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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Aging Neurosci.</journal-id>
<journal-title>Frontiers in Aging Neuroscience</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Aging Neurosci.</abbrev-journal-title>
<issn pub-type="epub">1663-4365</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/fnagi.2025.1622816</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Aging Neuroscience</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Music elicits different gene expression responses in the buccal cavity of age-related cognitive disorders patients and healthy controls</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname>G&#x00F3;mez-Carballa</surname>
<given-names>Alberto</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
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<contrib contrib-type="author">
<name>
<surname>Navarro</surname>
<given-names>Laura</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
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</contrib>
<contrib contrib-type="author">
<name>
<surname>Mallah</surname>
<given-names>Nour El Zahraa</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
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<contrib contrib-type="author">
<name>
<surname>Bello</surname>
<given-names>Xabier</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
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<contrib contrib-type="author">
<name>
<surname>Pischedda</surname>
<given-names>Sara</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
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<contrib contrib-type="author">
<name>
<surname>Viz-Lasheras</surname>
<given-names>Sandra</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
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<contrib contrib-type="author">
<name>
<surname>Curr&#x00E1;s</surname>
<given-names>Mar&#x00ED;a Jos&#x00E9;</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
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<contrib contrib-type="author">
<name>
<surname>Ferreir&#x00F3;s-Vidal</surname>
<given-names>Isabel</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
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<contrib contrib-type="author">
<name>
<surname>Mallah</surname>
<given-names>Narmeen</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
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<contrib contrib-type="author">
<name>
<surname>Montoto-Louzao</surname>
<given-names>Juli&#x00E1;n</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
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<contrib contrib-type="author">
<name>
<surname>Camino-Mera</surname>
<given-names>Alba</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
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<contrib contrib-type="author">
<name>
<surname>Castelo-Mart&#x00ED;nez</surname>
<given-names>L&#x00FA;a</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
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<contrib contrib-type="author">
<name>
<surname>Rey-V&#x00E1;zquez</surname>
<given-names>Sara</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
<xref ref-type="aff" rid="aff4"><sup>4</sup></xref>
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<contrib contrib-type="author">
<name>
<surname>Redondo</surname>
<given-names>Lorenzo</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
<xref ref-type="aff" rid="aff4"><sup>4</sup></xref>
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<contrib contrib-type="author">
<name>
<surname>Dacosta-Urbieta</surname>
<given-names>Ana</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
<xref ref-type="aff" rid="aff4"><sup>4</sup></xref>
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<contrib contrib-type="author">
<name>
<surname>Rivero-Calle</surname>
<given-names>Irene</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
<xref ref-type="aff" rid="aff4"><sup>4</sup></xref>
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<contrib contrib-type="author">
<name>
<surname>Rodr&#x00ED;guez-Tenreiro</surname>
<given-names>Carmen</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
<xref ref-type="aff" rid="aff4"><sup>4</sup></xref>
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<contrib contrib-type="author">
<name>
<surname>Martin&#x00F3;n-Torres</surname>
<given-names>Federico</given-names>
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<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
<xref ref-type="aff" rid="aff4"><sup>4</sup></xref>
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<contrib contrib-type="author" corresp="yes">
<name>
<surname>Salas</surname>
<given-names>Antonio</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
<xref ref-type="corresp" rid="c001"><sup>&#x002A;</sup></xref>
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<on-behalf-of>Sensogenomics Working Group</on-behalf-of>
</contrib-group>
<aff id="aff1"><sup>1</sup><institution>Genetics, Vaccines and Infections Research Group (GenViP), Instituto de Investigaci&#x00F3;n Sanitaria de Santiago, Universidade de Santiago de Compostela</institution>, <addr-line>Santiago de Compostela</addr-line>, <country>Spain</country></aff>
<aff id="aff2"><sup>2</sup><institution>Facultade de Medicina, GenPoB Research Group, Unidade de Xen&#x00E9;tica, Instituto de Ciencias Forenses, Universidade de Santiago de Compostela, Instituto de Investigaci&#x00F3;n Sanitaria (IDIS), Hospital Cl&#x00ED;nico Universitario de Santiago (SERGAS)</institution>, <addr-line>Santiago de Compostela</addr-line>, <country>Spain</country></aff>
<aff id="aff3"><sup>3</sup><institution>Centro de Investigaci&#x00F3;n Biom&#x00E9;dica en Red de Enfermedades Respiratorias (CIBERES)</institution>, <addr-line>Madrid</addr-line>, <country>Spain</country></aff>
<aff id="aff4"><sup>4</sup><institution>Department of Pediatrics, Translational Pediatrics and Infectious Diseases, Hospital Cl&#x00ED;nico Universitario de Santiago de Compostela</institution>, <addr-line>Santiago de Compostela</addr-line>, <country>Spain</country></aff>
<author-notes>
<fn fn-type="edited-by" id="fn0002">
<p>Edited by: <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/48263/overview">Elizabeta Blagoja Mukaetova-Ladinska</ext-link>, University of Leicester, United Kingdom</p>
</fn>
<fn fn-type="edited-by" id="fn0003">
<p>Reviewed by: <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/606704/overview">Ruma Raha-Chowdhury</ext-link>, University of Cambridge, United Kingdom</p>
<p><ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/2626704/overview">Rafaela Lacerda</ext-link>, National Health Institute Doutor Ricardo Jorge (INSA), Portugal</p>
</fn>
<corresp id="c001">&#x002A;Correspondence: Antonio Salas, <email>antonio.salas@usc.es</email></corresp>
</author-notes>
<pub-date pub-type="epub">
<day>23</day>
<month>09</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="collection">
<year>2025</year>
</pub-date>
<volume>17</volume>
<elocation-id>1622816</elocation-id>
<history>
<date date-type="received">
<day>06</day>
<month>05</month>
<year>2025</year>
</date>
<date date-type="accepted">
<day>02</day>
<month>09</month>
<year>2025</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#x00A9; 2025 G&#x00F3;mez-Carballa, Navarro, Mallah, Bello, Pischedda, Viz-Lasheras, Curr&#x00E1;s, Ferreir&#x00F3;s-Vidal, Mallah, Montoto-Louzao, Camino-Mera, Castelo-Mart&#x00ED;nez, Rey-V&#x00E1;zquez, Redondo, Dacosta-Urbieta, Rivero-Calle, Rodr&#x00ED;guez-Tenreiro, Martin&#x00F3;n-Torres and Salas.</copyright-statement>
<copyright-year>2025</copyright-year>
<copyright-holder>G&#x00F3;mez-Carballa, Navarro, Mallah, Bello, Pischedda, Viz-Lasheras, Curr&#x00E1;s, Ferreir&#x00F3;s-Vidal, Mallah, Montoto-Louzao, Camino-Mera, Castelo-Mart&#x00ED;nez, Rey-V&#x00E1;zquez, Redondo, Dacosta-Urbieta, Rivero-Calle, Rodr&#x00ED;guez-Tenreiro, Martin&#x00F3;n-Torres and Salas</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/">
<p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p>
</license>
</permissions>
<abstract>
<sec id="sec1001">
<title>Introduction</title>
<p>Recent evidence suggests that external stimuli can shape transcriptomes, a field emerging as sensogenomics. Specifically, the analysis of capillary blood samples has shown that musical stimuli can modulate gene expression patterns, not only in healthy individuals but also in those with age-related cognitive disorders (ACD).</p>
</sec>
<sec id="sec2001">
<title>Methods</title>
<p>Using targeted transcriptomics with Nanostring nCounter, we present groundbreaking evidence indicating that brief exposure to music can also impact the buccal transcriptome in both healthy donors and ACD patients.</p>
</sec>
<sec id="sec3001">
<title>Results</title>
<p>Our findings reveal that music elicits stronger transcriptomic effect on patients compared to controls, driving global upregulation in ACD patients but downregulation in controls. The most significantly dysregulated genes in ACD patients include <italic>LGALS3</italic> (downregulated) and <italic>CXCL8</italic> (upregulated), whereas in controls, <italic>THOP1</italic> was the top significant gene (downregulated). These genes play important roles in normal brain functions and are also altered in neurodegenerative conditions. Weighted Gene Co-expression network analysis reveals relevant and significant modules, both positive and negative correlated with music, implicated in neurodegenerative (e.g., autophagy) and immunological processes (e.g., IL-1, MHC).</p>
</sec>
<sec id="sec4001">
<title>Discussion</title>
<p>Collectively, these results suggest a complex interplay between music and molecular responses in the human body, and highlight the potential of musical stimuli to influence gene expression patterns outside systemic circulation, paving the way for further exploration of music&#x2019;s therapeutic applications.</p>
</sec>
</abstract>
<kwd-group>
<kwd>Alzheimer&#x2019;s disease</kwd>
