AAVS_13_11_2489-2497

Introduction

Subclinical mastitis in dairy cows is a major udder health issue that significantly impacts productivity, milk quality, and animal welfare, with serious economic implications (Azooz et al., 2020). Approximately 90 to 95% of mastitis cases in dairy cows are subclinical, often asymptomatic but capable of causing economic losses comparable to clinical mastitis (Wang et al., 2021). Subclinical mastitis is characterized by an elevated somatic cell count in milk and alterations in the milk microbiota composition, which can increase the risk of progression to clinical mastitis (Tong et al., 2025).

Field data reveal an alarming prevalence of subclinical mastitis in smallholder dairy farms in Indonesia. A study by Rifa’i et al. (2024) reported a prevalence rate of 69.84%, as determined by the California Mastitis Test (CMT), in small-scale dairy farms in East Java. These findings highlight persistent challenges in implementing proper milking hygiene practices. As noted by Surjowardojo et al. (2025), inadequate milking hygiene contributes to udder health risks, and in many cases, poor farm management practices further exacerbate the situation (Cheng and Han, 2020; Burakova et al., 2023).

The advancement of third-generation sequencing technologies, such as Oxford Nanopore, has expanded the potential for understanding udder microbiota dynamics associated with subclinical mastitis. While Nanopore sequencing enables high-resolution, full-length 16S rRNA analysis overcoming limitations of short-read platforms like Illumina its higher error rates necessitate rigorous bioinformatic correction (Wick et al., 2019; de Coster et al., 2018). This approach reveals microbial shifts previously obscured by conventional techniques (Wang et al., 2021; Burakova et al., 2023). Several studies have shown that alterations in milk microbiota composition during subclinical mastitis are often characterized by decreased diversity and increased dominance of opportunistic pathogens (Wang et al., 2021). However, the literature addressing microbial interactions in the early stages of subclinical mastitis (score 1), particularly within smallholder dairy farms in tropical regions, remains very limited.

Our previous study characterized the dominant taxa within bovine milk microbiota and demonstrated that Streptococcus parauberis was strongly associated with healthy milk, while Ralstonia pickettii emerged as a potential pathogen in subclinical mastitis (Rifa’i et al., 2025). These findings highlighted the importance of dominant microbial members in shaping udder health and food safety risks. However, dominant taxa alone cannot fully explain the complexity of microbial dynamics in the mammary gland. Rare and low-abundance taxa, though often overlooked, may act as keystone species, early indicators of dysbiosis, or reservoirs of functional traits that influence microbial community stability and host microbe interactions (Nguyen et al., 2020). Building upon our previous work, the present study therefore shifts the focus toward low-abundance taxa in healthy and subclinical mastitis milk, aiming to uncover microbial signatures that may provide novel ecological and clinical insights into the early stages of mastitis.

Accordingly, this study aims to profile the microbial communities of healthy dairy cow milk and early-stage subclinical mastitis (score 1) milk using full-length 16S rRNA Oxford Nanopore sequencing. The primary focus is to explore compositional shifts at both genus and species levels, assess the ecological flexibility of microorganisms inhabiting both conditions, and evaluate their potential role in modulating udder immune responses. This research is expected to address existing gaps in the literature regarding microbial dynamics in early subclinical mastitis and provide a scientific foundation for developing early detection and microbe-based intervention strategies in smallholder tropical dairy farms. Unlike our previous work which emphasized dominant taxa, this study uniquely focuses on low-abundance and rarely reported taxa, aiming to uncover their ecological roles and potential as early biomarkers of subclinical mastitis in tropical smallholder dairy systems.

