Biology subjects

Pinto, A. J.

Publications and source records attributed to Pinto, A. J..

At least 19 recordsLinked to original sources

bioRxiv2026

Accounting for DNA Recovery and Cell Culturability Enhances Quantitative Compatibility of Molecular and Legiolert Assays for Legionella pneumophila

Disagreement between molecular and culture-based assays for Legionella pneumophila detection is widely reported, yet comparisons have largely been based on direct assay-derived concentrations or binary positive/negative outcomes. However, it remains unclear whether molecular-culture disagreement reflects concentration-level incompatibility or unaccounted methodological and physiological differences related to DNA recovery and cell culturability. In this study, we also observed disagreement between molecular and Legiolert assays in source and finished drinking water samples collected from eight full-scale drinking water systems across the United States. Molecular thresholds adjusted for DNA recovery and cell culturability only partially resolved these discrepancies. We therefore developed a probabilistic Monte Carlo framework that incorporates sample-specific DNA recovery and cell culturability to evaluate the quantitative consistency of culturable L. pneumophila concentrations estimated by molecular and Legiolert assays. Quantitatively consistent and inconsistent samples occurred across both binary concordant and discordant classifications, demonstrating that positive/negative agreement poorly reflects concentration-level comparability. Overall, molecular and Legiolert assays showed strong quantitative consistency once sample-specific DNA recovery and cell culturability were considered. A small proportion of persistent inconsistencies at specific sampling sites, coupled with atypical microbial indicators, suggest that sample heterogeneity likely contributed to the remaining discrepancies. These findings demonstrate that integrating DNA recovery and cell culturability enhanced quantitative consistency between molecular and Legiolert assays and supports the use of molecular methods as rapid quantitative tools to complement culture-based L. pneumophila monitoring. SynopsisAccounting for DNA recovery and cell culturability revealed broad quantitative compatibility between molecular and Legiolert assays for Legionella pneumophila in drinking water. TOC O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=88 SRC="FIGDIR/small/739452v2_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@1e01cdcorg.highwire.dtl.DTLVardef@86bbc2org.highwire.dtl.DTLVardef@190fc66org.highwire.dtl.DTLVardef@1aa9ec8_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2026

High-accuracy hierarchical rRNA operon profiling resolves genomovar-level taxonomy and microdiversity using Nanopore sequencing.

Genomovar-level and intragenomic diversity cannot be resolved by conventional amplicon sequencing due to the limitation of fragment lengths and read accuracy, while the application of metagenomic profiling to a large number of samples can be resource intensive. Here, we report UltraRes-rrn, an integrated wet-lab and computational workflow for high-accuracy rrn (i.e., 16S- ITS-23S rRNA) operon profiling using Nanopore sequencing. By integrating ultra-long DNA recovery, long-read amplicon sequencing, and unique molecular identifiers (UMI)-based consensus correction, UltraRes-rrn obtains full-length 16S-ITS-23S rRNA operon consensus sequences with mean accuracies exceeding 99.98%. To achieve higher resolution, we propose a hierarchical rRNA operon profiling strategy in which concatenated 16S+23S rRNA genes (16S23S) serve as a primary and the internal transcribed spacer (ITS) provides a secondary marker. The 16S23S marker achieved discrimination at the genomovar level compared to either 16S or 23S rRNA genes alone, which mitigates the ITS-driven over-splitting observed with the full-length rrn operon and allows for larger proportion of data being classified at higher confidence thresholds. Further, ITS variation was strongly structured by tRNA occurrence patterns, suggesting that ITS can capture taxon microdiversity missed by either 16S or 23S rRNA gene sequences alone. The UltraRes-rrn workflow was applied to full-scale nitrogen removal reactors, revealing intraspecies diversity variation driven by different carbon regimes, which would not have been possible with a shorter gene sequence. In summary, UltraRes-rrn enables cost-effective community profiling at genomovar-level resolution in complex ecosystems.

microbiology↗

bioRxiv2026

Systematic comparisons between long-read and short-read based amplicon sequencing to characterize mixed microalgal communities.

