Analyzing postprandial metabolomics data using multiway models: a simulation study.

BACKGROUND: Analysis of time-resolved postprandial metabolomics data can improve the understanding of metabolic mechanisms, potentially revealing biomarkers for early diagnosis of metabolic diseases and advancing precision nutrition and medicine. Postprandial metabolomics measurements at several time points from multiple subjects can be arranged as a subjects by metabolites by time points array. Traditional analysis methods are limited in terms of revealing subject groups, related metabolites, and temporal patterns simultaneously from such three-way data. RESULTS: We introduce an unsupervised multiway analysis approach based on the CANDECOMP/PARAFAC (CP) model for improved analysis of postprandial metabolomics data guided by a simulation study. Because of the lack of ground truth in real data, we generate simulated data using a comprehensive human metabolic model. This allows us to assess the performance of CP models in terms of revealing subject groups and underlying metabolic processes. We study three analysis approaches: analysis of fasting-state data using principal component analysis, T0-corrected data (i.e., data corrected by subtracting fasting-state data) using a CP model and full-dynamic (i.e., full postprandial) data using CP. Through extensive simulations, we demonstrate that CP models capture meaningful and stable patterns from simulated meal challenge data, revealing underlying mechanisms and differences between diseased versus healthy groups. CONCLUSIONS: Our experiments show that it is crucial to analyze both fasting-state and T0-corrected data for understanding metabolic differences among subject groups. Depending on the nature of the subject group structure, the best group separation may be achieved by CP models of T0-corrected or full-dynamic data. This study introduces an improved analysis approach for postprandial metabolomics data while also shedding light on the debate about correcting baseline values in longitudinal data analysis.

Understanding repertoire sequencing data through a multiscale computational model of the germinal center.

Sequencing of B-cell and T-cell immune receptor repertoires helps us to understand the adaptive immune response, although it only provides information about the clonotypes (lineages) and their frequencies and not about, for example, their affinity or antigen (Ag) specificity. To further characterize the identified clones, usually with special attention to the particularly abundant ones (dominant), additional time-consuming or expensive experiments are generally required. Here, we present an extension of a multiscale model of the germinal center (GC) that we previously developed to gain more insight in B-cell repertoires. We compare the extent that these simulated repertoires deviate from experimental repertoires established from single GCs, blood, or tissue. Our simulations show that there is a limited correlation between clonal abundance and affinity and that there is large affinity variability among same-ancestor (same-clone) subclones. Our simulations suggest that low-abundance clones and subclones, might also be of interest since they may have high affinity for the Ag. We show that the fraction of plasma cells (PCs) with high B-cell receptor (BcR) mRNA content in the GC does not significantly affect the number of dominant clones derived from single GCs by sequencing BcR mRNAs. Results from these simulations guide data interpretation and the design of follow-up experiments.

From affinity selection to kinetic selection in Germinal Centre modelling.

Affinity maturation is an evolutionary process by which the affinity of antibodies (Abs) against specific antigens (Ags) increases through rounds of B-cell proliferation, somatic hypermutation, and positive selection in germinal centres (GC). The positive selection of B cells depends on affinity, but the underlying mechanisms of affinity discrimination and affinity-based selection are not well understood. It has been suggested that selection in GC depends on both rapid binding of B-cell receptors (BcRs) to Ags which is kinetically favourable and tight binding of BcRs to Ags, which is thermodynamically favourable; however, it has not been shown whether a selection bias for kinetic properties is present in the GC. To investigate the GC selection bias towards rapid and tight binding, we developed an agent-based model of GC and compared the evolution of founder B cells with initially identical low affinities but with different association/dissociation rates for Ag presented by follicular dendritic cells in three Ag collection mechanisms. We compared an Ag collection mechanism based on association/dissociation rates of B-cell interaction with presented Ag, which includes a probabilistic rupture of bonds between the B-cell and Ag (Scenario-1) with a reference scenario based on an affinity-based Ag collection mechanism (Scenario-0). Simulations showed that the mechanism of Ag collection affects the GC dynamics and the GC outputs concerning fast/slow (un)binding of B cells to FDC-presented Ags. In particular, clones with lower dissociation rates outcompete clones with higher association rates in Scenario-1, while remaining B cells from clones with higher association rates reach higher affinities. Accordingly, plasma cell and memory B cell populations were biased towards B-cell clones with lower dissociation rates. Without such probabilistic ruptures during the Ag extraction process (Scenario-2), the selective advantage for clones with very low dissociation rates diminished, and the affinity maturation level of all clones decreased to the reference level.

