Category Archives: Genetic Medicine

More than a decade ago, I was diagnosed with a string of autoimmune diseases, one after another, including a bone marrow disorder, thyroiditis, and then Guillain-Barr syndrome, which left me paralyzed while raising two young children.

I recovered from Guillain-Barr only to relapse, becoming paralyzed again. My immune system was repeatedly and mistakenly attacking my body, causing the nerves in my arms, legs, and those I needed to swallow to stop communicating with my brain, leaving me confined to and raising my children from bed.

As I slowly began to recover and learn to walk again, I noticed that along with residual physical losses I had experienced shifts in my mood and clarity of mind. Although Id always been an optimistic person, I felt a bleak unshakable dread, which didnt feel like the old me.

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I also noticed cognitive glitches. Names, words, and facts were hard to bring to mind. I can still recall cutting up slices of watermelon, putting them in a bowl, and staring down at them thinking, What is this again? I knew the word but couldnt remember it. I covered my lapse by bringing the bowl to the table and waiting for my children to call out, Yay! Watermelon! And I thought, Yes. Of course. Watermelon.

As a science journalist whose niche spans neuroscience, immunology, and human emotion, I knew at the time that it didnt make scientific sense that inflammation in the body could be connected to much less cause illness in the brain. At that time, scientific dogma held that the brain was the only organ in the body not ruled by the immune system. The brain was considered to be immune privileged.

That began to change in the early 2010s. As neuroscience and immunology started to merge, they began dismantling that century-old tenet. Scientists pivoted away from believing that the brain and body function as church and state entities, and developed an entirely new brain-body paradigm which acknowledges that the brain is also governed by the immune system.

Much of the revelatory science fueling this reversal in how we see brain health was due to a radically new understanding of tiny cells called microglia. In healthy brains, microglia act as humble housekeepers, removing dead cells and bathing neurons in protective factors. A new understanding of these cells tells us that when they go rogue, they destroy synapses and cause inflammation in the brain.

In 2012, Beth Stevens, a young researcher at Childrens Hospital and Harvard Medical School, and her then-postdoc, Dori Schafer, discovered that microglia also determine synaptic health, for good or ill, from cradle to grave a discovery for which Stevens was named a 2015 MacArthur genius award winner. They showed that these cells, which scientists had ignored since they were first noted in the 1920s, were actually powerful immune cells.

But just as the bodys immune system can rev into overdrive, causing inflammation and devastating physical symptoms, microglia can also become overexcited. When that happens, they can generate too much synaptic pruning, neuroinflammation, and symptoms of cognitive, mood, and behavioral disorders, from depression to Alzheimers disease.

This revelation, while scary to contemplate if you are a patient like me, is also the springboard for promise with a newly emerging and innovative set of tools that may help intervene in mental health disorders by treating the brains immune pathways much as we treat immune disorders of the body.

It turns out that people who have high levels of chronic inflammation, as measured by simple blood tests, also have higher levels of microglial activation in the brain, a keen and worrisome indicator that too many synapses are being lost.

This month, four hospitals from the National Network of Depression Centers the Mayo Clinic, University of Michigan, Johns Hopkins Hospital, and Emory University are wrapping up a clinical trial known as the Bio-K Study. It is investigating whether measuring individuals levels of inflammation and other related biomarkers can predict if infusions of ketamine (the K in the study name) will ease their depression. Originally used as an anesthetic, ketamine has been shown to have powerful antidepressant effects, and appears to work as an anti-inflammatory in the brain.

The Bio-K study is fueled by recent discoveries that individual differences in patients physical health can affect how well a treatment for depression helps the brain repair itself. Of particular interest is how inflammation, which can signal microglia to become overactive and destroy synapses in areas of the brain related to symptoms of depression and bipolar disorder, might limit someones response to treatment.

The Bio-K investigators are measuring participants levels of chronic inflammation before they receive ketamine infusions. After the infusions, blood samples are checked again to determine if changes in biomarkers are associated with improved outcomes.

If the findings are positive, it will help clinicians determine if inflammation is associated with patients positive, neutral, or negative responses to treatment, and may help them predict which treatments are best for which patients.

At Emory University School of Medicine, researchers have found that inflammation is linked to weakened reward circuits in depression and they can predict which patients neural circuits are going dark by measuring their level of inflammation via a simple blood draw. Also underway at Emory are clinical trials looking at the viability of using the same anti-inflammatories employed to treat autoimmune disease, such as infliximab, to treat depression. The hope is that by getting overexcited microglia to back off, important regions of the brain will be able to communicate again.

This new understanding of the working of the immune system in the brain is also leading to a clearer understanding of which oral antidepressant will work best in which patient. It turns out that for individuals who test positive for chronic inflammation, bupropion (Wellbutrin) may work better than drugs like escitalopram (Lexapro).

Immunotherapy also appears to show promise in treating Alzheimers disease. Last spring, news broke that Enbrel, Pfizers powerhouse anti-inflammatory drug for rheumatoid arthritis, appeared to help prevent Alzheimers disease. In data collected by a computer analysis of over 250,000 insurance claims, the drug reduced the risk of Alzheimers disease by 64% in patients who took it.

