A side-effect free method for identifying cancer drug targets

Chemical Composition and Quorum Sensing Inhibitory Effect of Nepeta curviflora Methanolic Extract against ESBL Pseudomonas aeruginosa

Objectives

Bacterial biofilm is regarded as a significant threat to the production of safe food and the arise of antibiotic-resistant bacteria. The objective of this investigation is to evaluate the quorum sensing inhibitory effect of Nepeta curviflora methanolic extract.

Methods

The effectiveness of the leaves at sub-inhibitory concentrations of 2.5, 1.25, and 0.6 mg/mL on the virulence factors and biofilm formation of P. aeruginosa was evaluated. The effect of N. curviflora methanolic extract on the virulence factors of P. aeruginosa, including pyocyanin, rhamnolipid, protease, and chitinase, was evaluated. Other tests including the crystal violet assay, scanning electron microscopy (SEM), swarming motility, aggregation ability, hydrophobicity and exopolysaccharide production were conducted to assess the effect of the extract on the formation of biofilm. Insight into the mode of anti-quorum sensing action was evaluated by examining the effect of the extract on the activity of N-Acyl homoserine lactone (AHL) and the expression of pslA and pelA genes.

Results

The results showed a significant attenuation in the production of pyocyanin and rhamnolipid and in the activities of protease and chitinase enzymes at 2.5 and 1.25 mg/mL. In addition, N. curviflora methanolic extract significantly inhibited the formation of P. aeruginosa biofilm by decreasing aggregation, hydrophobicity, and swarming motility as well as the production of exopolysaccharide (EPS). A significant reduction in AHL secretion and pslA gene expression was observed, indicating that the extract inhibited quorum sensing by disrupting the quorum-sensing systems. The quorum-sensing inhibitory effect of N. curviflora extract appears to be attributed to the presence of kaempferol, quercetin, salicylic acid, rutin, and rosmarinic acid, as indicated by LCMS analysis.

Conclusion

The results of the present study provide insight into the potential of developing anti-quorum sensing agents using the extract and the identified compounds to treat infections resulting from quorum sensing-mediated bacterial pathogenesis.

Promising Acinetobacter baumannii Vaccine Candidates and Drug Targets in Recent Years

In parallel to the uncontrolled use of antibiotics, the emergence of multidrug-resistant bacteria, like Acinetobacter baumannii, has posed a severe threat. A. baumannii predominates in the nosocomial setting due to its ability to persist in hospitals and survive antibiotic treatment, thereby eventually leading to an increasing prevalence and mortality due to its infection. With the increasing spectra of drug resistance and the incessant collapse of newly discovered antibiotics, new therapeutic countermeasures have been in high demand. Hence, recent research has shown favouritism towards the long-term solution of designing vaccines. Therefore, being a realistic alternative strategy to combat this pathogen, anti-A. Baumannii vaccines research has continued unearthing various antigens with variable results over the last decade. Again, other approaches, including pan-genomics, subtractive proteomics, and reverse vaccination strategies, have shown promise for identifying promiscuous core vaccine candidates that resulted in chimeric vaccine constructs. In addition, the integration of basic knowledge of the pathobiology of this drug-resistant bacteria has also facilitated the development of effective multiantigen vaccines. As opposed to the conventional trial-and-error approach, incorporating the in silico methods in recent studies, particularly network analysis, has manifested a great promise in unearthing novel vaccine candidates from the A. baumannii proteome. Some studies have used multiple A. baumannii data sources to build the co-functional networks and analyze them by k-shell decomposition. Additionally, Whole Genomic Protein Interactome (GPIN) analysis has utilized a rational approach for identifying essential proteins and presenting them as vaccines effective enough to combat the deadly pathogenic threats posed by A. baumannii. Others have identified multiple immune nodes using network-based centrality measurements for synergistic antigen combinations for different vaccination strategies. Protein-protein interactions have also been inferenced utilizing structural approaches, such as molecular docking and molecular dynamics simulation. Similar workflows and technologies were employed to unveil novel A. baumannii drug targets, with a similar trend in the increasing influx of in silico techniques. This review integrates the latest knowledge on the development of A. baumannii vaccines while highlighting the in silico methods as the future of such exploratory research. In parallel, we also briefly summarize recent advancements in A. baumannii drug target research.

