xbinbzy的个人博客分享 http://blog.sciencenet.cn/u/xbinbzy

博文

文章Sequencing-based methods and resources to study的阅读记录

已有 340 次阅读 2019-10-17 09:02 |个人分类:科研文章|系统分类:科研笔记

题目:Sequencing-based methods and resources to study antimicrobial resistance

杂志:Nature Reviews

时间:2019


一)文章目的

This Review provides a detailed overview of antimicrobial resistance identification and characterization methods, from traditional antimicrobial susceptibility testing to recent deep-learning methods.


二)耐药性引发的问题

在欧美国家的发病情况,Bacteria resistant to first-line antimicrobials infect 2 million people in the USA yearly, and these infections exact a US$20 billion health-care cost. In the European Union, antimicrobial resistance has accounted for >30,000 deaths and nearly 900,000 disability-adjusted life-years. Multiple national and global public health organizations categorize antimicrobial resistance as an imminent danger and uniformly agree that tracking its emergence and prevalence is critical to minimize the threat to human health.


三)耐药性的诱发机制

Genetically encoded antimicrobial resistance can occur through a number of mechanisms, including overexpression or duplication of existing genes, point mutations or the acquisition of entirely new genes via horizontal gene transfer.


四)耐药性的鉴定方法

    4.1 培养的策略(culture-based susceptibility testing)

    液体培养基,In liquid media microbroth dilution antimicrobial susceptibility testing, bacteria are challenged with decreasing antimicrobial concentrations to find the concentration at which they grow successfully.

    固体培养基,solid media techniques use antimicrobial Kirby–Bauer disc diffusion or gradient diffusion strips to measure the clearance of the bacteria from the source of the antimicrobial.

    当前发展趋势,更快的分析方法They include matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI–TOF-MS), fluorescence in situ hybridization (FISH) and microfluidics-based techniques, which reduced AST to ~30min in one study.

    培养方法的挑战在于需要有经验丰富的专家且通量较低,It is low throughput. For some bacteria (for example, M. tuberculosis), current laboratory diagnostic techniques feasible in low-resource areas have low sensitivity. Culture techniques can also fail in situations where multiple bacteria may cause symptomatic disease.

    自动化平台的研发,automation is rapidly being developed and implemented to conduct phenotypic assays, including AST. Several systems, including VITEK, ADAGIO, accelerate Pheno and others, are already moving into clinical spaces.

    4.2基于测序的策略(Sequencing-based resistance discovery)

    4.2.1 组装的方法(Assembly-based methods)

    微生物的组装难度较单基因组较高,Assembling WMS data is more complicated than single-isolate assembly, as the algorithms need to account for unknown abundances of different organisms with unknown phylogenetic relationships.

    针对微生物数据开发的工具,Some notable metagenomic assemblers are IDBA-UD, MEGAHIT, MetaSPAdes and MetaVelvet (extensions of SPAdes and Velvet for metagenomes).

    CAMI project, 尝试建立标准,seeks to benchmark these assemblers on highly complex and close to real data sets for users.

    当下对于数据分析没有统一的共识 currently, there is no single assembler that stands out as the best one that would accurately reconstruct known genomes and capture the majority of the taxonomic diversity in real data sets.

    组装策略的缺点:the process of de novo assembly and annotation is computationally expensive, time consuming and requires higher genome coverage than reference-based assembly or read mapping-based methods, which can be difficult to achieve for all samples, specifically when dealing with metagenomic samples with high microbial diversity and uneven taxonomic composition.

4.2.2 基于reads比对策略

工具SRST2,SRST2 is one widely used tool that aligns reads to a custom reference database using Bowtie2 to predict antimicrobial resistance genes in the sample.

工具KmerResistance,KmerResistance splits reads into k-mers, maps them and counts the co-occurrence of k-mers between reads and a reference database to predict resistance genes and associated species.

