Mikrobioomi väärtus terviseuuringutes

Väitekirja elektrooniline versioon ei sisalda publikatsiooneTehnoloogia areng on andnud inimesele võimaluse uurida ümbritsevat maailma nurkade alt, mille jaoks veel mõned kümnendid tagasi võimalused puudusid. Üks selliseid teadusvaldkondi on inimese mikrobioomi ehk meie kehal ja kehas elavate mikroorganismide nagu näiteks bakterite ja viiruste uurimine. On teada, et mikrobioomil on oluline funktsioon inimese tervisele ning mikrobioomi kooslust omakorda mõjutavad suurel määral meie elustiil, toitumisharjumused, ümbritsev keskkond ning tervislik seisund. Just seosed haigustega on tekitanud huvi mikrobioomi kasutamiseks meditsiinilistes rakendustes. Doktoritöö eesmärk oli uurida, millised faktorid lisaks teadaolevatele on seotud meie soolestiku mikrobioomi kooslusega ning kuidas on mikrobioomi andmeid võimalik kasutada haiguste diagnoosimiseks ning haigusriskide hindamiseks. Esiteks uurisime teist tüüpi diabeeti ning näitasime, et mikrobioom aitab senisest täpsemalt ennustada muutusi veresuhkru regulatsiooni kirjeldavates parameetrites, milleks olid eelkõige insuliini eritamisega seotud näitajaid. Järgmiseks eesmärgiks oli kirjeldada Eesti populatsiooni soolestiku mikrorbioomi profiiili ning tuvasatada mikrobioomi kooslust mõjutavad faktorid. Eesti Geenivaramu terviseandmestikku kasutades tuvastasime, et antibiootikumide pikaajalisel korduval kasutamisel on akkumuleeruv mõju mikrobioomi kooslusele olenemata sellest, kas antibiootikume on kasutatud viimase kuue kuu jooksul. Analüüsides pikaajalise antibiootikumide mõju arvesse võtmine võimaldas omakorda täpsustada haigusspetsiifilisi muutusi mikrobioomis. Lisaks uurisime, kas soolestiku mikrobioomi abil inimeste grupeerimine võimaldaks ka kasutust kliinilistes rakendustes. Selgus, et selliselt mikrobioomi kooslust lihtsustades on võimalik küll anda hinnang inimese üldisele elustiilile, kuid tõendid haiguste diagnoosimisel või haiguste riski hindamiseks pole piisavalt tugevad. Kokkuvõttes on mikrobioomi uurimisel meditsiinis suur potentsiaal, mis võimaldab täiendada olemasolevaid võimalusi haiguste diagnoosimiseks ning riskide hindamiseks, kuid see eeldab täiendavaid teadmisi ja uuringuid.The technological revolution allows us to study the world beyond the limits that were holding us back only a couple of decades ago. One of such fields is the study of the human microbiome. Tiny microorganisms making up the microbiome such as bacteria and viruses have been known to intervene with our health for centuries, but the whole microbial ecosystem has turned out to be more complex than previously thought. The extent of the role of the microbiome to our own functioning and well-being is just starting to unravel. Nevertheless, microbiome has been associated with a large variety of intrinsic and extrinsic factors, including various complex diseases. This evidence is leading a slow but steady progress towards clinical applications such as using microbiome for improving disease diagnostics or estimating the risk of developing a condition. This thesis aimed to expand the understanding of the factors influencing our gut microbiome composition and assess the possibility and challenges in using the microbiome composition for the clinical applications. Firstly, we identified novel microbial biomarkers for identifying the progression of type 2 diabetes (T2D), which can be used to improve the current risk estimation. Secondly, using the comprehensive health data available in the Estonian Biobank, we characterized the profile of the gut microbiome in the Estonian population and identified various factors affecting the microbiome. Our study indicated that the long-term antibiotics usage has an accumulative effect on the gut microbiome composition independent of recent usage. The novelty of this result has a significant impact on the microbiome field and the future analysis need to account for such drug effects. Lastly, we considered dividing the subjects into a few distinct clusters based on their microbiome composition and evaluated the clinical applicability of such representation. We showed that although this approach is desirable in its simplicity, it is not sufficient for clinical applications. In conclusion, the microbiome science is heading towards clinical applications, but exploratory analysis is still needed. Nevertheless, the challenges ahead do not overshadow the enthusiasm.https://www.ester.ee/record=b551831

