Clinical and laboratory characteristics of individuals included in the study.
Clinical, laboratorial, and nutritional characteristics of cases with obesity and eutrophic controls are shown in Table 1. There were no differences between cases and controls regarding age, gender, and energy intake. Moreover, both groups had a comparable dietary macronutrient composition. As expected, subjects with obesity presented higher waist circumference, glucose, total cholesterol, and triglyceride levels compared to normal weight individuals. Additionally, cases also presented elevated levels of metabolic markers such as insulin, leptin, TyG, and HOMA-IR indexes and, lower levels of METs compared to controls.
Table 1
Clinical, dietary, and laboratory characteristics of the sample included in the study.
Characteristics | Subjects with Obesity (cases, n = 78) | Eutrophic individuals (controls, n = 25) | P-value* |
Age (years) | 46.6 ± 9.4 | 44.7 ± 9.1 | 0.106 |
Gender (% male) | 36.1 | 40.0 | 0.443 |
Anthropometric and clinical data | | |
BMI (kg/m2) | 32.9 ± 2.4 | 18.6 ± 2.1 | - |
WC (cm) | 104.9 ± 10.2 | 75.2 ± 7.6 | 0.0001 |
SBP (mmHg) | 131 ± 16 | 111 ± 10 | 0.0001 |
DBP (mmHg) | 86 ± 9 | 70 ± 8 | 0.0001 |
Metabolic profile | | | |
FPG (mg/dl) | 97.4 ± 11.9 | 85.3 ± 6.8 | 0.0001 |
TC (mg/dl) | 222.5 ± 40.1 | 192.6 ± 37.1 | 0.001 |
HDL-c (mg/dl) | 54.2 ± 14.0 | 61.6 ± 12.7 | 0.022 |
TG (mg/dl) | 101.6 ± 54.1 | 65.6 ± 25.0 | 0.002 |
TyG index | 4.6 ± 0.3 | 4.2 ± 0.2 | 0.0001 |
HOMA-IR index | 1.6 (1.1–2.8) | 0.6 (0.4–1.0) | 0.0001 |
Adiponectin (ng/ml) | 10.9 (7.9–13.5) | 12.2 (9.3–15.7) | 0.067 |
Insulin (mU/L) | 6.8 (4.7–11.5) | 3.2 (2.8–4.8) | 0.0001 |
Leptin (ng/ml) | 33.1 (17.2–46.8) | 4.9 (2.1–11.7) | 0.0001 |
Body composition | | | |
Fat mass (%) | 34.7 ± 6.5 | 13.6 ± 5.7 | 0.0001 |
Lean mass (%) | 57.0 ± 11.7 | 47.6 ± 12.2 | 0.001 |
Dietary intake and energy expenditure | | |
Energy (Kcal) | 2,961 ± 1051 | 2,588 ± 701 | 0.101 |
Carbohydrates (%) | 41.4 ± 7.1 | 44.8 ± 6.4 | 0.034 |
Protein (%) | 16.7 ± 2.9 | 15.9 ± 3.4 | 0.245 |
Fat (%) | 40.1 ± 6.4 | 37.7 ± 4.9 | 0.100 |
Fiber (g/day) | 27.9 ± 11.4 | 32.7 ± 11.5 | 0.070 |
METs (kcal/kg/h) | 17.0 (7.5–27.0) | 33.2 (20.0–44.4) | 0.001 |
Variables are shown as mean ± SD, median (25th–75th percentiles) or %, as appropriate. *P-values were computed using χ2 or Student´s t-test, as appropriated. |
BMI: body mass index; DBP: diastolic blood pressure; FPG: fasting plasma glucose; HDL-c: high-density lipoprotein cholesterol; HOMA-IR index: homeostatic model assessment-insulin resistance index; METs: metabolic equivalents; SBP: systolic blood pressure; TC: total cholesterol; TG: triglycerides; TyG index: triglyceride glucose index; WC: waist circumference. |
Quality control of miRNA expression
The RNA spike-in expressions presented low variation in Cq among samples in RNA isolation and cDNA synthesis, demonstrating that extraction, reverse transcription, and qPCR were effective, and none of the samples contained inhibitors. As expected, the expression of UniSp2, UniSp4, UniSp5, and UniSp6 did not differ between groups (cases vs. controls). UniSp5 was expressed in all analyzed samples, demonstrating that miRNAs expressed in low levels was not lost during isolation. The ratio between miR-451a and miR-23a-3p ranged between 5 and − 1, indicating that the samples were not affected by hemolysis. Generally, these results showed a good and similar level of sample quality and reproducibility of the miRNA profiling processes.
