Plant-mSubP: a computational framework for the prediction of single- and multi-target protein subcellular localization using integrated machine-learning approaches

Protein subcellular localization prediction tools

Protein subcellular localization prediction is of great significance in bioinformatics and biological research. Most of the proteins do not have experimentally determined localization information, computational prediction methods and tools have been acting as an active research area for more than two decades now. Knowledge of the subcellular location of a protein provides valuable information about its functionalities, the functioning of the cell, and other possible interactions with proteins. Fast, reliable, and accurate predictors provides platforms to harness the abundance of sequence data to predict subcellular locations accordingly. During the last decade, there has been a considerable amount of research effort aimed at developing subcellular localization predictors. This paper reviews recent subcellular localization prediction tools in the Eukaryotic, Prokaryotic, and Virus-based categories followed by a detailed analysis. Each predictor is discussed based on its main features, strengths, weaknesses, algorithms used, prediction techniques, and analysis. This review is supported by prediction tools taxonomies that highlight their rele- vant area and examples for uncomplicated categorization and ease of understandability. These taxonomies help users find suitable tools according to their needs. Furthermore, recent research gaps and challenges are discussed to cover areas that need the utmost attention. This survey provides an in-depth analysis of the most recent prediction tools to facilitate readers and can be considered a quick guide for researchers to identify and explore the recent literature advancements.

Protein research in millets: current status and way forward

MAIN CONCLUSION

Millets' protein studies are lagging behind those of major cereals. Current status and future insights into the investigation of millet proteins are discussed. Millets are important small-seeded cereals majorly grown and consumed by people in Asia and Africa and are considered crops of future food security. Although millets possess excellent climate resilience and nutrient supplementation properties, their research advancements have been lagging behind major cereals. Although considerable genomic resources have been developed in recent years, research on millet proteins and proteomes is currently limited, highlighting a need for further investigation in this area. This review provides the current status of protein research in millets and provides insights to understand protein responses for climate resilience and nutrient supplementation in millets. The reference proteome data is available for sorghum, foxtail millet, and proso millet to date; other millets, such as pearl millet, finger millet, barnyard millet, kodo millet, tef, and browntop millet, do not have any reference proteome data. Many studies were reported on stress-responsive protein identification in foxtail millet, with most studies on the identification of proteins under drought-stress conditions. Pearl millet has a few reports on protein identification under drought and saline stress. Finger millet is the only other millet to have a report on stress-responsive (drought) protein identification in the leaf. For protein localization studies, foxtail millet has a few reports. Sorghum has the highest number of 40 experimentally proven crystal structures, and other millets have fewer or no experimentally proven structures. Further proteomics studies will help dissect the specific proteins involved in climate resilience and nutrient supplementation and aid in breeding better crops to conserve food security.

Sourcing DNA parts for synthetic biology applications in plants

Transgenic approaches are now standard in plant biology research aiming to characterize gene function or improve crops. Recent advances in DNA synthesis and assembly make constructing transgenes a routine task. What remains nontrivial is the selection of the DNA parts and optimization of the transgene design. Early career researchers and seasoned molecular biologists alike often face difficult decisions on what promoter or terminator to use, what tag to include, and where to place it. This review aims to inform about the current approaches being employed to identify and characterize DNA parts with the desired functionalities and give general advice on basic construct design. Furthermore, we hope to share the excitement about new experimental and computational tools being developed in this field.

Genome-wide characterization and expression analysis of GATA transcription factors under combination of light wavelengths and drought stress in potato

GATA is one of the prominent transcription factor families conserved among many organisms in eukaryotes and has different biological roles in many pathways, particularly in light regulation in plants. Although GATA transcription factors (TFs) have been identified in different crop species, their roles in abiotic stress tolerance have not been studied in potato. In this study, we identified 32 GATA TFs in potato (Solanum tuberosum) by in silico analyses, and expression levels of selected six genes were investigated in drought-tolerant (Sante) and sensitive (Agria) cultivars under light, drought, and combined (light + drought) stress conditions. According to the phylogenetic results, StGATA TFs were divided into four main groups (I, II, III, and IV) and different sub-groups in I and II (eight and five, respectively). StGATA genes were uniformly localized to each chromosome with a conserved exon/intron structure. The presence of cis-elements within the StGATA family further supported the possible involvement in abiotic stress tolerance and light response, tissue-specific expression, and hormonal regulation. Additional PPI investigations showed that these networks, especially for Groups I, II, and IV, play a significant role in response to light and drought stress. Six StGATAs were chosen from these groups for expressional profiling, and their expression in both Sante and Agria was mainly downregulated under purple and red lights, drought, and combined stress (blue + drought and purple + drought). The interactomes of selected StGATAs, StGATA3, StGATA24, and StGATA29 were analyzed, and the accessions with GATA motifs were checked for expression. The results showed that the target proteins, cyclin-P3-1, SPX domain-containing protein 1, mitochondrial calcium uniporter protein 2, mitogen-activated protein kinase kinase kinase YODA, and splicing factor 3 B subunit 4-like, mainly play a role in phytochrome-mediated stomatal patterning, development, and activity. Understanding the interactions between drought stress and the light response mechanisms in potato plants is essential. It will eventually be possible to enhance potato resilience to climate change by manipulating the TFs that play a role in these pathways.

Gossypium hirsutum gene of unknown function Gohir.A02G161000 encodes a potential transmembrane Root UVB Sensitive 4 Protein with a putative protein-protein interaction interface

A gene of unknown function, Gohir.A02G161000.1, identified in Gossypium hirsutum was studied using computational sequence and structure bioinformatics tools. The associated protein GhRUS4-A0A1U8JPV7 (UniProt A0A1U8JPV7) is predicted to be a plastid-localized, transmembrane root UVB-sensitive 4 (RUS4) protein with a newly identified potential dimerization surface. Evidence from homology and sequence conservation suggest involvement in auxin transport and pollen maturation.

Predictive Modeling of Proteins Encoded by a Plant Virus Sheds a New Light on Their Structure and Inherent Multifunctionality

Plant virus genomes encode proteins that are involved in replication, encapsidation, cell-to-cell, and long-distance movement, avoidance of host detection, counter-defense, and transmission from host to host, among other functions. Even though the multifunctionality of plant viral proteins is well documented, contemporary functional repertoires of individual proteins are incomplete. However, these can be enhanced by modeling tools. Here, predictive modeling of proteins encoded by the two genomic RNAs, i.e., RNA1 and RNA2, of grapevine fanleaf virus (GFLV) and their satellite RNAs by a suite of protein prediction software confirmed not only previously validated functions (suppressor of RNA silencing [VSR], viral genome-linked protein [VPg], protease [Pro], symptom determinant [Sd], homing protein [HP], movement protein [MP], coat protein [CP], and transmission determinant [Td]) and previously identified putative functions (helicase [Hel] and RNA-dependent RNA polymerase [Pol]), but also predicted novel functions with varying levels of confidence. These include a T3/T7-like RNA polymerase domain for protein 1AVSR, a short-chain reductase for protein 1BHel/VSR, a parathyroid hormone family domain for protein 1EPol/Sd, overlapping domains of unknown function and an ABC transporter domain for protein 2BMP, and DNA topoisomerase domains, transcription factor FBXO25 domain, or DNA Pol subunit cdc27 domain for the satellite RNA protein. Structural predictions for proteins 2AHP/Sd, 2BMP, and 3A? had low confidence, while predictions for proteins 1AVSR, 1BHel*/VSR, 1CVPg, 1DPro, 1EPol*/Sd, and 2CCP/Td retained higher confidence in at least one prediction. This research provided new insights into the structure and functions of GFLV proteins and their satellite protein. Future work is needed to validate these findings.

Artificial intelligence-driven systems engineering for next-generation plant-derived biopharmaceuticals

Recombinant biopharmaceuticals including antigens, antibodies, hormones, cytokines, single-chain variable fragments, and peptides have been used as vaccines, diagnostics and therapeutics. Plant molecular pharming is a robust platform that uses plants as an expression system to produce simple and complex recombinant biopharmaceuticals on a large scale. Plant system has several advantages over other host systems such as humanized expression, glycosylation, scalability, reduced risk of human or animal pathogenic contaminants, rapid and cost-effective production. Despite many advantages, the expression of recombinant proteins in plant system is hindered by some factors such as non-human post-translational modifications, protein misfolding, conformation changes and instability. Artificial intelligence (AI) plays a vital role in various fields of biotechnology and in the aspect of plant molecular pharming, a significant increase in yield and stability can be achieved with the intervention of AI-based multi-approach to overcome the hindrance factors. Current limitations of plant-based recombinant biopharmaceutical production can be circumvented with the aid of synthetic biology tools and AI algorithms in plant-based glycan engineering for protein folding, stability, viability, catalytic activity and organelle targeting. The AI models, including but not limited to, neural network, support vector machines, linear regression, Gaussian process and regressor ensemble, work by predicting the training and experimental data sets to design and validate the protein structures thereby optimizing properties such as thermostability, catalytic activity, antibody affinity, and protein folding. This review focuses on, integrating systems engineering approaches and AI-based machine learning and deep learning algorithms in protein engineering and host engineering to augment protein production in plant systems to meet the ever-expanding therapeutics market.

