The Essential Gene Set of a Photosynthetic Organism PNAS PLUS
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A Systems Biology Approach to Identify Adjunctive Drug Targets in Two Major Mycobacterial Pathogens
A Systems Biology Approach to Identify Adjunctive Drug Targets in Two Major Mycobacterial Pathogens by William M. Matern A dissertation submitted to Johns Hopkins University in conformity with the requirements for the degree of Doctor of Philosophy Baltimore, Maryland May, 2019 ⃝c William M. Matern 2019 All rights reserved Abstract This thesis explores novel drug targets to accelerate therapy for infections caused by the important human pathogens Mycobacterium avium (Mav) and Mycobacterium tuberculosis (Mtb). Infections with these bacterial species are notoriously difficult to treat - requiring months to years of intensive antibiotic therapy. Decreasing this length may help reduce expenditures necessary for monitoring therapy, improve patient outcomes, and reduce the significant morbidity and mortality caused by Mav and especially the global pathogen Mtb. Bacterial antibiotic persistence has been defined as the ability of bacteria to survive inhigh concentrations of antibiotics without genetic mutation (ie antibiotic resistance). Thus, infections caused by Mav and Mtb are highly persistent. A major focus of this work is the discovery of mechanisms underlying this persistence phenomenon in hopes that this knowledge can be exploited to improve available therapies. A major portion of the work is carried out using high-throughput genomic screens involving tech- niques such as transposon mutagenesis and transposon sequencing (Tn-seq). Statistical methods are developed and implemented to analyze this dataset with a focus on non-parametric methods. Novel discoveries include identification of the essential genes of Mav as well as particular genes that assist in bacterial survival during antibiotic exposure. Mechanisms underlying antibiotic persistence are discussed and explored in follow-up experiments guided by the high-throughput data. -
Essential Genes from Genome-Wide Screenings As a Resource for Neuropsychiatric Disorders Gene Discovery Wei Zhang1, Joao Quevedo 1,2,3,4 and Gabriel R
Zhang et al. Translational Psychiatry (2021) 11:317 https://doi.org/10.1038/s41398-021-01447-y Translational Psychiatry ARTICLE Open Access Essential genes from genome-wide screenings as a resource for neuropsychiatric disorders gene discovery Wei Zhang1, Joao Quevedo 1,2,3,4 and Gabriel R. Fries 1,2,5 Abstract Genome-wide screenings of “essential genes”, i.e., genes required for an organism or cell survival, have been traditionally conducted in vitro in cancer cell lines, limiting the translation of results to other tissues and non-cancerous cells. Recently, an in vivo screening was conducted in adult mouse striatum tissue, providing the first genome-wide dataset of essential genes in neuronal cells. Here, we aim to investigate the role of essential genes in brain development and disease risk with a comprehensive set of bioinformatics tools, including integration with transcriptomic data from developing human brain, publicly available data from genome-wide association studies, de novo mutation datasets for different neuropsychiatric disorders, and case–control transcriptomic data from postmortem brain tissues. For the first time, we found that the expression of neuronal essential genes (NEGs) increases before birth during the early development of human brain and maintains a relatively high expression after birth. On the contrary, common essential genes from cancer cell line screenings (ACEGs) tend to be expressed at high levels during development but quickly drop after birth. Both gene sets were enriched in neurodevelopmental disorders, but only NEGs were robustly associated with neuropsychiatric disorders risk genes. Finally, NEGs were more likely to show differential expression in the brains of neuropsychiatric disorders patients than ACEGs. -
Chilling Out: the Evolution and Diversification of Psychrophilic Algae with a Focus on Chlamydomonadales
