Frequency-based haplotype reconstruction from deep sequencing data of bacterial populations

Embracing Complexity: What Novel Sequencing Methods Are Teaching Us About Herpesvirus Genomic Diversity

The arrival of novel sequencing technologies throughout the past two decades has led to a paradigm shift in our understanding of herpesvirus genomic diversity. Previously, herpesviruses were seen as a family of DNA viruses with low genomic diversity. However, a growing body of evidence now suggests that herpesviruses exist as dynamic populations that possess standing variation and evolve at much faster rates than previously assumed. In this review, we explore how strategies such as deep sequencing, long-read sequencing, and haplotype reconstruction are allowing scientists to dissect the genomic composition of herpesvirus populations. We also discuss the challenges that need to be addressed before a detailed picture of herpesvirus diversity can emerge.

Floria: fast and accurate strain haplotyping in metagenomes

SUMMARY

Shotgun metagenomics allows for direct analysis of microbial community genetics, but scalable computational methods for the recovery of bacterial strain genomes from microbiomes remains a key challenge. We introduce Floria, a novel method designed for rapid and accurate recovery of strain haplotypes from short and long-read metagenome sequencing data, based on minimum error correction (MEC) read clustering and a strain-preserving network flow model. Floria can function as a standalone haplotyping method, outputting alleles and reads that co-occur on the same strain, as well as an end-to-end read-to-assembly pipeline (Floria-PL) for strain-level assembly. Benchmarking evaluations on synthetic metagenomes show that Floria is  > 3× faster and recovers 21% more strain content than base-level assembly methods (Strainberry) while being over an order of magnitude faster when only phasing is required. Applying Floria to a set of 109 deeply sequenced nanopore metagenomes took <20 min on average per sample and identified several species that have consistent strain heterogeneity. Applying Floria's short-read haplotyping to a longitudinal gut metagenomics dataset revealed a dynamic multi-strain Anaerostipes hadrus community with frequent strain loss and emergence events over 636 days. With Floria, accurate haplotyping of metagenomic datasets takes mere minutes on standard workstations, paving the way for extensive strain-level metagenomic analyses.

AVAILABILITY AND IMPLEMENTATION

Floria is available at https://github.com/bluenote-1577/floria, and the Floria-PL pipeline is available at https://github.com/jsgounot/Floria_analysis_workflow along with code for reproducing the benchmarks.

ColiSeq: a multiplex amplicon assay that provides strain level resolution of Escherichia coli directly from clinical specimens

Escherichia coli is a diverse pathogen, causing a range of disease in humans, from self-limiting diarrhea to urinary tract infections (UTIs). Uropathogenic E. coli (UPEC) is the most frequently observed uropathogen in UTIs, a common disease in high-income countries, incurring billions of dollars yearly in treatment costs. Although E. coli is easily grown and identified in the clinical laboratory, genotyping the pathogen is more complicated, yet critical for reducing the incidence of disease. These goals can be achieved through whole-genome sequencing of E. coli isolates, but this approach is relatively slow and typically requires culturing the pathogen in the laboratory. To genotype E. coli rapidly and inexpensively directly from clinical samples, including but not limited to urine, we developed and validated a multiplex amplicon sequencing assay, called ColiSeq. The assay consists of targets designed for E. coli species confirmation, high resolution genotyping, and mixture deconvolution. To demonstrate its utility, we screened the ColiSeq assay against 230 clinical urine samples collected from a hospital system in Flagstaff, Arizona, USA. A limit of detection analysis demonstrated the ability of ColiSeq to identify E. coli at a concentration of ~2 genomic equivalent (GEs)/mL and to generate high-resolution genotyping at a concentration of 1 × 105 GEs/mL. The results of this study suggest that ColiSeq could be a valuable method to understand the source of UPEC strains and guide infection mitigation efforts. As sequence-based diagnostics become accepted in the clinical laboratory, workflows such as ColiSeq will provide actionable information to improve patient outcomes.IMPORTANCEUrinary tract infections (UTIs), caused primarily by Escherichia coli, create an enormous health care burden in the United States and other high-income countries. The early detection of E. coli from clinical samples, including urine, is important to target therapy and prevent further patient complications. Additionally, understanding the source of E. coli exposure will help with future mitigation efforts. In this study, we developed, tested, and validated an amplicon sequencing assay focused on direct detection of E. coli from urine. The resulting sequence data were demonstrated to provide strain level resolution of the pathogen, not only confirming the presence of E. coli, which can focus treatment efforts, but also providing data needed for source attribution and contact tracing. This assay will generate inexpensive, rapid, and reproducible data that can be deployed by public health agencies to track, diagnose, and potentially mitigate future UTIs caused by E. coli.

SNP-slice resolves mixed infections: simultaneously unveiling strain haplotypes and linking them to hosts

MOTIVATION

Multi-strain infection is a common yet under-investigated phenomenon of many pathogens. Currently, biologists analyzing SNP information sometimes have to discard mixed infection samples as many downstream analyses require monogenomic inputs. Such a protocol impedes our understanding of the underlying genetic diversity, co-infection patterns, and genomic relatedness of pathogens. A scalable tool to learn and resolve the SNP-haplotypes from polygenomic data is an urgent need in molecular epidemiology.

