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Breakthroughs in the Health Effects of Plant Food Bioactives: A Perspective on Microbiomics, Nutri(epi)genomics, and Metabolomics.

J Agric Food Chem. 2018 Oct 17;66(41):10686-10692

Authors: Bayram B, González-Sarrías A, Istas G, Garcia-Aloy M, Morand C, Tuohy K, García-Villalba R, Mena P

Abstract
Plant bioactive compounds consumed as part of our diet are able to influence human health. They include secondary metabolites like (poly)phenols, carotenoids, glucosinolates, alkaloids, and terpenes. Although much knowledge has been gained, there is still need for studies unravelling the effects of plant bioactives on cardiometabolic health at the individual level, using cutting-edge high-resolution and data-rich holistic approaches. The aim of this Perspective is to review the prospects of microbiomics, nutrigenomics and nutriepigenomics, and metabolomics to assess the response to plant bioactive consumption while considering interindividual variability. Insights for future research in the field toward personalized nutrition are discussed.

PMID: 30208704 [PubMed – indexed for MEDLINE]

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Locus-specific Retention Predictor (LsRP): A Peptide Retention Time Predictor Developed for Precision Proteomics.

Sci Rep. 2017 03 17;7:43959

Authors: Lu W, Liu X, Liu S, Cao W, Zhang Y, Yang P

Abstract
The precision prediction of peptide retention time (RT) plays an increasingly important role in liquid chromatography-tandem mass spectrometry (LC-MS/MS) based proteomics. Owing to the high reproducibility of liquid chromatography, RT prediction provides promising information for both identification and quantification experiment design. In this work, we present a Locus-specific Retention Predictor (LsRP) for precise prediction of peptide RT, which is based on amino acid locus information and Support Vector Regression (SVR) algorithm. Corresponding to amino acid locus, each peptide sequence was converted to a featured locus vector consisting of zeros and ones. With locus vector information from LC-MS/MS data sets, an SVR computational process was trained and evaluated. LsRP finally provided a prediction correlation coefficient of 0.95~0.99. We compared our method with two common predictors. Results showed that LsRP outperforms these methods and tracked up to 30% extra peptides in an extraction RT window of 2 min. A new strategy by combining LsRP and calibration peptide approach was then proposed, which open up new opportunities for precision proteomics.

PMID: 28303880 [PubMed – indexed for MEDLINE]

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New additions to the cancer precision medicine toolkit.

Genome Med. 2018 04 13;10(1):28

Authors: Mardis ER

Abstract
New computational and database-driven tools are emerging to aid in the interpretation of cancer genomic data as its use becomes more common in clinical evidence-based cancer medicine. Two such open source tools, published recently in Genome Medicine, provide important advances to address the clinical cancer genomics data interpretation bottleneck.

PMID: 29653583 [PubMed – indexed for MEDLINE]

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TAS2R38 Predisposition to Bitter Taste Associated with Differential Changes in Vegetable Intake in Response to a Community-Based Dietary Intervention.

G3 (Bethesda). 2018 05 31;8(6):2107-2119

Authors: Calancie L, Keyserling TC, Taillie LS, Robasky K, Patterson C, Ammerman AS, Schisler JC

Abstract
Although vegetable consumption associates with decreased risk for a variety of diseases, few Americans meet dietary recommendations for vegetable intake. TAS2R38 encodes a taste receptor that confers bitter taste sensing from chemicals found in some vegetables. Common polymorphisms in TAS2R38 lead to coding substitutions that alter receptor function and result in the loss of bitter taste perception. Our study examined whether bitter taste perception TAS2R38 diplotypes associated with vegetable consumption in participants enrolled in either an enhanced or a minimal nutrition counseling intervention. DNA was isolated from the peripheral blood cells of study participants (N = 497) and analyzed for polymorphisms. Vegetable consumption was determined using the Block Fruit and Vegetable screener. We tested for differences in the frequency of vegetable consumption between intervention and genotype groups over time using mixed effects models. Baseline vegetable consumption frequency did not associate with bitter taste diplotypes (P = 0.937), however after six months of the intervention, we observed an interaction between bitter taste diplotypes and time (P = 0.046). Participants in the enhanced intervention increased their vegetable consumption frequency (P = 0.020) and within this intervention group, the bitter non-tasters and intermediate-bitter tasters had the largest increase in vegetable consumption. In contrast, in the minimal intervention group, the bitter tasting participants reported a decrease in vegetable consumption. Bitter-non tasters and intermediate-bitter tasters increased vegetable consumption in either intervention more than those who perceive bitterness. Future precision medicine applications could consider genetic variation in bitter taste perception genes when designing dietary interventions.

