Signal metrics analysis of oscillatory patterns in bacterial multi-omic networks

Motivation: One of the branches of Systems Biology is focused on a deep understanding of underlying regulatory networks through the analysis of the biomolecules oscillations and their interplay. Synthetic Biology exploits gene or/and protein regulatory networks towards the design of oscillatory networks for producing useful compounds. Therefore, at different levels of application and for different purposes, the study of biomolecular oscillations can lead to different clues about the mechanisms underlying living cells. It is known that network-level interactions involve more than one type of biomolecule as well as biological processes operating at multiple omic levels. Combining network/pathway-level information with genetic information it is possible to describe well-understood or unknown bacterial mechanisms and organism-specific dynamics. Results: Network multi-omic integration has led to the discovery of interesting oscillatory signals. Following the methodologies used in signal processing and communication engineering, a new methodology is introduced to identify and quantify the extent of the multi-omic oscillations of the signal. New signal metrics are designed to allow further biotechnological explanations and provide important clues about the oscillatory nature of the pathways and their regulatory circuits. Our algorithms designed for the analysis of multi-omic signals are tested and validated on 11 different bacteria for thousands of multi-omic signals perturbed at the network level by different experimental conditions. Information on the order of genes, codon usage, gene expression, and protein molecular weight is integrated at three different functional levels. Oscillations show interesting evidence that network-level multi-omic signals present a synchronized response to perturbations and evolutionary relations along with taxa.Comment: 8 pages, 5 figure, 3 algorithms, journal pape

A study on multi-omic oscillations in Escherichia coli metabolic networks.

BACKGROUND: Two important challenges in the analysis of molecular biology information are data (multi-omic information) integration and the detection of patterns across large scale molecular networks and sequences. They are are actually coupled beause the integration of omic information may provide better means to detect multi-omic patterns that could reveal multi-scale or emerging properties at the phenotype levels. RESULTS: Here we address the problem of integrating various types of molecular information (a large collection of gene expression and sequence data, codon usage and protein abundances) to analyse the E.coli metabolic response to treatments at the whole network level. Our algorithm, MORA (Multi-omic relations adjacency) is able to detect patterns which may represent metabolic network motifs at pathway and supra pathway levels which could hint at some functional role. We provide a description and insights on the algorithm by testing it on a large database of responses to antibiotics. Along with the algorithm MORA, a novel model for the analysis of oscillating multi-omics has been proposed. Interestingly, the resulting analysis suggests that some motifs reveal recurring oscillating or position variation patterns on multi-omics metabolic networks. Our framework, implemented in R, provides effective and friendly means to design intervention scenarios on real data. By analysing how multi-omics data build up multi-scale phenotypes, the software allows to compare and test metabolic models, design new pathways or redesign existing metabolic pathways and validate in silico metabolic models using nearby species. CONCLUSIONS: The integration of multi-omic data reveals that E.coli multi-omic metabolic networks contain position dependent and recurring patterns which could provide clues of long range correlations in the bacterial genome

Social Dynamics Modeling of Chrono-nutrition

Gut microbiota and human relationships are strictly connected to each other. What we eat reflects our body-mind connection and synchronizes with people around us. However, how this impacts on gut microbiota and, conversely, how gut bacteria influence our dietary behaviors has not been explored yet. To quantify the complex dynamics of this interplay between gut and human behaviors we explore the ``gut-human behavior axis'' and its evolutionary dynamics in a real-world scenario represented by the social multiplex network. We consider a dual type of similarity, homophily and gut similarity, other than psychological and unconscious biases. We analyze the dynamics of social and gut microbial communities, quantifying the impact of human behaviors on diets and gut microbial composition and, backwards, through a control mechanism. Meal timing mechanisms and ``chrono-nutrition'' play a crucial role in feeding behaviors, along with the quality and quantity of food intake. Considering a population of shift workers, we explore the dynamic interplay between their eating behaviors and gut microbiota, modeling the social dynamics of chrono-nutrition in a multiplex network. Our findings allow us to quantify the relation between human behaviors and gut microbiota through the methodological introduction of gut metabolic modeling and statistical estimators, able to capture their dynamic interplay. Moreover, we find that the timing of gut microbial communities is slower than social interactions and shift-working, and the impact of shift-working on the dynamics of chrono-nutrition is a fluctuation of strategies with a major propensity for defection (e.g. high-fat meals). A deeper understanding of the relation between gut microbiota and the dietary behavioral patterns, by embedding also the related social aspects, allows improving the overall knowledge about metabolic models and their implications for human health, opening the possibility to design promising social therapeutic dietary interventions

