Antibacterial Evaluation of Gallic Acid and its Derivatives against a Panel of Multi-drug Resistant Bacteria.

BACKGROUND: Infectious diseases are the second leading cause of deaths worldwide. Pathogenic bacteria have been developing tremendous resistance against antibiotics which has placed an additional burden on healthcare systems. Gallic acid belongs to a naturally occurring phenolic class of compounds and is known to possess a wide spectrum of antimicrobial activities. AIMS & OBJECTIVES: In this study, we synthesized thirteen derivatives of gallic acid and evaluated their antibacterial potential against seven multi-drug resistant bacteria, as well as cytotoxic effects against human embryonic kidney cell line in vitro. Methods: 13 compounds were successfully synthesized with moderate to good yield and evaluated. Synthesized derivatives were characterized by using nuclear magnetic resonance spectroscopy, mass spectrometry, and Fourier transformation infrared spectroscopy. Antibacterial activity was determined using microdilution while cytotoxicyt was assessed using MTT assay. RESULTS: The results of antibacterial assay showed that seven out of thirteen compounds exhibited antibacterial effects with compound 6 and 13 being most potent against Staphylococcus aureus (MIC 56 µg/mL) and Salmonella enterica (MIC 475 µg/mL) respectively. On the other hand, most of these compounds showed lower cytotoxicity against human embryonic kidney cells (HEK 293), with IC50 values ranging from over 700 µg/mL. CONCLUSION: Notably, compound 13 was found to be non-toxic at concentrations as high as 5000 µg/mL. These findings suggest that the present synthetic derivatives of gallic acid hold potential for further studies in the development of potent antibacterial agents.

Novel cationic cryptides in Penaeus vannamei demonstrate antimicrobial and anti-cancer activities.

Cryptides are a subfamily of bioactive peptides that exist in all living organisms. They are latently encrypted in their parent sequences and exhibit a wide range of biological activities when decrypted via in vivo or in vitro proteases. Cationic cryptides tend to be drawn to the negatively charged membranes of microbial and cancer cells, causing cell death through various mechanisms. This makes them promising candidates for alternative antimicrobial and anti-cancer therapies, as their mechanism of action is independent of gene mutations. In the current study, we employed an in silico approach to identify novel cationic cryptides with potential antimicrobial and anti-cancer activities in atypical and systematic strategy by reanalysis of a publicly available RNA-seq dataset of Pacific white shrimp (Penaus vannamei) in response to bacterial infection. Out of 12 cryptides identified, five were selected based on their net charges and potential for cell penetration. Following chemical synthesis, the cryptides were assayed in vitro to test for their biological activities. All five cryptides demonstrated a wide range of selective activity against the tested microbial and cancer cells, their anti-biofilm activities against mature biofilms, and their ability to interact with Gram-positive and negative bacterial membranes. Our research provides a framework for a comprehensive analysis of transcriptomes in various organisms to uncover novel bioactive cationic cryptides. This represents a significant step forward in combating the crisis of multi-drug-resistant microbial and cancer cells, as these cryptides neither induce mutations nor are influenced by mutations in the cells they target.

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.

Metabolite Profiling of Malaysian Gracilaria edulis Reveals Eplerenone as Novel Antibacterial Compound for Drug Repurposing Against MDR Bacteria.

With a continuous threat of antimicrobial resistance on human health worldwide, efforts for new alternatives are ongoing for the management of bacterial infectious diseases. Natural products of land and sea, being conceived to be having fewer side effects, pose themselves as a welcome relief. In this respect, we have taken a scaffolded approach to unearthing the almost unexplored chemical constituents of Malaysian red seaweed, Gracilaria edulis. Essentially, a preliminary evaluation of the ethyl acetate and acetone solvent extracts, among a series of six such, revealed potential antibacterial activity against six MDR species namely, Klebsiella pneumoniae, Pseudomonas aeruginosa, Salmonella enterica, methicillin-resistant Staphylococcus aureus (MRSA), Streptococcus pyogenes, and Bacillus subtilis. Detailed analyses of the inlying chemical constituents, through LC-MS and GC-MS chromatographic separation, revealed a library of metabolic compounds. These were led for further virtual screening against selected key role playing proteins in the virulence of the aforesaid bacteria. To this end, detailed predictive pharmacological analyses added up to reinforce Eplerenone as a natural alternative from the plethora of plausible bioactives. Our work adds the ongoing effort to re-discover and repurpose biochemical compounds to combat the antimicrobial resistance offered by the Gram-positive and the -negative bacterial species.

A scaffolded approach to unearth potential antibacterial components from epicarp of Malaysian Nephelium lappaceum L.

