Grandmother hypothesis: highly infectious diseases like gonorrhea might have impacted human evolution

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Researchers at University of California San Diego School of Medicine previously found a set of human gene mutations that protect older adults against cognitive decline and dementia.

In a new study, published July 9, 2022 in Molecular Biology and Evolution, they focus on one of these mutated genes and attempt to trace its evolution ⁠— when and why it appeared in the human genome.

The findings suggest selective pressure from infectious pathogens like gonorrhea may have promoted the emergence of this gene variant in Homo sapiens, and inadvertently supported the existence of grandparents in human society.

The biology of most animal species is optimized for reproduction, often at the expense of future health and longer lifespans. In fact, humans are one of the only species known to live well past menopause.

According to the “grandmother hypothesis,” this is because older women provide important support in raising human infants and children, who require more care than the young of other species. Scientists are now trying to understand what features of human biology make this longer-term health possible.

When researchers previously compared human and chimpanzee genomes, they found that humans have a unique version of the gene for CD33, a receptor expressed in immune cells. The standard CD33 receptor binds to a type of sugar called sialic acid that all human cells are coated with. When the immune cell senses the sialic acid via CD33, it recognizes the other cell as part of the body and does not attack it, preventing an autoimmune response.

The CD33 receptor is also expressed in brain immune cells called microglia, where it helps control neuroinflammation. However, microglia also have an important role in clearing away damaged brain cells and amyloid plaques associated with Alzheimer’s disease. By binding to the sialic acids on these cells and plaques, regular CD33 receptors actually suppress this important microglial function and increase the risk of dementia.

This is where the new gene variant comes in. Somewhere along the evolutionary line, humans picked up an additional mutated form of CD33 that is missing the sugar-binding site. The mutated receptor no longer reacts to sialic acids on damaged cells and plaques, allowing the microglia to break them down. Indeed, higher levels of this CD33 variant were independently found to be protective against late-onset Alzheimer’s.

In trying to understand when this gene variant first emerged, co-senior author Ajit Varki, MD, Distinguished Professor of Medicine and Cellular and Molecular Medicine at UC San Diego School of Medicine, and colleagues found evidence of strong positive selection, suggesting something was driving the gene to evolve more rapidly than expected.

They also discovered that this particular version of CD33 was not present in the genomes of Neanderthals or Denisovans, our closest evolutionary relatives.

“For most genes that are different in humans and chimps, Neanderthals usually have the same version as the humans, so this was really surprising to us,” said Varki. “These findings suggest the wisdom and care of healthy grandparents may have been an important evolutionary advantage that we had over other ancient hominin species.”

Varki led the study with Pascal Gagneux, PhD, professor of pathology at UC San Diego School of Medicine and professor in the Department of Anthropology. The authors said the study provides new evidence supporting the grandmother hypothesis.

Still, evolutionary theory says reproductive success is the main driver of genetic selection, not post-reproductive cognitive health. So what was pushing the prevalence of this mutated form of CD33 in humans?

One possibility, suggest the authors, is that highly infectious diseases like gonorrhea, which can be detrimental to reproductive health, might have impacted human evolution. Gonorrhea bacteria coat themselves in the same sugars that CD33 receptors bind to. Like a wolf in sheep’s clothing, the bacteria are able to trick human immune cells to not identify them as outside invaders.

The researchers suggest that the mutated version of CD33 without a sugar-binding site emerged as a human adaptation against such “molecular mimicry” by gonorrhea and other pathogens. Indeed, they confirmed that one of the human-specific mutations was able to completely abolish the interaction between the bacteria and CD33, which would allow immune cells to attack the bacteria again.

Altogether, the authors believe humans initially inherited the mutated form of CD33 to protect against gonorrhea during reproductive age, and this gene variant was later co-opted by the brain for its benefits against dementia.

“It is possible that CD33 is one of many genes selected for their survival advantages against infectious pathogens early in life, but that are then secondarily selected for their protective effects against dementia and other aging-related diseases,” said Gagneux.

