Person:

Hung, Deborah

Reconstructed models of metabolic networks are widely used for studying metabolism in various organisms. Many different reconstructions of the same organism often exist concurrently, forcing researchers to choose one of them at the exclusion of the others. We describe MetaMerge, an algorithm for semi-automatically reconciling a pair of existing metabolic network reconstructions into a single metabolic network model. We use MetaMerge to combine two published metabolic networks for Mycobacterium tuberculosis into a single network, which allows many reactions that could not be active in the individual models to become active, and predicts essential genes with a higher positive predictive value.

Mycobacterium tuberculosis, the causative agent of most human tuberculosis, infects one third of the world's population and kills an estimated 1.7 million people a year. With the world-wide emergence of drug resistance, and the finding of more functional genetic diversity than previously expected, there is a renewed interest in understanding the forces driving genome evolution of this important pathogen. Genetic diversity in M. tuberculosis is dominated by single nucleotide polymorphisms and small scale gene deletion, with little or no evidence for large scale genome rearrangements seen in other bacteria. Recently, a single report described a large scale genome duplication that was suggested to be specific to the Beijing lineage. We report here multiple independent large-scale duplications of the same genomic region of M. tuberculosis detected through whole-genome sequencing. The duplications occur in strains belonging to both M. tuberculosis lineage 2 and 4, and are thus not limited to Beijing strains. The duplications occur in both drug-resistant and drug susceptible strains. The duplicated regions also have substantially different boundaries in different strains, indicating different originating duplication events. We further identify a smaller segmental duplication of a different genomic region of a lab strain of H37Rv. The presence of multiple independent duplications of the same genomic region suggests either instability in this region, a selective advantage conferred by the duplication, or both. The identified duplications suggest that large-scale gene duplication may be more common in M. tuberculosis than previously considered.

Mycobacterium tuberculosis remains a significant threat to global health. Macrophages are the host cell for M. tuberculosis infection, and although bacteria are able to replicate intracellularly under certain conditions, it is also clear that macrophages are capable of killing M. tuberculosis if appropriately activated. The outcome of infection is determined at least in part by the host-pathogen interaction within the macrophage; however, we lack a complete understanding of which host pathways are critical for bacterial survival and replication. To add to our understanding of the molecular processes involved in intracellular infection, we performed a chemical screen using a high-content microscopic assay to identify small molecules that restrict mycobacterial growth in macrophages by targeting host functions and pathways. The identified host-targeted inhibitors restrict bacterial growth exclusively in the context of macrophage infection and predominantly fall into five categories: G-protein coupled receptor modulators, ion channel inhibitors, membrane transport proteins, anti-inflammatories, and kinase modulators. We found that fluoxetine, a selective serotonin reuptake inhibitor, enhances secretion of pro-inflammatory cytokine TNF-α and induces autophagy in infected macrophages, and gefitinib, an inhibitor of the Epidermal Growth Factor Receptor (EGFR), also activates autophagy and restricts growth. We demonstrate that during infection signaling through EGFR activates a p38 MAPK signaling pathway that prevents macrophages from effectively responding to infection. Inhibition of this pathway using gefitinib during in vivo infection reduces growth of M. tuberculosis in the lungs of infected mice. Our results support the concept that screening for inhibitors using intracellular models results in the identification of tool compounds for probing pathways during in vivo infection and may also result in the identification of new anti-tuberculosis agents that work by modulating host pathways. Given the existing experience with some of our identified compounds for other therapeutic indications, further clinically-directed study of these compounds is merited.

Certain bacterial pathogens are able to evade the host immune system and persist within the human host. The consequences of persistent bacterial infections potentially include increased morbidity and mortality from the infection itself as well as an increased risk of dissemination of disease. Eradication of persistent infections is difficult, often requiring prolonged or repeated courses of antibiotics. During persistent infections, a population or subpopulation of bacteria exists that is refractory to traditional antibiotics, possibly in a non-replicating or metabolically altered state. This review highlights the clinical significance of persistent infections and discusses different in vitro models used to investigate the altered physiology of bacteria during persistent infections. We specifically focus on recent work establishing increased protection against oxidative stress as a key element of the altered physiologic state across different in vitro models and pathogens.

Antibiotic resistance arising via chromosomal mutations is typically specific to a particular antibiotic or class of antibiotics. We have identified mutations in genes encoding ribosomal components in Mycobacterium smegmatis that confer resistance to several structurally and mechanistically unrelated classes of antibiotics and enhance survival following heat shock and membrane stress. These mutations affect ribosome assembly and cause large-scale transcriptomic and proteomic changes, including the downregulation of the catalase KatG, an activating enzyme required for isoniazid sensitivity, and upregulation of WhiB7, a transcription factor involved in innate antibiotic resistance. Importantly, while these ribosomal mutations have a fitness cost in antibiotic-free medium, in a multidrug environment they promote the evolution of high-level, target-based resistance. Further, suppressor mutations can then be easily acquired to restore wild-type growth. Thus, ribosomal mutations can serve as stepping-stones in an evolutionary path leading to the emergence of high-level, multidrug resistance. DOI: http://dx.doi.org/10.7554/eLife.20420.001