Mikrobioomi andmete analüüs

Inimese soolestikus on suur hulk erinevaid baktereid, mis täidavad organismi jaoks mitmeid olulisi funktsioone. Käesoleva magistritöö eesmärk on uurida, kas teist tüüpi diabeedi eelses seisundis indiviidide soolestiku bakterikoosluses on muudatusi võrreldes tervete indiviidide bakterikooslusega. Võrreldakse bakterikoosluse puhul huvitavaid α- ning β - mitmekesisuse näitajaid. Seejärel uuritakse Mendeli randomiseerimise skeemi abil, missugune võiks olla bakterikoosluse liigirikkuse põhjuslik mõju prediabeedile.Lisaks uuritakse, kas leidub üksikuid baktereid, mis esinevad tervete ja prediabeetikute mikrobioomides erineva sagedusega kasutades selleks kompositsionaalsete andmete analüüsimiseks mõeldud meetodeid. Kirjeldatakse kopositsionaalsete andmete jaoks mõeldud seose tugevuse näitajat uurimaks, kas soolestiku mikrobioomis on liike, mis esinevad soolestiku keskkonnas enamasti koos. Lisaks modelleeritakse prediabeedi esinemist logistilise regressiooni ning regulariseeritud logistilise regressiooniga

Geneetiliste mõjude hindamine kinnitava faktoranalüüsiga

Genoomikapõhise personaalse meditsiini väljatöötamiseks soovitakse inimese genotüübiandmete põhjal ennustada haiguste tekkimise riske. Geneetiliste mõjude hindamisel kasutatakse enim ühenukleotiidsete polümorfismide (SNP) markereid, mis on inimese geneetilise varieeruvuse põhilisemaid avaldumisviise. DNA-ahelal lähestikku paiknevad SNP-d on omavahel tugevasti korreleeritud, seetõttu kasutatakse geeni mõju hindamisel enamasti ainult piirkonna kõige olulisemat markerit. Käesoleva bakalaureusetöö eesmärk on anda ülevaade struktuurivõrrandite mudelitest ning rakendada metoodika ühte erijuhtu - kinnitavat faktoranalüüsi, hindamaks geenipiirkonna mõju, kasutades kõiki piirkonnas mõõdetud geneetilisi markereid

The Gut Microbiome in Polycystic Ovary Syndrome and Its Association with Metabolic Traits

Context: Despite the gut microbiome being widely studied in metabolic diseases, its role in polycystic ovary syndrome (PCOS) has been scarcely investigated. Objective: Compare the gut microbiome in late fertile age women with and without PCOS and investigate whether changes in the gut microbiome correlate with PCOSrelated metabolic parameters. Design: Prospective, case-control study using the Northern Finland Birth Cohort 1966. Setting: General community. Participants: A total of 102 PCOS women and 201 age- and body mass index (BMI)matched non-PCOS control women. Clinical and biochemical characteristics of the participants were assessed at ages 31 and 46 and analyzed in the context of gut microbiome data at the age of 46. Intervention(s): None Main outcome measure(s): Bacterial diversity, relative abundance, and correlations with PCOS-related metabolic measures. Results: Bacterial diversity indices did not differ significantly between PCOS and controls (Shannon diversity P =.979, unweighted UniFrac P =.175). Four genera whose balance helps to differentiate between PCOS and non-PCOS were identified. In the whole cohort, the abundance of 2 genera from Clostridiales, Ruminococcaceae UCG-002, and Clostridiales Family XIII AD3011 group, were correlated with several PCOS-related markers. Prediabetic PCOS women had significantly lower alpha diversity (Shannon diversity P =.018) and markedly increased abundance of genus Dorea (false discovery rate = 0.03) compared with women with normal glucose tolerance. Conclusion: PCOS and non-PCOS women at late fertile age with similar BMI do not significantly differ in their gut microbial profiles. However, there are significant microbial changes in PCOS individuals depending on their metabolic health.Peer reviewe

