Biological networks and analysis of experimental data in drug discovery

Heterogeneity and transcriptional drivers of triple-negative breast cancer

Triple-negative breast cancer (TNBC) is a heterogeneous disease with limited treatment options. To characterize TNBC heterogeneity, we defined transcriptional, epigenetic, and metabolic subtypes and subtype-driving super-enhancers and transcription factors by combining functional and molecular profiling with computational analyses. Single-cell RNA sequencing revealed relative homogeneity of the major transcriptional subtypes (luminal, basal, and mesenchymal) within samples. We found that mesenchymal TNBCs share features with mesenchymal neuroblastoma and rhabdoid tumors and that the PRRX1 transcription factor is a key driver of these tumors. PRRX1 is sufficient for inducing mesenchymal features in basal but not in luminal TNBC cells via reprogramming super-enhancer landscapes, but it is not required for mesenchymal state maintenance or for cellular viability. Our comprehensive, large-scale, multiplatform, multiomics study of both experimental and clinical TNBC is an important resource for the scientific and clinical research communities and opens venues for future investigation.

JAK-STAT Signaling in Inflammatory Breast Cancer Enables Chemotherapy-Resistant Cell States

Inflammatory breast cancer (IBC) is a difficult-to-treat disease with poor clinical outcomes due to high risk of metastasis and resistance to treatment. In breast cancer, CD44+CD24- cells possess stem cell-like features and contribute to disease progression, and we previously described a CD44+CD24-pSTAT3+ breast cancer cell subpopulation that is dependent on JAK2/STAT3 signaling. Here we report that CD44+CD24- cells are the most frequent cell type in IBC and are commonly pSTAT3+. Combination of JAK2/STAT3 inhibition with paclitaxel decreased IBC xenograft growth more than either agent alone. IBC cell lines resistant to paclitaxel and doxorubicin were developed and characterized to mimic therapeutic resistance in patients. Multi-omic profiling of parental and resistant cells revealed enrichment of genes associated with lineage identity and inflammation in chemotherapy-resistant derivatives. Integrated pSTAT3 chromatin immunoprecipitation sequencing and RNA sequencing (RNA-seq) analyses showed pSTAT3 regulates genes related to inflammation and epithelial-to-mesenchymal transition (EMT) in resistant cells, as well as PDE4A, a cAMP-specific phosphodiesterase. Metabolomic characterization identified elevated cAMP signaling and CREB as a candidate therapeutic target in IBC. Investigation of cellular dynamics and heterogeneity at the single cell level during chemotherapy and acquired resistance by CyTOF and single cell RNA-seq identified mechanisms of resistance including a shift from luminal to basal/mesenchymal cell states through selection for rare preexisting subpopulations or an acquired change. Finally, combination treatment with paclitaxel and JAK2/STAT3 inhibition prevented the emergence of the mesenchymal chemo-resistant subpopulation. These results provide mechanistic rational for combination of chemotherapy with inhibition of JAK2/STAT3 signaling as a more effective therapeutic strategy in IBC.

SIGNIFICANCE

Chemotherapy resistance in inflammatory breast cancer is driven by the JAK2/STAT3 pathway, in part via cAMP/PKA signaling and a cell state switch, which can be overcome using paclitaxel combined with JAK2 inhibitors.

Breast cancer prevention by short-term inhibition of TGFβ signaling

Cancer prevention has a profound impact on cancer-associated mortality and morbidity. We previously identified TGFβ signaling as a candidate regulator of mammary epithelial cells associated with breast cancer risk. Here, we show that short-term TGFBR inhibitor (TGFBRi) treatment of peripubertal ACI inbred and Sprague Dawley outbred rats induces lasting changes and prevents estrogen- and carcinogen-induced mammary tumors, respectively. We identify TGFBRi-responsive cell populations by single cell RNA-sequencing, including a unique epithelial subpopulation designated secretory basal cells (SBCs) with progenitor features. We detect SBCs in normal human breast tissues and find them to be associated with breast cancer risk. Interactome analysis identifies SBCs as the most interactive cell population and the main source of insulin-IGF signaling. Accordingly, inhibition of TGFBR and IGF1R decrease proliferation of organoid cultures. Our results reveal a critical role for TGFβ in regulating mammary epithelial cells relevant to breast cancer and serve as a proof-of-principle cancer prevention strategy.

The New Version of the ANDDigest Tool with Improved AI-Based Short Names Recognition

The body of scientific literature continues to grow annually. Over 1.5 million abstracts of biomedical publications were added to the PubMed database in 2021. Therefore, developing cognitive systems that provide a specialized search for information in scientific publications based on subject area ontology and modern artificial intelligence methods is urgently needed. We previously developed a web-based information retrieval system, ANDDigest, designed to search and analyze information in the PubMed database using a customized domain ontology. This paper presents an improved ANDDigest version that uses fine-tuned PubMedBERT classifiers to enhance the quality of short name recognition for molecular-genetics entities in PubMed abstracts on eight biological object types: cell components, diseases, side effects, genes, proteins, pathways, drugs, and metabolites. This approach increased average short name recognition accuracy by 13%.

Animal Models of Neurodegenerative Disease: Recent Advances in Fly Highlight Innovative Approaches to Drug Discovery

Neurodegenerative diseases represent a formidable challenge to global health. As advances in other areas of medicine grant healthy living into later decades of life, aging diseases such as Alzheimer's disease (AD) and other neurodegenerative disorders can diminish the quality of these additional years, owed largely to the lack of efficacious treatments and the absence of durable cures. Alzheimer's disease prevalence is predicted to more than double in the next 30 years, affecting nearly 15 million Americans, with AD-associated costs exceeding $1 billion by 2050. Delaying onset of AD and other neurodegenerative diseases is critical to improving the quality of life for patients and reducing the burden of disease on caregivers and healthcare systems. Significant progress has been made to model disease pathogenesis and identify points of therapeutic intervention. While some researchers have contributed to our understanding of the proteins and pathways that drive biological dysfunction in disease using in vitro and in vivo models, others have provided mathematical, biophysical, and computational technologies to identify potential therapeutic compounds using in silico modeling. The most exciting phase of the drug discovery process is now: by applying a target-directed approach that leverages the strengths of multiple techniques and validates lead hits using Drosophila as an animal model of disease, we are on the fast-track to identifying novel therapeutics to restore health to those impacted by neurodegenerative disease.

Identification of disease treatment mechanisms through the multiscale interactome

Most diseases disrupt multiple proteins, and drugs treat such diseases by restoring the functions of the disrupted proteins. How drugs restore these functions, however, is often unknown as a drug's therapeutic effects are not limited to the proteins that the drug directly targets. Here, we develop the multiscale interactome, a powerful approach to explain disease treatment. We integrate disease-perturbed proteins, drug targets, and biological functions into a multiscale interactome network. We then develop a random walk-based method that captures how drug effects propagate through a hierarchy of biological functions and physical protein-protein interactions. On three key pharmacological tasks, the multiscale interactome predicts drug-disease treatment, identifies proteins and biological functions related to treatment, and predicts genes that alter a treatment's efficacy and adverse reactions. Our results indicate that physical interactions between proteins alone cannot explain treatment since many drugs treat diseases by affecting the biological functions disrupted by the disease rather than directly targeting disease proteins or their regulators. We provide a general framework for explaining treatment, even when drugs seem unrelated to the diseases they are recommended for.

