Kenan Jijakli

Whole-Genome Resequencing Reveals Extensive Natural Variation in the Model Green Alga Chlamydomonas reinhardtii.

Whole-genome resequencing of Chlamydomonas reveals enormous intraspecific diversity and a reservoir of naturally occurring variation, including candidate loss-of-function alleles. We performed whole-genome resequencing of 12 field isolates and eight commonly studied laboratory strains of the model organism Chlamydomonas reinhardtii to characterize genomic diversity and provide a resource for studies of natural variation. Our data support previous observations that Chlamydomonas is among the most diverse eukaryotic species. Nucleotide diversity is ∼3% and is geographically structured in North America with some evidence of admixture among sampling locales. Examination of predicted loss-of-function mutations in field isolates indicates conservation of genes associated with core cellular functions, while genes in large gene families and poorly characterized genes show a greater incidence of major effect mutations. De novo assembly of unmapped reads recovered genes in the field isolates that are absent from the CC-503 assembly. The laboratory reference strains show a genomic pattern of polymorphism consistent with their origin as the recombinant progeny of a diploid zygospore. Large duplications or amplifications are a prominent feature of laboratory strains and appear to have originated under laboratory culture. Extensive natural variation offers a new source of genetic diversity for studies of Chlamydomonas, including naturally occurring alleles that may prove useful in studies of gene function and the dissection of quantitative genetic traits.

The in vitro selection world

Through iterative cycles of selection, amplification, and mutagenesis, in vitro selection provides the ability to isolate molecules of desired properties and function from large pools (libraries) of random molecules with as many as 10(16) distinct species. This review, in recognition of a quarter of century of scientific discoveries made through in vitro selection, starts with a brief overview of the method and its history. It further covers recent developments in in vitro selection with a focus on tools that enhance the capabilities of in vitro selection and its expansion from being purely a nucleic acids selection to that of polypeptides and proteins. In addition, we cover how next generation sequencing and modern biological computational tools are being used to complement in vitro selection experiments. On the very least, sequencing and computational tools can translate the large volume of information associated with in vitro selection experiments to manageable, analyzable, and exploitable information. Finally, in vivo selection is briefly compared and contrasted to in vitro selection to highlight the unique capabilities of each method.

Microalgal Metabolic Network Model Refinement through High-Throughput Functional Metabolic Profiling

Metabolic modeling provides the means to define metabolic processes at a systems level; however, genome-scale metabolic models often remain incomplete in their description of metabolic networks and may include reactions that are experimentally unverified. This shortcoming is exacerbated in reconstructed models of newly isolated algal species, as there may be little to no biochemical evidence available for the metabolism of such isolates. The phenotype microarray (PM) technology (Biolog, Hayward, CA, USA) provides an efficient, high-throughput method to functionally define cellular metabolic activities in response to a large array of entry metabolites. The platform can experimentally verify many of the unverified reactions in a network model as well as identify missing or new reactions in the reconstructed metabolic model. The PM technology has been used for metabolic phenotyping of non-photosynthetic bacteria and fungi, but it has not been reported for the phenotyping of microalgae. Here, we introduce the use of PM assays in a systematic way to the study of microalgae, applying it specifically to the green microalgal model species Chlamydomonas reinhardtii. The results obtained in this study validate a number of existing annotated metabolic reactions and identify a number of novel and unexpected metabolites. The obtained information was used to expand and refine the existing COBRA-based C. reinhardtii metabolic network model iRC1080. Over 254 reactions were added to the network, and the effects of these additions on flux distribution within the network are described. The novel reactions include the support of metabolism by a number of d-amino acids, l-dipeptides, and l-tripeptides as nitrogen sources, as well as support of cellular respiration by cysteamine-S-phosphate as a phosphorus source. The protocol developed here can be used as a foundation to functionally profile other microalgae such as known microalgae mutants and novel isolates.

Genome-wide expression analysis offers new insights into the origin and evolution of Physcomitrella patens stress response

Changes in the environment, such as those caused by climate change, can exert stress on plant growth, diversity and ultimately global food security. Thus, focused efforts to fully understand plant response to stress are urgently needed in order to develop strategies to cope with the effects of climate change. Because Physcomitrella patens holds a key evolutionary position bridging the gap between green algae and higher plants, and because it exhibits a well-developed stress tolerance, it is an excellent model for such exploration. Here, we have used Physcomitrella patens to study genome-wide responses to abiotic stress through transcriptomic analysis by a high-throughput sequencing platform. We report a comprehensive analysis of transcriptome dynamics, defining profiles of elicited gene regulation responses to abiotic stress-associated hormone Abscisic Acid (ABA), cold, drought, and salt treatments. We identified more than 20,000 genes expressed under each aforementioned stress treatments, of which 9,668 display differential expression in response to stress. The comparison of Physcomitrella patens stress regulated genes with unicellular algae, vascular and flowering plants revealed genomic delineation concomitant with the evolutionary movement to land, including a general gene family complexity and loss of genes associated with different functional groups.

