Evidence map›Paper›PMID 39713353›Full record

ArticlebioRxiv : the preprint server for biology2024

Data-driven discovery of cell-type-directed network-correcting combination therapy for Alzheimer's disease.

Yaqiao Li, Carlota Pereda Serras, Jessica Blumenfeld, Min Xie, Yanxia Hao, Elise Deng, You Young Chun, Julia Holtzman, Alice An, Seo Yeon Yoon and 11 more

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2024. 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

5 · Who and what money

Authors and funding

21 authors.

Yaqiao LiBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.ORCID 0000-0002-9433-7346
Carlota Pereda SerrasBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.
Jessica BlumenfeldGladstone Institute of Neurological Disease, Gladstone Institutes, San Francisco, CA, USA.
Min XieGladstone Institute of Neurological Disease, Gladstone Institutes, San Francisco, CA, USA.
Yanxia HaoGladstone Institute of Neurological Disease, Gladstone Institutes, San Francisco, CA, USA.
Elise DengGladstone Behavior Core, Gladstone Institutes, San Francisco, CA, USA.
You Young ChunGladstone Behavior Core, Gladstone Institutes, San Francisco, CA, USA.
Julia HoltzmanGladstone Behavior Core, Gladstone Institutes, San Francisco, CA, USA.
Alice AnGladstone Institute of Neurological Disease, Gladstone Institutes, San Francisco, CA, USA.
Seo Yeon YoonGladstone Institute of Neurological Disease, Gladstone Institutes, San Francisco, CA, USA.
Xinyu TangBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.
Antara RaoGladstone Institute of Neurological Disease, Gladstone Institutes, San Francisco, CA, USA.
Sarah WoldemariamBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.
Alice TangBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.ORCID 0000-0003-4745-0714
Alex ZhangGladstone Institute of Neurological Disease, Gladstone Institutes, San Francisco, CA, USA.
Jeffrey SimmsGladstone Behavior Core, Gladstone Institutes, San Francisco, CA, USA.
Iris LoGladstone Behavior Core, Gladstone Institutes, San Francisco, CA, USA.
Tomiko OskotskyBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.ORCID 0000-0001-7393-5120
Michael J KeiserBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.ORCID 0000-0002-1240-2192
Yadong HuangGladstone Institute of Neurological Disease, Gladstone Institutes, San Francisco, CA, USA.
Marina SirotaBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.

Funding

Rush Alzheimer's Disease Research CenterP30AG072975 · NIA · RUSH UNIVERSITY MEDICAL CENTER · PI Lisa L Barnes, Julie A. Schneider · 2021 to 2026
$24.7M
Project 4: Cross-species Dissection of Cellular Response to APOE Genotype and AD Pathology Using Single-cell Multi-omicsP01AG073082 · NIA · J. DAVID GLADSTONE INSTITUTES · PI HUANG, YADONG · 2021 to 2025
$23.4M
PREDOCTORAL TRAINING IN DEVELOPMENTAL BIOLOGYT32HD007470 · NICHD · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Todd Nystul · 1994 to 2026
$7.7M
Develop AD Connectivity Maps with Human iPSC-Derived Brain Cells and their UseR01AG078164 · NIA · J. DAVID GLADSTONE INSTITUTES · PI YADONG HUANG · 2022 to 2026
$4.7M
ApoE Genotype-Directed Drug Repositioning and Combination Therapy for Alzheimer's DiseaseR01AG057683 · NIA · J. DAVID GLADSTONE INSTITUTES · PI HUANG, YADONG, SIROTA, MARINA · 2017 to 2021
$4.3M
An Integrative Multi-Omics Approach to Elucidate Sex-Specific Differences in Alzheimers DiseaseR01AG060393 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI SIROTA, MARINA · 2018 to 2022
$4.1M
Study Susceptibility and Resistance to ApoE4 in Alzheimer's DiseaseRF1AG076647 · NIA · J. DAVID GLADSTONE INSTITUTES · PI HUANG, YADONG · 2022 to 2022
$2.6M
Leveraging Clinical Data for Phenotyping and Predictive Modelling of Alzheimer’s DiseaseF30AG079504 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI TANG, ALICE SUMMER · 2022 to 2025
$187k
NIA NIH HHS F30 AG079504NIA NIH HHS P01 AG073082NIA NIH HHS P30 AG072975NIA NIH HHS R01 AG057683NIA NIH HHS R01 AG060393NIA NIH HHS R01 AG078164NIA NIH HHS RF1 AG076647NICHD NIH HHS T32 HD007470
6 · The paper itself

Abstract

Alzheimer's disease (AD) is a multifactorial neurodegenerative disorder characterized by heterogeneous molecular changes across diverse cell types, posing significant challenges for treatment development. To address this, we introduced a cell-type-specific, multi-target drug discovery strategy grounded in human data and real-world evidence. This approach integrates single-cell transcriptomics, drug perturbation databases, and clinical records. Using this framework, letrozole and irinotecan were identified as a potential combination therapy, each targeting AD-related gene expression changes in neurons and glial cells, respectively. In an AD mouse model, this combination therapy significantly improved memory function and reduced AD-related pathologies compared to vehicle and single-drug treatments. Single-nuclei transcriptomic analysis confirmed that the therapy reversed disease-associated gene networks in a cell-type-specific manner. These results highlight the promise of cell-type-directed combination therapies in addressing multifactorial diseases like AD and lay the groundwork for precision medicine tailored to patient-specific transcriptomic and clinical profiles.

Identifiers

PMID39713353
PMCPMC11661161

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.