The Sequence 9/4-9/11
Differences Between Adult and Stem Cell-induced Endothelial Cells, Genetic Analysis Identifies Ancestry of Human Remains, Genetic Testing Result Reanalysis, Multi-population Asian Genomic Dataset
What are the differences between adult endothelial cells and stem cell-induced endothelial cells?
Fan et al. studied the differences between adult endothelial cells and endothelial cells derived from stem cells (i.e. a type of cell that has the ability to generate other types of specialty cells, like endothelial cells). They looked at the electrophysiological and functional properties of the ‘regular’ cells vs. the cells derived from stem cells.
Why does it matter?
Well, endothelial cells play an important role in regulating blood vessel and tissue function and are highly prevalent in the heart. Since cardiovascular disease remains one of the leading causes of death in the world, we really want to understand endothelial dysfunction, why it happens and how we can prevent or treat it. By using stem cells, we can create endothelial cells in the lab and use them to study all of that.
Makes sense. So we can we use them?
Likely, yes! The study indicated that stem cell-induced endothelial cells possess functional properties similar to native endothelial cells. Although there were some properties that were different, the mechanism that cells use for signaling was similar in both cell types.
Stem cell-induced endothelial cells may be useful for disease modeling, cell replacement therapy and drug screening in the future.
Genetic analysis identifies ancestry of human remains stumbled upon in the UK
Researchers used genomic analysis to identify the human remains of 6 adults and 11 children that were found at a construction site in Norwich, UK in 2004. The purpose was to understand the story of these individuals: Who were they? How did they die?
How did they find out all that?
To begin, genomic analysis helped the researchers to determine the ancestry of these individuals by comparing their DNA with the DNA of more than a dozen modern western Eurasian groups. The DNA most closely matched today's Ashkenazi Jewish populations.
This ‘ancestry matching’ is done by looking at short pieces of DNA across the genome, and comparing each piece of DNA to reference genomes (i.e. genomes of people with known ancestry). If the DNA matches the reference genome of a certain population, it’s assigned to that population.
Next, radiocarbon dating established that these individuals likely died between 1161 and 1216, leading the team to believe they were likely killed during a historically documented antisemitic massacre in Norwich in 1190.
Interesting. Anything else ground-breaking?
Yes! Just like there are genetic variants in the DNA that are more common in certain populations, there are also certain mutations, or harmful changes in the DNA, that are more common in certain populations. Following that logic, there are some conditions that are more common in the Ashkenazi Jewish population (just as there are some conditions that are more common in other populations).
It’s been thought that the specific genetic mutations that occur in the Ashkenazi Jewish population is due to a ‘bottleneck’ that happened over 600 years ago. A ‘bottleneck’ happens when a large population suddenly becomes much smaller, because as that smaller group of people recreate, there’s now a smaller genetic pool for their offspring to inherit. In the case of the Ashkenazi Jewish population, this sudden decrease in population was likely due to persecution. So, a lot of the variants that were present in that smaller group of people are ‘common’ in the Ashkenazi Jewish population.
The genetic analysis found that the newly identified individuals had genetic mutations that we see today in the Ashkenazi population. So, this whole thing suggests the bottleneck happened earlier on in history than we thought.
Interesting!
Yes. The use of genomic analysis can not only tell us about an individual’s DNA, but help us understand important historical events. The more we understand about ourselves, the more we understand where we came from.
Opinions on genetic testing result reanalysis
Berger et al. surveyed healthcare providers, lab personnel and patients/parents of patients to understand opinions on reinterpretation of genetic testing results. The team assessed which variants people felt should reported after a reanalysis, how long they felt it is the genetic testing lab’s responsibility to conduct reanalyses, and concerns about consent, cost and liability.
Why would we need to do a reanalysis?
Since our knowledge of genetics is limited, genetic testing results are often inconclusive. Sometimes, when an individual’s genetic data is reanalyzed after a period of time, say a couple of years, the test will identify a causative mutation that wasn’t originally identified. This happens because we’re constantly learning more about genes.
So we should be reanalyzing genetic testing data periodically, right?
That’s where this study comes in. There is really no existing consensus on when to reanalyze genetic testing data, and more importantly, who is responsible for doing it. The responsibility could fall on the genetic testing lab, the healthcare provider or even the patient. And every medical facility does it differently.
That’s a problem.
It is. But, it’s also not straightforward. Reanalyzing all patient genetic testing results is a huge undertaking. Barriers include lack of resources, concerns about the effects on patients, and variability in patient consent.
What did the survey say?
On which variants people felt should reported after a reanalysis: Patients/parents of patients, clinical genetic providers and laboratory genetic providers mostly felt any new result should be reported, while non-genetics providers mostly felt new results that could change the patient’s management should be reported.
On how long it is the genetic testing lab’s responsibility to conduct reanalyses: Most participants felt the lab should be indefinitely responsible for reinterpreting a previously reported variant. This was one of the most notable findings of the study.
On concerns about consent: Most genetics providers responded that consent should not be required before reinterpretation, however most patients/parents and non-genetics providers disagreed.
On cost: The cost of implementing reanalysis at scale is unknown. Most providers responded that the cost of reinterpretation should be included in the initial cost of testing, rather than as an additional cost at the time of initial testing or reinterpretation
On liability: Amongst providers, there was concern about legal liability associated with a duty to initiate reinterpretation and recontact patients about reinterpreted results. Clinical genetic providers were most concerned about liability related to initiating the process of reinterpretation.
What does this mean?
It means that there is a need to develop a consensus about the roles and responsibilities of parties involved in genetic testing reanalysis as genetic testing becomes more commonplace in clinical practice. The diversity of opinions suggests a need for open conversation and understanding between providers, genetic testing labs and patients.
Major partnership to create the largest multi-population Asian genomic dataset
Translational research company Anuva has partnered with healthcare technology company Helix to create the largest multi-population Asian genomic dataset.
Why is this important?
Some genetic variations are common in certain populations. At the moment, since most sample data available in public databases are European, it is difficult to do research on things like drug development and discovery that will be broadly applicable to all people. Researchers have been calling for greater genetic diversity in biological research for a long time. See this previous post on the genetic spectrum of genes associated with ALS in the Chinese population.
Got it. How will this partnership make a difference?
Anuva will be able to use Helix’s genetic sequencing technology to more efficiently analyze samples. This technology, combined with Anuva’s Genomic Bio / Data Bank and biorepository from diverse Asian subpopulations, will help to create the largest multi-population dataset from Asia.