Genome sequencing in clinical microbiology

Construction and evolution of artificial reef ecosystems: Response and regulation of marine microorganisms

Artificial reefs (ARs) are an important means of improving marine ecological environments and promoting the sustainable use of marine biological resources. After AR deployment, biological communities undergo dynamic changes as species succession and shifts in community structure. As the most sensitive frontier affected by the environment, the complex and dynamic changes of microbial communities play a crucial role in the health and stability of the ecosystem. This article reviews how AR construction affects the composition and function of marine microorganisms, their contributions to ecosystem stability, and the interaction mechanisms between microbial and macroecological systems. We focus on the responses and regulatory roles of microorganisms in AR ecosystems, including changes in microbial abundance, diversity, and distribution in the environment and on reef surfaces. Additionally, we examine their roles in nutrient cycling, the carbon sequestration, and their interactions with higher trophic organisms. We identify critical knowledge gaps and research deficiencies regarding microbial community risks that need to be addressed, which provide a framework for studying the complex relationships among marine environments, microbial communities and macrobiotic communities in the process of marine ranching construction.

The role of the microbiome in immune checkpoint inhibitor colitis and hepatitis

Immune checkpoint inhibitors have revolutionised management for a variety of different types of malignancies. However, gastrointestinal adverse effects, in particular colitis and hepatitis, are relatively common with up to 30 % of patients being affected. The gut microbiome has emerged as a potential contributor to both the effectiveness of immune checkpoint inhibitors and their side effects. This review will attempt to examine the impact the microbiome has on adverse effects as a result of immune checkpoint inhibitors as well as the potential for manipulation of the microbiome as a form of management for immune mediated colitis.

Quick detection of causative bacteria in cases of acute cholangitis and cholecystitis using a multichannel gene autoanalyzer

PURPOSES

Acute cholangitis and cholecystitis can become severe conditions as a result of inappropriate therapeutic administration and thereafter become increasingly resistant to antimicrobial treatment. The simultaneous detection of the bacterial nucleic acid and antimicrobial resistance gene is covered by the national health insurance program in Japan for sepsis. In this study, we evaluate the use of a multichannel gene autoanalyzer (Verigene system) for the quick detection of causative bacteria in cases of acute cholangitis and cholecystitis.

METHODS

This study included 108 patients diagnosed with acute cholangitis or cholecystitis between June 2015 and November 2018. A bacterial culture test and Verigene assay were used to evaluate the bile samples.

RESULTS

The most commonly isolated bacteria were Escherichia coli, which includes six extended-spectrum beta-lactamase (ESBL)-producing E. coli. Among the patients with positive bile cultures, bacteria were detected in 35.7% of cases via the Verigene system. The detection rates of the Verigene system significantly increased when the number of bacterial colonies was ≥ 10⁶ colony-forming unit (CFU)/mL (58.1%). Cases with a maximum colony quantity of ≥ 10⁶ CFU/mL exhibited higher inflammation, suggesting the presence of a bacterial infection.

CONCLUSIONS

The Verigene system might be a new method for the quick detection of causative bacteria in patients with infectious acute cholangitis and cholecystitis.

Performance and Application of 16S rRNA Gene Cycle Sequencing for Routine Identification of Bacteria in the Clinical Microbiology Laboratory

This review provides a state-of-the-art description of the performance of Sanger cycle sequencing of the 16S rRNA gene for routine identification of bacteria in the clinical microbiology laboratory. A detailed description of the technology and current methodology is outlined with a major focus on proper data analyses and interpretation of sequences. The remainder of the article is focused on a comprehensive evaluation of the application of this method for identification of bacterial pathogens based on analyses of 16S multialignment sequences. In particular, the existing limitations of similarity within 16S for genus- and species-level differentiation of clinically relevant pathogens and the lack of sequence data currently available in public databases is highlighted. A multiyear experience is described of a large regional clinical microbiology service with direct 16S broad-range PCR followed by cycle sequencing for direct detection of pathogens in appropriate clinical samples. The ability of proteomics (matrix-assisted desorption ionization-time of flight) versus 16S sequencing for bacterial identification and genotyping is compared. Finally, the potential for whole-genome analysis by next-generation sequencing (NGS) to replace 16S sequencing for routine diagnostic use is presented for several applications, including the barriers that must be overcome to fully implement newer genomic methods in clinical microbiology. A future challenge for large clinical, reference, and research laboratories, as well as for industry, will be the translation of vast amounts of accrued NGS microbial data into convenient algorithm testing schemes for various applications (i.e., microbial identification, genotyping, and metagenomics and microbiome analyses) so that clinically relevant information can be reported to physicians in a format that is understood and actionable. These challenges will not be faced by clinical microbiologists alone but by every scientist involved in a domain where natural diversity of genes and gene sequences plays a critical role in disease, health, pathogenicity, epidemiology, and other aspects of life-forms. Overcoming these challenges will require global multidisciplinary efforts across fields that do not normally interact with the clinical arena to make vast amounts of sequencing data clinically interpretable and actionable at the bedside.

