ABSTRACT
The gut microbial ecosystem underlies physiological relationships
between the gut and distal organs. Mechanisms remain elusive but rely
at least partially on the production of a diverse set of absorbable
metabolites and host gene expression regulation. Here we show that in
female mice, gut cecal microbiota profiles are related to microRNAs
(miRNAs) expressed in the mammary gland. A subset of these miRNAs were
found to regulate genes involved in breast cancer-related processes,
such as cell proliferation and migration. To determine if these
relationships could be exploited toward the reduction of breast cancer
risk, we studied if they are modifiable by dietary flaxseed (FS), a
source of lignan secoisolariciresinol diglucoside (SDG) and
alpha-linolenic acid (ALA)-rich oil (FSO), both with antitumor effects.
Importantly, SDG, but not ALA, needs microbial processing to release
bioactive metabolites. We found that the microbiota and mammary gland
miRNA are related, and FS modifies these relationships toward an
antioncogenic phenotype. FSO- and SDG-related miRNAs were found to be
involved in different pathways and neither FSO nor SDG alone could
recapitulate the effects of whole FS, affecting unique pathways related
to extracellular matrix processing. These findings highlight the
existence of inter-organ microbiota-miRNA relationships, show that
dietary interventions interact to affect them, and suggest a novel
route for breast cancer prevention.
IMPORTANCE
Breast cancer is a leading cause of cancer mortality worldwide. There
is a growing interest in using dietary approaches, including flaxseed
(FS) and its oil and lignan components, to mitigate breast cancer risk.
Importantly, there is recognition that pubertal processes and
lifestyle, including diet, are important for breast health throughout
life. Mechanisms remain incompletely understood. Our research uncovers
a link between mammary gland miRNA expression and the gut microbiota in
young female mice. We found that this relationship is modifiable via a
dietary intervention. Using data from The Cancer Genome Atlas, we also
show that the expression of miRNAs involved in these relationships is
altered in breast cancer in humans. These findings highlight a role for
the gut microbiome as a modulator, and thus a target, of interventions
aiming at reducing breast cancer risk. They also provide foundational
knowledge to explore the effects of early life interventions and
mechanisms programming breast health.
KEYWORDS: gut-breast axis, flaxseed, microbiota, microRNA, breast
cancer, lignan
INTRODUCTION
The gut microbiota is a bacteria-dominated community of trillions of
microorganisms that utilize dietary components to produce bioavailable
metabolites that affect host health. These metabolites at least
partially underlie the relationship between the gut and distal organs
([28]1). The role of microbial flaxseed (FS)-derived enterolignans in
the context of the gut-breast axis exemplifies this concept. FS is the
richest dietary source of the lignan secoisolariciresinol diglucoside
(SDG) ([29]2). The gut microbiota is necessary to convert SDG into the
enterolignans enterodiol (ED) and enterolactone (EL) via the
complementary action of members of the genera Bacteroides, Clostridium,
Eubacteriaceae, Peptostreptococcus, Eggerthella, and Enterobacter
([30]3). In mice, taxa such as the genus Lactobacillus and the family
Coriobacteriaceae have been positively correlated with serum ED and EL
levels ([31]4). In humans, dietary interventions with lignans
significantly increase urinary enterolignan excretion and are
positively associated with microbial taxa such as the genus
Ruminococcus, Roseburia, and members of the family Lachnospiraceae
([32]5). High concentrations of circulating lignans have been
associated with reduced mortality for breast cancer in postmenopausal
women ([33]6 [34]– [35]9). ED and EL are phytoestrogens that are
thought to have protective effects in postmenopausal breast cancer,
altering proliferation and apoptotic biomarkers as well as c-erbB2
expression in a clinical trial ([36]10). Urinary EL has been inversely
associated with proteins involved in pro-proliferative and apoptotic
pathways, such as the PI3k-Akt pathway ([37]11). Interestingly, we
found that microbiota metabolic activity, including that of non-lignan
metabolizers, is important for lignan processing ([38]4). This aligns
with the suggestion that the gut microbiota is a viable target to
increase the effectiveness of breast antitumor drugs ([39]12).
