Data-independent acquisition mass spectrometry for human gut microbiota metaproteome analysis

Human digestive tract microbiota is a diverse community of microorganisms having complex interactions between microbes and the human host. Observing the functions carried out by microbes is essential for gaining understanding on the role of gut microbiota in human health and associations to diseases. New methods and tools are needed for acquirement of functional information from complex microbial samples. Metagenomic approaches focus on taxonomy or gene based function potential but lack power in the discovery of the actual functions carried out by the microbes. Metaproteomic methods are required to uncover the functions. The current highthroughput metaproteomics methods are based on mass spectrometry which is capable of identifying and quantifying ionized protein fragments, called peptides. Proteins can be inferred from the peptides and the functions associated with protein expression can be determined by using protein databases. Currently the most widely used data-dependent acquisition (DDA) method records only the most intensive ions in a semi-stochastic manner, which reduces reproducibility and produces incomplete records impairing quantification. Alternative data-independent acquisition (DIA) systematically records all ions and has been proposed as a replacement for DDA. However, recording all ions produces highly convoluted spectra from multiple peptides and, for this reason, it has not been known if and how DIA can be applied to metaproteomics where the number of different peptides is high. This thesis work introduced the DIA method for metaproteomic data analysis. The method was shown to achieve high reproducibility enabling the usage of only a single analysis per sample while DDA requires multiple. An easy to use open source software package, DIAtools, was developed for the analysis. Finally, the DIA analysis method was applied to study human gut microbiota and carbohydrate-active enzymes expressed in members of gut microbiota.Ihmisen suolistomikrobiston analyysi DIAmassaspektrometriamenetelmällä Ihmisen suoliston mikrobisto on monien mikro-organismien yhteisö, joka on vuorovaikutuksessa ihmisen kehon kanssa. Suoliston mikrobien toiminnan ymmärtäminen on keskeistä niiden roolista ihmisen terveyteen ja sairauksiin. Uusia tutkimusmenetelmiä tarvitaan mikrobien toiminnallisuuden määrittämiseen monimutkaisista, useita mikrobeja sisältävistä, näytteistä. Yleisesti käytetyt metagenomiikan menetelmät keskittyvät taksonomiaan tai geenien perusteella ennustettuihin funktioihin, mutta metaproteomiikkaa tarvitaan mikrobien toiminnan selvittämiseen. Metaproteomiikka-analyysiin voidaan käyttää massaspektrometriaa, jolla pystytään tunnistamaan ja määrittämään ionisoitujen proteiinien osasten, peptidien, määrä. Proteiinit voidaan päätellä peptideistä ja näin pystytään määrittämään proteiineihin liittyviä toimintoja hyödyntäen proteiinitietokantoja. Nykyisin käytetty DDA-menetelmä tunnistaa vain runsaimmin esiintyvät ionit, mikä rajoittaa sen hyödyntämistä. Siinä mitattavien ionien valinta on jossain määrin satunnainen, mikä vähentää tulosten toistettavuutta. Vaihtoehtoinen DIA-menetelmä analysoi järjestelmällisesti kaikki ionit ja kyseistä menetelmää on ehdotettu DDA:n tilalle. DIA-menetelmä tuottaa päällekkäisiä peptidispektrejä ja siksi aiemmin ei ole ollut tiedossa, onko se soveltuva menetelmä tai miten sitä olisi mahdollista soveltaa metaproteomiikkaan, jossa on suuri määrä erilaisia peptidejä. Tämä tutkimus esittelee soveltuvia tapoja DIA-menetelmän käyttöön metaproteomiikkadatan analysoinnissa. Työssä osoitetaan, että DIA-metaproteomiikka tuottaa luotettavasti toistettavia tuloksia. DIA-menetelmää käyttäessä riittää, että näyte analysoidaan vain yhden kerran, kun vastaavasti DDA-menetelmän käyttö vaatii useamman analysointikerran. Tutkimuksessa kehitettiin avoimen lähdekoodin ohjelmisto DIAtools, joka toteuttaa kehitetyt DIA-datojen analysointimenetelmät. Lopuksi DIA-analyysiä sovellettiin ruoansulatuskanavan mikrobien ja niiden tuottamien CAZy-entsyymien tutkimiseksi

Introducing untargeted data-independent acquisition for metaproteomics of complex microbial samples

