The fecal microbiota of lactating Holstein dairy cows: a meta-analysis highlighting key microbial profiles and methodological challenges

The fecal microbiota of lactating Holstein dairy cows: a meta-analysis highlighting key microbial profiles and methodological challenges | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results The fecal microbiota of lactating Holstein dairy cows: a meta-analysis highlighting key microbial profiles and methodological challenges View ORCID Profile Lisa Arnalot , Géraldine Pascal , Sébastien Dejean , Gilles Foucras , Asma Zened doi: https://doi.org/10.1101/2025.07.21.666008 Lisa Arnalot 1 Ecole d’Ingénieurs de Purpan, Université de Toulouse , Toulouse, FRANCE 2 GenPhySE, Université de Toulouse, INRAE, ENVT , Castanet-Tolosan, FRANCE Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Lisa Arnalot For correspondence: lisa.arnalot{at}inrae.fr Géraldine Pascal 2 GenPhySE, Université de Toulouse, INRAE, ENVT , Castanet-Tolosan, FRANCE Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sébastien Dejean 3 Institut de Mathématiques de Toulouse , UMR5219, CNRS, UPS, Université de Toulouse , Toulouse, FRANCE Find this author on Google Scholar Find this author on PubMed Search for this author on this site Gilles Foucras 4 IHAP, Université de Toulouse , INRAE, ENVT, Toulouse, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Asma Zened 2 GenPhySE, Université de Toulouse, INRAE, ENVT , Castanet-Tolosan, FRANCE Find this author on Google Scholar Find this author on PubMed Search for this author on this site Abstract Full Text Info/History Metrics Preview PDF ABSTRACT In cattle, the digestive microbiota has been poorly studied outside the rumen, despite its recognized role in animal physiology, health, and production. This study aimed to provide a comprehensive meta-analysis of the fecal microbiota of lactating Holstein dairy cows. A systematic online search of PubMed was performed in June 2023 to identify studies on the fecal microbiota of lactating Holstein dairy cows. Of the 526 articles retrieved from the systematic online search, only 28 met our required inclusion criteria. Raw sequencing data targeting the 16S rRNA gene (V3-V4 region) were obtained either from public repositories or directly from the authors. Two recently published articles were included as they met the inclusion criteria. A total of 2,136 samples were included in the analysis. The number of sequences per sample varied considerably between studies, and the metadata were sparse. The core microbiota was identified as the most prevalent and shared taxa, comprising 21 microbiota families that accounted for 82% of the relative abundance. The observed clustering, which depended on study, highlighted the significant impact of environmental factors and experimental conditions on the microbial communities of cattle. A multi-group analysis was performed to correct for the study effect and successfully identified three microbiota profiles. The meta-analysis approach is a rigorous and systematic method of analyzing research findings that allows for the generation of reliable and reproducible results. This approach ensures the independence of results from a single experimental facility or condition, thereby enhancing the reliability and generalizability of the findings. Importance This meta-analysis provides the most comprehensive and up-to-date overview of the fecal microbiota of lactating Holstein dairy cows, based on a rigorous selection of over 2,000 samples. By controlling for inter-study variation, it successfully identified consistent microbial patterns and establishes a robust core microbiota. This work not only highlighted the microbial taxa most likely essential to bovine physiology and health, but also emphasized the need for standardized methodologies and improved data-sharing practices across studies. Ultimately, the findings pave the way for strategies that target the modulation of microbiota with potential benefits for animal health and environmental impacts. INTRODUCTION Over the last 10 years, numerous studies have linked the gastrointestinal microbiota to health and specific disease conditions across species ( Fan & Pedersen, 2021 ; O’Hara et al., 2020 ). In humans, mice and pigs, the digestive microbiota is typically characterized through fecal sampling ( Mishra et al., 2023 ; Seyoum et al., 2024 ; Xiao et al., 2016 ). Conversely, most research conducted on the microbiota of the bovine digestive tract has focused on the forestomach microbiota. This topic has been extensively reviewed and is the subject of numerous recent meta-analyses, with linked to various topics, such as methane