Inferring Admixture Histories of Human Populations Using Linkage Disequilibrium

Long-range migrations and the resulting admixtures between populations have been important forces shaping human genetic diversity. Most existing methods for detecting and reconstructing historical admixture events are based on allele frequency divergences or patterns of ancestry segments in chromosomes of admixed individuals. An emerging new approach harnesses the exponential decay of admixture-induced linkage disequilibrium (LD) as a function of genetic distance. Here, we comprehensively develop LD-based inference into a versatile tool for investigating admixture. We present a new weighted LD statistic that can be used to infer mixture proportions as well as dates with fewer constraints on reference populations than previous methods. We define an LD-based three-population test for admixture and identify scenarios in which it can detect admixture events that previous formal tests cannot. We further show that we can uncover phylogenetic relationships among populations by comparing weighted LD curves obtained using a suite of references. Finally, we describe several improvements to the computation and fitting of weighted LD curves that greatly increase the robustness and speed of the calculations. We implement all of these advances in a software package, ALDER, which we validate in simulations and apply to test for admixture among all populations from the Human Genome Diversity Project (HGDP), highlighting insights into the admixture history of Central African Pygmies, Sardinians, and Japanese.     

Genetics of the Pig Tapeworm in Madagascar Reveal a History of Human Dispersal and Colonization

An intricate history of human dispersal and geographic colonization has strongly affected the distribution of human pathogens. The pig tapeworm Taenia solium occurs throughout the world as the causative agent of cysticercosis, one of the most serious neglected tropical diseases. Discrete genetic lineages of T. solium in Asia and Africa/Latin America are geographically disjunct; only in Madagascar are they sympatric. Linguistic, archaeological and genetic evidence has indicated that the people in Madagascar have mixed ancestry from Island Southeast Asia and East Africa. Hence, anthropogenic introduction of the tapeworm from Southeast Asia and Africa had been postulated. This study shows that the major mitochondrial haplotype of T. solium in Madagascar is closely related to those from the Indian Subcontinent. Parasitological evidence presented here, and human genetics previously reported, support the hypothesis of an Indian influence on Malagasy culture coinciding with periods of early human migration onto the island. We also found evidence of nuclear-mitochondrial discordance in single tapeworms, indicating unexpected cross-fertilization between the two lineages of T. solium. Analyses of genetic and geographic populations of T. solium in Madagascar will shed light on apparently rapid evolution of this organism driven by recent (<2,000 yr) human migrations, following tens of thousands of years of geographic isolation.     

Inferring Demographic History from a Spectrum of Shared Haplotype Lengths

There has been much recent excitement about the use of genetics to elucidate ancestral history and demography. Whole genome data from humans and other species are revealing complex stories of divergence and admixture that were left undiscovered by previous smaller data sets. A central challenge is to estimate the timing of past admixture and divergence events, for example the time at which Neanderthals exchanged genetic material with humans and the time at which modern humans left Africa. Here, we present a method for using sequence data to jointly estimate the timing and magnitude of past admixture events, along with population divergence times and changes in effective population size. We infer demography from a collection of pairwise sequence alignments by summarizing their length distribution of tracts of identity by state (IBS) and maximizing an analytic composite likelihood derived from a Markovian coalescent approximation. Recent gene flow between populations leaves behind long tracts of identity by descent (IBD), and these tracts give our method power by influencing the distribution of shared IBS tracts. In simulated data, we accurately infer the timing and strength of admixture events, population size changes, and divergence times over a variety of ancient and recent time scales. Using the same technique, we analyze deeply sequenced trio parents from the 1000 Genomes project. The data show evidence of extensive gene flow between Africa and Europe after the time of divergence as well as substructure and gene flow among ancestral hominids. In particular, we infer that recent African-European gene flow and ancient ghost admixture into Europe are both necessary to explain the spectrum of IBS sharing in the trios, rejecting simpler models that contain less population structure.     

