Computational approaches for innovative antiepileptic drug discovery.

Expert Opin Drug Discov. 2016 Jul 25;

Authors: Talevi A

Abstract
INTRODUCTION: Despite the approval of a large number of antiepileptic agents over the past 25 years, there has been no significant improvement in efficacy of treatments, with one third of patients suffering from intractable epilepsy. This scenario has prompted the search for innovative drug discovery solutions. While network pharmacology and explanations of the drug resistance phenomena have been proposed to drive the search for more efficacious therapeutic solutions, such alternative approaches have not fully taken hold within the antiepileptic drug discovery community so far.
AREAS COVERED: Herein, the author discusses the impact that network pharmacology and the current hypotheses of refractory epilepsy and drug repurposing could have if integrated with anti-epileptic computer-aided discovery.
EXPERT OPINION: With many complex diseases, the advancement in the understanding of disorder pathophysiology in addition to the contribution of systems biology have rapidly translated into the discovery of novel drug candidates. However, antiepileptic drug developers have fallen a little behind in this regard, with fewer examples of computer-aided antiepileptic drug design and network-based approximations appearing in scientific literature. New generation single-target agents have so far shown limited success in terms of enhanced efficacy; although multi-target agents could yet demonstrate improved safety and efficacy.

PMID: 27454246 [PubMed - as supplied by publisher]

Getting the Most out of PubChem for Virtual Screening.

Expert Opin Drug Discov. 2016 Jul 25;

Authors: Kim S

Abstract
INTRODUCTION: With the emergence of the "big data" era, the biomedical research community has great interest in exploiting publicly available chemical information for drug discovery. PubChem is an example of public databases that provide a large amount of chemical information free of charge.
AREAS COVERED: This article provides an overview of how PubChem's data, tools, and services can be used for virtual screening and reviews recent publications that discuss important aspects of exploiting PubChem for drug discovery.
EXPERT OPINION: PubChem offers comprehensive chemical information useful for drug discovery. It also provides multiple programmatic access routes, which are essential to build automated virtual screening pipelines that exploit PubChem data. In addition, PubChemRDF allows users to download PubChem data and load them into a local computing facility, facilitating data integration between PubChem and other resources. PubChem resources have been used in many studies for developing bioactivity and toxicity prediction models, discovering polypharmacologic (multi-target) ligands, and identifying new macromolecule targets of compounds (for drug-repurposing or off-target side effect prediction). These studies demonstrate the usefulness of PubChem as a key resource for computer-aided drug discovery and related area.

PMID: 27454129 [PubMed - as supplied by publisher]

The human milk oligosaccharide 2'-fucosyllactose augments the adaptive response to extensive intestinal.

Am J Physiol Gastrointest Liver Physiol. 2016 Mar 15;310(6):G427-38

Authors: Mezoff EA, Hawkins JA, Ollberding NJ, Karns R, Morrow AL, Helmrath MA

Abstract
Intestinal resection resulting in short bowel syndrome (SBS) carries a heavy burden of long-term morbidity, mortality, and cost of care, which can be attenuated with strategies that improve intestinal adaptation. SBS infants fed human milk, compared with formula, have more rapid intestinal adaptation. We tested the hypothesis that the major noncaloric human milk oligosaccharide 2'-fucosyllactose (2'-FL) contributes to the adaptive response after intestinal resection. Using a previously described murine model of intestinal adaptation, we demonstrated increased weight gain from 21 to 56 days (P < 0.001) and crypt depth at 56 days (P < 0.0095) with 2'-FL supplementation after ileocecal resection. Furthermore, 2'-FL increased small bowel luminal content microbial alpha diversity following resection (P < 0.005) and stimulated a bloom in organisms of the genus Parabacteroides (log2-fold = 4.1, P = 0.035). Finally, transcriptional analysis of the intestine revealed enriched ontologies and pathways related to antimicrobial peptides, metabolism, and energy processing. We conclude that 2'-FL supplementation following ileocecal resection increases weight gain, energy availability through microbial community modulation, and histological changes consistent with improved adaptation.

PMID: 26702137 [PubMed - indexed for MEDLINE]

Coupling News Sentiment with Web Browsing Data Improves Prediction of Intra-Day Price Dynamics.

