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Agricultural And Food Sciences
Biology

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Investigating circulating miRNA in transition dairy cows: What miRNAomics tells about metabolic adaptation

In the current study, we investigated dairy cows’ circulating microRNA (miRNA) expression signature during several key time points around calving, to get insights into different aspects of metabolic adaptation. In a trial with 32 dairy cows, plasma samples were collected on days −21, 1, 28, and 63 relative to calving. Individually extracted total RNA was subjected to RNA sequencing using NovaSeq 6,000 (Illumina, CA) on the respective platform of IGA Technology Services, Udine, Italy. MiRDeep2 was used to identify known and novel miRNA according to the miRbase collection. Differentially expressed miRNA (DEM) were assessed at a threshold of fold-change > 1.5 and false discovery rate < 0.05 using the edgeR package. The MiRWalk database was used to predict DEM targets and their associated KEGG pathways. Among a total of 1,692 identified miRNA, 445 known miRNA were included for statistical analysis, of which 84, 59, and 61 DEM were found between days −21 to 1, 1 to 28, and 28 to 63, respectively. These miRNA were annotated to KEGG pathways targeting the insulin, MAPK, Ras, Wnt, Hippo, sphingolipid, T cell receptor, and mTOR signaling pathways. MiRNA-mRNA network analysis identified miRNA as master regulators of the biological process including miR-138, miR-149-5p, miR-2466-3p, miR-214, miR-504, and miR-6523a. This study provided new insights into the miRNA signatures of transition to the lactation period. Calving emerged as a critical time point when miRNA were most affected, while the following period appeared to be recovering from massive parturition changes. The primarily affected pathways were key signaling pathways related to establishing metabolic and immune adaptations.

Introduction
In dairy cows, the transition from pregnancy into lactation often coincides with metabolic stress associated with a negative energy balance (NEB), a result of an imbalance between feed intake and milk production (Drackley, 1999). Several factors such as excessive fat mobilization, impaired liver function, and systemic inflammation during this period may lead to metabolic disorders and also economic losses (Wankhade et al., 2017). Nevertheless, most dairy cows can adapt and maintain their internal organization to deal with transition period challenges, which require an orchestrated array of regulatory mechanisms at the hormonal, metabolic, and immunological levels (Horst et al., 2021).
MicroRNAs (miRNA) are evolutionary conserved small non-coding (approximately 17-24 nucleotides) RNA molecules involved in the post-transcriptional regulation of most protein-coding mRNA, including degradation and translational repression (O'Brien et al., 2018). Since miRNA are involved in almost all metabolic pathways, including energy metabolism, lipid metabolism, insulin sensitivity, and glucose homeostasis (Lin et al., 2020), it is not surprising that their function is crucial during the transition period. As tissuespecific miRNA can enter the circulation system (Barutta et al., 2018) embedded into stable structures such as exosomes and lipoproteins (Condrat et al., 2020), plasma and other body fluids may carry signatures related to the pathophysiological status. Various physiological conditions including heat stress (Lee et al., 2020), different levels of NEB (Hailay et al., 2019), and over-conditioning in dairy cows (Webb et al., 2020), induce changes to the circulating miRNA pattern. In particular, the stage of lactation and transition period affect the miRNA profile. In this regard, the microRNAome of dairy cows' dry secretions during the first 3 weeks of the dry period revealed differentially expressed miRNA related to reproduction and embryo development (i.e. bta-miR-130b and bta-miR-106a), lactation (i.e. bta-miR-29a, bta-miR-21-3p, bta-miR-130a), as well as inflammation and disease (bta-let-7 family) (Putz et al., 2019).
Markers of the metabolic adaptation during the transition period have been characterized in dairy cows by univariate analyses and, increasingly, via multivariate OMICs technologies (Ceciliani et al., 2018). On the other hand, the post-transcriptional regulation of gene expression has received relatively little attention in this context (Webb et al., 2020). Despite the progress in identifying the origin and the function of miRNA and developing databases, further knowledge of circulating miRNA in dairy cows is still lacking. We, therefore, aimed to characterize the longitudinal changes of the circulating miRNAome profile in dairy cows during the transition period using next-generation sequencing (NGS) and bioinformatics analysis.

Experimental design
The samples used herein were from an animal experiment described in detail by Vogel (Vogel et al., 2020), performed according to the animal welfare guidelines, and approved by the local authority, i.e. the State Mecklenburg-Western Pomerania, Germany (LALLF M-V/TSD/7221.3-1-038/15). In brief, 32 Holstein dairy cows in their second lactation and without clinical signs of disease were housed in free-stall barns with ad libitum access to a corn silage-based total mixed ration (TMR), formulated according to recommendations provided by the Society for Nutrition Physiology (GfE, 2001 ( The dairy cows were randomly assigned to one of four treatment groups receiving different fat supplements as abomasal infusion: CTRL (n = 8; coconut oil, Bio-Kokosöl #665, Kräuterhaus Sanct Bernhard, KG, Bad Ditzenbach, Germany; 38 and 76 g/d in AP and PP), Essential fatty acids (EFA, n = 8, a combination of linseed oil (DERBY ® Leinöl #4026921003087, DERBY Spezialfutter GmbH, Münster, Germany; 39 and 78 g/d in AP and PP), and safflower oil (GEFRO Distelöl, GEFRO Reformversand Frommlet KG, Memmingen, Germany; 2 and 4 g/d in AP and PP), conjugated linoleic acid (CLA, n = 8, Lutalin ® ; cis-9, trans-11, 10 g/d trans-10, cis-12 CLA, BASF SE, Lampertheim, Germany; 19 and 38 g/d in AP and PP), and EFA + CLA, a combination of linseed oil, safflower oil and Lutalin ® from day 63 AP to day 63 PP (Figure 1). The ingredients and the chemical composition of the basal diet were reported elsewhere (Vogel et al., 2020). The present investigation was focused on longitudinal changes only.

Blood sampling
Four blood samples were collected from each cow on days -21, 1, 28, and 63 relative to calving (Figure 1), immediately after the morning milking and before feeding, using the Vacuette system (Greiner Bio-One International GmbH, Kremsmünster, Austria) containing 1.8 g potassium-EDTA (K3EDTA)/L. Samples were immediately cooled on ice and transferred to the lab within 2 h of collection. The plasma fraction was harvested after centrifugation at 1,500 × g (4°C) for 20 min and stored at −80°C until analysis.

Isolation of total RNA
Total RNA was extracted from 128 plasma samples using the QIAGEN miRNeasy serum/plasma kit (Qiagen GmbH, Hilden, Germany) according to the manufacturer protocol. The miRNeasy Serum/Plasma Spike-In Control (lyophilized C. elegans miR-39 miRNA) was used to verify extraction quality and normalize TaqMan qPCR results. The RNA yield and purity were assessed using an Agilent Small RNA kit on a 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, United States). In samples with low miRNA concentration (<100 pg/μl), the isolation procedure was repeated using the double initial plasma volume (400 µl).

Library preparation and next-generation sequencing
The QIAseq miRNA library kit (QIAGEN, Hilden, Germany) was used for library preparation following the manufacturer's serum/plasma samples instructions. Final libraries were checked with Qubit 2.0 Fluorometer (Invitrogen, Carlsbad, CA) and Caliper (PerkinElmer, Waltham, MA).
Afterward, libraries were sequenced on single-end 100 bp mode on the NovaSeq 6,000 (Illumina, San Diego, CA) on IGA Technology Services platform, Udine, Italy (https:// igatechnology.com/). Raw-sequencing data was checked for quality (format conversion to FASTQ, demultiplexing, adapter trimming, and UMI removal) using the Illumina Bcl2fastq2 Conversion Software (version 2.20). A summary of nucleotide sequence data, including FastQC merge reads produced for each sample, sequence quality histograms, per sequence quality scores, per base sequence content, per sequence GC content, per base N content, sequence length distribution, alignment-free estimation of sequences duplication levels, overrepresented sequences, adapter content (multiQC file, version 1.6) is available in the ArrayExpress database (http://www.ebi.ac.uk/ arrayexpress) under the "ArrayExpress accession E-MTAB-11725".

