Relating perturbation magnitude to temporal gene expression in biological systems

<p>Abstract</p> <p>Background</p> <p>Most transcriptional activity is a result of environmental variability. This cause (environment) and effect (gene expression) relationship is essential to survival in any changing environment. The specific relationship between environmental perturbation and gene expression – and stability of the response – has yet to be measured in detail. We describe a method to quantitatively relate perturbation magnitude to response at the level of gene expression. We test our method using <it>Saccharomyces cerevisiae </it>as a model organism and osmotic stress as an environmental stress.</p> <p>Results</p> <p>Patterns of gene expression were measured in response to increasing sodium chloride concentrations (0, 0.5, 0.7, 1.0, and 1.2 M) for sixty genes impacted by osmotic shock. Expression of these genes was quantified over five time points using reverse transcriptase real-time polymerase chain reaction. Magnitudes of cumulative response for specific pathways, and the set of all genes, were obtained by combining the temporal response envelopes for genes exhibiting significant changes in expression with time. A linear relationship between perturbation magnitude and response was observed for the range of concentrations studied.</p> <p>Conclusion</p> <p>This study develops a quantitative approach to describe the stability of gene response and pathways to environmental perturbation and illustrates the utility of this approach. The approach should be applicable to quantitatively evaluate the response of organisms via the magnitude of response and stability of the transcriptome to environmental change.</p

Microbial biodiversity of Great Salt Lake, Utah

Microbial biodiversity is difficult to measure in extreme environments due to the inability to culture many of the species, especially from hypersaline environments. Great Salt Lake (GSL), Utah, USA offers a unique ecology to study microbial diversity across a salt gradient. GSL has increasing salt from South to North that varies from marine salt concentrations to saturation, respectively. We used three methods to examine the biodiversity of the GSL-traditional cultivation on solid media, 16s rRNA gene sequencing, multiplexed 16s rRNA gene hybridization to the phylochip, and DNA hybridization to the Geochip for metabolic diversity estimates. Over 40 isolates from the North Arm were obtained, while six were selected for identification. Isolates included gammaproteobacteria, bacilli, and actinobacteria. Sequencing the 16S rRNA genes for identification yielded 350 clones. Refraction curves indicated that this did not represent the bacterial diversity of the GSL, while estimation of the diversity with the Affymetrix phylochip produced over 1000 different genera in 31 different families. Estimation of the metabolic diversity found that genes for each activity were present in all three locations. The gene abundance was similar in all locations, except for metal use where the gene abundance declined as the salt gradient declined. This study provides the first evidence of the large microbial diversity supported by GSL to provide a large metabolic potential independent of the salt concentration

Using Machine Learning to Predict Obesity Based on Genome-Wide and Epigenome-Wide Gene-Gene and Gene-Diet Interactions.

Obesity is associated with many chronic diseases that impair healthy aging and is governed by genetic, epigenetic, and environmental factors and their complex interactions. This study aimed to develop a model that predicts an individual's risk of obesity by better characterizing these complex relations and interactions focusing on dietary factors. For this purpose, we conducted a combined genome-wide and epigenome-wide scan for body mass index (BMI) and up to three-way interactions among 402,793 single nucleotide polymorphisms (SNPs), 415,202 DNA methylation sites (DMSs), and 397 dietary and lifestyle factors using the generalized multifactor dimensionality reduction (GMDR) method. The training set consisted of 1,573 participants in exam 8 of the Framingham Offspring Study (FOS) cohort. After identifying genetic, epigenetic, and dietary factors that passed statistical significance, we applied machine learning (ML) algorithms to predict participants' obesity status in the test set, taken as a subset of independent samples (n = 394) from the same cohort. The quality and accuracy of prediction models were evaluated using the area under the receiver operating characteristic curve (ROC-AUC). GMDR identified 213 SNPs, 530 DMSs, and 49 dietary and lifestyle factors as significant predictors of obesity. Comparing several ML algorithms, we found that the stochastic gradient boosting model provided the best prediction accuracy for obesity with an overall accuracy of 70%, with ROC-AUC of 0.72 in test set samples. Top predictors of the best-fit model were 21 SNPs, 230 DMSs in genes such as CPT1A, ABCG1, SLC7A11, RNF145, and SREBF1, and 26 dietary factors, including processed meat, diet soda, French fries, high-fat dairy, artificial sweeteners, alcohol intake, and specific nutrients and food components, such as calcium and flavonols. In conclusion, we developed an integrated approach with ML to predict obesity using omics and dietary data. This extends our knowledge of the drivers of obesity, which can inform precision nutrition strategies for the prevention and treatment of obesity. Clinical Trial Registration: [www.ClinicalTrials.gov], the Framingham Heart Study (FHS), [NCT00005121].This research was funded by the United States Department of Agriculture (USDA), Agriculture Research Service (ARS) under agreement no. 8050-51000-107-000D. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the USDA. The USDA is an equal opportunity provider and employer. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the authors and do not necessarily reflect the view of the USDA.S

