1. Wochenbericht AL509

Wochenbericht FS Alkor Reise 509, Fahrtabschnitt 15.05 – 20.05.201

Cruise Report AL509 R.V. Alkor

Dates of Cruise: 15.05. – 30.05.2018 Areas of Research: Physical, chemical, biological and fishery oceanography Port Calls: Riga. Latvia, 22.05.201

1. Wochenbericht AL522

Wochenbericht FS Alkor Reise 522 "Ostsee Mai", 1. Fahrtabschnitt 15.05 – 25.05.201

Eco-Evolutionary Interaction in Competing Phytoplankton: Nutrient Driven Genotype Sorting Likely Explains Dominance Shift and Species Responses to CO2

How ecological and evolutionary processes interact and together determine species and community responses to climate change is poorly understood. We studied long-term dynamics (over approximately 200 asexual generations) in two phytoplankton species, a coccolithophore (Emiliania huxleyi), and a diatom (Chaetoceros affinis), to increased CO2 growing alone, or competing with one another in co-occurrence. To allow for rapid evolutionary responses, the experiment started with a standing genetic variation of nine genotypes in each of the species. Under co-occurrence of both species, we observed a dominance shift from C. affinis to E. huxleyi after about 120 generations in both CO2 treatments, but more pronounced under high CO2. Associated with this shift, we only found weak adaptation to high CO2 in the diatom and none in the coccolithophore in terms of species’ growth rates. In addition, no adaptation to interspecific competition could be observed by comparing the single to the two-species treatments in reciprocal assays, regardless of the CO2 treatment. Nevertheless, highly reproducible genotype sorting left only one genotype remaining for each of the species among all treatments. This strong evolutionary selection coincided with the dominance shift from C. affinis to E. huxleyi. Since all other conditions were kept constant over time, the most parsimonious explanation for the dominance shift is that the strong evolutionary selection was driven by the experimental nutrient conditions, and in turn potentially altered competitive ability of the two species. Thus, observed changes in the simplest possible two-species phytoplankton “community” demonstrated that eco-evolutionary interactions can be critical for predicting community responses to climate change in rapidly dividing organisms such as phytoplankton

An improved filtering algorithm for big read datasets and its application to single-cell assembly

Background: For single-cell or metagenomic sequencing projects, it is necessary to sequence with a very high mean coverage in order to make sure that all parts of the sample DNA get covered by the reads produced. This leads to huge datasets with lots of redundant data. A filtering of this data prior to assembly is advisable. Brown et al. (2012) presented the algorithm Diginorm for this purpose, which filters reads based on the abundance of their k-mers. Methods: We present Bignorm, a faster and quality-conscious read filtering algorithm. An important new algorithmic feature is the use of phred quality scores together with a detailed analysis of the k-mer counts to decide which reads to keep. Results: We qualify and recommend parameters for our new read filtering algorithm. Guided by these parameters, we remove in terms of median 97.15% of the reads while keeping the mean phred score of the filtered dataset high. Using the SDAdes assembler, we produce assemblies of high quality from these filtered datasets in a fraction of the time needed for an assembly from the datasets filtered with Diginorm. Conclusions: We conclude that read filtering is a practical and efficient method for reducing read data and for speeding up the assembly process. This applies not only for single cell assembly, as shown in this paper, but also to other projects with high mean coverage datasets like metagenomic sequencing projects. Our Bignorm algorithm allows assemblies of competitive quality in comparison to Diginorm, while being much faster. Bignorm is available for download at https://git.informatik.uni-kiel.de/axw/Bignorm

An improved filtering algorithm for big read datasets and its application to single-cell assembly

Background: For single-cell or metagenomic sequencing projects, it is necessary to sequence with a very high mean coverage in order to make sure that all parts of the sample DNA get covered by the reads produced. This leads to huge datasets with lots of redundant data. A filtering of this data prior to assembly is advisable. Brown et al. (2012) presented the algorithm Diginorm for this purpose, which filters reads based on the abundance of their k-mers. Methods: We present Bignorm, a faster and quality-conscious read filtering algorithm. An important new algorithmic feature is the use of phred quality scores together with a detailed analysis of the k-mer counts to decide which reads to keep. Results: We qualify and recommend parameters for our new read filtering algorithm. Guided by these parameters, we remove in terms of median 97.15% of the reads while keeping the mean phred score of the filtered dataset high. Using the SDAdes assembler, we produce assemblies of high quality from these filtered datasets in a fraction of the time needed for an assembly from the datasets filtered with Diginorm. Conclusions: We conclude that read filtering is a practical and efficient method for reducing read data and for speeding up the assembly process. This applies not only for single cell assembly, as shown in this paper, but also to other projects with high mean coverage datasets like metagenomic sequencing projects. Our Bignorm algorithm allows assemblies of competitive quality in comparison to Diginorm, while being much faster. Bignorm is available for download at https://git.informatik.uni-kiel.de/axw/Bignorm

Ca2+-controlled competitive diacylglycerol binding of protein kinase C isoenzymes in living cells

The cellular decoding of receptor-induced signaling is based in part on the spatiotemporal activation pattern of PKC isoforms. Because classical and novel PKC isoforms contain diacylglycerol (DAG)-binding C1 domains, they may compete for DAG binding. We reasoned that a Ca2+-induced membrane association of classical PKCs may accelerate the DAG binding and thereby prevent translocation of novel PKCs. Simultaneous imaging of fluorescent PKC fusion proteins revealed that during receptor stimulation, PKCα accumulated in the plasma membrane with a diffusion-limited kinetic, whereas translocation of PKCɛ was delayed and attenuated. In BAPTA-loaded cells, however, a selective translocation of PKCɛ, but not of coexpressed PKCα, was evident. A membrane-permeable DAG analogue displayed a higher binding affinity for PKCɛ than for PKCα. Subsequent photolysis of caged Ca2+ immediately recruited PKCα to the membrane, and DAG-bound PKCɛ was displaced. At low expression levels of PKCɛ, PKCα concentration dependently prevented the PKCɛ translocation with half-maximal effects at equimolar coexpression. Furthermore, translocation of endogenous PKCs in vascular smooth muscle cells corroborated the model that a competition between PKC isoforms for DAG binding occurs at native expression levels. We conclude that Ca2+-controlled competitive DAG binding contributes to the selective recruitment of PKC isoforms after receptor activation