Control of COVID-19 Outbreaks under Stochastic Community Dynamics, Bimodality, or Limited Vaccination

Reaching population immunity against COVID-19 is proving difficult even in countries with high vaccination levels. Thus, it is critical to identify limits of control and effective measures against future outbreaks. The effects of nonpharmaceutical interventions (NPIs) and vaccination strategies are analyzed with a detailed community-specific agent-based model (ABM). The authors demonstrate that the threshold for population immunity is not a unique number, but depends on the vaccination strategy. Prioritizing highly interactive people diminishes the risk for an infection wave, while prioritizing the elderly minimizes fatalities when vaccinations are low. Control over COVID-19 outbreaks requires adaptive combination of NPIs and targeted vaccination, exemplified for Germany for January–September 2021. Bimodality emerges from the heterogeneity and stochasticity of community-specific human–human interactions and infection networks, which can render the effects of limited NPIs uncertain. The authors' simulation platform can process and analyze dynamic COVID-19 epidemiological situations in diverse communities worldwide to predict pathways to population immunity even with limited vaccination.Peer Reviewe

Publisher Correction: MEMOTE for standardized genome-scale metabolic model testing

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MEMOTE for standardized genome-scale metabolic model testing

Supplementary information is available for this paper at https://doi.org/10.1038/s41587-020-0446-yReconstructing metabolic reaction networks enables the development of testable hypotheses of an organisms metabolism under different conditions1. State-of-the-art genome-scale metabolic models (GEMs) can include thousands of metabolites and reactions that are assigned to subcellular locations. Geneproteinreaction (GPR) rules and annotations using database information can add meta-information to GEMs. GEMs with metadata can be built using standard reconstruction protocols2, and guidelines have been put in place for tracking provenance and enabling interoperability, but a standardized means of quality control for GEMs is lacking3. Here we report a community effort to develop a test suite named MEMOTE (for metabolic model tests) to assess GEM quality.We acknowledge D. Dannaher and A. Lopez for their supporting work on the Angular parts of MEMOTE; resources and support from the DTU Computing Center; J. Cardoso, S. Gudmundsson, K. Jensen and D. Lappa for their feedback on conceptual details; and P. D. Karp and I. Thiele for critically reviewing the manuscript. We thank J. Daniel, T. Kristjánsdóttir, J. Saez-Saez, S. Sulheim, and P. Tubergen for being early adopters of MEMOTE and for providing written testimonials. J.O.V. received the Research Council of Norway grants 244164 (GenoSysFat), 248792 (DigiSal) and 248810 (Digital Life Norway); M.Z. received the Research Council of Norway grant 244164 (GenoSysFat); C.L. received funding from the Innovation Fund Denmark (project “Environmentally Friendly Protein Production (EFPro2)”); C.L., A.K., N. S., M.B., M.A., D.M., P.M, B.J.S., P.V., K.R.P. and M.H. received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement 686070 (DD-DeCaF); B.G.O., F.T.B. and A.D. acknowledge funding from the US National Institutes of Health (NIH, grant number 2R01GM070923-13); A.D. was supported by infrastructural funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), Cluster of Excellence EXC 2124 Controlling Microbes to Fight Infections; N.E.L. received funding from NIGMS R35 GM119850, Novo Nordisk Foundation NNF10CC1016517 and the Keck Foundation; A.R. received a Lilly Innovation Fellowship Award; B.G.-J. and J. Nogales received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no 686585 for the project LIAR, and the Spanish Ministry of Economy and Competitivity through the RobDcode grant (BIO2014-59528-JIN); L.M.B. has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement 633962 for project P4SB; R.F. received funding from the US Department of Energy, Offices of Advanced Scientific Computing Research and the Biological and Environmental Research as part of the Scientific Discovery Through Advanced Computing program, grant DE-SC0010429; A.M., C.Z., S.L. and J. Nielsen received funding from The Knut and Alice Wallenberg Foundation, Advanced Computing program, grant #DE-SC0010429; S.K.’s work was in part supported by the German Federal Ministry of Education and Research (de.NBI partner project “ModSim” (FKZ: 031L104B)); E.K. and J.A.H.W. were supported by the German Federal Ministry of Education and Research (project “SysToxChip”, FKZ 031A303A); M.K. is supported by the Federal Ministry of Education and Research (BMBF, Germany) within the research network Systems Medicine of the Liver (LiSyM, grant number 031L0054); J.A.P. and G.L.M. acknowledge funding from US National Institutes of Health (T32-LM012416, R01-AT010253, R01-GM108501) and the Wagner Foundation; G.L.M. acknowledges funding from a Grand Challenges Exploration Phase I grant (OPP1211869) from the Bill & Melinda Gates Foundation; H.H. and R.S.M.S. received funding from the Biotechnology and Biological Sciences Research Council MultiMod (BB/N019482/1); H.U.K. and S.Y.L. received funding from the Technology Development Program to Solve Climate Changes on Systems Metabolic Engineering for Biorefineries (grants NRF-2012M1A2A2026556 and NRF-2012M1A2A2026557) from the Ministry of Science and ICT through the National Research Foundation (NRF) of Korea; H.U.K. received funding from the Bio & Medical Technology Development Program of the NRF, the Ministry of Science and ICT (NRF-2018M3A9H3020459); P.B., B.J.S., Z.K., B.O.P., C.L., M.B., N.S., M.H. and A.F. received funding through Novo Nordisk Foundation through the Center for Biosustainability at the Technical University of Denmark (NNF10CC1016517); D.-Y.L. received funding from the Next-Generation BioGreen 21 Program (SSAC, PJ01334605), Rural Development Administration, Republic of Korea; G.F. was supported by the RobustYeast within ERA net project via SystemsX.ch; V.H. received funding from the ETH Domain and Swiss National Science Foundation; M.P. acknowledges Oxford Brookes University; J.C.X. received support via European Research Council (666053) to W.F. Martin; B.E.E. acknowledges funding through the CSIRO-UQ Synthetic Biology Alliance; C.D. is supported by a Washington Research Foundation Distinguished Investigator Award. I.N. received funding from National Institutes of Health (NIH)/National Institute of General Medical Sciences (NIGMS) (grant P20GM125503).info:eu-repo/semantics/publishedVersio

