What are the chances that there’s something else like Zika coming soon?



Global Zika-like outbreaks are increasingly inevitable largely due to unprecedented rates of

  • Population mobility and density
  • Rapid global transport
  • Human ecosystem alteration
  • Climate change

existing cheek by jowl with

  • Vast health and wealth disparities
  • Vast global differences in public health infrastructure including sanitation and active disease surveillance capacity
  • Surfeit of neglected and/or little known diseases, many of them tropical, that lack drugs or vaccines to treat or prevent them and whose geographic reach spreads as climate change expands the range of the vectors capable of transmitting them.

Several of these factors also increase human-wildlife interactions, which in turn increase risk of Zoonosis – Wikipedia, diseases transmissible between animals and humans.

Originally discovered in 1947 in the Zika forest adjacent to Lake Victoria in Uganda, for decades Zika was but one of several RNA viruses known only to a handful of aficionados. Its obscurity began changing starting in 2007 when it caused an outbreak on the Micronesian island of Yap (1) followed by a larger 2013-2014 outbreak in French Polynesia (2) culminating of course in the global headlines of the 2015-2016 outbreak in Brazil and beyond (3).

So, clearly a case of a virus that until quite recently remained confined to a remote African forest and yet in less than a decade it’s spread across the globe, not only infecting large swaths of previously unexposed human populations but also expanding its capability in terms of disease outcomes from GBS (Guillain–Barré syndrome – Wikipedia) to fetal abnormalities (Microcephaly – Wikipedia). While less than a decade may sound shocking, not being unprecedented is even more so since Zika’s only following in the footsteps of related viruses, Chikungunya – Wikipedia and Dengue virus – Wikipedia, all zoonoses. Having similarly triumphantly marched across the globe in recent decades, their spread eerily echoes that of their carrier mosquito, Aedes – Wikipedia.

A 2008 study (4) estimated 335 emerging infectious diseases (EID) between 1940 and 2004. Averaging yields a very deceptive ~5 per year, deceptive because frequency between Pandemic – Wikipedia has steadily shrunk in recent years. Consider for example the high-profile global outbreaks of SARS (Severe acute respiratory syndrome – Wikipedia) in 2003, Bird flu (Influenza A virus subtype H5N1 – Wikipedia) in 2007, Swine flu (2009 flu pandemic – Wikipedia) in 2009, MERS (Middle East respiratory syndrome – Wikipedia) in 2012, Ebola virus disease – Wikipedia in 2014 and Zika fever – Wikipedia in 2015.

~60% of EIDs are primarily zoonotic. For e.g., Chikungunya, Dengue Ebola, HIV, Lyme, SARS, West Nile, Zika, to mention just a few.

  • The 2008 analysis (4) concluded majority arose from wildlife and that human population density was the single common predictor for all types of EIDs (zoonotic or not, drug-resistant or not, vector-borne or not).
  • EIDs seem to be a ‘hidden’ cost of human economic development (see below from 4, 5).

Population Mobility & Rapid Global Transport

Practically anywhere in the world is today a mere plane ride away even as these anywheres remain vastly different in basic public health infrastructure and active disease surveillance capacity. One sick person is all it takes for infectious diseases to spread beyond borders, i.e., vastly expanded potential for diseases to rapidly spread globally.

Consider for example how global air travel has exponentially expanded in <100 years. A paltry 1205 total global plane tickets sold in 1914 had increased to a whopping 2,595,448,927 in 2010. Yet large swaths of the world still lack adequate sanitation (see below from 6 and 7).

Human-driven Ecosystem Alterations & Climate Change Tilt The Balance In Favor Of Increased Pandemic Risk

More than ever before, massive human -driven ecosystem alterations have become the norm since the Industrial Revolution – Wikipedia with this process only further globalizing in the current era (8; see below from 9, emphasis mine).

‘With roughly half the temperate and tropical forests cut down, nearly half the icefree, desert-free terrestrial landscape converted to croplands or pasture, and more than 800,000 dams impeding the flow through more than 60% of the world’s rivers, alterations to our planet’s land use and land cover represent some of the most pervasive changes humanity has made to Earth’s natural systems

Human-driven ecological changes increase human encroachments into wildlife habitat. Such human-wildlife interactions increase zoonoses risk.

  • For example, studies suggest such processes may have kick-started the initial Ebola and HIV outbreaks (10).
  • No surprise then that ~75% of EIDs are zoonoses (4, 5, 11).
  • New disease outbreaks become more inevitable when previously unimaginable mobility and enormous human-driven ecological changes exist alongside crippling poverty consisting of acute food scarcity and no sanitation, hygiene or running water since the more malnourished are weaker and likelier to get sick, especially with new EIDs.
  • A corollary is increased hunting and consumption of wild meat, Bushmeat – Wikipedia (12).
    • For example, ‘ground zero’ for the 2013-2016 West African Ebola outbreak is suspected to be hungry children living in the remote Meliandou – Wikipedia village in southern Guinea – Wikipedia who killed and ate infected fruit bats (13, 14).
    • Something so seemingly inconsequential and yet it triggered a global public health emergency with a total of 28616 cases and 11310 fatalities from 10 countries according to West African Ebola virus epidemic – Wikipedia.
  • Mapping such outbreaks only emphasizes that infectious disease transmissions have become that much easier given how fluidly, rapidly and easily humans can traverse the globe these days, and the increasingly porous divides between previously more strongly demarcated divisions such as affluence and poverty, sanitation and filth.

Thus, since lack of hygiene, sanitation and running water are today only a plane ride away so is pandemic risk.

The rapid, global expansion of mosquito species such as Aedes aegypti – Wikipedia and Aedes albopictus – Wikipedia is but one example of how climate change effects place greater selection pressures on vast numbers of species to adapt to these rapid changes, many of which end up increasing infectious disease risk not just in humans but for all types of life forms (see some other examples below from 15).

A 2009 analysis (16) concluded climate change may influence different arthropod-transmitted Arbovirus – Wikipedia diseases differently.

  • Chikungunya: A single mutation in the Chikungunya virus facilitated its adaptation to the fast expanding mosquito species, A. albopictus, i.e., Chikungunya’s spreading by latching on to this mosquito’s coat-tails, whose spread is facilitated by climate change. Human travel simply augments spread even more.
  • Rift Valley fever – Wikipedia, Bluetongue disease – Wikipedia: According to these authors, climate change helps mosquitoes spread in newly flooded areas while human activities such as irrigation projects, movements of herded animals and animal imports to feed large numbers of humans, for example during Mecca pilgrimages, also contribute to Rift Valley virus outbreaks.


1. Duffy, Mark R., et al. “Zika virus outbreak on Yap Island, federated states of Micronesia.” New England Journal of Medicine 360.24 (2009): 2536-2543. http://www.nejm.org/doi/pdf/10.1…

2. Cao-Lormeau, V. M., et al. “Zika virus, French polynesia, South pacific, 2013.” Emerging infectious diseases 20.6 (2014): 1085-1086. http://wwwnc.cdc.gov/eid/article…

3. Campos, Gubio S., Antonio C. Bandeira, and Silvia I. Sardi. “Zika virus outbreak, Bahia, Brazil.” Emerging infectious diseases 21.10 (2015): 1885. https://www.ncbi.nlm.nih.gov/pmc…

4. Jones, Kate E., et al. “Global trends in emerging infectious diseases.” Nature 451.7181 (2008): 990-993.

5. World Organisation for Animal Health

6. In flight

7. Total population: access to sanitation

8. Foley, Jonathan A., et al. “Global consequences of land use.” science 309.5734 (2005): 570-574. https://www.researchgate.net/pro…

9. Myers, Samuel S., et al. “Human health impacts of ecosystem alteration.” Proceedings of the National Academy of Sciences 110.47 (2013): 18753-18760. https://www.researchgate.net/pro…

10. Hahn, Beatrice H., et al. “AIDS as a zoonosis: scientific and public health implications.” Science 287.5453 (2000): 607-614. https://www.researchgate.net/pro…

11. Taylor, Louise H., Sophia M. Latham, and E. J. Mark. “Risk factors for human disease emergence.” Philosophical Transactions of the Royal Society of London B: Biological Sciences 356.1411 (2001): 983-989. http://rstb.royalsocietypublishi…

12. Wolfe, Nathan D., et al. “Naturally acquired simian retrovirus infections in central African hunters.” The Lancet 363.9413 (2004): 932-937. http://www.jhsph.edu/research/af…

13. Vogel, Gretchen. “Bat-filled tree source of Ebola epidemic?.” Science 347.6218 (2015): 142-143. Bat-filled tree may have been ground zero for the Ebola epidemic

14. Bausch, Daniel G., and Lara Schwarz. “Outbreak of Ebola virus disease in Guinea: where ecology meets economy.” PLoS Negl Trop Dis 8.7 (2014): e3056. http://journals.plos.org/plosntd…

15. Altizer, Sonia, et al. “Climate change and infectious diseases: from evidence to a predictive framework.” science 341.6145 (2013): 514-519. http://www.colorado.edu/eeb/facu…

16. Gould, Ernest A., and Stephen Higgs. “Impact of climate change and other factors on emerging arbovirus diseases.” Transactions of the Royal Society of Tropical Medicine and Hygiene 103.2 (2009): 109-121. http://www.idpublications.com/jo…


If a vaccine for Zika is developed, will it be freely available to everybody?



Primarily because their intended target is the healthy population, the economics of vaccines are unlike those for other medicines.

  • Most governments already factor in this difference and have specific policy guidelines and even laws to help fund mass immunizations campaigns.
  • An international two-tier pricing structure ensures that vaccine costs in poorer countries are far lower than those in their wealthier counterparts (1, 2).
  • Vaccine cost is also fundamentally different between countries with a publicly funded national health care system, e.g., Canada (lower), versus countries that don’t, e.g., USA (higher).
  • Difference between childhood and adult vaccination programs is another element that influences vaccine cost. While the former are the intense focus of governments and therefore have robust public-private partnerships to defray costs, adult vaccinations can be fee for service, i.e., a healthcare provider purchasing vaccines upfront and then getting reimbursed after administering them (3).

Whether a future Zika vaccine will be available for free or ~free to everyone or not thus depends on whether Zika presents an Epidemic or Pandemic threat (circumstance #1) or not (circumstance #2), if and when such a vaccine becomes available.

Circumstance #1. When a vaccine becomes finally available, if Zika presented an Epidemic or Pandemic threat, affected governments would likely support mass immunizations campaigns, meaning vaccine cost would be heavily subsidized or even free. Though the particulars of how vaccines are funded differ in different countries, by and large most governments heavily subsidize costs of vaccination. For e.g., in the US, vaccine costs were brought under a common umbrella through the 1962 Vaccination Assistance Act (Section 317 of the Public Health Service Act) (3, 4, 5). Section 317 has been continuously reauthorized since 1962. Thus, it’s now a mainstay of immunization support in the US (3).

The US Vaccine Assistance Act ended up doing several things,

  • One, it allowed the Centers for Disease Control and Prevention (CDC) to support mass immunizations through the National Immunization Program.
  • Two, it provided financial assistance ‘in lieu of cash‘ to state and local health departments to in turn support mass immunization programs. Specifically Section 317 allows the US federal government to provide vaccines and personnel such as CDC Public Health Advisors and epidemiologists to assist local and state health departments in managing these programs.
  • Three, through Section 317, the US federal government is able to negotiate down vaccine prices with manufacturers (3; also see 6 for price differences between CDC and private sector costs for pediatric and adult vaccines). Factors relevant to cost reduction include sales volume, limited distribution points, no-return policy, to name a few.
  • Four, in addition, the US 1993 Vaccines for Children (VFC) Act ensures free vaccines to uninsured children, those on Medicaid or American Indian or Alaska Natives (7).

