Mycoplasma pneumoniae is a significant cause of respiratory illness worldwide. Despite a minimal and highly conserved genome, genetic diversity within the species may impact disease. We performed whole genome sequencing (WGS) analysis of 107 M. pneumoniae isolates, including 67 newly sequenced using the Pacific BioSciences RS II and/or Illumina MiSeq sequencing platforms. Comparative genomic analysis of 107 genomes revealed >3,000 single nucleotide polymorphisms (SNPs) in total, including 520 type-specific SNPs. Population structure analysis supported the existence of six distinct subgroups, three within each type. We developed a predictive model to classify an isolate based on whole genome SNPs called against the reference genome into the identified subtypes, obviating the need for genome assembly. This study is the most comprehensive WGS analysis for M. pneumoniae to date, underscoring the power of combining complementary sequencing technologies to overcome difficult-to-sequence regions and highlighting potential differential genomic signatures in M. pneumoniae.

Legionella pneumophila was first recognized as a cause of severe and potentially fatal pneumonia during a large-scale outbreak of Legionnaires’ disease (LD) at a Pennsylvania veterans’ convention in Philadelphia, 1976. The ensuing investigation and recovery of four clinical isolates launched the fields of Legionella epidemiology and scientific research. Only one of the original isolates, “Philadelphia-1”, has been widely distributed or extensively studied. Here we describe the whole-genome sequencing (WGS), complete assembly, and comparative analysis of all Philadelphia LD strains recovered from that investigation, along with L. pneumophila isolates sharing the Philadelphia sequence type (ST36). Analyses revealed that the 1976 outbreak was due to multiple serogroup 1 strains within the same genetic lineage, differentiated by an actively mobilized, self-replicating episome that is shared with L. pneumophila str. Paris, and two large, horizontally-transferred genomic loci, among other polymorphisms. We also found a completely unassociated ST36 strain that displayed remarkable genetic similarity to the historical Philadelphia isolates. This similar strain implies the presence of a potential clonal population, and suggests important implications may exist for considering epidemiological context when interpreting phylogenetic relationships among outbreak-associated isolates. Additional extensive archival research identified the Philadelphia isolate associated with a non-Legionnaire case of “Broad Street pneumonia”, and provided new historical and genetic insights into the 1976 epidemic. This retrospective analysis has underscored the utility of fully-assembled WGS data for Legionella outbreak investigations, highlighting the increased resolution that comes from long-read sequencing and a sequence type-matched genomic data set.

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Bacterial 16S ribosomal gene was used to classify bacteria because it consists of both highly conservative region, as well as a hypervariable region, in its sequence. This hypervariable region serves as a discriminative factor to differentiate bacteria at taxonomic levels. In the past, many efforts have been made to correctly identify a bacterial species from environmental samples or human gut microbiome samples, yet this identification and subsequent classification task is challenging. For such bacterial taxonomic classification, several studies in the past have been performed based on k-mer frequency matching, assembly-based clustering, supervised/unsupervised machine learning models, and a very few studies with deep learning architectures. In this article, we study and propose six different deep learning architectures involving recurrent neural networks (RNNs) and convolutional neural networks to classify bacteria at a family, genus, and species taxonomic level using *12,900 16S ribosomal DNA sequences. The best classification accuracies achieved are 92%, 86%, and 70% at family, genus, and species taxonomic level, respectively, by variants of RNN.