Molecular Dynamics for Antimicrobial Peptide Discovery.

Although antimicrobial resistance is an increasingly significant public health concern, there have only been two new classes of antibiotics approved for human use since the 1960s. Understanding the mechanisms of action of antibiotics is critical for novel antibiotic discovery, but novel approaches are needed that do not exclusively rely on experiments. Molecular dynamics simulation is a computational tool that uses simple models of the atoms in a system to discover nanoscale insights into the dynamic relationship between mechanism and biological function. Such insights can lay the framework for elucidating the mechanism of action and optimizing antibiotic templates. Antimicrobial peptides represent a promising solution to escalating antimicrobial resistance, given their lesser tendency to induce resistance than that of small-molecule antibiotics. Simulations of these agents have already revealed how they interact with bacterial membranes and the underlying physiochemical features directing their structure and function. In this minireview, we discuss how traditional molecular dynamics simulation works and its role and potential for the development of new antibiotic candidates with an emphasis on antimicrobial peptides.

Learning Phenotypic Associations for Parkinson's Disease with Longitudinal Clinical Records.

Parkinson's disease (PD) is associated with multiple clinical motor and non-motor manifestations. Understanding of PD etiologies has been informed by a growing number of genetic mutations and various fluid-based and brain imaging biomarkers. However, the mechanisms underlying its varied phenotypic features remain elusive. The present work introduces a data-driven approach for generating phenotypic association graphs for PD cohorts. Data collected by the Parkinson's Progression Markers Initiative (PPMI), the Parkinson's Disease Biomarkers Program (PDBP), and the Fox Investigation for New Discovery of Biomarkers (BioFIND) were analyzed by this approach to identify heterogeneous and longitudinal phenotypic associations that may provide insight into the pathology of this complex disease. Findings based on the phenotypic association graphs could improve understanding of longitudinal PD pathologies and how these relate to patient symptomology.

Identification of Parkinson's disease PACE subtypes and repurposing treatments through integrative analyses of multimodal data.

Parkinson's disease (PD) is a serious neurodegenerative disorder marked by significant clinical and progression heterogeneity. This study aimed at addressing heterogeneity of PD through integrative analysis of various data modalities. We analyzed clinical progression data (≥5 years) of individuals with de novo PD using machine learning and deep learning, to characterize individuals' phenotypic progression trajectories for PD subtyping. We discovered three pace subtypes of PD exhibiting distinct progression patterns: the Inching Pace subtype (PD-I) with mild baseline severity and mild progression speed; the Moderate Pace subtype (PD-M) with mild baseline severity but advancing at a moderate progression rate; and the Rapid Pace subtype (PD-R) with the most rapid symptom progression rate. We found cerebrospinal fluid P-tau/α-synuclein ratio and atrophy in certain brain regions as potential markers of these subtypes. Analyses of genetic and transcriptomic profiles with network-based approaches identified molecular modules associated with each subtype. For instance, the PD-R-specific module suggested STAT3, FYN, BECN1, APOA1, NEDD4, and GATA2 as potential driver genes of PD-R. It also suggested neuroinflammation, oxidative stress, metabolism, PI3K/AKT, and angiogenesis pathways as potential drivers for rapid PD progression (i.e., PD-R). Moreover, we identified repurposable drug candidates by targeting these subtype-specific molecular modules using network-based approach and cell line drug-gene signature data. We further estimated their treatment effects using two large-scale real-world patient databases; the real-world evidence we gained highlighted the potential of metformin in ameliorating PD progression. In conclusion, this work helps better understand clinical and pathophysiological complexity of PD progression and accelerate precision medicine.

Molecular de-extinction of ancient antimicrobial peptides enabled by machine learning.

Molecular de-extinction could offer avenues for drug discovery by reintroducing bioactive molecules that are no longer encoded by extant organisms. To prospect for antimicrobial peptides encrypted within extinct and extant human proteins, we introduce the panCleave random forest model for proteome-wide cleavage site prediction. Our model outperformed multiple protease-specific cleavage site classifiers for three modern human caspases, despite its pan-protease design. Antimicrobial activity was observed in vitro for modern and archaic protein fragments identified with panCleave. Lead peptides showed resistance to proteolysis and exhibited variable membrane permeabilization. Additionally, representative modern and archaic protein fragments showed anti-infective efficacy against A. baumannii in both a skin abscess infection model and a preclinical murine thigh infection model. These results suggest that machine-learning-based encrypted peptide prospection can identify stable, nontoxic peptide antibiotics. Moreover, we establish molecular de-extinction through paleoproteome mining as a framework for antibacterial drug discovery.

Biomedical discovery through the integrative biomedical knowledge hub (iBKH).

