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

"systems biology"

These pubmed results were generated on 2018/11/07

PubMed comprises more than millions of 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.

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

"systems biology"

These pubmed results were generated on 2018/11/06

PubMed comprises more than millions of 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.

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

"systems biology"

These pubmed results were generated on 2018/11/06

PubMed comprises more than millions of 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.

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

"systems biology"

These pubmed results were generated on 2018/11/05

PubMed comprises more than millions of 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.

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

"systems biology"

These pubmed results were generated on 2018/11/03

PubMed comprises more than millions of 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.

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

"systems biology"

These pubmed results were generated on 2018/11/02

PubMed comprises more than millions of 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.

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

"systems biology"

These pubmed results were generated on 2018/11/02

PubMed comprises more than millions of 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.

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

"systems biology"

These pubmed results were generated on 2018/11/01

PubMed comprises more than millions of 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.

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

"systems biology"

These pubmed results were generated on 2018/10/31

PubMed comprises more than millions of 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.

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

"systems biology"

These pubmed results were generated on 2018/10/31

PubMed comprises more than millions of 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.

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

"systems biology"

These pubmed results were generated on 2018/10/30

PubMed comprises more than millions of 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.

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

"systems biology"

These pubmed results were generated on 2018/10/30

PubMed comprises more than millions of 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 National Cancer Institute Investment in Biomechanics in Oncology Research.

Adv Exp Med Biol. 2018;1092:1-10

Authors: Dickherber A, Hughes SK, Zahir N

Abstract
The qualitative description of tumors feeling stiffer than surrounding normal tissue has been long appreciated in the clinical setting. These empirical observations have been corroborated by the precise measurement and characterization of mechanical properties of cancerous tissues. Much of the advancement in our understanding of mechanics in oncology has been enabled by the development of innovative technologies designed to probe cells and tissues as well as integrative software analysis tools that facilitate biological interpretation and generation of testable hypotheses. While some mechanics in oncology research has been investigator-initiated and supported by the National Cancer Institute (NCI), several NCI programs described herein have helped to foster the growth of the burgeoning field. Programs highlighted in this chapter include Innovative Molecular Analysis Technologies (IMAT), Physical Sciences-Oncology Network (PS-ON), Tumor Microenvironment Network (TMEN), Integrative Cancer Biology Program (ICBP), and the Cancer Systems Biology Consortium (CSBC). This chapter showcases the scientific contributions of these programs to the field of biomechanics in oncology.

PMID: 30368745 [PubMed - in process]

De Novo Hepatic Transcriptome Assembly and Systems Level Analysis of Three Species of Dietary Fish, Sardinops sagax, Scomber japonicus, and Pleuronichthys verticalis.

Genes (Basel). 2018 Oct 25;9(11):

Authors: Richards DJ, Renaud L, Agarwal N, Starr Hazard E, Hyde J, Hardiman G

Abstract
The monitoring of marine species as sentinels for ecosystem health has long been a valuable tool worldwide, providing insight into how both anthropogenic pollution and naturally occurring phenomena (i.e., harmful algal blooms) may lead to human and animal dietary concerns. The marine environments contain many contaminants of anthropogenic origin that have sufficient similarities to steroid and thyroid hormones, to potentially disrupt normal endocrine physiology in humans, fish, and other animals. An appropriate understanding of the effects of these endocrine disrupting chemicals (EDCs) on forage fish (e.g., sardine, anchovy, mackerel) can lead to significant insight into how these contaminants may affect local ecosystems in addition to their potential impacts on human health. With advancements in molecular tools (e.g., high-throughput sequencing, HTS), a genomics approach offers a robust toolkit to discover putative genetic biomarkers in fish exposed to these chemicals. However, the lack of available sequence information for non-model species has limited the development of these genomic toolkits. Using HTS and de novo assembly technology, the present study aimed to establish, for the first time for Sardinops sagax (Pacific sardine), Scomber japonicas (Pacific chub mackerel) and Pleuronichthys verticalis (hornyhead turbot), a de novo global transcriptome database of the liver, the primary organ involved in detoxification. The assembled transcriptomes provide a foundation for further downstream validation, comparative genomic analysis and biomarker development for future applications in ecotoxicogenomic studies, as well as environmental evaluation (e.g., climate change) and public health safety (e.g., dietary screening).

PMID: 30366465 [PubMed]

Characterization of kinase gene expression and splicing profile in prostate cancer with RNA-Seq data.

