A multi-omics approach reveals function of Secretory Carrier-Associated Membrane Proteins in wood formation of​ ​​Populus​​ ​trees

Genome-Wide Identification of Rubber Tree SCAMP Genes and Functional Characterization of HbSCAMP3

Natural rubber produced by the rubber tree is a vital industrial raw material globally. Seven SCAMP gene family members were identified in the rubber tree, and the phylogenetic tree classified HbSCAMPs into three subfamilies. Significant differences were observed among HbSCAMPs in terms of gene length, number of exons, and composition of conserved motifs. The expansion of HbSCAMPs in the rubber tree genome is associated with segmental duplications. The high expression of HbSCAMP1-6 in petioles and HbSCAMP7 in stem tips, along with their distinct responses to drought, salt, and wound stresses, indicates their crucial roles in substance transport and stress adaptation. Transgenic poplar experiments demonstrated that overexpression of HbSCAMP3 significantly promotes plant height growth, with localization in the tobacco plasma membrane, suggesting its involvement in regulating plant growth through membrane transport processes. These findings enhance the understanding of HbSCAMPs in rubber trees and provide new insights into how plants finely tune gene family members to adapt to environmental changes.

Genome‑wide analysis of cotton SCAMP genes and functional characterization of GhSCAMP2 and GhSCAMP4 in salt tolerance

BACKGROUND

Secretory carrier membrane proteins (SCAMPs) form a family of integral membrane proteins and play a crucial role in mediating exocytosis in both animals and plants. While SCAMP genes have been studied in several plant species, their functions in cotton, particularly in response to abiotic stress, have not yet been reported.

RESULTS

In this study, a total of 53 SCAMP genes were identified in G. arboreum, G. raimondii, G. hirsutum, and G. barbadense. These genes were classified into five groups based on a phylogenetic analysis with SCAMPs from Arabidopsis thaliana. The main factor driving the expansion of the SCAMP gene family in G. hirsutum is tandem and segmental duplication events. Using MEME, in addition to the conserved SCAMP domain, we identified 3-13 other domains in each GhSCAMP. The cis-element analysis suggested that GhSCAMPs were widely involved in cotton growth and development, and responses to abiotic stresses. RNA sequencing (RNA-Seq) and reverse transcription-quantitative polymerase chain reaction (RT-qPCR) results showed that most GhSCAMPs were expressed highly in many tissues and had differential expression responses to drought, cold, and heat stresses. Knock-down of GhSCAMP2 and GhSCAMP4 by virus-induced gene silencing (VIGS) lead to a salt-sensitive phenotype and had a lower content of CAT, POD, and SOD.

CONCLUSIONS

This study identified SCAMP genes in four cotton species, enhancing our understanding of the potential biological functions of SCAMPs. Additionally, we demonstrated that GhSCAMP2 and GhSCAMP4 positively regulate cotton tolerance to salt stress.

Comparative Analysis of the Wood Metabolites of Three Poplar Clones Using UPLC-Triple-TOF-MS

Poplar, a woody tree species, is widely used for industrial production and as a protective forest belt. Different clones of poplar exhibit clear variation in terms of morphological and physiological features, however, the impact of the genetic variation on the composition and abundance of wood metabolite have not been fully determined. In this study, ultra-high pressure liquid chromatography-triple time of flight-mass spectrometer (UPLC-Triple-TOF-MS) was used to explore the metabolite changes in poplar wood from three clones, including Populus deltoides CL. '55/65', P. deltoides CL. 'Danhong', and P. nigra CL. 'N179'. A total of 699 metabolites were identified. Clustering analysis and principal component analysis display that the metabolic differences of wood have allowed distinguishing different species of poplar. Meanwhile, eight significantly different metabolites were screened between P. deltoides and P. nigra, which may be considered as valuable markers for chemotaxonomy. In addition, the highly discriminant 352 metabolites were obtained among the three clones, and those may be closely related to the distinction in unique properties (e.g., growth, rigidity and tolerance) of the poplar wood cultivars. This study provides a foundation for further studies on wood metabolomics in poplar, and offers chemotaxonomic markers that will stimulate the early screening of potentially superior trees.