The emergence and spread of antimicrobial resistance have been of serious concern to human health and the management of bacterial infectious diseases. Effective treatment of these diseases requires the development of novel therapeutics, preferably free of side effects. In this regard, natural products are frequently conceived to be potential alternative sources for novel antibacterial compounds. Herein, we have evaluated the antibacterial activity of the epicarp extracts of the Malaysian cultivar of yellow rambutan fruit (Nephelium lappaceum L.) against six pathogens namely, Bacillus subtilis, methicillin-resistant Staphylococcus aureus (MRSA), Streptococcus pyogenes, Pseudomonas aeruginosa, Klebsiella pneumoniae and Salmonella enterica. Among a series of solvent extracts, fractions of ethyl acetate and acetone have revealed significant activity towards all tested strains. Chemical profiling of these fractions, via HPLC, LC-MS and GC-MS, has generated a library of potentially bioactive compounds. Downstream virtual screening, pharmacological prediction, and receptor-ligand molecular dynamics simulation have eventually unveiled novel potential antibacterial compounds, which can be extracted for medicinal use. We report compounds like catechin, eplerenone and oritin-4-beta-ol to be computationally inhibiting the ATP-binding domain of the chaperone, DnaK of P. aeruginosa and MRSA. Thus, our work follows the objective to propose new antimicrobials capable of perforating the barrier of resistance posed by both the gram positives and the negatives.

A Comparative Genomics Approach for Shortlisting Broad-Spectrum Drug Targets in Nontuberculous Mycobacteria.

Many members of nontuberculous mycobacteria (NTM) are opportunistic pathogens causing several infections in animals. The incidence of NTM infections and emergence of drug-resistant NTM strains are rising worldwide, emphasizing the need to develop novel anti-NTM drugs. The present study is aimed to identify broad-spectrum drug targets in NTM using a comparative genomics approach. The study identified 537 core proteins in NTM of which 45 were pathogen specific and essential for the survival of pathogens. Furthermore, druggability analysis indicated that 15 were druggable among those 45 proteins. These 15 proteins, which were core proteins, pathogen-specific, essential, and druggable, were considered as potential broad-spectrum candidates. Based on their locations in cytoplasm and membrane, targets were classified as drug and vaccine targets. The identified 15 targets were different enzymes, carrier proteins, transcriptional regulator, two-component system protein, ribosomal, and binding proteins. The identified targets could further be utilized by researchers to design inhibitors for the discovery of antimicrobial agents.

The phylogenomics of CRISPR-Cas system and revelation of its features in Salmonella.

Salmonellae display intricate evolutionary patterns comprising over 2500 serovars having diverse pathogenic profiles. The acquisition and/or exchange of various virulence factors influences the evolutionary framework. To gain insights into evolution of Salmonella in association with the CRISPR-Cas genes we performed phylogenetic surveillance across strains of 22 Salmonella serovars. The strains differed in their CRISPR1-leader and cas operon features assorting into two main clades, CRISPR1-STY/cas-STY and CRISPR1-STM/cas-STM, comprising majorly typhoidal and non-typhoidal Salmonella serovars respectively. Serovars of these two clades displayed better relatedness, concerning CRISPR1-leader and cas operon, across genera than between themselves. This signifies the acquisition of CRISPR1/Cas region could be through a horizontal gene transfer event owing to the presence of mobile genetic elements flanking CRISPR1 array. Comparison of CRISPR and cas phenograms with that of multilocus sequence typing (MLST) suggests differential evolution of CRISPR/Cas system. As opposed to broad-host-range, the host-specific serovars harbor fewer spacers. Mapping of protospacer sources suggested a partial correlation of spacer content with habitat diversity of the serovars. Some serovars like serovar Enteritidis and Typhimurium that inhabit similar environment/infect similar hosts hardly shared their protospacer sources.

Common cancer biomarkers of breast and ovarian types identified through artificial intelligence.

Biomarkers can offer great promise for improving prevention and treatment of complex diseases such as cancer, cardiovascular diseases, and diabetes. These can be used as either diagnostic or predictive or as prognostic biomarkers. The revolution brought about in biological big data analytics by artificial intelligence (AI) has the potential to identify a broader range of genetic differences and support the generation of more robust biomarkers in medicine. AI is invigorating biomarker research on various fronts, right from the cataloguing of key mutations driving the complex diseases like cancer to the elucidation of molecular networks underlying diseases. In this study, we have explored the potential of AI through machine learning approaches to propose that these methods can act as recommendation systems to sort and prioritize important genes and finally predict the presence of specific biomarkers. Essentially, we have utilized microarray datasets from open-source databases, like GEO, for breast, lung, colon, and ovarian cancer. In this context, different clustering analyses like hierarchical and k-means along with random forest algorithm have been utilized to classify important genes from a pool of several thousand genes. To this end, network centrality and pathway analysis have been implemented to identify the most potential target as CREB1.

