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Citation: Patangia, D.V.; Grimaud, G.;
Linehan, K.; Ross, R.P.; Stanton, C.
Microbiota and Resistome Analysis
of Colostrum and Milk from Dairy
Cows Treated with and without Dry
Cow Therapies. Antibiotics 2023,12,
1315. https://doi.org/10.3390/
antibiotics12081315
Academic Editors: Aloysio de Mello
Figueiredo Cerqueira, Júlia
Peixoto Albuquerque and Renata
Fernandes Rabello
Received: 27 June 2023
Revised: 27 July 2023
Accepted: 4 August 2023
Published: 14 August 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
antibiotics
Article
Microbiota and Resistome Analysis of Colostrum and Milk
from Dairy Cows Treated with and without Dry Cow Therapies
Dhrati V. Patangia 1,2,3, Ghjuvan Grimaud 2,3 , Kevin Linehan 1,2,3, R. Paul Ross 1,3 and Catherine Stanton 2, 3, *
1
School of Microbiology, University College Cork, T12 K8AF Cork, Ireland; dhrati.patangia@teagasc.ie (D.V.P.);
p.ross@ucc.ie (R.P.R.)
2Biosciences Building, Teagasc Food Research Centre, P61 C996 Fermoy, Ireland
3APC Microbiome Ireland, University College Cork, T12 K8AF Cork, Ireland
*Correspondence: catherine.stanton@teagasc.ie
Abstract:
This study investigated the longitudinal impact of methods for the drying off of cows with
and without dry cow therapy (DCT) on the microbiota and resistome profile in colostrum and milk
samples from cows. Three groups of healthy dairy cows (n= 24) with different antibiotic treatments
during DCT were studied. Colostrum and milk samples from Month 0 (M0), 2 (M2), 4 (M4) and 6 (M6)
were analysed using whole-genome shotgun-sequencing. The microbial diversity from antibiotic-
treated groups was different and higher than that of the non-antibiotic group. This difference was
more evident in milk compared to colostrum, with increasing diversity seen only in antibiotic-treated
groups. The microbiome of antibiotic-treated groups clustered separately from the non-antibiotic
group at M2-, M4- and M6 milk samples, showing the effect of antibiotic treatment on between-group
(beta) diversity. The non-antibiotic group did not show a high relative abundance of mastitis-causing
pathogens during early lactation and was more associated with genera such as Psychrobacter,Serratia,
Gordonibacter and Brevibacterium. A high relative abundance of antibiotic resistance genes (ARGs) was
observed in the milk of antibiotic-treated groups with the Cephaguard group showing a significantly
high abundance of genes conferring resistance to cephalosporin, aminoglycoside and penam classes.
The data support the use of non-antibiotic alternatives for drying off in cows.
Keywords: cow milk microbiota; resistome; shotgun metagenomics; longitudinal
1. Introduction
The first milk produced after the non-lactating period, colostrum, is highly nutrient-
rich, provides the calf with both nutritional and immune benefits, and shapes the calf gut
microbiota [
1
]. It helps in the education, maturation and development of the immune,
organ system and intestinal function [
2
,
3
]. This is crucial for the calf as it is born agam-
maglobulinemic and depends on the colostrum for passive immunity and gut microbiota
development [
1
]. Colostrum and stable gut microbiota colonisation are important in early
life for ruminant development from the monogastric stage at birth, and impact the overall
health of the animal [
4
]. Due to the importance of animal health and its relation to dairy
products, research is now focused on bovine colostrum and milk microbiota composition
and its association with disease [5–8].
In cows, before the milking begins, there is a period called the dry period (six to eight
weeks long), which allows the teats to dry up. This can be performed by allowing the
drying to occur naturally, using teat sealants. Alternatively, drying of the teats can be
performed by using medications and antibiotics to ensure the teats are free of any infection,
called dry cow therapy (DCT). The objectives of DCT are to eliminate infections presenting
from previous lactation and to avoid new udder infections during the dry period—when
mammary glands are highly susceptible to new infections [
9
]. The use of DCT can be either
selective or for the entire herd. In the second approach, called the “blanket” approach, the
Antibiotics 2023,12, 1315. https://doi.org/10.3390/antibiotics12081315 https://www.mdpi.com/journal/antibiotics
Antibiotics 2023,12, 1315 2 of 16
entire herd is treated with antibiotics for preventive measures. It helps to achieve the goal
of attaining high quantities of good-quality milk [
9
,
10
]; however, prophylactic treatment of
animals with antibiotics is increasingly being frowned upon due to problems associated
with antibiotic resistance development.
The use of antimicrobials for DCT is common globally [
11
–
14
], although a declining
trend is now observed in many European countries [
15
]. Many antibiotics used in livestock
are medically relevant and based on the same anti-microbial compound used in human
medicine. Commonly used antibiotics include penicillin alone or in combination with
aminoglycosides and cephalosporins, tetracyclines, fluoroquinolones, macrolides and
sulfonamides [15,16].
Milk from cows treated with prophylactic antibiotics usually contains significant an-
tibiotic residues [
17
–
19
]. Sub-MIC (minimum inhibitory concentration) levels of antibiotics
can form resistant bacteria, which can be more problematic than those selected at higher
doses [
20
,
21
]. An indirect role of colostrum as the diet is suggested as a potential source of
antibiotic resistance genes (ARGs) for calf gut microbiota even in the absence of antibiotic
use in cows [
22
]. Further, reports suggest the presence of ARGs and a significantly altered
microbial and functional profile in the calf gut when fed with milk containing antibiotic
residues [
22
–
26
]. Antibiotic resistance in dairy milk is also an area of interest because of
the human consumption of raw milk or other dairy products made from contaminated raw
milk and issues of environmental pollution with ARGs making it a One-Health issue [
27
–
32
].
Prophylactic use of antibiotics on dairy farms is potentially an important contributor to
AMR spread in the environment. Thus, along with EU legislation to cease blanket DCT,
other national laws to reduce antibiotic use in other livestock and dairy activities might
help lower the development and spread of ARGs.
