Background: Nitrogen-starvation and other stresses induce triacylglycerol (TAG) accumulation in algae, but the relevant enzymes and corresponding signal transduction pathways are unknown. Results: RNA-Seq and genetic analysis revealed three acyltransferases that contribute to TAG accumulation. Conclusion: TAG synthesis results from recycling of membrane lipids and also by acylation of DAG. Significance: The genes are potential targets for manipulating TAG hyperaccumulation.

ORCID IDs: 0000-0002-7487-8014 (S.S.); 0000-0002-2594-509X (S.S.M.)Nitrogen (N) is a key nutrient that limits global primary productivity; hence, N-use efficiency is of compelling interest in agriculture and aquaculture. We used Chlamydomonas reinhardtii as a reference organism for a multicomponent analysis of the N starvation response. In the presence of acetate, respiratory metabolism is prioritized over photosynthesis; consequently, the N-sparing response targets proteins, pigments, and RNAs involved in photosynthesis and chloroplast function over those involved in respiration. Transcripts and proteins of the Calvin-Benson cycle are reduced in N-deficient cells, resulting in the accumulation of cycle metabolic intermediates. Both cytosolic and chloroplast ribosomes are reduced, but via different mechanisms, reflected by rapid changes in abundance of RNAs encoding chloroplast ribosomal proteins but not cytosolic ones. RNAs encoding transporters and enzymes for metabolizing alternative N sources increase in abundance, as is appropriate for the soil environmental niche of C. reinhardtii. Comparison of the N-replete versus N-deplete proteome indicated that abundant proteins with a high N content are reduced in N-starved cells, while the proteins that are increased have lower than average N contents. This sparing mechanism contributes to a lower cellular N/C ratio and suggests an approach for engineering increased N-use efficiency.

Label-free cell analysis is essential to personalized genomics, cancer diagnostics, and drug development as it avoids adverse effects of staining reagents on cellular viability and cell signaling. However, currently available label-free cell assays mostly rely only on a single feature and lack sufficient differentiation. Also, the sample size analyzed by these assays is limited due to their low throughput. Here, we integrate feature extraction and deep learning with high-throughput quantitative imaging enabled by photonic time stretch, achieving record high accuracy in label-free cell classification. Our system captures quantitative optical phase and intensity images and extracts multiple biophysical features of individual cells. These biophysical measurements form a hyperdimensional feature space in which supervised learning is performed for cell classification. We compare various learning algorithms including artificial neural network, support vector machine, logistic regression, and a novel deep learning pipeline, which adopts global optimization of receiver operating characteristics. As a validation of the enhanced sensitivity and specificity of our system, we show classification of white blood T-cells against colon cancer cells, as well as lipid accumulating algal strains for biofuel production. This system opens up a new path to data-driven phenotypic diagnosis and better understanding of the heterogeneous gene expressions in cells.

(S.S.M.).To understand the molecular basis underlying increased triacylglycerol (TAG) accumulation in starchless (sta) Chlamydomonas reinhardtii mutants, we undertook comparative time-course transcriptomics of strains CC-4348 (sta6 mutant), CC-4349, a cell wall-deficient (cw) strain purported to represent the parental STA6 strain, and three independent STA6 strains generated by complementation of sta6 (CC-4565/STA6-C2, CC-4566/STA6-C4, and CC-4567/STA6-C6) in the context of N deprivation. Despite N starvation-induced dramatic remodeling of the transcriptome, there were relatively few differences (5 3 10 2 ) observed between sta6 and STA6, the most dramatic of which were increased abundance of transcripts encoding key regulated or rate-limiting steps in central carbon metabolism, specifically isocitrate lyase, malate synthase, transaldolase, fructose bisphosphatase and phosphoenolpyruvate carboxykinase (encoded by ICL1, MAS1, TAL1, FBP1, and PCK1 respectively), suggestive of increased carbon movement toward hexose-phosphate in sta6 by upregulation of the glyoxylate pathway and gluconeogenesis. Enzyme assays validated the increase in isocitrate lyase and malate synthase activities. Targeted metabolite analysis indicated increased succinate, malate, and Glc-6-P and decreased Fru-1,6-bisphosphate, illustrating the effect of these changes. Comparisons of independent data sets in multiple strains allowed the delineation of a sequence of events in the global N starvation response in C. reinhardtii, starting within minutes with the upregulation of alternative N assimilation routes and carbohydrate synthesis and subsequently a more gradual upregulation of genes encoding enzymes of TAG synthesis. Finally, genome resequencing analysis indicated that (1) the deletion in sta6 extends into the neighboring gene encoding respiratory burst oxidase, and (2) a commonly used STA6 strain (CC-4349) as well as the sequenced reference (CC-503) are not congenic with respect to sta6 (CC-4348), underscoring the importance of using complemented strains for more rigorous assignment of phenotype to genotype.