Importantly, beyond providing lignans, FS is an important source of
fiber, protein, and oil (FSO). FS fiber and oil have been shown to
affect microbiota composition in the gut ([40]13, [41]14). FSO, in
particular, is among the highest plant-based sources of alpha-linolenic
acid (ALA), which delays mammary tumor onset, reduces tumor growth, and
tumor proliferation ([42]15 [43]– [44]17). Thus, breast anticancer
properties of FS may be the results of the combined action of its
components in the whole food, or the single components may act
independently. Interestingly, we found that FS, FSO, and SDG provided
in equal amounts as in FS, generate specific microRNA (miRNA) responses
in the mammary gland ([45]18). MiRNAs are short, non-coding RNAs that
are epigenetic regulators of transcription and by extension,
translation. MiRNAs are thought to regulate up to 90% of all genes by
mainly targeting the 3′ untranslated region of target mRNAs, leading to
degradation or inhibition of translation ([46]19, [47]20). Modulation
of miRNA expression may be a mechanism underlying host response to
metabolic signals, including those generated from microbial
metabolites. We recently showed that developmental patterns of mammary
gland miRNAs may provide clues to their dysregulated role in breast
cancer ([48]21). Here, we determined if the mammary gland miRNome and
the gut microbiota are related, if this relationship is modified by
dietary FS, and if FS-associated miRNAs are altered in human breast
cancer. Then, we determined if FSO and/or SDG components are
responsible for FS effects.
RESULTS
We used microbiota and miRNA data that we previously generated from the
cecal content and mammary gland, respectively, of 3-week-old C57BL/6
mice receiving either a control basal diet (BD), or the same isocaloric
diet modified to contain FS or its corresponding amount of oil (FSO),
or SDG, for a total of four dietary groups ([49]Fig. 1A). To
investigate the effect of FS and its components on the gut
microbiome-mammary gland miRNA axis, here, cecal microbiota data were
analyzed by Analysis of Compositions of Microbiomes with Bias
Correction (ANCOM-BC) and mammary gland miRNA expression data were
analyzed via a generalized linear model to estimate fold-change using
the dedicated package NanoStringDiff ([50]22). Seventy-three taxa were
identified at the genus level as represented in at least one of the
four dietary groups (Table S1). Of these, 10, 12, and 15 taxa were
found to be differentially abundant between the BD–FS, FS–FSO, and
FS–SDG diet comparisons ([51]Fig. 1B). Sixty-eight, 71, and 29 miRNAs
were found to be differentially expressed between the BD–FS, FS–FSO,
and FS–SDG diets ([52]Fig. 1C).
Fig 1.
[53]Fig 1
[54]Open in a new tab
Approach to investigate the cecal microbiota-mammary gland miRNA
relationship in mice fed a BD (control), FS diet, FSO diet, and SDG
diet. (A) Fourteen C57BL/J mice were assigned to a BD, FS, FSO, or SDG
diet. Cecal content and mammary glands were collected. Cecal content
was Illumina-sequenced for the 16S rRNA V3-V4 region ([55]23).
Differential abundances of cecal content between diets were analyzed
with ANCOM-BC. Mammary gland miRNA expression was quantified with
Nanostring Technologies ([56]24). Differential expression of mammary
gland miRNAs was identified with NanoStringDiff ([57]22). Relationships
between cecal relative abundance and mammary gland miRNAs within diet
were quantified with Spearman correlations with n = 5, 6, 5, and 5 for
the BD, FS, FSO, and SDG diets due to paired-sample availability.
Diet-induced differences were characterized by differential gene
correlation analysis (DGCA) ([58]25). Gene targets of miRNAs in
diet-altered correlations were identified with miRdb ([59]26), and
enriched pathways using pathDIP ([60]27). Panel (A) created with
BioRender.com. (B) Differential abundance of taxa compared between BD
vs FS (red), FS vs FSO (blue), and FS vs SDG (yellow) (left to right)
diet groups. (C) Differential expression of mammary gland miRNAs
relative to FS as identified with NanoStringDiff between the BD and FS
diets, FS and FSO diets, and FS and SDG diets (top to bottom).
Cecal microbial taxa and mammary gland miRNA are linked
To understand if the cecal microbial taxa and mammary gland miRNA are
linked, we identified significant Spearman correlations between the
cecal microbiota and mammary gland miRNA expression in the BD group.
One hundred and seventy one correlations were found to be significant
(Fig. S1A). In the BD, 8 of the 10 most abundant taxa were found to be
significantly correlated with miRNAs which targeted genes related to
developmental processes such as collagen biosynthesis and modifying
enzymes, developmental biology, transcriptional regulation of
pluripotent stem cells, and protein digestion and absorption.
