Biological upgrading of biogas assisted with membrane supplied hydrogen gas in a three-phase upflow reactor.

Biogas upgrading via CO2 conversion to CH4 is an emerging technology for renewable natural gas production and carbon management, but its development is limited by the low H2 gas to liquid phase transfer. Herein, an innovative biogas upgrading system employing a three-phase design was studied for CO2 conversion with H2 supply via gas-permeable membrane. The system produced biogas consisted of 74.1 ± 7.1 % CH4 and 25.9 ± 7.1 % CO2 with intermittent injection of H2. When H2 supply was continuous, the CH4 content increased to 91.6 ± 2.2 % at a H2:CO2 ratio of 4.4. Although a higher ratio of 5.5 could result in a higher CH4 percentage of 95.2 ± 2.5 %, biogas production rate started to decrease. The removal efficiency of organic contents remained above 90 % throughout the experiment. Microbial community analysis corroborated the findings, showing that hydrogenotrophic Methanobacteriaceae was more prevalent in the biofilm (71.9 %) compared to that in anaerobic digestion (15.8 %) and effluent (14.1 %).

Toward Characterizing Environmental Sources of Non-tuberculous Mycobacteria (NTM) at the Species Level: A Tutorial Review of NTM Phylogeny and Phylogenetic Classification.

Nontuberculous mycobacteria (NTM) are any mycobacteria that do not cause tuberculosis or leprosy. While the majority of NTM are harmless and some of them are considered probiotic, a growing number of people are being diagnosed with NTM infections. Therefore, their detection in the environment is of interest to clinicians, environmental microbiologists, and water quality researchers alike. This review provides a tutorial on the foundational approaches for taxonomic classifications, with a focus on the phylogenetic relationships among NTM revealed by the 16S rRNA gene, rpoB gene, and hsp65 gene, and by genome-based approaches. Recent updates on the Mycobacterium genus taxonomy are also provided. A synthesis on the habitats of 189 mycobacterial species in a genome-based taxonomy framework was performed, with attention paid to environmental sources (e.g., drinking water, aquatic environments, and soil). The 16S rRNA gene-based classification accuracy for various regions was evaluated (V3, V3-V4, V3-V5, V4, V4-V5, and V1-V9), revealing overall excellent genus-level classification (up to 100% accuracy) yet only modest performance (up to 63.5% accuracy) at the species level. Future research quantifying NTM species in water systems, determining the effects of water treatment and plumbing conditions on their variations, developing high throughput species-level characterization tools for use in the environment, and incorporating the characterization of functions in a phylogenetic framework will likely fill critical knowledge gaps. We believe this tutorial will be useful for researchers new to the field of molecular or genome-based taxonomic profiling of environmental microbiomes. Experts may also find this review useful in terms of the selected key findings of the past 30 years, recent updates on phylogenomic analyses, as well as a synthesis of the ecology of NTM in a phylogenetic framework.

Intra-hospital microbiome variability is driven by accessibility and clinical activities.

The hospital environmental microbiome, which can affect patients' and healthcare workers' health, is highly variable and the drivers of this variability are not well understood. In this study, we collected 37 surface samples from the neonatal intensive care unit (NICU) in an inpatient hospital before and after the operation began. Additionally, healthcare workers collected 160 surface samples from five additional areas of the hospital. All samples were analyzed using 16S rRNA gene amplicon sequencing, and the samples collected by healthcare workers were cultured. The NICU samples exhibited similar alpha and beta diversities before and after opening, which indicated that the microbiome there was stable over time. Conversely, the diversities of samples taken after opening varied widely by area. Principal coordinate analysis (PCoA) showed the samples clustered into two distinct groups: high alpha diversity [the pediatric intensive care unit (PICU), pathology lab, and microbiology lab] and low alpha diversity [the NICU, pediatric surgery ward, and infection prevention and control (IPAC) office]. Least absolute shrinkage and selection operator (LASSO) classification models identified 156 informative amplicon sequence variants (ASVs) for predicting the sample's area of origin. The testing accuracy ranged from 86.37% to 100%, which outperformed linear and radial support vector machine (SVM) and random forest models. ASVs of genera that contain emerging pathogens were identified in these models. Culture experiments had identified viable species among the samples, including potential antibiotic-resistant bacteria. Though area type differences were not noted in the culture data, the prevalences and relative abundances of genera detected positively correlated with 16S sequencing data. This study brings to light the microbial community temporal and spatial variation within the hospital and the importance of pathogenic and commensal bacteria to understanding dispersal patterns for infection control. IMPORTANCE: We sampled surface samples from a newly built inpatient hospital in multiple areas, including areas accessed by only healthcare workers. Our analysis of the neonatal intensive care unit (NICU) showed that the microbiome was stable before and after the operation began, possibly due to access restrictions. Of the high-touch samples taken after opening, areas with high diversity had more potential external seeds (long-term patients and clinical samples), and areas with low diversity and had fewer (short-term or newborn patients). Classification models performed at high accuracy and identified biomarkers that could be used for more targeted surveillance and infection control. Though culturing data yielded viability and antibiotic-resistance information, it disproportionately detected the presence of genera relative to 16S data. This difference reinforces the utility of 16S sequencing in profiling hospital microbiomes. By examining the microbiome over time and in multiple areas, we identified potential drivers of the microbial variation within a hospital.