Raffaele Fronza

Anti-gene peptide nucleic acid specifically inhibits MYCN expression in human neuroblastoma cells leading to cell growth inhibition and apoptosis

We developed an anti-gene peptide nucleic acid (PNA) for selective inhibition of MYCN transcription in neuroblastoma cells, targeted against a unique sequence in the antisense DNA strand of exon 2 of MYCN and linked at its NH2 terminus to a nuclear localization signal peptide. Fluorescence microscopy showed specific nuclear delivery of the PNA in six human neuroblastoma cell lines: GI-LI-N and IMR-32 (MYCN-amplified/overexpressed); SJ-N-KP and NB-100 (MYCN-unamplified/low-expressed); and GI-CA-N and GI-ME-N (MYCN-unamplified/unexpressed). Antiproliferative effects were observable at 24 hours (GI-LI-N, 60%; IMR-32, 70%) and peaked at 72 hours (GI-LI-N, 80%; IMR-32, 90%; SK-N-KP, 60%; NB-100, 50%); no reduction was recorded for GI-CA-N and GI-ME-N (controls). In MYCN-amplified/overexpressed IMR-32 cells and MYCN-unamplified/low-expressed SJ-N-KP cells, inhibition was recorded of MYCN mRNA (by real-time PCR) and N-Myc (Western blotting); these inhibitory effects increased over 3 days after single treatment in IMR-32. Anti-gene PNA induced G1-phase accumulation (39–53%) in IMR-32 and apoptosis (56% annexin V–positive cells at 24 hours in IMR-32 and 22% annexin V–positive cells at 48 hours in SJ-N-KP). Selective activity of the PNA was shown by altering three point mutations, and by the observation that an anti-gene PNA targeted against the noncoding DNA strand did not exert any effect. These findings could encourage research into development of an anti-gene PNA–based tumor-specific agent for neuroblastoma (and other neoplasms) with MYCN expression.

Identification and association analysis of several hundred single nucleotide polymorphisms within candidate genes for back fat thickness in Italian Large White pigs using a selective genotyping approach1

Combining different approaches (resequencing of portions of 54 obesity candidate genes, literature mining for pig markers associated with fat deposition or related traits in 77 genes, and in silico mining of porcine expressed sequence tags and other sequences available in databases), we identified and analyzed 736 SNP within candidate genes to identify markers associated with back fat thickness (BFT) in Italian Large White sows. Animals were chosen using a selective genotyping approach according to their EBV for BFT (276 with most negative and 279 with most positive EBV) within a population of ≈ 12,000 pigs. Association analysis between the SNP and BFT has been carried out using the MAX test proposed for case-control studies. The designed assays were successful for 656 SNP: 370 were excluded (low call rate or minor allele frequency <5%), whereas the remaining 286 in 212 genes were taken for subsequent analyses, among which 64 showed a P(nominal) value <0.1. To deal with the multiple testing problem in a candidate gene approach, we applied the proportion of false positives (PFP) method. Thirty-eight SNP were significant (P(PFP) < 0.20). The most significant SNP was the IGF2 intron3-g.3072G>A polymorphism (P(nominal) < 1.0E-50). The second most significant SNP was the MC4R c.1426A>G polymorphism (P(nominal) = 8.0E-05). The third top SNP (P(nominal) = 6.2E-04) was the intronic TBC1D1 g.219G>A polymorphic site, in agreement with our previous results obtained in an independent study. The list of significant markers also included SNP in additional genes (ABHD16A, ABHD5, ACP2, ALMS1, APOA2, ATP1A2, CALR, COL14A1, CTSF, DARS, DECR1, ENPP1, ESR1, GH1, GHRL, GNMT, IKBKB, JAK3, MTTP, NFKBIA, NT5E, PLAT, PPARG, PPP2R5D, PRLR, RRAGD, RFC2, SDHD, SERPINF1, UBE2H, VCAM1, and WAT). Functional relationships between genes were obtained using the Ingenuity Pathway Analysis (IPA) Knowledge Base. The top scoring pathway included 19 genes with a P(nominal) < 0.1, 2 of which (IKBKB and NFKBIA) are involved in the hypothalamic IKKβ/NFκB program that could represent a key axis to affect fat deposition traits in pigs. These results represent a starting point to plan marker-assisted selection in Italian Large White nuclei for BFT. Because of similarities between humans and pigs, this study might also provide useful clues to investigate genetic factors affecting human obesity.

