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Antibiotic resistance genes (ARGs) are widespread among bacteria. However, not all ARGs pose serious threats to public health, highlighting the importance of identifying those that are high-risk. Here, we developed an 'omics-based' framework to evaluate ARG risk considering human-associated-enrichment, gene mobility, and host pathogenicity. Our framework classifies human-associated, mobile ARGs (3.6% of all ARGs) as the highest risk, which we further differentiate as 'current threats' (Rank I; 3%) - already present among pathogens - and 'future threats' (Rank II; 0.6%) - novel resistance emerging from non-pathogens. Our framework identified 73 'current threat' ARG families. Of these, 35 were among the 37 high-risk ARGs proposed by the World Health Organization and other literature; the remaining 38 were significantly enriched in hospital plasmids. By evaluating all pathogen genomes released since framework construction, we confirmed that ARGs that recently transferred into pathogens were significantly enriched in Rank II ('future threats'). Lastly, we applied the framework to gut microbiome genomes from fecal microbiota transplantation donors. We found that although ARGs were widespread (73% of genomes), only 8.9% of genomes contained high-risk ARGs. Our framework provides an easy-to-implement approach to identify current and future antimicrobial resistance threats, with potential clinical applications including reducing risk of microbiome-based interventions. Antibiotic resistance genes are common but not all are of high risk to human health. Here, the authors develop an omics-based framework for ranking genes by risk that incorporates level of enrichment in human associated environments, gene mobility, and host pathogenicity. [ABSTRACT FROM AUTHOR]

In this study, we utilized the Illumina high-throughput metagenomic approach to investigate diversity and abundance of both microbial community and copper resistance genes (CuRGs) in activated sludge (AS) which was enriched under copper selective stress up to 800 mg/L. The raw datasets (~3.5 Gb for each sample, i.e., the copper-enriched AS and the control AS) were merged and normalized for the BLAST analyses against the SILVA SSU rRNA gene database and self-constructed copper resistance protein database (CuRD). Also, the raw metagenomic sequences were assembled into contigs and analyzed based on Open Reading Frames (ORFs) to identify potentially novel copper resistance genes. Among the different resistance systems for copper detoxification under the high copper stress condition, the Cus system was the most enriched system. The results also indicated that genes encoding multi-copper oxidase played a more important role than those encoding efflux proteins. More significantly, several potentially novel copper resistance ORFs were identified by Pfam search and phylogenic analysis. This study demonstrated a new understanding of microbial-mediated copper resistance under high copper stress using high-throughput shotgun sequencing technique. [ABSTRACT FROM AUTHOR]

The catechol-O-methyltransferase (COMT) gene contains a functional polymorphism (Val158Met) affecting the activity of the enzyme, and the monoamine oxidase A (MAOA) gene contains a VNTR polymorphism (MAOA-uVNTR) that affects the transcription of the gene. COMT and MAOA each contribute to the enzymatic degradation of dopamine and noradrenaline. Prefrontal cortical (PFC) function, which plays an important role in individual cognitive abilities, including intelligence, is modulated by dopamine. Since our previous association studies between attention deficit hyperactivity disorder (ADHD) and these two functional polymorphisms consistently showed the low activity alleles were preferentially transmitted to inattentive ADHD boys, the goal of the present study was to test the hypothesis that the interaction between COMT Val158Met and MAOA-uVNTR may affect the intelligence in a clinical sample of Chinese male ADHD subjects ( n = 264). We found that the COMT × MAOA interaction significantly predicted full scale (FSIQ) and performance (PIQ) IQ scores ( P = 0.039, 0.011); the MAOA-uVNTR significantly predicted FSIQ, PIQ and verbal IQ (VIQ) ( P = 0.009, 0.019, 0.038); COMT Val158Met independently had no effect on any of the IQ scores. Only the COMT × MAOA interaction for PIQ remained significant after a Bonferroni correction. Among all combined genotypes, the valval-3R genotype predicted higher intelligence, (average 106.7 ± 1.6, 95% C.I. 103.7–109.8 for FSIQ), and the valval-4R predicted lower intelligence (average 98.0 ± 2.3, 95% C.I. 93.5–102.6 for FSIQ). These results suggest that there is an inverted U-shaped relationship between intelligence and dopaminergic activity in our sample. Our finding that gene–gene interaction between COMT and MAOA predicts the intelligence of ADHD boys in China is intriguing but requires replication in other samples. [ABSTRACT FROM AUTHOR]