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Molecular association of pathogenetic contributors to pre-eclampsia (pre-eclampsia associome)

Overview of attention for article published in BMC Systems Biology, April 2015
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Title
Molecular association of pathogenetic contributors to pre-eclampsia (pre-eclampsia associome)
Published in
BMC Systems Biology, April 2015
DOI 10.1186/1752-0509-9-s2-s4
Pubmed ID
Authors

Andrey S Glotov, Evgeny S Tiys, Elena S Vashukova, Vladimir S Pakin, Pavel S Demenkov, Olga V Saik, Timofey V Ivanisenko, Olga N Arzhanova, Elena V Mozgovaya, Marina S Zainulina, Nikolay A Kolchanov, Vladislav S Baranov, Vladimir A Ivanisenko

Abstract

Pre-eclampsia is the most common complication occurring during pregnancy. In the majority of cases, it is concurrent with other pathologies in a comorbid manner (frequent co-occurrences in patients), such as diabetes mellitus, gestational diabetes and obesity. Providing bronchial asthma, pulmonary tuberculosis, certain neurodegenerative diseases and cancers as examples, we have shown previously that pairs of inversely comorbid pathologies (rare co-occurrences in patients) are more closely related to each other at the molecular genetic level compared with randomly generated pairs of diseases. Data in the literature concerning the causes of pre-eclampsia are abundant. However, the key mechanisms triggering this disease that are initiated by other pathological processes are thus far unknown. The aim of this work was to analyse the characteristic features of genetic networks that describe interactions between comorbid diseases, using pre-eclampsia as a case in point. The use of ANDSystem, Pathway Studio and STRING computer tools based on text-mining and database-mining approaches allowed us to reconstruct associative networks, representing molecular genetic interactions between genes, associated concurrently with comorbid disease pairs, including pre-eclampsia, diabetes mellitus, gestational diabetes and obesity. It was found that these associative networks statistically differed in the number of genes and interactions between them from those built for randomly chosen pairs of diseases. The associative network connecting all four diseases was composed of 16 genes (PLAT, ADIPOQ, ADRB3, LEPR, HP, TGFB1, TNFA, INS, CRP, CSRP1, IGFBP1, MBL2, ACE, ESR1, SHBG, ADA). Such an analysis allowed us to reveal differential gene risk factors for these diseases, and to propose certain, most probable, theoretical mechanisms of pre-eclampsia development in pregnant women. The mechanisms may include the following pathways: [TGFB1 or TNFA]-[IL1B]-[pre-eclampsia]; [TNFA or INS]-[NOS3]-[pre-eclampsia]; [INS]-[HSPA4 or CLU]-[pre-eclampsia]; [ACE]-[MTHFR]-[pre-eclampsia]. For pre-eclampsia, diabetes mellitus, gestational diabetes and obesity, we showed that the size and connectivity of the associative molecular genetic networks, which describe interactions between comorbid diseases, statistically exceeded the size and connectivity of those built for randomly chosen pairs of diseases. Recently, we have shown a similar result for inversely comorbid diseases. This suggests that comorbid and inversely comorbid diseases have common features concerning structural organization of associative molecular genetic networks.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 60 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 60 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 11 18%
Student > Bachelor 9 15%
Student > Postgraduate 6 10%
Other 4 7%
Researcher 4 7%
Other 9 15%
Unknown 17 28%
Readers by discipline Count As %
Medicine and Dentistry 22 37%
Nursing and Health Professions 5 8%
Biochemistry, Genetics and Molecular Biology 4 7%
Computer Science 3 5%
Pharmacology, Toxicology and Pharmaceutical Science 2 3%
Other 7 12%
Unknown 17 28%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 04 February 2016.
All research outputs
#17,758,492
of 22,805,349 outputs
Outputs from BMC Systems Biology
#770
of 1,142 outputs
Outputs of similar age
#180,106
of 264,037 outputs
Outputs of similar age from BMC Systems Biology
#9
of 13 outputs
Altmetric has tracked 22,805,349 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,142 research outputs from this source. They receive a mean Attention Score of 3.6. This one is in the 27th percentile – i.e., 27% of its peers scored the same or lower than it.
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We're also able to compare this research output to 13 others from the same source and published within six weeks on either side of this one. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.