epiG: statistical inference and profiling of DNA methylation from whole-genome bisulfite sequencing data.

The study of epigenetic heterogeneity at the level of individual cells and in whole populations is the key to understanding cellular differentiation, organismal development, and the evolution of cancer. We develop a statistical method, epiG, to infer and differentiate between different epi-allelic haplotypes, annotated with CpG methylation status and DNA polymorphisms, from whole-genome bisulfite sequencing data, and nucleosome occupancy from NOMe-seq data. We demonstrate the capabilities of the method by inferring allele-specific methylation and nucleosome occupancy in cell lines, and colon and tumor samples, and by benchmarking the method against independent experimental data.

Connecting Berezinskii-Kosterlitz-Thouless and BEC Phase Transitions by Tuning Interactions in a Trapped Gas.

We study the critical point for the emergence of coherence in a harmonically trapped two-dimensional Bose gas with tunable interactions. Over a wide range of interaction strengths we find excellent agreement with the classical-field predictions for the critical point of the Berezinskii-Kosterlitz-Thouless (BKT) superfluid transition. This allows us to quantitatively show, without any free parameters, that the interaction-driven BKT transition smoothly converges onto the purely quantum-statistical Bose-Einstein condensation transition in the limit of vanishing interactions.

Development and validation of a microRNA based diagnostic assay for primary tumor site classification of liver core biopsies.

Identification of the primary tumor site in patients with metastatic cancer is clinically important, but remains a challenge. Hence, efforts have been made towards establishing new diagnostic tools. Molecular profiling is a promising diagnostic approach, but tissue heterogeneity and inadequacy may negatively affect the accuracy and usability of molecular classifiers. We have developed and validated a microRNA-based classifier, which predicts the primary tumor site of liver biopsies, containing a limited number of tumor cells. Concurrently we explored the influence of surrounding normal tissue on classification. MicroRNA profiling was performed using quantitative Real-Time PCR on formalin-fixed paraffin-embedded samples. 278 primary tumors and liver metastases, representing nine primary tumor classes, as well as normal liver samples were used as a training set. A statistical model was applied to adjust for normal liver tissue contamination. Performance was estimated by cross-validation, followed by independent validation on 55 liver core biopsies with a tumor content as low as 10%. A microRNA classifier developed, using the statistical contamination model, showed an overall classification accuracy of 74.5% upon independent validation. Two-thirds of the samples were classified with high-confidence, with an accuracy of 92% on high-confidence predictions. A classifier trained without adjusting for liver tissue contamination, showed a classification accuracy of 38.2%. Our results indicate that surrounding normal tissue from the biopsy site may critically influence molecular classification. A significant improvement in classification accuracy was obtained when the influence of normal tissue was limited by application of a statistical contamination model.

Modeling tissue contamination to improve molecular identification of the primary tumor site of metastases.

MOTIVATION: Contamination of a cancer tissue by the surrounding benign (non-cancerous) tissue is a concern for molecular cancer diagnostics. This is because an observed molecular signature will be distorted by the surrounding benign tissue, possibly leading to an incorrect diagnosis. One example is molecular identification of the primary tumor site of metastases because biopsies of metastases typically contain a significant amount of benign tissue. RESULTS: A model of tissue contamination is presented. This contamination model works independently of the training of a molecular predictor, and it can be combined with any predictor model. The usability of the model is illustrated on primary tumor site identification of liver biopsies, specifically, on a human dataset consisting of microRNA expression measurements of primary tumor samples, benign liver samples and liver metastases. For a predictor trained on primary tumor and benign liver samples, the contamination model decreased the test error on biopsies from liver metastases from 77 to 45%. A further reduction to 34% was obtained by including biopsies in the training data. AVAILABILITY AND IMPLEMENTATION: http://www.math.ku.dk/∼richard/msgl/. CONTACT: vincent@math.ku.dk SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Efficient identification of miRNAs for classification of tumor origin.

Carcinomas of unknown primary origin constitute 3% to 5% of all newly diagnosed metastatic cancers, with the primary source difficult to classify with current histological methods. Effective cancer treatment depends on early and accurate identification of the tumor; patients with metastases of unknown origin have poor prognosis and short survival. Because miRNA expression is highly tissue specific, the miRNA profile of a metastasis may be used to identify its origin. We therefore evaluated the potential of miRNA profiling to identify the primary tumor of known metastases. Two hundred eight formalin-fixed, paraffin-embedded samples, representing 15 different histologies, were profiled on a locked nucleic acid-enhanced microarray platform, which allows for highly sensitive and specific detection of miRNA. On the basis of these data, we developed and cross-validated a novel classification algorithm, least absolute shrinkage and selection operator, which had an overall accuracy of 85% (CI, 79%-89%). When the classifier was applied on an independent test set of 48 metastases, the primary site was correctly identified in 42 cases (88% accuracy; CI, 75%-94%). Our findings suggest that miRNA expression profiling on paraffin tissue can efficiently predict the primary origin of a tumor and may provide pathologists with a molecular diagnostic tool that can improve their capability to correctly identify the origin of hitherto unidentifiable metastatic tumors and, eventually, enable tailored therapy.

Crimean-Congo hemorrhagic fever virus glycoprotein processing by the endoprotease SKI-1/S1P is critical for virus infectivity.

