Peptidomics analysis of serum in patients suffered from generalized anxiety disorder

Abstract

To identify the differentially expressed peptides that influenced the generalized anxiety disorder (GAD) by adopting peptidomics and to further provide new targets for the diagnosis and treatment of clinical GAD. 20 patients with GAD as the experimental group and 20 healthy volunteers as the control group were included in the experiment. Differentially expressed peptides were compared and analyzed after collecting serum samples from the two groups and performing peptidomics testing. In addition, GO analysis, COG analysis, and enrichment pathway analysis were utilized to explore the biological processes and function distribution of the differentially expressed peptides. A total of 1149 significantly differentially expressed peptides were identified in the sera of patients with GAD compared to healthy controls (p< 0.05 or P-value-chitest< 0.05), the up-regulated polypeptides are predominant among them. Bioinformatics analysis revealed that the differentially expressed peptide precursor proteins are primarily involved in biological processes such as cellular cytoskeleton organization, intracellular signal transduction, and vesicular trafficking, and are primarily localized in subcellular structures such as the cytoplasm and vesicles, suggesting a possible role in the pathogenesis of GAD by influencing cellular cytoskeletal dynamics, synaptic plasticity, and intercellular signal transduction. Further analysis revealed that the corresponding proteins of these significantly differentially expressed peptides (such as SLAM family member 5, Talin-1, Clusterin, and IGHG1) are involved in immunoregulation, integrin activation, neuroprotection, and antibody-mediated immune responses, indicating that immune-inflammatory imbalance, abnormal synaptic structure, and function may be important molecular mechanisms in the pathogenesis of GAD. GAD is closely associated with immune-inflammatory imbalance and synaptic abnormalities, with significant differences in key proteins such as SLAM family member 5, Talin-1, Clusterin, and IGHG1. This study preliminarily explores differentially expressed peptides in GAD, providing new insights for clinical diagnosis and the screening of therapeutic targets.

Introduction

Generalized anxiety disorder is generally characterized by excessive anxiety and worry, restlessness, and irritability, accompanied by physical symptoms such as sleep disorders, tension, fatigue, palpitations, dry mouth, sweating, etc., which usually last for more than six months¹. The incidence rate of GAD has been rising in recent years, and it commonly appears in adolescents and even children², and the incidence rate of GAD in females is higher than that in males³. GAD is characterized by slow onset, recurrence, and difficulty in recovery and will greatly affect the patient’s mental health, life, and work. The persistence of anxiety can affect the functions of various systems, aggravate the condition, and be the main cause of self-harm⁴; it can even lead to suicidal tendencies⁵. The onset of GAD is closely related to the environment and genetics, and its related neural mechanisms are related to the hypothalamic-pituitary-adrenal axis (HPA axis), neurotransmitter imbalance, neuroinflammation, and immune pathways^6–8.

The first-line treatment for anxiety disorders is behavioral cognitive therapy combined with psychotropic medication⁹. Benzodiazepines are commonly used drugs, which can significantly improve patients’ anxiety¹⁰. However, the problem with the aforementioned first-line treatment method is that the drug treatment time is long, and it is easy to relapse after drug withdrawal, and some patients may experience side effects or addiction. Therefore, the occurrence and pathogenesis of GAD need to be paid more attention to, and efforts should be made to suppress it from the source, reduce the incidence, and relieve people’s distress.

In recent years, many studies have been devoted to exploring the changes in the omics of anxiety disorders and exploring serum biomarkers to provide reliable diagnostic indicators for clinical practice^11–13. Peptidome refers to various endogenous polypeptides that are widely present in the intracellular and extracellular spaces of living organisms. Peptidomics summarized the visualization, quantification, and identification technologies of low molecular weight proteomes (<15 kDa)¹⁴. Protein is the basis for the body’s various functions. By expressing itself as various metabolites, it participates in the activities of the body’s nervous, immune, and endocrine systems and regulates the body. Peptides are intermediate substances in protein synthesis and degradation, which exist in all cells and body fluids and play a vital role in maintaining the body’s physiological functions and the internal environmental homeostasis¹⁵. Changes in peptide concentration and structure may contribute to the process of disease specificity formation, and they can serve as potential biomarkers of disease¹⁶. At present, the diagnosis of GAD relies on clinical manifestations and scale diagnosis, and clearly defined biochemical disease markers have not yet been found. Based on peptideomics analysis, this study was for the purpose of exploring the differentially expressed peptides in patients suffering from GAD, exploring the intrinsic pathogenesis, and providing a foundation for clinical diagnosis and therapeutics.

