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12.5: Examples of use of amino acids as biosynthetic precursor - Biology


12.5: Examples of use of amino acids as biosynthetic precursor

Amino acid homeostasis and signalling in mammalian cells and organisms

Cells have a constant turnover of proteins that recycle most amino acids over time. Net loss is mainly due to amino acid oxidation. Homeostasis is achieved through exchange of essential amino acids with non-essential amino acids and the transfer of amino groups from oxidised amino acids to amino acid biosynthesis. This homeostatic condition is maintained through an active mTORC1 complex. Under amino acid depletion, mTORC1 is inactivated. This increases the breakdown of cellular proteins through autophagy and reduces protein biosynthesis. The general control non-derepressable 2/ATF4 pathway may be activated in addition, resulting in transcription of genes involved in amino acid transport and biosynthesis of non-essential amino acids. Metabolism is autoregulated to minimise oxidation of amino acids. Systemic amino acid levels are also tightly regulated. Food intake briefly increases plasma amino acid levels, which stimulates insulin release and mTOR-dependent protein synthesis in muscle. Excess amino acids are oxidised, resulting in increased urea production. Short-term fasting does not result in depletion of plasma amino acids due to reduced protein synthesis and the onset of autophagy. Owing to the fact that half of all amino acids are essential, reduction in protein synthesis and amino acid oxidation are the only two measures to reduce amino acid demand. Long-term malnutrition causes depletion of plasma amino acids. The CNS appears to generate a protein-specific response upon amino acid depletion, resulting in avoidance of an inadequate diet. High protein levels, in contrast, contribute together with other nutrients to a reduction in food intake.


What Are Amino Acids?

Well, amino acids in food make up protein. When protein is digested it is once again broken down into specific amino acids, that are then selectively put together for different uses. These new proteins formed in the body are what make up most solid matter in the body: skin, eyes, heart, intestines, bones and, of course, muscle.

That's why understanding what each of these aminos can do and getting more of them in your diet can be very beneficial to reaching specific goals, such as muscle building. Of course, one mustn't exaggerate, because a good protein balance is what provides health and stability, without it any of the amino acids can become toxic.

An issue that has been brought up in the case of phenylalanine, but holds true for all amino acids. To counter potential harmful effects, getting enough vitamins and minerals is important because they insure proper conversion of protein to amino and vice versa.

Depending on who you talk to, there are around 20 to 22 standard amino acids. Of those 20-22, 8 to 10 of them are considered essential, which means that you need to get a certain amount of them in your diet to function properly - our bodies cannot synthesize them from other materials, so we only get them from food.

Since aminos are the building blocks of protein, I'm sure you get plenty of all of them, but this article will show you the benefits of supplementing with extra free form amino acids, going in to deep detail of what too much or too little of several of them can do, what they do in the body and how much and when you should use them.

Next to the 8 essential amino acids, there are around 14 non-essential amino acids and a whole host of other metabolites classed as amino acids which are derived from the 8 essential ones. Next to the 8 essential aminos, I will try to discuss a number of them that have made the headlines recently: L-Glutamine, L-Arginine, L-Carnitine, L-Cysteine, and HMB.


Amino Acids in Plants: Regulation and Functions in Development and Stress Defense

For quite a long time, research on amino acid metabolism received only limited attention in the areas of plant physiology and biochemistry. Owing to the essential function of amino acids in protein synthesis, it was tempting to assume that plants use and metabolize amino acids in the same manner as .

For quite a long time, research on amino acid metabolism received only limited attention in the areas of plant physiology and biochemistry. Owing to the essential function of amino acids in protein synthesis, it was tempting to assume that plants use and metabolize amino acids in the same manner as microorganism or humans do. However, as fully autotrophic organisms, plants face fundamentally different challenges compared to organisms that make a living on plant- (or algae-) produced biomass. Furthermore, plants produce literally hundreds of non-proteinogenic amino acids or amino acid-derived secondary metabolites and thus require a very distinct regulatory system to coordinate the needs of primary and secondary metabolism. The distribution of amino acid metabolism between roots and aboveground organs or among the different subcellular compartments adds another level of complexity further distinguishing plants from prokaryotes or animals.

From the perspective of human nutrition, the amino acids that we cannot synthesize by ourselves are the most interesting. Accordingly, the efforts to understand the routes and regulation of Lys, Met, and Trp biosynthesis has boosted amino acid research in plants, since these three amino acids are often contained in limiting amounts in staple crops. From an agricultural perspective, amino acid biosynthetic pathways that are exclusively found in plants were analyzed and used for the development of herbicides.

At present, the threats of climate change and groundwater contamination by excess nitrogen fertilization call for a re-focusing of our efforts. Optimal productivity is currently achieved by massive application of nitrogen-containing fertilizers and is yet limited by the efficiency of the plants in taking up, distributing, storing and assimilating the nitrogen, with all these processes being governed by amino acid metabolism and transport. Similarly, many defense responses of plants against biotic or abiotic stress involve metabolic adjustments in amino acid metabolism to counteract detrimental environmental impacts directly or to provide precursors for defense compounds. Several amino acids emerge as important signaling molecules that orchestrate plant growth and development by integrating the metabolic status of the plant with environmental signals, especially in stressful conditions. However, our knowledge about amino acid metabolism, the mechanisms that regulate the levels of free amino acids and the diverse functions of amino acids in plants is far from complete.

This Research Topic aims to collect contributions from different facets of amino acid metabolism, transport or signaling to bring together and integrate into a comprehensive view the latest advances in our understanding of the multiple functions of amino acids in plants. All types of articles related to this field of plant physiology, including Original Research, Reviews, Mini Reviews, Methods, Commentaries, and Opinions are welcome.

Keywords: Amino Acid Metabolism, Amino Acid Transport, Stress Responses, Nitrogen Sensing, Signaling and Assimilation, Nitrogen Use Efficiency, Crop Enhancement

Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.