Digital microbiology

BACKGROUND

Digitalization and artificial intelligence have an important impact on the way microbiology laboratories will work in the near future. Opportunities and challenges lie ahead to digitalize the microbiological workflows. Making efficient use of big data, machine learning, and artificial intelligence in clinical microbiology requires a profound understanding of data handling aspects.

OBJECTIVE

This review article summarizes the most important concepts of digital microbiology. The article gives microbiologists, clinicians and data scientists a viewpoint and practical examples along the diagnostic process.

SOURCES

We used peer-reviewed literature identified by a PubMed search for digitalization, machine learning, artificial intelligence and microbiology.

CONTENT

We describe the opportunities and challenges of digitalization in microbiological diagnostic processes with various examples. We also provide in this context key aspects of data structure and interoperability, as well as legal aspects. Finally, we outline the way for applications in a modern microbiology laboratory.

IMPLICATIONS

We predict that digitalization and the usage of machine learning will have a profound impact on the daily routine of laboratory staff. Along the analytical process, the most important steps should be identified, where digital technologies can be applied and provide a benefit. The education of all staff involved should be adapted to prepare for the advances in digital microbiology.

Metagenomic analysis of the effects of toll-like receptors on bacterial infection in the peritoneal cavity following cecum ligation and puncture in mice

Objective

To establish the composition of bacteria in mice following cecum ligation and puncture (CLP) through metagenomic analysis and investigate the role of TLRs on the composition of bacteria.

Methods

Total DNA extraction was done from the ascites, blood, and fecal samples from C57BL/6 mice sacrificed at 0, 4, 8, and 16 h, as well as from Tlr2^–/–, Tlr4^–/–, Tlr5^–/–, and NF-κB^–/–mice sacrificed at 16 h following CLP. Amplification of the V3–V4 regions of the bacterial 16S rRNA genes by PCR and the Illumina MiSeq sequencer was used for deep sequencing. Hierarchical clustering of the isolates was performed with Ward’s method using Euclidean distances. The relative abundance according to operational taxonomic unit (OTU) number or taxa was used to compare the richness among subgroups in the experiments.

Results

There were 18 taxa that had significantly different abundances among the different samples of the C57BL/6 mice at 16 h following CLP. Various dynamic changes in the infectious bacteria inside the peritoneal cavity after CLP were found. While knockout of Tlr5 and NF-κB impaired the ability of bacterial clearance inside the peritoneal cavity for some kinds of bacteria found in the C57BL/6 mice, the knockout of Tlr4 enhanced clearance for other kinds of bacteria, and they presented excessive abundance in the peritoneal cavity despite their scarce abundance in the stool.

Conclusion

NF-κB and TLRs are involved in bacterial clearance and in the expression pattern of the bacterial abundance inside the peritoneal cavity during polymicrobial infection.

Predicting bacterial resistance from whole-genome sequences using k-mers and stability selection

Background

Several studies demonstrated the feasibility of predicting bacterial antibiotic resistance phenotypes from whole-genome sequences, the prediction process usually amounting to detecting the presence of genes involved in antibiotic resistance mechanisms, or of specific mutations, previously identified from a training panel of strains, within these genes. We address the problem from the supervised statistical learning perspective, not relying on prior information about such resistance factors. We rely on a k-mer based genotyping scheme and a logistic regression model, thereby combining several k-mers into a probabilistic model. To identify a small yet predictive set of k-mers, we rely on the stability selection approach (Meinshausen et al., J R Stat Soc Ser B 72:417–73, 2010), that consists in penalizing logistic regression models with a Lasso penalty, coupled with extensive resampling procedures.