Thus, in the US, most recommended vaccines are covered by either private health insurance plans or government subsidies.

Circumstance #2. If, on the other hand, Zika threat in a particular country is so low that it would not be cost-effective for its government to subsidize it, then an individual may have to pay for it out of pocket, similar to the way they do now for travel-related vaccines, i.e., not covered by health insurance. Intended to protect against a mosquito-borne viral disease like Zika, the Yellow fever vaccine is a helpful guide for the cost of a travel-related vaccine in the US (see figure below from 8).


1. Batson, Amie, Sarah Glass, and Piers Whitehead. “Vaccine economics: from candidates to commercialized products in the developing world.” New Generation Vaccines (2004): 57-73.

2. Lieu, Tracy A., Thomas G. McGuire, and Alan R. Hinman. “Overcoming economic barriers to the optimal use of vaccines.” Health Affairs 24.3 (2005): 666-679. Overcoming Economic Barriers To The Optimal Use Of Vaccines

3. Hinman, Alan R., Walter A. Orenstein, and Lance Rodewald. “Financing immunizations in the United States.” Clinical infectious diseases 38.10 (2004): 1440-1446. Financing Immunizations in the United States

4. https://www.gpo.gov/fdsys/pkg/ST…

5. Hinman, Alan R., et al. “Vaccine-preventable diseases, immunizations, and MMWR: 1961-2011.” MMWR Surveill Summ 60.Suppl 4 (2011): 49-57. http://www.cdc.gov/mmwr/pdf/othe…

6. CDC Vaccine Price List

7. Robinson, Chester A., Stephen J. Sepe, and K. F. Lin. “The president’s child immunization initiative–a summary of the problem and the response.” Public Health Reports 108.4 (1993): 419. http://www.ncbi.nlm.nih.gov/pmc/…

8. Yellow Fever Vaccine Costs in the USA. http://www.okcopay.com/map/yello…


Why is cancer immunotherapy important?


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Cancer immunotherapy’s importance stems from its unique promise of specifically targeting cancer while minimizing collateral damage. Being non-specific is the glaring lacuna with Radiation therapy – Wikipedia and Chemotherapy – Wikipedia, which have dominated cancer Rx, especially since Nixon declared War on Cancer – Wikipedia in 1971 (1). While surgery is a third approach, it is only quasi-specific in so far as it removes as much of tumor and as little of normal tissue as possible. Working best when tumor’s diagnosed early, it’s invasive and therefore risk-laden and also not feasible for many cancers deemed inoperable such as blood cancers, types of melanoma, brain tumors and advanced metastases.

Exemplifying agents of war by any means, these approaches make rather tenuous distinctions between tumor and normal tissues. Chemotherapy mainly targets the cancer cell’s capacity to rapidly divide or for certain genetic and/or metabolic changes, attributes also shared by normal cells. Thus, with these Rx, the normal and the cancerous overlap to various degrees, i.e., these measures are non-specific and therefore entail considerable collateral damage, hair loss, nausea, vomiting, diarrhea, hematopoietic cell loss and therefore greater susceptibility to infections, to mention just a few. Depending on age, general physiological condition, extent of cancer, often collateral damage from these Rx is more damaging (toxic) than even the tumor itself. Resistance is another problem with chemotherapy since cancer cells often adapt to it by developing genetic alterations.

Radiation and chemotherapy became the standard arsenal in the war on cancer by displacing an earlier approach. Exploring cancer therapy landscape through the lens of metaphors proffers a sense of the wheel coming full circle because interestingly enough, the century prior to the war on cancer was dominated by the Magic bullet (medicine) – Wikipedia metaphor as exemplified by Coley’s toxins – Wikipedia, bacterial components used by William Coley – Wikipedia to drive a ‘fever’ dominated response that would get rid of the tumor, a notion premised on the well-substantiated historical observation that spontaneous tumor remissions frequently accompanied acute febrile infections (2, 3, 4, 5, 6).

Having a similar underlying premise, to harness the body’s own immune response capacity to specifically target and eliminate cancer, cancer immunotherapy of the early 21st century is in a way Coley’s Toxins 2.0 (2). Whether cancer immunotherapy’s promise will ever be fully realized, when, for which cancers better than for others, promising answers to such questions still remain very much up in the air because the first chapter of this story is still being written.

  • Cancer immunotherapy only truly fulfills its mandate if it specifically targets the cancer. Even after years of search, few confirmed tumor-specific targets have been identified (7), the first imperative in triggering a truly tumor-specific immune response.
  • The nuts and bolts of human adaptive immune responses themselves, let alone of tumor-specific immune responses, aren’t yet fully deciphered, at least not to the extent of being able to manipulate them at will and that too with sanguine control and predictability. Thus for example, while antibodies against Immune checkpoint – Wikipedia inhibitors such as PD-1 (Programmed cell death protein 1 – Wikipedia) or PD-L1 (PD-L1 – Wikipedia) are among the most prized magic bullets of the day, they don’t fulfill the promise of specificity since they merely remove the brakes from lymphocytes, all lymphocytes that express PD-1, not just those present within tumors or those that can specifically target them. Thus, such Rx too carry the danger of collateral damage (7, 8).
  • The premise of the Chimeric antigen receptor – Wikipedia T cell immunotherapy approach is to take out the patient’s own T cell – Wikipedia and genetically engineer them to target the tumor. So far such approaches have only worked best against B cell – Wikipedia tumors (8, 9, 10). Such customized approaches would also likely be quite costly. How such costs will be managed and paid for if and when such Rx become mainstream is very much unclear.


1. Hanahan, Douglas. “Rethinking the war on cancer.” The Lancet 383.9916 (2014): 558-563. http://www.amec.eu/images/docume…

2. Tirumalai Kamala’s answer to What is the relationship between tumors and immune tolerance?

3. Cann, SA Hoption, J. P. Van Netten, and C. Van Netten. “Dr William Coley and tumour regression: a place in history or in the future.” Postgraduate medical journal 79.938 (2003): 672-680. https://www.ncbi.nlm.nih.gov/pmc…

4. Cann, SA Hoption, et al. “Spontaneous regression: a hidden treasure buried in time.” Medical hypotheses 58.2 (2002): 115-119.

5. Jessy, Thomas. “Immunity over inability: The spontaneous regression of cancer.” Journal of Natural Science, Biology and Medicine 2.1 (2011): 43. http://www.jnsbm.org/temp/JNatSc…

6. Kienle, Gunver S. “Fever in cancer treatment: Coley’s therapy and epidemiologic observations.” Global advances in Health and Medicine 1.1 (2012): 92-100. http://www.gahmj.com/doi/pdfplus…

7. Tirumalai Kamala’s answer to What are the key immunological markers of successful cancer immunotherapy?

8. Tirumalai Kamala’s answer to Is immunotherapy the closest thing we have to a cure for cancer?

9. Tirumalai Kamala’s answer to Why is CAR T not very effective on solid tumors?

10. Tirumalai Kamala’s answer to Why is chimeric antigen receptor better than just injecting anti-CD19 antibodies?


Why does ancestry matter for some medical decisions?


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Short answer: Most often ancestry and environment together mould disease risk. Ancestry alone directly confers disease risk less often, specifically so in cases where single gene mutations have outsize effects. Tay–Sachs disease – Wikipedia, Sickle-cell disease – Wikipedia and Cystic fibrosis – Wikipedia are some well-known examples of the latter.

Longer answer if interested

Why It’s So Difficult to Scientifically Tease Apart The Role Of Ancestry In Disease Causes

Human ancestry is the outcome of choice that liberally pockmarks both past and present with gratuitous violence and calamities since it’s a choice contrived to mediate and enforce differential access to resources. This choice manifests itself as caste, class, ethnicity, linguistic group, race, sect, tribe, etc. They’re social, i.e., explicitly political and cultural, not biological categories (1) but they end up influencing biology anyway. Here’s how.

  • Human societies tend to practice some form of social stratification or another. Over time, differential access to critical healthful life-sustaining resources such as quality education, health care and nutrition impact health, especially since social stratification-engendered privations tend to be experienced across generations.
  • Thus, health disparity is the outcome of historical inequities a particular social category experiences at the hands of what usually tends to be a long-prevailing hegemony.
  • Persistence of social stratifications across generations thus end up influencing biology by differentially influencing disease risk through the human-created construct of health disparity.
  • The ethically unambiguous and appropriate place for ancestry in biomedicine lies in helping to try to tease apart the relative contributions of health disparity versus genetic predisposition to disease.
  • However, it’s well nigh impossible to disentangle genetic predisposition from the many environmental confounders health disparities impose on disease risk. This stymies the effort to accurately parse and pinpoint the role of ancestry in many disease causes.

Problem is by tending to examine it devoid of its inherent sociological context, biomedicine artificially insulates ancestry. That leaves its role vulnerable to exploitation by the scientific flavor of the day, which these days is genomics.

The genomic era is like the proverbial hammer primed to seek and find nails everywhere so it’s become quite the fashion to privilege or attempt to privilege genetic predilection even in the case of multi-factorial diseases. This problem is also grounded in the fact that biomedicine has evolved an inherently siloed approach such that super-specialists, be they molecular biologists, geneticists, epidemiologists, public health researchers, etc., examine a given health issue through the lens of their training often without simultaneously attempting to look beyond, especially at the sociological context of disease. Thus, ancestry in the form of race/ethnicity has been and continues to be used as a proxy for genotype, albeit devoid of sociological context.

Official guidelines add to the problem, being inadequate and/or inaccurate and/or too riddled with ambiguity. For example, in the US, when classifying their research subjects, NIH-funded scientists are required to adhere to the racial and ethnic categories specified by the Office of Management and Budget (OMB)’s Directive #15, the so-called NIH Inclusion Policy and Guidelines (see below from 2),

‘American Indian or Alaska Native, Asian, Black or African American, Hawaiian or Pacific Islander, and White, Hispanic or Latino and Not Hispanic or Latino’

  • At least one survey of 18 NIH-funded scientists (11 men, 7 women) (3) reports these guidelines
    • Are applied too unquestioningly regardless of their utility or accuracy.
    • Are bureaucratic, one size fits all, catch all, inflexible, an example cited being Barack Obama. Categorize as Black or Caucasian?
    • Are difficult to comply with in geographic areas with few minority residents.
    • Force minority inclusion numbers that yield data lacking sufficient statistical power to provide meaningful results, i.e., difficult to generate representative and therefore generalizable data sets.
  • A study from the UK (4) reports similar flawed approach to study of racial/ethnic contribution to health and disease.

Genomic Approaches To Assess Ancestry Remain Inherently Flawed

Ancestry-informative marker – Wikipedia (AIMs) are inherently misleading and fraught with flawed assumptions (5, 6, 7, 8, 9). So what are AIMs? They’re population-specific markers, typically Single-nucleotide polymorphism – Wikipedia (SNPs) that occur at different frequencies in different populations. Since they’re shared by all humans, rather than presence or absence, analysis focuses on their frequency.

These days TV ads about learning one’s ancestry are plentiful. Just send a saliva sample in and get genetic genealogical results back. Too deceptively simple but also flawed to boot.

