(2023) 73:31
Suzuki et al. Annals of Microbiology
https://doi.org/10.1186/s13213-023-01737-4

ORIGINAL ARTICLE

Annals of Microbiology

Open Access

Exploratory study of volatile fatty acids
and the rumen‑and‑gut microbiota of dairy
cows in a single farm, with respect to subclinicalinfection with bovine leukemia virus
Takehito Suzuki1†, Hironobu Murakami1†, Jumpei Uchiyama2*† , Reiichiro Sato3, Iyo Takemura‑Uchiyama2,
Masaya Ogata1, Kazuyuki Sogawa1, Hiroho Ishida1, Apichart Atipairin4, Osamu Matsushita2 and Makoto Nagai1

Abstract
Background Subclinical infection with bovine leukemia virus (BLV) in cows can cause economic losses in milk
and meat production in many countries, as BLV-related negative effects. The volatile fatty acids (VFAs) and microbiota
present in the digestive tracts of cows can contribute to cow health. Here, we exploratorily investigated the VFAs
and microbiota in the rumen and gut with respect to subclinical BLV infection using cows housed at a single farm.
Results We analyzed a herd of 38 cows kept at one farm, which included 15 uninfected and 23 BLV-infected cows.
First, the analysis of the VFAs in the rumen, gut, and blood revealed an absence of statistically significant differences
between the uninfected and BLV-infected groups. Thus, BLV infection did not cause major changes in VFA levels
in all tested specimens. Next, we analyzed the rumen and gut microbiota. The analysis of the microbial diversity
revealed a modest difference between the uninfected and BLV-infected groups in the gut; by contrast, no differenceswere observed in the rumen. In addition, the investigation of the bacteria that were predominant in the uninfected
and BLV-infected groups via a differential abundance analysis showed that no significant bacteria were present
in either of the microbiota. Thus, BLV infection possibly affected the gut microbiota to a small extent. Moreover,bacterial associations were compared between the uninfected and BLV-infected groups. The results of this analysis
suggested that BLV infection affected the equilibrium of the bacterial associations in both microbiota, which might
be related to the BLV-related negative effects. Thus, BLV infection may negatively affect the equilibrium of bacterial
associations in both microbiota.
Conclusions Subclinical BLV infection is likely to affect the rumen and gut microbiota, which may partly explain
the BLV-related negative effects.
Keywords Bovine leukemia virus, Volatile fatty acids, Rumen, Gut, Microbiota, Cows†

Takehito Suzuki, Hironobu Murakami, and Jumpei Uchiyama equallycontributed to this work.
*Correspondence:
Jumpei Uchiyama
uchiyama@okayama-u.ac.jp
Full list of author information is available at the end of the article© The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
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regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of thislicence, visit http://creativecommons.org/licenses/by/4.0/.

Suzuki et al. Annals of Microbiology(2023) 73:31

Background
Bovine leukemia virus (BLV) is a viral agent of the Deltaretrovirus genus in the Retroviridae family that infects
cattle. BLV is prevalent globally, with the exception ofcountries in which viral eradication of this agent wasachieved. Because about 70% of BLV-infected cattle are
asymptomatic, the control of infection with this virus isextremely difficult (LaDronka et al. 2018; Marawan et al.
2021; Murakami et al. 2011). About 30% of BLV-infected
cattle develop persistent lymphocytosis, and about 1–5%
can develop malignant B-cell lymphosarcoma after a prolonged latency (1–8 years), which is a malignancy thatis also known as enzootic bovine leucosis (Aida et al.
2013). Cows diagnosed with this condition are eliminated from commercial distribution, which can causesignificant economic damage. Moreover, subclinical BLV
infection in cows, which consists of an asymptomatic
infection over their lifetime without the development ofenzootic bovine leucosis, also appears to cause economic
losses, to a greater extent than that expected, as the BLVrelated negative effects caused by subclinical infection
include increased susceptibility to other infections anda decrease in lifetime milk production, meat production,and reproductive efficiency in cows (Brenner et al. 1989;
Nakada et al. 2023; Nekouei et al. 2016; Polat et al. 2017;
Schwartz and Levy 1994; Yang et al. 2016). However, thephysiological effects of subclinical BLV infection in cows
remain unknown, although subclinical BLV infection isbelieved to cause a systemic impairment of the immune
system of these animals (Konnai et al. 2017). Thus, theeffects of subclinical BLV infection on the body of cows
should be investigated.
The microbiota of digestive tracts, particularly therumen and gut microbiota, play an essential rolein maintaining health in cattle (Cholewinska et al.
2020; Welch et al. 2022). The fermentation of cellulose and other complex plant carbohydrates by therumen microbiota is essential in cattle because theseanimals do not possess digestive enzymes to break
down these complex carbohydrates. About 70% of theenergy requirements of cattle and about 50% of theirprotein requirements necessitate microbial fermentation in the rumen (Bergman 1990; Pitta et al. 2018).
In cattle, rumen microbes become a source of protein,
and microbial metabolites, such as volatile fatty acids(VFAs), are a source of energy via gluconeogenesis
from acetyl-CoA, as well as a source of fats. VFAs arealternatively termed short-chain fatty acids. The major
VFAs produced by rumen fermentation are acetic,
propionic, butyric, and valeric acids. The amount andratio of these VFAs in the rumen are important for
maintaining cattle health because, although acetic acid