emissions, nutrition and the functions of the rumen epithelium ( Ma et al., 2025 ; Plaizier et al., 2021 ; Susanto et al., 2024 ). Surprisingly, the fecal microbiota of dairy cows has not been studied as thoroughly. We could only identify one meta-analysis that focused specifically on feces ( Kim & Wells, 2016 ). More recently, Holman & Gzyl (2019) compared ruminal and fecal microbiota, including data from diverse breeds, types of cattle at different ages and physiological stages. The authors concluded that substantial variability was caused by the hypervariable region studied, the sampling location within the gastrointestinal tract and the study of origin. The fecal microbiota of dairy cows is perhaps of greater interest than the ruminal microbiota, because samples are easier to collect, with fewer ethical and technical constraints. This makes it a more reliable source of information that could improve cow’s production traits or health, resulting in more robust and important findings ( Monteiro et al., 2022 , 2024 ; Plaizier et al., 2017 ; G. Zhang et al., 2019 ). This reinforces the importance of studying its composition and central core, which was first characterized over a decade ago as a recurrent and shared microbial community among individuals ( Petri et al., 2013 ). Various cattle breeds are reared on all continents, but one breed has gained importance in almost all countries with dairying in the last fifty years. The Holstein breed has indeed established itself as the most productive dairy cow worldwide. With more than 25 million heads of cattle, this breed is primarily raised for milk production, in zero-grazing or pasture conditions. Thanks to intensive genetic selection, the Holstein breed is arguably the most suitable for milk production. The present study aimed to provide an overview of the fecal microbiota of the Holstein dairy breed, which is the most important category of cattle worldwide. We examined data collected during the lactation physiological stage. Raw data from studies that sequenced the V3-V4 regions of the 16S rRNA gene were gathered to minimize variability and draw robust conclusions by using the most published hypervariable region in bovine fecal microbiota. By alleviating biases related to the study, we described the most common and abundant taxa, approaching a definition of the core microbiota of dairy cattle. MATERIALS AND METHODS This work follows the Preferred Reporting Items for Systematic reviews and Meta-analyses (PRISMA) statement ( Page et al., 2021 ). Literature search Studies were identified through a systematic online search on PubMed ( https://pubmed.ncbi.nlm.nih.gov/ ) using the following search query: “(Microbiota OR Microbiome) AND (metagenomics OR 16S sequencing) AND (intestinal OR fecal) AND (Ruminant OR dairy cattle OR cow OR bovine)”. The search was conducted on June 15, 2023. Subsequently, from June to December 2023, a Google Scholar alert ( https://scholar.google.com/ ) was implemented to identify new publications meeting these criteria. Study selection To be included in the meta-analysis, the studies had to fit the following inclusion criteria: investigated the fecal microbiota of female cows. analyzed the V3-V4 region of the 16S rRNA gene, as this region has been the subject of the most extensive research. investigated lactating Holstein dairy cows. However, studies including other breeds were also accepted as long as the Holstein breed could be identified. include original raw data, excluding reviews or any kind of articles using data from other articles The raw data of the microbiota analysis were available in a public repository: ENA (European Nucleotide Archive: https://www.ebi.ac.uk/ena ), NCBI (National Center for Biotechnology Information: https://www.ncbi.nlm.nih.gov/ ), DDBJ (DNA Data Bank of Japan: https://www.ddbj.nig.ac.jp/ ). Alternatively, they could be obtained from the authors who were contacted. Research articles and notes that met with the above criteria, were included in the analysis. Two larger datasets published in 2024 ( Brulin et al., 2024 ) and 2025 (Arnalot et al., Animal Microbiome , accepted for publication) were also included in the analysis. Data extraction The raw sequences were obtained directly from the aforementioned databases. The authors of articles that met the inclusion criteria were contacted to request the raw data, further information on the materials and methods used in their study, or more details related to the cows. The following information was collected, where available or kindly provided by the authors: - Research topic: feed, health or microbiota description (including aging, transition period and