(Bear with me a bit, I promise I do get to the title eventually) One of the most formative classes I took in college was a class taught by Professor Doug Melton on stem cells. While truth be told, I’ve forgotten most of what I used to know about the growth factors and specifics of how stem cells work, the class left me with two powerful ideas.
The first is that true understanding requires you to overcome your own intellectual laziness. Its not enough to just take what a so-called expert says at face value — you should question her assumptions, her evidence, her interpretation, her controls (or lack thereof), and only after questioning these things can you properly make up your own mind. While I can’t say I’ve lived up to that challenge to the fullest extent, its been a helpful guide in my coursework and in my career as a consultant, then investor, and now entrepreneur.
The second was about the importance of personal passion as a motivating force. Professor Melton’s research and expertise into stem cells was driven in no small part by the desire to find a cure for diabetes, a condition which one of his kids suffers from. It was something which made him (and his lab) work harder at finding a way to take on the daunting task of taking stem cells and turning them into the beta islet cells in the pancreas that produce insulin. It made him advocate for the creation of the Harvard Stem Cell Institute and to strongly vocalize his opinions on legitimizing stem cell research (something which I had the pleasure of interviewing him on when I worked with Nextgen).
And, its paid off! Very recently, Melton’s lab published a paper in the journal Cell which claims to have devised a way to take stem cells and turn them into functioning beta islet cells capable of secreting insulin into the bloodstreams of diabetic mice that they’re transplanted in and reduce the high blood sugar levels that are a hallmark of the disease! While I have yet to read the paper (something I’ll try to get around to eventually) and this is still a ways off from a human therapy, its amazing to see the lab achieve this goal which seemed so challenging back when I was in college (not to mention, years earlier, when Melton first wanted to tackle the problem!)
Having met various members of the Melton lab (as well as the man himself), I can’t say how happy I am for the team and how great it is that we’ve made such a breakthrough in the fight against diabetes.
I’ve been fascinated by the scientific community’s growing understanding of the key role our gut flora plays in our health and wellbeing.
Interestingly, it seems that for some species, the gut flora may function as the type of reproductive barrier which drives speciation (the process by which new species arise from evolution). From this Nature News article (which is ironically about a Science paper)
Robert Brucker and Seth Bordenstein, biologists at Vanderbilt University in Nashville, Tennessee, have found that the gut bacteria of two recently diverged wasp species act as a living barrier that stops their evolutionary paths from reuniting. The wasps have subtly different collections of gut microbes, and when they cross-breed, the hybrids develop a distorted microbiome that causes their untimely deaths.
That gut flora may be partly to blame for the unique health/reproductive problems that hybrids (i.e., like a mule [horse + donkey] or a liger [lion + tiger]) experience! Or, as the article puts it:
“This is an important and potentially groundbreaking study,” says Jack Werren, an evolutionary geneticist at the University of Rochester in New York. “It reveals that problems in hybrids can be due not just to their genetic make-up, but to interactions between their genes and associated microbes.” The next step, he says, is to “determine which genes are involved in regulating which bacteria, and how this is disrupted in hybrids”.
This also means that gut flora (and hence diet and all the other factors which affect our flora) may be a major driver of evolution & speciation!
Hypothesizing that boa constrictors could sense the heartbeat of their prey, some enterprising researchers from Dickinson College decided to test the hypothesis by fitting dead rats with bulbs connected to water pumps (so that the researchers could simulate a heartbeat) and tracking how long and hard the boas would squeeze for:
rats without a “heartbeat” (white)
rats with a “heartbeat” for 10 min (gray)
rats with a continuous “heartbeat” (black)
The results are shown in figure 2 (to the right). The different color bars show the different experimental groups (white: no heartbeat, gray: heartbeat for 10 min before stopping, and black: continuous heartbeat). Figure 2a (on top) shows how long the boas squeezed for whereas Figure 2b (on bottom) shows the total “effort” exerted by the boas. As obvious from the chart, the longer the simulated heartbeat went, the longer and harder the boas would squeeze.
Conclusion? I’ll let the paper speak for itself: “snakes use the heartbeat in their prey as a cue to modulate constriction effort and to decide when to release their prey.”