Network-Based Analysis to Identify Hub Genes Involved in Spatial Root Response to Mechanical Constrains

Previous studies report that the asymmetric response, observed along the main poplar woody bent root axis, was strongly related to both the type of mechanical forces (compression or tension) and the intensity of force displacement. Despite a large number of targets that have been proposed to trigger this asymmetry, an understanding of the comprehensive and synergistic effect of the antistress spatially related pathways is still lacking. Recent progress in the bioinformatics area has the potential to fill these gaps through the use of in silico studies, able to investigate biological functions and pathway overlaps, and to identify promising targets in plant responses. Presently, for the first time, a comprehensive network-based analysis of proteomic signatures was used to identify functions and pivotal genes involved in the coordinated signalling pathways and molecular activities that asymmetrically modulate the response of different bent poplar root sectors and sides. To accomplish this aim, 66 candidate proteins, differentially represented across the poplar bent root sides and sectors, were grouped according to their abundance profile patterns and mapped, together with their first neighbours, on a high-confidence set of interactions from STRING to compose specific cluster-related subnetworks (I-VI). Successively, all subnetworks were explored by a functional gene set enrichment analysis to identify enriched gene ontology terms. Subnetworks were then analysed to identify the genes that are strongly interconnected with other genes (hub gene) and, thus, those that have a pivotal role in the bent root asymmetric response. The analysis revealed novel information regarding the response coordination, communication, and potential signalling pathways asymmetrically activated along the main root axis, delegated mainly to Ca2+ (for new lateral root formation) and ROS (for gravitropic response and lignin accumulation) signatures. Furthermore, some of the data indicate that the concave side of the bent sector, where the mechanical forces are most intense, communicates to the other (neighbour and distant) sectors, inducing spatially related strategies to ensure water uptake and accompanying cell modification. This information could be critical for understanding how plants maintain and improve their structural integrity-whenever and wherever it is necessary-in natural mechanical stress conditions.

Transcriptomic analysis of differentially expressed genes in leaves and roots of two alfalfa (Medicago sativa L.) cultivars with different salt tolerance

BACKGROUND

Alfalfa (Medicago sativa L.) production decreases under salt stress. Identification of genes associated with salt tolerance in alfalfa is essential for the development of molecular markers used for breeding and genetic improvement.

RESULT

An RNA-Seq technique was applied to identify the differentially expressed genes (DEGs) associated with salt stress in two alfalfa cultivars: salt tolerant 'Halo' and salt intolerant 'Vernal'. Leaf and root tissues were sampled for RNA extraction at 0 h, 3 h, and 27 h under 12 dS m^- 1 salt stress maintained by NaCl. The sequencing generated a total of 381 million clean sequence reads and 84.8% were mapped on to the alfalfa reference genome. A total of 237 DEGs were identified in leaves and 295 DEGs in roots of the two alfalfa cultivars. In leaf tissue, the two cultivars had a similar number of DEGs at 3 h and 27 h of salt stress, with 31 and 49 DEGs for 'Halo', 34 and 50 for 'Vernal', respectively. In root tissue, 'Halo' maintained 55 and 56 DEGs at 3 h and 27 h, respectively, while the number of DEGs decreased from 42 to 10 for 'Vernal'. This differential expression pattern highlights different genetic responses of the two cultivars to salt stress at different time points. Interestingly, 28 (leaf) and 31 (root) salt responsive candidate genes were highly expressed in 'Halo' compared to 'Vernal' under salt stress, of which 13 candidate genes were common for leaf and root tissues. About 60% of DEGs were assigned to known gene ontology (GO) categories. The genes were involved in transmembrane protein function, photosynthesis, carbohydrate metabolism, defense against oxidative damage, cell wall modification and protection against lipid peroxidation. Ion binding was found to be a key molecular activity for salt tolerance in alfalfa under salt stress.

