Comprehensive analysis of IgA nephropathy expression profiles: identification of potential biomarkers and therapeutic agents

A Study on Associations of Long Noncoding RNA HOTAIR Polymorphisms With Genetic Susceptibility to Chronic Kidney Disease

BACKGROUND

The importance of long noncoding RNAs (lncRNAs) in various biological processes has been increasingly recognized in recent years. This study investigated how gene polymorphism in HOX transcript antisense RNA (HOTAIR) lncRNA affects the predisposition to chronic kidney disease (CKD).

METHODS

This study comprised 150 patients with CKD and 150 healthy controls. A PCR-RFLP and ARMS-PCR techniques were used for genotyping the five target polymorphisms.

RESULTS

According to our findings, rs4759314 confers strong protection against CKD in allelic, dominant, and codominant heterozygote genetic patterns. Furthermore, rs3816153 decreased CKD risk by 78% when TT versus GG, 55% when GG+GT versus TT, and 74% when GT versus TT+GG. In contrast, the CC+CT genotype [odds ratio (OR) = 1.66, 95% confidence intervals (CIs) = 1.05-2.63] and the T allele (OR = 1.50, 95% CI = 1.06-2.11) of rs12826786, as well as the TT genotype (OR = 2.52, 95% CI = 1.06-5.98) of rs3816153 markedly increased the risk of CKD in the Iranian population. Although no linkage disequilibrium was found between the studied variants, the Crs12826786Trs920778Grs1899663Grs4759314Grs3816153 haplotype was associated with a decreased risk of CKD by 86% (OR = 0.14, 95% CI = 0.03-0.66).

CONCLUSION

The rs920778 was not correlated with CKD risk, whereas the HOTAIR rs4759314, rs12826786, rs1899663, and rs3816153 polymorphisms affected the risk of CKD in our population. It seems essential to conduct repeated studies across various ethnic groups to explore the link between HOTAIR variants and their impact on the disease outcome.

Identification of key genes and biological regulatory mechanisms in diabetic nephropathy: Meta-analysis of gene expression datasets

BACKGROUND

Diabetic nephropathy (DN) which refers to the cases with biopsy proven kidney lesions, is one of the main complications of diabetes all around the world; however, the underlying biological changes causing DN remain to be understood. Studying the alterations in gene expression profiles could give a holistic view of the molecular pathogenicity of DN and aid to discover key molecules as potential therapeutic targets. Here, we performed a meta-analysis study that included microarray gene expression profiles coming from glomerular samples of DN patients in order to acquire a list of consensus Differentially Expressed Genes (meta-DEGs) correlated with DN.

METHODS

After quality control and normalization steps, five gene expression datasets (GES1009, GSE30528, GSE47183, GSE104948, and GSE93804) were entered into the meta-analysis. The meta-analysis was performed by random effect size method and the meta-DEGs were put through network analysis and different pathway enrichment analyses steps. MiRTarBase and TRRUST databases were utilized to predict the meta-DEGs related miRNAs and transcription factors. A co-regulatory network including DEGs, transcription factors and miRNAs was constructed by Cytoscape, and top molecules were identified based on centrality scores in the network.

RESULTS

The identified meta-DEGs were 1364 DEGs including 665 downregulated and 669 upregulated DEGs. The results of pathway enrichment analysis showed, "immune system", "extracellular matrix organization", "hemostasis", "signal transduction", and "platelet activation" to be the top enriched terms with involvement of the meta-DEGs. After construction of the multilayer regulatory network, several top DEGs (TP53, MYC, BTG2, VEGFA, PTEN, etc.), as well as top miRNAs (miR-335, miR-16, miR-17, miR-20a, and miR-93), and transcription factors (SP1, STAT3, NF-KB1, RELA, E2F1), were introduced as potential therapeutic targets in DN. Among the regulatory molecules, miR-335-5p and SP1 were the most interactive miRNA and transcription factor molecules with the highest degree scores in the constructed network.

CONCLUSION

By performing a meta-analysis of available DN-related transcriptomics datasets, we reached a consensus list of DEGs for this complicated disorder. Further enrichment and network analyses steps revealed the involved pathways in the DN pathogenesis and marked the most potential therapeutic targets in this disease.

The Mission of Long Non-Coding RNAs in Human Adult Renal Stem/Progenitor Cells and Renal Diseases

Long non-coding RNAs (lncRNAs) are a large, heterogeneous class of transcripts and key regulators of gene expression at both the transcriptional and post-transcriptional levels in different cellular contexts and biological processes. Understanding the potential mechanisms of action of lncRNAs and their role in disease onset and development may open up new possibilities for therapeutic approaches in the future. LncRNAs also play an important role in renal pathogenesis. However, little is known about lncRNAs that are expressed in the healthy kidney and that are involved in renal cell homeostasis and development, and even less is known about lncRNAs involved in human adult renal stem/progenitor cells (ARPC) homeostasis. Here we give a thorough overview of the biogenesis, degradation, and functions of lncRNAs and highlight our current understanding of their functional roles in kidney diseases. We also discuss how lncRNAs regulate stem cell biology, focusing finally on their role in human adult renal stem/progenitor cells, in which the lncRNA HOTAIR prevents them from becoming senescent and supports these cells to secrete high quantities of α-Klotho, an anti-aging protein capable of influencing the surrounding tissues and therefore modulating the renal aging.

