Modelling colorectal cancer tumourigenesis using intestinal organoids

Colorectal cancer is one of the most common cancers leading to death, thus a better understanding of the mechanisms that lead to tumour initiation and progression are required to develop novel therapeutics. Current models for the study of this disease involve two-dimensional cell culture as well as animal studies. These have their advantages, however they do not contain complexity or species-specific implications. Human derived intestinal organoids provide a model that demonstrates the complexity and hierarchy of stem cells to differentiated cells that could allow for further insight into the disease, yet they are not without their own disadvantages. Production of human intestinal organoids is inefficient, and time consuming. I developed a screen to increase the efficiency of production using a small library of tool compounds targeting epigenetic regulators.M.A.S

SGA output for analysis sets 11-20.

Query strains that express one effector were mated to an array of ~330 effectors in groups of ~10 queries at a time ("Analysis Set"). The arrays were then imaged using a high-resolution camera and the spot sizes were quantified using SGAtools (http://sgatools.ccbr.utoronto.ca/). Outlier spot sizes flagged by the Jackknife filter (JK) in SGAtools were removed and the average and standard deviation of the remaining values were calculated and normalized to the average empty vector control. This .zip archive includes spreadsheets that encompass the raw SGAtools data output from the paper for analysis sets 11-20

SGA output for analysis sets 21-30.

Query strains that express one effector were mated to an array of ~330 effectors in groups of ~10 queries at a time ("Analysis Set"). The arrays were then imaged using a high-resolution camera and the spot sizes were quantified using SGAtools (http://sgatools.ccbr.utoronto.ca/). Outlier spot sizes flagged by the Jackknife filter (JK) in SGAtools were removed and the average and standard deviation of the remaining values were calculated and normalized to the average empty vector control. This .zip archive includes spreadsheets that encompass the raw SGAtools data output from the paper for analysis sets 21-30

SGA output for analysis sets 1-10.

Query strains that express one effector were mated to an array of ~330 effectors in groups of ~10 queries at a time ("Analysis Set"). The arrays were then imaged using a high-resolution camera and the spot sizes were quantified using SGAtools (http://sgatools.ccbr.utoronto.ca/). Outlier spot sizes flagged by the Jackknife filter (JK) in SGAtools were removed and the average and standard deviation of the remaining values were calculated and normalized to the average empty vector control. This .zip archive includes spreadsheets that encompass the raw SGAtools data output from the paper for analysis sets 1-10

SGA output for analysis sets 31-37.

Query strains that express one effector were mated to an array of ~330 effectors in groups of ~10 queries at a time ("Analysis Set"). The arrays were then imaged using a high-resolution camera and the spot sizes were quantified using SGAtools (http://sgatools.ccbr.utoronto.ca/). Outlier spot sizes flagged by the Jackknife filter (JK) in SGAtools were removed and the average and standard deviation of the remaining values were calculated and normalized to the average empty vector control. This .zip archive includes spreadsheets that encompass the raw SGAtools data output from the paper for analysis sets 31-37

Data from: Diverse mechanisms of metaeffector activity in an intracellular bacterial pathogen, Legionella pneumophila

Pathogens deliver complex arsenals of translocated effector proteins to host cells during infection, but the extent to which these proteins are regulated once inside the eukaryotic cell remains poorly defined. Among all bacterial pathogens, Legionella pneumophila maintains the largest known set of translocated substrates, delivering over 300 proteins to the host cell via its Type IVB, Icm/Dot translocation system. Backed by a few notable examples of effector–effector regulation in L. pneumophila, we sought to define the extent of this phenomenon through a systematic analysis of effector–effector functional interaction. We used Saccharomyces cerevisiae, an established proxy for the eukaryotic host, to query > 108,000 pairwise genetic interactions between two compatible expression libraries of ~330 L. pneumophila‐translocated substrates. While capturing all known examples of effector–effector suppression, we identify fourteen novel translocated substrates that suppress the activity of other bacterial effectors and one pair with synergistic activities. In at least nine instances, this regulation is direct—a hallmark of an emerging class of proteins called metaeffectors, or “effectors of effectors”. Through detailed structural and functional analysis, we show that metaeffector activity derives from a diverse range of mechanisms, shapes evolution, and can be used to reveal important aspects of each cognate effector's function. Metaeffectors, along with other, indirect, forms of effector–effector modulation, may be a common feature of many intracellular pathogens—with unrealized potential to inform our understanding of how pathogens regulate their interactions with the host cell

Global Analysis of the Regulon of the Transcriptional Repressor LexA, a Key Component of SOS Response in Mycobacterium tuberculosis*

Background: Mycobacterium tuberculosis LexA is thought to repress the expression of a small number of genes