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Enregistrement W4386114891 · doi:10.4103/0973-3698.274456

Lipidomics in Psoriatic Disease: The New Kid on the Omics Block

2019· article· en· W4386114891 sur OpenAlexaff
AshishJ Mathew, Vinod Chandran

Notice bibliographique

RevueIndian Journal of Rheumatology · 2019
Typearticle
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueMetabolomics and Mass Spectrometry Studies
Établissements canadiensUniversity of TorontoToronto Western HospitalUniversity Health Network
Organismes subventionnairesnon disponible
Mots-clésLipidomicsLipidomeMetabolomicsLipid metabolismComputational biologyChemistryBiochemistryBiologyBioinformatics

Résumé

récupéré en direct d'OpenAlex

Lipids are complex, hydrophobic molecules that are pivotal components of cellular membranes. Among their plethora of cellular functions, endogenous lipids act as major mediators in all phases of inflammation.[1] Psoriatic disease is a chronic, heterogeneous, inflammatory condition with varied presentations and multi-organ implications. Lipid metabolism abnormalities and oxidative stress have been described commonly in patients with psoriatic disease.[2] Fatty acid metabolism has a close relationship with the T-helper cell 17 function, which is known to play a critical role in psoriasis.[3] Omega-3 polyunsaturated fatty acid (PUFA) has been shown to suppress inflammatory cell infiltration and epidermal hyperplasia by inhibiting interleukin-23 production by dendritic cells in a mouse model.[4] Lipidomics is a subfield of metabolomics that works on the principles of analytical chemistry. It relates to the large-scale profiling and quantification of lipidome (complete profile of cellular lipids) in biological systems and its interaction with other lipids, proteins, and metabolites. Lipidomics has undergone rapid progress over the past decade, largely driven by the continuous technological advances in mass spectrometry (MS), nuclear magnetic resonance, fluorescence spectroscopy, and computational methods. Sample preparation, MS-based analysis, and data processing constitute the three main steps of a customary lipidomics workflow. MS-based techniques are quite popular, allowing separation and characterization of charged ionized analytes based on their mass-to-charge ratios.[5] These can be grouped into three categories: Global lipidomic analysis – identifying and stratifying thousands of cellular lipid species by a high-throughput basis. Shotgun lipidomics-based platforms play a major role in this analysis Targeted lipidomic analysis – identifying one or few lipid classes of interest. Liquid chromatography–mass spectrometry (LC-MS) and LC-MS/MS-based methods are used for this purpose. Novel lipid discovery – the discovery of lipid classes. LC coupled with MS methodology is applied in this area. Lipidomics has found utility in several diseases over the years. Metabolic syndrome and ischemic heart disease, considering their close bond with lipids, have applied lipidomics for risk stratification, population profiling, identification of biomarkers, and monitoring therapeutic responses.[6] Lipidomics has been useful in biomarker development for early diagnosis and prognosis of neurological disorders associated with lipid signaling and metabolism.[7] Bioactive lipids play essential roles in rapidly proliferating cancer cells. Biomarker discovery for early detection of cancers and monitoring of efficacy and toxicity of anticancer therapies has been the major application of lipidomics in cancer management.[8] Similar applications have been successfully tried in ophthalmic conditions.[9] Nutritional lipidomics has enhanced the understanding of the molecular mechanism underlying the health benefits of dietary PUFA and the regulatory roles of omega-3 and omega-6 fatty acids in inflammation.[10] It is early days for lipidomics in psoriatic disease. Targeted and untargeted LC-MS approaches quantifying bioactive lipid mediators in psoriasis patients and healthy controls have depicted disease-specific phenotype profiles represented by PUFA-oxidized derivatives in both skin and blood.[11] Untargeted lipidomics used to identify lipid metabolite signatures through LC-MS in psoriasis patients and healthy controls detected differential expression of several lipids in plasma of the diseased patients.[12] A recent study in patients with psoriatic arthritis (PsA) has described eicosanoid profiling and its association with joint inflammation using the LC-MS technique. Both pro- and anti-inflammatory eicosanoids were associated with joint disease scores.[13] In this issue of the Indian Journal of Rheumatology, Yaman et al. report the ratio of n-6/n-3 fatty acids in the erythrocyte membrane of psoriasis patients and its association with inflammatory markers.[14] Lipids were extracted using a freeze dryer and fatty acid composition was determined using gas chromatography. The authors noted a significantly higher n-6/n-3 PUFA ratio correlating positively with inflammatory markers in PsA patients compared to the controls. Besides, a differential correlation was noted between the individual fractions with disease activity. No association was noted with disease severity. This may underscore a sampling bias, as most patients were inactive. Although limited by small, homogeneous patients and specificity of lipid extraction techniques, this study spurs interest toward adopting lipidomics in psoriatic disease for better defining the role of n-6 and n-3 PUFA in pathogenesis. The implementation of lipidomics is often crippled by challenges. Lack of uniformity in methodologies and technologies has led to issues with reproducibility. Standardization of techniques and guidelines for the process is critical for better reporting. Profiling of low-abundant lipids in a minimal-sized sample is another limitation.[15] The utility of lipidomics in psoriatic disease for biomarker discovery, treatment efficacy, and side effect profile of newer therapeutic targets warrants further evaluation in the quest for precision medicine.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut 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: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,615
Score d'incertitude au seuil0,308

Scores Codex et Gemma par catégorie

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 savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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 Opus0,006
Tête enseignante GPT0,220
Écart entre enseignants0,214 · 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 tête enseignante, 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
GenreEmpirique

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
Publié2019
Routes d'admission1
Résumé présentoui

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