COSIFER: a Python package for the consensus inference of molecular interaction networks.

SUMMARY: The advent of high-throughput technologies has provided researchers with measurements of thousands of molecular entities and enable the investigation of the internal regulatory apparatus of the cell. However, network inference from high-throughput data is far from being a solved problem. While a plethora of different inference methods have been proposed, they often lead to non-overlapping predictions, and many of them lack user-friendly implementations to enable their broad utilization. Here, we present Consensus Interaction Network Inference Service (COSIFER), a package and a companion web-based platform to infer molecular networks from expression data using state-of-the-art consensus approaches. COSIFER includes a selection of state-of-the-art methodologies for network inference and different consensus strategies to integrate the predictions of individual methods and generate robust networks. AVAILABILITY AND IMPLEMENTATION: COSIFER Python source code is available at https://github.com/PhosphorylatedRabbits/cosifer. The web service is accessible at https://ibm.biz/cosifer-aas. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

R/PY-SUMMA: An R/Python Package for Unsupervised Ensemble Learning for Binary Classification Problems in Bioinformatics.

The increasing availability of complex data in biology and medicine has promoted the use of machine learning in classification tasks to address important problems in translational and fundamental science. Two important obstacles, however, may limit the unraveling of the full potential of machine learning in these fields: the lack of generalization of the resulting models and the limited number of labeled data sets in some applications. To address these important problems, we developed an unsupervised ensemble algorithm called strategy for unsupervised multiple method aggregation (SUMMA). By virtue of being an ensemble method, SUMMA is more robust to generalization than the predictions it combines. By virtue of being unsupervised, SUMMA does not require labeled data. SUMMA receives as input predictions from a diversity of models and estimates their classification performance even when labeled data are unavailable. It then uses these performance estimates to combine these different predictions into an ensemble model. SUMMA can be applied to a variety of binary classification problems in bioinformatics including but not limited to gene network inference, cancer diagnostics, drug response prediction, somatic mutation, and differential expression calling. In this application note, we introduce the R/PY-SUMMA packages, available in R or Python, that implement the SUMMA algorithm.

Summary of the DREAM8 Parameter Estimation Challenge: Toward Parameter Identification for Whole-Cell Models.

Whole-cell models that explicitly represent all cellular components at the molecular level have the potential to predict phenotype from genotype. However, even for simple bacteria, whole-cell models will contain thousands of parameters, many of which are poorly characterized or unknown. New algorithms are needed to estimate these parameters and enable researchers to build increasingly comprehensive models. We organized the Dialogue for Reverse Engineering Assessments and Methods (DREAM) 8 Whole-Cell Parameter Estimation Challenge to develop new parameter estimation algorithms for whole-cell models. We asked participants to identify a subset of parameters of a whole-cell model given the model's structure and in silico "experimental" data. Here we describe the challenge, the best performing methods, and new insights into the identifiability of whole-cell models. We also describe several valuable lessons we learned toward improving future challenges. Going forward, we believe that collaborative efforts supported by inexpensive cloud computing have the potential to solve whole-cell model parameter estimation.

Improving breast cancer survival analysis through competition-based multidimensional modeling.

Breast cancer is the most common malignancy in women and is responsible for hundreds of thousands of deaths annually. As with most cancers, it is a heterogeneous disease and different breast cancer subtypes are treated differently. Understanding the difference in prognosis for breast cancer based on its molecular and phenotypic features is one avenue for improving treatment by matching the proper treatment with molecular subtypes of the disease. In this work, we employed a competition-based approach to modeling breast cancer prognosis using large datasets containing genomic and clinical information and an online real-time leaderboard program used to speed feedback to the modeling team and to encourage each modeler to work towards achieving a higher ranked submission. We find that machine learning methods combined with molecular features selected based on expert prior knowledge can improve survival predictions compared to current best-in-class methodologies and that ensemble models trained across multiple user submissions systematically outperform individual models within the ensemble. We also find that model scores are highly consistent across multiple independent evaluations. This study serves as the pilot phase of a much larger competition open to the whole research community, with the goal of understanding general strategies for model optimization using clinical and molecular profiling data and providing an objective, transparent system for assessing prognostic models.