<kwd>dementia</kwd>
<kwd>saliva</kwd>
<kwd>mild cognitive impairment</kwd>
<kwd>music stimuli</kwd>
<kwd>RNA-Seq</kwd>
<kwd>transcriptome</kwd>
<kwd>sensogenomics</kwd>
</kwd-group>
<counts>
<fig-count count="4"/>
<table-count count="0"/>
<equation-count count="0"/>
<ref-count count="89"/>
<page-count count="13"/>
<word-count count="11386"/>
</counts>
<custom-meta-wrap>
<custom-meta>
<meta-name>section-at-acceptance</meta-name>
<meta-value>Neurocognitive Aging and Behavior</meta-value>
</custom-meta>
</custom-meta-wrap>
</article-meta>
</front>
<body>
<sec sec-type="intro" id="sec1">
<title>Introduction</title>
<p>Little is known about how musical stimuli impact on our gene expression. <xref ref-type="bibr" rid="ref53">Navarro et al. (2021)</xref> have recently highlighted the importance of deeper exploration into this still poorly understood field of biological science [sensogenomics (<xref ref-type="bibr" rid="ref53">Navarro et al., 2021</xref>; <xref ref-type="bibr" rid="ref29">G&#x00F3;mez-Carballa et al., 2023</xref>, <xref ref-type="bibr" rid="ref9003">2025</xref>; <xref ref-type="bibr" rid="ref52">Navarro et al., 2023</xref>; <xref ref-type="bibr" rid="ref9004">Salas et al., In press</xref>; <xref ref-type="bibr" rid="ref9002">Cavenaghi et al., 2025</xref>; <xref ref-type="bibr" rid="ref9001">Castelo-Mart&#x00ED;nez et al., 2025</xref>)], taking advantage of new technologies emerging in the &#x2018;-omic&#x2019; sciences, including genomics and transcriptomics. There have been only a few attempts to understand the gene expression mechanisms activated during music stimulation; the initial ones were carried out on healthy controls and professional musicians, with results indicating a few genes differentially expressed after exposure to classical music stimuli (<xref ref-type="bibr" rid="ref39">Kanduri et al., 2015</xref>; <xref ref-type="bibr" rid="ref38">J&#x00E4;rvel&#x00E4;, 2018</xref>). Recently, the exploratory study by <xref ref-type="bibr" rid="ref52">Navarro et al. (2023)</xref> provided suggestive evidence of the potential impact of music in the context of Alzheimer&#x2019;s disease (AD) and reviewed the current evidence overall, concurring on the beneficial effect of music on neurodegenerative diseases. To the best of our knowledge, our most recent study, <xref ref-type="bibr" rid="ref29">G&#x00F3;mez-Carballa et al. (2023)</xref>, is the only attempt to date to examine the impact of music in a disease context, specifically in capillary blood samples collected from age-related cognitive disorder (ACD) patients. This study found an increased effect of music in ACD patients compared to healthy controls, but most interestingly, it revealed that brief musical stimuli can modify the way patients express genes typically altered in this condition, but in the opposite direction.</p>
<p>Building upon previous findings, the present study represents the first attempt to analyze the impact of music in ACD patients, but this time exploring salivary instead of blood transcriptomes. The interest in analyzing saliva stems from the growing importance of this non-invasive biological source in biomedical studies, with several salivary biomarkers being explored as proxies for diagnosing and monitoring brain health, stress, mental disorders, and neurological diseases (<xref ref-type="bibr" rid="ref22">Farah et al., 2018</xref>; <xref ref-type="bibr" rid="ref5">Bauduin et al., 2021</xref>; <xref ref-type="bibr" rid="ref2">Ali and Nater, 2020</xref>; <xref ref-type="bibr" rid="ref21">Engeland et al., 2019</xref>; <xref ref-type="bibr" rid="ref69">Schepici et al., 2020</xref>). Saliva is primarily produced and secreted by the parotid, submandibular, and sublingual salivary glands and regulated by the autonomous nervous system (<xref ref-type="bibr" rid="ref17">Chibly et al., 2022</xref>). These major salivary glands are innervated by both sympathetic and parasympathetic nerves, with compact fibers encircled by Schwann cells (<xref ref-type="bibr" rid="ref28">Garrett and Kidd, 1993</xref>). The submandibular and sublingual glands are responsible for most unstimulated saliva production, whereas most of parotid gland saliva secretion occurs in response to stimuli. Previous studies have reported the presence of neurotransmitters in salivary glands extracted from mice and rats (<xref ref-type="bibr" rid="ref49">Murai et al., 1998</xref>; <xref ref-type="bibr" rid="ref50">Murai et al., 1995</xref>). Parasympathetic stimulation promotes the release of the neurotransmitter acetylcholine whereas sympathetic activation releases noradrenaline stimulating the secretion of proteins (<xref ref-type="bibr" rid="ref51">Nakamura et al., 2004</xref>; <xref ref-type="bibr" rid="ref60">Proctor and Carpenter, 2007</xref>). Given this connection between the salivary glands and the nervous system, the existence of a bidirectional oral-brain axis has been suggested (<xref ref-type="bibr" rid="ref67">Sansores-Espana et al., 2021</xref>), through which an inflammatory response in the oral cavity may impact brain homeostasis and vice-versa. Since there is growing evidence indicating an impact of music on blood transcriptomes as well as its beneficial effects on many disease conditions, it seems imperative to explore if musical stimuli have also the potential to regulate salivary transcriptomes.</p>
<p>Here, we propose exploring gene expression patterns in saliva obtained from ACD patients and healthy controls before and after brief musical stimuli. The samples analyzed in this study partially overlap with those used in <xref ref-type="bibr" rid="ref29">G&#x00F3;mez-Carballa et al. (2023)</xref>; and all of them were collected during the same experimental concerts. Therefore, this scenario offers a unique opportunity to evaluate the potential of saliva analysis in capturing expression changes triggered by music, and to undertake a comparative analysis with the capillary blood signatures reported in our previous study (<xref ref-type="bibr" rid="ref29">G&#x00F3;mez-Carballa et al., 2023</xref>).</p>
</sec>
<sec sec-type="methods" id="sec2">
<title>Methods</title>
<sec id="sec3">
<title>Participants and <italic>n</italic>Counter assay</title>
<p>Written informed consent was obtained from all the participants in the present study. The Ethics Committee of Xunta de Galicia approved the present project (Registration code: 2020/021), and the study was conducted in accordance to the guidelines of the Helsinki Declaration. We have followed the same experimental procedures described in <xref ref-type="bibr" rid="ref29">G&#x00F3;mez-Carballa et al. (2023)</xref> and within the framework of the Sensogenomics project (<ext-link xlink:href="https://www.sensogenomics.com/" ext-link-type="uri">www.sensogenomics.com</ext-link>).<xref ref-type="fn" rid="fn0001"><sup>1</sup></xref> Briefly, we collected saliva samples at two timepoints: before and after an experimental concert of classical music; (see Figure 1 of <xref ref-type="bibr" rid="ref29">G&#x00F3;mez-Carballa et al., 2023</xref>) for a schematic representation of the sampling and analysis procedures. Saliva samples were collected in <italic>Oragene DNA</italic> devices (ORE-100; DNAgenotek), comprising 10 ACD patients [aged 84&#x2013;92&#x202F;years old (mean 84); 5/5 male/female] and 14 healthy donors [aged 18&#x2013;88 (mean 57); 3/11 male/female]. Nearly all the ACD patients (9/10; 90%) and more than half of the controls (8/14; 57%) from the present study overlap with those included in the capillary blood experiment (<xref ref-type="bibr" rid="ref29">G&#x00F3;mez-Carballa et al., 2023</xref>).</p>
<p>RNA from saliva was isolated using 500&#x202F;&#x03BC;L of sample and the RNeasy microkit (Qiagen). We slightly modified the protocol provided by the extraction kit as recommended by the Oragene tubes supplier. RNA concentration step and an additional DNase treatment were undertaken using an RNA clean &#x0026; concentrator kit (Zymo Research). RNA amount and integrity were checked using TapeStation 4200 (Agilent), and DV200 values were calculated to ensure that &#x003E;50% of the RNA fragments were above 200&#x202F;nt and to estimate the optimal sample input.</p>
<p>Gene expression was evaluated through the <italic>n</italic>Counter <italic>MAX</italic> (NanoString Technologies) and the <italic>n</italic>Counter Host Response Panel, which includes 785 genes. We opted for NanoString over other methods such as RNAseq due to the intrinsic difficulties associated to sequencing endogenous RNA from saliva samples. Bacteria are naturally present in human saliva, making it challenging to analyze only the human component of the salivary transcriptome (<xref ref-type="bibr" rid="ref31">Gosch et al., 2024</xref>). We followed standard protocols; including 12&#x202F;&#x00D7;&#x202F;RNA hybridization with 5&#x202F;&#x03BC;L of RNA as input, and hybridization time of 18&#x202F;h for all samples. We also mixed controls and ACD patient samples in the same runs to avoid technical sample bias and batch effects. After filtering out genes expressing below the background (maximum expression value &#x003C; background), we detected a total of 566 and 672 genes (out of the total 785 in the NanoString panel) in ACD patients and healthy controls, respectively. In addition, 553 out of 566 genes in ACD and 648 out of 672 genes in healthy controls overlap with those detected in capillary blood samples from our previous study (<xref ref-type="bibr" rid="ref29">G&#x00F3;mez-Carballa et al., 2023</xref>).</p>
</sec>
<sec id="sec4">
<title>Statistical analysis</title>
<p>First, we carried out a quality control (QC) of the raw expression data checking technical parameters following manufacturer recommendations. Samples that did not pass technical QC, or with low number of genes detected, were excluded for downstream analysis.</p>
<p>Genes with counts below the background (defined as the mean + 2 standard deviations [SD] of the negative control spikes in the code set, disregarding negative control C, which typically yields a higher number of counts) were excluded from both normalization and the differential expression analysis.</p>
