Insertion sequence (Is) elements in E. coli have tra- ditionally been considered mostly “selfish” dna, i.e.,
evolutionarily neutral. Having a unique tool, a multi- deletional strain stripped of its mobile genetic ele- ments, we are investigating the potential role of Iss in adaptation to stressful conditions. measuring muta- tion rates under various conditions, we find evidence of stress-induced transposition of Iss, resulting in genomic rearrangements and inactivation of protein overexpressing plasmid clones. comparing isogenic strains with or without Is elements, their individual contribution to the adaptation of the cell can be in- vestigated.
Contact: [email protected]
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I. Mechanisms and evolution of
gene dispensability
Perhaps one of the most striking discoveries of modern molecular genetics was the extent by which organisms appear to tolerate mutations or even com- plete loss of their genes. systematic single gene dele- tion studies on microbes have revealed that 70–80% of the single mutant strains are viable with no apparent phenotypic deformation (see table 1).
our research concentrates largely on yeast (S. cer-
evisiae) and E. coli, and we seek to understand the
physiological and evolutionary mechanisms behind this pattern. The following questions sum up our re- search:
• Are these seemingly dispensable genes redundant or do they have important contribution under special environmental conditions not yet tested in the labo- ratory?
• How far can the deleterious impacts of gene dele- tions be mitigated during evolution, and what fac- tors limit the extent of compensatory evolution? • Is it likely that some of these genes increase the rate
of evolutionary adaptation?
to address these issues, we combine evolutionary genomics with systems biology and laboratory experi- mental evolution protocols.
The ability of cellular systems to adapt to genetic and environmental perturbations is a fundamental but poorly understood process both at the molecular and evolutionary level.
There are both physiological and evolutionary reasonings why mutations often have limited impact on cellular growth. First, perturbations that hit one target often have no effect on the overall performance of a complex system (such as metabolic networks), as perturbations can be adjusted by changed regulation and expression of the corre- sponding genes. Second, due to the fast evolvability of microbes, the effect of a perturbation can readily be alleviated by the evolution of compensatory mutations at other sites of the network. Understanding the extent of intrinsic and evolved robustness in cellular systems demands integrated analyses that combine functional genomics and computa- tional systems biology with microbial evolutionary experiments. In collaboration with several leading research teams in the field, we are investigating the following issues. First, we ask how accurately genome-scale metabolic network models can predict the impact of genetic deletions and non-heritable perturbations. Second, we investigate how far epistatic interaction networks are influenced by global regulatory modulators, such as Hsp90. Third, to understand how the impact of genetic and drug perturbations can be mitigated during evolution, we pursue large-scale lab evolu- tionary protocol, and compare results with computational model predictions and bioinformatics analyses.
evolUTIonaRY sYsTems bIoloGY / pal lab
(sYnTHeTIC/sYsTems bIoloGY UnIT)
Csaba PáL / Principal Investigator, Group Leader
Béla SZAMECZ / staff scientist Kálmán SOMOGYI / staff scientist Balazs BOGOS / staff scienstist Orsolya MÉHI / Phd student
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II. Evolution of epistatic interaction networks
redundant functions (and the extent of systems ro- bustness) can be uncovered by comparing the fitness of single and double knock-out strains. understanding the relevant genetic and environmental factors that in- fluence epistatic interactions across genes is of central importance for at least two reasons. first, it helps our understanding on the physiological and evolutionary contribution of genes with identified biochemical func- tions. second, understanding the impact of certain genes on the genetic interaction landscape will shed new lights on the cellular mechanisms of buffering. In collaboration with the Papp lab, we integrate machine learning protocols, metabolic network analyses with large-scale mapping of genetic interactions in yeast (S. cerevisiae). We ask (i) how reliably systems biology models can predict genetic interactions, (ii) to what extent genetic interactions depend on the cellular en- vironment investigated, and (iii) how far interactions between mutations are influenced by global regulatory modulators.
In the long run, a more complete picture of the interaction between multiple mutations and envi- ronmental conditions, and of the phenotypic conse- quences of these interactions would be required (i) to understand complex genetic diseases, (ii) to ration-
ally identify novel antimicrobial drug targets, (iii) to comprehend the accumulation of genetic variation in natural populations, (iv) to understand how cel- lular networks evolve and (v) to rationally construct simplified microbial cells by means of genome reduc- tion.
III. Evolution of drug resistance
evolution of antibiotic resistance is a problem that continues to challenge the healthcare sector. drug re- sistance mechanisms are often complex and involve many complementary changes that can affect trans- port processes, target enzymes and may also cause global reorganization of gene expression patterns. also, several case studies indicate the probability of acquired drug resistance when certain drug combina- tions are employed.
first, we ask how global transcriptional regulatory genes affect the potential for de novo evolution of re- sistance in E.coli. second, we are developing a heuris- tic algorithm with the aim to optimize the composi- tion of antimicrobial drug cocktails (for more details, see Papp lab projects).
Contact: [email protected]
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systems biology aims at understanding the design principles and multi-level properties of large cellular subsystems arising from numerous molecular interac- tions. While recent technological advancements has enabled the rapid collection of data on the molecular components of cells and their interactions, there is an increasing need for automated methods that can ex- tract useful knowledge from these data and build com- putational models that accurately describe both nor- mal cellular physiology and the phenotypic impact of mutations and environmental perturbations (e.g., drug treatments). The unicellular yeast Saccharomyces cer-
evisiae is an ideal candidate for systems biology studies
due to the availability of large and diverse sets of post- genomic information and experimental tools. We are developing novel computational methods to analyze functional genomic datasets and to automate scientific discovery in the fields of systems biology and drug dis- covery by focusing on the following research topics: