Task 1.5 Genomic Shift and Drift Trends of Emerging Pathogens

Borucki, M

关键词: ALGORITHMS;    ANTIBIOTICS;    BACTERIA;    DETECTION;    DISEASES;    ENZYMES;    EYES;    GENES;    GENETICS;    MUTATIONS;    PATHOGENS;    PROTEIN STRUCTURE;    RECOMBINATION;    RNA;    TRANSFORMATIONS;    VIRULENCE;    VIRUSES;   

DOI  :  10.2172/972402
RP-ID  :  LLNL-TR-422243
PID  :  OSTI ID: 972402
Others  :  TRN: US201005%%304

学科分类：生物科学（综合）

美国|英语

来源: SciTech Connect

【 摘 要 】

The Lawrence Livermore National Laboratory (LLNL) Bioinformatics group has recently taken on a role in DTRA's Transformation Medical Technologies Initiative (TMTI). The high-level goal of TMTI is to accelerate the development of broad-spectrum countermeasures. To achieve those goals, TMTI has a near term need to conduct analyses of genomic shift and drift trends of emerging pathogens, with a focused eye on select agent pathogens, as well as antibiotic and virulence markers. Most emerging human pathogens are zoonotic viruses with a genome composed of RNA. The high mutation rate of the replication enzymes of RNA viruses contributes to sequence drift and provides one mechanism for these viruses to adapt to diverse hosts (interspecies transmission events) and cause new human and zoonotic diseases. Additionally, new viral pathogens frequently emerge due to genetic shift (recombination and segment reassortment) which allows for dramatic genotypic and phenotypic changes to occur rapidly. Bacterial pathogens also evolve via genetic drift and shift, although sequence drift generally occurs at a much slower rate for bacteria as compared to RNA viruses. However, genetic shift such as lateral gene transfer and inter- and intragenomic recombination enables bacteria to rapidly acquire new mechanisms of survival and antibiotic resistance. New technologies such as rapid whole genome sequencing of bacterial genomes, ultra-deep sequencing of RNA virus populations, metagenomic studies of environments rich in antibiotic resistance genes, and the use of microarrays for the detection and characterization of emerging pathogens provide mechanisms to address the challenges posed by the rapid emergence of pathogens. Bioinformatic algorithms that enable efficient analysis of the massive amounts of data generated by these technologies as well computational modeling of protein structures and evolutionary processes need to be developed to allow the technology to fulfill its potential.