Download Adaptation to extreme environments

Zlatko Smole, Nela Nikolić, Ivo F. Sbalzarini, Anita Kriško
MedILS and ETH Zurich, Switzerland (Sbalzarini)
Adaptation to extreme environments - looking for the proteome patterns
Abstract
Extreme conditions of life are those that exceed conditions for growth and reproduction that are optimal for the
majority of organisms. Organisms that thrive in or require such conditions are termed extremophiles, each
corresponding to the way its environmental niche differs from those of the majority of mesophile organisms (live
in conditions optimal for the majority of organisms). These classifications are not exclusive, thus many
extremophiles fall under multiple categories.
Our main aim is to investigate if the environment in which a microorganism lives is encoded in the proteome and
can we make the connection between the two.
Namely, a total of 474 bacterial and archaeal full proteomic sequences are currently available in public
databases. In addition to the mesophilic organisms such databases also contain proteomes of different categories
of extremophiles. Recently, in silico studies on the biology of proteomes have focused mainly on functional
annotation of proteins, while characterization of species’ lifestyles at global levels has received far less attention.
Therefore, we have started our research by constructing and maintaining the database of the bacterial and
archaeal proteomes together with optimal growth conditions (temperature, pH, concentration of sodium chloride,
water activity and pressure) and information about lifestyle for each species.
The dominant part of our project is to address some of the following questions: Can we infer the environmental
niche and lifestyle of an organism from the characteristic properties of its proteome sequence? More precisely,
what are the relations between quantifiable properties of proteomic sequence, such as amino acid composition,
polarity, charge, acidity of entire proteomes and/or individual proteins, and many other properties concerning
lifestyle (pathogenicity, free living, symbiosis, etc.)? A number of parameters is defined based on the amino acid
sequence of proteins within a proteome to describe amino acid content of proteomes, at the same time placing
each amino acid in various contexts. The most challenging part has been to apply methods of supervised and
unsupervised machine learning to find characteristic patterns in our datasets. We will discuss our result in the
framework of adaptive evolution of prokaryotic species.