RESULTS

We develop a slice sampling Markov Chain Monte Carlo algorithm, named SNP-Slice, to learn not only the SNP-haplotypes of all strains in the populations but also which strains infect which hosts. Our method reconstructs SNP-haplotypes and individual heterozygosities accurately without reference panels and outperforms the state-of-the-art methods at estimating the multiplicity of infections and allele frequencies. Thus, SNP-Slice introduces a novel approach to address polygenomic data and opens a new avenue for resolving complex infection patterns in molecular surveillance. We illustrate the performance of SNP-Slice on empirical malaria and HIV datasets and provide recommendations for using our method on empirical datasets.

AVAILABILITY AND IMPLEMENTATION

The implementation of the SNP-Slice algorithm, as well as scripts to analyze SNP-Slice outputs, are available at https://github.com/nianqiaoju/snp-slice.

Are the predicted known bacterial strains in a sample really present? A case study

With mutations constantly accumulating in bacterial genomes, it is unclear whether the previously identified bacterial strains are really present in an extant sample. To address this question, we did a case study on the known strains of the bacterial species S. aureus and S. epidermis in 68 atopic dermatitis shotgun metagenomic samples. We evaluated the likelihood of the presence of all sixteen known strains predicted in the original study and by two popular tools in this study. We found that even with the same tool, only two known strains were predicted by the original study and this study. Moreover, none of the sixteen known strains was likely present in these 68 samples. Our study thus indicates the limitation of the known-strain-based studies, especially those on rapidly evolving bacterial species. It implies the unlikely presence of the previously identified known strains in a current environmental sample. It also called for de novo bacterial strain identification directly from shotgun metagenomic reads.

High-resolution strain-level microbiome composition analysis from short reads

BACKGROUND

Bacterial strains under the same species can exhibit different biological properties, making strain-level composition analysis an important step in understanding the dynamics of microbial communities. Metagenomic sequencing has become the major means for probing the microbial composition in host-associated or environmental samples. Although there are a plethora of composition analysis tools, they are not optimized to address the challenges in strain-level analysis: highly similar strain genomes and the presence of multiple strains under one species in a sample. Thus, this work aims to provide a high-resolution and more accurate strain-level analysis tool for short reads.

RESULTS

In this work, we present a new strain-level composition analysis tool named StrainScan that employs a novel tree-based k-mers indexing structure to strike a balance between the strain identification accuracy and the computational complexity. We tested StrainScan extensively on a large number of simulated and real sequencing data and benchmarked StrainScan with popular strain-level analysis tools including Krakenuniq, StrainSeeker, Pathoscope2, Sigma, StrainGE, and StrainEst. The results show that StrainScan has higher accuracy and resolution than the state-of-the-art tools on strain-level composition analysis. It improves the F1 score by 20% in identifying multiple strains at the strain level.

CONCLUSIONS

By using a novel k-mer indexing structure, StrainScan is able to provide strain-level analysis with higher resolution than existing tools, enabling it to return more informative strain composition analysis in one sample or across multiple samples. StrainScan takes short reads and a set of reference strains as input and its source codes are freely available at https://github.com/liaoherui/StrainScan . Video Abstract.

Pitfalls of genotyping microbial communities with rapidly growing genome collections

Detecting genetic variants in metagenomic data is a priority for understanding the evolution, ecology, and functional characteristics of microbial communities. Many tools that perform this metagenotyping rely on aligning reads of unknown origin to a database of sequences from many species before calling variants. In this synthesis, we investigate how databases of increasingly diverse and closely related species have pushed the limits of current alignment algorithms, thereby degrading the performance of metagenotyping tools. We identify multi-mapping reads as a prevalent source of errors and illustrate a trade-off between retaining correct alignments versus limiting incorrect alignments, many of which map reads to the wrong species. Then we evaluate several actionable mitigation strategies and review emerging methods showing promise to further improve metagenotyping in response to the rapid growth in genome collections. Our results have implications beyond metagenotyping to the many tools in microbial genomics that depend upon accurate read mapping.

Haplotype assignment of longitudinal viral deep sequencing data using covariation of variant frequencies

Longitudinal deep sequencing of viruses can provide detailed information about intra-host evolutionary dynamics including how viruses interact with and transmit between hosts. Many analyses require haplotype reconstruction, identifying which variants are co-located on the same genomic element. Most current methods to perform this reconstruction are based on a high density of variants and cannot perform this reconstruction for slowly evolving viruses. We present a new approach, HaROLD (HAplotype Reconstruction Of Longitudinal Deep sequencing data), which performs this reconstruction based on identifying co-varying variant frequencies using a probabilistic framework. We illustrate HaROLD on both RNA and DNA viruses with synthetic Illumina paired read data created from mixed human cytomegalovirus (HCMV) and norovirus genomes, and clinical datasets of HCMV and norovirus samples, demonstrating high accuracy, especially when longitudinal samples are available.

