Computational identification of protein-protein interactions in rice based on the predicted rice interactome network

Genome wide identification of LcC2DPs gene family in Lotus corniculatus provides insights into regulatory network in response to abiotic stresses

Low temperatures and drought reduce forage yield and quality, with protein kinases crucial for plant stress response. This study examines the LcC2DPs protein kinase family in Lotus corniculatus, identifying 90 members, with some tandemly distributed on chromosomes 2-6, and grouped into 5 subfamilies (I-V). 34 homologous gene pairs were found in Arabidopsis thaliana. LcC2DP genes promoters contain hormone and stress response elements. GO analysis highlights enrichment in hormone response and kinase activity. Transcriptomic analysis links 78 genes to environmental response and stress growth, with 10 validated by qRT-PCR after treatment with 100 μM ABA and IAA, 20% PEG6000, and 4 °C. Protein interaction analysis identifies 5 core proteins (LcC2DP5, 11, 15, 38, and 58) activated by drought and cold stress. Gene analysis revealed that only LcC2DP5 and LcC2DP15 share co-expression transcription factors, with bZIP, bHLH, WRKY, NAC, MYB-related, MYB, C3H, and C2H2 being prominent. These proteins are expressed under drought and cold conditions, highlighting LcC2DP5 and LcC2DP15 activity. NAC and C2H2 are vital for drought response, while bZIP and MYB-related are important for cold response. This suggests that various LcC2DPs in Lotus corniculatus respond to hormones and stress via a TF regulatory network.

Single-Cell Insights Into Cellular Response in Abdominal Aortic Occlusion-Induced Hippocampal Injury

OBJECTIVE

Ischemia-reperfusion of the abdominal aorta often results in damage to distant organs, such as the heart and brain. This cellular heterogeneity within affected tissues complicates the roles of specific cell subsets in abdominal aorta occlusion model (AAO) injury. However, cell type-specific molecular pathology in the hippocampus after ischemia is poorly understood.

AIMS

In this study, we adopted a mouse AAO to investigate the single-cell transcriptome in the hippocampi in AAO mice.

METHODS

Male C57BL/6 mice (8 weeks old) were used to create an AAO model, with animals divided into Sham and I/R groups. The I/R group was subjected to 2 h of ischemia followed by 24 h of reperfusion, after which hippocampal tissues were collected for single-cell RNA sequencing and histological analysis. Behavioral tests, including the Rotarod, Y-maze, and new object recognition tests, were performed daily for 28 days post-surgery to evaluate neurological function. A total of 62,624 cells were corresponding 7 cell types with neuronal, glial, and vascular lineages. We next analyzed cell-specific gene alterations in AAO mice and the function of these cell-specific Genes.

RESULTS

AAO injury upregulated astrocyte and oligodendrocyte precursor cell (OPC) proportions (p-value < 0.05). Astrocytes showed unique gene expression related to neurogenesis and mRNA processing. Five distinct astrocyte subtypes emerged post-injury. OPCs exhibited enhanced synapse organization. Microglia activation and the elevated expression level of the epithelial cell oxidative phosphorylation protein-protein interaction (PPI) module indicate an inflammatory response and metabolic changes in response to AAO injury.

CONCLUSIONS

Our scRNA-seq analysis provides insights into transcriptional changes at the single-cell level in response to AAO-induced hippocampal injury. This study illustrates how the hippocampal region responds to such injury and identifies potential therapeutic targets for intervention, thereby paving the way for future research and treatment strategies.

Homologous mapping yielded a comprehensive predicted protein-protein interaction network for peanut (Arachis hypogaea L.)

BACKGROUND

Protein-protein interactions are the primary means through which proteins carry out their functions. These interactions thus have crucial roles in life activities. The wide availability of fully sequenced animal and plant genomes has facilitated establishment of relatively complete global protein interaction networks for some model species. The genomes of cultivated and wild peanut (Arachis hypogaea L.) have also been sequenced, but the functions of most of the encoded proteins remain unclear.

RESULTS

We here used homologous mapping of validated protein interaction data from model species to generate complete peanut protein interaction networks for A. hypogaea cv. 'Tifrunner' (282,619 pairs), A. hypogaea cv. 'Shitouqi' (256,441 pairs), A. monticola (440,470 pairs), A. duranensis (136,363 pairs), and A. ipaensis (172,813 pairs). A detailed analysis was conducted for a putative disease-resistance subnetwork in the Tifrunner network to identify candidate genes and validate functional interactions. The network suggested that DX2UEH and its interacting partners may participate in peanut resistance to bacterial wilt; this was preliminarily validated with overexpression experiments in peanut.

