Systems Pharmacology Links GPCRs with Retinal Degenerative Disorders.

Annu Rev Pharmacol Toxicol. 2016;56:273-98

Authors: Chen Y, Palczewski K

Abstract
In most biological systems, second messengers and their key regulatory and effector proteins form links between multiple cellular signaling pathways. Such signaling nodes can integrate the deleterious effects of genetic aberrations, environmental stressors, or both in complex diseases, leading to cell death by various mechanisms. Here we present a systems (network) pharmacology approach that, together with transcriptomics analyses, was used to identify different G protein-coupled receptors that experimentally protected against cellular stress and death caused by linked signaling mechanisms. We describe the application of this concept to degenerative and diabetic retinopathies in appropriate mouse models as an example. Systems pharmacology also provides an attractive framework for devising strategies to combat complex diseases by using (repurposing) US Food and Drug Administration-approved pharmacological agents.

PMID: 25839098 [PubMed - indexed for MEDLINE]

Quantitative proteomic analysis reveals a simple strategy of global resource allocation in bacteria.

Mol Syst Biol. 2015 Jan;11(1):784

Authors: Hui S, Silverman JM, Chen SS, Erickson DW, Basan M, Wang J, Hwa T, Williamson JR

Abstract
A central aim of cell biology was to understand the strategy of gene expression in response to the environment. Here, we study gene expression response to metabolic challenges in exponentially growing Escherichia coli using mass spectrometry. Despite enormous complexity in the details of the underlying regulatory network, we find that the proteome partitions into several coarse-grained sectors, with each sector's total mass abundance exhibiting positive or negative linear relations with the growth rate. The growth rate-dependent components of the proteome fractions comprise about half of the proteome by mass, and their mutual dependencies can be characterized by a simple flux model involving only two effective parameters. The success and apparent generality of this model arises from tight coordination between proteome partition and metabolism, suggesting a principle for resource allocation in proteome economy of the cell. This strategy of global gene regulation should serve as a basis for future studies on gene expression and constructing synthetic biological circuits. Coarse graining may be an effective approach to derive predictive phenomenological models for other 'omics' studies.

PMID: 25678603 [PubMed - indexed for MEDLINE]

12 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

"Systems Biology"[Title/Abstract] AND ("2005/01/01"[PDAT] : "3000"[PDAT])

These pubmed results were generated on 2016/09/30

PubMed comprises more than 24 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

12 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

"Systems Biology"[Title/Abstract] AND ("2005/01/01"[PDAT] : "3000"[PDAT])

These pubmed results were generated on 2016/09/28

PubMed comprises more than 24 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

15 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

"Systems Biology"[Title/Abstract] AND ("2005/01/01"[PDAT] : "3000"[PDAT])

These pubmed results were generated on 2016/09/27

PubMed comprises more than 24 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

The Power of OMICs.

Biochem Biophys Res Commun. 2016 Sep 20;

Authors: Stagljar I

Abstract
Over the past two decades, the field of systems biology, which encompasses the numerous, widely popular "OMICs" approaches, has driven many significant advances in biomedical research, enabling researchers to generate huge datasets at multiple levels of biological organization. Despite such successes, some scientists still think that "OMICs"-based research introduces a lot of chaos into the biomedical field and claim that the resultant data are often not reproducible and do not reveal deep mechanistic aspects of biological processes. In this editorial, I argue the following: first, that "OMICs" technologies have improved significantly to yield much better datasets; and second, that follow-up studies on components identified in "OMICs" analyses have yielded many valuable biological insights.

PMID: 27663662 [PubMed - as supplied by publisher]

The effect of inhibition of PP1 and TNFα signaling on pathogenesis of SARS coronavirus.

