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. 2026 May 26;17:1835098. doi: 10.3389/fgene.2026.1835098

Characterization of circular RNAs in mammary tissue from Holstein cows at perinatal period and dry period

Yan Liang 1,2, Shuang Gu 1, Zhaozheng Zhang 1, Yanru Wang 2, Mingxun Li 2, Niel A Karrow 3, Jinling Hua 1, Yongjiang Mao 2,*
PMCID: PMC13245996  PMID: 42266409

Abstract

Introduction

Perinatal and dry periods are important physiological stages for cows to recover after calving and improve lactation performance. Exploring the expression characteristics of circRNAs as a molecular hotspot during the perinatal and dry periods of dairy cows is of great significance.

Methods

This study identified and compared circular RNAs (circRNAs) in the mammary tissue of three cows between the perinatal and dry periods. After analysis, we identified 10,388 circRNAs, ranging from 48 bp to 99,406 bp.

Results

Chromosome 1 had the most circRNAs, containing 597 circRNAs. Furthermore, 91.97% of the circRNAs belonged to sense-overlapping circRNA. CircRNAs contain different number of exons, ranging from 1 to 47 and most of the cirsRNAs harbored 1 to four exons. Compared with dry period, 132 circRNAs with significantly different expressions were identified in the perinatal period, 99 of which were upregulated and 33 downregulated. Enrichment analysis revealed the enrichment of circRNAs in the proliferation and differentiation of cells, such as regulation of chondrocyte differentiation and integral component of plasma membrane, phosphatidylinositol 3-kinase binding. The significantly enriched pathways further indicate that circRNAs play important roles in immunity and infection, such as cell adhesion molecules (CAMs), herpes simplex infection, and Kaposi’s sarcoma-associated herpesvirus infection.

Discussion

This study revealed the expression profile and characteristics of circRNAs in the perinatal and dry period of Holstein cows, thus providing rich information for studying circRNAs functions and mechanisms underlying perinatal and dry period diseases, suggesting a new avenue to investigate the regulatory mechanisms of dairy cow genetic breeding.

Keywords: circular RNAs, dry period, Holstein cows, mammary tissue, perinatal period, RNA sequencing

1. Introduction

The perinatal period of dairy cows (includes the 3 weeks before and after parturition) is the most important in their lives, closely related to their lactation and reproductive performance (Drackley, 1999). During this period, a series of significant changes have taken place in their hormone levels, rumen function, nutritional metabolism, and immunity (Mulligan and Doherty, 2008; LeBlanc et al., 2006). This prompted considerable research efforts in this area aimed at investigating physiological changes in cows during the perinatal period. These studies have shown that even in the absence of microbial infection and/or other identified signs of pathology, perinatal cows exhibit a pronounced inflammatory response, which increases metabolic stress and thus damages the host’s immune defenses (Trevisi et al., 2012). The transcriptomic analysis revealed that the cell cycle, DNA damage, and chromosomal conformation of perinatal neutrophils in dairy cows are strongly associated with the incidence of mastitis during lactation (Jiang et al., 2022). The dry period (traditionally about 6–8 weeks) has important impacts on the recovery of udders, limbs and feet, and rumen recovery, and can directly affect the milk production of cows in the next parity and the incidence of postpartum metabolic diseases (Kok et al., 2017). Similarly, cow dry period body condition scores (BCS) impact on blood biochemistry, liver triacylglycerol, and muscular monocarboxylate transporter-1 mRNA expression (Triwutanon and Rukkwamsuk, 2021). Additionally, relationships between plasma total antioxidant capacity (TAC) of and physiological stages such as dry period, have been evaluated (Omidi et al., 2017).

At present, most comparative studies on the perinatal and dry periods of dairy cows have rarely been analyzed at the molecular level. The multi-omics technology that has emerged in the past 10 years provides us with ideas for analyzing the physiological changes of dairy cows from the molecular level. As an emerging research hotspot, circRNA has also become the object of our attention. CircRNAs varies greatly in different developmental stages and tissue expression abundance (Gruner et al., 2016), has high tissue specificity, and is highly conserved in different species (Cortés-López et al., 2018). The biological production of circRNA competes with linear splicing and can regulate the production of linear RNA in cis-genes. During the regulation process of the body, covalently closed circular structures are formed through reverse splicing mechanism in cells, which are selectively loaded into extracellular vesicles such as exosomes and enter various body fluids for intercellular communication, exerting regulatory effects on distant cells or tissues (Zhang et al., 2025).