The 18S rRNA gene has emerged as the primary molecular marker for amplicon-based characterization of microalgal communities, including in wastewater treatment systems, yet trade-offs between short- and long-read approaches remain poorly defined. We systematically compared V8-V9 short-read sequencing (Illumina MiSeq), full-length long-read sequencing with ss5ss3 primers (PacBio Sequel II), and computationally extracted V8-V9 regions from long-read data. Both in silico and in vitro analyses confirmed V8-V9 captured broader taxonomic coverage than ss5ss3, though partial reference sequences and taxonomic mis-annotations biased in silico assessments. Long-reads taxonomic advantage was database-dependent, constrained by SILVA databases genus-level curation but fully realized when paired with the species-level-curated and eukaryotes-focused PR{superscript 2} (Protist Ribosomal Reference) database. Long-read sequencing uniquely identified amplicon sequence variants (ASVs) assigned to key phosphorus assimilators (Scenedesmus obliquus, Desmodesmus sp., and Acutodesmus sp.) at species level during successful phosphorus removal in a full-scale microalgal cultivation system, while V8-V9 short-read sequencing revealed ASVs assigned to algal-predatory (Leptophryidae) and bacterivorous (Choanoflagellata and Rhogostoma-lineage) protists when performance declined, suggesting grazing pressure on the phosphorus-removing community. Although both approaches performed comparably for operational monitoring, these complementary strengths support short-read sequencing for routine community profiling and long-read sequencing for detailed functional investigations of Chlorophyta.

microbiology↗

bioRxiv2026

SMC Motor Proteins Operate at the Near-Minimal Forces for DNA Loop Extrusion

Loop extrusion by structural maintenance of chromosomes (SMC) complexes is essential for genome organization, yet the forces driving this process remain poorly understood. We present a coarse-grained model enabling predictive simulations of in vitro loop extrusion experiments at experimentally relevant time and length scales by matching parameters with concrete experiments. Using this model, we demonstrate that the extrusion forces generated by SMC motor proteins are just sufficient to overcome initial entropic barriers and sustain loop extrusion, highlighting that motors operate in the thermal regime. By measuring stalling tension directly, we confirm that they can be reliably determined by the Marko-Siggia equation and that varying grafting distances in experimental setups has only a marginal effect on the resulting tension. These results provide a predictive computation method for dissecting the mechanics of SMC driven genome folding.

biophysics↗

bioRxiv2025

Gene-centric analysis of Raskinella chloraquaticus reveals a functionally conserved taxonomic group in global drinking water distribution systems

A recent metagenomic survey of drinking water systems revealed that a highly prevalent and dominant uncultured bacterial genus (Raskinella) was represented globally by a single species (Raskinella chloraquaticus). R. chloraquaticus comprises of two sub-species groups, Lineages 1 and 2, the former representing a globally prevalent genomovar. The objective of this study was to perform comparative analysis of the gene content of R. chloraquaticus to characterize the gene-level diversity and determine factors shaping the diversity of this species. Pangenome analysis revealed that R. chloraquaticus possesses a core set of genes that constitute a major portion (87.74%) of the known gene content of the genome. Furthermore, most of the gene diversity of R. chloraquaticus is associated with Lineage 2 organisms, which consists of at least four distinct genomovars. Lineage 1 organisms consist of a higher proportion of identical genes than would have been expected if changes primarily occurred through random mutations and thus is potentially indicative of recombination. In contrast, Lineage 2 organisms appear to have emerged through random mutations and display stronger geographic preference. These results indicate that homologous recombination and geographic isolation likely shape the genetic repertoire of R. chloraquaticus. Further, the high level of gene conservation in R. chloraquaticus may be reflective of highly selective environment in drinking water systems. Thus, R. chloraquaticus may represent a model organism to probe selective pressures shaping the drinking water microbiome.

microbiology↗

bioRxiv2025

Quantitative metagenomics using a portable protocol.