Obviously a test sample is compared to reference samples of Africans, Asians, Europeans, Native Americans, etc.

  • Who are the ‘reference populations’ for each race or ethnicity? To assign ‘purity’, ideally they should be groups that have remained immobile, isolated and endogamous for millennia till date since reference samples should represent ‘pure’ examples of different ethnic/racial categories, the standard against which the test sample would be compared.
  • Obviously such reference populations are an impossibility for much of the world’s population.
  • Instead ‘small groups of contemporary people‘ (9) are chosen as representative samples for a particular continent or region or ethnic, linguistic or tribal group.
  • Who is chosen? What are the criteria used to choose one individual and not another to supply the reference sample?
  • How many people from a given population (caste/ethnicity/linguistic group/race/sect/tribe) are sampled to develop a representative reference sample base?
  • How many are necessary? How many sufficient? 100, 1000, 10000, 100000, …?
  • How are thresholds set to determine how a given result is interpreted to include or exclude a particular population?

The answers to these scientifically crucial questions lie hidden behind the legal iron curtain of proprietary information preventing even the bare minimum in terms of rigorous science, namely to independently replicate and thereby verify (9). Thus, the widely advertised genomic categorization of ancestry using AIM touted by various commercial entities is more or less the outcome of a technological Sleight of hand – Wikipedia or two or three or more.

Diseases Found To Be More Prevalent in A Particular Race/Ethnicity Are Typically Monogenic

Tay–Sachs disease – Wikipedia & BRCA1 – Wikipedia mutations

At least 2 disease-causing/associated genes are more prevalent among Ashkenazi Jews – Wikipedia, infantile form of Tay–Sachs disease – Wikipedia and BRCA1 – Wikipedia mutations associated with higher risk for breast cancer.

Tay-Sachs is an autosomal recessive (Dominance (genetics) – Wikipedia) Genetic disorder – Wikipedia caused by a Mutation – Wikipedia in HEXA – Wikipedia gene on Chromosome 15 (human) – Wikipedia where children usually die by the age of 4. Disease is caused by the impaired function of lysosomal enzyme, Hex A.

Higher prevalence in these two instances is presumed to owe to the fact that the Jewish population descended from a small number of founders and remained largely endogamous (10).

Genetic counseling and prenatal screening are also advised for Cajuns – Wikipedia in Louisiana and French Canadians – Wikipedia since similar mutations have been identified among them.

Sickle-cell disease – Wikipedia

An adaptation to thwart malaria, sickle cell is more common among those with West African ancestry, specifically those with the globin S (betas) mutation (11).

Cystic fibrosis – Wikipedia

An autosomal recessive genetic disorder caused by mutations in Cystic fibrosis transmembrane conductance regulator – Wikipedia (CFTR) gene, it’s found to be more prevalent among people of European descent (12, 13).

More Accurate To Envision Ancestry As A Continuum Rather Than Groups Of Independently Evolving Discrete Units

An additional challenge is the fact that ancestry as a social construct is fast becoming less categorical as populations meet and meld as perhaps never before, even while they may have remained geographically isolated for varying lengths of time here and there in previous millennia.

Consider USA for example, a country that assesses race in its census.

  • The category ‘Other’ was first listed in the US 1910 census. Now listed as ‘Some Other Race’, in the 2010 census it had become the 3rd largest category after ‘White’ and ‘Black’ (14, 15).
  • In recent years, 15% of US marriages are between people of different ethnicities and races.
  • One in seven US infants is today born into a ethnically and/or racially mixed family.
  • A particular genomics example perfectly hints at the potentially vast complexity hidden underneath the surface of the race/ethnicity categories commonly used in our times. Complete genomic sequences of two famous European origin American scientists, James Watson – Wikipedia, Craig Venter – Wikipedia, and Seong-Jin Kim, an Asian-origin scientist, showed the former shared fewer (461000) SNPs with each other than they each shared with the Asian (569000 and 481000, respectively) (16, 17), something utterly unlikely to be discerned from physical appearance alone.
  • A genomic analysis of self-identified European Americans (n = 326), African Americans (n = 324) and Hispanics (n = 327) in Manhattan, New York, revealed such substantial ancestral mix in both African Americans and Hispanics, the authors concluded (18, emphasis mine; see figure below from 19).

‘A pooled analysis of the African Americans and Hispanics from NY demonstrated a broad continuum of ancestral origin making classification by race/ethnicity uninformative

  • Needless to say, such melding happened or is happening faster in some countries and especially faster in large cosmopolitan cities. Largely the mix of Native American (Amerindian), European (mainly Portugal) and African, Brazil is a country that famously embodies more than anything else racial ambiguity (20).

Finally, data also suggests higher genetic diversity within races (85%) rather than between races (15%) (21), which only further undermines the practical value of race/ethnicity in dissecting disease risks and causes at group level. This is especially the case for Africa, the continent with the greatest degree of genetic variation (see below from 22 quoted in 9).

‘For many regions of the human genome, there are more variants found among people of Africa than found among people in the rest of the world. This is probably because humans have resided in Africa for much longer than we have resided any place else in the world, so our species had time to accumulate genetic changes within the people in Africa.’

In other words, race/ethnic categories such as African, Asian, Caucasian, Hispanic, Latino, White poorly predict human biological similarity and diversity. As Cuban geneticist Dr. Beatriz Marcheco put it (23),

‘The classic mirror reflects skin color; but the DNA mirror reflects our common ancestors’

Race/ethnicity are thus becoming less and less relevant as proxies for genotype or rather the discernible truth about ancestry lies more and more between rather than within these commonly accepted social categories.

Some Examples Where Misapplication Of Ancestry Obfuscates Rather Than Clarifies Cause For Disease Predisposition

Hypertension – Wikipedia

Hypertension and its clinical outcomes such as heart disease, stroke and renal failure are so much more prevalent among African Americans that a racial predisposition ascribed back in the 20th century still erroneously prevails as a dogma (19). Erroneous because large studies comparing West African, Caribbean and American Blacks show high prevalence of hypertension among African Americans is an outlier, being lower among other Blacks (24, also see figures below from 19).

  • Low blood pressures in rural West Africa that change little with age.
  • Similar average blood pressures to White North Americans among West Indian Blacks.
  • Higher blood pressure among urban African Americans from Maywood in Chicago.
  • Obesity, high sodium and low potassium intake, the lifestyle factors known to increase blood pressure matched blood pressure averages among these three groups of Blacks. In other words, abrupt diet and lifestyle changes better explain hypertension rates among African Americans.
  • The specific example of hypertension reveals how difficult it is to assert which is more consequential, nature or environment, simply because it’s practically impossible to observe the obverse, people from Africa leading a US lifestyle without experiencing either racial or class inequities.

An even larger study of 85000 subjects including Whites from 8 surveys in the US, Canada and Europe, and 3 surveys among Blacks in Africa, the Caribbean and the US, showed preventable causes of hypertension overlap across races and ethnicities (25). Meantime a much smaller (n = 1056) US study (26) on African Americans served as the basis for the US FDA’s approval of a hypertension drug, BiDil (Isosorbide dinitrate/hydralazine – Wikipedia), supposedly designed for African Americans (27, 28).

Moral? Far from vaunted impartiality, an example of how economics (patents) and politics (tokenism) trump science.

The issue of causality is further complicated by the fact that blood pressure regulation is extremely complex and thus unlikely to be explained by genes alone. One of the largest blood pressure GWAS (Genome-wide association study – Wikipedia) examined 200000 subjects and found the 29 genetic markers most strongly associated with blood pressure could only account for 23% of risk for hypertension (29). Since lifetime hypertension risk in the US is ~85%, this means genomics has so far provided little by way of predictive value. Cherry on the cake is blood pressure susceptibility variants are similar among subjects with African, Asian, European and South Asian ancestry (29).


Judenkrankheit or Jews’ disease, as recently as 1904, this is how physicians in the US and Europe tended to perceive diabetes (see below from 30).

‘THERE IS NO RACE, WHICH is so subject to diabetes as the Jews,” wrote W. H. Thomas in 1904 in the eugenically obsessed language of his day. Thomas, a New York physician, was voicing an almost universally held belief in the United States that of all the “races,” Jews had the greatest likelihood of developing diabetes. At the same time, most members of the medical community considered the prevalence of diabetes among Blacks to be unusually low. In the words of a Johns Hopkins physician in 1898, “Diabetes is a rare disease in the colored race”.’

Fast forward 100 or so years and in the US,

  • Diabetes rates have sky-rocketed among African Americans to 2X those in Whites while they’ve declined among Jews.
  • Today, Pima people – Wikipedia have the highest rate of Diabetes mellitus type 2 – Wikipedia in the world and of course, since they form a homogenous group, unsurprisingly, mapping their genetics has become an intense focus of research interest. Laughable weren’t it so soul-crushingly tragic for the following reasons.
    • Before the advent of European American encroachers on their land after the American Civil War, the Pima had a reputation for excellent farming and lived independent, autonomous lives along the Gila river on lands presently known as Arizona (31). They even called themselves Akimel O’Tham or the River People.
    • White settlers directly competing for irrigation rights, the 1877 Desert Land Act which ‘required bona fide application of water to the land to obtain title‘, new dams that re-directed water away from traditional Pima farms (9), all these human-made interventions forced Pima to abandon their age-old ways of life in a matter of a few decades.
    • A health survey in 1902 found a single case of diabetes among the Pima. By the 1930s, this number had increased to >500.
    • With the completed Coolidge Dam not sending enough water their way, their traditional farming essentially going bust, the Pima quickly sank into abject poverty and started dying early.
    • Like a benighted god the US federal government rode to the rescue, providing Pima free government surplus food and what food it is! Refined white flour, processed cheese, lard, candy, chips, refined sugar, grape juice, macaroni when the Pima’s original diet consisted of (32 quoted in 9).

‘…seeds, buds, fruits and joints of various cacti; seeds of the mesquite, ironwood, palo verde, amaranth, salt bush, lambsquarter, horsebean and squash; acorns and other wild nuts; . . . roots and bulbs of the sandroot (wild potato) . . . deer, antelope, ..rabbits, quail, dove, wild ducks, wild turkey.’

    • By the mid-20th century, this ancient diet had been entirely supplanted by boxes and boxes of macaroni and cheese. Where Pima dietary fat intake was 15% in the 1890s, it had increased to an incredible 40% by the 1990s (33).
    • And yet it apparently sounds eminently reasonable and soundly scientific to probe and probe Pima genetics to sincerely try to understand their sky-high rates of diabetes these days (34). An exercise in callousness, ignorance, stupidity or all three.

At this point it becomes necessary to ask whether it is really reasonable to highlight ancestry as a mechanistic contributing factor to diabetes when rates can be evidently higher than the norm and drop to average in just 100 years in one group while they increase and increase in two other groups over the same period?

A simpler explanation is how abrupt diet and lifestyle changes impact life trajectories and chronic disease risk in the short-term. Plausible proof? Traditional rural dwelling societies practicing ‘traditional culture’ have vanishingly low rates of diabetes compared to their counterparts newly adapted to ‘westernized’ diets and lifestyle (see below from 9).

Attitudes ranging from the cavalier to sheer ineptitude suggest the prevailing culture of biomedical science is ill-equipped to deal with divisive political topics such as ancestry. Science exists within society, not outside of it and the prevalent untenable allegiance to the implausible notion of striving to be perceived as ahistorical and apolitical ill-serves biomedical science and society alike.

