A High-Resolution Genetic Map for the Laboratory Rat.
G3 (Bethesda). 2018 07 02;8(7):2241-2248
Authors: Littrell J, Tsaih SW, Baud A, Rastas P, Solberg-Woods L, Flister MJ
An accurate and high-resolution genetic map is critical for mapping complex traits, yet the resolution of the current rat genetic map is far lower than human and mouse, and has not been updated since the original Jensen-Seaman map in 2004. For the first time, we have refined the rat genetic map to sub-centimorgan (cM) resolution (<0.02 cM) by using 95,769 genetic markers and 870 informative meioses from a cohort of 528 heterogeneous stock (HS) rats. Global recombination rates in the revised sex-averaged map (0.66 cM/Mb) did not differ compared to the historical map (0.65 cM/Mb); however, substantial refinement was made to the localization of highly recombinant regions within the revised map. Also for the first time, sex-specific rat genetic maps were generated, which revealed both genomewide and fine-scale variation in recombination rates between male and female rats. Reanalysis of multiple quantitative trait loci (QTL) using the historical and refined rat genetic maps demonstrated marked changes to QTL localization, shape, and effect size. As a resource to the rat research community, we have provided revised centimorgan positions for all physical positions within the rat genome and commonly used genetic markers for trait mapping, including 44,828 SSLP markers and the RATDIV genotyping array. Collectively, this study provides a substantial improvement to the rat genetic map and an unprecedented resource for analysis of complex traits and recombination in the rat.
PMID: 29760201 [PubMed – indexed for MEDLINE]
Understanding and Predicting Antidepressant Response: Using Animal Models to Move Toward Precision Psychiatry.
Front Psychiatry. 2018;9:512
Authors: Herzog DP, Beckmann H, Lieb K, Ryu S, Müller MB
There are two important gaps of knowledge in depression treatment, namely the lack of biomarkers predicting response to antidepressants and the limited knowledge of the molecular mechanisms underlying clinical improvement. However, individually tailored treatment strategies and individualized prescription are greatly needed given the huge socio-economic burden of depression, the latency until clinical improvement can be observed and the response variability to a particular compound. Still, individual patient-level antidepressant treatment outcomes are highly unpredictable. In contrast to other therapeutic areas and despite tremendous efforts during the past years, the genomics era so far has failed to provide biological or genetic predictors of clinical utility for routine use in depression treatment. Specifically, we suggest to (1) shift the focus from the group patterns to individual outcomes, (2) use dimensional classifications such as Research Domain Criteria, and (3) envision better planning and improved connections between pre-clinical and clinical studies within translational research units. In contrast to studies in patients, animal models enable both searches for peripheral biosignatures predicting treatment response and in depth-analyses of the neurobiological pathways shaping individual antidepressant response in the brain. While there is a considerable number of animal models available aiming at mimicking disease-like conditions such as those seen in depressive disorder, only a limited number of preclinical or truly translational investigations is dedicated to the issue of heterogeneity seen in response to antidepressant treatment. In this mini-review, we provide an overview on the current state of knowledge and propose a framework for successful translational studies into antidepressant treatment response.
PMID: 30405454 [PubMed]
Evaluation of GWAS-Identified Genetic Variants for Gastric Cancer Survival.
EBioMedicine. 2018 Jul;33:82-87
Authors: Gu D, Zheng R, Xin J, Li S, Chu H, Gong W, Qiang F, Zhang Z, Wang M, Du M, Chen J
BACKGROUNDS: Genome-wide association studies (GWASs) have identified several gastric cancer (GC) susceptibility loci in Asians, but their effects on disease outcome are still unknown. This study aimed to investigate whether these GWAS-identified genetic variants could serve as robust prognostic biomarkers for GC.
METHODS: A multistage clinical cohort, including a total of 2432 GC patients in the Chinese population, was used to identify the association between GWAS-identified risk variants and overall survival of GC. Hazard ratios (HRs) and 95% confidence intervals (CIs) were computed by Cox regression analysis, and the log-rank P was calculated by the log-rank test with the Kaplan-Meier method.
RESULTS: We found that rs2274223 A>G in PLCE1 was associated with increased GC survival in both training set (P = .011), which was independently replicated in validation set 1 (P = .045), but not in validation set 2. The area under the curve (AUC) from receiver-operator characteristic (ROC) curve showed this clinical relevance with onset age-dependence, especially in the subgroup of early-onset cases. Moreover, a significant improvement in overall survival prediction was identified when the rs2274223 genetic effect was included in the estimation; this result was also supported by the prognostic nomogram. In addition, patients with lower expression of PLCE1 showed benefits via longer survival, potentially due to the functional effect of rs2274223.
INTERPRETATION: This preliminary study suggests that a GWAS-identified genetic variant in PLCE1 may serve as a potential biomarker for GC survival. Additional replication with larger samples size is warranted to further investigation.
PMID: 29983348 [PubMed – indexed for MEDLINE]
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
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]
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
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]