Quantitative modeling of the terminal differentiation of B cells and mechanisms of lymphomagenesis.

Mature B-cell exit from germinal centers is controlled by a transcriptional regulatory module that integrates antigen and T-cell signals and, ultimately, leads to terminal differentiation into memory B cells or plasma cells. Despite a compact structure, the module dynamics are highly complex because of the presence of several feedback loops and self-regulatory interactions, and understanding its dysregulation, frequently associated with lymphomagenesis, requires robust dynamical modeling techniques. We present a quantitative kinetic model of three key gene regulators, BCL6, IRF4, and BLIMP, and use gene expression profile data from mature human B cells to determine appropriate model parameters. The model predicts the existence of two different hysteresis cycles that direct B cells through an irreversible transition toward a differentiated cellular state. By synthetically perturbing the interactions in this network, we can elucidate known mechanisms of lymphomagenesis and suggest candidate tumorigenic alterations, indicating that the model is a valuable quantitative tool to simulate B-cell exit from the germinal center under a variety of physiological and pathological conditions.

Electrochemical protection of thin film electrodes in solid state nanopores.

Solid state nanopores are a core element of next-generation single molecule tools in the field of nano-biotechnology. Thin film electrodes integrated into a pore can interact with charges and fields within the pore. In order to keep the nanopore open and thus functional electrochemically induced surface alteration of electrode surfaces and bubble formation inside the pore have to be eliminated. This paper provides electrochemical analyses of nanopores drilled into TiN membranes which in turn were employed as thin film electrodes. We studied physical pore integrity and the occurrence of water decomposition yielding bubble formation inside pores by applying voltages between -4.5 and +4.5 V to membranes in various protection stages continuously for up to 24 h. During potential application pores were exposed to selected electrolyte-solvent systems. We have investigated and successfully eliminated electrochemical pore oxidation and reduction as well as water decomposition inside nanopores of various diameters ranging from 3.5 to 25 nm in 50 nm thick TiN membranes by passivating the nanopores with a plasma-oxidized layer and using a 90% solution of glycerol in water as KCl solvent. Nanopore ionic conductances were measured before and after voltage application in order to test for changes in pore diameter due to electrochemical oxidation or reduction. TEM imaging was used to confirm these observations. While non-passivated pores were electrochemically oxidized, neither electrochemical oxidation nor reduction was observed for passivated pores. Bubble formation through water decomposition could be detected in non-passivated pores in KCl/water solutions but was not observed in 90% glycerol solutions. The use of a protective self-assembled monolayer of hexadecylphosphonic acid (HDPA) was also investigated.

Electrochemical characterization of thin film electrodes toward developing a DNA transistor.

The DNA-Transistor is a device designed to control the translocation of single-stranded DNA through a solid-state nanopore. Functionality of the device is enabled by three electrodes exposed to the DNA-containing electrolyte solution within the pore and the application of a dynamic electrostatic potential well between the electrodes to temporarily trap a DNA molecule. Optimizing the surface chemistry and electrochemical behavior of the device is a necessary (but by no means sufficient) step toward the development of a functional device. In particular, effects to be eliminated are (i) electrochemically induced surface alteration through corrosion or reduction of the electrode surface and (ii) formation of hydrogen or oxygen bubbles inside the pore through water decomposition. Even though our motivation is to solve problems encountered in DNA transistor technology, in this paper we report on generic surface chemistry results. We investigated a variety of electrode-electrolyte-solvent systems with respect to their capability of suppressing water decomposition and maintaining surface integrity. We employed cyclic voltammetry and long-term amperometry as electrochemical test schemes, X-ray photoelectron spectroscopy, atomic force microscopy, and scanning, as well as transmission electron microscopy as analytical tools. Characterized electrode materials include thin films of Ru, Pt, nonstoichiometric TiN, and nonstoichiometric TiN carrying a custom-developed titanium oxide layer, as well as custom-oxidized nonstoichiometric TiN coated with a monolayer of hexadecylphosphonic acid (HDPA). We used distilled water as well as aqueous solutions of poly(ethylene glycol) (PEG-300) and glycerol as solvents. One millimolar KCl was employed as electrolyte in all solutions. Our results show that the HDPA-coated custom-developed titanium oxide layer effectively passivates the underlying TiN layer, eliminating any surface alterations through corrosion or reduction within a voltage window from -2 V to +2 V. Furthermore, we demonstrated that, by coating the custom-oxidized TiN samples with HDPA and increasing the concentration of PEG-300 or glycerol in aqueous 1 mM KCl solutions, water decomposition was suppressed within the same voltage window. Water dissociation was not detected when combining custom-oxidized HDPA-coated TiN electrodes with an aqueous 1 mM KCl-glycerol solution at a glycerol concentration of at least 90%. These results are applicable to any system that requires nanoelectrodes placed in aqueous solution at voltages that can activate electrochemical processes.

