with the speed of light, the cloud of dust, and a hearty high old silver, the Lone Ranger. With his faithful Indian companion, Tonto, the masked rider of the plains led the fight for law and order in the early west in the United States. The stories of his strength and courage, his daring and resourcefulness have come down to us through the generations, and nowhere in the pages of history can one find a greater champion of justice. Return with us now to those thrilling days of yesteryear. From out of the past come the thundering hoofbeats of the great horse Silver, the Lone Ranger rides again. Inside the home of the driver of the Overland Express stage in Prairie City, two boys stood admiring the faded yellow coach and the team of four horses which were waiting to make the regular run to Dolby. One of the youth, Danny Kern, was the stage driver's son, the other was his friend. Someday I'm going to be a stage driver, just like my dad. It takes muscle to drive a foreign hand. Oh, you bet, and I got it. Huh? Oh, look at this if you don't believe me. Yeah, that ain't so much. It's better than you can do. Well, I bet you can't drive a stage now. Oh, I bet you I can. I already done it. When? Well, not so times. Whenever dad takes me on one of his trips. You're just fooling. I am not. If dad wasn't going to drive that stage soon, I'd show you. Go on. I dare you to drive it. I tell you, dad's taking it out on his run. It's easy to talk.  You mean you'll drive it? If you go with me. Well, are you afraid? Of course not. Well, come on then. All right, come on. Quickly the two boys scurried to the driver's seat of the stage, and Danny scooped up the reins. With an air of authority, he spoke to the horses, then released the brake. Get out there. Get out, horse. We're moving. Of course we are. I told you I could drive. Where will we go? On a trail. Then I'll show you some real driving. Are you sure you know how? Oh, afraid he can't look at them turning green. I am not. Hang on then. Here we go. Get out. Get out there. The main street of town lumbered the stage, while the town folk gave it to the two boys who sat on the driver's seat. Needless of the warnings of the sheltered affluent, Danny headed the foreign hand to the Dobie trail. Oh, look at them horses. What? Say, this is five. I bet I could drive it clear to Dobie. You ain't going to the warrior. Well, not this time, dad. It's giving me a lie. Well, how far are you going? Oh, just for a spell down the road. Are you sure, dad? That'll end horses, Dan. Hey, you want to try your hand? You think I could? Oh, sure, as long as I'm here to help you. Well, maybe I better not. What are you scared of? Here, let me change places with you. As Dan Haft stood in the driver's seat at the fast-moving stage, two riders quickly rained in their horses.  The other was an Indian. The boy is driving their stage tunnel. Now he's changing places with the other. That's plenty dangerous. Yes, it's suicide. That stage is heading for a sharp curve. That's right. We must warn them. Come on, Sylvester. Urging the powerful white stallion and the sturdy paint forward, the masked man and his Indian friend rode winter-stepped the stage before it was too late. Master Sylvester, come on. It'll be close tunnel.  We've got to beat it while Sylvester is away. From the fast-moving stage, the boy has suddenly saw the dangerous curve ahead. Dan, look. Hey, give me those reins. Stop the horses. We'll overturn. Whoa, whoa, there. Whoa. They're running away. Whoa, whoa, horse. Pull up there. Look, a masked man and an Indian outlaws are overtaking us. As the thundering stage drew closer and closer to the sharp curve ahead, the great horse, Sylvester, drew even with the driver's seat and leaped alongside the lead horse of the team. Quickly the lone ranger grasped the team's reins. Whoa, whoa, hold up, whoa. Raining on the reins, he slowly but surely pulled the team to a halt, virtually on the brink of the turn. The coach lumbered to a halt. What are you boys doing with this stage? Well, we were just taking it for a ride. All right, huh? You might have killed yourself. We won't do it again. Oh, no. I better take it back now. My dad's a regular driver. He'll need it for the run to Doben. I'll see you on your way. Help me turn the horses, Tutto. A few days later, across the arid wastes of a sun-bleached prairie, a puff of dust moved along a new wagon trail. Then a sudden breeze whisked the dust away, revealing a stagecoach bound for Prairie City. As the stage lurched wide around a bend, two horsemen watched its approach. Something wrong with that stage, Tutto. Horses run way. Look at the garden driver. Let me see. Something's happened to the let's go. Half hour later, a strange procession filed down the main street of Prairie City toward the express office. The town folk gape as it slowly lumbered past, then buzzed with excitement. Hey, Sheriff. Stages here, Sheriff. It's been way late again. What's that? There it is. You can see for yourself. Look who's driving it. A mask man. And a ninja. What is it, Sheriff? No yet, Ram, but I'm aiming to fire now. I'll go with you. Me too. Come down off that stage, you two. I've got your cover. Put those guns back in the leather, Sheriff. How are our friends?  Meanwhile, if either one of you make a wrong move, you'll get a sludge for those six guns. Hey, Sheriff. Yeah? The driver and the guard. They're dead. Narita, mark on them. Mark on them. This is the third time that's happened inside a month. Maybe they died of bad heart or something, Sheriff. Are you a slow-witted maverick? If it had been just one, she might be right. But six corpse had been brought in by stage, and all without a mark on them. Right, Sheriff. Much more, the stage has been looted every time. That's right, Sheriff. There's no two ways about it. Those men were murdered. And this mask starving the engine of the ones who did it. You're right there, too. These two men were dead when we found them. A likely story, ain't it, Sheriff? Who are you? I'm District Superintendent of the Stage Company, Mr. And I'm charging you with murder and theft against the Oberlin Express. Oh, Dad, bring them up. It's Danny Kern, the stage driver's boy. Let me through. All right, get back there. Let the kids through.  Dad! Father's dead, Dad. Dad? You're the man? That's the man who killed him. That's the man who sent two murderers. Yeah! Come on, Tutto, steady silver. All of our way. Now, hold on there. I'll get him. What's so fast? The lone ranger and Tutto rode along the new stage trail, seeking a clue to the mysterious murders. Somewhere along this trail, six men were murdered, Tutto. Uh-huh. No sign of injury. Who's coming? Who's coming? Oh, oh, oh. Where is it, Tutto? You look there.  