Transcriptomic analysis of cultivated cotton Gossypium hirsutum provides insights into host responses upon whitefly-mediated transmission of cotton leaf curl disease

Genetic Analysis of Cotton Fiber Traits in Gossypium Hybrid Lines

Cotton plays a crucial role in the progress of the textile industry and the betterment of human life by providing natural fibers. In our study, we explored the genetic determinants of cotton architecture and fiber yield and quality by crossbreeding Gossypium hirsutum and Gossypium barbadense, creating a recombinant inbred line (RIL) population. Utilizing SNP markers, we constructed an extensive genetic map encompassing 7,730 markers over 2,784.2 cM. We appraised two architectural and seven fiber traits within six environments, identifying 58 QTLs, of which 49 demonstrated stability across these environments. These encompassed QTLs for traits such as lint percentage (LP), boll weight (BW), fiber strength (STRENGTH), seed index (SI), and micronaire (MIC), primarily located on chromosomes chr-A07, chr-D06, and chr-D07. Notably, chr-D07 houses a QTL region affecting SI, corroborated by multiple studies. Within this region, the genes BZIP043 and SEP2 were identified as pivotal, with SEP2 particularly showing augmented expression in developing ovules. These discoveries contribute significantly to marker-assisted selection, potentially elevating both the yield and quality of cotton fiber production. These findings provide valuable insights into marker-assisted breeding strategies, offering crucial information to enhance fiber yield and quality in cotton production.

Functional role of geminivirus encoded proteins in the host: Past and present

During plant-pathogen interaction, plant exhibits a strong defense system utilizing diverse groups of proteins to suppress the infection and subsequent establishment of the pathogen. However, in response, pathogens trigger an anti-silencing mechanism to overcome the host defense machinery. Among plant viruses, geminiviruses are the second largest virus family with a worldwide distribution and continue to be production constraints to food, feed, and fiber crops. These viruses are spread by a diverse group of insects, predominantly by whiteflies, and are characterized by a single-stranded DNA (ssDNA) genome coding for four to eight proteins that facilitate viral infection. The most effective means to managing these viruses is through an integrated disease management strategy that includes virus-resistant cultivars, vector management, and cultural practices. Dynamic changes in this virus family enable the species to manipulate their genome organization to respond to external changes in the environment. Therefore, the evolutionary nature of geminiviruses leads to new and novel approaches for developing virus-resistant cultivars and it is essential to study molecular ecology and evolution of geminiviruses. This review summarizes the multifunctionality of each geminivirus-encoded protein. These protein-based interactions trigger the abrupt changes in the host methyl cycle and signaling pathways that turn over protein normal production and impair the plant antiviral defense system. Studying these geminivirus interactions localized at cytoplasm-nucleus could reveal a more clear picture of host-pathogen relation. Data collected from this antagonistic relationship among geminivirus, vector, and its host, will provide extensive knowledge on their virulence mode and diversity with climate change.

Evaluation of BG, NPR1, and PAL in cotton plants through Virus Induced gene silencing reveals their role in whitefly stress

Whitefly is one of the most hazardous insect pests that infests a wide range of host plants and causes huge damage to crop worldwide. In order to engineer plants resilient to whitefly stress, it is important to identify and validate the responsive genes by exploring the molecular dynamics of plants under stress conditions. In this study three genes BG, NPR1, and PAL genes have been studied in cotton for elucidating their role in whitefly stress response. Initially, insilico approach was utilized to investigate the domains and phylogeny of BG, NPR1 and PAL genes and found out that these genes showed remarkable resemblance in four cotton species Gossypium hirsutum, G. barbadense, G. arboreum, and G. raimondii. In BG proteins the main functional domain was X8 belonging to glycohydro superfamily, in NPR1 two main functional domains were BTB_POZ at N terminal and NPR1_like_C at C terminal. In PAL functional domain PLN was found which belongs to Lyase class I superfamily. The promoter analysis of these genes displayed enrichment of hormone, stress and stimuli responsive cis elements. Through Virus Induced Gene Silencing (VIGS), these genes were targeted and kept under whitefly infestation. Overall, the whitefly egg and nymph production were observed 60-70% less on gene down regulated plants as compared to control plants. The qPCR-based expression analysis of certain stress-responsive genes showed that in BG down regulated plants the elevated expression of these whitefly responsive genes was detected, in NPR1 down regulated plants JAZ1 and HSP were found up regulated, ERF1 and WRKY40 didn't show significant differential expression, while MAPK6 was slightly down regulated. In PAL down regulated plants ERF1 and JAZ1 showed elevated expression while others didn't show significant alternation. Differential expression in gene down-regulated plants showed that whitefly responsive genes act in a complex inter signaling pathway and their expression impact each other. This study provides valuable insight into the structural and functional analysis of important whitefly responsive genes BG, NPR1, and PAL. The results will pave a path to future development of whitefly resilient crops.

