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. 2010 Aug;76(16):5533-40.
doi: 10.1128/AEM.00502-10. Epub 2010 Jun 18.

Metatranscriptome analysis of the human fecal microbiota reveals subject-specific expression profiles, with genes encoding proteins involved in carbohydrate metabolism being dominantly expressed

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Metatranscriptome analysis of the human fecal microbiota reveals subject-specific expression profiles, with genes encoding proteins involved in carbohydrate metabolism being dominantly expressed

Carien C G M Booijink et al. Appl Environ Microbiol. 2010 Aug.

Abstract

The human gastrointestinal (GI) tract provides home to a complex microbial community, collectively termed microbiota. Although major efforts have been made to describe the diversity and stability of the microbiota, functional studies have been largely restricted to intestinal isolates and include few community studies. The aim of this study was to explore the in situ gene expression of the fecal microbiota and to evaluate the RNA fingerprinting method cDNA-AFLP (cDNA amplified fragment length polymorphism) for this purpose. To this end, cDNA-AFLP analysis of enriched mRNA revealed that two healthy subjects showed highly divergent expression profiles with considerable fluctuations in time. Subsequent excision and sequence determination of bands from the mRNA-enriched profiles resulted in 122 identifiable sequences (transcripts and rRNAs). The classification of retrieved transcripts into functional clusters based on COG (cluster of orthologous genes) annotation showed that most assigned transcripts belonged to the metabolism cluster (26% of all sequences), underlining that even at the very end of the intestinal tract the microbiota is still very active. This study furthermore revealed that cDNA-AFLP is a useful tool to compare gene expression profiles in time in complex microbial communities.

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Figures

FIG. 1.
FIG. 1.
Schematic overview of the cDNA-AFLP procedure applied to RNA isolated from GI tract samples, adapted from Bachem et al. (4). (Step 1) Total RNA isolated from GI tract samples (feces) is enriched for mRNA by subtractive hybridization as indicated by the manufacturer (MicrobExpress; Ambion, Huntington, United Kingdom). (Step 2) cDNA is synthesized by reverse transcription using a mixture of random decamers, followed by second-strand synthesis. (Step 3) The double-stranded cDNA is digested by two different 4-bp cutting enzymes, followed by ligation of adaptors. The adaptors used are designed in such a way that once ligated to the sticky ends of the fragments, the sequence is changed and no longer recognized by the restriction enzymes. Therefore, restriction and ligation can be performed together. (Step 4) Nonselective primers (indicated by arrows), which anneal to the ligated adaptors, are used to nonselectively amplify the cDNA molecules. (Step 5) In the selective amplification, a small aliquot of the nonselectively amplified fragments is used in a second PCR. These selective primers extend one, two, or three bases inward. This reduces the number of fragments by a factor 64, 256, or 1,024, respectively. One of the primers contains a radioactive dye (33P). (Step 6) The fragments are size separated and visualized on a polyacrylamide gel. (Step 7) The fragments of the selected profiles are reamplified with the corresponding selective primer pairs used in step 5. (Step 8) Sequence analysis of reamplified fragments is performed.
FIG. 2.
FIG. 2.
Cluster analysis based on UPGMA of technical profiling duplicates of fecal cDNA-AFLP profiles from subjects 1 and 2. Enriched mRNA of two time samples (t0 and t3) were used as the starting material per subject. For cDNA-AFLP profiling of these four samples, two different primer pairs were used: TR18/M17, Taq+CT/Mse+CG; and TR14/M24, Taq+AT/Mse+TC. Similarity index (SI) values based on Pearson correlation for the technical profiling replicates are given with the overall average for +2/+2 primer selectivity. The tree on the left indicates the similarity; the cophenetic correlation coefficients indicated as error bars at the root of each cluster indicate the relatedness of the tree.
FIG. 3.
FIG. 3.
Cluster analysis based on UPGMA of the temporal fecal cDNA-AFLP profiles of subject 1. DNA, total RNA, and enriched mRNA were used as starting material. On the left, similarity coefficients based on Pearson correlation are indicated, whereas the gray bars at the root of each cluster indicate the cophenetic correlation coefficients. Four different primer combinations are displayed: TR14/B03, Taq+AT/Bfa+G; TR14/B04, Taq+AT/Bfa+T; TR18/M17, Taq+CT/Mse+CG; TR14/M24, Taq+AT/Mse+TC, respectively. The different time points are indicated by t0 and t3. The gray shading in the distance tree indicates the variation introduced by DNA or mRNA profiling, respectively.
FIG. 4.
FIG. 4.
Classification of the sequences retrieved from the mRNA enriched cDNA-AFLP profiles of fecal samples of subject 1 (32 sequences) and subject 2 (43 sequences), respectively, based on the COG as defined by Tatusov et al. (36).

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