115 research outputs found
The Water Extract of Juniperus communis L. Induces Cell Death and Sensitizes Cancer Cells to Cytostatic Drugs through p53 and PI3K/Akt Pathways
Juniper (Juniperus communis L.) is a northern coniferous plant generally used as a spice and for nutritional purposes in foods and drinks. It was previously reported that juniper extract (JE) affects p53 activity, cellular stress, and gene expression induced cell death in human neuroblastoma cells. Therefore, the effects of juniper on p53 and Akt signaling was examined further in A549 lung, 22RV1 and DU145 prostate, and HepG2 liver cancer cells using Western blot, confocal microscopy, and MTT analysis. We found that juniper simultaneously decreased cell viability, activated the p53 pathway, and inactivated the PI3K/Akt pathway. The p53 activation was associated with increased nuclear p53 level. Akt was dephosphorylated, and its inactivation was associated with increased levels of PHLPP1 and PHLPP2 phosphatases. Parallel increases of PARP suggest that JE decreased cell viability by activating cell death. In addition, JE potentiated the effects of gemcitabine and 5-fluorouracil anticancer drugs. Thus, JE can activate cell death in different cancer cell lines through p53 and Akt pathways
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The first step in the development of Text Mining technology for Cancer Risk Assessment: identifying and organizing scientific evidence in risk assessment literature.
BACKGROUND: One of the most neglected areas of biomedical Text Mining (TM) is the development of systems based on carefully assessed user needs. We have recently investigated the user needs of an important task yet to be tackled by TM -- Cancer Risk Assessment (CRA). Here we take the first step towards the development of TM technology for the task: identifying and organizing the scientific evidence required for CRA in a taxonomy which is capable of supporting extensive data gathering from biomedical literature. RESULTS: The taxonomy is based on expert annotation of 1297 abstracts downloaded from relevant PubMed journals. It classifies 1742 unique keywords found in the corpus to 48 classes which specify core evidence required for CRA. We report promising results with inter-annotator agreement tests and automatic classification of PubMed abstracts to taxonomy classes. A simple user test is also reported in a near real-world CRA scenario which demonstrates along with other evaluation that the resources we have built are well-defined, accurate, and applicable in practice. CONCLUSION: We present our annotation guidelines and a tool which we have designed for expert annotation of PubMed abstracts. A corpus annotated for keywords and document relevance is also presented, along with the taxonomy which organizes the keywords into classes defining core evidence for CRA. As demonstrated by the evaluation, the materials we have constructed provide a good basis for classification of CRA literature along multiple dimensions. They can support current manual CRA as well as facilitate the development of an approach based on TM. We discuss extending the taxonomy further via manual and machine learning approaches and the subsequent steps required to develop TM technology for the needs of CRA.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are
A Comparison and User-based Evaluation of Models of Textual Information Structure in the Context of Cancer Risk Assessment
BACKGROUND: Many practical tasks in biomedicine require accessing specific types of information in scientific literature; e.g. information about the results or conclusions of the study in question. Several schemes have been developed to characterize such information in scientific journal articles. For example, a simple section-based scheme assigns individual sentences in abstracts under sections such as Objective, Methods, Results and Conclusions. Some schemes of textual information structure have proved useful for biomedical text mining (BIO-TM) tasks (e.g. automatic summarization). However, user-centered evaluation in the context of real-life tasks has been lacking. METHODS: We take three schemes of different type and granularity - those based on section names, Argumentative Zones (AZ) and Core Scientific Concepts (CoreSC) - and evaluate their usefulness for a real-life task which focuses on biomedical abstracts: Cancer Risk Assessment (CRA). We annotate a corpus of CRA abstracts according to each scheme, develop classifiers for automatic identification of the schemes in abstracts, and evaluate both the manual and automatic classifications directly as well as in the context of CRA. RESULTS: Our results show that for each scheme, the majority of categories appear in abstracts, although two of the schemes (AZ and CoreSC) were developed originally for full journal articles. All the schemes can be identified in abstracts relatively reliably using machine learning. Moreover, when cancer risk assessors are presented with scheme annotated abstracts, they find relevant information significantly faster than when presented with unannotated abstracts, even when the annotations are produced using an automatic classifier. Interestingly, in this user-based evaluation the coarse-grained scheme based on section names proved nearly as useful for CRA as the finest-grained CoreSC scheme. CONCLUSIONS: We have shown that existing schemes aimed at capturing information structure of scientific documents can be applied to biomedical abstracts and can be identified in them automatically with an accuracy which is high enough to benefit a real-life task in biomedicine
Interactions between polycyclic aromatic hydrocarbons in complex mixtures and implications for cancer risk assessment.
