Transcriptomic Profile of Whole Blood Cells from Elderly Subjects Fed Probiotic Bacteria Lactobacillus rhamnosus GG ATCC 53103 (LGG) in a Phase I Open Label Study

Abstract

We examined gene expression of whole blood cells (WBC) from 11 healthy elderly volunteers participating on a Phase I open label study before and after oral treatment with Lactobacillus rhamnosus GG-ATCC 53103 (LGG)) using RNA-sequencing (RNA-Seq). Elderly patients (65-80 yrs) completed a clinical assessment for health status and had blood drawn for cellular RNA extraction at study admission (Baseline), after 28 days of daily LGG treatment (Day 28) and at the end of the study (Day 56) after LGG treatment had been suspended for 28 days. Treatment compliance was verified by measuring LGG-DNA copy levels detected in host fecal samples. Normalized gene expression levels in WBC RNA were analyzed using a paired design built within three analysis platforms (edgeR, DESeq2 and TSPM) commonly used for gene count data analysis. From the 25,990 transcripts detected, 95 differentially expressed genes (DEGs) were detected in common by all analysis platforms with a nominal significant difference in gene expression at Day 28 following LGG treatment (FDR<0.1; 77 decreased and 18 increased). With a more stringent significance threshold (FDR<0.05), only two genes (FCER2 and LY86), were down-regulated more than 1.5 fold and met the criteria for differential expression across two analysis platforms. The remaining 93 genes were only detected at this threshold level with DESeq2 platform. Data analysis for biological interpretation of DEGs with an absolute fold change of 1.5 revealed down-regulation of overlapping genes involved with Cellular movement, Cell to cell signaling interactions, Immune cell trafficking and Inflammatory response. These data provide evidence for LGG-induced transcriptional modulation in healthy elderly volunteers because pre-treatment transcription levels were restored at 28 days after LGG treatment was stopped. To gain insight into the signaling pathways affected in response to LGG treatment, DEG were mapped using biological pathways and genomic data mining packages to indicate significant biological relevance.

Trial registration: ClinicalTrials.gov NCT01274598.

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Fig 1. Participant flow diagram.

Fig 2. Differential Expression Analysis of RNA-seq Data.

Volcano plots depicting the fold difference in gene expression levels after consumption of LGG for 28 days. Volcano plots with DEGs generated from edge-R (Panel A), DESeq2 (Panel B) or TSPM (Panel C) analysis platforms. Colored points in red refer to down-regulated genes green for up-regulated genes according to their fold change (Log FC) in x-axis and p value (log 10 p-value) p<0.05 or p<0.1 in y-axis.

Fig 3. Ingenuity top gene network interaction reflecting immune response-related transcriptome changes after consumption of LGG.

Nodes in the interaction network are encoded by differentially expressed genes detected by edge-R in blood from subjects consuming LGG for 28 days, up-regulated genes are depicted in shades of green and down-regulated genes are in shades of red. Transcriptional information derived from IPA knowledge database on interactions between the nodes (activation, expression, molecular cleavage or phosphorylation) was projected onto the interaction map with predicted downregulation effects represented with blue dashed lines and upregulation effects with orange lines. From this interaction map it can be seen that several downstream genes including growth factors, peptidases, G-coupled receptors and cytokines that are known to be regulated by NF-KB transcription factor are down-regulated.

Fig 4. Downstream effect analysis (DEA) on whole blood cells of subjects consuming LGG for 28 days.

(A).The visualization is a hierarchical heat-map generated from edgeR analysis with filtered data where the major boxes represent a family (or category) of related functions. Each individual colored rectangle is a particular biological function or disease and the color indicates its predicted state: Increasing (orange), or decreasing (blue). Darker colors indicate higher absolute Z-scores. In this view the size of the rectangle is correlated with increasing overlap significance (p-value). The image has been cropped for better readability. (B) Heat-map comparison of Diseases and Biofunctions affected across all 4 analysis (edgeR 0.1 cpm/all, edgeR 0.1cpm/ 22, DESEq2, TSPM). Similarly color represents predicted state. (C). Individual Z-scores and mean Z-scores per each Bio Function affected. The Z-score algorithm is designed to reduce the chance that random data will generate significant predictions. Negative Z-scores indicate a down-regulation of Biofunction, positives Z-scores indicate an up-regulation of function. Absolute Z-score values higher than 2.0 can be used to make biological predictions.