The detection of bacterial-specific volatile metabolites may be a valuable tool to predict infection. Here we applied a real-time mass spectrometric technique to investigate differences in volatile metabolic profiles of oral bacteria that cause periodontitis. We coupled a secondary electrospray ionization (SESI) source to a commercial high-resolution mass spectrometer to interrogate the headspace from bacterial cultures and human saliva. We identified 120 potential markers characteristic for periodontal pathogens Aggregatibacter actinomycetemcomitans (n = 13), Porphyromonas gingivalis (n = 70), Tanerella forsythia (n = 30) and Treponema denticola (n = 7) in in vitro cultures. In a second proof-of-principle phase, we found 18 (P. gingivalis, T. forsythia and T. denticola) of the 120 in vitro compounds in the saliva from a periodontitis patient with confirmed infection with P. gingivalis, T. forsythia and T. denticola with enhanced ion intensity compared to two healthy controls. In conclusion, this method has the ability to identify individual metabolites of microbial pathogens in a complex medium such as saliva.
The detection of bacterial-specific volatile metabolites may be a valuable tool to predict infection. Here we applied a real-time mass spectrometric technique to investigate differences in volatile metabolic profiles of oral bacteria that cause periodontitis. We coupled a secondary electrospray ionization (SESI) source to a commercial high-resolution mass spectrometer to interrogate the headspace from bacterial cultures and human saliva. We identified 120 potential markers characteristic for periodontal pathogens Aggregatibacter actinomycetemcomitans (n = 13), Porphyromonas gingivalis (n = 70), Tanerella forsythia (n = 30) and Treponema denticola (n = 7) in in vitro cultures. In a second proof-of-principle phase, we found 18 (P. gingivalis, T. forsythia and T. denticola) of the 120 in vitro compounds in the saliva from a periodontitispatient with confirmed infection with P. gingivalis, T. forsythia and T. denticola with enhanced ion intensity compared to two healthy controls. In conclusion, this method has the ability to identify individual metabolites of microbial pathogens in a complex medium such as saliva.
Periodontal bacterial infections are one of the most severe dental diseases, often even leading to tooth loss if untreated1. The disease is characterized by a progressive damage of the periodontal soft and hard tissue and is frequently accompanied by four types of bacteria that can colonize the mouth: Aggregatibacter actinomycetemcomitans, Porphyromonas gingivalis, Tanerella forsythia and Treponema denticola. Often, patients suffering of periodontitis only show indicating symptoms at a late stage, when the development of the disease has led to enhanced tooth mobility.Today, dentists are identifying this disease mainly by visual inspections of the teeth, looking for specific signs of inflammation like bleeding upon probing and increased periodontal pocket depths2. In addition, bacteria samples from periodontal pockets can nowadays be analyzed using microbiological techniques to identify potential disease related bacterial species3. However there is still a lack of methods with the combined power of fast, sensitive and specific analysis of such bacteria. Early on, it was recognized that saliva is an ideal fluid to analyze the processes in the mouth and it is being used for clinical diagnostics. Saliva contains many different compound classes, including small molecules, proteins or enzymes, which also means that it contains rich information related to processes taking place in the oral cavity4.In the last decades, mass spectrometry has been shown to be a powerful tool to analyze biological samples like blood, urine, saliva, and breath, for applications in clinical chemistry and in toxicology567. Traditionally, hyphenated methods like gas chromatography – mass spectrometry (GC-MS) or liquid chromatography – mass spectrometry (LC-MS) were used to analyze the headspace and the culture solution of bacteria respectively8. After some pioneering developments9101112, several real-time techniques for the analysis of gas and vapor samples were developed in recent years, e.g., proton transfer reaction – mass spectrometry (PTR-MS) and selected ion flow tube – mass spectrometry (SIFT-MS). A similarly powerful analytical tool is secondary electrospray ionization – mass spectrometry (SESI-MS), where vapor species are ionized at atmospheric pressure and are subsequently detected by any commercial mass spectrometer of choice. It has shown promise in a number of applications calling for fast and sensitive analysis of vapors13141516. It has been extensively used for the analysis of volatile metabolic “fingerprints”, including bacterial species1718192021. A key element is that, with minor modifications, one can take advantage of the power of modern mass spectrometers, especially their high resolving power, sensitivity and MSMS capabilities. This is crucial in real-time analysis because, in the absence of prior chromatographic separation, metabolite detection and accurate identification relies exclusively in MS performance. As a result, SESI combined with high performance MS results in rich breathprints covering volatiles and semi-volatiles, e.g., fatty acids22.Following the idea of fast, sensitive and selective diagnostics, e.g., in dentistry, we show here the first untargeted headspace analyses of oral bacteria A. actinomycetemcomitans, P. gingivalis, T. denticola and T. forsythia with high-resolution SESI-MS. In a first in vitro part of our study, we analyzed the headspace of 5 independent biological replicate cultures from each bacterium. In a second phase, we tested whether the set of molecules found to be discriminatory in the in vitro study could also be found in saliva samples from one periodontitispatient.
