Diseases, symptoms, genes, and proteins linked together in giant network

July 18, 2014 by Lisa Zyga, Medical Xpress feature
Construction of the human symptoms-disease network consists of (a) extracting disease-symptom relationships from the PubMed bibliographic literature database, (b) constructing a disease network in which nodes represent diseases and links represent similarities between diseases, and (c) integrating both disease-gene associations and protein-protein interaction databases to obtain shared genes/protein-protein interactions between diseases. (d) The resulting disease network in which links represent shared genes/protein-protein interactions. (e) Highly clustered regions of diseases belong to the same broad disease category. Credit: ©2014 Zhou, et al. Nature Communications

(Medical Xpress)—The first indication that you're sick is typically one or more symptoms: perhaps a cough, fever, abdominal pain, etc. Symptoms are high-level clinical manifestations of a disease that, at a lower level, is caused by molecular-level components, such as genes and proteins. Understanding the complex ways in which symptoms, diseases, and their underlying molecular mechanisms are related can provide a valuable tool for medical researchers when designing better treatments.

However, this area of research is still very new and not well understood. In a new study published in Nature Communications, researchers XueZhong Zhou, et al., have constructed a human symptoms-disease (HSDN) that reveals the numerous and sometimes surprising connections between symptoms, diseases, genes, and proteins.

"Symptoms are the clinical manifestations that are closer to everyday activities and generally can be perceived by medical lay persons," coauthor Amitabh Sharma at Northeastern University, the Dana-Farber Cancer Institute, and Brigham and Women's Hospital, all in Boston, Massachusetts, told Phys.org. "The human symptoms-disease network represents a that has great potential in better research applications and clinical care. The HSDN definitely boosts the translational medicine and precision medicine field, where the data source can be used to identify the clinical phenotypes hidden in the large-scale to elaborate the clinical features of diseases."

The HSDN is a giant network, consisting of more than 4,000 diseases and 300 symptoms. The data was extracted from millions of PubMed bibliographic records with at least one disease or symptom term in the metadata field.

In the network, nodes represent diseases and links represent symptom similarities between diseases. For example, insulin resistance and metabolic syndrome are two diseases that share many of the same symptoms, such as obesity and hypertension, and therefore have a strongly weighted link between them. Overall, the network is very dense, with 94% of the nodes being connected to more than 50% of all other nodes (i.e., they have at least one shared symptom). The most highly connected disease is hyponatremia, an electrolyte disorder associated with a number of common symptoms that occur in many diseases, such as headache, nausea, and fatigue.

After constructing the network, the researchers then integrated genetic data from three genotype-phenotype databases as well as protein data from five protein-protein interaction databases. In the resulting networks, two diseases are connected if they share an associated gene or protein interaction, respectively. The integrated networks showed that diseases with more similar symptoms are more likely to have both common gene associations as well as shared protein interactions.

These associations among symptoms, diseases, genes, and proteins reveal a large amount of information, some that is widely known and some that is just beginning to be discovered in ground-breaking research.

Confirming what is widely known about disease categories, the network shows highly interconnected communities of diseases, such as those that involve the respiratory tract, digestive system, cardiovascular system, etc. In particular, the network shows that the three main disease risks—namely, infectious diseases, chronic inflammation diseases, and neoplasms (tumors)—are all highly interconnected.

As an example of less well-known associations, the network shows that Parkinson's disease has very similar symptoms, as well as correlated genes and protein interactions, with substance-related diseases such as mercury and manganese poisoning. In just the past few years, research has, in fact, suggested similarities between these diseases.

As another example, the network reveals that Alzheimer's disease shows high symptom similarity with epilepsy. Again, researchers have recently found that an antiepileptic drug (levetiracetam) can reverse deficits in learning and memory in mice with Alzheimer's disease, and might help do the same in humans.

Another major area where the network may be very useful is in comparing genetic and infectious diseases. For example, the network shows that Epstein-Barr virus, which causes mononucleosis, shares symptoms with several other diseases, including T-cell lymphoma, Hodgkin disease, and non-Hodgkin lymphoma, all of which have correlations between genes and protein interactions. The results suggest that symptom similarity scores could provide clues to understanding how viral/bacterial infections may affect genes and protein interactions, increasing susceptibility to .

In the future, the researchers plan to further expand the network by incorporating even more big data, from sources including electronic health records and clinical terminology systems. They predict that advances in the field of automated text mining will play a vital role in accumulating and analyzing this large amount of data.

