Mystery Rays from Outer Space

Meddling with things mankind is not meant to understand. Also, pictures of my kids

May 28th, 2009

On the Gelsinger case

Jesse Gelsinger
Jesse Gelsinger

Considering the sheer number of clinical trials out there, it’s surprising that more of them don’t end in tragedy.  One reason they don’t, is that those that do are scrutinized intensely.

One of the most famous clinical trial deaths was the death, in 1999, of Jesse Gelsinger, a participant in a gene therapy clinical trial (New York Times article here; Wikipedia summary, very short but not inaccurate, here).  Gelsinger’s death was clearly caused directly by the treatment, and he shouldn’t have been in the trial in the first place.  The death essentially shut down gene therapy for some time, and though the field has recovered, and has weathered other tragedies, anyone involved in gene therapy knows Gelsinger’s name and story.

The doctor in charge of the trial, James Wilson,  recently published “Lessons learned” from this case. 1  (The article is part of a settlement with the DoJ; the university involved was fined, but didn’t have to admit wrongdoing.)  No matter how you read the article — different people read it as a long excuse, as a confession, as a poignant reminiscence, as a warning — it’s worth thinking about; I’ve no doubt that the lessons Wilson talks about are still relevant to many clinical trials today.

  • Lesson #1: The clinical protocol is a contract with the research subjects and regulatory agencies that must be strictly and literally adhered to. (He points out the the complexities of a clinical trial, especially involving so many new components, made the protocols “a living document”. Even though the many changes were approved, it must have been difficult to follow the changes.  What’s more, changing the rules must make it seem that the rules are flexible.  They’re not.)
  • Lesson #2: If you think about reporting – then do so! (He notes that the trial was run by a committee of experts, which seemed like a good thing at the time. But that diluted some of the sense of personal responsibility. The person in charge has to be responsible.)
  • Lesson #3: It is very difficult to manage real or perceived financial conflicts of interest in clinical trials. (Is it possible to avoid financial conflicts? Is is possible to avoid the appearance of conflict? “I conclude that it is impossible to manage perceptions of conflicts of interest in the context of highly scrutinized clinical trials, particularly where there is a tragic outcome. It must be realized, however, that a zero tolerance for real or perceived financial conflicts of interest in clinical trials … can limit the contribution of the physician-scientist to the process of bench-to-bed-side or what we now call translational research.“)
  • Lesson #4: Informed consent may require objective third party participation. (The people who design the trial are invested in it — not financially, but personally; it’s their work. They are not the best people to explain the risks to potential participants. “The challenge is that the most qualified individuals to participate directly in the clinical trial are those who developed the technology and those with knowledge of the disease which unfortunately are also those with potential non-financial conflicts of interest. The crux of the problem is to assure that the subject receives a balanced and unbiased view of the risks and benefits of his/her participation in the trial.”)


  1. Wilson, J. (2009). Lessons learned from the gene therapy trial for ornithine transcarbamylase deficiency Molecular Genetics and Metabolism, 96 (4), 151-157 DOI: 10.1016/j.ymgme.2008.12.016[]
May 27th, 2009

How the aphid got its wings

Rosy Apple Aphid (Whalon lab)
Rosy Apple Aphid (Whalon lab, MSU)

While nothing can match the pure undiluted awesomeness that is the parasitoid wasp/bracovirus symbiosis,1 there are other symbioses that are at least in the same ballpark.  The latest one I’ve learned about is the relationship between a densovirus and the rosy apple aphid. 2  I can’t do better than to quote the abstract:

Winged morphs of aphids are essential for their dispersal and survival. We discovered that the production of the winged morph in asexual clones of the rosy apple aphid, Dysaphis plantaginea, is dependent on their infection with a DNA virus, Dysaphis plantaginea densovirus (DplDNV). Virus-free clones of the rosy apple aphid, or clones infected singly with an RNA virus, rosy apple aphid virus (RAAV), did not produce the winged morph in response to crowding and poor plant quality. DplDNV infection results in a significant reduction in aphid reproduction rate, but such aphids can produce the winged morph, even at low insect density, which can fly and colonize neighboring plants. Aphids infected with DplDNV produce a proportion of virus-free aphids, which enables production of virus-free clonal lines after colonization of a new plant.2

So without the virus, the aphids don’t grow wings, and they’re not able to disperse to new sites. When infected, they can sprout wings, and spread to a new site. Presumably without a flying aphid to carry them the virus can’t disperse, either.