FIGURE 1
Schematic representation of (A) the animal experimental design and sampling time points, consisting of four groups CTRL: control (n = 8), EFA: essential fatty acids (n = 8), CLA: conjugated linoleic acid (n = 8), and EFA + CLA (n = 8), and four plasma sampling (days −21, +1, +28, and +63 relative to parturition). The focus of this study is primarily on time comparisons since the effect of treatments was negligible (B) Sequencing analysis and bioinformatics. CTRL: control, EFA: essential fatty acids, CLA: conjugated linoleic acid.
Frontiers in Genetics frontiersin.org 03 contrast, the miRBase human, ovine, and goat miRNA datasets were added to support the identification of novel miRNA. Read counts for each known and novel miRNA were compiled using HTSeq-count (Putri et al., 2022).

Statistical analysis and bioinformatics
The quantified counts were normalized using the TMM method via the edgeR (version 3.12.0) package in R software (Version 4.0). Only miRNA with at least one count per million over at least two samples were considered for the analysis. After normalization, differentially expressed miRNA (DEM) during time were identified by performing generalized linear model (GLM) likelihood ratio tests (glmLRT) using the GLM approach in edgeR to deal with the time series paired effects. Between each two consecutive time points, miRNA with an FDR <0.05 and log2 (FC) > 1.3 were considered as DEM, and visualised by Volcano plot according to their expression (EnhancedVolcano R package). DEM between treatment groups were identified at each time point using the same criterion (FDR <0.05 and log2 (FC) > 1.3). The miRWalk database (2022_01, Bos Taurus, http://mirwalk.umm.uniheidelberg.de/) was used to compile consensus lists of predicted miRNA targets and to perform gene ontology and KEGG functional enrichment analysis. Network analysis was performed and visualized (yFiles Tree layout) using the combination of miRNet (version 2.0) web-based platform, StringApp, and Centiscape 2.2 in Cytoscape software (version 3.9) (Figure 1). The networks were filtered by two wellestablished criteria: degree centrality (number of connections with the other nodes) and betweenness centrality (the number of shortest paths connecting the node). The network has been constructed, visualized, and analyzed using the combination of miRanda, miRNet, and Cytoscape. The miRanda database was replaced with the miRWalk database to filter the number of target genes. As yet there is no possibility for target filtration in miRWalk based on Targetscan (http://www.targetscan.org/ vert_80/), miRtarbase (https://mirtarbase.cuhk.edu.cn/ miRTarBase/miRTarBase_2022/php/index.php), and mirdb (http://mirdb.org/faq.html) for dairy cows.

Results
In total, 183,772,507 cleaned reads were processed and mapped to the bovine reference genome; in detail, 51,086,403, 46,288,086, 44,508,614, and 41,889,404 reads were processed at days -21, 1, 28, and 63, respectively. From these sequences, 846 miRNA were matched to previously known mature bovine miRNA (equal to 846/1025 = 82.5% of total identified miRNA in cattle until January 2021 (Do et al., 2021), and 846/ 6808 = 12.42% total theoretical number available in the RumimiR 2022 database, http://rumimir.sigenae.org/). In addition, 1,274 novel miRNA hairpins were identified, including 836 completely novel bovine miRNA (named according to their absolute genomic position), 374 miRNA similar to known Homo sapiens miRNA (has-miR), 58 similar to Capra hircus known miRNA (ch-miR), and six similar to Ovis aries known miRNA (oar-miR) (Supplementary Table S1). The statistical and functional enrichment analyses were limited to 445 cleaned known miRNA with ten counts or more in at least 50% of samples (Supplementary Table S2). Figure 2 represents the 25 miRNA with the highest mean reads (ranging from 2. 1 × 10 5 to 7 × 10 6 ) across all samples and time points (sorted by time point). Oil treatments had negligible minor effects on miRNA profile and only one miRNA, bta-miR-1 (log2 (FC) = -5.65, FDR <0.001), was found to be differentially expressed between the CTRL and the CLA group at day -21 AP. There was no other difference in miRNA expression between CTRL, EFA, CLA, and EFA + CLA at days -21, 1, 28, and 63, respectively (Supplementary Table S3). Therefore, data from all four treatment groups were merged to study time-affected miRNA (regardless of the treatment effect).
Differentially expressed miRNA over time Figure 3 shows the multidimensional scaling (MDS) plot of the individual miRNA profiles obtained on the different sampling days. Using a two-dimensional scatterplot, the distances between samples were approximated by their expression differences. The analysis differentiates the AP (day -21) and PP (days 1, 28, and 63) periods as two well-separated clusters, suggesting that most of the variance in the miRNAome data resulted from the transition to lactation. Even if PP time points partially overlap, clustering according to lactation periods is observable.

Pattern identification of commonly affected miRNA during time
Venn diagram analysis of DEM across the time points revealed that the expression pattern of specific miRNA was time-dependent ( Figure 7). Among time-dependent DEM, a cluster ( Figure 8A) was affected at parturition and turned back to initial levels within 4 weeks, while another cluster ( Figure 8B) was affected at day 28 PP and returned to parturition level at day 63 PP.

miRNA-target interaction networks
The biological relevance of miRNA is based on their interaction with their target genes (Lee et al., 2020), although correctly interpreting miRNA-mRNA regulatory pathways is challenging due to numerous potential target genes. Based on the "graph theory" (León and Calligaris, 2017), we analyzed the miRNA-mRNA interaction networks to identify critical nodes and hubs that could act as master regulators, to narrow down the focus of the discussion.

Discussion
MiRNA play an essential role in the post-transcriptional regulation during the endocrine, metabolic, and immune reactions induced by an imbalance between energy requirements and supply during the transition to lactation. Previous studies have shown that severe states of NEB affect the expression of multiple hepatic miRNA and their target genes involved in lipid and glucose metabolism and homeostasis (McCarthy et al., 2010;McCabe et al., 2012;Fatima et al., 2014).
Nevertheless, less information is available regarding the circulating miRNA signature of late gestation and early lactation dairy cows. We used longitudinal miRNAome analysis to characterize DEM at critical time points around parturition and analyze the miRNA-gene network in plasma samples of transition dairy cows to identify candidate miRNA as master regulators of metabolic adaptation. The experimental dairy cows were abomasally supplemented with various fatty acids, and accordingly grouped to CTRL, EFA, CLA, and EFA + CLA. The effects of oil treatment on miRNA expression were negligible, except for the day -21 AP, when bta-miR-1 was differentially expressed between CTRL and CLA groups. No other significant differences were found between the treatment groups. Even though fatty acids treatment altered energy balance in cows of the present study (Vogel et al., 2020), it did not affect miRNA expression or post-transcriptional regulation of energy metabolism. Therefore, the discussion was limited to time-affected miRNA.
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Differentially expressed miRNA over time
To ensure comparability, the time points were chosen based on physiological reasons and previously collected data (zootechnical, performance, metabolite, hormone levels, as well as plasma proteomics data) (Vogel et al., 2020;Veshkini et al., 2022), under the identical experimental design. The MDS plot revealed a comprehensive perspective of miRNAome profile in individual samples over time, in which the AP period stands as a separate cluster, while there was a high degree of overlap between days 1 and 28 PP, and day 63 tended to be separated from them and form an independent cluster. Interestingly, the cluster separation was in line with dairy cows' energy balance (data are shown in (Vogel et al., 2020)), with positive energy balance (day -21) standing out as a distinct cluster, duration of the lowest energy balance (days 1 and 28 PP) as a second cluster, and the last cluster (day 63) appearing to correspond to a period when the energy balance was turning back to positive values. In this regard, miR-143 was differentially expressed in hepatic miRNA profiles of dairy cows at different levels of energy balance and was mainly related to lipid and energy metabolism (Fatima et al., 2014). Therefore, it might be concluded that energy balance is one of the significant determinants of critical pathways in which miRNA are subsequently necessary for initiating and regulating adaptation processes. As recently reviewed (Kinoshita et al., 2016), glucose FIGURE 8 (A) A bar chart shows differentially expressed miRNAs whose up-or downregulation occurred on day 1 postpartum and returned antepartum levels on day 28 postpartum (B) A bar chart shows differentially expressed miRNAs whose up-or downregulation occurred on day 28 postpartum and returned to day 1 levels on day 63 postpartum.
Frontiers in Genetics frontiersin.org Frontiers in Genetics frontiersin.org and lipid metabolism are systemically controlled by a complex orchestrated network containing a plethora of different molecules, such as hormones, lipoproteins, and miRNA. Although there are likely other players involved such as inflammation and immune activation, since in the current design fatty acid treatment significantly affected energy balance (Vogel et al., 2020), but not miRNA expression, which requires further investigation. Therefore, cluster separation was due to time-dependent miRNA expression, in which specific sets of miRNA were probably associated with the physiological state at that time. It should be noted that miRNA expression does not necessarily follow a linear order over time.
Thus, there might be other up or downregulated miRNA during the AP and PP periods which were not detected according to our selected time points.

