Abstract
Whole genome analysis based on next generation sequencing (NGS) now represents an affordable framework in public health systems. Robust analytical pipelines of genomic data provides in a short lapse of time (hours) information about taxonomy, comparative genomics (pan-genome) and single polymorphisms profiles. Pathogenic organisms of interest can be tracked at the genomic level, allowing monitoring at one-time several variables including: epidemiology, pathogenicity, resistance to antibiotics, virulence, persistence factors, mobile elements and adaptation features. Such information can be obtained not only at large spectra, but also at the “local” level, such as in the event of a recurrent or emergency outbreak. This paper reviews the state of the art in infection diagnostics in the context of modern NGS methodologies. We describe how actuation protocols in a public health environment will benefit from a “streaming approach” (pipeline). Such pipeline would include NGS data quality assessment, data mining for comparative analysis, searching differential genetic features, such as virulence, resistance persistence factors and mutation profiles (SNPs and InDels) and formatted “comprehensible” results. Such analytical protocols will enable a quick response to the needs of locally circumscribed outbreaks, providing information on the causes of resistance and genetic tracking elements for rapid detection, and monitoring actuations for present and future occurrences.
Highlights
We describe a “live” frame-work in microbial genome sequencing in which data mining from comparative genomics continuously populate a relational database with genetic information, allowing the extraction of useful differential data of interest such as virulence, resistance persistence factors, SNPs and InDels
Among the data which a microbiology unit needs to know in a “live” context, we suggest including properties linked to genes or to specific mutations, in other words, the pan-genome relationship of a given isolate within its species and the mutational (SNPs/InDels) profile
next generation sequencing (NGS) coupled with automatic data mining pipelines represent nowadays the future for promptly definition of actuation plans responding to outbreaks or recurrent infections in public health systems
Summary
Care units such as oncology and surgery, where patients are in most cases under conditions of immunodepression, are known for the presence of “the usual suspects” such as multi resistant. Without a deep knowledge of the organisms causing the sepsis, empirical antibiotic treatment is the first practice applied to stop the infection [1]. Intensive care units and hospitals are major reservoirs for pathogenic opportunistic organisms. Succeeding in eradicating the infection is mainly a race against time coupled with the selection of the proper empirical antibiotic treatment and the capabilities of bacteria in exchanging or evolving a variety of factors such as resistance, virulence and persistence [2]. The first data about bacteria susceptibility are provided within 48 h. This time frame allows antibiotic treatment adjustments to eventually be made. When eradication of a given infection is delayed or when an outbreak is extended, a more detailed gathering of information can help in resolving the emergency
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