Large-scale data mining pipeline for identifying novel soybean genes involved in resistance against the soybean cyst nematode

The soybean cyst nematode (SCN) [Heterodera glycines Ichinohe] is a devastating pathogen of soybean [Glycine max (L.) Merr.] that is rapidly becoming a global economic issue. Two loci conferring SCN resistance have been identified in soybean, Rhg1 and Rhg4; however, they offer declining protection. Therefore, it is imperative that we identify additional mechanisms for SCN resistance. In this paper, we develop a bioinformatics pipeline to identify protein–protein interactions related to SCN resistance by data mining massive-scale datasets. The pipeline combines two leading sequence-based protein–protein interaction predictors, the Protein–protein Interaction Prediction Engine (PIPE), PIPE4, and Scoring PRotein INTeractions (SPRINT) to predict high-confidence interactomes. First, we predicted the top soy interacting protein partners of the Rhg1 and Rhg4 proteins. Both PIPE4 and SPRINT overlap in their predictions with 58 soybean interacting partners, 19 of which had GO terms related to defense. Beginning with the top predicted interactors of Rhg1 and Rhg4, we implement a “guilt by association” in silico proteome-wide approach to identify novel soybean genes that may be involved in SCN resistance. This pipeline identified 1,082 candidate genes whose local interactomes overlap significantly with the Rhg1 and Rhg4 interactomes. Using GO enrichment tools, we highlighted many important genes including five genes with GO terms related to response to the nematode (GO:0009624), namely, Glyma.18G029000, Glyma.11G228300, Glyma.08G120500, Glyma.17G152300, and Glyma.08G265700. This study is the first of its kind to predict interacting partners of known resistance proteins Rhg1 and Rhg4, forming an analysis pipeline that enables researchers to focus their search on high-confidence targets to identify novel SCN resistance genes in soybean

Assessing and Improving Protein-Protein Interaction Prediction in E. coli

This thesis evaluates and extends the state-of-the-art in sequence-based binary protein-protein interaction (PPI) prediction for bacterial species. Accurately predicting PPIs for bacteria enables researchers to quickly identify targets for developing antimicrobial drugs and expand interactome knowledge for bacteria. E. coli is used here as a model organism for bacteria. A systematic and unbiased evaluation of four classifiers, SPRINT, DPPI, DEEPFE, and PIPR is conducted on new E. coli datasets. Classifier enhancement is accomplished using a stacked reciprocal perspective (RP) classifier, a technique recently developed by the cuBIC lab. Cross-validation results improve by 16.6% for the area under precision-recall (auPR) curve compared to the best base classifier, which increases to 262.5% when considering a 1:100 positive-to-negative sample imbalance. The results of this thesis also indicate the need for new benchmark datasets, more bacterial PPI data, and consistent evaluation protocols to be followed for new PPI predictions

SMCHD1 activates the expression of genes required for the expansion of human myoblasts

SMCHD1 is an epigenetic regulatory protein known to modulate the targeted repression of large chromatin domains. Diminished SMCHD1 function in muscle fibers causes Facioscapulohumeral Muscular Dystrophy (FSHD2) through derepression of the D4Z4 chromatin domain, an event which permits the aberrant expression of the disease-causing gene DUX4. Given that SMCHD1 plays a broader role in establishing the cellular epigenome, we examined whether loss of SMCHD1 function might affect muscle homeostasis through additional mechanisms. Here we show that acute depletion of SMCHD1 results in a DUX4-independent defect in myoblast proliferation. Genomic and transcriptomic experiments determined that SMCHD1 associates with enhancers of genes controlling cell cycle to activate their expression. Amongst these cell cycle regulatory genes, we identified LAP2 as a key target of SMCHD1 required for the expansion of myoblasts, where the ectopic expression of LAP2 rescues the proliferation defect of SMCHD1-depleted cells. Thus, the epigenetic regulator SMCHD1 can play the role of a transcriptional co-activator for maintaining the expression of genes required for muscle progenitor expansion. This DUX4-independent role for SMCHD1 in myoblasts suggests that the pathology of FSHD2 may be a consequence of defective muscle regeneration in addition to the muscle wasting caused by spurious DUX4 expression