Results and Discussion
Untargeted bacteria culture headspace analysis
Figure 1 shows that SESI-MS is a suitable real-time method to analyze volatiles accumulated in the headspace of bacteria medium. The total ion current from headspace injections of the four different bacteria strains and of the mixed medium is shown (total of 25 measurements; Panel A). Note how the total intensity rises sharply during the injection of the gas sample and decays within ~1 minute to the baseline level. Also of note is that the 25 mass spectrometric measurements were completed within 30 minutes, without any sample pretreatment. Extracted ion time-profiles for four selected mass peaks are plotted (B-E). Importantly, the intensities of the biological replicates are in most cases comparable, with the exception of the first T. denticola biological replicate at m/z 120.0641, which shows a lower intensity. By mere visual inspection of the four m/z time-profiles alone, one can already easily distinguish that each compound exhibit a different intensity in each of the bacterial strains investigated.
Figure 1
Headspace analysis showing the total ion chromatogram (A) and extracted ion chromatograms of four bacteria specific compounds (B - E): m/z 85.0630 (A. actinomycetemcomitans specific), m/z 92.0486 (T. forsythia specific), m/z 120.0641 (T. denticola specific) and m/z 132.0995 (P. gingivalis specific).
Figure 2 displays the data distribution per group for the selected features shown in Fig. 1. It can be noticed that the intensities of the biological replicates from one bacterial strain are significantly enhanced in comparison to the other three strains. With the exception of the T. denticola outlier for m/z 120.0641, these four volatiles alone could distinguish the four strains investigated. It is visible that the ion intensities for the non-specific strains is not equal to zero. This is reasonable, since it is unlikely that these metabolites are completely unique to one strain, but rather likely that they are produced in higher concentrations for a specific strain.
Figure 2
Plots showing the ion intensities of the headspace samples from all four bacteria cultures for four selected compounds m/z 85.0630 (Aa—A. actinomycetemcomitans specific), m/z 92.0486 (Tf – T. forsythia specific), m/z 120.0641 (Td – T. denticola specific) and m/z 132.0995 (Pg – P. gingivalis specific).
The horizontal red line represents the mean, while the vertical red line indicate standard deviation. The blue brackets connecting the boxes indicate a significant difference between the biological replicates originating from two bacterial strains. The number of stars indicate the range of p-values from the multiple comparison test (Tukey-Kramer procedure): * (0.01< p ≤ 0.05), ** (0.001 < p ≤ 0.01) and *** (p ≤ 0.001).
Apart from these four examples, the volatile fingerprints of the bacterial strains revealed a total of 120 bacteria-specific compounds (Table 1): 13 for A. actinomycetemcomitans, 70 for P. gingivalis, 7 for T. denticola and 30 for T. forsythia. The large number of P. gingivalis, A. actinomycetemcomitans and T. forsythia-specific compounds makes it very distinct, in contrast to T. denticola. Table 1 lists the molecular formulas of these compounds, along with p- and q-values. Supplementary Tables S1 and S2 provide further details on actual ion intensities for all bacterial cultures and culture medium; and pairwise comparisons, respectively.
Table 1
List of 120 discriminative compounds for in vitro bacteria cultures of A. actinomycetemcomitans (n = 13), P. gingivalis (n = 70), T. denticola (n = 7) and T. forsythia (n = 30).