The mammary gland is an important organ in Holstein cows as it is required for calf survival, passive immunity and early nutrition, and consumer products. The growth and development of the mammary glands are regulated by a variety of hormones (Rowson et al., 2012). And the expression changes of specific miRNAs also have certain impacts on the differentiation of mammary gland and epithelial cells (Nagaoka et al., 2013; Chen et al., 2020). In the study of circular RNA function in mammary gland tissue, researchers found that the expression level of specific circular RNAs (such as circR3HCC1) in the mammary glands of cows and goats is closely related to the synthesis of milk fat and milk protein. They directly affect the nutritional composition of milk by regulating downstream genes (Marei et al., 2025). Similarly, we compared circular RNAs in breast tissues from early lactation and non-lactating Holstein bovine and found that 87 circRNAs were significantly differentially expressed (Liang et al., 2022). Clearance of the above results, we speculate that there are also some special circRNAs in the perinatal and dry period mammary gland tissues of dairy cows. These special circRNAs have the potential to be molecular targets for us to study the different physiological characteristics of dairy cows during the perinatal and dry periods.

Therefore, this study used high-throughput RNA sequencing (RNA-seq) to study the expression profile of cirRNA from Holstein cows during perinatal and dry period, and identified the differential expressed cicrRNAs. In addition, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed for the parental mRNA genes of the differentially expressed cirRNA to investigate their potential roles. At the same time, we also predict the ceRNA mechanism between miRNA and circRNA. Through the characteristics of circRNAs in the perinatal and dry periods, circRNAs are expected to become novel molecular targets for the study of perinatal and dry periods diseases in dairy cows, and provide new research ideas for dairy cow genetic breeding.

2. Materials and methods

2.1. Statement of animal ethics

All experiments were conducted in accordance with the Guidelines for the Care and Use of Experimental Animals established by the Ministry of Science and Technology of the People’s Republic of China (Approval No. 2006–398). The collection of mammary gland tissue samples complied with the ethical standards governing experimental animal welfare. The experimental animal production license (SYDW-2019005) was obtained, and the study was approved by Yangzhou University, Yangzhou, China.

2.2. Tissue samples collection

Mammary gland tissue samples were collected from three Holstein cows during early lactation (n = 3; 7 days prepartum) and three during the non-lactating period (n = 3; 315 days postpartum) at a large-scale dairy farm in Jiangsu Province. Prior to tissue collection, milk was completely evacuated from the mammary glands of lactating cows confirmed to be free of mastitis. The biopsy procedure followed the methodology described by Li et al. (2016).

2.3. RNA preparation and circRNA sequencing

Mammary tissue samples (10–20 mg) were lysed in 300 μL RL buffer, homogenized, and incubated with 590 μL RNase-free ddH2O and 10 μL Proteinase K at 56 °C for 10–20 min. Total RNA was extracted using TRNzol reagent (Invitrogen, Carlsbad, CA) and purified with the RNAprep pure Tissue Kit (Tiangen, Beijing, China). The purified RNA was dissolved in DEPC-treated water, and its concentration (>400 ng/μL) and purity (A260/A280 = 1.9–2.2) were assessed using a NanoDrop® ND-1000 spectrophotometer (Thermo Scientific, DE).

Ribosomal RNA was removed from mRNA samples using a transcriptome isolation kit (Ribominus Bacteria 2.0, Thermo Fisher). The remaining RNA was used for library construction with the TruSeq RNA Library Preparation Kit (Illumina Inc., San Diego, US) and subjected to paired-end sequencing on an Illumina HiSeq Xten platform (Illumina Inc., San Diego, US) at Shanghai OE Biotechnology Company Ltd (Shanghai, China). Raw reads were filtered to remove low-quality reads (Q < 20, >50% of bases below threshold), reads with high error rate (>1%), ambiguous N bases, adaptor sequences, short reads (<20 bp), and rRNA-derived reads. Clean reads were used for circRNA identification using FIND_CIRC (Memczak et al., 2013), followed by prediction of known and novel circRNAs with CIRI software, referencing the circBase database (Gao et al., 2015). Chromosomal and length distributions of circRNAs were analyzed based on FIND_CIRC outputs. Junction read counts were normalized using DESeq, with expression levels estimated from mean base values. Differential expression was assessed using a negative binomial test (Anders and Huber, 2013), and differentially expressed circRNAs were identified based on fold change and significance thresholds.

