• A genomic timescale for the origin of eukaryotes

      Hedges, SB; Chen, H; Kumar, S; Wang, DYC; Thompson, AS; Watanabe, H; Kumar, Sudhir|0000-0002-9918-8212 (2001-09-12)
      Background: Genomic sequence analyses have shown that horizontal gene transfer occurred during the origin of eukaryotes as a consequence of symbiosis. However, details of the timing and number of symbiotic events are unclear. A timescale for the early evolution of eukaryotes would help to better understand the relationship between these biological events and changes in Earth's environment, such as the rise in oxygen. We used refined methods of sequence alignment, site selection, and time estimation to address these questions with protein sequences from complete genomes of prokaryotes and eukaryotes. Results: Eukaryotes were found to evolve faster than prokaryotes, with those eukaryotes derived from eubacteria evolving faster than those derived from archaebacteria. We found an early time of divergence (∼4 billion years ago, Ga) for archaebacteria and the archaebacterial genes in eukaryotes. Our analyses support at least two horizontal gene transfer events in the origin of eukaryotes, at 2.7 Ga and 1.8 Ga. Time estimates for the origin of cyanobacteria (2.6 Ga) and the divergence of an early-branching eukaryote that lacks mitochondria (Giardia) (2.2 Ga) fall between those two events. Conclusions: We find support for two symbiotic events in the origin of eukaryotes: one premitochondrial and a later mitochondrial event. The appearance of cyanobacteria immediately prior to the earliest undisputed evidence for the presence of oxygen (2.4-2.2 Ga) suggests that the innovation of oxygenic photosynthesis had a relatively rapid impact on the environment as it set the stage for further evolution of the eukaryotic cell. © 2001 Hedges et al; licensee BioMed Central Ltd.
    • A genomic timescale of prokaryote evolution: Insights into the origin of methanogenesis, phototrophy, and the colonization of land

      Battistuzzi, FU; Feijao, A; Hedges, SB (2004-11-09)
      Background: The timescale of prokaryote evolution has been difficult to reconstruct because of a limited fossil record and complexities associated with molecular clocks and deep divergences. However, the relatively large number of genome sequences currently available has provided a better opportunity to control for potential biases such as horizontal gene transfer and rate differences among lineages. We assembled a data set of sequences from 32 proteins (∼7600 amino acids) common to 72 species and estimated phylogenetic relationships and divergence times with a local clock method. Results: Our phylogenetic results support most of the currently recognized higher-level groupings of prokaryotes. Of particular interest is a well-supported group of three major lineages of eubacteria (Actinobacteria, Deinococcus, and Cyanobacteria) that we call Terrabacteria and associate with an early colonization of land. Divergence time estimates for the major groups of eubacteria are between 2.5-3.2 billion years ago (Ga) while those for archaebacteria are mostly between 3.1-4.1 Ga. The time estimates suggest a Hadean origin of life (prior to 4.1 Ga), an early origin of methanogenesis (3.8-4.1 Ga), an origin of anaerobic methanotrophy after 3.1 Ga, an origin of phototrophy prior to 3.2 Ga, an early colonization of land 2.8-3.1 Ga, and an origin of aerobic methanotrophy 2.5-2.8 Ga. Conclusions: Our early time estimates for methanogenesis support the consideration of methane, in addition to carbon dioxide, as a greenhouse gas responsible for the early warming of the Earths' surface. Our divergence times for the origin of anaerobic methanotrophy are compatible with highly depleted carbon isotopic values found in rocks dated 2.8-2.6 Ga. An early origin of phototrophy is consistent with the earliest bacterial mats and structures identified as stromatolites, but a 2.6 Ga origin of cyanobacteria suggests that those Archean structures, if biologically produced, were made by anoxygenic photosynthesizers. The resistance to desiccation of Terrabacteria and their elaboration of photoprotective compounds suggests that the common ancestor of this group inhabited land. If true, then oxygenic photosynthesis may owe its origin to terrestrial adaptations. © 2004 Battistuzzi et al; licensee BioMed Central Ltd.
    • A graph convolutional network-based deep reinforcement learning approach for resource allocation in a cognitive radio network

