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1.  CD163+ M2c-like macrophages predominate in renal biopsies from patients with lupus nephritis 
The role of macrophages in the pathogenesis of lupus nephritis, in particular their differentiation to a certain subtype (e.g., M1- or M2-like) modulating the inflammatory reaction, is unknown. Here we investigated whether the differentiation in M1- or M2-like macrophages depends on the stage of lupus nephritis and whether this correlates with clinical parameters.
Using immunohistochemical analysis we analyzed renal biopsies from 68 patients with lupus nephritis (ISN/RPS classes II–V) for infiltration with M1-like (iNOS+/CD68+), M2a-like (CD206+/CD68+), M2c-like macrophages (CD163+/CD68+), and FoxP3+ regulatory T-cells. In addition, clinical parameters at the time of renal biopsy, i.e., blood pressure, proteinuria and serum urea were correlated with the macrophage infiltration using the Spearman test.
The mean number of CD68+ macrophages was related to the diagnosed ISN/RPS class, showing the highest macrophage infiltration in biopsies with diffuse class IV and the lowest number in ISN/RPS class V. In all ISN/RPS classes we detected more M2c-like CD163+/CD68+ than M2a-like CD206+/CD68+ cells, while M1-macrophages played only a minor role. Cluster analysis using macrophage subtype numbers in different renal compartments revealed three main clusters showing cluster 1 dominated by class V. Clusters 2 and 3 were dominated by lupus class IV indicating that this class can be further differentiated by its macrophage population. The number of tubulointerstitial FoxP3+ cells correlated with all investigated macrophage subtypes showing the strongest association to numbers of M2a-like macrophages. Kidney function, as assessed by serum creatinine and serum urea, correlated positively with the number of total CD68+, M2a-like and M2c-like macrophages in the tubulointerstitium. In addition, total CD68+ and M2c-like macrophage numbers highly correlated with Austin activity score. Interestingly, in hypertensive lupus patients only the number of M2a-like macrophages was significantly increased compared to biopsies from normotensive lupus patients.
M2-like macrophages are the dominant subpopulation in human lupus nephritis and particularly, M2a subpopulations were associated with disease progression, but their role in disease progression remains unclear.
PMCID: PMC4835936  PMID: 27091114
Lupus nephritis; Macrophages; T-reg-cells; Macrophage subtypes
2.  Regeneration-associated WNT Signaling Is Activated in Long-term Reconstituting AC133bright Acute Myeloid Leukemia Cells12 
Neoplasia (New York, N.Y.)  2012;14(12):1236-2124.
Acute myeloid leukemia (AML) is a genetically heterogeneous clonal disorder characterized by two molecularly distinct self-renewing leukemic stem cell (LSC) populations most closely related to normal progenitors and organized as a hierarchy. A requirement for WNT/β-catenin signaling in the pathogenesis of AML has recently been suggested by a mouse model. However, its relationship to a specific molecular function promoting retention of self-renewing leukemia-initiating cells (LICs) in human remains elusive. To identify transcriptional programs involved in the maintenance of a self-renewing state in LICs, we performed the expression profiling in normal (n = 10) and leukemic (n = 33) human long-term reconstituting AC133+ cells, which represent an expanded cell population in most AML patients. This study reveals the ligand-dependent WNT pathway activation in AC133bright AML cells and shows a diffuse expression and release of WNT10B, a hematopoietic stem cell regenerative-associated molecule. The establishment of a primary AC133+ AML cell culture (A46) demonstrated that leukemia cells synthesize and secrete WNT ligands, increasing the levels of dephosphorylated β-catenin in vivo. We tested the LSC functional activity in AC133+ cells and found significant levels of engraftment upon transplantation of A46 cells into irradiated Rag2-/-γc-/- mice. Owing to the link between hematopoietic regeneration and developmental signaling, we transplanted A46 cells into developing zebrafish. This system revealed the formation of ectopic structures by activating dorsal organizer markers that act downstream of the WNT pathway. In conclusion, our findings suggest that AC133bright LSCs are promoted by misappropriating homeostatic WNT programs that control hematopoietic regeneration.
PMCID: PMC3540948  PMID: 23308055
3.  TGFβ1-Induced Baf60c Regulates both Smooth Muscle Cell Commitment and Quiescence 
PLoS ONE  2012;7(10):e47629.
