Subcellular Localization Prediction Research Articles

Structural proteomics guided annotation of vaccine targets and designing of multi-epitopes vaccine to instigate adaptive immune response against Francisella tularensis

Francisella tularensis can cause severe disease in humans via the respiratory or cutaneous routes and a case fatality ratio of up to 10% is reported due to lack of proper antibiotic treatment, while F. novicida causes disease in severely immunocompromised individuals. Efforts are needed to develop effective vaccine candidates against Francisella species. Thus, in this study, a systematic computational work frame was used to deeply investigate the whole proteome of Francisella novicida containing 1,728 proteins to develop vaccine against F. tularensis and related species. Whole-proteome analysis revealed that four proteins including (A0Q492) (A0Q7Y4), (A0Q4N4), and (A0Q5D9) are the suitable vaccine targets after the removal of homologous, paralogous and prediction of subcellular localization. These proteins were used to predict the T cell, B cell, and HTL epitopes which were joined together through suitable linkers to construct a multi-epitopes vaccine (MEVC). The MEVC was found to be highly immunogenic and non-allergenic while the physiochemical properties revealed the feasible expression and purification. Moreover, the molecular interaction of MEVC with TLR2, molecular simulation, and binding free energy analyses further validated the immune potential of the construct. According to Jcat analysis, the refined sequence demonstrates GC contents of 41.48% and a CAI value of 1. The in-silico cloning and optimization process ensured compatibility with host codon usage, thereby facilitating efficient expression. Computational immune simulation studies underscored the capacity of MEVC to induce both primary and secondary immune responses. The conservation analysis further revealed that the selected epitopes exhibit 100% conservation across different species and thus provides wider protection against Francisella.

Genome-Wide Analysis of Polygalacturonase Gene Family Reveals Its Role in Strawberry Softening.

Fruit softening is a prominent attribute governing both longevity on shelves and commercial worth. Polygalacturonase (PG) plays a major role in strawberry fruit softening. However, the PG gene family in strawberry has not been comprehensively analyzed. In this study, 75 FaPG genes were identified in the octoploid strawberry genome, which were classified into three groups according to phylogenetic analysis. Subcellular localization prediction indicated that FaPGs are mostly localized to the plasma membrane, cytoplasm, and chloroplasts. Moreover, the expression of FaPGs during strawberry development and ripening of 'Benihoppe' and its softer mutant was estimated. The results showed that among all 75 FaPGs, most genes exhibited low expression across developmental stages, while two group c members (FxaC_21g15770 and FxaC_20g05360) and one group b member, FxaC_19g05040, displayed relatively higher and gradual increases in their expression trends during strawberry ripening and softening. FxaC_21g15770 was selected for subsequent silencing to validate its role in strawberry softening due to the fact that it exhibited the highest and most changed expression level across different developmental stages in 'Benihoppe' and its mutant. Silencing FxaC_21g15770 could significantly improve strawberry fruit firmness without affecting fruit color, soluble solids, cellulose, and hemicellulose. Conversely, silencing FxaC_21g15770 could significantly suppress the expression of other genes related to pectin degradation such as FaPG-like, FaPL, FaPME, FaCX, FaCel, FaGlu, FaXET, and FaEG. These findings provide basic information on the FaPG gene family for further functional research and indicate that FxaC_21g15770 plays a vital role in strawberry fruit softening.

Genome-wide identification and molecular evolution of Dof transcription factors in Cyperus esculentus