PMID: 29686110 [PubMed – indexed for MEDLINE]

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Integrated time course omics analysis distinguishes immediate therapeutic response from acquired resistance.

Genome Med. 2018 05 23;10(1):37

Authors: Stein-O’Brien G, Kagohara LT, Li S, Thakar M, Ranaweera R, Ozawa H, Cheng H, Considine M, Schmitz S, Favorov AV, Danilova LV, Califano JA, Izumchenko E, Gaykalova DA, Chung CH, Fertig EJ

Abstract
BACKGROUND: Targeted therapies specifically act by blocking the activity of proteins that are encoded by genes critical for tumorigenesis. However, most cancers acquire resistance and long-term disease remission is rarely observed. Understanding the time course of molecular changes responsible for the development of acquired resistance could enable optimization of patients’ treatment options. Clinically, acquired therapeutic resistance can only be studied at a single time point in resistant tumors.
METHODS: To determine the dynamics of these molecular changes, we obtained high throughput omics data (RNA-sequencing and DNA methylation) weekly during the development of cetuximab resistance in a head and neck cancer in vitro model. The CoGAPS unsupervised algorithm was used to determine the dynamics of the molecular changes associated with resistance during the time course of resistance development.
RESULTS: CoGAPS was used to quantify the evolving transcriptional and epigenetic changes. Applying a PatternMarker statistic to the results from CoGAPS enabled novel heatmap-based visualization of the dynamics in these time course omics data. We demonstrate that transcriptional changes result from immediate therapeutic response or resistance, whereas epigenetic alterations only occur with resistance. Integrated analysis demonstrates delayed onset of changes in DNA methylation relative to transcription, suggesting that resistance is stabilized epigenetically.
CONCLUSIONS: Genes with epigenetic alterations associated with resistance that have concordant expression changes are hypothesized to stabilize the resistant phenotype. These genes include FGFR1, which was associated with EGFR inhibitors resistance previously. Thus, integrated omics analysis distinguishes the timing of molecular drivers of resistance. This understanding of the time course progression of molecular changes in acquired resistance is important for the development of alternative treatment strategies that would introduce appropriate selection of new drugs to treat cancer before the resistant phenotype develops.

PMID: 29792227 [PubMed – indexed for MEDLINE]

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PanDrugs: a novel method to prioritize anticancer drug treatments according to individual genomic data.

Genome Med. 2018 05 31;10(1):41

Authors: Piñeiro-Yáñez E, Reboiro-Jato M, Gómez-López G, Perales-Patón J, Troulé K, Rodríguez JM, Tejero H, Shimamura T, López-Casas PP, Carretero J, Valencia A, Hidalgo M, Glez-Peña D, Al-Shahrour F

Abstract
BACKGROUND: Large-sequencing cancer genome projects have shown that tumors have thousands of molecular alterations and their frequency is highly heterogeneous. In such scenarios, physicians and oncologists routinely face lists of cancer genomic alterations where only a minority of them are relevant biomarkers to drive clinical decision-making. For this reason, the medical community agrees on the urgent need of methodologies to establish the relevance of tumor alterations, assisting in genomic profile interpretation, and, more importantly, to prioritize those that could be clinically actionable for cancer therapy.
RESULTS: We present PanDrugs, a new computational methodology to guide the selection of personalized treatments in cancer patients using the variant lists provided by genome-wide sequencing analyses. PanDrugs offers the largest database of drug-target associations available from well-known targeted therapies to preclinical drugs. Scoring data-driven gene cancer relevance and drug feasibility PanDrugs interprets genomic alterations and provides a prioritized evidence-based list of anticancer therapies. Our tool represents the first drug prescription strategy applying a rational based on pathway context, multi-gene markers impact and information provided by functional experiments. Our approach has been systematically applied to TCGA patients and successfully validated in a cancer case study with a xenograft mouse model demonstrating its utility.
CONCLUSIONS: PanDrugs is a feasible method to identify potentially druggable molecular alterations and prioritize drugs to facilitate the interpretation of genomic landscape and clinical decision-making in cancer patients. Our approach expands the search of druggable genomic alterations from the concept of cancer driver genes to the druggable pathway context extending anticancer therapeutic options beyond already known cancer genes. The methodology is public and easily integratable with custom pipelines through its programmatic API or its docker image. The PanDrugs webtool is freely accessible at http://www.pandrugs.org .

