Phosphodiesterase 10A Inhibition Improves Cortico-Basal Ganglia Function in Huntington's Disease Models.

Huntington's disease (HD) symptoms are driven to a large extent by dysfunction of the basal ganglia circuitry. HD patients exhibit reduced striatal phoshodiesterase 10 (PDE10) levels. Using HD mouse models that exhibit reduced PDE10, we demonstrate the benefit of pharmacologic PDE10 inhibition to acutely correct basal ganglia circuitry deficits. PDE10 inhibition restored corticostriatal input and boosted cortically driven indirect pathway activity. Cyclic nucleotide signaling is impaired in HD models, and PDE10 loss may represent a homeostatic adaptation to maintain signaling. Elevation of both cAMP and cGMP by PDE10 inhibition was required for rescue. Phosphoproteomic profiling of striatum in response to PDE10 inhibition highlighted plausible neural substrates responsible for the improvement. Early chronic PDE10 inhibition in Q175 mice showed improvements beyond those seen with acute administration after symptom onset, including partial reversal of striatal deregulated transcripts and the prevention of the emergence of HD neurophysiological deficits. VIDEO ABSTRACT.

Comparative proteome analysis across non-small cell lung cancer cell lines.

Non-small cell lung cancer (NSCLC) cell lines are widely used model systems to study molecular aspects of lung cancer. Comparative and in-depth proteome expression data across many NSCLC cell lines has not been generated yet, but would be of utility for the investigation of candidate targets and markers in oncogenesis. We employed a SILAC reference approach to perform replicate proteome quantifications across 23 distinct NSCLC cell lines. On average, close to 4000 distinct proteins were identified and quantified per cell line. These included many known targets and diagnostic markers, indicating that our proteome expression data represents a useful resource for NSCLC pre-clinical research. To assess proteome diversity within the NSCLC cell line panel, we performed hierarchical clustering and principal component analysis of proteome expression data. Our results indicate that general proteome diversity among NSCLC cell lines supersedes potential effects common to K-Ras or epidermal growth factor receptor (EGFR) oncoprotein expression. However, we observed partial segregation of EGFR or KRAS mutant cell lines for certain principal components, which reflected biological differences according to gene ontology enrichment analyses. Moreover, statistical analysis revealed several proteins that were significantly overexpressed in KRAS or EGFR mutant cell lines.

Pareto Optimization Identifies Diverse Set of Phosphorylation Signatures Predicting Response to Treatment with Dasatinib.

Multivariate biomarkers that can predict the effectiveness of targeted therapy in individual patients are highly desired. Previous biomarker discovery studies have largely focused on the identification of single biomarker signatures, aimed at maximizing prediction accuracy. Here, we present a different approach that identifies multiple biomarkers by simultaneously optimizing their predictive power, number of features, and proximity to the drug target in a protein-protein interaction network. To this end, we incorporated NSGA-II, a fast and elitist multi-objective optimization algorithm that is based on the principle of Pareto optimality, into the biomarker discovery workflow. The method was applied to quantitative phosphoproteome data of 19 non-small cell lung cancer (NSCLC) cell lines from a previous biomarker study. The algorithm successfully identified a total of 77 candidate biomarker signatures predicting response to treatment with dasatinib. Through filtering and similarity clustering, this set was trimmed to four final biomarker signatures, which then were validated on an independent set of breast cancer cell lines. All four candidates reached the same good prediction accuracy (83%) as the originally published biomarker. Although the newly discovered signatures were diverse in their composition and in their size, the central protein of the originally published signature - integrin ß4 (ITGB4) - was also present in all four Pareto signatures, confirming its pivotal role in predicting dasatinib response in NSCLC cell lines. In summary, the method presented here allows for a robust and simultaneous identification of multiple multivariate biomarkers that are optimized for prediction performance, size, and relevance.

Identification of significant features by the Global Mean Rank test.

