热门疾病
不限 癌症 糖尿病 心血管疾病 精神/神经疾病 自身免疫性疾病 炎症及感染性疾病 代谢性疾病 呼吸系统疾病 其他
GWAS对“1.5型糖尿病”的分析揭示了免疫和代谢疾病之间的联系

新闻GWAS对“1.5型糖尿病”的分析揭示了免疫和代谢疾病之间的联系

Scientists who performed the largest-ever genetic study of a puzzling type of adult-onset diabetes have uncovered new connections to the two major types of diabetes, offering intriguing insights into more accurate diagnosis and better treatment. Informally termed "type 1.5 diabetes," latent automimmune diabetes in adults (LADA) is a relatively common disorder that shares features of type 1 diabetes (T1D) and type 2 diabetes (T2D). LADA is commonly misdiagnosed as T2D, as it presents in adulthood but doesn't initially require insulin treatment. New research, appearing online Sept. 25, 2018 in the journal Diabetes Care, reveals details of the underlying genetic influences in LADA, while leaving open many questions about how to classify the disorder. "This study lends support to the idea that LADA is a hybrid of type 1 and type 2 diabetes, but doesn't settle the question of the best way to precisely define the disorder," said co-first author Diana L. Cousminer, PhD, a geneticist at Children's Hospital of Philadelphia (CHOP). "Correctly characterizing LADA is important, because it may determine whether a patient receives the most appropriate treatment." The new research, the first genome-wide association study (GWAS) of LADA, represents a large international effort, with dozens of co-authors from nine countries. Many of the co-authors, including both co-corresponding authors of Cousminer's, Struan F.A. Grant, PhD, of CHOP and Richard David Leslie, MD, of the University of London, UK, were leaders of a 2017 study of candidate genes in LADA. That previous study implicated gene variants linked to LADA that also played roles in T1D and T2D. While the current study is larger than the 2017 analysis, and had genome-wide reach, both studies found the strongest genetic signals in LADA were associated with T1D, the autoimmune form of diabetes that usually presents in childhood and requires treatment with insulin. The current analysis also found genetic signals linked to T2D, the metabolic type of diabetes, more typically appearing first in adults, and by far the most common type of diabetes. The new study performed GWAS analyses in cohorts of European ancestry. The primary analysis compared 2,634 LADA cases to 5,947 control subjects. Secondary analyses consisted of the LADA cases vs. 968 T1D cases and the LADA cases vs. 10,396 T2D cases. Overall, the team found that the strongest genetic signals in LADA were mainly shared with established variants known to be linked to T1D. However, the researchers discovered a novel locus with genome-wide significance near the gene PFKFB3. This gene codes for a protein that regulates both insulin signaling and glycolisis, the chemical reaction that yields energy from glucose. "This finding points to how variants at PFKFB3 may help to drive LADA," said Cousminer, who added that because the gene's product not only impacts metabolism (a key feature of T2D), but also regulates inflammation in autoimmune disease (a key feature of T1D), "this protein therefore appears to sit at the intersection of both major types of diabetes." "Further study of underlying genetic interactions in LADA may reveal better biomarkers of the disease," said Rajashree Mishra, a co-first author of the current study, from CHOP's Division of Human Genetics and a graduate student in the Perelman School of Medicine at the University of Pennsylvania. "Currently, as high as 5 to 10 percent of patients diagnosed as adults with type 2 diabetes may actually be misdiagnosed, and in fact have a late-onset form of autoimmune diabetes," she said. "More accurate diagnosis may guide better clinical management. For instance, patients with LADA may require close monitoring, to detect the optimal point at which they require insulin." Better knowledge of the underlying genetics and biology of LADA could potentially lead to new treatments, said Cousminer. "The interaction of genes in LADA may modify the disease process by delaying the onset of more severe autoimmune diabetes into adulthood. If further research uncovers those mechanisms, we may be able to develop therapeutic methods to delay more severe disease."详情>>

