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.
Genomic sequencing continues to benefit patients through precision medicine clinical laboratory treatments and pharmacogenomic therapies
EDITOR’S UPDATE—Jan. 26, 2022: Since publication of this news briefing, officials from Genomics England contacted us to explain the following:
The “five million genome sequences” was an aspirational goal mentioned by then Secretary of State for Health and Social Care Matt Hancock, MP, in an October 2, 2018, press release issued by Genomics England.
As of this date a spokesman for Genomics England confirmed to Dark Daily that, with the initial goal of 100,000 genomes now attained, the immediate goal is to sequence 500,000 genomes.
This goal was confirmed in a tweet posted by Chris Wigley, CEO at Genomics England.
In accordance with this updated input, we have revised the original headline and information in this news briefing that follows.
What better proof of progress in whole human genome screening than the announcement that the United Kingdom’s 100,000 Genome Project has not only achieved that milestone, but will now increase the goal to 500,000 whole human genomes? This should be welcome news to clinical laboratory managers, as it means their labs will be positioned as the first-line provider of genetic data in support of clinical care.
Many clinical pathologists here in the United States are aware of the 100,000 Genome Project, established by the National Health Service (NHS) in England (UK) in 2012. Genomics England’s new goal to sequence 500,000 whole human genomes is to pioneer a “lasting legacy for patients by introducing genomic sequencing into the wider healthcare system,” according to Technology Networks.
The importance of personalized medicine and of the power of precise, accurate diagnoses cannot be understated. This announcement by Genomics England will be of interest to diagnosticians worldwide, especially doctors who diagnose and treat patients with chronic and life-threatening diseases.
Building a Vast Genomics Infrastructure
Genetic sequencing launched the era of precision medicine in healthcare. Through genomics, drug therapies and personalized treatments were developed that improved outcomes for all patients, especially those suffering with cancer and other chronic diseases. And so far, the role of genomics in healthcare has only been expanding, as Dark Daily covered in numerous ebriefings.
Genomics England, which is wholly owned by the Department of Health and Social Care in the United Kingdom, was formed in 2012 with the goal of sequencing 100,000 whole genomes of patients enrolled in the UK National Health Service. That goal was met in 2018, and now the NHS aspires to sequence 500,000 genomes.
“The last 10 years have been really exciting, as we have seen genetic data transition from being something that is useful in a small number of contexts with highly targeted tests, towards being a central part of mainstream healthcare settings,” Richard Scott, MD, PhD (above), Chief Medical Officer at Genomics England told Technology Networks. Much of the progress has found its way into clinical laboratory testing and precision medicine diagnostics. (Photo copyright: Genomics England.)
Genomics England’s initial goals included:
To create an ethical program based on consent,
To set up a genomic medicine service within the NHS to benefit patients,
To make new discoveries and gain insights into the use of genomics, and
To begin the development of a UK genomics industry.
To gain the greatest benefit from whole genome sequencing (WGS), a substantial amount of data infrastructure must exist. “The amount of data generated by WGS is quite large and you really need a system that can process the data well to achieve that vision,” said Richard Scott, MD, PhD, Chief Medical Officer at Genomics England.
In early 2020, Weka, developer of the WekaFS, a fully parallel and distributed file system, announced that it would be working with Genomics England on managing the enormous amount of genomic data. When Genomics England reached 100,000 sequenced genomes, it had already gathered 21 petabytes of data. The organization expects to have 140 petabytes by 2023, notes a Weka case study.
Putting Genomics England’s WGS Project into Action
WGS has significantly impacted the diagnosis of rare diseases. For example, Genomics England has contributed to projects that look at tuberculosis genomes to understand why the disease is sometimes resistant to certain medications. Genomic sequencing also played an enormous role in fighting the COVID-19 pandemic.
Scott notes that COVID-19 provides an example of how sequencing can be used to deliver care. “We can see genomic influences on the risk of needing critical care in COVID-19 patients and in how their immune system is behaving. Looking at this data alongside other omics information, such as the expression of different protein levels, helps us to understand the disease process better,” he said.
What’s Next for Genomics Sequencing?
As the research continues and scientists begin to better understand the information revealed by sequencing, other areas of scientific study like proteomics and metabolomics are becoming more important.
“There is real potential for using multiple strands of data alongside each other, both for discovery—helping us to understand new things about diseases and how [they] affect the body—but also in terms of live healthcare,” Scott said.
Along with expanding the target of Genomics England to 500,000 genomes sequenced, the UK has published a National Genomic Strategy named Genome UK. This plan describes how the research into genomics will be used to benefit patients. “Our vision is to create the most advanced genomic healthcare ecosystem in the world, where government, the NHS, research and technology communities work together to embed the latest advances in patient care,” according to the Genome UK website.