"We believe that a symptoms-level view of disease phenotypes can shed new light on the different aspects of disease manifestation," Zhou said. "Understanding the human symptoms-disease network in the future could help in revealing the underlying network behind the diseases, and this will eventually lead to clinical cures of the diseases. We are focusing on translating the human -disease network knowledge into wisdom that can yield clinically actionable results like predicting and controlling human . We believe it would contribute significantly to the new taxonomy of diseases and improved clinical care, which will be more elaborated and patient-oriented in this new information era."

Explore further: Hereditary disease genes found throughout the human body

More information: XueZhong Zhou, et al. "Human symptoms-disease network." Nature Communications. DOI: 10.1038/ncomms5212

Related Stories

Hereditary disease genes found throughout the human body

June 12, 2014
A new study published in PLOS Computational Biology shows that genes associated with hereditary diseases occur throughout the human body.

NIH creates network to tackle mysterious diseases

July 1, 2014
The government is expanding its "mystery disease" program, funding a network at six universities to help diagnose patients' super-rare diseases.

A Crohn's disease-associated gene expression profile and microbial community

July 8, 2014
Crohn's and other inflammatory bowel diseases (IBDs) can be painful and debilitating. There are no known cures for these diseases, but the symptoms can be managed. It is widely thought that IBDs develop as a result of an ...

New tool to identify genetic risk factors

January 30, 2014
Dartmouth researchers developed a new biological pathway-based computational model, called the Pathway-based Human Phenotype Network (PHPN), to identify underlying genetic connections between different diseases as reported ...

Hidden origins of pulmonary hypertension revealed by network modeling

June 24, 2014
In a groundbreaking study, researchers from Brigham and Women's Hospital (BWH) have identified a related family of molecules believed to be a major root cause of pulmonary hypertension, a deadly vascular disease with undefined ...

First large-scale PheWAS study using EMRs provides systematic method to discover new disease association

November 25, 2013
Vanderbilt University Medical Center researchers and co-authors from four other U.S. institutions from the Electronic Medical Records and Genomics (eMERGE) Network are repurposing genetic data and electronic medical records ...

Recommended for you

Team develops new way to grow blood vessels

August 17, 2018
Formation of new blood vessels, a process also known as angiogenesis, is one of the major clinical challenges in wound healing and tissue implants. To address this issue, researchers from Texas A&M University have developed ...

New imaging technique can spot tuberculosis infection in an hour

August 16, 2018
Guided by glowing bacteria, researchers have devised an imaging technique that can diagnose live tuberculosis in an hour and help monitor the efficacy of treatments. That's particularly critical because many TB strains have ...

Obesity, infertility and oxidative stress in mouse egg cells

August 16, 2018
Excessive body fat is associated with negative effects on female fertility and pregnancy. In mice, maternal obesity impairs proper development of egg precursor cells called oocytes. In a recent paper published in Molecular ...

Research shows it's possible to reverse damage caused by aging cells

August 15, 2018
What's the secret to aging well? University of Minnesota Medical School researchers have answered it- on a cellular level.

This matrix delivers healing stem cells to injured elderly muscles

August 15, 2018
A car accident leaves an aging patient with severe muscle injuries that won't heal. Treatment with muscle stem cells from a donor might restore damaged tissue, but doctors are unable to deliver them effectively. A new method ...

Male tobacco smokers have brain-wide reduction of CB1 receptors

August 15, 2018
Chronic, frequent tobacco smokers have a decreased number of cannabinoid CB1 receptors, the "pot receptor", when compared with non-smokers, reports a study in Biological Psychiatry.


Adjust slider to filter visible comments by rank

Display comments: newest first

1 / 5 (2) Jul 18, 2014
"Symptoms are high-level clinical manifestations of a disease that, at a lower level, is caused by molecular-level components, such as genes and proteins."

Some people might begin to realize that the proteins are nutrient-dependent and link ecological variation to pheromone-controlled ecological adaptations in species from microbes to man via the conserved molecular mechanisms of amino acid substitutions that differentiate cell types.

Other people might believe in the pseudoscientific nonsense of population genetics that they were taught links mutation-initiated natural selection to the evolution of biodiversity.

Some people understand the difference between cell type differentiation and mutations. Others are evolutionary theorists.
not rated yet Jul 19, 2014
IMPORTANT! Anyone that desires to remove heavy metals from their body needs to look into doing a detox with the natural mineral called Zeolite that has proven to safely remove both heavy metals and radiation from the body!

Please sign in to add a comment. Registration is free, and takes less than a minute. Read more

Click here to reset your password.
Sign in to get notified via email when new comments are made.