Computational model uncovers progression of HIV infection in brain

University of Alberta research team successfully uncovered the progression of HIV infection in the brain using a new mathematical model. The team is utilizing this model to develop a nasal spray to administer  antiretroviral medication effectively. Their research is published in Journal of Neurovirology.

Research was done by PhD student Weston Roda and Prof. Michael Li. They used data from patients who died five to 15 years after they were infected, as well as known biological processes for the HIV virus to build the model that predicts the growth and progression of HIV in the brain, from the moment of infection onward. It is the first model of an infectious disease in the brain.

“The nature of the HIV virus allows it to travel across the blood-brain barrier in infected macrophage–or white blood cell–as early as two weeks after infection. Antiretroviral drugs, the therapy of choice for HIV, cannot enter the brain so easily,” said Roda. This creates what is known as a viral reservoir, a place in the body where the virus can lay dormant and is relatively inaccessible to drugs.

Prior to this study, scientists could only study brain infection at autopsy. The new model allows scientists to backtrack, seeing the progression and development of HIV infection in the brain. Using this information, researchers can determine what level of effectiveness is needed for antiretroviral therapy in the brain to decrease active infection.

“The more we understand and can target treatment toward viral reservoirs, the closer we get to developing total suppression strategies for HIV infection,” said Roda. A research team led by Chris Power, Roda’s co-supervisor who is a professor in the Division of Neurology, is planning clinical trials for a nasal spray that would get the drugs into the brain faster, with critical information on dosage and improvement rate provided by Roda’s model.

“Our next steps are to understand other viral reservoirs, like the gut, and develop models similar to this one, as well as understand latently infected cell populations in the brain,” said Roda. “With the antiretroviral therapy, infected cells can go into a latent stage. The idea is to determine the size of the latently infected population so that clinicians can develop treatment strategies”

Citation: Roda, Weston C., Michael Y. Li, Michael S. Akinwumi, Eugene L. Asahchop, Benjamin B. Gelman, Kenneth W. Witwer, and Christopher Power. “Modeling brain lentiviral infections during antiretroviral therapy in AIDS.” Journal of NeuroVirology, 2017.
doi:10.1007/s13365-017-0530-3.
Adapted from press release by University of Alberta.

Computational model to track flu using Twitter data

An international team led by Alessandro Vespignani from Northeastern University has developed a computational model to predict the spread of the flu in real time. This unique model uses posts on Twitter in combination with key parameters of each season’s epidemic, including the incubation period of the disease, the immunization rate, how many people an individual with the virus can infect, and the viral strains present. When tested against official influenza surveillance systems, the model has been shown to accurately(70 to 90 percent) forecast the disease’s evolution up to six weeks in advance.

The paper on the novel model received a coveted Best Paper Honorable Mention award at the 2017 International World Wide Web Conference last month following its presentation.

While the paper reports results using Twitter data, the researchers note that the model can work with data from many other digital sources, too, as well as online surveys of individuals such as influenzanet, which is very popular in Europe.

“Our model is a work in progress,” emphasizes Vespignani. “We plan to add new parameters, for example, school and workplace structure.

Adapted from press release from the Northeastern University.

Dynamic undocking, a new computational method for efficient drug research

Researchers of the University of Barcelona have developed a more efficient computational method to identify new drugs. The study, published in the scientific journal Nature Chemistry, proposes a new way of facing the discovery of molecules with biological activity.

Researchers devised dynamic undocking (DUck), a fast computational method to calculate the work necessary to reach a quasi-bound state at which the ligand has just broken the most important native contact with the receptor. Since it is based on a different principle, this method complements conventional tools and allows going forward in the path of rational drug design. ICREA researcher Xavier Barril, from the Faculty of Pharmacy and Food Sciences and The Institute of Biomedicine of the University of Barcelona (IBUB), has led this project, which has the participation of professor Francesc Xavier Luque and PhD student Sergio Ruiz Carmona, members of the same Faculty.

The improvement on efficiency and effectiveness in the discovery of drugs is a key target in pharmaceutical research. In this process, the target are molecules that can be added to a target protein and modify its behavior according to clinical needs. “All current methods to predict if a molecule will join the wished protein are based on affinity, that is, in the complex’s thermodynamic stability. What we are proving is that molecules have to create complexes that are structurally stable, and that it is possible to distinguish between active and inactive by looking at what specific interactions are hard to break”, says Professor Xavier Barril.

This approach has been applied in software that identifies molecules with more possibilities to join the targeted protein. “The method allows selecting molecules that can be starting points to create new drugs”, says Barril. “Moreover, -he continues- the process is complementary with existing methods and allows multiplying five times the efficiency of the current processes with lower computational prices. We are actually using it successfully in several projects in the field of cancer and infectious diseases, among others”.