Methods

Study population

Patients who were treated at Henan University of Traditional Chinese Medicine between July and November 2023 were recruited, including 20 patients who conformed to the diagnosis of GAD and 20 healthy volunteers. All participants signed an informed consent form. The study was designed and conducted in conformity with the World Medical Association Declaration of Helsinki and was permitted by the Ethics Committee of the Third Affiliated Hospital of Henan University of Traditional Chinese Medicine (approval No. 2023HL-074). All participants have signed informed consent. This study has been registered on the International Traditional Medicine Clinical Trial Registry (Registration number: ITMCTR2025000970, 2025/01/12).

Exclusion criteria

Exclude patients with secondary anxiety due to physical diseases such as hyperthyroidism, hypertension, coronary heart disease, etc.; exclude patients with withdrawal reactions from overdose of stimulants, hypnotic sedatives, or antianxiety drugs, obsessive-compulsive disorder, phobia, hypochondriasis, neurasthenia, mania, depression, or anxiety associated with schizophrenia; exclude pregnant or breastfeeding women; exclude patients with severe skin diseases or severe damage to the treatment area; exclude subjects who could not tolerate the treatment.

Scale evaluation

Hamilton Anxiety Rating Scale (HAMA)

This scale is used for the diagnosis of GAD and evaluation of improvements in anxiety levels. HAMA has a total of 14 items, all of which are scored on a 5-point scale ranging from 0 to 4 points. A higher score indicates a more severe level of anxiety.

Self-Rating Anxiety Scale (SAS)

This scale is used to assess the degree of improvement in patients’ anxiety. The scale contains 20 items that reflect the subjective feeling of anxiety. Each item is scored on a 1-4 scale according to the frequency of symptoms. The higher the score, the more severe the anxiety.

Pittsburgh Sleep Quality Index (PSQI)

This scale is used to assess the patient’s sleep quality. This scale consists of 19 self-assessment items and 5 other-assessment items, of which the 19th self-assessment item and the 5th other-assessment item are not included in the scoring. The 18 self-assessment items are composed of 7 components, each component is scored on a scale of 0 to 3, and the cumulative scores of each component are the total PSQI score. The higher the score, the worse the sleep quality.

Serum peptidomics testing

Sample collection steps

The blood specimens were collected in a centrifuge tube and placed at 37 C (or room temperature) for 1 hour to coagulate and stratify. Samples were centrifuged at 3000 rpm at indoor temperature for 10 minutes, and then the liquid supernatants were transferred to a clean centrifuge tube. The obtained supernatant was centrifuged again at 12,000 rpm at 4 C and continued centrifuging for 10 minutes, and the liquid supernatant was dispensed into 1.5 mL centrifuge tubes, keeping 0.2 mL in each tube, and frozen samples at – 80 C. Sample volume requirement: 200 L/sample.

Peptide processing

The author weighed 100 l of the sample, added 400 l of 75% methanol-water to the sample, and placed the sample in the ice-water bath under ultrasound for 5 minutes. Then the sample was placed at 40 C and lasted 1 hour. Next, the sample was centrifuged again at 12,000 rpm and 4 C for 10 minutes, 400 l of liquid supernatant was collected, and the sample was vacuum-freeze-dried for later use. Afterward, the sample was dissolved with 400 l of 0.1% FA and 2% of acetonitrile solution (buffer A). The desalting column was enabled by using 400 l of 0.1% FA and 80% of acetonitrile (buffer B) and centrifuging at 1000*g for 1 minute. The desalting column was equilibrated by using 400 l of 0.1% FA and 2% of acetonitrile solution (buffer A) with centrifuging at 1000*g lasting 1 minute. The sample was injected into the salt removal column with centrifuging at 1000*g lasting 1 minute, and then the peptides were captured by the salt removal column. The salt removal column was cleaned by adding 2% of acetonitrile solution (buffer A) and 400 l of 0.1% of FA for centrifuging at 1000*g for 1 minute. Placed the salt removal column in an unused EP tube, added 80% of acetonitrile solution (buffer B) and 200 l of 0.1% FA, and centrifuged at 1000*g for 1 minute in order to elute the residual peptides. The eluent was vacuum dried, and 100 l of mobile phase A was injected into the redissolved sample, and then 15 l was taken for on-machine detection.