Polar Biol DOI 10.1007/s00300-016-2045-4 REVIEW Chilling out: the evolution and diversification of psychrophilic algae with a focus on Chlamydomonadales 1 1 1 Marina Cvetkovska • Norman P. A. Hu¨ner • David Roy Smith Received: 20 February 2016 / Revised: 20 July 2016 / Accepted: 10 October 2016 Ó Springer-Verlag Berlin Heidelberg 2016 Abstract The Earth is a cold place. Most of it exists at or Introduction below the freezing point of water. Although seemingly inhospitable, such extreme environments can harbour a Almost 80 % of the Earth’s biosphere is permanently variety of organisms, including psychrophiles, which can below 5 °C, including most of the oceans, the polar, and withstand intense cold and by definition cannot survive at alpine regions (Feller and Gerday 2003). These seemingly more moderate temperatures. Eukaryotic algae often inhospitable places are some of the least studied but most dominate and form the base of the food web in cold important ecosystems on the planet. They contain a huge environments. Consequently, they are ideal systems for diversity of prokaryotic and eukaryotic organisms, many of investigating the evolution, physiology, and biochemistry which are permanently adapted to the cold (psychrophiles) of photosynthesis under frigid conditions, which has (Margesin et al. 2007). The environmental conditions in implications for the origins of life, exobiology, and climate such habitats severely limit the spread of terrestrial plants, change. Here, we explore the evolution and diversification and therefore, primary production in perpetually cold of photosynthetic eukaryotes in permanently cold climates. environments is largely dependent on microbes. Eukaryotic We highlight the known diversity of psychrophilic algae algae and cyanobacteria are the dominant photosynthetic and the unique qualities that allow them to thrive in severe primary producers in cold habitats, thriving in a surprising ecosystems where life exists at the edge. -
1 Reflecting on a Decade of Transposon-Insertion Sequencing 1 2
1 Reflecting on a Decade of Transposon-Insertion Sequencing 2 3 Amy K. Cain1*, Lars Barquist2,3, Andrew L. Goodman4, Ian T. Paulsen1, Julian Parkhill5 and 4 Tim van Opijnen6* 5 6 Authors Affiliations: 7 1ARC Centre of Excellence in Synthetic Biology, Department of Molecular Sciences, 8 Macquarie University, Sydney, NSW, Australia 9 2 Helmholtz Institute for RNA-based Infection Research, Helmholtz Centre for Infection 10 Research, Würzburg, BY, Germany 11 3 Faculty of Medicine, University of Würzburg, Würzburg, BY, Germany 12 4 Department of Microbial Pathogenesis and Microbial Sciences Institute, Yale University 13 School of Medicine, New Haven, CT, USA 14 5Department of Veterinary Medicine, University of Cambridge, Cambridge, Cambs, UK 15 6 Department of Biology, Boston College, Boston, MA, USA 16 17 *corresponding authors: [email protected] or [email protected] 18 1 19 Abstract 20 It has been 10 years since the birth of modern transposon-insertion sequencing (TIS) 21 methods, which combine genome-wide transposon mutagenesis with high-throughput 22 sequencing to estimate the fitness contribution or essentiality of each genetic component 23 simultaneously in a bacterial genome. Four TIS variations were published in 2009: 24 transposon sequencing (Tn-Seq), transposon-directed insertion site sequencing (TraDIS), 25 insertion sequencing (INSeq) and high-throughput insertion tracking by deep sequencing 26 (HITS). TIS has since become an important tool for molecular microbiologists, being one of 27 the few genome-wide techniques that directly links phenotype to genotype and ultimately can 28 assign gene function. In this review, we discuss the recent applications of TIS to answer 29 overarching biological questions. -
Essential Genome of Campylobacter Jejuni Rabindra K
Mandal et al. BMC Genomics (2017) 18:616 DOI 10.1186/s12864-017-4032-8 RESEARCHARTICLE Open Access Essential genome of Campylobacter jejuni Rabindra K. Mandal1,3*, Tieshan Jiang1 and Young Min Kwon1,2 Abstract Background: Campylobacter species are a leading cause of bacterial foodborne illness worldwide. Despite the global efforts to curb them, Campylobacter infections have increased continuously in both developed and developing countries. The development of effective strategies to control the infection by this pathogen is warranted. The essential genes of bacteria are the most prominent targets for this purpose. In this study, we used transposon sequencing (Tn-seq) of a genome-saturating library of Tn5 insertion mutants to define the essential genome of C. jejuni at a high resolution. Result: We constructed a Tn5 mutant library of unprecedented complexity in C. jejuni NCTC 11168 with 95,929 unique insertions throughout the genome and used the genomic DNA of the library for the reconstruction of Tn5 libraries in the same (C. jejuni NCTC 11168) and different strain background (C. jejuni 81–176) through natural transformation. We identified 166 essential protein-coding genes and 20 essential transfer RNAs (tRNA) in C. jejuni NCTC 11168 which were intolerant to Tn5 insertions during in vitro growth. The reconstructed C. jejuni 81–176 library had 384 protein coding genes with no Tn5 insertions. Essential genes in both strain backgrounds were highly enriched in the cluster of orthologous group (COG) categories of ‘Translation, ribosomal structure and biogenesis (J)’, ‘Energy production and conversion (C)’,and‘Coenzyme transport and metabolism (H)’. Conclusion: Comparative analysis among this and previous studies identified 50 core essential genes of C. -