This work introduces a new way of thinking regarding the ligand-protein interaction. “We don’t look at the balancing situation, where two molecules make the best possible interactions, but we also think how the complex will break, which the breaking points are and how we can improve the drug to make it more resistant to separation. Now we have to focus on this phenomenon to understand it better and see if by creating more complex models we can still improve our predictions”, says the researcher. The team of the University of Barcelona is already using this method, which is open to all the scientific community.

Citation: “Dynamic undocking and the quasi-bound state as tools for drug discovery”. Sergio Ruiz-Carmona,  Peter Schmidtke, F. Javier Luque, Lisa Baker, Natalia Matassova, Ben Davis, Stephen Roughley, James Murray, Rod Hubbard & Xavier Barril. Nature Chemistry 2016.
DOI: 10.1038/nchem.2660
Adapted from press release by University of Barcelona.

Computer models to analyze Huntington disease pathology

Rice University scientists have uncovered new details about how a repeating nucleotide sequence in the gene for a mutant protein may trigger Huntington’s and other neurological diseases. Researchers used computer models to analyze proteins suspected of misfolding and forming plaques in the brains of patients with neurological diseases. Their simulations confirmed experimental results by other labs that showed the length of repeating polyglutamine sequences contained in proteins is critical to the onset of disease. The study led by Rice bioscientist Peter Wolynes appears in the Journal of the American Chemical Society.

Glutamine is the amino acid coded for by the genomic trinucleotide CAG. Repeating glutamines, called polyglutamines, are normal in huntingtin proteins, but when the DNA is copied incorrectly, the repeating sequence of glutamines can become too long. The result can be diseases like Huntington’s or spinocerebellar ataxia.

Simulations at Rice show how a repeating sequence in a mutant
 protein may trigger Huntington’s and other neurological diseases.
Credit:Mingchen Chen/Rice University

The number of repeats of glutamine can grow as the genetic code information is passed down through generations. That means a healthy parent whose huntingtin gene encodes proteins with 35 repeats may produce a child with 36 repeats. A person having the longer repeat is likely to develop Huntington’s disease.

Aggregation in Huntington’s typically begins only when polyglutamine chains reach a critical length of 36 repeats. Studies have demonstrated that longer repeat chains can make the disease more severe and its onset earlier.

The paper builds upon techniques used in an earlier study of amyloid beta proteins. That study was the lab’s first attempt to model the energy landscape of amyloid aggregation, which has been implicated in Alzheimer’s disease.  This time, Wolynes and his team were interested in knowing how the varying length of repeats, as few as 20 and as many as 50 influenced how aggregates form.

The Rice team found that at intermediate lengths between 20 and 30 repeats, polyglutamine sequences can choose between straight or hairpin configurations. While longer and shorter sequences form aligned fiber bundles, simulations showed intermediate sequences are more likely to form disordered, branched structures.

Mutations that would encourage polyglutamine sequences to remain unfolded would raise the energy barrier to aggregation, they found. “What’s ironic is that while Huntington’s has been classified as a misfolding disease, it seems to happen because the protein, in the bad case of longer repeats, carries out an extra folding process that it wasn’t supposed to be doing,” Wolynes said.

The team’s ongoing study is now looking at how the complete huntingtin protein, which contains parts in addition to the polyglutamine repeats, aggregates.

Citation: Chen, Mingchen, MinYeh Tsai, Weihua Zheng, and Peter G. Wolynes. “The Aggregation Free Energy Landscapes of Polyglutamine Repeats.” Journal of the American Chemical Society (2016).
DOI: 10.1021/jacs.6b08665
Research funding: NIH/National Institute of General Medical Sciences, Ministry of Science and Technology of Taiwan
Adapted from press release by Rice University.

New tool to discover bio-markers for aging using In-silico Pathway Activation Network Decomposition Analysis (iPANDA)

Today the Biogerontology Research Foundation announced the international collaboration on signaling pathway perturbation-based transcriptomic biomarkers of aging. On November 16th scientists at the Biogerontology Research Foundation alongside collaborators from Insilico Medicine, Inc, the Johns Hopkins University, Albert Einstein College of Medicine, Boston University, Novartis, Nestle and BioTime Inc. announced the publication of their proof of concept experiment demonstrating the utility of a novel approach for analyzing transcriptomic, metabolomic and signalomic data sets, titled iPANDA, in Nature Communications.

