Genome variation and molecular epidemiology of Salmonella enterica serovar Typhimurium pathovariants

Salmonella enterica serovar Typhimurium is one of approximately 2,500 distinct serovars of the genus Salmonella but is exceptional in its wide distribution in the environment, livestock, and wild animals. S. Typhimurium causes a large proportion of nontyphoidal Salmonella (NTS) infections, accounting for a quarter of infections, second only to S. enterica serovar Enteritidis in incidence. S. Typhimurium was once considered the archetypal broad-host-range Salmonella serovar due to its wide distribution in livestock and wild animals, and much of what we know of the interaction of Salmonella with the host comes from research using a small number of laboratory strains of the serovar (LT2, SL1344, and ATCC 14028). But it has become clear that these strains do not reflect the genotypic or phenotypic diversity of S. Typhimurium. Here, we review the epidemiological record of S. Typhimurium and studies of the host-pathogen interactions of diverse strains of S. Typhimurium. We present the concept of distinct pathovariants of S. Typhimurium that exhibit diversity of host range, distribution in the environment, pathogenicity, and risk to food safety. We review recent evidence from whole-genome sequencing that has revealed the extent of genomic diversity of S. Typhimurium pathovariants, the genomic basis of differences in the level of risk to human and animal health, and the molecular epidemiology of prominent strains. An improved understanding of the impact of genome variation of bacterial pathogens on pathogen-host and pathogen-environment interactions has the potential to improve quantitative risk assessment and reveal how new pathogens evolve

A Functional Metagenomic Approach for Expanding the Synthetic Biology Toolbox for Biomass Conversion

Sustainable biofuel alternatives to fossil fuel energy are hampered by recalcitrance and toxicity of biomass substrates to microbial biocatalysts. To address this issue, we present a culture-independent functional metagenomic platform for mining Nature's vast enzymatic reservoir and show its relevance to biomass conversion. We performed functional selections on 4.7 Gb of metagenomic fosmid libraries and show that genetic elements conferring tolerance toward seven important biomass inhibitors can be identified. We select two metagenomic fosmids that improve the growth of Escherichia coli by 5.7- and 6.9-fold in the presence of inhibitory concentrations of syringaldehyde and 2-furoic acid, respectively, and identify the individual genes responsible for these tolerance phenotypes. Finally, we combine the individual genes to create a three-gene construct that confers tolerance to mixtures of these important biomass inhibitors. This platform presents a route for expanding the repertoire of genetic elements available to synthetic biology and provides a starting point for efforts to engineer robust strains for biofuel generation

Emergence of robust growth laws from optimal regulation of ribosome synthesis

Bacteria must constantly adapt their growth to changes in nutrient availability; yet despite large‐scale changes in protein expression associated with sensing, adaptation, and processing different environmental nutrients, simple growth laws connect the ribosome abundance and the growth rate. Here, we investigate the origin of these growth laws by analyzing the features of ribosomal regulation that coordinate proteome‐wide expression changes with cell growth in a variety of nutrient conditions in the model organism Escherichia coli. We identify supply‐driven feedforward activation of ribosomal protein synthesis as the key regulatory motif maximizing amino acid flux, and autonomously guiding a cell to achieve optimal growth in different environments. The growth laws emerge naturally from the robust regulatory strategy underlying growth rate control, irrespective of the details of the molecular implementation. The study highlights the interplay between phenomenological modeling and molecular mechanisms in uncovering fundamental operating constraints, with implications for endogenous and synthetic design of microorganisms.ISSN:1744-429

Invariant Distribution of Promoter Activities in Escherichia coli

Cells need to allocate their limited resources to express a wide range of genes. To understand how Escherichia coli partitions its transcriptional resources between its different promoters, we employ a robotic assay using a comprehensive reporter strain library for E. coli to measure promoter activity on a genomic scale at high-temporal resolution and accuracy. This allows continuous tracking of promoter activity as cells change their growth rate from exponential to stationary phase in different media. We find a heavy-tailed distribution of promoter activities, with promoter activities spanning several orders of magnitude. While the shape of the distribution is almost completely independent of the growth conditions, the identity of the promoters expressed at different levels does depend on them. Translation machinery genes, however, keep the same relative expression levels in the distribution across conditions, and their fractional promoter activity tracks growth rate tightly. We present a simple optimization model for resource allocation which suggests that the observed invariant distributions might maximize growth rate. These invariant features of the distribution of promoter activities may suggest design constraints that shape the allocation of transcriptional resources

Choice of Bacterial Growth Medium Alters the Transcriptome and Phenotype of Salmonella enterica Serovar Typhimurium

