Bioinformaatika meetodid personaalses farmakoteraapias

Väitekirja elektrooniline versioon ei sisalda publikatsiooneKogutavate terviseandmete hulk kasvab kiiresti. Tänu neile andmetele on meditsiinilise ravi pakkumisel võimalik senisest enam arvesse võtta individuaalseid bioloogilisi andmeid. See doktoritöö käsitleb mitmeid personaalses meditsiinis esinevaid probleeme ja näitab, et ravi individualiseerimiseks kasutatavad andmed tulevad väga erinevatest allikatest. Inimestevahelised erinevused teevad ravimite metabolismi ennustamise keerukaks, siiski on ravi käigus kogutavad kontsentratsioonimõõtmised ravimiefekti hindamisel heaks allikaks. Me arendasime välja täppisdoseerimise tööriista, mis võimaldab vankomütsiini ravil vastsündinutele määrata ravi tõhustavat personaalseid doose kasutades selleks nende endi ravi käigus kogutud kontsentratsioone. Suurema osa ravimiteraapiate puhul ei ole võimalik pidevalt ravimi kontsentratsioone koguda. Nende ülejäänud ravimite puhul on heaks informatsiooniallikaks geneetika. Paljude ravimimetabolismiga seotud geneetiliste variantide mõju on piisav, et tingida muutuseid ravi läbiviimisel. Me uurisime geneetika ja ravimite kõrvalmõjude omavahelisi seoseid kasutades rahvastikupõhist lähenemist. See toetus Eesti Geenivaramu geeniandmetele ja teistele laiapõhjalistele terviseandmete registritele. Me leidsime ja valideerisime seose, et CTNNA3 geenis olev geenivariant tõstab oksikaamide ravil olevate inimeste jaoks kõrvalmõjude sagedust. Arvutuslik geneetika toetub kvantitatiivsetele meetoditele, millest kõige levinum on ülegenoomne assotsiatsiooni analüüs (GWAS). Sagedasti kasutatav GWASi järelsamm on aega nõudev GWASist ilmnenud p-väärtuste visuaalne hindamine teiste samas genoomi piirkonnas olevate geneetiliste variantide kontekstis. Selle sammu automatiseerimiseks arendasime me kaks tööriista, Manhattan Harvester ja Cropper, mis võimaldavad automaatselt huvipakkuvaid piirkondi tuvastada ja nende headust hinnata.The amount of collected health data is growing fast. Insights from these data allow using biological patient specifics to improve therapy management with further individualization. This thesis addresses problems in multiple sub-fields of personalised medicine and aims to illustrate that data for precision medicine emerges from different sources. Drug metabolism is difficult to predict because individual biological differences. Fortunately, drug concentrations are a good proxy for drug effect. To address the growing need for tools that allow on-line therapy adjustment based on individual concentrations we have developed and externally evaluated a precision dosing tool that allows individualised dosing of vancomycin in neonates. Other than drugs used in therapeutic drug monitoring, most pharmacotherapies can not rely on continuous concentration measurements but for such drugs genetics provides a valuable source of information for individualization. Effects of many genetic variants in drug metabolism pathways are often large enough to require changes in drug prescriptions or schedules. We have applied a population-based approach in testing relations between drug related adverse effects and genomic loci, and found and validated a novel variant in CTNNA3 gene that increases adverse drug effects in patients with oxicam prescriptions. This was done by leveraging the data in Estonian Genome Center and linking these to nation-wide electronic health data registries. Computational genetics relies on quantitative methods for which the most common is the genome-wide association analysis (GWAS). A common GWAS downstream step involves time-consuming visual assessment of the association study p-values in context with other variants in genomic vicinity. In order to streamline this step, we developed, Manhattan Harvester and Cropper, that allow for automated detection of peak areas and assign scores by emulating human evaluators.https://www.ester.ee/record=b524282

