Drager, A. | Search bioRxiv

bioRxiv2022

An integrated systems-biology platform for power-to-gas technology

Methanogenesis allows methanogenic archaea (methanogens) to generate cellular energy for their growth while producing methane. Hydrogenotrophic methanogens thrive on carbon dioxide and molecular hydrogen as sole carbon and energy sources. Thermophilic and hydrogenotrophic Methanothermobacter spp. have been recognized as robust biocatalysts for a circular carbon economy and are now applied in power-to-gas technology. Here, we generated the first manually curated genome-scale metabolic reconstruction for three Methanothermobacter spp.. We investigated differences in the growth performance of three wild-type strains and one genetically engineered strain in two independent chemostat bioreactor experiments. In the first experiment, with molecular hydrogen and carbon dioxide, we found the highest methane production rate for Methanothermobacter thermautotrophicus {Delta}H, while Methanothermobacter marburgensis Marburg reached the highest biomass growth rate. Systems biology investigations, including implementing a pan-model that contains combined reactions from all three microbes, allowed us to perform an interspecies comparison. This comparison enabled us to identify crucial differences in formate anabolism. In the second experiment, with sodium formate, we found stable growth with an M. thermautotrophicus {Delta}H plasmid-carrying strain with similar performance parameters compared to wild-type Methanothermobacter thermautotrophicus Z-245. Our findings reveal that formate anabolism influences the diversion of carbon to biomass and methane with implications for biotechnological applications of Methanothermobacter spp. in power-to-gas technology and for chemical production. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=103 SRC="FIGDIR/small/522236v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@1d468aeorg.highwire.dtl.DTLVardef@24a595org.highwire.dtl.DTLVardef@1f2dd60org.highwire.dtl.DTLVardef@1a3d8fc_HPS_FORMAT_FIGEXP M_FIG C_FIG Broader contextRenewable energy sources (e.g., wind and solar) provide carbon-free electric power. However, their intermittency and offset between peak production and demand generate the need to store this electric power. Furthermore, these technologies alone do not satisfy the demand for carbon-based commodities. Power-to-gas technology provides a means to store intermittent renewable electric power with concomitant carbon dioxide recycling into a chemical energy carrier, such as methane, on a centralized and decentralized scale. This is particularly important to establish equitable energy strategies for all countries, as is highlighted by the United Nations Sustainable Development Goals. With this work, we provide an integrated systems-biology platform for Methanothermobacter spp. to optimize biological power-to-gas technology and formulate strategies to produce other value-added products besides methane.

microbiology↗

bioRxiv2020

COVID-19 Disease Map, a computational knowledge repository of SARS-CoV-2 virus-host interaction mechanisms

We describe a large-scale community effort to build an open-access, interoperable, and computable repository of COVID-19 molecular mechanisms - the COVID-19 Disease Map. We discuss the tools, platforms, and guidelines necessary for the distributed development of its contents by a multi-faceted community of biocurators, domain experts, bioinformaticians, and computational biologists. We highlight the role of relevant databases and text mining approaches in enrichment and validation of the curated mechanisms. We describe the contents of the Map and their relevance to the molecular pathophysiology of COVID-19 and the analytical and computational modelling approaches that can be applied for mechanistic data interpretation and predictions. We conclude by demonstrating concrete applications of our work through several use cases and highlight new testable hypotheses.

systems biology↗

bioRxiv2018

Systematic discovery of uncharacterized transcription factors in Escherichia coli K-12 MG1655

Transcriptional regulation enables cells to respond to environmental changes. Yet, among the estimated 304 candidate transcription factors (TFs) in Escherichia coli K-12 MG1655, 185 have been experimentally identified and only a few tens of them have been fully characterized by ChIP methods. Understanding the remaining TFs is key to improving our knowledge of the E. coli transcriptional regulatory network (TRN). Here, we developed an integrated workflow for the computational prediction and comprehensive experimental validation of TFs using a suite of genome-wide experiments. We applied this workflow to: 1) identify 16 candidate TFs from over a hundred candidate uncharacterized genes; 2) capture a total of 255 DNA binding peaks for 10 candidate TFs resulting in six high-confidence binding motifs; 3) reconstruct the regulons of these 10 TFs by determining gene expression changes upon deletion of each TF; and 4) determine the regulatory roles of three TFs (YiaJ, YdcI, and YeiE) as regulators of L-ascorbate utilization, proton transfer and acetate metabolism, and iron homeostasis under iron limited condition, respectively. Together, these results demonstrate how this workflow can be used to discover, characterize, and elucidate regulatory functions of uncharacterized TFs in parallel.

microbiology↗