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Record W4315782361 · doi:10.3389/fcell.2023.1136992

Editorial: Post-translational mechanisms involved in regulating peroxisome biogenesis, functions and organelle-crosstalk

2023· editorial· en· W4315782361 on OpenAlexaff
Amr R. A. Kataya, Eric T. Fedosejevs, Yajin Ye

Bibliographic record

VenueFrontiers in Cell and Developmental Biology · 2023
Typeeditorial
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPeroxisome Proliferator-Activated Receptors
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of Calgary
Fundersnot available
KeywordsCrosstalkBiogenesisOrganelleCell biologyPeroxisomeOrganelle biogenesisSignal transductionBiologyChemistryPhysicsBiochemistryReceptor

Abstract

fetched live from OpenAlex

The topic presents 6 articles including two original research and reviews on peroxisomerelated research. We will represent in this editorial the aspects of these articles. Phosphorylation, ubiquitination, acetylation, and asparagine (N)-linked glycosylation PTMs were found associated and/or predicted with peroxisomal proteins (Sandalio et al. 2019;Infant et al. 2021). In the past decade, advanced proteomics helped to identify a huge number of PTMs such as phosphorylated peroxisomal proteins in several organisms ( (Kataya et al. 2019;Oeljeklaus et al. 2016). A limited number of studies have identified functional regulation of PTMs for example, yeast glycerol-3phosphate dehydrogenase 1 was found to change its localization to peroxisomes through phosphorylation of two serine residues downstream of its peroxisome targeting signal type 2 (PTS2). Ohsawa et al in this research topic have reviewed the recent findings highlighting the regulation of peroxisome homeostasis in the methylotrophic yeast Komagataella phaffii (Ohsawa et al. 2022). Methanol allows the yeast to grow with peroxisome proliferation that subsequently degrades by pexophagy upon methanol depletion, a mechanism that is repressed by the addition of ethanol. The authors discussed PTM-based signaling, where phosphorylation has been shown to inactivate Mxr1 transcription factor during ethanol repression and to trigger peroxisomemembrane-associated Atg30 to activate pexophagy under methanol depletion. The authors also have discussed how pexophagy is repressed during methanol signaling and the physiological significance of these types of machinery.In plants, only the photorespiratory enzyme, glycolate oxidase activity has been shown to be regulated by phosphorylation in vitro and its phospho-status differentiated in response to light and CO2 (Jossier et al. 2019;Dellero et al. 2013). So far, a limited number of protein kinases were identified in peroxisomes of humans, plants, and yeast, and three protein phosphatases were reported only in plant peroxisomes with little knowledge of their functions (reviewed in (Kataya et al. 2019)). In this research topic: original research by Kataya et al., has investigated bioinformatically peroxisome kinome in the model organism "Arabidopsis thaliana" for the presence of putative functional PTS type 1 (PTS1), and identified a considerable number of protein kinases that putatively harbors functional PTS1 (Kataya et al. 2022). The fact that the Arabidopsis genome encodes up to 1000 protein kinases and the identified protein kinases conservation of PTS1-like tripeptides aligned with experimental verification of 12 functional PTS1s. This was accomplished by the authors by fusing protein kinases terminal domains with EYFP and investigating their targeting into peroxisome in vivo. This study also investigated if calciumdependent protein kinase 1 (CDPK1) and glyoxysomal protein kinase 1 (GPK1), which were previously reported as protein kinases (reviewed in (Kataya et al. 2019)), can target peroxisomes in a PTS1-dependent fashion. In addition, the study successfully reported the ability of seven new full-length protein kinases to target peroxisomes in vivo and investigated their impact on peroxisomal fatty acid β-oxidation in the protein kinases isolated T-DNA mutants. Overall, this research opens more avenues to study the effect of protein phosphorylation on peroxisome functions and should help identify functional substrates involved in peroxisome signaling.As described above, the advancements in bioinformatics and predictive models help in identifying novel and rare PTSs or in other terms canonical and non-canonical PTSs. Kataya et al., used PTS1-developed models by (Wang et al. 2017;Lingner et al. 2011) to identify new PTS1s in Arabidopsis protein kinases. However, as Tarafdar and Chowdhary emphasized in their review of this research topic, investigating the peroxisome proteome of economically important crops was not fully investigated (Tarafdar and Chowdhary 2022). In their article, a reference peroxisomal matrix proteome map for Arabidopsis thaliana was generated including reported proteomic, bioinformatic predicted, and experimentally verified peroxisomal proteins. Employing this reference map, Tarafdar and Chowdhary identified putative peroxisome proteome in S. lycopersicum, which showed greater diversity in the composition of the signal tripeptide. These comparative studies are timely needed and should facilitate future studies investigating peroxisome signaling in such an important crop.The generation and signaling of peroxisomal reactive oxygen species are expected to be fine-tuned. The thiol side chain (RSH) of Cys is a