Genome-scale metabolic model of the diatom Thalassiosira pseudonana highlights the importance of nitrogen and sulfur metabolism in redox balance

Bioinformatics-Based Screening Approach for the Identification and Characterization of Lipolytic Enzymes from the Marine Diatom Phaeodactylum tricornutum

Oleaginous diatoms accumulate lipids of biotechnological interest when exposed to nutrient stress conditions such as nitrogen starvation. While accumulation mechanisms are well-known and have been engineered to improve lipid production, degradation mechanisms remain poorly investigated in diatoms. Identifying lipid-degrading enzymes is the initial step to understanding the catabolic processes. In this study, an in silico screening of the genome of Phaeodactylum tricornutum led to the identification of 57 putative triacylglycerol lipases (EC 3.1.1.3) grouped in 4 families. Further analysis revealed the presence of conserved domains and catalytic residues of lipases. Physico-chemical characteristics and subcellular localization predictions highlighted that a majority of these putative proteins are hydrophilic and cytosolic, suggesting they could be recruited to lipid droplets directly from the cytosol. Among the 57 identified putative proteins, three lipases were identified as possibly involved in lipophagy due to a potential vacuolar localization. The expression of the mRNA corresponding to the 57 proteins was then searched in 3 transcriptomic datasets obtained under nitrogen starvation. Nine genes were highly regulated and were considered as encoding enzymes with a probable important function in lipid catabolism. A tertiary structure prediction of these nine candidates yielded eight functional 3D models. Among those, two downregulated enzymes, Phatr3_J54974 and Phatr3_EG00720, were highlighted as good targets for future functional genomics and purification studies to investigate their role in lipid degradation.

Significant tipping points of sediment microeukaryotes forewarn increasing antibiotic pollution

Antibiotic pollution imposes urgent threats to public health and microbial-mediated ecological processes. Existing studies have primarily focused on bacterial responses to antibiotic pollution, but they ignored the microeukaryotic counterpart, though microeukaryotes are functionally important (e.g., predators and saprophytes) in microbial ecology. Herein, we explored how the assembly of sediment microeukaryotes was affected by increasing antibiotic pollution at the inlet (control) and across the outlet sites along a shrimp wastewater discharge channel. The structures of sediment microeukaryotic community were substantially altered by the increasing nutrient and antibiotic pollutions, which were primarily controlled by the direct effects of phosphate and ammonium (-0.645 and 0.507, respectively). In addition, tetracyclines exerted a large effect (0.209), including direct effect (0.326) and indirect effect (-0.117), on the microeukaryotic assembly. On the contrary, the fungal subcommunity was relatively resistant to antibiotic pollution. Segmented analysis depicted nonlinear responses of microeukaryotic genera to the antibiotic pollution gradient, as supported by the significant tipping points. We screened 30 antibiotic concentration-discriminatory taxa of microeukaryotes, which can quantitatively and accurately predict (98.7% accuracy) the in-situ antibiotic concentration. Sediment microeukaryotic (except fungal) community is sensitive to antibiotic pollution, and the identified bioindicators could be used for antibiotic pollution diagnosis.

Advances in engineering algae for biofuel production

While algae demonstrate potential as a sustainable fuel source, low productivities limit the economic realization of algal biofuels. High-throughput strain engineering, omics-informed genome-scale modeling, and microbiome engineering are key technologies for enabling algal biofuels. High-throughput strain engineering efforts generate improved traits, including high biomass productivity and lipid content, in diverse algal species. Genome-scale models, constructed with the aid of omics data, provide insight into metabolic limitations and guide rational algal strain engineering efforts. As outdoor cultivation systems introduce exogenous organisms, microbiome engineering seeks to eliminate harmful organisms and introduce beneficial species. Optimizing algal biomass production and lipid content using these technologies may overcome the productivity barrier for the commercialization of algal biofuels.

Recent Progress on Systems and Synthetic Biology of Diatoms for Improving Algal Productivity

Microalgae have drawn much attention for their potential applications as a sustainable source for developing bioactive compounds, functional foods, feeds, and biofuels. Diatoms, as one major group of microalgae with high yields and strong adaptability to the environment, have shown advantages in developing photosynthetic cell factories to produce value-added compounds, including heterologous bioactive products. However, the commercialization of diatoms has encountered several obstacles that limit the potential mass production, such as the limitation of algal productivity and low photosynthetic efficiency. In recent years, systems and synthetic biology have dramatically improved the efficiency of diatom cell factories. In this review, we discussed first the genome sequencing and genome-scale metabolic models (GEMs) of diatoms. Then, approaches to optimizing photosynthetic efficiency are introduced with a focus on the enhancement of biomass productivity in diatoms. We also reviewed genome engineering technologies, including CRISPR (clustered regularly interspaced short palindromic repeats) gene-editing to produce bioactive compounds in diatoms. Finally, we summarized the recent progress on the diatom cell factory for producing heterologous compounds through genome engineering to introduce foreign genes into host diatoms. This review also pinpointed the bottlenecks in algal engineering development and provided critical insights into the future direction of algal production.

