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Journal articles on the topic 'Microbial populations'

Author: Grafiati

Published: 4 June 2021

Last updated: 28 July 2024

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1

Gokhale, Chaitanya S., Stefano Giaimo, and Philippe Remigi. "Memory shapes microbial populations." PLOS Computational Biology 17, no. 10 (October 1, 2021): e1009431. http://dx.doi.org/10.1371/journal.pcbi.1009431.

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Correct decision making is fundamental for all living organisms to thrive under environmental changes. The patterns of environmental variation and the quality of available information define the most favourable strategy among multiple options, from randomly adopting a phenotypic state to sensing and reacting to environmental cues. Cellular memory—the ability to track and condition the time to switch to a different phenotypic state—can help withstand environmental fluctuations. How does memory manifest itself in unicellular organisms? We describe the population-wide consequences of phenotypic memory in microbes through a combination of deterministic modelling and stochastic simulations. Moving beyond binary switching models, our work highlights the need to consider a broader range of switching behaviours when describing microbial adaptive strategies. We show that memory in individual cells generates patterns at the population level coherent with overshoots and non-exponential lag times distributions experimentally observed in phenotypically heterogeneous populations. We emphasise the implications of our work in understanding antibiotic tolerance and, in general, bacterial survival under fluctuating environments.

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2

VÃ¡zquez, Francisco J., MarÃa J. Acea, and Tarsy Carballas. "Soil microbial populations after wildfire." FEMS Microbiology Ecology 13, no. 2 (December 1993): 93–103. http://dx.doi.org/10.1111/j.1574-6941.1993.tb00055.x.

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3

Haack, Sheridan K., and Barbara A. Bekins. "Microbial populations in contaminant plumes." Hydrogeology Journal 8, no. 1 (March 13, 2000): 63–76. http://dx.doi.org/10.1007/s100400050008.

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4

Koskella, Britt, and Michiel Vos. "Adaptation in Natural Microbial Populations." Annual Review of Ecology, Evolution, and Systematics 46, no. 1 (December 4, 2015): 503–22. http://dx.doi.org/10.1146/annurev-ecolsys-112414-054458.

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5

VAZQUEZ, F. "Soil microbial populations after wildfire." FEMS Microbiology Ecology 13, no. 2 (December 1993): 93–103. http://dx.doi.org/10.1016/0168-6496(93)90027-5.

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6

Oleskin, Alexander V. "Social behaviour of microbial populations." Journal of Basic Microbiology 34, no. 6 (1994): 425–39. http://dx.doi.org/10.1002/jobm.3620340608.

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7

Bennett, Albert F., and Bradley S. Hughes. "Microbial experimental evolution." American Journal of Physiology-Regulatory, Integrative and Comparative Physiology 297, no. 1 (July 2009): R17—R25. http://dx.doi.org/10.1152/ajpregu.90562.2008.

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Microbes have been widely used in experimental evolutionary studies because they possess a variety of valuable traits that facilitate large-scale experimentation. Many replicated populations can be cultured in the laboratory simultaneously along with appropriate controls. Short generation times and large population sizes make microbes ideal experimental subjects, ensuring that many spontaneous mutations occur every generation and that adaptive variants can spread rapidly through a population. Another highly useful experimental feature is the ability to preserve and store ancestral and evolutionarily derived clones. These can be revived in parallel to allow the direct measurement of the competitive fitness of a descendant compared with its ancestor. The extent of adaptation can thereby be measured quantitatively and compared statistically by direct competition among derived groups and with the ancestor. Thus, fitness and adaptation need not be matters of qualitative speculation, but are quantitatively measurable variables in these systems. Replication allows the quantification of heterogeneity in responses to imposed selection and thereby statistical distinction between changes that are systematic responses to the selective regimen and those that are specific to individual populations.

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8

Duan, Xing-Zhi, Guo-Sen Guo, Ling-Fei Zhou, Le Li, Ze-Min Liu, Cheng Chen, Bin-Hua Wang, and Lan Wu. "Enterobacteriaceae as a Key Indicator of Huanglongbing Infection in Diaphorina citri." International Journal of Molecular Sciences 25, no. 10 (May 9, 2024): 5136. http://dx.doi.org/10.3390/ijms25105136.