MicroRNAs differentially expressed in plasma of patients with obesity.
Expression of 86 target miRNAs was evaluated in plasma of subjects with obesity and in normal weight individuals. Of these 86 miRNAs, 61 were expressed in at least 20% of the sample with Cq values ≥ 35. Of these 61 miRNAs, 26 were differentially expressed between cases and controls after FDR correction (Table 2).
Table 2
Relation of 26 microRNAs whose expression profile in plasma is significantly different between cases with obesity and eutrophic controls.
miRNA | Subjects with obesity (cases, n = 78) | Eutrophic individuals (controls, n = 25) | P-value* | q-value** |
miR-103a-3p | 0.169 (0.057–0.443) | 0.567 (0.263–1.426) | 0.006 | 0.020 |
miR-107 | 0.201 (0.067–0.520) | 0.614 (0.309–1.386) | 0.014 | 0.038 |
miR-130a-3p | 19.321 (6.029–32.947) | 47.904 (31.421–79.317) | 0.005 | 0.004 |
miR-130b-3p | 0.208 (0.096–0.433) | 0.442 (0.221–1.426) | 0.003 | 0.015 |
miR-140-3p | 0.237 (0.076–0.578) | 0.872 (0.548–1.561) | 0.0001 | 0.0012 |
miR-142-5p | 0.118 (0.036–0.214) | 0.279 (0.162–0.941) | 0.002 | 0.007 |
miR-144-3p | 1.308 (0.276–2.872) | 6.903 (2.036–11.542) | 0.0001 | 0.0012 |
miR-148a-3p | 0.259 (0.096–0.479) | 0.647 (0.212–1.135) | 0.006 | 0.019 |
miR-181a-5p | 0.549 (0.229–1.107) | 1.633 (0.369–3.021) | 0.006 | 0.008 |
miR-183-5p | 0.374 (0.244–0.649) | 0.775 (0.476–1.928) | 0.001 | 0.009 |
miR-185-5p | 0.198 (0.083–0.480) | 0.665 (0.288–1.272) | 0.034 | 0.040 |
miR-200c-3p | 0.540 (0.257–1.118) | 1.001 (0.454–2.161) | 0.037 | 0.044 |
miR-205-5p | 0.163 (0.122–0.559) | 0.711 (0.319–1.322) | 0.005 | 0.020 |
miR-21-5p | 0.257 (0.137–0.615) | 0.559 (0.261–1.238) | 0.036 | 0.041 |
miR-210-3p | 0.110 (0.071–0.245) | 0.503 (0.117–0.672) | 0.030 | 0.078 |
miR-221-3p | 0.212 (0.065–0.454) | 0.555 (0.268–1.549) | 0.004 | 0.013 |
miR-222-3p | 0.311 (0.158–0.712) | 0.859 (0.470–1.396) | 0.005 | 0.019 |
miR-15a-5p | 0.118 (0.060–0.322) | 0.356 (0.155–0.877) | 0.022 | 0.054 |
miR-22-3p | 0.126 (0.056–0.286) | 0.320 (0.127–1.027) | 0.012 | 0.034 |
miR-29c-3p | 0.137 (0.068–0.350) | 0.453 (0.202–1.269) | 0.007 | 0.020 |
miR-30a-5p | 0.629 (0.294–1.347) | 1.426 (0.515–2.062) | 0.043 | 0.048 |
miR-30c-5p | 0.274 (0.093–0.625) | 0.694 (0.235–1.462) | 0.042 | 0.050 |
miR-33a-5p | 1.268 (0.573–2.425) | 4.54 (1.009–23.596) | 0.012 | 0.016 |
miR-375 | 0.228 (0.124–0.609) | 0.765 (0.484–1.563) | 0.0001 | 0.002 |
miR-424-3p | 0.892 (0.698–1.135) | 2.000 (0.834–3.127) | 0.016 | 0.030 |
miR-486-3p | 0.301 (0.190–0.570) | 0.645 (0.238–1.326) | 0.004 | 0.009 |
Data are shown as median (25th–75th percentiles) of n-fold values. *P-values were obtained using Student t test using the log-transformed variable. **P-values were corrected using false discovery rate (FDR; q-value). |
