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Study Finds Genetic Mutations in Healthy Human Tissues

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General mutations throughout the body

The standard view of biology is that every normal cell copies its DNA instruction book with complete accuracy every time it divides. And thus, with a few exceptions like the immune system, cells in normal, healthy tissue continue to contain exactly the same genome sequence as was present in the initial single-cell embryo that gave rise to that individual. But new evidence suggests it may be time to revise that view.

By analyzing genetic information collected throughout the bodies of nearly 500 different individuals, researchers discovered that almost all had some seemingly healthy tissue that contained pockets of cells bearing particular genetic mutations. Some even harbored mutations in genes linked to cancer. The findings suggest that nearly all of us are walking around with genetic mutations within various parts of our bodies that, under certain circumstances, may have the potential to give rise to cancer or other health conditions.

Efforts such as NIH’s The Cancer Genome Atlas (TCGA) have extensively characterized the many molecular and genomic alterations underlying various types of cancer. But it has remained difficult to pinpoint the precise sequence of events that lead to cancer, and there are hints that so-called normal tissues, including blood and skin, might contain a surprising number of mutations —perhaps starting down a path that would eventually lead to trouble.

In the study published in Science, a team from the Broad Institute at MIT and Harvard, led by Gad Getz and postdoctoral fellow Keren Yizhak, along with colleagues from Massachusetts General Hospital, decided to take a closer look. They turned their attention to the NIH’s Genotype-Tissue Expression (GTEx) project.

The GTEx is a comprehensive public resource that shows how genes are expressed and controlled differently in various tissues throughout the body. To capture those important differences, GTEx researchers analyzed messenger RNA sequences within thousands of healthy tissue samples collected from people who died of causes other than cancer.

Getz, Yizhak, and colleagues wanted to use that extensive RNA data in another way: to detect mutations that had arisen in the DNA genomes of cells within those tissues. To do it, they devised a method for comparing those tissue-derived RNA samples to the matched normal DNA. They call the new method RNA-MuTect.

All told, the researchers analyzed RNA sequences from 29 tissues, including heart, stomach, pancreas, and fat, and matched DNA from 488 individuals in the GTEx database. Those analyses showed that the vast majority of people—a whopping 95 percent—had one or more tissues with pockets of cells carrying new genetic mutations.

While many of those genetic mutations are most likely harmless, some have known links to cancer. The data show that genetic mutations arise most often in the skin, esophagus, and lung tissues. This suggests that exposure to environmental elements—such as air pollution in the lung, carcinogenic dietary substances in the esophagus, or the ultraviolet radiation in sunlight that hits the skin—may play important roles in causing genetic mutations in different parts of the body.

The findings clearly show that, even within normal tissues, the DNA in the cells of our bodies isn’t perfectly identical. Rather, mutations constantly arise, and that makes our cells more of a mosaic of different mutational events. Sometimes those altered cells may have a subtle growth advantage, and thus continue dividing to form larger groups of cells with slightly changed genomic profiles. In other cases, those altered cells may remain in small numbers or perhaps even disappear.

It’s not yet clear to what extent such pockets of altered cells may put people at greater risk for developing cancer down the road. But the presence of these genetic mutations does have potentially important implications for early cancer detection. For instance, it may be difficult to distinguish mutations that are truly red flags for cancer from those that are harmless and part of a new idea of what’s “normal.”

To further explore such questions, it will be useful to study the evolution of normal mutations in healthy human tissues over time. It’s worth noting that so far, the researchers have only detected these mutations in large populations of cells. As the technology advances, it will be interesting to explore such questions at the higher resolution of single cells.

Getz’s team will continue to pursue such questions, in part via participation in the recently launched NIH Pre-Cancer Atlas. It is designed to explore and characterize pre-malignant human tumors comprehensively. While considerable progress has been made in studying cancer and other chronic diseases, it’s clear we still have much to learn about the origins and development of illness to build better tools for early detection and control.

Reference:

[1] RNA sequence analysis reveals macroscopic somatic clonal expansion across normal tissues. Yizhak K, Aguet F, Kim J, Hess JM, Kübler K, Grimsby J, Frazer R, Zhang H, Haradhvala NJ, Rosebrock D, Livitz D, Li X, Arich-Landkof E, Shoresh N, Stewart C, Segrè AV, Branton PA, Polak P, Ardlie KG, Getz G. Science. 2019 Jun 7;364(6444).

