IOM’s Forum on Microbial Threats Workshop Vector-borne diseases: Exploring the
Environmental, Ecological, and Health Connections Sept 16-17, 2014 Washington, DC
Towards the diagnosis and prognosis of epidemic
vector-borne diseases
Barry J Beaty, University Distinguished Professor,
Department of Microbiology, Immunology,
and Pathology, Colorado State University, Fort
Collins, CO 80523 [email protected]
Dengue Virus (DENV)
• Family Flaviviridae • Genus Flavivirus • Genome: + sense, ssRNA, 11kb • Serotypes: DENV-1-4 • Infection produces lifelong immunity to
the serotype but not to other serotypes. • Primary infections typically lead to dengue
fever, secondary infections can lead to DHF/DSS
• Epidemic dengue and DHF/DSS emerged as major public health issues in Southeast Asia following world war II and subsequently in Latin America in 1980-90s
• Endemic in >100 countries, >300 million infections per year
• Mosquito-borne (Aedes sp.) • Urban mosquito vector and 4 serotypes
now hyperendemic throughout much of the tropics
VIRAL GENETIC DETERMINANTS OF EPIDEMIC DENGUE The four serotypes of DENV are now co-circulating in most of the tropical world
Phylogeny of DENV-2
0.1
MALAYSIA/70/P8 1407 IVORY COAST/80/DAKAr578 GUINEA/81/PM33974
NIGERIA/66/IBH11208* SENEGAL/90/DAKHD10674
THAILAND/64/16681 THAILAND/89/PUO218
THAILAND/93/ThNH7 MALAYSIA/87/M1
PHILIPPINES/83 PHILIPPINES/DOH 078
TAIWAN/87 CUBA/81/A15
VIETNAM/97/CTD113 CHINA/87/43 NEW GUINEA/44/NGC MX/GUERRERO/97/C932* MX/GUERRERO/97/C1077*
THAILAND/80/D80 141 CHINA/85/04
VIETNAM/98/CTD29 JAMAICA/83/1409
VENEZUELA/90/MARA4 VENEZUELA/98/3146
BRAZIL/90/40247 COLOMBIA/96/PTCOL96
VENEZUELA/97/1657 VENEZUELA/00/5207
MARTINIQUE/88/703 NICARAGUA/99/541* MX/YUCATAN/02/13381* MX/YUCATAN/02/13382*
MX/YUCATAN/02/13404* MX/OAXACA/00/468 MX/YUCATAN/01/11936* MX/YUCATAN/01/12914* MX/YUCATAN/01/12021*
SEYCHELLES/77/SEY42 SRI LANKA/85/1592 UGANDA/93/CAMR11
SAUDI ARABIA/92/CAMR16 AUSTRALIA/92/CAMR5
INDIA/94/CAMR10 AUSTRALIA/93/TSV01
THAILAND/98/CAMR14 BORNEO/88/620*
SOMALIA/94/S9* INDONESIA/76
MX/YUCATAN/96/ BC17*
100
87
100
100
100
100
81
57
100
96
98
100
100
96
100
98
100
100
100
100
100
100
100
100
100
100
100
100
TRINIDAD/53/TRI1751 INDIA/57
PUERTO RICO/69/159 MEXICOX/83/200787
PUERTO RICO 77 1328* PERU 96 IQT2913
MEXICOX/84/1482* VENEZUELA 87 VEN2 MEXICOX/83/1421* MX/94/QUINTANA ROO BC139*
MX/SONORA/92 131 MX/TAMAULIPAS/95/328298
100
100
100
100
100 66
80
American- Asian
Asian 1
Asian 2
Sylvatic
American
Cosmopolitan Diaz, et al. Dengue virus circulation
and evolution in Mexico: A
phylogenetic perspective. Arch. Med
Res. 37:760-773, 2006
Correlation Between Dengue Occurrence
and DENV Introductions in Mexico
Years
DHF cases DF cases
0
10,000
20,000
30,000
40,000
50,000
78 80 82 84 86 88 90 92 94 96 98 00 02
0
500
1,000
1,500
2,000
0
5000
10000
15000
20000
25000
30000
35000
40000
45000
50000
55000
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
Nu
mb
er
of
lab
ora
tory
-co
nfi
rmed
cases
Dengue Fever
Dengue Hemorrhagic Fever
0.000
0.050
0.100
0.150
0.200
0.250
0.300
0.350
0.400
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
Rati
o o
f la
bo
rato
ry-c
on