Differentially expressed miRNA and their associated pathways during the transition period
The transition from gestation to lactation considerably alters miRNA expression, with five times more upregulated miRNA than those downregulated. This might be biologically relevant since most miRNA induce mRNA degradation and translational repression (O'Brien et al., 2018), thereby as part of the adaptation process switching to several organs and tissues' catabolic status to prioritize milk production over other biological pathways (Veshkini et al., 2022). Although, in the following time points, some of the DEM showed reverse expression, which gradually inverted adaptations to restore normal metabolic and physiological conditions. In this regard, we have previously shown for various metabolites and the proteome that different aspects of the immune system (such as the complement system) (Veshkini et al., 2022) and lipid metabolism including lipoprotein content (Vogel et al., 2020) and apolipoprotein protein abundance (Veshkini et al., 2022) were temporarily downregulated at the time of parturition, and then gradually recovered within the next few weeks after parturition.
The DEM at day 1 PP were annotated to critical immune functions and energy metabolism pathways, including insulin, Ras, MAPK, Wnt, Hippo, sphingolipid, mTOR, and TCR signaling pathways. These signaling pathways are critical to establishing metabolic adaptation at the onset of lactation; therefore, they are strictly controlled. Their dysregulation results in immune and metabolic dysfunction and diseases (Sebastian-Leon et al., 2014). These results align with our recent findings, whereupon metabolic adaptations around calving are most prominent in two distinct but tightly interconnected physiological processes: lipid (energy) metabolism and immune function (Veshkini et al., 2022). The identified adaptation signaling pathways are tightly interconnected and exhibit functions that regulate a diverse array of physiological processes and cellular processes like proliferation, differentiation, transformation, inflammatory responses, apoptosis, and homeostasis in dairy cows (Sigdel et al., 2021). There is also evidence from a human study that circulating and tissue-derived miRNA play a crucial role in the cross-talk between different organs, acting as endocrine and paracrine messengers in intercellular communication (Kiran et al., 2021).
It is important to emphasize that targeted genes may overlap between up and downregulated miRNA; indeed, several pathways were annotated by both up and downregulated miRNA. Information regarding the source or target tissues of DEM is unavailable; therefore, discussing each pathway's direction according to the direction of miRNA expression seems inefficient. Instead, signaling pathways are discussed according to the literature and our previously published results at the proteome (OMICS based) and metabolite and hormones (classical kit measurements) level from the same experiment, in which the AP period was taken into consideration as the basis and calving as the critical time point for the initiation of the adaptation process.
A major component of immune adaptation was probably triggered by activating the TCR signaling pathway, which activates the NF-κb and downstream signaling cascades that regulate cytokine production, cell survival, proliferation, and differentiation (Hwang et al., 2020). In addition, the TCR signaling pathway initiates intracellular signals essential for T cell development and recruits macrophages for ingesting apoptotic and damaged cells (Moro-García et al., 2018;Shah et al., 2021), which is crucial during the rapid tissue remodeling at calving. Numerous miRNA have been reported to be associated with TCR signaling pathways (Rodríguez-Galán et al., 2018) of which miR-26b, miR-142, miR-146, miR-150, miR-342, and miR-451 were among our DEM.
Specific annotated pathways are downstream targets of the NF-κb pathway, and thus they might have ignited a state of inflammation already underway before parturition. In this regard, intracellular signaling pathways such as Wnt (Jeong et al., 2018), Hippo (Feng et al., 2017), TCR (Adachi and Davis, 2011), Rap1 (Pizon and Baldacci, 2000), and mTOR (Wang et al., 2013) are reportedly related to various physiological and pathological conditions depending on the specific cellular context. The Wnt pathway consists of a large family of highly conserved glycoproteins that cross-talk with MAPK in development, determining cell fate, tissue regeneration, and maintaining tissue homeostasis (Zhang et al., 2014). Previous studies have shown that Wnt and MAPK pathways are essential for normal mammary gland development (Roarty and Serra, 2007;Li et al., 2018) and are involved in immune system-related signaling pathways (Luoreng et al., 2021). These studies reported candidate miRNA including bta-miR-19a, bta-miR-19b, bta-miR-21-5p, bta-miR-29c, bta-miR-143, bta-miR-145, and bta-miR-146b are associated with Frontiers in Genetics frontiersin.org 15 Wnt signals and MAPK pathway (Li et al., 2018;Luoreng et al., 2021). Mounting evidence from recent human studies also suggested that Wnt is involved in immune signaling, thus regulating immune cell maintenance and renewal, modulation of inflammatory cytokine production, such as NF-κB signaling, and bridging innate and adaptive immunity (Patel et al., 2019;Jridi et al., 2020). Furthermore, Rap1 is shown to function as a transcriptional cofactor that regulates the NF-κB pathway (Yeung et al., 2013).
The current results follow a challenging hypothesis proposed by Bradford et al. (Bradford et al., 2015) as the presence of a subacute inflammation during the peripartum period, which has been reviewed later and related to tissue damage and remodeling during this prenatal period in critical organs such as the mammary glands, liver, and adipose tissue (for review, see (Horst et al., 2021)). An earlier study reported that low-grade systemic inflammation, also known as "metabolic inflammation" and "sub-acute inflammation", plays an important role in developing insulin resistance and hepatic steatosis in mice activating NF-κB signaling pathways (Zeng et al., 2016). As low-grade systemic inflammation in early lactation is a noninfectious inflammatory response, it may activate a type of noncanonical NF-κB that exhibits slower but continuous function (for review, see (Sun, 2011)). Coordinating these signaling pathways develops an immune adaptation process during the transition period in which miRNA play a crucial role (for review, see (Lawless et al., 2014;Chandan et al., 2019)). However, the tissue and cellular level of individual miRNA require further investigation. Further, the current study design did not determine precisely when the systemic inflammation began, reached its peak, and diminished.
Metabolic adaptations are at least partly mediated by alterations in the insulin signaling pathway during early lactation, which is a highly conserved regulatory network coordinating animal metabolism (Zachut et al., 2013). Insulin resistance in the periparturient cow has been ascribed to multiple factors, including low-grade inflammation as one of the primary ones (Rehman and Akash, 2016). Dairy cows in the periparturient period have moderately reduced peripheral tissue insulin sensitivity, which promotes the mobilization of body fat and protein reserves and prioritizes nutrients to the fetus and mammary gland (Zachut et al., 2013). In vivo measurements revealed a considerable drop (p < 0.001) in plasma insulin levels in the PP period over the AP period (Supplementary Figure S1) and there is a distinct decrease in the insulin release in the PP transition cow (Weber et al., 2016). As a result, we hypothesized that insulin metabolism pathways were partially downregulated. Some of the DEM found herein were previously reported to be associated with the insulin signaling pathway in humans (Nigi et al., 2018). Among those, miR-15b and miR-195 directly regulate the insulin receptor and miR-1 regulates the IGF-1 and  indirectly affect insulin signaling pathways (Nigi et al., 2018).
The insulin signaling pathways and the sphingolipid signaling pathways, are interrelated: sphingolipid accumulation at the onset of lactation may inhibit insulin signaling and cause insulin resistance in various tissues of dairy cows (McFadden and Rico, 2019), although they are not the sole mediator of insulin antagonism (Petersen and Jurczak, 2016). In that sense, modulating insulin signaling by the sphingolipid signaling pathway and in particular ceramides may considerably impact on regulating glucose utilization, maintaining PP health, and milk production in dairy cows (for review, see (McFadden and Rico, 2019)). Within the sphingolipid pathway, miR-1, miR-101, miR-125b, and miR-128 interact with sphingosine kinases, sphingosine-1 phosphate, and sphingosine-1 phosphate receptors (for review, see (Xu et al., 2021)). The knowledge of sphingolipids in inflammation and endocrine function of dairy cows will continue to grow with the increased accessibility of lipidomics technologies.
In association with the insulin signaling pathway, Ras/Raf/ MAPK (rat sarcoma/rapidly accelerated fibrosarcoma/mitogenactivated protein kinases) as major signaling pathways are triggered to transduce signals from the extracellular milieu to the cell nucleus where specific genes are activated to promote gluconeogenesis, lipolysis, re-adjust insulin sensitivity, and regulate the cellular response to various stimuli (Saltiel and Kahn, 2001;Wang et al., 2013). The MAPK family members were also shown to be involved in hepatic and systemic inflammation and apoptosis in dairy cows (Li et al., 2020;Horst et al., 2021), and thus in the initiation of the innate immune system and cytokine receptor-mediated responses, as well as in the adaptive immune system through regulating T and B lymphocyte differentiation (Krzyzowska et al., 2010). Several human miRNA were suggested to be associated with the MAPK signaling pathway (in chronic myeloid leukemia (Chakraborty et al., 2016)), including miR-196a, miR-196b, miR-30a, miR-138, miR-126, miR-221, miR-128, miR-15a, miR-17, and miR-19a that were found as DEM during different time points in the present study.
Also, mTOR is one of the main signaling factors for metabolic adaptation and responds to growth factors, energy and amino acid levels, and cellular stress to regulate transcription, protein and lipid synthesis, sphingolipid biosynthesis, membrane homeostasis, cell cycle progression, endocytosis, and nutrient transport and autophagy (Madrid et al., 2016;Sadri et al., 2016). The mTOR and MAPK signaling pathways may cross-talk to maintain essential cellular functions (Madrid et al., 2016) and probably direct protein synthesis toward the mammary gland and, thus, milk production in early lactating dairy cows. In accordance, a recent study on dairy goats suggested the involvement of the AMPK-mTOR pathway in mammary milk protein synthesis (Cai et al., 2020). The DEM identified herein were predominantly involved Frontiers in Genetics frontiersin.org 16 in signaling cross-talk regulatory mechanisms that determine energy homeostasis as a part of metabolic adaptations.
As discussed earlier, the expression level of several DEMs was reversed within a few weeks of lactation and then returned to AP expression, which resulted in normalized insulin, mTOR, and Ras signaling pathways. These results are consistent with our previous proteomics findings in which indicators of metabolic and immune adaptation such as acute phase proteins (haptoglobin, lipopolysaccharide-binding protein, alpha-2-Heremans-Schmid glycoprotein, and adiponectin) and various complement proteins at the onset of lactation were restored within four to 9 weeks of lactation (Veshkini et al., 2022).
Beside those affected pathways, the TNF signaling pathway was annotated by downregulated miRNA on day 28 PP. The NF-κB pathway is activated through TNF signalling pathways, producing various proinflammatory cytokines (including TNFα). Moreover, lipoprotein lipase is inhibited thus decreasing fatty acid uptake and lipogenesis, and increasing lipolysis in adipose tissue (Kim et al., 2014). Conversely, TNF-α promotes the hepatic expression of SREBP1c, a major regulatory transcription factor in hepatic lipogenesis and the development of hepatic steatosis in humans (Kim et al., 2014). In addition, TNF-α, besides being a well-known major proinflammatory cytokine, induces in part insulin resistance through phosphorylation of Jun and insulin receptors (Kim et al., 2014). Accordingly, a possible explanation could be that early lactation inflammation induces TNF-α, affecting downstream pathways, including insulin signaling pathways. Then, TNF signaling was also reduced in conjunction with attenuated systemic inflammation at 28 days PP.
We determined which miRNA are the central hub of those enriched pathways using miRNA-mRNA network analysis. Considering time-dependent expression and network analysis, bta-miR-138, miR-149-5p, bta-miR-214, bta-miR-504, bta-miR-2387, bta-miR-2466-3p, and bta-miR-6523a were selected as the central hub regulating annotated signaling pathways. Significant positive correlations were observed between bta-miR-504 and bta-miR-2466-3p (r = 0.51, p-value < 0.001), and bta-miR-504 and bta-miR-214 (r = 0.6, p-value < 0.001) (Supplementary Figure S2). According to bioinformatics analysis, these miRNA and their target genes are among the key regulators of the NF-κB signaling pathway, insulin secretion, sphingolipid, TNF, B cell receptor, and MAPK signaling pathways. Several target genes were common to these signaling pathways including, MAPK3, MAPK14, MAP3K14, MAP2K2, JUN, MYD88, NRAS (neuroblastoma RAS viral (V-ras) oncogene homolog), FOS, IKBKG (inhibitor of nuclear factor-kappa B kinase regulatory subunit gamma), IKBKB (inhibitor of nuclear factor-kappa B kinase subunit beta), and RELA (Supplementary Figure S3), which were the key genes in previously annotated metabolic and immune pathways. Also, protein-protein interaction network analysis revealed that all these genes are biologically connected (Supplementary Figure S4). Overall, bioinformatics provided a subset of miRNA considered the regulatory hub for metabolic and immune adaptation processes, which can be used as candidates to assess the metabolic health of dairy cows during the transition period. Nevertheless, future studies should focus on the specific role of these miRNA in metabolic disorders.