BMC Genomics. 2018 Aug 13;19(Suppl 6):564

Authors: Feng H, Li T, Zhang X

Abstract
BACKGROUND: Alternative splicing is a ubiquitous post-transcriptional regulation mechanism in most eukaryotic genes. Aberrant splicing isoforms and abnormal isoform ratios can contribute to cancer development. Kinase genes are key regulators of multiple cellular processes. Many kinases are found to be oncogenic and have been intensively investigated in the study of cancer and drugs. RNA-Seq provides a powerful technology for genome-wide study of alternative splicing in cancer besides the conventional gene expression profiling. But this potential has not been fully demonstrated yet.
METHODS: We characterized the transcriptome profile of prostate cancer using RNA-Seq data from viewpoints of both differential expression and differential splicing, with an emphasis on kinase genes and their splicing variations. We built a pipeline to conduct differential expression and differential splicing analysis, followed by functional enrichment analysis. We performed kinase domain analysis to identify the functionally important candidate kinase gene in prostate cancer, and calculated the expression levels of isoforms to explore the function of isoform switching of kinase genes in prostate cancer.
RESULTS: We identified distinct gene groups from differential expression and splicing analyses, which suggested that alternative splicing adds another level to gene expression regulation. Enriched GO terms of differentially expressed and spliced kinase genes were found to play different roles in regulation of cellular metabolism. Function analysis on differentially spliced kinase genes showed that differentially spliced exons of these genes are significantly enriched in protein kinase domains. Among them, we found that gene CDK5 has isoform switching between prostate cancer and benign tissues, which may affect cancer development by changing androgen receptor (AR) phosphorylation. The observation was validated in another RNA-Seq dataset of prostate cancer cell lines.
CONCLUSIONS: Our work characterized the expression and splicing profiles of kinase genes in prostate cancer and proposed a hypothetical model on isoform switching of CDK5 and AR phosphorylation in prostate cancer. These findings bring new understanding to the role of alternatively spliced kinases in prostate cancer and also demonstrate the use of RNA-Seq data in studying alternative splicing in cancer.

PMID: 30367578 [PubMed - in process]

CamurWeb: a classification software and a large knowledge base for gene expression data of cancer.

BMC Bioinformatics. 2018 Oct 15;19(Suppl 10):354

Authors: Weitschek E, Lauro SD, Cappelli E, Bertolazzi P, Felici G

Abstract
BACKGROUND: The high growth of Next Generation Sequencing data currently demands new knowledge extraction methods. In particular, the RNA sequencing gene expression experimental technique stands out for case-control studies on cancer, which can be addressed with supervised machine learning techniques able to extract human interpretable models composed of genes, and their relation to the investigated disease. State of the art rule-based classifiers are designed to extract a single classification model, possibly composed of few relevant genes. Conversely, we aim to create a large knowledge base composed of many rule-based models, and thus determine which genes could be potentially involved in the analyzed tumor. This comprehensive and open access knowledge base is required to disseminate novel insights about cancer.
RESULTS: We propose CamurWeb, a new method and web-based software that is able to extract multiple and equivalent classification models in form of logic formulas ("if then" rules) and to create a knowledge base of these rules that can be queried and analyzed. The method is based on an iterative classification procedure and an adaptive feature elimination technique that enables the computation of many rule-based models related to the cancer under study. Additionally, CamurWeb includes a user friendly interface for running the software, querying the results, and managing the performed experiments. The user can create her profile, upload her gene expression data, run the classification analyses, and interpret the results with predefined queries. In order to validate the software we apply it to all public available RNA sequencing datasets from The Cancer Genome Atlas database obtaining a large open access knowledge base about cancer. CamurWeb is available at http://bioinformatics.iasi.cnr.it/camurweb .
CONCLUSIONS: The experiments prove the validity of CamurWeb, obtaining many classification models and thus several genes that are associated to 21 different cancer types. Finally, the comprehensive knowledge base about cancer and the software tool are released online; interested researchers have free access to them for further studies and to design biological experiments in cancer research.

PMID: 30367574 [PubMed - in process]

An infrastructure for precision medicine through analysis of big data.

BMC Bioinformatics. 2018 Oct 15;19(Suppl 10):351

Authors: Moscatelli M, Manconi A, Pessina M, Fellegara G, Rampoldi S, Milanesi L, Casasco A, Gnocchi M

Abstract
BACKGROUND: Nowadays, the increasing availability of omics data, due to both the advancements in the acquisition of molecular biology results and in systems biology simulation technologies, provides the bases for precision medicine. Success in precision medicine depends on the access to healthcare and biomedical data. To this end, the digitization of all clinical exams and medical records is becoming a standard in hospitals. The digitization is essential to collect, share, and aggregate large volumes of heterogeneous data to support the discovery of hidden patterns with the aim to define predictive models for biomedical purposes. Patients' data sharing is a critical process. In fact, it raises ethical, social, legal, and technological issues that must be properly addressed.
RESULTS: In this work, we present an infrastructure devised to deal with the integration of large volumes of heterogeneous biological data. The infrastructure was applied to the data collected between 2010-2016 in one of the major diagnostic analysis laboratories in Italy. Data from three different platforms were collected (i.e., laboratory exams, pathological anatomy exams, biopsy exams). The infrastructure has been designed to allow the extraction and aggregation of both unstructured and semi-structured data. Data are properly treated to ensure data security and privacy. Specialized algorithms have also been implemented to process the aggregated information with the aim to obtain a precise historical analysis of the clinical activities of one or more patients. Moreover, three Bayesian classifiers have been developed to analyze examinations reported as free text. Experimental results show that the classifiers exhibit a good accuracy when used to analyze sentences related to the sample location, diseases presence and status of the illnesses.
CONCLUSIONS: The infrastructure allows the integration of multiple and heterogeneous sources of anonymized data from the different clinical platforms. Both unstructured and semi-structured data are processed to obtain a precise historical analysis of the clinical activities of one or more patients. Data aggregation allows to perform a series of statistical assessments required to answer complex questions that can be used in a variety of fields, such as predictive and precision medicine. In particular, studying the clinical history of patients that have developed similar pathologies can help to predict or individuate markers able to allow an early diagnosis of possible illnesses.

