Finite mixtures of matrix variate Poisson-log normal distributions for three-way count data.

MOTIVATION: Three-way data structures, characterized by three entities, the units, the variables and the occasions, are frequent in biological studies. In RNA sequencing, three-way data structures are obtained when high-throughput transcriptome sequencing data are collected for n genes across p conditions at r occasions. Matrix variate distributions offer a natural way to model three-way data and mixtures of matrix variate distributions can be used to cluster three-way data. Clustering of gene expression data is carried out as means of discovering gene co-expression networks. RESULTS: In this work, a mixture of matrix variate Poisson-log normal distributions is proposed for clustering read counts from RNA sequencing. By considering the matrix variate structure, full information on the conditions and occasions of the RNA sequencing dataset is simultaneously considered, and the number of covariance parameters to be estimated is reduced. We propose three different frameworks for parameter estimation: a Markov chain Monte Carlo-based approach, a variational Gaussian approximation-based approach, and a hybrid approach. Various information criteria are used for model selection. The models are applied to both real and simulated data, and we demonstrate that the proposed approaches can recover the underlying cluster structure in both cases. In simulation studies where the true model parameters are known, our proposed approach shows good parameter recovery. AVAILABILITY AND IMPLEMENTATION: The GitHub R package for this work is available at https://github.com/anjalisilva/mixMVPLN and is released under the open source MIT license.

A multivariate Poisson-log normal mixture model for clustering transcriptome sequencing data.

BACKGROUND: High-dimensional data of discrete and skewed nature is commonly encountered in high-throughput sequencing studies. Analyzing the network itself or the interplay between genes in this type of data continues to present many challenges. As data visualization techniques become cumbersome for higher dimensions and unconvincing when there is no clear separation between homogeneous subgroups within the data, cluster analysis provides an intuitive alternative. The aim of applying mixture model-based clustering in this context is to discover groups of co-expressed genes, which can shed light on biological functions and pathways of gene products. RESULTS: A mixture of multivariate Poisson-log normal (MPLN) model is developed for clustering of high-throughput transcriptome sequencing data. Parameter estimation is carried out using a Markov chain Monte Carlo expectation-maximization (MCMC-EM) algorithm, and information criteria are used for model selection. CONCLUSIONS: The mixture of MPLN model is able to fit a wide range of correlation and overdispersion situations, and is suited for modeling multivariate count data from RNA sequencing studies. All scripts used for implementing the method can be found at https://github.com/anjalisilva/MPLNClust .

Proanthocyanidin accumulation and transcriptional responses in the seed coat of cranberry beans (Phaseolus vulgaris L.) with different susceptibility to postharvest darkening.

BACKGROUND: Edible dry beans (Phaseolus vulgaris L.) that darken during postharvest storage are graded lower and are less marketable than their non-darkened counterparts. Seed coat darkening in susceptible genotypes is dependent upon the availability of proanthocyanidins, and their subsequent oxidation to reactive quinones. Mature cranberry beans lacking this postharvest darkening trait tend to be proanthocyanidin-deficient, although the underlying molecular and biochemical determinants for this metabolic phenomenon are unknown. RESULTS: Seed coat proanthocyanidin levels increased with plant maturation in a darkening-susceptible cranberry bean recombinant inbred line (RIL), whereas these metabolites were absent in seeds of the non-darkening RIL plants. RNA sequencing (RNA-seq) analysis was used to monitor changes in the seed coat transcriptome as a function of bean development, where transcript levels were measured as fragments per kilobase of exon per million fragments mapped. A total of 1336 genes were differentially expressed between darkening and non-darkening cranberry bean RILs. Structural and regulatory genes of the proanthocyanidin biosynthesis pathway were upregulated in seed coats of the darkening RIL. A principal component analysis determined that changes in transcript levels for two genes of unknown function and three proanthocyanidin biosynthesis genes, FLAVANONE 3-HYDROXYLASE 1, DIHYDROFLAVONOL 4-REDUCTASE 1 and ANTHOCYANIDIN REDUCTASE 1 (PvANR1) were highly correlated with proanthocyanidin accumulation in seed coats of the darkening-susceptible cranberry bean RIL. HPLC-DAD analysis revealed that in vitro activity of a recombinant PvANR1 was NADPH-dependent and assays containing cyanidin yielded epicatechin and catechin; high cyanidin substrate levels inhibited the formation of both of these products. CONCLUSION: Proanthocyanidin oxidation is a pre-requisite for postharvest-related seed coat darkening in dicotyledonous seeds. In model plant species, the accumulation of proanthocyanidins is dependent upon upregulation of biosynthetic genes. In this study, proanthocyanidin production in cranberry bean seed coats was strongly associated with an increase in PvANR1 transcripts during seed maturation. In the presence of NADPH, PvANR1 converted the physiologically relevant substrate cyanidin to epicatechin and catechin.