A translational approach to studying preterm labour

Preterm labour continues to be a major contributor to neonatal and infant morbidity. Recent data from the USA indicate that the number of preterm deliveries (including those associated with preterm labour) has risen in the last 20 years by 30%. This increase is despite considerable efforts to introduce new therapies for the prevention and treatment of preterm labour and highlights the need to assess research in this area from a fresh perspective. In this paper we discuss i) the limitations of our knowledge concerning prediction, prevention and treatment of preterm labour and ii) future multidisciplinary strategies for improving our approach

Single cell ecology

Cells are the building blocks of life, from single-celled microbes through to multi-cellular organisms. To understand a multitude of biological processes we need to understand how cells behave, how they interact with each other and how they respond to their environment. The use of new methodologies is changing the way we study cells allowing us to study them on minute scales and in unprecedented detail. These same methods are allowing researchers to begin to sample the vast diversity of microbes that dominate natural environments. The aim of this special issue is to bring together research and perspectives on the application of new approaches to understand the biological properties of cells, including how they interact with other biological entities

The Brain-Gut-Microbiome Axis.

Preclinical and clinical studies have shown bidirectional interactions within the brain-gut-microbiome axis. Gut microbes communicate to the central nervous system through at least 3 parallel and interacting channels involving nervous, endocrine, and immune signaling mechanisms. The brain can affect the community structure and function of the gut microbiota through the autonomic nervous system, by modulating regional gut motility, intestinal transit and secretion, and gut permeability, and potentially through the luminal secretion of hormones that directly modulate microbial gene expression. A systems biological model is proposed that posits circular communication loops amid the brain, gut, and gut microbiome, and in which perturbation at any level can propagate dysregulation throughout the circuit. A series of largely preclinical observations implicates alterations in brain-gut-microbiome communication in the pathogenesis and pathophysiology of irritable bowel syndrome, obesity, and several psychiatric and neurologic disorders. Continued research holds the promise of identifying novel therapeutic targets and developing treatment strategies to address some of the most debilitating, costly, and poorly understood diseases

Assigning ecological roles to the populations belonging to a phenanthrene-degrading bacterial consortium using omic approaches

The present study describes the behavior of a natural phenanthrene-degrading consortium (CON), a synthetic consortium (constructed with isolated strains from CON) and an isolated strain form CON (Sphingobium sp. AM) in phenanthrene cultures to understand the interactions among the microorganisms present in the natural consortium during phenanthrene degradation as a sole carbon and energy source in liquid cultures. In the contaminant degradation assay, the defined consortium not only achieved a major phenanthrene degradation percentage (> 95%) but also showed a more efficient elimination of the intermediate metabolite. The opposite behavior occurred in the CON culture where the lowest phenanthrene degradation and the highest HNA accumulation were observed, which suggests the presence of positive and also negative interaction in CON. To consider the uncultured bacteria present in CON, a metagenomic library was constructed with total CON DNA. One of the resulting scaffolds (S1P3) was affiliated with the Betaproteobacteria class and resulted in a significant similarity with a genome fragment from Burkholderia sp. HB1 chromosome 1. A complete gene cluster, which is related to one of the lower pathways (meta-cleavage of catechol) involved in PAH degradation (ORF 31–43), mobile genetic elements and associated proteins, was found. These results suggest the presence of at least one other microorganism in CON besides Sphingobium sp. AM, which is capable of degrading PAH through the meta-cleavage pathway. Burkholderiales order was further found, along with Sphingomonadales order, by a metaproteomic approach, which indicated that both orders were metabolically active in CON. Our results show the presence of negative interactions between bacterial populations found in a natural consortium selected by enrichment techniques; moreover, the synthetic syntrophic processing chain with only one microorganism with the capability of degrading phenanthrene was more efficient in contaminant and intermediate metabolite degradation than a generalist strain (Sphingobium sp. AM).Centro de Investigación y Desarrollo en Fermentaciones Industriale