CONCLUSION

Our results provide valuable new information for future analyses of gene and protein functions and regulatory networks in peanut.

iTRAQ based proteomic analysis of rice lines having single or stacked blast resistance genes: Pi54/Pi54rh during incompatible interaction with Magnaporthe oryzae

Deployment of single or multiple blast resistance (R) genes in rice plant is considered to be the most promising approach to enhance resistance against blast disease caused by fungus Magnaporthe oryzae. At the proteome level, relatively little information about R gene mediated defence mechanisms for single and stacking resistance characteristics is available. The overall objective of this study is to look at the proteomics of rice plants that have R genes; Pi54, Pi54rh and stacked Pi54 + Pi54rh in response to rice blast infection. In this study 'isobaric tag for relative and absolute quantification' (iTRAQ)-based proteomics analysis was performed in rice plants at 72-h post inoculation with Magnaporthe oryzae and various differentially expressed proteins were identified in these three transgenic lines in comparison to wild type during resistance response to blast pathogen. Through STRING analysis, the observed proteins were further examined to anticipate their linked partners, and it was shown that several defense-related proteins were co-expressed. These proteins can be employed as targets in future rice resistance breeding against Magnaporthe oryzae. The current study is the first to report a proteomics investigation of rice lines that express single blast R gene Pi54, Pi54rh and stacked (Pi54 + Pi54rh) during incompatible interaction with Magnaporthe oryzae. The differentially expressed proteins indicated that secondary metabolites, reactive oxygen species-related proteins, phenylpropanoid, phytohormones and pathogenesis-related proteins have a substantial relationship with the defense response against Magnaporthe oryzae.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12298-023-01327-3.

An Improved Genome-Wide Association Procedure Explores Gene-Allele Constitutions and Evolutionary Drives of Growth Period Traits in the Global Soybean Germplasm Population

In soybeans (Glycine max (L.) Merr.), their growth periods, DSF (days of sowing-to-flowering), and DFM (days of flowering-to-maturity) are determined by their required accumulative day-length (ADL) and active temperature (AAT). A sample of 354 soybean varieties from five world eco-regions was tested in four seasons in Nanjing, China. The ADL and AAT of DSF and DFM were calculated from daily day-lengths and temperatures provided by the Nanjing Meteorological Bureau. The improved restricted two-stage multi-locus genome-wide association study using gene-allele sequences as markers (coded GASM-RTM-GWAS) was performed. (i) For DSF and its related ADL_DSF and AAT_DSF, 130-141 genes with 384-406 alleles were explored, and for DFM and its related ADL_DFM and AAT_DFM, 124-135 genes with 362-384 alleles were explored, in a total of six gene-allele systems. DSF shared more ADL and AAT contributions than DFM. (ii) Comparisons between the eco-region gene-allele submatrices indicated that the genetic adaptation from the origin to the geographic sub-regions was characterized by allele emergence (mutation), while genetic expansion from primary maturity group (MG)-sets to early/late MG-sets featured allele exclusion (selection) without allele emergence in addition to inheritance (migration). (iii) Optimal crosses with transgressive segregations in both directions were predicted and recommended for breeding purposes, indicating that allele recombination in soybean is an important evolutionary drive. (iv) Genes of the six traits were mostly trait-specific involved in four categories of 10 groups of biological functions. GASM-RTM-GWAS showed potential in detecting directly causal genes with their alleles, identifying differential trait evolutionary drives, predicting recombination breeding potentials, and revealing population gene networks.

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.

CPIELA: Computational Prediction of Plant Protein–Protein Interactions by Ensemble Learning Approach From Protein Sequences and Evolutionary Information

Identification and characterization of plant protein–protein interactions (PPIs) are critical in elucidating the functions of proteins and molecular mechanisms in a plant cell. Although experimentally validated plant PPIs data have become increasingly available in diverse plant species, the high-throughput techniques are usually expensive and labor-intensive. With the incredibly valuable plant PPIs data accumulating in public databases, it is progressively important to propose computational approaches to facilitate the identification of possible PPIs. In this article, we propose an effective framework for predicting plant PPIs by combining the position-specific scoring matrix (PSSM), local optimal-oriented pattern (LOOP), and ensemble rotation forest (ROF) model. Specifically, the plant protein sequence is firstly transformed into the PSSM, in which the protein evolutionary information is perfectly preserved. Then, the local textural descriptor LOOP is employed to extract texture variation features from PSSM. Finally, the ROF classifier is adopted to infer the potential plant PPIs. The performance of CPIELA is evaluated via cross-validation on three plant PPIs datasets: Arabidopsis thaliana, Zea mays, and Oryza sativa. The experimental results demonstrate that the CPIELA method achieved the high average prediction accuracies of 98.63%, 98.09%, and 94.02%, respectively. To further verify the high performance of CPIELA, we also compared it with the other state-of-the-art methods on three gold standard datasets. The experimental results illustrate that CPIELA is efficient and reliable for predicting plant PPIs. It is anticipated that the CPIELA approach could become a useful tool for facilitating the identification of possible plant PPIs.