Identification of multiple phosphorylation sites on maize endosperm starch branching enzyme IIb, a key enzyme in amylopectin biosynthesis.

Starch branching enzyme IIb (SBEIIb) plays a crucial role in amylopectin biosynthesis in maize endosperm by defining the structural and functional properties of storage starch and is regulated by protein phosphorylation. Native and recombinant maize SBEIIb were used as substrates for amyloplast protein kinases to identify phosphorylation sites on the protein. A multidisciplinary approach involving bioinformatics, site-directed mutagenesis, and mass spectrometry identified three phosphorylation sites at Ser residues: Ser(649), Ser(286), and Ser(297). Two Ca(2+)-dependent protein kinase activities were partially purified from amyloplasts, termed K1, responsible for Ser(649) and Ser(286) phosphorylation, and K2, responsible for Ser(649) and Ser(297) phosphorylation. The Ser(286) and Ser(297) phosphorylation sites are conserved in all plant branching enzymes and are located at opposite openings of the 8-stranded parallel ß-barrel of the active site, which is involved with substrate binding and catalysis. Molecular dynamics simulation analysis indicates that phospho-Ser(297) forms a stable salt bridge with Arg(665), part of a conserved Cys-containing domain in plant branching enzymes. Ser(649) conservation appears confined to the enzyme in cereals and is not universal, and is presumably associated with functions specific to seed storage. The implications of SBEIIb phosphorylation are considered in terms of the role of the enzyme and the importance of starch biosynthesis for yield and biotechnological application.

Nitrogen limitation and high density responses in rice suggest a role for ethylene under high density stress.

BACKGROUND: High density stress, also known as intraspecies competition, causes significant yield losses in a wide variety of crop plants. At the same time, increases in density tolerance through selective breeding and the concomitant ability to plant crops at a higher population density has been one of the most important factors in the development of high yielding modern cultivars. RESULTS: Physiological changes underlying high density stress were examined in Oryza sativa plants over the course of a life cycle by assessing differences in gene expression and metabolism. Moreover, the nitrogen limitation was examined in parallel with high density stress to gain a better understanding of physiological responses specific to high density stress. While both nitrogen limitation and high density resulted in decreased shoot fresh weight, tiller number, plant height and chlorophyll content, high density stress alone had a greater impact on physiological factors. Decreases in aspartate and glutamate concentration were found in plants grown under both stress conditions; however, high density stress had a more significant effect on the concentration of these amino acids. Global transcriptome analysis revealed a large proportion of genes with altered expression in response to both stresses. The presence of ethylene-associated genes in a majority of density responsive genes was investigated further. Expression of ethylene biosynthesis genes ACC synthase 1, ACC synthase 2 and ACC oxidase 7 were found to be upregulated in plants under high density stress. Plants at high density were also found to up regulate ethylene-associated genes and senescence genes, while cytokinin response and biosynthesis genes were down regulated, consistent with higher ethylene production. CONCLUSIONS: High density stress has similar but greater impact on plant growth and development compared to nitrogen limitation. Global transcriptome changes implicate ethylene as a volatile signal used to communicate proximity in under dense population growth condition and suggest a role for phytohormones in high density stress response in rice plants.

Genome-wide expression profiling of maize in response to individual and combined water and nitrogen stresses.

