So today, as Jacques just said, we're going to talk about interaction with the host genome. But in particularly, what is interesting here is the variation in the host genome. So at the nucleotide level, so polymorphism sniffs in the host that can have an effect or that can modulate what happens, in particular in the gut, but we'll see a few examples of not necessarily limited to the gut. So this is just a brief outline. Basically we'll go over some of the measures of the microbiome that will be assessed and that we're going to try to correlate or associate with the genetic variance in the human. And we're going to go through three different kinds of host interaction in this concept. The first one is how the human sniffs, the human variations, can affect the gut microbiome. So in broad terms, as you can conceive of. The second one is how the gut microbiome can interact, how drugs have an effect on the body and vice versa, and I'll give you a few examples of that. But also we're going to end with a new kind of analysis, which I think is really exciting. It's a kind of work where you're doing genome to genome. It's like GWAS to GWAS analysis, looking at genetic variance throughout the genome in the human versus genetic variance throughout the genome of a bug. So before we go through some of those examples, let's start with a few words of wisdom. So first of all, the major differences between the human genome and the human microbiome. And I'm sure that by now you're quite familiar with the human microbiome, which has been the topic of conversation. So when we're considering the human genome, obviously there is only one genome and it's stable. And so when we do analyses with that, we're basically taking the DNA from anywhere, could be buccal swab, it could be blood, and basically we assume that the genome is going to be stable and that the variation in that genome represents the variation we had at birth and represents a variation that is having an impact on our health throughout the lifespan. But in the human microbiome, of course, there are many genomes and recombination occurs. So somatic recombination in human is not that frequent and when it happens, we tend to be concerned. But in the microbiome, it does happen and it happens, it can happen independently of reproduction. And so funny things can happen and also the human microbiome will change in response to the environment so it can evolve, it can adapt to changes. This is definitely not something that is happening to the human genome which is barely stable. And so those are some of the highlights of the difference. Now, when we do genetic association, when we're trying to establish a correlation between the human variation and the microbiome, we're going to have to define what we want to test. So what we call in statistics the endpoint or the outcome or the dependent variable. What are we trying to model? So in broad terms, this is what can be modeled and this, I hope you're familiar with, so hopefully this is the kind of outcomes you've been working with. So we can be interested in the diversity of the microbiome. This can be characterized as alpha diversity, beta diversity. We may be interested in the abundance of a specific species. So how much of one species is present? And how does that correlate with the human genetic variant? So remember, those are the outcomes. This is what we're trying to model in the microbiome. And we may be interested in functional profiling. So instead of asking how diverse it is or how much of one bug is present, we may be interested in asking, what does it do? Regardless of what the bugs are behind it, but what is the function? And this may be performed in a number of different ways. And we're going to go through a few examples of that. But I'm sure you're familiar with that by now. And of course, we may be interested in the strain level variation. So if you take one strain, that strain will have variability. And that variability will be dynamic over time. And we may be interested in using that as an endpoint. How does the human variation associate with specific variants in the bug of interest as it mutates over time? Few last words of wisdom. So what would be the advantages or what are the distinctions between doing microbial functional profiling versus just abundance? Well, when we work, when we do statistical associations, as you know your outcome that you're trying to model, you want it to be as stable and as replicable as possible. You want the variability within individuals and between the individual to not be so great that you can't model it, okay? See, some variability is essential, otherwise you can't model anything. But too much variability that is explained by unknown factors is just too much. So when we're looking at the abundance of specific species, we know and you probably know this, that there's a lot of variability, even within an individual and between individual. And the idea of doing functional profiling is that we're attempting to reduce that variability, where it's in a way we're trying to reduce features, to reduce error that couldn't be accounted for. So I think that's the major aspect of why one would be interested in functional profiling. Okay, so first example and so this is an example of how genetic variation in humans and people is modulating the microbiome. And this is the, I don't know if any of you prepared for this lecture, but I saw on the poster that there was a reading that was assigned to the cars. And this is the paper of the reading. Anybody looked at it? Okay, of course. So Jacques chose this paper and it's a good paper. It's not my favorite, Jacques, but we're going to go over it. So it was published in an issue of Nature Genetics in November 2016. And in that issue, in that specific issue, they had three papers back to back on this specific topic of how genetic variation can modulate the microbiome. And this is one of them and we're going to see a second one. We're not going to see all three. We're just going to look at two of them and it's going to give you a pretty good idea as to how this can happen. So this one is very simply called the effects of host genetics on the microbiome. So what did they do? Well, first of all, when we're studying human variation in the host, we need large numbers and this is in the world of genome-wide association studies, GWAS, we typically work with large cohorts. And here they have what would be considered a small cohort in the GWAS world. So typically when we do GWAS in order to be published in Nature Genetics those days, we need cohorts of above 150,000 samples. Okay, so this is a smallish cohort. Still it's providing interesting result and I think the reason why those three papers made it into Nature Genetics is that it's because of the novelty. It's just the mere fact of being able to tackle all of the variation, defining the outcome properly. So this was of high interest and that's why it made it there. So in this study, it's 1,500 individuals coming from three cohorts. And the three cohorts are homogeneous in terms of the geographical origin of the people sample their Dutch cohorts. And they use a funny statistical approach in the model and that's why I said it's not my favorite paper. They use like a staged approach. They had a discovery cohort and then they had replication cohorts. This is the kind of statistical analysis we used to do in the GWAS world 10 or 15 years ago. Nowadays, we tend to lump all the cohorts together over 100,000 samples and do a big meta-analysis and take the results at face value. You get the most power. When you do stage analysis, what happens is that at every stage, whenever you ignore to look at something, you obviously you lose power. If you're not looking at it, there's no chance you're going to find association with it. So the most amount of power you can have in the statistical analysis is if you put all your samples together. So here they use the stage approach. Okay, so they did germline genotyping. So basically they drew blood from those patients. They had, it was a cohort that already existed. I think they already had the blood and they genotyped. And this is typically what is done in the GWAS world. So you extract DNA and they did genotyping. And in this particular study, they genotyped less than a million SNPs. But as you see here, I said 8 million SNPs were tested sequentially. So what they did is a very standard procedure. And that's one of the differences between the microbiome and the human genome analysis is that in the human genome analysis, we have very standard procedures. Things are done, things that methods, approaches that we use have been tested and validated and there's robust documentation that as to what is the best statistical approach to use. So this is a very standard way of doing a genetic analysis in humans. They genotype 1 million SNPs and then they imputed probabilistically the genotypes of unseen SNPs based on external reference samples from the thousand genome. So thousands of genomes have been sequenced and those references are available in the public domain. So when you have genotyping on a subset of SNPs, you may ask, given my SNPs, what's the probability of unseen SNPs? And you assign a probability to those genotypes and you include those in your analysis. So that's what was done here. Yes, question? I sure do, yeah. So we've been using that for many years in my group and in many groups, I'm no exception. And the way it works is that, as you know, the human genome is not totally random. It's not like assigning SNPs at random because of the lineage, of the history of the people, of the populations. The chromosomes are shuffled in chunks. And those chunks create linkages equilibrium and the human genome. And we can exploit that linkages equilibrium to infer the probability of a neighboring SNP unseen, but so close that we're pretty confident as to what it would have been. And we can reconstruct those probabilistic predictions based on haplotypes. Not only on a single SNP, but you're actually using a series of haplotypes.  And what's the most likely SNPs in between those combined SNPs? So it works very well. And in my group, we made discoveries in the past based on imputed SNPs. And of course, I'm not going to publication until we validate the invitation. And each time we did, the correlation is super high. Just a few glitches here and there, but it has a minor impact on the statistics. Because remember, the models that we use for association take into account the probability of that value being correct or not. So it's all waged out.  I mean, I'm wrong, but I feel that we have, like, there's a real box of haplotypes in the human genome, like, around 30,000 weeks ago. So that probably helps in the imputation as well. It helps, yeah. It's totally different from the microbiome world. Not sure. In the microbiome, there are selection pressures that vary. And it's very dynamic. And it's a totally different environment. In humans, of course, the linkage disequilibrium patterns will vary from population to population. So other populations that have more diversity and populations that have less. And we know that allele frequencies will differ from one population to another. And so that's also very relevant. So whenever we do genetic analyses in humans, the most important covariates used in our association modeling is some sort of representation of the geographical origin of the samples. Otherwise, we could get confounding based on more diseases in some population. And we just assumed that because the SNP is more present, the SNP is causal, but it's not causal. So those situations may occur in human associations. And we have to take that in the video. Sorry. Yes? This could be a very basic question. But I'm curious about your last comment. How does that work if people aren't from mixed origins? OK, so how that works. I think you use similar approaches in the microbiome. So basically what we do is we try to take as homogeneous a group as possible. So let's say we define our analysis. If I'm doing an analysis, and 95% of my samples are Caucasian, and 5% would be of mixed origin, Asian, African origin, then because the representation of different population is so small, I may choose to ignore it. Otherwise, I wouldn't be able to model it properly. If I have a good representation, it would be a different story. But if there is a one dominating population, I may choose to homogenize my set and analyze this. But that's not the end of the story. You still need to take into account the population structure within that broad definition of Caucasian. And the way we do that is we typically use principle component analysis based on all SNPs throughout the genome or selection of SNPs throughout the genome. And we reconstruct the orthogonal similarities between the individuals. And we use that as cool variants in our model. That's how it's done. Okay, so back to our GWAS to microbiome analysis. So they did a very typical GWAS approach that was based on 8 million SNPs. And they did gut microbiome shotgun sequencing. The outcomes of interest, and remember we saw what are we trying to model in the microbiome that is going to be the target of our model. They used different factors. So one thing they looked