Molecular Genetic Techniques for Algal Bioengineering

The uniquely diverse metabolism of algae can make this group of organisms a prime target for biotechnological purposes and applications. To fully reap their biotechnological potential, molecular genetic techniques for manipulating algae must gain track and become more reliable. To this end, this chapter describes the currently available molecular genetic techniques and resources, as well as a number of relevant computational tools that can facilitate genetic manipulation of algae. Genetic transformation is perhaps the most elemental of such techniques and has become a well-established approach in algal-based genetic experiments. The utility of genetic transformations and other molecular genetic techniques is guided by phenotypic insights resulting from forward and reverse genetic analysis. As such, genetic transformations can form the building blocks for more complex genic manipulations. Herein, we describe currently available engineered homologous recombination or recombineering approaches, which allow for substitutions, insertions, and deletions of larger DNA segments, as well as manipulation of endogenous DNA. In addition, as reagent resources in the form of cloned open reading frames (ORFs) of transcription factors (TFs) and metabolic enzymes become more readily available, algal genetic manipulations can greatly increase the range of obtainable phenotypes for biotechnological applications. Such resources and a few case studies are highlighted in the context of candidate genes for algal bioengineering. On a final note, tools for computer-aided design (CAD) to prototype molecular genetic techniques and protocols are described. Such tools could greatly increase the reliability and efficiency of genetic molecular techniques for algal bioengineering.

Toward Applications of Genomics and Metabolic Modeling to Improve Algal Biomass Productivity

Genomic sequencing is the first step in a systems level study of an algal species, and sequencing studies have grown steadily in recent years. Completed sequences can be tied to algal phenotypes at a systems level through constructing genome-scale metabolic network models. Those models allow the prediction of algal phenotypes and genetic or metabolic modifications, and are constructed by tying the genes to reactions using enzyme databases, then representing those reactions in a concise mathematical form by means of stoichiometric matrices. This is followed by experimental validation using gene deletion or proteomics and metabolomics studies that may result in adding reactions to the model and filling phenotypic gaps. In this chapter, we offer a summary of completed and ongoing algal genomic projects before proceeding to holistically describing the process of constructing genome-scale metabolic models. Relevant examples of algal metabolic models are presented and discussed. The analysis of an alga’s emergent properties from metabolic models is also demonstrated using flux balance analysis (FBA) and related constraint-based approaches to optimize a given metabolic phenotype, or sets of phenotypes such as algal biomass. We also summarize readily available optimization tools rooted in constraint-based modeling that allow for optimizing bioproduction and algal strains. Examples include tools used to develop knockout strategies, identify optimal bioproduction strains, analyze gene deletions, and explore functional relationships within sets in a metabolic model. All in all, this systems level approach can lead to a better understanding and prediction of algal metabolism leading to more robust and cheaper applications.

Bioactive Compounds From Microalgae: Current Development and Prospects

Abstract Microalgae make up the largest and likely most diverse group of photosynthetic organisms in freshwater and marine systems. As new technologies are emerging for the study of bioactive compounds from microalgae, this group is drawing attention as a promising source of natural products that have wide applications in the food and pharmaceutical industries. Algae-derived bioactive compounds are attractive resources for drug screening, given their tremendous structural diversity and biological availability. In this chapter, we first discuss medicinally important products, such as carotenoids, including β-carotene, fucoxanthin, astaxanthin, and lutein, as well as essential fatty acids that originate in microalgae. We then briefly introduce screening assays for antioxidant, antimicrobial, antiviral, anticancer, and immunomodulatory effects, and explore biosynthesis of natural products, which have been widely used in food and cosmetics for their antioxidant effects and nutritional value, and we discuss the potential use of fucoxanthin and its derivatives as anticancer agents. In addition, we describe health benefits of the essential fatty acids eicosapentaenoic acid and docosahexaenoic acid. Further, this chapter emphasizes that microalgae provide a rich source of compounds for therapeutic drug screening and describes examples of screening assays for detection of biological activities of algae-derived compounds.

