Confocal image of an Alzheimer’s brain showing region of amyloid plaque. Courtesy of Wellcome Images. 
Understanding a Protein’s Role in Familial Alzheimer’s DiseaseNovel genomic approach reveals gene mutation isn’t simple answer
Researchers at the University of California, San Diego School of Medicine have used genetic engineering of human induced pluripotent stem cells to specifically and precisely parse the roles of a key mutated protein in causing familial Alzheimer’s disease (AD), discovering that simple loss-of-function does not contribute to the inherited form of the neurodegenerative disorder.
The findings, published online in the journal Cell Reports, could help elucidate the still-mysterious mechanisms of Alzheimer’s disease and better inform development of effective drugs, said principal investigator Lawrence Goldstein, PhD, professor in the Departments of Cellular and Molecular Medicine and Neurosciences and director of the UC San Diego Stem Cell Program.
“In some ways, this is a powerful technical demonstration of the promise of stem cells and genomics research in better understanding and ultimately treating AD,” said Goldstein, who is also director of the new Sanford Stem Cell Clinical Center at UC San Diego. “We were able to identify and assign precise limits on how a mutation works in familial AD. That’s an important step in advancing the science, in finding drugs and treatments that can slow, maybe reverse, the disease’s devastating effects.”
Familial AD is a subset of early-onset Alzheimer’s disease that is caused by inherited gene mutations. Most cases of Alzheimer’s disease – there are an estimated 5.2 million Americans with AD – are sporadic and do not have a precise known cause, though age is a primary risk factor.    
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Confocal image of an Alzheimer’s brain showing region of amyloid plaque. Courtesy of Wellcome Images.

Understanding a Protein’s Role in Familial Alzheimer’s Disease
Novel genomic approach reveals gene mutation isn’t simple answer

Researchers at the University of California, San Diego School of Medicine have used genetic engineering of human induced pluripotent stem cells to specifically and precisely parse the roles of a key mutated protein in causing familial Alzheimer’s disease (AD), discovering that simple loss-of-function does not contribute to the inherited form of the neurodegenerative disorder.

The findings, published online in the journal Cell Reports, could help elucidate the still-mysterious mechanisms of Alzheimer’s disease and better inform development of effective drugs, said principal investigator Lawrence Goldstein, PhD, professor in the Departments of Cellular and Molecular Medicine and Neurosciences and director of the UC San Diego Stem Cell Program.

“In some ways, this is a powerful technical demonstration of the promise of stem cells and genomics research in better understanding and ultimately treating AD,” said Goldstein, who is also director of the new Sanford Stem Cell Clinical Center at UC San Diego. “We were able to identify and assign precise limits on how a mutation works in familial AD. That’s an important step in advancing the science, in finding drugs and treatments that can slow, maybe reverse, the disease’s devastating effects.”

Familial AD is a subset of early-onset Alzheimer’s disease that is caused by inherited gene mutations. Most cases of Alzheimer’s disease – there are an estimated 5.2 million Americans with AD – are sporadic and do not have a precise known cause, though age is a primary risk factor.   

More here

A gelatinous mass of DNA precipitated out of solution. Courtesy of Wellcome Images. 
The Role of “Master Regulators” in Gene Mutations and DiseaseResearchers identify key proteins that help establish cell function
Researchers at the University of California, San Diego School of Medicine have developed a new way to parse and understand how special proteins called “master regulators” read the genome, and consequently turn genes on and off. 
Writing in the October 13, 2013 Advance Online Publication of Nature, the scientists say their approach could make it quicker and easier to identify specific gene mutations associated with increased disease risk – an essential step toward developing future targeted treatments, preventions and cures for conditions ranging from diabetes to neurodegenerative disease.   
“Given the emerging ability to sequence the genomes of individual patients, a major goal is to be able to interpret that DNA sequence with respect to disease risk. What diseases is a person genetically predisposed to?” said principal investigator Christopher Glass, MD, PhD, a professor in the departments of Medicine and Cellular and Molecular Medicine at UC San Diego.
“Mutations that occur in protein-coding regions of the genome are relatively straight forward, but most mutations associated with disease risk actually occur in regions of the genome that do not code for proteins,” said Glass. “A central challenge has been developing a strategy that assesses the potential functional impact of these non-coding mutations. This paper lays the foundation for doing so by examining how natural genetic variation alters the function of genomic regions controlling gene expression in a cell specific-manner.”
Cells use hundreds of different proteins called transcription factors to “read” the genome, employing those instructions to turn genes on and off. These factors tend to be bound close together on the genome, forming functional units called “enhancers.” Glass and colleagues hypothesized that while each cell has tens of thousands of enhancers consisting of myriad combinations of factors, most enhancers are established by just a handful of special transcription factors called “master regulators.” These master regulators play crucial, even disproportional, roles in defining each cell’s identity and function, such as whether it will be a muscle, skin or heart cell.
“Our main idea was that the binding of these master regulators is necessary for the co-binding of the other transcription factors that together enable enhancers to regulate the expression of nearby genes,” Glass said.
The scientists tested and validated their hypothesis by looking at the effects of approximately 4 million DNA sequence differences affecting master regulators in macrophage cells in two strains of mice. Macrophages are a type of immune response cell. They found that DNA sequence mutations deciphered by master regulators not only affected how they bound to the genome, but also impacted neighboring transcription factors needed to make functional enhancers.
The findings have practical importance for scientists and doctors investigating the genetic underpinnings of disease, said Glass. “Without actual knowledge of where the master regulator binds, there is relatively little predictive value of the DNA sequence for non-coding variants. Our work shows that by collecting a focused set of data for the master regulators of a particular cell type, one can greatly reduce the ‘search space’ of the genome in a particular cell type that would be susceptible to the effects of mutations. This allows prioritization of mutations for subsequent analysis, which can lead to new discoveries and real-world benefits.”