Conclusion
Investigating circulating miRNA revealed novel insights into miRNA signatures associated with metabolic and immune adaptation in late gestation and early lactation dairy cows. There were time-dependent differences in the expression of specific miRNA, in which the calving effect predominated, such that their expression was differential during the parturition time and returned to normal after a few weeks (days 28 and 63 PP). Calving prevailing was partially caused by DEM associated with a state of systemic inflammation. Bioinformatics analysis suggested that metabolic adaptation initiated by systemic inflammation, and DEM orchestrates critical signaling pathways, including TCR, NF-κB, MAPK, and insulin, which facilitate cross-talk between energy metabolism and immunity in target tissues across the whole body. In particular, the miRNA-mRNA network analysis revealed circulating DEM including bta-miR-138, miR-149-5p, bta-miR-2466-3p, bta-miR-214, bta-miR-504, and bta-miR-6523a and their targeted genes as central hubs that regulate key signal transduction pathways associated with energy homeostasis and immune response in transition dairy cows. However, a more indepth analysis of the identified miRNA and their target genes is required to develop them as biomarkers of metabolic homeostasis.
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Data availability statement
The data presented in the study are deposited in the ArrayExpress database (http://www.ebi.ac.uk/arrayexpress) repository, accession number E-MTAB-11725.