2018-10-18 00:00:00
迄今为止最大的自闭症测序研究产生了与自闭症谱系障碍相关的102个基因

新闻迄今为止最大的自闭症测序研究产生了与自闭症谱系障碍相关的102个基因

In the largest genetic sequencing study of autism spectrum disorder (ASD) to date, researchers have identified 102 genes associated with ASD, and report significant progress toward teasing apart the genes associated with ASD from those associated with intellectual disability and developmental delay, conditions between which there is often overlap. The findings were presented at the American Society of Human Genetics 2018 Annual Meeting in San Diego, Calif. Jack Kosmicki, Ph.D. candidate at Harvard University; Mark J. Daly, Ph.D., chief of the Analytic and Translational Genetics Unit at Massachusetts General Hospital; and collaborators studied 37,269 genetic samples collected from large research cohorts worldwide. "With about twice as many samples as any previous studies, we were able to substantially increase the number of genes studied, as well as incorporate recent improvements to the analytical methodology," said Dr. Daly. "By bringing together data from several existing sources, we hope to create a resource for definitive future analysis of genes associated with ASD." Indeed, the larger sample size enabled Mr. Kosmicki and colleagues to increase the number of genes associated with ASD from 65 in 2015 to 102 today. Of these 102 genes, 47 were found to be more strongly associated with intellectual disability and developmental delay than ASD, while 52 were more strongly related to ASD, and three were related to both. Statistically, the genes were identified at a 10 percent false discovery rate. "Being able to look at other disorders in connection to ASD is significant and valuable for being able to explain the genetics behind the variety of possible outcomes within ASD," said Mr. Kosmicki. Looking forward, the researchers believe these findings will help improve scientific understanding of the inheritance and biology of ASD, and the ability to characterize phenotypes into categories within and overlapping with ASD. They hope to connect the results of common- and rare-variant ASD research with those of larger genetic studies of intellectual disability, developmental delay, and psychiatric traits.详情>>

2018-10-18 00:00:00

论文骨髓增生性肿瘤的分类和个体化预后

Myeloproliferative neoplasms, such as polycythemia vera, essential thrombocythemia, and myelofibrosis, are chronic hematologic cancers with varied progression rates. The genomic characterization of patients with myeloproliferative neoplasms offers the potential for personalized diagnosis, risk stratification, and treatment.展开>><<收起

N Engl J Med 2018; 379:1416-1430  0
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论文肥胖中代谢组的严重紊乱与健康风险相关

Obesity is a heterogeneous phenotype that is crudely measured by body mass index (BMI). There is a need for a more precise yet portable method of phenotyping and categorizing risk in large numbers of people with obesity to advance clinical care and drug development. Here, we used non-targeted metabolomics and whole-genome sequencing to identify metabolic and genetic signatures of obesity. We find that obesity results in profound perturbation of the metabolome; nearly a third of the assayed metabolites associated with changes in BMI. A metabolome signature identifies the healthy obese and lean individuals with abnormal metabolomes—these groups differ in health outcomes and underlying genetic risk. Specifically, an abnormal metabolome associated with a 2- to 5-fold increase in cardiovascular events when comparing individuals who were matched for BMI but had opposing metabolome signatures. Because metabolome profiling identifies clinically meaningful heterogeneity in obesity, this approach could help select patients for clinical trials.展开>><<收起

Cell Metabolism,Published:October 11, 2018  0
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论文GSK3B的功能变体rs334558与抑郁症患者的缓解相关

Purpose: GSK3B and AKT1 genes have been implicated in the pathogenesis of a number of psychiatric and neurological disorders. Furthermore, their genetic variants are associated with response to antidepressant pharmacotherapy. As the evidence is still incomplete and inconsistent, continuing efforts to investigate the role of these two genes in the pathogenesis and treatment of brain disorders is necessary. The aim of our study was thus to evaluate the association of variants of these two genes with depressive disorders and drug treatment response. Patients and methods: In the present study, 222 patients with a depressive disorder who underwent pharmacological antidepressant treatment were divided into remitters and non-remitters following a 28-day course of pharmacotherapy. The association of a depressive disorder and remission rates with polymorphisms rs334558 in the GSK3B gene and rs1130214 and rs3730358 in the AKT1 gene was evaluated with a chi-square test. Results: Neither of the studied genetic variants was associated with a depressive disorder. Furthermore, frequencies of alleles and genotypes for rs1130214 and rs3730358 were not different in the groups of remitters and non-remitters. However, the activating allele T of the functional polymorphism rs334558 was significantly associated with remission, when all types of antidepressant drugs were included. This association continued as a trend when only patients taking selective serotonin reuptake inhibitors were considered. Conclusion: The present study provides support that the functional polymorphism rs334558 of GSK3B may play a role as a useful genetic and pharmacogenetic biomarker in the framework of personalized medicine approach.展开>><<收起