ANDDigest: a new web-based module of ANDSystem for the search of knowledge in the scientific literature

BACKGROUND

The rapid growth of scientific literature has rendered the task of finding relevant information one of the critical problems in almost any research. Search engines, like Google Scholar, Web of Knowledge, PubMed, Scopus, and others, are highly effective in document search; however, they do not allow knowledge extraction. In contrast to the search engines, text-mining systems provide extraction of knowledge with representations in the form of semantic networks. Of particular interest are tools performing a full cycle of knowledge management and engineering, including automated retrieval, integration, and representation of knowledge in the form of semantic networks, their visualization, and analysis. STRING, Pathway Studio, MetaCore, and others are well-known examples of such products. Previously, we developed the Associative Network Discovery System (ANDSystem), which also implements such a cycle. However, the drawback of these systems is dependence on the employed ontologies describing the subject area, which limits their functionality in searching information based on user-specified queries.

RESULTS

The ANDDigest system is a new web-based module of the ANDSystem tool, permitting searching within PubMed by using dictionaries from the ANDSystem tool and sets of user-defined keywords. ANDDigest allows performing the search based on complex queries simultaneously, taking into account many types of objects from the ANDSystem's ontology. The system has a user-friendly interface, providing sorting, visualization, and filtering of the found information, including mapping of mentioned objects in text, linking to external databases, sorting of data by publication date, citations number, journal H-indices, etc. The system provides data on trends for identified entities based on dynamics of interest according to the frequency of their mentions in PubMed by years.

CONCLUSIONS

The main feature of ANDDigest is its functionality, serving as a specialized search for information about multiple associative relationships of objects from the ANDSystem's ontology vocabularies, taking into account user-specified keywords. The tool can be applied to the interpretation of experimental genetics data, the search for associations between molecular genetics objects, and the preparation of scientific and analytical reviews. It is presently available at https://anddigest.sysbio.ru/ .

Characterization of Extracellular HSV-1 Virions by Proteomics

The analysis of HSV-1 mature extracellular virions by proteomics requires highly enriched samples to limit false-positives and favor the detection of true components. The protocol described below involves the removal of highly contaminating serum proteins and purification of the virions by a series of differential and density centrifugation steps. In addition, L-particles, which are viral particles devoid of a genome and capsid but present in the extracellular milieu, are depleted on Ficoll 400 gradients. As previously reported, the resulting viral particles are free of most contaminants and suitable for mass spectrometry.

The brain-adipocyte-gut network: Linking obesity and depression subtypes

Major depressive disorder (MDD) and obesity are dominant and inter-related health burdens. Obesity is a risk factor for MDD, and there is evidence MDD increases risk of obesity. However, description of a bidirectional relationship between obesity and MDD is misleading, as closer examination reveals distinct unidirectional relationships in MDD subtypes. MDD is frequently associated with weight loss, although obesity promotes MDD. In contrast, MDD with atypical features (MDD-AF) is characterised by subsequent weight gain and obesity. The bases of these distinct associations remain to be detailed, with conflicting findings clouding interpretation. These associations can be viewed within a systems biology framework-the psycho-immune neuroendocrine (PINE) network shared between MDD and metabolic disorders. Shared PINE subsystem perturbations may underlie increased MDD in overweight and obese people (obesity-associated depression), while obesity in MDD-AF (depression-associated obesity) involves more complex interactions between behavioural and biomolecular changes. In the former, the chronic PINE dysfunction triggering MDD is augmented by obesity-dependent dysregulation in shared networks, including inflammatory, leptin-ghrelin, neuroendocrine, and gut microbiome systems, influenced by chronic image-associated psychological stress (particularly in younger or female patients). In MDD-AF, behavioural dysregulation, including hypersensitivity to interpersonal rejection, fundamentally underpins energy imbalance (involving hyperphagia, lethargy, hypersomnia), with evolving obesity exaggerating these drivers via positive feedback (and potentially augmenting PINE disruption). In both settings, sex and age are important determinants of outcome, associated with differences in emotional versus cognitive dysregulation. A systems biology approach is recommended for further research into the pathophysiological networks underlying MDD and linking depression and obesity.

Subclonal cooperation drives metastasis by modulating local and systemic immune microenvironments

Most human tumours are heterogeneous, composed of cellular clones with different properties present at variable frequencies. Highly heterogeneous tumours have poor clinical outcomes, yet the underlying mechanism remains poorly understood. Here, we show that minor subclones of breast cancer cells expressing IL11 and FIGF (VEGFD) cooperate to promote metastatic progression and generate polyclonal metastases composed of driver and neutral subclones. Expression profiling of the epithelial and stromal compartments of monoclonal and polyclonal primary and metastatic lesions revealed that this cooperation is indirect, mediated through the local and systemic microenvironments. We identified neutrophils as a leukocyte population stimulated by the IL11-expressing minor subclone and showed that the depletion of neutrophils prevents metastatic outgrowth. Single-cell RNA-seq of CD45+ cell populations from primary tumours, blood and lungs demonstrated that IL11 acts on bone-marrow-derived mesenchymal stromal cells, which induce pro-tumorigenic and pro-metastatic neutrophils. Our results indicate key roles for non-cell-autonomous drivers and minor subclones in metastasis.

Identifying condition specific key genes from basal-like breast cancer gene expression data

Mining patterns of co-expressed genes across the subset of conditions help to narrow down the search space for the analysis of gene expression data. Identifying conditions specific key genes from the large-scale gene expression data is a challenging task. The conditions specific key gene signifies functional behavior of a group of co-expressed genes across the subset of conditions and can be act as biomarkers of the diseases. In this paper, we have propose a novel approach for identification of conditions specific key genes from Basal-Like Breast Cancer (BLBC) disease using biclustering algorithm and Gene Co-expression Network (GCN). The proposed approach is a two-stage approach. In the first stage, significant biclusters have been extracted with the help of 'runibic' biclustering algorithm. The second stage identifies conditions specific key genes from the extracted significant biclusters with the help of GCN. By using difference matrix and gene correlation matrix, we have constructed biologically meaningful and statistically strong GCN. Also, presented the proposed approach with the help of a process diagram and demonstrated the procedure with an example of bicluster number 93 (Bic93). From the experimental results, we observed that 95% and 85% of the extracted biclusters are found to be biologically significant at the p-values less than 0.05 and 0.01 respectively. We have compared proposed approach with the Weighted Gene Co-expression Network Analysis (WGCNA) based approach. From the comparison, our approach has performed effectively and extracted biologically significant biclusters. Also, identified conditions specific key genes which cannot be extracted using the WGCNA based approach. Some of the important identified known key genes are PIK3CA, SHC3, ERBB2, SHC4, PTOV1, STAG1, ZNF215 etc. These key genes can be used as a diagnostic and prognostic biomarker for the BLBC disease after the rigorous analysis. The identified conditions specific key genes can be helpful to reduce the analysis time and increase the accuracy of further research such as biomarker identification, drug target discovery etc.