The stage was driven off the trail and alongside that boulder. They follow tracks. Maybe find something. There are three sets of wagon tracks leading from the trail. Not right. Kimosabi, maybe find something. You look here on rock. Gold dust. At least it looks like it. Gold was stolen from the stage. Not right. What that mean? Working alone, the outlaw found the loot too heavy to handle, without dropping and breaking the boxes. Oh. I drove the stage off the trail and slid them onto this boulder, and probably onto pack horses. This dust trickled from the boxes. Oh, Tontosabi, no. That means he knew the garden driver would be dead when the stage got here. What killer? Easy, big fella. Answer lies on the trail ahead, Tutto. Come on, Tutto. Hit him up, Tutto. The next day, the sheriff was in the district office of the stage company, conferring with the manager. All right. As I size things up... Oh, just a minute, Sheriff. Who's there? It's me. Danny Kern. That's the son of the stage driver who was killed yesterday. I wonder what he wants. Ask him in and find out. All right. Come on in. Sorry about your poor death, son. Yeah, me too. We mean to make it up to you by catching the yellow-livered coyote who killed him. Yeah, that mask man is ancient friend. Well, that's what I come to see about, Mr. Rand. What? I want you to give me the chance to catch a killer that murdered my dad. What's that? Oh, can I give you the chance, boy? By giving me the job driving the stage my dad drove before he died. What? What? Well, don't laugh. Oh, sorry, son. But I thought I heard you say you wanted to drive the stage. I did. Horsifact, the kid's plum serious. How old are you, boy? 14. But I can handle a foreign hand. 14. You got size and weight enough to pass rating. Where'd you learn to handle a foreign hand? My dad taught me. He used to take me with him on some of his trips. And you think by taking your dad's place, you might meet up with a murderer, son? That's what I'm hoping. The new stage trail runs through some pretty dangerous and deserted country, Danny. Yeah, driving that coach is a man's job. Only one water hole in the whole stretch. When you come to it, you're so dog-gone thirsty you like to drown yourself getting enough to drink. I want that job, Mr. Rand. You got the spunk for a stage driver. I'll say that, son. I don't know. I heard you needed drivers for the new stage trail since some killings commenced, Mr. Rand. That's so. I heard you couldn't get no experience driver to take the job. The boy's got you there, Rand. Six men have been murdered riding the stage on that trail. Yeah. And do you want the job in spite of it? Yes, sir. What makes you think you'd bring the stage through when six grown men have been able to? I'll bring them horses through all right, Mr. Rand. And when I get to Prairie City, I'll be driving them. They won't be pulling my corpse. Dog-gone it, Rand. Let the boy try it. I like his nerve. Just give me one chance, Mr. Rand.  No, no, nothing but four good horses in a stage in Congo. Give the lad his chance. I've got an idea. All right, boy. The job's yours. Thanks, Mr. Rand. Report to me here tomorrow. You'll take the stage at Dobie in the morning and drive it back to Prairie City in the afternoon. Yes, sir. Goodbye. Goodbye, Jack. Good luck to you. Got the job, Rex. Now I'll get the critter that killed my dad.  Get up there. The youth rode away. A lone ranger and Tonto watched from a nearby concealment where they had listened to the conversation. I'm going in now, Tonto. I'll wait here. And what? Change your... I don't think so. I have a hunch there. I'll map out a plan after I get what I came for. I want you to overhear that plan. Tonto, Sandy. I'll wait for you at the fork outside of town. Adios. Adios. Hey, what the... The mask, man. Keep your guns in leather. They're covered. Put them chute knives down, stranger. And surrender in the name of the law. I didn't come to surrender, Sheriff. So you admit you're the killer. You'll think that whether I deny it or not, Rand. And you are the murder and coyote. No, I'm not, Sheriff. I came here for information. Information? What kind? I want to know when the stage leaves here for Dobie tomorrow morning. I'm going to schedule to leave Dobie in the afternoon. So you can murder more of our men and loot the coach again, huh? You're becoming insistent on that point, Rand. I don't like it. Pretty brave when you're standing behind them six guns, aren't you? You think it'd be a match for me if I weren't? I'd be twice the match of the likes of you, mister. Sheriff, here are my guns. What? Now, Rand, prove your point. Well, prove it. Miss that swing, Rand. Well, I won't miss this. That'll do for you, Rand.  Flush on the jaw. Now, Rand, I'll take that information. It gets the rules to give to anybody except them. The boy's a fair. Very well. I'll buy one. Oh, are you? I never mind, Rand. That chart on the wall seems to be the schedule. I'll just help myself. No, no, stop him, Sheriff. My gun, Sheriff, if you please. He's getting away. Stop him. Gornet, I've never seen anything like that mask man. Never. The curtain falls on the first act of our Lone Ranger story. Before the next exciting scenes, please permit us to pause for just a few moments. Now to continue our story. After the Lone Ranger escaped in a hail of bullets, Tonto crept closer outside the window and listened. It's the second time you've let that mask arm break it away from your sheriff. How are you going to hang on to Grease Lighthouse? You better do something to catch him pretty quick. Well, I name him, too. Oh. What's the stage freighten the doby tomorrow? Oh, nothing valuable. I'm not going to do that. What's the stage freighten the doby tomorrow? Oh, nothing valuable. What's it carrying on the return trip? Gold. Gold, eh? You think we better move the shipment back a couple of days? No. That gold's going to stick to schedule. But I can't afford... We'll use it as bait to lure the mask man into a trap. Oh. What's your plan, Sheriff? I'll ride the stage on the return trip myself. I'll take the job as guard of the gold. You mean... I'll swear in a lot of new deputies today to post along the trail. All along the trail? Yeah, but regular intervals. There ain't no sense in stationing them in dry gold, Sheriff. Part of the trail's clean deserted. Ain't even a shrub for the murderer to hide behind. I'm not taking any chances. Well, I guess you're right. We'll have that killer bottled up no matter where he strikes. Yeah. Well, I'll be getting along. I've got a lot of deputies to swear in before tomorrow. So you think you'll have the killer bottled up, Sheriff? Urging his sturdy paint ahead, Tonto rode to the fork outside of town with a lone ranger waited. He quickly reported what he had heard. Well, the sheriff is pushing men all along the trail, huh, Tonto? That's right. That's the surest way to scare the killer off. Ah. To capture the murderer, we'll have to get those deputies away from the trail. What do we do? Well, I have a plan, Tonto. That'll require a third person to carry it out. Someone we can trust. You know a person? Yes, I think I do, Kimosubbie. Come on, Silver.  