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0853 - Metabolite and Protein Variation Between Healthy and Degraded Alpaca Cervical Discs Analyzed by High Resolution Mass Spectrometry and Correlated to MR Imaging

2019· preprint· en· W4391553300 sur OpenAlexaboutno aff
Jared H. Bowden, Cédric M. John, Todd F. Robinson

Notice bibliographique

Revuenon disponible
Typepreprint
Langueen
DomaineMedicine
ThématiqueAcupuncture Treatment Research Studies
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMetaboliteMass spectrometryVariation (astronomy)Resolution (logic)High resolutionChemistryChromatographyPhysicsComputer scienceRemote sensingGeologyArtificial intelligenceBiochemistryAstrophysics

Résumé

récupéré en direct d'OpenAlex

INTRODUCTION: Naturally occurring degeneration of the intervertebral disc (IVD) tissue is a common condition associated with aging adults that has been linked with disability and pain. Much work has been done in evaluating the biomechanics, biotransport, genetics, histology, morphology, cellular variation, and even gene expression in the healthy and degenerated IVD. However, our current understanding of the IVD metabolome and proteome, and their adaptation in response to disc degeneration is primitive and largely based on work done on cartilaginous tissues from other regions of the body. Appropriate human tissue samples for research into this area are difficult to obtain [1]. Spontaneous intervertebral disc degeneration is a rarity in the animal kingdom, and nutrient transport mechanisms in the intervertebral disc have been shown to depend on both size and motion. Alpacas have been introduced as an appropriate large animal model suitable for studying natural disc degeneration from both a cellular and biomechanical perspective [2, 3]. This work presents the first known evaluation of the IVD metabolome and proteome from both the healthy and degenerated IVD using high resolution mass spectrometry of alpaca discs. Additionally, we have spatially correlated the metabolomic and proteomic data of the same IVD tissues with MR diffusion tensor imaging in order to investigate potential mechanisms for identifying which metabolic processes are perturbed between degenerated and healthy disc materials at various stages of degeneration. METHODS: T1, T2, and Diffusion Tensor MRI images of the cervical spines of four alpacas were taken less than 3-hours post-mortem and were used to classify disc degeneration using the Pfirrmann grading system. Following MRI imaging, the alpaca spines were dissected and relevant cervical disc tissues were removed and frozen in a -80u00b0 C freezer. The entire imaging and sample preparation process was completed less than 6-hours post-mortem. Samples of healthy (Pfirrmann grades 2 or 3) and degenerated (grades 4 or 5) IVDs from within the same animal were collected from each alpaca. A 50 mg sample of both the nucleus pulpous and annulus fibrous within each disc were removed. Hydrophobic metabolites were extracted with a modified Bligh and Dyer solvent system (Chloroform: Methanol: Isopropanol) (3: 1: 1.25) (V/V/V) [4] . Extracted hydrophobic metabolites were separated using reverse phase C18 chromatography and analyzed in both positive and negative ionization modes on an Agilent 6520 QTOF (Santa Clara, CA). Separate 5 mg samples of both the nucleus pulpous and annulus fibrous disc tissue for both degenerated and healthy disc tissue were reduced and denatured in 2.5 mM Dithiothreitol and 8 M urea overnight and then diluted and digested with trypsin. Peptides were desalted prior to mass spec analysis on a Thermo Orbitrap Fusion (Waltham, MA). Metabolite data was analyzed with XCMS Online (Scripts, La Jolla CA), and proteins were identified using PEAKS software (Bioinformatics Solutions Inc. Ontario, CA). RESULTS SECTION: XCMS comparison of hydrophobic metabolites between healthy and degenerated IVD tissues identified 26 valuable metabolites - primarily inflammation and oxidative stress lipid response markers - with a 1.5-fold change or greater and 0.05 p-value or less. An additional 45 metabolic features were noted of statistical significance between healthy and degenerated IVD tissues. The protein analysis revealed 1611 protein identifications; however, statistical analysis distilled these proteins into 49 valuable proteins with a p-value < 0.05. DISCUSSION: Inflammation proteins and metabolites give validity to the evidence of observed and expected pain in alpacas. Significant variations in collagen and ubiquitin were clearly noted. This may suggest ongoing restructuring and degeneration of compromised disc tissue. It is interesting to note that histone and methyl transferase proteins were also found. This may suggest evidence of alterations occurring at the DNA coding level related to disc degeneration. This has yet to be elucidated, but may lead to future research opportunities. Limitations of this pilot study include a limited number of alpaca samples, and the potential for inherent differences between alpacas and humans. Furthermore, additional hydrophilic and low level metabolites and proteins may be below the limit of detection. SIGNIFICANCE/CLINICAL RELEVANCE: In order to develop and evaluate strategies and therapeutics for long term prevention and regeneration of intervertebral disc tissues in humans, advances in understanding differences in metabolic pathways between degenerated and healthy disc tissue is crucial. REFERENCES: 1.tDaly, C., et al., A Review of Animal Models of Intervertebral Disc Degeneration: Pathophysiology, Regeneration, and Translation to the Clinic. Biomed Res Int, 2016. 2016: p. 5952165.2.tStolworthy, D.K., et al., MRI evaluation of spontaneous intervertebral disc degeneration in the alpaca cervical spine. J Orthop Res, 2015. 33(12): p. 1776-83.3.tStolworthy, D.K., et al., Biomechanical analysis of the camelid cervical intervertebral disc. J Orthop Translat, 2015. 3(1): p. 34-43.4.tBligh, E.G. and W.J. Dyer, A rapid method of total lipid extraction and purification. Can J Biochem Physiol, 1959. 37(8): p. 911-7.

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: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,003
Score d'incertitude au seuil0,008

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0020,001
Études des sciences et des technologies0,0000,001
Communication savante0,0010,000
Science ouverte0,0000,000
Intégrité de la recherche0,0010,000
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 Opus0,016
Tête enseignante GPT0,303
Écart entre enseignants0,287 · 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'étudeExpérimental (laboratoire)
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

Citations0
Publié2019
Routes d'admission1
Résumé présentoui

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