nanoLC–MS/MS analysis

For each sample, 2 g of total peptides were separated and analyzed with a nanoUPLC (nanoElute2) coupled to a timsTOF Pro2 instrument (Bruker) with a nanoelectrospray ion source. Separation was performed using a reversedphase column (PePSep C18, 1.9 m, 75 m × 15 cm, Bruker,Germany). Mobile phases were H₂O with 0.1% FA (phase A) and ACN with 0.1% FA (phase B). Separation of sample was executed with a 60 min gradient at 300 nL/min flow rate. Gradient B: 2% for 0 min, 222% for 45 min, 2237% for 5 min, 3780% for 5 min, 80% for 5 min.

The mass spectrometer adopts DDA PaSEF mode for DDA data acquisition, and the scanning range is from 100 to 1700 m/z for MS1. During PASEF MS/MS scanning, the impact energy increases linearly with ion mobility, from 20 eV (1/K0 = 0.6 Vs/cm2) to 59 eV (1/K0 = 1.6 Vs/cm2).

Data analysis

Proteome discoverer database search

Vendor’s raw MS files were processed using SpectroMine software (4.2.230428.52329) and the built-in Pulsar search engine. MS spectra lists were searched against their specieslevel UniProt FASTA databases (uniprot_Homo sapiens_9606_reviewed_2023_09.fasta), Carbamidomethyl [C] as a fixed modification, Oxidation (M) and Acetyl (Protein Nterm) as variable modifications. No-Enzyme (Unspecific) was used as enzyme. A maximum of 2 missed cleavage(s) was allowed. The false discovery rate (FDR) was set to 0.01 for both PSM and peptide levels. Peptide identification was performed with an initial precursor mass deviation of up to 20 ppm and a fragment mass deviation of 20 ppm. All the other parameters were reserved as default.

The sequences were mapped to the COG database using NCBI-BLAST (v2.12.0+) to obtain functional annotations, and the COG histogram was drawn using the ‘ggplot2’ package in R (v3.3.2) to analyze the functional clustering characteristics of genes/peptides.

At the same time, the R language (R (ggplot2, v3.3.2), (topGO, v2.36.0)), based on the EBI GOA protein annotation dataset (2023.07.12, http://ftp.ebi.ac.uk/pub/databases/GO/goa/proteomes), was used to complete the GO annotation enrichment analysis of the genes corresponding to the differentially expressed peptides and explore their functional distribution characteristics.

Quality control results

Each component of quantitative proteomics was detected on the machine independently based on LC–MS, and the detection had a high requirement on the repeatability and stability of the chromatography. Usually, a reasonable chromatographic condition should have the following characteristics: (1) The retention time should be relatively stable, as shown by the TIC/BPC with good redundancy. (2) The distribution of peptide peak time should be uniform, as shown by the resultant signal strength of TIC/BPC being basically the same in the anterior segment, middle segment, and posterior segment of the gradient. (3) The chromatographic resolution should be good, as shown by the narrow, sharp, and symmetrical chromatographic peaks without obvious tailing, and BPC should reflect the indicators generally. (4) The ion source spray should be stable, as shown by the TIC having no obvious fluctuations and the TIC curve being smooth. As shown in Fig. 1.

Fig. 1.

UPLC validity.

Results

Basic clinical information of two groups

There were 20 healthy volunteers collected in the control group, which included 11 females and 9 males between the ages of 21 years old and 60 years old, and the mean age of the group was 33.65 ± 12.35 years old. There were 20 volunteers collected in the GAD group, which included 16 females and 4 males between the ages of 22 years old and 55 years old, and the mean age of the group was 35.00 ± 11.02 years old. The course of GAD lasted 6-90 months; meanwhile, the average course of GAD was 31.55 ± 20.62 months. The scores of each scale of the patients in the experimental group were HAMA score 23.95 ± 4.52, SAS score 55.69±4.22, and PSQI score 12.65 ± 3.59. The details were shown in Table 1.

