Hopefully more states will follow.
Won't add anything else because I don't want to take away steam from the last hours of Donors Choose. Good job everyone!
Hopefully more states will follow.
Won't add anything else because I don't want to take away steam from the last hours of Donors Choose. Good job everyone!
Today we talk about… mating! Whoa -- did you just see that spike in the stats page? Haha, okay, but first you have to sit through the usual genetic lesson. Here it goes: we start talking about the major histocompatibility complex, or MHC.
I've mentioned many times in my previous posts that in order to trigger an immune response you have to make sure that the immune system recognizes the antigen, or "foreign" object. Antigens are made of proteins that, once inside the cell, are chopped into bits and pieces. The bits and pieces are then transported to the cell surface and "presented" to T-lymphocytes, which in turn recognize the bits as a "red flag" (as in, "ALARM! The cell has been infected with foreign and dangerous object!"), and destroy the cell. MHC molecules have the function of grabbing the bits of proteins inside the cell and presenting them to the cell surface. This is a very important step in the immune response, because without this "presentation" the immune system is unable to recognize the antigen.
Now, the genes that code the MHC molecules are highly polymorphic: what this means is that it's a DNA region that varies greatly across individuals. Why? Because the broader range of MHC molecules we have, the better chances to recognize even the rarest antigen that invades our body. Natural selection favors variety in the MHC genes. Having different alleles for these genes is beneficial for disease resistance. Basically, it makes our immune system stronger.
How do we get different alleles? Remember, we inherit one copy of each gene from our mother, and one from our father, and we get discordant alleles when the mother's copy is different from the father's. Studies have shown that mating occurs preferentially between MHC discordant alleles . And how do we detect people with different MHC alleles than ours? By sniffing. Seriously.
This is actually an old study (1995), and many of you may already know about it. Wedekind et al.  enrolled a sample of male and female students. They had the male participants wear the same T-shirt for two consecutive nights, and then they asked the ladies to rate the "odor pleasantness" of six T-shirts, chosen so that three came from MHC-similar males, and three from MHC-dissimilar males. A curious trivia is that the women in the study used nasal sprays to enhance their sense of smell, and they had all read Suskind's novel "The Perfume." Interestingly, the researchers found a correlation between odor preferences and discordant MHC.
Let's look at the genetics behind the scenes. The genes that regulate olfactory receptors are all over our genome. However, Ehlers et al.  found a very large cluster (36 genes) very close to the HLA complex on chromosome 6. This entire region is in strong linkage disequilibrium, which is a very complicated way geneticists use to say that basically we tend to inherit these genes together. You know how chromosomes split before they turn into oocytes or spermatocytes? Well, the splitting is not completely random, and it turns out that these olfactory receptor genes are highly correlated to the HLA genes (they tend to "stick" together), which would explain how odor preferences would correlate to discordant MHC types.
Wow, I've managed to weave a link through the first three items in my schizophrenic title. Now to the fourth one: birth control pills. Well, the Wedekind study found that the correlation varied depending on the women's hormonal status and, furthermore, the trend reversed for women on the pill. In other words, women on the pill seemed to prefer the T-shirts from MHC-similar men.
Okay, all of the above made a fantastic punchline, but… how about the caveats?
For starters, Roberts et al.  in 2008 repeated the exact same experiment Wedekind et al. did and, alas, couldn't reproduce the results. What they did find was that single women seemed to prefer the odor from MHC-similar men, while women in a relationship preferred odors form MHC-dissimilar men.
By the time I came to the end of the paper, they had done so many tests that any p-value they found would have to be taken with a grain of salt. And in all fairness, what they propose to measure in these studies are variables extremely hard to quantify. The likeability of a certain odor varies not only from person to person, but from day to day. Have you ever shopped for a fragrance? After spraying a few testers, don't they all smell the same?
As for the biology caveats (and for this part I have to thank my dad!):
1) In mammals sexual stimuli are regulated through pheromones. While generic smells are perceived through the olfactory receptors in the epithelium inside the nasal cavity, pheromones are sensed through the vomeronasal organ. The two are regulated by different genes.
2) In humans the vomeronasal organ has lost its function, mostly because it got replaced by the fact that we can see colors. This has caused a shift: most sexual stimuli in humans are perceived through sight, and the genes regulating the vomeronasal organ have become pseudogenes (non-coding).
3) Lastly, how we react to odors is not simply a genetic behavior, but it is highly correlated to the environment. In fact, it changes throughout our lifetime as we "learn" to like certain smells more than others, and this is due to the way our brain changes and reacts to the environment.
