Background: Photosynthetic oleaginous microalgae are considered promising feedstocks for biofuels. The marine microalga, Nannochloropsis oceanica, has been attracting ever-increasing interest because of its fast growth, high triacylglycerol (TAG) content, and available genome sequence and genetic tools. Diacylglycerol acyltransferase (DGAT) catalyzes the last and committed step of TAG biosynthesis in the acyl-CoA-dependent pathway. Previous studies have identified 13 putative DGAT-encoding genes in the genome of N. oceanica, but the functional role of DGAT genes, especially type-I DGAT (DGAT1), remains ambiguous.

Results: Nannochloropsis oceanica IMET1 possesses two DGAT1 genes: NoDGAT1A and NoDGAT1B. Functional complementation demonstrated the capability of NoDGAT1A rather than NoDGAT1B to restore TAG synthesis in a TAG-deficient yeast strain. In vitro DGAT assays revealed that NoDGAT1A preferred saturated/monounsaturated acyl-CoAs and eukaryotic diacylglycerols (DAGs) for TAG synthesis, while NoDGAT1B had no detectable enzymatic activity. Assisted with green fluorescence protein (GFP) fusion, fluorescence microscopy analysis indicated the localization of NoDGAT1A in the chloroplast endoplasmic reticulum (cER) of N. oceanica. NoDGAT1A knockdown caused ~25% decline in TAG content upon nitrogen depletion, accompanied by the reduced C16:0, C18:0, and C18:1 in TAG sn-1/sn-3 positions and C18:1 in the TAG sn-2 position. NoDGAT1A overexpression, on the other hand, led to ~39% increase in TAG content upon nitrogen depletion, accompanied by the enhanced C16:0 and C18:1 in the TAG sn-1/sn-3 positions and C18:1 in the TAG sn-2 position. Interestingly, NoDGAT1A overexpression also promoted TAG accumulation (by ~2.4-fold) under nitrogen-replete conditions without compromising cell growth, and TAG yield of the overexpression line reached 0.49 g L[superscript −1] at the end of a 10-day batch culture, 47% greater than that of the control line.

Conclusions: Taken together, our work demonstrates the functional role of NoDGAT1A and sheds light on the underlying mechanism for the biosynthesis of various TAG species in N. oceanica. NoDGAT1A resides likely in cER and prefers to transfer C16 and C18 saturated/monounsaturated fatty acids to eukaryotic DAGs for TAG assembly. This work also provides insights into the rational genetic engineering of microalgae by manipulating rate-limiting enzymes such as DGAT to modulate TAG biosynthesis and fatty acid composition for biofuel production.

Although significant progress has been made in understanding multisensory interactions at the behavioral level, their underlying neural mechanisms remain relatively poorly understood in cortical areas, particularly during the control of action. In recent experiments where animals reached to and actively maintained their arm position at multiple spatial locations while receiving either proprioceptive or visual-proprioceptive position feedback, multisensory interactions were shown to be associated with reduced spiking (i.e. subadditivity) as well as reduced intra-trial and across-trial spiking variability in the superior parietal lobule (SPL). To further explore the nature of such interaction-induced changes in spiking variability we quantified the spike train dynamics of 231 of these neurons. Neurons were classified as Poisson, bursty, refractory, or oscillatory (in the 13–30 Hz “beta-band”) based on their spike train power spectra and autocorrelograms. No neurons were classified as Poisson-like in either the proprioceptive or visual-proprioceptive conditions. Instead, oscillatory spiking was most commonly observed with many neurons exhibiting these oscillations under only one set of feedback conditions. The results suggest that the SPL may belong to a putative beta-synchronized network for arm position maintenance and that position estimation may be subserved by different subsets of neurons within this network depending on available sensory information. In addition, the nature of the observed spiking variability suggests that models of multisensory interactions in the SPL should account for both Poisson-like and non-Poisson variability.