It was dust when on the trail, not far from town, Danny Kern heard the thunder of distant hoops and, looking back, saw White Stallion with a masked rider rapidly approaching. For a moment, he sat transfixed as the great horse, Silver, raced toward him through the gathering shadows of night. Then, recognizing the rider as the stranger he and Tad had met on the trail, the mysterious masked man whom he believed had murdered his father, Dan spurred his horse ahead. The masked man? Get up, Rex! We ain't got a chance against him without a gun. Get up, boy! Wait, Diane! I want to talk to you! But try as he would to stretch the distance between him and the masked man, Dan soon found his efforts were of little avail. The onrushing Stallion rapidly bridged the gap with powerful, sinewy strides, and a lone ranger reached for Dan's bridle. Oh, Sir Silver!  Oh, Silver, before I expose!   You've got to believe that. I suppose this engine's innocent too. Tad and I would like to be your friends, Dan. Ah, you can't trick me like that. Dan, do you want to capture the man who killed your father? You bet I do, and you're him. Tad and I have a plan to trap the killer, but we need your help.  If you really wanted to catch the killer, you'd just walk into the Sheriff's Office and give yourself up. Perhaps, uh, this will change your mind about me. What? I'm just Silver Bullet. Silver Bullet? A masked man and a white horse. What?  Shall we talk about that plan now, Dan? You bet we will! Late the following afternoon, there was a bustle of activity before the express office in Dolby. It was almost time for the departure of the stage for Prairie City, and an exceptionally large and curious crowd of town folk had gathered to watch how the 14-year-old son of a slain stage driver handled the foreign hand on his return run. But then, made confident by the success of his trip from Prairie City, ignored their interest and whispered comments with the aplomb of a veteran. Whoa! Hold up there, horse! Whoa, whoa, there! Whoa! All right, Hank, hit some horses at the stage. Just as you say, Danny. You handle that foreign hand like a veteran already, sir. Thanks, Sheriff. A gold horse to board? Everything's in order. Good. Time to be moving into, Danny. Almost, Sheriff. We, uh, we got a passenger. A passenger? He's coming out of the office now. A tall man in nondescript clothes stepped out of the express office and into the stage. A secret preparation of wild berries and herbs applied to his face by Tonto had almost magically changed its appearance. Where not long before the lone ranger had stood, now walked an ordinary car ham. Horses are all hitched, Danny. Thanks, Hank. You ready, Sheriff? That passenger... You better get moving if you want to make it to Prairie City on time. We're moving right now. Come on, Sheriff. All set, Danny. Get up there, horse! Get up there! Come on!  Get up, horse!  Leaving the outskirts of Dobie, the stage gathered speed, then settled into a steady run under Dan's expert hands. Unknown to the Sheriff, the quarry he so eagerly sought as he fastened his eyes on the trail sat disguised as a passenger inside the coach. How far are we from the water hole, Sheriff? There are quite a ways to go yet, stranger. Have your men seen anything of the outlaw? No. The one at Maskspeller and his Indian friend hold at the stage this time they'll find a mesh of trouble. What's that? I don't know. It's coming from behind us. Why, it's your deputy, Sheriff. They're chasing someone. The injured. Oh, go and let Red Skin... And the Tonto dashed along the stage trail toward Prairie City with the Sheriff's deputies in hot pursuit. Attracted by the thundering hoops and the sound of gunfire, each man in turn left his post and joined the chase. You follow me! The express office in Prairie City sped the chase. Superintendent Rand went to the window to see what the excitement was. All of them leaving their post to chase one innocent engine. It's just what I needed to give me a chance at that gold. It was some time later when Danny Kern brought the stage to a stop at the water hole. The afternoon sun blazed down on the trail with an almost intolerable heat and dust clogged the hot air as the stage slowly rumbled to a halt. Pidgeting beside Dan on the driver's seat, the Sheriff eyed the water hole with keen anticipation of a drink from it to cool his part's throat. Pull up there! Whoa, easy there! Easy there! Whoa, whoa, there! Whoa! Man, I don't know about YouTube but I'm making for that water pronto. I'm so dry I can drown myself. Just a minute, Sheriff. Come on there, horses. Oh, hold on, Danny. If you've got no better sense than let them horses drink first, they'll muddy up the water. Them horses are mighty thirsty, Sheriff. So am I. I ain't hangin' to drink water after horses have stirred it up. Well, maybe you better wait till we get to Prairie City for a drink, Sheriff.  Even muddy water's better than none. Suddenly, from the direction of the stage, a tall man who wore a white hat and a black mask and silver-mounted six guns hung low at his hips, strode toward the Sheriff. Don't drink that water, Sheriff. The mask man! Also, your passengers. Look, you mean this... I removed my disguise and a coat, Sheriff. Jumping juniper. And all my deputies went chasing that engine. That was necessary to give the killer a chance to expose himself. You don't have to expose yourself to me, Mr. I know who you are. Dan, take this gun. Yes, sir. If he tries to drink that water, shoot him. What in thunder? Yes, sir. So you two rent cahoots all the time. The killer's as gritty for gold as I think he is, Sheriff. I'll bring him to you. What? Easy, big fella. Remember, Dan, don't let him drink that water. Yes, sir. Come on, gentlemen. Down the trail, thundered the powerful white stallion. His flying hoop struck sparks from jutting stones and his nostrils widened as the mask man urged him to even greater speed. Then partly concealed by a boulder, they sighted their quarry. Ho, sir, for who?  Keep your hands away from those holsters. What do you want? I want you for murder. Murder? You must be a love ghost. No use, stalling, Rand. I have proof. Will you ever take me out of my hands? The second time for you, Rand?   No, you don't. You won't be out of my saddle. You're coming with me, Rand. Come on, gentlemen. What are you taking me? To the waterhole. You'll talk there. At the waterhole, in the baking heat of the afternoon sun, Dan Kern kept his vigil. Watching the angry sheriff grow angrier by the minute as his eyes thirsted on the water, Dan's guns wouldn't permit him to drink. Please, arguments, offers of leniency to Dan in exchange for a mouthful, the water availed him nothing and he lapsed into bitterness. I think I encourage Rand to give you this job and you're working for the killer.  I reckon your pa would say different. Well, the masked man will be back soon, then you'll see. Well, I want a drink of that water. My throat is parched. Oh, no, you don't. You don't touch that water. My throat's parched. Can't help it, Sheriff. My orders are you aren't to drink that water. Oh, boy, who's over who? Don't call it. What's wrong with this water? It's poison, Sheriff. Well, Rand, what are you doing here? Well, I suspect, Sheriff. Rand is a mysterious killer, Sheriff. That's a lie. Well, he saw a tunnel chased by your deputies. He hurried to murder you and Dan as he did the others. Don't listen to him, Sheriff. I found him where I expected. At a boulder where he planned to rob the stage of the gold. Is that right, Rand? No, no, I... I asked him what he's doing on the trail with these pack horses, Sheriff. How could he kill six men and not leave a mark on them? He put poison in this water hole.  