Co-authors include: Sudeshna Saha, Naazneen Khan, Andrea Verhagen, Aniruddha Sasmal and Sandra Diaz at UC San Diego, Troy Comi and Joshua M. Akey at Princeton University, Hai Yu and Xi Chen at UC Davis, and Martin Frank at Biognos AB.


Macroevolutionary imprints on human disease
Systems involved in disease have ancient origins


Many of cellular life’s essential biological systems and processes, such as DNA replication, transcription and translation, represent ancient evolutionary innovations shared by all living organisms. Although essential, each of these ancient innovations generated the conditions for modern disease (Fig. 1).

In this section, we provide examples of how several ancient innovations have created substrates for dysfunction and disease, and how considering these histories contributes to understanding the biology of disease and extrapolating results from model systems to humans.

Fig. 1
Evolutionary events in both the deep evolutionary past and recent human evolution shape the potential for disease.
A timeline of evolutionary events (top) in the deep evolutionary past and on the human lineage that are relevant to patterns of human disease risk (bottom). The ancient innovations on this timeline (left) formed biological systems that are essential, but are also foundations for disease. During recent human evolution (right), the development of new traits and recent rapid demographic and environmental changes have created the potential for mismatches between genotypes and modern environments that can cause disease. The timeline is schematic and not shown to scale. bya, billion years ago; kya, thousand years ago; mya, million years ago.

As a foundational (if obvious) example, the origin of self-replicating molecules 4 billion years ago formed the basis of life, but also the root of genetic diseases12,14,15. Similarly, asymmetric cell division may have evolved as an efficient way to handle cellular damage, but it also established the basis for ageing in multicellular organisms16,17. Myriad age-related diseases in humans, and many other multicellular organisms, are a manifestation of this first evolutionary trade-off.

The evolution of multicellularity, which has occurred many times across the tree of life, illustrates the interplay between evolutionary innovation and disease18. The origin of multicellularity enabled complex body plans with trillions of cells, involving innovations associated with the ability of cells to regulate their cell cycles, modulate their growth and form intricate networks of communication. But multicellularity also established the foundation for cancer19,20. Genes that regulate cell cycle control are often divided into two groups: caretakers and gatekeepers21,22. The caretakers are involved in basic control of the cell cycle and DNA repair, and mutations in these genes often lead to increased mutation rates or genomic instability, both of which increase cancer risk. Caretaker genes are enriched for functions with origins dating back to the first cells23. The gatekeepers appeared later, at the genesis of metazoan multicellularity23. The gatekeepers are directly linked to tumorigenesis through their roles in regulating cell growth, death and communication. The progression of individual tumours in a given patient is likewise informed by an evolutionary perspective. Designing treatments that account for the evolution of drug resistance and heterogeneity in tumours is a tenet of modern cancer therapy24–29.

Like multicellularity, the evolution of immune systems also set the stage for dysregulation and disease. Mammalian innate and adaptive immune systems are both ancient. Components of the innate immune system are present across metazoans and even some plants30,31, whereas the adaptive immune system is present across jawed vertebrates32. These systems provided molecular mechanisms for self-/non-self-recognition and response to pathogens, but they evolved in a piecemeal fashion, using many different, pre-existing genes and processes. For example, co-option of endogenous retroviruses provided novel regulatory elements for interferon response33. As well, it is clear that the human immune system has co-evolved with parasites, such as helminths, over millions of years. Helminth infection both induces and modulates an immune response in humans34.

Evolutionary analyses of development have revealed that new anatomical structures often arise by co-opting existing structures and molecular pathways that were established earlier in the history of life. For example, animal eyes, limb structure in tetrapods and pregnancy in mammals (Box 3) each evolved by adapting and integrating ancient genes and regulatory circuits in new ways35–38. This integration of novel traits into the existing network of biological systems gives rise to links between diverse traits via the shared genes that underlie their development and function36. As a result, many genes are pleiotropic — they have effects on multiple, seemingly unrelated, traits. We do not have space here to cover the full evolutionary scope of these innovations and their legacies, but just as in each of the cases described above, innovations and adaptations spanning from the origin of metazoans to modern human populations shape the substrate upon which disease appears.

