Exploring the Darobactin Class of Antibiotics: A Comprehensive Review from Discovery to Recent Advancements.

The prevalence of antimicrobial resistance in Gram-negative bacteria poses a greater challenge due to their intrinsic resistance to many antibiotics. Recently, darobactins have emerged as a novel class of antibiotics originating from previously unexplored Gram-negative bacterial species such as Photorhabdus, Vibrio, Pseudoalteromonas and Yersinia. Darobactins belong to the ribosomally synthesized and post-translationally modified peptide (RiPP) class of antibiotics, exhibiting selective activity against Gram-negative bacteria. They target the ß-barrel assembly machinery (BAM), which is crucial for the maturation and insertion of outer membrane proteins in Gram-negative bacteria. The dar operon in the producer's genome encodes for the synthesis of darobactins, which are characterized by a fused ring system connected via an alkyl-aryl ether linkage (C-O-C) and a C-C cross-link. The enzyme DarE, using the radical S-adenosyl-l-methionine (rSAM), facilitates the formation of these bonds. Biosynthetic manipulation of the darobactin gene cluster, along with its expression in a surrogate host, has enabled access to diverse darobactin analogues with variable antibiotic activities. Recently, two independent research groups successfully achieved the total synthesis of darobactin, employing Larock heteroannulation to construct the bicyclic structure. This paper presents a comprehensive review of darobactins, encompassing their discovery through to the most recent advancements.

Implications of Artificial Intelligence in Addressing Antimicrobial Resistance: Innovations, Global Challenges, and Healthcare's Future.

Antibiotic resistance poses a significant threat to global public health due to complex interactions between bacterial genetic factors and external influences such as antibiotic misuse. Artificial intelligence (AI) offers innovative strategies to address this crisis. For example, AI can analyze genomic data to detect resistance markers early on, enabling early interventions. In addition, AI-powered decision support systems can optimize antibiotic use by recommending the most effective treatments based on patient data and local resistance patterns. AI can accelerate drug discovery by predicting the efficacy of new compounds and identifying potential antibacterial agents. Although progress has been made, challenges persist, including data quality, model interpretability, and real-world implementation. A multidisciplinary approach that integrates AI with other emerging technologies, such as synthetic biology and nanomedicine, could pave the way for effective prevention and mitigation of antimicrobial resistance, preserving the efficacy of antibiotics for future generations.

A review on the diversity of antimicrobial peptides and genome mining strategies for their prediction.

Antibiotic resistance has become one of the most serious threats to human health in recent years. In response to the increasing microbial resistance to the antibiotics currently available, it is imperative to develop new antibiotics or explore new approaches to combat antibiotic resistance. Antimicrobial peptides (AMPs) have shown considerable promise in this regard, as the microbes develop low or no resistance against them. The discovery and development of AMPs still confront numerous obstacles such as finding a target, developing assays, and identifying hits and leads, which are time-consuming processes, making it difficult to reach the market. However, with the advent of genome mining, new antibiotics could be discovered efficiently using tools such as BAGEL, antiSMASH, RODEO, etc., providing hope for better treatment of diseases in the future. Computational methods used in genome mining automatically detect and annotate biosynthetic gene clusters in genomic data, making it a useful tool in natural product discovery. This review aims to shed light on the history, diversity, and mechanisms of action of AMPs and the data on new AMPs identified by traditional as well as genome mining strategies. It further substantiates the various phases of clinical trials for some AMPs, as well as an overview of genome mining databases and tools built expressly for AMP discovery. In light of the recent advancements, it is evident that targeted genome mining stands as a beacon of hope, offering immense potential to expedite the discovery of novel antimicrobials.

Discovery of antimicrobial peptides in the global microbiome with machine learning.

