Neoteric advancement in TB drugs and an overview on the anti-tubercular role of peptides through computational approaches

Optimizing broiler performance, carcass traits, and health: evaluating thyme and/or garlic powders as natural growth promoters in antibiotic-free diets

The purpose of this study was to evaluate the effects of thyme powder (TP), garlic powder (GP), and their combination as natural alternatives to antibiotics on broiler growth performance, blood indices, and carcass characteristics. A completely randomized design was used, with four experimental groups consisting of 264 one-day-old Ross 308 broiler chicks (45.27±0.5 g). Each group received different levels of TP and GP in the diet as follows: a control group (basal diet with no additives), 2 g of GP/kg, 2 g of TP/kg, and a mixture treatment (1 g of GP/kg + 1 g of TP/kg). Each treatment included six replicates, with eleven unsexed chicks per replicate. The results showed that supplementation with TP, GP, and their combination significantly improved feed intake (FI), feed conversion ratio (FCR), and overall growth performance compared to the untreated group (UG). The different additives also significantly (P≤0.05) enhanced carcass characteristics, particularly carcass yield and heart weight, both in absolute and relative terms. Furthermore, the treatments significantly (P≤0.05) affected blood indices, leading to notable improvements in immune and antioxidant parameters, including increased levels of immunoglobulin Y (IgY), immunoglobulin M (IgM), and superoxide dismutase (SOD), as well as decreased levels of malondialdehyde (MDA), a marker of oxidative stress. These changes suggest that the supplemented groups had enhanced immunity and a better oxidative balance, contributing to improved overall health. Notably, the combined treatment of TP and GP (1% each) exhibited a synergistic effect, yielding benefits comparable to or even surpassing those of the individual TP or GP treatments. These findings support the potential of combining these natural additives to achieve a balanced improvement in growth, feed efficiency, and health status. In conclusion, supplementation with thyme powder, garlic powder, or a combination of both at appropriate levels (2% GP, 2% TP, or 1% each) can serve as an effective alternative to antibiotics, improving broiler growth performance, carcass quality, and health by enhancing immune responses and antioxidant status.

Compounds Derived from 5-Fluoropyridine and Benzo[b]thiophene: Killing Mycobacterium tuberculosis and Reducing its Virulence

The rise of drug-resistant Mycobacterium tuberculosis (Mtb) has extended the duration of tuberculosis (TB) treatment and reduced the likelihood of cure. One strategy to combat this issue is the development of inhibitors targeting the virulence factors of bacterial pathogens. Mtb' catalase (KatG) is crucial for its detoxification mechanisms and also serves as a significant virulence factor for the bacterium. In this study, twelve derivatives synthesized from 5-fluoropyridine and benzo[b]thiophene demonstrated antimycobacterial efficacy with minimum inhibitory concentrations (MICs) varying between 0.5 and 32 μg/mL. Compound 2, 1-(benzo[b]thiophen-2-ylmethylene) thiosemicarbazide, emerged as the most potent candidate. It effectively inhibited Mtb KatG, enhanced the production of reactive oxygen species (ROS) in Mtb, and achieved Mtb killing within 96 hours at a concentration of 2 μg/mL (4×MIC). Molecular docking simulations revealed that compound 2 binds tightly to the active site of Mtb-KatG with a docking score of 114, indicating that it may serve as a potent inhibitor of Mtb-KatG. The rabbit skin tuberculosis model was employed to assess the virulence of Mtb. Animal study results indicated that the granulomas induced by Mtb after treatment with compound 2 were reduced in size, exhibited a lower bacterial load, and the bacteria were no longer aggregated, in contrast to those caused by untreated Mtb. Hence, compound 2 can be regarded as a molecule capable of neutralizing the virulence factors of Mtb. This research offers insights into the design of anti-Mtb molecules with novel mechanisms of action.

