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Abstract Background As numerous diseases involve errors in signal transduction, modern therapeutics often target proteins involved in cellular signaling. Interpretation of the activity of signaling pathways during disease development or therapeutic intervention would assist in drug development, design of therapy, and target identification. Microarrays provide a global measure of cellular response, however linking these responses to signaling pathways requires an analytic approach tuned to the underlying biology. An ongoing issue in pattern recognition in microarrays has been how to determine the number of patterns (or clusters) to use for data interpretation, and this is a critical issue as measures of statistical significance in gene ontology or pathways rely on proper separation of genes into groups. Results Here we introduce a method relying on gene annotation coupled to decompositional analysis of global gene expression data that allows us to estimate specific activity on strongly coupled signaling pathways and, in some cases, activity of specific signaling proteins. We demonstrate the technique using the Rosetta yeast deletion mutant data set, decompositional analysis by Bayesian Decomposition, and annotation analysis using ClutrFree. We determined from measurements of gene persistence in patterns across multiple potential dimensionalities that 15 basis vectors provides the correct dimensionality for interpreting the data. Using gene ontology and data on gene regulation in the Saccharomyces Genome Database, we identified the transcriptional signatures of several cellular processes in yeast, including cell wall creation, ribosomal disruption, chemical blocking of protein synthesis, and, criticially, individual signatures of the strongly coupled mating and filamentation pathways. Conclusion This works demonstrates that microarray data can provide downstream indicators of pathway activity either through use of gene ontology or transcription factor databases. This can be used to investigate the specificity and success of targeted therapeutics as well as to elucidate signaling activity in normal and disease processes.

Both cell-intrinsic and niche-derived, cell-extrinsic cues drive the specification of hematopoietic multipotent progenitors (MPPs) in the bone marrow, which comprise multipotent MPP1 cells and lineage-restricted MPP2, MPP3, and MPP4 subsets. Patients with WHIM syndrome, a rare congenital immunodeficiency caused by mutations that prevent desensitization of the chemokine receptor CXCR4, have an excess of myeloid cells in the bone marrow. Here, we investigated the effects of increased CXCR4 signaling on the localization and fate of MPPs. Knock-in mice bearing a WHIM syndrome-associated CXCR4 mutation ( CXCR4 1013 ) phenocopied the myeloid skewing of bone marrow in patients. Whereas MPP4 cells in wild-type mice differentiated into lymphoid cells, MPP4s in CXCR4 1013 knock-in mice differentiated into myeloid cells. This myeloid rewiring of MPP4s in CXCR4 1013 knock-in mice was associated with enhanced signaling mediated by the kinase mTOR and increased oxidative phosphorylation (OXPHOS). MPP4s also localized further from arterioles in the bone marrow of knock-in mice compared with wild-type mice, suggesting that the loss of extrinsic cues from the perivascular niche may also contribute to their myeloid skewing. Chronic treatment with the CXCR4 antagonist AMD3100 or the mTOR inhibitor rapamycin restored the lymphoid potential of MPP4s in knock-in mice. Thus, CXCR4 desensitization drives the lymphoid potential of MPP4 cells by dampening the mTOR-dependent metabolic changes that promote myeloid differentiation.

Background: Pancreatic ductal adenocarcinoma (PDAC) remains a clinically challenging cancer, mainly due to limited therapeutic options and the presence of a highly prominent tumor microenvironment (TME), facilitating tumor progression. The TME is predominated by heterogeneous populations of cancer-associated fibroblasts (CAFs) and tumor associated macrophages (TAMs), in constant communication with each other and with tumor cells, influencing many tumoral abilities such as therapeutic resistance. However how the crosstalk between CAFs and macrophages evolves following chemotherapeutic treatment remains poorly understood, limiting our capacity to halt therapeutic resistance., Methods: We combined biological characterization of macrophages indirectly cocultured with human PDAC CAFs, under FOLFIRINOX treatment, with mRNAseq analyses of such macrophages and evaluated the relevance of the specific gene expression signature in a large series of primary PDAC patients to search for correlation with overall survival (OS) after FOLFIRINOX chemotherapy., Results: Firstly, we demonstrated that CAFs polarize naïve and M1 macrophages towards an M2-like phenotype with a specific increase of CD200R and CD209 M2 markers. Then, we demonstrated that CAFs counteract the pro-inflammatory phenotype induced by the FOLFIRINOX on Macrophages. Indeed, we highlighted that, under FOLFIRINOX, CAFs limit the FOLFIRINOX-induced cell death of macrophages and further reinforce their M2 phenotype as well as their immunosuppressive impact through specific chemokines production. Finally, we revealed that under FOLFIRINOX CAFs drive a specific macrophage gene expression signature involving SELENOP and GOS2 that correlates with shortened OS in FOLFIRINOX-treated PDAC patients., Conclusion: Our study provides insight into the complex interactions between TME cells under FOLFIRINOX treatment. It suggests potential novel candidates that could be used as therapeutic targets in combination with FOLFIRINOX to prevent and alleviate TME influx on therapeutic resistance as well as biomarkers to predict FOLFIRINOX response in PDAC patients. Video Abstract., (© 2023. The Author(s).)

