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Functional annotation of proteins for signaling network inference in non-model species

ABSTRACT: Molecular biology aims to understand the molecular basis of cellular responses, unravel dynamic regulatory networks, and model complex biological systems. However, these studies remain challenging in non-model species as a result of poor functional annotation of regulatory proteins, like kinases or phosphatases. To overcome this limitation, we developed a multi-layer neural network that annotates proteins by determining functionality directly from the protein sequence. We annotated the kinases and phosphatases in the non-model species, Glycine max (soybean), achieving a prediction sensitivity of up to 97%. To demonstrate the applicability, we used our functional annotations in combination with Bayesian network principles to predict signaling cascades using time series phosphoproteomics. We shed light on phosphorylation cascades in soybean seedlings upon cold treatment and identified Glyma.10G173000 (TOI5) and Glyma.19G007300 (TOT3) as key temperature response regulators in soybean. Importantly, the signaling cascade predictions do not rely upon known upstream kinases, kinase motifs, or protein interaction data, enabling de novo identification of kinase-substrate interactions. In addition to high accuracy and strong generalization, we showed that our functional prediction neural network is scalable to other model and non-model species, including Oryza sativa (rice), Zea mays (maize), Sorghum bicolor (sorghum), and Triticum aestivum (wheat). Overall, we demonstrated a data-driven systems biology approach for non-model species leveraging our predicted upstream kinases and phosphatases.

INSTRUMENT(S): Q Exactive

ORGANISM(S): Glycine Max

TISSUE(S): Leaf

SUBMITTER: Cassio Flavio Fonseca de Lima

LAB HEAD: Ive De Smet

PROVIDER: PXD037601 | Pride | 2023-06-13

REPOSITORIES: Pride

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Project description:Plant secondary cell walls constitute the majority of plant biomass and are an important source of biomaterials. Secondary cell walls are particularly prominent in xylem cells present in the vascular tissue. Although the process of vascularization has been extensively studied in the dicot Arabidopsis thaliana, remarkably little is known about these processes in grass species despite their emerging importance as biomass feedstocks. The targeted biofuel crop Sorghum bicolor has a sequenced and well-annotated genome, making it an ideal monocot model for addressing vascularization and biomass deposition. Here we generated tissue-specific transcriptome data using laser capture microdissection in the developing vascular and non-vascular tissues of the sorghum root. Many sorghum genes with enriched expression in developing vasculature were orthologous to genes previously associated with vascular development in other species. However, a number of transcription factor families, including NAC domain, MYB and ARF varied in their complement of vascular expressed genes to a considerable degree in sorghum compared to Arabidopsis and/or maize. Differential expression of genes associated with DNA methylation and chromatin modification were identified between vascular and non-vascular cell types, implying that changes in DNA methylation may be a feature of sorghum root vascularization. To profile DNA methylation in these tissues, sodium bisulfite sequencing of laser capture microdissected tissue was performed. DNA methylation was enriched in genic regions of genes demonstrating higher expression in non-vascular tissues. Methylation in genic and intergenic regions varied by tissue type and gene expression level. Furthermore, genes involved in cell elongation showed differences in methylation levels concomitant with expression between non-vascular and vascular tissue types suggesting a novel mode by which root growth in distinct tissues may be modulated. Our results provide both a genetic and epigenetic framework for studying vascularization and secondary cell wall development in sorghum.

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Functional annotation of proteins for signaling network inference in non-model species

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