Adaptation of E. coli to Ciprofloxacin and Enrofloxacin: Differential Proteomics of the SOS Response and RecA-Independent Mechanisms.

Antibiotic resistance is a growing global healthcare challenge, treatment of bacterial infections with fluoroquinolones being no exception. These antibiotics can induce genetic instability through several mechanisms, one of the most significant being the activation of the SOS response. During exposure to sublethal concentration, this stress response increases mutation rates, accelerating resistance evolution.

Organelles in the ointment: improved detection of cryptic mitochondrial reads resolves many unknown sequences in cross-species microbiome analyses.

The genomes of mitochondria and chloroplasts contain ribosomal RNA (rRNA) genes, reflecting their ancestry as free-living bacteria. These organellar rRNAs are often amplified in microbiome studies of animals and plants. If identified, they can be discarded, merely reducing sequencing depth.

Metagenomic assemblies tend to break around antibiotic resistance genes.

Background: Assembly of metagenomic samples can provide essential information about the mobility potential and taxonomic origin of antibiotic resistance genes (ARGs) and inform interventions to prevent further spread of resistant bacteria. However, similar to other conserved regions, such as ribosomal RNA genes and mobile genetic elements, almost identical ARGs typically occur in multiple genomic contexts across different species, representing a considerable challenge for the assembly process. Usually, this results in many fragmented contigs of unclear origin, complicating the risk assessment of ARG detections.

in urban marine sediments: interpreting virulence, biofilm formation, halotolerance, and antibiotic resistance to infer contamination or naturalization.

Marine sediments have been suggested as a reservoir for pathogenic bacteria, including . The origins, and properties promoting survival of in marine sediments (including osmotolerance, biofilm formation capacity, and antibiotic resistance), have not been well-characterized. Phenotypes and genotypes of 37 isolates from coastal marine sediments were characterized.

Aberrant microbiomes are associated with increased antibiotic resistance gene load in hybrid mice.

Antibiotic resistance is a priority public health problem resulting from eco-evolutionary dynamics within microbial communities and their interaction at a mammalian host interface or geographical scale. The links between mammalian host genetics, bacterial gut community, and antimicrobial resistance gene (ARG) content must be better understood in natural populations inhabiting heterogeneous environments. Hybridization, the interbreeding of genetically divergent populations, influences different components of the gut microbial communities.

Low coverage of species constrains the use of DNA barcoding to assess mosquito biodiversity.

Mosquitoes (Culicidae) represent the main vector insects globally, and they also inhabit many of the terrestrial and aquatic habitats of the world. DNA barcoding and metabarcoding are now widely used in both research and routine practices involving mosquitoes. However, these methodologies rely on information available in databases consisting of barcode sequences representing taxonomically identified voucher specimens.

A global baseline for qPCR-determined antimicrobial resistance gene prevalence across environments.

The environment is an important component in the emergence and transmission of antimicrobial resistance (AMR). Despite that, little effort has been made to monitor AMR outside of clinical and veterinary settings. Partially, this is caused by a lack of comprehensive reference data for the vast majority of environments.

Development of early life gut resistome and mobilome across gestational ages and microbiota-modifying treatments.

Background: Gestational age (GA) and associated level of gastrointestinal tract maturation are major factors driving the initial gut microbiota composition in preterm infants. Besides, compared to term infants, premature infants often receive antibiotics to treat infections and probiotics to restore optimal gut microbiota. How GA, antibiotics, and probiotics modulate the microbiota's core characteristics, gut resistome and mobilome, remains nascent.

Latent antibiotic resistance genes are abundant, diverse, and mobile in human, animal, and environmental microbiomes.

Background: Bacterial communities in humans, animals, and the external environment maintain a large collection of antibiotic resistance genes (ARGs). However, few of these ARGs are well-characterized and thus established in existing resistance gene databases. In contrast, the remaining latent ARGs are typically unknown and overlooked in most sequencing-based studies.

Microbial and human transcriptome in vaginal fluid at midgestation: Association with spontaneous preterm delivery.

Background: Intrauterine infection and inflammation caused by microbial transfer from the vagina are believed to be important factors causing spontaneous preterm delivery (PTD). Multiple studies have examined the relationship between the cervicovaginal microbiome and spontaneous PTD with divergent results. Most studies have applied a DNA-based assessment, providing information on the microbial composition but not transcriptional activity.

What Is the Role of the Environment in the Emergence of Novel Antibiotic Resistance Genes? A Modeling Approach.

