Biodegradable Nanoparticle-Entrapped Vaccine Induces Cross-Protective Immune Response against a Virulent Heterologous Respiratory Viral Infection in Pigs

Biodegradable nanoparticle-based vaccine development research is unexplored in large animals and humans. In this study, we illustrated the efficacy of nanoparticle-entrapped UV-killed virus vaccine against an economically important respiratory viral disease of pigs called porcine reproductive and respiratory syndrome virus (PRRSV). We entrapped PLGA [poly (lactide-co-glycolides)] nanoparticles with killed PRRSV antigens (Nano-KAg) and detected its phagocytosis by pig alveolar macrophages. Single doses of Nano-KAg vaccine administered intranasally to pigs upregulated innate and PRRSV specific adaptive responses. In a virulent heterologous PRRSV challenge study, Nano-KAg vaccine significantly reduced the lung pathology and viremia, and the viral load in the lungs. Immunologically, enhanced innate and adaptive immune cell population and associated cytokines with decreased secretion of immunosuppressive mediators were observed at both mucosal sites and blood. In summary, we demonstrated the benefits of intranasal delivery of nanoparticle-based viral vaccine in eliciting cross-protective immune response in pigs, a potential large animal model.     

Ultrasensitive cDNA detection of dengue virus RNA using electrochemical nanoporous membrane-based biosensor

A nanoporous alumina membrane-based ultrasensitive DNA biosensor is constructed using 5'-aminated DNA probes immobilized onto the alumina channel walls. Alumina nanoporous membrane-like structure is carved over platinum wire electrode of 76 μm diameter dimension by electrochemical anodization. The hybridization of complementary target DNA with probe DNA molecules attached inside the pores influences the pore size and ionic conductivity. The biosensor demonstrates linear range over 6 order of magnitude with ultrasensitive detection limit of 9.55×10(-12) M for the quantification of ss-31 mer DNA sequence. Its applicability is challenged against real time cDNA PCR sample of dengue virus serotype1 derived from asymmetric PCR. Excellent specificity down to one nucleotide mismatch in target DNA sample of DENV3 is also demonstrated.     

The origin of large molecules in primordial autocatalytic reaction networks

Large molecules such as proteins and nucleic acids are crucial for life, yet their primordial origin remains a major puzzle. The production of large molecules, as we know it today, requires good catalysts, and the only good catalysts we know that can accomplish this task consist of large molecules. Thus the origin of large molecules is a chicken and egg problem in chemistry. Here we present a mechanism, based on autocatalytic sets (ACSs), that is a possible solution to this problem. We discuss a mathematical model describing the population dynamics of molecules in a stylized but prebiotically plausible chemistry. Large molecules can be produced in this chemistry by the coalescing of smaller ones, with the smallest molecules, the 'food set', being buffered. Some of the reactions can be catalyzed by molecules within the chemistry with varying catalytic strengths. Normally the concentrations of large molecules in such a scenario are very small, diminishing exponentially with their size. ACSs, if present in the catalytic network, can focus the resources of the system into a sparse set of molecules. ACSs can produce a bistability in the population dynamics and, in particular, steady states wherein the ACS molecules dominate the population. However to reach these steady states from initial conditions that contain only the food set typically requires very large catalytic strengths, growing exponentially with the size of the catalyst molecule. We present a solution to this problem by studying 'nested ACSs', a structure in which a small ACS is connected to a larger one and reinforces it. We show that when the network contains a cascade of nested ACSs with the catalytic strengths of molecules increasing gradually with their size (e.g., as a power law), a sparse subset of molecules including some very large molecules can come to dominate the system.     

A viral dynamic model for treatment regimens with direct-acting antivirals for chronic hepatitis C infection

We propose an integrative, mechanistic model that integrates in vitro virology data, pharmacokinetics, and viral response to a combination regimen of a direct-acting antiviral (telaprevir, an HCV NS3-4A protease inhibitor) and peginterferon alfa-2a/ribavirin (PR) in patients with genotype 1 chronic hepatitis C (CHC). This model, which was parameterized with on-treatment data from early phase clinical studies in treatment-naïve patients, prospectively predicted sustained virologic response (SVR) rates that were comparable to observed rates in subsequent clinical trials of regimens with different treatment durations in treatment-naïve and treatment-experienced populations. The model explains the clinically-observed responses, taking into account the IC50, fitness, and prevalence prior to treatment of viral resistant variants and patient diversity in treatment responses, which result in different eradication times of each variant. The proposed model provides a framework to optimize treatment strategies and to integrate multifaceted mechanistic information and give insight into novel CHC treatments that include direct-acting antiviral agents.     