CONCLUSION

The identified DEGs are significant for understanding the genetic basis of salt tolerance in alfalfa. The generated genomic information is useful for molecular marker development for alfalfa genetic improvement for salt tolerance.

A metabolomic study of Gomphrena agrestis in Brazilian Cerrado suggests drought-adaptive strategies on metabolism

Drought is the main factor that limits the distribution and productivity of plant species. In the Brazilian Cerrado, the vegetation is adapted to a seasonal climate with long- and short-term periods of drought. To analyze the metabolic strategies under such conditions, a metabolomic approach was used to characterize Gomphrena agrestis Mart. (Amaranthaceae) a native species that grows under natural conditions, in a rock-field area. Roots and leaves material from native specimens were sampled along different seasons of the year and LC–MS and GC–MS analyzed for multiple chemical constituents. The datasets derived from the different measurements were combined and evaluated using multivariate analysis. Principal component analysis was used to obtain an overview of the samples and identify outliers. Later, the data was analyzed with orthogonal projection to latent structures discriminant analysis to obtain valid models that could explain the metabolite variations in the different seasons. Two hundred and eighty metabolites were annotated, generating a unique database to characterize metabolic strategies used to cope with the effects of drought. The accumulation of fructans in the thickened roots is consistent with the storage of carbons during the rainy season to support the energy demand during a long period of drought. The accumulation of Abscisic acid, sugars and sugar alcohols, phenolics, and pigment in the leaves suggests physiological adaptations. To cope with long-term drought, the data suggests that tissue water status and storage of reserves are important to support plant survival and regrowth. However, during short-term drought, osmoregulation and oxidative protection seems to be essential, probably to support the maintenance of active photosynthesis.

Mass spectrometry-based forest tree metabolomics

Research in forest tree species has advanced slowly when compared with other agricultural crops and model organisms, mainly due to the long-life cycles, large genome sizes, and lack of genomic tools. Additionally, trees are complex matrices, and the presence of interferents (e.g., oleoresins and cellulose) challenges the analysis of tree tissues with mass spectrometry (MS)-based analytical platforms. In this review, advances in MS-based forest tree metabolomics are discussed. Given their economic and ecological significance, particular focus is given to Pinus, Quercus, and Eucalyptus forest tree species to better understand their metabolite responses to abiotic and biotic stresses in the current climate change scenario. Furthermore, MS-based metabolomics technologies produce large and complex datasets that require expertize to adequately manage, process, analyze, and store the data in dedicated repositories. To ensure that the full potential of forest tree metabolomics data are translated into new knowledge, these data should comply with the FAIR principles (i.e., Findable, Accessible, Interoperable, and Re-usable). It is essential that adequate standards are implemented to annotate metadata from forest tree metabolomics studies as is already required by many science and governmental agencies and some major scientific publishers. © 2019 John Wiley & Sons Ltd. Mass Spec Rev 40:126-157, 2021.

Overexpression of vesicle-associated membrane protein PttVAP27-17 as a tool to improve biomass production and the overall saccharification yields in Populus trees

BACKGROUND

Bioconversion of wood into bioproducts and biofuels is hindered by the recalcitrance of woody raw material to bioprocesses such as enzymatic saccharification. Targeted modification of the chemical composition of the feedstock can improve saccharification but this gain is often abrogated by concomitant reduction in tree growth.

RESULTS

In this study, we report on transgenic hybrid aspen (Populus tremula × tremuloides) lines that showed potential to increase biomass production both in the greenhouse and after 5 years of growth in the field. The transgenic lines carried an overexpression construct for Populus tremula × tremuloides vesicle-associated membrane protein (VAMP)-associated protein PttVAP27-17 that was selected from a gene-mining program for novel regulators of wood formation. Analytical-scale enzymatic saccharification without any pretreatment revealed for all greenhouse-grown transgenic lines, compared to the wild type, a 20-44% increase in the glucose yield per dry weight after enzymatic saccharification, even though it was statistically significant only for one line. The glucose yield after enzymatic saccharification with a prior hydrothermal pretreatment step with sulfuric acid was not increased in the greenhouse-grown transgenic trees on a dry-weight basis, but increased by 26-50% when calculated on a whole biomass basis in comparison to the wild-type control. Tendencies to increased glucose yields by up to 24% were present on a whole tree biomass basis after acidic pretreatment and enzymatic saccharification also in the transgenic trees grown for 5 years on the field when compared to the wild-type control.