Interestingly, the paper goes a step further for those of us who aren’t ecology experts and notes that being attentive to heartbeat would probably be pretty irrelevant in the wild for small mammals (which, ironically, includes rats) and birds which die pretty quickly after being constricted. Where this type of attentiveness to heartrate is useful is in reptilian prey (crocodiles, lizards, other snakes, etc) which can survive with reduced oxygen for longer. From that observation, the researchers thus concluded that listening for heartrate probably evolved early in evolutionary history at a time when the main prey for snakes were other reptiles and not mammals and birds.
In terms of where I’d go next after this – my main point of curiosity is on whether or not boa constrictors are listening/feeling for any other signs of life (i.e. movement or breathing). Obviously, they’re sensitive to heart rate, but if an animal with simulated breathing or movement – would that change their constricting activity as well? After all, I’m sure the creative guys that made an artificial water-pump-heart can find ways to build an artificial diaphragm and limb muscles… right? 🙂
Its been known that the bacteria that live on our skin help give us our particular odors. So, the researchers wondered if the mosquitos responsible for passing malaria (Anopheles) were more or less drawn to different individuals based on the scent that our skin-borne bacteria impart upon us (also, for the record, before you freak out about bacteria on your skin, remember that like the bacteria in your gut, the bacteria on your skin are natural and play a key role in maintaining the health of your skin).
Looking at 48 individuals, they noticed a huge variation in terms of attractiveness to Anopheles mosquitos (measured by seeing how much mosquitos prefer to fly towards a chamber with a particular individual’s skin extract versus a control) which they were able to trace to two things. The first is the amount of bacteria on your skin. As shown in Figure 2 below, is that the more bacteria that you have on your skin (the higher your “log bacterial density”), the more attractive you seem to be to mosquitos (the higher your mean relative attractiveness).
The second thing they noticed was that the type of bacteria also seemed to be correlated with attractiveness to mosquitos. Using DNA sequencing technology, they were able to get a mini-census of what sort of bacteria were present on the skins of the different patients. Sadly, they didn’t show any pretty figures for the analysis they conducted on two common types of bacteria (Staphylococcus and Pseudomonas), but, to quote from the paper:
The abundance of Staphylococcus spp. was 2.62 times higher in the HA [Highly Attractive to mosquitoes] group than in the PA [Poorly Attractive to mosquitoes] group and the abundance of Pseudomonas spp. 3.11 times higher in the PA group than in the HA group.
Using further genetic analyses, they were also able to show a number of other types of bacteria that were correlated with one or the other.
So, what did I think? While I think there’s a lot of interesting data here, I think the story could’ve been tighter. First and foremost, for obvious reasons, correlation does not mean causation. This was not a true controlled experiment – we don’t know for a fact if more/specific types of bacteria cause mosquitos to be drawn to them or if there’s something else that explains both the amount/type of bacteria and the attractiveness of an individual’s skin scent to a mosquito. Secondly, Figure 2 leaves much to be desired in terms of establishing a strong trendline. Yes, if I squint (and ignore their very leading trendline) I can see a positive correlation – but truth be told, the scatterplot looks like a giant mess, especially if you include the red squares that go with “Not HA or PA”. For a future study, I think it’d be great if they could get around this to show stronger causation with direct experimentation (i.e. extracting the odorants from Staphylococcus and/or Pseudomonas and adding them to a “clean” skin sample, etc)
With that said, I have to applaud the researchers for tackling a fascinating topic by taking a very different angle. I’ve blogged before about papers on dealing with malaria, but the subject matter is usually focused on how to directly kill or impede the parasite (Plasmodium falciparums). This is the first treatment of the “ecology” of malaria – specifically the ecology of the bacteria on your skin! While the authors don’t promise a “cure for malaria”, you can tell they are excited about what they’ve found and the potential to find ways other than killing parasites/mosquitos to help deal with malaria, and I look forward to seeing the other ways that our skin bacteria impact our lives.
(Figure 2 from paper)
Paper: Verhulst et al. “Composition of Human Skin Microbiota Affects Attractiveness to Malaria Mosquitoes.” PLoS ONE 6(12). 17 Nov 2011. doi:10.1371/journal.pone.0028991
Well some researchers at McGill University in Canada want to take a page out of this playbook with a game they built called Phylo (HT: MedGadget) to help deal with another challenging issue in bioinformatics: multiple sequence alignment. In a nutshell, to better understand DNA and how it impacts life, we need to see how stretches of DNA line up with one another. Now, computers are extremely good at taking care of this problem for short stretches of DNA and for “roughly” aligning longer stretches of DNA – but its fairly difficult and costly to do it accurately for long stretches using computer algorithms.