Novel antibiotics are urgently needed to combat the antibiotic-resistance crisis. We present a machine-learning-based approach to predict antimicrobial peptides (AMPs) within the global microbiome and leverage a vast dataset of 63,410 metagenomes and 87,920 prokaryotic genomes from environmental and host-associated habitats to create the AMPSphere, a comprehensive catalog comprising 863,498 non-redundant peptides, few of which match existing databases. AMPSphere provides insights into the evolutionary origins of peptides, including by duplication or gene truncation of longer sequences, and we observed that AMP production varies by habitat. To validate our predictions, we synthesized and tested 100 AMPs against clinically relevant drug-resistant pathogens and human gut commensals both in vitro and in vivo. A total of 79 peptides were active, with 63 targeting pathogens. These active AMPs exhibited antibacterial activity by disrupting bacterial membranes. In conclusion, our approach identified nearly one million prokaryotic AMP sequences, an open-access resource for antibiotic discovery.

Two natural compounds as potential inhibitors against the Helicobacter pylori and Acinetobacter baumannii IspD enzymes.

In a vast majority of bacteria, protozoa and plants, the methylerythritol phosphate (MEP) pathway is utilized for the synthesis of isopentenyl diphosphate (IDP) and dimethylallyl diphosphate (DMADP), which are precursors for isoprenoids. Isoprenoids, such as cholesterol and coenzyme Q, play a variety of crucial roles in physiological activities, including cell-membrane formation, protein degradation, cell apoptosis, and transcription regulation. In contrast, humans employ the mevalonate (MVA) pathway for the production of IDP and DMADP, rendering proteins in the MEP pathway appealing targets for antimicrobial agents. This pathway consists of seven consecutive enzymatic reactions, of which 4-diphosphocytidyl-2C-methyl-D-erythritol synthase (IspD) and 2C-methyl-D-erythritol 2,4-cyclodiphosphate synthase (IspF) catalyze the third and fifth steps, respectively. In this study, we characterized the enzymatic activities and protein structures of Helicobacter pylori IspDF and Acinetobacter baumannii IspD. Then, using the direct interaction-based thermal shift assay, we conducted a compound screening of an approved drug library and identified 27 hit compounds potentially binding to AbIspD. Among them, two natural products, rosmarinic acid and tanshinone IIA sodium sulfonate, exhibited inhibitory activities against HpIspDF and AbIspD, by competing with one of the substrates, MEP. Moreover, tanshinone IIA sodium sulfonate also demonstrated certain antibacterial effects against H. pylori. In summary, we identified two IspD inhibitors from approved ingredients, broadening the scope for antibiotic discovery targeting the MEP pathway.

Corrigendum: Antimicrobial resistance in the COVID-19 landscape: is there an opportunity for anti-infective antibodies and antimicrobial peptides?

[This corrects the article DOI: 10.3389/fimmu.2022.921483.].

Silicon versus Superbug: Assessing Machine Learning's Role in the Fight against Antimicrobial Resistance.

In his 1945 Nobel Prize acceptance speech, Sir Alexander Fleming warned of antimicrobial resistance (AMR) if the necessary precautions were not taken diligently. As the growing threat of AMR continues to loom over humanity, we must look forward to alternative diagnostic tools and preventive measures to thwart looming economic collapse and untold mortality worldwide. The integration of machine learning (ML) methodologies within the framework of such tools/pipelines presents a promising avenue, offering unprecedented insights into the underlying mechanisms of resistance and enabling the development of more targeted and effective treatments. This paper explores the applications of ML in predicting and understanding AMR, highlighting its potential in revolutionizing healthcare practices. From the utilization of supervised-learning approaches to analyze genetic signatures of antibiotic resistance to the development of tools and databases, such as the Comprehensive Antibiotic Resistance Database (CARD), ML is actively shaping the future of AMR research. However, the successful implementation of ML in this domain is not without challenges. The dependence on high-quality data, the risk of overfitting, model selection, and potential bias in training data are issues that must be systematically addressed. Despite these challenges, the synergy between ML and biomedical research shows great promise in combating the growing menace of antibiotic resistance.

Biosensor-guided detection of outer membrane-specific antimicrobial activity against Pseudomonas aeruginosa from fungal cultures and medicinal plant extracts.

