Though the variant poses low risk thanks to modern HIV treatments, the scientists stress the importance of access to early clinical laboratory testing for at-risk individuals
With the global healthcare industry hyper focused on arrival of the next SARS-CoV-2 variant, pathologists and clinical laboratories may be relieved to learn that—though researchers in the Netherlands discovered a previously unknown “highly virulent” strain of HIV—the lead scientist of the study says there’s “no cause for alarm.”
In a news release, researchers at the University of Oxford Big Data Institute said the HIV variant got started in the Netherlands in the 1990s, spread quickly into the 2000s, and that prior to treatment, people with the new virulent subtype B (VB variant) had exceptionally high viral loads compared to people with other HIV variants.
Fortunately, the scientist also found that around 2010, thanks to antiretroviral drug therapy, the severe variant began to decline.
In an interview with NPR, Chris Wymant, PhD, the study’s lead author, said, “People with this variant have a viral load that is three to four times higher than usual for those with HIV. This characteristic means the virus progresses into serious illness twice as fast, and also makes it more contagious.”
Fortunately, he added, “Existing medications work very well to treat even very virulent variants like this one, cutting down on transmission and reducing the chance of developing severe illness.
“Nobody should be alarmed,” he continued. “It responds exactly as well to treatment as HIV normally does. There’s no need to develop special treatments for this variant.”
Wymant is senior researcher in statistical genetics and pathogen dynamics at the Big Data Institute (BDI).
Epidemiologist Chris Wymant, PhD (above), lead author of the Big Data Institute study at Oxford University, says today’s modern HIV antiretroviral drug therapies effectively treat for the “highly viral” HIV VB variant he and his team discovered. “Nobody should be alarmed,” he told NPR. “It responds exactly as well to treatment as HIV normally does.” Nevertheless, he stressed the importance of access to early clinical laboratory testing for at-risk individuals. (Photo copyright: Oxford Big Data Institute.)
In their published study, the BDI researchers reported that their analysis of genetic sequences of the VB variant suggested it “arose in the 1990s from de novo (of new) mutation, not recombination, with increased transmissibility and an unfamiliar molecular mechanism of virulence.
“By the time, they were diagnosed, these individuals were vulnerable to developing AIDS within two to three years. The virus lineage, which has apparently arisen de novo since around the millennium, shows extensive change across the genome affecting almost 300 amino acids, which makes it hard to discern the mechanism for elevated virulence,” the researchers noted.
The researchers analyzed a data set from the project BEEHIVE (Bridging the Epidemiology and Evolution of HIV in Europe and Uganda). They found 15 of 17 people positive for the VB variant residing in the Netherlands. That prompted them to focus on a cohort of more than 6,700 Dutch HIV positive people in the ATHENA (AIDS Therapy Evaluation in the Netherlands) cohort database, where they found 92 more individuals with the VB variant, bringing the total to 109.
According to a Medscape report on the study’s findings, people with the VB variant showed the following characteristics:
Double the rate of CD4-positive T-cell declines (indicator of immune system damage by HIV), compared to others with subtype-B strains.
Increased risk of infecting others with the virus based on transmissibility associated with variant branching.
Wymant says access to clinical laboratory testing is key to curtailing the number of people who contract the VB variant. “Getting people tested as soon as possible, getting them onto treatment as soon as possible, has helped reduce the numbers of this variant even though we didn’t know that it existed,” he told NPR.
The University of Oxford Big Data Institute study is another example of how constantly improving genome sequencing technology allows scientists to dig deeper into genetic material for insights that can advance the understanding of many diseases and health conditions.
DeepMind hopes its unrivaled collection of data, enabled by artificial intelligence, may advance development of precision medicines, new medical laboratory tests, and therapeutic treatments
‘Tis the season for giving, and one United Kingdom-based artificial intelligence (AI) research laboratory is making a sizeable gift. After using AI and machine learning to create “the most comprehensive map of human proteins,” in existence, DeepMind, a subsidiary of Alphabet Inc. (NASDAQ:GOOGL), parent company of Google, plans to give away for free its database of millions of protein structure predictions to the global scientific community and to all of humanity, The Verge reported.
Pathologists and clinical laboratory scientists developing proteomic assays understand the significance of this gesture. They know how difficult and expensive it is to determine protein structures using sequencing of amino acids. That’s because the various types of amino acids in use cause the [DNA] string to “fold.” Thus, the availability of this data may accelerate the development of more diagnostic tests based on proteomics.
“For decades, scientists have been trying to find a method to reliably determine a protein’s structure just from its sequence of amino acids. Attraction and repulsion between the 20 different types of amino acids cause the string to fold in a feat of ‘spontaneous origami,’ forming the intricate curls, loops, and pleats of a protein’s 3D structure. This grand scientific challenge is known as the protein-folding problem,” a DeepMind statement noted.
Enter DeepMind’s AlphaFold AI platform to help iron things out. “Experimental techniques for determining structures are painstakingly laborious and time consuming (sometimes taking years and millions of dollars). Our latest version [of AlphaFold] can now predict the shape of a protein, at scale and in minutes, down to atomic accuracy. This is a significant breakthrough and highlights the impact AI can have on science,” DeepMind stated.
Release of Data Will Be ‘Transformative’
In July, DeepMind announced it would begin releasing data from its AlphaFold Protein Structure Database which contains “predictions for the structure of some 350,000 proteins across 20 different organisms,” The Verge reported, adding, “Most significantly, the release includes predictions for 98% of all human proteins, around 20,000 different structures, which are collectively known as the human proteome. By the end of the year, DeepMind hopes to release predictions for 100 million protein structures.”
According to Edith Heard, PhD, Director General of the European Molecular Biology Laboratory (EMBL), the open release of such a dataset will be “transformative for our understanding of how life works,” The Verge reported.
“I see this as the culmination of the entire 10-year-plus lifetime of DeepMind,” company CEO and co-founder Demis Hassabis (above), told The Verge. “From the beginning, this is what we set out to do: to make breakthroughs in AI, test that on games like Go and Atari, [and] apply that to real-world problems, to see if we can accelerate scientific breakthroughs and use those to benefit humanity.” The release of DeepMind’s entire protein prediction database will certainly do that. Clinical laboratory scientists worldwide will have free access to use it in developing new precision medicine treatments based on proteomics. (Photo copyright: BBC.)
Free Data about Proteins Will Accelerate Research on Diseases, Treatments
Research into how protein folds and, thereby, functions could have implications to fighting diseases and developing new medicines, according to DeepMind.
“This will be one of the most important datasets since the mapping of the human genome,” said Ewan Birney, PhD, Deputy Director General of the EMBL, in the DeepMind statement. EMBL worked with DeepMind on the dataset.
DeepMind protein prediction data are already being used by scientists in medical research. “Anyone can use it for anything. They just need to credit the people involved in the citation,” said Demis Hassabis, DeepMind CEO and Co-founder, in The Verge.
In a blog article, Hassabis listed several projects and organizations already using AlphaFold. They include:
“As researchers seek cures for diseases and pursue solutions to other big problems facing humankind—including antibiotic resistance, microplastic pollution, and climate change—they will benefit from fresh insights in the structure of proteins,” Hassabis wrote.
Because of the deep financial backing that Alphabet/Google can offer, it is reasonable to predict that DeepMind will make progress with its AI technology that regularly adds capabilities and accuracy, allowing AlphaFold to be effective for many uses.
This will be particularly true for the development of new diagnostic assays that will give clinical laboratories better tools for diagnosing disease earlier and more accurately.