Novel Early Pregnancy Multimarker Screening Test for Preeclampsia Risk Prediction

Preeclampsia (PE) is a common pregnancy-linked disease, causing preterm births, complicated deliveries, and health consequences for mothers and offspring. We have previously developed 6PLEX, a multiplex assay that measures PE-related maternal serum biomarkers ADAM12, sENG, leptin, PlGF, sFlt-1, and PTX3 in a single test tube. This study investigated the potential of 6PLEX to develop novel PE prediction models for early pregnancy. We analyzed 132 serum samples drawn at 70–275 gestational days (g days) from 53 pregnant women (PE, n = 22; controls, n = 31). PE prediction models were developed using a machine learning strategy based on the stepwise selection of the most significant models and incorporating parameters with optimal resampling. Alternative models included also placental FLT1 rs4769613 T/C genotypes, a high-confidence risk factor for PE. The best performing PE prediction model using samples collected at 70–98 g days comprised of PTX3, sFlt-1, and ADAM12, the subject's parity and gestational age at sampling (AUC 0.94 [95%CI 0.84–0.99]). All cases, that developed PE several months later (onset 257.4 ± 15.2 g days), were correctly identified. The model's specificity was 80% [95%CI 65–100] and the overall accuracy was 88% [95%CI 73–95]. Incorporating additionally the placental FLT1 rs4769613 T/C genotype data increased the prediction accuracy to 93.5% [AUC = 0.97 (95%CI 0.89–1.00)]. However, 6PLEX measurements of samples collected at 100–182 g days were insufficiently informative to develop reliable PE prediction models for mid-pregnancy (accuracy <75%). In summary, the developed model opens new horizons for first-trimester PE screening, combining the easily standardizable 6PLEX assay with routinely collected antenatal care data and resulting in high sensitivity and specificity

Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment

The number of microbiome-related studies has notably increased the availability of data on human microbiome composition and function. These studies provide the essential material to deeply explore host-microbiome associations and their relation to the development and progression of various complex diseases. Improved data-analytical tools are needed to exploit all information from these biological datasets, taking into account the peculiarities of microbiome data, i.e., compositional, heterogeneous and sparse nature of these datasets. The possibility of predicting host-phenotypes based on taxonomy-informed feature selection to establish an association between microbiome and predict disease states is beneficial for personalized medicine. In this regard, machine learning (ML) provides new insights into the development of models that can be used to predict outputs, such as classification and prediction in microbiology, infer host phenotypes to predict diseases and use microbial communities to stratify patients by their characterization of state-specific microbial signatures. Here we review the state-of-the-art ML methods and respective software applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on the application of ML in microbiome studies related to association and clinical use for diagnostics, prognostics, and therapeutics. Although the data presented here is more related to the bacterial community, many algorithms could be applied in general, regardless of the feature type. This literature and software review covering this broad topic is aligned with the scoping review methodology. The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach

Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment

The number of microbiome-related studies has notably increased the availability of data on human microbiome composition and function. These studies provide the essential material to deeply explore host-microbiome associations and their relation to the development and progression of various complex diseases. Improved data-analytical tools are needed to exploit all information from these biological datasets, taking into account the peculiarities of microbiome data, i.e., compositional, heterogeneous and sparse nature of these datasets. The possibility of predicting host-phenotypes based on taxonomy-informed feature selection to establish an association between microbiome and predict disease states is beneficial for personalized medicine. In this regard, machine learning (ML) provides new insights into the development of models that can be used to predict outputs, such as classification and prediction in microbiology, infer host phenotypes to predict diseases and use microbial communities to stratify patients by their characterization of state-specific microbial signatures. Here we review the state-of-the-art ML methods and respective software applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on the application of ML in microbiome studies related to association and clinical use for diagnostics, prognostics, and therapeutics. Although the data presented here is more related to the bacterial community, many algorithms could be applied in general, regardless of the feature type. This literature and software review covering this broad topic is aligned with the scoping review methodology. The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach

