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Integrating Omics Data to Prioritize Target Genes in Pathogenic Bacteria

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Networks in Systems Biology

Part of the book series: Computational Biology ((COBO,volume 32))

Abstract

This review focuses on efforts toward target prioritization from several pathogenic bacteria applying integrated multi-omics approaches. In an integrative scheme, diverse layers of multi-omics data, genome-scale models (GSMs), and structural/functional data related to any pathogenic species can be used to prioritize genes and proteins with attractive target characteristics for the development of new antimicrobials agents. The reconstruction of genome-scale metabolic models (GSMMs) and transcriptional regulatory networks (TRNs) is described in detail. Also, we discuss the methods for the integration of GSMs and diverse web servers for drug targeting in pathogens. Structural approaches are also illustrated. We stress the clinical importance of the drug-resistant isolates related to severe nosocomial or community infections belonging to the species Klebsiella pneumoniae and Staphylococcus aureus, two of the six ESKAPE pathogens, as well as Mycobacterium tuberculosis, the causative agent of tuberculosis.

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Abbreviations

AMR:

Antimicrobial resistance

BEC:

Brazilian Epidemic Clone

BIGG:

Biochemical, Genetic and Genomic knowledge base

CAI:

Community-acquired infections

CA-MRSA:

Community-associated MRSA strains

cis-RE:

cis-regulatory elements

CCR:

Carbon Catabolite Repression

ChIP-seq:

Chromatin immunoprecipitation followed by sequencing

CG:

Clonal group

COBRA:

Constraint-based reconstruction and analysis (COBRA), a wide class of methods to analyze possible flux distributions in metabolic network

CP:

Choke-points

CRKp:

carbapenem-resistant Klebsiella pneumoniae

CRISPR-Cas:

Clustered Regularly Interspaced Short Palindromic Repeats

CWD:

Crystallized With Drugs

D:

Druggable

DBD:

Domain-Based Druggable

DCW:

Dry Cell Weight

DS:

Druggability Score

ESKAPE:

acronym encompassing the names of six bacterial pathogens commonly associated with antimicrobial resistance belonging to species Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp

FBA:

Flux Balance Analysis

FVA:

Flux Variability Analysis

GCNs:

Gene Co-expression Networks

GENRE:

Genome-Scale Reconstruction

GIMME:

Gene Inactivity Moderated by Metabolism and Expression

GIM3E:

Gene Inactivation Moderated by Metabolism, Metabolomics and Expression

GPRs:

Gene–Protein–Reactions

GSM:

Genome-scale Model also referred to as GEM

GSMM :

Genome-scale Metabolic Model also referred to as GEMM

GRN:

Gene Regulatory Network also referred to as TRN

HA-MRSA:

Healthcare-associated Methicillin-Resistant Staphylococcus aureus

HAIs:

Healthcare-associated infections

HD:

Highly Druggable

HIV:

Human Immunodeficiency Virus

HMM:

Hidden Markov Models

iFBA:

integrated Flux Balance Analysis

KEGG:

Kyoto Encyclopedia of Genes and Genomes

KL:

K-Locus, referred to as in silico predicted capsular serotype

KPC:

Klebsiella pneumoniae carbapenemase, also referred to as KPC-2

Kp13-MN:

Kp13 metabolic network

LPS:

Lipopolysaccharide

mAb:

monoclonal antibody

MADE:

Metabolic Adjustment by Differential Expression

MDR:

Multidrug-resistant, an organism resistant to multiple drugs

MRSA:

Methicillin-Resistant Staphylococcus aureus

MSSA:

Methicillin-Susceptible Staphylococcus aureus

MTB:

Mycobacterium tuberculosis

Mtb-MN:

Mycobacterium tuberculosis metabolic network

mRNA:

messenger RNA

PDB:

Protein Data Bank

PB:

Polymyxin B

PFAM:

Protein Family Database

PROM:

