Eran Elhaik

Plant–microbe interactions in the rhizosphere are central to nutrient cycling and ecosystem functioning. Sedimentary ancient DNA (sedaDNA) is a promising yet underexplored tool for reconstructing past microbial communities and investigating ecological interactions among plants, animals, and microorganisms. Here, we reanalyse the previously published Kap København Formation (Northern Greenland) sedaDNA dataset to move beyond taxonomic ecosystem reconstruction and test whether ancient sediments preserve structured, rhizosphere-compatible plant–microbe association signals. Our results show that this ancient boreal ecosystem hosted several rhizosphere-associated taxa, comparable to those in modern boreal soils. Several bacterial genera co-occurred repeatedly with specific plant families, forming a rhizosphere-like taxonomic core with predicted plant-growth-promoting traits related to nutrient acquisition, colonisation, and stress tolerance. Although sedaDNA co-occurrence cannot demonstrate direct symbiosis, the consistency of taxonomic, network, and functional signals suggests that ancient sediments preserve interconnected ecological structure. Our findings extend sedaDNA-based ecosystem reconstruction beyond taxonomy and provide a possibility for investigating plant–microbe association signals in deep time.

Trace evidence analysis is a specialized forensic science discipline that examines and interprets material transfers between objects, individuals, and environments using sources like hair, fibers, soil, and pollen. Identifying the recent geographic whereabouts of organisms has been challenging in ecology, microbiology, and forensics due to difficulties in uniquely associating biological material with specific sites. While human DNA can predict origins, it has limited forensic value as it remains constant and does not indicate recent movements. Only dynamic spatio-temporal types of information shared and exchanged between organisms and the environment can be used by tracing applications. However, no microbial biogeographical applications are available, and localizing microbes remains an unresolved challenge. We developed the Microbiome Geographic Population Structure (mGPS), the first machine-learning-based tool that leverages microbial relative sequence abundances to pinpoint the fine-scale source locations of microorganisms. mGPS was rigorously tested on microbiomes from the built (MetaSUB project), soil (Soil Atlas project), and marine (Tara Ocean) environments sequenced using different approaches. Applied to 40 global cities, mGPS predicted the sampling sites for 92% of the samples. At the resolution of a single city, mGPS predicted up to 82% of sampling sites, which often were only a few hundred meters apart. Applied to soil and marine microbiomes, mGPS predicted the sampling sites for 86% and 74% of the samples, respectively. We demonstrated that mGPS differentiated local from non-local microorganisms and used it to trace the global spread of antimicrobial resistance genes. mGPS’s ability to localize samples to their waterbody, country, city, and transit stations has forensics, medical, and epidemiological applications.

Motivation: Efforts to address health disparities are often limited by the lack of robust computational tools for inferring genetic ancestry by calculating an individual’s genetic similarity to continental groups. We have already shown that a preferred alternative to self-described race is using ancestry-informative markers (AIMs) that can be classified into ancestral components and used to estimate their similarity to those of known populations to identify continental groups. However, real-world genomic data can present challenges, including limited availability of germline DNA, a small number of AIMs for each sample, and the use of different variant calling software, limiting the application of existing solutions. Results: Here, we describe a novel supervised machine-learning tool AncestryGeni, which infers genetic ancestry for samples with even a hundred markers and is applicable to any genomic data, including whole exome sequencing (WES) and RNA sequencing (RNA-Seq) data. Applying AncestryGeni to a real-world genomic dataset obtained from the Multiple Myeloma Research Foundation (MMRF) CoMMpass study, we show that it is more accurate than the commonly used FastNGSadmix when using nonstandard genomic material. We also demonstrate that when using AncestryGeni, the tumor-derived sequence obtained from WES and RNA-Seq can be a robust data source to accurately estimate an individual’s genetic similarity to a continental group. Availability and implementation: AncestryGeni pipeline is available at https://github.com/eelhaik/AncestryGeni/tree/main.

