Omics-based approaches to understand mechanosensitive endothelial biology and atherosclerosis.

Wiley Interdiscip Rev Syst Biol Med. 2016 Jun 24;

Authors: Simmons RD, Kumar S, Thabet SR, Sur S, Jo H

Abstract
Atherosclerosis is a multifactorial disease that preferentially occurs in arterial regions exposed to d-flow can be used to indicate disturbed flow or disturbed blood flow. The mechanisms by which d-flow induces atherosclerosis involve changes in the transcriptome, methylome, proteome, and metabolome of multiple vascular cells, especially endothelial cells. Initially, we begin with the pathogenesis of atherosclerosis and the changes that occur at multiple levels owing to d-flow, especially in the endothelium. Also, there are a variety of strategies used for the global profiling of the genome, transcriptome, miRNA-ome, DNA methylome, and metabolome that are important to define the biological and pathophysiological mechanisms of endothelial dysfunction and atherosclerosis. Finally, systems biology can be used to integrate these 'omics' datasets, especially those that derive data based on a single animal model, in order to better understand the pathophysiology of atherosclerosis development in a holistic manner and how this integrative approach could be used to identify novel molecular diagnostics and therapeutic targets to prevent or treat atherosclerosis. For further resources related to this article, please visit the WIREs website.

PMID: 27341633 [PubMed - as supplied by publisher]

A Bacterial Component to Alzheimer's-Type Dementia Seen via a Systems Biology Approach that Links Iron Dysregulation and Inflammagen Shedding to Disease.

J Alzheimers Dis. 2016 Jun 18;

Authors: Pretorius E, Bester J, Kell DB

Abstract
The progression of Alzheimer's disease (AD) is accompanied by a great many observable changes, both molecular and physiological. These include oxidative stress, neuroinflammation, and (more proximal to cognitive decline) the death of neuronal and other cells. A systems biology approach seeks to organize these observed variables into pathways that discriminate those that are highly involved (i.e., causative) from those that are more usefully recognized as bystander effects. We review the evidence that iron dysregulation is one of the central causative pathway elements here, as this can cause each of the above effects. In addition, we review the evidence that dormant, non-growing bacteria are a crucial feature of AD, that their growth in vivo is normally limited by a lack of free iron, and that it is this iron dysregulation that is an important factor in their resuscitation. Indeed, bacterial cells can be observed by ultrastructural microscopy in the blood of AD patients. A consequence of this is that the growing cells can shed highly inflammatory components such as lipopolysaccharides (LPS). These too are known to be able to induce (apoptotic and pyroptotic) neuronal cell death. There is also evidence that these systems interact with elements of vitamin D metabolism. This integrative systems approach has strong predictive power, indicating (as has indeed been shown) that both natural and pharmaceutical iron chelators might have useful protective roles in arresting cognitive decline, and that a further assessment of the role of microbes in AD development is more than highly warranted.

PMID: 27340854 [PubMed - as supplied by publisher]

Gut microbiota drive the development of neuro-inflammatory response in cirrhosis.

Hepatology. 2016 Jun 23;

Authors: Kang DJ, Betrapally NS, Ghosh SA, Sartor RB, Hylemon PB, Gillevet PM, Sanyal AJ, Heuman DM, Carl D, Zhou H, Liu R, Wang X, Yang J, Jiao C, Herzog J, Lippmann HR, Sikaroodi M, Brown RR, Bajaj JS

Abstract
The mechanisms behind the development of hepatic encephalopathy (HE) are unclear although hyperammonemia and systemic inflammation through gut dysbiosis have been proposed.
AIM: Define the individual contribution of hyperammonemia and systemic inflammation on neuro-inflammation in cirrhosis using germ-free (GF) and conventional mice.
METHODS: GF and conventional C57BL/6 mice were made cirrhotic using CCl4 gavage. These were compared to their non-cirrhotic counterparts. Intestinal microbiota, systemic and neuro-inflammation (including microglial and glial activation), serum ammonia, intestinal glutaminase activity and cecal glutamine content were compared between groups.
RESULTS: GF-cirrhotic mice developed similar cirrhotic changes to the conventional mice after four extra weeks (16 vs. 12 weeks) of CCL4 gavage. GF-cirrhotic mice exhibited higher ammonia compared to the GF controls but this was not associated with systemic or neuro-inflammation. Ammonia was generated through increased small intestinal glutaminase activity with concomitantly reduced intestinal glutamine levels. However, conventional cirrhotic mice had intestinal dysbiosis as well as systemic inflammation, associated with increased serum ammonia compared to conventional controls. This was associated with neuro-inflammation and glial/microglial activation. Correlation network analysis in conventional mice showed significant linkages between systemic/neuro-inflammation, intestinal microbiota and ammonia. Specifically beneficial, autochthonous taxa were negatively linked with brain and systemic inflammation, ammonia and with Staphylococcaceae, Lactobacillaceae and Streptococcaceae. Enterobacteriaceae were positively linked with serum inflammatory cytokines Conclusions: Gut microbiota changes drive the development of neuro- and systemic inflammatory responses in cirrhotic animals. This article is protected by copyright. All rights reserved.

