiota with a distinct composition compared with Finnish infants. The environment, including diet and hygiene, may be among the factors influencing these differences.

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 FeNO < 25 ppb), non-T2 low150 (n = 138; BEC > 150 cells/µL and/or FeNO > 25 ppb), T2 low300 (n = 66; BEC < 300 cells/µL and FeNO < 25 ppb), and non-T2 low300 (n = 103; 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.

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.

The burden of chronic rhinosinusitis with nasal polyps and its relation to asthma in Finland.

Background: Chronic rhinosinusitis with nasal polyps (CRSwNP) is commonly associated with asthma. Treatment of CRSwNP includes intranasal and systemic corticosteroids, with non-responsive patients commonly considered for endoscopic sinus surgery (ESS). This nationwide register-based study evaluated the incidence, prevalence, and treatment burden of CRSwNP in Finland, and their association with the presence and severity of comorbid asthma. Methods: Electronic health records of patients diagnosed with CRSwNP between 1.1.2012 and 31.12.2018 in Finnish specialty and primary care were included in the study. The patients were divided into subgroups based on presence, severity, and control of asthma: no asthma, mild to moderate asthma, severe controlled asthma, and severe uncontrolled asthma. A mean cumulative count of ESS was calculated over time per subgroup. Results: The prevalence of CRSwNP increased from 602.2 to 856.7 patients per 100,000 population between years 2012 and 2019 (p < 0.001). A total of 18,563 patients (59.9% male) had incident CRSwNP between 2012 and 2019, with 27% having asthma, 6% having severe asthma, and 1.5% having severe uncontrolled asthma. In the no asthma, severe controlled asthma, and severe uncontrolled asthma subgroups, systemic corticosteroids were used by 54.1%, 94.9% and 99.3% (p < 0.001), respectively, while the ESS count 3 years post diagnosis was 0.49, 0.68 and 0.80, respectively. Conclusions: The prevalence of CRSwNP showed a significant increase in the recent decade in Finland. Comorbid asthma, and in particular severe asthma, increased the probability of receiving systemic corticosteroids and undergoing ESS. Thus, improved management of CRSwNP in patients with comorbid asthma is urgently needed.

Detailed Assessment of Sleep Architecture With Deep Learning and Shorter Epoch-to-Epoch Duration Reveals Sleep Fragmentation of Patients With Obstructive Sleep Apnea.

Traditional sleep staging with non-overlapping 30-second epochs overlooks multiple sleep-wake transitions. We aimed to overcome this by analyzing the sleep architecture in more detail with deep learning methods and hypothesized that the traditional sleep staging underestimates the sleep fragmentation of obstructive sleep apnea (OSA) patients. To test this hypothesis, we applied deep learning-based sleep staging to identify sleep stages with the traditional approach and by using overlapping 30-second epochs with 15-, 5-, 1-, or 0.5-second epoch-to-epoch duration. A dataset of 446 patients referred for polysomnography due to OSA suspicion was used to assess differences in the sleep architecture between OSA severity groups. The amount of wakefulness increased while REM and N3 decreased in severe OSA with shorter epoch-to-epoch duration. In other OSA severity groups, the amount of wake and N1 decreased while N3 increased. With the traditional 30-second epoch-to-epoch duration, only small differences in sleep continuity were observed between the OSA severity groups. With 1-second epoch-to-epoch duration, the hazard ratio illustrating the risk of fragmented sleep was 1.14 (p = 0.39) for mild OSA, 1.59 (p < 0.01) for moderate OSA, and 4.13 (p < 0.01) for severe OSA. With shorter epoch-to-epoch durations, total sleep time and sleep efficiency increased in the non-OSA group and decreased in severe OSA. In conclusion, more detailed sleep analysis emphasizes the highly fragmented sleep architecture in severe OSA patients which can be underestimated with traditional sleep staging. The results highlight the need for a more detailed analysis of sleep architecture when assessing sleep disorders.

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 recurrent Clostridioides difficile infection (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 of Bifidobacterium populations with cultivation, molecular typing, and whole genome sequencing (WGS) to isolate and identify strains that were transferred from donors to recipients. Several Bifidobacterium strains 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.

A carbohydrate-active enzyme (CAZy) profile links successful metabolic specialization of Prevotella to its abundance in gut microbiota.

Gut microbiota participates in diverse metabolic and homeostatic functions related to health and well-being. Its composition varies between individuals, and depends on factors related to host and microbial communities, which need to adapt to utilize various nutrients present in gut environment. We profiled fecal microbiota in 63 healthy adult individuals using metaproteomics, and focused on microbial CAZy (carbohydrate-active) enzymes involved in glycan foraging. We identified two distinct CAZy profiles, one with many Bacteroides-derived CAZy in more than one-third of subjects (n = 25), and it associated with high abundance of Bacteroides in most subjects. In a smaller subset of donors (n = 8) with dietary parameters similar to others, microbiota showed intense expression of Prevotella-derived CAZy including exo-beta-(1,4)-xylanase, xylan-1,4-beta-xylosidase, alpha-L-arabinofuranosidase and several other CAZy belonging to glycosyl hydrolase families involved in digestion of complex plant-derived polysaccharides. This associated invariably with high abundance of Prevotella in gut microbiota, while in subjects with lower abundance of Prevotella, microbiota showed no Prevotella-derived CAZy. Identification of Bacteroides- and Prevotella-derived CAZy in microbiota proteome and their association with differences in microbiota composition are in evidence of individual variation in metabolic specialization of gut microbes affecting their colonizing competence.