And so we’re back where we started, namely unable to parse environmental and genetic factors in assigning causes to many, especially multi-factorial diseases. Impasse largely owing to biomedical scientists ignoring sociology when probing the role of ancestry, specifically race/ethnicity, in diseases.


1. Schwartz, Robert S. “Racial profiling in medical research.” New England Journal of Medicine 344.18 (2001): 1392-1393.

2. Revisions to the Standards for the Classification of Federal Data on Race and Ethnicity

3. Knerr, Sarah, Dawn Wayman, and Vence L. Bonham. “Inclusion of racial and ethnic minorities in genetic research: advance the spirit by changing the rules?.” The Journal of Law, Medicine & Ethics 39.3 (2011): 502-512. https://www.ncbi.nlm.nih.gov/pmc…

4. Smart, Andrew, et al. “Social inclusivity vs analytical acuity? A qualitative study of UK researchers regarding the inclusion of minority ethnic groups in biobanks.” Medical Law International 9.2 (2008): 169-190.

5. Fullwiley, Duana. “The molecularization of race: institutionalizing human difference in pharmacogenetics practice.” Science as Culture 16.1 (2007): 1-30.

6. Fullwiley, Duana. “The Biologistical Construction Of RaceAdmixture’Technology And The New Genetic Medicine.” Social studies of science 38.5 (2008): 695-735. http://beck2.med.harvard.edu/wee…

7. TallBear, Kimberly. Native American DNA: Tribal belonging and the false promise of genetic science. 2013.

8. Fujimura, Joan H., and Ramya Rajagopalan. “Different differences: The use of’genetic ancestry’versus race in biomedical human genetic research.” Social Studies of Science (2010): 0306312710379170. http://www.ssc.wisc.edu/soc/facu…

9. Duster, Troy. “A post‐genomic surprise. The molecular reinscription of race in science, law and medicine.” The British journal of sociology 66.1 (2015): 1-27. http://geneticsandsociety.org/do…

10. Burchard, Esteban González, et al. “The importance of race and ethnic background in biomedical research and clinical practice.” New England Journal of Medicine 348.12 (2003): 1170-1175. https://www.researchgate.net/pro…

11. Grosse, Scott D., et al. “Sickle cell disease in Africa: a neglected cause of early childhood mortality.” American journal of preventive medicine 41.6 (2011): S398-S405. https://www.researchgate.net/pro…

12. Cutting, Garry R., et al. “Analysis of four diverse population groups indicates that a subset of cystic fibrosis mutations occur in common among Caucasians.” American journal of human genetics 50.6 (1992): 1185. https://www.ncbi.nlm.nih.gov/pmc…

13. Zvereff, Val V., et al. “Cystic fibrosis carrier screening in a North American population.” Genetics in Medicine 16.7 (2013): 539-546.

14. Black? White? Asian? More Young Americans Choose All of the Above. The New York Times, Susan Saulny, January 29, 2011. More Young Americans Identify as Mixed Race

15. The Rise of the American ‘Others’. The Atlantic, Sowmiya Ashok, August 27, 2016. More Americans Are Selecting “Some Other Race” on U.S. Census Forms

16. Levy, Samuel, et al. “The diploid genome sequence of an individual human.” PLoS Biol 5.10 (2007): e254. http://journals.plos.org/plosbio…

17. Ahn, Sung-Min, et al. “The first Korean genome sequence and analysis: full genome sequencing for a socio-ethnic group.” Genome research 19.9 (2009): 1622-1629. Full genome sequencing for a socio-ethnic group

18. Tayo, Bamidele O., et al. “Genetic background of patients from a university medical center in Manhattan: implications for personalized medicine.” PLoS One 6.5 (2011): e19166. http://journals.plos.org/plosone…

19. Cooper, Richard S. “Race in biological and biomedical research.” Cold Spring Harbor perspectives in medicine 3.11 (2013): a008573. Race in Biological and Biomedical Research

20. Santos, Hadassa C., et al. “A minimum set of ancestry informative markers for determining admixture proportions in a mixed American population: the Brazilian set.” European Journal of Human Genetics (2015). http://www.nature.com/ejhg/journ…

21. Mersha, Tesfaye B., and Tilahun Abebe. “Self-reported race/ethnicity in the age of genomic research: its potential impact on understanding health disparities.” Human genomics 9.1 (2015): 1. Self-reported race/ethnicity in the age of genomic research: its potential impact on understanding health disparities

22. Ossorio, Pilar N. “Myth and mystification: The science and race of IQ.” Race and the Genetic Revolution: Science, Myth, and Culture (2009).

23. Genes Prove Mixed Ancestry of All Cubans: Interview Director, National Medical Genetics Center, Havana

24. Cooper, R., et al. “Hypertension prevalence in seven populations of African origin.” Am J Public Health 87 (1997): 160-168. http://ajph.aphapublications.org…

25. Cooper, Richard S., et al. “An international comparative study of blood pressure in populations of European vs. African descent.” BMC medicine 3.1 (2005): 1. An international comparative study of blood pressure in populations of European vs. African descent

26. Taylor, Anne L., et al. “Combination of isosorbide dinitrate and hydralazine in blacks with heart failure.” New England Journal of Medicine 351.20 (2004): 2049-2057. http://www.nejm.org/doi/pdf/10.1…

27. Roberts, Dorothy. Fatal invention: How science, politics, and big business re-create race in the twenty-first century. The New Press, 2013.

28. Kahn, Jonathan. Race in a bottle: The story of BiDil and racialized medicine in a post-genomic age. Columbia University Press, 2013.

29. International Consortium for Blood Pressure Genome-Wide Association Studies. “Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk.” Nature 478.7367 (2011): 103-109. https://csg.sph.umich.edu/boehnk…

30. Tuchman, Arleen Marcia. “Diabetes and race a historical perspective.” American journal of public health 101.1 (2011): 24-33. https://www.researchgate.net/pro…

31. Dejong, David H. “Abandoned Little by Little:” The 1914 Pima Adjudication Survey, Water Deprivation, and Farming on the Pima Reservation.” Agricultural History (2007): 36-69.

32. Mark, Albyn K. “Ecological Change in the History of the Papago Indian Population.” Master of Arts thesis, University of Arizona (1960).

33. Demouy, J., et al. “The Pima Indians: Pathfinders of Health. Bethesda, MD: Nat. Inst.” Diabetes Digestive Kidney Diseases (1995).

34. Pearson, Ewan R. “Dissecting the etiology of type 2 diabetes in the Pima Indian population.” Diabetes 64.12 (2015): 3993-3995. http://diabetes.diabetesjournals…


Is the greater sickness in daycare outweighed by kids getting less sick when they start school?


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Many epidemiological studies have examined the link between Day care – Wikipedia attendance and illness. Problem is by a priori choosing to document/assess/quantify daycare-associated infections makes such examinations less neutral since they equate infections, an undesirable event, with microbial exposure, a plausibly neutral event. Thus, rather than daycare attendance being a proxy for increased microbial exposure per se which is what they actually are, they’ve largely been examined as a proxy for infections.

However, in the age of Human microbiota – Wikipedia, we increasingly appreciate that microbial exposure and infections are decidedly not the same. Rather, more frequent, more complex and greater microbial exposure in childhood may even be beneficial in the long-term. Thus, synthesizing the vast amount of information generated by these numerous epidemiological studies yields a decidedly mixed bag since the intent of many older studies is largely distorted by the flawed conflation of microbes and infections.

Keeping that glaring caveat in mind, with respect to protection, epidemiological data from various countries suggest daycare attendance reduces risk for ALL (Acute lymphoblastic leukemia – Wikipedia) and other forms of common Childhood leukemia – Wikipedia (CL), and for common cold and ear infections. On the other hand, data also shows children in daycare can experience more frequent acute respiratory tract infections (RTI) as well as greater number of doctor visits and antibiotic prescription rates compared to those in home care.

Childhood Infection Risks: Daycare Attendance One Of Many Factors

Over the course of the 20th century and beyond, more and more children the world over attend formal daycare. For example, >60% of US children aged 0 to 6 (1). Consequences of this early life difference are less clear because there are many other important confounding factors. These include

  • Age of daycare entry. Those who enter daycare after 1 year of age tend to get less sick.
  • Whether or not the child was born at term. Pre-term birth increases infection risk.
  • Whether the child was born through vaginal or C-section delivery. Lower risk of later life allergies and autoimmunities with vaginal delivery.
  • Whether or not the child has older siblings. More older siblings, lower the risk of later life allergies and autoimmunities (the original hygiene hypothesis). David P. Strahan followed >17000 children for >23 years and in 1989 first proposed what eventually became known as the Hygiene hypothesis – Wikipedia, i.e., larger the family and more older siblings a child had (birth order), lower their risk for Allergic rhinitis – Wikipedia (2).
  • Whether the child was exclusively breast-fed or not, at least during the 1st 3 to 4 months of life. The former are better protected against infections, especially upper respiratory tract infections.
  • Whether the child is exposed or not to pets at home. Pet exposure seems to protect against some allergies and infections.
  • Parental age. Younger better for the child, presumably because younger egg and sperm carry fewer deleterious genetic and epigenetic changes.
  • Parental life-history. Parental smoking, especially during pregnancy, increases child’s risk for asthma and allergies.

Beneficial Consequences Of Daycare Exposure

Epidemiological studies from Canada (3), Germany (4), and the US (5) suggest early life daycare is associated with protection against common cold and ear infection during some of the later school years.

Daycare Exposure & Protection From Childhood Cancer

According to the ‘delayed infection hypothesis’ proposed by Mel Greaves, early life ‘common’ infections could be protective against ALL, the idea being limited infections at this early life stage increase risk of abnormal immune responses to the same infections later on and some of these abnormal immune responses could trigger the B cell mutations characteristic of ALL (6, 7, 8). Even though childhood leukemias have well-defined genetic susceptibility, according to Greaves, rather than infections per se, their timing crucially impacts ALL risk. Earlier the infections, lower the risk. The underlying idea is the newborn immune system is evolutionarily programmed to ‘learn’ through infectious life history early in life. Of course, now it’s more appropriate to revise this view and argue instead that sequence, frequency and timing of exposure to microbes in early life is crucial to well-regulated immune function throughout life.

Indeed a preponderance of epidemiological evidence suggests children who attend daycare starting early in life are less likely to develop ALL.

  • Using daycare attendance as a proxy for exposure to infectious agents, a Meta-analysis – Wikipedia of 14 Case-control study – Wikipedia published in English found that daycare attendance, especially among children less than 2 years of age reduces ALL risk (9). The fact that these studies were conducted by different teams in different countries (1 each in Canada, Denmark, Germany, New Zealand and Hong Kong, 2 each in France, Greece, UK, and 3 in USA) only strengthens the data set.
  • Another meta-analysis of largely the same studies also concluded it reduced risk of childhood leukemia (10).
  • Another thorough meta-analysis assessed a total of 11 case-control studies (1 each from Australia, Canada, Greece, Italy, New Zealand, UK, 2 from USA, and 3 from France), i.e., a total of 7399 ALL cases and 11181 controls aged 2 to 14 years (11). It too found an inverse association between ALL and daycare attendance, especially lower risk for children who started daycare at an earlier age.

The fact that these analyses found ALL risk didn’t correlate with specific infections in infancy suggests that risk mitigation may be more a case of more exposure to diverse microbes and maybe the body learning to make well-regulated immune responses to them rather than exposure to specific infections per se.

