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Showing papers in "Genome Medicine in 2016"


Journal ArticleDOI
TL;DR: Several definitions of a ‘healthy microbiome’ that have emerged are reviewed, the current understanding of the ranges of healthy microbial diversity, and gaps such as the characterization of molecular function and the development of ecological therapies to be addressed in the future are reviewed.
Abstract: Humans are virtually identical in their genetic makeup, yet the small differences in our DNA give rise to tremendous phenotypic diversity across the human population. By contrast, the metagenome of the human microbiome—the total DNA content of microbes inhabiting our bodies—is quite a bit more variable, with only a third of its constituent genes found in a majority of healthy individuals. Understanding this variability in the “healthy microbiome” has thus been a major challenge in microbiome research, dating back at least to the 1960s, continuing through the Human Microbiome Project and beyond. Cataloguing the necessary and sufficient sets of microbiome features that support health, and the normal ranges of these features in healthy populations, is an essential first step to identifying and correcting microbial configurations that are implicated in disease. Toward this goal, several population-scale studies have documented the ranges and diversity of both taxonomic compositions and functional potentials normally observed in the microbiomes of healthy populations, along with possible driving factors such as geography, diet, and lifestyle. Here, we review several definitions of a ‘healthy microbiome’ that have emerged, the current understanding of the ranges of healthy microbial diversity, and gaps such as the characterization of molecular function and the development of ecological therapies to be addressed in the future.

1,164 citations


Journal ArticleDOI
TL;DR: Current knowledge about the mechanistic interactions between the gut microbiota, host energy metabolism, and the host immune system in the context of obesity and metabolic disease is discussed, with a focus on the importance of the axis that links gut microbes and host metabolic inflammation.
Abstract: The human gut harbors more than 100 trillion microbial cells, which have an essential role in human metabolic regulation via their symbiotic interactions with the host. Altered gut microbial ecosystems have been associated with increased metabolic and immune disorders in animals and humans. Molecular interactions linking the gut microbiota with host energy metabolism, lipid accumulation, and immunity have also been identified. However, the exact mechanisms that link specific variations in the composition of the gut microbiota with the development of obesity and metabolic diseases in humans remain obscure owing to the complex etiology of these pathologies. In this review, we discuss current knowledge about the mechanistic interactions between the gut microbiota, host energy metabolism, and the host immune system in the context of obesity and metabolic disease, with a focus on the importance of the axis that links gut microbes and host metabolic inflammation. Finally, we discuss therapeutic approaches aimed at reshaping the gut microbial ecosystem to regulate obesity and related pathologies, as well as the challenges that remain in this area.

941 citations


Journal ArticleDOI
TL;DR: This review summarizes current research on the short-term and long-term consequences of antibiotic use on the human microbiome, from early life to adulthood, and its effect on diseases such as malnutrition, obesity, diabetes, and Clostridium difficile infection.
Abstract: The widespread use of antibiotics in the past 80 years has saved millions of human lives, facilitated technological progress and killed incalculable numbers of microbes, both pathogenic and commensal. Human-associated microbes perform an array of important functions, and we are now just beginning to understand the ways in which antibiotics have reshaped their ecology and the functional consequences of these changes. Mounting evidence shows that antibiotics influence the function of the immune system, our ability to resist infection, and our capacity for processing food. Therefore, it is now more important than ever to revisit how we use antibiotics. This review summarizes current research on the short-term and long-term consequences of antibiotic use on the human microbiome, from early life to adulthood, and its effect on diseases such as malnutrition, obesity, diabetes, and Clostridium difficile infection. Motivated by the consequences of inappropriate antibiotic use, we explore recent progress in the development of antivirulence approaches for resisting infection while minimizing resistance to therapy. We close the article by discussing probiotics and fecal microbiota transplants, which promise to restore the microbiota after damage of the microbiome. Together, the results of studies in this field emphasize the importance of developing a mechanistic understanding of gut ecology to enable the development of new therapeutic strategies and to rationally limit the use of antibiotic compounds.

667 citations


Journal ArticleDOI
TL;DR: Dysbiosis in RA patients resulting from the abundance of certain rare bacterial lineages is suggested, suggesting a correlation between the intestinal microbiota and metabolic signatures could determine a predictive profile for disease causation and progression.
Abstract: The adaptive immune response in rheumatoid arthritis (RA) is influenced by an interaction between host genetics and environment, particularly the host microbiome. Association of the gut microbiota with various diseases has been reported, though the specific components of the microbiota that affect the host response leading to disease remain unknown. However, there is limited information on the role of gut microbiota in RA. In this study we aimed to define a microbial and metabolite profile that could predict disease status. In addition, we aimed to generate a humanized model of arthritis to confirm the RA-associated microbe. To identify an RA biomarker profile, the 16S ribosomal DNA of fecal samples from RA patients, first-degree relatives (to rule out environment/background as confounding factors), and random healthy non-RA controls were sequenced. Analysis of metabolites and their association with specific taxa was performed to investigate a potential mechanistic link. The role of an RA-associated microbe was confirmed using a human epithelial cell line and a humanized mouse model of arthritis. Patients with RA exhibited decreased gut microbial diversity compared with controls, which correlated with disease duration and autoantibody levels. A taxon-level analysis suggested an expansion of rare taxa, Actinobacteria, with a decrease in abundant taxa in patients with RA compared with controls. Prediction models based on the random forests algorithm suggested that three genera, Collinsella, Eggerthella, and Faecalibacterium, segregated with RA. The abundance of Collinsella correlated strongly with high levels of alpha-aminoadipic acid and asparagine as well as production of the proinflammatory cytokine IL-17A. A role for Collinsella in altering gut permeability and disease severity was confirmed in experimental arthritis. These observations suggest dysbiosis in RA patients resulting from the abundance of certain rare bacterial lineages. A correlation between the intestinal microbiota and metabolic signatures could determine a predictive profile for disease causation and progression.