Deep learning enables sleep staging from photoplethysmogram for patients with suspected sleep apnea.

STUDY OBJECTIVES: Accurate identification of sleep stages is essential in the diagnosis of sleep disorders (e.g. obstructive sleep apnea [OSA]) but relies on labor-intensive electroencephalogram (EEG)-based manual scoring. Furthermore, long-term assessment of sleep relies on actigraphy differentiating only between wake and sleep periods without identifying specific sleep stages and having low reliability in identifying wake periods after sleep onset. To address these issues, we aimed to develop an automatic method for identifying the sleep stages from the photoplethysmogram (PPG) signal obtained with a simple finger pulse oximeter. METHODS: PPG signals from the diagnostic polysomnographies of susptected OSA patients (n = 894) were utilized to develop a combined convolutional and recurrent neural network. The deep learning model was trained individually for three-stage (wake/NREM/REM), four-stage (wake/N1+N2/N3/REM), and five-stage (wake/N1/N2/N3/REM) classification of sleep. RESULTS: The three-stage model achieved an epoch-by-epoch accuracy of 80.1% with Cohen's κ of 0.65. The four- and five-stage models achieved 68.5% (κ = 0.54), and 64.1% (κ = 0.51) accuracies, respectively. With the five-stage model, the total sleep time was underestimated with a mean bias error (SD) of of 7.5 (55.2) minutes. CONCLUSION: The PPG-based deep learning model enabled accurate estimation of sleep time and differentiation between sleep stages with a moderate agreement to manual EEG-based scoring. As PPG is already included in ambulatory polygraphic recordings, applying the PPG-based sleep staging could improve their diagnostic value by enabling simple, low-cost, and reliable monitoring of sleep and help assess otherwise overlooked conditions such as REM-related OSA.

Accurate Deep Learning-Based Sleep Staging in a Clinical Population With Suspected Obstructive Sleep Apnea.

The identification of sleep stages is essential in the diagnostics of sleep disorders, among which obstructive sleep apnea (OSA) is one of the most prevalent. However, manual scoring of sleep stages is time-consuming, subjective, and costly. To overcome this shortcoming, we aimed to develop an accurate deep learning approach for automatic classification of sleep stages and to study the effect of OSA severity on the classification accuracy. Overnight polysomnographic recordings from a public dataset of healthy individuals (Sleep-EDF, n = 153) and from a clinical dataset (n = 891) of patients with suspected OSA were used to develop a combined convolutional and long short-term memory neural network. On the public dataset, the model achieved sleep staging accuracy of 83.7% (κ = 0.77) with a single frontal EEG channel and 83.9% (κ = 0.78) when supplemented with EOG. For the clinical dataset, the model achieved accuracies of 82.9% (κ = 0.77) and 83.8% (κ = 0.78) with a single EEG channel and two channels (EEG+EOG), respectively. The sleep staging accuracy decreased with increasing OSA severity. The single-channel accuracy ranged from 84.5% (κ = 0.79) for individuals without OSA diagnosis to 76.5% (κ = 0.68) for patients with severe OSA. In conclusion, deep learning enables automatic sleep staging for suspected OSA patients with high accuracy and expectedly, the accuracy decreased with increasing OSA severity. Furthermore, the accuracies achieved in the public dataset were superior to previously published state-of-the-art methods. Adding an EOG channel did not significantly increase the accuracy. The automatic, single-channel-based sleep staging could enable easy, accurate, and cost-efficient integration of EEG recording into diagnostic ambulatory recordings.

A Data Analysis Protocol for Quantitative Data-Independent Acquisition Proteomics.

Data-independent acquisition (DIA) mode of mass spectrometry, such as the SWATH-MS technology, enables accurate and consistent measurement of proteins, which is crucial for comparative proteomics studies. However, there is lack of free and easy to implement data analysis protocols that can handle the different data processing steps from raw spectrum files to peptide intensity matrix and its downstream analysis. Here, we provide a data analysis protocol, named diatools, covering all these steps from spectral library building to differential expression analysis of DIA proteomics data. The data analysis tools used in this protocol are open source and the protocol is distributed at Docker Hub as a complete software environment that supports Linux, Windows, and macOS operating systems.

The Impact of Storage Conditions on the Stability of Lactobacillus rhamnosus GG and Bifidobacterium animalis subsp. lactis Bb12 in Human Milk.

