Ivan Molineris

The Roles of Multiparametric Magnetic Resonance Imaging, PCA3 and Prostate Health Index—Which is the Best Predictor of Prostate Cancer after a Negative Biopsy?

PURPOSE In patients with a negative prostate biopsy and persistent suspicion of prostate cancer, additional analyses such as the PCA3 score, PHI and multiparametric magnetic resonance imaging have been proposed to reduce the number of unnecessary repeat biopsies. In this study we evaluate the diagnostic accuracy of PCA3, PHI, multiparametric magnetic resonance imaging and various combinations of these tests in the repeat biopsy setting. MATERIALS AND METHODS A total of 170 patients with an initial negative prostate biopsy and persistent suspicion of prostate cancer were enrolled in this prospective study. The patients underwent measurements of the total prostate specific antigen and free prostate specific antigen rate, along with PHI, PCA3 tests and multiparametric magnetic resonance imaging before standard repeat biopsy that was performed by urologists blinded to the multiparametric magnetic resonance imaging results. Multivariate logistic regression models with various combinations of PCA3, PHI and multiparametric magnetic resonance imaging were used to identify the predictors of prostate cancer with repeat biopsy, and the performance of these models was compared using ROC curves, AUC analysis and decision curve analysis. RESULTS In the ROC analysis the most significant contribution was provided by multiparametric magnetic resonance imaging (AUC 0.936), which was greater than the contribution of the PHI+PCA3 model (p <0.001). In the multivariate logistic regression analysis only multiparametric magnetic resonance imaging was a significant independent predictor of prostate cancer diagnosis with repeat biopsy (p <0.001). The results of the decision curve analysis confirmed that the most significant improvement in the net benefit was provided by multiparametric magnetic resonance imaging. CONCLUSIONS Multiparametric magnetic resonance imaging provides high diagnostic accuracy in identifying patients with prostate cancer in the repeat biopsy setting compared with PCA3 and PHI.

An atlas of tissue-specific conserved coexpression for functional annotation and disease gene prediction

Gene coexpression relationships that are phylogenetically conserved between human and mouse have been shown to provide important clues about gene function that can be efficiently used to identify promising candidate genes for human hereditary disorders. In the past, such approaches have considered mostly generic gene expression profiles that cover multiple tissues and organs. The individual genes of multicellular organisms, however, can participate in different transcriptional programs, operating at scales as different as single-cell types, tissues, organs, body regions or the entire organism. Therefore, systematic analysis of tissue-specific coexpression could be, in principle, a very powerful strategy to dissect those functional relationships among genes that emerge only in particular tissues or organs. In this report, we show that, in fact, conserved coexpression as determined from tissue-specific and condition-specific data sets can predict many functional relationships that are not detected by analyzing heterogeneous microarray data sets. More importantly, we find that, when combined with disease networks, the simultaneous use of both generic (multi-tissue) and tissue-specific conserved coexpression allows a more efficient prediction of human disease genes than the use of generic conserved coexpression alone. Using this strategy, we were able to identify high-probability candidates for 238 orphan disease loci. We provide proof of concept that this combined use of generic and tissue-specific conserved coexpression can be very useful to prioritize the mutational candidates obtained from deep-sequencing projects, even in the case of genetic disorders as heterogeneous as XLMR.

ZIKA virus elicits P53 activation and genotoxic stress in human neural progenitors similar to mutations involved in severe forms of genetic microcephaly and p53

Epidemiological evidence from the current outbreak of Zika virus (ZIKV) and recent studies in animal models indicate a strong causal link between ZIKV and microcephaly. ZIKV infection induces cell-cycle arrest and apoptosis in proliferating neural progenitors. However, the mechanisms leading to these phenotypes are still largely obscure. In this report, we explored the possible similarities between transcriptional responses induced by ZIKV in human neural progenitors and those elicited by three different genetic mutations leading to severe forms of microcephaly in mice. We found that the strongest similarity between all these conditions is the activation of common P53 downstream genes. In agreement with these observations, we report that ZIKV infection increases total P53 levels and nuclear accumulation, as well as P53 Ser15 phosphorylation, correlated with genotoxic stress and apoptosis induction. Interestingly, increased P53 activation and apoptosis are induced not only in cells expressing high levels of viral antigens but also in cells showing low or undetectable levels of the same proteins. These results indicate that P53 activation is an early and specific event in ZIKV-infected cells, which could result from cell-autonomous and/or non-cell-autonomous mechanisms. Moreover, we highlight a small group of P53 effector proteins that could act as critical mediators, not only in ZIKV-induced microcephaly but also in many genetic microcephaly syndromes.

KungFQ: A Simple and Powerful Approach to Compress fastq Files

Nowadays storing data derived from deep sequencing experiments has become pivotal and standard compression algorithms do not exploit in a satisfying manner their structure. A number of reference-based compression algorithms have been developed but they are less adequate when approaching new species without fully sequenced genomes or nongenomic data. We developed a tool that takes advantages of fastq characteristics and encodes them in a binary format optimized in order to be further compressed with standard tools (such as gzip or lzma). The algorithm is straightforward and does not need any external reference file, it scans the fastq only once and has a constant memory requirement. Moreover, we added the possibility to perform lossy compression, losing some of the original information (IDs and/or qualities) but resulting in smaller files; it is also possible to define a quality cutoff under which corresponding base calls are converted to N. We achieve 2.82 to 7.77 compression ratios on various fastq files without losing information and 5.37 to 8.77 losing IDs, which are often not used in common analysis pipelines. In this paper, we compare the algorithm performance with known tools, usually obtaining higher compression levels.