Results

Initial evaluation of urinary excretion patterns

Initially, 172 participants (aged 18 years or older) were recruited from the general public for stage 1 of the study. A final study group of 151 subjects provided urine samples and full responses to questionnaires and were reportedly free from any significant medical conditions. The participant group comprised 99 males with an average age of 29.5 ± 11.8 (mean ± SD) and 52 females with an average age of 35 ± 13.7. The first stage of the investigation involved appraising the pre-supplementation urine excretion profiles of the 151 recruits. The amino acid compositions were determined by GC-FID analyses and used to generate a dataset of relative percentage abundances of 29 amino acids and amino acid derivatives. These data were assessed by k-means clustering techniques to determine that the study participants could be partitioned into three clusters based the amino acid relative abundances with a minimum cluster membership of n > 10. Standard discriminant function analysis was then applied to determine whether each of the clusters had significantly different amino acid profiles (Wilks’ Lambda = 0.13, F(46,252) = 9.85, p < 0.0001) as shown in the canonical plot (Fig. 2) with glycine and histidine levels providing the major discrimination between the groups. The members in each cluster were tightly grouped based upon their urinary excretion profiles and each subgroup was clearly resolved from the other subgroups with minimal overlap.

Discriminant function canonical plot of subgroups generated via k-means clustering

Seventeen amino acids contributed towards differentiating the composition profiles of the clusters by displaying significant differences in relative abundances for the subgroups and the concentrations of the key components have been summarised in Table 2. Histidine and glycine were the most abundant amino acids in the urinary profiles and displayed different relative abundances for each of the three clusters. Cluster 1 was characterised by a high histidine to glycine ratio of 2.0, cluster 2 displayed a low ratio of 0.4 and cluster 3 had equivalent levels resulting in a ratio of 1.0. Cluster 1 had the highest total urinary concentration of excreted amino acids where histidine levels were 2.4 – 3.6 times those in clusters 3 and 2 respectively. Cluster 1 also had significantly higher concentrations of glutamine, tyrosine and the branch chain amino acids compared with both clusters 2 and 3. In addition, serine and alanine were higher in cluster 1 compared with cluster 3 (Table 2). Cluster 2 was equivalent to cluster 3 in these parameters but was clearly distinguished by the high level of glycine excretion in the urine.

The objective partitioning by k-means clustering of subjects based on similarities in their urinary excretion profile removed user-bias in group allocation. Cluster 3 had the highest frequency of membership (46%) followed by cluster 1 (32%) and then cluster 2 (22%) as shown in Table 2. The three subgroups within the study cohort had similar BMI values and were primarily Caucasian. Clusters 1 and 3 had similar mean ages but cluster 2 had a significantly higher mean age than both clusters 1 and 3. Gender was not equally balanced across the three groups where cluster 2 contained predominantly females (p < 0.05). Sixteen out of 27 males who identified themselves as athletes were assigned to cluster 1, ten to cluster 3 and only one to cluster 2. There were no significant differences in total amino acid concentrations between the genders in the whole study cohort where males (n = 99) had a mean level of 5185 ± 290 μmol/L (mean ± SEM) and females (n = 52) 4955 ± 452 μmol/L. Comparisons between the urinary compositions of amino acids in males and females revealed significant differences for glutamic acid (males 16.7 ± 2 vs females 25.0 ± 3 μmol/L, p < 0.05), glycine-proline dipeptide (78.0 ± 5 vs 54.7 ± 6 μmol/L, p < 0.01), proline-hydroxyproline dipeptide (234.3 ± 15 vs 154.7 ± 19 μmol/L, p < 0.01) and tyrosine (96.8 ± 7 vs 72.8 ± 9 μmol/L, p < 0.05).

Levels of fatigue were assessed using the Chalder fatigue scale [26] where the average total fatigue score was calculated to be 13.6 ± 5.9 (mean ± SD) for the whole group. However, evaluation of the clusters revealed that cluster 2 had a significantly higher level of total fatigue compared with cluster 1, while clusters 1 and 3 had similar scores (Table 2). The results of the Chalder fatigue scale were mirrored by the fatigue index results derived from the general health questionnaire. Cluster 2 also reported higher levels of symptoms assessed by the sleep index compared with cluster 1.

Correlation analyses were performed to evaluate potential associations between the levels of urinary metabolites and the various symptom indices for each of the clusters. There were distinctive arrays of correlations noted for each of the clusters with some strong positive associations (r > .50) noted for cluster 1 females (Table 3). It was evident for females belonging to cluster 1 (n = 13) that higher urinary concentrations of glutamic acid, hydroxylysine, methionine, ornithine, proline and valine were all positively correlated with values of the Chalder physical fatigue score (r > .55, p < 0.05). In the same group, increasing scores in the gastrointestinal index were very strongly associated with higher urinary levels of proline (r = .92), methionine (r = .82) and valine (r = .70). These amino acids were also correlated with a range of other symptoms as shown in Table 3. Females in Cluster 2 only exhibited positive correlations for two amino acids, where the urinary concentration for asparagine was associated with the gastrointestinal index and proline was associated with all four fatigue measures and the gastrointestinal index. In contrast, Cluster 3 females displayed negative associations between general fatigue and alanine, aspartic acid, isoleucine, phenylalanine, and tyrosine. The males in cluster 1 displayed nine negative correlations between amino acids and symptoms, where seven of these involved associations with the gastrointestinal index (Table 4). The males from cluster 2 had positive associations between α-aminobutyric acid and the four fatigue indices. The males from cluster 3 had six symptom indices (all four fatigue measures, pain and vitality) positively associated with β-aminoisobutyric acid and showed no negative correlations.

Evaluation of amino acid supplementation

Compliance for the supplementation trial was 51% where 37 participants successfully completed the trial by taking the amino acid supplement for a period of 25–30 days as well as returning general health questionnaires and providing post-supplement urine samples. The supplement trial cohort (n = 37) comprised 10 females and had an average age of 33.8 years ranging from 19 to 65 years.

Levels of fatigue were assessed using both the Chalder fatigue scale [26] and a general fatigue index developed by the current research team, where lower scores were indicative of a lower level of fatigue for both measures. After completion of the supplement trial, 30 out of the group of 37 participants (81%) reported an improvement in their levels of fatigue. A significant reduction in the mean total fatigue scores for the entire cohort was observed from 13.4 ± 0.8 (mean ± SEM) to 8.8 ± 0.6 (Wilcoxon matched pairs test, p < 0.0001) following supplementation. General health and wellbeing was also assessed using an 86 item questionnaire from which several indices were generated (Table 1). Scores were standardised to values out of 40, where a score of “0” indicated that the subject was not affected by any of the symptoms comprising that index, and a score of “40” indicating that the subject experienced all of the symptoms in the index group to a high degree. Following supplementation, significant reductions in questionnaire scores representing potential improvements were seen in the fatigue, sleep and vitality indices (Table 5). In response to the questions regarding their experience using the supplement, 25 responders (68%) indicated that they felt the supplement had improved their health and 28 (76%) stated that they would continue to take the supplement if given the opportunity.

