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Heterotrophy

Heterotrophy refers to the nutritional mode in which organisms obtain organic compounds from their environment as a source of carbon and energy, rather than synthesizing these compounds through autotrophy.
This process is essential for many lifeforms, including animals, fungi, and some protists.
Heterotrophs rely on the consumption of other organisms or organic matter to meet their metabolic needs.
Understanding the mechanisms and dynamics of heterotrophy is crucial for fields such as microbiology, ecology, and biotechnology, as it informs our knowledge of nutrient cycling, food webs, and potential applications in areas like bioremediation and bioenergy production.
The study of heterotrophy continues to provide valuable insights into the diversity and adaptability of living systems.

Most cited protocols related to «Heterotrophy»

Overcoming Methodological Biases in Plant Research

There are technical drivers of research priorities: the tools we have. As soon as certain aspects of plant functioning become measurable, we start using those tools and assign overarching significance to these measurements, perhaps, because we aim at doing important things, simply because we are important, at least to ourselves. Things we cannot measure or observe become a matter of unimportance. Although we will continue to depend on scientific tools and their availability, the challenge is, not to get trapped in studying what we have tools for, but to go beyond, based on the challenges posed by theory, developing novel approaches that will permit us entering the terrain that remained largely unexplored for methodological reasons.
As was explained above, water shortage and low temperature, and to some extent nutrient shortage, are not primarily affecting plant carbon capture (photosynthesis), but rather affect tissue formation directly. Well known in plant physiology, plant ecologists tend to overlook the great sensitivity of meristematic tissues to low turgor pressure, low temperature, and shortage in key nutrients. These tissues stop building new cells at water potentials, low temperatures, and critically low nutrient supply that still permit reasonably high rates of photosynthetic CO₂ uptake. Not surprisingly, the initial response of plants to such tissue-level growth constraints leads to an accumulation of non-structural carbon-metabolites (osmotically inactive ones such as starch and lipids), rather than to carbon-starvation (Körner, 2003 (link)). This discrepancy between awareness and reality roots in the convenient tools and techniques we have to measure photosynthesis and the absence of tools to monitor cell division and cell differentiation in situ, and/or to assess discrepancies between demand and supply of photoassimilates.
Another example for our methods driven priorities is the generally great significance attributed to air conditioning or to climate aspects in general when manipulative experiments are designed (e.g., CO₂-enrichment works), although soils exert far greater influences on plant responses to what ever treatment we apply. I invite readers to check the length authors spend on describing atmospheric conditions in their experiments versus the soil conditions. The simple reason is that we can engineer atmospheric conditions, but we have no means to engineer plant–soil or plant–soil–microbe interactions, as decisive these might be. To my knowledge, the only experiment where the response of plants to a high CO₂-environment were tested with plants growing in two different soil types (see Spinnler et al., 2002 (link)), revealed two different story lines, just depending on which soil was chosen. The challenge is to arrive at a broad appreciation that soil conditions (e.g., disturbed or undisturbed) are pre-determining experimental results. I join Högberg et al. (2005 ) in their viewing soil microbiota associated with roots as an integral part of plant functioning, to the extent, that they may actually be seen as part of the autotrophic system, rather than belonging to the heterotrophic world.
On a similar avenue, root research was and still is a minor fraction compared to leaf research, although there is no theoretical reason for such a posteriority. Both are equally significant, in fact roots may be more influential with respect to limiting resources. The only reason is methodology. While a leaf can be studied in isolation (e.g., some sensors mounted to it), a root does not function properly without its intact rhizosphere, apart from its poor visibility. We can “bring” the atmosphere into the lab (growth chambers), but we cannot bring a coupled rhizosphere to the lab. Any pot experiment is confounded as soon as plants respond differently to two treatments, because, inevitably, the treatment changes the root-space/plant size relationship. So the challenge here is testing hypothesis on plant responses with plants grown with unconstrained, well-developed soil biota in action. Most commonly, this can only be done in the field.

, & Körner C. (2011). The Grand Challenges in Functional Plant Ecology. Frontiers in plant science, 2, 1.

