Biology subjects

Gu, A. Z.

Publications and source records attributed to Gu, A. Z..

6 recordsLinked to original sources

bioRxiv2021

Spatial-temporal targeted and non-targeted surveys to assess microbiological composition of drinking water in Puerto Rico following Hurricane Maria.

Loss of basic utilities, such as drinking water and electricity distribution, were sustained for months in the aftermath of Hurricane Marias (HM) landfall in Puerto Rico (PR) in September 2017. The goal of this study was to assess if there was deterioration in biological quality of drinking water due to these disruptions. This study characterized the microbial composition of drinking water following HM across nine drinking water systems (DWSs) in PR and utilized an extended temporal sampling campaign to determine if changes in the drinking water microbiome were indicative of HM associated disturbance followed by recovery. In addition to monitoring water chemistry, the samples were subjected to culture independent targeted and non-targeted microbial analysis including quantitative PCR (qPCR) and genome-resolved metagenomics. The qPCR results showed that residual disinfectant was the major driver of bacterial concentrations in tap water with marked decrease in concentrations from early to late sampling timepoints. While Mycobacterium avium and Pseudomonas aeruginosa were not detected in any sampling locations and timepoints, genetic material from Leptospira and Legionella pneumophila were transiently detected in a few sampling locations. The majority of metagenome assembled genomes (MAGs) recovered from these samples were not associated with pathogens and were consistent with bacterial community members routinely detected in DWSs. Further, whole metagenome-level comparisons between drinking water samples collected in this study with samples from other full-scale DWS indicated no significant deviation from expected community membership of the drinking water microbiome. Overall, our results suggest that disruptions due to HM did not result in significant and sustained deterioration of biological quality of drinking water at our study sites. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=86 SRC="FIGDIR/small/442998v1_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@100c1e9org.highwire.dtl.DTLVardef@bae402org.highwire.dtl.DTLVardef@1cf41bborg.highwire.dtl.DTLVardef@1e46105_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2021

Combined Enhanced Biological Phosphorus Removal (EBPR) and Nitrite Accumulation for Treating High-strength Wastewater

The enhanced biological phosphorus removal (EBPR) has been widely applied in treating domestic wastewater, while the performance on high-strength P wastewater is less investigated and the feasibility of coupling with short-cut nitrogen removal process remains unknown. This study first achieved the simultaneous high-efficient P removal and stable nitrite accumulation in one sequencing batch reactor for treating the synthetic digested manure wastewater. The average effluent P could be down to 0.8 {+/-} 1.0 mg P/L and the P removal efficiency was 99.5 {+/-} 0.8%. Candidatus Accumulibacter was the dominant polyphosphate accumulating organism (PAO) with the relative abundance of 14.2-33.1% in the reactor. Examination of the micro-diversity of Candidatus Accumulibacter using 16s rRNA gene-based oligotyping analysis revealed one unique Accumulibacter oligotype that different from the conventional system, which accounted for 64.2-87.9% of the total Accumulibacter abundance. The presence of high-abundant glycogen accumulating organisms (GAO) (15.6-40.3%, Defluviicoccus and Candidatus Competibacter) did not deteriorate the EBPR performance. Moreover, nitrite accumulation happened in the system with the effluent nitrite up to 20.4 {+/-} 6.4 mg N/L and the nitrite accumulation ratio was nearly 100% maintained for 140 days (420 cycles). Nitrosomonas was the dominant ammonia-oxidizing bacteria with relative abundance of 0.3-2.4% while nitrite-oxidizing bacteria were almost undetected (<0.1%). The introduction of extended anaerobic phase and high volatile fatty acid concentrations were proposed to be the potential selector forces to promote partial nitrification. This is the first study that combined EBPR with nitrite-accumulation for digested manure wastewater treatment, and it provided new sights in strategies to combine the EBPR and short-cut nitrogen removal via nitrite to achieve simultaneous nitrogen and phosphorus removal.

