Identification of disturbed pathways in heart failure based on Gibbs sampling and pathway enrichment analysis

Chen, P; Guo, L H; Guo, You; Qu, Zhanli; Gao, Yong; Qiu, Huixian

doi:10.4238/gmr.15027956

Cited by 7 publications

(5 citation statements)

References 35 publications

(33 reference statements)

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“…• Neuroactive ligand-receptor interaction (hsa04080): cardiovascular diseases and aging are both associated with changes in the neurohumoral system, mainly adrenergic and renin-angiotensin systems. In particular, it has been reported a decrease in catecholamine-responsiveness in the elderly [28] and its regulatory effect in pathways disturbed in heart failure [29].…”

Section: Resultsmentioning

confidence: 99%

A computationally inferred regulatory heart aging model including post-transcriptional regulations

Politano

Logrand

Brancaccio

et al. 2016

2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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Cardiovascular diseases are one of the leading causes of death in most developed countries and aging is a dominant risk factor for their development. Among the different factors, miRNAs have been identified as relevant players in the development of cardiac pathologies and their ability to influence gene networks suggests them as potential therapeutic targets or diagnostic markers. This paper presents a computational study that applies data fusion techniques coupled with network analysis theory to identify a regulatory model able to represent the relationship between key genes and miRNAs involved in cardiac senescence processes. The model has been validated through an extensive literature analysis that was able to connect 94% of the identified genes and miRNAs with cardiac senescence related studies.

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Section: Resultsmentioning

confidence: 99%

A computationally inferred regulatory heart aging model including post-transcriptional regulations

Politano

Logrand

Brancaccio

et al. 2016

2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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show abstract

“…• Neuroactive ligand-receptor interaction (hsa04080): cardiovascular diseases and aging are both associated with changes in the neurohumoral system, mainly adrenergic and renin-angiotensin systems. In particular, it has been reported a decrease in catecholamine-responsiveness in the elderly [50] and its regulatory effect in pathways disturbed in heart failure [51].…”

Section: Resultsmentioning

confidence: 99%

In-silico cardiac aging regulatory model including microRNA post-transcriptional regulation

Politano

Logrand

Brancaccio

et al. 2017

Methods

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In most developed countries, cardiovascular diseases are among the top causes of death and their development has been shown closely related to aging. In this context, because of their ability to pervasively influence gene networks, miRs have been found as possible key players in the development of cardiac pathologies, suggesting their potential role as therapeutic targets or diagnostic markers. Based on these assumptions, we hereby present a computational study that applies data fusion techniques coupled with network analysis theory to identify a regulatory model able to represent the relationship between key genes and miRs involved in cardiac senescence processes. The proposed model has been validated through an extensive literature analysis, which confirmed that 94% of the identified genes and miRs are related with cardiac senescence. Furthermore, two relevant genes of the model have been also validated by Western blot experiments on heart samples from young and old mice, confirming in vitro their ectopic expression in aged hearts. The pure computationally inferred model presented in the paper is therefore a good candidate to represent the relationship between key genes and miRs involved in cardiac senescence processes, and represents a reliable selection of genes and miRs for further studies, in order to elucidate and better detail their involvement in cardiac aging.

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“…Next the adjusted probability of each pathway was calculated as follows: α adj = α × c . After accomplishing all the adjusted calculation probabilities, the pathways were all ranked according to their α adj value and pathways with α adj ≥0.8 were selected as differentially expressed pathways ( 12 ).…”

Section: Methodsmentioning

confidence: 99%

Screening pathways and hub genes involved in osteoclastogenesis by gene expression analysis of circulating monocytes based on Gibbs sampling

Xiao

Zhao²

2019

Exp Ther Med

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Differential expression pathways and hub genes in circulating monocytes from healthy Chinese women with high peak bone mass (PBM) vs. low PBM were explored using a Markov chain Monte Carlo (MCMC) algorithm. Human circulating monocytes transcription profiling (containing 14 samples with high PBM and 12 samples with low PBM) and KEGG pathways were all downloaded from the public database. Initial state of all the pathways were constructed and Gibbs sampling was performed to obtain a Markov chain and the posterior values of all the pathways were calculated. The probability (α) of occurrence of each pathway was calculated based on the posterior value and it was adjusted by taking gene expression variation into account. Pathways with αadj >0.8 were considered as differentially expressed pathways. Then, these steps were performed again on all the genes in the differentially expressed pathways to find the hub genes in the differential pathways. After Gibbs sampling, neuroactive ligand-receptor interaction (hsa04080) with αadj = 0.986 was screened out as the differentially expressed pathway. Analyzing the genes in this pathway, three genes (neurotensin, tachykinin receptor 3 and follicle-stimulating hormone receptor) with αadj >0.8 were identified as hub genes in circulating monocytes which may associate with osteoporosis development. One pathway and three genes which may possess close relationship with osteoporosis development were found in this study. These results provide insights into our understanding of the role of circulating monocytes in osteoporosis development.

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Identification of disturbed pathways in heart failure based on Gibbs sampling and pathway enrichment analysis

Cited by 7 publications

References 35 publications

A computationally inferred regulatory heart aging model including post-transcriptional regulations

A computationally inferred regulatory heart aging model including post-transcriptional regulations

In-silico cardiac aging regulatory model including microRNA post-transcriptional regulation

Screening pathways and hub genes involved in osteoclastogenesis by gene expression analysis of circulating monocytes based on Gibbs sampling

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