Natural Products for Drug Discovery in the 21st Century: Innovations for Novel Drug Discovery

The therapeutic properties of plants have been recognised since time immemorial. Many pathological conditions have been treated using plant-derived medicines. These medicines are used as concoctions or concentrated plant extracts without isolation of active compounds. Modern medicine however, requires the isolation and purification of one or two active compounds. There are however a lot of global health challenges with diseases such as cancer, degenerative diseases, HIV/AIDS and diabetes, of which modern medicine is struggling to provide cures. Many times the isolation of "active compound" has made the compound ineffective. Drug discovery is a multidimensional problem requiring several parameters of both natural and synthetic compounds such as safety, pharmacokinetics and efficacy to be evaluated during drug candidate selection. The advent of latest technologies that enhance drug design hypotheses such as Artificial Intelligence, the use of 'organ-on chip' and microfluidics technologies, means that automation has become part of drug discovery. This has resulted in increased speed in drug discovery and evaluation of the safety, pharmacokinetics and efficacy of candidate compounds whilst allowing novel ways of drug design and synthesis based on natural compounds. Recent advances in analytical and computational techniques have opened new avenues to process complex natural products and to use their structures to derive new and innovative drugs. Indeed, we are in the era of computational molecular design, as applied to natural products. Predictive computational softwares have contributed to the discovery of molecular targets of natural products and their derivatives. In future the use of quantum computing, computational softwares and databases in modelling molecular interactions and predicting features and parameters needed for drug development, such as pharmacokinetic and pharmacodynamics, will result in few false positive leads in drug development. This review discusses plant-based natural product drug discovery and how innovative technologies play a role in next-generation drug discovery.

Estrogenic Potentials of Traditional Chinese Medicine

Estrogen, a steroid hormone, is associated with several human activities, including environmental, industrial, agricultural, pharmaceutical and medical fields. In this review paper, estrogenic activity associated with traditional Chinese medicines (TCMs) is discussed first by focusing on the assays needed to detect estrogenic activity (animal test, cell assay, ligand-binding assay, protein assay, reporter-gene assay, transcription assay and yeast two-hybrid assay), and then, their sources, the nature of activities (estrogenic or anti-estrogenic, or other types), and pathways/functions, along with the assay used to detect the activity, which is followed by a summary of effective chemicals found in or associated with TCM. Applications of estrogens in TCM are then discussed by a comprehensive search of the literature, which include basic study/pathway analysis, cell functions, diseases/symptoms and medicine/supplements. Discrepancies and conflicting cases about estrogenicity of TCM among assays or between TCM and their effective chemicals, are focused on to enlarge estrogenic potentials of TCM by referring to omic knowledge such as transcriptome, proteome, glycome, chemome, cellome, ligandome, interactome and effectome.

Effect of Hyperglycemia on Gene Expression during Early Organogenesis in Mice

Background

Cardiovascular and neural malformations are common sequels of diabetic pregnancies, but the underlying molecular mechanisms remain unknown. We hypothesized that maternal hyperglycemia would affect the embryos most shortly after the glucose-sensitive time window at embryonic day (ED) 7.5 in mice.

Methods

Mice were made diabetic with streptozotocin, treated with slow-release insulin implants and mated. Pregnancy aggravated hyperglycemia. Gene expression profiles were determined in ED8.5 and ED9.5 embryos from diabetic and control mice using Serial Analysis of Gene Expression and deep sequencing.

Results

Maternal hyperglycemia induced differential regulation of 1,024 and 2,148 unique functional genes on ED8.5 and ED9.5, respectively, mostly in downward direction. Pathway analysis showed that ED8.5 embryos suffered mainly from impaired cell proliferation, and ED9.5 embryos from impaired cytoskeletal remodeling and oxidative phosphorylation (all P ≤ E-5). A query of the Mouse Genome Database showed that 20–25% of the differentially expressed genes were caused by cardiovascular and/or neural malformations, if deficient. Despite high glucose levels in embryos with maternal hyperglycemia and a ~150-fold higher rate of ATP production from glycolysis than from oxidative phosphorylation on ED9.5, ATP production from both glycolysis and oxidative phosphorylation was reduced to ~70% of controls, implying a shortage of energy production in hyperglycemic embryos.

Conclusion

Maternal hyperglycemia suppressed cell proliferation during gastrulation and cytoskeletal remodeling during early organogenesis. 20–25% of the genes that were differentially regulated by hyperglycemia were associated with relevant congenital malformations. Unexpectedly, maternal hyperglycemia also endangered the energy supply of the embryo by suppressing its glycolytic capacity.

A graph-based approach to construct target-focused libraries for virtual screening

Background

Due to exorbitant costs of high-throughput screening, many drug discovery projects commonly employ inexpensive virtual screening to support experimental efforts. However, the vast majority of compounds in widely used screening libraries, such as the ZINC database, will have a very low probability to exhibit the desired bioactivity for a given protein. Although combinatorial chemistry methods can be used to augment existing compound libraries with novel drug-like compounds, the broad chemical space is often too large to be explored. Consequently, the trend in library design has shifted to produce screening collections specifically tailored to modulate the function of a particular target or a protein family.

Methods

Assuming that organic compounds are composed of sets of rigid fragments connected by flexible linkers, a molecule can be decomposed into its building blocks tracking their atomic connectivity. On this account, we developed eSynth, an exhaustive graph-based search algorithm to computationally synthesize new compounds by reconnecting these building blocks following their connectivity patterns.

Results

We conducted a series of benchmarking calculations against the Directory of Useful Decoys, Enhanced database. First, in a self-benchmarking test, the correctness of the algorithm is validated with the objective to recover a molecule from its building blocks. Encouragingly, eSynth can efficiently rebuild more than 80 % of active molecules from their fragment components. Next, the capability to discover novel scaffolds is assessed in a cross-benchmarking test, where eSynth successfully reconstructed 40 % of the target molecules using fragments extracted from chemically distinct compounds. Despite an enormous chemical space to be explored, eSynth is computationally efficient; half of the molecules are rebuilt in less than a second, whereas 90 % take only about a minute to be generated.

Conclusions

eSynth can successfully reconstruct chemically feasible molecules from molecular fragments. Furthermore, in a procedure mimicking the real application, where one expects to discover novel compounds based on a small set of already developed bioactives, eSynth is capable of generating diverse collections of molecules with the desired activity profiles. Thus, we are very optimistic that our effort will contribute to targeted drug discovery. eSynth is freely available to the academic community at www.brylinski.org/content/molecular-synthesis.Graphical abstract

Temporal Phosphoproteome Dynamics Induced by an ATP Synthase Inhibitor Citreoviridin

Citreoviridin, one of toxic mycotoxins derived from fungal species, can suppress lung cancer cell growth by inhibiting the activity of ectopic ATP synthase, but has limited effect on normal cells. However, the mechanism of citreoviridin triggering dynamic molecular responses in cancer cells remains unclear. Here, we performed temporal phosphoproteomics to elucidate the dynamic changes after citreoviridin treatment in cells and xenograft model. We identified a total of 829 phosphoproteins and demonstrated that citreoviridin treatment affects protein folding, cell cycle, and cytoskeleton function. Furthermore, response network constructed by mathematical modeling shows the relationship between the phosphorylated heat shock protein 90 β and mitogen-activated protein kinase signaling pathway. This work describes that citreoviridin suppresses cancer cell growth and mitogen-activated protein kinase/extracellular signal-regulated kinase signaling by site-specific dephosphorylation of HSP90AB1 on Serine 255 and provides perspectives in cancer therapeutic strategies.