In this review we discuss the effects of exposure to complex PAH mixtures in
vitro and in vivo on mechanisms related to carcinogenesis. Of particular concern
regarding exposure to complex PAH mixtures is how interactions between different
constituents can affect the carcinogenic response and how these might be included
in risk assessment. Overall the findings suggest that the responses resulting
from exposure to complex PAH mixtures is varied and complicated. More- and
less-than additive effects on bioactivation and DNA damage formation have been
observed depending on the various mixtures studied, and equally dependent on the
different test systems that are used. Furthermore, the findings show that the
commonly used biological end-point of DNA damage formation is insufficient for
studying mixture effects. At present the assessment of the risk of exposure to
complex PAH mixtures involves comparison to individual compounds using either a
surrogate or a component-based potency approach. We discuss how future risk
assessment strategies for complex PAH mixtures should be based around whole
mixture assessment in order to account for interaction effects. Inherent to this
is the need to incorporate different experimental approaches using robust and
sensitive biological endpoints. Furthermore, the emphasis on future research
should be placed on studying real life mixtures that better represent the complex
PAH mixtures that humans are exposed to.FormasAccepte
Persistent activation of DNA damage signaling in response to complex mixtures of PAHs in air particulate matter
Complex mixtures of polycyclic aromatic hydrocarbons (PAHs) are present in air particulate
matter (PM) and have been associated with many adverse human health effects including
cancer and respiratory disease. However, due to their complexity, the risk of exposure to
mixtures is difficult to estimate. In the present study the effects of binary mixtures of
benzo[a]pyrene (BP) and dibenzo[a,l]pyrene (DBP) and complex mixtures of PAHs in urban
air PM extracts on DNA damage signaling was investigated. Applying a statistical model to
the data we observed a more than additive response for binary mixtures of BP and DBP on
activation of DNA damage signaling. Persistent activation of checkpoint kinase 1 (Chk1) was
observed at significantly lower concentrations of air PM extracts than BP alone. Activation of
DNA damage signaling was also more persistent in air PM fractions containing PAHs with
more than four aromatic rings suggesting larger PAHs contribute a greater risk to human
health. Altogether our data suggests that human health risk assessment based on additivity
such as toxicity equivalency factor scales may significantly underestimate the risk of
exposure to complex mixtures of PAHs. The data confirms our previous findings with PAHcontaminated
soil (Niziolek-Kierecka et al. 2012) and suggests a possible role for Chk1
Ser317 phosphorylation as a biological marker for future analyses of complex mixtures of
PAHsFormasCancer- och AllergifondenStockholm UniversityEU/FP7 Marie Curie IRG fellowshipAccepte
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A neural classification method for supporting the creation of BioVerbNet
Abstract
Background
VerbNet, an extensive computational verb lexicon for English, has proved useful for supporting a wide range of Natural Language Processing tasks requiring information about the behaviour and meaning of verbs. Biomedical text processing and mining could benefit from a similar resource. We take the first step towards the development of BioVerbNet: A VerbNet specifically aimed at describing verbs in the area of biomedicine. Because VerbNet-style classification is extremely time consuming, we start from a small manual classification of biomedical verbs and apply a state-of-the-art neural representation model, specifically developed for class-based optimization, to expand the classification with new verbs, using all the PubMed abstracts and the full articles in the PubMed Central Open Access subset as data.
Results
Direct evaluation of the resulting classification against BioSimVerb (verb similarity judgement data in biomedicine) shows promising results when representation learning is performed using verb class-based contexts. Human validation by linguists and biologists reveals that the automatically expanded classification is highly accurate. Including novel, valid member verbs and classes, our method can be used to facilitate cost-effective development of BioVerbNet.