at was just the abundance of different microbial taxonomies. And they defined over 200 such species. And then they looked at functional units, defined using different approaches. And they had over 1,000 different functional units that they were modeling. So how many statistical tests are we talking about here? We're talking about 8 million SNPs. Each one of those 8 million SNPs we're going to test with over 200 species. So that's already 8 million by 200 plus 8 million by 1,000 for each of the functional units. So when we do multiple testing like this, we expose ourselves to false positive association just because we're testing multiple, multiple, multiple times. And we need to adjust for that multiple testing. Am I going to get a microphone? Oh yeah, super. So in this paper, and the reason why I was so concerned about this paper is that the approach they use for multiple testing is unusual for adjustment for multiple testing is unusual. And in the methods section of the article, if you ever get curious enough to go look into it, they actually have almost a full page of justification of how they use multiple testing because would they have used the traditional approach to correction, none of this would have passed the threshold of evidence. Now what about covariates? So the covariates that they use here were limited. And I'm going to show you a different example right after where the covariates are more extensive to adjustment for agent sex. Okay, and it's not, they didn't actually rule covariates, but they didn't even model those things as covariates. They normalized their outcomes, normalized based on agent sex. Okay, so I was taken into account into this. What did they get? So this is the results. We're looking at a very typical, classical display of results for genome-wide association studies. It's called the min-hat and plot. So those blue dots represent p-values. So we should be looking at 8 million p-values or even more. I think this simplified the plot. It would be two dents on the page. And they drew a line, and that's the significance line. So you will understand that the higher the more significant, the higher the smaller the p-value. They actually use the log 10, negative log 10 of the p-value. So the higher, the smaller. And they're telling us that, I'm not sure how this works, that everything that passes the line would be significant. Is this on? Does this work? It's a fake. It's a sham. Question? Yeah. I have heard that there was like a consensus on the GWAS feeling that the threshold of p-value was to 10 to 16. Correct. So in the GWAS world, the threshold for significance is 10 to the negative 8. This represents correction for approximately how many multiple tests? Approximately 1 million. 5 times 10 to the negative 8 is correction for approximately 1 million genome-wide statistical tests. We used 8 million. Why are we adjusting for 1 million? Because there is correlation between the SNPs and the human genome. So they're not. That's a different point. So the SNPs are not independent tests. Each individual SNP is not entirely independent from its neighboring SNP. And we don't want to overcorrect. Otherwise, we'll never publish in Nature Genetics. So we use that standard. And it's been broadly accepted that we adjust for approximately 1 million tests when we do a genome-wide study, regardless, really, of how many SNPs we use. Now clearly here, they did not use that significantly. Or they did, but not taking into account the multiple species and the multiple functional units that they tested for. And this is just right in the paper through the staged approach that they use. So any more questions on this plot? OK, we're moving on. I didn't really want to go through each and every single result they have. I think this is not the point. Today, let's try to focus on the conception, understanding how those studies are conceived and how one would go about testing those studies. But just to highlight one result that they highlighted in their approach. And it's interesting that they decided to highlight that because it didn't directly stem out from their genome-wide statistical approach. It actually is the result of candidate genes that they had pre-specified saying, oh, we're also going to look at those genes because they have been reported in the past. And this is one example. It's the lactase gene, LCT, in human. We're talking about the gene in the human. And this gene in human is known. And it's broadly established that it is associated with adult ability to digest milk, so adult expression of lactase. And they wanted to see whether that gene would modulate the gut microbiome. And they say, well, we have some interesting signal here. And what they see is that people, individuals in the study who were homozygous GG at that specific SNP, previously known to be modulating lactase in adults, they had higher levels of one bacteria, difficile bacterium, abundance. And they tested the results in their three cohorts. They had three cohorts in their stage design. But what is particularly interesting in this model is that they saw what they call an interaction effect with the consumption of dairy products. So in fact, that association was mostly apparent in individuals who had higher intake of dairy products. So if individuals had low intake of dairy product, then they did not see that abundance, the correlation with abundance. But as the individuals were taking in more dairy products in a day, then they saw the separation. And they did a statistical test for interaction. And the statistical interaction was significant. This is probably the highlight of the results. Questions on this interaction? It's quite elegant. Yeah? Actually, sir, it's a question. Oh, fine. Oh, you don't have a question? I have a good question. Go ahead. OK, I'll be able to talk to you. Most of them, yeah. I think they are. Because I found the microbiome, sometimes getting this. Oh, the host DNA. Maybe they, so usually with the host DNA, what they typically do is they publish the summary statistics. So you don't get the individual level data of the genotypes, but you'll get the better value for the association of each endpoint, not knowing how each individual contributed to that better term. And the reason why they do that is to protect the identity of the individuals. And we can still use the statistical better terms to do meta-analyses. And actually, it's funny. It's interesting. The amount of things you can do with summary statistics, you can dissect them, you can. So it's still quite valuable. Yeah, question? So I haven't made a paper, so I'm going to ask you about the interaction itself. How did you go about missing that? Would it like just the product of the genotype times the integrity of the area and looking at the line? Well, basically, it's quite simple. So you have your outcome. And so the outcome here is just the abundance. And so you're testing for association with your SNP. And your SNP is basically dichotomous here. You have GG versus AG and AA. So you have two categories. So let's say 0, 1, or reverse 1, 0. And it's a regression model. And you add your diet. And here, the diet is portions of milk. And so you may have different levels. And then you add an interaction term. And so in statistics, it's just the multiplication of your things. And so you have your interaction term. And you can report the p-value here. And the p-values they reported was significant. So that's how you model it. And this was a meta-analysis because there were differences in how the diet was captured in the three cohorts. And so it's a meta-analysis p-value. So what they're saying is that it's a multiplicative effect rather than a derivative effect. So the more you. It's a GLM. It could be a super additive effect. Yes? I've been looking in this kind of study is an interaction between os-snips, like to check that there is an interaction between mutable snips or a joint-sniff effect. Because this is a very complex trait. So it's less probable that only one snake can have an effect on there. OK, so that's a very ambitious question. So we're asking, are there, throughout the 8 million sniffs, how are they modulating the outcome? But you want to take it one step further. Any of the 8 million in interaction with any other of the 8 million modeling your outcome in 1,500 people? I think that maybe in the future, when we put all those data sets together, we may start tackling those questions. So typically, gene-gene interaction models have been attempted in the GWAS world, but not been very successful. So typically, we'll do a targeted gene-gene interaction. If we have a very good reason to believe that two genes would interact with together, then we'll model that specific interaction, but not necessarily go out fishing for any snip-snip interaction, because there are too many possibilities. I mean, it's going to take all the snips which have the highest odds of reaching. So the question is, can we take only the snips that have the highest effect size as candidates for the gene-gene-snip-snip interaction? We can. But this brings me back to my comments initially about stage design. Whenever you do a stage design, anything you're not taking to the second stage, you lose power to discover. Question. So how exactly did you take into account the three different formats that you used, like a random effect in the past? Just remember, this is not my study. How did they take into account the three cohorts? So it was a meta-analysis. In the GWAS approach, it was a stage design. Anything that passes a certain p-value in the first cohort, then they would test in the second cohort. And then for this interaction model, they tested it in the three cohorts and used the combined in a meta-analysis. They combined the better terms and got a p-value on a combined approach using meta-analysis. Question. I have a question regarding the SNPs, which have a minority frequency, less than 5%. Okay. Is there any value to study them? Because since there is no enough space to stick a ball or tweak them. Okay, the last question on this topic, and then we'll move on. The question is, how do we deal with SNPs that have a small allele frequency? And this is a question that's probably very relevant to your microbiome analysis. What happens when you have a species in your analysis that has very low counts? And I've seen, so in genetics, how we deal with it, is for GWAS like this, if the number is too small, we won't have power to detect small numbers. And what happens when you're trying to establish associations with things for which you don't have power, what happens, you expose yourself to false positive findings, which become more likely than true positive findings. Okay, so typically we will not use statistical approaches that are underpowered to detect something. In your microbiome world, I've seen different approaches used for this. This may be a hot topic. So some people will say, well, if the strain is absent, we'll ignore it. And in individuals where we see the strain, then we'll count in that individual. So you're basically ignoring samples where the strain was not observed and putting that aside. This is, in my view, not the best approach. You need to have a modeling approach that can capture in your regression model, in your statistical test, those individuals where that species was not observed. Okay, so in genetics, what we do, we can use binning. And I'm not a big fan of binning, so you take a gene and you say, if I have mutation that's very rare, less than 5%, in one position, I will combine it with a mutation that's very rare in an other position and bin them together and use that in a test. In your world... But sorry, for these two positions, they have to be in linkage disequilibrium? No, they don't have, so the two rare variants don't have to be in linkage disequilibrium. There has to be a functional biological reason for binning them together. So typically we'll be, they're in the same exon, they're in the same gene. In your microbiome world, it would be they're in the same family of species, they're in the same, okay. So that would be the kind of binning that could be considered for low counts. Okay, I'm moving on because I'm never gonna feed. Before more questions. Okay, so this brings us to that second paper in the November Nature Genetics issue where they decided to go all out for microbiome. Statistically, I preferred this one. You may not like it as well for the reason here. So in this study, they had slightly more individuals, so 1,800 Europeans from two cohorts. The GWAS approach was very similar. They were a bit more strict on the quality of SNPs, which I like. But here's where it's the first. They didn't do shotgun sequencing, they used 16S.  