Dataset Information

Identification of contributing genes of Huntington's disease by machine learning.

ABSTRACT:

Background

Huntington's disease (HD) is an inherited disorder caused by the polyglutamine (poly-Q) mutations of the HTT gene results in neurodegeneration characterized by chorea, loss of coordination, cognitive decline. However, HD pathogenesis is still elusive. Despite the availability of a wide range of biological data, a comprehensive understanding of HD's mechanism from machine learning is so far unrealized, majorly due to the lack of needed data density.

Methods

To harness the knowledge of the HD pathogenesis from the expression profiles of postmortem prefrontal cortex samples of 157 HD and 157 controls, we used gene profiling ranking as the criteria to reduce the dimension to the order of magnitude of the sample size, followed by machine learning using the decision tree, rule induction, random forest, and generalized linear model.

Results

These four Machine learning models identified 66 potential HD-contributing genes, with the cross-validated accuracy of 90.79?±?4.57%, 89.49?±?5.20%, 90.45?±?4.24%, and 97.46?±?3.26%, respectively. The identified genes enriched the gene ontology of transcriptional regulation, inflammatory response, neuron projection, and the cytoskeleton. Moreover, three genes in the cognitive, sensory, and perceptual systems were also identified.

Conclusions

The mutant HTT may interfere with both the expression and transport of these identified genes to promote the HD pathogenesis.

SUBMITTER: Cheng J

PROVIDER: S-EPMC7684976 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Publications

Identification of contributing genes of Huntington's disease by machine learning.

Cheng Jack J Liu Hsin-Ping HP Lin Wei-Yong WY Tsai Fuu-Jen FJ

BMC medical genomics 20201123 1

<h4>Background</h4>Huntington's disease (HD) is an inherited disorder caused by the polyglutamine (poly-Q) mutations of the HTT gene results in neurodegeneration characterized by chorea, loss of coordination, cognitive decline. However, HD pathogenesis is still elusive. Despite the availability of a wide range of biological data, a comprehensive understanding of HD's mechanism from machine learning is so far unrealized, majorly due to the lack of needed data density.<h4>Methods</h4>To harness th ...[more]

PMID: 33228685

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Project description:BACKGROUND:With the global spread of multidrug resistance in pathogenic microbes, infectious diseases emerge as a key public health concern of the recent time. Identification of host genes associated with infectious diseases will improve our understanding about the mechanisms behind their development and help to identify novel therapeutic targets. RESULTS:We developed a machine learning techniques-based classification approach to identify infectious disease-associated host genes by integrating sequence and protein interaction network features. Among different methods, Deep Neural Networks (DNN) model with 16 selected features for pseudo-amino acid composition (PAAC) and network properties achieved the highest accuracy of 86.33% with sensitivity of 85.61% and specificity of 86.57%. The DNN classifier also attained an accuracy of 83.33% on a blind dataset and a sensitivity of 83.1% on an independent dataset. Furthermore, to predict unknown infectious disease-associated host genes, we applied the proposed DNN model to all reviewed proteins from the database. Seventy-six out of 100 highly-predicted infectious disease-associated genes from our study were also found in experimentally-verified human-pathogen protein-protein interactions (PPIs). Finally, we validated the highly-predicted infectious disease-associated genes by disease and gene ontology enrichment analysis and found that many of them are shared by one or more of the other diseases, such as cancer, metabolic and immune related diseases. CONCLUSIONS:To the best of our knowledge, this is the first computational method to identify infectious disease-associated host genes. The proposed method will help large-scale prediction of host genes associated with infectious-diseases. However, our results indicated that for small datasets, advanced DNN-based method does not offer significant advantage over the simpler supervised machine learning techniques, such as Support Vector Machine (SVM) or Random Forest (RF) for the prediction of infectious disease-associated host genes. Significant overlap of infectious disease with cancer and metabolic disease on disease and gene ontology enrichment analysis suggests that these diseases perturb the functions of the same cellular signaling pathways and may be treated by drugs that tend to reverse these perturbations. Moreover, identification of novel candidate genes associated with infectious diseases would help us to explain disease pathogenesis further and develop novel therapeutics.

Project description:Huntington's disease (HD) is an inherited neurodegenerative disease caused by a polyglutamine expansion in the huntington protein (htt). The neuropathological hallmark of HD is the loss of neurons in the striatum and, to a lesser extent, in the cortex. Foxp1 is a member of the Forkhead family of transcription factors expressed selectively in the striatum and the cortex. In the brain, three major Foxp1 isoforms are expressed: isoform-A (?90 kDa), isoform-D (?70 kDa), and isoform-C (?50 kDa). We find that expression of Foxp1 isoform-A and -D is selectively reduced in the striatum and cortex of R6/2 HD mice as well as in the striatum of HD patients. Furthermore, expression of mutant htt in neurons results in the downregulation of Foxp1 Elevating expression of isoform-A or -D protects cortical neurons from death caused by the expression of mutant htt On the other hand, knockdown of Foxp1 promotes death in otherwise healthy neurons. Neuroprotection by Foxp1 is likely to be mediated by the transcriptional stimulation of the cell-cycle inhibitory protein p21Waf1/Cip1 Consistently, Foxp1 activates transcription of the p21Waf1/Cip1 gene promoter, and overexpression of Foxp1 in neurons results in the elevation of p21 expression. Moreover, knocking down of p21Waf1/Cip1 blocks the ability of Foxp1 to protect neurons from mut-Htt-induced neurotoxicity. We propose that the selective vulnerability of neurons of the striatum and cortex in HD is related to the loss of expression of Foxp1, a protein that is highly expressed in these neurons and required for their survival.SIGNIFICANCE STATEMENT Although the mutant huntingtin gene is expressed widely, neurons of the striatum and cortex are selectively affected in Huntington's disease (HD). Our results suggest that this selectivity is attributable to the reduced expression of Foxp1, a protein expressed selectively in striatal and cortical neurons that plays a neuroprotective role in these cells. We show that protection by Foxp1 involves stimulation of the p21Waf1/Cip1 (Cdkn1a) gene. Although three major Foxp1 isoforms (A, C, and D) are expressed in the brain, only isoform-A has been studied in the nervous system. We show that isoform-D is also expressed selectively, neuroprotective and downregulated in HD mice and patients. Our results suggest that Foxp1 might be an attractive therapeutic target for HD.

Dataset Information

Identification of contributing genes of Huntington's disease by machine learning.

Background

Methods

Results

Conclusions

Publications

Identification of contributing genes of Huntington's disease by machine learning.

Similar Datasets

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