applications of targeted metabolic profiling by ......hdtg hdl triglycerides 6.75311e-23 22.25063647...

Elaine HolmesComputational and Systems Medicine, Imperial College, U.K.

Bruker Webinar

30th August 2018

Applications of targeted metabolic profiling by 1H NMR

spectroscopy in medicine and population screening

Your “phenome”

A phenome is represented by an integrated setof measureable physical and clinical featurescoupled to chemical, metabolic and physiological properties that define biological sub-classes and individuality.

Metabolic profiling

Why NMR Spectroscopy ?

➢Every spectroscopic platform has strengths and weaknesses. NMR is a robust platform that delivers information on atom-centredproperties.

➢With untargeted profiling there will always be some degree of inter-laboratory variation but NMR spectroscopy has repeatedly been shown to be robust and reproducible in high throughput mode. Because of the inherently quantitative basis of NMR both targeted (quantified metabolite concentrations) and untargeted profiles can be acquired at the same time allowing both hypothesis testing and hypothesis generation.

➢NMR can be used as a first line screen to detect outlier samples before progressing to other analytical platforms.

The National Phenome Centre employs high throughput 1H NMR profiling

• 96 sample assays per day per instrument = 1 rack (this is not at full capacity)

• 288/day• >100,000/year

• Each sampleprofiled with 3NMR experiments

>300 K data sets/yr

Sample workflow

Dona et al Anal Chem 2014

Harmonization across the metabolic profiling community (600 MHz)

• Ensuring SOPs and analytical pipelines are

consistent

• Sharing of SOPS and protocols

• Ring trials

• Sharing of databases

PLASMA

Spectral quality requirements

Lipoprotein Ring Test: quantification of lipoproteins with added set of 24 low molecular weight molecules

5 Institutions11 Different NMR Spectrometers

2 daily QCs6 days of analysis2 replicates NIST 1951c40 donor samples (20 sera, 20 plasma)

Ring trial partners

A) Cartoon of lipoprotein particle size and density.

B) Overlaid spectra of serum samples (C).D) Overlaid spectra of the 24 small molecules quantified with expansion of crowded region (E)

NMR-based metabolite quantification: schematic of fitted compounds in serum using Bruker B.I.LISA method

Schematic of the NMR lipoprotein subclass analysis approach: Plasma or serum is collected from a reference cohort; each sample is then ultracentrifuged in order to determine the main and subfractions of lipoproteins; NMR spectra are taken from each of the modelling samples; a regression model is developed from the combined information of both methods; Method is made available on the spectrum analysis server to be shared with other NMR laboratories.

electronic signal

Linear regression analysis of the Bruker I.LISA and clinical measurements (in mg/dL) of total cholesterol (total CH) (A), HDL-cholesterol (HDL-CH) (B), Apolipoprotein A (Apo-A) (C) and Apolipoprotein B (Apo-B) (D) in a healthy sub-cohort of the Airwave study (n=588) showing the accuracy of the Bruker methodology by comparison with the clinical data (ultracentrifugation).

b)

a) Intra-institution reproducibility of quantified lipoprotein concentrations: Regression curve where the mean value of each lipoprotein subclass, calculated for the different acquisitions of each institution QCs (2 replicate samples from the QC pool made up daily for 10 days for each of 11 instruments), is plotted against the values obtained for each of the 105 lipoprotein parameters in each of the measurements (R2=1, RMSE=0.8 mg/dL).

Intra-institution reproducibility of lipoprotein concentrations

b)

a)

Institution-specific QC means in mg/dl: Instrument-specific variability for lipoprotein quantification for six selected parameters. Each plot represents the standard deviation values for the main lipoprotein parameters obtained for each of the QC samples obtained daily. Green shaded regions represent percentage of variation of the lipoprotein parameter 1xSTD% (dark green), 2xSTD% (light green)

NPCNational Phenome Centre

CPC Clinical Phenome Centre

CSMImperial College academic

PBC Phenome Centre Birmingham

Bruker

KEY

In-depth Analysis: One Sample - 11 Spectrometers

Apo-Protein Profiles

Particle Numbers

Lipid Profiles

Figure 2. a) Intra-institution reproducibility: Regression curve where the mean value of each

lipoprotein subclass calculated for the different acquisitions of each institution QCs (20 measure in

total for 11 instruments) is plotted against each of the values obtained for the lipoprotein parameters

in each of the measurements (R2=, RMSE=). b) Instrument-specific variability for lipoprotein

quantification. Plot representing the standard deviation values for the main lipoprotein parameters

obtained for each of the daily QCs. Spectrometers from the five different participating institutions

have been plot colour coded: white diamonds, NPC, green diamonds, CPC, red diamonds, CSM, yellow

diamonds, PBC, blue diamonds, Bruker Germany. The green shaded regions represent percentage of

variation 1xSTD% (dark green), 2xSTD% (light green); c) Instrument specific variability for small

molecule quantification. Plot representing the standard deviation values for a selection of small

molecules quantified in the daily QCs. Colours as per b)

c) Institution-specific QC means in mg/dl for low molecular weight molecules

PCA scores, KODAMA (KNN classifier) and PLS scores plots of plasma 1H -NMR data, collectedlongitudinally at late-1st (in blue), early-2nd T (in yellow) and mid-2nd (in grey) trimester (a-b-c). Mean 1H-NMR plasma spectrum of the early pregnancy journey (12-21 g.w.) showing positive (red) and negative(green) metabolic correlations with advanced gestational age (d).