So they had 38 phyla and over 300 genera that were analyzed. And they did shotgun analysis, but it was follow-up work and it's not part of the main discovery. The outcomes, what they were modeling in this study here is beta diversity and bacterial abundance. So I think it's quite a classical kind of approach. And they labeled their paper, genome-wide association analysis, so GWAS, identifies variation in the vitamin D receptor and other host factors influencing gut microbiome. So they found a number of things, but they decided to highlight one of their findings in their title. So quite clearly they thought that the excitement of their discovery was in the vitamin D receptor. And I would tend to agree. What's really nice about this analysis is the care they took into modeling the different factors that can have an impact. And here it's the beta diversity. So they're basically looking at the factors of diet, age, body mass index, smoking or not, and sex and how that has an impact of what we're looking at here are principal coordinates analysis. Looking how age, BMI will push the principal coordinate analysis if you can plow them this in your imagination how the effect would be acting. And also with respect to the different elements in the diet. So they had a food frequency questionnaire and they structured the different parts of their food frequency questionnaires into units, protein, energy, sugar, fat, water. And so by creating those scores of units in the diet they could include those high school variants in the model. And so what we see here is the proportion of variance in the outcome, the beta diversity that can be explained by those variables.  So we see here that sex is only small. I'm moving on. This is a display of the results they have in a circular plot and we like colors in it. And what we're looking at on the right-hand side of that circle are the 22 human chromosomes. You'll say, wait a minute, there are 23 chromosomes and that's true. And these people made a major mistake. They did not analyze the X chromosome.  And on the other side of that circle what we're looking at is the mouse genome. Now they did not do a mouse genome analysis in here. They're basically cross-linking any of their hits that they had with hits previously known and established in the mouse as if this would be a reinforcement of evidence towards the hit that they're finding. Okay, so we see here the genes and it's hard to read but the number of the genes that we're associated with bit of diversity and individual taxon and whether or not they link. And they don't go through details of all of those hits but they focus on the vitamin D receptor.  So, why are we interested in the vitamin D receptor? Well, the vitamin D receptor is a human transcription factor. It plays many roles but it's a transcription factor and it forms a hetero-damaged, it's very well studied. It's implicated in the regulation of a number of genes and it forms a hetero-dimer with the retinoid X receptor and another nuclear transcription factor and it basically lives its life by activating the expression of genes. That's what it does. And so it's particularly interesting here because it's not the first time that it's been associated. So this is like a nice confirmation and they felt confident about this finding. What they saw here and they did model that individuals who did not have a specific, the presence of a specific type of species in their gut and they ignored those individuals, they used that which I don't agree with but they try to model it. So the percentage of people with non-zero value for the perebacteroid, so in their sample, so with respect to the vitamin D receptor genotype. So we see that TT individuals here behave differently from the CC and CT people and I'm gonna go back one slide. Oh no, oh I guess I didn't show you that. And so we see that those individuals have the TT individuals at this sniff that was associated with the abundance with a better diversity. We see that they have smaller abundance of that specific species. So this is what happened when people have been doing GWASs with diversity. Once they establish association of a sniff with diversity right away they wanna know, well which species is overexpressed in that diversity. So that's what they attempted to do and this is what they report. So we're looking at statistics that come out of over, so 1,800 individuals and this is the highlight of the paper. This brings us to the second set of examples that we're going to look at. Remember we said we were going to look at GWAS association, GWAS is an association with the gut microbiome, drug modulating the microbiome and lastly we're going to look at genome-genome interaction. So now we're on the drug second set of examples. So two things, drugs can impact the microbiome but the microbiome can impact the drug activity. And we have a few examples here of how this can occur. So first of all, for those of you who are not familiar, when we take medications, I'm not talking recreation, recreational drugs, I can't have been like this ever. I'm talking medication to treat diseases. When we take medications, some of those medications will be active, so we open the box, take it in, the medication we're taking in is active. Sometimes that medication is not active, it needs to be metabolized to become active. This second kind is called pro-drug. So it's like proto-drug. It will become a drug, but it's not drug-drug yet. So pro-drug. So pro-drugs typically need to be metabolized before they become active and there are examples of drugs that can be metabolized by the gut to make them active. And then there are active drugs that we take in that can be metabolized by the gut and then we become an active. And we're going to see one example of that. And there are examples also of drugs that may become toxic after being metabolized. Drugs that can inhibit microbiome, antibiotics, quite clearly we'll do that. And some drugs that could favor the growth of specific bugs. And we're going to see an example of that. So we're going to see the example of metformin with the selective bacterial growth and digoxin where the gut microbiome is making the drug inactive. Okay, so let's start by the example with metformin. This is a recent publication. It was published in May 2017, so quite fresh. And I was personally very excited when I saw this publication. Metformin alters the gut microbiome of people with type two diabetes. And this alteration contributes to the therapeutic effects of the drug. Now this was big news because metformin is a drug that's broadly used for the treatment of diabetes. And it's an old drug, it's cheap, and its mechanism of action is poorly understood. Now these most drugs that are developed and put on the market, we understand their mechanism of action, but the older drugs that passed approval years ago, sometimes they pass without really knowing how it works. And metformin is one of those drugs. And so this is why it was so interesting. Now, this is an example of microbiome drug interaction. It's not a GWAS. We're not looking at genetic variations in the host that would interplay. I'm sure there are variations that will impact metformin and impacts the microbiome, but this is not the question that was asked in this study. It was a randomized placebo control, intervention, double blind. So this is an experimental study. It's not observational. They had a hypothesis and they tested it. So this is generating solid evidence as a result of hypothesis testing, not a phishing experiment. We're looking for things that are modulated. This is a dedicated study where they wanted to see whether metformin affected diversity in the gut. Only less than 40 individuals in the study, small study on any genetic standard, but remember it's not a genetic study. First thing they saw, they drew blood and they saw that the interventions are okay, let's look at those graphs. We're looking at a measurement. So we have BMI, glycated hemoglobin and glucose. And on the X-axis, they're basically just plotting in a histogram, placebo measurements from individuals in the placebo arm and measurements from individuals in the metformin arm. Measurements were taken at baseline, so time zero after two months of treatment and after four months of treatment in both placebo and metformin. What we see here, and I have to say those individuals, they had never ever been exposed to any diabetes drug. And they were randomized, placebo or metformin, so metformin would be the first drug that they're using for the treatment of diabetes. Plus, they were put on a strict low-calorie diet. Obviously they had diabetes issues or something had to be done and lifestyle intervention is typically recommended. So this was a normal course for this intervention. First thing we saw is that in both arms, placebo or metformin, they lost weight. Good news, that's the impact of the diet. And this is very important for us because if we're going to see how metformin affects the gut, we want to know, is it just because the person is losing weight that the gut is changing? Or is it really because metformin is doing something? So people lost weight in both arms. The glycated hemoglobin, which is a measurement of how severe their diabetes is, improved in the intervention arm and the metformin arm, but not in the placebo arm until they did that extended crossover study. After six months, they took the individual in the placebo arm and put them all on metformin to see, to control for intra-individual variability. And fasting glucose improved also in that crossover arm of the placebo group. So that's not microbiome. That's just how are the patients doing? Now what happened? So they looked at different things. They looked at strain abundance. And here we're looking at the significant strains where there has been a change between baseline and two months or baseline and four months. In the placebo, there was no change in those that had a change in the metformin arm. And those symbols represent statistically significant findings. And here what we see is that in those significant findings, there was some enrichment of some species. They did something interesting in this study. They looked at microbial growth induced by metformin. And so previously we saw the results of the primary study where the intervention was happening. But they also did fecal transplant of those individuals that were exposed to metformin and put them in diabetic mice to see whether it could rescue the diabetic mice. And it did for the metformin treated individuals. And they did a number. They really wanted to understand what was happening in the metformin treated gut. And one thing, one analysis they did, and I'm not an expert and I'm just telling you because I thought it was quite neat, they did something they called the peak-to-through ratio. They're basically following shut-down sequencing, looking at reads, DNA reads that mapped at the origin of DNA replication versus the end. Every DNA replication within strains. So that they could identify those strains that were actively replicating more. And they saw a significant finding where there was a difference in the metformin treated gut versus the placebo cut for Bishido Adaliscentis, which is a very common bug. And they cultured also the fecal specimens and they saw that in the presence of metformin, this bug was growing more, so they supported this finding. And so this is part of the results and the conclusion of this study. That's all I had to say about metformin. I'm going to give you another example of how there can be a drug in a microbiome interaction. And it's the example of digoxin. Digoxin is a drug that's used for the treatment of heart failure. It's still used, that's another old drug, cheap, broadly used, and for the treatment of heart failure and some cardiac arrhythmia. And this drug, and this has been known for a while now, 1981, can be inactivated by bacteria in the gut, and in particular, elenta in the gut. And what happens when people take digoxin, if they have elenta, it will metabolize the drug and render it inactive. And so when the doctors are dosing, trying to find what the right dose is for the patient, they need to up the dose to get physiological effects. And so they up the dose in those patients that have more bug. And this could be of a concern because digoxin has a very narrow therapeutic index. And so if you change the dose, you can be exposing the patient to adverse events or if you're under treated, well, patients with heart failure are sick patients and they need treatment. This treatment is important for them. So monitoring is essential. And this led to the creation of a project that is ongoing between Jacques Carbezlade and my group. And we have Jeremy, who's a student with you in this course, who's working on this. So the hypothesis we put forth is maybe what happens when patients are using digoxin, one situation that could happen is that whenever those individuals are treated with antibiotics, this could change the metabolism of the drug and it could expose patients to overdose. So what Jeremy has been doing is looking at what happens to the abundance of elenta in gut samples of healthy volunteers exposed to a broad spectrum antibiotics. And this is what we see. So Cepherazole administration for seven days, we have day zero, day seven and day 90 after three months. And we have some individuals did not have the bug, some did. And what we see is we see a trend for a reduction in the presence of elenta after exposure to antibiotics and that this reduction is stable even after three months. Now sample size was on the small side and now of course remember those patients were not taking digoxin. So it's just a theoretical experiment. Now Frederic and Jacques have been