Production of $CO_2$ basal metabolism test

Production of $CO_2$ basal metabolism test

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Can the production of $CO_2$ be used as a type of basal metabolism test? Explain.

The answer to this is obviously yes, but I don't know how to explain it. Would the following be a good explanation?

The faster your metabolism the more cellular respiration your cells do, and you produce more $CO_2$, so $CO_2$ output can be used as a type of basal metabolism test.

Production of $CO_2$ basal metabolism test - Biology

To commercialize recombinant organisms for renewable chemical production, it is essential to characterize the cost and benefit of metabolic burden using metabolic flux analysis tools.

Genome-scale modeling can incorporate 13 C-fluxome information and machine learning to predict the metabolic burden of synthetic biology modules.

Modularized expression of native or recombinant pathways using a variety of experimental tools for controlling expression can substantially reduce the metabolic burden introduced by these pathways.

The development of a standard synthetic-biology publication database may allow the use of machine learning or artificial intelligence to harness past knowledge for future rational design.

Detailed computational methods have been developed to model macromolecule synthesis (DNA, RNA, proteins) to account for the maintenance costs associated with basal cellular function.

Systems-level dynamic simulations and design algorithms can inform new approaches to engineering microbial production strains.


Animals and sampling

Thirty adults of Limax maximus of approximately the same body mass (mean=6.56 g, SE=0.79) were collected under plants and rocks in public parks in Valdivia, Chile (39°49′S 73°15′W), and housed in plastic cages filled with 10 cm of humid soil. The slugs were fed with corn flour ad libitum and maintained at 20°C with photoperiod 12:12 L:D for 1 month before the measurements. Relative humidity was maintained at high levels by sprinkling the interior of the boxes with water every day.

Respirometry and water balance

10 min during measurements, which had a total duration of 45 min each. Activity was rarely observed during the respirometry measurements, and data from active animals were not included in the analysis. Records were automatically transformed by a macro recorded in the Expe Data software (Sable Systems), to transform the measurements from parts-per-million to mL-CO2 per hour, taking into account the flow rate. The respirometry equation used was as follows:

We eliminated the first 10 min of each of the 600 records to avoid any noise or erroneous recordings generated by animal manipulation. From each individual record, we extracted three variables: complete average of each transformed record (VCO2 Mean) which is used here as a proxy of SMR the average of the 1-min steady state of minimum VCO2 production (VCO2 Min) and the average of the 1-min steady state of maximum VCO2 production (VCO2 Max).

To determine the extent of evaporative water loss, the slugs were weighed at the beginning of each trial, when the slugs were fully hydrated, and at the end of the test period (i.e., 45 min), recording the mean Mb and also the differences between initial and final Mb as a proxy of body water loss. Precise measurements of repeatability can be accomplished with two repetitions (Nespolo & Franco 2007 ), although three or more are recommended for minimizing residual variance.


The design model included two predictor variables: one categorical variable (animals) and one continuous variable (Mb). Individual slugs were used as a random factor and measurements as replicates. Dependent variables were VCO2 Mean, VCO2 Min, VCO2 Max, and BD. Repeatability was evaluated by the intraclass correlation coefficient (τ see Lessells & Boag 1987 Falconer & Mackay 1997 ) by calculating the between- and within-individual variance component from one-way ANCOVA (mean Mb as covariate) for metabolic rates and BD and from one-way ANOVA for Mb. Then, τ=(between individuals variance component)/(between individuals variance component+within-individual variance component). The parallelism assumption (i.e., interaction with the covariate) was checked using an ANCOVA homogeneity-of-slopes model. Least-squares linear regressions were used for examining the relationship between body mass (Mb) and the three metabolic variables. We checked normality and homoscedasticity by the Lilliefors and Levene tests, respectively. A natural logarithm (Ln) transformation was applied to the three VCO2 rates and the Mb values, while BD was square-root transformed before the analysis. All statistical analyses were performed in Statistica 6.1 (Statsoft Inc 2004).

What affects my BMR?

Anything that results in an increase to your metabolic rate will increase your BMR. This includes stress, fear, illnesses and exercise or activity level.

Your BMR is relative to Height, Weight, Age, and Gender (men need more calories than women), but can be affected by other factos such as:

Age BMR decreases as age increases.
Gender Males have a higher BMR than females.
Pregnancy BMR increases during pregnancy and lactation.
Increased intake of foods Eating larges amounts of food requires more energy to process more material.
Environmental conditions Extreme temperatures require more energy to maintain body temperature.
Stress Increased heart rate or blood pressure need more energy.
Sleep time The more you sleep, the less energy your body needs.