Pancreatic ductal adenocarcinoma (PDA) tumor cells are deprived of oxygen and nutrients and therefore must adapt their metabolism to ensure proliferation. In some physiological states, cells rely on ketone bodies to satisfy their metabolic needs, especially during nutrient stress. Here, we show that PDA cells can activate ketone body metabolism and that β-hydroxybutyrate (βOHB) is an alternative cell-intrinsic or systemic fuel that can promote PDA growth and progression. PDA cells activate enzymes required for ketogenesis, utilizing various nutrients as carbon sources for ketone body formation. By assessing metabolic gene expression from spontaneously arising PDA tumors in mice, we find HMG-CoA lyase (HMGCL), involved in ketogenesis, to be among the most deregulated metabolic enzymes in PDA compared to normal pancreas. In vitro depletion of HMGCL impedes migration, tumor cell invasiveness, and anchorage-independent tumor sphere compaction. Moreover, disrupting HMGCL drastically decreases PDA tumor growth in vivo, while βOHB stimulates metastatic dissemination to the liver. These findings suggest that βOHB increases PDA aggressiveness and identify HMGCL and ketogenesis as metabolic targets for limiting PDA progression., (© 2022 The Authors.)

Mutation of the TET2 DNA-hydroxymethylase has been associated with a number of immune pathologies. The disparity in phenotype and clinical presentation among these pathologies leads to questions regarding the role of TET2 mutation in promoting disease evolution in different immune cell types. Here we show that, in primary mast cells, Tet2 expression is induced in response to chronic and acute activation signals. In TET2-deficient mast cells, chronic activation via the oncogenic KITD816V allele associated with mastocytosis, selects for a specific epigenetic signature characterized by hypermethylated DNA regions (HMR) at immune response genes. H3K27ac and transcription factor binding is consistent with priming or more open chromatin at both HMR and non-HMR in proximity to immune genes in these cells, and this signature coincides with increased pathological inflammation signals. HMR are also associated with a subset of immune genes that are direct targets of TET2 and repressed in TET2-deficient cells. Repression of these genes results in immune tolerance to acute stimulation that can be rescued with vitamin C treatment or reiterated with a Tet inhibitor. Overall, our data support a model where TET2 plays a direct role in preventing immune tolerance in chronically activated mast cells, supporting TET2 as a viable target to reprogram the innate immune response for innovative therapies.

Microbiotas play critical roles in human health, yet in most cases scientists lack standardized and reproducible methods from collection and preservation of samples, as well as the choice of omic analysis, up to the data processing. To date, stool sample preservation remains a source of technological bias in metagenomic sequencing, despite newly developed storage solutions. Here, we conducted a comparative study of 10 storage methods for human stool over a 14-day period of storage at fluctuating temperatures. We first compared the performance of each stabilizer with observed bacterial composition variation within the same specimen. Then, we identified the nature of the observed variations to determine which bacterial populations were more impacted by the stabilizer. We found that DNA stabilizers display various stabilizing efficacies and affect the recovered bacterial profiles thus highlighting that some solutions are more performant in preserving the true gut microbial community. Furthermore, our results showed that the bias associated with the stabilizers can be linked to the phenotypical traits of the bacterial populations present in the studied samples. Although newly developed storage solutions have improved our capacity to stabilize stool microbial content over time, they are nevertheless not devoid of biases hence requiring the implantation of standard operating procedures. Acknowledging the biases and limitations of the implemented method is key to better interpret and support true associated microbiome patterns that will then lead us towards personalized medicine, in which the microbiota profile could constitute a reliable tool for clinical practice., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Plauzolles, Toumi, Bonnet, Pénaranda, Bidaut, Chiche, Allardet-Servent, Retornaz, Goutorbe and Halfon.)

The MAPK/ERK pathway regulates a variety of physiological cellular functions, including cell proliferation and survival. It is abnormally activated in many types of human cancers in response to driver mutations in regulators of this pathway that trigger tumor initiation. The early steps of oncogenic progression downstream of ERK overactivation are poorly understood due to a lack of appropriate models. We show here that ERK1/2 overactivation in the trunk neural tube of the chicken embryo through expression of a constitutively active form of the upstream kinase MEK1 (MEK1ca), rapidly provokes a profound change in the transcriptional signature of developing spinal cord cells. These changes are concordant with a previously established role of the tyrosine kinase receptor ligand FGF8 acting via the ERK1/2 effectors to maintain an undifferentiated state. Furthermore, we show that MEK1ca-transfected spinal cord cells lose neuronal identity, retain caudal markers, and ectopically express potential effector oncogenes, such as AQP1. MEK1ca expression in the developing spinal cord from the chicken embryo is thus a tractable in vivo model to identify the mechanisms fostering neoplasia and malignancy in ERK-induced tumorigenesis of neural origins., Competing Interests: Declaration of competing interest The authors declare no competing or financial interests., (Copyright © 2021. Published by Elsevier Inc.)