Adverse Consequences Of Daycare Exposure

Several studies have shown children in daycare experience higher infection rates compared to those not in daycare.

  • As illustrative examples, epidemiological studies from Australia (12), Denmark (13, 14), Greenland (15), Italy (16) and Canada (17) show children in daycare experience more frequent acute respiratory tract infections (RTI) compared to those in home care.
  • However, some studies (18, 19) suggest such differences are most pronounced in the first two years of life and tend to fade away to no difference between the two groups by the 3rd year of life.
  • Also, one study (14) suggests risks were much higher for babies living in homes with no other children.
  • Epidemiological studies from Denmark (20), Sweden (21, 22), Spain (23), USA (24), the Netherlands (25) have noted greater doctor visits and antibiotic prescription rates for daycare attendees. In fact, already back in 1990, a US study had noted 2.4 to 3.6 times higher antibiotic prescription rates for children in daycare compared to those cared for at home (26).

Clearly, these data suggest care-givers and physicians haven’t yet heeded the clarion call to limit antibiotic use. Clearly, public health officials and scientists, microbiologists in particular, need to exert more effort in ensuring this counter-productive practice abates.


1. America’s Children in Brief: Key National Indicators of Well-Being, 2016

2. Strachan, David P. “Hay fever, hygiene, and household size.” BMJ: British Medical Journal 299.6710 (1989): 1259. http://www.ncbi.nlm.nih.gov/pmc/…

3. Côté, Sylvana M., et al. “Short-and long-term risk of infections as a function of group child care attendance: an 8-year population-based study.” Archives of pediatrics & adolescent medicine 164.12 (2010): 1132-1137. http://www.fyiliving.com/wp-cont…

4. Zutavern, Anne, et al. “Day care in relation to respiratory‐tract and gastrointestinal infections in a German birth cohort study.” Acta paediatrica 96.10 (2007): 1494-1499.

5. Ball, Thomas M., et al. “Influence of Attendance at Day Care on the Common Cold From Birth Through 13 Years of Age.” Archives of Pediatrics & Adolescent Medicine 156.2 (2002): 121. Influence of Attendance at Day Care on the Common Cold From Birth Through 13 Years of Age

6. Greaves, M. F. “Speculations on the cause of childhood acute lymphoblastic leukemia.” Leukemia 2.2 (1988): 120-125.

7. Greaves, Mel. “Childhood leukaemia.” British Medical Journal 324.7332 (2002): 283. http://pubmedcentralcanada.ca/pm…

8. Greaves, Mel. “Infection, immune responses and the aetiology of childhood leukaemia.” Nature Reviews Cancer 6.3 (2006): 193-203. http://www.ittumori.it/IttSanita…

9. Urayama, Kevin Y., et al. “A meta-analysis of the association between day-care attendance and childhood acute lymphoblastic leukaemia.” International journal of epidemiology 39.3 (2010): 718-732. A meta-analysis of the association between day-care attendance and childhood acute lymphoblastic leukaemia

10. Maia, Raquel da Rocha Paiva, and Victor Wunsch Filho. “Infection and childhood leukemia: review of evidence.” Revista de saude publica 47.6 (2013): 1172-1185. http://www.scielosp.org/pdf/rsp/…

11. Rudant, Jérémie, et al. “Childhood acute lymphoblastic leukemia and indicators of early immune stimulation: a childhood leukemia international consortium study.” American journal of epidemiology (2015): kwu298. A Childhood Leukemia International Consortium Study

12. Kusel, Merci MH, et al. “Occurrence and management of acute respiratory illnesses in early childhood.” Journal of paediatrics and child health 43.3 (2007): 139-146. https://www.researchgate.net/pro…

13. von Linstow, Marie‐Louise, et al. “Acute respiratory symptoms and general illness during the first year of life: A population‐based birth cohort study.” Pediatric pulmonology 43.6 (2008): 584-593. https://www.researchgate.net/pro…

14. Kamper-Jørgensen, Mads, et al. “Population-based study of the impact of childcare attendance on hospitalizations for acute respiratory infections.” Pediatrics 118.4 (2006): 1439-1446.

15. Koch, Anders, et al. “Risk factors for acute respiratory tract infections in young Greenlandic children.” American Journal of Epidemiology 158.4 (2003): 374-384. Risk Factors for Acute Respiratory Tract Infections in Young Greenlandic Children

16. De Martino, M., and S. Ballotti. “The child with recurrent respiratory infections: normal or not?.” Pediatric Allergy and Immunology 18.s18 (2007): 13-18.

17. Dales, Robert E., et al. “Respiratory illness in children attending daycare.” Pediatric pulmonology 38.1 (2004): 64-69.

18. Bradley, Robert. “Child care and common communicable illnesses.” Archives of Pediatrics and Adolescent Medicine 155.4 (2001): 481-488).

19. Lu, N., et al. “Child day care risks of common infectious diseases revisited.” Child: care, health and development 30.4 (2004): 361-368.

20. Thrane, Nana, et al. “Influence of day care attendance on the use of systemic antibiotics in 0-to 2-year-old children.” Pediatrics 107.5 (2001): e76-e76. http://pediatrics.aappublication…

21. Hjern, A., et al. “Socio‐economic differences in daycare arrangements and use of medical care and antibiotics in Swedish preschool children.” Acta Paediatrica 89.10 (2000): 1250-1256.

22. Hedin, Katarina, et al. “Physician consultation and antibiotic prescription in Swedish infants: population‐based comparison of group daycare and home care.” Acta Paediatrica 96.7 (2007): 1059-1063.

23. del Castillo‐Aguas, Guadalupe, et al. “Infectious morbidity and resource use in children under 2 years old at childcare centres.” Journal of Paediatrics and Child Health (2016).

24. Silverstein, Michael, Anne E. Sales, and Thomas D. Koepsell. “Health care utilization and expenditures associated with child care attendance: a nationally representative sample.” Pediatrics 111.4 (2003): e371-e375. https://www.researchgate.net/pro…

25. de Hoog, Marieke LA, et al. “Impact of early daycare on healthcare resource use related to upper respiratory tract infections during childhood: prospective WHISTLER cohort study.” BMC medicine 12.1 (2014): 1. Impact of early daycare on healthcare resource use related to upper respiratory tract infections during childhood: prospective WHISTLER cohort study

26. Reves, R. R., and J. Jones. “Antibiotic use and resistance patterns in day care centers.” Semin Pediatr Infect Dis 1 (1990): 212-221.


What is the current state of the HIV cure?


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Recent Documented Cases Of HIV Cure And Near Misses

  • As of 2016, there is one confirmed case of HIV cure, that of Timothy Ray Brown – Wikipedia, one of two The Berlin Patient – Wikipedia. Diagnosed with HIV in 1995 while studying in Berlin, Germany, in 2007 he underwent Hematopoietic stem cell transplantation – Wikipedia from a donor with a rare CCR5 – Wikipedia homozygous delta32 mutation, homozygous meaning in both alleles (1, 2). The CCR5-delta 32 mutation renders a person resistant to HIV infection by disabling its entry into susceptible cells that express CCR5 on their cell surface. Timothy Ray Brown is considered completely cured, sterile cure as in no longer harboring HIV in his body. He is off of antiretroviral therapy (ART), Management of HIV/AIDS – Wikipedia.
  • Two Boston patients were also treated using stem cell transplantation though not from CCR5-delta32 homozygous donors. However, they experienced relapse (3).
  • The VISCONTI cohort is a group of 14 French patients who started ART within the first few weeks of getting HIV infected (4). In remission now for 12 years and counting, they’re considered ‘functionally’ cured of HIV, i.e., continuing to harbor HIV but no longer needing to take ART. The same group’s also reported similar outcome for a perinatally infected case treated from birth for at least the first 5 years. This patient remained in remission when re-examined at length at 18 years of age (5).
  • The Mississippi baby – Wikipedia is a case diagnosed as HIV positive at birth and treated with powerful ART starting from the first hours of life. However, s/he suffered a relapse in 2014 (6), this after ART for the 1st 18 months followed by a full 27 months of remission.

Clearly, HIV’s ability to become latent, Virus latency – Wikipedia, is a challenge both for effective anti-HIV immunity and for accurate assessments of disease status. In such a situation a person may appear aviremic, i.e., no virus detected in circulation, and yet have sufficient virus within latently infected cells to trigger relapse.

Main Obstacle To HIV Cure: Virus latency – Wikipedia

CD4 T cells (T helper cell – Wikipedia) and other Myeloid – Wikipedia, i.e., bone-marrow-derived, cells are the main targets of HIV infection. Problem is replication-competent HIV provirus can persist in resting CD4 T cells and maybe in these other cells as well. In other words, virus latency.

Accurately assess latent virus load, reverse it and help sustain effective anti-HIV immunity to completely eradicate HIV, these are the key steps necessary to mediate ‘cure’ in HIV patients on ART.

To devise a cure in those on ART, an accurate measure of virus load is key. However, it’s technically difficult to do so.

  • Methods that measure integrated HIV virus DNA tend to overestimate latency since they can’t discriminate replication-defective HIV DNA (7).
  • Similar drawback attends methods that measure HIV RNA (8).
  • While the quantitative viral outgrowth assay (QVOA) is far better at measuring frequency of truly latent, replication-competent provirus (9, 10), it tends to underestimate the size of the latent reservoir (8).

Latency-reversing Agents (LRA) such as Disulfiram – Wikipedia, Panobinostat – Wikipedia, Romidepsin – Wikipedia, Valproate – Wikipedia, and Vorinostat – Wikipedia interfere with latency process. However, several technical challenges in accurately assessing their capacity to inhibit and/or reverse latency include

  • Data that suggests each one may reverse latency in only a subset of HIV-infected cells.
  • How accurately do data from animal models or in vitro studies recapitulate the situation in human patients?

Such doubts suggest combinations of LRAs may be needed to fully reverse latency. Obviously, if latency’s successfully reversed, anti-HIV immunity needs to kick in to completely eliminate the virus.

  • However, is that possible for HIV patients on ART?
  • Virus may persist as latent provirus in rare cells widely distributed within the body. How often does virus replicate under these conditions and does such replication engender sufficient antigen expression and presentation to trigger an effective immune response?
  • Were such reactivation even to occur, wouldn’t anti-HIV immunity have waned in the meantime?
  • Couldn’t such immunity also be depleted and/or dysfunctional as a result of HIV infection anyway? For e.g., latent virus sequestered within resting CD4 T cells does contain some CD8 T cell escape mutations (11), an important escape mechanism since CD8 T cells (Cytotoxic T cell – Wikipedia) are among the most effective in killing virus-infected cells.
  • Those on ART tend to have low frequencies of HIV-specific CD8 T cells (12). This means there’s a need to support efficient anti-HIV immunity in such patients through the use of therapeutic vaccines. However, none are as yet available. Two major obstacles they face
    • To work even in the presence of ART. After all, to stop ART would be too risky.
    • Identify epitopes relevant during latency reversal.

A recent surprising study was a monkey study that showed ‘cure’ using Vedolizumab – Wikipedia, a monoclonal antibody (mAb) against a gut-specific cell-surface receptor, a4b7 (13, 14). However, at this stage, this is really preliminary data. Has to be replicated by other groups then tested in humans. Another caveat as well, namely, it used not HIV but rather SIV, Simian immunodeficiency virus – Wikipedia.

Thus, current path to HIV cure is far from straightforward. However, cases such as the Berlin patient and the VISCONTI cohort have at least cure put on the map, something that wasn’t even a consideration until fairly recently.