firm
ed
DH
F:D
F c
ases
The clinical reality of epidemic dengue and DHF/SS: the great unmet needs for point of care noninvasive diagnosis and prognosis of dengue infection outcomes
Diagnosis is not enough: Prognosis of which patients are going to progress to severe dengue disease is critical
In many outbreaks now in the Americas (and Asia), 30% or more of patients are progressing to severe dengue disease In the absence of vaccines, aggressive and targeted supportive care could reduce disease severity and fatalities in these patients Prognosis capability could be utilized to appropriately triage patients and to target those who will progress to severe dengue disease for clinical care and in the future therapeutic intervention, New therapeutics and antivirals could be targeted to those destined to progress to severe disease. ,
Historia Natural del Dengue
Halstead SB. Dengue. Lancet. 2007 Nov 10;370(9599):1644-52.
The search for the Holy Grail for prognosis of severe dengue outcomes (a very selected list of publications)
Carrasco L, Leo Y, Cook A, Lee V, Thein T, Go C, and Lye D. Predictive tools for severe dengue conforming to World Health Organization 2009 criteria. PLOS Neglected Tropical Diseases 2014; 8(7):e2972.
Potts JA, Gibbons RV, Rothman AL, Srikiatkhachorn A, Thomas SJ, Supradish PO, et al. Prediction of dengue disease severity among pediatric Thai patients using early clinical laboratory indicators. PLoS Neglected Tropical Diseases 2010;4(8):e769.
Tanner L, Schreiber M, Low JG, Ong A, Tolfvenstam T, Lai YL, et al. Decision tree algorithms predict the diagnosis and outcome of dengue fever in the early phase of illness. PLoS Neglected Tropical Diseases 2008;2(3):e196.
Vasanwala F, Tun-Linn T, Leo Y, Gan VC, Hao Y, Lee L, and Lye D. Predictive value of proteinuria in adult dengue severity. PLOS Neglected Tropical Diseases, 2014;8(2);e2712.
Colbert JA, Gordon A, Roxelin R, Silva S, Silva J, Rocha C, Harris E, 2007. Ultrasound measurement of gallbladder wall thickening as a diagnostic test and prognostic indicator for severe dengue in pediatric patients. Pediatr Infect Dis J 26: 850-2.
Villar-Centeno LA, Diaz-Quijano FA, Martinez-Vega RA. Biochemical alterations as markers of dengue hemorrhagic fever. The American Journal of Tropical Medicine and Hygiene 2008;78(3):370-4
van Gorp EC, Suharti C, Mairuhu AT, Dolmans WM, van Der Ven J, Demacker PN, van Der Meer JW, 2002. Changes in the plasma lipid profile as a potential predictor of clinical outcome in dengue hemorrhagic fever. Clinical infectious diseases : an official publication of the Infectious Diseases Society of America 34: 1150-3.
Kumar Y, Liang C, Bo Z, Rajapakse JC, Ooi EE, Tannenbaum SR, 2012. Serum proteome and cytokine analysis in a longitudinal cohort of adults with primary dengue infection reveals predictive markers of DHF. PLoS Neglected Tropical Diseases 6: e1887.
Vejbaesya S, Luangtrakool P, Luangtrakool K, Kalayanarooj S, Vaughn DW, Endy TP, et al. TNF and LTA gene, allele, and extended HLA haplotype associations with severe dengue virus infection in ethnic Thais. The Journal of Infectious Diseases 2009;199(10):1442-8.