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 decade of sustained selection pressure on two surface sites of the VP1 protein of Enterovirus A71 suggests that immune evasion may be an indirect driver for virulence.

Enterovirus A71 (EV-A71) is an emerging pathogen in the Enterovirus A species group. EV-A71 causes hand, foot and mouth disease (HFMD), with virulent variants exhibiting polio-like acute flaccid paralysis and other central nervous system manifestations. We analysed all enterovirus A71 complete genomes with collection dates from 2008 to mid-2018. All sub-genotypes exhibit a strong molecular clock with omega (dN/dS) suggesting strong purifying selection. In sub-genotypes B5 and C4, positive selection can be detected at two surface sites on the VP1 protein, also detected in positive selection studies performed prior to 2008. Toggling of a limited repertoire of amino acids at these positively selected residues over the last decade suggests that EV-A71 may be undergoing a sustained frequency-dependent selection process for immune evasion, raising issues for vaccine development. These same sites have also been previously implicated in virus-host binding and strain-associated severity of HFMD, suggesting that immune evasion may be an indirect driver for virulence (154 words).

Paradigm Shift in Drug Re-purposing From Phenalenone to Phenaleno-Furanone to Combat Multi-Drug Resistant Salmonella enterica Serovar Typhi.

Over recent years, typhoid fever has gained increasing attention with several cases reporting treatment failure due to multidrug resistant (MDR) strains of Salmonella enterica serovar Typhi. While new drug development strategies are being devised to combat the threat posed by these MDR pathogens, drug repurposing or repositioning has become a good alternative. The latter is considered mainly due to its capacity for saving sufficient time and effort for pre-clinical and optimization studies. Owing to the possibility of an unsuccessful repositioning, due to the mismatch in the optimization of the drug ligand for the changed biochemical properties of "old" and "new" targets, we have chosen a "targeted" approach of adopting a combined chemical moiety-based drug repurposing. Using small molecules selected from a combination of earlier approved drugs having phenalenone and furanone moieties, we have computationally delineated a step-wise approach to drug design against MDR Salmonella. We utilized our network analysis-based pre-identified, essential chaperone protein, SicA, which regulates the folding and quality of several secretory proteins including the Hsp70 chaperone, SigE. To this end, another crucial chaperone protein, Hsp70 DnaK, was also considered due to its importance for pathogen survival under the stress conditions typically encountered during antibiotic therapies. These were docked with the 19 marketed anti-typhoid drugs along with two phenalenone-furanone derivatives, 15 non-related drugs which showed 70% similarity to phenalenone and furanone derivatives and other analogous small molecules. Furthermore, molecular dynamics simulation studies were performed to check the stability of the protein-drug complexes. Our results showed the best binding interaction and stability, under the parameters of a virtual human body environment, with XR770, a phenaleno-furanone moiety based derivative. We therefore propose XR770, for repurposing for therapeutic intervention against emerging and significant drug resistance conferred by pathogenic Salmonella strains.

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.

A side-effect free method for identifying cancer drug targets.

Identifying effective drug targets, with little or no side effects, remains an ever challenging task. A potential pitfall of failing to uncover the correct drug targets, due to side effect of pleiotropic genes, might lead the potential drugs to be illicit and withdrawn. Simplifying disease complexity, for the investigation of the mechanistic aspects and identification of effective drug targets, have been done through several approaches of protein interactome analysis. Of these, centrality measures have always gained importance in identifying candidate drug targets. Here, we put forward an integrated method of analysing a complex network of cancer and depict the importance of k-core, functional connectivity and centrality (KFC) for identifying effective drug targets. Essentially, we have extracted the proteins involved in the pathways leading to cancer from the pathway databases which enlist real experimental datasets. The interactions between these proteins were mapped to build an interactome. Integrative analyses of the interactome enabled us to unearth plausible reasons for drugs being rendered withdrawn, thereby giving future scope to pharmaceutical industries to potentially avoid them (e.g. ESR1, HDAC2, F2, PLG, PPARA, RXRA, etc). Based upon our KFC criteria, we have shortlisted ten proteins (GRB2, FYN, PIK3R1, CBL, JAK2, LCK, LYN, SYK, JAK1 and SOCS3) as effective candidates for drug development.

PNMA family: Protein interaction network and cell signalling pathways implicated in cancer and apoptosis.