It is generally accepted that intervention strategies to curb antibiotic resistance cannot solely focus on human and veterinary medicine but must also consider environmental settings. While the environment clearly has a role in transmission of resistant bacteria, its role in the emergence of novel antibiotic resistance genes (ARGs) is less clear. It has been suggested that the environment constitutes an enormous recruitment ground for ARGs to pathogens, but its extent is practically unknown.

Maximum levels of cross-contamination for 24 antimicrobial active substances in non-target feed. Part 13: .

The specific concentrations of trimethoprim in non-target feed for food-producing animals below which there would not be an effect on the emergence of, and/or selection for, resistance in bacteria relevant for human and animal health, as well as the specific antimicrobial concentrations in feed which have an effect in terms of growth promotion/increased yield were assessed by EFSA in collaboration with EMA. Details of the methodology used for this assessment, associated data gaps and uncertainties, are presented in a separate document. To address antimicrobial resistance, the Feed Antimicrobial Resistance Selection Concentration (FARSC) model developed specifically for the assessment was applied.

Maximum levels of cross-contamination for 24 antimicrobial active substances in non-target feed. Part 12: .

The specific concentrations of tetracycline, chlortetracycline, oxytetracycline and doxycycline in non-target feed for food-producing animals, below which there would not be an effect on the emergence of, and/or selection for, resistance in bacteria relevant for human and animal health, as well as the specific antimicrobial concentrations in feed which have an effect in terms of growth promotion/increased yield were assessed by EFSA in collaboration with EMA. Details of the methodology used for this assessment, associated data gaps and uncertainties are presented in a separate document. To address antimicrobial resistance, the Feed Antimicrobial Resistance Selection Concentration (FARSC) model developed specifically for the assessment was applied.

Maximum levels of cross-contamination for 24 antimicrobial active substances in non-target feed. Part 11: .

The specific concentrations of sulfonamides in non-target feed for food-producing animals, below which there would not be an effect on the emergence of, and/or selection for, resistance in bacteria relevant for human and animal health, as well as the specific antimicrobial concentrations in feed which have an effect in terms of growth promotion/increased yield were assessed by EFSA in collaboration with EMA. Details of the methodology used for this assessment, associated data gaps and uncertainties, are presented in a separate document. To address antimicrobial resistance, the Feed Antimicrobial Resistance Selection Concentration (FARSC) model developed specifically for the assessment was applied.

Maximum levels of cross-contamination for 24 antimicrobial active substances in non-target feed. Part 10: .

The specific concentrations of flumequine and oxolinic acid in non-target feed for food-producing animals, below which there would not be an effect on the emergence of, and/or selection for, resistance in bacteria relevant for human and animal health, as well as the specific antimicrobial concentrations in feed which have an effect in terms of growth promotion/increased yield were assessed by EFSA in collaboration with EMA. Details of the methodology used for this assessment, associated data gaps and uncertainties, are presented in a separate document. To address antimicrobial resistance, the Feed Antimicrobial Resistance Selection Concentration (FARSC) model developed specifically for the assessment was applied.

Maximum levels of cross-contamination for 24 antimicrobial active substances in non-target feed. Part 9: .

The specific concentrations of colistin in non-target feed for food-producing animals, below which there would not be an effect on the emergence of, and/or selection for, resistance in bacteria relevant for human and animal health, as well as the specific antimicrobial concentrations in feed which have an effect in terms of growth promotion/increased yield were assessed by EFSA in collaboration with EMA. Details of the methodology used for this assessment, associated data gaps and uncertainties, are presented in a separate document. To address antimicrobial resistance, the Feed Antimicrobial Resistance Selection Concentration (FARSC) model developed specifically for the assessment was applied.

Maximum levels of cross-contamination for 24 antimicrobial active substances in non-target feed. Part 8: .

The specific concentrations of tiamulin and valnemulin in non-target feed for food-producing animals, below which there would not be an effect on the emergence of, and/or selection for, resistance in bacteria relevant for human and animal health, as well as the specific antimicrobial concentrations in feed which have an effect in terms of growth promotion/increased yield were assessed by EFSA in collaboration with EMA. Details of the methodology used for this assessment, associated data gaps and uncertainties, are presented in a separate document. To address antimicrobial resistance, the Feed Antimicrobial Resistance Selection Concentration (FARSC) model developed specifically for the assessment was applied.

Maximum levels of cross-contamination for 24 antimicrobial active substances in non-target feed. Part 7: .