Apart from anything else, my kids, having learned about this at dinner,3 are now hoping to have their wings turned on the next time they’re infected with a virus.


  1. Bioweaponized wasps shooting mutualistic immune suppressive viruses at their prey! Pew! Pew! Pew! []
  2. Ryabov, E., Keane, G., Naish, N., Evered, C., & Winstanley, D. (2009). Densovirus induces winged morphs in asexual clones of the rosy apple aphid, Dysaphis plantaginea Proceedings of the National Academy of Sciences, 106 (21), 8465-8470 DOI: 10.1073/pnas.0901389106[][]
  3. We have interesting dinner conversations at my house[]
May 26th, 2009

Why (some) similar tumors are similar

Caco2 colon carcinoma cells
Caco2 colon carcinoma cells

One of my long-standing questions now has at least a partial answer, or maybe a pathway toward a partial answer.

My question was, “Why are different tumors the same?” That is, why do tumors of the same type often seem to have similar immunological changes?

Viruses of the same kind (all herpes simplex viruses, say) all avoid immunity in the same way because they all have a common ancestor from which they inherited their immune evasion molecules. But tumors have no common ancestor; each tumor has to grope around and find its own solution to common problems independently. So why should tumors of a particular tissue converge on common solutions? What would make a certain pathway a simple solution for colon carcinomas, but not for bladder tumors? And yet, apparently that’s what happens. Even though many different tumor types become non-immunogenic, tumors of a particular tissue type often reach non-immunogenicity by a similar path. For example:

… distinct molecular events underlie HLA class I loss, depending on the aetiology of the tumours; Lynch syndrome-related cancers presented with mutations in the β2-m molecule, while sporadic microsatellite-unstable tumours mainly showed alterations in the antigen-processing machinery components 1

When I posed this question a couple of months ago, I didn’t have an answer. I said

Part of the answer may be that the particular oncogenes associated with different tumor types lead to particular transcriptional hot-spots, and being a transcriptional hot-spot makes the region a mutational hot-spot as well, but at least as I understand it that’s not enough to account for the trends.

EH, in the comments, suggested a couple more possibilities: Perhaps destruction of a particular pathway is “a nice side benefit of destroying some yet undiscovered tumor suppressor important for melanoma or colon cancer? Or maybe the loss of certain repair genes common to a cancer type (like BRCA) leads to selective chromosomal instability in parts of chromosome 6?

It turns out, unsurprisingly, that I’m not the only one to ask the question. There is no general answer, but apparently a partial answer has been kicking around for a while now, and Hans Morreau’s group is starting to put some of the pieces together.2 As their model, Morreau’s group looked at MUTYH-associated polyposis (“MAP”)-associated tumors. (MAP is a heritable defect in DNA repair, so MAP patients have high rates of mutagenesis and usually develop colon carcinomas.) They reasoned that

MAP tumours could be more prone to stimulate a cytotoxic T-cell-mediated immune response, due to their frequent generation of aberrant peptides. Hence, these tumours could also be subjected to a strong selective pressure favouring the outgrowth of cancer cells that acquire an immune evasive phenotype.

DNA Repair
DNA Repair

In other words, the argument is that the group of tumors that lose (or reduce) DNA repair are much more likely to throw out mutant proteins. These mutant proteins are targets for the immune response (because they’re no longer “self” antigens), so to survive the immune attack the tumor has a strong selection for loss of immunogenicity (and also has the high mutation rate that allows them to rapidly mutate away from immunogenicity).