Smooth muscle cells (SMCs) play critical roles in a number of diseases; however, the molecular mechanism underlying their development is unclear. Although the role of TGFβ1 signaling in SMC development is well established, the downstream molecular signals are not fully understood. We used several rat multipotent adult progenitor cell ((r)MAPC) lines that express levels of Oct4 mRNA similar to hypoblast stem cells (HypoSC), and can differentiate robustly to mesodermal and endodermal cell types. TGFβ1 alone, or with PDGF-BB, induces differentiation of rMAPCs to SMCs, which expressed structural SMC proteins, including α-smooth muscle actin (αSMA), and contribute to the SMC coat of blood vessels in vivo. A genome-wide time-course transcriptome analysis revealed that transcripts of Baf60c, part of the SWI/SNF actin binding chromatin remodeling complex D-3 (SMARCD3/BAF60c), were significantly induced during MAPC-SMC differentiation. We demonstrated that BAF60c is a necessary co-regulator of TGFβ1 mediated induction of SMC genes. Knock-down of Baf60c decreased SMC gene expression in rMAPCs whereas ectopic expression of Baf60c was sufficient to commit rMAPCs to SMCs in the absence of exogenous cytokines. TGFβ1 activates Baf60c via the direct binding of SMAD2/3 complexes to the Baf60c promoter region. Chromatin- and co-immunoprecipitation studies demonstrated that regulation of SMC genes by BAF60c is mediated via interaction with SRF binding CArG box-containing promoter elements in SMC genes. We noted that compared with TGFβ1, Baf60c overexpression in rMAPC yielded SMC with a more immature phenotype. Similarly, Baf60c induced an immature phenotype in rat aortic SMCs marked by increased cell proliferation and decreased contractile marker expression. Thus, Baf60c is important for TGFβ-mediated commitment of primitive stem cells (rMAPCs) to SMCs and is associated with induction of a proliferative state of quiescent SMCs. The MAPC-SMC differentiation system may be useful for identification of additional critical (co-)regulators of SMC development.
PMCID: PMC3482188  PMID: 23110084
4.  Inferring cell cycle feedback regulation from gene expression data 
Journal of biomedical informatics  2011;44(4):565-575.
Feedback control is an important regulatory process in biological systems, which confers robustness against external and internal disturbances. Genes involved in feedback structures are therefore likely to have a major role in regulating cellular processes.
Here we rely on a dynamic Bayesian network approach to identify feedback loops in cell cycle regulation. We analyzed the transcriptional profile of the cell cycle in HeLa cancer cells and identified a feedback loop structure composed of 10 genes. In silico analyses showed that these genes hold important roles in system's dynamics. The results of published experimental assays confirmed the central role of 8 of the identified feedback loop genes in cell cycle regulation. In conclusion, we provide a novel approach to identify critical genes for the dynamics of biological processes. This may lead to the identification of therapeutic targets in diseases that involve perturbations of these dynamics.
PMCID: PMC3143236  PMID: 21310265
gene expression; cell cycle; dynamic Bayesian network; feedback
5.  R Engine Cell: integrating R into the i2b2 software infrastructure 
Informatics for Integrating Biology and the Bedside (i2b2) is an initiative funded by the NIH that aims at building an informatics infrastructure to support biomedical research. The University of Pavia has recently integrated i2b2 infrastructure with a registry of inherited arrhythmogenic diseases. Within this project, the authors created a novel i2b2 cell, named R Engine Cell, which allows the communication between i2b2 and the R statistical software. As survival analyses are routinely performed by cardiology researchers, the authors have first concentrated on making Kaplan–Meier analyses available within the i2b2 web interface. To this aim, the authors developed a web-client plug-in to select the patient set on which to perform the analysis and to display the results in a graphical, intuitive way. R Engine Cell has been designed to easily support the integration of other R-based statistical analyses into i2b2.
PMCID: PMC3078677  PMID: 21262924
6.  Nephronectin regulates atrioventricular canal differentiation via Bmp4-Has2 signaling in zebrafish 
Development (Cambridge, England)  2011;138(20):4499-4509.
The extracellular matrix is crucial for organogenesis. It is a complex and dynamic component that regulates cell behavior by modulating the activity, bioavailability and presentation of growth factors to cell surface receptors. Here, we determined the role of the extracellular matrix protein Nephronectin (Npnt) in heart development using the zebrafish model system. The vertebrate heart is formed as a linear tube in which myocardium and endocardium are separated by a layer of extracellular matrix termed the cardiac jelly. During heart development, the cardiac jelly swells at the atrioventricular (AV) canal, which precedes valve formation. Here, we show that Npnt expression correlates with this process. Morpholino-mediated knockdown of Npnt prevents proper valve leaflet formation and trabeculation and results in greater than 85% lethality at 7 days post-fertilization. The earliest observed phenotype is an extended tube-like structure at the AV boundary. In addition, the expression of myocardial genes involved in cardiac valve formation (cspg2, fibulin 1, tbx2b, bmp4) is expanded and endocardial cells along the extended tube-like structure exhibit characteristics of AV cells (has2, notch1b and Alcam expression, cuboidal cell shape). Inhibition of has2 in npnt morphants rescues the endocardial, but not the myocardial, expansion. By contrast, reduction of BMP signaling in npnt morphants reduces the ectopic expression of myocardial and endocardial AV markers. Taken together, our results identify Npnt as a novel upstream regulator of Bmp4-Has2 signaling that plays a crucial role in AV canal differentiation.