Rate-Limiting Enzymes in Cardiometabolic Health and Aging in Humans

Introduction: Rate-limiting enzymes (RLEs) are innate slow points in metabolic pathways, and many function in bio-processes related to nutrient sensing. Many RLEs carry causal mutations relevant to inherited metabolic disorders. Because the activity of RLEs in cardiovascular health is poorly characterized, our objective was to assess their involvement in cardiometabolic health and disease and where altered biophysical and biochemical functions can promote disease. Methods: A dataset of 380 human RLEs was compared to protein and gene datasets for factors likely to contribute to cardiometabolic disease, including proteins showing significant age-related altered expression in blood and genetic loci with variants that associate with common cardiometabolic phenotypes. The biochemical reactions catalyzed by RLEs were evaluated for metabolites enriched in RLE subsets associating with various cardiometabolic phenotypes. Most significance tests were based on Z-score enrichment converted to p values with a normal distribution function. Results: Of 380 RLEs analyzed, 112 function in mitochondria, and 53 are assigned to inherited metabolic disorders. There was a depletion of RLE proteins known as aging biomarkers. At the gene level, RLEs were assessed for common genetic variants that associated with important cardiometabolic traits of LDL-cholesterol or any of the five outcomes pertinent to metabolic syndrome. This revealed several RLEs with links to cardiometabolic traits, from a minimum of 26 for HDL-cholesterol to a maximum of 45 for plasma glucose. Analysis of these GWAS-linked RLEs for enrichment of the molecular constituents of the catalyzed reactions disclosed a number of significant phenotype-metabolite links. These included blood pressure with acetate (p = 2.2 × 10−4) and NADP+ (p = 0.0091), plasma HDL-cholesterol and triglyceride with diacylglycerol (p = 2.6 × 10−5, 6.4 × 10−5, respectively) and diolein (p = 2.2 × 10−6, 5.9 × 10−6), and waist circumference with d-glucosamine-6-phosphate (p = 1.8 × 10−4). Conclusion: In the context of cardiometabolic health, aging, and disease, these results highlight key diet-derived metabolites that are central to specific rate-limited processes that are linked to cardiometabolic health. These metabolites include acetate and diacylglycerol, pertinent to blood pressure and triglycerides, respectively, as well as diacylglycerol and HDL-cholesterol

Magnetic resonance microscopy-based analyses of the brains of normal and ethanol-exposed fetal mice

The application of magnetic resonance microscopy (MRM) to the study of normal and abnormal prenatal mouse development has facilitated discovery of dysmorphology following prenatal ethanol insult. The current analyses extend this work, providing a regional brain volume-based description of normal brain growth and illustrating the consequences of gestational day (GD) 10 ethanol exposure in the fetal mouse

Magnetic Resonance Microscopy Defines Ethanol-Induced Brain Abnormalities in Prenatal Mice: Effects of Acute Insult on Gestational Day 7

This magnetic resonance microscopy (MRM)-based report is the 2nd in a series designed to illustrate the spectrum of craniofacial and central nervous system (CNS) dysmorphia resulting from single- and multiple-day maternal ethanol treatment. The study described in this report examined the consequences of ethanol exposure on gestational day (GD) 7 in mice, a time in development when gastrulation and neural plate development begins; corresponding to the mid- to late 3rd week post-fertilization in humans. Acute GD 7 ethanol exposure in mice has previously been shown to result in CNS defects consistent with holoprosencephaly (HPE) and craniofacial anomalies typical of those in Fetal Alcohol Syndrome (FAS). MRM has facilitated further definition of the range of GD 7 ethanol-induced defects