Sure enough, the tumors did frequently (72%) have defects in antigen presentation. (In fact, because of the way they measured HLA expression — by immunohistochemistry rather than sequencing — I would bet that the rate of functional defects was actually much higher than that.) They conclude that this “provides additional evidence that tumours carrying defects in DNA base repair mechanisms are more prone to undergo immune escape mechanisms.2  Since they don’t formally compare to other tumor types, I don’t think they can really say “more prone” — you’d have to use the same techniques to look at tumors from non-MAP patients to be able to say that. Still, I do think that is a significantly higher rate than has been turned up in previous studies using similar techniques,3 so I’ll tentatively accept that conclusion.

This doesn’t really explain why similar tumors target similar components, but it’s at least a conceptual connection between different tumor types and an underlying pathway. I’d be interested in a more large-scale screen, looking at cancers with stronger and weaker mutator phenotypes to see if common pathways emerge. One may already have popped up, since the authors note here that expression of β2-m was frequently lost4 in several tumors with DNA repair defects:

Although speculative, it is interesting to underline that carcinomas derived from both MAP and Lynch syndromes preferentially lose β2-m expression coupled to HLA class I deficiencies. A functional explanation for these observations remains elusive, but perhaps distinct reactions (both qualitative and quantitative) by the immune system, depending on the age of onset of the tumours, could condition the type of mechanisms that lead to HLA class I expression deficiencies. 2

Again, we would need to compare to other tumor types to see if this really is more frequent, but overall it feels as if there’s a hint of a pathway here. At least there are some specific questions that can be asked.


  1. de Miranda, N., Nielsen, M., Pereira, D., van Puijenbroek, M., Vasen, H., Hes, F., van Wezel, T., & Morreau, H. (2009). MUTYH-associated polyposis carcinomas frequently lose HLA class I expression-a common event amongst DNA-repair-deficient colorectal cancers The Journal of Pathology DOI: 10.1002/path.2569
    Referencing
    Dierssen JWF, de Miranda NFCC, Ferrone S, van Puijenbroek M, Cornelisse CJ, Fleuren GJ, et al. HNPCC versus sporadic microsatellite-unstable colon cancers follow different routes toward loss of HLA class I expression. BMC Cancer 2007; 7: 33[]
  2. de Miranda, N., Nielsen, M., Pereira, D., van Puijenbroek, M., Vasen, H., Hes, F., van Wezel, T., & Morreau, H. (2009). MUTYH-associated polyposis carcinomas frequently lose HLA class I expression-a common event amongst DNA-repair-deficient colorectal cancers The Journal of Pathology DOI: 10.1002/path.2569[][][]
  3. Other studies, especially those of Soldano Ferrone, have turned up much higher rates of functional HLA class I defects, but they’ve used more focused, and more difficult and expensive, techniques to reach that conclusion.[]
  4. β2-m is a physical component of the MHC class I complex[]
May 22nd, 2009

On antigen processing

As my regular readers1 may have noticed, updates are a little sparse the last week or so.  The explanation is that I have an NIH grant application due at the beginning of June.  I hope to mostly finish it this weekend, but until I’m done struggling with paperwork and online submissions and budget predictions, the posts here are likely to be on the short side.

I’ll try to stall with this small version of the Nature Reviews Immunology antigen processing (link here [pdf], but I don’t know if it’s open-access) from Wearsch and Cresswell.

Nature Reviews Immunology Antigen Processing poster


  1. Who know what’s supposed to go in this footnote[]
May 18th, 2009

On the accuracy of the influenza databases

Lots of people have been analyzing the new H1N1 influenza virus by sequence analysis, comparing to influenza sequences in various databases (I used the NCBI‘s).  How reliable are these databases?

