high-throughput-metabolic-profiling-for-model-refinements

High-Throughput Metabolic Profiling for Model Refinements of Microalgae

Metabolic models are reconstructed based on an organism&#39;s available genome annotation and provide predictive tools to study metabolic processes at a ...

New York University Abu Dhabi

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. 

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

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. 

Computational approaches for microalgal biofuel optimization: a review.

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. 

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

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. 

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

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.

The in vitro selection world.

Metabolic networks, which are mathematical representations of organismal metabolism, are reconstructed to provide computational platforms to guide metabolic engineering experiments and explore fundamental questions on metabolism. Systems level analyses, such as interrogation of phylogenetic relationships within the network, can provide further guidance on the modification of metabolic circuitries. Chlamydomonas reinhardtii, a biofuel relevant green alga that has retained key genes with plant, animal, and protist affinities, serves as an ideal model organism to investigate the interplay between gene function and phylogenetic affinities at multiple organizational levels. Here, using detailed topological and functional analyses, coupled with transcriptomics studies on a metabolic network that we have reconstructed for C. reinhardtii, we show that network connectivity has a significant concordance with the co-conservation of genes; however, a distinction between topological and functional relationships is observable within the network. Dynamic and static modes of co-conservation were defined and observed in a subset of gene-pairs across the network topologically. In contrast, genes with predicted synthetic interactions, or genes involved in coupled reactions, show significant enrichment for both shorter and longer phylogenetic distances. Based on our results, we propose that the metabolic network of C. reinhardtii is assembled with an architecture to minimize phylogenetic profile distances topologically, while it includes an expansion of such distances for functionally interacting genes. This arrangement may increase the robustness of C. reinhardtii's network in dealing with varied environmental challenges that the species may face. The defined evolutionary constraints within the network, which identify important pairings of genes in metabolism, may offer guidance on synthetic biology approaches to optimize the production of desirable metabolites.

Systems level analysis of the Chlamydomonas reinhardtii metabolic network reveals variability in evolutionary co-conservation.

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.

Algal Cell Factories: Approaches, Applications, and Potentials.

To investigate the phenomic and genomic traits that allow green algae to survive in deserts, we characterized a ubiquitous species, , which we isolated from multiple locations in the United Arab Emirates (UAE). Metabolomic analyses of  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.

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

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 . Engineered  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.

Intracellular spectral recompositioning of light enhances algal photosynthetic efficiency.

Poor prognoses remain the most challenging aspect of hepatocellular carcinoma (HCC) therapy. Consequently, alternative therapeutics are essential to control HCC. This study investigated the anticancer effects of safranal against HCC using in vitro, in silico, and network analyses. Cell cycle and immunoblot analyses of key regulators of cell cycle, DNA damage repair and apoptosis demonstrated unique safranal-mediated cell cycle arrest at G2/M phase at 6 and 12 h, and at S-phase at 24 h, and a pronounced effect on DNA damage machinery. Safranal also showed pro-apoptotic effect through activation of both intrinsic and extrinsic initiator caspases; indicating ER stress-mediated apoptosis. Gene set enrichment analysis provided consistent findings where UPR is among the top terms of up-regulated genes in response to safranal treatment. Thus, proteins involved in ER stress were regulated through safranal treatment to induce UPR in HepG2 cells.

Safranal induces DNA double-strand breakage and ER-stress-mediated cell death in hepatocellular carcinoma cells.

The activities of microalgae support nutrient cycling that helps to sustain aquatic and terrestrial ecosystems. Most microalgal species, especially those from the subtropics, are genomically uncharacterized. Here we report the isolation and genomic characterization of 22 microalgal species from subtropical coastal regions belonging to multiple clades and three from temperate areas. Halotolerant strains including Halamphora, Dunaliella, Nannochloris, and Chloroidium comprised the majority of these isolates. The subtropical-based microalgae contained arrays of methyltransferase, pyridine nucleotide-disulfide oxidoreductase, abhydrolase, cystathionine synthase, and small-molecule transporter domains present at high relative abundance. We found that genes for sulfate transport, sulfotransferase, and glutathione S-transferase activities were especially abundant in subtropical, coastal microalgal species and halophytic species in general. Our metabolomics analyses indicate lineage- and habitat-specific sets of biomolecules implicated in niche-specific biological processes. This work effectively expands the collection of available microalgal genomes by ∼50%, and the generated resources provide perspectives for studying halophyte adaptive traits.

Potential for Heightened Sulfur-Metabolic Capacity in Coastal Subtropical Microalgae.

Surface colonization allows diatoms, a dominant group of phytoplankton in oceans, to adapt to harsh marine environments while mediating biofoulings to human-made underwater facilities. The regulatory pathways underlying diatom surface colonization, which involves morphotype switching in some species, remain mostly unknown. Here, we describe the identification of 61 signaling genes, including G-protein-coupled receptors (GPCRs) and protein kinases, which are differentially regulated during surface colonization in the model diatom species, Phaeodactylum tricornutum. We show that the transformation of P. tricornutum with constructs expressing individual GPCR genes induces cells to adopt the surface colonization morphology. P. tricornutum cells transformed to express GPCR1A display 30% more resistance to UV light exposure than their non-biofouling wild-type counterparts, consistent with increased silicification of cell walls associated with the oval biofouling morphotype. Our results provide a mechanistic definition of morphological shifts during surface colonization and identify candidate target proteins for the screening of eco-friendly, anti-biofouling molecules.

GPCR Genes as Activators of Surface Colonization Pathways in a Model Marine Diatom.

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.

Large-scale genome sequencing reveals the driving forces of viruses in microalgal evolution.

T cell exhaustion presents one of the major hurdles to cancer immunotherapy. Among exhausted CD8 tumor-infiltrating lymphocytes, the terminally exhausted subset contributes directly to tumor cell killing owing to its cytotoxic effector function. However, this subset does not respond to immune checkpoint blockades and is difficult to be reinvigorated with restored proliferative capacity. Here, we show that a half-life-extended interleukin-10-Fc fusion protein directly and potently enhanced expansion and effector function of terminally exhausted CD8 tumor-infiltrating lymphocytes by promoting oxidative phosphorylation, a process that was independent of the progenitor exhausted T cells. Interleukin-10-Fc was a safe and highly efficient metabolic intervention that synergized with adoptive T cell transfer immunotherapy, leading to eradication of established solid tumors and durable cures in the majority of treated mice. These findings show that metabolic reprogramming by upregulating mitochondrial pyruvate carrier-dependent oxidative phosphorylation can revitalize terminally exhausted T cells and enhance the response to cancer immunotherapy.

Amphun Chaiboonchoe

Laboratory of Algal,
Systems,
and Synthetic Biology (LASSB),
Division of Science and Math,
Laboratory of Algal, Systems, and Synthetic Biology (LASSB), Division of Science and Math

New York University Abu Dhabi

High-Throughput Metabolic Profiling for Model Refinements of Microalgae

Amphun Chaiboonchoe

.css-o250i3{font-size:18px;font-family:Open Sans,sans-serif;line-height:21.6px;}Laboratory of Algal,Systems,and Synthetic Biology (LASSB),Division of Science and Math,Laboratory of Algal, Systems, and Synthetic Biology (LASSB), Division of Science and Math

New York University Abu Dhabi

High-Throughput Metabolic Profiling for Model Refinements of Microalgae

Laboratory of Algal,
Systems,
and Synthetic Biology (LASSB),
Division of Science and Math,
Laboratory of Algal, Systems, and Synthetic Biology (LASSB), Division of Science and Math