Microbiome Analysis Exploring Taxonomic Diversity in Kasaragod Dwarf and Holstein Crossbred Cattle

The indigenous cattle are ecient in converting low quality feeds and forage into animal products. Kasaragod Dwarf cattle, a unique non-descriptive native cattle of Kerala, India, are noted for their unique qualities, such as low feed intake, thermotolerance, greater resistance to diseases and A2 allelic variant milk. However, owing to the higher milk yield, Holstein crossbred cattle are given more importance over Kasaragod Dwarf. The hindgut microbiota plays a major role in various biological processes such as the digestion, vitamins synthesis, and immunity in cattle. In this study, we compared the hindgut microbiota of the Kasaragod Dwarf with the highly found, Holstein crossbred utilizing 16S rRNA high-throughput sequencing for a better understanding of the relationship between the host and microbial community. Four replicates of each 20 samples comprising two cattle type (n=10) were sequenced and analyzed. Marker gene-based taxonomic analysis armed variations in their microbial composition. Principle Coordinate Analysis (PCoA) using weighted and unweighted UniFrac distance matrices showed the distinct microbial architecture of the two cattle type. Random Forest analysis further conrmed the distinctness and revealed the signature taxa in K-Dwarf. The study observed the predominance of feed eciency associated genera viz., Anaerovibrio, Succinivibrio, Roseburia, Coprococcus, Anaerostipes, Paludibacter, Elusimicrobium, Sutterella, Oribacterium, Coprobacillus, and Ruminobacter in Kasaragod Dwarf cattle. The study highlights the abundance of unique and benecial hindgut microora found in Kasaragod Dwarf, which may attest its importance over exotic cattle breeds viz., Holstein. To our knowledge, this is the rst report of Kasaragod Dwarf cattle gut microbiome proling. This study is pivotal towards developing genetic resources for the microbial population in K-Dwarf and how it could be differentiated from Holstein crossbred cattle. Comparison of the K-Dwarf and Holstein cattle gut microbiome based on diversity analysis and differential abundance analysis indicated substantial differences in their microbiome structure. The results implied that the cattle type signicantly contributes to the variation in fecal microbiome composition. Utilizing 16S rRNA high-throughput sequencing approach, our study performed a comparison between the gut bacterial community of native Indian cattle, K-Dwarf, and the crossbred variety Holstein. The study demonstrates the differential abundance analysis of microbial taxa and the degree to which the study groups differed. A considerable microbial diversity-wise wealth in K-Dwarf over Holstein cattle was highlighted. The study has also attempted to depict the association of potential gut microbes with the higher feed eciency of K-Dwarf. The study highlighted specic taxa that are potentially associated with different fermentation products and feed eciency phenotype. The potential of the cattle gut microbiome observed in this study could be used as a resource to improve feed eciency in exotic cattle breeds for both economic and environmental benets. Our study lays a foundation for further research to precisely determine the compositional variations in microbial communities that bring up differences in production-related phenotypic measures.

affected by factors such as lower population density and decreased milk production potential.
In recent years, gut microbial ecology has gained enormous attention due to its impact on host health and performance. The intestinal gut microbiota in cattle encompasses a very complex ecosystem that interacts symbiotically with the host and assists in various metabolic processes, such as the production of volatile fatty acids, vitamin synthesis, microbial protein synthesis, xenobiotic metabolism, and immune regulation (Malmuthuge et  In our study, we performed a comparative analysis between the two the native cattle, i.e., Kasaragod dwarf, and the Holstein crossbred exotic cattle, to elucidate the hindgut microbiota compositions. The selected cattle type represents different genetic background with an equal distribution of age and gender. Both the study groups were raised under the same environmental conditions and fed a common herbivore native diet to minimize non-genetic in uences. The study employed16S rRNA gene short variable tags of the V3 region generated through high-throughput sequencing to compare the hindgut bacterial communities' phylotype in K-Dwarf and Holstein cattle. This study provided a contrasting view of the microbial pro le in two cattle types and highlights the association of particular microbial taxa with K-Dwarf cattle. To our knowledge, this is the rst comparative study demonstrating the differences in microbial compositions between these two cattle, i.e., the Kasaragod Dwarf, non-descriptive native cattle of Kerala, India, and Holstein crossbred.