Others have picked up this research thread, and a number of labs are targeting the ways microglia express genes that increase the risk of Alzheimers. Piggybacking on work by an international team of researchers who identified a genetic mutation that seems to protect people from developing Alzheimers disease, Alector, an early-stage biotech company, identified a drug candidate called AL014 that shifts microglial gene expression in ways that prompt microglia to turn from the dark side to the light and begin clearing the brain of unwanted toxins. In theory, that may help stave off the onset of Alzheimers.

Why mental health disorders can be so difficult to treat in some people and not others is a mystery. The idea that microglia-led inflammation triggered by a combination of genes and environmental factors from emotional trauma to toxins can slowly brew within the brain throughout an individuals lifetime offers a clue to that enigma.

When microglia go haywire, they destroy synapses and neural connections in the brain that affect mood and behavior. There can be many consequences: overreaction to small problems, a dearth of joy, entrenched depression, pernicious anxiety, forgetfulness, lost memories. No two individuals brains are the same.

Over time, many small changes in neurocircuitry wrought by inflammation-led microglia can cause individuals to feel and behave very differently from the persons they once were or the ones they hoped to become.

Although the excessive forgetfulness I experienced in my post-paralysis years no longer plagues me, its been replaced by the kind of age-related glitches we all face. Having once struggled to add simple numbers and recall familiar words, Im eager to avoid going through that again. So Im keeping an eye out for where these trials lead, bearing in mind the first rule of medicine: We must first be sure to do no harm, and proceed with an abundance of caution.

As I get older, will I be brave enough to try an anti-inflammatory to help calm down the microglia that govern the 3-pound jelly universe that is my brain? I have a little time to decide. But not too much.

Donna Jackson Nakazawa is a science journalist and author of six books, including The Angel and The Assassin: The Tiny Brain Cell That Changed the Course of Medicine (Random House/Ballantine, January 2020).

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Microglia: a new brain target for depression and Alzheimer's? - STAT

Many valuable initiatives are outlined in the ambitious mandate letter from Justin Trudeau to his newly appointed health minister, Patty Hajdu. These include important responsibilities relating to effective, affordable medicines. While these plans have the potential to benefit all Canadians, a pharmaceutical policy viewed primarily through the current governments signature middle-class lens risks short-sightedly perpetuating serious existing issues.

These potential shortcomings are readily illustrated by a decidedly un-middle-class disease: tuberculosis. The archetypal disease of poverty, tuberculosis is generally found where the basic social determinants of health, from proper housing to adequate nutrition, are not. Vulnerable populations who already find themselves outside the remit of the Minister of Middle Class Prosperity are doubly failed when the Minister of Healths mandate for pharmaceutical policy overlooks them as well.

Take the mandates long-overdue inclusion of national universal pharmacareand implementing a national formulary and a rare disease drug strategy. Drugs that will eventually appear on the national formulary are presumably going to be drugs that are actually approved for sale in Canada. Yet approval isnt simply a matter of meeting certain standards for safety and efficacy. In the case of tuberculosis, many medicines considered the global standard of care, including the majority of medicines used to treat the more serious condition of drug-resistant tuberculosis, remain unavailable in Canada. This is in part because the process relies upon drug companies being willing to go to the effort of applying for approval in a country where there are too few cases of TB to make drugs profitable, regardless of whether the drugs are vital for patients and public health.

Virtually all of these missing medicines can be found on the World Health Organizations Model List of Essential Medicines, which outlines drugs every health system should have. Many countries, in keeping with WHO guidance, have created their own tailored essential medicines lists. Canada has not. The establishment of a Canadian list, as a prelude to a broader national formulary, was recommended in the recent Final Report of the Advisory Council on the Implementation of National Pharmacare. Hopefully its omission from the mandate letter does not mean this idea has been abandoned. Engaging directly with the WHO list when drawing up the Canadian list, as recommended in the report, would draw much-needed attention to important medicines absent from Canada, many of them for conditions like TB that disproportionately impact the most vulnerable Canadians.

Unfortunately, when the mandate letter highlights a rare disease drug strategy, it is not talking about ensuring access to globally recognized treatments for diseases that are uncommon in Canada even as they remain a public health scourge elsewhere. What, then, makes a disease rare? Health Canada has used a definition of a rare disease as one affecting fewer than 5 in 10,000 Canadians. The annual rate of tuberculosis is about 5 per 100,000; drug-resistant TB accounts for less than 10 percent of this total. However, rare disease in practice is used in relation to chronic genetic disorders like cystinosis and cystic fibrosis. These, not TB, are the diseases encountered in the heart-wrenching news stories we have all read about families advocating fiercely for treatment for a child or other family member requiring a sometimes unapproved, almost invariably expensive medicine.

In practice, TB and rare genetic disorders can face similar treatment barriers. Research into new treatments is underfunded in both cases. Similarly, in both instances, drug companies have been reluctant to take the steps necessary to enter the Canadian market. In turn, medical practitioners and their patients face similar barriers when navigating bureaucratic mechanisms, such as the Special Access Programme, to access drugs not officially available in Canada.

Federal action to provide access to otherwise unavailable TB drugs has been through temporary stopgap measures rather than structural reforms.