Nanoscale aggregation of doxorubicin-short peptide conjugates for enzyme-responsive delivery with various MOF carriers: In-silico steps towards smart cancer chemotherapy

Drug conjugation with enzyme-sensitive peptides is one of the innovative smart delivery systems for cancer therapy. This delivery method has some advantages, such as lowering side effects and increasing treatment selectivity. Herein, two conjugates of doxorubicin and small peptide are designed that are sensitive to Cathepsin B, a tumor homing enzyme. The formation of nanoparticles at three different numbers of drug peptide prodrugs (including 30, 50, and 70 prodrugs) was studied. In addition, three metal-organic frameworks (MOF) nanocarriers, including Zeolitic Imidazolate Frameworks (ZIF), Universitetet I Oslo MOF (UIO-66), and MOF of Hong Kong University of Science and Technology (HKUST-1), were used to increase the resistance of the prodrugs to decomposition during blood flow circulation. Then, the interactions between doxorubicin's prodrug and different MOFs were investigated. Furthermore, the impact of microfluidics on nanoparticle interactions was studied. Molecular dynamic simulation was used to investigate thermodynamic and conformational parameters. The results showed that the concentration of doxorubicin prodrugs affected cluster formation. Moreover, based on Gibb's free energy analysis, the interaction of these prodrugs with various types of MOFs revealed more spontaneous interactions in microfluidic modeling conditions. ZIF had the best and most stable interactions with the prodrugs in bulk and microfluidic modeling. As a result, the best and most stable state was associated with a lower concentration of these prodrugs with ZIF in the microfluidic condition.

Computational Identification of the Plausible Molecular Vaccine Candidates of Multidrug-Resistant Salmonella enterica

Salmonella enterica serovars are responsible for the life-threatening, fatal, invasive diseases that are common in children and young adults. According to the most recent estimates, globally, there are approximately 11–20 million cases of morbidity and between 128,000 and 161,000 mortality per year. The high incidence rates of diseases like typhoid, caused by the serovars Typhi and Paratyphi, and gastroenteritis, caused by the non-typhoidal Salmonellae, have become worse, with the ever-increasing pathogenic strains being resistant to fluoroquinolones or almost even the third generation cephalosporins, such as ciprofloxacin and ceftriaxone. With vaccination still being one of the chosen methods of eradicating this disease, identification of candidate proteins, to be utilized for effective molecular vaccines, has probably remained a challenging issue. In our study here, we portray the usage of computational tools to analyze and predict potential vaccine candidate(s) for the multi-drug resistant serovars of S. enterica.

Antibiofilm and anti-quorum sensing activities of eugenol and linalool from Ocimum tenuiflorum against Pseudomonas aeruginosa biofilm

AIMS

The aim of this study is to determine the ability of two bioactive compounds, namely, eugenol and linalool, purified from leaves of Ocimum tenuiflorum for eradication of biofilm produced by Pseudomonas aeruginosa.

METHODS AND RESULTS

The phytoextract of O. tenuiflorum (KT), a common ethno-botanical plant of India, was purified through high-performance liquid chromatography and was analysed using ultraviolet (UV) spectroscopy and gas chromatography-mass spectrometry (GC-MS). Eugenol and linalool were found to be the most active amongst all phytocompounds present in phytoextract and showed a significant reduction in the viability of sessile cells of P. aeruginosa and the minimum revival after withdrawal of phyto-challenge. They could bring about notable reduction in the protein and carbohydrate content of exopolysaccharide of biofilm. Eugenol and linalool could affect the synthesis of quorum sensing (QS) proteins like LasA and LasB as well as virulence factors such as pyocyanin, and rhamnolipids, which seriously hamper the formation of biofilm. The biofilm framework was extremely affected by the phytocompounds through the reduction of protein and carbohydrate content of extracellular polymeric substance (EPS). Another interesting found out was that they brought about maximum inhibition to the genomic DNA and RNA content. The studies were supported by in silico interaction between eugenol and linalool with the QS proteins. The antibiofilm efficacies of eugenol, linalool and phytoextract (KT) were further confirmed by microscopic studies with scanning electron microscopy (SEM), atomic force microscopy and fluorescence confocal microscopy microscopic studies.

