Packaging protein drugs as bacterial inclusion bodies for therapeutic applications

ABSTRACT: A growing number of insights on the biology of bacterial inclusion bodies (IBs) have revealed intriguing utilities of these protein particles. Since they combine mechanical stability and protein functionality, IBs have been already exploited in biocatalysis and explored for bottom-up topographical modification in tissue engineering. Being fully biocompatible and with tuneable bio-physical properties, IBs are currently emerging as agents for protein delivery into mammalian cells in protein-replacement cell therapies. So far, IBs formed by chaperones (heat shock protein 70, Hsp70), enzymes (catalase and dihydrofolate reductase), grow factors (leukemia inhibitory factor, LIF) and structural proteins (the cytoskeleton keratin 14) have been shown to rescue exposed cells from a spectrum of stresses and restore cell functions in absence of cytotoxicity. The natural penetrability of IBs into mammalian cells (reaching both cytoplasm and nucleus) empowers them as an unexpected platform for the controlled delivery of essentially any therapeutic polypeptide. Production of protein drugs by biopharma has been traditionally challenged by IB formation. However, a time might have arrived in which recombinant bacteria are to be engineered for the controlled packaging of therapeutic proteins as nanoparticulate materials (nanopills), for their extra- or intra-cellular release in medicine and cosmetics.     

Gaussian Mixture Models of Between-Source Variation for Likelihood Ratio Computation from Multivariate Data

In forensic science, trace evidence found at a crime scene and on suspect has to be evaluated from the measurements performed on them, usually in the form of multivariate data (for example, several chemical compound or physical characteristics). In order to assess the strength of that evidence, the likelihood ratio framework is being increasingly adopted. Several methods have been derived in order to obtain likelihood ratios directly from univariate or multivariate data by modelling both the variation appearing between observations (or features) coming from the same source (within-source variation) and that appearing between observations coming from different sources (between-source variation). In the widely used multivariate kernel likelihood-ratio, the within-source distribution is assumed to be normally distributed and constant among different sources and the between-source variation is modelled through a kernel density function (KDF). In order to better fit the observed distribution of the between-source variation, this paper presents a different approach in which a Gaussian mixture model (GMM) is used instead of a KDF. As it will be shown, this approach provides better-calibrated likelihood ratios as measured by the log-likelihood ratio cost (C_llr) in experiments performed on freely available forensic datasets involving different trace evidences: inks, glass fragments and car paints.     

Activation of ClpP Protease by ADEP Antibiotics: Insights from Hydrogen Exchange Mass Spectrometry

The bacterial protease ClpP consists of 14 subunits that assemble into two stacked heptameric rings. The central degradation chamber can be accessed via axial pores. In free ClpP, these pores are obstructed by the N-terminal regions of the seven subunits at either end of the barrel. Acyldepsipeptides (ADEPs) are antibacterial compounds that bind in hydrophobic clefts surrounding the pore region, causing the pores to open up. The ensuing uncontrolled degradation of intracellular proteins is responsible for the antibiotic activity of ADEPs. Recently published X-ray structures yielded conflicting models regarding the conformation adopted by the N-terminal regions in the open state. Here, we use hydrogen/deuterium exchange (HDX) mass spectrometry to obtain complementary insights into the ClpP behavior with and without ADEP1. Ligand binding causes rigidification of the equatorial belt, accompanied by destabilization in the vicinity of the binding clefts. The N-terminal regions undergo rapid deuteration with only minor changes after ADEP1 binding, revealing a lack of stable H-bonding. Our data point to a mechanism where the pore opening mechanism is mediated primarily by changes in the packing of N-terminal nonpolar side chains. We propose that a “hydrophobic plug” causes pore blockage in ligand-free ClpP. ADEP1 binding provides new hydrophobic anchor points that nonpolar N-terminal residues can interact with. In this way, ADEP1 triggers the transition to an open conformation, where nonpolar moieties are clustered around the rim of the pore. This proposed mechanism helps reconcile the conflicting models that had been put forward earlier.     

Structural basis of sequestration of the anti-Shine-Dalgarno sequence in the Bacteroidetes ribosome

Genomic studies have indicated that certain bacterial lineages such as the Bacteroidetes lack Shine-Dalgarno (SD) sequences, and yet with few exceptions ribosomes of these organisms carry the canonical anti-SD (ASD) sequence. Here, we show that ribosomes purified from Flavobacterium johnsoniae, a representative of the Bacteroidetes, fail to recognize the SD sequence of mRNA in vitro. A cryo-electron microscopy structure of the complete 70S ribosome from F. johnsoniae at 2.8 Å resolution reveals that the ASD is sequestered by ribosomal proteins bS21, bS18 and bS6, explaining the basis of ASD inhibition. The structure also uncovers a novel ribosomal protein—bL38. Remarkably, in F. johnsoniae and many other Flavobacteriia, the gene encoding bS21 contains a strong SD, unlike virtually all other genes. A subset of Flavobacteriia have an alternative ASD, and in these organisms the fully complementary sequence lies upstream of the bS21 gene, indicative of natural covariation. In other Bacteroidetes classes, strong SDs are frequently found upstream of the genes for bS21 and/or bS18. We propose that these SDs are used as regulatory elements, enabling bS21 and bS18 to translationally control their own production.     

Prediction of complex phenotypes using the Drosophila melanogaster metabolome

Understanding the genotype–phenotype map and how variation at different levels of biological organization is associated are central topics in modern biology. Fast developments in sequencing technologies and other molecular omic tools enable researchers to obtain detailed information on variation at DNA level and on intermediate endophenotypes, such as RNA, proteins and metabolites. This can facilitate our understanding of the link between genotypes and molecular and functional organismal phenotypes. Here, we use the Drosophila melanogaster Genetic Reference Panel and nuclear magnetic resonance (NMR) metabolomics to investigate the ability of the metabolome to predict organismal phenotypes. We performed NMR metabolomics on four replicate pools of male flies from each of 170 different isogenic lines. Our results show that metabolite profiles are variable among the investigated lines and that this variation is highly heritable. Second, we identify genes associated with metabolome variation. Third, using the metabolome gave better prediction accuracies than genomic information for four of five quantitative traits analyzed. Our comprehensive characterization of population-scale diversity of metabolomes and its genetic basis illustrates that metabolites have large potential as predictors of organismal phenotypes. This finding is of great importance, e.g., in human medicine, evolutionary biology and animal and plant breeding.Subject terms: Quantitative trait, Genetic association study