Crimean-Congo hemorrhagic fever virus (CCHFV) causes severe human disease. The CCHFV medium RNA encodes a polyprotein which is proteolytically processed to yield the glycoprotein precursors PreGn and PreGc, followed by structural glycoproteins Gn and Gc. Subtilisin kexin isozyme-1/site-1 protease (SKI-1/S1P) plays a central role in Gn processing. Here we show that CCHFV-infected cells deficient in SKI-1/S1P produce no infectious virus, although PreGn and PreGc accumulated normally in the Golgi apparatus, the site of virus assembly. Only nucleoprotein-containing particles which lacked virus glycoproteins (Gn/Gc or PreGn/PreGc) were secreted. Complementation of SKI-1/S1P-deficient cells with a SKI-1/S1P expression vector restored release of infectious virus (>10(6) PFU/ml), confirming that SKI-1/S1P processing is required for incorporation of viral glycoproteins. SKI-1/S1P may represent a promising antiviral target.

Crimean-congo hemorrhagic fever virus glycoprotein precursor is cleaved by Furin-like and SKI-1 proteases to generate a novel 38-kilodalton glycoprotein.

Crimean-Congo hemorrhagic fever virus (genus Nairovirus, family Bunyaviridae) genome M segment encodes an unusually large (in comparison to members of other genera) polyprotein (1,684 amino acids in length) containing the two major structural glycoproteins, Gn and Gc, that are posttranslationally processed from precursors PreGn and PreGc by SKI-1 and SKI-1-like proteases, respectively. The characteristics of the N-terminal 519 amino acids located upstream of the mature Gn are unknown. A highly conserved furin/proprotein convertase (PC) cleavage site motif (RSKR247) is located between the variable N-terminal region that is predicted to have mucin-like properties and the rest of PreGn. Mutational analysis of the RSKR247 motif and use of a specific furin/PC inhibitor and brefeldin A demonstrate that furin/PC cleavage occurs at the RSKR247 motif of PreGn as the protein transits the trans Golgi network and generates a novel glycoprotein designated GP38. Immunoprecipitation analysis identified two additional proteins, GP85 and GP160, which contain both mucin and GP38 domain regions, and whose generation does not involve furin/PC cleavage. Consistent with glycosylation predictions, heavy O-linked glycosylation and moderate levels of N-glycans were detected in the GP85 and GP160 proteins, both of which contain the mucin domain. GP38, GP85, and GP160 are likely soluble proteins based on the lack of predicted transmembrane domains, their detection in virus-infected cell supernatants, and the apparent absence from virions. Analogy with soluble glycoproteins and mucin-like proteins encoded by other hemorrhagic fever-associated RNA viruses suggests these proteins could play an important role in viral pathogenesis.

Chloroquine is a potent inhibitor of SARS coronavirus infection and spread.

BACKGROUND: Severe acute respiratory syndrome (SARS) is caused by a newly discovered coronavirus (SARS-CoV). No effective prophylactic or post-exposure therapy is currently available. RESULTS: We report, however, that chloroquine has strong antiviral effects on SARS-CoV infection of primate cells. These inhibitory effects are observed when the cells are treated with the drug either before or after exposure to the virus, suggesting both prophylactic and therapeutic advantage. In addition to the well-known functions of chloroquine such as elevations of endosomal pH, the drug appears to interfere with terminal glycosylation of the cellular receptor, angiotensin-converting enzyme 2. This may negatively influence the virus-receptor binding and abrogate the infection, with further ramifications by the elevation of vesicular pH, resulting in the inhibition of infection and spread of SARS CoV at clinically admissible concentrations. CONCLUSION: Chloroquine is effective in preventing the spread of SARS CoV in cell culture. Favorable inhibition of virus spread was observed when the cells were either treated with chloroquine prior to or after SARS CoV infection. In addition, the indirect immunofluorescence assay described herein represents a simple and rapid method for screening SARS-CoV antiviral compounds.

Characterization of the glycoproteins of Crimean-Congo hemorrhagic fever virus.

Crimean-Congo hemorrhagic fever (CCHF) virus is the cause of an important tick-borne disease of humans throughout regions of Africa, Europe, and Asia. Like other members of the genus Nairovirus, family Bunyaviridae, the CCHF virus M genome RNA segment encodes the virus glycoproteins. Sequence analysis of the CCHF virus (Matin strain) M RNA segment revealed one major open reading frame that potentially encodes a precursor polyprotein 1,689 amino acids (aa) in length. Comparison of the deduced amino acid sequences of the M-encoded polyproteins of Nigerian, Pakistani, and Chinese CCHF virus strains revealed two distinct protein regions. The carboxyl-terminal 1,441 aa are relatively highly conserved (up to 8.4% identity difference), whereas the amino-terminal 243 to 248 aa are highly variable (up to 56.4% identity difference) and have mucin-like features, including a high serine, threonine, and proline content (up to 47.3%) and a potential for extensive O-glycosylation. Analysis of released virus revealed two major structural glycoproteins, G2 (37 kDa) and G1 (75 kDa). Virus protein analysis by various techniques, including pulse-chase analysis and/or reactivity with CCHF virus-specific polyclonal and antipeptide antibodies, demonstrated that the 140-kDa (which contains the mucin-like region) and 85-kDa nonstructural proteins are the precursors of the mature G2 and G1 proteins, respectively. The amino termini of the CCHF virus (Matin strain) G2 and G1 proteins were established by microsequencing to be equivalent to aa 525 and 1046, respectively, of the encoded polyprotein precursor. The tetrapeptides RRLL and RKPL are immediately upstream of the cleavage site for mature G2 and G1, respectively. These are completely conserved among the predicted polyprotein sequences of all the CCHF virus strains and closely resemble the tetrapeptides that represent the major cleavage recognition sites present in the glycoprotein precursors of arenaviruses, such as Lassa fever virus (RRLL) and Pichinde virus (RKLL). These results strongly suggest that CCHF viruses (and other members of the genus Nairovirus) likely utilize the subtilase SKI-1/S1P-like cellular proteases for the major glycoprotein precursor cleavage events, as has recently been demonstrated for the arenaviruses.