Publication 2011

Atmosphere Awareness Biological Community Carbon Cells Climate Cold Temperature Differentiations, Cell Division, Cell Fixation, Carbon Heterotrophy Hypersensitivity isolation Lipids Meristem Microbial Community Microbial Interactions Nutrients Photosynthesis Plant Leaves Plant Roots Plants Pressure Rhizosphere Starch Tissues Vision

Freshwater Microbial Community Metabarcoding

The freshwater tag data sets used in this paper are all publicly available on the National Center for Biotechnology Information’s (NCBI) Sequence Read Archive (SRA). The accession numbers are as follows: Lake Mendota, ERP016591 (34 (link)); Trout Bog, ERP016854 (22 (link)); Lake Michigan, SRP056973 (20 (link)); and Danube River, SRP045083 (21 (link)). The Lake Michigan and bog project accession numbers include additional sample types, so only the Lake Michigan and Trout Bog samples were used. The mouse gut data set is the full version of the example data used by mothur’s miSeq SOP, and is available on the mothur website (https://www.mothur.org/wiki/MiSeq_SOP) (23 (link)). The Marathonas Reservoir clone library data set is available from GenBank under accession no. GQ340065–GQ340365 (19 (link)). The Marathonas Reservoir taxonomy determined by manual alignment to the FreshTrain is available from https://www.github.com/McMahonLab/TaxAss.
The taxonomy databases used in this article are also publicly available. The Freshwater Training Set (FreshTrain) version used was FreshTrain30Apr2018SILVAv132 (15 (link)), which includes 1,318 freshwater heterotrophic bacterial references and is available from https://www.github.com/McMahonLab/TaxAss. The SILVA database version used was version SSU 132 NR 99 (https://www.arb-silva.de) (8 (link)), which includes 213,119 bacterial and archaeal reference sequences clustered to 99% identity to avoid repeat references. A mothur-formatted version of this database obtained from https://www.mothur.org/wiki/Silva_reference_files was used for all analyses (accessed January 2018). Further details on download, versions, and formatting can be found in Text S2 in the supplemental material and in the detailed directions provided at https://www.github.com/McMahonLab/TaxAss.
Quality control of tag data set fastq files was performed according to mothur's MiSeq SOP (23 (link), accessed September 2017) through the chimera checking step with mothur version 1.39.5 (17 (link)). The resulting unique sequences were defined as OTUs for all further analyses. The single-end sequencing data sets (Lake Mendota and Trout Bog) were also preprocessed with vsearch version 2.3.4_osx_x86_64 (38 (link)) to trim to uniform lengths and remove low-quality sequences with >0.5 expected errors. During TaxAss, the percent identity cutoff used for all data sets was 98, and the Wang classifier’s bootstrap confidence was set at 80% for all classifications. Batch files that reproduce all download, quality control, and TaxAss processing for each data set are available in Text S2.
Manual alignment of the full-length Marathonas Reservoir clone library sequences to the FreshTrain database was performed using the program ARB (39 (link)). Chimeras were manually identified and removed from the analysis, and sequences without FreshTrain nomenclature were labeled unclassified. Tags were simulated by trimming full-length sequences to common primer regions with mothur version 1.39.5 (17 (link)). The primers used were V4 (515F, GTGCCAGCMGCCGCGGTAA; 806R, GGACTACHVGGGTWTCTAAT) (40 (link)), V4-V5 (515FB, GTGYCAGCMGCCGCGGTAA; 926R, CCGYCAATTYMTTTRAGTTT) (41 (link)), and V3-V4 (341F, CCTACGGGNGGCWGCAG; 805R, GACTACHVGGGTATCTAATCC) (42 (link)). A list of the processing commands used to trim sequences to the primer regions and classify them is available in Text S3 in the supplemental material.

Rohwer R.R., Hamilton J.J., Newton R.J, & McMahon K.D. (2018). TaxAss: Leveraging a Custom Freshwater Database Achieves Fine-Scale Taxonomic Resolution. mSphere, 3(5), e00327-18.