Quantitative Phosphoproteomic Analysis Provides Insights into the Sodium Bicarbonate Responsiveness of Glycine max

Sodium bicarbonate stress caused by NaHCO3 is one of the most severe abiotic stresses affecting agricultural production worldwide. However, little attention has been given to the molecular mechanisms underlying plant responses to sodium bicarbonate stress. To understand phosphorylation events in signaling pathways triggered by sodium bicarbonate stress, TMT-labeling-based quantitative phosphoproteomic analyses were performed on soybean leaf and root tissues under 50 mM NaHCO3 treatment. In the present study, a total of 7856 phosphopeptides were identified from cultivated soybeans (Glycine max L. Merr.), representing 3468 phosphoprotein groups, in which 2427 phosphoprotein groups were newly identified. These phosphoprotein groups contained 6326 unique high-probability phosphosites (UHPs), of which 77.2% were newly identified, increasing the current soybean phosphosite database size by 43.4%. Among the phosphopeptides found in this study, we determined 67 phosphopeptides (representing 63 phosphoprotein groups) from leaf tissue and 554 phosphopeptides (representing 487 phosphoprotein groups) from root tissue that showed significant changes in phosphorylation levels under sodium bicarbonate stress (fold change >1.2 or <0.83, respectively; p < 0.05). Localization prediction showed that most phosphoproteins localized in the nucleus for both leaf and root tissues. GO and KEGG enrichment analyses showed quite different enriched functional terms between leaf and root tissues, and more pathways were enriched in the root tissue than in the leaf tissue. Moreover, a total of 53 different protein kinases and 7 protein phosphatases were identified from the differentially expressed phosphoproteins (DEPs). A protein kinase/phosphatase interactor analysis showed that the interacting proteins were mainly involved in/with transporters/membrane trafficking, transcriptional level regulation, protein level regulation, signaling/stress response, and miscellaneous functions. The results presented in this study reveal insights into the function of post-translational modification in plant responses to sodium bicarbonate stress.

Evolution of the Tóxicos en Levadura 63 (TL63) gene family in plants and functional characterization of Arabidopsis thaliana TL63 under oxidative stress

MAIN CONCLUSION

TL63 orthologs were angiosperm specific and had undergone motifs loss and gain, and increased purifying selection. AtTL63 was involved in the response of yeast and Arabidopsis plants to oxidative stress. The Tóxicos en Levadura (TL) family, a class of E3 ubiquitin ligases with typical RING-H2 type zinc finger structure, plays a pivotal role in mediating physiological processes and responding to stress in plants. However, the evolution and function of TL63 remain unclear. In this study, TL63 homologs were dated roughly back to the origin of land plants and confirmed to have subjected to the gain and loss of motifs and increased purifying selection. Phylogenetic analysis displayed that 279 TL63s could be divided into four main clades (Clade A-D). Notably, the ancestral tandem TL40/41 cluster contributed to the expansion of modern Brassicaceae TL40/41. The substitution rate tests revealed that the TL63 lineage was evidently different from other lineages. The codon usage index exhibited that monocotyledons preferred to use not A3s and T3s, but C3s, G3s, CAI, CBI and Fop. Sequence analysis showed that the TL63 homologs had conserved TM and GLD motifs and RING-H2 domain whose key amino acid residues accounted for the high average abundance. Particularly, Arabidopsis thaliana TL63 (AtTL63) was located in the nuclei, cell membranes and peroxisomes and expressed universally and significantly throughout A. thaliana development. Under H2O2 treatment, low or moderate expression of the AtTL63 held beneficial effects on the growth and viability of yeast cells and the mutation or overexpression of the AtTL63 positively affected the growth of A. thaliana plants. In brief, this study could supply useful insight into the evolution of the plant TL63s and the AtTL63 functions under oxidative stress.

Genome-Wide Analysis and Expression Profiling of Trehalose-6-Phosphate Phosphatase (TPP) in Punica granatum in Response to Abscisic-Acid-Mediated Drought Stress

Trehalose, a nonreducing disaccharide, has been linked to plant growth and development as well as stress response. The enzyme trehalose-6-phosphate phosphatase (TPP) plays a crucial role in the production of trehalose in higher plants. This study identified a total of seven TPP family genes within the pomegranate species (PgTPP1-PgTPP7). Three subgroups of the seven PgTPPs were identified through phylogenetic analysis. The gene length, coding sequence (CD) length, and chromosomal location of the PgTPP genes were studied. In addition, the PgTPP proteins' length, isoelectric point (Ip), grand average of hydropathicity (GRAVY), conserved domains, conserved motifs, synteny, and phylogenetic relationships with Arabidopsis and tomato TPP proteins were examined. The cis-acting elements in the promoter region and the expression of the PgTPP genes under abscisic acid (ABA)-mediated drought stress as well as the differences in expression in the root, flower, and leaf tissues were also assessed. The PgTPP2 and PgTPP5 genes are involved in the response to abscisic-acid-mediated drought stress, as shown by drought-mediated stress transcriptomes. The PgTPP1 and PgTPP2 genes were expressed only in floral tissue and roots, respectively. The remaining PgTPPs did not exhibit any significant alterations in gene expression in roots, flowers, or leaves. The current study has the potential to provide a comprehensive understanding of the biological characteristics of PgTPP proteins in various developmental processes and their role in the pomegranate plant's response to different stressors. However, further research is required to explore their precise biological role. Hence, conducting a comprehensive functional validation study on PgTPPs could contribute to the development of stress-resistant agricultural cultivars.

Dynamics of annatto pigment synthesis and accumulation in seeds of Bixa orellana L. revealed by integrated chemical, anatomical, and RNA-Seq analyses

Bixin is a commercially valuable apocarotenoid pigment found in the seed aril of Bixa orellana. The dynamics and regulation of its biosynthesis and accumulation during seed development remain largely unknown. Here, we combined chemical, anatomical, and transcriptomic data to provide stage-specific resolution of the cellular and molecular events occurring during B. orellana seed development. Seeds at five developmental stages (S1-S5) were used for analysis of bixin content and seed anatomy, and three of them (S1, S3, and S4) were selected for Illumina HiSeq sequencing. Bixin accumulated in large quantities in seeds compared with other tissues analyzed, particularly during the S2 stage, peaking at the S4 stage, and then decreasing slightly in the S5 stage. Anatomical analysis revealed that bixin accumulated in the large central vacuole of specialized cells, which were scattered throughout the developing mesotesta at the S2 stage, but enlarged progressively at later stages, until they occupied most of the parenchyma in the aril. A total of 13 million reads were generated and assembled into 73,381 protein-encoding contigs, from which 312 were identified as containing 1-deoxy-D-xylulose-5-phosphate/2-C-methyl-D-erythritol-4-phosphate (DOXP/MEP), carotenoid, and bixin pathways genes. Differential transcriptome expression analysis of these genes revealed that 50 of them were sequentially and differentially expressed through the seed developmental stages analyzed, including seven carotenoid cleavage dioxygenases, eight aldehyde dehydrogenases, and 22 methyltransferases. Taken together, these results show that bixin synthesis and accumulation in seeds of B. orellana are a developmentally regulated process involving the coordinated expression of DOXP/MEP, carotenoid, and bixin biosynthesis genes.

NADP-Dependent Malic Enzyme Genes in Sweet Pepper Fruits: Involvement in Ripening and Modulation by Nitric Oxide (NO)

NADPH is an indispensable cofactor in a wide range of physiological processes that is generated by a family of NADPH dehydrogenases, of which the NADP-dependent malic enzyme (NADP-ME) is a member. Pepper (Capsicum annuum L.) fruit is a horticultural product consumed worldwide that has great nutritional and economic relevance. Besides the phenotypical changes that pepper fruit undergoes during ripening, there are many associated modifications at transcriptomic, proteome, biochemical and metabolic levels. Nitric oxide (NO) is a recognized signal molecule with regulatory functions in diverse plant processes. To our knowledge, there is very scarce information about the number of genes encoding for NADP-ME in pepper plants and their expression during the ripening of sweet pepper fruit. Using a data mining approach to evaluate the pepper plant genome and fruit transcriptome (RNA-seq), five NADP-ME genes were identified, and four of them, namely CaNADP-ME2 to CaNADP-ME5, were expressed in fruit. The time course expression analysis of these genes during different fruit ripening stages, including green immature (G), breaking point (BP) and red ripe (R), showed that they were differentially modulated. Thus, while CaNADP-ME3 and CaNADP-ME5 were upregulated, CaNADP-ME2 and CaNADP-ME4 were downregulated. Exogenous NO treatment of fruit triggered the downregulation of CaNADP-ME4. We obtained a 50-75% ammonium-sulfate-enriched protein fraction containing CaNADP-ME enzyme activity, and this was assayed via non-denaturing polyacrylamide gel electrophoresis (PAGE). The results allow us to identify four isozymes designated from CaNADP-ME I to CaNADP-ME IV. Taken together, the data provide new pieces of information on the CaNADP-ME system with the identification of five CaNADP-ME genes and how the four genes expressed in pepper fruits are modulated during ripening and exogenous NO gas treatment.

