A method for mining condition-specific co-expressed genes in Camellia sinensis based on k-means clustering.

BACKGROUND: As one of the world's most important beverage crops, tea plants (Camellia sinensis) are renowned for their unique flavors and numerous beneficial secondary metabolites, attracting researchers to investigate the formation of tea quality. With the increasing availability of transcriptome data on tea plants in public databases, conducting large-scale co-expression analyses has become feasible to meet the demand for functional characterization of tea plant genes. However, as the multidimensional noise increases, larger-scale co-expression analyses are not always effective. Analyzing a subset of samples generated by effectively downsampling and reorganizing the global sample set often leads to more accurate results in co-expression analysis. Meanwhile, global-based co-expression analyses are more likely to overlook condition-specific gene interactions, which may be more important and worthy of exploration and research. RESULTS: Here, we employed the k-means clustering method to organize and classify the global samples of tea plants, resulting in clustered samples. Metadata annotations were then performed on these clustered samples to determine the "conditions" represented by each cluster. Subsequently, we conducted gene co-expression network analysis (WGCNA) separately on the global samples and the clustered samples, resulting in global modules and cluster-specific modules. Comparative analyses of global modules and cluster-specific modules have demonstrated that cluster-specific modules exhibit higher accuracy in co-expression analysis. To measure the degree of condition specificity of genes within condition-specific clusters, we introduced the correlation difference value (CDV). By incorporating the CDV into co-expression analyses, we can assess the condition specificity of genes. This approach proved instrumental in identifying a series of high CDV transcription factor encoding genes upregulated during sustained cold treatment in Camellia sinensis leaves and buds, and pinpointing a pair of genes that participate in the antioxidant defense system of tea plants under sustained cold stress. CONCLUSIONS: To summarize, downsampling and reorganizing the sample set improved the accuracy of co-expression analysis. Cluster-specific modules were more accurate in capturing condition-specific gene interactions. The introduction of CDV allowed for the assessment of condition specificity in gene co-expression analyses. Using this approach, we identified a series of high CDV transcription factor encoding genes related to sustained cold stress in Camellia sinensis. This study highlights the importance of considering condition specificity in co-expression analysis and provides insights into the regulation of the cold stress in Camellia sinensis.

Genomes of multicellular algal sisters to land plants illuminate signaling network evolution.

Zygnematophyceae are the algal sisters of land plants. Here we sequenced four genomes of filamentous Zygnematophyceae, including chromosome-scale assemblies for three strains of Zygnema circumcarinatum. We inferred traits in the ancestor of Zygnematophyceae and land plants that might have ushered in the conquest of land by plants: expanded genes for signaling cascades, environmental response, and multicellular growth. Zygnematophyceae and land plants share all the major enzymes for cell wall synthesis and remodifications, and gene gains shaped this toolkit. Co-expression network analyses uncover gene cohorts that unite environmental signaling with multicellular developmental programs. Our data shed light on a molecular chassis that balances environmental response and growth modulation across more than 600 million years of streptophyte evolution.

PEO: Plant Expression Omnibus - a comparative transcriptomic database for 103 Archaeplastida.

The Plant Expression Omnibus (PEO) is a web application that provides biologists with access to gene expression insights across over 100 plant species, ~60 000 manually annotated RNA-seq samples, and more than 4 million genes. The tool allows users to explore the expression patterns of genes across different organs, identify organ-specific genes, and discover top co-expressed genes for any gene of interest. PEO also provides functional annotations for each gene, allowing for the identification of genetic modules and pathways. PEO is designed to facilitate comparative kingdom-wide gene expression analysis and provide a valuable resource for plant biology research. We provide two case studies to demonstrate the utility of PEO in identifying candidate genes in pollen coat biosynthesis in Arabidopsis and investigating the biosynthetic pathway components of capsaicin in Capsicum annuum. The database is freely available at https://expression.plant.tools/.

Comparative spatial proteomics of Plasmodium-infected erythrocytes.