Publication 2018

Archaea Bacteria Chimera Clone Cells DNA Library Heterotrophy Mice, House Oligonucleotide Primers Rivers Trout

Flux Balance Analysis Modeling of Cyanobacteria

Flux balance analysis (FBA) [99] was employed in both the model validation and model testing phases. Cyanothece iCyt773 and Synechocystis iSyn731 models were evaluated in terms of biomass production under several scenarios: light and dark phases, heterotrophic and mixotrophic conditions. Flux distributions for each one of these states were inferred using FBA:
Maximize
Subject to

Here, S_ij is the stoichiometric coefficient of metabolite i in reaction j and v_j is the flux value of reaction j. Parameters v_j,min and v_j,max denote the minimum and maximum allowable fluxes for reaction j, respectively. Light and dark phases in Cyanothece 51142 are represented via modifying the minimum or maximum allowable fluxes with the following constraints, respectively:

Here, v_Biomass is the flux of biomass reaction and v_Glytr, v_Glyctr and v_CO2tr are the fluxes of glycerol, glycogen and carbon dioxide transport reactions and v_light and v_cf are the fluxes of light reactions and carbon fixation reactions. For light phase, constraint (3) was included in the linear model, whereas for dark phase constraint (4) was included.
Once the Synechocystis iSyn731 model was validated, it was further tested for in silico gene essentiality. The following constraint(s) was included individually in the linear model to represent any mutant:
Here, v_mutant represents flux of reaction(s) associated with any genetic mutation.
Flux variability analysis [100] for the reactions (for which photoautotrophic ¹³C MFA measurements [35] (link) were available) was performed based on the following formulation:
Maximize/Minimize
Subject to

Here, is the minimum level of biomass production. In this case we fixed it to be the optimal value obtained under light condition for the Synechocystis iSyn731 model.
CPLEX solver (version 12.1, IBM ILOG) was used in the GAMS (version 23.3.3, GAMS Development Corporation) environment for implementing GapFind and GapFill [39] (link) and solving the aforementioned optimization models. All computations were carried out on Intel Xeon E5450 Quad-Core 3.0 GH and Intel Xeon E5472 Quad-Core 3.0 GH processors that are the part of the lionxj cluster (Intel Xeon E type processors and 96 GB memory) of High Performance Computing Group of The Pennsylvania State University.

Saha R., Verseput A.T., Berla B.M., Mueller T.J., Pakrasi H.B, & Maranas C.D. (2012). Reconstruction and Comparison of the Metabolic Potential of Cyanobacteria Cyanothece sp. ATCC 51142 and Synechocystis sp. PCC 6803. PLoS ONE, 7(10), e48285.

Publication 2012

Carbon dioxide Cyanothece Fixation, Carbon gamma-glutamylaminomethylsulfonic acid Genes, Essential Glycerin Glycogen Heterotrophy Light Memory Mutation Synechocystis

Genome Sequencing of 34 Synechococcus Strains

Thirty-four Synechococcus strains were chosen for genome sequencing based on their phylogenetic position, pigment content and isolation sites (Figure 1 and Supplementary Table S1). All but the three KORDI strains were retrieved from the Roscoff Culture Collection (RCC²) and transferred three times on 0.3% SeaPlaque Agarose (Lonza, Switzerland) to clone them and reduce contamination by heterotrophic bacteria. A first set of 25 Synechococcus genomes (including WH8103) were generated at the Genoscope (CEA, Paris-Saclay, France) by shotgun sequencing of two libraries: a short-insert forward-reverse pair-end (PE) library (50–150 bp) and a long-insert reverse-forward mate-pair library (4–10 kb), both sequenced by Illumina^TM technology. Additionally, seven other genomes were sequenced at the Center for Genomic Research (University of Liverpool, United Kingdom) by shotgun sequencing of 250 bp reads. Single or PE reads were first assembled into contigs using the CLC Assembly Cell© 4.10 (CLC Bio, Aarhus, Denmark). Synechococcus contigs were identified based on their different coverage compared to heterotrophic bacteria, scaffolded using WiseScaffolder and 28 out of 31 genomes were closed by manual finishing as described in Farrant et al. (2015) (link). Three genomes (BIOS-E4-1, BOUM118, and RS9915), had only one to three gaps in highly repeated genomic regions. The base numbering of the circularized genomes was set at 174 bp before the dnaN start, corresponding approximately to the origin of replication. Automatic structural and functional annotation of the genomes was then realized using the Institute of Genome Science (IGS) Annotation Engine³ (Galens et al., 2011 (link)). As concerns KORDI-49, KORDI-52 and KORDI-100 strains, genomes were sequenced from axenic cultures using a 454 GS-FLX Titanium sequencing system (Roche) at Macrogen (Seoul, South Korea). The obtained reads were assembled using the Newbler assembler (version 2.3, Roche). To fill contig gaps, additional PCR and primer walking was conducted. Sequences of all new Synechococcus genomes were deposited in GenBank under accession numbers CP047931-CP047961 (BioProject PRJNA596899), except Synechococcus sp. WH8103 that was previously deposited to illustrate the performance of the pipeline used to assemble, scaffold and manually finish these genomes as well as the three KORDI genomes that have been deposited in Genbank in August 2014 (see accession numbers in Supplementary Table S1).

