Conjugate vaccines are known to be one of the most effective and safest types of vaccines against bacterial pathogens. Previously, vaccine biosynthesis has been performed by using N-linked glycosylation systems. However, the structural specificity of these systems for sugar substrates has hindered their application. Here, we report a novel protein glycosylation system (O-linked glycosylation via Neisseria meningitidis) that can transfer virtually any glycan to produce a conjugate vaccine. We successfully established this system in Shigella spp., avoiding the construction of an expression vector for polysaccharide synthesis. We further found that different protein substrates can be glycosylated using this system and that the O-linked glycosylation system can also effectively function in other Gram-negative bacteria, including some strains whose polysaccharide structure was not suitable for conjugation using the N-linked glycosylation system. The results from a series of animal experiments show that the conjugate vaccine produced by this O-linked glycosylation system offered a potentially protective antibody response. Furthermore, we elucidated and optimized the recognition motif, named MOOR, for the O-glycosyltransferase PglL. Finally, we demonstrated that the fusion of other peptides recognized by major histocompatibility complex class II around MOOR had no adverse effects on substrate glycosylation, suggesting that this optimized system will be useful for future vaccine development. Our results expand the glycoengineering toolbox and provide a simpler and more robust strategy for producing bioconjugate vaccines against a variety of pathogens.

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In its own right, vaccinology has been undergoing a revolution, and there are now a large number of innovative projects seeking to develop both prophylactic and therapeutic vaccines against diseases such as Hepatitis B, influenza, HIV, and cancers. [4-6] Generally speaking, the major advantages conferred by nanovaccines include improving stability by protecting antigens from premature degradation, providing good adjuvant properties, and assisting in the targeted delivery of an antigen to antigen-presenting cells (APCs). [7] A large variety of nanoscale materials have been deployed in nanovaccine designs. Seminal work with inorganic nanoparticles (NPs, e.g., gold, carbon, and silica) established the capacity of nanovaccine-bound antigens to elicit desired immune responses. Subsequent technologies have elaborated beyond inorganic NPs, for example, use of inorganic/ organic hybrid NPs (e.g., PEI adopted silica NPs and biomimetic magnetosomes) to enhance antigen immunogenicity. [8,9] Recently, new types of organic NPs (e.g., lipoprotein-mimicking nanodisks, pickering emulsions, and nanogels) have also received great attention for their applications in vaccines. [10-16] Recent years have seen enormous advances in nanovaccines for both prophylactic and therapeutic applications, but most of these technologies employ chemical or hybrid semi-biosynthetic production methods. Thus, production of nanovaccines has to date failed to exploit biology-only processes like complex sequential post-translational biochemical modifications and scalability, limiting the realization of the initial promise for offering major performance advantages and improved therapeutic outcomes over conventional vaccines. A Nano-B5 platform for in vivo production of fully protein-based, self-assembling, stable nanovaccines bearing diverse antigens including peptides and polysaccharides is presented here. Combined with the self-assembly capacities of pentamer domains from the bacterial AB 5 toxin and unnatural trimer peptides, diverse nanovaccine structures can be produced in common Escherichia coli strains and in attenuated pathogenic strains. Notably, the chassis of these nanovaccines functions as an immunostimulant. After showing excellent lymph node targeting and immunoresponse elicitation and safety performance in both mouse and monkey models, the strong prophylactic effects of these nanovaccines against infection, as well as their efficient therapeutic effects against tumors are further demonstrated. Thus, the Nano-B5 platform can efficiently combine diverse modular components and antigen cargos to efficiently generate a potentially very large diversity of nanovaccine structures using many bacterial species.

In the present work, a theoretical study of five bipyrazolic-type organic compounds, 4-{bis[(3,5-dimethyl-1H-pyrazolyl-1-yl)methyl]-amino}phenol (1), N1,N1-bis[(3,5-dimethyl-1H-pyrazol-1-yl)methyl}]-N4,N4-dimethyl-1,4-benzenediamine (2), N,N-bis[(3,5-dimethyl-1H-pyrazol-1-yl)methyl]aniline (3), 4-[bis(3,5-dimethyl pyrazol-1-yl-methyl)-amino]butan-1-ol (4) and ethyl4-[bis(3,5-dimethyl-1H-pyrazol-1-yl-methyl) aminobenzoate] (5), has been performed using density functional theory (DFT) at the B3LYP/6-31G(d) level in order to elucidate the different inhibition efficiencies and reactive sites of these compounds as corrosion inhibitors. The efficiencies of corrosion inhibitors and the global chemical reactivity relate to some parameters, such as EHOMO, ELUMO, gap energy (DeltaE) and other parameters, including electronegativity (chi), global hardness (eta) and the fraction of electrons transferred from the inhibitor molecule to the metallic atom (DeltaN). The calculated results are in agreement with the experimental data on the whole. In addition, the local reactivity has been analyzed through the Fukui function and condensed softness indices.

We extend a recently developed time invariant (TIV) model order search criterion named the optimal parameter search algorithm (OPS) for identification of time varying (TV) autoregressive (AR) and autoregressive moving average (ARMA) models. Using the TV algorithm is facilitated by the fact that expanding each TV coefficient onto a finite set of basis sequences permits TV parameters to become TIV. Taking advantage of this TIV feature of expansion parameters exploits the features of the OPS, which has been shown to provide accurate model order selection as well as extraction of only the significant model terms. Another advantage of the new algorithm is its ability to discriminate insignificant basis sequences thereby reducing the number of expansion parameters to be estimated. Due to these features, the resulting algorithm can accurately estimate TV AR or ARMA models and determine their orders. Indeed, comparison via computer simulations of AR models between the proposed method and one of the well-known iterative methods, recursive least squares, shows the greater capability of the new method to track TV parameters. Furthermore, application of the new method to experimentally obtained renal blood flow signals shows that the new method provides higher-resolution time-varying spectral capability than does the short-time Fourier transform (STFT), concomitant with fewer spurious frequency peaks than obtained with the STFT spectrogram.