Identification of significant proxy variable for the physiological status affecting salt stress-induced lipid accumulation in Chlorella sorokiniana HS1

Trace metals exposure in three different coastal compartments show specific morphological and reproductive traits across generations in a sentinel copepod

The effect of exposure from several compartments of the environment at the level of individuals was rarely investigated. This study reports the effect of contaminants from varied compartments like sediment resuspension, elutriation from resuspended sediment (extract) and seawater spiked trace metal mixtures (TM) on morphological and reproductive traits of the pelagic bioindicator copepod Eurytemora affinis. At the population level of E. affinis, lowest survival was observed in dissolved exposures (TM and extract) in the first generation (G1), showing some adaptation in the second generation (G2). An opposite trend for resuspended sediment showed higher sensitivity in survival at G2. At the individual level, prosome length and volume proved to be sensitive parameters for resuspended sediments, whereas clutch size and egg diameter were more sensitive to TM and extract. Although the generation of decontamination (G3, no exposure), showed a significant recovery at the population level (survival % along with clutch size) of E. affinis exposed to resuspended sediment, morphological characteristics like prosome length and volume showed no such recovery (lower than control, p < 0.05). To the contrary, dissolved exposure showed no significant recovery from G1 to G3 on neither survival %, clutch size, egg diameter, prosome volume, but an increase of prosome length (p < 0.05). Such tradeoffs in combatting the stress from varied sources of toxicity were observed in all exposures, from G1 to G3. The number of lipid droplets inside the body cavity of E. affinis showed a significant positive correlation with trace metal bioaccumulation (p < 0.01) along with a negative correlation (p < 0.05) with survival and clutch size in each treatment. This confirms the inability of copepods to utilize lipids under stressful conditions. Our study tenders certain morphological and reproductive markers that show specificity to different compartments of exposure, promising an advantage in risk assessment and fish feed studies.

Efficient microalgal lipid production driven by salt stress and phytohormones synergistically

In this study, a novel method of coupling phytohormones with saline wastewater was proposed to drive efficient microalgal lipid production. All the six phytohormones effectively promoted microalgae growth in saline wastewater, and further increased the microalgal lipid content based on salt stress, so as to achieve a large increase in microalgal lipid productivity. Among the phytohormones used, abscisic acid had the most significant promoting effect. Under the synergistic effect of 20 g/L salt and 20 mg/L abscisic acid, the microalgal lipid productivity reached 3.7 times that of the control. Transcriptome analysis showed that differentially expressed genes (DEGs) of microalgae in saline wastewater were mainly up-regulated under the effects of phytohormones except brassinolide. Common DEGs analysis showed that phytohormones all regulated the expression of genes related to DNA repair and substance synthesis. In conclusion, synergistic effect of salt stress and phytohormones can greatly improve the microalgal lipid production efficiency.

Fluorescent Imaging and Sorting of High-Lipid-Content Strains of Green Algae by Using an Aggregation-Induced Emission Luminogen

Microalgae-based biofuels are receiving attention at the environmental, economic, and social levels because they are clean, renewable, and quickly produced. The green algae Chlorella vulgaris has been extensively studied in research laboratories and the biofuel industry as a model organism to increase lipid production to be cost-effective in commercial production. In this work, we utilized a lipid-droplet-specific luminogen with aggregation-induced emission (AIE) characteristics to increase the lipid production of C. vulgaris by fluorescent imaging and sorting of those algal cells with large and rich lipid droplets for subculturing. The AIE-active TPA-A enabled real-time monitoring of the size and number of lipid droplets in C. vulgaris during their growth period so that we can identify the best time for harvesting. Furthermore, the algae cells with high lipid content were identified and collected for subculturing by the technique of fluorescence-activated cell sorting (FACS). The lipid production in the generation of two successive selections was almost doubled compared to the generation with natural selection. This work demonstrated that the technologies of AIE and FACS could be applied together to improve the production of a third-generation biofuel.

Application of Systems Engineering Principles and Techniques in Biological Big Data Analytics: A Review

In the past few decades, we have witnessed tremendous advancements in biology, life sciences and healthcare. These advancements are due in no small part to the big data made available by various high-throughput technologies, the ever-advancing computing power, and the algorithmic advancements in machine learning. Specifically, big data analytics such as statistical and machine learning has become an essential tool in these rapidly developing fields. As a result, the subject has drawn increased attention and many review papers have been published in just the past few years on the subject. Different from all existing reviews, this work focuses on the application of systems, engineering principles and techniques in addressing some of the common challenges in big data analytics for biological, biomedical and healthcare applications. Specifically, this review focuses on the following three key areas in biological big data analytics where systems engineering principles and techniques have been playing important roles: the principle of parsimony in addressing overfitting, the dynamic analysis of biological data, and the role of domain knowledge in biological data analytics.