Bioinformatics Challenges and Potentialities in Studying Extreme Environments

Biological systems show impressive adaptations at extreme environments. In extreme environments, directional selection pressure mechanisms acting upon mutational events often produce functional and structural innovations. Examples are the antifreeze proteins in Antarctic fish and their lack of hemoglobin, and the thermostable properties of TAQ polymerase from thermophilic organisms. During the past decade, more than 4000 organisms have been part of genome-sequencing projects. This has enabled the retrieval of information about evolutionary relationships among all living organisms, and has increased the understanding of complex phenomena, such as evolution, adaptation, and ecology. Bioinformatics tools have allowed us to perform genome annotation, cross-comparison, and to understand the metabolic potential of living organisms. In the last few years, research in bioinformatics has started to migrate from the analysis of genomic sequences and structural biology problems to the analysis of genotype-phenotype mapping. We believe that the analysis of multi-omic information, particularly metabolic and transcriptomic data of organisms living in extreme environments, could provide important and general insights into the how natural selection in an ecosystem shapes the molecular constituents. Here we present a review of methods with the aim to bridge the gap between theoretical models, bioinformatics analysis and experimental settings. The amount of data suggests that bioinformatics could be used to investigate whether the adaptation is generated by interesting molecular inventions. We therefore review and discuss the methodology and tools to approach this challenge.

[1]  D. Gillespie Exact Stochastic Simulation of Coupled Chemical Reactions , 1977 .

[2]  Stuart A. Kauffman,et al.  The origins of order , 1993 .

[3]  O. White,et al.  Environmental Genome Shotgun Sequencing of the Sargasso Sea , 2004, Science.

[4]  J. Collins,et al.  Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles , 2007, PLoS biology.

[5]  Cristina Miceli,et al.  Biomonitoring of Lake Garda: Identification of ciliate species and symbiotic algae responsible for the "black-spot" bloom during the summer of 2004. , 2008, Environmental research.

[6]  James A. Eddy,et al.  Accomplishments in genome‐scale in silico modeling for industrial and medical biotechnology , 2009, Biotechnology journal.

[7]  Eugene V. Koonin,et al.  Constraints and plasticity in genome and molecular-phenome evolution , 2010, Nature Reviews Genetics.

[8]  Anders F. Andersson,et al.  Transitions in bacterial communities along the 2000 km salinity gradient of the Baltic Sea , 2011, The ISME Journal.

[9]  David Baker,et al.  Optimization of the In-silico-designed Kemp Eliminase Ke70 by Computational Design and Directed Evolution Journal of Molecular Biology , 2022 .

[10]  Haiying Yu,et al.  Genome Sequence and Transcriptome Analysis of the Radioresistant Bacterium Deinococcus gobiensis: Insights into the Extreme Environmental Adaptations , 2012, PloS one.

[11]  Y. Gilad,et al.  Comparative studies of gene expression and the evolution of gene regulation , 2012, Nature Reviews Genetics.

[12]  Ivan Merelli,et al.  Structural thermal adaptation of β‐tubulins from the Antarctic psychrophilic protozoan Euplotes focardii , 2012, Proteins.

[13]  Jonathan R. Karr,et al.  A Whole-Cell Computational Model Predicts Phenotype from Genotype , 2012, Cell.

[14]  R. Fani,et al.  Draft Genome Sequence of the Volatile Organic Compound-Producing Antarctic Bacterium Arthrobacter sp. Strain TB23, Able To Inhibit Cystic Fibrosis Pathogens Belonging to the Burkholderia cepacia Complex , 2012, Journal of bacteriology.

[15]  Jens Nielsen,et al.  Assessing the Human Gut Microbiota in Metabolic Diseases , 2013, Diabetes.

[16]  E. Kuijper,et al.  Current application and future perspectives of molecular typing methods to study Clostridium difficile infections. , 2013, Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin.

[17]  Ryan Calsbeek,et al.  The Adaptive Landscape in Evolutionary Biology , 2013 .

[18]  M. Slatkin,et al.  An Introduction to Population Genetics: Theory and Applications , 2013 .