BACKGROUND: Water and nitrogen are two of the most critical inputs required to achieve the high yield potential of modern corn varieties. Under most agricultural settings however they are often scarce and costly. Fortunately, tremendous progress has been made in the past decades in terms of modeling to assist growers in the decision making process and many tools are now available to achieve more sustainable practices both environmentally and economically. Nevertheless large gaps remain between our empirical knowledge of the physiological changes observed in the field in response to nitrogen and water stresses, and our limited understanding of the molecular processes leading to those changes. RESULTS: This work examines in particular the impact of simultaneous stresses on the transcriptome. In a greenhouse setting, corn plants were grown under tightly controlled nitrogen and water conditions, allowing sampling of various tissues and stress combinations. A microarray profiling experiment was performed using this material and showed that the concomitant presence of nitrogen and water limitation affects gene expression to an extent much larger than anticipated. A clustering analysis also revealed how the interaction between the two stresses shapes the patterns of gene expression over various levels of water stresses and recovery. CONCLUSIONS: Overall, this study suggests that the molecular signature of a specific combination of stresses on the transcriptome might be as unique as the impact of individual stresses, and hence underlines the difficulty to extrapolate conclusions obtained from the study of individual stress responses to more complex settings.

Clustering microbiome data using mixtures of logistic normal multinomial models.

Discrete data such as counts of microbiome taxa resulting from next-generation sequencing are routinely encountered in bioinformatics. Taxa count data in microbiome studies are typically high-dimensional, over-dispersed, and can only reveal relative abundance therefore being treated as compositional. Analyzing compositional data presents many challenges because they are restricted to a simplex. In a logistic normal multinomial model, the relative abundance is mapped from a simplex to a latent variable that exists on the real Euclidean space using the additive log-ratio transformation. While a logistic normal multinomial approach brings flexibility for modeling the data, it comes with a heavy computational cost as the parameter estimation typically relies on Bayesian techniques. In this paper, we develop a novel mixture of logistic normal multinomial models for clustering microbiome data. Additionally, we utilize an efficient framework for parameter estimation using variational Gaussian approximations (VGA). Adopting a variational Gaussian approximation for the posterior of the latent variable reduces the computational overhead substantially. The proposed method is illustrated on simulated and real datasets.

Exploring the Use of Raman Spectroscopy and Covariate-Adjusted Multivariate Analysis for the Detection of Irradiated Blood.

Biological dosimetry is a key technique for retrospective radiation dosimetry that provides individual estimates of absorbed dose of ionizing radiation, applicable for use in a large scale radiological/nuclear event. Current techniques for biodosimetry are labor intensive and time consuming and not high through-put. In this proof-of-concept study, we developed a new approach for detecting irradiated blood based on Raman spectroscopy of blood combined with multivariate analysis. Peripheral blood samples from 8 healthy male and female, anonymous donors, were exposed to either 5 Gy X ray radiation or unirradiated (0 Gy). At 3 h postirradiation, the blood was immediately frozen at -80°C. Raman spectra were measured from thawed blood using a portable spectrometer system. Data were preprocessed and analyzed using principal component analysis (PCA) to observe trends in the data, and by using partial least squares-discriminant analysis (PLS-DA) to build a model to discriminate between Raman spectra of control (0 Gy) and irradiated (5 Gy) blood. We found strong evidence of inter-donor variability in the form of donor-wise clustering of PCA scores corresponding to the control Raman spectra, in addition to the poor separation of PLS-DA scores associated with Raman intensities of 0 Gy vs. 5 Gy spectra. However, after adjustment for donor covariates using a linear mixed-effects model, we obtained a better separation between control and irradiated blood using PLS-DA. Evaluation of the coefficients of the PLS-DA loading vectors indicated radiation-induced changes in proteins, lipids and hemoglobin to be major contributors for this discrimination. This pilot study demonstrates the potential of application of Raman spectroscopy to support biodosimetry of blood and blood components.

Polygenic adaptation to overnutrition reveals a role for cholinergic signaling in longevity.

Overnutrition by high-sugar (HS) feeding reduces both the lifespan and healthspan across taxa. Pressuring organisms to adapt to overnutrition can highlight genes and pathways important for the healthspan in stressful environments. We used an experimental evolution approach to adapt four replicate, outbred population pairs of Drosophila melanogaster to a HS or control diet. Sexes were separated and aged on either diet until mid-life, then mated to produce the next generation, allowing enrichment for protective alleles over time. All HS-selected populations increased their lifespan and were therefore used as a platform to compare allele frequencies and gene expression. Pathways functioning in the nervous system were overrepresented in the genomic data and showed evidence for parallel evolution, although very few genes were the same across replicates. Acetylcholine-related genes, including the muscarinic receptor mAChR-A, showed significant changes in allele frequency in multiple selected populations and differential expression on a HS diet. Using genetic and pharmacological approaches, we show that cholinergic signaling affects Drosophila feeding in a sugar-specific fashion. Together, these results suggest that adaptation produces changes in allele frequencies that benefit animals under conditions of overnutrition and that it is repeatable at the pathway level.