Hyb_SEnc: An Antituberculosis Peptide Predictor Based on a Hybrid Feature Vector and Stacked Ensemble Learning

Tuberculosis has plagued mankind since ancient times, and the struggle between humans and tuberculosis continues. Mycobacterium tuberculosis is the leading cause of tuberculosis, infecting nearly one-third of the world's population. The rise of peptide drugs has created a new direction in the treatment of tuberculosis. Therefore, for the treatment of tuberculosis, the prediction of anti-tuberculosis peptides is crucial. This paper proposes an anti-tuberculosis peptide prediction method based on hybrid features and stacked ensemble learning. First, a random forest (RF) and extremely randomized tree (ERT) are selected as first-level learning of stacked ensembles. Then, the five best-performing feature encoding methods are selected to obtain the hybrid feature vector, and then the decision tree and recursive feature elimination (DT-RFE) are used to refine the hybrid feature vector. After selection, the optimal feature subset is used as the input of the stacked ensemble model. At the same time, logistic regression (LR) is used as a stacked ensemble secondary learner to build the final stacked ensemble model Hyb_SEnc. The prediction accuracy of Hyb_SEnc achieved 94.68% and 95.74% on the independent test sets of AntiTb_MD and AntiTb_RD, respectively.

Unlocking nature's arsenal: Nanotechnology for targeted delivery of venom toxins in cancer therapy

AIM

The aim of the present review is to shed light on the nanotechnological approaches adopted to overcome the shortcomings associated with the delivery of venom peptides which possess inherent anti-cancer properties.

BACKGROUND

Venom peptides although have been reported to demonstrate anti-cancer effects, they suffer from several disadvantages such as in vivo instability, off-target adverse effects, limited drug loading and low bioavailability. This review presents a comprehensive compilation of different classes of nanocarriers while underscoring their advantages, disadvantages and potential to carry such peptide molecules for in vivo delivery. It also discusses various nanotechnological aspects such as methods of fabrication, analytical tools to assess these nanoparticulate formulations, modulation of nanocarrier polymer properties to enhance loading capacity, stability and improve their suitability to carry toxic peptide drugs.

CONCLUSION

Nanotechnological approaches bear great potential in delivering venom peptide-based molecules as anticancer agents by enhancing their bioavailability, stability, efficacy as well as offering a spatiotemporal delivery approach. However, the challenges associated with toxicity and biocompatibility of nanocarriers must be duly addressed.

PERSPECTIVES

The everlasting quest for new breakthroughs for safer delivery of venom peptides in human subjects is fuelled by unmet clinical needs in the current landscape of chemotherapy. In addition, exhaustive efforts are required in obtaining and purifying the venom peptides followed by designing and optimizing scale up technologies.

Recent updates in natural terpenoids as potential anti-mycobacterial agents

Tuberculosis is considered as a leading health issue globally. Even though, the todays first line anti-mycobacterial treatments used in the hospital have low deaths, multidrug-resistance forms of the ailment have now spread globally and become a major issue. The wide-ranging biodiversity of medicinal plants, ocean animals have gained considerable attention for drug discovery in previous spans, and the emergence of TB drug resistance has inspired interest in judging natural products (NPs) to cure this disease. Till now, several compounds have been isolated from natural sources with anti-mycobacterial activity, few of which demonstrate significant activity and have the potential for further development. Worldwide huge natural flora and fauna are existing, this flora and fauna must be investigated for new potent lead against infectious TB. This review systematically surveys various classes of terpenoid molecules obtained from different medicinal plants, fungi, sponges, and sea plumes with anti-TB activity, which could be useful for further optimization and development in this field.

Codon usage divergence of important functional genes in Mycobacterium tuberculosis

