Biomedical subjects

Francesco Falciani

Publications and source records attributed to Francesco Falciani.

14 recordsLinked to original sources

PubMed2007

A genome-wide transcriptional analysis of producer and non-producer NS0 myeloma cell lines.

'Genome-wide' or 'global' gene expression profiling provides a powerful approach to the characterization of a cell's transcriptional state. Such technology has been used in animal cell culture to create genome-wide snapshots of transcriptional activity in response to environmental factors or cellular triggers under bioprocessing conditions. Furthermore, it allows us to have a fundamental understanding of genetic mechanisms involved in recombinant protein production. One such mechanism adversely affecting the growth of recombinant bacteria is the increased metabolic burden resulting from the maintenance of plasmid copy number and heterologous protein expression. There have also been some reports on the effect of metabolic burden in mammalian cell systems. In the present study, we have used a mouse array representing 6400 genes to assess the expression profile of a WT (wild-type) mouse plasmacytoma cell line, NS0 WT, and a GS (glutamine synthetase)-NS0 6A1-100 cell line expressing chimaeric monoclonal antibody. The producer cells did not exhibit a slower growth as the result of any metabolic burden, but showed differences in metabolic activity. Gene expression profiling revealed that the producer cell line was selected for a higher expression of chromosomal genes, genes for zinc-finger proteins as well as cell-cycle-related events. On the other hand, protein synthesis is greater and ribosomal genes were more expressed in the WT cells. A possible shift from expressing antigen presenting proteins to recombinant protein could also be seen. Hence, gene expression profiling suggests that the effect of the metabolic burden in slowing growth can be mostly negated in producer cell lines by careful clonal selection, where stable transfected cells are selected for both high productivity as well as high growth rates.

Animals↗

PubMed2006

GALGO: an R package for multivariate variable selection using genetic algorithms.

SUMMARY: The development of statistical models linking the molecular state of a cell to its physiology is one of the most important tasks in the analysis of Functional Genomics data. Because of the large number of variables measured a comprehensive evaluation of variable subsets cannot be performed with available computational resources. It follows that an efficient variable selection strategy is required. However, although software packages for performing univariate variable selection are available, a comprehensive software environment to develop and evaluate multivariate statistical models using a multivariate variable selection strategy is still needed. In order to address this issue, we developed GALGO, an R package based on a genetic algorithm variable selection strategy, primarily designed to develop statistical models from large-scale datasets.

Algorithms↗

PubMed2006

Synovial fluid leukocyte apoptosis is inhibited in patients with very early rheumatoid arthritis.

Synovial leukocyte apoptosis is inhibited in established rheumatoid arthritis (RA). In contrast, high levels of leukocyte apoptosis are seen in self-limiting crystal arthritis. The phase in the development of RA at which the inhibition of leukocyte apoptosis is first apparent, and the relationship between leukocyte apoptosis in early RA and other early arthritides, has not been defined. We measured synovial fluid leukocyte apoptosis in very early arthritis and related this to clinical outcome. Synovial fluid was obtained at presentation from 81 patients with synovitis of < or = 3 months duration. The percentages of apoptotic neutrophils and lymphocytes were assessed on cytospin preparations. Patients were assigned to diagnostic groups after 18 months follow-up. The relationship between leukocyte apoptosis and patient outcome was assessed. Patients with early RA had significantly lower levels of neutrophil apoptosis than patients who developed non-RA persistent arthritis and those with a resolving disease course. Similarly, lymphocyte apoptosis was absent in patients with early RA whereas it was seen in patients with other early arthritides. The inhibition of synovial fluid leukocyte apoptosis in the earliest clinically apparent phase of RA distinguishes this from other early arthritides. The mechanisms for this inhibition may relate to the high levels of anti-apoptotic cytokines found in the early rheumatoid joint (e.g. IL-2, IL-4, IL-15 GMCSF, GCSF). It is likely that this process contributes to an accumulation of leukocytes in the early rheumatoid lesion and is involved in the development of the microenvironment required for persistent RA.

Adult↗

PubMed2005

Over-expression of hTERT in CHO K1 results in decreased apoptosis and reduced serum dependency.