513 citations


Journal ArticleDOI
TL;DR: Key aspects of telomerase assembly, intracellular trafficking and recruitment to telomeres for completing DNA synthesis, which may provide novel targets for the development of anticancer agents are revealed.
Abstract: Telomeres maintain genomic integrity in normal cells, and their progressive shortening during successive cell divisions induces chromosomal instability. In the large majority of cancer cells, telomere length is maintained by telomerase. Thus, telomere length and telomerase activity are crucial for cancer initiation and the survival of tumors. Several pathways that regulate telomere length have been identified, and genome-scale studies have helped in mapping genes that are involved in telomere length control. Additionally, genomic screening for recurrent human telomerase gene hTERT promoter mutations and mutations in genes involved in the alternative lengthening of telomeres pathway, such as ATRX and DAXX, has elucidated how these genomic changes contribute to the activation of telomere maintenance mechanisms in cancer cells. Attempts have also been made to develop telomere length- and telomerase-based diagnostic tools and anticancer therapeutics. Recent efforts have revealed key aspects of telomerase assembly, intracellular trafficking and recruitment to telomeres for completing DNA synthesis, which may provide novel targets for the development of anticancer agents. Here, we summarize telomere organization and function and its role in oncogenesis. We also highlight genomic mutations that lead to reactivation of telomerase, and mechanisms of telomerase reconstitution and trafficking that shed light on its function in cancer initiation and tumor development. Additionally, recent advances in the clinical development of telomerase inhibitors, as well as potential novel targets, will be summarized.

447 citations


Journal ArticleDOI
TL;DR: A neural network-based machine-learning algorithm leveraging information across multiple receptor specificities and ligand length scales is developed and demonstrated how this approach significantly improves the accuracy for prediction of peptide binding and identification of MHC ligands.
Abstract: Binding of peptides to MHC class I molecules (MHC-I) is essential for antigen presentation to cytotoxic T-cells. Here, we demonstrate how a simple alignment step allowing insertions and deletions in a pan-specific MHC-I binding machine-learning model enables combining information across both multiple MHC molecules and peptide lengths. This pan-allele/pan-length algorithm significantly outperforms state-of-the-art methods, and captures differences in the length profile of binders to different MHC molecules leading to increased accuracy for ligand identification. Using this model, we demonstrate that percentile ranks in contrast to affinity-based thresholds are optimal for ligand identification due to uniform sampling of the MHC space. We have developed a neural network-based machine-learning algorithm leveraging information across multiple receptor specificities and ligand length scales, and demonstrated how this approach significantly improves the accuracy for prediction of peptide binding and identification of MHC ligands. The method is available at www.cbs.dtu.dk/services/NetMHCpan-3.0 .

444 citations


Journal ArticleDOI
TL;DR: A systematic review of the pertinent literature demonstrates a lack of evidence for an impact of probiotic supplementation on fecal microbiota composition in healthy adults.
Abstract: The effects of probiotic supplementation on fecal microbiota composition in healthy adults have not been well established. We aimed to provide a systematic review of the potential evidence for an effect of probiotic supplementation on the composition of human fecal microbiota as assessed by high-throughput molecular approaches in randomized controlled trials (RCTs) of healthy adults. The survey of peer-reviewed papers was performed on 17 August 2015 by a literature search through PubMed, SCOPUS, and ISI Web of Science. Additional papers were identified by checking references of relevant papers. Search terms included healthy adult, probiotic, bifidobacterium, lactobacillus, gut microbiota, fecal microbiota, intestinal microbiota, intervention, and (clinical) trial. RCTs of solely probiotic supplementation and placebo in healthy adults that examined alteration in composition of overall fecal microbiota structure assessed by shotgun metagenomic sequencing, 16S ribosomal RNA sequencing, or phylogenetic microarray methods were included. Independent collection and quality assessment of studies were performed by two authors using predefined criteria including methodological quality assessment of reports of the clinical trials based on revised tools from PRISMA/Cochrane and by the Jadad score. Seven RCTs investigating the effect of probiotic supplementation on fecal microbiota in healthy adults were identified and included in the present systematic review. The quality of the studies was assessed as medium to high. Still, no effects were observed on the fecal microbiota composition in terms of α-diversity, richness, or evenness in any of the included studies when compared to placebo. Only one study found that probiotic supplementation significantly modified the overall structure of the fecal bacterial community in terms of β-diversity when compared to placebo. This systematic review of the pertinent literature demonstrates a lack of evidence for an impact of probiotics on fecal microbiota composition in healthy adults. Future studies would benefit from pre-specifying the primary outcome and transparently reporting the results including effect sizes, confidence intervals, and P values as well as providing a clear distinction of between-group and within-group comparisons.

395 citations


Journal ArticleDOI
TL;DR: Several of the most intriguing diet-dependent metabolites that may impact host physiology and may therefore be appropriate targets for therapeutic interventions, such as short-chain fatty acids, trimethylamine N-oxide, tryptophan and tyrosine derivatives, and oxidized fatty acids are reviewed.
Abstract: Mass spectrometry- and nuclear magnetic resonance-based metabolomic studies comparing diseased versus healthy individuals have shown that microbial metabolites are often the compounds most markedly altered in the disease state. Recent studies suggest that several of these metabolites that derive from microbial transformation of dietary components have significant effects on physiological processes such as gut and immune homeostasis, energy metabolism, vascular function, and neurological behavior. Here, we review several of the most intriguing diet-dependent metabolites that may impact host physiology and may therefore be appropriate targets for therapeutic interventions, such as short-chain fatty acids, trimethylamine N-oxide, tryptophan and tyrosine derivatives, and oxidized fatty acids. Such interventions will require modulating either bacterial species or the bacterial biosynthetic enzymes required to produce these metabolites, so we briefly describe the current understanding of the bacterial and enzymatic pathways involved in their biosynthesis and summarize their molecular mechanisms of action. We then discuss in more detail the impact of these metabolites on health and disease, and review current strategies to modulate levels of these metabolites to promote human health. We also suggest future studies that are needed to realize the full therapeutic potential of targeting the gut microbiota.

