Geometry can provide long-range mechanical guidance for embryogenesis

Downstream of gene expression, effectors such as the actomyosin contractile machinery drive embryo morphogenesis. During $\textit{Drosophila}$ embryonic axis extension, actomyosin has a specific planar-polarised organisation, which is responsible for oriented cell intercalation. In addition to these cell rearrangements, cell shape changes also contribute to tissue deformation. While cell-autonomous dynamics are well described, understanding the tissue-scale behaviour challenges us to solve the corresponding mechanical problem at the scale of the whole embryo, since mechanical resistance of all neighbouring epithelia will feedback on individual cells. Here we propose a novel numerical approach to compute the whole-embryo dynamics of the actomyosin-rich apical epithelial surface. We input in the model specific patterns of actomyosin contractility, such as the planar-polarisation of actomyosin in defined ventro-lateral regions of the embryo. Tissue strain rates and displacements are then predicted over the whole embryo surface according to the global balance of stresses and the material behaviour of the epithelium. Epithelia are modelled using a rheological law that relates the rate of deformation to the local stresses and actomyosin anisotropic contractility. Predicted flow patterns are consistent with the cell flows observed when imaging $\textit{Drosophila}$ axis extension $\textit{in toto}$, using light sheet microscopy. The agreement between model and experimental data indicates that the anisotropic contractility of planar-polarised actomyosin in the ventro-lateral germband tissue can directly cause the tissue-scale deformations of the whole embryo. The three-dimensional mechanical balance is dependent on the geometry of the embryo, whose curved surface is taken into account in the simulations. Importantly, we find that to reproduce experimental flows, the model requires the presence of the cephalic furrow, a fold located anteriorly of the extending tissues. The presence of this geometric feature, through the global mechanical balance, guides the flow and orients extension towards the posterior end.All the computations presented in this paper were performed using the Cactus platform of the CIMENT infrastructure (https://ciment.ujf-grenoble.fr), which is supported by Région Rhône-Alpes (GRANT CPER07-13, http://en.rhonealpes.fr/370-the-quality-of-the-research.htm). MD, PS and JE thank Région Rhône-Alpes (CIBLE and IXXI, all authors; CMIRA, JE only), MD thanks Malian government and French embassy in Bamako "Bourse d’Excellences" programme, LIPHY and LJK (CNRS and Univ. Grenoble Alpes) for financial support. MD and JE thank ANR-12-BS09-0020-01 "Transmig" and ANR-11-LABX-0030 "Tec21", and are members of GDR 3570 MecaBio and GDR 3070 CellTiss of CNRS. JE thanks the Isaac Newton Institute for Mathematical Sciences for its hospitality during the programme "Coupling Geometric PDEs with Physics for Cell Morphology, Motility and Pattern Formation" supported by EPSRC Grant Number EP/K032208/1. CML, GBB and BS were supported by Wellcome Trust Investigator Award 099234/Z/12/Z to BS

Hypermethylated DNA, a circulating biomarker for colorectal cancer detection

<div><p>Background</p><p>Colorectal cancer (CRC) is one of the most common cancers in the western world. Screening is an efficient method of reducing cancer-related mortality. Molecular biomarkers for cancer in general and CRC in particular have been proposed, and hypermethylated DNA from stool or blood samples are already implemented as biomarkers for CRC screening.</p><p>We aimed to evaluate the performance of proven hypermethylated DNA promoter regions as plasma based biomarkers for CRC detection.</p><p>Methods</p><p>We conducted a cross-sectional case-control study of 193 CRC patients and 102 colonoscopy-verified healthy controls. Using methylation specific polymerase chain reaction, we evaluated 30 DNA promoter regions previously found to be CRC specific. We used multivariable logistic regression with stepwise backwards selection, and subsequent <i>leave-pair-out cross validation</i>, to calculate the optimism corrected area under the receiver operating characteristics curve (AUC) for all stage as well as early stage CRC.</p><p>Results</p><p>None of the individual DNA promoter regions provided an overall sensitivity above 30% at a reasonable specificity. However, seven hypermethylated promoter regions (<i>ALX4</i>, <i>BMP3</i>, <i>NPTX2</i>, <i>RARB</i>, <i>SDC2</i>, <i>SEPT9</i>, and <i>VIM</i>) along with the covariates sex and age yielded an optimism corrected AUC of 0.86 for all stage CRC and 0.85 for early stage CRC. Overall sensitivity for CRC detection was 90.7% at 72.5% specificity using a cut point value of 0.5.</p><p>Conclusions</p><p>Individual hypermethylated DNA promoter regions have limited value as CRC screening markers. However, a panel of seven hypermethylated promoter regions show great promise as a model for CRC detection.</p></div

Receiver operating characteristics curves for colorectal cancer.

<p>Note. A) ROC curve for all stage CRC with a non-optimism corrected AUC of 0.8870. B) ROC curve for stage I and II CRC with a non-optimism corrected AUC of 0.8775. Receiver operating characteristics (ROC). Colorectal cancer (CRC). Area under the receiver operating characteristics curve (AUC).</p

Stepwise backwards selection according to model number.

<p>Note. Logistic regression modelling with stepwise backwards selection. Potential predictor variables are located in the top row. Model number is recorded in the left column. Area under the receiver operating curve (AUC) according to model number and the P-value according to the removed predictor variable is located in the two rightward columns.</p

Model 12s sensitivity, specificity, PPV, and NPV for CRC.

<p>Model 12s sensitivity, specificity, PPV, and NPV for CRC.</p

Patient characteristics.

<p>Patient characteristics.</p

Number of methylated promoter regions according to patient group.

<p>Number of methylated promoter regions according to patient group.</p

Hypermethylation status according to gene names.

<p>Hypermethylation status according to gene names.</p

Additional file 1 of Colorectal cancer incidences in Lynch syndrome: a comparison of results from the prospective lynch syndrome database and the international mismatch repair consortium

Additional file 1