Preclinical validation of an Escherichia coli O-antigen glycoconjugate for the prevention of serotype O1 invasive disease.

A US collection of invasive Escherichia coli serotype O1 bloodstream infection (BSI) isolates were assessed for genotypic and phenotypic diversity as the basis for designing a broadly protective O-antigen vaccine. Eighty percent of the BSI isolate serotype O1 strains were genotypically ST95 O1:K1:H7. The carbohydrate repeat unit structure of the O1a subtype was conserved in the three strains tested representing core genome multi-locus sequence types (MLST) sequence types ST95, ST38, and ST59. A long-chain O1a CRM197 lattice glycoconjugate antigen was generated using oxidized polysaccharide and reductive amination chemistry. Two ST95 strains were investigated for use in opsonophagocytic assays (OPA) with immune sera from vaccinated animals and in murine lethal challenge models. Both strains were susceptible to OPA killing with O1a glycoconjugate post-immune sera. One of these, a neonatal sepsis strain, was found to be highly lethal in the murine challenge model for which virulence was shown to be dependent on the presence of the K1 capsule. Mice immunized with the O1a glycoconjugate were protected from challenges with this strain or a second, genotypically related, and similarly virulent neonatal isolate. This long-chain O1a CRM197 lattice glycoconjugate shows promise as a component of a multi-valent vaccine to prevent invasive E. coli infections. IMPORTANCE: The Escherichia coli serotype O1 O-antigen serogroup is a common cause of invasive bloodstream infections (BSI) in populations at risk such as newborns and the elderly. Sequencing of US BSI isolates and structural analysis of O polysaccharide antigens purified from strains that are representative of genotypic sub-groups confirmed the relevance of the O1a subtype as a vaccine antigen. O polysaccharide was purified from a strain engineered to produce long-chain O1a O-antigen and was chemically conjugated to CRM197 carrier protein. The resulting glycoconjugate elicited functional antibodies and was protective in mice against lethal challenges with virulent K1-encapsulated O1a isolates.

Deciphering regulatory architectures from synthetic single-cell expression patterns.

For the vast majority of genes in sequenced genomes, there is limited understanding of how they are regulated. Without such knowledge, it is not possible to perform a quantitative theory-experiment dialogue on how such genes give rise to physiological and evolutionary adaptation. One category of high-throughput experiments used to understand the sequence-phenotype relationship of the transcriptome is massively parallel reporter assays (MPRAs). However, to improve the versatility and scalability of MPRA pipelines, we need a "theory of the experiment" to help us better understand the impact of various biological and experimental parameters on the interpretation of experimental data. These parameters include binding site copy number, where a large number of specific binding sites may titrate away transcription factors, as well as the presence of overlapping binding sites, which may affect analysis of the degree of mutual dependence between mutations in the regulatory region and expression levels. To that end, in this paper we create tens of thousands of synthetic single-cell gene expression outputs using both equilibrium and out-of-equilibrium models. These models make it possible to imitate the summary statistics (information footprints and expression shift matrices) used to characterize the output of MPRAs and from this summary statistic to infer the underlying regulatory architecture. Specifically, we use a more refined implementation of the so-called thermodynamic models in which the binding energies of each sequence variant are derived from energy matrices. Our simulations reveal important effects of the parameters on MPRA data and we demonstrate our ability to optimize MPRA experimental designs with the goal of generating thermodynamic models of the transcriptome with base-pair specificity. Further, this approach makes it possible to carefully examine the mapping between mutations in binding sites and their corresponding expression profiles, a tool useful not only for better designing MPRAs, but also for exploring regulatory evolution. Author summary: With the rapid advancement of sequencing technology, there has been an exponential increase in the amount of data on the genomic sequences of diverse organisms. Nevertheless, deciphering the sequence-phenotype mapping of the genomic data remains a formidable task, especially when dealing with non-coding sequences such as the promoter. In current databases, annotations on transcription factor binding sites are sorely lacking, which creates a challenge for developing a systematic theory of transcriptional regulation. To address this gap in knowledge, high-throughput methods such as massively parallel reporter assays (MPRAs) have been employed to decipher the regulatory genome. In this work, we make use of thermodynamic models to computationally simulate MPRAs in the context of transcriptional regulation and produce thousands of synthetic MPRA datasets. We examine how well typical experimental and data analysis procedures of MPRAs are able to recover common regulatory architectures under different sets of experimental and biological parameters. By establishing a dialogue between high-throughput experiments and a physical theory of transcription, our efforts serve to both improve current experimental procedures and enhancing our broader understanding of the sequence-function landscape of regulatory sequences.