BMC Syst Biol. 2016;10(1):93

Authors: McDermott JE, Mitchell HD, Gralinski LE, Eisfeld AJ, Josset L, Bankhead A, Neumann G, Tilton SC, Schäfer A, Li C, Fan S, McWeeney S, Baric RS, Katze MG, Waters KM

Abstract
BACKGROUND: The complex interplay between viral replication and host immune response during infection remains poorly understood. While many viruses are known to employ anti-immune strategies to facilitate their replication, highly pathogenic virus infections can also cause an excessive immune response that exacerbates, rather than reduces pathogenicity. To investigate this dichotomy in severe acute respiratory syndrome coronavirus (SARS-CoV), we developed a transcriptional network model of SARS-CoV infection in mice and used the model to prioritize candidate regulatory targets for further investigation.
RESULTS: We validated our predictions in 18 different knockout (KO) mouse strains, showing that network topology provides significant predictive power to identify genes that are important for viral infection. We identified a novel player in the immune response to virus infection, Kepi, an inhibitory subunit of the protein phosphatase 1 (PP1) complex, which protects against SARS-CoV pathogenesis. We also found that receptors for the proinflammatory cytokine tumor necrosis factor alpha (TNFα) promote pathogenesis, presumably through excessive inflammation.
CONCLUSIONS: The current study provides validation of network modeling approaches for identifying important players in virus infection pathogenesis, and a step forward in understanding the host response to an important infectious disease. The results presented here suggest the role of Kepi in the host response to SARS-CoV, as well as inflammatory activity driving pathogenesis through TNFα signaling in SARS-CoV infections. Though we have reported the utility of this approach in bacterial and cell culture studies previously, this is the first comprehensive study to confirm that network topology can be used to predict phenotypes in mice with experimental validation.

PMID: 27663205 [PubMed - as supplied by publisher]

Compact and highly active next-generation libraries for CRISPR-mediated gene repression and activation.

Elife. 2016 Sep 23;5

Authors: Horlbeck MA, Gilbert LA, Villalta JE, Adamson B, Pak RA, Chen Y, Fields AP, Park CY, Corn JE, Kampmann M, Weissman JS

Abstract
We recently found that nucleosomes directly block access of CRISPR/Cas9 to DNA (Horlbeck et al., 2016). Here, we build on this observation with a comprehensive algorithm that incorporates chromatin, position, and sequence features to accurately predict highly effective single guide RNAs (sgRNAs) for targeting nuclease-dead Cas9-mediated transcriptional repression (CRISPRi) and activation (CRISPRa). We use this algorithm to design next-generation genome-scale CRISPRi and CRISPRa libraries targeting human and mouse genomes. A CRISPRi screen for essential genes in K562 cells demonstrates that the large majority of sgRNAs are highly active. We also find CRISPRi does not exhibit any detectable non-specific toxicity recently observed with CRISPR nuclease approaches. Precision-recall analysis shows that we detect over 90% of essential genes with minimal false positives using a compact 5 sgRNA/gene library. Our results establish CRISPRi and CRISPRa as premier tools for loss- or gain-of-function studies and provide a general strategy for identifying Cas9 target sites.

PMID: 27661255 [PubMed - as supplied by publisher]

The global effect of exposing bakers' yeast to 5-fluoruracil and nystatin; a view to Toxichip.

Chemosphere. 2016 Feb;145:470-9

Authors: Graziano S, Gullì M, Maestri E, Marmiroli N

Abstract
A genome-wide screen of a haploid deletion library of bakers' yeast (Saccharomyces cerevisiae) was conducted to document the phenotypic and transcriptional impact of exposure to each of the two pharmaceutical products 5-fluorouracil (an anti-tumor agent) and nystatin (an anti-fungal agent). The combined data set was handled by applying a systems biology perspective. A Gene Ontology analysis identified functional categories previously characterized as likely targets for both compounds. Induced transcription profiles were well correlated in yeast and human HepG2 cells. The identified molecular targets for both compounds were used to suggest a small set of human orthologues as appropriate for testing on human material. The yeast system developed here (denoted "Toxichip") has likely utility for identifying biomarkers relevant for health and environmental risk assessment applications required as part of the development process for novel pharmaceuticals.

PMID: 26694798 [PubMed - indexed for MEDLINE]

Promise and Pragmatism in Clinical Microbiome Research.