Oh, Dan's horses drank that water and there is healthy knowledge before. Horses aren't affected by the poison, Sheriff. Men are. That's why you and Dan were so anxious not to let me drink. That's it, Sheriff. One hour after the six men drank here, they were dead. Rand wetted at the boulder where he could be certain of it. He's lying, Sheriff. After looting the stage, Rand sent the horses on again. You'll find the stolen goods at his home. Why, you yellow-libered murderer! Don't touch me, Sheriff.  You won't further prove, Sheriff. Make him drink that water. Go on, Rand. Drink it. No. No, don't make me drink it. Oh, confess, I killed him. I killed him.  I told you I'd help catch some murder, didn't I, Sheriff? Yes, you did that, Danny. If it wasn't for you and the mask man, why, he's gone. Yep, he's gone. You have just heard as a copyrighted feature of the Lone Ranger Incorporated.


Hey everybody, this is Brett, and today I'm going to do a super-fast, ultra-quick walk around on this 2022 Jeep Wrangler Unlimited Rubicon in Sting Gray Clear Coat. This one has the 3.6-liter Pentastar V6 engine, has 285 horsepower, has the 8-speed automatic transmission, Sting Gray is the color, has the LED headlamp, LED running light, and LED fog lamp. It also has the front trail cam, has the metal bumpers on here. This one also has the Mopar cold air intake. Get the sport hood with this one. The course is seven slotted grille, the LED headlamps, LED running lights, and fog lamps. Color match fenders, color match hard top, gets the 17-inch polished and painted alloy rims, and it comes with BF Goodrich all-terrain TA tires. These are LT28570R17s. Definitely have an all-terrain tread pattern to them. This one also has the Gorilla Glass. You get the Jeep climbing up the window, as well as the sandals right there. Rock rails on the sides. You get the T50 indicators on the door hinges, how to take those doors off. We got a video showing you how to do that in the upper right-hand part of the screen. It has the 3M tape on the front of the back fenders, LED tail lamps as well, and that color matching hard top looks really, really good. Rear bumper is metal as well. You get the HD backup camera on this one. LED license plate lights. The enter and go system storage bag for your front hard top pieces. There's some more parts. I think this is the original air intake. It has the Alpine Premium sound system. Notice you get all the bars color matched. So when you take this hard top off, it all matches. You get two quick disconnects, four bolts on each side. We actually have a video showing you how to take your hard top off your JL, if you want to check that out. That's in the upper right-hand part of the screen as well. Get a JL logo right there. These doors close nice and easily. They made them a lot lighter than the JK ones that they used to have. Down the rest of the side looks really good. It does have blind spot monitoring and backup parking sensors in this vehicle. Those mirrors are heated as well. Inside you get the LED, or I'm sorry, the leather group has the red stitching, the Rubicon stitch into the backrest. These front seats do have side airbags, factory formats, auto headlamps, tilt telescopic steering wheel, power mirrors, and power locks.  Two USBs, two USB C's in a 115 volt, 150 watt plug-in. I love these seats. They fold down nice and flat. You can also just have the head rest down for better visibility when nobody's sitting back there. Child safety locks on the back doors. We'll take a look at the instrument cluster and the miles and the radio and everything that this Jeep has to offer on the interior before our final walk around on it. Get the 7-inch LCD display, 111 miles on this Jeep Wrangler. You get the heated leather-wrapped steering wheel, red stitching, cruise controls, Bluetooth information center controls. It has the 8.44C radio. This one does have the factory navigation system, AM, FM, and Sirius XM radio capabilities. Heated seats, heated steering wheel buttons. Check out your backup camera. That is HD and the front camera is HD as well. Those are dynamic grid lines on there so they move as you turn the steering wheel. You also have all your different apps including off-road pages, projection manager. We actually did a demo on projection manager with Android Auto if you want to check that out. And then you have your off-road pages. You get your more tactile climate and heated seat buttons there as well. There are your off-road pages. You got pitch and roll accessory gauges and your drive train. You have your steering angle on there as well. Latitude, longitude, and your altitude there. Window buttons, AUG's USB, USB-C. You get rear lockers or front and rear sway bar disconnect and off-road plus 8-speed automatic transmission and four-wheel drive. Heel as entry with remote start. There's the flipper key on there. I love that. And you get the red dash trim on this one. Some people like it. Some people don't. I'm indifferent. I think it looks okay.  It has SOS and assist buttons, home link buttons for your garage door security systems, lane systems, and auto dimming mirror. And yes, you can take just the front hardtop pieces off and just drive with those off without taking the whole back top off. Anyways, thanks for checking out the video. I hope it was fast enough for you. I'm gonna do a quick look at the original window sticker so you can pause that and check that out. There are all those LED headlamps, running lights, and fog lights on there. Love how those look. Let's just take a quick look at the original window sticker and then we'll be all done. Feel free to pause this because I'm not gonna go through anything on it. There's your MSRP, miles per gallon, and crash test radiance. I would personally like to thank you for checking out the video today. And if you like the video, give us a like, subscribe, and share on our YouTube channel. And to see more pictures of this Jeep or one of our other 450 new and used cars, trucks, SUVs, minivans, Wranglers, you name it, we gotta go to that website right there, summitauto.com. Full pictures and descriptions of every single vehicle from two locations all at summitauto.com. And if you'd like to check out more HD videos, you can go to youtube.com slash summitauto. Remember to like, subscribe, and share on this video and all the videos that you see there. In fact, in a second you will see a link to subscribe to my YouTube channel in the upper left to link to more Wrangler videos like this one in the upper right. Link to this vehicle on our website in the lower left and the link to one of our latest YouTube videos in the lower right. Click those, check us out. And we're super excited to help you with this ultra clean, brand new 2022 Jeep Wrangler Unlimited Rubicon in Stingray Clear Coat. Thanks again for checking out the video. Remember to like, subscribe, and share on my YouTube channel. I really appreciate it. Thanks again.