“Given the high volume of data being generated in the life sciences, there is a huge need for tools that make sense of that data. As such, this new method will have widespread applications in unraveling the molecular basis of age-related diseases and in revealing biomarkers that can be used in research and in clinical settings. In addition, tools that help reduce the complexity of biology and identify important players in disease processes are vital not only to better understand the underlying mechanisms of age-related disease but also to facilitate a personalized medicine approach. The future of medicine is in targeting diseases in a more specific and personalized fashion to improve clinical outcomes, and tools like iPANDA are essential for this emerging paradigm,” said João Pedro de Magalhães, PhD, a trustee of the Biogerontology Research Foundation.

The algorithm, iPANDA, applies deep learning algorithms to complex gene expression data sets and signal pathway activation data for the purposes of analysis and integration, and their proof of concept article demonstrates that the system is capable of significantly reducing noise and dimensionality of transcriptomic data sets and of identifying patient-specific pathway signatures associated with breast cancer patients that characterize their response to Toxicol-based neoadjuvant therapy.

The system represents a substantially new approach to the analysis of microarray data sets, especially as it pertains to data obtained from multiple sources, and appears to be more scalable and robust than other current approaches to the analysis of transcriptomic, metabolomic and signalomic data obtained from different sources. The system also has applications in rapid biomarker development and drug discovery, discrimination between distinct biological and clinical conditions, and the identification of functional pathways relevant to disease diagnosis and treatment, and ultimately in the development of personalized treatments for age-related diseases.

While the team predicted and compared the response of breast cancer patients to Taxol-based neoadjuvant therapy as their proof of concept, the application of this approach to patient-specific responses to biomedical gerontological interventions (e.g. to geroprotectors, which is a clear focus of the team’s past efforts), to the development of both generalized and personalized biomarkers of ageging, and to the characterization and analysis of minute differences in ageging over time, between individuals, and between different organisms would represent a promising and exciting future application” said Franco Cortese, Deputy Director of the Biogerontology Research Foundation.

Citation: “In silico Pathway Activation Network Decomposition Analysis (iPANDA) as a method for biomarker development”. Ivan V. Ozerov, Ksenia V. Lezhnina, Evgeny Izumchenko, Artem V. Artemov, Sergey Medintsev, Quentin Vanhaelen, Alexander Aliper, Jan Vijg, Andreyan N. Osipov, Ivan Labat, Michael D. West, Anton Buzdin, Charles R. Cantor, Yuri Nikolsky, Nikolay Borisov, Irina Irincheeva, Edward Khokhlovich, David Sidransky, Miguel Luiz Camargo & Alex Zhavoronkov. Nature Communications 2016 vol: 7 pp: 13427.
DOI: http://dx.doi.org/10.1038/NCOMMS13427
Adapted from press release by Biogerontology Research Foundation

Virtual clinical trials use mathematical modelling to predict melanoma response

Researchers from Moffitt Cancer Center’s Integrated Mathematical Oncology (IMO) Department are overcoming the limitations of common preclinical experiments and clinical trials by studying cancer through mathematical modeling. A study led by Alexander “Sandy” Anderson, Ph.D., chair of IMO, and Eunjung Kim, Ph.D., an applied research scientist, shows how mathematical modeling can accurately predict patient responses to cancer drugs in a virtual clinical trial. This study was recently published in the November issue of the European Journal of Cancer.

Cancer is a complicated process based on evolutionary principals and develops as a result of changes in both tumor cells and the surrounding tumor environment. Similar to how animals can change and adapt to their surroundings, tumor cells can also change and adapt to their surroundings and to cancer treatments. Those tumor cells that adapt to their environment or treatment will survive, while tumor cells that are unable to adapt will die.

Preclinical studies with tumor cell models cannot accurately measure these changes and adaptations in a context that accurately reflects what occurs in patients. “Purely experimental approaches are unpractical given the complexity of interactions and timescales involved in cancer. Mathematical modeling can capture the fine mechanistic details of a process and integrate these components to extract fundamental behaviors of cells and between cells and their environment,” said Anderson.

The research team wanted to demonstrate the power of mathematical modeling by developing a model that predicts the responses of melanoma to different drug treatments: no treatment, chemotherapy alone, AKT inhibitors, and AKT inhibitors plus chemotherapy in sequence and in combination. They then tested the model predictions in laboratory experiments with Keiran Smalley, Ph.D., director of the Donald A. Adam Comprehensive Melanoma and Skin Cancer Research Center of Excellence at Moffitt, to confirm that their model was accurate.

To determine the long-term outcome of therapy in different patients, the researchers developed a virtual clinical trial that tested different combinations of AKT inhibitors and chemotherapy in virtual patients. The researchers show that this Phase i trial (i for in silico, and representing the imaginary number) or virtual clinical trial was able to reproduce patient responses to those observed in the published results of an actual clinical trial. Importantly, their approach was able to stratify patient responses and predict a better treatment schedule for AKT inhibitors in melanoma patients that improves patient outcomes and reduces toxicities.