Clinical laboratories professionals with an understanding of diagnostics will recognize WGS’ impact on the healthcare industry. By following genomic sequencing initiatives, such as those coming from Genomics England, pathologists can keep their labs ready to take advantage of new discoveries and insights that will improve outcomes for patients.
Results of the UK study confirm for clinical laboratory professionals the importance of fully understanding the design and function of SNP chips they may be using in their labs
Here is another example of a long-established clinical laboratory test that—upon new evidence—turns out to be not as accurate as once thought. According to research conducted at the University of Exeter in Devon, UK, Single-nucleotide polymorphism (SNP) chips (aka, SNP microarrays)—technology commonly used in commercial genetic testing—is inadequate at detecting rare gene variants that can increase breast cancer risk.
A news release announcing the results of the large-scale study states, “A technology that is widely used by commercial genetic testing companies is ‘extremely unreliable’ in detecting very rare variants, meaning results suggesting individuals carry rare disease-causing genetic variants are usually wrong.”
Why is this a significant finding for clinical laboratories? Because medical laboratories performing genetic tests that use SNP chips should be aware that rare genetic variants—which are clinically relevant to a patient’s case—may not be detected and/or reported by the tests they are running.
UK Researchers Find ‘Shockingly High False Positives’
The conclusion reached by the Exeter researchers, the BMJ study states, is that “SNP chips are extremely unreliable for genotyping very rare pathogenic variants and should not be used to guide health decisions without validation.”
Leigh Jackson, PhD, Lecturer in Genomic Medicine at University of Exeter and co-author of the BMJ study, said in the news release, “The number of false positives on rare genetic variants produced by SNP chips was shockingly high. To be clear: a very rare, disease-causing variant detected using [an] SNP chip is more likely to be wrong than right.”
In the news release, Caroline Wright, PhD (above), Professor in Genomic Medicine at the University of Exeter Medical School and senior author of the BMJ study, said, “SNP chips are fantastic at detecting common genetic variants, yet we have to recognize that tests that perform well in one scenario are not necessarily applicable to others.” She added, “We’ve confirmed that SNP chips are extremely poor at detecting very rare disease-causing genetic variants, often giving false positive results that can have profound clinical impact. These false results had been used to schedule invasive medical procedures that were both unnecessary and unwarranted.” (Photo copyright: University of Exeter.)
Large-Scale Study Taps UK Biobank Data
The Exeter researchers were concerned about cases of unnecessary invasive medical procedures being scheduled by women after learning of rare genetic variations in BRCA1 (breast cancer type 1) and BRCA2 (breast cancer 2) tests.
“The inherent technical limitation of SNP chips for correctly detecting rare genetic variants is further exacerbated when the variants themselves are linked to very rare diseases. As with any diagnostic test, the positive predictive value for low prevalence conditions will necessarily be low in most individuals. For pathogenic BRCA variants in the UK Biobank, the SNP chips had an extremely low positive predictive value (1-17%) when compared with sequencing. Were these results to be fed back to individuals, the clinical implications would be profound. Women with a positive BRCA result face a lifetime of additional screening and potentially prophylactic surgery that is unwarranted in the case of a false positive result,” they wrote.
Using UK Biobank data from 49,908 participants (55% were female), the researchers compared next-generation sequencing (NGS) to SNP chip genotyping. They found that SNP chips—which test genetic variation at hundreds-of-thousands of specific locations across the genome—performed well when compared to NGS for common variants, such as those related to type 2 diabetes and ancestry assessment, the study noted.
“Because SNP chips are such a widely used and high-performing assay for common genetic variants, we were also surprised that the differing performance of SNP chips for detecting rare variants was not well appreciated in the wider research or medical communities. Luckily, we had recently received both SNP chip and genome-wide DNA sequencing data on 50,000 individuals through the UK Biobank—a population cohort of adult volunteers from across the UK. This large dataset allowed us to systematically investigate the performance of SNP chips across millions of genetic variants with a wide range of frequencies, down to those present in fewer than 1 in 50,000 individuals,” wrote Wright and Associate Professor of Bioinformatics and Human Genetics at Exeter, Michael Weedon, PhD, in a BMJ blog post.
The Exeter researchers also analyzed data from a small group of people in the Personal Genome Project who had both SNP genotyping and sequencing information available. They focused their analysis on rare pathogenic variants in BRCA1 and BRCA2 genes.
The researchers found:
The rarer the variant, the less reliable the test result. For example, for “very rare variants” in less than one in 100,000 people, 84% found by SNP chips were false positives.
Low positive predictive values of about 16% for very rare variants in the UK Biobank.
Nearly all (20 of 21) customers of commercial genetic testing had at least one false positive rare disease-causing variant incorrectly genotyped.
SNP chips detect common genetic variants “extremely well.”