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Extensive microbial interactions occur within insect hosts. However, the interactions between the Huanglongbing (HLB) pathogen and endosymbiotic bacteria within the Asian citrus psyllid (ACP, Diaphorina citri Kuwayama) in wild populations remain elusive. Thus, this study aimed to detect the infection rates of HLB in the ACP across five localities in China, with a widespread prevalence in Ruijin (RJ, 58%), Huidong (HD, 28%), and Lingui (LG, 15%) populations. Next, microbial communities of RJ and LG populations collected from citrus were analyzed via 16S rRNA amplicon sequencing. The results revealed a markedly higher microbial diversity in the RJ population compared to the LG population. Moreover, the PCoA analysis identified significant differences in microbial communities between the two populations. Considering that the inter-population differences of Bray–Curtis dissimilarity in the RJ population exceeded those between populations, separate analyses were performed. Our findings indicated an increased abundance of Enterobacteriaceae in individuals infected with HLB in both populations. Random forest analysis also identified Enterobacteriaceae as a crucial indicator of HLB infection. Furthermore, the phylogenetic analysis suggested a potential regulatory role of ASV4017 in Enterobacteriaceae for ACP, suggesting its possible attractant activity. This research contributes to expanding the understanding of microbial communities associated with HLB infection, holding significant implications for HLB prevention and treatment.

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9

Shooner, Frédéric, and Rajeshwar D. Tyagi. "Microbial ecology of simultaneous thermophilic microbial leaching and digestion of sewage sludge." Canadian Journal of Microbiology 41, no. 12 (December 1, 1995): 1071–80. http://dx.doi.org/10.1139/m95-150.

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The microbial population encountered during a simultaneous thermophilic microbial leaching and digestion process at 50 °C, based on microbial sulfur oxidation, was investigated. The cell count of the sulfuric acid producer Thiobacillus thermosulfatus increased, followed by a decrease. In the absence of sulfur (control: conventional thermophilic digestion), Thiobacillus thermosulfatus population decreased under the detection limit. Acidophilic and neutrophilic heterotrophic populations increased during the leaching process, and the final acidophilic population count was higher than the neutrophilic population. During the thermophilic digestion (control), the final neutrophilic population count was higher than the acidophilic. Six heterotrophic bacterial strains were isolated and partially characterized. Bacillus was the most predominant genus. The type of bacterial populations in thermophilic microbial leaching and digestion, as well as the thermophilic digestion process (control), were the same, while only the relative concentrations changed. In both processes, the bacterial indicators decreased under the detection limit after 12 h. Mesophilic heterotrophic population was more affected by the thermophilic microbial leaching process than by thermophilic digestion. Sludge mineralization was probably more influenced by the final cell concentration rather than the presence of an individual species or mixed population.Key words: Thiobacillus thermosulfatus, thermophilic metal leaching, thermophilic sludge digestion, indicator microorganisms.

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10

KOSEKI, SHIGENOBU, and KAZUHIKO ITOH. "Prediction of Microbial Growth in Fresh-Cut Vegetables Treated with Acidic Electrolyzed Water during Storage under Various Temperature Conditions." Journal of Food Protection 64, no. 12 (December 1, 2001): 1935–42. http://dx.doi.org/10.4315/0362-028x-64.12.1935.

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Effects of storage temperature (1, 5, and 10°C) on growth of microbial populations (total aerobic bacteria, coliform bacteria, Bacillus cereus, and psychrotrophic bacteria) on acidic electrolyzed water (AcEW)-treated fresh-cut lettuce and cabbage were determined. A modified Gompertz function was used to describe the kinetics of microbial growth. Growth data were analyzed using regression analysis to generate “best-fit” modified Gompertz equations, which were subsequently used to calculate lag time, exponential growth rate, and generation time. The data indicated that the growth kinetics of each bacterium were dependent on storage temperature, except at 1°C storage. At 1°C storage, no increases were observed in bacterial populations. Treatment of vegetables with AcEW produced a decrease in initial microbial populations. However, subsequent growth rates were higher than on nontreated vegetables. The recovery time required by the reduced microbial population to reach the initial (treated with tap water [TW]) population was also determined in this study, with the recovery time of the microbial population at 10°C being <3 days. The benefits of reducing the initial microbial populations on fresh-cut vegetables were greatly affected by storage temperature. Results from this study could be used to predict microbial quality of fresh-cut lettuce and cabbage throughout their distribution.

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11

Gendugov, V. M., G. P. Glazunov, and M. V. Yevdokimova. "Macrokinetics of microbial populations in soil." Moscow University Soil Science Bulletin 65, no. 3 (September 2010): 133–37. http://dx.doi.org/10.3103/s0147687410030075.

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12

Fowler, A. C. "Starvation kinetics of oscillating microbial populations." Mathematical Proceedings of the Royal Irish Academy 114A, no. 2 (2014): 173–89. http://dx.doi.org/10.1353/mpr.2014.0008.

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13

Henson, Michael A. "Dynamic modeling of microbial cell populations." Current Opinion in Biotechnology 14, no. 5 (October 2003): 460–67. http://dx.doi.org/10.1016/s0958-1669(03)00104-6.