      Zhao, D; Qin, H; Song, B; Han, B; Du, X; Guizani, M; Du, Xiaojiang|0000-0003-4235-9671 (2020-09-02)
      © 2020 by the authors. Licensee MDPI, Basel, Switzerland. Cognitive radio (CR) is a critical technique to solve the conflict between the explosive growth of traffic and severe spectrum scarcity. Reasonable radio resource allocation with CR can effectively achieve spectrum sharing and co-channel interference (CCI) mitigation. In this paper, we propose a joint channel selection and power adaptation scheme for the underlay cognitive radio network (CRN), maximizing the data rate of all secondary users (SUs) while guaranteeing the quality of service (QoS) of primary users (PUs). To exploit the underlying topology of CRNs, we model the communication network as dynamic graphs, and the random walk is used to imitate the users’ movements. Considering the lack of accurate channel state information (CSI), we use the user distance distribution contained in the graph to estimate CSI. Moreover, the graph convolutional network (GCN) is employed to extract the crucial interference features. Further, an end-to-end learning model is designed to implement the following resource allocation task to avoid the split with mismatched features and tasks. Finally, the deep reinforcement learning (DRL) framework is adopted for model learning, to explore the optimal resource allocation strategy. The simulation results verify the feasibility and convergence of the proposed scheme, and prove that its performance is significantly improved.
    • A guide to phylogenetic metrics for conservation, community ecology and macroecology

      Tucker, CM; Cadotte, MW; Carvalho, SB; Jonathan Davies, T; Ferrier, S; Fritz, SA; Grenyer, R; Helmus, MR; Jin, LS; Mooers, AO; Pavoine, S; Purschke, O; Redding, DW; Rosauer, DF; Winter, M; Mazel, F; Helmus, Matthew|0000-0003-3977-0507 (2017-05-01)
      © 2016 The Authors. The use of phylogenies in ecology is increasingly common and has broadened our understanding of biological diversity. Ecological sub-disciplines, particularly conservation, community ecology and macroecology, all recognize the value of evolutionary relationships but the resulting development of phylogenetic approaches has led to a proliferation of phylogenetic diversity metrics. The use of many metrics across the sub-disciplines hampers potential meta-analyses, syntheses, and generalizations of existing results. Further, there is no guide for selecting the appropriate metric for a given question, and different metrics are frequently used to address similar questions. To improve the choice, application, and interpretation of phylo-diversity metrics, we organize existing metrics by expanding on a unifying framework for phylogenetic information. Generally, questions about phylogenetic relationships within or between assemblages tend to ask three types of question: how much; how different; or how regular? We show that these questions reflect three dimensions of a phylogenetic tree: richness, divergence, and regularity. We classify 70 existing phylo-diversity metrics based on their mathematical form within these three dimensions and identify ‘anchor’ representatives: for α-diversity metrics these are PD (Faith’s phylogenetic diversity), MPD (mean pairwise distance), and VPD (variation of pairwise distances). By analysing mathematical formulae and using simulations, we use this framework to identify metrics that mix dimensions, and we provide a guide to choosing and using the most appropriate metrics. We show that metric choice requires connecting the research question with the correct dimension of the framework and that there are logical approaches to selecting and interpreting metrics. The guide outlined herein will help researchers navigate the current jungle of indices.
    • A guide to PIN1 function and mutations across cancers

