Search for: All records

Nontuberculous mycobacteria (NTM) are environmental organisms that can cause opportunistic pulmonary disease with species diversity showing significant regional variation. In the United States, Hawai’i shows the highest rate of NTM pulmonary disease. The need for improved understanding of NTM reservoirs led us to identify NTM from patient respiratory specimens and compare NTM diversity between outdoor and indoor locations in Hawai’i. A total of 545 water biofilm samples were collected from 357 unique locations across Kaua’i (n = 51), O’ahu (n = 202), Maui (n = 159), and Hawai’i Island (n = 133) and divided into outdoor (n = 179) or indoor (n = 366) categories. rpoB sequence analysis was used to determine NTM species and predictive modeling applied to develop NTM risk maps based on geographic characteristics between environments. M. chimaera was frequently identified from respiratory and environmental samples followed by M. chelonae and M. abscessus; yet significantly less NTM were consistently recovered from outdoor compared to indoor biofilms, as exemplified by showerhead biofilm samples. While the frequency of M. chimaera recovery was comparable between outdoor and indoor showerhead biofilms, phylogenetic analyses demonstrate similar rpoB gene sequences between all showerhead and respiratory M. chimaera isolates, supporting outdoor and indoor environments as possible sources for pulmonary M. chimaera infections. 

ABSTRACT Environmental nontuberculous mycobacteria (NTM), with the potential to cause opportunistic lung infections, can reside in soil. This might be particularly relevant in Hawai’i, a geographic hot spot for NTM infections and whose soil composition differs from many other areas of the world. Soil components are likely to contribute to NTM prevalence in certain niches as food sources or attachment scaffolds, but the particular types of soils, clays, and minerals that impact NTM growth are not well-defined. Hawai’i soil and chemically weathered rock (saprolite) samples were examined to characterize the microbiome and quantify 11 mineralogical features as well as soil pH. Machine learning methods were applied to identify important soil features influencing the presence of NTM. Next, these features were directly tested in vitro by incubating synthetic clays and minerals in the presence of Mycobacteroides abscessus and Mycobacterium chimaera isolates recovered from the Hawai'i environment, and changes in bacterial growth were determined. Of the components examined, synthetic gibbsite, a mineral form of aluminum hydroxide, inhibited the growth of both M. abscessus and M. chimaera , while other minerals tested showed differential effects on each species. For example, M. abscessus (but not M. chimaera ) growth was significantly higher in the presence of hematite, an iron oxide mineral. In contrast, M. chimaera (but not M. abscessus ) counts were significantly reduced in the presence of birnessite, a manganese-containing mineral. These studies shed new light on the mineralogic features that promote or inhibit the presence of Hawai’i NTM in Hawai’i soil. IMPORTANCE Globally and in the United States, the prevalence of NTM pulmonary disease—a potentially life-threatening but underdiagnosed chronic illness—is prominently rising. While NTM are ubiquitous in the environment, including in soil, the specific soil components that promote or inhibit NTM growth have not been elucidated. We hypothesized that NTM culture-positive soil contains minerals that promote NTM growth in vitro . Because Hawai’i is a hot spot for NTM and a unique geographic archipelago, we examined the composition of Hawai’i soil and identified individual clay, iron, and manganese minerals associated with NTM. Next, individual components were evaluated for their ability to directly modulate NTM growth in culture. In general, gibbsite and some manganese oxides were shown to decrease NTM, whereas iron-containing minerals were associated with higher NTM counts. These data provide new information to guide future analyses of soil-associated factors impacting persistence of these soil bacteria. 

A wealth of viral data sits untapped in publicly available metagenomic data sets when it might be extracted to create a usable index for the virological research community. We hypothesized that work of this complexity and scale could be done in a hackathon setting. Ten teams comprised of over 40 participants from six countries, assembled to create a crowd-sourced set of analysis and processing pipelines for a complex biological data set in a three-day event on the San Diego State University campus starting 9 January 2019. Prior to the hackathon, 141,676 metagenomic data sets from the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) were pre-assembled into contiguous assemblies (contigs) by NCBI staff. During the hackathon, a subset consisting of 2953 SRA data sets (approximately 55 million contigs) was selected, which were further filtered for a minimal length of 1 kb. This resulted in 4.2 million (Mio) contigs, which were aligned using BLAST against all known virus genomes, phylogenetically clustered and assigned metadata. Out of the 4.2 Mio contigs, 360,000 contigs were labeled with domains and an additional subset containing 4400 contigs was screened for virus or virus-like genes. The work yielded valuable insights into both SRA data and the cloud infrastructure required to support such efforts, revealing analysis bottlenecks and possible workarounds thereof. Mainly: (i) Conservative assemblies of SRA data improves initial analysis steps; (ii) existing bioinformatic software with weak multithreading/multicore support can be elevated by wrapper scripts to use all cores within a computing node; (iii) redesigning existing bioinformatic algorithms for a cloud infrastructure to facilitate its use for a wider audience; and (iv) a cloud infrastructure allows a diverse group of researchers to collaborate effectively. The scientific findings will be extended during a follow-up event. Here, we present the applied workflows, initial results, and lessons learned from the hackathon.