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How do absorbance readings indicate diauxic growth of bacteria?

How do absorbance readings indicate diauxic growth of bacteria?



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How is growth rate an indicator of carbohydrate use? How do absorbance readings from a spectrophotometer show growth of bacteria if the absorbance readings are taken several times over the course of a day [and plotted on a graph]? If the second absorbance reading of a sample with bacteria and particular substrate is lower than its initial absorbance reading is there less bacteria present or more? Do the presence of substrates affect the absorbance reading in other words do carbohydrates have any turbidity?

context if needed - experiment determines if a species tested in the presence of a carbohydrate and combination of carbohydrates is able to use each as its sole carbon& energy source and if any are used preferentially to others


How is growth rate an indicator of carbohydrate use?

Carbohydrates are the carbon and energy source available for the microbes, and their assimilation is correlated to biomass production. The relationship between microbial growth rates in an aqueous environment and the concentration of a limiting nutrient or carbon source is given by the Monod equation 1 : $$ mu = mu_{max} frac{[S]}{K_M + [S]} $$

This relation is linear when the substrate concentration is lower than the constant $K_M$, this is when it is not saturating.

Now, when the culture is in the log phase of growth biomass ($X$) can be described with the simple growth equation: $$ frac{dX}{dt} = mu X $$ where $mu$ is the growth rate of the culture, and it can be interpreted as the biomass production speed. So the fastest carbohydrate use is, the highest the growth rate will be.

How do absorbance readings from a spectrophotometer show growth of bacteria if the absorbance readings are taken several times over the course of a day [and plotted on a graph]?

This is explained by the Beer-Lambert law. Simply put: the absorbance ($A$) of an entity is proportional to its concentration ($c$) - all else being equal. $$ A = varepsilon ell c $$ where $varepsilon$ is the molar attenuation coefficient or absorptivity of the attenuating species, $ell$ is the optical path length in cm, and $c$ is the concentration of the attenuating species. So, the changes in the concentration of your bacterial population (its growth) are reflected in an increase in its absorbance.

If you find a lower absorbance value at time $t+1$ in comparison to time $t$, yes it could mean that the bacterial cell number at time $t+1$ is lower than at time $t$. However, we know neither the protocol you have been following, nor the values you have been obtaining, so there could be other explanations, such as error of measurement.

Do the presence of substrates affect the absorbance reading in other words do carbohydrates have any turbidity?

Yes, molecules in the medium can impact your Optical Density (OD) values. However, that would only be true if these molecules absorb light at the wavelength you are using to measure the absorbance (eg $600$ nm). This is why you should always blank with some filtered medium (or at least with some fresh medium).


Turbidity (measured as absorbance) is direct indicator of numerical density of microbes in culture. The more microbe-cells: the more light is diffuse-reflected and scattered in the path of light; so the less amount light reach the photo-sensor given at colorimeter/ photometer.

Now; if with time the turbidity goes up; then it means with time the number of cell increasing.

Growth rate is an indicator of nutrient use. If certain nutrient is difficult to metabolize for that bacteria; the growth rate would decrease. So turbidity will not increase in that rate. Visit wikipedia that says

"The preferred sugar is consumed first, which leads to rapid growth, followed by a lag phase. During the lag phase the cellular machinery used to metabolize the second sugar is activated and subsequently the second sugar is metabolized."

(graphs from wikipedia)

Reference: Wikipedia.


In silico evolution of diauxic growth

The glucose effect is a well known phenomenon whereby cells, when presented with two different nutrients, show a diauxic growth pattern, i.e. an episode of exponential growth followed by a lag phase of reduced growth followed by a second phase of exponential growth. Diauxic growth is usually thought of as a an adaptation to maximise biomass production in an environment offering two or more carbon sources. While diauxic growth has been studied widely both experimentally and theoretically, the hypothesis that diauxic growth is a strategy to increase overall growth has remained an unconfirmed conjecture.

Methods

Here, we present a minimal mathematical model of a bacterial nutrient uptake system and metabolism. We subject this model to artificial evolution to test under which conditions diauxic growth evolves.

Results

As a result, we find that, indeed, sequential uptake of nutrients emerges if there is competition for nutrients and the metabolism/uptake system is capacity limited.

Discussion

However, we also find that diauxic growth is a secondary effect of this system and that the speed-up of nutrient uptake is a much larger effect. Notably, this speed-up of nutrient uptake coincides with an overall reduction of efficiency.

