Interactive exploration of heterogeneous biological networks with Biomine Explorer

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Network modeling unravels mechanisms of crosstalk between ethylene and salicylate signaling in potato

To develop novel crop breeding strategies, it is crucial to understand the mechanisms underlying the interaction between plants and their pathogens. Network modeling represents a powerful tool that can unravel properties of complex biological systems. In this study, we aimed to use network modeling to better understand immune signaling in potato (Solanum tuberosum). For this, we first built on a reliable Arabidopsis (Arabidopsis thaliana) immune signaling model, extending it with the information from diverse publicly available resources. Next, we translated the resulting prior knowledge network (20,012 nodes and 70,091 connections) to potato and superimposed it with an ensemble network inferred from time-resolved transcriptomics data for potato. We used different network modeling approaches to generate specific hypotheses of potato immune signaling mechanisms. An interesting finding was the identification of a string of molecular events illuminating the ethylene pathway modulation of the salicylic acid pathway through Nonexpressor of PR Genesi gene expression. Functional validations confirmed this modulation, thus supporting the potential of our integrative network modeling approach for unraveling molecular mechanisms in complex systems. In addition, this approach can ultimately result in improved breeding strategies for potato and other sensitive crops

Proceedings of the “Think Tank Hackathon’’, Big Data Training School for Life Sciences Follow-up, Ljubljana 6th – 7th February 2018

On 6th and 7th February 2018, a Think Tank took place in Ljubljana, Slovenia. It was a follow-up of the “Big Data Training School for Life Sciences” held in Uppsala, Sweden, in September 2017. The focus was on identifying topics of interest and optimising the programme for a forthcoming “Advanced” Big Data Training School for Life Science, that we hope is again supported by the COST Action CHARME (Harmonising standardisation strategies to increase efficiency and competitiveness of European life-science research - CA15110). The Think Tank aimed to go into details of several topics that were - to a degree - covered by the former training school. Likewise, discussions embraced the recent experience of the attendees in light of the new knowledge obtained by the first edition of the training school and how it comes from the perspective of their current and upcoming work. The 2018 training school should strive for and further facilitate optimised applications of Big Data technologies in life sciences. The attendees of this hackathon entirely organised this workshop.Peer ReviewedPostprint (published version

The CHARME "Advanced Big Data Training School for Life Sciences": an example of good practices for training on current bioinformatics challenges

The CHARME “Advanced Big Data Training School for Life Sciences” took place during 3-7 September 2018, at the Campus Nord of the Technical University of Catalonia (UPC) in Barcelona (ES). The school was organised by the Data Management Group (DAMA) of the UPC in collaboration with EMBnet as a follow-up of the first CHARME-EMBnet “Big Data Training School for Life Sciences”, held in Uppsala, Sweden, in September 2017. The learning objectives of the school were defined and agreed during the CHARME “Think Tank Hackathon” that was held in Ljubljana, Slovenia, in February 2018. This article explains in detail the step forward organisation of the training school, the covered contents and the interaction/relationships that thanks to this school have been established between the trainees, the trainers and the organisers.Peer ReviewedPostprint (published version

Evidence-based unification of potato gene models with the UniTato collaborative genome browser

Potato (Solanum tuberosum) is the most popular tuber crop and a model organism. A variety of gene models for potato exist, and despite frequent updates, they are not unified. This hinders the comparison of gene models across versions, limits the ability to reuse experimental data without significant re-analysis, and leads to missing or wrongly annotated genes. Here, we unify the recent potato double monoploid v4 and v6 gene models by developing an automated merging protocol, resulting in a Unified poTato genome model (UniTato). We subsequently established an Apollo genome browser (unitato.nib.si) that enables public access to UniTato and further community-based curation. We demonstrate how the UniTato resource can help resolve problems with missing or misplaced genes and can be used to update or consolidate a wider set of gene models or genome information. The automated protocol, genome annotation files, and a comprehensive translation table are provided at github.com/NIB-SI/unitato

Orchestration of the stilbene synthase gene family and their regulators by subgroup 2 MYB genes