Nonetheless, these are certainly interesting experiments as they point to "trends" in human behavior. They give some insights on how much we are driven by hormonal changes, and on the complex ways physiology and environment weave together into making who we are.
 Milinski, M. (2006). The Major Histocompatibility Complex, Sexual Selection, and Mate Choice Annual Review of Ecology, Evolution, and Systematics, 37 (1), 159-186 DOI: 10.1146/annurev.ecolsys.37.091305.110242
 Wedekind C, Seebeck T, Bettens F, & Paepke AJ (1995). MHC-dependent mate preferences in humans. Proceedings. Biological sciences / The Royal Society, 260 (1359), 245-9 PMID: 7630893
 Ehlers A, Beck S, Forbes SA, Trowsdale J, Volz A, Younger R, & Ziegler A (2000). MHC-linked olfactory receptor loci exhibit polymorphism and contribute to extended HLA/OR-haplotypes. Genome research, 10 (12), 1968-78 PMID: 11116091
 Roberts SC, Gosling LM, Carter V, & Petrie M (2008). MHC-correlated odour preferences in humans and the use of oral contraceptives. Proceedings. Biological sciences / The Royal Society, 275 (1652), 2715-22 PMID: 18700206
Plastic surgery has become increasingly popular in the past decades. Fine. If you can't come to terms with your nose, ears, boobs, what-have-you, and you have the money, why not? Part of me would like to point out that that kind of money would feed a village of AIDS orphans in Africa, and in fact, I can even give out the routing number of a couple, but it's about self-esteem, so I understand.
Less so with people who get plastic surgery on their buttocks. I mean: it's behind you! You don't even see it!
Okay, okay, if I try hard enough, I think I can understand even people having plastic surgery on their butts.
But this? I mean... seriously??
Last week I talked about gene therapy and vaccines targeting tumor cells. Following those posts, a friend of mine (thanks, Alex!) pointed me to a recent case report published in the New England Journal of Medicine, which successfully used gene therapy to treat leukemia . Since you know I like to talk about chimeric viruses and all the wonderful things you can do with them, I was instantly drawn to the paper.
Leukemia is a type of cancer that causes an abnormal increase in white blood cells. The patient discussed in the NEJM case report was affected by a type of leukemia called B-cell neoplasm, which, as the name indicates, causes the abnormal proliferation of B-cells.
So, how do you address the problem using gene therapy?
This is what we need: (a) a target on the tumor cells that will tell the immune system to destroy them; (b) a weapon for the immune system to recognize and kill the tumor cells; (c) a way to "give" the weapon to the immune system.
The answer to (a) comes from a receptor called CD19, which is expressed by malignant B-cells. The "weapon" (b) is a genetically engineered anti-CD19 antigen receptor, which enables T-cells (our immune system "soldiers") to recognize the malignant B-cells and destroy it. The big question is (c): how do we make T-cells with the anti-CD19 antigen receptor?
This is where gene therapy and chimeric viruses come into play. How do we use gene therapy to transfer the genes that express the anti CD19 antigen receptor into the T-cells? We need "something" that does this for a living -- transfer genes into cells. Remember what that is?
Absolutely, a virus.
Now, remember what virus in particular targets T-cells?
HIV, of course!
And that's exactly what the authors of this study did: they created an HIV chimeric virus and endowed it with the genes of the anti-CD19 antigen receptor. T-cells were collected from the patient, transduced (which means that the genetic material was transferred inside the T-cells using the modified HIV virus), then infused back into the patient.
Like in all best stories, at first things seemed to go terribly wrong: two weeks after the transfusion, the patient started having high fevers; three weeks after treatment the patient had to be hospitalized and treated for metabolic complications consistent with leukemia treatment.
And then the miracle. One month after the infusion there were no more tumor cells in the patient's blood. At the time the paper was written -- ten months after the therapy -- the patient was still in remission, and the antigen recognizing T-cells were still proliferating.
Interestingly, this case report reminds of an almost symmetric case reported in 2008: an HIV-positive patient who developed leukemia was treated with a bone marrow transplant from a donor who had the Delta32 CCR5 mutation I discussed in this post. The mutation modifies T-cells in a way that they can no longer be infected by the HIV virus and, indeed, after the bone marrow transplant, the patient's viral load dropped and never recovered. As far as I know, the patient is the only one ever to be cured of AIDS.
