163 on-site and 78 virtual participants from all around the world attended the EMBO Workshop ‘From functional genomics to systems biology’ which took place 15 – 18 November 2022 in Heidelberg. 77 posters were on display around the ATC’s helices, with attendees and poster presenters chatting about their research, creating a lively buzz all around. Participants were then able to vote for their favorite posters. We’d like to introduce you to the poster prize winners: Congratulations to Daniela, Sophia, Ricardo, and Pedro!

One of the poster prizes was kindly sponsored by Molecular Systems Biology EMBO Press.

Identification of Transcription factor metabolite interactions by integrating gene expression and metabolomics

Presenter: Daniela Ledezma-Tejeida

Bacteria detect changes in their environment and adjust their gene expression accordingly, in order to survive. This process is partly mediated by Transcription Factors (TFs). In E. coli K12, the organism with arguably the best characterised Transcriptional Regulatory Network, there are 300 predicted TFs. Of those, 75% have a metabolite binding domain, suggesting that the most common mechanism of TF induction is via the binding of a metabolite. However, binding metabolites are only known for 95 TFs, which indicates there are over 100 TF metabolite interactions to be discovered.

The main limitation for their identification is the lack of high throughput approaches that allow the screening of several metabolites, or TFs, at once. To accelerate the pace of discovery, and systematically test over 100 metabolites, we integrated untargeted metabolomics and gene expression data. We calculated a correlation coefficient representing the relationship between the expression of genes regulated only by a TF of interest, and the intracellular abundance of the measured metabolites, across conditions. Our approach was validated by correctly identifying the known binding metabolites of TFs ArgR, CysB and TyrR in 8/9 independent tests.

Finally, we used our approach to identify potential interactors of 8 TFs for which no known binding metabolites have been reported: CdaR, CsgD, FlhDC, GadX, CecR, PgrR, MarA and MngR. By ranking our top results through their biological significance, we report 15 high confidence TF metabolite interactions.

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PHONEMeS 2.0: Systematic integration of phosphoproteomic data with comprehensive molecular interaction prior knowledge to model signalling networks

Presenter: Sophia Müller-Dott

Post translational modifications such as protein phosphorylation play an important role in regulating cellular processes. With the advantages of mass spectrometry, these modifications can be identified and quantified in a large scale manner, allowing the measurement of approximately 50,000 unique phospho peptides comprising over 75% of all cellular proteins. With that, mass spectrometry analysis of proteomic modifications offers enormous potential for elucidating phosphosignaling mechanisms in the cell. However, interpretation of these data in specific biological scenarios remains difficult due to the lack of methods that can translate site specific information into global maps of proteins within signaling networks.

In this work, we present an updated version of the method PHONEMeS (PHOsphorylation Networks for Mass Spectrometry) to contextualise signaling networks using phosphoproteomics data. This method generates mechanistic hypotheses that link a priori known or putative perturbed proteins to differentially regulated phosphopeptide measurements in a prior knowledge network that now includes known signed protein protein interactions in addition to kinase/phosphatase substrate relationships. PHONEMeS 2.0 takes the direction of changes of protein activities and phosphorylation sites into account and finds a coherent path to link them together. The value of PHONEMeS 2.0 is illustrated by several datasets of medical relevance, in which we shed light on signaling mechanisms that are deregulated in specific biological contexts.

Altogether, PHONEMeS 2.0 enables the functional and context specific interpretation of site specific post translational modifications in cellular responses and shows that it has enormous potential for the study of phosphosignal transduction and its therapeutic implications, especially to understand better the mode of action of therapeutic kinase inhibitors.

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Cross condition comparison of coordinated expression programs across cell types in tissues from single cell transcriptomics data

Presenter: Ricardo Ramirez

The availability of single cell transcriptomics atlases describing the pathological state of different tissues in humans has increased during the last years. However, cross condition comparisons within these studies have focused on identifying molecular changes of individual cell types, ignoring the fact that cells in tissues are organized in communities with shared functions. Here we propose a cross condition multicellular analysis from single cell transcriptomics data using Multi Omics Factor Analysis and apply it to a collection of public single cell atlases of human myocardial infarction and end stage heart failure. We demonstrate that the identification of multicellular gene expression programs from single cell cohorts allows for an unbiased unsupervised analysis of the global variability of patients that allows to prioritize coordinated transcriptional changes across cell types associated with clinical features. In different cardiovascular disease contexts, we quantify to what extent the gene expression deregulation across cell types is the product of a global tissue level response or a consequence of the emergence of new functional populations of cells. We explore the distribution of multicellular disease programs in spatial transcriptomics data and study if their coordinated expression is constrained by the tissue organization. Finally, we show that multicellular programs can be used to deconvolute disease signals from bulk transcriptomics data that are not related to compositional changes of tissues. Our proposed framework provides an alternative methodological perspective of single cell data analysis that facilitates the meta analysis and integration of multiple patient cohorts across multiple scales, generating a tissue centric description of disease processes.

Due to the confidentiality of the unpublished data, we cannot share the poster.

Cellular energy metabolism regulates mRNA translation and degradation in a codon specific manner

Presenter: Pedro Tomaz da Silva

The usage of fast over slow decoded codons is a major determinant of protein production and degradation rates of an mRNA. However, whether and through which mechanisms these effects are regulated remains poorly understood. While regulation of codon usage effects through tRNA regulation has been reported in cancer, this mechanism remains elusive in a broader context. Here we conducted the most extensive analysis of the regulatory impact of codon usage across tissues to date. We accomplished this by quantifying changes in the ratio between exonic and intronic RNA seq read coverage as a proxy for mRNA half life variations. We processed 7,771 post mortem RNA seq samples spanning 49 tissues and 528 human individuals from the GTEx consortium. While usage of slow decoded codons is universally associated with lower mRNA stability, we found the association to be attenuated in tissues with high energy metabolism, including brain and muscle. Moreover, the association strengthened for elder individuals, for which energy metabolism is weakened, and in samples collected with a longer ischemic time, i.e. upon longer oxygen deprivation.

These observations suggested a simple hypothesis: Optimal codon usage is more beneficial under scarcer ATP abundance.

To mechanistically explain this rule, we derived a biochemical model relating ATP & GTP concentrations with reaction kinetics of the tRNA life cycle and the translation elongation cycle. The model predictions supported the hypothesis for a wide range of plausible kinetic parameters. To experimentally validate our hypothesis, we quantified codon decoding times with 5P seq in S. cerevisiae in a time course following addition of the respiration inhibiting drug antimycin A and found that decoding times depended on intracellular ATP concentration in agreement with the model.

Altogether, our work uncovers a fundamental mechanistic link between cellular energy metabolism and eukaryotic gene expression, presenting as an alternative or complementary mechanism to tRNA pool regulation. The functional implications of this mechanism extend beyond tissue specific to altered energy metabolism states, such as in cancer and specific cellular environments, potentially providing a novel way for these abnormal states to shape gene expression.

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The EMBO Workshop ‘From functional genomics to systems biology’ took place from 15 – 18 November 2022 at EMBL Heidelberg and virtually.