Differential Gene Expression in Host Ubiquitination Processes in Childhood Malarial Anemia

Background: Malaria remains one of the leading global causes of childhood morbidity and mortality. In holoendemic Plasmodium falciparum transmission regions, such as western Kenya, severe malarial anemia [SMA, hemoglobin (Hb) < 6.0 g/dl] is the primary form of severe disease. Ubiquitination is essential for regulating intracellular processes involved in innate and adaptive immunity. Although dysregulation in ubiquitin molecular processes is central to the pathogenesis of multiple human diseases, the expression patterns of ubiquitination genes in SMA remain unexplored.Methods: To examine the role of the ubiquitination processes in pathogenesis of SMA, differential gene expression profiles were determined in Kenyan children (n = 44, aged <48 mos) with either mild malarial anemia (MlMA; Hb ≥9.0 g/dl; n = 23) or SMA (Hb <6.0 g/dl; n = 21) using the Qiagen Human Ubiquitination Pathway RT2 Profiler PCR Array containing a set of 84 human ubiquitination genes.Results: In children with SMA, 10 genes were down-regulated (BRCC3, FBXO3, MARCH5, RFWD2, SMURF2, UBA6, UBE2A, UBE2D1, UBE2L3, UBR1), and five genes were up-regulated (MDM2, PARK2, STUB1, UBE2E3, UBE2M). Enrichment analyses revealed Ubiquitin-Proteasomal Proteolysis as the top disrupted process, along with altered sub-networks involved in proteasomal, protein, and ubiquitin-dependent catabolic processes.Conclusion: Collectively, these novel results show that protein coding genes of the ubiquitination processes are involved in the pathogenesis of SMA.

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RankerGUI: A Computational Framework to Compare Differential Gene Expression Profiles Using Rank Based Statistics

International Journal of Molecular Sciences ◽

10.3390/ijms20236098 ◽

2019 ◽

Vol 20 (23) ◽

pp. 6098 ◽

Cited By ~ 1

Author(s):

Amarinder Singh Thind ◽

Kumar Parijat Tripathi ◽

Mario Rosario Guarracino

Keyword(s):

Gene Expression ◽

Differential Expression ◽

Differential Gene Expression ◽

Web Application ◽

Cellular Response ◽

Expression Profiles ◽

Expression Patterns ◽

Gene Expression Profiles ◽

Experimental Conditions ◽

Differential Gene

The comparison of high throughput gene expression datasets obtained from different experimental conditions is a challenging task. It provides an opportunity to explore the cellular response to various biological events such as disease, environmental conditions, and drugs. There is a need for tools that allow the integration and analysis of such data. We developed the “RankerGUI pipeline”, a user-friendly web application for the biological community. It allows users to use various rank based statistical approaches for the comparison of full differential gene expression profiles between the same or different biological states obtained from different sources. The pipeline modules are an integration of various open-source packages, a few of which are modified for extended functionality. The main modules include rank rank hypergeometric overlap, enriched rank rank hypergeometric overlap and distance calculations. Additionally, preprocessing steps such as merging differential expression profiles of multiple independent studies can be added before running the main modules. Output plots show the strength, pattern, and trends among complete differential expression profiles. In this paper, we describe the various modules and functionalities of the developed pipeline. We also present a case study that demonstrates how the pipeline can be used for the comparison of differential expression profiles obtained from multiple platforms’ data of the Gene Expression Omnibus. Using these comparisons, we investigate gene expression patterns in kidney and lung cancers.

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