Other factors: fasting, malnutrition, or increase secretion of certain hormones (testosterone, insulin).


The activity of SCs permits skeletal muscle to relentlessly regenerate itself and in this way preserve tonicity and fitness. SCs are therefore granted of major attention in the community working in muscle biology and their physiological patterns, together with regenerative potential, are widely exploited. According to recent discoveries, muscle SCs constitute a heterogeneous population of muscle precursors, dominated by finely tuned niche-mechanisms, which play a peculiar role in its global balance. The complete understanding of these is therefore fundamental to solve some basic questions, but in particular to set up some clinical protocols to treat some of the related pathologies. The identification, as shown in this paper, of key underlining metabolic parameters at the basis of the SCs’ intrinsic diversity is a substantial contribution toward a more exhaustive comprehension of SCs’ physiology and a fundamental step to interprete their phsyiopathology and rationalize strategies of intervention.

Food supply effects on the asexual reproduction and respiratory metabolism of Aurelia aurita polyps

Because Aurelia spp. blooms have important regional effects, it is urgent to determine factors that may affect their proliferation and their effects on food webs. The life cycle of most scyphozoans includes an attached stage (polyp or scyphistoma) that reproduces asexually. To test the effects of food availability (unfed, fed 1-, 2-, or 3-times weekly), we measured production rates, mass, and metabolism of Aurelia aurita polyps. Metabolic measurements were physiological respiratory O2 consumption (R), potential O2 consumption (Φ), and potential CO2 production (ψNADP). Φ and ψNADP were calculated from enzymatic activities of the respiratory electron transport system (ETS) and from the CO2-producing enzyme, NADP + -dependent isocitrate dehydrogenase (NADP-IDH), respectively. The production of polyps dramatically increased from

0.65 polyp day −1 (fed 3-times week −1 ) over 33 days. Mass and metabolism (R, Φ, ψNADP) per polyp were significantly lower in unfed polyps, but indistinguishable among fed polyps. Our results suggested that the polyps maintained low-metabolic rates, putting available energy into asexual reproduction. The polyps were adapted to survive and reproduce when unfed, confirming their contribution to population persistence and potential jellyfish blooms.


Mitochondria perform a diverse array of functions in mammalian cells, including production of ATP, induction of apoptosis, and calcium buffering (Anderson et al., 2019). Dysfunction of mitochondria is associated with many pathologies, including cancer, diabetes, and neurodegenerative disease. Mitochondrial diseases are genetic disorders that arise due to mutations in genes encoding proteins required for normal mitochondrial function (Jackson et al., 2018 Frazier et al., 2019). Mitochondria require 1200 nuclear-encoded proteins to function these proteins are delivered to specific mitochondrial subcompartments (outer membrane intermembrane space, inner membrane, and matrix) by translocation and sorting machineries (Wiedemann and Pfanner, 2017 Rath et al., 2020). Mutations in genes encoding various subunits of mitochondrial translocation machineries have been linked to a number of distinct mitochondrial diseases (Jackson et al., 2018).

The TIM22 complex is an inner membrane translocase that mediates the insertion of multipass transmembrane proteins into the mitochondrial inner membrane (Rehling et al., 2003). The TIM22 complex has been extensively studied in yeast however, recent analyses in human cells have revealed substantial divergence of the complex in higher eukaryotes. The human TIM22 complex consists of 1) Tim22, the core pore-forming subunit 2) the intermembrane space chaperones Tim9, Tim10, and Tim10b and 3) Tim29 (Callegari et al., 2016 Kang et al., 2016) and acylglycerol kinase (AGK) (Kang et al., 2017 Vukotic et al., 2017), which are metazoan-specific subunits of the complex. The main substrates of the TIM22 complex are members of the SLC25 family of metabolite carrier proteins, which possess six transmembrane domains (Palmieri, 2013). The TIM22 complex also mediates membrane insertion of “TIM substrates,” Tim17, Tim23, and Tim22, which possess four transmembrane domains (Káldi et al., 1998 Kurz et al., 1999). Based on the properties of these proteins, it was thought that all substrates of the TIM22 complex would contain an even number of transmembrane domains. Recently, subunits of the mitochondrial pyruvate carrier have been identified as TIM22 complex substrates in both yeast and humans (Gomkale et al., 2020 Rampelt et al., 2020). One subunit of this complex (MPC2) has three transmembrane domains, suggesting that the current model of TIM22 complex import may need revising and that further substrates with odd numbers of transmembrane domains may exist.