Transcription factor expression in lipopolysaccharide-activated peripheral-blood-derived mononuclear cells.

Transcription factors play a key role in integrating and modulating biological information. In this study, we comprehensively measured the changing abundances of mRNAs over a time course of activation of human peripheral-blood-derived mononuclear cells ("macrophages") with lipopolysaccharide. Global and dynamic analysis of transcription factors in response to a physiological stimulus has yet to be achieved in a human system, and our efforts significantly advanced this goal. We used multiple global high-throughput technologies for measuring mRNA levels, including massively parallel signature sequencing and GeneChip microarrays. We identified 92 of 1,288 known human transcription factors as having significantly measurable changes during our 24-h time course. At least 42 of these changes were previously unidentified in this system. Our data demonstrate that some transcription factors operate in a functional range below 10 transcripts per cell, whereas others operate in a range three orders of magnitude greater. The highly reproducible response of many mRNAs indicates feedback control. A broad range of activation kinetics was observed; thus, combinatorial regulation by small subsets of transcription factors would permit almost any timing input to cis-regulatory elements controlling gene transcription.

A plausible model for the digital response of p53 to DNA damage.

Recent observations show that the single-cell response of p53 to ionizing radiation (IR) is "digital" in that it is the number of oscillations rather than the amplitude of p53 that shows dependence on the radiation dose. We present a model of this phenomenon. In our model, double-strand break (DSB) sites induced by IR interact with a limiting pool of DNA repair proteins, forming DSB-protein complexes at DNA damage foci. The persisting complexes are sensed by ataxia telangiectasia mutated (ATM), a protein kinase that activates p53 once it is phosphorylated by DNA damage. The ATM-sensing module switches on or off the downstream p53 oscillator, consisting of a feedback loop formed by p53 and its negative regulator, Mdm2. In agreement with experiments, our simulations show that by assuming stochasticity in the initial number of DSBs and the DNA repair process, p53 and Mdm2 exhibit a coordinated oscillatory dynamics upon IR stimulation in single cells, with a stochastic number of oscillations whose mean increases with IR dose. The damped oscillations previously observed in cell populations can be explained as the aggregate behavior of single cells.

Formation of regulatory patterns during signal propagation in a Mammalian cellular network.

We developed a model of 545 components (nodes) and 1259 interactions representing signaling pathways and cellular machines in the hippocampal CA1 neuron. Using graph theory methods, we analyzed ligand-induced signal flow through the system. Specification of input and output nodes allowed us to identify functional modules. Networking resulted in the emergence of regulatory motifs, such as positive and negative feedback and feedforward loops, that process information. Key regulators of plasticity were highly connected nodes required for the formation of regulatory motifs, indicating the potential importance of such motifs in determining cellular choices between homeostasis and plasticity.

Report: workshop on mediastinal grey zone lymphoma.

