Degradation of Components of the Lpt Transenvelope Machinery Reveals LPS-Dependent Lpt Complex Stability in Escherichia coli

Lipopolysaccharide (LPS) is a peculiar component of the outer membrane (OM) of many Gram-negative bacteria that renders these bacteria highly impermeable to many toxic molecules, including antibiotics. LPS is assembled at the OM by a dedicated intermembrane transport system, the Lpt (LPS transport) machinery, composed of seven essential proteins located in the inner membrane (IM) (LptB2CFG), periplasm (LptA), and OM (LptDE). Defects in LPS transport compromise LPS insertion and assembly at the OM and result in an overall modification of the cell envelope and its permeability barrier properties. LptA is a key component of the Lpt machine. It connects the IM and OM sub-complexes by interacting with the IM protein LptC and the OM protein LptD, thus enabling the LPS transport across the periplasm. Defects in Lpt system assembly result in LptA degradation whose stability can be considered a marker of an improperly assembled Lpt system. Indeed, LptA recruitment by its IM and OM docking sites requires correct maturation of the LptB2CFG and LptDE sub-complexes, respectively. These quality control checkpoints are crucial to avoid LPS mistargeting. To further dissect the requirements for the complete Lpt transenvelope bridge assembly, we explored the importance of LPS presence by blocking its synthesis using an inhibitor compound. Here, we found that the interruption of LPS synthesis results in the degradation of both LptA and LptD, suggesting that, in the absence of the LPS substrate, the stability of the Lpt complex is compromised. Under these conditions, DegP, a major chaperone–protease in Escherichia coli, is responsible for LptD but not LptA degradation. Importantly, LptD and LptA stability is not affected by stressors disturbing the integrity of LPS or peptidoglycan layers, further supporting the notion that the LPS substrate is fundamental to keeping the Lpt transenvelope complex assembled and that LptA and LptD play a major role in the stability of the Lpt system.

Prediction of Novel Drug Targets and Vaccine Candidates against Human Lice (Insecta), Acari (Arachnida), and Their Associated Pathogens

The emergence of drug-resistant lice, acari, and their associated pathogens (APs) is associated with economic losses; thus, it is essential to find new appropriate therapeutic approaches. In the present study, a subtractive proteomics approach was used to predict suitable therapeutics against these vectors and their infectious agents. We found 9701 proteins in the lice (Pediculus humanus var. corporis) and acari (Ixodes scapularis, Leptotrombidium deliense), and 4822 proteins in the proteomes of their APs (Babesia microti, Borreliella mayonii, Borrelia miyamotoi, Borrelia recurrentis, Rickettsia prowazekii, Orientia tsutsugamushi str. Boryong) that were non-homologous to host proteins. Among these non-homologous proteins, 365 proteins of lice and acari, and 630 proteins of APs, were predicted as essential proteins. Twelve unique essential proteins were predicted to be involved in four unique metabolic pathways of lice and acari, and 103 unique proteins were found to be involved in 75 unique metabolic pathways of APs. The sub cellular localization analysis of 115 unique essential proteins of lice and acari and their APs revealed that 61 proteins were cytoplasmic, 42 as membrane-bound proteins and 12 proteins with multiple localization. The druggability analysis of the identified 73 cytoplasmic and multiple localization essential proteins revealed 22 druggable targets and 51 novel drug targets that participate in unique pathways of lice and acari and their APs. Further, the predicted 42 membrane bound proteins could be potential vaccine candidates. Screening of useful inhibitors against these novel targets may result in finding novel compounds efficient for the control of these parasites.

Subtractive genomics and molecular docking approach to identify drug targets against Stenotrophomonas maltophilia

Stenotrophomonas maltophilia is a multidrug resistant pathogen associated with high mortality and morbidity in patients having compromised immunity. The efflux systems of S. maltophilia include SmeABC and SmeDEF proteins, which assist in acquisition of multiple-drug-resistance. In this study, proteome based mapping was utilized to find out the potential drug targets for S. maltophilia strain k279a. Various tools of computational biology were applied to remove the human-specific homologous and pathogen-specific paralogous sequences from the bacterial proteome. The CD-HIT analysis selected 4315 proteins from total proteome count of 4365 proteins. Geptop identified 407 essential proteins, while the BlastP revealed approximately 85 non-homologous proteins in the human genome. Moreover, metabolic pathway and subcellular location analysis were performed for essential bacterial genes, to describe their role in various cellular processes. Only two essential proteins (Acyl-[acyl-carrier-protein]—UDP-N acetyl glucosamine O-acyltransferase and D-alanine-D-alanine ligase) as candidate for potent targets were found in proteome of the pathogen, in order to design new drugs. An online tool, Swiss model was employed to model the 3D structures of both target proteins. A library of 5000 phytochemicals was docked against those proteins through the molecular operating environment (MOE). That resulted in to eight inhibitors for both proteins i.e. enterodiol, aloin, ononin and rhinacanthinF for the Acyl-[acyl-carrier-protein]—UDP-N acetyl glucosamine O-acyltransferase, and rhazin, alkannin beta, aloesin and ancistrocladine for the D-alanine-D-alanine ligase. Finally the ADMET was done through ADMETsar. This study supported the development of natural as well as cost-effective drugs against S. maltophilia. These inhibitors displayed the effective binding interactions and safe drug profiles. However, further in vivo and in vitro validation experiment might be performed to check their drug effectiveness, biocompatibility and their role as effective inhibitors.