. . . . . .

Box 3 Pregnancy as a case study in evolutionary medicine
Mammalian pregnancy illustrates how consideration of a trait’s history across evolutionary time can inform our understanding of disease. Every human who ever lived experienced pregnancy, but its complexity is remarkable — it involves coordination between multiple genomes and physiological integration between individuals, and is administered by a transient organ, the placenta188. Furthermore, by ensuring the generational transmission of genetic information, it provides the substrate for all evolution and renewal of life itself189.

Macroevolutionary

Pregnancy in placental mammals, which appeared ~170 million years ago, involves physiological integration of fetal and maternal tissues via the placenta, a transient fetal-derived, extra-embryonic organ. Live birth and placentation open the door to interplay between mother and fetus over resource provisioning, with the potential for the mother to provide less than fetal demands because of other energetic needs, such as caring for other offspring. In some mammals, including humans, placentation is highly invasive, setting up a physiological tug of war between mother and fetus over provisioning. When this precarious balance is disrupted, diseases of pregnancy can occur. Poor maternal arterial remodelling during placentation limits placental invasion, which invokes a compensatory response by the distressed fetus. This imbalance results in inflammation, hypertension, kidney damage and proteinuria in the mother, and an increase in oxidative stress and spontaneous preterm birth in the fetus190. Pregnancy-associated maternal hypertension with proteinuria is clinically defined as pre-eclampsia with vascular aetiologies, with a poor prognosis for both mother and fetus if untreated. Understanding pre-eclampsia as the result of an evolutionary tug of war between mother and the fetus has medical implications191–194.

Human-specific

Timing of birth is key to a successful, healthy pregnancy, but little is known about the mechanisms governing the initiation of parturition. The steroid hormone progesterone and its receptors are involved in parturition in all viviparous species; however, how progesterone regulates parturition is likely to be species-specific. For example, the human progesterone receptor (PGR) exhibited rapid evolution after divergence from the last common ancestor with chimpanzees195,196. There are functional differences between the human and Neanderthal versions of the progesterone receptor197. The human-specific changes in the PGR influence its transcription and probably its phosphorylation198,199. Similarly, loci associated with human preterm birth have experienced diverse evolutionary forces, including balancing selection, positive selection and population differentiation200. The rapid and diverse types of evolutionary change observed in the PGR and some of the loci associated with preterm birth make it challenging to extrapolate analyses of their molecular functions in animal models, such as mice. In addition, humans and mice differ in reproductive strategies, morphology of the uterus, placentation, hormone production and the drivers of uterine activation201. For example, progesterone is produced maternally in mice throughout pregnancy, whereas in humans its production shifts to the placenta after the early stages of pregnancy. Given the unique evolutionary history of human pregnancy, many molecular aspects of pregnancy may be better studied in other model organisms or human cell-based systems.

Human population level

A central enigma of mammalian pregnancy is that the maternal immune system does not reject the foreign fetus; rather, it has not only evolved to accept the fetus but is also critical in the process of placentation202,203. The centrality of the maternal immune system in pregnancy has important medical implications. The modulation of the maternal immune system during pregnancy results in a lowered ability to clear certain infections204,205. Uterine natural killer (uNK) cells and their killer cell inhibitory receptors (KIRs) cooperate with fetal trophoblasts to regulate the maternal immune response. In addition, uNK cells are also involved in immune response to pathogens, and this dual role provides the substrate for evolutionary trade-offs. For example, the human-specific KIR AA haplotype is associated with lower birthweight and pre-eclampsia as well as with a more effective defence against Ebola virus and hepatitis206,207 (Fig. 4a). Modern human populations have variation in the diversity and identity of KIR haplotypes, probably due to selection on both placentation and host defence208. Infectious disease outbreaks, therefore, place a unique selective pressure on pregnancy. Severe outbreaks of infectious diseases, such as malaria, often produce significant shifts in population-level allele frequencies in pregnancy-related genes, such as FLT1 in malaria-endemic populations of Tanzania209. The varying pressures from infectious disease are likely to contribute to variation in risk of pregnancy-related diseases between modern populations.