There are several indications that classical Hodgkin lymphoma (cHL) and at least a proportion of cases of Primary Mediastinal B cell Lymphoma (PMBL) are derived from B cells at similar stages of differentiation and share common pathogenic mechanisms. The first indication was the existence of mediastinal grey zone lymphomas as identified in the 4th International Symposium on HL, with clinical, histological and immunohistochemical features intermediate between cHL and PMBL. Second, both tumor types resemble a cell that is developmentally situated in-between the germinal center reaction and a plasma cell. Third, cHL and PMBL were found to have similar gene expression profiles, including the lack of immunoglobulin expression and low levels of B cell receptor signalling molecules, and the secretion of molecules like the chemokine TARC and the prominent expression of IL-13 receptors. Fourth, both entities were found to have common genomic aberrancies, notably in 2p15 and 9p24, the sites of the REL oncogene and the tyrosine kinase gene JAK2, respectively. Further comparison of both lymphoma types may provide further insight in the pathogenic mechanisms and allow the design of diagnostic algorithms to sort out the small number of so-called mediastinal grey zone lymphomas, that appear to be intermediate between PMBL and cHL.

Tracking CD40 signaling during germinal center development.

Substantial evidence indicates that signaling through the CD40 receptor (CD40) is required for germinal center (GC) and memory B-cell formation. However, it is not fully understood at which stages of B-cell development the CD40 pathway is activated in vivo. To address this question, we induced CD40 signaling in human transformed GC B cells in vitro and identified a CD40 gene expression signature by DNA microarray analysis. This signature was then investigated in the gene expression profiles of normal B cells and found in pre- and post-GC B cells (naive and memory) but, surprisingly, not in GC B cells. This finding was validated in lymphoid tissues by showing that the nuclear factor-kappaB (NF-kappaB) transcription factors, which translocate to the nucleus upon CD40 stimulation, are retained in the cytoplasm in most GC B cells, indicating the absence of CD40 signaling. Nevertheless, a subset of centrocytes and B cells in the subepithelium showed nuclear staining of multiple NF-kappaB subunits, suggesting that a fraction of naive and memory B cells may be subject to CD40 signaling or to other signals that activate NF-kappaB. Together, these results show that GC expansion occurs in the absence of CD40 signaling, which may act only in the initial and final stages of the GC reaction.

Gene expression dynamics during germinal center transit in B cells.

The germinal center (GC) reaction in T cell dependent antibody responses is crucial for the generation of B cell memory and plays a critical role in B cell lymphomagenesis. To gain insight into the physiology of this reaction, we identified the transcriptional changes that occur in B cells during the GC-transit (naïve B cells --> CD77(+) centroblasts (CBs) --> CD77(-) centrocytes (CCs) --> memory B cells) by DNA microarray experiments and the subsequent data analysis employing unsupervised and supervised hierarchical clustering. The naïve B cell is characterized by a nonproliferative, anti-apoptotic phenotype and the expression of various chemokine and cytokine receptors. The transition from naïve B cells to CBs is associated with (1) the up-regulation of genes associated with cellular proliferation, DNA-repair, and chromatin remodeling; (2) the acquisition of a pro-apoptotic phenotype; (3) the down-regulation of cytokine, chemokine, and adhesion receptors expressed in the naïve cells; and (4) the expression of a distinct adhesion repertoire. The CB and the CC revealed surprisingly few gene expression differences, suggesting that the CC is heterogeneous in its cellular composition. The CB/CC to memory B cell transition shows a general reversion to the profile characteristic for the naïve B cells, with the exception of the up-regulation of several surface receptors, including CD27, CD80, and IL-2Rbeta, and the simultaneous expression of both anti- and pro-apoptotic genes. These gene expression profiles of the normal B cell subpopulations are being used to identify the signals occurring during GC development, the cellular derivation of various types of B cell malignancies, and the genes deregulated in GC-derived tumors.

Motif-based construction of a functional map for mammalian olfactory receptors.

