Unexpected Predictors of Antibiotic Resistance in Housekeeping Genes of Staphylococcus Aureus

Staphylococcus aureus epidemiology, pathophysiology, clinical manifestations and application of nano-therapeutics as a promising approach to combat methicillin resistant Staphylococcus aureus

Staphylococcus aureus is a Gram-positive bacterium and one of the most prevalent infectious disease-related causes of morbidity and mortality in adults. This pathogen can trigger a broad spectrum of diseases, from sepsis and pneumonia to severe skin infections that can be fatal. In this review, we will provide an overview of S. aureus and discuss the extensive literature on epidemiology, transmission, genetic diversity, evolution and antibiotic resistance strains, particularly methicillin resistant S. aureus (MRSA). While many different virulence factors that S. aureus produces have been investigated as therapeutic targets, this review examines recent nanotechnology approaches, which employ materials with atomic or molecular dimensions and are being used to diagnose, treat, or eliminate the activity of S. aureus. Finally, having a deeper understanding and clearer grasp of the roles and contributions of S. aureus determinants, antibiotic resistance, and nanotechnology will aid us in developing anti-virulence strategies to combat the growing scarcity of effective antibiotics against S. aureus.

The K-mer antibiotic resistance gene variant analyzer (KARGVA)

Characterization of antibiotic resistance genes (ARGs) from high-throughput sequencing data of metagenomics and cultured bacterial samples is a challenging task, with the need to account for both computational (e.g., string algorithms) and biological (e.g., gene transfers, rearrangements) aspects. Curated ARG databases exist together with assorted ARG classification approaches (e.g., database alignment, machine learning). Besides ARGs that naturally occur in bacterial strains or are acquired through mobile elements, there are chromosomal genes that can render a bacterium resistant to antibiotics through point mutations, i.e., ARG variants (ARGVs). While ARG repositories also collect ARGVs, there are only a few tools that are able to identify ARGVs from metagenomics and high throughput sequencing data, with a number of limitations (e.g., pre-assembly, a posteriori verification of mutations, or specification of species). In this work we present the k-mer, i.e., strings of fixed length k, ARGV analyzer - KARGVA - an open-source, multi-platform tool that provides: (i) an ad hoc, large ARGV database derived from multiple sources; (ii) input capability for various types of high-throughput sequencing data; (iii) a three-way, hash-based, k-mer search setup to process data efficiently, linking k-mers to ARGVs, k-mers to point mutations, and ARGVs to k-mers, respectively; (iv) a statistical filter on sequence classification to reduce type I and II errors. On semi-synthetic data, KARGVA provides very high accuracy even in presence of high sequencing errors or mutations (99.2 and 86.6% accuracy within 1 and 5% base change rates, respectively), and genome rearrangements (98.2% accuracy), with robust performance on ad hoc false positive sets. On data from the worldwide MetaSUB consortium, comprising 3,700+ metagenomics experiments, KARGVA identifies more ARGVs than Resistance Gene Identifier (4.8x) and PointFinder (6.8x), yet all predictions are below the expected false positive estimates. The prevalence of ARGVs is correlated to ARGs but ecological characteristics do not explain well ARGV variance. KARGVA is publicly available at https://github.com/DataIntellSystLab/KARGVA under MIT license.

KARGAMobile: Android app for portable, real-time, easily interpretable analysis of antibiotic resistance genes via nanopore sequencing

Nanopore technology enables portable, real-time sequencing of microbial populations from clinical and ecological samples. An emerging healthcare application for Nanopore includes point-of-care, timely identification of antibiotic resistance genes (ARGs) to help developing targeted treatments of bacterial infections, and monitoring resistant outbreaks in the environment. While several computational tools exist for classifying ARGs from sequencing data, to date (2022) none have been developed for mobile devices. We present here KARGAMobile, a mobile app for portable, real-time, easily interpretable analysis of ARGs from Nanopore sequencing. KARGAMobile is the porting of an existing ARG identification tool named KARGA; it retains the same algorithmic structure, but it is optimized for mobile devices. Specifically, KARGAMobile employs a compressed ARG reference database and different internal data structures to save RAM usage. The KARGAMobile app features a friendly graphical user interface that guides through file browsing, loading, parameter setup, and process execution. More importantly, the output files are post-processed to create visual, printable and shareable reports, aiding users to interpret the ARG findings. The difference in classification performance between KARGAMobile and KARGA is minimal (96.2% vs. 96.9% f-measure on semi-synthetic datasets of 1 million reads with known resistance ground truth). Using real Nanopore experiments, KARGAMobile processes on average 1 GB data every 23-48 min (targeted sequencing - metagenomics), with peak RAM usage below 500MB, independently from input file sizes, and an average temperature of 49°C after 1 h of continuous data processing. KARGAMobile is written in Java and is available at https://github.com/Ruiz-HCI-Lab/KargaMobile under the MIT license.