<p>Data normalization was performed through an iterative strategy that combines both <italic>DESeq2</italic> (<xref ref-type="bibr" rid="ref46">Love et al., 2014</xref>) and <italic>RUVSeq</italic> (<xref ref-type="bibr" rid="ref65">Risso et al., 2014</xref>) packages as described in (<xref ref-type="bibr" rid="ref10">Bhattacharya et al., 2021</xref>). Control reference genes for data normalization were detected by selecting invariable genes [<italic>p</italic>-value &#x003E; 0.1, BaseMean &#x003E; 100 and |log<sub>2</sub> Fold Change (FC)|&#x202F;&#x003C;&#x202F;0.2] after a na&#x00EF;ve differential expression analysis between timepoint 1 (TP1) and timepoint 2 (TP2) in both ACD and healthy controls separately. Genes expressed below the background were removed. We used a paired-sampling design to carry out analysis of transcriptome differences before the musical stimuli (Pretest; TP1) and after the musical stimuli (Posttest; TP2). Additionally, we evaluated if differentially expressed genes (DEGs) detected in saliva are also altered in ACD patients due to the condition by contrasting DEGs between TP1 and TP2 against DEGs that are altered in AD and mild cognitive impairment (MCI) patients. For this purpose, we downloaded from GEO (gene expression omnibus) microarray blood gene expression data from three independent microarray datasets analyzing MCI and AD patients and healthy controls, as done previously (<xref ref-type="bibr" rid="ref29">G&#x00F3;mez-Carballa et al., 2023</xref>). Data were processed and merged as explained in (<xref ref-type="bibr" rid="ref29">G&#x00F3;mez-Carballa et al., 2023</xref>; <xref ref-type="bibr" rid="ref52">Navarro et al., 2023</xref>).</p>
<p>We used the Weighted Gene Co-expression Network Analysis (<italic>WGCNA</italic>) R package (<xref ref-type="bibr" rid="ref44">Langfelder and Horvath, 2008</xref>) to investigate clusters of co-expressed genes potentially correlated to the musical stimuli in ACD patients and the healthy control groups separately. Normalized and corrected gene expression data, adjusted for patient-to-patient variability, served as input to construct a signed weighted correlation network. Following the package developers&#x2019; recommendations and considering the number of samples per group, we chose a soft-thresholding power of 18. We computed the Topological Overlap Matrix (TOM) and the corresponding dissimilarity (1&#x2013;TOM) values. We set a minimum module size of 30, and a dendrogram cut height threshold of 0.2 for module merging. Initially labeled by colors, the detected modules of co-expressed genes were later renamed using the name of the genes showing the highest connectivity within each module (hub genes). We identified modules of interest significantly associated with the musical stimuli by correlating module eigengenes with the timepoint (TP1 and TP2) data; and measuring gene significance (GS), a value that quantifies the biological significance of genes in modules. For each gene, Module Membership (MM) quantifies its intramodular connectivity within the module. Multiple test adjustment was carried out using the FDR method by Benjamini-Hochberg (<xref ref-type="bibr" rid="ref8">Benjamini and Hochberg, 1995</xref>).</p>
<p>Functional analysis of significantly correlated modules was carried out through an over-representation analysis with the <italic>Clusterprofiler</italic> R package (<xref ref-type="bibr" rid="ref79">Wu et al., 2021</xref>). The biological processes from the Gene Ontology (GO) and Reactome were used as reference databases for the analysis. The pool of genes included in the <italic>n</italic>Counter NanoString Host Response gene expression panel was employed as the gene universe for statistical calculations. To facilitate the interpretation of the results, redundant terms (similarity &#x003E; 0.7) were detected and removed after calculating the terms similarity matrix.</p>
<p>Different R packages were used to generate volcano plots [<italic>EnhancedVolcano</italic> (<xref ref-type="bibr" rid="ref11">Blighe et al., 2020</xref>)] and heatmaps [<italic>ComplexHeatmap</italic> (<xref ref-type="bibr" rid="ref33">Gu et al., 2016</xref>)]. Statistical significance was assessed using the Wilcoxon test.</p>
<p>Statistical analyses were performed using R version 4.2.2 (<xref ref-type="bibr" rid="ref61">R Core Team, 2019</xref>).</p>
</sec>
</sec>
<sec sec-type="results" id="sec5">
<title>Results</title>
<sec id="sec6">
<title>Differentially expression in saliva in response to music</title>
<p>To assess the impact of the musical stimuli on the transcriptomes of donors, we first conducted a paired TP1 <italic>vs</italic>. TP2 transcriptome analysis for the two groups of donors separately.</p>
<p>First, we observed a higher number of DEGs in ACD patients compared to controls. Specifically, we detected 31 (adjusted <italic>p</italic>-value &#x003C; 0.05) DEGs in the ACD group out of a total of 566 detected genes. In contrast, we found 7 adjusted DEGs in healthy controls out of a total of 672 detected genes. These different proportions (31/566 <italic>vs</italic>. 7/672) were statistically significant under a two-sample proportion test (<italic>p</italic>-value&#x202F;=&#x202F;1&#x202F;&#x00D7;&#x202F;10<sup>&#x2212;05</sup>); <xref rid="SM1" ref-type="supplementary-material">Supplementary Tables S1, S2</xref>; <xref ref-type="fig" rid="fig1">Figure 1A</xref>.</p>
<fig position="float" id="fig1">
<label>Figure 1</label>
<caption>
<p><bold>(A)</bold> Volcano plot showing the DEGs between TP1 and TP2 in both ADC and control donors (HC). Names of DEGs with |log<sub>2</sub>FC| value &#x003E; 0.5 and adjusted <italic>p</italic>-value &#x003C; 0.01 are displayed. <bold>(B)</bold> Density plots of log<sub>2</sub>FC; dashed lines indicate Log<sub>2</sub>FC&#x202F;=&#x202F;0; vertical solid lines indicate median values; <bold>(C)</bold> Principal components analysis (PCA) using DEGs with <italic>p</italic>-value &#x003C; 0.05. <bold>(D)</bold> Heatmap and cluster analysis of the DEGs (adjusted <italic>p</italic>-value &#x003C; 0.05) in ADC and HC.</p>
</caption>
<graphic xlink:href="fnagi-17-1622816-g001.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">(A) Two volcano plots displaying differential expression between TP1 and TP2 in ACD and healthy controls (HC), highlighting significant genes like LGALS3, CXCL8, CD59 and THOP1. (B) Two density plots comparing log2FC distributions for ACD and HC, showing medians. (C) Principal component analysis plot from expression profiles of DEGs with P-value &#x003C; 0.05, comparing timepoints TP1 and TP2 in ACD and HC samples. (D) Heatmap with hierarchical clustering of gene expression from DEGs (adjusted P-value &#x003C; 0.05) in ACD and HC over two timepoints, shown in different colors.</alt-text>
</graphic>
</fig>
<p>Secondly, the musical stimuli drive the transcriptome of ACD patients toward upregulation when compared to controls. Notably, there are more upregulated adjusted DEGs in ACD donors (11/31&#x202F;=&#x202F;0.35) compared to the lower proportion observed in the healthy controls (0/7). Moreover, upregulation appears to be the predominant overall response to music in the transcriptome of ACD patients (median log<sub>2</sub> Fold Change (log<sub>2</sub>FC)&#x202F;=&#x202F;0.032 of non-adjusted DEGs with <italic>p</italic>-value &#x003C; 0.05), whereas downregulation predominates in the transcriptome of healthy donors (median log<sub>2</sub>FC&#x202F;=&#x202F;&#x2212;0.037 of DEGs with non-adjusted <italic>p</italic>-value &#x003C; 0.05); <xref ref-type="fig" rid="fig1">Figure 1B</xref>. While these figures are inadequate for a two-sample proportion test (as it is an incorrect approximation to a Chi-square), we conducted the test using the observed non-adjusted DEGs, with proportions 52/97&#x202F;=&#x202F;0.54 in ACD patients <italic>vs</italic>. 45/128&#x202F;=&#x202F;0.35 in healthy controls; these different proportions were statistically significant with <italic>p</italic>-value&#x202F;=&#x202F;0.009.</p>
<p>The transcriptome profiles of non-adjusted DEGs reveal a segregation of samples into their two timepoints (TP1 and TP2) in ACD patients, as evidenced by Principal Component Analysis (PCA). This differentiation is particularly noticeable in Principal Component 1 (PC1), which account for most of the variation (79%); PC2 contributes minimally to this primary PC1 clustering, representing only 8% of the variation; <xref ref-type="fig" rid="fig1">Figure 1C</xref>. However, the differentiation between TP1 and TP2 is less pronounced in the healthy cohort, with PC1 and PC2 accounting for only 58 and 9% of the variation, respectively; <xref ref-type="fig" rid="fig1">Figure 1C</xref>. A heatmap of DEGs (adjusted <italic>p</italic>-value &#x003C; 0.05) in ACD clearly demonstrates a clear distinction between TP1 and TP2 in ACD patients. Despite the limited number of DEGs observed in healthy donors (<italic>n</italic>&#x202F;=&#x202F;7), the heatmap efficiently separates most of the transcriptomes into the two timepoints; <xref ref-type="fig" rid="fig1">Figure 1D</xref>.</p>
<p>The top downregulated gene in ACD was <italic>LGALS3</italic> (log<sub>2</sub>FC&#x202F;=&#x202F;&#x2212;0.83; adjusted <italic>p</italic>-value&#x202F;=&#x202F;0.007) whereas the DEG showing the lowest adjusted <italic>p</italic>-value was <italic>CXCL8</italic> (log<sub>2</sub>FC&#x202F;=&#x202F;0.78; adjusted <italic>p</italic>-value&#x202F;=&#x202F;0.004). In control samples, the top DEG, namely <italic>THOP1</italic>, was downregulated in TP2 with respect to TP1 (log<sub>2</sub>FC&#x202F;=&#x202F;&#x2212; 0.73; adjusted <italic>p</italic>-value&#x202F;=&#x202F;0.007); <xref rid="SM1" ref-type="supplementary-material">Supplementary Table S1</xref>.</p>