      El Boustani, M; De Stefano, L; Caligiuri, I; Mouawad, N; Granchi, C; Canzonieri, V; Tuccinardi, T; Giordano, A; Rizzolio, F; Giordano, Antonio|0000-0002-5959-016X (2019-01-01)
      © 2019 El Boustani, De Stefano, Caligiuri, Mouawad, Granchi, Canzonieri, Tuccinardi, Giordano and Rizzolio. PIN1 is a member of a family of peptidylprolyl isomerases that bind phosphoproteins and catalyze the rapid cis–trans isomerization of proline peptidyl bonds, resulting in an alteration of protein structure, function, and stability. PIN1 is overexpressed in human cancers, suggesting it promotes tumorigenesis, but depending on the cellular context, it also acts as a tumor suppressor. Here, we review the role of PIN1 in cancer and the regulation of PIN1 expression, and catalog the single nucleotide polymorphisms, and mutations in PIN1 gene associated with cancer. In addition, we provide a 3D model of the protein to localize the mutated residues.
    • A heuristic information cluster search approach for precise functional brain mapping

      Asadi, N; Wang, Y; Olson, I; Obradovic, Z (2020-06-15)
      © 2020 The Authors. Human Brain Mapping published by Wiley Periodicals, Inc. Detection of the relevant brain regions for characterizing the distinction between cognitive conditions is one of the most sought after objectives in neuroimaging research. A popular approach for achieving this goal is the multivariate pattern analysis which is currently conducted through a number of approaches such as the popular searchlight procedure. This is due to several advantages such as being automatic and flexible with regards to size of the search region. However, these approaches suffer from a number of limitations which can lead to misidentification of truly informative regions which in turn results in imprecise information maps. These limitations mainly stem from several factors such as the fact that the information value of the search spheres are assigned to the voxel at the center of them (in case of searchlight), the requirement for manual tuning of parameters such as searchlight radius and shape, and high complexity and low interpretability in commonly used machine learning-based approaches. Other drawbacks include overlooking the structure and interactions within the regions, and the disadvantages of using certain regularization techniques in analysis of datasets with characteristics of common functional magnetic resonance imaging data. In this article, we propose a fully data-driven maximum relevance minimum redundancy search algorithm for detecting precise information value of the clusters within brain regions while alleviating the above-mentioned limitations. Moreover, in order to make the proposed method faster, we propose an efficient algorithmic implementation. We evaluate and compare the proposed algorithm with the searchlight procedure as well as least absolute shrinkage and selection operator regularization-based mapping approach using both real and synthetic datasets. The analysis results of the proposed approach demonstrate higher information detection precision and map specificity compared to the benchmark approaches.
    • A Hydrogel-Based Ultrasonic Backscattering Wireless Biochemical Sensing

      Nam, J; Byun, E; Shim, H; Kim, E; Islam, S; Park, M; Kim, A; Song, SH; Kim, Albert|0000-0003-1539-1246 (2020-11-27)
      © Copyright © 2020 Nam, Byun, Shim, Kim, Islam, Park, Kim and Song. Wireless monitoring of the physio-biochemical information is becoming increasingly important for healthcare. In this work, we present a proof-of-concept hydrogel-based wireless biochemical sensing scheme utilizing ultrasound. The sensing system utilizes silica-nanoparticle embedded hydrogel deposited on a thin glass substrate, which presents two prominent interfaces for ultrasonic backscattering (tissue/glass and hydrogel/glass). To overcome the effect of the varying acoustic properties of the intervening biological tissues between the sensor and the external transducer, we implemented a differential mode of ultrasonic back-scattering. Here, we demonstrate a wireless pH measurement with a resolution of 0.2 pH level change and a wireless sensing range around 10 cm in a water tank.
    • A hypothetical molecular mechanism for TRPV1 activation that invokes rotation of an S6 asparagine