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is essential for the biosynthesis of body fat and milk
fat and for providing energy, an excessive amount ofbutyric acid leads to the generation of ketone bodies,
thus inducing ketosis (Miettinen and Huhtanen 1996).
Moreover, further down the intestinal tract, the hindgut—which is more generally called the gut—is anotherimportant intestinal locale. VFAs are also produced by
microbes in the gut (Yao et al. 2022), where they not
only function as an energy source for gut epithelial cells
but also regulate the function of innate immune cells,such as macrophages, neutrophils, and dendritic cells,
and regulate the differentiation of T and B cells (Yaoet al. 2022). Thus, the rumen and gut microbiota andthe VFAs generated by them are important for cattle
health.
Infections by some viruses are beginning to be shown
to modulate the microbiota of digestive tracts (Yuanet al. 2020). With respect to viruses for which such phenomena have not been examined, exploratory studies,
which are required in the early stages of research, arewarranted. One exploratory study alone using cows in
one farm reported that the gut microbiota of cows canbe modulated by BLV infection, even at the subclinicalstage (Uchiyama et al. 2020). However, integrative studies of the VFAs and microbiota of the digestive tract
with respect to subclinical BLV infection are scarce,
to the best of our knowledge. Here, we exploratorily
investigated the VFAs in the rumen fluid (rumen), feces(gut), and serum (blood) of uninfected and asymptomatic BLV-infected cows housed at a single farm andexamined the rumen and gut microbiota in the twogroups using 16S rRNA gene amplicon sequencing.

Results
Enrolled cows

We conducted the study in one commercial farm inJapan, which kept 70 cows, including 42 lactating cows.
These 42 lactating cows were housed in the same tiestall barn. We performed the sampling within 1 day
during the autumn season. The cows were fed the samediet. All cows were clinically healthy. We collected thesamples within 1 day but were unable to collect samples
from four cows because the sampling period coincidedwith the milking time. Finally, 38 cows were enrolled in
the present study.
Among these 38 cows (age, 24–101 months(mean ± standard deviation, 51.7 ± 19.9 months)), 23
were diagnosed as having subclinical BLV infection.
The mean ages of the uninfected and BLV-infected cows
were not significantly different (46.3 ± 22.9 months
and 55.3 ± 16.8 months, respectively; Mann–Whitney
U-test, P > 0.05).

Suzuki et al. Annals of Microbiology(2023) 73:31

VFA status in the rumen, gut, and blood in the uninfected
and BLV‑infected groups

To examine the effect of subclinical BLV infection,
we first compared the VFAs in the rumen, gut, andblood of the uninfected group with those of the BLVinfected group using a multivariate analysis of variance(MANOVA) and a principal component analysis (PCA).
There were no significant differences in the rumen, gut,and blood between the uninfected and BLV-infectedgroups (MANOVA, P ≥ 0.05). In addition, the comparison of the levels of each VFA (e.g., acetic acid) between
the uninfected and BLV-infected groups revealed anabsence of significant differences in the rumen, gut, andblood (Mann–Whitney U-test, P ≥ 0.05; Supplementary Table S1). A nonsignificant difference in the level ofisovaleric acid in the gut was observed between the uninfected and BLV-infected groups (Mann–Whitney U-test,
P < 0.1; Supplementary Table S1). Moreover, there were
no distinct observational differences in the in rumen, gut,and blood PCA score plots between uninfected and BLVinfected cows (Fig. 1).
Overview of the rumen and gut microbiota of uninfected
and BLV‑infected cows