Evolution of Promoter Affinity for Transcription Factors in the Human Lineage

Changes in gene regulation are believed to play an important role in the evolution of animals. It has been suggested that changes in cis-regulatory regions are responsible for many or most of the anatomical and behavioral differences between humans and apes. However, the study of the evolution of cis-regulatory regions is made problematic by the degeneracy of transcription factor (TF) binding sites and the shuffling of their positions. In this work, we use the predicted total affinity of a promoter for a large collection of TFs as the basis for studying the evolution of cis-regulatory regions in mammals. We introduce the human specificity of a promoter, measuring the divergence between the affinity profile of a human promoter and its orthologous promoters in other mammals. The promoters of genes involved in functional categories such as neural processes and signal transduction, among others, have higher human specificity compared with the rest of the genome. Clustering of the human-specific affinities (HSAs) of neural genes reveals patterns of promoter evolution associated with functional categories such as synaptic transmission and brain development and to diseases such as bipolar disorder and autism.

Candidate gene prioritization based on spatially mapped gene expression

Motivation: The identification of genes involved in specific phenotypes, such as human hereditary diseases, often requires the time-consuming and expensive examination of a large number of positional candidates selected by genome-wide techniques such as linkage analysis and association studies. Even considering the positive impact of next-generation sequencing technologies, the prioritization of these positional candidates may be an important step for disease-gene identification. Results: Here, we report a large-scale analysis of spatial, i.e. 3D, gene-expression data from an entire organ (the mouse brain) for the purpose of evaluating and ranking positional candidate genes, showing that the spatial gene-expression patterns can be successfully exploited for the prediction of gene–phenotype associations not only for mouse phenotypes, but also for human central nervous system-related Mendelian disorders. We apply our method to the case of X-linked mental retardation, compare the predictions to the results obtained from a previous large-scale resequencing study of chromosome X and discuss some promising novel candidates. Contact: rosario.piro@unito.it Supplementary information: Supplementary data are available at Bioinformatics online.

Genome-wide identification and characterization of fixed human-specific regulatory regions.

Changes in gene regulatory networks are believed to have played an important role in the development of human-specific anatomy and behavior. We identified the human genome regions that show the typical chromatin marks of regulatory regions but cannot be aligned to other mammalian genomes. Most of these regions have become fixed in the human genome. Their regulatory targets are enriched in genes involved in neural processes, CNS development, and diseases such as autism, depression, and schizophrenia. Specific transposable elements contributing to the rewiring of the human regulatory network can be identified by the creation of human-specific regulatory regions. Our results confirm the relevance of regulatory evolution in the emergence of human traits and cognitive abilities and the importance of newly acquired genomic elements for such evolution.

A new computational approach to analyze human protein complexes and predict novel protein interactions.

We propose a new approach to identify interacting proteins based on gene expression data. By using hypergeometric distribution and extensive Monte-Carlo simulations, we demonstrate that looking at synchronous expression peaks in a single time interval is a high sensitivity approach to detect co-regulation among interacting proteins. Combining gene expression and Gene Ontology similarity analyses enabled the extraction of novel interactions from microarray datasets. Applying this approach to p21-activated kinase 1, we validated α-tubulin and early endosome antigen 1 as its novel interactors.

Evaluation of Candidate Genes from Orphan FEB and GEFS+ Loci by Analysis of Human Brain Gene Expression Atlases

Febrile seizures, or febrile convulsions (FEB), represent the most common form of childhood seizures and are believed to be influenced by variations in several susceptibility genes. Most of the associated loci, however, remain ‘orphan’, i.e. the susceptibility genes they contain still remain to be identified. Further orphan loci have been mapped for a related disorder, genetic (generalized) epilepsy with febrile seizures plus (GEFS+). We show that both spatially mapped and ‘traditional’ gene expression data from the human brain can be successfully employed to predict the most promising candidate genes for FEB and GEFS+, apply our prediction method to the remaining orphan loci and discuss the validity of the predictions. For several of the orphan FEB/GEFS+ loci we propose excellent, and not always obvious, candidates for mutation screening in order to aid in gaining a better understanding of the genetic origin of the susceptibility to seizures.

Visual Search of Neuropil-Enriched RNAs from Brain In Situ Hybridization Data through the Image Analysis Pipeline Hippo-ATESC

Motivation RNA molecules specifically enriched in the neuropil of neuronal cells and in particular in dendritic spines are of great interest for neurobiology in virtue of their involvement in synaptic structure and plasticity. The systematic recognition of such molecules is therefore a very important task. High resolution images of RNA in situ hybridization experiments contained in the Allen Brain Atlas (ABA) represent a very rich resource to identify them and have been so far exploited for this task through human-expert analysis. However, software tools that may automatically address the same objective are not very well developed. Results In this study we describe an automatic method for exploring in situ hybridization data and discover neuropil-enriched RNAs in the mouse hippocampus. We called it Hippo-ATESC (Automatic Texture Extraction from the Hippocampal region using Soft Computing). Bioinformatic validation showed that the Hippo-ATESC is very efficient in the recognition of RNAs which are manually identified by expert curators as neuropil-enriched on the same image series. Moreover, we show that our method can also highlight genes revealed by microdissection-based methods but missed by human visual inspection. We experimentally validated our approach by identifying a non-coding transcript enriched in mouse synaptosomes. The code is freely available on the web at http://ibislab.ce.unipr.it/software/hippo/.