Population Genomics of the Immune Evasion (var) Genes of Plasmodium falciparum

Var genes encode the major surface antigen (PfEMP1) of the blood stages of the human malaria parasite Plasmodium falciparum. Differential expression of up to 60 diverse var genes in each parasite genome underlies immune evasion. We compared the diversity of the DBLα domain of var genes sampled from 30 parasite isolates from a malaria endemic area of Papua New Guinea (PNG) and 59 from widespread geographic origins (global). Overall, we obtained over 8,000 quality-controlled DBLα sequences. Within our sampling frame, the global population had a total of 895 distinct DBLα “types” and negligible overlap among repertoires. This indicated that var gene diversity on a global scale is so immense that many genomes would need to be sequenced to capture its true extent. In contrast, we found a much lower diversity in PNG of 185 DBLα types, with an average of approximately 7% overlap among repertoires. While we identify marked geographic structuring, nearly 40% of types identified in PNG were also found in samples from different countries showing a cosmopolitan distribution for much of the diversity. We also present evidence to suggest that recombination plays a key role in maintaining the unprecedented levels of polymorphism found in these immune evasion genes. This population genomic framework provides a cost effective molecular epidemiological tool to rapidly explore the geographic diversity of var genes

Defining Natural History: Assessment of the Ability of College Students to Aid in Characterizing Clinical Progression of Niemann-Pick Disease, Type C

Niemann-Pick Disease, type C (NPC) is a fatal, neurodegenerative, lysosomal storage disorder. It is a rare disease with broad phenotypic spectrum and variable age of onset. These issues make it difficult to develop a universally accepted clinical outcome measure to assess urgently needed therapies. To this end, clinical investigators have defined emerging, disease severity scales. The average time from initial symptom to diagnosis is approximately 4 years. Further, some patients may not travel to specialized clinical centers even after diagnosis. We were therefore interested in investigating whether appropriately trained, community-based assessment of patient records could assist in defining disease progression using clinical severity scores. In this study we evolved a secure, step wise process to show that pre-existing medical records may be correctly assessed by non-clinical practitioners trained to quantify disease progression. Sixty-four undergraduate students at the University of Notre Dame were expertly trained in clinical disease assessment and recognition of major and minor symptoms of NPC. Seven clinical records, randomly selected from a total of thirty seven used to establish a leading clinical severity scale, were correctly assessed to show expected characteristics of linear disease progression. Student assessment of two new records donated by NPC families to our study also revealed linear progression of disease, but both showed accelerated disease progression, relative to the current severity scale, especially at the later stages. Together, these data suggest that college students may be trained in assessment of patient records, and thus provide insight into the natural history of a disease

Biomarkers for cystic fibrosis lung disease: Application of SELDI-TOF mass spectrometry to BAL fluid

AbstractBackgroundFor cystic fibrosis (CF) patients there is a lack of good assays of disease activity and response to new therapeutic interventions, including gene therapy. Current measures of airways inflammation severity are insensitive or non-specific.MethodsBronchoalveolar lavage fluid from 39 CF children and 38 respiratory disease controls was obtained at bronchoscopy and analysed by surface enhanced laser desorption ionisation time of flight (SELDI-TOF) mass spectrometry. Recognized proteins were assessed for CF disease specificity. Individual protein identification of specific peaks was performed.Results1277 proteins/peptides, >4 kDa, were detected using 12 different surfaces and binding conditions. 202 proteins/peptides were differentially expressed in the CF samples (p<0.001), 167 up-regulated and 35 down-regulated. The most discriminatory biomarker had a mass of 5.163 kDa. The most abundant, with a mass of 10.6 kDa, was identified as s100 A8 (calgranulin A).ConclusionsThe application of SELDI-TOF mass spectrometry allows evaluation of proteins in BAL fluid avoiding the limitations of only analysing predetermined proteins and potentially identifying proteins not previously appreciated as biomarkers. Its application to cystic fibrosis should enable appropriate evaluation of evolving illness, of gene therapy and other new therapies

Sputum Proteomics in Inflammatory and Suppurative Respiratory Diseases

Rationale: Markers of inflammatory activity are important for assessment and management of many respiratory diseases. Markers that are currently unrecognized may be more valuable than those presently believed to be useful

SELDI-TOF biomarker signatures for cystic fibrosis, asthma and chronic obstructive pulmonary disease

OBJECTIVES: The aim of this work was to establish protein profiles in serum and nasal epithelial cells of cystic fibrosis individuals in comparison with controls, asthma and chronic obstructive pulmonary disease patients for specific biomarker signatures identification. DESIGN AND METHODS: Protein extracts were analyzed by Surface Enhanced Laser Desorption/Ionization Time-Of-Flight Mass-Spectrometry (SELDI-TOF-MS). RESULTS: The mass spectra revealed a set of peaks with differential expression in serum and nasal cells among the different groups studied, resulting into peak signatures representative/specific of each pathology. Logistic regressions were applied to those peaks; sensitivity, specificity, Youden's indexes and area under the curve (AUC) of the respective receiver operating characteristic (ROC) curves were compared. DISCUSSION: Multivariate analysis demonstrated that combination of peaks has a better predictive value than the individual ones. These protein signatures may serve as diagnostic/prognostic markers for the studied diseases with common clinical features, or as follow-up assessment markers of therapeutic interventions