Summary: IBM Watson helps researchers identify five new genes associated with amyotrophic lateral sclerosis.
Source: Barrow Neurological Institute.
Barrow Neurological Institute researchers have completed additional experiments that validate the identification of five new genes linked to Amyotrophic Lateral Sclerosis (ALS) – also known as Lou Gehrig’s disease. The new study results, validated through five different methods, were published in a full length manuscript in Acta Neuropathologica, validating earlier findings in the project.
ALS is a fatal neurological disorder affecting more than 220,000 patients worldwide1 with no cures and few treatments. Dr. Robert Bowser, who led the research, directs the Gregory W. Fulton ALS Research Center at Barrow, located at Dignity Health St. Joseph’s Hospital and Medical Center and considered one of the world’s leading neuroscience centers.
Dr. Bowser’s team used technologies provided by IBM Watson Health, including Watson for Drug Discovery, a novel research platform that harnesses the text of more than 28 million MEDLINE abstracts and other data sources. The solution applies advanced natural language processing, machine learning and predictive analytics to identify new relationships between genes, proteins, drugs and disease.
“Further validating and expanding on our earlier findings has been exciting, because in research of this nature, time is of the essence,” says Dr. Bowser, one of the nation’s top ALS researchers. “We could have individually looked at the 1,500 proteins and genes but it would have taken us much longer to do so. These findings inspire hope that, with this technology, we may someday identify new and more effective treatments for ALS.”
This research is important because it demonstrates the ability of artificial intelligence algorithms to accelerate wet lab research discoveries. It also provides further evidence that RNA metabolism plays an important role in ALS.
More than 30 genes have been linked to ALS, and mutations in the 11 genes that encode RNA binding proteins cause familial forms of ALS. These RNA binding proteins play a critical role in how genes encoded within the DNA in every cell are converted to the proteins that perform all the functions within a cell. Alterations in these proteins can lead to altered RNA metabolism and the generation of toxic protein aggregates within motor neurons that contribute to motor dysfunction and ultimately paralysis and death.
A person’s DNA encodes for over 1,500 RNA binding proteins, and it is unknown if other RNA binding proteins may contribute to ALS. With so many RNA binding proteins encoded in our genome, the cost and time required to examine all these RNA binding proteins would prohibitive. The Barrow laboratory studied whether IBM Watson for Drug Discovery could accelerate the identification of additional RNA binding proteins linked to ALS by helping scientists focus research efforts on the proteins that Watson ranked high and predicted to be altered in ALS.
Dr. Bowser and his team provided a list of 11 RNA binding proteins with known mutations that cause ALS. Watson for Drug Discovery used the list of proteins and cross referenced medical literature from 28 million MEDLINE abstracts to rank order all other 1,500 RNA binding proteins encoded by our genome to attempt to identify new RNA binding proteins linked to ALS.
The Barrow team validated the top 10 RNA binding proteins using five different methods that included use of patient tissue samples and patient derived stem cells differentiated into motor neurons. They also examined a smaller set of RNA binding proteins near the bottom of the list to demonstrate that any changes detected in the top 10 were not observed for those at the bottom of the list, demonstrating the ability of Watson for Drug Discovery to correctly predict RNA binding proteins linked to ALS.
The results were groundbreaking – and a painstaking process that would have taken researchers years was completed in only a few months.
Eight of the top 10 candidates were successfully validated and shown to be altered in ALS. Five of these genes had never been examined in ALS, indicating that IBM’s artificial intelligence platform could predict novel genes and proteins linked to this disease. RNA binding proteins at the bottom of the list were not altered in ALS.
By accelerating cell biological research, scientists hope to speed the discovery of new therapies for ALS.
Source: Carmelle Malkovich – Barrow Neurological Institute
Publisher: Organized by NeuroscienceNews.com.
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Original Research: Full open access research for “Artificial intelligence in neurodegenerative disease research: use of IBM Watson to identify additional RNA-binding proteins altered in amyotrophic lateral sclerosis” by Nadine Bakkar, Tina Kovalik, Ileana Lorenzini, Scott Spangler, Alix Lacoste, Kyle Sponaugle, Philip Ferrante, Elenee Argentinis, Rita Sattler, and Robert Bowser in Acta Neuropathologica. Published online November 13 2017 doi:10.1007/s00401-017-1785-8
Artificial intelligence in neurodegenerative disease research: use of IBM Watson to identify additional RNA-binding proteins altered in amyotrophic lateral sclerosis
Amyotrophic lateral sclerosis (ALS) is a devastating neurodegenerative disease with no effective treatments. Numerous RNA-binding proteins (RBPs) have been shown to be altered in ALS, with mutations in 11 RBPs causing familial forms of the disease, and 6 more RBPs showing abnormal expression/distribution in ALS albeit without any known mutations. RBP dysregulation is widely accepted as a contributing factor in ALS pathobiology. There are at least 1542 RBPs in the human genome; therefore, other unidentified RBPs may also be linked to the pathogenesis of ALS. We used IBM Watson® to sieve through all RBPs in the genome and identify new RBPs linked to ALS (ALS-RBPs). IBM Watson extracted features from published literature to create semantic similarities and identify new connections between entities of interest. IBM Watson analyzed all published abstracts of previously known ALS-RBPs, and applied that text-based knowledge to all RBPs in the genome, ranking them by semantic similarity to the known set. We then validated the Watson top-ten-ranked RBPs at the protein and RNA levels in tissues from ALS and non-neurological disease controls, as well as in patient-derived induced pluripotent stem cells. 5 RBPs previously unlinked to ALS, hnRNPU, Syncrip, RBMS3, Caprin-1 and NUPL2, showed significant alterations in ALS compared to controls. Overall, we successfully used IBM Watson to help identify additional RBPs altered in ALS, highlighting the use of artificial intelligence tools to accelerate scientific discovery in ALS and possibly other complex neurological disorders.
“Artificial intelligence in neurodegenerative disease research: use of IBM Watson to identify additional RNA-binding proteins altered in amyotrophic lateral sclerosis” by Nadine Bakkar, Tina Kovalik, Ileana Lorenzini, Scott Spangler, Alix Lacoste, Kyle Sponaugle, Philip Ferrante, Elenee Argentinis, Rita Sattler, and Robert Bowser in Acta Neuropathologica. Published online November 13 2017 doi:10.1007/s00401-017-1785-8