Doré H., Farrant G.K., Guyet U., Haguait J., Humily F., Ratin M., Pitt F.D., Ostrowski M., Six C., Brillet-Guéguen L., Hoebeke M., Bisch A., Le Corguillé G., Corre E., Labadie K., Aury J.M., Wincker P., Choi D.H., Noh J.H., Eveillard D., Scanlan D.J., Partensky F, & Garczarek L. (2020). Evolutionary Mechanisms of Long-Term Genome Diversification Associated With Niche Partitioning in Marine Picocyanobacteria. Frontiers in Microbiology, 11, 567431.

Publication 2020

Axenic Culture Bacteria Cells Clone Cells DNA Library Genome Heterotrophy isolation Oligonucleotide Primers Pigmentation Replication Origin Sepharose Strains Synechococcus Titanium

Viral Taxonomy and Novel Genes Discovery

Taxonomy was assigned using protein LAST alignments to known phages in RefSeq84 ([49 ]; Table S9), as well as five viral metagenomic databases available as of 2018: uvMED [50 ], uvDEEP [23 (link)], GOV [15 (link)], EV [16 (link)], and MED2017 [24 ]. To avoid inflating the number of novel populations, we performed broad taxonomic assignments at >60% average amino acid identity (AAI) across >50% of proteins to any reference genome or contig, with the best hit assigned based on the highest AAI (Table S3). A broader cut-off of >50% of proteins at any AAI was used to identify phages infecting heterotrophic bacterioplankton in RefSeq84, due to lower sequence representation than picocyanophages in these databases (Table S3).
Proteins were annotated using HMMsearch against PFAM (bit score >30). Proteins with functional domains not found in previously reported datasets [15 (link), 26 (link)] were considered novel viral genes (Table S10).
Putative temperate phages were identified using i. functional annotations to identify phages with the genomic potential for lysogeny and ii. VIRSorter to identify integrated prophages. i. Functional annotations identified 922 populations with temperate phage markers (>30 bit score to integrase, excisionase, Cro, or CI repressor). ii. VIRSorter identified prophages from original assemblies, of which 413 temperate phage populations shared significant homology (a minimum of 150 bp at 95% ANI); VIRSorter identified 73 final viral populations as prophages.

Luo E., Eppley J.M., Romano A.E., Mende D.R, & DeLong E.F. (2020). Double-stranded DNA virioplankton dynamics and reproductive strategies in the oligotrophic open ocean water column. The ISME Journal, 14(5), 1304-1315.

Publication 2020

Amino Acids Bacteriophages excisionase Genes, Viral Genome Heterotrophy Integrase Lysogeny Metagenome Population Group Prophages Protein Domain Proteins

Most recents protocols related to «Heterotrophy»

Estimating Ecosystem Carbon Fluxes

The trees’ NPP was estimated as the product of the annual biomass increment, woody components of litterfall, and the C content of each biomass component^{56 (link)}. Foliage NPP was estimated as the sum of the annual leaf litterfall and the annual foliage biomass increment, which in turn was estimated using the relevant allometric equation. Total soil CO₂ efflux and heterotrophic respiration were modelled as exponential functions of the soil temperature and moisture content in each treatment:

ln (R_{S i j k l}) = b_{0} + b_{1} \times T_{S i j k l} + b_{2} \times F_{j} + b_{3} \times {(T_{S} \times F)}_{ijkl} + b_{4} \times S_{k} + ϵ_{ijkl}