Growth-stage-related shifts in diatom endometabolome composition set the stage for bacterial heterotrophy

Phytoplankton-derived metabolites fuel a large fraction of heterotrophic bacterial production in the global ocean, yet methodological challenges have limited our understanding of the organic molecules transferred between these microbial groups. In an experimental bloom study consisting of three heterotrophic marine bacteria growing together with the diatom Thalassiosira pseudonana, we concurrently measured diatom endometabolites (i.e., potential exometabolite supply) by nuclear magnetic resonance (NMR) spectroscopy and bacterial gene expression (i.e., potential exometabolite uptake) by metatranscriptomic sequencing. Twenty-two diatom endometabolites were annotated, with nine increasing in internal concentration in the late stage of the bloom, eight decreasing, and five showing no variation through the bloom progression. Some metabolite changes could be linked to shifts in diatom gene expression, as well as to shifts in bacterial community composition and their expression of substrate uptake and catabolism genes. Yet an overall low match indicated that endometabolome concentration was not a good predictor of exometabolite availability, and that complex physiological and ecological interactions underlie metabolite exchange. Six diatom endometabolites accumulated to higher concentrations in the bacterial co-cultures compared to axenic cultures, suggesting a bacterial influence on rates of synthesis or release of glutamate, arginine, leucine, 2,3-dihydroxypropane-1-sulfonate, glucose, and glycerol-3-phosphate. Better understanding of phytoplankton metabolite production, release, and transfer to assembled bacterial communities is key to untangling this nearly invisible yet pivotal step in ocean carbon cycling.

Genome-Scale Metabolic Modeling Enables In-Depth Understanding of Big Data

Genome-scale metabolic models (GEMs) enable the mathematical simulation of the metabolism of archaea, bacteria, and eukaryotic organisms. GEMs quantitatively define a relationship between genotype and phenotype by contextualizing different types of Big Data (e.g., genomics, metabolomics, and transcriptomics). In this review, we analyze the available Big Data useful for metabolic modeling and compile the available GEM reconstruction tools that integrate Big Data. We also discuss recent applications in industry and research that include predicting phenotypes, elucidating metabolic pathways, producing industry-relevant chemicals, identifying drug targets, and generating knowledge to better understand host-associated diseases. In addition to the up-to-date review of GEMs currently available, we assessed a plethora of tools for developing new GEMs that include macromolecular expression and dynamic resolution. Finally, we provide a perspective in emerging areas, such as annotation, data managing, and machine learning, in which GEMs will play a key role in the further utilization of Big Data.

Potential for Biomass Production and Remediation by Cultivation of the Marine Model Diatom Phaeodactylum tricornutum in Oil Field Produced Wastewater Media

While oilfield produced water (PW) is one of the largest, unclaimed wastewater streams of the oil industry, it could potentially be used as a cultivation medium for microalgae. Microalgae could help with the remediation of this water while also delivering biomass that can be transformed into valuable byproducts such as biofuels. The coupling of these two purposes is expected to cut production costs of biofuels while aiding environmental protection. In this study, we compared the cultivation capacity of the marine model diatom Phaeodactylum tricornutum in media at varying salinities and in media composed of PW from two oilfields in the Central Valley of California that differed drastically in the concentration of inorganic and organic constituents. Specifically, we measured the carrying capacity of these media, the maximum growth rates of P. tricornutum, its cellular lipid accumulation capacity, and its capacity to remediate the most polluted PW source. Our study shows that P. tricornutum can successfully adjust to the tested cultivation media through processes of short-term acclimation and long-term adaptation. Furthermore, the cultivation of P. tricornutum in the most heavily polluted PW source led to significant increases in cell yield and improved photosynthetic capacity during the stationary phase, which could be attributed chiefly to the higher levels of nitrate present in this PW source. Chemical water analyses also demonstrated the capability of P. tricornutum to remediate major nutrient content and potentially harmful elements like fluorine and copper. Because P. tricornutum is amenable to advanced genetic engineering, which could be taken advantage of to improve its cultivation resilience and productivity in an economic setting, we propose this study as a step towards essential follow-up studies that will identify the genetic regulation behind its growth in oilfield PW media and its remediation of the PW constituents.

Deciphering Metabolic Currencies That Support Marine Microbial Networks

Microbes are omnipresent in the biosphere and perform biological and chemical processes critical to ecosystem function, nutrient cycling, and global climate regulation. In the ocean, microbes constitute more than two-thirds of biomass with abundances reaching over one million microbial cells per milliliter of seawater. Our understanding of the marine microbial world has rapidly expanded with use of innovative molecular and chemical 'omics tools to uncover previously hidden taxonomic diversity, spatiotemporal distributions, and novel metabolic functions. Recognition that specific microbial taxa cooccur in consistent patterns in the ocean has implicated microbe-microbe interactions as important, but poorly constrained, regulators of microbial activity. Here, I examine cooperative interactions among marine plankton, with a focus on the metabolic "currencies" that establish microbial partnerships in the surface-ocean trade economy. I discuss current and future directions to study microbial metabolic interactions in order to strengthen our understanding of ecosystem interdependencies and their impact on ocean biogeochemistry.