Mass spectrometry-based metaproteomics is a relatively new field of research that enables the characterization of the functionality of microbiota. Recently, we demonstrated the applicability of data-independent acquisition (DIA) mass spectrometry to the analysis of complex metaproteomic samples. This allowed us to circumvent many of the drawbacks of the previously used data-dependent acquisition (DDA) mass spectrometry, mainly the limited reproducibility when analyzing samples with complex microbial composition. However, the DDA-assisted DIA approach still required additional DDA data on the samples to assist the analysis. Here, we introduce, for the first time, an untargeted DIA metaproteomics tool that does not require any DDA data, but instead generates a pseudospectral library directly from the DIA data. This reduces the amount of required mass spectrometry data to a single DIA run per sample. The new DIA-only metaproteomics approach is implemented as a new open-source software package named glaDIAtor, including a modern web-based graphical user interface to facilitate wide use of the tool by the community.</p

PhosPiR: an automated phosphoproteomic pipeline in R

Large-scale phosphoproteome profiling using mass spectrometry (MS) provides functional insight that is crucial for disease biology and drug discovery. However, extracting biological understanding from these data is an arduous task requiring multiple analysis platforms that are not adapted for automated high-dimensional data analysis. Here, we introduce an integrated pipeline that combines several R packages to extract high-level biological understanding from large-scale phosphoproteomic data by seamless integration with existing databases and knowledge resources. In a single run, PhosPiR provides data clean-up, fast data overview, multiple statistical testing, differential expression analysis, phosphosite annotation and translation across species, multilevel enrichment analyses, proteome-wide kinase activity and substrate mapping and network hub analysis. Data output includes graphical formats such as heatmap, box-, volcano- and circos-plots. This resource is designed to assist proteome-wide data mining of pathophysiological mechanism without a need for programming knowledge.</p

Gut microbiota composition is associated with temperament traits in infants

Background: One of the key behavioral phenotypes in infancy are different temperament traits, and certain early life temperament traits have been shown to precede later mental health problems. Differences in the gut microbiota composition (GMC) have been suggested to link with neurodevelopment. For example, toddler temperament traits have been found to associate with differences in GMC; however, studies in infants are lacking although infancy is a rapid period of neurodevelopment as well as GM development. Thus, we aimed to investigate association between infant GMC and temperament. Methods: The study population (n = 301, 53% boys) was drawn from the FinnBrain Birth Cohort Study. Stool samples were collected from the 2.5-month-old infants and sequenced with 16S Illumina MiSeq platform. GMC taxonomic composition (at Genus and OTU level), observed sample clusters, diversity and richness were investigated in relation to the maternal reports of Infant Behavior Questionnaire -Revised (IBQ-R) at the age of 6 months. Results: Three sample clusters (Bifidobacterium/Enterobacteriaceae, Bacteroides, V. Dispar) based on GMC were identified, of which Bifidobacterium/Enterobacteriaceae–cluster presented with higher scores on the IBQ-R main dimension regulation and its subscale duration of orienting compared to Bacteroides-cluster. The clusters associated with temperament in a sex-dependent manner. The IBQ-R main dimension surgency (positive emotionality) was associated positively both with genus Bifidobacterium and Streptococcus. Alpha diversity had a negative association with negative emotionality and fear reactivity. Conclusion This is the first study demonstrating associations, but not causal connections, between GMC and temperament in young infants in a prospective design

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

Gut microbiota composition is associated with temperament traits in infants

BackgroundOne of the key behavioral phenotypes in infancy are different temperament traits, and certain early life temperament traits have been shown to precede later mental health problems. Differences in the gut microbiota composition (GMC) have been suggested to link with neurodevelopment. For example, toddler temperament traits have been found to associate with differences in GMC; however, studies in infants are lacking although infancy is a rapid period of neurodevelopment as well as GM development. Thus, we aimed to investigate association between infant GMC and temperament.MethodsThe study population (n=301, 53% boys) was drawn from the FinnBrain Birth Cohort Study. Stool samples were collected from the 2.5-month-old infants and sequenced with 16S Illumina MiSeq platform. GMC taxonomic composition (at Genus and OTU level), observed sample clusters, diversity and richness were investigated in relation to the maternal reports of Infant Behavior Questionnaire -Revised (IBQ-R) at the age of 6 months. ResultsThree sample clusters (Bifidobacterium/Enterobacteriaceae, Bacteroides, V. Dispar) based on GMC were identified, of which Bifidobacterium/Enterobacteriaceae–cluster presented with higher scores on the IBQ-R main dimension regulation and its subscale duration of orienting compared to Bacteroides-cluster. The clusters associated with temperament in a sex-dependent manner. The IBQ-R main dimension surgency (positive emotionality) was associated positively both with genus Bifidobacterium and Streptococcus. Alpha diversity had a negative association with negative emotionality and fear reactivity.ConclusionThis is the first study demonstrating associations, but not causal connections, between GMC and temperament in young infants in a prospective design. </p

Early fecal microbiota composition in children who later develop celiac disease and associated autoimmunity