Here, R_Sijkl is the lth observation of the total soil CO₂ efflux or heterotrophic respiration in replicated plot i (i = 1–3) of treatment j (F, j = control, PK, NPK fertilizations) within stand k (S, k = 1, 2). T_S is the soil temperature (°C) and T_S × F is the interaction term. The bs are coefficients to be estimated and ε represents the final residuals. After finding no effect of soil moisture content on the temperature sensitivity of R_S (b₁) or base R_S (b₀) (p = 0.3 for total CO₂ efflux and 0.5 for heterotrophic respiration; Table S3), only the soil temperature and plot-stand factors were included in the final model. The models included both terms separately for each year to enable propagation of spatiotemporal variation in the annual estimates within a treatment. Because we only measured the soil temperature manually once a month, hourly estimates of the soil temperature were obtained by deriving a relationship between the soil temperature and the corresponding air temperature (Fig. 2a; R² = 0.954, relationship unaffected by treatment, p = 0.99). Using the above model and the air temperature, we estimated the annual soil CO₂ efflux and heterotrophic respiration.
The effects of the fertilization treatments on above- and belowground variables were determined using a mixed effect ANOVA model to account for the completely randomized design used in the adjacent two stands. The stand term was treated as a random effect and the fertilization treatments were treated as fixed effects.

R e s p o n s e_{ijkl} = μ + F_{j} + Y_{l} + {(F \times Y)}_{jl} + S_{k} + ϵ_{ijkl}

Here, Response_ijkl is a response variable in replicated plot i of treatment j within stand k in year l (l = 1–4). µ is the grand population mean, F is the fixed effect of the treatment, Y is the fixed effect of the year as a dummy, and S is the random effect associated with stand. For the monthly soil N mineralization and nitrification models, we replaced year Y with month (l = 1–36).
All statistical analyses and parameter estimations for the devised models were performed using R (v. 4.1.3): the lm function was used to devise linear relationships and the lme function of the nlme package was used to determine the coefficients of the mixed effect ANOVA model (Eq. 2). Means were compared and separated using Tukey's test with a significance threshold of p < 0.05, which was performed using the lsmeans and cld functions of the lsmeans package. The final residuals (ε_ijkl) were normally distributed, which was confirmed visually by plotting them against predictions.

Baek G., Lim H., Noh N.J, & Kim C. (2023). No impact of nitrogen fertilization on carbon sequestration in a temperate Pinus densiflora forest. Scientific Reports, 13, 1743.

Publication 2023

Cell Respiration Fertilization Genetic Diversity Heterotrophy Hypersensitivity neuro-oncological ventral antigen 2, human Nitrification Physiologic Calcification Plant Leaves Trees

Soil Biogeochemical Fluxes Estimation

Annual fluxes of C and N were estimated from the measured monthly fluxes. Biomass stocks were estimated annually, while soil stocks were estimated only at the end of the investigation.
Soil C fluxes were characterized by determining the total soil CO₂ efflux and heterotrophic respiration, whereas soil N fluxes were characterized by determining the net mineralization and nitrification rates using an in situ incubation method. Total soil CO₂ efflux was separated into root- and heterotrophic respiration using a trench method^{59 (link)}. A root exclusion barrier (50 cm diameter, 30 cm depth, PVC) was installed in each plot, 4 weeks before the treatments were initiated. Two soil collars (10 cm diameter, 5 cm depth, PVC) were installed inside the rooting barrier to measure heterotrophic respiration and another two were installed outside the rooting barrier to measure heterotrophic + root respiration. Vegetation was removed within the rooting barriers but litterfall was kept. Soil CO₂ efflux was measured once a month using an infrared gas analyzer (Model EGM-4, PP-Systems, Hitchin, Hertfordshire, UK) equipped with a flow-through closed chamber (Model SRC-2). Measurements were performed between 10:00 and 13:00 over the study period. Soil temperature was also measured at a depth of 8 cm near the soil CO₂ efflux collar using a digital soil temperature probe (K-type, Summit SDT 200, Seoul, Korea).
On each day when the soil CO₂ efflux was measured, two soil cores were collected from each plot at a depth of 5 cm using a 100 cm³ core soil sampler. One sample was placed in a plastic bag and the other was returned to the soil and incubated to estimate the net N mineralization rate. We thus collected two soil samples from each plot every month, one fresh and one incubated. Both samples were transported to the laboratory and their fresh weight was measured. A 10 g portion of the fresh soil sample was oven-dried for 48 h at 105 °C to quantify the soil’s gravimetric water content and the rest was kept to determine the concentrations of nitrate and ammonium and the soil pH (measured using a 1:5 soil water suspension with an ion-selective glass electrode; Istec Model pH-220L, Seoul, Korea). The bulk density of each soil sample was calculated from its gravimetric water content and fresh weight.
Ammonium and nitrate were extracted from soil samples (5 g) using 50 ml of a 2 M KCl solution in a mechanical vacuum extractor (Model 24VE, SampleTek, Science Hill, KY, USA). The resulting solutions were then immediately placed in a cooler at 4 °C for storage. The ammonium and nitrate concentrations of the solutions were determined using an auto analyzer (AQ2 Discrete Analyzer, Southampton, UK). The mineral N concentration was measured throughout the period when fertilizer was applied, from April 2011 to April 2014. The net rates of ammonification and nitrification were estimated based on the differences in the ammonium and nitrate contents of the soil, respectively, between before and after the incubation. The sum of these two rates was taken as the net mineralization rate. If the net mineralization rate was negative, it was regarded as a net immobilization rate.
Additional soil samples were collected from four randomly selected points in each plot at depths of 0–15 cm both 4 weeks before starting the treatments and at the end of the investigation (2014). Samples were pooled within a plot and their concentrations of C, N, and available P were determined to capture the soil’s initial condition and the treatment response. C and N were analyzed with an elemental analyzer (vario Macro cube, Elementar Analysensysteme GmbH, Germany). The available P concentration was determined by extraction using NH₄F and HCl solutions⁶⁰ and analyzed using a UV spectrophotometer (Jenway 6505, Staffordshire, UK). C and N stocks were estimated by measuring the concentrations of both elements in a 100 cm³ core and dividing by the bulk density of the soil in the 0–5 cm layer.