Inferring Mathematical Equations Using Crowdsourcing

Crowdsourcing, understood as outsourcing work to a large network of people in the form of an open call, has been utilized successfully many times, including a very interesting concept involving the implementation of computer games with the objective of solving a scientific problem by employing users to play a game—so-called crowdsourced serious games. Our main objective was to verify whether such an approach could be successfully applied to the discovery of mathematical equations that explain experimental data gathered during the observation of a given dynamic system. Moreover, we wanted to compare it with an approach based on artificial intelligence that uses symbolic regression to find such formulae automatically. To achieve this, we designed and implemented an Internet game in which players attempt to design a spaceship representing an equation that models the observed system. The game was designed while considering that it should be easy to use for people without strong mathematical backgrounds. Moreover, we tried to make use of the collective intelligence observed in crowdsourced systems by enabling many players to collaborate on a single solution. The idea was tested on several hundred players playing almost 10,000 games and conducting a user opinion survey. The results prove that the proposed solution has very high potential. The function generated during weeklong tests was almost as precise as the analytical solution of the model of the system and, up to a certain complexity level of the formulae, it explained data better than the solution generated automatically by Eureqa, the leading software application for the implementation of symbolic regression. Moreover, we observed benefits of using crowdsourcing; the chain of consecutive solutions that led to the best solution was obtained by the continuous collaboration of several players.     

Inferring the Demographic History of African Farmers and Pygmy Hunter–Gatherers Using a Multilocus Resequencing Data Set

The transition from hunting and gathering to farming involved a major cultural innovation that has spread rapidly over most of the globe in the last ten millennia. In sub-Saharan Africa, hunter–gatherers have begun to shift toward an agriculture-based lifestyle over the last 5,000 years. Only a few populations still base their mode of subsistence on hunting and gathering. The Pygmies are considered to be the largest group of mobile hunter–gatherers of Africa. They dwell in equatorial rainforests and are characterized by their short mean stature. However, little is known about the chronology of the demographic events—size changes, population splits, and gene flow—ultimately giving rise to contemporary Pygmy (Western and Eastern) groups and neighboring agricultural populations. We studied the branching history of Pygmy hunter–gatherers and agricultural populations from Africa and estimated separation times and gene flow between these populations. We resequenced 24 independent noncoding regions across the genome, corresponding to a total of ~33 kb per individual, in 236 samples from seven Pygmy and five agricultural populations dispersed over the African continent. We used simulation-based inference to identify the historical model best fitting our data. The model identified included the early divergence of the ancestors of Pygmy hunter–gatherers and farming populations ~60,000 years ago, followed by a split of the Pygmies' ancestors into the Western and Eastern Pygmy groups ~20,000 years ago. Our findings increase knowledge of the history of the peopling of the African continent in a region lacking archaeological data. An appreciation of the demographic and adaptive history of African populations with different modes of subsistence should improve our understanding of the influence of human lifestyles on genome diversity.     

Evaluating Five Models of Human Colonization

Five patterns for the human colonization of unoccupied regions are tested for viability by the use of a new stochastic band simulation program, called ETHNOPOP ©, that incorporates four demographic variables: (1) sex ratio at birth, (2) distribution of sibships, (3) maternal risk of giving birth, and (4) risk of death; as well as three cultural variables: (1) marriage choice, (2) polygyny, and (3) marriage pool. It was discovered that small colonizing bands initially undergo a Stochastic Crisis because of a demographic version of the Sewall Wright Effect, followed most often by a Malthusian Takeoff in population size if fertility rates are high enough. Of the models tested, it was discovered that the "string of pearls" model, in which bands are arrayed along a stream or coastline, is the most viable colonizing pattern, and that the "outpost" model, in which a band has no contiguous bands, and the "beachhead" model, in which a group of bands are clustered against a body of water, should be subsumed under the "pulse" model, in which successive waves of colonists enter a previously unoccupied area, [colonization, modeling, simulation, sex ratio, fertility]     

Gastrointestinal Colonization Rates for Human Clinical Isolates of <Emphasis Type="Italic">Aeromonas Veronii</Emphasis> Using a Mouse Model

A variety of environment-associated gastrointestinal infections have been associated with the Aeromonas group of bacteria which contain both non-virulent strains as well as virulent strains within a particular species. This study monitors the colonization rates of colon tissue in a mouse-streptomycin dose/response model involving isolates of Aeromonas veronii biovar sobria obtained from human clinical specimens. The ability to successfully colonize mouse colon tissues by the human clinical isolates was then compared with the rates achieved in a previous study of Aeromonas isolates obtained from environmental drinking water samples. Results suggest that strains of Aeromonas isolated from drinking water environmental samples contain pathogenic and virulence capabilities similar to those seen in Aeromonas veronii clinical isolates from human infections.     

