Neighbourhood graph of the gene BRCA1 according to the Lieberman-Aiden et al. experiment.

<p>Gene expression data about colon cancer experiment GDS3160 have been mapped on the graph to show the enhanced description (and prediction) power that the graph representation has in relation to gene co-expression with respect to the approach relying on genomic coordinates.</p

Neighbourhood graph of the gene LMO2 according to the Lieberman-Aiden et al.

<p>Hi-C experiment.</p

Simulation details (left) and related statistics (right) of the stochastic analysis about the two components estimator for the graph describing the HOXB cluster of genes according to the Lieberman-Aiden et al.

<p>Hi-C experiment: in the top panel the topological distribution (edges) component; in the bottom panel the degree distribution (degree) component.</p

Goodness of fit diagnostics charts for three topological features of the stochastic estimator for the HOXB cluster of genes neighbourhood graph according to the Lieberman-Aiden et al.

<p>Hi-C experiment. The thick black line represents the real data concerning the analysed graph, while the boxplot shows the statistical properties of the estimator achieved by employing stochastic simulations. In the top panel the analysis of the estimated model in relation to the degree distribution of the HOXB neighbourhood graph; in the central panel the analysis of the estimated model in relation to weighted edge-wise shared partner statistic; in the bottom panel the analysis of the estimated model in relation to the minimum geodetic distance of the HOXB neighbourhood graph.</p

Representation of the OCT4 (official name POU5F1) neighbourhood graphs in four different runs from the Hi-C experiments of Dixon et al. to show inter and intra run modifications.

<p>In the panel a) and b) on the top part of the figure, the sequencing runs are from human embryonic stem cells (hESC), while panel c) and d) are from human foetal lung fibroblasts (IMR-90).</p

Normalization of chromosome 17 Hi-C data according to the Lieberman-Aiden et al. experiment.

<p>In panel a) the Hu et al. normalization is shown, while in panel b) the read-based normalization performed with NuChart (threshold 0.9) is presented to show the reproducibility with respect to the Hu et al. approach. Panel c) represents the NuChart read-based normalization performed using a more restrictive threshold (threshold 0.99).</p

NuChart: An R Package to Study Gene Spatial Neighbourhoods with Multi-Omics Annotations

<div><p>Long-range chromosomal associations between genomic regions, and their repositioning in the 3D space of the nucleus, are now considered to be key contributors to the regulation of gene expression and important links have been highlighted with other genomic features involved in DNA rearrangements. Recent Chromosome Conformation Capture (3C) measurements performed with high throughput sequencing (Hi-C) and molecular dynamics studies show that there is a large correlation between colocalization and coregulation of genes, but these important researches are hampered by the lack of biologists-friendly analysis and visualisation software. Here, we describe NuChart, an R package that allows the user to annotate and statistically analyse a list of input genes with information relying on Hi-C data, integrating knowledge about genomic features that are involved in the chromosome spatial organization. NuChart works directly with sequenced reads to identify the related Hi-C fragments, with the aim of creating gene-centric neighbourhood graphs on which multi-omics features can be mapped. Predictions about CTCF binding sites, isochores and cryptic Recombination Signal Sequences are provided directly with the package for mapping, although other annotation data in bed format can be used (such as methylation profiles and histone patterns). Gene expression data can be automatically retrieved and processed from the Gene Expression Omnibus and ArrayExpress repositories to highlight the expression profile of genes in the identified neighbourhood. Moreover, statistical inferences about the graph structure and correlations between its topology and multi-omics features can be performed using Exponential-family Random Graph Models. The Hi-C fragment visualisation provided by NuChart allows the comparisons of cells in different conditions, thus providing the possibility of novel biomarkers identification. NuChart is compliant with the Bioconductor standard and it is freely available at <a href="ftp://fileserver.itb.cnr.it/nuchart" target="_blank">ftp://fileserver.itb.cnr.it/nuchart</a>.</p> </div

DnaK as Antibiotic Target: Hot Spot Residues Analysis for Differential Inhibition of the Bacterial Protein in Comparison with the Human HSP70

<div><p>DnaK, the bacterial homolog of human Hsp70, plays an important role in pathogens survival under stress conditions, like antibiotic therapies. This chaperone sequesters protein aggregates accumulated in bacteria during antibiotic treatment reducing the effect of the cure. Although different classes of DnaK inhibitors have been already designed, they present low specificity. DnaK is highly conserved in prokaryotes (identity 50–70%), which encourages the development of a unique inhibitor for many different bacterial strains. We used the DnaK of <i>Acinetobacter baumannii</i> as representative for our analysis, since it is one of the most important opportunistic human pathogens, exhibits a significant drug resistance and it has the ability to survive in hospital environments. The <i>E</i>.<i>coli</i> DnaK was also included in the analysis as reference structure due to its wide diffusion. Unfortunately, bacterial DnaK and human Hsp70 have an elevated sequence similarity. Therefore, we performed a differential analysis of DnaK and Hsp70 residues to identify hot spots in bacterial proteins that are not present in the human homolog, with the aim of characterizing the key pharmacological features necessary to design selective inhibitors for DnaK. Different conformations of DnaK and Hsp70 bound to known inhibitor-peptides for DnaK, and ineffective for Hsp70, have been analysed by molecular dynamics simulations to identify residues displaying stable and selective interactions with these peptides. Results achieved in this work show that there are some residues that can be used to build selective inhibitors for DnaK, which should be ineffective for the human Hsp70.</p></div

Schematic representation of the Hsp70 allosteric cycle.

<p>NBD is in blue, βSBD is in green, helices HA and HB are in yellow and helices HC-HE are in red.</p

Structure-based pharmacophore.

<p>Pharmacophore built on residues important for the interaction between DnaK and inhibitor-peptides and not identified in human proteins. The yellow spheres represent the hydrophobic groups, red and green arrows identify, respectively, H-bonds acceptors and donors. Red and blue spheres are, respectively, negative and positive ionizable groups.</p