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Supplementary Tables S1 to S8. S1 shows de novo AML patient characteristics. S2 shows JAM-C expression in monocytic AML. S3 shows multivariate Cox regression data. S4 shows genes selected for single cell gene expression study. S5 shows statistical analysis of genes used in Fig 3C. S6 shows limit dilution assay data in vitro. S7 shows limit dilution assay data in vivo. S8 shows frequencies of human and mouse cells from Fig 5E.

Supplementary Figures S1 to S7. S1 shows JAM-C, CD90 and CD45RA expression in leukemic fraction. S2 shows JAM-C expression in leukemic cells and HSPCs. S3 shows JAM-C expression in cell lines and genes differentially expressed between KG1/JAM-CNeg and KG1/JAM-CPos variant cell lines. S4 shows characterization of engrafting and non-engrafting AML patient samples. S5 shows cell cycle and proliferation properties of KG1/JAM-CNeg and KG1/JAM-CPos variant cells. S6 shows signaling pathways in KG1/JAM-CNeg and KG1/JAM-CPos. S7 shows comparison of SFK phosphorylation in CD45dimCD34+ and CD45dimCD34- leukemic cells.

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.

Supplementary methods describing flow-cytometry, cumulative relapse incidence calculation and mass spectrometry.

Supplementary Figures. Fig.S1: Additional confocal images showing that expression of EFA6Bvsvg in MCF7 cells restores normal apico-basal polarity and promotes lumen formation Fig.S2: Analysis of the specificity of EFA6B repression using siRNAs Fig.S3: Additional confocal images showing that repression of EFA6B in MCF7 cells abrogates the assembly of TJ and leads to unorganized aggregates Fig.S4: Exogenous expression of EFA6Bvsvg abrogates the TGFβ-induced EMT in MDCK cells Fig.S5: Analysis of EFA6B/PSD4 mutations and copy number alterations from the cBioPortal website

Background Shallow shotgun metagenomics (SSM) has been recently suggested as a promising strategy to study human microbiota, providing nearly identical taxonomic profiles to deep shotgun metagenomics but at a sequencing cost as low as that of metabarcoding. To help clinical researchers determine whether shallow sequencing is appropriate for their projects, it is crucial to ascertain the accuracy of the information it provides, compared to deep sequencing. Here, we design a mapping-based workflow to build taxonomic profiles from SSM data and assess its accuracy at varying sequencing depths at both sample and cohort levels using extensive simulations and several public data sets. Results To identify genuinely present species and spuriously identified ones, we propose a novel data-driven filtering method based on machine learning techniques that largely outperforms basic filtering strategies based on pre defined thresholds, resulting in reliable taxonomic profiles at different sequencing depths, ranging from 50K to 10M reads/samples. Up to 90% of species with relative abundances higher than 4.10−4 were recovered correctly at 500K reads/sample showing that only information about rare taxa is lost at shallow depths. Furthermore, our results clearly show that SSM is able to correctly recover relevant biological signal from the confidently identified taxa, such as differences between groups of patients and diagnosis-like classification. Conclusions This study confirms that SSM is suitable for clinical research on human gut microbiota. We recommend that researchers should consider moving from 16S to SSM to limit biases in taxonomic profiles, or moving from deep to shallow sequencing, when functional analyses are not the main focus, to reduce costs and be able to include more patients in research projects.

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.

Background: Microbiotas play critical roles in human health, yet in most cases scientists lack standardized and reproducible methods. To date, stool sample preservation remains a source of technological bias in metagenomic sequencing, despite newly developed storage solutions. Studying those biases will help in identifying the appropriate stool preservation approach to promote the implementation of standard operating procedure and improve comparability across multiple microbiome studies. Results: Here, we conducted a comparative study of 10 storage methods for human stool over a 15-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. Furthermore, our results showed that the bias associated with the stabilizers can be linked to the phenotypical characteristics of the bacterial populations present in the studied samples. Conclusions: Although newly developed storage solutions have improved our capacity to stabilize stool microbial content over time, they are nevertheless not devoid of biases. We therefore recommend considering the nature of these technological biases before claiming whether some microbes are beneficial or even deleterious to human health.