The abundance of biomedical knowledge gained from biological experiments and clinical practices is an invaluable resource for biomedicine. The emerging biomedical knowledge graphs (BKGs) provide an efficient and effective way to manage the abundant knowledge in biomedical and life science. In this study, we created a comprehensive BKG called the integrative Biomedical Knowledge Hub (iBKH) by harmonizing and integrating information from diverse biomedical resources. To make iBKH easily accessible for biomedical research, we developed a web-based, user-friendly graphical portal that allows fast and interactive knowledge retrieval. Additionally, we also implemented an efficient and scalable graph learning pipeline for discovering novel biomedical knowledge in iBKH. As a proof of concept, we performed our iBKH-based method for computational in-silico drug repurposing for Alzheimer's disease. The iBKH is publicly available.

Accelerating antibiotic discovery through artificial intelligence.

By targeting invasive organisms, antibiotics insert themselves into the ancient struggle of the host-pathogen evolutionary arms race. As pathogens evolve tactics for evading antibiotics, therapies decline in efficacy and must be replaced, distinguishing antibiotics from most other forms of drug development. Together with a slow and expensive antibiotic development pipeline, the proliferation of drug-resistant pathogens drives urgent interest in computational methods that promise to expedite candidate discovery. Strides in artificial intelligence (AI) have encouraged its application to multiple dimensions of computer-aided drug design, with increasing application to antibiotic discovery. This review describes AI-facilitated advances in the discovery of both small molecule antibiotics and antimicrobial peptides. Beyond the essential prediction of antimicrobial activity, emphasis is also given to antimicrobial compound representation, determination of drug-likeness traits, antimicrobial resistance, and de novo molecular design. Given the urgency of the antimicrobial resistance crisis, we analyze uptake of open science best practices in AI-driven antibiotic discovery and argue for openness and reproducibility as a means of accelerating preclinical research. Finally, trends in the literature and areas for future inquiry are discussed, as artificially intelligent enhancements to drug discovery at large offer many opportunities for future applications in antibiotic development.

Rectal Swabs as an Alternative Sample Collection Method to Bulk Stool for the Real-Time PCR Detection of Giardia duodenalis.

Though bulk stool remains the gold standard specimen type for enteropathogen diagnosis, rectal swabs may offer comparable sensitivity with greater ease of collection for select pathogens. This study sought to evaluate the validity and reproducibility of rectal swabs as a sample collection method for the molecular diagnosis of Giardia duodenalis. Paired rectal swab and bulk stool samples were collected from 86 children ages 0-4 years living in southwest Niger, with duplicate samples collected among a subset of 50 children. Infection was detected using a previously validated real-time PCR diagnostic targeting the small subunit ribosomal RNA gene. Giardia duodenalis was detected in 65.5% (55/84) of bulk stool samples and 44.0% (37/84) of swab samples. The kappa evaluating test agreement was 0.81 (95% CI: 0.54-1.00) among duplicate stool samples (N = 49) and 0.75 (95% CI: 0.47-1.00) among duplicate rectal swabs (N = 48). Diagnostic sensitivity was 93% (95% CI: 84-98) by bulk stool and 63% (95% CI: 49-75) by rectal swabs. When restricting to the lowest three quartiles of bulk stool quantitation cycle values (an indication of relatively high parasite load), sensitivity by rectal swabs increased to 78.0% (95% CI: 64-89, P < 0.0001). These findings suggest that rectal swabs provide less sensitive and reproducible results than bulk stool for the real-time PCR diagnosis of G. duodenalis. However, their fair sensitivity for higher parasite loads suggests that swabs may be a useful tool for detecting higher burden infections when stool collection is excessively expensive or logistically challenging.

Comparison of multi-parallel qPCR and double-slide Kato-Katz for detection of soil-transmitted helminth infection among children in rural Bangladesh.

There is growing interest in local elimination of soil-transmitted helminth (STH) infection in endemic settings. In such settings, highly sensitive diagnostics are needed to detect STH infection. We compared double-slide Kato-Katz, the most commonly used copromicroscopic detection method, to multi-parallel quantitative polymerase chain reaction (qPCR) in 2,799 stool samples from children aged 2-12 years in a setting in rural Bangladesh with predominantly low STH infection intensity. We estimated the sensitivity and specificity of each diagnostic using Bayesian latent class analysis. Compared to double-slide Kato-Katz, STH prevalence using qPCR was almost 3-fold higher for hookworm species and nearly 2-fold higher for Trichuris trichiura. Ascaris lumbricoides prevalence was lower using qPCR, and 26% of samples classified as A. lumbricoides positive by Kato-Katz were negative by qPCR. Amplicon sequencing of the 18S rDNA from 10 samples confirmed that A. lumbricoides was absent in samples classified as positive by Kato-Katz and negative by qPCR. The sensitivity of Kato-Katz was 49% for A. lumbricoides, 32% for hookworm, and 52% for T. trichiura; the sensitivity of qPCR was 79% for A. lumbricoides, 93% for hookworm, and 90% for T. trichiura. Specificity was ≥ 97% for both tests for all STH except for Kato-Katz for A. lumbricoides (specificity = 68%). There were moderate negative, monotonic correlations between qPCR cycle quantification values and eggs per gram quantified by Kato-Katz. While it is widely assumed that double-slide Kato-Katz has few false positives, our results indicate otherwise and highlight inherent limitations of the Kato-Katz technique. qPCR had higher sensitivity than Kato-Katz in this low intensity infection setting.