Moreover, the presence of ARGs is not limited to pathogenic bacteria but may also
occur in commensals. Mostly, the antibiotic resistance of pathogens in bovine milk has been
studied in cases of mastitis [
33
–
35
], but not many studies have been conducted to study the
resistance pattern of the overall milk microbiota. Also, most studies focus on the resistance
patterns of pathogens at one time point; thus, temporal effects of antibiotics on the resistance
pattern in colostrum and milk are yet to be investigated. We hypothesised that antibiotic use
in the Cephaguard
TM
(CEF) (Cephaguard DC 150 mg intramammary ointment at drying
off, of which the active ingredient is Cefquinome—a fourth-generation cephalosporin)
and Ubro red (UBRO) (Ubro Red Dry Cow Intramammary Suspension at drying off;
of which the active ingredients are Framycetin Sulphate, Penethamate Hydriodide and
Procaine Penicillin) group during DCT will result in varying abundance and composition
of microbiota and resistance genes as compared to the non-antibiotic group (NOAB). Thus,
in this study, shotgun metagenomics sequencing was used to investigate the longitudinal
microbial composition and resistome profiles of colostrum and milk samples obtained from
healthy cows throughout lactation, which were previously treated with and without DCT.
2. Results
2.1. Microbiota Diversity Is Influenced by Antibiotic Treatment
Shotgun sequencing of the raw bovine colostrum and milk samples yielded 494,107,934
reads. Post trimming and host removal, the number of microbial reads remaining were
108,774,030 with an average of 1,121,382 reads (median = 310,854, min = 40,371,
max = 6,941,451). Both Shannon and Chao1 diversity indices did not show a varia-
tion between time points within the no-antibiotic group (Figures 1A and S1A). How-
ever, significant differences were observed for both Shannon and Chao1 diversity in-
dices between M0 and M2 (for UBRO: Shannon p-value = 0.026, Chao1 p-value = 0.011;
for CEF: Shannon p-value = 0.00016, Chao1 p-value = 0.003; Wilcoxon test), M0 and
M4 (for UBRO: Shannon p-value = 0.00058, Chao1 p-value = 0.0021; for CEF: Shannon
p-value = 0.00031, Chao1 p-value = 0.0061; Wilcoxon test), and M0 and M6 (for UBRO:
Shannon p-value = 0.00058, Chao1 p-value = 0.002; for CEF: Shannon p-value = 0.00016,
Chao1 p-value = 0.0044; Wilcoxon test) in both Cephaguard (CEF)- and UBRO red (UBRO)-
Antibiotics 2023,12, 1315 3 of 16
treated groups (Figures 1A and S1A). Additionally, a significant difference between M2
and M6 was observed for the Chao1 index in the UBRO group (Figure S1A). Concerning
individual time points, no significant differences were observed between the three groups
at M0, while CEF and NOAB groups showed differences at M2; UBRO and NOAB at M4;
and both antibiotics groups to NOAB at M6 (Figures 1B and S1B). The beta diversity of
milk microbiota between the NOAB and antibiotic groups (UBRO and CEF) demonstrated
distinct clustering overall and at all time-points after M0 (Figure 1C,D), including NOAB vs.
CEF (p= 0.0030) and NOAB vs. UBRO (p= 0.0045) at M2, NOAB vs. CEF (p= 0.0015) and
NOAB vs. UBRO (p= 0.0015) at M4, and NOAB vs. CEF (p= 0.0015) and NOAB vs. UBRO
at M6 (p= 0.0015). Furthermore, discrete clustering of samples was observed based on time
points, with samples at M0 clustering separately from all later time points (Figure 1D).
Antibiotics 2023, 12, x FOR PEER REVIEW 3 of 18
significant differences were observed for both Shannon and Chao1 diversity indices be-
tween M0 and M2 (for UBRO: Shannon p-value = 0.026, Chao1 p-value = 0.011; for CEF:
Shannon p-value = 0.00016, Chao1 p-value = 0.003; Wilcoxon test), M0 and M4 (for UBRO:
Shannon p-value = 0.00058, Chao1 p-value = 0.0021; for CEF: Shannon p-value = 0.00031,
Chao1 p-value = 0.0061; Wilcoxon test), and M0 and M6 (for UBRO: Shannon p-value =
0.00058, Chao1 p-value = 0.002; for CEF: Shannon p-value = 0.00016, Chao1 p-value =
0.0044; Wilcoxon test) in both Cephaguard (CEF)- and UBRO red (UBRO)-treated groups
(Figures 1A and S1A). Additionally, a significant difference between M2 and M6 was ob-
served for the Chao1 index in the UBRO group (Figure S1A). Concerning individual time
points, no significant differences were observed between the three groups at M0, while
CEF and NOAB groups showed differences at M2; UBRO and NOAB at M4; and both
antibiotics groups to NOAB at M6 (Figures 1B and S1B). The beta diversity of milk micro-
biota between the NOAB and antibiotic groups (UBRO and CEF) demonstrated distinct
clustering overall and at all time-points after M0 (Figure 1C,D), including NOAB vs. CEF
(p = 0.0030) and NOAB vs. UBRO (p = 0.0045) at M2, NOAB vs. CEF (p = 0.0015) and NOAB
vs. UBRO (p = 0.0015) at M4, and NOAB vs. CEF (p = 0.0015) and NOAB vs. UBRO at M6
(p = 0.0015). Furthermore, discrete clustering of samples was observed based on time
points, with samples at M0 clustering separately from all later time points (Figure 1D).
Figure 1. (A) Alpha diversity of milk microbiota using Shannon index between all time-points for
each group. The plot shows significant differences in diversity between milk obtained at M0, M2,
M4 and M6 for both the antibiotic-treated groups (UBRO and CEF). (B) Alpha diversity using Shan-
non index between the three groups (NOAB, UBRO and CEF) at all time-points. Plot shows no sig-
nificant difference between groups at M0, while significance between antibiotic and no-antibiotic
group was seen at later time points. (C) PCoA plot using Bray–Curtis distance matrix between the
three groups at all time-points, showing distinct clustering between groups. (D) PCoA plot using
Bray–Curtis distances (a) showing distinct clustering of all three groups, with antibiotic groups clus-
tering discretely compared to the no-antibiotic group; and plot (b) showing separate grouping of
Figure 1.