Comparative analysis, diversification, and functional validation of plant nucleotide-binding site domain genes

Nucleotide-binding site (NBS) domain genes are one of the superfamily of resistance genes involved in plant responses to pathogens. The current study identified 12,820 NBS-domain-containing genes across 34 species covering from mosses to monocots and dicots. These identified genes are classified into 168 classes with several novel domain architecture patterns encompassing significant diversity among plant species. Several classical (NBS, NBS-LRR, TIR-NBS, TIR-NBS-LRR, etc.) and species-specific structural patterns (TIR-NBS-TIR-Cupin_1-Cupin_1, TIR-NBS-Prenyltransf, Sugar_tr-NBS etc.) were discovered. We observed 603 orthogroups (OGs) with some core (most common orthogroups; OG0, OG1, OG2, etc.) and unique (highly specific to species; OG80, OG82, etc.) OGs with tandem duplications. The expression profiling presented the putative upregulation of OG2, OG6, and OG15 in different tissues under various biotic and abiotic stresses in susceptible and tolerant plants to cotton leaf curl disease (CLCuD). The genetic variation between susceptible (Coker 312) and tolerant (Mac7) Gossypium hirsutum accessions identified several unique variants in NBS genes of Mac7 (6583 variants) and Coker312 (5173 variants). The protein-ligand and proteins-protein interaction showed a strong interaction of some putative NBS proteins with ADP/ATP and different core proteins of the cotton leaf curl disease virus. The silencing of GaNBS (OG2) in resistant cotton through virus-induced gene silencing (VIGS) demonstrated its putative role in virus tittering. The presented study will be further helpful in understanding the plant adaptation mechanism.

A comprehensive review on Gossypium hirsutum resistance against cotton leaf curl virus

Cotton (Gossypium hirsutum L.) is a significant fiber crop. Being a major contributor to the textile industry requires continuous care and attention. Cotton is subjected to various biotic and abiotic constraints. Among these, biotic factors including cotton leaf curl virus (CLCuV) are dominant. CLCuV is a notorious disease of cotton and is acquired, carried, and transmitted by the whitefly (Bemisia tabaci). A cotton plant affected with CLCuV may show a wide range of symptoms such as yellowing of leaves, thickening of veins, upward or downward curling, formation of enations, and stunted growth. Though there are many efforts to protect the crop from CLCuV, long-term results are not yet obtained as CLCuV strains are capable of mutating and overcoming plant resistance. However, systemic-induced resistance using a gene-based approach remained effective until new virulent strains of CLCuV (like Cotton Leaf Curl Burewala Virus and others) came into existence. Disease control by biological means and the development of CLCuV-resistant cotton varieties are in progress. In this review, we first discussed in detail the evolution of cotton and CLCuV strains, the transmission mechanism of CLCuV, the genetic architecture of CLCuV vectors, and the use of pathogen and nonpathogen-based approaches to control CLCuD. Next, we delineate the uses of cutting-edge technologies like genome editing (with a special focus on CRISPR-Cas), next-generation technologies, and their application in cotton genomics and speed breeding to develop CLCuD resistant cotton germplasm in a short time. Finally, we delve into the current obstacles related to cotton genome editing and explore forthcoming pathways for enhancing precision in genome editing through the utilization of advanced genome editing technologies. These endeavors aim to enhance cotton's resilience against CLCuD.

Omics-driven exploration and mining of key functional genes for the improvement of food and fiber crops

The deployment of omics technologies has obtained an incredible boost over the past few decades with the advances in next-generation sequencing (NGS) technologies, innovative bioinformatics tools, and the deluge of available biological information. The major omics technologies in the limelight are genomics, transcriptomics, proteomics, metabolomics, and phenomics. These biotechnological advances have modernized crop breeding and opened new horizons for developing crop varieties with improved traits. The genomes of several crop species are sequenced, and a huge number of genes associated with crucial economic traits have been identified. These identified genes not only provide insights into the understanding of regulatory mechanisms of crop traits but also decipher practical grounds to assist in the molecular breeding of crops. This review discusses the potential of omics technologies for the acquisition of biological information and mining of the genes associated with important agronomic traits in important food and fiber crops, such as wheat, rice, maize, potato, tomato, cassava, and cotton. Different functional genomics approaches for the validation of these important genes are also highlighted. Furthermore, a list of genes discovered by employing omics approaches is being represented as potential targets for genetic modifications by the latest genome engineering methods for the development of climate-resilient crops that would in turn provide great impetus to secure global food security.

