Examination of the fecal microbiota in dairy cows infected with bovine leukemia virus

Veterinary Microbiology 240 (2020) 108547

Contents lists available at ScienceDirect

Veterinary Microbiology journal homepage: www.elsevier.com/locate/vetmic

Examination of the fecal microbiota in dairy cows infected with bovine leukemia virus

T

Jumpei Uchiyamaa,*,1, Hironobu Murakamia,1, Reiichiro Satoa, Keijiro Mizukamia, Takehito Suzukia, Ayaka Shimab, Genki Ishiharab, Kazuyuki Sogawaa, Masahiro Sakaguchia a b

School of Veterinary Medicine, Azabu University, Fuchinobe 1-17-71, Chuo-ku Sagamihara-shi, Kanagawa, Japan Anicom Insurance, Inc., 8-17-1 Nishishinjuku, Shinjuku-ku, Tokyo, 171-0033, Japan

A R T I C LE I N FO

A B S T R A C T

Keywords: Gut microbiota Feces Bovine leukemia virus Retrovirus Latent infection Lactating cows

Infection of cattle by bovine leukemia virus (BLV) causes significant economic losses in terms of milk and meat production in many countries. Because the gut microbiota may be altered by immunomodulation resulting from viral infections, we hypothesized that latent BLV infection would change the gut (i.e., rumen and hindgut) microbiota of infected cattle. In this study, we compared the gut microbiota of 22 uninfected and 29 BLVinfected Holstein-Friesian cows kept on the same farm, by 16S rRNA amplicon sequence analysis of fecal samples. First, we found that the fecal microbial diversity of BLV-infected cows differed slightly from that of uninfected cows. According to differential abundance analysis, some bacterial taxa associated with ruminal fermentation, such as Lachnospiraceae and Veillonellaceae families, were enriched in the fecal microbiota of uninfected cows. Second, the virus propagation ability of BLV strains was examined in vitro, and the correlation of the fecal microbiota with this virus propagation ability was analyzed. Higher virus propagation was shown to lead to less diversity in the microbiota. Differential abundance analysis showed that one bacterial taxon of genus Sanguibacteroides was negatively correlated with the virus propagation ability of BLV strains. Considering these results, BLV infection was speculated to decrease energy production efficiency in the cows via modification of rumen and hindgut microbiota, which partly relies on the virus propagation ability of BLV strains. This may explain the secondary negative effects of BLV infections such as increased susceptibility to other infections and decreased lifetime milk production and reproductive efficiency.

1. Introduction Bovine leukemia virus (BLV) is a viral species that is taxonomically classified in the family Retroviridae (Lefkowitz et al., 2018). It infects domestic cattle and is highly prevalent in several geographic regions (Aida et al., 2013). Once BLV infects a cow, it cannot be eliminated (Murakami et al., 2011). BLV infection is characterized by three progressive disease stages: the latent stages of aleukemia (AL) and persistent lymphocytosis (PL), followed eventually by the onset of leukemia or lymphoma, which is also known as enzootic bovine leucosis (EBL) (Aida et al., 2013). Approximately 30 % of infected cattle at the AL stage develop PL, and 0.1–10 % of infected cattle develop EBL (Aida et al., 2013). Because the immune system of host cattle can be impaired even during the AL stage, BLV infection consistently results in the inability of cattle to maintain normal health (Frie and Coussens, 2015). Although cattle with AL and PL do not show distinct clinical symptoms,

BLV infection causes secondary negative effects such as increased susceptibility to other infections and decreased lifetime milk production and reproductive efficiency (Brenner et al., 1989; Nekouei et al., 2016; Norby et al., 2016; Polat et al., 2017; Schwartz and Levy, 1994; Wellenberg et al., 2002; Yang et al., 2016). Cattle diagnosed with EBL are generally slaughtered (EFSA Panel on Animal Health and Welfare, 2015). Thus, BLV infection causes enormous economic losses to the livestock industry. The gut microbiota, which plays an essential role in maintaining the health of the gastrointestinal tract (Kinross et al., 2011; Shreiner et al., 2015), can be influenced by interaction with many factors, including food intake, host genetics, age, environment, health state (e.g., infections and chronic disease), and stress (Allaband et al., 2019; Miyoshi et al., 2018; Moloney et al., 2014). In cattle, the lower intestines (i.e., hindgut) are believed to be also important in maintaining health, in addition to the rumen. In recent years, disease-associated changes in the

⁎

Corresponding author. E-mail address: [email protected] (J. Uchiyama). 1 These authors equally contributed to this work. https://doi.org/10.1016/j.vetmic.2019.108547 Received 18 September 2019; Received in revised form 3 December 2019; Accepted 3 December 2019 0378-1135/ © 2019 Elsevier B.V. All rights reserved.