A review from biological mapping to computation-based subcellular localization

Subcellular localization is crucial to the study of virus and diseases. Specifically, research on protein subcellular localization can help identify clues between virus and host cells that can aid in the design of targeted drugs. Research on RNA subcellular localization is significant for human diseases (such as Alzheimer's disease, colon cancer, etc.). To date, only reviews addressing subcellular localization of proteins have been published, which are outdated for reference, and reviews of RNA subcellular localization are not comprehensive. Therefore, we collated (the most up-to-date) literature on protein and RNA subcellular localization to help researchers understand changes in the field of protein and RNA subcellular localization. Extensive and complete methods for constructing subcellular localization models have also been summarized, which can help readers understand the changes in application of biotechnology and computer science in subcellular localization research and explore how to use biological data to construct improved subcellular localization models. This paper is the first review to cover both protein subcellular localization and RNA subcellular localization. We urge researchers from biology and computational biology to jointly pay attention to transformation patterns, interrelationships, differences, and causality of protein subcellular localization and RNA subcellular localization.

An exonuclease V homologue is expressed predominantly during early megasporogenesis in apomictic Brachiaria brizantha

MAIN CONCLUSION

An exonuclease V homologue from apomictic Brachiaria brizantha is expressed and localized in nucellar cells at key moments when these cells differentiate to give rise to unreduced gametophytes. Brachiaria is a genus of forage grasses with economical and agricultural importance to Brazil. Brachiaria reproduces by aposporic apomixis, in which unreduced embryo sacs, derived from nucellar cells, other than the megaspore mother cell (MMC), are formed. The unreduced embryo sacs produce an embryo without fertilization resulting in clones of the mother plant. Comparative gene expression analysis in ovaries of sexual and apomictic Brachiaria spp. revealed a sequence from B. brizantha that showed a distinct pattern of expression in ovaries of sexual and apomictic plants. In this work, we describe a gene named BbrizExoV with strong identity to exonuclease V (Exo V) genes from other grasses. Sequence analysis in signal prediction tools showed that BbrizExoV might have dual localization, depending on the translation point. A longer form to the nucleus and a shorter form which would be directed to the chloroplast. This is also the case for monocot sequences analyzed from other species. The long form of BbrizExoV protein localizes to the nucleus of onion epidermal cells. Analysis of ExoV proteins from dicot species, with exception of Arabidopsis thaliana ExoVL protein, showed only one localization. Using a template-based AlphaFold 2 modelling approach the structure of BbrizExoV in complex with metal and ssDNA was predicted based on the holo structure of the human counterpart. Features predicted to define ssDNA binding but a lack of sequence specificity are shared between the human enzyme and BbrizExoV. Expression analyses indicated the precise site and timing of transcript accumulation during ovule development, which coincides with the differentiation of nucelar cells to form the typical aposporic four-celled unreduced gametophyte. A putative function for this protein is proposed based on its homology and expression pattern.

Genome-wide analysis of pathogenesis-related protein-1 (PR-1) genes from Qingke (Hordeum vulgare L. var. nudum) reveals their roles in stress responses

Proteins that are pathogenesis-related 1 (PR-1) can accumulate to high levels when plants employ defenses, being major participants in processes critical for stress responses as well as development of many species. Yet we still lack information concerning PR-1 family members in Qingke plants (Hordeum vulgare L. var. nudum). In this work, we distinguished 20 PR-1s from the Qingke genome whose encoded proteins often featured at the N-terminus a signal peptide; all 20 PR-1s were predicted to localize either periplasmically or extracellularly. The CAP domain was confirmed as being highly conserved in all these PR-1s. Phylogeny-based inference revealed that PR-1 proteins clustered into four major clades, with the majority of Qingke PR-1s distributed in clade I (17 out 20), and the other 3 distributed in clade II. Gene structure analysis showed that 16 PR-1s did not contain any introns, whereas the other four had 1-4 introns. We identified a variety of motifs that are cis-acting in the promoter regions of PR-1s; these included those potentially involved in Qingke's light response, hormonal and stress responses, circadian control and regulation of development and growth, in addition to sites where transcription factors bind to. Expression analysis uncovered several members of PR-1 genes that were strongly and rapidly induced by powdery mildew infection, phytohormones, and cold stimulus. Altogether, our study's findings enhance what is known about genetic features of PR-1 family members in H. vulgare plants, especially Qingke, and could thereby facilitate further exploration aiming to elucidate the functioning of these proteins.

Gossypium hirsutum gene of unknown function Gohir.A03G007700.1 encodes a potential VAN3-binding protein with a phosphoinositide-binding site

A gene of unknown function, Gohir.A03G007700.1 (gene ID: Gohir.A03G007700_UTX-TM1_v2.1; transcript ID: Gohir.A03G007700.1_UTX-TM1_v2.1), identified in Gossypium hirsutum was studied using bioinformatic analyses of the sequence and structure of its encoded protein. Results from domain prediction, conserved residues and structure comparison indicate the encoded plant-specific protein (UniProt A0A1U8N485) is part of the VAN3-binding protein family with a conserved phosphoinositide-binding site. Homology comparison suggests functional similarity with Arabidopsis FORKED-like FL5 and 6, which localize to the Golgi apparatus and are linked to vein development and leaf size phenotypes.

Computational models for prediction of protein-protein interaction in rice and Magnaporthe grisea

INTRODUCTION

Plant-microbe interactions play a vital role in the development of strategies to manage pathogen-induced destructive diseases that cause enormous crop losses every year. Rice blast is one of the severe diseases to rice Oryza sativa (O. sativa) due to Magnaporthe grisea (M. grisea) fungus. Protein-protein interaction (PPI) between rice and fungus plays a key role in causing rice blast disease.

METHODS

In this paper, four genomic information-based models such as (i) the interolog, (ii) the domain, (iii) the gene ontology, and (iv) the phylogenetic-based model are developed for predicting the interaction between O. sativa and M. grisea in a whole-genome scale.

RESULTS AND DISCUSSION

A total of 59,430 interacting pairs between 1,801 rice proteins and 135 blast fungus proteins are obtained from the four models. Furthermore, a machine learning model is developed to assess the predicted interactions. Using composition-based amino acid composition (AAC) and conjoint triad (CT) features, an accuracy of 88% and 89% is achieved, respectively. When tested on the experimental dataset, the CT feature provides the highest accuracy of 95%. Furthermore, the specificity of the model is verified with other pathogen-host datasets where less accuracy is obtained, which confirmed that the model is specific to O. sativa and M. grisea. Understanding the molecular processes behind rice resistance to blast fungus begins with the identification of PPIs, and these predicted PPIs will be useful for drug design in the plant science community.

Ion-pumping microbial rhodopsin protein classification by machine learning approach

BACKGROUND

Rhodopsin is a seven-transmembrane protein covalently linked with retinal chromophore that absorbs photons for energy conversion and intracellular signaling in eukaryotes, bacteria, and archaea. Haloarchaeal rhodopsins are Type-I microbial rhodopsin that elicits various light-driven functions like proton pumping, chloride pumping and Phototaxis behaviour. The industrial application of Ion-pumping Haloarchaeal rhodopsins is limited by the lack of full-length rhodopsin sequence-based classifications, which play an important role in Ion-pumping activity. The well-studied Haloarchaeal rhodopsin is a proton-pumping bacteriorhodopsin that shows promising applications in optogenetics, biosensitized solar cells, security ink, data storage, artificial retinal implant and biohydrogen generation. As a result, a low-cost computational approach is required to identify Ion-pumping Haloarchaeal rhodopsin sequences and its subtype.

RESULTS

This study uses a support vector machine (SVM) technique to identify these ion-pumping Haloarchaeal rhodopsin proteins. The haloarchaeal ion pumping rhodopsins viz., bacteriorhodopsin, halorhodopsin, xanthorhodopsin, sensoryrhodopsin and marine prokaryotic Ion-pumping rhodopsins like actinorhodopsin, proteorhodopsin have been utilized to develop the methods that accurately identified the ion pumping haloarchaeal and other type I microbial rhodopsins. We achieved overall maximum accuracy of 97.78%, 97.84% and 97.60%, respectively, for amino acid composition, dipeptide composition and hybrid approach on tenfold cross validation using SVM. Predictive models for each class of rhodopsin performed equally well on an independent data set. In addition to this, similar results were achieved using another machine learning technique namely random forest. Simultaneously predictive models performed equally well during five-fold cross validation. Apart from this study, we also tested the own, blank, BLAST dataset and annotated whole-genome rhodopsin sequences of PWS haloarchaeal isolates in the developed methods. The developed web server ( https://bioinfo.imtech.res.in/servers/rhodopred ) can identify the Ion Pumping Haloarchaeal rhodopsin proteins and their subtypes. We expect this web tool would be useful for rhodopsin researchers.

CONCLUSION

The overall performance of the developed method results show that it accurately identifies the Ionpumping Haloarchaeal rhodopsin and their subtypes using known and unknown microbial rhodopsin sequences. We expect that this study would be useful for optogenetics, molecular biologists and rhodopsin researchers.

