Akash Chandra Baidya

Projects: Group Data Science, Targeting the immunoproteasome: Analyzing the biological effects of specific versus pan inhibition, Macrophage and neutrophil redox proteome in infection with Mycobacterium tuberculosis

Institutions: Forschungszentrum Borstel

https://orcid.org/0009-0008-0344-2740
Ylva Wedmark

Projects: DigiSal, GenoSysFat, SEEK tutorial for DigiSal

Institutions: Norwegian University of Life Sciences

https://orcid.org/0000-0002-7450-619X
Marie Gulla

Projects: DigiSal, SEEK tutorial for DigiSal

Institutions: Norwegian University of Life Sciences

https://orcid.org/0000-0002-5680-4855
Hannington Ochieng

Projects: Establishing an innovative and transnational feed production approach for reduced climate impact of the aquaculture sector and future food supply

Institutions: Kenya Agricultural and Livestock Research Organisation (KALRO)

https://orcid.org/0000-0002-4694-7895
Bettina Heinlein

Projects: BioCreative VII

Institutions: Heidelberg Institute for Theoretical Studies (HITS gGmbH)

Lukrécia Mertová project_administrator

Projects: FAIRDOM, BioCreative VII, The BeeProject, SDBV/HITS, Semantic Table Interpretation in Chemistry

Institutions: Heidelberg Institute for Theoretical Studies (HITS gGmbH)

https://orcid.org/0000-0002-7585-4479
Steve Baeyen project_administrator

Projects: hybrid sequencing

Institutions: ILVO

https://orcid.org/0000-0002-7192-2403
Mitsuhiro Odaka

Projects: COVID-19 Disease Map

Institutions: National Institute of Informatics

https://orcid.org/0000-0001-8725-3366
Wolfgang Hess

Projects: SCyCode The Autotrophy-Heterotrophy Switch in Cyanobacteria: Coherent Decision-Making at Multiple Regulatory Layers

Institutions: University of Freiburg

https://orcid.org/0000-0002-5340-3423
Nils Wildt

Projects: Working Group Nicole Radde

Institutions: University of Stuttgart

https://orcid.org/0000-0003-3954-8981
Carissa Bleker

Projects: ADAPT - Accelerated Development of multiple-stress tolerAnt PoTato

Institutions: National Institute of Biology

https://orcid.org/0000-0003-1617-7145
Eirini Tsirvouli

Projects: C19DM - Macrophage logical model, COVID-19 Disease Map

Institutions: Norwegian University of Science and Technology

https://orcid.org/0000-0002-6456-6463
Tyrone Chen

Projects: COVID-19 Disease Map

Institutions: Monash University

https://orcid.org/0000-0002-9207-0385
Pranavathiyani G

Projects: COVID-19 Disease Map

Institutions: Pondicherry University

https://orcid.org/0000-0003-4854-8238
Bhanwar Lal Puniya

Projects: COVID-19 Disease Map, Covid-19 Interferon pathway modelling and analysis

Institutions: University of Nebraska-Lincoln

https://orcid.org/0000-0001-7528-3568
Rupert Overall

Projects: COVID-19 Disease Map

Institutions: independent researcher

https://orcid.org/0000-0002-3882-6073
Gina Crovetto

Projects: Not specified

Institutions: Not specified

Denes Turei

Projects: COVID-19 Disease Map

Institutions: European Molecular Biology Laboratory

https://orcid.org/0000-0002-7249-9379
Johann Rohwer

Projects: EnzymeML, Standardization of enzyme-catalyzed reaction measurement, Standardization of enzyme-catalyzed reaction modelling

Institutions: University of Stellenbosch

https://orcid.org/0000-0001-6288-8904
Mohammed Abdallah

Projects: COVID-19 Disease Map

Institutions: University of Cape Town

https://orcid.org/0000-0002-5374-0657
Chris Evelo project_administratorprogramme_administrator

Projects: HUMET Startup, COMBINE Multicellular Modelling, COVID-19 Disease Map, VHP project

Institutions: University Maastricht

https://orcid.org/0000-0002-5301-3142
Carsten Oliver Schmidt

Projects: COVID-19 related studies and tools in Germany, nfdi4health - German National Research Data Infrastructure for Personal Health Data