The gut microbiome in polycystic ovary syndrome and its association with metabolic traits

Abstract Context: Despite the gut microbiome being widely studied in metabolic diseases, its role in polycystic ovary syndrome (PCOS) has been scarcely investigated. Objective: Compare the gut microbiome in late fertile age women with and without PCOS and investigate whether changes in the gut microbiome correlate with PCOS-related metabolic parameters. Design: Prospective, case–control study using the Northern Finland Birth Cohort 1966. Setting: General community. Participants: A total of 102 PCOS women and 201 age- and body mass index (BMI)-matched non-PCOS control women. Clinical and biochemical characteristics of the participants were assessed at ages 31 and 46 and analyzed in the context of gut microbiome data at the age of 46. Intervention: (s): None Main outcome measure(s): Bacterial diversity, relative abundance, and correlations with PCOS-related metabolic measures. Results: Bacterial diversity indices did not differ significantly between PCOS and controls (Shannon diversity P = .979, unweighted UniFrac P = .175). Four genera whose balance helps to differentiate between PCOS and non-PCOS were identified. In the whole cohort, the abundance of 2 genera from Clostridiales, Ruminococcaceae UCG-002, and Clostridiales Family XIII AD3011 group, were correlated with several PCOS-related markers. Prediabetic PCOS women had significantly lower alpha diversity (Shannon diversity P = .018) and markedly increased abundance of genus Dorea (false discovery rate = 0.03) compared with women with normal glucose tolerance. Conclusions: PCOS and non-PCOS women at late fertile age with similar BMI do not significantly differ in their gut microbial profiles. However, there are significant microbial changes in PCOS individuals depending on their metabolic health

Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment

The number of microbiome-related studies has notably increased the availability of data on human microbiome composition and function. These studies provide the essential material to deeply explore host-microbiome associations and their relation to the development and progression of various complex diseases. Improved data-analytical tools are needed to exploit all information from these biological datasets, taking into account the peculiarities of microbiome data, i.e., compositional, heterogeneous and sparse nature of these datasets. The possibility of predicting host-phenotypes based on taxonomy-informed feature selection to establish an association between microbiome and predict disease states is beneficial for personalized medicine. In this regard, machine learning (ML) provides new insights into the development of models that can be used to predict outputs, such as classification and prediction in microbiology, infer host phenotypes to predict diseases and use microbial communities to stratify patients by their characterization of state-specific microbial signatures. Here we review the state-of-the-art ML methods and respective software applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on the application of ML in microbiome studies related to association and clinical use for diagnostics, prognostics, and therapeutics. Although the data presented here is more related to the bacterial community, many algorithms could be applied in general, regardless of the feature type. This literature and software review covering this broad topic is aligned with the scoping review methodology. The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach

A toolbox of machine learning software to support microbiome analysis

The human microbiome has become an area of intense research due to its potential impact on human health. However, the analysis and interpretation of this data have proven to be challenging due to its complexity and high dimensionality. Machine learning (ML) algorithms can process vast amounts of data to uncover informative patterns and relationships within the data, even with limited prior knowledge. Therefore, there has been a rapid growth in the development of software specifically designed for the analysis and interpretation of microbiome data using ML techniques. These software incorporate a wide range of ML algorithms for clustering, classification, regression, or feature selection, to identify microbial patterns and relationships within the data and generate predictive models. This rapid development with a constant need for new developments and integration of new features require efforts into compile, catalog and classify these tools to create infrastructures and services with easy, transparent, and trustable standards. Here we review the state-of-the-art for ML tools applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on ML based software and framework resources currently available for the analysis of microbiome data in humans. The aim is to support microbiologists and biomedical scientists to go deeper into specialized resources that integrate ML techniques and facilitate future benchmarking to create standards for the analysis of microbiome data. The software resources are organized based on the type of analysis they were developed for and the ML techniques they implement. A description of each software with examples of usage is provided including comments about pitfalls and lacks in the usage of software based on ML methods in relation to microbiome data that need to be considered by developers and users. This review represents an extensive compilation to date, offering valuable insights and guidance for researchers interested in leveraging ML approaches for microbiome analysis.</p