Computational method for aromatase-related proteins using machine learning approach

Human aromatase enzyme is a microsomal cytochrome P450 and catalyzes aromatization of androgens into estrogens during steroidogenesis. For breast cancer therapy, third-generation aromatase inhibitors (AIs) have proven to be effective; however patients acquire resistance to current AIs. Thus there is a need to predict aromatase-related proteins to develop efficacious AIs. A machine learning method was established to identify aromatase-related proteins using a five-fold cross validation technique. In this study, different SVM approach-based models were built using the following approaches like amino acid, dipeptide composition, hybrid and evolutionary profiles in the form of position-specific scoring matrix (PSSM); with maximum accuracy of 87.42%, 84.05%, 85.12%, and 92.02% respectively. Based on the primary sequence, the developed method is highly accurate to predict the aromatase-related proteins. Prediction scores graphs were developed using the known dataset to check the performance of the method. Based on the approach described above, a webserver for predicting aromatase-related proteins from primary sequence data was developed and implemented at https://bioinfo.imtech.res.in/servers/muthu/aromatase/home.html. We hope that the developed method will be useful for aromatase protein related research.

In Silico Comparative Exploration of Allergens of Periplaneta americana, Blattella germanica and Phoenix dactylifera for the Diagnosis of Patients Suffering from IgE-Mediated Allergic Respiratory Diseases

The burden of allergic illnesses is continuously rising, and patient diagnosis is a significant problem because of how intricately hereditary and environmental variables interact. The past three to four decades have seen an outbreak of allergies in high-income countries. According to reports on the illness, asthma affects around 300 million individuals worldwide. Identifying clinically important allergens for the accurate classification of IgE-mediated allergy respiratory disease diagnosis would be beneficial for implementing standardized allergen-associated therapy. Therefore, the current study includes an in silico analysis to identify potential IgE-mediated allergens in date palms and cockroaches. Such an immunoinformatic approach aids the prioritization of allergens with probable involvement in IgE-mediated allergic respiratory diseases. Immunoglobulin E (IgE) was used for molecular dynamic simulations, antigen-antibody docking analyses, epitope identifications, and characterizations. The potential of these allergens (Per a7, Per a 1.0102, and Bla g 1.0101) in IgE-mediated allergic respiratory diseases was explored through the evaluation of physicochemical characteristics, interaction observations, docking, and molecular dynamics simulations for drug and vaccine development.

TRustDB: A comprehensive bioinformatics resource for understanding the complete Wheat-Stem rust host-pathogen interactome

The increasing infectious diseases in wheat immensely reduce crop yield and quality, thus affecting global wheat production. The evolution in phytopathogens hinders the understanding of the disease infection mechanisms. TRustDB is an open-access, comprehensive database that is specifically focused on the disease stem rust (also known as black rust) in Triticum aestivum, which is caused by the fungal pathogen Puccinia graminis (Pgt), strains 'Ug99' and '21-0'. The database aims at a broader focus of providing the researchers with comprehensive tools to predict the protein-protein interactions and avail the functional annotations of the proteins involved in the interactions that cause the disease. The network of the predicted interactome can also be visualized on the browser. Various modules for the functional annotations of the host and pathogen proteins such as subcellular localization, functional domains, gene ontology annotations, pathogen orthologs and effector proteins have been implemented. The host proteins that serve as transcription factors, along with the respective Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways are also available, which further enhance the understanding of the disease infection mechanisms and the defense responses of the host. The database is also linked with several other databases such as InterPro, KEGG pathways, Ensembl and National Center for Biotechnology Information (NCBI). TRustDB has a user-friendly web interface, which can be accessed through . Database URL http://bioinfo.usu.edu/trustdb/.

Crop Proteomics under Abiotic Stress: From Data to Insights

Food security is a major challenge in the present world due to erratic weather and climatic changes. Environmental stress negatively affects plant growth and development which leads to reduced crop yields. Technological advancements have caused remarkable improvements in crop-breeding programs. Proteins have an indispensable role in developing stress resilience and tolerance in crops. Genomic and biotechnological advancements have made the process of crop improvement more accurate and targeted. Proteomic studies provide the information required for such targeted approaches. The crosstalk among cellular components is being analyzed by subcellular proteomics. Additionally, the functional diversity of proteins is being unraveled by post-translational modifications during abiotic stress. The exploration of precise cellular responses and the networking among different cellular organelles help in the prediction of signaling pathways and protein-protein interactions. High-throughput mass-spectrometry-based protein studies are now possible due to incremental advancements in mass-spectrometry techniques, sample protocols, and bioinformatic tools as well as the increasing availability of plant genome sequence information for multiple species. In this review, the key role of proteomic analysis in identifying the abiotic-stress-responsive mechanisms in various crops was summarized. The development and availability of advanced computational tools were discussed in detail. The highly variable protein responses among different crops have provided a wide avenue for molecular-marker-assisted genetic buildup studies to develop smart, high-yielding, and stress-tolerant varieties to cope with food-security challenges.

The moss-specific transcription factor PpERF24 positively modulates immunity against fungal pathogens in Physcomitrium patens

APETALA2/ethylene response factors (AP2/ERFs) transcription factors (TFs) have greatly expanded in land plants compared to algae. In angiosperms, AP2/ERFs play important regulatory functions in plant defenses against pathogens and abiotic stress by controlling the expression of target genes. In the moss Physcomitrium patens, a high number of members of the ERF family are induced during pathogen infection, suggesting that they are important regulators in bryophyte immunity. In the current study, we analyzed a P. patens pathogen-inducible ERF family member designated as PpERF24. Orthologs of PpERF24 were only found in other mosses, while they were absent in the bryophytes Marchantia polymorpha and Anthoceros agrestis, the vascular plant Selaginella moellendorffii, and angiosperms. We show that PpERF24 belongs to a moss-specific clade with distinctive amino acids features in the AP2 domain that binds to the DNA. Interestingly, all P. patens members of the PpERF24 subclade are induced by fungal pathogens. The function of PpERF24 during plant immunity was assessed by an overexpression approach and transcriptomic analysis. Overexpressing lines showed increased defenses to infection by the fungal pathogens Botrytis cinerea and Colletotrichum gloeosporioides evidenced by reduced cellular damage and fungal biomass compared to wild-type plants. Transcriptomic and RT-qPCR analysis revealed that PpERF24 positively regulates the expression levels of defense genes involved in transcriptional regulation, phenylpropanoid and jasmonate pathways, oxidative burst and pathogenesis-related (PR) genes. These findings give novel insights into potential mechanism by which PpERF24 increases plant defenses against several pathogens by regulating important players in plant immunity.

Lignin biosynthesis regulated by CsCSE1 is required for Cucumis sativus defence to Podosphaera xanthii

Lignin is a complex phenolic compound that can enhance the stiffness, hydrophobicity, and antioxidant capacity of the cell wall; it thus provides a critical barrier against pathogen and insect invaders. Caffeoyl shikimate esterase (CSE) is a key novel enzyme involved in lignin biosynthesis that is associated with genetic improvements in lignocellulosic biomass; however, no research thus far have revealed the role of CSE in resistance to pathogenic stress. CsCSE1 (Cucsa.134370) has previously been shown to highly associated with the response of cucumber to attack by Podosphaera xanthii through RNA sequencing. Here, we detected the exactly role of CsCSE1 in the defence of cucumber to P. xanthii infection. Homologous sequence alignment revealed that CsCSE1 contains two highly conserved lyase domains (GXSXG), suggesting that CsCSE1 possesses CSE activity. Subcellular localization analysis manifested that CsCSE1 was localized to the plasma membrane and endoplasmic reticulum (ER). Functional analysis demonstrated that the transient silencing of CsCSE1 in cucumber dramatically attenuated resistance to P. xanthii, whereas overexpression of CsCSE1 in cucumber markedly increased resistance to P. xanthii. Further investigation of the abundance of lignin in transient transgenic plants revealed that CsCSE1 might actively mediate the disease resistance of cucumber by promoting lignin biosynthesis. CsCSE1 also affects the expression of its downstream lignin biosynthesis-related genes, like CsLAC, CsCOMT, CsCCR, and CsCAD. The results of this study provide targets for the genetic breeding of tolerant cucumber cultivars as well as new insights that could aid the control of plant diseases.

Pathogenesis-related protein-4 (PR-4) gene family in Qingke (Hordeum vulgare L. var. nudum): genome-wide identification, structural analysis and expression profile under stresses

BACKGROUND

Pathogenesis-related (PR) proteins are active participants of plant defense against biotic and abiotic stresses. The PR-4 family features a Barwin domain at the C-terminus, which endows the host plant with disease resistance. However, comprehensive analysis of PR-4 genes is still lacking in Qingke (Hordeum vulgare L. var. nudum).

METHODS AND RESULTS

Herein, a total of four PR-4 genes were identified from the genome of Qingke through HMM profiling. Devoid of the chitin-binding domain, these 4 proteins were grouped as class II PR-4s. Phylogenic analysis revealed that 127 PR-4s from 47 species were clustered into 3 major groups, among which the four Qingke PR-4s were claded into group I. Analysis of gene structure demonstrated that no intron was found in 3 out of the 4 Qingke PR-4s, and HOVUSG0928500 was the only gene contained one intron. An array of cis-acting motifs were detected in promoters of Qingke PR-4 genes, including elements associated with hormone response, light response, stress response, growth and development processes and binding sites of transcription factors, implying their diverse role. Expression profiling confirmed that Qingke PR-4s were involved in defense response against drought, cold and powdery mildews infection, and transcription of HOVUSG1974300 and HOVUSG5705400 was differentially regulated by MeJA and SA.