Our observations show that a fraction of the sequences in the database exhibit anomalous properties that point to either radically new biology or, more likely, problems with the data. … We speculate that perhaps the most likely explanation for both of the anomalies reported here is stock contamination in the sequencing laboratories … 1

Influenza Virus Sequence Distribution
IVDB: Influenza Virus Sequence Distribution

(My emphasis) The data are worst for older viruses and for non-human, especially swine, influenza viruses.

As of 2008, the authors identified around 100 of 3300 genomes (about 3%) of the genomes in the influenza databases that were problematic, but they noted that because of the way they identified the probable mixups this is likely a significant underestimate of the problems: “If stock contamination is indeed to blame for these anomalies, the results reported here could represent just the tip of the iceberg.

Thanks to Vincent Racaniello of The Virology Blog and This Week in Virology for  pointing me to the paper. 1   I don’t know if the databases have been cleaned up in the year since the problems were noted, but I doubt it. That said, I think this type of error shouldn’t have a huge impact on tracking the evolution and origins of the new H1N1 virus; it would probably have the same effect as if no sequence was deposited for a particular strain. (In other words, it leaves gaps, but for the most part doesn’t actively steer research in the wrong direction.)


  1. Krasnitz, M., Levine, A., & Rabadan, R. (2008). Anomalies in the Influenza Virus Genome Database: New Biology or Laboratory Errors? Journal of Virology, 82 (17), 8947-8950 DOI: 10.1128/JVI.00101-08[][]
May 11th, 2009

On emerging pathogens

The recent much-publicized report World at Risk predicts that we are soon to experience a biological or nuclear weapons attack.  … we are at least equally likely, if not more likely, to soon experience large-scale morbidity through epidemics of emergent pathogens. As was illustrated by the severe acute respiratory syndrome–associated coronavirus, when a ubiquitous nuisance pathogen suddenly becomes more virulent, its reign of destruction needs little help from rogue nations or terrorist cells. Humankind is quite efficient in spreading such pathogens around.

Adenovirus (Mapposity)
“Adenovirus” (by Mappposity)

That paragraph was written last December and published in early April, a couple of weeks before the new H1N1 influenza cases were detected in San Diego, making the author look prescient.  Except, of course, the article has nothing to do with influenza.

This is from an editorial1 in the Journal of Infectious Disease, and refers to a series of papers2 showing that adenovirus strain 14 has suddenly exploded throughout Oregon and Texas in the past year or two, hospitalizing dozens and causing at least 17 deaths.

Adenovirus infections are very common in humans, and usually don’t cause much in the way of disease; they’re one of the “common cold” cluster of agents (see an earlier post on adenoviruses here, and other posts here).  But there are over 50 different human adenoviruses, and some are both more severe, and less common, than others.  Adenovirus Type 14 (Ad14) was actually one of the sporadic, fairly moderate, strains, until 2005 when a new variant of the virus (poetically called “Ad14a”) abruptly started causing epidemics.  It’s this new variant that’s continued to cause these outbreaks.

Most Ad14a infections are probably minor.  Probably; we don’t really know, because “Ad surveillance is generally passive. Additionally, relatively few laboratories look for Ad, and even fewer can distinguish Ad14 from other Ad types.” 1  So we don’t know how many Ad14a infections there actually are.  This is the same “missing denominator” problem I alluded to with the Mexican cases of the new H1N1 — when we find that 18% of patients with Ad14a infection die, does that mean it’s a very serious disease, or does it mean doctors only look for Ad14a in seriously ill patients?  In all likelihood, there are a vast number of Ad14a infections that are missed (“With its propensity for rapid transmission, it seems likely that Ad14a is now circulating throughout the United States and may have been introduced from another country“).

Still, the virus clearly can cause serious problems.  It’s just another warning that we’re constantly under siege by pathogens.  We probably should understand better what’s attacking us.


