Human Single-Cell RNA-Sequencing Data Supports the Hypothesis of X Chromosome Insensitivity but Is Ineffective in Testing the Dosage Compensation Model

被引:0
作者
Chen, Jiabi [1 ,2 ]
Chen, Xiaoshu [1 ,2 ]
机构
[1] Sun Yat Sen Univ, Zhongshan Sch Med, Dept Immunol & Microbiol, Guangzhou, Peoples R China
[2] Sun Yat sen Univ, Minist Educ, Key Lab Trop Dis Control, Guangzhou, Peoples R China
基金
中国国家自然科学基金;
关键词
dosage compensation; single-cell RNA-sequencing; dosage-insensitive genes; LINKED GENES; EXPRESSION; INACTIVATION; EVOLUTION; NOISE; REVEALS; MAMMALS;
D O I
10.1093/molbev/msaf004
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
A controversy in evolutionary genetics is whether active dosage compensation is necessary to resolve the gene dosage imbalance between the X chromosome and autosomes. ScRNA-seq data could provide insight into this issue. However, it's crucial to carefully evaluate whether inherent characteristics of scRNA-seq, such as the sparsity of detected genes, might bias the X:AA expression ratio in mammals. This study evaluated two common strategies for selecting genes in the calculation of X:AA, namely, filter-by-expression and filter-by-fraction, with simulated scRNA-seq and bulk RNA-seq datasets. We found that both strategies produce an inflated X:AA, thus artifactually supporting dosage compensation. Analyzing empirical human Smart-seq2 data, results from the filter-by-expression strategy suggested that X-linked genes were more highly expressed than autosomal genes, a pattern that is neither predicted by dosage compensation nor explained by genes escaping X chromosome inactivation. However, the results of the filter-by-fraction strategy are consistent with the simulation. Furthermore, despite biasing for mean expression levels, we found that scRNA-seq data could be used to detect X-to-autosome expression noise differences as small as 10%, which enabled investigation into the distribution of genes that are more likely insensitive to gene dosage changes. Analysis of the empirical Smart-seq2 data revealed a 10% to 15% increase in expression noise for X chromosomes compared with autosomes and a significant depletion of dosage-sensitive genes on X chromosomes. Overall, these results highlight the need to be cautious when interpreting scRNA-seq data, particularly when comparing the expression of different genes, and provide additional evidence for the hypothesis of X chromosome insensitivity.
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页数:11
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共 38 条
  • [1] [Anonymous], 1967, Sex chromosomes and sex-linked genes
  • [2] The eXceptional nature of the X chromosome
    Balaton, Bradley P.
    Dixon-McDougall, Thomas
    Peeters, Samantha B.
    Brown, Carolyn J.
    [J]. HUMAN MOLECULAR GENETICS, 2018, 27 (R2) : R242 - R249
  • [3] Noise in protein expression scales with natural protein abundance
    Bar-Even, Arren
    Paulsson, Johan
    Maheshri, Narendra
    Carmi, Miri
    O'Shea, Erin
    Pilpel, Yitzhak
    Barkai, Naama
    [J]. NATURE GENETICS, 2006, 38 (06) : 636 - 643
  • [4] Consequences and opportunities arising due to sparser single-cell RNA-seq datasets
    Bouland, Gerard A.
    Mahfouz, Ahmed
    Reinders, Marcel J. T.
    [J]. GENOME BIOLOGY, 2023, 24 (01)
  • [5] X-inactivation profile reveals extensive variability in X-linked gene expression in females
    Carrel, L
    Willard, HF
    [J]. NATURE, 2005, 434 (7031) : 400 - 404
  • [6] The evolution of sex chromosome dosage compensation in animals
    Chen, Jiabi
    Wang, Menghan
    He, Xionglei
    Yang, Jian-Rong
    Chen, Xiaoshu
    [J]. JOURNAL OF GENETICS AND GENOMICS, 2020, 47 (11) : 681 - 693
  • [7] fastp: an ultra-fast all-in-one FASTQ preprocessor
    Chen, Shifu
    Zhou, Yanqing
    Chen, Yaru
    Gu, Jia
    [J]. BIOINFORMATICS, 2018, 34 (17) : 884 - 890
  • [8] The X to Autosome Expression Ratio in Haploid and Diploid Human Embryonic Stem Cells
    Chen, Xiaoshu
    Zhang, Jianzhi
    [J]. MOLECULAR BIOLOGY AND EVOLUTION, 2016, 33 (12) : 3104 - 3107
  • [9] The Genomic Landscape of Position Effects on Protein Expression Level and Noise in Yeast
    Chen, Xiaoshu
    Zhang, Jianzhi
    [J]. CELL SYSTEMS, 2016, 2 (05) : 347 - 354
  • [10] NCBI GEO: archive for gene expression and epigenomics data sets: 23-year update
    Clough, Emily
    Barrett, Tanya
    Wilhite, Stephen E.
    Ledoux, Pierre
    Evangelista, Carlos
    Kim, Irene F.
    Tomashevsky, Maxim
    Marshall, Kimberly A.
    Phillippy, Katherine H.
    Sherman, Patti M.
    Lee, Hyeseung
    Zhang, Naigong
    Serova, Nadezhda
    Wagner, Lukas
    Zalunin, Vadim
    Kochergin, Andrey
    Soboleva, Alexandra
    [J]. NUCLEIC ACIDS RESEARCH, 2023, : D138 - D144