Epigenetic and Genetic Influences on DNA Methylation Variation in Maize Populations

DNA methylation is a chromatin modification that is frequently associated with epigenetic regulation in plants and mammals. However, genetic changes such as transposon insertions can also lead to changes in DNA methylation. Genome-wide profiles of DNA methylation for 20 maize (Zea mays) inbred lines were used to discover differentially methylated regions (DMRs). The methylation level for each of these DMRs was also assayed in 31 additional maize or teosinte genotypes, resulting in the discovery of 1966 common DMRs and 1754 rare DMRs. Analysis of recombinant inbred lines provides evidence that the majority of DMRs are heritable. A local association scan found that nearly half of the DMRs with common variation are significantly associated with single nucleotide polymorphisms found within or near the DMR. Many of the DMRs that are significantly associated with local genetic variation are found near transposable elements that may contribute to the variation in DNA methylation. Analysis of gene expression in the same samples used for DNA methylation profiling identified over 300 genes with expression patterns that are significantly associated with DNA methylation variation. Collectively, our results suggest that DNA methylation variation is influenced by genetic and epigenetic changes that are often stably inherited and can influence the expression of nearby genes.     

Methods for Measuring Specific Rates of Mercury Methylation and Degradation and Their Use in Determining Factors Controlling Net Rates of Mercury Methylation

A method was developed to estimate specific rates of demethylation of methyl mercury in aquatic samples by measuring the volatile ¹⁴C end products of ¹⁴CH₃HgI demethylation. This method was used in conjunction with a ²⁰³Hg²⁺ radiochemical method which determines specific rates of mercury methylation. Together, these methods enabled us to examine some factors controlling the net rate of mercury methylation. The methodologies were field tested, using lake sediment samples from a recently flooded reservoir in the Southern Indian Lake system which had developed a mercury contamination problem in fish. Ratios of the specific rates of methylation/demethylation were calculated. The highest ratios of methylation/demethylation were calculated. The highest ratios of methylation/demethylation occurred in the flooded shorelines of Southern Indian Lake. These results provide an explanation for the observed increases in the methyl mercury concentrations in fish after flooding.     

中国南北方汉族人群DNA甲基化表观遗传差异研究

目的 族群地域、体貌特征等表型是基因型与环境共同作用的结果。大量基因组学研究表明,汉族人群具有混合特征,内部存在明显的南北遗传差异。本研究旨在探索研究表观基因组在中国南北方汉族人群之间是否存在差异,并筛选差异遗传位点。方法 使用GLINT软件对483份汉族样本的全基因组甲基化芯片数据进行EWAS分析,使用Lasso回归方法筛选位点。使用多元逻辑回归算法构建南北方汉族人群预测模型,通过十折交叉验证的方法评估。结果 筛选出一组南北方汉族之间差异显著的CpG位点,准确性为99.03%,Kappa系数为0.979 6。结论 本研究表明南北方汉族人群之间存在表观遗传差异,本研究为进一步开展不同地域汉族人群之间的表观遗传差异研究奠定了基础。     

Inheritance and Variation of Cytosine Methylation in Three Populus Allotriploid Populations with Different Heterozygosity

DNA methylation is an epigenetic mechanism with the potential to regulate gene expression and affect plant phenotypes. Both hybridization and genome doubling may affect the DNA methylation status of newly formed allopolyploid plants. Previous studies demonstrated that changes in cytosine methylation levels and patterns were different among individual hybrid plant, therefore, studies investigating the characteristics of variation in cytosine methylation status must be conducted at the population level to avoid sampling error. In the present study, an F1 hybrid diploid population and three allotriploid populations with different heterozygosity [originating from first-division restitution (FDR), second-division restitution (SDR), and post-meiotic restitution (PMR) 2n eggs of the same female parent] were used to investigate cytosine methylation inheritance and variation relative to their common parents using methylation-sensitive amplification polymorphism (MSAP). The variation in cytosine methylation in individuals in each population exhibited substantial differences, confirming the necessity of population epigenetics. The total methylation levels of the diploid population were significantly higher than in the parents, but those of the three allotriploid populations were significantly lower than in the parents, indicating that both hybridization and polyploidization contributed to cytosine methylation variation. The vast majority of methylated status could be inherited from the parents, and the average percentages of non-additive variation were 6.29, 3.27, 5.49 and 5.07% in the diploid, FDR, SDR and PMR progeny populations, respectively. This study lays a foundation for further research on population epigenetics in allopolyploids.     