Analyzing the Heterogeneous Structure of the Genes Interaction Network Through the Random Matrix Theory
Analyzing the heterogeneous structure of the genes interaction network through the random matrix theory Nastaran Allahyari1, Ali Hosseiny1, Nima Abedpour2, and G. Reza Jafari1* 1Department of Physics, Shahid Beheshti University, G.C., Evin, Tehran 19839, Iran 2Department of Translational Genomics, University of Cologne, Weyertal 115b, 50931 Cologne, Germany * g [email protected] ABSTRACT There have been massive efforts devoted to understanding the collective behavior of genes. In this regard, a wide range of studies has focused on pairwise interactions. Understanding collective performance beyond pairwise interactions is a great goal in this field of research. In this work, we aim to analyze the structure of the genes interaction network through the random matrix theory. We focus on the Pearson Correlation Coefficient network of about 6000 genes of the yeast Saccharomyces cerevisiae. By comparing the spectrum of the eigenvalues of the interaction networks itself with the spectrum of the shuffled ones, we observe clear evidence that unveils the existence of the structure beyond pairwise interactions in the network of genes. In the global network, we identify 140 eigenvectors that have unnormal large eigenvalues.It is interesting to observe that when the essential genes as the influential and hubs of the network are excluded, still the special spectrum of the eigenvalues is preserved which is quite different from the random networks. In another analysis we derive the spectrum of genes based on the node participation ratio (NPR) index. We again observe noticeable deviation from a random structure. We indicate about 500 genes that have high values of NPR. Comparing with the records of the shuffled network, we present clear pieces of evidence that these high values of NPR are a consequence of the structures of the network. -
Genetic Network Complexity Shapes
TIGS 1479 No. of Pages 9 Opinion Genetic Network Complexity Shapes Background-Dependent Phenotypic Expression 1, 1 1, 1, Jing Hou, * Jolanda van Leeuwen, Brenda J. Andrews, * and Charles Boone * The phenotypic consequences of a given mutation can vary across individuals. Highlights This so-called background effect is widely observed, from mutant fitness of The same mutation often does not lead loss-of-function variants in model organisms to variable disease penetrance to the same phenotype in different indi- and expressivity in humans; however, the underlying genetic basis often viduals due to other genetic variants that are specific to each individual remains unclear. Taking insights gained from recent large-scale surveys of genome. genetic interaction and suppression analyses in yeast, we propose that the Background modifiers can arise genetic network context for a given mutation may shape its propensity of through gain- or loss-of-function exhibiting background-dependent phenotypes. We argue that further efforts mutations in genes involved in related in systematically mapping the genetic interaction networks beyond yeast will cellular functions. provide not only key insights into the functional properties of genes, but also a Our ability to predict phenotypes relies better understanding of the background effects and the (un)predictability of on expanding our knowledge of the traits in a broader context. complex networks of genetic interac- tions underlying traits; however, the structure and complexity of the net- Genetic Background and the (Un)predictability of Traits work itself may be variable across A particular mutation in a given gene often leads to different phenotypes in different individuals. -
Gene Essentiality Prediction with Graph Attention Networks