Natural Killer (NK) cells are potent anti-leukemic immune effectors. However, they display multiple defects in acute myeloid leukemia (AML) patients leading to reduced anti-tumor potential. Our limited understanding of the mechanisms underlying these defects hampers the development of strategies to restore NK cell potential. Here, we have used a mouse model of AML to gain insight into these mechanisms. We found that leukemia progression resulted in NK cell maturation defects and functional alterations. Next, we assessed NK cell cytokine signaling governing their behavior. We showed that NK cells from leukemic mice exhibit constitutive IL-15/mTOR signaling and type I IFN signaling. However, these cells failed to respond to IL-15 stimulation in vitro as illustrated by reduced activation of the mTOR pathway. Moreover, our data suggest that mTOR-mediated metabolic responses were reduced in NK cells from AML-bearing mice. Noteworthy, the reduction of mTOR-mediated activation of NK cells during AML development partially rescued NK cell metabolic and functional defects. Altogether, our data strongly suggest that NK cells from leukemic mice are metabolically and functionally exhausted as a result of a chronic cytokine activation, at least partially IL-15/mTOR signaling. NK cells from AML patients also displayed reduced IL-2/15Rβ expression and showed cues of reduced metabolic response to IL-15 stimulation in vitro , suggesting that a similar mechanism might occur in AML patients. Our study pinpoints the dysregulation of cytokine stimulation pathways as a new mechanism leading to NK cell defects in AML., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Bou-Tayeh, Laletin, Salem, Just-Landi, Fares, Leblanc, Balzano, Kerdiles, Bidaut, Hérault, Olive, Aurrand-Lions, Walzer, Nunès and Fauriat.)

Therapeutic resistance is a major clinical challenge in oncology. Evidence identifies cancer stem cells (CSCs) as a driver of tumor evolution. Accordingly, the key stemness property unique to CSCs may represent a reservoir of therapeutic target to improve cancer treatment. Here, we carried out a genome-wide RNA interference screen to identify genes that regulate breast CSCs-fate (bCSC). Using an interactome/regulome analysis, we integrated screen results in a functional mapping of the CSC-related processes. This network analysis uncovered potential therapeutic targets controlling bCSC-fate. We tested a panel of 15 compounds targeting these regulators. We showed that mifepristone, salinomycin, and JQ1 represent the best anti-bCSC activity. A combination assay revealed a synergistic interaction of salinomycin/JQ1 association to deplete the bCSC population. Treatment of primary breast cancer xenografts with this combination reduced the tumor-initiating cell population and limited metastatic development. The clinical relevance of our findings was reinforced by an association between the expression of the bCSC-related networks and patient prognosis. Targeting bCSCs with salinomycin/JQ1 combination provides the basis for a new therapeutic approach in the treatment of breast cancer., (© 2019 The Authors. Published under the terms of the CC BY 4.0 license.)

In the bone marrow, CXCL12 and IL-7 are essential for B cell differentiation, whereas hematopoietic stem cell (HSC) maintenance requires SCF and CXCL12. Peri-sinusoidal stromal (PSS) cells are the main source of IL-7, but their characterization as a pro-B cell niche remains limited. Here, we characterize pro-B cell supporting stromal cells and decipher the interaction network allowing pro-B cell retention. Preferential contacts are found between pro-B cells and PSS cells, which homogeneously express HSC and B cell niche genes. Furthermore, pro-B cells are frequently located in the vicinity of HSCs in the same niche. Using an interactome bioinformatics pipeline, we identify Nidogen-1 as essential for pro-B cell retention in the peri-sinusoidal niche as confirmed in Nidogen-1 -/- mice. Finally, human pro-B cells and hematopoietic progenitors are observed close to similar IL-7 + stromal cells. Thus, a multispecific niche exists in mouse and human supporting both early progenitors and committed hematopoietic lineages., (Copyright © 2019 The Author(s). Published by Elsevier Inc. All rights reserved.)

Acute myeloid leukemia (AML) originates from hematopoietic stem and progenitor cells that acquire somatic mutations, leading to disease and clonogenic evolution. AML is characterized by accumulation of immature myeloid cells in the bone marrow and phenotypic cellular heterogeneity reflective of normal hematopoietic differentiation. Here, we show that JAM-C expression defines a subset of leukemic cells endowed with leukemia-initiating cell activity (LIC). Stratification of de novo AML patients at diagnosis based on JAM-C-expressing cells frequencies in the blood served as an independent prognostic marker for disease outcome. Using publicly available leukemic stem cell (LSC) gene expression profiles and gene expression data generated from JAM-C-expressing leukemic cells, we defined a single cell core gene expression signature correlated to JAM-C expression that reveals LSC heterogeneity. Finally, we demonstrated that JAM-C controls Src family kinase (SFK) activation in LSC and that LIC with exacerbated SFK activation was uniquely found within the JAM-C-expressing LSC compartment. Cancer Res; 77(23); 6627-40. ©2017 AACR ., (©2017 American Association for Cancer Research.)

