A Meta-Analysis of Global Urban Land Expansion

The conversion of Earth's land surface to urban uses is one of the most irreversible human impacts on the global biosphere. It drives the loss of farmland, affects local climate, fragments habitats, and threatens biodiversity. Here we present a meta-analysis of 326 studies that have used remotely sensed images to map urban land conversion. We report a worldwide observed increase in urban land area of 58,000 km2 from 1970 to 2000. India, China, and Africa have experienced the highest rates of urban land expansion, and the largest change in total urban extent has occurred in North America. Across all regions and for all three decades, urban land expansion rates are higher than or equal to urban population growth rates, suggesting that urban growth is becoming more expansive than compact. Annual growth in GDP per capita drives approximately half of the observed urban land expansion in China but only moderately affects urban expansion in India and Africa, where urban land expansion is driven more by urban population growth. In high income countries, rates of urban land expansion are slower and increasingly related to GDP growth. However, in North America, population growth contributes more to urban expansion than it does in Europe. Much of the observed variation in urban expansion was not captured by either population, GDP, or other variables in the model. This suggests that contemporary urban expansion is related to a variety of factors difficult to observe comprehensively at the global level, including international capital flows, the informal economy, land use policy, and generalized transport costs. Using the results from the global model, we develop forecasts for new urban land cover using SRES Scenarios. Our results show that by 2030, global urban land cover will increase between 430,000 km2 and 12,568,000 km2, with an estimate of 1,527,000 km2 more likely

Amino Acid Metabolic Origin as an Evolutionary Influence on Protein Sequence in Yeast

The metabolic cycle of Saccharomyces cerevisiae consists of alternating oxidative (respiration) and reductive (glycolysis) energy-yielding reactions. The intracellular concentrations of amino acid precursors generated by these reactions oscillate accordingly, attaining maximal concentration during the middle of their respective yeast metabolic cycle phases. Typically, the amino acids themselves are most abundant at the end of their precursor’s phase. We show that this metabolic cycling has likely biased the amino acid composition of proteins across the S. cerevisiae genome. In particular, we observed that the metabolic source of amino acids is the single most important source of variation in the amino acid compositions of functionally related proteins and that this signal appears only in (facultative) organisms using both oxidative and reductive metabolism. Periodically expressed proteins are enriched for amino acids generated in the preceding phase of the metabolic cycle. Proteins expressed during the oxidative phase contain more glycolysis-derived amino acids, whereas proteins expressed during the reductive phase contain more respiration-derived amino acids. Rare amino acids (e.g., tryptophan) are greatly overrepresented or underrepresented, relative to the proteomic average, in periodically expressed proteins, whereas common amino acids vary by a few percent. Genome-wide, we infer that 20,000 to 60,000 residues have been modified by this previously unappreciated pressure. This trend is strongest in ancient proteins, suggesting that oscillating endogenous amino acid availability exerted genome-wide selective pressure on protein sequences across evolutionary time

Cell-cycle-dependent transcriptional and translational DNA-damage response of 2 ribonucleotide reductase genes in S. cerevisiae

The ribonucleotide reductase (RNR) enzyme catalyzes an essential step in the production of deoxyribonucleotide triphosphates (dNTPs) in cells. Bulk biochemical measurements in synchronized Saccharomyces cerevisiae cells suggest that RNR mRNA production is maximal in late G1 and S phases; however, damaged DNA induces RNR transcription throughout the cell cycle. But such en masse measurements reveal neither cell-to-cell heterogeneity in responses nor direct correlations between transcript and protein expression or localization in single cells which may be central to function. We overcame these limitations by simultaneous detection of single RNR transcripts and also Rnr proteins in the same individual asynchronous S. cerevisiae cells, with and without DNA damage by methyl methanesulfonate (MMS). Surprisingly, RNR subunit mRNA levels were comparably low in both damaged and undamaged G1 cells and highly induced in damaged S/G2 cells. Transcript numbers became correlated with both protein levels and localization only upon DNA damage in a cell cycle-dependent manner. Further, we showed that the differential RNR response to DNA damage correlated with variable Mec1 kinase activity in the cell cycle in single cells. The transcription of RNR genes was found to be noisy and non-Poissonian in nature. Our results provide vital insight into cell cycle-dependent RNR regulation under conditions of genotoxic stress.Massachusetts Institute of Technology. Center for Environmental Health Sciences (deriving from NIH P30-ES002109)National Institutes of Health (U.S.) (grant R01-CA055042)National Institutes of Health (U.S.) (grant DP1-OD006422)Massachusetts Institute of Technology (CSBi Merck-MIT Fellowship