1 EPGAT: Gene Essentiality Prediction With Graph Attention Networks Joo Schapke, Anderson Tavares, and Mariana Recamonde-Mendoza Abstract The identification of essential genes/proteins is a critical step towards a better understanding of human biology and pathology. Computational approaches helped to mitigate experimental constraints by exploring machine learning (ML) methods and the correlation of essentiality with biological infor- mation, especially protein-protein interaction (PPI) networks, to predict essential genes. Nonetheless, their performance is still limited, as network-based centralities are not exclusive proxies of essentiality, and traditional ML methods are unable to learn from non-Euclidean domains such as graphs. Given these limitations, we proposed EPGAT, an approach for essentiality prediction based on Graph Attention Networks (GATs), which are attention-based Graph Neural Networks (GNNs) that operate on graph- structured data. Our model directly learns patterns of gene essentiality from PPI networks, integrating additional evidence from multiomics data encoded as node attributes. We benchmarked EPGAT for four organisms, including humans, accurately predicting gene essentiality with AUC score ranging from 0.78 to 0.97. Our model significantly outperformed network-based and shallow ML-based methods and achieved a very competitive performance against the state-of-the-art node2vec embedding method. Notably, EPGAT was the most robust approach in scenarios with limited and imbalanced training data. Thus, the proposed approach offers a powerful and effective way to identify essential genes and proteins. Index Terms arXiv:2007.09671v1 [q-bio.MN] 19 Jul 2020 Bioinformatics, deep learning, essential genes, essential proteins, graph neural networks, multiomics data, prediction J. Schapke, A. Tavares, and M. -
Genome-Wide Determination of Gene Essentiality by Transposon Insertion Sequencing in Yeast Pichia Pastoris
www.nature.com/scientificreports OPEN Genome-Wide Determination of Gene Essentiality by Transposon Insertion Sequencing in Yeast Pichia Received: 8 December 2017 Accepted: 19 June 2018 pastoris Published: xx xx xxxx Jinxiang Zhu1, Ruiqing Gong1, Qiaoyun Zhu1, Qiulin He1, Ning Xu1, Yichun Xu1, Menghao Cai1, Xiangshan Zhou1, Yuanxing Zhang1,2 & Mian Zhou1 In many prokaryotes but limited eukaryotic species, the combination of transposon mutagenesis and high-throughput sequencing has greatly accelerated the identifcation of essential genes. Here we successfully applied this technique to the methylotrophic yeast Pichia pastoris and classifed its conditionally essential/non-essential gene sets. Firstly, we showed that two DNA transposons, TcBuster and Sleeping beauty, had high transposition activities in P. pastoris. By merging their insertion libraries and performing Tn-seq, we identifed a total of 202,858 unique insertions under glucose supported growth condition. We then developed a machine learning method to classify the 5,040 annotated genes into putatively essential, putatively non-essential, ambig1 and ambig2 groups, and validated the accuracy of this classifcation model. Besides, Tn-seq was also performed under methanol supported growth condition and methanol specifc essential genes were identifed. The comparison of conditionally essential genes between glucose and methanol supported growth conditions helped to reveal potential novel targets involved in methanol metabolism and signaling. Our fndings suggest that transposon mutagenesis and Tn-seq could be applied in the methylotrophic yeast Pichia pastoris to classify conditionally essential/non-essential gene sets. Our work also shows that determining gene essentiality under diferent culture conditions could help to screen for novel functional components specifcally involved in methanol metabolism. -
High-Throughput Parallel Sequencing to Measure Fitness of Leptospira Interrogans Transposon Insertion Mutants During Acute Infection
UCLA UCLA Previously Published Works Title High-Throughput Parallel Sequencing to Measure Fitness of Leptospira interrogans Transposon Insertion Mutants during Acute Infection. Permalink https://escholarship.org/uc/item/33s941jw Journal PLoS neglected tropical diseases, 10(11) ISSN 1935-2727 Authors Lourdault, Kristel Matsunaga, James Haake, David A Publication Date 2016-11-08 DOI 10.1371/journal.pntd.0005117 Peer reviewed eScholarship.org Powered by the California Digital Library University of California RESEARCH ARTICLE High-Throughput Parallel Sequencing to Measure Fitness of Leptospira interrogans Transposon Insertion Mutants during Acute Infection Kristel Lourdault1,2*, James Matsunaga1,2, David A. Haake1,2,3,4 1 Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, California, United States of a11111 