People, however, are curiously intuitive about patterns and shapes. So, the researchers turned the multiple sequence alignment problem into a puzzle game they’ve called Phylo (see image below) where the goal is to line up multiple colored blocks. Players tackle the individual puzzles (in a browser or even on their mobile phone) and the researchers aggregate all of this into improved sequence alignments which help them better understand the underlying genetics of disease.
So far, it has been working very well. Since the game was launched in November 2010, the researchers have received more than 350,000 solutions to alignment sequence problems. “Phylo has contributed to improving our understanding of the regulation of 521 genes involved in a variety of diseases. It also confirms that difficult computational problems can be embedded in a casual game that can easily be played by people without any scientific training,” Waldispuhl said. “What we’re doing here is different from classical citizen science approaches. We aren’t substituting humans for computers or asking them to compete with the machines. They are working together. It’s a synergy of humans and machines that helps to solve one of the most fundamental biological problems.”
With the new games and platforms, the researchers are hoping to encourage even more gamers to join the fun and contribute to a better understanding of genetically-based diseases at the same time.
Try it out – I have to admit I’m not especially good with puzzle games, so I haven’t been doing particularly well, but the researchers have done a pretty good job with the design of the game (esp. relative to many other academic-inspired gaming programs that I’ve seen) – and who knows, you might be a key contributor to the next big drug treatment!
The idea that our bodies are, in some ways, more bacteria than human (there are 10x more gut bacteria – or flora — than human cells on our bodies) and that those bacteria can play a key role on our health is not only mind-blowing, it opens up another potential area for medical/life sciences research and future medicines/treatments.
In the paper, a genetics team from Washington University in St. Louis explored a very basic question: are the gut bacteria from obese individuals different from those from non-obese individuals? To study the question, they performed two types of analyses on a set of mice with a genetic defect leading to an inability of the mice to “feel full” (and hence likely to become obese) and genetically similar mice lacking that defect (the s0-called “wild type” control).
The first was a series of genetic experiments comparing the bacteria found within the gut of obese mice with those from the gut of “wild-type” mice (this sort of comparison is something the field calls metagenomics). In doing so, the researchers noticed a number of key differences in the “genetic fingerprint” of the two sets of gut bacteria, especially in the genes involved in metabolism.
But, what did that mean to the overall health of the animal? To answer that question, the researchers did a number of experiments, two of which I will talk about below. First, they did a very simple chemical analysis (see figure 3b to the left) comparing the “leftover energy” in the waste (aka poop) of the obese mice to the waste of wild-type mice (and, yes, all of this was controlled for the amount of waste/poop). Lo and behold, the obese mice (the white bar) seemed to have gut bacteria which were significantly better at pulling calories out of the food, leaving less “leftover energy”.
While an interesting result, especially when thinking about some of the causes and effects of obesity, a skeptic might look at that data and say that its inconclusive about the role of gut bacteria in obesity – after all, obese mice could have all sorts of other changes which make them more efficient at pulling energy out of food. To address that, the researchers did a very elegant experiment involving fecal transplant: that’s right, colonize one mouse with the bacteria from another mouse (by transferring poop). The figure to the right (figure 3c) shows the results of the experiment. After two weeks, despite starting out at about the same weight and eating similar amounts of the same food, wild type mice that received bacteria from other wild type mice showed an increase in body fat of about 27%, whereas the wild type mice that received bacteria from the obese mice showed an increase of about 47%! Clearly, gut bacteria in obese mice are playing a key role in calorie uptake!
In terms of areas of improvement, my main complaint about this study is just that it doesn’t go far enough. The paper never gets too deep on what exactly were the bacteria in each sample and we didn’t really get a sense of the real variation: how much do bacteria vary from mouse to mouse? Is it the completely different bacteria? Is it the same bacteria but different numbers? Is it the same bacteria but they’re each functioning differently? Do two obese mice have the same bacteria? What about a mouse that isn’t quite obese but not quite wild-type either? Furthermore, the paper doesn’t show us what happens if an obese mouse has its bacteria replaced with the bacteria from a wild-type mouse. These are all interesting questions that would really help researchers and doctors understand what is happening.
But, despite all of that, this was a very interesting finding and has major implications for doctors and researchers in thinking about how our complicated flora impact and are impacted by our health.