关于复制中医动物模型的思考

指出复制中医动物模型需要遵守4个基本的原则,即以中医理论为指导;复制的模型症状符合中医临床;相应的药物能够反证治疗;模型稳定,能重复。同时,认为复制理想的中医动物模型可以采取以下几个步骤,即：选择研究的病、证模型;了解相应病、证临床表现、病因、病理和病机;紧密关注临床,找寻适当造模因素;选择恰当造模动物;评判指标的选择;药物反证;重复验证。     

Oxalobacter formigenes Colonization and Oxalate Dynamics in a Mouse Model

Animal and human studies have provided compelling evidence that colonization of the intestine with Oxalobacter formigenes reduces urinary oxalate excretion and lowers the risk of forming calcium oxalate kidney stones. The mechanism providing protection appears to be related to the unique ability of O. formigenes to rely on oxalate as a major source of carbon and energy for growth. However, much is not known about the factors that influence colonization and host-bacterium interactions. We have colonized mice with O. formigenes OxCC13 and systematically investigated the impacts of diets with different levels of calcium and oxalate on O. formigenes intestinal densities and urinary and intestinal oxalate levels. Measurement of intestinal oxalate levels in mice colonized or not colonized with O. formigenes demonstrated the highly efficient degradation of soluble oxalate by O. formigenes relative to other microbiota. The ratio of calcium to oxalate in diets was important in determining colonization densities and conditions where urinary oxalate and fecal oxalate excretion were modified, and the results were consistent with those from studies we have performed with colonized and noncolonized humans. The use of low-oxalate purified diets showed that 80% of animals retained O. formigenes colonization after a 1-week dietary oxalate deprivation. Animals not colonized with O. formigenes excreted two times more oxalate in feces than they had ingested. This nondietary source of oxalate may play an important role in the survival of O. formigenes during periods of dietary oxalate deprivation. These studies suggest that the mouse will be a useful model to further characterize interactions between O. formigenes and the host and factors that impact colonization.     

Using Human iPSC-Derived Neurons to Model TAU Aggregation

Alzheimer’s disease and frontotemporal dementia are amongst the most common forms of dementia characterized by the formation and deposition of abnormal TAU in the brain. In order to develop a translational human TAU aggregation model suitable for screening, we transduced TAU harboring the pro-aggregating P301L mutation into control hiPSC-derived neural progenitor cells followed by differentiation into cortical neurons. TAU aggregation and phosphorylation was quantified using AlphaLISA technology. Although no spontaneous aggregation was observed upon expressing TAU-P301L in neurons, seeding with preformed aggregates consisting of the TAU-microtubule binding repeat domain triggered robust TAU aggregation and hyperphosphorylation already after 2 weeks, without affecting general cell health. To validate our model, activity of two autophagy inducers was tested. Both rapamycin and trehalose significantly reduced TAU aggregation levels suggesting that iPSC-derived neurons allow for the generation of a biologically relevant human Tauopathy model, highly suitable to screen for compounds that modulate TAU aggregation.     

Effects of Age and Strain on the Microbiota Colonization in an Infant Human Flora-Associated Mouse Model

The establishment of human flora-associated animal models allows the in vivo manipulation of host, microbial, and environmental parameters to influence the gut microbial community. However, it is difficult to simulate infant gut microbiota in germ-free animals because of the variation and dynamic state of infant microbial communities. In this study, the effects of age and strain on intestinal microbiota were observed in an infant human flora-associated (IHFA) mouse model. To establish an IHFA model, postnatal day (PND) 1 germ-free mice (Kunming, n = 10; BALB/c, n = 10) were infected with feces from a breast-fed infant. Microbiota in the feces of BALB/c mice (at PND 7, 14, and 21), and Kunming mice (at PND 14) were analyzed by PCR-denaturing gradient gel electrophoresis. Bifidobacteria and lactobacilli levels in the feces of BALB/c and Kunming mice (PND 7/14/21) were detected by quantitative real-time PCR. The Dice similarity coefficient (Cs) for the fecal microbiota of IHFA mice in comparison with the HD donor sample was higher for BALB/c mice than for Kunming mice (P < 0.05). In addition, the DCs at PND 7 were lower than those at PND 14 and PND 21 in both mouse strains (P < 0.05). The Bifidobacteria and Lactobacillus species colonizing the BALB/c mice were similar to those in the Kunming mice (at PND 7/14/21). The bifidobacteria counts increased with age in both mouse strains, whereas the lactobacilli counts decreased with age in both strains. These results suggest that both age and strain influence microbiota patterns in the IHFA mouse model.     