Dof transcription factor family in Cyperus esculentus genome was identified and analyzed using bioinformatics. The analysis results revealed that C.esculentus genome contains 29 Dof genes (CesDof), all of which are located in the nucleus according to subcellular localization prediction. CesDof proteinrs have a range of 124 to 512 amino acids, with most being basic proteins. Their secondary structure was mainly irregular curl. The promoter sequence of CesDof genes contains cis-acting elements that respond to light, drought, hormones, low temperature, and circadian rhythm. Codon preference analysis showed that CesDof genes' codon preference ends in T/A. Collinearity analysis revealed that C.esculentus had three pairs of collinear CesDof genes. Additionally, there were 15 pairs of collinear genes between C.esculentus and Arabidopsis thaliana. The genetic relationship between C.esculentus and Rhynchospora pubera was found to be the closest. Phylogenetic tree analysis revealed that 29 CesDof genes of C.esculentus can be classified into 4 subgroups. Additionally, 144 miRNAs were predicted to target these CesDof genes. Furthermore, protein interaction analysis indicated that 15 Dof proteins in C.esculentus had interactions. The qRT-PCR verification results of drought stress and salt stress treatment experiments showed that most CesDof genes were involved in drought stress and salt stress responses, and the gene expression trends under drought stress and salt stress conditions were consistent. These results lay a theoretical foundation for further studying the molecular functions of Dof gene family in C.esculentus and its molecular mechanisms in regulating the life activities of C.esculentus.

Compilation of resources on subcellular localization of lncRNA

Long non-coding RNAs (lncRNAs) play a vital role in biological processes, and their dysfunctions lead to a wide range of diseases. Due to advancements in sequencing technology, more than 20,000 lncRNA transcripts have been identified in humans, almost equivalent to coding transcripts. One crucial aspect in annotating lncRNA function is predicting their subcellular localization, which often determines their functional roles within cells. This review aims to cover the experimental techniques, databases, and in silico tools developed for identifying subcellular localization. Firstly, we discuss the experimental methods employed to determine the subcellular localization of lncRNAs. These techniques provide valuable insights into the precise cellular compartments where lncRNAs reside. Secondly, we explore the available computational resources and databases contributing to our understanding of lncRNAs, including information on their subcellular localization. These computational methods utilize algorithms and machine learning approaches to predict lncRNA subcellular locations using sequence and structural features. Lastly, we discuss the limitations of existing methodologies, future challenges, and potential applications of subcellular localization prediction for lncRNAs. We highlight the need for further advancements in computational methods and experimental validation to enhance the accuracy and reliability of subcellular localization predictions. To support the scientific community, we have developed a platform called LncInfo, which offers comprehensive information on lncRNAs, including their subcellular localization. This platform aims to consolidate and provide accessible resources to researchers studying lncRNAs and their functional roles (http://webs.iiitd.edu.in/raghava/lncinfo).

SCLpred-ECL: Subcellular Localization Prediction by Deep N-to-1 Convolutional Neural Networks.

The subcellular location of a protein provides valuable insights to bioinformaticians in terms of drug designs and discovery, genomics, and various other aspects of medical research. Experimental methods for protein subcellular localization determination are time-consuming and expensive, whereas computational methods, if accurate, would represent a much more efficient alternative. This article introduces an ab initio protein subcellular localization predictor based on an ensemble of Deep N-to-1 Convolutional Neural Networks. Our predictor is trained and tested on strict redundancy-reduced datasets and achieves 63% accuracy for the diverse number of classes. This predictor is a step towards bridging the gap between a protein sequence and the protein's function. It can potentially provide information about protein-protein interaction to facilitate drug design and processes like vaccine production that are essential to disease prevention.

The apple MdGA2ox7 modulates the balance between growth and stress tolerance in an anthocyanin-dependent manner

Apple (Malus domestica Borkh.) is a widely cultivated fruit crop worldwide but often suffers from abiotic stresses such as salt and cold. Gibberellic acid (GA) plays a pivotal in controlling plant development, environmental adaptability, and secondary metabolism. The GA2-oxidase (GA2ox) is responsible for the deactivation of bioactive GA. In this study, seventeen GA2-oxidase genes were identified in the apple genome, and these members could be clustered into four clades based on phylogenetic relationships and conserved domain structures. MdGA2ox7 exhibited robust expression across various tissues, responded to cold and salt treatments, and was triggered in apple fruit peels via light-induced anthocyanin accumulation. Subcellular localization prediction and experiments confirmed that MdGA2ox7 was located in the cytoplasm. Overexpression of MdGA2ox7 in Arabidopsis caused a lower level of active GA and led to GA-deficient phenotypes, such as dwarfism and delayed flowering. MdGA2ox7 alleviated cold and salt stress damage in both Arabidopsis and apple in concert with melatonin (MT). Additionally, MdGA2ox7 enhanced anthocyanin biosynthesis in apple calli and activated genes involved in anthocyanin synthesis. These findings provide new insights into the functions of apple GA2ox in regulating development, stress tolerance, and secondary metabolism.