Deciphering regulatory architectures from synthetic single-cell expression patterns.

For the vast majority of genes in sequenced genomes, there is limited understanding of how they are regulated. Without such knowledge, it is not possible to perform a quantitative theory-experiment dialogue on how such genes give rise to physiological and evolutionary adaptation. One category of high-throughput experiments used to understand the sequence-phenotype relationship of the transcriptome is massively parallel reporter assays (MPRAs). However, to improve the versatility and scalability of MPRA pipelines, we need a "theory of the experiment" to help us better understand the impact of various biological and experimental parameters on the interpretation of experimental data. These parameters include binding site copy number, where a large number of specific binding sites may titrate away transcription factors, as well as the presence of overlapping binding sites, which may affect analysis of the degree of mutual dependence between mutations in the regulatory region and expression levels. To that end, in this paper we create tens of thousands of synthetic single-cell gene expression outputs using both equilibrium and out-of-equilibrium models. These models make it possible to imitate the summary statistics (information footprints and expression shift matrices) used to characterize the output of MPRAs and from this summary statistic to infer the underlying regulatory architecture. Specifically, we use a more refined implementation of the so-called thermodynamic models in which the binding energies of each sequence variant are derived from energy matrices. Our simulations reveal important effects of the parameters on MPRA data and we demonstrate our ability to optimize MPRA experimental designs with the goal of generating thermodynamic models of the transcriptome with base-pair specificity. Further, this approach makes it possible to carefully examine the mapping between mutations in binding sites and their corresponding expression profiles, a tool useful not only for better designing MPRAs, but also for exploring regulatory evolution.

Pervasive, conserved secondary structure in highly charged protein regions.

Understanding how protein sequences confer function remains a defining challenge in molecular biology. Two approaches have yielded enormous insight yet are often pursued separately: structure-based, where sequence-encoded structures mediate function, and disorder-based, where sequences dictate physicochemical and dynamical properties which determine function in the absence of stable structure. Here we study highly charged protein regions (>40% charged residues), which are routinely presumed to be disordered. Using recent advances in structure prediction and experimental structures, we show that roughly 40% of these regions form well-structured helices. Features often used to predict disorder-high charge density, low hydrophobicity, low sequence complexity, and evolutionarily varying length-are also compatible with solvated, variable-length helices. We show that a simple composition classifier predicts the existence of structure far better than well-established heuristics based on charge and hydropathy. We show that helical structure is more prevalent than previously appreciated in highly charged regions of diverse proteomes and characterize the conservation of highly charged regions. Our results underscore the importance of integrating, rather than choosing between, structure- and disorder-based approaches.

Pervasive, conserved secondary structure in highly charged protein regions.

Understanding how protein sequences confer function remains a defining challenge in molecular biology. Two approaches have yielded enormous insight yet are often pursued separately: structure-based, where sequence-encoded structures mediate function, and disorder-based, where sequences dictate physicochemical and dynamical properties which determine function in the absence of stable structure. Here we study highly charged protein regions (>40% charged residues), which are routinely presumed to be disordered. Using recent advances in structure prediction and experimental structures, we show that roughly 40% of these regions form well-structured helices. Features often used to predict disorder-high charge density, low hydrophobicity, low sequence complexity, and evolutionarily varying length-are also compatible with solvated, variable-length helices. We show that a simple composition classifier predicts the existence of structure far better than well-established heuristics based on charge and hydropathy. We show that helical structure is more prevalent than previously appreciated in highly charged regions of diverse proteomes and characterize the conservation of highly charged regions. Our results underscore the importance of integrating, rather than choosing between, structure- and disorder-based approaches.