Genetic Studies of Human DNA Repair Proteins Using Yeast as a Model System

Understanding the roles of human DNA repair proteins in genetic pathways is a formidable challenge to many researchers. Genetic studies in mammalian systems have been limited due to the lack of readily available tools including defined mutant genetic cell lines, regulatory expression systems, and appropriate selectable markers. To circumvent these difficulties, model genetic systems in lower eukaryotes have become an attractive choice for the study of functionally conserved DNA repair proteins and pathways. We have developed a model yeast system to study the poorly defined genetic functions of the Werner syndrome helicase-nuclease (WRN) in nucleic acid metabolism. Cellular phenotypes associated with defined genetic mutant backgrounds can be investigated to clarify the cellular and molecular functions of WRN through its catalytic activities and protein interactions. The human WRN gene and associated variants, cloned into DNA plasmids for expression in yeast, can be placed under the control of a regulatory plasmid element. The expression construct can then be transformed into the appropriate yeast mutant background, and genetic function assayed by a variety of methodologies. Using this approach, we determined that WRN, like its related RecQ family members BLM and Sgs1, operates in a Top3-dependent pathway that is likely to be important for genomic stability. This is described in our recent publication [1] at www.impactaging.com. Detailed methods of specific assays for genetic complementation studies in yeast are provided in this paper.Download video file.^{(158M, mp4)}     

Inferring the History of the Polyploid Silene aegaea (Caryophyllaceae) Using Plastid and Homoeologous Nuclear DNA Sequences

The origin of the rare allotetraploid Silene aegaea was inferred from plastid rps16 intron sequences, homoeologous copies of nuclear ribosomal internal transcribed spacer (ITS) sequences, and an intron from the nuclear gene coding for the second largest subunit of RNA polymerase II (RPB2). The nuclear DNA regions support the S. sedoides and S. pentelica lineages as most closely related to the two S. aegaea paralogues. A few recombinant ITS sequences were found, but as PCR recombination could be demonstrated, no true recombination could be demonstrated. No recombination was found in the RPB2 sequences. Plastid rps16 intron sequences strongly support S. pentelica as the maternal lineage. The strength of the approach of using homoeologous sequences of several loci is demonstrated, and its usefulness for the study of phylogenies of groups including polyploids is emphasized.     

Inferring Regulatory Networks from Expression Data Using Tree-Based Methods

One of the pressing open problems of computational systems biology is the elucidation of the topology of genetic regulatory networks (GRNs) using high throughput genomic data, in particular microarray gene expression data. The Dialogue for Reverse Engineering Assessments and Methods (DREAM) challenge aims to evaluate the success of GRN inference algorithms on benchmarks of simulated data. In this article, we present GENIE3, a new algorithm for the inference of GRNs that was best performer in the DREAM4 In Silico Multifactorial challenge. GENIE3 decomposes the prediction of a regulatory network between p genes into p different regression problems. In each of the regression problems, the expression pattern of one of the genes (target gene) is predicted from the expression patterns of all the other genes (input genes), using tree-based ensemble methods Random Forests or Extra-Trees. The importance of an input gene in the prediction of the target gene expression pattern is taken as an indication of a putative regulatory link. Putative regulatory links are then aggregated over all genes to provide a ranking of interactions from which the whole network is reconstructed. In addition to performing well on the DREAM4 In Silico Multifactorial challenge simulated data, we show that GENIE3 compares favorably with existing algorithms to decipher the genetic regulatory network of Escherichia coli. It doesn''t make any assumption about the nature of gene regulation, can deal with combinatorial and non-linear interactions, produces directed GRNs, and is fast and scalable. In conclusion, we propose a new algorithm for GRN inference that performs well on both synthetic and real gene expression data. The algorithm, based on feature selection with tree-based ensemble methods, is simple and generic, making it adaptable to other types of genomic data and interactions.     