A gelatinous mass of DNA precipitated out of solution. Courtesy of Wellcome Images.

The Role of “Master Regulators” in Gene Mutations and Disease
Researchers identify key proteins that help establish cell function

Researchers at the University of California, San Diego School of Medicine have developed a new way to parse and understand how special proteins called “master regulators” read the genome, and consequently turn genes on and off. 

Writing in the October 13, 2013 Advance Online Publication of Nature, the scientists say their approach could make it quicker and easier to identify specific gene mutations associated with increased disease risk – an essential step toward developing future targeted treatments, preventions and cures for conditions ranging from diabetes to neurodegenerative disease.   

“Given the emerging ability to sequence the genomes of individual patients, a major goal is to be able to interpret that DNA sequence with respect to disease risk. What diseases is a person genetically predisposed to?” said principal investigator Christopher Glass, MD, PhD, a professor in the departments of Medicine and Cellular and Molecular Medicine at UC San Diego.

“Mutations that occur in protein-coding regions of the genome are relatively straight forward, but most mutations associated with disease risk actually occur in regions of the genome that do not code for proteins,” said Glass. “A central challenge has been developing a strategy that assesses the potential functional impact of these non-coding mutations. This paper lays the foundation for doing so by examining how natural genetic variation alters the function of genomic regions controlling gene expression in a cell specific-manner.”

Cells use hundreds of different proteins called transcription factors to “read” the genome, employing those instructions to turn genes on and off. These factors tend to be bound close together on the genome, forming functional units called “enhancers.” Glass and colleagues hypothesized that while each cell has tens of thousands of enhancers consisting of myriad combinations of factors, most enhancers are established by just a handful of special transcription factors called “master regulators.” These master regulators play crucial, even disproportional, roles in defining each cell’s identity and function, such as whether it will be a muscle, skin or heart cell.

“Our main idea was that the binding of these master regulators is necessary for the co-binding of the other transcription factors that together enable enhancers to regulate the expression of nearby genes,” Glass said.

The scientists tested and validated their hypothesis by looking at the effects of approximately 4 million DNA sequence differences affecting master regulators in macrophage cells in two strains of mice. Macrophages are a type of immune response cell. They found that DNA sequence mutations deciphered by master regulators not only affected how they bound to the genome, but also impacted neighboring transcription factors needed to make functional enhancers.

The findings have practical importance for scientists and doctors investigating the genetic underpinnings of disease, said Glass. “Without actual knowledge of where the master regulator binds, there is relatively little predictive value of the DNA sequence for non-coding variants. Our work shows that by collecting a focused set of data for the master regulators of a particular cell type, one can greatly reduce the ‘search space’ of the genome in a particular cell type that would be susceptible to the effects of mutations. This allows prioritization of mutations for subsequent analysis, which can lead to new discoveries and real-world benefits.”

The figure above depicts the complexity of genes with high network-smoothed mutation scores in a single subtype of ovarian cancer. Patients in this subtype exhibited the lowest survival and highest chemotherapy resistance rates in the cohort. Node size corresponds to smoothed mutation scores. Node color corresponds to the gene’s functional  category. Thickened node outlines indicate known cancer genes. An underlined gene symbol in the network indicates that somatic mutations were found for that gene in the examined subtype. Image courtesy of Trey Ideker, UC San Diego. 
“Wildly Heterogeneous Genes”New approach subtypes cancers by shared genetic effects; a step toward personalized medicine
Cancer tumors almost never share the exact same genetic mutations, a fact that has confounded scientific efforts to better categorize cancer types and develop more targeted, effective treatments.
In a paper published in the September 15 advanced online edition of Nature Methods, researchers at the University of California, San Diego propose a new approach called network-based stratification (NBS), which identifies cancer subtypes not by the singular mutations of individual patients, but by how those mutations affect shared genetic networks or systems.  
“Subtyping is the most basic step toward the goal of personalized medicine,” said principal investigator Trey Ideker, PhD, division chief of Genetics in the UC San Diego School of Medicine and a professor in the Departments of Medicine and Bioengineering at UC San Diego. “Based on patient data, patients are placed into subtypes with associated treatments. For example, one subtype of cancer is known to respond well to drug A, but not drug B. Without subtyping, every patient looks the same by definition, and you have no idea how to treat them differently.” 
Recent advances in knowledge and technology have made it easier (and less expensive) to sequence individual genomes, especially in the treatment of cancer, which is fundamentally a disease of genes. 
But genes are “wildly heterogeneous,” said Ideker. It is in combination, influenced by other factors, that mutated genes cause diseases like cancer. Every patient’s cancer is genetically unique, which can affect the efficacy and outcomes of clinical treatment.
More here

