Biomedical subjects

Francesca Chiaromonte

Publications and source records attributed to Francesca Chiaromonte.

14 recordsLinked to original sources

PubMed2007

Qualitative network models and genome-wide expression data define carbon/nitrogen-responsive molecular machines in Arabidopsis.

BACKGROUND: Carbon (C) and nitrogen (N) metabolites can regulate gene expression in Arabidopsis thaliana. Here, we use multi-network analysis of microarray data to identify molecular networks regulated by C and N in the Arabidopsis root system. RESULTS: We used the Arabidopsis whole genome Affymetrix gene chip to explore global gene expression responses in plants exposed transiently to a matrix of C and N treatments. We used ANOVA analysis to define quantitative models of regulation for all detected genes. Our results suggest that about half of the Arabidopsis transcriptome is regulated by C, N or CN interactions. We found ample evidence for interactions between C and N that include genes involved in metabolic pathways, protein degradation and auxin signaling. To provide a global, yet detailed, view of how the cell molecular network is adjusted in response to the CN treatments, we constructed a qualitative multi-network model of the Arabidopsis metabolic and regulatory molecular network, including 6,176 genes, 1,459 metabolites and 230,900 interactions among them. We integrated the quantitative models of CN gene regulation with the wiring diagram in the multi-network, and identified specific interacting genes in biological modules that respond to C, N or CN treatments. CONCLUSION: Our results indicate that CN regulation occurs at multiple levels, including potential post-transcriptional control by microRNAs. The network analysis of our systematic dataset of CN treatments indicates that CN sensing is a mechanism that coordinates the global and coordinated regulation of specific sets of molecular machines in the plant cell.

Arabidopsis↗

PubMed2006

ESPERR: learning strong and weak signals in genomic sequence alignments to identify functional elements.

Genomic sequence signals - such as base composition, presence of particular motifs, or evolutionary constraint - have been used effectively to identify functional elements. However, approaches based only on specific signals known to correlate with function can be quite limiting. When training data are available, application of computational learning algorithms to multispecies alignments has the potential to capture broader and more informative sequence and evolutionary patterns that better characterize a class of elements. However, effective exploitation of patterns in multispecies alignments is impeded by the vast number of possible alignment columns and by a limited understanding of which particular strings of columns may characterize a given class. We have developed a computational method, called ESPERR (evolutionary and sequence pattern extraction through reduced representations), which uses training examples to learn encodings of multispecies alignments into reduced forms tailored for the prediction of chosen classes of functional elements. ESPERR produces a greatly improved Regulatory Potential score, which can discriminate regulatory regions from neutral sites with excellent accuracy ( approximately 94%). This score captures strong signals (GC content and conservation), as well as subtler signals (with small contributions from many different alignment patterns) that characterize the regulatory elements in our training set. ESPERR is also effective for predicting other classes of functional elements, as we show for DNaseI hypersensitive sites and highly conserved regions with developmental enhancer activity. Our software, training data, and genome-wide predictions are available from our Web site (http://www.bx.psu.edu/projects/esperr).

Algorithms↗

PubMed2006

Experimental validation of predicted mammalian erythroid cis-regulatory modules.

Multiple alignments of genome sequences are helpful guides to functional analysis, but predicting cis-regulatory modules (CRMs) accurately from such alignments remains an elusive goal. We predict CRMs for mammalian genes expressed in red blood cells by combining two properties gleaned from aligned, noncoding genome sequences: a positive regulatory potential (RP) score, which detects similarity to patterns in alignments distinctive for regulatory regions, and conservation of a binding site motif for the essential erythroid transcription factor GATA-1. Within eight target loci, we tested 75 noncoding segments by reporter gene assays in transiently transfected human K562 cells and/or after site-directed integration into murine erythroleukemia cells. Segments with a high RP score and a conserved exact match to the binding site consensus are validated at a good rate (50%-100%, with rates increasing at higher RP), whereas segments with lower RP scores or nonconsensus binding motifs tend to be inactive. Active DNA segments were shown to be occupied by GATA-1 protein by chromatin immunoprecipitation, whereas sites predicted to be inactive were not occupied. We verify four previously known erythroid CRMs and identify 28 novel ones. Thus, high RP in combination with another feature of a CRM, such as a conserved transcription factor binding site, is a good predictor of functional CRMs. Genome-wide predictions based on RP and a large set of well-defined transcription factor binding sites are available through servers at http://www.bx.psu.edu/.

