Genetic Factors in Mammalian Prion Diseases

Mammalian prion diseases are a group of neurodegenerative conditions caused by infection of the central nervous system with proteinaceous agents called prions, including sporadic, variant, and iatrogenic Creutzfeldt-Jakob disease; kuru; inherited prion disease; sheep scrapie; bovine spongiform encephalopathy; and chronic wasting disease. Prions are composed of misfolded and multimeric forms of the normal cellular prion protein (PrP). Prion diseases require host expression of the prion protein gene (PRNP) and a range of other cellular functions to support their propagation and toxicity. Inherited forms of prion disease are caused by mutation of PRNP, whereas acquired and sporadically occurring mammalian prion diseases are controlled by powerful genetic risk and modifying factors. Whereas some PrP amino acid variants cause the disease, others confer protection, dramatically altered incubation times, or changes in the clinical phenotype. Multiple mechanisms, including interference with homotypic protein interactions and the selection of the permissible prion strains in a host, play a role. Several non-PRNP factors have now been uncovered that provide insights into pathways of disease susceptibility or neurotoxicity.

Prion Protein Interactome: Identifying Novel Targets in Slowly and Rapidly Progressive Forms of Alzheimer's Disease

Rapidly progressive Alzheimer's disease (rpAD) is a variant of AD distinguished by a rapid decline in cognition and short disease duration from onset to death. While attempts to identify rpAD based on biomarker profile classifications have been initiated, the mechanisms which contribute to the rapid decline and prion mimicking heterogeneity in clinical signs are still largely unknown. In this study, we characterized prion protein (PrP) expression, localization, and interactome in rpAD, slow progressive AD, and in non-dementia controls. PrP along with its interacting proteins were affinity purified with magnetic Dynabeads Protein-G, and were identified using Q-TOF-ESI/MS analysis. Our data demonstrated a significant 1.2-fold decrease in di-glycosylated PrP isoforms specifically in rpAD patients. Fifteen proteins appeared to interact with PrP and only two proteins3/4histone H2B-type1-B and zinc alpha-2 protein3/4were specifically bound with PrP isoform isolated from rpAD cases. Our data suggest distinct PrP involvement in association with the altered PrP interacting protein in rpAD, though the pathophysiological significance of these interactions remains to be established.

A quantum leap in the reproducibility, precision, and sensitivity of gene expression profile analysis even when sample size is extremely small

Transcript-level quantification is often measured across two groups of patients to aid the discovery of biomarkers and detection of biological mechanisms involving these biomarkers. Statistical tests lack power and false discovery rate is high when sample size is small. Yet, many experiments have very few samples (≤ 5). This creates the impetus for a method to discover biomarkers and mechanisms under very small sample sizes. We present a powerful method, ESSNet, that is able to identify subnetworks consistently across independent datasets of the same disease phenotypes even under very small sample sizes. The key idea of ESSNet is to fragment large pathways into smaller subnetworks and compute a statistic that discriminates the subnetworks in two phenotypes. We do not greedily select genes to be included based on differential expression but rely on gene-expression-level ranking within a phenotype, which is shown to be stable even under extremely small sample sizes. We test our subnetworks on null distributions obtained by array rotation; this preserves the gene–gene correlation structure and is suitable for datasets with small sample size allowing us to consistently predict relevant subnetworks even when sample size is small. For most other methods, this consistency drops to less than 10% when we test them on datasets with only two samples from each phenotype, whereas ESSNet is able to achieve an average consistency of 58% (72% when we consider genes within the subnetworks) and continues to be superior when sample size is large. We further show that the subnetworks identified by ESSNet are highly correlated to many references in the biological literature. ESSNet and supplementary material are available at: http://compbio.ddns.comp.nus.edu.sg:8080/essnet .

Prion neuropathology follows the accumulation of alternate prion protein isoforms after infective titre has peaked

Prions are lethal infectious agents thought to consist of multi-chain forms (PrP^Sc) of misfolded cellular prion protein (PrP^C). Prion propagation proceeds in two distinct mechanistic phases: an exponential phase 1, which rapidly reaches a fixed level of infectivity irrespective of PrP^C expression level, and a plateau (phase 2), which continues until clinical onset with duration inversely proportional to PrP^C expression level. We hypothesized that neurotoxicity relates to distinct neurotoxic species produced following a pathway switch when prion levels saturate. Here we show a linear increase of proteinase K-sensitive PrP isoforms distinct from classical PrP^Sc at a rate proportional to PrP^C concentration, commencing at the phase transition and rising until clinical onset. The unaltered level of total PrP during phase 1, when prion infectivity increases a million-fold, indicates that prions comprise a small minority of total PrP. This is consistent with PrP^C concentration not being rate limiting to exponential prion propagation and neurotoxicity relating to critical concentrations of alternate PrP isoforms whose production is PrP^C concentration dependent.

