The Essential Gene Set of a Photosynthetic Organism PNAS PLUS
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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. -
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. -
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. -
Alcohol Dehydrogenase) from Cylindrospermopsis Raciborskii T3 by Zymography and Mass Spectrometry
Investigation of the Putative Enzyme Function of SxtL (GDSL-lipase) and SxtU (Alcohol dehydrogenase) from Cylindrospermopsis raciborskii T3 by Zymography and Mass Spectrometry by Kulbhushan N Ugemuge z3274021 Supervisor: Prof. Brett A. Neilan Co supervisors: Dr. Sohail Siddiqui and Dr. Michelle Gehringer UNSW 21 April 2011 A.D. In Partial Fulfilment of the Requirements for the Award of Master of Philosophy (Research) School of Biotechnology and Biomolecular Sciences Centre for Cyanobacteria and Astrobiology The University of New South Wales, Sydney, Australia i ii Acknowledgement I would like to thank my supervisor Brett Neilan for giving me this wonderful opportunity to learn and prosper in the field of cyanobacterial research as an MPhil student. I couldn’t have gotten a better supervisor. Long back I dreamed of becoming a scientist and was seeking a role model. Thank you for being that inspiration for me. I am thankful for your support and guidance during the research, your knowledge in the field of cyanobacteria is extraordinary. I am grateful for your friendship during the ups and downs of my life and also for guiding me throughout. Thank you so much. I am grateful to my co-supervisors Michelle Gehringer and Sohail Siddiqui who have been the heart of my project. This thesis wouldn’t have been the same without your tremendous knowledge and expertise in the field. Thank you for all the discussions and valuable inputs while trying to solve the mysteries in the project. Your ideas and suggestions have been remarkable. Thank you for all the technical support and also for making this thesis presentable. -
A Comprehensive Database of Bacterial Srna Targets Verified by Experiments
Downloaded from rnajournal.cshlp.org on September 27, 2021 - Published by Cold Spring Harbor Laboratory Press BIOINFORMATICS sRNATarBase: A comprehensive database of bacterial sRNA targets verified by experiments YUAN CAO,1,2,3 JIAYAO WU,1,3 QIAN LIU,1 YALIN ZHAO,1 XIAOMIN YING,1 LEI CHA,1 LIGUI WANG,1 and WUJU LI1 1Center of Computational Biology, Beijing Institute of Basic Medical Sciences, Beijing 100850, China 2Department of Clinical Laboratory, The 90th Hospital of Jinan, Jinan, Shandong 250031, China ABSTRACT Bacterial sRNAs are an emerging class of small regulatory RNAs, 40;500 nt in length, which play a variety of important roles in many biological processes through binding to their mRNA or protein targets. A comprehensive database of experimentally confirmed sRNA targets would be helpful in understanding sRNA functions systematically and provide support for developing prediction models. Here we report on such a database—sRNATarBase. The database holds 138 sRNA–target interactions and 252 noninteraction entries, which were manually collected from peer-reviewed papers. The detailed information for each entry, such as supporting experimental protocols, BLAST-based phylogenetic analysis of sRNA–mRNA target interaction in closely related bacteria, predicted secondary structures for both sRNAs and their targets, and available binding regions, is provided as accurately as possible. This database also provides hyperlinks to other databases including GenBank, SWISS-PROT, and MPIDB. The database is available from the web page http://ccb.bmi.ac.cn/srnatarbase/. Keywords: sRNA; sRNA targets; database; experimental supports INTRODUCTION throughput experimental technologies and bioinformatics methods (Livny et al. 2006, 2008; Pichon and Felden 2008; Bacterial sRNAs are an emerging class of small regulatory Huang et al. -
Gokul Jarishma Keriuscia Vol1
SUPRAGLACIAL SYSTEMS BIOLOGY OF DYNAMIC ARCTIC MICROBIAL ECOSYSTEMS A thesis submitted for the degree of Philosophiae Doctor (Doctor of Philosophy) by Jarishma Keriuscia Gokul (MSc, BTech (Hons), BSc) Interdisciplinary Centre for Environmental Microbiology Institute for Biological, Environmental and Rural Sciences Aberystwyth University May 2017 DECLARATION Word count of thesis: .……………………………………………………………………………….…………….57 348 DECLARATION This work has not previously been accepted in substance for any degree and is not being concurrently submitted in candidature for any degree. Candidate name ……………………………………………………………………………………………………….Jarishma Keriuscia Gokul Signature …………………………………………………………………………………………………………………. Date ………………………………………………………………………………………………………………………….30 May 2017 STATEMENT 1 This thesis is the result of my own investigations, except where otherwise stated. Where correction services have been used, the extent and nature of the correction is clearly marked in a footnote(s). Other sources are acknowledged by footnotes giving explicit references. A bibliography is appended. Signature …………………………………………………………………………………………………………………. Date ………………………………………………………………………………………………………………………….30 May 2017 STATEMENT 2 I hereby give consent for my thesis, if accepted, to be available for photocopying and for inter-library loan, and for the title and summary to be made available to outside organisations. Signature …………………………………………………………………………………………………………………. Date ………………………………………………………………………………………………………………………….30 May 2017 i SUMMARY Arctic glacier surfaces -
Nitric Oxide Overproduction by Cue1 Mutants Differs on Developmental
plants Article Nitric Oxide Overproduction by cue1 Mutants Differs on Developmental Stages and Growth Conditions Tamara Lechón, Luis Sanz, Inmaculada Sánchez-Vicente and Oscar Lorenzo * Department of Botany and Plant Physiology, Instituto Hispano-Luso de Investigaciones Agrarias (CIALE), Facultad de Biología, Universidad de Salamanca, C/Río Duero 12, 37185 Salamanca, Spain; [email protected] (T.L.); [email protected] (L.S.); elfi[email protected] (I.S.-V.) * Correspondence: [email protected]; Tel.: +34-923294500-5117 Received: 29 September 2020; Accepted: 2 November 2020; Published: 4 November 2020 Abstract: The cue1 nitric oxide (NO) overproducer mutants are impaired in a plastid phosphoenolpyruvate/phosphate translocator, mainly expressed in Arabidopsis thaliana roots. cue1 mutants present an increased content of arginine, a precursor of NO in oxidative synthesis processes. However, the pathways of plant NO biosynthesis and signaling have not yet been fully characterized, and the role of CUE1 in these processes is not clear. Here, in an attempt to advance our knowledge regarding NO homeostasis, we performed a deep characterization of the NO production of four different cue1 alleles (cue1-1, cue1-5, cue1-6 and nox1) during seed germination, primary root elongation, and salt stress resistance. Furthermore, we analyzed the production of NO in different carbon sources to improve our understanding of the interplay between carbon metabolism and NO homeostasis. After in vivo NO imaging and spectrofluorometric quantification of the endogenous NO levels of cue1 mutants, we demonstrate that CUE1 does not directly contribute to the rapid NO synthesis during seed imbibition. Although cue1 mutants do not overproduce NO during germination and early plant development, they are able to accumulate NO after the seedling is completely established. -
Chilling Out: the Evolution and Diversification of Psychrophilic Algae with a Focus on Chlamydomonadales
Polar Biol (2017) 40:1169–1184 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 / Published online: 21 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.