<p>Using a pathways enrichment approach and DEGs (adjusted <italic>p</italic>-value &#x003C; 0.05) a single GO significant term was detected in ACD patients: &#x201C;unsaturated fatty acid metabolic process&#x201D; (adjusted <italic>p</italic>-value&#x202F;=&#x202F;0.03), involving the DEGs <italic>IL1B</italic>, <italic>PTGS2</italic>, <italic>ACOX1</italic>, <italic>MIF</italic> and <italic>ALOX12</italic>. Nonsignificant pathway resulted from the analysis in control donors, most likely due to the low number of DEGs detected.</p>
</sec>
<sec id="sec7">
<title>Comparative transcriptomic response to music in capillary blood and saliva</title>
<p>We observed few similarities between transcriptomic response in the capillary blood and saliva of donors exposed to musical stimuli comparing the values mentioned above with results reported in (<xref ref-type="bibr" rid="ref29">G&#x00F3;mez-Carballa et al., 2023</xref>; see also <xref rid="SM1" ref-type="supplementary-material">Supplementary Table S2</xref>); namely: (i) There is a statistically significant higher number of DEGs in ACD patients compared to controls, (ii) Global upregulation is the predominant reaction to music in the transcriptome of ACD patients, while downregulation predominates in the transcriptome of healthy donors, and (iii) The transcriptome profile of DEGs shows a clear distinct differentiation between TP1 and TP2 in both healthy controls and, even more marked, in ACD patients.</p>
<p>However, there are also a few differences between the transcriptomes in saliva and capillary blood of patients and controls that are worth highlighting.</p>
<p>Firstly, the most notable finding is that the proportion of adjusted DEGs is substantially higher in saliva than in blood (<xref rid="SM1" ref-type="supplementary-material">Supplementary Table S2</xref>), for both ACD patients and healthy controls. When referred to the total number of transcripts captured by the different techniques [NanoString in saliva and RNAseq in capillary blood (<xref ref-type="bibr" rid="ref29">G&#x00F3;mez-Carballa et al., 2023</xref>)], the proportions are as follows: (i) in ACD patients: 31/566&#x202F;=&#x202F;0.05 [saliva] vs. 328/36155&#x202F;=&#x202F;0.01 [capillary blood]; and (ii) in healthy controls: 7/672&#x202F;=&#x202F;0.01 [saliva] vs. 1/35865&#x202F;=&#x202F;0.00 [capillary blood]. For these comparisons, the two-sample proportion test is highly significant, p-value &#x003C; 2&#x202F;&#x00D7;&#x202F;10<sup>&#x2212;16</sup>; <xref rid="SM1" ref-type="supplementary-material">Supplementary Table S2</xref>. In addition, to mitigate potential bias arising from the different techniques employed to generate salivary and capillary blood transcriptomes, we can consider only the common genes in both studies (553 in ACD patients and 648 in healthy controls; see Material and Methods). Although the proportions are more attenuated, they remain consistent: (i) in ACD patients: 30/553&#x202F;=&#x202F;0.05 [saliva] vs. 21/553&#x202F;=&#x202F;0.04 [capillary blood], and (ii) in healthy controls: 7/648&#x202F;=&#x202F;0.01[saliva] <italic>vs</italic>. 0/648&#x202F;=&#x202F;0.00 [capillary blood]. This difference is statistically significant in healthy controls (<italic>p</italic>-value&#x202F;=&#x202F;0.02); but it is highly significant in both groups when computing proportions using non-adjusted DEGs; <xref rid="SM1" ref-type="supplementary-material">Supplementary Table S2</xref>.</p>
<p>Secondly, we observed a significant proportion of genes that express in different directions in capillary blood and saliva. Specifically, there are 24 common (non-adjusted) DEGs in the two tissues for ACD patients, and 7 in healthy controls. Among these, 11/24&#x202F;=&#x202F;0.46 [ACD patients], and 2/7&#x202F;=&#x202F;0.29 [controls] were found to be negatively correlated (<xref ref-type="fig" rid="fig2">Figure 2</xref>). Additionally, there are only two DEGs (adjusted <italic>p</italic>-value &#x003C; 0.05) in ACD patients (none in controls), namely <italic>KDM6B</italic> and <italic>TIMP2,</italic> that overlap between saliva and blood transcriptomes. While <italic>TIMP2</italic> expressed similarly in saliva and capillary blood, <italic>KDM6B</italic> is upregulated in saliva but downregulated in blood (<xref ref-type="fig" rid="fig2">Figure 2</xref>).</p>
<fig position="float" id="fig2">
<label>Figure 2</label>
<caption>
<p>Correlation between gene expression changes observed in capillary blood and saliva samples after musical stimulation in ACD and control donors (HC). Blue dots indicate positive correlation whereas red dots indicate negative correlation between tissues. Only common DEGs (<italic>p</italic>-value &#x003C; 0.05) in both saliva and capillary blood samples are being displayed. Ranked values refer to the value of the test statistic for a gene obtained from <italic>DESeq2</italic>.</p>
</caption>
<graphic xlink:href="fnagi-17-1622816-g002.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Scatter plots comparing ranked gene expressions in saliva versus blood for two conditions, ACD and HC. In ACD, genes like KDM6B and TIMP2 are highlighted. Blue dots indicate positive correlation whereas red dots indicate negative correlation between tissues. Only common DEGs (P-value &#x003C; 0.05) in both saliva and capillary blood samples are being displayed. Ranked values refer to the value of the test statistic for a gene obtained from DESeq2.</alt-text>
</graphic>
</fig>
</sec>
<sec id="sec8">
<title>Music-related DEGs in saliva from ACD patients compared to DEGs in AD/MCI condition</title>
<p>To investigate if some of the genes affected by musical stimuli in ACD were also dysregulated in AD/MCI patients due to their condition, we contrasted the DEGs detected in ACD after the musical stimuli with DEGs resulted from comparing transcriptomes from AD/MCI patients and healthy controls. We observed that some genes targeted by music were also affected in both neurodegenerative conditions. However, music appeared to impact more significantly on genes dysregulated in MCI (<italic>n</italic>&#x202F;=&#x202F;13) than in genes dysregulated in AD (<italic>n</italic>&#x202F;=&#x202F;8); <xref rid="SM1" ref-type="supplementary-material">Supplementary Figure S1</xref>. There were few genes that showed a negative correlation in these contrasts, more in MCI (<italic>n</italic>&#x202F;=&#x202F;7) than in AD (<italic>n</italic>&#x202F;=&#x202F;3); suggesting that music has a compensatory effect for those altered in the two disease conditions.</p>
<p>Interestingly, the only significant genes altered by music in both capillary blood and saliva (see above) were also differentially expressed in AD and MCI, namely <italic>KDM6B</italic> and <italic>TIMP2</italic> (<xref rid="SM1" ref-type="supplementary-material">Supplementary Figure S1</xref>). Both genes showed a significant higher expression in MCI and AD compared to healthy controls. However, music induced opposite expression changes in saliva for the <italic>KDM6B</italic> (over-expression as in MCI/AD) and <italic>TIMP2</italic> (under-expression) genes.</p>
</sec>
<sec id="sec9">
<title>Co-expression modules in response to the musical stimuli in ACD</title>
<p>The <italic>WGCNA</italic> analysis generated six modules of co-expressed genes from the ACD expression data (<xref ref-type="fig" rid="fig3">Figure 3A</xref>; <xref rid="SM1" ref-type="supplementary-material">Supplementary Figures S2A,B</xref>; <xref rid="SM1" ref-type="supplementary-material">Supplementary Table S3</xref>). Correlation of the modules eigengenes with TP1 and TP2 revealed four modules significantly correlated with the expression changes produced by the music stimuli (<xref ref-type="fig" rid="fig3">Figure 3A</xref>). Three of them were positively correlated: <italic>PIK3CD</italic> [(blue) <italic>p</italic>-value&#x202F;=&#x202F;0.004], <italic>NOTCH1</italic> [(brown) <italic>p</italic>-value&#x202F;=&#x202F;5&#x202F;&#x00D7;&#x202F;10<sup>&#x2212;06</sup>], and <italic>CXCR1</italic> [(green) <italic>p</italic>-value&#x202F;=&#x202F;0.002]; whereas the module <italic>CD59</italic> [(turquoise) <italic>p</italic>-value&#x202F;=&#x202F;7&#x202F;&#x00D7;&#x202F;10<sup>&#x2212;05</sup>] showed a negative correlation. <italic>NOTCH1</italic> and <italic>CD59</italic> modules showed the highest correlation with the musical stimuli (<italic>R</italic>&#x202F;=&#x202F;0.83 and <italic>R</italic>&#x202F;=&#x202F;&#x2212;0.77, respectively).</p>
<fig position="float" id="fig3">
<label>Figure 3</label>
<caption>
<p>Co-expression analysis in ACD patients. <bold>(A)</bold> Correlation and <italic>p</italic>-values values heatmap obtained from the co-expression analysis. Upper value corresponds to the correlation with musical stimuli and <italic>p</italic>-values are shown in brackets <bold>(B)</bold> Boxplots of samples eigengenes values between TP1 and TP2 from <italic>NOTCH1</italic>, <italic>CD59</italic> and <italic>PIK3CD</italic> modules. <bold>(C)</bold> Pathways enrichment analysis results using GO and Reactome databases.</p>
</caption>
<graphic xlink:href="fnagi-17-1622816-g003.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Co-expression analysis in ACD patients. Panel A displays a heatmap showing correlations values and significance between TP1-TP2 comparison and NOTCH1, PIK3CD, CXCR1, CD59, CREBBP, and CFLAR co-expression modules. Panel B shows box plots for NOTCH1, PIK3CD, and CD59 modules eigengenes values in TP1 and TP2, with p-values indicating statistical significance. Panel C presents a dot plot for GO and Reactome pathways significantly associated with CD59 and NOTCH1 co-expression modules, with gene ratios and adjusted p-values, focusing on intracellular transport and immune system processes.</alt-text>
</graphic>
</fig>
