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.

ResistoMap developed to track world wide microbial drug resistance

Scientists from the Federal Research and Clinical Center of Physical-Chemical Medicine, the Moscow Institute of Physics and Technology, and Data Laboratory have created the ResistoMap, an interactive visualization of gut resistome. Gut resistome is human gut microbiota potential to resist antibiotics and includes the set of all antibiotic resistance genes in the genomes of human gut microbes. Their ResistoMap will help identify national trends in antibiotic use and control antibiotic resistance on the global scale. This research is published in journal Bioinformatics.

Resistomap - interactive world map of human gut resistome
Resistomap –  an interactive world map of human gut microbiota potential to resist antibiotics.
Credit: Bioinformatics

Microbial drug resistance is caused by the extensive uncontrolled use of antibiotics in medicine and agriculture. It has been predicted that by 2050 around 10 million people will die annually due to reasons associated with drug resistance.

The ResistoMap has two main interactive work fields: a geographic map and a heat map. A user can choose the antibiotic group or country of interest to be displayed on the heat map and obtain a resistome cross section. The data can be filtered by the country of origin, gender, age, and diagnosis. The current version of the interactive map developed by the authors draws on a dataset that includes over 1600 individuals from 12 studies covering 15 countries. However, the dataset can be expanded by additional input from users reflecting the findings of new published studies in a unified format.

Using the ResistoMap, researchers fee that it is possible to estimate the global variation of the resistance to different groups of antibiotics and explore the associations between specific drugs and clinical factors or other metadata. For example, the Danish gut metagenomes tend to demonstrate the lowest resistome among the European groups, whereas the French samples have the highest levels, particularly of the fluoroquinolones, a group of broad-spectrum anti-bacterial drugs. This is in agreement with the fact that France has the highest total antibiotic use across Western Europe, while the use of antimicrobial drugs in Denmark and Germany is moderate, both in health care and agriculture. At the opposite end of the spectrum, Chinese and Russian populations appear to have increased levels of resistome, which is likely due to looser regulation policies, frequent prescription of broad-spectrum antibiotics, and their over-the-counter availability without prescription. The lowest levels of microbiota resistome are observed in the native population of Venezuela who have no documented contacts with populations of the developed countries. ResistoMap-informed analysis reveals certain novel trends that await further interpretation from the clinical standpoint.

Konstantin Yarygin, one of the creators of the visualization tool, says, “We anticipate that the exploratory analysis of global gut resistome enabled by the ResistoMap will provide new insights into how the use of antibiotics in medicine and agriculture could be optimized.”

Citation: Yarygin, Konstantin S., Boris A. Kovarsky, Tatyana S. Bibikova, Damir S. Melnikov, Alexander V. Tyakht, and Dmitry G. Alexeev. “ResistoMap—online visualization of human gut microbiota antibiotic resistome.” Bioinformatics, 2017.
doi:10.1093/bioinformatics/btx134.
Research funding: Russian Scientific Foundation.
Adapted from press release by the Moscow Institute of Physics and Technology.

Dutch universities collaborate on big data in health to understand disease process

Patients with the same illness often receive the same treatment, even if the cause of the illness is different for each person. This represents a new step towards ultimately being able to offer every patient more personalized treatment.

Six Dutch universities are combining forces to chart the different disease processes for a range of common conditions. This represents a new step towards ultimately being able to offer every patient more personalized treatment. The results of this study have been published in two articles in the authoritative scientific journal Nature Genetics.

The researchers were able to make their discoveries thanks to new techniques that make it possible to simultaneously measure the regulation and activity of all the genes of thousands of people, and to link these data to millions of genetic differences in their DNA. The combined analysis of these ‘big data’ made it possible to determine which molecular processes in the body become dysregulated for a range of disparate diseases, from prostate cancer to ulcerative bowel disease, before the individuals concerned actually become ill.

“The emergence of ‘big data’, ever faster computers and new mathematical techniques means it’s now possible to conduct extremely large-scale studies and gain an understanding of many diseases at the same time,” explains Lude Franke (UMCG), head of the research team in Groningen. The researchers show how thousands of disease-related DNA differences disrupt the internal working of a cell and how their effect can be influenced by environmental factors. And all this was possible without the need for a single lab experiment.

The success of this research is the result of the decision taken six years ago by biobanks throughout the Netherlands to share data and biomaterials within the BBMRI consortium. This decision meant it became possible to gather, store and analyze data from blood samples of a very large number of volunteers. The present study illustrates the tremendous value of large-scale collaboration in the field of medical research in the Netherlands.

Heijmans (LUMC), research leader in Leiden and initiator of the partnership: “The Netherlands is leading the field in sharing molecular data. This enables researchers to carry out the kind of large-scale studies that are needed to gain a better understanding of the causes of diseases. This result is only just the beginning: once they have undergone a screening, other researchers with a good scientific idea will be given access to this enormous bank of anonymized data. Our Dutch ‘polder mentality’ is also advancing science.”

Mapping the various molecular causes for a disease is the first step towards a form of medical treatment that better matches the disease process of individual patients. To reach that ideal, however, we still have a long way to go. The large-scale molecular data that have been collected for this research are the cornerstone of even bigger partnerships, such as the national Health-RI initiative. The third research leader, Peter-Bram ’t Hoen (LUMC), says: “Large quantities of data should eventually make it possible to give everyone personalized health advice, and to determine the best treatment for each individual patient.”

The research has been made possible thanks to the cooperation within the BBMRI biobank consortium of six long-running Dutch population studies carried out by the university medical centres in Groningen (LifeLines), Leiden (Leiden Longevity Study), Maastricht (CODAM Study), Rotterdam (Rotterdam Study), Utrecht (Netherlands Prospective ALS Study) and by the Vrije Universiteit (Netherlands Twin Register). The molecular data were generated in a standardized fashion at a central site (Human Genomics Facility HuGE-F, ErasmusMC) and subsequently securely stored and analyzed at a second central site (SURFSara). The study links in with the Personalised Medicine route of the National Research Agenda and the Health-RI and M3 proposals on the large-scale research infrastructure agenda of the Royal Netherlands Academy of Arts and Sciences (KNAW).

Citations:
1. Bonder, Marc Jan, René Luijk, Daria Zhernakova, Matthijs Moed, Patrick Deelen, Martijn Vermaat, Maarten van Iterson et al. “Disease variants alter transcription factor levels and methylation of their binding sites.” bioRxiv (2015): 033084. Nature Genetics 2016.
DOI: 10.1038/ng.3721

2.  Zhernakova, Daria V,  Patrick Deelen, Martijn Vermaat, Maarten van Iterson, Michiel van Galen, Wibowo Arindrarto et al. “Identification of context-dependent expression quantitative trait loci in whole blood”. Nature Genetics 2016.
DOI: doi:10.1038/ng.3737

Adapted from press release by Leiden University.