Conclusion
This work constitutes the first effort on applying a state-of-the-art architecture for neural representation learning to biomedical verb classification. While we discuss future optimization of the method, our promising results suggest that the automatic classification released with this article can be used to readily support application tasks in biomedicine
Benzo[a]pyrene-specific online high-performance liquid chromatography fractionation of air particulate extracts : a tool for evaluating biological interactions.
Benzo[a]pyrene (B[a]P) is a known human carcinogen and is commonly used as a
surrogate for assessing the carcinogenic risk posed by complex mixtures of
polycyclic aromatic hydrocarbons (PAHs) present in air particulate matter (PM).
However, studies have shown that using B[a]P as a surrogate may underestimate the
carcinogenic potential of PAH mixtures, as the risk assessment approach does not
consider interaction effects. Thus, toxicological studies using B[a]P to assess
its carcinogenic potential in environmentally derived complex mixtures, as
opposed to single compound experiments, could improve risk assessment. The
intention of the present study was to develop an online HPLC fractionation system
for the selective removal of B[a]P from air PM extracts. Two serial pyrenylethyl
(PYE) columns enabled selective separation of B[a]P from its isomers and other
PAHs as well as a short fractionation cycle of 30min. One run consisted of three
collection steps: the first fraction contained PAHs eluting earlier than B[a]P,
the second contained B[a]P and the last contained later-eluting PAHs. The
selectivity and recovery of the system was investigated using extracts of
Stockholm air PM samples. The overall recovery for all PAHs was approximately
80%, and the system proved to be selective, as it removed 94% of B[a]P and less
than 3% of benzo[b]fluoranthene from the complex PAH mixture. Exposing human
cells to blanks generated by the fractionation system did not induce cytotoxicity
or DNA damage signalling. In conclusion, the online HPLC system was selective for
B[a]P fractionation whilst minimising run-to-run variation and allowing repeated
fractionations for larger samples due to its relatively short run time.FormasAccepte
Nanomolar levels of PAHs in extracts from urban air induce MAPK signaling in HepG2 cells.
Polycyclic aromatic hydrocarbons (PAHs) are common environmental pollutants that
occur naturally in complex mixtures. Many of the adverse health effects of PAHs
including cancer are linked to the activation of intracellular stress response
signaling. This study has investigated intracellular MAPK signaling in response
to PAHs in extracts from urban air collected in Stockholm, Sweden and Limeira,
Brazil, in comparison to BP in HepG2 cells. Nanomolar concentrations of PAHs in
the extracts induced activation of MEK4 signaling with down-stream increased gene
expression of several important stress response mediators. Involvement of the
MEK4/JNK pathway was confirmed using siRNA and an inhibitor of JNK signaling
resulting in significantly reduced MAPK signaling transactivated by the AP-1
transcription factors ATF2 and c-Jun. ATF2 was also identified as a sensitive
stress responsive protein with activation observed at extract concentrations
equivalent to 0.1 nM BP. We show that exposure to low levels of environmental PAH
mixtures more strongly activates these signaling pathways compared to BP alone
suggesting effects due to interactions. Taken together, this is the first study
showing the involvement of MEK4/JNK/AP-1 pathway in regulating the intracellular
stress response after exposure to nanomolar levels of PAHs in environmentalFormasAccepte
Estrogen-Like Effects of Cadmium in Vivo Do Not Appear to be Mediated via the Classical Estrogen Receptor Transcriptional Pathway
Cadmium is a toxic metal classified as human carcinogen and ubiquitously found in our
environment mainly from anthropogenic activities. Exposure to cadmium has been
associated with increased risk of certain hormone-dependent cancers in humans, and the
metal has been proposed to possess endocrine disruptive properties by mimicking the
physiological actions of estrogens. However, the mechanisms behind these effects are
unclear.
The overall aim of this thesis was to provide mechanistic insights into the
estrogenicity of cadmium that may have implications for the human health. To achieve
this aim, investigations on the estrogen-like effects of cadmium as well as possible
involvement of classical/non-classical estrogen receptor signaling was studied in mice,
and these mechanisms were further scrutinized in cell-based models. Furthermore,
associations of biomarker of cadmium exposure with endogenous circulating sex
hormones were evaluated in a population-based study of women.
