Genomic analysis of Caldalkalibacillus thermarum TA2.A1 reveals aerobic alkaliphilic metabolism and evolutionary hallmarks linking alkaliphilic bacteria and plant life.

The aerobic thermoalkaliphile Caldalkalibacillus thermarum strain TA2.A1 is a member of a separate order of alkaliphilic bacteria closely related to the Bacillales order. Efforts to relate the genomic information of this evolutionary ancient organism to environmental adaptation have been thwarted by the inability to construct a complete genome. The existing draft genome is highly fragmented due to repetitive regions, and gaps between and over repetitive regions were unbridgeable. To address this, Oxford Nanopore Technology's MinION allowed us to span these repeats through long reads, with over 6000-fold coverage. This resulted in a single 3.34 Mb circular chromosome. The profile of transporters and central metabolism gives insight into why the organism prefers glutamate over sucrose as carbon source. We propose that the deamination of glutamate allows alkalization of the immediate environment, an excellent example of how an extremophile modulates environmental conditions to suit its own requirements. Curiously, plant-like hallmark electron transfer enzymes and transporters are found throughout the genome, such as a cytochrome b6c1 complex and a CO2-concentrating transporter. In addition, multiple self-splicing group II intron-encoded proteins closely aligning to those of a telomerase reverse transcriptase in Arabidopsis thaliana were revealed. Collectively, these features suggest an evolutionary relationship to plant life.

De novo detection of copy number variation by co-assembly.

MOTIVATION: Comparing genomes of individual organisms using next-generation sequencing data is, until now, mostly performed using a reference genome. This is challenging when the reference is distant and introduces bias towards the exact sequence present in the reference. Recent improvements in both sequencing read length and efficiency of assembly algorithms have brought direct comparison of individual genomes by de novo assembly, rather than through a reference genome, within reach. RESULTS: Here, we develop and test an algorithm, named Magnolya, that uses a Poisson mixture model for copy number estimation of contigs assembled from sequencing data. We combine this with co-assembly to allow de novo detection of copy number variation (CNV) between two individual genomes, without mapping reads to a reference genome. In co-assembly, multiple sequencing samples are combined, generating a single contig graph with different traversal counts for the nodes and edges between the samples. In the resulting 'coloured' graph, the contigs have integer copy numbers; this negates the need to segment genomic regions based on depth of coverage, as required for mapping-based detection methods. Magnolya is then used to assign integer copy numbers to contigs, after which CNV probabilities are easily inferred. The copy number estimator and CNV detector perform well on simulated data. Application of the algorithms to hybrid yeast genomes showed allotriploid content from different origin in the wine yeast Y12, and extensive CNV in aneuploid brewing yeast genomes. Integer CNV was also accurately detected in a short-term laboratory-evolved yeast strain.

Combinatorial effects of environmental parameters on transcriptional regulation in Saccharomyces cerevisiae: a quantitative analysis of a compendium of chemostat-based transcriptome data.

BACKGROUND: Microorganisms adapt their transcriptome by integrating multiple chemical and physical signals from their environment. Shake-flask cultivation does not allow precise manipulation of individual culture parameters and therefore precludes a quantitative analysis of the (combinatorial) influence of these parameters on transcriptional regulation. Steady-state chemostat cultures, which do enable accurate control, measurement and manipulation of individual cultivation parameters (e.g. specific growth rate, temperature, identity of the growth-limiting nutrient) appear to provide a promising experimental platform for such a combinatorial analysis. RESULTS: A microarray compendium of 170 steady-state chemostat cultures of the yeast Saccharomyces cerevisiae is presented and analyzed. The 170 microarrays encompass 55 unique conditions, which can be characterized by the combined settings of 10 different cultivation parameters. By applying a regression model to assess the impact of (combinations of) cultivation parameters on the transcriptome, most S. cerevisiae genes were shown to be influenced by multiple cultivation parameters, and in many cases by combinatorial effects of cultivation parameters. The inclusion of these combinatorial effects in the regression model led to higher explained variance of the gene expression patterns and resulted in higher function enrichment in subsequent analysis. We further demonstrate the usefulness of the compendium and regression analysis for interpretation of shake-flask-based transcriptome studies and for guiding functional analysis of (uncharacterized) genes and pathways. CONCLUSION: Modeling the combinatorial effects of environmental parameters on the transcriptome is crucial for understanding transcriptional regulation. Chemostat cultivation offers a powerful tool for such an approach.