      Kasimova, MA; Yazici, AT; Yudin, Y; Granata, D; Klein, ML; Rohacs, T; Carnevale, V; Carnevale, Vincenzo|0000-0002-0447-1278 (2018-11-01)
      © 2018 Kasimova et al. The transient receptor potential channel vanilloid type 1 (TRPV1) is activated by a variety of endogenous and exogenous stimuli and is involved in nociception and body temperature regulation. Although the structure of TRPV1 has been experimentally determined in both the closed and open states, very little is known about its activation mechanism. In particular, the conformational changes that occur in the pore domain and result in ionic conduction have not yet been identified. Here we suggest a hypothetical molecular mechanism for TRPV1 activation, which involves rotation of a conserved asparagine in S6 from a position facing the S4-S5 linker toward the pore. This rotation is associated with hydration of the pore and dehydration of the four peripheral cavities located between each S6 and S4-S5 linker. In light of our hypothesis, we perform bioinformatics analyses of TRP and other evolutionary related ion channels, evaluate newly available structures, and reexamine previously reported water accessibility and mutagenesis experiments. These analyses provide several independent lines of evidence to support our hypothesis. Finally, we show that our proposed molecular mechanism is compatible with the prevailing theory that the selectivity filter acts as a secondary gate in TRPV1.
    • A large-scale evaluation of computational protein function prediction

      Radivojac, P; Clark, WT; Oron, TR; Schnoes, AM; Wittkop, T; Sokolov, A; Graim, K; Funk, C; Verspoor, K; Ben-Hur, A; Pandey, G; Yunes, JM; Talwalkar, AS; Repo, S; Souza, ML; Piovesan, D; Casadio, R; Wang, Z; Cheng, J; Fang, H; Gough, J; Koskinen, P; Törönen, P; Nokso-Koivisto, J; Holm, L; Cozzetto, D; Buchan, DWA; Bryson, K; Jones, DT; Limaye, B; Inamdar, H; Datta, A; Manjari, SK; Joshi, R; Chitale, M; Kihara, D; Lisewski, AM; Erdin, S; Venner, E; Lichtarge, O; Rentzsch, R; Yang, H; Romero, AE; Bhat, P; Paccanaro, A; Hamp, T; Kaßner, R; Seemayer, S; Vicedo, E; Schaefer, C; Achten, D; Auer, F; Boehm, A; Braun, T; Hecht, M; Heron, M; Hönigschmid, P; Hopf, TA; Kaufmann, S; Kiening, M; Krompass, D; Landerer, C; Mahlich, Y; Roos, M; Björne, J; Salakoski, T; Wong, A; Shatkay, H; Gatzmann, F; Sommer, I; Wass, MN; Sternberg, MJE; Škunca, N; Supek, F; Bošnjak, M; Panov, P; Džeroski, S; Šmuc, T; Kourmpetis, YAI; Van Dijk, ADJ; Ter Braak, CJF; Zhou, Y; Gong, Q; Dong, X; Tian, W; Falda, M; Fontana, P; Lavezzo, E; Di Camillo, B; Toppo, S; Lan, L; Djuric, N; Guo, Y; Vucetic, S; Bairoch, A; Linial, M; Babbitt, PC; Brenner, SE; Orengo, C; Rost, B (2013-03-01)
      Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the overwhelming majority of protein products can only be annotated computationally. If computational predictions are to be relied upon, it is crucial that the accuracy of these methods be high. Here we report the results from the first large-scale community-based critical assessment of protein function annotation (CAFA) experiment. Fifty-four methods representing the state of the art for protein function prediction were evaluated on a target set of 866 proteins from 11 organisms. Two findings stand out: (i) today's best protein function prediction algorithms substantially outperform widely used first-generation methods, with large gains on all types of targets; and (ii) although the top methods perform well enough to guide experiments, there is considerable need for improvement of currently available tools. © 2013 Nature America, Inc. All rights reserved.
    • A likelihood approach to estimate the number of co-infections