Veterinary Microbiology 240 (2020) 108547

J. Uchiyama, et al.

measured using quantitative polymerase chain reaction (PCR) (Murakami et al., 2019). Titers of virus produced from molecular-clonetransfected cells were transformed with log10 to be parametric (P < 0.05; Kolmogorov–Smirnov test), and were further used for the microbial diversity comparison and differential abundance analysis.

gut microbiota have been studied extensively, including those associated with viral infections (Kinross et al., 2011; Li et al., 2019). Retroviral infections seem to lead to the modulation of gut microbiota as a consequence of virus–host interaction. For example, the latent infection with human immunodeficiency virus (HIV), which like BLV is a retrovirus, has been shown to change the gut microbiota via virus-mediated immunomodulation (Vujkovic-Cvijin and Somsouk, 2019; Williams et al., 2016). Because immunomodulation by BLV infection has also been demonstrated (Frie and Coussens, 2015; Konnai et al., 2017), we hypothesized that the hindgut microbiota may be changed after BLV infection. Thus, not only changes in immunity but also in the gut microbiota may perturb the health of BLV-infected cattle that do not develop EBL (i.e., cattle with AL and PL). Recently, 16S rRNA gene amplicon sequence analysis using nextgeneration sequencing technology has allowed the examination of microbial communities with high accuracy and throughput. The bovine gut (i.e., rumen and hindgut) microbiota has recently been investigated in dairy science and veterinary medicine using this method, particularly focusing on the rumen microbiota in investigations of nutrition and health (Zeineldin et al., 2018). However, the effects of BLV infection on the gut microbiota have not been examined. Because a massive outflow of fermentable materials from the rumen to the hindgut is considered to lead to post-rumen fermentation in the hindgut, we believe that the fecal microbiota not only partly reflects ruminal microbiota but also represents hindgut microbiota. In this study, we investigated and compared the fecal microbiota of BLV-infected and uninfected cattle.

2.4. DNA extraction from fecal samples First, 1.0–2.0 g of fecal samples was suspended into 10 ml of phosphate-buffered saline (PBS). After filtration of fecal suspension with pluriStrainer 100 μm (pluriSelect Life Science, Leipzig, Germany), the microbial cells were pelleted by centrifugation (9000×g, 10 min, 4 °C). The cell pellets were washed in PBS twice, and suspended in 2 ml of PBS. The DNA was extracted from the microbial cell suspension, using Chemagic DNA Stool 200 Kit (PerkinElmer, Waltham, MA, USA) according to the manufacturer’s instructions. Briefly, 400 μl of microbial cell suspension was mixed with 810 μl of lysis buffer (PerkinElmer) containing 0.25 mg/ml proteinase K in vials filled with 1.4-mm ceramic beads (CK14 Soft Tissue Homogenizing Kit, Bertin Instruments, Montigny-le-Bretonneux, France). The sample was homogenized twice at 6000 rpm for 30 s each with a 30-s pause in between, using Precellys Evolution (Bertin Instruments). After homogenization, the suspension was sequentially incubated at 70 °C for 10 min and then 95 °C for 5 min. Finally, DNA was isolated using a Chemagic 360 instrument (PerkinElmer). 2.5. 16S rRNA gene amplicon sequencing

2. Methods and materials The DNAs were subjected to 16S rRNA gene amplicon sequencing. The V3–V4 regions of the 16S rRNA gene were amplified by PCR using the primers described in the Illumina 16S Sample Preparation Guide (Illumina, San Diego, CA, USA) as follows: forward, 5′-TCGTCGGCAG CGTCAGATGTGTATAAGAGACAG−CCTACGGGNGGCWGCAG-3′; reverse, 5′-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACA-GGACTACHVGGGTATCTAATCC-3′. PCR amplification was performed using the KAPA HiFi HotStart Library Amplification Kit (Kapa Biosystems, Wilmington, MA, USA). Each PCR product was purified using an Agencourt AMPure XP Beads Kit (Beckman Coulter, Pasadena, CA, USA) and quantified using a Qubit dsDNA BR Assay Kit (Thermo Fisher Scientific, Waltham, MA, USA). One hundred nanograms of each amplicon were subjected to a second PCR round for indexing, using a Nextera XT Index Kit v2 (Illumina). After purification, the PCR products were quantified with a NanoPhotometer (Implen, Westlake Village, CA, USA) and pooled into one tube at a final concentration of 1.6 ng/μl. The concentration of the pooled DNA library was validated using an Agilent 2100 Bioanalyzer (Agilent, Santa Clara, CA, USA). After denaturation with NaOH, 850 μl of a 9 pM DNA library and 150 μl of 9 pM PhiX were mixed and subjected to pair-end sequencing using Illumina MiSeq with a MiSeq Reagent Kit v3 (600 cycles; Illumina).

