Missing Pieces in the Puzzle of Plant MicroRNAs.

Trends Plant Sci. 2015 Nov;20(11):721-8

Authors: Reis RS, Eamens AL, Waterhouse PM

Abstract
Plant microRNAs (miRNAs) are important regulatory switches. Recent advances have revealed many regulatory layers between the two essential processes, miRNA biogenesis and function. However, how these multilayered regulatory processes ultimately control miRNA gene regulation and connects miRNAs and plant responses with the surrounding environment is still largely unknown. In this opinion article, we propose that the miRNA pathway is highly dynamic and plastic. The apparent flexibility of the miRNA pathway in plants appears to be controlled by a number recently identified proteins and poorly characterized signaling cascades. We further propose that altered miRNA accumulation can be a direct consequence of the rewiring of interactions between proteins that function in the miRNA pathway, an avenue that remains largely unexplored.

PMID: 26442682 [PubMed - indexed for MEDLINE]

Knowns and unknowns in metabolomics identified by multidimensional NMR and hybrid MS/NMR methods.

Curr Opin Biotechnol. 2016 Aug 20;43:17-24

Authors: Bingol K, Brüschweiler R

Abstract
Metabolomics continues to make rapid progress through the development of new and better methods and their applications to gain insight into the metabolism of a wide range of different biological systems from a systems biology perspective. Customization of NMR databases and search tools allows the faster and more accurate identification of known metabolites, whereas the identification of unknowns, without a need for extensive purification, requires new strategies to integrate NMR with mass spectrometry, cheminformatics, and computational methods. For some applications, the use of covalent and non-covalent attachments in the form of labeled tags or nanoparticles can significantly reduce the complexity of these tasks.

PMID: 27552705 [PubMed - as supplied by publisher]

Transcriptional landscapes at the intersection of neuronal apoptosis and substance P-induced survival: exploring pathways and drug targets.

Cell Death Discov. 2016;2:16050

Authors: Paparone S, Severini C, Ciotti MT, D'Agata V, Calissano P, Cavallaro S

Abstract
A change in the delicate equilibrium between apoptosis and survival regulates the neurons fate during the development of nervous system and its homeostasis in adulthood. Signaling pathways promoting or protecting from apoptosis are activated by multiple signals, including those elicited by neurotrophic factors, and depend upon specific transcriptional programs. To decipher the rescue program induced by substance P (SP) in cerebellar granule neurons, we analyzed their whole-genome expression profiles after induction of apoptosis and treatment with SP. Transcriptional pathways associated with the survival effect of SP included genes encoding for proteins that may act as pharmacological targets. Inhibition of one of these, the Myc pro-oncogene by treatment with 10058-F4, reverted in a dose-dependent manner the rescue effect of SP. In addition to elucidate the transcriptional mechanisms at the intersection of neuronal apoptosis and survival, our systems biology-based perspective paves the way towards an innovative pharmacology based on targets downstream of neurotrophic factor receptors.

PMID: 27551538 [PubMed]

pH determines the energetic efficiency of the cyanobacterial CO2 concentrating mechanism.

Proc Natl Acad Sci U S A. 2016 Aug 22;

Authors: Mangan NM, Flamholz A, Hood RD, Milo R, Savage DF

Abstract
Many carbon-fixing bacteria rely on a CO2 concentrating mechanism (CCM) to elevate the CO2 concentration around the carboxylating enzyme ribulose bisphosphate carboxylase/oxygenase (RuBisCO). The CCM is postulated to simultaneously enhance the rate of carboxylation and minimize oxygenation, a competitive reaction with O2 also catalyzed by RuBisCO. To achieve this effect, the CCM combines two features: active transport of inorganic carbon into the cell and colocalization of carbonic anhydrase and RuBisCO inside proteinaceous microcompartments called carboxysomes. Understanding the significance of the various CCM components requires reconciling biochemical intuition with a quantitative description of the system. To this end, we have developed a mathematical model of the CCM to analyze its energetic costs and the inherent intertwining of physiology and pH. We find that intracellular pH greatly affects the cost of inorganic carbon accumulation. At low pH the inorganic carbon pool contains more of the highly cell-permeable H2CO3, necessitating a substantial expenditure of energy on transport to maintain internal inorganic carbon levels. An intracellular pH ≈8 reduces leakage, making the CCM significantly more energetically efficient. This pH prediction coincides well with our measurement of intracellular pH in a model cyanobacterium. We also demonstrate that CO2 retention in the carboxysome is necessary, whereas selective uptake of HCO3 (-) into the carboxysome would not appreciably enhance energetic efficiency. Altogether, integration of pH produces a model that is quantitatively consistent with cyanobacterial physiology, emphasizing that pH cannot be neglected when describing biological systems interacting with inorganic carbon pools.

PMID: 27551079 [PubMed - as supplied by publisher]

Paradoxical signaling regulates structural plasticity in dendritic spines.

Proc Natl Acad Sci U S A. 2016 Aug 22;

Authors: Rangamani P, Levy MG, Khan S, Oster G

Abstract
Transient spine enlargement (3- to 5-min timescale) is an important event associated with the structural plasticity of dendritic spines. Many of the molecular mechanisms associated with transient spine enlargement have been identified experimentally. Here, we use a systems biology approach to construct a mathematical model of biochemical signaling and actin-mediated transient spine expansion in response to calcium influx caused by NMDA receptor activation. We have identified that a key feature of this signaling network is the paradoxical signaling loop. Paradoxical components act bifunctionally in signaling networks, and their role is to control both the activation and the inhibition of a desired response function (protein activity or spine volume). Using ordinary differential equation (ODE)-based modeling, we show that the dynamics of different regulators of transient spine expansion, including calmodulin-dependent protein kinase II (CaMKII), RhoA, and Cdc42, and the spine volume can be described using paradoxical signaling loops. Our model is able to capture the experimentally observed dynamics of transient spine volume. Furthermore, we show that actin remodeling events provide a robustness to spine volume dynamics. We also generate experimentally testable predictions about the role of different components and parameters of the network on spine dynamics.

PMID: 27551076 [PubMed - as supplied by publisher]

TRIENNIAL LACTATION SYMPOSIUM: Systems biology of regulatory mechanisms of nutrient metabolism in lactation.

J Anim Sci. 2015 Dec;93(12):5575-85

Authors: McNamara JP

Abstract
A major role of the dairy cow is to convert low-quality plant materials into high-quality protein and other nutrients for humans. We must select and manage cows with the goal of having animals of the greatest efficiency matched to their environment. We have increased efficiency tremendously over the years, yet the variation in productive and reproductive efficiency among animals is still large. In part, this is because of a lack of full integration of genetic, nutritional, and reproductive biology into management decisions. However, integration across these disciplines is increasing as the biological research findings show specific control points at which genetics, nutrition, and reproduction interact. An ordered systems biology approach that focuses on why and how cells regulate energy and N use and on how and why organs interact through endocrine and neurocrine mechanisms will speed improvements in efficiency. More sophisticated dairy managers will demand better information to improve the efficiency of their animals. Using genetic improvement and animal management to improve milk productive and reproductive efficiency requires a deeper understanding of metabolic processes throughout the life cycle. Using existing metabolic models, we can design experiments specifically to integrate data from global transcriptional profiling into models that describe nutrient use in farm animals. A systems modeling approach can help focus our research to make faster and larger advances in efficiency and determine how this knowledge can be applied on the farms.

PMID: 26641166 [PubMed - indexed for MEDLINE]

TRIENNIAL LACTATION SYMPOSIUM: Nutrigenomics in livestock: Systems biology meets nutrition.