Bacterial strain
m/z
Sum formula
Mass deviation from experimental mass (ppm)
in vitro comparisons
Present in patient’s saliva (IAI Pado-Test 4.5)
Enhanced in patient vs. controls
Enhancement ratio
p value
q value
patient/control 1
patient/control 2
Aa
43.0180
C2H3O
3.7
1.93E-05
1.32E-07
no
not present
–
–
Aa
58.8706
–
–
1.85E-03
4.13E-06
no
no
0.18
0.15
Aa
58.9993
–
–
2.04E-03
4.50E-06
no
not present
–
–
Aa
59.0134
C2H3O2
11
3.32E-03
6.59E-06
no
not present
–
–
Aa
59.0481
C3H7O
11
1.21E-03
3.11E-06
no
no
0.22
0.15
Aa
65.0369
C5H5
26
3.35E-05
1.97E-07
no
yes
27.42
88.89
Aa
67.0522
C5H7
1.9
8.09E-05
3.87E-07
no
no
0.43
0.31
Aa
84.8031
–
–
1.24E-03
3.13E-06
no
not present
–
–
Aa
85.0630
C5H9O
3.4
8.50E-06
7.04E-08
no
no
0.50
0.42
Aa
87.0667
C2H7N4
2
3.18E-04
1.16E-06
no
not present
–
–
Aa
99.0778
C6H11O
12
1.92E-04
7.69E-07
no
no
0.33
0.18
Aa
117.0886
C6H13O2
1.8
4.28E-04
1.43E-06
no
no
1.23
1.33
Aa
144.1094
C6H14N3O4
24
1.29E-04
5.71E-07
no
no
0.82
0.86
Pg
79.0189
C5H3O
1.8
6.18E-04
1.91E-06
yes
yes
3.28
4.65
Pg
88.0738
C4H10NO
2.2
7.25E-08
2.20E-09
yes
no
1.55
2.21
Pg
97.0258
C5H5O2
0.1
6.27E-06
5.50E-08
yes
no
0.34
0.30
Pg
97.0619
C6H9O
0.9
1.48E-09
2.17E-10
yes
no
0.47
0.48
Pg
100.0450
C4H6NO2
57
1.03E-04
4.61E-07
yes
no
1.15
1.23
Pg
107.0669
C4H11O3
31
9.38E-04
2.64E-06
yes
yes
3.06
3.53
Pg
110.0579
C6H8NO
19
3.39E-04
1.19E-06
yes
no
1.13
1.40
Pg
111.0414
C6H7O2
3.2
8.31E-08
2.33E-09
yes
no
0.37
0.38
Pg
111.0771
C7H11O
0.2
8.71E-09
4.82E-10
yes
no
1.02
1.12
Pg
113.0573
C6H9O2
1.8
3.02E-07
5.55E-09
yes
no
0.94
1.38
Pg
115.0355
C5H7O3
23
7.75E-04
2.29E-06
yes
no
0.20
0.23
Pg
115.0730
C6H11O2
0.2
2.48E-07
4.87E-09
yes
no
0.75
0.85
Pg
116.0498
C8H6N
9.3
1.00E-06
1.23E-08
yes
no
0.62
0.44
Pg
116.0680
C5H10NO2
1.8
3.62E-09
3.11E-10
yes
no
0.84
1.08
Pg
121.0274
C7H5O2
8.3
1.42E-05
1.07E-07
yes
no
1.19
1.26
Pg
125.0568
C7H9O2
3.2
4.59E-05
2.50E-07
yes
no
0.33
0.40
Pg
125.0931
C8H13O
24
4.84E-07
7.30E-09
yes
no
1.37
1.55
Pg
127.0732
C7H11O2
2
9.02E-09
4.82E-10
yes
no
0.60
0.81
Pg
128.0754
C3H6N5O
2.2
1.30E-09
2.17E-10
yes
no
0.67
0.84
Pg
128.1042
C7H14NO
0.2
1.11E-07
2.97E-09
yes
yes
2.41
3.03
Pg
129.0511
C2H5N6O
1
7.85E-06
6.60E-08
yes
no
0.68
0.68
Pg
129.0881
C7H13O2
0.8
2.28E-07
4.63E-09
yes
no
0.77
0.83
Pg
130.0611
C9H8N
3.3
6.46E-06
5.51E-08
yes
yes
2.50
2.67
Pg
130.0838
C6H12NO2
2.7
3.04E-04
1.11E-06
yes
no
1.21
1.68
Pg
132.0995
C6H14NO2
3.1
1.61E-08
7.90E-10
yes
no
1.34
1.73
Pg
134.0785
C5H12NO3
2.8
9.89E-04