Fluoxetine-induced regulation of heat shock protein 90 and 14-3-3ε in human embryonic carcinoma cells

Fluoxetine, a serotonin-selective reuptake inhibitor, exerts antidepressant and antianxiety effects on major depressive and anxiety disorders. Previous studies suggest that treatment with fluoxetine influences the expression of various proteins that are involved in proliferation, differentiation, and apoptosis in the neuronal cells of the brain. However, many aspects of the molecular pathways that modulate antidepressant action are not well understood. Here, with the aim of identifying proteins involved in antidepressant action, we examined the protein expression profile of human embryonic carcinoma (NCCIT) cells in response to fluoxetine treatment using proteomic techniques such as two-dimensional gel electrophoresis and matrix-assisted laser desorption ionization-time-of-flight mass spectrometry (MALDI-TOF MS). We found several upregulated and downregulated proteins in fluoxetine-treated NCCIT cells, and then biochemically confirmed the increased expression of heat shock protein 90 and 14-3-3ε, which play an essential role in many cellular mechanisms including cell cycle control and other signaling pathways. Our data suggest that the regulated expression of heat shock protein 90, 14-3-3ε, and other identified proteins may be associated with the therapeutic action of fluoxetine.

In-silico prediction of drug targets, biological activities, signal pathways and regulating networks of dioscin based on bioinformatics

Background

Inverse docking technology has been a trend of drug discovery, and bioinformatics approaches have been used to predict target proteins, biological activities, signal pathways and molecular regulating networks affected by drugs for further pharmacodynamic and mechanism studies.

Methods

In the present paper, inverse docking technology was applied to screen potential targets from potential drug target database (PDTD). Then, the corresponding gene information of the obtained drug-targets was applied to predict the related biological activities, signal pathways and processes networks of the compound by using MetaCore platform. After that, some most relevant regulating networks were considered, which included the nodes and relevant pathways of dioscin.

Results

71 potential targets of dioscin from humans, 7 from rats and 8 from mice were screened, and the prediction results showed that the most likely targets of dioscin were cyclin A2, calmodulin, hemoglobin subunit beta, DNA topoisomerase I, DNA polymerase lambda, nitric oxide synthase and UDP-N-acetylhexosamine pyrophosphorylase, etc. Many diseases including experimental autoimmune encephalomyelitis of human, temporal lobe epilepsy of rat and ankylosing spondylitis of mouse, may be inhibited by dioscin through regulating immune response alternative complement pathway, G-protein signaling RhoB regulation pathway and immune response antiviral actions of interferons, etc. The most relevant networks (5 from human, 3 from rat and 5 from mouse) indicated that dioscin may be a TOP1 inhibitor, which can treat cancer though the cell cycle– transition and termination of DNA replication pathway. Dioscin can down regulate EGFR and EGF to inhibit cancer, and also has anti-inflammation activity by regulating JNK signaling pathway.

Conclusions

The predictions of the possible targets, biological activities, signal pathways and relevant regulating networks of dioscin provide valuable information to guide further investigation of dioscin on pharmacodynamics and molecular mechanisms, which also suggests a practical and effective method for studies on the mechanism of other chemicals.

Proteomics, metabolomics, and protein interactomics in the characterization of the molecular features of major depressive disorder

Omics technologies emerged as complementary strategies to genomics in the attempt to understand human illnesses. In general, proteomics technologies emerged earlier than those of metabolomics for major depressive disorder (MDD) research, but both are driven by the identification of proteins and/or metabolites that can delineate a comprehensive characterization of MDD's molecular mechanisms, as well as lead to the identification of biomarker candidates of all types—prognosis, diagnosis, treatment, and patient stratification. Also, one can explore protein and metabolite interactomes in order to pinpoint additional molecules associated with the disease that had not been picked up initially. Here, results and methodological aspects of MDD research using proteomics, metabolomics, and protein interactomics are reviewed, focusing on human samples.

A logical model of HIV-1 interactions with the T-cell activation signalling pathway

MOTIVATION

Human immunodeficiency virus type 1 (HIV-1) hijacks host cellular processes to replicate within its host. Through interactions with host proteins, it perturbs and interrupts signaling pathways that alter key cellular functions. Although networks of viral-host interactions have been relatively well characterized, the dynamics of the perturbation process is poorly understood. Dynamic models of infection have the potential to provide insights into the HIV-1 host interaction.

RESULTS

We employed a logical signal flow network to model the dynamic interactions between HIV-1 proteins and key human signal transduction pathways necessary for activation of CD4+ T lymphocytes. We integrated viral-host interaction and host signal transduction data into a dynamic logical model comprised of 137 nodes (16 HIV-1 and 121 human proteins) and 336 interactions collected from the HIV-1 Human Interaction Database. The model reproduced expected patterns of T-cell activation, co-stimulation and co-inhibition. After simulations, we identified 26 host cell factors, including MAPK1&3, Ikkb-Ikky-Ikka and PKA, which contribute to the net activation or inhibition of viral proteins. Through in silico knockouts, the model identified a further nine host cell factors, including members of the PI3K signalling pathway that are essential to viral replication. Simulation results intersected with the findings of three siRNA gene knockout studies and identified potential drug targets. Our results demonstrate how viral infection causes the cell to lose control of its signalling system. Logical Boolean modelling therefore provides a useful approach for analysing the dynamics of host-viral interactions with potential applications for drug discovery.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Enteric bacterial metabolites propionic and butyric acid modulate gene expression, including CREB-dependent catecholaminergic neurotransmission, in PC12 cells--possible relevance to autism spectrum disorders

Alterations in gut microbiome composition have an emerging role in health and disease including brain function and behavior. Short chain fatty acids (SCFA) like propionic (PPA), and butyric acid (BA), which are present in diet and are fermentation products of many gastrointestinal bacteria, are showing increasing importance in host health, but also may be environmental contributors in neurodevelopmental disorders including autism spectrum disorders (ASD). Further to this we have shown SCFA administration to rodents over a variety of routes (intracerebroventricular, subcutaneous, intraperitoneal) or developmental time periods can elicit behavioral, electrophysiological, neuropathological and biochemical effects consistent with findings in ASD patients. SCFA are capable of altering host gene expression, partly due to their histone deacetylase inhibitor activity. We have previously shown BA can regulate tyrosine hydroxylase (TH) mRNA levels in a PC12 cell model. Since monoamine concentration is known to be elevated in the brain and blood of ASD patients and in many ASD animal models, we hypothesized that SCFA may directly influence brain monoaminergic pathways. When PC12 cells were transiently transfected with plasmids having a luciferase reporter gene under the control of the TH promoter, PPA was found to induce reporter gene activity over a wide concentration range. CREB transcription factor(s) was necessary for the transcriptional activation of TH gene by PPA. At lower concentrations PPA also caused accumulation of TH mRNA and protein, indicative of increased cell capacity to produce catecholamines. PPA and BA induced broad alterations in gene expression including neurotransmitter systems, neuronal cell adhesion molecules, inflammation, oxidative stress, lipid metabolism and mitochondrial function, all of which have been implicated in ASD. In conclusion, our data are consistent with a molecular mechanism through which gut related environmental signals such as increased levels of SCFA's can epigenetically modulate cell function further supporting their role as environmental contributors to ASD.