  1. Gray, G., & Chorazy, M. (2009). Human Adenovirus 14a: A New Epidemic Threat The Journal of Infectious Diseases, 199 (10), 1413-1415 DOI: 10.1086/598522[][]
  2. Lewis PF, Schmidt MA, Lu X, et al. A community-based outbreak of severe respiratory illness caused by human adenovirus serotype 14. J Infect Dis 2009;199:1427–34

    Tate JE, Bunning ML, Lott L, et al. Outbreak of severe respiratory disease associated with emergent human adenovirus serotype 14 at a US Air Force training facility in 2007. J Infect Dis 2009;199:1419–26

    Centers for Disease Control and Prevention. Acute respiratory disease associated with adenovirus serotype 14—four states, 2006–2007. MMWR Morb Mortal Wkly Rep 2007;56:1181–4.[]

May 6th, 2009

Predicting influenza virulence from sequences

Sometimes, scientists manage to be in the right place at the right time.  For example, a group from Lawrence Livermore National Laboratory submitted a paper1 early in January, and it just turned up in pre-publication form.  When they submitted it I really doubt they expected to have it appear in the midst of a new influenza pandemic, even though that’s exactly what they were trying to predict.

What they are actually trying to predict is exactly what the CDC and WHO are now looking at.  If you have the genome sequence of an influenza virus, can you predict how dangerous it’s going to be? Can it infect humans? Will it be mild, or highly virulent?

The answer is probably “Not so much,” but the question is an excellent and very relevant one.  In fact (as far as I know), there’s not a lot understood about how genome sequences correlate with an influenza strain’s ability to infect and cause disease. So that puts public health essentially in a reactive role, waiting to see what the virus does and then going back to look at the sequences involved.  It would be nice to have some kind of prediction, where we could see that the virus is moving toward a more or less dangerous capability.

What the authors of this paper did is gather flu sequences from high- and -low-mortality strains and from pandemic and non-pandemic strains, and then ask what amino acids they had in common.

They found 34 markers of specificity and virulence. They then asked how likely it is for all 34 danger markers to arise in a single strain of influenza, pointing out that:

While marker re-emergence in a single strain does not predict pandemic potential, their presence could highlight unexpected evolutionary events in circulating strains that warrant closer scrutiny.2  … The high mortality rate markers appeared in a wide variety of avian strains but the recent avian to human strain crossovers lacked most of the human strain specific markers. Human persistent strains retained human specific markers (by definition) but lacked most of the high mortality rate markers. Swine strains fell in the middle, carrying both high mortality rate and host specificity markers but with no single strain containing all 34 markers. 1

In other words, swine influenza strains look as if they have the potential to either infect humans and cause a pandemic, or to cause high mortality in humans, but not both (without a lot of mutation or recombination). So that kind of matches what we’re seeing in the present outbreak.

I haven’t looked at the present outbreak to see which of these markers the new H1N1 has — it’s going to be a lot of work to match them up because of the way they’re presenting the data, unfortunately.  In any case, they emphasize that their markers are not absolutely predictive, especially the high-mortality markers:

Finding that classification accuracy for high mortality rate strains is lower than the host type classification weakens support for the notion of a single essential common set of high mortality rate markers.

(It’s those high-mortality markers that we’d be most interested in with the present H1N1 strain, because it’s obvious that the virus has already become fairly comfortable with humans, so the specificity markers are unnecessary.)


  1. Allen, J., Gardner, S., Vitalis, E., & Slezak, T. (2009). Conserved amino acid markers from past influenza pandemic strains BMC Microbiology, 9 (1) DOI: 10.1186/1471-2180-9-77[][]
  2. This is exactly the scenario we’re in now, monitoring the present outbreak.[]
May 4th, 2009

Relationships

If anyone’s interested in looking at relationships between the new H1N1 and other influenza viruses, I’ve put up a (large!) table here showing relatives of each of the segments.

Knock yourselves out.