De Novo Multi-Omics Pathway Analysis Designed for Prior Data Independent Inference of Cell Signaling Pathways

New tools for cell signaling pathway inference from multi-omics data that are independent of previous knowledge are needed. Here, we propose a new de novo method, the de novo multi-omics pathway analysis (DMPA), to model and combine omics data into network modules and pathways. DMPA was validated with published omics data and was found accurate in discovering reported molecular associations in transcriptome, interactome, phosphoproteome, methylome, and metabolomics data, and signaling pathways in multi-omics data. DMPA was benchmarked against module discovery and multi-omics integration methods and outperformed previous methods in module and pathway discovery especially when applied to datasets of relatively low sample sizes. Transcription factor, kinase, subcellular location, and function prediction algorithms were devised for transcriptome, phosphoproteome, and interactome modules and pathways, respectively. To apply DMPA in a biologically relevant context, interactome, phosphoproteome, transcriptome, and proteome data were collected from analyses carried out using melanoma cells to address gamma-secretase cleavage-dependent signaling characteristics of the receptor tyrosine kinase TYRO3. The pathways modeled with DMPA reflected the predicted function and its direction in validation experiments.

MRNA-CLA: An interpretable deep learning approach for predicting mRNA subcellular localization

Messenger RNA (mRNA) is vital for post-transcriptional gene regulation, acting as the direct template for protein synthesis. However, the methods available for predicting mRNA subcellular localization need to be improved and enhanced. Notably, few existing algorithms can annotate mRNA sequences with multiple localizations. In this work, we propose the mRNA-CLA, an innovative multi-label subcellular localization prediction framework for mRNA, leveraging a deep learning approach with a multi-head self-attention mechanism. The framework employs a multi-scale convolutional layer to extract sequence features across different regions and uses a self-attention mechanism explicitly designed for each sequence. Paired with Position Weight Matrices (PWMs) derived from the convolutional neural network layers, our model offers interpretability in the analysis. In particular, we perform a base-level analysis of mRNA sequences from diverse subcellular localizations to determine the nucleotide specificity corresponding to each site. Our evaluations demonstrate that the mRNA-CLA model substantially outperforms existing methods and tools.

GP-HTNLoc: A graph prototype head-tail network-based model for multi-label subcellular localization prediction of ncRNAs

Numerous research results demonstrated that understanding the subcellular localization of non-coding RNAs (ncRNAs) is pivotal in elucidating their roles and regulatory mechanisms in cells. Despite the existence of over ten computational models dedicated to predicting the subcellular localization of ncRNAs, a majority of these models are designed solely for single-label prediction. In reality, ncRNAs often exhibit localization across multiple subcellular compartments. Furthermore, the existing multi-label localization prediction models are insufficient in addressing the challenges posed by the scarcity of training samples and class imbalance in ncRNA dataset. To address these limitations, this study proposes a novel multi-label localization prediction model for ncRNAs, named GP-HTNLoc. To mitigate class imbalance, GP-HTNLoc adopts separate training approaches for head and tail location labels. Additionally, GP-HTNLoc introduces a pioneering graph prototype module to enhance its performance in small-sample, multi-label scenarios. The experimental results based on 10-fold cross-validation on benchmark datasets demonstrate that GP-HTNLoc achieves competitive predictive performance. The average results from 10 rounds of testing on an independent dataset show that GP-HTNLoc outperforms the best existing models on the human lncRNA, human snoRNA, and human miRNA subsets, with average precision improvements of 31.5%, 14.2%, and 5.6%, respectively, reaching 0.685, 0.632, and 0.704. A user-friendly online GP-HTNLoc server is accessible at https://56s8y85390.goho.co.

