Human mucosal in vivo transcriptome responses to three lactobacilli indicate how probiotics may modulate human cellular pathways

Abstract

Probiotic bacteria, specific representatives of bacterial species that are a common part of the human microbiota, are proposed to deliver health benefits to the consumer by modulation of intestinal function through largely unknown molecular mechanisms. To explore in vivo mucosal responses of healthy adults to probiotics, we obtained transcriptomes in an intervention study after a double-blind placebo-controlled cross-over design. In the mucosa of the proximal small intestine of healthy volunteers, probiotic strains from the species Lactobacillus acidophilus, L. casei, and L. rhamnosus each induced differential gene-regulatory networks and pathways in the human mucosa. Comprehensive analyses revealed that these transcriptional networks regulate major basal mucosal processes and uncovered remarkable similarity to response profiles obtained for specific bioactive molecules and drugs. This study elucidates how intestinal mucosa of healthy humans perceives different probiotics and provides avenues for rationally designed tests of clinical applications.

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.

Dendrogram visualizing similarity and distances between the microarray data. The second number of the array identification indicates each individual volunteer. Note that the clusters, representing the array datasets with higher similarity, are basically consisting of data from individual volunteers, not interventions. Clusters representing individuals are boxed. From the dendrogram, it is apparent that the differences between individuals, represented by the node-to-node distances, are larger than the differences between interventions.

Fig. 2.

Ingenuity protein–protein interaction network reflecting immune response-related transcriptome changes after consumption of L. acidophilus. Nodes in the interaction network are encoded by differentially expressed genes with the following functional annotations: immune response, infectious disease, and inflammatory disease. Interactions between the nodes represent protein–protein interactions (binding and phosphorylation) as well as regulation of gene transcription by transcription factors. Chemokines without direct interactions are depicted as well (upper right corner). Transcriptional information was projected onto the interaction map such that up-regulated genes are depicted in shades of red and down-regulated genes are in shades of green. From this interaction map, it can be seen that the up-regulated transcription factors STAT3, CCAAT/enhancer binding protein (C/EBP), beta (CEBPB), CEBPD, RELB, and NFKB2 connect, by multiple outward pointing arrows, to multiple nodes. These nodes represent downstream genes that are known to be regulated by these transcription factors. Genes that participate in the immuno-regulatory pathways, NF-κB and IL-10 signaling, are indicated by the blue lines.

Fig. 3.

Heat map visualization of transcriptional change (fold change) and coefficients of variation (CoVars) for those genes that encode the proteins that are represented in the interaction network depicted in Fig. 2. The values listed in the table correspond with the heat-map colors. An expression value represented in black indicates that the respective gene was not differentially expressed. From this, it can be seen that genes with functional annotations relating to the immune response are mainly regulated on consumption of L. acidophilus, not on consumption of the other two lactobacilli. Note that genes encoding proteins that occupy more central regulatory functions in the network of Fig. 2 tend to have lower CoVars compared with genes that encode proteins with more acute functions such as chemokines. This trend is also apparent in the responses to the other two lactobacilli (SI Appendix, SI Results, Figs. S8 and S9, and Tables S11 and S12).