Links:

Genotype-Tissue Expression Program

The Cancer Genome Atlas (National Cancer Institute/NIH)

Pre-Cancer Atlas (National Cancer Institute/NIH)

Getz Lab (Broad Institute, Cambridge, MA)

NIH Support: Common Fund; National Heart, Lung, and Blood Institute; National Human Genome Research Institute; National Institute of Mental Health; National Cancer Institute; National Library of Medicine; National Institute on Drug Abuse; National Institute of Neurological Diseases and Stroke


Whole-Genome Sequencing Plus AI Yields Same-Day Genetic Diagnoses

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Sebastiana
Caption: Rapid whole-genome sequencing helped doctors diagnose Sebastiana Manuel with Ohtahara syndrome, a neurological condition that causes seizures. Her data are now being used as part of an effort to speed the diagnosis of other children born with unexplained illnesses. Credits: Getty Images (left); Jenny Siegwart (right).



Back in April 2003, when the international Human Genome Project successfully completed the first reference sequence of the human DNA blueprint, we were thrilled to have achieved that feat in just 13 years. Sure, the U.S. contribution to that first human reference sequence cost an estimated $400 million, but we knew (or at least we hoped) that the costs would come down quickly, and the speed would accelerate. How far we’ve come since then! A new study shows that whole genome sequencing—combined with artificial intelligence (AI)—can now be used to diagnose genetic diseases in seriously ill babies in less than 24 hours.

Take a moment to absorb this. I would submit that there is no other technology in the history of planet Earth that has experienced this degree of progress in speed and affordability. And, at the same time, DNA sequence technology has achieved spectacularly high levels of accuracy. The time-honored adage that you can only get two out of three for “faster, better, and cheaper” has been broken—all three have been dramatically enhanced by the advances of the last 16 years.

Rapid diagnosis is critical for infants born with mysterious conditions because it enables them to receive potentially life-saving interventions as soon as possible after birth. In a study in Science Translational Medicine, NIH-funded researchers describe development of a highly automated, genome-sequencing pipeline that’s capable of routinely delivering a diagnosis to anxious parents and health-care professionals dramatically earlier than typically has been possible [1].

While the cost of rapid DNA sequencing continues to fall, challenges remain in utilizing this valuable tool to make quick diagnostic decisions. In most clinical settings, the wait for whole-genome sequencing results still runs more than two weeks. Attempts to obtain faster results also have been labor intensive, requiring dedicated teams of experts to sift through the data, one sample at a time.

In the new study, a research team led by Stephen Kingsmore, Rady Children’s Institute for Genomic Medicine, San Diego, CA, describes a streamlined approach that accelerates every step in the process, making it possible to obtain whole-genome test results in a median time of about 20 hours and with much less manual labor. They propose that the system could deliver answers for 30 patients per week using a single genome sequencing instrument.

Here’s how it works: Instead of manually preparing blood samples, his team used special microbeads to isolate DNA much more rapidly with very little labor. The approach reduced the time for sample preparation from 10 hours to less than three. Then, using a state-of-the-art DNA sequencer, they sequence those samples to obtain good quality whole genome data in just 15.5 hours.

The next potentially time-consuming challenge is making sense of all that data. To speed up the analysis, Kingsmore’s team took advantage of a machine-learning system called MOON. The automated platform sifts through all the data using artificial intelligence to search for potentially disease-causing variants.

The researchers paired MOON with a clinical language processing system, which allowed them to extract relevant information from the child’s electronic health records within seconds. Teaming that patient-specific information with data on more than 13,000 known genetic diseases in the scientific literature, the machine-learning system could pick out a likely disease-causing mutation out of 4.5 million potential variants in an impressive 5 minutes or less!

To put the system to the test, the researchers first evaluated its ability to reach a correct diagnosis in a sample of 101 children with 105 previously diagnosed genetic diseases. In nearly every case, the automated diagnosis matched the opinions reached previously via the more lengthy and laborious manual interpretation of experts.

Next, the researchers tested the automated system in assisting diagnosis of seven seriously ill infants in the intensive care unit, and three previously diagnosed infants. They showed that their automated system could reach a diagnosis in less than 20 hours. That’s compared to the fastest manual approach, which typically took about 48 hours. The automated system also required about 90 percent less manpower.

