Metabolomic correlation-network modules in Arabidopsis based on a graph-clustering approach

<p>Abstract</p> <p>Background</p> <p>Deciphering the metabolome is essential for a better understanding of the cellular metabolism as a system. Typical metabolomics data show a few but significant correlations among metabolite levels when data sampling is repeated across individuals grown under strictly controlled conditions. Although several studies have assessed topologies in metabolomic correlation networks, it remains unclear whether highly connected metabolites in these networks have specific functions in known tissue- and/or genotype-dependent biochemical pathways.</p> <p>Results</p> <p>In our study of metabolite profiles we subjected root tissues to gas chromatography-time-of-flight/mass spectrometry (GC-TOF/MS) and used published information on the aerial parts of 3 <it>Arabidopsis </it>genotypes, Col-0 wild-type, <it>methionine over-accumulation 1 </it>(<it>mto1</it>), and <it>transparent testa4 </it>(<it>tt4</it>) to compare systematically the metabolomic correlations in samples of roots and aerial parts. We then applied graph clustering to the constructed correlation networks to extract densely connected metabolites and evaluated the clusters by biochemical-pathway enrichment analysis. We found that the number of significant correlations varied by tissue and genotype and that the obtained clusters were significantly enriched for metabolites included in biochemical pathways.</p> <p>Conclusions</p> <p>We demonstrate that the graph-clustering approach identifies tissue- and/or genotype-dependent metabolomic clusters related to the biochemical pathway. Metabolomic correlations complement information about changes in mean metabolite levels and may help to elucidate the organization of metabolically functional modules.</p

EXPLORATION OF REACTANT-PRODUCT LIPID PAIRS IN MUTANT-WILD TYPE LIPIDOMICS EXPERIMENTS

High-throughput metabolite analysis is very important for biologists to identify the functions of genes. A mutation in a gene encoding an enzyme is expected to alter the level of the metabolites which serve as the enzyme’s reactant(s) (also known as substrate) and product(s). To find the function of a mutated gene, metabolite data from a wild-type organism and a mutant are compared and candidate reactants and products are identified. The screening principle is that the concentration of reactants will be higher and the concentration of products will be lower in the mutant than in wild type. This is because the mutation reduces the reaction between the reactant and the product in the mutant organism. Based upon this principle, we suggest a method to screen metabolite pairs for candidate reactant-product pairs. Metrics are defined that quantify the effect of a mutation on each potential reaction, represented by a metabolite pair. For reactions catalyzed by well-characterized enzymes, one or more biologically functioning reactant-product pairs are known. Knowledge of the functional reactant-product pairs informs the development of the metrics. The goal is for ranking of the metrics for all possible pairs to reflect the likelihood that a particular metabolite pair is a functional reactant-product pair

Potential metabolic mechanism of girls' central precocious puberty: a network analysis on urine metabonomics data

BACKGROUND: Central precocious puberty (CPP) is a common pediatric endocrine disease caused by early activation of hypothalamic-putuitary-gonadal (HPG) axis, yet the exact mechanism was poorly understood. Although there were some proofs that an altered metabolic profile was involved in CPP, interpreting the biological implications at a systematic level is still in pressing need. To gain a systematic understanding of the biological implications, this paper analyzed the CPP differential urine metabolites from a network point of view. RESULTS: In this study, differential urine metabolites between CPP girls and age-matched normal ones were identified by LC-MS. Their basic topological parameters were calculated in the background network. The network decomposition suggested that CPP differential urine metabolites were most relevant to amino acid metabolism. Further proximity analysis of CPP differential urine metabolites and neuro-endocrine metabolites showed a close relationship between CPP metabolism and neuro-endocrine system. Then the core metabolic network of CPP was successfully constructed among all these differential urine metabolites. As can be demonstrated in the core network, abnormal aromatic amino acid metabolism might influence the activity of HPG and hypothalamic pituitary adrenal (HPA) axis. Several adjustments to the early activation of puberty in CPP girls could also be revealed by urine metabonomics. CONCLUSIONS: The present article demonstrated the ability of urine metabonomics to provide several potential metabolic clues for CPP's mechanism. It was revealed that abnormal metabolism of amino acid, especially aromatic amino acid, might have a close correlation with CPP's pathogenesis by activating HPG axis and suppressing HPA axis. Such a method of network-based analysis could also be applied to other metabonomics analysis to provide an overall perspective at a systematic level