Pharmacogenomics and Personalized Medicine, 2018, 11: 121-126  0
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Cell Metabolism:代谢物预测与肥胖相关的疾病风险

新闻Cell Metabolism:代谢物预测与肥胖相关的疾病风险

Scientists at Scripps Research and collaborating corporate and academic partners have found a new way to use distinct molecular “signatures” from people with obesity to predict risk of developing diabetes and cardiovascular disease, an advance that could broaden the way doctors and scientists think about diagnosing and treating disease. The research, led by Amalio Telenti, MD, PhD, professor of genomics at Scripps Research and previously a scientific leader at Human Longevity Inc. (HLI), shows that predictors of future diabetes and cardiovascular disease for a person with obesity can be found among their body’s metabolites, molecules that all of us produce as we live, breathe and eat. Using cutting edge technologies, the scientists were able to assess the relationship between disease risk and the “metabolome,” a person’s collection of hundreds of metabolites, identifying specific signatures that predicted higher risk, according to results published in Cell Metabolism. “By looking at metabolome changes, we could identify individuals with a several-fold increase in their risk of developing of diabetes and cardiovascular disease over the ensuing years,” says Telenti. The ability to identify patterns in the metabolome that are associated with increased disease risk potentially represents a powerful tool for better understanding and preventing these diseases. For the new study, Telenti and his colleagues from HLI, the J. Craig Venter Institute and other partner organizations analyzed 2,396 people and found that obesity profoundly alters the metabolome, with the most medically important changes affecting how the body distributes fat. They found that certain metabolites are associated with an increase in intra-abdominal fat, which sits behind the abdominal wall and is associated with health risks. In total, the researchers found 49 metabolites with a strong association to body mass index (BMI), an indicator of obesity. By looking at these metabolite levels, scientists could predict a person’s obesity status with a 80 to 90 percent accuracy rate. Interestingly, changes in the metabolome didn’t always match up with whether a person was actually obese. In these cases, the researchers may have identified people who were obese but healthy and people who were lean but still at risk of disease. This is important information for doctors who want to predict future disease risk or enroll patients in clinical trials. To Telenti, the study shows how new technologies can broaden the way scientists think about disease. Instead of looking at a single metabolite or biomarker to predict disease, researchers today can combine many measurements to create a “signature” of a disease. For example, the researchers also sequenced the genomes of study participants. They found that while genetics are not great predictors of health conditions related to obesity, a few individuals had genetic variants associated with morbid obesity—a data point that adds to their individual “signature.” Next, the researchers hope to use these tools to study other metabolic diseases. “We generated a signature of obesity, but with different experimental and machine learning approaches, we could have also generated more targeted biomarkers for diseases like diabetes and liver steatosis,” says Telenti.  The study, “Profound perturbation of the metabolome in obesity associates with health risk,” included authors at Human Longevity, Inc., Metabolon, Inc., King’s College London, Baylor College of Medicine, University of Lausanne and the J. Craig Venter Institute. This research was supported by Human Longevity Inc., by the Wellcome Trust, the European Community’s Seventh Framework Programme; the National Institute for Health Research (NIHR) Clinical Research Facility at Guy’s & St Thomas’ NHS Foundation Trust and NIHR Biomedical Research Centre based at Guy's and St Thomas' NHS Foundation Trust and King's College London. A.T. is supported by the Qualcomm Foundation and a grant from NIH’s National Center for Advancing Translational Sciences.详情>>