The seven participants who did not report any improvements in fatigue were all males and had an average Chalder total fatigue score of 10.0 ± 1.4 (mean ± SEM) prior to supplementation whereas the remainder of the cohort (n = 30) had a significantly higher mean Chalder fatigue score of 14.2 ± 0.8 (Mann-Whitney U test, p < 0.02). The participants that showed improvements in fatigue thus had a higher level of fatigue prior to commencing the supplementation. The larger group who reported having experienced relief from fatigue ultimately reported post-supplementation fatigue scores that were significantly lower (8.1 ± 0.6, mean ± SEM) than those who did not report improved fatigue (11.7 ± 1.4, p < 0.04). Six out of the seven males who did not respond to the amino acid supplementation were classified by their pre-supplementation urinary profile as belonging to cluster 3 and the seventh non-responder was assigned to cluster 1.


Conclusions

Herein, we have studied codon and amino acid use in a cricket model system and proposed a significant role of selection within its most highly transcribed genes, at the organism-wide level (Table 1) and in different tissue types (Additional file 1: Table S2). Future research should include the direct quantification of tRNAs in different tissue types [39, 48, 74, 113], to assess whether those results add support to the conclusion of similar relative tRNA abundances across tissue type and sex in this cricket. Such an approach will also help discern why this cricket species may have less propensity for tissue-related optimal codons than other organisms studied to date [20, 36, 38, 40]. While our data suggest that mutational AT biases may partly contribute towards genome-wide codon use patterns in G. bimaculatus, and we do not exclude a role of BGC in the variation in GC/AT content among genes, the collective patterns are consistent with the hypothesis that translational selection significantly contributes to optimal codon use under high transcription. Further studies should rigorously evaluate the possible roles of BGC in codon use in this cricket species [85], including approaches that consider meiotic recombination rates, expression level in meiotic cells, and their relationships to GC (and thus AT) content (cf. [83, 114, 115]), as more genomic, population data, and recombination data begin to emerge in this taxon.

Another meaningful direction for future study may include the identification of ramping of codons in CDS [116], which may cause a slow-down in translation, particularly at the beginning of CDS, and may potentially increase translational efficiency downstream of the ramp [26, 45, 51, 52, 116]. In particular, ramps using the codons with Nonopt-codon↓tRNAs and Opt-codonwobble status identified herein (Table 1) are candidates to play roles in regulating translation elongation rates using ramping in CDS, and may vary with high versus low expression. In addition, recent research suggests codon use and hydrogen bonding ramps may have roles in dsDNA unwinding and transcriptional regulation, as inferred in Bacteria and Archaea (but not Fungi) [117], and thus this also provides a meaningful avenue for further study in this cricket model and other multicellular animals. Finally, further studies should be conducted of the frequencies of optimal, as well as non-optimal, codons and their relationships to tRNA abundances and gene functionalities in a wider range of multicellular organisms. Such research will reveal whether the phenomena observed herein are shared across divergent systems.


What to know about essential amino acids

The body needs 20 different amino acids to maintain good health and normal functioning. People must obtain nine of these amino acids, called the essential amino acids, through food. Good dietary sources include meat, eggs, tofu, soy, buckwheat, quinoa, and dairy.

Amino acids are compounds that combine to make proteins. When a person eats a food that contains protein, their digestive system breaks the protein down into amino acids. The body then combines the amino acids in various ways to carry out bodily functions.

A healthy body can manufacture the other 11 amino acids, so these do not usually need to enter the body through the diet.

Amino acids build muscles, cause chemical reactions in the body, transport nutrients, prevent illness, and carry out other functions. Amino acid deficiency can result in decreased immunity, digestive problems, depression, fertility issues, lower mental alertness, slowed growth in children, and many other health issues.

Each of the essential amino acids plays a different role in the body, and the symptoms of deficiency vary accordingly.

There are many types of essential amino acids, including:

Lysine

Lysine plays a vital role in building muscle, maintaining bone strength, aiding recovery from injury or surgery, and regulating hormones, antibodies, and enzymes. It may also have antiviral effects.

There is not a lot of research available on lysine deficiency, but a study on rats indicates that lysine deficiency can lead to stress-induced anxiety.

Histidine

Share on Pinterest High protein foods, such as tofu and quinoa, contain amino acids.

Histidine facilitates growth, the creation of blood cells, and tissue repair. It also helps maintain the special protective covering over nerve cells, which is called the myelin sheath.

The body metabolizes histidine into histamine, which is crucial for immunity, reproductive health, and digestion. The results of a study that recruited women with obesity and metabolic syndrome suggest that histidine supplements may lower BMI and insulin resistance.

Deficiency can cause anemia, and low blood levels appear to be more common among people with arthritis and kidney disease.

Threonine

Threonine is necessary for healthy skin and teeth, as it is a component in tooth enamel, collagen, and elastin. It helps aid fat metabolism and may be beneficial for people with indigestion, anxiety, and mild depression.

A 2018 study found that threonine deficiency in fish led to these animals having a lowered resistance to disease.

Methionine

Methionine and the nonessential amino acid cysteine play a role in the health and flexibility of skin and hair. Methionine also helps keep nails strong. It aids the proper absorption of selenium and zinc and the removal of heavy metals, such as lead and mercury.

Valine

Valine is essential for mental focus, muscle coordination, and emotional calm. People may use valine supplements for muscle growth, tissue repair, and energy.

Deficiency may cause insomnia and reduced mental function.

Isoleucine

Isoleucine helps with wound healing, immunity, blood sugar regulation, and hormone production. It is primarily present in muscle tissue and regulates energy levels.

Older adults may be more prone to isoleucine deficiency than younger people. This deficiency may cause muscle wasting and shaking.

Leucine

Leucine helps regulate blood sugar levels and aids the growth and repair of muscle and bone. It is also necessary for wound healing and the production of growth hormone.

Leucine deficiency can lead to skin rashes, hair loss, and fatigue.