<p>The highly significant correlation values of these four modules suggest a functional role of these gene sets in the buccal molecular response to music in ACD patients. This functional relevance is likewise visible by examining the global correlation between gene MM and gene-trait correlation values for the individual genes within each module, indicating that genes with higher trait-correlation values are also important functional drivers of the modules (high MM values) (<italic>R</italic>&#x202F;=&#x202F;0.73 and <italic>p</italic>-value&#x202F;=&#x202F;9&#x202F;&#x00D7;&#x202F;10<sup>&#x2212;34</sup> for <italic>CD59</italic> module; <italic>R</italic>&#x202F;=&#x202F;0.74 and <italic>p</italic>-value&#x202F;=&#x202F;3&#x202F;&#x00D7;&#x202F;10<sup>&#x2212;14</sup> for <italic>NOTCH1</italic> module); <xref rid="SM1" ref-type="supplementary-material">Supplementary Figure S2C</xref>. Modules <italic>NOTCH1</italic>, <italic>CD59</italic> and <italic>PIK3CD</italic> are the ones showing the most significant expression changes between TP1 and TP2 <xref rid="SM1" ref-type="supplementary-material">Supplementary Figure S2D</xref>; this is particularly clear when examining their module eigengene values (<xref ref-type="fig" rid="fig3">Figure 3B</xref>).</p>
<p>Pathways enrichment analysis of the significant modules identified relevant biological routes involved in the musical stimuli for three of the modules; <italic>CD59</italic>, <italic>NOTCH1</italic> and <italic>PIK3CD</italic> (<xref rid="SM1" ref-type="supplementary-material">Supplementary Table S4</xref>; <xref ref-type="fig" rid="fig3">Figure 3C</xref>). The most relevant pathways detected for the downregulated <italic>CD59</italic> module were related to intracellular protein transport and localization (in GO) and, most remarkable, autophagic machinery/vacuole organization (in GO and Reactome; <xref ref-type="fig" rid="fig3">Figure 3C</xref>). Genes from the upregulated <italic>NOTCH1</italic> and <italic>PIK3CD</italic> modules were involved in the detection of biotic stimulus (<italic>NOTCH1</italic> in GO) and innate immune system (<italic>PIK3CD</italic> in Reactome; <xref ref-type="fig" rid="fig3">Figure 3C</xref>).</p>
</sec>
<sec id="sec10">
<title>Co-expression modules in response to the musical stimuli in controls</title>
<p>The co-expression module analysis in control donors also detected significant modules altered in response to music, but the correlations were generally lower than those reported for the ACD patients (<xref ref-type="fig" rid="fig4">Figure 4A</xref>; <xref rid="SM1" ref-type="supplementary-material">Supplementary Figures S3A,B</xref>; <xref rid="SM1" ref-type="supplementary-material">Supplementary Table S3</xref>). Three modules yielded positive correlation values [<italic>LTF</italic> &#x2013; (yellow) <italic>p</italic>-value&#x202F;=&#x202F;0.002, <italic>GZMA</italic> &#x2013; (brown) <italic>p</italic>-value&#x202F;=&#x202F;0.004, <italic>CCR2</italic> &#x2013; (red) <italic>p</italic>-value&#x202F;=&#x202F;0.01], indicating upregulation after receiving musical stimuli (in TP2), whereas two of them showed negative correlation values [<italic>SERPINA1</italic> &#x2013; (turquoise) <italic>p</italic>-value&#x202F;=&#x202F;0.0002, <italic>NCF4</italic> &#x2013; (green) <italic>p</italic>-value&#x202F;=&#x202F;0.005], indicating downregulation after listening to music; all of them survived adjustment for multiple test (<xref rid="SM1" ref-type="supplementary-material">Supplementary Table S3</xref>). Among the topmost significant modules (<italic>SERPINA1</italic>, <italic>GZMA</italic> and <italic>LTF</italic>), the most significant one was also the most negatively correlated with musical stimulation (<italic>SERPINA1</italic>; <italic>R</italic>&#x202F;=&#x202F;&#x2212;0.64), and the module displaying the most extreme differences in eigengenes values between both timepoints (<italic>p</italic>-value&#x202F;=&#x202F;0.0002; <xref ref-type="fig" rid="fig4">Figure 4B</xref>). Although the overall gene expression profiles of the top three modules clustered reasonably well the samples into TP1 and TP2 (<xref rid="SM1" ref-type="supplementary-material">Supplementary Figure S3C</xref>), this separation by TP was not as optimal as in the case of the modules from ACD patients. The importance of the <italic>SERPINA1</italic> module and its core genes in the gene expression response of control donors can also be noted in the high correlation and low significance values observed when contrasting gene MMs with GSs individual values (<italic>R</italic>&#x202F;=&#x202F;0.70; <italic>p</italic>-value&#x202F;=&#x202F;1&#x202F;&#x00D7;&#x202F;10<sup>&#x2212;34</sup>) in comparison to the values obtained for the other modules (<xref rid="SM1" ref-type="supplementary-material">Supplementary Figure S3D</xref>).</p>
<fig position="float" id="fig4">
<label>Figure 4</label>
<caption>
<p>Co-expression analysis in control donors. <bold>(A)</bold> Correlation and <italic>p</italic>-values values heatmap obtained from the co-expression analysis. Upper value corresponds to the correlation with musical stimuli and <italic>p</italic>-values are shown in brackets <bold>(B)</bold> Boxplots of samples eigengenes values between TP1 and TP2 from <italic>GZMA</italic>, <italic>LTF</italic> and <italic>SERPINA1</italic> modules. <bold>(C)</bold> Pathways enrichment analysis results using GO and Reactome databases.</p>
</caption>
<graphic xlink:href="fnagi-17-1622816-g004.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Co-expression analysis in control donors. Panel A displays a heatmap showing correlations values and significance between TP1-TP2 comparison and HLA-A, GZMA, NCF4, CCR2, LTF and SERPINA1 co-expression modules. Panel B shows box plots for GZMA, LTF, and SERPINA1 modules eigengenes values in TP1 and TP2 with with p-values indicating statistical significance. Panel C presents a dot plot for GO and Reactome pathways significantly associated with co-expression modules GZMA and SERPINA1, showing gene ratios and p-values, with pathways like immune response and signaling highlighted.</alt-text>
</graphic>
</fig>
<p>Several pathways were found to be related to <italic>GZMA</italic>, <italic>SERPINA1</italic> and <italic>LTF</italic> modules after a functional assessment of each of the significantly correlated modules (<xref ref-type="fig" rid="fig4">Figure 4C</xref>; <xref rid="SM1" ref-type="supplementary-material">Supplementary Table S4</xref>). The <italic>GZMA</italic> module showed a strong involvement of the adaptative immune system. Thus, most significant pathways can be condensed into three sets of adaptive immune-related processes: antigen processing and presentation <italic>via</italic> MHC class II, cell adhesion, and T-cell metabolism routes (activation, proliferation, regulation, differentiation). Consistently, enrichment analysis with Reactome database yielded equivalent results, with MHC class II antigen presentation as the top significant pathway, and other pathways with a key role modulating T-cell activity (CD28, PD-1 signaling) or triggering downstream cascades after T-cell receptor activation (ZAP-70); (<xref ref-type="fig" rid="fig4">Figure 4C</xref>; <xref rid="SM1" ref-type="supplementary-material">Supplementary Table S3</xref>). GO enrichment analysis of <italic>SERPINA1</italic> module resulted in intracellular protein transport and related terms significantly associated with this module. However, Reactome enrichment analysis found several significant pathways engaged in the innate immune system, locating at the top different Toll-Like Receptors (TLR) cascades and related pathways, such as IL-1 signaling, MyD88: MAL (TIRAP) cascade or TRAF6 mediated induction of NFkB and MAP kinases. Most noticeable, autophagy pathways were also significantly associated with the <italic>SERPINA1</italic> module (autophagy, macro-autophagy and ER-Phagosome pathways) suggesting functional similarities with the negatively correlated module <italic>CD59</italic> detected in ACD patients (see above). Processes associated with the <italic>LTF</italic> module were only detected in Reactome; related to the GPCR signal transduction events, and more specifically to the sub-family A/1 (Rhodopsin-like) receptors.</p>
</sec>
</sec>
<sec sec-type="discussion" id="sec11">
<title>Discussion</title>
<p>Musical stimulation is a multifaceted cognitive phenomenon that intricately engages various brain regions, eliciting a spectrum of cognitive, emotional, and physiological responses. Elucidating the molecular changes triggered by music can help to understanding its effect on brain function and mental health in the general population, as well as its therapeutic potential in the context of various neurological and psychiatric conditions. Moreover, disentangling the complexity of gene networks (and molecular pathways involved in these networks) could reveal new targets for pharmacological interventions or provide guidance to develop new personalized music-based therapies.</p>
<p>Recently, we demonstrated that musical stimuli have an important impact on the capillary blood transcriptomes of ACD patients and healthy individuals, providing insights into the systemic gene expression response to music. We reported that music stimulation in ACD patients compensates for the expression of genes and pathways dysregulated due to cognitive impairment. Elaborating on this groundwork and given the known connection between the oral cavity and the brain, namely oral-brain axis, we aimed for the first time to investigate gene expression changes elicited by music in saliva samples in healthy donors and ACD patients. For this purpose, we have followed the same experimental design that has already been successfully used in our previous study on capillary blood samples, but this time employing a specific saliva collection device and a hybridization-based and PCR-free <italic>n</italic>Counter assay from NanoString. Currently, this is the most appropriate technology to deal with transcriptomes isolated from saliva samples, which are usually enriched with abundant genetic material from microbial species and poor in terms of quantity/quality.</p>