Advantages and Capabilities of SNP Chips
Compared to next-gen genetic sequencing, SNP chips are less costly. The chips use “grids of hundreds of thousands of beads that react to specific gene variants by glowing in different colors,” New Scientist explained.
Common variants of BRCA1 and BRCA2 can be found using SNP chips with 99% accuracy, New Scientist reported based on study data.
However, when the task is to find thousands of rare variants in BRCA1 and BRCA2 genes, SNP chips do not fare so well.
“It is just not the right technology for the job when it comes to rare variants. They’re excellent for the common variants that are present in lots of people. But the rarer the variant is, the less likely they are to be able to correctly detect it,” Wright told CNN.
SNP chips can’t detect all variants because they struggle to cluster needed data, the Exeter researchers explained.
“SNP chips perform poorly for genotyping rare genetic variants owing to their reliance on data clustering. Clustering data from multiple individuals with similar genotypes works very well when variants are common,” the researchers wrote. “Clustering becomes more difficult as the number of people with a particular genotype decreases.”
Clinical laboratories Using SNP Chips
The researchers at Exeter unveiled important information that pathologists and medical laboratory professionals will want to understand and monitor. Cancer patients with rare genetic variants may not be diagnosed accurately because SNP chips were not designed to identify specific genetic variants. Those patients may need additional testing to validate diagnoses and prevent harm.
The scientist also employed machine learning “to gauge how easily accessible genes are for transcription” in research that could lead to new clinical laboratory diagnostic tests
Anatomic pathologists and clinical laboratories are of course familiar with the biological science of genomics, which, among other things, has been used to map the human genome. But did you know that a three-dimensional (3D) map of a genome has been created and that it is helping scientists understand how DNA regulates its organization—and why?
The achievement took place at St. Jude Children’s Research Hospital (St. Jude) in Memphis, Tenn. Scientists there created “the first 3D map of a mouse genome” to study “the way cells organize their genomes during development,” a St. Jude news release noted.
Some experts predict that this new approach to understanding how changes happen in a genome could eventually provide new insights that anatomic pathologists and clinical laboratory scientists could find useful when working with physicians to diagnose patients and using the test results to identify the most appropriate therapy for those patients.
In addition to 3D modeling, the researchers applied machine learning to data from multiple sources to see how the organization of the genome changed at different times during development. “The changes are not random, but part of the developmental program of cells,” Dyer said in the news release.
The St. Jude study focused on the rod cells in a mouse retina. That may seem like a narrow scope, but there are more than 8,000 genes involved in retinal development in mice, during which those genes are either turned on or off.
To see what was happening among the cells, the researchers used HI-C analysis, an aspect of ultra-deep chromosome conformation capture, in situ. They found that the loops in the DNA bring together regions of the genome, allowing them to interact in specific ways.
Until this study, how those interactions took place was a
mystery.
“Understanding the way cells organize their genomes during development will help us to understand their ability to respond to stress, injury and disease,”Michael Dyer, PhD (above), Chair of St. Jude’s Developmental Neurobiology Department, co-leader of the Developmental Biology and Solid Tumor Program, and Investigator at Howard Hughes Medical Institute (HHMI), said in the news release. (Photo copyright: St. Jude Children’s Research Hospital.)
The scientists also discovered there were DNA promoters, which encourage gene expression, and also DNA enhancers that increase the likelihood gene expression will occur.
“The research also included the first report of a powerful regulator of gene expression, a super enhancer, that worked in a specific cell at a specific stage of development,” the news release states. “The finding is important because the super enhancers can be hijacked in developmental cancers of the brain and other organs.”
St. Jude goes on to state, “In this study, the scientists determined that when a core regulatory circuit super-enhancer for the VSX2 gene was deleted, an entire class of neurons (bipolar neurons) was eliminated. No other defects were identified. Deletion of the VSX2 gene causes many more defects in retinal development, so the super-enhancer is highly specific to bipolar neurons.”
The St. Jude researchers developed a genetic mouse model of
the defect that scientists are using to study neural circuits in the retina,
the news release states.
Research Technologist Victoria Honnell (left); Developmental Neurobiologist Jackie Norrie, PhD (center); and Postdoctoral Researcher Marybeth Lupo, PhD (right), work in the St. Jude clinical laboratory of Michael Dyer, PhD, using 3D genomic mapping to study gene regulation during development and disease. (Photo copyright: St. Jude Children’s Research Hospital.)
DNA Loops May Matter to Pathology Sooner Rather than
Later
Previous researcher studies primarily used genomic sequencing technology to locate and investigate alterations in genes that lead to disease. In the St. Jude study, the researchers examined how DNA is packaged. If the DNA of a single cell could be stretched out, it would be more than six feet long. To fit into the nucleus of a cell, DNA is looped and bundled into a microscopic package. The St. Jude scientists determined that how these loops are organized regulates how the cell functions and develops.