PLoS One. 2016;11(1):e0146576

Authors: Ranco G, Bordino I, Bormetti G, Caldarelli G, Lillo F, Treccani M

Abstract
The new digital revolution of big data is deeply changing our capability of understanding society and forecasting the outcome of many social and economic systems. Unfortunately, information can be very heterogeneous in the importance, relevance, and surprise it conveys, affecting severely the predictive power of semantic and statistical methods. Here we show that the aggregation of web users' behavior can be elicited to overcome this problem in a hard to predict complex system, namely the financial market. Specifically, our in-sample analysis shows that the combined use of sentiment analysis of news and browsing activity of users of Yahoo! Finance greatly helps forecasting intra-day and daily price changes of a set of 100 highly capitalized US stocks traded in the period 2012-2013. Sentiment analysis or browsing activity when taken alone have very small or no predictive power. Conversely, when considering a news signal where in a given time interval we compute the average sentiment of the clicked news, weighted by the number of clicks, we show that for nearly 50% of the companies such signal Granger-causes hourly price returns. Our result indicates a "wisdom-of-the-crowd" effect that allows to exploit users' activity to identify and weigh properly the relevant and surprising news, enhancing considerably the forecasting power of the news sentiment.

PMID: 26808833 [PubMed - indexed for MEDLINE]

Pharmacogenomics of platinum-based chemotherapy sensitivity in NSCLC: toward precision medicine.

Pharmacogenomics. 2016 Jul 27;

Authors: Yin JY, Li X, Zhou HH, Liu ZQ

Abstract
Lung cancer is one of the leading causes of cancer-related death in the world. Platinum-based chemotherapy is the first-line treatment for non-small-cell lung cancer (NSCLC), however, the therapeutic efficiency varies remarkably among individuals. A large number of pharmacogenomics studies aimed to identify genetic variations which can be used to predict platinum response. Those studies are leading NSCLC treatment to the new era of precision medicine. In the current review, we provided a comprehensive update on the main recent findings of genetic variations which can be used to predict platinum sensitivity in the NSCLC patients.

PMID: 27462924 [PubMed - as supplied by publisher]

Genetic variation in catechol-O-methyltransferase modifies effects of clonidine treatment in chronic fatigue syndrome.

Pharmacogenomics J. 2016 Jul 26;

Authors: Hall KT, Kossowsky J, Oberlander TF, Kaptchuk TJ, Saul JP, Wyller VB, Fagermoen E, Sulheim D, Gjerstad J, Winger A, Mukamal KJ

Abstract
Clonidine, an α2-adrenergic receptor agonist, decreases circulating norepinephrine and epinephrine, attenuating sympathetic activity. Although catechol-O-methyltransferase (COMT) metabolizes catecholamines, main effectors of sympathetic function, COMT genetic variation effects on clonidine treatment are unknown. Chronic fatigue syndrome (CFS) is hypothesized to result in part from dysregulated sympathetic function. A candidate gene analysis of COMT rs4680 effects on clinical outcomes in the Norwegian Study of Chronic Fatigue Syndrome in Adolescents: Pathophysiology and Intervention Trial (NorCAPITAL), a randomized double-blinded clonidine versus placebo trial, was conducted (N=104). Patients homozygous for rs4680 high-activity allele randomized to clonidine took 2500 fewer steps compared with placebo (Pinteraction=0.04). There were no differences between clonidine and placebo among patients with COMT low-activity alleles. Similar gene-drug interactions were observed for sleep (Pinteraction=0.003) and quality of life (Pinteraction=0.018). Detrimental effects of clonidine in the subset of CFS patients homozygous for COMT high-activity allele warrant investigation of potential clonidine-COMT interaction effects in other conditions.The Pharmacogenomics Journal advance online publication, 26 July 2016; doi:10.1038/tpj.2016.53.

PMID: 27457818 [PubMed - as supplied by publisher]

Promoter region variation in NFE2L2 influences susceptibility to ototoxicity in patients exposed to high cumulative doses of cisplatin.

Pharmacogenomics J. 2016 Jul 26;

Authors: Spracklen TF, Vorster AA, Ramma L, Dalvie S, Ramesar RS

Abstract
Ototoxicity is a disabling reaction to cisplatin chemotherapy. Much of the inter-individual variability in the development of hearing impairment among cisplatin-receiving patients has not been fully accounted for. In particular, little is known about the pharmacogenomics of cisplatin-induced ototoxicity. This study sought to investigate the role of variation in five candidate genes in a cohort of South African cancer patients. Five variants within the candidate genes were genotyped in 214 patients, of which SLC22A2 rs316019 and NFE2L2 rs6721961 associated with reduced rates of ototoxicity. In the patients who were exposed to cumulative cisplatin doses ⩾200 mg m(-)(2) (n=113), the variant rs6721961 associated with ototoxicity according to three different grading scales of hearing loss (ASHA, P=0.005; Chang, P=0.028; CTCAE, P=0.004). The NFE2L2 promotor variant rs6721961 may therefore be protective against hearing loss in cisplatin-receiving cancer patients.The Pharmacogenomics Journal advance online publication, 26 July 2016; doi:10.1038/tpj.2016.52.