<p>Overall, the results suggest that music significantly impacts on the salivary transcriptomes of patients and controls; with this impact being higher than in the capillary blood transcriptomes of donors (as evidenced by the number of DEGs captured, both non-adjusted and adjusted; <xref rid="SM1" ref-type="supplementary-material">Supplementary Table S2</xref>). This finding highlights, for the first time, the relevance of the host transcriptome response to music in saliva. Additionally, the results indicate that the relative impact of music on individual genes may vary considerably between tissues, leading to specific genes being upregulated in one tissue but downregulated in another; this differential behavior between tissues is consistent with responses to other significant external stimuli in the host, such as an infection (<xref ref-type="bibr" rid="ref30">G&#x00F3;mez-Carballa et al., 2022</xref>).</p>
<p>Three significant observations from the present study on the impact of music on salivary transcriptomes have already been reported for capillary blood transcriptomes (<xref ref-type="bibr" rid="ref29">G&#x00F3;mez-Carballa et al., 2023</xref>), namely, music elicits: (i) greater transcriptomic changes in ACD patients than in controls, (ii) a transcriptomic response toward upregulation in ACD patients compared to healthy donors, and (iii) music modifies the salivary expression of a few genes that are known to be altered in AD/MCI conditions. Despite the relatively low overlap existing between DEGs from saliva and blood capillary samples, <italic>KDM6B</italic> (Lysine Demethylase 6B) and <italic>TIMP2</italic> (TIMP Metallopeptidase Inhibitor 2) genes emerged as the only common DEGs (adjusted <italic>p</italic>-value &#x003C; 0.05) in both tissues from ACD patients. <italic>TIMP2</italic> was downregulated in both tissues whereas <italic>KDM6B</italic> showed opposite regulation patterns between tissues after musical stimuli. Furthermore, these genes were also found significantly upregulated in MCI/AD patients compared to healthy controls (<xref rid="SM1" ref-type="supplementary-material">Supplementary Figure S1</xref>). As reported in the literature, both <italic>KDM6B</italic> and <italic>TIMP2</italic> are required for a normal brain function, as alterations in these genes might lead to neurological conditions. <italic>TIMP2</italic> protein regulates extracellular matrix (ECM) remodeling and is particularly enriched in the hippocampus in comparison to other TIMP proteins (<xref ref-type="bibr" rid="ref15">Castellano et al., 2017</xref>). In the adult brain, <italic>TIMP2</italic> participates in neurogenesis, neuronal differentiation and in hippocampus-dependent memory (<xref ref-type="bibr" rid="ref23">Ferreira et al., 2023</xref>; <xref ref-type="bibr" rid="ref58">Perez-Martinez and Jaworski, 2005</xref>). Moreover, TIMP2 level disturbances were found across several neurodegenerative disorders such as AD (<xref ref-type="bibr" rid="ref45">Lorenzl et al., 2003</xref>; <xref ref-type="bibr" rid="ref76">Wang et al., 2020</xref>; <xref ref-type="bibr" rid="ref1">Aksnes et al., 2023</xref>). <italic>KDM6B</italic> cooperates with Tau in regulating synaptic plasticity and cognitive function (<xref ref-type="bibr" rid="ref77">Wang et al., 2022</xref>) and is present in excitatory neurons. Deleting <italic>KDM6B</italic> in neurons led to impaired synaptic activity, resulting in learning and memory deficits in mice (<xref ref-type="bibr" rid="ref77">Wang et al., 2022</xref>). Indeed, <italic>KDM6B</italic> has been recently reported as a risk gene for intellectual disability (<xref ref-type="bibr" rid="ref73">Stolerman et al., 2019</xref>), highlighting its importance for an adequate brain activity.</p>
<p>In ACD patients, the <italic>CXCL8</italic> and <italic>LGALS3</italic> genes emerged as the most upregulated and downregulated genes in TP2, respectively. <italic>CXCL8</italic> gene encodes the pro-inflammatory IL-8 cytokine and is primarily expressed in neurons, astrocytes, and microglia in the nervous system. Chronic inflammation has been reported to be a pivotal factor in AD development (<xref ref-type="bibr" rid="ref80">Xia and Hyman, 1999</xref>), even from an early stage of the disease progression (MCI) and potentially preceding clinical symptoms. Significantly higher levels of CXCL8 have been reported in cerebrospinal fluid (CSF), brain and plasma from AD patients in comparison to levels from healthy controls (<xref ref-type="bibr" rid="ref3">Alsadany et al., 2013</xref>; <xref ref-type="bibr" rid="ref4">Ashutosh et al., 2011</xref>; <xref ref-type="bibr" rid="ref18">Correa et al., 2011</xref>; <xref ref-type="bibr" rid="ref27">Galimberti et al., 2006</xref>), suggesting a probable detrimental role of CXCL8 in AD. <italic>CXCL8</italic> has been negatively correlated with cognitive scores from AD patients (<xref ref-type="bibr" rid="ref3">Alsadany et al., 2013</xref>), and positively correlated with CSF amyloid beta (A&#x03B2;) levels (<xref ref-type="bibr" rid="ref18">Correa et al., 2011</xref>). However, a neuro-protective role of CXCL8 in AD has also been suggested. While stimulation with A&#x03B2; triggers <italic>CXCL8</italic> production, it exhibits neuroprotective effects against A&#x03B2;-induced toxicity, possibly through a <italic>CXCL8</italic>-induced intracellular signaling and the production of neurotrophic factors, such as brain-derived neurotrophic factor (<xref ref-type="bibr" rid="ref4">Ashutosh et al., 2011</xref>). Thus, it is tempting to interpret that the upregulation of the <italic>CXCL8</italic> gene observed in saliva might represent a neuroprotective response triggered by music in the brains of ACD patients. Nevertheless, the specific role of <italic>CXCL8</italic> in AD pathogenesis is still unclear, and the disparity in results reporting both neuroprotective and detrimental effects may reflect issues related to, e.g., different experimental conditions and tissues.</p>
<p>The main pathological characteristics of AD include the formation of A&#x03B2; plaques and neurofibrillary tangles, neuronal loss, inflammation, oxidative stress, and microglial activation. Galectin-3, encoded by <italic>LGALS3</italic> gene, has been extensively associated with the activation of microglial cells around A&#x03B2; plaques in AD, indicating a major involvement in disease pathogenesis (<xref ref-type="bibr" rid="ref12">Boza-Serrano et al., 2019</xref>; <xref ref-type="bibr" rid="ref36">Holtman et al., 2015</xref>; <xref ref-type="bibr" rid="ref43">Krasemann et al., 2017</xref>). Molecular signatures of microglial activation in AD and aging have been described <italic>LGALS3</italic> as one of the most upregulated genes (<xref ref-type="bibr" rid="ref43">Krasemann et al., 2017</xref>; <xref ref-type="bibr" rid="ref40">Keren-Shaul et al., 2017</xref>). Although there are no data on the Galectin-3 measurements in microglia from MCI patients, elevated galectin levels have also been reported in the serum of both AD and MCI patients (<xref ref-type="bibr" rid="ref37">Ijsselstijn et al., 2013</xref>; <xref ref-type="bibr" rid="ref47">Ma et al., 2020</xref>; <xref ref-type="bibr" rid="ref82">Yazar et al., 2021</xref>; <xref ref-type="bibr" rid="ref78">Wang et al., 2015</xref>), suggesting a role of Galectin-3 in the disease and/or a risk factor for disease development. These pieces of evidence support the usefulness of Galectin-3 as a biomarker for AD, and its inhibition could have significant therapeutic benefits (<xref ref-type="bibr" rid="ref12">Boza-Serrano et al., 2019</xref>). Therefore, the under-expression of <italic>LGALS3</italic> might suggest a beneficial effect of music in ACD by compensating for the pathological effect of <italic>LGALS3</italic> over-expression due to the disease condition.</p>
<p>The top DEG in healthy controls, the Thimet Oligopeptidase (<italic>THOP1</italic>) gene, showed a significantly lower expression after the musical stimuli. <italic>THOP1</italic> is responsible for encoding a metallopeptidase, which participates in the metabolism of different neuropeptides expressed in neurons and glial cells, and plays a role in the brain neuropeptide degradation (<xref ref-type="bibr" rid="ref41">Kim et al., 2003</xref>). Dysregulation of <italic>THOP1</italic> has been associated with an unbalance in dopamine and serotonin turnover (<xref ref-type="bibr" rid="ref24">Ferro et al., 2020</xref>) and it is widely understood that listening to music can influence both dopaminergic and serotoninergic pathways. Thus, under-expression observed in the <italic>THOP1</italic> gene may be indicative of a music-mediated regulation of these neurotransmission systems. In addition, some studies have reported an over-regulation of <italic>THOP1</italic> in brain and CSF of AD patients (<xref ref-type="bibr" rid="ref20">Del Campo et al., 2023</xref>; <xref ref-type="bibr" rid="ref71">Shi et al., 2020</xref>), indicating a potential association with A&#x03B2;-mediated toxicity. However, this over-expression of <italic>THOP1</italic> in AD patients has been attributed to a protective response against A&#x03B2; toxicity (<xref ref-type="bibr" rid="ref59">Pollio et al., 2008</xref>).</p>
<p>Pathways analysis of DEGs suggests an influence of musical stimuli on unsaturated fatty acid metabolism in ACD patients. The brain, predominantly composed of lipids, necessitates proper lipid homeostasis for a normal brain function and development. Within the brain, unsaturated fatty acids, particularly polyunsaturated fatty acids (PUFAs) govern critical processes such as cell survival, neurogenesis, brain inflammation and synaptic function (<xref ref-type="bibr" rid="ref6">Bazinet and Laye, 2014</xref>). With normal aging, there is a decline in cholesterol and PUFAs levels in lipid rafts, affecting cell&#x2013;cell communication, signal transduction, and synaptic plasticity. However, this reduction is significantly more prominent in AD and other neurodegenerative diseases, leading to dysregulation in unsaturated fatty acid metabolism, increased Amyloid Precursor Protein (APP) processing, and rapid formation of A&#x03B2; aggregates (<xref ref-type="bibr" rid="ref32">Grassi et al., 2020</xref>; <xref ref-type="bibr" rid="ref72">Snowden et al., 2017</xref>). Reduced levels of unsaturated fatty acids have been detected in the brain and plasma of AD patients (<xref ref-type="bibr" rid="ref72">Snowden et al., 2017</xref>; <xref ref-type="bibr" rid="ref19">Cunnane et al., 2012</xref>). Interestingly, various therapeutic approaches targeting lipid metabolism are being considered in the context of AD (<xref ref-type="bibr" rid="ref75">Tong et al., 2024</xref>); our finding indicating a role of music in lipid homeostasis deserves further exploration.</p>