Table 1.

Basic clinical information of two groups.

		Experimental group	Control group
Gender	Male	9	4	0.091
	Female	11	16
Age	Average age	35.00± 11.02	33.65± 12.35	0.717
	Minimum value	22	21
	Maximum value	55	60
Medication status		None	None
Course of Disease		31.55± 20.62	–
HAMA Score		23.95± 4.524	–
SAS Score		55.69± 4.224	–
PSQI Score		12.65± 3.588	–

Peptide data analysis

Raw result preprocessing

First, the original data was normalized by using median normalization. At the same time, the missing values in the original data were simulated. The missing values were imputed using the half-minimum value method in the numerical simulation. After preprocessing, 14,904 detected peptides were retained.

Identification of differentially expressed peptides in patients with GAD

In contrast to the control group, 1149 differentially expressed peptides were distinguished in the serum of patients with GAD. Then the differentially expressed peptides were subjected to hierarchical cluster analysis and revealed visually in a thermodynamic diagram, which is shown in Fig. 2. Among the aforementioned differentially expressed peptides, 1078 were upregulated and 71 were downregulated, belonging to a total of 344 proteins. The volcano plot of all differential expression peptides was shown in Fig. 3.

Fig. 2.

Cluster analysis heat map. The screening conditions for differential peptides were Foldchange 1.2 or 0.83 and P-value or P-value-chitest < 0.05. The abscissa in the figure represented different groups of experiments; A represents the GAD group, and C represents healthy subjects. The ordinate represented the differentially expressed peptides, and the blocks of color in different places represented the relative expression levels of the peptides in the corresponding places; the red color represented high expression quantity, and the blue color represented low expression quantity. The consequence can be seen in that there was an obvious grouping pattern of differentially expressed peptides in the figure.

Fig. 3.

Volcano map. Every point in the figure represented a detected peptide, the horizontal coordinates represented the multiple changes of every peptide in the group (taken as the logarithm with base number 2), and the vertical coordinates represented the P-Value of Student’s t-test (taken as the negative number of the logarithm with base number 10); the color of the scatter represented the result of the final screening. In the results, significantly up-regulated differential expression peptides appeared as red, significantly down-regulated differential expression peptides appeared as blue, and non-significantly different peptides appeared as gray.

Location distribution of differentially expressed peptides

The proteins corresponding to the differential expression peptides were primarily distributed in the cytoplasm (146, 43.84%), secretion (87, 26.13%), nucleus (49, 14.71%), plasma membrane (21, 6.31%), cytoplasm (9, 2.70%), endoplasmic reticulum membrane (7, 2.1%), endoplasmic reticulum (3, 0.9%), golgiosome (2, 0.6%), and others included mitochondria, mitochondrial membrane, peroxisome, Golgi membrane, vacuole, etc. (Fig. 4).

Fig. 4.

Analysis pie chart of subcellular localization. Sub-cellular localization means the specific location of a protein in a cell and also reflects the place where it performs functions. The pie chart of subcellular localization analysis can reflect the proportion and number of differentially expressed peptide subordinate proteins mapped in every subcellular intuitively.

Differentially expressed peptides and precursor proteins, gene information

According to the multiple relationships between two experimental groups, the top 20 differential peptide expression information was screened out, as shown in Table 2. The UniProt database showed that CD84 and IGHG1 were closely related to human immune function and participated in the regulation and interconnection of immune responses, including geneogenous and adaptive; TLN1 and CLU are involved in neural signal transduction and synaptic plasticity regulation.

Table 2.

Information of differentially expressed peptide.