All these 26 miRNAs were negatively correlated with BMI (P ≤0.05). Moreover, miR-107, miR-130a-3p, miR-140-3p, miR-142-5p, miR-144-3p, miR-181a-3p, miR-21-5p, miR-221-3p, miR-375, and miR-424-3p expressions were negatively associated with glucose levels (P < 0.05). Otherwise, miR-200c-3p and miR-375 positively correlated with HDL-c levels (r = 0.232, P = 0.047; and r = 0.295, P = 0.015, respectively). Regarding hormone levels, miR-140-3p and miR-144-3p were negatively associated with leptin levels (r= -0.222, P = 0.034; and r= -0.245, P = 0.025, respectively), miR-144-3p and miR-183-5p were inversely correlated with insulin (r= -0.245, P = 0.032; and r= -0.264, P = 0.033, respectively) and adiponectin levels were positively associated with miR-375 (r = 0.272, P = 0.025) and miR-424-3p (r = 0.405, P = 0.012).
Gut microbiota profile in subjects with obesity compared to eutrophic individuals
The effect of obesity on gut microbiota composition was investigated at the genus and species levels. The levels of eighteen bacterial genera were significantly different when comparing obese and normal weight individuals, being nine bacterial genera significantly increased in obese subjects when compared to controls (Fig. 1A). Twelve bacterial species were statistically different between obese and normal weight individuals, being ten of them more abundant in subjects with obesity compared to eutrophic individuals (Fig. 1B and Table 3).
Table 3
Bacterial species whose abundance is statistically different when comparing cases with obesity and eutrophic controls.
Bacteria | Subjects with obesity (cases, n = 78) | Eutrophic individuals (controls, n = 25) | P-value* | q-value** |
Abiotrophia defectiva | 0.0001 (0.0001–0.2825) | 0.0001 (0.0001–0.001) | 0.0001 | - |
Actinomyces odontolyticus | 0.0001 (0.0001–1.643) | 0.0001 (0.0001–0.652) | 0.010 | - |
Allisonella histaminiformans | 0.0001 (0.0001- 5.260) | 0.0001 (0.0001–0.001) | 0.0001 | - |
Bacteroides eggerthii | 3.573 (1.903–10.086) | 9.769 (3.32–11.892) | 0.039 | 0.042 |
Barnesiella intestinihominis | 2.885 (0.730–5.809) | 1.889 (0.001–3.361) | 0.019 | 0.036 |
Dorea longicatena | 9.959 (9.295–10.557) | 9.547 (8.887–10.268) | 0.039 | 0.045 |
Haemophilus parainfluenzae | 5.834 (2.990–7.775) | 8.254 (6.181–10.708) | 0.001 | 0.002 |
Howardella ureilytica | 2.439 (0.0001–6.303) | 0.0001 (0.0001–2.476) | 0.010 | 0.023 |
Lactobacillus curvatus | 0.0001 (0.0001–1.626) | 0.0001 (0.0001–0.001) | 0.0001 | - |
Megamonas funiformis | 0.0001 (0.0001–0.974) | 0.0001 (0.0001–0.001) | 0.0001 | - |
Mitsuokella jaladudinii | 0.0001 (0.0001–2.521) | 0.0001 (0.0001–0.001) | 0.002 | - |
Odoribacter laneus | 0.0001 (0.0001–2.707) | 0.0001 (0.0001–1.015) | 0.027 | - |
Data are shown as median (25th − 75th percentiles). *P-values were obtained using Student t test using the log-transformed variable. **P-values were corrected for false discovery rate (FDR; q-value) using the Benjamini-Hochberg test. |
Shannon index, which reflects the alpha diversity, was not different between obese and normal weight groups (Supplementary Fig. 1). However, the beta diversity values of gut microbiota, based on Jaccard index (PERMANOVA, P = 0.025; Supplementary Fig. 2A) and Bray-Curtis dissimilarity (PERMANOVA, P = 0.015; Supplementary Fig. 2B), was significantly different between groups.