<p>Co-expression modules analysis pointed out to an impact of music on the salivary transcriptomes, higher in ACD than in healthy controls (both in correlation values and lower <italic>p</italic>-values). Overall, the molecular response to music was characterized by a stronger involvement of both adaptative and innate immune systems in healthy controls than in ACD patients. Specifically, the over-regulated <italic>GZMA</italic> module was found to be engaged in several T-cell adaptative related responses whereas the under-regulated <italic>SERPINA1</italic> module participates in different innate processes, such as IL-1 and TLR signaling. Interactions between the immune and nervous systems are bidirectional, with each directly influencing the behavior of the other. Furthermore, both the adaptive and innate immune systems play complex and dynamic roles in learning and memory, brain function, and neurostimulation and collaborate closely to preserve immune homeostasis (<xref ref-type="bibr" rid="ref25">Filiano et al., 2015</xref>). Dysregulation of these immune components can have significant repercussions on brain function and development, underscoring the importance of proper immune regulation in maintaining neurological health. For instance, microglia are macrophage-like innate resident immune cells of the central nervous system (CNS) with essential functions in the brain, ranging from immune surveillance and response to synaptic pruning and neuroprotection (<xref ref-type="bibr" rid="ref56">Paolicelli et al., 2011</xref>; <xref ref-type="bibr" rid="ref16">Chen et al., 2014</xref>; <xref ref-type="bibr" rid="ref63">Ransohoff and Cardona, 2010</xref>). Microglia interact with neurons modulating synaptic transmissions and, therefore, directly influencing synaptic plasticity and neuronal excitability (<xref ref-type="bibr" rid="ref57">Pascual et al., 2012</xref>; <xref ref-type="bibr" rid="ref84">Zhan et al., 2014</xref>). Dysregulation of microglial activity has been implicated in neurological and neurodegenerative disorders, such as AD (<xref ref-type="bibr" rid="ref68">Scheltens et al., 2021</xref>). Another innate immune component, the TLR family, is expressed in microglia, astrocytes and oligodendrocytes, but neurons also express TLRs, regulating proliferation, differentiation, outgrowth and neuron survival (<xref ref-type="bibr" rid="ref55">Okun et al., 2011</xref>). TLRs cascades are involved in different brain-related functions contributing to neurogenesis, modulation of CNS plasticity and learning (<xref ref-type="bibr" rid="ref66">Rolls et al., 2007</xref>; <xref ref-type="bibr" rid="ref54">Okun et al., 2010</xref>). Disturbances in TLR signaling might have either a negative or positive impact on nervous system homeostasis. Studies also provide evidence of the important role of cytokines considered pro-inflammatory, such as the innate immunity mediators IL-1 and TNF, for normal synaptic function (<xref ref-type="bibr" rid="ref7">Ben Menachem-Zidon et al., 2011</xref>; <xref ref-type="bibr" rid="ref83">Yirmiya and Goshen, 2011</xref>). Similarly, adaptative immunity T-cells have showed to be important for normal brain functioning with a beneficial role in cognition and behavior (<xref ref-type="bibr" rid="ref42">Kipnis et al., 2012</xref>; <xref ref-type="bibr" rid="ref14">Brynskikh et al., 2008</xref>; <xref ref-type="bibr" rid="ref62">Radjavi et al., 2014</xref>). A decreased number or dysfunction of T-cells may contribute to the etiology of different neurological disorders like autism or AD.</p>
<p>Notably, <italic>CD59</italic> gene emerged as both one of the top downregulated DEGs and the hub gene of the under-regulated module in ACD patients. CD59 is a glycoprotein that plays a crucial role in regulating the complement system by preventing the formation of the Membrane Attack Complex (MAC). In the brain, complement proteins regulate neurodevelopment, neural migration, proliferation and synaptic pruning. Downregulation of <italic>CD59</italic> (<xref ref-type="bibr" rid="ref81">Yang et al., 2000</xref>) and upregulation of the complement system have been reported in several studies involving AD mouse models and brain tissue from AD patients (<xref ref-type="bibr" rid="ref34">Hammond et al., 2019</xref>; <xref ref-type="bibr" rid="ref70">Shi et al., 2017</xref>; <xref ref-type="bibr" rid="ref64">Reichwald et al., 2009</xref>). However, it is still unclear if the changes in complement activity observed in AD are harmful or beneficial, as studies report both neuroprotective and neurodegenerative roles (<xref ref-type="bibr" rid="ref70">Shi et al., 2017</xref>; <xref ref-type="bibr" rid="ref48">Maier et al., 2008</xref>; <xref ref-type="bibr" rid="ref9">Benoit et al., 2013</xref>; <xref ref-type="bibr" rid="ref74">Toledo et al., 2014</xref>; <xref ref-type="bibr" rid="ref13">Brucato and Benjamin, 2020</xref>). Therefore, the biological interpretation of the expression changes induced by music is complex. The two downregulated modules in ACD patients and controls (<italic>CD59</italic> and <italic>SERPINA1</italic>) showed some functional commonalities, with intracellular transport and autophagic pathways emerging as common processes associated with the musical stimuli in both modules. Autophagy promotes the clearance of misfolded proteins and pathological aggregates, and in the brain, helps to maintain neuronal cellular morphology and physiological activities for a proper CNS function, prevents cellular toxicity and plays a crucial role in synaptic plasticity (<xref ref-type="bibr" rid="ref35">Haynes et al., 2015</xref>). In fact, deficient autophagic machinery is one of the most relevant hallmarks found in neurodegenerative diseases like AD (<xref ref-type="bibr" rid="ref26">Filippone et al., 2022</xref>; <xref ref-type="bibr" rid="ref85">Zhang et al., 2021</xref>). The effect of music on the downregulation of modules involved in autophagy could be related to a local response, as these pathways are crucial for the homeostasis of most, if not all, tissues. However, this issue deserves further investigation in future studies due to the significant role of autophagy in neurodegenerative processes.</p>
<p>After our initial and recent attempt to investigate the impact of music on neurodegenerative diseases, the present follow-up study is pioneering in revealing several aspects: (i) It demonstrates that music has the ability to influence the expression patterns captured from saliva donors, (ii) It establishes parallels between gene expression observed in saliva and blood; (iii) It highlights that music has a stronger impact on the transcriptome of ACD patients compared to healthy individuals, as, e.g., measured by the number of DEGs altered by music, and (iv) It reveals that music overall triggers the upregulation of gene expression in patients compared to healthy controls.</p>
<p>Among the limitations of the present study, we echo those already discussed in our previous study on sensogenomics22 experimental concerts carried out on capillary blood samples (<xref ref-type="bibr" rid="ref29">G&#x00F3;mez-Carballa et al., 2023</xref>). Additionally, we acknowledge the challenge posed by the analysis of saliva in RNAseq analysis. Fortunately, the methodology employed in the present study, although it evaluates a lower number of genes for expression, offers the advantage of being a gold standard for gene expression studies. Therefore, the high quality of the gene expression results provided by NanoString compensates somewhat for the limitation of analyzing fewer genes.</p>
<p>The present study has demonstrated the power of short-duration musical stimuli in modifying the salivary transcriptome of ACD patients and healthy donors. The impact of music on saliva tissue is comparable to, or even greater than, that observed in blood with a more pronounced effect seen in patients than in healthy controls. Of note is the discovery that music influences the expression of genes and modules commonly altered in neurodegenerative diseases, a finding that may help to elucidate the known beneficial effects of music as reported by specialists in neuroscience and cognitive sciences. Further efforts to validate these findings in larger cohorts and other disease scenarios and to explore the impact of music not only on the gene expression level but also on other &#x2018;-omic&#x2019; layers are warranted.</p>
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<sec sec-type="data-availability" id="sec12">
<title>Data availability statement</title>
<p>The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found at: <ext-link xlink:href="https://www.ncbi.nlm.nih.gov/geo/" ext-link-type="uri">https://www.ncbi.nlm.nih.gov/geo/</ext-link>, GSE268683.</p>
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<sec sec-type="ethics-statement" id="sec13">
<title>Ethics statement</title>
<p>The studies involving humans were approved by the Ethics Committee of Xunta de Galicia approved the present project (Registration code: 2020/021), and the study was conducted in accordance to the guidelines of the Helsinki Declaration. The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation in this study was provided by the participants&#x2019; legal guardians/next of kin. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.</p>
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<sec sec-type="author-contributions" id="sec14">
<title>Author contributions</title>
