Transcriptional noise in intact and TGF-beta treated human kidney cells; the importance of time-series designs

Olfactory receptors contribute to progression of kidney fibrosis

Olfactory receptors (ORs) which are mainly known as odor-sensors in the olfactory epithelium are shown to be expressed in several non-sensory tissues. Despite the specified role of some of these receptors in normal physiology of the kidney, little is known about their potential effect in renal disorders. In this study, using the holistic view of systems biology, it was determined that ORs are significantly changed during the progression of kidney fibrosis. For further validation, common differentially expressed ORs resulted from reanalysis of two time-course microarray datasets were selected for experimental evaluation in a validated murine model of unilateral ureteral obstruction (UUO). Transcriptional analysis by real-time quantitative polymerase chain reaction demonstrated considerable changes in the expression pattern of Olfr433, Olfr129, Olfr1393, Olfr161, and Olfr622 during the progression of kidney fibrosis. For localization of these ORs, single-cell RNA-sequencing datasets of normal and UUO mice were reanalyzed. Results showed that Olfr433 is highly expressed in macrophages in day-2 and 7 post-injury in UUO mice and not in normal subgroups. Besides, like previous findings, Olfr1393 was shown to be expressed prominently in the proximal tubular cells of the kidney. In conclusion, our combinatorial temporal approach to the underlying mechanisms of chronic kidney disease highlighted the potential role of ORs in progression of fibrosis. The expression of Olfr433 in the macrophages provides some clue about its relation to molecular mechanisms promoted in the fibrotic kidney. The proposed ORs in this study could be the subject of further functional assessments in the future.

In vitro versus in vivo models of kidney fibrosis: Time-course experimental design is crucial to avoid misinterpretations of gene expression data

Background:

In vitro models are common tools in nephrology research. However, their validity has rarely been scrutinized.

Materials and Methods:

Considering the critical role of transforming growth factor (TGF)-β and hypoxia pathways in kidney fibrosis, kidney-derived cells were exposed to TGF-β and/or hypoxic conditions and the expression levels of some genes related to these two signaling pathways were quantified in a time-course manner. Furthermore, a unilateral ureteral obstruction mouse model was generated, and the expressions of the same genes were assessed.

Results:

In all in vitro experimental groups, the expression of the genes was noisy with no consistent pattern. However, in the animal model, TGF-β pathway-related genes demonstrated considerable overexpression in the ureteral obstruction group compared with the sham controls. Interestingly, hypoxia pathway genes had prominent fluctuations with very similar patterns in both animal groups, suggesting a periodical pattern not affected by the intervention.

Conclusion:

The findings of this study suggest that in vitro findings should be interpreted cautiously and if possible are substituted or supported by animal models that are more consistent and reliable. Furthermore, we underscore the importance of time-course evaluation of both case and control groups in gene expression studies to avoid misconceptions caused by gene expression noise or intrinsic rhythms.

Big data to knowledge: common pitfalls in transcriptomics data analysis and representation

The omics technologies provide an invaluable opportunity to employ a global view towards human diseases. However, the appropriate translation of big data to knowledge remains a major challenge. In this study, we have performed quality control assessments for 91 transcriptomics datasets deposited in gene expression omnibus database and also have evaluated the publications derived from these datasets. This survey shows that drawbacks in the analyses and reports of transcriptomics studies are more common than one may assume. This report is concluded with some suggestions for researchers and reviewers to enhance the minimal requirements for gene expression data generation, analysis and report.