This month’s paper is about stem cells: those unique cells within the body which have the capacity to assume different roles. While people have talked at lengths about the potential for stem cells to function as therapies, one thing holding them back (with the main exception being bone marrow cells) is that its very difficult to get stem cells to exactly where they need to be.
With bone marrow transplants, hematopoietic stem cells naturally “home” (like a missile) to where they need to be (in the blood-making areas of the body). But with other types of stem cells, that is not so readily true, making it difficult or impossible to use the bloodstream as a means of administering stem cell therapies. Of course, you could try to inject, say, heart muscle stem cells directly into the heart, but that’s not only risky/difficult, its also artificial enough that you’re not necessarily providing the heart muscle stem cells with the right triggers/indicators to push them towards becoming normal, functioning heart tissue.
Researchers at Brigham & Women’s Hospital and Mass General Hospital published an interesting approach to this problem in the journal Blood (yes, that’s the real name). They used a unique feature of white blood cells that I blogged about very briefly before called leukocyte extravasation, which lets white blood cells leave the bloodstream towards areas of inflammation.
The process is described in the image above, but it basically involves the sugars on the white blood cell’s surface, called Sialyl Lewis X (SLeX), sticking to the walls of blood vessels near sites of tissue damage. This causes the white blood cell to start rolling (rather than flowing through the blood) which then triggers other chemical and physical changes which ultimately leads to the white blood cell sticking to the blood vessel walls and moving through.
The researchers “borrowed” this ability of white blood cells for their mesenchymal stem cells. The researchers took mesenchymal stem cells from a donor mouse and chemically coated them with SLeX – the hope being that the stem cells would start rolling anytime they were in the bloodstream and near a site of inflammation/tissue damage. After verifying that these coated cells still functioned (they could still become different types of cells, etc), they then injected them into mice (who received injections in their ears with a substance called LPS to simulate inflammation) and used video microscopes to measure the speed of different mesenchymal stem cells in the bloodstream. In Figures 2A and 2B to the left, the mesenchymal stem cell coated in SLeX is shown in green and a control mesenchymal stem cell is shown in red. What you’re seeing is the same spot in the ear of a mouse under inflammation with the camera rolling at 30 frames per second. As you can see, the red cell (the untreated) moves much faster than the green – in the same number of frames, its already left the vessel area! That, and a number of other measurements, made the researchers conclude that their SLeX coat actually got their mesenchymal stem cells to slow down near points of inflammation.
But, does this slowdown correspond with the mesenchymal stem cells exiting the bloodstream? Unfortunately, the researchers didn’t provide any good pictures, but they did count the number of different types of cells that they observed in the tissue. When it came to ears with inflammation (what Figure 4A below refers to as “LPS ear”), the researchers saw an average of 48 SLeX-coated mesenchymal stem cells versus 31 uncoated mesenchymal stem cells within their microscopic field of view (~50% higher). When it came to the control (the “saline ear”), the researchers saw 31 SLeX-coated mesenchymal stem cells versus 29 uncoated (~7% higher). Conclusion: yes, coating mesenchymal stem cells with SLeX and introducing them into the bloodstream lets them “home” to areas of tissue damage/inflammation.
As you can imagine, this is pretty cool – a simple chemical treatment could help us turn non-bone-marrow-stem cells into treatments you might receive via IV someday!
But, despite the cool finding, there were a number of improvements that this paper needs. Granted, I received it pre-print (so I’m sure there are some more edits that need to happen), but my main concerns are around the quality of the figures presented. Without any clear time indicators or pictures, its hard to know what exactly the researchers are seeing. Furthermore, its difficult to see for sure whether or not the treatment did anything to the underlying stem cell function. The supplemental figures of the paper are only the first step in, to me, what needs to be a long and deep investigation into whether or not those cells do what they’re supposed to – otherwise, this method of administering stem cell therapies is dead in the water.
“Omics” is the hot buzz-suffix in the life sciences for anything which uses the new sequencing/array technologies we now have available. You don’t study genes anymore, you study genomics. You don’t study proteins anymore – that’s so last century, you study proteomics now. And, who studies metabolism? Its all about metabolomics. There’s even a (pretty nifty) blog post covering this with the semi-irreverent name “Omics! Omics!”.