CONCLUSIONS

The results demonstrate the usefulness of gene-mining programs to identify novel genes with the potential to improve biofuel production in tree biotechnology programs. Furthermore, multi-omic analyses, including transcriptomic, proteomic and metabolomic analyses, performed here provide a toolbox for future studies on the function of VAP27 proteins in plants.

Wood profiling by non-targeted high-resolution mass spectrometry: Part 1, Metabolite profiling in Cedrela wood for the determination of the geographical origin

The determination of the geographical origin of wood can be highly relevant for several reasons: On the one hand, it can help to prevent illegal logging and timber trade, on the other hand, it is of special interest for archaeological artefacts made of wood, as well as for a variety of biological questions. For this reason, different extraction methods were first tested for the analysis of polar and non-polar metabolites using liquid chromatography coupled electrospray ionization quadrupole time-of-flight mass spectrometry (UHPLC-ESI-QTOF-MS). A two-phase extraction with chloroform, methanol and water proved to be particularly successful. Subsequently, cedrela (Cedrela odorata) samples from South America were measured to distinguish geographic origin. Using multivariate data analysis, numerous origin-dependent differences could be extracted. The identification of the marker substances indicated that several metabolic pathways were affected by the geographical influences, some of them probably indicating pest infections.

EgPHI-1, a PHOSPHATE-INDUCED-1 gene from Eucalyptus globulus, is involved in shoot growth, xylem fiber length and secondary cell wall properties

MAIN CONCLUSION

EgPHI-1 is a member of PHI-1/EXO/EXL protein family. Its overexpression in tobacco resulted in changes in biomass partitioning, xylem fiber length, secondary cell wall thickening and composition, and lignification. Here, we report the functional characterization of a PHOSPHATE-INDUCED PROTEIN 1 homologue showing differential expression in xylem cells from Eucalyptus species of contrasting phenotypes for wood quality and growth traits. Our results indicated that this gene is a member of the PHI-1/EXO/EXL family. Analysis of the promoter cis-acting regulatory elements and expression responses to different treatments revealed that the Eucalyptus globulus PHI-1 (EgPHI-1) is transcriptionally regulated by auxin, cytokinin, wounding and drought. EgPHI-1 overexpression in transgenic tobacco changed the partitioning of biomass, favoring its allocation to shoots in detriment of roots. The stem of the transgenic plants showed longer xylem fibers and reduced cellulose content, while the leaf xylem had enhanced secondary cell wall thickness. UV microspectrophotometry of individual cell wall layers of fibers and vessels has shown that the transgenic plants exhibit differences in the lignification of S2 layer in both cell types. Taken together, the results suggest that EgPHI-1 mediates the elongation of secondary xylem fibers, secondary cell wall thickening and composition, and lignification, making it an attractive target for biotechnological applications in forestry and biofuel crops.

Application of Multiblock Analysis on Small Metabolomic Multi-Tissue Dataset

Data integration has been proven to provide valuable information. The information extracted using data integration in the form of multiblock analysis can pinpoint both common and unique trends in the different blocks. When working with small multiblock datasets the number of possible integration methods is drastically reduced. To investigate the application of multiblock analysis in cases where one has a few number of samples and a lack of statistical power, we studied a small metabolomic multiblock dataset containing six blocks (i.e., tissue types), only including common metabolites. We used a single model multiblock analysis method called the joint and unique multiblock analysis (JUMBA) and compared it to a commonly used method, concatenated principal component analysis (PCA). These methods were used to detect trends in the dataset and identify underlying factors responsible for metabolic variations. Using JUMBA, we were able to interpret the extracted components and link them to relevant biological properties. JUMBA shows how the observations are related to one another, the stability of these relationships, and to what extent each of the blocks contribute to the components. These results indicate that multiblock methods can be useful even with a small number of samples.