Exploring dynamic metabolomics data with multiway data analysis: a simulation study.

BACKGROUND: Analysis of dynamic metabolomics data holds the promise to improve our understanding of underlying mechanisms in metabolism. For example, it may detect changes in metabolism due to the onset of a disease. Dynamic or time-resolved metabolomics data can be arranged as a three-way array with entries organized according to a subjects mode, a metabolites mode and a time mode. While such time-evolving multiway data sets are increasingly collected, revealing the underlying mechanisms and their dynamics from such data remains challenging. For such data, one of the complexities is the presence of a superposition of several sources of variation: induced variation (due to experimental conditions or inborn errors), individual variation, and measurement error. Multiway data analysis (also known as tensor factorizations) has been successfully used in data mining to find the underlying patterns in multiway data. To explore the performance of multiway data analysis methods in terms of revealing the underlying mechanisms in dynamic metabolomics data, simulated data with known ground truth can be studied. RESULTS: We focus on simulated data arising from different dynamic models of increasing complexity, i.e., a simple linear system, a yeast glycolysis model, and a human cholesterol model. We generate data with induced variation as well as individual variation. Systematic experiments are performed to demonstrate the advantages and limitations of multiway data analysis in analyzing such dynamic metabolomics data and their capacity to disentangle the different sources of variations. We choose to use simulations since we want to understand the capability of multiway data analysis methods which is facilitated by knowing the ground truth. CONCLUSION: Our numerical experiments demonstrate that despite the increasing complexity of the studied dynamic metabolic models, tensor factorization methods CANDECOMP/PARAFAC(CP) and Parallel Profiles with Linear Dependences (Paralind) can disentangle the sources of variations and thereby reveal the underlying mechanisms and their dynamics.

Human Blood Lipoprotein Predictions from 1H NMR Spectra: Protocol, Model Performances, and Cage of Covariance.

Lipoprotein subfractions are biomarkers for the early diagnosis of cardiovascular diseases. The reference method, ultracentrifugation, for measuring lipoproteins is time-consuming, and there is a need to develop a rapid method for cohort screenings. This study presents partial least-squares regression models developed using 1H nuclear magnetic resonance (NMR) spectra and concentrations of lipoproteins as measured by ultracentrifugation on 316 healthy Danes. This study explores, for the first time, different regions of the 1H NMR spectrum representing signals of molecules in lipoprotein particles and different lipid species to develop parsimonious, reliable, and optimal prediction models. A total of 65 lipoprotein main and subfractions were predictable with high accuracy, Q2 of >0.6, using an optimal spectral region (1.4-0.6 ppm) containing methylene and methyl signals from lipids. The models were subsequently tested on an independent cohort of 290 healthy Swedes with predicted and reference values matching by up to 85-95%. In addition, an open software tool was developed to predict lipoproteins concentrations in human blood from standardized 1H NMR spectral recordings.

Coupled Antigen and BLIMP1 Asymmetric Division With a Large Segregation Between Daughter Cells Recapitulates the Temporal Transition From Memory B Cells to Plasma Cells and a DZ-to-LZ Ratio in the Germinal Center.

Memory B cells and antibody-secreting plasma cells are generated within germinal centers during affinity maturation in which B-cell proliferation, selection, differentiation, and self-renewal play important roles. The mechanisms behind memory B cell and plasma cell differentiation in germinal centers are not well understood. However, it has been suggested that cell fate is (partially) determined by asymmetric cell division, which involves the unequal distribution of cellular components to both daughter cells. To investigate what level and/or probability of asymmetric segregation of several fate determinant molecules, such as the antigen and transcription factors (BCL6, IRF4, and BLIMP1) recapitulates the temporal switch and DZ-to-LZ ratio in the germinal center, we implemented a multiscale model that combines a core gene regulatory network for plasma cell differentiation with a model describing the cellular interactions and dynamics in the germinal center. Our simulations show that BLIMP1 driven plasma cell differentiation together with coupled asymmetric division of antigen and BLIMP1 with a large segregation between the daughter cells results in a germinal center DZ-to-LZ ratio and a temporal switch from memory B cells to plasma cells that have been observed in experiments.