CONCLUSION

Findings of the study provided insights into the genetic basis of the PR-4 family genes, and would promote further investigation on protein function and utilization.

Transcriptome Analyses in a Selected Gene Set Indicate Alternative Oxidase (AOX) and Early Enhanced Fermentation as Critical for Salinity Tolerance in Rice

Plants subjected to stress need to respond rapidly and efficiently to acclimatize and survive. In this paper, we investigated a selected gene set potentially involved in early cell reprogramming in two rice genotypes with contrasting salinity tolerance (Pokkali tolerant and IR29 susceptible) in order to advance knowledge of early molecular mechanisms of rice in dealing with salt stress. Selected genes were evaluated in available transcriptomic data over a short period of 24 h and involved enzymes that avoid ROS formation (AOX, UCP and PTOX), impact ATP production (PFK, ADH and COX) or relate to the antioxidant system. Higher transcript accumulation of AOX (ROS balancing), PFK and ADH (alcohol fermentation) was detected in the tolerant genotype, while the sensitive genotype revealed higher UCP and PTOX transcript levels, indicating a predominant role for early transcription of AOX and fermentation in conferring salt stress tolerance to rice. Antioxidant gene analyses supported higher oxidative stress in IR29, with transcript increases of cytosolic CAT and SOD from all cell compartments (cytoplasm, peroxisome, chloroplast and mitochondria). In contrast, Pokkali increased mRNA levels from the AsA-GSH cycle as cytosolic/mitochondrial DHAR was involved in ascorbate recovery. In addition, these responses occurred from 2 h in IR29 and 10 h in Pokkali, indicating early but ineffective antioxidant activity in the susceptible genotype. Overall, our data suggest that AOX and ADH can play a critical role during early cell reprogramming for improving salt stress tolerance by efficiently controlling ROS formation in mitochondria. We discuss our results in relation to gene engineering and editing approaches to develop salinity-tolerant crops.

Genome-wide analysis of the Thaumatin-like gene family in Qingke (Hordeum vulgare L. var. nudum) uncovers candidates involved in plant defense against biotic and abiotic stresses

Thaumatin-like proteins (TLPs) participate in the defense responses of plants as well as their growth and development processes, including seed germination. Yet the functioning of TLP family genes, in addition to key details of their encoded protein products, has not been thoroughly investigated for Qingke (Hordeum vulgare L. var. nudum). Here, a total of 36 TLP genes were identified in the genome of Qingke via HMM profiling. Of them, 25 TLPs contained a signal peptide at the N-terminus, with most proteins predicted to localize in the cytoplasm or outer membrane. Sequence alignment and motif analysis revealed that the five REDDD residues required for β-1,3-glucanase activity were conserved in 21 of the 36 Qingke TLPs. Phylogenetically, the TLPs in plants are clustered in 10 major groups. Our analysis of gene structure did not detect an intron in 15 Qingke TLPs whereas the other 21 did contain 1-7 introns. A diverse set of cis-acting motifs were found in the promoters of the 36 TLPs, including elements related to light, hormone, and stress responses, growth and development, circadian control, and binding sites of transcription factors, thus suggesting a multifaceted role of TLPs in Qingke. Expression analyses revealed the potential involvement of TLPs in plant defense against biotic and abiotic stresses. Taken together, the findings of this study deepen our understanding of the TLP family genes in Qingke, a staple food item in Tibet, which could strengthen future investigations of protein function in barley and its improved genetic engineering.

TritiKBdb: A Functional Annotation Resource for Deciphering the Complete Interaction Networks in Wheat-Karnal Bunt Pathosystem

The study of molecular interactions, especially the inter-species protein-protein interactions, is crucial for understanding the disease infection mechanism in plants. These interactions play an important role in disease infection and host immune responses against pathogen attack. Among various critical fungal diseases, the incidences of Karnal bunt (Tilletia indica) around the world have hindered the export of the crops such as wheat from infected regions, thus causing substantial economic losses. Due to sparse information on T. indica, limited insight is available with regard to gaining in-depth knowledge of the interaction mechanisms between the host and pathogen proteins during the disease infection process. Here, we report the development of a comprehensive database and webserver, TritiKBdb, that implements various tools to study the protein-protein interactions in the Triticum species-Tilletia indica pathosystem. The novel ‘interactomics’ tool allows the user to visualize/compare the networks of the predicted interactions in an enriched manner. TritiKBdb is a user-friendly database that provides functional annotations such as subcellular localization, available domains, KEGG pathways, and GO terms of the host and pathogen proteins. Additionally, the information about the host and pathogen proteins that serve as transcription factors and effectors, respectively, is also made available. We believe that TritiKBdb will serve as a beneficial resource for the research community, and aid the community in better understanding the infection mechanisms of Karnal bunt and its interactions with wheat. The database is freely available for public use at http://bioinfo.usu.edu/tritikbdb/.

Genome-Wide Identification and Characterization of Receptor-Like Protein Kinase 1 (RPK1) Gene Family in Triticum aestivum Under Drought Stress

Receptor-like protein kinase1 (RPK1) genes play crucial roles in plant growth and development processes, root architecture, and abiotic stress regulation. A comprehensive study of the RPK1 gene family has not been reported in bread wheat (Triticum aestivum). Here, we reported the genome-wide identification, characterization, and expression patterns of the RPK1 gene family in wheat. Results confirmed 15 TaRPK1 genes, classified mainly into three sub-clades based on a phylogenetic tree. The TaRPK1 genes were mapped on chromosomes 1–3 in the respective A, B, and D genomes. Gene structure, motif conservation, collinearity prediction, and synteny analysis were carried out systematically. A Gene ontology study revealed that TaRPK1 genes play a vital role during molecular and biological processes. We also identified 18 putative miRNAs targeting TaRPK1 genes, suggesting their roles in growth, development, and stress responses. Cis-Regulatory elements interpreted the presence of light-related elements, hormone responsiveness, and abiotic stress-related motifs in the promoter regions. The SWISS_MODEL predicted the successful models of TaRPK1 proteins with at least 30% identity to the template, a widely accepted threshold for successful modeling. In silico expression analysis in different tissues and stages suggested that TaRPK1 genes exhibited the highest expression in root tissues. Moreover, qRT-PCR further validated the higher expression of TaRPK1 genes in roots of drought-tolerant varieties compared to the drought-susceptible variety. Collectively, the present study renders valuable information on the functioning of TaRPK1 genes in wheat that will be useful in further functional validation of these genes in future studies.

WeCoNET: a host-pathogen interactome database for deciphering crucial molecular networks of wheat-common bunt cross-talk mechanisms

BACKGROUND

Triticum aestivum is the most important staple food grain of the world. In recent years, the outbreak of a major seed-borne disease, common bunt, in wheat resulted in reduced quality and quantity of the crop. The disease is caused by two fungal pathogens, Tilletia caries and Tilletia laevis, which show high similarity to each other in terms of life cycle, germination, and disease symptoms. The host-pathogen protein-protein interactions play a crucial role in initiating the disease infection mechanism as well as in plant defense responses. Due to the availability of limited information on Tilletia species, the elucidation of infection mechanisms is hampered.

RESULTS

We constructed a database WeCoNET ( http://bioinfo.usu.edu/weconet/ ), providing functional annotations of the pathogen proteins and various tools to exploit host-pathogen interactions and other relevant information. The database implements a host-pathogen interactomics tool to predict protein-protein interactions, followed by network visualization, BLAST search tool, advanced 'keywords-based' search module, etc. Other features in the database include various functional annotations of host and pathogen proteins such as gene ontology terms, functional domains, and subcellular localization. The pathogen proteins that serve as effector and secretory proteins have also been incorporated in the database, along with their respective descriptions. Additionally, the host proteins that serve as transcription factors were predicted, and are available along with the respective transcription factor family and KEGG pathway to which they belong.

CONCLUSION

WeCoNET is a comprehensive, efficient resource to the molecular biologists engaged in understanding the molecular mechanisms behind the common bunt infection in wheat. The data integrated into the database can also be beneficial to the breeders for the development of common bunt-resistant cultivars.

Recent Advances in the Prediction of Subcellular Localization of Proteins and Related Topics

Prediction of subcellular localization of proteins from their amino acid sequences has a long history in bioinformatics and is still actively developing, incorporating the latest advances in machine learning and proteomics. Notably, deep learning-based methods for natural language processing have made great contributions. Here, we review recent advances in the field as well as its related fields, such as subcellular proteomics and the prediction/recognition of subcellular localization from image data.

Single-molecule real-time sequencing of the full-length transcriptome of purple garlic (Allium sativum L. cv. Leduzipi) and identification of serine O-acetyltransferase family proteins involved in cysteine biosynthesis

BACKGROUND

Garlic (Allium sativum L.), whose bioactive components are mainly organosulfur compounds (OSCs), is a herbaceous perennial widely consumed as a green vegetable and a condiment. Yet, the metabolic enzymes involved in the biosynthesis of OSCs are not identified in garlic.

