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Haematococcus lacustris is a ubiquitous unicellular green alga with industrial bioproduct applications, namely, as feedstock for natural astaxanthin. We report the annotated 291.5 Mbp genome for H. lacustris Liv1 to support future algal research in the areas of carotenoid biosynthesis and crop protection.
Ex situ seed banking was first conceptualized and implemented in the early 20th century to maintain and protect crop lines. Today, ex situ seed banking is important for the preservation of heirloom strains, biodiversity conservation and ecosystem restoration, and diverse research applications. However, these efforts primarily target microalgae and terrestrial plants. Although some collections include macroalgae (i.e., seaweeds), they are relatively few and have yet to be connected via any international, coordinated initiative. In this piece, we provide a brief introduction to macroalgal germplasm banking and its application to conservation, industry, and mariculture. We argue that concerted effort should be made globally in germline preservation of marine algal species via germplasm banking with an overview of the technical advances for feasibility and ensured success.
This report briefly summarizes the spectroradiometric approach adapted and deployed as part of Task 4.2 within the BETO-funded D evelopment of I ntegrated S creening, C ultivar O ptimization, and V erification R esearch (DISCOVR) project, specifically task 4, “Increase crop protection and culture stability.” The goals of this subtask were to monitor ponds cultivating a variety of DISCOVR strains at the Arizona Center for Algae Technology and Innovation (AzCATI) with spectroradiometric technology to: • Predict biomass concentrations with high temporal frequency. • Optimize automated analysis methods that enabled feedback for pond automation strategies • Identify pond crash indicators from primary (pest/predator) and indirect (change in algal host) spectroscopic signals with potential for early treatment/intervention • Provide recommendations for technology deployment at scale. This report fulfills the deliverable requirement for the final bullet “provide recommendations for technology deployment at scale”.
Crop protection presentation for the Algal BBB conference.
Microalgae are amongst the most efficient phototrophs for the reduction of CO2 to industrially relevant products and product intermediates. We have recently identified a novel species in the genus Picochlorum (Picochlorum renovo) that demonstrates high areal productivity (>30 g/m2/day), high temperature and salinity tolerances, and rapid growth rate (-2 hour doubling time): attributes essential for economically viable industrial scale deployment for low value commodities (e.g. fuel and chemical precursors). Following screening of >300 algal isolates to identify Picochlorum renovo, we developed baseline genetic tools necessary for genetic engineering of the nucleus and chloroplast, and further built upon these tools to establish a protein secretion system. We have also deployed genetic engineering tools to establish the utilization of phosphite as a selectable marker in the chloroplast and nuclear genomes, while concurrently acting as a potential crop protection strategy. We have further implemented these tools to enable the utilization of formate as a novel carbon source for phototroph cultivation. Explicitly, formate toxicity was assayed, followed by introduction of a formate dehydrogenase to allow formate utilization. Cultures supplemented with formate grew to a higher density when cultivated under ambient CO2, highlighting the potential for this strategy to increase growth. Additionally, we have established a synthetic transcription factor and core promoter system which lays the foundation for tunable, high expression engineering in the nuclear genome, and potentially can be applied for universal algal genetic engineering. Finally, current work has focused on direct photosynthetic production of chemicals, and generation of mutant libraries. Combined, our work has established a robust genetic toolbox for Picochlorum renovo, which we have deployed for strain development purposes to introduce biotechnologically relevant traits.
Chemical agents are commonly used to protect algal crops. Yet, few studies have characterized the effects of these agents on associated microbial communities to understand effects on microbial functions relevant to algal crop production and protection. Here, we used shotgun metagenomic sequencing and untargeted exometabolite profiling to link the application of bleach, a -cidal agent used to protect algae from pests, to changes in community composition, metabolic pathways, and exometabolies - at a whole community level. Bleach protected the algal crop from crashing but altered bacterial diversity. Analysis of metagenome-assembled genomes (MAGs) revealed a classic predator-prey cycle between Oligoflexus and our target alga Nannochloropsis. Olifoflexus genomes from our study were notably similar to a previously identified BALO (Bdellovibrio and like organism), FD111, known to kill Nannochloropsis cultures, providing strong evidence that an FD111-like organism was responsible for the crash. Metabolic pathway composition differed between bleached and unbleached ponds, with abundance of twelve pathways related to stress tolerance, including the superpathway of methylglyoxal degradation, lipid IVA biosynthesis, and ectoine biosynthesis, greater in bleached ponds compared to unbleached ponds. Virulence factors related to adherence, biofilm formation, motility, and pathogenicity increased dramatically in bleached ponds with time, although this increase was not coupled with an increase in pathogens - algal or otherwise - or a decline in algal health. Our study highlights the importance of coupling 16S rRNA gene sequencing with whole genome data and other -omics tools to sketch a larger picture of community structure and function in crop systems. Moreover, our results highlight that continued long-term bleaching may lead to negative effects to crop health or downstream adverse health effects to humans or animals, depending on the algal product (i.e. human supplements or animal feedstocks). Future work on alternative treatment methods that would reduce resistance is necessary in the field.
