We report here the 98.5 Mbp haploid genome (12,924 protein coding genes) of Ulva mutabilis, a ubiquitous and iconic representative of the Ulvophyceae or green seaweeds. Ulva's rapid and abundant growth makes it a key contributor to coastal biogeochemical cycles; its role in marine sulfur cycles is particularly important because it produces high levels of dimethylsulfoniopropionate (DMSP), the main precursor of volatile dimethyl sulfide (DMS). Rapid growth makes Ulva attractive biomass feedstock but also increasingly a driver of nuisance "green tides." Ulvophytes are key to understanding the evolution of multicellularity in the green lineage, and Ulva morphogenesis is dependent on bacterial signals, making it an important species with which to study cross-kingdom communication. Our sequenced genome informs these aspects of ulvophyte cell biology, physiology, and ecology. Gene family expansions associated with multicellularity are distinct from those of freshwater algae. Candidate genes, including some that arose following horizontal gene transfer from chromalveolates, are present for the transport and metabolism of DMSP. The Ulva genome offers, therefore, new opportunities to understand coastal and marine ecosystems and the fundamental evolution of the green lineage.

Light and nutrients are critical regulators of photosynthesis and metabolism in plants and algae. Many algae have the metabolic flexibility to grow photoautotrophically, heterotrophically, or mixotrophically. Here, we describe reversible Glc-dependent repression/activation of oxygenic photosynthesis in the unicellular green alga Chromochloris zofingiensis. We observed rapid and reversible changes in photosynthesis, in the photosynthetic apparatus, in thylakoid ultrastructure, and in energy stores including lipids and starch. Following Glc addition in the light, C. zofingiensis shuts off photosynthesis within days and accumulates large amounts of commercially relevant bioproducts, including triacylglycerols and the high-value nutraceutical ketocarotenoid astaxanthin, while increasing culture biomass. RNA sequencing reveals reversible changes in the transcriptome that form the basis of this metabolic regulation. Functional enrichment analyses show that Glc represses photosynthetic pathways while ketocarotenoid biosynthesis and heterotrophic carbon metabolism are upregulated. Because sugars play fundamental regulatory roles in gene expression, physiology, metabolism, and growth in both plants and animals, we have developed a simple algal model system to investigate conserved eukaryotic sugar responses as well as mechanisms of thylakoid breakdown and biogenesis in chloroplasts. Understanding regulation of photosynthesis and metabolism in algae could enable bioengineering to reroute metabolism toward beneficial bioproducts for energy, food, pharmaceuticals, and human health.

An expected outcome of climate change is intensification of the global water cycle, which magnifies surface water fluxes, and consequently alters salinity patterns. It is therefore important to understand the adaptations and limits of microalgae to survive changing salinities. To this end, we sequenced the 13.5 Mbp genome of the halotolerant green alga Picochlorum SENEW3 (SE3) that was isolated from a brackish water pond subject to large seasonal salinity fluctuations. Picochlorum SE3 encodes 7367 genes, making it one of the smallest and most gene dense eukaryotic genomes known. Comparison with the pico-prasinophyte Ostreococcus tauri, a species with a limited range of salt tolerance, reveals the enrichment of transporters putatively involved in the salt stress response in Picochlorum SE3. Analysis of cultures and the protein complement highlight the metabolic flexibility of Picochlorum SE3 that encodes genes involved in urea metabolism, acetate assimilation and fermentation, acetoin production and glucose uptake, many of which form functional gene clusters. Twenty-four cases of horizontal gene transfer from bacterial sources were found in Picochlorum SE3 with these genes involved in stress adaptation including osmolyte production and growth promotion. Our results identify Picochlorum SE3 as a model for understanding microalgal adaptation to stressful, fluctuating environments.

The broadly halotolerant green alga, Picochlorum strain SENEW3, has a highly reduced nuclear genome of 13.5 Mbp that encodes only 7,367 genes. It was originally isolated from a shallow, mesophilic brackish-water lagoon that experiences extreme changes in temperature, light, and in particular, salinity (freshwater to 3-fold seawater). We challenged Picochlorum cells with high or low salinity shock and used transcriptomic and chlorophyll fluorescence analyses to elucidate tolerance to salinity fluctuation. The transcriptome analysis showed that one-half of the coding regions are differentially expressed in response to salinity changes. In addition, a significant number of co-expressed genes (usually from different metabolic pathways) are co-localized in the genome, forming 2-10 gene clusters. Whereas the overall salt stress response in Picochlorum SENEW3 is similar to that in other salt-tolerant algae, the "operon-like" structure in this species likely contributes to rapid recovery during salinity fluctuation. In summary, our work elucidates how evolutionary forces play out in a streamlined genome. Picochlorum SENEW3 relies on a broad array of adaptations from the reliance on horizontally transferred adaptive genes to the colocalization of stress response genes and a robust photosystem II to deal with a fluctuating environment. These attributes make Picochlorum SENEW3 of great biotechnological interest.

