Network-based analysis of comorbidities risk during an infection: SARS and HIV case studies
Identifieur interne : 001004 ( Pmc/Checkpoint ); précédent : 001003; suivant : 001005Network-based analysis of comorbidities risk during an infection: SARS and HIV case studies
Auteurs : Mohammad Ali Moni [Bangladesh] ; Pietro LiSource :
- BMC Bioinformatics [ 1471-2105 ] ; 2014.
Abstract
Infections are often associated to comorbidity that increases the risk of medical conditions which can lead to further morbidity and mortality. SARS is a threat which is similar to MERS virus, but the comorbidity is the key aspect to underline their different impacts. One UK doctor says "I’d rather have HIV than diabetes" as life expectancy among diabetes patients is lower than that of HIV. However, HIV has a comorbidity impact on the diabetes.
We present a quantitative framework to compare and explore comorbidity between diseases. By using neighbourhood based benchmark and topological methods, we have built comorbidity relationships network based on the OMIM and our identified significant genes. Then based on the gene expression, PPI and signalling pathways data, we investigate the comorbidity association of these 2 infective pathologies with other 7 diseases (heart failure, kidney disorder, breast cancer, neurodegenerative disorders, bone diseases, Type 1 and Type 2 diabetes). Phenotypic association is measured by calculating both the Relative Risk as the quantified measures of comorbidity tendency of two disease pairs and the
Our method presents a ripe opportunity to use data-driven approaches for advancing our current knowledge on disease mechanism and predicting disease comorbidities in a quantitative way.
The online version of this article (doi:10.1186/1471-2105-15-333) contains supplementary material, which is available to authorized users.
Url:
DOI: 10.1186/1471-2105-15-333
PubMed: 25344230
PubMed Central: 4363349
Affiliations:
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PMC:4363349Le document en format XML
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<sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Network-based analysis of comorbidities risk during an infection: SARS and HIV case studies</title>
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<front><div type="abstract" xml:lang="en"><sec><title>Background</title>
<p>Infections are often associated to comorbidity that increases the risk of medical conditions which can lead to further morbidity and mortality. SARS is a threat which is similar to MERS virus, but the comorbidity is the key aspect to underline their different impacts. One UK doctor says "I’d rather have HIV than diabetes" as life expectancy among diabetes patients is lower than that of HIV. However, HIV has a comorbidity impact on the diabetes.</p>
</sec>
<sec><title>Results</title>
<p>We present a quantitative framework to compare and explore comorbidity between diseases. By using neighbourhood based benchmark and topological methods, we have built comorbidity relationships network based on the OMIM and our identified significant genes. Then based on the gene expression, PPI and signalling pathways data, we investigate the comorbidity association of these 2 infective pathologies with other 7 diseases (heart failure, kidney disorder, breast cancer, neurodegenerative disorders, bone diseases, Type 1 and Type 2 diabetes). Phenotypic association is measured by calculating both the Relative Risk as the quantified measures of comorbidity tendency of two disease pairs and the <italic>ϕ</italic>
-correlation to measure the robustness of the comorbidity associations. The differential gene expression profiling strongly suggests that the response of SARS affected patients seems to be mainly an innate inflammatory response and statistically dysregulates a large number of genes, pathways and PPIs subnetworks in different pathologies such as chronic heart failure (21 genes), breast cancer (16 genes) and bone diseases (11 genes). HIV-1 induces comorbidities relationship with many other diseases, particularly strong correlation with the neurological, cancer, metabolic and immunological diseases. Similar comorbidities risk is observed from the clinical information. Moreover, SARS and HIV infections dysregulate 4 genes (ANXA3, GNS, HIST1H1C, RASA3) and 3 genes (HBA1, TFRC, GHITM) respectively that affect the ageing process. It is notable that HIV and SARS similarly dysregulated 11 genes and 3 pathways. Only 4 significantly dysregulated genes are common between SARS-CoV and MERS-CoV, including NFKBIA that is a key regulator of immune responsiveness implicated in susceptibility to infectious and inflammatory diseases.</p>
</sec>
<sec><title>Conclusions</title>
<p>Our method presents a ripe opportunity to use data-driven approaches for advancing our current knowledge on disease mechanism and predicting disease comorbidities in a quantitative way.</p>
</sec>
<sec><title>Electronic supplementary material</title>
<p>The online version of this article (doi:10.1186/1471-2105-15-333) contains supplementary material, which is available to authorized users.</p>
</sec>
</div>
</front>
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<pmc article-type="research-article"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">BMC Bioinformatics</journal-id>
<journal-id journal-id-type="iso-abbrev">BMC Bioinformatics</journal-id>
<journal-title-group><journal-title>BMC Bioinformatics</journal-title>
</journal-title-group>
<issn pub-type="epub">1471-2105</issn>
<publisher><publisher-name>BioMed Central</publisher-name>
<publisher-loc>London</publisher-loc>
</publisher>
</journal-meta>
<article-meta><article-id pub-id-type="pmid">25344230</article-id>
<article-id pub-id-type="pmc">4363349</article-id>
<article-id pub-id-type="publisher-id">6782</article-id>
<article-id pub-id-type="doi">10.1186/1471-2105-15-333</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject>
</subj-group>
</article-categories>