In human cells, mutations in the AGK encoded subunit of the TIM22 complex are linked to Sengers syndrome, a severe mitochondrial disease characterized by congenital cataracts, hypertrophic cardiomyopathy, exercise intolerance, and lactic acidosis (Calvo et al., 2012 Mayr et al., 2012). As well as contributing to protein import at the TIM22 complex, AGK also functions as a lipid kinase, able to phosphorylate monoacylglycerol and diacylglycerol to produce phosphatidic acid and lysophosphatidic acid, respectively (Waggoner et al., 2004 Bektas et al., 2005). The lipid kinase activity of AGK is dispensable for its function at the TIM22 complex (Kang et al., 2017 Vukotic et al., 2017). Despite advances in the understanding of AGK function, how the protein’s dysfunction contributes to the molecular pathogenesis underlying Sengers syndrome is unclear.

Using quantitative proteomics, we set out to identify which mitochondrial functions and/or pathways are dysregulated in Sengers syndrome, with a view to expand our understanding of AGK function and Sengers syndrome pathology in an unbiased manner. We mapped the mitochondrial proteome of Sengers syndrome patient fibroblasts (Calvo et al., 2012 Kang et al., 2017), in addition to MCF7 and HEK293 cell lines lacking AGK (Kang et al., 2017), and identified extensive remodeling of the mitochondrial proteome. Consistent with the function of AGK at TIM22 and the known features of Sengers syndrome pathology, we observed a down-regulation of mitochondrial carrier proteins and Complex I subunits, in addition to down-regulation of enzymes involved in mitochondrial one-carbon (1C) metabolism (Ducker and Rabinowitz, 2017). By analyzing proteomic data sets from multiple systems of AGK or TIM22 complex dysfunction, we identified a list of candidate TIM22 complex substrates. We focused on sideroflexins (SFXNs), a family of proteins that mediate transport of serine into mitochondria and are required for efficient 1C metabolism (Kory et al., 2018). The proteomic data from Sengers patient fibroblasts and AGK KO HEK293 and MCF7 cell lines indicated down-regulation of SFXN proteins in the absence of functional AGK, and we show that these proteins rely on the TIM22 complex for their biogenesis. Consequently, loss of AGK in HEK293 cells leads to dependency on exogenous serine for normal proliferation. These data add further evidence to recent developments that suggest that mitochondrial dysfunction leads to changes in mitochondrial 1C metabolism (Bao et al., 2016 Nikkanen et al., 2016 Khan et al., 2017), and also provide a comprehensive list of candidate substrates of the human TIM22 complex.

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Our findings indicate that miR-378 and miR-378* participate in the regulation of mitochondrial metabolism and energy homeostasis in mice through the transcriptional network controlled by PGC-1β (Fig. 5). MiR-378/378* KO mice are protected against HFD-induced obesity and exhibit enhanced mitochondrial fatty acid metabolism in liver and slow muscle fibers.

Schematic diagram of the roles of miR-378 and miR-378* in energy homeostasis and mitochondrial functions. PGC-1β is a transcriptional coactivator of nuclear hormone receptors (NRs), which modulate metabolism via the Mediator complex and the basal transcription machinery. MED13, a subunit of the Mediator complex, is a target of miR-378*, and Crat is a target of miR-378. CRAT is a mitochondrial enzyme involved in fatty acid oxidative metabolism via the TCA cycle. Thus, miR-378 and miR-378* are integral components of a regulatory circuit that functions under conditions of metabolic stress to control the overall oxidative capacity of insulin target tissues.