Nascimento EJ, Braga-Neto U, Calzavara-Silva CE, Gomes AL, Abath FG, Brito CA, Cordeiro MT, Silva AM, Magalhaes C, Andrade R, Gil LH, Marques ET, Jr., 2009. Gene expression profiling during early acute febrile stage of dengue infection can predict the disease outcome. PloS One 4: e7892
Libraty DH, Young PR, Pickering D, Endy TP, Kalayanarooj S, Green S, et al. High circulating levels of the dengue virus nonstructural protein NS1 early in dengue illness correlate with the development of dengue hemorrhagic fever. The Journal of Infectious Diseases 2002;186(8):1165-8.
Vaughn DW, Green S, Kalayanarooj S, Innis BL, Nimmannitya S, Suntayakorn S, et al. Dengue viremia titer, antibody response pattern, and virus serotype correlate with disease severity. The Journal of Infectious Diseases 2000;181(1):2-9.
Guzman MG, Alvarez M, Halstead SB. Secondary infection as a risk factor for dengue hemorrhagic fever/dengue shock syndrome: an historical perspective and role of antibody-dependent enhancment of infection. Arch Virol. 2013 Jul;158(7):1445-59. doi: 10.1007/s00705-013-1645-3. Epub 2013 Mar 8. Review.
Dengue Non Invasive Diagnosis and Prognosis
Diagnostico y Pronostico No Invasivo de Dengue
Metabolomic Profile of Serum, Urine and Saliva as Diagnostic and Prognostic Tools
Perfil Metabolomico de Suero, Orina y
Saliva como Herramienta Para El Diagnostico Clinico
Dra. Natalia Voge (PhD) CSU/UADY/UNI
Metabolomics • Constitutes one of the new tools of the post genomic era, a very recent discipline, truly
interdisciplinary: works with pathobiochemistry, systems biology, medicine, platform technology, sophisticated data analysis; bioinformatics, multivariate statistics, among many others.
• Metabolomics is the analysis of low molecular weight biological molecules that are the result of cellular processes. The metabolic activity of a cell or tissue is strongly influenced by its environment and physiological state. Disease states result in changes in metabolism of a system that affect the profile of metabolites. Thus, analysis of metabolomic profiles in disease conditions and comparison with the profiles of non-diseased individuals can be used in the development of diagnostics.
• Metabolomics using liquid chromatography-tandem mass spectrometry (LC-MS/MS) is the key analytical technique that provides both structural and quantitative data and can be used in a targeted or untargeted manner to detect molecular features (potential small molecule biomarkers (SMBs)) diagnostic of infection and prognostic of disease progression in acute phase clinical specimens, theoretically before a detectable acquired immune response.
• SMBs could be detectable in noninvasive clinical specimens (eg, saliva and urine), which have been problematic in conventional assays for viral antigens, antibodies, etc., thereby providing novel analytes and approaches for diagnosis and prognosis.
• Advances in mass spectrometry, metabolomic databases, and analytical software provide exciting new opportunities to detect metabolites at levels of a few parts per billion and to identify host SMBs of DEN outcome in acute phase serum and non-invasive clinical specimens.
Metabolomics Workflow
Urine
Serum
Dengue and non-dengue diagnosed patient’s
Sample preparation
Serum - methanol extraction, dry, resuspend in acetonitrile; Saliva –precipitation in acetonitrile; Urine - precipitation
50 μl
Q-TOF, LC-MS, HILIC*. Highly polar metabolites are captured earlier in the analysis. Each sample is analyzed in Q-TOF during 45 min.
m/z, RT Total Ion Chromatogram
Mass Hunter and offline reprocessor
Significant features
Metabolite and pathway identification
Sample analysis
Database search and LC/MS-MS on samples and analytical standards
Statistical Analysis • Molecular feature extraction • Normalization, alignment, filtering • Statistical Analysis: Ttest, fold change. • Validation of peaks quantitatively. • compound ID.