Organization and integration of large-scale datasets for designing a metabolic model and re-annotating the genome of mycoplasma pneumoniae

Mycoplasma pneumoniae, einer der kleinsten lebenden Organismen, ist ein erfolgversprechender Modellorganismus der Systembiologie um eine komplette lebende Zelle zu verstehen. Wichtig dahingehend ist die Konstruktion mathematischer Modelle, die zelluläre Prozesse beschreiben, indem sie beteiligte Komponenten vernetzen und zugrundeliegende Mechanismen entschlüsseln. Für Mycoplasma pneumoniae wurden genomweite Datensätze für Genomics, Transcriptomics, Proteomics und Metabolomics produziert. Allerdings fehlten ein effizientes Informationsaustauschsystem und mathematische Modelle zur Datenintegration. Zudem waren verschiedene Beobachtungen im metabolischen Verhalten ungeklärt. Diese Dissertation präsentiert einen kombinatorischen Ansatz zur Entwicklung eines metabolischen Modells für Mycoplasma pneumoniae. Zuerst haben wir eine Datenbank, MyMpn, entwickelt, um Zugang zu strukturierten, organisierten Daten zu schaffen. Danach haben wir ein genomweites, Constraint-basiertes metabolisches Modell mit Vorhersagekapazitäten konstruiert und parallel dazu das Metabolome experimentell charakterisiert. Wir haben die Biomasse einer Mycoplasma pneumoniae Zelle definiert, das Netzwerk korrigiert, gezeigt, dass ein Grossteil der produzierten Energie auf zelluläre Homeostase verwendet wird, und das Verhalten unter verschiedenen Wachstumsbedingungen analysiert. Schließlich haben wir manuell das Genom reannotiert. Die Datenbank, obwohl noch nicht öffentlich zugänglich, wird bereits intern für die Analyse experimenteller Daten und die Modellierung genutzt. Die Entdeckung von Kontrollprinzipien des Energiemetabolismus und der Anpassungsfähigkeiten bei Genausfall heben den Einfluss der reduktiven Genomevolution hervor und erleichtert die Entwicklung von Manipulationstechniken und dynamischen Modellen. Überdies haben wir gezeigt, dass die Genomorganisation in Mycoplasma pneumoniae komplexer ist als bisher für möglich gehalten, und 32 neue, noch nicht annotierte Gene entdeckt.Mycoplasma pneumoniae, one of the smallest known self-replicating organisms, is a promising model organism in systems biology when aiming to assess understanding of an entire living cell. One key step towards this goal is the design of mathematical models that describe cellular processes by connecting the involved components to unravel underlying mechanisms. For Mycoplasma pneumoniae, a wealth of genome-wide datasets on genomics, transcriptomics, proteomics, and metabolism had been produced. However, a proper system facilitating information exchange and mathematical models to integrate the different datasets were lacking. Also, different in vivo observations of metabolic behavior remained unexplained. This thesis presents a combinatorial approach to design a metabolic model for Mycoplasma pneumoniae. First, we developed a database, MyMpn, in order to provide access to structured and organized data. Second, we built a predictive, genome-scale, constraint-based metabolic model and, in parallel, we explored the metabolome in vivo. We defined the biomass composition of a Mycoplasma pneumoniae cell, corrected the wiring diagram, showed that a large proportion of energy is dedicated to cellular homeostasis, and analyzed the metabolic behavior under different growth conditions. Finally, we manually re-annotated the genome of Mycoplasma pneumoniae. The database, despite not yet being released to the public, is internally already used for data analysis, and for mathematical modeling. Unraveling the principles governing energy metabolism and adaptive capabilities upon gene deletion highlight the impact of the reductive genome evolution and facilitates the development of engineering tools and dynamic models for metabolic sub-systems. Furthermore, we revealed that the degree of complexity in which the genome of Mycoplasma pneumoniae is organized far exceeds what has been considered possible so far and we identified 32 new, previously not annotated genes

