In addition to other nuclear factor regulatory genes, the SNW domain containing 1 gene plays an important role in the pathogenesis of rheumatoid arthritis (RA) and may represent a potential biomarker for the disease, according to research published in Frontiers in Genetics. Researchers indicated that this may lead to a potential pathway for the development of effective, targeted, RA therapies.

Using both statistical and knowledge-based systemic interventions, researchers sought to study the expression status of nuclear factor kappa B (NF-κb) regulators in synovial tissues and to trace the molecular pathways to assess how these regulators contribute to RA.

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With large-scale secondary RNA interference screening, investigators identified the top 40 NF-κb regulators. They created a microarray gene expression profile for patients’ synovial tissues, with analysis conducted using R/Bioconductor.

Investigators collected the top 20 hits from each tumor necrosis factor (TNF)-α and lipopolysaccharide secondary screen analysis, as well as the 5 genes from the NF-κb family and identified the genes common to both TNF-α and lipopolysaccharide, finding that the RELA proto-oncogene, NF-kB subunit gene was present in all 3 categories. They selected 36 genes for analysis and retrieved high-throughput experimental gene expression profiles. Using a Robust Multiarray Average algorithm, the researchers standardized the raw signal intensities of 45,056 probes, from which they identified 3573 nonredundant differentially expressed genes.

Researchers who analyzed seed genes and nucleoside/nucleotide kinase protein families indicated that a majority of seed genes are downregulated (22 downregulated vs 14 upregulated).

They used microarray data to build a protein-protein interaction map, extracting relationships between the genes. The complex protein-protein interaction map included 2742 genes, 37,032 interactions, and 13.51 mean edge-node fractions. From there, the investigators classified differentially expressed genes as either bottlenecks or hubs, according to properties for the construction of the Regulators Allied Protein Interaction Network. In total, 652 genes were bottlenecks and 131 were hubs.

Using Pearson’s correlation algorithm, researchers found that 601 genes had higher correlation, whereas 461 genes were coexpressed in disease samples. All seed genes were coexpressed in the RA sample; however, 2 genes — lysine demethylase 4A and limb and CNS expressed 1 — were not coexpressed in normal samples.

Researchers also conducted a genome-wide association studies analysis to compare susceptibility loci of inflammation-associated disease conditions with the driver genes of RA. One set of genes — TNF receptor superfamily member 1A; toll like receptor 4; nuclear factor kappa B subunit 1; REL proto-oncogene; NF-kB subunit; RELA proto-oncogene; NF-kB subunit; autophagy related 7; FosB proto-oncogene; AP-1 transcription factor subunit;  karyopherin subunit alpha 1; and transient receptor potential cation channel subfamily C member 6 — was significantly associated with RA. According to investigators, this analysis substantiated the role of the identified genes.

“Outcomes from this analysis provide new indications for clarifying the genetic mechanism of RA,” they wrote.

Study limitations include the exclusion of any interactions not involved in the Bisogenet databases, as well as potentially insufficient evidence provided for some genes engaged in either molecular functions or gene ontology.

“A detailed parametric downstream analysis based on biological insight highlights 11 candidate genes that can act as potential biomarkers or drug targets for RA,” the researchers wrote, adding that one remarkable result was the identification of the SNW domain containing 1 gene as a potential RA biomarker.

“Overall, our research analysis has presented the effectiveness of linking genetic expression with their functional relationship in the identification of RA candidate genes,” they concluded. “By experimentally authenticating the results using in vitro and in vivo experiments, this can be further extended in order to pinpoint more selective therapeutic agents.”

Reference

Sabir JSM, El Omri A, Banaganapalli B, et al. Dissecting the role of NF-κb protein family and its regulators in rheumatoid arthritis using weighted gene co-expression networkFront Genet. 2019;10:1163.