working by with the cultures in the lab. So there's been growth of samples from those patients in the lab. And what we see is that when we expose the samples to another anti-broad spectrum antibiotics, we also see a reduction in the presence of elenta in those samples. And this time it was significant because the sample size was slightly larger. And I want to finish with another example which I think is quite exciting for going forward with this, the kind of interaction and analysis we can do between the host genome and the microbiome. And they've been called genome to genome analysis. Now we're not talking about gut microbiome. We're talking about hepatitis C virus in circulation and patients. So genome to genome analysis, published in May 2017 highlights the effect of human innate and adaptive immune system on hepatitis C virus. Remember we said that the microbiome virus is adapt. We're not born with it, they adapt. So whenever we treat ourselves there's an adaptation. And so this adaptation and evolution occurs for hepatitis C as well. So they did this very interesting study using 600 hepatitis C infected patients who were taking part in a clinical trial but this study is not a clinical trial. This study is an observational study of samples that were sampled as part of a clinical trial. And so they had baseline plasma that they used for whole genome sequencing of the virus. So they sequenced the virus. They also had the host DNA. So they did genotyping. And as we saw before, genotype imputed so they had genetic variants for it for the 600 individuals in this study. And one thing to remember is that human key lymphocytes will attack the virus. And depending on the kind of immune system the patient has, HLA variants can have an effect on how well it attacks the virus. And what escape mechanisms are left for the virus to survive. So this was the major interest in conducting this study. So a few words on the, and I'm not an expert on the hepatitis C virus. There are seven major types. And hepatitis C three is the type that was of particular interest as the most prevalent virus. And also it's the type that has the most resistance to treatment. So they did this genome to genome analysis. It's a busy slide, but it's a beautiful slide. So again, we see the circle plot and we have the human chromosome and again they only have 22 chromosomes. And basically what they're doing is that with each one of those SNPs, they're asking the question, is my SNP one going over millions of SNPs? SNP number one, is it associated with specific positions in the virus genome? But they didn't use the genome. They did a very elegant thing here. So they had sequenced the genome of the virus, remember? And of course viruses are dynamic. They don't have one strain copy of the virus. There's multiple different copies and different copies of the virus. And so what they did is they asked the question, does that nucleotide change after an amino acid? And what amino acid is encoded? So the association was performed at the amino acid level. So that was one way of reducing the variation in the viral genome. Now, amino acid's not like four bases of nucleotides. It's like many amino acids. So they had declinations of observed encoding of amino acid at each position and they normalized this to have a quantitative endpoint. So what we're looking at here on the top circle are the genes or the proteins encoded by the virus. And the viral genome is pretty small. It has over 3,000 amino acid and they saw variation only in 1,200 amino acids in the virus and they tested for association each position with each amino acid. So how many statistical tests are we talking about here? We said when we do a genome wide association analysis, we adjust for a million tests. So we have at least a million SNPs in the genome and we have 1,000 amino acids, so a million by 1,000. So the significant threshold here was established at 10 to the negative 11. So we have 10 to, five times 10 to the negative eight and we add a few zeros to that, brings us to 11. Okay, outer-minitonal viral diversity. So they tested for viral diversity. They had, oh, this is what we're looking at. And so this plot is the density of SNPs. Obviously the distribution of SNPs in the genome is not uniform, those tests that we do, the SNPs can lump in specific region and they wanted to express that. Typically in the genome, we have less diversity near the centromeres. So we have fewer and from a fewer variants located near the centromere and this is what this is depicted. So what they did is the analysis is they did logistic regression, also it was model zero one, sorry, logistic regression, additive model for the SNP and they adjusted for sex. There are sex-based difference in the human genome, so it's quite relevant, even though you may think the microbiome is not influenced by sex, here we're talking about viruses and it's not even the cat, the way that the host genome will act in the body can very well be modulated by sex. And we're safer to add sex in our model than to confound our results by sex, risk confounding our results by sex. And they adjusted for geographical origin of the individuals by using principal component analysis based on genetics of the people. And they also adjusted for the diversity of the virus by using principal components from the virus. Ascovarius in the model. Finding, so it's tables and tables and this is a truncated table of the findings. So very few signals past the pre-established significant threshold, but in the tables they're showing the sub-significant also because people like to look at data, there's a fascination with data. So what are they reporting? They're reporting the P value and they did for the association whole population and they did a subgroup analysis in Caucasian white population just because genetic associations are so sensitive to confounding by geographical origin that you wanna do a subgroup analysis just to be safe that what you're seeing is not a fake result. Okay, so what's the take home message from this study? Here we're looking at again a Manhattan plot, all of the chromosomes in the human sort of and looking at viral load and they found an interesting association with one gene, an SNL4 involved in the immune response and they show how different genotypes will affect the different viral load of their patients. And I'm ending on this, I think that this study is quite promising looking forward for the possibilities that we have to do genome to genome analysis with large number and you will have understood by now that we're going to be needing large numbers, large cohorts. So Jacques, if you want to do shotgun sequencing on thousands of sample, I'll analyze the data. And what have we seen? We saw host genetics can impact the gut microbiome. We had two examples from that nature genetics edition. We've seen that drug microbiome, there are drug microbiome interactions. We saw two examples metformin and digoxin and we saw an example of genome to genome analysis. Very few of those, only two in the literature. So the hepatitis C virus as we saw, there was also an analysis with the HIV virus which I didn't talk about here. Looking forward, sequencing is cheap. It's getting cheaper and cheaper. So there is a real possibility for large data availability. Longitudinal analysis and repeated measures of gut samples and microbial samples is very important. It helps reduce the variability. It helps us explain the observed variability that are due to the within individual effect and account for that in the statistical model. I've heard talks, I've heard things and there's interest from the pharmaceutical industry to study the microbiome, to see how it affects drugs that are in development, but also the potential for developing drugs that target the microbiome, which is now a real factor in health and disease. And for you guys in this workshop, my two cents of wisdom, is you should look forward to the establishment of guidelines, to standardize methods, to benchmark novel methods when you develop a new tool. Don't stop there. Benchmark it against as many tools available, making the data available, and promoting benchmarking will help establish useful guidelines for methodologies. That's it, questions. Question. Thanks for the talk, great talk. So we mentioned earlier, it was a genome-wide association study. There was a high risk of false positive, just based on the number of associations made. So we define a lot of findings, or the next thing you would expect because of that. Okay, so in the field of human genetics, we had to tackle the problem of false positive association due to multiple testing in early 2000s, late 90s, when SNPs appeared, when genotyping panels appeared, people started testing small cohorts of 200s with many, many variants. And we realized that a lot of the findings weren't being replicated. And it was at the time when we did not have guidelines, strict guidelines to help us into developing the methodologies for approach. And it's with years of hindsight that I can tell you that you will benefit from those methodologies to make sure that there's some sort of community-wide standardized methods that can be used and can be looked for. So in the human genetics world, there was a time, an epoch, where false positive signals were rampant, around 2008, 2007, the major journals said, that's it, from now on, we're not publishing a single GWAS paper unless it has a replication embedded within the study design. And so all of a sudden, the number of publications dropped radically, and it promoted collaborations between teams and the generation of super-large cohorts. So this is how our host genomics community has adapted to the pressure of making sure that we're not publishing false things. I'm not sure that answers your question now. Well, I was just curious, from your opinion, did they find a lot of things or little findings? So there's a lot of GWAS findings. There's an online, if you're curious, there's something called the GWAS catalog. And it lists thousands of associations that have been established. And I think, and actually going forward, as I was looking at the microbiome publications coming out, a lot of people are now interested in focusing on SNPs in the host that are known to be correlated with disease and asking precisely, that specific SNP, how does it affect the microbiome and how could that relate to health and disease? So I think that that's also one venue that we may be seeing in the near future. I had a question here. Am I, no? In regards to the hepatitis C system, have you looked for sort of an increase in sniffer ability in the regions that the MHC would find too in order to escape the... Remember, this is not my study. I'm just teaching you guys what's out there. Have they looked at how specific variability in the viral genome that may be adapting? If I was a bacteria and I entered a human that had a specific MHC finding complex, I would try to mutate my regions and apply it to that more so than the other ones. Yeah, and that finding that they highlighted, the IFNL4 finding was precisely about that regarding the host variant and how that has an impact on how well the virus adapts. Sorry, I went quickly. I wanted to be on time. But that was precisely what they were looking for in the paper. Question. I have a question. It's not related to the paper's technology question, and I was interested in your take on viral volume when you started to redress the words of, you know, wisdom. You did highlight the fact that it's this process. So from an analysis standpoint, how do you see the future of, like, how we analyze data that changes when you're going to have examples at time one, time two, time three, time four. So once it's forward, people are going to collaborate. We're going to get more data. So what type of methodology in terms of statistical analysis or just how do you deal with this type of data? Are there going to be able to do this anymore? Oh, we can. We can use hierarchical modeling. We can do a lot of nasty things with it. My take on it is that we're going to need to understand better how the dynamics work. So we, as humans, we like to simplify things so that we can model them. But we first, I think, need to understand what is the dynamics of the different strains, different situations, the daily cycles, how that is taken into account, but also the longitudinal. Some factors will replicate faster than others. Some will adapt faster than others. And going longitudinally, as we saw with the digoxin, some with elenta in the presence of antibiotics, some may not come back that easily. Some will come back easily, but some won't. And what are the factors involved in that? So the factors involved in the dynamic and how different strains, the types of that cycling. Is it daily? Is it weekly? Is it monthly? And how fast it bounces back from an intervention? I think that that will help us model the dynamics of the data. And going forward, in terms of longitudinal analysis, it's the more data, the better. That's always the bottom line. And it's the sampling, if it can be. And that's methodologies, it's not analysis. As systematic as possible, you know, and as consistent between sites as possible and on as many people as possible.