(
A
) Alpha diversity of milk microbiota using Shannon index between all time-points
for each group. The plot shows significant differences in diversity between milk obtained at M0,
M2, M4 and M6 for both the antibiotic-treated groups (UBRO and CEF). (
B
) Alpha diversity using
Shannon index between the three groups (NOAB, UBRO and CEF) at all time-points. Plot shows no
significant difference between groups at M0, while significance between antibiotic and no-antibiotic
group was seen at later time points. (
C
) PCoA plot using Bray–Curtis distance matrix between the
three groups at all time-points, showing distinct clustering between groups. (
D
) PCoA plot using
Bray–Curtis distances (
a
) showing distinct clustering of all three groups, with antibiotic groups
clustering discretely compared to the no-antibiotic group; and plot (
b
) showing separate grouping of
points between groups and time-points. * p-value ≤0.05; ** p-value ≤0.01; *** p-value ≤0.001.
Similar to distinct clustering of the NOAB group from antibiotic-treated groups overall,
we also observed clustering based on farms, where farm “C” (i.e., the farm without DCT)
clusters separately from the other antibiotic-treated farms (farm “D” and “P” are CEF,
while farm “L” and “T” are UBRO). Furthermore, samples from nulliparous cows, which
corresponded to the NOAB group, showed a similar clustering pattern because of an
overlapping between the antibiotics groups and parity (Figure S2A–C, Table S1). Based on
Antibiotics 2023,12, 1315 4 of 16
PERMANOVA tests, the variable farms and antibiotics were the most explanatory variables
regarding beta-diversity (Bray–Curtis distance) in our dataset (PERMANOVA R
2
= 0.15
and R
2
= 0.11, reciprocally). To test the interactions between antibiotics and farms, we
performed a PERMANOVA on the two most explanatory grouping variables. The results
showed that grouping based on antibiotic exposure explains a higher variance compared to
farms (R
2
= 0.11 and 0.03 and p-values = 0.001 and 0.02 for group and farm study variables,
respectively; PERMANOVA using Bray–Curtis distance ~ Antibiotic groups + farm). These
results considering the effect of farm variables must be interpreted cautiously due to the
low subject size for each farm.
2.2. Taxonomic Composition Is Associated with Dry Cow Therapy Treatment
Differences in the relative abundance of the microbial composition of milk between
the three groups at all time points were observed using plots at phyla and genera levels
(Figure 2). Overall, the phylum Actinobacteria was observed to have high relative abun-
dance in both colostrum and milk samples in all groups over time (Figure 2A). The next
most abundant phylum was Proteobacteria, followed by Firmicutes and Bacteroidetes
(Figure 2A).
Antibiotics 2023, 12, x FOR PEER REVIEW 4 of 18
points between groups and time-points. * p-value ≤ 0.05; ** p-value ≤ 0.01; *** p-value ≤ 0.001; **** p-
value ≤ 0.0001.
Similar to distinct clustering of the NOAB group from antibiotic-treated groups over-
all, we also observed clustering based on farms, where farm “C” (i.e., the farm without
DCT) clusters separately from the other antibiotic-treated farms (farm “D” and “P” are
CEF, while farm “L” and “T” are UBRO). Furthermore, samples from nulliparous cows,
which corresponded to the NOAB group, showed a similar clustering paern because of
an overlapping between the antibiotics groups and parity (Figure S2A–C, Table S1). Based
on PERMANOVA tests, the variable farms and antibiotics were the most explanatory var-
iables regarding beta-diversity (Bray–Curtis distance) in our dataset (PERMANOVA R2 =
0.15 and R2 = 0.11, reciprocally). To test the interactions between antibiotics and farms, we
performed a PERMANOVA on the two most explanatory grouping variables. The results
showed that grouping based on antibiotic exposure explains a higher variance compared
to farms (R2 = 0.11 and 0.03 and p-values = 0.001 and 0.02 for group and farm study varia-
bles, respectively; PERMANOVA using Bray–Curtis distance ~ Antibiotic groups + farm).
These results considering the effect of farm variables must be interpreted cautiously due
to the low subject size for each farm.
2.2. Taxonomic Composition Is Associated with Dry Cow Therapy Treatment
Differences in the relative abundance of the microbial composition of milk between
the three groups at all time points were observed using plots at phyla and genera levels
(Figure 2). Overall, the phylum Actinobacteria was observed to have high relative abun-
dance in both colostrum and milk samples in all groups over time (Figure 2A). The next
most abundant phylum was Proteobacteria, followed by Firmicutes and Bacteroidetes
(Figure 2A).
Figure 2. (A) Phylum level distribution of taxa in all three groups across all time points. (B) Overall
top 10 genera in all three groups (NOAB, UBRO and CEF) at all time-points. (C) Trend plots at
Figure 2. (A) Phylum level distribution of taxa in all three groups across all time points. (B) Overall
top 10 genera in all three groups (NOAB, UBRO and CEF) at all time-points. (
C
) Trend plots at genera
level, showing relative abundance of top 10 genera from all three groups (NOAB, UBRO and CEF)
over time with confidence interval of 95% (with different Y-axis scale for each taxa).
The top 10 genera found in milk from each group over all time points were examined
and several genera identified in cow milk in our study include Acinetobacter,Brevibacterium,
Corynebacterium,Lactobacillus,Lactococcus,Microbacterium,Pseudomonas,Propionibacterium,
Kocuria and Staphylococcus (Figure 2B). In the NOAB group, the genera Actinoallotecihus,
Corynebacterium,Brachybacterium and Microbacterium were amongst the top 10 over all time
points. Brachybacterium and Brevibacterium showed an increase in the no-antibiotic group,
compared with the antibiotic-treated groups. Further, Corynebacterium was found in both
Antibiotics 2023,12, 1315 5 of 16
antibiotic-administered and NOAB groups with higher initial abundance in the NOAB
group. Actinoalloteichus was also present in all three groups in high relative abundance at
M0 and showed a declining trend with time of lactation (Figure 2C). A higher abundance
of Acinetobacter was observed in the antibiotic-administered groups compared to NOAB.
