Bonfante, P

Chialva, Matteo

Ghignone, Stefano

Novero, M

Salvioli, A

English

Institutional Research Information System University of Turin

16 October 2023AperTO - Archivio Istituzionale Open Access dell'Università di TorinoOriginal Citation:Reconstructing the tomato root-associated metatranscriptome using host-targeted RNAseq dataTerms of use:Open Access(Article begins on next page)Anyone can freely access the full text of works made available as "Open Access". Works made availableunder a Creative Commons license can be used according to the terms and conditions of said license. Useof all other works requires consent of the right holder (author or publisher) if not exempted from copyrightprotection by the applicable law.Availability:This is the author's manuscriptThis version is available http://hdl.handle.net/2318/1692193 since 2019-02-13T14:42:33ZThe abstract must include the Motivation, Methods, and Results sections. It cannot be longer than 5,000 characters (including spaces) or longer than 4,000 characters (including spaces) when a figure is also included (see Figure field below)Title:Reconstructing the tomato root-associated metatranscriptome using host-targeted RNAseq dataChialva M(1), Salvioli A(1), Ghignone S(2), Novero M(1), Bonfante P(1)Affiliation(1) Department of Life Sciences and Systems Biology, University of Torino, Viale P.A. Mattioli 25, Torino, Italy(2) Institute for Sustainable Plant Protection (IPSP), Italian National Research Council (CNR), Viale P.A. Mattioli 25, I-10125 Torino, ItalyMotivationPlants, like humans, have their own microbiota, which may exert a powerful effect on their health.In  the  last  years,  Next  Generation  Sequencing  technologies,  applied  to  develop  16S/18Smetabarcoding data sets and metagenomes have become a standard to routinely describe microbiotadiversity. As a step forward, metatranscriptomic approaches applied to host-associated niches areproviding details on the functions activated by microbial communities and their potential interactionwith their host. However, such approaches are limited by several factors, among which the high-coverage  sequencing  needed  which  results  into  major  economic  efforts  as  well  as  technicallimitations, such as RNA extraction from environmental samples, rRNA depletion procedures, andcomputationally intensive bioinformatics. However, understanding how soil microbiota (one of themost diverse ecosystem on Earth) influences plant responses is becoming a priority in plant sciencesand will pave the way for next-generation agriculture.In this work, we analysed the tomato root-associated fungal metatranscriptome using host-targetedshort-reads RNA-seq data from Chialva et al. (2018) and set up a new bioinformatic pipeline takingadvantage of published tools and R programming environment. Furthermore, we tried to link thereconstructed metatranscriptome at both diversity and functional level with the host transcriptionalprofile,  testing  the  hypothesis  that  molecular  functions  in  the  plant-microbiota  complex  (plantholobiont) are connected.MethodsTotal  RNA was  extracted  from tomato  roots  grown on native  soils  as  well  as  on an  artificialsubstrate, and including two tomato genotypes as detailed in Chialva et al. (2018). Poly-A enrichedRNA was converted in cDNA and sequenced using Illumina HiScanSQ (50 bp reads).Reads were trimmed using TRIMMOMATIC v0.35 and host sequences removed by aligning ontomato genome (SL.2.50) using STAR c2.2.5a. Residual host (transcript variants) and contaminantreads were detected mapping on a NCBI-NT subset containing all tomato and human sequencesusing Bowtie2 v2.2.9 and removed. rRNA sequences were removed with SortMeRNA v2.1. Theremaining reads were considered as metatranscriptome and annotated. Due to the short read length,we chose to annotate reads rather than generating an assembly to avoid the assembly of chimerictranscripts. Sequences were mapped on the full NCBI Nucleotide database using Taxoner64 v1.7software and Bowtie2. For taxonomical assignment, the LCA (last common ancestor) algorithm at97% sequence identity  on the reference database using NCBI taxonomy was adopted.  Residualcontaminants (Viridiplantae and Metazoa) as well as low-abundance taxa (<5 raw reads in at least 3libraries) were discarded.Reads  were  functionally  annotated  by  mapping  them  to  the  eggNOG  database  v4.5.  Sincesequencing  length  did  not  allow  functional  assignment,  we  generated  a  set  of  ‘pseudo-reads’extending  taxa-annotated  reads  50  bp  upstream  and  downstream  on  their  mapping  referencegenerating 150 bp fragments using 'getfasta' function in BEDTools v2.26.0-19. Pseudo-reads fromdifferent samples were than pooled and mapped on eggNOG using DIAMOND-BLASTX v0.8.24.Plant transcriptome was analysed mapping reads on tomato reference genome with TopHat2 andnormalizing reads count with DESeq2 as described in Chialva et al. (2018).Analysis and statistical  tests were performed in R statistical programming environment (R CoreTeam, 2017).ResultsUsing a custom pipeline we were able to reconstruct tomato root metatranscriptome using host-targeted RNA-seq libraries. Even though the sequencing coverage for microbial transcripts was low,we successfully reconstructed fungal functional diversity associated with plant host. As reported inliterature, only a small part of non-host reads can be successfully assigned and, in our case, this wasdue to the short reads length. The fungal microbiota, irrespectively of the soil type, was dominatedby Ascomycota  and other  taxa commonly  associated  to  rhizosphere such as  Glomeromycotina.However,  differential  abundance  analysis  revealed  that  these taxa  were more  abundant  in  bothnative soils when compared to an artificial substrate.  Even if the pipeline did not allow to assignfunctions to taxa, we successfully reconstruct overall expressed microbial functions (1,568-56,515reads per library mapped). Among functional categories, in native soils, we found as upregulateddifferent  COGs  corresponding  to  ribosomal  proteins,  mirroring  a  higher  microbial  metabolicactivity.  Finally,  to  validate  our  hypothesis,  we  performed  a  variance  partitioning  analysis:  itrevealed  that  microbial  functional  categories,  more than diversity,  hold a statistically  supportedcorrelation with the tomato transcriptome explaining more than 40% of its variability.

Reconstructing the tomato root-associated metatranscriptome using host-targeted RNAseq data

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Reconstructing the tomato root-associated metatranscriptome using host-targeted RNAseq data

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