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Metabolic models are reconstructed based on an organism's available genome annotation and provide predictive tools to study metabolic processes at a systems-level. Genome-scale metabolic models may include gaps as well as reactions that are unverified experimentally. Reconstructed models of newly isolated microalgal species will result in weaknesses due to these gaps, as there is usually sparse biochemical evidence available for the metabolism of such isolates. The phenotype microarray (PM) technology is an effective, high-throughput method that functionally determines cellular metabolic activities in response to a wide array of entry metabolites. Combining the high throughput phenotypic assays with metabolic modeling can allow existing metabolic network models to be rapidly reconstructed or optimized by providing biochemical evidence to support and expand genomic evidence. This work will show the use of PM assays for the study of microalgae by using the green microalgal model species Chlamydomonas reinhardtii as an example. Experimental evidence for over 254 reactions obtained by PM was used in this study to expand and refine a genome-scale C. reinhardtii metabolic network model, iRC1080, by approximately 25 percent. The protocol created here can be used as a basis for functionally profiling the metabolism of other microalgae, including known microalgae mutants and new isolates.

Being integral primary producers in diverse ecosystems, microalgal genomes could be mined for ecological insights, but representative genome sequences are lacking for many phyla. We cultured and sequenced 107 microalgae species from 11 different phyla indigenous to varied geographies and climates. This collection was used to resolve genomic differences between saltwater and freshwater microalgae. Freshwater species showed domain-centric ontology enrichment for nuclear and nuclear membrane functions, while saltwater species were enriched in organellar and cellular membrane functions. Further, marine species contained significantly more viral families in their genomes (p = 8e-4). Sequences from Chlorovirus, Coccolithovirus, Pandoravirus, Marseillevirus, Tupanvirus, and other viruses were found integrated into the genomes of algal from marine environments. These viral-origin sequences were found to be expressed and code for a wide variety of functions. Together, this study comprehensively defines the expanse of protein-coding and viral elements in microalgal genomes and posits a unified adaptive strategy for algal halotolerance., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Microalgae have been investigated for the photosynthetic production of natural products with industrial and biomedical applications. Their rapid growth offers an advantage over higher plants, while their complex metabolic capacities allow for the production of various molecules. Despite their potentials, molecular techniques are underdeveloped in microalgae compared to higher plants, fungi, and bacteria. However, recent advances in genome sequencing, strain development, and genome editing technologies, are providing thrust to enhance research on microalgal species that have branched out from several focal model organisms to encompass a great diversity of species. In this review, we highlight the recent, significant advances in microalgal research, with a focus on the development of new resources that can enhance work on model and non-model species., (Copyright © 2019 The Authors. Published by Elsevier Ltd.. All rights reserved.)

To convert waste CO 2 from flue gases of power plants into value-added products, bio-mitigation technologies show promise. In this study, we cultivated a fast-growing species of green microalgae, Chlorella vulgaris, in different sizes of photobioreactors (PBRs) and developed a strategy using small doses of sugars for enhancing CO 2 sequestration under light-emitting diode illumination. Glucose supplementation at low levels resulted in an increase of photoautotrophic growth-driven biomass generation as well as CO 2 capture by 10% and its enhancement corresponded to an increase of supplied photon flux. The utilization of urea instead of nitrate as the sole nitrogen source increased photoautotrophic growth by 14%, but change of nitrogen source didn't compromise glucose-induced enhancement of photoautotrophic growth. The optimized biomass productivity achieved was 30.4% higher than the initial productivity of purely photoautotrophic culture. The major pigments in the obtained algal biomass were found comparable to its photoautotrophic counterpart and a high neutral lipids productivity of 516.6 mg/(L·day) was achieved after optimization. A techno-economic model was also developed, indicating that LED-based PBRs represent a feasible strategy for converting CO 2 into value-added algal biomass., (Copyright © 2018 Elsevier B.V. All rights reserved.)

