MapMan4: A Refined Protein Classification and Annotation Framework Applicable to Multi-Omics Data Analysis

Parallel Metabolomic and Transcriptomic Analysis Reveals Key Factors for Quality Improvement of Tea Plants

As one of the most popular beverages globally, tea has enormous economic, cultural, and medicinal importance that necessitates a comprehensive metabolomics study of this species. In this study, a large-scale targeted metabolomics analysis on two types of leaf tissues of nine tea cultivars from five representative geographical origins within China was carried out using the liquid chromatography-mass spectrometry technique. RNA-seq-based transcriptomic analysis was in parallel conducted on the same samples, and gene expression and metabolic differentiation between tissues as well as between the multiple tea cultivars were investigated. The data obtained provide an accessible resource for further studies of naturally occurring metabolic variation of tea plants, which will aid in thoroughly interpreting the underlying genetic and molecular mechanisms of biosynthesis of specialized metabolites in this critical species. Candidate genes including a transcription factor (CsMYB5-like), which were highly correlated with both the content of flavonoids and the expression level of genes participating in the phenylpropanoid and flavonoid biosynthesis pathway, were identified as potential targets for quality improvement of tea.

Phenotyping: New Windows into the Plant for Breeders

Plant phenotyping enables noninvasive quantification of plant structure and function and interactions with environments. High-capacity phenotyping reaches hitherto inaccessible phenotypic characteristics. Diverse, challenging, and valuable applications of phenotyping have originated among scientists, prebreeders, and breeders as they study the phenotypic diversity of genetic resources and apply increasingly complex traits to crop improvement. Noninvasive technologies are used to analyze experimental and breeding populations. We cover the most recent research in controlled-environment and field phenotyping for seed, shoot, and root traits. Select field phenotyping technologies have become state of the art and show promise for speeding up the breeding process in early generations. We highlight the technologies behind the rapid advances in proximal and remote sensing of plants in fields. We conclude by discussing the new disciplines working with the phenotyping community: data science, to address the challenge of generating FAIR (findable, accessible, interoperable, and reusable) data, and robotics, to apply phenotyping directly on farms.

LSTrAP-Cloud: A User-Friendly Cloud Computing Pipeline to Infer Coexpression Networks

As genomes become more and more available, gene function prediction presents itself as one of the major hurdles in our quest to extract meaningful information on the biological processes genes participate in. In order to facilitate gene function prediction, we show how our user-friendly pipeline, the Large-Scale Transcriptomic Analysis Pipeline in Cloud (LSTrAP-Cloud), can be useful in helping biologists make a shortlist of genes involved in a biological process that they might be interested in, by using a single gene of interest as bait. The LSTrAP-Cloud is based on Google Colaboratory, and provides user-friendly tools that process quality-control RNA sequencing data streamed from the European Nucleotide Archive. The LSTRAP-Cloud outputs a gene coexpression network that can be used to identify functionally related genes for any organism with a sequenced genome and publicly available RNA sequencing data. Here, we used the biosynthesis pathway of Nicotiana tabacum as a case study to demonstrate how enzymes, transporters, and transcription factors involved in the synthesis, transport, and regulation of nicotine can be identified using our pipeline.

Sugar Beet (Beta vulgaris) Guard Cells Responses to Salinity Stress: A Proteomic Analysis