FS, FSO, and SDG modify cecal microbiota-mammary gland miRNA correlations in
a distinctive manner
In the FS, FSO, and SDG diets, respectively, 25, 139, and, 163
correlations between cecal microbiota and mammary gland miRNA were
found to be significant (q < 0.05) (Fig. S1B through D). To determine
if FS or its components affect the basal microbiota-miRNA relationship,
we found correlations that were significantly changed between diet
groups using differential gene correlation analysis (DGCA) ([61]25). FS
changed 5 BD correlations to positive, 6 correlations to negative, and
89 correlations were nullified (Fig. S2A). Of these changed
correlations, the family Enterobacteriaceae, Coriobacteriaceae,
Lachnospiraceae, and genus Lactobacillus were also found to be
differentially abundant in response to FS ([62]Fig. 1B, left). Compared
to BD, FSO changed 4 correlations to positive, 79 to negative, and 80
were nullified (Fig. S2D) and SDG changed 4 correlations to positive,
63 to negative, and 73 were nullified (Fig. S2E). Family
Coriobacteriaceae was found to be significantly decreased in the FSO
and SDG diet compared to the BD and was also found in the differential
correlations for both comparisons (Fig. S3).
FS effects on gut microbiota-mammary gland miRNA correlations are not
explained by its isolated FSO and SDG components
To investigate the extent to which FSO and SDG contribute to FS
effects, we compared microbiota-miRNA correlations between the FS–FSO
and FS–SDG diet groups. When compared to the FS group, only
correlations between family Ruminococcaceae and miRNAs-140 and 30e were
maintained in the FSO group; FSO changed 9 correlations to positive, 81
to negative, and 5 were nullified (Fig. S2B). Four taxa, including
family Clostridiaceae, Mogibacteriaceae, order RF32, and genus
Adlercreutzia ([63]Fig. 1B, middle), and 14 miRNAs were also found to
be differentially abundant between FSO and FS. When compared to the FS
group, 1 microbiota-miRNA correlation was maintained in the SDG group;
SDG changed 5 FS correlations to positive, 10 to negative, and 7 were
nullified (Fig. S2C). Family Clostridiaceae, Rikenellaceae,
Coriobacteriaceae, genus Anaerotruncus, and Clostridium in
significantly changed correlations were also found to be differentially
abundant ([64]Fig. 1B, right) and 5 miRNAs were also found to be
differentially expressed between SDG and FS. A comparison of FSO to SDG
revealed 9 correlations changed to positive, 65 changed to negative,
and 80 correlations nullified (Fig. S2F). No taxa were found to be
significantly different between the FSO and SDG diet groups.
FS alters relationships related to the PI3K-Akt-mTOR pathway
In order to understand the potential downstream effects of miRNAs
involved in the diet-altered correlations, miRdb was used to predict
miRNA gene targets. Predicted gene targets were used for gene
enrichment pathway analysis with pathDIP, which integrates 23 pathway
databases. Specifically, gene targets of the BD–FS, FS–FSO, and FS–SDG
comparisons were investigated. Between BD and FS, 3750 gene targets of
miRNAs in significantly changed correlations were found (Fig. S4A). Of
these, 2012 were unique gene targets, 161 of which were involved in 16
significantly enriched pathways (Fig. S5, top). Between FS and FSO,
2903 gene targets of significantly changed miRNAs were found (Fig.
S4B), with 1665 unique targets and 252 of these gene targets involved
in 22 significantly enriched pathways (Fig. S5, middle). Between FS and
SDG, 3996 gene targets of significantly changed miRNAs were found (Fig.
S4C), with 2491 unique targets. Of the unique gene targets, 87 were
involved in 11 significantly enriched pathways (Fig. S5, bottom). Three
pathways were shared between the BD–FS, FS–FSO, and FS–SDG comparisons:
the PI3K-Akt-mTOR, purine metabolism, and the regulation of RUNX1
expression and activity ([65]Fig. 2A, top). A network was constructed
to visualize these shared pathways ([66]Fig. 2B). There were 22 genes
in common between all three comparisons involved in the PI3K-Akt-mTOR
and purine metabolism pathways ([67]Fig. 2B) including Runx2 and Skp2.
