The DOI matches its Crossref record; 74 of the 74 references it cites were found in a registry or an index.
Integrated genomics and proteomics define huntingtin CAG length-dependent networks in mice
Open accessAbstract
To gain insight into how mutant huntingtin (mHtt) CAG repeat length modifies Huntington's disease (HD) pathogenesis, we profiled mRNA in over 600 brain and peripheral tissue samples from HD knock-in mice with increasing CAG repeat lengths. We found repeat length-dependent transcriptional signatures to be prominent in the striatum, less so in cortex, and minimal in the liver. Coexpression network analyses revealed 13 striatal and 5 cortical modules that correlated highly with CAG length and age, and that were preserved in HD models and sometimes in patients. Top striatal modules implicated mHtt CAG length and age in graded impairment in the expression of identity genes for striatal medium spiny neurons and in dysregulation of cyclic AMP signaling, cell death and protocadherin genes. We used proteomics to confirm 790 genes and 5 striatal modules with CAG length-dependent dysregulation at the protein level, and validated 22 striatal module genes as modifiers of mHtt toxicities in vivo.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“nearly all differential expression signatures from HD mice (35 of 37) (Langfelder et al., 2016) failed to overlap with microglial-enriched coexpression modules (Figure S5).”
From Meta-Analysis of the Alzheimer’s Disease Human Brain Transcriptome and Functional Dissection in Mouse Models (2020) · cites this paper as Langfelder et al., 2016
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology · doi:10.1186/s13059-014-0550-8 · Cited by 95,106
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709
WGCNA: an R package for weighted correlation network analysis
Langfelder, P., & Horvath, S. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-559 · Cited by 27,252
Self-citationHTSeq—a Python framework to work with high-throughput sequencing data
Anders, S., Pyl, P. T., & Huber, W. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btu638 · Cited by 21,464
Bioconductor: open software development for computational biology and bioinformatics
Gentleman, R. C., Carey, V. J., Bates, D. M., Bolstad, B., Dettling, M., Dudoit, S., Ellis, B., Gautier, L., Ge, Y., Gentry, J., et al. (2004) · Genome biology · doi:10.1186/gb-2004-5-10-r80 · Cited by 12,082
Adjusting batch effects in microarray expression data using empirical Bayes methods
Johnson, W. E., Li, C., & Rabinovic, A. (2007) · Biostatistics · doi:10.1093/biostatistics/kxj037 · Cited by 8,658
MACDONALD, M. (1993) · Cell · doi:10.1016/0092-8674(93)90585-e · Cited by 8,366
Bolstad, B. M., Irizarry, R. A., Åstrand, M., & Speed, T. P. (2003) · Bioinformatics · doi:10.1093/bioinformatics/19.2.185 · Cited by 8,328
Mitochondrial Dysfunction and Oxidative Stress in Neurodegenerative Diseases
Lin, M. T., & Beal, M. F. (2006) · Nature · doi:10.1038/nature05292 · Cited by 6,455
Flagged first, then by citations
Research works printed but not linked: 2
- Stouffer, S.A. The American Soldier (Princeton Univ. Press, 1949).
- Gu, X. et al. N17 modifies mutant huntingtin nuclear pathogenesis and severity of disease in HD BAC transgenic mice. Neuron 85, 726–741 (2015). · DOI · Paper page
Cited by
Sorted by citations
Negative feedback control of neuronal activity by microglia
Badimon, A., Strasburger, H. J., Ayata, P., Chen, X., Nair, A., Ikegami, A., Hwang, P., Chan, A. T., Graves, S. M., Uweru, J. O., et al. (2020) · Nature · doi:10.1038/s41586-020-2777-8 · Cited by 979
Protein misfolding in neurodegenerative diseases: Implications and strategies
Sweeney, P., Park, H., Baumann, M., Dunlop, J., Frydman, J., Kopito, R., McCampbell, A., Leblanc, G., Venkateswaran, A., Nurmi, A., et al. (2017) · Translational Neurodegeneration · doi:10.1186/s40035-017-0077-5 · Cited by 675
Lee, C. Y. D., Daggett, A., Gu, X., Jiang, L.-L., Langfelder, P., Li, X., Wang, N., Zhao, Y., Park, C. S., Cooper, Y., et al. (2018) · Neuron · doi:10.1016/j.neuron.2018.02.002 · Cited by 396
Self-citationYan, S., Tu, Z., Liu, Z., Fan, N., Yang, H., Yang, S., Yang, W., Zhao, Y., Ouyang, Z., Lai, C., et al. (2018) · Cell · doi:10.1016/j.cell.2018.03.005 · Cited by 349
Self-citationTabrizi, S. J., Estevez‐Fraga, C., van Roon‐Mom, W., Flower, M. D., Scahill, R. I., Wild, E. J., Muñoz-Sanjuán, I., Sampaio, C., Rosser, A., & Leavitt, B. R. (2022) · The Lancet Neurology · doi:10.1016/s1474-4422(22)00121-1 · Cited by 354
Metadata
- Authors
- Langfelder, P., Cantle, J. P., Chatzopoulou, D., Wang, N., Gao, F., Al-Ramahi, I., Lu, X.-H., Ramos, E. M., El-Zein, K., Zhao, Y., Deverasetty, S., Tebbe, A., Schaab, C., Lavery, D. J., Howland, D., Kwak, S., Botas, J., Aaronson, J. S., Rosinski, J., Coppola, G., Horvath, S., & Yang, X. W.
- Affiliations
- University of California, Los Angeles (US)
- Evotec (Germany) (DE)
- CHDI Foundation (US)
- Baylor College of Medicine (US)
- Corresponding author
- Horvath, Steve
- Yang, X William
- ORCID
- Venue
- Nature Neuroscience
- Published
- 2016-02-22
- Volume
- 19
- Issue
- 4
- Pages
- 623-633
- ISSN
- 1097-6256 (ISSN-L)
- 1546-1726
- PMC
- PMC5984042
- OpenAlex
- W2274495055
- Cited by
- 385
- References
- 74
- Citations by year
- 2026: 29 · 2025: 27 · 2024: 30 · 2023: 30 · 2022: 60 · 2021: 40 · 2020: 40 · 2019: 36 · 2018: 36 · 2017: 41 · 2016: 16
- FWCI
- 19.76
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- Hereditary Disease Foundation
- CHDI Foundation
- National Institutes of Health
- National Institute of Neurological Disorders and Stroke (R01NS084298, R01NS049501, R01NS074312, P30NS062691)
- Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Genetic Neurodegenerative Diseases
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetics and Neurodevelopmental Disorders
- Life Sciences › Neuroscience › Neurology › Neurological diseases and metabolism
- Keywords
- Huntington's disease, mutant huntingtin, CAG repeat length, repeat expansion, transcriptome, proteome, co-expression network, striatum, medium spiny neurons, cAMP signaling, protocadherins, cell death, age-related gene expression, disease-modifying genes, knock-in mice
- MeSH terms
- Huntingtin Protein, Animals, Cerebral Cortex, Corpus Striatum, Female, Male, Mice, Inbred C57BL, Nerve Tissue Proteins, Nuclear Proteins, Genomics, Proteomics, Mice, Gene Regulatory Networks, Gene Knock-In Techniques
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review