The figure above depicts the complexity of genes with high network-smoothed mutation scores in a single subtype of ovarian cancer. Patients in this subtype exhibited the lowest survival and highest chemotherapy resistance rates in the cohort. Node size corresponds to smoothed mutation scores. Node color corresponds to the gene’s functional  category. Thickened node outlines indicate known cancer genes. An underlined gene symbol in the network indicates that somatic mutations were found for that gene in the examined subtype. Image courtesy of Trey Ideker, UC San Diego.

“Wildly Heterogeneous Genes”
New approach subtypes cancers by shared genetic effects; a step toward personalized medicine

Cancer tumors almost never share the exact same genetic mutations, a fact that has confounded scientific efforts to better categorize cancer types and develop more targeted, effective treatments.

In a paper published in the September 15 advanced online edition of Nature Methods, researchers at the University of California, San Diego propose a new approach called network-based stratification (NBS), which identifies cancer subtypes not by the singular mutations of individual patients, but by how those mutations affect shared genetic networks or systems. 

“Subtyping is the most basic step toward the goal of personalized medicine,” said principal investigator Trey Ideker, PhD, division chief of Genetics in the UC San Diego School of Medicine and a professor in the Departments of Medicine and Bioengineering at UC San Diego. “Based on patient data, patients are placed into subtypes with associated treatments. For example, one subtype of cancer is known to respond well to drug A, but not drug B. Without subtyping, every patient looks the same by definition, and you have no idea how to treat them differently.”

Recent advances in knowledge and technology have made it easier (and less expensive) to sequence individual genomes, especially in the treatment of cancer, which is fundamentally a disease of genes.

But genes are “wildly heterogeneous,” said Ideker. It is in combination, influenced by other factors, that mutated genes cause diseases like cancer. Every patient’s cancer is genetically unique, which can affect the efficacy and outcomes of clinical treatment.

More here

Boosting the Powers of Genomic ScienceWith two new methods, UC San Diego scientists hope to improve genome-wide association studies
As scientists probe and parse the genetic bases of what makes a human a human (or one human different from another), and vigorously push for greater use of whole genome sequencing, they find themselves increasingly threatened by the unthinkable: Too much data to make full sense of. 
In a pair of papers published in the April 25, 2013 issue of PLOS Genetics, two diverse teams of scientists, both headed by researchers at the University of California, San Diego School of Medicine, describe novel statistical models that more broadly and deeply identify associations between bits of sequenced DNA called single nucleotide polymorphisms or SNPs and say lead to a more complete and accurate understanding of the genetic underpinnings of many diseases and how best to treat them.      
“It’s increasingly evident that highly heritable diseases and traits are influenced by a large number of genetic variants in different parts of the genome, each with small effects,” said Anders M. Dale, PhD, a professor in the departments of Radiology, Neurosciences and Psychiatry at the UC San Diego School of Medicine. “Unfortunately, it’s also increasingly evident that existing statistical methods, like genome-wide association studies (GWAS) that look for associations between SNPs and diseases, are severely underpowered and can’t adequately incorporate all of this new, exciting and exceedingly rich data.”
Dale cited, for example, a recent study published in Nature Genetics in which researchers used traditional GWAS to raise the number of SNPs associated with primary sclerosing cholangitis from four to 16. The scientists then applied the new statistical methods to identify 33 additional SNPs, more than tripling the number of genome locations associated with the life-threatening liver disease.
Generally speaking, the new methods boost researchers’ analytical powers by incorporating a priori or prior knowledge about the function of SNPs with their pleiotrophic relationships to  multiple phenotypes. Pleiotrophy occurs when one gene influences multiple sets of observed traits or phenotypes.
Dale and colleagues believe the new methods could lead to a paradigm shift in CWAS analysis, with profound implications across a broad range of complex traits and disorders.
“There is ever-greater emphasis being placed on expensive whole genome sequencing efforts,” he said, “but as the science advances, the challenges become larger. The needle in the haystack of traditional GWAS involves searching through about one million SNPs. This will increase 10- to 100-fold, to about 3 billion positions. We think these new methodologies allow us to more completely exploit our resources, to extract the most information possible, which we think has important implications for gene discovery, drug development and more accurately assessing a person’s overall genetic risk of developing a certain disease.”