A field-deployable DNA sequencing approach for quantitative microbial community profiling can enable rapid responses for a range of applications in the water sector - from process control to wastewater surveillance. Current quantitative approaches have complex instrumentation requirements and long turnaround time for DNA recovery and absolute quantitation. In this study, we report a field-deployable rapid detection and absolute quantitation (rD+rQ) workflow that leverages the real-time sequencing capabilities of Nanopore sequencing for quantitative metagenomics. This workflow integrates a high-molecular-weight DNA recovery protocol for diverse environmental matrices of relevance to the water sector and multiplexed Nanopore sequencing with barcoded spike-in-based calibration (BSINC). BSINC using multi-species genomic spike-in controls exhibits significantly higher calibration accuracy compared to conventional approaches that utilize either single DNA fragment or single organism spike in controls. Dynamic detection and quantitation limits were established based on coverage fraction of sequenced genomes and the coefficient of variation of genome copy numbers across replicates to enhance the accuracy and precision of microbial quantitation. The rD+rQ workflow achieves species-level identification and absolute quantitative results comparable to digital PCR in environmental samples. This portable laboratory and easy-to-use rD+rQ workflow should facilitate rapid decision-making for the water industry.

microbiology↗

bioRxiv2025

Encoding Cell Phenotype from Label-Free Imaging Flow Cytometry with Unsupervised Deep Learning

Phenotype characterization with single-cell resolution can enable deep and nuanced insights into microbiological systems. Currently, Flow Cytometry and Imaging Flow Cytometry (IFC) offer numerous advantages, but are marred by barriers to accessibility: (1) high instrument costs; (2) labor-intensive, technically demanding sample preparation; and (3) reliance on consumable reagents (i.e., fluorescent labels). To achieve phenotype characterization without these constraints, we evaluated the low-cost, low-input ARTiMiS IFC as a potential alternative instrument technology. To demonstrate this approach, we used intracellular lipid content in microalgae, an important phenotype for production of biofuels and high-value bioproducts, as the phenotype of interest. Variational Auto-Encoder (VAE) unsupervised deep learning methodology was implemented to encode phenotype variation from un-annotated training data. The VAE embeddings were compared with other label-free predictor modalities to evaluate the stability of VAE data encoding across replicates and its predictive power to estimate the target phenotype. The VAE embeddings were robust and consistent between culture batches, and yielded accurate, consistent predictions of the demonstration phenotype in a high-throughput, non-destructive, dye-free methodology. In this proof-of-concept study, we demonstrate that VAE-enabled ARTiMiS IFC may serve as a viable alternative for cell phenotype characterization while overcoming several of the key drawbacks of traditional high-fidelity techniques. SynopsisLabel-free Imaging Flow Cytometry data was processed by a Variational Auto-Encoder to accurately predict lipid content in microalgal cells.

microbiology↗

bioRxiv2025

The Mycobacterium tuberculosis long-chain fatty acid resistome reveals the universal stress protein TB15.3 as essential for infection

The human pathogen Mycobacterium tuberculosis (Mtb) thrives in lipid-rich microenvironments. Long-chain fatty acids (LCFA) are some of the most abundant lipids encountered by Mtb during infection. Mtb has evolved to utilize LCFA as a preferred carbon source, however, LCFA are also known to be potent antimicrobials. Mtb must therefore employ mechanisms to utilize LCFA as a carbon source while avoiding its bactericidal properties. We used transposon sequencing (TnSeq) to define a Mtb LCFA resistome, and found it to be composed by 38 genes. Surprisingly, Mtb requires a diverse set of metabolic pathways to avoid LCFA toxicity, indicating pleiotropic effects of LCFA in Mtb metabolism. As a functional follow-up on the TnSeq screen, we investigated the function of the universal stress protein TB15.3 in LCFA metabolism and during infection. We show that TB15.3 acts as a "metabolic break" for LCFA uptake and catabolism, avoiding deleterious membrane hyperpolarization. This was associated with loss of viability in the chronic phase of infection in mice and in an in vitro caseum model. Our work highlights Mtb LCFA resistance mechanisms as an important adaptation to the host and a promising target space to be exploited for drug development.