PMID: 27457817 [PubMed - as supplied by publisher]

Ayurgenomics for stratified medicine: TRISUTRA consortium initiative across ethnically and geographically diverse Indian populations.

J Ethnopharmacol. 2016 Jul 22;

Authors: Prasher B, Varma B, Kumar A, Khuntia BK, Pandey R, Narang A, Tiwari P, Kutum R, Guin D, Kukreti R, Dash D, TRISUTRA Ayurgenomics Consortium, Mukerji M

Abstract
BACKGROUND: Genetic differences in the target proteins, metabolizing enzymes and transporters that contribute to inter-individual differences in drug response are not integrated in contemporary drug development programs. Ayurveda, that has propelled many drug discovery programs albeit for the search of new chemical entities incorporates inter-individual variability "Prakriti" in development and administration of drug in an individualized manner. Prakriti of an individual largely determines responsiveness to external environment including drugs as well as susceptibility to diseases. Prakriti has also been shown to have molecular and genomic correlates. We highlight how integration of Prakriti concepts can augment the efficiency of drug discovery and development programs through a unique initiative of Ayurgenomics TRISUTRA consortium.
METHODS: Five aspects that have been carried out are (1) analysis of variability in FDA approved pharmacogenomics genes/SNPs in exomes of 72 healthy individuals including predominant Prakriti types and matched controls from a North Indian Indo-European cohort (2) establishment of a consortium network and development of five genetically homogeneous cohorts from diverse ethnic and geo-climatic background (3) identification of parameters and development of uniform standard protocols for objective assessment of Prakriti types (4) development of protocols for Prakriti evaluation and its application in more than 7500 individuals in the five cohorts (5) Development of data and sample repository and integrative omics pipelines for identification of genomic correlates.
RESULTS: Highlight of the study are (1) Exome sequencing revealed significant differences between Prakriti types in 28 SNPs of 11 FDA approved genes of pharmacogenomics relevance viz CYP2C19 CYP2B6, ESR1, F2, PGR, HLA-B, HLA-DQA1, HLA-DRB1, LDLR, CFTR, CPS1. These variations are polymorphic in diverse Indian and world populations included in 1000 genomes project. (2) Based on the phenotypic attributes of Prakriti we identified anthropometry for anatomical features, biophysical parameters for skin types, HRV for autonomic function tests, spirometry for vital capacity and gustometry for taste thresholds as objective parameters. (3) Comparison of Prakriti phenotypes across different ethnic, age and gender groups led to identification of invariant features as well as some that require weighted considerations across the cohorts.
CONCLUSION: Considering the molecular and genomics differences underlying Prakriti and relevance in disease pharmacogenomics studies, this novel integrative platform would help in identification of differently susceptible and drug responsive population. Additionally, integrated analysis of phenomic and genomic variations would not only allow identification of clinical and genomic markers of Prakriti for application in personalized medicine but also its integration in drug discovery and development programs.

PMID: 27457695 [PubMed - as supplied by publisher]

Personalized Therapy of Hypertension: the Past and the Future.

Curr Hypertens Rep. 2016 Mar;18(3):24

Authors: Manunta P, Ferrandi M, Cusi D, Ferrari P, Staessen J, Bianchi G

Abstract
During the past 20 years, the studies on genetics or pharmacogenomics of primary hypertension provided interesting results supporting the role of genetics, but no actionable finding ready to be translated into personalized medicine. Two types of approaches have been applied: a "hypothesis-driven" approach on the candidate genes, coding for proteins involved in the biochemical machinery underlying the regulation of BP, and an "unbiased hypothesis-free" approach with GWAS, based on the randomness principles of frequentist statistics. During the past 10-15 years, the application of the latter has overtaken the application of the former leading to an enlargement of the number of previously unknown candidate loci or genes but without any actionable result for the therapy of hypertension. In the present review, we summarize the results of our hypothesis-driven approach based on studies carried out in rats with genetic hypertension and in humans with essential hypertension at the pre-hypertensive and early hypertensive stages. These studies led to the identification of mutant adducin and endogenous ouabain as candidate genetic-molecular mechanisms in both species. Rostafuroxin has been developed for its ability to selectively correct Na(+) pump abnormalities sustained by the two abovementioned mechanisms and to selectively reduce BP in rats and in humans carrying the gene variants underlying the mutant adducin and endogenous ouabain (EO) effects. A clinical trial is ongoing to substantiate these findings. Future studies should apply both the candidate gene and GWAS approaches to fully exploit the potential of genetics in optimizing the personalized therapy.