Phenylalanine

Share on Pinterest Some diet sodas contain sweeteners with phenylalanine.

Phenylalanine helps the body use other amino acids as well as proteins and enzymes. The body converts phenylalanine to tyrosine, which is necessary for specific brain functions.

Phenylalanine deficiency, though rare, can lead to poor weight gain in infants. It may also cause eczema, fatigue, and memory problems in adults.

Phenylalanine is often in the artificial sweetener aspartame, which manufacturers use to make diet sodas. Large doses of aspartame can increase the levels of phenylalanine in the brain and may cause anxiety and jitteriness and affect sleep.

People with a rare genetic disorder called phenylketonuria (PKU) are unable to metabolize phenylalanine. As a result, they should avoid consuming foods that contain high levels of this amino acid.

Tryptophan

Tryptophan is necessary for proper growth in infants and is a precursor of serotonin and melatonin. Serotonin is a neurotransmitter that regulates appetite, sleep, mood, and pain. Melatonin also regulates sleep.

Tryptophan is a sedative, and it is an ingredient in some sleep aids. One study indicates that tryptophan supplementation can improve mental energy and emotional processing in healthy women.

Tryptophan deficiency can cause a condition called pellagra, which can lead to dementia, skin rashes, and digestive issues.

Many studies show that low levels of protein and essential amino acids affect muscle strength and exercise performance.

According to a 2014 study, not getting enough essential amino acids may cause lower muscle mass in older adults.

An additional study shows that amino acid supplements can help athletes recover after exercise.

Doctors previously believed that people had to eat foods that provided all nine essential amino acids in one meal.

As a result, unless an individual was eating meat, eggs, dairy, tofu, or another food with all the essential amino acids, it was necessary to combine two or more plant foods containing all nine, such as rice and beans.

Today, however, that recommendation is different. People who eat vegetarian or vegan diets can get their essential amino acids from various plant foods throughout the day and do not necessarily have to eat them all together at one meal.

Although 11 of the amino acids are nonessential, humans may require some of them if they are under stress or have an illness. During these times, the body may not be able to make enough of these amino acids to keep up with the increased demand. These amino acids are “conditional,” which means that a person may require them in certain situations.

People may sometimes wish to take essential amino acid supplements. It is best to seek advice from a doctor first regarding safety and dosage.

Although it is possible to be deficient in essential amino acids, most people can obtain enough of them by eating a diet that includes protein.

The foods in the following list are the most common sources of essential amino acids:

  • Lysine is in meat, eggs, soy, black beans, quinoa, and pumpkin seeds.
  • Meat, fish, poultry, nuts, seeds, and whole grains contain large amounts of histidine.
  • Cottage cheese and wheat germ contain high quantities of threonine.
  • Methionine is in eggs, grains, nuts, and seeds.
  • Valine is in soy, cheese, peanuts, mushrooms, whole grains, and vegetables.
  • Isoleucine is plentiful in meat, fish, poultry, eggs, cheese, lentils, nuts, and seeds.
  • Dairy, soy, beans, and legumes are sources of leucine.
  • Phenylalanine is in dairy, meat, poultry, soy, fish, beans, and nuts.
  • Tryptophan is in most high-protein foods, including wheat germ, cottage cheese, chicken, and turkey.

These are just a few examples of foods that are rich in essential amino acids. All foods that contain protein, whether plant-based or animal-based, will contain at least some of the essential amino acids.

Consuming essential amino acids is crucial for good health.

Eating a variety of foods that contain protein each day is the best way for people to ensure that they are getting adequate amounts of essential amino acids. With today’s modern diet and access to a wide variety of foods, deficiency is rare for people who are generally in good health.


Consequences of Amino-Acid Catabolizing Enzyme Activity on T-Cell Differentiation and Function

Most amino-acid catabolizing enzymes, including IDO1 and IL4I1, decrease T-cell proliferation and modify the balance of effector versus regulatory T-cell differentiation (Figure 4). Plasmacytoid dendritic cells stimulated by CpG induce IDO activity, which stabilizes the suppressor phenotype of Tregs, while simultaneously blocking the IL-6 expression required for Th17 cell differentiation (Baban et al., 2009). During fungal infection of mice with Paracoccidioides brasiliensis, the absence of IDO1 is associated with an increased influx of Th17 cells to the infected lung and a concomitant reduction of the number of Th1 and Treg cells (de Araújo et al., 2017). Kyns, which are produced both by IDO and TDO, have been shown to bind to the aryl hydrocarbon receptor (AHR), a highly conserved ligand-activated transcription factor involved in controlling the balance of Treg versus Th17 differentiation (Mezrich et al., 2010 Opitz et al., 2011). Although certain AHR ligands promote the differentiation of Th17 cells, AHR activation by Kyns leads to Treg generation (Mezrich et al., 2010). In addition, tryptophan depletion can enhance the suppressive functions of Tregs by excluding PKCθ from the immune synapse, thus inhibiting its signaling activity (Zanin-Zhorov et al., 2010 Metz et al., 2012).

Figure 4. Simplified scheme of the influence of immunosuppressive enzymes on T-cell priming, differentiation, and function in secondary lymphoid organs and in the periphery in humans. Mature dendritic cells in the T-cell zone (e.g., activated by IFNγ) can present antigens, as well as produce cytoplasmic IDO and secreted IL4I1. IDO degrades Trp and IL4I1 degrades Phe and, to a lesser extent, Trp. The level of these two essential amino acids declines in the T-cell microenvironment, whereas Kyn, phenylpyruvate (PP), IAA (indole-3 acetic acid), H2O2, and NH3 accumulate. The combined effect limits the activation of naïve T cells or, in the case of CD4 T cells, favors their differentiation into regulatory T cells. By enhancing the activation threshold, IL4I1 can also restrain the repertoire of primed CD8 T cells to the high-affinity clones. In inflamed tissues, Arg-catabolizing enzymes can also be expressed, thus diminishing the concentration of available Arg (Arg1) and producing NO (iNOS) and peroxynitrite. Peroxynitrite (ONOO – ) results from the reaction of NO with O2 – , which is produced by iNOS under conditions of low Arg levels. The combined effect of amino-acid starvation and the production of the various catabolites by Trp-, Phe-, and Arg-catabolizing enzymes diminishes the recruitment, proliferation and function of effector CD4 and CD8 T cells and increases the inhibitory function of regulatory T cells. Overall, this leads to lowering of the local T-cell response. The enzymatic reactions are indicated by arrows. Catabolic products that have no known specific impact on T-cell activation are shown in light gray. Some of these products are used for amino-acid regeneration (arginine from citrulline, proline from ornithine) or the production of polyamines (ornithine), which serve as building blocks for cell growth.