Baek G., Lim H., Noh N.J, & Kim C. (2023). No impact of nitrogen fertilization on carbon sequestration in a temperate Pinus densiflora forest. Scientific Reports, 13, 1743.

Publication 2023

Ammonification Ammonium Cell Respiration Dietary Fiber Fingers Heterotrophy Immobilization Ion-Selective Electrodes Minerals Nitrates Nitrification Physiologic Calcification Vacuum

Heterotrophic Growth of Axenic Isolates

To assess the heterotrophic growth of axenic isolates, BG11 agar plates were prepared at final concentrations of 10 mM with different carbon sources: glucose, sucrose, lactose, arabinose, maltose, fructose, galactose, mannose, and glycerol. The tests were performed with spots of 10 μL at OD_750nm = 1 onto BG11 plates to reduce the possibility of contamination. Plates were incubated at 30°C in darkness for 30 days. Furthermore, the photosynthesis inhibitor DCMU [3-(3,4-dichlorophenyl)-1,1-dimethylurea] was added for a final concentration of 10 μM to make sure that the observed growth was heterotrophic. Isolates were checked to ensure that they were free of contaminant bacteria before the experiments.

Baldanta S., Arnal R., Blanco-Rivero A., Guevara G, & Navarro Llorens J.M. (2023). First characterization of cultivable extremophile Chroococcidiopsis isolates from a solar panel. Frontiers in Microbiology, 14, 982422.

Publication 2023

Agar Arabinose Bacteria Carbon Darkness Diuron Exanthema Fructose Galactose Glucose Glycerin Heterotrophic Growth Heterotrophy Lactose Maltose Mannose Photosynthesis Sucrose

Calculating Net Ecosystem Productivity

Net Ecosystem Productivity (NEP) is defined as the difference between NPP and soil heterotrophic respiration:
where NEP is net ecosystem productivity of vegetation (gC·m^-2); NPP is net primary productivity of vegetation (gC·m^-2); RH is soil heterotrophic respiration (gC·m^-2).
Soil heterotrophic respiration is calculated by the empirical equation developed by Pei et al. (2003) (link).
Where RH is soil heterotrophic respiration (gC·m^-2); T is air temperature (°C); R is precipitation (mm).

Lu X., Chen Y., Sun Y., Xu Y., Xin Y, & Mo Y. (2023). Spatial and temporal variations of net ecosystem productivity in Xinjiang Autonomous Region, China based on remote sensing. Frontiers in Plant Science, 14, 1146388.