The enzyme telomerase plays a crucial role in cellular proliferation. By adding hexameric repeats to the chromosome ends, it prevents telomeric loss and, thus entry into senescence of limited life span cells. It is unclear, however, what would be the effect of over-expressing telomerase in an immortalised cell line, characterised by unlimited life span and high levels of apoptosis under sub-optimal growth conditions. In order to address this question, we have transfected the immortal cell line CHO K1 with the human telomerase reverse transcriptase (hTERT) catalytic subunit. Differences in the growth profile and apoptosis levels between the cells over-expressing hTERT (Telo) and the cells containing mock vector were found under standard growth conditions. Similarly, the Telo cells showed lower levels of apoptosis, greater attachment tendency and higher viable cell density under serum-deprived conditions compared to the control cell line, suggesting a major role for hTERT over-expression in stressed cultures. Using a mouse cDNA microarray, the collagen type III and V genes were shown to have at least a 10-fold higher expression in the Telo cells than the control cells, suggesting a role of hTERT in the cell attachment pathways.

Animals↗

PubMed2005

Biomarker discovery in microarray gene expression data with Gaussian processes.

MOTIVATION: In clinical practice, pathological phenotypes are often labelled with ordinal scales rather than binary, e.g. the Gleason grading system for tumour cell differentiation. However, in the literature of microarray analysis, these ordinal labels have been rarely treated in a principled way. This paper describes a gene selection algorithm based on Gaussian processes to discover consistent gene expression patterns associated with ordinal clinical phenotypes. The technique of automatic relevance determination is applied to represent the significance level of the genes in a Bayesian inference framework. RESULTS: The usefulness of the proposed algorithm for ordinal labels is demonstrated by the gene expression signature associated with the Gleason score for prostate cancer data. Our results demonstrate how multi-gene markers that may be initially developed with a diagnostic or prognostic application in mind are also useful as an investigative tool to reveal associations between specific molecular and cellular events and features of tumour physiology. Our algorithm can also be applied to microarray data with binary labels with results comparable to other methods in the literature.

Algorithms↗

PubMed2005

Early rheumatoid arthritis is characterized by a distinct and transient synovial fluid cytokine profile of T cell and stromal cell origin.

Pathological processes involved in the initiation of rheumatoid synovitis remain unclear. We undertook the present study to identify immune and stromal processes that are present soon after the clinical onset of rheumatoid arthritis (RA) by assessing a panel of T cell, macrophage, and stromal cell related cytokines and chemokines in the synovial fluid of patients with early synovitis. Synovial fluid was aspirated from inflamed joints of patients with inflammatory arthritis of duration 3 months or less, whose outcomes were subsequently determined by follow up. For comparison, synovial fluid was aspirated from patients with acute crystal arthritis, established RA and osteoarthritis. Rheumatoid factor activity was blocked in the synovial fluid samples, and a panel of 23 cytokines and chemokines measured using a multiplex based system. Patients with early inflammatory arthritis who subsequently developed RA had a distinct but transient synovial fluid cytokine profile. The levels of a range of T cell, macrophage and stromal cell related cytokines (e.g. IL-2, IL-4, IL-13, IL-17, IL-15, basic fibroblast growth factor and epidermal growth factor) were significantly elevated in these patients within 3 months after symptom onset, as compared with early arthritis patients who did not develop RA. In addition, this profile was no longer present in established RA. In contrast, patients with non-rheumatoid persistent synovitis exhibited elevated levels of interferon-gamma at initiation. Early synovitis destined to develop into RA is thus characterized by a distinct and transient synovial fluid cytokine profile. The cytokines present in the early rheumatoid lesion suggest that this response is likely to influence the microenvironment required for persistent RA.

Adult↗

PubMed2005

Analysis of host response to bacterial infection using error model based gene expression microarray experiments.

A key step in the analysis of microarray data is the selection of genes that are differentially expressed. Ideally, such experiments should be properly replicated in order to infer both technical and biological variability, and the data should be subjected to rigorous hypothesis tests to identify the differentially expressed genes. However, in microarray experiments involving the analysis of very large numbers of biological samples, replication is not always practical. Therefore, there is a need for a method to select differentially expressed genes in a rational way from insufficiently replicated data. In this paper, we describe a simple method that uses bootstrapping to generate an error model from a replicated pilot study that can be used to identify differentially expressed genes in subsequent large-scale studies on the same platform, but in which there may be no replicated arrays. The method builds a stratified error model that includes array-to-array variability, feature-to-feature variability and the dependence of error on signal intensity. We apply this model to the characterization of the host response in a model of bacterial infection of human intestinal epithelial cells. We demonstrate the effectiveness of error model based microarray experiments and propose this as a general strategy for a microarray-based screening of large collections of biological samples.