Differentiation of naïve CD4 + T cells in the presence of IL4I1 also skews their polarization toward Tregs, whereas it does not substantially affect Th17 differentiation. This effect appears to involve diminution of mTORC1 signaling (Cousin et al., 2015). However, it has also been recently observed that IL4I1 degradation of tryptophan [a minor substrate in comparison to phenylalanine (Boulland et al., 2007)] produces indole derivatives that can activate the AHR pathway (Sadik et al., 2020 Zhang et al., 2020). Finally, IL4I1 modulates the priming of CD8 + T cells. Indeed, the absence of IL4I1 lowered the activation threshold of cognate CD8 + T cells in a mouse model of acute infection with the lymphocytic choriomeningitis virus, leading to extension of the responding repertoire to low-affinity clones and increased memory T-cell differentiation. Thus, IL4I1 may represent a mechanism to restrain T-cell activation to high-affinity CD8 + T-cell clones (Puiffe et al., 2020).

Arg1 produced by MDSCs has also been suggested to play a role in Th17 differentiation. Indeed, RORγT and IL-17A expression decrease in T cells cultured with MDSCs treated with the Arg1 inhibitor Nor-NOHA (Wu et al., 2016). Consistent with this observation, mice with a conditional deletion of Arg1 in myeloid cells show decreased expression of IL-17A in the colorectum during experimentally induced colitis (Ma et al., 2020). High concentrations of NO provided by the NO donor NOC-18 can suppress the proliferation and function of polarized murine and human Th17 cells by inhibiting the expression of AHR (Niedbala et al., 2011). In accordance with this result, iNOS-deficient mice exhibit enhanced Th17 cell differentiation but no changes in Th1 or Th2 polarization (Yang et al., 2013). Conversely, the use of NOC-18 induces the proliferation and sustained survival of CD4 + CD25 – T cells, which acquire the expression of CD25 but not Foxp3 and present regulatory functions (Niedbala et al., 2007). In sharp contrast with these findings, physiological NO levels produced by the MDSCs of cancer patients or endogenously by CD4 + T cells expressing iNOS can induce and stabilize the Th17 phenotype (Obermajer et al., 2013). Mouse γδ T cells also express iNOS, in particular following stimulation by inflammatory cytokines (Douguet et al., 2018). The enzyme is essential for promoting optimal IL-2 production and proliferation of γδ T cells, but drives IL-17 production, which is associated with pro-tumor properties in a murine model of melanoma (Douguet et al., 2016a,b). These findings illustrate the dual role of NO on T cell activation at the level of T-cell differentiation, depending on its concentration.


What do amino acids do?

Build, repair, regulate, energise

Amino acids are commonly known for their role in muscle protein synthesis. However, the benefits stretch beyond your workout and muscles.

Amino acids make proteins, which make up about 20% of our bodies. Our skin, hair, muscles, internal organs, red and white blood cells all depend on proteins for structure and function. Amino acids also help regulate and maintain most bodily processes by becoming proteins, enzymes or hormones, and by supplying energy to our cells.

Cognitive function & nervous system

The central nervous system controls most bodily functions and our minds. It consists of two parts: the brain and the spinal cord. To function adequately, the brain and central nervous system need a number of amino acids, including histidine, tryptophan, tyrosine, and arginine. The brain uses these to produce various neurotransmitters and neuromodulators.

Hormone production

Simply put, hormones are signalling molecules and they encourage the body to respond to stimuli. Hormones raise our blood pressure during exercise, allow us to sweat when we're hot and more. Amino acids can either be used as building blocks for specific hormones or to encourage the release of hormones.

There is evidence that the amino acids lysine and arginine help to trigger the release of growth hormones, encouraging the growth and development of cells, which can be particularly useful for children and sports people looking to gain muscle mass.

Digestive system

Amino acids are the major fuel of the small intestine membrane and they are also used to support the intestinal immune and anti-oxidative responses. Glutamate, glutamine and aspartate, in particular, are vital. You may have heard the phrase that your gut is your first line of defence against disease and pathogens and amino acids play a major role in these defenses.

Reproductive system

The availability and metabolism of amino acids, which interact with other macronutrients, is vital for various reproductive processes, including gametogenesis, fertilisation, implantation, placentation, foetal growth and development.

Immune system

The immune system protects your body from outside invaders, such as bacteria, viruses, fungi, and toxins. The system is made up of different organs, cells and proteins that work together.

A deficiency of dietary protein or amino acids impairs immune function and increases susceptibility to infectious disease. That’s because protein is broken down to provide amino acids for the creation of new cells, proteins and peptides, vital for the immune response. Amino acids can be used as fuel by the immune system either directly, or following their conversion to other amino acids (e.g., glutamine) or to glucose for energy.

Mounting evidence shows that giving specific amino acid supplements to animals and humans with malnutrition and infectious disease improves the immune response. Arginine, glutamine and cysteine precursors are the best used examples in recent studies.


Materials and methods

Contact for reagent and resource sharing

Further information and requests for resources and reagents should be directed to and will be fulfilled by Kenneth Dyar ([email protected]).

Ethics statement

All experimental procedures were performed according to European Commission guidelines and were reviewed and approved by the local Veterinary Central Service, University of Padova, and the relevant Italian authority (Ministero della Salute, Ufficio VI), in compliance with Italian Animal Welfare Law (Law n 116/1992 and subsequent modifications) and Directive 2010/63/EU of the European Parliament. Accordingly, experiments performed before 2012 were sent to the Italian Ministry of Health (Project# 27/09). Experiments performed after 2012 were approved by the Italian Ministry of Health (Decree# 164/2012-B of 09.08.12).