2.4. Enrichment analysis

Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses for the differential expressed circRNA source gene were performed using the DAVID biometric analysis tool (Kanehisa et al., 2008; Sun et al., 2014).

2.5. ceRNA

The miRNA-targets of each differentially expressed circRNA were predicted using the miRanda algorithm (Miranda et al., 2006) and the interaction network of the circRNAs and their target miRNAs was analyzed using starBase and then drawn using Cytoscape (Smoot et al., 2011). The calculation method of ceRNA_score and P value (Das et al., 2014) is as follows:

ceRNA_score=#MRE_for_share_miRNA2a#MRE_for_circRNA_miRNA

Where circRNA denotes the circRNA identifier; ceRNA_score refers to the predicted interaction score; #shared_miRNA indicates the number of shared miRNAs; miRNAs lists the names of those miRNAs; and pvalue represents the significance level for ceRNA prediction.

The calculation formula of P-value is as follows:

P=∑i=mcminmp.mnmniMt−mnmp−iMtmp

Where MT represents the number of all miRNAs; mp represents the number of miRNAs that regulate this mRNA. mn represents the number of miRNAs that have regulatory effects on the circRNA; and mc represents the number of common miRNAs.

3. Results

3.1. Identification and sequence characteristics of circRNAs in mammary tissue from Holstein cows

A total of 10,388 circRNAs were identified in mammary tissue RNA from Holstein cows by library construction, sequencing, and bioinformatics analysis. Count the number of circRNAs identified by each sample, as shown in Figure 1, the number of circRNAs predicted in the three samples at −7 days was 3331, 1345, 3454, and the number of unique circRNAs were 570, 394, 752, respectively. The number of circRNAs predicted in the three samples at 315 days was 4,131, 3765, 3359, and the number of unique circRNAs were 850, 669, 604, respectively. The chromosome 1 contained most circRNAs (n = 597) (Figures 2A). CircRNAs contains different number of exons, ranging from 1 to 47 and most of cirsRNAs harbored 1 to four exons (Figures 2B). The size of circRNAs ranged from 48 bp to 99,406 bp and the average size was 3027.79 bp. The circRNA lengths were mainly in the 201–500 bp, and lengths were more than 2000 bp (Figures 2C). Variable shear signals AT reverse shear sites in circRNA sequences were counted, and all of them were GT-AG (Figures 2D). There are three types of circRNA identified: exonic (3.28%), intergenic (4.75%), and sense-overlapping (91.97%) (Figures 2E).

FIGURE 1.

Horizontal bar chart comparing uniq_circRNA_number and circRNA_number for six samples, with sample names on the y-axis and circRNA numbers on the x-axis. Each sample has two bars: purple for uniq_circRNA_number and blue for circRNA_number. Sample_4 (315 d) has the highest circRNA_number at 4131 and uniq_circRNA_number at 850, while sample_2 (-7 d) has the lowest values. Legend lists both categories in the upper right.

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CircRNA numbers predicted in each sample. Legend: The horizontal axis is the number of circRNAs; the vertical axis shows the individual samples from cows on postpartum days −7 or 315; the numbers above each bar refer to the number of circRNAs predicted in each sample; the Uniq_circRNA_numbers refers to the number of circRNAs specifically predicted in each sample compared to other samples in the project.

FIGURE 2.

Panel A is a bar graph displaying circRNA numbers distributed across chromosomes and scaffolds, with counts ranging from 1 to 597. Panel B is a bar graph showing circRNA numbers by exon count, with the majority having one to four exons. Panel C is a bar graph of circRNA numbers by length interval, most commonly between two hundred and three hundred nucleotides. Panel D is a bar graph indicating that all circRNAs have GT-AG backsplicing signals, total ten thousand three hundred eighty-eight, with zero in the other category. Panel E is a pie chart illustrating exonic circRNAs at ninety-one point ninety-seven percent, intergenic at four point seventy-five percent, and sense-overlapping at three point twenty-eight percent.