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14

Takhaveev, Vakil, and Matthias Heinemann. "Metabolic heterogeneity in clonal microbial populations." Current Opinion in Microbiology 45 (October 2018): 30–38. http://dx.doi.org/10.1016/j.mib.2018.02.004.

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15

Fowler. "Starvation kinetics of oscillating microbial populations." Mathematical Proceedings of the Royal Irish Academy 114A, no. 2 (2014): 173. http://dx.doi.org/10.3318/pria.2014.114.09.

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16

Nichols, T. D., D. C. Wolf, H. B. Rogers, C. A. Beyrouty, and C. M. Reynolds. "Rhizosphere microbial populations in contaminated soils." Water, Air, & Soil Pollution 95, no. 1-4 (April 1997): 165–78. http://dx.doi.org/10.1007/bf02406163.

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17

Marshall, Timothy R., and Joseph S. Devinny. "The Microbial Ecosystem in Petroleum Waste Land Treatment." Water Science and Technology 20, no. 11-12 (November 1, 1988): 285–91. http://dx.doi.org/10.2166/wst.1988.0297.

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Microbial populations, microbial activity and environmental conditions in an operating petroleum waste land treatment facility were monitored for eighteen months. Seasonal influences are apparent for both bacterial and fungal populations. During the cooler, wetter seasons, microbe populations were smaller, less variable and inhibited by the adverse environmental conditions. The hotter, drier months supported large, active populations which experienced large swings in numbers and respiratory output. Microenvironments within aggregates were investigated. Analysis of various aggregate sizes revealed differences in population, activity and distribution of microorganisms. Optimization of waste biodegradation in treatment soils requires monitoring the factors affecting the microbial community at the system level and an awareness of the microenvironmental influences.

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18

Hwang, Chiachi, Fangqiong Ling, Gary L. Andersen, Mark W. LeChevallier, and Wen-Tso Liu. "Microbial Community Dynamics of an Urban Drinking Water Distribution System Subjected to Phases of Chloramination and Chlorination Treatments." Applied and Environmental Microbiology 78, no. 22 (August 31, 2012): 7856–65. http://dx.doi.org/10.1128/aem.01892-12.

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ABSTRACTWater utilities in parts of the U.S. control microbial regrowth in drinking water distribution systems (DWDS) by alternating postdisinfection methods between chlorination and chloramination. To examine how this strategy influences drinking water microbial communities, an urban DWDS (population ≅ 40,000) with groundwater as the source water was studied for approximately 2 years. Water samples were collected at five locations in the network at different seasons and analyzed for their chemical and physical characteristics and for their microbial community composition and structure by examining the 16S rRNA gene via terminal restriction fragment length polymorphism and DNA pyrosequencing technology. Nonmetric multidimension scaling and canonical correspondence analysis of microbial community profiles could explain >57% of the variation. Clustering of samples based on disinfection types (free chlorine versus combined chlorine) and sampling time was observed to correlate to the shifts in microbial communities. Sampling location and water age (<21.2 h) had no apparent effects on the microbial compositions of samples from most time points. Microbial community analysis revealed that among major core populations,Cyanobacteria,Methylobacteriaceae,Sphingomonadaceae, andXanthomonadaceaewere more abundant in chlorinated water, andMethylophilaceae,Methylococcaceae, andPseudomonadaceaewere more abundant in chloraminated water. No correlation was observed with minor populations that were detected frequently (<0.1% of total pyrosequences), which were likely present in source water and survived through the treatment process. Transient microbial populations includingFlavobacteriaceaeandClostridiaceaewere also observed. Overall, reversible shifts in microbial communities were especially pronounced with chloramination, suggesting stronger selection of microbial populations from chloramines than chlorine.

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19

VanInsberghe, David, Philip Arevalo, Diana Chien, and Martin F. Polz. "How can microbial population genomics inform community ecology?" Philosophical Transactions of the Royal Society B: Biological Sciences 375, no. 1798 (March 23, 2020): 20190253. http://dx.doi.org/10.1098/rstb.2019.0253.

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Populations are fundamental units of ecology and evolution, but can we define them for bacteria and archaea in a biologically meaningful way? Here, we review why population structure is difficult to recognize in microbes and how recent advances in measuring contemporary gene flow allow us to identify clearly delineated populations among collections of closely related genomes. Such structure can arise from preferential gene flow caused by coexistence and genetic similarity, defining populations based on biological mechanisms. We show that such gene flow units are sufficiently genetically isolated for specific adaptations to spread, making them also ecological units that are differentially adapted compared to their closest relatives. We discuss the implications of these observations for measuring bacterial and archaeal diversity in the environment. We show that operational taxonomic units defined by 16S rRNA gene sequencing have woefully poor resolution for ecologically defined populations and propose monophyletic clusters of nearly identical ribosomal protein genes as an alternative measure for population mapping in community ecological studies employing metagenomics. These population-based approaches have the potential to provide much-needed clarity in interpreting the vast microbial diversity in human and environmental microbiomes. This article is part of the theme issue ‘Conceptual challenges in microbial community ecology’.