Re-using public RNA-Seq data

"Järgmise põlvkonna sekveneerimismeetodid"(NGS) on geeniandmete analüüsil kiiresti populaarsust kogumas. RNA-Seq on NGS tehnika, mis võimaldab geeniekspressiooni tasemete hindamist. Eksperimentidest kogutuid andmeid arhiveeritakse jõudsalt avalikesse andmebaasidesse, kuna toorandmete neisse edastamine on üheks eeltingimuseks akadeemilistes ajakirjades avaldamiseks. RNA-Seq toorandmed on mahult üsna suured ja üksikute eksperimentide analüüs üsnagi aeganõudev. Sekveneerimise toorandmeid taaskasutatakse praegu veel üsna vähe. Andmebaasidesse leiduvate andmete taaskasutamisele avaldavad pärssivat mõju ebatäpsed katseplaneerimise kirjeldused ja kindlate standardite puudumine analüüsimeetodites. Tööriistade vahelised algoritmilised eripärad tähendavad erinevatel meetoditel teostatud analüüside vähest võrreldavust. Lihtne kollektsioonide agregeerimine ei tööta, kuna analüüsitud andmed pole võrreldavad. Seega tuleb analüüs kõikide eksperimentide jaoks teostada alates toorandmetest. Iga eksperimendi analüüs on aga üsna aeganõudev ning nõuab kuldsete standardite puudumisel konkreetseid valikuid. Suuremahuliste analüüsiandmete kollektsiooni nõuab seega efektiivset töövoo implementatsiooni. Toimimise tingimusteks on minimaalne inimsekkumine, fikseeritud tööriistade valik ja robustne eksperimentide käsitsemismetoodika. Väga erinevates tingimustes teostatud eksperimentide ekspressiooniandmete agregeerimine loob võimaluse andmekaeve meetodite rakendamiseks. Lokaalselt ilmnevad mustrid võivad taustsüsteemis osutuda signaaliks. Üheks analüüsivallaks, mis selliseid mustreid uurib on koekspressioonianalüüs. Selles magistritöös arendasime ja implementeerisime raamistiku suuremahuliseks avalike RNA-Seq andmete analüüsiks. Analüüs ei vaja eksperimentide analüüsimisele eelnevalt konfiguratsioonifaili vaid toetub ühekordselt konstrueeritud andmebaasile. Kasutajapoolne sekkumine on minimaalne, kõik parameetrid määratakse andmetest lähtuvalt. See võimaldab järjestikulist analüüsi üle arvukate eksperimentide. Loodavat RNA-Seq ekspressiooniandmete kollektsiooni kasutatakse sisendina BIIT töörühma poolt arenda- tud koekspressiooni uurimise tööriistas - MEM. Algselt oli see ehitatud üksnes mikrokiip andmetelt sondide koekspressiooni hindamiseks, kuid RNA-Seq ekspressiooniandmed laiendavad selle rakendusampluaad.Next Generation Sequencing (NGS) methods are rapidly becoming the most popular paradigm for exploring genomic data. RNA-Seq is a NGS method that enables gene expression analyses. Raw sequencing data generated by researchers is actively submitted to public databases as part of the requirements for publishing in academic journals. Raw sequencing data is quite large in size and analysis of each experiment is time consuming. Therefore published raw files are currently not re-used much. Repetitive analysis of uploaded data is also complicated by negligent experiment set-up write-ups and lack of clear standards for the analysis process. Publicly available analysis results have been obtained using a varying set of tools and parameters. There are biases introduced by algorithmic differences of tools which greatly decreases the comparability of results between experiments. This is due because of lack of golden analysis standards. Comprehensive collections of expression data have to account for computational expenses and time limits. Therefore collection set-up needs an effective pipeline implementation with automatic parameter estimation, a defined subset of tools and a robust handling mechanism to ensure minimal required user input. Aggregating expression data from individual experiments with varying experimental conditions creates many new opportunities for data aggregation and mining. Pattern discovery over larger collections generalises local tendencies. One such analysis sub-field is assessing gene co-expression over a broader set of experiments. In this thesis, we have designed and implemented a framework for performing large scale analysis of publicly available RNA-Seq experiments. No separate configuration file for analysis is required, instead a pre-built database is employed. User intervention is minimal and the process is self-guiding. All parameters within the analysis process are determined automatically. This enables unsupervised sequential analysis of numerous experiments. Analysed datasets can be used as an input for co-expression analysis tool MEM which was developed by BIIT research group and was originally designed for public microarray data. RNA-Seq data adds a new application field for the tool. Other than co-expression analysis with MEM, the data can also be used in other downstream analysis applications

Ravi sihtmärgi saavutamise tõenäosus vankomütsiini korduval manustamisel enneaegsetel vastsündinutel