Sequence characteristics are usually used to explain the adaptive ability to hosts, metabolism, genetic diversity, drug resistance, and infectivity of Mycobacterium tuberculosis. Exploring the codon usage pattern of coding sequences in Mycobacterium tuberculosis is of great significance. In the present study, two hundred random complete genomes of Mycobacterium tuberculosis were downloaded from the National Center for Biotechnology Information database. The important codon usage pattern, such as the codon bias index, the effective number of codons, the relative synonymous codon usage as well as the base component, of twenty one specific functional genes were counted or calculated. The differences of the relative synonymous codon usage values among those functional genes, and the summation of the standard deviations of codon usage parameters were used to evaluate the divergence degree of the concerned genes. The results show that among the concerned genes, 1) all genes are high GC sequences, the codon usage frequency corresponding to each amino acid of these functional genes had a significant bias; 2) the genes of those with high effective number of codons, such as the coding sequences of Myco-bacterial membrane protein large family, usually have higher divergences; and 3) genes with lower divergences, such as the ag85A and the sigH, are usually highly conserved and are often used as drug target genes. The findings of the present work would improve new understandings on the evolution of Mycobacterium tuberculosis and on the measures to prevent and control tuberculosis from the gene engineering.

Bio-chemical markers of chronic, non-infectious disease in the human tear film

The tear film is a thin, moist layer covering the ocular surface and is laden with proteins, peptides, lipids, mucins, electrolytes and cellular debris which function to maintain the healthy status of the ocular surface. In many cases of ocular or systemic disease, the integrity of this layer is changed and/or the balance of its constituents is disturbed. Since tears are easy and quick to collect and can be stored for long periods, they have the potential to be a valuable source of information relevant to many disease states. The purpose of this review is to collate information on the known biomarkers of systemic disease that have been identified in tears. The range of conditions covered includes diabetes mellitus, diabetic retinopathy, diabetic peripheral neuropathy, multiple sclerosis, Parkinson's disease, Alzheimer's disease, migraine, systemic sclerosis, cystic fibrosis, thyroid disorders and cancer.

iAtbP-Hyb-EnC: Prediction of antitubercular peptides via heterogeneous feature representation and genetic algorithm based ensemble learning model

Tuberculosis (TB) is a worldwide illness caused by the bacteria Mycobacterium tuberculosis. Owing to the high prevalence of multidrug-resistant tuberculosis, numerous traditional strategies for developing novel alternative therapies have been presented. The effectiveness and dependability of these procedures are not always consistent. Peptide-based therapy has recently been regarded as a preferable alternative due to its excellent selectivity in targeting specific cells without affecting the normal cells. However, due to the rapid growth of the peptide samples, predicting TB accurately has become a challenging task. To effectively identify antitubercular peptides, an intelligent and reliable prediction model is indispensable. An ensemble learning approach was used in this study to improve expected results by compensating for the shortcomings of individual classification algorithms. Initially, three distinct representation approaches were used to formulate the training samples: k-space amino acid composition, composite physiochemical properties, and one-hot encoding. The feature vectors of the applied feature extraction methods are then combined to generate a heterogeneous vector. Finally, utilizing individual and heterogeneous vectors, five distinct nature classification models were used to evaluate prediction rates. In addition, a genetic algorithm-based ensemble model was used to improve the suggested model's prediction and training capabilities. Using Training and independent datasets, the proposed ensemble model achieved an accuracy of 94.47% and 92.68%, respectively. It was observed that our proposed "iAtbP-Hyb-EnC" model outperformed and reported ~10% highest training accuracy than existing predictors. The "iAtbP-Hyb-EnC" model is suggested to be a reliable tool for scientists and might play a valuable role in academic research and drug discovery. The source code and all datasets are publicly available at https://github.com/Farman335/iAtbP-Hyb-EnC.

Modulators of protein-protein interactions as antimicrobial agents

Protein-Protein interactions (PPIs) are involved in a myriad of cellular processes in all living organisms and the modulation of PPIs is already under investigation for the development of new drugs targeting cancers, autoimmune diseases and viruses. PPIs are also involved in the regulation of vital functions in bacteria and, therefore, targeting bacterial PPIs offers an attractive strategy for the development of antibiotics with novel modes of action. The latter are urgently needed to tackle multidrug-resistant and multidrug-tolerant bacteria. In this review, we describe recent developments in the modulation of PPIs in pathogenic bacteria for antibiotic development, including advanced small molecule and peptide inhibitors acting on bacterial PPIs involved in division, replication and transcription, outer membrane protein biogenesis, with an additional focus on toxin-antitoxin systems as upcoming drug targets.