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20

Milani, Christian, Giulia Alessandri, Leonardo Mancabelli, Gabriele Andrea Lugli, Giulia Longhi, Rosaria Anzalone, Alice Viappiani, et al. "Bifidobacterial Distribution Across Italian Cheeses Produced from Raw Milk." Microorganisms 7, no. 12 (November 21, 2019): 599. http://dx.doi.org/10.3390/microorganisms7120599.

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Cheese microbiota is of high industrial relevance due to its crucial role in defining the organoleptic features of the final product. Nevertheless, the composition of and possible microbe–microbe interactions between these bacterial populations have never been assessed down to the species-level. For this reason, 16S rRNA gene microbial profiling combined with internally transcribed spacer (ITS)-mediated bifidobacterial profiling analyses of various cheeses produced with raw milk were performed in order to achieve an in-depth view of the bifidobacterial populations present in these microbially fermented food matrices. Moreover, statistical elaboration of the data collected in this study revealed the existence of community state types characterized by the dominance of specific microbial genera that appear to shape the overall cheese microbiota through an interactive network responsible for species-specific modulatory effects on the bifidobacterial population.

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21

Gilbert, Rosalind, and Diane Ouwerkerk. "The Genetics of Rumen Phage Populations." Proceedings 36, no. 1 (April 7, 2020): 165. http://dx.doi.org/10.3390/proceedings2019036165.

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The microbial populations of the rumen are widely recognised as being essential for ruminant nutrition and health, utilising and breaking down fibrous plant material which would otherwise be indigestible. The dense and highly diverse viral populations which co-exist with these microbial populations are less understood, despite their potential impacts on microbial lysis and gene transfer. In recent years, studies using metagenomics, metatranscriptomics and proteomics have provided new insights into the types of viruses present in the rumen and the proteins they produce. These studies however are limited in their capacity to fully identify and classify the viral sequence information obtained, due to the absence of rumen-specific virus genomes in current sequence databases. The majority of commensal viruses found in the rumen are those infecting bacteria (phages), therefore we genome sequenced phage isolates from our phage culture collection infecting the common rumen microbial genera Bacteroides, Ruminococcus and Streptococcus. We also created a pan-genome using 39 whole genome sequences of predominantly livestock-derived Streptococcus isolates (representing S. bovis, S. equinus, S. henryi, and S. gallolyticus), to identify and characterise integrated viral genomes (prophage sequences). Collectively this approach has provided novel rumen phage sequences to increase the accuracy of rumen metagenomics analyses. It has also provided new insights into how viruses or virus-encoded proteins can potentially be used to modulate specific microbial populations within the rumen microbiome.

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22

Logares, Ramiro. "Population genetics: the next stop for microbial ecologists?" Open Life Sciences 6, no. 6 (December 1, 2011): 887–92. http://dx.doi.org/10.2478/s11535-011-0086-9.

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AbstractMicrobes play key roles in the functioning of the biosphere. Still, our knowledge about their total diversity is very limited. In particular, we lack a clear understanding of the evolutionary dynamics occurring within their populations (i.e. among members of the same biological species). Unlike animals and plants, microbes normally have huge population sizes, high reproductive rates and the potential for unrestricted dispersal. As a consequence, the knowledge of population genetics acquired from studying animals and plants cannot be applied without extensive testing to microbes. Next generation molecular tools, like High Throughput Sequencing (e.g. 454 and Illumina) coupled to Single Cell Genomics, now allow investigating microbial populations at a very fine scale. Such techniques have the potential to shed light on several ecological and evolutionary processes occurring within microbial populations that so far have remained hidden. Furthermore, they may facilitate the identification of microbial species. Eventually, we may find an answer to the question of whether microbes and multicellular organisms follow the same or different rules in their population diversification patterns.

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23

Gao, P. K., G. Q. Li, H. M. Tian, Y. S. Wang, H. W. Sun, and T. Ma. "Differences in microbial community composition between injection and production water samples of water flooding petroleum reservoirs." Biogeosciences 12, no. 11 (June 5, 2015): 3403–14. http://dx.doi.org/10.5194/bg-12-3403-2015.

