The nasal microbiome in patients with chronic rhinosinusitis: Analyzing the effects of atopy and bacterial functional pathways in 111 patients

• Published in: Journal of allergy and clinical immunology Vol. 142; no. 1; pp. 287 - 290.e4
• Main Authors: Mahdavinia, Mahboobeh, Engen, Phillip A., LoSavio, Phillip S., Naqib, Ankur, Khan, Rafsa J., Tobin, Mary C., Mehta, Arpita, Kota, Raj, Preite, Nailliw Z., Codispoti, Christopher D., Tajudeen, Bobby A., Schleimer, Robert P., Green, Stefan J., Keshavarzian, Ali, Batra, Pete S.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.07.2018; Elsevier Limited
• Subjects: Actinobacteria; Adolescent; Adult; Asthma; Atopy; Bacteria; Biosynthesis; Child; Chronic Disease; Chronic illnesses; Clostridia; Corynebacterium; Eczema; Endoscopy; Enterobacteriaceae; Female; Food allergies; Humans; Inflammation; Male; Microbiota; Middle Aged; Moraxella; Nose; Nose - microbiology; Pseudomonas; Rhinitis, Allergic - microbiology; Rhinosinusitis; Sinusitis - microbiology; Skin; Streptococcus; Studies; Taxonomy; Young Adult
• ISSN: 0091-6749; 1097-6825; 1097-6825
• DOI: 10.1016/j.jaci.2018.01.033

The information, including patients' demographics; prevalence of allergic rhinitis, asthma, eczema, and food allergy; and CRS-related factors, including history of nasal polyps, number of FESSs, duration of CRS, SNOT-22 scores, and LMSs are detailed in Table E1 in this article's Online Repository at www.jacionline.org. Raw FASTQ files for each sample were processed to merge reads, remove low-quality data and chimeras, and perform annotation with the Greengenes 13_8 reference database, as previously described.E3,E4 Data were then clustered into OTUs at 97% similarity, and the sample sequence set was rarefied to 4400 sequences.E4 α-Diversity indices were calculated by using the software package Primer7.E5 We applied both conventional statistical bioinformatics analyses to interrogate microbiota composition in nasal cavity and also used an in silico approach called PICRUSt to infer microbiota functional pathways.E6 During the process of DNA amplification in nasal microbiome analysis, a significant number of short DNA fragments are generated. PICRUSt allows identification and measurement of the RA of each sample's metagenome and potential involvement in different metabolic and functional pathways needed for invasion and metabolism of bacteria, including epithelial invasion, antibacterial resistance properties, and production of LPS.Biostatistics Microbial community analysis was done in a tiered fashion from the taxonomic levels of phylum to species. Taxonomic level Patients with CRS Sex Age Race/ethnic groups FDR P value RA mean in patients with CRS (n = 111) RA mean in control subjects (n = 21) FDR P value RA mean in male subjects (n = 58) RA mean in female subjects (n = 53) FDR P value RA mean in <30 y (n = 21) RA mean in 31-60 y (n = 25) RA mean in >61 y (n = 64) FDR P value RA mean in white subjects (n = 75) RA mean in African American subjects (n = 22) RA mean in Hispanic subjects (n = 11) Phyla Actinobacteria .006* 1884.06 3548.19 .14 1432.79 2377.91 .14 1624.19 1546.36 2130.23 .52 1710.64 2314.27 2069.09 Bacteroidetes .895 194.32 161.90 .46 167.72 223.42 .46 294.05 202.24 153.52 .99 195.23 208.73 193.55 Firmicutes .863 3630.49 3548.43 .82 3575.71 3690.43 .82 3246.71 3331.64 3864.73 .91 