Periodic Reporting for period 1 - MetChromTx (Macrophage metabolism and signal-induced chromatin and transcription changes: an integrated, multi-layer approach)
Reporting period: 2018-10-01 to 2020-09-30
Cells are special devices with the ability to integrate environmental signals. Prototypical example are macrophages, key components of the innate immune system, which are capable of reacting to environmental changes with distinct activities. Macrophages play key roles during homeostatic processes and immunity but are also involved in a variety of inflammatory diseases and cancers. As stated by the World Health Organization (WHO) chronic inflammatory diseases are the most significant cause of death in the world and macrophages are undoubtedly the major player. For these reasons a lot of effort has been invested in research studies aiming at better understanding macrophage biology and regulation. Nevertheless, the reciprocal influences that signal-induced transcriptional and metabolic changes have on one another have been overlooked.
The action “Macrophage metabolism and signal-induced chromatin and transcription changes: an integrated, multi-layer approach” aims to unravel key mechanistic principles during macrophage activation and to develop computational approaches for the integration of “big-omics” datasets into interpretable models. The generation of predictive models is essential for basic research to become more exploitable.
Specific objectives of this action have been to:
(a) Relate transcriptional changes to metabolic pathways activation and assess the role of specific transcription factors (TFs) through model assembly.
(b) Dissect the transcriptional effects driven by metabolic pathways focusing on specific key metabolites.
Given the challenging task to both develop and perform bench experiments and use my computational expertise to integrate the generated datasets Insilco this action allowed the development and scientific growth of the researcher.
The action “Macrophage metabolism and signal-induced chromatin and transcription changes: an integrated, multi-layer approach” aims to unravel key mechanistic principles during macrophage activation and to develop computational approaches for the integration of “big-omics” datasets into interpretable models. The generation of predictive models is essential for basic research to become more exploitable.
Specific objectives of this action have been to:
(a) Relate transcriptional changes to metabolic pathways activation and assess the role of specific transcription factors (TFs) through model assembly.
(b) Dissect the transcriptional effects driven by metabolic pathways focusing on specific key metabolites.
Given the challenging task to both develop and perform bench experiments and use my computational expertise to integrate the generated datasets Insilco this action allowed the development and scientific growth of the researcher.
Work was conducted via 3 main workpackages (WP). WP1 and WP2 have been dedicated to tackle the two objectives namely “How are transcriptional and metabolic changes coordinated over time during macrophage activation?” and “How are metabolic pathways co-opted to control signal-induced chromatin and transcriptional changes?” while WP3 aimed at the assembly of a database for the laboratory/institute first, and for dissemination upon publication of relevant articles.
WP1 comprised the main experimental and computational steps to relate transcriptional and metabolic changes. I was able to:
(WP1.1) Record transcriptional and metabolomic changes in activated macrophages in time. High-throughput metabolomics was conducted during the secondment in the lab of Prof. Nico Mitro at UNIMI. Interaction with the Mitro’s lab allowed the fellow to learn novel techniques and identifying more that 200 metabolites constitutive or changing across. Data integration was performed using Constrained-base reconstruction and analysis methods (COBRA). This strategy was adopted as it produces human interpretable models which I was able to appreciate during the Metabolic Pathway Analysis 2019 conference (Riga University, Aug 2019) and the advanced COBRA course (Leiden University, Nov 2019). Modelling metabolic changes using this strategy allowed the fellow to both learn new modelling strategies and produce a metabolic model of macrophage activation with unprecedent levels of accuracy. (WP1.2) Identifying transcriptional regulators directly controlling metabolic enzymes and nutrient transporters encoded within the model assembled in WP1.1. (WP1.3) Selection of Irf8 transcriptional regulator from a panel of candidates identified from WP1.2. Perturbation experiment were performed to assess the role of Irf8 in the metabolic regulation of activated macrophages.
WP2 was also structure into sub-workpackages and aimed at evaluating the impact of metabolic pathways onto transcriptional changes. Although a full screen of a panel of metabolic inhibitors have been impossible to achieve due to the limited laboratory-time caused by the COVID19 emergency, I was able to team up with members of the Natoli’s lab and assess the effects of nitric oxide on gene transcription in activated macrophage.
WP3 was dedicated to the assembly of computational resources developed during the action. To make these resources immediately available to potential users, together with the supervisor the researcher decided to make the data available to all the institute and train biologist of the lab to use these resources. In particular the fellow assembled a general workflow, which was developed and used for the analysis of all genomic datasets related to the objective of the action, into a flexible and versatile pipeline so that more data-sets could be easily analysed. This produced a valuable resource that speeded up the analysis of datasets of more that 15 different projects allowing direct transfer of knowledge to the about 20 members of the lab. Supporting the efficacy of this strategy three members of the lab started independent learning of both basic and advanced computational languages (learning R and Python) and are actively mentored by the fellow.