Author contributions
HMH and AT suggested the main idea of the basic animal study; AT supplied materials. HMH designed and performed the animal study and collected performance and blood and tissue data. HMH, FC, and HS elaborated the concept for the current analyses, contributed to the research's design and implementation, and supervised the related works. VV conducted the NGS analysis and the related works. BL designed and conducted the statistical analyses. LV and MG planned and performed the farm trial. AV and HaS performed sample preparation and laboratory analysis and provided methodologies, respectively. AV curated and visualized the NGS data, did the bioinformatics analysis, and wrote the paper. HaS, BL, HMH, FC, and HS revised the manuscript. All authors reviewed the results and approved the final version of the manuscript.

Funding
This project has received funding from the European Union's Horizon 2020 research and innovation programme H2020-MSCA-ITN-2017-EJD: Marie Skłodowska-Curie Innovative Training Networks (European Joint Doctorate)-Grant agreement n°: 765423. The animal study was supported by BASF SE (Ludwigshafen, Germany).


provenance: AgriculturalAndFoodSciences,2022-2022-Agricultural And Food Sciences-2022.jsonl.gz.gz

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The use of teat disinfectants and milking machine cleaning products in commercial Holstein-Friesian farms

The aim of our study was to survey and analyze the use of pre- and post-milking teat disinfectants and milking machine cleaning products in large commercial Holstein-Friesian farms. A total of 43 Hungarian dairy farms with 31,430 cows with an average herd size of 731 cows were surveyed in 2014 by using a questionnaire via personal interviews. In the statistical analysis, we used ANOVA models and Tukey's multiple comparison method. Furthermore, seven in-depth individual interviews were conducted with farm managers. The results showed that 83.7% of the farms used different pre-milking disinfection methods (65.1% teat dips) and all of them applied post-milking disinfection. In the herds, chlorhexidine (42.9%) and other chlorine (21.4%) compounds were the most widely used active ingredients in the pre-milking disinfection, while iodine in the post-milking disinfection (53.8%). Lactic acid was ranked second in both disinfections (25.0 vs. 41.0%). In post-milking teat disinfection, the use of iodine and lactic acid combined with other active ingredients showed a significant relationship with SCC (p = 0.0454; p = 0.0113). In the milking machine cleaning process, the most frequently used active ingredients were sodium hypochlorite (80.0%) and sodium hydroxide (60.0%) as caustic detergents, while phosphoric acid (81.3%) as an acidic product. A significant relationship was found between the use of phosphoric acid combined with nitric acid, and the use of a combination of phosphoric acid, nitric acid, and organic acid and SCC (p = 0.0483; p = 0.0477). For farm decision-makers, the most decisive factor in the procurement of teat disinfectants was the active ingredient (3.4 on a scale of 1 to 10 where 1 was the most important), while regarding milking machine cleaning products the price (3.2).

Introduction
Mastitis is one of the most frequent diseases of dairy cows and has wellrecognized detrimental effects on animal wellbeing and dairy farm profitability (1). According to the National Mastitis Council (NMC) Recommended Mastitis Control Program, routine application of pre-and post-milking teat disinfectants during each

Study design
The survey was drafted to define the use of teat disinfectants and milking machine cleaning products in commercial Holstein-Friesian farms and the views of farm managers on teat disinfection and cleaning practices. The drafted survey was reviewed by farm managers (n = 2), dairy cattle veterinary practitioners (n = 3), academic professionals (n = 3), and veterinary and animal science Ph.D. students (n = 3) to receive feedback on content. Based on collected feedback, revisions were made before the survey was sent to potential respondents. This study used a mixed-method approach, which combines the collection and analysis of quantitative and qualitative data. In the first part of this work, we collected data about the total number of cows, type of milking system and parlor, number of daily milkings, milk production parameters [lactation milk yield, somatic cell count (SCC), percentage share of marketed milk, and days in milk (DIM)], and we also surveyed the active ingredients of the used pre-and post-milking teat disinfectants and milking machine cleaning products (detergents and disinfectants), and their applications (e.g., disinfection and cleaning processes, the concentration of substances). The key factors in the procurement of teat disinfectants and milking machine cleaning products were also surveyed by evaluating their different characteristics on a scale of 1 to 10 ("1" for the most important, "10" for the least important factor). In the second part of the in-depth survey, structured individual interviews were conducted with dairy cattle farm managers. We used a questionnaire with open-ended questions that allowed the participants to convey their views on the aforementioned teat disinfection and milking machine cleaning practices.

Data collection
Commercial Holstein-Friesian farms were included in this survey based on the following criteria: use of computerized onfarm records, participation in milk recording, and willingness to provide data to the authors. A total of 43 Hungarian dairy farms were surveyed between September and October 2014 by using a questionnaire via personal interviews with farm managers (n = 21; 48.8%), veterinarians (n = 14; 32.6%), shift supervisors (n = 5; 11.6%), or division heads (n = 3; 7.0%), who had access to farm records and were familiar with the milking procedures in the studied dairy units. Furthermore, we had in-depth, structured individual interviews with seven farm managers (out of the 21). The participants took part in the survey voluntarily and remained anonymous. Each participant was required to sign a written consent before they began the survey. Each questionnaire has been coded to detect inaccuracies in data entry. The obtained data were processed in MS Excel (Microsoft Corporation, Redmond, WA, USA).
A total of 31,430 cows were kept on the 43 farms, which corresponded to 17.8% of the 176,753 Hungarian dairy cow population on 458 performance-tested farms according to the official statistical data during the time of the survey (8,9). The smallest surveyed farm had 56 cows, whereas the largest had 2,500; the average herd size was 731 ± 508 (milking + dry cows). All of the seven regions of Hungary were covered in the survey (min. 3 and max. 14 dairy farms per region were involved). The seven, individually interviewed farm managers represented More than half of the milking parlors had a herringbone design (57.8%), followed by parallel (20.0%), rotary (17.8%), and polygon (4.4%) milking systems. The average age of the milking systems was 11.7 years (n = 41; min. 1 year; max. 28 years). The cows were mostly milked twice a day (62.8%), but 41.9% of the farms milked the cows three times and 4.7% four times a day. On four farms different cow groups were milked differently (usually cows were milked more frequently until 30 DIM). The vast majority of the farms (85.4%) used traditional elastic teat liners and 14.6% silicone ones. The teat cups were disconnected automatically on all farms, except for the smallest one.
All herds (n = 43) were free from tuberculosis, brucellosis, and bovine leukosis, but 34.9% of the farms were also free from either IBR (25.6%), BVD (4.7%) or the five diseases (4.7%). The diseased cows (e.g., clinical mastitis cases) were kept in separate hospital barns on 59.5% of the surveyed farms (n = 42). In the other herds, they were isolated within the maternity barn.