It is widely accepted that humans interbred with Neanderthals and other extinct hominins, leaving a lasting genetic legacy. However, much of the supporting evidence was developed using the statistic D, which assumes, without testing, both that mutation rate is constant and that recurrent mutations are vanishingly rare. These assumptions together preclude an alternative explanation based on variation in mutation rates across human populations. Here we critically evaluate the assumptions underlying D and confirm that neither is valid. Over 40% of SNPs in dbSNP carry recurrent mutations. Theory indicates that D does not vary with mutation rate as long as the mutation rate does not vary between populations. In practice, D calculated separately for different sequence motifs varies greatly, implying strongly that mutation rates do vary between populations. We show that most, if not all, D-informative sites result from two mutations rather than the one mutation expected under the introgression hypothesis. Moreover, individual non-Africans carry a signal in more than five times as many genomic windows as can be accounted for by the 2% legacy they are thought to carry, indicating a signal that is radically more diffuse than expected. Remarkably, partitioning the data by whether the chimpanzee or Neanderthal allele is the major allele in humans reveals that the overall reported D-value of ∼5% actually comprises two opposing components: one with D ∼ 30% and another with D ∼ -25%. Tellingly, the positive component is produced by sites where the Neanderthal allele is the major allele, the exact opposite of what should be the case under introgression, where introgressed alleles should be rare. We show further that the entire D signal can be accounted for by sites where the Neanderthal allele is fixed outside Africa and the chimpanzee allele is rare inside Africa. Investigating potential mechanisms, we extend the published observation that the mutability of three-base combinations across human populations is influenced by flanking sequence heterozygosity to reveal how genomic regions that lost more heterozygosity out of Africa exhibit higher D-values. This correlation supports a model where loss of heterozygosity slowed the mutation rate, thereby reducing the divergence between Neanderthals and non-Africans. Across independent tests, our findings consistently indicate that the mutation rate variation hypothesis provides a more compelling explanation for the observed patterns in human-Neanderthal genetic relationships than the introgression hypothesis. We argue that the mutation rate variation hypothesis would help settle a number of conflicting patterns in the literature and, hence, that the concept of archaic introgression into humans and its implications for hominin-derived traits warrants reconsideration.

Evolutionary biologists, primarily palaeoanthropologists, anatomists and ontogenists, employ modern geometric morphometrics to quantitatively analyse physical forms (e.g., skull morphology) and explore relationships, variations, and differences between samples and taxa using landmark coordinates. The standard approach comprises two steps: Generalised Procrustes Analysis (GPA) followed by Principal Component Analysis (PCA). PCA projects the superimposed data produced by GPA onto a set of uncorrelated variables, which can be visualised on scatterplots and used to draw phenetic, evolutionary, and ontogenetic conclusions. Recently, the use of PCA in genetic studies has been challenged. Due to PCA’s central role in morphometrics, we sought to evaluate the standard approach and claims based on PCA outcomes. To test PCA’s accuracy, robustness, and reproducibility using benchmark data of the crania of five papionin genera, we developed MORPHIX, a Python package for processing superimposed landmark data with classifier and outlier detection methods, which can be further visualised using various plots. Throughout this manuscript, we address the recent and contentious use of PCA in physical anthropology and phylogenetic inference, such as the case of Homo Nesher Ramla, an archaic hominin with a questionable taxonomy. We found that PCA outcomes are artefacts of the input data and are neither reliable, robust, nor reproducible as field members may assume. We also found that supervised machine learning classifiers are more accurate both for classification and detecting new taxa. Our findings raise concerns about PCA-based findings applied in 18,400 to 35,200 Physical anthropology studies. Our work can be used to evaluate prior and novel claims concerning the origins and relatedness of inter- and intra-species and improve phylogenetic and taxonomic reconstructions.

Over the past decade, sequencing data generated by large microbiome projects showed that taxa exhibit patchy geographical distribution, raising questions about the geospatial dynamics that shape natural microbiomes and the spread of antimicrobial resistance genes. Answering these questions requires distinguishing between local and nonlocal microorganisms and identifying the source sites for the latter. Predicting the source sites and migration routes of microbiota has been envisioned for decades but was hampered by the lack of data, tools, and understanding of the processes governing biodiversity. State-of-the-art biogeographical tools suffer from low resolution and cannot predict biogeographical patterns at a scale relevant to ecological, medical, or epidemiological applications. Analyzing urban, soil, and marine microorganisms, we found that some taxa exhibit regional-specific composition and abundance, suggesting they can be used as biogeographical biomarkers. We developed the microbiome geographic population structure, a machine learning–based tool that utilizes microbial relative sequence abundances to yield a fine-scale source site for microorganisms. Microbiome geographic population structure predicted the source city for 92% of the samples and the within-city source for 82% of the samples, though they were often only a few hundred meters apart. Microbiome geographic population structure also predicted soil and marine sampling sites for 86% and 74% of the samples, respectively. We demonstrated that microbiome geographic population structure differentiated local from nonlocal microorganisms and used it to trace the global spread of antimicrobial resistance genes. Microbiome geographic population structure's ability to localize samples to their water body, country, city, and transit stations opens new possibilities in tracing microbiomes and has applications in forensics, medicine, and epidemiology.