High-throughput RNAi screenings (HTS) allow quantifying the impact of the deletion of each gene in any particular function, from virus-host interactions to cell differentiation. However, there has been less development for functional analysis tools dedicated to RNAi analyses. HTS-Net, a network-based analysis program, was developed to identify gene regulatory modules impacted in high-throughput screenings, by integrating transcription factors-target genes interaction data (regulome) and protein-protein interaction networks (interactome) on top of screening z-scores. HTS-Net produces exhaustive HTML reports for results navigation and exploration. HTS-Net is a new pipeline for RNA interference screening analyses that proves better performance than simple gene rankings by z-scores, by re-prioritizing genes and replacing them in their biological context, as shown by the three studies that we reanalyzed. Formatted input data for the three studied datasets, source code and web site for testing the system are available from the companion web site at http://htsnet.marseille.inserm.fr/. We also compared our program with existing algorithms (CARD and hotnet2).

Natural-killer (NK) cells are important immune effectors during a viral infection. Latent CMV infection is widely spread and was demonstrated to shape the NK cell repertoire through the NKG2C receptor. An expansion of NKG2C + NK cells has been reported during primary HIV infection (PHI), but their role is not known. We previously found a correlation between the maturation state of the NK cell compartment and a lower viral load by studying patients from the ANRS 147 Optiprim trial. We investigated here extensively the NKG2C + NK cells at the time of PHI and its evolution after 3 months of early antiretroviral therapy (combination antiretroviral therapy (cART)). Multiparametric cytometry combined with bioinformatics was used to determine subsets. NK bright NKG2C + progenitor, NK dim NKG2C + effector and NK dim NKG2C + CD57 + memory-like populations were identified. Two groups of patients were unraveled according to the distribution of the NKG2C + subsets skewed toward either progenitor/effector or memory-like phenotype. Patients with high NKG2C + CD57 + NK cell frequencies showed lower HIV-RNA, lower immune activation, higher pDC counts and reached more rapidly undetectable levels of HIV-RNA at M1 under cART. NKG2C + CD57 + NK cell frequency was the only factor strongly correlated to low viral load among other clinical features. While the patients were cytomegalovirus (CMV) infected, there was no sign of reactivation of CMV during PHI suggesting that memory-like NK cells were already present at the time of HIV infection and constituted a preexisting immune response able to contribute to natural control of HIV. This parameter appears to be a good candidate in the search of predictive markers to monitor HIV remission., Competing Interests: The authors declare no conflict of interest.

Breast cancer stem cells (bCSCs) have been implicated in tumor progression and therapeutic resistance; however, the molecular mechanisms that define this state are unclear. We have performed two microRNA (miRNA) gain- and loss-of-function screens to identify miRNAs that regulate the choice between bCSC self-renewal and differentiation. We find that micro-RNA (miR)-600 silencing results in bCSC expansion, while its overexpression reduces bCSC self-renewal, leading to decreased in vivo tumorigenicity. miR-600 targets stearoyl desaturase 1 (SCD1), an enzyme required to produce active, lipid-modified WNT proteins. In the absence of miR-600, WNT signaling is active and promotes self-renewal, whereas overexpression of miR-600 inhibits the production of active WNT and promotes bCSC differentiation. In a series of 120 breast tumors, we found that a low level of miR-600 is correlated with active WNT signaling and a poor prognosis. These findings highlight a miR-600-centered signaling network that governs bCSC-fate decisions and influences tumor progression., (Copyright © 2017 The Author(s). Published by Elsevier Inc. All rights reserved.)

Natural killer (NK) cells are major effectors of the innate immune response. Despite an overall defect in their function associated with chronic human immunodeficiency virus (HIV) infection, their role in primary HIV infection is poorly understood. We investigated the modifications of the NK cell compartment in patients from the ANRS-147-Optiprim trial, a study designed to examine the benefits of intensive combination antiretroviral therapy (cART) in patients with acute or early primary HIV infection. Multiparametric flow cytometry combined with bioinformatics analyses identified the NK phenotypes in blood samples from 30 primary HIV-infected patients collected at inclusion and after 3 months of cART. NK phenotypes were revealed by co-expression of CD56/CD16/NKG2A/NKG2C and CD57, five markers known to delineate stages of NK maturation. Three groups of patients were formed according to their distributions of the 12 NK cell phenotypes identified. Their virological and immunological characteristics were compared along with the early outcome of cART. At inclusion, HIV-infected individuals could be grouped into those with predominantly immature/early differentiated NK cells and those with predominantly mature NK cells. Several virological and immunological markers were improved in patients with mature NK profiles, including lower HIV viral loads, lower immune activation markers on NK and dendritic cell (DC), lower levels of plasma IL-6 and IP-10, and a trend to normal DC counts. Whereas all patients showed a decrease of viremia higher than 3 log 10  copies/ml after 3 months of treatment, patients with a mature NK profile at inclusion reached this threshold more rapidly than patients with an immature NK profile (70 vs. 38%). In conclusion, a better early response to cART is observed in patients whose NK profile is skewed to maturation at inclusion. Whether the mature NK cells contributed directly or indirectly to HIV control through a better immune environment under cART is unknown. The NK maturation status of primary infected patients should be considered as a relevant marker of an immune process contributing to the early outcome of cART that could help in the management of HIV-infected patients.

