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The importance of improving the FAIRness (findability, accessibility, interoperability, reusability) of research data is undeniable, especially in the face of large, complex datasets currently being produced by omics technologies. Facilitating the integration of a dataset with other types of data increases the likelihood of reuse, and the potential of answering novel research questions. Ontologies are a useful tool for semantically tagging datasets as adding relevant metadata increases the understanding of how data was produced and increases its interoperability. Ontologies provide concepts for a particular domain as well as the relationships between concepts. By tagging data with ontology terms, data becomes both human- and machine- interpretable, allowing for increased reuse and interoperability. However, the task of identifying ontologies relevant to a particular research domain or technology is challenging, especially within the diverse realm of fundamental plant research. In this review, we outline the ontologies most relevant to the fundamental plant sciences and how they can be used to annotate data related to plant-specific experiments within metadata frameworks, such as Investigation-Study-Assay (ISA). We also outline repositories and platforms most useful for identifying applicable ontologies or finding ontology terms.

Abstract Corynebacterium glutamicum is the major host for the industrial production of amino acids and has become one of the best studied model organisms in microbial biotechnology. Rational strain construction has led to an improvement of producer strains and to a variety of novel producer strains with a broad substrate and product spectrum. A key factor for the success of these approaches is detailed knowledge of transcriptional regulation in C. glutamicum. Here, we present a large compendium of 927 manually curated microarray-based transcriptional profiles for wild-type and engineered strains detecting genome-wide expression changes of the 3,047 annotated genes in response to various environmental conditions or in response to genetic modifications. The replicates within the 927 experiments were combined to 304 microarray sets ordered into six categories that were used for differential gene expression analysis. Hierarchical clustering confirmed that no outliers were present in the sets. The compendium provides a valuable resource for future fundamental and applied research with C. glutamicum and contributes to a systemic understanding of this microbial cell factory. Measurement(s) Gene Expression Analysis Technology Type(s) Two Color Microarray Factor Type(s) WT condition A vs. WT condition B • Plasmid-based gene overexpression in parental strain vs. parental strain with empty vector control • Deletion mutant vs. parental strain Sample Characteristic - Organism Corynebacterium glutamicum Sample Characteristic - Environment laboratory environment Sample Characteristic - Location Germany

Research data management (RDM) combines a set of practices for the organization, storage and preservation of data from research projects. The RDM strategy of a project is usually formalized as a data management plan (DMP)—a document that sets out procedures to ensure data findability, accessibility, interoperability and reusability (FAIR-ness). Many aspects of RDM are standardized across disciplines so that data and metadata are reusable, but the components of DMPs in the plant sciences are often disconnected. The inability to reuse plant-specific DMP content across projects and funding sources requires additional time and effort to write unique DMPs for different settings. To address this issue, we developed DataPLAN—an open-source tool incorporating prewritten DMP content for the plant sciences that can be used online or offline to prepare multiple DMPs. The current version of DataPLAN supports Horizon 2020 and Horizon Europe projects, as well as projects funded by the German Research Foundation (DFG). Furthermore, DataPLAN offers the option for users to customize their own templates. Additional templates to accommodate other funding schemes will be added in the future. DataPLAN reduces the workload needed to create or update DMPs in the plant sciences by presenting standardized RDM practices optimized for different funding contexts.

Abstract Background 5-Ketofructose (5-KF) has recently been identified as a promising non-nutritive natural sweetener. Gluconobacter oxydans strains have been developed that allow efficient production of 5-KF from fructose by plasmid-based expression of the fructose dehydrogenase genes fdhSCL of Gluconobacter japonicus. As plasmid-free strains are preferred for industrial production of food additives, we aimed at the construction of efficient 5-KF production strains with the fdhSCL genes chromosomally integrated. Results For plasmid-free 5-KF production, we selected four sites in the genome of G. oxydans IK003.1 and inserted the fdhSCL genes under control of the strong P264 promoter into each of these sites. All four recombinant strains expressed fdhSCL and oxidized fructose to 5-KF, but site-specific differences were observed suggesting that the genomic vicinity influenced gene expression. For further improvement, a second copy of the fdhSCL genes under control of P264 was inserted into the second-best insertion site to obtain strain IK003.1::fdhSCL 2 . The 5-KF production rate and the 5-KF yield obtained with this double-integration strain were considerably higher than for the single integration strains and approached the values of IK003.1 with plasmid-based fdhSCL expression. Conclusion We identified four sites in the genome of G. oxydans suitable for expression of heterologous genes and constructed a strain with two genomic copies of the fdhSCL genes enabling efficient plasmid-free 5-KF production. This strain will serve as basis for further metabolic engineering strategies aiming at the use of alternative carbon sources for 5-KF production and for bioprocess optimization.

γ-Aminobutyric acid (GABA) is a non-proteinogenic amino acid mainly formed by decarboxylation of L-glutamate and is widespread in nature from microorganisms to plants and animals. In this study, we analyzed the regulation of GABA utilization by the Gram-positive soil bacterium Corynebacterium glutamicum, which serves as model organism of the phylum Actinobacteria. We show that GABA usage is subject to both specific and global regulatory mechanisms. Transcriptomics revealed that the gabTDP genes encoding GABA transaminase, succinate semialdehyde dehydrogenase, and GABA permease, respectively, were highly induced in GABA-grown cells compared to glucose-grown cells. Expression of the gabTDP genes was dependent on GABA and the PucR-type transcriptional regulator GabR, which is encoded divergently to gabT. A ΔgabR mutant failed to grow with GABA, but not with glucose. Growth of the mutant on GABA was restored by plasmid-based expression of gabR or of gabTDP, indicating that no further genes are specifically required for GABA utilization. Purified GabR (calculated mass 55.75 kDa) formed an octamer with an apparent mass of 420 kDa and bound to two inverted repeats in the gabR-gabT intergenic region. Glucose, gluconate, and myo-inositol caused reduced expression of gabTDP, presumably via the cAMP-dependent global regulator GlxR, for which a binding site is present downstream of the gabT transcriptional start site. C. glutamicum was able to grow with GABA as sole carbon and nitrogen source. Ammonium and, to a lesser extent, urea inhibited growth on GABA, whereas L-glutamine stimulated it. Possible mechanisms for these effects are discussed.