PMID: 30367571 [PubMed - in process]

Uric acid enhances alteplase-mediated thrombolysis as an antioxidant.

Sci Rep. 2018 Oct 26;8(1):15844

Authors: Kikuchi K, Setoyama K, Tanaka E, Otsuka S, Terashi T, Nakanishi K, Takada S, Sakakima H, Ampawong S, Kawahara KI, Nagasato T, Hosokawa K, Harada Y, Yamamoto M, Kamikokuryo C, Kiyama R, Morioka M, Ito T, Maruyama I, Tancharoen S

Abstract
Uric acid (UA) therapy may prevent early ischemic worsening after acute stroke in thrombolysis patients. The aim of this study was to examine the influence of UA on the thrombolytic efficacy of alteplase in human blood samples by measuring thrombolysis under flow conditions using a newly developed microchip-based flow-chamber assay. Human blood samples from healthy volunteers were exposed to UA, alteplase, or a combination of UA and alteplase. Whole blood and platelet-rich plasma were perfused over a collagen- and thromboplastin-coated microchip, and capillary occlusion was monitored with a video microscope and flow-pressure sensor. The area under the curve (extent of thrombogenesis or thrombolysis) at 30 minutes was 92% lower in the UA-alteplase-treated group compared with the alteplase-treated group. D-dimers were measured to evaluate these effects in human platelet-poor plasma samples. Although hydrogen peroxide significantly decreased the elevation of D-dimers by alteplase, UA significantly inhibited the effect of hydrogen peroxide. Meanwhile, rat models of thromboembolic cerebral ischemia were treated with either alteplase or UA-alteplase combination therapy. Compared with alteplase alone, the combination therapy reduced the infarct volume and inhibited haemorrhagic transformation. UA enhances alteplase-mediated thrombolysis, potentially by preventing oxidative stress, which inhibits fibrinolysis by alteplase in thrombi.

PMID: 30367108 [PubMed - in process]

Optimization of carbon and energy utilization through differential translational efficiency.

Nat Commun. 2018 Oct 26;9(1):4474

Authors: Al-Bassam MM, Kim JN, Zaramela LS, Kellman BP, Zuniga C, Wozniak JM, Gonzalez DJ, Zengler K

Abstract
Control of translation is vital to all species. Here we employ a multi-omics approach to decipher condition-dependent translational regulation in the model acetogen Clostridium ljungdahlii. Integration of data from cells grown autotrophically or heterotrophically revealed that pathways critical to carbon and energy metabolism are under strong translational regulation. Major pathways involved in carbon and energy metabolism are not only differentially transcribed and translated, but their translational efficiencies are differentially elevated in response to resource availability under different growth conditions. We show that translational efficiency is not static and that it changes dynamically in response to mRNA expression levels. mRNAs harboring optimized 5'-untranslated region and coding region features, have higher translational efficiencies and are significantly enriched in genes encoding carbon and energy metabolism. In contrast, mRNAs enriched in housekeeping functions harbor sub-optimal features and have lower translational efficiencies. We propose that regulation of translational efficiency is crucial for effectively controlling resource allocation in energy-deprived microorganisms.

PMID: 30367068 [PubMed - in process]

Determinants of promoter and enhancer transcription directionality in metazoans.

Nat Commun. 2018 Oct 26;9(1):4472

Authors: Ibrahim MM, Karabacak A, Glahs A, Kolundzic E, Hirsekorn A, Carda A, Tursun B, Zinzen RP, Lacadie SA, Ohler U

Abstract
Divergent transcription from promoters and enhancers is pervasive in many species, but it remains unclear if it is a general feature of all eukaryotic cis regulatory elements. To address this, here we define cis regulatory elements in C. elegans, D. melanogaster and H. sapiens and investigate the determinants of their transcription directionality. In all three species, we find that divergent transcription is initiated from two separate core promoter sequences and promoter regions display competition between histone modifications on the + 1 and -1 nucleosomes. In contrast, promoter directionality, sequence composition surrounding promoters, and positional enrichment of chromatin states, are different across species. Integrative models of H3K4me3 levels and core promoter sequence are highly predictive of promoter and enhancer directionality and support two directional classes, skewed and balanced. The relative importance of features to these models are clearly distinct for promoters and enhancers. Differences in regulatory architecture within and between metazoans are therefore abundant, arguing against a unified eukaryotic model.

PMID: 30367057 [PubMed - in process]