Biology subjects

Wloch-Salamon, D.

Publications and source records attributed to Wloch-Salamon, D..

2 recordsLinked to original sources

bioRxiv2022

How to determine microbial lag phase duration?

The lag phase is a temporary non-replicative period observed when a microbial population is introduced to a new nutrient-rich environment. Its duration can have a pronounced effect on population fitness, and it is often measured in laboratory conditions. However, calculating the lag phase length may be challenging and method and parameters dependent. Moreover, the details of these methods and parameters used throughout experimental studies are often under-reported. Here we discuss the most frequently used methods in experimental and theoretical studies, and we point out some inconsistencies between them. Using experimental and simulated data we study the performance of these methods depending on the frequency of population size measurements, and parameters determining the growth curve shape, such as growth rate. It turns out that the sensitivity to each of these parameters depends on the lag calculation methods. For example, lag duration calculation by parameter fitting to a logistic model is very robust to low frequency of measurements, but it may be highly biased for growth curves with low growth rate. On the contrary, the method based on finding the point where growth acceleration is the highest, is robust to low growth rate, but highly sensitive to low frequency of measurements and the level of noise in the data. Based on our results, we propose a decision tree to choose a method most suited to ones data. Finally, we developed a web tool where the lag duration can be calculated based on the user-specified growth curve data, and for various explicitly specified methods, parameters, and data pre-processing techniques. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=100 SRC="FIGDIR/small/516631v1_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@1867dc4org.highwire.dtl.DTLVardef@ab04fborg.highwire.dtl.DTLVardef@1d43aaeorg.highwire.dtl.DTLVardef@58963f_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2020

Forest Saccharomyces paradoxus are robust to seasonal biotic and abiotic changes

Microorganisms are famous for adapting quickly to new environments. However, most evidence for rapid microbial adaptation comes from laboratory experiments or domesticated environments, and it is unclear how rates of adaptation scale from human-influenced environments to the great diversity of wild microorganisms. We examined potential monthly-scale selective pressures in the model forest yeast Saccharomyces paradoxus. Contrary to expectations of seasonal adaptation, the S. paradoxus population was stable over four seasons in the face of abiotic and biotic environmental changes. While the S. paradoxus population was diverse, including 41 unique genotypes among 192 sampled isolates, there was no correlation between S. paradoxus genotypes and seasonal environments. Consistent with observations from other S. paradoxus populations, the forest population was highly clonal and inbred. This lack of recombination, paired with population stability, implies that S. paradoxus evolved the phenotypic plasticity needed to resist seasonal environmental fluctuations long ago, and that individual S. paradoxus are generalists with regard to seasonal environments. Similarly, while the forest population included diversity among phenotypes related to intraspecific interference competition, there was no evidence for active coevolution among these phenotypes. At least ten percent of the forest S. paradoxus individuals produced "killer toxins", which kill sensitive Saccharomyces cells, but the presence of a toxin-producing isolate did not predict resistance to the toxin among nearby isolates. How forest yeasts acclimate to changing environments remains an open question, and future studies should investigate the physiological responses that allow microbial cells to cope with environmental fluctuations in their native habitats.

evolutionary biology↗