The specific concentrations of florfenicol and thiamphenicol in non-target feed for food-producing animals, below which there would not be an effect on the emergence of, and/or selection for, resistance in bacteria relevant for human and animal health, as well as the specific antimicrobial concentrations in feed which have an effect in terms of growth promotion/increased yield, were assessed by EFSA in collaboration with EMA. Details of the methodology used for this assessment, associated data gaps and uncertainties, are presented in a separate document. To address antimicrobial resistance, the Feed Antimicrobial Resistance Selection Concentration (FARSC) model developed specifically for the assessment was applied.

Maximum levels of cross-contamination for 24 antimicrobial active substances in non-target feed. Part 6: .

The specific concentrations of tilmicosin, tylosin and tylvalosin in non-target feed for food-producing animals, below which there would not be an effect on the emergence of, and/or selection for, resistance in bacteria relevant for human and animal health, as well as the specific antimicrobial concentrations in feed which have an effect in terms of growth promotion/increased yield, were assessed by EFSA in collaboration with EMA. Details of the methodology used for this assessment, associated data gaps and uncertainties, are presented in a separate document. To address antimicrobial resistance, the Feed Antimicrobial Resistance Selection Concentration (FARSC) model developed specifically for the assessment was applied.

Maximum levels of cross-contamination for 24 antimicrobial active substances in non-target feed. Part 5: .

The specific concentrations of lincomycin in non-target feed for food-producing animals, below which there would not be an effect on the emergence of, and/or selection for, resistance in bacteria relevant for human and animal health, as well as the specific antimicrobial concentrations in feed which have an effect in terms of growth promotion/increased yield were assessed by EFSA in collaboration with EMA. Details of the methodology used for this assessment, associated data gaps and uncertainties, are presented in a separate document. To address antimicrobial resistance, the Feed Antimicrobial Resistance Selection Concentration (FARSC) model developed specifically for the assessment was applied.

Maximum levels of cross-contamination for 24 antimicrobial active substances in non-target feed. Part 4: .

The specific concentrations of amoxicillin and penicillin V in non-target feed for food-producing animals, below which there would not be an effect on the emergence of, and/or selection for, resistance in bacteria relevant for human and animal health, as well as the specific antimicrobial concentrations in feed which have an effect in terms of growth promotion/increased yield were assessed by EFSA in collaboration with EMA. Details of the methodology used for this assessment, associated data gaps and uncertainties, are presented in a separate document. To address antimicrobial resistance, the Feed Antimicrobial Resistance Selection Concentration (FARSC) model developed specifically for the assessment was applied.

Maximum levels of cross-contamination for 24 antimicrobial active substances in non-target feed. Part 3: .

The specific concentrations of amprolium in non-target feed for food-producing animals, below which there would not be an effect on the emergence of, and/or selection for, resistance in bacteria relevant for human and animal health, as well as the specific antimicrobial concentrations in feed which have an effect in terms of growth promotion/increased yield were assessed by EFSA in collaboration with EMA. Details of the methodology used for this assessment, associated data gaps and uncertainties, are presented in a separate document. To address antimicrobial resistance, the Feed Antimicrobial Resistance Selection Concentration (FARSC) model developed specifically for the assessment was applied.

Maximum levels of cross-contamination for 24 antimicrobial active substances in non-target feed. Part 2: .

The specific concentrations of apramycin, paromomycin, neomycin and spectinomycin in non-target feed for food-producing animals, below which there would not be an effect on the emergence of, and/or selection for, resistance in bacteria relevant for human and animal health, as well as the specific antimicrobial concentrations in feed which have an effect in terms of growth promotion/increased yield, were assessed by EFSA in collaboration with EMA. Details of the methodology used for this assessment, associated data gaps and uncertainties, are presented in a separate document. To address antimicrobial resistance, the Feed Antimicrobial Resistance Selection Concentration (FARSC) model developed specifically for the assessment was applied.

Maximum levels of cross-contamination for 24 antimicrobial active substances in non-target feed. Part 1: .

The European Commission requested EFSA to assess, in collaboration with EMA, the specific concentrations of antimicrobials resulting from cross-contamination in non-target feed for food-producing animals below which there would not be an effect on the emergence of, and/or selection for, resistance in microbial agents relevant for human and animal health, as well as the levels of the antimicrobials which could have a growth promotion/increase yield effect. The assessment was performed for 24 antimicrobial active substances, as specified in the mandate. This scientific opinion describes the methodology used, and the main associated data gaps and uncertainties.