Crosstalk between host miRNAs and gut microbiota
To further investigate the relationships between circulating miRNAs and the gut microbiota composition, interactions between bacteria and miRNAs differentially expressed in obesity were analyzed. At the genus level, of the 18 genera differently expressed in obesity, 9 were significantly correlated with the expression of 10 miRNAs out of 26 miRNAs differently expressed in subjects with obesity (Fig. 2A). Fourteen of these miRNAs were significantly associated with 4 bacterial species (Dorea longicatena, Banesiela intestinihominis, Bacteroides eggerthii, and Haemophillus parainfluenzae), as illustrated in Fig. 2B and Fig. 2C.
A diagram was built to visualize the relationships between the miRNAs and their significantly correlated bacteria (Fig. 2C). The correlation network shows a highly interconnected relationship between these miRNAs and bacterial species. Interestingly, B. eggerthii negatively correlated with miR-103a-3p, miR-21-5p, miR-130a-3p, miR-185-5p, miR-144-3p, miR-210-3p, miR-33a-5p, miR-15a-5p, miR-130b-3p, miR-183-5p, miR-221-3p, miR-222-3p, and miR-142-5p. Moreover, an interaction among miRNAs, B. eggerthi and BMI levels was found. Individually, the expression of miR-103a-3p (r2 = 0.1229, P = 0.051), miR-130b-3p (r2 = 0.0933, P = 0.021), miR-185-5p (r2 = 0.0894, P = 0.035), miR-21-5p (r2 = 0.1124, P = 0.008), and miR-210 (r2 = 0.0866, P = 0.052) interacted with these bacterial species and BMI levels. Furthermore, an interaction among three miRNAs levels (miR-130b-3p, miR-185-5p, and miR-21-5p), B. eggerthi and BMI levels was also evidenced (r2 = 0.148, P = 0.004). Interestingly, there was also an interaction among B. eggerthi, adiponectin levels, and miR-183-5p (r2 = 0.1294, P = 0.009).
In the same way, B. intestinihominis abundance was negatively associated with miR-107, miR-103a-3p, miR-222-3p, and miR-142-5p expressions. The expression of miR-15a-5p was inversely associated with the abundance of H. parainfluenzae, and an interaction with insulin levels (r2 = 0.0592, P = 0.027) was found. In contrast, D. longicatena was positively associated with miR-21-5p, miR-130a-3p, miR-185-5p, and miR-144-3p. However, interactions among the bacterial abundance, miRNA expression, and BMI levels were not found for these three bacterial species. No association among the bacterial species, miRNAs, and leptin was found.
Predicted functions of miRNAs correlated with obesity-associated bacteria
Target gene prediction of the 14 miRNAs that correlated with the 4 bacterial species associated with obesity were investigated (Supplementary Table 2). Of the total 9,584 genes identified as potential targets of these miRNAs, 5,381 were found to be regulated by two or more miRNAs; however only 719 were experimentally validated (Supplementary Table 2). After that, functional enrichment analysis of miRNA targets was carried out to explore biological pathways possibly regulated by this set of miRNAs. A total of 248 pathways were significantly enriched (q-value < 0.05) for these miRNAs (Supplementary Table 3). However, considering only the experimentally validated target genes, 98 pathways were significantly enriched (Supplementary Table 3).
As shown in Fig. 3, H. parainfluenzae, D. longicatena, B. intestinihominis, and B. eggerthii correlated with miRNAs associated with pathways related to obesity and metabolic processes, including carbohydrate and lipid turnover, endocrine and inflammatory signaling pathways. More specifically, the target genes of miRNAs associated with the four bacterial species related to obesity participate in the fatty acid degradation, mineral absorption, carbohydrate digestion and absorption, insulin signaling pathway, and glycerolipid metabolism.