Integrative Analysis of Proteome and Transcriptome Dynamics during Bacillus subtilis Spore Revival.

Bacillus subtilis spores can reactivate their metabolism through germination upon contact with germinants and can develop into vegetative cells upon outgrowth. However, the mechanisms at the basis of the molecular machinery that triggers the spore germination and outgrowth processes are still largely unclear. To gain further insights into these processes, the transcriptome and proteome changes occurring during the conversion of spores to vegetative cells were analyzed in the present study. For each time point sampled, the changes in the spore proteome were quantitatively monitored relative to the proteome of metabolically 15N-labeled vegetative cells. Of the quantified proteins, 60% are shared by vegetative cells and spores, indicating that the spores have a minimal protein set, sufficient to resume metabolism upon completion of germination. These shared proteins thus represent the most basic "survival kit" for spore-based life. We observed no significant change in the proteome or the transcriptome until the spore's completion of germination. Our analysis identified 34 abundant mRNA transcripts in the dormant spores, 31 of which are rapidly degraded after germination. In outgrowing spores, we identified 3,152 differentially expressed genes and have demonstrated the differential expression of 322 proteins with our mass spectrometry analyses. Our data also showed that 173 proteins from dormant spores, including both proteins unique to spores and proteins shared with vegetative cells, were lost after completion of germination. The observed diverse timings of synthesis of different protein sets in spore outgrowth revealed a putative core strategy underlying the revival of 'life' from the B. subtilis spore.IMPORTANCE This study demonstrated the progress of macromolecular synthesis during Bacillus subtilis spore germination and outgrowth. The transcriptome analysis has additionally allowed us to trace gene expression during this transformation process. For the first time, the basic survival kit for spore-based life has been identified. In addition, in this analysis based on monitoring of protein levels in germinating and outgrowing spores, the transition from (ribo)nucleotide and amino acid biosynthesis to the restoration of all metabolic pathways can be clearly seen. The integrative multi-omics approach applied in this study thus has helped us to achieve a comprehensive overview of the molecular mechanisms at the basis of spore germination and outgrowth as well as to identify important knowledge gaps in need of further study.

Multiscale Modeling of Germinal Center Recapitulates the Temporal Transition From Memory B Cells to Plasma Cells Differentiation as Regulated by Antigen Affinity-Based Tfh Cell Help.

Germinal centers play a key role in the adaptive immune system since they are able to produce memory B cells and plasma cells that produce high affinity antibodies for an effective immune protection. The mechanisms underlying cell-fate decisions are not well understood but asymmetric division of antigen, B-cell receptor affinity, interactions between B-cells and T follicular helper cells (triggering CD40 signaling), and regulatory interactions of transcription factors have all been proposed to play a role. In addition, a temporal switch from memory B-cell to plasma cell differentiation during the germinal center reaction has been shown. To investigate if antigen affinity-based Tfh cell help recapitulates the temporal switch we implemented a multiscale model that integrates cellular interactions with a core gene regulatory network comprising BCL6, IRF4, and BLIMP1. Using this model we show that affinity-based CD40 signaling in combination with asymmetric division of B-cells result in switch from memory B-cell to plasma cell generation during the course of the germinal center reaction. We also show that cell fate division is unlikely to be (solely) based on asymmetric division of Ag but that BLIMP1 is a more important factor. Altogether, our model enables to test the influence of molecular modulations of the CD40 signaling pathway on the production of germinal center output cells.

Repeatability and reproducibility of lipoprotein particle profile measurements in plasma samples by ultracentrifugation.