Animals

Animals were housed in a temperature-controlled room (22 °C) under a 12-hr light/dark regimen, with lights on at ZT0 (6 AM) and lights off at ZT12 (6 PM), with standard chow diet (Mucedola, Settimo Milanese, Italy) and water provided ad libitum. Muscle-specific inactivation of Bmal1 (mKO) was obtained as described [7] by crossing a floxed Bmal1 C57BL/6 mouse line with a C57BL/6 mouse line carrying a Cre recombinase transgene under control of the Mlc1f promoter (Mlc1f-Cre). In the resulting mKO mice, the region coding for the BMAL1 bHLH DNA binding domain is excised. Cre-negative littermates were used as controls. All mice used in this study were 3–5-mo-old male littermates, unless specified otherwise. Littermates were randomly assigned to experimental groups. Tissues were collected immediately after cervical dislocation at ZT0, 4, 8, 12, 16, and 20, snap frozen in liquid nitrogen, and stored at −80 °C until subsequent use. S4 Table details the various muscles and tissues used for this paper.

Metabolic phenotyping

Total body fat and lean tissue mass were quantified by nuclear magnetic resonance (EchoMRI). EE, RER (VCO2/VO2), locomotor activity, and feeding behavior were monitored on a TSE system (Bad Homburg, Germany).

Exercise and endurance training experiments

To measure exercise endurance, forced running on a motorized treadmill was used (Harvard Apparatus PANLAB, LE 8710 M). The protocol was based on [135], with some minor modifications. Briefly, mice were first acclimated to low-speed running (22 cm/s and 10% incline) for 10 min each day for 2 d prior to the test. Acclimation was always performed under dim red light around ZT12 (lights off), the normal start of the circadian activity phase. On the day of the endurance test, food was removed 3 hr before starting at ZT12 to ensure consistent baseline blood glucose levels. For the test, mice began running at 22 cm/s and 10% incline under dim red light. Speed was gradually increased by 5 cm/s every 30 min up to 45 cm/s. Mice were encouraged to run by humanely stimulating their tail with a soft brush. The operator was blind to genotype of the mice, and exhaustion was defined by mice refusing to run for 10 sec and confirmed by blood glucose <76 g/dl [75]. Time and running distance were recorded for each mouse. For endurance training, mice trotted on a motorized treadmill 25 cm/s and 0% incline 1 hr daily for 4 wk, starting around ZT12. After humanely encouraging mice to run with a soft brush on their tail during the first few acclimation sessions, mice ran without much need for further encouragement.

Clinical blood chemistry

Sedentary mice were either fed ad libitum, and blood was collected at ZT14 (“fed ZT14”), or fasted for 4 hr late in the afternoon (ZT7–ZT11), and blood was collected at ZT11 (“fasted ZT11”). A separate cohort of endurance-trained WT and mKO littermates was measured after 4 wk training on a motorized treadmill 25 cm/s and 0% incline 1 hr daily, starting around ZT12. Blood was collected after running 25 cm/s and 0% incline for 1 hr. Blood was collected from the orbital sinus in heparin-coated Pasteur pipettes and centrifuged immediately after collection. Plasma samples were kept at −20 °C until dosing. FFAs, β-OH-B, and AcAc were dosed using an automated spectrophotometer, Cobas Fara II (Roche), according to the manufacturer’s instructions. Blood glucose and lactate levels were measured with a YSI 2300 STAT Plus glucose and lactate analyzer (YSI Life Sciences, Yellow Springs, OH) according to the manufacturer’s instructions.

Quantification of serum amino acids

Mice were fasted 6 hr (8 AM–2 PM), and blood was collected at ZT8 (2 PM). Blood was collected from the orbital sinus in Pasteur pipettes, allowed to clot at room temperature (RT) for 30 min, centrifuged, and kept at −20 °C until use. Amino acid concentrations were assessed by GC/MS (HP 5890 Agilent Technologies, Santa Clara, CA), using the internal standard technique, as previously reported [136]. Briefly, known amounts of internal standards (L-[ 15 N]glycine, L-[ 15 N]glutamate, L-[ 15 N]glutamine, L-[1- 13 C, methyl- 2 H3]methionine, L-[3,3- 2 H2]cysteine, L-[ 15 N]alanine, L-[1- 13 C]leucine, L-[1- 13 C]phenylalanine, L-[3,3- 2 H2]tyrosine, L-[ 15 N]threonine, L-[ 15 N]serine, and L-[ 15 N]proline [Cambridge Isotope Laboratories]) were added to plasma samples. Amino acid concentrations were determined considering the following mass-to-charge ratios (m/z): 218/219 for glycine, 432/433 for glutamate, 431/432 for glutamine, 320/324 for methionine, 406/408 for cysteine, 158/159 for alanine, 302/303 for leucine, 336/337 for phenylalanine, 466/468 for tyrosine, 404/405 for threonine, 362/363 for serine, and 184/185 for proline.

In vivo ChIP assays

In vivo skeletal muscle ChIP was performed with sonicated nuclear extract prepared from formaldehyde-cross-linked gastrocnemius muscle according to [137]. For immunoprecipitation, we used anti-BMAL1 (ab93806, Abcam), anti-REV-ERBα (generous gift from Ron Evans), anti-RNA Polymerase II (#MMS-126R clone 8WG1, Biolegends), anti-NCOR1 (#20018-1-AP, Protein Tech), anti-HDAC3 (ab7030, Abcam), or rabbit IgG (2027x, Santa Cruz). DNA was column purified and used for sequencing or real-time qPCR (enrichment expressed as fold-change relative to IgG primer sequences used are listed in S4 Table).

ChIP-seq library prep

Libraries from ChIP and input DNA were prepared with the KAPA Hyperprep Kit (Kapa Biosystems, KK8504) Illumina-compatible adapters were synthesized by Integrated DNA Technologies (IDT) and used at a final concentration of 68 nM. Adapter-ligated libraries were size selected (360–610 bp) in a Pippin Gel station (Sage Science) using 2% dye free gels (Sage Science, CDF2010). Library concentration was estimated by real-time PCR with the KAPA Library Quantification Kit (Kapa Biosystems, KK4873). Library quality was evaluated with the Agilent High Sensitivity DNA Kit on a 2100 Bioanalyzer (Agilent). Libraries were run on a HighSeq2500/HighSeq4000 sequencers (Illumina) at the NGS Core Facility at HMGU.

Bioinformatics pipeline for ChIP-Seq data analysis

Pre-processing.