Application of B.I.LISA quantification method to establish longitudinal changes in plasma lipoproteins in a cohort of ‘healthy’ pregnant women.

➢ Since lipid metabolism showed the largest gestation-associated variation, additional lipoproteins subfraction distribution analysis was carried using the proprietary Bruker B.I.-LISA (Bruker IVDr Lipoprotein Subclass Analysis) platform which decomposes each standard 1D spectrum, collected from all plasma samples, to 105 lipoprotein subfractions.

➢ Univariate statistical data analysis performed in R showed that 95 lipoprotein subfractions, out of the 105 (i.e., 90.4%), significantly changed from 1st to 3rd

trimester reinforcing the pregnancy-related shift in lipid metabolism during a healthy uncomplicated pregnancy journey.

➢ Of the 95 significantly changing lipoprotein subfractions, the top 38 were selected to build a model for prediction of stage of pregnancy. These models of ‘normal’ pregnancy profiles were later used to predict preterm birth.

Name Matrix Analyte FDR Median A Median C Fold change (A/C)

L1TG LDL-1 Triglycerides 1.55152E-32 6.411739498 10.229517 -0.673950311

L1AB LDL-1 Apo-B 1.72876E-28 7.545602839 11.88636651 -0.6555997

L1PL LDL-1 Phospholipids 4.39869E-28 9.812772425 15.30592089 -0.641357141

H1TG HDL-1 Triglycerides 7.0213E-25 9.919108954 13.60292274 -0.455634232

LDTG LDL Triglycerides 7.21554E-25 25.9665034 34.16169926 -0.395727982

HDTG HDL Triglycerides 6.75311E-23 22.25063647 28.06827196 -0.335093642

TPCH Total Plasma Cholesterol 1.87101E-19 228.5560812 271.1508109 -0.24654728

TPAB Total Plasma Apo-B 4.9906E-19 76.35389967 95.83244215 -0.327812292

V4PL VLDL-4 Phospholipids 1.47539E-18 4.579973459 6.658487168 -0.539855191

VLAB VLDL Apo-B 9.50076E-18 5.342830648 7.874982146 -0.559672364

TPTG Total Plasma Triglycerides 4.34084E-17 135.2469929 182.2074504 -0.429985434

V2CH VLDL-2 Cholesterol 1.67619E-16 2.138461777 3.494487036 -0.708507274

H4TG HDL-4 Triglycerides 6.39443E-16 4.050716646 5.050992188 -0.318389641

IDTG IDL Triglycerides 6.54167E-15 9.433530633 15.73172294 -0.737806957

V4TG VLDL-4 Triglycerides 2.93509E-14 9.130169958 12.40547762 -0.44226366

L3AB LDL-3 Apo-B 3.9324E-14 12.40875637 14.14174898 -0.188602023

L2AB LDL-2 Apo-B 1.12046E-13 10.50597212 12.00451733 -0.192367736

LDAB LDL Apo-B 1.93331E-13 62.04603274 73.8368698 -0.251002427


H1PL HDL-1 Phospholipids 7.95279E-12 48.23686067 59.78681649 -0.309691375


LDFC LDL Free Cholesterol 3.91015E-11 41.19125416 48.81001246 -0.244839067


LDCH LDL Cholesterol 2.76937E-10 130.9105873 149.0523921 -0.187237751


HDFC HDL Free Cholesterol 2.82915E-08 25.17179536 27.37825409 -0.12122233

HDPL HDL Phospholipids 4.76328E-08 116.197352 125.1076657 -0.106592998

H1FC HDL-1 Free Cholesterol 8.21366E-08 10.86516142 12.80709129 -0.237233244


H3A2 HDL-3 Apo-A2 3.20507E-07 7.876211576 6.874186457 0.196312882

V6TG VLDL-6 Triglycerides 5.58628E-07 2.873218051 3.26932015 -0.186323176

L6PL LDL-6 Phospholipids 3.08271E-06 15.83420671 18.27893021 -0.207137047

L5CH LDL-5 Cholesterol 3.42224E-06 15.71871033 18.2826374 -0.217991351

L2CH LDL-2 Cholesterol 0.000234073 23.01711364 26.1872662 -0.186158529

L3CH LDL-3 Cholesterol 0.001328901 19.28368122 21.32140492 -0.144922019

HDA2 HDL Apo-A2 0.006745372 37.52937025 36.28607196 0.048604191

L4FC LDL-4 Free Cholesterol 0.025469886 6.971512971 7.42305726 -0.090541711

H4A2 HDL-4 Apo-A2 0.03838242 15.56741207 15.001663 0.053406692

Partial list of lipoprotein subfractions and their statistical significance characteristics (FDR, base-2 log change) identified via logistic regression analysis as the strongest biomarkers to discriminate the late 1st vs mid-2nd trimester of normal uncomplicated gestation.