Additionally, Rhodococcus,Ottowia and Lactobacillus were observed in higher relative abun-
dance in both the antibiotic-treated groups and demonstrated an increasing trend over time
of lactation. Pseudomonas,Microbacterium,Corynebacterium and Kocuria were also present at
all time points in all three groups, with Kocuria and Microbacterium increasing over lactation
time in the antibiotic-treated groups.
A closer look at the relative abundance of major mastitis-causing pathogens in dairy
cows revealed a lack of high relative abundance of potential pathogens such as Escherichia
and Staphylococcus in the non-antibiotic group at M0 and M2 (Figure 3A). The relative
abundance of Streptococcus was close to zero in all three groups (plot not shown). The
relative abundance of Corynebacterium was higher in the NOAB group than the CEF group
at M0, and higher than both antibiotic-treated groups at M2 (Figure 3A).
Antibiotics 2023, 12, x FOR PEER REVIEW 6 of 18
Figure 3. (A) Boxplots showing abundance of potential mastitis-causing pathogens in milk of all
three groups (NOAB, UBRO and CEF) over time of lactation. Significance was determined using
Wilcoxon test in R, and p.adj values < 0.05 are considered significant. The black dots correspond to
the outliers. (B) Songbird differentials obtained using the formula C (Group, Treatment(‘NOAB’))
were sorted by ranks and the top 10 positive and negative features are ploed and depicted using
bar charts with NOAB groups as reference (negative here is more associated with reference group—
NOAB here).
2.3. Impact of Dry Cow Therapy on Antibiotic Resistance Gene Reservoir
RGI-bwt was used to predict antibiotic resistance genes in filtered and quality-con-
trolled shotgun sequencing reads. ARGs belonging to 189 AMR gene families conferring
resistance to 43 different classes of antibiotics were found. Of these, several genes con-
ferred resistance to more than one class and genes conferring resistance to three or more
classes were termed multi-drug-resistance (MDR) genes (Figure 4A). Further, resistance
was also observed for classes not administered in the present cycle of DCT with a very
high abundance of MDR genes (Figure 4B).
Figure 3.
(
A
) Boxplots showing abundance of potential mastitis-causing pathogens in milk of all
three groups (NOAB, UBRO and CEF) over time of lactation. Significance was determined using
Wilcoxon test in R, and p.adj values < 0.05 are considered significant. The black dots correspond to
the outliers. (
B
) Songbird differentials obtained using the formula C (Group, Treatment(‘NOAB’))
were sorted by ranks and the top 10 positive and negative features are plotted and depicted using bar
charts. NOAB group is reference (negative here is more associated with reference group—NOAB
here) and (
a
) and (
b
) are top 10 positive and negative associations with treatment group CEF and
UBRO respectively.
Antibiotics 2023,12, 1315 6 of 16
Relative associations of genera to each group were estimated with Songbird [
36
] using
the formula: C(Group, Treatment(‘NOAB’)) (Figure 3B). Upon comparing the antibiotic-
treated group with the no-antibiotic group, Brevibacterium,Brachybacterium,Serratia and
Gordonibacter were more associated with the NOAB group, while Ottowia,Kocuria,Rhodod-
coccus,Microbacterium and Pseudomonas were more associated with the antibiotic-treated
groups. A comparison of the differentially abundant genera between the antibiotic-treated
groups (UBRO and CEF) did not show any pattern, highlighting a stronger association of
any genera to a particular antibiotic group.
2.3. Impact of Dry Cow Therapy on Antibiotic Resistance Gene Reservoir
RGI-bwt was used to predict antibiotic resistance genes in filtered and quality-controlled
shotgun sequencing reads. ARGs belonging to 189 AMR gene families conferring resistance
to 43 different classes of antibiotics were found. Of these, several genes conferred resistance
to more than one class and genes conferring resistance to three or more classes were termed
multi-drug-resistance (MDR) genes (Figure 4A). Further, resistance was also observed for
classes not administered in the present cycle of DCT with a very high abundance of MDR
genes (Figure 4B).
Antibiotics 2023, 12, x FOR PEER REVIEW 7 of 18
Figure 4. (A) Boxplot showing log-transformed abundance of the different classes of antibiotics to
which resistance was observed in this study. (B) Bar plot showing representation of ARGs in all
three groups (NOAB, UBRO and CEF) over all time-points. The classes highlighted in blue boxes
correspond to the class of antibiotics administered to the UBRO group, while those in red boxes
correspond to the CEF group.
The alpha diversity of ARGs over time was not different in milk samples from all
groups (Shannon index), while the CEF and NOAB groups showed significant differences
(Chao1 index), pointing towards higher richness in the CEF group. No significant differ-
ences were observed between groups or between time points in individual groups (Figure
5A). Beta diversity analysis showed a discrete clustering of milk microbiota between the
CEF and NOAB groups (p.adj = 0.03, pairwiseAdonis) (Figure 5B). No significant cluster-
ing was observed at any individual time points except M6 (p.adj values: NOAB vs. CEF:
0.039, NOAB vs. UBRO: 0.015, pairwiseAdonis) (Figure 5C).
Figure 4.
(
A
) Boxplot showing log-transformed abundance of the different classes of antibiotics to
which resistance was observed in this study. (
B
) Bar plot showing representation of ARGs in all
three groups (NOAB, UBRO and CEF) over all time-points. The classes highlighted in blue boxes
correspond to the class of antibiotics administered to the UBRO group, while those in red boxes
correspond to the CEF group.