Construction and analysis of an interologous protein-protein interaction network of Camellia sinensis leaf (TeaLIPIN) from RNA-Seq data sets

An interologous PPI network of tea leaf is designed by developing reference transcriptome assembly and using experimentally validated PPIs in plants. Key regulatory proteins are proposed and potential TFs are predicted. Worldwide, tea (Camellia sinensis) is the most consumed beverage primarily due to the taste, flavour, and aroma of its newly formed leaves; and has been used as an important ingredient in several traditional medicinal systems because of its antioxidant properties. For this medicinally and commercially important plant, design principles of gene-regulatory and protein-protein interaction (PPI) networks at sub-cellular level are largely un-characterized. In this work, we report a tea leaf interologous PPI network (TeaLIPIN) consisting of 11,208 nodes and 197,820 interactions. A reference transcriptome assembly was first developed from all the 44 samples of 6 publicly available leaf transcriptomes (1,567,288,290 raw reads). By inferring the high-confidence interactions among potential proteins coded by these transcripts using known experimental information about PPIs in 14 plants, an interologous PPI network was constructed and its modular architecture was explored. Comparing this network with 10,000 realizations of two types of corresponding random networks (Erdős-Rényi and Barabási-Albert models) and examining over three network centrality metrics, we predict 2750 bottleneck proteins (having p values < 0.01). 247 of these are deduced to have transcription factor domains by in-house developed HMM models of known plant TFs and these were also mapped to the draft tea genome for searching their probable loci of origin. Co-expression analysis of the TeaLIPIN proteins was also performed and top ranking modules are elaborated. We believe that the proposed novel methodology can easily be adopted to develop and explore the PPI interactomes in other plant species by making use of the available transcriptomic data.

Large Scale Proteomic Data and Network-Based Systems Biology Approaches to Explore the Plant World

The investigation of plant organisms by means of data-derived systems biology approaches based on network modeling is mainly characterized by genomic data, while the potential of proteomics is largely unexplored. This delay is mainly caused by the paucity of plant genomic/proteomic sequences and annotations which are fundamental to perform mass-spectrometry (MS) data interpretation. However, Next Generation Sequencing (NGS) techniques are contributing to filling this gap and an increasing number of studies are focusing on plant proteome profiling and protein-protein interactions (PPIs) identification. Interesting results were obtained by evaluating the topology of PPI networks in the context of organ-associated biological processes as well as plant-pathogen relationships. These examples foreshadow well the benefits that these approaches may provide to plant research. Thus, in addition to providing an overview of the main-omic technologies recently used on plant organisms, we will focus on studies that rely on concepts of module, hub and shortest path, and how they can contribute to the plant discovery processes. In this scenario, we will also consider gene co-expression networks, and some examples of integration with metabolomic data and genome-wide association studies (GWAS) to select candidate genes will be mentioned.

Prediction and functional analysis of the sweet orange protein-protein interaction network

BACKGROUND

Sweet orange (Citrus sinensis) is one of the most important fruits world-wide. Because it is a woody plant with a long growth cycle, genetic studies of sweet orange are lagging behind those of other species.

RESULTS

In this analysis, we employed ortholog identification and domain combination methods to predict the protein-protein interaction (PPI) network for sweet orange. The K-nearest neighbors (KNN) classification method was used to verify and filter the network. The final predicted PPI network, CitrusNet, contained 8,195 proteins with 124,491 interactions. The quality of CitrusNet was evaluated using gene ontology (GO) and Mapman annotations, which confirmed the reliability of the network. In addition, we calculated the expression difference of interacting genes (EDI) in CitrusNet using RNA-seq data from four sweet orange tissues, and also analyzed the EDI distribution and variation in different sub-networks.

CONCLUSIONS

Gene expression in CitrusNet has significant modular features. Target of rapamycin (TOR) protein served as the central node of the hormone-signaling sub-network. All evidence supported the idea that TOR can integrate various hormone signals and affect plant growth. CitrusNet provides valuable resources for the study of biological functions in sweet orange.

Network modeling to understand plant immunity

Deciphering the networks that underpin complex biological processes using experimental data remains a significant, but promising, challenge, a task made all the harder by the added complexity of host-pathogen interactions. The aim of this article is to review the progress in understanding plant immunity made so far by applying network modeling algorithms and to show how this computational/mathematical strategy is facilitating a systems view of plant defense. We review the different types of network modeling that have been used, the data required, and the type of insight that such modeling can provide. We discuss the current challenges in modeling the regulatory networks that underlie plant defense and the future developments that may help address these challenges.

Plant protein interactomes

Protein-protein interactions are a critical element of biological systems, and the analysis of interaction partners can provide valuable hints about unknown functions of a protein. In recent years, several large-scale protein interaction studies have begun to unravel the complex networks through which plant proteins exert their functions. Two major classes of experimental approaches are used for protein interaction mapping: analysis of direct interactions using binary methods such as yeast two-hybrid or split ubiquitin, and analysis of protein complexes through affinity purification followed by mass spectrometry. In addition, bioinformatics predictions can suggest interactions that have evaded detection by other methods or those of proteins that have not been investigated. Here we review the major approaches to construct, analyze, use, and carry out quality control on plant protein interactome networks. We present experimental and computational approaches for large-scale mapping, methods for validation or smaller-scale functional studies, important bioinformatics resources, and findings from recently published large-scale plant interactome network maps.