Cytosine Methylation Alteration in Natural Populations of Leymus chinensis Induced by Multiple Abiotic Stresses

Background

Human activity has a profound effect on the global environment and caused frequent occurrence of climatic fluctuations. To survive, plants need to adapt to the changing environmental conditions through altering their morphological and physiological traits. One known mechanism for phenotypic innovation to be achieved is environment-induced rapid yet inheritable epigenetic changes. Therefore, the use of molecular techniques to address the epigenetic mechanisms underpinning stress adaptation in plants is an important and challenging topic in biological research. In this study, we investigated the impact of warming, nitrogen (N) addition, and warming+nitrogen (N) addition stresses on the cytosine methylation status of Leymus chinensis Tzvel. at the population level by using the amplified fragment length polymorphism (AFLP), methylation-sensitive amplified polymorphism (MSAP) and retrotransposon based sequence-specific amplification polymorphism (SSAP) techniques.

Methodology/Principal Findings

Our results showed that, although the percentages of cytosine methylation changes in SSAP are significantly higher than those in MSAP, all the treatment groups showed similar alteration patterns of hypermethylation and hypomethylation. It meant that the abiotic stresses have induced the alterations in cytosine methylation patterns, and the levels of cytosine methylation changes around the transposable element are higher than the other genomic regions. In addition, the identification and analysis of differentially methylated loci (DML) indicated that the abiotic stresses have also caused targeted methylation changes at specific loci and these DML might have contributed to the capability of plants in adaptation to the abiotic stresses.

Conclusions/Significance

Our results demonstrated that abiotic stresses related to global warming and nitrogen deposition readily evoke alterations of cytosine methylation, and which may provide a molecular basis for rapid adaptation by the affected plant populations to the changed environments.     

System for Determining the Relative Fitness of Multiple Bacterial Populations without Using Selective Markers

A device for simultaneously measuring the relative fitness of multiple bacterial populations was developed and evaluated. The new device eliminates the need to construct strains with selectively neutral markers so that strains can be readily distinguished, and it provides a means to perform multispecies competition experiments.     

Detection of Cytosine Methylation in Ancient DNA from Five Native American Populations Using Bisulfite Sequencing

While cytosine methylation has been widely studied in extant populations, relatively few studies have analyzed methylation in ancient DNA. Most existing studies of epigenetic marks in ancient DNA have inferred patterns of methylation in highly degraded samples using post-mortem damage to cytosines as a proxy for cytosine methylation levels. However, this approach limits the inference of methylation compared with direct bisulfite sequencing, the current gold standard for analyzing cytosine methylation at single nucleotide resolution. In this study, we used direct bisulfite sequencing to assess cytosine methylation in ancient DNA from the skeletal remains of 30 Native Americans ranging in age from approximately 230 to 4500 years before present. Unmethylated cytosines were converted to uracils by treatment with sodium bisulfite, bisulfite products of a CpG-rich retrotransposon were pyrosequenced, and C-to-T ratios were quantified for a single CpG position. We found that cytosine methylation is readily recoverable from most samples, given adequate preservation of endogenous nuclear DNA. In addition, our results indicate that the precision of cytosine methylation estimates is inversely correlated with aDNA preservation, such that samples of low DNA concentration show higher variability in measures of percent methylation than samples of high DNA concentration. In particular, samples in this study with a DNA concentration above 0.015 ng/μL generated the most consistent measures of cytosine methylation. This study presents evidence of cytosine methylation in a large collection of ancient human remains, and indicates that it is possible to analyze epigenetic patterns in ancient populations using direct bisulfite sequencing approaches.     

Eco-physiological Characterization of Soil Bacterial Populations in Different States of Growth

The method based on characterization of microbial populations in terms of their growth rate in agar plates has been used for testing the prediction of the theory of r- and K-selection in a microbial community from a tropical soil. Conditions which could lead bacterial populations to grow exponentially or to enter into a stationary phase were obtained by growing soil microbial populations in a chemostat and in a chemostat with recycle, respectively. Significant differences in population distribution patterns were observed by comparing results from the two growth systems. When soil community was grown in a chemostat and subjected specifically to well-defined r- and K-conditions, stable associations of organisms with r- and K-type characteristics developed as a consequence of environmental pressure. In contrast, when cultivated in chemostat with recycle under the same r- and K-conditions imposed on chemostat cultures, distribution patterns of r- and K-selected populations appeared very little affected by changes in substrate availability.     