However, these shared barriers have not been effectively recognized as such in reforming pharmaceutical policy. Progress on improving access to drugs for rare diseases, including bringing more rare disease drugs to the Canadian market and shouldering at least some of their often exorbitant costs, is a real success story for effective advocacy with a middle-class face. By contrast, ensuring proper tuberculosis treatment has not received similar attention, even though for too many Canadian communities, particularly in the North, TB is in reality an all too common disease.

A better approach to pharmaceutical policy would involve taking steps to ensure that any drugs forming the global standard of care for any condition are available in Canada. Instead, federal action to provide access to otherwise unavailable TB drugs has been through temporary stopgap measures rather than structural reforms. One such drug, rifapentine, has been widely accessed by the majority of provinces and territories over the past few years under the Access to Drugs in Exceptional Circumstances mechanism, which has permitted temporary bulk importation in response to what has ended up being officially listed as the tuberculosis crisis. That rifapentine is nevertheless still not officially available in Canada has even drawn international attention to the inadequacies of Canadas system.

Similarly, even those tuberculosis drugs that are officially available in Canada are not always available in practice. Like many other drugs for a wide range of conditions, tuberculosis drugs have been prone to recent shortages. Of particular concern, over the past year Canada has faced shortages of both rifampin and ethambutol, two cornerstones of tuberculosis treatment. It is thus welcome news that another key element of the Ministers mandate is to ensure that Canadians have access to the medicines they need by taking actionto address drug shortages.

Such action, which should be undertaken promptly, must include seriously examining options like stockpiling and alternative sourcing; in another sign of policy incoherence, even though Canada has been a major funder of the Global Drug Facility to ensure access to essential medicines for tuberculosis in low- and middle-income countries, it does not draw upon the mechanism itself for drugs in shortage, let alone drugs not otherwise available in Canada, even though all countries are encouraged to do so. Furthermore, the response must reflect the fact that not all shortages are created equal; while structural reforms are necessary, drugs for conditions like TB that have serious public health consequences and that do not have effective, accessible substitutes must be prioritized.

Ironically, tuberculosis also offers a stark reminder for a third component of the Ministers mandate, to address the serious and growing public health threat of antimicrobial resistance. Drug-resistant tuberculosis exists only because insufficient attention was paid to tuberculosis treatment in the first place; prioritizing its prevention (through ensuring an uninterrupted supply of basic tuberculosis drugs) and treatment (through removing barriers to patient access to drugs for treating drug-resistant TB) is an essential component of successfully addressing the public health threat of antimicrobial resistance.

Tuberculosis was the leading cause of death in Canada at the time of Confederation; better living standards and access to effective medicines drove its precipitous decline over the next century. Unfortunately, attention from policy-makers has similarly declined. That rates of this curable disease in Canada have remained relatively steady since the 1980s, and that numerous drugs for the worlds top infectious killer are in shortage or simply not officially available here, underscore the fact that pharmaceutical policy that fails to identify the particular needs of those outside the middle class can be at best a middling success.

Photo: Shutterstock, byMaxx-Studio.

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Pharmaceutical policy excludes the most vulnerable - Policy Options

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Newswise A comprehensive analysis of 10,575 genomes as part of a multi-national study led by researchers at UC San Diego has revealed close evolutionary proximity between the microbial domains at the base of the tree of life, the branching pattern of evolution described by Charles Darwin more than 160 years ago in his book, On the Origin of Species.

The currently accepted tree of life consists of two microbial domains, Bacteria and Archaea, and a third domain, Eukaryota, of higher organisms whose cells have nuclei to enclose their DNA and that may have evolved from Archaea.

The study, published last month in Nature Communications, found much closer evolutionary proximity between Archaea and Bacteria than have most previous studies. This new result arises from the use of a comprehensive set of 381 marker genes versus a couple of dozen core genes such as ribosomal proteins typically used in previous studies, according to Qiyun Zhu, a postdoctoral scholar in the UC San Diego School of Medicine's Department of Pediatrics and lead author of the paper.

Our work shows that insufficient or uneven sampling of genetic information, as in most previous work, results in a biased view of the tree of life, therefore limiting our ability to establish evolutionary relationships, said Zhu.

The researchers also generated time-calibrated trees, assuming a universal molecular clock and that the split between Cyanobacteria and Melainabacteria occurred about 2.5 billion years ago when the atmosphere became oxygenated. The base of these trees implies that the origin of life occurred about 4 billion years ago when 381 marker genes are considered, versus about 7 billion years ago when 30 ribosomal proteins are considered. The latter time is not credible, said researchers, since it is older than the age of the Earth, which further supports the choice of genes adopted in the study.

Rob Knight, founding director of the Center for Microbiome Innovation and Professor of Pediatrics and Computer Science & Engineering at UC San Diego, and senior author of the new study, said that its significance from a pediatric standpoint is that many diseases that strike in adulthood have their roots in the human microbiome in childhood.

Our ability to collect DNA sequences from the human microbiome has expanded dramatically in the past 15 years, but our ability to interpret the data relies on reference databases that are highly incomplete, said Knight. Improving the precision of our understanding of evolutionary relationships among microbes gives us better precision in understanding how these changes occur, and how to target them to improve the microbiome in childhood to address not only microbiome-based early-life diseases, but to improve health throughout a persons lifespan.