Institutions: University Medicine of Greifswald

https://orcid.org/0000-0001-5266-9396
Inna Kuperstein

Projects: COVID-19 Disease Map

Institutions: Institut Curie

https://orcid.org/0000-0001-8086-8915
Rex D A B

Projects: COVID-19 Disease Map

Institutions: Yenepoya University

Arnau Montagud

Projects: COVID-19 Disease Map

Institutions: Barcelona Supercomputing Center

https://orcid.org/0000-0002-7696-1241
Noriko Hiroi

Projects: COVID-19 Disease Map

Institutions: Keio University

https://orcid.org/0000-0002-0214-373X
Kary Ocaña asset_housekeeper

Projects: WG Infrastructure for Translational Research, GMDS Project Group "FAIRe Dateninfrastrukturen für die Biomedizinische Informatik"

Institutions: University of Goettingen, University Medical Center Göttingen

https://orcid.org/0000-0002-2151-7418
Maja Zagorscak project_administratorasset_housekeeperasset_gatekeeperprogramme_administrator

Projects: pISA-tree, HYp - Spatiotemporal analysis of hypersensitive response to Potato virus Y in potato, INDIE - Biotechnological production of sustainable indole, _p_stRT, ADAPT - Accelerated Development of multiple-stress tolerAnt PoTato, tst, tst2

Institutions: National Institute of Biology, tst

https://orcid.org/0000-0002-1669-6482
Anuradha Balasundaram

Projects: Not specified

Institutions: Not specified

https://orcid.org/0000-0001-8357-2159
Steven Watterson

Projects: HUMET Startup, COVID-19 Disease Map

Institutions: University of Ulster

https://orcid.org/0000-0002-2750-6410
Niko Beerenwinkel

Projects: SysVirDrug

Institutions: ETH Zurich

https://orcid.org/0000-0002-0573-6119
Jörg Schaber

Projects: SulfoSys - Biotec, ICYSB 2015 - International Practical Course in Systems Biology

Institutions: Otto-von-Guericke University Magdeburg, University of Gothenburg

https://orcid.org/0000-0001-6971-2530
Kay Nieselt

Projects: STREAM, SYSTERACT

Institutions: University of Tuebingen, University of Tübingen

Maikel Verouden pal

Projects: SUMO

Institutions: University of Amsterdam

Sven Even Borgos pal

Projects: SCaRAB, STREAM, SYSTERACT, INBioPharm

Institutions: SINTEF, Norwegian University of Science and Technology

Thomas Millat

Projects: COSMIC, BaCell-SysMO

Institutions: University of Rostock

PhotoBoost

Engineering next level photosynthesis-PhotoBoost is a 4-year research and innovation action that aims to significantly improve the efficiency of photosynthesis in plants. The optimisation of photosynthesis will be achieved by capitalising on multidisciplinary approaches including computational biology, metabolic modelling, systems biology, enzyme and pathway engineering, synthetic biology, and the multigene transformation of two major C3 crops: potato and rice. The PhotoBoost project will deliver ...

Programme: Independent Projects

Public web page: http://www.photoboost.org

_A_04_TransRate

Assay: _A_04_TransRate Short Name: 04_TransRate Assay Class: DRY Assay Type: TransRate Title: TransRate post filtering and reassignment Description: TransRate post filtering and reassignment pISA Assay creation date: 2019-10-22 pISA Assay creator: Maja Zagorscak Phenodata: None Featuredata: None Data: see ./input/path_to_files.txt

Submitter: Maja Zagorscak

Biological problem addressed: Validation

Investigation: _I_STRT

Study: _S_03_stCuSTr

_A_03.2_components

Assay: _A_03.2_components Short Name: 03.2_components Assay Class: DRY Assay Type: components Title: Components: tr2aacds headers, cdhit-2d Description: Components: tr2aacds headers, cdhit-2d; post-filtering redefinition of paralogue clusters pISA Assay creation date: 2019-10-22 pISA Assay creator: Ziva Ramsak Phenodata: None Featuredata: Data:

Submitter: Maja Zagorscak

Biological problem addressed: Annotation

Investigation: _I_STRT

Study: _S_03_stCuSTr

_A_03.1_filtering

Assay: _A_03.1_filtering Short Name: 03.1_filtering Assay Class: DRY Assay Type: filtering Title: Filtering according to biological evidence; removal of contaminants, chimeras and suspicious constructs Description: Filtering according to biological evidence; removal of contaminants, chimeras and suspicious constructs pISA Assay creation date: 2019-10-22 pISA Assay creator: Maja Zagorscak Phenodata: None Featuredata: None Data: see ./input/path_to_files.txt