But yeah it's a Chalmers University in Gothenburg and we had the first Haskell compiler written by Leonard Auguston. Apparently he just took a lazy ML compiler and then made it parse Haskell syntax so it was like technically the first Haskell compiler but you know who's gonna argue with Leonard huh I mean we don't have the Glasgow Haskell compiler they're pretty you know that's the one we're all using but was it the first one I don't know you know after the Americans landed on the moon the Russians just didn't go because you know why bother if you're not first am I right anyway anyway I'm just kidding


I'd like to thank the organizers for giving me the opportunity to come and talk to you guys about what we've been thinking about in my lab. So what I'm going to talk about primarily is stuff that's going on. So all of this is unpublished. Feel free to think about it, share it, whatever, but it's very much work in progress. Some of it's hot off the press, so do take it with a pinch of salt. So what we think about a lot is autoimmune diseases in my lab. And we want, we kind of want to think about which genes go wrong in disease and we think about dysregulated genes, but actually what we're interested in are the causal genes. And my pointer doesn't work, I can use this pointer, it's all coming up, Chris, today. So we're thinking more about causality than anything else. So when we say dysregulation, we're interested in pathogenesis, right? That's ultimately what we're after. And so just a 30,000-foot view of the immune system, if you remember, you start with a stem cell, you have two major lineages in the immune system, the lymphoid and the myeloid lineages. So things like macrophages are all the way down here and your T cells and B cells are down here. You think of them as adaptive versus innate. And what happens is every now and then this goes wrong. So the immune system's primary function is to protect the body from things that are foreign. And so it's got this amazing capacity to tell the difference between your cells and the rest of the world. And it's really good at this, but occasionally it screws up. And it kind of, what happens is that it starts attacking certain tissues. So if it doesn't like myelin, you get multiple sclerosis. The immune systems manage to go into the brain and attack the myelin sheath very specifically around neurons, chew it up, and you get lesions into your brain. You can get things like skin attacks, which give you chogren syndrome, scleroderma. You can get type 1 diabetes, which we now know is an immune disease. If it doesn't like aspects of the GI tract, you wind up with Crohn's disease, ulcerative colitis, or celiac disease, if it doesn't like the epithelia. Joint, specific joint dislikes, shall we say, give you rheumatoid arthritis, or ankylosing spondylitis. And if it just doesn't like DNA, if it doesn't like nucleic acid, it attacks everything and you wind up with something called lupus. What's really interesting is that these are very, very specific dislikes. So MS is not rheumatoid arthritis. It's a very specific attack against myelin. It's not a specific attack against anything else. And what we really want to understand is what these diseases are. So something's going wrong with the immune system. We don't really understand what it is. What we do know is that all of these diseases are common. They're complex genetic diseases. There's a large portion of heritability. They track in family, but they're not Mendelian. It's not one catastrophic mutation. And of course, as GWAS came along, I'm going to talk about multiple sclerosis, which is something that I work on. But you can take this as red for any immune disease. As GWAS came along, we hadn't really gotten a lot of traction on the genetics of these diseases. And then we barely managed to identify two loci in the genome in one of the first GWAS studies. Then a little while later, we managed to get another one. A meta-analysis of these two sets of studies from International Consortia kind of gave six new hits. And we're starting to climb this power curve of discovery. Then a further meta-analysis with more markers and a few more samples gave us an additional three new hits. Even more samples gave us another 25 new hits. The immunorship gave us 47. That took us up to 100. And our current studies, which are about 16,000 cases, 26,000 controls, and replication in another 36,000 samples, we've got another 100 odd new hits. So we're standing at around 200 loci right now in GWAS, right? That explains, including the HLA, it explains about 55% of the heritability. We estimate that in the common space, there's probably another six to 800 loci that we don't know about yet. We kind of do know about them. They're not genome-wide significant yet, but we know they're there. And we know the approximate complexity of the disease is about 1,000 independent variants. And so when ENCODE came along and we did, we were a very small part of this paper from John Stam, sort of showing that in Crohn's disease and in multiple sclerosis, there is strong enrichment of the risk snips on regulatory regions active in very specific subsets of the immune cells. In multiple sclerosis in particular, you can see CD3 cells, CD19s, B lymphocytes, and CD14s, which is interesting. There's a lot of pathogenesis coming out of T cells as well, but these are more B cell-like ends. So dysregulation in multiple sets of immune cells seems to be an issue here, but this kind of sends us chasing down this idea that is now extremely common. And this is one of the great things, right? So 10 years ago GWAS wasn't going to work. And five years ago, everyone was asking why we haven't solved disease yet. Five years ago, everything was coding and now everything's regulatory and it seems really obvious. But even two, three years ago, this was not that obvious. And so this starts us chasing down this rabbit hole of which genes are getting dysregulated and how does that cause disease? And so that's what we're going to talk about today. Further evidence that in specific immune cells, you get dysregulation that maps into specific transcription factor binding sites. This is from Kyle Farr and Brad Bernstein, showing that the MS snips are particularly enriched for NF-Kappa B transcription factor chip peaks, for instance. And so there's something that's fairly specific dysregulation in immune cells, which is great in bulk, hard when you actually want to identify specific effects on specific genes in specific cells. And so that's the task at hand. And so when you look at some of these loci, you know, you put up a GWAS locus, here's a classic locus in MS. Well, there's NF-Kappa B1 and mannose binding protein A. And you could sort of make a case for mannose binding protein A, but really, everyone's going to assume that NF-Kappa B1 is the appropriate gene. And it turns out that that's right for various reasons, and so you can start working on that because you're kind of reasonably sure that's the gene. When you look at another locus, of course, that gets a lot more difficult.  