Conclusions

Our two main conclusions are: (i) Cells competing for the same nutrients evolve rapid but inefficient growth dynamics. (ii) In the deterministic models we use here no substantial lag-phase evolves. This suggests that the lag-phase is a consequence of stochastic gene expression.


Abstract

The phenotype-genotype landscape is a projection coming from detailed phenotypic and genotypic data under environmental pressure. Although phenome of microbes or microbial consortia mirrors the functional expression of a genome or set of genomes, metabolic traits rely on the phenotype. Phenomics has the potential to revolution functional genomics. In this review, we discuss why and how phenomics was developed. We described how phenomics may extend our understanding of the assembly of microbial consortia and their functionality, and then we outlined the novel applications within the study of phenomes using Omnilog platform together with a revision of its current application to study lactic acid bacteria (LAB) metabolic traits during food processing. LAB were proposed as a suitable model system to analyze and discuss the implementation and exploitation of this emerging omics approach. We introduced the ‘phenotype switching’, as a new phenotype microarray approach to get insights in bacterial physiology. An overview of methodologies and tools to manage and analyze the generated data was provided. Finally, pro and cons of pipelines developed so far, including the most innovative ones were critically analyzed. We propose an R pipeline, recently deposited, which allows to automatically analyze Omnilog data integrating the latest approaches and implementing the new concepts described here.


How do absorbance readings indicate diauxic growth of bacteria? - Biology

Growth of Saccharomyces cerevisiae following glucose depletion (the diauxic shift) depends on a profound metabolic adaptation accompanied by a global reprogramming of gene expression. In this study, we provide evidence for a heretofore unsuspected role for Isc1p in mediating this reprogramming. Initial studies revealed that yeast cells deleted in ISC1, the gene encoding inositol sphingolipid phospholipase C, which resides in mitochondria in the post-diauxic phase, showed defective aerobic respiration in the post-diauxic phase but retained normal intrinsic mitochondrial functions, including intact mitochondrial DNA, normal oxygen consumption, and normal mitochondrial polarization. Microarray analysis revealed that the Δisc1 strain failed to up-regulate genes required for nonfermentable carbon source metabolism during the diauxic shift, thus suggesting a mechanism for the defective supply of respiratory substrates into mitochondria in the post-diauxic phase. This defect in regulating nuclear gene induction in response to a defect in a mitochondrial enzyme raised the possibility that mitochondria may initiate diauxic shift-associated regulation of nucleus-encoded genes. This was established by demonstrating that in respiratory-deficient petite cells these genes failed to be up-regulated across the diauxic shift in a manner similar to the Δisc1 strain. Isc1p- and mitochondrial function-dependent genes significantly overlapped with Adr1p-, Snf1p-, and Cat8p-dependent genes, suggesting some functional link among these factors. However, the retrograde response was not activated in Δisc1, suggesting that the response of Δisc1 cannot be simply attributed to mitochondrial dysfunction. These results suggest a novel role for Isc1p in allowing the reprogramming of gene expression during the transition from anaerobic to aerobic metabolism.

The abbreviations used are: WT, wild type FACS, fluorescence-activated cell sorter RT, reverse transcription MOPS, 4-morpholinepropanesulfonic acid.


2 Experimental Procedures

2.1 Isolation and identification of Persicobacter sp. CCB-QB2

Persicobacter QB2 was isolated from seaweed (genus Ulva) from Queens Bay of Penang Island, Malaysia, using a method described previously (Furusawa, Lau, Shu-Chien, Jaya-Ram, & Amirul, 2015 ). A piece of seaweed was transferred to a L-ASWM (0.05% tryptone, 2.4% artificial sea water [ASW] with 10 mmol L −1 HEPES) agar plate. After incubating 2 days at 30°C, colonies exhibiting agarolytic activity formed a clear zone around the colonies. The colony were purified two times by single colony isolation on H-ASWM (0.5% tryptone, 2.4% ASW with 10 mmol L −1 HEPES) agar plates (1.5% agar). QB2 cells grew to a density of 3 × 10 8 cells ml −1 (OD600 = 1) in H-ASWM at 30°C for overnight. Five microliters of the bacterial suspension was spotted on 1.5% agar H-ASWM plates and incubated at 30°C for 24 hr. To confirm the agarolytic activity, this plate was stained with Lugol's solution (0.2 g iodine crystal and 2 g potassium iodine in 20 mL distilled water) (Cui et al., 2014 ).