RNA-seq analysis reveals an early defense response to tomato leaf curl New Delhi virus in potato cultivar Kufri Bahar

Potatoes in India are very susceptible to apical leaf curl disease, which causes severe symptoms and greater yield losses. Because the majority of potato cultivars are susceptible to the virus, it is crucial to discover sources of resistance and investigate the mechanism of resistance/susceptibility in potato cultivars. In this study, the gene expression profile of two potato cultivars, Kufri Bahar (resistant) and Kufri Pukhraj (susceptible), varying in their level of resistance to ToLCNDV, was analyzed using RNA-Seq. The Ion ProtonTM system was used to sequence eight RiboMinus RNA libraries from inoculated and uninoculated potato plants at 15 and 20 days after inoculation (DAI). The findings indicated that the majority of differentially expressed genes (DEGs) were cultivar-or time-specific. These DEGs included genes for proteins that interact with viruses, genes linked with the cell cycle, genes for proteins involved in defense, transcription and translation initiation factors, and plant hormone signaling pathway genes. Interestingly, defense responses were generated early in Kufri Bahar, at 15 DAI, which may have impeded the replication and spread of ToLCNDV. This research provides a genome-wide transcriptional analysis of two potato cultivars with variable levels of ToLCNDV resistance. At an early stage, we observed suppression of genes that interact with viral proteins, induction of genes associated with restriction of cell division, genes encoding defense proteins, AP2/ERF transcription factors, and altered expression of zinc finger protein genes, HSPs, JA, and SA pathway-related genes. Our findings add to a greater comprehension of the molecular basis of potato resistance to ToLCNDV and may aid in the development of more effective disease management techniques.

Transcriptomic and metabolomic analyses reveal the potential mechanism of waterlogging resistance in cotton (Gossypium hirsutum L.)

Introduction

Cotton (Gossypium hirsutum L.) is susceptible to long-term waterlogging stress; however, genomic information of cotton response mechanisms toward long days of waterlogging is quite elusive.

Methods

Here, we combined the transcriptome and metabolome expression level changes in cotton roots after 10 and 20 days of waterlogging stress treatment pertaining to potential resistance mechanisms in two cotton genotypes.

Results and discussion

Numerous adventitious roots and hypertrophic lenticels were induced in CJ1831056 and CJ1831072. Transcriptome analysis revealed 101,599 differentially expressed genes in cotton roots with higher gene expression after 20 days of stress. Reactive oxygen species (ROS) generating genes, antioxidant enzyme genes, and transcription factor genes (AP2, MYB, WRKY, and bZIP) were highly responsive to waterlogging stress among the two genotypes. Metabolomics results showed higher expressions of stress-resistant metabolites sinapyl alcohol, L-glutamic acid, galactaric acid, glucose 1-phosphate, L-valine, L-asparagine, and melibiose in CJ1831056 than CJ1831072. Differentially expressed metabolites (adenosine, galactaric acid, sinapyl alcohol, L-valine, L-asparagine, and melibiose) significantly correlated with the differentially expressed PRX52, PER1, PER64, and BGLU11 transcripts. This investigation reveals genes for targeted genetic engineering to improve waterlogging stress resistance to enhance abiotic stress regulatory mechanisms in cotton at the transcript and metabolic levels of study.

Network Biology Analyses and Dynamic Modeling of Gene Regulatory Networks under Drought Stress Reveal Major Transcriptional Regulators in Arabidopsis

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.

Transcriptome and Metabolome Profiling Unveil Pigment Formation Variations in Brown Cotton Lines (Gossypium hirsutum L.)

