Quantification of macromolecular biomass composition for constraint-based metabolic modeling

Genome-scale metabolic models (GEMs) are mathematical representations of metabolism that allow for in silico simulation of metabolic phenotypes and capabilities. A prerequisite for these predictions is an accurate representation of the biomolecular composition of the cell necessary for replication and growth, implemented in GEMs as the so-called biomass objective function (BOF). The BOF contains the metabolic precursors required for synthesis of the cellular macro- and micromolecular constituents (e.g. protein, RNA, DNA), and its composition is highly dependent on the particular organism, strain, and growth condition. Despite its critical role, the BOF is rarely constructed using specific measurements of the modeled organism, drawing the validity of this approach into question. Thus, there is a need to establish robust and reliable protocols for experimental condition-specific biomass determination. Here, we address this challenge by presenting a general pipeline for biomass quantification, evaluating its performance on Escherichia coli K-12 MG1655 sampled during balanced exponential growth under controlled conditions in a batch-fermentor set-up. We significantly improve both the coverage and molecular resolution compared to previously published workflows, quantifying 91.6% of the biomass. Our measurements display great correspondence with previously reported measurements, and we were also able to detect subtle characteristics specific to the particular E. coli strain. Using the modified E. coli GEM iML1515a, we compare the feasible flux ranges of our experimentally determined BOF with the original BOF, finding that the changes in BOF coefficients considerably affect the attainable fluxes at the genome-scale.

Cylindrospermopsin- and Deoxycylindrospermopsin-Producing Raphidiopsis raciborskii and Microcystin-Producing Microcystis spp. in Meiktila Lake, Myanmar

Meiktila Lake is a shallow reservoir located close to Meiktila city in central Myanmar. Its water is used for irrigation, domestic purposes and drinking water. No detailed study of the presence of cyanobacteria and their potential toxin production has been conducted so far. To ascertain the cyanobacterial composition and presence of cyanobacterial toxins in Meiktila Lake, water samples were collected in March and November 2017 and investigated for physico-chemical and biological parameters. Phytoplankton composition and biomass determination revealed that most of the samples were dominated by the cyanobacterium Raphidiopsis raciborskii. In a polyphasic approach, seven isolated cyanobacterial strains were classified morphologically and phylogenetically as R. raciborskii, and Microcystis spp. and tested for microcystins (MCs), cylindrospermopsins (CYNs), saxitoxins and anatoxins by enzyme-linked immunosorbent assay (ELISA) and liquid chromatography–mass spectrometry (LC–MS). ELISA and LC–MS analyses confirmed CYNs in three of the five Raphidiopsis strains between 1.8 and 9.8 μg mg−1 fresh weight. Both Microcystis strains produced MCs, one strain 52 congeners and the other strain 20 congeners, including 22 previously unreported variants. Due to the presence of CYN- and MC-producing cyanobacteria, harmful effects on humans, domestic and wild animals cannot be excluded in Meiktila Lake.

Effects of Fertilization on Biomass and Macronutrient Content of Eucalyptus urophylla S.T. Blake in Arenized Soil of Pampa Biome

The objective of this study was to quantify the biomass and the macronutrient stock in an experiment of fertilization with Eucalyptus urophylla, planted in arenized soil at 12 months-old, in Rio Grande do Sul, Brazil. The experiment had a completely randomized design with five treatments (T1, T2, T3, T4 and T5) with three replications. The treatments T2, T3, T4 and T5, received increasing doses of triple superphosphate. On the other hand, the T1 treatment was the only one to receive natural phosphate in planting. For the determination of the biomass, fifteen trees were felled and separated in the following components: leaves, branches, stem bark, stem wood and roots. Samples of the components were collected and transported to the laboratory for biomass determination and chemical analysis. The total biomass varied between the treatments, with highest biomass accumulation of 6.83 Mg ha-1 occurring in T5. The T1 presented the highest biomass for roots representing 33.4% of the total biomass. The biomass distribution among the different components in the decreasing order was: roots > stem wood > leaves > branches > stem bark, for all the treatments. The treatment with higher doses of fertilizers (T5) presented the highest amount of nutrient accumulation in the total biomass (131.26 kg ha-1). The concentration and accumulation of nutrients presented the following trend K > N > Ca > Mg > P > S. Analyzing the different components of biomass, the highest amounts of nutrients followed the order: leaves > roots > stem wood > branches > stem bark. Fertilization influenced the biomass production of E. urophylla in arenized soil in the Pampa biome, but without significant differences to date (12 months). The leaves had the highest concentration of macronutrients, with the exception of Ca, which was higher in the bark. The K was the element that presented highest accumulation in whole tree. The plantation of eucalyptus with fertilization management may be an alternative for the economic use of arenized soil.

