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Scaling-up a fragment-based protein-protein interaction method using a human reference interaction set.

ABSTRACT: Protein-protein interactions (PPIs) are essential in understanding numerous aspects of protein function. Here, we significantly scaled and modified analyses of the recently developed all-vs-all sequencing (AVA-Seq) approach using a gold-standard human protein interaction set (hsPRS-v2) containing 98 proteins. Binary interaction analyses recovered 20 of 47 (43%) binary PPIs from this positive reference set (PRS), comparing favorably with other methods. However, the increase of 20× in the interaction search space for AVA-Seq analysis in this manuscript resulted in numerous changes to the method required for future use in genome-wide interaction studies. We show that standard sequencing analysis methods must be modified to consider the possible recovery of thousands of positives among millions of tested interactions in a single sequencing run. The PRS data were used to optimize data scaling, auto-activator removal, rank interaction features (such as orientation and unique fragment pairs), and statistical cutoffs. Using these modifications to the method, AVA-Seq recovered >500 known and novel PPIs, including interactions between wild-type fragments of tumor protein p53 and minichromosome maintenance complex proteins 2 and 5 (MCM2 and MCM5) that could be of interest in human disease.

SUBMITTER: Schaefer-Ramadan S

PROVIDER: S-EPMC9299658 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Purpose: To demonstrate an interaction-based method for the refinement of Gene Set Enrichment Analysis (GSEA) results. Method: Intravitreal injection of miR-124-3p antagomir was used to knockdown the expression of miR-124-3p in mouse retina at postnatal day 3 (P3). Whole retinal RNA was extracted for mRNA transcriptome sequencing at P9. After preprocessing the dataset, GSEA was performed, and the leading-edge subsets were obtained. The Apriori algorithm was used to identify the frequent genes or gene sets from the union of the leading-edge subsets. A new statistic d was introduced to evaluate the frequent genes or gene sets. Reverse transcription quantitative PCR (RT-qPCR) was performed to validate the expression trend of candidate genes after the knockdown of miR-124-3p. Results: A total of 115,140 assembled transcript sequences were obtained from the clean data. With GSEA, the NOD-like receptor signaling pathway, C-type-like lectin receptor signaling pathway, phagosome, necroptosis, JAK-STAT signaling pathway, Toll-like receptor signaling pathway, leukocyte transendothelial migration, chemokine signaling pathway, NF-kappa B signaling pathway and RIG-I-like signaling pathway were identified as the top 10 enriched pathways, and their leading-edge subsets were obtained. After being refined by the Apriori algorithm and sorted by the value of the modulus of d , Prkcd, Irf9, Stat3, Cxcl12, Stat1, Stat2, Isg15, Eif2ak2, Il6st, Pdgfra, Socs4 and Csf2ra had the significant number of interactions and the greatest value of d to downstream genes among all frequent transactions. Results of RT-qPCR validation for the expression of candidate genes after the knockdown of miR-124-3p showed a similar trend to the RNA-Seq results. Conclusion: This study indicated that using the Apriori algorithm and defining the statistic d was a novel way to refine the GSEA results. We hope to convey the intricacies from the computational results to the low-throughput experiments, and to plan experimental investigations specifically.

Project description:BackgroundDuring the last decade, investigations have focused on revealing genes or proteins that are involved in HCC carcinogenesis using either genetic or proteomic techniques. However, these studies are overshadowed by a lack of good internal reference standards. The need to identify "housekeeping" markers, whose expression is stable in various experimental and clinical conditions, is therefore of the utmost clinical relevance in quantitative studies. This is the first study employed 2-DE analysis to screen for potential reference markers and aims to correlate the abundance of these proteins with their level of transcript expression.MethodsA Chinese cohort of 224 liver tissues samples (105 cancerous, 103 non-tumourous cirrhotic, and 16 normal) was profiled using 2-DE analysis. Expression of the potential reference markers was confirmed by western blot, immunohistochemistry and real-time quantitative PCR. geNorm algorithm was employed for gene stability measure of the identified reference markers.ResultsThe expression levels of three protein markers beta-actin (ACTB), heat shock protein 60 (HSP60), and protein disulphide isomerase (PDI) were found to be stable using p-values (p > 0.99) as a ranking tool in all 224 human liver tissues examined by 2-DE analysis. Of high importance, ACTB and HSP 60 were successfully validated at both protein and mRNA levels in human hepatic tissues by western blot, immunohistochemistry and real-time quantitative PCR. In addition, no significant correlation of these markers with any clinicopathological features of HCC and cirrhosis was found. Gene stability measure of these two markers with other conventionally applied housekeeping genes was assessed by the geNorm algorithm, which ranked ACTB and HSP60 as the most stable genes among this cohort of clinical samples.ConclusionOur findings identified 2 reference markers that exhibited stable expression across human liver tissues with different conditions thus should be regarded as reliable reference moieties for normalisation of gene and protein expression in clinical research employing human hepatic tissues.

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Scaling-up a fragment-based protein-protein interaction method using a human reference interaction set.

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