AtbPpred: A Robust Sequence-Based Prediction of Anti-Tubercular Peptides Using Extremely Randomized Trees

Mycobacterium tuberculosis is one of the most dangerous pathogens in humans. It acts as an etiological agent of tuberculosis (TB), infecting almost one-third of the world's population. Owing to the high incidence of multidrug-resistant TB and extensively drug-resistant TB, there is an urgent need for novel and effective alternative therapies. Peptide-based therapy has several advantages, such as diverse mechanisms of action, low immunogenicity, and selective affinity to bacterial cell envelopes. However, the identification of anti-tubercular peptides (AtbPs) via experimentation is laborious and expensive; hence, the development of an efficient computational method is necessary for the prediction of AtbPs prior to both in vitro and in vivo experiments. To this end, we developed a two-layer machine learning (ML)-based predictor called AtbPpred for the identification of AtbPs. In the first layer, we applied a two-step feature selection procedure and identified the optimal feature set individually for nine different feature encodings, whose corresponding models were developed using extremely randomized tree (ERT). In the second-layer, the predicted probability of AtbPs from the above nine models were considered as input features to ERT and developed the final predictor. AtbPpred respectively achieved average accuracies of 88.3% and 87.3% during cross-validation and an independent evaluation, which were ~8.7% and 10.0% higher than the state-of-the-art method. Furthermore, we established a user-friendly webserver which is currently available at http://thegleelab.org/AtbPpred. We anticipate that this predictor could be useful in the high-throughput prediction of AtbPs and also provide mechanistic insights into its functions.

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We developed a novel computational framework for the identification of anti-tubercular peptides using Extremely randomized tree.

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AtbPpred displayed superior performance compared to the existing method on both benchmark and independent datasets.

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We constructed a user-friendly web server that implements the proposed AtbPpred method.

Computational Approaches as Rational Decision Support Systems for Discovering Next-Generation Antitubercular Agents: Mini-Review

Tuberculosis, malaria, dengue, chikungunya, leishmaniasis etc. are a large group of neglected tropical diseases that prevail in tropical and subtropical countries, affecting one billion people every year. Minimal funding and grants for research on these scientific problems challenge many researchers to find a different way to reduce the extensive time and cost involved in the drug discovery cycle of these problems. Computer-aided drug design techniques have already been proved successful in the discovery of new molecules rationally by reducing the time and cost involved in the development of drugs. In the current minireview, we are highlighting on the molecular modeling studies published during 2010-2018 for target specific antitubercular agents. This review includes the studies of Structure-Based (SB) and Ligand-Based (LB) modeling and those involving Machine Learning (ML) techniques against different antitubercular targets such as dihydrofolate reductase (DHFR), enoyl Acyl Carrier Protein (ACP) reductase (InhA), catalase-peroxidase (KatG), enzyme antigen 85C, protein tyrosine phosphatases (PtpA and PtpB), dUTPase, thioredoxin reductase (MtTrxR), etc. The information presented in this review will help the researchers to get acquainted with the recent progress in the modeling studies of antitubercular agents.

A Systematic Overview of Type II and III Toxin-Antitoxin Systems with a Focus on Druggability

Toxin-antitoxin (TA) systems are known to play various roles in physiological processes, such as gene regulation, growth arrest and survival, in bacteria exposed to environmental stress. Type II TA systems comprise natural complexes consisting of protein toxins and antitoxins. Each toxin and antitoxin participates in distinct regulatory mechanisms depending on the type of TA system. Recently, peptides designed by mimicking the interfaces between TA complexes showed its potential to activate the activity of toxin by competing its binding counterparts. Type II TA systems occur more often in pathogenic bacteria than in their nonpathogenic kin. Therefore, they can be possible drug targets, because of their high abundance in some pathogenic bacteria, such as Mycobacterium tuberculosis. In addition, recent bioinformatic analyses have shown that type III TA systems are highly abundant in the intestinal microbiota, and recent clinical studies have shown that the intestinal microbiota is linked to inflammatory diseases, obesity and even several types of cancer. We therefore focused on exploring the putative relationship between intestinal microbiota-related human diseases and type III TA systems. In this paper, we review and discuss the development of possible druggable materials based on the mechanism of type II and type III TA system.