The type of bacterial culture medium is an important consideration during design of any experimental protocol. The aim of this study was to understand the impact of medium choice on bacterial gene expression and physiology by comparing the transcriptome of Salmonella enterica SL1344 after growth in the widely used LB broth or the rationally designed MOPS minimal medium. Transcriptomics showed that after growth in MOPS minimal media, compared to LB, there was increased expression of 42 genes involved in amino acid synthesis and 23 genes coding for ABC transporters. Seven flagellar genes had decreased expression after growth in MOPS minimal medium and this correlated with a decreased motility. In both MOPS minimal medium and MEM expression of genes from SPI-2 was increased and the adhesion of S. Typhimurium to intestinal epithelial cells was higher compared to the levels after growth in LB. However, SL1344 invasion was not significantly altered by growth in either MOPs minimal media or MEM. Expression of SPI-2 was also measured using chromosomal GFP reporter fusions followed by flow cytometry which showed, for the first time, that the reduction in SPI-2 transcript after growth in different media related to a reduction in the proportion of the bacterial population expressing SPI-2. These data highlight the profound differences in the global transcriptome after in vitro growth in different media and show that choice of medium should be considered carefully during experimental design, particularly when virulence related phenotypes are being measured

Membranes by the Numbers

Many of the most important processes in cells take place on and across membranes. With the rise of an impressive array of powerful quantitative methods for characterizing these membranes, it is an opportune time to reflect on the structure and function of membranes from the point of view of biological numeracy. To that end, in this article, I review the quantitative parameters that characterize the mechanical, electrical and transport properties of membranes and carry out a number of corresponding order of magnitude estimates that help us understand the values of those parameters.Comment: 27 pages, 12 figure

Shifts in growth strategies reflect tradeoffs in cellular economics

The growth rate-dependent regulation of cell size, ribosomal content, and metabolic efficiency follows a common pattern in unicellular organisms: with increasing growth rates, cell size and ribosomal content increase and a shift to energetically inefficient metabolism takes place. The latter two phenomena are also observed in fast growing tumour cells and cell lines. These patterns suggest a fundamental principle of design. In biology such designs can often be understood as the result of the optimization of fitness. Here we show that in basic models of self-replicating systems these patterns are the consequence of maximizing the growth rate. Whereas most models of cellular growth consider a part of physiology, for instance only metabolism, the approach presented here integrates several subsystems to a complete self-replicating system. Such models can yield fundamentally different optimal strategies. In particular, it is shown how the shift in metabolic efficiency originates from a tradeoff between investments in enzyme synthesis and metabolic yields for alternative catabolic pathways. The models elucidate how the optimization of growth by natural selection shapes growth strategies

Total synthesis of Escherichia coli with a recoded genome

Nature uses 64 codons to encode the synthesis of proteins from the genome, and chooses 1 sense codon—out of up to 6 synonyms—to encode each amino acid. Synonymous codon choice has diverse and important roles, and many synonymous substitutions are detrimental. Here we demonstrate that the number of codons used to encode the canonical amino acids can be reduced, through the genome-wide substitution of target codons by defined synonyms. We create a variant of Escherichia coli with a four-megabase synthetic genome through a high-fidelity convergent total synthesis. Our synthetic genome implements a defined recoding and refactoring scheme—with simple corrections at just seven positions—to replace every known occurrence of two sense codons and a stop codon in the genome. Thus, we recode 18,214 codons to create an organism with a 61-codon genome; this organism uses 59 codons to encode the 20 amino acids, and enables the deletion of a previously essential transfer RNA

Genome-scale gene/reaction essentiality and synthetic lethality analysis

Synthetic lethals are to pairs of non-essential genes whose simultaneous deletion prohibits growth. One can extend the concept of synthetic lethality by considering gene groups of increasing size where only the simultaneous elimination of all genes is lethal, whereas individual gene deletions are not. We developed optimization-based procedures for the exhaustive and targeted enumeration of multi-gene (and by extension multi-reaction) lethals for genome-scale metabolic models. Specifically, these approaches are applied to iAF1260, the latest model of Escherichia coli, leading to the complete identification of all double and triple gene and reaction synthetic lethals as well as the targeted identification of quadruples and some higher-order ones. Graph representations of these synthetic lethals reveal a variety of motifs ranging from hub-like to highly connected subgraphs providing a birds-eye view of the avenues available for redirecting metabolism and uncovering complex patterns of gene utilization and interdependence. The procedure also enables the use of falsely predicted synthetic lethals for metabolic model curation. By analyzing the functional classifications of the genes involved in synthetic lethals, we reveal surprising connections within and across clusters of orthologous group functional classifications