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Abstract. Microbial communities in injected water are expected to have significant influence on those of reservoir strata in long-term water flooding petroleum reservoirs. To investigate the similarities and differences in microbial communities in injected water and reservoir strata, high-throughput sequencing of microbial partial 16S rRNA of the water samples collected from the wellhead and downhole of injection wells, and from production wells in a homogeneous sandstone reservoir and a heterogeneous conglomerate reservoir were performed. The results indicate that a small number of microbial populations are shared between the water samples from the injection and production wells in the sandstone reservoir, whereas a large number of microbial populations are shared in the conglomerate reservoir. The bacterial and archaeal communities in the reservoir strata have high concentrations, which are similar to those in the injected water. However, microbial population abundance exhibited large differences between the water samples from the injection and production wells. The number of shared populations reflects the influence of microbial communities in injected water on those in reservoir strata to some extent, and show strong association with the unique variation of reservoir environments.

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24

Tiedje, James M., Suzanne M. Thiem, Arturo Massol-Deya, Jong-Ok Ka, and Marcos R. Fries. "Tracking Microbial Populations Effective in Reducing Exposure." Environmental Health Perspectives 103 (June 1995): 117. http://dx.doi.org/10.2307/3432493.

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25

Waipara, N. W., F. O. Obanor, and M. Walter. "Impact of phylloplane management on microbial populations." New Zealand Plant Protection 55 (August 1, 2002): 125–28. http://dx.doi.org/10.30843/nzpp.2002.55.3940.

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The impact of apple orchard management on leaf microbial populations was investigated during the 2001/2002 growing season Apple leaves were collected in spring and autumn from two certified organic (BioGro) and IFP (Integrated Fruit Production) managed apple orchards at each of three New Zealand sites (Hawkes Bay Nelson and Canterbury) Phylloplane epiphytes were recovered by leaf washing using a stomacher blender and the microorganisms enumerated using serial plate dilutions The microorganisms were separated into recognisable taxonomic units (RTUs) based on colony morphology Analysis of both spring and autumn samples showed that leaves from all three sites from organic orchards harboured significantly more colony forming units than were found on leaves from IFP orchards Overall population richness (based on RTUs/ leaf sample) was also significantly higher in organic than IFP orchards

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26

Tiedje, J. M., S. M. Thiem, A. Massol-Deyá, J. O. Ka, and M. R. Fries. "Tracking microbial populations effective in reducing exposure." Environmental Health Perspectives 103, suppl 5 (June 1995): 117–20. http://dx.doi.org/10.1289/ehp.95103s4117.

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27

Lenski, R. E. "Assessing the genetic structure of microbial populations." Proceedings of the National Academy of Sciences 90, no. 10 (May 15, 1993): 4334–36. http://dx.doi.org/10.1073/pnas.90.10.4334.

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28

Pfeiffer, Thomas, and Sebastian Bonhoeffer. "Evolution of Cross‐Feeding in Microbial Populations." American Naturalist 163, no. 6 (June 2004): E126—E135. http://dx.doi.org/10.1086/383593.

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29

Delarue, Morgan, Jörn Hartung, Carl Schreck, Pawel Gniewek, Lucy Hu, Stephan Herminghaus, and Oskar Hallatschek. "Self-driven jamming in growing microbial populations." Nature Physics 12, no. 8 (May 9, 2016): 762–66. http://dx.doi.org/10.1038/nphys3741.

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30

Childs, Lauren M., Whitney E. England, Mark J. Young, Joshua S. Weitz, and Rachel J. Whitaker. "CRISPR-Induced Distributed Immunity in Microbial Populations." PLoS ONE 9, no. 7 (July 7, 2014): e101710. http://dx.doi.org/10.1371/journal.pone.0101710.

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31

Cannon, Matthew V., Joseph Craine, James Hester, Amanda Shalkhauser, Ernest R. Chan, Kyle Logue, Scott Small, and David Serre. "Dynamic microbial populations along the Cuyahoga River." PLOS ONE 12, no. 10 (October 19, 2017): e0186290. http://dx.doi.org/10.1371/journal.pone.0186290.

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32

De Leenheer, Patrick, and N. G. Cogan. "Failure of antibiotic treatment in microbial populations." Journal of Mathematical Biology 59, no. 4 (December 16, 2008): 563–79. http://dx.doi.org/10.1007/s00285-008-0243-6.

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33

Nodar, R., M. J. Acea, and T. Carballas. "Microbial populations of poultry pine-sawdust litter." Biological Wastes 33, no. 4 (1990): 295–306. http://dx.doi.org/10.1016/0269-7483(90)90133-d.

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34

Chuang, John S. "Engineering multicellular traits in synthetic microbial populations." Current Opinion in Chemical Biology 16, no. 3-4 (August 2012): 370–78. http://dx.doi.org/10.1016/j.cbpa.2012.04.002.

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35

Palmer, C. "Rapid quantitative profiling of complex microbial populations." Nucleic Acids Research 34, no. 1 (January 8, 2006): e5-e5. http://dx.doi.org/10.1093/nar/gnj007.