Specifically, Runx2 has been associated with spatial and temporal
epithelial differentiation in the developing mammary gland and
metastatic breast cancer ([68]28). The taxa-miRNA correlation change
was in the same direction relative to the FS group for the
PI3K-Akt-mTOR pathway and the purine metabolism pathways ([69]Fig. 2A).
For the former, correlations changed from negative in the BD, FSO, and
SDG diets to null in the FS diet. For the latter, correlations changed
from null in the BD, FSO, and SDG diets to null in the FS diet.
Fig 2.
[70]Fig 2
[71]Open in a new tab
Shared and unique pathways significantly enriched by gene targets of
miRNAs in diet-changed microbiota-miRNA correlations for comparisons
between the BD–FS (red), FS–FSO (blue), and FS–SDG (yellow) diets. (A)
Significantly enriched pathways (q < 0.05) in response to genes
targeted by miRNAs (MiRTarget score >95) involved significantly changed
correlations in the BD–FS, FS–FSO, and FS–SDG diet comparisons (q <
0.05). + indicates a positive correlation, 0 a null correlation, and −
a negative correlation/indicates a correlation change between diets
(e.g., −/0 indicates a change from − correlation in one diet to 0
correlation in another diet). (B) A network representation of the
taxa-miRNA pairs with significantly changed correlations (q < 0.05)
between diet groups and their gene targets identified with miRdb
related to enriched pathways identified with miRDIP which were shared
between the BD–FS, FS–FSO, and FS–SDG comparisons. Red: unique to the
BD–FS comparison; blue: unique to the FS–FSO comparison;
yellow/lime-green: unique to the FS–SDG comparison; purple: in common
between BD–FS and FS–FSO comparisons; orange: in common between BD–FS
and FS–SDG comparisons; green: in common between FS–FSO and FS–SDG
comparisons; brown: in common between BD–FS, FS–FSO, and FS–SDG
comparisons. (C) qPCR quantification of Runx2 (top) and Skp2 (bottom),
members of the PI3k-Akt-mTOR pathway in the BD (n = 7), FS (n = 5), FSO
(n = 8), and SDG (n = 8) diets. Groups that are significantly different
(P < 0.05) are denoted with different letters (a–b) above each bar.
Gene expression related to the PI3K-Akt-mTOR pathway is reduced
To determine modulated miRNAs that may be affecting pathways altered by
the FS diet, we found the miRNAs that both targeted genes involved in
either the PI3K-Akt-mTOR or purine metabolism pathway pathways and were
differentially expressed. In total, we found five, six, and four
significantly differentially expressed miRNAs in the BD–FS, FS–FSO, and
FS–SDG comparisons, which were involved in genes targeting either the
PI3K-Akt-mTOR or purine metabolism pathways. Two of the miRNAs
differentially expressed between BD and FS, miR-137 and miR-340-5p,
were a part of the differential correlations related to the
PI3K-Akt-mTOR pathways and were increased in the FS when compared to
the BD diet ([72]Fig. 1C). In the FS–FSO comparison, miR-137 was
significantly lower in the FSO when compared to the FS diet ([73]Fig.
1C). In the FS–SDG comparison, none of these two miRNAs were
significantly differentially expressed ([74]Fig. 1C). The miRNA
expression was increased in FS with respect to all other diet groups
([75]Fig. 1C). MiR-137 was predicted by miRdb to target Runx2 and
miR-340-5p to target Skp2 ([76]Fig. 2B). To determine expression levels
of Runx2 and Skp2, we used quantitative PCR. We found significantly
lower expression of Runx2 in the FS diet relative to the BD, FSO, and
SDG diets ([77]Fig. 2C, top). No other diet comparisons had
significantly different expression of Runx2. Skp2 was not significantly
differentially expressed between any diet groups ([78]Fig. 2C, bottom).
