Hat-tip: @younglandis who tweeted yesterday about this fun interactive illustration.
...and note that Posterous' game attempt at capturing the infographic didn't quite catch all of the interactive elements (try the slider under the picture), so you really should click on the image to visit the original page if you want to see and play with this, and read more about it!
Another wonderful TED talk! Relevant to our discussion of Bacterial diversity and communication in Biol 1B this week.
If you prefer to read the story rather than listen to it aloud, here's the transcript via npr.org.
Courtesy of Lillian Fritz-Laylan
Naegleria gruberi grows a pair of flagella when under stress. But unlike a sperm tail, it puts these appendages out front, and swims by breast stroke. The organism is stained to emphasize its anatomy.
According to Nathan Wolfe, a virus hunter interviewed last week in another TED Q&A, "We've created a perfect storm for viruses". An excerpt:
SARS, avian flu, swine flu ... what's going on here? Why are we suddenly seeing so many more outbreaks of viruses from animals?
Viruses have always passed from humans to animals. In fact, the vast majority of human diseases have animal origins. But the human population is different from what it once was. For most of our history, we lived in geographically disparate populations. So viruses could enter from animals into humans, spread locally and go extinct. But the human population has gone through a connectivity explosion. All humans on the planet are now connected to each other spatially and temporally in a way that's unprecedented in the history of vertebrate biology. Humans -- as well as our domestic animals and wild animals we trade -- move around the planet at biological warp speed. This provides new opportunities for viruses that would have gone extinct locally to have the population density fuel they need to establish themselves and spread globally.
We've created a "perfect storm" for viruses. And we'll continue to see -- as we have in the past few years -- a whole range of new animal diseases as outbreaks in human populations. But we have to stop being surprised by them. Right now, global public health is like cardiology in the '50s -- just waiting for the heart attack, without understanding why they occur or the many ways to monitor for them, detect them early and ultimately prevent them. Swine flu is not an anomaly. We know that swine flu -- like the vast majority of new outbreaks -- comes from animals. We should be monitoring those animals and the humans that come into contact with them, so we can catch these viruses early, before they infect major cities and spread throughout the world.
And here's Wolfe's TED talk:
As you may have read by now, the new swine flu virus, this new strain H1N1 which is threatening to turn into a pandemic according to the WHO (even though there is much confusion about the actual number of infected/dead victims confirmed to have this strain), is actually an interesting genetic mixture, a chimera if you will with a potpourri of genes from different influenza strains: these may be from multiple host taxa — birds (thought to be the original source of all influenza viruses), humans, and, of course, pigs (from North America and Asia!) — or perhaps all from pigs (the picture is still cloudy). In any case, if you are wondering how such reassorted viruses form, the following video paints a cartoon picture to help you understand. The key thing to remember is that viruses, like many other microbes, are rather promiscuous when it comes to swapping bits of DNA - even across "species" - and such lateral transfer can allow new strains to evolve, even drug-resistant ones, much more rapidly. Here's how influenza may be able to shift the shape of its antigens, and potentially jump between host species:
But, that is not the only trick up this virus' sleeve. Ever wonder why, unlike with other vaccinations which are often a once-in-a-lifetime deal, you've got to take that flu shot over every single year? Because, even in the absence of opportunities to hook-up and swap genes with other viruses, i.e., even when a host is infected by just a single strain of the flu, the virus is constantly changing shape. Mutations arise all the time, and those that change the shape of the antigen such that it is no longer recognized by host antibodies will be naturally selected. Antigen shape therefore drifts around constantly, making it a much more fun game for the host immune system - and our vaccines - to keep up with the flu! It's evolution in action, right within our own bodies and those of animals we cohabit with! You didn't think we had quite liberated ourselves from the clutches of evolutionary processes now, did you? As you ponder that, here's another cartoon depicting antigenic drift:
[Hat-tip: GrrlScientist for finding the videos]
Just as we (in Biol 105) finish up studying how phylogenetic trees are estimated, and how they might be used to answer interesting questions, comes this highly topical example - microbiologist and science blogger Sandra Porter spent a happy afternoon applying phylogenetic analyses to try and answer the health scare du jour:
This afternoon, I was working on educational activities and suddenly realized that the H1N1 strain that caused the California outbreak might be the same strain that caused an outbreak in 2007 at an Ohio country fair. Here's the data.