Results

Using public datasets, we applied the resulting classifiers to two bacterial species and achieved predictive performance equivalent to state of the art. The models are extremely sparse, involving 1 to 8 k-mers per antibiotic, hence are remarkably easy and fast to evaluate on new genomes (from raw reads to assemblies).

Conclusion

Our proof of concept therefore demonstrates that stability selection is a powerful approach to investigate bacterial genotype-phenotype relationships.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2403-z) contains supplementary material, which is available to authorized users.

Metagenome Analysis as a Tool to Study Bacterial Infection Associated with Acute Surgical Abdomen

Background: The purpose of this study was to profile the bacterium in the ascites and blood of patients with acute surgical abdomen by metagenome analysis. Methods: A total of 97 patients with acute surgical abdomen were included in this study. Accompanied with the standard culture procedures, ascites and blood samples were collected for metagenome analysis to measure the relative abundance of bacteria among groups of patients and between blood and ascites. Results: Metagenomic analysis identified 107 bacterial taxa from the ascites of patients. A principal component analysis (PCA) could separate the bacteria of ascites into roughly three groups: peptic ulcer, perforated or non-perforated appendicitis, and a group which included cholecystitis, small bowel lesion, and colon perforation. Significant correlation between the bacteria of blood and ascites was found in nine bacterial taxa both in blood and ascites with more than 500 sequence reads. However, the PCA failed to separate the variation in the bacteria of blood into different groups of patients, and the bacteria of metagenomic analysis is only partly in accordance with those isolated from a conventional culture method. Conclusion: This study indicated that the metagenome analysis can provide limited information regarding the bacteria in the ascites and blood of patients with acute surgical abdomen.

Rapid Classification and Identification of Multiple Microorganisms with Accurate Statistical Significance via High-Resolution Tandem Mass Spectrometry

Rapid and accurate identification and classification of microorganisms is of paramount importance to public health and safety. With the advance of mass spectrometry (MS) technology, the speed of identification can be greatly improved. However, the increasing number of microbes sequenced is complicating correct microbial identification even in a simple sample due to the large number of candidates present. To properly untwine candidate microbes in samples containing one or more microbes, one needs to go beyond apparent morphology or simple "fingerprinting"; to correctly prioritize the candidate microbes, one needs to have accurate statistical significance in microbial identification. We meet these challenges by using peptide-centric representations of microbes to better separate them and by augmenting our earlier analysis method that yields accurate statistical significance. Here, we present an updated analysis workflow that uses tandem MS (MS/MS) spectra for microbial identification or classification. We have demonstrated, using 226 MS/MS publicly available data files (each containing from 2500 to nearly 100,000 MS/MS spectra) and 4000 additional MS/MS data files, that the updated workflow can correctly identify multiple microbes at the genus and often the species level for samples containing more than one microbe. We have also shown that the proposed workflow computes accurate statistical significances, i.e., E values for identified peptides and unified E values for identified microbes. Our updated analysis workflow MiCId, a freely available software for Microorganism Classification and Identification, is available for download at https://www.ncbi.nlm.nih.gov/CBBresearch/Yu/downloads.html . Graphical Abstract ᅟ.

A Molecular Window into the Biology and Epidemiology of Pneumocystis spp

Pneumocystis, a unique atypical fungus with an elusive lifestyle, has had an important medical history. It came to prominence as an opportunistic pathogen that not only can cause life-threatening pneumonia in patients with HIV infection and other immunodeficiencies but also can colonize the lungs of healthy individuals from a very early age. The genus Pneumocystis includes a group of closely related but heterogeneous organisms that have a worldwide distribution, have been detected in multiple mammalian species, are highly host species specific, inhabit the lungs almost exclusively, and have never convincingly been cultured in vitro, making Pneumocystis a fascinating but difficult-to-study organism. Improved molecular biologic methodologies have opened a new window into the biology and epidemiology of Pneumocystis. Advances include an improved taxonomic classification, identification of an extremely reduced genome and concomitant inability to metabolize and grow independent of the host lungs, insights into its transmission mode, recognition of its widespread colonization in both immunocompetent and immunodeficient hosts, and utilization of strain variation to study drug resistance, epidemiology, and outbreaks of infection among transplant patients. This review summarizes these advances and also identifies some major questions and challenges that need to be addressed to better understand Pneumocystis biology and its relevance to clinical care.