2.1. Samples Fecal and blood samples were collected on December 28, 2017 from cattle maintained by one owner, who kept 70 Holstein-Friesian cows on one farm. These cattle were fed a mixture of 34.7 % (w/w) roughage, 64.1 % concentrate feed, and 1.2 % supplements. The owner provided informed consent before collection of the biological samples. Fecal samples were collected directly from the interior of the anus, and immediately placed in a sterile propylene tube with a filter cap (Cellstar CELLreactor; Greiner Bio-One, Rainbach im Mühlkreis, Austria) under anaerobic conditions using the AnaeroPack Kenki system (Mitsubishi Gas Chemical Company Inc., Tokyo, Japan), and transported on ice to the laboratory on the same day. Within one day after transportation to the laboratory, the samples were stored at −80 °C until use. Blood samples were also collected from the tail veins and transported at 4 °C to the laboratory on the same day. This study was conducted in accordance with the Guidelines for Laboratory Animal Welfare and Animal Experiment Control promulgated by the School of Veterinary Medicine of Azabu University (Approval No. 17113-3). 2.2. Blood examination

2.6. Processing of 16S rRNA gene amplicon sequences Within one day after transportation to the laboratory, the levels of white blood cells, red blood cells, platelets, hemoglobin, and hematocrit were measured using an automatic cytometer (PCE-170; ERMA Inc., Tokyo, Japan). BLV infection was diagnosed as described previously using both agar gel immunodiffusion assays and measurement of BLV proviral genomes (Murakami et al., 2019).

The sequence data were processed using Quantitative Insights into Microbial Ecology 2 (QIIME 2) v2019.4.0 (Bolyen et al., 2019). The DADA2 software package v2019.4.0 incorporated in QIIME 2 was used to correct the amplicon sequence errors and to construct an amplicon sequence variant (ASV) table. The ASV table was rarefied. Microbial taxonomy was assigned using a Naïve Bayes classifier trained on the SILVA 132 99 % database.

2.3. Measurement of the viral propagation ability of BLV strains

2.7. Microbial diversity

Molecular clones were constructed as described previously using the genomes of BLVs listed in Supplementary Table S1 (Murakami et al., 2019). The virus propagation ability of BLV strains was measured in vitro using transfection of these molecular clones into human embryonic kidney 293 T cells, as described elsewhere (Murakami et al., 2019). Titers of virus produced from molecular-clone-transfected cells were

Metrics of alpha diversity, including Faith’s phylogenetic diversity (Faith-PD), Chao1 index (Chao1), and Shannon’s index (Shannon), and those of beta diversity, including unweighted UniFrac and weighted unnormalized UniFrac (weighted UniFrac), were examined using QIIME 2

Veterinary Microbiology 240 (2020) 108547

J. Uchiyama, et al.

3.2. Overall description of fecal microbiota in the herd of cattle

2. These diversity metrics were statistically analyzed by the method described below.

The fecal microbiota of 51 cows was investigated using the 16S rRNA amplicon sequencing method. A total of 2,032,860 nonchimeric reads (39,860.0 ± 7,307.8 nonchimeric reads/sample; mean ± SD) were used in this study. To rarefy the data, we used 24,000 reads from each sample. According to the taxonomic annotation of the sequence data, two phyla of Firmicutes and Bacteroidetes comprised 93.71 ± 1.53 % (mean ± SD; Table 1) of the bacteria identified. These phyla of Firmicutes and Bacteroidetes comprised 93.20 ± 1.74 % and 94.09 ± 1.22 % of the feces of uninfected and BLV-infected cows, respectively. The remainder of the microbiota comprised 16 phyla (Table 1). Previous analyses of the fecal microbiota of dairy cows have also shown the two predominant phyla of Firmicutes and Bacteroidetes (Wells et al., 2014). In addition, the abundance of these taxa at the phylum level was compared between BLV-infected and uninfected cows. No significant differences were observed in all taxa at the phylum level. Thus, because of the overall similarity of our data with previously reported bovine data, the data collected in this study were deemed reliable to study the fecal microbiota associated with BLV infection, and the fecal microbiota associated with BLV infection was further analyzed using methods from environmental ecology.

2.8. Statistical analyses The statistical analysis was performed using statistical software R, unless otherwise stated. In all statistical analyses, significance was set at P < 0.05, unless otherwise stated. The age, and the levels of white blood cells, red blood cells, platelets, hemoglobin, and hematocrit were statistically analyzed by Mann–Whitney U test between BLV-infected and uninfected cows. The bacterial taxa at the phylum level were statistically analyzed by Mann–Whitney U test with Bonferroni correction between BLV-infected and uninfected cows. In the statistical analysis of microbial diversity, diversity metrics were analyzed using statistics implemented in the QIIME 2 pipeline. When metadata were categorical data, alpha and beta diversity metrics were analyzed by Kruskal–Wallis test and permutational analysis of variance test, respectively. When metadata were parametric numerical data, alpha and beta diversity metrics were analyzed by Pearson’s correlation test and Mantel test with Pearson’s correlation coefficient, respectively. The diversity metrics were statistically analyzed with the transformed data of the titer of virus produced from molecular-clonetransfected cells.

3.3. Fecal bacterial diversity associated with BLV infection Microbiota diversity can be measured by alpha and beta diversities. Alpha diversity is the diversity within a particular ecosystem (i.e., species richness); beta diversity is a comparison of diversity between ecosystems. In this study, we examined and compared three alpha diversity metrics, Faith-PD, Shannon, and Chao1, and two beta diversity metrics, unweighted UniFrac and weighted UniFrac, between BLV-infected and uninfected cows. The comparison of microbiota diversity in feces of BLV-infected and uninfected cows demonstrated that only the weighted UniFrac showed a significant difference (P < 0.01), while no significant difference was observed in the alpha diversity metrics FaithPD, Shannon, and Chao1 or the beta diversity metric unweighted UniFrac (P ≥ 0.01). These results suggested that BLV infection altered the proportions of some bacterial species without changing bacterial species richness in the gut.