Objectives: Several studies have reported that the intestinal microbiota composition of celiac disease (CD) patients differs from healthy individuals. The possible role of gut microbiota in the pathogenesis of the disease is, however, not known. Here, we aimed to assess the possible differences in early fecal microbiota composition between children that later developed CD and healthy controls matched for age, sex and HLA risk genotype.Materials and methods: We used 16S rRNA gene sequencing to examine the fecal microbiota of 27 children with high genetic risk of developing CD. Nine of these children developed the disease by the age of 4 years. Stool samples were collected at the age of 9 and 12 months, before any of the children had developed CD. The fecal microbiota composition of children who later developed the disease was compared with the microbiota of the children who did not have CD or associated autoantibodies at the age of 4 years. Delivery mode, early nutrition, and use of antibiotics were taken into account in the analyses.Results: No statistically significant differences in the fecal microbiota composition were found between children who later developed CD (n = 9) and the control children without disease or associated autoantibodies (n = 18).Conclusions: Based on our results, the fecal microbiota composition at the age of 9 and 12 months is not associated with the development of CD. Our results, however, do not exclude the possibility of duodenal microbiota changes or a later microbiota-related trigger for the disease.</p

An Infancy-Onset 20-Year Dietary Counselling Intervention and Gut Microbiota Composition in Adulthood

The randomized controlled Special Turku Coronary Risk Factor Intervention Project (STRIP) has completed a 20-year infancy-onset dietary counselling intervention to reduce exposure to atherosclerotic cardiovascular disease risk factors via promotion of a heart-healthy diet. The counselling on, e.g., low intake of saturated fat and cholesterol and promotion of fruit, vegetable, and whole-grain consumption has affected the dietary characteristics of the intervention participants. By leveraging this unique cohort, we further investigated whether this long-term dietary intervention affected the gut microbiota bacterial profile six years after the intervention ceased. Our sub-study comprised 357 individuals aged 26 years (intervention n = 174, control n = 183), whose gut microbiota were profiled using 16S rRNA amplicon sequencing. We observed no differences in microbiota profiles between the intervention and control groups. However, out of the 77 detected microbial genera, the Veillonella genus was more abundant in the intervention group compared to the controls (log(2) fold-change 1.58, p < 0.001) after adjusting for multiple comparison. In addition, an association between the study group and overall gut microbiota profile was found only in males. The subtle differences in gut microbiota abundances observed in this unique intervention setting suggest that long-term dietary counselling reflecting dietary guidelines may be associated with alterations in gut microbiota

Six-Week Endurance Exercise Alters Gut Metagenome That Is not Reflected in Systemic Metabolism in Over-weight Women

Recent studies suggest that exercise alters the gut microbiome. We determined whether six-weeks endurance exercise, without changing diet, affected the gut metagenome and systemic metabolites of overweight women. Previously sedentary overweight women (n = 19) underwent a six-weeks endurance exercise intervention, but two were excluded due to antibiotic therapy. The gut microbiota composition and functions were analyzed by 16S rRNA gene amplicon sequencing and metagenomics. Body composition was analyzed with DXA X-ray densitometer and serum metabolomics with NMR metabolomics. Total energy and energy-yielding nutrient intakes were analyzed from food records using Micro-Nutrica software. Serum clinical variables were determined with KONELAB instrument. Soluble Vascular Adhesion Protein 1 (VAP-1) was measured with ELISA and its' enzymatic activity as produced hydrogen peroxide. The exercise intervention was effective, as maximal power and maximum rate of oxygen consumption increased while android fat mass decreased. No changes in diet were observed. Metagenomic analysis revealed taxonomic shifts including an increase in Akkermansia and a decrease in Proteobacteria. These changes were independent of age, weight, fat % as well as energy and fiber intake. Training slightly increased Jaccard distance of genus level β-diversity. Training did not alter the enriched metagenomic pathways, which, according to Bray Curtis dissimilarity analysis, may have been due to that only half of the subjects' microbiomes responded considerably to exercise. Nevertheless, tranining decreased the abundance of several genes including those related to fructose and amino acid metabolism. These metagenomic changes, however, were not translated into major systemic metabolic changes as only two metabolites, phospholipids and cholesterol in large VLDL particles, decreased after exercise. Training also decreased the amine oxidase activity of pro-inflammatory VAP-1, whereas no changes in CRP were detected. All clinical blood variables were within normal range, yet exercise slightly increased glucose and decreased LDL and HDL. In conclusion, exercise training modified the gut microbiome without greatly affecting systemic metabolites or body composition. Based on our data and existing literature, we propose that especially Akkermansia and Proteobacteria are exercise-responsive taxa. Our results warrant the need for further studies in larger cohorts to determine whether exercise types other than endurance exercise also modify the gut metagenome