America, 2 Departments of Medicine, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, California, United States of America, 3 Departments of Urology, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, California, United States of America, 4 Departments of Microbiology, Immunology, and Molecular Genetics, University of California Los Angeles, Los Angeles, California, United States of America * [email protected] OPEN ACCESS Citation: Lourdault K, Matsunaga J, Haake DA Abstract (2016) High-Throughput Parallel Sequencing to Measure Fitness of Leptospira interrogans Pathogenic species of Leptospira are the causative agents of leptospirosis, a zoonotic dis- Transposon Insertion Mutants during Acute ease that causes mortality and morbidity worldwide. The understanding of the virulence Infection. PLoS Negl Trop Dis 10(11): e0005117. mechanisms of Leptospira spp is still at an early stage due to the limited number of genetic doi:10.1371/journal.pntd.0005117 tools available for this microorganism. -
The Non-Essentiality of Essential Genes in Yeast Provides Therapeutic Insights Into a Human Disease
Downloaded from genome.cshlp.org on September 28, 2021 - Published by Cold Spring Harbor Laboratory Press Jun 21, 2016 The non-essentiality of essential genes in yeast provides therapeutic insights into a human disease Piaopiao Chen1, Dandan Wang1, Han Chen1, Zhenzhen Zhou1, and Xionglei He1,2 1 Key Laboratory of Gene Engineering of Ministry of Education, Collaborative Innovation Center for Cancer Medicine, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, China 2 State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, China Correspondence to: Xionglei He School of Life Sciences Sun Yat-sen University 135 Xingang West Guangzhou 510275 China Email: [email protected] Running title: Non-essentiality of essential genes Keywords: Essential gene; Conditional essentiality; Rescuing interaction; ADSL 1 Downloaded from genome.cshlp.org on September 28, 2021 - Published by Cold Spring Harbor Laboratory Press Abstract Essential genes refer to those whose null mutation leads to lethality or sterility. Theoretical reasoning and empirical data both suggest that the fatal effect of inactivating an essential gene can be attributed to either the loss of indispensable core cellular function (Type I), or the gain of fatal side effects after losing dispensable periphery function (Type II). In principle, inactivation of Type I essential genes can be rescued only by re-gain of the core functions, whereas inactivation of Type II essential genes could be rescued by a further loss of function of another gene to eliminate the otherwise fatal side effects. Because such loss-of-function rescuing mutations may occur spontaneously, Type II essential genes may become non-essential in a few individuals of a large population. -
A Deep Learning Framework for Predicting Human Essential Genes by Integrating Sequence and Functional Data
bioRxiv preprint doi: https://doi.org/10.1101/2020.08.04.236646; this version posted August 5, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. A Deep Learning Framework for Predicting Human Essential Genes by Integrating Sequence and Functional data Wangxin Xiao1#, Xue Zhang2,3#*, Weijia Xiao4 1Faculty of Transportation Engineering, Huaiyin Institute of Technology, Huai’an, Jiangsu 223003, China 2Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huai’an, Jiangsu 223003, China 3School of Medicine, Tufts University, Boston, MA 02111, USA 4Boston Latin School, Boston, MA 02115, USA Abstract Motivation: Essential genes are necessary to the survival or reproduction of a living organism. The prediction and analysis of gene essentiality can advance our understanding to basic life and human diseases, and further boost the development of new drugs. Wet lab methods for identifying essential genes are often costly, time consuming, and laborious. As a complement, computational methods have been proposed to predict essential genes by integrating multiple biological data sources. Most of these methods are evaluated on model organisms. However, prediction methods for human essential genes are still limited and the relationship between human gene essentiality and different biological information still needs to be explored. In addition, exploring suitable deep learning techniques to overcome the limitations of traditional machine learning methods and improve the prediction accuracy is also important and interesting. Results: We propose a deep learning based method, DeepSF, to predict human essential genes.