PMID: 27339732 [PubMed - as supplied by publisher]

Reverse Engineering of Gene Regulatory Network Using Restricted Gene Expression Programming.

J Bioinform Comput Biol. 2016 May 26;:1650021

Authors: Yang B, Liu S, Zhang W

Abstract
Inference of gene regulatory networks has been becoming a major area of interest in the field of systems biology over the past decade. In this paper, we present a novel representation of S-system model, named restricted gene expression programming (RGEP), to infer gene regulatory network. A new hybrid evolutionary algorithm based on structure-based evolutionary algorithm and cuckoo search (CS) is proposed to optimize the architecture and corresponding parameters of model, respectively. Two synthetic benchmark datasets and one real biological dataset from SOS DNA repair network in E. coli are used to test the validity of our method. Experimental results demonstrate that our proposed method performs better than previously proposed popular methods.

PMID: 27338130 [PubMed - as supplied by publisher]

Prediction of core cancer genes using a hybrid of feature selection and machine learning methods.

Genet Mol Res. 2015;14(3):8871-82

Authors: Liu YX, Zhang NN, He Y, Lun LJ

Abstract
Machine learning techniques are of great importance in the analysis of microarray expression data, and provide a systematic and promising way to predict core cancer genes. In this study, a hybrid strategy was introduced based on machine learning techniques to select a small set of informative genes, which will lead to improving classification accuracy. First feature filtering algorithms were applied to select a set of top-ranked genes, and then hierarchical clustering and collapsing dense clusters were used to select core cancer genes. Through empirical study, our approach is capable of selecting relatively few core cancer genes while making high-accuracy predictions. The biological significance of these genes was evaluated using systems biology analysis. Extensive functional pathway and network analyses have confirmed findings in previous studies and can bring new insights into common cancer mechanisms.

PMID: 26345818 [PubMed - indexed for MEDLINE]

Identification of a thienopyrimidine derivatives target by a kinome and chemical biology approach.

Arch Pharm Res. 2015 Sep;38(9):1575-81

Authors: Lee C, Yang JS, Han G

Abstract
Target identification through chemical biology has been considered one of the most efficient approaches for drug discovery. Thienopyrimidine derivatives were designed to discover potent IκB kinase β (IKKβ) inhibitors based on a known IKKβ inhibitor library. Most of the thienopyrimidine derivatives inhibited nitric oxide and tumor necrosis factor alpha, which are downstream of the NF-κB signaling pathway, but not IKKβ. To identify the appropriate targets of thienopyrimidine analogues, chemical biology approaches, including text mining and a subsequent kinase panel assay from the kinome profiling were used. Based on the results, Fms-like tyrosine kinase 3 was found to be the target for thienopyrimidine derivatives, and was confirmed to be a potent inhibitor for acute myeloid leukemia.

PMID: 26186885 [PubMed - indexed for MEDLINE]

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

("orphan disease" OR "rare disease" OR "orphan diseases" OR "rare diseases")

These pubmed results were generated on 2016/06/24

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.

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

"Cystic Fibrosis"

These pubmed results were generated on 2016/06/24

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.

Molecular Docking for Identification of Potential Targets for Drug Repurposing.

Curr Top Med Chem. 2016 May 30;

Authors: Luo H, Mattes W, Mendrick DL, Hong H

Abstract
Using existing drugs for new indications (drug repurposing) is an effective method not only to reduce drug development time and costs but also to develop treatments for new disease including those that are rare. In order to discover novel indications, potential target identification is a necessary step. One widely used method to identify potential targets is through molecule docking. It requires no prior information except structure inputs from both the drug and the target, and can identify potential targets for a given drug, or identify potential drugs for a specific target. Though molecular docking is popular for drug development and repurposing, challenges remain for the method. In order to improve the prediction accuracy, optimizing the target conformation, considering the solvents and adding co-binders to the system are possible solutions.