Key achievements transversal to all workpackages included: (i) the submission of a seminal review on macrophage metabolism (submitted); (ii) training of a PhD student and a master student dedicating individual tasks and WPs; (iii) dissemination of the action at two international conferences, an advanced workshop and a lecture at the University of Bologna; (iv) engage in productive scientific collaborations expanding the researcher scientific community.
WP1 comprised the main experimental and computational steps to relate transcriptional and metabolic changes. I was able to:
(WP1.1) Record transcriptional and metabolomic changes in activated macrophages in time. High-throughput metabolomics was conducted during the secondment in the lab of Prof. Nico Mitro at UNIMI. Interaction with the Mitro’s lab allowed the fellow to learn novel techniques and identifying more that 200 metabolites constitutive or changing across. Data integration was performed using Constrained-base reconstruction and analysis methods (COBRA). This strategy was adopted as it produces human interpretable models which I was able to appreciate during the Metabolic Pathway Analysis 2019 conference (Riga University, Aug 2019) and the advanced COBRA course (Leiden University, Nov 2019). Modelling metabolic changes using this strategy allowed the fellow to both learn new modelling strategies and produce a metabolic model of macrophage activation with unprecedent levels of accuracy. (WP1.2) Identifying transcriptional regulators directly controlling metabolic enzymes and nutrient transporters encoded within the model assembled in WP1.1. (WP1.3) Selection of Irf8 transcriptional regulator from a panel of candidates identified from WP1.2. Perturbation experiment were performed to assess the role of Irf8 in the metabolic regulation of activated macrophages.
WP2 was also structure into sub-workpackages and aimed at evaluating the impact of metabolic pathways onto transcriptional changes. Although a full screen of a panel of metabolic inhibitors have been impossible to achieve due to the limited laboratory-time caused by the COVID19 emergency, I was able to team up with members of the Natoli’s lab and assess the effects of nitric oxide on gene transcription in activated macrophage.
WP3 was dedicated to the assembly of computational resources developed during the action. To make these resources immediately available to potential users, together with the supervisor the researcher decided to make the data available to all the institute and train biologist of the lab to use these resources. In particular the fellow assembled a general workflow, which was developed and used for the analysis of all genomic datasets related to the objective of the action, into a flexible and versatile pipeline so that more data-sets could be easily analysed. This produced a valuable resource that speeded up the analysis of datasets of more that 15 different projects allowing direct transfer of knowledge to the about 20 members of the lab. Supporting the efficacy of this strategy three members of the lab started independent learning of both basic and advanced computational languages (learning R and Python) and are actively mentored by the fellow.
Key achievements transversal to all workpackages included: (i) the submission of a seminal review on macrophage metabolism (submitted); (ii) training of a PhD student and a master student dedicating individual tasks and WPs; (iii) dissemination of the action at two international conferences, an advanced workshop and a lecture at the University of Bologna; (iv) engage in productive scientific collaborations expanding the researcher scientific community.
This MSCA has tackled challenging objectives adopting a multidisciplinary approach enhancing the researcher career. Unique computational skills have been assimilated. In particular the construction and development of constrained models requires advanced mathematical and computational skills that are rare in the field pursued by the fellow. In addition, the possibility to both generate experimentally and analyse computationally the hundreds genome-wide datasets allowed the fellow to productively communicate to both computational and experimental biologist engaging in productive collaborations. The submitted review have shed new light and ordered the various information regarding macrophage metabolic changes during polarization. The implementation of genome-wide metabolic network via constrained-based reconstruction and analysis (COBRA) produced a general metabolic model with unprecedent accuracy. The systematic recording of genome-wide changes (both transcriptional and metabolic) in activated macrophages constitute an unbiased resource to evaluate early events during polarization. The assessment of the role of Irf8 in the activation of homeostatic macrophages add a new layer of regulation. The assessment genome-wide of the impact that nitric oxide synthesis has on enhancer acetylation using advanced experimental and computational approaches tackles a biological question which is vastly unexplored. The flexible pipeline developed by the researcher helped experimental biologist to learn computational approaches, become more independent and speeding up their work saving therefore time and money.
This MSCA allowed the Fellow to develop new skills and agility with many computational methodologies. The possibility to mentor master and PhD students was highly productive and allowed an immediate transfer of knowledge.
This MSCA allowed the Fellow to develop new skills and agility with many computational methodologies. The possibility to mentor master and PhD students was highly productive and allowed an immediate transfer of knowledge.