Mini Rev Med Chem. 2015;16(3):222-4

Authors: Ajami NJ, Hutchinson DS, Petrosino JF

Abstract
The evolution of human microbiome research has lead to a systems biology approach that encompasses multidisciplinary investigations. The implementation of next generation sequencing technologies has allowed researchers to study unculturable organisms, discover novel ones, and provide insights into the role of the human microbiome in health and disease. When these approaches are applied to large-scale longitudinal studies designed to interrogate the association of the microbiome with specific clinical outcomes, the development of new therapeutics and diagnostics intended to modulate or detect changes in microbiome composition to improve human health are born. We are just starting to unravel the role of the microbiome in a wide-variety of diseases, and while some of it appears to be related to causation and provide opportunities for intervention, a good dose of pragmatism is warranted as the field is still in its infancy.

PMID: 26202196 [PubMed - indexed for MEDLINE]

Systems Biology and Biomarkers of Early Effects for Occupational Exposure Limit Setting.

J Occup Environ Hyg. 2015;12 Suppl 1:S41-54

Authors: DeBord DG, Burgoon L, Edwards SW, Haber LT, Kanitz MH, Kuempel E, Thomas RS, Yucesoy B

Abstract
In a recent National Research Council document, new strategies for risk assessment were described to enable more accurate and quicker assessments. This report suggested that evaluating individual responses through increased use of bio-monitoring could improve dose-response estimations. Identification of specific biomarkers may be useful for diagnostics or risk prediction as they have the potential to improve exposure assessments. This paper discusses systems biology, biomarkers of effect, and computational toxicology approaches and their relevance to the occupational exposure limit setting process. The systems biology approach evaluates the integration of biological processes and how disruption of these processes by chemicals or other hazards affects disease outcomes. This type of approach could provide information used in delineating the mode of action of the response or toxicity, and may be useful to define the low adverse and no adverse effect levels. Biomarkers of effect are changes measured in biological systems and are considered to be preclinical in nature. Advances in computational methods and experimental -omics methods that allow the simultaneous measurement of families of macromolecules such as DNA, RNA, and proteins in a single analysis have made these systems approaches feasible for broad application. The utility of the information for risk assessments from -omics approaches has shown promise and can provide information on mode of action and dose-response relationships. As these techniques evolve, estimation of internal dose and response biomarkers will be a critical test of these new technologies for application in risk assessment strategies. While proof of concept studies have been conducted that provide evidence of their value, challenges with standardization and harmonization still need to be overcome before these methods are used routinely.

PMID: 26132979 [PubMed - indexed for MEDLINE]

13 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

"Systems Biology"[Title/Abstract] AND ("2005/01/01"[PDAT] : "3000"[PDAT])

These pubmed results were generated on 2016/09/23

PubMed comprises more than 24 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

Systemic therapies in neuroendocrine tumors and novel approaches towards personalised medicine.

Endocr Relat Cancer. 2016 Sep 20;

Authors: Pavel M, Sers C

Abstract
Neuroendocrine tumors (NET) are a group of heterogenous neoplasms. Evidence-based treatment options for antiproliferative therapy include somatostatin analogs, the mTOR inhibitor everolimus, the multiple tyrosine kinase inhibitor sunitinib and peptide receptor radionuclide therapy with 177-Lu-octreotate. In the absence of definite predictive markers therapeutic decision making follows clinical and pathological criteria. Since objective reponse rates with targeted drugs are rather low, and response duration is limited in most patients, numerous combination therapies targeting multiple pathways have been explored in the field. Upfront combination of drugs, however, is associated with increasing toxicity and has shown little benefit. Major advancements in the molecular understanding of NET based on genomic, epigenomic, and transcriptomic analysis have been achieved with prognostic and therapeutic impact. New insight into molecular alterations has paved the way to biomarker-driven clinical trials and may facilitate treatment stratification towards personalized medicine in the near future. However, an improved understandiing of the complexity of pathway interactions is required for successful treatment. A systems biology approach is one of the tools that may help to achieve this endeavour.