The rumen and gut microbiota of the 38 cows were analyzed using 16S rRNA gene amplicon sequencing. A totalof 4,915,395 reads (129,353 ± 44,757 reads/sample) and
5,009,119 reads (131,819 ± 43,641 reads/sample) wereobtained from rumen and gut samples, respectively. Readtrimming and exclusion of chimeric reads of the rumen
and gut samples were performed using the DADA2Page 3 of 10

software (Callahan et al. 2016), which produced a totalof 1,843,202 reads (48,505 ± 19,152 reads/sample) and
1,954,142 reads (51,425 ± 17,723 reads/sample), respectively. The relative abundance of the bacterial community at the phylum level was calculated using these data.
In both the rumen and gut microbiota, Bacteroidota andFirmicutes were predominantly present, at about 40‒50%(Fig. 2). The other major phyla, Proteobacteria, Patescibacteria, Actinobacteriota, Planctomycetota, Spirochaetota, and Euryarchaeota (Archaea), were present at a
relative abundance of less than about 7% in the rumen
microbiota, whereas Verrucomicrobiota, Spirochaetota,
and Proteobacteria were present at a relative abundanceof less than about 6% in the gut microbiota.
Analyses of microbial diversities and differentiallyabundant microbes in the rumen and gut microbiota
between the uninfected and BLV‑infected groups

Microbial diversity was compared between the uninfected and BLV-infected groups using the alpha andbeta-diversity metrics (Table 1); the alpha-diversity metrics were evenness and the Shannon and Chao1 indices,whereas the beta-diversity metrics were the weighted andunweighted UniFrac metrics. First, in the rumen analysis, no significant differences in alpha or beta diversity
were observed. Similarly, in the fecal analysis, no significant differences were observed for either of the metrics.
However, nonsignificant differences were observed in
the gut: a lower evenness value was obtained in the BLVinfected vs. the uninfected group (P < 0.1); similarly, there
was a difference in unweighted UniFrac values (P < 0.1).

Fig. 1 PCA score plots of VFAs in A rumen, B gut, and C blood. The uninfected and BLV-infected groups are shown as dots of different colors, which
are coded in the bottom box. The eclipses with 95% reliability are also plotted on each graph, which are represented in the same color as the dot
colorSuzuki et al. Annals of Microbiology(2023) 73:31

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Fig. 2 Relative abundance at the phylum level of taxonomy. The average bacterial abundances in all cows are shown in taxa bar plots. A Rumen
and B gut. The bacterial taxa are indicated using different colors, as shown on the right of the taxa bar plots

Table 1 Comparison of microbial diversity of the uninfected and BLV-infected groups
Diversity metrica
Rumen
Microbiota

Uninfected group

BLV-infected groupp-value

Alpha diversity
Evenness

0.89 ± 0.04

0.89 ± 0.03

0.332

Shannon

9.15 ± 0.62

9.25 ± 0.59

0.560

Chao1

1395.64 ± 423.77

1408.81 ± 369.73

0.940

Beta diversity

Gut
Microbiota

Unweighted UniFrac

0.903

Weighted UniFrac

0.532

Alpha diversity
Evenness

0.89 ± 0.01

0.87 ± 0.04

0.071

Shannon9.01 ± 0.21

8.69 ± 0.73

0.317

Chao1

1167.94 ± 213.95

1081.47 ± 327.68

0.560

Beta diversity
Unweighted UniFrac

0.094

Weighted UniFrac

0.244

a

Mean ± standard deviations are shown as evenness, Shannon, or Chao1 indicesaAlpha- and beta-diversity metrics were compared by Kruskal–Wallis test and permutational multivariate analysis of variance (PERMANOVA), respectively