bioengineering↗

bioRxiv2020

Machine Learning-based Biomarkers Identification and Validation from Toxicogenomics - Bridging to Regulatory Relevant Phenotypic Endpoints

High-throughput in vitro assays and AOP-based approach is promising for the assessment of health and ecotoxicological risks from exposure to pollutants and their mixtures. However, one of the major challenges in realization and implementations of the Tox21 vision is the urgent need to establish quantitative link between in-vitro assay molecular endpoint and in-vivo phenotypic toxicity endpoint. Here, we demonstrated that, using time series toxicomics in-vitro assay along with machine learning-based feature selection (MRMR) and classification method (SVM), an "optimal" number of biomarkers with minimum redundancy can be identified for prediction of phenotypic endpoints with good accuracy. We included two case studies for in-vivo carcinogenicity and Ames genotoxicity prediction with 20 selected chemicals including model genotoxic chemicals and negative controls, respectively, using an in-vitro toxicogenomic assay that captures real-time proteomic response data of 38 GFP-fused proteins of S. cerevisiae strains covering biomarkers indicative of all known DNA damage and repair pathways in yeast. The results suggested that, employing the adverse outcome pathway (AOP) concept, molecular endpoints based on a relatively small number of properly selected biomarker-ensemble involved in the conserved DNA-damage and repair pathways among eukaryotes, were able to predict both in-vivo carcinogenicity in rats and Ames genotoxicity endpoints. The specific biomarkers identified are different for the two different phenotypic genotoxicity assays. The top-ranked five biomarkers for the in-vivo carcinogenicity prediction mainly focused on double strand break repair and DNA recombination, whereas the selected top-ranked biomarkers for Ames genotoxicity prediction are associated with base- and nucleotide-excision repair. Current toxicomics approach still mostly rely on large number of redundant markers without pre-selection or ranking, therefore, selection of relevant biomarkers with minimal redundancy would reduce the number of markers to be monitored and reduce the cost, time, and complexity of the toxicity screening and risk monitoring. The method developed in this study will help to fill in the knowledge gap in phenotypic anchoring and predictive toxicology, and contribute to the progress in the implementation of tox 21 vision for environmental and health applications. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=112 SRC="FIGDIR/small/423486v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@11bc876org.highwire.dtl.DTLVardef@105ebc2org.highwire.dtl.DTLVardef@1b91a63org.highwire.dtl.DTLVardef@1738486_HPS_FORMAT_FIGEXP M_FIG TOC Art C_FIG

pharmacology and toxicology↗

bioRxiv2020

Machine Leaning-based Determination of Sampling Depth for Complex Environmental Systems: Case Study with Single-Cell Raman Spectroscopy Data in EBPR Systems

Rapid progress in various advanced analytical methods such as single-cell technologies enable unprecedented and deeper understanding of microbial ecology beyond the resolution of conventional approaches. A major application challenge exists in the determination of sufficient sample size without sufficient prior knowledge of the community complexity and, the need to balance between statistical power and limited time or resources. This hinders the desired standardization and wider application of these technologies. Here, we proposed, tested and validated a computational sampling size assessment protocol taking advantage of a metric, named kernel divergence. This metric has two advantages: First, it directly compares dataset-wise distributional differences with no requirements on human intervention or prior knowledge-based pre-classification. Second, minimal assumptions in distribution and sample space are made in data processing to enhance its application domain. This enables test-verified appropriate handling of datasets with both linear and non-linear relationships. The model was then validated in a case study with eight SCRS phenotyping datasets each sampled from a different enhanced biological phosphorus removal (EBPR) activated sludge community located across North America. The model allows the determination of sufficient sampling size for any targeted or customized information capture capacity or resolution level. For example, an approximated sampling size of 50 or 100 spectra for full-scale EBPR-related ecosystems at 5% or 2% OPU cluster resolution. Promised by its flexibility and minimal restriction of input data types, the proposed method is expected to be a standardized approach for sampling size optimization, enabling more comparable and reproducible experiments and analysis on complex environmental samples. Finally, these advantages exhibit the capability of generalizing to other single-cell technologies or environmental applications, provided that the input datasets contain only continuous features. TOC O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=182 SRC="FIGDIR/small/423496v1_ufig1.gif" ALT="Figure 1"> View larger version (56K): org.highwire.dtl.DTLVardef@1261a6eorg.highwire.dtl.DTLVardef@91369aorg.highwire.dtl.DTLVardef@197f64dorg.highwire.dtl.DTLVardef@1ab3c19_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2020