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36

Mancino, C. F., and W. A. Torello. "Enumeration of denitrifying microbial populations in turf." Plant and Soil 96, no. 1 (February 1986): 149–51. http://dx.doi.org/10.1007/bf02375006.

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37

Liao, Xiaobin, Bingxin Li, Rusen Zou, Shuguang Xie, and Baoling Yuan. "Antibiotic sulfanilamide biodegradation by acclimated microbial populations." Applied Microbiology and Biotechnology 100, no. 5 (November 13, 2015): 2439–47. http://dx.doi.org/10.1007/s00253-015-7133-9.

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38

Atlas, Ronald M., Ami Horowitz, Micah Krichevsky, and Asim K. Bej. "Response of microbial populations to environmental disturbance." Microbial Ecology 22, no. 1 (December 1991): 249–56. http://dx.doi.org/10.1007/bf02540227.

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39

Peretti, S. W., and J. E. Bailey. "Transient response simulations of recombinant microbial populations." Biotechnology and Bioengineering 32, no. 4 (August 5, 1988): 418–29. http://dx.doi.org/10.1002/bit.260320403.

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40

Viljoen, Clint R., and Alexander von Holy. "Microbial populations associated with commercial bread production." Journal of Basic Microbiology 37, no. 6 (1997): 439–44. http://dx.doi.org/10.1002/jobm.3620370612.

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41

Blum, Udo, and Steven R. Shafer. "Microbial populations and phenolic acids in soil." Soil Biology and Biochemistry 20, no. 6 (January 1988): 793–800. http://dx.doi.org/10.1016/0038-0717(88)90084-3.

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42

Pflug, Florian G., Deepak Bhat, and Simone Pigolotti. "Genome replication in asynchronously growing microbial populations." PLOS Computational Biology 20, no. 1 (January 5, 2024): e1011753. http://dx.doi.org/10.1371/journal.pcbi.1011753.

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Biological cells replicate their genomes in a well-planned manner. The DNA replication program of an organism determines the timing at which different genomic regions are replicated, with fundamental consequences for cell homeostasis and genome stability. In a growing cell culture, genomic regions that are replicated early should be more abundant than regions that are replicated late. This abundance pattern can be experimentally measured using deep sequencing. However, a general quantitative theory linking this pattern to the replication program is still lacking. In this paper, we predict the abundance of DNA fragments in asynchronously growing cultures from any given stochastic model of the DNA replication program. As key examples, we present stochastic models of the DNA replication programs in budding yeast and Escherichia coli. In both cases, our model results are in excellent agreement with experimental data and permit to infer key information about the replication program. In particular, our method is able to infer the locations of known replication origins in budding yeast with high accuracy. These examples demonstrate that our method can provide insight into a broad range of organisms, from bacteria to eukaryotes.

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43

N, Pruthviraj, and Geetha K N. "Impact of Nanofertilizers on Soil Microbial Populations." International Journal of Environment and Climate Change 14, no. 6 (June 20, 2024): 406–35. http://dx.doi.org/10.9734/ijecc/2024/v14i64240.

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A field experiment was conducted at Zonal Agricultural Research Station, GKVK, Bengaluru during 2019 and 2020 to know the Impact of Nanofertilizers on Soil Microbial Populations. The experiment was laid out in Randomized Complete Block Design (Factorial concept) with two factors [Factor I -Seed treatment) [Factor II (F- foliar application of nutrients at ray floret stage) with two control C1 : Recommended dose of fertilizers (RDF) only and C2 : Recommended package of practices (RPP) treatments replicated thrice. In this experiment treatment seed priming with 1500 ppm nano boron nitride (Green synthesized particle) + foliar application of 600 ppm nano sulphur (GsP) + 1500 ppm nano boron nitride (Green synthesized particle) significantly recorded higher dehydrogenase enzyme (2.61 µ g TPF g-1 h-1), microbial population (13.1×105, 22.4×103, 12.5×103, 12.0×104 and 8.8×104 cfu/g of soil of bacteria, fungi, actinomycetes, azotobacter and PSB, respectively) but azospirillum population was found to be non significant. The same treatment also recorded higher nitrogen uptake (114.9 kg ha-1), phosphorus uptake (34.30 kg ha-1), potassium uptake (79.20 kg ha-1) plant height (270.6 cm at harvest stage) and seed yield (3588 kg ha-1) compared to other treatments.

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44

Tsoi, Ryan, Feilun Wu, Carolyn Zhang, Sharon Bewick, David Karig, and Lingchong You. "Metabolic division of labor in microbial systems." Proceedings of the National Academy of Sciences 115, no. 10 (February 20, 2018): 2526–31. http://dx.doi.org/10.1073/pnas.1716888115.