Diarrheum DL3 is here, IKZF3, which is Helios, which is a transcription factor that controls T-regulatory cell differentiation, is there a bunch of other immune cells, and so you're kind of going, what's going on here? So we kind of thought, okay, if there is regulation and we have SNPs, how do we unite the genetics with the epigenomics? And a lot of people are thinking about this. You're going to hear a lot more stories about this. You've already heard some. Here's how we've been thinking about it. So we're kind of amateur math geeks, and so we think about how we can transfer some of this probability and do some functional fine mapping. So you have a set of SNPs in the genome. We're going to talk about hypersensitive sites now, but instead of DHS, you can think of any regulatory mark. We've been working along with hypersensitive sites, because we like them. They're stable. They're nice. They tell you a lot. We're going to expand this to the other sets, but think about DHS for now. And you've got a gene in the locus. So this is my, like, tiered view of a locus. So each of these guys is associated to disease. Ah, oh, this is going to chop off my things. Oh, well. So what that says is posterior probability of association, or PPA, okay? So when you do a GWAS, for each of these SNPs, you get a P value of whether it's associated to disease or not. You can convert that simple P value into basically a posterior probability, which tells you what is the likelihood that this SNP is the one driving the signal, okay? We're not going to talk about the math magic that underlies that. I'll bore you with it in person over coffee, if you like. But basically, for each of these SNPs, you can do a magical transformation and get the probability that that's the SNP that's driving signal. If it's very associated and nothing else is associated, it's going to be really probable to drive the signal. If there's a whole bunch of SNPs that are equally associated, you're going to have to spread the probability that it's causal over all of those guys, right? That's the intuition here. So of course, some of these SNPs are actually on DHSs. And so you can transfer that probability. I can't even talk anymore, sorry. That probability to the DHS. You could also do something fancy like, say, this guy is about this far away from this DHS, so I'm going to give it some proportion here. That's, we're not doing that right now. But basically what I can do is come up with a way to score every regulatory region for what their probability of explaining the association in that region is, right? And if I sum all of those, of course not every SNP is on those. But if I sum all of these posteriors, that gives me the global probability that in this locus, association is mediated by these regulatory regions. It doesn't have to be all of it. But if most of the signal is on DHSs, then you're going to get a high percentage, right? It's going to be close to one. If it doesn't look like it's being mediated by regulatory regions, you're going to get a low proportion. So, so much is easy. What's cool is you can then think about how you correlate these guys to the genes they control. So if I had a magic way of saying, well, this DHS is correlated to this gene this much, then I can wait how much of the posterior of association gets transferred into this gene, right? So if this guy's perfectly correlated, if this is what determines whether this gene is expressed, then if this explains all of the association to a trait, then presumably it's acting on this gene. Because the DHS isn't just a DHS, it's regulating something, right? So that's the intuition. And you can partition this all this way. And what it says here is CP times PPA, okay? So that's just the correlation posterior between this DHS and this gene times how much weight you've given it from the association data. And that way you wind up building this model of this gene posterior. So if I sum all of these, all of the contribution of each DHS from the SNPs going into this gene, I can get a sense of what the probability that this gene is driving association in this region is. And I can do that for any gene. So I now derive a score, basically, for how likely this gene is to be pathogenic if that pathogenesis is mediated by DHS regions. And we know they're enriched, so that's a reasonable hypothesis, okay? It's not the only way to do it, but it's one way to think about this. And so you have to solve a couple of technical problems to do this. One is you've got to correlate your DHSs to your genes. And so that's really simple. You just observe if there's a peak and what the level of expression of a gene is, and then you do a correlation, on, off versus level of expression of a gene. And you do that for each DHS you find. Two issues. First of all, you've got to decide what the same DHS is. And secondly, you need measurements where you've measured both DHS and gene expression, okay? So to do this thing, we use an alignment approach. This is what real DHS data looks like out of hotspot. These are peaks. This is an arbitrary part of the genome, and your job is to figure out which ones of these represent the same element across samples. We're not terribly good at that as human beings. Fortunately, computers are a lot better at this than we are. So you can put in a clustering approach and kind of decide that these look the same, that are a little jittered, but they kind of look similar. And then these guys are kind of the same, but you're maybe a little bit less confident because there's more spread. And these guys are kind of the same as well, but there's even more spread. Okay, and the way we do this is with Markov clustering. It's a way to cluster stuff. There are other ways to do it. It works reasonably well.  That's brilliant. Okay, so one way you might want to do this is to say, is this detectable? And so you go into the roadmap data. Unfortunately, there are replicates. And here's my assertion. If I see a peak here in replica one of a tissue, then I should expect to see that peak in replica two of a tissue as well, right? Biological replication, just like we do in any other experiment. Really simple. And so once I decide this is my cluster, that's what comes out of the algorithm, you don't just go and apply that mindlessly to data. That's not how you do analysis, right? You check and you see what you can detect. And of course, the wider and the sloppier this peak is, the less likely it is to be true. And so you can do a statistical test. And so once you've decided what the cluster is, if there's a peak anywhere in that cluster, you mark that sample as a one. And if there's no peak, you mark it as a zero. If you have replicates where the labels are somewhere over here on that wall, you can then say, okay, if I see ones in both replicates, I'm going to score that tissue as a two. I'm going to score it as a one if there's only one replicate. So if it's discordant, I'm going to score it as a zero if there's none there. And then you can do a test. So I've done this without knowing about replicates. And then I add the information about what goes with what. And I ask are they consistent? So if I get things like, look, in cell type one, I get a one. And in two, I get a one. I get all ones. That suggests this isn't consistent. It's not replicating. And if I get a lot of twos and a lot of zeros and very few ones, that looks consistent. So it's replicating. It's either not there or it's there. And so I can do a statistical test. It's not terribly important what the test is. It's a simple chi-square approximation. We do this over 57 tissue replicates from roadmap. And we find that just feeding this in when we cluster, we can get about a million out of 1.99 million. So about 54% of our clusters pass a fairly stringent threshold, a fairly lenient threshold. And that's because very often these things are kind of diffuse. The clusters don't look really good. And so we're probably not doing great at the clustering. And it's unreliable.  