目的 通過整合網(wǎng)絡(luò)推理算法、機(jī)器學(xué)習(xí)、類藥性評估、分子對接和動(dòng)力學(xué)模擬等方法，旨在篩選阿爾茨海默?。ˋlzheimer’s disease，AD）的生物標(biāo)志物，闡明其發(fā)病機(jī)制，并探索其潛在治療靶點(diǎn)及可入血成分（blood-absorbed constituents，BACs）。方法 基于中藥入血成分及代謝產(chǎn)物數(shù)據(jù)庫(Database of Constituents Absorbed Into Blood and Metabolites of Traditional Chinese Medicine，DCABM-TCM）篩選含有BACs的經(jīng)典名方，通過證候本體與方劑數(shù)據(jù)庫（Syndrome Ontology and Formula Database，SoFDA）檢索經(jīng)典名方所對應(yīng)的證候及其靶點(diǎn)。利用基于加權(quán)有向圖網(wǎng)絡(luò)的推理（weighted signed directed tensor network-based inference，wSDTNBI）算法預(yù)測BACs靶點(diǎn)。通過基因表達(dá)綜合數(shù)據(jù)庫（gene expression omnibus，GEO）挖掘篩選AD差異性表達(dá)基因（differential gene expression，DEGs），并采用基因本體（gene ontology，GO）和京都基因與基因組百科全書（Kyoto encyclopedia of genes and genomes，KEGG）分析DEGs靶向證候基因所涉及參與的主要生物學(xué)過程和信號通路。采用最小絕對收縮和選擇算法（least absolute shrinkage and selection operator，LASSO）、支持向量機(jī)遞歸特征消除（support vector machine-recursive feature elimination，SVM-RFE）方法、蛋白質(zhì)-蛋白質(zhì)相互作用網(wǎng)絡(luò)（protein-protein interaction，PPI）和文本挖掘的方法篩選AD核心基因。結(jié)合泛分析干擾化合物（employ pan-assay interference compounds，PAINS）、Lipinski（Ro5）和Lipinski（Ro3）的過濾和分子對接來篩選候選BACs。結(jié)果 通過篩選193個(gè)經(jīng)典名方，最終納入10個(gè)含94個(gè)BACs的方劑及對應(yīng)15種證候。預(yù)測得到1 520個(gè)證候基因和552個(gè)BACs靶點(diǎn)。進(jìn)一步篩選出證候可靶向的528個(gè)上調(diào)及697個(gè)下調(diào)DEGs。富集分析顯示DEGs主要參與神經(jīng)元抗凋亡及突觸功能等生物學(xué)過程，并顯著富集于磷脂酰肌醇3-激酶（phosphatidylinositol 3-kinase，PI3K）-蛋白激酶B（protein kinase B，Akt）信號通路、黏著斑及AD發(fā)生通路。BACs-DEGs-AD網(wǎng)絡(luò)表明上調(diào)和下調(diào)的DEGs分別可以靶向90和74個(gè)BACs，與9種證候相關(guān)。進(jìn)一步通過PPI網(wǎng)絡(luò)共分析得到度值較大的AD核心基因5個(gè)，分別是β2腎上腺素能受體（β2 adrenergic receptor，ADRB2）、P物質(zhì)受體1（substance-P receptor 1，TACR1）、前列腺素G/H合酶2（prostaglandin G/H synthase 2，PTGS2）、絲氨酸蛋白酶HTRA1A（serine protease，HTRA1A）和代謝型谷氨酸受體1（metabotropic glutamate receptor 1，GRM1）。類藥性評估篩選得到22個(gè)候選BACs，其中藥理學(xué)文獻(xiàn)驗(yàn)證有11個(gè)BACs具有抗AD活性。通過分子對接與動(dòng)力學(xué)模擬結(jié)果表明，與上市藥物多奈哌齊、加蘭他敏和卡巴拉汀比較，unii-x87dcb9gst與5個(gè)AD核心基因中的乙酰膽堿脂酶（acetylcholinesterase，AChE）具有最穩(wěn)定的綜合結(jié)合能。結(jié)論 通過多模態(tài)算法篩選出AD的生物標(biāo)志物，通過富集分析揭示AD相關(guān)的生物過程及信號通路，從分子層面闡釋中醫(yī)證候-AD基因的交互作用機(jī)制。同時(shí)，篩選得到的unii-x87dcb9gst可能為經(jīng)典名方中治療AD的候選BACs。不僅多維度解析AD發(fā)病的分子機(jī)制，更為抗AD藥物研發(fā)提供創(chuàng)新性的生物標(biāo)志物篩選體系和研究范式。

