A nanocompartment system contributes to defense against oxidative stress in Mycobacterium tuberculosis

Encapsulin nanocompartments are an emerging class of prokaryotic protein-based organelle consisting of an encapsulin protein shell that encloses a protein cargo. Genes encoding nanocompartments are widespread in bacteria and archaea, and recent works have characterized the biochemical function of several cargo enzymes. However, the importance of these organelles to host physiology is poorly understood. Here, we report that the human pathogen Mycobacterium tuberculosis (Mtb) produces a nanocompartment that contains the dye-decolorizing peroxidase DyP. We show that this nanocompartment is important for the ability of Mtb to resist oxidative stress in low pH environments, including during infection of host cells and upon treatment with a clinically relevant antibiotic. Our findings are the first to implicate a nanocompartment in bacterial pathogenesis and reveal a new mechanism that Mtb uses to combat oxidative stress.

Computational Design and Development of Benzodioxane-Benzamides as Potent Inhibitors of FtsZ by Exploring the Hydrophobic Subpocket

Multidrug resistant Staphylococcus aureus is a severe threat, responsible for most of the nosocomial infections globally. This resistant strain is associated with a 64% increase in death compared to the antibiotic-susceptible strain. The prokaryotic protein FtsZ and the cell division cycle have been validated as potential targets to exploit in the general battle against antibiotic resistance. Despite the discovery and development of several anti-FtsZ compounds, no FtsZ inhibitors are currently used in therapy. This work further develops benzodioxane-benzamide FtsZ inhibitors. We seek to find more potent compounds using computational studies, with encouraging predicted drug-like profiles. We report the synthesis and the characterization of novel promising derivatives that exhibit very low MICs towards both methicillin-susceptible and -resistant S. aureus, as well as another Gram positive species, Bacillus subtilis, while possessing good predicted physical-chemical properties in terms of solubility, permeability, and chemical and physical stability. In addition, we demonstrate by fluorescence microscopy that Z ring formation and FtsZ localization are strongly perturbed by our derivatives, thus validating the target.

AbhemC encoding porphobilinogen deaminase plays an important role in chlorophyll biosynthesis and function in albino Ananas comosus var. bracteatus leaves

Background The chimeric leaves of Ananas comosus var. bracteatus are composed of normal green parts (Grs) and albino white parts (Whs). Although the underlying mechanism of albinism in A. comosus var. bracteatus leaves is not fully understood, it is likely associated with the chlorophyll (Chl) biosynthesis. In this biosynthetic process, porphobilinogen deaminase (PBGD) plays a crucial role by catalyzing the conversion of porphobilinogen (PBG) to uroporphyrinogen III (Urogen III). Therefore, its encoding gene AbhemC was investigated here in association with Chl biosynthesis and albinism in chimeric A. comosus var. bracteatus leaves. Methods The Chl content, main Chl biosynthesis precursor content, and main enzyme activity were determined and compared between the Whs and Grs of A. comosus var. bracteatus leaves. In addition, AbhemC was cloned and its transcriptional expression and prokaryotic protein expression were analyzed. Furthermore, RNAi-mediated silencing of AbhemC was produced and assessed in tobacco plants. Results The concentration of Chl a and Chl b in the Grs was significantly higher than that in the Whs, respectively. Additionally, the content of the Chl biosynthesis precursor Urogen III decreased significantly in the Whs compared with the Grs. Thus, the transition of PBG to Urogen III may be the first rate-limiting step leading to albinism in the chimeric leaves of A. comosus var. bracteatus. The gene AbhemC comprised 1,135 bp and was encoded into a protein with 371 amino acids; phylogenetically, AbhemC was most closely related to hemC of pineapple. Prokaryotic expression and in vitro enzyme activity analysis showed that the cloned mRNA sequence of AbhemC was successfully integrated and had PBGD activity. Compared with control plants, transgenic tobacco leaves with pFGC5941-AbhemC-RNAi vector were substantially less green with significantly reduced hemC expression and Chl content, as well as reduced PBGD enzyme activity and significantly decreased content of Chl biosynthesis precursors from Urogen III onwards. Our results suggest that the absence of hemC expression reduces the enzyme activity of PBGD, which blocks the transition of PBG to Urogen III, and in turn suppresses Chl synthesis leading to the pale-green leaf color. Therefore, we suggest that AbhemC plays an important role in Chl synthesis and may be an important factor in the albinism of A. comosus var. bracteatus leaves.

Analysis of Stop Codons within Prokaryotic Protein-Coding Genes Suggests Frequent Readthrough Events

Nonsense mutations turn a coding (sense) codon into an in-frame stop codon that is assumed to result in a truncated protein product. Thus, nonsense substitutions are the hallmark of pseudogenes and are used to identify them. Here we show that in-frame stop codons within bacterial protein-coding genes are widespread. Their evolutionary conservation suggests that many of them are not pseudogenes, since they maintain dN/dS values (ratios of substitution rates at non-synonymous and synonymous sites) significantly lower than 1 (this is a signature of purifying selection in protein-coding regions). We also found that double substitutions in codons—where an intermediate step is a nonsense substitution—show a higher rate of evolution compared to null models, indicating that a stop codon was introduced and then changed back to sense via positive selection. This further supports the notion that nonsense substitutions in bacteria are relatively common and do not necessarily cause pseudogenization. In-frame stop codons may be an important mechanism of regulation: Such codons are likely to cause a substantial decrease of protein expression levels.