Combination of Whole Genome Sequencing and Metagenomics for Microbiological Diagnostics

Whole genome sequencing (WGS) provides the highest resolution for genome-based species identification and can provide insight into the antimicrobial resistance and virulence potential of a single microbiological isolate during the diagnostic process. In contrast, metagenomic sequencing allows the analysis of DNA segments from multiple microorganisms within a community, either using an amplicon- or shotgun-based approach. However, WGS and shotgun metagenomic data are rarely combined, although such an approach may generate additive or synergistic information, critical for, e.g., patient management, infection control, and pathogen surveillance. To produce a combined workflow with actionable outputs, we need to understand the pre-to-post analytical process of both technologies. This will require specific databases storing interlinked sequencing and metadata, and also involves customized bioinformatic analytical pipelines. This review article will provide an overview of the critical steps and potential clinical application of combining WGS and metagenomics together for microbiological diagnosis.

A revisit to universal single-copy genes in bacterial genomes

Universal single-copy genes (USCGs) are widely used for species classification and taxonomic profiling. Despite many studies on USCGs, our understanding of USCGs in bacterial genomes might be out of date, especially how different the USCGs are in different studies, how well a set of USCGs can distinguish two bacterial species, whether USCGs can separate different strains of a bacterial species, to name a few. To fill the void, we studied USCGs in the most updated complete bacterial genomes. We showed that different USCG sets are quite different while coming from highly similar functional categories. We also found that although USCGs occur once in almost all bacterial genomes, each USCG does occur multiple times in certain genomes. We demonstrated that USCGs are reliable markers to distinguish different species while they cannot distinguish different strains of most bacterial species. Our study sheds new light on the usage and limitations of USCGs, which will facilitate their applications in evolutionary, phylogenomic, and metagenomic studies.

Comparative 3D genome architecture in vertebrates

BACKGROUND

The three-dimensional (3D) architecture of the genome has a highly ordered and hierarchical nature, which influences the regulation of essential nuclear processes at the basis of gene expression, such as gene transcription. While the hierarchical organization of heterochromatin and euchromatin can underlie differences in gene expression that determine evolutionary differences among species, the way 3D genome architecture is affected by evolutionary forces within major lineages remains unclear. Here, we report a comprehensive comparison of 3D genomes, using high resolution Hi-C data in fibroblast cells of fish, chickens, and 10 mammalian species.

RESULTS

This analysis shows a correlation between genome size and chromosome length that affects chromosome territory (CT) organization in the upper hierarchy of genome architecture, whereas lower hierarchical features, including local transcriptional availability of DNA, are selected through the evolution of vertebrates. Furthermore, conservation of topologically associating domains (TADs) appears strongly associated with the modularity of expression profiles across species. Additionally, LINE and SINE transposable elements likely contribute to heterochromatin and euchromatin organization, respectively, during the evolution of genome architecture.

CONCLUSIONS

Our analysis uncovers organizational features that appear to determine the conservation and transcriptional regulation of functional genes across species. These findings can guide ongoing investigations of genome evolution by extending our understanding of the mechanisms shaping genome architecture.

The evolution of bacterial genome assemblies - where do we need to go next?

Genome sequencing has fundamentally changed our ability to decipher and understand the genetic blueprint of life and how it changes over time in response to environmental and evolutionary pressures. The pace of sequencing is still increasing in response to advances in technologies, paving the way from sequenced genes to genomes to metagenomes to metagenome-assembled genomes (MAGs). Our ability to interrogate increasingly complex microbial communities through metagenomes and MAGs is opening up a tantalizing future where we may be able to delve deeper into the mechanisms and genetic responses emerging over time. In the near future, we will be able to detect MAG assembly variations within strains originating from diverging sub-populations, and one of the emerging challenges will be to capture these variations in a biologically relevant way. Here, we present a brief overview of sequencing technologies and the current state of metagenome assemblies to suggest the need to develop new data formats that can capture the genetic variations within strains and communities, which previously remained invisible due to sequencing technology limitations.

Computational analyses of bacterial strains from shotgun reads

Shotgun sequencing is routinely employed to study bacteria in microbial communities. With the vast amount of shotgun sequencing reads generated in a metagenomic project, it is crucial to determine the microbial composition at the strain level. This study investigated 20 computational tools that attempt to infer bacterial strain genomes from shotgun reads. For the first time, we discussed the methodology behind these tools. We also systematically evaluated six novel-strain-targeting tools on the same datasets and found that BHap, mixtureS and StrainFinder performed better than other tools. Because the performance of the best tools is still suboptimal, we discussed future directions that may address the limitations.

Reconstruction of evolving gene variants and fitness from short sequencing reads

Directed evolution can generate proteins with tailor-made activities. However, full-length genotypes, their frequencies and fitnesses are difficult to measure for evolving gene-length biomolecules using most high-throughput DNA sequencing methods, as short read lengths can lose mutation linkages in haplotypes. Here we present Evoracle, a machine learning method that accurately reconstructs full-length genotypes (R² = 0.94) and fitness using short-read data from directed evolution experiments, with substantial improvements over related methods. We validate Evoracle on phage-assisted continuous evolution (PACE) and phage-assisted non-continuous evolution (PANCE) of adenine base editors and OrthoRep evolution of drug-resistant enzymes. Evoracle retains strong performance (R² = 0.86) on data with complete linkage loss between neighboring nucleotides and large measurement noise, such as pooled Sanger sequencing data (~US$10 per timepoint), and broadens the accessibility of training machine learning models on gene variant fitnesses. Evoracle can also identify high-fitness variants, including low-frequency 'rising stars', well before they are identifiable from consensus mutations.

mixtureS: a novel tool for bacterial strain genome reconstruction from reads

MOTIVATION

It is essential to study bacterial strains in environmental samples. Existing methods and tools often depend on known strains or known variations, cannot work on individual samples, not reliable, or not easy to use, etc. It is thus important to develop more user-friendly tools that can identify bacterial strains more accurately.

