A systematic review of epidemiologic studies of styrene and cancer

<p>Previous epidemiology reviews of exposure to styrene and the risk of cancer considered studies published through 13 November 2013. Since then, additional relevant research has been published. No review has included meta-analyses. The current systematic review considered research published through June 2017; included meta-analyses of the relationship between any exposure to styrene and cancers identified as being of concern, including non-Hodgkin lymphoma (NHL), leukemia and cancers of the esophagus, pancreas, lung and kidney; and evaluated several other forms of cancer. Meta-relative risks for all studies were 1.14 (95% confidence interval (CI), 0.91–1.43) for NHL, 1.00 (95% CI, 0.80–1.26) for multiple myeloma, 0.98 (95% CI, 0.87–1.09) for all leukemia, 1.03 (95% CI, 0.92–1.15) for esophageal cancer, 1.02 (95% CI, 0.93–1.12) for pancreatic cancer, 1.09 (95% CI, 0.95–1.24) for lung cancer and 1.10 (95% CI, 0.99–1.22) for kidney cancer. Individual studies provided little evidence of exposure-response or induction time trends. Limitations of the available research and of the meta-analyses included reliance in most studies on mortality data rather than on incidence data, lack of quantitative estimates of styrene exposure for individual subjects and lack of information on lifestyle factors. Consideration of all pertinent data, including substantial recent research, indicates that the epidemiologic evidence on the potential carcinogenicity of styrene is inconclusive and does not establish that styrene causes any form of cancer in humans.</p

Identifying reprogramming candidates.

<p>For a given cell fate, we plot every differentially expressed transcription factor's (TF) predictivity (aka energy projection-contribution, ) vs TF expression level (z-score normalized). Unless otherwise stated all existing reprogramming protocols to a given cell fate are labeled. (A) Schematic illustrating predictivity vs expression level plots. The large positive (negative) predictivity and large positive (negative) gene expression TFs are candidates for over expression (knock out) in a reprogramming protocol. The TFs with z-score between and are highlighted in gray because <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003734#pcbi-1003734-g002" target="_blank">Figure 2B</a> suggests these TFs predictivity may be prone to extra noise induced by the data discretization. (B) Embryonic stem cell, ESC (induced pluripotent stem cells, iPSC). Original Takahashi and Yamanaka factors <i>Pou5f1</i> (<i>Oct 4</i>), <i>Sox2</i>, <i>Klf4</i>, and <i>Myc </i><a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003734#pcbi.1003734-Takahashi1" target="_blank">[1]</a>. (C) Inset of ESC positive predictivity and gene expression. <i>Zfp42</i> (<i>Rex1</i>) <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003734#pcbi.1003734-Masui1" target="_blank">[40]</a> and <i>Nr0b1</i> (<i>Dax1</i>) <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003734#pcbi.1003734-Khalfallah1" target="_blank">[41]</a> are pluripotency markers that are not necessary to overexpress for reprogramming, while combinations of the remaining labeled TFs have been successfully used in reprogramming protocols <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003734#pcbi.1003734-Gonzlez1" target="_blank">[8]</a>. (D) Heart (induced cardiomyocytes, iCM) <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003734#pcbi.1003734-Ieda1" target="_blank">[3]</a>. (E) Liver (induced hepatocytes, iHep). There are two published protocols. One protocol used <i>Hnf4a</i> plus any of <i>Foxa1</i>, <i>Foxa2</i>, or <i>Foxa3 </i><a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003734#pcbi.1003734-Sekiya1" target="_blank">[4]</a> while another used <i>Gata4</i>, <i>Foxa3</i>, <i>Hnf1a</i>, and deletion of <i>p19Arf </i><a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003734#pcbi.1003734-Huang1" target="_blank">[5]</a>. <i>p19Arf</i> was not differentially expressed in our microarrays and is not shown. (F) Thyroid <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003734#pcbi.1003734-Antonica1" target="_blank">[7]</a>. (G) Neural Progenitor Cells, NPC (induced NPC, iNPC) used <i>Pou3f2</i> (<i>Brn2</i>), <i>Sox2</i>, and <i>Foxg1 </i><a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003734#pcbi.1003734-Lujan1" target="_blank">[6]</a>. With our microarrays we find that <i>Foxg1</i> is not predictive for NPC but is predictive of neural stem cells (NSC) (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003734#pcbi.1003734.s003" target="_blank">Figure S3</a>). (H) Neurons (induced neuron, iN) <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003734#pcbi.1003734-Vierbuchen1" target="_blank">[2]</a>. The reprogramming protocol used a combination of factors that were known to be important to ether mature neurons (<i>Myt1l</i>) or NPCs (<i>Pou3f2</i>, <i>Ascl1</i>). (G) shows that <i>Pou3f2</i> and <i>Ascl1</i> are predictive of NPCs.</p

Partially reprogrammed cells as spurious attractors.

<p>Partially reprogrammed cell lines (first column) and their significant projections (2 std above noise or ) onto “natural” cell fates based on microarray data. Bold indicates 3 std above noise or . Abbreviations: CLP, Common Lymphoid Progenitor; CMP, Common Myeloid Progenitor; EpiSC, epiblast stem cell; ESC, embryonic stem cell; GMP, Granulocyte-Monocyte Progenitor; MEF, mouse embryonic fibroblast; MEP, Megakaryocyte-Erythroid Progenitor; MSC, Mesenchymal stem cells; NK, Natural Killer cells; NSC, neural stem cells.</p

Overview of model.

