No abstract

Pyridoxal 59-phosphate (PLP) is an essential cofactor for nearly 60 Escherichia coli enzymes but is a highly reactive molecule that is toxic in its free form. How PLP levels are regulated and how PLP is delivered to target enzymes are still open questions. The COG0325 protein family belongs to the fold-type III class of PLP enzymes and binds PLP but has no known biochemical activity although it occurs in all kingdoms of life. Various pleiotropic phenotypes of the E. coli COG0325 (yggS) mutant have been reported, some of which were reproduced and extended in this study. Comparative genomic, genetic and metabolic analyses suggest that these phenotypes reflect an imbalance in PLP homeostasis. The E. coli yggS mutant accumulates the PLP precursor pyridoxine 59-phosphate (PNP) and is sensitive to an excess of pyridoxine but not of pyridoxal. The pyridoxine toxicity phenotype is complemented by the expression of eukaryotic yggS orthologs. It is also suppressed by the presence of amino acids, specifically isoleucine, threonine and leucine, suggesting the PLP-dependent enzyme transaminase B (IlvE) is affected. These genetic results lay a foundation for future biochemical studies of the role of COG0325 proteins in PLP homeostasis.

Point-scanning imaging systems are among the most widely used tools for high-resolution cellular and tissue imaging, benefitting from arbitrarily defined pixel sizes. The resolution, speed, sample preservation, and signal-to-noise ratio (SNR) of point-scanning systems are difficult to optimize simultaneously. We show these limitations can be mitigated via the use of Deep Learning-based supersampling of undersampled images acquired on a point-scanning system, which we term point-scanning super-resolution (PSSR) imaging. We designed a “crappifier” that computationally degrades high SNR, high pixel resolution ground truth images to simulate low SNR, low-resolution counterparts for training PSSR models that can restore real-world undersampled images. For high spatiotemporal resolution fluorescence timelapse data, we developed a “multi-frame” PSSR approach that utilizes information in adjacent frames to improve model predictions. In conclusion, PSSR facilitates point-scanning image acquisition with otherwise unattainable resolution, speed, and sensitivity. All the training data, models, and code for PSSR are publicly available at 3DEM.org . Editor’s summary Point-scanning super-resolution imaging uses deep learning to supersample undersampled images and enable time-lapse imaging of subcellular events. An accompanying “crappifier” rapidly generates quality training data for robust performance.

Mitochondria are essential and dynamic organelles undergoing constant fission and fusion. The primary players in mitochondrial morphology (MFN1/2, OPA1, DRP1) have been identified, but their mechanism(s) of regulation are still being elucidated. ARL2 is a regulatory GTPase that has previously been shown to play a role in the regulation of mitochondrial morphology. Here we demonstrate that ELMOD2, an ARL2 GTPase-activating protein (GAP), is necessary for ARL2 to promote mitochondrial elongation. We show that loss of ELMOD2 causes mitochondrial fragmentation and a lower rate of mitochondrial fusion, while ELMOD2 overexpression promotes mitochondrial tubulation and increases the rate of fusion in a mitofusin-dependent manner. We also show that a mutant of ELMOD2 lacking GAP activity is capable of promoting fusion, suggesting that ELMOD2 does not need GAP activity to influence mitochondrial morphology. Finally, we show that ELMOD2, ARL2, Mitofusins 1 and 2, Miros 1 and 2, and mitochondrial phospholipase D (mitoPLD) all localize to discrete, regularly spaced puncta along mitochondria. These results suggest that ELMOD2 is functioning as an effector downstream of ARL2 and upstream of the mitofusins to promote mitochondrial fusion. Our data provide insights into the pathway by which mitochondrial fusion is regulated in the cell.