Amoeba/amoebal symbiont genetic transfers: lessons from giant virus neighbours.

Free-living amoebae serve as hosts for a variety of amoebae-resisting microorganisms, including giant viruses and certain bacteria. The latter include symbiotic bacteria as well as bacteria exhibiting a pathogenic phenotype towards amoebae. Amoebae-resisting bacteria have been shown to be widespread in water and to use the amoebae as a reservoir, a replication niche, a protective armour as well as a training ground to select virulence traits allowing survival in the face of microbicidal effects of macrophages, the first line of defense against invading pathogens. More importantly, amoebae play a significant role as a melting pot for genetic exchanges. These ecological and evolutionary roles of amoebae might also be at play for giant viruses and knowledge derived from the study of amoebae-resisting bacteria is useful for the study and understanding of interactions between amoebae and giant viruses. This is especially important since some genes have spread in all domains of life and the exponential availability of eukaryotic genomes and metagenomic sequences will allow researchers to explore these genetic exchanges in a more comprehensive way, thus completely changing our perception of the evolutionary history of organisms. Thus, a large part of this review is dedicated to report current known gene exchanges between the different amoebae-resisting organisms and between amoebae and the internalized bacteria.

Aitchison Geometry for Probability and Likelihood as a new approach to mathematical statistics

The Aitchison vector space structure for the simplex is generalized to a Hilbert space structure A2(P) for distributions and likelihoods on arbitrary spaces. Centralnotations of statistics, such as Information or Likelihood, can be identified in the algebraical structure of A2(P) and their corresponding notions in compositional data analysis, such as Aitchison distance or centered log ratio transform.In this way very elaborated aspects of mathematical statistics can be understoodeasily in the light of a simple vector space structure and of compositional data analysis. E.g. combination of statistical information such as Bayesian updating,combination of likelihood and robust M-estimation functions are simple additions/perturbations in A2(Pprior). Weighting observations corresponds to a weightedaddition of the corresponding evidence.Likelihood based statistics for general exponential families turns out to have aparticularly easy interpretation in terms of A2(P). Regular exponential families formfinite dimensional linear subspaces of A2(P) and they correspond to finite dimensionalsubspaces formed by their posterior in the dual information space A2(Pprior).The Aitchison norm can identified with mean Fisher information. The closing constant itself is identified with a generalization of the cummulant function and shown to be Kullback Leiblers directed information. Fisher information is the local geometry of the manifold induced by the A2(P) derivative of the Kullback Leibler information and the space A2(P) can therefore be seen as the tangential geometry of statistical inference at the distribution P.The discussion of A2(P) valued random variables, such as estimation functionsor likelihoods, give a further interpretation of Fisher information as the expected squared norm of evidence and a scale free understanding of unbiased reasoning

Valtioneuvos K.-A.Fagerholm 8/6 1970

Kirje 8.6.1970

Statsrådet K.-A. Fagerholm 8.6.1970

Kirje

Mutation identification by direct comparison of whole-genome sequencing data from mutant and wild-type individuals using k-mers.

Genes underlying mutant phenotypes can be isolated by combining marker discovery, genetic mapping and resequencing, but a more straightforward strategy for mapping mutations would be the direct comparison of mutant and wild-type genomes. Applying such an approach, however, is hampered by the need for reference sequences and by mutational loads that confound the unambiguous identification of causal mutations. Here we introduce NIKS (needle in the k-stack), a reference-free algorithm based on comparing k-mers in whole-genome sequencing data for precise discovery of homozygous mutations. We applied NIKS to eight mutants induced in nonreference rice cultivars and to two mutants of the nonmodel species Arabis alpina. In both species, comparing pooled F2 individuals selected for mutant phenotypes revealed small sets of mutations including the causal changes. Moreover, comparing M3 seedlings of two allelic mutants unambiguously identified the causal gene. Thus, for any species amenable to mutagenesis, NIKS enables forward genetics without requiring segregating populations, genetic maps and reference sequences.

Osastopäällikkö, tohtori A.K. Kivivuori 4/9 1972

Kirje 4.9.1972