Results presented here indicate that exposure to cadmium does not affect the genomic
estrogen response in vivo in mice, suggesting that classical estrogen signaling is not
targeted by cadmium. However, some estrogen-like effects were observed in cadmium
exposed mice, i.e. significant thickening of uterine epithelia, in the absence of uterine
weight increase, and activation of ERK1/2 MAPKs in the liver. This suggests the
existence of alternative signaling pathways modulated by cadmium. In addition,
exposure to a wide dose range of cadmium, dose-dependently increased the expression
of the endogenous genes Mt1, Mt2, p53, c-fos, and Mdm2 in mouse liver, with p53 being
the most sensitive gene. However, phosphorylation of ERK1/2 was already induced at
the lowest exposure level (0.5µg/kg body weight), rendering ERK1/2 a more sensitive
marker of exposure than any change in gene expression. Furthermore, in vivo findings
suggest that cadmium-induced effects are markedly concentration dependent: low-level
exposure activates protein-kinases whereas high-level exposure turns on cellular stress
responses. The data from in vitro studies indicate that cadmium at regular human
exposure levels activates protein-kinase signaling through Raf-MEK-ERK/MAPKs, and
we identified EGFR and GPR30 as the mediating receptors. This cadmium-induced
activation of protein-kinases further leads to a disturbance in Mdm2/p53 balance, with a
significant increase in the Mdm2/p53 ratio in the presence of genotoxic compounds,
which in turn suggest that cadmium may disrupt stress response to genotoxins. In 438
postmenopausal women, a positive association was observed between the concentrations
of cadmium in blood and testosterone in serum, while an inverse association was
observed with estradiol. This may suggest that cadmium affects steroidogenesis.
In conclusion, data presented in this thesis collectively suggests that cadmium-induced
estrogen-like effects do not involve classical estrogen receptor signaling but rather
appear to be mediated via membrane-associated signaling. The activation/
transactivation of GPR30/EGFR-Raf-MEK-ERK/MAPKs and Mdm2 represent a general
mechanism by which cadmium may exert its effects. Since EGFR, ERK and Mdm2 are
all known key players in cancer promotion, cadmium-induced activation of these and
disturbance in the estradiol/testosterone balance in women may have implications for the
promotion/development of hormone-related cancers
Sensitivity of Salmonella YG5161 for detecting PAH-associated mutagenicity in air particulate matter.
The Salmonella/microsome assay is the most used assay for the evaluation of air
particulate matter (PM) mutagenicity and a positive correlation between strain
TA98 responses and benzo[a]pyrene (B[a]P) levels in PM has been found. However,
it seems that the major causes of PM mutagenicity in this assay are the nitro and
oxy-PAHs. Salmonella YG5161, a 30-times more responsive strain to B[a]P has been
developed. To verify if YG5161 strain was sufficiently sensitive to detect
mutagenicity associated with B[a]P mutagenicity, PM samples were collected in
Brazil and Sweden, extracted with toluene and tested in the Salmonella/microsome
microsuspension assay. PAHs and B[a]P were determined and the extracts were
tested with YG5161 and its parental strain TA1538. The extracts were also tested
with YG1041 and its parental strain TA98. For sensitivity comparisons, we tested
B[a]P and 1-nitropyrene (1-NP) using the same conditions. The minimal effective
dose of B[a]P was 155 ng/plate for TA1538 and 7 ng/plate for YG5161. Although the
maximum tested dose, 10 m(3) /plate containing 9 ng of B[a]P in the case of
Brazilian sample, was sufficient to elicit a response in YG5161, mutagenicity was
detected at a dose as low as 1 m(3) /plate (0.9 ng). This is probably caused by
nitro-compounds that have been shown to be even more potent than B[a]P for
YG5161. It seems that the mutagenicity of B[a]P present in PM is not detectable
even with the use of YG5161 unless more efficient separation to remove the
nitro-compounds from the PAH extract is performed.FormasAccepte
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