1. Hütter, Gero, et al. “Long-term control of HIV by CCR5 Delta32/Delta32 stem-cell transplantation.” New England Journal of Medicine 360.7 (2009): 692-698. http://www.nejm.org/doi/pdf/10.1…

2. Yukl, Steven A., et al. “Challenges in detecting HIV persistence during potentially curative interventions: a study of the Berlin patient.” PLoS Pathog 9.5 (2013): e1003347. http://journals.plos.org/plospat…

3. Henrich, Timothy J., et al. “Antiretroviral-free HIV-1 remission and viral rebound after allogeneic stem cell transplantation: report of 2 cases.” Annals of internal medicine 161.5 (2014): 319-327. https://www.researchgate.net/pro…

4. Sáez-Cirión, Asier, et al. “Post-treatment HIV-1 controllers with a long-term virological remission after the interruption of early initiated antiretroviral therapy ANRS VISCONTI Study.” PLoS Pathog 9.3 (2013): e1003211. http://journals.plos.org/plospat…

5. Frange, Pierre, et al. “HIV-1 virological remission lasting more than 12 years after interruption of early antiretroviral therapy in a perinatally infected teenager enrolled in the French ANRS EPF-CO10 paediatric cohort: a case report.” The Lancet HIV 3.1 (2016): e49-e54. http://www.natap.org/2015/HIV/PI…

6. Luzuriaga, Katherine, et al. “Viremic relapse after HIV-1 remission in a perinatally infected child.” New England Journal of Medicine 372.8 (2015): 786-788. http://www.nejm.org/doi/pdf/10.1…

7. Eriksson, Susanne, et al. “Comparative analysis of measures of viral reservoirs in HIV-1 eradication studies.” PLoS Pathog 9.2 (2013): e1003174. http://journals.plos.org/plospat…

8. Ho, Ya-Chi, et al. “Replication-competent noninduced proviruses in the latent reservoir increase barrier to HIV-1 cure.” Cell 155.3 (2013): 540-551. http://www.cell.com/cell/pdf/S00…

9. Siliciano, Janet D., et al. “Long-term follow-up studies confirm the stability of the latent reservoir for HIV-1 in resting CD4+ T cells.” Nature medicine 9.6 (2003): 727-728.

10. Crooks, Amanda M., et al. “Precise quantitation of the latent HIV-1 reservoir: implications for eradication strategies.” Journal of Infectious Diseases 212.9 (2015): 1361-1365. Implications for Eradication Strategies

11. Deng, Kai, et al. “Broad CTL response is required to clear latent HIV-1 due to dominance of escape mutations.” Nature 517.7534 (2015): 381-385. https://www.researchgate.net/pro…

12. Shan, Liang, et al. “Stimulation of HIV-1-specific cytolytic T lymphocytes facilitates elimination of latent viral reservoir after virus reactivation.” Immunity 36.3 (2012): 491-501. http://www.cell.com/immunity/pdf…

13. Byrareddy, Siddappa N., et al. “Sustained virologic control in SIV+ macaques after antiretroviral and α4β7 antibody therapy.” Science 354.6309 (2016): 197-202.

14. Cohen, Jon. “Surprising treatment ‘cures’ monkey HIV infection.” (2016): 157-158.

Further reading

Margolis, David M., et al. “Latency reversal and viral clearance to cure HIV-1.” Science 353.6297 (2016): aaf6517.


Why does the same virus cause one symptom in one person and another in a different one?



Question details: For example, for a GI bug, some of my kids will have vomiting, others will have diarrhea, or one unfortunate one will end up with both.

Differences in either genetics and/or exposure (dose) might lead siblings to have different symptoms to the same illness and the two reasons aren’t mutually exclusive.

No two individuals have the exact same biomedical profile, not even identical (monozygotic) twins*.

These differences form the essential basis for differences in symptoms and severity for the same illness among individuals. However, apart from genetic and immunity differences between hosts, differences in severity of exposure could also explain why this happens, with the sibling with greatest exposure, i.e., dose, more likely to suffer the most severe symptoms. The same sibling repeatedly suffering more severe outcomes for shared illnesses would suggest greater likelihood of underlying genetic difference(s) being at play.

To successfully infect means to first successfully enter a cell, replicate inside it and finally spread to neighboring cells and beyond. Each disease-causing microbe thus evolves to express molecules that mimic those that bind certain cell-surface receptors and this process then serves as its entryway into a particular cell. This preference of a particular microbe to infect certain tissues is its Tissue tropism – Wikipedia. For e.g., the cold virus, Rhinovirus – Wikipedia, and Influenza – Wikipedia primarily target the epithelial cells of the upper respiratory tract, Viral hepatitis – Wikipedia the liver, etc.

Though cells obviously differ in the panoply of what they express on their surface, there’s also considerable overlap between different tissues and these overlaps differ between individuals, both in strength and variety. Thus, given the processes of genetic and somatic recombination, and epigenetics, even related individuals differ in their relative susceptibility to various disease-causing microbes.

This is why some people might get Zika fever – Wikipedia and never know since their symptoms stayed so mild while others may get fever (systemic involvement), joint pain (musculoskeletal) and rashes on their torso (skin) while still others could develop debilitating muscle weakness and pain, Guillain–Barré syndrome – Wikipedia, symptoms that could even be life-threatening.

With a GI tract illness, only vomiting suggests a stringently self-limiting infection that stayed restricted to the upper GI tract, diarrhea suggests a spread into the lower GI tract while both vomiting and diarrhea suggest the most severe outcome, i.e., persistent infection effects in both upper and lower GI tract. Again, simple dose differences in initial exposure could be a trivial explanation for a one-off outcome difference of this kind.

* Tim D. Spector’s long-term studies on identical and fraternal twins show that ‘twins rarely die of the same disease‘ (Why do identical twins end up having such different lives?).


How do T-cells recognize foreign MHC molecules?


Question details: How do T-cells recognize foreign MHC molecules (and get activated thereafter), when the T-cells are positively selected in the thymus to recognize only own MHC (and foreign peptide, and get activated thereafter)?

T cell development occurs in the Thymus – Wikipedia. T cells bind pMHC (peptides bound to Major histocompatibility complex – Wikipedia (MHC)). In fact they can bind peptides presented both by MHC expressed by cells in the Thymus – Wikipedia of the body in which they develop, MHC restriction – Wikipedia, as well as by MHC expressed in genetically non-identical transplants, for e.g. during Allotransplantation – Wikipedia (1) *. The latter phenomenon is called alloreactivity.

As-Yet Unresolved Conundrums About T cell Development

The remarkable feature about the repertoire of B and T cells bearing unique B-cell receptor – Wikipedia (BCRs) and TCRs is they’re generated blind, i.e., in the absence of foreknowledge of antigens and antigen-derived peptides a person may encounter and need to respond to through their lifetime. T cell repertoire refers to the diversity of clonotypic T cells expressing unique TCRs, clonotypic meaning when a given T cell divides it creates a multitude of T cells bearing that same unique somatically generated TCR (Somatic recombination – Wikipedia).

T cells aren’t ‘positively selected in the thymus to recognize only own MHC (and foreign peptide)‘. To understand how that’s not even possible, consider someone who gets infected with flu. Thymic selection of T cells that ‘recognize only own MHC and foreign peptide‘ implies that to even have flu-specific T cells in the first place, the thymus should express flu antigens to positively select T cells expressing flu-specific TCRs, and so on ad nauseam for every one of the millions of peptides derived from the multitude of different kinds of entities a body might encounter and need to prevail over in the course of a lifetime. Crux is the thymus needs to select myriad T cell specificities during thymic T cell development even though the body can’t predict what antigen-derived peptides and antigens it would encounter in future. After all, the human adaptive immune system does have T (and B) cells that can specifically recognize and bind any number and variety of them. A conundrum indeed in that mature T cells bind specifically to both peptides (the norm in any immune response) and MHC molecules (the case in ‘direct’ response to allogeneic transplants) that they “couldn’t” have encountered during their development in the thymus.

  • How are TCR specificities selected during T cell development, as in the nature of the selecting thymic peptides. Cross-reactivity – Wikipedia is implicit in this process since clearly flu-derived peptides cannot have selected for a flu-specific T cell and so on.
  • Alloreactvity only adds to the conundrum since T cells also appear capable of binding MHC molecules they’ve obviously never previously encountered as happens with genetically mismatched transplants.

MHC Restriction Of T cells

Two principal models explain MHC restriction of T cells (2, 3, 4, 5, 6, 7).

Though not mutually exclusive, these models make different predictions. For the first one it doesn’t matter whether the T cell repertoire contains TCRs that bind pMHC or not while the second one requires TCRs biased to bind MHC, regardless of its class (I or II) or allele.

T cells That Make It Through Thymic Development Appear Wired to Bind MHC

Back in 1971, Niels Kaj Jerne – Wikipedia hypothesized ‘parallel evolution’ (8) of MHC and the then-undiscovered T-cell receptor – Wikipedia (TCR). After all, how else to explain alloreactivity other than by coevolution of TCR and MHC (9)?

As a recent illustrative example, a clever in vitro cellular model from 2016 demonstrates that TCRs may indeed be hard-wired to bind MHC (10; see figure below from 11).

This 2016 study thus concurs with many previous experimental (mostly mouse and some human) studies that found some Germline – Wikipedia amino acid residues on TCR alpha and beta chains to be crucial for binding MHC (4, 12, 13, 14, 15).

Cumulative data thus allows to infer that T cell development in the thymus may be mainly to ensure that only T cells with a ‘functional’ TCR get through developmental bottlenecks to be released into the ‘periphery’, i.e., a TCR capable of binding pMHC and delivering a modicum of signals downstream into the T cell, just enough, not too much nor too little, a la Goldilocks.

How To Explain T cell Alloreactivity (Ability of T cells to bind and respond to MHCs other than those that selected them in the thymus)

TCRs clearly bind both MHC molecules and the peptides they present. Two prevalent models to explain alloreactivity largely differ in which is more important, recognition of the peptides that allo MHC present, peptide-centric model, or the MHC molecules themselves, MHC-centric model (see figure below from (1).

Structural and some functional data from different experimental studies (16, 17, 18, 19) support either model.

One mouse TCR (17) was found to assume different conformations to accommodate binding to selecting versus novel MHC and in that study interfering with TCR’s ability to engage the peptide had little effect, i.e., support for the MHC-centric model. One mouse TCR was found to assume different conformations to accommodate binding to different peptides (16), i.e., support for the peptide-centric model. Meantime studies with human TCRs and HLA class I (18, 19) also support the MHC-centric model. Rather than one or the other, both approaches likely play their part in physiology.

* Note this answer deliberately avoids using ‘self’ and ‘non-self’/‘foreign’, mainstay words in immunology that obfuscate rather than clarify. In the age of Human microbiota – Wikipedia they’re also obviously unsuitable.


1. Boardman, Dominic A., et al. “What Is Direct Allorecognition?.” Current Transplantation Reports (2016): 1-9. What Is Direct Allorecognition?