Fig. 4
Illustrations of the need to consider diverse human populations in the genetic analysis of disease.
a | Interactions between the maternal killer cell inhibitory receptor (KIR) genotype and the fetal trophoblasts illustrate evolutionary trade-offs in pregnancy. Birthweight is under stabilizing selection in human populations. The interaction between maternal KIR genotypes (a diversity of which are maintained in the population) and the fetal trophoblasts influence birthweight. African (AFR) populations, relative to European (EUR) populations, maintain larger proportions of the KIR AA haplotype176, which is associated with improved maternal immune response to some viral challenges; however, it is also associated with low birthweight. Alternatively, the KIR BB haplotype is associated with higher birthweight but increased risk of pre-eclampsia. b | Current strategies for predicting genetic risk are confounded by a lack of inclusion of diverse human populations. Thus, they are more likely to fail in genetic risk prediction in populations that are under-represented in genetic databases. For example, polygenic risk score (PRS) models trained on European populations often perform poorly when applied to African populations. This poor performance stems from the fact that the genetic diversity of African populations, differences in effect sizes between populations and differential evolutionary pressures are not taken into account. The weights for each variant (blue circles) in the PRS derived from genome-wide association studies are signified by w1, w2 and w3. c | Population-specific adaptation and genetic hitch-hiking can produce different disease risk between populations. Haplotypes with protective effects against disease may rise to high frequency in specific populations through genetic hitch-hiking with nearby alleles under selection for a different trait. For example, selection for lighter skin pigmentation caused a haplotype that carried a variant associated with lighter skin (blue circle) to increase in frequency in European populations compared with African populations. This haplotype also carried a variant protective against prostate cancer (blue triangle).

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Medical implications
Although ancient macroevolutionary innovations may seem far removed from modern human phenotypes, their imprint remains on the human body and genome. Understanding the constraints they impose can provide insight into mechanisms of disease.

Mapping the origins and evolution of traits and identifying the genetic networks that underlie them are critical to the accurate selection of model systems and extrapolation to human populations. Failure to consider the evolutionary history of homologous systems, their phylogenetic relationships and their functional contexts in different organisms can lead to inaccurate generalization. Instead, when considering a model system, key evolutionary questions about both the organism and the trait of interest can indicate how translatable the research will be to humans39,40. For example, is the similarity between the trait in humans and the trait in the model system due to shared ancestry, that is, homology? The presence of homology in a human gene or system of study suggests potential as a model system; however, homology alone is not sufficient justification. Environmental and life history factors shape traits, and divergence between species complicates the simple assumption that homology provides genetic or mechanistic similarity. Thus, homology must be supplemented by understanding of whether the evolutionary divergence between humans and the proposed model led to functional divergence. For example, the rapid evolution of the placenta and variation in reproductive strategy across mammals have made it challenging to extrapolate results about the regulation of birth timing from model organisms, such as mouse, to humans (Box 3). More broadly, differences in genetic networks that underlie the development of homologous traits across mammals explain why the majority of successful animal trials fail to translate to human clinical trials41,42. Molecular mechanisms of ancient systems, such as DNA replication, can be studied using phylogenetically distant species; however, ‘humanizing’ these models to research human-specific aspects of traits may not be possible and comparative studies of closely related species may be required40.