[19]  Sarah R. Smith,et al.  The Marine Microbial Eukaryote Transcriptome Sequencing Project (MMETSP): Illuminating the Functional Diversity of Eukaryotic Life in the Oceans through Transcriptome Sequencing , 2014, PLoS biology.

[20]  Osman Ugur Sezerman,et al.  PANOGA: a web server for identification of SNP-targeted pathways from genome-wide association study data , 2014, Bioinform..

[21]  O. Nolte Antimicrobial resistance in the 21st century: a multifaceted challenge. , 2014, Protein and peptide letters.

[22]  Genomic analysis of three sponge-associated Arthrobacter Antarctic strains, inhibiting the growth of Burkholderia cepacia complex bacteria by synthesizing volatile organic compounds. , 2014, Microbiological research.

[23]  Kathleen Marchal,et al.  COLOMBOS v2.0: an ever expanding collection of bacterial expression compendia , 2013, Nucleic Acids Res..

[24]  Pietro Liò,et al.  Bioaccumulation modelling and sensitivity analysis for discovering key players in contaminated food webs: The case study of PCBs in the Adriatic Sea , 2015 .

[25]  P. Lio’,et al.  Bioremediation in marine ecosystems: a computational study combining ecological modeling and flux balance analysis , 2014, Front. Genet..

[26]  A. Demain,et al.  Microbial Enzymes: Tools for Biotechnological Processes , 2014, Biomolecules.

[27]  Ilias Tagkopoulos,et al.  An integrative, multi-scale, genome-wide model reveals the phenotypic landscape of Escherichia coli , 2014, Molecular systems biology.

[28]  Sofia Morfopoulou,et al.  Bayesian mixture analysis for metagenomic community profiling , 2014, bioRxiv.

[29]  Naruemon Pratanwanich,et al.  A Hybrid of Metabolic Flux Analysis and Bayesian Factor Modeling for Multiomic Temporal Pathway Activation. , 2015, ACS synthetic biology.

[30]  Claudio Angione,et al.  Predictive analytics of environmental adaptability in multi-omic network models , 2015, Scientific Reports.

[31]  L. Harris,et al.  Microarray transcriptional profiling of Arctic Mesorhizobium strain N33 at low temperature provides insights into cold adaption strategies , 2015, BMC Genomics.

[32]  D. Weinreich,et al.  Quantitative Description of a Protein Fitness Landscape Based on Molecular Features. , 2015, Molecular biology and evolution.

[33]  C. Miceli,et al.  Microbial Consortium Associated with the Antarctic Marine Ciliate Euplotes focardii: An Investigation from Genomic Sequences , 2015, Microbial Ecology.

[34]  Giuseppe Nicosia,et al.  Multi-Target Analysis and Design of Mitochondrial Metabolism , 2015, PloS one.

[35]  O. U. Sezerman,et al.  Zinc Modulates Self-Assembly of Bacillus thermocatenulatus Lipase. , 2015, Biochemistry.

[36]  Fabian J. Theis,et al.  RAMONA: a Web application for gene set analysis on multilevel omics data , 2015, Bioinform..

[37]  Luca Cardelli,et al.  Stochastic analysis of Chemical Reaction Networks using Linear Noise Approximation , 2015, Biosyst..

[38]  Bernhard O. Palsson,et al.  Solving Puzzles With Missing Pieces: The Power of Systems Biology [Point of View] , 2016, Proc. IEEE.

[39]  Daniel J Verdini,et al.  Retrospective analysis of nosocomial infections in an Italian tertiary care hospital. , 2016, The new microbiologica.

[40]  A. Mitschler,et al.  Probing the roles of two tryptophans surrounding the unique zinc coordination site in lipase family I.5 , 2015, Proteins.

[41]  Zhenzhen Yi,et al.  Utility of combining morphological characters, nuclear and mitochondrial genes: An attempt to resolve the conflicts of species identification for ciliated protists. , 2016, Molecular phylogenetics and evolution.

[42]  Kouros Owzar,et al.  Exploiting expression patterns across multiple tissues to map expression quantitative trait loci , 2016, BMC Bioinformatics.

[43]  Pietro Liò,et al.  Multiplex methods provide effective integration of multi-omic data in genome-scale models , 2016, BMC Bioinformatics.