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Identification, characterization, and chromosomal distribution of circRNAs. Legend: (A) Number of circRNAs per chromosome; (B) The number of exons by circRNAs; (C) The length distribution density of circRNAs; (D) Statistical diagram of circRNA shear signal, the number of circRNA on the vertical axis, and the type of shear signal on the horizontal axis; (E) Percentage of different types of circRNAs.

3.2. Differential expression of circRNAs analysis in mammary tissue from Holstein cows

Compared with dry-period, 132 circRNAs with significantly different expressions were identified in perinatal-period, 99 of which were upregulated and 33 were downregulated (Supplementary Table S1). Unsupervised hierarchical clustering of differentially expressed circRNAs was carried out, the distance between pairs of multiple samples was calculated to form a distance matrix, and the expression of selected differential circRNAs was used to calculate the direct correlation of samples. The two clusters are up- and downregulated circRNAs, clustered in the same cluster may have similar biological functions (Figure 3).

FIGURE 3.

Panel A shows a heatmap with hierarchical clustering of gene expression levels for two groups labeled three hundred fifteen days and seven days, using a color gradient from blue to pink. Panel B presents a volcano plot comparing log two fold change and negative log ten p-value, highlighting significant gene changes. Panel C displays an MA plot relating mean normalized counts to log two fold changes, with significant points highlighted in red.

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Differentially expressed circRNAs in mammary tissue from Holstein cows. Legend: (A) Differential circRNA expression level clustering. Red indicates high expression and blue indicates low expression. (B) Gray and blue circRNAs with non-significant differences, red and green circRNAs with upregulated and downregulated significant differences, respectively. The X-axis is the display of log2 Fold Change, and the Y-axis is the display of log10 P - value. (C) The X-axis is the mean expression of all samples used for comparison after standardization, and the Y-axis is Log2 Fold Change, and the red highlights are significant difference expressed circRNAs.

3.3. circRNA enrichment

To investigate the possible functions of differentially expressed circRNAs, GO enrichment analysis was performed on the target genes of differentially expressed circRNAs. Through the enrichment analysis of 1094 GO term, revealed the enrichment of circRNAs in the proliferation and differentiation of cells, such as regulation of chondrocyte differentiation and integral component of plasma membrane, phosphatidylinositol 3-kinase binding (Figures 4A). The significantly enriched pathways further indicate that circRNAs play important roles in immunity and infection, such as cell adhesion molecules (CAMs), herpes simplex infection and kaposi’s sarcoma-associated herpesvirus infection (Figures 4B).

FIGURE 4.

Panel A displays a bar chart categorizing top gene ontology terms in red for biological_process, green for cellular_component, and blue for molecular_function. Panel B contains a bubble plot showing KEGG pathway enrichment for the top twenty pathways, with dot colors representing p-value ranges and dot sizes reflecting gene counts.

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Go and KEGG Enrichment analysis. Legend: (A) The basic information for each node is displayed in the corresponding graph, which is the GO ID and GO term. The X-axis is the Go entry name, and the Y-axis is -log10 P - value. (B) Top 20 categories of KEGG pathway analysis, the X-axis Enrichment Score is the Enrichment Score. The larger the bubble is, the more circRNAs the item contains, and the color of the bubble changes from purple to blue to green to red. The smaller the Enrichment P - value is, the greater the significance is.

3.4. circRNA-miRNA interaction research

Hypergeometric distribution testing was used to identify miRNAs with a large influence in differential circRNAs. The result of the calculation returns a P-value for enrichment significance. For the total difference circRNA enrichment results, the top 300 miRNA-circRNA interaction pairs with smaller P value were extracted by P value ordered, and the circRNA-miRNA target interaction network diagram was plotted using Cytoscape software (Figure 5).

FIGURE 5.

Network diagram illustrating interactions between circRNAs (blue nodes) and miRNAs (red nodes) with connecting lines representing molecular relationships. Nodes are densely clustered in the center and sparser at the edges.

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Top300 miRNA-circRNA ceRNA network.