Background Characterization of lipoprotein particle profiles (LPPs) (including main classes and subclasses) by means of ultracentrifugation (UC) is highly requested given its clinical potential. However, rapid methods are required to replace the very labor-intensive UC method and one solution is to calibrate rapid nuclear magnetic resonance (NMR)-based prediction models, but the reliability of the UC-response method required for the NMR calibration has been largely overlooked. Methods This study provides a comprehensive repeatability and reproducibility study of various UC-based lipid measurements (cholesterol, triglycerides [TGs], free cholesterol, phospholipids, apolipoprotein [apo]A1 and apoB) in different main classes and subclasses of 25 duplicated fresh plasma samples and of 42 quality control (QC) frozen pooled plasma samples of healthy individuals. Results Cholesterol, apoA1 and apoB measurements were very repeatable in all classes (intraclass correlation coefficient [ICC]: 92.93%-99.54%). Free cholesterol and phospholipid concentrations in main classes and subclasses and TG concentrations in high-density lipoproteins (HDL), HDL subclasses and low-density lipoproteins (LDL) subclasses, showed worse repeatability (ICC: 19.21%-99.08%) attributable to low concentrations, variability introduced during UC and assay limitations. On frozen QC samples, the reproducibility of cholesterol, apoA1 and apoB concentrations was found to be better than for the free cholesterol, phospholipids and TGs concentrations. Conclusions This study shows that for LPPs measurements near or below the limit of detection (LOD) in some of the subclasses, as well as the use of frozen samples, results in worsened repeatability and reproducibility. Furthermore, we show that the analytical assay coupled to UC for free cholesterol and phospholipids have different repeatability and reproducibility. All of this needs to be taken into account when calibrating future NMR-based models.

Proof of concept study of age-dependent DNA methylation markers across different tissues by massive parallel sequencing.

The use of DNA methylation (DNAm) for chronological age determination has been widely investigated within the last few years for its application within the field of forensic genetics. The majority of forensic studies are based on blood, saliva, and buccal cell samples, respectively. Although these types of samples represent an extensive amount of traces found at a crime scene or are readily available from individuals, samples from other tissues can be relevant for forensic investigations. Age determination could be important for cases involving unidentifiable bodies and based on remaining soft tissue e.g. brain and muscle, or completely depend on hard tissue such as bone. However, due to the cell type specificity of DNAm, it is not evident whether cell type specific age-dependent CpG positions are also applicable for age determination in other cell types. Within this pilot study, we investigated whether 13 previously selected age-dependent loci based on whole blood analysis including amongst others ELOVL2, TRIM59, F5, and KLF14 also have predictive value in other forensically relevant tissues. Samples of brain, bone, muscle, buccal swabs, and whole blood of 29 deceased individuals (age range 0-87 years) were analyzed for these 13 age-dependent markers using massive parallel sequencing. Seven of these loci did show age-dependency in all five tissues. The change of DNAm during lifetime was different in the set of tissues analyzed, and sometimes other CpG sites within the loci showed a higher age-dependency. This pilot study shows the potential of existing blood DNAm markers for age-determination to analyze other tissues than blood. We identified seven known blood-based DNAm markers for use in muscle, brain, bone, buccal swabs, and blood. Nevertheless, a different reference set for each tissue is needed to adapt for tissue-specific changes of the DNAm over time.

Training for translation between disciplines: a philosophy for life and data sciences curricula.

Motivation: Our society has become data-rich to the extent that research in many areas has become impossible without computational approaches. Educational programmes seem to be lagging behind this development. At the same time, there is a growing need not only for strong data science skills, but foremost for the ability to both translate between tools and methods on the one hand, and application and problems on the other. Results: Here we present our experiences with shaping and running a masters' programme in bioinformatics and systems biology in Amsterdam. From this, we have developed a comprehensive philosophy on how translation in training may be achieved in a dynamic and multidisciplinary research area, which is described here. We furthermore describe two requirements that enable translation, which we have found to be crucial: sufficient depth and focus on multidisciplinary topic areas, coupled with a balanced breadth from adjacent disciplines. Finally, we present concrete suggestions on how this may be implemented in practice, which may be relevant for the effectiveness of life science and data science curricula in general, and of particular interest to those who are in the process of setting up such curricula. Supplementary information: Supplementary data are available at Bioinformatics online.

Dynamic elementary mode modelling of non-steady state flux data.