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

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

Role of chymase in cigarette smoke-induced pulmonary artery remodeling and pulmonary hypertension in hamsters

<p>Abstract</p> <p>Background</p> <p>Cigarette smoking is an important risk factor for pulmonary arterial hypertension (PAH) in chronic obstructive pulmonary disease (COPD). Chymase has been shown to function in the enzymatic production of angiotensin II (AngII) and the activation of transforming growth factor (TGF)-β1 in the cardiovascular system. The aim of this study was to determine the potential role of chymase in cigarette smoke-induced pulmonary artery remodeling and PAH.</p> <p>Methods</p> <p>Hamsters were exposed to cigarette smoke; after 4 months, lung morphology and tissue biochemical changes were examined using immunohistochemistry, Western blotting, radioimmunoassay and reverse-transcription polymerase chain reaction.</p> <p>Results</p> <p>Our results show that chronic cigarette smoke exposure significantly induced elevation of right ventricular systolic pressures (RVSP) and medial hypertrophy of pulmonary arterioles in hamsters, concurrent with an increase of chymase activity and synthesis in the lung. Elevated Ang II levels and enhanced TGF-β1/Smad signaling activation were also observed in smoke-exposed lungs. Chymase inhibition with chymostatin reduced the cigarette smoke-induced increase in chymase activity and Ang II concentration in the lung, and attenuated the RVSP elevation and the remodeling of pulmonary arterioles. Chymostatin did not affect angiotensin converting enzyme (ACE) activity in hamster lungs.</p> <p>Conclusions</p> <p>These results suggest that chronic cigarette smoke exposure can increase chymase activity and expression in hamster lungs. The capability of activated chymase to induce Ang II formation and TGF-β1 signaling may be part of the mechanism for smoking-induced pulmonary vascular remodeling. Thus, our study implies that blockade of chymase might provide benefits to PAH smokers.</p

Variations in Stress Sensitivity and Genomic Expression in Diverse S. cerevisiae Isolates

Interactions between an organism and its environment can significantly influence phenotypic evolution. A first step toward understanding this process is to characterize phenotypic diversity within and between populations. We explored the phenotypic variation in stress sensitivity and genomic expression in a large panel of Saccharomyces strains collected from diverse environments. We measured the sensitivity of 52 strains to 14 environmental conditions, compared genomic expression in 18 strains, and identified gene copy-number variations in six of these isolates. Our results demonstrate a large degree of phenotypic variation in stress sensitivity and gene expression. Analysis of these datasets reveals relationships between strains from similar niches, suggests common and unique features of yeast habitats, and implicates genes whose variable expression is linked to stress resistance. Using a simple metric to suggest cases of selection, we found that strains collected from oak exudates are phenotypically more similar than expected based on their genetic diversity, while sake and vineyard isolates display more diverse phenotypes than expected under a neutral model. We also show that the laboratory strain S288c is phenotypically distinct from all of the other strains studied here, in terms of stress sensitivity, gene expression, Ty copy number, mitochondrial content, and gene-dosage control. These results highlight the value of understanding the genetic basis of phenotypic variation and raise caution about using laboratory strains for comparative genomics

Genetic Architecture of Highly Complex Chemical Resistance Traits across Four Yeast Strains

Many questions about the genetic basis of complex traits remain unanswered. This is in part due to the low statistical power of traditional genetic mapping studies. We used a statistically powerful approach, extreme QTL mapping (X-QTL), to identify the genetic basis of resistance to 13 chemicals in all 6 pairwise crosses of four ecologically and genetically diverse yeast strains, and we detected a total of more than 800 loci. We found that the number of loci detected in each experiment was primarily a function of the trait (explaining 46% of the variance) rather than the cross (11%), suggesting that the level of genetic complexity is a consistent property of a trait across different genetic backgrounds. Further, we observed that most loci had trait-specific effects, although a small number of loci with effects in many conditions were identified. We used the patterns of resistance and susceptibility alleles in the four parent strains to make inferences about the allele frequency spectrum of functional variants. We also observed evidence of more complex allelic series at a number of loci, as well as strain-specific signatures of selection. These results improve our understanding of complex traits in yeast and have implications for study design in other organisms