Use of whole-genome sequencing for Campylobacter surveillance from NARMS retail poultry in the United States in 2015

Whole genome sequencing (WGS) has become a rapid and affordable tool for public health surveillance and outbreak detection. In this study, we used the Illuminia MiSeq^® to sequence 589 Campylobacter isolates obtained in 2015 from retail poultry meats as part of the National Antimicrobial Resistance Monitoring System (NARMS). WGS data were used to identify the Campylobacter species and to compare the concordance between resistance genotypes and phenotypes. WGS accurately identified 386 C. jejuni and 203 C. coli using gyrA sequence information. Ten resistance genes, including tetO, bla_OXA-61, aph(2″)-Ic, aph(2″)-If, aph(2″)-Ig, aph(3')-III, ant(6)-1a, aadE, aph(3")-VIIa, and Inu(C), plus mutations in housekeeping genes (gyrA at position 86, 23S rRNA at position 2074 and 2075), were identified by WGS analysis. Overall, there was a high concordance between phenotypic resistance to a given drug and the presence of known resistance genes. Concordance between both resistance and susceptible phenotypes and genotype was 100% for ciprofloxacin, nalidixic acid, gentamicin, azithromycin, and florfenicol. A few discrepancies were observed for tetracycline, clindamycin, and telithromycin. The concordance between resistance phenotype and genotype ranged from 67.9% to 100%; whereas, the concordance between susceptible phenotype and genotype ranged from 98.0% to 99.6%. Our study demonstrates that WGS can correctly identify Campylobacter species and predict antimicrobial resistance with a high degree of accuracy.

Comprehensive Diagnosis of Bacterial Infection Associated with Acute Cholecystitis Using Metagenomic Approach

Acute cholecystitis (AC), which is strongly associated with retrograde bacterial infection, is an inflammatory disease that can be fatal if inappropriately treated. Currently, bacterial culture testing, which is basically recommended to detect the etiological agent, is a time-consuming (4–6 days), non-comprehensive approach. To rapidly detect a potential pathogen and predict its antimicrobial susceptibility, we undertook a metagenomic approach to characterize the bacterial infection associated with AC. Six patients (P1–P6) who underwent cholecystectomy for AC were enrolled in this study. Metagenome analysis demonstrated possible single or multiple bacterial infections in four patients (P1, P2, P3, and P4) with 24-h experimental procedures; in addition, the CTX-M extended-spectrum ß-lactamase (ESBL) gene was identified in two bile samples (P1 and P4). Further whole genome sequencing of Escherichia coli isolates suggested that CTX-M-27-producing ST131 and CTX-M-14-producing novel-ST were identified in P1 and P4, respectively. Metagenome analysis of feces and saliva also suggested some imbalance in the microbiota for more comprehensive assessment of patients with AC. In conclusion, metagenome analysis was useful for rapid bacterial diagnostics, including assessing potential antimicrobial susceptibility, in patients with AC.

Genomes analysis and bacteria identification: The use of overlapping genes as molecular markers

The growing number of available microbial genomes offers the possibility to identify features that could be used for identification. In this work, the possibility to exploit overlapping genes to develop a simple PCR based method of identification, was explored. Using the Burkholderia cepacia complex as a model, genomic analyses were performed to check the phylogenetic distribution of an overlap between marC and hisH genes and then, a PCR specific for Burkholderia was designed, set up and tested on a panel of strains and on DNA extracted from the sputum of cystic fibrosis patients. Results obtained revealed the usefulness of this approach, which could then be used to develop PCR for the identification of specific bacteria species or genera.

TreeSeq, a Fast and Intuitive Tool for Analysis of Whole Genome and Metagenomic Sequence Data

Next-generation sequencing is not yet commonly used in clinical laboratories because of a lack of simple and intuitive tools. We developed a software tool (TreeSeq) with a quaternary tree search structure for the analysis of sequence data. This permits rapid searches for sequences of interest in large datasets. We used TreeSeq to screen a gut microbiota metagenomic dataset and a whole genome sequencing (WGS) dataset of a strain of Klebsiella pneumoniae for antibiotic resistance genes and compared the results with BLAST and phenotypic resistance determination. TreeSeq was more than thirty times faster than BLAST and accurately detected resistance gene sequences in complex metagenomic data and resistance genes corresponding with the phenotypic resistance pattern of the Klebsiella strain. Resistance genes found by TreeSeq were visualized as a gene coverage heat map, aiding in the interpretation of results. TreeSeq brings analysis of metagenomic and WGS data within reach of clinical diagnostics.