The malignant progression of pancreatic ductal adenocarcinoma (PDAC) is accompanied by a profound desmoplasia, which forces proliferating tumor cells to metabolically adapt to this new microenvironment. We established the PDAC metabolic signature to highlight the main activated tumor metabolic pathways. Comparative transcriptomic analysis identified lipid-related metabolic pathways as being the most highly enriched in PDAC, compared with a normal pancreas. Our study revealed that lipoprotein metabolic processes, in particular cholesterol uptake, are drastically activated in the tumor. This process results in an increase in the amount of cholesterol and an overexpression of the low-density lipoprotein receptor (LDLR) in pancreatic tumor cells. These findings identify LDLR as a novel metabolic target to limit PDAC progression. Here, we demonstrate that shRNA silencing of LDLR, in pancreatic tumor cells, profoundly reduces uptake of cholesterol and alters its distribution, decreases tumor cell proliferation, and limits activation of ERK1/2 survival pathway. Moreover, blocking cholesterol uptake sensitizes cells to chemotherapeutic drugs and potentiates the effect of chemotherapy on PDAC regression. Clinically, high PDAC Ldlr expression is not restricted to a specific tumor stage but is correlated to a higher risk of disease recurrence. This study provides a precise overview of lipid metabolic pathways that are disturbed in PDAC. We also highlight the high dependence of pancreatic cancer cells upon cholesterol uptake, and identify LDLR as a promising metabolic target for combined therapy, to limit PDAC progression and disease patient relapse.

Pancreatic ductal adenocarcinoma (PDA) is a critical health issue in the field of cancer, with few therapeutic options. Evidence supports an implication of the intratumoral microenvironment (stroma) on PDA progression. However, its contribution to the role of neuroplastic changes within the pathophysiology and clinical course of PDA, through tumor recurrence and neuropathic pain, remains unknown, neglecting a putative, therapeutic window. Here, we report that the intratumoral microenvironment is a mediator of PDA-associated neural remodeling (PANR), and we highlight factors such as 'SLIT2' (an axon guidance molecule), which is expressed by cancer-associated fibroblasts (CAFs), that impact on neuroplastic changes in human PDA. We showed that 'CAF-secreted SLIT2' increases neurite outgrowth from dorsal root ganglia neurons as well as from Schwann cell migration/proliferation by modulating N-cadherin/β-catenin signaling. Importantly, SLIT2/ROBO signaling inhibition disrupts this stromal/neural connection. Finally, we revealed that SLIT2 expression and CAFs are correlated with neural remodeling within human and mouse PDA. All together, our data demonstrate the implication of CAFs, through the secretion of axon guidance molecule, in PANR. Furthermore, it provides rationale to investigate the disruption of the stromal/neural compartment connection with SLIT2/ROBO inhibitors for the treatment of pancreatic cancer recurrence and pain.

One of the earliest events in epithelial carcinogenesis is the dissolution of tight junctions and cell polarity signals that are essential for normal epithelial barrier function. Here, we report that EFA6B, a guanine nucleotide exchange factor for the Ras superfamily protein Arf6 that helps assemble and stabilize tight junction, is required to maintain apico-basal cell polarity and mesenchymal phenotypes in mammary epithelial cells. In organotypic three-dimensional cell cultures, endogenous levels of EFA6B were critical to determine epithelial-mesenchymal status. EFA6B downregulation correlated with a mesenchymal phenotype and ectopic expression of EFA6B hampered TGFβ-induced epithelial-to-mesenchymal transition (EMT). Transcriptomic and immunohistochemical analyses of human breast tumors revealed that the reduced expression of EFA6B was associated with loss of tight junction components and with increased signatures of EMT, cancer stemness, and poor prognosis. Accordingly, tumors with low levels of EFA6B were enriched in the aggressive triple-negative and claudin-low breast cancer subtypes. Our results identify EFA6B as a novel antagonist in breast cancer and they point to its regulatory and signaling pathways as rational therapeutic targets in aggressive forms of this disease., (©2014 American Association for Cancer Research.)