Antibiotics 2023,12, 1315 7 of 16
The alpha diversity of ARGs over time was not different in milk samples from all
groups (Shannon index), while the CEF and NOAB groups showed significant differences
(Chao1 index), pointing towards higher richness in the CEF group. No significant dif-
ferences were observed between groups or between time points in individual groups
(Figure 5A). Beta diversity analysis showed a discrete clustering of milk microbiota be-
tween the CEF and NOAB groups (p.adj = 0.03, pairwiseAdonis) (Figure 5B). No significant
clustering was observed at any individual time points except M6 (p.adj values: NOAB vs.
CEF: 0.039, NOAB vs. UBRO: 0.015, pairwiseAdonis) (Figure 5C).
Antibiotics 2023, 12, x FOR PEER REVIEW 8 of 18
Figure 5. (A) Alpha diversity with Shannon index shows no difference between groups (NOAB,
UBRO and CEF) at any time points for ARG abundance. (B) Beta diversity of ARGs using PCoA and
Bray–Curtis distance. (C) Beta diversity of ARGs at each time point using PCoA plot with Bray–
Curtis distance.
The CEF group showed the highest ARG abundance, with both antibiotic-treated
groups showing a higher ARG abundance compared to the NOAB group (Figure S3). The
abundance of genes conferring resistance to the drug class cephalosporin at M0 was
higher in abundance in the CEF group. Multiple comparisons between groups (NOAB
group as control) were performed for each class and high resistance was found in the an-
tibiotic groups (Figure 6).
Figure 5.
(
A
) Alpha diversity with Shannon index shows no difference between groups (NOAB, UBRO
and CEF) at any time points for ARG abundance. (
B
) Beta diversity of ARGs using PCoA and Bray–Curtis
distance. (C) Beta diversity of ARGs at each time point using PCoA plot with Bray–Curtis distance.
The CEF group showed the highest ARG abundance, with both antibiotic-treated
groups showing a higher ARG abundance compared to the NOAB group (Figure S3). The
abundance of genes conferring resistance to the drug class cephalosporin at M0 was higher
Antibiotics 2023,12, 1315 8 of 16
in abundance in the CEF group. Multiple comparisons between groups (NOAB group
as control) were performed for each class and high resistance was found in the antibiotic
groups (Figure 6).
Antibiotics 2023, 12, x FOR PEER REVIEW 9 of 18
Figure 6. Heatmap showing log-transformed abundance (cpm) of various ARG classes per group at
each timepoint. Significance was calculated with NOAB group as reference group against each an-
tibiotic-treated group using Dunn test for multiple comparisons. P.adj values below 0.05 were con-
sidered significant. * p-value ≤ 0.05; ** p-value ≤ 0.01; *** p-value ≤ 0.001; **** p-value≤ 0.0001.
3. Discussion
In this study, we compared the microbial and resistome profiles of colostrum and
milk from healthy cows throughout lactation, separated into three groups treated with
differing dry cow therapies—two antibiotic-treated groups and one non-antibiotic-treated
group using shotgun metagenomics. Studying the bovine milk microbiome is considered
challenging as it is a low-biomass sample, difficult to collect without contamination, along
with the possibility of kit contaminants, with an inter-individual variation in microbiota
[6], different farming practices [37] and a lack of studies to develop a standardised proto-
col. Shotgun metagenomics is rarely used to study the resistance paern of the microbiota
in dairy colostrum and milk; however, this approach provides high resolution and allows
functional profiling [38]. The antibiotics used were Cephaguard (cefquinome—fourth-
Figure 6.
Heatmap showing log-transformed abundance (cpm) of various ARG classes per group
at each timepoint. Significance was calculated with NOAB group as reference group against each
antibiotic-treated group using Dunn test for multiple comparisons. P.adj values below 0.05 were
considered significant and denoted with *.
3. Discussion
In this study, we compared the microbial and resistome profiles of colostrum and
milk from healthy cows throughout lactation, separated into three groups treated with
differing dry cow therapies—two antibiotic-treated groups and one non-antibiotic-treated
group using shotgun metagenomics. Studying the bovine milk microbiome is considered
challenging as it is a low-biomass sample, difficult to collect without contamination, along
with the possibility of kit contaminants, with an inter-individual variation in microbiota [
6
],
Antibiotics 2023,12, 1315 9 of 16
different farming practices [
37
] and a lack of studies to develop a standardised protocol.
Shotgun metagenomics is rarely used to study the resistance pattern of the microbiota in
dairy colostrum and milk; however, this approach provides high resolution and allows func-
tional profiling [
38
]. The antibiotics used were Cephaguard (cefquinome—fourth-generation
cephalosporin) and Ubro red (active ingredient Framycetin Sulphate, Penethamate Hydroio-
dide and Procaine Penicillin), which are broad-spectrum antibiotics. Their target species
include major mastitis-causing organisms such as Streptococcus spp. (uberis,dysgalactiae,
agalactiae) and Staphylococcus aureus, which are coagulase-negative staphylococci for Cepha-
guard, while UBRO red targets Staphylococcus spp., Streptococcus spp., Corynebacterium spp.,
Escherichia spp., Klebsiella and Pseudomonas spp. The European Medicine Agency (EMA)
classifies cefquinome as a category B (restricted use) antibiotic, while Framycetin is to be
used with caution [39].
This study demonstrated significant differences in the diversity of microbial genera
within the antibiotic-treated groups over the time points (Shannon diversity) similar to [
40
].
Similar results of increasing microbial diversity were also observed by Hermansson et al.,
(2019) [
41
] in breast milk of mothers treated with antibiotics, though the reason for it was
unknown. However, the transition from colostrum to milk in the non-antibiotic group
did not demonstrate significant changes in microbial diversity over time. This suggests
that low initial microbial diversity in the antibiotic-groups could be due to antibiotic
use. Further, the diversity between groups was significantly different at M2, M4 and M6
(beta diversity), with the NOAB group clustering distinctly compared to antibiotic-treated
groups. Additionally, we do not report a high prevalence of mastitis-causing pathogens
such as Escherichia spp., Streptococcus spp. and Staphylococcus spp. in the groups, especially
in the NOAB group. Similar to other studies [
40
,
42
–
44
], our data support the use of only
teat sealants for drying off, reducing antibiotic use.