Amino Acid Motifs↗

PubMed2006

Genomic environment predicts expression patterns on the human inactive X chromosome.

What genomic landmarks render most genes silent while leaving others expressed on the inactive X chromosome in mammalian females? To date, signals determining expression status of genes on the inactive X remain enigmatic despite the availability of complete genomic sequences. Long interspersed repeats (L1s), particularly abundant on the X, are hypothesized to spread the inactivation signal and are enriched in the vicinity of inactive genes. However, both L1s and inactive genes are also more prevalent in ancient evolutionary strata. Did L1s accumulate there because of their role in inactivation or simply because they spent more time on the rarely recombining X? Here we utilize an experimentally derived inactivation profile of the entire human X chromosome to uncover sequences important for its inactivation, and to predict expression status of individual genes. Focusing on Xp22, where both inactive and active genes reside within evolutionarily young strata, we compare neighborhoods of genes with different inactivation states to identify enriched oligomers. Occurrences of such oligomers are then used as features to train a linear discriminant analysis classifier. Remarkably, expression status is correctly predicted for 84% and 91% of active and inactive genes, respectively, on the entire X, suggesting that oligomers enriched in Xp22 capture most of the genomic signal determining inactivation. To our surprise, the majority of oligomers associated with inactivated genes fall within L1 elements, even though L1 frequency in Xp22 is low. Moreover, these oligomers are enriched in parts of L1 sequences that are usually underrepresented in the genome. Thus, our results strongly support the role of L1s in X inactivation, yet indicate that a chromatin microenvironment composed of multiple genomic sequence elements determines expression status of X chromosome genes.

Algorithms↗

PubMed2005

Strong and weak male mutation bias at different sites in the primate genomes: insights from the human-chimpanzee comparison.

Male mutation bias is a higher mutation rate in males than in females thought to result from the greater number of germ line cell divisions in males. If errors in DNA replication cause most mutations, then the magnitude of male mutation bias, measured as the male-to-female mutation rate ratio (alpha), should reflect the relative excess of male versus female germ line cell divisions. Evolutionary rates averaged among all sites in a sequence and compared between mammalian sex chromosomes were shown to be indeed higher in males than in females. However, it is presently unknown whether individual classes of substitutions exhibit such bias. To address this issue, we investigated male mutation bias separately at non-CpG and CpG sites using human-chimpanzee whole-genome alignments. We observed strong male mutation bias at non-CpG sites: alpha in the X-autosome comparison was approximately 6-7, which was similar to the male-to-female ratio in the number of germ line cell divisions. In contrast, mutations at CpG sites exhibited weak male mutation bias: alpha in the X-autosome comparison was only approximately 2-3. This is consistent with the methylation-induced and replication-independent mechanism of CpG transitions, which constitute the majority of mutations at CpG sites. Interestingly, our study also indicated weak male mutation bias for transversions at CpG sites, implying a spontaneous mechanism largely not associated with replication. Male mutation bias was equally strong at CpG and non-CpG sites located within unmethylated "CpG islands," suggesting the replication-dependent origin of these mutations. Thus, we found that the strength of male mutation bias is nonuniform in the primate genomes. Importantly, we discovered that male mutation bias depends on the proportion of CpG sites in the loci compared. This might explain the differences in the magnitude of primate male mutation bias observed among studies.

Animals↗

PubMed2005

Evaluation of regulatory potential and conservation scores for detecting cis-regulatory modules in aligned mammalian genome sequences.

Techniques of comparative genomics are being used to identify candidate functional DNA sequences, and objective evaluations are needed to assess their effectiveness. Different analytical methods score distinctive features of whole-genome alignments among human, mouse, and rat to predict functional regions. We evaluated three of these methods for their ability to identify the positions of known regulatory regions in the well-studied HBB gene complex. Two methods, multispecies conserved sequences and phastCons, quantify levels of conservation to estimate a likelihood that aligned DNA sequences are under purifying selection. A third function, regulatory potential (RP), measures the similarity of patterns in the alignments to those in known regulatory regions. The methods can correctly identify 50%-60% of noncoding positions in the HBB gene complex as regulatory or nonregulatory, with RP performing better than do other methods. When evaluated by the ability to discriminate genomic intervals, RP reaches a sensitivity of 0.78 and a true discovery rate of approximately 0.6. The performance is better on other reference sets; both phastCons and RP scores can capture almost all regulatory elements in those sets along with approximately 7% of the human genome.