      Schneider, KA; Escalante, AA (2014-07-02)
      The number of co-infections of a pathogen (multiplicity of infection or MOI) is a relevant parameter in epidemiology as it relates to transmission intensity. Notably, such quantities can be built into a metric in the context of disease control and prevention. Having applications to malaria in mind, we develop here a maximum-likelihood (ML) framework to estimate the quantities of interest at low computational and no additional costs to study designs or data collection. We show how the ML estimate for the quantities of interest and corresponding confidence-regions are obtained from multiple genetic loci. Assuming specifically that infections are rare and independent events, the number of infections per host follows a conditional Poisson distribution. Under this assumption, we show that a unique ML estimate for the parameter (λ) describing MOI exists which is found by a simple recursion. Moreover, we provide explicit formulas for asymptotic confidence intervals, and show that profile-likelihood-based confidence intervals exist, which are found by a simple twodimensional recursion. Based on the confidence intervals we provide alternative statistical tests for the MOI parameter. Finally, we illustrate the methods on three malaria data sets. The statistical framework however is not limited to malaria. © 2014 Schneider, Escalante.
    • A likelihood-based approach to mixed modeling with ambiguity in cluster identifiers

      Foulkes, AS; Yucel, R; Li, X (2008-10-01)
      This manuscript describes a novel, linear mixed-effects model-fitting technique for the setting in which correlated data indicators are not completely observed. Mixed modeling is a useful analytical tool for characterizing genotype-phenotype associations among multiple potentially informative genetic loci. This approach involves grouping individuals into genetic clusters, where individuals in the same cluster have similar or identical multilocus genotypes. In haplotype-based investigations of unrelated individuals, corresponding cluster assignments are unobservable since the alignment of alleles within chromosomal copies is not generally observed. We derive an expectation conditional maximization approach to estimation in the mixed modeling setting, where cluster assignments are ambiguous. The approach has broad relevance to the analysis of data with missing correlated data identifiers. An example is provided based on data arising from a cohort of human immunodeficiency virus type-1-infected individuals at risk for antiretroviral therapy-associated dyslipidemia. © 2008 The Authors.
    • A line profile-based double partial fusion method for acquiring planning CT of oversized patients in radiation treatment

      Wu, H; Zhao, Q; Cao, M; Das, I; Wu, Huanmei|0000-0003-0346-6044 (2012-01-01)
      True 3D CT dataset for treatment planning of an oversized patient is difficult to acquire due to the bore size and field of view (FOV) reconstruction. This project aims to provide a simple approach to reconstruct true CT data for oversize patients using CT scanner with limited FOV by acquiring double partial CT (left and right side) images. An efficient line profile-based method has been developed to minimize the difference of the CT numbers in the overlapping region between the right and left images and to generate a complete true 3D CT dataset in the natural state. New image processing modules have been developed and integrated to the Insight Segmentation & Registration Toolkit (ITK 3.6) package. For example, different modules for image cropping, line profile generation, line profile matching, and optimized partial image fusion have been developed. The algorithm has been implemented for images containing the bony structure of the spine and tested on 3D CT planning datasets from both phantom and real patients with satisfactory results in both cases. The proposed optimized line profile-based partial registration method provides a simple and accurate method for acquiring a complete true 3D CT dataset for an oversized patient using CT scanning with small bore size, that can be used for accurate treatment planning.
    • A locomotor innovation enables water-land transition in a marine fish

      Hsieh, STT (2010-08-11)
      Background: Morphological innovations that significantly enhance performance capacity may enable exploitation of new resources and invasion of new ecological niches. The invasion of land from the aquatic realm requires dramatic structural and physiological modifications to permit survival in a gravity-dominated, aerial environment. Most fishes are obligatorily aquatic, with amphibious fishes typically making slow-moving and short forays on to land. Methodology/Principal Findings: Here I describe the behaviors and movements of a little known marine fish that moves extraordinarily rapidly on land. I found that the Pacific leaping blenny, Alticus arnoldorum, employs a tail-twisting movement on land, previously unreported in fishes. Focal point behavioral observations of Alticus show that they have largely abandoned the marine realm, feed and reproduce on land, and even defend terrestrial territories. Comparisons of these blennies' terrestrial kinematic and kinetic (i.e., force) measurements with those of less terrestrial sister genera show A. arnoldorum move with greater stability and locomotor control, and can move away more rapidly from impending threats. Conclusions/Significance: My results demonstrate that axial tail twisting serves as a key innovation enabling invasion of a novel marine niche. This paper highlights the potential of using this system to address general evolutionary questions about water-land transitions and niche invasions. © 2010 Shi-Tong Tonia Hsieh.
    • A longitudinal study of post-traumatic growth and psychological distress in colorectal cancer survivors