Bhalin, an Essential Cytoskeleton-Associated Protein of Trypanosoma brucei Linking TbBILBO1 of the Flagellar Pocket Collar with the Hook Complex

Background: In most trypanosomes, endo and exocytosis only occur at a unique organelle called the flagellar pocket (FP) and the flagellum exits the cell via the FP. Investigations of essential cytoskeleton-associated structures located at this site have revealed a number of essential proteins. The protein TbBILBO1 is located at the neck of the FP in a structure called the flagellar pocket collar (FPC) and is essential for biogenesis of the FPC and parasite survival. TbMORN1 is a protein that is present on a closely linked structure called the hook complex (HC) and is located anterior to and overlapping the collar. TbMORN1 is essential in the bloodstream form of T. brucei. We now describe the location and function of BHALIN, an essential, new FPC-HC protein. Methodology/Principal Findings: Here, we show that a newly characterised protein, BHALIN (BILBO1 Hook Associated LINker protein), is localised to both the FPC and HC and has a TbBILBO1 binding domain, which was confirmed in vitro. Knockdown of BHALIN by RNAi in the bloodstream form parasites led to cell death, indicating an essential role in cell viability. Conclusions/Significance: Our results demonstrate the essential role of a newly characterised hook complex protein, BHALIN, that influences flagellar pocket organisation and function in bloodstream form T. brucei parasites.

Comparing protein-protein interaction networks of SARS-CoV-2 and (H1N1) influenza using topological features

Background: SARS-CoV-2 pandemic first emerged in late 2019 in China. It has since infected more than 183 million individuals and caused about 4 million deaths globally. A protein-protein interaction network (PPIN) and its analysis can provide insight into the behavior of cells and lead to advance the procedure of drug discovery. The identification of essential proteins is crucial to understand for cellular survival. There are many centrality measures to detect influential nodes in complex networks. Since SARS-CoV-2 and (H1N1) influenza PPINs pose 553 common proteins. Analyzing influential proteins and comparing these networks together can be an effective step helping biologists in drug design. Results: We used 21 centrality measures on SARS-CoV-2 and (H1N1) influenza PPINs to identify essential proteins. PCA-based dimensionality reduction was applied on normalized centrality values. Some measures demonstrated a high level of contribution in comparison to others in both PPINs, like Barycenter, Decay, Diffusion degree, Closeness (Freeman), Closeness (Latora), Lin, Radiality, and Residual. Using validation measures, the appropriate clustering method was chosen for centrality measures. We also investigated some graph theory-based properties like the power law, exponential distribution, and robustness. Conclusions: Through analysis and comparison, both networks exhibited remarkable experimental results. The network diameters were equal and in terms of heterogeneity, SARS-CoV-2 PPIN tends to be more heterogeneous. Both networks under study display a typical power-law degree distribution. Dimensionality reduction and unsupervised learning methods were so effective to reveal appropriate centrality measures.

Regulation of skeletal muscle metabolism and contraction performance via teneurin-latrophilin action.

Skeletal muscle regulation is responsible for voluntary muscular movement in vertebrates. The genes of two essential proteins, teneurins and latrophilins (LPHN), evolving in ancestors of multicellular animals, form a ligand-receptor pair, and are now shown to be required for skeletal muscle function. Teneurins possess a bioactive peptide, termed the teneurin C-terminal associated peptide (TCAP) that interacts with the LPHNs to regulate skeletal muscle contractility strength and fatigue by an insulin-independent glucose importation mechanism. CRISPR-based knockouts and siRNA-associated knockdowns of LPHN-1 and-3 shows that TCAP stimulates an LPHN-mediated cytosolic Ca 2+ signal transduction cascade to increase energy metabolism and enhance skeletal muscle function via increases in type-1 oxidative fiber formation and reduce the fatigue response. Thus, the teneurin/TCAP-LPHN system is presented as a novel mechanism likely to regulate the energy requirements and performance of skeletal muscle.