Pancreatic ductal adenocarcinoma (PDAC) is a very aggressive malignancy characterized by an excessive resistance to all known anticancer therapies, a still largely elusive phenomenon. To identify original mechanisms, we have explored the role of post-translational modifications (PTMs) mediated by members of the ubiquitin family. Although alterations of these pathways have been reported in different cancers, no methodical search for these kinds of anomalies has been performed so far. Therefore, we studied the ubiquitin-, Nedd8-, and SUMO1-specific proteomes of a pancreatic cancer cell line (MiaPaCa-2) and identified changes induced by gemcitabine, the standard PDAC's chemotherapeutic drug. These PTMs profiles contained both known major substrates of all three modifiers as well as original ones. Gemcitabine treatment altered the PTM profile of proteins involved in various biological functions, some known cancer associated genes, many potentially cancer-associated genes, and several cancer-signaling networks, including canonical and noncanonical WNT and PI3K/Akt/MTOR pathways. Some of these altered PTMs formed groups of functionally and physically associated proteins. Importantly, we could validate the gemcitabine-induced PTMs variations of relevant candidates and we could demonstrate the biological significance of such altered PTMs by studying in detail the sumoylation of SNIP1, one of these new targets.

The junctional adhesion molecules Jam-b and Jam-c interact together at interendothelial junctions and have been involved in the regulation of immune response, inflammation, and leukocyte migration. More recently, Jam-c has been found to be expressed by hematopoietic stem and progenitor cells (HSPC) in mouse. Conversely, we have reported that Jam-b is present on bone marrow stromal cells and that Jam-b-deficient mice have defects in the regulation of hematopoietic stem cell pool. In this study, we have addressed whether interaction between Jam-b and Jam-c participates to HSPC mobilization or hematopoietic reconstitution after irradiation. We show that a blocking monoclonal antibody directed against Jam-c inhibits hematopoietic reconstitution, progenitor homing to the bone marrow, and induces HSPC mobilization in a Jam-b dependent manner. In the latter setting, antibody treatment over a period of 3 days does not alter hematopoietic differentiation nor induce leukocytosis. Results are translated to human hematopoietic system in which a functional adhesive interaction between JAM-B and JAM-C is found between human HSPC and mesenchymal stem cells. Such an interaction does not occur between HSPC and human endothelial cells or osteoblasts. It is further shown that anti-JAM-C blocking antibody interferes with CD34(+) hematopoietic progenitor homing in mouse bone marrow suggesting that monoclonal antibodies inhibiting JAM-B/JAM-C interaction may represent valuable therapeutic tools to improve stem cell mobilization protocols., (© AlphaMed Press.)

With the development of high-throughput gene expression profiling technologies came the opportunity to define genomic signatures predicting clinical condition or cancer patient outcome. However, such signatures show dependency on training set, lack of generalization, and instability, partly due to microarray data topology. Additional issues for analyzing tumor gene expression are that subtle molecular perturbations in driver genes leading to cancer and metastasis (masked in typical differential expression analysis) may provoke expression changes of greater amplitude in downstream genes (easily detected). In this chapter, we are describing an interactome-based algorithm, Interactome-Transcriptome Integration (ITI) that is used to find a generalizable signature for prediction of breast cancer relapse by superimposition of a large-scale protein-protein interaction data (human interactome) over several gene expression datasets. ITI extracts regions in the interactome whose expression is discriminating for predicting relapse-free survival in cancer and allow detection of subnetworks that constitutes a generalizable and stable genomic signature. In this chapter, we describe the practical aspects of running the full ITI pipeline (subnetwork detection and classification) on six microarray datasets.

Background: With the advance of post-genomic technologies, the need for tools to manage large scale data in biology becomes more pressing. This involves annotating and storing data securely, as well as granting permissions flexibly with several technologies (all array types, flow cytometry, proteomics) for collaborative work and data sharing. This task is not easily achieved with most systems available today., Findings: We developed Djeen (Database for Joomla!'s Extensible Engine), a new Research Information Management System (RIMS) for collaborative projects. Djeen is a user-friendly application, designed to streamline data storage and annotation collaboratively. Its database model, kept simple, is compliant with most technologies and allows storing and managing of heterogeneous data with the same system. Advanced permissions are managed through different roles. Templates allow Minimum Information (MI) compliance., Conclusion: Djeen allows managing project associated with heterogeneous data types while enforcing annotation integrity and minimum information. Projects are managed within a hierarchy and user permissions are finely-grained for each project, user and group.Djeen Component source code (version 1.5.1) and installation documentation are available under CeCILL license from http://sourceforge.net/projects/djeen/files and supplementary material.

Motivation: High-throughput gene expression profiling yields genomic signatures that allow the prediction of clinical conditions including patient outcome. However, these signatures have limitations, such as dependency on the training set, and worse, lack of generalization., Results: We propose a novel algorithm called ITI (interactome-transcriptome integration), to extract a genomic signature predicting distant metastasis in breast cancer by superimposition of large-scale protein-protein interaction data over a compendium of several gene expression datasets. Training on two different compendia showed that the estrogen receptor-specific signatures obtained are more stable (11-35% stability), can be generalized on independent data and performs better than previously published methods (53-74% accuracy)., Availability: The ITI algorithm source code from analysis are available under CeCILL from the ITI companion website: http://bioinformatique.marseille.inserm.fr/iti., Supplementary Information: Supplementary data are available at Bioinformatics online.

