[SOUND] [MUSIC] In this lecture, I will discuss the concept of the Connectivity Map. This lecture aims to provide a historical prospective about LINCS and to get you into the fundamentals of this course. So at the beginning of time in 2006, Justin Lamb et al., published an important paper in Science called the Connectivity Map. The idea was to create a system where people will be able to submit a disease signature into a web based tool and find drug signatures that can either mimic or reverse the expression signature of the disease. So what is a disease signature? What is a drug signature? Why this is so important and we think it's transformative. These are some of the questions that we try to answer in LINCS, and we will try to answer in this lecture, and in general, in this course. So let's start with the question: What is a disease signature? So when we compare the same data points collected from normal, healthy, wild type tissue, or cells, or people, to the disease state. We can identify differences between the two types of samples. And by this we can define a disease signature. We need to have a good sample size so we can assess the variance in the data. How much the value of the variables spread within each group, the disease group and normal group. We also need to measure the relevant data points. We need to measure many data points. We want to have a vector of variables not just a single biomarker. This allows to identify correlates, but what we really want is mechanisms. We want to be able to explain the observed changes in a theory that is based on our understanding of how the system actually works, at the molecular level. In the case of the Connectivity Map project, we measure genome wide mRNA expression to define a disease signature as well as developing drug expression signatures. And then we try to map them. So I mentioned mRNA expression. So what is mRNA, for those of you who are not coming with a biology background. So, mRNA is an abbreviation for messenger ribonucleic acid. It is a type of molecule that can be found inside cells. When a gene, coded in our DNA, is transcribed, an mRNA molecule is created. The mRNA molecule travels from the nucleus to the ribosomes, which typically live outside of the nucleus. In the ribosomes, mRNA molecules are translated to proteins. Each gene give rise to one or few unique mRNA molecules. Each mRNA molecule can be mapped to a gene and there could be many mRNA molecules from the same gene. Gene expression assays are methods to count the different levels of the different types of mRNAs inside cells. In each human cell, there are at least 30,000 different species of mRNA, each with many copies. So what are those assays that can measure genome-wide mRNA levels? So the short story is that we are not going to get into those assays, because this is out of the scope of this course. We're going to talk about the L1000 assay, which is a unique assay for LINCS. However, in the midterm and final you will be asked about those technologies. So, I recommend to the research and find out information about each of those technologies, if you are interested in taking those exams. So this is the Connectivity Map concept. This figure is commonly used to describe the idea of the Connectivity Map. Initially you start from a disease model, and that can be cells from a person. Those can be cell lines or it can be a mouse model of disease. Then using gene expression assays, you can identify the genes that are upregulated or downregulated in the disease compared to the normal tissue. Then you want to query that disease signature against a database of drug signatures. Those are experiments that were done in high throughput to measure gene expression where individual drugs were used to stimulate cells and expression signatures were developed when comparing unstimulated cells with stimulated cells. This allows us to prioritize small molecules that can either reverse or mimic the expression in the disease. On this website, which was a part of that Science paper from 2006, users can upload their up and down disease signatures to obtain a ranked list of drugs from the old version of the Connectivity Map. There was much success in using this tool for many projects. A recent publication in cell identified a drug for obesity. So, the paper provided some lay summary about how that drug was identified using the Connectivity Map and then validated using a mouse model of the disease. So let's read it together. Despite all modern advances in medicine, an effective drug treatment of obesity has not been found yet. Discovery of leptin two decades ago created hopes for treatment for obesity. However, development of leptin resistance has been a big obstacle, mitigating a leptin-centric treatment of obesity. Here, by using in silico drug-screening methods, we discovered that Celastrol, a pentacyclic triterpene, extracted from the roots of the thunder god vine plant, is a powerful anti-obesity agent. Celastrol suppresses food intake, blocks reduction of energy expenditure, and leads to up to 45% weight loss in hyperleptinemic diet-induced obese mice by increasing leptin sensitivity. But, it is ineffective in leptin-deficient ob/ob and leptin receptor-deficient db/db mouse models. These results indicate that Celastrol is a leptin sensitizer and a promising agent for the pharmaceutical treatment of obesity. So this is an example of how a mouse model of obesity was used to query the connectivity map to prioritize drugs and then test the top hit drug in mouse models and provide not only a potential treatment, but also some mechanisms of how the drug actually works. A word of caution though, is that it worked in mice, it worked in one study, so people should not start chewing the roots of this thunder god vine yet, but it is a promising lead.