Its in the spirit of “Omics” that I chose a Sciencepaper from researchers at the NIH because it was the first time I have ever encountered the term “antibodyome”. For those of you who don’t know, antibodies are the “smart missiles” of your immune system – they are built to recognize and attack only one specific target (i.e. a particular protein on a bacteria/virus). This ability is so remarkable that, rather than rely on human-generated constructs, researchers and biotech companies oftentimes choose to use antibodies to make research tools (i.e. using fluorescent antibodies to label specific things) and therapies (i.e. using antibodies to proteins associated with cancer as anti-cancer drugs).
How the immune system does this is a fascinating story in and of itself. In a process called V(D)J recombination – the basic idea is that your immune system’s B-cells mix, match, and scramble certain pieces of your genetic code to try to produce a wide range of antibodies to hit potentially every structure they could conceivably see. And, once they see something which “kind of sticks”, they undergo a process called affinity maturation to introduce all sorts of mutations in the hopes that you create an even better antibody.
Which brings us to the paper I picked – the researchers analyzed a couple of particularly effective antibodies targeted at HIV, the virus which causes AIDS. What they found was that these antibodies all bound the same part of the HIV virus, but when they took a closer look at the 3D structures/the B-cell genetic code which made them, they found that the antibodies were quite different from one another (see Figure 3C below)
What’s more, not only were they fairly distinct from one another, they each showed *significant* affinity maturation – while a typical antibody has 5-15% of their underlying genetic code modified, these antibodies had 20-50%! To get to the bottom of this, the researchers looked at all the antibodies they could pull from the patient – in effect, the “antibodyome”, in the same way that the patient’s genome would be all of his/her genes, — and along with data from other patients, they were able to construct a “family tree” of these antibodies (see Figure 6C below)
The analysis shows that many of the antibodies were derived from the same initial genetic VDJ “mix-and-match” but that afterwards, there were quite a number of changes made to that code to get the situation where a diverse set of structures/genetic codes could attack the same spot on the HIV virus.
While I wish the paper probed deeper into actual experimentation to take this analysis further (i.e. artificially using this method to create other antibodies with similar behavior), this paper goes a long way into establishing an early picture of what “antibodyomics” is. Rather than study the total impact of an immune response or just the immune capabilities of one particular B-cell/antibody, this sort of genetic approach lets researchers get a very detailed, albeit comprehensive look at where the body’s antibodies are coming from. Hopefully, longer term this also turns into a way for researchers to make better vaccines.
(Figure 2 and 6 from paper)
Paper: Wu et al., “Focused Evolution of HIV-1 Neutralizing Antibodies Revealed by Structures and Deep Sequencing.” Science (333). 16 Sep 2011. doi: 10.1126/science.1207532
Another month, another paper, and like with last month’s, I picked another genetics paper, this time covering an interesting quirk of immunology.
This month’s paper from Nature talks about a species of fish that has made it to the dinner plates of many: the Atlantic Cod (Gadus morhua). The researchers applied shotgun sequencing techniques to look at the DNA of the Atlantic Cod. What they found about the Atlantic Cod’s immune system was very puzzling: animals with vertebra (so that includes fish, birds, reptiles, mammals, including humans!) tend to rely on proteins called Major Histocompatibility Complex (MHC) to trigger our adaptive immune systems. There tend to be two kinds of MHC proteins, conveniently called MHC I and MHC II:
MHC I is found on almost every cell in the body – they act like a snapshot X-ray of sorts for your cells, revealing what’s going on inside. If a cell has been infected by an intracellular pathogen like a virus, the MHC I complexes on the cell will reveal abnormal proteins (an abnormal snapshot X-ray), triggering an immune response to destroy the cell.
MHC II is found only on special cells called antigen-presenting cells. These cells are like advance scouts for your immune system – they roam your body searching for signs of infection. When they find it, they reveal these telltale abnormal proteins to the immune system, triggering an immune response to clear the infection.
The genome of the Atlantic cod, however, seemed to be completely lacking in genes for MHC II! In fact, when the researchers used computational methods to see how the Atlantic cod’s genome aligned with another fish species, the Stickleback (Gasterosteus aculeatus), it looked as if someone had simply cut the MHCII genes (highlighted in yellow) out! (see Supplemental Figure 17 below)
Yet, despite not having MHC II, Atlantic cod do not appear to suffer any serious susceptibility to disease. How could this be if they’re lacking one entire arm of their disease detection?One possible answer: they seemed to have compensated for their lack of MHC II by beefing up on MHC I! By looking at the RNA (the “working copy” of the DNA that is edited and used to create proteins) from Atlantic cod, the researchers were able to see a diverse range of MHC I complexes, which you can see in how wide the “family tree” of MHCs in Atlantic cod is relative to other species (see figure 3B, below).