3742.24 3433.41 3068.09 Proteobacteria .281 2223.83 1126.86 .29 2459.52 1965.91 .29 2846.43 2088.08 2061.19 .94 2311.97 2168.64 1988.36 Genera Corynebacterium .012* 1766.97 3471.86 .08 1275.67 2304.62 .33 1560.00 2045.73 1296.96 .45 1574.17 2242.68 1969.55 Prevotella .870 53.95 63.62 .19 34.67 75.06 .94 69.43 54.25 42.32 .99 55.28 51.86 63.00 Staphylococcus .593 2174.83 1403.24 .69 2085.22 2272.89 .28 1360.52 2453.70 2067.24 .88 2211.03 2150.23 1990.09 Alloiococcus .194 123.56 587.19 .23 63.33 189.47 .89 91.76 158.98 64.52 .00 90.61 93.36 13.36 Lactobacillus .780 5.50 1.24 .19 8.36 2.36 .36 14.00 3.64 3.28 .56 3.21 13.27 6.18 Streptococcus .560 616.49 77.33 .16 822.40 391.15 .87 829.48 530.63 681.92 .83 724.63 265.23 638.55 Ruminococcus .780 21.10 10.10 .25 13.55 29.36 .91 12.67 21.67 27.48 .68 17.41 41.18 11.09 Anaerococcus .478 219.45 461.90 .47 191.05 250.53 .26 328.14 238.89 87.08 .74 212.48 306.82 101.91 Finegoldia .104 94.16 281.71 .96 93.03 95.40 .13 203.19 67.56 74.32 .40 124.47 47.77 5.36 Peptoniphilus .007* 144.41 490.90 .54 162.26 124.89 .42 223.05 149.98 69.80 .96 145.92 158.09 145.91 Burkholderia .880 308.02 282.90 .15 392.43 215.64 .75 366.57 312.06 256.84 .56 281.23 362.41 438.82 Enterobacteriaceae species .890 370.20 187.81 .85 393.38 344.83 .72 86.57 460.42 392.20 .67 259.96 580.36 553.91 Haemophilus .881 198.51 41.10 .23 85.07 322.66 .85 179.24 145.59 358.08 .80 286.49 9.73 28.91 Moraxella .890 156.12 32.33 .43 219.55 86.70 .46 1.05 117.33 391.92 .81 229.11 3.18 4.91 Pseudomonas .672 507.42 44.14 .97 512.76 501.58 .02* 1565.62 339.27 69.20 .96 537.40 622.73 209.36 Table I RA of selected sequences derived from individual taxa in the sinus cavities of 111 patients with CRS and 21 control subjects Taxonomic level Asthma AR Eczema Polyps FDR P value Asthmatic patients (n = 46) Nonasthmatic subjects (n = 65) FDR P value Patients with AR (n = 45) Subjects without AR (n = 51) FDR P value Patient with eczema (n = 12) Subjects with without eczema (n = 99) FDR P value Patients with CRSwNP (n = 39) Patients with CRSsNP (n = 72) Phyla Actinobacteria .22 1602.59 2083.26 .01∗ 1179.51 2450.90 .19 1146.67 1973.44 .90 1902.14 1850.69 Bacteroidetes .93 190.50 197.02 .43 137.98 210.00 .39 101.50 205.57 .48 213.85 158.26 Firmicutes .59 3467.02 3746.17 .20 3789.33 3167.61 .01 5497.42 3404.19 .57 3526.38 3822.69 Proteobacteria .81 2291.85 2175.69 .57 2401.20 2308.08 .42 1678.08 2289.98 .65 2145.35 2368.72 Genera Corynebacterium .29 1521.13 1940.95 .01∗ 1089.82 2298.43 .24 1100.58 1847.75 .72 1818.00 1672.77 Prevotella .22 31.83 69.62 .33 32.29 55.25 .29 7.00 59.65 .72 57.97 46.54 Staphylococcus .20 1819.52 2426.28 .69 2366.00 1885.33 .08 3343.50 2033.17 .12 1911.29 2661.36 Alloiococcus .34 63.87 165.80 .37 40.09 225.61 .42 1.67 138.33 .70 138.42 96.13 Lactobacillus .41 3.24 7.09 .69 3.29 8.49 .75 3.42 5.75 .48 6.69 3.28 Streptococcus .02∗ 1037.24 318.72 .09 753.98 310.80 .01∗ 1770.08 476.66 .35 722.26 421.21 Ruminococcus .14 32.93 12.72 .05 14.18 13.86 .83 17.00 21.60 .39 25.43 13.10 Anaerococcus .09 138.89 276.46 .98 223.78 212.96 .27 89.83 235.16 .65 233.29 193.90 Finegoldia .22 61.20 117.49 .71 101.87 107.10 .33 31.50 101.76 .46 81.82 116.95 Peptoniphilus .23 101.17 175.02 .87 170.04 135.37 .48 81.83 152.00 .40 125.42 179.49 Burkholderia .05 383.04 254.92 .16 396.29 273.61 .85 327.75 305.63 .24 339.81 249.33 