Evidence map›Paper›PMID 33998547›Full record

ReviewJournal of Huntington's disease2021

Taming the Huntington's Disease Proteome: What Have We Learned?

Connor Seeley, Kimberly B Kegel-Gleason

Open access · hybridAbstract readReview
In one paragraph

Review in Journal of Huntington's disease, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
0.5field-weighted citation impact, top 44% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 1 synthesis or guideline pooled it, 8 citations in OpenAlex.

  1. Systematic Review with Meta-Analysis of Biofluid Markers for Huntington's Disease.Movement disorders : official journal of the Movement Disorder Society · 2025
    Pooled it
  2. Article
  3. Review
  4. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors at 1 institution in 1 country.

Connor SeeleyLaboratory of Cellular Neurobiology, Department of Neurology, Massachusetts General Hospital, Charlestown, MA, USA.
Kimberly B Kegel-GleasonLaboratory of Cellular Neurobiology, Department of Neurology, Massachusetts General Hospital, Charlestown, MA, USA.
Massachusetts General Hospital · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Mass spectrometry (MS) is a physical technique used to identify specific chemicals and molecules by precise analysis of their mass and charge; this technology has been adapted for biological sciences applications. Investigators have used MS to identify differential expressions of proteins in Huntington's disease (HD), to discover Huntingtin (HTT) interacting proteins and to analyze HTT proteoforms. Using systems biology and computational approaches, data from MS screens have been leveraged to find differentially expressed pathways. This review summarizes the data from most of the MS studies done in the HD field in the last 20 years and compares it to the protein data reported before the use of MS technology. The MS results validate early findings in the field such as differential expression of PDE10a and DARPP-32 and identify new changes. We offer a perspective on the MS approach in HD, particularly for identification of disease pathways, the challenges in interpreting data across different studies, and its application to protein studies moving forward.

Indexed as

Huntingtin ProteinHuntington DiseaseProteomeAmino AcidsAnimalsBrain ChemistryHumansMass SpectrometryMiceProteomicsAmino AcidsHTT protein, humanHuntingtin ProteinProteomeHTTHuntingtinHuntington’s diseaseinteractomemass spectrometryproteomeproteomics

Identifiers

PMID33998547
PMCPMC8293646
OpenAlexW3161063945

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.