378 citations


Journal ArticleDOI
TL;DR: A flexible, streamlined computational workflow for identification of personalized Variant Antigens by Cancer Sequencing (pVAC-Seq) that integrates tumor mutation and expression data (DNA- and RNA-Sequ) is presented.
Abstract: Cancer immunotherapy has gained significant momentum from recent clinical successes of checkpoint blockade inhibition. Massively parallel sequence analysis suggests a connection between mutational load and response to this class of therapy. Methods to identify which tumor-specific mutant peptides (neoantigens) can elicit anti-tumor T cell immunity are needed to improve predictions of checkpoint therapy response and to identify targets for vaccines and adoptive T cell therapies. Here, we present a flexible, streamlined computational workflow for identification of personalized Variant Antigens by Cancer Sequencing (pVAC-Seq) that integrates tumor mutation and expression data (DNA- and RNA-Seq). pVAC-Seq is available at https://github.com/griffithlab/pVAC-Seq .

310 citations


Journal ArticleDOI
TL;DR: This study presents a novel, scalable, and scalable approach that allows for real-time assessment of the impact of infectious disease-related adverse events on survival and quality of life at the clinic level.
Abstract: Author details Department of Laboratory Medicine, University of California, 185 Berry Street, San Francisco 94107 CA, USA. UCSF-Abbott Viral Diagnostics and Discovery Center, San Francisco 91407 CA, USA. Children’s Hospital Colorado and University of Colorado School of Medicine, Aurora, CO, USA. California Department of Public Health, Richmond, CA, USA. Kaiser Permanente Hospital, Oakland, CA, USA. John Muir Hospital, Walnut Creek, CA, USA. Department of Medicine, Division of Infectious Diseases, University of California, San Francisco 94107 CA, USA.

304 citations


Journal ArticleDOI
TL;DR: There was a striking negative association between frailty and gut microbiota diversity, underpinned by specific taxonomic associations that represent targets for diagnostic surveillance, or for intervention studies to improve vitality in ageing.
Abstract: Frailty is arguably the biggest problem associated with population ageing, and associates with gut microbiome composition in elderly and care-dependent individuals. Here we characterize frailty associations with the gut microbiota in a younger community dwelling population, to identify targets for intervention to encourage healthy ageing. We analysed 16S rRNA gene sequence data derived from faecal samples obtained from 728 female twins. Frailty was quantified using a frailty index (FI). Mixed effects models were used to identify associations with diversity, operational taxonomic units (OTUs) and taxa. OTU associations were replicated in the Eldermet cohort. Phenotypes were correlated with modules of OTUs collapsed by co-occurrence. Frailty negatively associated with alpha diversity of the gut microbiota. Models considering a number of covariates identified 637 OTUs associated with FI. Twenty-two OTU associations were significant independent of alpha diversity. Species more abundant with frailty included Eubacterium dolichum and Eggerthella lenta. A Faecalibacterium prausnitzii OTU was less abundant in frailer individuals, and retained significance in discordant twin analysis. Sixty OTU associations were replicated in the Eldermet cohort. OTU co-occurrence modules had mutually exclusive associations between frailty and alpha diversity. There was a striking negative association between frailty and gut microbiota diversity, underpinned by specific taxonomic associations. Whether these relationships are causal or consequential is unknown. Nevertheless, they represent targets for diagnostic surveillance, or for intervention studies to improve vitality in ageing.

Journal ArticleDOI
TL;DR: Similar to the primate, independent of maternal body mass index, a maternal high-fat diet is associated with distinct changes in the neonatal gut microbiome at birth which persist through 4–6 weeks of age, and underscore the importance of counseling pregnant mothers on macronutrient consumption during pregnancy and lactation.
Abstract: Emerging evidence suggests that the in utero environment is not sterile as once presumed. Work in the mouse demonstrated transmission of commensal bacteria from mother to fetus during gestation, though it is unclear what modulates this process. We have previously shown in the nonhuman primate that, independent of obesity, a maternal high-fat diet during gestation and lactation persistently shapes the juvenile gut microbiome. We therefore sought to interrogate in a population-based human longitudinal cohort whether a maternal high-fat diet similarly alters the neonatal and infant gut microbiome in early life. A representative cohort was prospectively enrolled either in the early third trimester or intrapartum (n = 163), with a subset consented to longitudinal sampling through the postpartum interval (n = 81). Multiple body site samples, including stool and meconium, were collected from neonates at delivery and by 6 weeks of age. A rapid dietary questionnaire was administered to estimate intake of fat, added sugars, and fiber over the past month (National Health and Examination Survey). DNA was extracted from each infant meconium/stool sample (MoBio) and subjected to 16S rRNA gene sequencing and analysis. On average, the maternal dietary intake of fat ranged from 14.0 to 55.2 %, with an average intake of 33.1 % (σ = 6.1 %). Mothers whose diets significantly differed from the mean (±1 standard deviation) were separated into two distinct groups, a control group (n = 13, μ = 24.4 %) and a high-fat group (n = 13, μ = 43.1 %). Principal coordinate analysis revealed that the microbiome of the neonatal stool at birth (meconium) clustered differently by virtue of maternal gestational diet (PERMANOVA p = 0.001). LEfSe feature selection identified several taxa that discriminated the groups, with a notable relative depletion of Bacteroides in the neonates exposed to a maternal high-fat gestational diet (Student’s t-test, p < 0.05) that persisted to 6 weeks of age. Similar to the primate, independent of maternal body mass index, a maternal high-fat diet is associated with distinct changes in the neonatal gut microbiome at birth which persist through 4–6 weeks of age. Our findings underscore the importance of counseling pregnant mothers on macronutrient consumption during pregnancy and lactation.