Statistical analysis
The surveyed farms represented all farm sizes, milking systems, milking parlor types, and geographical regions in Hungary. Teat disinfection and milking machine cleaning practice outcome measures were analyzed with ANOVA models. All models included the herd size (1-400, 401-800, and >800 cows), milking parlor type (herringbone, parallel, rotary, polygon), and number of daily milkings (two times, more than two times) as explanatory variables. Consequently, bias caused by data imbalance related to these variables was eliminated from the resulting estimates. For each pre-and post-milking teat disinfection and milking machine cleaning practice outcome, the basic model included only the three main management variables listed above. Next, each management explanatory variable was added to the basic model one by one separately (Tables 1, 2). The normality of the residuals (the difference between the raw data and the model prediction) is checked by quantile comparison plots (QQ-plot), and no deviation from normality was found. Differences between the means of the outcome variables in the layers of the explanatory variables were evaluated by Tukey's multiple comparison method applying the R package multcomp. Statistical analyses were performed in R version 4.1.2 (10). The level of significance was set to 0.05. To support confirmatory analysis with newly collected data, in cases of marginally significant differences (i.e., when the pvalues were >0.05 and <0.1), we carried out power analysis assuming equal sample sizes in the groups compared. The calculations were carried out applying the method of Cohen (11), implemented in the package pwr of R version 4.1.2 (10). The effect sizes (i.e., the ratios of mean differences to residual standard deviations) were set to 0.88, which is the smallest effect size obtained from the ANOVA models in this paper. The required power was set to 0.8, and the family-wise error rate related to the multiple comparisons of group means was set to . /fvets. .

Results
There was no significant relationship between the herd size and the milking parlor type, and the lactation milk yield and SCC (p ≥ 0.108 and p ≥ 0.192). The percentage share of marketed milk tended to be higher in parallel parlors vs. herringbone (p = 0.0772) and DIM tended to be higher in herringbone parlors vs. polygon (p = 0.0729), and in herds with >800 cows vs. 1-400 cows (p = 0.0892) by Tukey's multiple comparison method. A significant relationship was found between the number of daily milkings and lactation milk yield (p = 0.0182).

Pre-milking teat disinfectants
Vast majority of the surveyed farms (83.7%) used different pre-milking teat disinfection procedures, 65.1% used disinfectant teat dips, 14.0% disinfectant wash or foaming, and 4.7% impregnated papers (Supplementary Table 1). No significant association was found between the use of pre-milking disinfection and the studied milk production parameters (lactation milk yield, percentage share of marketed milk, DIM, SCC; p ≥ 0.3356). Six out of seven (85.7%) personally interviewed farm managers said that pre-milking teat disinfection should always be applied and four out of seven (57.1%) considered disinfectant dip or foaming disinfectant wash to be the ideal method.
Out of the farms applying pre-milking teat disinfection 72.2% used one-component pre-milking disinfectants exclusively, 8.3% both one-and two-component disinfectants, 13.9% only two-component products, while two large dairies (5.6%) used disinfectant impregnated paper (Supplementary Table 2). The use of one component premilking disinfectant and the use of impregnated paper showed significant association with DIM (p = 0.0125; p = 0.0376) and the use of two-component pre-milking disinfectant tended to associate with SCC and DIM (p = 0.0865; p = 0.0716). All seven personally interviewed farm managers preferred onecomponent pre-milking disinfectants, because of their practical use primarily (71.4%), that is, they exclude the possibility of human errors, but the longer shelf life and sales discounts were also mentioned (14.3% each).
The most used active ingredient of the pre-milking disinfectants was chlorhexidine (42.9%), followed by lactic acid (25.0%) and other compounds of chlorine (chlorine dioxide and triclosan together: 21.4%). The pre-milking disinfectants containing compounds of chlorine were used in almost twothirds (64.3%) of the commercial dairy units, while disinfectants with iodine were the least preferred products (7.1%) ( Figure 1). No significant association was found between the active ingredients and the studied milk production parameters (p ≥ 0.1535) (Tables 3-6). Several farms used multiple premilking teat disinfectants at the same time.
Three out of the seven interviewed farm managers (42.9%) considered chlorhexidine to be the optimal active ingredient in pre-milking disinfection, although another three managers (42.9%) considered iodine to be equally good. One manager .
(14.3%) also mentioned lactic acid as an optimal active ingredient. Six out of seven farm managers (85.7%) named efficacy as the major reason for choosing a certain active ingredient for pre-milking disinfection.

Post-milking teat disinfectants
All the surveyed farms applied post-milking teat disinfection and 67.5% of the farms exclusively used one-component postmilking disinfectants, 27.5% two-component products alone, and a further 5.0% both (Supplementary Table 3). No significant relationship was found between the component number of the post-milking disinfectant and the studied milk production parameters (p ≥ 0.1300). Six out of seven interviewed farm managers (85.7%) preferred one-component post-milking disinfectants, because of their practical use primarily (57.1%), but the efficacy, sales discount, and longer shelf life were also mentioned (14.3% each).
Iodine was the most used active ingredient in post-milking disinfection on the farms (53.8%), followed by lactic acid (41.0%) and various compounds of chlorine (chlorhexidine, chlorine dioxide and sodium chlorite) with a total share of 36.0% ( Figure 2). The use of iodine and lactic acid combined with other active ingredients showed a significant relationship with SCC (p = 0.0454; p = 0.0113) (Tables 3-6). Several farms used multiple post-milking teat disinfectants at the same time. Six out of seven individually interviewed farm managers (85.7%) considered iodine as the optimal active ingredient in post-milking disinfection, although two managers (28.6%) also named lactic acid and chlorhexidine. The major reason for the choice of an active ingredient was the efficacy (100%), but one farm manager (14.3%) underlined the effectiveness against Staphylococcus aureus and another one (14.3%) mentioned the importance of skin integrity, referring to the fact that iodine dries the skin.
The key factors in the procurement of teat disinfectants were also surveyed, the farm decision-makers evaluated the different characteristics of pre-and post-milking disinfectants on a scale of 1 to 10 ("1" for the most important, "10" for the least important factor). Figure 3 shows that the active ingredient was the most decisive procurement factor for a teat disinfectant, followed by price and ease of use. The advertisement was considered as the least important factor in the procurement, and sales discounts were not that important either.
The seven personal interviews confirmed the results of the survey, since the most important teat disinfectant characteristic for all seven farm managers was efficacy, followed by color of the product (85.7%), good value/price ratio (71.4%), reliable supply (57.1%), no skin drying effect (42.9%), and proper teat skin coverage (28.6%). The solution color makes it easy for the farm managers to check if the disinfectant was applied correctly.  Two farm managers (28.6%) underlined the importance of skin coverage and the ability of the product to stay on the skin, that is, the disinfectant should not drip off, it should stay on the teat for a long enough time so that it can take full effect. Other factors, namely, number of milking sessions, package size, easy to use, small per-unit cost, viscosity and distributor's recommendation were mentioned once by the farm managers (14.3% each).