Time-to-event prediction is a key task for biological discovery, experimental medicine, and clinical care. This is particularly true for neurological diseases where development of reliable biomarkers is often limited by difficulty visualising and sampling relevant cell and molecular pathobiology. To date, much work has relied on Cox regression because of ease-of-use, despite evidence that this model includes incorrect assumptions. We have implemented a set of deep learning and spline models for time-to-event modelling within a fully customizable ‘app’ and accompanying online portal, both of which can be used for any time-to-event analysis in any disease by a non-expert user. Our online portal includes capacity for end-users including patients, Neurology clinicians, and researchers, to access and perform predictions using a trained model, and to contribute new data for model improvement, all within a data-secure environment. We demonstrate a pipeline for use of our app with three use-cases including imputation of missing data, hyperparameter tuning, model training and independent validation. We show that predictions are optimal for use in downstream applications such as genetic discovery, biomarker interpretation, and personalised choice of medication. We demonstrate the efficiency of an ensemble configuration, including focused training of a deep learning model. We have optimised a pipeline for imputation of missing data in combination with time-to-event prediction models. Overall, we provide a powerful and accessible tool to develop, access and share time-to-event prediction models; all software and tutorials are available at www.predictte.org .

Evolutionary biologists, primarily palaeoanthropologists, anatomists and ontogenists, employ modern geometric morphometrics to quantitatively analyse physical forms (e.g., skull morphology) and explore relationships, variations, and differences between samples and taxa using landmark coordinates. The standard approach comprises two steps: Generalised Procrustes Analysis (GPA) followed by Principal Component Analysis (PCA). PCA projects the superimposed data produced by GPA onto a set of uncorrelated variables, which can be visualised on scatterplots and used to draw phenetic, evolutionary, and ontogenetic conclusions. Recently, the use of PCA in genetic studies has been challenged. Due to PCA’s central role in morphometrics, we sought to evaluate the standard approach and claims based on PCA outcomes. To test PCA’s accuracy, robustness, and reproducibility using benchmark data of the crania of five papionin genera, we developed MORPHIX, a Python package for processing superimposed landmark data with classifier and outlier detection methods, which can be further visualised using various plots. Throughout this manuscript, we address the recent and contentious use of PCA in physical anthropology and phylogenetic inference, such as the case of Homo Nesher Ramla , an archaic hominin with a questionable taxonomy. We found that PCA outcomes are artefacts of the input data and are neither reliable, robust, nor reproducible as field members may assume. We also found that supervised machine learning classifiers are more accurate both for classification and detecting new taxa. Our findings raise concerns about PCA-based findings applied in 18,400 to 35,200 Physical anthropology studies. Our work can be used to evaluate prior and novel claims concerning the origins and relatedness of inter- and intra-species and improve phylogenetic and taxonomic reconstructions.

Glucocorticoid resistance is commonly observed in depression, and has been linked to reduced expression and/or function of the glucocorticoid receptor (NR3C1 in human, hereafter referred to as GR). Previous studies have shown that GR-mutant zebrafish exhibit behavioural abnormalities that are indicative of an affective disorder, suggesting that GR plays a role in brain function. We compared the brain methylomes and brain transcriptomes of adult wild-type and GR-mutant zebrafish, and identified 249 differentially methylated regions (DMRs) that are regulated by GR. These include a cluster of CpG sites within the first intron of fkbp5, the gene encoding the glucocorticoid-inducible heat shock protein co-chaperone Fkbp5. RNA-sequencing analysis revealed that genes associated with chaperone-mediated protein folding, the regulation of circadian rhythm and the regulation of metabolism are particularly sensitive to loss of GR function. In addition, we identified subsets of genes exhibiting GR-regulated transcription that are known to regulate behaviour, and are linked to unipolar depression and anxiety. Taken together, our results identify key biological processes and novel molecular mechanisms through which the GR is likely to mediate responses to stress in the adult zebrafish brain, and they provide further support for the zebrafish GR mutant as a model for the study of affective disorders.