PMID: 26915067 [PubMed - indexed for MEDLINE]

Endogenous glucose production increases in response to metformin treatment in the glycogen-depleted state in humans: a randomised trial.

Diabetologia. 2015 Nov;58(11):2494-502

Authors: Christensen MM, Højlund K, Hother-Nielsen O, Stage TB, Damkier P, Beck-Nielsen H, Brøsen K

Abstract
AIMS/HYPOTHESIS: Metformin is believed to reduce glucose levels primarily by inhibiting hepatic glucose production. Recent data indicate that metformin antagonises glucagon-dependent glucose output, suggesting that compensatory mechanisms protect against hypoglycaemia. Here, we examined the effect of metformin on glucose metabolism in humans after a glycogen-depleting fast and the role of reduced-function alleles in OCT1 (also known as SLC22A1).
METHODS: In a randomised, crossover trial, healthy individuals with or without reduced-function alleles in OCT1 were fasted for 42 h twice, either with or without prior treatment with 1 g metformin twice daily. Participants were recruited from the Pharmacogenomics Biobank of the University of Southern Denmark. Treatment allocation was generated by the Good Clinical Practice Unit, Odense University Hospital, Denmark. Variables of whole-body glucose metabolism were assessed using [3-(3)H]glucose, indirect calorimetry and measurement of substrates and counter-regulatory hormones. The primary outcome was endogenous glucose production (EGP).
RESULTS: Thirty-seven individuals were randomised. Thirty-four completed the study (12 had none, 13 had one and nine had two reduced-function alleles in OCT1). Three were excluded from the analysis because of early dropout. Metformin significantly stimulated glucose disposal rates and non-oxidative glucose metabolism with no effect on glucose oxidation. This increase in glucose utilisation was explained by a concomitant increase in glycolytic flux and accompanied by increased EGP, most likely mediated by increased plasma lactate, glucagon and cortisol levels. There was no effect of reduced-function OCT1 alleles on any of these measures. All individuals completed the glycogen-depleting fast without hypoglycaemia.
CONCLUSIONS/INTERPRETATION: Metformin stimulates glycolytic glucose utilisation and lactate production in the glycogen-depleted state. This may trigger a rise in glucose counter-regulatory hormones and subsequently an increase in EGP, which protects against hypoglycaemia.
TRIAL REGISTRATION: ClinicalTrials.gov NCT01400191 FUNDING: Danish Research Council for Health and Disease (0602-02695B) and Odense University Hospital Free Research Fund, 2012.

PMID: 26271344 [PubMed - indexed for MEDLINE]

Establishing a baseline for literature mining human genetic variants and their relationships to disease cohorts.

BMC Med Inform Decis Mak. 2016;16 Suppl 1:68

Authors: Verspoor KM, Heo GE, Kang KY, Song M

Abstract
BACKGROUND: The Variome corpus, a small collection of published articles about inherited colorectal cancer, includes annotations of 11 entity types and 13 relation types related to the curation of the relationship between genetic variation and disease. Due to the richness of these annotations, the corpus provides a good testbed for evaluation of biomedical literature information extraction systems.
METHODS: In this paper, we focus on assessing performance on extracting the relations in the corpus, using gold standard entities as a starting point, to establish a baseline for extraction of relations important for extraction of genetic variant information from the literature. We test the application of the Public Knowledge Discovery Engine for Java (PKDE4J) system, a natural language processing system designed for information extraction of entities and relations in text, on the relation extraction task using this corpus.
RESULTS: For the relations which are attested at least 100 times in the Variome corpus, we realise a performance ranging from 0.78-0.84 Precision-weighted F-score, depending on the relation. We find that the PKDE4J system adapted straightforwardly to the range of relation types represented in the corpus; some extensions to the original methodology were required to adapt to the multi-relational classification context. The results are competitive with state-of-the-art relation extraction performance on more heavily studied corpora, although the analysis shows that the Recall of a co-occurrence baseline outweighs the benefit of improved Precision for many relations, indicating the value of simple semantic constraints on relations.
CONCLUSIONS: This work represents the first attempt to apply relation extraction methods to the Variome corpus. The results demonstrate that automated methods have good potential to structure the information expressed in the published literature related to genetic variants, connecting mutations to genes, diseases, and patient cohorts. Further development of such approaches will facilitate more efficient biocuration of genetic variant information into structured databases, leveraging the knowledge embedded in the vast publication literature.