BACKGROUND: A novel framework is proposed to analyse metabolic fluxes in non-steady state conditions, based on the new concept of dynamic elementary mode (dynEM): an elementary mode activated partially depending on the time point of the experiment. RESULTS: Two methods are introduced here: dynamic elementary mode analysis (dynEMA) and dynamic elementary mode regression discriminant analysis (dynEMR-DA). The former is an extension of the recently proposed principal elementary mode analysis (PEMA) method from steady state to non-steady state scenarios. The latter is a discriminant model that permits to identify which dynEMs behave strongly different depending on the experimental conditions. Two case studies of Saccharomyces cerevisiae, with fluxes derived from simulated and real concentration data sets, are presented to highlight the benefits of this dynamic modelling. CONCLUSIONS: This methodology permits to analyse metabolic fluxes at early stages with the aim of i) creating reduced dynamic models of flux data, ii) combining many experiments in a single biologically meaningful model, and iii) identifying the metabolic pathways that drive the organism from one state to another when changing the environmental conditions.

Global testing of shifts in metabolic phenotype.

INTRODUCTION: Current metabolomics approaches to unravel impact of diet- or lifestyle induced phenotype variation and shifts predominantly deploy univariate or multivariate approaches, with a posteriori interpretation at pathway level. This however often provides only a fragmented view on the involved metabolic pathways. OBJECTIVES: To demonstrate the feasibility of using Goeman's global test (GGT) for assessment of variation and shifts in metabolic phenotype at the level of a priori defined pathways. METHODS: Two intervention studies with identified phenotype variations and shifts were examined. In a weight loss (WL) intervention study obese subjects received a mixed meal challenge before and after WL. In a polyphenol (PP) intervention study obese subjects received a high fat mixed meal challenge (61E% fat) before and after a PP intervention. Plasma samples were obtained at fasting and during the postprandial response. Besides WL- and PP-induced phenotype shifts, also correlation of plasma metabolome with phenotype descriptors was assessed at pathway level. The plasma metabolome covered organic acids, amino acids, biogenic amines, acylcarnitines and oxylipins. RESULTS: For the population of the WL study, GGT revealed that HOMA correlated with the fasting levels of the TCA cycle, BCAA catabolism, the lactate, arginine-proline and phenylalanine-tyrosine pathways. For the population of the PP study, HOMA correlated with fasting metabolite levels of TCA cycle, fatty acid oxidation and phenylalanine-tyrosine pathways. These correlations were more pronounced for metabolic pathways in the fasting state, than during the postprandial response. The effect of the WL and PP intervention on a priori defined metabolic pathways, and correlation of pathways with insulin sensitivity as described by HOMA was in line with previous studies. CONCLUSION: GGT confirmed earlier biological findings in a hypothesis led approach. A main advantage of GGT is that it provides a direct view on involvement of a priori defined pathways in phenotype shifts.

Forensic DNA methylation profiling from minimal traces: How low can we go?

Analysis of human DNA methylation (DNAm) can provide additional investigative leads in crime cases, e.g. the type of tissue or body fluid, the chronological age of an individual, and differentiation between identical twins. In contrast to the genetic profile, the DNAm level is not the same in every cell. At the single cell level, DNAm represents a binary event at a defined CpG site (methylated versus non-methylated). The DNAm level from a DNA extract however represents the average level of methylation of the CpG of interest of all molecules in the forensic sample. The variance of DNAm levels between replicates is often attributed to technological issues, i.e. degradation of DNA due to bisulfite treatment, preferential amplification of DNA, and amplification failure. On the other hand, we show that stochastic variations can lead to gross fluctuation in the analysis of methylation levels in samples with low DNA levels. This stochasticity in DNAm results is relevant since low DNA amounts (1pg - 1ng) is rather the norm than the exception when analyzing forensic DNA samples. This study describes a conceptual analysis of DNAm profiling and its dependence on the amount of input DNA. We took a close look at the variation of DNAm analysis due to DNA input and its consequences for different DNAm-based forensic applications. As can be expected, the 95%-confidence interval of measured DNAm becomes narrower with increasing amounts of DNA. We compared this aspect for two different DNAm-based forensic applications: body fluid identification and chronological age determination. Our study shows that DNA amount should be well considered when using DNAm for forensic applications.

Stoichiometry, Absolute Abundance, and Localization of Proteins in the Bacillus cereus Spore Coat Insoluble Fraction Determined Using a QconCAT Approach.

