DOI: 10.5061/dryad.qv9s4mwbc
title: Impact of smoking cessation, coffee and bread consumption on the intestinal microbial composition among Saudis: A cross-sectional study

description: The gut microbiota is often affected by the dietary and lifestyle habits
 of the host, resulting in a better efficacy that favors energy harvesting
 from the consumed food. Our objective was to characterize the composition
 of gut microbiota in adult Saudis and investigate possible association
 with lifestyle and dietary practices. Feces from 104 Saudi volunteers (48%
 males) were tested for microbiota by sequencing the V3-V4 region of
 bacterial 16S ribosomal RNA (rRNA). For all participants, data were
 collected related to their lifestyle habits and dietary practices. The
 relative abundance (RA) of Fusobacteria was significantly higher in normal
 weight Saudis ( p =0.005, FDR=0.014). Individuals who consumed more coffee
 presented marginally significant more RA of Fusobacteria ( p =0.02,
 FDR=0.20) in their gut microbiota compared to those reporting low or no
 coffee intake, but the RA of Fusobacteria was significantly higher in
 smokers compared to non-smokers p =0.009, FDR=0.027) . The RA of
 Fusobacteria was also significantly higher in those reporting daily
 consumption of bread ( p =0.005, FDR=0.015). At the species level, the gut
 microbiota of people who consumed coffee was dominated by Bacteroidetes
 thetaiotaomicron followed by Phascolarctobacterium faecium and
 Eubacteriumrectale . Similarly, the gut microbiota of smokers was also
 enriched by B. thetaiotaomicron and Lactobacillus amylovorus. Smoking
 cessation, bread and coffee consumption induce changes in the intestinal
 microbial composition of Saudis. This indicates the significance of diet
 and lifestyle practices in the determination of the composition of the gut
 microbiota, which could possibly lead later to changes in metabolic
 profile and weight.
description: All
 participants were asked to sign a written informed consent after being
 informed about the purpose of the study and ensured about confidentiality
 of the data. They were then requested to fill out a questionnaire covering
 their socio-demographic information, medical history and lifestyle
 practices. In addition, a structured food frequency questionnaire (FFQ)
 was administered to evaluate their dietary practices, and weight and
 height measurements were taken using standardized techniques. The used
 questionnaire was previously described partially or fully and used in
 other manuscripts [12-15]. Weight and height were used to calculate body
 mass index (BMI =  kg m<sup>-2</sup>) and the WHO criteria
 [16] were used to classify participants as underweight, normal, overweight
 and obese. Weight categories were defined according to BMI as follows:
 normal 20-25 kg m<sup>−2</sup>, underweight 18-20 kg
 m<sup>−2</sup>, overweight 25-30 kg
 m<sup>−2</sup>, and obese &gt;30 kg
 m<sup>−2</sup>. Stool samples were collected in aseptic
 conditions with clean, dry screw-top containers and immediately stored at
 -20 °C. <b>Extraction of DNA from
 stool samples and 16S rRNA sequencing using MiSeq
 technology</b> All
 participants' stool samples were extracted using a deglycosylation
 protocol as follows: 250 µL of each sample was placed in a 2 mL tube
 containing a mixture of acid-washed glass beads (Sigma,
 Aldrich) and with two or three 0.5 mm glass
 beads. Mechanical
 lysis was performed by bead-beating the mixture using a Fast Prep BIO 101
 apparatus (Qbiogene, Strasbourg, France) at maximum speed (6.5) for 3×30
 seconds. The supernatant was centrifuged at 12,000 rpm for 10 min and the
 pellet retained. A mixture containing 2 µL of 10×glycoprotein denaturing
 buffer EndoHf (New England Biolabs) and 17 µL of
 H<sub>2</sub>O was added and heated at 100 °C for 10 minutes.
 Deglycosylation was performed adding a mixture of 2 µL of 10×G5 reaction
 buffer (ref B1702 New England Biolabs), 2 µL of EndoHf (New England
 Biolabs), 2 µL of cellulase (Sigma) and 16 µL of
 H<sub>2</sub>O. The preparation was then incubated overnight
 at 37 °C. Finally, DNA was extracted using the NucleoSpin® Tissue Mini Kit
 (Macherey Nagel, Hoerdt, France) according to a previously described
 protocol [17]. The quantity, purity, integrity and size of DNA and its
 amenability to PCR amplification were assessed. The concentration of each
 DNA extraction was measured by a Qubit assay with the high sensitivity kit
 (Life technologies, Carlsbad, CA, USA) according to the Nextera XT DNA
 sample prep kit (Illumina) and diluted to 1 ng aliquots of each metagenome
 for paired end sequencing analysis. DNA extracts were dispensed into 10-
 to 20-μL single-use aliquots and frozen at -20 °C to avoid repeat
 freeze-thaw cycles prior to downstream analyses.  Samples were then
 sequenced targeting the V3–V4 regions of the 16S rRNA gene using MiSeq
 technology as previously described [18, 19]. <b>Data processing:
 Filtering the reads, dereplication and clustering</b>
 Paired end
 fastq files were assembled using FLASH [20]. A total of 7518258 joined
 reads were filtered and then analyzed in QIIME by choosing chimera slayer
 for  removing chimera and Uclust [16, 20] for Operational Taxonomic Units
 (OTU) extraction as described previously [18, 19]. All reads were
 clustered with a threshold of 97% identity to obtain OTU. Extracted OTUs
 were blasted against SILVA123 SSU database
 [21] of release and taxonomy were assigned to a species if they matched one with at least 97% identity, as previously described [22, 23]. Briefly, for each OTU, representative sequences were extracted and were searched against the reference database. For each unique representative sequence, we extracted the best matches from the reference database and sorted them by decreasing percentage of similarity rounded to the nearest integer. We used the reference sequences with &gt;97% similarity (or the highest available) for taxonomic assignments into species. When multiple matches with the same percentage of similarity were present, the taxonomy of each rank was obtained by consensus [16, 24]. OTU not assigned to any species were considered "unidentified". As several OTUs matched identical species, the total number of identified species and the number of unidentified OTU was expected to be smaller than the total number of OTUs.