EIEPCF: accurate inference of functional gene regulatory networks by eliminating indirect effects from confounding factors.

Reconstructing functional gene regulatory networks (GRNs) is a primary prerequisite for understanding pathogenic mechanisms and curing diseases in animals, and it also provides an important foundation for cultivating vegetable and fruit varieties that are resistant to diseases and corrosion in plants. Many computational methods have been developed to infer GRNs, but most of the regulatory relationships between genes obtained by these methods are biased. Eliminating indirect effects in GRNs remains a significant challenge for researchers. In this work, we propose a novel approach for inferring functional GRNs, named EIEPCF (eliminating indirect effects produced by confounding factors), which eliminates indirect effects caused by confounding factors. This method eliminates the influence of confounding factors on regulatory factors and target genes by measuring the similarity between their residuals. The validation results of the EIEPCF method on simulation studies, the gold-standard networks provided by the DREAM3 Challenge and the real gene networks of Escherichia coli demonstrate that it achieves significantly higher accuracy compared to other popular computational methods for inferring GRNs. As a case study, we utilized the EIEPCF method to reconstruct the cold-resistant specific GRN from gene expression data of cold-resistant in Arabidopsis thaliana. The source code and data are available at https://github.com/zhanglab-wbgcas/EIEPCF.

Comparative network analysis reveals the dynamics of organic acid diversity during fruit ripening in peach (Prunus persica L. Batsch).

BACKGROUND: Organic acids are important components that determine the fruit flavor of peach (Prunus persica L. Batsch). However, the dynamics of organic acid diversity during fruit ripening and the key genes that modulate the organic acids metabolism remain largely unknown in this kind of fruit tree which yield ranks sixth in the world. RESULTS: In this study, we used 3D transcriptome data containing three dimensions of information, namely time, phenotype and gene expression, from 5 different varieties of peach to construct gene co-expression networks throughout fruit ripening of peach. With the network inferred, the time-ordered network comparative analysis was performed to select high-acid specific gene co-expression network and then clarify the regulatory factors controlling organic acid accumulation. As a result, network modules related to organic acid synthesis and metabolism under high-acid and low-acid comparison conditions were identified for our following research. In addition, we obtained 20 candidate genes as regulatory factors related to organic acid metabolism in peach. CONCLUSIONS: The study provides new insights into the dynamics of organic acid accumulation during fruit ripening, complements the results of classical co-expression network analysis and establishes a foundation for key genes discovery from time-series multiple species transcriptome data.

PiTLiD: Identification of Plant Disease From Leaf Images Based on Convolutional Neural Network.

With the development of plant phenomics, the identification of plant diseases from leaf images has become an effective and economic approach in plant disease science. Among the methods of plant diseases identification, the convolutional neural network (CNN) is the most popular one for its superior performance. However, CNN's representation power is still a challenge in dealing with small datasets, which greatly affects its popularization. In this work, we propose a new method, namely PiTLiD, based on pretrained Inception-V3 convolutional neural network and transfer learning to identify plant leaf diseases from phenotype data of plant leaf with small sample size. To evaluate the robustness of the proposed method, the experiments on several datasets with small-scale samples were implemented. The results show that PiTLiD performs better than compared methods. This study provides a plant disease identification tool based on a deep learning algorithm for plant phenomics. All the source data and code are accessible at https://github.com/zhanglab-wbgcas/PiTLiD.

Genome-wide identification and expression analysis of peach multiple organellar RNA editing factors reveals the roles of RNA editing in plant immunity.

BACKGROUND: Multiple organellar RNA editing factor (MORF) genes play key roles in chloroplast developmental processes by mediating RNA editing of Cytosine-to-Uracil conversion. However, the function of MORF genes in peach (Prunus persica), a perennial horticultural crop species of Rosaceae, is still not well known, particularly the resistance to biotic and abiotic stresses that threaten peach yield seriously. RESULTS: In this study, to reveal the regulatory roles of RNA editing in plant immunity, we implemented genome-wide analysis of peach MORF (PpMORF) genes in response to biotic and abiotic stresses. The chromosomal and subcellular location analysis showed that the identified seven PpMORF genes distributed on three peach chromosomes were mainly localized in the mitochondria and chloroplast. All the PpMORF genes were classified into six groups and one pair of PpMORF genes was tandemly duplicated. Based on the meta-analysis of two types of public RNA-seq data under different treatments (biotic and abiotic stresses), we observed down-regulated expression of PpMORF genes and reduced chloroplast RNA editing, especially the different response of PpMORF2 and PpMORF9 to pathogens infection between resistant and susceptible peach varieties, indicating the roles of MORF genes in stress response by modulating the RNA editing extent in plant immunity. Three upstream transcription factors (MYB3R-1, ZAT10, HSFB3) were identified under both stresses, they may regulate resistance adaption by modulating the PpMORF gene expression. CONCLUSION: These results provided the foundation for further analyses of the functions of MORF genes, in particular the roles of RNA editing in plant immunity. In addition, our findings will be conducive to clarifying the resistance mechanisms in peaches and open up avenues for breeding new cultivars with high resistance.