Paraneoplastic Ma Family (PNMA) comprises a growing number of family members which share relatively conserved protein sequences encoded by the human genome and is localized to several human chromosomes, including the X-chromosome. Based on sequence analysis, PNMA family members share sequence homology to the Gag protein of LTR retrotransposon, and several family members with aberrant protein expressions have been reported to be closely associated with the human Paraneoplastic Disorder (PND). In addition, gene mutations of specific members of PNMA family are known to be associated with human mental retardation or 3-M syndrome consisting of restrictive post-natal growth or dwarfism, and development of skeletal abnormalities. Other than sequence homology, the physiological function of many members in this family remains unclear. However, several members of this family have been characterized, including cell signalling events mediated by these proteins that are associated with apoptosis, and cancer in different cell types. Furthermore, while certain PNMA family members show restricted gene expression in the human brain and testis, other PNMA family members exhibit broader gene expression or preferential and selective protein interaction profiles, suggesting functional divergence within the family. Functional analysis of some members of this family have identified protein domains that are required for subcellular localization, protein-protein interactions, and cell signalling events which are the focus of this review paper.

Computational analysis of protein interaction networks for infectious diseases.

Infectious diseases caused by pathogens, including viruses, bacteria and parasites, pose a serious threat to human health worldwide. Frequent changes in the pattern of infection mechanisms and the emergence of multidrug-resistant strains among pathogens have weakened the current treatment regimen. This necessitates the development of new therapeutic interventions to prevent and control such diseases. To cater to the need, analysis of protein interaction networks (PINs) has gained importance as one of the promising strategies. The present review aims to discuss various computational approaches to analyse the PINs in context to infectious diseases. Topology and modularity analysis of the network with their biological relevance, and the scenario till date about host-pathogen and intra-pathogenic protein interaction studies were delineated. This would provide useful insights to the research community, thereby enabling them to design novel biomedicine against such infectious diseases.

Interactome analyses of Salmonella pathogenicity islands reveal SicA indispensable for virulence.

BACKGROUND: Serovars of Salmonella enterica, namely Typhi and Typhimurium, reportedly, are the bacterial pathogens causing systemic infections like gastroenteritis and typhoid fever. To elucidate the role and importance in such infection, the proteins of the Type III secretion system of Salmonella pathogenicity islands and two component signal transduction systems, have been mainly focused. However, the most indispensable of these virulent ones and their hierarchical role has not yet been studied extensively. RESULTS: We have adopted a theoretical approach to build an interactome comprising the proteins from the Salmonella pathogeneicity islands (SPI) and two component signal transduction systems. This interactome was then analyzed by using network parameters like centrality and k-core measures. An initial step to capture the fingerprint of the core network resulted in a set of proteins which are involved in the process of invasion and colonization, thereby becoming more important in the process of infection. These proteins pertained to the Inv, Org, Prg, Sip, Spa, Ssa and Sse operons along with chaperone protein SicA. Amongst them, SicA was figured out to be the most indispensable protein from different network parametric analyses. Subsequently, the gene expression levels of all these theoretically identified important proteins were confirmed by microarray data analysis. Finally, we have proposed a hierarchy of the proteins involved in the total infection process. This theoretical approach is the first of its kind to figure out potential virulence determinants encoded by SPI for therapeutic targets for enteric infection. CONCLUSIONS: A set of responsible virulent proteins was identified and the expression level of their genes was validated by using independent, published microarray data. The result was a targeted set of proteins that could serve as sensitive predictors and form the foundation for a series of trials in the wet-lab setting. Understanding these regulatory and virulent proteins would provide insight into conditions which are encountered by this intracellular enteric pathogen during the course of infection. This would further contribute in identifying novel targets for antimicrobial agents.

A novel gene cluster soxSRT is essential for the chemolithotrophic oxidation of thiosulfate and tetrathionate by Pseudaminobacter salicylatoxidans KCT001.

Chemolithotrophic sulfur oxidation (Sox) in the alpha-proteobacterium Pseudaminobacter salicylatoxidans KCT001 was found to be governed by the gene cluster soxSRT-soxVWXYZABCD. Independent transposon-insertion mutations in the genes soxB, soxC, soxD, and also in a novel open reading frame (ORF), designated as soxT, afforded revelation of the entire sox locus of this bacterium. The deduced amino acid sequence of the novel ORF soxT comprised 362 residues and exhibited significant homology with hypothetical proteins of diverse origin, including a permease-like transport protein of Escherichia coli. Two contiguous ORFs, soxR and soxS, immediately preceded the soxT gene. The gene cluster soxSRT was located upstream of soxVWXYZABCD and was transcribed divergently with respect to the latter. Chemolithotrophic utilization of both thiosulfate and tetrathionate was observed to have been impaired in all of these Sox- mutants, implicating the involvement of the gene cluster soxSRT-soxVWXYZABCD in the oxidation of both thiosulfate and tetrathionate.