Chromosomal DNA from mid-log phase QB2 cells (3 × 10 8 cells ml −1 ) was prepared using the Mygen Genomic DNA Prep Kit (Gene Xpress PLT). The protocol for phylogenetic analysis based on 16S rRNA gene sequence was described previously (Furusawa et al., 2015 ). The sequence data have been submitted to the DDBJ/EMBL/GenBank databases under the accession number KT285294.

2.2 Genome sequencing, assembly, and annotation

Chromosomal DNA from mid-log phase QB2 cells (3 × 10 8 cells ml −1 ) was prepared using the DNeasy Blood & Tissue Kit (QIAGEN). Whole-genome sequencing for QB2 was performed with a PacBio RSII platform (Pacific Biosciences). A 10 kb Single Molecule Real-Time (SMRT) bell library was prepared and sequenced using P5-C3 chemistry according to the manufacturer's instructions. The library was sequenced using three SMRT cells, yielding 830 Mb of sequences from 117,232 reads, with an average read length of 7,081 bp. De novo assembly of the reads was performed following the Hierarchical Genome Assembly Process (HGAP) (v 2.2.0) workflow with default parameters (Chin et al., 2013 ). Using the workflow, the QB2 genome was assembled into two contigs of 3,843,562 and 31,064 bp, respectively. The genome was annotated using the Rapid Annotation using Subsystem Technology (RAST) server (Aziz et al., 2008 ) with default parameters. The genome sequence of the QB2 is available in DDBJ/EMBL/GenBank database under the accession number LBGV00000000.

2.3 Determination of growth curves and agarase activity

Two hundred microliters of a QB2 cell suspension (3 × 10 8 cells ml −1 ) was transferred to 100 ml H-ASWM, H-ASWM with 0.2% agarose (Promega), L-ASWM and L-ASWM with 0.2% agarose. The samples were incubated at 30°C for 56 hr on a rotary shaker at 200 rpm. The growth of the bacterium at different incubation periods was measured by counting the colony-forming units on H-ASWM agar plates. Simultaneously, the agarase activity was also measured by the release of the reducing sugar equivalent using the 3,5-dinitrosalicylic acid (DNS) method (Miller, 1959 ). One milliliter cell suspension was centrifuged and 10 μl of the supernatant was incubated in 90 μl of 20 mmol L −1 Tris-HCl buffer (pH 7.6) containing 1.5% melted agarose at 50°C for 30 min. Subsequently, 200 μl DNS solution was mixed into the reaction solution and incubated at 100°C for 10 min. After heat treatment, 1 mL deionized water was added, and the absorbance of the reducing sugar was measured at 540 mm. The value was evaluated with d -galactose as the standard. One unit (U) of enzymatic activity was defined as the amount of enzyme that released 1 μmol of reducing sugar per minute under this condition.

2.4 Quantitative real-time PCR

Two hundred microliters of precultured QB2 cells (3 × 10 8 cells mL −1 ) was transferred to 100 ml H-ASWM, H-ASWM with 0.2% agarose (Promega), L-ASWM and L-ASWM with 0.2% agarose. Samples were taken after 6, 9, and 30 hr incubation (roughly corresponding to the lag, first growth, and second growth phase observed for cells exhibiting diauxic growth). After harvesting cells, total RNA was extracted using TRIZOL Reagent (Ambion) and the QIAGEN RNeasy Mini Kit (QIAGEN) following the protocol described by Lopez and Bohuski (Lopez & Bohuski, 2007 ). cDNA was synthesized using the RevertAid H Minus First Strand cDNA Synthesis Kit (Thermo Scientific), and 20 μg of the resulting cDNA was added to a 20 μl PCR mixture prepared from Fast SYBR ® Green Master Mix (Applied Biosystems), which contained 10 μmol L −1 of each primer listed in Table S1 at a final concentration of 0.5 μmol L −1 . The experiment was conducted in duplicate and three independent runs were performed for each experiment. The following thermal cycling parameters were used: denaturation at 95°C for 25 s, followed by 40 cycles of denaturation at 95°C for 3 s, annealing and extension at 60°C for 30 s. Melting curve analysis was conducted at temperature range of 45 to 95°C with a slope of 0.05°C per second. Fold change in gene expression was calculated for each gene in treated and control samples. All obtained data were normalized to the 16S rRNA gene.