MeDaX Prototype v0.2

The MeDaX project aims to develop and implement concepts and tools for bioMedical Data eXploration using graph technologies. Here, we present v0.2 of our prototype, representing FHIR formatted clinical data in a graph format. We build on the pre-existing CyFHIR tool for generic conversion, optimise the resulting graph structure to lessen complexity, and incorporate the BioCypher framework, integrating the clinical data with ontology information. This makes the data more accessible and convenient for querying, information retrieval and analysis

MyMpn: a database for the systems biology model organism Mycoplasma pneumoniae

MyMpn (http://mympn.crg.eu) is an online resource devoted to studying the human pathogen Mycoplasma pneumoniae, a minimal bacterium causing lower respiratory tract infections. Due to its small size, its ability to grow in vitro, and the amount of data produced over the past decades, M. pneumoniae is an interesting model organisms for the development of systems biology approaches for unicellular organisms. Our database hosts a wealth of omics-scale datasets generated by hundreds of experimental and computational analyses. These include data obtained from gene expression profiling experiments, gene essentiality studies, protein abundance profiling, protein complex analysis, metabolic reactions and network modeling, cell growth experiments, comparative genomics and 3D tomography. In addition, the intuitive web interface provides access to several visualization and analysis tools as well as to different data search options. The availability and--even more relevant--the accessibility of properly structured and organized data are of up-most importance when aiming to understand the biology of an organism on a global scale. Therefore, MyMpn constitutes a unique and valuable new resource for the large systems biology and microbiology community.European Union through the European Research Council (ERC) [‘Celldoctor’ GA 232913]; Spanish Ministerio de Economía y Competitividad Plan Nacional [BIO2007-61762]; Fundaci´on Marcelino Botin [to L.S.]. laCaixa-CRG Fellowship, Fundació laCaixa [to J.W.]; Spanish Ministry of Economy and Competitiveness No [PTA2010-4446-I to A.H.; PTA2011-6729-I to L.C.]. Funding for open access charge: ERC [to L.S.]