Naturally brown colored cotton (NBCC) is becoming increasingly popular due to its natural properties of coloration. However, poor fiber quality and color fading are key issues that are hindering the cultivation of naturally colored cotton. In this study, based on transcriptome and metabolome of 18 days post-anthesis (DPA), we compared the variations of pigment formation in two brown cotton fibers (DCF and LCF), with white cotton fiber (WCF) belonging to a near-isogenic line. A transcriptome study revealed a total of 15,785 differentially expressed genes significantly enriched in the flavonoid biosynthesis pathway. Furthermore, for flavonoid biosynthesis-related genes, such as flavonoid 3′5′-hydroxylase (F3′5′H), anthocyanidin synthase (ANS), anthocyanidin reductase (ANR), chalcone synthase (CHS), dihydroflavonol 4-reductase (DFR), and chalcone isomerase (CHI), their expressions significantly increased in LCF compared with DCF and WCF. Moreover, transcription factors MYB and bHLH were significantly expressed in LCF and DCF. Most flavonoid-related metabolites (myricetin naringenin, catechin, epicatechin-epiafzelechin, and epigallocatechin) were found to be more highly up-regulated in LCF and DCF than WCF. These findings reveal the regulatory mechanism controlling different brown pigmentation in cotton fibers and elucidate the need for the proper selection of high-quality brown cotton fiber breeding lines for promising fiber quality and durable brown color pigmentation.

Transcriptome diversity assessment of Gossypium arboreum (FDH228) leaves under control, drought and whitefly infestation using PacBio long reads

Alternative splicing (AS) and alternative polyadenylation (APA) are common mechanisms in eukaryotes to increase the complexity of transcriptomes and subsequently proteomes. Analysis of long reads transcriptomics data can result in the discovery of novel transcripts, splice sites, AS or APA events. Gossypium arboreum is an important cultivated cotton species and a putative contributor of the A sub-genome to the modern tetraploid cotton; and inherently tolerant to several biotic and abiotic stresses. Specifically, its variety 'FDH228' is considered to be an important resistance source. In this study, we sequenced the G. arboreum (var. FDH228) transcriptome using PacBio IsoSeq and illumina short read sequencing under three different conditions i.e. untreated/healthy, treated with biotic stress through whitefly infestation, and treated with abiotic stress via water deprivation, for the discovery and surveying of canonical and non-canonical AS, APA and transcript fusion events. We were able to obtain 15,419 unique transcripts from all samples representing 11,343 genes, out of which 10,832 were annotated and 520 were novel with respect to the published reference genome. These transcripts were grouped into different structural categories including 60 Antisense, 11,959 having a full-splice match, 999 with incomplete-splice match, 30 fusion transcripts, 177 genic, 479 intergenic, 771 novels in the catalog, and 944 Novel but not found in the catalog. Subsequently, randomly selected candidate transcripts were experimentally validated using qRT-PCR. Our comprehensive identification of canonical and non-canonical splicing events, and novel and fusion transcripts aids in the understanding of the resistance mechanisms for this specific germplasm.

Whole-Genome Resequencing Deciphers New Insight Into Genetic Diversity and Signatures of Resistance in Cultivated Cotton Gossypium hirsutum

Cotton is an important crop that produces fiber and cottonseed oil for the textile and oil industry. However, cotton leaf curl virus disease (CLCuD) stress is limiting its yield in several Asian countries. In this study, we have sequenced Mac7 accession, a Gossypium hirsutum resistance source against several biotic stresses. By aligning with the Gossypium hirsutum (AD1) 'TM-1' genome, a total of 4.7 and 1.2 million SNPs and InDels were identified in the Mac7 genome. The gene ontology and metabolic pathway enrichment indicated SNPs and InDels role in nucleotide bindings, secondary metabolite synthesis, and plant-pathogen interaction pathways. Furthermore, the RNA-seq data in different tissues and qPCR expression profiling under CLCuD provided individual gene roles in resistant and susceptible accessions. Interestingly, the differential NLR genes demonstrated higher expression in resistant plants rather than in susceptible plants expression. The current resequencing results may provide primary data to identify DNA resistance markers which will be helpful in marker-assisted breeding for development of Mac7-derived resistance lines.

A pipeline of integrating transcriptome and interactome to elucidate central nodes in host-pathogens interactions

Investigating the complexity of host-pathogen interactions is challenging. Here, we outline a pipeline to identify important proteins and signaling molecules in human-viral interactomes. Firstly, we curate a comprehensive human interactome. Subsequently, we infer viral targets and transcriptome-specific human interactomes (VTTSHI) for papillomavirus and herpes viruses by integrating viral targets and transcriptome data. Finally, we reveal the common and shared nodes and pathways in viral pathogenesis following network topology and pathway enrichment analyses.

For complete details on the use and execution of this protocol, please refer to Kumar et al. (2020).