2.71E-06
yes
not present
–
–
Pg
136.0722
C8H10NO
21
2.11E-05
1.39E-07
yes
no
0.93
1.32
Pg
137.0216
C7H5O3
7.9
1.13E-05
8.99E-08
yes
yes
2.35
2.75
Pg
138.0143
C6H4NO3
31
6.63E-05
3.33E-07
yes
yes
2.49
2.89
Pg
140.1120
C8H14NO
31
1.70E-06
1.96E-08
yes
no
0.55
0.64
Pg
141.0990
C8H13O2
2.9
2.54E-04
9.63E-07
yes
not present
–
–
Pg
143.1035
C8H15O2
1.8
1.18E-05
9.23E-08
yes
no
0.87
0.80
Pg
144.1349
C8H18NO
4.1
7.70E-05
3.73E-07
yes
no
1.75
2.51
Pg
145.0988
C6H13N2O2
1.8
9.63E-04
2.68E-06
yes
no
0.41
0.43
Pg
147.0421
C9H7O2
2.4
8.27E-04
2.42E-06
yes
no
0.27
0.31
Pg
148.0756
C9H10NO
0.6
7.67E-04
2.28E-06
yes
yes
4.07
4.39
Pg
149.0780
C6H13O4
2.2
5.36E-04
1.71E-06
yes
no
1.27
2.02
Pg
153.0607
C8H9O3
2.1
1.06E-03
2.87E-06
yes
not present
–
–
Pg
155.1042
C9H15O2
1.7
6.00E-06
5.35E-08
yes
no
0.60
0.92
Pg
157.1191
C9H17O2
1.9
3.70E-09
3.11E-10
yes
no
0.56
0.65
Pg
158.0824
C7H12NO3
13
2.31E-05
1.51E-07
yes
not present
–
–
Pg
158.1515
C9H20NO
17
2.82E-04
1.06E-06
yes
yes
7.06
8.68
Pg
159.1093
C7H15N2O2
2.5
2.63E-05
1.63E-07
yes
yes
2.04
2.96
Pg
162.0949
C7H16NOS
1.2
3.69E-04
1.28E-06
yes
not present
–
–
Pg
163.0716
C6H7N6
2.3
9.74E-05
4.41E-07
yes
no
0.63
0.76
Pg
165.0992
C10H13O2
4.3
6.82E-03
1.20E-05
yes
yes
2.06
2.76
Pg
171.0982
C5H11N6O
0.5
5.19E-05
2.73E-07
yes
no
0.94
1.33
Pg
171.1351
C6H15N6
1
1.29E-10
3.80E-11
yes
no
0.64
0.79
Pg
171.1461
C9H19N2O
2.3
7.37E-08
2.20E-09
yes
no
1.91
2.42
Pg
172.1664
C10H22NO
2.3
2.57E-03
5.41E-06
yes
no
1.65
1.82
Pg
175.1297
C5H15N6O
1.1
1.39E-04
5.97E-07
yes
no
1.98
2.21
Pg
179.0607
C9H11N2S
3.6
6.86E-05
3.42E-07
yes
no
1.06
1.69
Pg
179.1042
C7H11N6
1
1.26E-06
1.51E-08
yes
no
0.48
0.59
Pg
182.1148
C10H16NO2
15
1.97E-05
1.32E-07
yes
not present
–
–
Pg
185.1506
C11H21O2
3.8
4.34E-07
7.09E-09
yes
no
0.47
0.53
Pg
189.1455
C10H21O3
3.8
1.37E-03
3.34E-06
yes
yes
2.34
2.52
Pg
191.1394
C13H19O
3.4
1.46E-07
3.43E-09
yes
no
0.67
0.88
Pg
192.1645
C9H22NO3
26
6.97E-05
3.42E-07
yes
not present
–
–
Pg
193.1416
C9H21O4
3.8
3.64E-05
2.12E-07
yes
no
3.10
0.91
Pg
195.1349
C12H19O2
4.9
2.13E-04
8.45E-07
yes
no
0.60
0.67
Pg
199.1299
C7H15N6O
8.5
3.30E-04
1.18E-06
yes
no
0.62
0.88
Pg
199.1676
C12H23O2
1.3
8.04E-07
1.06E-08
yes
no
1.15
1.28
Pg
201.1095
C10H17O4
4.2
4.80E-07
7.30E-09
yes
no
1.59
2.37
Pg
204.1399
C13H18NO
7.9
5.05E-09
3.30E-10
yes
yes
3.35
4.18
Pg
205.1409
C10H21O4
3.6
2.29E-04
8.82E-07
yes
no
1.30
2.33
Pg
206.1358
C9H20NO4
14
2.28E-06
2.48E-08
yes
not present
–
–
Pg
214.0884
C10H16NO2S
3.4
1.90E-04