G1 cell-cycle arrest and apoptosis by histone deacetylase inhibition in MLL-AF9 acute myeloid leukemia cells is p21 dependent and MLL-AF9 independent

G1 cell-cycle arrest and apoptosis by histone deacetylase inhibition in MLL-AF9 acute myeloid leukemia cells is p21 dependent and MLL-AF9 independent

The bologna annotation resource: a non hierarchical method for the functional and structural annotation of protein sequences relying on a comparative large-scale genome analysis.

Protein sequence annotation is a major challenge in the postgenomic era. Thanks to the availability of complete genomes and proteomes, protein annotation has recently taken invaluable advantage from cross-genome comparisons. In this work, we describe a new non hierarchical clustering procedure characterized by a stringent metric which ensures a reliable transfer of function between related proteins even in the case of multidomain and distantly related proteins. The method takes advantage of the comparative analysis of 599 completely sequenced genomes, both from prokaryotes and eukaryotes, and of a GO and PDB/SCOP mapping over the clusters. A statistical validation of our method demonstrates that our clustering technique captures the essential information shared between homologous and distantly related protein sequences. By this, uncharacterized proteins can be safely annotated by inheriting the annotation of the cluster. We validate our method by blindly annotating other 201 genomes and finally we develop BAR (the Bologna Annotation Resource), a prediction server for protein functional annotation based on a total of 800 genomes (publicly available at http://microserf.biocomp.unibo.it/bar/).

Joint analysis of transcriptional and post- transcriptional brain tumor data: searching for emergent properties of cellular systems

BackgroundAdvances in biotechnology offer a fast growing variety of high-throughput data for screening molecular activities of genomic, transcriptional, post-transcriptional and translational observations. However, to date, most computational and algorithmic efforts have been directed at mining data from each of these molecular levels (genomic, transcriptional, etc.) separately. In view of the rapid advances in technology (new generation sequencing, high-throughput proteomics) it is important to address the problem of analyzing these data as a whole, i.e. preserving the emergent properties that appear in the cellular system when all molecular levels are interacting. We analyzed one of the (currently) few datasets that provide both transcriptional and post-transcriptional data of the same samples to investigate the possibility to extract more information, using a joint analysis approach.ResultsWe use Factor Analysis coupled with pre-established knowledge as a theoretical base to achieve this goal. Our intention is to identify structures that contain information from both mRNAs and miRNAs, and that can explain the complexity of the data. Despite the small sample available, we can show that this approach permits identification of meaningful structures, in particular two polycistronic miRNA genes related to transcriptional activity and likely to be relevant in the discrimination between gliosarcomas and other brain tumors.ConclusionsThis suggests the need to develop methodologies to simultaneously mine information from different levels of biological organization, rather than linking separate analyses performed in parallel.

Brain cancer prognosis: independent validation of a clinical bioinformatics approach

Translational and evidence based medicine can take advantage of biotechnology advances that offer a fast growing variety of high-throughput data for screening molecular activities of genomic, transcriptional, post-transcriptional and translational observations. The clinical information hidden in these data can be clarified with clinical bioinformatics approaches. We have recently proposed a method to analyze different layers of high-throughput (omic) data to preserve the emergent properties that appear in the cellular system when all molecular levels are interacting. We show here that this method applied to brain cancer data can uncover properties (i.e. molecules related to protective versus risky features in different types of brain cancers) that have been independently validated as survival markers, with potential important application in clinical practice.

The FAGenomicH project: towards a whole candidate gene approach to identify markers associated with fatness and production traits in pigs and investigate the pig as a model for human obesity

Abstract Fatness in pigs is a complex trait for which a large number of genes are expected to be involved. Genetics of human obesity could take advantages from genetic information coming from the pig and vice versa. To these aims, a comprehensive candidate gene approach could be helpful. We catalogued all genes affecting fatness on both species, and identified in silico and by resequencing porcine SNPs on a large number of candidate genes. In addition, we applied a selective genotyping approach to identify markers associated with fat deposition in pigs. This approach was tested genotyping the IGF2 intron3-g.3072G>A mutation and novel markers in the PCSK1 and TBC1D1 genes. Polymorphisms in these genes resulted associated with back fat thickness in Italian Large White pigs.