After months of a chase for him, Nigeria now has a leader of the indigenous people of Biafra, Nambi Kano. The upper leader is being held in custody of the Department of State Services and was arraigned in court today. Attorney-General of the Federation and Minister of Justice, Abu Bakar Malami, says Kano was detained two days ago and brought back to Nigeria, but he did not say from where. A journalist talks about saying Kano appeared in court earlier today. He was brought to court by the DSS and the lawyer to the government told the court that the man Kano had been at large. I was granted bail earlier in 2017, but since then he jumped bail and has flouted the bail conditions. So he now applied that Kano be remanded in the custody of the DSS and that the trial be accelerated so that the case can be concluded on time.


Hi everyone, Joe for jasby's case rakes.com. Thank you, sir. Joe for jasby's case rakes.com. Happy Friday. Brand new release date coming at you with 2020 Panini Contenders football. Six box. Pick your team number four. Second half of the case. Remember I marked these four a little bit earlier. And there's everybody right here getting in on the action. This time it was Townsend with the double last spot mojo. Thanks to him and thanks to everybody for getting into this and making this happen. Appreciate it. Five autos box including one on-card auto. The first half we saw a couple points, cards and a couple redemptions. So I think that might that might be the the the general trend for for your contenders breaks. jasby's case rakes.com we got we got break five I think with 14 15 teams left if you're watching live. Visit jasby's case rakes.com and keep chipping away at it. Remember there's there since uh contenders it really doesn't have a lot of cards with numbers in it. I wouldn't hold my breath for like number blocks for teams. So just grab this is one of those days where we're just having to do it the old-fashioned way. Grab your team straight up throwing these cards around. Sorry about that. So I bought I bought the tickets. Listen to this. All right. Mega million folks. Someone good Calvin. Someone came in before me. Uh-oh. Bought tickets. Didn't want to pay. What do you mean didn't want to what do you mean didn't want to pay? She wanted a hundred dollars worth. So she said a hundred tickets. That's oh that's 200 dollars right. She didn't realize it left. So that means I'm gonna win on her ticket. Whoa yeah that's probably true. Now that was that's gonna be a news story.  She didn't know it was two dollars a ticket. What a noob. Got got my megas right here. All right so a lot of these are vet commons. Most of these are gonna be vet commons. These aren't gonna ship. Obviously stuff like that that's an insert that will ship and this is a bird right here. Eno Benjamin obviously an auto will ship victor with the cardinals and these will do left right randomizers at the end. If they're numbered we'll randomize those separately. Obviously winning tickets are inserts that'll ship and these these are inserts that'll ship and this will ship for the Ravens. Justin Matibuki for Aaron and the purple birds. Yeah Nick that's definitely gonna happen now because that'll be the news story. It'll be like local man went to a grocery store to buy by lottery tickets. The woman in front of them did not want to pay. No no I'm good thank you though. Get your mega millions. Gotta get more. Lottery fever has struck. Jasper's ladies and gentlemen there's a Darnell Mooney two out of 99. A bit of a playmaker here for the bear for moan with Chicago and we've got a Justin Herbert here rookie of the year contenders. I thought you'd be here more often Kelvin with a with a lot of schooling at home nowadays or maybe you're just busy or not. Any guesses on that redemption for bragging rights? We got Jake from State Farm on card autograph for the bills. Greg Munson with Buffalo. Buffalo. Obviously that's gonna ship too. Those are inserts. J.K. Dobbins number that'll ship. All right Herbert Ortua guesses Travis. It's Jeff Gladney instead. I'm getting a lot of interesting redemptions here. Gladney I feel like we've not seen as a as a redemption. Where's my checklist? I have no idea where he goes. He is a Viking Minnesota that's gonna go to Matthew Shearer. They do redemptions there. They're printing directly on the card now instead of the stickers they used to do. I guess that saves time. Saves the time for someone to actually sticker all these redemptions right? Makes sense. All right we'll do a recap at the end as well. Next box. Good luck. No, no Broncos yet. But we only just finished our first box so you're just you're just in time. And nothing sold out at the moment folks after this. I see a couple orders coming in. Picker team five is the very next contenders that we can do and that's holding at 15 teams at the moment. Picker team five is from a fresh case. So visit jaspeascasebrakes.com. Point your browsers there. Do people say that anymore? Point your browser to jaspeascasebrakes.com. Packs are awful this year. Let's pack. There we go. Second box. Good luck. This is picker team four. Plenty more in the store folks.  We've got Rookie of the Year contenders AJ Dillon to 149. For Green Bay Matthew Shearer. And we've got Ed Two Tall Jones. Nice one for Townsend and the Cowboys. We saw an old school cowboy in uh in the first half and here's another one here. The second half. And there's Joe Burrell Rookie of the Year contenders for the Bengals. Sean Mattick with that one. There's our next autograph. It's going to be it's going to be Raymond Calais. Rookie ticket auto for the Rams. That's going to be for Nick Koba. Another Ram. Jordan Fuller this time. Rams just filled some coordinator positions lately. Jalen Hertz Rookie of the Year contenders for Nathaniel and the Fly Eagles Fly. And we've got another one for the Eagles. John Hightower IV. Nathaniel Murphy with the Eagles. And there's your Broncos autograph on card auto. Jerry Judy Rookie ticket autograph for Ramon and the Broncos. Well horse team mojo. Next box. Again more in the store. Check it out jaskyscasebreaks.com. We've got a late night going on too. We might have to go in late into that if every pack's going to be this difficult. I would gladly pay an extra nickel per pack for better pack quality. Well I don't know maybe maybe I'm maybe I'm just not using a maybe I need a lot. I've adjusted at an in game adjustment.  Maybe maybe I'm just being too eager and aggressive with it in game adjustment. I'm already seeing improvements. Next box. Good luck everybody. And we've got these will randomize separately because their number we've got Henry Ruggs on top and Jerry Judy on the bottom. A little rivalry randomizer here. And we've got Devon Asiasi for the Patriots Karen Steele and her Pats. We've got Dearness Johnson 17 out of 49 championship ticket autograph for the Browns. That'll be for Jerry Bennington. We've got DJ Dallas Seattle Dallas going to Seattle. That'll be for Robert Briggs. Which maybe you never heard those guys. You gotta watch more football. There's Patrick Queen rookie autograph for Aaron and the Ravens. Actually I had no idea who this guy was. Who's Dearness Johnson. Other guys than you. All right. Next redemption is Clyde Edwards E. Lair. We've all heard of him. That's going to go to Casey J. Goins and the Chiefs. I think he's been he's going to play this. He's been practicing. This would be good for the hobby. Hashtag good for the hobby. If we can get some Clyde Edwards E. Lair rumbling out there. All right. Three boxes to go. Yeah. JB. Contenders is kind of like that. Just because I mean five autographs a box in a pretty card heavy set. It's going to reach towards the towards the deeper parts of the draft. See some guys. But what's unique about this is that if you're like personally PCing a particular team or something like that, there's going to be autos of these rookies that you'll probably never see in any other set again. You know. So there's there's a little bit of advantage to that. Like we saw that we will actually this is pretty good player. We saw that that Bechton offensive lineman the big offensive lineman for the Jets and and we were commenting during that part of the break. We're just like, you know, it's kind of weird. It's you know, you're not going to see offensive lineman autographs too often in very other very many other sets.  It'll be kind of kind of cool to get to add that to your team PC just for just for funsies because you're not going to see him in like prism or anything. So I think that that's where there's like a little fun value added to stuff like contenders.  Contenders is is kind of like the contenders prism even select or kind of like the rookie cards to get for a particular year. And then the RPAs, of course, would be like Immaculate and Flawless and N.T. But just the auto onies. I, you know, I'll be honest with you. Clyde Edwards E. Lair started off the season hot. And I know everyone was all over Clyde Edwards E. Lair. He cooled off a little bit. And then I think it was just banged up. So I think it was just sort of out of sight out of mind for a lot of for part of the hobby. But I think there's still some solid value there. People like collecting running backs. There's Alexander Johnson for the Broncos. So maybe we wait and see on the Edwards E. Lair to see how he does in the AFC Championship game. And if the Chiefs get to the Super Bowl and if he's healthy for the Super Bowl and he has a good game, you know, then you then I think then you'll see another then you'll see another big boost in value for him again. It's maybe more similar to early in this earlier in the season. There's Bryce Perkins, Rams, goes Nick. But I think he isn't he the Clyde Edwards E. Lair. I think still might be the leading rusher, leading rookie rusher. There's Justin Herbert for Franz and the Chargers. Another redemption there. Third redemption. We got Donovan People's Jones cracked ice autograph five out of 25. Nice. Jerry Bennington with the Browns. I feel like People's Jones showed some flashes of his talent and what he could do possibly in the NFL. It's T Higgins, Rookie of the Year contenders card. We got Gabriel Davis. Here's a good one on card autograph. Gabriel Davis, Greg Munson, Buffalo Bills. I think he's starting to I think a couple drops in the end zone. He could have he could have two touchdowns in a divisional game I think. So he could he's on the cusp of breaking out. E. Lair had 800 yards and then oh Gibson and Robinson both had a better year by the end. It's about a 199 big Ben. The redemption is going to be Kenneth Murray. Chargers. That's going to be for Franz. LAC Los Angeles Chargers. 10 more autos to go. Two more boxes.  AFC and NFC championship games? Conference games. Some players you're taking a strong position on that you feel like sharing. Daily fantasy is always is always fun. Start thinking thinking about all these game scenarios in your head and everything. Allen any time TD Ramon saying? Allen Lazard or Josh Allen? JB likes a chief teaser teasing him down to down to plus 13 minus 13 plus 13. Or you like minus 13 and getting better odds. Josh Allen says Ramon. Anything for daily fantasy though? Draft Kings lineups? Fandal Linus? Buccaneers are kind of a pass funnel defense I think. They're running game. They're running their run defense is pretty strong so that people end up passing more on them. Aaron Jones could be sneaky good in the past game. I don't know. I feel like those guys are kind of easy to figure out like the bigger names but then it's always always like this is where this is where I get got in like draft Kings lineups is like is like the third wide receiver like when you have like $4,000 left is like finding that guy.  