Serum TCA cycle metabolites in Lewy bodies dementia and Alzheimer's disease: Network analysis and cognitive prognosis

Se han documentado anomalías en el ciclo del ácido tricarboxílico (TCA) en la demencia. A través del análisis de redes, los metabolitos del ciclo TCA podrían reflejar indirectamente anomalías conocidas relacionadas con la demencia en las vías bioquímicas, y los metabolitos clave podrían estar asociados con el pronóstico. Este estudio analizó los metabolitos del ciclo de TCA como predictores del deterioro cognitivo en una cohorte de demencia leve y exploró las posibles interacciones con el diagnóstico de demencia con cuerpos de Lewy (LBD) o enfermedad de Alzheimer (EA) y el genotipo APOE-ε4. Se incluyeron 145 pacientes con demencia leve (LBD = 59; AD = 86). Los metabolitos del ciclo de TCA en suero se analizaron al inicio del estudio y se realizaron redes de correlación parcial. El rendimiento cognitivo se midió anualmente durante 5 años con el Mini-examen del estado mental. Los modelos Tobit de efectos mixtos longitudinales evaluaron cada metabolito de referencia como predictor del deterioro cognitivo a los 5 años. Se exploraron las interacciones APOE-ε4 y de diagnóstico. Los resultados mostraron concentraciones de metabolitos comparables en LBD y AD. Las redes corregidas de pruebas múltiples mostraron coeficientes más grandes para una correlación negativa entre piruvato-succinato y correlaciones positivas entre fumarato-malato y citrato-isocitrato tanto en LBD como en AD. En la muestra total, los modelos mixtos ajustados mostraron asociaciones significativas entre la concentración inicial de citrato y las puntuaciones longitudinales del MMSE. En los portadores de APOE-ε4, el isocitrato inicial predijo las puntuaciones del MMSE. Concluimos que, en la demencia leve, las concentraciones de citrato sérico podrían estar asociadas con el deterioro cognitivo posterior, así como las concentraciones de isocitrato en los portadores de APOE-ε4. La regulación a la baja de la actividad enzimática en la primera mitad del ciclo TCA (deshidrogenasas descarboxiladoras), con regulación al alza en la segunda mitad (solo deshidrogenasas), podría reflejarse indirectamente en las redes de metabolitos del ciclo TCA sérico.Q2Abnormalities in the Tri-Carboxylic-Acid (TCA) cycle have been documented in dementia. Through network analysis, TCA cycle metabolites could indirectly reflect known dementia-related abnormalities in biochemical pathways, and key metabolites might be associated with prognosis. This study analyzed TCA cycle metabolites as predictors of cognitive decline in a mild dementia cohort and explored potential interactions with the diagnosis of Lewy Body Dementia (LBD) or Alzheimer's Disease (AD) and APOE-ε4 genotype. We included 145 mild dementia patients (LBD = 59; AD = 86). Serum TCA cycle metabolites were analyzed at baseline, and partial correlation networks were conducted. Cognitive performance was measured annually over 5-years with the Mini-mental State Examination. Longitudinal mixed-effects Tobit models evaluated each baseline metabolite as a predictor of 5-year cognitive decline. APOE-ε4 and diagnostic interactions were explored. Results showed comparable metabolite concentrations in LBD and AD. Multiple testing corrected networks showed larger coefficients for a negative correlation between pyruvate – succinate and positive correlations between fumarate – malate and citrate – Isocitrate in both LBD and AD. In the total sample, adjusted mixed models showed significant associations between baseline citrate concentration and longitudinal MMSE scores. In APOE-ε4 carriers, baseline isocitrate predicted MMSE scores. We conclude that, in mild dementia, serum citrate concentrations could be associated with subsequent cognitive decline, as well as isocitrate concentrations in APOE-ε4 carriers. Downregulation of enzymatic activity in the first half of the TCA cycle (decarboxylating dehydrogenases), with upregulation in the latter half (dehydrogenases only), might be indirectly reflected in serum TCA cycle metabolites' networks.https://orcid.org/0000-0001-5832-0603https://scholar.google.com/citations?user=MrICwaMAAAAJ&hl=enhttps://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001429659Revista Internacional - IndexadaS