J Anim Sci. 2015 Dec;93(12):5554-74

Authors: Loor JJ, Vailati-Riboni M, McCann JC, Zhou Z, Bionaz M

Abstract
The advent of high-throughput technologies to study an animal's genome, proteome, and metabolome (i.e., "omics" tools) constituted a setback to the use of reductionism in livestock research. More recent development of "next-generation sequencing" tools was instrumental in allowing in-depth studies of the microbiome in the rumen and other sections of the gastrointestinal tract. Omics, along with bioinformatics, constitutes the foundation of modern systems biology, a field of study widely used in model organisms (e.g., rodents, yeast, humans) to enhance understanding of the complex biological interactions occurring within cells and tissues at the gene, protein, and metabolite level. Application of systems biology concepts is ideal for the study of interactions between nutrition and physiological state with tissue and cell metabolism and function during key life stages of livestock species, including the transition from pregnancy to lactation, in utero development, or postnatal growth. Modern bioinformatic tools capable of discerning functional outcomes and biologically meaningful networks complement the ever-increasing ability to generate large molecular, microbial, and metabolite data sets. Simultaneous visualization of the complex intertissue adaptations to physiological state and nutrition can now be discerned. Studies to understand the linkages between the microbiome and the absorptive epithelium using the integrative approach are emerging. We present examples of new knowledge generated through the application of functional analyses of transcriptomic, proteomic, and metabolomic data sets encompassing nutritional management of dairy cows, pigs, and poultry. Published work to date underscores that the integrative approach across and within tissues may prove useful for fine-tuning nutritional management of livestock. An important goal during this process is to uncover key molecular players involved in the organismal adaptations to nutrition.

PMID: 26641165 [PubMed - indexed for MEDLINE]

Dual Protective and Cytotoxic Benefits of Mesenchymal Stem Cell Therapy in Combination with Chemotherapy/Radiotherapy for Cancer Patients.

Crit Rev Eukaryot Gene Expr. 2015;25(3):203-7

Authors: Hendijani F, Javanmard SH

Abstract
Cancer is a major health problem in the world, and scientists seek innovative treatment strategies with higher efficacy and lower toxicity than the existing therapeutic agents. In this way, stem cell researchers try to reveal new pathways that will eventually benefit patients. Stem cell research has proven that mesenchymal stem cells (MSCs) possess anticancer activities, and their protein-rich secretome showed similar effects. MSCs also secrete cytokines that play an active role in healing and regeneration processes. Because of their known plasticity, MSCs display a variety of characteristics and functions in different environments, depending on their interactions with various cell types and tissues. Therefore, we hypothesize that MSC therapy in combination with anticancer medicines can potentiate cytotoxic effects on cancer cells. In addition, because of their regenerative capacity, MSCs can protect normal tissues from adverse cytotoxic drug reactions. They may also help rescue injured tissues from these toxic damages or systemic pathological events that occur during cancer treatment. MSC therapy may double the beneficial effects on cancer and normal cells. As our knowledge of systems biology and biotechnological methodology is progressing, this idea can move forward as a treatment option.

PMID: 26558944 [PubMed - indexed for MEDLINE]

20 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 2016/08/23

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.

Metabolic modelling in a dynamic evolutionary framework predicts adaptive diversification of bacteria in a long-term evolution experiment.

BMC Evol Biol. 2016;16(1):163

Authors: Großkopf T, Consuegra J, Gaffé J, Willison JC, Lenski RE, Soyer OS, Schneider D

Abstract
BACKGROUND: Predicting adaptive trajectories is a major goal of evolutionary biology and useful for practical applications. Systems biology has enabled the development of genome-scale metabolic models. However, analysing these models via flux balance analysis (FBA) cannot predict many evolutionary outcomes including adaptive diversification, whereby an ancestral lineage diverges to fill multiple niches. Here we combine in silico evolution with FBA and apply this modelling framework, evoFBA, to a long-term evolution experiment with Escherichia coli.
RESULTS: Simulations predicted the adaptive diversification that occurred in one experimental population and generated hypotheses about the mechanisms that promoted coexistence of the diverged lineages. We experimentally tested and, on balance, verified these mechanisms, showing that diversification involved niche construction and character displacement through differential nutrient uptake and altered metabolic regulation.
CONCLUSION: The evoFBA framework represents a promising new way to model biochemical evolution, one that can generate testable predictions about evolutionary and ecosystem-level outcomes.