Identification of HSP70 gene family members in Fritillaria cirrhosa and expression analysis in different tissues under high temperature stress

The heat shock protein 70 family contains the stress proteins ubiquitous in plants. These proteins are involved in the responses to different abiotic stress conditions and have highly conserved gene sequences. However, little is known about the molecular mechanisms of Fritillaria cirrhosa in response to high-temperature stress. Here, 26 HSP70s, FcHSP70-1 to FcHSP70-26, were identified from the transcriptome data of root, bulb, stem, leaf, and fruit samples of F. cirrhosa. The proteins encoded by FcHSP70s had the lengths ranging from 560 aa to 944 aa, with the molecular weight of 61.64-100.01 kDa and the theoretical isoelectric point between 5.00 and 6.59. The secondary structural elements of HSP70s were mainly random coils and α-helixes. Subcellular localization prediction revealed that FcHSP70s were distributed in mitochondria, chloroplasts, nuclei, endoplasmic reticulum, and cytoplasm. The phylogenetic tree showed that 7 members of the HSP70 family belonged to the Dnak subfamily and 19 members belonged to the HSP110/SSE subfamily. In addition, the qRT-PCR results showed that the expression of FcHSP70-5, FcHSP70-8, FcHSP70-17, FcHSP70-18, and FcHSP70-23 in F. cirrhosa was significantly up-regulated at 35 ℃, which indicated that these genes might play a role in the response to high temperature stress. In addition, compared with other tissues, stems and leaves were sensitive to high temperature stress, with the expression of 18 genes up-regulated by 18.18 and 8.03 folds on average, respectively. These findings provide valuable information about the molecular mechanism of HSP70s of F. cirrhosa in response to high temperature stress.

Identification and expression profiles of Tubby‑like proteins coding genes in walnut (Juglans regia L.) in response to stress and hormone treatments

Tubby-like proteins (TLPs) constitute a diverse multigene family found across a broad range of eukaryotic organisms, from single-celled to multicellular eukaryotes. Despite their importance, the existence of TLPs in walnuts (Juglans regia L.) has not been reported until now. In this study, we identified 12 members of the JrTLP gene family in the walnut genome, revealing a pattern of gene replication events. Notably, all JrTLPs exhibited a split gene structure, containing two to eight introns, and these genes could be divided into five distinct categories based on their evolutionary relationships. The majority of TLP proteins possess two signature domains: the F-box and Tubby domains. Subcellular localization prediction and experiments confirmed that JrTLP2 was located in the nucleus and membrane. Expression profiling revealed that multiple JrTLPs respond to a range of stimuli, including pathogen infection, hormonal treatment (such as auxin, ABA, MeJA, and GA), and abiotic stress conditions such as cold, heat, and salt. This study represents the first comprehensive identification of the walnut TLP family, providing valuable insights into the function and regulation of TLP in walnut and laying the foundation for future functional dissection, particularly in the context of stress tolerance.

Genome-wide identification of toll-like receptors in Octopus sinensis and expression analysis in response to different PAMPs stimulation

Toll-like receptors (TLRs) are one of the extensively studied pattern recognition receptors (PRRs) and play crucial roles in the immune responses of vertebrates and invertebrates. In this study, 14 TLR genes were identified from the genome-wide data of Octopus sinensis. Protein structural domain analysis showed that most TLR proteins had three main structural domains: extracellular leucine-rich repeats (LRR), transmembrane structural domains, and intracellular Toll/IL-1 receptor domain (TIR). The results of subcellular localization prediction showed that the TLRs of O. sinensis were mainly located on the plasma membrane. The results of quantitative real-time PCR (qPCR) showed that the detected TLR genes were differentially expressed in the hemolymph, white bodies, hepatopancreas, gills, gill heart, intestine, kidney, and salivary gland of O. sinensis. Furthermore, the present study investigated the expression changes of O. sinensis TLR genes in hemolymph, white bodies, gills, and hepatopancreas in different phases (6 h, 12 h, 24 h, 48 h) after stimulation with PGN, poly(I: C) and Vibrio parahaemolyticus. The expression of most of the TLR genes was upregulated at different time points after infection with pathogens or stimulation with PAMPs, a few genes were unchanged or even down-regulated, and many of the TLR genes were much higher after V. parahaemolyticus infection than after PGN and poly(I:C) stimulation. The results of this study contribute to a better understanding of the molecular immune mechanisms of O. sinensis TLRs genes in resistance to pathogen stimulation.