microbiology↗

bioRxiv2024

Morphotype-Resolved Characterization of Microalgal Communities in a Nutrient Recovery Process with ARTiMiS Flow Imaging Microscopy

Microalgae-driven nutrient recovery represents a promising technology to reduce effluent phosphorus while simultaneously generating biomass that can be valorized to offset treatment costs. As full-scale processes come online, system parameters including biomass composition must be carefully monitored to optimize performance and prevent culture crashes. In this study, flow imaging microscopy (FIM) was leveraged to characterize microalgal community composition in near real-time at a full-scale municipal wastewater treatment plant (WWTP) in Wisconsin, USA, and population and morphotype dynamics were examined to identify relationships between water chemistry, biomass composition, and system performance. Two FIM technologies, FlowCam and ARTiMiS, were evaluated as monitoring tools. ARTiMiS provided a more accurate estimate of total system biomass, and estimates derived from particle area as a proxy for biovolume yielded better approximations than particle counts. Deep learning classification models trained on annotated image libraries demonstrated equivalent performance between FlowCam and ARTiMiS, and convolutional neural network (CNN) classifiers proved significantly more accurate when compared to feature table-based deep neural network (DNN) models. Across a two-year study period, Scenedesmus spp. appeared most important for phosphorus removal, which were negatively associated with elevated temperatures and nitrite/nitrate concentrations. Chlorella and Monoraphidium also played an important role in system performance. For both Scenedesmus and Chlorella, smaller morphological types were more often associated with high system performance, whereas larger morphotypes implied a stress response correlating with poor phosphorus recovery rates. These results demonstrate the potential of FIM as a critical technology for high-resolution characterization of industrial microalgal processes. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=76 SRC="FIGDIR/small/628756v1_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@1023934org.highwire.dtl.DTLVardef@f9b4a0org.highwire.dtl.DTLVardef@1c193a6org.highwire.dtl.DTLVardef@1c48ec1_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2024

Integrating Machine Learning with Flow-Imaging Microscopy for Automated Monitoring of Algal Blooms

Real-time monitoring of phytoplankton in freshwater systems is critical for early detection of harmful algal blooms so as to enable efficient response by water management agencies. This paper presents an image processing pipeline developed to adapt ARTiMiS, a low-cost automated flow-imaging device, for real-time algal monitoring specifically in freshwater and environmental systems. This pipeline addresses several challenges associated with autonomous imaging of aquatic samples such as flow-imaging artifacts (i.e., out-of-focus and background objects), as well as specific challenges associated with monitoring of open environmental systems (i.e., identification of novel objects). The pipeline leverages a Random Forest model to identify out- of-focus particles with an accuracy of 89% and a custom background particle detection algorithm to identify and remove particles that erroneously appear in consecutive images with >97{+/-}2.8% accuracy. Furthermore, a convolutional neural network (CNN), trained to classify distinct classes comprising both taxonomical and morphological categories, achieved 94% accuracy in a closed dataset. Nonetheless, the supervised closed-set classifiers struggled with the accurate classification of objects when challenged with debris and novel particles which are common in complex open environments; this limits real-time monitoring applications by requiring extensive manual oversight. To mitigate this, three methods incorporating classification with rejection were tested to improve model precision by excluding irrelevant or unknown classes. Combined, these advances present a fully integrated, end-to-end solution for real-time HAB monitoring in open environmental systems thus enhancing the scalability of automated detection in dynamic aquatic environments. HighlightsO_LIRandom Forest model is more generalizable than Convolutional Neural Networks to remove out-of-focus particles. C_LIO_LIA two-stage clustering algorithm is effective at removing background particles in flow imaging microscopy. C_LIO_LIClosed-set CNN classifier performance deteriorates when challenged with unknown particles. C_LIO_LIClassification with rejection improves both precision and accuracy for environmental samples. C_LI

ecology↗

bioRxiv2024

The Fc fragment of soluble IgMs binds C1q to activate the classical complement pathway, while inhibiting complement-dependent cytotoxicity