Boosting the Powers of Genomic Science
With two new methods, UC San Diego scientists hope to improve genome-wide association studies

As scientists probe and parse the genetic bases of what makes a human a human (or one human different from another), and vigorously push for greater use of whole genome sequencing, they find themselves increasingly threatened by the unthinkable: Too much data to make full sense of. 

In a pair of papers published in the April 25, 2013 issue of PLOS Genetics, two diverse teams of scientists, both headed by researchers at the University of California, San Diego School of Medicine, describe novel statistical models that more broadly and deeply identify associations between bits of sequenced DNA called single nucleotide polymorphisms or SNPs and say lead to a more complete and accurate understanding of the genetic underpinnings of many diseases and how best to treat them.      

“It’s increasingly evident that highly heritable diseases and traits are influenced by a large number of genetic variants in different parts of the genome, each with small effects,” said Anders M. Dale, PhD, a professor in the departments of Radiology, Neurosciences and Psychiatry at the UC San Diego School of Medicine. “Unfortunately, it’s also increasingly evident that existing statistical methods, like genome-wide association studies (GWAS) that look for associations between SNPs and diseases, are severely underpowered and can’t adequately incorporate all of this new, exciting and exceedingly rich data.”

Dale cited, for example, a recent study published in Nature Genetics in which researchers used traditional GWAS to raise the number of SNPs associated with primary sclerosing cholangitis from four to 16. The scientists then applied the new statistical methods to identify 33 additional SNPs, more than tripling the number of genome locations associated with the life-threatening liver disease.

Generally speaking, the new methods boost researchers’ analytical powers by incorporating a priori or prior knowledge about the function of SNPs with their pleiotrophic relationships to  multiple phenotypes. Pleiotrophy occurs when one gene influences multiple sets of observed traits or phenotypes.

Dale and colleagues believe the new methods could lead to a paradigm shift in CWAS analysis, with profound implications across a broad range of complex traits and disorders.

“There is ever-greater emphasis being placed on expensive whole genome sequencing efforts,” he said, “but as the science advances, the challenges become larger. The needle in the haystack of traditional GWAS involves searching through about one million SNPs. This will increase 10- to 100-fold, to about 3 billion positions. We think these new methodologies allow us to more completely exploit our resources, to extract the most information possible, which we think has important implications for gene discovery, drug development and more accurately assessing a person’s overall genetic risk of developing a certain disease.”

An electrophoresis gel separates nucleic acid molecules into constituent DNA, RNA and proteins, based on size and electrical charge. 
More Links Found Between Schizophrenia and Cardiovascular Disease
A new study, to be published in the Feb. 7, 2013 issue of the American Journal of Human Genetics, expands and deepens the biological and genetic links between cardiovascular disease and schizophrenia. Cardiovascular disease (CVD) is the leading cause of premature death among schizophrenia patients, who die from heart and blood vessel disorders at a rate double that of persons without the mental disorder.
“These results have important clinical implications, adding to our growing awareness that cardiovascular disease is under-recognized and under-treated in mentally ill individuals,” said study first author Ole Andreassen, MD, PhD, an adjunct professor at the University of California, San Diego School of Medicine and professor of psychiatry at the University of Oslo. “Its presence in schizophrenia is not solely due to lifestyle or medication side effects. Clinicians must recognize that individuals with schizophrenia are at risk for cardiovascular disease independent of these factors.”
Led by principal investigator Anders M. Dale, PhD, professor of radiology, neurosciences, psychiatry and cognitive science at UC San Diego School of Medicine, an international team of researchers used a novel statistical model to magnify the analytical powers of genome-wide association studies or GWAS.
These are studies in which differing bits of sequential DNA – called single nucleotide polymorphisms or SNPs – in persons and groups are compared to find common genetic variants that might be linked to a trait or disease. The researchers boosted the power of GWAS by adding information based on genetic pleiotropy, the concept that at least some genes influence multiple traits or phenotypes.
“Our approach is different in that we use all available genetic information for multiple traits and diseases, not just SNPs below a given statistical threshold,” said Dale. “This significantly increases the power to discover new genes by leveraging the combined power across multiple GWAS of pleiotropic traits and diseases.”
The scientists confirmed nine SNPs linked to schizophrenia in prior studies, but also identified 16 new loci – some of which are also associated with CVD. Among these shared risk factors: triglyceride and lipoprotein levels, waist-hip ratio, systolic blood pressure and body mass index.
“Our findings suggest that shared biological and genetic mechanisms can help explain why schizophrenia patients have a greater risk of cardiovascular disease,” said study co-author Rahul S. Desikan, MD, PhD, research fellow and radiology resident at the UC San Diego School of Medicine.
“In addition to schizophrenia, this new analysis method can be used to examine the genetic overlap between a number of diseases and traits,” Desikan said. “Examining overlap in common variants can shed insight into disease mechanisms and help identify potential therapeutic targets for common diseases.”

