Comparison of methods for measurement of organic compounds at ultra-trace level: analytical criteria and application to analysis of amino acids in extraterrestrial samples

Investigation of neutron radiation effects on polyclonal antibodies (IgG) and fluorescein dye for astrobiological applications

Detecting life in the Solar System is one of the great challenges of new upcoming space missions. Biochips have been proposed as a way to detect organic matter on extraterrestrial objects. A biochip is a miniaturized device composed of biologically sensitive systems, such as antibodies, which are immobilized on a slide. In the case of in situ measurements, the main concern is to ensure the survival of the antibodies under space radiation. Our recent computing simulation of cosmic ray interactions with the martian environment shows that neutrons are one of the dominant species at soil level. Therefore, we have chosen, in a first approach, to study antibody resistance to neutrons by performing irradiation experiments at the Applications Interdisciplinaires des Faisceaux d'Ions en Région Aquitaine (AIFIRA) platform, a French ion beam facility at the Centre d'Etudes Nucléaires de Bordeaux-Gradignan in Bordeaux. Antibodies and fluorescent dyes, freeze-dried and in buffer solution, were irradiated with 0.6 MeV and 6 MeV neutrons. Sample analyses demonstrated that, in the conditions tested, antibody recognition capability and fluorescence dye intensity are not affected by the neutrons.

An examination of the carbon isotope effects associated with amino acid biosynthesis

Stable carbon isotope ratios (delta(13)C) were determined for alanine, proline, phenylalanine, valine, leucine, isoleucine, aspartate (aspartic acid and asparagine), glutamate (glutamic acid and glutamine), lysine, serine, glycine, and threonine from metabolically diverse microorganisms. The microorganisms examined included fermenting bacteria, organotrophic, chemolithotrophic, phototrophic, methylotrophic, methanogenic, acetogenic, acetotrophic, and naturally occurring cryptoendolithic communities from the Dry Valleys of Antarctica. Here we demonstrated that reactions involved in amino acid biosynthesis can be used to distinguish amino acids formed by life from those formed by nonbiological processes. The unique patterns of delta(13)C imprinted by life on amino acids produced a biological bias. We also showed that, by applying discriminant function analysis to the delta(13)C value of a pool of amino acids formed by biological activity, it was possible to identify key aspects of intermediary carbon metabolism in the microbial world. In fact, microorganisms examined in this study could be placed within one of three metabolic groups: (1) heterotrophs that grow by oxidizing compounds containing three or more carbon-to-carbon bonds (fermenters and organotrophs), (2) autotrophs that grow by taking up carbon dioxide (chemolitotrophs and phototrophs), and (3) acetoclastic microbes that grow by assimilation of formaldehyde or acetate (methylotrophs, methanogens, acetogens, and acetotrophs). Furthermore, we demonstrated that cryptoendolithic communities from Antarctica grouped most closely with the autotrophs, which indicates that the dominant metabolic pathways in these communities are likely those utilized for CO(2 )fixation. We propose that this technique can be used to determine the dominant metabolic types in a community and reveal the overall flow of carbon in a complex ecosystem.

Amino acid distribution in meteorites: diagenesis, extraction methods, and standard metrics in the search for extraterrestrial biosignatures

The relative abundance of the protein amino acids has been previously investigated as a potential marker for biogenicity in meteoritic samples. However, these investigations were executed without a quantitative metric to evaluate distribution variations, and they did not account for the possibility of interdisciplinary systematic error arising from inter-laboratory differences in extraction and detection techniques. Principal component analysis (PCA), hierarchical cluster analysis (HCA), and stochastic probabilistic artificial neural networks (ANNs) were used to compare the distributions for nine protein amino acids previously reported for the Murchison carbonaceous chondrite, Mars meteorites (ALH84001, Nakhla, and EETA79001), prebiotic synthesis experiments, and terrestrial biota and sediments. These techniques allowed us (1) to identify a shift in terrestrial amino acid distributions secondary to diagenesis; (2) to detect differences in terrestrial distributions that may be systematic differences between extraction and analysis techniques in biological and geological laboratories; and (3) to determine that distributions in meteoritic samples appear more similar to prebiotic chemistry samples than they do to the terrestrial unaltered or diagenetic samples. Both diagenesis and putative interdisciplinary differences in analysis complicate interpretation of meteoritic amino acid distributions. We propose that the analysis of future samples from such diverse sources as meteoritic influx, sample return missions, and in situ exploration of Mars would be less ambiguous with adoption of standardized assay techniques, systematic inclusion of assay standards, and the use of a quantitative, probabilistic metric. We present here one such metric determined by sequential feature extraction and normalization (PCA), information-driven automated exploration of classification possibilities (HCA), and prediction of classification accuracy (ANNs).