GeneName	Accession	Sequence	FOLD CHANGE	LOG_FOLDCHANGE	P-VALUE	Q-VALUE	P-VALUE-chitest
CD84	Q9UIB8	SRNTQPAESRIYDEILQ_2162	594.7373	9.2161	0.0045	0.5041	0.0036
TLN1	Q9Y490	SMPPAQQQITSGQMHRGHMPPLT_6251	251.4856	7.9743	0.0198	0.5902	0.0168
CLU	P10909	DQTVSDNELQEMSNQGSKYVNKEIQNAVNGVKQIK_1354	244.1122	7.9314	0.0096	0.5494	0.0079
IGHG1	P01857	TQKSLSLSPG_2024	191.5966	7.5819	0.0021	0.3934	0.0016
CRACD	Q6ZU35	HPGPPPASSQTPAPEHDKAANKMPLA_3008	174.3286	7.4457	0.0021	0.3934	0.0016
IRAG1	Q9Y6F6	VESELGKQLL_3648	125.5016	6.9716	0.0198	0.5902	0.0168
SEC31A	O94979	NIEGAPGAPIGNTFQHVQ_9906	124.4298	6.9592	0.0402	0.5902	0.0350
F2	P00734	TERELLESYIDG_8544	83.1810	6.3782	0.0096	0.5494	0.0079
F2	P00734	EDKTERELLES_8548	72.6482	6.1829	0.0402	0.5902	0.0350
ADD1	P35611	TNPKEVQEMRNKIREQNLQDIK_5492	64.2371	6.0053	0.0198	0.5902	0.0168
CTTN	Q14247	STFEDVTQVSSAYQ_4486	62.3495	5.9623	0.0198	0.5902	0.0169
TLN1	Q9Y490	VDDSKTVTDMLM_6644	55.8631	5.8038	0.0096	0.5494	0.0079
FGA	P02671	GSEADHEGTHSTKRGHA_3498	53.7240	5.7475	0.0045	0.5041	0.0036
CAPNS1	P04632	AAAQYNPEPPPPRTHYSNIE_2643	52.2463	5.7073	0.0021	0.3934	0.0016
PTPN12	Q05209	LTPSPTTQVETPDLVDHDNT_4213	51.7399	5.6932	0.0402	0.5902	0.0350
SEC22B	O75396	ELQDVQRIMVANIE_9029	51.6335	5.6902	0.0402	0.5902	0.0350
LCP2	Q13094	ALRNVPFRSEVLG_4418	50.4096	5.6556	0.0096	0.5494	0.0079
CASS4	Q9NQ75	TLPNPQKSEWIYDTPVSPGKASVRN_2915	49.6285	5.6331	0.0021	0.3934	0.0016
FGA	P02671	YGTGSETESPR_484	49.0803	5.6171	0.0004	0.2474	0.0002
DMTN	Q08495	SPGSVSPSRDSSVPGSPSSIVAKMDNQVLG_4230	48.0179	5.5855	0.0021	0.3934	0.0016

GeneName: The gene name of the polypeptide’s dependent protein,Accession: The ID of the peptide’s subordinate protein in the Uniprot database, Sequence: Amino acid sequence information of the polypeptide, FOLD CHANGE: The ratio of the quantitative values of the two groups of experimental substances, P-VALUE: P-VALUE from the Mann-Whitney U test, Q-VALUE: P-VALUE adjusted using the false discovery rate (FDR) method.

Bioinformatics functional testing

The authors performed GO and COG analyses for the purpose of determining the characteristics and features of the differentially expressed peptides. GO analysis was mainly conducted from three aspects: the biological process (GO_BP) in which the gene participates, the cellular component (GO_CC), and the molecular function (GO_MF), to more comprehensively explore the function of the gene (Fig. 5A). COG analysis performed homology analysis on the differentially expressed peptides and classified them according to their functions, which intuitively reflected the functional distribution of the subordinate proteins of the differentially expressed peptides (Fig. 5B).

Fig. 5.

A Histogram of GO annotation enrichment analysis. In the figure, the horizontal axis is GO Term, and the vertical axis is the number of mapped differentially expressed peptide subordinate proteins. Red represents BP annotation information, green represents CC annotation information, and blue represents MF annotation information. The transparency represents the P-value size. The darker the color, the smaller the P-value. The red color represents the biological processes in which the genes expressing the relevant polypeptides participate, mainly regulation of cellular component organization, cytoskeleton organization, vesicle-mediated transport, actin cytoskeleton organization, actin filament-based process. Green represents the cellular components, mainly cytoplasm, vesicle, cytoplasmic vesicle, intracellular vesicle, and cytoskeleton. The blue color represents the molecular functions, mainly protein binding, protein-containing complex binding, enzyme binding, cytoskeletal protein binding, and cell adhesion molecule binding. B Histogram of COG-dependent protein analyses of differentially expressed peptides. The horizontal axis of the COG analysis represents the different COG categories, while the vertical axis represents the COG/KOG frequency. The functions of the COGs are primarily focused on signal transduction mechanisms, cytoskeleton, intracellular trafficking, secretion, and vesicular transport, posttranslational modification, protein turnover, and chaperones. C KEGG enrichment analysis bubble plot. The horizontal axis represents the enrichment factor, and the vertical axis represents the KEGG pathway information. The size of the circle indicates the number of differentially expressed peptides in the mapped pathway; larger circles indicate a greater number of proteins; the color of the circle indicates the P-value; the redder the circle, the smaller the P-value.