Materials and Methods

Sample collection and preparation

Fresh milk samples were aseptically collected from healthy Friesian Holstein (FH) dairy cows and those diagnosed with early-stage subclinical mastitis (score 1). Cows were selected based on the following criteria: (1) parity of 2–4, (2) lactation stage between 60–150 days in milk, and (3) no antibiotic treatment within 30 days prior to sampling. Subclinical mastitis was identified using the California Mastitis Test (CMT) as described by Roberts (2025) and Ramuada et al. (2024). Sampling was performed per individual udder quarter (non-pooled), and only quarters showing consistent CMT score 1 results across three consecutive tests were included. A total of 250 mL of milk per quarter was collected into sterile containers from smallholder dairy farms in Toyomerto Hamlet, Pesanggrahan Village, Batu City, East Java, Indonesia (see Figure 1). All samples were transported under refrigerated conditions (4 °C) and processed within 24 hours. Microbiological analysis was initiated immediately upon arrival at the laboratory, while remaining aliquots were stored at 4 °C for subsequent DNA extraction (Usui et al., 2023; Faust et al., 2024). Strict aseptic procedures were followed throughout to minimize contamination risk (Supamri et al., 2025).

Geographical location of the dairy farm in Toyomerto Hamlet, Pesanggrahan Village, Batu City, East Java, Indonesia, where milk samples were collected. The site represents a typical smallholder dairy farming system in tropical regions. Coordinates: 7°53′10.5″ S, 112°30′03.5″ E.

Table 1: CMT scoring system.

CMT score	Description	SCC Range (cells/mL)
N(negative)	No thickening of the mixture	0 - 200,000
T (trace)	Slight thickening of the mixture. Trace reaction seems to disappear with the continued rotation of the paddle.	200,000 - 400,000
1	Distinct thickening of the mixture, but no tendency to form a gel. If the CMT paddle is rotated for more than 20 seconds, thickening may disappear.	400,000 - 1,200,000
2	Immediate thickening of the mixture, with a slight gel formation. As the mixture is swirled, it moves toward the center of the cup, exposing the bottom of the outer edge. When the motion stops, the mixture levels out and covers the bottom of the cup.	1,200,000 - 5,000,000
3	Gel is formed and the surface of the mixture becomes elevated (like a fried egg). The central peak remains projected even after the CMT paddle rotation is stopped.	> 5,000,000

Source: Burton, 2021; Surjowardojo et al., 2025

Subclinical mastitis analysis

The analysis of subclinical mastitis was performed using a California Mastitis Test (CMT) paddle, CMT reagent, and fresh milk collected from each udder quarter. Approximately 2 mL (equivalent to one teaspoon) of milk from each quarter was dispensed into the respective wells of the CMT paddle. This volume corresponds to the residual milk that reaches the external calibration line when the paddle is held in a nearly vertical position. An equal volume of CMT reagent was then added to each well. The contents were gently mixed by swirling the paddle in a circular motion for approximately 10 seconds. The reaction was interpreted promptly, as visible gel formation begins to disintegrate after about 20 seconds. The degree of gel formation was scored visually, with higher scores indicating higher somatic cell counts and a greater likelihood of subclinical mastitis. The paddle was thoroughly rinsed between tests to prevent cross-contamination (Burton, 2021; Rifa’i et al., 2024).

16S rRNA gene amplification and sequencing

DNA isolation and purity assessment

Genomic DNA was isolated from 200 μL of centrifuged milk pellet (from 250 mL raw milk) using the ZymoBIOMICS DNA Miniprep Kit (Zymo Research, D4300), a validated method widely used for high-quality microbial DNA extraction from complex biological samples (Zymo Research, 2022; Isuosuo et al., 2024). This protocol effectively separates genomic DNA from cellular components such as proteins, lipids, and RNA, thus ensuring high purity suitable for next-generation sequencing applications (Versmessen et al., 2024). DNA concentration and purity were assessed using a Nanodrop 2000 spectrophotometer (Thermo Scientific), with A260/280 and A260/230 absorbance ratios serving as critical quality indicators (Uddin et al., 2025). All samples exhibited A260/280 values within the acceptable range of 1.8–2.0, indicating minimal protein contamination, while A260/230 ratios exceeding 1.8 suggested low levels of residual organic compounds. DNA yields varied across samples, with the highest concentration reaching 209.2 ng/μL, demonstrating the efficiency of the extraction procedure.

