While some bacteria survive independently, others reduce their metabolic expenditures by utilizing the nutrients available to them in their environment. These bacteria choose to adapt the concept of simple living or “less is more,” meaning one can survive on minimal requirements (we could definitely learn from them). Auxotrophy, a.k.a nutritional dependencies, are a characteristic of host adaptation. They are hard to characterize experimentally because there are too many nutrients to choose from, and also because they differ from one strain to another.
In a study published Mar. 5 in PNAS, we develop a computational workflow that uses both flux balance analysis and comparative genomics to predict nutrient requirements de novo and from sequences alone.
In our workflow, we compare the gene content across several strains of bacteria, and build metabolic networks tailored to each genetic background. Next, we simulate for growth on a minimal medium, and when that cannot be achieved, we run our algorithm called AuxoFind, to search for possible nutrients that would restore growth in silico.
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| Metabolic networks were tailored to the gene content of different bacteria and nutrient dependencies were predicted and validated experimentally. Image courtesy of Systems Biology Research Group |
We find that when the same gene is missing, the nutrient requirements change across species, because they have different metabolic networks and combinations of alternative pathways. We also observed that the absences are manifested as a result of a large range of genetic modifications going from simple and small mutations (like single nucleotide polymorphisms) to large and complex genetic changes (whole genome rearrangements and multi-gene deletions).
The significance of this work is as follows:
Patients with certain diseases (such as Crohn’s disease or cystic fibrosis) tend to be chronically infected with bacteria. Over time, these bugs become more vicious because they slowly adapt to the in vivo environment. Understanding how these adaptations occur is a first step towards devising therapeutic solutions.
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Yara Seif is a UC San Diego bioengineering Ph.D. student. As a member of Bernhard Palsson's Systems Biology Research Group, she studies the metabolism of bacterial strains as well as the evolution of metabolic traits across strains especially in relation to their lifestyle. Her research so far has included multi-strain genomic and metabolic analysis of gram-negative strains using a combination of constraint-based metabolic modeling, comparative genomics and machine learning.