CONCLUSIONS

The phytocompounds are proved to be more effective than conventional antibiotics in inhibiting the biofilm forming sessile cells and can be used as a replacement for antibiotic.

SIGNIFICANCE AND IMPACT OF THE STUDY

Pure eugenol extracted from common basil leaves can be used as a safe substitute for common antibiotic for treatment of chronic infections caused by P. aeruginosa. It will be cost effective, devoid of notable side effects and will not generate antibiotic resistance in host body.

Computational medicine: quantitative modeling of complex diseases

Biological complex systems are composed of numerous components that interact within and across different scales. The ever-increasing generation of high-throughput biomedical data has given us an opportunity to develop a quantitative model of nonlinear biological systems having implications in health and diseases. Multidimensional molecular data can be modeled using various statistical methods at different scales of biological organization, such as genome, transcriptome and proteome. I will discuss recent advances in the application of computational medicine in complex diseases such as network-based studies, genome-scale metabolic modeling, kinetic modeling and support vector machines with specific examples in the field of cancer, psychiatric disorders and type 2 diabetes. The recent advances in translating these computational models in diagnosis and identification of drug targets of complex diseases are discussed, as well as the challenges researchers and clinicians are facing in taking computational medicine from the bench to bedside.

Landscape of cancer diagnostic biomarkers from specifically expressed genes

Although there has been great progress in cancer treatment, cancer remains a serious health threat to humans because of the lack of biomarkers for diagnosis, especially for early-stage diagnosis. In this study, we comprehensively surveyed the specifically expressed genes (SEGs) using the SEGtool based on the big data of gene expression from the The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) projects. In 15 solid tumors, we identified 233 cancer-specific SEGs (cSEGs), which were specifically expressed in only one cancer and showed great potential to be diagnostic biomarkers. Among them, three cSEGs (OGDH, MUDENG and ACO2) had a sample frequency >80% in kidney cancer, suggesting their high sensitivity. Furthermore, we identified 254 cSEGs as early-stage diagnostic biomarkers across 17 cancers. A two-gene combination strategy was applied to improve the sensitivity of diagnostic biomarkers, and hundreds of two-gene combinations were identified with high frequency. We also observed that 13 SEGs were targets of various drugs and nearly half of these drugs may be repurposed to treat cancers with SEGs as their targets. Several SEGs were regulated by specific transcription factors in the corresponding cancer, and 39 cSEGs were prognosis-related genes in 7 cancers. This work provides a survey of cancer biomarkers for diagnosis and early diagnosis and new insights to drug repurposing. These biomarkers may have great potential in cancer research and application.

Delineating the Plausible Molecular Vaccine Candidates and Drug Targets of Multidrug-Resistant Acinetobacter baumannii

Nosocomial infections have become alarming with the increase of multidrug-resistant bacterial strains of Acinetobacter baumannii. Being the causative agent in ~80% of the cases, these pathogenic gram-negative species could be deadly for hospitalized patients, especially in intensive care units utilizing ventilators, urinary catheters, and nasogastric tubes. Primarily infecting an immuno-compromised system, they are resistant to most antibiotics and are the root cause of various types of opportunistic infections including but not limited to septicemia, endocarditis, meningitis, pneumonia, skin, and wound sepsis and even urinary tract infections. Conventional experimental methods including typing, computational methods encompassing comparative genomics, and combined methods of reverse vaccinology and proteomics had been proposed to differentiate and develop vaccines and/or drugs for several outbreak strains. However, identifying proteins suitable enough to be posed as drug targets and/or molecular vaccines against the multidrug-resistant pathogenic bacterial strains has probably remained an open issue to address. In these cases of novel protein identification, the targets either are uncharacterized or have been unable to confer the most coveted protection either in the form of molecular vaccine candidates or as drug targets. Here, we report a strategic approach with the 3,766 proteins from the whole genome of A. baumannii ATCC19606 (AB) to rationally identify plausible candidates and propose them as future molecular vaccine candidates and/or drug targets. Essentially, we started with mapping the vaccine candidates (VaC) and virulence factors (ViF) of A. baumannii strain AYE onto strain ATCC19606 to identify them in the latter. We move on to build small networks of VaC and ViF to conceptualize their position in the network space of the whole genomic protein interactome (GPIN) and rationalize their candidature for drugs and/or molecular vaccines. To this end, we propose new sets of known proteins unearthed from interactome built using key factors, KeF, potent enough to compete with VaC and ViF. Our method is the first of its kind to propose, albeit theoretically, a rational approach to identify crucial proteins and pose them for candidates of vaccines and/or drugs effective enough to combat the deadly pathogenic threats of A. baumannii.