IMPORTANCE: New approaches are needed to discover novel antimicrobials, particularly antibiotics that target the Gram-negative outer membrane. By exploiting bacterial sensing and responses to outer membrane (OM) damage, we used a biosensor approach consisting of polymyxin resistance gene transcriptional reporters to screen natural products and a small drug library for biosensor activity that indicates damage to the OM. The diverse antimicrobial compounds that cause induction of the polymyxin resistance genes, which correlates with outer membrane damage, suggest that these LPS and surface modifications also function in short-term repair to sublethal exposure and are required against broad membrane stress conditions.

Adapting antibacterial display to identify serum-active macrocyclic peptide antibiotics.

The lack of available treatments for many antimicrobial-resistant infections highlights the critical need for antibiotic discovery innovation. Peptides are an underappreciated antibiotic scaffold because they often suffer from proteolytic instability and toxicity toward human cells, making in vivo use challenging. To investigate sequence factors related to serum activity, we adapt an antibacterial display technology to screen a library of peptide macrocycles for antibacterial potential directly in human serum. We identify dozens of new macrocyclic peptide antibiotic sequences and find that serum activity within our library is influenced by peptide length, cationic charge, and the number of disulfide bonds present. Interestingly, an optimized version of our most active lead peptide permeates the outer membrane of Gram-negative bacteria without strong inner-membrane disruption and kills bacteria slowly while causing cell elongation. This contrasts with traditional cationic antimicrobial peptides, which kill rapidly via lysis of both bacterial membranes. Notably, this optimized variant is not toxic to mammalian cells and retains its function in vivo, suggesting therapeutic promise. Our results support the use of more physiologically relevant conditions when screening peptides for antimicrobial activity which retain in vivo functionality.

Repurposing sunscreen as an antibiotic: zinc-activated avobenzone inhibits methicillin-resistant Staphylococcus aureus.

Methicillin-resistant Staphylococcus aureus (MRSA) is a major healthcare concern with associated healthcare costs reaching over ${\$}$1 billion in a single year in the USA. Antibiotic resistance in S. aureus is now observed against last line of defense antibiotics, such as vancomycin, linezolid, and daptomycin. Unfortunately, high throughput drug discovery approaches to identify new antibiotics effective against MRSA have not resulted in much tangible success over the last decades. Previously, we demonstrated the feasibility of an alternative drug discovery approach, the identification of metallo-antibiotics, compounds that gain antibacterial activity only after binding to a transition metal ion and as such are unlikely to be detected in standard drug screens. We now report that avobenzone, the primary active ingredient of most sunscreens, can be activated by zinc to become a potent antibacterial compound against MRSA. Zinc-activated avobenzone (AVB-Zn) potently inhibited a series of clinical MRSA isolates [minimal inhibitory concentration (MIC): 0.62-2.5 µM], without pre-existing resistance and activity without zinc (MIC: >10 µM). AVB-Zn was also active against clinical MRSA isolates that were resistant against the commonly used zinc-salt antibiotic bacitracin. We found AVB-Zn exerted no cytotoxicity on human cell lines and primary cells. Last, we demonstrate AVB-Zn can be deployed therapeutically as lotion preparations, which showed efficacy in a mouse wound model of MRSA infection. AVB-Zn thus demonstrates Zn-activated metallo-antibiotics are a promising avenue for future drug discovery.

Making a chink in their armor: Current and next-generation antimicrobial strategies against the bacterial cell envelope.

Gram-negative bacteria are uniquely equipped to defeat antibiotics. Their outermost layer, the cell envelope, is a natural permeability barrier that contains an array of resistance proteins capable of neutralizing most existing antimicrobials. As a result, its presence creates a major obstacle for the treatment of resistant infections and for the development of new antibiotics. Despite this seemingly impenetrable armor, in-depth understanding of the cell envelope, including structural, functional and systems biology insights, has promoted efforts to target it that can ultimately lead to the generation of new antibacterial therapies. In this article, we broadly overview the biology of the cell envelope and highlight attempts and successes in generating inhibitors that impair its function or biogenesis. We argue that the very structure that has hampered antibiotic discovery for decades has untapped potential for the design of novel next-generation therapeutics against bacterial pathogens.