(This took me a good 15 minutes to produce, what with running the blast searches and writing the 15 lines of Python that parsed the output and printed out the table, so I hope you appreciate it.)

May 4th, 2009

On influenza and handwashing

A reader asked (I feel like Dear Abby) about alcohol-based handwashes. Do they work against flu? The answer is Yes, but plain old soap is just as good, if not better.

News you can use, folks.

Conclusions. HH [hand hygiene] with SW [soap and water hand washing] or alcohol-based hand rub is highly effective in reducing influenza A virus on human hands, although SW is the most effective intervention. Appropriate HH may be an important public health initiative to reduce pandemic and avian influenza transmission.

–Efficacy of Soap and Water and Alcohol-Based Hand-Rub Preparations against Live H1N1 Influenza Virus on the Hands of Human Volunteers
Lindsay Grayson, Sharmila Melvani, Julian Druce4 Ian G. Barr, Susan A. Ballard, Paul D. R. Johnson, Tasoula Mastorakos, and Christopher Birch
Clinical Infectious Diseases 2009;48:285–291

May 3rd, 2009

Has the new H1N1 been hiding for 11 years? (No.)

Here are the results of a quick BLAST lineup of some of the segments from the new H1N1 influenza viruses.

gsgs said in comments that “we had the reassortment event from 1998 (or some years earlier ?) when this type of virus emerged. The ancestor of modern American Swine-flu.
Several other progenies from USA of this reassortment are at genbank.
But the new virus (in 123458) is more similar to the 1998 virus than to any of these, suggesting that it evolved separately (in Mexico ?) since ~1998. 511445 is more similar to several other viruses than to 1998.

But as far as I can see that’s simply not true.  Homology lineups — even just looking at H1N1 strains — pull out recent viruses long before the 1998. The closest H1N1 I find matching the HA is A/swine/OH/511445/2007(H1N1); for polymerase (which is probably better for lineups) it’s A/Iowa/CEID23/2005, just three years before the new H1N1. Only a NP search finds a 98 H1N1 as a best match, and that’s only a fraction of a percent better match than a 2005 Korean strain — ten more residues out of 1500.

What’s more, it’s wrong-headed.  The differences in homology between different viruses here represent only a tiny handful of nucleic acid changes.  And that kind of difference represents a week or two in the replication of a single virus.  Look at the HA from isolates from the current outbreak!  There are half a dozen changes between two different California isolates!  That’s just about the difference that makes gsgs say the parent must be a 98 virus and not a 2005 virus, and yet that’s the same virus. There are a bunch of H3N2 viruses that are closer than any H1N1– are these therefore the parental viruses?

I’m sorry, it’s craziness to look at these kinds of changes and make the claim that it’s somehow informative of parental lines.  The virus changes too fast to put any parent/progeny relationship on a handful of residues, because that takes like a week in the virus cycle.