2.9. Differential abundance analyses The data for genus clusters were obtained, and bacterial taxa that were significantly enriched in a certain sample group were extracted by linear discriminant analysis (LDA) effect size (LEfSe) analysis using the online interface Galaxy (http://huttenhower.sph.harvard.edu/lefse/) (Segata et al., 2011). For LEfSe analysis, the Kruskal–Wallis test (alpha value of 0.05) and LDA score of > 2.0 (P < 0.01) were used as thresholds. The change in bacterial taxa associated with the virus propagation ability of BLV strains was also examined. The ASV count table was converted at the levels of order, family, and genus, and was analyzed with the transformed data of the titer of virus produced from molecularclone-transfected cells, using the differential abundance analysis software package DAtest v2.7.12 (Russel et al., 2018). According to the comparison of differential abundance analysis methods with 1,000 repeats by DAtest v2.7.12, Spearman’s correlation test was selected as an appropriate statistical method (Supplementary Table S2). The microbiota data together with the transformed data of the titer of virus produced from molecular-clone-transfected cells were analyzed by Spearman’s correlation test implemented in DAtest v2.7.12.

3.4. Bacterial taxa associated with BLV infection Because of the detection of a significant difference in the weighted UniFrac of uninfected and BLV-infected microbiota diversity, the bacterial taxa that differed between BLV-infected and uninfected cows were examined. First, we determined the taxa that best characterized each population using LEfSe analysis. LEfSe is a method that is most likely to identify the bacterial taxa featured in comparable groups (Segata et al., 2011). As a result, 14 taxa were detected in the LEfSe analysis (Fig. 2A). In the BLV-infected cows, three bacterial taxa were identified (Fig. 2B and C). The genera of Cellulosilyticum (family Lachnospiraceae) and Sutterella (family Burkholderiaceae), and the family Methanobacteriaceae (class Methanobacteria, order Methanobacteriales) were assumed to be enriched. On the other hand, in the uninfected cows, 11 taxa were identified (Fig. 2B and C). These taxa included genus Methanocorpusculum (class Methanomicrobia order Methanomicrobiales family Methanocorpusculaceae), order Bacteroidales, genus Helcococcus (order Clostridiales family XI), genus Eubacterium nodatum group (order Clostridiales family XIII), genus Agathobacter (family Lachnospiraceae), genus Coprococcus (family Lachnospiraceae), genus Angelakisella (family Ruminococcaceae), genus Megasphaera (family Veillonellaceae), unclassified genus of family Veillonellaceae, order WCHB1-41 (class Kiritimatiellae), and order Mollicutes/RF39 (class Mollicutes). The relative abundances of these bacteria are shown in Fig. 2D.

3. Results 3.1. Brief description of cows used in this study To avoid confounding factors such as environmental factors and other diseases, as well as age, we selected 51 healthy lactating cows aged 25–85 months (mean ± standard deviation [SD], 46.3 ± 15.2 months) from a herd of cattle housed in the same barn. The BLV diagnosis divided these cows into 29 BLV-infected and 22 uninfected cows (mean ± SD, 42.6 ± 13.5 months and 49.1 ± 15.7 months, respectively). The age of BLV-infected cows was not statistically different from that of uninfected cows (Fig. 1A). The levels of white blood cells, red blood cells, platelets, hemoglobin, and hematocrit were statistically compared between BLV-infected and uninfected cows. BLV-infected cows showed significantly higher numbers of white blood cells and platelets than the uninfected cows (Fig. 1B and D, respectively); significant differences were not seen in other variables such as red blood cells, platelets, hemoglobin, and hematocrit (Fig. 1C, E, and F). 3

Veterinary Microbiology 240 (2020) 108547

J. Uchiyama, et al.

Fig. 1. Comparison of (A) age, (B) levels of white blood cells, (C) red blood cells, (D) platelets, (E) hemoglobin, and (F) hematocrit between BLV-infected and uninfected cows. The Mann–Whitney U test was used to compare the two groups. Average values with standard deviations are shown as bar graphs with error bars. Statistical significance is indicated by single and double asterisks (P < 0.05 and 0.0005, respectively). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).

decrease as the virus propagation ability of BLV strains strengthened.