The ability to simultaneously characterize the bacterial and host expression programs during infection would facilitate a comprehensive understanding of pathogen-host interactions. While RNA-Seq has greatly advanced our ability to study the transcriptomes of prokaryote and eukaryotes separately, limitations in existing protocols for generating and analyzing RNA-Seq data have hindered simultaneous profiling of host and bacterial pathogen transcripts from the same sample. Here we provide a detailed protocol for simultaneous analysis of host and bacterial transcripts by RNA-Seq. Importantly, this protocol details the steps required for efficient host and bacteria lysis, barcoding of samples, technical advances in sample preparation for low yield sample inputs and a computational pipeline to analyze both mammalian and microbial reads from mixed hostpathogen RNA-Seq data. Sample preparation takes 3 d from cultured cells to pooled libraries. Data analysis takes an additional day. Compared with previous methods, the protocol detailed here provides a sensitive, facile and generalizable approach, suitable for large-scale studies, which will enable the field to obtain in-depth analysis of hostpathogen interactions in infection models.

Zebrafish (Danio rerio) have a number of strengths as a host model for infection, including genetic tractability, a vertebrate immune system similar to that of mammals, ease and scale of laboratory handling, which allows analysis with reasonable throughput, and transparency, which facilitates visualization of the infection. With these advantages in mind, we examined whether zebrafish could be used to study Pseudomonas aeruginosa pathogenesis and found that infection of zebrafish embryos with live P. aeruginosa (PA14 or PAO1) by microinjection results in embryonic death, unlike infection with Escherichia coli or heat-killed P. aeruginosa, which has no effect. Similar to studies with mice, P. aeruginosa mutants deficient in type three secretion (pscD) or quorum sensing (lasR and mvfR) are attenuated in zebrafish embryos infected at 50 h postfertilization (hpf), a developmental stage when both macrophages and neutrophils are present. In contrast, embryos infected at 28 hpf, when only macrophages are initially present, succumb to lethal challenge with far fewer P. aeruginosa cells than those required for embryos infected at 50 hpf, are susceptible to infection with lasR and pscD deletion mutants, and are moderately resistant to infection with an mvfR mutant. Finally, we show that we can control the outcome of infection through the use of morpholinos, which allow us to shift immune cell numbers, or small molecules (antibiotics), which rescue embryos from lethal challenge. Thus, zebrafish are a novel host model that is well suited for studying the interactions among individual pathogenic functions of P. aeruginosa, the role of individual components of host immune defense, and small-molecule modulators of infection.

Our variant ascertainment algorithm, VAAL, uses massively parallel DNA sequence data to identify differences between bacterial genomes with high sensitivity and specificity. VAAL detected B98% of differences (including large insertiondeletions) between pairs of strains from three species while calling no false positives. VAAL also pinpointed a single mutation between Vibrio cholerae genomes, identifying an antibiotic’s site of action by identifying sequence differences between drug-sensitive strains and drug-resistant derivatives.

A key to the pathogenic success of Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis, is the capacity to survive within host macrophages. Although several factors required for this survival have been identified, a comprehensive knowledge of such factors and how they work together to manipulate the host environment to benefit bacterial survival are not well understood. To systematically identify Mtb factors required for intracellular growth, we screened an arrayed, non-redundant Mtb transposon mutant library by high-content imaging to characterize the mutant-macrophage interaction. Based on a combination of imaging features, we identified mutants impaired for intracellular survival. We then characterized the phenotype of infection with each mutant by profiling the induced macrophage cytokine response. Taking a systems-level approach to understanding the biology of identified mutants, we performed a multiparametric analysis combining pathogen and host phenotypes to predict functional relationships between mutants based on clustering. Strikingly, mutants defective in two well-known virulence factors, the ESX-1 protein secretion system and the virulence lipid phthiocerol dimycocerosate (PDIM), clustered together. Building upon the shared phenotype of loss of the macrophage type I interferon (IFN) response to infection, we found that PDIM production and export are required for coordinated secretion of ESX-1-substrates, for phagosomal permeabilization, and for downstream induction of the type I IFN response. Multiparametric clustering also identified two novel genes that are required for PDIM production and induction of the type I IFN response. Thus, multiparametric analysis combining host and pathogen infection phenotypes can be used to identify novel functional relationships between genes that play a role in infection.

The degree to which molecular epidemiology reveals information about the sources and transmission patterns of an outbreak depends on the resolution of the technology used and the samples studied. Isolates of Escherichia coli O104:H4 from the outbreak centered in Germany in May–July 2011, and the much smaller outbreak in southwest France in June 2011, were indistinguishable by standard tests. We report a molecular epidemiological analysis using multiplatform whole-genome sequencing and analysis of multiple isolates from the German and French outbreaks. Isolates from the German outbreak showed remarkably little diversity, with only two single nucleotide polymorphisms (SNPs) found in isolates from four individuals. Surprisingly, we found much greater diversity (19 SNPs) in isolates from seven individuals infected in the French outbreak. The German isolates form a clade within the more diverse French outbreak strains. Moreover, five isolates derived from a single infected individual from the French outbreak had extremely limited diversity. The striking difference in diversity between the German and French outbreak samples is consistent with several hypotheses, including a bottleneck that purged diversity in the German isolates, variation in mutation rates in the two E. coli outbreak populations, or uneven distribution of diversity in the seed populations that led to each outbreak.