A Six-Gene-Based Prognostic Model Predicts Survival in Head and Neck Squamous Cell Carcinoma Patients

Background and Objective

Head and neck cancer is a malignant tumor that begins in the head and neck region, and has the sixth highest incidence worldwide. Previous studies have indicated several prognostic markers for head and neck squamous cell carcinoma (HNSCC), but due to poor accuracy and sensitivity of these clinical characteristic markers attention has been gradually switched to molecular biomarkers. This study aimed to sort out the mRNAs correlated with patient survival time to establish an mRNA combination prognostic biomarker model for HNSCC patient risk stratification, providing optimal therapeutic regimens and improving patient prognosis.

Methods

Clinical data and transcriptome sequencing data of HNSCC were retrieved from TCGA database and were allocated into training and validation datasets. The prognostic model was established using the mRNAs, which were sorted out from training dataset by a significant correlation with survival time. Eventually, the prediction property of the model was evaluated by Kaplan-Meier survival analysis and receiver operating characteristic (ROC) curve.

Results

An optimal prognostic model by the combination of six mRNAs was established. Kaplan-Meier survival analysis revealed effective risk stratification by this model for patients in the two datasets. The area under ROC curve (AUC) was > 0.65 for training and validation datasets, indicating good sensitivity and specificity of this model. Moreover, prominent superiority of this model to investigate prognostic biomarkers was demonstrated.

Conclusion

Our model provided effective prognostication in terms of death risk stratification and evaluation in HNSCC patients. Combination of this prognostic model with current treatment measures is expected to greatly improve the patients' prognosis.

Transcriptomic data for analyzing global gene expression patterns in Methicillin-resistance Staphylococcus aureus in response to spermine and oxacillin stress

Methicillin-resistant Staphylococcus aureus (MRSA) is a rapidly emerging bacteria causing infection, which has developed resistance to most of the beta-lactam antibiotics because of newly acquired low-affinity penicillin binding protein (PBP2a), which can continue to build the cell wall when other PBPs are blocked by beta-lactams. Exogenous spermine exerts a dose dependent inhibition effect on the growth of E. coli, Salmonella enterica serovar and Staphylococcus aureus. We have selected an MRSA Mu50 derivative which harbors mutation on PBP2 gene (named as MuM) showing spermine resistance and which confers a complete abolishment of spermine-beta-lactam synergy. A transcriptomic profiling of MuM against Mu50 (wild type) without any treatment, MuM and Mu50 in response to high dose spermine and Mu50 in response to spermine-beta-lactam synergy is provided in this article. These comparisons will enhance our current understanding of mechanisms of spermine-beta-lactam synergy sensitization effects on MRSA.