At the same time, analysis of metabolic pathways with significant enrichment of differentially expressed proteins (Fig. 5C) showed that the top five were Platelet activation, Endocytosis, Cytoskeleton in muscle cells, Pathogenic Escherichia coli infection, and Regulation of actin cytoskeleton.

Correlation analysis

Through Spearman correlation analysis, we investigated correlations between selected differential peptides and baseline data/assessment scales. The results revealed varying degrees of association among peptides and their corresponding metrics (Fig. 6). Notably, PTPN12 showed a significant positive correlation with disease progression, suggesting that the disease course is linked to PTPN12 expression. Additionally, certain peptides demonstrated correlations with scores from anxiety severity scales (SAS) and post-traumatic stress inventory (PSQI), indicating that immune-related peptides may play roles in regulating pathological processes such as anxiety and sleep disorders.

Fig. 6.

Heatmap of correlation analysis. In the heatmap, a deeper red color indicates a stronger positive correlation between differential peptides and baseline data or scales, while a deeper blue color indicates a stronger negative correlation. Data with a correlation P-value less than 0.05 are marked with “*” in the graph.

Discussion

Anxiety disorder is a common mental illness with a long course and an incidence rate as high as 33.7%. The onset of anxiety disorder can be seen in adolescence, adulthood, and even childhood, but the incidence rate decreases with age¹⁷. Peptidomics is used to identify peptides that change during the development of anxiety disorders. It can also quantify them and indicate the corresponding genes and proteins, providing a basis for explaining the pathogenesis of anxiety disorders¹⁸. It also reveals serum biomarkers that are closely related to the onset of anxiety disorders, providing scientific evidence for clinical diagnosis.

In this study, the authors collected serum samples from patients with GAD and healthy controls and compared the peptides in these samples. A total of 1149 peptides were identified that were significantly different in GAD patient sera compared to healthy controls (p < 0.05 or p-value-chitest < 0.05). These included 1078 upregulated and 71 downregulated peptides, belonging to 334 proteins.

By analyzing the protein locations corresponding to the differentially expressed peptides, it was found that these peptides exist in various cellular components, such as cytoplasm (146, 43.84%), secreted (87, 26.13%), nucleus (49, 14.71%), plasma membrane (21, 6.31%), endoplasmic reticulum membrane (9, 2.70%), endoplasmic reticulum (7, 2.1%), Golgi apparatus (3, 0.9%), mitochondria (2, 0.6%), etc. There are three major categories of receptors involved in the signal transduction of various regulatory factors within cells, mainly in the cell nucleus, cell membrane, and mitochondria. They all use the cytoplasm as a medium and regulate the cell cycle, adjust the cytoskeleton, and mediate cell proliferation through signal transduction¹⁹. Neurotransmitters are secreted between cells in the nervous system through the presynaptic neurotransmitters of the cell membrane, stored in separate vesicles, and released through exocytosis. Postsynaptic neurotransmitters are recognized by receptors and receive related neurotransmitters through endocytosis, thereby conducting signal transduction and exerting excitatory or inhibitory effects²⁰. The occurrence of mental illness is closely related to changes in synaptic plasticity, which is one of the first structures affected during the onset of the disease²¹.

To explore the biological functions of the differentially expressed peptides, the authors conducted bioinformatics analysis to reveal the functions and mechanisms of the significantly differentially expressed peptides in the pathogenesis of GAD. First, by analyzing the cellular components in which they were located, the authors found that they were located throughout the subcellular regions, primarily in the cytoplasm, vesicle, cytoplasmic vesicle, intracellular vesicle, and cytoskeleton.