Moreover, to gain further insight, we performed a differential abundance analysis of the rumen and gut microbiota at the genus level using an analysis of the compositionof microbiome (ANCOM) (Mandal et al. 2015). No bacterial taxa exhibited significant differences in abundance
between the uninfected and BLV-infected groups (Supplementary Fig. S1).
Comparison of microbial co‑occurrence patterns
in the rumen and gut microbiota between the uninfected
and BLV‑infected groups

The importance of bacterial associations in microbiota
has recently been suggested. These associations can beinferred from metataxonomic data via a co-occurrence
network analysis (Dai et al. 2019; Matchado et al. 2021).
The analysis of the bacterial associations related to BLV
infection in the rumen and gut using a co-occurrence
network analysis revealed that the overall patterns of
the bacterial association networks in the rumen andgut microbiota were topologically different between theuninfected and BLV-infected groups (Supplementary
Fig. S2). The comparison of the co-occurrence networksof the uninfected group with those of the BLV-infectedgroup led to the detection of differential bacterial associations. Because of the complexity of the differential
networks derived from all detected differential bacterial

Suzuki et al. Annals of Microbiology(2023) 73:31

associations, representative differential networks in therumen and gut microbiota were simply constructed using
the nine differential bacterial associations with the highest weights (Fig. 3).
In the differential network of the rumen microbiota, seven and two differential bacterial associations were detected that were predominant in theuninfected and the BLV-infected groups, respectively (Fig. 3A). The seven associations that werepredominant in the uninfected group included Christensenellaceae–Rikenellaceae,
Lachnospiraceae–Oli-

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gosphaeraceae,
Bdellovibrionota–Moraxellaceae,
Ruminococcaceae–Oligosphaeraceae, Rhizobiaceae–Erysipelatoclostridiaceae, Leptotrichiaceae–Weeksellaceae,
and Brevibacteriaceae–Lachnospiraceae. The two associations that were predominant in the BLV-infected group
included Lachnospiraceae–Methanomicrobiaceae andLachnospiraceae–Veillonellaceae.
In the differential network of the gut microbiota, fiveand four differential bacterial associations were predominant in the uninfected and the BLV-infectedgroups, respectively (Fig. 3B). The five associations that

Fig. 3 Differential networks between the uninfected and the BLV-infected groups. A Rumen and B gut. In the networks, each node showsa bacterium at genus level. The types of edges are described on the bottom dotted box. The taxonomic units at genus level (bacterial
classifications at family level) shown in A are as follows: Lachnospiraceae_UCG-002 (Lachnospiraceae), horsej-a03 (Oligosphaeraceae), uncultured(Bdellovibrionota), Moraxella (Moraxellaceae), Incertae_Sedis (Ruminococcaceae), Z20 (Oligosphaeraceae), Allorhizobium-Neorhizobium-Pararhizob
ium-Rhizobium (Rhizobiaceae), Asteroleplasma (Erysipelatoclostridiaceae), Caviibacter (Leptotrichiaceae), Bergeyella (Weeksellaceae), Rikenellaceae_
RC9_gut_group (Rikenellaceae), Christensenellaceae_R-7_group (Christensenellaceae), Brevibacterium (Brevibacteriaceae), possible_genus_
Sk018 (Lachnospiraceae), Methanomicrobium (Methanomicrobiaceae), Lachnospiraceae_NC2004_group (Lachnospiraceae), and Megasphaera(Veillonellaceae). The taxonomic units (bacterial classifications) shown in Fig. 3B are as follows: Bradymonadales (Bradymonadales), Lysinibacillus(Planococcaceae), Methanocorpusculum (Methanocorpusculaceae), Howardella (Lachnospiraceae), Mogibacterium (Anaerovoracaceae), Megasphaera(Veillonellaceae), Raoultibacter (Eggerthellaceae), Hydrogenoanaerobacterium (Oscillospiraceae), Catenisphaera (Erysipelotrichaceae), uncultured(Verrucomicrobiota), Sanguibacteroides (Marinifilaceae), [Clostridium]_methylpentosum_group (Oscillospirales), [Eubacterium]_xylanophilum_
group (Lachnospiraceae), Anaerofustis (Anaerofustaceae), uncultured (Actinobacteriota), Syntrophococcus (Lachnospiraceae), and Kandleria(Erysipelatoclostridiaceae)