<title-group><article-title>Network-based analysis of comorbidities risk during an infection: SARS and HIV case studies</article-title>
</title-group>
<contrib-group><contrib contrib-type="author" corresp="yes"><name><surname>Moni</surname>
<given-names>Mohammad Ali</given-names>
</name>
<address><email>Mohammad.Moni@cl.cam.ac.uk</email>
</address>
<xref ref-type="aff" rid="Aff1">1</xref>
<xref ref-type="aff" rid="Aff2">2</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Liò</surname>
<given-names>Pietro</given-names>
</name>
<address><email>pietro.lio@cl.cam.ac.uk</email>
</address>
<xref ref-type="aff" rid="Aff1">1</xref>
</contrib>
<aff id="Aff1"><label>1</label>
<institution-wrap><institution-id institution-id-type="GRID">grid.5335.0</institution-id>
<institution-id institution-id-type="ISNI">0000000121885934</institution-id>
<institution>Computer Laboratory,</institution>
<institution>University of Cambridge,</institution>
</institution-wrap>
William Gates Building, 15 JJ Thomson Avenue, Cambridge, CB3 0FD UK</aff>
<aff id="Aff2"><label>2</label>
<institution-wrap><institution-id institution-id-type="GRID">grid.449168.6</institution-id>
<institution>Department of Computer Science & Engineering,</institution>
<institution>Pabna University of Science & Technology,</institution>
</institution-wrap>
Pabna, Bangladesh</aff>
</contrib-group>
<pub-date pub-type="epub"><day>24</day>
<month>10</month>
<year>2014</year>
</pub-date>
<pub-date pub-type="pmc-release"><day>24</day>
<month>10</month>
<year>2014</year>
</pub-date>
<pub-date pub-type="collection"><year>2014</year>
</pub-date>
<volume>15</volume>
<elocation-id>333</elocation-id>
<history><date date-type="received"><day>30</day>
<month>12</month>
<year>2013</year>
</date>
<date date-type="accepted"><day>19</day>
<month>9</month>
<year>2014</year>
</date>
</history>
<permissions><copyright-statement>© Moni and Liò; licensee BioMed Central. 2014</copyright-statement>
<license license-type="OpenAccess"><license-p>This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0">http://creativecommons.org/licenses/by/4.0</ext-link>
), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/publicdomain/zero/1.0/">http://creativecommons.org/publicdomain/zero/1.0/</ext-link>
) applies to the data made available in this article, unless otherwise stated.</license-p>
</license>
</permissions>
<abstract id="Abs1"><sec><title>Background</title>
<p>Infections are often associated to comorbidity that increases the risk of medical conditions which can lead to further morbidity and mortality. SARS is a threat which is similar to MERS virus, but the comorbidity is the key aspect to underline their different impacts. One UK doctor says "I’d rather have HIV than diabetes" as life expectancy among diabetes patients is lower than that of HIV. However, HIV has a comorbidity impact on the diabetes.</p>
</sec>
<sec><title>Results</title>
<p>We present a quantitative framework to compare and explore comorbidity between diseases. By using neighbourhood based benchmark and topological methods, we have built comorbidity relationships network based on the OMIM and our identified significant genes. Then based on the gene expression, PPI and signalling pathways data, we investigate the comorbidity association of these 2 infective pathologies with other 7 diseases (heart failure, kidney disorder, breast cancer, neurodegenerative disorders, bone diseases, Type 1 and Type 2 diabetes). Phenotypic association is measured by calculating both the Relative Risk as the quantified measures of comorbidity tendency of two disease pairs and the <italic>ϕ</italic>
-correlation to measure the robustness of the comorbidity associations. The differential gene expression profiling strongly suggests that the response of SARS affected patients seems to be mainly an innate inflammatory response and statistically dysregulates a large number of genes, pathways and PPIs subnetworks in different pathologies such as chronic heart failure (21 genes), breast cancer (16 genes) and bone diseases (11 genes). HIV-1 induces comorbidities relationship with many other diseases, particularly strong correlation with the neurological, cancer, metabolic and immunological diseases. Similar comorbidities risk is observed from the clinical information. Moreover, SARS and HIV infections dysregulate 4 genes (ANXA3, GNS, HIST1H1C, RASA3) and 3 genes (HBA1, TFRC, GHITM) respectively that affect the ageing process. It is notable that HIV and SARS similarly dysregulated 11 genes and 3 pathways. Only 4 significantly dysregulated genes are common between SARS-CoV and MERS-CoV, including NFKBIA that is a key regulator of immune responsiveness implicated in susceptibility to infectious and inflammatory diseases.</p>
</sec>
<sec><title>Conclusions</title>
<p>Our method presents a ripe opportunity to use data-driven approaches for advancing our current knowledge on disease mechanism and predicting disease comorbidities in a quantitative way.</p>
</sec>
<sec><title>Electronic supplementary material</title>
<p>The online version of this article (doi:10.1186/1471-2105-15-333) contains supplementary material, which is available to authorized users.</p>
</sec>
</abstract>
<kwd-group xml:lang="en"><title>Keywords</title>
<kwd>Comorbidities infections</kwd>
<kwd>Disease associations</kwd>
<kwd>SARS</kwd>
<kwd>HIV</kwd>
</kwd-group>
<custom-meta-group><custom-meta><meta-name>issue-copyright-statement</meta-name>
<meta-value>© The Author(s) 2014</meta-value>
</custom-meta>
</custom-meta-group>
</article-meta>
</front>
</pmc>
<affiliations><list><country><li>Bangladesh</li>
</country>
</list>
<tree><noCountry><name sortKey="Li, Pietro" sort="Li, Pietro" uniqKey="Li P" first="Pietro" last="Li">Pietro Li</name>
</noCountry>
<country name="Bangladesh"><noRegion><name sortKey="Moni, Mohammad Ali" sort="Moni, Mohammad Ali" uniqKey="Moni M" first="Mohammad Ali" last="Moni">Mohammad Ali Moni</name>
</noRegion>
</country>
</tree>
</affiliations>
</record>
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