PMID: 27544664 [PubMed - as supplied by publisher]

Networks and Games for Precision Medicine.

Biosystems. 2016 Aug 16;

Authors: Biane C, Delaplace F, Klaudel H

Abstract
Recent advances in omics technologies provide the leverage for the emergence of precision medicine that aims at personalizing therapy to patient. In this undertaking, computational methods play a central role for assisting physicians in their clinical decision-making by combining data analysis and systems biology modelling. Complex diseases such as cancer or diabetes arise from the intricate interplay of various biological molecules. Therefore, assessing drug efficiency requires to study the effects of elementary perturbations caused by diseases on relevant biological networks. In this paper, we propose a computational framework called Network-Action Game applied to best drug selection problem combining Game Theory and discrete models of dynamics (Boolean networks). Decision-making is modelled using Game Theory that defines the process of drug selection among alternative possibilities, while Boolean networks are used to model the effects of the interplay between disease and drugs actions on the patient's molecular system. The actions/strategies of disease and drugs are focused on arc alterations of the interactome. The efficiency of this framework has been evaluated for drug prediction on a model of breast cancer signalling.

PMID: 27543134 [PubMed - as supplied by publisher]

DEsubs: an R package for flexible identification of differentially expressed subpathways using RNA-seq experiments.

Bioinformatics. 2016 Aug 19;

Authors: Vrahatis AG, Balomenos P, Tsakalidis AK, Bezerianos A

Abstract
DEsubs is a network-based systems biology R package that extracts disease-perturbed subpathways within a pathway network as recorded by RNA-seq experiments. It contains an extensive and customized framework with a broad range of operation modes at all stages of the subpathway analysis, enabling so a case-specific approach. The operation modes include pathway network construction and processing, subpathway extraction, visualization and enrichment analysis with regard to various biological and pharmacological features. Its capabilities render it as a tool-guide for both the modeler and experimentalist for the identification of more robust systems-level drug targets and biomarkers for complex diseases.
AVAILABILITY AND IMPLEMENTATION: DEsubs is implemented as an R package following Bioconductor guidelines (permanently available in URL: http://biosignal.med.upatras.gr/wordpress/desubs).
CONTACT: tassos.bezerianos@nus.edu.sg SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID: 27542770 [PubMed - as supplied by publisher]

Robust de novo pathway enrichment with KeyPathwayMiner 5.

F1000Res. 2016;5:1531

Authors: Alcaraz N, List M, Dissing-Hansen M, Rehmsmeier M, Tan Q, Mollenhauer J, Ditzel HJ, Baumbach J

Abstract
Identifying functional modules or novel active pathways, recently termed de novo pathway enrichment, is a computational systems biology challenge that has gained much attention during the last decade. Given a large biological interaction network, KeyPathwayMiner extracts connected subnetworks that are enriched for differentially active entities from a series of molecular profiles encoded as binary indicator matrices. Since interaction networks constantly evolve, an important question is how robust the extracted results are when the network is modified. We enable users to study this effect through several network perturbation techniques and over a range of perturbation degrees. In addition, users may now provide a gold-standard set to determine how enriched extracted pathways are with relevant genes compared to randomized versions of the original network.

PMID: 27540470 [PubMed]

Not low hanging but still sweet: Metabolic proteomes in cardiovascular disease.