major target for reactive oxygen species that can be sulfenylated (Cys-SOH). Scarce information is present about the targets of peroxisomal hydrogen peroxide and the regulatory role of peroxisomes involving reactive oxygen species in peroxisomal signaling. Lismont et al., in this research topic, have shown the sulfenylation profiles of key redox signaling proteins are indeed affected by peroxisome-derived hydrogen peroxide (Lismont et al. 2022). The authors employed a cell system, Flp-In T-REx 293, where they can modulate peroxisomal hydrogen peroxide in combination with a yeast AP-1-like-based sulfenome mining strategy and identified around 400 thiol targets. These targets, as reported, are derived in response to peroxisome-derived hydrogen peroxide and reside not only in peroxisomes but also in cytosol and mitochondria. The sulfenylation profiles and kinetics in response to peroxisomederived hydrogen peroxide have shown considerable differentiation of peroxiredoxins and tubulins. Moreover, the authors have shown putative evidence of the involvement of the redoxrelay mechanism to oxidize peroxisomal hydrogen peroxide targets such as transcription factors. These results take one step forward in understanding how peroxisomes interact with cell constituents and integrate into the cellular hydrogen signaling network.Peroxisome mutants in humans cause peroxisomal biogenesis disorders such as Zellweger syndrome which is a metabolic disorder with severe pathology in multiple organs with little known about pathogenesis. Jiang and Okazaki in this research topic have reviewed recent findings and discussed how peroxisomes are involved in regulating intrinsic apoptotic pathways and upstream fission-fusion processes that could cause multiple organ dysfunctions of Zellweger syndrome (Jiang and Okazaki 2022). The authors also provided mammalian cells-based findings implicating the regulatory roles of peroxisomes in mitochondrial fission-fusion dynamics and intrinsic apoptotic pathways through redox control.Peroxisomes are known to be highly dynamic as they proliferate by de novo biosynthesis and division, can increase in number due to environmental stresses, and extend to form peroxules to interact with other organelles. This versatility in peroxisome morphological appearances requires complex signaling that putatively includes PTMs. For example, peroxisomes proliferation has been shown to be influenced by the protein kinase MPK17 (Frick and Shrader, 2018) due to salt stress. Salt stress also has been shown to affect the expression of peroxisomal genes in a process that requires ethylene, jasmonate, and abscisic acid signaling pathways (CHARLTON et al. 2005). Also, in an intriguing study, peroxisomes were found to extend peroxules in response to stress via reactive oxygen species involving the induction of the peroxin 11a, which leads to higher proliferation and execute its proliferation (Rodríguez-Serrano et al. 2016). The need to systematically study these peroxisome features is crucial, and the Goto-Yamada et al., review article on this research topic has thoroughly investigated molecular dynamics or peroxisomes in plants through image-based analysis (Goto-Yamada et al. 2022). The authors have addressed and compared molecular mechanisms that putatively regulate peroxisomes dynamics and morphological appearances utilizing fluorescence imaging, especially in Arabidopsis thaliana aberrant peroxisome morphology (apem) and peroxisome unusual positioning (peup) mutants and the liverwort Marchantia polymorpha that is emerging as useful plant model. They have analyzed peroxisome biogenesis, proliferation and quality control, and physical organelle-organelle interactions and conclude that Marchantia polymorpha is a useful organism to identify genes bioinformatically, mutate, and investigate dynamics and diversity of peroxisomes in land plants. The authors also highlighted the demanding need to systematically screen and phenotypically investigate large number of mutants. Therefore, they have highlighted the study by Li et al., which categorized mutant phenotypes and identified abnormal morphology using a newly established Deep Learning of the Morphology of Organelles (DeepLearnMOR) tool (Li et al. 2021). Taken together, these advances in studying molecular mechanisms regulating peroxisome dynamics will facilitate future studies investigating PTM involvement.The PTM investigation in peroxisomes remains largely unknown. This research topic, for the first time, specifically focused on the regulation of peroxisomes by PTMs. This topic covered studies in plants, yeast, and human peroxisomes, discussed and supplied evidence of PTM-dependent regulation of peroxisome proteome, the presence of peroxisome-PTM modifiers, and the putative regulatory effect of peroxisomes on mitochondrial biogenesis and pathogenesis. Moreover, utilizing bioinformatic and prediction algorithms and image-based analyses were thoroughly discussed which will help future analyses of this research field.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.029
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0020.001
Research integrity0.0060.007
Insufficient payload (model declined to judge)0.0290.014

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.006
GPT teacher head0.217
Teacher spread0.211 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2023
Admission routes1
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