Biology subjects

Mau, R. L.

Publications and source records attributed to Mau, R. L..

3 recordsLinked to original sources

bioRxiv2026

Quantitative stable isotope probing to measure in situ growth of protist populations

Soil protists shape microbial food webs and nutrient cycling, yet methods for measuring their population growth in soil have lagged behind the taxonomic resolution available from 18S rRNA gene sequencing. Microscopy-based approaches can estimate abundance and growth, but with limited taxonomic resolution. Here, we tested whether quantitative stable isotope probing (qSIP) can provide reproducible, sequencing-resolved growth measurements for soil protists. We incubated soil with natural-abundance or {superscript 1} O-labeled water and measured taxon-specific 18O enrichment in DNA using three common 18S rRNA gene primer sets. 18O enrichment values were positively correlated across datasets, with relationships closest to 1:1 after poorly resolved taxonomic assignments were excluded, indicating that qSIP provides reproducible population-level growth signals across marker choices. We then compared 18S amplicon profiles from unfractionated DNA with qSIP-derived growth measurements across a soil moisture gradient. Amplicon profiles showed small shifts in relative abundances of major protist groups, whereas qSIP revealed a large moisture response in the growing community: 7 ASVs were growing at 20% field capacity compared with 143 at 80% field capacity, representing 1.6% and 63.3% of total protist relative abundance, respectively. Average growth was <1% day{square}1 in the two driest treatments, increasing to 2.1% day{square}1 at 60% and 5.6% day{square}1 at 80% field capacity; among growing taxa, rates averaged 8.7% and 8.3% day{square}1 in the two wetter treatments. By pairing taxonomic resolution with isotope-based growth estimates, qSIP with 18O-H2O moves soil protist ecology toward quantitative population dynamics: identifying which taxa grow, how fast, and how growth responds to the environment.

microbiology↗

bioRxiv2021

The Functional Significance of Bacterial Predators

Predation structures food webs, influences energy flow, and alters rates and pathways of nutrient cycling through ecosystems, effects that are well documented for macroscopic predators. In the microbial world, predatory bacteria are common, yet little is known about their rates of growth and roles in energy flows through microbial food webs, in part because these are difficult to quantify. Here, we show that growth and carbon uptake were higher in predatory bacteria compared to non-predatory bacteria, a finding across 15 sites, synthesizing 82 experiments and over 100,000 taxon-specific measurements of element flow into newly synthesized bacterial DNA. Obligate predatory bacteria grew 36% faster and assimilated carbon at rates 211% higher than non-predatory bacteria. These differences were less pronounced for facultative predators (6% higher growth rates, 17% higher carbon assimilation rates), though high growth and carbon assimilation rates were observed for some facultative predators, such as members of the genera Lysobacter and Cytophaga, both capable of gliding motility and wolfpack hunting behavior. Added carbon substrates disproportionately stimulated growth of obligate predators, with responses 63% higher than non-predators for the Bdellovibrionales and 81% higher for the Vampirovibrionales, whereas responses of facultative predators to substrate addition were no different from non-predators. This finding supports ecological theory that higher productivity increases predator control of lower trophic levels. These findings also indicate that the functional significance of bacterial predators increases with energy flow, and that predatory bacteria influence element flow through microbial food webs.

ecology↗

bioRxiv2020

Measurement error and resolution in quantitative stable isotope probing: implications for experimental design

Quantitative stable isotope probing (qSIP) estimates the degree of incorporation of an isotope tracer into nucleic acids of metabolically active organisms and can be applied to microorganisms growing in complex communities, such as the microbiomes of soil or water. As such, qSIP has the potential to link microbial biodiversity and biogeochemistry. As with any technique involving quantitative estimation, qSIP involves measurement error; a more complete understanding of error, precision and statistical power will aid in the design of qSIP experiments and interpretation of qSIP data. We used several existing qSIP datasets of microbial communities found in soil and water to evaluate how variance in the estimate of isotope incorporation depends on organism abundance and on the resolution of the density fractionation scheme. We also assessed statistical power for replicated qSIP studies, and sensitivity and specificity for unreplicated designs. We found that variance declines as taxon abundance increases. Increasing the number of density fractions reduces variance, although the benefit of added fractions declines as the number of fractions increases. Specifically, nine fractions appear to be a reasonable tradeoff between cost and precision for most qSIP applications. Increasing replication improves power and reduces the minimum detectable threshold for inferring isotope uptake to 5 atom%. Finally, we provide evidence for the importance of internal standards to calibrate the %GC to mean weighted density regression per sample. These results should benefit those designing future SIP experiments, and provide a reference for metagenomic SIP applications where financial and computational limitations constrain experimental scope. ImportanceOne of the biggest challenges in microbial ecology is correlating the identity of microorganisms with the roles they fulfill in natural environmental systems. Studies of microbes in pure culture reveal much about genomic content and potential functions, but may not reflect an organisms activity within its natural community. Culture-independent studies supply a community-wide view of composition and function in the context of community interactions, but fail to link the two. Quantitative stable isotope probing (qSIP) is a method that can link the identity and function of specific microbes within a naturally occurring community. Here we explore how the resolution of density-gradient fractionation affects the error and precision of qSIP results, how they may be improved via additional replication, and cost-benefit balanced scenarios for SIP experimental design.

microbiology↗