Gain of function of a metalloproteinase associated with multiple myeloma, bicuspid aortic valve, and Von Hippel-Lindau syndrome.

A patient diagnosed with multiple myeloma, bicuspid aortic valve, and Von Hippel-Lindau syndrome underwent whole-exome sequencing seeking a unified genetic cause for these three pathologies. The patient possessed a single-point mutation of arginine to cysteine (R24C) in the N-terminal region(pro-domain) of matrix metalloproteinase 9 (MMP-9). The pro-domain interacts with the catalytic site of this enzyme rendering it inactive.

Naturally occurring combinations of receptors from single cell transcriptomics in endothelial cells.

VEGF inhibitor drugs are part of standard care in oncology and ophthalmology, but not all patients respond to them. Combinations of drugs are likely to be needed for more effective therapies of angiogenesis-related diseases. In this paper we describe naturally occurring combinations of receptors in endothelial cells that might help to understand how cells communicate and to identify targets for drug combinations.

LIF, a mitogen for choroidal endothelial cells, protects the choriocapillaris: implications for prevention of geographic atrophy.

In the course of our studies aiming to discover vascular bed-specific endothelial cell (EC) mitogens, we identified leukemia inhibitory factor (LIF) as a mitogen for bovine choroidal EC (BCE), although LIF has been mainly characterized as an EC growth inhibitor and an anti-angiogenic molecule. LIF stimulated growth of BCE while it inhibited, as previously reported, bovine aortic EC (BAE) growth. The JAK-STAT3 pathway mediated LIF actions in both BCE and BAE cells, but a caspase-independent proapoptotic signal mediated by cathepsins was triggered in BAE but not in BCE.

Digital Cell Sorter (DCS): a cell type identification, anomaly detection, and Hopfield landscapes toolkit for single-cell transcriptomics.

Motivation: Analysis of singe cell RNA sequencing (scRNA-seq) typically consists of different steps including quality control, batch correction, clustering, cell identification and characterization, and visualization. The amount of scRNA-seq data is growing extremely fast, and novel algorithmic approaches improving these steps are key to extract more biological information. Here, we introduce: (i) two methods for automatic cell type identification (i.

Modeling disease progression in Multiple Myeloma with Hopfield networks and single-cell RNA-seq.

Unlabelled: Associative memories in Hopfield's neural networks are mapped to gene expression pattern to model different paths of disease progression towards Multiple Myeloma (MM). The model is built using single cell RNA-seq data from bone marrow aspirates of MM patients as well as patients diagnosed with Monoclonal Gammopathy of Undetermined Significance (MGUS) and Smoldering Multiple Myeloma (SMM), two medical conditions that often progress to full MM.

Results: We identify different clusters of MGUS, SMM, and MM cells, map them to Hopfield associative memory patterns, and model the dynamics of transition between the different patterns.

Polled Digital Cell Sorter (p-DCS): Automatic identification of hematological cell types from single cell RNA-sequencing clusters.

Background: Single cell RNA sequencing (scRNA-seq) brings unprecedented opportunities for mapping the heterogeneity of complex cellular environments such as bone marrow, and provides insight into many cellular processes. Single cell RNA-seq has a far larger fraction of missing data reported as zeros (dropouts) than traditional bulk RNA-seq, and unsupervised clustering combined with Principal Component Analysis (PCA) can be used to overcome this limitation. After clustering, however, one has to interpret the average expression of markers on each cluster to identify the corresponding cell types, and this is normally done by hand by an expert curator.

Cell cycle time series gene expression data encoded as cyclic attractors in Hopfield systems.

Modern time series gene expression and other omics data sets have enabled unprecedented resolution of the dynamics of cellular processes such as cell cycle and response to pharmaceutical compounds. In anticipation of the proliferation of time series data sets in the near future, we use the Hopfield model, a recurrent neural network based on spin glasses, to model the dynamics of cell cycle in HeLa (human cervical cancer) and S. cerevisiae cells.