The system nailed a rapid diagnosis for 3 of 7 infants without returning any false-positive results. Those diagnoses were made with an average time savings of more than 22 hours. In each case, the early diagnosis immediately influenced the treatment those children received. That’s key given that, for young children suffering from serious and unexplained symptoms such as seizures, metabolic abnormalities, or immunodeficiencies, time is of the essence.

Of course, artificial intelligence may never replace doctors and other healthcare providers. Kingsmore notes that 106 years after the invention of the autopilot, two pilots are still required to fly a commercial aircraft. Likewise, health care decisions based on genome interpretation also will continue to require the expertise of skilled physicians.

Still, such a rapid automated system will prove incredibly useful. For instance, this system can provide immediate provisional diagnosis, allowing the experts to focus their attention on more difficult unsolved cases or other needs. It may also prove useful in re-evaluating the evidence in the many cases in which manual interpretation by experts fails to provide an answer.

The automated system may also be useful for periodically reanalyzing data in the many cases that remain unsolved. Keeping up with such reanalysis is a particular challenge considering that researchers continue to discover hundreds of disease-associated genes and thousands of variants each and every year. The hope is that in the years ahead, the combination of whole genome sequencing, artificial intelligence, and expert care will make all the difference in the lives of many more seriously ill babies and their families.

Reference:

[1] Diagnosis of genetic diseases in seriously ill children by rapid whole-genome sequencing and automated phenotyping and interpretation. Clark MM, Hildreth A, Batalov S, Ding Y, Chowdhury S, Watkins K, Ellsworth K, Camp B, Kint CI, Yacoubian C, Farnaes L, Bainbridge MN, Beebe C, Braun JJA, Bray M, Carroll J, Cakici JA, Caylor SA, Clarke C, Creed MP, Friedman J, Frith A, Gain R, Gaughran M, George S, Gilmer S, Gleeson J, Gore J, Grunenwald H, Hovey RL, Janes ML, Lin K, McDonagh PD, McBride K, Mulrooney P, Nahas S, Oh D, Oriol A, Puckett L, Rady Z, Reese MG, Ryu J, Salz L, Sanford E, Stewart L, Sweeney N, Tokita M, Van Der Kraan L, White S, Wigby K, Williams B, Wong T, Wright MS, Yamada C, Schols P, Reynders J, Hall K, Dimmock D, Veeraraghavan N, Defay T, Kingsmore SF. Sci Transl Med. 2019 Apr 24;11(489).

Links:

DNA Sequencing Fact Sheet (National Human Genome Research Institute/NIH)

Genomics and Medicine (NHGRI/NIH)

Genetic and Rare Disease Information Center (National Center for Advancing Translational Sciences/NIH)

Stephen Kingsmore (Rady Children’s Institute for Genomic Medicine, San Diego, CA)

NIH Support: National Institute of Child Health and Human Development; National Human Genome Research Institute; National Center for Advancing Translational Sciences


Largest-Ever Alzheimer’s Gene Study Brings New Answers

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Alzheimer's Risk Genes

Predicting whether someone will get Alzheimer’s disease (AD) late in life, and how to use that information for prevention, has been an intense focus of biomedical research. The goal of this work is to learn not only about the genes involved in AD, but how they work together and with other complex biological, environmental, and lifestyle factors to drive this devastating neurological disease.

It’s good news to be able to report that an international team of researchers, partly funded by NIH, has made more progress in explaining the genetic component of AD. Their analysis, involving data from more than 35,000 individuals with late-onset AD, has identified variants in five new genes that put people at greater risk of AD [1]. It also points to molecular pathways involved in AD as possible avenues for prevention, and offers further confirmation of 20 other genes that had been implicated previously in AD.

The results of this largest-ever genomic study of AD suggests key roles for genes involved in the processing of beta-amyloid peptides, which form plaques in the brain recognized as an important early indicator of AD. They also offer the first evidence for a genetic link to proteins that bind tau, the protein responsible for telltale tangles in the AD brain that track closely with a person’s cognitive decline.

The new findings are the latest from the International Genomics of Alzheimer’s Project (IGAP) consortium, led by a large, collaborative team including Brian Kunkle and Margaret Pericak-Vance, University of Miami Miller School of Medicine, Miami, FL. The effort, spanning four consortia focused on AD in the United States and Europe, was launched in 2011 with the aim of discovering and mapping all the genes that contribute to AD.