Suzuki et al. Annals of Microbiology(2023) 73:31

were predominant in the uninfected group included
Erysipelotrichaceae–Verrucomicrobiota,
Bradymonadales–Planococcaceae,
Methanocorpusculaceae–
Lachnospiraceae, Anaerovoracaceae–Veillonellaceae, andEggerthellaceae–Oscillospiraceae. Finally, the four associations that were predominant in the BLV-infected group
included Verrucomicrobiota–Marinifilaceae, Oscillospirales–Lachnospiraceae, Anaerofustaceae–Actinobacteriota, and Lachnospiraceae–Erysipelatoclostridiaceae.

Discussion
This was an exploratory study for the integrative analysis of VFAs in the rumen, gut, and blood, as well as therumen and gut microbiota, with respect to subclinical
BLV infection, using cows housed at a single farm. No
estimation of sample size was performed prior to sample collection in this study. However, we believe that theherd yielded the appropriate BLV-related results with two
reasons. First, the size of the herd used in this study was
similar to that of a previous exploratory study (i.e., 22
uninfected and 29 BLV-infected cows) (Uchiyama et al.
2020). Second, the confounding factors were considered
to be minimized as much as possible. As described in
the previous study (Uchiyama et al. 2020), because factors such as diet, host phenotype, genetics, age, environment, and health status affect the VFAs and microbiota
of digestive tracts (Gupta et al. 2020; Kurilshikov et al.
2021; Na and Guan 2022; Wang and LeCao 2020), we collected samples from cows housed in the same environment (i.e., housing style and feeding management) over
1 day. Of note, because older cows generally have more
opportunities to be exposed to BLV infection, the age of
BLV-infected cows is older than that of uninfected cows.
The age of the animals in the two groups was not significantly different, although the average age of the cows in
the BLV-infected group was 9 months older than that ofthose in the uninfected group.
Using the herd, we first compared the VFAs of therumen, gut, and blood between the uninfected and BLVinfected groups based on statistics and the observation of
PCA plots. The uninfected and BLV-infected groups did
not differ in the VFAs of the rumen, gut, and blood. Thus,
a drastic change in the VFA status was not likely to be
triggered by subclinical BLV infection.
We then investigated the rumen and gut microbiota.
The composition and abundance of the major bacterial taxa in the rumen and gut microbiota in this study
appeared to be similar to those reported for other cows
(Li et al. 2021; Uchiyama et al. 2020), which suggeststhat these cows had typical rumen and gut microbiota. In our subsequent analysis of microbial diversity,
the gut microbiota appeared to be minimally alteredby BLV infection, whereas the rumen microbiota wasPage 6 of 10

hardly altered by BLV infection. The small change
observed in the gut microbiota after BLV infection
was similar to the results of a previous study (Uchiyama et al. 2020). In addition, we performed a differential abundance analysis with respect to BLV infectionin the rumen and gut microbiota. No bacteria in association with BLV infection were detected. Thus, the gut
was very slightly affected by BLV infection, whereas therumen hardly appeared to be affected by it.
Next, the impact of BLV infection on the equilibrium ofbacterial associations was examined via a differential network analysis of the rumen and gut microbiota. Becauseof the detection of differential bacterial associations inboth microbiota, subclinical BLV infection was likely totrigger a disequilibrium in the bacterial associations in
the rumen and gut microbiota. We predicted the functions of bacterial associations from the bacteria present
in the differential networks. First, the differential network of the rumen microbiota was examined. Among theassociations that were predominant in the BLV-infectedgroup, the associations between methanogenic bacteria(Methanomicrobiaceae) with cellulose-degrading bacteria (Lachnospiraceae), and between cellulose-degrading
bacteria (Lachnospiraceae) and VFA-producing bacteria (Veiollonellaceae), were assessed. Among the associations that were predominant in the uninfected group,
cellulose-degrading bacteria and VFA-producing bacteria appeared to form associations. Based on our observation, the cooperation of methane-producing bacteriawith cellulose-degrading bacteria may be more likely tooccur in the rumen microbiota of BLV-infected groups.
The rumen methanogenic bacteria reduce the feed efficiency by releasing some of the energy in the feed asmethane (Martinez-Fernandez et al. 2016). Thus, BLV
infection may reduce energy production efficiency inthe rumen microbiota at an undetectable low level overa long period. Moreover, the differential network of thegut microbiota was examined. Among the associations
that were predominant in the BLV-infected or uninfectedgroup, the functions of most bacteria are hardly deducible based solely on bacterial classification. However,cellulose-degrading bacteria (Lachnospiraceae) and VFAproducing bacteria (Veiollonellaceae) were detected in
the associations that were predominant in the uninfectedgroup. The VFAs produced by gut microbiota play animportant role in host physiology by maintaining homeostasis with the host’s immune system (Welch et al. 2022).
Thus, the fermentation in the uninfected group thatis required for VFA production may have been imbalanced by BLV infection at an undetectable low level overa long period. Considering these assumptions of differential bacterial associations, we may grasp hints that thedisequilibrium of the rumen and gut microbiota caused