Identifying aging-related genes in mouse hippocampus using gateway nodes

BACKGROUND: High-throughput studies continue to produce volumes of metadata representing valuable sources of information to better guide biological research. With a stronger focus on data generation, analysis models that can readily identify actual signals have not received the same level of attention. This is due in part to high levels of noise and data heterogeneity, along with a lack of sophisticated algorithms for mining useful information. Networks have emerged as a powerful tool for modeling high-throughput data because they are capable of representing not only individual biological elements but also different types of relationships en masse. Moreover, well-established graph theoretic methodology can be applied to network models to increase efficiency and speed of analysis. In this project, we propose a network model that examines temporal data from mouse hippocampus at the transcriptional level via correlation of gene expression. Using this model, we formally define the concept of “gateway” nodes, loosely defined as nodes representing genes co-expressed in multiple states. We show that the proposed network model allows us to identify target genes implicated in hippocampal aging-related processes. RESULTS: By mining gateway genes related to hippocampal aging from networks made from gene expression in young and middle-aged mice, we provide a proof-of-concept of existence and importance of gateway nodes. Additionally, these results highlight how network analysis can act as a supplement to traditional statistical analysis of differentially expressed genes. Finally, we use the gateway nodes identified by our method as well as functional databases and literature to propose new targets for study of aging in the mouse hippocampus. CONCLUSIONS: This research highlights the need for methods of temporal comparison using network models and provides a systems biology approach to extract information from correlation networks of gene expression. Our results identify a number of genes previously implicated in the aging mouse hippocampus related to synaptic plasticity and apoptosis. Additionally, this model identifies a novel set of aging genes previously uncharacterized in the hippocampus. This research can be viewed as a first-step for identifying the processes behind comparative experiments in aging that is applicable to any type of temporal multi-state network

Seeing the forest for the trees : retrieving plant secondary biochemical pathways from metabolome networks

Over the last decade, a giant leap forward has been made in resolving the main bottleneck in metabolomics, i.e., the structural characterization of the many unknowns. This has led to the next challenge in this research field: retrieving biochemical pathway information from the various types of networks that can be constructed from metabolome data. Searching putative biochemical pathways, referred to as biotransformation paths, is complicated because several flaws occur during the construction of metabolome networks. Multiple network analysis tools have been developed to deal with these flaws, while in silico retrosynthesis is appearing as an alternative approach. In this review, the different types of metabolome networks, their flaws, and the various tools to trace these biotransformation paths are discussed

Natural mutagenesis-enabled global proteomic study of metabolic and carbon source implications in mutant thermoacidophillic Archaeon Sulfolobus solfataricus PBL2025

The thermoacidophilic crenarchaeon Sulfolobus solfataricus has been widely used as a model organism for archaeal systems biology research. Investigation using its spontaneous mutant PBL2025 provides an effective metabolic baseline to study subsequent mutagenesis-induced functional process shifts as well as changes in feedback inhibitions. Here, an untargeted metabolic investigation using quantitative proteomics and metabolomics was performed to correlate changes in S. solfataricus strains P2 against PBL2025 and under both glucose and tryptone. The study is combined with pathway enrichment analysis to identify prominent proteins with differential stoichiometry. Proteome level quantification reveals that over 20% of the observed overlapping proteome is differentially expressed under these conditions. Metabolic-induced differential expressions are observed along the central carbon metabolism, along with 12 other significantly regulated pathways. Current findings suggest that PBL2025 is able to compensate through the induction of carbon metabolism, as well as other anabolic pathways such as Val, Leu and iso-Leu biosynthesis. Studying protein abundance changes after changes in carbon sources also reveals distinct differences in metabolic strategies employed by both strains, whereby a clear down-regulation of carbohydrate and nucleotide metabolism is observed for P2, while a mixed response through down-regulation of energy formation and up-regulation of glycolysis is observed for PBL2025. This study contributes, to date, the most comprehensive network of changes in carbohydrate and amino acid pathways using the complementary systems biology observations at the protein and metabolite levels. Current findings provide a unique insight into molecular processing changes through natural (spontaneous) metabolic rewiring, as well as a systems biology understanding of the metabolic elasticity of thermoacidophiles to environmental carbon source change, potentially guiding more efficient directed mutagenesis in archaea

