A Pilot Study of Chicago Waterways as Reservoirs of Multidrug-Resistant Enterobacteriaceae (MDR-Ent) in a High-Risk Region for Community-Acquired MDR-Ent Infection in Children.

Community-acquired multidrug resistant Enterobacteriaceae (MDR-Ent) infections continue to increase in the United States. In prior studies, we identified neighboring regions in Chicago, Illinois, where children have 5 to 6 times greater odds of MDR-Ent infections. To prevent community spread of MDR-Ent, we need to identify the MDR-Ent reservoirs. A pilot study of 4 Chicago waterways for MDR-Ent and associated antibiotic resistance genes (ARGs) was conducted. Three waterways (A1 to A3) are labeled safe for "incidental contact recreation" (e.g., kayaking), and A4 is a nonrecreational waterway that carries nondisinfected water. Surface water samples were collected and processed for standard bacterial culture and shotgun metagenomic sequencing. Generally, A3 and A4 (neighboring waterways which are not hydraulically connected) were strikingly similar in bacterial taxa, ARG profiles, and abundances of corresponding clades and genera within the Enterobacteriaceae Additionally, total ARG abundances recovered from the full microbial community were strongly correlated between A3 and A4 (R2 = 0.97). Escherichia coli numbers (per 100 ml water) were highest in A4 (783 most probable number [MPN]) and A3 (200 MPN) relative to A2 (84 MPN) and A1 (32 MPN). We found concerning ARGs in Enterobacteriaceae such as MCR-1 (colistin), Qnr and OqxA/B (quinolones), CTX-M, OXA and ACT/MIR (beta-lactams), and AAC (aminoglycosides). We found significant correlations in microbial community composition between nearby waterways that are not hydraulically connected, suggesting cross-seeding and the potential for mobility of ARGs. Enterobacteriaceae and ARG profiles support the hypothesized concerns that recreational waterways are a potential source of community-acquired MDR-Ent.

MetaMLP: A Fast Word Embedding Based Classifier to Profile Target Gene Databases in Metagenomic Samples.

The functional profile of metagenomic samples enables improved understanding of microbial populations in the environment. Such analysis consists of assigning short sequencing reads to a particular functional category. Normally, manually curated databases are used for functional assignment, and genes are arranged into different classes. Sequence alignment has been widely used to profile metagenomic samples against curated databases. However, this method is time consuming and requires high computational resources. While several alignment-free methods based on k-mer composition have been developed in recent years, they still require large amounts of computer main memory. In this article, MetaMLP (Metagenomics Machine Learning Profiler), a machine learning method that represents sequences as numerical vectors (embeddings) and uses a simple one hidden layer neural network to profile functional categories, is developed. Unlike other methods, MetaMLP enables partial matching by using a reduced alphabet to build sequence embeddings from full and partial k-mers. MetaMLP is able to identify a slightly larger number of reads compared with DIAMOND (one of the fastest sequence alignment methods), as well as to perform accurate predictions with 0.99 precision and 0.99 recall. MetaMLP can process 100M reads in ∼10 minutes on a laptop computer, which is 50 times faster than DIAMOND.