Algorithms↗

PubMed2004

Insertions and deletions are male biased too: a whole-genome analysis in rodents.

It is presently accepted that, in mammals, due to the greater number of cell divisions in the male germline than in the female germline, nucleotide substitutions occur more frequently in males. The data on mutation bias in insertions and deletions (indels) are contradictory, with some studies indicating no sex bias and others indicating either female or male bias. The sequenced rat and mouse genomes provide a unique opportunity to investigate a potential sex bias for different types of mutations. Indeed, mutation rates can be accurately estimated from a large number of orthologous loci in organisms similar in generation time and in the number of germline cell divisions. Here we compare the mutation rates between chromosome X and autosomes for likely neutral sites in eutherian ancestral interspersed repetitive elements present at orthologous locations in the rat and mouse genomes. We find that small indels are male biased: The male-to-female mutation rate ratio (alpha) for indels in rodents is approximately 2. Similarly, our whole-genome analysis in rodents indicates an approximately twofold excess of nucleotide substitutions originating in males over that in females. This is the same as the male-to-female ratio of the number of germline cell divisions in rat and mouse. Thus, this is consistent with nucleotide substitutions and small indels occurring primarily during DNA replication.

Animals↗

PubMed2004

Regulatory potential scores from genome-wide three-way alignments of human, mouse, and rat.

We generalize the computation of the Regulatory Potential (RP) score from two-way alignments of human and mouse to three-way alignments of human, mouse, and rat. This requires overcoming technical challenges that arise because the complexity of the models underlying the score increases exponentially with the number of species. Despite the close evolutionary proximity of rat to mouse, we find that adding the rat sequence increases our ability to predict genomic sites that regulate gene transcription. A variant of the RP scoring scheme that accounts for local variation in neutral mutational patterns further improves our predictions.

Actins↗

PubMed2004

Patterns of insertions and their covariation with substitutions in the rat, mouse, and human genomes.

The rates at which human genomic DNA changes by neutral substitution and insertion of certain families of transposable elements covary in large, megabase-sized segments. We used the rat, mouse, and human genomic DNA sequences to examine these processes in more detail in comparisons over both shorter (rat-mouse) and longer (rodent-primate) times, and demonstrated the generality of the covariation. Different families of transposable elements show distinctive insertion preferences and patterns of variation with substitution rates. SINEs are more abundant in GC-rich DNA, but the regional GC preference for insertion (monitored in young SINEs) differs between rodents and humans. In contrast, insertions in the rodent genomes are predominantly LINEs, which prefer to insert into AT-rich DNA in all three mammals. The insertion frequency of repeats other than SINEs correlates strongly positively with the frequency of substitutions in all species. However, correlations with SINEs show the opposite effects. The correlations are explained only in part by the GC content, indicating that other factors also contribute to the inherent tendency of DNA segments to change over evolutionary time.

Animals↗

PubMed2003

Gene length and proximity to neighbors affect genome-wide expression levels.

Steady-state levels of mRNA in cells theoretically depend on the rate and efficiency of transcription and posttranscriptional processing, on mRNA stability, on transcriptional interference from other genes, and on poorly defined long-range chromatin effects. Although each of these cellular processes has been studied in detail for a few genes, it is not possible to predict expression levels by simply examining gene sequences. In this report, we have used a bioinformatics approach to identify critical factors that influence expression levels. To simplify the problem, we have limited our analysis to the collection of genes expressed in all tissues, because such genes provide a unique opportunity to distinguish the role of general genomic features that constrain gene expression from the effect of tissue-specific factors. Using correlation and regression techniques, we have investigated the dependence between expression level and morphological parameters (distance to neighbors, gene, mRNA or 3'-UTR length, number of exons, etc.) that can be directly related to transcription, posttranscriptional processing, mRNA stability, or transcriptional interference. We found that, on a genome-wide scale, highly expressed genes are significantly farther from their closest neighboring genes, are smaller, contain a moderate number of exons, and produce shorter mRNAs with shorter 3'-UTRs. This confirms that transcriptional and posttranscriptional processes are highly interrelated and implies that transcriptional interference plays a role in determining steady-state levels of mRNA in cells.