Process for building quantitative diagnostic

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-0.06 -0.04 -0.02 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14

Term

Preterm

12+0-14+6 weeks

15+0-17+6 weeks

19+0-21+6 weeks

12+0-14+6 weeks

15+0-17+6 weeks

19+0-21+6 weeks

Use diagnostic to predict term vs preterm birth

Creatinine (n=7,579)Colour: distance to linear model fit

Red point colour indicates acceptable analytical correspondence(distance point to linear model fit < 30% of mean)

cyan square defines reference ranges in either method, using only corresponding concentration (red dots)

Method A: 1.2 – 17.5 mMMethod B: 1.5 – 20.3 mM

Normal Range:

Comparison of creatinine concentrations for 2 independent peak fitting methods

Identify outliers

Quantification method for urine samples

Selection of ‘good’ and ‘bad’ metabolites based on correlation between the 2 methods.Total shared = 48, Bruker = 150+, in-house = 76

Subset of top reliably fitted compounds (n=17) HR 1H NMR profilestorth,cv: noise level after PQN normalization

Insets: Kernel density estimates (KDE) of tpred,cv class memberships

Comparison of quantified metabolites versus untargeted profiling method for sex differentiation.

Lactic acid as an examples of an age-dependent metabolite that changes in females but not in males

Inset: Kernel density estimates (KDE) of tpred,cv class memberships, Cliff’s d = effect size estimate (max range = -1 to 1)

Top reliably fitted compounds (n=17)

Cliff’s d P value

F: (40-60] vs (60-100]

Gender (F vs M) -0.58 4.9 x 10-37

-0.25

-0.10 1.2 x 10-3

3.1 x 10-13

M: (40-60] vs (60-100]

Comparison of quantified metabolites versus profiles for age differentiation

Cliff’s d P valueF: (40-60] vs (60-100]

Gender (F vs M) -0.02 0.04

-0.20-0.15 4.1 x 10-8

4 x 10-12

M: (40-60] vs (60-100]

Cliff’s d P value

F: all ages (young vs old)

Gender (F vs M) -0.33 3.9 x 10-34

0.22

0.22 1.4 x 10-13

1.9 x 10-11

M: all ages (young vs old)

Cliff’s d = effect size estimate (max range = -1 to 1)

Metabolite-specific behaviour with age

weak effect Age and gender effect

Summary

➢ Accurate quantification of lipoproteins and small molecules in plasma and serum is possible using the B.I.LISA fitting method.

➢ Quantified plasma metabolites can be used to form biomarker panels for prediction of physiological and pathological states.

➢ This is suited to high throughput profiling and provides an easy set of data for clinicians to interpret

➢ We have shown significant changes in lipoprotein profiles thoughout healthy pregnancy and have further shown that the model for this ‘healthy’ trajectory can be used to indicate risk of preterm birth.

➢ The Bruker quantification method for urinary metabolites is consistent with other peak fitting methods for ascertaining metabolite concentrations and can be conducted for a range of metabolites.

➢ We have used this method to establish normal ranges of physiological variation for a range of metabolites stratified by age and gender.

Acknowledgements

➢ Dr Beatriz Jimenez (Imperial College London) for development of ring trial and provision of slides.

➢ Prof Mark Viant and Dr. Wawrick Dunn (University of Birmingham), Dr Manfred Spraul and Hartmut Schaefer (Bruker Biospin) for design of methods and design of ring trial.

➢ Prof Jeremy Nicholson and Prof John Lindon for design of ring trial and data interpretation

➢ Dr Torben Kimhofer and Dr Joram Posma for design of urine range quantification experiment

➢ Dr Manfred Spraul and Hartmut Schaefer (Bruker Biospin) and Dr Joram Posma for provision of urinary quantification method

➢ Prof. Philip Bennet and Dr. David MacIntyre for design of pregnancy study and collection of samples.

➢ Dr Nancy Georgakopoulu for analysis of longitudinal pregnancy samples

➢ Dr Matthew Lewis and the MRC-NIHR Phenome Centre team for analyisis of samples and provision of slides relating to the Phenome centre.

applications of targeted metabolic profiling by ......hdtg hdl triglycerides 6.75311e-23 22.25063647...

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