      Occhipinti, S; Chambers, SK; Lepore, S; Aitken, J; Dunn, J; Lepore, Stephen J.|0000-0001-7370-6280 (2015-09-29)
      © 2015 Occhipinti et al. The stability of post-traumatic growth overtime and the relationship between post-traumatic growth and traditional distress outcomes remains unclear. We tracked post-traumatic growth in a population-based sample of colorectal cancer patients from soon after diagnosis to five years subsequently to assess the heterogeneity of a post-traumatic growth response to cancer over time and describe the simultaneous and longitudinal relationships between post-traumatic growth and psychological distress. 1966 colorectal patients who were five months post diagnosis were assessed six times over a five year period. There was considerable heterogeneity associated with both psychological distress and benefit finding scores over time. However, both for benefit finding and psychological distress, the variation in individual scores suggested an underlying positive linear trend and both lagged and lagged change components. Specifically, benefit finding and psychological distress are mutual leading indicators of each other. First, benefit finding served as a leading indicator of distress, in that increases in reported benefit finding from year to year predicted higher future increases in psychological distress. As well, in an inverse relationship, psychological distress served as a leading indicator of benefit finding, such that increases in reported distress from year to year predicted lower future increases in benefit finding. Post-traumatic growth may reflect patients coping efforts to enhance perceptions of wellbeing in response to escalating cancer-related threats, acting as harbinger of increasing trajectories of psychological distress. This explanation is consistent with a cognitive dissonance response in which threats to the integrity of the self then lead to a tendency to accentuate positive aspects of the self.
    • A Loss-Of-Function Analysis Reveals That Endogenous Rem2 Promotes Functional Glutamatergic Synapse Formation and Restricts Dendritic Complexity

      Moore, AR; Ghiretti, AE; Paradis, S; Moore, Anna|0000-0001-6183-906X (2013-08-26)
      Rem2 is a member of the RGK family of small Ras-like GTPases whose expression and function is regulated by neuronal activity in the brain. A number of questions still remain as to the endogenous functions of Rem2 in neurons. RNAi-mediated Rem2 knockdown leads to an increase in dendritic complexity and a decrease in functional excitatory synapses, though a recent report challenged the specificity of Rem2-targeted RNAi reagents. In addition, overexpression in a number of cell types has shown that Rem2 can inhibit voltage-gated calcium channel (VGCC) function, while studies employing RNAi-mediated knockdown of Rem2 have failed to observe a corresponding enhancement of VGCC function. To further investigate these discrepancies and determine the endogenous function of Rem2, we took a comprehensive, loss-of-function approach utilizing two independent, validated Rem2-targeted shRNAs to analyze Rem2 function. We sought to investigate the consequence of endogenous Rem2 knockdown by focusing on the three reported functions of Rem2 in neurons: regulation of synapse formation, dendritic morphology, and voltage-gated calcium channels. We conclude that endogenous Rem2 is a positive regulator of functional, excitatory synapse development and a negative regulator of dendritic complexity. In addition, while we are unable to reach a definitive conclusion as to whether the regulation of VGCCs is an endogenous function of Rem2, our study reports important data regarding RNAi reagents for use in future investigation of this issue. © 2013 Moore et al.
    • A low-latency and energy-efficient neighbor discovery algorithm for wireless sensor networks