Suzuki et al. Annals of Microbiology(2023) 73:31

by BLV infection is related to the BLV-related negative
effects associated with subclinical infection.
In this study, we observed that subclinical BLV infection appeared to slightly affect the gut microbiota while
having hardly any effect on the rumen microbiota; it
did not cause a major change in the VFA compositionof blood and digestive tract. Subclinical BLV infection
appeared to affect the equilibrium of the bacterial associations in both the rumen and gut microbiota for a long
period, particularly the gut microbiota. Considering theimportance of the rumen and gut in the health status ofcows together with the results of this study, the digestive
tract may become a target for the management of BLVrelated negative effects. Because this was an exploratory
study with low statistical power, our results warrant replication across larger samples based on sample size calculation to confirm the phenomena in the future.

Conclusions
This study investigated not only the VFAs in the rumen,gut, and blood but also the rumen and gut microbiota
with respect to subclinical BLV infection. Subclinical
BLV infection did not cause major changes in either the
VFA levels or the microbiota of the digestive tract. However, the microbiota in the digestive tract, particularly thegut, might be in a slight disequilibrium, which implies a
potential relation to BLV-related negative effects. In conclusion, subclinical BLV infection is likely to affect therumen and gut microbiota, which may partly explain the
BLV-related negative effects.
Methods
Animals

One dairy farm located in Tokyo, Japan, which was maintained by one owner, kept 70 cows, including 42 lactatingcows. These 42 lactating cows were at different lactation
stages and were kept in the same tie-stall barn. Theselactating cows were fed a mixture of 39.0% (w/w) roughage, 59.8% concentrate feed, and 1.2% supplements. Veterinarians interviewed farm owners about animals withchanges in milk production or abnormal symptoms.
After checking the rectal temperature, auscultation wasused to check the heart rate, rumen, pinging test, respiratory rate, adventitious sounds, and abnormal respiratory
sounds. Other tests were performed to check for external abnormalities, to evaluate the locomotor system incases of lameness, and to check for abnormalities in thepelvic cavity and female genitalia via rectal examination,
to ensure that all cows were in good clinical condition.
Cows that were diagnosed as clinically healthy after successful sampling were enrolled in this study.

Page 7 of 10

Samples

Blood samples were collected from the tail veins. In
this study, the enrolled cows were commercial production cows, and rumen fluid was not collected byruminocentesis, to avoid the potential risk of peritonitis. Rumen fluids were collected using a pump aftera hose was placed in the mouth of the animal throughto the rumen. After 200 mL of the first fluid was discarded, to avoid saliva contamination, 10 mL of rumen
fluid was collected into sterilized plastic tubes. Fecalmaterial near the anus was discarded during the rectal
palpation, and fecal material in the deep rectum wascollected. Immediately after collection, the fecal samples were placed in sterile polypropylene tubes withfilter caps (CELLSTAR CELLreactor; Greiner Bio-One,
Rainbach im Mühlkreis, Austria) under anaerobic conditions using the AnaeroPack Kenki system (Mitsubishi
Gas Chemical Company, Japan). All samplings were
performed on September 2, 2019.
Immediately after collection, the samples were temporally stored and transported within a day at 4 °C forblood samples and − 20 °C for rumen fluid and fecal
samples. The samples were then stored in the laboratory at 4 °C (blood) and − 80 °C (rumen fluid, feces).
Diagnosis of BLV infection