Adverse outcome pathways and linkages to transcriptomic effects relevant to ionizing radiation injury.

BACKGROUND: In the past three decades, a large body of data on the effects of exposure to ionizing radiation and the ensuing changes in gene expression has been generated. These data have allowed for an understanding of molecular-level events and shown a level of consistency in response despite the vast formats and experimental procedures being used across institutions. However, clarity on how this information may inform strategies for health risk assessment needs to be explored. An approach to bridge this gap is the adverse outcome pathway (AOP) framework. AOPs represent an illustrative framework characterizing a stressor associated with a sequential set of causally linked key events (KEs) at different levels of biological organization, beginning with a molecular initiating event (MIE) and culminating in an adverse outcome (AO). Here, we demonstrate the interpretation of transcriptomic datasets in the context of the AOP framework within the field of ionizing radiation by using a lung cancer AOP (AOP 272: https://www.aopwiki.org/aops/272) as a case example. METHODS: Through the mining of the literature, radiation exposure-related transcriptomic studies in line with AOP 272 related to lung cancer, DNA damage response, and repair were identified. The differentially expressed genes within relevant studies were collated and subjected to the pathway and network analysis using Reactome and GeneMANIA platforms. Identified pathways were filtered (p < .001, ≥3 genes) and categorized based on relevance to KEs in the AOP. Gene connectivities were identified and further grouped by gene expression-informed associated events (AEs). Relevant quantitative dose-response data were used to inform the directionality in the expression of the genes in the network across AEs. RESULTS: Reactome analyses identified 7 high-level biological processes with multiple pathways and associated genes that mapped to potential KEs in AOP 272. The gene connectivities were further represented as a network of AEs with associated expression profiles that highlighted patterns of gene expression levels. CONCLUSIONS: This study demonstrates the application of transcriptomics data in AOP development and provides information on potential data gaps. Although the approach is new and anticipated to evolve, it shows promise for improving the understanding of underlying mechanisms of disease progression with a long-term vision to be predictive of adverse outcomes.

Bacteria Remediate the Effects of Food Additives on Intestinal Function in an in vitro Model of the Gastrointestinal Tract.

As the site of nutrient absorption, the small intestine is continuously exposed to preservatives and additives present in consumed food. While the effects of diet on the lower gastrointestinal tract are widely studied, the effects of food additives on the small intestinal epithelium and microbiota are less clearly understood. The goal of this work was to develop and establish a physiologically relevant model of the upper gastrointestinal tract to study the complex interactions between food additives, individual bacterial species, and intestinal function. To achieve this, an in vitro model incorporating simulated digestion, human intestinal epithelial cells, and the commensal, Gram-positive Lactobacillus rhamnosus, or the opportunistic, Gram-negative Escherichia coli was developed. This model was used to assess intestinal permeability and alkaline phosphatase activity following exposure to high glucose (HG), salt, emulsifier (TWEEN 20), food (milk chocolate candies) or chemical grade titanium dioxide nanoparticles (TiO2-NP), and food (whole wheat bread) or chemical grade gluten. It was found that HG increased intestinal permeability, the presence of bacteria remediated the negative effects of HG on intestinal permeability, and a decrease in permeability and IAP activity was observed with increasing concentration of TWEEN 20 both in the presence and absence of bacteria. While L. rhamnosus influenced the activity of intestinal alkaline phosphatase and tight junction protein distribution, E. coli produced indole to reinstate intestinal permeability. The source of TiO2 and gluten led to altered impacts on permeability and IAP activity. The growth of E. coli and L. rhamnosus was found to depend on the type of food additive used. Overall, the presence of bacteria in the in vitro model influenced the effects of food additives on intestinal function, suggesting a complex association between diet and upper GI microbiota. This model provides a method to study small intestinal function and host-microbe interactions in vitro in both healthy and diseased conditions.