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Metabolic pathways are often engineered in single microbial populations. However, the introduction of heterologous circuits into the host can create a substantial metabolic burden that limits the overall productivity of the system. This limitation could be overcome by metabolic division of labor (DOL), whereby distinct populations perform different steps in a metabolic pathway, reducing the burden each population will experience. While conceptually appealing, the conditions when DOL is advantageous have not been rigorously established. Here, we have analyzed 24 common architectures of metabolic pathways in which DOL can be implemented. Our analysis reveals general criteria defining the conditions that favor DOL, accounting for the burden or benefit of the pathway activity on the host populations as well as the transport and turnover of enzymes and intermediate metabolites. These criteria can help guide engineering of metabolic pathways and have implications for understanding evolution of natural microbial communities.

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Li, Xiang-Yi, Cleo Pietschke, Sebastian Fraune, Philipp M. Altrock, Thomas C. G. Bosch, and Arne Traulsen. "Which games are growing bacterial populations playing?" Journal of The Royal Society Interface 12, no. 108 (July 2015): 20150121. http://dx.doi.org/10.1098/rsif.2015.0121.

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Microbial communities display complex population dynamics, both in frequency and absolute density. Evolutionary game theory provides a natural approach to analyse and model this complexity by studying the detailed interactions among players, including competition and conflict, cooperation and coexistence. Classic evolutionary game theory models typically assume constant population size, which often does not hold for microbial populations. Here, we explicitly take into account population growth with frequency-dependent growth parameters, as observed in our experimental system. We study the in vitro population dynamics of the two commensal bacteria ( Curvibacter sp. (AEP1.3) and Duganella sp. (C1.2)) that synergistically protect the metazoan host Hydra vulgaris (AEP) from fungal infection. The frequency-dependent, nonlinear growth rates observed in our experiments indicate that the interactions among bacteria in co-culture are beyond the simple case of direct competition or, equivalently, pairwise games. This is in agreement with the synergistic effect of anti-fungal activity observed in vivo . Our analysis provides new insight into the minimal degree of complexity needed to appropriately understand and predict coexistence or extinction events in this kind of microbial community dynamics. Our approach extends the understanding of microbial communities and points to novel experiments.

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Fernando, S. C., H. T. Purvis, F. Z. Najar, L. O. Sukharnikov, C. R. Krehbiel, T. G. Nagaraja, B. A. Roe, and U. DeSilva. "Rumen Microbial Population Dynamics during Adaptation to a High-Grain Diet." Applied and Environmental Microbiology 76, no. 22 (September 17, 2010): 7482–90. http://dx.doi.org/10.1128/aem.00388-10.

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ABSTRACT High-grain adaptation programs are widely used with feedlot cattle to balance enhanced growth performance against the risk of acidosis. This adaptation to a high-grain diet from a high-forage diet is known to change the rumen microbial population structure and help establish a stable microbial population within the rumen. Therefore, to evaluate bacterial population dynamics during adaptation to a high-grain diet, 4 ruminally cannulated beef steers were adapted to a high-grain diet using a step-up diet regimen containing grain and hay at ratios of 20:80, 40:60, 60:40, and 80:20. The rumen bacterial populations were evaluated at each stage of the step-up diet after 1 week of adaptation, before the steers were transitioned to the next stage of the diet, using terminal restriction fragment length polymorphism (T-RFLP) analysis, 16S rRNA gene libraries, and quantitative real-time PCR. The T-RFLP analysis displayed a shift in the rumen microbial population structure during the final two stages of the step-up diet. The 16S rRNA gene libraries demonstrated two distinct rumen microbial populations in hay-fed and high-grain-fed animals and detected only 24 common operational taxonomic units out of 398 and 315, respectively. The 16S rRNA gene libraries of hay-fed animals contained a significantly higher number of bacteria belonging to the phylum Fibrobacteres, whereas the 16S rRNA gene libraries of grain-fed animals contained a significantly higher number of bacteria belonging to the phylum Bacteroidetes. Real-time PCR analysis detected significant fold increases in the Megasphaera elsdenii, Streptococcus bovis, Selenomonas ruminantium, and Prevotella bryantii populations during adaptation to the high-concentrate (high-grain) diet, whereas the Butyrivibrio fibrisolvens and Fibrobacter succinogenes populations gradually decreased as the animals were adapted to the high-concentrate diet. This study evaluates the rumen microbial population using several molecular approaches and presents a broader picture of the rumen microbial population structure during adaptation to a high-grain diet from a forage diet.