Of course, that’s just a working theory – the researchers also found evidence of other adaptations on the part of Atlantic cod. The key question the authors don’t answer, presumably because they are fish genetics guys rather than fish immunologists, is how these adaptations work? Is it really an increase in MHC I diversity that helps the Atlantic cod compensate for the lack of MHC II? That sort of functional analysis rather than a purely genetic one would be very interesting to see.
The paper is definitely a testament to the interesting sorts of questions and investigations that genetic analysis can reveal and give a nice tantalizing clue to how alternative immune systems might work.
In my life in venture capital, I’ve started more seriously looking at new bioinformatics technologies so I decided to dig into a topic that is right up that alley. This month’s paper from Nature Biotechnologycovers the use of next-generation DNA sequencing technologies to look into something which had been previously extremely difficult to study with past sequencing technologies.
As the vast majority of human DNA is the same from person to person, one would expect that the areas of our genetic code which tend to vary the most from person to person, locations which are commonly known as Single Nucleotide Polymorphisms, or SNPs, would be the biggest driver of the variation we see in the human race (at least the variations that we can attribute to genes). This paper from researchers at the Beijing Genomics Institute (now the world’s largest sequencing facility – yes, its in China) adds another dimension to this – its not just SNPs that make us different from one another: humans also appear to have a wide range of variations on an individual level in the “structure” of our DNA, what are called Structural Variations, or SVs.
Whereas SNPs represent changes at the individual DNA code level (for instance, turning a C into a T), SVs are examples where DNA is moved (i.e., between chromosomes), repeated, inverted (i.e., large stretches of DNA reversed in sequence), or subject to deletions/insertions (i.e., where a stretch of DNA is removed or inserted into the original code). Yes, at the end of the day, these are changes to the underlying genetic code, but because of the nature of these changes, they are more difficult to detect with “old school” sequencing technologies which rely on starting at one position in the DNA and “reading” a stretch of DNA from that point onward. Take the example of a stretch of DNA that is moved – unless you start your “reading” right before or right at the end of where the new DNA has been moved to, you’d never know as the DNA would read normally everywhere else and in the middle of the DNA fragment.
What the researchers figured out is that new sequencing technologies let you tackle the problem of detecting SVs in a very different way. Instead of approaching each SV separately (trying to structure your reading strategy to catch these modifications), why not use the fact that so-called “next generation sequencing” is far faster and cheaper to read an individual’s entire genome and then look at the overall structure that way?
And that’s exactly what they did (see figures 1b and 1c above). They applied their sequencing technologies to the genomes of an African individual (1c) and an Asian individual (1b) and compared them to some of the genomes we have on file. The circles above map out the chromosomes for each of the individuals on the outer-most ring. On the inside, the lines show spots where DNA was moved or copied from place to place. The blue histogram shows where all the insertions are located, and the red histogram does the same thing with deletions. All in all: there looks to be a ton of structural variation between individuals. The two individuals had 80-90,000 insertions, 50-60,000 deletions, 20-30 inversions, and 500-800 copy/moves.
The key question that the authors don’t answer (mainly because the paper was about explaining how they did this approach, which I heavily glossed over here partly because I’m no expert, and how they know this approach is a valid one) is what sort of effect do these structural variations have on us biologically? The authors did a little hand-waving to show, with the limited data that they have, that humans seem to have more rare structural variations than we do rare SNPs – in other words, that you and I are more likely to have different SVs than different SNPs: a weak, but intriguing argument that structural variations drive a lot of the genetic-related individual variations between people. But that remains to be validated.
Suffice to say, this was an interesting technique with a very cool “million dollar figure” and I’m looking forward to seeing further research in this field as well as new uses that researchers and doctors dig up for the new DNA sequencing technology that is coming our way.
(All figures from paper)
Paper: Li et al., “Structural Variation in Two Human Genomes Mapped at Single-Nucleotide Resolution by Whole Genome Assembly.” Nature Biotechnology29 (Jul 2011) — doi:10.1038/nbt.1904
A brief intro to microfinance: in many developing countries, the banking system is underdeveloped. And, even if a mature banking system were to exist, banks themselves typically do not lend small amounts of money to small businesses/families who don’t have much by the way of credit history. The idea being microfinance is that you can do a lot to help people in developing countries by providing their smallest businesses, especially those run by women who are traditionally excluded from their local economies, with “micro” loans. Organizations like Kiva have sprung up to pursue this sort of work, and the 2006 Nobel Peace Prize was even awarded to Muhammad Yunus for his role in popularizing it.
