ARGA, a pipeline for primer evaluation on antibiotic resistance genes

NCRD: A non-redundant comprehensive database for detecting antibiotic resistance genes

Antibiotic resistance genes (ARGs) are emerging pollutants present in various environments. Identifying ARGs has become a growing concern in recent years. Several databases, including the Antibiotic Resistance Genes Database (ARDB), Comprehensive Antibiotic Resistance Database (CARD), and Structured Antibiotic Resistance Genes (SARG), have been applied to detect ARGs. However, these databases have limitations, which hinder the comprehensive profiling of ARGs in environmental samples. To address these issues, we constructed a non-redundant antibiotic resistance genes database (NRD) by consolidating sequences from ARDB, CARD, and SARG. We identified the homologous proteins of NRD from Non-redundant Protein Database (NR) and the Protein DataBank Database (PDB) and clustered them to establish a non-redundant comprehensive antibiotic resistance genes database (NCRD) with similarities of 100% (NCRD100) and 95% (NCRD95). To demonstrate the advantages of NCRD, we compared it with other databases by using metagenome datasets. Results revealed its strong ability in detecting potential ARGs.

A Comprehensive Bioinformatics Resource Guide for Genome-Based Antimicrobial Resistance Studies

The use of high-throughput sequencing technologies and bioinformatic tools has greatly transformed microbial genome research. With the help of sophisticated computational tools, it has become easier to perform whole genome assembly, identify and compare different species based on their genomes, and predict the presence of genes responsible for proteins, antimicrobial resistance, and toxins. These bioinformatics resources are likely to continuously improve in quality, become more user-friendly to analyze the multiple genomic data, efficient in generating information and translating it into meaningful knowledge, and enhance our understanding of the genetic mechanism of AMR. In this manuscript, we provide an essential guide for selecting the popular resources for microbial research, such as genome assembly and annotation, antibiotic resistance gene profiling, identification of virulence factors, and drug interaction studies. In addition, we discuss the best practices in computer-oriented microbial genome research, emerging trends in microbial genomic data analysis, integration of multi-omics data, the appropriate use of machine-learning algorithms, and open-source bioinformatics resources for genome data analytics.

QSP: An open sequence database for quorum sensing related gene analysis with an automatic annotation pipeline

Quorum sensing (QS) has attracted great attention due to its important role in the bacterial interactions and its relevance to water management. With the development of high-throughput sequencing technology, a specific database for QS-related sequence annotation is urgently needed. Here, Hidden Markov Model (HMM) profiles for 38 types of QS-related proteins were built using a total of 4024 collected seed sequences. Based on both homolog search and keywords confirmation against the non-redundant database, we established a QS-related protein (QSP) database, that includes 809,721 protein sequences and 186,133 nucleotide sequences, downloaded available at: https://github.com/chunxiao-dcx/QSP. The entries were classified into 38 types and 315 subtypes among 91 bacterial phyla. Furthermore, an automatic annotation pipeline, named QSAP, was developed for rapid annotation, classification and abundance quantification of QSP-like sequences from sequencing data. This pipeline provided the two homolog alignment strategies offered by Diamond (Blastp) or HMMER (Hmmscan), as well as a data cleansing function for a subset or union set of the hits. The pipeline was tested using 14 metagenomic samples from various water environments, including activated sludge, deep-sea sediments, estuary water, and reservoir water. The QSAP pipeline is freely available for academic use in the code repository at: https://github.com/chunxiao-dcx/QSAP. The establishment of this database and pipeline, provides a useful tool for QS-related sequence annotation in a wide range of projects, and will increase our understanding of QS communication in aquatic environments.

Bioinformatics toolbox for exploring target mutation-induced drug resistance

Drug resistance is increasingly among the main issues affecting human health and threatening agriculture and food security. In particular, developing approaches to overcome target mutation-induced drug resistance has long been an essential part of biological research. During the past decade, many bioinformatics tools have been developed to explore this type of drug resistance, and they have become popular for elucidating drug resistance mechanisms in a low cost, fast and effective way. However, these resources are scattered and underutilized, and their strengths and limitations have not been systematically analyzed and compared. Here, we systematically surveyed 59 freely available bioinformatics tools for exploring target mutation-induced drug resistance. We analyzed and summarized these resources based on their functionality, data volume, data source, operating principle, performance, etc. And we concisely discussed the strengths, limitations and application examples of these tools. Specifically, we tested some predictive tools and offered some thoughts from the clinician's perspective. Hopefully, this work will provide a useful toolbox for researchers working in the biomedical, pesticide, bioinformatics and pharmaceutical engineering fields, and a good platform for non-specialists to quickly understand drug resistance prediction.