Characterization of extracellular HSV-1 virions by proteomics

The analysis of herpes simplex virus type 1 mature extracellular virions by proteomics requires highly enriched samples to limit false positives and favor the detection of true components. The protocol described below involves the removal of highly contaminating serum proteins and purification of the virions by a series of differential and density centrifugation steps. In addition, L-particles, which are viral particles devoid of genome and capsid but present in the extracellular milieu, are depleted on Ficoll 400 gradients. As previously reported, the resulting viral particles are free of most contaminants and suitable for mass spectrometry.

Leucine-rich repeat kinase 2 (LRRK2)-deficient rats exhibit renal tubule injury and perturbations in metabolic and immunological homeostasis

Genetic evidence links mutations in the LRRK2 gene with an increased risk of Parkinson's disease, for which no neuroprotective or neurorestorative therapies currently exist. While the role of LRRK2 in normal cellular function has yet to be fully described, evidence suggests involvement with immune and kidney functions. A comparative study of LRRK2-deficient and wild type rats investigated the influence that this gene has on the phenotype of these rats. Significant weight gain in the LRRK2 null rats was observed and was accompanied by significant increases in insulin and insulin-like growth factors. Additionally, LRRK2-deficient rats displayed kidney morphological and histopathological alterations in the renal tubule epithelial cells of all animals assessed. These perturbations in renal morphology were accompanied by significant decreases of lipocalin-2, in both the urine and plasma of knockout animals. Significant alterations in the cellular composition of the spleen between LRRK2 knockout and wild type animals were identified by immunophenotyping and were associated with subtle differences in response to dual infection with rat-adapted influenza virus (RAIV) and Streptococcus pneumoniae. Ontological pathway analysis of LRRK2 across metabolic and kidney processes and pathological categories suggested that the thioredoxin network may play a role in perturbing these organ systems. The phenotype of the LRRK2 null rat is suggestive of a complex biology influencing metabolism, immune function and kidney homeostasis. These data need to be extended to better understand the role of the kinase domain or other biological functions of the gene to better inform the development of pharmacological inhibitors.

A network pharmacology approach to evaluating the efficacy of chinese medicine using genome-wide transcriptional expression data

The research of multicomponent drugs, such as in Chinese Medicine, on both mechanism dissection and drug discovery is challenging, especially the approaches to systematically evaluating the efficacy at a molecular level. Here, we presented a network pharmacology-based approach to evaluating the efficacy of multicomponent drugs by genome-wide transcriptional expression data and applied it to Shenmai injection (SHENMAI), a widely used Chinese Medicine composed of red ginseng (RG) and Radix Ophiopogonis (RO) in clinically treating myocardial ischemia (MI) diseases. The disease network, MI network in this case, was constructed by combining the protein-protein interactions (PPI) involved in the MI enriched pathways. The therapeutic efficacy of SHENMAI, RG, and RO was therefore evaluated by a network parameter, namely, network recovery index (NRI), which quantitatively evaluates the overall recovery rate in MI network. The NRI of SHENMAI, RG, and RO were 0.865, 0.425, and 0.271 respectively [corrected], which indicated SHENMAI exerts protective effects and the synergistic effect of RG and RO on treating myocardial ischemia disease. The successful application of SHENMAI implied that the proposed network pharmacology-based approach could help researchers to better evaluate a multicomponent drug on a systematic and molecular level.

Knowledge-Driven, Data-Assisted Integrative Pathway Analytics

Target and biomarker selection in drug discovery relies extensively on the use of various genomics platforms. These technologies generate large amounts of data that can be used to gain novel insights in biology. There is a strong need to mine these information-rich datasets in an effective and efficient manner. Pathway and network based approaches have become an increasingly important methodology to mine bioinformatics datasets derived from ‘omics’ technologies. These approaches also find use in exploring the unknown biology of a disease or functional process. This chapter provides an overview of pathway databases and network tools, network architecture, text mining and existing methods used in knowledge-driven data analysis. It shows examples of how these databases and tools can be used integratively to apply existing knowledge and network-based approach in data analytics.

Temporal transcriptional response during infection of type II alveolar epithelial cells with Francisella tularensis live vaccine strain (LVS) supports a general host suppression and bacterial uptake by macropinocytosis

Pneumonic tularemia is caused by inhalation of Francisella tularensis, one of the most infectious microbes known. We wanted to study the kinetics of the initial and early interactions between bacterium and host cells in the lung. To do this, we examined the infection of A549 airway epithelial cells with the live vaccine strain (LVS) of F. tularensis. A549 cells were infected and analyzed for global transcriptional response at multiple time points up to 16 h following infection. At 15 min and 2 h, a strong transcriptional response was observed including cytoskeletal rearrangement, intracellular transport, and interferon signaling. However, at later time points (6 and 16 h), very little differential gene expression was observed, indicating a general suppression of the host response consistent with other reported cell lines and murine tissues. Genes for macropinocytosis and actin/cytoskeleton rearrangement were highly up-regulated and common to the 15 min and 2 h time points, suggesting the use of this method for bacterial entry into cells. We demonstrate macropinocytosis through the uptake of FITC-dextran and amiloride inhibition of Francisella LVS uptake. Our results suggest that macropinocytosis is a potential mechanism of intracellular entry by LVS and that the host cell response is suppressed during the first 2-6 h of infection. These results suggest that the attenuated Francisella LVS induces significant host cell signaling at very early time points after the bacteria's interaction with the cell.

Differentiating vitreous proteomes in proliferative diabetic retinopathy using high-performance liquid chromatography coupled to tandem mass spectrometry

Proliferative diabetic retinopathy (PDR) is a serious microangiopathic complication of diabetes mellitus and a major cause of blindness in working-age adults. Diabetes-induced alterations in the vitreous protein composition in diabetic patients with PDR may be responsible for the presence of PDR. Therefore, we performed a comprehensive proteomic analysis and compared the protein profiles of vitreous humor from type 2 diabetic patients with PDR (n = 8) and that from normal human eyes donated for corneal transplant (n = 8). Using reversed phase high-performance liquid chromatography (RP-HPLC) coupled to electrospray Ionization tandem mass spectrometry (ESI-MS/MS), we identified 96 significant differentially expressed proteins (abundance ratio > 1.5, p < 0.05), including 37 and 59 proteins up- and downregulated in PDR vitreous compared with the control, respectively. Biological pathway analysis revealed 44 proteins involved in 56 biological pathways; among them, the most remarkable pathways differentially represented between PDR and normal vitreous were the glycolysis/gluconeogenesis, complement and coagulation cascades, gap junction, and phagosome pathways. The differential expressions of angiopoietin-related protein 6, apolipoprotein A-I, estrogen receptor alpha, and tubulin were confirmed by western blot analysis. These data provide insight into the molecular events possibly involved in the pathogenesis of PDR and widen the scope of potential avenues for new therapies for PDR.

