Project description:The first GSSM of V. vinifera was reconstructed (MODEL2408120001). Tissue-specific models for stem, leaf, and berry of the Cabernet Sauvignon cultivar were generated from the original model, through the integration of RNA-Seq data. These models have been merged into diel multi-tissue models to study the interactions between tissues at light and dark phases.

2024-09-02 | MODEL2408160001 | BioModels

Identification of levoglucosan degradation pathways in bacteria

Project description:Levoglucosan is produced in the pyrolysis of cellulose and starch, including from bushfires or the burning of biofuels, and is deposited from the atmosphere across the surface of the earth. We describe two levoglucosan degrading Paenarthrobacter spp. (Paenarthrobacter nitrojuajacolis LG01 and Paenarthrobacter histidinolovorans LG02) that were isolated by metabolic enrichment on levoglucosan as sole carbon source. Genome sequencing and proteomics analysis revealed expression of a series of gene clusters encoding known levoglucosan degrading enzymes, levoglucosan dehydrogenase (LGDH, LgdA), 3-keto-levoglucosan b-eliminase (LgdB1) and glucose 3-dehydrogenase (LgdC), along with an ABC transporter cassette and associated solute binding protein. However, no homologues of 3-ketoglucose dehydratase (LgdB2) were evident. The expressed gene clusters contained a range of putative sugar phosphate isomerase/xylose isomerases with weak similarity to LgdB2. Sequence similarity network analysis of genome neighbors revealed that homologues of LgdA, LgdB1 and LgdC are generally conserved in a range of bacteria in the phyla Firmicutes, Actinobacteria and Proteobacteria. One sugar phosphate isomerase/xylose isomerase cluster (LgdB3) was identified with limited distribution mutually exclusive with LgdB2. LgdB1, LgdB2 and LgdB3 adopt similar predicted 3D folds suggesting overlapping function in processing intermediates in LG metabolism. Our findings highlight the diversity within the LGDH pathway through which bacteria utilize levoglucosan as a nutrient source.

2023-05-10 | PXD039086 | Pride

Paenarthrobacter ilicis strain:CECT 4207 | isolate:CECT 4207

Project description:Arthrobacter ilicis CECT 4207 genome sequencing

| PRJNA546687 | ENA

A multi-omics dataset of heat-shock response in the yeast RNA binding protein Mip6 (NMR assay)

Project description:<p>Gene expression is a biological process regulated at different molecular levels, including chromatin accessibility, transcription, and RNA maturation and transport. In addition, these regulatory mechanisms have strong links with cellular metabolism. Here we present a multi-omics dataset that captures different aspects of this multi-layered process in yeast. We obtained RNA-seq, metabolomics, and H4K12Ac ChIP-seq data for wild-type and mip6delta strains during a heat-shock time course. Mip6 is an RNA-binding protein that contributes to RNA export during environmental stress and is informative of the contribution of post-transcriptional regulation to control cellular adaptations to environmental changes. The experiment was performed in quadruplicate, and the different omics measurements were obtained from the same biological samples, which facilitates the integration and analysis of data using covariance-based methods. We validate our dataset by showing that ChIP-seq, RNA-seq and metabolomics signals recapitulate existing knowledge about the response of ribosomal genes and the contribution of trehalose metabolism to heat stress.</p>

2020-02-14 | MTBLS1320 | MetaboLights

Benchmarking differential expression, imputation and quantification methods for proteomics data

Project description:Data analysis is a critical part of quantitative proteomics studies in interpreting biological questions. Numerous computational tools including protein quantification, imputation, and differential expression (DE) analysis were generated in the past decade. However, searching optimized tools is still an unsolved issue. Moreover, due to the rapid development of RNA-Seq technology, a vast number of DE analysis methods are created. Applying these newly developed RNA-Seq-oriented tools to proteomics data is still a question that needs to be addressed. In order to benchmark these analysis methods, a proteomics dataset constituted the proteins derived from human, yeast, and drosophila with different ratios were generated. Based on this dataset, DE analysis tools (including array-based and RNA-Seq based), imputation algorithms, and protein quantification methods were compared and benchmarked. This study provided useful information on analyzing quantitative proteomics datasets. All the methods used in this study were integrated into Perseus which are available at https://www.maxquant.org/perseus.

2022-11-16 | PXD029891 | JPOST Repository

Project description:Paenarthrobacter nitroguajacolicus LDT1

| PRJNA1182526 | ENA

Paenarthrobacter ureafaciens

Project description:Paenarthrobacter ureafaciens overview

| PRJNA655966 | ENA