MRG Chip: A High-Throughput qPCR-Based Tool for Assessment of the Heavy Metal(loid) Resistome

Bacterial metal detoxification mechanisms have been well studied for centuries in pure culture systems. However, profiling metal resistance determinants at the community level is still a challenge due to the lack of comprehensive and reliable quantification tools. Here, a novel high-throughput quantitative polymerase chain reaction (HT-qPCR) chip, termed the metal resistance gene (MRG) chip, has been developed for the quantification of genes involved in the homeostasis of 9 metals. The MRG chip contains 77 newly designed degenerate primer sets and 9 published primer sets covering 56 metal resistance genes. Computational evaluation of the taxonomic coverage indicated that the MRG chip had a broad coverage matching 2 kingdoms, 29 phyla, 64 classes, 130 orders, 226 families, and 382 genera. Temperature gradient PCR and HT-qPCR verified that 57 °C was the optimal annealing temperature, with amplification efficiencies of over 94% primer sets achieving 80-110%, with R² > 0.993. Both computational evaluation and the melting curve analysis of HT-qPCR validated a high specificity. The MRG chip has been successfully applied to characterize the distribution of diverse metal resistance determinants in natural and human-related environments, confirming its wide scope of application. Collectively, the MRG chip is a powerful and efficient high-throughput quantification tool for exploring the microbial metal resistome.

Organic fertilizer potentiates the transfer of typical antibiotic resistance gene among special bacterial species

The propagation of antibiotic resistance genes (ARGs) in environments has evoked many attentions, however, how to identify their host pathogenic bacteria in situ remains a great challenge. Here we explored the bacterial host distribution and dissemination of a typical ARG, sul1 gene, in agricultural soils through the simultaneous detection of sul1 and its host 16S rRNA gene by emulsion paired isolation and concatenation PCR (epicPCR). Compared to chemical fertilizer, organic fertilizer (chicken manure) led to a higher prevalence of sul1 gene in the soil, and dominant bacterial hosts of sul1 gene were classified into Proteobacteria and Bacteroidetes phyla. Additionally, significant higher diversity of antibiotic resistance bacteria (ARB), higher rate of horizontal gene transfer (HGT), higher rate of mobile genetic elements (MGE) and higher proportion of pathogens were all observed in the treatment of organic fertilizer. This study alerts potential health risks of manure applications in agricultural soils.

Responses of sediment resistome, virulence factors and potential pathogens to decades of antibiotics pollution in a shrimp aquafarm

Aquaculture ecosystem has become a hotspot of antibiotics resistance genes (ARGs) dissemination, owing to the abuse of prophylactic antibiotics. However, it is still unclear how and to what extent ARGs respond to the increasing antibiotic pollution, a trend as expected and as has occurred. Herein, a significant sediment antibiotic pollution gradient was detected along a drainage ditch after decades of shrimp aquaculture. The increasing antibiotic pollution evidently promoted the diversities and tailored the community structures of ARGs, mobile genetic elements (MGEs), virulence factors and pathogens. The profiles of ARGs and MGEs were directly altered by the concentrations of terramycin and sulphadimidine. By contrast, virulence factors were primarily affected by nutrient variables in sediment. The pathogens potentially hosted diverse virulence factors and ARGs. More than half of the detected ARGs subtypes non-linearly responded to increasing antibiotic pollution, as supported by significant tipping points. However, we screened seven antibiotic concentration discriminatory ARGs that could serve as independent variable for quantitatively diagnosing total antibiotic concentration. Co-occurrence analysis depicted that notorious aquaculture pathogens of Vibrio harveyi and V. parahaemolyticus potentially hosted ARGs that confer resistance to multiple antibiotics, while priority pathogens for humankind, e.g., Helicobacter pylori and Staphylococcus aureus, could have harbored redundant virulence factors. Collectively, the significant tipping points and antibiotic concentration-discriminatory ARGs may translate into warning index and diagnostic approach for diagnosing antibiotic pollution. Our findings provided novel insights into the interplay among ARGs, MGEs, pathogens, virulence factors and geochemical variables under the scenario of increasing antibiotic pollution.