microbiota stability). - Type of farm: commercial, experimental or demonstration. - DNA extraction method and commercial kit information, if applicable. - Sequencing platform - For individual cows, when applicable, whether the animal was a control or treated/diseased animal, if applicable, and other zootechnical information such as parity and/or lactation stage. Any missing or unclear information was noted as “NA” for Not Available. Bioinformatic processing The raw sequences were analyzed using the FROGS v4.1.0 software ( Escudié et al., 2018 ). We successively used preprocessing , clustering , remove_chimera , cluster_filter , taxonomic_affiliation tools with the parameters shown in Supplementary Table 1 for samples processed only with read 1, due to inability to contig reads some or for all of the samples from the articles ( Table 1 ), and in Supplementary Table 2 for the others. View this table: View inline View popup Download powerpoint Table 1: List of samples extracted where only read1 was used for downstream analysis Three samples from Huang et al., (2020) were processed separately because they could not be properly merged with the other datasets (SRR11617225, SRR11617226 and SRR11617227). Five samples were neither processed nor used in the analysis of the data of Scarsella et al., (2021) : SRR14584731, SRR14584692 and SRR14584693 had an unequal number of read1 and read2, while SRR14584729 and SRR14584631 were excluded due to atypical read lengths that differed from the other samples. Two samples (SRR9901317 and SRR9901341) from Zhang et al., (2022) , three samples (SRR26046274, SRR26046275 and SRR26046276) from Li et al., (2023) , and six samples (SRR12183056, SRR12183065, SRR12183074, SRR12183077, SRR12183096 and SRR12183098) from Dankwa et al., (2021) were also excluded from the analysis, as no sequences were obtained after processing and filtering. Each bioinformatic process generated a phyloseq object ( McMurdie & Holmes, 2013 ). Statistical analysis Statistical analysis was performed using the R software, version 4.3.3 (R Core Team, 2025). First, the generated phyloseq objects were merged based on taxonomic affiliation at the genus level. When the samples were merged, NA counts were replaced with 0. As our focus was on bacteria, clusters allocated to kingdoms other than bacteria and unclassified bacterial phyla were removed. Following the approach of Holman and Gzyl. (2019) , samples containing fewer than 1,000 sequences were excluded from further analysis. The diversity indicators (observed richness and Bray-Curtis) were described with the vegan package (Oksanen et al., 2022). Noise removal was performed by filtering out clusters that contributed less than 1% to the total sum. The microbiome package was used in order to define the core microbiota ( Lahti & Shetty, 2018 ), which was present in a minimum of 90% of the samples, as previously described by Holman et al. (2017) . The mixOmics package was used to perform a Principal Component Analysis (PCA) after Centered Log-Ratio (CLR) transformation and for multi-group integration (function mint.pca ) which is designed to identify reproducible molecular signatures across different datasets, in order to correct for the article effect (Rohart, Eslami, et al., 2017; Rohart, Gautier, et al., 2017). Partition around medoid clustering was performed using the cluster package ( Maechler, 2018 ), to determine the microbiota profile. The ancombc2 from the package ANCOM-BC (prvcut =0.5) was used in order to identify differential taxa across the microbiota profiles (Lin & Peddada, 2020). RESULTS Selection criteria and the results of the literature search A total of 28 articles were selected as fitting the required inclusion criteria (see Figure 1 for the selection process), along with two additional recently published datasets from our institute. The articles are presented in Supplementary Table 3 . All the articles contained samples from individual cows, except for those by Ossa-Trujillo et al., (2023) which included pooled fecal samples from lactating Holstein cows, and by Sun et al., (2017) which represented mixed samples from the same cow. Download figure Open in new tab Figure 1: Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram for article inclusion The metadata retrieved was found to be inconsistent with little information on the animals available in the articles. Information regarding factors such as parity, lactation stage, ration, feed intake, or health status was indeed lacking for some cows. This prevented a thorough description of the entire dataset and differential analysis according to age, lactation rank or health status. For the