PMID: 27334201 [PubMed - as supplied by publisher]

Drug repositioning through network pharmacology.

Curr Top Med Chem. 2016 May 30;

Authors: Ye H, Wei J, Tang K, Feuers R, Hong H

Abstract
Low drug productivity has been a significant problem of the pharmaceutical industry for several decades even though numerous novel technologies were introduced during this period. Currently pharmacologic dogma, "single drug, single target, single disease", is at the root of the lack of drug productivity. From a systems biology viewpoint, network pharmacology has been proposed to complement established and guiding pharmacologic approaches. The rationale for network pharmacology as a major component of drug discovery and development is that a disease can be caused by perturbation of the disease-causing network and a drug may be designed to interact with multiple targets for modulation of such a network from the disease status toward normal status. Therefore, network pharmacology has been applied to guide and assist in drug repositioning. Drugs exerting their therapeutic effects may directly target disease-associated proteins, but they may also modulate the pathways involved in the pathological process. In this review, we discuss the progresses and prospects in network pharmacology, focusing on drug off-targets discovery, disease-associated protein identification, and pathway analysis for elucidating relationships between drug targets and disease-associated proteins.

PMID: 27334200 [PubMed - as supplied by publisher]

Methods to Profile the Macromolecular Targets of Small Compounds.

Curr Top Med Chem. 2016 May 30;

Authors: Zhu J, Wang W, Chen X

Abstract
Small compounds constitute most of the available medicines. However, their stereophysical and stereochemical properties are relatively simple, which typically results in promiscuity in their interactions with human proteins. Such promiscuity has caused problems in our past efforts to discover and develop new drugs, but at the same time, it also brought new opportunities to exploit the off-target interactions between small compounds and human proteins for novel or improved therapeutics, e.g. in the applications of polypharmacology, drug repositioning, and designing drug combinations. In this direction, identifying the full profile of macromolecules that a small compound may interact with is of fundamental importance to harnessing the positive side of small compound promiscuity. This review summarizes available experimental and computational approaches that identify macromolecular targets for small compounds. The principle, application, performance, limitation and availability of these approaches are discussed.

PMID: 27334197 [PubMed - as supplied by publisher]

Using the Semantic Web for Rapid Integration of WikiPathways with Other Biological Online Data Resources.

PLoS Comput Biol. 2016 Jun;12(6):e1004989

Authors: Waagmeester A, Kutmon M, Riutta A, Miller R, Willighagen EL, Evelo CT, Pico AR

Abstract
The diversity of online resources storing biological data in different formats provides a challenge for bioinformaticians to integrate and analyse their biological data. The semantic web provides a standard to facilitate knowledge integration using statements built as triples describing a relation between two objects. WikiPathways, an online collaborative pathway resource, is now available in the semantic web through a SPARQL endpoint at http://sparql.wikipathways.org. Having biological pathways in the semantic web allows rapid integration with data from other resources that contain information about elements present in pathways using SPARQL queries. In order to convert WikiPathways content into meaningful triples we developed two new vocabularies that capture the graphical representation and the pathway logic, respectively. Each gene, protein, and metabolite in a given pathway is defined with a standard set of identifiers to support linking to several other biological resources in the semantic web. WikiPathways triples were loaded into the Open PHACTS discovery platform and are available through its Web API (https://dev.openphacts.org/docs) to be used in various tools for drug development. We combined various semantic web resources with the newly converted WikiPathways content using a variety of SPARQL query types and third-party resources, such as the Open PHACTS API. The ability to use pathway information to form new links across diverse biological data highlights the utility of integrating WikiPathways in the semantic web.

PMID: 27336457 [PubMed - as supplied by publisher]

TarNet: An Evidence-Based Database for Natural Medicine Research.