Journal ArticleDOI
TL;DR: A random forest classification model that detects colonic lesions using the relative abundance of gut microbiota and the concentration of hemoglobin in stool found that the loss of potentially beneficial organisms, such as members of the Lachnospiraceae, was more predictive for identifying patients with adenomas when used in combination with FIT.
Abstract: Colorectal cancer (CRC) is the second leading cause of death among cancers in the United States. Although individuals diagnosed early have a greater than 90 % chance of survival, more than one-third of individuals do not adhere to screening recommendations partly because the standard diagnostics, colonoscopy and sigmoidoscopy, are expensive and invasive. Thus, there is a great need to improve the sensitivity of non-invasive tests to detect early stage cancers and adenomas. Numerous studies have identified shifts in the composition of the gut microbiota associated with the progression of CRC, suggesting that the gut microbiota may represent a reservoir of biomarkers that would complement existing non-invasive methods such as the widely used fecal immunochemical test (FIT). We sequenced the 16S rRNA genes from the stool samples of 490 patients. We used the relative abundances of the bacterial populations within each sample to develop a random forest classification model that detects colonic lesions using the relative abundance of gut microbiota and the concentration of hemoglobin in stool. The microbiota-based random forest model detected 91.7 % of cancers and 45.5 % of adenomas while FIT alone detected 75.0 % and 15.7 %, respectively. Of the colonic lesions missed by FIT, the model detected 70.0 % of cancers and 37.7 % of adenomas. We confirmed known associations of Porphyromonas assaccharolytica, Peptostreptococcus stomatis, Parvimonas micra, and Fusobacterium nucleatum with CRC. Yet, we found that the loss of potentially beneficial organisms, such as members of the Lachnospiraceae, was more predictive for identifying patients with adenomas when used in combination with FIT. These findings demonstrate the potential for microbiota analysis to complement existing screening methods to improve detection of colonic lesions.

Journal ArticleDOI
TL;DR: Within 3 months after morbidly obese individuals had undergone RYGB, their gut microbiota featured an increased diversity, an altered composition, an increased potential for oxygen tolerance, and an increased possible for microbial utilization of macro- and micro-nutrients.
Abstract: Roux-en-Y gastric bypass (RYGB) is an effective means to achieve sustained weight loss for morbidly obese individuals. Besides rapid weight reduction, patients achieve major improvements of insulin sensitivity and glucose homeostasis. Dysbiosis of gut microbiota has been associated with obesity and some of its co-morbidities, like type 2 diabetes, and major changes of gut microbial communities have been hypothesized to mediate part of the beneficial metabolic effects observed after RYGB. Here we describe changes in gut microbial taxonomic composition and functional potential following RYGB. We recruited 13 morbidly obese patients who underwent RYGB, carefully phenotyped them, and had their gut microbiomes quantified before (n = 13) and 3 months (n = 12) and 12 months (n = 8) after RYGB. Following shotgun metagenomic sequencing of the fecal microbial DNA purified from stools, we characterized the gut microbial composition at species and gene levels followed by functional annotation. In parallel with the weight loss and metabolic improvements, gut microbial diversity increased within the first 3 months after RYGB and remained high 1 year later. RYGB led to altered relative abundances of 31 species (P < 0.05, q < 0.15) within the first 3 months, including those of Escherichia coli, Klebsiella pneumoniae, Veillonella spp., Streptococcus spp., Alistipes spp., and Akkermansia muciniphila. Sixteen of these species maintained their altered relative abundances during the following 9 months. Interestingly, Faecalibacterium prausnitzii was the only species that decreased in relative abundance. Fifty-three microbial functional modules increased their relative abundance between baseline and 3 months (P < 0.05, q < 0.17). These functional changes included increased potential (i) to assimilate multiple energy sources using transporters and phosphotransferase systems, (ii) to use aerobic respiration, (iii) to shift from protein degradation to putrefaction, and (iv) to use amino acids and fatty acids as energy sources. Within 3 months after morbidly obese individuals had undergone RYGB, their gut microbiota featured an increased diversity, an altered composition, an increased potential for oxygen tolerance, and an increased potential for microbial utilization of macro- and micro-nutrients. These changes were maintained for the first year post-RYGB. Current controlled trials (ID NCT00810823 , NCT01579981 , and NCT01993511 ).

Journal ArticleDOI
TL;DR: In this article, a complete diagnostic workflow for the identification of infectious microorganisms from seven septic patients based on unbiased sequence analyses of free circulating DNA from plasma by next-generation sequencing was described.
Abstract: Bloodstream infections remain one of the major challenges in intensive care units, leading to sepsis or even septic shock in many cases. Due to the lack of timely diagnostic approaches with sufficient sensitivity, mortality rates of sepsis are still unacceptably high. However a prompt diagnosis of the causative microorganism is critical to significantly improve outcome of bloodstream infections. Although various targeted molecular tests for blood samples are available, time-consuming blood culture-based approaches still represent the standard of care for the identification of bacteria. Here we describe the establishment of a complete diagnostic workflow for the identification of infectious microorganisms from seven septic patients based on unbiased sequence analyses of free circulating DNA from plasma by next-generation sequencing. We found significant levels of DNA fragments derived from pathogenic bacteria in samples from septic patients. Quantitative evaluation of normalized read counts and introduction of a sepsis indicating quantifier (SIQ) score allowed for an unambiguous identification of Gram-positive as well as Gram-negative bacteria that exactly matched with blood cultures from corresponding patient samples. In addition, we also identified species from samples where blood cultures were negative. Reads of non-human origin also comprised fragments derived from antimicrobial resistance genes, showing that, in principle, prediction of specific types of resistance might be possible. The complete workflow from sample preparation to species identification report could be accomplished in roughly 30 h, thus making this approach a promising diagnostic platform for critically ill patients suffering from bloodstream infections.

