In pectin regulation, polygalacturonases (PGs) and pectin methylesterases (PMEs) are critical components in the transformation, disassembly network, and remodeling of plant primary cell walls. In the current study, we identified 36 PG and 47 PME genes using the available genomic resources of grapevine. Herein, we provide a comprehensive overview of PGs and PMEs, including phylogenetic and collinearity relationships, motif and gene structure compositions, gene duplications, principal component analysis, and expression profiling during developmental stages. Phylogenetic analysis of PGs and PMEs revealed similar domain composition patterns with Arabidopsis. The collinearity analysis showed high conservation and gene duplications with purifying selection. The type of duplications also varied in terms of gene numbers in PGs (10 dispersed, 1 proximal, 12 tandem, and 13 segmental, respectively) and PMEs (23 dispersed, 1 proximal, 16 tandem, and 7 segmental, respectively). The tissue-specific response of PG and PME genes based on the reported transcriptomic data exhibited diverged expression patterns in various organs during different developmental stages. Among PGs, VvPG8, VvPG10, VvPG13, VvPG17, VvPG18, VvPG19, VvPG20, VvPG22, and VvPG23 showed tissue- or organ-specific expression in majority of the tissues during development. Similarly, in PMEs, VvPME3, VvPME4, VvPME5, VvPME6, VvPME19, VvPME21, VvPME23, VvPME29, VvPME31, and VvPME32 suggested high tissue-specific response. The gene ontology (GO), Kyoto Encyclopedia of Genes and Genomics (KEGG) enrichment, and cis-elements prediction analysis also suggested the putative functions of PGs and PMEs in plant development, such as pectin and carbohydrate metabolism, and stress activities. Moreover, qRT-PCR validation of 32 PG and PME genes revealed their role in various organs of grapevines (i.e., root, stem, tendril, inflorescence, flesh, skins, and leaves). Therefore, these findings will lead to novel insights and encourage cutting-edge research on functional characterization of PGs and PMEs in fruit crop species.

Leaf rust caused by Puccinia triticina is the most widespread rust disease of wheat. As pathogen populations are constantly evolving, identification of novel sources of resistance is necessary to maintain disease resistance and stay ahead of this plant-pathogen evolutionary arms race. The wild genepool of wheat is a rich source of genetic diversity, accounting for 44% of the Lr genes identified. Here we performed a genome-wide association study (GWAS) on a diverse germplasm of 385 accessions, including 27 different Triticum and Aegilops species. Genetic characterization using the wheat 90 K array and subsequent filtering identified a set of 20,501 single nucleotide polymorphic (SNP) markers. Of those, 9,570 were validated using exome capture and mapped onto the Chinese Spring reference sequence v1.0. Phylogenetic analyses illustrated four major clades, clearly separating the wild species from the T. aestivum and T. turgidum species. GWAS was conducted using eight statistical models for infection types against six leaf rust isolates and leaf rust severity rated in field trials for 3–4 years at 2–3 locations in Canada. Functional annotation of genes containing significant quantitative trait nucleotides (QTNs) identified 96 disease-related loci associated with leaf rust resistance. A total of 21 QTNs were in haplotype blocks or within flanking markers of at least 16 known Lr genes. The remaining significant QTNs were considered loci that putatively harbor new Lr resistance genes. Isolation of these candidate genes will contribute to the elucidation of their role in leaf rust resistance and promote their usefulness in marker-assisted selection and introgression.

Boolean modelling of biological networks is a well-established technique for abstracting dynamical biomolecular regulation in cells. Specifically, decoding linkages between salient regulatory network states and corresponding cell fate outcomes can help uncover pathological foundations of diseases such as cancer. Attractor landscape analysis is one such methodology which converts complex network behavior into a landscape of network states wherein each state is represented by propensity of its occurrence. Towards undertaking attractor landscape analysis of Boolean networks, we propose an Attractor Landscape Analysis Toolbox (ATLANTIS) for cell fate discovery, from biomolecular networks, and reprogramming upon network perturbation. ATLANTIS can be employed to perform both deterministic and probabilistic analyses. It has been validated by successfully reconstructing attractor landscapes from several published case studies followed by reprogramming of cell fates upon therapeutic treatment of network. Additionally, the biomolecular network of HCT-116 colorectal cancer cell line has been screened for therapeutic evaluation of drug-targets. Our results show agreement between therapeutic efficacies reported by ATLANTIS and the published literature. These case studies sufficiently highlight the in silico cell fate prediction and therapeutic screening potential of the toolbox. Lastly, ATLANTIS can also help guide single or combinatorial therapy responses towards reprogramming biomolecular networks to recover cell fates.

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