Zhu further noted: We expect that our tree with 10,575 genomes selected in a statistically even way will be a valuable resource. We have made our results publicly available in a reference database and have developed computational tools to explore it. In multiple microbiome studies currently taking place in the Knight Lab, we have already witnessed remarkable improvements by using this resource.

Scalable Algorithm and Powerful Supercomputer

The availability of a scalable algorithm and a powerful supercomputer were essential for carrying out the study.

Phylogenetic trees were generated using two algorithmic approaches: concatenation and summary. Summary methods, which are relatively new, combine potentially different evolutionary histories of different genes to obtain a master "species tree". A leading summary method is ASTRAL, developed by (among others) Siavash Mirarab, an assistant professor in the Electrical and Computer Engineering Departmemt at UC San Diego.

Both approaches gave similar trees, but the summary approach better resolved the basal relationships among major microbial lineages because it is inherently scalable and can use all genomic data, whereas the concatenation approach requires subsampling to be computationally feasible. To facilitate analysis of the very large amount of data in the study, Uyen Mai, a PhD student in the Mirarab Lab and co-first author of the paper, developed new methods to extend the summary approach.

Most of the computations were done on the Comet supercomputer of the San Diego Supercomputer Center (SDSC) at UC San Diego. Wayne Pfeiffer, Distinguished Scientist at SDSC, made more than 2,000 runs on the standard compute nodes of Comet to generate the gene trees, while Mai combined these trees using ASTRAL on the GPU nodes of Comet.

Zhu summarized: We advanced the state-of-the-art of phylogenetic research along three dimensions: larger and more even representation of microbial life forms, more comprehensive use of whole-genome information, and improved methodology for accurate resolution of evolutionary relationships. This was made possible with the supercomputing power at SDSC.

About SDSC

As an Organized Research Unit of UC San Diego, SDSC is considered a leader in data-intensive computing and cyberinfrastructure, providing resources, services, and expertise to the national research community, including industry and academia. SDSC supports hundreds of multidisciplinary programs spanning a wide variety of domains, from earth sciences and biology to astrophysics, bioinformatics, and health IT. SDSCs petascale Comet supercomputer is a key resource within the National Science Foundations XSEDE (Extreme Science and Engineering Discovery Environment) program.

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UC San Diego-led Study Finds Close Evolutionary Proximity Between Microbial Domains in the Tree of Life - Newswise

Jesse Bloom, left, and Lea Starita are genetic scientists pursuing advances with the technique known as Deep Mutational Scanning, which will be the subject of a symposium and workshop at the University of Washington in Seattle on Jan. 13 and 14. (GeekWire Photo / Todd Bishop)

It has been nearly two decades since scientists accomplished the first complete sequencing of the human genome. This historic moment gave us an unprecedented view of human DNA, the genetic code that determines everything from our eye color to our chance of disease, unlocking some of the biggest mysteries of human life.

Twenty years later, despite the prevalence of genetic sequencing, considerable work remains to fulfill the promise of these advances to alleviate and cure human illness and disease.

Scientists and researchers are actually extremely good at reading genomes, but were very, very bad at understanding what were reading, said Lea Starita, co-director of Brotman Baty Institute for Precision Medicines Advanced Technology Lab, and research assistant professor in the Department of Genome Sciences at the University of Washington.

But that is changing thanks to new tools and approaches, including one called Deep Mutational Scanning. This powerful technique for determining genetic variants is generating widespread interest in the field of genetics and personalized medicine, and its the subject of a symposium and workshop on Jan. 13 and 14 at the University of Washington.

I think approaches like Deep Mutational Scanning will eventually allow us to make better countermeasures, both vaccines and drugs that will help us combat even these viruses that are changing very rapidly said Jesse Bloom, an evolutionary and computational biologist at the Fred Hutchinson Cancer Research Center, the Howard Hughes Medical Institute and the University of Washington Department of Genome Sciences.

Bloom, who researches the evolution of viruses, will deliver the keynote at the symposium, held by the Brotman Baty Institute and the Center for the Multiplex Assessment of Phenotype.

On this episode of the GeekWire Health Tech Podcast, we get a preview and a deeper understanding of Deep Mutational Scanning from Bloom and Starita.

Listen to the episode above, or subscribe in your favorite podcast app, and continue reading for an edited transcript.

Todd Bishop: Lets start with the landscape for precision medicine and personalized medicine. Can you give us a laypersons understanding of how personalized medicine differs from the medicine that most of us have encountered in our lives?

Lea Starita: One of the goals of precision medicine is to use the genomic sequence, the DNA sequence of the human in front of the doctor, to inform the best course of action that would be tailored to that person given their set of genes and the mutations within them.

TB: Some people in general might respond to certain treatments in certain ways and others might not. Today we dont know necessarily why thats the case, but personalized medicine is a quest to tailor the treatment or

Starita: To the individual. Exactly. Thats kind of personalized medicine, but you could also extend that to infectious disease to make sure that youre actually treating the pathogen that the person has, not the general pathogen, if you would. How would you say that, Jesse?