Abstract Background Gluconobacter oxydans is a strictly aerobic Gram-negative acetic acid bacterium used industrially for oxidative biotransformations due to its exceptional type of catabolism. It incompletely oxidizes a wide variety of carbohydrates regio- and stereoselectively in the periplasm using membrane-bound dehydrogenases with accumulation of the products in the medium. As a consequence, only a small fraction of the carbon and energy source enters the cell, resulting in a low biomass yield. Additionally, central carbon metabolism is characterized by the absence of a functional glycolysis and absence of a functional tricarboxylic acid (TCA) cycle. Due to these features, G. oxydans is a highly interesting model organism. Here we analyzed global mRNA decay in G. oxydans to describe its characteristic features and to identify short-lived mRNAs representing potential bottlenecks in the metabolism for further growth improvement by metabolic engineering. Results Using DNA microarrays we estimated the mRNA half-lives in G. oxydans. Overall, the mRNA half-lives ranged mainly from 3 min to 25 min with a global mean of 5.7 min. The transcripts encoding GroES and GroEL required for proper protein folding ranked at the top among transcripts exhibiting both long half-lives and high abundance. The F-type H+-ATP synthase transcripts involved in energy metabolism ranked among the transcripts with the shortest mRNA half-lives. RNAseq analysis revealed low expression levels for genes of the incomplete TCA cycle and also the mRNA half-lives of several of those were short and below the global mean. The mRNA decay analysis also revealed an apparent instability of full-length 23S rRNA. Further analysis of the ribosome-associated rRNA revealed a 23S rRNA fragmentation pattern exhibiting new cleavage regions in 23S rRNAs which were previously not known. Conclusions The very short mRNA half-lives of the H+-ATP synthase, which is likely responsible for the ATP-proton motive force interconversion in G. oxydans under many or most conditions, is notably in contrast to mRNA decay data from other bacteria. Together with the short mRNA half-lives and low expression of some other central metabolic genes it could limit intended improvements of G. oxydans’ biomass yield by metabolic engineering. Also, further studies are needed to unravel the multistep process of the 23S rRNA fragmentation in G. oxydans.

Abstract Background The acetic acid bacterium Gluconobacter oxydans 621H is characterized by its exceptional ability to incompletely oxidize a great variety of carbohydrates in the periplasm. The metabolism of this α-proteobacterium has been characterized to some extent, yet little is known about its transcriptomes and related data. In this study, we applied two different RNAseq approaches. Primary transcriptomes enriched for 5′-ends of transcripts were sequenced to detect transcription start sites, which allow subsequent analysis of promoter motifs, ribosome binding sites, and 5´-UTRs. Whole transcriptomes were sequenced to identify expressed genes and operon structures. Results Sequencing of primary transcriptomes of G. oxydans revealed 2449 TSSs, which were classified according to their genomic context followed by identification of promoter and ribosome binding site motifs, analysis of 5´-UTRs including validation of predicted cis-regulatory elements and correction of start codons. 1144 (41%) of all genes were found to be expressed monocistronically, whereas 1634 genes were organized in 571 operons. Together, TSSs and whole transcriptome data were also used to identify novel intergenic (18), intragenic (328), and antisense transcripts (313). Conclusions This study provides deep insights into the transcriptional landscapes of G. oxydans. The comprehensive transcriptome data, which we made publicly available, facilitate further analysis of promoters and other regulatory elements. This will support future approaches for rational strain development and targeted gene expression in G. oxydans. The corrections of start codons further improve the high quality genome reference and support future proteome analysis.

Abstract Background The influence of both individual factors and, in particular, the regional labour market on the return to work after medical rehabilitation is to be analyzed based on comprehensive administrative data from the German Pension Insurance and Employment Agencies. Method For rehabilitation in 2016, pre- and post-rehabilitation employment was determined from German Pension Insurance data for 305,980 patients in 589 orthopaedic rehabilitation departments and 117,386 patients in 202 psychosomatic rehabilitation departments. Labour market data was linked to the district of residence and categorized into 257 labour market regions. RTW was operationalized as the number of employment days in the calendar year after medical rehabilitation. Predictors are individual data (socio-demographics, rehabilitation biography, employment biography) and contextual data (regional unemployment rate, rehabilitation department level: percentage of patients employed before). The estimation method used was fractional logit regression in a cross-classified multilevel model. Results The effect of the regional unemployment rate on RTW is significant yet small. It is even smaller (orthopaedics) or not significant (psychosomatics) when individual employment biographies (i.e., pre-rehabilitation employment status) are inserted into the model as the most important predictors. The interaction with pre-rehabilitation employment status is not substantial. Conclusions Database and methods are of high quality, however due to the nonexperimental design, omitted variables could lead to bias and limit causal interpretation. The influence of the labour market on RTW is small and proxied to a large extent by individual employment biographies. However, if no (valid) employment biographies are available, the labour market should be included in RTW analyses.