Bioinformatic approaches to study the metabolic effect on Gene Regulation

La adaptación celular a ambientes dinámicos constituye un mecanismo esencial para la supervivencia celular. Las células responden a condiciones externas modulando los mecanismos moleculares que regulan expresión génica o la actividad proteica, confiriendo una respuesta rápida a cambios metabólicos externos. Por ello, los mecanismos celulares que captan los cambios metabólicos consistuyen un paso importante en adaptación celular, siendo la epigenética el mecanismo que une el metabolismo con la regulación génica. Las marcas epigenéticas confieren a la célula la capacidad de moldear la conformacion de la cromatina, lo que permite la regulación de la expresión génica. Por tanto, un correcto funcionamiento de la regulación epigenética de la célula, es crucial para la adaptación celular a ambientes con cambios metabólicos. Los moduladores epigenéticos dependen de la disponibilidad meta\-bólica para poder modificar la epigenética de la célula. Estudios recientes han señalado que la acumulación de ciertos metabolitos es clave para que moduladores epigenéticos actúen sobre las marcas de la cromatina. Un ejemplo claro se ve en los ritmos circadianos, donde los mecanismos epigenéticos median la relación que existe entre las oscilaciones metabólicas y los cambios en expresión génica; la falta de mecanismos epigenéticos desconecta estos relojes moleculares, provocando enfermedades como en el caso del síndrome metabólico. El estudio del control metabólico del epigenoma y el transcriptoma es un área de conocimiento emergente. Muchos estudios han generado información a través de las tecnologías de alto rendimiento, que miden la expresión génica, los metabolitos o las modificaciones de histonas entre otros tipos de moleculas para medir esta conexión, y aunque se ha desarrollado mucha literatura al respecto, los mecanismos que ejercen la regulación de distintos tipos moleculares es todavía desconocida. Una necesidad en el ámbito de la bioinformática es el análisis integrativo de datos moleculares que propongan modelos de regulación detallados para conocer la relación entre metabolismo, cromatina y la transcripción. En este trabajo se ha aproximado la integración estadística de meta\-bolómica y distintos datos epigenéticos con la expresión génica. Hemos realizado estos análisis integrativos en el sistema modelo del ciclo metabólico de la levadura (YMC), en el cual la expresión génica se coordina con cambios en modificaciones de histonas y oscilaciones metabólicas. Primero analizamos el impacto de las modificaciones de histonas sobre la expresión génica, lo cual nos permitió identificar las marcas de histonas que coordinan los cambios en expresión. Después creamos un conjunto de datos multiómico obteniendo muestras de metabolómica y ATAC-seq en el YMC, e incorporamos un set de datos de NET-seq. Estos datos fueron usados para modelar el impacto de los cambios metabólicos y de la cromatina en la expresión génica y, por primera vez en ritmos biológicos, integramos los tres tipos de datos moleculares en un solo modelo usando PLS-Path Modelling, una estrategia multivariante que permite encontrar relaciones entre muchos conjuntos de datos multi dimensionales. Esta herramienta nos ha permitido conocer que la expresión génica en la fase oxidativa está regulada principalmente por la marca de histona H3K9ac, y la acumulación de ATP en esta parte del ciclo sugiere una regulación de la cromatina activando la enzima dependiente de ATP INO80. El resultado de PLS-PM también nos muestra que los derivados de la nicotinamida podrían afectar los niveles de H3K18ac en a fase RC del ciclo a través de la regulación de las sirtuinas, activando la respuesta de degradación de ácidos grasos. El aspartato también se ha asociado a la regulación epigenética de la fase RC, pero los mecanismos por los que esta asociación novedosa tienen lugar son aún desconocidos. Finalmente, hemos creado Padhoc, una herramienta computacional capaz de combinar el conocimiento existente en nuevos ámbitos de investigación -como el de este trabajo- para proponer modelos de redes metabólicas que compleneten el conocimiento de las bases de datos actuales. Esta tesis recopila la extracción de un conjunto de datos multiómicos que cubre metabolismo, epigenética y expresión génica, así como su análisis integrativo usando estrategias multivariantes novedosas que modelan la coordinación de las distintas moléculas estudiadas. Además, incluimos una herramienta para la reconstrucción de redes biológicas. En conjunto, esta tesis