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

Palmitate impairs circadian transcriptomics in muscle cells through histone modification of enhancers

Acknowledgements The authors are supported by grants from the Novo Nordisk Foundation (NNF14OC0011493 and NNF17OC0030088), EFSD/Novo Nordisk Foundation Future Leader Award (NNF21SA0072747), Swedish Diabetes Foundation (DIA2021-641 and DIA2021-645), Swedish Research Council (2015-00165 and 2018-02389), KID-funding (2-3591/2014), the Strategic Research Program in Diabetes at Karolinska Institutet (2009-1068), Marie Skłodowska-Curie Actions (European Commission, 675610 and 704978), and Novo Nordisk postdoctoral fellowship run in partnership with Karolinska Institutet. Additional support was received from the Novo Nordisk Foundation Center for Basic Metabolic Research at the University of Copenhagen (NNF18CC0034900).Peer reviewedPublisher PD

Elucidating the Role of Chromatin State and Transcription Factors on the Regulation of the Yeast Metabolic Cycle: A Multi-Omic Integrative Approach

The Yeast Metabolic Cycle (YMC) is a model system in which levels of around 60% of the yeast transcripts cycle over time. The spatial and temporal resolution provided by the YMC has revealed that changes in the yeast metabolic landscape and chromatin status can be related to cycling gene expression. However, the interplay between histone modifications and transcription factor activity during the YMC is still poorly understood. Here we apply an innovative statistical approach to integrate chromatin state (ChIP-seq) and gene expression (RNA-seq) data to investigate the transcriptional control during the YMC. By using the multivariate regression models N-PLS (Partial Least Squares) and MORE (Multi-Omics REgulation) methodologies, we assessed the contribution of histone marks and transcription factors to the regulation of gene expression in the YMC. We found that H3K18ac and H3K9ac were the most important histone modifications, whereas Sfp1, Hfi1, Pip2, Mig2, and Yhp1 emerged as the most relevant transcription factors. A significant association in the co-regulation of gene expression was found between H3K18ac and the transcription factors Pip2 (involved in fatty acids metabolism), Xbp1 (cyclin implicated in the regulation of carbohydrate and amino acid metabolism), and Hfi1 (involved in the formation of the SAGA complex). These results evidence the crucial role of histone lysine acetylation levels in the regulation of gene expression in the YMC through the coordinated action of transcription factors and lysine acetyltransferases

Systematic decoding of cis gene regulation defines context-dependent control of the multi-gene costimulatory receptor locus in human T cells

Cis-regulatory elements (CREs) interact with trans regulators to orchestrate gene expression, but how transcriptional regulation is coordinated in multi-gene loci has not been experimentally defined. We sought to characterize the CREs controlling dynamic expression of the adjacent costimulatory genes CD28, CTLA4 and ICOS, encoding regulators of T cell-mediated immunity. Tiling CRISPR interference (CRISPRi) screens in primary human T cells, both conventional and regulatory subsets, uncovered gene-, cell subset- and stimulation-specific CREs. Integration with CRISPR knockout screens and assay for transposase-accessible chromatin with sequencing (ATAC-seq) profiling identified trans regulators influencing chromatin states at specific CRISPRi-responsive elements to control costimulatory gene expression. We then discovered a critical CCCTC-binding factor (CTCF) boundary that reinforces CRE interaction with CTLA4 while also preventing promiscuous activation of CD28. By systematically mapping CREs and associated trans regulators directly in primary human T cell subsets, this work overcomes longstanding experimental limitations to decode context-dependent gene regulatory programs in a complex, multi-gene locus critical to immune homeostasis