A comprehensive analysis of gene expression profiling data in COVID-19 patients for discovery of specific and differential blood biomarker signatures

COVID-19 is a newly recognized illness with a predominantly respiratory presentation. Although initial analyses have identified groups of candidate gene biomarkers for the diagnosis of COVID-19, they have yet to identify clinically applicable biomarkers, so we need disease-specific diagnostic biomarkers in biofluid and differential diagnosis in comparison with other infectious diseases. This can further increase knowledge of pathogenesis and help guide treatment. Eight transcriptomic profiles of COVID-19 infected versus control samples from peripheral blood (PB), lung tissue, nasopharyngeal swab and bronchoalveolar lavage fluid (BALF) were considered. In order to find COVID-19 potential Specific Blood Differentially expressed genes (SpeBDs), we implemented a strategy based on finding shared pathways of peripheral blood and the most involved tissues in COVID-19 patients. This step was performed to filter blood DEGs with a role in the shared pathways. Furthermore, nine datasets of the three types of Influenza (H1N1, H3N2, and B) were used for the second step. Potential Differential Blood DEGs of COVID-19 versus Influenza (DifBDs) were found by extracting DEGs involved in only enriched pathways by SpeBDs and not by Influenza DEGs. Then in the third step, a machine learning method (a wrapper feature selection approach supervised by four classifiers of k-NN, Random Forest, SVM, Naïve Bayes) was utilized to narrow down the number of SpeBDs and DifBDs and find the most predictive combination of them to select COVID-19 potential Specific Blood Biomarker Signatures (SpeBBSs) and COVID-19 versus influenza Differential Blood Biomarker Signatures (DifBBSs), respectively. After that, models based on SpeBBSs and DifBBSs and the corresponding algorithms were built to assess their performance on an external dataset. Among all the extracted DEGs from the PB dataset (from common PB pathways with BALF, Lung and Swab), 108 unique SpeBD were obtained. Feature selection using Random Forest outperformed its counterparts and selected IGKC, IGLV3-16 and SRP9 among SpeBDs as SpeBBSs. Validation of the constructed model based on these genes and Random Forest on an external dataset resulted in 93.09% Accuracy. Eighty-three pathways enriched by SpeBDs and not by any of the influenza strains were identified, including 87 DifBDs. Using feature selection by Naive Bayes classifier on DifBDs, FMNL2, IGHV3-23, IGLV2-11 and RPL31 were selected as the most predictable DifBBSs. The constructed model based on these genes and Naive Bayes on an external dataset was validated with 87.2% accuracy. Our study identified several candidate blood biomarkers for a potential specific and differential diagnosis of COVID-19. The proposed biomarkers could be valuable targets for practical investigations to validate their potential.

Comprehensive analysis of diabetic nephropathy expression profile based on weighted gene co-expression network analysis algorithm

Background

Diabetic nephropathy (DN) is the major complication of diabetes mellitus, and leading cause of end-stage renal disease. The underlying molecular mechanism of DN is not yet completely clear. The aim of this study was to analyze a DN microarray dataset using weighted gene co-expression network analysis (WGCNA) algorithm for better understanding of DN pathogenesis and exploring key genes in the disease progression.

Methods

The identified differentially expressed genes (DEGs) in DN dataset GSE47183 were introduced to WGCNA algorithm to construct co-expression modules. STRING database was used for construction of Protein-protein interaction (PPI) networks of the genes in all modules and the hub genes were identified considering both the degree centrality in the PPI networks and the ranked lists of weighted networks. Gene ontology and Reactome pathway enrichment analyses were performed on each module to understand their involvement in the biological processes and pathways. Following validation of the hub genes in another DN dataset (GSE96804), their up-stream regulators, including microRNAs and transcription factors were predicted and a regulatory network comprising of all these molecules was constructed.

Results

After normalization and analysis of the dataset, 2475 significant DEGs were identified and clustered into six different co-expression modules by WGCNA algorithm. Then, DEGs of each module were subjected to functional enrichment analyses and PPI network constructions. Metabolic processes, cell cycle control, and apoptosis were among the top enriched terms. In the next step, 23 hub genes were identified among the modules in genes and five of them, including FN1, SLC2A2, FABP1, EHHADH and PIPOX were validated in another DN dataset. In the regulatory network, FN1 was the most affected hub gene and mir-27a and REAL were recognized as two main upstream-regulators of the hub genes.

Conclusions

The identified hub genes from the hearts of co-expression modules could widen our understanding of the DN development and might be of targets of future investigations, exploring their therapeutic potentials for treatment of this complicated disease.

Non-Coding RNAs in Kidney Diseases: The Long and Short of Them

Recent progress in genomic research has highlighted the genome to be much more transcribed than expected. The formerly so-called junk DNA encodes a miscellaneous group of largely unknown RNA transcripts, which contain the long non-coding RNAs (lncRNAs) family. lncRNAs are instrumental in gene regulation. Moreover, understanding their biological roles in the physiopathology of many diseases, including renal, is a new challenge. lncRNAs regulate the effects of microRNAs (miRNA) on mRNA expression. Understanding the complex crosstalk between lncRNA–miRNA–mRNA is one of the main challenges of modern molecular biology. This review aims to summarize the role of lncRNA on kidney diseases, the molecular mechanisms involved, and their function as emerging prognostic biomarkers for both acute and chronic kidney diseases. Finally, we will also outline new therapeutic opportunities to diminish renal injury by targeting lncRNA with antisense oligonucleotides.