On the PhyloQuant protein expression profiles approach to the taxonomic problem

IF 3.4 4区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS
Proteomics Pub Date : 2024-08-15 DOI:10.1002/pmic.202400117
Ayixon Sánchez Reyes, Cesar Ayala-Ruan
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引用次数: 0

Abstract

Inferring evolutionary relationships among organisms has been a fundamental problem in evolutionary biology. The current phylogenetic molecular methods serve as the baseline model to test new evolutionary hypotheses with taxonomic purposes. Leishmaniinae trypanosomatids subfamily includes protozoan parasites of clinical importance to humans. They have an intricate taxonomic history defined by morphological elements, host range, and molecular phylogenies. Unraveling the increasing diversity of this group has shown limitations in reconstructing the true evolutionary relationships among Trypanosomatidae species. Toward the goal of inferring evolutionary relationships that help to resolve phylogenetic and taxonomic controversies among parasites of the subfamily Leishmaniinae, Mule et al. propose the method PhyloQuant as a valuable approach, based on differential protein expression obtained from high throughput mass spectrometry data. Employing a pioneering methodological approach, Mule et al. assess the taxonomic problem for species hypothesis within Leishmaniinae, from quantitative phenetic protein expression profiles, in contrast to the standard multilocus phylogenetic approaches.

用 PhyloQuant 蛋白表达谱方法解决分类问题。
推断生物之间的进化关系一直是进化生物学的一个基本问题。目前的系统发育分子方法是检验新的进化假说的基础模型,具有分类学的目的。利什曼尼亚科锥虫亚科包括对人类具有重要临床意义的原生动物寄生虫。它们的分类历史错综复杂,由形态要素、宿主范围和分子系统发育所决定。由于该亚科的多样性不断增加,因此在重建锥虫科物种之间的真实进化关系方面存在局限性。为了推断有助于解决利什曼病亚科寄生虫之间的系统发育和分类争议的进化关系,Mule 等人提出了 PhyloQuant 方法,这是一种基于高通量质谱数据获得的差异蛋白表达的有价值的方法。与标准的多焦点系统发生学方法不同,Mule 等人采用了一种开创性的方法,从定量表观蛋白质表达谱评估利什曼病科内物种假说的分类问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Proteomics
Proteomics 生物-生化研究方法
CiteScore
6.30
自引率
5.90%
发文量
193
审稿时长
3 months
期刊介绍: PROTEOMICS is the premier international source for information on all aspects of applications and technologies, including software, in proteomics and other "omics". The journal includes but is not limited to proteomics, genomics, transcriptomics, metabolomics and lipidomics, and systems biology approaches. Papers describing novel applications of proteomics and integration of multi-omics data and approaches are especially welcome.
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