Joint Inferences of Natural Selection from Population Genomic Data
DateTuesday, February 5, 2019 - 4:00pm
AbstractThe distribution of fitness effects (DFE) among new mutations quantifies how likely a mutation is to have severe, moderate, or minor effect. The DFE is a key determinant of how functional genetic variation is distributed among and within populations, and much research has focused on inferring the DFE from population genetic data. Here, I describe ongoing work on two novel mathematical approaches for inferring the DFE. First, we have extended the conventional one-dimensional DFE to a two-dimensional DFE, to account for differences in fitness affects between populations. Second, we have developed a diffusion model of how selection acts on pairs of nearby mutations, which offers the prospect of inferring how mutations interact.
The Control of Growth, Patterning and Drug Response of the Intestinal Epithelium
DateTuesday, February 12, 2019 - 4:00pm
AbstractThe epithelial lining of the human intestine is a prime example of tight homeostatic control of cell proliferation, organization and fate determination. Estimated to have a surface area the size of a tennis court, it continuously receives mechanical, chemical and pathogen-derived insults and is in constant turnover, completely renewing every five days. Amazingly, this active process produces multiple cell types at just the right ratios and locations throughout our life span. Failure of this exquisite control is the basis for diseases including inflammatory bowel disease and cancers of the esophagus, stomach, small and large intestine. To accelerate our ability to study control of cell fate and discover new therapies, we have developed the culture of intestinal stem cells (ISCs) in a high-throughput, quantitative, two-dimensional format. Using this novel “enteroid monolayer” system, we have systematically perturbed intrinsic and extrinsic WNT/BMP signaling to reveal a core morphogenic feedback pathway that controls tissue growth and patterning. Our work demonstrates that the intestinal epithelium, without contributions from the mesenchyme or 3-D crypt geometry, has the intrinsic ability to regulate proliferation and patterning through morphogen-mediated feedback. Additionally we explored Glycogen Synthase Kinase 3 (GSK-3), a protein kinase that is uniquely positioned to act as a signaling by-pass for cancer cells to evade targeted therapies. We found GSK-3 suppression can affect the cellular sensitivities to a broad spectrum of chemotherapies and targeted oncology drugs (e.g., inhibitors of RTKs, mTOR, PLK1). Combined with a kinome-wide RNAi screen, we have shown GSK-3 is a central drug response modulator that affects potency of ~50% of current, clinically relevant kinase-targeted drugs. Our findings suggest small molecule activators of GSK-3 would have potent anti-tumor activity as single agents or in combination therapies.
Normalization Methods in Single-cell RNA Sequencing
DateTuesday, February 19, 2019 - 4:00pm
AbstractThrough gene sequencing experiments, researchers can analyze the genetic content of tumors or developing embryos and better understand the importance of particular genes during stages of development. Single-cell RNA-sequencing (scRNA-seq) provides a means to assess transcriptomic variations among individual cells, rather than over the tumor as a whole, giving an advantage over bulk sequencing methods that fail to detect subgroups and rare cell types. However, restrictions such as amplification bias, technical noise, and dropout events often limit the power of scRNA-seq results. To address these issues, various normalization methods have been developed that correct observed gene counts to account for existing noise and more accurately represent the true biological signal of interest. Eliminating technical noise and amplification error often involves the use of a set of exogenous genes injected into the cell in known quantities, referred to as “spike-in genes”. By statistically modeling the difference between observed gene counts and known gene counts, the resulting model can then apply to all other genes present in the cell, adjusting observed gene counts accordingly. I propose a novel scRNA-seq normalization method that normalizes between a data set’s groups while also using dropout imputation to adjust for missing values. I compare this method with existing normalization approaches, using real data sets to support my results.