Genomic and resistome analysis of Alcaligenes faecalis strain PGB1 by Nanopore MinION and Illumina Technologies

Antibiotic resistance gene distribution in Shine Muscat grapes and health risk assessment of streptomycin residues in mice

Antibiotic residues and antibiotic resistance genes (ARGs) in fruits and vegetables pose public health risks via the food chain, attracting increased attention. Antibiotics such as streptomycin, used directly on seedless grapes or introduced into vineyard soil through organic fertilizers. However, extensive data supporting the risk assessment of antibiotic residues and resistance in these produce remains lacking. Utilizing metagenomic sequencing, we characterized Shine Muscat grape antibiotic resistome and mobile genetic elements (MGEs). Abundant MGEs and ARGs were found in grapes, with 174 ARGs on the grape surface and 32 in the fruit. Furthermore, our data indicated that soil is not the primary source of these MGEs and ARGs. Escherichia was identified as an essential carrier and potential transmitter of ARGs. In our previous study, streptomycin residue was identified in grapes. Further short-term exposure experiments in mice revealed no severe physiological or histological damage at several environment-related concentrations. However, with increased exposure, some ARGs levels in mouse gut microbes increased, indicating a potential threat to animal health. Overall, this study provides comprehensive insights into the resistance genome and potential hosts in grapes, supporting the risk assessment of antibiotic resistance in fruits and vegetables.

Functional genomic analysis of an efficient indole degrading bacteria strain Alcaligenes faecalis IITR89 and its biodegradation characteristics

Indole is a nitrogenous heterocyclic aromatic pollutant often detected in various environments. An efficient indole degrading bacterium strain IITR89 was isolated from River Cauvery, India, and identified as Alcaligenes faecalis subsp. phenolicus. The bacterium was found to degrade ~ 95% of 2.5 mM (293.75 mg/L) of indole within 18 h utilizing it as a sole carbon and energy source. Based on metabolite identification, the metabolic route of indole degradation is indole → (indoxyl) → isatin → (anthranilate) → salicylic acid → (catechol) → (Acetyl-CoA) → and further entering into TCA cycle. Genome sequencing of IITR89 revealed the presence of gene cluster dmpKLMNOP, encoding multicomponent phenol hydroxylase; andAbcd gene cluster, encoding anthranilate 1,2-dioxygenase ferredoxin subunit (andAb), anthranilate 1,2-dioxygenase large subunit (andAc), and anthranilate 1,2-dioxygenase small subunit (andAd); nahG, salicylate hydroxylase; catA, catechol 1,2-dioxygenase; catB, cis, cis-muconate cycloisomerase; and catC, muconolactone D-isomerase which play an active role in indole degradation. The findings strongly support the degradation potential of strain IITR89 and its possible application for indole biodegradation.

Evaluation of pre-treated healthcare wastes during COVID-19 pandemic reveals pathogenic microbiota, antibiotics residues, and antibiotic resistance genes against beta-lactams

The disposal of healthcare waste without prior elimination of pathogens and hazardous contaminants has negative effects on the environment and public health. This study aimed to profile the complete microbial community and correlate it with the antibiotic compounds identified in microwave pre-treated healthcare wastes collected from three different waste operators in Peninsular Malaysia. The bacterial and fungal compositions were determined via amplicon sequencing by targeting the full-length 16S rRNA gene and partial 18S with full-length ITS1-ITS2 regions, respectively. The antibiotic compounds were characterized using high-throughput spectrometry. There was significant variation in bacterial and fungal composition in three groups of samples, with alpha- (p-value = 0.04) and beta-diversity (p-values <0.006 and < 0.002), respectively. FC samples were found to acquire more pathogenic microorganisms than FA and FV samples. Paenibacillus and unclassified Bacilli genera were shared among three groups of samples, meanwhile, antibiotic-resistant bacteria Proteus mirabilis, Enterococcus faecium, and Enterococcus faecalis were found in modest quantities. A total of 19 antibiotic compounds were discovered and linked with the microbial abundance detected in the healthcare waste samples. The principal component analysis demonstrated a positive antibiotic-bacteria correlation for genera Pseudomonas, Aerococcus, Comamonas, and Vagococcus, while the other bacteria were negatively linked with antibiotics. Nevertheless, deep bioinformatic analysis confirmed the presence of bla_TEM-1 and penP which are associated with the production of class A beta-lactamase and beta-lactam resistance pathways. Microorganisms and contaminants, which serve as putative indicators in healthcare waste treatment evaluation revealed the ineffectiveness of microbial inactivation using the microwave sterilization method. Our findings suggested that the occurrence of clinically relevant microorganisms, antibiotic contaminants, and associated antibiotic resistance genes (ARGs) represent environmental and human health hazards when released into landfills via ARGs transmission.