The top 10 genera observed in our study were similar to those reported by
earlier studies and include Acinetobacter, Brevibacterium, Corynebacterium, Lactobacillus,
Lactococcus, Microbacterium, Pseudomonas, Propionibacterium, Kocuria and Staphylococcus
(Figure 2B) [
32
,
43
,
45
–
50
]. Acinetobacter was high in antibiotic-treated groups while Corynebac-
terium was higher in the no-antibiotic group. The presence of Corynebacterium in the top 10
throughout lactation in the NOAB group is not unusual as it is thought to be controlled by
various anti-sepsis measures [
51
,
52
]. Its high prevalence could also be because it is observed
to be a dominant bacterium on teat skin [
53
]. Some studies suggest an association between
Corynebacterium and intramammary infections, suggesting its role as a mastitis-causing
pathogen, though this association is mainly with Corynebacterium bovis, while most other
species are reported to be present in environmental niches [
54
]. Others suggest that the
high abundance of Corynebacterium may provide protective effects against infections caused
by major mammary gland pathogens such as Staphylococcus and Streptococcus [
55
,
56
]. To
confirm the role of the high relative abundance of Corynebacterium in the NOAB group,
further studies including a mix of culture and sequencing-based methods are needed.
Actinoalloteichus was observed to have high relative abundance in all groups in the study.
Actinoalloteichus was found in milk aliquots in a recent study [
46
], but not many other
studies have reported its presence. Actinoalloteichus is reported to be isolated from marine
sponges [
57
], seashores [
58
] and soil [
59
] and is considered to be a source of secondary
metabolites due to the presence of several secondary metabolite biosynthetic gene clus-
ters [
60
]. The high relative abundance of this genus in all three groups is surprising, and
further studies are needed on Irish farms to study its origins.
The microbial diversity in milk from cows is affected by several factors. For in-
stance, [
45
] demonstrated changes in microbial diversity longitudinally. The authors
observed an increased abundance of Streptococcus and Kocuria over the summer months.
The antibiotic-treated groups in our study show an increasing trend of Kocuria over lac-
tation time, and these time points overlap with summertime in the given geographical
area. Similarly, parity of the cow [
61
] and seasonal housing (indoor or outdoor sampling
and housing) [
62
] can impact the milk microbial composition. Stage of lactation, milking
Antibiotics 2023,12, 1315 10 of 16
hygiene, bedding material and feeding habits affect cow milk microbial composition and
diversity [
37
,
48
,
53
,
63
]. For instance, Doyle et al. (2017) [
62
] observed that Acinetobacter and
Pseudomonas were in lower proportions in milk from indoor-housed animals as compared
to outdoor milk samples. An increase in these genera over M2 and M4 was observed in this
study, which could be attributed to the fact that once the cows calve and are put on grass,
they are outdoors full-time (later time points—M4 and M6—in our study correspond to
outdoor samples). However, Acinetobacter and Pseudomonas are also psychrotrophic and ob-
served in stored bulk milk samples [
47
] and are common dairy environment contaminants;
thus, further studies are needed to investigate their origin in raw quarter milk samples.
Another possible explanation as described by Oikonomou et al. (2020) [
5
] is that the milk
microbiota was not alive and DNA from dead bacteria was thus not affected by treatments.
As reported earlier, we observed the presence of ARGs in cow colostrum; however,
changes in ARG diversity over time were not observed in any group in this study. There
are certain antibiotic resistance genes (ARGs) found in the NOAB group, while other
ARGs that do not belong to commonly used antibiotic classes might be attributed to past
antibiotic exposure or environmental factors such as contaminated water or manure, as
well as horizontal gene transfer within the herd. Further, a high abundance of ARGs
in the antibiotic-treated groups was observed. Certain genes conferring resistance to
classes like beta-lactams, penams and cephalosporin and MDR genes were observed. The
presence of ARGs (those that confer resistance to the antibiotic in question) in the antibiotic-
treated groups can be justified due to the use of antibiotics, while many other resistance
genes conferring resistance to drug classes like tetracycline, intercalating dye, disinfecting
agents, triclosan and glycopeptide antibiotics could be due to contaminating bacteria
from environmental sources such as irrigation [
64
], groundwater [
65
,
66
], slurry waste [
67
],
manure and interaction with other animals in the herd [
68
,
69
]. This high ARG abundance
detected in the antibiotic-treated groups could also be a result of the selection of MDR
bacteria possessing multiple ARGs post-antibiotic use. Certain resistance could be due
to previous antibiotic use and might be horizontally transferred between bacteria over
time. The presence of ARGs in colostrum could result in the transfer to calves, leading
to the further spread and dissemination of ARGs in the environment. Thus, this study
lends further support to reducing the use of antimicrobials in dairy cows as well as their
cautionary use in other farm practices, to reduce the development and spread of antibiotic-
resistant bacteria.
The limitations of this study include the relatively small number of animals included
per group, although we would emphasise that these animals were all followed longitu-
dinally for six months of lactation. Another limitation of the study was the long sample
intervals, which spans various seasonal and feeding variations; however, all cows belonged
to the same geographic location; thus, the effect of these variables, if any, was similar
throughout the dataset. We suggest that this study demonstrates the value of conducting
temporal microbial and resistome studies on bovine colostrum and milk microbiota to
examine the effect of DCT and antibiotic use. More studies with larger cohort sizes and
shorter between-sample intervals are needed to understand the longitudinal effect of an-
tibiotics on bovine colostrum and milk samples. This will help reduce antibiotic use in
livestock and decrease the generation and transfer of ARGs.