We applied an automatic and unsupervised system to a nearly complete database of mammalian odor receptor genes. The generated motifs and gene classification were subjected to extensive and systematic downstream analysis to obtain biological insights. Two major results from this analysis were: (1) a map of sequence motifs that may correlate with function and (2) the corresponding receptor classes in which members of each class are likely to share specific functions. We have discovered motifs that have been implicated in structural integrity and posttranslational modification, as well as motifs very likely to be directly involved in ligand binding. We further propose a combinatorial molecular hypothesis, based on unique combinations of the observed motifs, that provides a foundation for understanding the generation of a large number of ligand binding sites.

Identification of a global gene expression signature of B-chronic lymphocytic leukemia.

B-chronic lymphocytic leukemia (B-CLL) is an adult-onset leukemia characterized by significant accumulation of apoptosis-resistant monoclonal B lymphocytes. In this study, we performed gene expression profiling on B cells obtained from 10 healthy age-matched individuals and CLL B cells from 38 B-CLL patients to identify key genetic differences between CLL and normal B cells. In addition, we leveraged recent independent studies to assess the reproducibility of our molecular B-CLL signature. We used a novel combination of several methods of data analysis including our own software and identified 70 previously unreported genes that differentiate leukemic cells from normal B cells, as well as confirmed recently reported B-CLL specific expression levels of an additional 10 genes. Importantly, many of these genes have previously been linked with other cancers, thus lending further support to their importance as candidate genes leading to B-CLL pathogenesis. We have also validated a subset of these genes using independent methodologies. Moreover, we show that our genes can be used to create a diagnostics signature that performs with perfect sensitivity and specificity in an independent cohort of 21 B-CLL and 20 normal subjects, thus strongly validating the informative nature of our set of genes. Finally, we identified a group of 31 genes that distinguish between low (Rai stage 0) and high (Rai stage 4) risk patients, suggesting that there may also be a gene expression signature that associates with disease progression.

Transcriptional analysis of the B cell germinal center reaction.

The germinal center (GC) reaction is crucial for T cell-dependent immune responses and is targeted by B cell lymphomagenesis. Here we analyzed the transcriptional changes that occur in B cells during GC transit (naive B cells --> centroblasts --> centrocytes --> memory B cells) by gene expression profiling. Naive B cells, characterized by the expression of cell cycle-inhibitory and antiapoptotic genes, become centroblasts by inducing an atypical proliferation program lacking c-Myc expression, switching to a proapoptotic program, and down-regulating cytokine, chemokine, and adhesion receptors. The transition from GC to memory cells is characterized by a return to a phenotype similar to that of naive cells except for an apoptotic program primed for both death and survival and for changes in the expression of cell surface receptors including IL-2 receptor beta. These results provide insights into the dynamics of the GC reaction and represent the basis for the analysis of B cell malignancies.

Identification of Hodgkin and Reed-Sternberg cell-specific genes by gene expression profiling.

Hodgkin lymphoma (HL) is a malignancy of unknown pathogenesis. The malignant Hodgkin and Reed/Sternberg (HRS) cells derive from germinal center B cells (or rarely, T cells) but have a heterogeneous and largely uncharacterized phenotype. Using microarrays, we compared the gene expression profile of four HL cell lines with profiles of the main B cell subsets and B cell non-HLs to find out whether HRS cells, despite their described heterogeneity, show a distinct gene expression, to study their relationship to other normal and malignant B cells, and to identify genes aberrantly or overexpressed by HRS cells. The HL lines indeed clustered as a distinct entity, irrespective of their B or T cell derivation, and their gene expression was most similar to that of EBV-transformed B cells and cell lines derived from diffuse large cell lymphomas showing features of in vitro-activated B cells. Twenty-seven genes, most of which were previously unknown to be expressed by HRS cells, showed aberrant expression specifically in these cells, e.g., the transcription factors GATA-3, ABF1, EAR3, and Nrf3. For five genes, expression in primary HRS cells was confirmed. The newly identified HL-specific genes may play important roles in the pathogenesis of HL, potentially represent novel diagnostic markers, and can be considered for therapeutic targeting.