PMCID: PMC3253110  PMID: 21937601
Nephronectin; Atrioventricular canal; Bmp4; Zebrafish
7.  Cardiac Deletion of Smyd2 Is Dispensable for Mouse Heart Development 
PLoS ONE  2010;5(3):e9748.
Chromatin modifying enzymes play a critical role in cardiac differentiation. Previously, it has been shown that the targeted deletion of the histone methyltransferase, Smyd1, the founding member of the SET and MYND domain containing (Smyd) family, interferes with cardiomyocyte maturation and proper formation of the right heart ventricle. The highly related paralogue, Smyd2 is a histone 3 lysine 4- and lysine 36-specific methyltransferase expressed in heart and brain. Here, we report that Smyd2 is differentially expressed during cardiac development with highest expression in the neonatal heart. To elucidate the functional role of Smyd2 in the heart, we generated conditional knockout (cKO) mice harboring a cardiomyocyte-specific deletion of Smyd2 and performed histological, functional and molecular analyses. Unexpectedly, cardiac deletion of Smyd2 was dispensable for proper morphological and functional development of the murine heart and had no effect on global histone 3 lysine 4 or 36 methylation. However, we provide evidence for a potential role of Smyd2 in the transcriptional regulation of genes associated with translation and reveal that Smyd2, similar to Smyd3, interacts with RNA Polymerase II as well as to the RNA helicase, HELZ.
PMCID: PMC2840034  PMID: 20305823
8.  Phenotype forecasting with SNPs data through gene-based Bayesian networks 
BMC Bioinformatics  2009;10(Suppl 2):S7.
Bayesian networks are powerful instruments to learn genetic models from association studies data. They are able to derive the existing correlation between genetic markers and phenotypic traits and, at the same time, to find the relationships between the markers themselves. However, learning Bayesian networks is often non-trivial due to the high number of variables to be taken into account in the model with respect to the instances of the dataset. Therefore, it becomes very interesting to use an abstraction of the variable space that suitably reduces its dimensionality without losing information. In this paper we present a new strategy to achieve this goal by mapping the SNPs related to the same gene to one meta-variable. In order to assign states to the meta-variables we employ an approach based on classification trees.
We applied our approach to data coming from a genome-wide scan on 288 individuals affected by arterial hypertension and 271 nonagenarians without history of hypertension. After pre-processing, we focused on a subset of 24 SNPs. We compared the performance of the proposed approach with the Bayesian network learned with SNPs as variables and with the network learned with haplotypes as meta-variables. The results were obtained by running a hold-out experiment five times. The mean accuracy of the new method was 64.28%, while the mean accuracy of the SNPs network was 58.99% and the mean accuracy of the haplotype network was 54.57%.
The new approach presented in this paper is able to derive a gene-based predictive model based on SNPs data. Such model is more parsimonious than the one based on single SNPs, while preserving the capability of highlighting predictive SNPs configurations. The prediction performance of this approach was consistently superior to the SNP-based and the haplotype-based one in all the test sets of the evaluation procedure. The method can be then considered as an alternative way to analyze the data coming from association studies.
PMCID: PMC2646249  PMID: 19208195
9.  Bayesian approaches to reverse engineer cellular systems: a simulation study on nonlinear Gaussian networks 
BMC Bioinformatics  2007;8(Suppl 5):S2.
Reverse engineering cellular networks is currently one of the most challenging problems in systems biology. Dynamic Bayesian networks (DBNs) seem to be particularly suitable for inferring relationships between cellular variables from the analysis of time series measurements of mRNA or protein concentrations. As evaluating inference results on a real dataset is controversial, the use of simulated data has been proposed. However, DBN approaches that use continuous variables, thus avoiding the information loss associated with discretization, have not yet been extensively assessed, and most of the proposed approaches have dealt with linear Gaussian models.
We propose a generalization of dynamic Gaussian networks to accommodate nonlinear dependencies between variables. As a benchmark dataset to test the new approach, we used data from a mathematical model of cell cycle control in budding yeast that realistically reproduces the complexity of a cellular system. We evaluated the ability of the networks to describe the dynamics of cellular systems and their precision in reconstructing the true underlying causal relationships between variables. We also tested the robustness of the results by analyzing the effect of noise on the data, and the impact of a different sampling time.
The results confirmed that DBNs with Gaussian models can be effectively exploited for a first level analysis of data from complex cellular systems. The inferred models are parsimonious and have a satisfying goodness of fit. Furthermore, the networks not only offer a phenomenological description of the dynamics of cellular systems, but are also able to suggest hypotheses concerning the causal interactions between variables. The proposed nonlinear generalization of Gaussian models yielded models characterized by a slightly lower goodness of fit than the linear model, but a better ability to recover the true underlying connections between variables.
PMCID: PMC1892090  PMID: 17570861

Results 1-9 (9)