Although evolutionary divergence in homologous traits is an impediment to the direct translation of findings from a model system to humans, understanding how these evolutionary differences came about can also yield insights into disease mechanisms. For example, intuition would suggest that large animals (many cells and cell divisions) with long lifespans (many ageing cells), such as elephants and whales, would be at increased risk for developing cancer. However, size and lifespan are not significantly correlated with cancer risk across species; despite their large size, elephants and whales do not have a higher risk of developing cancer43,44. Why is this so? Recent studies of the evolution of genes involved in the DNA damage response in elephants have revealed mechanisms that may contribute to cancer resistance. An ancient leukaemia inhibiting factor pseudogene (LIF6) regained its function in the ancestor of modern elephants. This gene works in conjunction with the tumour suppressor gene TP53, which has increased in copy number in elephants, to reduce elephants’ risk for cancer despite their large body size45,46. This illustrates a basic life history trade-off: selection has created mechanisms for cancer suppression and somatic maintenance in large vertebrates that are not needed in small short-lived vertebrates. Studying such seeming paradoxes, especially those with clear contrasts to human disease risk, will shed light on broader disease mechanisms and suggest targets for functional interventions with translation potential.

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Human-specific evolution
Human adaptation, trade-offs and disease
The macroevolutionary events described above created the foundation of genetic disease, but considering the more recent changes that occurred during the evolutionary history of the human lineage is necessary to illuminate the full context of human disease. Comparisons between humans and their closest living primate relatives, such as chimpanzees, have revealed diseases that either do not appear in other species or take very different courses47. We are beginning to understand the genetic differences underlying some of these human-specific conditions, with particular insights into infectious diseases.

The last common ancestors of humans and chimpanzees underwent a complex speciation event that is likely to have involved multiple rounds of gene flow between ~12 and 6 million years ago (mya)48. Over the millions of years after this divergence, climatic, demographic and social pressures drove the evolution of many physical and behavioural traits unique to the human lineage, including bipedalism (~7 mya), lack of body hair (~2–3 mya) and larger brain volume relative to body size (~2 mya)12,47. These traits evolved in a diverse array of hominin groups, mainly in Africa, although some of these species, such as Homo erectus, ventured into Europe and Asia.

These human adaptations developed on the substrate of tightly integrated systems shaped by billions of years of evolution, and thus beneficial adaptations with respect to one system often incurred trade-offs in the form of costs on other linked systems49. The trade-off concept derives from a branch of evolutionary biology known as life history theory. It is based on the observation that organisms contain combinations of traits that cannot be simultaneously optimized by natural selection50,51. For example, many fitness-related traits draw on common energetic reserves, and investment in one comes at the expense of another52. Large body size may improve survival in certain environments, but it comes at the expense of longer development and lower numerical investment in reproduction.

The trade-off concept is clinically relevant because it dispenses with the notion of a single ‘optimal’ phenotype or fitness state for an individual49,53,54. Given the interconnected deep evolution of the human body, many diseases are tightly linked, in the sense that decreasing the risk for one increases the risk for the other. Such diametric diseases and the trade-offs that produce them are the starkest when there is competition within the body for limited resources; for example, energy used for reproduction cannot be used for growth, immune function or other energy-consuming survival processes54. The molecular basis for diametric diseases often results from antagonistic pleiotropy at the genetic level — when a variant has contrasting effects on multiple bodily systems. In extreme cases, some diseases that manifest well after reproductive age, for example, Alzheimer disease, have been less visible to selection and, thus, potentially more susceptible to trade-offs. Cancer and neurodegenerative disorders also exhibit this diametric pattern, where cancer risk is inversely associated with Alzheimer disease, Parkinson disease and Huntington disease. This association is hypothesized to be mediated by differences in the neuronal energy use and trade-offs in cell proliferation and apoptosis pathways49. Similarly, osteoarthritis (breakdown of cartilage in joints often accompanied by high bone mineral density) and osteoporosis (low bone mineral density) rarely co-occur. Their diametric pattern reflects, at least in part, different probabilities across individuals of mesenchymal stem cells within bone marrow to develop into osteoblasts versus non-bone cells such as adipocytes49,55. In another example, a history of selection for a robust immune response can now lead to an increased risk for autoimmune and inflammatory diseases, especially when coupled with new environmental mismatches49,54. Other examples of trade-offs are found throughout the human body, manifesting in risk for diverse diseases, including psychiatric and rheumatoid disorders49,56.

Just as adaptations in deep evolutionary time created new substrates for disease, evolutionary pressures exerted on the human lineage established the foundation for complex cognitive capabilities, but they also established the potential for many neuropsychiatric or neurodevelopmental diseases. For example, genomic structural variants enabled functional innovation in the brain through the emergence of novel genes57–60. Many human-specific segmental duplications influence genes that are essential to the development of the human brain, such as SRGAP2C and ARHGAP11B. Both of these genes function in cortical development and may be involved in the expansion of human brain size61–63. The human-specific NOTCH2NL is also hypothesized to have evolved from a partial duplication event, and is implicated in increased output during human corticogenesis, another potential key contributor to human brain size59,60. Although these structural variants were probably adaptive58, they may have also predisposed humans to neuropsychiatric diseases and developmental disorders. Copy number variation in the region flanking ARHGAP11B, specifically a microdeletion at 15q13.3, is associated with risk for intellectual disability, autism spectrum disorder (ASD), schizophrenia and epilepsy58,64. Duplications and deletions of NOTCH2NL and surrounding regions are implicated in macrocephaly and ASD or microcephaly and schizophrenia, respectively59. These trade-offs also play out at the protein domain level. For example, the Olduvai domain (previously known as DUF1220) is a 1.4-kb sequence that appears in ~300 copies in the human genome; this domain has experienced a large human-specific increase in copy number. These domains appear in tandem arrays in neuroblastoma breakpoint family (NBPF) genes, and have been associated with both increased brain size and neuropsychiatric diseases, including autism and schizophrenia65. These examples suggest that the genomic organization of these human-specific duplications may have enabled human-specific changes in brain development while also increasing the likelihood of detrimental rearrangements that cause human disease59,64. Furthermore, genomic regions associated with neuropsychiatric diseases have experienced human-specific accelerated evolution and recent positive selection, providing additional evidence for the role of recent evolutionary pressures on human disease risk66,67. Schizophrenia-associated loci, for example, are enriched near human accelerated regions (HARs) that are conserved in non-human primates68. Variation in HARs has also been associated with risk for ASD, possibly through perturbations of gene regulatory architecture69.

Human immune systems have adapted in response to changes in environment and lifestyles over the past few million years; however, the rapid evolution of the immune system may have left humans vulnerable to certain diseases, such as HIV-1 infection. A similar virus, simian immunodeficiency virus (SIV), is found in chimpanzees and other primates, and studies in the early 2000s found evidence of AIDS-like symptoms (primarily a reduction in CD4+ T cells) in chimpanzees infected with SIV. Although the effects of SIV in chimpanzees mirror some of the effects of HIV in humans70, captive chimpanzees infected with HIV-1 do not typically develop AIDS and have better clinical outcomes. The differences in outcome are influenced by human-specific immune evolution. For example, humans have lost expression of several Siglecs, cell surface proteins that binds sialic acids, in T lymphocytes compared with great apes71. In support of this hypothesis, human T cells with high Siglec-5 expression survive longer after HIV-1 infection72. Moreover, there is a possible role for the rapidly evolving Siglecs in other diseases, such as epithelial cancers, that differentially affect humans relative to closely related primates73,74.