J Mol Cell Cardiol. 2016 Jan;90:70-3

Authors: Monte E, Lopez R, Vondriska TM

Abstract
The application of proteomics in biology and medicine has reached a moment of truth. The demand of biologists for transformative insights into how cells work, plus the mandate of basic science research to ultimately impact clinical medicine, crystallize as a test on the rigor and reproducibility of any 'omics measurement. Studies like that by Boylston et al. indicate that proteomics can pass that test.

PMID: 26611885 [PubMed - indexed for MEDLINE]

Coexpression Network Analysis of miRNA-142 Overexpression in Neuronal Cells.

Biomed Res Int. 2015;2015:921517

Authors: Thapa I, Fox HS, Bastola D

Abstract
MicroRNAs are small noncoding RNA molecules, which are differentially expressed in diverse biological processes and are also involved in the regulation of multiple genes. A number of sites in the 3' untranslated regions (UTRs) of different mRNAs allow complimentary binding for a microRNA, leading to their posttranscriptional regulation. The miRNA-142 is one of the microRNAs overexpressed in neurons that is found to regulate SIRT1 and MAOA genes. Differential analysis of gene expression data, which is focused on identifying up- or downregulated genes, ignores many relationships between genes affected by miRNA-142 overexpression in a cell. Thus, we applied a correlation network model to identify the coexpressed genes and to study the impact of miRNA-142 overexpression on this network. Combining multiple sources of knowledge is useful to infer meaningful relationships in systems biology. We applied coexpression model on the data obtained from wild type and miR-142 overexpression neuronal cells and integrated miRNA seed sequence mapping information to identify genes greatly affected by this overexpression. Larger differences in the enriched networks revealed that the nervous system development related genes such as TEAD2, PLEKHA6, and POGLUT1 were greatly impacted due to miRNA-142 overexpression.

PMID: 26539539 [PubMed - indexed for MEDLINE]

A systematic approach to prioritize drug targets using machine learning, a molecular descriptor-based classification model, and high-throughput screening of plant derived molecules: a case study in oral cancer.

Mol Biosyst. 2015 Dec;11(12):3362-77

Authors: Randhawa V, Kumar Singh A, Acharya V

Abstract
Systems-biology inspired identification of drug targets and machine learning-based screening of small molecules which modulate their activity have the potential to revolutionize modern drug discovery by complementing conventional methods. To utilize the effectiveness of such pipelines, we first analyzed the dysregulated gene pairs between control and tumor samples and then implemented an ensemble-based feature selection approach to prioritize targets in oral squamous cell carcinoma (OSCC) for therapeutic exploration. Based on the structural information of known inhibitors of CXCR4-one of the best targets identified in this study-a feature selection was implemented for the identification of optimal structural features (molecular descriptor) based on which a classification model was generated. Furthermore, the CXCR4-centered descriptor-based classification model was finally utilized to screen a repository of plant derived small-molecules to obtain potential inhibitors. The application of our methodology may assist effective selection of the best targets which may have previously been overlooked, that in turn will lead to the development of new oral cancer medications. The small molecules identified in this study can be ideal candidates for trials as potential novel anti-oral cancer agents. Importantly, distinct steps of this whole study may provide reference for the analysis of other complex human diseases.

PMID: 26467789 [PubMed - indexed for MEDLINE]

Determining Associations between Human Diseases and non-coding RNAs with Critical Roles in Network Control.

Sci Rep. 2015;5:14577

Authors: Kagami H, Akutsu T, Maegawa S, Hosokawa H, Nacher JC

Abstract
Deciphering the association between life molecules and human diseases is currently an important task in systems biology. Research over the past decade has unveiled that the human genome is almost entirely transcribed, producing a vast number of non-protein-coding RNAs (ncRNAs) with potential regulatory functions. More recent findings suggest that many diseases may not be exclusively linked to mutations in protein-coding genes. The combination of these arguments poses the question of whether ncRNAs that play a critical role in network control are also enriched with disease-associated ncRNAs. To address this question, we mapped the available annotated information of more than 350 human disorders to the largest collection of human ncRNA-protein interactions, which define a bipartite network of almost 93,000 interactions. Using a novel algorithmic-based controllability framework applied to the constructed bipartite network, we found that ncRNAs engaged in critical network control are also statistically linked to human disorders (P-value of P = 9.8 × 10(-109)). Taken together, these findings suggest that the addition of those genes that encode optimized subsets of ncRNAs engaged in critical control within the pool of candidate genes could aid disease gene prioritization studies.