Background: Inflammatory breast cancer (IBC) is an aggressive form of BC poorly defined at the molecular level. We compared the molecular portraits of 63 IBC and 134 non-IBC (nIBC) clinical samples., Methodology/findings: Genomic imbalances of 49 IBCs and 124 nIBCs were determined using high-resolution array-comparative genomic hybridization, and mRNA expression profiles of 197 samples using whole-genome microarrays. Genomic profiles of IBCs were as heterogeneous as those of nIBCs, and globally relatively close. However, IBCs showed more frequent "complex" patterns and a higher percentage of genes with CNAs per sample. The number of altered regions was similar in both types, although some regions were altered more frequently and/or with higher amplitude in IBCs. Many genes were similarly altered in both types; however, more genes displayed recurrent amplifications in IBCs. The percentage of genes whose mRNA expression correlated with CNAs was similar in both types for the gained genes, but ∼7-fold lower in IBCs for the lost genes. Integrated analysis identified 24 potential candidate IBC-specific genes. Their combined expression accurately distinguished IBCs and nIBCS in an independent validation set, and retained an independent prognostic value in a series of 1,781 nIBCs, reinforcing the hypothesis for a link with IBC aggressiveness. Consistent with the hyperproliferative and invasive phenotype of IBC these genes are notably involved in protein translation, cell cycle, RNA processing and transcription, metabolism, and cell migration., Conclusions: Our results suggest a higher genomic instability of IBC. We established the first repertory of DNA copy number alterations in this tumor, and provided a list of genes that may contribute to its aggressiveness and represent novel therapeutic targets.

To further characterize the molecular events supporting the tumor suppressor activity of Scrib in mammals, we aim to identify new binding partners. We isolated MCC, a recently identified binding partner for beta-catenin, as a new interacting protein for Scrib. MCC interacts with both Scrib and the NHERF1/NHERF2/Ezrin complex in a PDZ-dependent manner. In T47D cells, MCC and Scrib proteins colocalize at the cell membrane and reduced expression of MCC results in impaired cell migration. By contrast to Scrib, MCC inhibits cell directed migration independently of Rac1, Cdc42 and PAK activation. Altogether, these results identify MCC as a potential scaffold protein regulating cell movement and able to bind Scrib, beta-catenin and NHERF1/2.

Stem cells represent not only a potential source of treatment for degenerative diseases but can also shed light on developmental biology and cancer. It is believed that stem cells differentiation and fate is triggered by a common genetic program that endows those cells with the ability to differentiate into specialized progenitors and fully differentiated cells. To extract the stemness signature of several cells types at the transcription level, we integrated heterogeneous datasets (microarray experiments) performed in different adult and embryonic tissues (liver, blood, bone, prostate and stomach in Homo sapiens and Mus musculus). Data were integrated by generalization of the hematopoietic stem cell hierarchy and by homology between mouse and human. The variation-filtered and integrated gene expression dataset was fed to a single-layered neural network to create a classifier to (i) extract the stemness signature and (ii) characterize unknown stem cell tissue samples by attribution of a stem cell differentiation stage. We were able to characterize mouse stomach progenitor and human prostate progenitor samples and isolate gene signatures playing a fundamental role for every level of the generalized stem cell hierarchy.

A wide variety of stem cells has been reported to exist and renew several adult tissues, raising the question of the existence of a stemness signature-that is, a common molecular program of differentiation. To detect such a signature, we applied a data integration algorithm on several DNA microarray datasets generated by the Stem Cell Genome Anatomy Project (SCGAP) Consortium on several mouse and human tissues, to generate a cross-organism compendium that we submitted to a single layer artificial neural network (ANN) trained to attribute differentiation labels-from totipotent stem cells to differentiated ones (five labels in total were used). The inherent architecture of the system allowed studing the biology behind stem cells differentiation stages and the ANN isolated a 63 gene stemness signature. This chapter presents technological details on DNA microarray integration, ANN training through leave-one-out cross-validation, and independent testing on uncharacterized adult tissues by automated detection of differentiation capabilities on human prostate and mouse stomach progenitors. All scripts of the Stem Cell Analysis and characterization by Neural Networks (SCANN) project are available on the SourceForge Web site: http://scann.sourceforge.net.

Typical microarray or GeneChip experiments now provide genome-wide measurements on gene expression across many conditions. Analysis often focuses on only a few of the genes, looking for those that are "differentially expressed" between conditions or groups of conditions. However, the large number of measurements both present statistical problems to such single gene approaches and offers a tremendous amount of information for methods focused on biological processes rather than individual genes. Here we provide a method to utilize biological annotations in the form of gene ontologies to interpret the results of individual or multiple pattern recognition analyses of a microarray experiment.