In silico analysis of the wheat BBX gene family and identification of candidate genes for seed dormancy and germination

BackgroundB-box (BBX) proteins are a type of zinc finger proteins containing one or two B-box domains. They play important roles in development and diverse stress responses of plants, yet their roles in wheat remain unclear.ResultsIn this study, 96 BBX genes were identified in the wheat genome and classified into five subfamilies. Subcellular localization prediction results showed that 68 TaBBXs were localized in the nucleus. Protein interaction prediction analysis indicated that interaction was one way that these proteins exerted their functions. Promoter analysis indicated that TaBBXs may play important roles in light signal, hormone, and stress responses. qRT-PCR analysis revealed that 14 TaBBXs were highly expressed in seeds compared with other tissues. These were probably involved in seed dormancy and germination, and their expression patterns were investigated during dormancy acquisition and release in the seeds of wheat varieties Jing 411 and Hongmangchun 21, showing significant differences in seed dormancy and germination phenotypes. Subcellular localization analysis confirmed that the three candidates TaBBX2-2 A, TaBBX4-2 A, and TaBBX11-2D were nuclear proteins. Transcriptional self-activation experiments further demonstrated that TaBBX4-2A was transcriptionally active, but TaBBX2-2A and TaBBX11-2D were not. Protein interaction analysis revealed that TaBBX2-2A, TaBBX4-2A, and TaBBX11-2D had no interaction with each other, while TaBBX2-2A and TaBBX11-2D interacted with each other, indicating that TaBBX4-2A may regulate seed dormancy and germination by transcriptional regulation, and TaBBX2-2A and TaBBX11-2D may regulate seed dormancy and germination by forming a homologous complex.ConclusionsIn this study, the wheat BBX gene family was identified and characterized at the genomic level by bioinformatics analysis. These observations provide a theoretical basis for future studies on the functions of BBXs in wheat and other species.

Genome-wide identification of polyamine metabolism and ethylene synthesis genes in Chenopodium quinoa Willd. and their responses to low-temperature stress

BackgroundQuinoa (Chenopodium quinoa Willd.) is valued for its nutritional richness. However, pre-harvest sprouting poses a significant threat to yield and grain quality. This study aims to enhance our understanding of pre-harvest sprouting mitigation strategies, specifically through delayed sowing and avoiding rainy seasons during quinoa maturation. The overarching goal is to identify cold-resistant varieties and unravel the molecular mechanisms behind the low-temperature response of quinoa. We employed bioinformatics and genomics tools for a comprehensive genome-wide analysis of polyamines (PAs) and ethylene synthesis gene families in quinoa under low-temperature stress.ResultsThis involved the identification of 37 PA biosynthesis and 30 PA catabolism genes, alongside 227 ethylene synthesis. Structural and phylogenetic analyses showcased conserved patterns, and subcellular localization predictions indicated diverse cellular distributions. The results indicate that the PA metabolism of quinoa is closely linked to ethylene synthesis, with multiple genes showing an upregulation in response to cold stress. However, differential expression within gene families suggests a nuanced regulatory network.ConclusionsOverall, this study contributes valuable insights for the functional characterization of the PA metabolism and ethylene synthesis of quinoa, which emphasize their roles in plant low-temperature tolerance and providing a foundation for future research in this domain.