Soil salinity is a major environmental constraint affecting crop growth and threatening global food security. Plants adapt to salinity by optimizing the performance of stomata. Stomata are formed by two guard cells (GCs) that are morphologically and functionally distinct from the other leaf cells. These microscopic sphincters inserted into the wax-covered epidermis of the shoot balance CO₂ intake for photosynthetic carbon gain and concomitant water loss. In order to better understand the molecular mechanisms underlying stomatal function under saline conditions, we used proteomics approach to study isolated GCs from the salt-tolerant sugar beet species. Of the 2088 proteins identified in sugar beet GCs, 82 were differentially regulated by salt treatment. According to bioinformatics analysis (GO enrichment analysis and protein classification), these proteins were involved in lipid metabolism, cell wall modification, ATP biosynthesis, and signaling. Among the significant differentially abundant proteins, several proteins classified as "stress proteins" were upregulated, including non-specific lipid transfer protein, chaperone proteins, heat shock proteins, inorganic pyrophosphatase 2, responsible for energized vacuole membrane for ion transportation. Moreover, several antioxidant enzymes (peroxide, superoxidase dismutase) were highly upregulated. Furthermore, cell wall proteins detected in GCs provided some evidence that GC walls were more flexible in response to salt stress. Proteins such as L-ascorbate oxidase that were constitutively high under both control and high salinity conditions may contribute to the ability of sugar beet GCs to adapt to salinity by mitigating salinity-induced oxidative stress.

Transcriptome Analysis of Apple Leaves Infected by the Rust Fungus Gymnosporangium yamadae at Two Sporulation Stages

Apple rust disease caused by Gymnosporangium yamadae is one of the major threats to apple orchards. In this study, dual RNA-seq analysis was conducted to simultaneously monitor gene expression profiles of G. yamadae and infected apple leaves during the formation of rust spermogonia and aecia. The molecular mechanisms underlying this compatible interaction at 10 and 30 days postinoculation (dpi) indicate a significant reaction from the host plant and comprise detoxication pathways at the earliest stage and the induction of secondary metabolism pathways at 30 dpi. Such host reactions have been previously reported in other rust pathosystems and may represent a general reaction to rust infection. G. yamadae transcript profiling indicates a conserved genetic program in spermogonia and aecia that is shared with other rust fungi, whereas secretome prediction reveals the presence of specific secreted candidate effector proteins expressed during apple infection. Unexpectedly, the survey of fungal unigenes in the transcriptome assemblies of inoculated and mock-inoculated apple leaves reveals that G. yamadae infection may modify the fungal community composition in the apple phyllosphere at 30 dpi. Collectively, our results provide novel insights into the compatible apple-G. yamadae interaction and advance the knowledge of this heteroecious demicyclic rust fungus.

Gene expression analysis of Cyanophora paradoxa reveals conserved abiotic stress responses between basal algae and flowering plants

The glaucophyte Cyanophora paradoxa represents the most basal member of the kingdom Archaeplastida, but the function and expression of most of its genes are unknown. This information is needed to uncover how functional gene modules, that is groups of genes performing a given function, evolved in the plant kingdom. We have generated a gene expression atlas capturing responses of Cyanophora to various abiotic stresses. The data were included in the CoNekT-Plants database, enabling comparative transcriptomic analyses across two algae and six land plants. We demonstrate how the database can be used to study gene expression, co-expression networks and gene function in Cyanophora, and how conserved transcriptional programs can be identified. We identified gene modules involved in phycobilisome biosynthesis, response to high light and cell division. While we observed no correlation between the number of differentially expressed genes and the impact on growth of Cyanophora, we found that the response to stress involves a conserved, kingdom-wide transcriptional reprogramming, which is activated upon most stresses in algae and land plants. The Cyanophora stress gene expression atlas and the tools found in the https://conekt.plant.tools/ database thus provide a useful resource to reveal functionally related genes and stress responses in the plant kingdom.

Comparative transcriptome analysis reveals the molecular regulation underlying the adaptive mechanism of cherry (Cerasus pseudocerasus Lindl.) to shelter covering

BACKGROUND

Rain-shelter covering is widely applied during cherry fruit development in subtropical monsoon climates with the aim of decreasing the dropping and cracking of fruit caused by excessive rainfall. Under rain-shelter covering, the characteristics of the leaves and fruit of the cherry plant may adapt to the changes in the microclimate. However, the molecular mechanism underlying such adaptation remains unclear, although clarifying it may be helpful for improving the yield and quality of cherry under rain-shelter covering.