Ouabain Mimics Low Temperature Rescue of F508del-CFTR in Cystic Fibrosis Epithelial Cells

Most cases of cystic fibrosis (CF) are caused by the deletion of a single phenylalanine residue at position 508 of the cystic fibrosis transmembrane conductance regulator (CFTR). The mutant F508del-CFTR is retained in the endoplasmic reticulum and degraded, but can be induced by low temperature incubation (29°C) to traffic to the plasma membrane where it functions as a chloride channel. Here we show that, cardiac glycosides, at nanomolar concentrations, can partially correct the trafficking of F508del-CFTR in human CF bronchial epithelial cells (CFBE41o-) and in an F508del-CFTR mouse model. Comparison of the transcriptional profiles obtained with polarized CFBE41o-cells after treatment with ouabain and by low temperature has revealed a striking similarity between the two corrector treatments that is not shared with other correctors. In summary, our study shows a novel function of ouabain and its analogs in the regulation of F508del-CFTR trafficking and suggests that compounds that mimic this low temperature correction of trafficking will provide new avenues for the development of therapeutics for CF.

NetWalker: a contextual network analysis tool for functional genomics

Background

Functional analyses of genomic data within the context of a priori biomolecular networks can give valuable mechanistic insights. However, such analyses are not a trivial task, owing to the complexity of biological networks and lack of computational methods for their effective integration with experimental data.

Results

We developed a software application suite, NetWalker, as a one-stop platform featuring a number of novel holistic (i.e. assesses the whole data distribution without requiring data cutoffs) data integration and analysis methods for network-based comparative interpretations of genome-scale data. The central analysis components, NetWalk and FunWalk, are novel random walk-based network analysis methods that provide unique analysis capabilities to assess the entire data distributions together with network connectivity to prioritize molecular and functional networks, respectively, most highlighted in the supplied data. Extensive inter-operability between the analysis components and with external applications, including R, adds to the flexibility of data analyses. Here, we present a detailed computational analysis of our microarray gene expression data from MCF7 cells treated with lethal and sublethal doses of doxorubicin.

Conclusion

NetWalker, a detailed step-by-step tutorial containing the analyses presented in this paper and a manual are available at the web site http://netwalkersuite.org.

Proteomic Analysis of the Copper Ion-Induced Stress Response in a Human Embryonic Carcinoma Cell Line

Excessive exposure to copper, a redox-active metal, generates free radicals, which can cause cellular damage. In this study, we aim to identify the proteins that are up- or downregulated by copper exposure in human embryonic carcinoma (NCCIT) cells and to understand the mechanisms that play a role in the copper-induced stress response. After exposure to copper ions, the cells showed upregulated levels of 78 kDa glucose-regulated protein, fibrillin 1, CWC22 spliceosome-associated protein (KIAA1604), heat shock protein (HSP) 60, and HSP70, while the tumor necrosis factor receptor-associated factor 6, vimentin, 14-3-3 protein zeta, and RAC-beta (AKT2) serine/threonine protein kinase were downregulated. The GeneGo Process Networks of the proteins upregulated by copper ions were analyzed, and the 3 highest-scoring networks from the proteins upregulated by copper ions are presented here. In particular, the increased level of HSP70 in response to copper ions occurred in a dose-dependent manner, indicating that HSP70 could be a potential biomarker for copper toxicity in mammalian cells.

Nkx3.1 and Myc crossregulate shared target genes in mouse and human prostate tumorigenesis

Cooperativity between oncogenic mutations is recognized as a fundamental feature of malignant transformation, and it may be mediated by synergistic regulation of the expression of pro- and antitumorigenic target genes. However, the mechanisms by which oncogenes and tumor suppressors coregulate downstream targets and pathways remain largely unknown. Here, we used ChIP coupled to massively parallel sequencing (ChIP-seq) and gene expression profiling in mouse prostates to identify direct targets of the tumor suppressor Nkx3.1. Further analysis indicated that a substantial fraction of Nkx3.1 target genes are also direct targets of the oncoprotein Myc. We also showed that Nkx3.1 and Myc bound to and crossregulated shared target genes in mouse and human prostate epithelial cells and that Nkx3.1 could oppose the transcriptional activity of Myc. Furthermore, loss of Nkx3.1 cooperated with concurrent overexpression of Myc to promote prostate cancer in transgenic mice. In human prostate cancer patients, dysregulation of shared NKX3.1/MYC target genes was associated with disease relapse. Our results indicate that NKX3.1 and MYC coregulate prostate tumorigenesis by converging on, and crossregulating, a common set of target genes. We propose that coregulation of target gene expression by oncogenic/tumor suppressor transcription factors may represent a general mechanism underlying the cooperativity of oncogenic mutations during tumorigenesis.

Bioinformatics and systems biology

Delivering personalized therapeutic options to cancer patients based on the genetic and molecular aberrations of the tumor offers great promise to improve the outcomes of cancer therapy. Significant progress in biotechnology has allowed the measurement of tens of thousands of "omic" data points across multiple levels (DNA, RNA protein, metabolomics) from a single tumor biopsy sample in a reasonable time frame for making clinical decisions. With this data in hand, the challenge from the bioinformatics and systems biology point of view is how does one convert data into information and knowledge that can improve the delivery of personalized therapy to the patient.

Directing human embryonic stem cell differentiation by non-viral delivery of siRNA in 3D culture

Human embryonic stem cells (hESCs) hold great potential as a resource for regenerative medicine. Before achieving therapeutic relevancy, methods must be developed to control stem cell differentiation. It is clear that stem cells can respond to genetic signals, such as those imparted by nucleic acids, to promote lineage-specific differentiation. Here we have developed an efficient system for delivering siRNA to hESCs in a 3D culture matrix using lipid-like materials. We show that non-viral siRNA delivery in a 3D scaffolds can efficiently knockdown 90% of GFP expression in GFP-hESCs. We further show that this system can be used as a platform for directing hESC differentiation. Through siRNA silencing of the KDR receptor gene, we achieve concurrent downregulation (60-90%) in genes representative of the endoderm germ layer and significant upregulation of genes representative of the mesoderm germ layer (27-90 fold). This demonstrates that siRNA can direct stem cell differentiation by blocking genes representative of one germ layer and also provides a particularly powerful means to isolate the endoderm germ layer from the mesoderm and ectoderm. This ability to inhibit endoderm germ layer differentiation could allow for improved control over hESC differentiation to desired cell types.

A topographical map of the causal network of mechanisms underlying the relationship between major depressive disorder and coronary heart disease

OBJECTIVE

Major depressive disorder (MDD) and coronary heart disease (CHD) are both clinically important public health problems. Depression is linked with a higher incidence of ischaemic cardiac events and MDD is more prevalent in patients with CHD. No single comprehensive model has yet described the causal mechanisms linking MDD to CHD. Several key mechanisms have been put forward, comprising behavioural mechanisms, genetic mechanisms, dysregulation of immune mechanisms, coagulation abnormalities and vascular endothelial dysfunction, polyunsaturated omega-3 free fatty acid deficiency, and autonomic mechanisms. It has been suggested that these mechanisms form a network, which links MDD and CHD. The aim of this review is to examine the causal mechanisms underlying the relationship between MDD and CHD, with the aim of constructing a topological map of the causal network which describes the relationship between MDD and CHD.