Interplay between Constraints,Objectives, and Optimality for Genome-Scale Stoichiometric Models

High-throughput data generation and genome-scale stoichiometric models have greatly facilitated the comprehensive study of metabolic networks. The computation of all feasible metabolic routes with these models, given stoichiometric, thermodynamic, and steady-state constraints, provides important insights into the metabolic capacities of a cell. How the feasible metabolic routes emerge from the interplay between flux constraints, optimality objectives, and the entire metabolic network of a cell is, however, only partially understood. We show how optimal metabolic routes, resulting from flux balance analysis computations, arise out of elementary flux modes, constraints, and optimization objectives. We illustrate our findings with a genome-scale stoichiometric model of Escherichia coli metabolism. In the case of one flux constraint, all feasible optimal flux routes can be derived from elementary flux modes alone. We found up to 120 million of such optimal elementary flux modes. We introduce a new computational method to compute the corner points of the optimal solution space fast and efficiently. Optimal flux routes no longer depend exclusively on elementary flux modes when we impose additional constraints; new optimal metabolic routes arise out of combinations of elementary flux modes. The solution space of feasible metabolic routes shrinks enormously when additional objectives---e.g. those related to pathway expression costs or pathway length---are introduced. In many cases, only a single metabolic route remains that is both feasible and optimal. This paper contributes to reaching a complete topological understanding of the metabolic capacity of organisms in terms of metabolic flux routes, one that is most natural to biochemists and biotechnologists studying and engineering metabolism.     

Comparative Genome-Scale Reconstruction of Gapless Metabolic Networks for Present and Ancestral Species

We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/.     

Enumeration of Smallest Intervention Strategies in Genome-Scale Metabolic Networks

One ultimate goal of metabolic network modeling is the rational redesign of biochemical networks to optimize the production of certain compounds by cellular systems. Although several constraint-based optimization techniques have been developed for this purpose, methods for systematic enumeration of intervention strategies in genome-scale metabolic networks are still lacking. In principle, Minimal Cut Sets (MCSs; inclusion-minimal combinations of reaction or gene deletions that lead to the fulfilment of a given intervention goal) provide an exhaustive enumeration approach. However, their disadvantage is the combinatorial explosion in larger networks and the requirement to compute first the elementary modes (EMs) which itself is impractical in genome-scale networks.We present MCSEnumerator, a new method for effective enumeration of the smallest MCSs (with fewest interventions) in genome-scale metabolic network models. For this we combine two approaches, namely (i) the mapping of MCSs to EMs in a dual network, and (ii) a modified algorithm by which shortest EMs can be effectively determined in large networks. In this way, we can identify the smallest MCSs by calculating the shortest EMs in the dual network. Realistic application examples demonstrate that our algorithm is able to list thousands of the most efficient intervention strategies in genome-scale networks for various intervention problems. For instance, for the first time we could enumerate all synthetic lethals in E.coli with combinations of up to 5 reactions. We also applied the new algorithm exemplarily to compute strain designs for growth-coupled synthesis of different products (ethanol, fumarate, serine) by E.coli. We found numerous new engineering strategies partially requiring less knockouts and guaranteeing higher product yields (even without the assumption of optimal growth) than reported previously. The strength of the presented approach is that smallest intervention strategies can be quickly calculated and screened with neither network size nor the number of required interventions posing major challenges.     

Identifying All Moiety Conservation Laws in Genome-Scale Metabolic Networks

The stoichiometry of a metabolic network gives rise to a set of conservation laws for the aggregate level of specific pools of metabolites, which, on one hand, pose dynamical constraints that cross-link the variations of metabolite concentrations and, on the other, provide key insight into a cell''s metabolic production capabilities. When the conserved quantity identifies with a chemical moiety, extracting all such conservation laws from the stoichiometry amounts to finding all non-negative integer solutions of a linear system, a programming problem known to be NP-hard. We present an efficient strategy to compute the complete set of integer conservation laws of a genome-scale stoichiometric matrix, also providing a certificate for correctness and maximality of the solution. Our method is deployed for the analysis of moiety conservation relationships in two large-scale reconstructions of the metabolism of the bacterium E. coli, in six tissue-specific human metabolic networks, and, finally, in the human reactome as a whole, revealing that bacterial metabolism could be evolutionarily designed to cover broader production spectra than human metabolism. Convergence to the full set of moiety conservation laws in each case is achieved in extremely reduced computing times. In addition, we uncover a scaling relation that links the size of the independent pool basis to the number of metabolites, for which we present an analytical explanation.     