MBGD update 2015: microbial genome database for flexible ortholog analysis utilizing a diverse set of genomic data

The microbial genome database for comparative analysis (MBGD) (available at http://mbgd.genome.ad.jp/) is a comprehensive ortholog database for flexible comparative analysis of microbial genomes, where the users are allowed to create an ortholog table among any specified set of organisms. Because of the rapid increase in microbial genome data owing to the next-generation sequencing technology, it becomes increasingly challenging to maintain high-quality orthology relationships while allowing the users to incorporate the latest genomic data available into an analysis. Because many of the recently accumulating genomic data are draft genome sequences for which some complete genome sequences of the same or closely related species are available, MBGD now stores draft genome data and allows the users to incorporate them into a user-specific ortholog database using the MyMBGD functionality. In this function, draft genome data are incorporated into an existing ortholog table created only from the complete genome data in an incremental manner to prevent low-quality draft data from affecting clustering results. In addition, to provide high-quality orthology relationships, the standard ortholog table containing all the representative genomes, which is first created by the rapid classification program DomClust, is now refined using DomRefine, a recently developed program for improving domain-level clustering using multiple sequence alignment information.

MePIC, metagenomic pathogen identification for clinical specimens

Next-generation DNA sequencing technologies have led to a new method of identifying the causative agents of infectious diseases. The analysis comprises three steps. First, DNA/RNA is extracted and extensively sequenced from a specimen that includes the pathogen, human tissue and commensal microorganisms. Second, the sequenced reads are matched with a database of known sequences, and the organisms from which the individual reads were derived are inferred. Last, the percentages of the organisms' genomic sequences in the specimen (i.e., the metagenome) are estimated, and the pathogen is identified. The first and last steps have become easy due to the development of benchtop sequencers and metagenomic software. To facilitate the middle step, which requires computational resources and skill, we developed a cloud-computing pipeline, MePIC: "Metagenomic Pathogen Identification for Clinical specimens." In the pipeline, unnecessary bases are trimmed off the reads, and human reads are removed. For the remaining reads, similar sequences are searched in the database of known nucleotide sequences. The search is drastically sped up by using a cloud-computing system. The webpage interface can be used easily by clinicians and epidemiologists. We believe that the use of the MePIC pipeline will promote metagenomic pathogen identification and improve the understanding of infectious diseases.

Enabling large-scale next-generation sequence assembly with Blacklight

A variety of extremely challenging biological sequence analyses were conducted on the XSEDE large shared memory resource Blacklight, using current bioinformatics tools and encompassing a wide range of scientific applications. These include genomic sequence assembly, very large metagenomic sequence assembly, transcriptome assembly, and sequencing error correction. The data sets used in these analyses included uncategorized fungal species, reference microbial data, very large soil and human gut microbiome sequence data, and primate transcriptomes, composed of both short-read and long-read sequence data. A new parallel command execution program was developed on the Blacklight resource to handle some of these analyses. These results, initially reported previously at XSEDE13 and expanded here, represent significant advances for their respective scientific communities. The breadth and depth of the results achieved demonstrate the ease of use, versatility, and unique capabilities of the Blacklight XSEDE resource for scientific analysis of genomic and transcriptomic sequence data, and the power of these resources, together with XSEDE support, in meeting the most challenging scientific problems.

Clinical PathoScope: rapid alignment and filtration for accurate pathogen identification in clinical samples using unassembled sequencing data

Background

The use of sequencing technologies to investigate the microbiome of a sample can positively impact patient healthcare by providing therapeutic targets for personalized disease treatment. However, these samples contain genomic sequences from various sources that complicate the identification of pathogens.