Venom as therapeutic weapon to combat dreadful diseases of 21st century: A systematic review on cancer, TB, and HIV/AIDS

Cancer and infectious diseases are the preeminent causes of human morbidities and mortalities worldwide. At present, chemotherapy, radiotherapy, immunotherapy, and gene therapy are considered as predominant options in order to treat cancer. But these therapies provide inadequate consequences by affecting both the normal and tumor cells. On the other hand, tuberculosis (TB), and HIV (human immunodeficiency virus) infections are significant threats, causing over a million mortalities each year. The extensive applications of antibiotics have caused the microbes to acquire resistance to the existing antibiotics. With the emerging dilemma of drug resistant microbes, it has become imperative to identify novel therapeutic agents from natural sources as emphatic alternative approach. Over the past few decades, venoms derived from several reptiles, amphibians, and arthropods including snakes, scorpions, frogs, spiders, honey bees, wasps, beetles, caterpillars, ants, centipedes, and sponges have been identified as efficient therapeutics. Venoms constitute plethora of bioactive components, particularly peptides, enzymes, and other chemical entities, which exhibit a large array of anticancer and anti-pathogenic activities. This review highlights the panorama of bioactive components of animal venoms divulging the anticancer, anti-tubercular, and anti-HIV activities. In a nutshell, this context discloses the decisive role of animal venoms as alternative natural resources to combat these deadly diseases of 21^st century, and propounding the plausible development of new therapeutic drugs in the present era.

Recent advances of imidazole-containing derivatives as anti-tubercular agents

Tuberculosis still remains one of the most common, communicable, and leading deadliest diseases known to mankind throughout the world. Drug-resistance in Mycobacterium tuberculosis which threatens to worsen the global tuberculosis epidemic has caused great concern in recent years. To overcome the resistance, the development of new drugs with novel mechanisms of actions is of great importance. Imidazole-containing derivatives endow with various biological properties, and some of them demonstrated excellent anti-tubercular activity. As the most emblematic example, 4-nitroimidazole delamanid has already received approval for treatment of multidrug-resistant tuberculosis infected patients. Thus, imidazole-containing derivatives have caused great interests in discovery of new anti-tubercular agents. Numerous of imidazole-containing derivatives were synthesized and screened for their in vitro and in vivo anti-mycobacterial activities against both drug-sensitive and drug-resistant Mycobacterium tuberculosis pathogens. This review aims to outline the recent advances of imidazole-containing derivatives as anti-tubercular agents, and summarize the structure-activity relationship of these derivatives. The enriched structure-activity relationship may pave the way for the further rational development of imidazole-containing derivatives as anti-tubercular agents.

Bactericidal effect of silver nanoparticles against propagation of Clavibacter michiganensis infection in Lycopersicon esculentum Mill

This study explored the use of silver nanoparticle as a bactericidal against the propagation of Clavibacter michiganensis onto tomatoes (Lycopersicon esculentum Mill). In Mexico, tomato production covers about 73% of the total vegetable production but it is affected by outbreak of bacteria canker caused by Clavibacter michiganensis subspecies michiganensis (Cmm). Silver ions possess inhibitor properties, bactericides and high specter antimicrobials. In this study, 6 groups of culture were prepared using 6 different petri dishes where silver nanoparticles of varying concentrations (120, 84, 48, 24, 12 and 0 μg) were added. Furthermore, each group was observed for 20 min, 1, 2, 12 and 24 h. The optimum concentration is 84 μg, which shows an average of 2 Cmm colonies after 20 min. Further increase to 120 μg shows no significant change. However, the average colonies was observed for 48 μg after 1, 2, 12, and 24 h. The obtained results indicate that silver nanoparticles are a promising inhibitor, bactericide and high a specter antimicrobial for treatment or prevention of Cmm.