3.5. Fecal microbiota diversity associated with the virus propagation ability of BLV strains

3.6. Bacterial taxa associated with the virus propagation ability of BLV strains

The BLV genomes were cloned from blood DNAs of 29 infected cows, and the molecular clones were constructed. Because the virus propagation ability of BLV strains can be evaluated in vitro by measuring the virus titer in the culture supernatant of cells transfected with BLV molecular clones (Murakami et al., 2016), the virus propagation ability of 29 BLV strains was examined (Supplementary Table S1). We then analyzed the correlation of microbiota diversity with the virus propagation ability of BLV strains. A total of 1,108,272 nonchimeric reads (38,216.3 ± 7,131.2 nonchimeric reads/sample; mean ± SD) were used. To rarefy the data, 25,000 reads were used from each sample. Using these rarefied read data, the correlation of microbial diversity with the virus propagation ability of BLV strains was examined (Table 2). First, the alpha diversity metric Faith-PD was negatively correlated with the virus propagation ability of BLV strains with statistical significance, while the alpha diversity metrics Chao1 and Shannon and the beta diversity metric weighted UniFrac were not. Second, the beta diversity metric unweighted UniFrac was significantly correlated with the virus propagation ability of BLV strains, while weighted UniFrac was not. These results indicated that the species richness of the microbiota was likely to

We searched for the bacterial taxa for which abundance was correlated with the virus propagation ability of BLV strains. The correlation of microbiota data at the order, family, and genus levels with the virus propagation ability of BLV strains was analyzed. Only when the metadata at the genus level was examined, the relative abundance of class Bacteroidia order Bacteroidales family Marinifilaceae genus Sanguibacteroides was shown to be negatively correlated with statistical significance in the BLV-infected cows (correlation coefficient ρ = –0.6252; Supplementary Table S3). 4. Discussion In this study, we analyzed the fecal microbiota of BLV-infected cows. The cow cohort studied was kept in the same rearing environment (i.e., farm and feeding). According to the blood examination, the BLV-infected cows showed a higher number of white blood cells, and such a clinical manifestation is typically seen in BLV-infected cows (Alvarez et al., 2013). The increased numbers of white blood cells are 4

Veterinary Microbiology 240 (2020) 108547

J. Uchiyama, et al.

Table 1 Taxonomical abundance of bovine feces microbiota examined in this study.

Table 2 Correlation of fecal microbiota diversity with virus propagation ability of BLV strains.

Relative abundance (%) Taxonomy level

Cows used in this study (n = 51)

Uninfected cows (n = 22)

BLV-infected cows (n = 29)

Kingdom

Phylum

Mean

SD

Mean

SD

Mean

SD

Bacteria Bacteria Bacteria Bacteria Bacteria Bacteria Bacteria Bacteria Archaea Bacteria Bacteria Bacteria Bacteria Bacteria Bacteria Bacteria

Firmicutes Bacteroidetes Spirochaetes Patescibacteria Proteobacteria Verrucomicrobia Tenericutes Actinobacteria Euryarchaeota Planctomycetes Cyanobacteria Fibrobacteres Kiritimatiellaeota Chloroflexi Epsilonbacteraeota Othersa

54.97 38.74 1.71 1.02 0.95 0.94 0.53 0.27 0.25 0.21 0.21 0.12 0.03 0.02 0.01 0.02

2.95 3.01 0.94 0.55 0.42 0.61 0.20 0.14 0.15 0.13 0.18 0.13 0.04 0.02 0.05 0.02

55.76 37.44 2.01 1.08 0.94 1.11 0.53 0.26 0.24 0.22 0.2 0.13 0.03 0.02 0.02 0.02

3.06 2.99 1.07 0.63 0.38 0.79 0.18 0.12 0.13 0.11 0.17 0.11 0.04 0.02 0.07 0.02

54.37 39.73 1.47 0.97 0.95 0.82 0.53 0.28 0.26 0.21 0.21 0.12 0.03 0.02 0.01 0.02

2.72 2.62 0.74 0.47 0.45 0.37 0.21 0.15 0.17 0.14 0.18 0.13 0.04 0.02 0.02 0.02

Alpha diversity metrica Faith-PD Shannon Chao1 Beta diversity metricb Unweighted UniFrac Weighted UniFrac

Correlation r

P valuec

−0.4023 −0.3584 −0.3323

0.0305* 0.0562 0.0782

0.2792 0.1241

0.036* 0.368

a Alpha diversity metric was analyzed with metadata using Pearson’s correlation test. b Beta diversity metric was analyzed with metadata using the Mantel test with Pearson’s correlation coefficient. c Statistical significance is indicated by an asterisk.

partly explained as a consequence of PL (Alvarez et al., 2013). Unfortunately, we did not determine the disease development of AL or PL in the individual cows. Moreover, the BLV-infected cows showed a higher number of platelets than the uninfected cows. Although this higher level of platelets may reflect secondary disease, it is difficult to speculate on the cause as a health check by a veterinarian confirmed that these cows did not have health issues. However, given the bovine physiological data, we suspect the influence of BLV infection on fecal microbiota.

a

Others included Elusimicrobia, Fusobacteria, Lentisphaerae, and unclassified phyla.