      Gu, Z; Cao, Z; Tian, Z; Wang, Y; Du, X; Mohsen, G; Du, Xiaojiang|0000-0003-4235-9671 (2020-02-01)
      © 2020 by the authors. Licensee MDPI, Basel, Switzerland. Wireless sensor networks have been widely adopted, and neighbor discovery is an essential step to construct the networks. Most existing studies on neighbor discovery are designed on the assumption that either all nodes are fully connected or only two nodes compose the network. However, networks are partially connected in reality: some nodes are within radio range of each other, while others are not. Low latency and energy efficiency are two common goals, which become even more challenging to achieve at the same time in partially connected networks. We find that the collision caused by simultaneous transmissions is the main obstruction of achieving the two goals. In this paper, we present an efficient algorithm called Panacea to address these challenges by alleviating collisions. To begin with, we design Panacea-NCD (Panacea no collision detection) for nodes that do not have a collision detection mechanism. When n is large, we show the discovery latency is bounded by O(n · ln n) for any duty cycle (the percentage time to turn on the radio), where each node has n neighbors on average. For nodes that can detect collisions, we then present Panacea-WCD which also bounds the latency within O(n · ln n) slots. Finally, we conduct extensive evaluations and the results also corroborate our analyses.
    • A machine learning method for detecting autocorrelation of evolutionary rates in large phylogenies

      Tao, Q; Tamura, K; Battistuzzi, FU; Kumar, S; Kumar, Sudhir|0000-0002-9918-8212 (2019-01-01)
      © The Author(s) 2019. New species arise from pre-existing species and inherit similar genomes and environments. This predicts greater similarity of the tempo of molecular evolution between direct ancestors and descendants, resulting in autocorrelation of evolutionary rates in the tree of life. Surprisingly, molecular sequence data have not confirmed this expectation, possibly because available methods lack the power to detect autocorrelated rates. Here, we present a machine learning method, CorrTest, to detect the presence of rate autocorrelation in large phylogenies. CorrTest is computationally efficient and performs better than the available state-of-the-art method. Application of CorrTest reveals extensive rate autocorrelation in DNA and amino acid sequence evolution of mammals, birds, insects, metazoans, plants, fungi, parasitic protozoans, and prokaryotes. Therefore, rate autocorrelation is a common phenomenon throughout the tree of life. These findings suggest concordance between molecular and nonmolecular evolutionary patterns, and they will foster unbiased and precise dating of the tree of life.
    • A massively parallel sequencing approach uncovers ancient origins and high genetic variability of endangered Przewalski's horses

      Goto, H; Ryder, OA; Fisher, AR; Schultz, B; Pond, SLK; Nekrutenko, A; Makova, KD; Pond, Sergei L. Kosakovsky|0000-0003-4817-4029 (2011-12-01)
      The endangered Przewalski's horse is the closest relative of the domestic horse and is the only true wild horse species surviving today. The question of whether Przewalski's horse is the direct progenitor of domestic horse has been hotly debated. Studies of DNA diversity within Przewalski's horses have been sparse but are urgently needed to ensure their successful reintroduction to the wild. In an attempt to resolve the controversy surrounding the phylogenetic position and genetic diversity of Przewalski's horses, we used massively parallel sequencing technology to decipher the complete mitochondrial and partial nuclear genomes for all four surviving maternal lineages of Przewalski's horses. Unlike single-nucleotide polymorphism (SNP) typing usually affected by ascertainment bias, the present method is expected to be largely unbiased. Three mitochondrial haplotypes were discovered-two similar ones, haplotypes I/II, and one substantially divergent from the other two, haplotype III. Haplotypes I/II versus III did not cluster together on a phylogenetic tree, rejecting the monophyly of Przewalski's horse maternal lineages, and were estimated to split 0.117-0.186 Ma, significantly preceding horse domestication. In the phylogeny based on autosomal sequences, Przewalski's horses formed a monophyletic clade, separate from the Thoroughbred domestic horse lineage. Our results suggest that Przewalski's horses have ancient origins and are not the direct progenitors of domestic horses. The analysis of the vast amount of sequence data presented here suggests that Przewalski's and domestic horse lineages diverged at least 0.117 Ma but since then have retained ancestral genetic polymorphism and/or experienced gene flow. © The Author(s) 2010.
    • A mesh generation and machine learning framework for Drosophila gene expression pattern image analysis