 Porter, D., Levine, B., Kalos, M., Bagg, A., & June, C. (2011). Chimeric Antigen Receptor–Modified T Cells in Chronic Lymphoid Leukemia New England Journal of Medicine, 365 (8), 725-733 DOI: 10.1056/NEJMoa1103849
They're called Sangre de Cristo Mountains because they turn red at night (yes, the Conquistadores were very devout people!), but tonight... they turned into stripes! Or at least, the sky above them did.
Photos: focal length 61mm, shutter speed 1/8, f-stop 6.3, ISO 100.
I generate most of my figures in R. What do you guys use? I suppose it changes from field to field. However, no matter what your field is, there comes a time when you have to "beautify" through Adobe Illustrator.
I have a love/hate relationship with AI.
I love it because it lets me do so many things.
I hate it because it won't let me do so many things.
So, the other day I was having one of my AI fits when everything around me got blurry, a heavy fog lifted in my office, and I was propelled back to
many, ahem, some years back when I was in elementary school and my dad was preparing his paper figures...
Okay, the blurring and the fog I added for special effects, but going back to my dad: he is a developmental biologist, and back in the days when Apples only came in black screens with fixed menus in a green font, my dad would start by developing his pictures in the camera obscura. He would then trim the pictures and mount them on a paper board -- one board for each figure, and each figure had different panels.
Now, for the labeling, this is what he'd use:
Remember those? (I'm dating myself, aren't I? Well, I can always say that this was in Italy, and we've always been a few years behind in Italy 😉 ) As a kid, I loved them! We'd get comic books where you could add your characters to a scene -- it was fun! But those tiny letters my dad would use to label his photos -- man, they were a pain in the ***! And the letters were a piece of cake compared to the thin lines and geometrical shapes he'd use for the graphs. You'd have to press very delicately. If you pressed too hard you'd ruin the photo, or the lines would break, and you'd have to start over. If you'd press too lightly they'd come off and you'd find them all over your hand, the lines especially, sticking out like misplaced hairs.
So, on second thoughts... I love AI. I really, really love AI.
(As always, I post fascinating stuff I learn at work, and I try to put it in simple terms as much as I can. Please feel free to add/clarify in the comments as you see fit.)
The September issue of the Cancer Journal is dedicated to cancer vaccines and how they may hold the key for cancer treatment and prevention. This is not to be confused with vaccines against cancer-causing viruses, like HPV. In that case the vaccine elicits antibody responses against the virus. In the context of cancer, though, a vaccine would use the immune system's own weapons in order to destroy tumor cells. An example is the vaccine to treat advanced prostate cancer that was approved by the FDA in April 2010, after a Phase III trial showed that patients who received the treatment survived longer than the controls.
The main question in order to create a vaccine that targets cancer cells is: how do we tell the immune system which are the cells to destroy? There is a particular class of cancer cells that offers a potential candidate: cancer stem cells.
Stem cells are a class of very special cells because they are undifferentiated, which means they have the potential to generate any kind of tissue (heart, lung, skin, etc.) Seems almost a paradox, doesn't it? Stem cells can remain undifferentiated and at the same time differentiate into specialized tissue cells. This is possible through an asymmetric cell division: every time a stem cell divides, it generates two cells, an undifferentiated stem cell, and a differentiated one. This way, the differentiated cells produce the specialized tissue, while the stem cell population remains intact.
In a healthy individual, cells with this capacity are found in the bone marrow and in umbilical cords. Unfortunately, they have also been found in solid tumors, such as breast cancer, prostate cancer, and melanoma. You can immediately see the problem: if a cancer cell remains undifferentiated, it means it can preserve its population while generating new cancers in other parts of the body -- the process known as metastasis.
Therefore, one way to produce a cancer vaccine is to have it elicit immune responses against cancer stem cells . How? The idea is to use proteins, or even bits of proteins (peptides) that are over-expressed on tumor cells. Vaccines that use peptides as antigens are called anticancer peptide vaccines , and right as I was reading about them, one of the authors of this paper  wrote this wonderful article on Scientific American, which describes in great detail the history and ideas behind a cancer vaccine. Quoted from the S.A. article:
"Basically, there are three elements to making a cancer vaccine. The first is to decide precisely what molecular feature, or antigen, in a malignant tumor the immune system should recognize as foreign and target for killing. The second is to decide how to deliver a triggering agent (or vaccine) to the immune system that ramps it up to attack cancer cells. And the third is to decide which cancer patients to treat and when during the course of their disease to administer the vaccine."