Integrity verification and quantification

DNA integrity was evaluated using 1% agarose gel electrophoresis in TBE buffer, which enabled clear separation of intact genomic DNA, visualized as sharp bands at approximately 1500 bp. Quality control of PCR amplification was performed using primers incorporating Illumina universal adapters, with the expected amplicon size being the target length expected ~100 bp. Quantitative assessment of DNA concentration was carried out using the Qubit™ dsDNA HS Assay Kit (Thermo Scientific), a sensitive fluorometric method optimized for double-stranded DNA. DNA concentrations ranged from 32.6 to 125 ng/μL, exceeding the minimum input requirement of 20 ng per 10 μL for Oxford Nanopore sequencing. All samples passed the quality control (QC) criteria and were deemed suitable for 16S rRNA gene sequencing (Bahram et al., 2018).

Sequencing workflow

The sequencing workflow, encompassing DNA extraction and Nanopore sequencing processes, is summarized schematically in Figure 2.

The Figure 2 outlines the step-by-step workflow for full-length 16S rRNA gene sequencing, including DNA extraction, quality control, library preparation, sequencing using Oxford Nanopore technology, and bioinformatics analysis.

Nanopore sequencing

Nanopore sequencing was conducted to characterize the bacterial community structure by targeting the full-length 16S rRNA gene. Initially, DNA concentration and purity were assessed using a NanoDrop spectrophotometer and

Qubit fluorometer to ensure adequate sample quality for downstream applications. Amplification of the 16S rRNA gene was performed using the universal primers 27F (5’ AGAGTTTGATCMTGGCTCAG 3’) and 1492R (5’ TACGGYTACCTTGTTACGACTT 3’). PCR cycling conditions consisted of 35 cycles with pre-denaturation at 95 °C, denaturation at 95 °C, annealing at 60 °C, extension at 72 °C, and post-extension at 72 °C, ensuring robust amplification of near full-length amplicons.

Library preparation was performed using an Oxford Nanopore Technologies (ONT) kit, following standard protocols comprising end-prep DNA repair, barcode ligation, and adaptor attachment. The inclusion of barcodes enabled multiplexing, allowing simultaneous sequencing of multiple samples in a single run.

Prepared libraries were subsequently loaded into a MinION sequencing device, operated via MinKNOW software (version 24.02.16), which managed sequencing performance parameters. Raw sequencing output was processed through Dorado (version 7.3.11) for basecalling, utilizing high-accuracy models to enhance nucleotide sequence identification (Wick et al., 2019). Resulting FASTQ files were quality-checked using NanoPlot, and low-quality reads were filtered using NanoFilt, applying stringent quality thresholds to retain only high-confidence sequences for further analysis (de Coster et al., 2018; Nygaard et al., 2020). Bacteria and Archaea index was built using NCBI 16S RefSeq database (https://ftp.ncbi.nlm.nih.gov/refseq/TargetedLoci/).

Centrifuge was selected over EPI2ME/QIIME2 for its superior accuracy in classifying full-length 16S rRNA reads at species level (Kim et al., 2016), using a minimum alignment length of 1,400 bp and confidence threshold of 97% identity. Negative controls were included at all stages: Extraction blanks and PCR no-template controls. Contaminant taxa (e.g., Delftia, Pelomonas) identified in controls were removed from final datasets. Final steps included metagenomic data visualization through interactive tools to explore microbial diversity profiles.

Data analysis

Statistical analysis was performed to compare the bacterial diversity between healthy milk and subclinical mastitis milk (CMT score 1) at both genus and species levels. Descriptive statistics were calculated, including mean and standard deviation. To assess the equality of variances between groups, Levene’s test was conducted. Subsequently, independent samples t-tests were applied to determine statistically significant differences between the two groups. The results were presented with 95% confidence intervals (CI) and p-values, with a significance level set at p < 0.05. All statistical analyses were performed using IBM SPSS Statistics version 29.0 (IBM Corp., Armonk, NY, USA).