High-throughput screening and bioinformatic analysis to ascertain compounds that prevent saturated fatty acid-induced β-cell apoptosis.

Pancreatic β-cell lipotoxicity is a central feature of the pathogenesis of type 2 diabetes. To study the mechanism by which fatty acids cause β-cell death and develop novel approaches to prevent it, a high-throughput screen on the β-cell line INS1 was carried out. The cells were exposed to palmitate to induce cell death and compounds that reversed palmitate-induced cytotoxicity were ascertained.

Evolutionary and Topological Properties of Genes and Community Structures in Human Gene Regulatory Networks.

The diverse, specialized genes present in today's lifeforms evolved from a common core of ancient, elementary genes. However, these genes did not evolve individually: gene expression is controlled by a complex network of interactions, and alterations in one gene may drive reciprocal changes in its proteins' binding partners. Like many complex networks, these gene regulatory networks (GRNs) are composed of communities, or clusters of genes with relatively high connectivity.

Combinations of Kinase Inhibitors Protecting Myoblasts against Hypoxia.

Cell-based therapies to treat skeletal muscle disease are limited by the poor survival of donor myoblasts, due in part to acute hypoxic stress. After confirming that the microenvironment of transplanted myoblasts is hypoxic, we screened a kinase inhibitor library in vitro and identified five kinase inhibitors that protected myoblasts from cell death or growth arrest in hypoxic conditions. A systematic, combinatorial study of these compounds further improved myoblast viability, showing both synergistic and additive effects.

A scalable method for molecular network reconstruction identifies properties of targets and mutations in acute myeloid leukemia.

A key aim of systems biology is the reconstruction of molecular networks. We do not yet, however, have networks that integrate information from all datasets available for a particular clinical condition. This is in part due to the limited scalability, in terms of required computational time and power, of existing algorithms.

Control of asymmetric Hopfield networks and application to cancer attractors.

The asymmetric Hopfield model is used to simulate signaling dynamics in gene regulatory networks. The model allows for a direct mapping of a gene expression pattern into attractor states. We analyze different control strategies aimed at disrupting attractor patterns using selective local fields representing therapeutic interventions.

Identification of drug combinations containing imatinib for treatment of BCR-ABL+ leukemias.

The BCR-ABL translocation is found in chronic myeloid leukemia (CML) and in Ph+ acute lymphoblastic leukemia (ALL) patients. Although imatinib and its analogues have been used as front-line therapy to target this mutation and control the disease for over a decade, resistance to the therapy is still observed and most patients are not cured but need to continue the therapy indefinitely. It is therefore of great importance to find new therapies, possibly as drug combinations, which can overcome drug resistance.

Prediction of kinase inhibitor response using activity profiling, in vitro screening, and elastic net regression.

Background: Many kinase inhibitors have been approved as cancer therapies. Recently, libraries of kinase inhibitors have been extensively profiled, thus providing a map of the strength of action of each compound on a large number of its targets. These profiled libraries define drug-kinase networks that can predict the effectiveness of untested drugs and elucidate the roles of specific kinases in different cellular systems.

Cellular protection using Flt3 and PI3Kα inhibitors demonstrates multiple mechanisms of oxidative glutamate toxicity.

Glutamate-induced oxidative stress is a major contributor to neurodegenerative diseases. Here, we identify small-molecule inhibitors of this process. We screen a kinase inhibitor library on neuronal cells and identify Flt3 and PI3Kα inhibitors as potent protectors against glutamate toxicity.

Metabolomics of the tumor microenvironment in pediatric acute lymphoblastic leukemia.

The tumor microenvironment is emerging as an important therapeutic target. Most studies, however, are focused on the protein components, and relatively little is known of how the microenvironmental metabolome might influence tumor survival. In this study, we examined the metabolic profiles of paired bone marrow (BM) and peripheral blood (PB) samples from 10 children with acute lymphoblastic leukemia (ALL).

Statistical properties and robustness of biological controller-target networks.