Mutated cancer cells arise normally (in small quantities) in the body and a healthy immune system is normally capable of recognizing them and destroying them. A vaccine would make this kind of response stronger and robust enough to wipe out all malignant cells. Unfortunately, as cancer progresses, the immune system gets severely damaged. Therefore, the key for this strategy would be to either act fast enough (when the tumor is still small), or combine it with other strategies like chemotherapy.
In the September issue of the Cancer Journal, Dhodapkar et al.  review what the future holds in cancer vaccine research, whereas Larocca et al.  discuss viral vectors, in other words, how viruses could be engineered to deliver a cancer vaccine.
 Dhodapkar MV, & Dhodapkar KM (2011). Vaccines targeting cancer stem cells: are they within reach? Cancer journal (Sudbury, Mass.), 17 (5), 397-402 PMID: 21952290
 Perez SA, von Hofe E, Kallinteris NL, Gritzapis AD, Peoples GE, Papamichail M, & Baxevanis CN (2010). A new era in anticancer peptide vaccines. Cancer, 116 (9), 2071-80 PMID: 20187092
 Larocca C, & Schlom J (2011). Viral vector-based therapeutic cancer vaccines. Cancer journal (Sudbury, Mass.), 17 (5), 359-71 PMID: 21952287
So I finally did it. I put my blog on Facebook. And then I instantly became needy and sent out a bulk of emails begging people to like me. I sent out five and since they're very nice friends of mine, they all liked me. And then I thought, "Well, now, my friends' friends' will like me, and then my friends' friends' friends', and then..."
Hmm. That got me thinking. Does it work like with viruses? No, seriously, do "likes" spread like a viral infection in the body? If not, what kind of network do they resemble? Neurons? Random walks? Traffic network? Surely somebody has thought of modeling this -- does anybody know?
I really got curious about this. So I logged onto PubMed and did a search under the keyword "Facebook." I got around 200 hits, none of which answered my questions, but I did find a few papers that captured my attention, so I thought I'd list them below.
* In my literature search, unfortunately, there were numerous papers I didn't have access to.
All of the above is fascinating and interesting, but what about the networking model? I still think a viral infection model might work: you need to re-define parameters such as fitness cost and effective population size. For example, you might send the request to "like you" to, say, 10 friends, but only the ones who will actually click on the like button are the ones who actually "replicate." Say you get 7 likes. Now, all the 7 friends' friends will see the likes, but how many will go ahead and click the like button in turn? That's the effective size population, how many "likes" will actually generate new "likes." In this model there's no immune pressure, but if the effective size is too small, then the infection doesn't take off.
Obviously, this is just my speculations, so I did a second PubMed search and this time I typed "Facebook viral," hoping I'd get some insight on whether Facebook "likes" spread like a virus. This is the only entry I got:
That's all for today. Short post, I know, but hey, all those refreshing clicks on FB to check the number of likes, it's a lot of work, you know?
 Benkler Y (2011). The unselfish gene. Harvard business review, 89 (7-8) PMID: 21800472
 Mgweba L, Dlamini S, Kassim J, Planting T, & Smith D (2009). Facebook is smoking. South African medical journal = Suid-Afrikaanse tydskrif vir geneeskunde, 99 (11) PMID: 20222194
 Gonzales AL, & Hancock JT (2011). Mirror, mirror on my Facebook wall: effects of exposure to Facebook on self-esteem. Cyberpsychology, behavior and social networking, 14 (1-2), 79-83 PMID: 21329447
 Purdy CH (2011). Using the Internet and social media to promote condom use in Turkey. Reproductive health matters, 19 (37), 157-65 PMID: 21555096
What a great honor to be here!
I'm relatively new to blogging, as I started last July after much debating on whether I would have enough things to say. Turns out, to my own surprise, I do. (Kinda scary the things we don't really know about ourselves, huh?) I blog about genetics, DNA, and HIV genetics in particular, since that's what my research focuses on at the moment.
As a first post, I thought I'd tell you how I ended up being a computational biologist. Forewarning: there are better and more linear ways to become a computational biologist. But as we all know, life is rarely linear.
When I finished college I decided I wanted to be a mathematician. Math is pure and beautiful. It's like a Michelangelo painting, perfect all around. You follow the steps dictated by logic and you can't be wrong. It's Socratic. I got accepted into graduate school, and my husband arranged to finish his dissertation off site so we could both go. We fit all our belongings into two suitcases (that's all we had) and left. We were young, enthusiastic, and clueless.
