Evidence map›Paper›PMID 41890040›Full record

ArticlebioRxiv : the preprint server for biology2026

Interactome mapping in human excitatory neurons reveals novel risk genes and pathways in Alzheimer's disease.

Xiaomu Wei, Katie Munechika, Yu Sun, Yuansong Wan, Tianyu Xia, Yuan Hou, Wenqiang Song, Kumar Yugandhar, Yiwen Wang, Se-In Lee and 9 more

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

19 authors.

Xiaomu WeiDepartment of Computational Biology, Cornell University, Ithaca, New York, USA.
Katie MunechikaWeill Institute for Cellular and Molecular Biology, Cornell University, Ithaca, New York, USA.
Yu SunDepartment of Computational Biology, Cornell University, Ithaca, New York, USA.
Yuansong WanHelen and Robert Appel Alzheimer's Disease Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, New York, USA.
Tianyu XiaDepartment of Computational Biology, Cornell University, Ithaca, New York, USA.
Yuan HouDepartment of Genomic Sciences and Systems Biology, Cleveland Clinic Research, Cleveland, Ohio, USA.
Wenqiang SongDepartment of Genomic Sciences and Systems Biology, Cleveland Clinic Research, Cleveland, Ohio, USA.
Kumar YugandharDepartment of Computational Biology, Cornell University, Ithaca, New York, USA.
Yiwen WangDepartment of Computational Biology, Cornell University, Ithaca, New York, USA.
Se-In LeeHelen and Robert Appel Alzheimer's Disease Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, New York, USA.
Zhengdong ShaDepartment of Genomic Sciences and Systems Biology, Cleveland Clinic Research, Cleveland, Ohio, USA.
Yadi ZhouDepartment of Genomic Sciences and Systems Biology, Cleveland Clinic Research, Cleveland, Ohio, USA.
Weixi FengHelen and Robert Appel Alzheimer's Disease Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, New York, USA.
Jingjie ZhuHelen and Robert Appel Alzheimer's Disease Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, New York, USA.
Yuliang TangWeill Institute for Cellular and Molecular Biology, Cornell University, Ithaca, New York, USA.
Wenjie LuoHelen and Robert Appel Alzheimer's Disease Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, New York, USA.
Feixiong ChengDepartment of Genomic Sciences and Systems Biology, Cleveland Clinic Research, Cleveland, Ohio, USA.
Li GanHelen and Robert Appel Alzheimer's Disease Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, New York, USA.
Haiyuan YuDepartment of Computational Biology, Cornell University, Ithaca, New York, USA.ORCID 0000-0001-7597-6049

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Alzheimer's disease (AD) is an irreversible neurodegenerative disease defined by its molecular hallmarks - amyloid beta peptide plaques and neurofibrillary Tau tangles. Despite significant progress that has been made in uncovering a large number of genetic risk factors through extensive genomic sequencing and genetic studies, the molecular mechanisms driving AD-associated pathology and cognitive decline remain poorly understood. Therefore, alongside the identification of more risk genes, it is also paramount to study how these genes function and influence each other within the cellular pathways and overall molecular networks in AD-relevant brain cell types. However, current human protein-protein interactome datasets were all generated in either yeast or generic human cell lines. Consequently, many important neuronal interactions, especially neuron-specific ones, have yet been discovered. To address this critical gap, we developed a highly scalable, high-quality interactome mapping pipeline in human excitatory neurons derived from induced pluripotent stem cells (iPSC), and generated a comprehensive, neuron-specific interactome map, named ADNeuronNet, for key AD risk genes. ADNeuronNet consists of 1,767 high-confidence interactions among 1,189 proteins and is the only dataset enriched with neuron-specific genes when compared to known protein interactions, including previous large-scale interactome maps, for the same baits in the literature. Within ADNeuronNet, we identified 1,375 novel interactions, many of which are likely neuron specific. For example, we identified a neuron-specific interactor, RIN2, for major AD risk factor BIN1 and confirmed RIN2's function in recruiting BIN1 to RAB5 positive early endosomes, a process that has been well-associated with AD etiology. Additionally, we performed quantitative interaction perturbation analyses on AD risk genes with AD-associated mutations or isoforms and identified significant changes in 99 protein interactions among 11 different protein variants. Finally, we found that subunits from the anaphase-promoting complex/cyclosome (APC/C), another novel BIN1 interactors identified by ADNeuronNet, mediated modulation of Tau-aggregation in neurons via regulation of APOE expression, uncovering a previously unrecognized BIN1-APC/C-APOE regulatory axis in AD pathobiology. In summary, these findings illustrate how our neuron-specific ADNeuronNet can be leveraged to uncover new risk gene candidates and cellular pathways that help advance our understanding of molecular mechanisms underlying AD etiology.

Identifiers

PMID41890040
PMCPMC13015489

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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 Socratic graph.