Genome-Scale Discovery of DNA-Methylation Biomarkers for Blood-Based Detection of Colorectal Cancer

Background

There is an increasing demand for accurate biomarkers for early non-invasive colorectal cancer detection. We employed a genome-scale marker discovery method to identify and verify candidate DNA methylation biomarkers for blood-based detection of colorectal cancer.

Methodology/Principal Findings

We used DNA methylation data from 711 colorectal tumors, 53 matched adjacent-normal colonic tissue samples, 286 healthy blood samples and 4,201 tumor samples of 15 different cancer types. DNA methylation data were generated by the Illumina Infinium HumanMethylation27 and the HumanMethylation450 platforms, which determine the methylation status of 27,578 and 482,421 CpG sites respectively. We first performed a multistep marker selection to identify candidate markers with high methylation across all colorectal tumors while harboring low methylation in healthy samples and other cancer types. We then used pre-therapeutic plasma and serum samples from 107 colorectal cancer patients and 98 controls without colorectal cancer, confirmed by colonoscopy, to verify candidate markers. We selected two markers for further evaluation: methylated THBD (THBD-M) and methylated C9orf50 (C9orf50-M). When tested on clinical plasma and serum samples these markers outperformed carcinoembryonic antigen (CEA) serum measurement and resulted in a high sensitive and specific test performance for early colorectal cancer detection.

Conclusions/Significance

Our systematic marker discovery and verification study for blood-based DNA methylation markers resulted in two novel colorectal cancer biomarkers, THBD-M and C9orf50-M. THBD-M in particular showed promising performance in clinical samples, justifying its further optimization and clinical testing.     

Likelihood-Based Gene Annotations for Gap Filling and Quality Assessment in Genome-Scale Metabolic Models

Genome-scale metabolic models provide a powerful means to harness information from genomes to deepen biological insights. With exponentially increasing sequencing capacity, there is an enormous need for automated reconstruction techniques that can provide more accurate models in a short time frame. Current methods for automated metabolic network reconstruction rely on gene and reaction annotations to build draft metabolic networks and algorithms to fill gaps in these networks. However, automated reconstruction is hampered by database inconsistencies, incorrect annotations, and gap filling largely without considering genomic information. Here we develop an approach for applying genomic information to predict alternative functions for genes and estimate their likelihoods from sequence homology. We show that computed likelihood values were significantly higher for annotations found in manually curated metabolic networks than those that were not. We then apply these alternative functional predictions to estimate reaction likelihoods, which are used in a new gap filling approach called likelihood-based gap filling to predict more genomically consistent solutions. To validate the likelihood-based gap filling approach, we applied it to models where essential pathways were removed, finding that likelihood-based gap filling identified more biologically relevant solutions than parsimony-based gap filling approaches. We also demonstrate that models gap filled using likelihood-based gap filling provide greater coverage and genomic consistency with metabolic gene functions compared to parsimony-based approaches. Interestingly, despite these findings, we found that likelihoods did not significantly affect consistency of gap filled models with Biolog and knockout lethality data. This indicates that the phenotype data alone cannot necessarily be used to discriminate between alternative solutions for gap filling and therefore, that the use of other information is necessary to obtain a more accurate network. All described workflows are implemented as part of the DOE Systems Biology Knowledgebase (KBase) and are publicly available via API or command-line web interface.     

Determining spatially varying profit-maximizing management practices for miscanthus and switchgrass production in the rainfed United States

