345 research outputs found
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Metabolome-Informed Microbiome Analysis Refines Metadata Classifications and Reveals Unexpected Medication Transfer in Captive Cheetahs.
Even high-quality collection and reporting of study metadata in microbiome studies can lead to various forms of inadvertently missing or mischaracterized information that can alter the interpretation or outcome of the studies, especially with nonmodel organisms. Metabolomic profiling of fecal microbiome samples can provide empirical insight into unanticipated confounding factors that are not possible to obtain even from detailed care records. We illustrate this point using data from cheetahs from the San Diego Zoo Safari Park. The metabolomic characterization indicated that one cheetah had to be moved from the non-antibiotic-exposed group to the antibiotic-exposed group. The detection of the antibiotic in this second cheetah was likely due to grooming interactions with the cheetah that was administered antibiotics. Similarly, because transit time for stool is variable, fecal samples within the first few days of antibiotic prescription do not all contain detected antibiotics, and the microbiome is not yet affected. These insights significantly altered the way the samples were grouped for analysis (antibiotic versus no antibiotic) and the subsequent understanding of the effect of the antibiotics on the cheetah microbiome. Metabolomics also revealed information about numerous other medications and provided unexpected dietary insights that in turn improved our understanding of the molecular patterns on the impact on the community microbial structure. These results suggest that untargeted metabolomic data provide empirical evidence to correct records and aid in the monitoring of the health of nonmodel organisms in captivity, although we also expect that these methods may be appropriate for other social animals, such as cats.IMPORTANCE Metabolome-informed analyses can enhance omics studies by enabling the correct partitioning of samples by identifying hidden confounders inadvertently misrepresented or omitted from carefully curated metadata. We demonstrate here the utility of metabolomics in a study characterizing the microbiome associated with liver disease in cheetahs. Metabolome-informed reinterpretation of metagenome and metabolome profiles factored in an unexpected transfer of antibiotics, preventing misinterpretation of the data. Our work suggests that untargeted metabolomics can be used to verify, augment, and correct sample metadata to support improved grouping of sample data for microbiome analyses, here for nonmodel organisms in captivity. However, the techniques also suggest a path forward for correcting clinical information in microbiome studies more broadly to enable higher-precision analyses
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Human Skin, Oral, and Gut Microbiomes Predict Chronological Age.
Human gut microbiomes are known to change with age, yet the relative value of human microbiomes across the body as predictors of age, and prediction robustness across populations is unknown. In this study, we tested the ability of the oral, gut, and skin (hand and forehead) microbiomes to predict age in adults using random forest regression on data combined from multiple publicly available studies, evaluating the models in each cohort individually. Intriguingly, the skin microbiome provides the best prediction of age (mean ± standard deviation, 3.8 ± 0.45 years, versus 4.5 ± 0.14 years for the oral microbiome and 11.5 ± 0.12 years for the gut microbiome). This also agrees with forensic studies showing that the skin microbiome predicts postmortem interval better than microbiomes from other body sites. Age prediction models constructed from the hand microbiome generalized to the forehead and vice versa, across cohorts, and results from the gut microbiome generalized across multiple cohorts (United States, United Kingdom, and China). Interestingly, taxa enriched in young individuals (18 to 30 years) tend to be more abundant and more prevalent than taxa enriched in elderly individuals (>60 yrs), suggesting a model in which physiological aging occurs concomitantly with the loss of key taxa over a lifetime, enabling potential microbiome-targeted therapeutic strategies to prevent aging.IMPORTANCE Considerable evidence suggests that the gut microbiome changes with age or even accelerates aging in adults. Whether the age-related changes in the gut microbiome are more or less prominent than those for other body sites and whether predictions can be made about a person's age from a microbiome sample remain unknown. We therefore combined several large studies from different countries to determine which body site's microbiome could most accurately predict age. We found that the skin was the best, on average yielding predictions within 4 years of chronological age. This study sets the stage for future research on the role of the microbiome in accelerating or decelerating the aging process and in the susceptibility for age-related diseases
Cardiac tumors in a tertiary care cancer hospital: clinical features, echocardiographic findings, treatment and outcomes
Cardiac tumors are a rare entity, comprised of tumors with diverse histology and natural history. We report the clinical characteristics, echocardiograhic findings, therapy and outcome of 59 patients with primary and metastatic cardiac tumors. Our institutional echocardiogram data base from 1993 through 2005 was reviewed to identify patients diagnosed with intra-cardiac tumor. A total of 59 patients with cardiac tumors were identified and included in the study. The patients' characteristics, presenting symptoms, diagnostic tests, location, histology of the tumor, treatment and the one year survival rate of this population was collected from the medical records. Of the 59 cardiac tumor cases, 16 (27%) were primary cardiac tumors and 43 (73%) were secondary cardiac tumors. The most common primary tumor was sarcoma affecting 13 (81%) of the 16 cases. Of these, 5 patients were angiosarcoma, 5 unclassified sarcoma, one myxoid sarcoma and 2 malignant fibrous histiocytoma. The mean age at presentation was 41.1 years, and the most common location was right atrium affecting 6 cases (37.5%). The most common symptom of dyspnea was present in 10 (62.5%) cases. Eleven (25.6%) of the 43 secondary cardiac tumors were metastasis from renal cell carcinoma. The mean age at presentation was 55.4 years. Right atrium was the most frequent location affecting 18 (42%) of the 43 patients. The most common presenting symptom was dyspnea in 15 (35%) cases. For both primary and secondary tumors, dyspnea was the most common symptom and right atrium was most frequently involved. Sarcoma was the most common primary cardiac tumor while metastasis from renal cell carcinoma was the most common secondary tumor
Testing Consumer Rationality using Perfect Graphs and Oriented Discs
Given a consumer data-set, the axioms of revealed preference proffer a binary
test for rational behaviour. A natural (non-binary) measure of the degree of
rationality exhibited by the consumer is the minimum number of data points
whose removal induces a rationalisable data-set.We study the computational
complexity of the resultant consumer rationality problem in this paper. This
problem is, in the worst case, equivalent (in terms of approximation) to the
directed feedback vertex set problem. Our main result is to obtain an exact
threshold on the number of commodities that separates easy cases and hard
cases. Specifically, for two-commodity markets the consumer rationality problem
is polynomial time solvable; we prove this via a reduction to the vertex cover
problem on perfect graphs. For three-commodity markets, however, the problem is
NP-complete; we prove thisusing a reduction from planar 3-SAT that is based
upon oriented-disc drawings
An allele of IKZF1 (Ikaros) conferring susceptibility to childhood acute lymphoblastic leukemia protects against type 1 diabetes.