The control of plant specialised metabolism is exerted by transcription factors and co-regulators acting on cis-regulatory DNA sequences of pathway-structural genes, determining when, where, and how metabolites are accumulated. A particularly interesting case for studying the transcriptional control of metabolism is represented by stilbenoids, produced within the phenylpropanoid pathway, as their ability to inhibit infection by coronaviruses MERS-CoV and SARS-CoV has been recently demonstrated in vitro. Integrative omic studies in grapevine (Vitis vinifera L.), including gene co-expression networks, have previously highlighted several transcription factors (TFs) from different gene families as potential modulators of stilbenoid accumulation, offering an ideal framework for gene function characterisation using genome-wide approaches. In the context of non-model plant species, DNA affinity purification sequencing (DAP-Seq) results a novel and potentially powerful tool for the analysis of novel uncharacterised regulators, however, it has not yet been applied in fruit crops. Accordingly, we tested as a proof-of-concept the binding of two previously characterised R2R3-MYB TFs to their known targets of the stilbene pathway, MYB14 and MYB15, obtaining 5,222 and 4,502 binding events assigned to 4,038 and 3,645 genes for each TF, respectively. Bound genes (putative targets) were overlapped with aggregated gene centred co-expression networks resulting in shared and exclusive High Confidence Targets (HCTs) suggesting a high, but not complete, redundancy. Our results show that in addition to the previously known but few STS targets, these regulators bind to almost half of the complete STS family in addition to other phenylpropanoid- and stilbenoid-related genes. We also suggest they are potentially involved in other processes such as the circadian rhythm or the synthesis of biotin. We searched the activated transcriptomes of transiently MYB15-overexpressing grapevine plants and observed a large activation of its high confidence targets, validating our methodological approach. Our results also show that MYB15 seems to play a role in regulating other stilbenoid-related TFs such as WRKY03.This work was supported by Grant PGC2018-099449-A-I00 and by the Ramón y Cajal program grant RYC-2017-23645, both awarded to J.T.M. and to the FPI scholarship PRE2019-088044 granted to L.O. from the Ministerio de Ciencia, Innovaci´on y Universidades (MCIU, Spain), Agencia Estatal de Investigaci´on (AEI, Spain), and Fondo Europeo de Desarrollo Regional (FEDER, European Union). C.Z. is supported by China Scholarship Council (CSC) no. 201906300087. This article is based upon work from COST Action CA 17111 INTEGRAPE, supported by COST (European Cooperation in Science and Technology). Data has been treated and uploaded in public repositories according to the FAIR principles.N

Direct regulation of shikimate, early phenylpropanoid, and stilbenoid pathways by subgroup 2 R2R3-MYBs in grapevine

The stilbenoid pathway is responsible for the production of resveratrol in grapevine (Vitis vinifera L.). A few transcription factors (TFs) have been identified as regulators of this pathway but the extent of this control has not been deeply studied. Here we show how DNA affinity purification sequencing (DAP-Seq) allows for the genome-wide TF-binding site interrogation in grape. We obtained 5190 and 4443 binding events assigned to 4041 and 3626 genes for MYB14 and MYB15, respectively (approximately 40% of peaks located within −10 kb of transcription start sites). DAP-Seq of MYB14/MYB15 was combined with aggregate gene co-expression networks (GCNs) built from more than 1400 transcriptomic datasets from leaves, fruits, and flowers to narrow down bound genes to a set of high confidence targets. The analysis of MYB14, MYB15, and MYB13, a third uncharacterized member of Subgroup 2 (S2), showed that in addition to the few previously known stilbene synthase (STS) targets, these regulators bind to 30 of 47 STS family genes. Moreover, all three MYBs bind to several PAL, C4H, and 4CL genes, in addition to shikimate pathway genes, the WRKY03 stilbenoid co-regulator and resveratrol-modifying gene candidates among which ROMT2-3 were validated enzymatically. A high proportion of DAP-Seq bound genes were induced in the activated transcriptomes of transient MYB15-overexpressing grapevine leaves, validating our methodological approach for delimiting TF targets. Overall, Subgroup 2 R2R3-MYBs appear to play a key role in binding and directly regulating several primary and secondary metabolic steps leading to an increased flux towards stilbenoid production. The integration of DAP-Seq and reciprocal GCNs offers a rapid framework for gene function characterization using genome-wide approaches in the context of non-model plant species and stands up as a valid first approach for identifying gene regulatory networks of specialized metabolism.This work was supported by Grant PGC2018-099449-A-I00 and by the Ramón y Cajal program (grant RYC-2017-23 645), both awarded to JTM, and to the FPI scholarship (PRE2019-088044) granted to LO from the Ministerio de Ciencia, Innovación y Universidades (MCIU, Spain), Agencia Estatal de Investigación (AEI, Spain), and Fondo Europeo de Desarrollo Regional (FEDER, European Union). CZ is supported by China Scholarship Council (CSC; no. 201906300087). KG and ZR were supported by the Slovenian Research Agency (grants P4-0165 and Z7-1888). SCH is partially supported by the National Science Foundation (grant PGRP IOS-1916804). This article is based upon work from COST Action CA 17111 INTEGRAPE, supported by COST (European Cooperation in Science and Technology).Peer reviewe