Milking machine cleaning products (detergents and disinfectants)
About 59.5% of the surveyed farms used a 5-phase cleaning system (pre-rinse; caustic wash; rinse; disinfection; final rinse), 35.7% applied three-phase systems (pre-rinse; sanitizing wash including cleaning and disinfection; final rinse), and 4.8% used both systems depending on timing and duration of the milking session (Supplementary Table 4). We found no significant relationship between the type of cleaning and the studied milk production parameters (p ≥ 0.1522). Four out of seven interviewed farm managers (57.1%) considered the threephase cleaning systems better than the 5-phase ones. 74.4% of the farms performed the final rinsing cycle immediately after disinfection and 25.6% directly before the next milking session (n = 39). The time of the final rising tended to associate with SCC (p = 0.0634). All the interviewed farm managers considered rinsing to be optimal directly after disinfection.
Milking machines were cleaned twice a day in 73.8% of the farms, three times a day in 21.4%, but only once a day in 4.8%, respectively (Supplementary Table 5). It can be stated that the milking machinery was not cleaned after every milking on many farms (especially in the case of three milking sessions a day), despite the fact, that all seven interviewed farm managers said that cleaning should be performed after each milking optimally. There was no significant relationship between daily cleaning and the studied milk production parameters (p ≥ 0.1254).
The number of acid descaling washes per week revealed a very diverse picture: it ranged from once a week (in 11.9% Frontiers in Veterinary Science frontiersin.org . /fvets. .  Table 6). There was no significant relationship between the number of weekly acid cleaning and the studied milk production parameters (p ≥ 0.2588). Even the opinions of the interviewed farm managers on the optimal number of acid descaling washes per week differed: three out of seven (42.9%) said an acid wash should be performed after every milking, another three (42.9%) were in favor of an acid wash after every fourth caustic wash, and one manager (14.3%) said that one acid wash a day would be optimal.
There was an even greater deviation regarding the average yearly number of occasions when the milking machine was disassembled so that its parts could be cleaned thoroughly, manually. In 29.4% of the farms, this took place every week (52 × a year) and in 17.6% every month (12 × a year). At the same time, 11.8% of the farms never cleaned the milking machines manually (Supplementary Table 7). There was no significant relationship between the number of manual machine cleaning per year and the studied milk production parameters (p ≥ 0.1113). The opinions of the interviewed farm managers on the optimal number of manual cleaning sessions per year also greatly varied. Most commonly (28.6% each) a manual cleaning in every 6 months (that is, 2 × a year) and every 3 months (that is, 4 × a year) was considered as optimal, but one manager (14.3%) said that the weekly (52×) cleaning would be optimal.
Sodium hypochlorite was the most preferred active ingredient of caustic detergents (80.0% of the farms used it), followed by sodium hydroxide (60.0%) and potassium hydroxide (20.0%) (Figure 4). There was no significant relationship between the used caustic detergent and the studied milk production parameters (p ≥ 0.426). Three out of seven personally interviewed farm managers (42.9%) were not aware of the active ingredient or the name of the caustic detergent they used on their farm; one (14.3%) could name the product, another (14.3%) could name the distributor, and two of them (28.6%) knew that they used a detergent containing chlorine.
. /fvets. . Phosphoric acid was the most used acid (in 81.3% of the farms), followed by nitric acid (in 28.3%), sulfuric acid (in 21.9%), and organic (acetic and citric) acids (in 12.5%) ( Figure 5). A significant relationship was found between the use of phosphoric acid combined with nitric acid and the use of a combination of phosphoric acid, nitric acid, and organic acid and SCC (p = 0.0483; p = 0.0477). The use of phosphoric acid alone tended to associate with SCC (p = 0.05698). Only one personally interviewed farm manager (14.3%) had no information regarding the acid cleaning agent, which was used on the farm, three managers (42.9%) knew the name of the distributor, two (28.6%) could name the product but there was only one manager (14.3%), who could name the active ingredient. Thus, it can be concluded that farm managers have only vague information about the active ingredients of the cleaning products used on their farms.
The survey contained questions regarding the concentrations of caustics, acids, and disinfectants. In the surveyed dairies, the most used (38.9%) concentration for caustic solutions was 1% by far, while concentrations of 1.5 and 2% (min. 0.2%; max. 10%) were also frequently used, in 8.3 and 11.1% of the farms, respectively (Supplementary Table 8). The caustic concentration had no significant relationship with any of the studied milk production parameters (p ≥ 0.4369). Four out of seven managers (57.1%) did not know the concentration of the caustic solutions, which were used on their farms, the other three (42.9%) said 2-3% concentration, which-based on the results of the questionnaire-was probably greater than the actual values; therefore, they were not aware of the exact numbers. However, all of them stated that they used them according to the product manuals.
The acids were mostly (40.0%) used in a concentration of 1% also, but concentrations of 0.5%; 2% and 5% (min. 0.2%; max. 10%) were also quite common: in 8.6, 11.4, and 8.8% of the farms, respectively (Supplementary Table 9). The acid concentration had no significant relationship with any of the studied milk production parameters (p ≥ 0.6543). Five out of seven farm managers (71.4%) were not aware of the concentration of the acid solutions, which were used on their farms, one (14.3%) manager said 0.2% and another .
/fvets. . 0.3%, which-based on the results of the questionnaire-were probably lower than the actual ones. However, all of them stated that they used the acids according to the product manuals. Out of 43 surveyed farms, only 17 (39.5%) reported the use of a separate disinfectant for the milking machines. Peracetic acid was the most used disinfectant (88.2%), and 29.4% of the dairies also reported the use of hydrogen peroxide (n = 17). The use of peracetic acid was significantly associated with the percentage share of marketed milk (p = 0.0432). The results of the personal interviews corresponded with the results of the questionnaires; four farm managers (57.1%) said that they did not use a separate disinfectant while three (42.9%) reported using peracetic acid. The disinfectants were used in concentrations between 0.1 and 10%, but usually (in 61.5% of the farms) between 0.3 and 2% (n = 13) (Supplementary Table 10). The concentration of disinfectant had a significant relationship with the percentage share of marketed milk (p = 0.0356). In the dairies, where a separate disinfectant was used, none of the farm managers was aware of its concentration, but they all stated that the disinfectants were used according to the product manuals.
The key drivers in the procurement of milking machine cleaning products were also surveyed, and the respondents evaluated the different characteristics of the cleaning products on a scale of 1-10 ("1" for the most important, "10" for the least important characteristic). Figure 6 shows that the price was the most decisive procurement factor for a milking machine cleaning product, followed by reliable supply and the type of the cleaning process (one/two-component). The sale discounts were considered as the least important factor in the procurement process, and the corrosive impact did not prove to be important either.
The results of the personal interviews partly complied with the findings of the survey, because according to the farm managers the most important milking machine cleaning product characteristics were efficacy (all seven managers), followed by good value/price ratio (85.7%), reliable supply (71.4%) and non-corrosive impact (57.1%). Every personally interviewed manager emphasized efficacy, which corresponds to the factors named "active ingredient" and "duration of activity" in the questionnaire and was found to be of moderate importance only Frontiers in Veterinary Science frontiersin.org . /fvets. .     in the survey. The reliable distributor, not damaging to teat cups and one/two-component product, was of far smaller importance (28.6% each). Other factors, namely, package size, easy to use, long shelf life, small per-unit cost, safe application, active at low temperatures, and no residues were mentioned once (14.3%) by the farm managers.

Pre-milking teat disinfection
Effective pre-milking teat sanitation reduces the number of bacteria on teat skin, thus decreasing bacterial contamination of milk and improving milk quality (12,13). It has been wellestablished that reducing teat end exposure to microorganisms can result in a reduced incidence of intramammary infection (14). Almost two-thirds of the surveyed farms (62.8%) used disinfectant teat dips prior to milking, but almost one-quarter of the farms (23.3%) still washed the udder with water. Washing with water is only indicated when the udder is very dirty, but it is not recommended as a routine part of udder preparation (15). The udder was wiped with dry paper towels in almost three-quarters of the farms (73.8%), which cannot play a role in spreading infection (16). A teat cleaning procedure that includes wet cleaning followed by manual drying with a paper towel will result in the lowest bacterial counts (4). In situations where herd infection levels are considered high and where the risk of spread of infection is greater, pre-milking disinfection of clean teats followed by teat drying can be beneficial. However, the routine application of pre-milking teat disinfectants in pasturegrazed herds is unlikely to be of benefit where herd SCC is below 200 × 10 3 cells/mL (17). In general, when cows were housed indoors, the procedure was found to reduce the incidence of new intramammary infection caused by environmental pathogens by >50% (17).
The pre-milking disinfectants containing compounds of chlorine are preferred in two-thirds (64.3%) of the surveyed commercial Holstein-Friesian herds, which complies with the international findings from Europe (16). The use of chlorohexidine digluconate is shown to have a significant efficacy against Staphylococcus aureus and Streptococcus agalactiae under experimental challenge conditions (17). The second most common pre-milking disinfectant was lactic acid in the surveyed Hungarian dairies (25.0%). The application of probiotic lactic acid bacteria (LAB) is now considered as the best choice for the treatment of many infectious human diseases and the control of bovine mastitis, and the use of probiotic LAB teat disinfectant as a protective barrier to inhibit pathogens and to improve the microbial balance of the teat proved to be beneficial (3). Therefore, the use of lactic acid as a pre-milking disinfectant might be further strengthened in dairy cattle farms .
to achieve a better microbial balance on the teat skin, thus, have a greater resistance against udder infections.