Submitter: Maja Zagorscak

Biological problem addressed: Validation

Investigation: _I_STRT

Study: _S_03_stCuSTr

_A_02.8_DIAMOND

Assay: _A_02.8_DIAMOND Short Name: 02.8_DIAMOND Assay Class: DRY Assay Type: DIAMOND Title: DIAMOND on raw tr2aacds output Description: DIAMOND on raw tr2aacds output, both .cds and .tr; including lost and found pISA Assay creation date: 2019-10-22 pISA Assay creator: Maja Zagorscak Phenodata: None Featuredata: None Data: see ./input/path_to_files.txt

Submitter: Maja Zagorscak

Biological problem addressed: Annotation

Investigation: _I_STRT

Study: _S_03_stCuSTr

_A_02.7_VecScreen

Assay: _A_02.7_VecScreen Short Name: 02.7_VecScreen Assay Class: DRY Assay Type: VecScreen Title: VecScreen on raw tr2aacds output Description: VecScreen (contamination screening) and blastn (contaminants annotation) on raw (initial) tr2aacds output pISA Assay creation date: 2019-10-22 pISA Assay creator: Maja Zagorscak Phenodata: None Featuredata: None Data: see ./input/path_to_files.txt

Submitter: Maja Zagorscak

Biological problem addressed: Validation

Investigation: _I_STRT

Study: _S_03_stCuSTr

_A_02.5_STARlong_matchAnnot

Assay: _A_02.5_STARlong_matchAnnot Short Name: 02.5_STARlong_matchAnnot Assay Class: DRY Assay Type: matchAnnot Title: potato transcriptome v2 (evigene) STARlong mapping of transcripts to reference genome and MatchAnnot to get transcript annotations Description: potato initial transcriptome (evigene) STARlong mapping of transcripts to reference genome and MatchAnnot to get transcript annotations pISA Assay creation date: 2019-10-22 pISA Assay creator: Marko Petek Phenodata: None Featuredata: None ...

Submitter: Maja Zagorscak

Biological problem addressed: Annotation

Investigation: _I_STRT

Study: _S_03_stCuSTr

_A_02.4_STAR

Assay: _A_02.4_STAR Short Name: 02.4_STAR Assay Class: DRY Assay Type: STAR Title: mapping reads back to evigene .tr output Description: mapping reads back to evigene .tr output using STAR to check the percentage of reads that map pISA Assay creation date: 2019-10-22 pISA Assay creator: Marko Petek Phenodata: None Featuredata: None Data: see ./input/path_to_files.txt

Submitter: Maja Zagorscak

Biological problem addressed: Validation

Investigation: _I_STRT

Study: _S_03_stCuSTr

_A_02.3_InterProScan

Assay: _A_02.3_InterProScan Short Name: 02.3_InterProScan Assay Class: DRY Assay Type: InterProScan Title: InterProScan on tr2aacds output Description: InterProScan on tr2aacds output pISA Assay creation date: 2019-10-22 pISA Assay creator: Marko Petek Phenodata: None Featuredata: None Data: see ./input/path_to_files.txt

Submitter: Maja Zagorscak

Biological problem addressed: Annotation

Investigation: _I_STRT

Study: _S_03_stCuSTr

_A_01_evigene

Assay: _A_01_evigene Short Name: 01_evigene Assay Class: DRY Assay Type: evigene Title: EvidentialGene tr2aacds.pl VERSION 2016.07.11 Description: See http://eugenes.org/EvidentialGene/about/EvidentialGene_trassembly_pipe.html pISA Assay creation date: 2019-10-22 pISA Assay creator: Maja Zagorscak Phenodata: None Featuredata: None Data: see ./input/path_to_files.txt

Submitter: Maja Zagorscak

Biological problem addressed: Integration

Investigation: _I_STRT

Study: _S_03_stCuSTr

/presentations/Figure2.pdf

_p_stRT/_I_STRT pipeline

Creators: Maja Zagorscak, Marko Petek

Submitter: Maja Zagorscak

DOI: 10.15490/fairdomhub.1.datafile.3157.1

Download
Instructions for using the workflow

Instructions and details on the data analysis workflow.

Creators: Gudrun Gygli, Juergen Pleiss, Xinmeng Xu

Submitter: Gudrun Gygli

Download
Regulation of Rubisco activity in crops.