2018-10-17 00:00:00
微流体设备帮助科学家识别癌症的早期遗传标记

新闻微流体设备帮助科学家识别癌症的早期遗传标记

As anyone who has played "Where's Waldo" knows, searching for a single item in a landscape filled with a mélange of characters and objects can be a challenge. Chrissy O'Keefe, a Ph.D. student in the Department of Biomedical Engineering, understands this all too well: She spends her days searching for subtle DNA changes in cancer cells hiding among many healthy cells. O'Keefe uses a device she and a team developed to analyze blood samples at the molecular level. Her goal? To detect cancer in the very early stages, long before symptoms arise. "Since blood reaches every tissue in the body, including cancer cells, it picks up pieces of cancer DNA," she explains. "While molecular analysis has seen great advances, there are still limitations in their ability to detect rare or infrequent gene changes and rare biomarkers." In a recent issue of Science Advances, O'Keefe describes the molecular analysis hardware and software techniques her team developed to make finding these DNA changes easy and efficient. Her team includes Jeff Wang, core faculty member of the Institute for NanoBioTechnology and a professor in the Department of Mechanical Engineering, and Tom Pisanic, INBT senior research scientist. O'Keefe explains that cancer DNA, like the DNA of all organisms, changes in response to its environment, favoring changes that help it survive long-term. These changes, which include mutations, deletions, frameshifts, and methylation, can be large and obvious, but some can be small and almost unnoticeable, she says. Small changes, even a single nucleotide on a single gene, can give cancer DNA an advantage. Therefore, detecting those changes early can inform physicians of potential problems, allowing medical intervention to begin immediately and improving patient survival rates. "Biology's secret to success is its ability to adapt and diversify. It maintains a strict balance between some variability and strict regulation to ensure stable growth," says O'Keefe. "However, cancer works by promoting growth-favoring instability. To detect this instability early, we need a technique that can compare molecule-to-molecule, and use variability itself as an indicator of the loss of strict regulation." To detect these small modifications, O'Keefe and her team created a digital platform called HYPER-Melt, which stands for high-density profiling and enumeration by melt. HYPER-Melt is a microfluidics platform, meaning it focuses on manipulating and analyzing small volumes of fluid. The team's device begins by separating blood samples into ever smaller portions and in doing so, makes it easier to analyze, identify, and separate diseased DNA from healthy DNA. Then the device digitizes and analyzes thousands of individual molecules. Other technologies do exist to provide the same information, but O'Keefe's device—capable of multidimensional detection of genetic and epigenetic changes—is more efficient and cost-effective for routine use. O'Keefe maintains that the device can be extremely helpful to physicians for early detection of cancers, especially those requiring invasive procedures like biopsies, endoscopies, and colonoscopies. O'Keefe's hope is that the testing method can lead to early detection of precancerous material and other disease detection and lead to better understanding of tumor progression.详情>>

2018-10-17 00:00:00

论文PARP-1的多态性表明缺血性卒中的风险增加和初始严重程度加重

Aim: Polymorphisms of DNA repair enzyme gene may alter the ability to repair damage and in turn may contribute to ischemic stroke susceptibility and outcome. Methods: We selected 316 ischemic stroke patients and 302 healthy controls. Then we genotyped SNPs of PARP-1 rs3219119, rs2271347 and APE1 rs1130409 in patient and control groups. Results: Polymorphism in PARP-1 rs2271347 was significantly associated with increased ischemic stroke risk (additive model: OR: 1.74; 95% CI: 1.03–2.93; p = 0.037). Patients harboring the PARP-1 rs2271347 GA/AA genotype had a worse initial stroke (additive model: OR: 1.85; 95% CI: 1.10–3.11; p = 0.021). Conclusion: Our study suggests that genetic variant of rs2271347 may contribute to the etiology of ischemic stroke.展开>><<收起

