DNA from KI,WU and Merkel Cell Polyomaviruses Is Not Detected in Childhood Central Nervous System Tumours or Neuroblastomas

Background

BK and JC polyomaviruses (BKV and JCV) are potentially oncogenic and have in the past inconclusively been associated with tumours of the central nervous system (CNS), while BKV has been hinted, but not confirmed to be associated with neuroblastomas. Recently three new polyomaviruses (KIPyV, WUPyV and MCPyV) were identified in humans. So far KIPyV and WUPyV have not been associated to human diseases, while MCPyV was discovered in Merkel Cell carcinomas and may have neuroepithelial cell tropism. However, all three viruses can be potentially oncogenic and this compelled us to investigate for their presence in childhood CNS and neuroblastomas.

Methodology

The presence of KI, WU and MCPyV DNA was analysed, by a joint WU and KI specific PCR (covering part of VP1) and by a MCPyV specific regular and real time quantitative PCR (covering part of Large T) in 25 CNS tumour biopsies and 31 neuroblastoma biopsies from the Karolinska University Hospital, Sweden. None of the three new human polyomaviruses were found to be associated with any of the tumours, despite the presence of PCR amplifiable DNA assayed by a S14 housekeeping gene PCR.

Conclusion

In this pilot study, the presence of MCPyV, KI and WU was not observed in childhood CNS tumours and neuroblastomas. Nonetheless, we suggest that additional data are warranted in tumours of the central and peripheral nervous systems and we do not exclude that other still not yet detected polyomaviruses could be present in these tumours.     

Metabolic network prediction through pairwise rational kernels

Background

Metabolic networks are represented by the set of metabolic pathways. Metabolic pathways are a series of biochemical reactions, in which the product (output) from one reaction serves as the substrate (input) to another reaction. Many pathways remain incompletely characterized. One of the major challenges of computational biology is to obtain better models of metabolic pathways. Existing models are dependent on the annotation of the genes. This propagates error accumulation when the pathways are predicted by incorrectly annotated genes. Pairwise classification methods are supervised learning methods used to classify new pair of entities. Some of these classification methods, e.g., Pairwise Support Vector Machines (SVMs), use pairwise kernels. Pairwise kernels describe similarity measures between two pairs of entities. Using pairwise kernels to handle sequence data requires long processing times and large storage. Rational kernels are kernels based on weighted finite-state transducers that represent similarity measures between sequences or automata. They have been effectively used in problems that handle large amount of sequence information such as protein essentiality, natural language processing and machine translations.

Results

We create a new family of pairwise kernels using weighted finite-state transducers (called Pairwise Rational Kernel (PRK)) to predict metabolic pathways from a variety of biological data. PRKs take advantage of the simpler representations and faster algorithms of transducers. Because raw sequence data can be used, the predictor model avoids the errors introduced by incorrect gene annotations. We then developed several experiments with PRKs and Pairwise SVM to validate our methods using the metabolic network of Saccharomyces cerevisiae. As a result, when PRKs are used, our method executes faster in comparison with other pairwise kernels. Also, when we use PRKs combined with other simple kernels that include evolutionary information, the accuracy values have been improved, while maintaining lower construction and execution times.

Conclusions

The power of using kernels is that almost any sort of data can be represented using kernels. Therefore, completely disparate types of data can be combined to add power to kernel-based machine learning methods. When we compared our proposal using PRKs with other similar kernel, the execution times were decreased, with no compromise of accuracy. We also proved that by combining PRKs with other kernels that include evolutionary information, the accuracy can also also be improved. As our proposal can use any type of sequence data, genes do not need to be properly annotated, avoiding accumulation errors because of incorrect previous annotations.     

A novel artificial intelligence-based approach for identification of deoxynucleotide aptamers

The selection of a DNA aptamer through the Systematic Evolution of Ligands by EXponential enrichment (SELEX) method involves multiple binding steps, in which a target and a library of randomized DNA sequences are mixed for selection of a single, nucleotide-specific molecule. Usually, 10 to 20 steps are required for SELEX to be completed. Throughout this process it is necessary to discriminate between true DNA aptamers and unspecified DNA-binding sequences. Thus, a novel machine learning-based approach was developed to support and simplify the early steps of the SELEX process, to help discriminate binding between DNA aptamers from those unspecified targets of DNA-binding sequences. An Artificial Intelligence (AI) approach to identify aptamers were implemented based on Natural Language Processing (NLP) and Machine Learning (ML). NLP method (CountVectorizer) was used to extract information from the nucleotide sequences. Four ML algorithms (Logistic Regression, Decision Tree, Gaussian Naïve Bayes, Support Vector Machines) were trained using data from the NLP method along with sequence information. The best performing model was Support Vector Machines because it had the best ability to discriminate between positive and negative classes. In our model, an Accuracy (A) of 0.995, the fraction of samples that the model correctly classified, and an Area Under the Receiving Operating Curve (AUROC) of 0.998, the degree by which a model is capable of distinguishing between classes, were observed. The developed AI approach is useful to identify potential DNA aptamers to reduce the amount of rounds in a SELEX selection. This new approach could be applied in the design of DNA libraries and result in a more efficient and faster process for DNA aptamers to be chosen during SELEX.     

Brain abnormalities and glioma-like lesions in mice overexpressing the long isoform of PDGF-A in astrocytic cells

Background

Deregulation of platelet-derived growth factor (PDGF) signaling is a hallmark of malignant glioma. Two alternatively spliced PDGF-A mRNAs have been described, corresponding to a long (L) and a short (S) isoform of PDGF-A. In contrast to PDGF-A(S), the PDGF-A(L) isoform has a lysine and arginine rich carboxy-terminal extension that acts as an extracellular matrix retention motif. However, the exact role of PDGF-A(L) and how it functionally differs from the shorter isoform is not well understood.

Methodology/Principal Findings

We overexpressed PDGF-A(L) as a transgene under control of the glial fibrillary acidic protein (GFAP) promoter in the mouse brain. This directs expression of the transgene to astrocytic cells and GFAP expressing neural stem cells throughout the developing and adult central nervous system. Transgenic mice exhibited a phenotype with enlarged skull at approximately 6-16 weeks of age and they died between 1.5 months and 2 years of age. We detected an increased number of undifferentiated cells in all areas of transgene expression, such as in the subependymal zone around the lateral ventricle and in the cerebellar medulla. The cells stained positive for Pdgfr-α, Olig2 and NG2 but this population did only partially overlap with cells positive for Gfap and the transgene reporter. Interestingly, a few mice presented with overt neoplastic glioma-like lesions composed of both Olig2 and Gfap positive cell populations and with microvascular proliferation, in a wild-type p53 background.

Conclusions

Our findings show that PDGF-A(L) can induce accumulation of immature cells in the mouse brain. The strong expression of NG2, Pdgfr-α and Olig2 in PDGF-A(L) brains suggests that a fraction of these cells are oligodendrocyte progenitors. In addition, accumulation of fluid in the subarachnoid space and skull enlargement indicate that an increased intracranial pressure contributed to the observed lethality.     

Zika virus infection of the placenta alters extracellular matrix proteome

Journal of Molecular Histology - Zika virus (ZIKV) infection has been associated with fetal abnormalities by compromising placental integrity, but the mechanisms by which this occurs are unknown....