The recent widespread emergence of carbapenem resistance in Enterobacteriaceae is a major public health concern, as carbapenems are a therapy of last resort against this family of common bacterial pathogens. Resistance genes can mobilize via various mechanisms, including conjugation and transposition; however, the importance of this mobility in short-term evolution, such as within nosocomial outbreaks, is unknown. Using a combination of short- and long-read whole-genome sequencing of 281 blaKPC-positive Enterobacteriaceae isolates from a single hospital over 5 years, we demonstrate rapid dissemination of this carbapenem resistance gene to multiple species, strains, and plasmids. Mobility of blaKPC occurs at multiple nested genetic levels, with transmission of blaKPC strains between individuals, frequent transfer of blaKPC plasmids between strains/species, and frequent transposition of blaKPC transposon Tn4401 between plasmids. We also identify a common insertion site for Tn4401 within various Tn2-like elements, suggesting that homologous recombination between Tn2-like elements has enhanced the spread of Tn4401 between different plasmid vectors. Furthermore, while short-read sequencing has known limitations for plasmid assembly, various studies have attempted to overcome this by the use of reference-based methods. We also demonstrate that, as a consequence of the genetic mobility observed in this study, plasmid structures can be extremely dynamic, and therefore these reference-based methods, as well as traditional partial typing methods, can produce very misleading conclusions. Overall, our findings demonstrate that nonclonal resistance gene dissemination can be extremely rapid, presenting significant challenges for public health surveillance and achieving effective control of antibiotic resistance.

In the search for new drug targets, we evaluated the biotin synthetic pathway of Mycobacterium tuberculosis (Mtb) and constructed an Mtb mutant lacking the biotin biosynthetic enzyme 7,8-diaminopelargonic acid synthase, BioA. In biotin-free synthetic media, ΔbioA did not produce wild-type levels of biotinylated proteins, and therefore did not grow and lost viability. ΔbioA was also unable to establish infection in mice. Conditionally-regulated knockdown strains of Mtb similarly exhibited impaired bacterial growth and viability in vitro and in mice, irrespective of the timing of transcriptional silencing. Biochemical studies further showed that BioA activity has to be reduced by approximately 99% to prevent growth. These studies thus establish that de novo biotin synthesis is essential for Mtb to establish and maintain a chronic infection in a murine model of TB. Moreover, these studies provide an experimental strategy to systematically rank the in vivo value of potential drug targets in Mtb and other pathogens.

The control of Clostridium difficile infection is a major international healthcare priority, hindered by a limited understanding of transmission epidemiology for these bacteria. However, transmission studies of bacterial pathogens are rapidly being transformed by the advent of next generation sequencing.

Bacterial pathogens impose a heavy burden of disease on human populations worldwide. The gravest threats are posed by highly virulent respiratory pathogens, enteric pathogens, and HIV-associated infections. Tuberculosis alone is responsible for the deaths of 1.5 million people annually. Treatment options for bacterial pathogens are being steadily eroded by the evolution and spread of drug resistance. However, population-level whole genome sequencing offers new hope in the fight against pathogenic bacteria. By providing insights into bacterial evolution and disease etiology, these approaches pave the way for novel interventions and therapeutic targets. Sequencing populations of bacteria across the whole genome provides unprecedented resolution to investigate (i) within-host evolution, (ii) transmission history, and (iii) population structure. Moreover, advances in rapid benchtop sequencing herald a new era of real-time genomics in which sequencing and analysis can be deployed within hours in response to rapidly changing public health emergencies. The purpose of this review is to highlight the transformative effect of population genomics on bacteriology, and to consider the prospects for answering abiding questions such as why bacteria cause disease.

There is currently no comprehensive meningococcal vaccine, due to difficulties in immunizing against organisms expressing serogroup B capsules. To address this problem, subcapsular antigens, particularly the outer-membrane proteins (OMPs), are being investigated as candidate vaccine components. If immunogenic, however, such antigens are often antigenically variable, and knowledge of the extent and structuring of this diversity is an essential part of vaccine formulation. Factor H-binding protein (fHbp) is one such protein and is included in two vaccines under development. A survey of the diversity of the fHbp gene and the encoded protein in a representative sample of meningococcal isolates confirmed that variability in this protein is structured into two or three major groups, each with a substantial number of alleles that have some association with meningococcal clonal complexes and serogroups. A unified nomenclature scheme was devised to catalogue this diversity. Analysis of recombination and selection on the allele sequences demonstrated that parts of the gene are subject to positive selection, consistent with immune selection on the protein generating antigenic variation, particularly in the C-terminal region of the peptide sequence. The highest levels of selection were observed in regions corresponding to epitopes recognized by previously described bactericidal monoclonal antibodies.

Selection plays a key role in the spread of antibiotic resistance, but the evolutionary drivers of clinically important resistant strains remain poorly understood. Here, we use genomic analyses and competition experiments to study Staphylococcus aureus ST239, a prominent MRSA strain that is thought to have been formed by large-scale recombination between ST8 and ST30. Genomic analyses allowed us to refine the hybrid model for the origin of ST239 and to date the origin of ST239 to 1920 to 1945, which predates the clinical introduction of methicillin in 1959. Although purifying selection has dominated the evolution of ST239, parallel evolution has occurred in genes involved in antibiotic resistance and virulence, suggesting that ST239 has evolved toward an increasingly pathogenic lifestyle. Crucially, ST239 isolates have low competitive fitness relative to both ST8 and ST30 isolates, supporting the idea that fitness costs have driven the demise of this once-dominant pathogen strain. IMPORTANCE The rise of antibiotic resistance in most pathogenic bacteria has been driven by the spread of a small number of epidemically successful resistant strains. However, the processes that drive the rise and fall of these "superbugs" remain poorly understood. In our study, we investigated Staphylococcus aureus ST239, an important MRSA strain that has been a leading cause of serious hospital-acquired infections. We show here that ST239 was formed by the exchange of a very large fragment of DNA carrying resistance genes between strains of S. aureus sometime before 1945. The introduction of the antibiotic methicillin in 1959 provided a key advantage for Methicillin-Resistant Staphylococcus aureus (MRSA) strains. Interestingly, we found that ST239 has low competitive ability compared to other strains of S. aureus, and this evolutionary hindrance may explain why the prevalence of ST239 has declined precipitously at a global scale.