PMID: 27649723 [PubMed - as supplied by publisher]

iTAP: integrated transcriptomics and phenotype database for stress response of Escherichia coli and Saccharomyces cerevisiae.

BMC Res Notes. 2015;8:771

Authors: Sundararaman N, Ash C, Guo W, Button R, Singh J, Feng X

Abstract
BACKGROUND: Organisms are subject to various stress conditions, which affect both the organism's gene expression and phenotype. It is critical to understand microbial responses to stress conditions and uncover the underlying molecular mechanisms. To this end, it is necessary to build a database that collects transcriptomics and phenotypic data of microbes growing under various stress factors for in-depth systems biology analysis. Despite of numerous databases that collect gene expression profiles, to our best knowledge, there are few, if any, databases that collect both transcriptomics and phenotype data simultaneously. In light of this, we have developed an open source, web-based database, namely integrated transcriptomics and phenotype (iTAP) database, that records and links the transcriptomics and phenotype data for two model microorganisms, Escherichia coli and Saccharomyces cerevisiae in response to exposure of various stress conditions.
RESULTS: To collect the data, we chose relevant research papers from the PubMed database containing all the necessary information for data curation including experimental conditions, transcriptomics data, and phenotype data. The transcriptomics data, including the p value and fold change, were obtained through the comparison of test strains against control strains using Gene Expression Omnibus's GEO2R analyzer. The phenotype data, including the cell growth rate and the productivity, volumetric rate, and mass-based yield of byproducts, were calculated independently from charts or graphs within the reference papers. Since the phenotype data was never reported in a standardized format, the curation of correlated transcriptomics-phenotype datasets became extremely tedious and time-consuming. Despite the challenges, till now, we successfully correlated 57 and 143 datasets of transcriptomics and phenotype for E. coli and S. cerevisiae, respectively, and applied a regression model within the iTAP database to accurately predict over 93 and 73 % of the growth rates of E. coli and S. cerevisiae, respectively, directly from the transcriptomics data.
CONCLUSION: This is the first time that transcriptomics and phenotype data are categorized and correlated in an open-source database. This allows biologists to access the database and utilize it to predict the phenotype of microorganisms from their transcriptomics data. The iTAP database is freely available at https://sites.google.com/a/vt.edu/biomolecular-engineering-lab/software .

PMID: 26653323 [PubMed - indexed for MEDLINE]

Reproducible quantitative proteotype data matrices for systems biology.

Mol Biol Cell. 2015 Nov 5;26(22):3926-31

Authors: Röst HL, Malmström L, Aebersold R

Abstract
Historically, many mass spectrometry-based proteomic studies have aimed at compiling an inventory of protein compounds present in a biological sample, with the long-term objective of creating a proteome map of a species. However, to answer fundamental questions about the behavior of biological systems at the protein level, accurate and unbiased quantitative data are required in addition to a list of all protein components. Fueled by advances in mass spectrometry, the proteomics field has thus recently shifted focus toward the reproducible quantification of proteins across a large number of biological samples. This provides the foundation to move away from pure enumeration of identified proteins toward quantitative matrices of many proteins measured across multiple samples. It is argued here that data matrices consisting of highly reproducible, quantitative, and unbiased proteomic measurements across a high number of conditions, referred to here as quantitative proteotype maps, will become the fundamental currency in the field and provide the starting point for downstream biological analysis. Such proteotype data matrices, for example, are generated by the measurement of large patient cohorts, time series, or multiple experimental perturbations. They are expected to have a large effect on systems biology and personalized medicine approaches that investigate the dynamic behavior of biological systems across multiple perturbations, time points, and individuals.

PMID: 26543201 [PubMed - indexed for MEDLINE]

A comprehensive model to predict mitotic division in budding yeasts.