RESULTS

Here, a full-length transcriptome of purple garlic was generated via PacBio and Illumina sequencing, to characterize the garlic transcriptome and identify key proteins mediating the biosynthesis of OSCs. Overall, 22.56 Gb of clean data were generated, resulting in 454 698 circular consensus sequence (CCS) reads, of which 83.4% (379 206) were identified as being full-length non-chimeric reads - their further transcript clustering facilitated identification of 36 571 high-quality consensus reads. Once corrected, their genome-wide mapping revealed that 6140 reads were novel isoforms of known genes, and 2186 reads were novel isoforms from novel genes. We detected 1677 alternative splicing events, finding 2902 genes possessing either two or more poly(A) sites. Given the importance of serine O-acetyltransferase (SERAT) in cysteine biosynthesis, we investigated the five SERAT homologs in garlic. Phylogenetic analysis revealed a three-tier classification of SERAT proteins, each featuring a serine acetyltransferase domain (N-terminal) and one or two hexapeptide transferase motifs. Template-based modeling showed that garlic SERATs shared a common homo-trimeric structure with homologs from bacteria and other plants. The residues responsible for substrate recognition and catalysis were highly conserved, implying a similar reaction mechanism. In profiling the five SERAT genes' transcript levels, their expression pattern varied significantly among different tissues.

CONCLUSION

This study's findings deepen our knowledge of SERAT proteins, and provide timely genetic resources that could advance future exploration into garlic's genetic improvement and breeding. © 2021 Society of Chemical Industry.

A tau class GST, OsGSTU5, interacts with VirE2 and modulates the Agrobacterium-mediated transformation in rice

OsGSTU5 interacts and glutathionylates the VirE2 protein of Agrobacterium and its (OsGSTU5) overexpression and downregulation showed a low and high AMT efficiency in rice, respectively. During Agrobacterium-mediated transformation (AMT), T-DNA along with several virulence proteins such as VirD2, VirE2, VirE3, VirD5, and VirF enter the plant cytoplasm. VirE2 serves as a single-stranded DNA binding (SSB) protein that assists the cytoplasmic trafficking of T-DNA inside the host cell. Though the regulatory roles of VirE2 have been established, the cellular reaction of their host, especially in monocots, has not been characterized in detail. This study identified a cellular interactor of VirE2 from the cDNA library of rice. The identified plant protein encoded by the gene cloned from rice was designated OsGSTU5, it interacted specifically with VirE2 in the host cytoplasm. OsGSTU5 was upregulated during Agrobacterium infection and involved in the post-translational glutathionylation of VirE2 (gVirE2). Interestingly, the in silico analysis showed that the 'gVirE2 + ssDNA' complex was structurally less stable than the 'VirE2 + ssDNA' complex. The gel shift assay also confirmed the attenuated SSB property of gVirE2 over VirE2. Moreover, knock-down and overexpression of OsGSTU5 in rice showed increased and decreased T-DNA expression, respectively after Agrobacterium infection. The present finding establishes the role of OsGSTU5 as an important target for modulation of AMT efficiency in rice.

Deciphering the Host-Pathogen Interactome of the Wheat-Common Bunt System: A Step towards Enhanced Resilience in Next Generation Wheat

Common bunt, caused by two fungal species, Tilletia caries and Tilletia laevis, is one of the most potentially destructive diseases of wheat. Despite the availability of synthetic chemicals against the disease, organic agriculture relies greatly on resistant cultivars. Using two computational approaches—interolog and domain-based methods—a total of approximately 58 M and 56 M probable PPIs were predicted in T. aestivum–T. caries and T. aestivum–T. laevis interactomes, respectively. We also identified 648 and 575 effectors in the interactions from T. caries and T. laevis, respectively. The major host hubs belonged to the serine/threonine protein kinase, hsp70, and mitogen-activated protein kinase families, which are actively involved in plant immune signaling during stress conditions. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of the host proteins revealed significant GO terms (O-methyltransferase activity, regulation of response to stimulus, and plastid envelope) and pathways (NF-kappa B signaling and the MAPK signaling pathway) related to plant defense against pathogens. Subcellular localization suggested that most of the pathogen proteins target the host in the plastid. Furthermore, a comparison between unique T. caries and T. laevis proteins was carried out. We also identified novel host candidates that are resistant to disease. Additionally, the host proteins that serve as transcription factors were also predicted.

Diversity and conservation of plant small secreted proteins associated with arbuscular mycorrhizal symbiosis

Arbuscular mycorrhizal symbiosis (AMS) is widespread mutualistic association between plants and fungi, which plays an essential role in nutrient exchange, enhancement in plant stress resistance, development of host, and ecosystem sustainability. Previous studies have shown that plant small secreted proteins (SSPs) are involved in beneficial symbiotic interactions. However, the role of SSPs in the evolution of AMS has not been well studied yet. In this study, we performed computational analysis of SSPs in 60 plant species and identified three AMS-specific ortholog groups containing SSPs only from at least 30% of the AMS species in this study and three AMS-preferential ortholog groups containing SSPs from both AMS and non-AMS species, with AMS species containing significantly more SSPs than non-AMS species. We found that independent lineages of monocot and eudicot plants contained genes in the AMS-specific ortholog groups and had significant expansion in the AMS-preferential ortholog groups. Also, two AMS-preferential ortholog groups showed convergent changes, between monocot and eudicot species, in gene expression in response to arbuscular mycorrhizal fungus Rhizophagus irregularis. Furthermore, conserved cis-elements were identified in the promoter regions of the genes showing convergent gene expression. We found that the SSPs, and their closely related homologs, in each of three AMS-preferential ortholog groups, had some local variations in the protein structural alignment. We also identified genes co-expressed with the Populus trichocarpa SSP genes in the AMS-preferential ortholog groups. This first plant kingdom-wide analysis on SSP provides insights on plant-AMS convergent evolution with specific SSP gene expression and local diversification of protein structures.

Multilocation proteins in organelle communication: Based on protein-protein interactions

Protein-protein interaction (PPI) plays a crucial role in most biological processes, including signal transduction and cell apoptosis. Importantly, the knowledge of PPIs can be useful for identification of multimeric protein complexes and elucidation of uncharacterized protein functions. Arabidopsis thaliana, the best-characterized dicotyledonous plant, the steadily increasing amount of information on the levels of its proteome and signaling pathways is progressively enabling more researchers to construct models for cellular processes for the plant, which in turn encourages more experimental data to be generated. In this study, we performed an overview analysis of the 10 major organelles and their associated proteins of the dicotyledonous model plant Arabidopsis thaliana via PPI network, and found that PPI may play an important role in organelle communication. Further, multilocation proteins, especially phosphorylation-related multilocation proteins, can function as a "needle and thread" via PPIs and play an important role in organelle communication. Similar results were obtained in a monocotyledonous model crop, rice. Furthermore, we provide a research strategy for multilocation proteins by LOPIT technique, proteomics, and bioinformatics analysis and also describe their potential role in the field of plant science. The results provide a new view that the phosphorylation-related multilocation proteins play an important role in organelle communication and provide new insight into PPIs and novel directions for proteomic research. The research of phosphorylation-related multilocation proteins may promote the development of organelle communication and provide an important theoretical basis for plant responses to external stress.

The SAUR gene family in coffee: genome-wide identification and gene expression analysis during somatic embryogenesis

BACKGROUND

Small auxin-up RNA (SAUR) genes form a wide family supposedly involved in different physiological and developmental processes in plants such as leaf senescence, auxin signaling and transport, hypocotyl development and tolerance to abiotic stresses. The transcription of SAUR genes is quickly induced by auxins, a group of phytohormones of major importance on embryo development. To better understand the distribution and expression profile of such still not explored family in Coffea sp., especially during the development of somatic embryogenesis (SE), SAUR members were characterized in silico using the available Coffea canephora genome data and analyzed for gene expression by RT-qPCR in C. arabica embryogenic samples.

METHODS AND RESULTS

Over C. canephora genome 31 CcSAURs were distributed by 11 chromosomes. Out of these 31 gene members, 5 SAURs were selected for gene expression analysis in C. arabica embryogenic materials. CaSAUR12 and CaSAUR18 were the members highly expressed through almost all plant materials. The other genes had more expression in at least one of the developing embryo stages or plantlets. The CaSAUR12 was the only member to exhibit an increased expression in both non-embryogenic calli and the developing embryo stages.

CONCLUSION

The identification of SAUR family on C. canephora genome followed by the analysis of gene expression profile across coffee somatic embryogenesis process on C. arabica represents a further additional step towards a better comprehension of molecular components acting on SE. Along with new research about this gene family such knowledge may support studies about clonal propagation methods via somatic embryogenesis to help the scientific community towards improvements into coffee crop.