PLoS One. 2016;11(6):e0157222

Authors: Hu R, Ren G, Sun G, Sun X

Abstract
BACKGROUND: Complex diseases seriously threaten human health. Drug discovery approaches based on "single genes, single drugs, and single targets" are limited in targeting complex diseases. The development of new multicomponent drugs for complex diseases is imperative, and the establishment of a suitable solution for drug group-target protein network analysis is a key scientific problem that must be addressed. Herbal medicines have formed the basis of sophisticated systems of traditional medicine and have given rise to some key drugs that remain in use today. The search for new molecules is currently taking a different route, whereby scientific principles of ethnobotany and ethnopharmacognosy are being used by chemists in the discovery of different sources and classes of compounds.
RESULTS: In this study, we developed TarNet, a manually curated database and platform of traditional medicinal plants with natural compounds that includes potential bio-target information. We gathered information on proteins that are related to or affected by medicinal plant ingredients and data on protein-protein interactions (PPIs). TarNet includes in-depth information on both plant-compound-protein relationships and PPIs. Additionally, TarNet can provide researchers with network construction analyses of biological pathways and protein-protein interactions (PPIs) associated with specific diseases. Researchers can upload a gene or protein list mapped to our PPI database that has been manually curated to generate relevant networks. Multiple functions are accessible for network topological calculations, subnetwork analyses, pathway analyses, and compound-protein relationships.
CONCLUSIONS: TarNet will serve as a useful analytical tool that will provide information on medicinal plant compound-affected proteins (potential targets) and system-level analyses for systems biology and network pharmacology researchers. TarNet is freely available at http://www.herbbol.org:8001/tarnet, and detailed tutorials on the program are also available.

PMID: 27337171 [PubMed - as supplied by publisher]

Principles of Systems Biology-No. 6.

Cell Syst. 2016 Jun 22;2(6):356-359

Authors:

Abstract
This month's Cell Systems Call (Cell Systems 1, 307) shows how interdisciplinary approaches can provide leverage against problems as diverse as tracing cell lineage and understanding massive cellular machines.

PMID: 27336964 [PubMed - as supplied by publisher]

Schizophrenia interactome with 504 novel protein-protein interactions.

NPJ Schizophr. 2016;2:16012

Authors: Ganapathiraju MK, Thahir M, Handen A, Sarkar SN, Sweet RA, Nimgaonkar VL, Loscher CE, Bauer EM, Chaparala S

Abstract
Genome-wide association studies of schizophrenia (GWAS) have revealed the role of rare and common genetic variants, but the functional effects of the risk variants remain to be understood. Protein interactome-based studies can facilitate the study of molecular mechanisms by which the risk genes relate to schizophrenia (SZ) genesis, but protein-protein interactions (PPIs) are unknown for many of the liability genes. We developed a computational model to discover PPIs, which is found to be highly accurate according to computational evaluations and experimental validations of selected PPIs. We present here, 365 novel PPIs of liability genes identified by the SZ Working Group of the Psychiatric Genomics Consortium (PGC). Seventeen genes that had no previously known interactions have 57 novel interactions by our method. Among the new interactors are 19 drug targets that are targeted by 130 drugs. In addition, we computed 147 novel PPIs of 25 candidate genes investigated in the pre-GWAS era. While there is little overlap between the GWAS genes and the pre-GWAS genes, the interactomes reveal that they largely belong to the same pathways, thus reconciling the apparent disparities between the GWAS and prior gene association studies. The interactome including 504 novel PPIs overall, could motivate other systems biology studies and trials with repurposed drugs. The PPIs are made available on a webserver, called Schizo-Pi at http://severus.dbmi.pitt.edu/schizo-pi with advanced search capabilities.

PMID: 27336055 [PubMed]

Proteomic systems evaluation of the molecular validity of preclinical psychosis models compared to schizophrenia brain pathology.

Schizophr Res. 2016 Jun 19;

Authors: Cox DA, Gottschalk MG, Wesseling H, Ernst A, Cooper JD, Bahn S

Abstract
Pharmacological and genetic rodent models of schizophrenia play an important role in the drug discovery pipeline, but quantifying the molecular similarity of such models with the underlying human pathophysiology has proved difficult. We developed a novel systems biology methodology for the direct comparison of anterior prefrontal cortex tissue from four established glutamatergic rodent models and schizophrenia patients, enabling the evaluation of which model displays the greatest similarity to schizophrenia across different pathophysiological characteristics of the disease. Liquid chromatography coupled tandem mass spectrometry (LC-MS(E)) proteomic profiling was applied comparing healthy and "disease state" in human post-mortem samples and rodent brain tissue samples derived from models based on acute and chronic phencyclidine (PCP) treatment, ketamine treatment or NMDA receptor knockdown. Protein-protein interaction networks were constructed from significant abundance changes and enrichment analyses enabled the identification of five functional domains of the disease such as "development and differentiation", which were represented across all four rodent models and were thus subsequently used for cross-species comparison. Kernel-based machine learning techniques quantified that the chronic PCP model represented schizophrenia brain changes most closely for four of these functional domains. This is the first study aiming to quantify which rodent model recapitulates the neuropathological features of schizophrenia most closely, providing an indication of face validity as well as potential guidance in the refinement of construct and predictive validity. The methodology and findings presented here support recent efforts to overcome translational hurdles of preclinical psychiatric research by associating functional dimensions of behaviour with distinct biological processes.