Once I realized that the genome sequences from the H1N1 swine flu were in the NCBI's virus genome resources database, I had to take a look.
And, like eating potato chips, making phylogenetic trees is a little bit addictive. Or maybe it was just the adrenaline rush that hit when I realized that every tree was telling me the same thing.
What did those trees say?
Read the full blog post to study the results yourself, and see what you think of the remarkable concordance between the trees, providing a plausible answer to the question of where this virus may have originated.
In the process, Dr. Porter has also given us all a glimpse at the working product of a fresh analysis - raw results hot off the computer before they are published in a peer-reviewed journal! Is this a first for the blogosphere? I don't know, but given the high level of public interest, I can see why one might want to get the results out quickly. Surely some top science journal would be interested in publishing this quickly as well?
Thanks to Porter's blog, we all get to see how genomic data available in the public domain can be used to help address problems that might affect us in real time! How cool is that?! As I try to impress upon my students every time we discuss the subject: Phylogenies are not just static graphic depictions of inferred relationships between organisms long gone - trees of dead wood, so to speak: they also serve as working models of ongoing evolutionary processes! And often enough, they help us pinpoint the origins of new diseases, in turn helping us develop treatment strategies before the outbreak gets too far out of hand. And how is that for putting those phylogenetic trees to work?
Meanwhile, Tara Smith, of Aetiology (also on ScienceBlogs) following up on Porter's big discovery, notes that the peer-reviewed paper describing the Ohio swine flu strain came out only recently. And here's the bit that really raises the eyebrow, if not the hair on your head:
I also assume this is where the human-avian-swine reassortant claim came from. The authors note that:
The H1N1 viruses contain the HA and NA from the classical swine virus and the internal genes from the triple reassortant H3N2 viruses (rH1N1); the H1N2 viruses contain the HA from the classical swine virus and the NA and internal genes from the triple reassortant H3N2 viruses (Karasin et al., 2002; Webby et al., 2004). Contemporary triple reassortant viruses were demonstrated to have acquired a PB1 gene of human virus origin; PA and PB2 genes of avian virus origin; and the remaining internal genes, M, NS, and NP, of swine virus origin, thus giving rise to the triple reassortant designation (Zhou et al., 1999).
So what it looks like to me is that this isn't a *new* reassortant virus, but is closely related to one that had already been identified in swine--and that had already caused an outbreak in humans right here in the US.
So why is the virus getting so much more media attention this time around? Is the strain in Mexico really the same or different? And if it is the same (or close) how did it get from Ohio to Mexico City and back to Texas and California? Gotta love that globalization, eh!
Rebecca Freeman submitted this essay for the Evolution class.
According to the World Health Organization (WHO), a “hot zone” is an area with >5% prevalence (or incidence) of Multi-Drug Resistant Tuberculosis (MDRtb). Sally M Blower and Tom Chou have been using a mathematical method to track the emergence and evolution of multiple strains of drug resistant tuberculosis, but they have now developed a new, more complex mathematical model. Before this model, there was only a two strain model, meaning it was only relevant to individuals that can be infected with a wild type pansensitive strain or a drug resistant strain, but there are many more strains then this. There are a resistant strains only to one drug and some resistant to multiple drugs. This means there is a multitude of strains in these hot zones and there was a need for a better way to track this (Blower and Chou 2004). Blower and Chou realized that a more complex mathematical model is necessary to capture the complexity of the epidemiology of the hot zones, and the evolution of hot zones was very unclear
Understanding drug resistance is important to understanding the, and Blower and Chou explain the evolving of resistance very well. They give three processes that are involved in generating drug resistance: Transmission of drug resistant strains to uninfected individuals, which is transmitted resistance; Conversion of wild pansensitive cases to drug resistant cases, which is acquired resistance; finally, cases where they have drug resistant strains and it becomes resistant to more antibiotics during treatment, which is amplified resistance. What everyone has had to do in the past is just study acquired and transmitted resistance, and now with the new model, they can incorporate amplification resistance. This was a big problem because it has been shown that inadequate treatment of DRtb can result in the amplification of drug resistant strains, which may be an important process of MDR epidemics (Blower and Chou 2004). So this is where Blower and Chou came in. They created a model, the call the amplifier model, that enables the tracking of emergence and evolution of MDR strains, the transmission of these strains and the amplification of these strains during repeated episodes of treatment.