One day after blood sample collection, sera were prepared from the blood by centrifugation. Genomic DNA
was extracted from blood cells using a Wizard Genomic
DNA Purification Kit (Promega, Madison, USA). BLV
infection was diagnosed using the serum as described
previously via both agar gel immunodiffusion assays
and detection of BLV proviral genomes (Murakami
et al. 2016).
Measurement of VFAs

Ruminal VFAs were analyzed by gas chromatography (GC). After centrifugation of the rumen fluids, 20
μL of 85% phosphoric acid was added to about 1 mL of
the supernatant and incubated overnight at 4 °C. Aftercentrifugation (13,000 × g, 10 min, 4 °C) and filtration
through an 0.45-μm filter, 2 μL of sample was injected
into a 30 m × 0.25-mm FFAP capillary column with a
0.25-μm film thickness (Agilent Technologies, Germany)
fitted to a 7820A GC system (Agilent) with flame ionization detection. The GC operation conditions were as follows. Helium was used as the carrier gas with a constantpressure of 57 kPa. Sample injection was carried out insplit mode (10:1), with an injection volume of 1 μL andan injector temperature of 250 °C. The initial oven temperature was set to 60 °C and increased to 240 °C in stepsof 10 °C/min and then 20 °C/min to 325 °C.

Suzuki et al. Annals of Microbiology(2023) 73:31

Fecal and serum VFAs were analyzed by gas chromatography–mass spectrometry (GC–MS) after derivatization (tert-butyldimethylsilylation). Fecal samples(50 mg) and serum samples (450 μL) were made to 500
μL with Milli-Q water. To these samples, 5 μL of internal standard (crotonic acid, 200 μg/mL) was added followed by vortexing with 10-μL 50% sulfosalicylic acidto precipitate protein. After centrifugation at 13,000 × g
for 10 min, the deproteinized samples were extractedwith diethyl ether/hydrochloric acid (1:0.003, v/v) byshaking for 30 min. Subsequently, 8 μL of 1-tert-butyldimethylsilylimidazole was added and allowed to reactat 60 °C for 30 min. Two microliters of the mixture
were analyzed by GC–MS on a 7890A chromatographequipped with automated sample injection (MPS fromGERSTEL, Germany), a 30 m × 0.25 mm DB-5 ms capillary column with a 0.25-μm film thickness (Agilent),
and a 5975B mass spectral detector (Agilent). The mass
spectrometer was used in the electrospray ionization mode with an ion source temperature of 250 °C;
the temperature of the quadrupole and mass spectralinterface was 150 °C and 290 °C, respectively. The mass
spectral detector was used in the selected ion monitoring mode, to search for acetic, propionic, butyric,valeric, and crotonic acids, which have m/z values of
159.0, 131.0, 145.0, 159.0, and 143.0, respectively.
Statistical analysis

The JMP Pro 16.2.0 software (SAS Institute, USA) wasused for the statistical analysis of cattle age and VFAlevels.
DNA extraction and sequencing

DNA was extracted from rumen and fecal samples using
an ISOSPIN Fecal DNA Kit (Nippon Gene, Japan). The
extracts were subjected to 16S rRNA gene ampliconsequencing, in which the V3–V4 region of the 16S rRNA
gene, amplified by polymerase chain reaction, was analyzed using an MiSeq Reagent Kit v3 in 600 cycles in an
Illumina MiSeq instrument (Illumina, USA), as described
elsewhere (Uchiyama et al. 2022).
Microbiota data analysis

The sequence data were processed using the Quantitative
Insights into Microbial Ecology 2 (QIIME2) pipeline v.
2022.2.0 (Bolyen et al. 2019). The DADA2 software package v2021.8.0 incorporated in QIIME 2 was used to correct the amplicon sequence errors and to construct anamplicon sequence variant table (Callahan et al. 2016).
The amplicon sequence variant table was rarefied. Microbial taxonomy was assigned using a Naïve-Bayes classifier
trained on the SILVA 138 99% database.