An electrophoresis gel separates nucleic acid molecules into constituent DNA, RNA and proteins, based on size and electrical charge.

More Links Found Between Schizophrenia and Cardiovascular Disease

A new study, to be published in the Feb. 7, 2013 issue of the American Journal of Human Genetics, expands and deepens the biological and genetic links between cardiovascular disease and schizophrenia. Cardiovascular disease (CVD) is the leading cause of premature death among schizophrenia patients, who die from heart and blood vessel disorders at a rate double that of persons without the mental disorder.

“These results have important clinical implications, adding to our growing awareness that cardiovascular disease is under-recognized and under-treated in mentally ill individuals,” said study first author Ole Andreassen, MD, PhD, an adjunct professor at the University of California, San Diego School of Medicine and professor of psychiatry at the University of Oslo. “Its presence in schizophrenia is not solely due to lifestyle or medication side effects. Clinicians must recognize that individuals with schizophrenia are at risk for cardiovascular disease independent of these factors.”

Led by principal investigator Anders M. Dale, PhD, professor of radiology, neurosciences, psychiatry and cognitive science at UC San Diego School of Medicine, an international team of researchers used a novel statistical model to magnify the analytical powers of genome-wide association studies or GWAS.

These are studies in which differing bits of sequential DNA – called single nucleotide polymorphisms or SNPs – in persons and groups are compared to find common genetic variants that might be linked to a trait or disease. The researchers boosted the power of GWAS by adding information based on genetic pleiotropy, the concept that at least some genes influence multiple traits or phenotypes.

“Our approach is different in that we use all available genetic information for multiple traits and diseases, not just SNPs below a given statistical threshold,” said Dale. “This significantly increases the power to discover new genes by leveraging the combined power across multiple GWAS of pleiotropic traits and diseases.”

The scientists confirmed nine SNPs linked to schizophrenia in prior studies, but also identified 16 new loci – some of which are also associated with CVD. Among these shared risk factors: triglyceride and lipoprotein levels, waist-hip ratio, systolic blood pressure and body mass index.

“Our findings suggest that shared biological and genetic mechanisms can help explain why schizophrenia patients have a greater risk of cardiovascular disease,” said study co-author Rahul S. Desikan, MD, PhD, research fellow and radiology resident at the UC San Diego School of Medicine.

“In addition to schizophrenia, this new analysis method can be used to examine the genetic overlap between a number of diseases and traits,” Desikan said. “Examining overlap in common variants can shed insight into disease mechanisms and help identify potential therapeutic targets for common diseases.”

Genomic “Hotspots” Offer Clues to Causes of Autism, Other Disorders

An international team, led by researchers from the University of California, San Diego School of Medicine, has discovered that “random” mutations in the genome are not quite so random after all. Their study, to be published in the journal Cell on December 21, shows that the DNA sequence in some regions of the human genome is quite volatile and can mutate ten times more frequently than the rest of the genome. Genes that are linked to autism and a variety of other disorders have a particularly strong tendency to mutate.

Clusters of mutations or “hotspots” are not unique to the autism genome but instead are an intrinsic characteristic of the human genome, according to principal investigator Jonathan Sebat, PhD, professor of psychiatry and cellular and molecule medicine, and chief of the Beyster Center for Molecular Genomics of Neuropsychiatric Diseases at UC San Diego.

“Our findings provide some insights into the underlying basis of autism—that, surprisingly, the genome is not shy about tinkering with its important genes” said Sebat.  “To the contrary, disease-causing genes tend to be hypermutable.”

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Image courtesy of University of Washington
Bugging out
Next time you’re visiting a hospital or medical center and step inside to wash your hands, take a look at the label on the antibiotic soap you’re using. Often it will sport a disclaimer saying it does not work against Clostridium difficile, a bacterium so difficult to control these days that its resistance to modern medicine is duly noted in its name.
Drug-resistant microbes are a huge and growing problem. More than 60,000 Americans die each year from infections caused by bacteria unaffected by modern antibiotics. C. difficile is among the most worrisome superbugs, responsible for killing 14,000 Americans each year. Some reports say the actual mortality number is more than double that.
In a new paper, published in the journal Nature Genetics, British geneticists sequenced a variety of C. difficile strains, producing a genomic chart that maps how the drug-resistant bacteria first appeared in North America, and then spread to other parts of the world.
The researchers determined that a particularly virulent form of C. difficile emerged in Pittsburgh around 2001, most likely in a hospital where patients received heavy doses of antibiotic treatment. It quickly spread to Oregon, Arizona, New Jersey and Maryland, with major hospital outbreaks in those states.
Then it spread beyond, the strain appearing in South Korea and Switzerland after 2007.
Given the remarkable abilities of bacteria to mutate quickly and often – an evolutionary adaptation that has rendered them the singularly most successful life form in Earth’s history – the emergence of the Pittsburgh superbug wasn’t an unfortunate, isolated event.
Genomic data reveals that another C. difficile superbug arose independently in the United States around 2001 (exact whereabouts unknown) and subsequently spread to Montreal in 2003 and the Netherlands in 2006.
Both strains of C. difficile enjoy a single enzyme mutation that binds to fluroquinolones, a subset of a common class of powerful antibiotics. The binding renders the antibiotics ineffective. The strains also have genes that pump the antibiotics out of their cells. Fluroquinolones were widely prescribed in the late-1990s and early 2000s; much less so now due to drug-resistance and associated side effects.
The Nature Genetics paper is another cautionary tale (one of many) about the risks and consequences of overusing antibiotics. It suggests that bacteria of all sorts, not just C. difficile, develop resistance faster, more easily and more often than previously suspected.
Unfortunately, solutions to this growing global health threat are far less forthcoming.