recovered data, different bioinformatics processing methods were applied; the process and results are indicated in Supplementary Tables 1 and 2 . The characteristics of the 34 phyloseq objects produced are presented in Table 2 . View this table: View inline View popup Download powerpoint Table 2: Characteristics of the output of the different bio-informatic processes Merging the 34 phyloseq datasets resulted in 514 clusters at the genus level and 2,160 samples. Archaea (n=5), unclassified kingdom (n=1) and undefined bacterial phyla (n=2) were discarded. A total of 24 samples yielding fewer than 1,000 sequences were removed; including those from Brulin et al., (2024) : ERR13304407, ERR13304726, ERR13304740,ERR13304742, from Dankwa et al., (2021) : SRR12183083, SRR12183104, SRR12183106, SRR12183108, SRR12183110, from Li et al., (2023) : SRR26046271, SRR26046272, SRR26046273, SRR26046277, SRR26046279, SRR26046290, SRR26046296, SRR26046297, SRR26046300, SRR26046301, SRR26046302 and from J. Zhang et al., (2022) : SRR9901338, SRR9901342, SRR9901344, SRR9901345. The final phyloseq dataset comprise 506 clusters and 2,136 samples. The number of sequences and clusters per sample varied considerably between studies. In addition to this disparity, it is important to note that, during the bioinformatic processing, certain samples did not share any sequences with others from the same study, despite likely being processed in the same way (DNA extraction, amplification, and sequencing run). After implementing the denoising process, the genus-level dataset, which initially contained 506 clusters, produced 185 clusters for the 2,136 samples at the genus level. The number of sequences per sample ranged from 1,060 to 965,946, with a mean of 42,986. After filtering, the range was 229 to 963,739, with a mean of 42,783 sequences. The cluster size (number of sequences per genus) originally ranged from 19 to 12,763,111, with a mean of 181,459. After filtering, this ranged from 9,428 to 12,763,111 sequences per genus, with a mean of 493,974. Fecal microbiota diversity and composition The observed richness exhibited significant variation between articles ( Supplementary Figure 1 ). This makes it difficult to identify discernible trends across the diverse sample sets. A similar outcome was observed concerning beta diversity ( Supplementary Figure 2 ). A total of 11 bacterial phyla were detected, with the Firmicutes phylum identified as the most abundant, accounting for an average of 66.4% of the total bacteria, followed by the Bacteroidota phylum, accounting for 21.9%. Other notable phyla included Proteobacteria (5.00%,), Actinobacteriota (3.77%) and Spirochaetota (1.56%). Furthermore, several low-abundance phyla were consistently detected in over 1,000 samples, including Patescibacteria (0.67%), Verrucomicrobiota (0.41%) and Cyanobacteria (0.10%). Other notable phyla included Fibrobacterota (0.07%), Desulfobacterota (0.06%) and Planctomycetota (0.03%) were detected in fewer than half of the 2,136 analyzed samples (958 and 563 samples, respectively). Identification of the core microbiota in lactating dairy cattle The core microbiota was investigated at the family level ( Figure 2 ). Download figure Open in new tab Figure 2: Core microbiota plot for the 2,136 samples across the 81 families (21 of which were identified as part of the core microbiota shown on the prevalence versus abundance plot). The families Oscillospiraceae , Prevotellaceae, Rikenellaceae , Lachnospiraceae , Muribaculaceae, Erysipelotrichaceae, [Eubacterium] coprostanoligenes group, Spirochaetaceae , Ruminococcaceae , Bacteroidaceae, Peptostreptococcaceae , and Christensenellaceae, UCG-010 from the Oscillospirales order, Clostridiaceae , Monoglobaceae , Bacteroidales RF16 group, Anaerovoraceae , Bifidobacteriaceae , Clostridia UCG-014 ; unknown family, and Butyricicoccaceae were identified as part of the core microbiota. Excluding the Clostridia; unknown order; unknown family and Clostridia UCG-014; unknown family, these taxa represented 23% of the identified families in the dataset and 82% of the total relative abundance. Identification and description of different microbiota profiles As shown in Figure 3 , a strong source or article effect was observed. To alleviate this effect, a PCA-based correction was applied to the dataset after performing a CLR transformation. Download figure Open in new tab Figure 3: Principal component analysis (PCA) after centered log ratio (CLR) transformation not corrected (1a and 1b) and corrected with multi-group analysis according to the study (2a and 2b). Following the correction of the article, the effect of microbiota