Sample collection
The study cohort belonged to two major groups, Kasaragod dwarf (K-Dwarf), non-descriptive native dwarf cattle (n=10), and Holstein crossbred cattle (Holstein), (n=10). The average age of K-Dwarf cattle and Holstein crossbred cattle were 4.4 ± 1.07 years and 4.2 ± 2.09 years respectively. Samples for both the study groups were collected from Kasaragod, Kerala, India (12˚33'41" NL & 75˚9'59" EL). K-Dwarf cattle samples were collected from the cattle pure breed rich region in Kasaragod i.e., Badiadka. Samples were collected from dairy farms belonging to nonpublic individuals with their due consent. Holstein cattle dung samples were collected from the Kanhangad region of Kasaragod District. Prior to sample collection, all the subjects' samples were examined and certi ed t by a veterinary specialist. Breed purity based on the morphological characteristics described by Anilkumar and Raghunandan (2003) was carried out with a certi ed veterinarian's assistance. All subjects included in the study were fed a common herbivore native diet (including green grass, hay, concentrate mixture, and ad libitum water supply), and their metadata is provided in Table S1. It was ensured that only subjects with no prior history of having any antibiotics for the past two months were considered part of the cohort. Before the morning feed, fecal samples were collected in a sterile container just after defecation. To avoid contamination, cattle were made to defecate into a sterile tray, which was then quickly transferred into a small sterile container. The samples were transported to the laboratory in cooler boxes (4°C) and processed immediately to minimize systematic bias that can be introduced during preprocessing step (Choo et al. 2015).

DNA Extraction and amplicon Sequencing
Metagenomic DNA was extracted from about 180 mg of the fecal sample using the QIAamp stool mini kit (Qiagen, USA) as per the manufacturer's instructions. The Quality of extracted genomic DNA was evaluated by electrophoresis in 1% agarose gel, and the 260 nm/280 nm ratio was determined by Nanodrop (Thermo Fisher Scienti c, Waltham, USA). The V3 region of the 16S rRNA gene from each genomic DNA sample was ampli ed using a unique barcoded universal primer (341F and 534R) as described previously (Saxena et al. 2017). After examining the ampli ed products on 2% agarose gel, the amplicons were puri ed using the Ampure XP kit (Beckman Coulter, Brea, CA USA). Amplicon libraries were then prepared by following the Illumina 16S metagenomic library preparation guide and evaluated on 2100 Bioanalyzer using the DNA1000 kit (Agilent Technologies, Santa Clara, CA, USA). The libraries were further quanti ed using a Qubit dsDNA broad-range assay kit (Life Technologies, United States). Equal concentration of the libraries was sequenced using the Illumina NextSeq 500 platform in Next-Generation Sequencing (NGS) facility at the Indian Institute of Science Education and Research (IISER), Bhopal, India. In total, 80 samples comprising four technical replicates for all 20 samples (10 samples from each cohort) were sequenced and analyzed in this study.

Sequence data processing
The amplicon reads obtained from the Illumina sequencing platform were quality trimmed using the NGS QC tool kit (Patel and Jain 2012), and the reads with ≥3 ambiguous bases were discarded. The pairedend reads were joined together using FLASH V1.

Comparative microbiome analysis
Comparative analysis of microbiome between K-Dwarf and Holstein samples was prepared at alpha and beta diversity levels. At the alpha level, analysis was carried out based on Observed OTUs, and Chao 1.
Mean alpha diversity estimates for the study groups were compared and statistically tested using the Mann-Whitney test. A signi cant difference level was set at p-value <0.05. Subsequently, beta diversity was calculated based on the weighted and unweighted Unifrac distance metrics (Lozupone et al. 2011), and plotted by Principal Coordinate Analysis (PCoA) to identify the microbial community associated with each cattle type. This was followed by Analysis of similarity (ANOSIM) (Clarke et al. 2006) test to statistically evaluate the signi cant differences or similarities between the cattle type's bacterial communities. Dendrogram clustering was carried out using the Bray Curtis index and ward clustering algorithm at the OTU level. MetagenomeSeq (Paulson et al. 2014), which uses a zero-in ated Gaussian t model with an adjusted p-value cut off at 0.05, was used to classify genera differed signi cantly in abundance between K-Dwarf and Holstein group.Volcano plots were generated to illustrate signi cant differences between individual genera in K-Dwarf and Holstein communities, using the relative abundance data from the 16S rRNA gene surveys with the R-code supplied with the METAGENassist software

Correlation of bacterial genera
Correlation between the genera in K-Dwarf and Holstein samples was determined based on spearman's rank correlation. For the Spearman rank correlation, the signi cant correlation was considered with coe cient values ranges -1 to 1, and FDR adjusted p-value < 0.05.