To address challenges in early detection of pond pests, we have extended a spectroradiometric monitoring method, initially demonstrated for measurement of pigment optical activity and biomass, to the detection of algal competitors and grazers. The method relies upon measurement and interpretation of pond reflectance spectra spanning from the visible into the near-infrared. Reflectance spectra are acquired every 5 min with a multi-channel, fiber-coupled spectroradiometer, providing monitoring of algal pond conditions with high temporal frequency. The spectra are interpreted via numerical inversion of a reflectance model, in which the above-water reflectance is expressed in terms of the absorption and backscatter coefficients of the cultured species, with additional terms accounting for the pigment fluorescence features and for the water-surface reflection of sunlight and skylight. With this method we demonstrate detection of diatoms and the predator Poteriochromonas in outdoor cultures of Nannochloropsis oceanica and Chlorella vulgaris, respectively. Further, the relative strength of these signatures is compared to microscopy and sequencing analysis. Spectroradiometric detection of diatoms is then further assessed on beaker-contained mixtures of Microchloropsis salina with Phaeodactylum tricornutum, Thalassiosira weissflogii, and Thalassiosira pseudonana, respectively, providing an initial evaluation of the sensitivity and specificity of detecting pond competitors.
Wide adoption of algae cultivation to produce environmentally sustainable biofuels and fine chemicals is currently hampered by large losses (10 to 30%) incurred by grazer infections. We show the usage of real-time chemical ionization mass spectrometry to rapidly identify gaseous indicators of grazer infections in cyanobacteria cultures. Grazing was detected significantly faster (up to 3 d) using real-time mass spectrometry than the current methods of microscopy and qPCR. By employing this technology, cultivators may be empowered to treat grazer infestations sooner, thereby protecting the crop and enhancing profitability.
Specialized or secondary metabolites are small molecules of biological origin, often showing potent biological activities with applications in agriculture, engineering and medicine. Usually, the biosynthesis of these natural products is governed by sets of co-regulated and physically clustered genes known as biosynthetic gene clusters (BGCs). To share information about BGCs in a standardized and machine-readable way, the Minimum Information about a Biosynthetic Gene cluster (MIBiG) data standard and repository was initiated in 2015. Since its conception, MIBiG has been regularly updated to expand data coverage and remain up to date with innovations in natural product research. Here, we describe MIBiG version 4.0, an extensive update to the data repository and the underlying data standard. In a massive community annotation effort, 267 contributors performed 8304 edits, creating 557 new entries and modifying 590 existing entries, resulting in a new total of 3059 curated entries in MIBiG. Particular attention was paid to ensuring high data quality, with automated data validation using a newly developed custom submission portal prototype, paired with a novel peer-reviewing model. MIBiG 4.0 also takes steps towards a rolling release model and a broader involvement of the scientific community. MIBiG 4.0 is accessible online at https://mibig.secondarymetabolites.org/.
Abstract Spray‐induced gene silencing (SIGS) is an emerging tool for crop pest protection. It utilizes exogenously applied double‐stranded RNA to specifically reduce pest target gene expression using endogenous RNA interference machinery. In this study, SIGS methods were developed and optimized for powdery mildew fungi, which are widespread obligate biotrophic fungi that infect agricultural crops, using the known azole‐fungicide target cytochrome P450 51 (CYP51) in the Golovinomyces orontii–Arabidopsis thaliana pathosystem. Additional screening resulted in the identification of conserved gene targets and processes important to powdery mildew proliferation: apoptosis‐antagonizing transcription factor in essential cellular metabolism and stress response; lipid catabolism genes lipase a , lipase 1 , and acetyl‐CoA oxidase in energy production ; and genes involved in manipulation of the plant host via abscisic acid metabolism ( 9‐cis‐epoxycarotenoid dioxygenase , xanthoxin dehydrogenase , and a putative abscisic acid G‐protein coupled receptor ) and secretion of the effector protein, effector candidate 2 . Powdery mildew is the dominant disease impacting grapes and extensive powdery mildew resistance to applied fungicides has been reported. We therefore developed SIGS for the Erysiphe necator–Vitis vinifera system and tested six successful targets identified using the G. orontii–A. thaliana system. For all targets tested, a similar reduction in powdery mildew disease was observed between systems. This indicates screening of broadly conserved targets in the G. orontii–A. thaliana pathosystem identifies targets and processes for the successful control of other powdery mildew fungi. The efficacy of SIGS on powdery mildew fungi makes SIGS an exciting prospect for commercial powdery mildew control.