Image courtesy of University of Washington

Bugging out

Next time you’re visiting a hospital or medical center and step inside to wash your hands, take a look at the label on the antibiotic soap you’re using. Often it will sport a disclaimer saying it does not work against Clostridium difficile, a bacterium so difficult to control these days that its resistance to modern medicine is duly noted in its name.

Drug-resistant microbes are a huge and growing problem. More than 60,000 Americans die each year from infections caused by bacteria unaffected by modern antibiotics. C. difficile is among the most worrisome superbugs, responsible for killing 14,000 Americans each year. Some reports say the actual mortality number is more than double that.

In a new paper, published in the journal Nature Genetics, British geneticists sequenced a variety of C. difficile strains, producing a genomic chart that maps how the drug-resistant bacteria first appeared in North America, and then spread to other parts of the world.

The researchers determined that a particularly virulent form of C. difficile emerged in Pittsburgh around 2001, most likely in a hospital where patients received heavy doses of antibiotic treatment. It quickly spread to Oregon, Arizona, New Jersey and Maryland, with major hospital outbreaks in those states.

Then it spread beyond, the strain appearing in South Korea and Switzerland after 2007.

Given the remarkable abilities of bacteria to mutate quickly and often – an evolutionary adaptation that has rendered them the singularly most successful life form in Earth’s history – the emergence of the Pittsburgh superbug wasn’t an unfortunate, isolated event.

Genomic data reveals that another C. difficile superbug arose independently in the United States around 2001 (exact whereabouts unknown) and subsequently spread to Montreal in 2003 and the Netherlands in 2006.

Both strains of C. difficile enjoy a single enzyme mutation that binds to fluroquinolones, a subset of a common class of powerful antibiotics. The binding renders the antibiotics ineffective. The strains also have genes that pump the antibiotics out of their cells. Fluroquinolones were widely prescribed in the late-1990s and early 2000s; much less so now due to drug-resistance and associated side effects.

The Nature Genetics paper is another cautionary tale (one of many) about the risks and consequences of overusing antibiotics. It suggests that bacteria of all sorts, not just C. difficile, develop resistance faster, more easily and more often than previously suspected.

Unfortunately, solutions to this growing global health threat are far less forthcoming.

Biomarking TimeMethylome modifications offer new measure of our “biological” ageWomen live longer than men. Individuals can appear or feel years younger – or older – than their chronological age. Diseases can affect our aging process. When it comes to biology, our clocks clearly tick differently. In a new study, researchers at the University of California, San Diego School of Medicine, with colleagues elsewhere, describe markers and a model that quantify how aging occurs at the level of genes and molecules, providing not just a more precise way to determine how old someone is, but also perhaps anticipate or treat ailments and diseases that come with the passage of time. The findings are published in the November 21 online issue of the journal Molecular Cell. “It’s well known that people age at different rates,” said Kang Zhang, MD, PhD, professor of ophthalmology and human genetics at the Shiley Eye Center and director of the Institute for Genomic Medicine, both at UC San Diego. “Some people in their 70s look like they’re in their 50s, while others in their 50s look like they’re in their 70s.”However, identifying markers and precisely quantifying the actual rate of aging in individuals has been challenging. For example, researchers have looked at telomeres – repeating nucleotide sequences that cap the ends of chromosomes and which shorten with age – but have found that other factors like stress can affect them as well.In the new Molecular Cell paper, Zhang and colleagues focus on DNA methylation, a fundamental, life-long process in which a methyl group is added or removed from the cytosine molecule in DNA to promote or suppress gene activity and expression. The researchers measured more than 485,000 genome-wide methylation markers in blood samples of 656 persons ranging in age from 19 to 101. “It’s a very robust way of predicting aging,” said Zhang, one that was subsequently validated on a second sampling of several hundred blood samples from another cohort of human individuals.The scientists found that an individual’s “methylome” – the entire set of human methylation markers and changes across a whole genome – predictably varies over time, providing a way to determine a person’s actual biological age from just a blood sample. “It’s the majority of the methylome that accurately predicts age, not just a few key genes,” said co-senior author Trey Ideker, PhD, a professor of medicine and chief of the Division of Medical Genetics in the UC San Diego School of Medicine and professor of bioengineering in the Jacobs School of Engineering. “The methylation state decays over time along the entire genome. You look in the body, into the cells, of young people and methylation occurs very distinctly in some spots and not in others. It’s very structured. Over time, though, methylation sites get fuzzier; the boundaries blur.”They do not, however, blur at the same rate in everybody. At the molecular level of the methylome, the researchers said it was clear that individual bodies age at varying rates, and even within the same body, different organs age differently. Moreover, cancer cells age differently than their surrounding normal cells.  The findings, according to the study authors, have broad practical implications. Most immediately, they could be used in forensics to determine a person’s age based only upon a blood or tissue sample. More profoundly, said Zhang, the methylome provides a measure of biological age – how quickly or slowly a person is experiencing the passage of time. That information has potentially huge medical import. “For example, you could serially profile patients to compare therapies, to see if a treatment is making people healthier and ‘younger.’ You could screen compounds to see if they retard the aging process at the tissue or cellular level.”Ideker said assessing an individual’s methylome state could improve preventive medicine by identifying lifestyle changes that might slow molecular aging. He noted, however, that much more research remains to be done. “The next step is to look to see whether methylation can predict specific health factors, and whether this kind of molecular diagnosis is better than existing clinical or physical markers. We think it’s very promising,” Ideker said. 

