Orbitofrontal Cortex and the Early Processing of Visual Novelty in Healthy Aging

Event-related potential (ERP) studies have previously found that scalp topographies of attention-related ERP components show frontal shifts with age, suggesting an increased need for compensatory frontal activity to assist with top-down facilitation of attention. However, the precise neural time course of top-down attentional control in aging is not clear. In this study, 20 young (mean: 22 years) and 14 older (mean: 64 years) adults completed a three-stimulus visual oddball task while high-density ERPs were acquired. Colorful, novel distracters were presented to engage early visual processing. Relative to young controls, older participants exhibited elevations in occipital early posterior positivity (EPP), approximately 100 ms after viewing colorful distracters. Neural source models for older adults implicated unique patterns of orbitofrontal cortex (BA 11) activity during early visual novelty processing (100 ms), which was positively correlated with subsequent activations in primary visual cortex (BA 17). Older adult EPP amplitudes and OFC activity were associated with performance on tests of complex attention and executive function. These findings are suggestive of age-related, compensatory neural changes that may driven by a combination of weaker cortical efficiency and increased need for top-down control over attention. Accordingly, enhanced early OFC activity during visual attention may serve as an important indicator of frontal lobe integrity in healthy aging

Revealing Complex Traits with Small Molecules and Naturally Recombinant Yeast Strains

SummaryHere we demonstrate that natural variants of the yeast Saccharomyces cerevisiae are a model system for the systematic study of complex traits, specifically the response to small molecules. As a complement to artificial knockout collections of S. cerevisiae widely used to study individual gene function, we used 314- and 1932-member libraries of mutant strains generated by meiotic recombination to study the cumulative, quantitative effects of natural mutations on phenotypes induced by 23 small-molecule perturbagens (SMPs). This approach reveals synthetic lethality between SMPs, and genetic mapping studies confirm the involvement of multiple quantitative trait loci in the response to two SMPs that affect respiratory processes. The systematic combination of natural variants of yeast and small molecules that modulate evolutionarily conserved cellular processes can enable a better understanding of the general features of complex traits

Evolutionary rescue of phosphomannomutase deficiency in yeast models of human disease

The most common cause of human congenital disorders of glycosylation (CDG) are mutations in the phosphomannomutase gene PMM2, which affect protein N-linked glycosylation. The yeast gene SEC53 encodes a homolog of human PMM2. We evolved 384 populations of yeast harboring one of two human-disease-associated alleles, sec53-V238M and sec53-F126L, or wild-type SEC53. We find that after 1000 generations, most populations compensate for the slow-growth phenotype associated with the sec53 human-disease-associated alleles. Through whole-genome sequencing we identify compensatory mutations, including known SEC53 genetic interactors. We observe an enrichment of compensatory mutations in other genes whose human homologs are associated with Type 1 CDG, including PGM1, which encodes the minor isoform of phosphoglucomutase in yeast. By genetic reconstruction, we show that evolved pgm1 mutations are dominant and allele-specific genetic interactors that restore both protein glycosylation and growth of yeast harboring the sec53-V238M allele. Finally, we characterize the enzymatic activity of purified Pgm1 mutant proteins. We find that reduction, but not elimination, of Pgm1 activity best compensates for the deleterious phenotypes associated with the sec53-V238M allele. Broadly, our results demonstrate the power of experimental evolution as a tool for identifying genes and pathways that compensate for human-disease-associated alleles

Effect of angiotensin receptor blockade on insulin sensitivity and endothelial function in abdominally obese hypertensive patients with impaired fasting glucose

AngII (angiotensin II) may contribute to cardiovascular risk in obesity via adverse effects on insulin sensitivity and endothelial function. In the present study, we examined the effects of ARB (angiotensin receptor blocker) therapy (losartan, 100 mg/day) on insulin sensitivity and endothelial function in 53 subjects with stage I hypertension, abdominal obesity and impaired fasting glucose. The study design was a randomized double-blinded parallel design placebo-controlled multi-centre trial of 8 weeks duration. We used the hyperinsulinaemic-euglycaemic clamp technique to measure insulin sensitivity (expressed as the 'M/I' value) and RH-PAT (reactive hyperaemia-peripheral arterial tonometry) to measure endothelial function. Additional measures included HOMA (homoeostasis model assessment)-B, an index of pancreatic β-cell function, and markers of inflammation [e.g. CRP (C-reactive protein)] and oxidative stress (e.g. F2-isoprostanes). ARB therapy did not alter insulin sensitivity [5.2 (2.7) pre-treatment and 4.6 (1.6) post-treatment] compared with placebo therapy [6.1 (2.9) pre-treatment and 5.3 (2.7) post-treatment; P value not significant], but did improve the HOMA-B compared with placebo therapy (P=0.05). ARB therapy also did not change endothelial function [RH-PAT, 2.15 (0.7) pre-treatment and 2.11 (0.7) post-treatment] compared with placebo therapy [RH-PAT, 1.81 (0.5) pre-treatment and 1.76 (0.7) post-treatment; P value not significant]. Markers of inflammation and oxidative stress were not significantly changed by ARB therapy. In conclusion, ARB therapy did not alter peripheral insulin sensitivity or endothelial function in this cohort of patients with essential hypertension, abdominal obesity and impaired fasting glucose, but did improve pancreatic β-cell function