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新基因研究揭示心脏病发作的新风险

新闻新基因研究揭示心脏病发作的新风险

Loss of a protein that regulates mitochondrial function can greatly increase the risk of myocardial infarction (heart attack), Vanderbilt scientists reported Oct. 3 in the journal eLife. The study illustrates how "integrative genomics," a combination of basic research, a human biobank linked to electronic health records and novel computational genetic approaches can identify genetically determined changes in gene expression that contribute to complex diseases. "It's that end-to-end type of study, looking at findings in the lab and translating them into something that has important clinical implications," said Eric Gamazon, PhD, the study's co-corresponding author with Sandra Zinkel, MD, PhD. Gamazon is a computational geneticist and research instructor in Medicine in the Vanderbilt University School of Medicine. Zinkel is associate professor of Medicine and of Cell and Developmental Biology. Mitochondria are the energy-producing "power plants" of the cell. When they are not working properly, the tissues can become starved for energy. In the heart, mitochondrial dysfunction can lead to susceptibility to heart attack or heart failure. Mitochondria also play an important role in preventing cancer. They amplify and execute apoptosis, or programmed cell death, which is a way of preventing the growth of abnormal cells. 'Bid' is a protein that can trigger mitochondrial-induced apoptosis and thus is a potential target in the development of new anti-cancer drugs. But what does it do when it's not killing cells? To find out, Zinkel and colleagues led by graduate student Christi Salisbury-Ruf knocked out the gene for Bid in mice and studied what happens to their mitochondria. "We found that these mice … in the absence of Bid had mitochondrial defects," Salisbury-Ruf said. "No one had shown that before." When stressed, their hearts also showed evidence of damage and loss of function similar to what happens to humans who have had a heart attack. Detailed studies showed a loss of structures inside the mitochondria called cristae that are critical for normal mitochondrial function. This intrigued Gamazon, who wondered if a loss of Bid in humans also was associated with increased predisposition to heart attack. While at the University of Chicago, Gamazon and Nancy Cox, PhD, who currently directs the Vanderbilt Genetics Institute, developed a method for predicting whether the expression or activity level of certain genes may contribute to diseases as diverse as type 1 diabetes, rheumatoid arthritis and bipolar disorder. Their technique, called PrediXcan, focuses on gene expression that is turned up or down like a light switch by genetic variations. It was developed with the help of GTEx (Genotype-Tissue Expression), a National Institutes of Health reference data set of genetic variations and gene activity in multiple healthy tissues. The researchers applied a PrediXcan model for predicting the expression of the BID gene to BioVU, Vanderbilt's DNA databank, and to available GWAS (genome-wide association studies) of myocardial infarction. BioVU consists of nearly 250,000 samples of DNA that have been extracted from discarded blood samples donated by Vanderbilt patients and linked to their "de-identified" electronic health records, which have been scrubbed of personal identifying information. The researchers determined the level of BID expression in DNA samples from more than 29,000 patients, of whom more than 5,000 had experienced a heart attack. Those with the lowest level of BID expression, in the bottom 5 percent, had a better-than fourfold increased risk of myocardial infarction. Lower BID expression was also associated with greater risk of heart attack in additional (non-BioVU) GWAS samples. The finding suggests genotyping may be a way to identify patients with low BID expression who are at increased risk of having a heart attack. "If you know someone's at risk then you might tell them, 'You should be more careful about your cholesterol and blood pressure,'" Zinkel said.详情>>

2018-10-16 00:00:00
科学家发现有助于ADHD发展的基因

新闻科学家发现有助于ADHD发展的基因

A team from I.M. Sechenov First Moscow State Medical University, together with foreign colleagues, analyzed the genomes of several families that have members with ADHD. The results have shown that all patients had specific features in certain genes. The identification of such patterns may help diagnose ADHD. The work was published in Molecular Psychiatry. ADHD, or attention deficit and hyperactivity disorder, is a common neurodevelopmental disorder with manifestation usually occurring in childhood. According to various sources, this condition affects from 3 percent to 30 percent of the population. Children with ADHD are easily distracted, hyperactive and impulsive. ADHD patients may be aggressive and have higher risks for substance abuse and the development of other mental disorders. Only 40 percent show improvement with age. The pathological mechanisms behind ADHD are still unknown, but scientists prefer the version of joint influence of genetic factors and environmental adversities (e.g., stress or illness of the mother during pregnancy, complications during labor, or diseases suffered in early childhood). "We believe the development of ADHD may be caused by alterations in the structure of genes encoding proteins which participate in signal transmission in the nervous system using special biologically active substances—neuromediators. In the case of ADHD, the changes affect, among others, serotonin and dopamine pathways in the nervous system that play an important role in the processes of attention, motivation, and learning. The disorder has a complex genetic nature that hasn't been addressed until recently," says Evgeniy Svirin, junior research associate of the laboratory of psychiatric neurobiology at I.M. Sechenov First Moscow State Medical University. The scientists focused on studying the rare functional variants in whole set of genes in several generations of families with running ADHD. They managed to follow the patterns for certain genes, e.g., AAED1. The protein produced by this gene binds with several other proteins that participate in dopaminergic and glutamatergic transmission. Changes in its activity can potentially influence the excitability of some structures of the central nervous system leading to behavioral alterations. Moreover, to the association with ADHD was discovered for another gene with an unknown function—ATAD2. Notably, these rare variants were also found in ADHD patients outside the studied families. These findings give further evidence for considering them as ADHD risk genes. "We have discovered new rare functional variants associated with the risk of ADHD development. Our results may be of use for the development of genetic diagnostics methods for this complex disorder. Further studies of the rare AAED1 gene variant on brain development that are already carried out by our joint team with German researchers, may help better understand pathological mechanisms that lead to the occurrence of this disease," concludes Evgeniy Svirin.详情>>

medicalxpress
2018-10-16 00:00:00