7.69E-07
yes
no
0.18
0.22
Pg
235.1863
C15H23O2
3.6
2.60E-05
1.62E-07
yes
not present
–
–
Pg
236.1563
C10H22NO5
30
8.80E-05
4.08E-07
yes
not present
–
–
Pg
251.1842
C12H27O5
2.8
1.55E-04
6.45E-07
yes
yes
7.77
7.74
Td
119.0570
C7H7N2
28
1.84E-04
7.57E-07
yes
not present
–
–
Td
120.0641
C4H10NO3
1.8
7.35E-04
2.22E-06
yes
no
0.02
0.03
Td
120.0796
C3H10N3O2
11
5.00E-04
1.63E-06
yes
no
0.67
0.41
Td
121.0819
C5H13O3
3.5
4.33E-04
1.44E-06
yes
yes
2.47
2.36
Td
136.1085
C9H14N
2
7.73E-05
3.73E-07
yes
no
0.53
1.09
Td
136.1299
–
–
1.18E-03
3.06E-06
yes
no
0.02
0.03
Td
150.1237
C5H16N3O2
25
2.55E-03
5.40E-06
yes
no
0.57
0.60
Tf
44.0492
C2H6N
6.2
6.46E-05
3.30E-07
yes
not present
–
–
Tf
50.0164
–
–
9.77E-04
2.70E-06
yes
not present
–
–
Tf
53.0378
C4H5
15
5.14E-04
1.65E-06
yes
not present
–
–
Tf
55.0279
C2H3N2
21
4.49E-09
3.30E-10
yes
not present
–
–
Tf
55.0530
C4H7
24
5.54E-08
2.04E-09
yes
no
0.65
0.52
Tf
60.0801
C3H10N
1.3
3.22E-05
1.92E-07
yes
no
0.55
0.92
Tf
70.0716
–
–
4.08E-07
6.86E-09
yes
not present
–
–
Tf
73.0441
C2H5N2O
43
8.28E-07
1.06E-08
yes
no
0.22
0.22
Tf
73.0623
C4H9O
33
1.30E-07
3.19E-09
yes
yes
3.37
2.97
Tf
76.0809
–
–
1.57E-07
3.56E-09
yes
not present
–
–
Tf
77.0362
C6H5
31
7.41E-07
1.04E-08
yes
no
0.37
0.31
Tf
79.0515
C6H7
34
2.72E-07
5.17E-09
yes
no
0.34
0.27
Tf
87.0785
C5H11O
13
6.23E-08
2.16E-09
yes
no
1.51
1.09
Tf
89.0577
C4H9O2
2.3
4.73E-04
1.54E-06
yes
no
1.31
1.19
Tf
92.0486
C6H6N
9.5
6.21E-07
8.91E-09
yes
no
0.20
0.07
Tf
96.0859
C6H10N
53
9.65E-05
4.40E-07
yes
no
0.49
0.47
Tf
100.0180
C2H3N2O2
38
3.23E-04
1.16E-06
yes
yes
5.12
2.79
Tf
103.0630
C2H7N4O
13
1.75E-05
1.23E-07
yes
not present
–
–
Tf
103.0729
C5H11O2
1.5
1.77E-06
2.01E-08
yes
no
0.64
0.60
Tf
107.0463
C2H7N2O3
14
3.30E-07
5.88E-09
yes
no
0.29
0.29
Tf
109.0516
C3H9O4
19
4.33E-05
2.42E-07
yes
not present
–
–
Tf
109.0981
C8H13
2.5
5.06E-06
4.73E-08
yes
no
1.26
0.81
Tf
115.1081
C7H15O
3
2.78E-08
1.17E-09
yes
no
0.98
0.66
Tf
116.1120
C6H14NO
43
4.00E-04
1.36E-06
yes
not present
–
–
Tf
123.1131
C9H15
1.8
5.57E-06
5.04E-08
yes
no
0.29
0.43
Tf
127.1082
C8H15O
1.1
1.70E-05
1.20E-07
yes
yes
12.17
8.27
Tf
142.0290
C9H4NO
1.8
4.13E-12
2.43E-12
yes
yes
17.36
13.13
Tf
142.0474
C6H8NO3
17
2.52E-05
1.59E-07
yes
not present
–
–
Tf
183.1711
C12H23O
4.6
1.65E-05
1.19E-07
yes
no
0.31
0.32
Tf
189.1261
C13H17O
6.8
7.80E-04
2.29E-06
yes
no
0.78
0.59
Compounds in bold were found at least a factor of two more intense in the saliva of the patient vs. two controls.