Results

Here we present Clinical PathoScope, a pipeline to rapidly and accurately remove host contamination, isolate microbial reads, and identify potential disease-causing pathogens. We have accomplished three essential tasks in the development of Clinical PathoScope. First, we developed an optimized framework for pathogen identification using a computational subtraction methodology in concordance with read trimming and ambiguous read reassignment. Second, we have demonstrated the ability of our approach to identify multiple pathogens in a single clinical sample, accurately identify pathogens at the subspecies level, and determine the nearest phylogenetic neighbor of novel or highly mutated pathogens using real clinical sequencing data. Finally, we have shown that Clinical PathoScope outperforms previously published pathogen identification methods with regard to computational speed, sensitivity, and specificity.

Conclusions

Clinical PathoScope is the only pathogen identification method currently available that can identify multiple pathogens from mixed samples and distinguish between very closely related species and strains in samples with very few reads per pathogen. Furthermore, Clinical PathoScope does not rely on genome assembly and thus can more rapidly complete the analysis of a clinical sample when compared with current assembly-based methods. Clinical PathoScope is freely available at: http://sourceforge.net/projects/pathoscope/.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-262) contains supplementary material, which is available to authorized users.

Bacterial strain typing

Over the course of the past several decades, rapid advancements in molecular technologies have revolutionized the practice of public health microbiology, and have fundamentally changed the nature, accuracy, and timeliness of laboratory data for outbreak investigation and response. Whole-genome sequencing, in particular, is becoming an increasingly feasible and cost-effective approach for near real-time high-resolution strain typing, genomic characterization, and comparative analyses. This review discusses the current state of the art in bacterial strain typing for outbreak investigation and infectious disease surveillance, and the impact of emerging genomic technologies on the field of public health microbiology.

High-Throughput Sequencing, a VersatileWeapon to Support Genome-Based Diagnosis in Infectious Diseases: Applications to Clinical Bacteriology

The recent progresses of high-throughput sequencing (HTS) technologies enable easy and cost-reduced access to whole genome sequencing (WGS) or re-sequencing. HTS associated with adapted, automatic and fast bioinformatics solutions for sequencing applications promises an accurate and timely identification and characterization of pathogenic agents. Many studies have demonstrated that data obtained from HTS analysis have allowed genome-based diagnosis, which has been consistent with phenotypic observations. These proofs of concept are probably the first steps toward the future of clinical microbiology. From concept to routine use, many parameters need to be considered to promote HTS as a powerful tool to help physicians and clinicians in microbiological investigations. This review highlights the milestones to be completed toward this purpose.

Diagnosis of Clostridium difficile infection: an ongoing conundrum for clinicians and for clinical laboratories

Clostridium difficile is a formidable nosocomial and community-acquired pathogen, causing clinical presentations ranging from asymptomatic colonization to self-limiting diarrhea to toxic megacolon and fulminant colitis. Since the early 2000s, the incidence of C. difficile disease has increased dramatically, and this is thought to be due to the emergence of new strain types. For many years, the mainstay of C. difficile disease diagnosis was enzyme immunoassays for detection of the C. difficile toxin(s), although it is now generally accepted that these assays lack sensitivity. A number of molecular assays are commercially available for the detection of C. difficile. This review covers the history and biology of C. difficile and provides an in-depth discussion of the laboratory methods used for the diagnosis of C. difficile infection (CDI). In addition, strain typing methods for C. difficile and the evolving epidemiology of colonization and infection with this organism are discussed. Finally, considerations for diagnosing C. difficile disease in special patient populations, such as children, oncology patients, transplant patients, and patients with inflammatory bowel disease, are described. As detection of C. difficile in clinical specimens does not always equate with disease, the diagnosis of C. difficile infection continues to be a challenge for both laboratories and clinicians.

Using phage display selected antibodies to dissect microbiomes for complete de novo genome sequencing of low abundance microbes

BACKGROUND

Single cell genomics has revolutionized microbial sequencing, but complete coverage of genomes in complex microbiomes is imperfect due to enormous variation in organismal abundance and amplification bias. Empirical methods that complement rapidly improving bioinformatic tools will improve characterization of microbiomes and facilitate better genome coverage for low abundance microbes.

METHODS

We describe a new approach to sequencing individual species from microbiomes that combines antibody phage display against intact bacteria with fluorescence activated cell sorting (FACS). Single chain (scFv) antibodies are selected using phage display against a bacteria or microbial community, resulting in species-specific antibodies that can be used in FACS for relative quantification of an organism in a community, as well as enrichment or depletion prior to genome sequencing.