工具ARIBA集合了组装和比对的策略,Clustering reference sequences and using a representative sequence from the cluster to map reads considerably reduce ambiguous alignments. 这种挑选代表序列的方法可能会丢失一部分信息

为此工具Graphing Resistance Out of Metagenomes (GROOT), a newly established tool for resistome profiling of metagenomes, builds a variation graph for reference gene sets and aligns sequence reads to these graphs.

方法.png

4.2.3 总结

总体来讲,对于两种分析策略来讲,未来研究中需要根据项目本身的需求选择

Presently, there is no consensus on which sequence analysis approach is better, and the choice of analysis mainly depends on the type of sequencing (WGS versus WMS), availability of computational resources and the study objective.

assembly causes information loss compared with direct read analysis 58 but enables identification of protein-coding genes and for investigation of upstream and downstream regulatory elements, whereas direct read analysis lacks the positional information required to analyse upstream and downstream factors of identified resistance genes.

read-based approaches enable identification of antimicrobial resistance genes from low-abundance organisms present in complex communities, which may be missed by assembly-based methods owing to incomplete or poor assemblies. mapping reads directly to large data sets can inflate false-positive predictions, as reads derived from protein-coding sequences may spuriously align to other genes as a result of local sequence homology.


五)耐药性的相关数据库(Generalized versus specialized databases)

    5.1 通用型数据库

    Generalized antimicrobial resistance databases, such as the now archived Antibiotic Resistance Genes Database (ARDB) or the active Antibiotic Resistance Gene Annotation (ARG- ANNOT) and Comprehensive Antibiotic Resistance Database (CARD), cover broad spectrums of anti-microbial resistance genes and mechanism information.

    5.2 特定型数据库

    specialized antimicrobial resistance databases provide comprehensive information for specific gene families or species. For example, targeted databases such as Lactamase Engineering Database (LacED), the Lahey database of β-lactamases, National Center for Biotechnology Information (NCBI)β-Lactamase Alleles Initiative, and the Comprehensiveβ-Lactamase Molecular Annotation Resource (CBMAR) focus on β-lactamases, a family of antimicrobial resistance enzymes that facilitate hydrolysation of the key β-lactam rings in β-lactam antimicrobials, thus protecting the bacteria from the antimicrobial activity.

    5.3 Hidden Markov model-based databases

    One major limitation of these databases is that the antimicrobial resistance genes they contain are heavily biased towards human pathogens and easily cultivable model organisms, making it difficult to identify remote homologues or novel resistance sequences present in fastid-ious or uncultured bacteria.

针对此特点,有新的数据库开始出现

One potential solution to overcome this bias is to use hidden Markov model (HMM) databases. Derived from the multiple sequence alignment of known sequences, an HMM can find sequences with similar function but low sequence identity.

Resfams is an HMM database of antimicrobial resistance proteins derived from multiple sequence alignments of manually curated sets of representative antimicrobial resistance protein sequences obtained from the generalized CARD and the specialized LacED and Lahey database.

HMM数据库的特点,This increased sensitivity demonstrates the versatility of the HMM in annotating sequences from non-clinical samples with sparser representation in publicly available resistance gene databases. However, HMM-based approaches may have poor specificity (yield higher number of false-positive hits) and may not be able to distinguish between protein families with closely related functions.

Functional Antibiotic Resistance Metagenomic Element (FARME) database comprises a curated set of microbial sequences excluded from current databases but functionally screened to confer resistance in various functional metagenomics studies of different habitats. Apart from predicted protein-coding antimicrobial resistance sequences, the FARME database also includes regulatory elements, mobile genetic elements and predicted proteins flanking antimicrobial resistance genes.

database.png

目前数据库存在的问题,

One major bottleneck is the lack of effective curation strategies. With few exceptions, antimicrobial resistance databases lack efficient and sustainable curation pipelines, so they tend to receive active maintenance for a few years before becoming outdated.