Biomarking Time
Methylome modifications offer new measure of our “biological” age

Women live longer than men. Individuals can appear or feel years younger – or older – than their chronological age. Diseases can affect our aging process. When it comes to biology, our clocks clearly tick differently.

In a new study, researchers at the University of California, San Diego School of Medicine, with colleagues elsewhere, describe markers and a model that quantify how aging occurs at the level of genes and molecules, providing not just a more precise way to determine how old someone is, but also perhaps anticipate or treat ailments and diseases that come with the passage of time.

The findings are published in the November 21 online issue of the journal Molecular Cell.

“It’s well known that people age at different rates,” said Kang Zhang, MD, PhD, professor of ophthalmology and human genetics at the Shiley Eye Center and director of the Institute for Genomic Medicine, both at UC San Diego. “Some people in their 70s look like they’re in their 50s, while others in their 50s look like they’re in their 70s.”

However, identifying markers and precisely quantifying the actual rate of aging in individuals has been challenging. For example, researchers have looked at telomeres – repeating nucleotide sequences that cap the ends of chromosomes and which shorten with age – but have found that other factors like stress can affect them as well.

In the new Molecular Cell paper, Zhang and colleagues focus on DNA methylation, a fundamental, life-long process in which a methyl group is added or removed from the cytosine molecule in DNA to promote or suppress gene activity and expression. The researchers measured more than 485,000 genome-wide methylation markers in blood samples of 656 persons ranging in age from 19 to 101.

“It’s a very robust way of predicting aging,” said Zhang, one that was subsequently validated on a second sampling of several hundred blood samples from another cohort of human individuals.

The scientists found that an individual’s “methylome” – the entire set of human methylation markers and changes across a whole genome – predictably varies over time, providing a way to determine a person’s actual biological age from just a blood sample.

“It’s the majority of the methylome that accurately predicts age, not just a few key genes,” said co-senior author Trey Ideker, PhD, a professor of medicine and chief of the Division of Medical Genetics in the UC San Diego School of Medicine and professor of bioengineering in the Jacobs School of Engineering. “The methylation state decays over time along the entire genome. You look in the body, into the cells, of young people and methylation occurs very distinctly in some spots and not in others. It’s very structured. Over time, though, methylation sites get fuzzier; the boundaries blur.”

They do not, however, blur at the same rate in everybody. At the molecular level of the methylome, the researchers said it was clear that individual bodies age at varying rates, and even within the same body, different organs age differently. Moreover, cancer cells age differently than their surrounding normal cells.  The findings, according to the study authors, have broad practical implications. Most immediately, they could be used in forensics to determine a person’s age based only upon a blood or tissue sample.

More profoundly, said Zhang, the methylome provides a measure of biological age – how quickly or slowly a person is experiencing the passage of time. That information has potentially huge medical import. “For example, you could serially profile patients to compare therapies, to see if a treatment is making people healthier and ‘younger.’ You could screen compounds to see if they retard the aging process at the tissue or cellular level.”

Ideker said assessing an individual’s methylome state could improve preventive medicine by identifying lifestyle changes that might slow molecular aging. He noted, however, that much more research remains to be done.

“The next step is to look to see whether methylation can predict specific health factors, and whether this kind of molecular diagnosis is better than existing clinical or physical markers. We think it’s very promising,” Ideker said. 

