Biomedical subjects

Diego di Bernardo

Publications and source records attributed to Diego di Bernardo.

9 recordsLinked to original sources

PubMed2006

Computational framework for the prediction of transcription factor binding sites by multiple data integration.

Control of gene expression is essential to the establishment and maintenance of all cell types, and its dysregulation is involved in pathogenesis of several diseases. Accurate computational predictions of transcription factor regulation may thus help in understanding complex diseases, including mental disorders in which dysregulation of neural gene expression is thought to play a key role. However, biological mechanisms underlying the regulation of gene expression are not completely understood, and predictions via bioinformatics tools are typically poorly specific. We developed a bioinformatics workflow for the prediction of transcription factor binding sites from several independent datasets. We show the advantages of integrating information based on evolutionary conservation and gene expression, when tackling the problem of binding site prediction. Consistent results were obtained on a large simulated dataset consisting of 13050 in silico promoter sequences, on a set of 161 human gene promoters for which binding sites are known, and on a smaller set of promoters of Myc target genes. Our computational framework for binding site prediction can integrate multiple sources of data, and its performance was tested on different datasets. Our results show that integrating information from multiple data sources, such as genomic sequence of genes' promoters, conservation over multiple species, and gene expression data, indeed improves the accuracy of computational predictions.

Animals↗

PubMed2006

Transcriptional network dynamics in macrophage activation.

Transcriptional regulatory networks govern cell differentiation and the cellular response to external stimuli. However, mammalian model systems have not yet been accessible for network analysis. Here, we present a genome-wide network analysis of the transcriptional regulation underlying the mouse macrophage response to bacterial lipopolysaccharide (LPS). Key to uncovering the network structure is our combination of time-series cap analysis of gene expression with in silico prediction of transcription factor binding sites. By integrating microarray and qPCR time-series expression data with a promoter analysis, we find dynamic subnetworks that describe how signaling pathways change dynamically during the progress of the macrophage LPS response, thus defining regulatory modules characteristic of the inflammatory response. In particular, our integrative analysis enabled us to suggest novel roles for the transcription factors ATF-3 and NRF-2 during the inflammatory response. We believe that our system approach presented here is applicable to understanding cellular differentiation in higher eukaryotes.

Algorithms↗

PubMed2006

Inference of gene regulatory networks and compound mode of action from time course gene expression profiles.

MOTIVATION: Time series expression experiments are an increasingly popular method for studying a wide range of biological systems. Here we developed an algorithm that can infer the local network of gene-gene interactions surrounding a gene of interest. This is achieved by a perturbation of the gene of interest and subsequently measuring the gene expression profiles at multiple time points. We applied this algorithm to computer simulated data and to experimental data on a nine gene network in Escherichia coli. RESULTS: In this paper we show that it is possible to recover the gene regulatory network from a time series data of gene expression following a perturbation to the cell. We show this both on simulated data and on a nine gene subnetwork part of the DNA-damage response pathway (SOS pathway) in the bacteria E. coli. CONTACT: dibernardo@tigem.it SUPLEMENTARY INFORMATION: Supplementary data are available at http://dibernado.tigem.it

Algorithms↗

PubMed2005

DG-CST (Disease Gene Conserved Sequence Tags), a database of human-mouse conserved elements associated to disease genes.

The identification and study of evolutionarily conserved genomic sequences that surround disease-related genes is a valuable tool to gain insight into the functional role of these genes and to better elucidate the pathogenetic mechanisms of disease. We created the DG-CST (Disease Gene Conserved Sequence Tags) database for the identification and detailed annotation of human-mouse conserved genomic sequences that are localized within or in the vicinity of human disease-related genes. CSTs are defined as sequences that show at least 70% identity between human and mouse over a length of at least 100 bp. The database contains CST data relative to over 1088 genes responsible for monogenetic human genetic diseases or involved in the susceptibility to multifactorial/polygenic diseases. DG-CST is accessible via the internet at http://dgcst.ceinge.unina.it/ and may be searched using both simple and complex queries. A graphic browser allows direct visualization of the CSTs and related annotations within the context of the relative gene and its transcripts.

Animals↗

PubMed2005

Chemogenomic profiling on a genome-wide scale using reverse-engineered gene networks.

A major challenge in drug discovery is to distinguish the molecular targets of a bioactive compound from the hundreds to thousands of additional gene products that respond indirectly to changes in the activity of the targets. Here, we present an integrated computational-experimental approach for computing the likelihood that gene products and associated pathways are targets of a compound. This is achieved by filtering the mRNA expression profile of compound-exposed cells using a reverse-engineered model of the cell's gene regulatory network. We apply the method to a set of 515 whole-genome yeast expression profiles resulting from a variety of treatments (compounds, knockouts and induced expression), and correctly enrich for the known targets and associated pathways in the majority of compounds examined. We demonstrate our approach with PTSB, a growth inhibitory compound with a previously unknown mode of action, by predicting and validating thioredoxin and thioredoxin reductase as its target.