For a better visualization, the 120 filtered mass features from all biological replicates from the four different bacteria strains were subjected to principal component analysis (PCA). Figure 3 shows the score plot for the first two principal components, explaining ~76% of the variance. It can be observed that the five biological replicates per strain tend to cluster together and each bacterial type occupies a distinct area. The first PC separates P. gingivalis and A. actinomycetemcomitans from the rest, whereas PC 2 separates T. forsythia from the other three strains. With the combined information of these two axes it is possible to differentiate all four bacterial strains.
Figure 3
Projection of the mass spectra from the biological replicates of the four different bacteria strains A. actinomycetemcomitans (circles), P. gingivalis (squares), T. denticola (triangles) and T. forsythia (rhomboids) onto a two-dimensional PCA subspace.
The replicates are clustering and the strains distinguishable from each other.
Targeted bacteria analysis in human saliva
In the second phase of this study, the set of bacteria-specific compounds found in vitro (Table 1, Tables S1 and S2) were sought in saliva samples from one patient and two healthy controls. The patient suffered a severe periodontitis, and P. gingivalis, T. denticola and T. forsythia bacteria were present. The number of bacteria was determined by the standard IAI Pado-Test 4.5: 0.45 ± 0.9 E6 Pg, 0.28 ± 0.53 E6 Td and 0.51 ± 1.02 E6 Tf bacteria (mean of the number of bacteria in four dental pockets). It has to be stated that only in 2 of 4 pockets the bacteria were present, although all pockets had a periodontal screening index equal to 4, which is the highest possible score for the disease. As expected, none of the four oral bacteria strains were found in the dental pockets of the controls. Out of the 120 bacteria-specific compounds identified in vitro, 94 were found to be present in saliva. This is consistent with previous studies showing that the transfer of potential markers from in vitro to in vivo is not always possible, due to different conditions such as different media23. Remarkably, 18 of these compounds were systematically present with enhanced ion intensities in the patient in comparison to the healthy controls. 13 compounds were related to P. gingivalis, 4 to T. forsythia and 1 to T. denticola. These compounds were systematically enhanced in the patient’s saliva as compared to the two controls (average patient/control ratio 4.7 and 4.6 for both controls). Supplementary Table S3 lists ion intensities and patient/controls ratios for each of the 120 molecules of interest. While the number of participants in this study is limited, the fact that 18 molecules are systematically detected both in in vitro cultures and enhanced in the saliva of a patient suffering periodontitis, supports the hypothesis that pathogen-related volatiles could be used as indicators of periodontitis development.
Conclusions
We have shown that with simple modifications of the atmospheric pressure interface of commercial mass spectrometers, a rapid screening of volatiles found in the headspace of bacterial cultures and saliva is feasible. SESI-MS produced rich mass spectrometric fingerprints of volatiles with masses up to >200 Da. In addition, the high accuracy and high mass resolution of the MS systems used in this study enabled us to provide molecular formulae of the bacteria-related chemicals with high confidence.During the initial headspace analysis of pure bacterial cultures, we were able to differentiate four oral bacteria strains: A. actinomycetemcomitans, P. gingivalis, T. denticola, and T. forsythia. The 120 most discriminative compounds found in vitro were then used for targeted analysis of the saliva samples from a severe periodontitispatient and two healthy controls. As a result, we found a set of 18 compounds highly increased in the saliva of the patient as compared to the controls. We conclude that this method has potential for clinical diagnosis of bacterial infections in the oral cavity such as periodontitis. Follow-up measurements with a larger cohort of patients and healthy controls should be accomplished to validate these preliminary results and to correlate the absolute number of oral bacteria with the volatile compounds abundance.
Methods
Bacteria cultures
Aggregatibacter actinomycetemcomitans and P. gingivalis were cultivated on Colombia Blood Agar (CBA) plates and afterwards used to inoculate 10 mL of a Brain Heart Infusion (BHI) liquid medium, under aerobic (A. actinomycetemcomitans) or anaerobic (P. gingivalis) conditions, at 37 °C. In the next step, 5% of the liquid culture was sub-cultivated under the same conditions for 24 hours. Treponema denticola was cultivated under anaerobic conditions in 10 mL of spirochetes medium OMIZ-W6824 for 5 days, and thereafter 10% of this volume were sub-cultured into the same medium and cultivated anaerobically at 37 °C for 8 days. Tannerella forsythia was cultivated under anaerobic conditions for 3 days. Thereafter, 10% volume was transferred in modified25 spirochetes medium OMIZ-W6824, and thereafter sub-cultured anaerobically at 37 °C for 3 days. All bacteria suspensions were adjusted to an optical density (OD) 550 nm = 0.5 and centrifuged at 4’200 rpm (3’600 g) for 10 minutes, at 4 °C. The supernatants were finally sterilized by filtration (pore diameter 0.2 μm), transferred into 20 mL glass vials with septa (Infochroma, Zug, Switzerland) and stored at −20 °C until further use. From each bacterial strain, five biological replicates were produced. It is important to note the bacteria were cultured in vitro in different specific media: BHI for A. actinomycetemcomitans and P. gingivalis; OMIZ-W68 for T. denticola and modified OMIZ-W68 for T. forsythia. To counteract artefact volatiles resulting from different media, the media were pooled prior headspace analysis.
