Literature DB >> 28544375

Proteomic analysis of hair shafts from monozygotic twins: Expression profiles and genetically variant peptides.

Pei-Wen Wu1, Katelyn E Mason2, Blythe P Durbin-Johnson3, Michelle Salemi4, Brett S Phinney4, David M Rocke3, Glendon J Parker2,5, Robert H Rice1.   

Abstract

Forensic association of hair shaft evidence with individuals is currently assessed by comparing mitochondrial DNA haplotypes of reference and casework samples, primarily for exclusionary purposes. Present work tests and validates more recent proteomic approaches to extract quantitative transcriptional and genetic information from hair samples of monozygotic twin pairs, which would be predicted to partition away from unrelated individuals if the datasets contain identifying information. Protein expression profiles and polymorphic, genetically variant hair peptides were generated from ten pairs of monozygotic twins. Profiling using the protein tryptic digests revealed that samples from identical twins had typically an order of magnitude fewer protein expression differences than unrelated individuals. The data did not indicate that the degree of difference within twin pairs increased with age. In parallel, data from the digests were used to detect genetically variant peptides that result from common nonsynonymous single nucleotide polymorphisms in genes expressed in the hair follicle. Compilation of the variants permitted sorting of the samples by hierarchical clustering, permitting accurate matching of twin pairs. The results demonstrate that genetic differences are detectable by proteomic methods and provide a framework for developing quantitative statistical estimates of personal identification that increase the value of hair shaft evidence.
© 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Entities:  

Keywords:  Agglomerative hierarchical clustering; Forensic evidence; Genetically variant peptides; Monozygotic twin pairs; Protein profiling

Mesh:

Substances:

Year:  2017        PMID: 28544375      PMCID: PMC5540574          DOI: 10.1002/pmic.201600462

Source DB:  PubMed          Journal:  Proteomics        ISSN: 1615-9853            Impact factor:   3.984


  24 in total

Review 1.  Mitochondrial dynamics and inheritance during cell division, development and disease.

Authors:  Prashant Mishra; David C Chan
Journal:  Nat Rev Mol Cell Biol       Date:  2014-09-17       Impact factor: 94.444

2.  Comparing the Diagnostic Classification Accuracy of iTRAQ, Peak-Area, Spectral-Counting, and emPAI Methods for Relative Quantification in Expression Proteomics.

Authors:  Adam A Dowle; Julie Wilson; Jerry R Thomas
Journal:  J Proteome Res       Date:  2016-09-06       Impact factor: 4.466

3.  The Twin Research Registry at SRI International.

Authors:  Ruth E Krasnow; Lisa M Jack; Christina N Lessov-Schlaggar; Andrew W Bergen; Gary E Swan
Journal:  Twin Res Hum Genet       Date:  2012-10-19       Impact factor: 1.587

4.  Extensive variation in chromatin states across humans.

Authors:  Maya Kasowski; Sofia Kyriazopoulou-Panagiotopoulou; Fabian Grubert; Judith B Zaugg; Anshul Kundaje; Yuling Liu; Alan P Boyle; Qiangfeng Cliff Zhang; Fouad Zakharia; Damek V Spacek; Jingjing Li; Dan Xie; Anthony Olarerin-George; Lars M Steinmetz; John B Hogenesch; Manolis Kellis; Serafim Batzoglou; Michael Snyder
Journal:  Science       Date:  2013-10-17       Impact factor: 47.728

5.  Benchmarking quantitative label-free LC-MS data processing workflows using a complex spiked proteomic standard dataset.

Authors:  Claire Ramus; Agnès Hovasse; Marlène Marcellin; Anne-Marie Hesse; Emmanuelle Mouton-Barbosa; David Bouyssié; Sebastian Vaca; Christine Carapito; Karima Chaoui; Christophe Bruley; Jérôme Garin; Sarah Cianférani; Myriam Ferro; Alain Van Dorssaeler; Odile Burlet-Schiltz; Christine Schaeffer; Yohann Couté; Anne Gonzalez de Peredo
Journal:  J Proteomics       Date:  2015-11-14       Impact factor: 4.044

6.  A global reference for human genetic variation.

Authors:  Adam Auton; Lisa D Brooks; Richard M Durbin; Erik P Garrison; Hyun Min Kang; Jan O Korbel; Jonathan L Marchini; Shane McCarthy; Gil A McVean; Gonçalo R Abecasis
Journal:  Nature       Date:  2015-10-01       Impact factor: 49.962

7.  A comparison of methods for differential expression analysis of RNA-seq data.

Authors:  Charlotte Soneson; Mauro Delorenzi
Journal:  BMC Bioinformatics       Date:  2013-03-09       Impact factor: 3.169

8.  Differentiating inbred mouse strains from each other and those with single gene mutations using hair proteomics.

Authors:  Robert H Rice; Katie M Bradshaw; Blythe P Durbin-Johnson; David M Rocke; Richard A Eigenheer; Brett S Phinney; John P Sundberg
Journal:  PLoS One       Date:  2012-12-14       Impact factor: 3.240

9.  Human hair shaft proteomic profiling: individual differences, site specificity and cuticle analysis.

Authors:  Chelsea N Laatsch; Blythe P Durbin-Johnson; David M Rocke; Sophie Mukwana; Abby B Newland; Michael J Flagler; Michael G Davis; Richard A Eigenheer; Brett S Phinney; Robert H Rice
Journal:  PeerJ       Date:  2014-08-05       Impact factor: 2.984

10.  Demonstration of Protein-Based Human Identification Using the Hair Shaft Proteome.

Authors:  Glendon J Parker; Tami Leppert; Deon S Anex; Jonathan K Hilmer; Nori Matsunami; Lisa Baird; Jeffery Stevens; Krishna Parsawar; Blythe P Durbin-Johnson; David M Rocke; Chad Nelson; Daniel J Fairbanks; Andrew S Wilson; Robert H Rice; Scott R Woodward; Brian Bothner; Bradley R Hart; Mark Leppert
Journal:  PLoS One       Date:  2016-09-07       Impact factor: 3.240

View more
  3 in total

1.  Age-Related Changes in Hair Shaft Protein Profiling and Genetically Variant Peptides.

Authors:  Tempest J Plott; Noreen Karim; Blythe P Durbin-Johnson; Dionne P Swift; R Scott Youngquist; Michelle Salemi; Brett S Phinney; David M Rocke; Michael G Davis; Glendon J Parker; Robert H Rice
Journal:  Forensic Sci Int Genet       Date:  2020-05-22       Impact factor: 4.882

2.  Hair Proteome Variation at Different Body Locations on Genetically Variant Peptide Detection for Protein-Based Human Identification.

Authors:  Fanny Chu; Katelyn E Mason; Deon S Anex; A Daniel Jones; Bradley R Hart
Journal:  Sci Rep       Date:  2019-05-21       Impact factor: 4.379

3.  Potential use of human hair shaft keratin peptide signatures to distinguish gender and ethnicity.

Authors:  Nurdiena Mohamed Nasir; Jumriah Hiji; Jaime Jacqueline Jayapalan; Onn Haji Hashim
Journal:  PeerJ       Date:  2020-01-30       Impact factor: 2.984

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.