Distribution Patterns of Antibiotic Resistance Genes and Their Bacterial Hosts in a Manure Lagoon of a Large-Scale Swine Finishing Facility

The spread of antibiotic resistance genes (ARGs) that are present in livestock manures, which are discharged into the environment, is a severe threat to human and animal health. Here, we used 16S rRNA gene profiling and metagenomic analysis to characterize microbial community composition and antibiotic resistance in a manure storage lagoon from a large-scale swine finishing facility. Manure samples were collected at intervals of two years. Both the prokaryotic community and the resistome were dominated by the Firmicutes, Proteobacteria and Bacteroidota. Metagenomic analysis of two samples revealed 726 and 641 ARGs classified into 59 and 46 AMR gene families. Besides multidrug efflux pumps, the predominating ARGs potentially encoded resistance to tetracyclines, macrolide-lincosamide-streptogramin, aminoglycosides, peptide antibiotics, rifamycin, chloramphenicol, and beta-lactams. Genes from all predominant AMR gene families were found in both samples indicating overall long-term stability of the resistome. Antibiotic efflux pumps were the primary type of ARGs in the Proteobacteria, while antibiotic target alteration or protection was the main mechanism of resistance in the Firmicutes, Actinobacteriota and Bacteroidota. Metagenome-assembled genomes (MAG) of four multidrug-resistant strains were assembled. The first MAG, assigned to Escherichia flexneri, contained 46 ARGs, including multidrug efflux pumps, modified porins, beta-lactamases, and genes conferring resistance to peptide antibiotics. The second MAG, assigned to the family Alcaligenaceae, contained 18 ARGs encoding resistance to macrolide-lincosamide-streptogramin, tetracyclines, aminoglycosides and diaminopyrimidins. Two other MAGs representing the genera Atopostipes and Prevotella, contained four and seven ARGs, respectively. All these MAGs represented minor community members and accounted for less than 0.3% of the whole metagenome. Overall, a few lineages originated from the gut but relatively rare in the manure storage lagoon, are the main source of ARGs and some of them carry multiple resistance determinants.

Right Lower Extremity Osteomyelitis With Alcaligenes faecalis in a Patient With Poorly Controlled Type 2 Diabetes Mellitus

Alcaligenes faecalis (A. faecalis) is a Gram-negative, rod-shaped, oxidase(+) and catalase(+), obligate aerobe commonly found in soil or water. It has also been found in human intestinal microbiota and more rarely in hospital settings and is typically associated with opportunistic infections. Although it has intrinsic resistance to many commonly used antibiotics, it is increasingly found to have developed antibiotic resistance. We present a rare case of A. faecalis osteomyelitis in a patient with a chronic diabetic foot ulcer.

NanoCoV19: An analytical pipeline for rapid detection of severe acute respiratory syndrome coronavirus 2

Nanopore sequencing technology (NST) has become a rapid and cost-effective method for the diagnosis and epidemiological surveillance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) during the coronavirus disease 2019 (COVID-19) pandemic. Compared with short-read sequencing platforms (e.g., Illumina’s), nanopore long-read sequencing platforms effectively shorten the time required to complete the detection process. However, due to the principles and data characteristics of NST, the accuracy of sequencing data has been reduced, thereby limiting monitoring and lineage analysis of SARS-CoV-2. In this study, we developed an analytical pipeline for SARS-CoV-2 rapid detection and lineage identification that integrates phylogenetic-tree and hotspot mutation analysis, which we have named NanoCoV19. This method not only can distinguish and trace the lineages contained in the alpha, beta, delta, gamma, lambda, and omicron variants of SARS-CoV-2 but is also rapid and efficient, completing overall analysis within 1 h. We hope that NanoCoV19 can be used as an auxiliary tool for rapid subtyping and lineage analysis of SARS-CoV-2 and, more importantly, that it can promote further applications of NST in public-health and -safety plans similar to those formulated to address the COVID-19 outbreak.

MAECI: A pipeline for generating consensus sequence with nanopore sequencing long-read assembly and error correction

Nanopore sequencing produces long reads and offers unique advantages over next-generation sequencing, especially for the assembly of draft bacterial genomes with improved completeness. However, assembly errors can occur due to data characteristics and assembly algorithms. To address these issues, we developed MAECI, a pipeline for generating consensus sequences from multiple assemblies of the same nanopore sequencing data and error correction. Systematic evaluation showed that MAECI is an efficient and effective pipeline to improve the accuracy and completeness of bacterial genome assemblies. The available codes and implementation are at https://github.com/langjidong/MAECI.