Spores of Bacillus cereus pose a threat to food safety due to their high resistance to the heat or acid treatments commonly used to make food microbiologically safe. Spores may survive these treatments and later resume growth either on foodstuffs or, after ingestion, upon entering the gut they are capable of producing toxins, which cause either vomiting or diarrhea. The outer layers of the spore, the spore coat and exosporium, consist primarily of proteins that may serve as potential biomarkers for detection. The major morphogenetic protein CotE is important for correct assembly and attachment of the outermost layer, the exosporium, and by extension retention of many proteins. However, characterization of the proteins affected by deletion of CotE has been limited to electrophoretic patterns. Here we report the effect of CotE deletion on the insoluble fraction of the spore proteome through liquid chromatography-Fourier transform tandem mass spectrometry (LC-FTMS/MS) analysis. A total of 560 proteins have been identified in both mutant and wild-type spore coat isolates. A further 163 proteins were identified exclusively in wild-type spore isolates indicating that they are dependent on CotE for their association with the spore. Several of these are newly confirmed as associated with the exosporium, namely BC_2569 (BclF), BC_3345, BC_2427, BC_2878, BC_0666, BC_2984, BC_3481, and BC_2570. A total of 153 proteins were only identified in ΔCotE spore isolates. This was observed for proteins that are known or likely to be interacting with or are encased by CotE. Crucial spore proteins were quantified using a QconCAT reference standard, the first time this was used in a biochemically heterogeneous system. This allowed us to determine the absolute abundance of 21 proteins, which spanned across three orders of magnitude and together covered 5.66% ± 0.51 of the total spore weight. Applying the QconCAT methodology to the ΔCotE mutant allowed us to quantify 4.13% ± 0.14 of the spore total weight and revealed a reduction in abundance for most known exosporium associated proteins upon CotE deletion. In contrast, several proteins, either known or likely to be interacting with or encased by CotE (i.e., GerQ), were more abundant. The results obtained provide deeper insight into the layered spore structure such as which proteins are exposed on the outside of the spore. This information is important for developing detection methods for targeting spores in a food safety setting. Furthermore, protein stoichiometry and determination of the abundance of germination mediating enzymes provides useful information for germination and outgrowth model development.

Genetic Components of Root Architecture Remodeling in Response to Salt Stress.

Salinity of the soil is highly detrimental to plant growth. Plants respond by a redistribution of root mass between main and lateral roots, yet the genetic machinery underlying this process is still largely unknown. Here, we describe the natural variation among 347 Arabidopsis thaliana accessions in root system architecture (RSA) and identify the traits with highest natural variation in their response to salt. Salt-induced changes in RSA were associated with 100 genetic loci using genome-wide association studies. Two candidate loci associated with lateral root development were validated and further investigated. Changes in CYP79B2 expression in salt stress positively correlated with lateral root development in accessions, and cyp79b2 cyp79b3 double mutants developed fewer and shorter lateral roots under salt stress, but not in control conditions. By contrast, high HKT1 expression in the root repressed lateral root development, which could be partially rescued by addition of potassium. The collected data and multivariate analysis of multiple RSA traits, available through the Salt_NV_Root App, capture root responses to salinity. Together, our results provide a better understanding of effective RSA remodeling responses, and the genetic components involved, for plant performance in stress conditions.

Chronological age prediction based on DNA methylation: Massive parallel sequencing and random forest regression.