What does soil-transmitted helminth elimination look like? Results from a targeted molecular detection survey in Japan.

BACKGROUND: Japan is one of the few countries believed to have eliminated soil-transmitted helminths (STHs). In 1949, the national prevalence of Ascaris lumbricoides was 62.9%, which decreased to 0.6% in 1973 due to improvements in infrastructure, socioeconomic status, and the implementation of national STH control measures. The Parasitosis Prevention Law ended in 1994 and population-level screening ceased in Japan; therefore, current transmission status of STH in Japan is not well characterized. Sporadic cases of STH infections continue to be reported, raising the possibility of a larger-scale recrudescence of STH infections. Given that traditional microscopic detection methods are not sensitive to low-intensity STH infections, we conducted targeted prevalence surveys using sensitive PCR-based assays to evaluate the current STH-transmission status and to describe epidemiological characteristics of areas of Japan believed to have achieved historical elimination of STHs. METHODS: Stool samples were collected from 682 preschool- and school-aged children from six localities of Japan with previously high prevalence of STH. Caregivers of participants completed a questionnaire to ascertain access to water, sanitation and hygiene (WASH), and potential exposures to environmental contamination. For fecal testing, multi-parallel real-time PCR assays were used to detect infections of Ascaris lumbricoides, Necator americanus, Ancylostoma duodenale and Trichuris trichiura. RESULTS: Among the 682 children, no positive samples were identified, and participants reported high standards of WASH. CONCLUSIONS: To our knowledge, this is the first STH-surveillance study in Japan to use sensitive molecular techniques for STH detection. The results suggest that recrudescence of STH infections has not occurred, and that declines in prevalence have been sustained in the sampled areas. These findings suggest that reductions in prevalence below the elimination thresholds, suggestive of transmission interruption, are possible. Additionally, this study provides circumstantial evidence that multi-parallel real-time PCR methods are applicable for evaluating elimination status in areas where STH prevalence is extremely low.

Targeting a highly repeated germline DNA sequence for improved real-time PCR-based detection of Ascaris infection in human stool.

BACKGROUND: With the expansion of soil transmitted helminth (STH) intervention efforts and the corresponding decline in infection prevalence, there is an increased need for sensitive and specific STH diagnostic assays. Previously, through next generation sequencing (NGS)-based identification and targeting of non-coding, high copy-number repetitive DNA sequences, we described the development of a panel of improved quantitative real-time PCR (qPCR)-based assays for the detection of Necator americanus, Ancylostoma duodenale, Ancylostoma ceylanicum, Trichuris trichiura, and Strongyloides stercoralis. However, due to the phenomenon of chromosome diminution, a similar assay based on high copy-number repetitive DNA was not developed for the detection of Ascaris lumbricoides. Recently, the publication of a reference-level germline genome sequence for A. lumbricoides has facilitated our development of an improved assay for this human pathogen of vast global importance. METHODOLOGY/PRINCIPAL FINDINGS: Repurposing raw DNA sequence reads from a previously published Illumina-generated, NGS-based A. lumbricoides germline genome sequencing project, we performed a cluster-based repeat analysis utilizing RepeatExplorer2 software. This analysis identified the most prevalent repetitive DNA element of the A. lumbricoides germline genome (AGR, Ascaris germline repeat), which was then used to develop an improved qPCR assay. During experimental validation, this assay demonstrated a fold increase in sensitivity of ~3,100, as determined by relative Cq values, when compared with an assay utilizing a previously published, frequently employed, ribosomal internal transcribed spacer (ITS) DNA target. A comparative analysis of 2,784 field-collected samples was then performed, successfully verifying this improved sensitivity. CONCLUSIONS/SIGNIFICANCE: Through analysis of the germline genome sequence of A. lumbricoides, a vastly improved qPCR assay has been developed. This assay, utilizing a high copy-number repeat target found in eggs and embryos (the AGR repeat), will improve prevalence estimates that are fundamental to the programmatic decision-making process, while simultaneously strengthening mathematical models used to examine STH infection rates. Furthermore, through the identification of an optimal target for PCR, future assay development efforts will also benefit, as the identity of the optimized repeat DNA target is likely to remain unchanged despite continued improvement in PCR-based diagnostic technologies.