cluster could be identified. The choice of three clusters is supported by the hierarchical clustering dendrogram using the corrected dataset (distance = 1-correlation of the first 50 components of the corrected PCA, which explains 71% of the variance, using Ward’s method with squared distances as linkage method). A natural separation emerged at this level ( Figure 4 ). The distribution of the samples across the microbiota profiles in the included articles is presented in Supplementary Table 4 . Download figure Open in new tab Figure 4: Dendrogram determining the three microbiota profiles. The distance is based on 1-correlation of the first 50 components of the corrected PCA, which explains 71% of the variance, using Ward’s linkage method with squared distances. As shown in Figure 5 , the ANCOM-BC analysis revealed substantial disparities among the profiles. Download figure Open in new tab Figure 5: ANCOM-BC output for the pairwise comparison of the three microbiota profiles identified at the phylum level, exclusively for significant log fold change (LFC). The size of the dot corresponds to the magnitude of the LFC. Negative LFC is indicated by blue, while positive LFC is indicated by red. Profile 1 exhibited a decrease in the abundance of Proteobacteria and Actinobacteriota , accompanied by a modest decrease in Patescibacteria . In contrast, a significant increase of Verrucomicrobiota , Cyanobacteria , and Fibrobacterota was observed, compared to Profile 3 . Profile 2 exhibited a higher abundance of Proteobacteria compared to the other profiles; however, for other phyla, it was mainly an intermediate between the other two profiles. Profile 3 was characterized by elevated levels of Actinobacteriota andlower levels of Verrucomicrobiota , Cyanobacteria , and Fibrobacterota . The two phyla with the lowest abundance, Desulfobacterota and Planctomycetota, did not exhibit any difference across microbiota profiles. The differential abundance of bacterial species across microbial profiles was also determined at the family level ( Figure 6 ). Profile 1 was enriched in Flavobacteriaceae , Fibrobacteraceae , and Gammaproteobacteria; unknown order; unknown family . However, it depleted in Bifidobacteriaceae, Clostridiaceae and Akkermansiaceae compared to Profile 3 . Download figure Open in new tab Figure 6: ANCOM-BC output for pairwise comparison of the three microbiota profiles identified at the family level, exclusively for significant log fold change (LFC). The size of the dot corresponds to the magnitude of the LFC. Negative LFC is indicated by blue, while positive LFC is indicated by red. Profile 2 seemed to be more of a transitional profile between the others. DISCUSSION This study investigated the fecal microbiota of lactating Holstein dairy cows by examining existing literature. Following corrections of the variability due to data sources, three different microbiota profiles were identified according to these data, as well as 21 microbiota families that constitute the overall core microbiota. Among the recovered data from publication search and direct contact with the authors, we had to apply different bioinformatic processing methods for several articles, as the reported primers were incompatible with the analytical tool. Additionally, some samples had to be excluded due to inconsistencies within the same article, such as atypical read lengths and discrepancies between read1 and read2. These issues suggest that the quality of certain samples may be questionable. The variability in the quality of the data can also be discussed as the number of sequences per sample and shared sequences between samples from the same origin varied considerably across studies. To minimize the variability previously reported by Holman et al. (2017) and Holman and Gzyl. (2019), the investigation was conducted exclusively on the same hypervariable region. Nevertheless, there was a strong variability across samples according to the source of origin, which led us to use batch correction via multivariate integration, a process combining multiple independent studies that assessed the same predictors (Rohart, Eslami, et al., 2017; Rohart, Gautier, et al., 2017). Profile 1 was characterized by a significant depletion of Actinobacteriota, including the family Bifidobacteriaceae , known to have beneficial functions in carbohydrate fermentation ( Turroni et al., 2011 ), suggesting a potential association of this profile with negative outcomes. Furthermore, the slightly reduced levels of Firmicutes , along with the associated Lachnospiraceae , indicated a limited capacity for fiber degradation compared to other profiles ( Flint et al., 