Jesse Bloom: I would elaborate on what Lea said when it comes to infectious diseases and other diseases. Not everybody gets equally sick when they are afflicted with the same underlying thing, and people tend to respond very differently to treatments. That obviously goes for genetic diseases caused by changes in our own genes like cancer, and it also happens with infectious diseases. For instance, the flu virus. Different people will get flu in the same year and some of them will get sicker than others, and thats personalized variation. Obviously wed like to be able to understand what the basis of that variation is and why some people get more sick in some years than others.

TB: Where are we today as a society, as a world, in the evolution of personalized medicine?

Starita: Pretty close to the starting line still. Theres been revolutions in DNA sequencing, for example. Weve got a thousand dollar genome, right? So were actually extremely good at reading genomes, but were very, very bad at understanding what were reading. So you could imagine youve got a human genome, its three billion base pairs times two, because youve got two copies of your genome, one from your mother, one from your father, and within that theres going to be millions of changes, little spelling mistakes all over the genome. We are right now very, very, very I cant even use enough verys bad at predicting which ones of those spelling mistakes are going to either be associated with disease or predictive of disease, even for genes where we know a lot about it. Even if that spelling mistake is in a spot in the genome we know a lot about, say breast cancer genes or something like that, we are still extraordinarily bad at understanding or predicting what effects those changes might have on health.

Bloom: In our research, were obviously also interested in how the genetics of a person influences how sick they get with an infectious disease, but we especially focus on the fact that the viruses themselves are changing a lot, as well. So theres changes in the virus as well as the fact that were all genetically different and those will interact with each other. In both cases, it really comes back to what Lea is saying is that I think weve reached the point in a lot of these fields where we can now determine the sequences of a humans genome or we can determine the sequence of a virus genome relatively easily. But its still very hard to understand what those changes mean. And so, thats really the goal of what were trying to do.

TB: What is deep mutational scanning in this context?

Lea Starita: A mutation is a change in the DNA sequence. DNA is just As, Cs, Ts and Gs. Some mutations which are called variants are harmless. You can think of a spelling mistake or a difference in spelling that wouldnt change the word, right? So the American gray, which is G-R-A-Y versus the British grey, G-R-E-Y. If you saw that in a sentence, its gray. Its the color.

But then it could be a spelling mistake that completely blows up the function of a protein, and then in that case, somebody could have a terrible genetic disease or could have an extremely high risk of cancer, or a flu virus could now be resistant to a drug or something like that, or resistant to your immune response. Or, mutations could also be beneficial, right? This is what allows evolution. This is how flu viruses of all the bacteria evolve to become drug resistant or gain some new enzymatic function that it needs to survive.

Bloom: For instance, in the case of mutations in the human genome, we know that everybody has mutations relative to the average human. Some of those mutations will have really major effects, some of them wont. The very traditional way or the way that people have first tried to understand what those mutations do is to sequence the genomes of a group of people and then compare them. Maybe here are people who got cancer and here are people who didnt get cancer and now you look to see which mutations are in the group that got cancer versus the group that didnt, and youll try to hypothesize that the mutations that are enriched in the group that did get cancer are associated with causing cancer.

This is a really powerful approach, but it comes with a shortcoming which is that theres a lot of mutations, and it gets very expensive to look across very, very large groups of people. And so the idea of a technique like deep mutational scanning is that we could simply do an experiment where we test all of the mutations on their own and we wouldnt have to do these sort of complicated population level comparisons to get at the answer. Because when youre comparing two people in the population, they tend to be different in a lot of ways, and its not a very well-controlled comparison. Whereas you can set up something in the lab where you have a gene that does have this mutation and does not have this mutation, and you can really directly see what the effect of that mutation is. Really, people have been doing that sort of experiment for many decades now. Whats new about deep mutational scanning is the idea that you can do that experiment on a lot of mutations all at once.

Starita: And its called deep because we try to make every possible spelling mistake. So every possible change in the amino acid sequence or the nucleotide sequence, which is the A, C, Ts and Gs, across the entire gene or the sequence were looking at.

Bloom: Lets say we were to compare me and Lea to figure out why one of us had some disease and other ones didnt. We could compare our genomes and theres going to be a lot of differences between them, and were not really going to know what difference is responsible. We dont even really know if it would be a change in their genomes thats responsible. It could be a change in something about our environment. So the idea behind deep mutational scanning is we would just take one gene. So in the case of Lea, she studies a particular gene thats related to breast cancer, and we would just make all of the individual changes in that gene and test what they do one by one. And then subsequently if we were to see that a mutation has some effect, if we were to then observe that mutation when we sequenced someones genome, we would have some idea of what it does.

Starita: The deep mutational scanning, the deep part is making all possible changes. We have all of that information at hand in an Excel file somewhere in the lab that says that this mutation is likely to cause damage to the function of the protein or the activity of the protein that it encodes. Making all of the possible mutations. Thats where the deep comes from.

TB: How exactly are you doing this? Is it because of advances in computer processing or is it because of a change in approach that has enabled this increase in volume of the different mutations you can look at?

Bloom: I would say that theres a number of technologies that have improved, but the really key one is the idea that the whole experiment can be done all at once. The traditional, if you were to go back a few decades way of doing an experiment like this, would be take one tube and put, lets say the normal or un-mutated gene variant in that, and then have another tube which has the mutant that you care about, and have somehow do an experiment on each of those two tubes and that works well.