Blood production is a tightly regulated process that starts with hematopoietic stem cells (HSCs). In adults, HSCs are unique in their capacity to self-renew and replenish the entire blood system through production of a series of increasingly committed progenitor cells within the bone marrow (BM) microenvironment. HSCs form a rare, quiescent population that displays a metabolism skewed towards anaerobic glycolysis at the expense of mitochondrial oxidative phosphorylation (OXPHOS) to preserve its quiescent state and long-term reconstitution capacity. However, when HSCs differentiate, they undergo a metabolic switch from anaerobic glycolysis to mitochondrial OXPHOS, a process that is in part mediated by the metabolic sensor mTOR. It is well-established that HSCs in the BM adapt the production of myeloid and lymphoid cells depending on the needs of the body and that metabolic plasticity is a critical driver of HSC fate decisions. This has never been assessed for multipotent progenitors (MPPs) which constitute the stage at which the major divergence of lymphoid and myeloid lineages occurs. In mice, common lymphoid progenitors (CLPs) and common myeloid progenitors (CMPs) are generated from phenotypically and functionally distinct subpopulations of lineage-biased MPPs, i.e. MPP2 and MPP3 are reported as distinct myeloid-biased MPP subsets that operate together with lymphoid-primed MPP4 to control blood leukocyte production. This question is thus of paramount importance to understand how the lympho-myeloid specification process is regulated. Signaling by the G protein-coupled receptor CXCR4 on MPPs in response to stimulation by its natural ligand, the chemokine CXCL12, produced by BM perivascular stromal cells constitutes a key pathway through which the niches and MPPs communicate. However, the mechanisms whereby CXCR4 signaling regulates MPP specification are still unknown. We addressed this point using BM samples of patients with WHIM Syndrome (WS), a rare immunodeficiency caused by inherited heterozygous autosomal gain-of-CXCR4-function mutations affecting desensitization of CXCR4 and characterized by chronic lympho-neutropenia, as well as a unique WS mouse model which phenocopies severe pan-leukopenia. We unraveled myeloid skewing of the hematopoietic stem and progenitor cell (HSPC) compartment in BM of patients with WS and of WS mice. This relied on CXCR4 signaling strength that controls the output of the lymphoid and myeloid lineages by coordinating the composition and molecular identity of the MPP compartment. The fate of the lymphoid-biased MPP4 subset was central in such a process. Indeed, CXCR4 signaling termination was required for efficient generation and maintenance of the MPP4 pool, while regulating intrinsically their cell cycle status and lymphoid-myeloid gene landscape. In fact, we demonstrated for the first time that enhanced mTOR signaling, accumulation of damaged mitochondria and overactive OXPHOS-driven metabolism promoted cell-autonomous molecular changes that reprogram mutant MPP4 away from lymphoid differentiation. Consistent with this, in vivo chronic treatment with the CXCR4 antagonist AMD3100/Plerixafor or the mTOR inhibitor Rapamycin normalized mitochondrial metabolism and MPP4 differentiation. Thus, our study shows that CXCR4 signaling acts through the mTOR pathway as an essential gatekeeper for integrity of the mitochondrial machinery, which in turn controls lymphoid potential of MPP4. Disclosures No relevant conflicts of interest to declare.

Summary: 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. : El Helou et al. identify miRNAs that are able to balance bCSC fate. They find that miR-600 silencing results in bCSC expansion, while its overexpression reduces bCSC self-renewal. miR-600 was further found to regulate WNT signaling through SCD1, and miR-600 expression correlates with clinical outcome.

The wide possibilities opened by the developments of multi-parametric cytometry are limited by the inadequacy of the classical methods of analysis to the multi-dimensional characteristics of the data. While new computational tools seemed ideally adapted and were applied successfully, their adoption is still low among the flow cytometrists. In the purpose to integrate unsupervised computational tools for the management of multi-stained samples, we investigated their advantages and limits by comparison to manual gating on a typical sample analyzed in immunomonitoring routine. A single tube of PBMC, containing 11 populations characterized by different sizes and stained with 9 fluorescent markers, was used. We investigated the impact of the strategy choice on manual gating variability, an undocumented pitfall of the analysis process, and we identified rules to optimize it. While assessing automatic gating as an alternate, we introduced the Multi-Experiment Viewer software (MeV) and validated it for merging clusters and annotating interactively populations. This procedure allowed the finding of both targeted and unexpected populations. However, the careful examination of computed clusters in standard dot plots revealed some heterogeneity, often below 10%, that was overcome by increasing the number of clusters to be computed. MeV facilitated the identification of populations by displaying both the MFI and the marker signature of the dataset simultaneously. The procedure described here appears fully adapted to manage homogeneously high number of multi-stained samples and allows improving multi-parametric analyses in a way close to the classic approach. © 2016 International Society for Advancement of Cytometry.