Blower and Chou are really studying the effects of inadequate treatment programs, and how this may lead to a higher prevalence in MDRtb. One problem that this research cannot completely take into account yet is the transmittance ability of MDRtb compared to pansensitive tuberculosis. This is an area that is hazy right now, and so this cannot completely be incorporated into the model. Amazingly, they have measured a general fitness of MDRtb vs. pansensitive tuberculosis, by calculating the treatment fail rates and treatment cure rates of the each category of strains.
The authors were very clear with the purpose of the model. Even though the mathematical model is very complex, the idea and how they explain it is easily understandable. They use R0 to stand for the average number of secondary cases caused by one infectious case in a population where treatments are available. Their model breaks this up into four categories of strains: The wild type pansensitive [R0(1)], which is sensitive to all drugs; Pre-MDR [R0(2)], which is sensitive to one of the main drugs used to treat tuberculosis; MDR [R0(3)], which is resistant to both of the main treatment drugs; and post-MDR [R0(4)], which is resistant to both of the main antibiotics and others as well (Blower and Chou 2004). With the information gathered from over 30 years of date they constructed likely evolutionary trajectories of hot zones, and with this they also took into account low cure rates vs. high amplification probabilities in many areas. They also tried to incorporate which strains are more transmissible, but as I said before this was not really possible with their model and there was a large degree of uncertainty.
The results of their model matched the WHO predictions well, but there were some distinct differences, and I think these differences are what make this research so important. By using all for types (R01-4) they found great variability in incidence and prevalence. When treatments were originally started strains of pre-MDR strains emerged quickly, so incidence and prevalence of pre-MDR strains increased, and this subsequently led to possible amplification of resistance and MDRtb epidemics in certain areas. The question is: Why certain areas and not others? This question is explained by Blower and Chou. Interestingly, areas with bad treatment programs do not necessarily have a really high incidence of MDRtb, it has stayed pretty steady at a 5%-14% (Blower and Chou 2004). This to me seems like an argument that MDRtb is not as easily transmissible, because its rates overall have stayed pretty low, but there was no significant evidence for this. The WHO predictions state that a >5% prevalence OR incidence in MDRtb equals a hot zone. Blower and Chou found the mathematical relationship between MDR prevalence and incidence. MDR prevalence can be three times greater then MDR incidence. They used the results to evaluate the hot zones on prevalence or incidence. If it is by incidence then only 20% of those areas would be considered hot zones and 51% if criterion is prevalence (Blower and Chou 2004). I see this as an argument for the fitness of MDRtb to be very high and transmissible ability to be lower, because there are less new cases, and more cases that have just become more resistant.
When looking at the four strains the hot zones had a much lower R0 for pansensitive strains (median=.82), which suggests that the wild type strain should be slowly eradicated. The R0 for the pansensitive strains in non-hot zones were all above 1 (median=1.39) Looking at the rate of detection of cases and treatment rates in hot zones versus non-hot zones it is 55% to 25% (Blower and Chou 2004). This shows that places where they have control programs were successful at fighting pansensitive strains but ironically it created more MDRtb strains, making it more likely to become a hot zone.
The importance of this research is that they have figured out that the difference between incidence and prevalence rates is significant enough to change the view of an area as being a hot zone or not. Their research looks at many factors that go into the evolution of these hot zones. Out of the many factors they actually saw that case detection and treatment rates were the most important factors. They came to this conclusion because if case detection and treatment rates were low, and the amplification was high, it still did not generate a hot zone. Vise versa, if the case detection and treatment rates were high and the amplification rates were low; it was likely to become a hot zone. The point is that these areas with high case detection and treatment rates should not increase these rates unless high cure rates are achieved first. Blower and Chou have created a model that has multiple dimensions and can help the WHO in the future to prevent hot zones from popping up in high risk regions. The WHO already had a model for this but it was nowhere complex enough to correctly calculate prevalence and incidence of MDRtb, and how their mathematical relationship.