PMID: 29848362 [PubMed – indexed for MEDLINE]

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Human carbonic anhydrase-8 AAV8 gene therapy inhibits nerve growth factor signaling producing prolonged analgesia and anti-hyperalgesia in mice.

Gene Ther. 2018 07;25(4):297-311

Authors: Zhuang GZ, Upadhyay U, Tong X, Kang Y, Erasso DM, Fu ES, Sarantopoulos KD, Martin ER, Wiltshire T, Diatchenko L, Smith SB, Maixner W, Levitt RC

Abstract
Carbonic anhydrase-8 (Car8; murine gene symbol) is an allosteric inhibitor of inositol trisphosphate receptor-1 (ITPR1), which regulates neuronal intracellular calcium release. We previously reported that wild-type Car8 overexpression corrects the baseline allodynia and hyperalgesia associated with calcium dysregulation in the waddle (wdl) mouse due to a 19 bp deletion in exon 8 of the Car8 gene. In this report, we provide preliminary evidence that overexpression of the human wild-type ortholog of Car8 (CA8WT), but not the reported CA8 S100P loss-of-function mutation (CA8MT), inhibits nerve growth factor (NGF)-induced phosphorylation of ITPR1, TrkA (NGF high-affinity receptor), and ITPR1-mediated cytoplasmic free calcium release in vitro. In addition, we show that gene transfer using AAV8-V5-CA8WT viral particles via sciatic nerve injection demonstrates retrograde transport to dorsal root ganglia (DRG) producing prolonged V5-CA8WT expression, pITPR1 and pTrkA inhibition, and profound analgesia and anti-hyperalgesia in male C57BL/6J mice. AAV8-V5-CA8WT-mediated overexpression prevented and treated allodynia and hyperalgesia associated with chronic neuropathic pain produced by the spinal nerve ligation (SNL) model. These AAV8-V5-CA8 data provide a proof-of-concept for precision medicine through targeted gene therapy of NGF-responsive somatosensory neurons as a long-acting local analgesic able to prevent and treat chronic neuropathic pain through regulating TrkA signaling, ITPR1 activation, and intracellular free calcium release by ITPR1.

PMID: 29789638 [PubMed – indexed for MEDLINE]

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Identification of outcome-related driver mutations in cancer using conditional co-occurrence distributions.

Sci Rep. 2017 02 27;7:43350

Authors: Treviño V, Martínez-Ledesma E, Tamez-Peña J

Abstract
Previous methods proposed for the detection of cancer driver mutations have been based on the estimation of background mutation rate, impact on protein function, or network influence. In this paper, we instead focus on those factors influencing patient survival. To this end, an approximation of the log-rank test has been systematically applied, even though it assumes a large and similar number of patients in both risk groups, which is violated in cancer genomics. Here, we propose VALORATE, a novel algorithm for the estimation of the null distribution for the log-rank, independent of the number of mutations. VALORATE is based on conditional distributions of the co-occurrences between events and mutations. The results, achieved through simulations, comparisons with other methods, analyses of TCGA and ICGC cancer datasets, and validations, suggest that VALORATE is accurate, fast, and can identify both known and novel gene mutations. Our proposal and results may have important implications in cancer biology, bioinformatics analyses, and ultimately precision medicine.

PMID: 28240231 [PubMed – indexed for MEDLINE]