But you can imagine if you had 10,000 tubes, it might start to become a little bit more difficult. And so the idea is that really the same way that people have gotten very good at sequencing all of these genomes, you can also use to make all of these measurements at once. The idea is you would now put all of different mutants together in the same tube and you would somehow set up the experiment, and this is really the crucial part of the whole thing, set up the experiment such that the cell or the virus or whatever youre looking at, how well it can grow in that tube depends on the effect of that mutation. And then you can just use the sequencing to read out how the frequencies of all of these mutations have changed. You would see that a good mutation that lets say helped the cell grow better would be more representative in the tube at the end, and a bad mutation would be less representative in the tube. And by doing this you could in principle group together tens of thousands or even hundreds of thousands or millions of mutations all at once and read it all out in one experiment.

Starita: This has been enabled by that same revolution that has given us the thousand dollar genome. These DNA sequencers that were now using, not really to sequence human genomes, but were using them as very expensive counting machines. So, were identifying the mutation and were counting it. Thats basically what were using the sequencers for. Instead of sequencing human genomes, were using them as a tool to count all of these different pieces of DNA that are in these cells.

TB: At what stage of development is deep mutational scanning?

Starita: It started about 10 years ago. The first couple of papers came out in 2009 and 2010 actually from the Genome Sciences department at University of Washington. Those started with short sequences and very simplified experiments, and we have been working over the years to build mutational scanning into better and more accurate model systems, but that are increasing the complexity of these experiments. And so weve gone from almost, Hey, thats a cute experiment you guys did, to doing impactful work that people are using in clinical genetics and things like that.

TB: When youre at a holiday party and somebody asks you what you do and then they get really into it and they ask you, Wait, what are the implications of not only personalized medicine but this deep mutational scanning? Whats this going to mean for my life?

Starita: Right now it hasnt been systematically used in the clinic, but well get phone calls from UW pathology that says, Hey, I have a patient that has this variant. We found the sequence variant and this patient has this phenotype. What does this mutation look like in your assay? And were like, Well, it looks like its damaging. And then they put all of that information together and they can actually go back to that patient and say, You are at high risk of cancer. Were going to take medical action. That has happened multiple times. Were working right now to try to figure out how to use the information that we are creating. So these maps of the effect of mutations on these very important proteins and how to systematically use them as evidence for or against their pathogenicity. Right now for a decent percentage of these people who are telling them, Well, youve got changes but we dont know what they do. We want those tests to be more informative. So you go, you get the test, they say, That is a bad one. That ones fine. That mutation is good. That ones OK. That one, though. That ones going to cause you problems. We want more people to have more informative genetic testing because right now in a decent proportion of tests come back with an I have no idea, answer.

Bloom: You can also think about mutations that affect resistance to some sort of drug. For many, many types of drugs, these include drugs against viruses, drugs against cancers and so on, the viruses and the cancers can become resistant by giving mutations that allow them to escape from that drug. In many cases there are even multiple drugs out there and you might have options of which drug to administer, but you might not really know which one. Clinicians have sort of built up lore that this drug tends to work more often or you try this one and then you try this other one, but because how well the drug works is probably in general determined by either the genetic mutations in lets say the cancer or the person or the genetic mutations in the virus or pathogen, if you knew what the effects of those mutations were ahead of time, you could make much more intelligent decisions about which drugs to administer. And there really shouldnt be a drug that works only 50 percent of the time; youre probably just not giving it in the right condition 50 perfect of the time. Wed like to be able to pick the right drug for the right condition all the time.

TB: And thats what precision medicine is about.

Starita: Yes.

TB: Deep mutational scanning as a tool.

Starita: To inform precision medicine.

Bloom: These deep mutational scanning techniques were really developed by people like Jay Shendure and Stan Fields, and Lea and Doug Fowler to look at these questions of precision medicine from the perspective of changes in our human genomes affecting our susceptibility to diseases. I actually work on mutations in a different context, which has mutations in the viruses that infect us and make us sick. These viruses evolve quite rapidly. In the case of flu virus, youre supposed to get the flu vaccine every year. The reason why you have to get it every year is the virus is always changing and we have to make the vaccine keep up with the virus. The same thing is true with drugs against viruses like flu or HIV. Sometimes the viruses will be resistant, sometimes the drugs will work. These again have to do with the very rapid genetic changes that are happening in the virus. So, were trying to use deep mutational scanning to understand how these mutations to these viruses will affect their ability to, lets say, escape someones immunity or escape a drug that might be used to treat that person.

TB: How far along are you on that path?

Bloom: Were making progress. One of the key things weve found is that the same mutation of the virus might have a different impact for different people. So we found using these approaches that the ways that you mutate a virus will allow the virus sometimes to escape from one persons immunity much better than from another persons immunity. And so were really right now trying to map out the heterogeneity across different people. And hopefully that could be used to understand what makes some people susceptible to a very specific viral strain versus other people.

TB: And so then would your research extend into the mutations in human genes in addition to the changes in the virus?