presenta distintas herramientas para estudiar el impacto metabólico en la expresión génica usando la biología computacional.Cellular adaptation to changing environments constitutes a critical mechanism for cell survival. Cells primarily respond to external conditions by modulating the molecular mechanisms that regulate gene expression or protein activity, granting a rapid response to external metabolic changes. Therefore, metabolic sensing constitutes an important step in cell adaptation, and epigenetics is now considered the mechanism that connects metabolic shifts with gene regulation. Epigenetic marks give cells the capability of shaping chromatin conformation, which in turn regulates gene expression. Consequently, the correct functioning of a cell's epigenetic program is critical for cellular adaptation to changing conditions. Different epigenetic modifiers rely on metabolite availability to modify the cell's epigenetic landscape. Recent studies point towards the accumulation of key metabolites as the critical mechanism by which epigenetic modifiers modulate the chromatin marks. This can be appreciated in circadian rhythms, where epigenetic changes mediate the cross-talk between metabolic oscillations and gene expression. Deficiencies that disconnect this molecular regulation lead to diseases, such as metabolic syndrome. The study of the metabolic control of the epigenome and transcriptome is an emerging field of research. Multiple studies have generated large, high-throughput datasets that measure gene expression, metabolites and histone modifications, among others, to study these interconnections; although a wealth of literature is accumulating, the precise mechanisms of these multi-layered regulations are still to be fully elucidated. Also, a consensus pathway describing these processes cannot yet be found in any of the common biological pathway databases. One critical need in the field is the integrative analysis of existing molecular data to propose detailed regulatory models for the interplay between metabolism, chromatin state and transcription. This thesis addresses the statistical integration of metabolomics and epigenetics measurements with gene expression. We approached this data analysis challenge using the Yeast Metabolic Cycle (YMC) as a model system. Gene expression at the YMC can be divided into three, well-defined phases where transcription is coordinated with histone modifications and metabolomics oscillations. First, we analyzed the impact of histone modifications on gene expression, which led to the identification of the histone marks that have a higher impact on gene expression changes. Next, we created a comprehensive, multi-layered, multi-omics dataset for this system by obtaining metabolomics and ATAC-Seq data of the YMC and incorporating an existing nascent transcription (NET-seq) dataset. Moreover, we modeled the impact of chromatin conformation and metabolic changes on gene expression, and created a regulatory model for gene expression, epigenetics and metabolomics by applying PLS Path Modeling, a multivariate strategy suitable for finding relationships across multiple high-dimensional datasets. To our knowledge, this is the first time that PLS-PM is used for the modelling of molecular regulatory layers. We found that gene expression in OX phase was mainly controlled by H3K9ac histone mark and ATP accumulation at this phase, suggesting INO80 ATP-dependent chromatin remodeling activity. We also found an enrichment of H3K18ac during RC phase, together with accumulation of nicotinamide and its derivatives, suggesting that sirtuins may regulate H3K18ac levels at RC to activate fatty acid oxidation response. Aspartate was also associated with RC phase epigenetic regulation, but the mechanisms by which this amino acid may control the epigenome are still unanswered. Finally, in this work, we have also created Padhoc, a computational pipeline to integrate the existing published knowledge in emerging research fields -such as those studied in this thesis- to propose pathway models that can complement current pathway databases. Altogether, this thesis involves the generation of a multi-omics dataset that covers metabolic, epigenetic and gene expression information, and their integrative analysis using novel multivariate strategies that model their mechanistic coordination. Moreover, it includes a framework for the reconstruction of biological pathways. All in all, we have presented different strategies by which to study the impact of metabolic changes in chromatin using computational biology approaches