COMMENTARY

Background Information

The aerobic A. vinelandii is an appealing model organism for laboratory work due to its relative ease of maintenance, genetic tractability, and non-pathogenicity (Biosafety Level 1), as compared with other pathogenic or anaerobically cultivated diazotroph models (e.g., Clostridium pasteurianum, Klebsiella pneumoniae, Rhodopseudomonas palustris). A. vinelandii can be maintained in ambient, benchtop conditions, though growth is typically optimized and standardized in an incubator. Natural competency for transformation in A. vinelandii can be induced via metal starvation and visually confirmed due to the associated production of fluorescent green siderophores (McRose et al., 2017 ). This characteristic makes it a highly suitable organism for genetic manipulation, as reviewed in (Dos Santos, 2019 ). Growth rate assessments of genetically modified A. vinelandii can, therefore, reveal the physiological contributions of nitrogenase and nitrogenase-related genes (Arragain et al., 2017 Garcia, McShea, Kolaczkowski, & Kaçar, 2020 McRose et al., 2017 Mus, Colman, Peters, & Boyd, 2019 Plunkett et al., 2020 ).

Critical Parameters and Troubleshooting

It is important to optimize conditions for A. vinelandii growth on a microplate reader prior to growth rate assessment to ensure consistency and reproducibility across biological and technical replicates (see Strategic Planning). Growth conditions that should be considered include temperature, shaking speed, culture volume, and inoculum preparation.

A common problem encountered during growth rate assessment on a microplate reader (Basic Protocol 3) is well evaporation over the approximately 48 to 72 hr needed for A. vinelandii cultures to reach saturation. Evaporation can be minimized by use of a lid or gas-permeable membrane. However, significant evaporation can still occur at plate edges when using a lid (Chavez et al., 2017 ), and improper application of the membrane can result in variable growth rates across the plate. Since such variability is challenging to eliminate entirely, it is important to include several technical replicates across the plate and avoid measuring wells at the plate edges when using a lid.

Time Considerations

The initial recovery of Azotobacter strains (Basic Protocol 1) and preparation of isogenic plate cultures (Basic Protocol 2) takes approximately 6 days. Preculture preparation (Basic Protocol 2) takes approximately 24 hr. This time can be optimized but should be made consistent across replicates. Each microplate reader growth experiment takes approximately 48 to 72 hr, though this time may vary depending on the tested strain and growth conditions. The experiment should be maintained until cultures reach saturation to ensure reliable curve-fitting during subsequent growth data analysis.

Understanding results

This protocol can also be used to compare the growth rates and characteristics of different wild-type and engineered A. vinelandii strains, as well as different physical and nutritional growth conditions. To provide an example of anticipated results for the protocol described here, we conducted diazotrophic growth rate experiments on wild-type Azotobacter vinelandii DJ and three strains harboring modifications to the nitrogenase nifD gene. The nifD gene encodes the active site-containing subunit of the nitrogenase enzyme. Modifications to this gene are expected to influence nitrogenase N2-reduction and, thus, the ability of A. vinelandii to grow diazotrophically. The modified strains include “AK013” and “AK014”, which have 93% and 81% nifD DNA identity to nifD of the wild-type DJ strain, respectively, and a DJΔnifD deletion strain.

Figure 4 shows growth curves for each A. vinelandii strain, and Table 1 reports mean doubling times calculated with the R package GrowthCurver (Sprouffske & Wagner, 2016 ). We did not detect diazotrophic growth for DJΔnifD. For Trials 1 and 2, AK014 grew slower than both DJ and AK013 (p < 0.05 calculated from a post-hoc Tukey's HSD test following a one-way ANOVA), but no difference in doubling times was found for DJ and AK013. However, for Trial 3, DJ grew significantly slower than both AK013 and AK014. This result highlights the need to repeat growth experiments on multiple days to account for day-to-day instrument variability. This automated protocol for evaluating diazotrophic growth differences across different A. vinelandii strains can be adapted for a variety of additional applications.