METHODS

The search term 'depression and heart disease' was entered into an electronic multiple database search engine. Abstracts were screened for relevance and individually selected articles were collated.

RESULTS

This review introduces the first topological map of the causal network which describes the relationship between MDD and CHD.

CONCLUSIONS

Viewing the causal pathways as an interdependent network presents a new paradigm in this field and provides fertile ground for further research. The causal network can be studied using the methodology of systems biology, which is briefly introduced. Future research should focus on the creation of a more comprehensive topological map of the causal network and the quantification of the activity between each node of the causal network.

Interorgan coordination of the murine adaptive response to fasting

Starvation elicits a complex adaptive response in an organism. No information on transcriptional regulation of metabolic adaptations is available. We, therefore, studied the gene expression profiles of brain, small intestine, kidney, liver, and skeletal muscle in mice that were subjected to 0-72 h of fasting. Functional-category enrichment, text mining, and network analyses were employed to scrutinize the overall adaptation, aiming to identify responsive pathways, processes, and networks, and their regulation. The observed transcriptomics response did not follow the accepted "carbohydrate-lipid-protein" succession of expenditure of energy substrates. Instead, these processes were activated simultaneously in different organs during the entire period. The most prominent changes occurred in lipid and steroid metabolism, especially in the liver and kidney. They were accompanied by suppression of the immune response and cell turnover, particularly in the small intestine, and by increased proteolysis in the muscle. The brain was extremely well protected from the sequels of starvation. 60% of the identified overconnected transcription factors were organ-specific, 6% were common for 4 organs, with nuclear receptors as protagonists, accounting for almost 40% of all transcriptional regulators during fasting. The common transcription factors were PPARα, HNF4α, GCRα, AR (androgen receptor), SREBP1 and -2, FOXOs, EGR1, c-JUN, c-MYC, SP1, YY1, and ETS1. Our data strongly suggest that the control of metabolism in four metabolically active organs is exerted by transcription factors that are activated by nutrient signals and serves, at least partly, to prevent irreversible brain damage.

Transcriptome analysis of common gene expression in human mesenchymal stem cells derived from four different origins

We have used Affymetrix oligonucleotide microarrays to analyze common transcriptomes and thereby learn about the core gene expression profile in human mesenchymal stem cells (MSC) from different tissues, including fetal amniotic fluid-derived MSC, term pregnancy amniotic membrane-derived MSC, term pregnancy umbilical cord blood-derived MSC, and adult bone marrow-derived MSC. The beauty of microarray analysis of gene expression (MAGE) is that it can be used to discover associating genes that were previously thought to be unrelated to a physiological or pathological event. However, interpreting complex biological processes from gene expression profiles often requires extensive knowledge mining in biomedical literature. In this chapter, we describe, step-by-step, how to use a commercially available biological database and software program, MetaCore (GeneGo Inc.), for functional network analysis.

Use of data-biased random walks on graphs for the retrieval of context-specific networks from genomic data

Extracting network-based functional relationships within genomic datasets is an important challenge in the computational analysis of large-scale data. Although many methods, both public and commercial, have been developed, the problem of identifying networks of interactions that are most relevant to the given input data still remains an open issue. Here, we have leveraged the method of random walks on graphs as a powerful platform for scoring network components based on simultaneous assessment of the experimental data as well as local network connectivity. Using this method, NetWalk, we can calculate distribution of Edge Flux values associated with each interaction in the network, which reflects the relevance of interactions based on the experimental data. We show that network-based analyses of genomic data are simpler and more accurate using NetWalk than with some of the currently employed methods. We also present NetWalk analysis of microarray gene expression data from MCF7 cells exposed to different doses of doxorubicin, which reveals a switch-like pattern in the p53 regulated network in cell cycle arrest and apoptosis. Our analyses demonstrate the use of NetWalk as a valuable tool in generating high-confidence hypotheses from high-content genomic data.

Analysis of high-content genomic data within the context of known networks of interactions of genes can lead to a better understanding of the underlying biological processes. However, finding the networks of interactions that are most relevant to the given data is a challenging task. We present a random walk-based algorithm, NetWalk, which integrates genomic data with networks of interactions between genes to score the relevance of each interaction based on both the data values of the genes as well as their local network connectivity. This results in a distribution of Edge Flux values, which can be used for dynamic reconstruction of user-defined networks. Edge Flux values can be further subjected to statistical analyses such as clustering, allowing for direct numerical comparisons of context-specific networks between different conditions. To test NetWalk performance, we carried out microarray gene expression analysis of MCF7 cells subjected to lethal and sublethal doses of a DNA damaging agent. We compared NetWalk to other network-based analysis methods and found that NetWalk was superior in identifying coherently altered sub-networks from the genomic data. Using NetWalk, we further identified p53-regulated networks that are differentially involved in cell cycle arrest and apoptosis, which we experimentally tested.

Functional classification of proteins based on projection of amino acid sequences: application for prediction of protein kinase substrates

Background

The knowledge about proteins with specific interaction capacity to the protein partners is very important for the modeling of cell signaling networks. However, the experimentally-derived data are sufficiently not complete for the reconstruction of signaling pathways. This problem can be solved by the network enrichment with predicted protein interactions. The previously published in silico method PAAS was applied for prediction of interactions between protein kinases and their substrates.

Results

We used the method for recognition of the protein classes defined by the interaction with the same protein partners. 1021 protein kinase substrates classified by 45 kinases were extracted from the Phospho.ELM database and used as a training set. The reasonable accuracy of prediction calculated by leave-one-out cross validation procedure was observed in the majority of kinase-specificity classes. The random multiple splitting of the studied set onto the test and training set had also led to satisfactory results. The kinase substrate specificity for 186 proteins extracted from TRANSPATH^® database was predicted by PAAS method. Several kinase-substrate interactions described in this database were correctly predicted. Using the previously developed ExPlain™ system for the reconstruction of signal transduction pathways, we showed that addition of the newly predicted interactions enabled us to find the possible path between signal trigger, TNF-alpha, and its target genes in the cell.

Conclusions

It was shown that the predictions of protein kinase substrates by PAAS were suitable for the enrichment of signaling pathway networks and identification of the novel signaling pathways. The on-line version of PAAS for prediction of protein kinase substrates is freely available at http://www.ibmc.msk.ru/PAAS/.

Databases of protein-protein interactions and complexes

In the current understanding, translation of genomic sequences into proteins is the most important path for realization of genome information. In exercising their intended function, proteins work together through various forms of direct (physical) or indirect interaction mechanisms. For a variety of basic functions, many proteins form a large complex representing a molecular machine or a macromolecular super-structural building block. After several high-throughput techniques for detection of protein-protein interactions had matured, protein interaction data became available in a large scale and curated databases for protein-protein interactions (PPIs) are a new necessity for efficient research. Here, their scope, annotation quality, and retrieval tools are reviewed. In addition, attention is paid to portals that provide unified access to a variety of such databases with added annotation value.