RESULTS

To better understand the regulation and adaptive mechanism of cherry under rain-shelter covering, 38,621 and 3584 differentially expressed genes were identified with a combination of Illumina HiSeq and single-molecule real-time sequencing in leaves and fruits, respectively, at three developmental stages. Among these, key genes, such as those encoding photosynthetic-antenna proteins (Lhca and Lhcb) and photosynthetic electron transporters (PsbP, PsbR, PsbY, and PetF), were up-regulated following the application of rain-shelter covering, leading to increased efficiency of light utilization. The mRNA levels of genes involved in carbon fixation, namely, rbcL and rbcS, were clearly increased compared with those under shelter-free conditions, resulting in improved CO₂ utilization. Furthermore, the transcription levels of genes involved in chlorophyll (hemA, hemN, and chlH) and carotenoid synthesis (crtB, PDS, crtISO, and lcyB) in the sheltered leaves peaked earlier than those in the unsheltered leaves, thereby promoting organic matter accumulation in leaves. Remarkably, the expression levels of key genes involved in the metabolic pathways of phenylpropanoid (PAL, C4H, and 4CL) and flavonoid (CHS, CHI, F3'H, DFR, and ANS) in the sheltered fruits were also up-regulated earlier than of those in the unsheltered fruits, conducive to an increase in anthocyanin content in the fruits.

CONCLUSIONS

According to the physiological indicators and transcriptional expression levels of the related genes, the adaptive regulation mechanism of cherry plants was systematically revealed. These findings can help understand the effect of rain-shelter covering on Chinese cherry cultivation in rainy regions.

Plant Reactome: a knowledgebase and resource for comparative pathway analysis

Plant Reactome (https://plantreactome.gramene.org) is an open-source, comparative plant pathway knowledgebase of the Gramene project. It uses Oryza sativa (rice) as a reference species for manual curation of pathways and extends pathway knowledge to another 82 plant species via gene-orthology projection using the Reactome data model and framework. It currently hosts 298 reference pathways, including metabolic and transport pathways, transcriptional networks, hormone signaling pathways, and plant developmental processes. In addition to browsing plant pathways, users can upload and analyze their omics data, such as the gene-expression data, and overlay curated or experimental gene-gene interaction data to extend pathway knowledge. The curation team actively engages researchers and students on gene and pathway curation by offering workshops and online tutorials. The Plant Reactome supports, implements and collaborates with the wider community to make data and tools related to genes, genomes, and pathways Findable, Accessible, Interoperable and Re-usable (FAIR).

Low UV-C stress modulates Chlamydomonas reinhardtii biomass composition and oxidative stress response through proteomic and metabolomic changes involving novel signalers and effectors

BACKGROUND

The exposure of microalgae and plants to low UV-C radiation dosages can improve their biomass composition and stress tolerance. Despite UV-C sharing these effects with UV-A/B but at much lower dosages, UV-C sensing and signal mechanisms are still mostly unknown. Thus, we have described and integrated the proteometabolomic and physiological changes occurring in Chlamydomonas reinhardtii-a simple Plantae model-into the first 24 h after a short and low-intensity UV-C irradiation in order to reconstruct the microalgae response system to this stress.

RESULTS

The microalgae response was characterized by increased redox homeostasis, ROS scavenging and protein damage repair/avoidance elements. These processes were upregulated along with others related to the modulation of photosynthetic electron flux, carbon fixation and C/N metabolism. These changes, attributed to either direct UV-C-, ROS- or redox unbalances-associated damage, trigger a response process involving novel signaling intermediaries and effectors such as the translation modulator FAP204, a PP2A-like protein and a novel DYRK kinase. These elements were found linked to the modulation of Chlamydomonas biomass composition (starch accumulation) and proliferation, within an UV-C response probably modulated by different epigenetic factors.

