A high-throughput assay for quantifying phenotypic traits of microalgae

High-throughput methods for phenotyping microalgae are in demand across a variety of research and commercial purposes. Many microalgae can be readily cultivated in multi-well plates for experimental studies which can reduce overall costs, while measuring traits from low volume samples can reduce handling. Here we develop a high-throughput quantitative phenotypic assay (QPA) that can be used to phenotype microalgae grown in multi-well plates. The QPA integrates 10 low-volume, relatively high-throughput trait measurements (growth rate, cell size, granularity, chlorophyll a, neutral lipid content, silicification, reactive oxygen species accumulation, and photophysiology parameters: ETRmax, Ik, and alpha) into one workflow. We demonstrate the utility of the QPA on Thalassiosira spp., a cosmopolitan marine diatom, phenotyping six strains in a standard nutrient rich environment (f/2 media) using the full 10-trait assay. The multivariate phenotypes of strains can be simplified into two dimensions using principal component analysis, generating a trait-scape. We determine that traits show a consistent pattern when grown in small volume compared to more typical large volumes. The QPA can thus be used for quantifying traits across different growth environments without requiring exhaustive large-scale culturing experiments, which facilitates experiments on trait plasticity. We confirm that this assay can be used to phenotype newly isolated diatom strains within 4 weeks of isolation. The QPA described here is highly amenable to customisation for other traits or unicellular taxa and provides a framework for designing high-throughput experiments. This method will have applications in experimental evolution, modelling, and for commercial applications where screening of phytoplankton traits is of high importance

Multitrait diversification in marine diatoms in constant and warmed environments

Phytoplankton are photosynthetic marine microbes that affect food webs, nutrient cycles and climate regulation. Their roles are determined by correlated phytoplankton functional traits including cell size, chlorophyll content and cellular composition. Here, we explore patterns of evolution in interrelated trait values and correlations. Because both chance events and natural selection contribute to phytoplankton trait evolution, we used population bottlenecks to diversify six genotypes of Thalassiosirid diatoms. We then evolved them as large populations in two environments. Interspecific variation and within-species evolution were visualized for nine traits and their correlations using reduced axes (a trait-scape). Our main findings are that shifts in trait values resulted in movement of evolving populations within the trait-scape in both environments, but were more frequent when large populations evolved in a novel environment. Which trait relationships evolved was population-specific, but greater departures from ancestral trait correlations were associated with lower population growth rates. There was no single master trait that could be used to understand multi-trait evolution. Instead, repeatable multi-trait evolution occurred along a major axis of variation defined by several diatom traits and trait relationships. Because trait-scapes capture changes in trait relation-ships and values together, they offer an insightful way to study multi-trait variatio

Laporan Praktek Kerja Profesi Apoteker di PT.Bayer Indonesia-Cimanggis Plant Jalan Raya Bogor KM 32 Depok (03 April 2017 - 31 Mei 2017)

Experimental Data of Exp.1.2 (6-16°C

Patterns of vertical cyst distribution and survival in 100-year-old sediment archives of three spring dinoflagellate species from the Northern Baltic Sea

The history of expansion of bloom-forming cold water dinoflagellates in the Northern Baltic Sea was studied using 100-year-old sediment archives of their resting cysts. Vertical cyst distributions of Biecheleria baltica and Apocalathium malmogiense, two dinoflagellates indistinguishable by light microscopy and not recognized as distinct species in monitoring, and chain-forming Peridiniella catenata were analysed in Pb210 and Cs137 dated layers of a sediment core from deep, hypoxic accumulation bottoms of the Gulf of Finland. Cyst profiles showed that B. baltica and A. malmogiense were already present in the Baltic spring phytoplankton community at the beginning of the 20th century. This confirms that B. baltica, which was only recognized in the late 1980s, is a native species in the area. A drastic increase in B. baltica cyst concentrations in the 1930s to 1960s coincided with the acceleration of anthropogenic eutrophication. Large cyst deposits accumulated over several decades in the sediment which, by the 1980s, amounted to the seed stock necessary to inoculate dominant blooms. In the cyst records A. malmogiense always contributed a minor fraction of the two species. P. catenata had a relatively short cyst record in Gulf of Finland sediments despite demonstrated long-term presence in the plankton, which emphasizes that cyst-based historic surveys are not suitable for all cyst-forming dinoflagellates. This was corroborated by correspondence analyses of long-term plankton and cyst records which validated the trends from the sediment archive for B. baltica and A. malmogiense, but failed to do so for P. catenata. Germination experiments with 100-year-old cysts revealed a remarkable long-term survival capacity of A. malmogiense, making this species a suitable model for resurrection studies testing adaptation in heavily impacted systems such as the Baltic Sea

The evolution of trait correlations constrains phenotypic adaptation to high CO 2 in a eukaryotic alga

Microbes form the base of food webs and drive biogeochemical cycling. Predicting the effects of microbial evolution on global elemental cycles remains a significant challenge due to the sheer number of interacting environmental and trait combinations. Here, we present an approach for integrating multivariate trait data into a predictive model of trait evolution. We investigated the outcome of thousands of possible adaptive walks parameterized using empirical evolution data from the alga Chlamydomonas exposed to high CO(2). We found that the direction of historical bias (existing trait correlations) influenced both the rate of adaptation and the evolved phenotypes (trait combinations). Critically, we use fitness landscapes derived directly from empirical trait values to capture known evolutionary phenomena. This work demonstrates that ecological models need to represent both changes in traits and changes in the correlation between traits in order to accurately capture phytoplankton evolution and predict future shifts in elemental cycling

The marine biodiversity observation network plankton workshops : plankton ecosystem function, biodiversity, and forecasting—research requirements and applications

Plankton is a massive and phylogenetically diverse group of thousands of prokaryotes, protists (unicellular eukaryotic organisms), and metazoans (multicellular eukaryotic organisms; Fig. 1). Plankton functional diversity is at the core of various ecological processes, including productivity, carbon cycling and sequestration, nutrient cycling (Falkowski 2012), interspecies interactions, and food web dynamics and structure (D'Alelio et al. 2016). Through these functions, plankton play a critical role in the health of the coastal and open ocean and provide essential ecosystem services. Yet, at present, our understanding of plankton dynamics is insufficient to project how climate change and other human-driven impacts affect the functional diversity of plankton. That limits our ability to predict how critical ecosystem services will change in the future and develop strategies to adapt to these changes

The Marine Biodiversity Observation Network Plankton Workshops: Plankton Ecosystem Function, Biodiversity, and Forecasting—Research Requirements and Applications

ISSN:1539-6088ISSN:1539-607

The Marine Biodiversity Observation Network Plankton Workshops:Plankton Ecosystem Function, Biodiversity, and Forecasting—Research Requirements and Applications

Plankton is a massive and phylogenetically diverse group of thousands of prokaryotes, protists (unicellular eukaryotic organisms), and metazoans (multicellular eukaryotic organisms; Fig. 1). Plankton functional diversity is at the core of various ecological processes, including productivity, carbon cycling and sequestration, nutrient cycling (Falkowski 2012), interspecies interactions, and food web dynamics and structure (D'Alelio et al. 2016). Through these functions, plankton play a critical role in the health of the coastal and open ocean and provide essential ecosystem services. Yet, at present, our understanding of plankton dynamics is insufficient to project how climate change and other human-driven impacts affect the functional diversity of plankton. That limits our ability to predict how critical ecosystem services will change in the future and develop strategies to adapt to these changes