Ravimiga organismis toimuvaid protsesse uurib farmakokineetika. Ravimi mõju uurimisega organismi funktsioonidele tegeleb aga farmakodünaamika. Arvutusintensiivsete meetodite abil saab farmakokineetika ja farmakodünaamika tulemusi kombineerides ravi mõju ning kasutatavaid doose efektiivsemalt ja personaalsemalt hinnata. Vankomütsiin on antibiootikum, mis töötati välja üle 50 aasta tagasi, kuid seoses resistentsuse kasvuga tavapärastele ravimitele on selle kasutusvaldkond tugevatoimelise antibiootikumina viimasel ajal laienenud. Käesolev bakalaureusetöö annab ülevaate farmakokineetilisest taustsüsteemist ning seni teostatud selle alastest uuringutest vankomütsiiniga. Monte-Carlo simulatsioonidel Andersoni mudelist (Anderson et al., 2006) genereeritud farmakokineetilisi parameetrite väärtuseid kasutatakse enneaegsete vastsündinute ravi 90% sihtmärgi saavutamise tõenäosuste jaoks vaja minevate dooside hindamisel. Töö eesmärgiks on TÜ mikrobioloogia instituudis kogutud minimaalsete inhibeerivate kontsentratsioonide põhjal hinnata 90% ravi sihtmärgi saavutamiseks vajaminevaid doose erinevate farmakokineetilis-farmakodünaamiliste parameetrite piirväärtuste korral. Leitud dooside põhjal hinnatakse saavutatavate terapeutiliste kontsentratsioonide sobivust. Töös sisalduvad mitmed lisad. Erinevate lühendite loend on toodud lisas 1. Ravi sihtmärgi saavutamise tõenäosuse hindamise graafikud ning leitud dooside juures esinevad kontsentratsioonide muutused konkreetsetel valitud juhtudel on toodud vastavalt lisades 2 ja 3. Töös esitatud jooniste ning simulatsioonide teostamiseks tarkvarale R (R Core Team, 2013) kirjutatud kood on lisas 4

Ampitsilliini farmakokineetika enneaegsetel ja ajalistel vastsündinutel esimesel elunädalal

Eesti Arst 2022; 101(2):12

High-throughput mRNA sequencing of stromal cells from endometriomas and endometrium

The aetiology of endometriosis is still unclear and to find mechanisms behind the disease development, it is important to study each cell type from endometrium and ectopic lesions independently. The objective of this study was to uncover complete mRNA profiles in uncultured stromal cells from paired samples of endometriomas and eutopic endometrium. High-throughput mRNA sequencing revealed over 1300 dysregulated genes in stromal cells from ectopic lesions, including several novel genes in the context of endometriosis. Functional annotation analysis of differentially expressed genes highlighted pathways related to cell adhesion, extracellular matrix–receptor interaction and complement and coagulation cascade. Most importantly, we found a simultaneous upregulation of complement system components and inhibitors, indicating major imbalances in complement regulation in ectopic stromal cells. We also performed in vitro experiments to evaluate the effect of endometriosis patients’ peritoneal fluid (PF) on complement system gene expression levels, but no significant impact of PF on C3, CD55 and CFH levels was observed. In conclusion, the use of isolated stromal cells enables to determine gene expression levels without the background interference of other cell types. In the future, a new standard design studying all cell types from endometriotic lesions separately should be applied to reveal novel mechanisms behind endometriosis pathogenesis

Manhattan Harvester and Cropper: a system for GWAS peak detection

Abstract Background Selection of interesting regions from genome wide association studies (GWAS) is typically performed by eyeballing of Manhattan Plots. This is no longer possible with thousands of different phenotypes. There is a need for tools that can automatically detect genomic regions that correspond to what the experienced researcher perceives as peaks worthwhile of further study. Results We developed Manhattan Harvester, a tool designed for “peak extraction” from GWAS summary files and computation of parameters characterizing various aspects of individual peaks. We present the algorithms used and a model for creating a general quality score that evaluates peaks similarly to that of a human researcher. Our tool Cropper utilizes a graphical interface for inspecting, cropping and subsetting Manhattan Plot regions. Cropper is used to validate and visualize the regions detected by Manhattan Harvester. Conclusions We conclude that our tools fill the current void in automatically screening large number of GWAS output files in batch mode. The interesting regions are detected and quantified by various parameters by Manhattan Harvester. Cropper offers graphical tools for in-depth inspection of the regions. The tools are open source and freely available

Genetic variation in the Estonian population : pharmacogenomics study of adverse drug effects using electronic health records

Pharmacogenomics aims to tailor pharmacological treatment to each individual by considering associations between genetic polymorphisms and adverse drug effects (ADEs). With technological advances, pharmacogenomic research has evolved from candidate gene analyses to genome-wide association studies. Here, we integrate deep whole-genome sequencing (WGS) information with drug prescription and ADE data from Estonian electronic health record (EHR) databases to evaluate genome- and pharmacome-wide associations on an unprecedented scale. We leveraged WGS data of 2240 Estonian Biobank participants and imputed all single-nucleotide variants (SNVs) with allele counts over 2 for 13,986 genotyped participants. Overall, we identified 41 (10 novel) loss-of-function and 567 (134 novel) missense variants in 64 very important pharmacogenes. The majority of the detected variants were very rare with frequencies below 0.05%, and 6 of the novel lossof-function and 99 of the missense variants were only detected as single alleles (allele count = 1). We also validated documented pharmacogenetic associations and detected new independent variants in known gene-drug pairs. Specifically, we found that CTNNA3 was associated with myositis and myopathies among individuals taking nonsteroidal anti-inflammatory oxicams and replicated this finding in an extended cohort of 706 individuals. These findings illustrate that population-based WGS-coupled EHRs are a useful tool for biomarker discovery