With the introduction of omics-technologies such as transcriptomics and proteomics, numerous methods for the reliable identification of significantly regulated features (genes, proteins, etc.) have been developed. Experimental practice requires these tests to successfully deal with conditions such as small numbers of replicates, missing values, non-normally distributed expression levels, and non-identical distributions of features. With the MeanRank test we aimed at developing a test that performs robustly under these conditions, while favorably scaling with the number of replicates. The test proposed here is a global one-sample location test, which is based on the mean ranks across replicates, and internally estimates and controls the false discovery rate. Furthermore, missing data is accounted for without the need of imputation. In extensive simulations comparing MeanRank to other frequently used methods, we found that it performs well with small and large numbers of replicates, feature dependent variance between replicates, and variable regulation across features on simulation data and a recent two-color microarray spike-in dataset. The tests were then used to identify significant changes in the phosphoproteomes of cancer cells induced by the kinase inhibitors erlotinib and 3-MB-PP1 in two independently published mass spectrometry-based studies. MeanRank outperformed the other global rank-based methods applied in this study. Compared to the popular Significance Analysis of Microarrays and Linear Models for Microarray methods, MeanRank performed similar or better. Furthermore, MeanRank exhibits more consistent behavior regarding the degree of regulation and is robust against the choice of preprocessing methods. MeanRank does not require any imputation of missing values, is easy to understand, and yields results that are easy to interpret. The software implementing the algorithm is freely available for academic and commercial use.

Transient domain interactions enhance the affinity of the mitotic regulator Pin1 toward phosphorylated peptide ligands.

The mitotic regulator Pin1 plays an important role in protein quality control and age-related medical conditions such as Alzheimer disease and Parkinson disease. Although its cellular role has been thoroughly investigated during the past decade, the molecular mechanisms underlying its function remain elusive. We provide evidence for interactions between the two domains of Pin1. Several residues displayed unequivocal peak splits in nuclear magnetic resonance spectra, indicative of two different conformational states in equilibrium. Pareto analysis of paramagnetic relaxation enhancement data demonstrates that the two domains approach each other upon addition of a nonpeptidic ligand. Titration experiments with phosphorylated peptides monitored by fluorescence anisotropy and chemical shift perturbation indicate that domain interactions increase Pin1's affinity toward peptide ligands. We propose this interplay of the domains and ligands to be a general mechanism for a large class of two-domain proteins.

Improved Bevirimat resistance prediction by combination of structural and sequence-based classifiers.

BACKGROUND: Maturation inhibitors such as Bevirimat are a new class of antiretroviral drugs that hamper the cleavage of HIV-1 proteins into their functional active forms. They bind to these preproteins and inhibit their cleavage by the HIV-1 protease, resulting in non-functional virus particles. Nevertheless, there exist mutations in this region leading to resistance against Bevirimat. Highly specific and accurate tools to predict resistance to maturation inhibitors can help to identify patients, who might benefit from the usage of these new drugs. RESULTS: We tested several methods to improve Bevirimat resistance prediction in HIV-1. It turned out that combining structural and sequence-based information in classifier ensembles led to accurate and reliable predictions. Moreover, we were able to identify the most crucial regions for Bevirimat resistance computationally, which are in line with experimental results from other studies. CONCLUSIONS: Our analysis demonstrated the use of machine learning techniques to predict HIV-1 resistance against maturation inhibitors such as Bevirimat. New maturation inhibitors are already under development and might enlarge the arsenal of antiretroviral drugs in the future. Thus, accurate prediction tools are very useful to enable a personalized therapy.

Computational Design of a DNA- and Fc-Binding Fusion Protein.

Computational design of novel proteins with well-defined functions is an ongoing topic in computational biology. In this work, we generated and optimized a new synthetic fusion protein using an evolutionary approach. The optimization was guided by directed evolution based on hydrophobicity scores, molecular weight, and secondary structure predictions. Several methods were used to refine the models built from the resulting sequences. We have successfully combined two unrelated naturally occurring binding sites, the immunoglobin Fc-binding site of the Z domain and the DNA-binding motif of MyoD bHLH, into a novel stable protein.

Parental origin and functional relevance of a de novo UBE3A variant.