Spermine and oxacillin stress response on the cell wall synthesis and the global gene expression analysis in Methicillin-resistance Staphylococcus aureus

Methicillin-resistant Staphylococcus aureus (MRSA) is a rapidly emerging bacteria causing infection, which has developed resistance to most of the beta-lactam antibiotics because of newly acquired low-affinity penicillin-binding protein (PBP2a), which can continue to build the cell wall when beta-lactams block other PBPs. Exogenous spermine exerts a dose-dependent inhibition effect on the growth of Escherichia coli, Salmonella enterica serovar, and S. aureus. Selection of an MRSA Mu50 derivative which harbors mutation on PBP2 gene (named as MuM) showing spermine resistance and which confers a complete abolishment of spermine-beta-lactam synergy was identified. To further investigate the gene expression changes, a transcriptome profiling of MuM against Mu50 (wild-type) without any treatment, MuM and Mu50 in response to high dose spermine and Mu50 in response to spermine-beta-lactam synergy at 15, 30 and 60 min time points was performed. Functional annotation was further performed to delineate the metabolic pathways associated with the significant genes. A significant down-regulation in the iron regulatory system, potassium channel uptake and polyamine transport system with an up-regulation in general stress response sigB dependent operon in MuM strain at 15, 30 and 60 min time points with spermine treatment compared to Mu50 strain was observed. Analysis of spermine-dependent synergy with beta-lactams on cell wall synthesis revealed that it significantly reduces the degree of cross-linkage on cell wall with no change in trypsin digestion pattern of purified PBPs and without affecting PBPs expression or PBPs acylation by Bocillin. A strong relation between PBP2 protein and general stress sigB response, iron, potassium and polyamine transport systems was observed. SigB regulon should be activated on stress, which was not seen in some of our previous studies where it was down-regulated in wild-type Mu50 strain with spermine stress. Here, an intriguing finding is made where there seems to be a correction of this abnormal response of no SigB induction to a significant induction by PBP2 mutation. In MuM strain, a significant down-regulation of KdpABC operon genes at 15, 30 and 60 min time points on spermine stress is seen, which seems to be absent without spermine treatment. Since KCL has been found to protect the cell against spermine stress in wild-type strain by induction of KdpABC operon, it fails to do so in MuM strain underlying the importance of PBP2 protein in spermine stress. Analysis of spermine-dependent synergy with beta-lactams on cell wall synthesis revealed that it significantly reduces the degree of cross-linkage on cell wall with no change in trypsin digestion patterns of purified PBPs and without affecting PBPs expression or PBPs acylation by Bocillin. Furthermore, spermine does not help in enhancing the binding of beta-lactams to PBPs and binding of spermine to PBPs does not cause conformational changes to PBPs, as tested with trypsin digestion patterns. Future studies on the molecular mechanism of spermine interactions with these systems hold great potential for the development of new therapeutics for MRSA infections.

In silico Identification of the Indispensable Quorum Sensing Proteins of Multidrug Resistant Proteus mirabilis

Catheter-associated urinary tract infections (CAUTI) is an alarming hospital based disease with the increase of multidrug resistance (MDR) strains of Proteus mirabilis. Cases of long term hospitalized patients with multiple episodes of antibiotic treatments along with urinary tract obstruction and/or undergoing catheterization have been reported to be associated with CAUTI. The cases are complicated due to the opportunist approach of the pathogen having robust swimming and swarming capability. The latter giving rise to biofilms and probably inducible through autoinducers make the scenario quite complex. High prevalence of long-term hospital based CAUTI for patients along with moderate percentage of morbidity, cropping from ignorance about drug usage and failure to cure due to MDR, necessitates an immediate intervention strategy effective enough to combat the deadly disease. Several reports and reviews focus on revealing the important genes and proteins, essential to tackle CAUTI caused by P. mirabilis. Despite longitudinal countrywide studies and methodical strategies to circumvent the issues, effective means of unearthing the most indispensable proteins to target for therapeutic uses have been meager. Here, we report a strategic approach for identifying the most indispensable proteins from the genome of P. mirabilis strain HI4320, besides comparing the interactomes comprising the autoinducer-2 (AI-2) biosynthetic pathway along with other proteins involved in biofilm formation and responsible for virulence. Essentially, we have adopted a theoretical network model based approach to construct a set of small protein interaction networks (SPINs) along with the whole genome (GPIN) to computationally identify the crucial proteins involved in the phenomenon of quorum sensing (QS) and biofilm formation and thus, could be therapeutically targeted to fight out the MDR threats to antibiotics of P. mirabilis. Our approach utilizes the functional modularity coupled with k-core analysis and centrality scores of eigenvector as a measure to address the pressing issues.