Post-milking teat disinfection
According to Oliver et al. (18), almost on every farm iodine is considered as the optimal active ingredient followed by lactic acid in post-milking disinfection, which can largely reduce the incidence of clinical mastitis. These findings correspond to the results of our survey which showed that 53.8% of the surveyed large Holstein-Friesian farms used iodine and 41.0% used lactic acid as active ingredients in their post-milking teat disinfection process, and the use of iodine and of lactic acid combined with other active ingredients resulted in significantly lower SCC. In the United States, iodine-based teat disinfectants are used in 66% of pre-milking and in 84% of post-milking disinfections in large dairy units, and the post-milking teat disinfection with barrier properties and higher free iodine content reduced the risk of clinical mastitis (4). Fitzpatrick et al. (19) stated that the most effective post-milking products to reduce the bacterial load on teat skin contained 0.6% diamine and 0.5% chlorhexidine, 0.6% diamine, and 0.5% w/w iodine in the case of staphylococcal, streptococcal, and coliform isolates, respectively. This is in harmony with other studies where iodine-based products were shown to be effective against a wide range of udder pathogens (20)(21)(22), although it should be emphasized that higher iodine concentrations may occur in milk by using these products, which might be an important food safety factor in infant formula production (23).
According to the personally interviewed farms managers, the drying impact on teat skin is an important characteristic of the teat disinfectants that should not dry the skin too much. This is an issue mainly with disinfectants containing iodine, as they can significantly dry the skin and manufacturers try to counterbalance this unwanted effect by using different additives. Chemical disinfectants can reduce the major pathogen infections, but the high concentration of chemical substances raised the concern of potential residues in milk (24). Therefore, during teat disinfections, the farm managers might be encouraged to use products with lower risk to human health. Introducing effective teat disinfectants with chemicals, which occur naturally in milk, is an opportunity in the udder health management, because concerns of residues in milk are minimized (2). Nevertheless, farm conditions and udder health management practices have a significant impact on the effectiveness of teat disinfection (21).

Milking machine cleaning
The efficacy of milking machine cleaning depends on the working solution content, the temperature of the water, and the application of sanitizer (18). Meanwhile, four out of seven (57.1%) personally interviewed farm managers considered three-phase cleaning systems better than five-phase ones, the five-phase cleaning systems were more common in the Holstein-Friesian dairy units, and it can be stated that the fivephase washing and disinfection are considered equally efficient, and the number of cleaning phases depends mainly on the specifications of the milking equipment manufacturers.
The typical automatic three-phase cleaning process can be divided into three different main phases: pre-rinse, washing phase, and post-rinse. During the washing phase, alkaline or acid detergents should be used. The alkaline detergent helps to remove organic deposits, such as milk protein and fat. Most detergents can work effectively at hot temperatures. Acid detergent is used periodically to remove mineral deposits from water and milk. The frequency of acid washing depends on the hardness of the water used on the farm (7). The number of acid descaling washes per week revealed a very diverse picture in our study: it ranged from once a week to 21 times a week, that is, after every milking, but most commonly it was performed daily or two to three times per week. The use of daily cold caustic cleaning in conjunction with daily hot acid cleaning or cleaning with a hot detergent/sanitizer twice a day maintained the lowest total bacterial count in milk and on plastic surfaces (18).
Sodium hypochlorite was the most preferred active ingredient of caustic detergents, 80.0% of the surveyed commercial dairy farms used it. While the addition of sodium hypochlorite to the pre-milking rinse water may have benefits in sanitizing internal milking equipment surfaces, it may also result in the formation of the residue of trichloromethane (TCM) in milk (25). Farms using cleaning products that contain a high sodium hypochlorite content (>8%) are highlighted as being more likely to result in TCM residues in bulk tank milk (26). The international agency for research on cancer states that TCM is possibly carcinogenic to humans (27). In the surveyed Holstein-Friesian dairies, the most used concentration of caustic solutions was 1% by far, while concentrations of 1.5 and 2% were also frequently used. A potential alternative sanitizer for milking machines might be peracetic acid. The addition of peracetic acid in the final rinse water and as a replacement for sodium hypochlorite would also reduce or eliminate TCM residues (5). 39.5% of the surveyed farms reported using a separate disinfectant for the milking machines which was peracetic acid in 88.2%. Peracetic acid was also more effective against Prototheca zopfii than sodium hypochlorite or iodine (28). A further potential alternative sanitizer is quaternary ammonium compounds (QAC) that are non-oxidizing surfactant-based disinfectants. Some of these compounds (diethyl ammonium chloride and dimethyl benzyl ammonium chloride) are now being promoted as an alternative to traditional sanitizer products; however, Gleeson et al. (5) found that the QAC product is not suitable for the cleaning of milking .
/fvets. . equipment due to the foaming effect of the product during the wash circulation.

Conclusions
A significant share (16.3%) of the surveyed Holstein-Friesian farms did not use any pre-milking disinfectants, which was still a considerable percentage, and applying pre-milking disinfection might improve the udder health in these dairy units. Many products of both pre-and post-milking teat disinfectants are available on the market, but compounds of chlorine are the most preferred active ingredients for premilking disinfectants while iodine in post-milking disinfection. Lactic acid is ranked second in both disinfections. The decisionmakers on the farms prefer these active ingredients primarily because of their efficacy, which is the most decisive factor in buying teat disinfectants. Price and continuous, reliable supply are also important factors during procurement. Teat disinfectants that are colored, that is, easy to see on the teat skin, and consist of one component are also more preferred by the farm managers. Advertisements and sales discounts have no significant effect on the procurement of teat disinfectants.
The decision-makers on the Holstein-Friesian farms are not as well-informed regarding milking machine cleaning products (caustics and acids) and their use as they are as regards to teat disinfectants. In many cases, they are not aware of their active ingredients and/or concentrations, they only refer to the product manuals. Their opinions greatly differ on that how frequently an acid wash is needed. In some herds (11.9%), acid wash was performed after every milking, but more than half of the farms (52.4%) only run an acid wash every other day or even less frequently. Several milking machine cleaning products, both caustics and acids, are available on the market, but sodium hypochlorite and sodium hydroxide are the most preferred active ingredients of caustic detergents while phosphoric acid is the most used acidic product. Furthermore, 39.5% of the large commercial dairies used a separate milking machine disinfectant, in most cases, peracetic acid. The use of milking machine detergents is greatly influenced by the milking equipment because milking machine distributors often specify which sanitizers are allowed.
According to our knowledge, this was the first scientific, large-scale study assessing the use of teat disinfectants and milking machine cleaning products in Hungarian dairy cattle farms, but the limitation of the survey is the non-representative nature of the sample. As most of the teat disinfectants and milking machine cleaning products proved to be similarly effective in milk production, the use of those, which may pose less risks to human health and the environment, might be more encouraged in the future.

Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement
The revised survey was reviewed by the Scientific Research Committee of the Faculty of Veterinary Science, Budapest, and found exempt from human subject protection regulations. The participants provided their written informed consent to participate in this survey.

Author contributions
LÓ conceived and designed the study, collected the data, and acquired funding. DI developed the statistical models and analyzed the data. DI and LÓ contributed to the conceptualization and writing the paper. All authors contributed to manuscript revision, read and approved the submitted version. of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.