      Zhang, W; Feng, D; Li, R; Chernikov, A; Chrisochoides, N; Osgood, C; Konikoff, C; Newfeld, S; Kumar, S; Ji, S; Kumar, Sudhir|0000-0002-9918-8212 (2013-12-28)
      Background: Multicellular organisms consist of cells of many different types that are established during development. Each type of cell is characterized by the unique combination of expressed gene products as a result of spatiotemporal gene regulation. Currently, a fundamental challenge in regulatory biology is to elucidate the gene expression controls that generate the complex body plans during development. Recent advances in high-throughput biotechnologies have generated spatiotemporal expression patterns for thousands of genes in the model organism fruit fly Drosophila melanogaster. Existing qualitative methods enhanced by a quantitative analysis based on computational tools we present in this paper would provide promising ways for addressing key scientific questions.Results: We develop a set of computational methods and open source tools for identifying co-expressed embryonic domains and the associated genes simultaneously. To map the expression patterns of many genes into the same coordinate space and account for the embryonic shape variations, we develop a mesh generation method to deform a meshed generic ellipse to each individual embryo. We then develop a co-clustering formulation to cluster the genes and the mesh elements, thereby identifying co-expressed embryonic domains and the associated genes simultaneously. Experimental results indicate that the gene and mesh co-clusters can be correlated to key developmental events during the stages of embryogenesis we study. The open source software tool has been made available at http://compbio.cs.odu.edu/fly/.Conclusions: Our mesh generation and machine learning methods and tools improve upon the flexibility, ease-of-use and accuracy of existing methods. © 2013 Zhang et al.; licensee BioMed Central Ltd.
    • A Mitochondrial-targeted purine-based HSP90 antagonist for leukemia therapy

      Bryant, KG; Chae, YC; Martinez, RL; Gordon, JC; Elokely, KM; Kossenkov, AV; Grant, S; Childers, WE; Abou-Gharbia, M; Altieri, DC; Elokely, Khaled M.|0000-0002-2394-021X (2017-01-01)
      © Bryant et al. Reprogramming of mitochondrial functions sustains tumor growth and may provide therapeutic opportunities. Here, we targeted the protein folding environment in mitochondria by coupling a purine-based inhibitor of the molecular chaperone Heat Shock Protein-90 (Hsp90), PU-H71 to the mitochondrial-targeting moiety, triphenylphosphonium (TPP). Binding of PU-H71-TPP to ADP-Hsp90, Hsp90 cochaperone complex or mitochondrial Hsp90 homolog, TRAP1 involved hydrogen bonds, p-p stacking, cation-p contacts and hydrophobic interactions with the surrounding amino acids in the active site. PU-H71-TPP selectively accumulated in mitochondria of tumor cells (17-fold increase in mitochondria/cytosol ratio), whereas unmodified PU-H71 showed minimal mitochondrial localization. Treatment of tumor cells with PU-H71-TPP dissipated mitochondrial membrane potential, inhibited oxidative phosphorylation in sensitive cell types, and reduced ATP production, resulting in apoptosis and tumor cell killing. Unmodified PU-H71 had no effect. Bioinformatics analysis identified a "mitochondrial Hsp90" signature in Acute Myeloid Leukemia (AML), which correlates with worse disease outcome. Accordingly, inhibition of mitochondrial Hsp90s killed primary and cultured AML cells, with minimal effects on normal peripheral blood mononuclear cells. These data demonstrate that directing Hsp90 inhibitors with different chemical scaffolds to mitochondria is feasible and confers improved anticancer activity. A potential "addiction" to mitochondrial Hsp90s may provide a new therapeutic target in AML.