Principal Component Analysis
Nicaragua / Mexico Dengue Patients
(DF, DHF, DSS) Non-dengue Patients
Serum / Saliva / Urine
LC-MS Metabolic Profiling
Reverse Phase Normal Phase (HILIC)
Super learner Analyses
Serum Nica:253
Mexico:101
Urine Nica:96
Mexico:88
Saliva Nica:94
Mexico:84
Serum Nica:100
Urine Nica:100
Saliva Nica:100
Compounds detected in 50% of samples in at least one diagnosis group
Schema for sample processing and LC-MS analyses
Laboratory, epidemiological and clinical data
Laboratory, epidemiological and clinical data
Data Processing Baseline correction
Filtering Normalization
One Way ANOVA Fold Change
determination Identification
HILIC LC-MS and MS/MS analysis of Nicaraguan and Mexican serum specimens
MFs detected in serum after normalization, baselining and alignment
Nicaragua: 15,930 / Mexico: 17,665
MFs detected in 50% of samples of at least one diagnosis group
Nicaragua: 744 / Mexico: 866
Identified compounds that statistically differentiate:
DHF/DSS vs. DF
Nicaragua: 85 / Mexico: 36 DHF/DSS vs. ND
Nicaragua: 211 / Mexico: 325 DF vs. ND
Nicaragua: 208 / Mexico: 326
Compounds that were subjected to LC-MS/MS analysis
50
Structurally confirmed compounds that differentiate
disease states
Nicaragua & Mexico: 11 *Results based upon pairwise comparisons of molecular features using cut off values of FC of >2 and corrected P-value of <0.05.
HILIC LC-MS/MS ANALYSIS OF MEXICAN NON-INVASIVE SPECIMENS
MFs detected after normalization, baselining and alignment
SALIVA: 1,041 / URINE: 972
MFs detected in 50% of samples of at least one diagnosis group
SALIVA: 511 / URINE: 676
Identified compounds that statistically differentiate:
DHF/DSS vs. DF
SALIVA: 18 / URINE: 21 DHF/DSS vs. ND
SALIVA: 106 / URINE :22
DF vs. ND
SALIVA: 100 / URINE: 25
Compounds that were subjected to LC-MS/MS analysis
SALIVA: 20 / URINE: 12
Structurally confirmed compounds that differentiate disease states
SALIVA: 2 / URINE: 2
*Results based upon pairwise comparisons of molecular features using cut off values of FC of >1, and corrected P-value of < 0.05.
Total # MFs detected in acute phase clinical specimens by HILIC-LC-MS that statistically differentiated dengue disease groups
Clinical
Sample Country DHF/DSS-DF
DHF/DSS-
ND DF-ND
Serum Nicaragua 85 211 208
Mexico 36 325 326
Saliva Nicaragua 52 73 74
Mexico 18 106 100
Urine Nicaragua 105 144 66
Mexico 21 22 25
Selected compounds identified in Nicaraguan serum specimens by HILIC LC-MS analysis that differentiate the DHF/DSS, DF and ND diagnostic groups
Mass
RT Potential compound Calculated
formula # DB hits
% Detected in samples DHF/DSS-DF DHF/DSS-ND DF-ND DHF DF ND P value FC P value FC P value FC
Amino acids
115.0635 16.08 Proline C5H9NO2 7
7 57 21 - -2.15 6.65E-05 -8.88 5.37E-03 -6.74
Fatty acids