Whole genomes of deep-sea sponge-associated bacteria exhibit high novel natural product potential.

Global antimicrobial resistance is a health crisis that can change the face of modern medicine. Exploring diverse natural habitats for bacterially-derived novel antimicrobial compounds has historically been a successful strategy. The deep-sea presents an exciting opportunity for the cultivation of taxonomically novel organisms and exploring potentially chemically novel spaces. In this study, the draft genomes of 12 bacteria previously isolated from the deep-sea sponges Phenomena carpenteri and Hertwigia sp. are investigated for the diversity of specialized secondary metabolites. In addition, early data support the production of antibacterial inhibitory substances produced from a number of these strains, including activity against clinically relevant pathogens Acinetobacter baumannii, Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, and Staphylococcus aureus. Draft whole-genomes are presented of 12 deep-sea isolates, which include four potentially novel strains: Psychrobacter sp. PP-21, Streptomyces sp. DK15, Dietzia sp. PP-33, and Micrococcus sp. M4NT. Across the 12 draft genomes, 138 biosynthetic gene clusters were detected, of which over half displayed less than 50% similarity to known BGCs, suggesting that these genomes present an exciting opportunity to elucidate novel secondary metabolites. Exploring bacterial isolates belonging to the phylum Actinomycetota, Pseudomonadota, and Bacillota from understudied deep-sea sponges provided opportunities to search for new chemical diversity of interest to those working in antibiotic discovery.

Dynamics-Based Regulatory Switches of Type II Antitoxins: Insights into New Antimicrobial Discovery.

Type II toxin-antitoxin (TA) modules are prevalent in prokaryotes and are involved in cell maintenance and survival under harsh environmental conditions, including nutrient deficiency, antibiotic treatment, and human immune responses. Typically, the type II TA system consists of two protein components: a toxin that inhibits an essential cellular process and an antitoxin that neutralizes its toxicity. Antitoxins of type II TA modules typically contain the structured DNA-binding domain responsible for TA transcription repression and an intrinsically disordered region (IDR) at the C-terminus that directly binds to and neutralizes the toxin. Recently accumulated data have suggested that the antitoxin's IDRs exhibit variable degrees of preexisting helical conformations that stabilize upon binding to the corresponding toxin or operator DNA and function as a central hub in regulatory protein interaction networks of the type II TA system. However, the biological and pathogenic functions of the antitoxin's IDRs have not been well discussed compared with those of IDRs from the eukaryotic proteome. Here, we focus on the current state of knowledge about the versatile roles of IDRs of type II antitoxins in TA regulation and provide insights into the discovery of new antibiotic candidates that induce toxin activation/reactivation and cell death by modulating the regulatory dynamics or allostery of the antitoxin.

Nutrient stress is a target for new antibiotics.

Novel approaches are required to address the looming threat of pan-resistant Gram-negative pathogens and forestall the rise of untreatable infections. Unconventional targets that are uniquely important during infection and tractable to high-throughput drug discovery methods hold high potential for innovation in antibiotic discovery programs. In this context, inhibitors of bacterial nutrient stress are particularly exciting candidates for future antibiotic development. Amino acid, nucleotide, and vitamin biosynthesis pathways are critical for bacterial growth in nutrient-limiting conditions in the laboratory and the host. Although historically dismissed as dispensable for pathogens, a wealth of transposon mutagenesis and single-mutant studies have emerged which demonstrate that several such pathways are critical for infection. Indeed, high-throughput screens of diverse synthetic compounds and natural products have uncovered inhibitors of nutrient biosynthesis. Herein, we review bacterial nutrient biosynthesis and its role during host infection. Further, we explore screening platforms developed to search for inhibitors of these targets and highlight successes among these. Finally, we feature important and sometimes surprising connections between bacterial nutrient biosynthesis, antibiotic activity, and antibiotic resistance.