Using Expression and Genotype to Predict Drug Response in Yeast

Personalized, or genomic, medicine entails tailoring pharmacological therapies according to individual genetic variation at genomic loci encoding proteins in drug-response pathways. It has been previously shown that steady-state mRNA expression can be used to predict the drug response (i.e., sensitivity or resistance) of non-genotyped mammalian cancer cell lines to chemotherapeutic agents. In a real-world setting, clinicians would have access to both steady-state expression levels of patient tissue(s) and a patient's genotypic profile, and yet the predictive power of transcripts versus markers is not well understood. We have previously shown that a collection of genotyped and expression-profiled yeast strains can provide a model for personalized medicine. Here we compare the predictive power of 6,229 steady-state mRNA transcript levels and 2,894 genotyped markers using a pattern recognition algorithm. We were able to predict with over 70% accuracy the drug sensitivity of 104 individual genotyped yeast strains derived from a cross between a laboratory strain and a wild isolate. We observe that, independently of drug mechanism of action, both transcripts and markers can accurately predict drug response. Marker-based prediction is usually more accurate than transcript-based prediction, likely reflecting the genetic determination of gene expression in this cross

A Cohort Study of Serum Bilirubin Levels and Incident Non-Alcoholic Fatty Liver Disease in Middle Aged Korean Workers

BACKGROUND: Serum bilirubin may have potent antioxidant and cytoprotective effects. Serum bilirubin levels are inversely associated with several cardiovascular and metabolic endpoints, but their association with nonalcoholic fatty liver disease (NAFLD) has not been investigated except for a single cross-sectional study in a pediatric population. We assessed the prospective association between serum bilirubin concentrations (total, direct, and indirect) and the risk for NAFLD. METHODS AND FINDINGS: We performed a cohort study in 5,900 Korean men, 30 to 59 years of age, with no evidence of liver disease and no major risk factors for liver disease at baseline. Study participants were followed in annual or biennial health examinations between 2002 and 2009. The presence of fatty liver was determined at each visit by ultrasonography. We observed 1,938 incident cases of NAFLD during 28,101.8 person-years of follow-up. Increasing levels of serum direct bilirubin were progressively associated with a decreasing incidence of NAFLD. In age-adjusted models, the hazard ratio for NAFLD comparing the highest to the lowest quartile of serum direct bilirubin levels was 0.61 (95% CI 0.54-0.68). The association persisted after adjusting for multiple metabolic parameters (hazard ratio comparing the highest to the lowest quartile 0.86, 95% CI 0.76-0.98; P trend = 0.039). Neither serum total nor indirect bilirubin levels were significantly associated with the incidence of NAFLD. CONCLUSIONS: In this large prospective study, higher serum direct bilirubin levels were significantly associated with a lower risk of developing NAFLD, even adjusting for a variety of metabolic parameters. Further research is needed to elucidate the mechanisms underlying this association and to establish the role of serum direct bilirubin as a marker for NAFLD risk

Using Stochastic Causal Trees to Augment Bayesian Networks for Modeling eQTL Datasets

<p>Abstract</p> <p>Background</p> <p>The combination of genotypic and genome-wide expression data arising from segregating populations offers an unprecedented opportunity to model and dissect complex phenotypes. The immense potential offered by these data derives from the fact that genotypic variation is the sole source of perturbation and can therefore be used to reconcile changes in gene expression programs with the parental genotypes. To date, several methodologies have been developed for modeling eQTL data. These methods generally leverage genotypic data to resolve causal relationships among gene pairs implicated as associates in the expression data. In particular, leading studies have augmented Bayesian networks with genotypic data, providing a powerful framework for learning and modeling causal relationships. While these initial efforts have provided promising results, one major drawback associated with these methods is that they are generally limited to resolving causal orderings for transcripts most proximal to the genomic loci. In this manuscript, we present a probabilistic method capable of learning the causal relationships between transcripts at all levels in the network. We use the information provided by our method as a prior for Bayesian network structure learning, resulting in enhanced performance for gene network reconstruction.</p> <p>Results</p> <p>Using established protocols to synthesize eQTL networks and corresponding data, we show that our method achieves improved performance over existing leading methods. For the goal of gene network reconstruction, our method achieves improvements in recall ranging from 20% to 90% across a broad range of precision levels and for datasets of varying sample sizes. Additionally, we show that the learned networks can be utilized for expression quantitative trait loci mapping, resulting in upwards of 10-fold increases in recall over traditional univariate mapping.</p> <p>Conclusions</p> <p>Using the information from our method as a prior for Bayesian network structure learning yields large improvements in accuracy for the tasks of gene network reconstruction and expression quantitative trait loci mapping. In particular, our method is effective for establishing causal relationships between transcripts located both proximally and distally from genomic loci.</p