Like other miRNAs, miR-378 and miR378* have numerous targets. Thus, although the repressive influence of these miRNAs on Crat and Med13 in the liver likely contributes to their metabolic actions, the combined functions of these miRNAs in multiple tissues and on multiple targets are undoubtedly involved as well. In this regard, we detected no changes in expression of Crat or Med13 in muscle tissues of miR-378/378* mutant mice. Given the sensitivity of miRNA-dependent repression to stress signaling (35), the apparent restriction of Crat and Med13 regulation to the liver may indicate greater sensitivity of this tissue to the adverse consequences of an HFD. In agreement with our findings, miR-378* has been shown to target the mRNAs encoding estrogen-related receptor-γ and GA-binding protein-α, which associate with PGC-1β to control oxidative metabolism (10). Up-regulation of miR-378* in cancer cells has been proposed to mediate increased lactate production owing to the shift from oxidative to glycolytic metabolism, known as the Warburg effect, which is associated with tumorigenesis (10, 45). Thus, the phenotype resulting from increased miR-378* expression is the reciprocal to the shift toward oxidative metabolism that we observed on genetic deletion of miR-378*. In addition, miR-378 targets the transcriptional repressor MyoR during myoblast differentiation, increasing the activity of the myogenic transcription factor MyoD, which in turn up-regulates miR-378 within a regulatory feed-forward loop (46). Furthermore, miR-378 has been reported to target the insulin-like growth factor 1 receptor and to promote apoptosis in cardiomyocytes (28).

Our global genetic deletion of the miR-378/378* cluster leaves open questions regarding the relative contributions of miR-378 and miR-378* in different insulin target tissues. For example, these miRNAs might have unrecognized functions in the brain that influence energy homeostasis. In addition, we noted a reduction of adipocyte size in mice lacking miR-378/378*, raising the possibility that these miRNAs are required for efficient hypertrophy and lipid uptake in white adipocytes. In this regard, overexpression of miR-378/378* in mesenchymal precursor cells has been shown to increase lipogenesis (26). The modulation of fatty acid metabolism by miR-378/378* is crucial not only in the context of adipogenesis, but also in the maintenance of cardiac function, given that the adult heart depends primarily on fatty acids as an energy source (47). Thus, it will be of eventual interest to investigate the potential influence of these miRNAs on cardiac function and pathological remodeling under conditions of nutritional and other forms of stress.

We recently reported that MED13 is also a target of miR-208a and other muscle-specific miRNAs encoded by introns of myosin heavy-chain genes (24). Up-regulation of MED13 in the heart through transgenic overexpression or miR-208a inhibition was found to confer resistance to HFD. Thus, MED13 serves as a nodal point for the convergence of multiple miRNAs to modulate energy balance and metabolism.

MiR-378/378* mutant mice, like other mouse strains lacking specific miRNAs (35), do not display overt phenotypes under normal laboratory conditions, but phenotypes become apparent under conditions of stress—in this case, in response to excessive calorie intake. Thus, miR-378 and 378* represent intriguing targets for disease modulation and pharmacologic intervention in the treatment of metabolic syndrome. However, the myriad targets of these miRNAs, as well as their expression in multiple tissues raises questions about systemic inhibition of these miRNAs with inhibitory oligonucleotides and their potential to modulate both pathological and beneficial processes in different tissues. Studies assessing the potential efficacy of systemically delivered miR-378/378* inhibitors in the setting of obesity, as well as their potential adverse side effects, are underway.

Lipid Metabolism Signaling Pathway

Lipid is a general term for fats and lipids. It is an ester formed by the action of fatty acids and alcohols and its derivatives, collectively known as lipids. It is a major component of animals and plants and is also widely found in nature. Despite their much different chemical composition, structural physicochemical properties and biological functions, lipids all have a common feature, which can be extracted from cells and tissues with non-polar organic solutions. Fat metabolism is one of the three major material metabolisms, and its signal transduction pathway has a complex and fine regulatory network, which is mainly involved in the energy supply and storage of the organism, the composition of biofilm and other important life processes. Lipid metabolism mainly includes triglyceride (TG) metabolism, metabolism of cholesterol and its esters, and phospholipid and glycolipid metabolism. The stability of lipid metabolism is particularly important for the steady state maintenance of the body.

Lipid metabolism family

Lipid metabolism mainly includes triglyceride (TG) metabolism, metabolism of cholesterol and its esters, and phospholipid and glycolipid metabolism. In these metabolic processes, many proteases, receptors, transcription factors, etc. are involved, and they are regulated by some signal transduction pathways, forming a complex and fine regulatory network to maintain the lipid metabolism balance of cells and the whole body. Lipid metabolism transduction signal pathways mainly include peroxisome proliferator-activated receptor (PPARs) signal transduction pathway, liver X receptor (LXRs) signal transduction pathway, sterol regulatory element binding protein (SREBPs) signal transduction guide route and so on. The lipid metabolism signal transduction pathways are complex, and there are many downstream target genes regulated by each pathway, and each pathway is also regulated by each other. Many of these problems remain to be elucidated.


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