Lab website: http://group.szbk.u-szeged.hu/sysbiol/

EVOLUTIONARY SYSTEMS BIOLOGY

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 corresponding 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 computational 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 evolutionary protocol, and compare results with computational model predictions and bioinformatics analyses.

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 complete loss of their genes. Systematic single gene deletion 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. cerevisiae) and E. coli, and we seek to understand the physiological and evolutionary mechanisms behind this pattern. The following questions sum up our research:

• Are these seemingly dispensable genes redundant or do they have important contribution under special environmental conditions not yet tested in the laboratory?
• How far can the deleterious impacts of gene deletions be mitigated during evolution, and what factors 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 experimental evolution protocols.

II. Evolution of epistatic interaction networks

Redundant functions (and the extent of systems robustness) can be uncovered by comparing the fitness of single and double knock-out strains. Understanding the relevant genetic and environmental factors that influence 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 functions. 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 environment 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 environmental conditions, and of the phenotypic consequences of these interactions would be required (i) to understand complex genetic diseases, (ii) to rationally identify novel antimicrobial drug targets, (iii) to comprehend the accumulation of genetic variation in natural populations, (iv) to understand how cellular networks evolve and (v) to rationally construct simplified microbial cells by means of genome reduction.

III. Evolution of drug resistance

Evolution of antibiotic resistance is a problem that continues to challenge the healthcare sector. Drug resistance mechanisms are often complex and involve many complementary changes that can affect transport 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 combinations are employed.

First, we ask how global transcriptional regulatory genes affect the potential for de novo evolution of resistance in E.coli. Second, we are developing a heuristic algorithm with the aim to optimize the composition of antimicrobial drug cocktails (for more details, see Papp lab projects).