RESULTS

We developed a new tool called mixtureS that can de novo identify bacterial strains from shotgun reads of a clonal or metagenomic sample, without prior knowledge about the strains and their variations. Tested on 243 simulated datasets and 195 experimental datasets, mixtureS reliably identified the strains, their numbers and their abundance. Compared with three tools, mixtureS showed better performance in almost all simulated datasets and the vast majority of experimental datasets.

AVAILABILITY AND IMPLEMENTATION

The source code and tool mixtureS is available at http://www.cs.ucf.edu/˜xiaoman/mixtureS/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Multiple haplotype reconstruction from allele frequency data

Because haplotype information is of widespread interest in biomedical applications, effort has been put into their reconstruction. Here, we propose an efficient method, called haploSep, that is able to accurately infer major haplotypes and their frequencies just from multiple samples of allele frequency data. Even the accuracy of experimentally obtained allele frequencies can be improved by re-estimating them from our reconstructed haplotypes. From a methodological point of view, we model our problem as a multivariate regression problem where both the design matrix and the coefficient matrix are unknown. Compared to other methods, haploSep is very fast, with linear computational complexity in the haplotype length. We illustrate our method on simulated and real data focusing on experimental evolution and microbial data.

Strain-Resolved Dynamics of the Lung Microbiome in Patients with Cystic Fibrosis

Cystic fibrosis patients frequently suffer from recurring respiratory infections caused by colonizing pathogenic and commensal bacteria. Although modern therapies can sometimes alleviate respiratory symptoms by ameliorating residual function of the protein responsible for the disorder, management of chronic respiratory infections remains an issue.

In cystic fibrosis, dynamic and complex communities of microbial pathogens and commensals can colonize the lung. Cultured isolates from lung sputum reveal high inter- and intraindividual variability in pathogen strains, sequence variants, and phenotypes; disease progression likely depends on the precise combination of infecting lineages. Routine clinical protocols, however, provide a limited overview of the colonizer populations. Therefore, a more comprehensive and precise identification and characterization of infecting lineages could assist in making corresponding decisions on treatment. Here, we describe longitudinal tracking for four cystic fibrosis patients who exhibited extreme clinical phenotypes and, thus, were selected from a pilot cohort of 11 patients with repeated sampling for more than a year. Following metagenomics sequencing of lung sputum, we find that the taxonomic identity of individual colonizer lineages can be easily established. Crucially, even superficially clonal pathogens can be subdivided into multiple sublineages at the sequence level. By tracking individual allelic differences over time, an assembly-free clustering approach allows us to reconstruct multiple lineage-specific genomes with clear structural differences. Our study showcases a culture-independent shotgun metagenomics approach for longitudinal tracking of sublineage pathogen dynamics, opening up the possibility of using such methods to assist in monitoring disease progression through providing high-resolution routine characterization of the cystic fibrosis lung microbiome.

Reconstruction of Microbial Haplotypes by Integration of Statistical and Physical Linkage in Scaffolding

DNA sequencing technologies provide unprecedented opportunities to analyze within-host evolution of microorganism populations. Often, within-host populations are analyzed via pooled sequencing of the population, which contains multiple individuals or "haplotypes." However, current next-generation sequencing instruments, in conjunction with single-molecule barcoded linked-reads, cannot distinguish long haplotypes directly. Computational reconstruction of haplotypes from pooled sequencing has been attempted in virology, bacterial genomics, metagenomics, and human genetics, using algorithms based on either cross-host genetic sharing or within-host genomic reads. Here, we describe PoolHapX, a flexible computational approach that integrates information from both genetic sharing and genomic sequencing. We demonstrated that PoolHapX outperforms state-of-the-art tools tailored to specific organismal systems, and is robust to within-host evolution. Importantly, together with barcoded linked-reads, PoolHapX can infer whole-chromosome-scale haplotypes from 50 pools each containing 12 different haplotypes. By analyzing real data, we uncovered dynamic variations in the evolutionary processes of within-patient HIV populations previously unobserved in single position-based analysis.

Computational Methods for Strain-Level Microbial Detection in Colony and Metagenome Sequencing Data

Metagenomic sequencing is a powerful tool for examining the diversity and complexity of microbial communities. Most widely used tools for taxonomic profiling of metagenomic sequence data allow for a species-level overview of the composition. However, individual strains within a species can differ greatly in key genotypic and phenotypic characteristics, such as drug resistance, virulence and growth rate. Therefore, the ability to resolve microbial communities down to the level of individual strains within a species is critical to interpreting metagenomic data for clinical and environmental applications, where identifying a particular strain, or tracking a particular strain across a set of samples, can help aid in clinical diagnosis and treatment, or in characterizing yet unstudied strains across novel environmental locations. Recently published approaches have begun to tackle the problem of resolving strains within a particular species in metagenomic samples. In this review, we present an overview of these new algorithms and their uses, including methods based on assembly reconstruction and methods operating with or without a reference database. While existing metagenomic analysis methods show reasonable performance at the species and higher taxonomic levels, identifying closely related strains within a species presents a bigger challenge, due to the diversity of databases, genetic relatedness, and goals when conducting these analyses. Selection of which metagenomic tool to employ for a specific application should be performed on a case-by case basis as these tools have strengths and weaknesses that affect their performance on specific tasks. A comprehensive benchmark across different use case scenarios is vital to validate performance of these tools on microbial samples. Because strain-level metagenomic analysis is still in its infancy, development of more fine-grained, high-resolution algorithms will continue to be in demand for the future.

Strain-Level Metagenomic Data Analysis of Enriched In Vitro and In Silico Spiked Food Samples: Paving the Way towards a Culture-Free Foodborne Outbreak Investigation Using STEC as a Case Study

Culture-independent diagnostics, such as metagenomic shotgun sequencing of food samples, could not only reduce the turnaround time of samples in an outbreak investigation, but also allow the detection of multi-species and multi-strain outbreaks. For successful foodborne outbreak investigation using a metagenomic approach, it is, however, necessary to bioinformatically separate the genomes of individual strains, including strains belonging to the same species, present in a microbial community, which has up until now not been demonstrated for this application. The current work shows the feasibility of strain-level metagenomics of enriched food matrix samples making use of data analysis tools that classify reads against a sequence database. It includes a brief comparison of two database-based read classification tools, Sigma and Sparse, using a mock community obtained by in vitro spiking minced meat with a Shiga toxin-producing Escherichia coli (STEC) isolate originating from a described outbreak. The more optimal tool Sigma was further evaluated using in silico simulated metagenomic data to explore the possibilities and limitations of this data analysis approach. The performed analysis allowed us to link the pathogenic strains from food samples to human isolates previously collected during the same outbreak, demonstrating that the metagenomic approach could be applied for the rapid source tracking of foodborne outbreaks. To our knowledge, this is the first study demonstrating a data analysis approach for detailed characterization and phylogenetic placement of multiple bacterial strains of one species from shotgun metagenomic WGS data of an enriched food sample.

BHap: a novel approach for bacterial haplotype reconstruction

MOTIVATION

The bacterial haplotype reconstruction is critical for selecting proper treatments for diseases caused by unknown haplotypes. Existing methods and tools do not work well on this task, because they are usually developed for viral instead of bacterial populations.

RESULTS

In this study, we developed BHap, a novel algorithm based on fuzzy flow networks, for reconstructing bacterial haplotypes from next generation sequencing data. Tested on simulated and experimental datasets, we showed that BHap was capable of reconstructing haplotypes of bacterial populations with an average F1 score of 0.87, an average precision of 0.87 and an average recall of 0.88. We also demonstrated that BHap had a low susceptibility to sequencing errors, was capable of reconstructing haplotypes with low coverage and could handle a wide range of mutation rates. Compared with existing approaches, BHap outperformed them in terms of higher F1 scores, better precision, better recall and more accurate estimation of the number of haplotypes.

AVAILABILITY AND IMPLEMENTATION

The BHap tool is available at http://www.cs.ucf.edu/∼xiaoman/BHap/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Rare mutations in Pfmdr1 gene of Plasmodium falciparum detected in clinical isolates from patients treated with anti-malarial drug in Nigeria

Background

Plasmodium falciparum, the deadliest causative agent of malaria, has high prevalence in Nigeria. Drug resistance causing failure of previously effective drugs has compromised anti-malarial treatment. On this basis, there is need for a proactive surveillance for resistance markers to the currently recommended artemisinin-based combination therapy (ACT), for early detection of resistance before it become widespread.

Methods

This study assessed anti-malarial resistance genes polymorphism in patients with uncomplicated P. falciparum malaria in Lagos, Nigeria. Sanger and Next Generation Sequencing (NGS) methods were used to screen for mutations in thirty-seven malaria positive blood samples targeting the P. falciparum chloroquine-resistance transporter (Pfcrt), P. falciparum multidrug-resistance 1 (Pfmdr1), and P. falciparum kelch 13 (Pfk13) genes, which have been previously associated with anti-malarial resistance.

Results

Expectedly, the NGS method was more proficient, detecting six Pfmdr1, seven Pfcrt and three Pfk13 mutations in the studied clinical isolates from Nigeria, a malaria endemic area. These mutations included rare Pfmdr1 mutations, N504K, N649D, F938Y and S967N, which were previously unreported. In addition, there was moderate prevalence of the K76T mutation (34.6%) associated with chloroquine and amodiaquine resistance, and high prevalence of the N86 wild type allele (92.3%) associated with lumefantrine resistance.

Conclusion

Widespread circulation of mutations associated with resistance to current anti-malarial drugs could potentially limit effective malaria therapy in endemic populations.

