Research 

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 the deleterious impacts of gene deletions can 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. Combating the evolution of drug resistance

Emergence of multi drug resistance pathogenic microbial strains is a problem that continues to challenge the healthcare sector. Therefore, understanding evolutionary mechanisms underpinning acquired drug resistance is of high critical importance. We concentrate on the following two issues:

Modulators of evolvability. It is well established that genes that increased rates of mutation and recombination have large influence on evolutionary adaptation. Similarly, molecular chaperones govern evolutionary trajectories in diverse species by modulating the translation of genotype into phenotype. Are these examples only the tip of a huge iceberg? Are there numerous genes and independent molecular mechanisms that modulate the evolution of drug resistance?

Automated discovery of optimal antimicrobial drug combinations. In addition to discover new compounds, there is an increasing need to identify optimal combinations of existing drugs (‘drug cocktails’) that are highly effective against resistant strains. Most high-throughput experimental screens aim to test all possible pair-wise combinations in a given drug compound library, which is not feasible for large libraries, or when the goal is to test the synergistic effects between three or more compounds. Therefore, robotic protocols coupled with intelligent experimental selection are needed to explore the vast chemical space of drug compounds in rapid and cost-effective ways. We are developing a heuristic algorithm that would optimize the composition of antimicrobial drug cocktails by iteratively performing experiments, using automated laboratory equipments, and automatically evaluating them. The project is run in collaboration with the Papp lab (BRC, Szeged).

III. 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 model 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-term, 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.