OBJECTIVE: IKZF1 encoding Ikaros, an essential regulator of lymphopoiesis and immune homeostasis, has been implicated in the development of childhood acute lymphoblastic leukemia (C-ALL). Because recent genome-wide association (GWA) studies have linked a region of the 3'-UTR of IKZF1 with C-ALL susceptibility, we tested whether IKZF1 is associated with the autoimmune disease type 1 diabetes. RESEARCH DESIGN AND METHODS: rs10272724 (T>C) near IKZF1 at 7p12 was genotyped in 8,333 individuals with type 1 diabetes, 9,947 control subjects, and 3,997 families of European ancestry. Association was tested using logistic regression in the case-control data and by the transmission disequilibrium test in the families. Expression data for IKZF1 by rs10272724 genotype were obtained using quantitative PCR of mRNA/cDNA generated from peripheral blood mononuclear cells from 88 individuals, whereas expression data for five other neighboring genes were obtained from the online Genevar dataset. RESULTS: The minor allele of rs10272724 (C) was found to be protective from type 1 diabetes (odds ratio 0.87 [95% CI 0.83-0.91]; P = 1.1 × 10(-11)). rs10272724 was not correlated with levels of two transcripts of IKZF1 in peripheral blood mononuclear cells. CONCLUSIONS: The major susceptibility genotype for C-ALL confers protection from type 1 diabetes. Our finding strengthens the link between autoimmunity and lymphoid cancers. Further investigation is warranted for the genetic effect marked by rs10272724, its impact on IKZF1, and the role of Ikaros and other family members, Ailios (IKZF3) and Eos (IKZF4), in autoimmunity
Phylogeny-Aware Analysis of Metagenome Community Ecology Based on Matched Reference Genomes while Bypassing Taxonomy
We introduce the operational genomic unit (OGU) method, a metagenome analysis strategy that directly exploits sequence alignment hits to individual reference genomes as the minimum unit for assessing the diversity of microbial communities and their relevance to environmental factors. This approach is independent of taxonomic classification, granting the possibility of maximal resolution of community composition, and organizes features into an accurate hierarchy using a phylogenomic tree. The outputs are suitable for contemporary analytical protocols for community ecology, differential abundance, and supervised learning while supporting phylogenetic methods, such as UniFrac and phylofactorization, that are seldom applied to shotgun metagenomics despite being prevalent in 16S rRNA gene amplicon studies. As demonstrated in two real-world case studies, the OGU method produces biologically meaningful patterns from microbiome data sets. Such patterns further remain detectable at very low metagenomic sequencing depths. Compared with taxonomic unit-based analyses implemented in currently adopted metagenomics tools, and the analysis of 16S rRNA gene amplicon sequence variants, this method shows superiority in informing biologically relevant insights, including stronger correlation with body environment and host sex on the Human Microbiome Project data set and more accurate prediction of human age by the gut microbiomes of Finnish individuals included in the FINRISK 2002 cohort. We provide Woltka, a bioinformatics tool to implement this method, with full integration with the QIIME 2 package and the Qiita web platform, to facilitate adoption of the OGU method in future metagenomics studies. IMPORTANCE Shotgun metagenomics is a powerful, yet computationally challenging, technique compared to 16S rRNA gene amplicon sequencing for decoding the composition and structure of microbial communities. Current analyses of metagenomic data are primarily based on taxonomic classification, which is limited in feature resolution. To solve these challenges, we introduce operational genomic units (OGUs), which are the individual reference genomes derived from sequence alignment results, without further assigning them taxonomy. The OGU method advances current read-based metagenomics in two dimensions: (i) providing maximal resolution of community composition and (ii) permitting use of phylogeny-aware tools. Our analysis of real-world data sets shows that it is advantageous over currently adopted metagenomic analysis methods and the finest-grained 16S rRNA analysis methods in predicting biological traits. We thus propose the adoption of OGUs as an effective practice in metagenomic studies.Peer reviewe
Efficient computation of Faith's phylogenetic diversity with applications in characterizing microbiomes
The number of publicly available microbiome samples is continually growing. As data set size increases, bottlenecks arise in standard analytical pipelines. Faith's phylogenetic diversity (Faith's PD) is a highly utilized phylogenetic alpha diversity metric that has thus far failed to effectively scale to trees with millions of vertices. Stacked Faith's phylogenetic diversity (SFPhD) enables calculation of this widely adopted diversity metric at a much larger scale by implementing a computationally efficient algorithm. The algorithm reduces the amount of computational resources required, resulting in more accessible software with a reduced carbon footprint, as compared to previous approaches. The new algorithm produces identical results to the previous method. We further demonstrate that the phylogenetic aspect of Faith's PD provides increased power in detecting diversity differences between younger and older populations in the FINRISK study's metagenomic data.Peer reviewe
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