MyMpn: a database for the systems biology model organism Mycoplasma pneumoniae

MyMpn (http://mympn.crg.eu) is an online resource devoted to studying the human pathogen Mycoplasma pneumoniae, a minimal bacterium causing lower respiratory tract infections. Due to its small size, its ability to grow in vitro, and the amount of data produced over the past decades, M. pneumoniae is an interesting model organisms for the development of systems biology approaches for unicellular organisms. Our database hosts a wealth of omics-scale datasets generated by hundreds of experimental and computational analyses. These include data obtained from gene expression profiling experiments, gene essentiality studies, protein abundance profiling, protein complex analysis, metabolic reactions and network modeling, cell growth experiments, comparative genomics and 3D tomography. In addition, the intuitive web interface provides access to several visualization and analysis tools as well as to different data search options. The availability and--even more relevant--the accessibility of properly structured and organized data are of up-most importance when aiming to understand the biology of an organism on a global scale. Therefore, MyMpn constitutes a unique and valuable new resource for the large systems biology and microbiology community.European Union through the European Research Council (ERC) [‘Celldoctor’ GA 232913]; Spanish Ministerio de Economía y Competitividad Plan Nacional [BIO2007-61762]; Fundaci´on Marcelino Botin [to L.S.]. laCaixa-CRG Fellowship, Fundació laCaixa [to J.W.]; Spanish Ministry of Economy and Competitiveness No [PTA2010-4446-I to A.H.; PTA2011-6729-I to L.C.]. Funding for open access charge: ERC [to L.S.]

Loss of hepatic Mboat7 leads to liver fibrosis

OBJECTIVE: The rs641738C>T variant located near the membrane-bound O-acyltransferase domain containing 7 (MBOAT7) locus is associated with fibrosis in liver diseases, including non-alcoholic fatty liver disease (NAFLD), alcohol-related liver disease, hepatitis B and C. We aim to understand the mechanism by which the rs641738C>T variant contributes to pathogenesis of NAFLD. DESIGN: Mice with hepatocyte-specific deletion of MBOAT7 (Mboat7$^{Δhep}$) were generated and livers were characterised by histology, flow cytometry, qPCR, RNA sequencing and lipidomics. We analysed the association of rs641738C>T genotype with liver inflammation and fibrosis in 846 NAFLD patients and obtained genotype-specific liver lipidomes from 280 human biopsies. RESULTS: Allelic imbalance analysis of heterozygous human liver samples pointed to lower expression of the MBOAT7 transcript on the rs641738C>T haplotype. Mboat7$^{Δhep}$ mice showed spontaneous steatosis characterised by increased hepatic cholesterol ester content after 10 weeks. After 6 weeks on a high fat, methionine-low, choline-deficient diet, mice developed increased hepatic fibrosis as measured by picrosirius staining (pT was associated with fibrosis (p=0.004) independent of the presence of histological inflammation. Liver lipidomes of Mboat7$^{Δhep}$ mice and human rs641738TT carriers with fibrosis showed increased total lysophosphatidylinositol levels. The altered lysophosphatidylinositol and phosphatidylinositol subspecies in MBOAT7$^{Δhep}$ livers and human rs641738TT carriers were similar. CONCLUSION: Mboat7 deficiency in mice and human points to an inflammation-independent pathway of liver fibrosis that may be mediated by lipid signalling and a potentially targetable treatment option in NAFLD

Integration of multi-omics data of a genome-reduced bacterium: Prevalence of post-transcriptional regulation and its correlation with protein abundances

We developed a comprehensive resource for the genome-reduced bacterium Mycoplasma pneumoniae comprising 1748 consistently generated '-omics' data sets, and used it to quantify the power of antisense non-coding RNAs (ncRNAs), lysine acetylation, and protein phosphorylation in predicting protein abundance (11%, 24% and 8%, respectively). These factors taken together are four times more predictive of the proteome abundance than of mRNA abundance. In bacteria, post-translational modifications (PTMs) and ncRNA transcription were both found to increase with decreasing genomic GC-content and genome size. Thus, the evolutionary forces constraining genome size and GC-content modify the relative contributions of the different regulatory layers to proteome homeostasis, and impact more genomic and genetic features than previously appreciated. Indeed, these scaling principles will enable us to develop more informed approaches when engineering minimal synthetic genomes.The research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP7/2007–2013)/ERC Grant Agreement No. 232913. We acknowledge support from the Spanish Ministry of Economy and Competitiveness, ‘Centro de Excelencia Severo Ochoa 2013–2017’, SEV-2012–0208. This project has received funding from the European Union's Horizon 2020 research and innovation program under Grant Agreement No. 634942. Funding for open access charge: European Research council (ERC) advanced Grant Agreement No. 232913, the Fundación Marcelino Botin, the Spanish Ministerio de Economía y Competitividad BIO2007-61762, the ISCIII, Subdirección General de evaluación y fomento de la investigación PI10/01702 to the ICREA researcher L.S