RESULTS

We selected antibodies against Lactobacillus acidophilus and demonstrate a FACS-based approach for identification and enrichment of the organism from both laboratory-cultured and commercially derived bacterial mixtures. The ability to selectively enrich for L. acidophilus when it is present at a very low abundance (<0.2%) leads to complete (>99.8%) de novo genome coverage whereas the standard single-cell sequencing approach is incomplete (<68%). We show that specific antibodies can be selected against L. acidophilus when the monoculture is used as antigen as well as when a community of 10 closely related species is used demonstrating that in principal antibodies can be generated against individual organisms within microbial communities.

CONCLUSIONS

The approach presented here demonstrates that phage-selected antibodies against bacteria enable identification, enrichment of rare species, and depletion of abundant organisms making it tractable to virtually any microbe or microbial community. Combining antibody specificity with FACS provides a new approach for characterizing and manipulating microbial communities prior to genome sequencing.

Recent advances in understanding enteric pathogenic Escherichia coli

Although Escherichia coli can be an innocuous resident of the gastrointestinal tract, it also has the pathogenic capacity to cause significant diarrheal and extraintestinal diseases. Pathogenic variants of E. coli (pathovars or pathotypes) cause much morbidity and mortality worldwide. Consequently, pathogenic E. coli is widely studied in humans, animals, food, and the environment. While there are many common features that these pathotypes employ to colonize the intestinal mucosa and cause disease, the course, onset, and complications vary significantly. Outbreaks are common in developed and developing countries, and they sometimes have fatal consequences. Many of these pathotypes are a major public health concern as they have low infectious doses and are transmitted through ubiquitous mediums, including food and water. The seriousness of pathogenic E. coli is exemplified by dedicated national and international surveillance programs that monitor and track outbreaks; unfortunately, this surveillance is often lacking in developing countries. While not all pathotypes carry the same public health profile, they all carry an enormous potential to cause disease and continue to present challenges to human health. This comprehensive review highlights recent advances in our understanding of the intestinal pathotypes of E. coli.

A modified RNA-Seq approach for whole genome sequencing of RNA viruses from faecal and blood samples

To date, very large scale sequencing of many clinically important RNA viruses has been complicated by their high population molecular variation, which creates challenges for polymerase chain reaction and sequencing primer design. Many RNA viruses are also difficult or currently not possible to culture, severely limiting the amount and purity of available starting material. Here, we describe a simple, novel, high-throughput approach to Norovirus and Hepatitis C virus whole genome sequence determination based on RNA shotgun sequencing (also known as RNA-Seq). We demonstrate the effectiveness of this method by sequencing three Norovirus samples from faeces and two Hepatitis C virus samples from blood, on an Illumina MiSeq benchtop sequencer. More than 97% of reference genomes were recovered. Compared with Sanger sequencing, our method had no nucleotide differences in 14,019 nucleotides (nt) for Noroviruses (from a total of 2 Norovirus genomes obtained with Sanger sequencing), and 8 variants in 9,542 nt for Hepatitis C virus (1 variant per 1,193 nt). The three Norovirus samples had 2, 3, and 2 distinct positions called as heterozygous, while the two Hepatitis C virus samples had 117 and 131 positions called as heterozygous. To confirm that our sample and library preparation could be scaled to true high-throughput, we prepared and sequenced an additional 77 Norovirus samples in a single batch on an Illumina HiSeq 2000 sequencer, recovering >90% of the reference genome in all but one sample. No discrepancies were observed across 118,757 nt compared between Sanger and our custom RNA-Seq method in 16 samples. By generating viral genomic sequences that are not biased by primer-specific amplification or enrichment, this method offers the prospect of large-scale, affordable studies of RNA viruses which could be adapted to routine diagnostic laboratory workflows in the near future, with the potential to directly characterize within-host viral diversity.

Software for selecting the most informative sets of genomic loci for multi-target microbial typing

Background

High-throughput sequencing can identify numerous potential genomic targets for microbial strain typing, but identification of the most informative combinations requires the use of computational screening tools. This paper describes novel software – Automated Selection of Typing Target Subsets (AuSeTTS) - that allows intelligent selection of optimal targets for pathogen strain typing. The objective of this software is to maximise both discriminatory power, using Simpson’s index of diversity (D), and concordance with existing typing methods, using the adjusted Wallace coefficient (AW). The program interrogates molecular typing results for panels of isolates, based on large target sets, and iteratively examines each target, one-by-one, to determine the most informative subset.