Bacterial Infections↗

PubMed2005

Multiplex bead immunoassay analysis of aqueous humor reveals distinct cytokine profiles in uveitis.

PURPOSE: To extensively characterize the complex network of cytokines present in uveitis aqueous humor (AqH), and the relationships between cytokines and the cellular infiltrate. METHODS: AqH from noninflammatory control subjects and patients with idiopathic, Fuchs' heterochromic cyclitis (FHC), and herpes-viral or Behçet's uveitis were analyzed for IL-1beta, -2, -4, -5, -7, -8, -10, -12, -13, -15, TNFalpha, IFNgamma, CCL2 (MCP-1), CCL5 (RANTES), CCL11 (Eotaxin), TGFbeta2, and CXCL12 (SDF-1), using multiplex bead immunoassays. The cellular infiltrate was also determined for each sample. RESULTS: Idiopathic uveitis AqH, compared with noninflammatory controls, was characterized by high levels of IL-6, IL-8, CCL2 and IFNgamma, the levels of which correlated with each other. For IL-6 and IL-8 these levels were proportional to the number of neutrophils present. By contrast, the levels of both TGFbeta2 and CXCL12 decreased in idiopathic uveitis AqH with increasing inflammation. Cluster analysis showed a degree of segregation between noninflammatory and idiopathic uveitis AqH. Further examination using random forest analysis yielded a complete distinction between these two groups. The minimum cytokines required for this classification were IL-6, IL-8, CCL2, IL-13, TNFalpha, and IL-2. CONCLUSIONS: Application of multiplex bead immunoassays has allowed us to identify distinct patterns of cytokines that relate to both clinical disease and the cellular infiltrates present. Bioinformatics analysis allowed identification of cytokines that differentiate idiopathic uveitis from noninflammatory control AqH and are likely to be important for the pathogenesis of uveitis.

Adolescent↗

PubMed2004

A Bayesian approach to reconstructing genetic regulatory networks with hidden factors.

MOTIVATION: We have used state-space models (SSMs) to reverse engineer transcriptional networks from highly replicated gene expression profiling time series data obtained from a well-established model of T cell activation. SSMs are a class of dynamic Bayesian networks in which the observed measurements depend on some hidden state variables that evolve according to Markovian dynamics. These hidden variables can capture effects that cannot be directly measured in a gene expression profiling experiment, for example: genes that have not been included in the microarray, levels of regulatory proteins, the effects of mRNA and protein degradation, etc. RESULTS: We have approached the problem of inferring the model structure of these state-space models using both classical and Bayesian methods. In our previous work, a bootstrap procedure was used to derive classical confidence intervals for parameters representing 'gene-gene' interactions over time. In this article, variational approximations are used to perform the analogous model selection task in the Bayesian context. Certain interactions are present in both the classical and the Bayesian analyses of these regulatory networks. The resulting models place JunB and JunD at the centre of the mechanisms that control apoptosis and proliferation. These mechanisms are key for clonal expansion and for controlling the long term behavior (e.g. programmed cell death) of these cells. AVAILABILITY: Supplementary data is available at http://public.kgi.edu/wild/index.htm and Matlab source code for variational Bayesian learning of SSMs is available at http://www.cse.ebuffalo.edu/faculty/mbeal/software.html.

Bayes Theorem↗

PubMed2004

Modeling T-cell activation using gene expression profiling and state-space models.

MOTIVATION: We have used state-space models to reverse engineer transcriptional networks from highly replicated gene expression profiling time series data obtained from a well-established model of T-cell activation. State space models are a class of dynamic Bayesian networks that assume that the observed measurements depend on some hidden state variables that evolve according to Markovian dynamics. These hidden variables can capture effects that cannot be measured in a gene expression profiling experiment, e.g. genes that have not been included in the microarray, levels of regulatory proteins, the effects of messenger RNA and protein degradation, etc. RESULTS: Bootstrap confidence intervals are developed for parameters representing 'gene-gene' interactions over time. Our models represent the dynamics of T-cell activation and provide a methodology for the development of rational and experimentally testable hypotheses. AVAILABILITY: Supplementary data and Matlab computer source code will be made available on the web at the URL given below. SUPPLEMENTARY INFORMATION: http://public.kgi.edu/~wild/LDS/index.htm

Computer Simulation↗

PubMed2004

Bayesian variable selection in multinomial probit models to identify molecular signatures of disease stage.