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.

International audience; 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 log10 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.

International audience; 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.

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.

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.

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. NKbright NKG2C+ progenitor, NKdim NKG2C+ effector and NKdim 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.

PcG methylation of the HIST1 cluster defines an epigenetic marker of acute myeloid leukemia

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.

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. Cancer Res; 74(19); 5493–506. ©2014 AACR.

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.

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.

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. Stem Cells 2014;32:1043–1054

The introduction of high-throughput gene expression profiling technologies (DNA microarrays) in molecular biology and their expected applications to the clinic have allowed the design of predictive signatures linked to a particular clinical condition or patient outcome in a given clinical setting. However, it has been shown that such signatures are prone to several problems: (i) they are heavily unstable and linked to the set of patients chosen for training; (ii) data topology is problematic with regard to the data dimensionality (too many variables for too few samples); (iii) diseases such as cancer are provoked by subtle misregulations which cannot be readily detected by current analysis methods. To find a predictive signature generalizable for multiple datasets, a strategy of superimposition of a large scale of protein-protein interaction data (human interactome) was devised over several gene expression datasets (a total of 2,464 breast cancer tumors were integrated), to find discriminative regions in the interactome (subnetworks) predicting metastatic relapse in breast cancer. This method, Interactome-Transcriptome Integration (ITI), was applied to several breast cancer DNA microarray datasets and allowed the extraction of a signature constituted by 119 subnetworks. All subnetworks have been stored in a relational database and linked to Gene Ontology and NCBI EntrezGene annotation databases for analysis. Exploration of annotations has shown that this set of subnetworks reflects several biological processes linked to cancer and is a good candidate for establishing a network-based signature for prediction of metastatic relapse in breast cancer.

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. Contact: maxime.garcia@inserm.fr; ghislain.bidaut@inserm.fr Supplementary information: Supplementary data are available at Bioinformatics online.

In large-scale transcriptome analysis with DNA microarrays, experimentalists are typically using clustering or pattern recognition algorithms that group genes with similar expression profiles into clusters of biological significance, which aid in interpreting the data. The choice of clustering algorithm and their parameters is essential, since they have a large impact on the final results. However, no standards have been established with regard to these choices, leading to the development of bioinformatics methods to quantify their final impact on the analysis.

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 β-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, β-catenin and NHERF1/2.Structured summaryMINT-7211022: SCRIB (uniprotkb:Q14160) and MCC (uniprotkb:P23508) colocalize (MI:0403) by fluorescence microscopy (MI:0416)MINT-7210609: SCRIB (uniprotkb:Q14160) physically interacts (MI:0915) with MCC (uniprotkb:P23508) by two hybrid (MI:0018)MINT-7210759, MINT-7210792: SCRIB (uniprotkb:Q14160) physically interacts (MI:0914) with PIX beta (uniprotkb:Q14155) by pull down (MI:0096)MINT-7210883, MINT-7210820: SCRIB (uniprotkb:Q14160) physically interacts (MI:0914) with MCC (uniprotkb:P23508) by anti bait coimmunoprecipitation (MI:0006)MINT-7210634, MINT-7210690, MINT-7210731: SCRIB (uniprotkb:Q14160) physically interacts (MI:0914) with MCC (uniprotkb:P23508) by pull down (MI:0096)MINT-7211267: E6 (uniprotkb:P06463) physically interacts (MI:0915) with SCRIB (uniprotkb:Q14160), SNX27 (uniprotkb:Q96L92), UTRN (uniprotkb:P46939), CASK (uniprotkb:O14936), DMD (uniprotkb:P11532) and Dlg (uniprotkb:Q12959) by pull down (MI:0096)MINT-7211237: MCC (uniprotkb:P23508) physically interacts (MI:0915) with SCRIB (uniprotkb:Q14160), EZR (uniprotkb:P15311), SNX27 (uniprotkb:Q96L92), NHERF1 (uniprotkb:O14745) and NHERF2 (uniprotkb:Q15599) by pull down (MI:0096)

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

International audience; 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.

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.

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.