<p>(A) Histone 3 tri-methylation at lysine 4 (K4) is associated with active genes, while histone 3 tri-methylation at lysine 27 (K27) is associated with repressed genes. (B) Conditional probability distribution of histone modification (HM) given transcription factor (TF) expression levels derived by comparing microarray data with HM data from <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003734#pcbi.1003734-Mikkelsen2" target="_blank">[36]</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003734#pcbi.1003734-Meissner1" target="_blank">[37]</a>. Notice the sharp threshold (black line) between expression levels of active and inactive TFs. (C) For mathematical convenience, we take the continuous TF expression levels and convert it to binary states (z-score to and z-score to ). This binarization is consistent with the result from (B). (D) An arbitrary state is represented by a vector of , with each dimension in the vector space representing the state of a TF. The natural cell fates form a subspace (gray plane). The landscape model is based on the orthogonal projection of the TF state onto this subspace. (E) The dynamics of the landscape model for different initial conditions for a fully connected interaction matrix and a diluted (non-equilibrium) interaction matrix where 20% of interactions have been randomly deleted. Plot shows the projection of on embryonic stem cells (ESC) as function of time. Notice the large basins of attraction (red bracket). Parameters used were and burst errors of every spin updates. (F) Simulations showing how a common myeloid progenitor (CMP) can differentiate into either granulo-monocytic progenitors (GMP) or megakaryocyte-erythroid progenitors (MEP) in response to two distinct external signals. All trajectories used . For signal 1, we set and all other . For signal 2, we set and all other .</p

Mathematical model of cell identity landscape.

<p>This table provides a summary of the landscape model and the biological interpretation of each term. The first column is written in index notation, while the second column is the same term in matrix notation with the dimension of the term given in parenthesis. If no dimension is listed, the term is a single number.</p

Phenotypic landscape.

<p>These are illustrative cartoons of the cell fate attractor landscape. (A) The minimal cellular identity landscape. Each cell fate is a basin of attraction (black circles). Reprogramming between different cell fates (1 and 2) can occur probabilistically via different trajectories (black paths). Partially reprogrammed cells (PRC) exist as smaller, spurious, basins of attraction (red circle) that can be experimentally observed by reprogramming experiments (example trajectory in red). (B) Same cellular identity landscape in the presence of a stabilizing environment (ex. favorable culturing medium) for cell fate 2. The environment increases the radius and depth of the cell fate 2 basin of attraction. (C) Landscape in the presence of an external signal that gives rise to differentiation from cell fate 1 to cell fate 2 (ex. growth factors associated with differentiation). Notice the low energy path between the cell fates that drives switching from cell fate 1 to cell fate 2.</p

The <i>che-3(am178)</i> mutation caused temperature-sensitive defects in DiO staining of amphid neurons.

∞<p>For lines 1–4, hermaphrodites were cultured from conception to L4 at the indicated temperature. For lines 5–8, hermaphrodites were cultured from conception to L4 at 20°C and then shifted to the indicated temperature for approximately 24 hours.</p

Ethosuximide stimulated dauer formation in a low-density, well-fed population at 27°C.

***<p>p<0.0001, comparisons are to the same genotype with no drug treatment. p values determined by Student T-test.</p

Mutants with defects in cilia structure were resistant to ethosuximide toxicity.

∞<p>Animals cultured at 20°C were classified as normal (+) or defective (Dyf) for amphid neuron dye filling (see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1000230#s4" target="_blank">Materials and Methods</a>).</p>#<p>Experiments were done with 12 mg/ml ethosuximide. The differences in percent resistance between <i>che-3</i> alleles were not statistically significant except that <i>che-3(am162)</i> was significantly different from <i>che-3(am178)</i>, <i>che-3(am165)</i> and <i>che-3(e1124)</i> (p<0.0001). The differences in percent resistance between <i>osm-3</i> alleles were not statistically significant except that <i>osm-3(p802)</i> was significantly different from <i>osm-3(am161)</i> and <i>osm-3(am177) (p<0.0001)</i>.</p>†<p>The dye-filling phenotype of these mutants was described previously <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1000230#pgen.1000230-Perkins1" target="_blank">[28]</a>.</p>*<p>These values may underestimate the fraction of animals that were resistant to ethosuximide, since we observed multiple animals that were mature, indicating they were ethosuximide resistant, but they were desiccated on the side of the dish and not included in the data. We have observed that mutants with severe chemotaxis defects have a propensity to leave the agar surface.</p

<i>am177</i> is a missense mutation of <i>osm-3</i>.

<p>(A) A schematic of the OSM-3 protein showing amino acid numbers (below) and four functional regions: the motor (left diagonal lines), neck (shaded), rod (open) and tail (right diagonal lines) <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1000230#pgen.1000230-Snow1" target="_blank">[47]</a>. The position of <i>am177</i> and six previously characterized mutations are shown above with the mutation name and molecular change <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1000230#pgen.1000230-Snow1" target="_blank">[47]</a>. (B) The predicted amino acid sequence of OSM-3 (M02B7.1B). The <i>am177</i> mutation changes codon 329 from AAC (N) to CAC (H), affecting the neck region of the protein.</p