Determining optimal management practices for the profitable production of perennial energy crops is critical for scaling up production beyond experimental levels. Although many experimental field studies have examined the effects of management practices on the performance of miscanthus and switchgrass, there are no recommendations for economically optimal nitrogen (N) application rates and how they should vary spatially and with the age of the energy crop as well as on optimal rotation age of the energy crop to maximize profits. We develop a modeling framework to determine economically optimal crop management decisions and simulate the variability under various scenarios for miscanthus and switchgrass production across 2287 counties in the rainfed United States. We find that profit-maximizing N recommendations for these crops vary across maturity stages and regions and can increase the landowner's profits compared with a uniform N rate across ages and regions. We also find that the optimal rotation for these crops is shorter than the productive physical lifespan (15–20 and 10 years for miscanthus and switchgrass, respectively). Specifically, the N rate that maximizes the economic returns is negligible for miscanthus and 111 kg ha⁻¹ for switchgrass production at age 2. The mean profit-maximizing N rate increases with age for miscanthus, peaking at 151 kg ha⁻¹ at age 11 before declining to 114 kg ha⁻¹ at the optimal rotation age of 13 years while that for switchgrass is 150 kg ha⁻¹ for middle-aged stands and declines to 114 kg ha⁻¹ at the optimal rotation of 8–9 years. We find that miscanthus is the most profitable energy crop in the northern region of the rainfed United States while switchgrass is most profitable in the south of the rainfed United States. Our findings are useful for improving assessments of the profitability of energy crops and guiding future management decisions by landowners.     

Genome-Scale Model of Streptococcus thermophilus LMG18311 for Metabolic Comparison of Lactic Acid Bacteria

In this report, we describe the amino acid metabolism and amino acid dependency of the dairy bacterium Streptococcus thermophilus LMG18311 and compare them with those of two other characterized lactic acid bacteria, Lactococcus lactis and Lactobacillus plantarum. Through the construction of a genome-scale metabolic model of S. thermophilus, the metabolic differences between the three bacteria were visualized by direct projection on a metabolic map. The comparative analysis revealed the minimal amino acid auxotrophy (only histidine and methionine or cysteine) of S. thermophilus LMG18311 and the broad variety of volatiles produced from amino acids compared to the other two bacteria. It also revealed the limited number of pyruvate branches, forcing this strain to use the homofermentative metabolism for growth optimization. In addition, some industrially relevant features could be identified in S. thermophilus, such as the unique pathway for acetaldehyde (yogurt flavor) production and the absence of a complete pentose phosphate pathway.Lactic acid bacteria (LAB) are of great importance in the food industry because of their lactic acid production and their characteristic impact (e.g., texture, flavor) on the final product (19). LAB, as fastidious organisms, require a complex medium (such as milk) and are dependent on their proteolytic system for their supply of essential amino acids (34). Amino acids are not only the building blocks for proteins and peptides, but they also serve as precursors for many other biomolecules (1). Amino acids are also important for the final flavor of a product. Most amino acids do not directly influence the product flavor, but they will contribute indirectly to it because they are precursors of aromatic compounds (36). The conversion of amino acids to flavor compounds is initiated mainly by amino acid transamination, which uses an α-keto acid as an amino group acceptor for the aminotransferases (27). The presence (or absence) of the α-keto acid either by endogenous production or by addition to the medium is an important factor in flavor formation (13). The α-keto acids are decarboxylated into aldehydes, which are the precursors of other flavor compounds such as alcohols, esters, and carboxylic acids (27). A large variation in flavor formation between strains and species is observed. Different studies have reported this biodiversity (25, 27, 32, 33); van Hylckama Vlieg et al. studied, for instance, the difference between dairy and nondairy lactococcal strains, since the latter group has some unique flavor-forming activities (33).Amino acid catabolism and anabolism are complex processes, and thus, metabolic models will be helpful for their understanding. Genome-scale metabolic models provide an overview of all metabolic conversions in an organism based on its genome sequence and make it possible to visualize different metabolic pathways, such as amino acid metabolism. These models can be used to understand the metabolism and can then be applied for a directed study of functionality. For Lactobacillus plantarum and Lactococcus lactis, such genome-scale models have already been developed (18, 29); the construction of such a model for Streptococcus thermophilus LMG18311 is described in this paper. The characterization of the genome sequence of this S. thermophilus strain has revealed the presence of a large number of incomplete or truncated genes. These so-called pseudogenes amount to 10% of the total genes, and most of them relate to carbohydrate metabolism, transport, and regulation (2, 11). S. thermophilus is an important starter for the dairy industry. It is used in combination with Lactobacillus delbrueckii subsp. bulgaricus for the production of yogurt. It is also used for the manufacture of cheeses in which high cooking temperatures are applied (11). The objective of this paper is to study the metabolism of S. thermophilus with the use of genome-scale models and experimental data in a comparative way. This comparison with other LAB may reveal important differences. This study showed the simple primary metabolism and the extensive amino acid metabolism of S. thermophilus.