Respiratory-deficient mutants of the unicellular green alga Chlamydomonas: A review.

Genetic manipulation of the unicellular green alga Chlamydomonas reinhardtii is straightforward. Nuclear genes can be interrupted by insertional mutagenesis or targeted by RNA interference whereas random or site-directed mutagenesis allows the introduction of mutations in the mitochondrial genome. This, combined with a screen that easily allows discriminating respiratory-deficient mutants, makes Chlamydomonas a model system of choice to study mitochondria biology in photosynthetic organisms. Since the first description of Chlamydomonas respiratory-deficient mutants in 1977 by random mutagenesis, many other mutants affected in mitochondrial components have been characterized. These respiratory-deficient mutants increased our knowledge on function and assembly of the respiratory enzyme complexes. More recently some of these mutants allowed the study of mitochondrial gene expression processes poorly understood in Chlamydomonas. In this review, we update the data concerning the respiratory components with a special focus on the assembly factors identified on other organisms. In addition, we make an inventory of different mitochondrial respiratory mutants that are inactivated either on mitochondrial or nuclear genes

Inactivation of genes coding for mitochondrial Nd7 and Nd9 complex I subunits in Chlamydomonas reinhardtii. Impact of complex I loss on respiration and energetic metabolism.

In Chlamydomonas, unlike in flowering plants, genes coding for Nd7 (NAD7/49kDa) and Nd9 (NAD9/30kDa) core subunits of mitochondrial respiratory-chain complex I are nucleus-encoded. Both genes possess all the features that facilitate their expression and proper import of the polypeptides in mitochondria. By inactivating their expression by RNA interference or insertional mutagenesis, we show that both subunits are required for complex I assembly and activity. Inactivation of complex I impairs the cell growth rate, reduces the respiratory rate, leads to lower intracellular ROS production and lower expression of ROS scavenging enzymes, and is associated to a diminished capacity to concentrate CO2 without compromising photosynthetic capacity.Peer reviewe

Inactivation of genes coding for mitochondrial Nd7 and Nd9 complex I subunits in Chlamydomonas reinhardtii. Impact of complex I loss on respiration and energetic metabolism.

In Chlamydomonas, unlike in flowering plants, genes coding for Nd7 (NAD7/49kDa) and Nd9 (NAD9/30kDa) core subunits of mitochondrial respiratory-chain complex I are nucleus-encoded. Both genes possess all the features that facilitate their expression and proper import of the polypeptides in mitochondria. By inactivating their expression by RNA interference or insertional mutagenesis, we show that both subunits are required for complex I assembly and activity. Inactivation of complex I impairs the cell growth rate, reduces the respiratory rate, leads to lower intracellular ROS production and lower expression of ROS scavenging enzymes, and is associated to a diminished capacity to concentrate CO2 without compromising photosynthetic capacity.Peer reviewe

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino de tector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower-or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches