eXplainable Artificial Intelligence (XAI) in aging clock models

eXplainable Artificial Intelligence (XAI) is a rapidly progressing field of machine learning, aiming to unravel the predictions of complex models. XAI is especially required in sensitive applications, e.g. in health care, when diagnosis, recommendations and treatment choices might rely on the decisions made by artificial intelligence systems. AI approaches have become widely used in aging research as well, in particular, in developing biological clock models and identifying biomarkers of aging and age-related diseases. However, the potential of XAI here awaits to be fully appreciated. We discuss the application of XAI for developing the "aging clocks" and present a comprehensive analysis of the literature categorized by the focus on particular physiological systems

Age-related DNA methylation changes are sex-specific: a comprehensive assessment

The existence of a sex gap in human health and longevity has been widely documented. Autosomal DNA methylation differences between males and females have been reported, but so far few studies have investigated if DNA methylation is differently affected by aging in males and females. We performed a meta-analysis of 4 large whole blood datasets, comparing 4 aspects of epigenetic age-dependent remodeling between the two sexes: differential methylation, variability, epimutations and entropy. We reported that a large fraction (43%) of sex-associated probes undergoes age-associated DNA methylation changes, and that a limited number of probes show age-by-sex interaction. We experimentally validated 2 regions mapping in FIGN and PRR4 genes and showed sex-specific deviations of their methylation patterns in models of decelerated (centenarians) and accelerated (Down syndrome) aging. While we did not find sex differences in the age-associated increase in epimutations and entropy, we showed that the number of probes having an age-related increase in methylation variability is 15 times higher in males compared to females. Our results can offer new epigenetic tools to study the interaction between aging and sex and can pave the way to the identification of molecular triggers of sex differences in longevity and age-related diseases prevalence

Analysis of human mitochondrial genome co-occurrence networks of Asian population at varying altitudes

Networks have been established as an extremely powerful framework to understand and predict the behavior of many large-scale complex systems. We studied network motifs, the basic structural elements of networks, to describe the possible role of co-occurrence of genomic variations behind high altitude adaptation in the Asian human population. Mitochondrial DNA (mtDNA) variations have been acclaimed as one of the key players in understanding the biological mechanisms behind adaptation to extreme conditions. To explore the cumulative effects of variations in the mitochondrial genome with the variation in the altitude, we investigated human mt-DNA sequences from the NCBI database at different altitudes under the co-occurrence motifs framework. Analysis of the co-occurrence motifs using similarity clustering revealed a clear distinction between lower and higher altitude regions. In addition, the previously known high altitude markers 3394 and 7697 (which are definitive sites of haplogroup M9a1a1c1b) were found to co-occur within their own gene complexes indicating the impact of intra-genic constraint on co-evolution of nucleotides. Furthermore, an ancestral 'RSRS50' variant 10,398 was found to co-occur only at higher altitudes supporting the fact that a separate route of colonization at these altitudes might have taken place. Overall, our analysis revealed the presence of co-occurrence interactions specific to high altitude at a whole mitochondrial genome level. This study, combined with the classical haplogroups analysis is useful in understanding the role of co-occurrence of mitochondrial variations in high altitude adaptation.11Nsciescopu

Investigating Mitonuclear Genetic Interactions Through Machine Learning: A Case Study on Cold Adaptation Genes in Human Populations From Different European Climate Regions

© 2020 Kalyakulina et al.Cold climates represent one of the major environmental challenges that anatomically modern humans faced during their dispersal out of Africa. The related adaptive traits have been achieved by modulation of thermogenesis and thermoregulation processes where nuclear (nuc) and mitochondrial (mt) genes play a major role. In human populations, mitonuclear genetic interactions are the result of both the peculiar genetic history of each human group and the different environments they have long occupied. This study aims to investigate mitonuclear genetic interactions by considering all the mitochondrial genes and 28 nuclear genes involved in brown adipose tissue metabolism, which have been previously hypothesized to be crucial for cold adaptation. For this purpose, we focused on three human populations (i.e., Finnish, British, and Central Italian people) of European ancestry from different biogeographical and climatic areas, and we used a machine learning approach to identify relevant nucDNA–mtDNA interactions that characterized each population. The obtained results are twofold: (i) at the methodological level, we demonstrated that a machine learning approach is able to detect patterns of genetic structure among human groups from different latitudes both at single genes and by considering combinations of mtDNA and nucDNA loci; (ii) at the biological level, the analysis identified population-specific nuclear genes and variants that likely play a relevant biological role in association with a mitochondrial gene (such as the “obesity gene” FTO in Finnish people). Further studies are needed to fully elucidate the evolutionary dynamics (e.g., migration, admixture, and/or local adaptation) that shaped these nucDNA–mtDNA interactions and their functional role11sciescopu

Drift speed without rotational diffusion.

<p>(a) Analytically obtained function <i>v</i>_<i>d</i>(<i>α</i>, <i>β</i>), see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0190434#pone.0190434.e020" target="_blank">Eq (11)</a> is shown as a green surface and numerically obtained results as symbols for <i>D</i>_<i>r</i> = 0.0 rad²s⁻¹, |∇<i>c</i>| = 0.05 <i>μm</i>⁻⁴ and <i>W</i> = 0.0458 <i>μ</i>m³. (b) Comparison of analytically (lines) and numerically (symbols) obtained drift speed dependences on the parameter <i>α</i> for four values of <i>β</i>: <i>β</i> = 1.0 (yellow), <i>β</i> = <i>α</i> (red), <i>β</i> = −1.0 (blue), <i>β</i> = 0.0 (purple).</p

Drift speed with rotational diffusion.

<p>(a) Analytically obtained function <i>v</i>_<i>d</i>(<i>α</i>, <i>β</i>), shown as surface, and numerically obtained result as points for <i>D</i>_<i>r</i> = 0.2 rad²s⁻¹, |∇<i>c</i>| = 0.05 <i>μm</i>⁻⁴ and <i>W</i> = 0.0458 <i>μ</i>m³. (b) Comparison of analytically (lines) and numerically (symbols) obtained drift speed dependences on the parameter <i>α</i> for four values of <i>β</i>: <i>β</i> = 1.0 (yellow), <i>β</i> = <i>α</i> (red), <i>β</i> = −1.0 (blue), <i>β</i> = 0.0 (purple).</p

Variability of the drift velocity with the cell body size.

<p>(a) Histograms showing the distribution of flick angles with various body lengths (data from [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0190434#pone.0190434.ref017" target="_blank">17</a>]). Red histogram represents all measured flick angles—170 events for 20 cells with various body lengths. Flick angles for four particular values of cellular body lengths are shown by narrow color histograms (<i>l</i> = 3.45 <i>μ</i>m, orange; <i>l</i> = 2.31 <i>μ</i>m, yellow; <i>l</i> = 1.72 <i>μ</i>m, green; <i>l</i> = 1.35 <i>μ</i>m, cyan histogram). The data obtained for 20 individuals (each of which is displayed at least 6 flicks in their trajectory) are shown as red points. Small blue ticks show the mean flick angles obtained from individual distributions. Black tick labels of the vertical axis correspond to the histograms of individual cells, whereas the red tick labels correspond to the red histogram for all cells. (b) Analytically obtained drift speed calculated for the mean flick angle of the considered cells, as a function of the angle and the corresponding body length. For results the following parameters were used: |∇<i>c</i>| = 0.5 <i>μ</i>m⁻⁴, λ = 3.3 s⁻¹, <i>v</i>₀ = 45 <i>μ</i>ms⁻¹, <i>D</i>_<i>r</i> = 0.2 rad²s⁻¹, <i>W</i> = 0.0458 <i>μ</i>m³ and <i>β</i> = cos 172°.</p

Analytically obtained drift speed and diffusion constant as functions of the mean flick angle and the corresponding body length.

<p>The curves represent the angle-dependent characteristics obtained from Eqs <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0190434#pone.0190434.e020" target="_blank">(11)</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0190434#pone.0190434.e022" target="_blank">(12)</a>. To plot the drift speed and the diffusion constant as a function of cell size, we use the data of [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0190434#pone.0190434.ref017" target="_blank">17</a>] to relate the size to the turning angle, and use that angle in analytical results Eqs <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0190434#pone.0190434.e020" target="_blank">(11)</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0190434#pone.0190434.e022" target="_blank">(12)</a> (these values are shown by symbols). The fact that data based on cell sizes line up with the theoretical curves as functions of angles indicates an approximately linear relation between the cell size and the cosine of the turning angle, which is in agreement with data presented in [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0190434#pone.0190434.ref017" target="_blank">17</a>]. The parameters are λ = 3.3 s⁻¹, <i>v</i>₀ = 45 <i>μ</i>ms⁻¹, <i>D</i>_<i>r</i> = 0.2 rad²s⁻¹ and <i>β</i> = cos 172°.</p