Computational Biology↗

PubMed2003

Distinguishing regulatory DNA from neutral sites.

We explore several computational approaches to analyzing interspecies genomic sequence alignments, aiming to distinguish regulatory regions from neutrally evolving DNA. Human-mouse genomic alignments were collected for three sets of human regions: (1) experimentally defined gene regulatory regions, (2) well-characterized exons (coding sequences, as a positive control), and (3) interspersed repeats thought to have inserted before the human-mouse split (a good model for neutrally evolving DNA). Models that potentially could distinguish functional noncoding sequences from neutral DNA were evaluated on these three data sets, as well as bulk genome alignments. Our analyses show that discrimination based on frequencies of individual nucleotide pairs or gaps (i.e., of possible alignment columns) is only partially successful. In contrast, scoring procedures that include the alignment context, based on frequencies of short runs of alignment columns, dramatically improve separation between regulatory and neutral features. Such scoring functions should aid in the identification of putative regulatory regions throughout the human genome.

Animals↗

PubMed2003

Covariation in frequencies of substitution, deletion, transposition, and recombination during eutherian evolution.

Six measures of evolutionary change in the human genome were studied, three derived from the aligned human and mouse genomes in conjunction with the Mouse Genome Sequencing Consortium, consisting of (1) nucleotide substitution per fourfold degenerate site in coding regions, (2) nucleotide substitution per site in relics of transposable elements active only before the human-mouse speciation, and (3) the nonaligning fraction of human DNA that is nonrepetitive or in ancestral repeats; and three derived from human genome data alone, consisting of (4) SNP density, (5) frequency of insertion of transposable elements, and (6) rate of recombination. Features 1 and 2 are measures of nucleotide substitutions at two classes of "neutral" sites, whereas 4 is a measure of recent mutations. Feature 3 is a measure dominated by deletions in mouse, whereas 5 represents insertions in human. It was found that all six vary significantly in megabase-sized regions genome-wide, and many vary together. This indicates that some regions of a genome change slowly by all processes that alter DNA, and others change faster. Regional variation in all processes is correlated with, but not completely accounted for, by GC content in human and the difference between GC content in human and mouse.

Animals↗

PubMed2002

Initial sequencing and comparative analysis of the mouse genome.

The sequence of the mouse genome is a key informational tool for understanding the contents of the human genome and a key experimental tool for biomedical research. Here, we report the results of an international collaboration to produce a high-quality draft sequence of the mouse genome. We also present an initial comparative analysis of the mouse and human genomes, describing some of the insights that can be gleaned from the two sequences. We discuss topics including the analysis of the evolutionary forces shaping the size, structure and sequence of the genomes; the conservation of large-scale synteny across most of the genomes; the much lower extent of sequence orthology covering less than half of the genomes; the proportions of the genomes under selection; the number of protein-coding genes; the expansion of gene families related to reproduction and immunity; the evolution of proteins; and the identification of intraspecies polymorphism.

Animals↗

PubMed2002

Dimension reduction strategies for analyzing global gene expression data with a response.

The analysis of global gene expression data from microarrays is breaking new ground in genetics research, while confronting modelers and statisticians with many critical issues. In this paper, we consider data sets in which a categorical or continuous response is recorded, along with gene expression, on a given number of experimental samples. Data of this type are usually employed to create a prediction mechanism for the response based on gene expression, and to identify a subset of relevant genes. This defines a regression setting characterized by a dramatic under-resolution with respect to the predictors (genes), whose number exceeds by orders of magnitude the number of available observations (samples). We present a dimension reduction strategy that, under appropriate assumptions, allows us to restrict attention to a few linear combinations of the original expression profiles, and thus to overcome under-resolution. These linear combinations can then be used to build and validate a regression model with standard techniques. Moreover, they can be used to rank original predictors, and ultimately to select a subset of them through comparison with a background 'chance scenario' based on a number of independent randomizations. We apply this strategy to publicly available data on leukemia classification.

Gene Expression Profiling↗