276.2087 1.11 3,6-octadecadienoic acid C18H28O2 118 50 4 4 2.02E-05 8.38 2.93E-05 8.06 _
278.2245 1.1 α-linolenic acid C18H30O2 82 58 0 8 1.20E-06 10.94 7.82E-06 10.01 _
328.238 1.09 Docosahexaenoic acid C22H32O2 18 50 8 4 1.98E-04 7.44 8.39E-05 7.61 _
Eicosanoids
302.2243 1.13 5,6-dehydro arachidonic acid
C20H30O2 66 73 38 46 7.49E-03 6.49 4.18E-02 4.98 _ -1.52
455.3088 2.36 Hydroxy-eicosatetraenoyl-dopamine
C28H41NO4 2 65 42 0 -- 1.65 3.27E-07 8.49 9.68E-05 6.84
Phospholipids
579.3536 13.23 LysoPS(22:1/0:0) C28H54NO9P 1 15 42 92 1.88E-02 -4.47 4.19E-12 -12.28 4.54E-05 -7.81
819.5418 11.83 PS(20:2/22:4) C46H78NO9P 1 8 4 77 _ 0.47 1.15E-09 -12.98 3.96E-10 -13.45
863.5598 11.81 PS(20:2/22:4) C48H82NO10P 8 0 0 58 _ -0.03 4.30E-07 -9.82 1.17E-06 -9.79
642.3612 1.11 LysoPI(21:0/0:0) C30H59O12P 1 58 54 0 _ -0.58 6.21E-07 9.49 1.98E-06 10.07
495.3329 13.67 LysoPC(16:0/0:0) C24H50NO7P 11 65 27 27 9.29E-03 7.55 4.97E-03 7.95 _ 0.40
521.348 13.74 LysoPC(18:1(9Z)/0:0) C26H52NO7P 19 65 15 15 1.02E-03 9.16 4.03E-04 9.50 _ 0.34
509.3834 14.80 PC(O-16:0/O-2:0) C26H56NO6P 12 62 29 35 9.46E-03 5.88 2.45E-02 5.05 _ _
Sphingolipids
647.6205 1.71 Ceramide C42H81NO3 3 46 88 42 2.04E-03 -6.91 0.75 0.77 5.61E-04 7.68
Vitamin D derivatives
368.3435 1.66 3-Deoxyvitamin D3 C27H44 2 69 27 23 2.89E-03 8.37 6.98E-04 9.33 0.7 0.96
400.3312 1.25 25-hydroxyvitamin D3 C27H44O2 54 65 96 42 6.90E-02 4.27 0.062994 -3.76 9.91E-05 -8.03
416.3282 1.45 25-dihydroxyvitamin D3 C27H44O3 70 70 88 96 5.33E-03 -5.03 0.007352 -4.96 -- 0.07
Unknown
295.2869 1.42 Unknown C19 H37 N O 0 65 54 0 -- 1.32 1.29E-08 10.95 1.88E-06 9.64
343.22 4.5 Unknown C11 H25 N11 O2 3 85 75 12 -- 1.45 1.07E-05 9.31 5.23E-04 7.86
113.0842 1.92 Unknown C6 H11 NO 5 80 66 15 _ 4.22 1.27E-09 16.20 1.19E-05 11.98
Selected compounds identified in Mexican serum specimens by HILIC LC-MS analysis that differentiate the DHF/DSS, DF, and ND diagnostic groups
Mexican serum Calculated formula
# DB hits
Detected in samples DHF/DSS-DF DHF/DSS-ND DF-ND
Mass RT Potential compound % DHF
% DF
% ND
P value FC P value FC P value FC
Unknowns 226.1682 7.64 Unknown C17H22 2 0 26 69 7.77E-03 -4.74 5.90E-03 -12.4 3.64E-08 -7.65
Fatty acids 366.3293 1.29 Alpha-linoleoylcholine C23H44NO2 1 57 30 6 4.30E-02 5.02 1.06E-03 8.87 9.04E-02 3.85
Eicosanoids 350.2098 1.10 Prostaglandin D3 C20H30O5 44 0 0 50 _ _ 9.28E-06 -7.98 4.04E-05 -7.98
370.2348 1.21 Hydroxy prostaglandin F2 C20H34O6 18 0 0 50 _ _ 1.03E-05 -7.85 4.41E-05 -7.85
410.2585 1.36 Prostaglandin F2α C23H38O6 3 65 26 6 7.06E-03 6.82 7.21E-05 10.59 9.93E-02 3.77
Phospholipids 477.3211 14.55 LysoPC(O-16:2/0:0) C24H48NO6P 3 17 41 63 7.66E-02 -3.66 3.52E-03 -6.92 1.95E-01 -3.26
523.365 14.72 LysoPC(18:0/0:0) C26H54NO7P 15 52 48 0 _ _ 5.71E-04 10.67 2.44E-04 10.20