Semisynthetic Glycoconjugate Vaccine Candidates against Escherichia coli O25B Induce Functional IgG Antibodies in Mice.

Extraintestinal pathogenic Escherichia coli (ExPEC) is a major health concern due to emerging antibiotic resistance. Along with O1A, O2, and O6A, E. coli O25B is a major serotype within the ExPEC group, which expresses a unique O-antigen. Clinical studies with a glycoconjugate vaccine of the above-mentioned O-types revealed O25B as the least immunogenic component, inducing relatively weak IgG titers. To evaluate the immunological properties of semisynthetic glycoconjugate vaccine candidates against E. coli O25B, we here report the chemical synthesis of an initial set of five O25B glycan antigens differing in length, from one to three repeat units, and frameshifts of the repeat unit. The oligosaccharide antigens were conjugated to the carrier protein CRM197. The resulting semisynthetic glycoconjugates induced functional IgG antibodies in mice with opsonophagocytic activity against E. coli O25B. Three of the oligosaccharide-CRM197 conjugates elicited functional IgGs in the same order of magnitude as a conventional CRM197 glycoconjugate prepared with native O25B O-antigen and therefore represent promising vaccine candidates for further investigation. Binding studies with two monoclonal antibodies (mAbs) revealed nanomolar anti-O25B IgG responses with nanomolar K D values and with varying binding epitopes. The immunogenicity and mAb binding data now allow for the rational design of additional synthetic antigens for future preclinical studies, with expected further improvements in the functional antibody responses. Moreover, acetylation of a rhamnose residue was shown to be likely dispensable for immunogenicity, as a deacylated antigen was able to elicit strong functional IgG responses. Our findings strongly support the feasibility of a semisynthetic glycoconjugate vaccine against E. coli O25B.

Preclinical Immunogenicity and Efficacy of Optimized O25b O-Antigen Glycoconjugates To Prevent MDR ST131 E. coli Infections.

Multivalent O-antigen polysaccharide glycoconjugate vaccines are under development to prevent invasive infections caused by pathogenic Enterobacteriaceae. Sequence type 131 (ST131) Escherichia coli of serotype O25b has emerged as the predominant lineage causing invasive multidrug-resistant extraintestinal pathogenic E. coli (ExPEC) infections. We observed the prevalence of E. coli O25b ST131 among a contemporary collection of isolates from U.S. bloodstream infections from 2013 to 2016 (n = 444) and global urinary tract infections from 2014 to 2017 (n = 102) to be 25% and 24%, respectively. To maximize immunogenicity of the serotype O25b O antigen, we investigated glycoconjugate properties, including CRM197 carrier protein cross-linking (single-end versus cross-linked "lattice") and conjugation chemistry (reductive amination chemistry in dimethyl sulfoxide [RAC/DMSO] versus ((2-((2-oxoethyl)thio)ethyl)carbamate [eTEC] linker). Using opsonophagocytic assays (OPAs) to measure serum functional antibody responses to vaccination, we observed that higher-molecular-mass O25b long-chain lattice conjugates showed improved immunogenicity in mice compared with long- or short-chain O antigens conjugated via single-end attachment. The lattice conjugates protected mice from lethal challenge with acapsular O25b ST131 strains as well as against hypervirulent O25b isolates expressing K5 or K100 capsular polysaccharides. A single 1-µg dose of long-chain O25b lattice conjugate constructed with both chemistries also elicited robust serum IgG and OPA responses in cynomolgus macaques. Our findings show that key properties of the O-antigen carrier protein conjugate such as saccharide epitope density and degree of intermolecular cross-linking can significantly enhance functional immunogenicity.