Insights into horizontal acquisition patterns of dormancy and reactivation regulon genes in mycobacterial species using a partitioning-based framework

Horizontal Gene Transfer (HGT) events, initially thought to be rare in Mycobacterium tuberculosis, have recently been shown to be involved in the acquisition of virulence operons in M. tuberculosis. We have developed a new partitioning framework based HGT prediction algorithm, called Grid3M, and applied the same for the prediction of HGTs in Mycobacteria. Validation and testing using simulated and real microbial genomes indicated better performance of Grid3M as compared with other widely used HGT prediction methods. Specific analysis of the genes belonging to dormancy/reactivation regulons across 14 mycobacterial genomes indicated that horizontal acquisition is specifically restricted to important accessory proteins. The results also revealed Burkholderia species to be a probable source of HGT genes belonging to these regulons. The current study provides a basis for similar analyses investigating the functional/evolutionary aspects of HGT genes in other pathogens. A database of Grid3M predicted HGTs in completely sequenced genomes is available at https://metagenomics.atc.tcs.com/Grid3M/ .     

CFD of mixing of multi‐phase flow in a bioreactor using population balance model

Mixing in bioreactors is known to be crucial for achieving efficient mass and heat transfer, both of which thereby impact not only growth of cells but also product quality. In a typical bioreactor, the rate of transport of oxygen from air is the limiting factor. While higher impeller speeds can enhance mixing, they can also cause severe cell damage. Hence, it is crucial to understand the hydrodynamics in a bioreactor to achieve optimal performance. This article presents a novel approach involving use of computational fluid dynamics (CFD) to model the hydrodynamics of an aerated stirred bioreactor for production of a monoclonal antibody therapeutic via mammalian cell culture. This is achieved by estimating the volume averaged mass transfer coefficient (k_La) under varying conditions of the process parameters. The process parameters that have been examined include the impeller rotational speed and the flow rate of the incoming gas through the sparger inlet. To undermine the two‐phase flow and turbulence, an Eulerian‐Eulerian multiphase model and k‐ε turbulence model have been used, respectively. These have further been coupled with population balance model to incorporate the various interphase interactions that lead to coalescence and breakage of bubbles. We have successfully demonstrated the utility of CFD as a tool to predict size distribution of bubbles as a function of process parameters and an efficient approach for obtaining optimized mixing conditions in the reactor. The proposed approach is significantly time and resource efficient when compared to the hit and trial, all experimental approach that is presently used. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:613–628, 2016     

Tracking dynamics of enzyme activities and their gene expression in <Emphasis Type="Italic">Picrorhiza kurroa</Emphasis> with respect to picroside accumulation

Picrosides, the terpenoids synthesized by Picrorhiza kurroa, have ample usage in medicine. Identification of the regulatory enzymes involved in picroside biosynthesis needs to be explored for improving the level of these secondary metabolites. Current efforts are based on the analysis of secondary metabolism in picroside biosynthesis but its interpretation is limited by the lack of information on the involvement of primary metabolic pathways. The present study investigated the connection of primary metabolic enzymes with the picrosides levels in P. kurroa. The results showed changes in the catalytic activities as well as in the gene expression profiles of hexokinase, pyruvate kinase, isocitrate dehydrogenase, malate dehydrogenase, and NADP⁺-malic enzyme in congruence with picroside-I content under different conditions of P. kurroa growth, which indicates the role of these enzymes in the accumulation of picrosides. The significant correlation coefficients (p?<?0.05) observed between gene expression and enzyme activity underline the role of integrative studies for a better understanding of connecting links between metabolic pathways leading to picroside biosynthesis. This is apparently the first report on the involvement of glycolytic and TCA cycle enzymes in the accumulation of picrosides in P. kurroa.