Journal ArticleDOI
TL;DR: This transcriptomic network analysis of 19 brain regions provides a comprehensive assessment of the critical molecular pathways associated with AD pathology and offers new insights into molecular mechanisms underlying selective regional vulnerability to AD at different stages of the progression of cognitive compromise and development of the canonical neuropathological lesions of AD.
Abstract: Alzheimer’s disease (AD) is the most common form of dementia, characterized by progressive cognitive impairment and neurodegeneration. However, despite extensive clinical and genomic studies, the molecular basis of AD development and progression remains elusive. To elucidate molecular systems associated with AD, we developed a large scale gene expression dataset from 1053 postmortem brain samples across 19 cortical regions of 125 individuals with a severity spectrum of dementia and neuropathology of AD. We excluded brain specimens that evidenced neuropathology other than that characteristic of AD. For the first time, we performed a pan-cortical brain region genomic analysis, characterizing the gene expression changes associated with a measure of dementia severity and multiple measures of the severity of neuropathological lesions associated with AD (neuritic plaques and neurofibrillary tangles) and constructing region-specific co-expression networks. We rank-ordered 44,692 gene probesets, 1558 co-expressed gene modules and 19 brain regions based upon their association with the disease traits. The neurobiological pathways identified through these analyses included actin cytoskeleton, axon guidance, and nervous system development. Using public human brain single-cell RNA-sequencing data, we computed brain cell type-specific marker genes for human and determined that many of the abnormally expressed gene signatures and network modules were specific to oligodendrocytes, astrocytes, and neurons. Analysis based on disease severity suggested that: many of the gene expression changes, including those of oligodendrocytes, occurred early in the progression of disease, making them potential translational/treatment development targets and unlikely to be mere bystander result of degeneration; several modules were closely linked to cognitive compromise with lesser association with traditional measures of neuropathology. The brain regional analyses identified temporal lobe gyri as sites associated with the greatest and earliest gene expression abnormalities. This transcriptomic network analysis of 19 brain regions provides a comprehensive assessment of the critical molecular pathways associated with AD pathology and offers new insights into molecular mechanisms underlying selective regional vulnerability to AD at different stages of the progression of cognitive compromise and development of the canonical neuropathological lesions of AD.

Journal ArticleDOI
TL;DR: Clinicians, researchers, and citizens need improved methods, tools, and training to generate, analyze, and query data effectively and contribute to creating the European Single Market for health, which will improve health and healthcare for all Europeans.
Abstract: Medicine and healthcare are undergoing profound changes. Whole-genome sequencing and high-resolution imaging technologies are key drivers of this rapid and crucial transformation. Technological innovation combined with automation and miniaturization has triggered an explosion in data production that will soon reach exabyte proportions. How are we going to deal with this exponential increase in data production? The potential of “big data” for improving health is enormous but, at the same time, we face a wide range of challenges to overcome urgently. Europe is very proud of its cultural diversity; however, exploitation of the data made available through advances in genomic medicine, imaging, and a wide range of mobile health applications or connected devices is hampered by numerous historical, technical, legal, and political barriers. European health systems and databases are diverse and fragmented. There is a lack of harmonization of data formats, processing, analysis, and data transfer, which leads to incompatibilities and lost opportunities. Legal frameworks for data sharing are evolving. Clinicians, researchers, and citizens need improved methods, tools, and training to generate, analyze, and query data effectively. Addressing these barriers will contribute to creating the European Single Market for health, which will improve health and healthcare for all Europeans.

Journal ArticleDOI
TL;DR: The microbiome sequencing revealed that the microbiomes of all organs (vagina, cervix, Fallopian tubes, and ovaries) are significantly correlated and that there is a structural microbiome shift in the cancer and hyperplasia cases, distinguishable from the benign cases.
Abstract: Endometrial cancer studies have led to a number of well-defined but mechanistically unconnected genetic and environmental risk factors. One of the emerging modulators between environmental triggers and genetic expression is the microbiome. We set out to inquire about the composition of the uterine microbiome and its putative role in endometrial cancer. We undertook a study of the microbiome in samples taken from different locations along the female reproductive tract in patients with endometrial cancer (n = 17), patients with endometrial hyperplasia (endometrial cancer precursor, n = 4), and patients afflicted with benign uterine conditions (n = 10). Vaginal, cervical, Fallopian, ovarian, peritoneal, and urine samples were collected aseptically both in the operating room and the pathology laboratory. DNA extraction was followed by amplification and high-throughput next generation sequencing (MiSeq) of the 16S rDNA V3-V5 region to identify the microbiota present. Microbiota data were summarized using both α-diversity to reflect species richness and evenness within bacterial populations and β-diversity to reflect the shared diversity between bacterial populations. Statistical significance was determined through the use of multiple testing, including the generalized mixed-effects model. The microbiome sequencing (16S rDNA V3-V5 region) revealed that the microbiomes of all organs (vagina, cervix, Fallopian tubes, and ovaries) are significantly correlated (p 4.5). Our results suggest that the detection of A. vaginae and the identified Porphyromonas sp. in the gynecologic tract combined with a high vaginal pH is statistically associated with the presence of endometrial cancer. Given the documented association of the identified microorganisms with other pathologies, these findings raise the possibility of a microbiome role in the manifestation, etiology, or progression of endometrial cancer that should be further investigated.

Journal ArticleDOI
TL;DR: Changes both in composition and in function of the sub-clinical gut microbiome are found, including a decrease in Akkermansia muciniphila suggesting a role prior to the onset of disease, and functional changes reflecting a response to oxidative stress comparable to that previously observed in chronic T2D and inflammatory bowel diseases.
Abstract: Obesity and type 2 diabetes (T2D) are linked both with host genetics and with environmental factors, including dysbioses of the gut microbiota. However, it is unclear whether these microbial changes precede disease onset. Twin cohorts present a unique genetically-controlled opportunity to study the relationships between lifestyle factors and the microbiome. In particular, we hypothesized that family-independent changes in microbial composition and metabolic function during the sub-clinical state of T2D could be either causal or early biomarkers of progression. We collected fecal samples and clinical metadata from 20 monozygotic Korean twins at up to two time points, resulting in 36 stool shotgun metagenomes. While the participants were neither obese nor diabetic, they spanned the entire range of healthy to near-clinical values and thus enabled the study of microbial associations during sub-clinical disease while accounting for genetic background. We found changes both in composition and in function of the sub-clinical gut microbiome, including a decrease in Akkermansia muciniphila suggesting a role prior to the onset of disease, and functional changes reflecting a response to oxidative stress comparable to that previously observed in chronic T2D and inflammatory bowel diseases. Finally, our unique study design allowed us to examine the strain similarity between twins, and we found that twins demonstrate strain-level differences in composition despite species-level similarities. These changes in the microbiome might be used for the early diagnosis of an inflamed gut and T2D prior to clinical onset of the disease and will help to advance toward microbial interventions.