2. Feng, Dan, et al. “Structural evidence for a germline-encoded T cell receptor–major histocompatibility complex interaction’codon’.” Nature immunology 8.9 (2007): 975-983. https://www.researchgate.net/pro…

3. Dai, Shaodong, et al. “Crossreactive T Cells spotlight the germline rules for αβ T cell-receptor interactions with MHC molecules.” Immunity 28.3 (2008): 324-334. http://www.cell.com/immunity/pdf…

4. Garcia, K. Christopher, et al. “The molecular basis of TCR germline bias for MHC is surprisingly simple.” Nature immunology 10.2 (2009): 143-147. https://www.ncbi.nlm.nih.gov/pmc…

5. Garcia, K. Christopher. “Reconciling views on T cell receptor germline bias for MHC.” Trends in immunology 33.9 (2012): 429-436. https://www.ncbi.nlm.nih.gov/pmc…

6. Yin, Lei, et al. “T cells and their eons‐old obsession with MHC.” Immunological reviews 250.1 (2012): 49-60. https://www.ncbi.nlm.nih.gov/pmc…

7. Van Laethem, François, Anastasia N. Tikhonova, and Alfred Singer. “MHC restriction is imposed on a diverse T cell receptor repertoire by CD4 and CD8 co-receptors during thymic selection.” Trends in immunology 33.9 (2012): 437-441. https://www.ncbi.nlm.nih.gov/pmc…

8. Jerne, Niels Kaj. “The somatic generation of immune recognition.” European journal of immunology 1.1 (1971): 1-9. http://onlinelibrary.wiley.com/d…

9. Felix, Nathan J., and Paul M. Allen. “Specificity of T-cell alloreactivity.” Nature Reviews Immunology 7.12 (2007): 942-953.

10. Parrish, Heather L., et al. “Functional evidence for TCR-intrinsic specificity for MHCII.” Proceedings of the National Academy of Sciences 113.11 (2016): 3000-3005. http://www.pnas.org/content/113/…

11. Krovi, Sai Harsha, and Laurent Gapin. “Revealing the TCR bias for MHC molecules.” Proceedings of the National Academy of Sciences 113.11 (2016): 2809-2811. http://www.pnas.org/content/113/…

12. Huseby, Eric S., et al. “How the T cell repertoire becomes peptide and MHC specific.” Cell 122.2 (2005): 247-260. http://www.cell.com/cell/pdf/S00…

13. Marrack, Philippa, et al. “Evolutionarily conserved amino acids in TCR V regions and MHC control their interaction.” Annual review of immunology 26 (2008): 171. https://www.ncbi.nlm.nih.gov/pmc…

14. Scott-Browne, James P., et al. “Germline-encoded amino acids in the αβ T cell receptor control thymic selection.” Nature 458.7241 (2009): 1043. https://www.ncbi.nlm.nih.gov/pmc…

15. Adams, Jarrett J., et al. “Structural interplay between germline interactions and adaptive recognition determines the bandwidth of TCR-peptide-MHC cross-reactivity.” Nature immunology 17.1 (2016): 87-94. https://www.ncbi.nlm.nih.gov/pmc…

16. Reiser, Jean-Baptiste, et al. “Crystal structure of a T cell receptor bound to an allogeneic MHC molecule.” Nature immunology 1.4 (2000): 291-297. https://www.researchgate.net/pro…

17. Colf, Leremy A., et al. “How a single T cell receptor recognizes both self and foreign MHC.” Cell 129.1 (2007): 135-146. https://www.researchgate.net/pro…

18. Archbold, Julia K., et al. “Alloreactivity between disparate cognate and allogeneic pMHC-I complexes is the result of highly focused, peptide-dependent structural mimicry.” Journal of Biological Chemistry 281.45 (2006): 34324-34332. Alloreactivity between Disparate Cognate and Allogeneic pMHC-I Complexes Is the Result of Highly Focused, Peptide-dependent Structural Mimicry

19. Macdonald, Whitney A., et al. “T cell allorecognition via molecular mimicry.” Immunity 31.6 (2009): 897-908. https://www.researchgate.net/pro…


Why does rabies vaccine cause sarcoma in cats but not human?


What Is Feline Injection-Site Sarcoma (FISS)

  • First reported in the scientific literature in 1991 on cats originating from the US states of Pennsylvania, New Jersey and Maryland (1) but actually observed since at least 1987, feline injection-site sarcoma (FISS, Vaccine-associated sarcoma – Wikipedia) isn’t unique to inactivated rabies vaccine (2) but can also develop following FeLV (Feline leukemia virus – Wikipedia) vaccine (3), aluminum-adjuvanted vaccines (1), vaccines against feline panleukopenia virus (FPV), feline herpes virus-1 (FHV-1), feline calcivirus (FCV) or even just injections themselves in the absence of vaccines (4).
  • Multiple studies found no relationship between vaccine type, brand, nature (modified-live or inactivated) and FISS risk (5, 6, 7).
  • Injection-site sarcomas in cat occur even in the absence of vaccines (3, 8), for example following injections of glucocorticoids, antibiotics, anti-flea and painkillers (4).
  • Incidence of FISS ranged from 1.3 per 1000 (0.13%) in Canada from 1982 to 1993 (9) to 1 per 10000 (0.01%) in Canada and USA from 1998 to 2000 (10). In other words, relatively low with further reduction in recent years, perhaps as awareness of injection-related risk spread within the veterinary community and efforts were made to identify and mitigate risks.

FISS (Feline Injection-Site Sarcoma) Risk Factors

So what could trigger FISS?

  • Risk factors documented thus far include number of injections given in one site (more injections, higher the risk), route (interscapular, scruff of the neck, more risky) and temperature (cold more risky compared to room temperature) (5, see below from 11).
  • Adjuvanted vaccines may be more risky since histology and ultrastructural studies of FISS have shown adjuvants like aluminum concentrated within them (12, 13).
  • One risk factor may be genetic since risk is higher in siblings of cats with FISS (4). However, as of 2016 FISS genetic risk factors remain as yet undetermined (see below from 14),

‘Dr. Boston: Most affected cats are not purebreds; they’re domestic shorthairs from the pound. They’re spayed and neutered. No one knows where their littermates are. It’s difficult to show a genetic predisposition, but we presume it’s there. Some studies show that these cats are predisposed genetically for one of the cancer-promoter genes’

  • Research on linking FISS to tumor suppressor p53 – Wikipedia have so far been contradictory and hence inconclusive (15, 16, 17). This might have been due to different studies examining genetically disparate cat populations.

How To Minimize FISS Risk

Finally injection recommendations to minimize FISS occurrence include

  • Choosing non-adjuvanted, modified-live or recombinant vaccines over adjuvanted or killed vaccines (18).
  • Injecting only at recommended sites, hindlimbs, right forelimb and stringently avoiding interscapular (scruff of the neck) (see below from 11, 18).


1. Hendrick, Mattie J., et al. “Postvaccinal sarcomas in the cat: epidemiology and electron probe microanalytical identification of aluminum.” Cancer Research 52.19 (1992): 5391-5394. http://cancerres.aacrjournals.or…

2. Hendrick, M. J., and J. J. Brooks. “Postvaccinal sarcomas in the cat: histology and immunohistochemistry.” Veterinary Pathology 31 (1994): 126-126. https://www.researchgate.net/pro…

3. Kass, Philip H., et al. “Epidemiologic evidence for a causal relation between vaccination and fibrosarcoma tumorigenesis in cats.” Journal of the American Veterinary Medical Association 203.3 (1993): 396-405.

4. Hartmann, Katrin, et al. “Feline injection-site sarcoma ABCD guidelines on prevention and management.” Journal of feline medicine and surgery 17.7 (2015): 606-613. https://www.researchgate.net/pro…

5. Kass, Philip H., et al. “Multicenter case-control study of risk factors associated with development of vaccine-associated sarcomas in cats.” Journal of the American Veterinary Medical Association 223.9 (2003): 1283-1292. https://www.researchgate.net/pro…

6. Wilcock, Brian, Anne Wilcock, and Katherine Bottoms. “Feline postvaccinal sarcoma: 20 years later.” The Canadian Veterinary Journal 53.4 (2012): 430. https://www.researchgate.net/pro…

7. Srivastav, Anup, et al. “Comparative vaccine-specific and other injectable-specific risks of injection-site sarcomas in cats.” Journal of the American Veterinary Medical Association 241.5 (2012): 595-602. https://www.researchgate.net/pro…

8. Martano, Marina, Emanuela Morello, and Paolo Buracco. “Feline injection-site sarcoma: past, present and future perspectives.” The Veterinary Journal 188.2 (2011): 136-141. http://www.ad-domus.it/app/downl…

9. Lester, Sally, Terri Clemett, and Alf Burt. “Vaccine site-associated sarcomas in cats: clinical experience and a laboratory review (1982-1993).” Journal of the American Animal Hospital Association 32.2 (1995): 91-95.

10. Gobar, Glenna M., and Philip H. Kass. “World wide web-based survey of vaccination practices, postvaccinal reactions, and vaccine site-associated sarcomas in cats.” Journal of the American Veterinary Medical Association 220.10 (2002): 1477-1482. http://citeseerx.ist.psu.edu/vie…

11. Ladlow, Jane. “Injection Site-Associated Sarcoma in the Cat Treatment recommendations and results to date.” Journal of feline medicine and surgery 15.5 (2013): 409-418. http://felineasthma.org/vas/vasc…

12. Hendrick, M. J., et al. “Comparison of fibrosarcomas that developed at vaccination sites and at nonvaccination sites in cats: 239 cases (1991-1992).” Journal of the American Veterinary Medical Association 205.10 (1994): 1425-1429.

13. Madewell, B. R., et al. “Feline vaccine-associated fibrosarcoma: an ultrastructural study of 20 tumors (1996–1999).” Veterinary Pathology Online 38.2 (2001): 196-202. An Ultrastructural Study of 20 Tumors (1996-1999)

14. Boston, Sarah. “The Feline Sarcoma Controversy: Where Do We Stand?.” http://www.cliniciansbrief.com/s…

15. Banerji, Nilanjana, and Sagarika Kanjilal. “Somatic alterations of the p53 tumor suppressor gene in vaccine-associated feline sarcoma.” American journal of veterinary research 67.10 (2006): 1766-1772.

16. Banerji, Nilanjana, Vivek Kapur, and Sagarika Kanjilal. “Association of germ-line polymorphisms in the feline p53 gene with genetic predisposition to vaccine-associated feline sarcoma.” Journal of Heredity 98.5 (2007): 421-427. Association of Germ-line Polymorphisms in the Feline p53 Gene with Genetic Predisposition to Vaccine-Associated Feline Sarcoma

17. Mucha, D., et al. “Lack of association between p53 SNP and FISS in a cat population from Germany.” Veterinary and comparative oncology 12.2 (2014): 130-137.

18. Scherk, Margie A., et al. “2013 AAFP feline vaccination advisory panel report.” Journal of feline medicine and surgery 15.9 (2013): 785-808. https://www.researchgate.net/pro…


How is it possible that a T Cell Receptor (TCR) recognises as few as 1-3 residues of the MHC-associated peptide?


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Question details: In Basic Immunology, 5th Edition, it was mentioned that “each TCR recognizes as few as one to three residues of the MHC-associated peptide”. How is that possible while retaining specificity to the antigen? It seems to me that any single amino acid could be common to more than 1 peptide.

Short answer: Structural constraints on how many amino acid residues TCRs can bind on short peptide sequences include

  • Several of the peptide’s amino acid residues are already engaged in binding firmly to an MHC (Major histocompatibility complex) molecule creating the pMHC (peptide bound to MHC molecule).
  • At the same time the TCR also needs to bind some amino acid residues of the MHC molecule itself.