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Protocol for integrative analysis of transcriptome and proteome network data

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Network subgroup enrichment of two host-pathogen interaction networks

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Preprocessing of data and heterogeneous network integration

Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

A genome-wide comparative evolutionary analysis of zinc finger-BED transcription factor genes in land plants

Zinc finger (Zf)-BED proteins are a novel superfamily of transcription factors that controls numerous activities in plants including growth, development, and cellular responses to biotic and abiotic stresses. Despite their important roles in gene regulation, little is known about the specific functions of Zf-BEDs in land plants. The current study identified a total of 750 Zf-BED-encoding genes in 35 land plant species including mosses, bryophytes, lycophytes, gymnosperms, and angiosperms. The gene family size was somewhat proportional to genome size. All identified genes were categorized into 22 classes based on their specific domain architectures. Of these, class I (Zf-BED_DUF-domain_Dimer_Tnp_hAT) was the most common in the majority of the land plants. However, some classes were family-specific, while the others were species-specific, demonstrating diversity at different classification levels. In addition, several novel functional domains were also predicated including WRKY and nucleotide-binding site (NBS). Comparative genomics, transcriptomics, and proteomics provided insights into the evolutionary history, duplication, divergence, gene gain and loss, species relationship, expression profiling, and structural diversity of Zf-BEDs in land plants. The comprehensive study of Zf-BEDs in Gossypium sp., (cotton) also demonstrated a clear footprint of polyploidization. Overall, this comprehensive evolutionary study of Zf-BEDs in land plants highlighted significant diversity among plant species.

Genome wide study of cysteine rich receptor like proteins in Gossypium sp

Cysteine-rich receptor-like-kinases (CRKs), a transmembrane subfamily of receptor-like kinase, play crucial roles in plant adaptation. As such cotton is the major source of fiber for the textile industry, but environmental stresses are limiting its growth and production. Here, we have performed a deep computational analysis of CRKs in five Gossypium species, including G. arboreum (60 genes), G. raimondii (74 genes), G. herbaceum (65 genes), G. hirsutum (118 genes), and G. barbadense (120 genes). All identified CRKs were classified into 11 major classes and 43 subclasses with the finding of several novel CRK-associated domains including ALMT, FUSC_2, Cript, FYVE, and Pkinase. Of these, DUF26_DUF26_Pkinase_Tyr was common and had elevated expression under different biotic and abiotic stresses. Moreover, the 35 land plants comparison identified several new CRKs domain-architectures. Likewise, several SNPs and InDels were observed in CLCuD resistant G. hirsutum. The miRNA target side prediction and their expression profiling in different tissues predicted miR172 as a major CRK regulating miR. The expression profiling of CRKs identified multiple clusters with co-expression under certain stress conditions. The expression analysis under CLCuD highlighted the role of GhCRK057, GhCRK059, GhCRK058, and GhCRK081 in resistant accession. Overall, these results provided primary data for future potential functional analysis as well as a reference study for other agronomically important crops.

Biochemical evidence of epicuticular wax compounds involved in cotton-whitefly interaction

Sucking insects require a surface of plants on which the legs and the eggs of insects will adhere and to which insect mouthparts will access. The primary plant protection against insects is their surface property, which hinders the attachment of the insect’s legs and eggs. The epicuticular waxes chemistry influences the fine structure of the cuticular surface. In current study, an attempt was made to investigate the variation of chemical compounds in epicuticular waxes of four cotton species that classify them resistant or susceptible i.e., Gossypium abroreum, G. hirsutum, G. arboreum wax deficient mutant (GaWM3) and G. harknessi which were evaluated for their interaction with whitefly and CLCuV transmission. Gossypium hirsutum an insect and CLCuV susceptible cotton variety, was found to have four compounds namely Trichloroacetic acid, hexadecylester, P-xylenolpthalein, 2-cyclopentene-1-ol, 1-phenyl-and Phenol, 2,5-bis [1,1- dimethyl] which could interact with chitin of whitefly while only two compounds in Gossypium arboreum an insect and CLCuV resistant cotton variety could interact with chitin of whitefly. Similarly, GaWM3 and Gossypium harkasnessi were found to have only a single compound. Number of whiteflies found on leaves of G. hirsutum was much higher as compared to other cotton species. Keeping this fact in mind a wax biosynthetic gene CER3, from Arabidopsis thaliana was transformed into G. hirsutum and the plants were evaluated for their resistance against whitefly and CLCuV transmission. In microscopic analysis transgenic plants clearly showed higher amounts of leaf waxes as compared to non-transgenics. The least whitefly population and CLCuV titer of <10,000 units was found in transgenic plants compared to non-transgenic cotton where it was ≈4.5X106 units that confirmed the role of wax in insect interaction and ultimately to CLCuV transmission. This study provides novel insight on wax related compounds involved in cotton-whitefly interaction, which potentially can help in developing more efficient control strategies for this destructive pest.