Deep Learning and Antibiotic Resistance.

Antibiotic resistance (AR) is a naturally occurring phenomenon with the capacity to render useless all known antibiotics in the fight against bacterial infections. Although bacterial resistance appeared before any human life form, this process has accelerated in the past years. Important causes of AR in modern times could be the over-prescription of antibiotics, the presence of faulty infection-prevention strategies, pollution in overcrowded areas, or the use of antibiotics in agriculture and farming, together with a decreased interest from the pharmaceutical industry in researching and testing new antibiotics. The last cause is primarily due to the high costs of developing antibiotics. The aim of the present review is to highlight the techniques that are being developed for the identification of new antibiotics to assist this lengthy process, using artificial intelligence (AI). AI can shorten the preclinical phase by rapidly generating many substances based on algorithms created by machine learning (ML) through techniques such as neural networks (NN) or deep learning (DL). Recently, a text mining system that incorporates DL algorithms was used to help and speed up the data curation process. Moreover, new and old methods are being used to identify new antibiotics, such as the combination of quantitative structure-activity relationship (QSAR) methods with ML or Raman spectroscopy and MALDI-TOF MS combined with NN, offering faster and easier interpretation of results. Thus, AI techniques are important additional tools for researchers and clinicians in the race for new methods of overcoming bacterial resistance.

Direct Detection of Antibacterial-Producing Soil Isolates Utilizing a Novel High-Throughput Screening Assay.

The ever-increasing global threat of common infections developing resistance to current therapeutics is rapidly accelerating the onset of a primitive post-antibiotic era in medicine. The prevention of further antimicrobial resistance development is unlikely due to the continued misuse of antibiotics, augmented by the lack of discovery of novel antibiotics. Screening large libraries of synthetic compounds have yet to offer effective replacements for current antibiotics. Due to historical successes, discovery from large and diverse natural sources and, more specifically, environmental bacteria, may still yield novel alternative antibiotics. However, the process of antibiotic discovery from natural sources is laborious and time-consuming as a result of outdated methodologies. Therefore, we have developed a simple and rapid preliminary screening assay to identify antibacterial-producing bacteria from natural sources. In brief, the assay utilizes the presence or absence of luminescence in bioluminescent reporter bacteria and test bacterium co-cultures in a 96-well plate format to determine the absence or presence of antibacterial compound production. Our assay, called the bioluminescent simultaneous antagonism (BSLA) assay, can accurately distinguish between known antibacterial-producing and non-producing test bacteria. The BSLA assay was validated by screening 264 unknown soil isolates which resulted in the identification of 10 antibacterial-producing isolates, effectively decreasing the pool of isolates for downstream analysis by 96%. By design, the assay is simple and requires only general laboratory equipment; however, we have shown that the assay can be scaled to automated high-throughput screening systems. Taken together, the BSLA assay allows for the rapid pre-screening of unknown bacterial isolates which, when coupled with innovative downstream dereplication and identification technologies, can effectively fast-track antimicrobial discovery.

L007-0069 kills Staphylococcus aureus in high resistant phenotypes.

Staphylococcus aureus, a common gram-positive pathogenic bacterium, is a main cause of hospital infection. The prevalence rate of methicillin-resistant S. aureus (MRSA) has made its treatment difficult in recent decades. Moreover, S. aureus in the highly tolerant format of biofilm or persister often renders infections refractory. Thus, developing new active compounds against resistant S. aureus is urgently needed. In this study, by a high-throughput screening assay, we identified a small molecule, L007-0069, that exhibited strong and effective bactericidal activity against S. aureus and its high resistance patterns, such as biofilms and persisters, with a low probability of inducing resistance. By molecular dynamics and fluorescent probe analysis, mechanistic studies revealed that the bactericidal activity of L007-0069 was mainly mediated by membrane disruption and metabolic disorder induction. Furthermore, L007-0069 showed effective anti-MRSA effects in vivo in both a wound infection model and a peritonitis-sepsis model, with no detectable toxicity observed at the therapeutic dosage. In conclusion, L007-0069 has the potential to become an alternative for the treatment of highly resistant S. aureus-related infections.

