Developing a bioinformatics pipeline gDAT to analyse arbuscular mycorrhizal fungal communities using sequence data from different marker regions
Date
2020-07-06
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Abstract
Mullas on palju mikroorganisme, ning neil on oluline roll ökosüsteemide toimimisel. Üheks oluliseks mikroorganismide rühmaks on arbuskulaarset mükoriisat (AM) moodustavad seened (krohmseened). AM on seenjuure vorm, mida moodustavad krohmseened enamuse roht- ja puittaimedega, sealhulgas paljude kultuurtaimedega. Mükoriisses kooselus saab peremeestaim seene abil kasvuks vajalikke mineraalaineid ja vett, seen omakorda taimelt fotosünteesil tekkinud süsivesikuid. Lisaks parandavad AM seened taimede toimetulekut stressitingimustega, näiteks veepuuduse ja haigustekitajatega.
Antud doktoritöös uuriti AM seente määramise efektiivsust kasutades kolme mikroorganismide määramiseks enim kasutatud genoomset markerpiirkonda (SSU, ITS, LSU) ja erinevate sekveneerimisplatvormide sobivust AM seente määramiseks ökoloogilistest proovidest. Doktoritöö raames valmis graafilise liidesega bioinformaatiline töövahend gDAT (graphical downstream analyse tool), mis aitab ökoloogidel analüüsida suuremahulisi DNA järjestusandmeid.
Doktoritöö peamised tulemused ja järeldused on: 1) SSU markerpiirkond on piisavalt varieeruv AM seeneliikide määramiseks. Teisisõnu, selle markeri liigisisene varieeruvus on AM seentel väiksem kui liikidevaheline varieeruvus; 2) uute sekveneerimisplatvormide tulekuga on järjestuste maht proovi kohta mitmekordistunud, kuid liigirikkus proovi kohta püsib sama ja saadud ökoloogiline teave (elurikkuse hinnang) on võrreldav eelmise põlvkonna sekveneerimisplatvormil saaduga. Seega on metoodiliselt optimaalne proovipõhine järjestuste arv saavutatud AM seeneliikide määramiseks ning elurikkuse hindamiseks looduskeskkonnast; 3) lisaks SSU markerpiirkonnale saab arvukamaid AM seeni edukalt määrata ka järjestades koguseenekooslust ITS piirkonna praimersüsteeme kasutades; 4) arendatud bioinformaatiline töövahend gDAT võimaldab kiirelt, tõhusalt teostada AM seente uurimusi pärilikkusaine põhjal. See töövahend on kasutatav ka teiste organismide DNA-põhiseks määramiseks.
Soils harbour vast numbers of microorganisms that play important roles in ecosystems. One important group of microorganisms is the arbuscular mycorrhizal (AM) fungi. AM is a type of mycorrhiza, where fungi forms a symbiosis with most of the herbaceous and woody plants including crop plants. AM fungi provide nutrients and water that are needed for plant growth and in exchange receive carbon fixed by the plant in photosynthesis. AM fungi also improve plant tolerance to stress, such as drought and pathogen attacks. This thesis analysed the efficiency of popular DNA sequencing marker regions (SSU, ITS, LSU) used with microorganisms and different sequencing platforms to identify AM fungi in environmental samples. Outcome of the thesis is a bioinformatics tool gDAT (graphical downstream analysis tool) with graphical interface that allows ecologists to easily analyse vast amounts of DNA sequence data. The main results and conclusions of the thesis are the following: 1) the SSU marker region is sufficiently variable to identify AM fungi; and the variation within species is lower than the variation between species; 2) newer sequencing technologies provide increased sequencing depth, but this does not increase species richness estimates per sample and produces similar community patterns compared to previous generation sequencing. Thus, the optimal sample sequencing depth has been achieved for AM fungal diversity assessments; 3) the ITS region can be used successfully to identify abundant AM fungal species; 4) the newly developed bioinformatics tool gDAT allows rapid and efficient analysis of AM fungi from sequenced DNA. This tool is not limited to AM fungi and is applicable for other organisms to be identified through DNA sequencing.
Soils harbour vast numbers of microorganisms that play important roles in ecosystems. One important group of microorganisms is the arbuscular mycorrhizal (AM) fungi. AM is a type of mycorrhiza, where fungi forms a symbiosis with most of the herbaceous and woody plants including crop plants. AM fungi provide nutrients and water that are needed for plant growth and in exchange receive carbon fixed by the plant in photosynthesis. AM fungi also improve plant tolerance to stress, such as drought and pathogen attacks. This thesis analysed the efficiency of popular DNA sequencing marker regions (SSU, ITS, LSU) used with microorganisms and different sequencing platforms to identify AM fungi in environmental samples. Outcome of the thesis is a bioinformatics tool gDAT (graphical downstream analysis tool) with graphical interface that allows ecologists to easily analyse vast amounts of DNA sequence data. The main results and conclusions of the thesis are the following: 1) the SSU marker region is sufficiently variable to identify AM fungi; and the variation within species is lower than the variation between species; 2) newer sequencing technologies provide increased sequencing depth, but this does not increase species richness estimates per sample and produces similar community patterns compared to previous generation sequencing. Thus, the optimal sample sequencing depth has been achieved for AM fungal diversity assessments; 3) the ITS region can be used successfully to identify abundant AM fungal species; 4) the newly developed bioinformatics tool gDAT allows rapid and efficient analysis of AM fungi from sequenced DNA. This tool is not limited to AM fungi and is applicable for other organisms to be identified through DNA sequencing.
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Keywords
mushrooms, mycorrhiza, nucleotide sequence, DNA tandem repeats, bioinformatics, computer programmes