Novel statistical framework to identify differentially expressed genes allowing transcriptomic background differences

A disease-related gene mining method based on weakly supervised learning model

BACKGROUND

Predicting disease-related genes is helpful for understanding the disease pathology and the molecular mechanisms during the disease progression. However, traditional methods are not suitable for screening genes related to the disease development, because there are some samples with weak label information in the disease dataset and a small number of genes are known disease-related genes.

RESULTS

We designed a disease-related gene mining method based on the weakly supervised learning model in this paper. The method is separated into two steps. Firstly, the differentially expressed genes are screened based on the weakly supervised learning model. In the model, the strong and weak label information at different stages of the disease progression is fully utilized. The obtained differentially expressed gene set is stable and complete after the algorithm converges. Then, we screen disease-related genes in the obtained differentially expressed gene set using transductive support vector machine based on the difference kernel function. The difference kernel function can map the input space of the original Huntington's disease gene expression dataset to the difference space. The relation between the two genes can be evaluated more accurately in the difference space and the known disease-related gene information can be used effectively.

CONCLUSIONS

The experimental results show that the disease-related gene mining method based on the weakly supervised learning model can effectively improve the precision of the disease-related gene prediction compared with other excellent methods.

How many differentially expressed genes: A perspective from the comparison of genotypic and phenotypic distances

Identifying differentially expressed genes is critical in microarray data analysis. Many methods have been developed by combining p-value, fold-change, and various statistical models to determine these genes. When using these methods, it is necessary to set up various pre-determined cutoff values. However, many of these cutoff values are somewhat arbitrary and may not have clear connections to biology. In this study, a genetic distance method based on gene expression level was developed to analyze eight sets of microarray data extracted from the GEO database. Since the genes used in distance calculation have been ranked by fold-change, the genetic distance becomes more stable when adding more genes in the calculation, indicating there is an optimal set of genes which are sufficient to characterize the stable difference between samples. This set of genes is differentially expressed genes representing both the genotypic and phenotypic differences between samples.

A model for aryl hydrocarbon receptor-activated gene expression shows potency and efficacy changes and predicts squelching due to competition for transcription co-activators

A stochastic model of nuclear receptor-mediated transcription was developed based on activation of the aryl hydrocarbon receptor (AHR) by 2,3,7,8-tetrachlorodibenzodioxin (TCDD) and subsequent binding the activated AHR to xenobiotic response elements (XREs) on DNA. The model was based on effects observed in cells lines commonly used as in vitro experimental systems. Following ligand binding, the AHR moves into the cell nucleus and forms a heterodimer with the aryl hydrocarbon nuclear translocator (ARNT). In the model, a requirement for binding to DNA is that a generic coregulatory protein is subsequently bound to the AHR-ARNT dimer. Varying the amount of coregulator available within the nucleus altered both the potency and efficacy of TCDD for inducing for transcription of CYP1A1 mRNA, a commonly used marker for activation of the AHR. Lowering the amount of available cofactor slightly increased the EC50 for the transcriptional response without changing the efficacy or maximal response. Further reduction in the amount of cofactor reduced the efficacy and produced non-monotonic dose-response curves (NMDRCs) at higher ligand concentrations. The shapes of these NMDRCs were reminiscent of the phenomenon of squelching. Resource limitations for transcriptional machinery are becoming apparent in eukaryotic cells. Within single cells, nuclear receptor-mediated gene expression appears to be a stochastic process; however, intercellular communication and other aspects of tissue coordination may represent a compensatory process to maintain an organism’s ability to respond on a phenotypic level to various stimuli within an inconstant environment.

Gene profiling of Chikungunya virus arthritis in a mouse model reveals significant overlap with rheumatoid arthritis

OBJECTIVE

Chikungunya virus (CHIKV) is a mosquito-borne alphavirus that causes a chronic debilitating polyarthralgia/polyarthritis, for which current treatments are often inadequate. To assess whether new drugs being developed for rheumatoid arthritis (RA) might find utility in the treatment of alphaviral arthritides, we sought to determine whether the inflammatory gene expression signature of CHIKV arthritis shows any similarities with RA or collagen-induced arthritis (CIA), a mouse model of RA.

METHODS

Using a recently developed animal model of CHIKV arthritis in adult wild-type mice, we generated a consensus CHIKV arthritis gene expression signature, which was used to interrogate publicly available microarray studies of RA and CIA. Pathway analyses were then performed using the overlapping gene signatures.

RESULTS

Gene set enrichment analysis showed that there was a highly significant overlap in the differentially expressed genes in the CHIKV arthritis model and in RA. This concordance also increased with the severity of RA, as measured by the inflammation score. A highly significant overlap was also seen between CHIKV arthritis and CIA. Pathway analysis revealed that the overlap between these arthritides was spread over a range of different inflammatory processes. Involvement of T cells and interferon-γ (IFNγ) in CHIKV arthritis was confirmed in studies of MHCII-deficient mice and IFNγ-deficient mice, respectively.

CONCLUSION

These results suggest that RA, a chronic autoimmune arthritis, and CHIKV disease, usually a self-limiting viral arthropathy, share multiple inflammatory processes. New drugs and biologic therapies being developed for RA may thus find application in the treatment of alphaviral arthritides.