Flaxseed oil and SDG have unique roles in flaxseed effects
In order to determine shared or specific FSO and SDG effects as
components of (in relation to) FS, pathways that were in common between
the FS–FSO and FS–SDG comparisons, but not in the BD–FS comparisons
were investigated. There were no pathways in common between the FS–FSO
and FS–SDG comparison groups. To investigate how FSO may contribute to
FS effects, pathways unique to the comparison between FS–FSO (not in
the BD–FS or FS–SDG comparisons) were identified. There were 16
enriched pathways ([79]Fig. 2A), 12 of which were also found in the
BD–FSO comparison (Fig. S6). The pathways enriched included axon
guidance, collagen synthesis and degradation, extracellular matrix
signaling and degradation, and NCAM1 signaling. To investigate how SDG
may uniquely contribute to FS effects, pathways unique to the
comparison between FS and SDG were found. There were five pathways
enriched ([80]Fig. 2A). Pathways enriched included starvation
autophagy, CREB3 factors, and mTOR signaling. There are 11 fewer
pathways enriched compared to the FS–FSO pathways, potentially
suggesting that there are fewer differences between the mechanism of
action of FS and SDG when compared to FSO. Furthermore, the BD–SDG and
FSO–SDG comparison also found the PI3k-Akt-mTOR pathway to be enriched,
similar to the BD–FS comparison (Fig. S6).
FS-associated miRNAs and predicted gene targets are altered in human breast
cancer
To investigate if the miRNAs and gene targets predicted may be relevant
in human cancer, differential expression of publicly-available human
miRNA- and RNA-Seq data between breast cancer and matched normal
samples in The Cancer Genome Atlas (TCGA) ([81]29) was determined with
DESeq2; human–mouse orthologues were identified with the HGNC
Comparison of Orthology Predictions Search
([82]https://www.genenames.org/tools/hcop/). In breast cancer,
28,983/60,661 genes and 562/1,882 miRNAs were found to be
differentially expressed (Table S2; [83]Fig. 3). Of the 28,983
differentially expressed genes found in breast cancer, 14,889 had mouse
orthologues ([84]Fig. 3; Table S2). Out of the 161 genes involved in
enriched pathways between BD and FS found in this study, 129 (80%) were
found to be differentially expressed in breast cancer ([85]Fig. 3),
including significantly higher expression of Runx2 and Skp2 in both the
BD and breast cancer groups (Table S2). Of the 44 miRNAs involved in
enriched pathways between BD and FS, 31 were found to be differentially
expressed in breast cancer ([86]Fig. 3). Within these miRNAs, miR-137
and miR-340 expression were also decreased in breast cancer (Table S2).
Fig 3.
[87]Fig 3
[88]Open in a new tab
Number of significantly altered microbiota-miRNA correlations between
the control BD and FS diets with miRNAs targeting genes within
significantly enriched pathways in our mouse study. The TCGA-BRCA
([89]https://portal.gdc.cancer.gov/projects/TCGA-BRCA) data set was
used to determine differentially expressed miRNAs and genes between
breast cancer tissues and matched normal samples. Mouse orthologues of
differentially expressed miRNAs and genes were compared with those
found in the differential correlation network between mice fed the BD
vs FS diets. Created with BioRender.com
DISCUSSION
Compounds found in FS, such as SDG, require the microbiota to convert
them into their bioactive forms which have been shown to have
preventative benefits in breast cancer. These effects may act through
mammary gland miRNAs, but the relationship between the gut microbiota
and mammary gland miRNAs has never been explored. In this study, it was
found that there is a relationship between the cecal microbiota and
mammary gland miRNA and that this relationship is altered by an FS
diet. The FS diet altered microbiota-miRNA relationships which targeted
pathways related to the PI3K-Akt-mTOR pathway and genes previously
found to be related to breast cancer. It was also found that within FS,
FSO may act through different pathways than SDG, as a FSO diet had an
increased number of altered correlations when compared to SDG, and FSO
and SDG had no shared pathways related to altered correlations.
The PI3K-Akt-mTOR and Purine degradation/3′,5′ cyclic amp degradation
pathways were enriched in response to FS. The PI3K-Akt-mTOR pathway has
been implicated in increased ductal branching and TEB number in
response to 0.1% arginine exposure in pubertal mice ([90]30). TEBs are
the most likely site for mammary tumors to develop, indicating a
potential role of this pathway in pubertal development and cancer risk
in later life. This pathway is activated in puberty and dysregulated in
breast cancer ([91]21). The PI3K-Akt-mTOR pathway has also been shown
to play a role in breast cancer therapy resistance, with inhibition of
mTOR resulting in restored sensitivity to tamoxifen ([92]31). FS has
previously been shown to have a synergistic effect on breast cancer
with tamoxifen in vivo, with 10% FS and tamoxifen treatment
significantly reducing tumor growth in comparison with tamoxifen alone
([93]32). Previous and current clinical trials have tested inhibitors
of mTOR ([94]33) as well as upstream PI3K/Akt inhibitors ([95]34).