4. Methods
4.1. Enrolment Criteria and Treatment for Cows
Colostrum and subsequent milk samples at month 2 (M2), month 4 (M4) and month 6
(M6) were collected from healthy cows from five different Irish farms between February
and September 2020 as detailed below. All cows were housed in different herds in farms
located in County Cork, Ireland. The farms included in our study were labelled farm D,
farm P, farm L, farm T and farm C. Cows from each farm were selected randomly and
divided into three groups based on the DCT treatment. Cows on all farms were treated
with a teat sealant—Boviseal—in addition to antibiotics as described during the drying-off
Antibiotics 2023,12, 1315 11 of 16
period. NOAB did not use any antibiotics during the drying-off period (natural drying off
using teat sealant) and was assigned as the control group. The UBRO group was treated
with blanket DCT using Ubro Red Dry Cow Intramammary Suspension at drying off. The
CEF group was treated with blanket DCT using Cephaguard DC 150 mg intramammary
ointment at drying off. Twenty-four cows divided into three groups were included in the
study (NOAB: n= 9, CEF: n= 8, UBRO: n= 7), with four samples collected longitudinally
from each cow throughout the first six months of lactation. In terms of the farm-wise
distribution of the cows included in our study, all NOAB cows were from a single farm,
Farm C (n= 9), while farm D and P constituted the CEF group and had n= 4 and n= 4,
respectively. Farm L and T formed the UBRO group and had n= 5 and n= 2, respectively.
Farms D and P reported routinely using Cephaguard while farms L and T routinely used
Ubro red therapy. Cows showing signs of mastitis or any other infections were excluded
from the study and milk samples were collected only from healthy cows. Cows were not
subjected to antibiotic treatment during the time interval of sample collection.
4.2. Sample and Data Collection
All farms and personnel who collected samples included in this study were quality-
assurance-certified members of the Irish Food Board—Bord Bia. Standard recommen-
dations from the National Mastitis Council’s Laboratory Handbook on bovine mastitis
were followed for sample collection [
70
]. Instructions to farmers were provided for sterile
sample collection. Briefly, teats were disinfected with iodine tincture, dried and thoroughly
disinfected with wipes soaked in 70% alcohol. Initial milk streams were discarded and
then 15 mL of colostrum within the first hour post partum and milk samples from the front
right quarter of each cow’s udder were collected into sterile 15 mL falcon tubes (Sarstedt)
without preservatives. Colostrum (M0), and milk samples at month 2 (M2), month 4 (M4)
and month 6 (M6) were collected from each cow. The samples were immediately frozen at
−
20
◦
C in chest freezers on the farms. Within a week of sample collection, the samples were
transported to the laboratory where they were stored at
−
80
◦
C until further use. DNA
extractions were performed within six months of sample collection. Metadata regarding
the parity of cows, any previous infections, the treatment used for DCT and any other
medications were collected from the farms (Table S2).
4.3. DNA Extraction
Milk samples were thawed by placing them at 4
◦
C the night prior to DNA extractions.
The samples were homogenised by inverting the tubes and centrifuged at 4000
×
gfor
30 min at 4
◦
C. The fatty/cream layer that accumulated at the top was discarded using
sterile cotton swabs and the supernatant was discarded. DNA was extracted from the
pellet according to the manufacturer’s instructions using the PowerFood microbial DNA
Isolation Kit (MoBIO Laboratories Inc., Carlsbad, CA, USA) with modifications as follows:
Briefly, the cell pellets were washed with PBS (phosphate-buffered saline) (Sigma Aldrich,
St. Louis, MO, USA) and centrifuged at 13,000
×
gfor one minute at 20
◦
C to discard
the supernatants. This process was repeated until the supernatant was no longer cloudy.
The pellet was then re-suspended in PBS and treated with 90
µ
L of 50 mg/mL lysozyme
(Sigma Aldrich, Lysozyme activity:
≥
40,000 units/mg protein) and 50
µ
L of 5 KU/mL
mutanolysin (Sigma Aldrich) followed by 15 min incubation at 55
◦
C with vortexing at
intervals of 5 min. Further, samples were treated with Proteinase k (Qiagen, UK, 28
µ
L of
20 mg/mL, >600 mAU/mL), incubated at 55
◦
C for 15 min and then treated according to
the manufacturer’s instructions using the PowerFood microbial DNA Isolation Kit protocol.
The extracted DNA was stored at −30 ◦C until further use.
4.4. Amplification of DNA, Library Preparation and Shotgun Metagenomics Sequencing
The quantification of DNA was performed using the Qubit double-stranded DNA
(dsDNA) high-sensitivity assay kit (Invitrogen). Some samples had less than 0.2 ng/
µ
L of
DNA; thus, a modified low-input Nextera XP protocol was used for shotgun sequencing
Antibiotics 2023,12, 1315 12 of 16
library preparation as described previously [
71
]. Briefly, 5
µ
L of DNA was used for the
tagmentation run along with 10
µ
L of Tagment DNA (TD) buffer and 5
µ
L of 1:10 diluted
Amplicon Tagment Mix (ATM), making the total reaction volume 20
µ
L. The samples were
incubated at 55
◦
C for 5 min on the thermal cycler for tagmentation reaction, with the
immediate addition of 5
µ
L of NT (Neutralise Tagment) buffer to stop the tagmentation.
Subsequent amplification was performed using 20 amplification cycles to ensure high
quantities of amplified product for downstream reactions. A 1.6
×
ratio of Ampure XP
beads was used to purify the libraries and the purified product was eluted in 20
µ
L of re-
suspension buffer. The quality of purified DNA was determined using the High-Sensitivity
DNA kit on the Agilent Bioanalyzer. The amplified DNA was quantified using Qubit
and the samples were then pooled and outsourced for sequencing to the Teagasc Next-
Generation DNA Sequencing Facility (Teagasc, Ireland).