4. Discussion

Cows are a significant source of dairy products and an ideal large animal model to study the transcriptome and expression characteristics of mammary tissues. Perinatal and dry period, as important physiological stages of cow recovery, play a key role in the prevention of immunity, lactation, and postpartum diseases. Therefore, it is necessary to explore the characteristics of the molecular mechanism of dairy cows at these stage. As a new type of endogenous non-coding RNA, circRNAs have been studied in humans (Yang et al., 2021), mice (Fan et al., 2015), pigs (Sun et al., 2017), cattle (Li et al., 2018), sheep (Wang et al., 2021), and other species (Zhang et al., 2019).

High-throughput sequencing was used to explore the presence and expression of circRNAs in mammary tissues from Holstein cows during perinatal and dry period, and to screen and identify circRNAs that may play an important role in lactation. Through systematic identification and analysis of circRNAs, it was found that 1716 and 2,123 unique circRNAs were predicted in mammary tissues from Holstein cows during perinatal and dry period. More interestingly, a total of 132 differential expressions were found in the two sets of circRNAs. We previously identified circRNAs in Holstein cows' mammary tissues during early lactating and non-lactating and found 10,686 circRNAs were predicted in the mammary tissue of Holstein cows at 30 days and 315 days postpartum (Liang et al., 2022). In sheep mammary gland tissues, Hao et al. (2020) identified 4,906 circRNAs in two sheep mammary gland tissues with different lactation performances and 33 of these were differentially expressed between breeds. Another study showed that 6,621 circRNAs were differentially expressed in the mammary tissue of Holstein cows at postpartum 90 days and 250 days, of which 2,231 were co-expressed (Zhang et al., 2016). Through these different results, we found that different physiological stages have a certain influence on the expression of circRNAs.

The first chromosome of cows contains the most circRNAs. This may be related to gene density and functional enrichment. Cow chromosome 1 has the most microsatellite loci and is one of the largest chromosomes in cattle, carrying the highest density of protein coding genes. Due to the fact that the vast majority of circular RNAs come from exons of protein coding genes, the denser the region of genes, the greater the potential for circular RNA production (Qi et al., 2013). Most circRNAs are short in length and are concentrated between 201 and 500 bp, while some circRNAs were greater than 2 kb. This is also consistent with many research findings (Liang et al., 2022). In addition, we found three different types of circRNAs in mammary tissue, of which 91.97% belonged to sense-overlapping, while Wang et al. (2021) identified six types of circRNAs in sheep mammary tissue, among which EciRNA was the main one. Sense-overlapping circRNAs (sometimes defined as EIciRNA) have overlapping regions with mRNA exons and are transcribed in the same direction. Related studies have shown that long flank introns are considered crucial to exon cyclization, and they contain ALU repeats (Jeck et al., 2013) and possibly help determine the production rate of circRNAs (Ashwal-Fluss et al., 2014). Finally, introns between the encircled exons are retained, which Li termed EIciRNAs (Li et al., 2015). Most of the circRNAs in this study are sense - overlapping, which may be related to the above reasons.

GO and KEGG enrichment analysis can illustrate the related functions of genes. Enrichment analysis revealed the enrichment of circRNAs in proliferation and differentiation of cells, such as regulation of chondrocyte differentiation and integral component of plasma membrane, phosphatidylinositol 3-kinase binding. The significantly enriched pathways further indicate that circRNAs play important roles in immunity and infection, such as cell adhesion molecules (CAMs), herpes simplex infection and Kaposi’s sarcoma-associated herpesvirus infection. Cows suffer severe physiological stress during the perinatal period, during which increased nutrient requirements, loss of appetite and milk production lead to energy deficiency in cows, which leads to negative energy balance and metabolic disorders (Lv et al., 2022). Compared with the dry milk period, perinatal cows need to be prepared for lactation initiation, and mammary gland related functions are more active, which may be one of the reasons why GO enrichment is associated with cell proliferation and differentiation. In addition, the immunity of dairy cows in the perinatal period began to decline, and immune-related neutrophils, lymphocytes, and monocytes were suppressed in the perinatal period (Khan et al., 2020). The KEGG pathways of immune response is the most significantly pathways, which is important for fighting pathogen infections (Korkmaz et al., 2018), and the special physiological characteristics of the perinatal period led to enrichment in pathways related to inflammation and immunity.