A validation strategy to assess the role of phase separation as a determinant of macromolecular localization

Liquid-liquid phase separation (LLPS) of putative assembly scaffolds has been proposed to drive the biogenesis of membraneless compartments. LLPS scaffolds are usually identified through invitro LLPS assays with single macromolecules (homotypic), but the predictive value of these assays remains poorly characterized. Here, we apply a strategy to evaluate the robustness of homotypic LLPS assays. When applied to the chromosomal passenger complex (CPC), which undergoes LLPS invitro and localizes to centromeres to promote chromosome biorientation, LLPS propensity invitro emerged as an unreliable predictor of subcellular localization. Invitro CPC LLPS in aqueous buffers was enhanced by commonly used crowding agents. Conversely, diluted cytomimetic media dissolved condensates of the CPC and of several other proteins. We also show that centromeres do not seem to nucleate LLPS, nor do they promote local, spatially restrained LLPS of the CPC. Our strategy can be adapted to purported LLPS scaffolds of other membraneless compartments.

Vislocas: Vision transformers for identifying protein subcellular mis-localization signatures of different cancer subtypes from immunohistochemistry images

Proteins must be sorted to specific subcellular compartments to perform their functions. Abnormal protein subcellular localizations are related to many diseases. Although many efforts have been made in predicting protein subcellular localization from various static information, including sequences, structures and interactions, such static information cannot predict protein mis-localization events in diseases. On the contrary, the IHC (immunohistochemistry) images, which have been widely applied in clinical diagnosis, contains information that can be used to find protein mis-localization events in disease states. In this study, we create the Vislocas method, which is capable of finding mis-localized proteins from IHC images as markers of cancer subtypes. By combining CNNs and vision transformer encoders, Vislocas can automatically extract image features at both global and local level. Vislocas can be trained with full-sized IHC images from scratch. It is the first attempt to create an end-to-end IHC image-based protein subcellular location predictor. Vislocas achieved comparable or better performances than state-of-the-art methods. We applied Vislocas to find significant protein mis-localization events in different subtypes of glioma, melanoma and skin cancer. The mis-localized proteins, which were found purely from IHC images by Vislocas, are in consistency with clinical or experimental results in literatures. All codes of Vislocas have been deposited in a Github repository (https://github.com/JingwenWen99/Vislocas). All datasets of Vislocas have been deposited in Zenodo (https://zenodo.org/records/10632698).

Identification and Expression Pattern Analysis of the SOS Gene Family in Tomatoes

SOSs are key genes in the SOS (salt overly sensitive) signaling pathway, which plays an important role in maintaining ion homeostasis in plants under salt stress. Our aim was to clarify the biological function of the SOS gene family in tomato plants. We identified 14 SpeSOS genes, 10 SpiSOS genes, 11 SpmSOS genes, 9 SlmSOS genes, and 11 SlySOS genes from the genomes of “LA0716” (Spe), “LA2093” (Spi), “LA1589” (Spm), “M82” (Slm), and “Heinz 1706” (Sly) separately. The SOS protein family in tomatoes was divided into five subgroups (SOS1, SOS2, SOS3, SOS4, and SOS5) through phylogenetic analysis. The SOS proteins of the same subgroup in tomatoes contained similar conserved domains and motif structures. A subcellular localization prediction showed that the SOS1, SOS3, and SOS5 proteins in tomatoes were located on the cell membrane, while the SOS2 and SOS4 proteins in tomatoes were located on the cytoplasm and chloroplast, respectively. SlSOS1 contained the most exons and introns (23 and 22, respectively), while SlSOS5 contained only one exon. Via the analysis of the cis-elements in the promoters of those SOS genes in tomatoes, several hormone-, light-, and abiotic stress-related cis-elements were found. In addition, qRT-PCR revealed that the SpeSOS, SpiSOS, and SlySOS genes were induced by salt stress with similar expression patterns. Additionally, the expressions of SOS1-1, SOS1-2, SOS2-2, SOS3-3, SOS4-1, and SOS5-2 were higher in salt-tolerant tomatoes compared with salt-sensitive tomatoes under salt stress. In the salt-sensitive “LA1698” tomato and salt-tolerant “LA0516” tomato, most SOS genes had the highest expression in the roots. The expressions of SOS1-1, SOS1-2, SOS2-1, SOS2-2, SOS3-2, SOS3-3, and SOS5-1 in the leaves of salt-tolerant tomatoes were significantly higher than those in salt-sensitive tomatoes. Thereby, the SOS genes in tomatoes were induced by salt stress, indicating that they participated in the regulation mechanism of tomato salt tolerance. This study laid the foundation for further study on the function of the SOS gene family and revealed the molecular mechanism of tomato salt resistance.