Inferring Metabolic States in Uncharacterized Environments Using Gene-Expression Measurements

The large size of metabolic networks entails an overwhelming multiplicity in the possible steady-state flux distributions that are compatible with stoichiometric constraints. This space of possibilities is largest in the frequent situation where the nutrients available to the cells are unknown. These two factors: network size and lack of knowledge of nutrient availability, challenge the identification of the actual metabolic state of living cells among the myriad possibilities. Here we address this challenge by developing a method that integrates gene-expression measurements with genome-scale models of metabolism as a means of inferring metabolic states. Our method explores the space of alternative flux distributions that maximize the agreement between gene expression and metabolic fluxes, and thereby identifies reactions that are likely to be active in the culture from which the gene-expression measurements were taken. These active reactions are used to build environment-specific metabolic models and to predict actual metabolic states. We applied our method to model the metabolic states of Saccharomyces cerevisiae growing in rich media supplemented with either glucose or ethanol as the main energy source. The resulting models comprise about 50% of the reactions in the original model, and predict environment-specific essential genes with high sensitivity. By minimizing the sum of fluxes while forcing our predicted active reactions to carry flux, we predicted the metabolic states of these yeast cultures that are in large agreement with what is known about yeast physiology. Most notably, our method predicts the Crabtree effect in yeast cells growing in excess glucose, a long-known phenomenon that could not have been predicted by traditional constraint-based modeling approaches. Our method is of immediate practical relevance for medical and industrial applications, such as the identification of novel drug targets, and the development of biotechnological processes that use complex, largely uncharacterized media, such as biofuel production.     

Immunization Using GroEL Decreases Clostridium difficile Intestinal Colonization

Clostridium difficile is a pathogen which is responsible for diarrhea and colitis, particularly after treatment with antibiotics. Clinical signs are mainly due to two toxins, TcdA and TcdB. However, the first step of pathogenesis is the colonization process. We evaluated C. difficile surface proteins as vaccine antigens in the hamster model to prevent intestinal colonization. This vaccination induced a partial protection of hamsters against death after a C. difficile challenge. A proteomic analysis of animal sera allowed us to identify proteins which could be responsible for the protection observed. Among these proteins, we identified the GroEL heat shock protein. To confirm the role of the specific GroEL antibodies in the delayed C. difficile colonization of hamsters, we performed an immunization assay in a mouse model. After intranasal immunization with the recombinant protein GroEL, we observed a lower C. difficile intestinal colonization in the immunized group as compared to the control group.     

Inferring Selective History from Multilocus Frequency Data: Wright Meets the Hamiltonian

We describe a method to study characteristics of the dynamics of multilocus population genetic models without specifying the form of selection a priori. Our approach consists of specifying initial and final genotypic frequencies (either completely or partially) and then determining the minimum time to go from the initial condition to the final condition according to a continuous time genetic model, with arbitrary constraints on the strength and possibly the form of selection. In analyzing a two-locus, two-allele model with this approach, we show that--so long as r is not much larger than s--substantial linkage disequilibrium can be generated from an initial state of linkage equilibrium in a few hundred generations. We also show that unless recombination is much larger than selection, there is only weak dependence on r of the minimum time to reach a specified state. Thus, similar strengths of selection can lead to similar levels of disequilibrium over a fixed time and a range of small recombination rates. This implies that, within the level of a single gene, selection cannot in general be assumed to lead to any particular relationship between recombination rate and levels of disequilibrium. We indicate a number of other ways in which our method can be useful in asking theoretical questions and in interpreting data.     

Inferring Pairwise Interactions from Biological Data Using Maximum-Entropy Probability Models

Maximum entropy-based inference methods have been successfully used to infer direct interactions from biological datasets such as gene expression data or sequence ensembles. Here, we review undirected pairwise maximum-entropy probability models in two categories of data types, those with continuous and categorical random variables. As a concrete example, we present recently developed inference methods from the field of protein contact prediction and show that a basic set of assumptions leads to similar solution strategies for inferring the model parameters in both variable types. These parameters reflect interactive couplings between observables, which can be used to predict global properties of the biological system. Such methods are applicable to the important problems of protein 3-D structure prediction and association of gene–gene networks, and they enable potential applications to the analysis of gene alteration patterns and to protein design.     

A Model for Bacterial Colonization of Sinking Aggregates

Sinking aggregates provide important nutrient-rich environments for marine bacteria. Quantifying the rate at which motile bacteria colonize such aggregations is important in understanding the microbial loop in the pelagic food web. In this paper, a simple analytical model is presented to predict the rate at which bacteria undergoing a random walk encounter a sinking aggregate. The model incorporates the flow field generated by the sinking aggregate, the swimming behavior of the bacteria, and the interaction of the flow with the swimming behavior. An expression for the encounter rate is computed in the limit of large Péclet number when the random walk can be approximated by a diffusion process. Comparison with an individual-based numerical simulation is also given.