2008 ). Compared to the other profiles, Profile 2 exhibited an increase in Proteobacteria and Patescibacteria . The main difference at the family level was related to the Gammaproteobacteria ; unknown order; unknown family. Profile 2 appeared to be a more transient profile that is less distinct than Profiles 1 and 3 , from each other. Profile 3 exhibited a preponderance of Actinobacteriota , specifically Bifidobacteriaceae. These taxa play a role in complex carbohydrate metabolism, short-chain fatty acid (SCFA) production and bile acid metabolism ( Clavel et al., 2014 ; Turroni et al., 2011 ). Firmicutes , including Lachnospiraceae , also demonstrated moderate abundance, providing fibre degradation and butyrate production, which are crucial for colonocyte health and anti-inflammatory effects ( Flint et al., 2008 ). Profile 3 has also a lower abundance in Akkermansiaceae ( Verrucomicrobiota ) compared to other groups, suggesting decreased mucin degradation, which plays a key role in maintaining gut barrier integrity and modulating immune responses ( Everard et al., 2013 ). However, Akkermansia has also been associated with diseased cows such as left displacement of the abomasum and subclinical mastitis (S. Luo et al., 2022 ; Zhao et al., 2023 ). A recent meta-analysis in human microbiota revealed the fact that its composition and diversity differ between world regions. The most important technical factor being related to DNA extraction and primers ( Abdill et al., 2025 ). In our study, the majority of the samples originated from the same countries, consequently, the potential for variation due to regional differences was not assessed. However, it should be noted that the effects of diet and region may be less variable overall in dairy cows, as they are managed similarly, compared to meat cattle, where management is much more different (feedlots compared to free pasture). Unlike humans, for whom considerable variability is observed across a range of factors, including country of residence, ethnicity, lifestyle, and age, such variability is less pronounced in dairy cows. Based on Shade and Stopnisek, (2019) description, Neu, Allen and Roy, (2021) hypothesized that investigating the core microbiota at a higher taxonomic level might be more relevant than at a lower one (e.g., species level). We identified several families as part of the core microbiota of the lactating Holstein dairy cow. Interestingly, the core constituted merely a minority fraction of the families(representing only 23% of the entire families in the dataset), yet it accounts for the majority of the relative abundance of the samples, leaving only 18% of the relative abundance to other taxa. We would also like to emphasize the challenges involved in conducting this meta-analysis. In the current era of Open Science — defined as an effort to make scientific research more accessible, transparent, reproducible, and reliable ( Bertram et al., 2023 ) — we encountered persistent issues with data availability. In numerous cases, the raw data were not publicly accessible, essential metadata were missing, or the authors did not respond to data requests. As a consequence, 13 studies meeting our inclusion criteria could not be incorporated into the analysis, and even the included datasets lacked complete metadata. This highlights a significant gap between the principles of Open Science and the practical reality of data sharing in microbiota research. Similar concerns have recently been raised in ecology, calling for clearer and more enforceable guidelines on data availability and management ( Koivisto & Mäntylä, 2024 ). The present study provides a valuable foundation for future research on the dairy cow microbiota. However, the limited availability and precision of metadata prevented a more in-depth investigation into potential associations with health status, lactation rank or lactation stage. We recommend that future studies address this limitation by ensuring that metadata are both comprehensive and publicly available, in order to facilitate more robust and generalizable conclusions on these important topics. CONCLUSION This meta-analysis demonstrated the significant impact of the sample reference origin on results, highlighting the challenge of deriving global conclusions. Nevertheless, the study successfully diminished the article effect and identified three distinct microbiota profiles in lactating Holstein dairy cows. The core microbiota identified within the datasets was characterized, with 21 microbiota families representing 82% of the relative abundance. The study also underscores the scarcity of high-quality data, in terms of both raw