PMID: 27335180 [PubMed - as supplied by publisher]

Drug repositioning through network pharmacology.

Curr Top Med Chem. 2016 May 30;

Authors: Ye H, Wei J, Tang K, Feuers R, Hong H

Abstract
Low drug productivity has been a significant problem of the pharmaceutical industry for several decades even though numerous novel technologies were introduced during this period. Currently pharmacologic dogma, "single drug, single target, single disease", is at the root of the lack of drug productivity. From a systems biology viewpoint, network pharmacology has been proposed to complement established and guiding pharmacologic approaches. The rationale for network pharmacology as a major component of drug discovery and development is that a disease can be caused by perturbation of the disease-causing network and a drug may be designed to interact with multiple targets for modulation of such a network from the disease status toward normal status. Therefore, network pharmacology has been applied to guide and assist in drug repositioning. Drugs exerting their therapeutic effects may directly target disease-associated proteins, but they may also modulate the pathways involved in the pathological process. In this review, we discuss the progresses and prospects in network pharmacology, focusing on drug off-targets discovery, disease-associated protein identification, and pathway analysis for elucidating relationships between drug targets and disease-associated proteins.

PMID: 27334200 [PubMed - as supplied by publisher]

JAK inhibition in the treatment of diabetic kidney disease.

Diabetologia. 2016 Jun 22;

Authors: Brosius FC, Tuttle KR, Kretzler M

Abstract
Diabetic kidney disease (DKD) is the most common cause of kidney failure in many countries today, but treatments have not improved in the last 20 years. Recently, systems biology methods have allowed the elucidation of signalling pathways and networks involved in the progression of DKD that were not well appreciated previously. A prominent pathway found to be integrally associated with DKD progression is the Janus kinase-signal transducer and activator of transcription (JAK-STAT) pathway. Increased expression of JAK-STAT genes was found in multiple cells in the kidney, including glomerular podocytes, in both early and progressive DKD. Subsequent experiments in a mouse diabetic model showed that enhanced expression of JAK2 selectively in glomerular podocytes increased functional and pathological features of DKD. Finally, a yet unpublished Phase 2 multicentre, randomised, double-blind, placebo-controlled study of the efficacy of a selective JAK1 and JAK2 inhibitor has been conducted in type 2 diabetic participants with DKD. In this trial there was a reduction of albuminuria in participants who received the active inhibitor compared with those who received a placebo These results support the further study of JAK inhibitors as a new therapy for DKD. This review summarises a presentation given at the 'Anti-inflammatory interventions in diabetes' symposium at the 2015 annual meeting of the EASD. It is accompanied by an overview by the Session Chair, Hiddo Heerspink (DOI: 10.1007/s00125-016-4030-4 ).

PMID: 27333885 [PubMed - as supplied by publisher]

Supporting systematic reviews using LDA-based document representations.

Syst Rev. 2015;4:172

Authors: Mo Y, Kontonatsios G, Ananiadou S

Abstract
BACKGROUND: Identifying relevant studies for inclusion in a systematic review (i.e. screening) is a complex, laborious and expensive task. Recently, a number of studies has shown that the use of machine learning and text mining methods to automatically identify relevant studies has the potential to drastically decrease the workload involved in the screening phase. The vast majority of these machine learning methods exploit the same underlying principle, i.e. a study is modelled as a bag-of-words (BOW).
METHODS: We explore the use of topic modelling methods to derive a more informative representation of studies. We apply Latent Dirichlet allocation (LDA), an unsupervised topic modelling approach, to automatically identify topics in a collection of studies. We then represent each study as a distribution of LDA topics. Additionally, we enrich topics derived using LDA with multi-word terms identified by using an automatic term recognition (ATR) tool. For evaluation purposes, we carry out automatic identification of relevant studies using support vector machine (SVM)-based classifiers that employ both our novel topic-based representation and the BOW representation.
RESULTS: Our results show that the SVM classifier is able to identify a greater number of relevant studies when using the LDA representation than the BOW representation. These observations hold for two systematic reviews of the clinical domain and three reviews of the social science domain.
CONCLUSIONS: A topic-based feature representation of documents outperforms the BOW representation when applied to the task of automatic citation screening. The proposed term-enriched topics are more informative and less ambiguous to systematic reviewers.

PMID: 26612232 [PubMed - indexed for MEDLINE]

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

("orphan disease" OR "rare disease" OR "orphan diseases" OR "rare diseases")

These pubmed results were generated on 2016/06/23

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.