But, does it work at building communities and improving economies? If you’re a scientist, to answer that question conclusively, you need a controlled experiment. So, the authors of the study worked with a for-profit microfinance organization in the Philippines, First Macro Bank (FMB), to do a double-blinded randomized trial. Using a computer program, they automatically categorized a series of microcredit applicants by their creditworthiness. Obviously credit-worthy and obviously credit-unworthy applicants (combined, 26% of applicants) were taken care of quickly. For the 74% of applicants that the program considered “marginal” (not obviously one way or the other), they were randomly assigned to two groups: a control group that did not receive a microloan, and a treatment group who would receive a microloan. Following the “treatment”, the participants in the experiment were then surveyed along on a number of economic and lifestyle metrics.
How was this double-blinded? Neither the applicants nor the FMB employees who interfaced with were aware that this was an experiment. The surveyors were not even aware this was an experiment or that FMB was involved.
Why focus on “marginal” applicants? A couple of reasons: first, the most likely changes to microfinance policy will impact these applicants the most, so they are the most relevant group to study. Secondly, you want to try to make apples-to-apples comparisons. Rejecting some obviously credit-worthy (or credit-unworthy) individuals may have raised red flags that some sort of algorithmic flaw or artificial experiment was happening. To really understand the impact of microfinance, you need to start on even footing in a realistic setting (esp. not comparing obviously credit-worthy individuals with so-so- credit-worthy individuals)
So, what did the researchers find? They found a lot of interesting things – many of which will require us to re-think the advantages of microfinance. The data is presented in a lot of boring tables so, unlike most of my science paper posts, I’m not going to cut and paste figures, but I will summarize the statistically significant findings:
Receiving microfinance increases amount of borrowing. The “treatment group” had, on average, 9% more loans from institutions (rather than friends/family) than the control group (excluding the microloan itself, of course)
Microfinance does not seem to go towards aggressive hiring. The “treatment group” had, on average, 0.273 fewer paid employees than the control group. Whether or not this reflected the original size of the businesses is beyond me, but I am willing to give the researchers the benefit of the doubt for now.
Microfinance does not seem to have a major impact on subjective measures of quality of life except elevated stress levels of male microfinance recipients. Most of the subjective quality of life measures showed no statistically significant differences except that one.
Receiving microfinance reduces likelihood of getting non-health insurance by 7.9%
There don’t appear to be significantly different or larger impacts of microfinance on women vs. men.
So, when’s all said and done, what does it all mean? First, it appears that instead of leading to aggressive business expansion as it is widely believed, microfinance itself actually seems to have a small, but slightly negative impact on employment at those businesses. While I don’t have a perfect explanation, combining all the observations above would suggest that the main impact of microfinance is not business expansion so much as risk management: entrepreneurs who received microloans seemed more willing to consolidate their business activities (i.e., firing “extra” workers who might have been “spare capacity”), to avoid purchases of insurance, and to reach out to other banks for more loans — very different than the story that we usually hear from the typical microfinance supporter.
The fundamental unknowns of this well-crafted study, though, are around whether or not these findings are that useful. While the researchers did an admirable job controlling for extraneous factors to reach a certain conclusion for a certain set of people in the Philippines, its not necessarily obvious that the study’s findings hold true in another country/culture. The surveys were also conducted only a few months after receipt of the microloans — it is possible that the impacts on businesses and local communities need more time to manifest. Finally, the data collected from the study does almost too good of a job stripping out selection bias. Microfinance organizations today can be fairly selective, picking only the best entrepreneurs or potentially coaching/forcing the entrepreneurs to allocate their resources differently than the mostly hands-off approach that was taken here.
All in all, an interesting paper, and something worth reading and thinking about by anyone who works in/with microfinance organizations.
I’ve known Mike Lee since we were both in high school doing debate. He’s a great guy, and I’ve enjoyed talking to him over the years about comic books, science, religion, and politics. He and I don’t always see eye-to-eye (translation: sometimes I think he’s nuts – come on, Mike, Kyle Rayner as the greatest Green Lantern ever?), but he’s one of the most thoughtful and intellectually humble guys I know.