Results

Microbial differences between healthy milk and subclinical mastitis milk (Score 1)

The analysis revealed distinct differences in microbial composition between healthy bovine milk and milk affected by subclinical mastitis (score 1). Among bacterial genera, Rothia showed relatively balanced abundance between both groups (42% in healthy milk vs. 45% in subclinical mastitis milk). In contrast, genera such as Parablastomonas, Sutterella, Negativibacillus, Agaribacter, Mediterraneibacter, and Paraphocaeicola consistently dominated both conditions (100%). Notably, Bartonella was highly prevalent in healthy milk samples (100%) but decreased to 50% in subclinical mastitis samples. This high abundance is unusual, as Bartonella species are not typically considered dominant commensals in bovine milk. Nevertheless, Bartonella spp. have been sporadically detected in cattle through PCR and culture-based studies (Gutiérrez et al., 2014). For instance, B. bovis DNA was found in over 80% of bovine blood samples in one study using genus-specific and species-specific assays, suggesting potential for systemic presence (Cherry et al., 2009). In stark contrast, Epilithonimonas was detected in 100% of subclinical mastitis samples but only 40% of healthy milk samples. Additionally, Caulobacter exhibited higher abundance in healthy milk (50%) compared to subclinical mastitis milk (19%).

At the species level, Lactococcus lactis was detected with 284 reads in healthy milk versus 179 reads in subclinical mastitis milk, while Streptococcus australis was found with 270 reads in healthy milk but only 1 read in subclinical mastitis milk. Conversely, Staphylococcus chromogenes (425 reads), Rothia nasimurium (77 reads), and Kocuria tytonis (297 reads) predominantly appeared in subclinical mastitis samples. Particularly, Epilithonimonas vandammei was almost exclusively found in healthy milk (2,474 reads) compared to just 1 read in subclinical mastitis milk, whereas Phyllobacterium pellucidum was predominantly observed in subclinical mastitis samples (267 reads) versus only 1 read in healthy milk. The presence of Epilithonimonas vandammei was nearly exclusive to healthy milk (2,474 reads), suggesting a potential role in maintaining udder health. To date, E. vandammei has not been reported in association with bovine mastitis, underscoring the novelty of its identification in this study. While other Epilithonimonas species such as E. lactis sp. nov., isolated from raw cow’s milk in Israeli dairy farms (Shakéd et al., 2010) have been detected in the dairy environment, no published studies have linked E. vandammei to either health or disease states in the bovine mammary gland. This finding warrants further investigation into its ecological role, including potential probiotic activity or competitive exclusion mechanisms against mastitis-associated pathogens.

The Figure 3 illustrates the relative abundance of bacterial taxa at both genus and species levels, comparing healthy bovine milk with milk affected by subclinical mastitis (score 1). Noticeable shifts in microbial composition highlight potential indicator taxa associated with udder health status.

Discussion

Bartonella exhibited a 2-fold reduction in prevalence, decreasing from 100% in healthy milk to 50% in subclinical mastitis milk, while Epilithonimonas showed a 2.5-fold increase, rising from 40% in healthy samples to 100% in mastitic milk. Remarkably, Epilithonimonas vandammei demonstrated a drastic fold-change, with 2,474 reads detected in healthy milk and only 1 read in mastitis samples, highlighting its potential relevance to udder health. These findings provide valuable insights into the milk microbiota of dairy cows under healthy and early-stage subclinical mastitis (score 1) conditions, as assessed through full-length 16S rRNA gene sequencing. Although independent t-tests revealed no statistically significant differences in overall microbial diversity at the genus (p= 0.892) and species (p= 0.449) levels, the observed compositional shifts in specific taxa suggest biologically meaningful alterations in the mammary microbial ecosystem during the early onset of subclinical mastitis. Notably, the detection of genus Bartonella at 100% prevalence in healthy milk, although atypical for bovine-associated microbiomes, is not without

precedent in other mammalian reservoirs. Gutiérrez et al. (2014) reported high prevalence and complex co-infections of Bartonella lineages in wild rodents and their fleas, underscoring the ecological capacity of this genus to establish persistent subclinical infections across hosts. In this context, our findings may reflect either a true biological signal of Bartonella colonization in the bovine udder or an early ecological interaction that warrants further exploration.