Results

AuSeTTS was evaluated using three target sets: 51 binary targets (13 toxin genes, 16 phage-related loci and 22 SCCmec elements), used for multilocus typing of 153 methicillin-resistant Staphylococcus aureus (MRSA) isolates; 17 MLVA loci in 502 Streptococcus pneumoniae isolates from the MLVA database (http://www.mlva.eu) and 12 MLST loci for 98 Cryptococcus spp. isolates.

The maximum D for MRSA, 0.984, was achieved with a subset of 20 targets and a D value of 0.954 with 7 targets. Twelve targets predicted MLST with a maximum AW of 0.9994. All 17 S. pneumoniae MLVA targets were required to achieve maximum D of 0.997, but 4 targets reached D of 0.990. Twelve targets predicted pneumococcal serotype with a maximum AW of 0.899 and 9 predicted MLST with maximum AW of 0.963. Eight of the 12 MLST loci were sufficient to achieve the maximum D of 0.963 for Cryptococcus spp.

Conclusions

Computerised analysis with AuSeTTS allows rapid selection of the most discriminatory targets for incorporation into typing schemes. Output of the program is presented in both tabular and graphical formats and the software is available for free download from http://www.cidmpublichealth.org/pages/ausetts.html.

Genomics and outbreak investigation: from sequence to consequence

Outbreaks of infection can be devastating for individuals and societies. In this review, we examine the applications of new high-throughput sequencing approaches to the identification and characterization of outbreaks, focusing on the application of whole-genome sequencing (WGS) to outbreaks of bacterial infection. We describe traditional epidemiological analysis and show how WGS can be informative at multiple steps in outbreak investigation, as evidenced by many recent studies. We conclude that high-throughput sequencing approaches can make a significant contribution to the investigation of outbreaks of bacterial infection and that the integration of WGS with epidemiological investigation, diagnostic assays and antimicrobial susceptibility testing will precipitate radical changes in clinical microbiology and infectious disease epidemiology in the near future. However, several challenges remain before WGS can be routinely used in outbreak investigation and clinical practice.

Viral surveillance and discovery

The field of virus discovery has burgeoned with the advent of high throughput sequencing platforms and bioinformatics programs that enable rapid identification and molecular characterization of known and novel agents, investments in global microbial surveillance that include wildlife and domestic animals as well as humans, and recognition that viruses may be implicated in chronic as well as acute diseases. Here we review methods for viral surveillance and discovery, strategies and pitfalls in linking discoveries to disease, and identify opportunities for improvements in sequencing instrumentation and analysis, the use of social media and medical informatics that will further advance clinical medicine and public health.

Contact investigations for outbreaks of Mycobacterium tuberculosis: advances through whole genome sequencing

The control of tuberculosis depends on the identification and treatment of infectious patients and their contacts, who are currently identified through a combined approach of genotyping and epidemiological investigation. However, epidemiological data are often challenging to obtain, and genotyping data are difficult to interpret without them. Whole genome sequencing (WGS) technology is increasingly affordable, and offers the prospect of identifying plausible transmission events between patients without prior recourse to epidemiological data. We discuss the current approaches to tuberculosis control, and how WGS might advance public health efforts in the future.

Pathogen typing in the genomics era: MLST and the future of molecular epidemiology

Multi-locus sequence typing (MLST) is a high-resolution genetic typing approach to identify species and strains of pathogens impacting human health, agriculture (animals and plants), and biosafety. In this review, we outline the general concepts behind MLST, molecular approaches for obtaining MLST data, analytical approaches for MLST data, and the contributions MLST studies have made in a wide variety of areas. We then look at the future of MLST and their relative strengths and weaknesses with respect to whole genome sequence typing approaches that are moving into the research arena at an ever-increasing pace. Throughout the paper, we provide exemplar references of these various aspects of MLST. The literature is simply too vast to make this review comprehensive, nevertheless, we have attempted to include enough references in a variety of key areas to introduce the reader to the broad applications and complications of MLST data.