PMID: 27454860 [PubMed - in process]

Protein-protein interaction extraction with feature selection by evaluating contribution levels of groups consisting of related features.

BMC Bioinformatics. 2016;17 Suppl 7:246

Authors: Thuy Phan TT, Ohkawa T

Abstract
BACKGROUND: Protein-protein interaction (PPI) extraction from published scientific articles is one key issue in biological research due to its importance in grasping biological processes. Despite considerable advances of recent research in automatic PPI extraction from articles, demand remains to enhance the performance of the existing methods.
RESULTS: Our feature-based method incorporates the strength of many kinds of diverse features, such as lexical and word context features derived from sentences, syntactic features derived from parse trees, and features using existing patterns to extract PPIs automatically from articles. Among these abundant features, we assemble the related features into four groups and define the contribution level (CL) for each group, which consists of related features. Our method consists of two steps. First, we divide the training set into subsets based on the structure of the sentence and the existence of significant keywords (SKs) and apply the sentence patterns given in advance to each subset. Second, we automatically perform feature selection based on the CL values of the four groups that consist of related features and the k-nearest neighbor algorithm (k-NN) through three approaches: (1) focusing on the group with the best contribution level (BEST1G); (2) unoptimized combination of three groups with the best contribution levels (U3G); (3) optimized combination of two groups with the best contribution levels (O2G).
CONCLUSIONS: Our method outperforms other state-of-the-art PPI extraction systems in terms of F-score on the HPRD50 corpus and achieves promising results that are comparable with these PPI extraction systems on other corpora. Further, our method always obtains the best F-score on all the corpora than when using k-NN only without exploiting the CLs of the groups of related features.

PMID: 27454611 [PubMed - in process]

CLASH: Complementary Linkage with Anchoring and Scoring for Heterogeneous biomolecular and clinical data.

BMC Med Inform Decis Mak. 2016;16 Suppl 3:72

Authors: Nam Y, Kim M, Lee K, Shin H

Abstract
BACKGROUND: The study on disease-disease association has been increasingly viewed and analyzed as a network, in which the connections between diseases are configured using the source information on interactome maps of biomolecules such as genes, proteins, metabolites, etc. Although abundance in source information leads to tighter connections between diseases in the network, for a certain group of diseases, such as metabolic diseases, the connections do not occur much due to insufficient source information; a large proportion of their associated genes are still unknown. One way to circumvent the difficulties in the lack of source information is to integrate available external information by using one of up-to-date integration or fusion methods. However, if one wants a disease network placing huge emphasis on the original source of data but still utilizing external sources only to complement it, integration may not be pertinent. Interpretation on the integrated network would be ambiguous: meanings conferred on edges would be vague due to fused information.
METHODS: In this study, we propose a network based algorithm that complements the original network by utilizing external information while preserving the network's originality. The proposed algorithm links the disconnected node to the disease network by using complementary information from external data source through four steps: anchoring, connecting, scoring, and stopping.
RESULTS: When applied to the network of metabolic diseases that is sourced from protein-protein interaction data, the proposed algorithm recovered connections by 97%, and improved the AUC performance up to 0.71 (lifted from 0.55) by using the external information outsourced from text mining results on PubMed comorbidity literatures. Experimental results also show that the proposed algorithm is robust to noisy external information.
CONCLUSION: This research has novelty in which the proposed algorithm preserves the network's originality, but at the same time, complements it by utilizing external information. Furthermore it can be utilized for original association recovery and novel association discovery for disease network.

PMID: 27454118 [PubMed - in process]

15 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")

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"Cystic Fibrosis"

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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.

Drug screening: Drug repositioning needs a rethink.

Nature. 2016 Jul 21;535(7612):355

Authors: Ding X

PMID: 27443733 [PubMed - in process]

Prospects for Moxidectin as a New Oral Treatment for Human Scabies.

PLoS Negl Trop Dis. 2016 Mar;10(3):e0004389

Authors: Mounsey KE, Bernigaud C, Chosidow O, McCarthy JS

PMID: 26985995 [PubMed - indexed for MEDLINE]

Web Image Search Re-ranking with Click-based Similarity and Typicality.