Page 8 of 10

Metrics of alpha diversity, including Pielou’s evenness (Evenness) index, Shannon’s (Shannon) index, andthe Chao1 index, and those of beta diversity, including
unweighted UniFrac and weighted UniFrac, were calculated and statistically analyzed using the QIIME 2 pipeline. A differential abundance analysis was performed
using ANCOM implemented in the QIIME 2 pipeline.
Networks were analyzed with the NetCoMi packagein the R software version 1.0.2 and in the R software
version 4.0.2 using the SPIEC-EASI metric for network
construction (Peschel et al. 2021). Associations were
estimated via the SPRING approach. The associationsin the networks were then compared using the discordant method.
Abbreviations
BLV	Bovine leukemia virus
VFAs	Volatile fatty acids
MANOVA	Multivariate analysis of variance
PCA	Principal component analysis
ANCOM	Analysis of composition of microbiome
GC	Gas chromatography
GC–MS	Gas chromatography–mass spectrometry
QIIME2	Quantitative Insights into Microbial Ecology 2

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s13213-​023-​01737-4.
Additional file 1: Fig. S1. ANCOM volcano plots. The centered log ratio(clr) on the horizontal axis is the clr representative of the difference in theabundance of a given taxonomical unit at the genus level between theuninfected and the BLV-infected groups. The W value on the vertical axis
indicates the value of the statistical test corresponding to the numberof times the null hypothesis was rejected for each taxonomical unit. The
positive and negative values on the horizontal axis indicate the abun‑dance of bacteria in the uninfected and BLV-infected groups, respectively.
(A) Rumen and (B) gut microbiota. Each dot represents one bacterial
taxonomy unit at the genus level; dots with W = 1 are labeled. Fig. S2.
Microbial co-occurrence network of (A) rumen and (B) gut microbiota. The
150 genera with the highest variance were selected from each group, andthe co-occurrence networks of the uninfected and BLV-infected groups
were constructed using the 100 edges with the highest weights. The net‑
works of uninfected and BLV-infected groups are shown on the left andright, respectively. Each node is a bacterial taxonomic unit at genus level.
The node positions in the left and right networks are the same, and nodes
in the same position indicate the same bacterial species. The clusters wereinferred using the Louvain method, which are shown by different nodecolors. All the edges shown in the networks are positive associations,shown by green lines. Table S1. Measurement of metabolites in rumen,
gut and blood. Supplementary protocol 1.
Acknowledgements
We thank the undergraduate students, School of Veterinary Medicine,
Azabu University, for experimental assistance. We especially thank Anicom
Pafe, Inc. for the next-generation sequencing and online English for English
proofreading.
Authors’ contributions
TS, HM, JU, and RS designed the study. TS, HM, JU, RS, IT, and MO collected thesamples. TS, HM, JU, and IT performed the experiments, analyzed the data,
and wrote the manuscript. All authors reviewed and critically revised themanuscript.

Suzuki et al. Annals of Microbiology(2023) 73:31

Funding
This study is funded by Grants-in-Aid for the Promotion of the Livestock Indus‑
try from the Japan Racing Association in 2021.
Availability of data and materials
Raw sequence data were deposited in the DNA Data Bank of Japan (DDBJ)
sequence read archive (DRA014764) under the BioProject no. PRJDB14276.

Declarations
Ethics approval and consent to participate
The study farm was willing to cooperate with us. Our care and handling of theanimals followed the Azabu University Animal Experiment Guidelines, which
are in accordance with the ARRIVE guidelines. All experiments complied withthe guidelines of the Ethics Committee of Azabu University, which reviewed
and approved this study (approval number: 17113–3).
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Author details
1
School of Veterinary Medicine, Azabu University, Sagamihara, Kanagawa,Japan. 2 Department of Bacteriology, Graduate School of Medicine Dentistry
and Pharmaceutical Sciences, Okayama University, Shikata‑cho 2‑5‑1, Kita‑ku
Okayama‑shi, Okayama 700‑8558, Japan. 3 Faculty of Agriculture, Universityof Miyazaki, Miyazaki, Japan. 4 School of Pharmacy, Walailak University, Thasala,Nakhon Si Thammarat, Thailand.
Received: 7 June 2023 Accepted: 8 September 2023

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