But most of this is actually the clustering. So we can get about a million features out the genome. And we don't worry about recovering more stuff and improving the clustering. Right now, we're just working with these million. So the other thing is you've got relatively low power. And so what's nice about this is this, what you can clearly read here, what you can do is estimate how much of the heritability you're still explaining. So this is just a sanity check. If you use all of these clusters, it's about 14% of the genome. And it explains the proportion of heritability. And what I want to know is if I reduce this to the half of the clusters that I'm using now, what proportion of heritability am I still explaining? And to a first approximation, what you can see here is in red is all the peaks. And in blue is just the clusters that we define. Pretty much we're capturing all of the signal. It varies as wiggle room. There's a little bit of error on these things. But we're capturing just about all of the heritability. But we've gone from 14% of the genome to 8% of the genome. So rather than do the 500 base pair either side, which is what most of the previous heritability estimates have done, which a lot of the summary papers have kind of shown, oh, there's enrichment in DHS or in regulatory regions or whatever, but they actually bracket each feature by 500 bases and so they cover 50% of the genome. So yes, all of the heritabilities explained by 50% of the genome. I'm telling you that a lot of the heritabilities explained by 8% of the genome. So it's a little bit more specific. And so the second challenge is to now correlate these guys, now that we've decided what clusters are, to correlate them to gene expression. So you need matched data. We use 22 sets of matched DHS and exon array data from roadmap again. And the problem is there's massive inflation because gene expression data, of course, is highly correlated. And so you just get this massive inflation in the expected distribution of these tests and we can correct this. We just go through and normalize it and basically you kind of start off with this massive inflation. I'm showing you lambda here. It's supposed to be a nice straight line here and we can correct all of that out. So now that we have all of these statistics, we can go back and do our little approach. So now we have this part, we already have this part from credible interval, set mapping and posterior estimation and we can now estimate gene-wise scores. And so big red exclamation point here you can't see means this is really fresh as in last Friday's results, hot off the presses. Here's a region. We're talking about MSGWAS. This is actually the immunochip data from 2013. Chromosome six, one megabase region and I'm doing this for all of the genes in the region. DHSs explain 94.5% of the signal in the region. So whatever it is, it is really, really likely to be acting through a DHS, right? MNDN1, which is one of the genes in the region, explains 55.5% not of this 94% but of the total signal in the region. That's how it feeds through, right? So that's what I'm doing. So buck two is 16%. Between these two genes, you'd be hard pressed to say that any of these other genes are really sort of pushing the signal but it's probably this one. So this is a way to prioritize genes based on regulatory potential. Now it's really important to look at this number as well. If this number is low, you kind of think well, it's not really likely to be regulatory in the first place. If it is, it's gonna be one of these guys but it's not likely to be. In this case, it's really likely to be, right?  Okay, that reads 0.029, 0.022 and they're ranked and it goes down from there, okay? So you'll see that in this region, about 30% of the association signal is explained by DHS clusters as we've defined them, okay? So it's not a lot of it. That 30% is now basically smeared over a whole bunch of genes. There's no one gene that explains that signal. So even if you accept that I'm willing to take this 30% as a gamble, there's no one obvious gene you look at. And the reason for that is actually that we suspect that what's going on in this region is there's an entire element, sort of something like an accessibility element. Some people call them super enhancers, they can mean different things to different folks. What we suspect is going on is there's an element that sets whether the entire locus is accessible or not accessible and that affects the transcription of multiple genes. And so what the effect may be is actually that you're changing whether this entire locus is available or not available. And there's a whole bunch of genes in there that then do different things and set a risk state or multiple risk states. And so sadly it's not always one locus, well, one gene, but these are probably gonna be really interesting. It's unclear whether we can solve such loci, but they're gonna be really interesting. So you're gonna get examples like this and it doesn't work all the time, this approach won't work all the time because not all loci are simple in the one gene thing. So we're gonna have to think harder about these. So I'm gonna switch gears in the dying moments and just give you another flavor of how we're thinking about the other way around of epigenomics. So so far we've talked about how to analyze these data and make inferences so we can then go and work on certain genes. But what we really wanna know at some stage, if changes to gene regulation are what is creating disease states in the immune system, well, you're not born with an immune disease. Most of these diseases occur in the third, even fourth decade of life. So what's the risk state in immune cells that predisposes you to disease? That's a hard question, that's a really hard question. So I told you before that you can see in multiple sclerosis a fairly strong enrichment in NF-Kappa B binding sites that are near associated SNPs. Okay, so there seems to be something about NF-Kappa B. And I also told you that there's an NF-Kappa B1 locus that harbors a lot of a very strong association. So when you look at MS patients versus controls, if you look at CD4 cells, you find that in response to stimulus, in response to TNF alpha, ex vivo CD4 cells actually signal much more strongly through NF-Kappa B. And this is a measure of phosphorylation of P65, which is one of the NF-Kappa B subunits, okay? If you look at how inducible CD4 cells are, how easy it is to activate CD4 cells from MS patients versus controls, you find that these are controls, the black circles, the filled ones are MS patients. You find that in general, the CD4 cells are easier to activate through NF-Kappa B. You can just hit them and they'll go. Correlations, no causation. This could be an epiphenomenon of disease state. And so what we did is we took this NF-Kappa B1 and we stratified people by genotype there. There's no implication of causality for the SNP we used. It's actually one of these really long haplotypes. It's identical. We just used it to stratify risk, non-risk. And we're looking at opposite homozygotes. And so when you look at the three genotype classes, without stimulation, this is your baseline, I'm sorry, it's chopped off again, but this is your baseline I-Kappa B degradation. So I-Kappa B gets degraded when NF-Kappa B signaling starts, right? And what you see is