PMID: 26459019 [PubMed - indexed for MEDLINE]

An integrated pathway interaction network for the combination of four effective compounds from ShengMai preparations in the treatment of cardio-cerebral ischemic diseases.

Acta Pharmacol Sin. 2015 Nov;36(11):1337-48

Authors: Li F, Lv YN, Tan YS, Shen K, Zhai KF, Chen HL, Kou JP, Yu BY

Abstract
AIM: SMXZF (a combination of ginsenoside Rb1, ginsenoside Rg1, schizandrin and DT-13) derived from Chinese traditional medicine formula ShengMai preparations) is capable of alleviating cerebral ischemia-reperfusion injury in mice. In this study we used network pharmacology approach to explore the mechanisms of SMXZF in the treatment of cardio-cerebral ischemic diseases.
METHODS: Based upon the chemical predictors, such as chemical structure, pharmacological information and systems biology functional data analysis, a target-pathway interaction network was constructed to identify potential pathways and targets of SMXZF in the treatment of cardio-cerebral ischemia. Furthermore, the most related pathways were verified in TNF-α-treated human vascular endothelial EA.hy926 cells and H2O2-treated rat PC12 cells.
RESULTS: Three signaling pathways including the NF-κB pathway, oxidative stress pathway and cytokine network pathway were demonstrated to be the main signaling pathways. The results from the gene ontology analysis were in accordance with these signaling pathways. The target proteins were found to be associated with other diseases such as vision, renal and metabolic diseases, although they exerted therapeutic actions on cardio-cerebral ischemic diseases. Furthermore, SMXZF not only dose-dependently inhibited the phosphorylation of NF-κB, p50, p65 and IKKα/β in TNF-α-treated EA.hy926 cells, but also regulated the Nrf2/HO-1 pathway in H2O2-treated PC12 cells.
CONCLUSION: NF-κB signaling pathway, oxidative stress pathway and cytokine network pathway are mainly responsible for the therapeutic actions of SMXZF against cardio-cerebral ischemic diseases.

PMID: 26456587 [PubMed - indexed for MEDLINE]

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 2016/08/19

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.

A Skyline Plugin for Pathway-Centric Data Browsing.

J Am Soc Mass Spectrom. 2016 Aug 16;

Authors: Degan MG, Ryadinskiy L, Fujimoto GM, Wilkins CS, Lichti CF, Payne SH

Abstract
For targeted proteomics to be broadly adopted in biological laboratories as a routine experimental protocol, wet-bench biologists must be able to approach selected reaction monitoring (SRM) and parallel reaction monitoring (PRM) assay design in the same way they approach biological experimental design. Most often, biological hypotheses are envisioned in a set of protein interactions, networks, and pathways. We present a plugin for the popular Skyline tool that presents public mass spectrometry data in a pathway-centric view to assist users in browsing available data and determining how to design quantitative experiments. Selected proteins and their underlying mass spectra are imported to Skyline for further assay design (transition selection). The same plugin can be used for hypothesis-driven data-independent acquisition (DIA) data analysis, again utilizing the pathway view to help narrow down the set of proteins that will be investigated. The plugin is backed by the Pacific Northwest National Laboratory (PNNL) Biodiversity Library, a corpus of 3 million peptides from >100 organisms, and the draft human proteome. Users can upload personal data to the plugin to use the pathway navigation prior to importing their own data into Skyline. Graphical Abstract ᅟ.

PMID: 27530777 [PubMed - as supplied by publisher]