Soluble type-M immunoglobulins (IgMs), among the most potent activators of the classical pathway, are key mediators of complement-dependent cytotoxicity, which render them promising drug candidates for the development of alternative drugs in treating autoimmune or inflammatory diseases. In this study, we investigated the biochemical and in vitro functional properties of recombinant fragments from IgMs corresponding to the Fc-core in their pentameric or hexameric forms. Biophysical experiments confirmed the crucial role of the IgM Joining chain (J) in favoring homogenous pentamers, while its absence led to heterogeneous population with a mixture of oligomeric forms. By combining size-exclusion chromatography with mass photometry, isolation of enriched samples with IgM hexamers or IgM pentamers without the J chain was possible. Biolayer interferometry demonstrated that both IgM-Fc forms bind C1q and ELISA showed that they induce the in vitro C4b deposition when in solid phase. Additionally, our data confirmed the higher efficacy of IgM hexamers compared to pentamers in activating the first component of the classical pathway. Finally, hemolytic assays demonstrate the ability of IgM-Fc constructs to inhibit Ig-induced complement-dependent cytotoxicity, which is likely made possible by the absence of Fab. These findings suggest a possible mechanism of C1 sequestration in plasma by IgM cores and consumption of the initial complement component C4. Our data thus provide important information for the development of IgM-based anti-inflammatory molecules that target specifically complement activation.

biochemistry↗

bioRxiv2024

Genetically diverse Mycobacterium tuberculosis isolates manipulate inflammasome activation and IL-1β secretion independently of macrophage metabolic rewiring

The natural diversity of Mycobacterium tuberculosis is gaining relevance in dictating the outcome of tuberculosis (TB). We previously revealed a link between TB severity and M. tuberculosis-driven evasion of the macrophage cytosolic surveillance systems, with isolates from severe TB cases reducing inflammasome activation and interleukin (IL)-1{beta} production by infected cells. IL-1{beta} production and inflammasome activation are commonly associated with the metabolic reprogramming of stimulated macrophages. Thus, we questioned whether the differential modulation of the inflammasome and IL-1{beta} by M. tuberculosis isolates depended on distinct macrophage metabolic reprogramming. Using metabolic inhibitors, mice deficient for key metabolic regulators, and a metabolomics approach, we found that the macrophage metabolic landscape was similar regardless of the infecting M. tuberculosis isolate. Paralleling single-TLR activated macrophages, inhibition of glycolysis during infection impaired IL-1{beta} secretion. However, departing from TLR based models, in M. tuberculosis-infected macrophages IL-1{beta} secretion was independent of macrophage mitochondrial metabolic changes and the transcription factor hypoxia-inducible factor (HIF)-1. Additionally, we found a previously unappreciated impact of host metabolic inhibitors on the pathogen, and show that inhibition of the mycobacteria metabolism dampened both inflammasome activation and IL-1{beta} production. Collectively, our study raises awareness of the potential confounding effect of host metabolic inhibitors acting on the pathogen itself and demonstrates that the modulation of the inflammasome by M. tuberculosis may be uncoupled from the host metabolic reprogramming. Author SummaryMycobacterium tuberculosis is the causative agent of tuberculosis and one of the top infectious killers in the world, with around 1.3 million deaths reported annually. The genetic variability of this pathogen can shape its interaction with the host and modulate disease outcomes. We previously found that M. tuberculosis clinical isolates from patients with severe forms of tuberculosis evade cytosolic surveillance systems in macrophages. Here, we explored whether this evasion tactic was linked to metabolic alterations in the infected macrophages. We found that different M. tuberculosis isolates induced similar metabolic changes in infected macrophages. Additionally, we demonstrate that both host glycolysis and pathogens metabolism were pivotal for maximum IL-1{beta} production. These findings highlight the complexity of macrophage-pathogen interactions and emphasize that bacterial metabolism should be considered in metabolic studies and may be amenable to therapeutic intervention against tuberculosis.