Mol Biol Cell. 2015 Nov 5;26(22):3954-65

Authors: Sutradhar S, Yadav V, Sridhar S, Sreekumar L, Bhattacharyya D, Ghosh SK, Paul R, Sanyal K

Abstract
High-fidelity chromosome segregation during cell division depends on a series of concerted interdependent interactions. Using a systems biology approach, we built a robust minimal computational model to comprehend mitotic events in dividing budding yeasts of two major phyla: Ascomycota and Basidiomycota. This model accurately reproduces experimental observations related to spindle alignment, nuclear migration, and microtubule (MT) dynamics during cell division in these yeasts. The model converges to the conclusion that biased nucleation of cytoplasmic microtubules (cMTs) is essential for directional nuclear migration. Two distinct pathways, based on the population of cMTs and cortical dyneins, differentiate nuclear migration and spindle orientation in these two phyla. In addition, the model accurately predicts the contribution of specific classes of MTs in chromosome segregation. Thus we present a model that offers a wider applicability to simulate the effects of perturbation of an event on the concerted process of the mitotic cell division.

PMID: 26310442 [PubMed - indexed for MEDLINE]

A stepwise integrated approach to personalized risk predictions in stage III colorectal cancer.

Clin Cancer Res. 2016 Sep 20;

Authors: Salvucci M, Würstle ML, Morgan C, Curry S, Cremona M, Lindner AU, Bacon O, Resler AJ, Murphy AC, O'Byrne R, Flanagan L, Dasgupta S, Rice N, Pilati C, Zink E, Schöller LM, Toomey S, Lawler M, Johnston PG, Wilson RH, Camilleri-Broët S, Salto-Tellez M, McNamara DA, Kay EW, Laurent-Puig P, Van Schaeybroeck S, Hennessy BT, Longley DB, Rehm M, Prehn JH

Abstract
PURPOSE: Apoptosis is essential for chemotherapy responses. In this discovery and validation study, we evaluated the suitability of a mathematical model of apoptosis execution (APOPTO-CELL) as a stand-alone signature and as a constituent of further refined prognostic stratification tools.
EXPERIMENTAL DESIGN: Apoptosis competency of primary tumor samples from n=120 stage III colorectal cancer patients was calculated by APOPTO-CELL from measured protein concentrations of Procaspase-3, Procaspase-9, SMAC and XIAP. An enriched APOPTO-CELL signature (APOPTO-CELL-PC3) was synthesized to capture apoptosome-independent effects of Caspase-3. Furthermore, a machine learning Random Forest approach was applied to APOPTO-CELL-PC3 and available molecular and clinicopathological data to identify a further enhanced signature. Association of the signature with prognosis was evaluated in an independent colon adenocarcinoma cohort (TCGA COAD, n=136).
RESULTS: We identified three prognostic biomarkers (p=0.04, p=0.006 and p=0.0004 for APOPTO-CELL, APOPTO-CELL-PC3 and Random Forest signatures, respectively) with increasing stratification accuracy for stage III colorectal cancer patients. The APOPTO-CELL-PC3 signature ranked highest among all features. The prognostic value of the signatures was independently validated in stage III TCGA COAD patients (p=0.01, p=0.04 and p=0.02 for APOPTO-CELL, APOPTO-CELL-PC3 and Random Forest signatures, respectively). The signatures provided further stratification for patients of CMS1-3 molecular subtype.
CONCLUSIONS: The integration of a systems-biology-based biomarker for apoptosis competency with machine learning approaches is an appealing and innovative strategy towards refined patient stratification. The prognostic value of apoptosis competency is independent of other available clinicopathological and molecular factors, with tangible potential of being introduced in the clinical management of stage III colorectal patients.

PMID: 27649552 [PubMed - as supplied by publisher]

A systems biology approach to identify niche determinants of cellular phenotypes.