Abstract (Expand)

Efficient plant acclimation to changing environmental conditions relies on fast adjustments of the transcriptome, proteome, and metabolome. Regulation of enzyme activity depends on the activity of … specific chaperones, chemical post-translational modifications (PTMs) of amino acid residues, and changes in the cellular and organellar microenvironment. Central to carbon assimilation, and thus plant growth and yield, Rubisco activity is regulated by its chaperone Rubisco activase (Rca) and by adjustments in the chloroplast stroma environment. Focused on crops, this review highlights the main PTMs and stromal ions and metabolites affecting Rubisco and Rca in response to environmental stimuli. Rca isoforms differ in regulatory properties and heat sensitivity, with expression changing according to the surrounding environment. Much of the physiological relevance of Rubisco and Rca PTMs is still poorly understood, though some PTMs have been associated with Rubisco regulation in response to stress. Ion and metabolite concentrations in the chloroplast change in response to variations in light and temperature. Some of these changes promote Rubisco activation while others inhibit activation, deactivate the enzyme, or change the rates of catalysis. Understanding these regulatory mechanisms will aid the development of strategies to improve carbon fixation by Rubisco under rapidly changing environments as experienced by crop plants.

Authors: J. Amaral, A. K. M. Lobo, E. Carmo-Silva

Date Published: 11th Dec 2023

Publication Type: Journal

Kinetic modeling identifies targets for engineering improved photosynthetic efficiency in potato (Solanum tuberosum cv. Solara).

Abstract (Expand)

Potato (Solanum tuberosum) is a significant non-grain food crop in terms of global production. However, its yield potential might be raised by identifying means to release bottlenecks within photosynthetic … metabolism, from the capture of solar energy to the synthesis of carbohydrates. Recently, engineered increases in photosynthetic rates in other crops have been directly related to increased yield - how might such increases be achieved in potato? To answer this question, we derived the photosynthetic parameters V(cmax) and J(max) to calibrate a kinetic model of leaf metabolism (e-Photosynthesis) for potato. This model was then used to simulate the impact of manipulating the expression of genes and their protein products on carbon assimilation rates in silico through optimizing resource investment among 23 photosynthetic enzymes, predicting increases in photosynthetic CO(2) uptake of up to 67%. However, this number of manipulations would not be practical with current technologies. Given a limited practical number of manipulations, the optimization indicated that an increase in amounts of three enzymes - Rubisco, FBP aldolase, and SBPase - would increase net assimilation. Increasing these alone to the levels predicted necessary for optimization increased photosynthetic rate by 28% in potato.

Authors: S. Vijayakumar, Y. Wang, G. Lehretz, S. Taylor, E. Carmo-Silva, S. Long

Date Published: 30th Jan 2024

Publication Type: Journal

Periportal steatosis in mice affects distinct parameters of pericentral drug metabolism

Abstract (Expand)

Abstract Little is known about the impact of morphological disorders in distinct zones on metabolic zonation. It was described recently that periportal fibrosis did affect the expression of CYP proteins, …xpression of CYP proteins, a set of pericentrally located drug-metabolizing enzymes. Here, we investigated whether periportal steatosis might have a similar effect. Periportal steatosis was induced in C57BL6/J mice by feeding a high-fat diet with low methionine/choline content for either two or four weeks. Steatosis severity was quantified using image analysis. Triglycerides and CYP activity were quantified in photometric or fluorometric assay. The distribution of CYP3A4, CYP1A2, CYP2D6, and CYP2E1 was visualized by immunohistochemistry. Pharmacokinetic parameters of test drugs were determined after injecting a drug cocktail (caffeine, codeine, and midazolam). The dietary model resulted in moderate to severe mixed steatosis confined to periportal and midzonal areas. Periportal steatosis did not affect the zonal distribution of CYP expression but the activity of selected CYPs was associated with steatosis severity. Caffeine elimination was accelerated by microvesicular steatosis, whereas midazolam elimination was delayed in macrovesicular steatosis. In summary, periportal steatosis affected parameters of pericentrally located drug metabolism. This observation calls for further investigations of the highly complex interrelationship between steatosis and drug metabolism and underlying signaling mechanisms.