sequencing reads and associated metadata. While some authors shared their datasets upon request, broader data availability—including comprehensive metadata—would greatly enhance the comparability of results across studies. Such improvements are essential to generate more robust conclusions and to advance our understanding of key factors influencing cattle productivity, health and welfare. DATA AVAILABILITY All data included in this article were obtained from public repositories with the following accession numbers: PRJNA1170208, PRJNA628713, PRJNA644954, PRJNA657329, PRJNA682766, PRJNA725200, PRJNA760802, PRJNA774499, PRJNA790039, PRJNA815875, PRJNA822933, PRJNA838477, PRJNA860705, PRJNA881400, PRJNA928233, PRJNA943133, PRJNA973208, PRJNA824686, PRJNA625290, PRJNA351736, PRJNA525989, PRJNA540088, PRJNA526913, PRJNA550022, PRJNA558571, PRJNA828386, PRJEB75421 from the National Centre for Biotechnology Information Sequence Red Archive and DRA004106 and DRR401736 from DNA Data Bank of Japan. Data from ( Dong et al., 2022 ) was directly obtained from the authors. Authors’ contributions A.Z., G.F., and L.A. contributed to the design of the study. G.P. and L.A. conducted the bioinformatic processing. L.A. performed the statistical analysis for which S.D. provided direction. A.Z., G.F. and L.A. interpreted the data. The first version of the manuscript was drafted by L. A., with substantial revisions thereafter by A. Z. and G. F.. All authors approved the final version submitted for publication. CONFLICTS OF INTEREST The authors declare no conflict of interest. Supplementary data list View this table: View inline View popup Supplementary Table 1: FROGS 4.1.0 parameters used for the samples treated with only read1 for 16S rRNA gene V3V4 amplicons from literature View this table: View inline View popup Supplementary Table 2: FROGS 4.1.0 parameters used for the samples treated with both reads for 16S rRNA gene V3V4 amplicons from literature View this table: View inline View popup Supplementary Table 3: Information on the studies included in the meta-analysis View this table: View inline View popup Download powerpoint Supplementary Table 4: Number of samples per microbiota profile according to the reference of origin Download figure Open in new tab Supplementary Figure 1: Observed richness according to the reference of origin Download figure Open in new tab Supplementary Figure 2: Bray-Curtis distance according to the reference of origin ACKNOWLEDGMENTS Expressions of profound gratitude are extended to all authors who graciously responded to our requests and provided us with their data, thereby demonstrating remarkable receptiveness and transparency. 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Rumen and hindgut bacteria are potential indicators for mastitis of mid-lactating holstein dairy cows . Microorganisms , 8 ( 12 ), 1 – 13 . doi: 10.3390/microorganisms8122042 OpenUrl CrossRef View the discussion thread. Back to top Previous Next Posted July 25, 2025. Download PDF Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following The fecal microbiota of lactating Holstein dairy cows: a meta-analysis highlighting key microbial profiles and methodological challenges Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website. 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Share The fecal microbiota of lactating Holstein dairy cows: a meta-analysis highlighting key microbial profiles and methodological challenges Lisa Arnalot , Géraldine Pascal , Sébastien Dejean , Gilles Foucras , Asma Zened bioRxiv 2025.07.21.666008; doi: https://doi.org/10.1101/2025.07.21.666008 Share This Article: Copy Citation Tools The fecal microbiota of lactating Holstein dairy cows: a meta-analysis highlighting key microbial profiles and methodological challenges Lisa Arnalot , Géraldine Pascal , Sébastien Dejean , Gilles Foucras , Asma Zened bioRxiv 2025.07.21.666008; doi: https://doi.org/10.1101/2025.07.21.666008 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Systems Biology Subject Areas All Articles Animal Behavior and Cognition (7642) Biochemistry (17715) Bioengineering (13907) Bioinformatics (42005) Biophysics (21472) Cancer Biology (18624) Cell Biology (25534) Clinical Trials (138) Developmental Biology (13391) Ecology (19935) Epidemiology (2067) Evolutionary Biology (24356) Genetics (15617) Genomics (22529) Immunology (17753) Microbiology (40437) Molecular Biology (17200) Neuroscience (88697) Paleontology (667) Pathology (2840) Pharmacology and Toxicology (4829) Physiology (7653) Plant Biology (15171) Scientific Communication and Education (2046) Synthetic Biology (4304) Systems Biology (9827) Zoology (2272)