DeepLoc 2.1: multi-label membrane protein type prediction using protein language models.

DeepLoc 2.0 is a popular web server for the prediction of protein subcellular localization and sorting signals. Here, we introduce DeepLoc 2.1, which additionally classifies the input proteins into the membrane protein types Transmembrane, Peripheral, Lipid-anchoredand Soluble. Leveraging pre-trained transformer-based protein language models, the server utilizes a three-stage architecture for sequence-based, multi-label predictions. Comparative evaluations with other established tools on a test set of 4933 eukaryotic protein sequences, constructed following stringent homology partitioning, demonstrate state-of-the-art performance. Notably, DeepLoc 2.1 outperforms existing models, with the larger ProtT5 model exhibiting a marginal advantage over the ESM-1B model. The web server is available at https://services.healthtech.dtu.dk/services/DeepLoc-2.1.

Genome-Wide Identification and Expression Analysis of YTH Gene Family for Abiotic Stress Regulation in Camellia chekiangoleosa.

N6-methyladenosine (m6A) is essential for RNA metabolism in cells. The YTH domain, conserved in the kingdom of Eukaryotes, acts as an m6A reader that binds m6A-containing RNA. In plants, the YTH domain is involved in plant hormone signaling, stress response regulation, RNA stability, translation, and differentiation. However, little is known about the YTH genes in tea-oil tree, which can produce edible oil with high nutritional value. This study aims to identify and characterize the YTH domains within the tea-oil tree (Camellia chekiangoleosa Hu) genome to predict their potential role in development and stress regulation. In this study, 10 members of the YTH family containing the YTH domain named CchYTH1-10 were identified from C. chekiangoleosa. Through analysis of their physical and chemical properties and prediction of subcellular localization, it is known that most family members are located in the nucleus and may have liquid-liquid phase separation. Analysis of cis-acting elements in the CchYTH promoter region revealed that these genes could be closely related to abiotic stress and hormones. The results of expression profiling show that the CchYTH genes were differentially expressed in different tissues, and their expression levels change under drought stress. Overall, these findings could provide a foundation for future research regarding CchYTHs in C. chekiangoleosa and enrich the world in terms of epigenetic mark m6A in forest trees.

In silico approaches for the identification of potential allergens among hypothetical proteins from Alternaria alternata and its functional annotation

Direct exposure to the fungal species Alternaria alternata is a major risk factor for the development of asthma, allergic rhinitis, and inflammation. As of November 23rd 2020, the NCBI protein database showed 11,227 proteins from A. alternata genome as hypothetical proteins (HPs). Allergens are the main causative of several life-threatening diseases, especially in fungal infections. Therefore, the main aim of the study is to identify the potentially allergenic inducible proteins from the HPs in A. alternata and their associated functional assignment for the complete understanding of the complex biological systems at the molecular level. AlgPred and Structural Database of Allergenic Proteins (SDAP) were used for the prediction of potential allergens from the HPs of A. alternata. While analyzing the proteome data, 29 potential allergens were predicted by AlgPred and further screening in SDAP confirmed the allergic response of 10 proteins. Extensive bioinformatics tools including protein family classification, sequence-function relationship, protein motif discovery, pathway interactions, and intrinsic features from the amino acid sequence were used to successfully predict the probable functions of the 10 HPs. The functions of the HPs are characterized as chitin-binding, ribosomal protein P1, thaumatin, glycosyl hydrolase, and NOB1 proteins. The subcellular localization and signal peptide prediction of these 10 proteins has further provided additional information on localization and function. The allergens prediction and functional annotation of the 10 proteins may facilitate a better understanding of the allergenic mechanism of A. alternata in asthma and other diseases. The functional domain level insights and predicted structural features of the allergenic proteins help to understand the pathogenesis and host immune tolerance. The outcomes of the study would aid in the development of specific drugs to combat A. alternata infections.