Dynamic Network Biomarker Analysis Reveals the Critical Phase Transition of Fruit Ripening in Grapevine.

Grapevine (Vitisvinifera L.) fruit ripening is a complex biological process involving a phase transition from immature to mature. Understanding the molecular mechanism of fruit ripening is critical for grapevine fruit storage and quality improvement. However, the regulatory mechanism for the critical phase transition of fruit ripening from immature to mature in grapevine remains poorly understood. In this work, to identify the key molecular events controlling the critical phase transition of grapevine fruit ripening, we performed an integrated dynamic network analysis on time-series transcriptomic data of grapevine berry development and ripening. As a result, we identified the third time point as a critical transition point in grapevine fruit ripening, which is consistent with the onset of veraison reported in previous studies. In addition, we detected 68 genes as being key regulators involved in controlling fruit ripening. The GO (Gene Ontology) analysis showed that some of these genes participate in fruit development and seed development. This study provided dynamic network biomarkers for marking the initial transcriptional events that characterizes the transition process of fruit ripening, as well as new insights into fruit development and ripening.

Diversity and Functional Evolution of Terpene Synthases in Rosaceae.

Terpenes are organic compounds and play important roles in plant development and stress response. Terpene synthases (TPSs) are the key enzymes for the biosynthesis of terpenes. For Rosaceae species, terpene composition represents a critical quality attribute, but limited information is available regarding the evolution and expansion occurring in the terpene synthases gene family. Here, we selected eight Rosaceae species with sequenced and annotated genomes for the identification of TPSs, including three Prunoideae, three Maloideae, and two Rosoideae species. Our data showed that the TPS gene family in the Rosaceae species displayed a diversity of family numbers and functions among different subfamilies. Lineage and species-specific expansion of the TPSs accompanied by frequent domain loss was widely observed within different TPS clades, which might have contributed to speciation or environmental adaptation in Rosaceae. In contrast to Maloideae and Rosoideae species, Prunoideae species owned less TPSs, with the evolution of Prunoideae species, TPSs were expanded in modern peach. Both tandem and segmental duplication significantly contributed to TPSs expansion. Ka/Ks calculations revealed that TPSs genes mainly evolved under purifying selection except for several pairs, where the divergent time indicated TPS-e clade was diverged relatively anciently. Gene function classification of TPSs further demonstrated the function diversity among clades and species. Moreover, based on already published RNA-Seq data from NCBI, the expression of most TPSs in Malus domestica, Prunus persica, and Fragaria vesca displayed tissue specificity and distinct expression patterns either in tissues or expression abundance between species and TPS clades. Certain putative TPS-like proteins lacking both domains were detected to be highly expressed, indicating the underlying functional or regulatory potentials. The result provided insight into the TPS family evolution and genetic information that would help to improve Rosaceae species quality.

Genome-Wide Analysis of Multiple Organellar RNA Editing Factor (MORF) Family in Kiwifruit (Actinidia chinensis) Reveals Its Roles in Chloroplast RNA Editing and Pathogens Stress.

Kiwifruit (Actinidia chinensis) is well known for its high vitamin C content and good taste. Various diseases, especially bacterial canker, are a serious threat to the yield of kiwifruit. Multiple organellar RNA editing factor (MORF) genes are pivotal factors in the RNA editosome that mediates Cytosine-to-Uracil RNA editing, and they are also indispensable for the regulation of chloroplast development, plant growth, and response to stresses. Although the kiwifruit genome has been released, little is known about MORF genes in kiwifruit at the genome-wide level, especially those involved in the response to pathogens stress. In this study, we identified ten MORF genes in the kiwifruit genome. The genomic structures and chromosomal locations analysis indicated that all the MORF genes consisted of three conserved motifs, and they were distributed widely across the seven linkage groups and one contig of the kiwifruit genome. Based on the structural features of MORF proteins and the topology of the phylogenetic tree, the kiwifruit MORF gene family members were classified into six groups (Groups A-F). A synteny analysis indicated that two pairs of MORF genes were tandemly duplicated and five pairs of MORF genes were segmentally duplicated. Moreover, based on analysis of RNA-seq data from five tissues of kiwifruit, we found that both expressions of MORF genes and chloroplast RNA editing exhibited tissue-specific patterns. MORF2 and MORF9 were highly expressed in leaf and shoot, and may be responsible for chloroplast RNA editing, especially the ndhB genes. We also observed different MORF expression and chloroplast RNA editing profiles between resistant and susceptible kiwifruits after pathogen infection, indicating the roles of MORF genes in stress response by modulating the editing extend of mRNA. These results provide a solid foundation for further analyses of the functions and molecular evolution of MORF genes, in particular, for clarifying the resistance mechanisms in kiwifruits and breeding new cultivars with high resistance.