Development of a Machine Learning-Based Cysticidal Assay and Identification of an Amebicidal and Cysticidal Marine Microbial Metabolite against Acanthamoeba

High-Throughput Screen of Microbial Metabolites Identifies F1FO ATP Synthase Inhibitors as New Leads for Naegleria fowleri Infection

Primary amebic meningoencephalitis (PAM), a brain infection caused by a free-living ameba Naegleria fowleri, leads to an extensive inflammation of the brain and death within 1-18 (median 5) days after symptoms begin. Although natural products have played a significant role in the development of drugs for over a century, research focusing on identifying new natural product-based anti-N. fowleri agents is limited. We undertook a large-scale ATP bioluminescence-based screen of about 10,000 unique marine microbial metabolite mixtures against the trophozoites of N. fowleri. Our screen identified about 100 test materials with >90% inhibition at 50 μg/mL and a dose-response study found 20 of these active test materials exhibiting an EC50 ranging from 0.2 to 2 μg/mL. Examination of four of these potent metabolite mixtures, derived from our actinomycete strains CNT671, CNT756, and CNH301, resulted in the isolation of a pure metabolite identified as oligomycin D. Oligomycin D exhibited nanomolar potency on multiple genotypes of N. fowleri, and it was five- or 850-times more potent than the recommended drugs amphotericin B or miltefosine. Oligomycin D is fast-acting and reached its EC50 in 10 h, and it was also able to inhibit the invasiveness of N. fowleri significantly when tested on a matrigel invasion assay. Since oligomycin is known to manifest inhibitory activity against F1FO ATP synthase, we tested different F1FO ATP synthase inhibitors and identified a natural peptide leucinostatin as a fast-acting amebicidal compound with nanomolar potency on multiple strains.

New Frontiers in Acanthamoeba Keratitis Diagnosis and Management

Acanthamoeba Keratitis (AK) is a severe corneal infection caused by the Acanthamoeba species of protozoa, potentially leading to permanent vision loss. AK requires prompt diagnosis and treatment to mitigate vision impairment. Diagnosing AK is challenging due to overlapping symptoms with other corneal infections, and treatment is made complicated by the organism's dual forms and increasing virulence, and delayed diagnosis. In this review, new approaches in AK diagnostics and treatment within the last 5 years are discussed. The English-language literature on PubMed was reviewed using the search terms "Acanthamoeba keratitis" and "diagnosis" or "treatment" and focused on studies published between 2018 and 2023. Two hundred sixty-five publications were initially identified, of which eighty-seven met inclusion and exclusion criteria. This review highlights the findings of these studies. Notably, advances in PCR-based diagnostics may be clinically implemented in the near future, while antibody-based and machine-learning approaches hold promise for the future. Single-drug topical therapy (0.08% PHMB) may improve drug access and efficacy, while oral medication (i.e., miltefosine) may offer a treatment option for patients with recalcitrant disease.

Malabaricones from the fruit of Myristica cinnamomea King as potential agents against Acanthamoeba castellanii

Acanthamoeba castellanii is an opportunistic free-living amoeba (FLA) pathogen which can cause fatal central nervous system (CNS) infection, granulomatous amoebic encephalitis (GAE) and potentially blinding ocular infection, Acanthamoeba keratitis (AK). Acanthamoeba species remain a challenging protist to treat due to the unavailability of safe and effective therapeutic drugs and their ability to protect themselves in the cyst stage. Natural products and their secondary metabolites play a pivotal role in drug discovery against various pathogenic microorganisms. In the present study, the ethyl acetate extract of Myristica cinnamomea King fruit was evaluated against A. castellanii (ATCC 50492), showing an IC50 of 45.102 ± 4.62 µg/mL. Previously, the bio-guided fractionation of the extract resulted in the identification of three active compounds, namely Malabaricones (A-C). The isolated and thoroughly characterized acylphenols were evaluated for their anti-amoebic activity against A. castellanii for the first time. Among tested compounds, Malabaricone B (IC50 of 101.31 ± 17.41 µM) and Malabaricone C (IC50 of 49.95 ± 6.33 µM) showed potent anti-amoebic activity against A. castellanii trophozoites and reduced their viability up-to 75 and 80 %, respectively. Moreover, both extract and Malabaricones also significantly (p < 0.05) inhibit the encystation and excystation of A. castellanii, while showed minimal toxicity against human keratinocyte cells (HaCaT cells) at lower tested concentrations. Following that, the explanation of the possible mechanism of action of purified compounds were assessed by detection of the state of chromatin. Hoechst/PI 33342 double staining showed that necrotic cell death occurred in A. castellanii trophozoites after 8 h treatment of Malabaricones (A-C). These findings demonstrate that Malabaricones B and C could serve as promising therapeutic options against A. castellanii infections.

A critical review of machine-learning for "multi-omics" marine metabolite datasets

During the last decade, genomic, transcriptomic, proteomic, metabolomic, and other omics datasets have been generated for a wide range of marine organisms, and even more are still on the way. Marine organisms possess unique and diverse biosynthetic pathways contributing to the synthesis of novel secondary metabolites with significant bioactivities. As marine organisms have a greater tendency to adapt to stressed environmental conditions, the chance to identify novel bioactive metabolites with potential biotechnological application is very high. This review presents a comprehensive overview of the available "-omics" and "multi-omics" approaches employed for characterizing marine metabolites along with novel data integration tools. The need for the development of machine-learning algorithms for "multi-omics" approaches is briefly discussed. In addition, the challenges involved in the analysis of "multi-omics" data and recommendations for conducting "multi-omics" study were discussed.