Journal ArticleDOI
TL;DR: In this article, the authors evaluated the clinical utility of molecular profiling of tumor tissue to guide treatment of patients with advanced solid tumors and evaluated the frequency of genomic alterations, clinical "actionability" of somatic variants, enrollment in mutation-targeted or other clinical trials.
Abstract: The clinical utility of molecular profiling of tumor tissue to guide treatment of patients with advanced solid tumors is unknown. Our objectives were to evaluate the frequency of genomic alterations, clinical “actionability” of somatic variants, enrollment in mutation-targeted or other clinical trials, and outcome of molecular profiling for advanced solid tumor patients at the Princess Margaret Cancer Centre (PM). Patients with advanced solid tumors aged ≥18 years, good performance status, and archival tumor tissue available were prospectively consented. DNA from archival formalin-fixed paraffin-embedded tumor tissue was tested using a MALDI-TOF MS hotspot panel or a targeted next generation sequencing (NGS) panel. Somatic variants were classified according to clinical actionability and an annotated report included in the electronic medical record. Oncologists were provided with summary tables of their patients’ molecular profiling results and available mutation-specific clinical trials. Enrolment in genotype-matched versus genotype-unmatched clinical trials following release of profiling results and response by RECIST v1.1 criteria were evaluated. From March 2012 to July 2014, 1893 patients were enrolled and 1640 tested. After a median follow-up of 18 months, 245 patients (15 %) who were tested were subsequently treated on 277 therapeutic clinical trials, including 84 patients (5 %) on 89 genotype-matched trials. The overall response rate was higher in patients treated on genotype-matched trials (19 %) compared with genotype-unmatched trials (9 %; p < 0.026). In a multi-variable model, trial matching by genotype (p = 0.021) and female gender (p = 0.034) were the only factors associated with increased likelihood of treatment response. Few advanced solid tumor patients enrolled in a prospective institutional molecular profiling trial were treated subsequently on genotype-matched therapeutic trials. In this non-randomized comparison, genotype-enrichment of early phase clinical trials was associated with an increased objective tumor response rate. NCT01505400 (date of registration 4 January 2012).

Journal ArticleDOI
TL;DR: A GFD changes the gut microbiome composition and alters the activity of microbial pathways and observes strong relations between the predicted activity of pathways and biomarker measurements.
Abstract: A gluten-free diet (GFD) is the most commonly adopted special diet worldwide. It is an effective treatment for coeliac disease and is also often followed by individuals to alleviate gastrointestinal complaints. It is known there is an important link between diet and the gut microbiome, but it is largely unknown how a switch to a GFD affects the human gut microbiome. We studied changes in the gut microbiomes of 21 healthy volunteers who followed a GFD for four weeks. We collected nine stool samples from each participant: one at baseline, four during the GFD period, and four when they returned to their habitual diet (HD), making a total of 189 samples. We determined microbiome profiles using 16S rRNA sequencing and then processed the samples for taxonomic and imputed functional composition. Additionally, in all 189 samples, six gut health-related biomarkers were measured. Inter-individual variation in the gut microbiota remained stable during this short-term GFD intervention. A number of taxon-specific differences were seen during the GFD: the most striking shift was seen for the family Veillonellaceae (class Clostridia), which was significantly reduced during the intervention (p = 2.81 × 10−05). Seven other taxa also showed significant changes; the majority of them are known to play a role in starch metabolism. We saw stronger differences in pathway activities: 21 predicted pathway activity scores showed significant association to the change in diet. We observed strong relations between the predicted activity of pathways and biomarker measurements. A GFD changes the gut microbiome composition and alters the activity of microbial pathways.

Journal ArticleDOI
TL;DR: Patient dysbiosis improved over time but persisted even among those who responded to treatment and achieved mucosal healing, and it was found that pretreatment microbiome signatures are a promising avenue for prediction of remission and response to treatment.
Abstract: Gut microbiome dysbiosis has been demonstrated in subjects with newly diagnosed and chronic inflammatory bowel disease (IBD). In this study we sought to explore longitudinal changes in dysbiosis and ascertain associations between dysbiosis and markers of disease activity and treatment outcome. We performed a prospective cohort study of 19 treatment-naive pediatric IBD subjects and 10 healthy controls, measuring fecal calprotectin and assessing the gut microbiome via repeated stool samples. Associations between clinical characteristics and the microbiome were tested using generalized estimating equations. Random forest classification was used to predict ultimate treatment response (presence of mucosal healing at follow-up colonoscopy) or non-response using patients’ pretreatment samples. Patients with Crohn’s disease had increased markers of inflammation and dysbiosis compared to controls. Patients with ulcerative colitis had even higher inflammation and dysbiosis compared to those with Crohn’s disease. For all cases, the gut microbial dysbiosis index associated significantly with clinical and biological measures of disease severity, but did not associate with treatment response. We found differences in specific gut microbiome genera between cases/controls and responders/non-responders including Akkermansia, Coprococcus, Fusobacterium, Veillonella, Faecalibacterium, and Adlercreutzia. Using pretreatment microbiome data in a weighted random forest classifier, we were able to obtain 76.5 % accuracy for prediction of responder status. Patient dysbiosis improved over time but persisted even among those who responded to treatment and achieved mucosal healing. Although dysbiosis index was not significantly different between responders and non-responders, we found specific genus-level differences. We found that pretreatment microbiome signatures are a promising avenue for prediction of remission and response to treatment.

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TL;DR: The ZiBRA project is a United Kingdom–Brazil collaboration that aims to improve knowledge of Zika virus genomic epidemiology using new sequencing technologies.
Abstract: The World Health Organization has declared Zika virus an international public health emergency. Knowledge of Zika virus genomic epidemiology is currently limited due to challenges in obtaining and processing samples for sequencing. The ZiBRA project is a United Kingdom–Brazil collaboration that aims to improve this situation using new sequencing technologies.