Fig. 2. Differential abundance of bacterial taxa in fecal microbiota of BLV-infected and uninfected cows. Bacterial taxa in uninfected and BLV-infected cows are shown in red and green, respectively. (A) List of bacterial taxa detected in LEfSe analysis. The bacterial taxa are marked from “a” to “n”, as also shown in B–D. The bacterial taxa showing positive LDA are shown as "a" to "c" in blue, which those showing negative LDA are shown as "d" to "n" in purple. (B) Differently abundant bacterial taxa detected with an LDA score cutoff value of > 2.0. The bacterial taxa in BLV-infected and uninfected cows are indicated with positive and negative LDA scores, respectively. (C) Taxonomic cladogram generated from LEfSe analysis. (D) Relative abundance of bacterial taxa detected in LEfSe analysis. On each graph, the bar with error bar indicates the mean relative abundance as a percentage with standard deviation. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).

5

Veterinary Microbiology 240 (2020) 108547

J. Uchiyama, et al.

including virus propagation, which stimulates the host immunity and is then assumed to alter gut microbiota (Gillet et al., 2007; Murakami et al., 2019, 2016). Assuming that the gut microbiota alteration by BLV infection caused the secondary negative effects of the BLV infection, the genetics of BLV strains should also be carefully examined. In the future, rumen microbiota need to be systematically investigated with rumen nutrition status, such as the level of volatile fatty acids together with BLV strain genetics.

Analysis of the fecal microbiota of BLV-infected cows against that of uninfected cows indicated that the BLV-infected cows showed slightly different microbiota diversity from the uninfected cows. Of note, although the correlations of alpha and beta diversity metrics were examined with the other variables such as the levels of white blood cells, red blood cells, platelets, hemoglobin, hematocrit, and age, no significant correlations with alpha and beta diversity metrics were observed for these factors (Supplementary Table S4). BLV infection was the only variable showing statistical significance among these variables in the microbial diversity analysis. Accordingly, we believe that BLV infection can lead to a slight change of gut microbiota. Using LEfSe analysis, we searched for the bacterial taxa that were enriched in the BLV-infected and uninfected cows. In the analysis, 14 bacterial taxa were detected, which included bacteria associated with rumen fermentation. The uninfected cows showed a higher number of bacterial taxa associated with rumen fermentation than BLV-infected cows. The relative abundance of these bacterial taxa tended to be higher in uninfected cows than in BLV-infected cows. These bacteria included Lachnospiraceae and Veillonellaceae families. The Lachnospiraceae family is known to contribute to volatile fatty acid production from diverse polysaccharides (Seshadri et al., 2018; Ziemer, 2014). Some genera of the Veillonellaceae family, including genus Megasphaera, can convert lactate into volatile fatty acids such as acetate and propionate as a major fermentation product in the rumen (EsquivelElizondo et al., 2017; Marchandin and Jumas-Bilak, 2014; Weimer and Kohn, 2016) and also consume lactate to maintain the pH in the gut (Kleen et al., 2003; Plaizier et al., 2008). We believe that the bacterial taxa detected in this analysis are the tip of the iceberg of the bacterial taxa associated with BLV infection, because the fecal microbiota has a complex community of bacteria. Thus, the fecal microbiota of uninfected cows might have a greater abundance of bacterial community associated with rumen fermentation than that of BLV-infected cows. Next, we showed that the virus propagation ability of BLV strains was negatively correlated with fecal microbiota diversity. In other words, higher virus production was shown to lead to reduced fecal microbial diversity. After BLV infection of peripheral blood mononuclear cells in cows, the infected cells produce BLV particles for several weeks, and then they reduce the viral production (Juliarena et al., 2017; Kabeya et al., 1996). During the latent infection period, BLV is reactivated with some frequency (Alvarez et al., 2019; Jaworski et al., 2019). The BLVs released from the infected cells constantly stimulate the host immune system throughout the animals’ lives (Gillet et al., 2007). A recent investigation demonstrated that the genetics of BLV strains contributes to the degree of virus activity including virus propagation, which was shown using molecular clones (Murakami et al., 2019). Thus, the virus propagation ability of BLV strains was assumed to alter the gut microbiota depending on the corresponding response of the host immunity. Subsequently, we searched for the bacterial taxa correlated with the virus propagation ability of BLV strains. One bacterial taxon of genus Sanguibacteroides was shown to be negatively correlated with the virus propagation ability of BLV strains. The bacterium is typically found in the guts of homeothermic animals (Ormerod et al., 2016). Unfortunately, the biological function of this bacterium remains unclear. The reasons why the proportions of this bacterial taxon in the bovine gut microbiota were negatively correlated with the virus propagation ability of BLV strains have not been clarified. In the present study, alteration of the fecal microbiota by BLV infection was observed. According to function speculation of the bacteria detected in the differential abundance analysis, energy production loss in the rumen and hindgut was assumed, which may explain the secondary negative effects such as increased susceptibility to other infections and decreased lifetime milk production and reproductive efficiency (Brenner et al., 1989; Nekouei et al., 2016; Norby et al., 2016; Polat et al., 2017; Schwartz and Levy, 1994; Wellenberg et al., 2002; Yang et al., 2016). Moreover, BLV genetics influences virus activity