Terrien, a metabolite made by Aspergillus terreus, has activity against Cryptococcus neoformans.

Antimicrobial compounds, including antibiotics, have been a cornerstone of modern medicine being able to both treat infections and prevent infections in at-risk people, including those who are immune-compromised and those undergoing routine surgical procedures. Their intense use, including in people, animals, and plants, has led to an increase in the incidence of resistant bacteria and fungi, resulting in a desperate need for novel antimicrobial compounds with new mechanisms of action. Many antimicrobial compounds in current use originate from microbial sources, such as penicillin from the fungus Penicillium chrysogenum (renamed by some as P. rubens). Through a collaboration with Aotearoa New Zealand Crown Research Institute Manaaki Whenua-Landcare Research we have access to a collection of thousands of fungal cultures known as the International Collection of Microorganisms from Plants (ICMP). The ICMP contains both known and novel species which have not been extensively tested for their antimicrobial activity. Initial screening of ICMP isolates for activity against Escherichia coli and Staphylococcus aureus directed our interest towards ICMP 477, an isolate of the soil-inhabiting fungus, Aspergillus terreus. In our investigation of the secondary metabolites of A. terreus, through extraction, fractionation, and purification, we isolated nine known natural products. We evaluated the biological activity of selected compounds against various bacteria and fungi and discovered that terrein (1) has potent activity against the important human pathogen Cryptococcus neoformans.

Structure-Based Ligand Design Targeting Pseudomonas aeruginosa LpxA in Lipid A Biosynthesis.

Enzymes involved in lipid A biosynthesis are promising antibacterial drug targets in Gram-negative bacteria. In this study, we use a structure-based design approach to develop a series of novel tetrazole ligands with low µM affinity for LpxA, the first enzyme in the lipid A pathway. Aided by previous structural data, X-ray crystallography, and surface plasmon resonance bioanalysis, we identify 17 hit compounds. Two of these hits were subsequently modified to optimize interactions with three regions of the LpxA active site. This strategy ultimately led to the discovery of ligand L13, which had a KD of 3.0 µM. The results reveal new chemical scaffolds as potential LpxA inhibitors, important binding features for ligand optimization, and protein conformational changes in response to ligand binding. Specifically, they show that a tetrazole ring is well-accommodated in a small cleft formed between Met169, the "hydrophobic-ruler" and His156, both of which demonstrate significant conformational flexibility. Furthermore, we find that the acyl-chain binding pocket is the most tractable region of the active site for realizing affinity gains and, along with a neighboring patch of hydrophobic residues, preferentially binds aliphatic and aromatic groups. The results presented herein provide valuable chemical and structural information for future inhibitor discovery against this important antibacterial drug target.

Antimicrobial Resistance in the COVID-19 Landscape: Is There an Opportunity for Anti-Infective Antibodies and Antimicrobial Peptides?

Although COVID-19 has captured most of the public health attention, antimicrobial resistance (AMR) has not disappeared. To prevent the escape of resistant microorganisms in animals or environmental reservoirs a "one health approach" is desirable. In this context of COVID-19, AMR has probably been affected by the inappropriate or over-use of antibiotics. The increased use of antimicrobials and biocides for disinfection may have enhanced the prevalence of AMR. Antibiotics have been used empirically in patients with COVID-19 to avoid or prevent bacterial coinfection or superinfections. On the other hand, the measures to prevent the transmission of COVID-19 could have reduced the risk of the emergence of multidrug-resistant microorganisms. Since we do not currently have a sterilizing vaccine against SARS-CoV-2, the virus may still multiply in the organism and new mutations may occur. As a consequence, there is a risk of the appearance of new variants. Nature-derived anti-infective agents, such as antibodies and antimicrobial peptides (AMPs), are very promising in the fight against infectious diseases, because they are less likely to develop resistance, even though further investigation is still required.