Genes involved in the enrichment of the PI3K-Akt-mTOR pathway included
Runx2 and Skp2, which are involved in mammary gland development and
breast cancer. Runx2, a transcription factor, has been shown to promote
phosphorylated Akt levels through mammalian targets of rapamycin
complex-2 in vitro ([96]35). Runx2 expression is reduced during late
pregnancy, suggesting that it is necessary for full alveolar
development in the mammary gland; moreover, loss of Runx2 expression
increases mammary tumor survival ([97]36). Furthermore, deletion of
Runx2 has been shown to impair mammary stem regeneration, with a
potential parallel to mammary tumors and their regenerative potential
([98]37). Runx2 expression was found to be significantly decreased in
the FS diet only, which suggests that miR-137 may be upregulated with
an FS intervention and contribute to cancer-protective effects. Skp2
may also promote PI3K inhibitor resistance in aggressive breast cancer
cells ([99]38, [100]39). Runx2 and Skp2 are targeted by miR-137 and
miR-340-5p, which were found to be significantly increased 2 and
1.8-log-fold, respectively, in the FS diet when compared to the BD diet
([101]Fig. 1C). This corresponds to the change in correlation from
negative in BD to nullified in FS. With a significant increase in
miRNA, one might expect a change from a negative to a positive
correlation between BD and FS. Here, a nullified correlation is
observed, which may suggest that the miRNA reaches a steady-state
maximal expression in response to microbial modulation from FS
consumption. Further studies are needed to understand the time dynamics
of this miRNA. The increased miRNA expression suggests that Runx2 and
Skp2 may be downregulated in response to FS consumption. MiR-137 was
associated with the family Lachnospiraceae and the genus Oscillospira,
the former of which was found to be significantly enriched in the FS
diet (0.66 log-fold change, q < 0.05). MiR-340-5p was associated with
the genus Lactobacillus which was found to be enriched in the FS diet
with a 2-log-fold increase (Fig. S1). Lactobacillus has been found to
positively correlate with serum ED concentration ([102]4). Species of
the family Lachnospiraceae and genus Lactobacillus produce SCFA
([103]40 [104]– [105]42). However, Skp2 expression was found to be
unchanged by diet, and thus may not contribute to these effects.
Interestingly, we found that in human breast cancer samples, Runx2 and
Skp2 are significantly increased and miR-137 and miR-340 are decreased,
which is in line with the findings from the FS to BD diet comparison.
In addition, 70% of miRNAs were found in differential correlations
which led to altered pathways between BD and FS, and 80% of their gene
targets were found to be differentially expressed in these breast
cancer samples. When comparing differentially expressed miRNAs between
BD and FS, only 37/59 (
[MATH: ∼
:MATH]
54%) were in common with those found to be differentially expressed in
human breast cancer. This may indicate that FS drives benefits in the
mammary gland during puberty which may have relevance to breast cancer,
and these effects can be best elucidated when investigating miRNAs and
genes within the context of their network connecting the microbiota and
affected pathways.
Next, FS effects dependence on its FSO and/or SDG components was
determined. To determine this, pathways enriched by correlation changes
between the FS–FSO diets and FS–SDG diets which were not found in the
BD–FS comparison were identified. There were no correlation changes in
common between the FS–FSO and FS–SDG comparisons, suggesting that the
FSO and SDG components have diverging roles in FS as a whole food.
Indeed, FSO and SDG act through different mechanisms. SDG is digested
by the gut microbiota to form ED and EL, bioactive molecules implicated
in mammary gland development, breast cancer, and cardiovascular disease
([106]43). Rather than digestion by the microbiota, FSO is rich in ALA
and is thought to influence factors that interact with cellular
receptors or influence the composition of the cell membrane ([107]44).
Many of the enriched pathways related to FS–FSO changed correlations
were related to extracellular matrix and collagen synthesis,
regulation, and degradation. During pubertal development, the
development of TEB ([108]45) and the direction of branching
morphogenesis are aligned with collagen-1 orientation ([109]46).
Mammary tumor cells also respond to collagen density and alignment,
with collagen facilitating invasion and metastases of tumor cells
([110]47 [111]– [112]50). Tumor-associated collagen signature-3 is
known to be associated with recurrence and poor survival ([113]51).