An earlier IGAP study including about 25,500 people with late-onset AD identified 20 common gene variants that influence a person’s risk for developing AD late in life [2]. While that was terrific progress to be sure, the analysis also showed that those gene variants could explain only a third of the genetic component of AD. It was clear more genes with ties to AD were yet to be found.

So, in the study reported in Nature Genetics, the researchers expanded the search. While so-called genome-wide association studies (GWAS) are generally useful in identifying gene variants that turn up often in association with particular diseases or other traits, the ones that arise more rarely require much larger sample sizes to find.

To increase their odds of finding additional variants, the researchers analyzed genomic data for more than 94,000 individuals, including more than 35,000 with a diagnosis of late-onset AD and another 60,000 older people without AD. Their search led them to variants in five additional genes, named IQCK, ACE, ADAM10, ADAMTS1, and WWOX, associated with late-onset AD that hadn’t turned up in the previous study.

Further analysis of those genes supports a view of AD in which groups of genes work together to influence risk and disease progression. In addition to some genes influencing the processing of beta-amyloid peptides and accumulation of tau proteins, others appear to contribute to AD via certain aspects of the immune system and lipid metabolism.

Each of these newly discovered variants contributes only a small amount of increased risk, and therefore probably have limited value in predicting an average person’s risk of developing AD later in life. But they are invaluable when it comes to advancing our understanding of AD’s biological underpinnings and pointing the way to potentially new treatment approaches. For instance, these new data highlight intriguing similarities between early-onset and late-onset AD, suggesting that treatments developed for people with the early-onset form also might prove beneficial for people with the more common late-onset disease.

It’s worth noting that the new findings continue to suggest that the search is not yet over—many more as-yet undiscovered rare variants likely play a role in AD. The search for answers to AD and so many other complex health conditions—assisted through collaborative data sharing efforts such as this one—continues at an accelerating pace.

References:

[1] Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing. Kunkle BW, Grenier-Boley B, Sims R, Bis JC, et. al. Nat Genet. 2019 Mar;51(3):414-430.

[2] Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease. Lambert JC, Ibrahim-Verbaas CA, Harold D, Naj AC, Sims R, Bellenguez C, DeStafano AL, Bis JC, et al. Nat Genet. 2013 Dec;45(12):1452-8.

Links:

Alzheimer’s Disease Genetics Fact Sheet (National Institute on Aging/NIH)

Genome-Wide Association Studies (NIH)

Margaret Pericak-Vance (University of Miami Health System, FL)

NIH Support: National Institute on Aging; National Heart, Lung, and Blood Institute; National Human Genome Research Institute; National Institute of Allergy and Infectious Diseases; Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute of Diabetes and Digestive and Kidney Disease; National Institute of Neurological Disorders and Stroke


Accelerating Cures in the Genomic Age: The Sickle Cell Example

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Sickle Cell Disease Symbol
Credit: Jill George, NIH

Forty-five years ago, when I was a first-year medical student, a lecturer introduced me to a young man with sickle cell disease (SCD). Sickle cell disease is the first “molecular disease”, with its cause having been identified decades ago. That helped me see the connection between the abstract concepts of molecular genetics and their real-world human consequences in a way no textbook could. In fact, it inspired some of my earliest research on human hemoglobin disorders, which I conducted as a postdoctoral fellow.

Today, I’m heartened to report that, thanks to decades of biomedical advances, we stand on the verge of a cure for SCD. While at the American Society of Hematology meeting in San Diego last week, I was excited to be part of a discussion about how the tools and technologies arising from the Human Genome Project are accelerating the quest for cures.

The good news at the meeting included some promising, early results from human clinical trials of SCD gene therapies, including new data from the NIH Clinical Center. Researchers also presented very encouraging pre-clinical work on how gene-editing technologies, such as CRISPR, can be used in ways that may open the door to curing everyone with SCD. In fact, just days before the meeting, the first clinical trial for a CRISPR approach to SCD opened.

One important note: the gene editing research aimed at curing SCD is being done in non-reproductive (somatic) cells. The NIH does not support the use of gene editing technologies in human embryos (germline). I recently reiterated our opposition to germline gene editing, in response to an unethical experiment by a researcher in China who claims to have used CRISPR editing on embryos to produce twin girls resistant to HIV.