Here we focus on discrimination problems where the number of predictors substantially exceeds the sample size and we propose a Bayesian variable selection approach to multinomial probit models. Our method makes use of mixture priors and Markov chain Monte Carlo techniques to select sets of variables that differ among the classes. We apply our methodology to a problem in functional genomics using gene expression profiling data. The aim of the analysis is to identify molecular signatures that characterize two different stages of rheumatoid arthritis.

Arthritis, Rheumatoid↗

PubMed2004

Regulators encoded in the Escherichia coli type III secretion system 2 gene cluster influence expression of genes within the locus for enterocyte effacement in enterohemorrhagic E. coli O157:H7.

Enterohemorrhagic Escherichia coli (EHEC) O157:H7 subverts host cells through a type III secretion system encoded by the locus for enterocyte effacement (LEE). Genome sequencing of this pathotype revealed the existence of a gene cluster encoding components of a second cryptic type III secretion system, E. coli type III secretion system 2 (ETT2). Recently, we showed that the ETT2 gene cluster is present in whole or in part in the majority of E. coli strains but is unable to encode a functional secretion system in most strains, including EHEC O157:H7. However, here we show that mutational inhibition of two regulatory genes (ECs3720 or etrA and ECs3734 or eivF) from the ETT2 cluster in EHEC O157:H7 leads to greatly increased secretion of proteins encoded by the LEE and to increased adhesion to human intestinal cells. Studies in which transcriptional fusions and microarrays were used indicated that EtrA and EivF exert profound negative effects on gene transcription within the LEE. Consistent with these observations, expression of these regulators in an EHEC O26:H- strain led to suppression of protein secretion under LEE-inducing conditions. These findings provide fresh examples of the influence of mobile genetic elements on regulation of the LEE and of cross talk between type III secretion system gene clusters. In addition, they provide a cautionary tale because they show that the effects of regulatory genes can outlive widespread decay of other genes in a functionally coherent gene cluster, a phenomenon that we have named the "Cheshire cat effect." It also seems likely that variations in the ETT2 regulator repertoire might account for strain-to-strain variation in secretion of LEE-encoded proteins.

Bacterial Adhesion↗

PubMed2004

Making sense of molecular signatures in the immune system.

The development of Functional Genomics technologies has opened new avenues to investigate the complexity of the immune system. Microarray technology has been particularly successful because of its relatively low cost and high genome coverage. Consequently to our ability to monitor the expression of a significant proportion of an organism genome, our understanding of the molecular dynamics behind cell differentiation and cell response has greatly improved. Molecular signatures associated to immune cells have provided important tools to investigate the molecular basis of diseases and have been often associated to diagnostic and prognostic markers. The availability of such large collection of data has stimulated the application of complex machine learning techniques in the attempt to link molecular signatures and cell physiology. Here we review the most recent developments in the analysis of molecular signatures in the immune system.

Animals↗

PubMed2003

Global gene expression profiles in fibroblasts from synovial, skin and lymphoid tissue reveals distinct cytokine and chemokine expression patterns.

We investigated the extent to which fibroblasts isolated from diverse tissues differ in their capacity to modulate inflammation by comparing the global gene expression profiles of cultured human fibroblasts from skin, acute and chronically inflamed synovium, lymph node and tonsil. The responses of these fibroblasts to TNF-alpha, IFN-gamma and IL-4 stimulation were markedly different, as revealed by hierarchical cluster analysis and principal component analysis. In the absence of exogenous cytokine, synovial and skin fibroblasts exhibited similar patterns of gene expression. However their transcriptional profiles diverged upon treatment with TNF-alpha. This proved to be biologically relevant, as TNF-alpha induced the secretion of different patterns and amounts of IL-6, IL-8 and CCL2 (MCP-1) in the two fibroblast types. Co-culture of skin or synovial fibroblasts with synovial fluid-derived mononuclear cells provided further evidence that these transcriptional differences were functionally significant in an ex vivo setting. Interestingly, the transcriptional response of skin fibroblasts to IL-4 converged with that of TNF-alpha-treated synovial fibroblasts, suggesting resident tissue fibroblasts and their blood-borne precursors may be imprinted by inflammatory cytokines that are characteristic of different tissues. Our data supports the concept that fibroblasts are heterogeneous, and that they contribute to the tissue-specificity of inflammatory reactions. Fibroblasts are therefore likely to play an active role in the persistence of chronic inflammatory reactions.

Adjuvants, Immunologic↗