663.4458 12.99 LysoPS(P-16:0/12:0) C34H66NO9P 2 0 0 56 _ _ 1.04E-06 -8.66 5.98E-06 -8.66
729.5309 12.96 PC(14:0/18:2) C40H76NO8P 41 52 30 0 1.53E-01 3.44 1.56E-02 8.66 2.45E-04 5.22
755.5477 12.98 PC(15:0/19:3) C42H78NO8P 45 52 30 0 8.73E-02 4.79 1.61E-02 10.51 2.52E-04 5.72
763.5513 11.72 PS(22:0/12:0) C40H78NO10P 20 17 41 75 7.81E-02 -3.56 6.81E-05 -9.63 1.53E-02 -6.06
781.5647 12.57 PC(16:0/20:4) C44H80NO8P 69 52 37 0 2.95E-01 3.45 4.70E-03 11.95 2.38E-04 8.50
805.5636 12.46 PC(16:0/22:6) C46H80NO8P 62 52 41 0 4.15E-01 2.54 2.50E-03 11.26 2.40E-04 8.72
833.5936 12.37 PC(18:0/22:6) C48H84NO8P 49 52 37 0 3.51E-01 2.57 4.74E-03 9.79 2.45E-04 7.22
495.3337 14.97 LysoPC(16:0/0:0) C24H50NO7P 11 52 44 0 _ _ 1.22E-03 11.69 2.42E-04 10.07
521.3493 14.8 LysoPC(18:1/0:0) C26H52NO7P 19 52 44 0 _ _ 1.24E-03 9.60 2.47E-04 8.49
Sphingolipids 647.6207 1.30 Ceramide C42H81NO3 3 26 44 69 _ _ 2.35E-03 -7.10 0.01988 -5.28
Vitamin D derivatives 384.3387 1.14 Vitamin D3 C27H44O 47 57 67 94 _ _ 2.27E-02 -6.53 6.53E-03 -5.02
400.3339 1.16 25-hydroxyvitamin D3 C27H44O2 54 57 67 94 _ _ 1.25E-02 -7.64 2.16E-03 -5.79
416.3275 1.31 25-dihydroxyvitamin D3 C27H44O3 70 43 44 94 - - 3.06E-04 -8.30 2.52E-04 -8.08
Selected phospholipids that were detected in both Nicaraguan and Mexican serum samples and that differentiate disease diagnosis
groups*
*Phospholipid metabolite fold change (increase or decrease) in abundance in pairwise comparisons of different diagnosis groups
ᵃ Ionized form of the compound [M+H-Na]+ was 781.5627, accurate mass is 759.5778; b Ionized form of the compound [M+H-Na]+ was 784.5827
accurate mass is 761.5934; ᵈIonized form of the compound [M+H-H2O]+ was 226.1932, accurate mass is 208.149; ᵉ Ionized form of the
compound [M+H-H2O]+ was 369.3516, accurate mass is 386.3548. Abbreviations: RT: retention time; DHF/DSS: dengue hemorrhagic
fever/dengue shock syndrome; DF: dengue fever; ND: non dengue; FC: fold change., LC-MS/MS: liquid chromatography tandem mass
spectrometry. NIST; National Institute of Standards and Technology, METLIN; Metabolite and Tandem Mass Spectrometry Database, Standard;
commercially available chemical standard.
HILIC LC-MS/MS confirmation of candidate small molecule biomarkers for dengue diagnosis and prognosis of severe disease in acute phase serum specimens
HILIC LC-MS/MS identification of 1-oleoyl-lysophosphatidylcholine in serum
Native sample
Purchased standard
60.0810
60.0818
69.0
69
71.0
76
4
69.0
699
71.0
777
86.0971
86.0961
95.0
86
3
95
.085
1
125.0004
124.9987
135.1179
135.1156
166.0636
166.0616
181.
02
74
1
81
.02
49
184.0741
184.0720
199.0376
199.0343
240.
08
76
240.
0988
258.1107
258.1084
309.2644
309.2744
339.2895
339.2862
s
387.8051
387.1370
445.2694
445.2676
504.3474
504.3400 522.