CONCLUSION

Chosen multiomics integration approach was able to describe many fast changes, including biomass composition and ROS stress tolerance, as a response to a low-intensity UV-C stress. Moreover, the employed omics and systems biology approach placed many previously unidentified protein and metabolites at the center of these changes. These elements would be promising targets for the characterization of this stress response in microalgae and plants and the engineering of more productive microalgae strains.

RNA-seq and ChIP-seq as Complementary Approaches for Comprehension of Plant Transcriptional Regulatory Mechanism

The availability of data produced from various sequencing platforms offer the possibility to answer complex questions in plant research. However, drawbacks can arise when there are gaps in the information generated, and complementary platforms are essential to obtain more comprehensive data sets relating to specific biological process, such as responses to environmental perturbations in plant systems. The investigation of transcriptional regulation raises different challenges, particularly in associating differentially expressed transcription factors with their downstream responsive genes. In this paper, we discuss the integration of transcriptional factor studies through RNA sequencing (RNA-seq) and Chromatin Immunoprecipitation sequencing (ChIP-seq). We show how the data from ChIP-seq can strengthen information generated from RNA-seq in elucidating gene regulatory mechanisms. In particular, we discuss how integration of ChIP-seq and RNA-seq data can help to unravel transcriptional regulatory networks. This review discusses recent advances in methods for studying transcriptional regulation using these two methods. It also provides guidelines for making choices in selecting specific protocols in RNA-seq pipelines for genome-wide analysis to achieve more detailed characterization of specific transcription regulatory pathways via ChIP-seq.

Cucumber mosaic virus resistance: Comparative proteomics of contrasting Cucumis sativus cultivars after long-term infection

Plant viruses are a significant threat to a wide range of host species, causing substantial losses in agriculture. Particularly, Cucumber mosaic virus (CMV) evokes severe symptoms, thus dramatically limiting yield. Activation of plant defense reactions is associated with changes in the cellular proteome to ensure virus resistance. Herein, we studied two cultivars of cucumber (Cucumis sativus) resistant host Heliana and susceptible host Vanda. Plant cotyledons were mechanically inoculated with CMV isolate PK1, and systemic leaves were harvested at 33 days post-inoculation. Proteome was profiled by ultrahigh-performance liquid chromatography and comprehensively quantified by ion mobility enhanced mass spectrometry. From 1516 reproducibly quantified proteins using a label-free approach, 133 were differentially abundant among cultivars or treatments by strict statistic and effect size criteria. Pigments and hydrogen peroxide measurements corroborated proteomic findings. Comparison of both cultivars in the uninfected state highlighted more abundant photosynthetic and development-related proteins in resistant cucumber cultivar. Long-term CMV infection caused worse preservation of energy processes and less robust translation in the susceptible cultivar. Contrary, compatible plants had numerous more abundant stress and defense-related proteins. We proposed promising targets for functional validation in transgenic lines: A step toward durable virus resistance in cucurbits and other crops. SIGNIFICANCE: Sustainable production of crops requires an understanding of natural mechanisms of resistance/susceptibility to ubiquitous viral infections. We report original findings of comparative analysis of plant genotypes exposed to CMV. Deep discovery proteomics of resistant and susceptible cucumber cultivars, inoculated with widespread phytovirus, allowed to suggest several novel molecular targets for functional testing in plant protection strategies.

The de.NBI / ELIXIR-DE training platform - Bioinformatics training in Germany and across Europe within ELIXIR