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TL;DR: Rapid shotgun and robust technology can now simultaneously characterize the human and microbiome contributions to the proteome of a body fluid and is therefore a valuable complement to genomic studies.
Abstract: The oral cavity is home to one of the most diverse microbial communities of the human body and a major entry portal for pathogens. Its homeostasis is maintained by saliva, which fulfills key functions including lubrication of food, pre-digestion, and bacterial defense. Consequently, disruptions in saliva secretion and changes in the oral microbiome contribute to conditions such as tooth decay and respiratory tract infections. Here we set out to quantitatively map the saliva proteome in great depth with a rapid and in-depth mass spectrometry-based proteomics workflow. We used recent improvements in mass spectrometry (MS)-based proteomics to develop a rapid workflow for mapping the saliva proteome quantitatively and at great depth. Standard clinical cotton swabs were used to collect saliva form eight healthy individuals at two different time points, allowing us to study inter-individual differences and interday changes of the saliva proteome. To accurately identify microbial proteins, we developed a method called “split by taxonomy id” that prevents peptides shared by humans and bacteria or between different bacterial phyla to contribute to protein identification. Microgram protein amounts retrieved from cotton swabs resulted in more than 3700 quantified human proteins in 100-min gradients or 5500 proteins after simple fractionation. Remarkably, our measurements also quantified more than 2000 microbial proteins from 50 bacterial genera. Co-analysis of the proteomics results with next-generation sequencing data from the Human Microbiome Project as well as a comparison to MALDI-TOF mass spectrometry on microbial cultures revealed strong agreement. The oral microbiome differs between individuals and changes drastically upon eating and tooth brushing. Rapid shotgun and robust technology can now simultaneously characterize the human and microbiome contributions to the proteome of a body fluid and is therefore a valuable complement to genomic studies. This opens new frontiers for the study of host–pathogen interactions and clinical saliva diagnostics.

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TL;DR: It is shown that the majority of the bacteria identified by 16S sequencing of the human gut microbiota can be cultured and this method revealed greater bacterial diversity compared to culture-independent sequencing.
Abstract: The human gut microbiota has been implicated in most aspects of health and disease; however, most of the bacteria in this community are considered unculturable, so studies have relied on molecular-based methods. These methods generally do not permit the isolation of organisms, which is required to fully explore the functional roles of bacteria for definitive association with host phenotypes. Using a combination of culture and 16S rRNA gene sequencing, referred to as culture-enriched molecular profiling, we show that the majority of the bacteria identified by 16S sequencing of the human gut microbiota can be cultured. Five fresh, anaerobic fecal samples were cultured using 33 media and incubation of plates anaerobically and aerobically resulted in 66 culture conditions for culture-enriched molecular profiling. The cultivable portion of the fecal microbiota was determined by comparing the operational taxonomic units (OTUs) recovered by 16S sequencing of the culture plates to OTUs from culture-independent sequencing of the fecal sample. Targeted isolation of Lachnospiraceae strains using conditions defined by culture-enriched molecular profiling was carried out on two fresh stool samples. We show that culture-enriched molecular profiling, utilizing 66 culture conditions combined with 16S rRNA gene sequencing, allowed for the culturing of an average of 95 % of the OTUs present at greater than 0.1 % abundance in fecal samples. Uncultured OTUs were low abundance in stool. Importantly, comparing culture-enrichment to culture-independent sequencing revealed that the majority of OTUs were detected only by culture, highlighting the advantage of culture for studying the diversity of the gut microbiota. Applying culture-enriched molecular profiling to target Lachnospiraceae strains resulted in the recovery of 79 isolates, 12 of which are on the Human Microbiome Project’s “Most Wanted” list. We show that, through culture-enriched molecular profiling, the majority of the bacteria in the human gut microbiota can be cultured and this method revealed greater bacterial diversity compared to culture-independent sequencing. Additionally, this method could be applied for the targeted recovery of a specific bacterial group. This approach allows for the isolation of bacteria of interest from the gut microbiota, providing new opportunities to explore mechanisms of microbiota–host interactions and the diversity of the human microbiota.

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TL;DR: In this paper, the authors performed a genome-wide screen of DNA methylation using the Illumina Infinium HumanMethylation450 platform on bulk tissue samples from the superior temporal gyrus of patients with Alzheimer's disease and non-demented controls.
Abstract: Alzheimer’s disease affects ~13 % of people in the United States 65 years and older, making it the most common neurodegenerative disorder. Recent work has identified roles for environmental, genetic, and epigenetic factors in Alzheimer’s disease risk. We performed a genome-wide screen of DNA methylation using the Illumina Infinium HumanMethylation450 platform on bulk tissue samples from the superior temporal gyrus of patients with Alzheimer’s disease and non-demented controls. We paired a sliding window approach with multivariate linear regression to characterize Alzheimer’s disease-associated differentially methylated regions (DMRs). We identified 479 DMRs exhibiting a strong bias for hypermethylated changes, a subset of which were independently associated with aging. DMR intervals overlapped 475 RefSeq genes enriched for gene ontology categories with relevant roles in neuron function and development, as well as cellular metabolism, and included genes reported in Alzheimer’s disease genome-wide and epigenome-wide association studies. DMRs were enriched for brain-specific histone signatures and for binding motifs of transcription factors with roles in the brain and Alzheimer’s disease pathology. Notably, hypermethylated DMRs preferentially overlapped poised promoter regions, marked by H3K27me3 and H3K4me3, previously shown to co-localize with aging-associated hypermethylation. Finally, the integration of DMR-associated single nucleotide polymorphisms with Alzheimer’s disease genome-wide association study risk loci and brain expression quantitative trait loci highlights multiple potential DMRs of interest for further functional analysis. We have characterized changes in DNA methylation in the superior temporal gyrus of patients with Alzheimer’s disease, highlighting novel loci that facilitate better characterization of pathways and mechanisms underlying Alzheimer’s disease pathogenesis, and improve our understanding of epigenetic signatures that may contribute to the development of disease.