Mean doubling time ± 1σ (hr)
Strain DJ (wild-type) AK013 AK014
Trial 1 2.95 ± 0.13 2.83 ± 0.11 3.62 ± 0.20
Trial 2 2.88 ± 0.17 2.74 ± 0.11 3.47 ± 0.30
Trial 3 4.63 ± 0.18 3.23 ± 0.10 4.36 ± 0.24

Acknowledgments

We thank Dennis Dean and Valerie Cash for providing the A. vinelandii DJ and DJΔnifD strains, as well as for support and helpful discussions. We acknowledge funding from the NASA Early Career Faculty (ECF) Award No. 80NSSC19K1617, NSF Emerging Frontiers Program Award No. 1724090, and the University of Arizona Foundation Small Grants Program. BC acknowledges support from the NASA Arizona Space Grant and the Blue Marble Space Institute of Science Young Scientist Program and AKG acknowledges support from a NASA Postdoctoral Program Fellowship.

Conflict of Interest Statement

The authors declare no conflicts of interest.

Author Contributions

Brooke M. Carruthers: Conceptualization Data curation Formal analysis Investigation Methodology Validation writing-original draft writing-review & editing. Amanda K. Garcia: Data curation Formal analysis Investigation Methodology Supervision writing-original draft writing-review & editing. Alex Rivier: Data curation Formal analysis Investigation Validation writing-review & editing. Betul Kacar: Conceptualization Investigation Methodology Project administration Resources Supervision writing-review & editing.


Chapter 7 Methods for Monitoring the Growth of Yeast Cultures and for Dealing with the Clumping Problem

This chapter discusses methods for monitoring the growth of yeast cultures. Most biochemical, physiological, cytological, and developmental studies of yeasts require monitoring the growth of yeast cultures. In some cases, determination of the rate or extent of growth provides important experimental results in many other cases, it is necessary to know the amount of cellular material analyzed or the physiological state of the cells, or both. The methods used for determining amounts of yeast and for monitoring the growth of yeast cultures are the same as those used with other microorganisms. This chapter focuses on the virtues and limitations of (and the relationships among) various methods of monitoring growth. It discusses the complications imposed by the budding mode of reproduction and by the clumping problem. The chapter describes the clumping problem, methods for measuring biomass and its components and cytokinesis.


Affiliations

School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, 2052, Australia

Bat-Erdene Jugder, Zhiliang Chen, Darren Tan Tek Ping, Helene Lebhar, Jeffrey Welch & Christopher P Marquis

Systems Biology Initiative, University of New South Wales, Sydney, 2052, Australia

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Contents

In this example (Figure 1, see Lac operon for details) the number of bacteria present in a nutrient-containing broth was measured during the course of an 8-hour cell growth experiment. The observed pattern of bacterial growth is bi-phasic because two different sugars were present, glucose and lactose. The bacteria prefer to consume glucose (Phase I) and only use the lactose (Phase II) after the glucose has been depleted. Analysis of the molecular basis for this bi-phasic growth curve led to the discovery of the basic mechanisms that control gene expression.

Cancer research is an area of biology where growth curve analysis plays an important role. In many types of cancer, the rate at which tumors shrink following chemotherapy is related to the rate of tumor growth before treatment. Tumors that grow rapidly are generally more sensitive to the toxic effects that conventional anticancer drugs have on the cancer cells. Many conventional anticancer drugs (for example, 5-Fluorouracil) interfere with DNA replication and can cause the death of cells that attempt to replicate their DNA and divide. A rapidly growing tumor will have more actively dividing cells and more cell death upon exposure to such anticancer drugs.

In the example shown in Figure 2, a tumor is found after the cell growth rate has slowed. Most of the cancer cells are removed by surgery. The remaining cancer cells begin to proliferate rapidly and cancer chemotherapy is started. Many tumor cells are killed by the chemotherapy, but eventually some cancer cells that are resistant to the chemotherapy drug begin to grow rapidly. The chemotherapy is no longer useful and is discontinued.

Children who fall significantly below the normal range of growth curves for body height can be tested for growth hormone deficiency and might be treatable with hormone injections. [1]


Conclusions

To summarize our experience, we have found that close reading of papers that made a significant contribution to basic biology through the use of quantitative reasoning and theory is useful in the setting of first-year graduate students with diverse educational backgrounds. The papers help those students with a biology background to focus on the mathematical and computational ideas and methods that are most relevant not only to current practice, but also to the students’ areas of interest.

In a complementary way, the students with physics and computational backgrounds are exposed to many issues of significance in basic biology. They also become familiar with research areas to which they might well be able to contribute, if they continue to pursue their interests in biology. Last, we are hopeful that the emerging practice of teaching students with such diverse backgrounds together will facilitate communication and professional relationships that will serve these future members of the genomic and systems biology community well.


Watch the video: Growth and Control of Microbial Growth (August 2022).