Studies have shown that circRNA can act as a “sponge” competitive binding target site for miRNA, affecting the translation of mRNA, representing a new class of ceRNA regulators (Zhao et al., 2020). Sun et al. (2017) identified 68 sponge modulators participating in 26 miRNA-mediated ceRNA interactions, including 40 circRNAs, and 9 mRNAs. Li et al. (2018) found that circFGFR4 binding miR-107 promotes cell differentiation via targeting Wnt3a in bovine primary myoblasts. Another study also reported circ11103 regulates milk fat metabolism in dairy cows through the ceRNA mechanism (Chen et al., 2021). Through the above studies, we found that in different organisms and different physio-logical stages, circRNA has a specific expression regulation mechanism and plays rich and important functions, not just as a by-product of transcription. We also proved this in the circRNA-miRNA interaction research. For the total difference circRNA enrichment results, the top 300 miRNA-circRNA interaction pairs with smaller P value were extracted by P value ordered, and the circRNA-miRNA target interaction network diagram was plotted using Cytoscape software. In the following study, based on the feature analysis of circRNAs and the enrichment of differentially expressed circRNAs, we will screen out specific differentially expressed circRNAs, miRNAs, and mRNAs with targeted relationships for functional validation and targeted relationship validation, further exploring how differentially expressed circRNAs affect milk production levels and quality in cows at different lactation stages.

5. Conclusion

In this study, through high-throughput sequencing of circRNAs in Holstein cow mammary tissues at perinatal and dry period, 10,388 circRNAs were detected, mainly distributed on chromosomes 1 and mainly sense-overlapping. Among the 132 differentially expressed circRNAs detected, enrichment analysis revealed the enrichment of circRNAs in proliferation and differentiation of cells and significantly enriched pathways further indicate that circRNAs play important roles in immunity and infection.

This study revealed the characteristics of DEcircRNAs in the mammary gland tissues of cows during the perinatal and dry milk periods. These DEcircRNAs can serve as molecular signals for the switching between the “stop milk production” and “prepare for lactation” states in the mammary gland of cows, directly participating in immune activation, inflammation regulation, and tissue remodeling processes. This provides a scientific basis for the development of new disease resistant breeding markers, early disease diagnosis tools, and precise health management strategies. The ultimate goal is to help cows navigate the high-risk stage of the perinatal period more smoothly, reduce disease occurrence, and improve animal welfare and production efficiency.

Funding Statement

The author(s) declared that financial support was received for this work and/or its publication. The research received financial support from the National Natural Science Foundation of China (31972555); the Talent Project of Anhui Science and Technology University (DKYJ202406), the Innovation Project for College Students in Anhui Province (S202510879040).

Footnotes

Edited by: Peter Dovc, University of Ljubljana, Slovenia

Reviewed by: Sudarshan Mahala, Indian Veterinary Research Institute (IVRI), India

Shijun Li, Guizhou University, China

Data availability statement

The original contributions presented in the study are publicly available. This data can be found in the NCBI repository with the BioProject accession number PRJNA766152 and the BioSample accession number SAMN21619997.

Ethics statement

The animal study was approved by the experimental animal production license (SYDW-2019005) was obtained, and the study was approved by Yangzhou University, Yangzhou, China. The study was conducted in accordance with the local legislation and institutional requirements.

Author contributions

YL: Visualization, Writing – original draft, Writing – review and editing. SG: Software, Writing – review and editing. ZZ: Validation, Writing – review and editing. YW: Software, Writing – review and editing. ML: Methodology, Visualization, Writing – review and editing. NK: Writing – review and editing. JH: Visualization, Writing – review and editing. YM: Conceptualization, Funding acquisition, Project administration, Resources, Writing – review and editing.

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that generative AI was not used in the creation of this manuscript.

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.

Publisher’s note

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fgene.2026.1835098/full#supplementary-material

Table1.xlsx (80.9KB, xlsx)

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Table1.xlsx (80.9KB, xlsx)

Data Availability Statement

The original contributions presented in the study are publicly available. This data can be found in the NCBI repository with the BioProject accession number PRJNA766152 and the BioSample accession number SAMN21619997.

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