Authors: Mohamed Albadry, Sebastian Höpfl, Nadia Ehteshamzad, Matthias König, Michael Böttcher, Jasna Neumann, Amelie Lupp, Olaf Dirsch, Nicole Radde, Bruno Christ, Madlen Christ, Lars Ole Schwen, Hendrik Laue, Robert Klopfleisch, Uta Dahmen

Date Published: 1st Dec 2022

Publication Type: Journal

Gene Network Inference from Single-Cell Omics Data and Domain Knowledge for Constructing COVID-19-Specific ICAM1-Associated Pathways

Abstract (Expand)

Abstract ICAM-1 is critical for interactions between cells. Previous studies have suggested that ICAM-1 triggers cell-to-cell transmission of HIV-1 or HTLV-1. SARS-CoV-2 shares several features with … several features with these viruses in interactions between cells, and SARS-CoV-2 cell-to-cell transmission is associated with COVID-19 severity. However, ICAM-1 and its associated pathways are not identified as essential factors in interactions between cells in COVID-19. For example, the current COVID-19 Disease Map has no entry for those pathways. Therefore, discovering unknown ICAM-1 pathways will be indispensable for clarifying the mechanism of COVID-19. This study builds ICAM1-associated pathways by gene network inference from single-cell omics data and multiple knowledge bases. First, data analyses extracted coexpressed genes with significant differences in expression levels with spurious correlations removed. Second, knowledge bases validate models. Finally, mapping the models onto existing pathways identifies new ICAM1-associated pathways. These pathways indicate that (1) upstream pathways include proteins in noncanonical NF-kappaB pathway and that (2) downstream pathways contain integrins and cytoskeleton or motor proteins for cell transformation. In this way, data-driven and knowledge-based approaches are integrated into gene network inference for ICAM1-associated pathway construction. The results can contribute to repairing and completing the COVID-19 Disease Map, thereby improving our understanding of the mechanisms of COVID-19.

Authors: Mitsuhiro Odaka, Morgan Magnin, Katsumi Inoue

Date Published: 11th Feb 2022

Publication Type: Journal

Cultivar-specific transcriptome and pan-transcriptome reconstruction of tetraploid potato

Abstract (Expand)

Background: Although the reference genome of Solanum tuberosum group Phureja double-monoploid (DM) clone is available, knowledge on the genetic diversity of the highly heterozygous tetraploid group … Tuberosum, representing most cultivated varieties, remains largely unexplored. This lack of knowledge hinders further progress in potato research and its subsequent applications in breeding. Results: For the DM genome assembly, two only partially-overlapping gene models exist differing in a unique set of genes and intron/exon structure predictions. First step was to merge and manually curate the merged gene model, creating a union of genes in Phureja scaffold. We next compiled available RNA-Seq datasets (cca. 1.5 billion reads) for three tetraploid potato genotypes (cultivar Désirée, cultivar Rywal, and breeding clone PW363) with diverse breeding pedigrees. Short-read transcriptomes were assembled using CLC, Trinity, Velvet, and rnaSPAdes de novo assemblers using different settings to test for optimal outcome. In addition, for cultivar Rywal, PacBio Iso-Seq full-length transcriptome sequencing was also performed. Revised EvidentialGene redundancy-reducing pipeline was employed to produce accurate and complete cultivar-specific transcriptomes from assemblers output, as well as to attain the pan-transcriptome. Due to being the most diverse dataset in terms of tissues (stem, seedlings and roots) and experimental conditions, cv. Désirée was the most complete transcriptome (95.8% BUSCO completeness). For cv. Rywal and breeding clone PW363 data were available for leaf samples only and the resulting transcriptomes were less complete than cv. Désirée (89.8% and 89.3% BUSCO completeness, respectively). Cross comparison of these cultivar-specific transcriptomes and merged DM gene model suggests that the core potato transcriptome is comprised of 16,339 genes. The pan-transcriptome contains a total of 95,779 transcripts, of which 54,614 transcripts are not present in the Phureja genome. These represent the variants of the novel genes found in the potato pan-genome. Conclusions: Our analysis shows that the available gene model of double-monoploid potato from group Phureja is, to some degree, not complete. The generated transcriptomes and pan-transcriptome represent a valuable resource for potato gene variability exploration, high-throughput -omics analyses, and future breeding programmes.

Authors: Marko Petek, Maja Zagorščak, Živa Ramšak, Sheri Sanders, Elizabeth Tseng, Mohamed Zouine, Anna Coll, Kristina Gruden

Date Published: No date defined

Publication Type: Not specified

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