ChIP-Seq FASTQ files were mapped against the mouse mm10 genome with BWA version 7.12 using MEM algorithm [138]. Duplicate reads were removed using samtools version 0.1.19 [139]. Multi-mapping reads were removed with bamtools version 2.4.0 [140] using read threshold of MAPQ ≥ 24.

Adjusting sequencing depth.

Sequencing read depth was adjusted by down sampling replicate BAM files to the replicate with lowest read count, resulting in about 13 million unique reads for RNAP2 and about 20 million unique reads each for BMAL1 and REV-ERBα replicates (see Table 1 below for further details).

Peak calling.

Macs2 version 2.1.1 [141] peak caller was used to perform peak calling on the replicates using input DNA. Peak calling cutoff was set to p-value 0.05. In addition to narrow peak files, read density distribution (Bedgraphs) was used to visualize ChIP-seq tracks using Integrated Genome Browser [142].

Peak universe, “high confidence” and “confident” peak tables, and overlapping peaks.

A peak table was created for each sample. Replicate sample tables were then combined into a unified peak universe for each factor with unique ranges across the genome and containing overlapping peaks information including the tag counts (enrichment score). “High confidence” peaks were identified as reproducible peaks common between replicate samples. “Confident” peaks were reproducible peaks common between BMAL1 and REV-ERBα and present in ≥3 of the 4 samples.

To assess overlapping peaks between liver and muscle, liver peak location coordinates for BMAL1 [14] and REV-ERBα [15] were first converted from mm9 to mm10 using UCSC liftOver.

Heatmaps.

Bedgraphs were used to plot the read density map near the universe peak centers using deepTools version 2.2.4 [143]. Replicates for each factor were merged using UCSC tools bedGraphToBigWig and bigWigMerge (http://hgdownload.cse.ucsc.edu/admin/exe/macOSX.x86_64/).

Then, deepTools computeMatrix tool was used followed by plotHeatmap tool [143].

Peak annotation.

Peak annotation was performed with HOMER version v4.8 [144]. GENCODE database for mm10 [145] was used as a reference for assigning feature level annotation.

Motif discovery.

To minimize bias, a combination of two different methods was used for motif discovery:

  1. HOMER version v4.8 [144]. For BMAL1 we searched for motifs of 8-, 10-, 12-, 14-, 15-, and 16-mers. For REV-ERBα, we searched for motifs of 8-, 10-, and 12-mers.
  2. Clover version published on Jan 14, 2016 [146], was used with default settings.

Functional enrichment analysis of peaks.

Genomic Regions Enrichment of Annotations Tool (GREAT) [16] was used to analyze functional significance of BMAL1 and REV-ERBα peaks using mouse NCBI build 38 genome assembly (mm10), whole genome as background, and associating genomic regions with genes using “basal plus extension” and the following parameters: proximal 20 kb upstream, 2 kb downstream, plus distal up to 500 kb.

Global metabolite profiling

Metabolite profiling, peak identification, and curation were performed by Metabolon using described methods [147]. Briefly, the nontargeted metabolic profiling platform used by Metabolon combines 3 independent platforms: UHPLC/MS/MS optimized for basic species, UHPLC/MS/MS optimized for acidic species, and GC/MS. We analyzed a total of 60 TA muscles from 60 male muscle-specific Bmal1 KO and control littermates (5 × group × time point).

Metabolomics data processing and analysis

Metabolomics data (“origscale”) can be found in supporting file S3 Data. The data were first normalized according to raw area counts and processed according to [148]. Run day correction was performed for each metabolite by setting the run day medians equal to 1. We removed metabolites with more than 50% missing values and transformed data to log10. Data points outside 4 times the standard deviation for each metabolite were considered as outliers and removed. Missing data were imputed by k-nearest-neighbor algorithm. To identify metabolites that show significant change over time and/or genotype, we fit data to a linear mixed effects model. Significant changes were estimated by performing F-test statistics to each fixed effect term (ANOVA) Genotype, Time, Genotype × Time. Calculations were done using MATLAB R2015b, Statistics Toolbox. Heatmaps were generated using the mean of 5 replicates. Hierarchical clustering was performed with squared euclidean distance and Ward’s minimum variance algorithm. Data were sorted by phase according to WT muscle and aligned between groups to show effect in mKO. Metabolites were categorized according to Metabolon superpathways: Amino Acids, Carbohydrates, Cofactors and Vitamins, Energy, Lipids, Nucleotides, Peptides, and Xenobiotics. To identify significantly enriched KEGG pathways in our data, we performed a hypergeometric distribution test on metabolites showing a Genotype effect p < 0.05. To identify 24-hr cycling metabolites, we used the nonparametric test JTK_CYCLE as described in [147], using an adjusted p < 0.05.

Extraction of total lipids

Muscles were weighed and homogenized in 800 μl dH2O. Lipids were extracted twice according to Folch and colleagues [149] using chloroform/methanol/water (2/1/0.6, v/v/v) containing 500 pmol butylated hydroxytoluene, 1% acetic acid, and 100 pmol of internal standards (ISTD, 17:0–17:0 PC, 17:0 LPC, 17:0–17:0–17:0 TG, Avanti Polar Lipids) per sample. Extraction was performed under constant shaking for 90 min at RT. After centrifugation at 1,000 x g for 15 min at RT, the lower organic phase was collected. Then, 2.5 ml chloroform was added to the remaining aqueous phase, and the second extraction was performed as described above. Combined organic phases of the double extraction were dried under a stream of nitrogen and resolved in 900 μl 2-propanol/chloroform/methanol (7/2/1, v/v/v).

Quantitative analysis of TG by HPLC with light scattering detection

For HPLC-ELSD, 20 μl of the resolved extract was evaporated and dissolved in 100 μl chloroform/methanol (2/1, v,v). The chromatographic setup for lipid separation consisted of an Agilent 1100 combining pump, injector, precooled sample manager (4 °C), and column oven (40 °C) (Agilent, Santa Clara, CA). For detection of lipids, a Sedex 85 evaporative light scattering detector (Sedere, Alfortville, France) was used. Data acquisition was performed by the Chemstation software (B 04.01, Agilent, Santa Clara, CA). A ternary gradient with a Betasil Diol column (100 × 4.6 mm, particle size 5 μm, Thermo Fisher Scientific, Waltham, MA) was used for chromatographic separation. The solvent system consisted of eluent A (isooctane/ethylacetate, 99.8/0.2, v/v), eluent B (acetone/ethylacetate, 2/1, v/v, containing 0.02% [v/v] acetic acid), and eluent C (isopropanol/water, 85/15, v/v, containing 0.05% [v/v] acetic acid and 0.3% [v/v] ammonium acetate). For external calibration, TG 54:3 (Larodan, Solna, Sweden) was prepared in chloroform:methanol (2:1, v/v), and the final concentration ranged from 1 μM to 2.5 μM. Injection volume for all samples including external calibration was 10 μl.