TMT-based quantitative proteomic analysis reveals defense mechanism of wheat against the crown rot pathogen Fusarium pseudograminearum

BACKGROUND

Fusarium crown rot is major disease in wheat. However, the wheat defense mechanisms against this disease remain poorly understood.

RESULTS

Using tandem mass tag (TMT) quantitative proteomics, we evaluated a disease-susceptible (UC1110) and a disease-tolerant (PI610750) wheat cultivar inoculated with Fusarium pseudograminearum WZ-8A. The morphological and physiological results showed that the average root diameter and malondialdehyde content in the roots of PI610750 decreased 3 days post-inoculation (dpi), while the average number of root tips increased. Root vigor was significantly increased in both cultivars, indicating that the morphological, physiological, and biochemical responses of the roots to disease differed between the two cultivars. TMT analysis showed that 366 differentially expressed proteins (DEPs) were identified by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment in the two comparison groups, UC1110_3dpi/UC1110_0dpi (163) and PI610750_3dpi/PI610750_0dpi (203). It may be concluded that phenylpropanoid biosynthesis (8), secondary metabolite biosynthesis (12), linolenic acid metabolites (5), glutathione metabolism (8), plant hormone signal transduction (3), MAPK signaling pathway-plant (4), and photosynthesis (12) contributed to the defense mechanisms in wheat. Protein-protein interaction network analysis showed that the DEPs interacted in both sugar metabolism and photosynthesis pathways. Sixteen genes were validated by real-time quantitative polymerase chain reaction and were found to be consistent with the proteomics data.

CONCLUSION

The results provided insight into the molecular mechanisms of the interaction between wheat and F. pseudograminearum.

Inference of Gene Regulatory Network from Single-Cell Transcriptomic Data Using pySCENIC

With the advent of recent next-generation sequencing (NGS) technologies in genomics, transcriptomics, and epigenomics, profiling single-cell sequencing became possible. The single-cell RNA sequencing (scRNA-seq) is widely used to characterize diverse cell populations and ascertain cell type-specific regulatory mechanisms. The gene regulatory network (GRN) mainly consists of genes and their regulators-transcription factors (TF). Here, we describe the lightning-fast Python implementation of the SCENIC (Single-Cell reEgulatory Network Inference and Clustering) pipeline called pySCENIC. Using single-cell RNA-seq data, it maps TFs onto gene regulatory networks and integrates various cell types to infer cell-specific GRNs. There are two fast and efficient GRN inference algorithms, GRNBoost2 and GENIE3, optionally available with pySCENIC. The pipeline has three steps: (1) identification of potential TF targets based on co-expression; (2) TF-motif enrichment analysis to identify the direct targets (regulons); and (3) scoring the activity of regulons (or other gene sets) on single cell types.

Dynamic Regulatory Event Mining by iDREM in Large-Scale Multi-omics Datasets During Biotic and Abiotic Stress in Plants

The system-wide complexity of genome regulation encoding the organism phenotypic diversity is well understood. However, a major challenge persists about the appropriate method to describe the systematic dynamic genome regulation event utilizing enormous multi-omics datasets. Here, we describe Interactive Dynamic Regulatory Events Miner (iDREM) which reconstructs gene-regulatory networks from temporal transcriptome, proteome, and epigenome datasets during stress to envisage "master" regulators by simulating cascades of temporal transcription-regulatory and interactome events. The iDREM is a Java-based software that integrates static and time-series transcriptomics and proteomics datasets, transcription factor (TF)-target interactions, microRNA (miRNA)-target interaction, and protein-protein interactions to reconstruct temporal regulatory network and identify significant regulators in an unsupervised manner. The hidden Markov model detects specialized manipulated pathways as well as genes to recognize statistically significant regulators (TFs/miRNAs) that diverge in temporal activity. This method can be translated to any biotic or abiotic stress in plants and animals to predict the master regulators from condition-specific multi-omics datasets including host-pathogen interactions for comprehensive understanding of manipulated biological pathways.