PlantCV is an open-source Python project aimed at developing tools to address a range of image-based, plant phenotyping questions. PlantCV has been used for more than 10 years to automate trait collection from image data, and the newest release, PlantCV version 4, continues to lower the barrier to entry for users without substantial coding experience through extensive example use-case tutorials and simplified installation. In addition to usability, we document added functionality since the release of PlantCV v2, including support for more image types such as fluorescence, thermal, and hyperspectral data. Finally, we describe the development of a new subpackage focused on morphological trait measurements like leaf angle, and demonstrate its utility as compared to more manual methods of data collection.
To address major knowledge gaps and barriers to the commercial development of algal biomass for biofuels and co-products, a collaborative consortium, Development of Integrated Screening, Cultivar Optimization, and Verification Research (DISCOVR), was established in 2016. Funded by the U.S. Department of Energy (DOE) Bioenergy Technologies Office (BETO), this consortium constitutes a partnership between four DOE national laboratories - Pacific Northwest National Laboratory (PNNL), Los Alamos National Laboratory (LANL), the National Renewable Energy Laboratory (NREL), and Sandia National Laboratories (SNL) - and the Arizona Center for Algae Technology and Innovation (AzCATI) at Arizona State University. To address the barriers of strain selection for achieving high seasonal productivities with a suitable composition and culture resilience, a tiered strain down-selection pipeline is implemented. At Tier I, the temperature and salinity tolerance of strains is determined in flask cultures; at Tier II, the areal biomass productivity and composition is determined in climate-simulation photobioreactors; at Tier III, the productivity and culture stability are determined in outdoor raceways. The top performing strains move forward to long-term testing at the algae testbed site at AzCATI to generate annual biomass productivity data. Concurrent to the strain down-selection in the DISCOVR pipeline, hypotheses for increasing biomass productivity, shifting biomass composition to enhance intrinsic value, and improving culture stability and resistance to pests are also tested. Techno-economic analyses are carried out to determine whether promising findings from laboratory studies or proposed modifications in outdoor pond cultivation conditions translate into reductions in the minimum biomass selling price (MBSP). Notably, in the three years following the launch of DISCOVR, annual biomass productivity has increased from 11.7 to 17.6 g m -2 day -1 , resulting in an MBSP decrease from 824 to 611 $ ton -1 .
Contaminating organisms (grazers, pathogens, competitors) and self-inhibition by algae-produced allelopathic chemicals are two issues that may limit the productivity of algal cultivation for bioproducts. One potential solution is to identify algal strains that are not affected by allelopathic inhibition even while undesirable organisms are suppressed. Here we used two experiments to test how sensitivity to allelopathy varies across algae. In the first experiment, we tested the sensitivity of five biofuel candidate green algae strains to two allelopathic compounds (i.e., free fatty acids) and found that the degree of inhibition depends strongly on both the species and specific compound. In the second experiment, we exposed one alga (Chlorella) to the sterile-filtered medium of each species, and found that the concentration of free fatty acids released into the media predicted Chlorella’s growth response. This provides a better understanding of how the production of, and sensitivity to, allelopathic compounds determines algal productivity.
For outdoor cultivation of algal feedstocks to become a commercially viable and sustainable option for biofuel production, algal cultivation must maintain high yields and temporal stability in environmentally variable outdoor ponds. One of the main challenges is mitigating disease outbreaks that leads to culture crashes. Drawing on predictions from the ‘dilution effect’ hypothesis, in which increased biodiversity is thought to reduce disease risk in a community, a teste of whether algal polycultures would reduce disease risk and improve feedstock production efficiencies compared to monocultures was performed. While the positive benefits of biodiversity on disease risk have been demonstrated in various systems, to the best of our knowledge this is the first test in an algal biofuel system. Here, the results a before-after-control-impact (BACI) experimental design to compare mean monoculture (control) and polyculture (impact) yield, stability, and productivity before and after fungal infection when grown in 400-L outdoor raceway ponds are presented. It has been found that polycultures did not experience a reduction in disease risk compared to monocultures or differ in production efficiencies throughout the course of the 43-day experiment. These results show that polyculture feedstocks can maintain similar levels of productivity, stability, and disease resistance to that of a monoculture. Determining whether these results are generalizable or represent one case study requires additional outdoor experiments using a larger variety of host and pathogen species.