Algorithms↗

PubMed2003

ddbRNA: detection of conserved secondary structures in multiple alignments.

MOTIVATION: Structured non-coding RNAs (ncRNAs) have a very important functional role in the cell. No distinctive general features common to all ncRNA have yet been discovered. This makes it difficult to design computational tools able to detect novel ncRNAs in the genomic sequence. RESULTS: We devised an algorithm able to detect conserved secondary structures in both pairwise and multiple DNA sequence alignments with computational time proportional to the square of the sequence length. We implemented the algorithm for the case of pairwise and three-way alignments and tested it on ncRNAs obtained from public databases. On the test sets, the pairwise algorithm has a specificity greater than 97% with a sensitivity varying from 22.26% for Blast alignments to 56.35% for structural alignments. The three-way algorithm behaves similarly. Our algorithm is able to efficiently detect a conserved secondary structure in multiple alignments.

Algorithms↗

PubMed2003

Inferring genetic networks and identifying compound mode of action via expression profiling.

The complexity of cellular gene, protein, and metabolite networks can hinder attempts to elucidate their structure and function. To address this problem, we used systematic transcriptional perturbations to construct a first-order model of regulatory interactions in a nine-gene subnetwork of the SOS pathway in Escherichia coli. The model correctly identified the major regulatory genes and the transcriptional targets of mitomycin C activity in the subnetwork. This approach, which is experimentally and computationally scalable, provides a framework for elucidating the functional properties of genetic networks and identifying molecular targets of pharmacological compounds.

Algorithms↗

PubMed2002

Origin on the electrocardiogram of U-waves and abnormal U-wave inversion.

AIMS: Soon after the initial development of electrocardiography, U-waves were discovered in many normal subjects following the T-wave repolarisation waveform on the electrocardiogram. Various explanations have been offered for their origin, but none is universally accepted. We used our model of left ventricular repolarisation to explore the most common hypotheses for the genesis of U-waves. METHODS: Recently, we have shown that a computer model of left ventricular repolarisation was able to explain the formation of the characteristic shape of the T-wave, and we have now used this model to explore the most common hypotheses for the genesis of U-waves. The repolarisation phase of the action potentials in the model exhibited an after-potential. We investigated separately the effect on the 12-lead electrocardiogram of three different features of the model: the amplitude of the after-potential; dispersion of repolarisation in the left ventricle ranging from 20 to 100 ms; the timing of the after-potential, relative to the end of the principal action potential component, ranging from -100 to 100 ms. RESULTS: We show that delaying repolarisation in different regions of the heart cannot explain the U-wave, but show that the presence of after-potentials on the cardiac action potential do explain the U-wave polarity and other characteristic U-wave features. We also show that abnormal after-potential timing corresponds with abnormal U-wave inversion. CONCLUSION: Our model provides a realistic and simple solution to the problem of U-wave genesis.

Action Potentials↗

PubMed2002

Effect of changes in heart rate and in action potential duration on the electrocardiogram T wave shape.

The mechanisms responsible for changes in T wave symmetry and amplitude with changes in heart rate and action potential duration were investigated. A computer model of normal left ventricular repolarization was used to simulate the T waves on the surface 12-lead ECG. The effect of heart rate changes was studied by varying the ratio between dispersion of repolarization (Disp) and action potential repolarization duration (APRD). With constant dispersion. as heart rate increases, APRD decreases and the ratio Disp/APRD increases. T waves were simulated while varying the Disp/APRD ratio from 3.6% to 100%. The T wave symmetry ratio measured from the areas either side of the peak (SRarea), the symmetry ratio from the times either side of the peak (SRtime) and the T wave amplitude (Tamplitude) were calculated from each simulated ECG. SRarea decreased from 1.42 to 0.77, SRtime from 1.75 to 1.04 and the Tamplitude increased from 0. 19 mV to 2.30 mV. The stability of results with variation in model characteristics was also investigated, by moving the heart +/- 20 mm on all three axes, rotating the heart axes by +/- 10 and by modifying all constants defining the action potential by +/- 5% and +/- 10%. T wave amplitude was sensitive to changes in heart position, as the heart was moved towards the body surface. However, T wave shape changed very little with heart position or rotation, with the SD of SRarea varying by less than 0.05 over an SRarea range of 0.65 for different values of Disp/APRD ratio. We have shown from our model that cardiac T waves increase in amplitude, and become more symmetric with their peaks becoming central as APRD shortens with increasing heart rate, agreeing with clinical observations. These results help to explain the T wave shape changes which occur when heart rate increases.

Action Potentials↗