Probabilistic Regulation Of Metabolism

PWM:

Position Weight Matrix

rFBA:

regulated Flux Balance Analysis

RNAi:

RNA interference

RNA-seq:

RNA sequencing

RNOS:

Reactive Nitrogen and Oxygen Species

RSA:

Regulatory Steady-state Analysis

SBML:

Systems Biology Markup Language

SBML-qual:

Systems Biology Markup Language Qualitative

ST:

Sequence Type

SR-FBA:

Steady-State Regulated Flux Balance Analysis

TB:

Tuberculosis

TF:

Transcription Factor

TP:

Target-Pathogen database

TFBS:

Transcription Factor Binding Site

TRN:

Transcriptional Regulatory Network

TU:

Transcription Unit

WGCNA:

Weighted Gene Co-expression Network Analysis

WHO:

World Health Organization’s

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Acknowledgments

This work was supported by fellowships from CNPq (process no. 306894/2019-0) and grant by CAPES (process no. 88887.368759/2019-00) to M.F.N. E.P-R was supported by Dirección General de Asuntos del Personal Académico-Universidad Nacional Autónoma de México (IN-209620) and Programa Iberoamericano de Ciencia y Tecnologìa para el Desarrollo (P918PTE0261).

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  1. National Laboratory for Scientific Computation, LNCC/MCTIC, Petrópolis, RJ, Brazil

    Marisa Fabiana Nicolás, Maiana de Oliveira Cerqueira e Costa & Marcelo Trindade dos Santos

  2. Center for Data and Knowledge Integration for Health (CIDACS), Instituto Gonçalo Moniz, Fiocruz-Bahia, Salvador, BA, Brazil

    Pablo Ivan P. Ramos

  3. Instituto de Investigaciones En Matemáticas Aplicadas Y En Sistemas (IIMAS) Universidad, Nacional Autónoma de México, Unidad Académica Yucatán, Mérida City, Mexico

    Ernesto Perez-Rueda

  4. Departamento de Química Biológica, Facultad de Ciencias Exactas Y Naturales, Universidad de Buenos Aires (FCEyN-UBA), Ciudad de Buenos Aires C1428EHA, Buenos Aires, Argentina

    Marcelo A. Marti, Dario Fernandez Do Porto & Adrian G. Turjanski

  5. Instituto de Química Biológica de La Facultad de Ciencias Exactas Y Naturales (IQUIBICEN) CONICET, Pabellòn 2 de Ciudad Universitaria, Ciudad de Buenos Aires C1428EHA, Buenos Aires, Argentina

    Marcelo A. Marti & Adrian G. Turjanski

  6. Instituto de Cálculo, Facultad de Ciencias Exactas Y Naturales, Universidad de Buenos Aires (FCEyN-UBA), Ciudad de Buenos Aires C1428EHA, Buenos Aires, Argentina

    Dario Fernandez Do Porto

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Correspondence to Marisa Fabiana Nicolás .

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  1. Scientific Computing Program (PROCC), Oswaldo Cruz Foundation, Rio de Janeiro, Brazil

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  2. CDTS, Oswaldo Cruz Foundation, Rio de Janeiro, Brazil

    Nicolas Carels

  3. Department of Computational Modeling, National Laboratory of Scientific Computing, Petrópolis, Rio de Janeiro, Brazil

    Marcelo Trindade dos Santos

  4. Graduate Program in Nanobiosystems, Federal University of Rio de Janeiro, Duque de Caxias, Rio de Janeiro, Brazil

    Francisco José Pereira Lopes

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Nicolás, M.F. et al. (2020). Integrating Omics Data to Prioritize Target Genes in Pathogenic Bacteria. In: da Silva, F.A.B., Carels, N., Trindade dos Santos, M., Lopes, F.J.P. (eds) Networks in Systems Biology. Computational Biology, vol 32. Springer, Cham. https://doi.org/10.1007/978-3-030-51862-2_10

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