Objective This study aims to screen biomarkers for Alzheimer’s disease (AD), elucidate its pathogenesis, and explore potential therapeutic targets and blood-absorbed constituents (BACs) by integrating network inference algorithms, machine learning, drug-likeness evaluation, molecular docking, and molecular dynamics simulations. Methods Classical TCM formulas containing BACs were screened based on the database of constituents absorbed into the blood and metabolites of traditional Chinese medicine (DCABM-TCM) and the corresponding syndromes and their targets were retrieved from the from syndrome ontology to network-based evaluation of syndrome ontology and formula database (SoFDA). The weighted signed directed tensor network-based inference (wSDTNBI) algorithm was used to predict BACs targets. Differential expressed genes (DEGs) related to AD were identified by mining the GEO database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to explore the biological processes and pathways associated with up- and down-regulated DEGs. Core AD genes were screened using the least absolute shrinkage and selection operator (LASSO), support vector machine-recursive feature elimination (SVM-RFE), protein-protein interaction (PPI) network analysis, and text mining. Candidate BACs were further filtered using pan-assay interference compounds (PAINS), Lipinski’s Rule of Five (Ro5), and Rule of Three (Ro3), followed by molecular docking. Results First, a total of 10 classical TCM formulas containing 94 BACs and corresponding to 15 syndromes were selected from 193 formulas. A total of 1 520 syndrome-related genes and 552 BAC targets were predicted. Additionally, 528 up-regulated and 697 down-regulated DEGs targeted by syndromes were identified. Enrichment analysis revealed that these DEGs were primarily involved in biological processes such as positive regulation of gene expression, neuronal anti-apoptosis, and synaptic function, and were significantly enriched in pathways such as phosphatidylinositol 3-kinase (PI3K)-protein kinase B signaling pathway (Akt) signaling, focal adhesion, and AD pathways. The BACs-DEGs-AD network indicated that up- and down-regulated DEGs could target 90 and 74 BACs, respectively, associated with nine syndromes. PPI network analysis identified five core AD genes with high degrees: beta-2 adrenergic receptor (ADRB2), substance-P receptor (TACR1), prostaglandin G/H synthase 2 (PTGS2), serine protease HTRA1A, and metabotropic glutamate receptor 1 (GRM1). Drug-likeness evaluation screened 22 candidate BACs, 11 of which were pharmacologically validated to have anti-AD activity. Molecular docking results showed that unii-x87dcb9gst exhibited superior comprehensive binding energy with the five core AD genes compared to marketed drugs such as donepezil, galantamine, and rivastigmine. Finally, molecular dynamics simulations further confirmed the stable binding of unii-x87dcb9gst to the acetylcholinesterase (AChE) complex. Conclusion This study identified AD biomarkers through multimodal algorithms and revealed AD-related biological processes and signaling pathways through enrichment analysis, providing molecular insights into the interaction mechanisms between TCM syndromes and AD genes. Additionally, unii-x87dcb9gst, screened as a candidate BAC from classical TCM formulas, may serve as a potential therapeutic agent for AD. This research not only offers a multidimensional understanding of AD pathogenesis but also establishes an innovative biomarker screening system and research paradigm for anti-AD drug development.

TP18;R285

國家科技部重大新藥創(chuàng)制項(xiàng)目（2017ZX09301001）；深圳市科技計(jì)劃重點(diǎn)項(xiàng)目（JCYJ20220818101806014）；國家自然科學(xué)基金資助項(xiàng)目（81574038）；深圳大學(xué)第一附屬醫(yī)院有組織醫(yī)學(xué)科學(xué)研究基金項(xiàng)目（2024YZZ11）