Presence of a Mitovirus Is Associated with Alteration of the Mitochondrial Proteome, as Revealed by Protein–Protein Interaction (PPI) and Co-Expression Network Models in Chenopodium quinoa Plants

Simple Summary

Plants often harbor persistent plant virus infection transmitted only vertically through seeds, resulting in no obvious symptoms (cryptic infections). Several studies have shown that such cryptic infections provide resilience against abiotic (and biotic) stress. We have recently discovered a new group of cryptic plant viruses infecting mitochondria (plant mitovirus). Mitochondria are cellular organelles displaying a pivotal role in protecting cells from the stress of nature . Here, we look at the proteomic alterations caused by the mitovirus cryptic infection of Chenopodium quinoa by Systems Biology approaches allowing one to evaluate data at holistic level. Quinoa is a domesticated plant species with many exciting features of abiotic stress resistance, and it is distinguished by its exceptional nutritional characteristics, such as the content and quality of proteins, minerals, lipids, and tocopherols. These features determined the growing interest for the quinoa crop by the scientific community and international organizations since they provide opportunities to produce high-value grains in arid, high-salt and high-UV agroecological environments. We discovered that quinoa lines hosting mitovirus activate some metabolic processes that might help them face drought. These findings present a new perspective for breeding crop plants through the augmented genome provided by accessory cryptic viruses to be investigated in the future.

Abstract

Plant mitoviruses belong to Mitoviridae family and consist of positive single-stranded RNA genomes replicating exclusively in host mitochondria. We previously reported the biological characterization of a replicating plant mitovirus, designated Chenopodium quinoa mitovirus 1 (CqMV1), in some Chenopodium quinoa accessions. In this study, we analyzed the mitochondrial proteome from leaves of quinoa, infected and not infected by CqMV1. Furthermore, by protein–protein interaction and co-expression network models, we provided a system perspective of how CqMV1 affects mitochondrial functionality. We found that CqMV1 is associated with changes in mitochondrial protein expression in a mild but well-defined way. In quinoa-infected plants, we observed up-regulation of functional modules involved in amino acid catabolism, mitochondrial respiratory chain, proteolysis, folding/stress response and redox homeostasis. In this context, some proteins, including BCE2 (lipoamide acyltransferase component of branched-chain alpha-keto acid dehydrogenase complex), DELTA-OAT (ornithine aminotransferase) and GR-RBP2 (glycine-rich RNA-binding protein 2) were interesting because all up-regulated and network hubs in infected plants; together with other hubs, including CAT (catalase) and APX3 (L-ascorbate peroxidase 3), they play a role in stress response and redox homeostasis. These proteins could be related to the higher tolerance degree to drought we observed in CqMV1-infected plants. Although a specific causative link could not be established by our experimental approach at this stage, the results suggest a new mechanistic hypothesis that demands further in-depth functional studies.

Identification of plant vacuole proteins by exploiting deep representation learning features

Plant vacuoles are the most important organelles for plant growth, development, and defense, and they play an important role in many types of stress responses. An important function of vacuole proteins is the transport of various classes of amino acids, ions, sugars, and other molecules. Accurate identification of vacuole proteins is crucial for revealing their biological functions. Several automatic and rapid computational tools have been proposed for the subcellular localization of proteins. Regrettably, they are not specific for the identification of plant vacuole proteins. To the best of our knowledge, there is only one computational software specifically trained for plant vacuolar proteins. Although its accuracy is acceptable, the prediction performance and stability of this method in practical applications can still be improved. Hence, in this study, a new predictor named iPVP-DRLF was developed to identify plant vacuole proteins specifically and effectively. This prediction software is designed using the light gradient boosting machine (LGBM) algorithm and hybrid features composed of classic sequence features and deep representation learning features. iPVP-DRLF achieved fivefold cross-validation and independent test accuracy values of 88.25 % and 87.16 %, respectively, both outperforming previous state-of-the-art predictors. Moreover, the blind dataset test results also showed that the performance of iPVP-DRLF was significantly better than the existing tools. The results of comparative experiments confirmed that deep representation learning features have an advantage over other classic sequence features in the identification of plant vacuole proteins. We believe that iPVP-DRLF would serve as an effective computational technique for plant vacuole protein prediction and facilitate related future research. The online server is freely accessible at https://lab.malab.cn/~acy/iPVP-DRLF. In addition, the source code and datasets are also accessible at https://github.com/jiaoshihu/iPVP-DRLF.

Deciphering the Crosstalk Mechanisms of Wheat-Stem Rust Pathosystem: Genome-Scale Prediction Unravels Novel Host Targets

Triticum aestivum (wheat), a major staple food grain, is affected by various biotic stresses. Among these, fungal diseases cause about 15-20% of yield loss, worldwide. In this study, we performed a comparative analysis of protein-protein interactions between two Puccinia graminis races (Pgt 21-0 and Pgt Ug99) that cause stem (black) rust in wheat. The available molecular techniques to study the host-pathogen interaction mechanisms are expensive and labor-intensive. We implemented two computational approaches (interolog and domain-based) for the prediction of PPIs and performed various functional analysis to determine the significant differences between the two pathogen races. The analysis revealed that T. aestivum-Pgt 21-0 and T. aestivum-Pgt Ug99 interactomes consisted of ∼90M and ∼56M putative PPIs, respectively. In the predicted PPIs, we identified 115 Pgt 21-0 and 34 Pgt Ug99 potential effectors that were highly involved in pathogen virulence and development. Functional enrichment analysis of the host proteins revealed significant GO terms and KEGG pathways such as O-methyltransferase activity (GO:0008171), regulation of signal transduction (GO:0009966), lignin metabolic process (GO:0009808), plastid envelope (GO:0009526), plant-pathogen interaction pathway (ko04626), and MAPK pathway (ko04016) that are actively involved in plant defense and immune signaling against the biotic stresses. Subcellular localization analysis anticipated the host plastid as a primary target for pathogen attack. The highly connected host hubs in the protein interaction network belonged to protein kinase domain including Ser/Thr protein kinase, MAPK, and cyclin-dependent kinase. We also identified 5,577 transcription factors in the interactions, associated with plant defense during biotic stress conditions. Additionally, novel host targets that are resistant to stem rust disease were also identified. The present study elucidates the functional differences between Pgt 21-0 and Pgt Ug99, thus providing the researchers with strain-specific information for further experimental validation of the interactions, and the development of durable, disease-resistant crop lines.

Predicting the multi-label protein subcellular localization through multi-information fusion and MLSI dimensionality reduction based on MLFE classifier

MOTIVATION

Multi-label (ML) protein subcellular localization (SCL) is an indispensable way to study protein function. It can locate a certain protein (such as the human transmembrane protein that promotes the invasion of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)) or expression product at a specific location in a cell, which can provide a reference for clinical treatment of diseases such as coronavirus disease 2019 (COVID-19).

RESULTS

The article proposes a novel method named ML-locMLFE. First of all, six feature extraction methods are adopted to obtain protein effective information. These methods include pseudo amino acid composition, encoding based on grouped weight, gene ontology, multi-scale continuous and discontinuous, residue probing transformation and evolutionary distance transformation. In the next part, we utilize the ML information latent semantic index method to avoid the interference of redundant information. In the end, ML learning with feature-induced labeling information enrichment is adopted to predict the ML protein SCL. The Gram-positive bacteria dataset is chosen as a training set, while the Gram-negative bacteria dataset, virus dataset, newPlant dataset and SARS-CoV-2 dataset as the test sets. The overall actual accuracy of the first four datasets are 99.23%, 93.82%, 93.24% and 96.72% by the leave-one-out cross validation. It is worth mentioning that the overall actual accuracy prediction result of our predictor on the SARS-CoV-2 dataset is 72.73%. The results indicate that the ML-locMLFE method has obvious advantages in predicting the SCL of ML protein, which provides new ideas for further research on the SCL of ML protein.

AVAILABILITY AND IMPLEMENTATION

The source codes and datasets are publicly available at https://github.com/QUST-AIBBDRC/ML-locMLFE/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Subcellular Localization of Seed-Expressed LEA_4 Proteins Reveals Liquid-Liquid Phase Separation for LEA9 and for LEA48 Homo- and LEA42-LEA48 Heterodimers

LEA proteins are involved in plant stress tolerance. In Arabidopsis, the LEA_4 Pfam group is the biggest group with the majority of its members being expressed in dry seeds. To assess subcellular localization in vivo, we investigated 11 seed-expressed LEA_4 proteins in embryos dissected from dry seeds expressing LEA_4 fusion proteins under its native promoters with the Venus fluorescent protein (proLEA_4::LEA_4:Venus). LEA_4 proteins were shown to be localized in the endoplasmic reticulum, nucleus, mitochondria, and plastids. LEA9, in addition to the nucleus, was also found in cytoplasmic condensates in dry seeds dependent on cellular hydration level. Most investigated LEA_4 proteins were detected in 4-d-old seedlings. In addition, we assessed bioinformatic tools for predicting subcellular localization and promoter motifs of 11 seed-expressed LEA_4 proteins. Ratiometric bimolecular fluorescence complementation assays showed that LEA7, LEA29, and LEA48 form homodimers while heterodimers were formed between LEA7-LEA29 and LEA42-LEA48 in tobacco leaves. Interestingly, LEA48 homodimers and LEA42-LEA48 heterodimers formed droplets structures with liquid-like behavior. These structures, along with LEA9 cytoplasmic condensates, may have been formed through liquid-liquid phase separation. These findings suggest possible important roles of LLPS for LEA protein functions.