Graph methods for the investigation of metabolic networks in parasitology

Recently, a way was opened with the development of many mathematical methods to model and analyze genome-scale metabolic networks. Among them, methods based on graph models enable to us quickly perform large-scale analyses on large metabolic networks. However, it could be difficult for parasitologists to select the graph model and methods adapted to their biological questions. In this review, after briefly addressing the problem of the metabolic network reconstruction, we propose an overview of the graph-based approaches used in whole metabolic network analyses. Applications highlight the usefulness of this kind of approach in the field of parasitology, especially by suggesting metabolic targets for new drugs. Their development still represents a major challenge to fight against the numerous diseases caused by parasites.

Integrated network analysis of transcriptomic and proteomic data in psoriasis

BACKGROUND

Psoriasis is complex inflammatory skin pathology of autoimmune origin. Several cell types are perturbed in this pathology, and underlying signaling events are complex and still poorly understood.

RESULTS

In order to gain insight into molecular machinery underlying the disease, we conducted a comprehensive meta-analysis of proteomics and transcriptomics of psoriatic lesions from independent studies. Network-based analysis revealed similarities in regulation at both proteomics and transcriptomics level. We identified a group of transcription factors responsible for overexpression of psoriasis genes and a number of previously unknown signaling pathways that may play a role in this process. We also evaluated functional synergy between transcriptomics and proteomics results.

CONCLUSIONS

We developed network-based methodology for integrative analysis of high throughput data sets of different types. Investigation of proteomics and transcriptomics data sets on psoriasis revealed versatility in regulatory machinery underlying pathology and showed complementarities between two levels of cellular organization.

Modulation of glycosphingolipid metabolism significantly improves hepatic insulin sensitivity and reverses hepatic steatosis in mice

Nonalcoholic fatty liver disease (NAFLD) is associated with obesity, insulin resistance, and type 2 diabetes. The hyperinsulinemia that occurs as a consequence of insulin resistance is thought to be an important contributor to the development of fatty liver. We have shown that the iminosugar N-(5'-adamantane-1'-yl-methoxy)-pentyl-1-deoxynojirimycin (AMP-DNM), an inhibitor of the enzyme glucosylceramide synthase, is a potent enhancer of insulin signaling in rodent models for insulin resistance and type 2 diabetes. The present study was designed to assess the impact of AMP-DNM on insulin levels, liver triglyceride synthesis, and gene expression profile. Treatment of ob/ob mice with AMP-DNM restored insulin signaling in the liver, corrected blood glucose values to levels found in lean mice, and decreased insulin concentration. The expression of sterol regulatory element-binding protein 1c target genes involved in fatty acid synthesis normalized. AMP-DNM treatment significantly reduced liver to body weight ratio and reversed hepatic steatosis, comprising fat as well as inflammatory markers. In addition, AMP-DNM treatment corrected to a large extent the gene expression profile of ob/ob mice livers toward the profile of lean mice.

CONCLUSION

Pharmacological lowering of glycosphingolipids with the iminosugar AMP-DNM is a promising approach to restore insulin signaling and improve glucose homeostasis as well as hepatic steatosis.

Meta-analysis of gene expression in human pancreatic islets after in vitro expansion

Pancreatic islet transplantation as a potential cure for type 1 diabetes (T1D) cannot be scaled up due to a scarcity of human pancreas donors. In vitro expansion of beta-cells from mature human pancreatic islets provides an alternative source of insulin-producing cells. The exact nature of the expanded cells produced by diverse expansion protocols and their potential for differentiation into functional beta-cells remain elusive. We performed a large-scale meta-analysis of gene expression in human pancreatic islet cells, which were processed using three different previously described protocols for expansion and for which redifferentiation was attempted. All three expansion protocols induced dramatic changes in the expression profiles of pancreatic islets; many of these changes are shared among the three protocols. Attempts at redifferentiation of expanded cells induce a limited number of gene expression changes. Nevertheless, these fail to restore a pancreatic islet-like gene expression pattern. Comparison with a collection of public microarray datasets confirmed that expanded cells are highly comparable to mesenchymal stem cells. Genes induced in expanded cells are also enriched for targets of transcription factors important for pluripotency induction. The present data increase our understanding of the active pathways in expanded and redifferentiated islets. Knowledge of the mesenchymal stem cell potential may help development of drug therapeutics to restore beta-cell mass in T1D patients.

Influence of protein abundance on high-throughput protein-protein interaction detection

Experimental protein-protein interaction (PPI) networks are increasingly being exploited in diverse ways for biological discovery. Accordingly, it is vital to discern their underlying natures by identifying and classifying the various types of deterministic (specific) and probabilistic (nonspecific) interactions detected. To this end, we have analyzed PPI networks determined using a range of high-throughput experimental techniques with the aim of systematically quantifying any biases that arise from the varying cellular abundances of the proteins. We confirm that PPI networks determined using affinity purification methods for yeast and Escherichia coli incorporate a correlation between protein degree, or number of interactions, and cellular abundance. The observed correlations are small but statistically significant and occur in both unprocessed (raw) and processed (high-confidence) data sets. In contrast, the yeast two-hybrid system yields networks that contain no such relationship. While previously commented based on mRNA abundance, our more extensive analysis based on protein abundance confirms a systematic difference between PPI networks determined from the two technologies. We additionally demonstrate that the centrality-lethality rule, which implies that higher-degree proteins are more likely to be essential, may be misleading, as protein abundance measurements identify essential proteins to be more prevalent than nonessential proteins. In fact, we generally find that when there is a degree/abundance correlation, the degree distributions of nonessential and essential proteins are also disparate. Conversely, when there is no degree/abundance correlation, the degree distributions of nonessential and essential proteins are not different. However, we show that essentiality manifests itself as a biological property in all of the yeast PPI networks investigated here via enrichments of interactions between essential proteins. These findings provide valuable insights into the underlying natures of the various high-throughput technologies utilized to detect PPIs and should lead to more effective strategies for the inference and analysis of high-quality PPI data sets.

Network analysis of human glaucomatous optic nerve head astrocytes

Background

Astrocyte activation is a characteristic response to injury in the central nervous system, and can be either neurotoxic or neuroprotective, while the regulation of both roles remains elusive.

Methods

To decipher the regulatory elements controlling astrocyte-mediated neurotoxicity in glaucoma, we conducted a systems-level functional analysis of gene expression, proteomic and genetic data associated with reactive optic nerve head astrocytes (ONHAs).

Results

Our reconstruction of the molecular interactions affected by glaucoma revealed multi-domain biological networks controlling activation of ONHAs at the level of intercellular stimuli, intracellular signaling and core effectors. The analysis revealed that synergistic action of the transcription factors AP-1, vitamin D receptor and Nuclear Factor-kappaB in cross-activation of multiple pathways, including inflammatory cytokines, complement, clusterin, ephrins, and multiple metabolic pathways. We found that the products of over two thirds of genes linked to glaucoma by genetic analysis can be functionally interconnected into one epistatic network via experimentally-validated interactions. Finally, we built and analyzed an integrative disease pathology network from a combined set of genes revealed in genetic studies, genes differentially expressed in glaucoma and closely connected genes/proteins in the interactome.

Conclusion

Our results suggest several key biological network modules that are involved in regulating neurotoxicity of reactive astrocytes in glaucoma, and comprise potential targets for cell-based therapy.