Pathobiological classification of severe malaria based on an integrated approach of high-throughput proteomics and syste
Lead Research Organisation:
University of Oxford
Department Name: Wellcome Trust Centre for Human Genetics
Abstract
Severe malaria (SM) is caused by the parasite Plasmodium falciparum. It is an important cause of death mainly in children under five years of age. The current definition of SM includes various severe syndromes with associated mortalities ranging from 4% to 35% despite adequate treatment.
Proteins are involved in almost any biological function. The comprehensive study of all the proteins (the proteome) provides a global perspective of how a biological system works. Therefore, emerging technologies like proteomics offer powerful new tools to disentangle the mechanisms of complex diseases and help identify new therapeutic targets.
The plasma proteome includes all the proteins that can be found in the fluid phase of the blood. In this study we aim to identify the plasma proteome of children with SM, which will help us identify key molecules that participate in the development of severe disease. The identification of these molecules or biomarkers will provide a better understanding of the underlying mechanisms of SM, which may be translated into better diagnosis, management and eventually decrease the mortality of severe malaria.
Proteins are involved in almost any biological function. The comprehensive study of all the proteins (the proteome) provides a global perspective of how a biological system works. Therefore, emerging technologies like proteomics offer powerful new tools to disentangle the mechanisms of complex diseases and help identify new therapeutic targets.
The plasma proteome includes all the proteins that can be found in the fluid phase of the blood. In this study we aim to identify the plasma proteome of children with SM, which will help us identify key molecules that participate in the development of severe disease. The identification of these molecules or biomarkers will provide a better understanding of the underlying mechanisms of SM, which may be translated into better diagnosis, management and eventually decrease the mortality of severe malaria.
Technical Summary
Aim: The overall aim of this research proposal is to study the molecules involved in the pathophysiology of severe malaria to improve its diagnosis and clinical management.
Objectives: 1-To identify and quantitate the peptides and proteins that constitute the plasma proteome of children with Plasmodium falciparum severe malaria (CM, SRD, SMA and other severe infections) and mild malaria and healthy controls; 2- to assess the diagnostic performance of distinctive peptide/proteins identified; and 3-to integrate these data into predictive models that may explain differences in clinical outcome in severe malaria.
Design: I will use shotgun proteomics to describe the plasma proteome in each of the severe malaria groups (n= 200 per group) and controls (n=100) and a systems biology approach to integrate three levels of biological information (proteomics, clinical and genome-wide data) into predictive models that may explain differences in clinical outcome in severe malaria and may provide a better framework for risk stratification.
Methods: This study will analyse plasma samples that have been collected in The Gambia from 1997 to 2006.
1- Data acquisition: a combination of off-gel electrophoresis, high performance liquid chromatography and tandem mass spectrometry (LC-MS/MS) will be performed to study the plasma proteome of patients and controls; the acquired MS/MS spectra will be interpreted with Mascot algorithms using target-decoy database strategies. Peptide/proteins will be quantified using label-free methods.
2- Identification of potential biomarkers:
a) The classification performance of distinctive peptides/proteins (potential biomarkers) in each group will be carried out using support vector machine (SVM) algorithms. The diagnostic performance of potential biomarkers will be based on sensitivity, specificity, positive and negative-predictive values and receiver-operator curves (ROC) for individual and combinations of biomarkers. 3- Data validation:
a) Biochemical validation of biomarkers will be carried out by repeating MS post-immuno-depletion of identified peptides (subject to antibody availability) and b) clinical validation will be carried out in an independent set of severe malaria cases and controls. This study will lay the foundations for the prospective validation of these biomarkers in a larger clinical study.
4- Data integration: Supervised and unsupervised machine-learning algorithms will be used for feature extraction at three levels of information (proteomic, clinical and genome-wide data). These features will be used to create predictive models for pathobiological classification. These data will be used to generate theoretical network models. The topology of these networks will be used to identify critical pathophysiological steps leading to different pathophenotypes.
Objectives: 1-To identify and quantitate the peptides and proteins that constitute the plasma proteome of children with Plasmodium falciparum severe malaria (CM, SRD, SMA and other severe infections) and mild malaria and healthy controls; 2- to assess the diagnostic performance of distinctive peptide/proteins identified; and 3-to integrate these data into predictive models that may explain differences in clinical outcome in severe malaria.
Design: I will use shotgun proteomics to describe the plasma proteome in each of the severe malaria groups (n= 200 per group) and controls (n=100) and a systems biology approach to integrate three levels of biological information (proteomics, clinical and genome-wide data) into predictive models that may explain differences in clinical outcome in severe malaria and may provide a better framework for risk stratification.
Methods: This study will analyse plasma samples that have been collected in The Gambia from 1997 to 2006.
1- Data acquisition: a combination of off-gel electrophoresis, high performance liquid chromatography and tandem mass spectrometry (LC-MS/MS) will be performed to study the plasma proteome of patients and controls; the acquired MS/MS spectra will be interpreted with Mascot algorithms using target-decoy database strategies. Peptide/proteins will be quantified using label-free methods.
2- Identification of potential biomarkers:
a) The classification performance of distinctive peptides/proteins (potential biomarkers) in each group will be carried out using support vector machine (SVM) algorithms. The diagnostic performance of potential biomarkers will be based on sensitivity, specificity, positive and negative-predictive values and receiver-operator curves (ROC) for individual and combinations of biomarkers. 3- Data validation:
a) Biochemical validation of biomarkers will be carried out by repeating MS post-immuno-depletion of identified peptides (subject to antibody availability) and b) clinical validation will be carried out in an independent set of severe malaria cases and controls. This study will lay the foundations for the prospective validation of these biomarkers in a larger clinical study.
4- Data integration: Supervised and unsupervised machine-learning algorithms will be used for feature extraction at three levels of information (proteomic, clinical and genome-wide data). These features will be used to create predictive models for pathobiological classification. These data will be used to generate theoretical network models. The topology of these networks will be used to identify critical pathophysiological steps leading to different pathophenotypes.