Many of the miRNAs in the correlation changes targeting the
collagen/extracellular matrix (ECM) pathways changed from no
correlation in FS to a negative correlation in FSO, suggesting FSO may
alter collagen synthesis, regulation, or degradation when compared to
FS. This highlights the parallel role collagen may play between
pubertal mammary gland development and cancer, both of which are
characterized by increased cellular invasion and proliferation.
These findings support previous evidence that FS may provide maximum
benefit to the mammary gland when consumed as a whole food ([114]4,
[115]18). The direction of correlation change related to the
PI3K-Akt-mTOR pathway and related genes were the same for BD, FSO, and
SDG relative to the FS diet (nullified in FS, negative correlation in
the other diets), suggesting that the pathway is an FS-specific
taxa-miRNA change. A previous paper from our group found that ED and EL
production positively correlated with FS, but not BD, FSO, and SDG
diets ([116]4), which aligns with our finding that FS uniquely alters
taxa-miRNA relationships. Although the FS and SDG diets have the same
amounts of SDG and fiber, the ED and EL production with the SDG diet
was lower than with the FS diet, which may contribute to a diminished
effect from an FS-equivalent SDG diet ([117]18). This may be due to the
higher proportion of fermentable soluble fiber in the FS diet than in
the SDG diet. Indeed, previous studies have found that urinary lignan
excretion is lower in rats consuming SDG compared to FS, even at an
equivalent amount ([118]52). These differences warrant further study
into the mechanism of difference between FS and its components, not
only in fiber but also in protein. In mice fed the FS diet, protein
processing functions were enriched in the microbiome ([119]4); FS
protein may have antioxidant properties and cardiovascular benefits
([120]21). FS fiber, which includes both soluble and insoluble fiber,
can slow FS transit through the gastrointestinal tract, potentially
increasing the absorption time of the lignan and oil components. The
results further highlight the need for more research to disentangle
these relationships. This knowledge may help in designing specific
dietary strategies including the provision of fiber or FS-based
synbiotics for mammary gland benefits.
Overall, this study showed the cecal gut microbiota is related to
mammary gland miRNAs and consumption of FS may modulate mammary gland
development pathways which may reduce breast cancer risk in later life.
This study confirmed that FS components, FSO and SDG, target different
physiological pathways and elucidate potential biological mechanisms
for these differences. This study is a foundation for mechanistic
studies of how FS acts through the gut microbiota to alter steady-state
mammary miRNA expression. This may provide novel guidance for
functional food interventions.
MATERIALS AND METHODS
Approach
We used mammary gland miRNA expression and gut microbiota data that we
have previously generated in a mouse study where female C57BL/6 mice at
4–5 weeks of age were randomized to one of four isocaloric diets (n =
14/group): (i) basal modified AIN-93G, (ii) 10%FS, (iii) 3.67% FSO, and
(iv) 0.15% SDG for 3 weeks until sacrifice ([121]23, [122]24). The
diets were formulated using the AIN-93G diet as a basis so that the
amount of FSO and SDG in diets II and III, respectively, would equal
their amount in diet I ([123]23). At sacrifice, mammary gland, cecum
contents were collected from n = 8–14/group, selected based on body
weight and representing different cages. Cecal microbiota data were
obtained via 16S rRNA Illumina sequencing using primers targeting the
V3–V4 region (available at the NCBI SRA database, BioProject ID:
PRJNA683934) ([124]23). For this study, relative abundances of
operational taxanomic units (OTUs) were copy-number corrected using the
ribosomal RNA operons database (rrnDB) ([125]53). For copy numbers not
found at the desired taxonomic level, the copy number of the first
available higher rank was used. The correction was adapted from Jian et
al. ([126]54) and is described in [127]equation 1 where is one taxon
within a taxonomic level, is the corrected relative abundance of taxon
i,
[MATH: ri0
:MATH]
is the uncorrected relative abundance of taxon i, is the copy number of
the taxon i as found in rrnDB.
[MATH: ri=ri0
ci∑j0n
rj0
cj
:MATH]
(1)
The mammary gland miRNome (578 miRNAs) data were generated using the
nCounter Mouse v1.5 miRNA Expression Assay Kit and the NanoString
Technology (NanoString Technologies, Seattle, WA, USA) as previously
described ([128]18, [129]24) and are available with the Gene Expression
Omnibus ID [130]GSE193847 (GEO ID [131]GSE193847) ([132]18).