Pertumbuhan dan Kandungan Lutein Dunaliella salina pada Salinitas yang Berbeda

Dunaliella salina adalah salah satu mikroalga yang mengandung pigmen lutein. Lutein memiliki manfaat sebagai antioksidan untuk melawan radikal bebas pada mata. Pertumbuhan mikroalga dipengaruhi oleh berbagai macam faktor lingkungan, salah satunya adalah salinitas. Penelitian ini bertujuan untuk menentukan salinitas terbaik guna mengoptimalkan pertumbuhan dan produksi pigmen lutein pada D. salina. Metode yang digunakan adalah eksperimen laboratoris. Mikroalga D. salina dikultivasi dengan lima perlakuan salinitas yang berbeda yaitu 20, 25, 30, 35, dan 40 ppt. Pertumbuhan sel D. salina diamati selama 9 x 24 jam kemudian dipanen untuk perhitungan biomassanya. Biomassa basah hasil kultivasi diekstraksi menggunakan pelarut aseton. Ekstrak aseton D. salina kemudian dianalisis kandungan pigmen luteinnya secara spektrofotometrik. Hasil penelitian menunjukkan bahwa perlakuan salinitas berpengaruh secara signifikan terhadap pertumbuhan sel dan kandungan pigmen lutein D. salina. Pertumbuhan sel D. salina optimum pada perlakuan salinitas 30 ppt,yaitu sebesar 125,86 x 104 sel/mL, sedangkan untuk kandungan pigmen lutein D. salina diproduksi optimum pada salinitas 25 ppt, yaitu sebesar 0,0077 µg/g. Dunaliella salina is a microalga containing lutein pigment. Lutein has the role of being an antioxidant to fight free radicals in the eye. Microalgae growth is influenced by a variety of environmental factors, such as salinity. The purpose of this research is to determine the best salinity to optimize the growth and production of lutein pigments in D. salina. The method used in this research was a laboratory experiment. Microalgae D.salina was cultivated with five different salinity treatments, which 20, 25, 30, 35, and 40 ppt. Growth of D. salina cells was observed for 9 x 24 hours and then harvested for the biomass determination. The wet biomass from the cultivation results was extracted using acetone solvent. D. salina acetone extract was then analyzed for its lutein pigment content spectrophotometrically. The results showed that salinity treatment had a significant effect on cell growth and pigment content of lutein D. salina. The optimum growth of D. salina cell is optimally achieved in 30 ppt salinity treatment at the amount of 125,86 x 104 cell/mL, while for the lutein pigment content of D. salina is optimally achieved in 25 ppt salinity at the amount of 0,0077 µg/g.

WATCHING GRASS GROW- A PILOT STUDY ON THE SUITABILITY OF PHOTOGRAMMETRIC TECHNIQUES FOR QUANTIFYING CHANGE IN ABOVEGROUND BIOMASS IN GRASSLAND EXPERIMENTS

Grassland ecology experiments in remote locations requiring quantitative analysis of the biomass in defined plots are becoming increasingly widespread, but are still limited by manual sampling methodologies. To provide a cost-effective automated solution for biomass determination, several photogrammetric techniques are examined to generate 3D point cloud representations of plots as a basis, to estimate aboveground biomass on grassland plots, which is a key ecosystem variable used in many experiments. Methods investigated include Structure from Motion (SfM) techniques for camera pose estimation with posterior dense matching as well as the usage of a Time of Flight (TOF) 3D camera, a laser light sheet triangulation system and a coded light projection system. In this context, plants of small scales (herbage) and medium scales are observed. In the first pilot study presented here, the best results are obtained by applying dense matching after SfM, ideal for integration into distributed experiment networks.