The use of DNA methylation (DNAm) to obtain additional information in forensic investigations showed to be a promising and increasing field of interest. Prediction of the chronological age based on age-dependent changes in the DNAm of specific CpG sites within the genome is one such potential application. Here we present an age-prediction tool for whole blood based on massive parallel sequencing (MPS) and a random forest machine learning algorithm. MPS allows accurate DNAm determination of pre-selected markers and neighboring CpG-sites to identify the best age-predictive markers for the age-prediction tool. 15 age-dependent markers of different loci were initially chosen based on publicly available 450K microarray data, and 13 finally selected for the age tool based on MPS (DDO, ELOVL2, F5, GRM2, HOXC4, KLF14, LDB2, MEIS1-AS3, NKIRAS2, RPA2, SAMD10, TRIM59, ZYG11A). Whole blood samples of 208 individuals were used for training of the algorithm and a further 104 individuals were used for model evaluation (age 18-69). In the case of KLF14, LDB2, SAMD10, and GRM2, neighboring CpG sites and not the initial 450K sites were chosen for the final model. Cross-validation of the training set leads to a mean absolute deviation (MAD) of 3.21 years and a root-mean square error (RMSE) of 3.97 years. Evaluation of model performance using the test set showed a comparable result (MAD 3.16 years, RMSE 3.93 years). A reduced model based on only the top 4 markers (ELOVL2, F5, KLF14, and TRIM59) resulted in a RMSE of 4.19 years and MAD of 3.24 years for the test set (cross validation training set: RMSE 4.63 years, MAD 3.64 years). The amplified region was additionally investigated for occurrence of SNPs in case of an aberrant DNAm result, which in some cases can be an indication for a deviation in DNAm. Our approach uncovered well-known DNAm age-dependent markers, as well as additional new age-dependent sites for improvement of the model, and allowed the creation of a reliable and accurate epigenetic tool for age-prediction without restriction to a linear change in DNAm with age.

Toward Reliable Lipoprotein Particle Predictions from NMR Spectra of Human Blood: An Interlaboratory Ring Test.

Lipoprotein profiling of human blood by 1H nuclear magnetic resonance (NMR) spectroscopy is a rapid and promising approach to monitor health and disease states in medicine and nutrition. However, lack of standardization of measurement protocols has prevented the use of NMR-based lipoprotein profiling in metastudies. In this study, a standardized NMR measurement protocol was applied in a ring test performed across three different laboratories in Europe on plasma and serum samples from 28 individuals. Data was evaluated in terms of (i) spectral differences, (ii) differences in LPD predictions obtained using an existing prediction model, and (iii) agreement of predictions with cholesterol concentrations in high- and low-density lipoproteins (HDL and LDL) particles measured by standardized clinical assays. ANOVA-simultaneous component analysis (ASCA) of the ring test spectral ensemble that contains methylene and methyl peaks (1.4-0.6 ppm) showed that 97.99% of the variance in the data is related to subject, 1.62% to sample type (serum or plasma), and 0.39% to laboratory. This interlaboratory variation is in fact smaller than the maximum acceptable intralaboratory variation on quality control samples. It is also shown that the reproducibility between laboratories is good enough for the LPD predictions to be exchangeable when the standardized NMR measurement protocol is followed. With the successful implementation of this protocol, which results in reproducible prediction of lipoprotein distributions across laboratories, a step is taken toward bringing NMR more into scope of prognostic and diagnostic biomarkers, reducing the need for less efficient methods such as ultracentrifugation or high-performance liquid chromatography (HPLC).

Genome-wide mapping of transcriptional enhancer candidates using DNA and chromatin features in maize.

BACKGROUND: While most cells in multicellular organisms carry the same genetic information, in each cell type only a subset of genes is being transcribed. Such differentiation in gene expression depends, for a large part, on the activation and repression of regulatory sequences, including transcriptional enhancers. Transcriptional enhancers can be located tens of kilobases from their target genes, but display characteristic chromatin and DNA features, allowing their identification by genome-wide profiling. Here we show that integration of chromatin characteristics can be applied to predict distal enhancer candidates in Zea mays, thereby providing a basis for a better understanding of gene regulation in this important crop plant. RESULT: To predict transcriptional enhancers in the crop plant maize (Zea mays L. ssp. mays), we integrated available genome-wide DNA methylation data with newly generated maps for chromatin accessibility and histone 3 lysine 9 acetylation (H3K9ac) enrichment in young seedling and husk tissue. Approximately 1500 intergenic regions, displaying low DNA methylation, high chromatin accessibility and H3K9ac enrichment, were classified as enhancer candidates. Based on their chromatin profiles, candidate sequences can be classified into four subcategories. Tissue-specificity of enhancer candidates is defined based on the tissues in which they are identified and putative target genes are assigned based on tissue-specific expression patterns of flanking genes. CONCLUSIONS: Our method identifies three previously identified distal enhancers in maize, validating the new set of enhancer candidates and enlarging the toolbox for the functional characterization of gene regulation in the highly repetitive maize genome.