Human subjects and standard oral bacteria tests
All three study participants were non-smoking male volunteers. One patient with severe periodontitis and two healthy controls were selected for the explorative targeted analysis of the human saliva samples. The participants were examined for their periodontitis status by a dentist. During a periodontal basic examination (PGU) the periodontal screening index (PSI) in six dental areas was measured. The criteria to be patient was to have a PSI of four (periodontal pocket depth deeper than 5.5 mm) in at least two of the dental sextants. A healthy control should have a maximum PSI of one (periodontal pocket depth not deeper than 3.5 mm) in all sextants. In addition, the absolute number of bacteria for all four strains (A. actinomycetemcomitans, P. gingivalis, T. denticola, T. forsythia) was determined in four periodontal pockets (teeth no. 16, 25, 36 and 46) with the commercially available IAI Pado-Test 4.5 (IAI AG, Zuchwil, Switzerland). The ethical committee of the Kanton Zürich (KEK, Stampfenbachstrasse 121, 8090 Zürich) approved the experiments (KEK-ZH-Nr. 2013–0353) and all volunteers gave written informed consent to participate. All experiments were carried out in accordance with the approved protocol.
Sample preparation
In the cultivation experiments three different media (BHI, OMIZ-W86, modified OMIZ-W86) were used. To avoid the assignment of media compounds as potential bacteria strain markers, all samples were spiked with the two remaining media. For example, 100 μL of the Aa or Pg samples were spiked with 100 μL OMIZ-W86 and 100 μL modified OMIZ-W86 medium, and likewise for the other two media. And 100 μL of each Tf sample was spiked with 100 μL BHI and 100 μL OMIZ-W86 medium. After vortexing, the sample vials were flushed with pressurized air (medicinal air, Pangas, Dagmersellen, Switzerland) with a flow rate of 2 L min−1.
Human saliva samples
From all test subjects 1–2 mL saliva were sampled into 20 mL glass vials (Infochroma, Zug, Switzerland) after they had not drunk, eaten, smoked or cleaned their teeth for one hour. The samples were stored at −18 °C between sampling and analysis. Before the measurements, the sample vials were brought to room temperature and flushed with pressurized air (medicinal air, Pangas, Dagmersellen, Switzerland) with a flow rate of 2 L min−1.
Secondary electrospray ionization – mass spectrometry (SESI-MS)
For this type of metabolomic analysis, a quadrupole time-of-flight instrument was chosen because of its high resolution and sensitivity. We interfaced a home-built SESI source with a TripleTOF 5600+ mass spectrometer (10’000 resolution at m/z 40 to 32’000 resolution at m/z 450/Applied Biosystems Sciex, Toronto, ON, Canada/Fig. 4). The standard ESI source was removed, the SESI source was installed on the “curtain plate” and the original curtain gas was replaced by an auxiliary gas supply (2.4 L min−1 of high purity nitrogen, heated to 60 °C). The SESI source consisted of a cylindrical stainless steel reaction chamber with two observation windows (glass), two inlets (nano electrospray and sample delivery) and one outlet (backpressure vent). Coaxially with the inlet of the mass spectrometer, an uncoated fused silica capillary (id 20 μm, TaperTip Emitters, New Objectives, Woburn, MA, USA) was fixed in the chamber wall to establish a nano electrospray. The spray was pneumatically (approx. 500 mbar overpressure of air) supplied with nanopure water (resistivity 18.2 MΩ cm, Barnstead Nanopure, Thermo Fisher Scientific, Waltham, MA, USA) and 0.1% formic acid (98%, for MS, Fluka, Sigma-Aldrich, Buchs, Switzerland) as solvent. To establish the nanoES, high voltage (3.6 kV) was taken from the mass spectrometer and applied to the solvent reservoir via a platinum wire. The nanoES was optically and electrically checked by a microscope (Specwell) and a multimeter (Uni-Trend, China). The spray current was optimized to 60–80 nA. The backpressure vent (Legris, Parker, Mesa, AZ, USA) was optimized for maximum signal intensity while introducing air with an overpressure of 10 mbar into the reaction chamber.