Cells are regulated by networks of controllers having many targets, and targets affected by many controllers, in a "many-to-many" control structure. Here we study several of these bipartite (two-layer) networks. We analyze both naturally occurring biological networks (composed of transcription factors controlling genes, microRNAs controlling mRNA transcripts, and protein kinases controlling protein substrates) and a drug-target network composed of kinase inhibitors and of their kinase targets.

Metabolomic high-content nuclear magnetic resonance-based drug screening of a kinase inhibitor library.

Metabolism is altered in many highly prevalent diseases and is controlled by a complex network of intracellular regulators. Monitoring cell metabolism during treatment is extremely valuable to investigate cellular response and treatment efficacy. Here we describe a nuclear magnetic resonance-based method for screening of the metabolomic response of drug-treated mammalian cells in a 96-well format.

Systems approaches and algorithms for discovery of combinatorial therapies.

Effective therapy of complex diseases requires control of highly nonlinear complex networks that remain incompletely characterized. In particular, drug intervention can be seen as control of cellular network activity. Identification of control parameters presents an extreme challenge due to the combinatorial explosion of control possibilities in combination therapy and to the incomplete knowledge of the systems biology of cells.

Metabolism as means for hypoxia adaptation: metabolic profiling and flux balance analysis.

Background: Cellular hypoxia is a component of many diseases, but mechanisms of global hypoxic adaptation and resistance are not completely understood. Previously, a population of Drosophila flies was experimentally selected over several generations to survive a chronically hypoxic environment. NMR-based metabolomics, combined with flux-balance simulations of genome-scale metabolic networks, can generate specific hypotheses for global reaction fluxes within the cell.

Search algorithms as a framework for the optimization of drug combinations.

Combination therapies are often needed for effective clinical outcomes in the management of complex diseases, but presently they are generally based on empirical clinical experience. Here we suggest a novel application of search algorithms -- originally developed for digital communication -- modified to optimize combinations of therapeutic interventions. In biological experiments measuring the restoration of the decline with age in heart function and exercise capacity in Drosophila melanogaster, we found that search algorithms correctly identified optimal combinations of four drugs using only one-third of the tests performed in a fully factorial search.

Metabolomic and flux-balance analysis of age-related decline of hypoxia tolerance in Drosophila muscle tissue.

The fruitfly Drosophila melanogaster is increasingly used as a model organism for studying acute hypoxia tolerance and for studying aging, but the interactions between these two factors are not well known. Here we show that hypoxia tolerance degrades with age in post-hypoxic recovery of whole-body movement, heart rate and ATP content. We previously used (1)H NMR metabolomics and a constraint-based model of ATP-generating metabolism to discover the end products of hypoxic metabolism in flies and generate hypotheses for the biological mechanisms.

Discovering regulators of the Drosophila cardiac hypoxia response using automated phenotyping technology.

Necrosis and apoptosis during acute myocardial infarction result in part from the inability of hypoxic cardiac myocytes to match ATP supply and demand. In contrast, hypoxia-tolerant organisms, such as Drosophila, can rapidly regulate cellular metabolism to survive large oxygen fluctuations. A genetic screen of fly heart function during acute hypoxia can be an unbiased way to discover essential enzymes and novel signaling proteins involved in this response.

Integrating metabolomics and phenomics with systems models of cardiac hypoxia.

Hypoxia is the major cause of necrotic cell death in myocardial infarction. Cellular energy supply and demand under hypoxic conditions is regulated by many interacting signaling and transcriptional networks, which complicates studies on individual proteins and pathways. We apply an integrated systems approach to understand the metabolic and functional response to hypoxia in muscle cells of the fruit fly Drosophila melanogaster.

Selective control of the apoptosis signaling network in heterogeneous cell populations.

Background: Selective control in a population is the ability to control a member of the population while leaving the other members relatively unaffected. The concept of selective control is developed using cell death or apoptosis in heterogeneous cell populations as an example. Control of apoptosis is essential in a variety of therapeutic environments, including cancer where cancer cell death is a desired outcome and Alzheimer's disease where neuron survival is the desired outcome.