Plasmodium parasites contribute to one of the highest global infectious disease burdens. To achieve this success, the parasite has evolved a range of specialized subcellular compartments to extensively remodel the host cell for its survival. The information to fully understand these compartments is likely hidden in the so far poorly characterized Plasmodium species spatial proteome. To address this question, we determined the steady-state subcellular location of more than 12,000 parasite proteins across five different species by extensive subcellular fractionation of erythrocytes infected by Plasmodium falciparum, Plasmodium knowlesi, Plasmodium yoelii, Plasmodium berghei, and Plasmodium chabaudi. This comparison of the pan-species spatial proteomes and their expression patterns indicates increasing species-specific proteins associated with the more external compartments, supporting host adaptations and post-transcriptional regulation. The spatial proteome offers comprehensive insight into the different human, simian, and rodent Plasmodium species, establishing a powerful resource for understanding species-specific host adaptation processes in the parasite.

New molecular components that regulates the transcriptional hub in root hairs: coupling environmental signals to endogenous hormones to coordinate growth.

Root hairs (RH) have become an important model system for studying plant growth and how plants modulate their growth in response to cell-intrinsic and environmental stimuli. Here, we will discuss recent advances in our understanding of the molecular mechanisms underlying the growth of Arabidopsis thaliana RH in the interface between responses to environmental cues (e.g. nutrients such as nitrates, phosphate and microorganism) and hormonal stimuli (e.g. auxin). RH growth is under the control of several transcription factors that are also under strong regulation at different levels. In this review we highlight recent new discoveries along these transcriptional pathways that may increase our capacity to enhance nutrient uptake by the roots in the context of abiotic stresses. We used text-mining capacities of the PlantConnectome database to generate the most updated view of RH growth in these complex biological contexts.

Transcriptomic and metabolomic characterization of antibacterial activity of Melastoma dodecandrum.

Antibacterial resistance poses a significant global threat, necessitating the discovery of new therapeutic agents. Plants are a valuable source of secondary metabolites with demonstrated anticancer and antibacterial properties. In this study, we reveal that Melastoma dodecandrum exhibits both bacteriostatic and bactericidal effects against Pseudomonas aeruginosa and Staphylococcus aureus. Treatment with plant extracts results in membrane damage and a reduction in P.aeruginosa swimming and swarming motility. A comparative analysis of bacterial transcriptomes exposed to M.dodecandrum extracts and four distinct antibiotics indicates that the extracts may trigger similar transcriptomic responses as triclosan, a fatty acid synthesis inhibitor. Activity-guided fractionation suggests that the antibacterial activity is not attributable to hydrolyzable tannins, but to unidentified minor compounds. Additionally, we identified 104 specialized metabolic pathways and demonstrated a high level of transcriptional coordination between these biosynthetic pathways and phytohormones, highlighting potential regulatory mechanisms of antibacterial metabolites in M.dodecandrum.

Environmental gradients reveal stress hubs pre-dating plant terrestrialization.

Plant terrestrialization brought forth the land plants (embryophytes). Embryophytes account for most of the biomass on land and evolved from streptophyte algae in a singular event. Recent advances have unravelled the first full genomes of the closest algal relatives of land plants; among the first such species was Mesotaenium endlicherianum. Here we used fine-combed RNA sequencing in tandem with a photophysiological assessment on Mesotaenium exposed to a continuous range of temperature and light cues. Our data establish a grid of 42 different conditions, resulting in 128 transcriptomes and ~1.5 Tbp (~9.9 billion reads) of data to study the combinatory effects of stress response using clustering along gradients. Mesotaenium shares with land plants major hubs in genetic networks underpinning stress response and acclimation. Our data suggest that lipid droplet formation and plastid and cell wall-derived signals have denominated molecular programmes since more than 600 million years of streptophyte evolution-before plants made their first steps on land.

Locally Isolated Trichoderma harzianum Species Have Broad Spectrum Biocontrol Activities against the Wood Rot Fungal Species through Both Volatile Inhibition and Mycoparasitism.