Bloom: You could imagine eventually wanting to look at all of those combinations together, and we are very interested in this, but the immediate research were focusing on right now actually probably is not so much driven by the genetics of the humans. In the case of influenza virus, like I was saying, we found that if theres a virus that has some particular mutation, it might, lets say, allow it to escape from your immunity but not allow it to escape from the immunity of me or Lea. That doesnt seem to be driven as much we think by our genetics, but rather our exposure histories. So in the case of influenza, were not born with any immunity to influenza virus. We build up that immunity over the course of our lifetime because we either get infected with flu or we get vaccinated with flu and then our body makes an immune response, which includes antibodies which block the virus. Each of us have our own personal history, not genetic history, but life history of which vaccinations and which infections weve gotten. And so, that will shape how our immune response sees the virus. As a result, we think that that doesnt really have so much of a genetic component as a historical component.

TB: Just going with the flu example, could this result in a future big picture where I go in to get my flu vaccine and its different than the one the next person might go in to get?

Bloom: What we would most like to do is use this knowledge to just design a vaccine that works for everybody. So that would just be the same vaccine that everyone could get. But its a very interesting I think at this point I would say its almost in the thought experiment stage to think about this. When you think of something like cancer, like Lea was saying, you can use these tools to understand when people have mutations that might make them at risk for a cancer, but thats actually often a very hard thing to intervene for, right? Its not so easy to prevent someone from getting cancer even if you know theyre at risk. But obviously if people are able to do that, theyre interested in spending a lot of money to do it, because cancer is a very severe thing and you often have a very long window to treat it.

Something like a flu virus is very much at the other end. If I had the omniscient capability to tell you that three days from now youre going to get infected with flu and youre going to get really sick, we could prevent that. We have the technology basically right now to prevent that, if its nothing else than just telling you to put on a bunch of Purell and dont leave your bedroom. But theres also actually some pretty good interventions including prophylactics to flu that work quite well. But the key thing is, right now we think of everyone in the world as being at risk all the time and you cant be treating everybody in the world all the time against flu. Theres just too many people and the risk that any person

Starita: Not that much Tamiflu on the market.

Bloom: Not that much, and the risk of it So I think to the extent that we could really identify whos at the most risk in any given year, that might allow us to use these interventions in a more targeted way. Thats the idea.

TB: And how does deep mutational scanning lead to that potentially?

Bloom: Yeah. So the idea, and at this point, this is really in the research phase, but the idea is if we could identify that say certain people or certain segments of the population, that because of the way their immunity, lets say, is working makes them very susceptible to the viral mutant that happens to have arisen in this particular year, we could then somehow either suggest that theyre more at risk or, as you suggested, design a vaccine thats specifically tailored to work for them. So thats the idea. I should make clear that that is not anywhere close to anybody even thinking of putting it into economic practice at this point because even the concepts behind it are really quite new. But I do think that theres a lot of potential if we think of these infectious diseases not so much as an act of God, where you just happened to someone sneezed on you as youre walking down the street, but actually a complex interaction between the mutations in the virus and your own either genetics or immune system, we can start to identify who might be more at risk for certain things in certain years, and that would at least open the door to using a lot of interventions we already have.

Starita: The first year was three years ago, and some very enthusiastic graduate students started it. Basically, it was almost like a giant lab meeting where everybody who is interested in this field came. Somebody tweeted it out and then all of a sudden people from UCSF were there and were like, What the heck? It was great and we all talked about the technology and how we were using it. The next year, the Brotman Baty Institute came in and were like, OK, well, maybe if we use some of this gift to support this, we can have a bigger meeting. And then it was 200 people in a big auditorium and that was great. And now this year, its a two-day symposium and workshop, and its also co-sponsored by a grant from the National Human Genome Research Institute. But now weve got hundreds of people, so about 200 people again, but now flying in from all over the world. Weve got invited speakers, and the workshop, which is Tuesday, is a more practical, If youre interested in this, how do you actually do these experiments?

TB: Whats driving the interest in deep mutational scanning?

Bloom: We are starting to have so much genetic information about really everything. It used to be, going back a couple of decades, a big deal to determine even the sequence of a single flu virus. It was totally unthinkable to determine the sequence of a human genome, right? If you dont know what mutations are there, you dont really care that much what they do. Now we can determine the sequence of tens of thousands of flu viruses. I mean, this is happening all the time, and we can determine the sequence of thousands, even tens of thousands of human genomes. So now it becomes, as Lea said, really important to go from just getting these sequences to understanding what the mutations that you observe in these sequences actually will mean for human health.

See this site for more on the Brotman Baty Institute for Precision Medicine and the Deep Mutational Scanning Symposium and Workshop, Jan. 13 and 14 in Seattle. The symposium is free to attend if youre in the Seattle area, and it will also be livestreamed, with archived video available afterward.