A general approach to explore prokaryotic protein glycosylation reveals the unique surface layer modulation of an anammox bacterium

The enormous chemical diversity and strain variability of prokaryotic protein glycosylation makes a large-scale exploration exceptionally challenging. Therefore, despite the universal relevance of protein glycosylation across all domains of life, the understanding of their biological significance and the evolutionary forces shaping oligosaccharide structures remains highly limited.Here, we report on a newly established mass binning glycoproteomics approach that establishes the chemical identity of the carbohydrate components and performs untargeted exploration of prokaryotic oligosaccharides from large-scale proteomics data directly. We demonstrate our approach by exploring an enrichment culture of the globally relevant anaerobic ammonium-oxidizing bacterium Ca. Kuenenia stuttgartiensis. By doing so we resolved a remarkable array of oligosaccharides, produced by two entirely unrelated glycosylation machineries targeting the same surface-layer protein (SLP) simultaneously. More intriguingly, the investigated strain also accomplished modulation of highly specialized sugars, supposedly in response to its energy metabolism—the anaerobic oxidation of ammonium —which depends on the acquisition of substrates of opposite charge. Ultimately, we provide a systematic approach for the compositional exploration of prokaryotic protein glycosylation, and reveal for the first time a remarkable balance between maximising cellular protection through a complex array of oligosaccharides and adhering to the requirements of the ‘metabolic lifestyle’.

A nanocompartment containing the peroxidase DypB contributes to defense against oxidative stress in M. tuberculosis

Encapsulin nanocompartments are an emerging class of prokaryotic protein-based organelles consisting of an encapsulin protein shell that encloses a protein cargo1. Genes encoding nanocompartments are widespread in bacteria and archaea, and recent works have characterized the biochemical function of several cargo enzymes2. However, the importance of these organelles to host physiology is poorly understood. Here, we report that the human pathogen Mycobacterium tuberculosis (Mtb) produces a nanocompartment that contains the dye-decolorizing peroxidase DypB. We show that this nanocompartment is important for the ability of Mtb to resist oxidative stress in low pH environments, including during infection of host cells and upon treatment with a clinically relevant antibiotic. Our findings are the first to implicate a nanocompartment in bacterial pathogenesis and reveal a new mechanism that Mtb uses to combat oxidative stress.

CoRNeA: A Pipeline to Decrypt the Inter-Protein Interfaces from Amino Acid Sequence Information

Decrypting the interface residues of the protein complexes provides insight into the functions of the proteins and, hence, the overall cellular machinery. Computational methods have been devised in the past to predict the interface residues using amino acid sequence information, but all these methods have been majorly applied to predict for prokaryotic protein complexes. Since the composition and rate of evolution of the primary sequence is different between prokaryotes and eukaryotes, it is important to develop a method specifically for eukaryotic complexes. Here, we report a new hybrid pipeline for predicting the protein-protein interaction interfaces in a pairwise manner from the amino acid sequence information of the interacting proteins. It is based on the framework of Co-evolution, machine learning (Random Forest), and Network Analysis named CoRNeA trained specifically on eukaryotic protein complexes. We use Co-evolution, physicochemical properties, and contact potential as major group of features to train the Random Forest classifier. We also incorporate the intra-contact information of the individual proteins to eliminate false positives from the predictions keeping in mind that the amino acid sequence of a protein also holds information for its own folding and not only the interface propensities. Our prediction on example datasets shows that CoRNeA not only enhances the prediction of true interface residues but also reduces false positive rates significantly.

Benzamide Derivatives Targeting the Cell Division Protein FtsZ: Modifications of the Linker and the Benzodioxane Scaffold and Their Effects on Antimicrobial Activity

Filamentous temperature-sensitive Z (FtsZ) is a prokaryotic protein with an essential role in the bacterial cell division process. It is widely conserved and expressed in both Gram-positive and Gram-negative strains. In the last decade, several research groups have pointed out molecules able to target FtsZ in Staphylococcus aureus, Bacillus subtilis and other Gram-positive strains, with sub-micromolar Minimum Inhibitory Concentrations (MICs). Conversely, no promising derivatives active on Gram-negatives have been found up to now. Here, we report our results on a class of benzamide compounds, which showed comparable inhibitory activities on both S. aureus and Escherichia coli FtsZ, even though they proved to be substrates of E. coli efflux pump AcrAB, thus affecting the antimicrobial activity. These surprising results confirmed how a single molecule can target both species while maintaining potent antimicrobial activity. A further computational study helped us decipher the structural features necessary for broad spectrum activity and assess the drug-like profile and the on-target activity of this family of compounds.

CoRNeA: A pipeline to decrypt the inter protein interfaces from amino acid sequence information

Computational methods have been devised in the past to predict the interface residues using amino acid sequence information but have been majorly applied to predict for prokaryotic protein complexes. Since the composition and rate of evolution of the primary sequence are different between prokaryotes and eukaryotes, it is important to develop a method specifically for eukaryotic complexes. Here we report a new hybrid pipeline for the prediction of protein-protein interaction interfaces from the amino acid sequence information alone based on the framework of Co-evolution, machine learning (Random forest) and Network Analysis named CoRNeA trained specifically on eukaryotic protein complexes. We incorporate the intra contact information of the individual proteins to eliminate false positives from the predictions as the amino acid sequence also holds information for its own folding along with the interface propensities. Our prediction on various case studies shows that CoRNeA can successfully identify minimal interacting regions of two partner proteins with higher precision and recall.