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Enregistrement W2464739734 · doi:10.1371/journal.pgen.1006173

Gene Regulation in Developing Chloroplasts Disentangled

2016· letter· en· W2464739734 sur OpenAlexaff
William Zerges

Notice bibliographique

RevuePLoS Genetics · 2016
Typeletter
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiquePhotosynthetic Processes and Mechanisms
Établissements canadiensConcordia University
Organismes subventionnairesnon disponible
Mots-clésPlastidBiologyChloroplastEndosymbiosisGenomeCell biologyGeneBotanyGenetics

Résumé

récupéré en direct d'OpenAlex

Distant cousins of photosynthetic bacteria reside in plant cells where, as organelles called plastids, they give color to fruit and flowers, make starch in roots, and carry out photosynthesis in leaves. Plastids acquire these tissue-specific functions during plant development by undergoing programed differentiation from totipotent proplastids of meristematic tissues. Plastid differentiation involves the regulated expression of the 100–200 genes of the plastid genome, which have been retained from the ancestral cyanobacterium that founded the plastids by endosymbiosis 1–2 x 109 years ago [1]. Plastid genes are expressed by bacterial-type transcription and translation systems, which plastids also have retained during evolution [2]. The differentiation of chloroplasts, the photosynthetic plastid in green tissues, requires expression of plastid genes encoding polypeptide subunits of the photosynthesis apparatus and components of the organellar gene expression system. Photosynthesis subunits are produced in precise stoichiometries which, in the green alga Chlamydomonas reinhardtii, are brought about by feedback controls at the level of translation [3]. In C4 plants, distinct chloroplast types develop in mesophyll and bundle sheath cells through, in part, differential expression of the plastid genome. However, surprisingly little is known about how plastid genes are regulated during the transformation of proplastids into chloroplasts in the context of leaf development. Developmental regulation of gene expression in plants is difficult to characterize. Young plants have many cell types in the gamut of developmental stages, and in many plant species, developing cells are in arrangements that complicate the physical dissection of tissues in specific stages for analyses. An excellent experimental system is provided by seedling leaves of the grasses, e.g., maize, rice, and barley. Each leaf has a developmental gradient along its long axis, with nonphotosynthetic meristematic cells (and proplastids) at the base and differentiated photosynthetic cells (and chloroplasts) at the tip (Fig 1) [4]. Analysis of the abundance of a subset of plastid mRNAs along this leaf gradient in maize and barley has revealed programmed changes in mRNA levels [5,6,7]. However, the degree to which mRNA levels predict the actual protein output of each gene has been unclear because of lack of data on translation rates. Fig 1 Plastid transcriptome and translatome profiles during chloroplast development in the maize seedling leaf reveal developmental patterns of plastid gene expression and the relative contributions of mRNA level and translational control in establishing them ... Chotewutmontri and Barkan report in this issue [8] plastid transcriptome and translatome profiles at several points during the proplastid to chloroplast transition in young maize leaves, thereby revealing developmental patterns of protein production from each plastid gene and the relative contributions of mRNA level and translation rate in establishing them (Fig 1). They first optimized the ribosome profiling or “Riboseq” method for analysis of the plastid translatome in leaf tissue and in separated bundle sheath and mesophyll cells. Riboseq uses deep sequencing to tally footprints of translating ribosomes on specific mRNAs, revealing ribosome numbers and their precise locations on all mRNAs [9]. The abundance of ribosome footprints from each gene (normalized to mRNA length) serves as a proxy for the overall rate of protein synthesis from that gene. By comparing the Riboseq data to RNAseq data from the same tissue samples, the authors calculated relative translation efficiencies (i.e., ribosome footprint abundance divided by mRNA abundance). They describe the profiles of mRNA abundance, translational efficiency, and protein output for all plastid genes as a function of developmental stage (Fig 1). The major importance of this work is the deciphering of the contributions of mRNA abundance and translational efficiency in the expression of the plastid genome during the proplastid to chloroplast transition. In this context, the results support five major conclusions. First, during chloroplast development, protein output from most plastid genes increases early and declines later. Changes in mRNA level account for many of these changes, but they are further refined by changes in translational efficiency. Second, two translational regulons are revealed, which are characterized by distinct developmental profiles at the level of translational efficiency during the proplastid to chloroplast transition. One regulon exhibits prioritized translation early in development and consists primarily of mRNAs encoding components of the chloroplast gene expression machinery. The other regulon exhibits peak translational efficiency later in development and consists primarily of mRNAs encoding photosynthesis proteins. Third, in bundle sheath and mesophyll chloroplasts, different patterns of protein output reflect primarily differences in mRNA abundance, although these are amplified in several cases by differences in translational efficiency. Fourth, photosynthesis complex subunit stoichiometry is reflected by relative rates of protein synthesis, and this tuning is programmed by a balancing of translational efficiencies with mRNA abundance. Fifth, the editing of plastid mRNAs, a process that changes specific C to U residues, generally is not a prerequisite for their translation. In addition, readers will find in the wealth of data something relevant to their particular interests in plastid gene expression. Future research will use the groundwork and methods of Chotewutmontri and Barkan to advance further our understanding of plastid gene expression in development. One likely outcome is the discovery of developmental regulatory functions of known translation factors in the chloroplasts [10,11]. Also of interest will be to expand the analyses to the transcriptome and translatome of the nuclear-cytoplasmic compartments—for example, to describe how expression of nucleus-encoded and plastid-encoded subunits of photosynthetic complexes is coordinated during development. This work also opens avenues to explore relationships between translation and the polycistronic organization of plastid genes, mRNA splicing, the repair of photosynthesis complexes, and acclimation to changing environmental conditions. The disentangling of our understanding of the regulated steps in plastid gene expression in chloroplast development has begun.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Commentaire · Signal consensuel: aucune
Score de désaccord entre enseignants0,002
Score d'incertitude au seuil0,006

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0010,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,015
Tête enseignante GPT0,228
Écart entre enseignants0,213 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2016
Routes d'admission1
Résumé présentoui

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