immunology↗

bioRxiv2024

New drinking water genome catalog identifies a globally distributed bacterial genus adapted to disinfected drinking water systems.

Genome-resolved insights into the structure and function of the drinking water microbiome can advance the effective management of drinking water quality. To enable this, we constructed and curated thousands of metagenome-assembled and isolate genomes from drinking water distribution systems globally to develop a Drinking Water Genome Catalog (DWGC). The current DWGC disproportionately represents disinfected drinking water systems due to a paucity of metagenomes from non-disinfected systems. Using the DWGC, we identify core genera of the drinking water microbiome including a genus (UBA4765) within the order Rhizobiales that is frequently detected and highly abundant in disinfected drinking water systems. We demonstrate that this genus has been widely detected but incorrectly classified in previous amplicon sequencing-based investigations of the drinking water microbiome. Further, we show that a single genome variant (genomevar) within this genus is detected in >82% of drinking water systems included in this study. We propose a provisional name for this uncultured bacterium as "Raskinella chlorumaquaticus" and describe the genus as "Raskinella" (pending SeqCode approval). Metabolic annotation and modeling-based predictions indicate that this bacterium is capable of necrotrophic growth, able to metabolize halogenated compounds, proliferates in a biofilm-based environment and shows clear indications of disinfection-mediated selection. SynopsisCreation and analysis of a new curated drinking water genome catalog identifies uncharacterized bacterial species that is widely distributed in disinfected drinking water systems.

microbiology↗

bioRxiv2024

Introducing ARTiMiS: A low-cost flow imaging microscope for phytoplankton monitoring in engineered and natural environments

Manual microscopy is the gold standard for phytoplankton monitoring in diverse engineered and natural environments. However, it is both labor-intensive and requires specialized training for accuracy and consistency, and therefore difficult to implement on a routine basis without significant time investment. Automation can reduce this burden by simplifying the measurement to a single indicator (e.g., chlorophyll fluorescence) measurable by a probe, or by processing samples on an automated cytometer for more granular information. The cost of commercially available flow imaging cytometers, however, poses a steep financial barrier to adoption. To overcome these labor and cost barriers, we developed ARTiMiS: the Autonomous Real-Time Microbial Scope. The ARTiMiS is a low-cost flow imaging microscopy-based platform with onboard software capable of providing species-level quantitation of phytoplankton communities in real-time. ARTiMiS leverages novel multi-modal imaging and onboard machine learning-based data processing that is currently optimized for a curated and expandable database of industrially relevant microalgae. We demonstrate its operational limits, performance in identification of laboratory-cultivated microalgae, and potential for continuous monitoring of complex microalgal communities in full-scale cultivation systems. SynopsisWe introduce a platform for low-cost real-time imaging monitoring of phytoplankton and demonstrate its utility in real-time monitoring of laboratory- and full-scale microalgal cultivation systems.

bioengineering↗

bioRxiv2024

Community Structure and Function During Periods of High Performance and System Upset in a Full-Scale Mixed Microalgal Wastewater Resource Recovery Facility