Modeling Side-Stream Enhanced Biological Phosphorus Removal (S2EBPR) System Using Agent-based Model with Adaptive Maintenance, Decay and TCA Metabolism

Side-stream enhanced biological phosphorus removal process (S2EBPR) has been demonstrated to improve performance stability and offer a suite of advantages compared to conventional EBPR design. Design and optimization of S2EBPR require modification of the current EBPR models that were not able to fully reflect the metabolic functions of and competition between the polyphosphate-accumulating organisms (PAOs) and glycogen-accumulating organisms (GAOs) under extended anaerobic conditions as in the S2EBPR conditions. In this study, we proposed and validated an improved model (iEBPR) for simulating PAO and GAO competition that incorporated heterogeneity and versatility in PAO sequential polymer usage, staged maintenance-decay, and glycolysis-TCA pathway shifts. The iEBPR model was first calibrated against bulk batch testing experiment data, being proved to perform better than the previous EBPR model for predicting the soluble orthoP, ammonia, biomass glycogen, and PHA temporal profiles in a batch starvation testing under prolonged anaerobic conditions. We further validated the model with another independent set of batch anaerobic batch testing data that included high-resolution single-cell and specific population level intracellular polymer measurements enabled by the single-cell Raman micro-spectroscopy technique. The model accurately predicted the temporal changes in the intracellular polymers at cellular and population levels within PAOs and GAOs, and further confirmed the proposed mechanism of sequential polymer utilization, and polymer availability-dependent and staged maintenance-decay in PAOs. These results indicate that under extended anaerobic phases as in S2EBPR, the PAOs may gain competitive advantages over GAOs due to the possession of multiple intracellular polymers and the adaptive switching of the anaerobic metabolic pathways that consequently lead to the later and slower decay in PAOs than GAOs. The iEBPR model can be applied to facilitate and optimize the design and operations of S2EBPR for more reliable nutrient removal and recovery from wastewater.

microbiology↗

bioRxiv2019

Oligotyping and Genome-Resolved Metagenomics Reveal Distinct Candidatus Accumulibacter Communities in Full-Scale Side-Stream versus Conventional Enhanced Biological Phosphorus Removal (EBPR) Configurations

Candidatus Accumulibacter phosphatis (CAP) and its sub-clades-level diversity has been associated and implicated in successful phosphorus removal performance in enhanced biological phosphorus removal (EBPR). Development of high-throughput untargeted methods to characterize clades of CAP in EBPR communities can enable a better understanding of Accumulibacter ecology at a higher-resolution beyond OTU-level in wastewater resource recovery facilities (WRRFs). In this study, for the first time, using integrated 16S rRNA gene sequencing, oligotyping and genome-resolved metagenomics, we were able to reveal clade-level differences in Accumulibacter communities and associate the differences with two different full-scale EBPR configurations. The results led to the identification and characterization of a distinct and dominant Accumulibacter oligotype - Oligotype 2 (belonging to Clade IIC) and its matching MAG (RC14) associated with side-stream EBPR configuration. We are also able to extract MAGs belonging to CAP clades IIB (RCAB4-2) and II (RC18) which did not have representative genomes before. This study demonstrates and validates the use of a high-throughput approach of oligotyping analysis of 16S rRNA gene sequences to elucidate CAP clade-level diversity. We also show the existence of a previously uncharacterized diversity of CAP clades in full-scale EBPR communities through extraction of MAGs, for the first time from full-scale facilities.

microbiology↗