Prions (PrP) are infectious agents that cause lethal neurodegenerative diseases. Here the authors study the kinetics of prion propagation in mice and show that the onset of neuropathology occurs during the late phase of disease and is hypothesized to be due to increases in a toxic isoform of PrP that is different from the infectious species.

Finding consistent disease subnetworks using PFSNet

MOTIVATION

Microarray data analysis is often applied to characterize disease populations by identifying individual genes linked to the disease. In recent years, efforts have shifted to focus on sets of genes known to perform related biological functions (i.e. in the same pathways). Evaluating gene sets reduces the need to correct for false positives in multiple hypothesis testing. However, pathways are often large, and genes in the same pathway that do not contribute to the disease can cause a method to miss the pathway. In addition, large pathways may not give much insight to the cause of the disease. Moreover, when such a method is applied independently to two datasets of the same disease phenotypes, the two resulting lists of significant pathways often have low agreement.

RESULTS

We present a powerful method, PFSNet, that identifies smaller parts of pathways (which we call subnetworks), and show that significant subnetworks (and the genes therein) discovered by PFSNet are up to 51% (64%) more consistent across independent datasets of the same disease phenotypes, even for datasets based on different platforms, than previously published methods. We further show that those methods which initially declared some large pathways to be insignificant would declare subnetworks detected by PFSNet in those large pathways to be significant, if they were given those subnetworks as input instead of the entire large pathways.

AVAILABILITY

http://compbio.ddns.comp.nus.edu.sg:8080/pfsnet/

Genetics of prion diseases

Prion diseases are transmissible, fatal neurodegenerative diseases that include scrapie and bovine spongiform encephalopathy (BSE) in animals and Creutzfeldt-Jakob disease (CJD) in human. The prion protein gene (PRNP) is the major genetic determinant of susceptibility, however, several studies now suggest that other genes are also important. Two recent genome wide association studies in human have identified four new loci of interest: ZBTB38-RASA2 in UK CJD cases and MTMR7 and NPAS2 in variant CJD. Complementary studies in mouse have used complex crosses to identify new modifiers such as Cpne8 and provided supporting evidence for previously implicated genes (Rarb and Stmn2). Expression profiling has identified new candidates, including Hspa13, which reduces incubation time in a transgenic model.

Networks in proteomics analysis of cancer

Proteomics provides direct biological information on proteins but is still a limited platform. Borrowing from genomics, its cancer-specific applications can be broadly categorized as (1) pure diagnostics, (2) biomarkers, (3) identification of root causes and (4) identification of cancer-specific network rewirings. Biological networks capture complex relationships between proteins and provide an appropriate means of contextualization. While playing significantly larger roles, especially in 1 and 3, progress in proteomics-specific network-based methods is lagging as compared to genomics. Rapid hardware advances and improvements in proteomic identification and quantification have given rise to much better quality data alongside advent of new network-based analysis methods. However, a tighter integration between analytics and hardware is still essential for network analysis to play more significant roles in proteomics analysis.

Detection of transcriptional triggers in the dynamics of microbial growth: application to the respiratorily versatile bacterium Shewanella oneidensis

The capacity of microorganisms to respond to variable external conditions requires a coordination of environment-sensing mechanisms and decision-making regulatory circuits. Here, we seek to understand the interplay between these two processes by combining high-throughput measurement of time-dependent mRNA profiles with a novel computational approach that searches for key genetic triggers of transcriptional changes. Our approach helped us understand the regulatory strategies of a respiratorily versatile bacterium with promising bioenergy and bioremediation applications, Shewanella oneidensis, in minimal and rich media. By comparing expression profiles across these two conditions, we unveiled components of the transcriptional program that depend mainly on the growth phase. Conversely, by integrating our time-dependent data with a previously available large compendium of static perturbation responses, we identified transcriptional changes that cannot be explained solely by internal network dynamics, but are rather triggered by specific genes acting as key mediators of an environment-dependent response. These transcriptional triggers include known and novel regulators that respond to carbon, nitrogen and oxygen limitation. Our analysis suggests a sequence of physiological responses, including a coupling between nitrogen depletion and glycogen storage, partially recapitulated through dynamic flux balance analysis, and experimentally confirmed by metabolite measurements. Our approach is broadly applicable to other systems.