Figure 4
(a) Gas tight syringe with a headspace sample from the bacteria cultures (b) SESI reaction chamber with nano electrospray mounted coaxially with the mass spectrometer inlet. The sample was injected into the chamber, where secondary ionization takes place. (c) Quadrupole time-of-flight hybrid mass spectrometer for analyzing the ionized headspace samples in real-time. (d) Schematic diagram of the sample introduction system into the SESI-MS system. Photos are reproduced with permission, courtesy of AB Sciex Pte. Ltd.
For analysis, 10 mL headspace were extracted from the sample vials with a gas tight syringe (10 mL, Hamilton, Reno, NV, USA) and injected into the reaction chamber were secondary ionization by the nano-electrospray took place. For each bacterial strain and human subject, a clean syringe was used. The mass spectrometer was acquiring mass spectra (m/z 40–450) in positive ionization mode with an accumulation time of 1 s.
Compound identification
For further identification of the found biomarker with sum formula determination, the SESI source described for the TripleTOF 5600+ was adapted to a LTQ Orbitrap high-resolution mass analyzer (Thermo Fisher Scientific, Waltham, MA, USA) that has a resolution of 300’000 at m/z 60 to 100’000 at m/z 400. 100 μL of all 4 in vivo cultured bacteria samples spiked with the different media were mixed together and flushed with pressurized air (medicinal air, Pangas, Dagmersellen, Switzerland) with a flow rate of 2 L min−1. Afterwards 10 mL headspace were extracted and injected into the SESI – Orbitrap mass analyzer. The mass spectrometer was acquiring mass spectra (m/z 50–450) in positive ionization mode with an accumulation time of 1 s. Based on the exact mass sum formulae were provided based on the seven golden rules for sum formula determination by mass spectrometry26.
Data Analysis
Data pre-processing
The data was acquired and mass calibrated with the Analyst TF 1.7 and PeakView 2.1 software (Applied Biosystems Sciex, Toronto, ON, Canada). The mass spectrometric spectra of all bacteria culture supernatant and individual medium sample injections were exported as txt files. All subsequent data analysis was done with Matlab 2014a (MathWorks, Natick, MA, USA). (i) All the spectra were resampled using a linear interpolation function (2’000’000 data points across the 40–450 m/z range); (ii) to remove systematic variation between spectra, we applied median normalization; (iii) The spectra were centroided. An intensity threshold of 50 counts was set resulting in 1,966 features. Subsequently, signals that rise with time upon sample injection were identified. As a result, 547 of the 1’966 features were retained for further data analysis.
Statistical analysis
The next step was to filter out the most informative features to discriminate one bacterial strain from the others. We pursued a univariate approach for this task. Thus, a comparison of differences in each of the detected compounds in the headspace of the bacterial samples was assessed by ANOVA or Kruskal-Wallis tests for normally and non-normally distributed data, respectively. Normality of the data was evaluated using a Lilliefors test. It followed a pairwise comparison by using a Tukey-Kramer procedure. Statistical significance was set at p-value ≤ 0.05. The false discovery rate for multiple hypothesis testing was estimated using the procedure introduced by Storey27.This procedure delivered 464 statistically significant features. We further selected those which had significantly low intensity for three bacterial strains and an enhanced intensity for one bacterial strain which means that the p-values (from multiple comparison) between the specific strain and the other strains had to be lower than 0.05. In addition, the feature intensity in the medium sample had to be lower than in the bacteria samples. This procedure reduced the list of discriminative features to 149. A closer inspection of these features revealed the presence of redundant 13C isotopes, finally reducing the list to 120 discriminatory signals. Further dimensionality reduction was accomplished by subjecting the normalized 20 x 120 (samples x features) matrix to a principal component analysis (PCA) for better visualization of the data. For the targeted saliva analysis, the ion intensities of the 120 in vitro signals were compared between the patient and the two healthy controls. An arbitrary 2-fold intensity enhancement cut-off value was used to determine whether the compounds were enhanced in the saliva of the patient vs. the healthy controls.
Additional Information
How to cite this article: Bregy, L. et al. Differentiation of oral bacteria in in vitro cultures and human saliva by secondary electrospray ionization – mass spectrometry. Sci. Rep. 5, 15163; doi: 10.1038/srep15163 (2015).
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