3495
258.1107 5
22
.35
55
10
4.1
07
6
104
.106
4
Subset of Molecular features (N = 85) that differentiate DHF/DSS from DF in Nicaraguan serum
Accurate Mass Calculated Formula Compound Name Database identifier
MSI level 1. Compounds structurally identified based on spectrum similarity with a standard, high confidence identification
115.0635 C5H9NO2 Proline
278.2245 C18H30O2 α-linolenic acid
328.238 C22 H32 O2 Docosahexaenoic acid
495.3329 C24 H50 NO7 P Lysophosphatidylcholine (16:0)
521.348 C26 H52 N O7P Lysophosphatidylcholine (18:1)
416.3282 C27H44O3 1,25-dihydroxyvitamin D3
MSI Level 2. Compound identified based on spectrum similarity with spectrum libraries, high confidence identification
226.1932 C14 H26 O2 Myristoleic acid NIST 1114112
386.3548 C27 H46 O Cholesterol NIST 1057003
759.5778 C42 H82 NO8 P Phosphatidylcholine (34:1) NIST 112638
761.5934 C42H84 NO8P Phosphatidylcholine (34:0) NIST 112620
771.5415 C44H86NO7P Phosphatidylcholine (36:1) NIST 1047871
MSI Level 3. Compound characterized by class based only on physicochemical characteristics
136.0383 C5H4N4O Hypoxanthine Metlin ID 83
253.2397 C16 H31 N O Palmitoleamide Metlin ID 97431
255.1834 C14 H25 N O3 N-decanoyl-L-homoserine lactone Metlin ID 45310
276.2087 C18H28O2 3,9,12,15-octadecatetraenoic acid Metlin ID 34837
279.2558 C18H33NO Linoleamide Metlin ID 43435
294.2196 C18H30O3 17-hydroxy-linolenic acid
Metlin ID 45842
300.2088 C20H28O2 Retinoate KEGG C00777
302.2243 C20H30O2 4,8,12,15,18-eicosapentaenoic acid Metlin ID 34848
312.23 C18H32O4 9-hydroxy-12-oxo-10-octadecenoic acid
Metlin ID 74524
318.2209 C20H30O3 Leukotriene A4 KEGG C00909
366.3279 C23H44NO2 α -Linoleoylcholine HMDB13213
385.2669 C24H35NO3 Docosahexaenoyl Glycine Metlin ID 62993
400.3312 C27H44O2 25-hydroxyvitamin D3 METLIN ID 42183
428.2406 C18H32N6O6 Ala Lys Asn Pro Metlin ID 106912
429.2929 C20H39N5O5 Lys Val Ala Leu Metlin ID 174288
430.3758 C29H50O2 α-tocopherol LMPR02020001
445.267 C18H35N7O6 Trp Ile Lys Metlin ID 16425
586.3006 C26H38N10O6 Gln Arg Phe His Metlin ID 213165
646.4514 C35H67O8P Diacylphosphatidylglycerol (32:1) Metlin ID 81645
647.6205 C42H81NO3 Ceramide (42:2) HMDB04953
672.5202 C37H73N2O6P Sphingomyelin (32:2) LMSP03010034
789.5524 C42H80NO10P Phosphatidylserine (36:1) HMDB10163
791.5504 C45H78NO8P Phosphatidylethanolamine (40:6) HMDB09012
815.567 C44H82NO10P Phosphatidylserine (38:2) LMGP03010200
823.5718 C46H82NO9P Phosphatidylserine (40:5) LMGP03020064
824.6873 C53H92O6 Triacylglycerol (50:5) LMGL03010056
826.7007 C53H94O6 Triacylglycerol (50:4) LMGL03010046
848.6875 C48H97O9P Diacylphosphatidylglycerol(42:0) LMGP04020069
850.7041 C55H94O6 Triacylglycerol (52:6) LMGL03010166
852.7204 C55H96O6 Triacylglycerol (52:5) LMGL03010140
878.7344 C57H98O6 Triacylglycerol (54:6) LMGL03010350
887.5658 C50H82NO10P Phosphatidylserine (44:8) LMGP03010786
Abbreviations. MSI level: Metabolomics standard initiative (1-4); Rows bolded and highlighted in blue represent the compounds identified by LC-MS/MS from Table 2.A. Compound searches were performed using 4 databases including Metlin; Metabolite and Tandem Mass Spectrometry Database , Human Metabolome Database (HMDB), LipidMaps (LM) and Kyoto encyclopedia of genes and genomes (KEGG). NIST; National Institutes of Standards and Technology.