Despite the lack of statistically significant quantitative differences, qualitative changes in microbial composition hold important biological implications for subclinical mastitis pathogenesis. A study by Wang et al. (2020) demonstrated that even minor shifts in genus- or species-level microbial composition could indicate disruptions in udder microbiome homeostasis, potentially increasing susceptibility to clinical mastitis. These findings align with Porcellato et al. (2020), who reported that early subclinical mastitis is often associated with decreased microbial community stability. Although the overall species count differences may not always reach statistical significance, the observed species diversity provides critical insights into udder health and the potential risk of elevated infection susceptibility.

Several genera, including Parablastomonas, Sutterella, and Negativibacillus, were consistently abundant in both healthy and mastitic milk, suggesting their potential role as core members of the bovine mammary microbiome. Differences in the relative abundance of specific genera, such as Bartonella (100% in healthy milk vs. 50% in mastitis milk) and Epilithonimonas (100% in mastitis milk vs. 40% in healthy milk), highlight their potential as indicator taxa for mastitis status. These findings are consistent with recent studies indicating that shifts in low-abundance genera can significantly impact the immunological environment of the udder (Urrutia-Angulo et al., 2024).

At the species level, healthy milk was dominated by Lactococcus lactis and Streptococcus australis, known for their roles in fermentation processes and probiotic functions. The decreased abundance of these species in subclinical mastitis milk indicates dysbiosis driven by inflammatory responses, leading to a reduction in beneficial commensal populations (Frece et al., 2014; Rodríguez-Figueroa et al., 2013). Conversely, the increased abundance of Staphylococcus chromogenes, a well-recognized mastitis pathogen, and Rothia nasimurium reinforces previous findings on the dominance of opportunistic pathogens in subclinical mastitis conditions (Yu et al., 2025). Beyond their role as starter cultures in dairy fermentation, Lactococcus lactis has also been reported to produce bioactive metabolites contributing to host health (Mendoza-Salazar et al., 2021; Qi et al., 2020). Therefore, understanding the dynamics of bacterial community shifts in healthy and subclinical mastitis milk is crucial, considering its implications for dairy cow health and milk safety for consumers (Odamaki et al., 2011; Olaide et al., 2020).

A notable finding in this study is the near-exclusive presence of Epilithonimonas vandammei in healthy milk (over 2,474 reads) compared to only 1 read in mastitis samples. Similarly, Phyllobacterium pellucidum was predominantly detected in subclinical mastitis milk. These species may serve as potential microbial biomarkers for udder health status, although their diagnostic utility requires validation in larger and more diverse populations. The broader species diversity observed in healthy milk compared to mastitis milk is also supported by Rahmeh et al. (2022), who reported significantly higher bacterial community diversity in healthy milk samples relative to those from mastitic cows. These findings underscore the importance of a balanced microbiota in maintaining udder health.