Polymorphism analysis of the apxIA gene of Actinobacillus pleuropneumoniae serovar 5 isolated in swine herds from Brazil

The bacterium Actinobacillus pleuropneumoniae is the etiological agent of Contagious Porcine Pleuropneumonia, a disease responsible for economic losses in the swine industry worldwide. A. pleuropneumoniae is capable of producing proteinaceous exotoxins responsible for inducing hemorrhagic lesions, one of which is ApxI. Few studies have conducted an in-depth evaluation of polymorphisms of the nucleotides that make up the ApxI toxin gene. Here we analyze the polymorphisms of the apxIA gene region of A. pleuropneumoniae serovar 5 isolated from swine in different regions in Brazil and report the results of molecular sequencing and phylogenetic analysis. Analysis of the apxIA gene in 60 isolates revealed the presence of genetic diversity and variability. The polymorphisms in the nucleotide sequences determined the grouping of the Brazilian sequences and five more sequences from the GenBank database into 14 different haplotypes, which formed three main groups and revealed the presence of mutations in the nucleotide sequences. The estimation of selection pressures suggests the occurrence of genetic variations by positive selective pressure on A. pleuropneumoniae in large groups of animals in relatively small spaces. These conditions presumably favor the horizontal dissemination of apxIA gene mutations within bacterial populations with host reservoirs. As a result, the same serovar can demonstrate different antigenic capacities due to mutations in the apxIA gene. These alterations in sequences of the apxIA gene could occur in other areas of countries with intense swine production, which could lead to differences in the pathogenicity and immunogenicity of each serovar and have implications for the clinical status or diagnosis of A. pleuropneumoniae.

Identifying Mixed Mycobacterium tuberculosis Infection and Laboratory Cross-Contamination during Mycobacterial Sequencing Programs

The detection of laboratory cross-contamination and mixed tuberculosis infections is an important goal of clinical mycobacteriology laboratories. The objective of this study was to develop a method to detect mixtures of different Mycobacterium tuberculosis lineages in laboratories performing mycobacterial next-generation sequencing (NGS). The setting was the Public Health England National Mycobacteriology Laboratory Birmingham, which performs Illumina sequencing on DNA extracted from positive mycobacterial growth indicator tubes. We analyzed 4,156 samples yielding M. tuberculosis from 663 MiSeq runs, which were obtained during development and production use of a diagnostic process using NGS. The counts of the most common (major) variant and all other variants (nonmajor variants) were determined from reads mapping to positions defining M. tuberculosis lineages. Expected variation was estimated during process development. For each sample, we determined the nonmajor variant proportions at 55 sets of lineage-defining positions. The nonmajor variant proportion in the two most mixed lineage-defining sets (F2 metric) was compared with that of the 47 least-mixed lineage-defining sets (F47 metric). The following three patterns were observed: (i) not mixed by either metric; (ii) high F47 metric, suggesting mixtures of multiple lineages; and (iii) samples compatible with mixtures of two lineages, detected by differential F2 metric elevations relative to F47. Pattern ii was observed in batches, with similar patterns in the M. tuberculosis H37Rv control present in each run, and is likely to reflect cross-contamination. During production, the proportions of samples in the patterns were 97%, 2.8%, and 0.001%, respectively. The F2 and F47 metrics described could be used for laboratory process control in laboratories sequencing M. tuberculosis genomes.

Experimental Design, Population Dynamics, and Diversity in Microbial Experimental Evolution

In experimental evolution, laboratory-controlled conditions select for the adaptation of species, which can be monitored in real time. Despite the current popularity of such experiments, nature's most pervasive biological force was long believed to be observable only on time scales that transcend a researcher's life-span, and studying evolution by natural selection was therefore carried out solely by comparative means. Eventually, microorganisms' propensity for fast evolutionary changes proved us wrong, displaying strong evolutionary adaptations over a limited time, nowadays massively exploited in laboratory evolution experiments. Here, we formulate a guide to experimental evolution with microorganisms, explaining experimental design and discussing evolutionary dynamics and outcomes and how it is used to assess ecoevolutionary theories, improve industrially important traits, and untangle complex phenotypes. Specifically, we give a comprehensive overview of the setups used in experimental evolution. Additionally, we address population dynamics and genetic or phenotypic diversity during evolution experiments and expand upon contributing factors, such as epistasis and the consequences of (a)sexual reproduction. Dynamics and outcomes of evolution are most profoundly affected by the spatiotemporal nature of the selective environment, where changing environments might lead to generalists and structured environments could foster diversity, aided by, for example, clonal interference and negative frequency-dependent selection. We conclude with future perspectives, with an emphasis on possibilities offered by fast-paced technological progress. This work is meant to serve as an introduction to those new to the field of experimental evolution, as a guide to the budding experimentalist, and as a reference work to the seasoned expert.

