Amphun Chaiboonchoe

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.

Algal Cell Factories: Approaches, Applications, and Potentials

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.

Computational Approaches for Microalgal Biofuel Optimization: A Review

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.

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.

Integrated analysis of gene network in childhood leukemia from microarray and pathway databases

Glucocorticoids (GCs) have been used as therapeutic agents for children with acute lymphoblastic leukaemia (ALL) for over 50 years. However, much remains to be understood about the molecular mechanism of GCs actions in ALL subtypes. In this study, we delineate differential responses of ALL subtypes, B- and T-ALL, to GCs treatment at systems level by identifying the differences among biological processes, molecular pathways, and interaction networks that emerge from the action of GCs through the use of a selected number of available bioinformatics methods and tools. We provide biological insight into GC-regulated genes, their related functions, and their networks specific to the ALL subtypes. We show that differentially expressed GC-regulated genes participate in distinct underlying biological processes affected by GCs in B-ALL and T-ALL with little to no overlap. These findings provide the opportunity towards identifying new therapeutic targets.

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.

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.

The genome and phenome of the green alga Chloroidium sp. UTEX 3007 reveal adaptive traits for desert acclimatization

To investigate the phenomic and genomic traits that allow green algae to survive in deserts, we characterized a ubiquitous species, Chloroidium sp. UTEX 3007, which we isolated from multiple locations in the United Arab Emirates (UAE). Metabolomic analyses of Chloroidium sp. UTEX 3007 indicated that the alga accumulates a broad range of carbon sources, including several desiccation tolerance-promoting sugars and unusually large stores of palmitate. Growth assays revealed capacities to grow in salinities from zero to 60 g/L and to grow heterotrophically on >40 distinct carbon sources. Assembly and annotation of genomic reads yielded a 52.5 Mbp genome with 8153 functionally annotated genes. Comparison with other sequenced green algae revealed unique protein families involved in osmotic stress tolerance and saccharide metabolism that support phenomic studies. Our results reveal the robust and flexible biology utilized by a green alga to successfully inhabit a desert coastline. DOI: http://dx.doi.org/10.7554/eLife.25783.001