Reference:
Sally M Blower, Tom Chou (2004). Modeling the emergence of the 'hot zones': tuberculosis and the amplification dynamics of drug resistance Nature Medicine, 10 (10), 1111-1116 DOI: 10.1038/nm1102
Student post submitted by Trevor Clark.
The article, Evolution and pathogenesis of Staphylococcus aureus: lessons learned from genotyping and comparative genomics, is well written and also well diagramed. The article is looking to obtain an understanding of what the exact biologic role of methicillin resistant S. aureus (MRSA) is. The authors want to understand the existence and the mechanism of evolution for the genesis of C-MRSA. The paper gives a clear background of MRSA but it does not go into depth about the history of how it has evolved to what it is now. It is clearly stated by the authors that, not much is known about how MRSA is able to evolve as quickly as it does to medications.
The body of the paper dives right into the evolution of the core genome of MRSA. My focus is more towards how MRSA has evolved in the hospital and medial setting and the effects that it has had on humans. The research conducted for the paper was vital for them to understand the clonal structure so they could compare it to the strains that common today. They were able to obtain an understanding of what genes it had by running it through a multilocus sequence typing (MLST). “MLST is currently the most popular typing method through the sequencing of seven housekeeping genes (arcC, aroE, glpF, gmk, pta, tpi, and yqiL). For each gene, the different sequences are assigned as alleles and the alleles at the seven loci provide an allelic profile, which unambiguously define the sequence type (ST) of each isolate.” From the data collected from the MLST they were able to analyze and conclude that point mutations have given rise to new alleles more frequently than recombination. Their data shown in figure 1 is good, but I still do not have a clear understanding on how their calculations came about. I would have liked to see a side-by-side comparison of the genes from each individual strains that they were able to sequence successfully. Anyone can talk about what they have found, but I need to see proof through more data.
Fig. 1. Protein homology between nine sequenced Staphylococcus aureus genomes. In each box is the number of orthologues shared by the corresponding strains and median nucleotide divergence that reflects divergence between the two strains. The orthologue was constructed by the orthomcl program (Li et al., 2003). Nucleotide divergence is defined as the number of mismatch bases divided by the number of comparable bases. The color intensity in each box is in inverse proportion to the nucleotide divergence. The accession numbers of the S. aureus genomes are: NC_002745 (N315), NC_002758 (Mu50), NC_003923 (MW2), NC_002953 (MSSA476), NC_002951 (COL), NC_007795 (NCTC8325), NC_007793 (USA300), NC_002952 (MRSA252), NC_007622 (RF122).
The second half of the paper was geared more towards what I was interested in. They talked about how MRSA enters a host and clearly explained the life cycle of MRSA on what they gathered from their studies. Community-acquired MRSA (C-MRSA) has become a problem and is considered to be a super bug since doctors and scientist have not yet found a cure or a drug to fight it. The evolution of MRSA to become what it has become today has interested the medical community and also has sparked an interest in me. This paper does a good job in bringing some light towards possible ways to fight MRSA; “staphylococcal species are one of the most important topics in the research of the evolution and pathogenesis of S. aureus”.
Fig. 4. Illustration of the hypothetical Staphylococcus aureus evolutionary history. The whole S. aureus species can be divided into two putative subspecies (Robinson et al., 2005a). The circles with different colors represent different agr groups, and the circles with numbers inside represent the corresponding clonal complexes. The arrows on the right side indicate the important phases during the S. aureus evolution.
The table and figures that were used were well done overall. They were clearly explained and could be understood. The way the paper was written and put together made is so those who have taken an entry-level genetic course would be able to understand and follow what was being said.
References:
Feng, Y., Chen, C., Su, L., Hu, S., Yu, J., Chiu, C. (2007). Evolution and pathogenesis of Staphylococcus aureus: lessons learned from genotyping and comparative genomics. FEMS Microbiology Reviews DOI: 10.1111/j.1574-6976.2007.00086.x
Robinson, D.A. (2005). Evolutionary Genetics of the Accessory Gene Regulator (agr) Locus in Staphylococcus aureus. Journal of Bacteriology, 187(24), 8312-8321. DOI: 10.1128/JB.187.24.8312-8321.2005
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