Hemagglutinin
Strain Identities/total length Percent identity
A/California/04/2009(H1N1) 1701/1701     100.00
A/California/07/2009(H1N1) 1700/1701     99.94
A/California/08/2009(H1N1) 1700/1701     99.94
A/California/07/2009(H1N1) 1700/1701     99.94
A/California/07/2009(H1N1) 1698/1701     99.82
A/California/10/2009(H1N1) 1696/1701     99.71
A/California/05/2009(H1N1) 1696/1701     99.71
A/Regensburg/Germany/01/2009(H1N1) 1441/1446     99.65
A/Texas/04/2009(H1N1) 1695/1701     99.65
A/Texas/04/2009(H1N1) 1695/1701     99.65
A/New York/19/2009(H1N1) 1695/1701     99.65
A/Texas/04/2009(H1N1) 1695/1701     99.65
A/California/06/2009(H1N1) 1695/1701     99.65
A/Texas/05/2009(H1N1) 1695/1701     99.65
A/Auckland/1/2009(H1N1) 1571/1576     99.68
A/Swine/Indiana/P12439/00 (H1N2) 1624/1704     95.31
A/Swine/Indiana/9K035/99 (H1N2) 1624/1706     95.19
A/Turkey/MO/24093/99(H1N2) 1619/1704     95.01
A/swine/Minnesota/1192/2001(H1N2) 1618/1704     94.95
A/Swine/Ohio/891/01(H1N2) 1617/1704     94.89
A/swine/Guangxi/17/2005(H1N2) 1617/1705     94.84
A/SW/MN/23124-T/01(H1N2) 1617/1705     94.84
A/SW/MN/16419/01(H1N2) 1615/1705     94.72
A/SW/MN/23124-S/01(H1N2) 1615/1705     94.72
A/Swine/Illinois/100085A/01 (H1N2) 1614/1705     94.66
A/Swine/Illinois/100084/01 (H1N2) 1612/1706     94.49
A/swine/Guangxi/13/2006(H1N2) 1608/1704     94.37
A/swine/Minnesota/00194/2003(H1N2) 1609/1706     94.31
A/swine/Kansas/00246/2004(H1N2) 1603/1706     93.96
A/swine/OH/511445/2007(H1N1) 1598/1704     93.78
A/Swine/North Carolina/93523/01 (H1N2) 1597/1706     93.61
Polymerase
Strain Identities/total length Percent identity
A/California/07/2009(H1N1) 2151/2151 100.00
A/California/04/2009(H1N1) 2151/2151 100.00
A/California/07/2009(H1N1) 2151/2151 100.00
A/California/04/2009(H1N1) 2151/2151 100.00
A/California/07/2009(H1N1) 2150/2151 99.95
A/Texas/04/2009(H1N1) 2148/2151 99.86
A/Texas/05/2009(H1N1) 2148/2151 99.86
A/Texas/04/2009(H1N1) 2148/2151 99.86
A/Texas/05/2009(H1N1) 2148/2151 99.86
A/California/06/2009(H1N1) 2147/2151 99.81
A/California/05/2009(H1N1) 2147/2151 99.81
A/Swine/Illinois/100084/01 (H1N2) 2070/2154 96.10
A/Swine/Minnesota/593/99 (H3N2) 2069/2153 96.10
A/Swine/Iowa/569/99 (H3N2) 2069/2153 96.10
A/Swine/Iowa/533/99 (H3N2) 2069/2153 96.10
A/Swine/Nebraska/209/98 (H3N2) 2069/2153 96.10
A/pintail duck/South Dakota/Sg-00126/2007(H3N2) 2052/2129 96.38
A/Swine/Minnesota/55551/00 (H1N2) 2067/2153 96.01
A/Swine/North Carolina/93523/01 (H1N2) 2064/2153 95.87
A/mallard duck/South Dakota/Sg-00125/2007(H3N2) 2038/2115 96.36
A/Swine/Korea/CY02/02(H1N2) 2063/2153 95.82
A/Swine/Indiana/P12439/00 (H1N2) 2063/2153 95.82
A/duck/NC/91347/01(H1N2) 2062/2153 95.77
A/Swine/North Carolina/98225/01(H1N2) 2061/2153 95.73
