Description of different steps of human filtration of pipeline utilized to compare sensitivity/specificity of detection and performance of runtime and computational resources of the simulated reads.

<p>Dash (-) represents that the option was not utilized.</p><p>Description of different steps of human filtration of pipeline utilized to compare sensitivity/specificity of detection and performance of runtime and computational resources of the simulated reads.</p

MetaGeniE: Characterizing Human Clinical Samples Using Deep Metagenomic Sequencing

<div><p>With the decreasing cost of next-generation sequencing, deep sequencing of clinical samples provides unique opportunities to understand host-associated microbial communities. Among the primary challenges of clinical metagenomic sequencing is the rapid filtering of human reads to survey for pathogens with high specificity and sensitivity. Metagenomes are inherently variable due to different microbes in the samples and their relative abundance, the size and architecture of genomes, and factors such as target DNA amounts in tissue samples (i.e. human DNA versus pathogen DNA concentration). This variation in metagenomes typically manifests in sequencing datasets as low pathogen abundance, a high number of host reads, and the presence of close relatives and complex microbial communities. In addition to these challenges posed by the composition of metagenomes, high numbers of reads generated from high-throughput deep sequencing pose immense computational challenges. Accurate identification of pathogens is confounded by individual reads mapping to multiple different reference genomes due to gene similarity in different taxa present in the community or close relatives in the reference database. Available global and local sequence aligners also vary in sensitivity, specificity, and speed of detection. The efficiency of detection of pathogens in clinical samples is largely dependent on the desired taxonomic resolution of the organisms. We have developed an efficient strategy that identifies “all against all” relationships between sequencing reads and reference genomes. Our approach allows for scaling to large reference databases and then genome reconstruction by aggregating global and local alignments, thus allowing genetic characterization of pathogens at higher taxonomic resolution. These results were consistent with strain level SNP genotyping and bacterial identification from laboratory culture.</p></div

Effect of human filtration on percent genome coverage and read recall percentage of pathogen detection.

<p>The legends of the figure are prefixed with the number of reads (0.1K = 100; 1K = 1000; 10K = 10000; 100K = 100000; 1M = 1000000) followed by mg_bw2 for only fast alignment feature of human read reduction; mg_dc for all features of human read reduction except data compression; mgall_bw2 for all features of human read reduction module).</p

Sequential reduction of the metagenome reads for 4 clinical samples from cystic fibrosis patients.

<p>Data points represent the remaining reads after each processing step of the pipeline. First six data points (Initial, Quality Filter, BWT Alignment, Data Compression, Sensitive Alignment, Human Repeat Alignment) represent the Human Read Reduction and BWA Bacteria and BLAT Bacteria represent Pathogen Detection against bacterial database.</p

Comparison of detection of close relative in co-infection versus single infection.

<p><b>A.</b> Comparison of percent genome coverage of true detection in co-infection versus false detection of <i>S. aureus</i> Newman. <b>B.</b> Comparison of percent genome coverage of <i>S. aureus</i> TCH1516 in co-infection versus simple infection.</p

Benchmarking the human read reduction module of the pipeline.

<p><b>A.</b> Total numbers of reads remaining after human read reduction with different filtration parameters <b>B.</b> Runtime for human read filtration with different aligner and filtration parameters (in minutes).</p

Relationship between percent genome coverage and read recall percentage with incremental divergence (i.e. error).

<p>Relationship between percent genome coverage and read recall percentage with incremental divergence (i.e. error).</p

Bacterial infection detected by MetaGeniE confirmed with the laboratory culture media.

<p><b>MRSA:</b> Methicillin resistant <i>Staphylococcus aureus</i>; <b>ENCL:</b><i>Enterobacter cloacae</i>; <b>PSAR:</b><i>Pseudomonas aeruginosa</i>; <b>MSSA:</b> Methicillin sensitive <i>S</i>. <i>aureus</i>; <b>ECOL:</b><i>Escherichia coli</i>; <b>ENSP:</b><i>Enterococcus</i> sp.; <b>HAEM:</b><i>Haemophilus influenza</i>.</p><p>Bacterial infection detected by MetaGeniE confirmed with the laboratory culture media.</p

Detection of genomes in complex community.

<p>Relationship between genome size and genome coverage with increasing sequencing reads. Effect of detection on <i>E. coli</i> APEC O1 in simple and complex community.</p

Microbial Ecology and Water Chemistry Impact Regrowth of Opportunistic Pathogens in Full-Scale Reclaimed Water Distribution Systems

Need for global water security has spurred growing interest in wastewater reuse to offset demand for municipal water. While reclaimed (i.e., nonpotable) microbial water quality regulations target fecal indicator bacteria, opportunistic pathogens (OPs), which are subject to regrowth in distribution systems and spread via aerosol inhalation and other noningestion routes, may be more relevant. This study compares the occurrences of five OP gene markers (<i>Acanthamoeba</i> spp., <i>Legionella</i> spp., <i>Mycobacterium</i> spp., <i>Naegleria fowleri</i>, <i>Pseudomonas aeruginosa</i>) in reclaimed versus potable water distribution systems and characterizes factors potentially contributing to their regrowth. Samples were collected over four sampling events at the point of compliance for water exiting treatment plants and at five points of use at four U.S. utilities bearing both reclaimed and potable water distribution systems. Reclaimed water systems harbored unique water chemistry (e.g., elevated nutrients), microbial community composition, and OP occurrence patterns compared to potable systems examined here and reported in the literature. <i>Legionella</i> spp. genes, <i>Mycobacterium</i> spp. genes, and total bacteria, represented by 16S rRNA genes, were more abundant in reclaimed than potable water distribution system samples (<i>p</i> ≤ 0.0001). This work suggests that further consideration should be given to managing reclaimed water distribution systems with respect to nonpotable exposures to OPs