Many antimicrobial resistance genes can be assigned names on the basis of nucleotide sequences and protein sequences, leading to conflicting naming schemes.

Antimicrobial resistance genomic data are an ever expanding data source, demonstrates the need for frequent database updating and curation.

Another important limitation of current antimicrobial resistance databases is their focus on the identification and characterization of protein-coding resistance genes; they ignore other potential antimicrobial resistance mechanisms such as genomic changes or de novo mutations in ribosomal RNA (rRNA) genes and regulatory elements and drug target mutations.


六)功能的研究(Functional metagenomics)

    In addition to sequence-based metagenomics, functional metagenomics is a powerful, culture-independent, sequence-unbiased approach for characterizing resistomes.

    Parallel Annotation and Reassembly of Functional Metagenomic Selections (PARFuMS) is a custom computational pipeline that assembles reads from functional metagenomic selections into contigs using the Velvet and Phrap assemblers and annotates the assemblies for antimicrobial resistance genes using MetaGeneMark and Resfams.


七)基于机器学习策略的预测(Machine learning for resistance prediction)

In one study, a logistic regression approach was used to develop a model based on 14 gene parameters and 3 molecular typing markers that can differentiate between vancomycin-susceptible and vancomycin-intermediate Staphylococcus aureus using publicly available genomic data and patient isolates.

Another study evaluated a rules-based and a machine learning-based approach (that is, logistic regression) for predicting antimicrobial resistance profiles and showed that the machine learning-based approach had higher accuracy with novel variants in known antimicrobial resistance genes than the rules-based approach.

A fast k-mer screening tool, is used to identify antimicrobial resistance genes and SNPs in S. aureus and M. tuberculosis.

Rapid Annotation using Subsystem Technology (RAST) is a k-mer-based tool that uses a machine learning classifier (AdaBoost) based on the Pathosystems Resource Integration Center (PATRIC) database to identify target-specific antimicrobial resistance genes in a specific collection of pathogens.

DeepArgs is a newly established tool that applies deep learning to identify antimicrobial resistance genes.

机器学习未来的临床应用还有很长的工作要做Although the application of machine learning to antimicrobial resistance prediction and classification is promising, these techniques have a long way to go before they can be used for rapid diagnostic purposes and replace traditional culture techniques and AST, which can take days or weeks to yield results.


八)文章总结(Conclusions and future perspectives)

测序费用目前偏高,Further cost reductions for these technologies will be important for widespread adoption.

抗性基因数据需要进一步完善,While progress has been made in building comprehensive antimicrobial resistance gene databases, lack of standardization across databases and long update intervals hold back their potential. Moreover, complex resistance mechanisms are difficult to capture in antimicrobial resistance databases.

To realize the goal of making phenotypic predictions from genotypic data, we need more comprehensive databases that link specific antimicrobial resistance genes to specific AST results.

Rapid and accurate identification of resistance genes in isolate and metagenomic samples would augment the ability of clinicians to make treatment plans for bacterial infections, facilitating a future where sequence-based personized medicine is routine. It would also ease anti-microbial resistance surveillance efforts and enable low-resource areas to benefit more fully from rapidly decreasing sequencing costs.


个人理解:

      1)对于目前基于测序技术对于耐药分析的方法、数据库有着充分详细总结和介绍

      2)期待未来有篇文章讨论各种策略和方法的准确度、灵敏度等



http://blog.sciencenet.cn/blog-306699-1202263.html

上一篇:RGI在Linux安装过程中遇到的问题
下一篇:QIIME2 feature-classifier提示错误[Errno 28] No space left on

0

该博文允许注册用户评论 请点击登录 评论 (0 个评论)

数据加载中...

Archiver|手机版|科学网 ( 京ICP备14006957 )

GMT+8, 2019-11-19 16:49

Powered by ScienceNet.cn

Copyright © 2007- 中国科学报社

返回顶部