Human milk is the optimal source of complete nutrition for neonates and it also guides the development of infant gut microbiota. Importantly, human milk can be supplemented with probiotics to complement the health benefits of breastfeeding. Storage of human milk for limited periods of time is often unavoidable, but little is known about the effect of different storage conditions (temperature) on the viability of the added probiotics. Therefore, in this study, we evaluated how different storage conditions affect the viability of two specific widely used probiotics, Lactobacillus rhamnosus GG (LGG) and Bifidobacterium animalis subsp. lactis (Bb12), in human milk by culturing and quantitative polymerase chain reaction. Our results indicate that LGG and Bb12 remained stable throughout the storage period. Thus, we conclude that human milk offers an appropriate matrix for probiotic supplementation.

Human gut colonisation may be initiated in utero by distinct microbial communities in the placenta and amniotic fluid.

Interaction with intestinal microbes in infancy has a profound impact on health and disease in later life through programming of immune and metabolic pathways. We collected maternal faeces, placenta, amniotic fluid, colostrum, meconium and infant faeces samples from 15 mother-infant pairs in an effort to rigorously investigate prenatal and neonatal microbial transfer and gut colonisation. To ensure sterile sampling, only deliveries at full term by elective caesarean section were studied. Microbiota composition and activity assessment by conventional bacterial culture, 16S rRNA gene pyrosequencing, quantitative PCR, and denaturing gradient gel electrophoresis revealed that the placenta and amniotic fluid harbour a distinct microbiota characterised by low richness, low diversity and the predominance of Proteobacteria. Shared features between the microbiota detected in the placenta and amniotic fluid and in infant meconium suggest microbial transfer at the foeto-maternal interface. At the age of 3-4 days, the infant gut microbiota composition begins to resemble that detected in colostrum. Based on these data, we propose that the stepwise microbial gut colonisation process may be initiated already prenatally by a distinct microbiota in the placenta and amniotic fluid. The link between the mother and the offspring is continued after birth by microbes present in breast milk.

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.

Evaluation of strain-specific primers for identification of Lactobacillus rhamnosus GG.

Lactobacillus rhamnosus strain GG (ATCC 53103) is one of the most widely studied and commercialized probiotic strains, and thus strain-specific identification for the strain is highly valuable. In this study, two published PCR-based identification methods for strain GG, a transposase gene-targeting system and a phage-related gene-targeting system, were evaluated. The former produced amplicons from eight of the 41 strains tested and the phage-related system from five of the tested strains, including the strain GG. Fingerprinting analysis indicated that the strains LMG 18025, LMG 18030, and LMG 18038, which had an amplicon by the former system but none by the latter, were genetically distinguishable from L. rhamnosus GG at strain level. Strains LMG 23320, LMG 23325, LMG 23534, and LMG 25859 showed profiles very similar to that of the strain GG, suggesting that these strains might be identical to GG or derivative strains of it. The results here indicated that the phage-related gene-targeting system is a good tool for accurate identification of L. rhamnosus GG. This system would be able to detect both the original L. rhamnosus GG and its derivative strains.

Probiotic cheese containing Lactobacillus rhamnosus HN001 and Lactobacillus acidophilus NCFM® modifies subpopulations of fecal lactobacilli and Clostridium difficile in the elderly.

Aging is associated with alterations in the intestinal microbiota and with immunosenescence. Probiotics have the potential to modify a selected part of the intestinal microbiota as well as improve immune functions and may, therefore, be particularly beneficial to elderly consumers. In this randomized, controlled cross-over clinical trial, we assessed the effects of a probiotic cheese containing Lactobacillus rhamnosus HN001 and Lactobacillus acidophilus NCFM on the intestinal microbiota and fecal immune markers of 31 elderly volunteers and compared these effects with the administration of the same cheese without probiotics. The probiotic cheese was found to increase the number of L. rhamnosus and L. acidophilus NCFM in the feces, suggesting the survival of the strains during the gastrointestinal transit. Importantly, probiotic cheese administration was associated with a trend towards lower counts of Clostridium difficile in the elderly, as compared with the run-in period with the plain cheese. The effect was statistically significant in the subpopulation of the elderly who harbored C. difficile at the start of the study. The probiotic cheese was not found to significantly alter the levels of the major microbial groups, suggesting that the microbial changes conferred by the probiotic cheese were limited to specific bacterial groups. Despite that the administration of the probiotic cheese to the study population has earlier been shown to significantly improve the innate immunity of the elders, we did not observe measurable changes in the fecal immune IgA concentrations. No increase in fecal calprotectin and ß-defensin concentrations suggests that the probiotic treatment did not affect intestinal inflammatory markers. In conclusion, the administration of probiotic cheese containing L. rhamnosus HN001 and L. acidophilus NCFM, was associated with specific changes in the intestinal microbiota, mainly affecting specific subpopulations of intestinal lactobacilli and C. difficile, but did not have significant effects on the major microbial groups or the fecal immune markers.