Understanding how microalgae adapt to rapidly changing environments is not only important to science but can help clarify the potential impact of climate change on the biology of primary producers. We sequenced and analyzed the nuclear genome of multiple Picochlorum isolates (Chlorophyta) to elucidate strategies of environmental adaptation. It was previously found that coordinated gene regulation is involved in adaptation to salinity stress, and here we show that gene gain and loss also play key roles in adaptation. We determined the extent of horizontal gene transfer (HGT) from prokaryotes and their role in the origin of novel functions in the Picochlorum clade. HGT is an ongoing and dynamic process in this algal clade with adaptation being driven by transfer, divergence, and loss. One HGT candidate that is differentially expressed under salinity stress is indolepyruvate decarboxylase that is involved in the production of a plant auxin that mediates bacteria-diatom symbiotic interactions. Large differences in levels of heterozygosity were found in diploid haplotypes among Picochlorum isolates. Biallelic divergence was pronounced in P. oklahomensis (salt plains environment) when compared with its closely related sister taxon Picochlorum SENEW3 (brackish water environment), suggesting a role of diverged alleles in response to environmental stress. Our results elucidate how microbial eukaryotes with limited gene inventories expand habitat range from mesophilic to halophilic through allelic diversity, and with minor but important contributions made by HGT. We also explore how the nature and quality of genome data may impact inference of nuclear ploidy.

Separating specific cell phenotypes from a heterotypic mixture is a critical step in many research projects. Traditional methods usually require a large sample volume and a complex preparation process that may alter cell property during the sorting process. Here we present the use of electrical impedance as an indicator of cell health and for identifying specific microalgal phenotypes. We developed a microfluidic platform for measuring electrical impedance at different frequencies using the bacterium-sized green alga Picochlorum SE3. The cells were cultured under different salinity conditions and sampled at four different time points. Our results demonstrate the utility of electrical impedance as an indicator of cell phenotype by providing results that are consistent with known changes in cell size and physiology. Outliers in the cell data distribution are particularly useful because they represent phenotypes that have the ability to maintain size and/or membrane ionic permeability under prolonged salt stress. This suggests that our device can be used to identify and sort desired (e.g., experimentally evolved, mutant) cell phenotypes based on their electrical impedance properties.

Diverging from the classic paradigm of random gene order in eukaryotes, gene proximity can be leveraged to systematically identify functionally related gene neighborhoods in eukaryotes, utilizing techniques pioneered in bacteria. Current methods of identifying gene neighborhoods typically rely on sequence similarity to characterized gene products. However, this approach is not robust for non-model organisms like algae, which are evolutionarily distant from well-characterized model organisms. Here, we utilize a comparative genomic approach to identify evolutionarily conserved Proximal Orthologous Gene (POG) pairs conserved across at least two taxonomic classes of green algae. A total of 317 gene neighborhoods were identified. In some cases, gene proximity appears to have been conserved since before the streptophyte-chlorophyte split, 1,000 million years ago. Using functional inferences derived from reconstructed evolutionary relationships, we identified several novel functional clusters. A putative mycosporine-like amino acid (MAA), “sunscreen”, neighborhood contains genes similar to either vertebrate or cyanobacterial pathways, suggesting a novel mosaic biosynthetic pathway in green algae. One of two putative arsenic-detoxification neighborhoods includes an organoarsenical transporter (ArsJ), a glyceraldehyde 3-phosphate dehydrogenase-like gene, homologs of which are involved in arsenic detoxification in bacteria, and a novel algal-specific phosphoglycerate kinase-like gene (PGK). Mutants of the ArsJ-like transporter and PGK-like genes in Chlamydomonas reinhardtii were found to be sensitive to arsenate, providing experimental support for the role of these identified neighbors in resistance to arsenate. Potential evolutionary origins of neighborhoods are discussed, and updated annotations for formerly poorly annotated genes are presented, highlighting the potential of this strategy for functional annotation.

Microbial communities are increasingly recognized as key drivers in animal health, agricultural productivity, industrial operations, and ecological systems. The abundance of chemical interactions in these complex communities, however, can complicate or evade experimental studies, which hinders basic understanding and limits efforts to rationally design communities for applications in the aforementioned fields. Numerous computational approaches have been proposed to deduce these metabolic interactions -- notably including flux balance analysis (FBA) and systems of ordinary differential equations (ODEs) -- yet, these methods either fail to capture the dynamic phenotype expression of community members or lack the abstractions required to fit or explain the diverse experimental omics data that can be acquired today. We therefore developed a dynamic model (CommPhitting) that deduces phenotype abundances and growth kinetics for each community member, concurrent with metabolic concentrations, by coupling flux profiles for each phenotype with experimental growth and -omics data of the community. These data are captured as variables and coefficients within a mixed integer linear optimization problem (MILP) designed to represent the associated biological processes. This problem finds the globally optimized fit to all experimental data of a trial, thereby most accurately computing aspects of the community: (1) species and phenotype abundances over time; (2) a linearized growth kinetic constant for each phenotype; and (3) metabolite concentrations over time. We exemplify CommPhitting by applying it to study batch growth of an idealized two-member community of the model organisms (Escherichia coli and Pseudomonas flourescens) that exhibits cross-feeding in maltose media. Measurements of this community from our accompanying experimental studies -- including total biomass, species biomass, and metabolite abundances over time -- were parameterized into a CommPhitting simulation. The resultant kinetics constants and biomass proportions for each member phenotype would be difficult to ascertain experimentally, yet are important for understanding community responses to environmental perturbations and therefore engineering applications: e.g. for bioproduction. We believe that CommPhitting -- which is generalized for a diversity of data types and formats, and is further available and amply documented as a Python API -- will augment basic understanding of microbial communities and will accelerate the engineering of synthetic communities for diverse applications in medicine, agriculture, industry, and ecology.