Microalgae have the potential to exceed current nutrient recovery limits from wastewater, enabling water resource recovery facilities (WRRFs) to achieve increasingly stringent effluent permits. The use of photobioreactors (PBRs) and the separation of hydraulic retention and solids residence time (HRT/SRT) further enables increased biomass in a reduced physical footprint while allowing operational parameters (e.g., SRT) to select for desired functional communities. However, as algal technology transitions to full-scale, there is a need to understand the effect of operational and environmental parameters on complex microbial dynamics among mixotrophic microalgae, bacterial groups, and pests (i.e., grazers and pathogens) and to implement robust process controls for stable long-term performance. Here, we examine the first full-scale, intensive WRRF utilizing mixed microalgal for tertiary treatment in the US (EcoRecover, Clearas Water Recovery Inc.) during a nine-month monitoring campaign. We investigated the temporal variations in microbial community structure (18S and 16S rRNA genes), which revealed that stable system performance of the EcoRecover system was marked by a low-diversity microalgal community (DINVSIMPSON = 2.01) dominated by Scenedesmus sp. (MRA = 55%-80%) that achieved strict nutrient removal (effluent TP < 0.04 mg{middle dot}L-1) and steady biomass production (TSSmonthly avg. = 400-700 mg{middle dot}L-1). Operational variables including pH, alkalinity, and influent ammonium (NH4+), correlated positively (p < 0.05, method = Spearman) with algal community during stable performance. Further, the use of these parameters as operational controls along with N/P loading and SRT allowed for system recovery following upset events. Importantly, the presence or absence of bacterial nitrification did not directly impact algal system performance and overall nutrient recovery, but partial nitrification (potentially resulting from NO2- accumulation) inhibited algal growth and should be considered during long-term operation. The microalgal communities were also adversely affected by zooplankton grazers (ciliates, rotifers) and fungal parasites (Aphelidium), particularly during periods of upset when algal cultures were experiencing culture turnover or stress conditions (e.g., nitrogen limitation, elevated temperature). Overall, the active management of system operation in order to maintain healthy algal cultures and high biomass productivity can result in significant periods (>4 months) of stable system performance that achieve robust nutrient recovery, even in winter months in northern latitudes (WI, USA). Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=72 SRC="FIGDIR/small/576871v1_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@15ef2a4org.highwire.dtl.DTLVardef@8c1de6org.highwire.dtl.DTLVardef@ff1160org.highwire.dtl.DTLVardef@1cded87_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIMicrobial dynamics were examined for first full-scale, intensive (small footprint) algal wastewater treatment process (EcoRecover) for advanced P removal. C_LIO_LIMixed microbial communities during stable performance were dominated by Scenedesmus and Cyanobacteria and positively correlated with pH, alkalinity, and influent NH4+, among other parameters. C_LIO_LIBacterial nitrification did not benefit or hinder nutrient recovery, but partial nitrification and NO2- accumulation inhibited algal growth. C_LIO_LITaxa specific pest dynamics are described, with major outbreaks occurring during high temperature in summer months. C_LIO_LIControl of operational parameters, and recovery of stable system performance and algal biomass was achieved following system upsets. C_LI

bioengineering↗

bioRxiv2022

Identifying eukaryotes in drinking water metagenomes and factors influencing their biogeography.

The biogeography of eukaryotes in drinking water systems is poorly understood relative to prokaryotes or viruses. A common challenge with studying complex eukaryotic communities from natural and engineered systems is that the metagenomic analysis workflows are currently not as mature as those that focus on prokaryotes or even viruses. In this study, we benchmarked different strategies to recover eukaryotic sequences and genomes from metagenomic data and applied the best-performing workflow to explore eukaryotic communities present in drinking water distribution systems (DWDSs). We developed an ensemble approach that exploits k-mer and reference-based strategies to improve eukaryotic sequence identification from metagenomes and identified MetaBAT2 as the best performing binning approach for clustering of eukaryotic sequences. Applying this workflow on the DWDSs metagenomes showed that eukaryotic sequences typically constituted a small proportion (i.e., <1%) of the overall metagenomic data. Eukaryotic sequences showed higher relative abundances in surface water-fed and chlorine disinfected systems. Further, the alpha and beta-diversity of eukaryotes were correlated with prokaryotic and viral communities. Finally, a co-occurrence analysis highlighted clusters of eukaryotes whose presence and abundance in DWDSs is affected by disinfection strategies, climate conditions, and source water types. SynopsisAfter benchmarking tools and developing a dedicated consensus workflow for eukaryotic sequence detection in metagenomes, the experimental, environmental, and engineering factors affecting their biogeography in drinking water distribution systems were investigated Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=108 SRC="FIGDIR/small/518372v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@1ae8423org.highwire.dtl.DTLVardef@653471org.highwire.dtl.DTLVardef@5606b7org.highwire.dtl.DTLVardef@1d30cde_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2022