HILIC LC-MS/MS identification of candidate small molecule biomarkers for dengue diagnosis and prognosis
of severe disease in acute phase urine and saliva specimens
LC-MS/MS IDENTIFICATION OF ARGININE IN URINE
Comparison of spectra from the NIST library database and from a NATIVE urine sample
NIST Library
Native sample
Potential biological significance of selected candidate SMBs
• Lysophosphatidylcholine. LysoPCs are involved in alteration of membrane structures and mediation of inflammation. Interestingly, lysoPC alters homeostasis of vascular endothelium, causing endothelial cell instability, barrier dysfunction, and vascular leakage, a major component of the pathophysiology of DSS.
• 1-25-dihydroxyvitamin D3. The active form of vitamin D3 (1,25-VitD3) is synthesized in vascular endothelium following stimulation of 1α-hydroxylase activity by inflammatory cytokines. 1,25-VitD3, with its extensive roles in immunoregulation and vascular barrier function and sepsis, could help condition the immunopathophysiology associated with DHF/DSS. A decrease in serum 1,25-VitD3 level is associated with increased mortality in sepsis patients, which agrees with our findings of lower levels of this compound in DHF/DSS patients. Interestingly, polymorphisms in the vitamin D receptor are linked with severe dengue disease outcomes.
• Docosahexaenoic acid has been shown to decrease the production of
inflammatory eicosanoids, cytokines, and reactive oxygen species . DHA can act both directly by inhibiting arachidonic acid metabolism and indirectly by altering the expression of inflammatory genes. DHA also is a precursor to a family of anti-inflammatory mediators called D-series resolvins . The increases in DHA levels we observed in dengue patient serum might represent the host attempt to mitigate immunopathology of dengue disease.
• Arginine abundance was increased in urine specimens of Mexican patients. DENV is known to activate the arginine-nitric oxide pathway, which could lead to increased release of NO, vasodilation and reduced aggregation of platelets, which could favor the onset of DHF/DSS.
SMBs for DIAGNOSIS AND PROGNOSIS OF DENGUE INFECTIONS – NEXT STEPS
•Confirm current and identify new candidate SMBs in studies using serum, saliva, and urine samples from patients with well defined clinical course and disease outcomes and clearly define clinical correlates of respective SMBs in diverse populations.
•Generate metabolic small molecule biomarker biosignatures for dengue prognosis and diagnosis and use Super Learner to identify most efficacious and parsimonisous clinical/laboratory biomarkers for dengue diagnosis and prognosis.
•Conduct prospective clinical trials when available of the diagnostic potential of the candidate battery of SMBs using exploratory diagnostic tests.
•Exploit candidate SMB and metabolic pathway knowledge to develop rapid POC diagnostics (eg, lateral flow or micro-fluidics) for dengue diagnosis and prognosis.
•Provide a paradigm shift for dengue diagnosis and prognosis.
Metabolomics-based discovery of small molecule biomarkers for noninvasive dengue diagnosis and
prognosis Colorado State University: Barry Beaty, PI, Carol Blair and Rushika Perera, Co – PIs, Natalia Voge U.C. Berkeley/SSI: Eva Harris, PI, Lionel Gresh, Carolyn Cotterman National Virology Laboratory: Angel Balmaseda, PI Managua, Nicaragua Universidad Autonoma de: Maria Alba Lorono-Pino, PI Yucatan, Merida, Mexico
NIH Grant: AI100186. Metabolomics-based discovery of small molecule biomarkers for noninvasive dengue diagnosis
and prognosis
We gratefully acknowledge the participation of the wonderful people of Merida, Mexico and Managua,
Nicaragua in these studies