IEEE Trans Image Process. 2016 Jul 20;

Authors: Yang X, Mei T, Zhang YD, Liu J, Satoh S

Abstract
In image search re-ranking, besides the well known semantic gap, intent gap, which is the gap between the representation of users' query/demand and the real intent of the users, is becoming a major problem restricting the development of image retrieval. To reduce human effects, in this paper, we use image click-through data, which can be viewed as the "implicit feedback" from users, to help overcome the intention gap, and further improve the image search performance. Generally, the hypothesis visually similar images should be close in a ranking list and the strategy images with higher relevance should be ranked higher than others are widely accepted. To obtain satisfying search results, thus, image similarity and the level of relevance typicality are determinate factors correspondingly. However, when measuring image similarity and typicality, conventional re-ranking approaches only consider visual information and initial ranks of images, while overlooking the influence of click-through data. This paper presents a novel re-ranking approach, named spectral clustering re-ranking with click-based similarity and typicality (SCCST). First, to learn an appropriate similarity measurement, we propose click-based multi-feature similarity learning algorithm (CMSL), which conducts metric learning based on clickbased triplets selection, and integrates multiple features into a unified similarity space via multiple kernel learning. Then based on the learnt click-based image similarity measure, we conduct spectral clustering to group visually and semantically similar images into same clusters, and get the final re-rank list by calculating click-based clusters typicality and withinclusters click-based image typicality in descending order. Our experiments conducted on two real-world query-image datasets with diverse representative queries show that our proposed reranking approach can significantly improve initial search results, and outperform several existing re-ranking approaches.

PMID: 27448362 [PubMed - as supplied by publisher]

MicroRNAs in hereditary diffuse gastric cancer.

Biomed Rep. 2016 Aug;5(2):151-154

Authors: Suárez-Arriaga MC, Ribas-Aparicio RM, Ruiz-Tachiquín ME

Abstract
In 2012, gastric cancer (GC) was the third cause of mortality due to cancer in men and women. In Central and South America, high mortality rates have been reported. A total of 95% of tumors developed in the stomach are of epithelial origin; thus, these are denominated adenocarcinomas of the stomach. Diverse classification systems have been established, among which two types of GC based on histological type and growth pattern have been described as follows: Intestinal (IGC) and diffuse (DGC). Approximately 1-3% of GC cases are associated with heredity. Hereditary-DGC (HDGC), with 80% penetrance, is an autosomal-type, dominant syndrome in which 40% of cases are carriers of diverse mutations of the CDH1 gene, which encodes for the cadherin protein. By contrast, microRNA are non-encoded, single-chain RNA molecules. These molecules regulate the majority of cellular functions at the post-transcriptional level. However, analysis of these interactions by means of Systems Biology has allowed the understanding of complex and heterogeneous diseases, such as cancer. These molecules are ubiquitous; however, their expression can be specific in different tissues either temporarily or permanently, depending on the stage of the cell. Due to the participation of microRNA in the processes of cellular proliferation, cell cycle control, apoptosis, differentiation and metabolism, these have been indicated to have a role in the development of cancerous processes, finding specific patterns of expression in different neoplasms, including GC, in which the microRNA expression profile is different in samples of non-cancerous versus cancerous tissues. A difference has been observed in the expression patterns of DGC and IGC. However, the role of microRNA in HDGC has not yet been established. The present study reviews the investigations that describe the participation of microRNA in the regulation of genes CDH1, RHOA, CTNNA1, INSR and TGF-β in different neoplasms, such as HDGC.

PMID: 27446532 [PubMed - as supplied by publisher]

The Significance of an Enhanced Concept of the Organism for Medicine.

Evid Based Complement Alternat Med. 2016;2016:1587652

Authors: Rosslenbroich B

Abstract
Recent developments in evolutionary biology, comparative embryology, and systems biology suggest the necessity of a conceptual shift in the way we think about organisms. It is becoming increasingly evident that molecular and genetic processes are subject to extremely refined regulation and control by the cell and the organism, so that it becomes hard to define single molecular functions or certain genes as primary causes of specific processes. Rather, the molecular level is integrated into highly regulated networks within the respective systems. This has consequences for medical research in general, especially for the basic concept of personalized medicine or precision medicine. Here an integrative systems concept is proposed that describes the organism as a multilevel, highly flexible, adaptable, and, in this sense, autonomous basis for a human individual. The hypothesis is developed that these properties of the organism, gained from scientific observation, will gradually make it necessary to rethink the conceptual framework of physiology and pathophysiology in medicine.

PMID: 27446221 [PubMed]