4.5. Bioinformatics and Statistical Analysis
Raw metagenomics shotgun reads received post sequencing were quality-checked
using FastQC (0.11.8) and MultiQC (v1.9). The reads were then filtered and trimmed using
Trim galore (v0.6.1) and Bowtie2 (v 2.4.4) using the Bos taurus reference genome database
from NCBI (Genome assembly ARS-UCD1.3) to remove any host contaminant reads. Reads
obtained post-host removal step were considered microbial and were used for further analy-
sis. The filtered and trimmed reads were then used for taxonomic assignment with Kraken2
(v2.1.1) and kraken-biom (https://github.com/smdabdoub/kraken-biom). Further, antibi-
otic resistance analysis was performed using RGI (Resistance Gene Identifier) (RGI-bwt)
(v3.1.4) and CARD (Comprehensive Antibiotic Resistance Database) [72]. The RGI results
were normalised to copies per million based on read counts using all mapped reads from
the gene mapping output file of RGI-bwt. The results were further log-transformed and
the abundance of ARGs at each time point was visualised using the heatmap, boxplots
and bar plot in R. Songbird [
36
], using the formula C (Group, Treatment (‘NOAB’)), was
used to generate differentials that described the association of taxa to the study groups.
Songbird provides a comparatively greater ability to detect associations in compositional
data analysis methods [
73
]. The differentials obtained (Table S3) were ordered by rank
and the top 10 most positive and negative genera were plotted and visualised using R
(Figure 3B). Negative coefficient values denote stronger associations of the feature (genera
in our analysis) with the reference. The phyloseq object [
74
] formed from Kraken2 output
was normalised and filtered to keep reads with an abundance greater than 5
×
10
−4
reads
per taxa, and those belonging to viruses, Euryarchaeota and Archaea, and not classified at
phylum level were removed. Initially, 5194 taxa were obtained, which were filtered down
to 235 taxa. Stringent filtering parameters were applied to make sure that no taxa arising
as contaminants/singletons remain in downstream analysis. Further, decontam package
(v1.14.0) in R was used to look for contaminants in samples and remove them. Using the
default parameters, no PCR blank reads were observed in any of the study samples. The
negative control sample (with less than 500 reads) was thus excluded from further analysis
and the phyloseq object with all study samples was used for further analysis. Down-
stream analysis was performed in R (v4.0.2) using packages phyloseq (v1.38.0), microbiome
(v1.16.0) and ggplot2 (v3.3.2). Shannon and Chao1 diversity indices were used to calculate
the alpha diversity of the microbiota within the groups. The Shannon diversity index pro-
vides details about richness and evenness while the Chao1 index estimates total richness
only. The Wilcoxon test was performed to test if the difference between any two groups or
time points was significant. Beta diversity analysis was performed using the Bray–Curtis
dissimilarity matrix to determine compositional dissimilarities between the samples based
on group, time, farm and parity variables in the study. The Bray–Curtis distance matrix
takes into account abundance/occurrence data and does not rely solely on the presence
or absence of data. The impact of various study variables on the microbiota diversity was
examined using permutational multivariate analysis of variance (PERMANOVA). Statistical
analysis was also performed in R using the Wilcoxon test and Dunn test with the function
Antibiotics 2023,12, 1315 13 of 16
stat_compare_means and stat_pwc from package ggpubr (v0.4.0) and Permanova using the
pairwiseAdonis (v0.4) package. p-values were adjusted using the false discovery rate (fdr)
method in R unless specified otherwise.
5. Conclusions
Antibiotic treatment during DCT was associated with high abundances of ARGs in
milk compared to the non-antibiotic control group, and altered microbial composition
throughout lactation. Using teat sealant without antibiotics did not lead to the presence
of a high abundance of mastitis-causing pathogens. Furthermore, the microbial diversity
(alpha diversity) in antibiotic-treated groups increased over time, which was not observed
in the non-antibiotic group. The reason for this is unknown; however, it could be due to
effect of antibiotics on highly abundant bacteria, leaving a higher diversity of unaffected
species. The presence of ARGs at high abundance in cow colostrum and milk is alarming
because of the threat of passage of ARGs to the calf and to humans through the food chain.
Both previous and current antibiotic use may result in the selection of bacteria that carry
resistance genes, thus increasing the overall resistome profile diversity of the microbiota.
It is challenging to trace the source of bacteria and thus the resistance genes in question;
thus, preventive and protective approaches to reduce the use of antibiotics on farms and
associated resources is crucial.
Supplementary Materials:
The following supporting information can be downloaded at: https://www.
mdpi.com/article/10.3390/antibiotics12081315/s1, Figure S1: Alpha diversity for microbial composition
as calculated by Chao1 index A) Between groups and B) Between time points; Figure S2: PCoA using
Bray-Curtis dissimilarity measure showing distinct clustering based on A. Farms, B. Study groups and
C. Parity; Figure S3: Boxplot showing overall ARG abundance in all three groups. P.adj values were
calculated using Dunn test in R, and p.adj values below 0.05 were considered significant; Table S1: Table
showing PERMANOVA results for tests based on farms, study groups and parity Table S2: Metadata
showing details of all cows included in this study; Table S3: Table showing coefficients obtained from
differential abundance analysis using Songbird tool with formula C (Group, Treatment (0NOAB0)).
Author Contributions:
Conceptualization, C.S. and R.P.R. and D.V.P.; methodology, D.V.P.; software,
D.V.P. and G.G.; validation, D.V.P., G.G..; formal analysis, D.V.P. and G.G.; investigation, C.S. and
R.P.R.; resources, K.L.; data curation, D.V.P. and G.G; writing—original draft preparation, D.V.P.;
writing—review and editing, D.V.P. and G.G. and K.L and C.S. and R.P.R.; visualization, D.V.P. and
G.G.; supervision, C.S. and R.P.R.; project administration, C.S., R.P.R.; funding acquisition, C.S. and
R.P.R. All authors have read and agreed to the published version of the manuscript.
Funding:
This research was co-funded by Science Foundation Ireland (SFI) under Grant Number
SFI/ 12/RC/2273_P2 and Vistamilk and the European Union (ERC, BACtheWINNER, 101054719).
Views and opinions expressed are however those of the author(s) only and do not necessarily reflect
those of the European Union or the European Research Council. Neither the European Union nor the
granting authority can be held responsible for them.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement:
The data generated by shotgun sequencing as a part of this study were
submitted to NCBI and are available under the project number PRJNA945243.
Acknowledgments:
We would like to extend our thanks to the farmers who assisted with the
sample collections.
Conflicts of Interest: The authors declare no conflict of interest.
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