The cytoplasm contains a large number of enzymes, proteins, etc.²² and is the site where all cellular functions are performed, including protein folding, enzymatic, intracellular signal transduction, cytokine secretion and release, etc. Many regulatory factors exert their regulatory effects by being secreted into the cytoplasm or entering and exiting the cell through vesicles²³. Cells receive signals by endocytosing vesicles to incorporate substances into the cell, thereby generating a series of biochemical reactions²⁴. Secondly, GO analysis showed that the biological processes involved in genes controlling the expression of related polypeptides were mainly related to the regulation of cellular component organization, cytoskeleton organization, vesicle-mediated transport, and actin cytoskeleton organization, and the molecular functions they played were mainly involved in the binding of proteins, enzymes, and skeletal proteins. In addition, COG analysis results showed that the functions of differentially expressed peptide precursor proteins were mainly concentrated in signal transduction mechanisms, cytoskeleton, intracellular trafficking, secretion, vesicular transport, posttranslational modification, protein turnover, chaperones, etc. Signal transduction is essential for the growth and development of the body and is involved in the biological processes of cell proliferation, differentiation, movement, and survival²⁵. The normal function of signaling pathways plays a key role in the communication between neurotransmitters and neuropeptides. The occurrence of mental illness is closely related to abnormalities in intracellular signaling pathways, which manifest as abnormalities in cells involved in regulating neurotransmitter release or abnormalities in signal transduction systems²⁶. Cytoskeleton regulatory factors can regulate cell structure and function, play a role in maintaining cell morphology, mediating cell movement, regulating material transport, and can participate in regulating neuronal synaptic plasticity and neural circuit construction. Studies have shown that the differential expression of cytoskeleton regulatory factors in the adolescent prefrontal cortex is closely related to the onset of mental illnesses such as depression and anxiety²⁷.

Then, in the mining of differentially expressed peptide precursor proteins and genes, it was found that some of the peptides with significant differences are closely related to the body’s immune function and can participate in the body’s innate and acquired immune processes. Some are related to neural signal transduction and synaptic plasticity, which can change the morphology and function of neurons and regulate neural excitability. The most significantly different Q9UIB8 corresponding protein is member 5 of the signal transduction lymphocyte activation molecule (SLAM) receptor family. The SLAM receptor family belongs to the type I transmembrane glycoprotein and is mainly present in NK cells, T cells and myeloid cells²⁸. SLAM family receptors play important roles in cytotoxicity, humoral immune responses, autoimmune diseases, lymphocyte development, cell survival, and cell adhesion²⁹.

SLAM receptors co-localized with T cell receptor (TCR) complexes of immune synapse in the activated T cells. The tyrosine phosphorylation of the SLAM tail in the cytoplasm initiated the signal transduction cascade and regulated downstream signal transduction after receptor cross-linking. SLAM and the adaptor protein SLAM-associated protein (SAP) had the function of promoting apoptosis, which contributed to maintaining T cell homeostasis and humoral immunity in proper functioning^30,31. A study showed that neuroinflammation will cause immune disorders and increase susceptibility to mental disease. At the same time, repeated attacks of mental disease will cause continuous immune inflammation, increase nerve tissue damage, and aggravate symptoms³². In addition, SLAM family receptors can stimulate the adhesion of NK cells, affect the activity of NK cells, promote their functional maturation, and play a role in anti-infection and immune regulation³³.

Q9Y490 corresponds to Talin-1, an adhesion plaque-associated protein encoded by the TLN1 gene^34,35. Talin-1 can participate in the activation of integrins and connect the cytoplasmic domain of integrins to the actin cytoskeleton to form adhesion plaques (FAs)³⁶. At the same time, as an intracellular molecule of a mechanical sensor, it can respond to mechanical stress, convert mechanical signals into intracellular biochemical signals, and affect intracellular signal transduction³⁷. It also participates in regulating dendritic morphology and synaptic plasticity, affecting synaptic transmission efficiency, and regulating the occurrence and development of many neurological diseases³⁸. Increased Talin1 expression activates integrins and actin, thereby improving cardiac remodeling after pressure load; it can also activate integrins and NF-KB signaling pathways, leading to vascular endothelial cell inflammation, and is closely related to the onset of many diseases³⁹. Studies have shown that Talin-1 can be used as a new biomarker for human myalgic encephalomyelitis/chronic fatigue syndrome⁴⁰.