Funding This work was supported by a research project grant awarded by the Azabu University Research Services Division, Azabu University, Kanagawa, Japan. Declaration of Competing Interest The authors declare that they have no conflicts of interest. Appendix A. Supplementary data Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.vetmic.2019.108547. References Aida, Y., Murakami, H., Takahashi, M., Takeshima, S.N., 2013. Mechanisms of pathogenesis induced by bovine leukemia virus as a model for human T-cell leukemia virus. Front. Microbiol. 4, 328. Allaband, C., McDonald, D., Vazquez-Baeza, Y., Minich, J.J., Tripathi, A., Brenner, D.A., Loomba, R., Smarr, L., Sandborn, W.J., Schnabl, B., Dorrestein, P., Zarrinpar, A., Knight, R., 2019. Microbiome 101: studying, analyzing, and interpreting gut microbiome data for clinicians. Clin. Gastroenterol. Hepatol. 17, 218–230. Alvarez, I., Gutierrez, G., Gammella, M., Martinez, C., Politzki, R., Gonzalez, C., Caviglia, L., Carignano, H., Fondevila, N., Poli, M., Trono, K., 2013. Evaluation of total white blood cell count as a marker for proviral load of bovine leukemia virus in dairy cattle from herds with a high seroprevalence of antibodies against bovine leukemia virus. Am. J. Vet. Res. 74, 744–749. Alvarez, I., Porta, N.G., Trono, K., 2019. Detection of bovine leukemia virus RNA in blood samples of naturally infected dairy cattle. Vet. Sci. 6. Bolyen, E., Rideout, J.R., Dillon, M.R., Bokulich, N.A., Abnet, C.C., Al-Ghalith, G.A., et al., 2019. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857. Brenner, J., Van-Haam, M., Savir, D., Trainin, Z., 1989. The implication of BLV infection in the productivity, reproductive capacity and survival rate of a dairy cow. Vet. Immunol. Immunopathol. 22, 299–305. EFSA Panel on Animal Health and Welfare, 2015. Enzootic bovine leukosis. EFSA J. 4188. Esquivel-Elizondo, S., Ilhan, Z.E., Garcia-Pena, E.I., Krajmalnik-Brown, R., 2017. Insights into butyrate production in a controlled fermentation system via gene predictions. mSystems 2, e00051–00017. Frie, M.C., Coussens, P.M., 2015. Bovine leukemia virus: a major silent threat to proper immune responses in cattle. Vet. Immunol. Immunopathol. 163, 103–114. Gillet, N., Florins, A., Boxus, M., Burteau, C., Nigro, A., Vandermeers, F., Balon, H., Bouzar, A.B., Defoiche, J., Burny, A., Reichert, M., Kettmann, R., Willems, L., 2007. Mechanisms of leukemogenesis induced by bovine leukemia virus: prospects for novel anti-retroviral therapies in human. Retrovirology 4, 18. Jaworski, J.P., Petersen, M.I., Carignano, H.A., Trono, K.G., 2019. Spontaneous virus reactivation in cattle chronically infected with bovine leukemia virus. BMC Vet. Res. 15, 150. Juliarena, M.A., Barrios, C.N., Lützelschwab, C.M., Esteban, E.N., Gutiérrez, S.E., 2017. Bovine leukemia virus: current perspectives. Virus Adapt. Treat. 2017 (9), 13–26. Kabeya, H., Ohashi, K., Ohishi, K., Sugimoto, C., Amanuma, H., Onuma, M., 1996. An effective peptide vaccine to eliminate bovine leukaemia virus (BLV) infected cells in carrier sheep. Vaccine 14, 1118–1122. Kinross, J.M., Darzi, A.W., Nicholson, J.K., 2011. Gut microbiome-host interactions in health and disease. Genome Med. 3, 14. Kleen, J.L., Hooijer, G.A., Rehage, J., Noordhuizen, J.P., 2003. Subacute ruminal acidosis (SARA): a review. J. Vet. Med. A Physiol. Pathol. Clin. Med. 50, 406–414. Konnai, S., Murata, S., Ohashi, K., 2017. Immune exhaustion during chronic infections in cattle. J. Vet. Med. Sci. 79, 1–5. Lefkowitz, E.J., Dempsey, D.M., Hendrickson, R.C., Orton, R.J., Siddell, S.G., Smith, D.B., 2018. Virus taxonomy: the database of the International Committee on Taxonomy of Viruses (ICTV). Nucleic Acids Res. 46, D708–D717. Li, N., Ma, W.T., Pang, M., Fan, Q.L., Hua, J.L., 2019. The commensal microbiota and viral Infection: a comprehensive review. Front. Immunol. 10, 1551. Marchandin, H., Jumas-Bilak, E., 2014. . The family Veillonellaceae. In: Rosenberg, E., DeLong, E.F., Lory, S., Stackebrandt, E., Thompson, F. (Eds.), The Prokaryotes Firmicutes and Tenericutes. Springer, Berlin, Heidelberg, German, pp. 433–453.

