Computational Systems Biology of Alfalfa – Bacterial Blight Host-Pathogen Interactions: Uncovering the Complex Molecular Networks for Developing Durable Disease Resistant Crop

Abstract

Medicago sativa (also known as alfalfa), a forage legume, is widely cultivated due to its high yield and high-value hay crop production. Infectious diseases are a major threat to the crops, owing to huge economic losses to the agriculture industry, worldwide. The protein-protein interactions (PPIs) between the pathogens and their hosts play a critical role in understanding the molecular basis of pathogenesis. Pseudomonas syringae pv. syringae ALF3 suppresses the plant’s innate immune response by secreting type III effector proteins into the host cell, causing bacterial stem blight in alfalfa. The alfalfa-P. syringae system has little information available for PPIs. Thus, to understand the infection mechanism, we elucidated the genome-scale host-pathogen interactions (HPIs) between alfalfa and P. syringae using two computational approaches: interolog-based and domain-based method. A total of ∼14 M putative PPIs were predicted between 50,629 alfalfa proteins and 2,932 P. syringae proteins by combining these approaches. Additionally, ∼0.7 M consensus PPIs were also predicted. The functional analysis revealed that P. syringae proteins are highly involved in nucleotide binding activity (GO:0000166), intracellular organelle (GO:0043229), and translation (GO:0006412) while alfalfa proteins are involved in cellular response to chemical stimulus (GO:0070887), oxidoreductase activity (GO:0016614), and Golgi apparatus (GO:0005794). According to subcellular localization predictions, most of the pathogen proteins targeted host proteins within the cytoplasm and nucleus. In addition, we discovered a slew of new virulence effectors in the predicted HPIs. The current research describes an integrated approach for deciphering genome-scale host-pathogen PPIs between alfalfa and P. syringae, allowing the researchers to better understand the pathogen’s infection mechanism and develop pathogen-resistant lines.

Deciphering the complete human-monkeypox virus interactome: Identifying immune responses and potential drug targets

“…Therefore, to gain deeper insights into the disease infection mechanism of MPXV in humans, we aim to develop computational models to decipher genome-scale protein-protein interactions in human-monkeypox virus pathosystem. These models include homology-based interolog and domain-based prediction, which have been widely used in several host-pathogen interaction studies in the past ( 15 , 16 ). The molecular techniques available for the detection and/or validation of PPIs are laborious and costly, while the computational approaches, on the other hand, provide a comprehensive understanding of the biological function and cellular behaviour of the proteins involved in the interactions in rapid and economical manner ( 17 ).…”

A correlation coefficient-based feature selection approach for virus-host protein-protein interaction prediction

“…There is an armamentarium of computational methods for PPI identification. These include approaches based on protein co-evolution [ 6 ], sequence similarity, and domain-domain interaction patterns devised to, for example, predict genome-scale host-pathogen PPIs [ 7 ], structural annotation and modeling [ 8 ] and self-adjustable Gaussian Network Model to determine binding pockets for small peptides or molecules, which is particularly useful in the discovery of PPI-inhibitory pharmaceutical compounds [ 9 ]. Among the computational methods, machine learning-based virus-host PPI prediction approaches handle PPI identification as a binary classification problem.…”

Diverse Virulence Attributes of Pantoea alfalfae sp. nov. CQ10 Responsible for Bacterial Leaf Blight in Alfalfa Revealed by Genomic Analysis

“…Pseudomonas syringae pv. syringae is the causative pathogen of bacterial stem blight and causes 50% or more loss of alfalfa yield in Iran, Europe, Australia, and the United States [4]. The extent of risk of new or re-emerging bacterial diseases has increased as a result of increased trade of products between countries [5].…”