Software Dedicated to Virus Sequence Analysis "Bioinformatics Goes Viral"

Computer-assisted technologies of the genomic structure, biological function, and evolution of viruses remain a largely neglected area of research. The attention of bioinformaticians to this challenging field is currently unsatisfying in respect to its medical and biological importance. The power of new genome sequencing technologies, associated with new tools to handle "big data", provides unprecedented opportunities to address fundamental questions in virology. Here, we present an overview of the current technologies, challenges, and advantages of Next-Generation Sequencing (NGS) in relation to the field of virology. We present how viral sequences can be detected de novo out of current short-read NGS data. Furthermore, we discuss the challenges and applications of viral quasispecies and how secondary structures, commonly shaped by RNA viruses, can be computationally predicted. The phylogenetic analysis of viruses, as another ubiquitous field in virology, forms an essential element of describing viral epidemics and challenges current algorithms. Recently, the first specialized virus-bioinformatic organizations have been established. We need to bring together virologists and bioinformaticians and provide a platform for the implementation of interdisciplinary collaborative projects at local and international scales. Above all, there is an urgent need for dedicated software tools to tackle various challenges in virology.

Adaptive tuning of mutation rates allows fast response to lethal stress in Escherichia coli

While specific mutations allow organisms to adapt to stressful environments, most changes in an organism's DNA negatively impact fitness. The mutation rate is therefore strictly regulated and often considered a slowly-evolving parameter. In contrast, we demonstrate an unexpected flexibility in cellular mutation rates as a response to changes in selective pressure. We show that hypermutation independently evolves when different Escherichia coli cultures adapt to high ethanol stress. Furthermore, hypermutator states are transitory and repeatedly alternate with decreases in mutation rate. Specifically, population mutation rates rise when cells experience higher stress and decline again once cells are adapted. Interestingly, we identified cellular mortality as the major force driving the quick evolution of mutation rates. Together, these findings show how organisms balance robustness and evolvability and help explain the prevalence of hypermutation in various settings, ranging from emergence of antibiotic resistance in microbes to cancer relapses upon chemotherapy.

Molecular Epidemiology of Plasmodium falciparum kelch13 Mutations in Senegal Determined by Using Targeted Amplicon Deep Sequencing

The emergence of Plasmodium falciparum resistance to artemisinin in Southeast Asia threatens malaria control and elimination activities worldwide. Multiple polymorphisms in the P. falciparum kelch gene found in chromosome 13 (Pfk13) have been associated with artemisinin resistance. Surveillance of potential drug resistance loci within a population that may emerge under increasing drug pressure is an important public health activity. In this context, P. falciparum infections from an observational surveillance study in Senegal were genotyped using targeted amplicon deep sequencing (TADS) for Pfk13 polymorphisms. The results were compared to previously reported Pfk13 polymorphisms from around the world. A total of 22 Pfk13 propeller domain polymorphisms were identified in this study, of which 12 have previously not been reported. Interestingly, of the 10 polymorphisms identified in the present study that were also previously reported, all had a different amino acid substitution at these codon positions. Most of the polymorphisms were present at low frequencies and were confined to single isolates, suggesting they are likely transient polymorphisms that are part of naturally evolving parasite populations. The results of this study underscore the need to identify potential drug resistance loci existing within a population, which may emerge under increasing drug pressure.

Variant profiling of evolving prokaryotic populations

Genomic heterogeneity of bacterial species is observed and studied in experimental evolution experiments and clinical diagnostics, and occurs as micro-diversity of natural habitats. The challenge for genome research is to accurately capture this heterogeneity with the currently used short sequencing reads. Recent advances in NGS technologies improved the speed and coverage and thus allowed for deep sequencing of bacterial populations. This facilitates the quantitative assessment of genomic heterogeneity, including low frequency alleles or haplotypes. However, false positive variant predictions due to sequencing errors and mapping artifacts of short reads need to be prevented. We therefore created VarCap, a workflow for the reliable prediction of different types of variants even at low frequencies. In order to predict SNPs, InDels and structural variations, we evaluated the sensitivity and accuracy of different software tools using synthetic read data. The results suggested that the best sensitivity could be reached by a union of different tools, however at the price of increased false positives. We identified possible reasons for false predictions and used this knowledge to improve the accuracy by post-filtering the predicted variants according to properties such as frequency, coverage, genomic environment/localization and co-localization with other variants. We observed that best precision was achieved by using an intersection of at least two tools per variant. This resulted in the reliable prediction of variants above a minimum relative abundance of 2%. VarCap is designed for being routinely used within experimental evolution experiments or for clinical diagnostics. The detected variants are reported as frequencies within a VCF file and as a graphical overview of the distribution of the different variant/allele/haplotype frequencies. The source code of VarCap is available at https://github.com/ma2o/VarCap. In order to provide this workflow to a broad community, we implemeted VarCap on a Galaxy webserver, which is accessible at http://galaxy.csb.univie.ac.at.

Genomic diversity in Onchocerca volvulus and its Wolbachia endosymbiont

Ongoing elimination efforts have altered the global distribution of Onchocerca volvulus, the agent of river blindness, and further population restructuring is expected as efforts continue. Therefore, a better understanding of population genetic processes and their effect on biogeography is needed to support elimination goals. We describe O. volvulus genome variation in 27 isolates from the early 1990s (before widespread mass treatment) from four distinct locales: Ecuador, Uganda, the West African forest and the West African savanna. We observed genetic substructuring between Ecuador and West Africa and between the West African forest and savanna bioclimes, with evidence of unidirectional gene flow from savanna to forest strains. We identified forest:savanna-discriminatory genomic regions and report a set of ancestry informative loci that can be used to differentiate between forest, savanna and admixed isolates, which has not previously been possible. We observed mito-nuclear discordance possibly stemming from incomplete lineage sorting. The catalogue of the nuclear, mitochondrial and endosymbiont DNA variants generated in this study will support future basic and translational onchocerciasis research, with particular relevance for ongoing control programmes, and boost efforts to characterize drug, vaccine and diagnostic targets.

