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In plants, the mitochondrial complex I is the protein complex encompassing the largest number of iron-sulfur (Fe-S) clusters. The whole, membrane-embedded, holo-complex is assembled stepwise from assembly intermediates. The Q and N modules are combined to form a peripheral arm in the matrix, whereas the so-called membrane arm is formed after merging a carbonic anhydrase (CA) module with so-called Pp (proximal) and the Pd (distal) domains. A ferredoxin bridge connects both arms. The eight Fe-S clusters present in the peripheral arm for electron transfer reactions are synthesized via a dedicated protein machinery referred to as the iron-sulfur cluster (ISC) machinery. The de novo assembly occurs on ISCU scaffold proteins from iron, sulfur and electron delivery proteins. In a second step, the preformed Fe-S clusters are transferred, eventually converted and inserted in recipient apo-proteins. Diverse molecular actors, including a chaperone-cochaperone system, assembly factors among which proteins with LYR motifs, and Fe-S cluster carrier/transfer proteins, have been identified as contributors to the second step. This mini-review highlights the recent progresses in our understanding of how specificity is achieved during the delivery of preformed Fe-S clusters to complex I subunits., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 López-López, Keech and Rouhier.)

Gene co-expression networks (GCNs) can be prepared using a variety of mathematical approaches based on data sampled across diverse developmental processes, tissue types, pathologies, mutant backgrounds, and stress conditions. These networks are used to identify genes with similar expression dynamics but are prone to introducing false-positive and false-negative relationships, especially in the instance of large and heterogenous datasets. With the aim of optimizing the relevance of edges in GCNs and enhancing global biological insight, we propose a novel approach that involves a data-centering step performed simultaneously per gene and per sub-experiment, called centralization within sub-experiments (CSE). Using a gene set encoding the plant mitochondrial proteome as a case study, our results show that all CSE-based GCNs assessed had significantly more edges within the majority of the considered functional sub-networks, such as the mitochondrial electron transport chain and its complexes, than GCNs not using CSE; thus demonstrating that CSE-based GCNs are efficient at predicting canonical functions and associated pathways, here referred to as the core gene network. Furthermore, we show that correlation analyses using CSE-processed data can be used to fine-tune prediction of the function of uncharacterized genes; while its use in combination with analyses based on non-CSE data can augment conventional stress analyses with the innate connections underpinning the dynamic system being examined. Therefore, CSE is an effective alternative method to conventional batch correction approaches, particularly when dealing with large and heterogenous datasets. The method is easy to implement into a pre-existing GCN analysis pipeline and can provide enhanced biological relevance to conventional GCNs by allowing users to delineate a core gene network., Author Summary: Gene co-expression networks (GCNs) are the product of a variety of mathematical approaches that identify causal relationships in gene expression dynamics but are prone to the misdiagnoses of false-positives and false-negatives, especially in the instance of large and heterogenous datasets. In light of the burgeoning output of next-generation sequencing projects performed on a variety of species, and developmental or clinical conditions; the statistical power and complexity of these networks will undoubtedly increase, while their biological relevance will be fiercely challenged. Here, we propose a novel approach to generate a "core" GCN with enhanced biological relevance. Our method involves a data-centering step that effectively removes all primary treatment/tissue effects, which is simple to employ and can be easily implemented into pre-existing GCN analysis pipelines. The gain in biological relevance resulting from the adoption of this approach was assessed using a plant mitochondrial case study., (Copyright © 2020 Law, Kellgren, Björk, Ryden and Keech.)

Glutathione transferases (GSTs) belong to a ubiquitous multigenic family of enzymes involved in diverse biological processes including xenobiotic detoxification and secondary metabolism. A canonical GST is formed by two domains, the N-terminal one adopting a thioredoxin (TRX) fold and the C-terminal one an all-helical structure. The most recent genomic and phylogenetic analysis based on this domain organization allowed the classification of the GST family into 14 classes in terrestrial plants. These GSTs are further distinguished based on the presence of the ancestral cysteine (Cys-GSTs) present in TRX family proteins or on its substitution by a serine (Ser-GSTs). Cys-GSTs catalyze the reduction of dehydroascorbate and deglutathionylation reactions whereas Ser-GSTs catalyze glutathione conjugation reactions and eventually have peroxidase activity, both activities being important for stress tolerance or herbicide detoxification. Through non-catalytic, so-called ligandin properties, numerous plant GSTs also participate in the binding and transport of small heterocyclic ligands such as flavonoids including anthocyanins, and polyphenols. So far, this function has likely been underestimated compared to the other documented roles of GSTs. In this review, we compiled data concerning the known enzymatic and structural properties as well as the biochemical and physiological functions associated to plant GSTs having a conserved serine in their active site.