Metabolic Alterations in Postharvest Pear Fruit As Influenced by 1-Methylcyclopropene and Controlled Atmosphere Storage.

This study assessed the impact of 1-methylcyclopropene (1-MCP) and controlled atmosphere (CA) on the metabolism of targeted amino acids, organic acids, and antioxidants in stored 'AC Harrow Crisp' pears and their relationships to storage disorders. Pears were treated with 0 or 300 nL L-1 1-MCP and stored at 0 °C under ambient air or CA. Spectrophotometric assays demonstrated that glutathione levels fluctuated with storage and were most preserved by 1-MCP under ambient air. HPLC analysis revealed that ascorbate concentrations declined with storage and were little affected by 1-MCP and CA. Citrate, lactate, and fumarate accumulated with storage but were differentially affected by 1-MCP. Aspartate and glutamate concentrations were greater with 1-MCP; γ-aminobutyrate accumulated in disordered fruit. Principal component analysis demonstrated that alterations in citrate and fumarate were, respectively, correlated with internal breakdown and senescent scald. γ-Aminobutyrate and alanine were associated with internal cavities. All disorders were associated with antioxidant depletion.

Targeted quantitative profiling of metabolites and gene transcripts associated with 4-aminobutyrate (GABA) in apple fruit stored under multiple abiotic stresses.

4-Aminobutyrate accumulates in plants under abiotic stress. Here, targeted quantitative profiling of metabolites and transcripts was conducted to monitor glutamate- and polyamine-derived 4-aminobutyrate production and its subsequent catabolism to succinate or 4-hydroxybutyrate in apple (Malus x domestica Borkh.) fruit stored at 0 °C with 2.5 kPa O2 and 0.03 or 5 kPa CO2 for 16 weeks. Low-temperature-induced protein hydrolysis appeared to be responsible for the enhanced availability of amino acids during early storage, and the resulting higher glutamate level stimulated 4-aminobutyrate levels more than polyamines. Elevated CO2 increased the levels of polyamines, as well as succinate and 4-hydroxybutyrate, during early storage, and 4-aminobutyrate and 4-hydroxybutyrate over the longer term. Expression of all of the genes likely involved in 4-aminobutyrate metabolism from glutamate/polyamines to succinate/4-hydroxybutyrate was induced in a co-ordinated manner. CO2-regulated expression of apple GLUTAMATE DECARBOXYLASE 2, AMINE OXIDASE 1, ALDEHYDE DEHYDROGENASE 10A8 and POLYAMINE OXIDASE 2 was evident with longer term storage. Evidence suggested that respiratory activities were restricted by the elevated CO2/O2 environment, and that decreasing NAD+ availability and increasing NADPH and NADPH/NADP+, respectively, played key roles in the regulation of succinate and 4-hydroxybutyate accumulation. Together, these findings suggest that both transcriptional and biochemical mechanisms are associated with 4-aminobutyrate and 4-hydroxybutyrate metabolism in apple fruit stored under multiple abiotic stresses.

Marine fish oil is more potent than plant-based n-3 polyunsaturated fatty acids in the prevention of mammary tumors.

Marine-derived n-3 polyunsaturated fatty acids (PUFAs), such as eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), have been shown to inhibit mammary carcinogenesis. However, evidence regarding plant-based α-linolenic acid (ALA), the major n-3 PUFA in the Western diet, remains equivocal. The objective of this study was to examine the effect of lifelong exposure to plant- or marine-derived n-3 PUFAs on pubertal mammary gland and tumor development in MMTV-neu(ndl)-YD5 mice. It is hypothesized that lifelong exposure to n-3 PUFA reduces terminal end buds during puberty leading to delayed tumor onset, volume and multiplicity. It is further hypothesized that plant-derived n-3 PUFAs will exert dose-dependent effects. Harems of MMTV-FVB males were bred with wild-type females and fed either a (1) 10% safflower (10% SF, n-6 PUFA, control), (2) 10% flaxseed (10% FS), (3) 7% safflower plus 3% flaxseed (3% FS) or (4) 7% safflower plus 3% menhaden (3% FO) diet. Female offspring were maintained on parental diets. Compared to SF, 10% FS and 3% FO reduced (P<.05) terminal end buds at 6 weeks and tumor volume and multiplicity at 20 weeks. A dose-dependent reduction of tumor volume and multiplicity was observed in mice fed 3% and 10% FS. Antitumorigenic effects were associated with altered HER2, pHER-2, pAkt and Ki-67 protein expression. Compared to 10% SF, 3% FO significantly down-regulated expression of genes involved in eicosanoid synthesis and inflammation. From this, it can be estimated that ALA was 1/8 as potent as EPA+DHA. Thus, marine-derived n-3 PUFAs have greater potency versus plant-based n-3 PUFAs.

Evaluating Changes in Omega-3 Fatty Acid Intake after Receiving Personal FADS1 Genetic Information: A Randomized Nutrigenetic Intervention.

Nutrigenetics research is anticipated to lay the foundation for personalized dietary recommendations; however, it remains unclear if providing individuals with their personal genetic information changes dietary behaviors. Our objective was to evaluate if providing information for a common variant in the fatty acid desaturase 1 (FADS1) gene changed omega-3 fatty acid (FA) intake and blood levels in young female adults (18-25 years). Participants were randomized into Genetic (intervention) and Non-Genetic (control) groups, with measurements taken at Baseline and Final (12 weeks). Dietary intake of eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) was assessed using an omega-3 food frequency questionnaire. Red blood cell (RBC) FA content was quantified by gas chromatography. Implications of participation in a nutrigenetics study and awareness of omega-3 FAs were assessed with online questionnaires. Upon completion of the study, EPA and DHA intake increased significantly (p = 1.0 × 10-4) in all participants. This change was reflected by small increases in RBC %EPA. Participants in the Genetic group showed increased awareness of omega-3 terminology by the end of the study, reported that the dietary recommendations were more useful, and rated cost as a barrier to omega-3 consumption less often than those in the Non-Genetic group. Providing participants FADS1 genetic information did not appear to influence omega-3 intake during the 12 weeks, but did change perceptions and behaviors related to omega-3 FAs in this timeframe.

A genome-wide association study of multiple longitudinal traits with related subjects.

Pleiotropy is a phenomenon that a single gene inflicts multiple correlated phenotypic effects, often characterized as traits, involving multiple biological systems. We propose a two-stage method to identify pleiotropic effects on multiple longitudinal traits from a family-based data set. The first stage analyzes each longitudinal trait via a three-level mixed-effects model. Random effects at the subject-level and at the family-level measure the subject-specific genetic effects and between-subjects intraclass correlations within families, respectively. The second stage performs a simultaneous association test between a single nucleotide polymorphism and all subject-specific effects for multiple longitudinal traits. This is performed using a quasi-likelihood scoring method in which the correlation structure among related subjects is adjusted. Two simulation studies for the proposed method are undertaken to assess both the type I error control and the power. Furthermore, we demonstrate the utility of the two-stage method in identifying pleiotropic genes or loci by analyzing the Genetic Analysis Workshop 16 Problem 2 cohort data drawn from the Framingham Heart Study and illustrate an example of the kind of complexity in data that can be handled by the proposed approach. We establish that our two-stage method can identify pleiotropic effects whilst accommodating varying data types in the model.

The LASSO and sparse least square regression methods for SNP selection in predicting quantitative traits.

Recent work concerning quantitative traits of interest has focused on selecting a small subset of single nucleotide polymorphisms (SNPs) from amongst the SNPs responsible for the phenotypic variation of the trait. When considered as covariates, the large number of variables (SNPs) and their association with those in close proximity pose challenges for variable selection. The features of sparsity and shrinkage of regression coefficients of the least absolute shrinkage and selection operator (LASSO) method appear attractive for SNP selection. Sparse partial least squares (SPLS) is also appealing as it combines the features of sparsity in subset selection and dimension reduction to handle correlations amongst SNPs. In this paper we investigate application of the LASSO and SPLS methods for selecting SNPs that predict quantitative traits. We evaluate the performance of both methods with different criteria and under different scenarios using simulation studies. Results indicate that these methods can be effective in selecting SNPs that predict quantitative traits but are limited by some conditions. Both methods perform similarly overall but each exhibit advantages over the other in given situations. Both methods are applied to Canadian Holstein cattle data to compare their performance.