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Systematic Evaluation of Competing Brain Transcriptomic Representations Reveals Reciprocal Patterns Across Heterogeneous Contexts.

Overview

Authors: Zongnan Lyu1, Chunxue Shao1, Qi Yu1, Renyu Yang1, Guang Yang1, Ziheng Wang1
ORCID iDs: Ziheng Wang
  1. Division of Computational Biology, Chinese Center of Exercise Epidemiology, Northeast Normal University, Changchun 130024, China
Institutions: Northeast Normal University (China)
Journal: International journal of molecular sciences, volume 27, issue 13, article 6083
Dates: received 25 May 2026; accepted 30 June 2026; published online 7 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.3390/ijms27136083 · PMID 42450348 · PMCID PMC13362261 · OpenAlex W7167597987
Open access: gold, a free copy (OpenAlex)
Status: data only
Categories: genetics / omics (modality), human (organism), cellular / molecular (subfield)
Methods: Preprocessing, Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning
Keywords: brain transcriptomics, direction classes, intervention-like contrasts, adversity-like contrasts, reciprocal representation, cross-study integration, hippocampus
MeSH: Brain*, Transcriptome*, Gene Expression Profiling, Humans (* major topic)
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Citations: not cited yet (Europe PMC); 63 references in the paper

Abstract

Adaptive and adverse brain states are often assumed to lie on a shared molecular continuum, but this assumption has rarely been evaluated against explicit transcriptomic alternatives. This study aimed to compare two representations of cross-context brain transcriptomic organization: a transcriptome-wide global-axis model and a low-dimensional reciprocal model. We benchmarked these models across a curated cross-study brain cohort spanning exercise, alcohol-related adversity-like contexts, stress, aging, and neurodegeneration, using prespecified intervention-like and adversity-like directional contrast labels rather than assuming homogeneous biological states. We assessed the competing representations using signed-effect correlations, permutation analyses, non-linear fitting, and held-out reconstruction, and we then examined the resulting structure through region-specific human bulk evaluation and exploratory cellular, single-nucleus, spatial, and chromatin projection analyses. These downstream analyses were used to examine localization and biological interpretability and were not treated as independent evaluation of the module 1/module 2 (M1/M2) partition. The combined signed-effect statistics were interpreted as representation-level directional summaries rather than estimates of a homogeneous cross-study biological effect. The global-axis model received limited support: intervention-like and adversity-like signed-effect summaries were only weakly correlated, were not stronger than permutation null expectations, and were not improved by non-linear fitting. Within the selected reciprocal-gene space, a rank-1 latent profile reconstructed held-out genes more accurately than the hard M1/M2 partition, whereas the M1/M2 discretization provided a more interpretable but selection-conditioned directional summary. Human analyses yielded an asymmetric pattern: a significant M1 association was observed only in the hippocampal dataset, whereas M2, the reciprocal index, and the other examined brain regions showed no consistent corresponding effects; leave-one-stratum-out analyses indicated poor cross-stratum reproducibility of the exact gene-level partition. These findings motivate a low-dimensional reciprocal representation as an exploratory framework while emphasizing context dependence, cohort dependence, and heterogeneity.

Reproduced under the paper's license (CC BY), from the paper cited above.

Code

The paper links to its data, not to its authors' code: see the Data section.

Tracing map

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Data

Datasets cited

Data Availability Statement

The public accessions used for primary discovery, human evaluation, cellular localization, spatial analysis, and chromatin projection are listed in Table 1. The 21 unique GEO accessions that contributed directly to this study are available from the NCBI Gene Expression Omnibus (GEO). Direct GEO record links for all accessions are provided in accession-and-URL format below. All URLs were accessed on 3 April 2026: GSE299436 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE299436), https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE299436; GSE111212 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE111212), https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE111212; GSE29075 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE29075), https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE29075; GSE220980 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE220980), https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE220980; GSE315740 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE315740), https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE315740; GSE164798 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE164798), https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE164798; GSE203554 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE203554), https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE203554; GSE159136 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE159136), https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE159136; GSE49040 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE49040), https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE49040; GSE49041 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE49041), https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE49041; GSE60966 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE60966), https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE60966; GSE31705 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE31705), https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE31705; GSE126705 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE126705), https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE126705; GSE115746 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE115746), https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE115746; GSE237885 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE237885), https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE237885; GSE277313 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE277313), https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE277313; GSE181804 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE181804), https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE181804; GSE253155 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE253155), https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE253155; GSE208633 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE208633), https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE208633; GSE264692 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE264692), https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE264692; and GSE271564 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE271564), https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE271564. Thirteen accessions formed the primary bulk discovery cohort, whereas eight accessions were used for human evaluation, cellular or single-nucleus localization, chromatin projection, and spatial follow-up. GSE253155 contributed two analysis rows because DLPFC and nucleus accumbens samples were modeled separately. GSE111212 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE111212) and GSE271564 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE271564) are cited as GEO records because no clearly linked peer-reviewed primary article was used. No new datasets were generated in this study.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 7 keywords, 4 MeSH terms, 60 references.

Cite

This paper

Lyu, Z., Shao, C., Yu, Q., Yang, R., Yang, G., & Wang, Z. (2026). Systematic Evaluation of Competing Brain Transcriptomic Representations Reveals Reciprocal Patterns Across Heterogeneous Contexts. International journal of molecular sciences, 27(13), 6083. https://doi.org/10.3390/ijms27136083

BibTeX

@article{lyu2026systematic,
author = {Lyu, Zongnan and Shao, Chunxue and Yu, Qi and Yang, Renyu and Yang, Guang and Wang, Ziheng},
title = {{Systematic Evaluation of Competing Brain Transcriptomic Representations Reveals Reciprocal Patterns Across Heterogeneous Contexts}},
journal = {International journal of molecular sciences},
year = {2026},
month = jul,
volume = {27},
number = {13},
pages = {6083},
publisher = {Multidisciplinary Digital Publishing Institute (MDPI)},
issn = {1422-0067},
doi = {10.3390/ijms27136083},
url = {https://doi.org/10.3390/ijms27136083},
pmid = {42450348},
pmcid = {PMC13362261}
}

RIS

TY - JOUR
AU - Lyu, Zongnan
AU - Shao, Chunxue
AU - Yu, Qi
AU - Yang, Renyu
AU - Yang, Guang
AU - Wang, Ziheng
TI - Systematic Evaluation of Competing Brain Transcriptomic Representations Reveals Reciprocal Patterns Across Heterogeneous Contexts
T2 - International journal of molecular sciences
J2 - Int J Mol Sci
PY - 2026
DA - 2026/07/07
VL - 27
IS - 13
SP - 6083
SN - 1422-0067
PB - Multidisciplinary Digital Publishing Institute (MDPI)
DO - 10.3390/ijms27136083
UR - https://doi.org/10.3390/ijms27136083
LA - en
ER -

CSL-JSON

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