The German Network for Bioinformatics Infrastructure (de.NBI) is a national and academic infrastructure funded by the German Federal Ministry of Education and Research (BMBF). The de.NBI provides (i) service, (ii) training, and (iii) cloud computing to users in life sciences research and biomedicine in Germany and Europe and (iv) fosters the cooperation of the German bioinformatics community with international network structures. The de.NBI members also run the German node (ELIXIR-DE) within the European ELIXIR infrastructure. The de.NBI / ELIXIR-DE training platform, also known as special interest group 3 (SIG 3) ‘Training & Education’, coordinates the bioinformatics training of de.NBI and the German ELIXIR node. The network provides a high-quality, coherent, timely, and impactful training program across its eight service centers. Life scientists learn how to handle and analyze biological big data more effectively by applying tools, standards and compute services provided by de.NBI. Since 2015, more than 300 training courses were carried out with about 6,000 participants and these courses received recommendation rates of almost 90% (status as of July 2020). In addition to face-to-face training courses, online training was introduced on the de.NBI website in 2016 and guidelines for the preparation of e-learning material were established in 2018. In 2016, ELIXIR-DE joined the ELIXIR training platform. Here, the de.NBI / ELIXIR-DE training platform collaborates with ELIXIR in training activities, advertising training courses via TeSS and discussions on the exchange of data for training events essential for quality assessment on both the technical and administrative levels. The de.NBI training program trained thousands of scientists from Germany and beyond in many different areas of bioinformatics.

Regulation of Pyridine Nucleotide Metabolism During Tomato Fruit Development Through Transcript and Protein Profiling

Central metabolism is the engine of plant biomass, supplying fruit growth with building blocks, energy, and biochemical cofactors. Among metabolic cornerstones, nicotinamide adenine dinucleotide (NAD) is particularly pivotal for electron transfer through reduction-oxidation (redox) reactions, thus participating in a myriad of biochemical processes. Besides redox functions, NAD is now assumed to act as an integral regulator of signaling cascades involved in growth and environmental responses. However, the regulation of NAD metabolism and signaling during fruit development remains poorly studied and understood. Here, we benefit from RNAseq and proteomic data obtained from nine growth stages of tomato fruit (var. Moneymaker) to dissect mRNA and protein profiles that link to NAD metabolism, including de novo biosynthesis, recycling, utilization, and putative transport. As expected for a cofactor synthesis pathway, protein profiles failed to detect enzymes involved in NAD synthesis or utilization, except for nicotinic acid phosphoribosyltransferase (NaPT) and nicotinamidase (NIC), which suggested that most NAD metabolic enzymes were poorly represented quantitatively. Further investigations on transcript data unveiled differential expression patterns during fruit development. Interestingly, among specific NAD metabolism-related genes, early de novo biosynthetic genes were transcriptionally induced in very young fruits, in association with NAD kinase, while later stages of fruit growth rather showed an accumulation of transcripts involved in later stages of de novo synthesis and in NAD recycling, which agreed with augmented NAD(P) levels. In addition, a more global overview of 119 mRNA and 78 protein significant markers for NAD(P)-dependent enzymes revealed differential patterns during tomato growth that evidenced clear regulations of primary metabolism, notably with respect to mitochondrial functions. Overall, we propose that NAD metabolism and signaling are very dynamic in the developing tomato fruit and that its differential regulation is certainly critical to fuel central metabolism linking to growth mechanisms.

Modeling Protein Destiny in Developing Fruit

Protein synthesis and degradation are essential processes that regulate cell status. Because labeling in bulky organs, such as fruits, is difficult, we developed a modeling approach to study protein turnover at the global scale in developing tomato (Solanum lycopersicum) fruit. Quantitative data were collected for transcripts and proteins during fruit development. Clustering analysis showed smaller changes in protein abundance compared to mRNA abundance. Furthermore, protein and transcript abundance were poorly correlated, and the coefficient of correlation decreased during fruit development and ripening, with transcript levels decreasing more than protein levels. A mathematical model with one ordinary differential equation was used to estimate translation (kt ) and degradation (kd ) rate constants for almost 2,400 detected transcript-protein pairs and was satisfactorily fitted for >1,000 pairs. The model predicted median values of ∼2 min for the translation of a protein, and a protein lifetime of ∼11 d. The constants were validated and inspected for biological relevance. Proteins involved in protein synthesis had higher kt and kd values, indicating that the protein machinery is particularly flexible. Our model also predicts that protein concentration is more strongly affected by the rate of translation than that of degradation.