6

Veterinary Microbiology 240 (2020) 108547

J. Uchiyama, et al.

biorXiv. Schwartz, I., Levy, D., 1994. Pathobiology of bovine leukemia virus. Vet. Res. 25, 521–536. Segata, N., Izard, J., Waldron, L., Gevers, D., Miropolsky, L., Garrett, W.S., Huttenhower, C., 2011. Metagenomic biomarker discovery and explanation. Genome Biol. 12, R60. Seshadri, R., Leahy, S.C., Attwood, G.T., Teh, K.H., Lambie, S.C., Cookson, A.L., et al., 2018. Cultivation and sequencing of rumen microbiome members from the Hungate1000 Collection. Nat. Biotechnol. 36, 359–367. Shreiner, A.B., Kao, J.Y., Young, V.B., 2015. The gut microbiome in health and in disease. Curr. Opin. Gastroenterol. 31, 69–75. Vujkovic-Cvijin, I., Somsouk, M., 2019. HIV and the gut microbiota: composition, consequences, and avenues for amelioration. Curr. HIVAIDS Rep. 16, 204–213. Weimer, P.J., Kohn, R.A., 2016. Impacts of ruminal microorganisms on the production of fuels: how can we intercede from the outside? Appl. Microbiol. Biotechnol. 100, 3389–3398. Wellenberg, G.J., van der Poel, W.H., Van Oirschot, J.T., 2002. Viral infections and bovine mastitis: a review. Vet. Microbiol. 88, 27–45. Wells, J.E., Kim, M., Kuehn, L.A., Bono, J.L., 2014. Fecal microbiome in the bovine animal. In: In:67th Annual Reciprocal Meat Conference. University of Wisconsin, Madison, WI. pp. 14–19. Williams, B., Landay, A., Presti, R.M., 2016. Microbiome alterations in HIV infection a review. Cell. Microbiol. 18, 645–651. Yang, Y., Fan, W., Mao, Y., Yang, Z., Lu, G., Zhang, R., Zhang, H., Szeto, C., Wang, C., 2016. Bovine leukemia virus infection in cattle of China: association with reduced milk production and increased somatic cell score. J. Dairy Sci. 99, 3688–3697. Zeineldin, M., Barakat, R., Elolimy, A., Salem, A.Z.M., Elghandour, M.M.Y., Monroy, J.C., 2018. Synergetic action between the rumen microbiota and bovine health. Microb. Pathog. 124, 106–115. Ziemer, C.J., 2014. Newly cultured bacteria with broad diversity isolated from eight-week continuous culture enrichments of cow feces on complex polysaccharides. Appl. Environ. Microbiol. 80, 574–585.

Miyoshi, J., Leone, V., Nobutani, K., Musch, M.W., Martinez-Guryn, K., Wang, Y., Miyoshi, S., Bobe, A.M., Eren, A.M., Chang, E.B., 2018. Minimizing confounders and increasing data quality in murine models for studies of the gut microbiome. PeerJ 6, e5166. Moloney, R.D., Desbonnet, L., Clarke, G., Dinan, T.G., Cryan, J.F., 2014. The microbiome: stress, health and disease. Mamm. Genome 25, 49–74. Murakami, H., Todaka, H., Uchiyama, J., Sato, R., Sogawa, K., Sakaguchi, M., Tsukamoto, K., 2019. A point mutation to the long terminal repeat of bovine leukemia virus related to viral productivity and transmissibility. Virology 537, 45–52. Murakami, H., Uchiyama, J., Nikaido, S., Sato, R., Sakaguchi, M., Tsukamoto, K., 2016. Inefficient viral replication of bovine leukemia virus induced by spontaneous deletion mutation in the G4 gene. J. Gen. Virol. 97, 2753–2762. Murakami, H., Yamada, T., Suzuki, M., Nakahara, Y., Suzuki, K., Sentsui, H., 2011. Bovine leukemia virus integration site selection in cattle that develop leukemia. Virus Res. 156, 107–112. Nekouei, O., VanLeeuwen, J., Stryhn, H., Kelton, D., Keefe, G., 2016. Lifetime effects of infection with bovine leukemia virus on longevity and milk production of dairy cows. Prev. Vet. Med. 133, 1–9. Norby, B., Bartlett, P.C., Byrem, T.M., Erskine, R.J., 2016. Effect of infection with bovine leukemia virus on milk production in Michigan dairy cows. J. Dairy Sci. 99, 2043–2052. Ormerod, K.L., Wood, D.L., Lachner, N., Gellatly, S.L., Daly, J.N., Parsons, J.D., Dal’Molin, C.G., Palfreyman, R.W., Nielsen, L.K., Cooper, M.A., Morrison, M., Hansbro, P.M., Hugenholtz, P., 2016. Genomic characterization of the uncultured Bacteroidales family S24-7 inhabiting the guts of homeothermic animals. Microbiome 4, 36. Plaizier, J.C., Krause, D.O., Gozho, G.N., McBride, B.W., 2008. Subacute ruminal acidosis in dairy cows: the physiological causes, incidence and consequences. Vet. J. 176, 21–31. Polat, M., Takeshima, S.N., Aida, Y., 2017. Epidemiology and genetic diversity of bovine leukemia virus. Virol. J. 14, 209. Russel, J., Thorsen, J., Brejnrod, A.D., Bisgaard, H., Sørensen, S.J., Burmølle, M., 2018. DAtest: a framework for choosing differential abundance or expression method.

7