Single-Cell Characterization of Microalgal Lipid Contents with Confocal Raman Microscopy

The environmental impacts from consumption of fossil fuels have raised interest in finding renewable energy resources throughout the globe. Much focus has been placed on optimizing microalgae to efficiently produce compounds that can substitute for fossil fuels. However, the path to achieving economical feasibility of this substitution is likely to require strain optimization through mutagenesis screens as well as other available approaches and tools. Rapid characterization of the type of fatty acid expressed at a single-cell level can help identify screened cells with the desired lipid characteristics such as chain length and saturation status. Confocal Raman microscopy is a powerful tool for physicochemical characterization of biological samples. It enables single-cell, in vivo monitoring of various cellular components in a rapid, quantitative, label-free, and nondestructive manner. In this chapter, we describe recent advances in this method, which have resulted in remarkable enhancements in the sensitivity, specificity, and spatiotemporal resolution of the technique. We utilize this technique for analyzing lipid content of algal isolates obtained through a mutagenesis screen of the green alga, Chlamydomonas reinhardtii, for increased lipid production at the single-cell level. Our results demonstrate cell-to-cell variation in structural features of expressed lipids among the screened C. reinhardtii mutants, while clonal isolates show little to no variability in expressed lipids. The lack of stochasticity in expression of lipids in clonal populations of C. reinhardtii is a desired feature when accompanied by expression of fatty acids suitable for use as biofuel feedstock.

Prospective Applications of Synthetic Biology for Algal Bioproduct Optimization

Synthetic Biology is an interdisciplinary approach combining biotechnology, evolutionary biology, molecular biology, systems biology and biophysics. While the exact definition of Synthetic Biology might still be debatable, its focus on design and construction of biological devices that perform useful functions is clear and of great utility to engineering algae. This relies on the re-engineering of biological circuits and optimization of certain metabolic pathways to reprogram algae and introduce new functions in them via the use of genetic modules. Genetic editing tools are primary enabling techniques in Synthetic Biology and this chapter discusses common techniques that show promise for algal gene editing. The genetic editing tools discussed in this chapter include RNA interference (RNAi) and artificial microRNAs, RNA scaffolds, transcription activator-like effector nucleases (TALENs), RNA guided Cas9 endonucleases (CRISPR), and multiplex automated genome engineering (MAGE). DNA and whole genome synthesis is another enabling technology in Synthetic Biology and might present an alternative approach to drastically and readily modify algae. Clear and powerful examples of the potential of whole genome synthesis for algal engineering are presented. Also, the development of relevant computational tools, and genetic part registries has stimulated further advancements in the field and their utility in algal research and engineering is described. For now, the majority of synthetic biology efforts are focused on microbes as many pressing problems, such as sustainability in food and energy production rely on modification of microorganisms. Synthetic modifications of algal strains to enhance desired physiological properties could lead to improvements in their utility.

Intracellular spectral recompositioning of light enhances algal photosynthetic efficiency

Spectral conversion of light enhances algal photosynthesis and enables technologies for sustainable energy and food production. Diatoms, considered as one of the most diverse and largest groups of algae, can provide the means to reach a sustainable production of petrochemical substitutes and bioactive compounds. However, a prerequisite to achieving this goal is to increase the solar-to-biomass conversion efficiency of photosynthesis, which generally remains less than 5% for most photosynthetic organisms. We have developed and implemented a rapid and effective approach, herein referred to as intracellular spectral recompositioning (ISR) of light, which, through absorption of excess blue light and its intracellular emission in the green spectral band, can improve light utilization. We demonstrate that ISR can be used chemogenically, by using lipophilic fluorophores, or biogenically, through the expression of an enhanced green fluorescent protein (eGFP) in the model diatom Phaeodactylum tricornutum. Engineered P. tricornutum cells expressing eGFP achieved 28% higher efficiency in photosynthesis than the parental strain, along with an increased effective quantum yield and reduced nonphotochemical quenching (NPQ) induction levels under high-light conditions. Further, pond simulator experiments demonstrated that eGFP transformants could outperform their wild-type parental strain by 50% in biomass production rate under simulated outdoor sunlight conditions. Transcriptome analysis identified up-regulation of major photosynthesis genes in the engineered strain in comparison with the wild type, along with down-regulation of NPQ genes involved in light stress response. Our findings provide a proof of concept for a strategy of developing more efficient photosynthetic cell factories to produce algae-based biofuels and bioactive products.