Enterobacteriaceae .80 331.72 397.43 .70 221.56 428.35 .33 17.33 412.97 .75 399.07 316.90 Haemophilus .41 102.24 266.65 .94 271.58 161.22 .54 23.08 219.78 .13 89.94 398.95 Moraxella .71 119.30 182.17 .99 123.09 180.24 .52 2.42 174.75 .79 139.46 186.87 Pseudomonas .49 649.96 406.55 .74 528.18 629.22 .57 787.42 473.48 .46 412.44 682.77 Table II RA of selected sequences derived from individual taxa in the sinus cavities of 111 patients with CRS in relation to allergic rhinitis, eczema, asthma, and nasal polyps Characteristic Patients with CRS Control subjects P value, χ2 or t test Sex Male 58 13 .28 Female 53 8 Age (y), mean ± SD 47.34 ± 15.47 52 ± 13.7 .31 BMI (kg/m2), mean ± SD 29.42 ± 6.22 31.30 ± 7.23 .21 Nasal polyps CRSsNP 39 NA — CRSwNP 72 Asthma Yes 46 3 .006 No 65 171 Not known 0 1 AERD Yes 18 NA — No 93 Atopy Negative skin test result 52 15 .015 Positive skin test result 45 1 No skin test∗ 14 5 Eczema Yes 12 1 .004 No 99 20 Food allergy Yes 12 2 .093 No 99 19 LMS, mean ± SD 9.46 ± 7.07 NA — No. of surgeries, mean ± SD† 1.81 ± 1.82 NA — Duration of CRS (y), mean ± SD 12.43 ± 10.28 NA — Total SNOT-22 scores 32.57 ± 24.65 13.28 ± 20.9 .001 Table E1 Demographic and clinical characteristics of 111 patients with CRS and 21 control subjects SNOT-22 score LMS Taxonomic level Correlation coefficient (R) FDR P value Correlation coefficient (R) FDR P value Phyla Actinobacteria −0.043 .651 −0.054 .576 Bacteroidetes −0.015 .874 −0.002 .984 Firmicutes 0.041 .670 −0.070 .465 Proteobacteria 0.052 .586 0.138 .150 Genera Corynebacterium −0.050 .600 −0.053 .578 Prevotella −0.233 .014* 0.064 .507 Staphylococcus 0.039 .684 −0.117 .220 Alloiococcus 0.040 .675 0.019 .840 Lactobacillus −0.114 .233 0.097 .312 Streptococcus 0.111 .246 0.036 .709 Ruminococcus 0.040 .679 −0.055 .566 Anaerococcus −0.158 .097 −0.032 .740 Finegoldia −0.100 .297 −0.119 .213 Peptoniphilus −0.131 .171 −0.080 .405 Burkholderia 0.070 .463 −0.095 .322 Enterobacteriaceae −0.043 .657 0.189 .045* Haemophilus −0.004 .963 0.065 .496 Moraxella 0.029 .760 0.092 .337 Pseudomonas −0.112 .243 −0.164 .086 Table E2 Correlation between RA of selected sequences derived from individual taxa and CRS symptom scores measured by using SNOT-22 scores and LMSs in 111 patients with CRS Pathway Patients with CRS vs control subjects Patients with CRS with allergic rhinitis vs patients with CRS without allergic rhinitis Patients with CRS, RA mean Control subjects, RA mean Patients with CRS/control subjects, ratio P value∗ Patients with CRS with AR, RA mean Patients with CRS without AR, RA mean Patients with CRS with AR/CRS without AR, ratio P value∗ Bacterial invasion of epithelial cells 191,003.76 63,446.76 3.01 .03* 228,467.99 154,631.88 1.47 .11 LPS biosynthesis proteins 74,341.98 32,890.76 2.26 .04* 98,073.42 34,233.58 2.86 .04* Bacterial toxin production 49,787.94 21,994.04 2.26 .09 42,846.76 53,209.27 1.24 .67 RNA transport 20,471.63 11,123.90 1.84 .26 23,423.63 17,433.20 1.34 .24 β-Lactam resistance 9,167.56 5,300.62 1.72 .23 10,178.52 8,350.42 0.82 .86 Bacterial motility 152,008.65 108,989.38 1.39 .40 142,468.64 154,781.02 1.08 .87 Bacterial chemotaxis 234.18 174.80 1.33 .54 150.31 165.18 1.09 .79 Bacterial secretion system 17,863.96 15,339.47 1.16 .87 17,346.11 17,887.11 1.03 .91 Table E3 Differences in selected KEGG pathways using PICRUSt analysis of nasal microbiomes