Genome-wide identification of ascorbate-glutathione cycle gene families in soybean (Glycine max) reveals gene duplication events and specificity of gene members linked to development and stress conditions

Ascorbate-glutathione (AsA-GSH) cycle plays an important role in tuning beneficial ROS accumulation for intracellular signals and imparts plant tolerance to oxidative stress by detoxifying excess of ROS. Here, we present genome-wide identification of AsA-GSH cycle genes (APX, MDHAR, DHAR, and GR) in several leguminous species and expression analyses in G. max during stress, germination and tissue development. Our data revealed 24 genes in Glycine genus against the maximum of 15 in other leguminous species, which was due to 9 pars of duplicated genes mostly originated from sub/neofunctionalization. Cytosolic APX and MDHAR genes were highly expressed in different tissues and physiological conditions. Germination induced genes encoding AsA-GSH proteins from different cell compartments, whereas vegetative phase (leaves) stimulated predominantly genes related to chloroplast/mitochondria proteins. Moreover, cytosolic APX-1, 2, MDHAR-1a, 1b and GR genes were the primary genes linked to senescence and biotic stresses, while stAPX-a, b and GR (from organelles) were the most abiotic stress related genes. Biotic and abiotic stress tolerant genotypes generally showed increased MDHAR, DHAR and/or GR mRNA levels compared to susceptible genotypes. Overall, these data clarified evolutionary events in leguminous plants and point to the functional specificity of duplicated genes of the AsA-GSH cycle in G. max.

The phosphoproteome of rice leaves responds to water and nitrogen supply

The scarcity of freshwater is an increasing concern in flood-irrigated rice, whilst excessive use of nitrogen fertilizers is costly and contributes to environmental pollution. To co-ordinate growth adaptation under prolonged exposure to limited water or excess nitrogen supply, plants employ complex systems for signalling and regulation of metabolic processes. There is limited information on the involvement of one of the most important post-translational modifications (PTMs), protein phosphorylation, in plant adaptation to long-term changes in resource supply. Oryza sativa cv. Nipponbare was grown under two regimes of nitrogen from the time of germination to final harvest. Twenty-five days after germination, water was withheld from half the pots in each nitrogen treatment and low water supply continued for an additional 26 days, while the remaining pots were well watered. Leaves from all four groups of plants were harvested after 51 days in order to test whether phosphorylation of leaf proteins responded to prior abiotic stress events. The dominant impact of these resources is exerted in leaves, where PTMs have been predicted to occur. Proteins were extracted and phosphopeptides were analysed by nanoLC-MS/MS analysis, coupled with label-free quantitation. Water and nitrogen regimes triggered extensive changes in phosphorylation of proteins involved in membrane transport, such as the aquaporin OsPIP2-6, a water channel protein. Our study reveals phosphorylation of several peptides belonging to proteins involved in RNA-processing and carbohydrate metabolism, suggesting that phosphorylation events regulate the signalling cascades that are required to optimize plant response to resource supply.

Advances and perspectives in discovery and functional analysis of small secreted proteins in plants

Small secreted proteins (SSPs) are less than 250 amino acids in length and are actively transported out of cells through conventional protein secretion pathways or unconventional protein secretion pathways. In plants, SSPs have been found to play important roles in various processes, including plant growth and development, plant response to abiotic and biotic stresses, and beneficial plant-microbe interactions. Over the past 10 years, substantial progress has been made in the identification and functional characterization of SSPs in several plant species relevant to agriculture, bioenergy, and horticulture. Yet, there are potentially a lot of SSPs that have not been discovered in plant genomes, which is largely due to limitations of existing computational algorithms. Recent advances in genomics, transcriptomics, and proteomics research, as well as the development of new computational algorithms based on machine learning, provide unprecedented capabilities for genome-wide discovery of novel SSPs in plants. In this review, we summarize known SSPs and their functions in various plant species. Then we provide an update on the computational and experimental approaches that can be used to discover new SSPs. Finally, we discuss strategies for elucidating the biological functions of SSPs in plants.

Co-expressing Eranthis hyemalis lysophosphatidic acid acyltransferase 2 and elongase improves two very long chain polyunsaturated fatty acid production in Brassica carinata

Docosadienoic acid (DDA, 22:2–13,16) and docosatrienoic acid (DTA, 22:3–13,16,19) are two very long chain polyunsaturated fatty acids (VLCPUFAs) that are recently shown to possess strong anti-inflammatory and antitumor properties. An ELO type elongase (EhELO1) from wild plant Eranthis hyemalis can synthesize the two fatty acids by sequential elongation of linoleic acid and alpha-linolenic acid, respectively. Seed-specific expression of this gene in oilseed crop Brassica carinata produced a considerable amount of DDA and DTA in transgenic seeds. However, these fatty acids were excluded from the sn-2 position of triacylglycerols (TAGs). To improve the production level and nutrition value of the VLCPUFAs in the transgenic oilseed crop, a cytoplasmic lysophosphatidic acid acyltransferase (EhLPAAT2) for the incorporation of the two fatty acids into the sn-2 position of triacylglycerols was identified from E. hyemalis. RT-PCR analysis showed that it was preferentially expressed in developing seeds where EhELO1 was exclusively expressed in E. hyemalis. Seed specific expression of EhLPAAT2 along with EhELO1 in B. carinata resulted in the effective incorporation of DDA and DTA at the sn-2 position of TAGs, thereby increasing the total amount of DDA and DTA in transgenic seeds. To our knowledge, this is the first plant LPAAT that can incorporate VLCPUFAs into TAGs. Improved production of DDA and DTA in the oilseed crop using EhLPAAT2 and EhELO1 provides a real commercial opportunity for high value agriculture products for nutraceutical uses.

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The first plant LPAAT able to acylate VLCPUFAs was identified from winter aconite.

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It could complement the defective phenotype of E. coli LPAAT mutant.

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It could improve the incorporation of two VLCPUFAs into TAGs in oilseeds.

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It could enhance the total production of two VLCPUFAs in oilseeds.

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Seed-specific expression of it could also increase seed oil and seed weight.

Ensemble of Multiple Classifiers for Multilabel Classification of Plant Protein Subcellular Localization

The accurate prediction of protein localization is a critical step in any functional genome annotation process. This paper proposes an improved strategy for protein subcellular localization prediction in plants based on multiple classifiers, to improve prediction results in terms of both accuracy and reliability. The prediction of plant protein subcellular localization is challenging because the underlying problem is not only a multiclass, but also a multilabel problem. Generally, plant proteins can be found in 10–14 locations/compartments. The number of proteins in some compartments (nucleus, cytoplasm, and mitochondria) is generally much greater than that in other compartments (vacuole, peroxisome, Golgi, and cell wall). Therefore, the problem of imbalanced data usually arises. Therefore, we propose an ensemble machine learning method based on average voting among heterogeneous classifiers. We first extracted various types of features suitable for each type of protein localization to form a total of 479 feature spaces. Then, feature selection methods were used to reduce the dimensions of the features into smaller informative feature subsets. This reduced feature subset was then used to train/build three different individual models. In the process of combining the three distinct classifier models, we used an average voting approach to combine the results of these three different classifiers that we constructed to return the final probability prediction. The method could predict subcellular localizations in both single- and multilabel locations, based on the voting probability. Experimental results indicated that the proposed ensemble method could achieve correct classification with an overall accuracy of 84.58% for 11 compartments, on the basis of the testing dataset.

Computational Systems Biology of Alfalfa - Bacterial Blight Host-Pathogen Interactions: Uncovering the Complex Molecular Networks for Developing Durable Disease Resistant Crop

Medicago sativa (also known as alfalfa), a forage legume, is widely cultivated due to its high yield and high-value hay crop production. Infectious diseases are a major threat to the crops, owing to huge economic losses to the agriculture industry, worldwide. The protein-protein interactions (PPIs) between the pathogens and their hosts play a critical role in understanding the molecular basis of pathogenesis. Pseudomonas syringae pv. syringae ALF3 suppresses the plant's innate immune response by secreting type III effector proteins into the host cell, causing bacterial stem blight in alfalfa. The alfalfa-P. syringae system has little information available for PPIs. Thus, to understand the infection mechanism, we elucidated the genome-scale host-pathogen interactions (HPIs) between alfalfa and P. syringae using two computational approaches: interolog-based and domain-based method. A total of ∼14 M putative PPIs were predicted between 50,629 alfalfa proteins and 2,932 P. syringae proteins by combining these approaches. Additionally, ∼0.7 M consensus PPIs were also predicted. The functional analysis revealed that P. syringae proteins are highly involved in nucleotide binding activity (GO:0000166), intracellular organelle (GO:0043229), and translation (GO:0006412) while alfalfa proteins are involved in cellular response to chemical stimulus (GO:0070887), oxidoreductase activity (GO:0016614), and Golgi apparatus (GO:0005794). According to subcellular localization predictions, most of the pathogen proteins targeted host proteins within the cytoplasm and nucleus. In addition, we discovered a slew of new virulence effectors in the predicted HPIs. The current research describes an integrated approach for deciphering genome-scale host-pathogen PPIs between alfalfa and P. syringae, allowing the researchers to better understand the pathogen's infection mechanism and develop pathogen-resistant lines.