P10909 corresponds to clusterin. Clusterin, also known as apolipoprotein, is expressed in various body tissues and fluids and participates in physiological processes such as complement regulation, lipid transport, and microglial activation^41–43. As a molecular chaperone, clusterin increases the aggregation and clearance rate of amyloid beta (A), preventing its accumulation in neurons and causing toxic damage, thereby playing a role in the pathogenesis of Alzheimer’s disease^44,45. At the same time, it plays an important role in neuroprotection, neurodegeneration, and synaptic plasticity, and can affect the stability of neural circuits^46–48. It can also inhibit microglial activation and reduce the release of proinflammatory factors, thereby avoiding excessive stimulation of the brain by neuroinflammation and regulating neuronal excitability.

The protein corresponding to P01857 was Immunoglobulin Heavy Constant Gamma 1 (IGHG1). As an IgG subclass member, IGHG1 was an important functional isomer of immunoglobulins generated by the human immunity system^49,50. Immunoglobulins, also known as antibodies, are glycoproteins synthesized and secreted by B lymphocytes. They participate in the immune response process by binding to specific antigens, activating immune effector mechanisms, and regulating immune balance⁵¹. Immunoglobulins can participate in the pathogenesis of various diseases such as autoimmune diseases and neurological diseases through immune inflammation and tissue damage⁵². In neurological diseases, IGHG1 mainly activates microglia through the blood-brain barrier, inducing neuroinflammation, and thus participating in the pathogenesis of diseases such as anxiety^53,54.

In correlation analysis, PTPN12 showed a significant positive correlation with disease progression. PTPN12 regulates cellular signaling through dephosphorylation, inhibiting excessive cell proliferation, migration, and immune activation to maintain cellular homeostasis⁵⁵. As a crucial “regulator” in cellular physiology, PTPN12 participates in cell adhesion and negative regulation of signaling pathways⁵⁶. Abnormal function of this protein can disrupt signaling pathways and accelerate disease progression⁵⁷. Extended disease duration may continuously activate immune cells (e.g., T cells and macrophages), leading to overexpression of PTPN12 that suppresses immune responses⁵⁸. However, sustained high expression may disrupt immune balance and conversely exacerbate the condition. Correlation analysis revealed complex associations between differential peptides and clinical phenotypes. Subsequent studies should delve deeper into the relationships between various factors and differential peptides to provide clues for further exploration of disease molecular mechanisms and screening of potential biomarkers.

However, this study has several limitations. Regarding the research design, we conducted a preliminary population study first. Subsequent trials will categorize participants by region, gender, disease duration, and age, while also considering factors such as clinical subtypes, symptom severity (e.g., HAMA score stratification), and comorbid conditions (e.g., co-occurring depression, sleep disorders, etc.). At the sampling and verification level, there is a lack of independent verification cohorts, and the representativeness and balance of the sample size may be insufficient, limiting the extrapolation of the results to a wider population. At the technical level, the detection results of this study may vary on different platforms. In subsequent experiments, combined analysis of transcriptome, proteome and other omics data will be conducted to reveal the regulatory mechanism of peptide expression changes.

Conclusion

In conclusion, this study screened differentially expressed peptides preliminarily in patients with GAD with combined clinical practice based on the peptideomics method and provided new insights for finding diagnostic biomarkers and therapeutic targets of GAD. In future research, the authors will further collect a large number of samples, and refine experimental designs to verify differential peptides in the above findings and offer a basis for early clinical treatment targets and diagnosis.

Author contributions

X.G., J.Z. and S.D. wrote the manuscript. X.G., X.W. and S.D. designed the research. X.G., J.Z., X.W., W.L., X.J., and Y.Z. performed the research. J.Z., W.L. S.D., X.J. and Y.Z. analyzed the data. W.L. and X.W. contributed new reagents/analytical tools. All authors read and approved the final manuscript.

Funding

The study was funded by the Henan Province “Double First-class” establishment Discipline of Chinese Medical Science Research Project (HSRP-DFCTCM-2023-1-09).

Data availability

The datasets generated and analyzed during the current study have not been fully completed for information extraction, so they are not yet publicly available. but are available from the corresponding author on reasonable request.

Declarations

Competing interests

The authors declare no competing interests.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-025-32707-2.