Different parts of the TCR bind peptide and MHC. The unique process of somatic nucleotide insertions and deletions makes the TCR’s Complementarity-determining region (CDR)3 region hypervariable and thus quite flexible and effective in binding a few conserved residues on a MHC-bound peptide. This feature also makes TCRs cross-reactive, i.e., capable of binding >1 unique pMHC.

Longer answer on theoretical basis for why it should be so and some data on how TCRs actually bind peptides

The adaptive immune system is predicated on the notion of anticipatory defense. CD4 and CD8 T cells express the alphabeta TCR, composed of two protein chains, alpha and beta. Each undergoes somatic gene rearrangement, specifically V(D)J recombination, i.e., nucleotide insertions and deletions at the V(D) J junctions in the Complementarity-determining region (CDR)3 regions of each chain of the TCR. This means each human body iterates from scratch its adaptive defense armamentarium, something that a 2015 immune parameter analysis of 210 healthy monozygotic twin pairs only confirms (1).

At first glance, this seems reasonable enough. After all, TCRs on CD4 and CD8 T cells have evolved to recognize and bind not whole protein molecules, ‘antigens’, but rather presented by MHC molecules, tiny pieces thereof, ‘peptides’, 12- to 20-mer (12-20 amino acids in length) in the case of CD4s, and 8- to 14-mer in the case of CD8s.

On second glance, this imposes a heavy burden on such a recognition system. Zooming in from larger structures to much smaller ones means the number of peptides presented to T cells by MHC class I and II molecules should be very large. Why should? Zooming in to such magnification simply exponentially increases the number of targets, i.e., peptides. If MHC molecules didn’t present as diverse of a peptide pool as possible from within a cell, missed pathogen-derived peptides become missed opportunities weakening the anticipatory potential of the adaptive immune system.

Given that TCRs bind short peptide sequences presented by MHC molecules, how could recognition be based on other than a handful of amino acid residues? Much of the rest of the peptide binds the MHC molecule. Peptide binding to MHC is itself a critical filtering event of great consequence in adaptive immunity. Antigen processing generates many peptides of varying lengths during protein digestion but only a handful succeed in making it past several bottlenecks to successfully and tightly bind MHC in such a way that they get presented on the cell surface, a feature called Immunodominance.

Why Most T cell Receptors (TCRs) Need To Be Inherently Cross-Reactive, Capable Of Binding >1 pMHC Complex

T cell receptors (TCRs) bind bits of both MHC and peptide. TCRs are hypothesized to have been evolutionarily selected to recognize and bind MHC. Thus, considering just the TCR engagement with the peptide cuts off too small a slice of the pie since outcome, TCR-mediated biochemical activation of T cells, depends on other critical factors,

  • How the peptide is bound within the MHC, which in turn determines which of its amino acid residues are available to make contact with the TCR and how optimally that could happen.
  • How the peptide is bound within the MHC also determines how optimally key anchor residues of the MHC molecule itself are available to bind the TCR.
  • How optimally can T cell co-receptors CD3 (immunology), CD4 (for CD4+ T cells)/CD8 (for CD8+ T cells) bind MHC at the same time.

Corollary of the fact that TCRs recognize and bind peptides presented by MHC molecules is the need for correspondingly large repertoire of specific T cells, one specific enough to recognize and bind each peptide processed and presented by each MHC molecule. However, if each TCR bound only one specific peptide, a logical extension of the Clonal selection theory (2, 3), each individual would theoretically need >10^15 T cells. Why? Because the 20 amino acid alphabet predicates the possible number of peptides that could bind to MHC molecules to be in the range of 10^15 (4). But 10^15 monospecific T cells necessary for optimal anticipatory defense entails a body weight of >500kgs (4), clearly and simply a physical impossibility, something that Don Mason already demonstrated in 1998 with his absurd mouse cartoon (see below from 5).

Given the constraint that there can only be far fewer T cells with unique TCRs compared to the potential diversity of possible unique pMHC complexes, no surprise TCRs tend to be cross-reactive, i.e., capable of binding >1 pMHC complex. An elegant 2014 experimental study found each of 5 different mouse and human TCRs even capable of binding >100 different peptides presented by one MHC molecule (6). Thus far crystal structures of ~120+ TCR- pMHC complexes have been published. One of the most striking observations of such crystal structures is the tremendous flexibility in how TCRs bind to pMHC (7). Rather than binding unique peptides, a given TCR can bind many but TCR binding’s supposed to be very specific. Cross-reactivity is antithetical to specificity. Can the two be reconciled? Yes, by the fact that the common thread linking cross-reactive peptides that bind a given TCR is the presence of conserved motifs, i.e., one, two, three or more conserved residues at specific TCR-binding positions (see quote, emphasis mine, and figure below from 6).

‘TCR cross-reactivity is not achieved by each receptor recognizing a large number of unrelated peptide epitopes but rather through greater tolerance for substitutions to peptide residues outside of the TCR interface, differences in residues that contact the MHC, and relatively conservative changes to the residues that contact the TCR CDR loops. The segregation of TCR recognition and MHC binding allows for TCRs to simultaneously accommodate needs for specificity and cross-reactivity.’

Thus, this example shows that even though peptides like 2A and MCC are very different in their sequences, both successfully bind the same TCR 2B4 because the process of somatic nucleotide insertions and deletions makes the region that primarily makes contact with a peptide, the TCR’s CDR3 region, hypervariable. This endows TCRs with the capacity to be quite flexible in accommodating different peptide sequences and also be able to bind firmly by contacting only a handful of conserved residues on any given MHC-bound peptide.

Human epidemiological studies reveal the implications of T cell recognition being the way it is. A relatively obscure set of data epitomize not only the extent of T cell cross-reactivity but also suggest that such functionality enables a vast, connected immunoprotective landscape against disparate entities ranging from bacteria to virus to cancer.

~85% of malignant melanocytes express an antigen called HERV-K-MEL (8, 9, 10), product of a pseudo-gene incorporated in the HERV-K env gene. HERV (Human Endogenous retrovirus) in turn are endogenous retroviruses incorporated into the human genome over millions of years. Acquired between 3 and 6 million years back, HERV-K are the latest family (11), making them the only HERVs still capable of replicating in the human population within the last few million years. HERV-K appears to be involved in several stages of melanoma formation (12, 13, 14).

Spontaneous melanoma regressions have occasionally been reported in the literature, suggesting effective anti-melanoma immune responses occur in nature. But what are the coordinates of such immunity? Taking a leaf out of William Coley and his Coley’s toxins, the European Organization for Research and Treatment of Cancer (EORTC) established the Febrile Infections and Melanoma (FEBIM) working group, tasked to explore how prior infectious diseases and vaccines influenced melanoma risk.

Their studies thus far suggest the Tuberculosis (TB) BCG vaccine, the Vaccinia vaccine against small pox and the 17D Yellow fever vaccine provide some degree of protection against melanoma (15, 16, 17).

What could possibly link such disparate characters as BCG, Vaccinia, Yellow fever and Melanoma and what connects this story to TCRs and peptides? Turns out each of these really disparate agents, a bacterium and two unrelated viruses, express peptides with high sequence homology to the melanoma HERV-K-MEL peptide (see below from 18).

BCG, vaccinia and yellow fever vaccines are of course expected to induce specific immune responses against themselves. However, given they express proteins with high sequence homology, vaccine-specific cytotoxic CD8+ and helper CD4+ T cells would also include those cross-reactive to melanoma HERV-K-MEL peptide. This could prevent melanoma development in those vaccinees who retain robust memory immune responses against this cross-reactive peptide.

Such phenomena may underlie the observation that certain live vaccines like BCG and measles can protect against unrelated pathogens and even reduce rates of all-cause mortality (19, 20). And as more microbiota-immunity interactions get mined, such examples that at present seem unanticipated will become more commonplace.


1. Brodin, Petter, et al. “Variation in the human immune system is largely driven by non-heritable influences.” Cell 160.1 (2015): 37-47.

2. Jerne, Niels K. “The natural-selection theory of antibody formation.” Proceedings of the National Academy of Sciences 41.11 (1955): 849-857. http://www.pnas.org/content/41/1…

3. Jerne, Niels Kaj. “The somatic generation of immune recognition.” European journal of immunology 1.1 (1971): 1-9.

4. Sewell, Andrew K. “Why must T cells be cross-reactive?.” Nature Reviews Immunology 12.9 (2012): 669-677. http://www.tcells.org/scientific…

5. Mason, Don. “A very high level of crossreactivity is an essential feature of the T-cell receptor.” Immunology today 19.9 (1998): 395-404.

6. Birnbaum, Michael E., et al. “Deconstructing the peptide-MHC specificity of T cell recognition.” Cell 157.5 (2014): 1073-1087. http://www.cell.com/cell/pdf/S00…

7. Rudolph, Markus G., Robyn L. Stanfield, and Ian A. Wilson. “How TCRs bind MHCs, peptides, and coreceptors.” Annu. Rev. Immunol. 24 (2006): 419-466. http://nfs.unipv.it/nfs/minf/dis…

8. Kölmel, K. F., O. Gefeller, and B. Haferkamp. “Febrile infections and malignant melanoma: results of a case-control study.” Melanoma research 2.3 (1992): 207-212

9. Schiavetti, Francesca, et al. “A human endogenous retroviral sequence encoding an antigen recognized on melanoma by cytolytic T lymphocytes.” Cancer research 62.19 (2002): 5510-5516. https://www.researchgate.net/pro…

10. Grange, John M., et al. “Can prior vaccinations against certain infections confer protection against developing melanoma?.” Medical Journal of Australia 191.9 (2009): 478. http://citeseerx.ist.psu.edu/vie…

11. Sverdlov, Eugene D. “Retroviruses and primate evolution.” Bioessays 22.2 (2000): 161-171.

12. Muster, Thomas, et al. “An endogenous retrovirus derived from human melanoma cells.” Cancer research 63.24 (2003): 8735-8741. https://www.researchgate.net/pro…

13. Serafino, A., et al. “The activation of human endogenous retrovirus K (HERV-K) is implicated in melanoma cell malignant transformation.” Experimental cell research 315.5 (2009): 849-862. https://www.researchgate.net/pro…

14. Singh, Sarita, et al. “The role of human endogenous retroviruses in melanoma.” British Journal of Dermatology 161.6 (2009): 1225-1231.

15. Grange, John M., Bernd Krone, and John L. Stanford. “Immunotherapy for malignant melanoma–tracing Ariadne’s thread through the labyrinth.” European Journal of Cancer 45.13 (2009): 2266-2273.

16. Krone, Bernd, et al. “Protection against melanoma by vaccination with Bacille Calmette-Guerin (BCG) and/or vaccinia: an epidemiology-based hypothesis on the nature of a melanoma risk factor and its immunological control.” European Journal of Cancer 41.1 (2005): 104-117.

17. Mastrangelo, G., et al. “Does yellow fever 17D vaccine protect against melanoma?.” Vaccine 27.4 (2009): 588-591.

18. Cegolon, Luca, et al. “Human endogenous retroviruses and cancer prevention: evidence and prospects.” BMC cancer 13.1 (2013): 1. http://bmccancer.biomedcentral.c…

19. Goodridge, Helen S., et al. “Harnessing the beneficial heterologous effects of vaccination.” Nature Reviews Immunology (2016).

20. Muraille, Eric. “The Unspecific Side of Acquired Immunity Against Infectious Disease: Causes and Consequences.” Frontiers in microbiology 6 (2015). https://www.ncbi.nlm.nih.gov/pmc…