High-Throughput Single-Cell Technology Reveals the Contribution of Horizontal Gene Transfer to Typical Antibiotic Resistance Gene Dissemination in Wastewater Treatment Plants

The spread of antibiotic resistance genes (ARGs) has gained much attention worldwide, while the contribution of vertical gene transfer (VGT) and horizontal gene transfer (HGT) is still elusive. Here, we improved an emerging high-throughput single-cell-based technology, emulsion, paired isolation, and concatenation polymerase chain reaction (epicPCR), by lengthening the sequence of ARG in the fused ARG-16S rRNA fragments to cover the variance of both ARG and its hosts. The improved epicPCR was applied to track the hosts of a widely detected ARG, sul1 gene, in five urban wastewater treatment plants (UWTPs) during two seasons. The sul1 host bacteria were highly diverse and mostly classified as Proteobacteria and Bacteroidetes. Clear seasonal divergence of α-diversity and interaction networks were present in the host community. The consensus phylogenetic trees of the sul1 gene and their host demonstrated incorrespondence on the whole and regularity on abundant groups, suggesting the important role of both HGT and VGT, respectively. The relative importance of these two ways was further measured; HGT (54%) generally played an equal or even more important role as VGT (46%) in UWTPs. The application of the improved epicPCR technology provides a feasible approach to quantify the relative contributions of VGT and HGT in environmental dissemination of ARGs.

Current Trends in Experimental and Computational Approaches to Combat Antimicrobial Resistance

A multitude of factors, such as drug misuse, lack of strong regulatory measures, improper sewage disposal, and low-quality medicine and medications, have been attributed to the emergence of drug resistant microbes. The emergence and outbreaks of multidrug resistance to last-line antibiotics has become quite common. This is further fueled by the slow rate of drug development and the lack of effective resistome surveillance systems. In this review, we provide insights into the recent advances made in computational approaches for the surveillance of antibiotic resistomes, as well as experimental formulation of combinatorial drugs. We explore the multiple roles of antibiotics in nature and the current status of combinatorial and adjuvant-based antibiotic treatments with nanoparticles, phytochemical, and other non-antibiotics based on synergetic effects. Furthermore, advancements in machine learning algorithms could also be applied to combat the spread of antibiotic resistance. Development of resistance to new antibiotics is quite rapid. Hence, we review the recent literature on discoveries of novel antibiotic resistant genes though shotgun and expression-based metagenomics. To decelerate the spread of antibiotic resistant genes, surveillance of the resistome is of utmost importance. Therefore, we discuss integrative applications of whole-genome sequencing and metagenomics together with machine learning models as a means for state-of-the-art surveillance of the antibiotic resistome. We further explore the interactions and negative effects between antibiotics and microbiomes upon drug administration.

Fate and toxicity of pharmaceuticals in water environment: An insight on their occurrence in South Asia

Pharmaceutically active compounds are newly recognized micropollutants which are ubiquitous in aquatic environment mainly due to direct discharge of treated and untreated wastewater from wastewater treatment plants. These contaminants have attracted mounted attention due to their toxic effects on aquatic life. They disrupt biological processes in non-target lower organisms upon exposure. Biodegradation, photo-degradation, and sorption are key processes which determine their fate in the environment. A variety of conventional and advanced treatment processes had been extensively investigated for the removal of pharmaceuticals from wastewater. However, due to structural complexity and varying operating parameters, complete removal seems ideal. Generally, due to high energy requirement of advanced treatment technology, it is considered cost ineffective. Transport of pharmaceutical compounds occurs via aquatic channels whereas sediments and aquatic colloids play a significant role as sinks for these contaminants. The current review provides a critical understanding of fate and toxicity of pharmaceutical compounds and highlights their vulnerability and occurrence in South Asia. Antibiotics, analgesics, and psychiatric drugs were found predominantly in the water environment of South Asian regions. Despite significant advances in understanding pharmaceuticals fate, toxicity, and associated risks since the 1990s, still substantial data gaps in terms of monitoring, human health risks, and legislation exist which presses the need to develop a more in-depth and interdisciplinary understanding of the subject.