The bus left us in the middle of nowhere in Massachusetts. The motel we'd booked was five miles away. A lady took pity on us and gave us a ride. I forgot the lady's name, but not her baby's: Timothy. He was the cutest baby.
I soon grew tired of doing pure math. Yes, it's beautiful and perfect. There's Banach spaces, and then Hilbert spaces, and then Banach spaces of Hilbert spaces, and Hilberts of Banachs of Hilberts... I felt lost in one of Dr. Seuss's pictures. Oh, the thinks you can think... Yes you can, but do you want to?
(My mathematician friends, please don't hate me. I'm in confession mode, so bear with me.)
So when my husband got a postdoc in Vienna, Austria, we packed again and left. By then we had four suitcases and a baby. In Vienna I started freelance writing. I wasn't paid a penny but it was fun.
Vienna is beautiful, by the way. If you have enough money to enjoy it. We didn't.
The following year we moved to Valencia, Spain. We had four suitcases, five boxes, and a baby. Two years later we moved to Pasadena, California. We had four suitcases, twenty boxes, and two babies. Gosh, it's exponential, isn't it? Not the baby part, though. We stopped at two and glad we did.
In Pasadena I started missing my job. Did I mention I felt poor in Vienna? Haha, that was nothing compared to Pasadena! That's what Southern California does to you. I looked around but as it turns out with a degree in pure math there's not much you can do besides teaching. And I wasn't much into teaching. I continued to do freelance writing, and even though by now I was getting paid a little, it definitely wasn't enough.
So I decided to go back to school.
I applied to the biomath program at UCLA, the computational biology program at USC, and the biostat program at USC. I got accepted to all three of them. At the time I was stubbornly convinced that I could survive in Southern California without ever getting on a freeway.
I got up one morning at 4 a.m., took a bus, and three hours later I was at the UCLA campus, which is a whole city within a city. Never seen a campus that huge. The commute drained me. The next day I went to downtown to check out the USC computational biology department. The faculty there is impressive--gods in the field. Now that I knew I wasn't going to be a mathematician, I really wanted to become a computational biologist. That's where all the cool stuff was happening--genetics, protein folding, sequencing. And, they even offered me a scholarship.
Unfortunately, the USC downtown campus is not in a charming part of town (to put it in mild terms). And again, the commute from where I lived was going to be a killer.
Finally, I went to the USC medical campus, which turned out to be ten miles down Huntington Drive from where we lived (no freeway! Can you believe it? I could get somewhere in LA county without getting on the freeway!), and met another god.
There's many gods in my life.
Stan is fantastic. If you live in Pasadena, go listen to him play the piano at the Parkway Grill on Thursday nights. He's amazing.
So, anyways, I got into the biostat program. Like I said, Stan is fantastic, and the no-freeway thing sealed the deal.
Statistics is not beautiful. You know the old saying: "There's lies, there's damn lies, and then there's statistics"? Well, it's true. I set off wanting to do pure math, which is perfect and beautiful, and here I was, doing dirty and very much imperfect stuff. But it's useful. And life, the way we describe it, is very much imperfect. So there. You can't apply perfect and beautiful to real life.
I decided I wanted to be a biostatistician. Even got a job as one. By then I could handle Californian freeways. Sort of. I still screamed from time to time. And I still got off at the wrong exits and stuff like that. But hey, the adrenaline high the morning commute down the Two-Ten gives you is unbeatable! (I don't miss it, BTW).
And then my husband had us move again. This time we filled a truck. Heck, give it enough time, you start buying furniture! No, let me rephrase that: give it enough time and a close enough Ikea.
On a side note, my husband hates Ikea. He's the one who has to decipher the cryptic drawings.
We moved to a remote part of Northern New Mexico, so remote that for a while it was only known as a "mail stop." But that's another story. For now, all I will say is that it's not the desert. It's got mountains, and trees, and forests, although the forests do tend to burn down every ten years or so...
Anyways, I'm getting carried away. The point I wanted to make is that we came out here and I met yet another god, my wonderful, amazing, gracious mentor. And guess what I ended up doing? Computational biology. Yeah, where all the cool stuff is happening. (Not that cool stuff isn't happening elsewhere too... We're scientists, we all do cool stuff!)
Looking back, I did choose to become a computational biologist, didn't I?
Well Scientopia, my time is up! Thank you so much for the opportunity, I really enjoyed being here, it was a challenge for me to write posts this often as I usually write one piece every week or two but I feel I accomplished and learned a lot, and hope you got something out of it too. I especially enjoyed chatting with you all a bit! Keep in touch with me on Facebook and twitter!