Deep Whole-Genome Sequencing to Detect Mixed Infection of Mycobacterium tuberculosis

Mixed infection by multiple Mycobacterium tuberculosis (MTB) strains is associated with poor treatment outcome of tuberculosis (TB). Traditional genotyping methods have been used to detect mixed infections of MTB, however, their sensitivity and resolution are limited. Deep whole-genome sequencing (WGS) has been proved highly sensitive and discriminative for studying population heterogeneity of MTB. Here, we developed a phylogenetic-based method to detect MTB mixed infections using WGS data. We collected published WGS data of 782 global MTB strains from public database. We called homogeneous and heterogeneous single nucleotide variations (SNVs) of individual strains by mapping short reads to the ancestral MTB reference genome. We constructed a phylogenomic database based on 68,639 homogeneous SNVs of 652 MTB strains. Mixed infections were determined if multiple evolutionary paths were identified by mapping the SNVs of individual samples to the phylogenomic database. By simulation, our method could specifically detect mixed infections when the sequencing depth of minor strains was as low as 1× coverage, and when the genomic distance of two mixed strains was as small as 16 SNVs. By applying our methods to all 782 samples, we detected 47 mixed infections and 45 of them were caused by locally endemic strains. The results indicate that our method is highly sensitive and discriminative for identifying mixed infections from deep WGS data of MTB isolates.

Next-generation sequencing (NGS) in the microbiological world: How to make the most of your money

The Sanger sequencing method produces relatively long DNA sequences of unmatched quality and has been considered for long time as the gold standard for sequencing DNA. Many improvements of the Sanger method that culminated with fluorescent dyes coupled with automated capillary electrophoresis enabled the sequencing of the first genomes. Nevertheless, using this technology to sequence whole genomes was costly, laborious and time consuming even for genomes that are relatively small in size. A major technological advance was the introduction of next-generation sequencing (NGS) pioneered by 454 Life Sciences in the early part of the 21th century. NGS allowed scientists to sequence thousands to millions of DNA molecules in a single machine run. Since then, new NGS technologies have emerged and existing NGS platforms have been improved, enabling the production of genome sequences at an unprecedented rate as well as broadening the spectrum of NGS applications. The current affordability of generating genomic information, especially with microbial samples, has resulted in a false sense of simplicity that belies the fact that many researchers still consider these technologies a black box. In this review, our objective is to identify and discuss four steps that we consider crucial to the success of any NGS-related project. These steps are: (1) the definition of the research objectives beyond sequencing and appropriate experimental planning, (2) library preparation, (3) sequencing and (4) data analysis. The goal of this review is to give an overview of the process, from sample to analysis, and discuss how to optimize your resources to achieve the most from your NGS-based research. Regardless of the evolution and improvement of the sequencing technologies, these four steps will remain relevant.

Experimental evolution in biofilm populations

Biofilms are a major form of microbial life in which cells form dense surface associated communities that can persist for many generations. The long-life of biofilm communities means that they can be strongly shaped by evolutionary processes. Here, we review the experimental study of evolution in biofilm communities. We first provide an overview of the different experimental models used to study biofilm evolution and their associated advantages and disadvantages. We then illustrate the vast amount of diversification observed during biofilm evolution, and we discuss (i) potential ecological and evolutionary processes behind the observed diversification, (ii) recent insights into the genetics of adaptive diversification, (iii) the striking degree of parallelism between evolution experiments and real-life biofilms and (iv) potential consequences of diversification. In the second part, we discuss the insights provided by evolution experiments in how biofilm growth and structure can promote cooperative phenotypes. Overall, our analysis points to an important role of biofilm diversification and cooperation in bacterial survival and productivity. Deeper understanding of both processes is of key importance to design improved antimicrobial strategies and diagnostic techniques.

This review paper provides an overview of (i) the different experimental models used to study biofilm evolution, (ii) the vast amount of diversification observed during biofilm evolution (including potential causes and consequences) and (iii) recent insights in how growth in biofilms can lead to the evolution of cooperative phenotypes.

Synthetic long-read sequencing reveals intraspecies diversity in the human microbiome

Identifying bacterial strains in metagenome and microbiome samples using computational analyses of short-read sequence remains a difficult problem. Here, we present an analysis of a human gut microbiome using on Tru-seq synthetic long reads combined with new computational tools for metagenomic long-read assembly, variant-calling and haplotyping (Nanoscope and Lens). Our analysis identifies 178 bacterial species of which 51 were not found using short sequence reads alone. We recover bacterial contigs that comprise multiple operons, including 22 contigs of >1Mbp. Extensive intraspecies variation among microbial strains in the form of haplotypes that span up to hundreds of Kbp can be observed using our approach. Our method incorporates synthetic long-read sequencing technology with standard shotgun approaches to move towards rapid, precise and comprehensive analyses of metagenome and microbiome samples.