Systematic Multi-Omics Integration (MOI) Approach in Plant Systems Biology

Across all facets of biology, the rapid progress in high-throughput data generation has enabled us to perform multi-omics systems biology research. Transcriptomics, proteomics, and metabolomics data can answer targeted biological questions regarding the expression of transcripts, proteins, and metabolites, independently, but a systematic multi-omics integration (MOI) can comprehensively assimilate, annotate, and model these large data sets. Previous MOI studies and reviews have detailed its usage and practicality on various organisms including human, animals, microbes, and plants. Plants are especially challenging due to large poorly annotated genomes, multi-organelles, and diverse secondary metabolites. Hence, constructive and methodological guidelines on how to perform MOI for plants are needed, particularly for researchers newly embarking on this topic. In this review, we thoroughly classify multi-omics studies on plants and verify workflows to ensure successful omics integration with accurate data representation. We also propose three levels of MOI, namely element-based (level 1), pathway-based (level 2), and mathematical-based integration (level 3). These MOI levels are described in relation to recent publications and tools, to highlight their practicality and function. The drawbacks and limitations of these MOI are also discussed for future improvement toward more amenable strategies in plant systems biology.

Joint and unique multiblock analysis of biological data - multiomics malaria study

Modern profiling technologies enable us to obtain large amounts of data which can be used later for a comprehensive understanding of the studied system. Proper evaluation of such data is challenging, and cannot be carried out by bare analysis of separate data sets. Integrated approaches are necessary, because only data integration allows us to find correlation trends common for all studied data sets and reveal hidden structures not known a priori. This improves the understanding and interpretation of complex systems. Joint and Unique MultiBlock Analysis (JUMBA) is an analysis method based on the OnPLS-algorithm that decomposes a set of matrices into joint parts containing variations shared with other connected matrices and variations that are unique for each single matrix. Mapping unique variations is important from a data integration perspective, since it certainly cannot be expected that all variation co-varies. In this work we used JUMBA for the integrated analysis of lipidomic, metabolomic and oxylipins data sets obtained from profiling of plasma samples from children infected with P. falciparum malaria. P. falciparum is one of the primary contributors to childhood mortality and obstetric complications in the developing world, which makes the development of new diagnostic and prognostic tools, as well as a better understanding of the disease, of utmost importance. In the presented work, JUMBA made it possible to detect already known trends related to the disease progression, but also to discover new structures in the data connected to food intake and personal differences in metabolism. By separating the variation in each data set into joint and unique, JUMBA reduced the complexity of the analysis and facilitated the detection of samples and variables corresponding to specific structures across multiple data sets, and by doing this enabled fast interpretation of the studied system. All of this makes JUMBA a perfect choice for multiblock analysis of systems biology data.

Experimental Design and Sample Preparation in Forest Tree Metabolomics

Appropriate experimental design and sample preparation are key steps in metabolomics experiments, highly influencing the biological interpretation of the results. The sample preparation workflow for plant metabolomics studies includes several steps before metabolite extraction and analysis. These include the optimization of laboratory procedures, which should be optimized for different plants and tissues. This is particularly the case for trees, whose tissues are complex matrices to work with due to the presence of several interferents, such as oleoresins, cellulose. A good experimental design, tree tissue harvest conditions, and sample preparation are crucial to ensure consistency and reproducibility of the metadata among datasets. In this review, we discuss the main challenges when setting up a forest tree metabolomics experiment for mass spectrometry (MS)-based analysis covering all technical aspects from the biological question formulation and experimental design to sample processing and metabolite extraction and data acquisition. We also highlight the importance of forest tree metadata standardization in metabolomics studies.