Pathogenic root/wood rot fungal species infect multiple urban tree species in Singapore. There is a need for sustainable and environmentally friendly mitigation. We report the local Trichoderma strains as potential biocontrol agents (BCAs) for pathogenic wood rot fungal species such as Phellinus noxius, Rigidoporus microporus, and Fulvifomes siamensis. Isolated Trichoderma strains were DNA-barcoded for their molecular identities and assessed for their potential as a BCA by their rate of growth in culture and effectiveness in inhibiting the pathogenic fungi in in vitro dual culture assays. Trichoderma harzianum strain CE92 was the most effective in inhibiting the growth of the pathogenic fungi tested. Preliminary results suggested both volatile organic compound (VOC) production and direct hyphal contact contributed to inhibition. SPME GC-MS identified known fungal inhibitory volatiles. Trichoderma harzianum strain CE92 hyphae were found to coil around Phellinus noxius and Lasiodiplodia theobromae upon contact in vitro and were possibly a part of the mycoparasitism. In summary, the work provides insight into Trichoderma inhibition of pathogenic fungi and identifies local strains with good potential for broad-spectrum BCAs against root/wood rot fungi in Singapore.

Volatile-Based Diagnosis for Pathogenic Wood-Rot Fungus Fulvifomes siamensis by Electronic Nose (E-Nose) and Solid-Phase Microextraction/Gas Chromatography/Mass Spectrometry.

Wood rot fungus Fulvifomes siamensis infects multiple urban tree species commonly planted in Singapore. A commercial e-nose (Cyranose 320) was used to differentiate some plant and fungi volatiles. The e-nose distinctly clustered the volatiles at 0.25 ppm, and this sensitivity was further increased to 0.05 ppm with the use of nitrogen gas to purge the system and set up the baseline. Nitrogen gas baseline resulted in a higher magnitude of sensor responses and a higher number of responsive sensors. The specificity of the e-nose for F. siamensis was demonstrated by distinctive clustering of its pure culture, fruiting bodies collected from different tree species, and in diseased tissues infected by F. siamensis with a 15-min incubation time. This good specificity was supported by the unique volatile profiles revealed by SPME GC-MS analysis, which also identified the signature volatile for F. siamensis-1,2,4,5-tetrachloro-3,6-dimethoxybenzene. In field conditions, the e-nose successfully identified F. siamensis fruiting bodies on different tree species. The findings of concentration-based clustering and host-tree-specific volatile profiles for fruiting bodies provide further insights into the complexity of volatile-based diagnosis that should be taken into consideration for future studies.

A Metagenomic Survey of Wood Decay Fungi in the Urban Trees of Singapore.

Mature tropical urban trees are susceptible to root and trunk rot caused by pathogenic fungi. A metagenomic survey of such fungi was carried out on 210 soil and tissue samples collected from 134 trees of 14 common species in Singapore. Furthermore, 121 fruiting bodies were collected and barcoded. Out of the 22,067 OTUs (operational taxonomic units) identified, 10,646 OTUs had annotation information, and most were either ascomycetes (63.4%) or basidiomycetes (22.5%). Based on their detection in the diseased tissues and surrounding soils and/or the presence of fruiting bodies, fourteen basidiomycetes (nine Polyporales, four Hymenochaetales, one Boletales) and three ascomycetes (three species of Scytalidium) were strongly associated with the diseased trees. Fulvifomes siamensis affected the largest number of tree species surveyed. The association of three fungi was further supported by in vitro wood decay studies. Genetic heterogeneity was common in the diseased tissues and fruiting bodies (Ganoderma species especially). This survey identified the common pathogenic fungi of tropical urban trees and laid the foundation for early diagnosis and targeted mitigation efforts. It also illustrated the complexity of fungal ecology and pathogenicity.

Redesigning plant specialized metabolism with supervised machine learning using publicly available reactome data.

The immense structural diversity of products and intermediates of plant specialized metabolism (specialized metabolites) makes them rich sources of therapeutic medicine, nutrients, and other useful materials. With the rapid accumulation of reactome data that can be accessible on biological and chemical databases, along with recent advances in machine learning, this review sets out to outline how supervised machine learning can be used to design new compounds and pathways by exploiting the wealth of said data. We will first examine the various sources from which reactome data can be obtained, followed by explaining the different machine learning encoding methods for reactome data. We then discuss current supervised machine learning developments that can be employed in various aspects to help redesign plant specialized metabolism.

Chromosome-level genomes of multicellular algal sisters to land plants illuminate signaling network evolution.