Towards routine employment of computational tools for antimicrobial resistance determination via high-throughput sequencing

Antimicrobial resistance (AMR) is a growing threat to public health and farming at large. In clinical and veterinary practice, timely characterization of the antibiotic susceptibility profile of bacterial infections is a crucial step in optimizing treatment. High-throughput sequencing is a promising option for clinical point-of-care and ecological surveillance, opening the opportunity to develop genotyping-based AMR determination as a possibly faster alternative to phenotypic testing. In the present work, we compare the performance of state-of-the-art methods for detection of AMR using high-throughput sequencing data from clinical settings. We consider five computational approaches based on alignment (AMRPlusPlus), deep learning (DeepARG), k-mer genomic signatures (KARGA, ResFinder) or hidden Markov models (Meta-MARC). We use an extensive collection of 585 isolates with available AMR resistance profiles determined by phenotypic tests across nine antibiotic classes. We show how the prediction landscape of AMR classifiers is highly heterogeneous, with balanced accuracy varying from 0.40 to 0.92. Although some algorithms-ResFinder, KARGA and AMRPlusPlus-exhibit overall better balanced accuracy than others, the high per-AMR-class variance and related findings suggest that: (1) all algorithms might be subject to sampling bias both in data repositories used for training and experimental/clinical settings; and (2) a portion of clinical samples might contain uncharacterized AMR genes that the algorithms-mostly trained on known AMR genes-fail to generalize upon. These results lead us to formulate practical advice for software configuration and application, and give suggestions for future study designs to further develop AMR prediction tools from proof-of-concept to bedside.

Transcriptional Profiling of Exosomes Derived from Staphylococcus aureus-Infected Bovine Mammary Epithelial Cell Line MAC-T by RNA-Seq Analysis

Mastitis is a common disease in the dairy industry that causes huge economic losses worldwide. Exosomes (carrying proteins, miRNA, lncRNA, etc.) play a vital role in the regulation of immune response. lncRNA can play a variety of regulatory roles by combining with protein, RNA, and DNA. The expression of mRNA and lncRNA in exosomes derived from bovine mammary epithelial cells infected by S. aureus is rarely understood. To explore this issue, RNA sequencing analysis was performed on exosomes derived from S. aureus-infected and noninfected MAC-T cells. Analysis of the sequencing results showed that there were 186 differentially expressed genes, 431 differentially expressed mRNAs and 19 differentially expressed lncRNAs in the exosomes derived from S. aureus-infected and noninfected MAC-T cells. By predicting lncRNA target genes, it was found that 19 differentially expressed lncRNAs all acted on multiple mRNAs in cis and trans. GO analysis revealed that differentially expressed genes and lncRNA target genes played significant roles in such metabolism (reactive oxygen species metabolic processes), transmembrane transport, cellular response to DNA damage stimulus, and response to cytokines. KEGG enrichment indicated that lncRNA target genes gathered in the TNF pathway, Notch pathway, MAPK pathway, NF-kappa B pathway, Hippo pathway, p53 pathway, reactive oxygen species metabolic processes, and longevity regulating pathway. In summary, all data indicated that differentially expressed gene, mRNA, and lncRNA in transcriptional profiling of exosomes participated in bacterial invasion and adhesion, oxidative stress, inflammation, and apoptosis-related signaling pathway. The data obtained in this study would provide valuable resource for understanding the lncRNA information in exosomes derived from dairy cow mammary epithelial cells and conduced to the study of S. aureus infection in dairy cow mammary glands.

Propensity score synthetic augmentation matching using generative adversarial networks (PSSAM-GAN)

Understanding causality is of crucial importance in biomedical sciences, where developing prediction models is insufficient because the models need to be actionable. However, data sources, such as electronic health records, are observational and often plagued with various types of biases, e.g. confounding. Although randomized controlled trials are the gold standard to estimate the causal effects of treatment interventions on health outcomes, they are not always possible. Propensity score matching (PSM) is a popular statistical technique for observational data that aims at balancing the characteristics of the population assigned either to a treatment or to a control group, making treatment assignment and outcome independent upon these characteristics. However, matching subjects can reduce the sample size. Inverse probability weighting (IPW) maintains the sample size, but extreme values can lead to instability. While PSM and IPW have been historically used in conjunction with linear regression, machine learning methods -including deep learning with propensity dropout- have been proposed to account for nonlinear treatment assignments. In this work, we propose a novel deep learning approach -the Propensity Score Synthetic Augmentation Matching using Generative Adversarial Networks (PSSAM-GAN)- that aims at keeping the sample size, without IPW, by generating synthetic matches. PSSAM-GAN can be used in conjunction with any other prediction method to estimate treatment effects. Experiments performed on both semi-synthetic (perinatal interventions) and real-world observational data (antibiotic treatments, and job interventions) show that the PSSAM-GAN approach effectively creates balanced datasets, relaxing the weighting/dropout needs for downstream methods, and providing competitive performance in effects estimation as compared to simple GAN and in conjunction with other deep counterfactual learning architectures, e.g. TARNet.