SCD affects approximately 100,000 people in the United States, and another 20 million worldwide, mostly in developing nations. This inherited, potentially life-threatening disorder is caused by a specific point mutation in a gene that codes for the beta chain of hemoglobin, a molecule found in red blood cells that deliver oxygen throughout the body. In people with SCD, the mutant hemoglobin forms insoluble aggregates when de-oxygenated. As a result the red cells assume a sickle shape, rather than the usual donut shape. These sickled cells clump together and stick in small blood vessels, resulting in severe pain, blood cell destruction, anemia, stroke, pulmonary hypertension, organ failure, and much too often, early death.

The need for a widespread cure for SCD is great. Since 1998, doctors have used a drug called hydroxyurea to reduce symptoms, but it can cause serious side effects and increase the risk of certain cancers. Blood transfusions can also ease symptoms in certain instances, but they too come with risks and complications. At the present time, the only way to cure SCD is a bone marrow transplant. However, transplants are not an option for many patients due to lack of matched marrow donors.

The good news is that novel genetic approaches have raised hopes of a widespread cure for SCD, possibly even within five to 10 years. So, in September, NIH’s National Heart, Lung, and Blood Institute launched the Cure Sickle Cell Initiative to accelerate development of the most promising of these next generation of therapies

At the ASH meeting, that first wave of this progress was evident. A team led by NHLBI’s John Tisdale, in collaboration with Bluebird bio, Cambridge, MA, was among the groups that presented impressive early results from human clinical trials testing novel gene replacement therapies for SCD. In the NIH trial, researchers removed blood precursor cells, called hematopoietic stem cells (HSCs), from a patient’s own bone marrow or bloodstream and used a harmless virus to insert a sickle-resistant hemoglobin gene. Then, after a chemotherapy infusion to condition the patient’s existing bone marrow, they returned the corrected cells to the patient.

So far, nine SCD patients have received the most advanced form of the experimental gene therapy, and Tisdale presented data on those who were farthest out from treatment [1,2]. His team found that in the four patients who were at least six months out, levels of gene therapy-derived hemoglobin were found to equal or exceed their levels of SCD hemoglobin.

Very cool science, but what does this mean for SCD patients’ health and well-being? Well, none of the gene therapy trial participants have required a blood transfusion during the follow-up period. In addition, improvements were seen in their hemoglobin levels and key markers of blood-cell destruction (total bilirubin concentration, lactate dehydrogenase, and reticulocyte counts) compared to baseline. Most importantly, in the years leading up to the clinical trial, all of the participants had experienced frequent painful sickle crises, in which sickled cells blocked their blood vessels. No such episodes were reported among the participants in the months after they received the gene therapy.

Researchers did report that one patient receiving this form of gene therapy developed myelodysplastic syndrome (MDS), a serious condition in which the blood-forming cells in the bone marrow become abnormal. However, there is no indication that the gene replacement technology itself caused the problem, and MDS has previously been linked to the chemotherapy drugs used in conditioning regimens before bone marrow transplants.

The NIH trial is just one of several clinical trials for SCD that are using viral vectors to deliver a variety of genes with therapeutic potential. Other trials actively recruiting are led by researchers at Boston Children’s Hospital, Cincinnati Children’s Hospital, and the University of California, Los Angeles.

While it’s hoped that genes inserted by viral vectors will provide long-lasting or curative treatment, other researchers are betting that new gene-editing technologies, such as CRISPR, will offer the best chance for developing a widespread cure for SCD. One strategy being eyed by these “gene editors” is to correct the SCD mutation, replacing it with a normal gene. Another strategy involves knocking out certain DNA sequences to reactivate production of fetal hemoglobin (HbF).

The HbF protein is produced in the developing fetus to give it better access to oxygen from the mother’s bloodstream. But shortly after birth, the production of fetal hemoglobin shuts down, and the adult form kicks in. Adults normally have very low levels of fetal hemoglobin, which makes sense. However, from genome-wide association studies of human genetic variation, we know that that actual levels of HbF are under genetic control.