Sequence analysis of the imprinted UBE3A gene in a 3-year-old girl suspected of having Angelman syndrome had revealed a de novo 3bp in frame deletion predicted to encode a protein lacking the amino acid G538 (based on sequence NM_130838). In order to assess the clinical relevance of this unknown variant, we determined the parental origin and the functional consequences of the deletion. We separated the two chromosomes 15 by microdissection of metaphase spreads and used cytogenetic and molecular markers to demonstrate that the deletion is on the maternal chromosome. For determining the functional consequences of the deletion, we modelled the structure of the deletion mutant based on the wildtype X-ray structure and simulated the molecular dynamics of the wildtype and mutant protein in complex with UcbH7. Our simulations showed that deletion of G538 destroys a network of salt bridges between highly conserved residues in the catalytic cleft of UBE3A. In conclusion, our results strongly suggest that the 3bp deletion is a loss-of-function mutation of the maternal UBE3A allele that has caused Angelman syndrome in our patient. Our study may serve as a paradigm to determine the parental origin of a de novo mutation.

Structure of HIV-1 quasi-species as early indicator for switches of co-receptor tropism.

Deep sequencing is able to generate a complete picture of the retroviral quasi-species in a patient. We demonstrate that the unprecedented power of deep sequencing in conjunction with computational data analysis has great potential for clinical diagnostics and basic research. Specifically, we analyzed longitudinal deep sequencing data from patients in a study with Vicriviroc, a drug that blocks the HIV-1 co-receptor CCR5. Sequences covered the V3-loop of gp120, known to be the main determinant of co-receptor tropism. First, we evaluated this data with a computational model for the interpretation of V3-sequences with respect to tropism, and we found complete agreement with results from phenotypic assays. Thus, the method could be applied in cases where phenotypic assays fail. Second, computational analysis led to the discovery of a characteristic pattern in the quasi-species that foreshadows switches of co-receptor tropism. This analysis could help to unravel the mechanism of tropism switches, and to predict these switches weeks to months before they can be detected by a phenotypic assay.

Prediction of co-receptor usage of HIV-1 from genotype.

Human Immunodeficiency Virus 1 uses for entry into host cells a receptor (CD4) and one of two co-receptors (CCR5 or CXCR4). Recently, a new class of antiretroviral drugs has entered clinical practice that specifically bind to the co-receptor CCR5, and thus inhibit virus entry. Accurate prediction of the co-receptor used by the virus in the patient is important as it allows for personalized selection of effective drugs and prognosis of disease progression. We have investigated whether it is possible to predict co-receptor usage accurately by analyzing the amino acid sequence of the main determinant of co-receptor usage, i.e., the third variable loop V3 of the gp120 protein. We developed a two-level machine learning approach that in the first level considers two different properties important for protein-protein binding derived from structural models of V3 and V3 sequences. The second level combines the two predictions of the first level. The two-level method predicts usage of CXCR4 co-receptor for new V3 sequences within seconds, with an area under the ROC curve of 0.937+/-0.004. Moreover, it is relatively robust against insertions and deletions, which frequently occur in V3. The approach could help clinicians to find optimal personalized treatments, and it offers new insights into the molecular basis of co-receptor usage. For instance, it quantifies the importance for co-receptor usage of a pocket that probably is responsible for binding sulfated tyrosine.

Rpd3-dependent boundary formation at telomeres by removal of Sir2 substrate.

Boundaries between euchromatic and heterochromatic regions until now have been associated with chromatin-opening activities. Here, we identified an unexpected role for histone deacetylation in this process. Significantly, the histone deacetylase (HDAC) Rpd3 was necessary for boundary formation in Saccharomyces cerevisiae. rpd3Delta led to silent information regulator (SIR) spreading and repression of subtelomeric genes. In the absence of a known boundary factor, the histone acetyltransferase complex SAS-I, rpd3Delta caused inappropriate SIR spreading that was lethal to yeast cells. Notably, Rpd3 was capable of creating a boundary when targeted to heterochromatin. Our data suggest a mechanism for boundary formation whereby histone deacetylation by Rpd3 removes the substrate for the HDAC Sir2, so that Sir2 no longer can produce O-acetyl-ADP ribose (OAADPR) by consumption of NAD(+) in the deacetylation reaction. In essence, OAADPR therefore is unavailable for binding to Sir3, preventing SIR propagation.