Stem Cell Res. 2016 Sep 15;17(2):406-412

Authors: Ravichandran S, Okawa S, Martínez Arbas S, Del Sol A

Abstract
Recent reports indicate a dominant role for cellular microenvironment or niche for stably maintaining cellular phenotypic states. Identification of key niche mediated signaling that maintains stem cells in specific phenotypic states remains a challenge, mainly due to the complex and dynamic nature of stem cell-niche interactions. In order to overcome this, we consider that stem cells maintain their phenotypic state by experiencing a constant effect created by the niche by integrating its signals via signaling pathways. Such a constant niche effect should induce sustained activation/inhibition of specific stem cell signaling pathways that controls the gene regulatory program defining the cellular phenotypic state. Based on this view, we propose a computational approach to identify the most likely receptor mediated signaling responsible for transmitting niche signals to the transcriptional regulatory network that maintain cell-specific gene expression patterns, termed as niche determinants. We demonstrate the utility of our method in different stem cell systems by identifying several known and novel niche determinants. Given the key role of niche in several degenerative diseases, identification of niche determinants can aid in developing strategies for potential applications in regenerative medicine.

PMID: 27649532 [PubMed - as supplied by publisher]

Differential tandem mass spectrometry-based cross-linker: a new approach for high confidence in identifying protein cross-linking.

Anal Chem. 2016 Sep 20;

Authors: Chakrabarty JK, Naik AG, Fessler MB, Munske GR, Chowdhury SM

Abstract
Chemical cross-linking and mass spectrometry are now widely used to analyze large-scale protein-protein interactions. The major challenge in cross-linking approaches is the complexity of the mass spectrometric data. New approaches are required that can identify cross-linked peptides with high-confidence and establish a user-friendly analysis protocol for the biomedical scientific community. Here, we introduce a novel cross-linker that can be selectively cleaved in the gas phase using two differential tandem mass-spectrometric fragmentation methods, such as collision-induced or electron transfer dissociation (CID and ETD). This technique produces two signature mass spectra of the same cross-linked peptide, thereby producing high confidence in identifying the sites of interaction. Further tandem mass spectrometry can also give additional confidence on the peptide sequences. We demonstrate a proof-of-concept for this method using standard peptides and proteins. Peptides and proteins were cross-linked and their fragmentation characteristics were analyzed using CID and ETD tandem mass spectrometry. Two sequential cleavages unambiguously identified cross-linked peptides. In addition, the labeling efficiency of the new cross-linker was evaluated in macrophage immune cells after stimulation with the microbial ligand lipopolysaccharide and subsequent pulldown experiments with biotin-avidin affinity chromatography. We believe this strategy will help advance insights into the structural biology and systems biology of cell signaling.

PMID: 27649375 [PubMed - as supplied by publisher]

Bayesian model for detection and classification of meningioma nuclei in microscopic images.

J Microsc. 2016 Sep 20;

Authors: Wirjadi O, Kim YJ, Stech F, Bonfert L, Wagner M

Abstract
Image segmentation aims to determine structures of interest inside a digital picture in biomedical sciences. State-of-the art automatic methods however still fail to provide the segmentation quality achievable by humans who employ expert knowledge and use software to mark target structures on an image. Manual segmentation is time-consuming, tedious and suffers from interoperator variability, thus not serving the requirements of daily use well. Therefore, the approach presented here abandons the goal of full-fledged segmentation and settles for the localization of circular objects in photographs (10 training images and 20 testing images with several hundreds of nuclei each). A fully trainable softcore interaction point process model was hence fit to the most likely locations of nuclei of meningioma cells. The Broad Bioimage Benchmark Collection/SIMCEP data set of virtual cells served as controls. A 'colour deconvolution' algorithm was integrated to determine (based on anti-Ki67 immunohistochemistry) which real cells might have the potential to proliferate. In addition, a density parameter of the underlying Bayesian model was estimated. Immunohistochemistry results were 'simulated'for the virtual cells. The system yielded true positive (TP) rates in the detection and classification of real nuclei and their virtual counterparts. These hits outnumbered those obtained from the public domain image processing software ImageJ by 10%. The method introduced here can be trained to function not only in medicine and morphology-based systems biology but in other application domains as well. The algorithm lends itself to an automated approach that constitutes a valuable tool which is easy to use and generates acceptable results quickly.

PMID: 27649284 [PubMed - as supplied by publisher]