Treatment effects on the host gut transcriptome.
To determine if antibiotic and probiotic-induced changes in the
microbiome led changes in the host gut transcriptome, RNA-Seq was used
to determine host transcript levels in the hindgut. Pairwise treatment
comparisons resulted in 96 (control vs antibiotic; 35 control
upregulated and 61 control downregulated), 105 (control vs probiotic; 61
control upregulated, and 44 control downregulated), 120 (antibiotic vs
probiotic; 84 antibiotic upregulated, and 36 antibiotic downregulated)
transcripts that were differentially expressed among treatments
(Benjamini-Hochberg false-discovery rate (BH FDR) 0.1,
|log2 FC| > 0.25).
However, for selecting candidate genes for the OpenArray high-throughput
qPCR analyses, we took a conservative approach and we only selected
genes with transcripts that were significantly expressed at
|log2 FC| > 1 and FDR P
value < 0.05 (Supplementary Figure S3). This decreased the
differentially expressed transcripts to 29 (control vs antibiotic), 29
(control vs probiotic), and 27 transcripts (antibiotic vs probiotic)
(Table 2). For the control versus antibiotic group comparisons, the
selected genes related to cellular process (e.g., cell activation, cell
communication, cell cycle, and cell death) were upregulated and genes
related to metabolism and response to stimuli and stress were
downregulated in antibiotic group (Table 2). While in the control versus
probiotic group comparisons, genes related to regulation of a variety of
functions (regulation of meiosis, intracellular protein transport,
angiogenesis, transmembrane transporter, cell adhesion, negative
regulation of apoptotic process) were downregulated and genes related to
post-translation modifications were over expressed in the probiotic
treated fish (Table 2). Moreover, when we compared antibiotic against
probiotic group transcription, genes related to cellular process (mostly
apoptotic process) were over expressed in antibiotic group while genes
related to cell adhesion, regulation of transcription were over
expressed in probiotic group (Table 2).
OpenArray high-throughput qRT‐PCR
The LMM analysis showed PCs 4, 5, 6, 7 and 9 were significantly affected
by treatment (Table 3). We identified only those genes whose
contributions to the significantly affected principal component axes
were important (Supplementary
Figure S4) and selected them for analyses. In our analysis we also
included tank, body weight, and OpenArray chip ID as random effects to
correct for possible technical, environmental, and body size effects.
Chip and body weight were not significant for any of the genes and were
dropped from our analyses. Sire effects (nested within dam) were not
significant after FDR correction. Moreover, a significant tank effect
was observed for only one gene (anxa1 , p < 0.05) before
FDR correction. We found no significant effects for dam‐by‐treatment or
sire-by‐treatment interactions. After including FDR correction into our
model, aifm3 , manf ,
and prmt3 still showed a significant treatment effect (Table 4).