a baseline, this is 100%, and by genotype, you find that there's a difference in how strong I-Kappa B degradation is, suggesting that there's different amounts of signaling going on in these cells. If you do the obverse and look at the phosphorylation of the P65 subunit, again, you see the same sort of thing that this GG, which is the risk state, overphosphorylates compared to the other genotypes, suggesting that there's more signaling through NF-Kappa B happening for unit activation. That's kind of interesting, but actually, if you look at the expression by Western, so this is protein expression, if you try and quantitate how much P50, which is an NF-Kappa B subunit you're seeing, you see like a 20-fold increase in how much P50 exists, just a baseline in these cells. What's really interesting is that after activation, if you measure nuclear localization of phosphorylated NF-Kappa B, you see that there's about a three-fold change between with the GG risk homozygot putting a lot more phosphorylated NF-Kappa B into the nucleus following stimulus in CD4 cells compared to the A. And so what it looks like is for a given dose of stimulus, if you have the risk genotype, you signal a lot more, which probably does two things. It probably decreases the activation threshold required to kick these cells over into an activated state. And it may also smear the phenotype that you see because there's so much transcription factor going into the nucleus that it's activating everything. And we'll talk about that in a second. This is not quite as simple as just a single effect at the NF-Kappa B1 locus. If you look at the TNF receptor, there are two subunits, there's a variant in the first subunit, TNF-R1, now called TNF-SF1A. There's a coding variant where if you hit cells with TNF-alpha, you get different amounts of signaling through the TNF receptor, which leads to different amounts of phosphorylation of NF-Kappa B. Again, you're getting different amounts of signaling. I won't belabor this. It also turns out there's a whole bunch of other genes in the MS risk flow side that are directly related to the NF-Kappa B signaling. And so I suspect one of the things that's happening here is you're getting this global effect on NF-Kappa B signaling. It's not that simple as just a linear effect, but there are multiple things that feed into NF-Kappa B signaling, at least in CD4 cells, maybe in multiple other subunits that are kind of really setting the real stat of how the immune system responds, and maybe that's how, partly, how risk is determined. And so, oh great, these are chopped off as well. So here's the model, right? Sort of, with external stimulus, you get phosphorylation of NF-Kappa B. NF-Kappa B translocates to the nucleus, and it does what it does best. It activates a bunch of its targets. It activates the transcription, and that leads to activation, proliferation, and survival of these genes. Here's what happens when you change this. If you increase phosphorylation, you're gonna get more NF-Kappa B going into the nucleus. That's probably gonna activate its targets more easily. There's probably spillover, right? NF-Kappa B only activates a subset of its targets in any one given cell, or cell type. It's got a bunch of other targets, which it doesn't activate because the cofactors aren't there. Transcriptional activation is a multi-cofactor process.  And so, this, I believe, this is an assertion at this stage, right? But I think you're gonna get context and appropriate gene activation as a result of just putting a lot more NF-Kappa B into the nucleus. I showed you before right at the beginning that there's also risk variants that localize close to NF-Kappa B binding sites in the genome. So promoters where those variants exist, you're gonna get differential activation of those promoters in a way that's probably unrelated to the total amount of NF-Kappa B, but that's an additional modulation. And so, here's what you can do. You can take a bunch of cells, take people who are risk variant homozygotes and people who are non-risk homozygotes, so people who have different amounts of NF-Kappa B in the nucleus, hit them with TNF. In 15 minutes you get signaling, so you measure how much phosphorylation you're getting. In 30 minutes you get translocation, so you can actually do chip seek and see where NF-Kappa B is going into the nucleus. Within two hours you get gene activation in CD4 cells, so you can do enhancer mapping and RNA seek and see what's changing in the regulation between these two groups. And within three days you can get cell phenotype by producing the full activation stimulus and you can measure that by flow and actually see what these cells are doing. So these are the level of experiments that you really need to do and this is what we're doing right now to actually see what the differential risk states are in various T cells. And I'm way over time, so I'm gonna stop and I will just acknowledge a bunch of my colleagues at the IMSGC, the International MS Genetics Consortium, a lot of partners including Brad and John where we do a lot of these genomics things and people in my lab. Most of the causal mapping that I showed you is from Parissa, a postdoc in my lab. All of the immunology is from Will Housley, who's a fellow with David Halfler and with me and I will stop and take a couple of questions if I'm allowed. Thank you very much. Great work. So quick question with regards to, maybe you've seen Peter Skacheri described MEVs a couple of years ago, so multiple enhancer variants where there's multiple DHS or whatever measurement. And there's a wide range there. You can have MEVs with only two DHS, with three, with four, with five. So I'm just wondering how you take that into account in your pipeline. Are the genes that you find at the top of your list biased towards risk low side or risk low cost that have a high MEV nature as opposed to those that have a lower MEV nature? And the second thing, there's a large collections of risk low cost that actually are singletons that will only have like one DHS sites in them. How are you taking those into account? Right, so this is not about a single peak. This is about whether the peak is consistent across cell types. Right, if there is only one peak in the entire collection of samples and you don't see it in the replica, we throw it out. It's a singleton in one sample, so if I have two CD4s and I only see a peak in one CD4 and never in any other cell type, I'm throwing that one out. So that's gonna be true for one DHS site. Right. But an MEV would have two of those, or three of those. So what we're not doing is the combinatorics of the clusters yet. We're basically not thinking about MEVs, but the correlation should still be there. It should be multiple of them correlating. So that gets naturally taken in to the correlation towards the gene, because all three of those DHSs should be correlated. Right, but they might not go to the same genes. So you might have one risk-locust multiple genes regulated differently by different subset of their DHS. But if they're regulating different genes, then the risk that they're imparting should only go to the gene that they regulate. Yes. Does that make sense? Yes. Because you're trying to figure out which gene is being altered by whatever the risk effect is. And so if DHS one is correlated to gene three, I don't care about transmitting its risk quotient to gene two, because it's not, there's no evidence that it's controlling it. Yeah.