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Scientists pursue new genetic insights for health: Inside the world of deep mutational scanning - GeekWire

Ahmad Farajzadeh-Sheikh, 1, 2 Mohammad Savari, 1, 2 Khadijeh Ahmadi, 2, 3 Hossein Hosseini Nave, 4 Mojtaba Shahin, 5 Maryam Afzali 2

1Department of Microbiology, School of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran; 2Infectious and Tropical Diseases Research Center, Health Research Institute, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran; 3Abadan Faculty of Medical Sciences, Abadan, Iran; 4Department of Microbiology and Virology, School of Medicine, Kerman University of Medical Sciences, Kerman, Iran; 5Department of Medical Laboratory Sciences, Faculty of Medical Sciences, Islamic Azad University, Arak, Iran

Correspondence: Maryam AfzaliDepartment of Microbiology, School of Medicine, Ahvaz Jundishapur University of Medical Sciences, Golestan Blvd 39345-61355, Ahvaz, IranTel +09156127753Fax +98-61-3333 2036Email afzalimaryam@ymail.com

Background: Entero-invasive E. coli (EIEC) is one of the causes of bacillary dysentery in adults and children. The ability of EIEC to invade and colonize the surface of epithelial cells is influenced by many virulence factors. This study aimed to investigate the distribution of virulence factor genes in EIEC strains isolated from patients with diarrhea in Ahvaz, Iran, as well as the genetic diversity between these isolates by Multilocus variable-number tandem repeat analysis (MLVA).Materials and Methods: A total of 581 diarrheic stool samples were collected from patients with diarrhea attending two hospitals, in Ahvaz, Iran. The E. coli strains were identified by biochemical methods. Subsequently, all E. coli isolates were identified as EIEC by polymerase chain reaction (PCR) for the ipaH gene. The EIEC isolates evaluated by PCR for the presence of 8 virulence genes (ial, sen, virF, invE, sat, sigA, pic, and sepA). All EIEC strains were genotyped by the MLVA typing method.Results: A total of 13 EIEC isolates were identified. The presence of ial, virF, invE, sen, sigA, pic, and sat genes was confirmed among 92.3%, 84.6%, 84.6%, 76.9%, 69.2%, and 15.3% of EIEC isolates, respectively. On the other hand, none of the isolates were positive for the sepA gene. The EIEC isolates were divided into 11 MLVA types.Conclusion: Our results showed a high distribution of virulence genes among EIEC isolates in our region. This study showed that MLVA is a promising typing technique for epidemiological studies. MLVA can supply data in the form of codes that can be saved in the database and easily shared among laboratories, research institutes, and even hospitals.

Keywords: entero-invasive Escherichia coli, diarrhea, virulence factor, MLVA

This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution - Non Commercial (unported, v3.0) License.By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms.

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Distribution of Genes Encoding Virulence Factors and the Genetic Diver | IDR - Dove Medical Press

Doctors say digital technology and data are driving change that will create a different world of medicine in the next couple of decades, a new report from Stanford Medicine finds.

In a survey, physicians, residents and medical students say they expect almost a third of their current duties could be automated in 20 years. And doctors are preparing for that very different healthcare future now, according to the report (PDF).

Nearly half of physicians (73%) and most medical students (73%) are seeking additional training in areas such as advanced statistics, genetic counseling, population health and coding. One-third are studying artificial intelligence, according to the national survey of more than 700 physicians, residents and medical students commissioned by Stanford Medicine to understand how changing trends will reach the doctors office and shape patient care.

"We found that current and future physicians are not only open to new technologies but are actively seeking training in subjects such as data science to enhance care for their patients," saidLloyd Minor, M.D., dean of theStanford UniversitySchool of Medicine, in a statement.

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"We are encouraged by these findings and the opportunity they present to improve patient outcomes. At the same time, we must be clear-eyed about the challenges that may stymie progress, he said.

Key trends that are reshaping healthcare include a maturing digital health market, new health laws opening patient access to data and AI gaining regulatory traction for medical use.

And the jurys still out when it comes to whether the private industrys foray into healthcarein the form of companies such as Amazon, Google and Apple will solve problems.

Physicians, residents and students had mixed views about the impact these companies will have on healthcare, with 30% of students and residents and 21% of physicians still undecided. While patient outcomes are likely to improve, respondents are divided on whether physician effectiveness will improve and say physician job satisfaction will likely decrease, while healthcare costs likely increase.

Other findings include:

The value of data. The survey also showed that providers are heavy digital users and they believe patient data from wearables can be clinically valuable. Nearly half the survey respondents wear a health monitoring device, and most of them use the data to inform their personal healthcare decisions (71% of physicians, 60% of students and residents). A majority of students and residents (78%) and physicians (80%) say self-reported data from a patients health app would be clinically valuable in supporting their care. They also see value in data from consumer genetic testing reports.

Doctors arent prepared to implement innovations. However, most providers dont believe the current generation of practitioners is ready for the data-driven future, even current medical students and residents. When asked to rate the effectiveness of their education to prepare them for these developments, only 18% of current medical students and residents surveyed said that their education was very helpful. And 44% of physicians surveyed said their education was either not very helpful or not helpful at all.

The report pointed to the need to modernize curriculum and training programs so current and future physicians can make the most of new technologies.

The ongoing struggle with medical practice burdens. And, no surprise, physicians and residents say they are struggling under medical practice burdens. Nearly 1 in 5 would change their career path if given the opportunity, citing poor work-life balance and administrative burdens as the top reasons to reconsider their decision.

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Physicians expect almost one-third of their jobs to be automated by 2040, Stanford Medicine report finds - FierceHealthcare