There's Rashad Lawrence. Yeah we should Jack I'm sure we will. Cardinals Victor with the Cardinals. Jaylen Hertz and Raymond Calais. Rams that goes to Nick Koba signed by the Rams in early September. I mean House Patrick Mahomes gonna do in Daily Fantasy on draft Kings. He's he's a past concussion protocol but I don't know. They're saying he's his foot spanked that maybe his mobility is banged up a little bit. There's JK Dobbins rookie ticket autograph. Nice one for Aaron Billingsley and the Ravens. Jay Dogg I'm on the I'm on the Packers. I have financial interest in the Packers minus three. Another Raven for Aaron James Proch. Jay Dogg you have a small investment on the Buccaneers. It's gonna be a good it's gonna be a good game. I think both games are I'm looking forward to both games are both gonna be pretty exciting I think. There's Jerry Judy another on-card auto for Ramon and the Broncos. La Poe Joe Mojo and I think you know Vegas is Vegas for a reason you know. So I think they I think those lines are actually kind of spot-on. The minus three for the two home teams I think are pretty spot-on. Like I wish I could get you know Green Bay minus two and a half but I don't think that's ever gonna happen right. You know or if you're on the Buccaneers side you would love them at plus three and a half plus four right. I think you could have gotten that very early in the week but that's you're not gonna get there you know so so I think the lines are kind of sharp. I wouldn't be surprised if there's some pushes that happen with those lines. So I don't know maybe me do you pay do you pay for like a half point one way or one one direction or the other. Actually now that Mahomes is back you think that she's line moves JB. He's thinking he's thinking down to minus six and a half by game time. I don't know if it would it move that much because I think the I think Vegas had it at minus three just knowing that Mahomes I think they always assumed that Mahomes was gonna was gonna play. What did I add? He's got a little narrative here and the he's thinking that the chiefs play for behind from behind for a while and then come back and win. The regular season builds that score of points by yeah 119 points in the first half only seven points in the second half and that was boosted by the big wins at the end of the season so they let a lot of teams back in the games. Meanwhile Casey trailed in 10 of their 14 wins and then we saw the comebacks that they had to make last year in the playoffs true. First half Buffalo that's I think see the problem with Buffalo is that they don't run very often they don't run very much I guess they have plays that that are like passing plays that are kind of that are pretty much runs but but yeah they don't do that so they don't really so it's just a lot of Josh Allen slinging it. All right there's another one we're going to randomize separately Isaiah Simmons and AJ Terrell, Cardinals, Falcons, round numbers. I heard uh trying to remember what what stat this was but I heard a a teams don't score a lot in the first quarter in like these playoff games in these conference championship games I heard something about that that you could take like an under 10 in the first quarter and do pretty well more often than not and then more points start to get scored as the game goes on but there's an interesting like first quarter trend in championship and and Super Bowl games too I think. There's Marlon Davidson for the Falcons that'll be for Bryant. Hey there's Rodrigo Blankenship, Colts kicker going to Mark. We got CJ Henderson for the Jacksonville Jaguars, Rookie Otto for Mark and the Jags, little Cat T Mojo, a redemption and the last redemption is going to be Tyler Johnson who I think is a Buccaneer. Are we going to see more Tyler Johnson in action without Antonio Brown? Maybe Scotty Miller gets uh some more looks as well those could be some cheap guys in uh DraftKings. Interesting. Bill's lead at the half plus Chief's win game is plus 525. Kind of like that based on the trends. All right no points here but we've got some round number randomizing to do so let's go flip back to random.org. I've got a blank list here I need a couple more here one and two. All right so the first is going to be left right randomizers for the round numbers. We're going to go by the back of the card. The first one is a randomizer versus bird teams. It's Arizona versus Atlanta and then the next one is uh almost put Oakland Las Vegas Raiders and Denver. Same dice roll for all three lists and that's going to be come on 11 times 11 times 1 2 3 4 5 6 7 8 9 10 and 11 the final time after 11 left side we'll get him I think left side got him last time so left side cards we'll get those round number cards and 11 times for the teams 2 3 and 11th and final time after 11 uh Atlanta Brian with the Falcons so Brian you'll get this card that numbered 136 to 149 and we've got Henry Ruggs versus Jerry Judy to 149 1 2 3 4 5 6 7 and 11 the final time after 11 goes to Las Vegas little Raider Joe Mojo going out to Townsend there you go Jack Townsend you get the 105 to 149 round numbers card and there you go ladies and gentlemen oh let's do a quick recap all right here for pick your team number four pick your team five is in the store will be from a fresh case we haven't seen autos of the well I haven't seen autos of the big quarterbacks yet I think Jason Jaspy did uh pulled a Jalen Hertz earlier but pretty solid break overall let's keep hunting folks hunting for the big names I'm Joe for jaspyscasebreaks.com I'll see you next time for the next break


I'm really excited to be here and thank you all for having me, and I'm going to talk about comics, how I got into the comics business, talk about the process of making them and discuss how comics can be used to educate through my group reading with pictures. But I also don't want to monopolize this. I want this to be more of a conversation and a discussion, rather than just me standing here, well, not here, but back in the States and being projected there. But I want this to be a conversation and not just a lecture. So I want you guys involved in this as well. So I guess we'll sort of start with what reading with pictures is and sort of my secret origin and how I got into comics and how I ended up founding reading pictures, sort of why I'm here with you guys today. So I'm going to try a screen share and so bear with me and we'll hope it goes well. Get the right file up here. Oh sure, I said we're going to try a screen share so I can show some slides and I just have to sort of get everything right here so just give me one moment. I think. And is that coming through alright? Yes. Alright, so like I said my group is called Reading with Pictures and we're a non-profit that was founded in 2009 and we're based here in the United States. And we promote the use of comics in the classroom and we do that because we believe that comics have a unique power and ability to improve educational outcomes in every subject.  And you can see up there that's little Josh and he's loved comics for as long as he can remember. Now his mother who is a school librarian she taught him to value reading but it was comics that taught him to love it. And also I would like to introduce you to my mother who is currently working as a school librarian who actually taught me how to value reading. And there are also his scholastic secret weapons. There are his sort of special tools in school. And she also taught me how to love reading using some secret weapons. So my mother would read to me every night before putting me to bed and occasionally she'd let me pick the book and I always picked a comic. And there's mom. I can probably tell from the Michael Jackson shirt you know when that picture was taken. So she would always you know read to me she'd let me pick the book and one day I picked this comic in particular. And obviously the Transformers were pretty awesome. Robots very cool. Cars and planes very cool. Robots that were also cars and planes very very cool. So mom was reading the story to me and things were going great. You can see how excited I was. But then tragedy struck because about halfway through mom lost her voice and she couldn't finish the story. This was completely unacceptable. Optimus Prime here was in a lot of danger. And I had to make sure that he was going to be okay. And so with mom out of the picture the only person left to finish the story was me. And so I had to use the comic to teach myself how to read the comic. I was able to follow the narrative all the story through the images alone and use that visual context to sort of figure out all the words that I didn't already know. And pretty quickly you know really just like that I was reading. And pretty quickly you know really just like that I was reading. And so mom got retired. She got full pension and benefits of course. We took care of her. Oh sorry go ahead. But I started reading everything and get my hands on. Comics books magazines newspapers everything. And this is actually my graduation photo from when I graduated high school. By the time I was in the fifth grade I was already reading at the college level because of comics. By the time I was in the seventh grade by the time I was 12 years old I was taking college courses. And it's because comics made reading easy and it made reading fun. And once reading is easy and reading is fun then learning anything becomes easy and becomes fun. So you could say that comics made me a super student. So after I graduated college I went on to work in the comics business myself. Here are some of the things that I've worked on. Mail order ninja iron man starcraft the scribble knots. I've been very very fortunate that I get to play with all of my favorite toys. This is a museum exhibit in the states that was dedicated to Mail Order Ninja and that was pretty darn amazing. And it's always fun when you get to kind of force teenagers to do silly poses and photographs with you. I always I enjoy that I don't know I'm weird. I mean in the end I guess you could say that comics made me the man that I am today. Here you can see a picture of one of the cosplay events and you can also see that we did a great job. Is it good or bad? So that's sort of my story. And reading with pictures. Turn the screen share off I hope. There I'm back. So that's sort of my story and how I got into comics. How I learned to love them and reading with pictures grew out of that experience where if comics could make me a great student and make me love learning then maybe they could do that for everyone. So what I'd like to do is see if we can get some sort of conversation stuff going now. You know anything about the creation of comics anything about working in that business or anything about reading with pictures. I'm happy to address any and all. I have a question about the project reading with pictures. How do you work in in in this project? How do you publish some comics where there are pictures about some math or some other topics like physics? Or do you introduce some extracurricular activities in schools? So I guess kind