Campylobacteriosis is among the world's most common foodborne illnesses, caused predominantly by the bacterium Campylobacter jejuni. Effective interventions require determination of the infection source which is challenging as transmission occurs via multiple sources such as contaminated meat, poultry, and drinking water. Strain variation has allowed source tracking based upon allelic variation in multi-locus sequence typing (MLST) genes allowing isolates from infected individuals to be attributed to specific animal or environmental reservoirs. However, the accuracy of probabilistic attribution models has been limited by the ability to differentiate isolates based upon just 7 MLST genes. Here, we broaden the input data spectrum to include core genome MLST (cgMLST) and whole genome sequences (WGS), and implement multiple machine learning algorithms, allowing more accurate source attribution. We increase attribution accuracy from 64% using the standard iSource population genetic approach to 71% for MLST, 85% for cgMLST and 78% for kmerized WGS data using the classifier we named aiSource. To gain insight beyond the source model prediction, we use Bayesian inference to analyse the relative affinity of C. jejuni strains to infect humans and identified potential differences, in source-human transmission ability among clonally related isolates in the most common disease causing lineage (ST-21 clonal complex). Providing generalizable computationally efficient methods, based upon machine learning and population genetics, we provide a scalable approach to global disease surveillance that can continuously incorporate novel samples for source attribution and identify fine-scale variation in transmission potential.

Quantitative evidence for the risk of zoonoses and the spread of antimicrobial resistance remains lacking. Here, as part of the UrbanZoo project, we sampled Escherichia coli from humans, livestock and peri-domestic wildlife in 99 households across Nairobi, Kenya, to investigate its distribution among host species in this rapidly developing urban landscape. We performed whole-genome sequencing of 1,338 E. coli isolates and found that the diversity and sharing patterns of E. coli were heavily structured by household and strongly shaped by host type. We also found evidence for inter-household and inter-host sharing and, importantly, between humans and animals, although this occurs much less frequently. Resistome similarity was differently distributed across host and household, consistent with being driven by shared exposure to antimicrobials. Our results indicate that a large, epidemiologically structured sampling framework combined with WGS is needed to uncover strain-sharing events among different host populations in complex environments and the major contributing pathways that could ultimately drive the emergence of zoonoses and the spread of antimicrobial resistance.

Studies have shown that hepatitis C virus subtype 3a (HCV-3a) is likely to have been circulating in South Asia before its global spread. However, the time and route of this dissemination remain unclear. For the first time, we generated host and virus genome-wide data for more than 500 patients infected with HCV-3a from the UK, North America, Australia, and New Zealand. We used the host genomic data to infer the ancestry of the patients and used this information to investigate the epidemic history of HCV-3a. We observed that viruses from hosts of South Asian ancestry clustered together near the root of the tree, irrespective of the sampling country, and that they were more diverse than viruses from other host ancestries. We hypothesized that South Asian hosts are more likely to have been infected in South Asia and used the inferred host ancestries to distinguish between the location where the infection was acquired and where the sample was taken. Next, we inferred that three independent transmission events resulted in the spread of the virus from South Asia to the UK, North America, and Oceania. This initial spread happened during or soon after the end of World War II. This was subsequently followed by many independent transmissions between the UK, North America, and Oceania. Using both host and virus genomic information can be highly informative in studying the virus epidemic history, especially in the context of chronic infections where migration histories need to be accounted for.

There has been a growing interest in exploring the applications of stretched-exponential (SEM) and intravoxel incoherent motion (IVIM) models of diffusion-weighted imaging (DWI) in breast imaging, with the focus on differentiation of breast lesions. However, the use of SEM and IVIM models to predict early response to neoadjuvant chemotherapy (NACT) has received less attention.

Development of paper-based microfluidic devices that perform colorimetric measurements requires quantitative image analysis. Because the design geometries of paper-based microfluidic devices are not standardized, conventional methods for performing batch measurements of regularly spaced areas of signal intensity, such as those for well plates, cannot be used to quantify signal from most of these devices. To streamline the device development process, we have developed an open-source program called ColorScan that can automatically recognize and measure signal-containing zones from images of devices, regardless of output zone geometry or spatial arrangement. This program, which measures color intensity with the same accuracy as standard manual approaches, can rapidly process scanned device images, simultaneously measure identified output zones, and effectively manage measurement results to eliminate requirements for time-consuming and user-dependent image processing procedures.

Quantifying pathogen transmission in multi-host systems is difficult, as exemplified in bovine tuberculosis (bTB) systems, but is crucial for control. The agent of bTB, Mycobacterium bovis, persists in cattle populations worldwide, often where potential wildlife reservoirs exist. However, the relative contribution of different host species to bTB persistence is generally unknown. In Britain, the role of badgers in infection persistence in cattle is highly contentious, despite decades of research and control efforts. We applied Bayesian phylogenetic and machine-learning approaches to bacterial genome data to quantify the roles of badgers and cattle in M. bovis infection dynamics in the presence of data biases. Our results suggest that transmission occurs more frequently from badgers to cattle than vice versa (10.4x in the most likely model) and that within-species transmission occurs at higher rates than between-species transmission for both. If representative, our results suggest that control operations should target both cattle and badgers.