Publication 2023

Cell Respiration Ecosystem Heterotrophy

Phage Impact on Beneficial Soil Microbes

The total microbial count (control) of beneficial rhizosphere bacteria (heterotrophs and nitrogen-fixing, ammonifying, nitrifying, and denitrifying bacteria) was calculated in a native soil suspension prepared in saline (10 g soil/90 mL saline). The media, methods, and cultivation conditions applied in the analyses are listed in Table 2. Aerobic heterotrophic bacteria and nitrogen-fixing bacteria were enumerated by the plate counting method. A series of tenfold dilutions (up to 10⁻⁷) of the soil suspension in saline were prepared, and an aliquot of 100 µL of each dilution (in triplicate) was plated on the surface of the appropriate culture media (Table 2). The number of ammonifying, nitrifying, and denitrifying bacteria was determined by the most probable number method (MPN) [40 ,41 (link),42 (link)].
To determine the influence of the phage isolate BsXeu269p/3 on the beneficial rhizosphere microorganisms, a pure lysate with a titer of 10¹⁰ pfu/mL was used. The analysis was carried out by two methods—DAOPA and MPN. We used DAOPA to determine the ability of the phage isolate to form clear plaques on a lawn of previously enriched soil heterotrophs (in NAII medium) and nitrogen-fixing bacteria (in nitrogen-free broth medium) (Table 2). A mixture of 100 µL enriched bacterial suspension, 10 mM CaCl₂, and 3 mL soft NAII (0.45% agar) or NFM agar (0.45% agar) for heterotrophs and nitrogen-fixing bacteria, respectively, was poured onto the surface of the respective agar media in previously prepared petri dishes. The phage lysate was diluted up to 10⁻⁷, and an aliquot of 10 µL of each dilution was spotted on the surface of the prepared double agar plates, which were cultivated at 28 °C for 24 h. The formation of clear plaques after cultivation indicated the presence of heterotrophic and nitrogen-fixing bacteria susceptible to the phage isolate. The MPN method was applied to study the ability of the phage isolate to reduce (by lysis) the amount of soil microorganisms in liquid media (Table 2). A mixture of 100 µL soil suspension (diluted to 10⁻⁷), 100 µL pure phage lysate (10¹⁰ pfu/mL), and 10 mM CaCl₂ was added to each test tube containing liquid media.

Kizheva Y., Urshev Z., Dimitrova M., Bogatzevska N., Moncheva P, & Hristova P. (2023). Phenotypic and Genotypic Characterization of Newly Isolated Xanthomonas euvesicatoria-Specific Bacteriophages and Evaluation of Their Biocontrol Potential. Plants, 12(4), 947.

Publication 2023

Agar Bacteria Bacteria, Aerobic Bacteriophages Heterotrophy Hyperostosis, Diffuse Idiopathic Skeletal Nitrogen Nitrogen-Fixing Bacteria Rhizosphere Saline Solution Senile Plaques Technique, Dilution

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More about "Heterotrophy"

Heterotrophy is the nutritional mode in which organisms obtain organic compounds from their environment as a source of carbon and energy, rather than synthesizing these compounds through autotrophy.
This heterotrophic process is essential for many lifeforms, including animals, fungi, and some protists.
Heterotrophs rely on the consumption of other organisms or organic matter, such as dead plant and animal matter, to meet their metabolic needs.
Understanding the mechanisms and dynamics of heterotrophy is crucial for fields like microbiology, ecology, and biotechnology, as it informs our knowledge of nutrient cycling, food webs, and potential applications in areas like bioremediation and bioenergy production.
The study of heterotrophy continues to provide valuable insights into the diversity and adaptability of living systems.
Researchers often use techniques like SYBR Green I staining, FACSCalibur flow cytometry, and SYTO13 to study heterotrophic microorganisms.
Additionally, tools such as the LI-8100 soil CO2 flux chamber and Biolog EcoPlates can be employed to assess heterotrophic activity in soil and aquatic environments.
Scintillation counters are used to measure the uptake of radiolabeled substrates by heterotrophs, while Attune Acoustic Focusing Flow Cytometers can provide high-throughput analysis of heterotrophic populations.
Ringer's solution is a commonly used medium for culturing and maintaining heterotrophic organisms in the laboratory.
Exploring the diverse world of heterotrophy continues to yield fascinating insights into the intricate relationships and adaptations within ecosystems, with implications for fields ranging from microbiology and ecology to biotechnology and environmental science.