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Record W4386114891 · doi:10.4103/0973-3698.274456

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

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

Bibliographic record

VenueIndian Journal of Rheumatology · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolomics and Mass Spectrometry Studies
Canadian institutionsUniversity of TorontoToronto Western HospitalUniversity Health Network
Fundersnot available
KeywordsLipidomicsLipidomeMetabolomicsLipid metabolismComputational biologyChemistryBiochemistryBiologyBioinformatics

Abstract

fetched live from 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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.615
Threshold uncertainty score0.308

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.220
Teacher spread0.214 · 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 teacher head, not a consensus.

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

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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Citations1
Published2019
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