Low diversity and microdiversity of comammox bacteria in wastewater systems suggests wastewater-specific adaptation within the Ca. Nitrospira nitrosa cluster.

Studies have found Ca. Nitrospira nitrosa-like bacteria to be the principal or sole comammox bacteria in nitrogen removal systems for wastewater treatment. In contrast, multiple populations of strict ammonia and nitrite oxidizers co-exist in similar systems. This apparent lack of diversity is surprising and could impact the feasibility of leveraging comammox bacteria for nitrogen removal. We used full-length 16S rRNA gene sequencing and genome-resolved metagenomics to compare population-level (i.e., species) diversity of comammox bacteria with that of strict nitrifiers in full-scale wastewater treatment systems and assess whether these observations were consistent or diverged at the strain-level. Full-length 16S rRNA gene sequencing indicated that while Nitrosomonas-like bacteria exhibited higher population-level diversity, the effective microdiversity of most Nitrospira-like bacteria were comparatively higher except for one Nitrospira Lineage II population. Comammox bacterial metagenome assembled genomes (MAGs) were associated with Ca. Nitrospira nitrosa. The average amino acid identity between comammox bacterial MAGs (93% {+/-} 3) across systems was significantly higher than that of the Nitrosomonas-like ammonia oxidizers (73%{+/-}8) and the Nitrospira-like nitrite oxidizer MAGs (75%{+/-}13), suggesting that the same comammox population was detected in all systems. Comammox bacteria and some ammonia oxidizers MAGs were significantly less microdiverse than most ammonia and nitrite oxidizers. Interestingly, strain-resolved analysis also indicates that different nitrogen removal systems harbor different comammox bacterial strains within the Ca. Nitrospira nitrosa cluster. These results suggest that comammox bacteria associated with Ca. Nitrospira nitrosa have low species- and strain-level diversity in nitrogen removal systems and may thus harbor specific adaptations to the wastewater ecosystem.

microbiology↗

bioRxiv2021

A Snapshot of the Global Drinking Water Virome: Diversity and Metabolic Potential Vary with Residual Disinfectant Use

Viruses are important drivers of microbial community ecology and evolution, influencing microbial mortality, metabolism, and horizontal gene transfer. However, the effects of viruses remain largely unknown in many environments, including in drinking water systems. Drinking water metagenomic studies have offered a whole community perspective of bacterial impacts on water quality, but have not yet considered the influences of viruses. In this study, we address this gap by mining viral DNA sequences from publicly available drinking water metagenomes from distribution systems in six countries around the world. These datasets provide a snapshot of the taxonomic diversity and metabolic potential of the global drinking water virome; and provide an opportunity to investigate the effects of geography, climate, and drinking water treatment practices on viral diversity. Both environmental conditions and differences in sample processing were found to influence the viral composition. Using free chlorine as the residual disinfectant was associated with clear differences in viral taxonomic diversity and metabolic potential, with significantly fewer viral populations and less even viral community structures than observed in distribution systems without residual disinfectant. Additionally, drinking water viruses carry antibiotic resistance genes (ARGs), as well as genes to survive oxidative stress and nitrogen limitation. Through this study, we have demonstrated that viral communities are diverse across drinking water systems and vary with the use of residual disinfectant. Our findings offer directions for future research to develop a more robust understanding of how virus-bacteria interactions in drinking water distribution systems affect water quality.

microbiology↗