Identifying and addressing critical improvements in biomass, bioproduct and biofuel productivity is a priority for the nascent algae-based bioeconomy. Economic and sustainability principles should guide these developing improvements and help to unravel the contentious water–food–energy–environment nexus that algae inhabit. Understanding the biochemistry of the storage carbon metabolism of algae to produce biofuels and bioproducts can bring to light the key barriers that currently limit the overall carbon efficiency and the photosynthetic efficiency, and ultimately guide productivity and commercial viability in the context of limiting resources. In the analysis reported here, we present different potential pathways for a conceptual algae biorefinery framework, with each pathway addressing one of the main identified barriers to future deployment. We highlight the molecular identification, in the form of an extensive literature review, of potential bioproducts that may be derived directly from both biomass and fractions produced through a conversion pathway, for three important commercially-relevant genera of algae, Scenedesmus, Chlorella and Nannochloropsis. We establish a relationship between each of the potential bioproducts, describe relevant conversion and extraction processes, and discuss market opportunities with values and sizes as they relate to commercial development of the products.

The model yeast, Saccharomyces cerevisiae, is not known to be oleaginous. However, an industrial wild-type strain, D5A, was shown to accumulate over 20% storage lipids from glucose when growth is nitrogen-limited compared to no more than 7% lipid accumulation without nitrogen stress. To elucidate the mechanisms of S. cerevisiae D5A oleaginicity, we compared physiological and metabolic changes; as well as the transcriptional profiles of the oleaginous industrial strain, D5A, and a non-oleaginous laboratory strain, BY4741, under normal and nitrogen-limited conditions using analytic techniques and next-generation sequencing-based RNA-Seq transcriptomics. Transcriptional levels for genes associated with fatty acid biosynthesis, nitrogen metabolism, amino acid catabolism, as well as the pentose phosphate pathway and ethanol oxidation in central carbon (C) metabolism, were up-regulated in D5A during nitrogen deprivation. Despite increased carbon flux to lipids, most gene-encoding enzymes involved in triacylglycerol (TAG) assembly were expressed at similar levels regardless of the varying nitrogen concentrations in the growth media and strain backgrounds. Phospholipid turnover also contributed to TAG accumulation through increased precursor production with the down-regulation of subsequent phospholipid synthesis steps. Our results also demonstrated that nitrogen assimilation via the glutamate–glutamine pathway and amino acid metabolism, as well as the fluxes of carbon and reductants from central C metabolism, are integral to the general oleaginicity of D5A, which resulted in the enhanced lipid storage during nitrogen deprivation. This work demonstrated the disequilibrium and rebalance of carbon and nitrogen contribution to the accumulation of lipids in the oleaginous yeast S. cerevisiae D5A. Rather than TAG assembly from acyl groups, the major switches for the enhanced lipid accumulation of D5A (i.e., fatty acid biosynthesis) are the increases of cytosolic pools of acetyl-CoA and NADPH, as well as alternative nitrogen assimilation.

Opportunities associated with biomass production and bioproduct isolation from algae-derived feedstocks are plentiful and promising; however, there are challenges associated with realizing these applications. One of the most important, and often overlooked, challenges is the lack of availability of a strong foundation of compositional analysis methods validated on microalgal biomass. Currently, compositional analysis in algae is dominated by the use of interference-prone methods, a lack of full mass balance accounting, and the use of top-down approaches that bin all unaccounted-for mass into a single category, such as carbohydrates. We present here an approach based on a bottom-up algal biomass characterization aimed at moving towards, and highlighting the importance of, full and accurate mass closure to achieve the maximum economic potential from a sustainable and renewable feedstock. Algal biomass representing three genera, Nannochloropsis, Scenedesmus, and Monoraphidium, was subjected to a cell rupture and fractionation process, followed by detailed characterization of each fraction to determine the partitioning of measured and unknown components. The goal of this work is to identify where the missing components partition, and develop a strategy to close the mass balance or identify the unknowns, while utilizing a rigorous characterization approach for characterizing algal biomass. Although only 75–80% of the biomass was accounted for, the fractionation approach utilized here provides key insight into possible chemical components for future investigations.

