Investigating the relationship between microbial network features of giant kelp "seedbank" cultures and subsequent farm performance.

Microbial inoculants can increase the yield of cultivated crops and are successful in independent trials; however, efficacy drops in large-scale applications due to insufficient consideration of microbial community dynamics. The structure of microbiomes, in addition to the impact of individual taxa, is an important factor to consider when designing growth-promoting inoculants. Here, we investigate the microbial network and community assembly patterns of Macrocystis pyrifera gametophyte germplasm cultures (collectively referred to as a "seedbank") used to cultivate an offshore farm in Santa Barbara, California, and identify network features associated with increased biomass of mature sporophytes. We found that [1] several network features, such as clustering coefficient and edge ratios, significantly vary with biomass outcomes; [2] gametophytes that become low- or high-biomass sporophytes have different hub taxa; and [3] microbial community assembly of gametophyte germplasm cultures is niche-driven. Overall, this study describes microbial community dynamics in M. pyrifera germplasm cultures and ultimately supports the development of early life stage inoculants that can be used on seaweed cultivars to increase biomass yield.

Natural variation of Macrocystis pyrifera gametophyte germplasm culture microbiomes and applications for improving yield in offshore farms.

With national interest in seaweed-based biofuels as a sustainable alternative to fossil fuels, there is a need for tools that produce high-yield seaweed cultivars and increase the efficiency of offshore farms. Several agricultural studies have demonstrated that the application of microbial inoculants at an early life stage can improve crop yield, and there is an opportunity to use similar techniques in seaweed aquaculture. However, there is a critical knowledge gap regarding host-microbiome associations of macroalgae gametophytes in germplasm cultures. Here, we investigate the microbial community of Macrocystis pyrifera gametophyte germplasm cultures that were used to cultivate an offshore farm in Santa Barbara, California and identify key taxa correlated with increased biomass of mature sporophytes. This work provides a valuable knowledge base for the development of microbial inoculants that produce high-biomass M. pyrifera cultivars to ultimately be used as biofuel feedstocks.

Understanding the metabolome and metagenome as extended phenotypes: The next frontier in macroalgae domestication and improvement.

"Omics" techniques (including genomics, transcriptomics, metabolomics, proteomics, and metagenomics) have been employed with huge success in the improvement of agricultural crops. As marine aquaculture of macroalgae expands globally, biologists are working to domesticate species of macroalgae by applying these techniques tested in agriculture to wild macroalgae species. Metabolomics has revealed metabolites and pathways that influence agriculturally relevant traits in crops, allowing for informed crop crossing schemes and genomic improvement strategies that would be pivotal to inform selection on macroalgae for domestication. Advances in metagenomics have improved understanding of host-symbiont interactions and the potential for microbial organisms to improve crop outcomes. There is much room in the field of macroalgal biology for further research toward improvement of macroalgae cultivars in aquaculture using metabolomic and metagenomic analyses. To this end, this review discusses the application and necessary expansion of the omics tool kit for macroalgae domestication as we move to enhance seaweed farming worldwide.

Removal of Toxic Volatile Compounds in Batch Culture Prolongs Stationary Phase and Delays Death of Escherichia coli.

The mechanisms controlling entry into and exit from the death phase in the bacterial life cycle remain unclear. Although bacterial growth studies in batch cultures traditionally focus on the first three phases during incubation, two additional phases, the death phase and the long-term stationary phase, are less understood. Although there are a number of stressors that arise during long-term batch culture, including nutrient depletion and the accumulation of metabolic toxins such as reactive oxidative species, their roles in cell death are not well-defined. By manipulating the environmental conditions of Escherichia coli incubated in long-term batch culture through chemical and mechanical means, we investigated the role of volatile metabolic toxins in modulating the onset of the death phase. Here, we demonstrate that with the introduction of substrates with high binding affinities for volatile compounds, toxic by-products of normal cell metabolism, into the headspace of batch cultures, cells display a prolonged stationary phase and delayed entry into the death phase. The addition of these substrates allows cultures to maintain a high cell density for hours to days longer than cultures incubated under standard growth conditions. A similar effect is observed when the gaseous headspace in culture flasks is continuously replaced with sterile air, mechanically preventing the accumulation of metabolic by-products in batch cultures. We establish that toxic compound(s) are produced during the exponential phase, demonstrate that buildup of toxic by-products influence entry into the death phase, and present a novel tool for improving high-density growth in batch culture that may be used in future research or industrial or biotechnology applications. IMPORTANCE Bacteria, such as Escherichia coli, are routinely used in the production of biomaterials because of their efficient and sustainable capacity for synthesis of bioproducts. Industrial applications of microbial synthesis typically utilize cells in the stationary phase, when cultures have the greatest density of viable cells. By manipulating culture conditions to delay the transition from the stationary phase to the death phase, we can prolong the stationary phase on a scale of hours to days, thereby maintaining the maximum density of cells that would otherwise quickly decline. Characterization of the mechanisms that control entry into the death phase for the model organism E. coli not only deepens our understanding of the bacterial life cycle but also presents an opportunity to enhance current protocols for batch culture growth and explore similar effects in a variety of widely used bacterial strains.

Landscape analyses using eDNA metabarcoding and Earth observation predict community biodiversity in California.

Ecosystems globally are under threat from ongoing anthropogenic environmental change. Effective conservation management requires more thorough biodiversity surveys that can reveal system-level patterns and that can be applied rapidly across space and time. Using modern ecological models and community science, we integrate environmental DNA and Earth observations to produce a time snapshot of regional biodiversity patterns and provide multi-scalar community-level characterization. We collected 278 samples in spring 2017 from coastal, shrub, and lowland forest sites in California, a complex ecosystem and biodiversity hotspot. We recovered 16,118 taxonomic entries from eDNA analyses and compiled associated traditional observations and environmental data to assess how well they predicted alpha, beta, and zeta diversity. We found that local habitat classification was diagnostic of community composition and distinct communities and organisms in different kingdoms are predicted by different environmental variables. Nonetheless, gradient forest models of 915 families recovered by eDNA analysis and using BIOCLIM variables, Sentinel-2 satellite data, human impact, and topographical features as predictors, explained 35% of the variance in community turnover. Elevation, sand percentage, and photosynthetic activities (NDVI32) were the top predictors. In addition to this signal of environmental filtering, we found a positive relationship between environmentally predicted families and their numbers of biotic interactions, suggesting environmental change could have a disproportionate effect on community networks. Together, these analyses show that coupling eDNA with environmental predictors including remote sensing data has capacity to test proposed Essential Biodiversity Variables and create new landscape biodiversity baselines that span the tree of life.