of all of the above. We try to create material. We have a book coming out called comics that make kids smarter that's coming out in just a few months that have stories about every subject that you can think of. And you know we also have we're developing after school programs and summer reading programs. In fact we're partnering with the international school in Minsk to develop what we hope will be a summer reading program that can go all over the world. We also organize post-school programs. There's a summer camp on this topic. We cooperate with international schools. We also promote our idea of reading with comics. In Russian, Russian parents don't actually like comics. And I would like to know what do American parents think of comics and do you receive any feedback from them? Well you know it's been we've been doing this I've been doing this for for quite a few years now and there's been kind of this movement in the United States towards comics and and what we call visual literacy. So the idea that you can teach and learn from images as well as words. And that's pretty that's pretty advanced here in the States. I would like to say that in the United States there has been a movement that is that is making use of visual content in education, that is making use of education in visual form and in text form. And this movement has received a lot of development and now it's at a fairly professional level. But there's still a lot of resistance and there's still a lot of understandable concern because it's sort of like saying that you know candy is good for you I guess is kind of our argument and that's a hard one for parents to accept. And so one of the things that we try to do is we collect research and we actually are trying to put new research together looking at how the brain actually interprets data, interprets information. So when you think about how the brain works we have different parts of the brain are specialized in different tasks. One part of the brain is concerned about language and other parts concerned about image and pattern recognition. And so when you're using both of them together you get a better outcome. There's a whole theory behind this called the dual coding theory of cognition. I don't know how that necessarily translates but it's the idea that when you put an image and text together you're more likely to not only understand but to remember what you saw than if you saw either alone. So something I'd like to see if I can find something here. So this is something I like to show when I talk about how the real world uses comics. Now if any of you have ever been on a plane you've probably seen something like this right? And so in the real world when it's absolutely essential that you learn how to do something in just a few seconds or you'll die we use a comic. But this isn't when we think about something like this when we think about comics we don't think about something like this right? This isn't what comes to our mind. But comics are just pictures in a sequence.  And if all those things are comics then we use comics in our lives all the time we just don't think of it that way. And so what we try to do at Reading with Pictures is you know point out these things and then try to find the best ways to use this material. Because I mean the you know my own story is that you know hooked on comics worked for me you know I was I was a college student when you know I was a college student at 12 years old you know something worked there and if we can do this and replicate it for everyone then we can do something really special. And there are two questions from our online audience. Okay my first one what academic type of literature do you recommend about reading and pictures visual literacy etc. Okay so probably the best repository of that can be found on our website readingwithpictures.org.org so readingwithpictures.org and that's we have a database that's full of research and rationales and lesson plans. The second question the second question is at what age can children start learning reading with pictures? The second question is at what age can children start learning reading with pictures? I mean I think we we should start them learning with pictures and I think we do start them at incredibly young ages because we all learn to read by associating words with pictures right I mean that's that's how everyone learns to read everywhere on earth. And we do that because it's a normal part of human cognitive neurological development to understand that a picture of a thing can also represent a thing. So think about like when a dog sees a its reflection in a mirror and it doesn't understand that it's looking at itself it thinks it's another dog and babies are the same way but once we get to about two years old we start to understand that that picture in the glass is us and so then they can see a picture of an apple and know that that stands for or represents an apple even though they can't eat it so this is natural but language the idea that you make some lines on a paper and that that is apple the same way that that picture is apple that is something we all have to learn so I mean right from the beginning we're always putting you know words and pictures are together like this it's just as we get older we start to pull them apart right and think how easy it was to learn language especially but to learn all sorts of things when you're very young and think how much harder it gets the older you get and we believe that a big part of that is that we're just not using our entire brain you know when you take images out of the equation it's like running a race by hopping on one foot I mean you could do it but I wouldn't recommend it and Mr. Elder there's a question from our audience well creating comics is a quite a difficult task and what do you do to simplify the process do you have any secrets and could you please give us some examples of it sure so I actually have some stuff I'll show you here let me let me queue it up so this is one of my issues of the batman strikes from a few years ago and the process that I went through for this it starts with an outline and considering I also had to get this approved by DC comics it also was sort of a pitch as well I was both saying this is what the story would be and telling them why it's going to be good and worth uh worth them publishing and you know every story has to start with an outline right it's like you know a body without a skeleton it's not very useful you know so once you've got your outline figured out you know what your story is about and you know basically the sort of the framework of it then it goes to this stage so this is uh very similar to what you would see in a screenplay for a movie uh the script for a stageplay you know we describe the action we uh we walk out the dialogue um there are a few key differences uh mostly related to the format of the comics itself you'll typically describe what happens in every panel you'll take account of for things like sound effects or narrative captions basically anything you would see on the page you're going to want to try to account for at the script cadre and this is true whether you're drawing comics yourself whether you're writing and drawing or whether you're working with collaborators uh and this is primarily because it's much easier to make changes here where you're just cutting and pasting then if you have to erase and redraw an entire page worth of artwork so once you have the script and everyone likes it and you're ready to start drawing it it goes to this stage and uh these are the pencils and so this is typically done by uh an art team especially with the with comics like this uh it's like an assembly line and everyone has a role to play uh so i came up with the story i wrote the script and then it goes to the penciler uh who drew what you see in front of you and this is sort of the blueprint or the foundation for the rest of the art the you know all the elements are there but it's not finished yet and uh there's a question sure how much time does it take to create such a such a work such a page so professionals who are who are doing this as their job uh they typically can turn one of these pages around in a day that's the rule um summer faster summer certainly slower uh but the the sweet spot is that you can do one page a day uh so once the penciler is finished with his work it goes on to an anchor and what was sort of semi-defined now has now it's there you know now every all the details are there and uh you can also see that they put in the lettering uh like the the word balloons uh for the characters dialogue the narrative captions uh up there at the top left and if this had sound effects those would be in there as well and then the final step this is the finished colored page and now we can see the setting sun uh we you know now know that back girl is a redhead uh you know all of these details that are now coming to life through the colors and this page is a good example as well of a specific type of storytelling that you often only see in stories about characters like batman and how much time does it take to create such a picture uh so this so colorists and anchors can typically do between um two and three pages a day so the penciler takes about a paid a day to do his work the anchor can do his job in about a half a day and the colorist can do you know about a third of a day and we need to turn out 20 pages of this every month at least and so that's why we have to have a team working on it instead of just one person usually and and with characters like batman what's you know with stories that you created on your own anything can happen um but with batman you're kind of limited um so uh every batman story starts the same um a bad guy shows up every story ends the same batman stops him and the key is can you make all the parts in between uh fun interesting and unique so in this story the you know there is a villain and batman's going to fight it and uh but the the fun part of the story the thing that you know where anything can happen has to do with batgirl who's in the batmobile with batman in the beginning of this story oh sure sure so the the part that's interesting is has to do with the relationship between batgirl and batman and no problem uh so they're they're uh driving across the city to go take out this bad guy and batgirl saying you know i have my driver's license now i could drive us and batman's like you're not driving this car it's my batman voice and batgirl's like i bet you let robin drive it when he turns 16 and so batman's like believe me robin is definitely not driving this car and batgirl's all like fine whatever i didn't want to drive your stupid old batmobile anyway so this is what we call in dramatic terms um check-offs gun um you may have heard of that guy Anton check-off um and he said that if you have a gun uh on the mantle piece in the first act of your story it had better go off by the third so batgirl wanting to drive the car is what we call check-offs gun from the playwright Anton check-off who said that if you have a gun on a mantle piece in the beginning of your story it had better go off it had better someone had better fire it by the end and so batgirl really wants to drive the car right and batman does not want to later drive the car so we know she's gonna have to drive the car and unlike the fight with um the super villain where we know batman's going to win