The persistence of certain bacterial genera and species across both health conditions indicates high ecological flexibility and adaptive potential within the mammary gland environment. Taxa such as Rothia, Sutterella, and Negativibacillus appear resistant to host immune modulation, nutrient availability changes, and inflammatory conditions, suggesting facultative ecological roles. This plasticity allows these taxa to shift between commensalism and opportunism depending on udder health status. In particular, the consistent detection of Rothia across both healthy and mastitic milk suggests that this genus possesses adaptive traits that facilitate survival under diverse conditions. Previous studies have reported the presence of Rothia in bovine subclinical mastitis (Riggio et al., 2017), while genomic evidence has demonstrated that Rothia species exhibit metabolic versatility and the ability to form biofilms, supporting persistence during inflammatory stress (Maruyama et al., 2009). These adaptive features likely underpin the ecological plasticity of Rothia observed in our dataset, allowing it to act either as a commensal stabilizer of the udder microbiome or as a potential opportunistic pathogen under mastitis conditions. These observations align with recent studies emphasizing microbial adaptability as a key feature of dysbiosis in mastitis (Guo et al., 2024). Biologically, such microbial shifts can influence udder immune responses, milk quality, and susceptibility to secondary infections. Practically, early detection of specific microbial signatures may enhance mastitis surveillance programs and support the development of targeted probiotic interventions aimed at restoring a healthy mammary microbiome (Khan et al., 2024).

The strengths of this study include the use of high-resolution sequencing technology and a specific focus on early-stage subclinical mastitis (score 1), a condition often overlooked despite its importance for early detection and intervention. However, several limitations must be acknowledged. The small sample size (N = 10 per group) limits statistical power and generalizability. Additionally, the high standard deviation observed in species-level diversity (particularly in healthy samples, SD= 762.8) reflects significant inter-individual variability, potentially obscuring critical patterns. Future research should address these limitations by employing larger longitudinal cohorts, incorporating somatic cell count (SCC) analysis, clinical outcomes, and functional metagenomics. Although intergroup statistical differences were not significant, compositional shifts at the genus and species levels highlight the potential of microbial biomarkers and the importance of in-depth investigations linking microbial dynamics to the pathophysiology of subclinical mastitis.

CONCLUSION

This study revealed distinct shifts in milk microbiota composition between healthy cows and those with early-stage subclinical mastitis (score 1), with a particular focus on low-abundance taxa, using full-length 16S rRNA Oxford Nanopore sequencing. Key taxa such as Epilithonimonas vandammei and Phyllobacterium pellucidum were identified as potential microbial biomarkers, while the persistence of certain taxa across both conditions highlighted their ecological plasticity. These findings provide new insights into mastitis pathogenesis and offer a scientific basis for microbiota-based early detection and targeted probiotic interventions in smallholder dairy systems. Further validation in larger cohorts and integration with clinical and functional data are warranted. Future studies should also explore the practical application of these microbial biomarkers as on-farm diagnostic tools to support early mastitis detection and guide management strategies in real-world dairy production systems

ACKNOWLEDGEMENT

This work was supported by the Beasiswa Pendidikan Indonesia (BPI) program from the Indonesia Endowment Fund for Education (LPDP) through the Center for Higher Education Funding and Assessment (PPAPT), Ministry of Higher Education, Science, and Technology of Republic Indonesia.

NOVELTY STATEMENT

This study is the first to profile and compare milk microbiota of healthy cows and early-stage subclinical mastitis (score 1) under tropical smallholder systems using full-length 16S rRNA Oxford Nanopore sequencing.
By focusing on low-abundance and rarely reported taxa, it identifies Epilithonimonas vandammei and Phyllobacterium pellucidum as potential early-stage biomarkers.
The persistence of taxa such as Rothia and Sutterella highlights ecological plasticity, underscoring the potential of milk microbiota for early mastitis detection and microbiome-based interventions.

AUTHOR’S CONTRIBUTION

R: Conceptualization, writing-original draft, formal analysis, visualization. LER: Supervision, methodology, critical review, revision. LCH: Supervision, project administration, critical review, revision. TES: Critical review, revision. PS: Conceptualization, critical review, approved and validation.

Generative AI and AI-assisted technology statement

The authors confirm that generative AI and AI-assisted tools (such as Grammarly and ChatGPT) were used only to refine language, improve grammar, and reduce typographical errors to ensure the clarity and quality of the manuscript. No AI tools were involved in data generation, analysis, or interpretation. The authors take full responsibility for the accuracy and integrity of the manuscript’s content and conclusions.

Conflict of interest

The authors have declared no conflict of interest.

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