Data Availability
The 16s rRNA high-throughput sequencing data generated in this study have been deposited at NCBI under the accession number: SRR11213013 -SRR11213032.

Results
Statistics for 16S rRNA data Output data for microbiota sequencing included a total of 7.5 million raw reads with an average value of 2,57,096.85 ± 83008.43 reads per sample (Table S2). After eliminating both short and low-quality reads, adaptors, and other redundant sequences, a total of 5.1 million high-quality reads (~77.3% of total raw sequences) were obtained. To minimize the variation, the high quality reads were further rare ed to the minimum library size of 1,43,291 sequences. After quality ltering and rarefaction, the reads were classi ed into 1,54,790 unique OTUs by a 97% sequence similarity cutoff. For all samples, rarefaction curve showed an increase in OTU numbers as a function of the number of samples. The curve became asymptotically stable along with the OTU number being saturated, and an increasingly smaller number of new OTUs were added in each sample (Fig S1), indicating an adequate sequencing depth to obtain an accurate estimate of the OTU richness (with the Good's coverage > 99.7%). After eliminating low abundance and low variance features, a total of 3,847 OTUs were considered for downstream analysis.

Comparative microbiome analysis between K-Dwarf and Holstein samples
Alpha and Beta diversity analysis Signi cant difference in the alpha diversity (Chao1 and Observed species) between the K-Dwarf and Holstein samples (Mann-Whitney, p< 0.0001) was observed that revealed a notable higher bacterial richness in K-Dwarf, compared to Holstein cattle (Fig 1a-b, Table 1). Further, the beta diversity projection on the PCoA plot revealed that the K-Dwarf and Holstein groups' fecal microbial communities were signi cantly different (ANOSIM, p-value < 0.001 , Fig 2a-b).
Differential taxonomic abundance and signature taxa analysis To demonstrate the differential abundance analysis of microbial taxa and the degree to which the study groups differed at the phylum and genus level, the metagenomeSeq ( tZIG) approach was employed. Signi cant variations were observed at the phylum and genus level abundance between the K-Dwarf and Holstein groups (FDR adjusted p-value < 0.05).
At the phylum level, the relative abundance of nine phyla varied signi cantly between the study groups (MetagenomeSeq, FDR adjusted p-value < 0.001, Table S4). The phylum Proteobacteria, TM7, Cyanobacteria, Elusimicrobia, Firmicutes, and Spirochaetes were predominantly found in K-Dwarf, whereas in the Holstein group, Bacteroidetes, Actinobacteria, and Tenericutes were abundant.
At the genus level, 28 signi cant genera were differentially abundant between K-Dwarf and Holstein (MetagenomeSeq, FDR adjusted P < 0.05, Table S5) and highlighted through the heat map (Fig S3). The volcano plot displayed the existence of different community dynamics between K-Dwarf and Holstein samples with fold changes >2 and p values <0.05 (Fig 5, Table S3). Following Random Forest analysis, signature taxa were identi ed that potentially differentiate the K-Dwarf from Holstein groups (Fig 6). The signature genera investigated for K-Dwarf included Succinivibrio, Roseburia, Coprobacillus, Anaerovibrio, Anaerofustis, Paludibacter, Elusimicrobium, Candidatus-Azobacteroidetes, Sutterella, Ruminobacter, Oribacterium, and Coprococcus. On the other hand, Bi dobacterium, Prevotella, and L7A were found to be the marker taxa for the Holstein group. The mentioned features were ranked by their contribution to classi cation accuracy. The noticeable coherence observed in both the study groups showed the critical role of these genera in forming a distinct gut microbiome.

Correlation analysis of taxa between cattle type
Using the Spearman rank correlation coe cient (Rs), correlation analysis of highly abundant genera in K-Dwarf and Holstein cattle was performed (Fig 7). The results showed the taxa exhibiting a signi cant relationship with cattle type at the genus level. More than 10 bene cial bacterial genera such as Sutterella, Succinivibrio, Paludibacter, Coprobcillus, Anaerovibrio, Roseburia, Dorea, Oribacterium, Blautia, Anaerofustis, and Coprococcus was found to have a positive correlation with the K-Dwarf, (FDR adjusted p-value 0.05). Whereas, in the Holstein only three bene cial bacterial genera such as Ruminococcus, Oscillospira, and Prevotella were observed to be positively correlated (FDR adjusted p-value < 0.05).