The filamentous and unicellular algae of the class Zygnematophyceae are the closest algal relatives of land plants. Inferring the properties of the last common ancestor shared by these algae and land plants allows us to identify decisive traits that enabled the conquest of land by plants. We sequenced four genomes of filamentous Zygnematophyceae (three strains of Zygnema circumcarinatum and one strain of Z. cylindricum) and generated chromosome-scale assemblies for all strains of the emerging model system Z. circumcarinatum. Comparative genomic analyses reveal expanded genes for signaling cascades, environmental response, and intracellular trafficking that we associate with multicellularity. Gene family analyses suggest that Zygnematophyceae share all the major enzymes with land plants for cell wall polysaccharide synthesis, degradation, and modifications; most of the enzymes for cell wall innovations, especially for polysaccharide backbone synthesis, were gained more than 700 million years ago. In Zygnematophyceae, these enzyme families expanded, forming co-expressed modules. Transcriptomic profiling of over 19 growth conditions combined with co-expression network analyses uncover cohorts of genes that unite environmental signaling with multicellular developmental programs. Our data shed light on a molecular chassis that balances environmental response and growth modulation across more than 600 million years of streptophyte evolution.

Cross-stress gene expression atlas of Marchantia polymorpha reveals the hierarchy and regulatory principles of abiotic stress responses.

Abiotic stresses negatively impact ecosystems and the yield of crops, and climate change will increase their frequency and intensity. Despite progress in understanding how plants respond to individual stresses, our knowledge of plant acclimatization to combined stresses typically occurring in nature is still lacking. Here, we used a plant with minimal regulatory network redundancy, Marchantia polymorpha, to study how seven abiotic stresses, alone and in 19 pairwise combinations, affect the phenotype, gene expression, and activity of cellular pathways. While the transcriptomic responses show a conserved differential gene expression between Arabidopsis and Marchantia, we also observe a strong functional and transcriptional divergence between the two species. The reconstructed high-confidence gene regulatory network demonstrates that the response to specific stresses dominates those of others by relying on a large ensemble of transcription factors. We also show that a regression model could accurately predict the gene expression under combined stresses, indicating that Marchantia performs arithmetic multiplication to respond to multiple stresses. Lastly, two online resources ( https://conekt.plant.tools and http://bar.utoronto.ca/efp_marchantia/cgi-bin/efpWeb.cgi ) are provided to facilitate the study of gene expression in Marchantia exposed to abiotic stresses.

Genomic, transcriptomic, and metabolomic analysis of Oldenlandia corymbosa reveals the biosynthesis and mode of action of anti-cancer metabolites.

Plants accumulate a vast array of secondary metabolites, which constitute a natural resource for pharmaceuticals. Oldenlandia corymbosa belongs to the Rubiaceae family, and has been used in traditional medicine to treat different diseases, including cancer. However, the active metabolites of the plant, their biosynthetic pathway and mode of action in cancer are unknown. To fill these gaps, we exposed this plant to eight different stress conditions and combined different omics data capturing gene expression, metabolic profiles, and anti-cancer activity. Our results show that O. corymbosa extracts are active against breast cancer cell lines and that ursolic acid is responsible for this activity. Moreover, we assembled a high-quality genome and uncovered two genes involved in the biosynthesis of ursolic acid. Finally, we also revealed that ursolic acid causes mitotic catastrophe in cancer cells and identified three high-confidence protein binding targets by Cellular Thermal Shift Assay (CETSA) and reverse docking. Altogether, these results constitute a valuable resource to further characterize the biosynthesis of active metabolites in the Oldenlandia group, while the mode of action of ursolic acid will allow us to further develop this valuable compound.

Toward kingdom-wide analyses of gene expression.

Gene expression data for Archaeplastida are accumulating exponentially, with more than 300 000 RNA-sequencing (RNA-seq) experiments available for hundreds of species. The gene expression data stem from thousands of experiments that capture gene expression in various organs, tissues, cell types, (a)biotic perturbations, and genotypes. Advances in software tools make it possible to process all these data in a matter of weeks on modern office computers, giving us the possibility to study gene expression in a kingdom-wide manner for the first time. We discuss how the expression data can be accessed and processed and outline analyses that take advantage of cross-species analyses, allowing us to generate powerful and robust hypotheses about gene function and evolution.