Singular achievement
Most recent advances in sequencing have celebrated the big picture: the successful mapping, for example, of entire genomes or large, significant series of gene.
In a paper published in the July 22 issue of Nature Biotechnology, an international team of researchers that includes Gregory A. Daniels at the UC San Diego Moores Cancer Center and Louise C. Laurant in the UCSD School of Medicine’s Department of Reproductive Medicine uses a novel sequencing method called Smart-Seq to deeply scrutinize the genetic information contained in a single cell.
The achievement is important. Many clinically relevant cells exist in only small numbers and require singular analysis.  “Cancer researchers around the world will now be able to analyze these cells more systematically to enable them to produce better methods of diagnosis and therapy in the future,” said senior study author Rickard Sandberg of the Ludwig Institute for Cancer Research and Karolinska Institutet in Sweden. 
You can read the full news release from LICR here.
Pictured: A prostate cancer cell

Singular achievement

Most recent advances in sequencing have celebrated the big picture: the successful mapping, for example, of entire genomes or large, significant series of gene.

In a paper published in the July 22 issue of Nature Biotechnology, an international team of researchers that includes Gregory A. Daniels at the UC San Diego Moores Cancer Center and Louise C. Laurant in the UCSD School of Medicine’s Department of Reproductive Medicine uses a novel sequencing method called Smart-Seq to deeply scrutinize the genetic information contained in a single cell.

The achievement is important. Many clinically relevant cells exist in only small numbers and require singular analysis.  “Cancer researchers around the world will now be able to analyze these cells more systematically to enable them to produce better methods of diagnosis and therapy in the future,” said senior study author Rickard Sandberg of the Ludwig Institute for Cancer Research and Karolinska Institutet in Sweden. 

You can read the full news release from LICR here.

Pictured: A prostate cancer cell

Epigenetics Alters Genes in Rheumatoid Arthritis
It’s not just our DNA that makes us susceptible to disease and influences its impact and outcome. Scientists are beginning to realize more and more that important changes in genes that are unrelated to changes in the DNA sequence itself – a field of study known as epigenetics – are equally influential.
A research team at the University of California, San Diego – led by Gary S. Firestein, MD, professor in the Division of Rheumatology, Allergy and Immunology at UC San Diego School of Medicine – investigated a mechanism usually implicated in cancer and in fetal development, called DNA methylation, in the progression of rheumatoid arthritis (RA). They found that epigenetic changes due to methylation play a key role in altering genes that could potentially contribute to inflammation and joint damage.  Their study is currently published in the online edition of the Annals of the Rheumatic Diseases.
“Genomics has rapidly advanced our understanding of susceptibility and severity of rheumatoid arthritis,” said Firestein.  “While many genetic associations have been described in this disease, we also know that if one identical twin develops RA that the other twin only has a 12 to 15 percent chance of also getting the disease.  This suggests that other factors are at play – epigenetic influences.”
DNA methylation is one example of epigenetic change, in which a strand of DNA is modified after it is duplicated by adding a methyl to any cytosine molecule (C) – one of the 4 main bases of DNA.  This is one of the methods used to regulate gene expression, and is often abnormal in cancers and plays a role in organ development.
While DNA methylation of individual genes has been explored in autoimmune diseases, this study represents a genome-wide evaluation of the process in fibroblast-like synoviocytes (FLS), isolated from the site of the disease in RA. FLS are cells that interact with the immune cells in RA, an inflammatory disease in the joints that damages cartilage, bone and soft tissues of the joint.
More here

Epigenetics Alters Genes in Rheumatoid Arthritis

It’s not just our DNA that makes us susceptible to disease and influences its impact and outcome. Scientists are beginning to realize more and more that important changes in genes that are unrelated to changes in the DNA sequence itself – a field of study known as epigenetics – are equally influential.

A research team at the University of California, San Diego – led by Gary S. Firestein, MD, professor in the Division of Rheumatology, Allergy and Immunology at UC San Diego School of Medicine – investigated a mechanism usually implicated in cancer and in fetal development, called DNA methylation, in the progression of rheumatoid arthritis (RA). They found that epigenetic changes due to methylation play a key role in altering genes that could potentially contribute to inflammation and joint damage.  Their study is currently published in the online edition of the Annals of the Rheumatic Diseases.

“Genomics has rapidly advanced our understanding of susceptibility and severity of rheumatoid arthritis,” said Firestein.  “While many genetic associations have been described in this disease, we also know that if one identical twin develops RA that the other twin only has a 12 to 15 percent chance of also getting the disease.  This suggests that other factors are at play – epigenetic influences.”

DNA methylation is one example of epigenetic change, in which a strand of DNA is modified after it is duplicated by adding a methyl to any cytosine molecule (C) – one of the 4 main bases of DNA.  This is one of the methods used to regulate gene expression, and is often abnormal in cancers and plays a role in organ development.

While DNA methylation of individual genes has been explored in autoimmune diseases, this study represents a genome-wide evaluation of the process in fibroblast-like synoviocytes (FLS), isolated from the site of the disease in RA. FLS are cells that interact with the immune cells in RA, an inflammatory disease in the joints that damages cartilage, bone and soft tissues of the joint.

More here

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