SysMO LAB (Hugenholtz)-Westerhoff
Lead Research Organisation:
University of Manchester
Department Name: Chem Eng and Analytical Science
Abstract
Molecular and genomics approaches have contributed greatly to our understanding of bacteria. They have also led to a number of surprises. One is the fact that organisms that appear to be highly different are rather similar in terms of their molecules. Many of the molecular components of humans are similar to components of bacteria. Even closer similarities have been found between bacteria that are highly different in the way they interact with human beings. A recent development in the sciences, called Systems Biology, recognizes that the functioning of living organisms is not merely determined by their components, but also by properties that emerge from the interactions between those components, just like the scoring by a football player is not just determined by that player himself. Here we propose to test our hypothesis that much of the differences between organisms derives from differences in the interactions between their components. Three related organisms will be compared, one that produces our cheese, one that is a fecal contaminant of food, and one that causes sore throats. The networks of the three bacteria will be unraveled, the flow of carbon and energy through those networks will be determined, and the extent to which the bacteria are able to adjust their networks to altered environments will be measured. The results will be assembled into computer models that should then reproduce the behavior of the bacteria. It will be examined whether and how these computer versions of the organisms explain the differences in the extent to which they cause disease or function as organisms that help produce our food. These explanations will then be tested experimentally.
Technical Summary
Challenge: The central principle of this proposal is that important aspects of the functional differences between organisms derive from the interactions between their components. This project will develop Comparative Systems Biology (CSB). Model system: The project focuses on three relatively simple and highly related microorganisms, which nevertheless exhibit stark and important differences in their functional relationship with human beings: These homofermentative lactic acid bacteria are Lactococcus lactis, the major microorganism used in the dairy industry, Enterococcus faecalis, a major (fecal) contaminant in food and water as well as a contributor to food fermentation, and Streptococcus pyogenes, an important human pathogen. These organisms have similar primary metabolism, but persist in completely different environments (milk, faeces, blood). Lactococcus lactis will be used as the reference microorganism, since: (i) it is by far the best studied lactic acid bacterium, (ii) three different genomes have been sequenced, (iii) a kinetic model has been developed for its complete glycolysis including some branching pathways, (iv) genetic and metabolic engineering tools are available and (v) a functional genomics platform has been set-up including DNA microarray and metabolic databases. Methodology: The project will focus on carbon metabolism and its response to aeration, change of sugar-source, addition of heme and elimination of the crucial metabolic enzyme, lactate dehydrogenase. Cells will be grown in a non-nutrient-limited turbidostat that will allow maximal growth under defined conditions. The major regulatory events at the genetic level (adaptive mutations), the transcription, translation, enzymatic and metabolic level up to the final output (functional) level, will be quantified and then integrated in iterations between experimentation (all~omics) and modeling (network structure, flux balances, dynamics, control, regulation).
Publications
Armitage, Emily
(2012)
Systems biology of chemotherapy in hypoxia environments
in Mutagenesis
Armitage, Emily
(2012)
Systems biology of HIF metabolism in cancer
in Mutagenesis
Bakker, S. J. L.
(2007)
Adenine nucleotide translocator - A candidate for mediating long chain acyl-CoA effects in type 2 diabetes
in Febs Journal
Bruggeman FJ
(2007)
Introduction to systems biology.
in EXS
Bruggeman, Frank
(2010)
Nuclear receptors as controlling factors in chemical metabolism: Determination of regulatory signal network crucial for co-ordinating cellular response to chemicals
in Drug Metabolism Reviews
Bryant, H.
(2008)
Structural and dynamic features of the eukaryotic translation initiation pathway
in Febs Journal
Dols JA
(2016)
Molecular assessment of bacterial vaginosis by Lactobacillus abundance and species diversity.
in BMC infectious diseases
García-Contreras R
(2012)
Why in vivo may not equal in vitro - new effectors revealed by measurement of enzymatic activities under the same in vivo -like assay conditions
in FEBS Journal
Haanstra JR
(2017)
Targeting pathogen metabolism without collateral damage to the host.
in Scientific reports
Hornberg JJ
(2007)
Metabolic control analysis to identify optimal drug targets.
in Progress in drug research. Fortschritte der Arzneimittelforschung. Progres des recherches pharmaceutiques
Jonker C
(2010)
Transactions on Computational Collective Intelligence I
Kolodkin, Alexey
(2012)
Sulfolobus Systems Biology: Cool Hot Design for Metabolic Pathways
in Systems Microbiology: Current Topics and Applications
Westerhoff HV
(2011)
Systems biology left and right.
in Methods in enzymology
Westerhoff HV
(2009)
Systems biology towards life in silico: mathematics of the control of living cells.
in Journal of mathematical biology
Westerhoff HV
(2007)
Systems biology: new paradigms for cell biology and drug design.
in Ernst Schering Research Foundation workshop
Westerhoff HV
(2009)
Matrix method for determining steps most rate-limiting to metabolic fluxes in biotechnological processes. 1987.
in Biotechnology and bioengineering
Westerhoff, H. V.
(2008)
Battling robustness: to make microbes weak
in Febs Journal
Westerhoff, H. V.
(2007)
Multi-factorial disease and robustness: Where Systems Biology makes a difference
in Febs Journal
Wruck W
(2015)
Multi-omic profiles of human non-alcoholic fatty liver disease tissue highlight heterogenic phenotypes.
in Scientific data
Wu H
(2016)
MUFINS: multi-formalism interaction network simulator.
in NPJ systems biology and applications
| Description | This early systems biology project was an international collaboration. We discovered how important it was to develop standard protocols for experimentation as well as for modelling, in the latter case for using more of mathematics to understand more of biology. We also found ways to assess differences between in vivo function and in vitro observation, ways that use modelling. |
| Exploitation Route | Consultation of our publications and even more so the subsequent projects. |
| Sectors | Agriculture, Food and Drink,Education,Manufacturing, including Industrial Biotechology,Other |
| Description | The findings have been used mainly in many subsequent projects |
| First Year Of Impact | 2011 |
| Sector | Education,Other |
| Impact Types | Cultural,Societal,Economic |
| Title | Candidate pathway finding |
| Description | Steatosis or fatty liver disease is an important disease sometimes leading to hepatocarcinoma. Most researchers engaged in genomics are searching for so-called candidate genes in their data, which then should identify single-gene causes and single target strategies. We have developed a way to identify/examine 'candidate pathways'. More inn general, the portfolio of projects ahs led to a great increase in number of detailed kinetic models of metabolic pathways (as reported in JWS-Online). these are now of great use for other organisms and the same pathways or other pathways in the same organisms. All these models are also of use for the development of the Infrastructure Systems Biology Europe (ISBE). |
| Type Of Material | Model of mechanisms or symptoms - human |
| Year Produced | 2016 |
| Provided To Others? | Yes |
| Impact | This is now used in multiple research projects. Through JWS online and BioModels our models are used by many. |
| Description | Snoep |
| Organisation | University of Stellenbosch |
| Country | South Africa |
| Sector | Academic/University |
| PI Contribution | Ideas, models, data |
| Collaborator Contribution | Ideas, models, data management |
| Impact | Publications Models Grant proposals |
| Description | VU Amsterdam |
| Organisation | Free University of Amsterdam |
| Country | Netherlands |
| Sector | Academic/University |
| PI Contribution | Expertise, information, data |
| Collaborator Contribution | Expertise, information, data |
| Impact | Publications Grant proposals Learned students Understanding of Biology |