Functional Biogeography as Evidence of Gene Transfer in Hypersaline Microbial Communities

We are grateful to David Naftz and the United States Geological Survey for access to sampling sites, Chris Garrard and Robert Baskin for geospatial images, and Ethan White for statistical input.Conceived and designed the experiments: JJP GR MEP BCW. Performed the experiments: JJP GR JDVN. Analyzed the data: JJP LCL MEP JDVN BG. Contributed reagents/materials/analysis tools: JJP GR LCL MEP JDVN ZH JZ GLA BCW. Wrote the paper: JJP MEP PC BCW.BackgroundHorizontal gene transfer (HGT) plays a major role in speciation and evolution of bacteria and archaea by controlling gene distribution within an environment. However, information that links HGT to a natural community using relevant population-genetics parameters and spatial considerations is scarce. The Great Salt Lake (Utah, USA) provides an excellent model for studying HGT in the context of biogeography because it is a contiguous system with dispersal limitations due to a strong selective salinity gradient. We hypothesize that in spite of the barrier to phylogenetic dispersal, functional characteristics—in the form of HGT—expand beyond phylogenetic limitations due to selective pressure.Methodology and ResultsTo assay the functional genes and microorganisms throughout the GSL, we used a 16S rRNA oligonucleotide microarray (Phylochip) and a functional gene array (GeoChip) to measure biogeographic patterns of nine microbial communities. We found a significant difference in biogeography based on microarray analyses when comparing Sørensen similarity values for presence/absence of function and phylogeny (Student's t-test; p = 0.005).Conclusion and SignificanceBiogeographic patterns exhibit behavior associated with horizontal gene transfer in that informational genes (16S rRNA) have a lower similarity than functional genes, and functional similarity is positively correlated with lake-wide selective pressure. Specifically, high concentrations of chromium throughout GSL correspond to an average similarity of chromium resistance genes that is 22% higher than taxonomic similarity. This suggests active HGT may be measured at the population level in microbial communities and these biogeographic patterns may serve as a model to study bacteria adaptation and speciation.Yeshttp://www.plosone.org/static/editorial#pee

Mismatches in Scale Between Highly Mobile Marine Megafauna and Marine Protected Areas

Marine protected areas (MPAs), particularly large MPAs, are increasing in number and size around the globe in part to facilitate the conservation of marine megafauna under the assumption that large-scale MPAs better align with vagile life histories; however, this alignment is not well established. Using a global tracking dataset from 36 species across five taxa, chosen to reflect the span of home range size in highly mobile marine megafauna, we show most MPAs are too small to encompass complete home ranges of most species. Based on size alone, 40% of existing MPAs could encompass the home ranges of the smallest ranged species, while only \u3c 1% of existing MPAs could encompass those of the largest ranged species. Further, where home ranges and MPAs overlapped in real geographic space, MPAs encompassed \u3c 5% of core areas used by all species. Despite most home ranges of mobile marine megafauna being much larger than existing MPAs, we demonstrate how benefits from MPAs are still likely to accrue by targeting seasonal aggregations and critical life history stages and through other management techniques

Mismatches in Scale Between Highly Mobile Marine Megafauna and Marine Protected Areas

Marine protected areas (MPAs), particularly large MPAs, are increasing in number and size around the globe in part to facilitate the conservation of marine megafauna under the assumption that large-scale MPAs better align with vagile life histories; however, this alignment is not well established. Using a global tracking dataset from 36 species across five taxa, chosen to reflect the span of home range size in highly mobile marine megafauna, we show most MPAs are too small to encompass complete home ranges of most species. Based on size alone, 40% of existing MPAs could encompass the home ranges of the smallest ranged species, while only \u3c 1% of existing MPAs could encompass those of the largest ranged species. Further, where home ranges and MPAs overlapped in real geographic space, MPAs encompassed \u3c 5% of core areas used by all species. Despite most home ranges of mobile marine megafauna being much larger than existing MPAs, we demonstrate how benefits from MPAs are still likely to accrue by targeting seasonal aggregations and critical life history stages and through other management techniques