Feature importance network reveals novel functional relationships between biological features in Arabidopsis thaliana.

Understanding how the different cellular components are working together to form a living cell requires multidisciplinary approaches combining molecular and computational biology. Machine learning shows great potential in life sciences, as it can find novel relationships between biological features. Here, we constructed a dataset of 11,801 gene features for 31,522 Arabidopsis thaliana genes and developed a machine learning workflow to identify linked features. The detected linked features are visualised as a Feature Important Network (FIN), which can be mined to reveal a variety of novel biological insights pertaining to gene function. We demonstrate how FIN can be used to generate novel insights into gene function. To make this network easily accessible to the scientific community, we present the FINder database, available at finder.plant.tools.

Exploiting plant transcriptomic databases: Resources, tools, and approaches.

There are now more than 300 000 RNA sequencing samples available, stemming from thousands of experiments capturing gene expression in organs, tissues, developmental stages, and experimental treatments for hundreds of plant species. The expression data have great value, as they can be re-analyzed by others to ask and answer questions that go beyond the aims of the study that generated the data. Because gene expression provides essential clues to where and when a gene is active, the data provide powerful tools for predicting gene function, and comparative analyses allow us to study plant evolution from a new perspective. This review describes how we can gain new knowledge from gene expression profiles, expression specificities, co-expression networks, differential gene expression, and experiment correlation. We also introduce and demonstrate databases that provide user-friendly access to these tools.

Protist.guru: A Comparative Transcriptomics Database for Protists.

During the last few decades, the study of microbial ecology has been enabled by molecular and genomic data. DNA sequencing has revealed the surprising extent of microbial diversity and how microbial processes run global ecosystems. However, significant gaps in our understanding of the microbial world remain, and one example is that microbial eukaryotes, or protists, are still largely neglected. To address this gap, we used gene expression data from 17 protist species to create protist.guru: an online database equipped with tools for identifying co-expressed genes, gene families, and co-expression clusters enriched for specific biological functions. Here, we show how our database can be used to reveal genes involved in essential pathways, such as the synthesis of secondary carotenoids in Haematococcus lacustris. We expect protist.guru to serve as a valuable resource for protistologists, as well as a catalyst for discoveries and new insights into the biological processes of microbial eukaryotes. AVAILABILITY: The database and co-expression networks are freely available from http://protist.guru/. The expression matrices and sample annotations are found in the supplementary data.

The Arabidopsis electron-transfer flavoprotein:ubiquinone oxidoreductase is required during normal seed development and germination.

The importance of the alternative donation of electrons to the ubiquinol pool via the electron-transfer flavoprotein/electron-transfer flavoprotein:ubiquinone oxidoreductase (ETF/ETFQO) complex has been demonstrated. However, the functional significance of this pathway during seed development and germination remains to be elucidated. To assess the function of this pathway, we performed a detailed metabolic and transcriptomic analysis of Arabidopsis mutants to test the molecular consequences of a dysfunctional ETF/ETFQO pathway. We demonstrate that the disruption of this pathway compromises seed germination in the absence of an external carbon source and also impacts seed size and yield. Total protein and storage protein content is reduced in dry seeds, whilst sucrose levels remain invariant. Seeds of ETFQO and related mutants were also characterized by an altered fatty acid composition. During seed development, lower levels of fatty acids and proteins accumulated in the etfqo-1 mutant as well as in mutants in the alternative electron donors isovaleryl-CoA dehydrogenase (ivdh-1) and d-2-hydroxyglutarate dehydrogenase (d2hgdh1-2). Furthermore, the content of several amino acids was increased in etfqo-1 mutants during seed development, indicating that these mutants are not using such amino acids as alternative energy source for respiration. Transcriptome analysis revealed alterations in the expression levels of several genes involved in energy and hormonal metabolism. Our findings demonstrated that the alternative pathway of respiration mediated by the ETF/ETFQO complex affects seed germination and development by directly adjusting carbon storage during seed filling. These results indicate a role for the pathway in the normal plant life cycle to complement its previously defined roles in the response to abiotic stress.