Although it is well known that low nitrogen stimulates lipid accumulation, especially for algae and some oleaginous yeast, few studies have been conducted in fungal species, especially on the impact of different nitrogen deficiency strategies. In this study, we use two promising consolidated bioprocessing (CBP) candidates to examine the impact of two nitrogen deficiency strategies on lipid production, which are the extensively investigated oleaginous yeast Yarrowia lipolytica, and the commercial cellulase producer Trichoderma reesei. We first utilized bioinformatics approaches to reconstruct the fatty acid metabolic pathway and demonstrated the presence of a triacylglycerol (TAG) biosynthesis pathway in Trichoderma reesei. We then examined the lipid production of Trichoderma reesei and Y. lipomyces in different media using two nitrogen deficiency strategies of nitrogen natural repletion and nitrogen depletion through centrifugation. Our results demonstrated that nitrogen depletion was better than nitrogen repletion with about 30% lipid increase for Trichoderma reesei and Y. lipomyces, and could be an option to improve lipid production in both oleaginous yeast and filamentous fungal species. The resulting distinctive lipid composition profiles indicated that the impacts of nitrogen depletion on yeast were different from those for fungal species. Under three types of C/N ratio conditions, C16 and C18 fatty acids were the predominant forms of lipids for both Trichoderma reesei and Y. lipolytica. While the overall fatty acid methyl ester (FAME) profiles of Trichoderma reesei were similar, the overall FAME profiles of Y. lipolytica observed a shift. The fatty acid metabolic pathway reconstructed in this work supports previous reports of lipid production in T. reesei, and provides a pathway for future omics studies and metabolic engineering efforts. Further investigation to identify the genetic targets responsible for the effect of nitrogen depletion on lipid production improvement will facilitate strain engineering to boost lipid production under more optimal conditions for productivity than those required for nitrogen depletion.

To create carbon efficient sources of bioenergy feedstocks and feedstuff for aquaculture and terrestrial livestock, it is critical to develop and commercialize the most efficient seaweed cultivation approach with a sustainable nutrient input supply. Here, we present data for a novel, onshore tropical macroalgae cultivation system, based on influent deep seawater as the nutrient and carbon sources. Two red algal species were selected, Agardhiella subulata and Halymenia hawaiiana, as the basis for growth optimization. Highest productivity in small-scale cultivation was demonstrated with A. subulata in the 10% deep seawater (64.7 µg N L⁻¹) treatment, growing at up to 26% specific growth rate day⁻¹ with highest yields observed at 247.5 g m⁻² day⁻¹ fresh weight. The highest yields for H. hawaiiana were measured with the addition of 10% deep seawater up to 8.8% specific growth rate day⁻¹ and yields at 63.3 g fresh weight m⁻² day⁻¹ equivalent. Biomass should be culled weekly or biweekly to avoid density limitations, which likely contributed to a decrease in SGR over time. With a measured 30–40% carbon content of the ash-free dry weight (20–30% of the dry weight) biomass, this translates to an almost 1:1 CO₂ capture to biomass ratio. The compositional fingerprint of the high carbohydrate content of both Agardhiella and Halymenia makes for an attractive feedstock for downstream biorefinery applications. By focusing on scaling and optimizing seaweed farming technologies for large-scale onshore farms, the opportunities for yield potential, adaptability to cultivation conditions, and meeting global sustainability goals through novel, carbon-negative biomass sources such as seaweed can be realized.

Sustainable biofuels and bioproducts technologies are being developed by fermentation of sugars present and released from pretreated cellulosic biomass to lipids using oleaginous yeasts. Detailed analytical characterization of lipid content through cultivation under different scenarios not only is a bottleneck that slows down development of improved strains and processes, this process also creates significant chemical waste. Since lipids exhibit a dominant, distinct, and unique fingerprint in the NIR spectrum, the use of multivariate linear regression of respective wavelengths can be used for the prediction of intracellular lipid content present in the yeast biomass. We present data on the multivariate quantitative correlation of NIR spectra with measured lipid content in different oleaginous yeast strains. This work is the first demonstration of the rapid, non-destructive, lipid quantification on as little as 10 mg of yeast biomass in a 96-well format, preventing significant chemical pollution by applying a real-time monitoring process. We demonstrate a distinct correlation of lipid content with the accumulation of select fatty acids of the lipids for 5 different yeast species, among which, for S. cerevisiae and L. starkeyi, in-depth calibration curves were developed from 65 and 154 unique samples, respectively. We demonstrate that NIR spectra can be used to accurately predict intracellular lipid content using multivariate linear regression analysis in a manner of minutes, avoiding the need for lengthy chemical analyses that are resource intensive.