Molecular analysis of somatic embryogenesis through proteomic approach and optimization of protocol in recalcitrant Musa spp

Somatic embryogenesis (SE) is a complex stress related process regulated by numerous biological factors. SE is mainly applicable to mass propagation and genetic improvement of plants through gene transfer technology and induced mutations. In banana, SE is highly genome dependent as the efficiency varies with cultivars. To understand the molecular mechanism of SE, a proteomics approach was carried out to identify proteins expressed during embryogenic calli (EC) induction, regeneration and germination of somatic embryos in the banana cultivar cv. Rasthali (AAB). In total, 70 spots were differentially expressed in various developmental stages of SE, of which 16 were uniquely expressed and 17 were highly abundant in EC compared to non-embryogenic calli and explants. Also, four spots were uniquely expressed in germinating somatic embryos. The functional annotation of identified proteins revealed that calcium signaling along with stress and endogenous hormones related proteins played a vital role in EC induction and germination of somatic embryos. Thus, based on this outcome, the callus induction media was modified and tested in five cultivars. Among them, cultivars Grand Naine (AAA), Monthan (ABB) and Ney Poovan (AB) showed a better response in tryptophan added media, whereas Red Banana (AAA) and Karpuravalli (ABB) showed maximum EC induction in kinetin and CaCl₂ supplemented media respectively. Simultaneously, germination media were modified to induce proteins responsible for germination. In cv. Rasthali, media supplemented with 10 mM CaCl₂ showed a maximum increase in germination (51.79%) over control plants. Thus, the present study revealed that media modification based on proteomic analysis can induce SE in recalcitrant cultivars and also enhance germination in cultivars amenable for SE.

Data Integration in Poplar: 'Omics Layers and Integration Strategies

Populus trichocarpa is an important biofuel feedstock that has been the target of extensive research and is emerging as a model organism for plants, especially woody perennials. This research has generated several large ‘omics datasets. However, only few studies in Populus have attempted to integrate various data types. This review will summarize various ‘omics data layers, focusing on their application in Populus species. Subsequently, network and signal processing techniques for the integration and analysis of these data types will be discussed, with particular reference to examples in Populus.

Lignin Engineering in Forest Trees

Wood is a renewable resource that is mainly composed of lignin and cell wall polysaccharides. The polysaccharide fraction is valuable as it can be converted into pulp and paper, or into fermentable sugars. On the other hand, the lignin fraction is increasingly being considered a valuable source of aromatic building blocks for the chemical industry. The presence of lignin in wood is one of the major recalcitrance factors in woody biomass processing, necessitating the need for harsh chemical treatments to degrade and extract it prior to the valorization of the cell wall polysaccharides, cellulose and hemicellulose. Over the past years, large research efforts have been devoted to engineering lignin amount and composition to reduce biomass recalcitrance toward chemical processing. We review the efforts made in forest trees, and compare results from greenhouse and field trials. Furthermore, we address the value and potential of CRISPR-based gene editing in lignin engineering and its integration in tree breeding programs.

Analytical Enzymatic Saccharification of Lignocellulosic Biomass for Conversion to Biofuels and Bio-Based Chemicals

Lignocellulosic feedstocks are an important resource for biorefining of renewables to bio-based fuels, chemicals, and materials. Relevant feedstocks include energy crops, residues from agriculture and forestry, and agro-industrial and forest-industrial residues. The feedstocks differ with respect to their recalcitrance to bioconversion through pretreatment and enzymatic saccharification, which will produce sugars that can be further converted to advanced biofuels and other products through microbial fermentation processes. In analytical enzymatic saccharification, the susceptibility of lignocellulosic samples to pretreatment and enzymatic saccharification is assessed in analytical scale using high-throughput or semi-automated techniques. This type of analysis is particularly relevant for screening of large collections of natural or transgenic varieties of plants that are dedicated to production of biofuels or other bio-based chemicals. In combination with studies of plant physiology and cell wall chemistry, analytical enzymatic saccharification can provide information about the fundamental reasons behind lignocellulose recalcitrance as well as about the potential of collections of plants or different fractions of plants for industrial biorefining. This review is focused on techniques used by researchers for screening the susceptibility of plants to pretreatment and enzymatic saccharification, and advantages and disadvantages that are associated with different approaches.