Discussion
High-throughput sequencing of 16S rRNA gene has greatly contributed to the appraisal and understanding the diversity of gut microbiome. This approach has been widely used to provide a deep understanding of fecal microbiome in different cattle type ( Compositional variance of microbial communities between K-Dwarf and Holstein cattle Comparison of the K-Dwarf and Holstein cattle gut microbiome based on diversity analysis and differential abundance analysis indicated substantial differences in their microbiome structure. The results implied that the cattle type signi cantly contributes to the variation in fecal microbiome composition. Alpha diversity metrics showed higher microbial diversity in K-Dwarf than Holstein cattle, suggesting a microbial diversity-wise wealth in K-Dwarf over Holstein. Supporting our results, a previous study conducted in a native cattle variety, Vechur, reported a higher alpha diversity index than the crossbred cattle. Various studies suggested that an increase in microbial diversity contributing to a stable microbial ecosystem and improved host health . Their higher prevalence has been reported in cattle producing less methane; besides, their abundance has been directly correlated to the high feede ciency of cattle (Løvendahl et al. 2018;Stolze et al. 2015). Thus, the predominance of Succinivibrio observed in K-Dwarf may be proposed to have a major role in their high-feed e ciency.
In comparison to the Holstein, the K-Dwarf has a higher abundance of other speci c genera such as Coprococcus, Paludibacter, Sutterella, Roseburia, and Oribacterium. Coprococcus plays a pivotal role in energy homeostasis (Laydenet al. 2013) and was observed signi cantly higher in K-Dwarf. Paludibacter, a propionate producing fermentative bacteria that helps enhance the production of inositol phosphate metabolism, is abundantly found in feed-e cient animals (Gardiner et al. 2020). Also, Sutterella plays an essential role in the biosynthesis of amino acids, pantothenate, and biotin metabolism ). Roseburia is a major bovine gut bacterium that produces bene cial butyrate required for a healthy gut Our study advances our knowledge about the composition of the hindgut microbiota in K-Dwarf and Holstein samples. Subsequent translational validation is warranted to determine whether changes in bacterial community composition lead to differences in production-related phenotypic measures.

Conclusion
Utilizing 16S rRNA high-throughput sequencing approach, our study performed a comparison between the gut bacterial community of native Indian cattle, K-Dwarf, and the crossbred variety Holstein. The study demonstrates the differential abundance analysis of microbial taxa and the degree to which the study groups differed. A considerable microbial diversity-wise wealth in K-Dwarf over Holstein cattle was highlighted. The study has also attempted to depict the association of potential gut microbes with the higher feed e ciency of K-Dwarf. The study highlighted speci c taxa that are potentially associated with different fermentation products and feed e ciency phenotype. The potential of the cattle gut microbiome observed in this study could be used as a resource to improve feed e ciency in exotic cattle breeds for both economic and environmental bene ts. Our study lays a foundation for further research to precisely determine the compositional variations in microbial communities that bring up differences in productionrelated phenotypic measures.

Declarations Con icts of interest
The authors declare that they have no con ict of interest.

Data Availability
The raw data les (reads in FASTQ format) were deposited at NCBI SRA database under accession number: SRR11213013 -SRR11213032.

Competing Interests
The authors declare that they have no competing interests.  Dendrogram showing the hierarchical cluster analysis using Bray Curtis distance and ward clustering algorithm for bacterial abundance data at the OTU level. Two main clusters are found, K-Dwarf (green) and Holstein (red), each one represents cattle type.
Page 20/24 Random Forest The gure shows genera that are responsible for the differences between groups of K-Dwarf and Holstein. Red elds show higher abundance, blue elds a low abundance of the particular genus based on cattle type. The plot shows each variable on the Y-axis and its importance on the X-axis. Random forest calculates feature importance by removing each feature from the model and measuring the decrease in accuracy Correlation between cattle type and genus abundance. (a) The cladogram shows the correlation occurring between bacterial genus and cattle type. X-axis represent genus and y axis represent correlation coe cients. Taxa associated with the K-Dwarf are represented with the red and Holstein with the blue color. The darker color represents stronger correlations. The bar size represents the effect of the size of speci c taxa in the particular group at genus level. (b) Heat map with color key shows differential abundance of feed e cient bacteria. X-axis represent the samples and y-axis represent the genus