Biology subjects

Tellier, L.

Publications and source records attributed to Tellier, L..

2 recordsLinked to original sources

bioRxiv2026

DLC1 loss drives multicellular streaming invasion by enforcing spatially coordinated Rho and β1 integrin signaling

Efficient cancer cell invasion requires coordinated control of actomyosin contractility, extracellular matrix (ECM) engagement and remodeling, yet how these processes are integrated in complex, three-dimensional (3D) environments remains unclear. Here, we identify the tumor suppressor and RhoGAP protein DLC1 as molecular brake on multicellular streaming invasion in collagen-rich ECM. Using CRISPRoff-engineered breast cancer spheroids, DLC1 reconstitution models and patient-derived organoids embedded in collagen gels, we show that DLC1 downregulation promotes an efficient multicellular streaming phenotype. This invasion program requires matrix metalloproteinase activity, {beta}1 integrin engagement and Rho-ROCK-dependent actomyosin contractility. Mechanistically, DLC1 downregulation stabilized the rear-polarization of RhoA activity and increased {beta}1 integrin abundance, plasma membrane localization and activation. Separation-of-function mutants revealed that DLC1 restrains invasion through a dual mechanism: its RhoGAP activity limited actomyosin-driven streaming, whereas its LD-like talin-binding motif controlled {beta}1 integrin enrichment at the plasma membrane. Together, our findings provide a molecular basis for the prominent role of DLC1 as a metastasis suppressor.

Cancer Biology↗

bioRxiv2017

Accurate Genomic Prediction Of Human Height

We construct genomic predictors for heritable and extremely complex human quantitative traits (height, heel bone density, and educational attainment) using modern methods in high dimensional statistics (i.e., machine learning). Replication tests show that these predictors capture, respectively, ~40, 20, and 9 percent of total variance for the three traits. For example, predicted heights correlate ~0.65 with actual height; actual heights of most individuals in validation samples are within a few cm of the prediction. The variance captured for height is comparable to the estimated SNP heritability from GCTA (GREML) analysis, and seems to be close to its asymptotic value (i.e., as sample size goes to infinity), suggesting that we have captured most of the heritability for the SNPs used. Thus, our results resolve the common SNP portion of the \"missing heritability\" problem - i.e., the gap between prediction R-squared and SNP heritability. The ~20k activated SNPs in our height predictor reveal the genetic architecture of human height, at least for common SNPs. Our primary dataset is the UK Biobank cohort, comprised of almost 500k individual genotypes with multiple phenotypes. We also use other datasets and SNPs found in earlier GWAS for out-of-sample validation of our results.

genomics↗