anything could happen when batgirl gets behind the wheel the wheel of that car and you know she drives the car they get into an accident she doesn't have any insurance it's a it's a big mess um but you know it's all okay in the end um but that's the that's what makes this story fun to me and to the reader there's ways you can introduce uh different types of story elements even when with a character like batman you're kind of put in a little box into what you can do there's still ways to make it fun I noticed that the pages that were in pencils and ink are done manually and do you use any computers any new technologies to optimize the process so a lot of people do uh most people still pencil and ink and by hand but almost all coloring is done in uh on a computer usually using photoshop or a program called manga studio and a lot of penciling and inking is now done uh on like a wacom tablet uh or you know any kind of other anything where you can draw directly on a screen they can now use that for the penciling and inking you know so as long as it's really up to you however you want to do it everything uh everybody uses different tools and another question from the audience well you have a lot of characters and each character has a story but um how can you how can you find the main story how can you find the main one so when you have a story with very strong characters um and whether it's your story or they're your characters or their characters that someone else has created like batman or superman um the key is deciding what you want to say what you know the meaning of it all so uh i did a story called mail order ninja um about a boy who gets a ninja in the mail and takes him to school with him and so it's pretty funny and it's pretty silly but it was actually about um uh sort of being being bullied and being picked on and um what what happens when you become the strong one do you become a bully too and you know with stories like um i wrote a superman story that uh was all about you know in in the story superman fights a like an evil robot uh but that wasn't the point of it the that was just a way to illustrate the idea that um no matter how powerful you are you still have to struggle uh and that people who have been um who have been blessed with you know talents and abilities whatever they are uh they can be inspired by people who have been given handicaps by life instead but still struggle anyway still keep fighting and so the i guess the the thing that the best way to do this is to ask yourself the question why am i writing this you know why am i putting my heart and my soul into this story and then why should i expect someone else to care um and if you can't answer that question then you don't really have a story uh so the the i guess the summation is you know you have you can have great characters who have their own voices and uh their own stories that you know that they'll tell but if you're gonna put them in a story then you need to make sure that that it's a story we're telling that means something two questions from our online audience okay and the first one is the first one from Margot's uh Margot's at the Moor other than superhero type comics how receptive to other types of comics are the publishers in the states or other countries so i mean it's been a the the kinds of comics and the kinds of of stories and genres that you find in comics has has just really been exploding over probably the last 20 years i'd say and so you can find comics about just about anything now um whether it's non-fiction you know biographies uh books about economics and science you can find you know comics uh comics for kids comics for adults comics for everything in between comics for adults i mean the real key is uh finding something that you're passionate about really you know a topic uh an art style something that where your enthusiasm and your excitement shows through uh and if you can do that then you have a pretty good shot at getting published somewhere and you don't have to and the great thing about the modern era is that you don't have to wait on someone to publish you you can take your comics to the internet um and one real quick thing about that i was going to self publish my mail order ninja series and i randomly entered it into a contest and won the grand prize and then i got a book deal out of that and that's how i started working in comics professionally but it was all because i believed in my story and i was going to put it out there regardless of what anyone else thought and there is another question from our audience a question about your project and graphic textbook um how um how did you work on this graphic textbook did you follow some already existing educational programs or did you just take some situations and display them in your textbook okay so we wanted to make something that um you wanted to make a textbook that kids would actually want to read uh so we recruited uh authors and illustrators who were popular with the age ranges of the students we were trying to reach and so these were the people that these kids were already spending their own time and their own money to read uh the work of these people and uh what age are you working for what a target audience this first uh this first book is uh ages really about nine to twelve our first book was published for children from nine to twelve years but we plan to uh again at this book is a success we plan to uh start focusing on very specific grade levels um and specific subjects so we do math for um people in grade five for kids in grade five and so the process is that we have uh the we have these really talented uh cartoonists and we give them a list of topics and these are the things that someone has to learn in math at grade five and we say pick one and if it's we we want them to be excited about the topic and be like i can tell a great story about that and then we let them do that and our education team is sort of looking over their shoulder to make sure that all their facts are right and it's doing all the stuff that the educators the teachers needed to do uh but otherwise we just sort of get out of their way and let them do what they normally do which is make comics that kids want to read and another question from our online audience we're starting from Nasta Danilova are there any color keys used for story coloring for one issue as for there is in animation or how does it happen uh that's a great question um so what you'll often see especially when you're talking about um um characters that are established uh like batman uh you'll we typically have what's called a style guide uh with color keys that's like batman's cape is this color and if it's stories of our own uh and characters of our own uh we will create those as a part of the uh the process so the answer is uh is yes um you know and and it's not exactly like it is in animation but it's pretty close well the first question is about those who are new in comic business and what can you advise for those who are good at creating characters at drawing characters but like skills necessary for drawing buildings or drawing surroundings do these people should these people start drawing some simple comics like Snoopy or should they develop their skills at drawing buildings and create some professional comics well i mean i think you have you have three choices the first is you can just play to your strengths you can just draw what you like and find a way and find characters in a world that let you just draw what you want to draw one of the most popular comics in the world is called xkcd uh and it's uh has stick figures uh you know it doesn't hurt that the the guy who creates xkcd uh he works for nasa and so he's a he's a rocket scientist he's a pretty smart guy um but you know he uses stick figures to tell all sorts of amazing stories so you can just draw to your strengths and be very successful um the second option is that you become a part of a studio uh the way that most manga is created in japan is by a studio so i showed you how in american comics someone will pencil the whole page and then someone will ink on top of that whole page uh in manga they typically break it down even more so one person draws the characters and one person draws the backgrounds and so you don't have to be an expert on everything because you have a studio mate sitting right next to you who is and the third option is that yeah you can basically just you know keep working at the stuff that you're not strong at um and eventually you know master all of it and that takes a lot of time but it can certainly be done i'd like to quickly recommend a few books uh that i think would be very helpful for anyone wanting to make comics and i don't know if these have been translated into russian um but they might have um but i'll i'll give you the titles and then we can hope for the best the first is called drawing words and writing pictures and it's uh authors are jessica abel abel and after jessica abel and matt madden madden and mad man uh and then another title that i would very much recommend is called understanding comics by scott mclout and he has also written two other books about reinventing comics and making comics that i also highly recommend between those uh i think you you're off to a pretty good start sorry i couldn't hear you sure i said between those i think you'd be off to a pretty good start right and um and last question and we're done um no uh there was a second question from that lady and uh the question about the balance between text and images in comics what is that balance and um how does the text impact on images and vice versa and what do professional comics makers think about it so fundamentally you want them to both add something unique uh you don't simply want to the text shouldn't simply describe what you can see and the images shouldn't show what you're telling with the text there sometimes it actually is very useful for the two of them to say essentially the same thing and to you for you to repeat yourself and text an image especially when you're trying to teach because that repetition and that connection is valuable excuse me could I repeat please so if you're trying to teach something uh you actually often want to repeat yourself you want the text and the image to essentially say the same thing because that's the point of it you want you want someone to understand something and so you kind of tell them twice in the same space but when you're uh trying to be artistic when you're trying to create a uh a compelling piece of of art and literature you want them to complement each other uh you want one one does one thing and one does the other and when you put them together you get something that's greater than either would be alone and uh the the ideal is that the text if the text was seen alone there would clearly be something missing and if the images were seen alone there would be something missing but when you put them together then it feels like uh everything is there but again this is more art than science uh so it really honestly it depends on you and what feels right to you and that's all for today thank you very much for for the lecture for answering questions thank you everyone this has been a this has been a lot of fun i hope you guys had fun too and i would like to pass the microphone to alexander he wants to say a few words uh thank you josh thank you mr. elder so have a nice evening i'm good