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Hu, Hong-Ying, Koichi Fujie, and Kohei Urano. "Dynamic Behaviour of Aerobic Submerged Biofilter." Water Science and Technology 28, no. 7 (October 1, 1993): 179–85. http://dx.doi.org/10.2166/wst.1993.0160.

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Dynamic behaviour of microbial film and BOD removal characteristics in an aerobic submerged biofilter packed with ceramic balls were investigated. The effects of BOD loading and temperature on the populations of bacteria and protozoa inhabiting microbial film were investigated. It was ascertained that the BOD removal rate by the microbial film was controlled by the bacterial population, while the microbial concentration in the biofilter was due to the growth of protozoa when the temperature and the BOD loading were low. The analysis of bacterial quinone mixtures was successfully applied to identify the bacterial population in the microbial film.

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Borse, Florian, Dovydas Kičiatovas, Teemu Kuosmanen, Mabel Vidal, Guillermo Cabrera-Vives, Johannes Cairns, Jonas Warringer, and Ville Mustonen. "Quantifying massively parallel microbial growth with spatially mediated interactions." PLOS Computational Biology 20, no. 7 (July 22, 2024): e1011585. http://dx.doi.org/10.1371/journal.pcbi.1011585.

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Quantitative understanding of microbial growth is an essential prerequisite for successful control of pathogens as well as various biotechnology applications. Even though the growth of cell populations has been extensively studied, microbial growth remains poorly characterised at the spatial level. Indeed, even isogenic populations growing at different locations on solid growth medium typically show significant location-dependent variability in growth. Here we show that this variability can be attributed to the initial physiological states of the populations, the interplay between populations interacting with their local environment and the diffusion of nutrients and energy sources coupling the environments. We further show how the causes of this variability change throughout the growth of a population. We use a dual approach, first applying machine learning regression models to discover that location dominates growth variability at specific times, and, in parallel, developing explicit population growth models to describe this spatial effect. In particular, treating nutrient and energy source concentration as a latent variable allows us to develop a mechanistic resource consumer model that captures growth variability across the shared environment. As a consequence, we are able to determine intrinsic growth parameters for each local population, removing confounders common to location-dependent variability in growth. Importantly, our explicit low-parametric model for the environment paves the way for massively parallel experimentation with configurable spatial niches for testing specific eco-evolutionary hypotheses.

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Jingang, Liang, Luan Ying, Jiao Yue, Sun Shi, Wu Cunxiang, Wu Haiying, Zhang Mingrong, Zhang Haifeng, Zheng Xiaobo, and Zhang Zhengguang. "High-methionine soybean has no significant effect on nitrogen-transforming bacteria in rhizosphere soil." Plant, Soil and Environment 64, No. 3 (March 21, 2018): 108–13. http://dx.doi.org/10.17221/750/2017-pse.

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Transgenic plants may induce shifts in the microbial community composition that in turn alter microbially-mediated nutrient cycling in soil. Studies of how specific microbial groups respond to genetically modified (GM) planting help predict potential impacts upon processes performed by these groups. This study investigated the effect of transgenic high-methionine soybean cv. ZD91 on nitrogen-fixing and ammonia-oxidizing bacterial populations. A difference in nitrogen-fixing or ammonia-oxidizing bacteria community composition was not found, suggesting that cv. ZD91 does not alter the bacterial populations in rhizosphere soil. This study increases our understanding of the potential effect of transgenic soybean on microbial functional groups within soil by suggesting that nitrogen-transforming bacteria may be useful for future investigations on the GM crops impact in the soil ecosystem.

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Green, Rowan, Hejie Wang, Carol Botchey, Siu Nam Nancy Zhang, Charles Wadsworth, Francesca Tyrrell, James Letton, et al. "Collective peroxide detoxification determines microbial mutation rate plasticity in E. coli." PLOS Biology 22, no. 7 (July 15, 2024): e3002711. http://dx.doi.org/10.1371/journal.pbio.3002711.

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Mutagenesis is responsive to many environmental factors. Evolution therefore depends on the environment not only for selection but also in determining the variation available in a population. One such environmental dependency is the inverse relationship between mutation rates and population density in many microbial species. Here, we determine the mechanism responsible for this mutation rate plasticity. Using dynamical computational modelling and in culture mutation rate estimation, we show that the negative relationship between mutation rate and population density arises from the collective ability of microbial populations to control concentrations of hydrogen peroxide. We demonstrate a loss of this density-associated mutation rate plasticity (DAMP) when Escherichia coli populations are deficient in the degradation of hydrogen peroxide. We further show that the reduction in mutation rate in denser populations is restored in peroxide degradation-deficient cells by the presence of wild-type cells in a mixed population. Together, these model-guided experiments provide a mechanistic explanation for DAMP, applicable across all domains of life, and frames mutation rate as a dynamic trait shaped by microbial community composition.

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