Prediction methods for statistical inference in graph signal processing

This thesis studies the problem of inferring topology from signal graphs. For this reason, the Master's Thesis is part of the current of thought, growing in recent years, in which the structure of the network is not assumed to be known. The problem of inferring topology is approached from two angles. The first one, studies how to find the structure of a graph from spectral templates which can be noisy. Thus, from observations of the network, the spectral template of the graph that makes up the network is inferred. In previous works, like my Degree's Thesis, the algorithms for the inference of incomplete spectral templates were studied. In this Master's thesis, we go one step further by demonstrating why the techniques studied do not always work and proposing an algorithm based on LASSO to infer the network topology when the spectral templates are noisy. The proposed algorithm is compared with those previously studied obtaining better results in terms of RMSE and reliability. The second point of view addressed in this thesis is the inference of the network from statistical techniques. It is common to find networks whose nodes have some relation. These techniques are based on, from some observations of the network, trying to find the existing relationships between the different nodes of the graph. These techniques can be used in a more generic way than those based on spectral templates. Statistical methods are studied in more depth in this Master's Thesis. Initially, the Pearson correlation coefficient is explained. After studying it, some limitations are found. Thus, a new approach is proposed based on the conditional covariance. Then, it is assumed that the signals follow a Gaussian distribution which brings us to study the Maximum Likelihood estimator while considering the graph's sparsity. Although, the previous approach was improved, we are interested in finding even a better one. Hence, we study an approach based on linear regression. In this last algorithm, we include a term to promote sparsity when finding the solution. To conclude, the statistical methods studied, are compared by performing some simulations. By performing these simulations, it is observed that the best technique to infer the graph's topology is the one based on linear regression.Esta tesis estudia el problema de inferir la topología de la red a partir de las señales grafo. Por esta razón, la Tesis Final de Máster se inscribe en la corriente actual de pensamiento, creciente en los últimos años, en la que se supone no conocida la estructura de la red. El problema de la inferencia de la topología se aborda desde dos ángulos. El primero, estudia cómo encontrar la estructura de un grafo a partir de plantillas espectrales que pueden ser ruidosas o no. Así, a partir de las observaciones, se infiere la plantilla espectral del grafo que compone la red. En trabajos anteriores, como mi Tesis de Final de Grado, se estudiaron los algoritmos para la inferencia de plantillas espectrales incompletas. En esta trabajo, vamos un paso más allá demostrando por qué las técnicas estudiadas no siempre funcionan. Seguimos, proponiendo un algoritmo basado en LASSO para inferir la topología de la red cuando las plantillas espectrales son ruidosas. El algoritmo propuesto se compara con los anteriormente estudiados obteniendo mejores resultados en términos de RMSE y fiabilidad. El segundo punto de vista abordado en esta tesis es la inferencia de la red a partir de técnicas estadísticas. Es común encontrar redes cuyos nodos tienen alguna relación entre ellos. Estas técnicas se basan, a partir de algunas observaciones de la red, en tratar de encontrar las relaciones existentes entre los diferentes nodos del grafo. Estas técnicas pueden ser utilizadas de manera más genérica que las basadas en plantillas espectrales. Por ese motivo, los métodos estadísticos se estudian con más profundidad en esta Tesis de Máster. Inicialmente, se explica el coeficiente de correlación de Pearson. Después de estudiarlo, se detecta una limitación. Por ese motivo, se propone un nuevo enfoque basado en la covarianza condicional. Luego, se asume que las señales siguen una distribución Gaussiana, lo que nos lleva a estudiar el estimador de Máxima Verosimilitud, también considerando que la matriz solución es dispersa. Aunque con esta técnica se mejora el enfoque anterior, estamos interesados en encontrar una técnica aún mejor. Por lo tanto, estudiamos un enfoque basado en la regresión lineal. En este último algoritmo, incluimos un término para promover la que la solución sea una matriz dispersa. Para concluir, los métodos estadísticos estudiados, se comparan realizando algunas simulaciones. Al realizarlas, se observa que la mejor técnica para inferir la topología del grafo es la que se basa en la regresión lineal.Aquesta tesi estudia el problema d'inferir la topologia d'una xarxa a partir dels senyals graf. Per aquesta raó, la Tesi Final de Màster s'inscriu en el corrent actual de pensament, creixent en els darrers anys, en la que se suposa no coneguda l'estructura de la xarxa. El problema d'inferència de la topologia s'aborda des de dos angles. El primer, estudia com trobar l'estructura d'un graf a partir de plantilles espectrals que poden ser sorolloses o no. Així, a partir de les observacions, s'infereix la plantilla espectral del graf que compon la xarxa. En treballs anteriors, com la meva Tesi de Final de Grau, es van estudiar els algoritmes per a la inferència de plantilles espectrals incompletes. En aquest treball, anem un pas més enllà demostrant per què les tècniques estudiades no sempre funcionen. Seguim, proposant un algoritme basat en LASSO per inferir la topologia de la xarxa quan les plantilles espectrals són sorolloses. L'algoritme proposat es compara amb els anteriorment estudiats obtenint millors resultats en termes de RMSE i fiabilitat. El segon punt de vista abordat en aquesta tesi és la inferència de la xarxa a partir de tècniques estadístiques. És comú trobar xarxes on els nodes tenen alguna relació entre ells. Aquestes tècniques es basen, a partir d'algunes observacions de la xarxa, a tractar de trobar les relacions existents entre els diferents nodes del graf. Aquestes tècniques poden ser utilitzades de manera més genèrica que les basades en plantilles espectrals. Per aquest motiu, els mètodes estadístics s'estudien amb més profunditat en aquesta Tesi de Màster. Inicialment, s'explica el coeficient de correlació de Pearson. Després d'estudiar-lo, es detecta una limitació. Per aquest motiu, es proposa un nou enfocament basat en la covariància condicional. Després, s'assumeix que els senyals segueixen una distribució Gaussiana, el que ens porta a estudiar l'estimador de Màxima Versemblança, també considerant que la matriu solució és dispersa. Encara que amb aquesta tècnica es millora l'enfocament anterior, estem interessats a trobar una tècnica encara millor. Per tant, estudiem un enfocament basat en la regressió lineal. En aquest últim algoritme, incloem un terme per promoure que la solució sigui una matriu dispersa. Per concloure, els mètodes estadístics estudiats, es comparen realitzant algunes simulacions. En realitzar-les, s'observa que la millor tècnica per inferir la topologia del graf és la que es basa en la regressió lineal