A/swine/Korea/CY05/2007(H3N2) 2061/2154 95.68
A/swine/Korea/CY04/2007(H3N2) 2061/2154 95.68
A/Swine/Illinois/100085A/01 (H1N2) 2060/2154 95.64
A/swine/Korea/CY09/2007(H3N2) 2059/2154 95.59
A/swine/North Carolina/2003(H3N2) 2059/2154 95.59
A/swine/Korea/CAS05/2004(H3N2) 2056/2153 95.49
A/swine/Korea/Hongsong2/2004(H1N2) 2056/2153 95.49
A/swine/Korea/JNS06/2004(H3N2) 2055/2153 95.45
A/swine/Korea/CAS09/2006(H3N2) 2055/2153 95.45
A/swine/Korea/PZ14/2006(H1N2) 2055/2153 95.45
A/swine/Korea/PZ7/2006(H1N2) 2055/2153 95.45
A/swine/Korea/PZ4/2006(H1N2) 2055/2153 95.45
A/swine/Korea/JL02/2005(H1N2) 2055/2153 95.45
A/swine/Korea/CAS07/2005(H3N2) 2053/2153 95.36
A/swine/Korea/CAN04/2005(H3N2) 2053/2153 95.36
A/Iowa/CEID23/2005(H1N1) 2054/2154 95.36
Nucleoprotein
Strain Identities/total length Percent identity
A/Texas/04/2009(H1N1) 1497/1497 100.00
A/Texas/05/2009(H1N1) 1497/1497 100.00
A/California/04/2009(H1N1) 1497/1497 100.00
A/Texas/04/2009(H1N1) 1497/1497 100.00
A/Texas/05/2009(H1N1) 1497/1497 100.00
A/California/04/2009(H1N1) 1497/1497 100.00
A/California/06/2009(H1N1) 1496/1497 99.93
A/California/07/2009(H1N1) 1495/1497 99.87
A/California/05/2009(H1N1) 1494/1497 99.80
A/Swine/Iowa/533/99 (H3N2) 1450/1498 96.80
A/swine/Korea/CY05/2007(H3N2) 1449/1498 96.73
A/swine/Korea/CY04/2007(H3N2) 1449/1498 96.73
A/swine/Korea/CY09/2007(H3N2) 1448/1498 96.66
A/Swine/Indiana/P12439/00 (H1N2) 1447/1497 96.66
A/Swine/Iowa/569/99 (H3N2) 1447/1498 96.60
A/swine/Korea/JNS06/2004(H3N2) 1445/1497 96.53
A/Swine/Ohio/891/01(H1N2) 1446/1498 96.53
A/Swine/Minnesota/593/99 (H3N2) 1445/1498 96.46
A/swine/Korea/CAS09/2006(H3N2) 1443/1497 96.39
A/swine/Korea/CAN04/2005(H3N2) 1443/1497 96.39
A/Swine/Indiana/9K035/99 (H1N2) 1445/1499 96.40
A/Swine/Minnesota/55551/00 (H1N2) 1442/1497 96.33
A/Wisconsin/10/98 (H1N1) 1445/1500 96.33
A/swine/Korea/CAS07/2005(H3N2) 1441/1497 96.26
A/Swine/Illinois/100084/01 (H1N2) 1442/1498 96.26
A/Swine/Illinois/100085A/01 (H1N2) 1442/1498 96.26
A/swine/Korea/CAS05/2004(H3N2) 1440/1497 96.19
A/swine/Shanghai/1/2007(H1N2) 1440/1498 96.13
A/Swine/North Carolina/93523/01 (H1N2) 1437/1497 95.99
A/Swine/North Carolina/98225/01(H1N2) 1432/1495 95.79
A/swine/Guangxi/17/2005(H1N2) 1436/1498 95.86
A/swine/Korea/CAS08/2005(H1N1) 1435/1498 95.79
A/turkey/IA/21089-3/1992(H1N1) 1434/1497 95.79
A/swine/Guangxi/13/2006(H1N2) 1435/1498 95.79
A/duck/NC/91347/01(H1N2) 1433/1496 95.79
A/swine/Korea/CAN01/2004(H1N1) 1434/1498 95.73
A/turkey/Ohio/313053/04(H3N2) 1434/1498 95.73
A/turkey/OH/313053/2004(H3N2) 1433/1498 95.66
A/swine/California/T9001707/1991(H1N1) 1432/1497 95.66
A/swine/North Carolina/2003(H3N2) 1433/1498 95.66
A/swine/MI/PU243/04 (H3N1) 1433/1498 95.66