Shedding Light on the African Enigma: In Vitro Testing of Homo sapiens-Helicobacter pylori Coevolution

The continuous characterization of genome-wide diversity in population and case–cohort samples, allied to the development of new algorithms, are shedding light on host ancestry impact and selection events on various infectious diseases. Especially interesting are the long-standing associations between humans and certain bacteria, such as the case of Helicobacter pylori, which could have been strong drivers of adaptation leading to coevolution. Some evidence on admixed gastric cancer cohorts have been suggested as supporting Homo-Helicobacter coevolution, but reliable experimental data that control both the bacterium and the host ancestries are lacking. Here, we conducted the first in vitro coinfection assays with dual human- and bacterium-matched and -mismatched ancestries, in African and European backgrounds, to evaluate the genome wide gene expression host response to H. pylori. Our results showed that: (1) the host response to H. pylori infection was greatly shaped by the human ancestry, with variability on innate immune system and metabolism; (2) African human ancestry showed signs of coevolution with H. pylori while European ancestry appeared to be maladapted; and (3) mismatched ancestry did not seem to be an important differentiator of gene expression at the initial stages of infection as assayed here.

High-depth African genomes inform human migration and health

The African continent is regarded as the cradle of modern humans and African genomes contain more genetic variation than those from any other continent, yet only a fraction of the genetic diversity among African individuals has been surveyed1. Here we performed whole-genome sequencing analyses of 426 individuals—comprising 50 ethnolinguistic groups, including previously unsampled populations—to explore the breadth of genomic diversity across Africa. We uncovered more than 3 million previously undescribed variants, most of which were found among individuals from newly sampled ethnolinguistic groups, as well as 62 previously unreported loci that are under strong selection, which were predominantly found in genes that are involved in viral immunity, DNA repair and metabolism. We observed complex patterns of ancestral admixture and putative-damaging and novel variation, both within and between populations, alongside evidence that Zambia was a likely intermediate site along the routes of expansion of Bantu-speaking populations. Pathogenic variants in genes that are currently characterized as medically relevant were uncommon—but in other genes, variants denoted as ‘likely pathogenic’ in the ClinVar database were commonly observed. Collectively, these findings refine our current understanding of continental migration, identify gene flow and the response to human disease as strong drivers of genome-level population variation, and underscore the scientific imperative for a broader characterization of the genomic diversity of African individuals to understand human ancestry and improve health.

By Their Flags Ye Shall Know Them: National Flag Symbolism and Colors Predict Average Country IQ and HDI

That human ancestry predicts average IQ and socioeconomic outcomes is amongst the most thoroughly replicated findings of the social sciences. Since human ethnic and cultural descent is usually represented on national flags, it was hypothesized herein that national flag symbolism and colors would be predictive of a nation’s average IQ and socioeconomic development. In order to test this hypothesis, national flag symbols and colors were coded, quantified, and correlated with country IQ and Human Development Index (HDI). Both country-level IQ and HDI are positively associated with Christian symbolism, and negatively associated with symbols representing celestial bodies. The color green predicts lower IQ and HDI, while the color white predicts higher IQ and HDI. The color red predicts higher IQ, but not higher HDI, and the color yellow predicts lower HDI, but not lower IQ. The correlations are generally higher for HDI than IQ. With the exception of the color yellow, the correlations with HDI are significant even when controlling for the correlation between HDI and IQ. The present study suggests national flag symbolism and colors as yet another correlate of average group intelligence.

The Evolution of Aesthetics and Beauty

For over two millennia philosophical discussions tied aesthetics mainly to art and led to meager insights, if any, into why humans experience aesthetic reactions. Current scientific discussions on the issue, on the other hand, have turned to biology, evolution, and neuroscience. They link aesthetics with beauty in art, faces, scenery, and a variety of other sources. The biology of beauty is now viewed as having roots that extend far into the human ancestry, specifically to animal mate selection strategies. This chapter traces and explains the biological role in the human beauty response and its usefulness in various domains of human interactions.

The Edge of Respectability

This chapter concerns the insights of Elaine Morgan, particularly in her 1972 book, The Descent of Woman. In this book, she argues that all theories of human evolution to date were based on a male-centered notion of human evolution. The Descent of Woman was one of the earliest public rejoinders to masculinist narratives of human ancestry. An Oxford-educated Welsh writer for BBC Radio, Morgan chose as her target several of the most prominent books on human evolution circulating through the United Kingdom. By the early 1970s, in other words, evolutionary approaches to human nature were sufficiently current for other writers (and publishers) to deem them worthy of ridicule. Yet humor was a double-edged sword. Sarcasm might garner readers but for authors with no formal training in anthropology or zoology, like Morgan, maintaining a respectable public persona as a popularizer of science proved difficult.

Emergent populations derived with unsupervised learning of human whole genomes

Artificial intelligence (AI) holds great promise to precisely classify human ancestry and the genetic causes of complex diseases. I have constructed an unsupervised machine learning paradigm that examines the whole genome as a hyper-dense, nonlinear, multidimensional feature space. The AI system culminates in 26 neural network neurons each sensitive to a specific heritage that can identify an individual’s component genetic heritages with a top-5 error of <0.5%. Importantly, I observed some populations previously thought to belong to single stratum are composed of multiple strata – for instance Japan is defined as a uniform population using previous methods. I found that the Japanese individuals segregate to two very distinct populations. This work represents an essential step towards understanding the genetic background of patients to enable precision medicine causal disease gene identification.