Genes for seed quality : integrating physiology and genetical genomics to mine for seed quality genes in tomato

Seed quality in tomato is associated with many complex physiological and genetical traits. The performance of seeds is determined by three interlinked and interactive components that constitute a performance triangle of genetics, physiological quality and the environment. So far, there has been little or no discussion about the genetic analysis of seed and seedling traits in tomato at a systems level. To the best of our knowledge, the present study is the first systemic analysis of the genetics of seed and seedling traits, adding to a growing body of information on tomato seed quality. With the aim of improving the production of high-quality tomato seeds, a multidisciplinary study (physiology, genetics and genomics) was undertaken to develop and evaluate methods for improving the percentage, rate and uniformity of germination and early seedling development, and for increasing the range of environmental conditions for germination. Primarily, we explored the natural variation present in a Solanum lycopersicum x Solanum pimpinellifolium RIL population to dissect the molecular-genetic mechanisms controlling seed quality. Although previous solutions to issues associated with seed quality phenotypes seemed promising, none have utilized the integration of genomic, phenotypic and metabolic datasets to understand seed quality in tomato.Thus, the integration of metabolic and genomic analysis contributed to a comprehensive biological understanding of observed phenotypic differences between RILs of S. lycopersicumx S. pimpinellifolium. Here we describe, for the first time, the use of a generalized genetical genomics (GGG) model in tomato seeds that incorporates genetics, as well as environmental effects, and we applied this approach to map traditional quantitative trait loci (Genetic QTLs) and QTLs that are the result of interaction between the genetics and environmental changes (Genetic x Environmental QTLs). This model uses chosen environmental perturbations (different seed developmental stages, i.e. dry and 6h imbibed seeds) in combination with the analysis of genetic variation present in the RIL population, to study the change of metabolites over the multiple environments and to identify genotype-by-environment interactions. This thesis gives an account of the integration of genotyping, phenotyping and a molecular phenotype using metabolomics in generating a novel understanding of seed phenotypes and their interaction with the environment. In summary, the integration of phenotypic and metabolomics data has facilitated the identification of potential biomarkers for better understanding of the complex nature of tomato seed quality.</p