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Forensic Species Identification Using Phylogenetic Proteomics

2022· other· en· W6981889237 sur OpenAlexaboutno aff

Notice bibliographique

RevueeScholarship (California Digital Library) · 2022
Typeother
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueIdentification and Quantification in Food
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPhylogenetic treeProteomeIdentification (biology)WildlifeForensic identificationSpecies identificationAnimal species
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Wildlife trafficking is a global issue with devastating ecological effects and synergistic relationships with other forms of international illicit networks. Furs and pelts are a major component of this trade and are more practical to transport. Proper identification of such items is important to be able to prosecute and punish poachers. Wildlife forensic investigators depend on reliable tools and methods to either include or exclude illegal versus legal species. However, due to the chemically and physically harsh production process, DNA-based methods often fail because the sample DNA is degraded. Morphological-based methods have poor species resolution, are time-consuming, and are dependent on a limited number of highly trained individuals. However, these challenges may be by-passed by focusing on the phylogenetic information in protein. DNA changes between closely related species are reflected at the protein level, allowing the phylogenetic information between species to lead to a species-level identification. Proteins are chemically more stable than DNA. Therefore, proteomic analysis of heavily processed items may provide an alternative, and robust, method of species identification. However, determining the amino acid sequences in mass spectrometry data requires the precise sequences to be present in a reference protein database. The hypothesis is that the protein sequences, or reference proteome, of a given species would match more than proteomes from other species and be more efficient at identifying peptide sequences in fur digests from the same species. This research focuses on the phylogenetic relationship between six cat species, lion (Panthera leo), leopard (Panthera pardus), tiger (Panthera tigris), cheetah (Acinonyx jubatus), Canada lynx (Lynx canadensis), and domestic cat (Felis catus); of which the first four had good-quality, morphologically-identified materials that were selected for analysis. The remaining two species, Canada lynx and domestic cat, were included in the phylogenetic comparison using their reference proteome databases. The selected fur or pelt reference material items were digested based on a previously-published method for protein extraction from human hair and analyzed by LC-MS/MS and PEAKS DB software. Species identification was achieved at three different levels: total peptide yield, protein coverage patterns, and presence of peptides containing species-specific markers. For each item sampled, the maximum number of total peptides identified using the PEAKS scoring algorithm was obtained using the corresponding theoretical database. For example, the raw peptides generated from the lion paw pelt yielded 13928 total identified peptides using the lion database. As evolutionary distance between the lion and related felid species increases, the total peptide yield decreases. Using the leopard, tiger, Canada lynx, domestic cat, and cheetah databases, the total peptide yields were less - 13766, 13758, 12604, 12307, and 11774, respectively. Similar results were obtained with the leopard pelt, tiger pelt, and cheetah coat sampled, demonstrating how species identification can be achieved through comparison of total peptide yields. Protein coverage patterns also indicated species of origin. Many keratin and keratin-associated proteins were identified from the hair and skin samples, including, but not limited to, Keratin 14, Keratin 16, Keratin 32, Keratin 35, Keratin 38, Keratin 75, Keratin 82, Keratin 85, Desmoplakin, and Corneodesmosin. Coverage patterns demonstrated the highest percent coverage when analyzing the raw peptides from a sample with its corresponding database. For example, the raw peptides generated from the lion sample that matched with Keratin 35 resulted in 84% coverage using the lion database, while the tiger, leopard, domestic cat, Canada lynx, and cheetah databases resulted in 77%, 75%, 77%, 75%, and 65%, respectively. Species-specific marker locations were identified by protein alignments between the sequences of all six species. Peptides containing these species-specific markers were then identified in the raw data and found to be present only in the samples that correlated with the species of origin. This paper demonstrates four individual examples of species-specific markers within Keratin 35 corresponding to the respective species of origin for all four sampled items: lion, leopard, tiger, and cheetah. In summary, the three approaches used in this project – the total peptide approach, the protein coverage pattern approach, and the species-specific peptide approach – individually demonstrated that species identification is plausible using phylogenetic proteomics. This data demonstrates the robustness and reliability of proteomic approaches to species identification from fur and lays the foundation for future research, such as the development of targeted proteomic assays, for broader application to wildlife forensic casework.

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 candidatesMéta-épidémiologie (sens strict), Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesCharge utile insuffisante (le modèle a refusé de juger)
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Autre · Signal consensuel: Autre
Score de désaccord entre enseignants0,144
Score d'incertitude au seuil1,000

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 savante0,0010,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0060,001

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 Opus0,021
Tête enseignante GPT0,233
Écart entre enseignants0,212 · 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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeSans objet
Domainenon disponible
GenreAutre

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
Publié2022
Routes d'admission1
Résumé présentoui

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