Intein Inhibitors as Novel Antimicrobials: Protein Splicing in Human Pathogens, Screening Methods, and Off-Target Considerations

Protein splicing is a post-translational process by which an intervening polypeptide, or intein, catalyzes its own removal from the flanking polypeptides, or exteins, concomitant with extein ligation. Although inteins are highly abundant in the microbial world, including within several human pathogens, they are absent in the genomes of metazoans. As protein splicing is required to permit function of essential proteins within pathogens, inteins represent attractive antimicrobial targets. Here we review key proteins interrupted by inteins in pathogenic mycobacteria and fungi, exciting discoveries that provide proof of concept that intein activity can be inhibited and that this inhibition has an effect on the host organism’s fitness, and bioanalytical methods that have been used to screen for intein activity. We also consider potential off-target inhibition of hedgehog signaling, given the similarity in structure and function of inteins and hedgehog autoprocessing domains.

Using informative features in machine learning based method for COVID-19 drug repurposing

Coronavirus disease 2019 (COVID-19) is caused by a novel virus named Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). This virus induced a large number of deaths and millions of confirmed cases worldwide, creating a serious danger to public health. However, there are no specific therapies or drugs available for COVID-19 treatment. While new drug discovery is a long process, repurposing available drugs for COVID-19 can help recognize treatments with known clinical profiles. Computational drug repurposing methods can reduce the cost, time, and risk of drug toxicity. In this work, we build a graph as a COVID-19 related biological network. This network is related to virus targets or their associated biological processes. We select essential proteins in the constructed biological network that lead to a major disruption in the network. Our method from these essential proteins chooses 93 proteins related to COVID-19 pathology. Then, we propose multiple informative features based on drug–target and protein−protein interaction information. Through these informative features, we find five appropriate clusters of drugs that contain some candidates as potential COVID-19 treatments. To evaluate our results, we provide statistical and clinical evidence for our candidate drugs. From our proposed candidate drugs, 80% of them were studied in other studies and clinical trials.

Root proteome alterations in sugarcane promoted by the regrowth cycle in commercial production

The decrease in agricultural productivity in successive cutting of sugarcane plants is associated with several extrinsic and intrinsic factors. However, no studies have focused on the physiological potential of sett roots in successive cuts in sugarcane culture. There have been no proteomic studies on sugarcane sett roots at different stages of cutting. In this study, the UPLC-ESI-TOF-MS system and bioinformatics tools were used to identify proteins of sett roots in the first and fifth cuts of sugarcane cultivar RB966928 in the sprouting stage. Differences in the proteome of sett roots of RB966928 in the first and fifth cuts detected in this study supports the hypothesis that the proteome of sett roots may change after successive cuts in sugarcane culture. A reduction in the number of proteins was observed in the roots of the fifth cut, whereas 34% of proteins, identified exclusively in the first cut, were absent in the fifth cut. Proteome analysis of sett roots in the first and fifth cuts showed that the changes after successive cuts were quantitative (number of proteins) and mainly qualitative. In this study, the detailed list of proteins identified in the first cut but absent in the fifth cut is relevant. The findings of this study may aid further research that employ biotic or abiotic elicitors to induce gene expression of essential proteins absent in sett roots of the fifth cut, and thus increasing the agricultural productivity and longevity of cane fields

An iteration model for identifying essential proteins by combining comprehensive PPI network with biological information

Background Essential proteins have great impacts on cell survival and development, and played important roles in disease analysis and new drug design. However, since it is inefficient and costly to identify essential proteins by using biological experiments, then there is an urgent need for automated and accurate detection methods. In recent years, the recognition of essential proteins in protein interaction networks (PPI) has become a research hotspot, and many computational models for predicting essential proteins have been proposed successively. Results In order to achieve higher prediction performance, in this paper, a new prediction model called TGSO is proposed. In TGSO, a protein aggregation degree network is constructed first by adopting the node density measurement method for complex networks. And simultaneously, a protein co-expression interactive network is constructed by combining the gene expression information with the network connectivity, and a protein co-localization interaction network is constructed based on the subcellular localization data. And then, through integrating these three kinds of newly constructed networks, a comprehensive protein–protein interaction network will be obtained. Finally, based on the homology information, scores can be calculated out iteratively for different proteins, which can be utilized to estimate the importance of proteins effectively. Moreover, in order to evaluate the identification performance of TGSO, we have compared TGSO with 13 different latest competitive methods based on three kinds of yeast databases. And experimental results show that TGSO can achieve identification accuracies of 94%, 82% and 72% out of the top 1%, 5% and 10% candidate proteins respectively, which are to some degree superior to these state-of-the-art competitive models. Conclusions We constructed a comprehensive interactive network based on multi-source data to reduce the noise and errors in the initial PPI, and combined with iterative methods to improve the accuracy of necessary protein prediction, and means that TGSO may be conducive to the future development of essential protein recognition as well.