Microbiota and miRNA differential analysis
For the microbiota data, ANCOM-BC version 1.6.4 was used to identify
differentially abundant taxa between diet groups with default
parameters, structural zero detection, a prevalence cutoff of 0.2, a
conservative variance estimator, and global test ([133]55).
For the miRNA data, NanoString count data were normalized using the
estNormalizationFactors function, and differential expression was
assessed using a generalized linear model likelihood ratio test via the
glm.LRT function in NanoStringDiff version 1.24.0. Benjamini-Hochberg
corrected q-values of less than 0.1 were considered significant.
NanoStringDiff is a differential abundance method developed in R
specifically for NanoString data, which differs from other differential
abundance methods such as DESeq and edgeR as it takes into account
positive control and housekeeping genes from the NanoString assay
([134]22).
Correlation analysis
Mammary gland miRNome data and cecal microbiota data matched by mice
were used, resulting in n = 5–6/group. The associations between paired
microbiota and miRNA were first assessed via Spearman correlation
within each dietary group. Correlations were conducted in Python
version 3.9.5 using the scipy.stats library at the genus level, using
the lowest available rank above the genus when genus information was
not available. Then, microbiota-miRNA correlations were compared
between diets using DGCA via the DGCA v.1.0.2 package in R
(Benjamini-Hochberg q < 0.05) ([135]25). There were six diet
comparisons: BD–FS (where “–” is “compared to”), FS–FSO, FS–SDG,
BD–FSO, BD–SDG, and FSO–SDG. For DGCA analysis, microbiota data were
filtered to ensure presence of all taxa in at least one mouse per diet
group. The Fisher z-transformation was used to transform Spearman
correlation values and compare z-scores of microbiota-miRNA
correlations from different diets. There were nine possible outcomes
from DGCA when comparing correlations in different diets: change from
negative to positive correlation (−/+); change from positive to
negative correlation (+/−); change from no correlation to positive
correlation (0/+); change from no correlation to negative correlation
(0/−); change from positive to no correlation (+/0); change from
negative to no correlation (−/0); no change from no correlation to no
correlation (0/0); no change from positive to positive correlation
(+/+); and no change from negative to negative correlation (−/−).
MiRNA gene target identification
Predicted gene targets of miRNAs involved in microbiota-miRNA
correlations significantly modified by diet were identified using miRDB
version 6.0 ([136]26). Predicted gene targets with a minimum MirTarget
score of 95 were included in downstream analyses.
Pathway enrichment analysis and visualization of findings
Mouse genes were mapped to human orthologs and their corresponding
pathways were identified with pathDIP 4.0 ([137]27). Pathways with
q-values (Bonferroni) less than 0.05 were considered significant.
Taxa-miRNA-pathway networks were constructed and visualized with
NAViGaTOR version 3.0 ([138]56).
Real-time quantitative PCR
Total RNA extracted as described previously ([139]18) was reverse
transcribed using the TaqManMicroRNA Reverse Transcription Kit for
miR-137 and the High-Capacity cDNA Reverse Transcription Kit (Catalog
no. 4368814, Applied Biosystems, ThermoFisher Scientific) for genes.
The expression of B2m (Assay ID: Mm00437762_m1), Runx2 (Assay ID:
Mm00501584_m1), and Skp2 (Assay ID: Mm00449925_m1) was quantified in
triplicate using the QuantStudio5 Real-Time PCR System (Thermo Fisher
Scientific). Expression data were normalized to the endogenous control
B2m. Differential expression was assessed via two-tailed Welch’s
unequal variances t-test (P-value < 0.05). Data were presented as mean
relative expression relative to the reference diet.
Human miRNA-Seq and RNA-Seq data
Publicly available miRNA-Seq and RNA-Seq data from non-metastatic
breast cancer patients (n = 1111) in The Cancer Genome Atlas (TCGA-BRCA
data set) ([140]29) and matched normal samples (n = 113) were retrieved
([141]https://tcga-data.nci.nih.gov/tcga/). Differential expression of
miRNAs and RNA were assessed using DESeq2 ([142]57). MiRNAs and genes
with q-values less than 0.05 were considered significant. Microbiota
data are available at the NCBI SRA database, BioProject ID
[143]PRJNA683934 ([144]23). MicroRNA data are available at the NCBI
Gene Expression Omnibus, ID [145]GSE193847 ([146]24).
ACKNOWLEDGMENTS