With the advent of modern biotechnology, microorganisms from diverse lineages have been used to produce bio-based feedstocks and bioactive compounds. Many of these compounds are currently commodities of interest, in a variety of markets and their utility warrants investigation into improving their production through strain development. In this review, we address the issue of strain improvement in a group of organisms with strong potential to be productive "cell factories": the photosynthetic microalgae. Microalgae are a diverse group of phytoplankton, involving polyphyletic lineage such as green algae and diatoms that are commonly used in the industry. The photosynthetic microalgae have been under intense investigation recently for their ability to produce commercial compounds using only light, CO₂, and basic nutrients. However, their strain improvement is still a relatively recent area of work that is under development. Importantly, it is only through appropriate engineering methods that we may see the full biotechnological potential of microalgae come to fruition. Thus, in this review, we address past and present endeavors towards the aim of creating productive algal cell factories and describe possible advantageous future directions for the field., Competing Interests: The authors declare no conflict interest.

The increased demand and consumption of fossil fuels have raised interest in finding renewable energy sources throughout the globe. Much focus has been placed on optimizing microorganisms and primarily microalgae, to efficiently produce compounds that can substitute for fossil fuels. However, the path to achieving economic feasibility is likely to require strain optimization through using available tools and technologies in the fields of systems and synthetic biology. Such approaches invoke a deep understanding of the metabolic networks of the organisms and their genomic and proteomic profiles. The advent of next generation sequencing and other high throughput methods has led to a major increase in availability of biological data. Integration of such disparate data can help define the emergent metabolic system properties, which is of crucial importance in addressing biofuel production optimization. Herein, we review major computational tools and approaches developed and used in order to potentially identify target genes, pathways, and reactions of particular interest to biofuel production in algae. As the use of these tools and approaches has not been fully implemented in algal biofuel research, the aim of this review is to highlight the potential utility of these resources toward their future implementation in algal research.

Microalgae are emerging as a sustainable source of bioproducts, including food, animal feed, nutraceuticals, and biofuels. This review emphasizes the need to carefully select suitable species and highlights the importance of strain optimization to enhance the feasibility of developing algae as a sustainable resource for food and biomaterial production. It discussesmicroalgal bioprospecting methods, different types of cultivation systems, microalgal biomass yields, and cultivation using wastewater. The paper highlights advances in artificial intelligence that can optimize algal productivity and overcome the limitations faced in current microalgal industries. Additionally, the potential of UV mutagenesis combined with high-throughput screening is examined as a strategy for generating improved strains without introducing foreign genetic material. The necessity of a multifaceted optimization approach for enhanced productivity is acknowledged. This review provides an overview of recent developments crucial for the commercial success of microalgal production. [ABSTRACT FROM AUTHOR]

Background: Oils and bioproducts extracted from cultivated algae can be used as sustainable feedstock for fuels, nutritional supplements, and other bio-based products. Discovery and isolation of new algal species and their subsequent optimization are needed to achieve economical feasibility for industrial applications. Here we describe and validate a workflow for in situ analysis of algal lipids through confocal Raman microscopy. We demonstrate its effectiveness to characterize lipid content of algal strains isolated from the environment as well as algal cells screened for increased lipid accumulation through UV mutagenesis combined with Fluorescence Activated Cell Sorting (FACS). Results: To establish and validate our workflow, we refined an existing Raman platform to obtain better discrimination in chain length and saturation of lipids through ratiometric analyses of mixed fatty acid lipid standards. Raman experiments were performed using two different excitation lasers (? = 532 and 785 nm), with close agreement observed between values obtained using each laser. Liquid chromatography coupled with mass spectrometry (LC- MS) experiments validated the obtained Raman spectroscopic results. To demonstrate the utility and effectiveness of the improved Raman platform, we carried out bioprospecting for algal species from soil and marine environments in both temperate and subtropical geographies to obtain algal isolates from varied environments. Further, we carried out two rounds of mutagenesis screens on the green algal model species, Chlamydomonas reinhardtii, to obtain cells with increased lipid content. Analyses on both environmental isolates and screened cells were conducted which determined their respective lipids. Different saturation states among the isolates as well as the screened C. reinhardtii strains were observed. The latter indicated the presence of cell-to cell variations among cells grown under identical condition. In contrast, non-mutagenized C. reinhardtii cells showed no significant heterogeneity in lipid content. Conclusions: We demonstrate the utility of confocal Raman microscopy for lipid analysis on novel aquatic and soil microalgal isolates and for characterization of lipid-expressing cells obtained in a mutagenesis screen. Raman microscopy enables quantitative determination of the unsaturation level and chain lengths of microalgal lipids, which are key parameters in selection and engineering of microalgae for optimal production of biofuels. [ABSTRACT FROM AUTHOR]