This project was proposed in response to the FY19 Bioenergy Technologies Office Multi-Topic Funding Opportunity Announcement DE-FOA-0002029, Area of Interest Subtopic 1 (AOI1): Cultivation Intensification Processes for Algae. The main challenge identified by the FOA was “in translating results between laboratory research systems and larger-scale outdoor (or mass culture) systems. These difficulties limit reliable experimental durations, adequate and representative experimental volumes of material, and results that can be reproduced reliably. By overcoming the challenge in translating results between laboratory and mass cultures, the objective of AOI 1 is to increase the harvest yield, robustness, and quality of algae cultivation for biofuels and bioproducts”. This report marks the final technical deliverable of this project and reviews all the major findings of the research program.
Datasets are land use and land cover (LULC) rasterized base maps at 30-m resolution for the conterminous United States (CONUS) for the years 2008, 2011, 2016, and 2019. Separate base maps are provided where LULC classifications are thematically congruent with Community Land Model (CLM), Land Use Harmonization (LUH2), and Global Change Analysis Model (GCAM), and a detailed decomposition of all combined land classes into a Multisector Dynamics (MSD) LULC product. Base maps were developed using empirically derived satellite (National Land Cover Dataset, MODIS) and combined observation datasets (Crop Data Layer, Protected Areas Database) and represent the most up-to-date accurate information on LULC in the CONUS. The four datasets encompass four different landcover classification systems: MSD Layers - The raw landcover classes obtained from reclassifying NLCD and USDA Crop data layers into a respective landcover class GCAM Layers - The MSD classes mosaiced, reclassified, and combined into the respective GCAM landcover classes CLM Layers - Similar process to GCAM layers, but mosaiced, reclassified, and combined MSD layers to their respective PFT classes LUH2 Layers - Similar process to both GCAM and CLM Layers, but mosaiced, reclassified and combined the MSD layers to align with the respective states
Abstract Many freshwater wetlands and riparian systems are protected within agricultural landscapes. Yet, pollinator ecosystem services are seldom considered key ecosystem services provided by these conservation easements. The purpose of this study is to explore the extent of protected aquatic lands to provide pollination ecosystem services by assessing pollinator abundances, crop yield changes, and value estimations of increased soybean yields from a subset of common native solitary bees. We created a novel geodatabase of United States Department of Agriculture (USDA) conservation easements and used this database in the InVEST crop pollination model to model wild solitary bee pollination. We then estimated the monetary value of yield increases provided by pollinators. We found that wetland uplands provided the greatest potential for pollination services for ground nesting bees, followed by herbaceous and forested riparian respectively. Stem nesters preferred forested riparian, then upland habitats. In soybeans fields, we found wild pollinators can provide up to 5.5% yield response from current private aquatic conservation lands. The current landscape is not optimized to use wetlands and riparian conservation lands as pollinator habitat, but these results suggest protected aquatic lands can sustainably increase wild pollination services to agricultural crops if landscapes are managed, protected, and optimized with pollinator services as co-benefit.
Abstract Projection of land use and land‐cover change is highly uncertain yet drives critical estimates of carbon emissions, climate change, and food and bioenergy production. We use new, spatially explicit land availability data in conjunction with a model sensitivity analysis to estimate the effects of additional land protection on land use and land cover. The land availability data include protected land and agricultural suitability and is incorporated into the Moirai land data system for initializing the Global Change Analysis Model. Overall, decreasing land availability is relatively inefficient at preserving undeveloped land while having considerable regional land‐use impacts. Current amounts of protected area have little effect on land and crop production estimates, but including the spatial distribution of unsuitable (i.e., unavailable) land dramatically shifts bioenergy production from high northern latitudes to the rest of the world, compared with uniform availability. This highlights the importance of spatial heterogeneity in understanding and managing land change. Approximately doubling the current protected area to emulate a 30% protected area target may avoid land conversion by 2050 of less than half the newly protected extent while reducing bioenergy feedstock land by 10.4% and cropland and grazed pasture by over 3%. Regional bioenergy land may be reduced (increased) by up to 46% (36%), cropland reduced by up to 61%, pasture reduced by up to 100%, and harvested forest reduced by up to 35%. Only a few regions show notable gains in some undeveloped land types of up to 36%. Half of the regions can reach the target using only unsuitable land, which would minimize impacts on agriculture but may not meet conservation goals. Rather than focusing on an area target, a more robust approach may be to carefully select newly protected land to meet well‐defined conservation goals while minimizing impacts to agriculture.