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TL;DR: It is argued that it is necessary and possible to bridge between precision medicine and evidence-based medicine, and that, through collection, analyses and sharing of standardized medically relevant data globally, evidence- based precision medicine will shift progressively from therapy to prevention, thus leading eventually to improved, clinician-to-patient communication, citizen-centered healthcare and sustained well-being.
Abstract: This era of groundbreaking scientific developments in high-resolution, high-throughput technologies is allowing the cost-effective collection and analysis of huge, disparate datasets on individual health. Proper data mining and translation of the vast datasets into clinically actionable knowledge will require the application of clinical bioinformatics. These developments have triggered multiple national initiatives in precision medicine—a data-driven approach centering on the individual. However, clinical implementation of precision medicine poses numerous challenges. Foremost, precision medicine needs to be contrasted with the powerful and widely used practice of evidence-based medicine, which is informed by meta-analyses or group-centered studies from which mean recommendations are derived. This “one size fits all” approach can provide inadequate solutions for outliers. Such outliers, which are far from an oddity as all of us fall into this category for some traits, can be better managed using precision medicine. Here, we argue that it is necessary and possible to bridge between precision medicine and evidence-based medicine. This will require worldwide and responsible data sharing, as well as regularly updated training programs. We also discuss the challenges and opportunities for achieving clinical utility in precision medicine. We project that, through collection, analyses and sharing of standardized medically relevant data globally, evidence-based precision medicine will shift progressively from therapy to prevention, thus leading eventually to improved, clinician-to-patient communication, citizen-centered healthcare and sustained well-being.

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TL;DR: The identification of chronic intraocular rubella virus infection highlights the eye’s role as a long-term pathogen reservoir, which has implications for virus eradication and emerging global epidemics.
Abstract: Ocular infections remain a major cause of blindness and morbidity worldwide. While prognosis is dependent on the timing and accuracy of diagnosis, the etiology remains elusive in ~50 % of presumed infectious uveitis cases. The objective of this study is to determine if unbiased metagenomic deep sequencing (MDS) can accurately detect pathogens in intraocular fluid samples of patients with uveitis. This is a proof-of-concept study, in which intraocular fluid samples were obtained from five subjects with known diagnoses, and one subject with bilateral chronic uveitis without a known etiology. Samples were subjected to MDS, and results were compared with those from conventional diagnostic tests. Pathogens were identified using a rapid computational pipeline to analyze the non-host sequences obtained from MDS. Unbiased MDS of intraocular fluid produced results concordant with known diagnoses in subjects with (n = 4) and without (n = 1) uveitis. Samples positive for Cryptococcus neoformans, Toxoplasma gondii, and herpes simplex virus 1 as tested by a Clinical Laboratory Improvement Amendments-certified laboratory were correctly identified with MDS. Rubella virus was identified in one case of chronic bilateral idiopathic uveitis. The subject’s strain was most closely related to a German rubella virus strain isolated in 1992, one year before he developed a fever and rash while living in Germany. The pattern and the number of viral identified mutations present in the patient’s strain were consistent with long-term viral replication in the eye. MDS can identify fungi, parasites, and DNA and RNA viruses in minute volumes of intraocular fluid samples. The identification of chronic intraocular rubella virus infection highlights the eye’s role as a long-term pathogen reservoir, which has implications for virus eradication and emerging global epidemics.

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TL;DR: These studies form the basis for new models of type 2 diabetes and NAFLD that involve microbiome perturbation and showed topological shifts concurrent with growth promotion and suggest the presence of keystone species.
Abstract: Obesity, type 2 diabetes, and non-alcoholic fatty liver disease (NAFLD) are serious health concerns, especially in Western populations. Antibiotic exposure and high-fat diet (HFD) are important and modifiable factors that may contribute to these diseases. To investigate the relationship of antibiotic exposure with microbiome perturbations in a murine model of growth promotion, C57BL/6 mice received lifelong sub-therapeutic antibiotic treatment (STAT), or not (control), and were fed HFD starting at 13 weeks. To characterize microbiota changes caused by STAT, the V4 region of the 16S rRNA gene was examined from collected fecal samples and analyzed. In this model, which included HFD, STAT mice developed increased weight and fat mass compared to controls. Although results in males and females were not identical, insulin resistance and NAFLD were more severe in the STAT mice. Fecal microbiota from STAT mice were distinct from controls. Compared with controls, STAT exposure led to early conserved diet-independent microbiota changes indicative of an immature microbial community. Key taxa were identified as STAT-specific and several were found to be predictive of disease. Inferred network models showed topological shifts concurrent with growth promotion and suggest the presence of keystone species. These studies form the basis for new models of type 2 diabetes and NAFLD that involve microbiome perturbation.

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TL;DR: Large tumor-only targeted panels are sufficient for most somatic variant identification and mutational load prediction if paired with expanded germline analysis strategies and molecular pathologist review.
Abstract: The diversity of clinical tumor profiling approaches (small panels to whole exomes with matched or unmatched germline analysis) may engender uncertainty about their benefits and liabilities, particularly in light of reported germline false positives in tumor-only profiling and use of global mutational and/or neoantigen data. The goal of this study was to determine the impact of genomic analysis strategies on error rates and data interpretation across contexts and ancestries. We modeled common tumor profiling modalities—large (n = 300 genes), medium (n = 48 genes), and small (n = 15 genes) panels—using clinical whole exomes (WES) from 157 patients with lung or colon adenocarcinoma. We created a tumor-only analysis algorithm to assess germline false positive rates, the impact of patient ancestry on tumor-only results, and neoantigen detection. After optimizing a germline filtering strategy, the germline false positive rate with tumor-only large panel sequencing was 14 % (144/1012 variants). For patients whose tumor-only results underwent molecular pathologist review (n = 91), 50/54 (93 %) false positives were correctly interpreted as uncertain variants. Increased germline false positives were observed in tumor-only sequencing of non-European compared with European ancestry patients (p < 0.001; Fisher’s exact) when basic germline filtering approaches were used; however, the ExAC database (60,706 germline exomes) mitigated this disparity (p = 0.53). Matched and unmatched large panel mutational load correlated with WES mutational load (r2 = 0.99 and 0.93, respectively; p < 0.001). Neoantigen load also correlated (r2 = 0.80; p < 0.001), though WES identified a broader spectrum of neoantigens. Small panels did not predict mutational or neoantigen load. Large tumor-only targeted panels are sufficient for most somatic variant identification and mutational load prediction if paired with expanded germline analysis strategies and molecular pathologist review. Paired germline sequencing reduced overall false positive mutation calls and WES provided the most neoantigens. Without patient-matched germline data, large germline databases are needed to minimize false positive mutation calling and mitigate ethnic disparities.