A major factor has been mapped to the BCL11A gene, which has subsequently been found to be a master mediator for the fetal to adult hemoglobin switch. Specifically, variations in a red cell specific enhancer of BCL11A affect an adult’s level of HbF— levels of BCL11A protein lead to higher amounts of fetal hemoglobin. Furthermore, it’s been known for some time that rare individuals keep on producing relatively high levels of hemoglobin into adulthood. If people with SCD happen to have a rare mutation that keeps fetal hemoglobin production active in adulthood (the first of these was found as part of my postdoctoral research), their SCD symptoms are much less severe.

Currently, two groups—CRISPR Therapeutics/Vertex Pharmaceuticals and Sangamo Therapeutics/Bioverativ—are gearing up to begin the first U.S. human clinical trials of gene-editing for SCD within the next few months. While they employ different technologies, both approaches involve removing a patient’s HSCs, using gene editing to knock out the BCL11A red cell enhancer, and then returning the gene-edited cells to the patient. The hope is that the gene-edited cells will greatly boost fetal hemoglobin production, thereby offsetting the effects of SCD.

All of this is exciting news for the 100,000 people living in the United States who have SCD. But what about the 300,000 babies born with SCD every year in other parts of the world, mostly in low- and middle-income countries?

The complicated, high-tech procedures that I just described may not be practical for a very long time in places like sub-Saharan Africa. That’s one reason why NIH recently launched a new effort to speed the development of safe, effective genome-editing approaches that could be delivered directly into a patient’s body (in vivo), perhaps by infusion of the CRISPR gene editing apparatus. Recent preclinical experiments demonstrating the promise of in vivo gene editing for Duchenne muscular dystrophy make me optimistic that NIH’s Somatic Cell Genome Editing Program, which is hosting its first gathering of investigators this week, will be able to develop similar approaches for SCD and many other conditions.

While moving forward in this fast-paced field, it is important that we remain ethical, but also remain bold on behalf of the millions of patients with genetic diseases who are still waiting for a cure. We must continue to assess and address the very serious ethical concerns raised by germline gene editing of human embryos, which will irreversibly alter the DNA instruction book of future children and affect future generations. I continue to argue that we are not ready to undertake such experiments.

But the use of gene editing to treat, perhaps even to cure, children and adults with genetic diseases, by correcting the mutation in their relevant tissues (so-called somatic cell gene editing), without risk of passing those changes on to a future generation, holds enormous promise. Somatic cell gene editing is associated with ethical issues that are much more in line with decades of deep thinking about benefits and risks of therapeutic trials.

Finally, we must recognize that somatic cell gene editing is a profoundly promising approach not only for people with SCD, but for all who are struggling with the thousands of diseases that still have no treatments or cures. Real hope for cures has never been greater.

References:

[1] NIH researcher presents encouraging results for gene therapy for severe sickle cell disease. NIH News Release. December 4, 2018 

[2] Bluebird bio presents new data for LentiGlobin gene therapy in sickle cell disease at 60th annual meeting of the American Society of Hematology. Bluebird bio. December 3, 2018 

Links:

Sickle Cell Disease (National Heart, Lung, and Blood Institute/NIH)

Cure Sickle Cell Initiative (NHLBI)

John Tisdale (NHLBI)

Somatic Cell Genome Editing Program (Common Fund/NIH)

What are genome editing and CRISPR-Cas9? (National Library of Medicine/NIH)

ClinicalTrials.gov (NIH) 

NIH Support: National Heart, Lung, and Blood Institute; Common Fund


Some ‘Hospital-Acquired’ Infections Traced to Patient’s Own Microbiome

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Bacteria in both blood and gut

Caption: New computational tool determines whether a gut microbe is the source of a hospital-acquired bloodstream infection
Credit: Fiona Tamburini, Stanford University, Palo Alto, CA

While being cared for in the hospital, a disturbingly large number of people develop potentially life-threatening bloodstream infections. It’s been thought that most of the blame lies with microbes lurking on medical equipment, health-care professionals, or other patients and visitors. And certainly that is often true. But now an NIH-funded team has discovered that a significant fraction of these “hospital-acquired” infections may actually stem from a quite different source: the patient’s own body.

In a study of 30 bone-marrow transplant patients suffering from bloodstream infections, researchers used a newly developed computational tool called StrainSifter to match microbial DNA from close to one-third of the infections to bugs already living in the patients’ large intestines [1]. In contrast, the researchers found little DNA evidence to support the notion that such microbes were being passed around among patients.


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