Scientists around the world will continue studying how the loops in DNA impact gene regulation and how that affects the gene’s response to disease. At St. Jude Children’s Research Hospital, Dyer and his colleagues “used the same approach to create a 3D genomic map of the mouse cerebellum, a brain structure where medulloblastoma can develop. Medulloblastoma is the most common malignant pediatric brain tumor,” noted the St. Jude’s news release.
In addition to providing an understanding of how genes
function, these 3D studies are providing valuable insight into how some
diseases develop and mature. While nascent research such as this may not impact
pathologists and clinical laboratories at the moment, it’s not a stretch to
think that this work may lead to greater understanding of the pathology of
diseases in the near future.
Using animal blood, the researchers hope to improve the accuracy of AI driven diagnostic technology
What does a cheetah, a tortoise, and a Humboldt penguin have
in common? They are zoo animals helping scientists at Saarland University in
Saarbrücken, Germany, find biomarkers that can help computer-assisted diagnoses
of diseases in humans at early stages. And they are not the only animals
lending a paw or claw.
In their initial research, the scientists used blood samples
that had been collected during routine examinations of 21 zoo animals between
2016 and 2018, said a news
release. The team of bioinformatics
and human genetics experts
worked with German zoos Saarbrücken and Neunkircher for the study. The project
progresses, and thus far, they’ve studied the blood of 40 zoo animals, the
release states.
This research work may eventually add useful biomarkers and
assays that clinical
laboratories can use to support physicians as they diagnose patients,
select appropriate therapies, and monitor the progress of their patients. As medical
laboratory scientists know, for many decades, the animal kingdom has been
the source of useful insights and biological materials that have been
incorporated into laboratory assays.
“Measuring the molecular blood profiles of animals has never
been done before this way,” said Andreas
Keller, PhD, Saarland University Bioinformatics Professor and Chair for
Clinical Bioinformatics, in the news release. The Saarland researchers published
their findings in Nucleic Acids
Research, an Oxford
Academic journal.
“Studies on sncRNAs [small non-coding RNAs] are often largely based on homology-based information, relying on genomic sequence similarity and excluding actual expression data. To obtain information on sncRNA expression (including miRNAs, snoRNAs, YRNAs and tRNAs), we performed low-input-volume next-generation sequencing of 500 pg of RNA from 21 animals at two German zoological gardens,” the article states.
Can Animals Improve the Accuracy of AI to Detect Disease
in Humans?
However, the researchers perceived an inability for AI and machine learning to
discern real biomarker patterns from those that just seemed to fit.
“The machine learning methods recognize the typical
patterns, for example for a lung tumor or Alzheimer’s disease. However, it is
difficult for artificial intelligence to learn which biomarker patterns are
real and which only seem to fit the respective clinical picture. This is where
the blood samples of the animals come into play,” Keller states in the news
release.
“If a biomarker is evolutionarily conserved, i.e. also
occurs in other species in similar form and function, it is much more likely
that it is a resilient biomarker,” Keller explained. “The new findings are now
being incorporated into our computer models and will help us to identify the
correct biomarkers even more precisely in the future.”
Andreas Keller, PhD (left), and zoo director Richard Francke (right), hold a pair of radiated tortoises that participated in the Saarland University study. (Photo copyright: Oliver Dietze/Saarland University.)
“Because blood can be obtained in a standardized manner and
miRNA expression patterns are technically very stable, it is easy to accurately
compare expression between different animal species. In particular, dried blood
spots or microsampling devices appear to be well suited as containers for
miRNAs,” the researchers wrote in Nucleic Acids Research.
Animal species that participated in the study include:
Additionally, human volunteers contributed blood specimens
for a total of 19 species studied. The scientists reported success in capturing
data from all of the species. They are integrating the information into their
computer models and have developed a public database of their
findings for future research.
“With our study, we provide a large collection of small RNA
NGS expression data of species that have not been analyzed before in great
detail. We created a comprehensive publicly available online resource for
researchers in the field to facilitate the assessment of evolutionarily
conserved small RNA sequences,” the researchers wrote in their paper.
Clinical Laboratory Research and Zoos: A Future
Partnership?
This novel involvement of zoo animals in research aimed at improving
the ability of AI driven diagnostics to isolate and identify human disease is
notable and worth watching. It is obviously pioneering work and needs much
additional research. At the same time, these findings give evidence that there
is useful information to be extracted from a wide range of unlikely sources—in
this case, zoo animals.
Also, the use of artificial intelligence to search for
useful patterns in the data is a notable part of what these researchers
discovered. It is also notable that this research is focused on sequencing DNA
and RNA of the animals involved with the goal of identifying sequences that are
common across several species, thus demonstrating the common, important
functions they serve.
In coming years, those clinical laboratories doing genetic
testing in support of patient care may be incorporating some of this research
group’s findings into their interpretation of certain gene sequences.