Another human-specific immune change is the deletion of an exon of CMP-N-acetylneuraminic acid hydroxylase (CMAH) leading to a difference in human cell surface sialoglycans compared with other great apes75–77. The change in human sialic acid to an N-acetylneuraminic acid (Neu5Ac) termination, rather than N-glycolylneuraminic acid (Neu5Gc), may have been driven by pressure to escape infection by Plasmodium reichenowi, a parasite that binds Neu5Gc and causes malaria in chimpanzees. Conversely, the prevalence of Neu5Ac probably made humans more susceptible to infection by the malaria parasite Plasmodium falciparum, which binds to Neu5Ac78,79, and another human-specific pathology: typhoid fever80. Typhoid toxin binds specifically and is cytotoxic to cells expressing Neu5Ac glycans. Thus, the deletion of CMAH was likely to have been selected for by pressure from pathogens, but has in turn enabled other human-specific diseases such as malaria and typhoid fever81. The rapid evolution of the human immune system creates the potential for human-specific disease. As a result, human-specific variation in many other human immune genes influences human-specific disease risk82,83.

Medical implications
These examples from recent human evolution highlight the ongoing interplay of genetic variation, adaptation and disease. Understanding the evolutionary history of traits along with the aetiology of related diseases can help identify and evaluate risks for unintended consequences of treatments due to trade-offs. For example, ovarian steroids have pleiotropic effects stimulating both bone growth and mitosis in breast tissues to mobilize calcium stores during lactation54. However, later in life this link gives rise to a clinical trade-off. Hormone replacement therapy in postmenopausal women reduces the risk for osteoporosis and ovarian cancer, but also, as a result of its effects on breast tissue, increases the risk for breast cancer. Given the commonality of the trade-off between maintenance and proliferation, this is just one of many examples of cancer risk emerging as a result of trade-offs in immune, reproductive and metabolic systems56,84. Pregnancy is also rife with clinically relevant trade-offs given the interaction between multiple individuals and genomes (mother, father and fetus) with different objectives (Box 3). Trade-offs at the cellular level also have medical implications. For example, cellular senescence is a necessary and beneficial part of many basic bodily responses, but the accumulation of senescent cells underlies many ageing-related disorders. Thus, individuals with different solutions to this trade-off may have very different ‘molecular’ versus ‘chronological’ ages85.

Identifying such trade-offs by studying disease and treatment response is of great interest, but is challenging for several reasons: the number of possible combinations of traits to consider is large; many humans must have experienced the negative effects; and data must be available on both traits in the same individuals. Here, evolution paired with massive electronic health record (EHR)-linked biobanks5,86,87 provides a possible solution. By considering the evolutionary context and potential linkages between traits, the search space of possible trade-offs can be constrained. Then, diametric traits can be tested for among individuals in the EHRs by performing phenome-wide association studies (PheWAS) either on traits or genetic loci of interest and looking for inverse relationships88. The mechanisms underlying the observed associations could then be evaluated in model systems and, if validated, anticipated in future human treatments.

In addition to trade-offs, evolutionary analyses can help us identify therapeutic targets for uniquely human diseases. A small subset of humans infected with HIV never progress to AIDS — a resistance phenotype that has been generally attributed to host genomics89–91. Identifying and understanding the genes that contribute to non-progression is of great interest in the development of vaccines and treatments for HIV infection. Genome-wide association studies (GWAS) and functional studies have supported the role of the MHC class I region, specifically the HLA-B27/B57 molecules, in HIV non-progression92–94. Comparative genomics with chimpanzees identified a chimpanzee MHC class I molecule functionally analogous to that of the non-progressors that contains amino acid substitutions that change binding affinity for conserved areas of the HIV-1 and SIV viruses. Evolutionary analysis of this region suggests that these substitutions are the result of an ancient selective sweep in chimpanzee genomes that did not occur in humans95. This analysis not only helps us understand how humans are uniquely susceptible to HIV progression but also highlights functional variation in the MHC that are potential targets of medical intervention.

reference link: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7787134/


reference link :

Original Research: Open access.
Evolution of Human-specific Alleles Protecting Cognitive Function of Grandmothers” by Ajit Varki et al. Molecular Biology and Evolution

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