<p>AG-C: Conceptualization, Methodology, Writing &#x2013; original draft, Data curation, Investigation, Visualization, Supervision, Validation, Writing &#x2013; review &#x0026; editing, Formal analysis, Software. LN: Writing &#x2013; review &#x0026; editing, Conceptualization. NoM: Writing &#x2013; review &#x0026; editing, Software, Formal analysis. XB: Writing &#x2013; review &#x0026; editing, Data curation, Methodology. SP: Writing &#x2013; review &#x0026; editing, Data curation, Methodology. SV-L: Methodology, Writing &#x2013; review &#x0026; editing, Data curation. MC: Writing &#x2013; review &#x0026; editing, Methodology. IF-V: Writing &#x2013; review &#x0026; editing, Supervision. NaM: Writing &#x2013; review &#x0026; editing, Methodology. JM-L: Writing &#x2013; review &#x0026; editing, Methodology. AC-M: Writing &#x2013; review &#x0026; editing. LC-M: Writing &#x2013; review &#x0026; editing, Methodology. SR-V: Methodology, Writing &#x2013; original draft. LR: Writing &#x2013; original draft, Methodology. AD-U: Methodology, Writing &#x2013; review &#x0026; editing. IR-C: Methodology, Writing &#x2013; review &#x0026; editing. CR-T: Writing &#x2013; review &#x0026; editing, Methodology, FM-T: Project administration, Resources, Writing &#x2013; review &#x0026; editing, Funding acquisition. AS: Supervision, Validation, Conceptualization, Investigation, Project administration, Funding acquisition, Writing &#x2013; review &#x0026; editing, Resources, Methodology, Software, Formal analysis, Writing &#x2013; original draft, Visualization.</p>
</sec>
<sec id="sec15">
<title>Group member of sensogenomics working group</title>
<p>Antonio Salas Ellacuriaga &#x2013; PI; Federico Martin&#x00F3;n-Torres &#x2013; PI; Laura Navarro Ram&#x00F3;n &#x2013; Coordinator. <italic>GenPoB/GenVip - Instituto de Investigaci&#x00F3;n Sanitaria (IDIS) (alphabetic order)</italic>. Alba Camino Mera, Albert Pad&#x00ED;n Villar, Alberto G&#x00F3;mez Carballa, Alejandro P&#x00E9;rez L&#x00F3;pez, Alicia Carballal Fern&#x00E1;ndez, Ana Cotovad Bellas, Ana Isabel Dacosta Urbieta, Narmeen Mallah, Ana Mar&#x00ED;a Pastoriza Mourelle, Ana Mar&#x00ED;a Sen&#x00ED;n Ferreiro, Andr&#x00E9;s Muy P&#x00E9;rez, Ant&#x00ED;a Rivas Oural, Antonio Justicia Grande, Antonio Pi&#x00F1;eiro Garc&#x00ED;a, Anxela Cristina Delgado Garc&#x00ED;a, Bel&#x00E9;n Mosquera P&#x00E9;rez, Blanca D&#x00ED;az Esteban, Carlos Dur&#x00E1;n Su&#x00E1;rez, Carmen Curros Novo, Carmen G&#x00F3;mez Vieites, Carmen Rodr&#x00ED;guez-Tenreiro S&#x00E1;nchez, Celia Varela P&#x00E1;jaro, Claudia Navarro Gonzalo, Cristina Ser&#x00E9;n Trasorras, Cristina Talavero Gonz&#x00E1;lez, Ein&#x00E9;s Monteagudo Vilavedra, Estefan&#x00ED;a Rey Campos, Esther Montero Campos, Fernando &#x00C1;lvez Gonz&#x00E1;lez, Fernando Caama&#x00F1;o Vi&#x00F1;as, Francisco Garc&#x00ED;a Iglesias, Gloria <italic>Viz</italic> Rodr&#x00ED;guez, Hugo Alberto Tovar Velasco, Irene &#x00C1;lvarez Rodr&#x00ED;guez, Irene Garc&#x00ED;a Zuazola, Irene Rivero Calle, Iria Afonso Carrasco, Isabel Ferreir&#x00F3;s Vidal, Isabel Lista Garc&#x00ED;a, Isabel Rego Lijo, Iv&#x00E1;n Prieto G&#x00F3;mez, Iv&#x00E1;n Quintana Cepedal, Jacobo Pardo Seco, Jes&#x00FA;s Eir&#x00ED;s Pu&#x00F1;al, Jos&#x00E9; G&#x00F3;mez Rial, Jos&#x00E9; Manuel Fern&#x00E1;ndez Garc&#x00ED;a, Jos&#x00E9; Mar&#x00ED;a Martin&#x00F3;n Mart&#x00ED;nez, Julia Cela Mosquera, Julia Garc&#x00ED;a Curr&#x00E1;s, Juli&#x00E1;n Montoto Louzao, Lara Mart&#x00ED;nez Mart&#x00ED;nez, Laura Navarro Marr&#x00F3;n, Lidia Pi&#x00F1;eiro Rodr&#x00ED;guez, Lorenzo Redondo Collazo, L&#x00FA;a Castelo Mart&#x00ED;nez, Luc&#x00ED;a Company Arciniegas, Luis Crego Rodr&#x00ED;guez, Luisa Garc&#x00ED;a Vicente, Manuel V&#x00E1;zquez Donsi&#x00F3;n, Mar&#x00ED;a Dolores Mart&#x00ED;nez Garc&#x00ED;a, Mar&#x00ED;a Elena Gamborino Caram&#x00E9;s, Mar&#x00ED;a Elena Sobrino Fern&#x00E1;ndez, Mar&#x00ED;a Jos&#x00E9; Curr&#x00E1;s Tuala, Mar&#x00ED;a Mart&#x00ED;nez Leis, Mar&#x00ED;a Soledad Vilas Iglesias, Mar&#x00ED;a Sol Rodriguez Calvo, Mar&#x00ED;a Teresa Autran Garc&#x00ED;a, Marina Casas P&#x00E9;rez, Marta Aldonza Torres, Marta Bouz&#x00F3;n Alejandro, Marta Lendoiro Fuentes, Miriam Ben Garc&#x00ED;a, Miriam Cebey L&#x00F3;pez, Montserrat L&#x00F3;pez Franco, Nour El Zahraa Mallah, Narmeen Mallah, Natalia Garc&#x00ED;a S&#x00E1;nchez, Natalia Vieito Perez, Patricia Regueiro Casuso, Ricardo Su&#x00E1;rez Camacho, Rita Garc&#x00ED;a Fern&#x00E1;ndez, Rita Varela Est&#x00E9;vez, Rosaura Pic&#x00E1;ns Leis, Ruth Barral Arca, Sandra Carnota Antonio, Sandra <italic>Viz</italic> Lasheras, Sara Pischedda, Sara Rey V&#x00E1;zquez, Sonia Marcos Alonso, Sonia Ser&#x00E9;n Fern&#x00E1;ndez, Susana Rey Garc&#x00ED;a, Vanesa &#x00C1;lvarez Iglesias, Victoria Redondo Cervantes, Vanesa &#x00C1;lvarez Iglesias, Wiktor Dominik Nowak, Xabier Bello Paderne, and Xabier Mazaira L&#x00F3;pez. <italic>Nursing volunteers (alphabetic order)</italic>. Alejandra Fern&#x00E1;ndez M&#x00E9;ndez, Ana Isabel Abad&#x00ED;n Campa&#x00F1;a, Ana Mar&#x00ED;a Le&#x00F3;n Caama&#x00F1;o, Ana Mar&#x00ED;a Buide Illobre, &#x00C1;ngeles Mera Cores, Carmen Nieves Vastro, Carolina Suarez Crego, Concepci&#x00F3;n Rey Iglesias, Cristina Candal Regueira, Dolores Barreiro Puente, Elvira Rodr&#x00ED;guez Rodr&#x00ED;guez, Eugenia Gonz&#x00E1;lez Budi&#x00F1;o, Eva Rey &#x00C1;lvarez, Fernando Rodr&#x00ED;guez Gerpe, Gemma Albela Silva, Isabel Castro P&#x00E9;rez, Isabel Dom&#x00ED;nguez R&#x00ED;os, Jos&#x00E9; &#x00C1;ngel Fern&#x00E1;ndez de la Iglesia, Jos&#x00E9; Cruces V&#x00E1;zquez, Jos&#x00E9; Luis Cambeiro Quintela, Jos&#x00E9; Ram&#x00F3;n Magari&#x00F1;os Iglesias, Julia Rey Brandariz, Julio Abel Fern&#x00E1;ndez L&#x00F3;pez, Luisa Garc&#x00ED;a Vicente, Manuel Gonz&#x00E1;lez Lito, Manuel Gonz&#x00E1;lez Lij&#x00F3;, Manuela P&#x00E9;rez Rivas, Margarita Turnes Paredes, Mar&#x00ED;a Aurora M&#x00E9;ndez L&#x00F3;pez, Mar&#x00ED;a Bego&#x00F1;a Tom&#x00E9; Arufe, Mar&#x00ED;a Campos Torres, Mar&#x00ED;a del Carmen Baloira Nogueira, Mar&#x00ED;a del Carmen Garc&#x00ED;a juan, Mar&#x00ED;a Esther Moricosa Garc&#x00ED;a, Mar&#x00ED;a Luz Chao Jarel, Mar&#x00ED;a Mart&#x00ED;nez Leis, Mar&#x00ED;a Mercedes Jim&#x00E9;nez Santos, Mar&#x00ED;a Salom&#x00E9; Buide Illobre, Mar&#x00ED;a Victoria L&#x00F3;pez Pereira, Mercedes Jorge Gonz&#x00E1;lez, Mercedes Isolina Rodr&#x00ED;guez Rodr&#x00ED;guez, Miren Payo Puente, Natalia Carter Dom&#x00ED;nguez, Olga Mar&#x00ED;a Reyes Gonz&#x00E1;lez, Pilar Mera Rodr&#x00ED;guez, Purificaci&#x00F3;n Sebio Brandariz, Salom&#x00E9; Quint&#x00E1;ns lago, Yolanda Rodr&#x00ED;guez Taboada, and Mar&#x00ED;a Pereira Grau. <italic>Other volunteers (alphabetic order)</italic>. Alba Arias G&#x00F3;mez, Alejandro Moreno D&#x00ED;az, Ana Arca Mar&#x00E1;n, Astro Gonz&#x00E1;lez Guirado, Brais Garc&#x00ED;a Iglesias, Carlos S&#x00E1;nchez Rub&#x00ED;n, Carmen Otero de Andr&#x00E9;s, Clara P&#x00E9;rez Errazquin Barrera, Claudia Rey Posse, Cristina Rojas Garc&#x00ED;a, Eduardo Xavier Gim&#x00E9;nez Bargiela, Elena Gloria Morales Garc&#x00ED;a, Fabio Izquierdo Garc&#x00ED;a Escribano, Gabriel Guisande Garc&#x00ED;a, Jaime L&#x00F3;pez Mart&#x00ED;n, Lara Pais Ramiro, Luc&#x00ED;a Rico Montero, Lu&#x00ED;s Est&#x00E9;vez Mart&#x00ED;nez, Manuel Est&#x00E9;vez Casal, Mar&#x00ED;a Ar&#x00E1;nzazu Palomino Ca&#x00F1;o, Mar&#x00ED;a Rubio Vald&#x00E9;s, Marisol Nogales Ben&#x00ED;tez, Miryam Tilve P&#x00E9;rez, Nuria Villar Mui&#x00F1;os, Pablo Del Cerro Rodr&#x00ED;guez, Pablo Pozuelo Mart&#x00ED;nez Carde&#x00F1;oso, Salma Ouahabi El Ouahabi, and Santiago V&#x00E1;zquez Calvache.</p>
</sec>
<sec sec-type="funding-information" id="sec16">
<title>Funding</title>
<p>The author(s) declare that financial support was received for the research and/or publication of this article. This study received support from Grupos de Referencia Competitiva GRC GAIN (IN607A2023/02) and Programa de Cooperaci&#x00F3;n Interreg-VI POCTEP (0313_EUTERPE_ADN_1_E) [AS], IIN607A2021/05 [FM-T]) and IN677D 2024/06 [AG-C]).</p>
</sec>
<ack>
<p>We would like to kindly acknowledge all participants in the present study, the AGADEA association of Alzheimer disease patients, and the musicians of SANARTE, who kindly agreed to participate in this project. We would also like to acknowledge the Real Filharmon&#x00ED;a de Galicia (<ext-link xlink:href="http://www.rfgalicia.org" ext-link-type="uri">www.rfgalicia.org</ext-link>; and Sabela Garc&#x00ED;a Fonte in particular), and the Auditorio de Galicia for their support.</p>
</ack>
<sec sec-type="COI-statement" id="sec17">
<title>Conflict of interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec sec-type="ai-statement" id="sec18">
<title>Generative AI statement</title>
<p>The authors declare that no Gen AI was used in the creation of this manuscript.</p>
<p>Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.</p>
</sec>
<sec sec-type="disclaimer" id="sec19">
<title>Publisher&#x2019;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
<sec sec-type="supplementary-material" id="sec20">
<title>Supplementary material</title>
<p>The Supplementary material for this article can be found online at: <ext-link xlink:href="https://www.frontiersin.org/articles/10.3389/fnagi.2025.1622816/full#supplementary-material" ext-link-type="uri">https://www.frontiersin.org/articles/10.3389/fnagi.2025.1622816/full#supplementary-material</ext-link></p>
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<supplementary-material xlink:href="Table_4.xlsx" id="SM4" mimetype="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet" xmlns:xlink="http://www.w3.org/1999/xlink"/>
<supplementary-material xlink:href="Image_1.TIF" id="SM5" mimetype="image/TIFF" xmlns:xlink="http://www.w3.org/1999/xlink"/>
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<supplementary-material xlink:href="Image_3.TIF" id="SM7" mimetype="image/TIFF" xmlns:xlink="http://www.w3.org/1999/xlink"/>
</sec>
<fn-group>
<fn id="fn0001"><p><sup>1</sup><ext-link xlink:href="http://sensogenomics.com" ext-link-type="uri">http://sensogenomics.com</ext-link></p></fn>
</fn-group>
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