“By using a range of mathematical modeling approaches targeted at specific types of cancer, Moffitt’s IMO Department is aiding in the development and testing of new treatment strategies, as well as facilitating a deeper understanding of why they fail. This multi-model, multi-scale approach has led to a diverse and rich interdisciplinary environment within our institution, one that is creating many novel approaches for the treatment and understanding cancer,” Anderson said.

Citation: Kim, Eunjung, Vito W. Rebecca, Keiran SM Smalley, and Alexander RA Anderson. “Phase i trials in melanoma: A framework to translate preclinical findings to the clinic.” bioRxiv (2015): 015925. European journal of cancer 2016 vol: 67 pp: 213-222.
DOI: http://dx.doi.org/10.1016/j.ejca.2016.07.024
Adapted from press release by Moffitt Cancer Center

Computer based model InFlo predicts cell signals and network activity

A multi-institution academic-industrial partnership of researchers led by Case Western Reserve University School of Medicine has developed a new method to broadly assess cell communication networks and identify disease-specific network anomalies. The computer-based method, called InFlo, was developed in collaboration with researchers at Philips and Princeton University and predicts how cells send signals across networks to cause cancer or other disease. Details about the new method were recently published in Oncogene.

“Cellular signaling networks are the mechanisms that cells use to transfer, process, and respond to biological information derived from their immediate surroundings,” said Vinay Varadan, PhD, assistant professor at Case Western Reserve University School of Medicine, member of the Case Comprehensive Cancer Center, and senior corresponding author on the study. “InFlo can be viewed as modeling the flow of information within these signaling networks.”

InFlo works by assessing gene activity levels in tissue samples and predicting corresponding protein levels. It then uses statistical probabilities and other mathematical models to build activity webs showing how the proteins interact. Researchers can use InFlo to compare diseased and healthy tissues and pinpoint signaling differences. InFlo is tissue-specific and accounts for genetic alterations associated with disease, unlike other methods. It represents a major step forward in deciphering the activities of multi-tiered signaling networks commonly used by cells.

“Complex diseases such as cancer involve the simultaneous disruptions of multiple cellular processes acting in tandem,” said Varadan. “We developed InFlo to robustly integrate multiple molecular data streams and develop an integrative molecular portrait of an individual cancer sample.”

InFlo incorporates data related to each level of cell communication within a single sample, including DNA, RNA, proteins, and molecules commonly attached to proteins such as chemical methyl groups. The method also includes strategies to reduce “noise” and only highlight the signaling networks most likely to cause disease.

Analisa DiFeo, PhD, senior co-corresponding author on the study, Norma C. and Al I. Geller Designated Professor of Ovarian Cancer Research at Case Western Reserve University School of Medicine, and member of the Case Comprehensive Cancer Center, validated InFlo using ovarian cancer tumor cells that were resistant to platinum-based chemotherapy. InFlo pinpointed the interaction between two proteins called cAMP and CREB1 as a key mechanism associated with platinum resistance.

“Following up on InFlo’s predictions, we showed that inhibiting CREB1 potently sensitizes ovarian cancer cells to platinum therapy and is also effective in killing ovarian cancer stem cells. We are therefore excited about this discovery and are currently evaluating whether this could lead to a potential therapeutic target for the treatment of platinum-resistant ovarian cancer,” said DiFeo.

InFlo is being incorporated into Philips IntelliSpace Genomics platform, and will soon be available for widespread use in basic and translational research settings. Case Western Reserve University researchers will continue to develop the IntelliSpace Genomics InFlo module and the next step will be to expand InFlo to incorporate other data streams. “We are currently collaborating with the Imaging Informatics research group in the Center for Computational Imaging and Personalized Diagnostics at Case Western Reserve University to integrate InFlo with imaging-features derived from pathology and radiology data,” said Varadan. Such an addition would result in one of the most comprehensive tools available to researchers to infer mechanisms underlying complex diseases such as cancer.

Citation: Dimitrova, N., Nagaraj, A.B., Razi, A., Singh, S., Kamalakaran, S., Banerjee, N., Joseph, P., Mankovich, A., Mittal, P., DiFeo, A. and Varadan, V., 2016. InFlo: a novel systems biology framework identifies cAMP-CREB1 axis as a key modulator of platinum resistance in ovarian cancer. Oncogene.
DOI: http://dx.doi.org/10.1038/onc.2016.398
Research funding: Career Development Program of Case GI SPORE, Career Development Program in Computational Genomic Epidemiology of Cancer, Philips Healthcare, Rosalie and Morton Cohen Family Memorial Genomics Fund of University Hospitals, and others
Adapted from press release by  Case Western Reserve University School of Medicine