Expression data from knockout mutants is a powerful tool for gene function inference, permitting observation of the phenotype of a deleted gene on the organismal scale. A computational method is demonstrated herein to assess gene function from gene expression measured in deletion mutants using Bayesian decomposition, a matrix factorization technique that permits the extraction of patterns and functional units from the data, i.e., sets of genes belonging to the same pathways shared by sets of knockout mutants. ClutrFree, a cluster visualization program is used to aid in the interpretation of functional units and the assessment of gene functions for a subset of unknown genes.

Gene expression microarrays monitor the expression levels of thousands of genes in an experiment simultaneously. To utilize the information generated, each of the thousands of spots on a microarray image must be properly quantified, including background correction. Most present methods require manual alignment of grids to the image data, and still often require additional minor adjustments on a spot by spot basis to correct for spotting irregularities. Such intervention is time consuming and also introduces inconsistency in the handling of data. A fully automatic, tested system would increase throughput and reliability in this field. In this paper, we describe WaveRead, a fully automated, standalone, open-source system for quantifying gene expression array images. Through the use of wavelet analysis to identify the spot locations and diameters, the system is able to automatically grid the image and quantify signal intensities and background corrections without any user intervention. The ability of WaveRead to perform proper quantification is demonstrated by analysis of both simulated images containing spots with donut shapes, elliptical shapes, and Gaussian intensity distributions, as well as of standard images from the National Cancer Institute.

Background: The past two decades have seen the appearance of new infectious diseases and the reemergence of old diseases previously thought to be under control. At the same time, the effectiveness of the existing antibacterials is rapidly decreasing due to the spread of multidrug-resistant pathogens., Aim: The aim of this study was to the identify candidate molecular targets (e.g. enzymes) within essential metabolic pathways specific to a significant subset of bacterial pathogens as the first step in the rational design of new antibacterial drugs., Methods: We constructed a dataset of phylogenomic profiles (vectors that encode the similarity, measured by BLAST scores, of a gene across many species) for a series of 31 pathogenic bacteria of interest with 1073 genes taken from the reference organisms Escherichia coli and Mycobacterium tuberculosis. We applied Bayesian Decomposition, a matrix decomposition algorithm, to identify functional metabolic units comprising overlapping sets of genes in this dataset., Results: Although no information on phylogeny was provided to the system, Bayesian Decomposition retrieved the known bacteria phylogenic relationships on the basis of the proteins necessary for survival. In addition, a set of genes required by all bacteria was identified, as well as components and enzymes specific to subsets of bacteria., Conclusion: The use of phylogenomic profiles and Bayesian Decomposition provide important insights for the design of new antibacterial therapeutics.

Unlabelled: ClutrFree facilitates the visualization and interpretation of clusters or patterns computed from microarray data through a graphical user interface that displays patterns, membership information of the genes and annotation statistics simultaneously. ClutrFree creates a tree linking the patterns based on similarity, permitting the navigation among patterns identified by different algorithms or by the same algorithm with different parameters, and aids the inferring of conclusions from a microarray experiment., Availability: The ClutrFree Java source code and compiled bytecode are available as a package under the GNU General Public License at http://bioinformatics.fccc.edu

The detection and correct identification of cancer, especially at an early stage, are vitally important for patient survival and quality of life. Since signaling pathways play critical roles in cancer development and metastasis, methods that reliably assess the activity of these pathways are critical to understand cancer and the response to therapy. Bayesian Decomposition (BD) identifies signatures of expression that can be linked directly to signaling pathway activity, allowing the changes in mRNA levels to be used as downstream indicators of pathway activity. Here, we demonstrate this ability by identifying the downstream expression signal associated with the mating response in Saccharomyces cerevisiae and showing that this signal disappears in deletion mutants of genes critical to the MAPK signaling cascade used to trigger the response. We also show the use of BD in the context of supervised learning, by analyzing the Mus musculus tissue-specific data set provided by Project Normal. The algorithm correctly removes routine metabolic processes, allowing tissue-specific signatures of expression to be identified. Gene ontology is used to interpret these signatures. Since a number of modern therapeutics specifically target signaling proteins, it is important to be able to identify changes in signaling pathways in order to use microarray data to interpret cancer response. By removing routine metabolic signatures and linking specific signatures to signaling pathway activity, BD makes it possible to link changes in microarray results to signaling pathways.

Unlabelled: Gene expression microarrays and oligonucleotide GeneChips have provided biologists with a means of measuring, in a single experiment, the expression levels of entire genomes under a variety of conditions. As with any nascent field, there is no single accepted method for analyzing the new data types, with new methods appearing monthly. Investigators using the new technology must constantly seek access to the latest tools and explore their data in multiple ways. The functional genomics data pipeline provides an integrated, extendable analysis environment permitting multiple, simultaneous analyses to be automatically performed and provides a web server and interface for presenting results., Availability: Source code and executables are available under the GNU public license at http://bioinformatics.fccc.edu/