Systems biology is an inclusive approach to study the static and dynamic emergent properties on a global scale by integrating multiomics datasets to establish qualitative and quantitative associations among multiple biological components. With an abundance of improved high throughput -omics datasets, network-based analyses and machine learning technologies are playing a pivotal role in comprehensive understanding of biological systems. Network topological features reveal most important nodes within a network as well as prioritize significant molecular components for diverse biological networks, including coexpression, protein–protein interaction, and gene regulatory networks. Machine learning techniques provide enormous predictive power through specific feature extraction from biological data. Deep learning, a subtype of machine learning, has plausible future applications because a domain expert for feature extraction is not needed in this algorithm. Inspired by diverse domains of biology, we here review classic systems biology techniques applied in plant immunity thus far. We also discuss additional advanced approaches in both graph theory and machine learning, which may provide new insights for understanding plant–microbe interactions. Finally, we propose a hybrid approach in plant immune systems that harnesses the power of both network biology and machine learning, with a potential to be applicable to both model systems and agronomically important crop plants.

Summary: Cultivated cotton (Gossypium hirsutum) is the most important fibre crop in the world. Cotton leaf curl disease (CLCuD) is the major limiting factor and a threat to textile industry in India and Pakistan. All the local cotton cultivars exhibit moderate to no resistance against CLCuD. In this study, we evaluated an exotic cotton accession Mac7 as a resistance source to CLCuD by challenging it with viruliferous whiteflies and performing qPCR to evaluate the presence/absence and relative titre of CLCuD‐associated geminiviruses/betasatellites. The results indicated that replication of pathogenicity determinant betasatellite is significantly attenuated in Mac7 and probably responsible for resistance phenotype. Afterwards, to decipher the genetic basis of CLCuD resistance in Mac7, we performed RNA sequencing on CLCuD‐infested Mac7 and validated RNA‐Seq data with qPCR on 24 independent genes. We performed co‐expression network and pathway analysis for regulation of geminivirus/betasatellite‐interacting genes. We identified nine novel modules with 52 hubs of highly connected genes in network topology within the co‐expression network. Analysis of these hubs indicated the differential regulation of auxin stimulus and cellular localization pathways in response to CLCuD. We also analysed the differential regulation of geminivirus/betasatellite‐interacting genes in Mac7. We further performed the functional validation of selected candidate genes via virus‐induced gene silencing (VIGS). Finally, we evaluated the genomic context of resistance responsive genes and found that these genes are not specific to A or D sub‐genomes of G. hirsutum. These results have important implications in understanding CLCuD resistance mechanism and developing a durable resistance in cultivated cotton.

Cotton leaf curl disease (CLCuD), caused by cotton leaf curl viruses (CLCuVs), is among the most devastating diseases in cotton. While the widely cultivated cotton speciesGossypium hirsutumis generally susceptible, the diploid speciesG. arboreumis a natural source for resistance against CLCuD. However, the influence of CLCuD on theG. arboreumtranscriptome and the interaction of CLCuD withG. arboreumremains to be elucidated. Here we have used an RNA-Seq based study to analyze differential gene expression inG. arboreumunder CLCuD infestation.G. arboreumplants were infested by graft inoculation using a CLCuD infected scion ofG. hirsutum. CLCuD infested asymptomatic and symptomatic plants were analyzed with RNA-seq using an Illumina HiSeq. 2500. Data analysis revealed 1062 differentially expressed genes (DEGs) inG. arboreum. We selected 17 genes for qPCR to validate RNA-Seq data. We identified several genes involved in disease resistance and pathogen defense. Furthermore, a weighted gene co-expression network was constructed from the RNA-Seq dataset that indicated 50 hub genes, most of which are involved in transport processes and might have a role in the defense response ofG. arboreumagainst CLCuD. This fundamental study will improve the understanding of virus-host interaction and identification of important genes involved inG. arboreumtolerance against CLCuD.

Drought is one of the most serious abiotic stressors in the environment, restricting agricultural production by reducing plant growth, development, and productivity. To investigate such a complex and multifaceted stressor and its effects on plants, a systems biology-based approach is necessitated, entailing the generation of co-expression networks, identification of high-priority transcription factors (TFs), dynamic mathematical modeling, and computational simulations. Here, we studied a high-resolution drought transcriptome of Arabidopsis. We identified distinct temporal transcriptional signatures and demonstrated the involvement of specific biological pathways. Generation of a large-scale co-expression network followed by network centrality analyses identified 117 TFs that possess critical properties of hubs, bottlenecks, and high clustering coefficient nodes. Dynamic transcriptional regulatory modeling of integrated TF targets and transcriptome datasets uncovered major transcriptional events during the course of drought stress. Mathematical transcriptional simulations allowed us to ascertain the activation status of major TFs, as well as the transcriptional intensity and amplitude of their target genes. Finally, we validated our predictions by providing experimental evidence of gene expression under drought stress for a set of four TFs and their major target genes using qRT-PCR. Taken together, we provided a systems-level perspective on the dynamic transcriptional regulation during drought stress in Arabidopsis and uncovered numerous novel TFs that could potentially be used in future genetic crop engineering programs.

Cotton is a commercial and economically important crop that generates billions of dollars in annual revenue worldwide. However, cotton yield is affected by a sap-sucking insect Bemisia tabaci (whitefly), and whitefly-borne cotton leaf curl disease (CLCuD). The causative agent of devastating CLCuD is led by the viruses belonging to the genus Begomovirus (family Geminiviridae), collectively called cotton leaf curl viruses. Unfortunately, the extensively cultivated cotton (Gossypium hirsutum) species are highly susceptible and vulnerable to CLCuD. Yet, the concomitant influence of whitefly and CLCuD on the susceptible G. hirsutum transcriptome has not been interpreted. In the present study we have employed an RNA Sequencing (RNA-Seq) transcriptomics approach to explore the differential gene expression in susceptible G. hirsutum variety upon infection with viruliferous whiteflies. Comparative RNA-Seq of control and CLCuD infected plants was done using Illumina HiSeq 2500. This study yielded 468 differentially expressed genes (DEGs). Among them, we identified 220 up and 248 downregulated DEGs involved in disease responses and pathogen defense. We selected ten genes for downstream RT-qPCR analyses on two cultivars, Karishma and MNH 786 that are susceptible to CLCuD. We observed a similar expression pattern of these genes in both susceptible cultivars that was also consistent with our transcriptome data further implying a wider application of our global transcription study on host susceptibility to CLCuD. We next performed weighted gene co-expression network analysis that revealed six modules. This analysis also identified highly co-expressed genes as well as 55 hub genes that co-express with ≥ 50 genes. Intriguingly, most of these hub genes are shown to be downregulated and enriched in cellular processes. Under-expression of such highly co-expressed genes suggests their roles in favoring the virus and enhancing plant susceptibility to CLCuD. We also discuss the potential mechanisms governing the establishment of disease susceptibility. Overall, our study provides a comprehensive differential gene expression analysis of G. hirsutum under whitefly-mediated CLCuD infection. This vital study will advance the understanding of simultaneous effect of whitefly and virus on their host and aid in identifying important G. hirsutum genes which intricate in its susceptibility to CLCuD.