Qualitative analysis of lipids by ultra-performance liquid chromatography (UPLC) with qTOF detection

For UPLC-qTOF, 120 μl of the resolved extract was transferred to an autosampler vial for analysis. Chromatographic separation was performed using an AQUITY UPLC system (Waters), equipped with a BEH-C18-column (2.1 × 150 mm, 1.7 μm Waters) as previously described [150]. A SYNAPTG1 qTOF HD mass spectrometer (Waters) equipped with an ESI source was used for detection. For positive and negative ionization mode, 5 μl and 10 μl were injected, respectively. Data acquisition was done by the MassLynx 4.1 software (Waters Corporation). Lipid classes were analyzed with the “Lipid Data Analyzer 1.6.2” software [151]. Extraction efficacy and lipid recovery were normalized using ISTD, and lipid classes were expressed as percent composition.

Separation of neutral lipids by thin-layer chromatography

Extracted lipids were spotted on a silica gel 60 (Merck, Darmstadt, Germany). For comparison, a standard solution containing TG 54:3 was used. The silica gel was developed using n-hexane/diethylether/acetic acid (80/20/2, v/v/v) as solvent system, and lipids were visualized by charring using concentrated sulfuric acid.

Palmitate oxidation

Gastrocnemius muscles were immediately removed after cervical dislocation. Tissues were quickly weighed, and placed in ice-cold homogenization buffer (250 mM Sucrose, 10 mM Tris-HCL, 1 mM EDTA, pH = 7.4). Muscles were then thoroughly minced with scissors and transferred to a 10 ml glass homogenization mortar on ice, and ice-cold homogenization buffer was added up to a 20-fold dilution (w/v) suspension. Samples were then homogenized with 10 strokes of a motor-driven, tightly fitting glass mortar/Teflon pestle Potter Elvehjem homogenizer operated at 1,600 rpm. Reactions were initiated by adding 50 μl of muscle homogenates to 450 μl of prewarmed oxidation medium (30 °C 111 mM sucrose, 11.1 mM Tris-HCl, 5.56 mM KH2PO4, 1.11 mM MgCl2, 88.9 mM KCl, 0.222 mM EDTA, 1.11 mM DTT, 2.22 mM ATP, 0.33% fatty acid–free BSA, 2.22 mM Carnitine, 0.056 mM CoA, 0.111 mM Malate, 222 uM cold Palmitate, + 0.5 uCi [1- 14 C]-palmitic acid prepared fresh daily pH 7.4). After incubation for 90 min in a shaking water bath (100 rpm 30 °C), reactions were terminated by addition of 100 μl 1 M perchloric acid, and the CO2 produced during the incubation was trapped in 100 μl NaOH that had been added to a small tube inside the reaction vial. Palmitate oxidation rates were determined by measuring incorporation into 14 CO2 and 14 C-acid-soluble metabolites (ASM) by liquid scintillation counting.

Analysis of mitochondrial function

Spectrophotometric activity of mitochondrial respiratory chain complexes CI, CII, CIII, CII+III, CIV, as well as CS, was measured according to [152] using muscle homogenates from frozen vastus lateralis muscles from the same cohort of mice used for gene expression and metabolomics analyses. Mitochondrial membrane potential was measured by epifluorescence microscopy based on the accumulation of TMRM fluorescence in isolated muscle fibers from flexor digitorum brevis (FDB) muscles as previously described [153], with minor modifications. Briefly, fresh FDB muscles from adult mice were digested in type I collagenase at 37 °C for 2 hr and dissociated into single fibers by gentle pipetting. Isolated FDB myofibers were then placed in 1 ml Tyrode’s buffer (Sigma) and loaded with 2.5 nM TMRM (Molecular Probes) supplemented with 1 μM cyclosporine H (a P-glycoprotein inhibitor) for 30 min at 37 °C. At the times indicated by arrows, oligomycin (Olm, 5 μM Sigma) or the protonophore FCCP (4 μM Sigma) was added to the culture medium.

Assessment of muscle protein synthesis

Quantification of muscle protein synthesis rates was performed using the nonradioactive IV-SUnSET technique as described in [78].

Gene expression profiling and analyses

Microarray sample processing, quality control, and data analysis are reported in [7].

SDS-PAGE and western blotting

Muscle lysates were prepared in RIPA buffer (Sigma) supplemented with Halt protease and phosphatase inhibitor cocktail (#78446, Thermo Scientific), protein concentration determined using the bicinchoninic acid assay (Pierce, Rockford, IL, USA), and SDS-PAGE performed using NuPAGE 4%–12% gels (Invitrogen). Proteins were transferred onto a PVDF membrane and incubated with rabbit anti-REV-ERBα (kind gift from Ron Evans), mouse anti-REV-ERBβ (D-8 sc-398252, Santa Cruz), or mouse anti-GAPDH (ab8245, Abcam). Membranes were then incubated with HRP-conjugated donkey anti-rabbit (sc-2317, Santa Cruz) or goat anti-mouse (170–6516, Biorad) secondary antibodies. HRP activity was measured by chemiluminescence (Immobilon Western, Millipore), and bands visualized on CP-BU Medical X-Ray Film (Agfa HealthCare NV, Gevaert, Belgium).

Plasmids

Mouse promoter regions of Rev-erbα (forward, 5′-CCCCTAGTCACCACTAACCTC-3′ reverse, 5′-AGAGACGTGTGCCCTGCTA-3′), Rev-erbβ (forward, 5′-ATGTAGGAGGGAGGCTCGG-3′ reverse, 5′-GCCTCGCGCAGACTATGG-3′), Dgat2 (forward, 5′-AGCTGCTAGGATTGTAGGATTACAG-3′ reverse, 5′-AGAGCTGAGGTAGGTAGCCG-3′), and Coq10b (forward, 5′-GCTAACCAAATGCAGCAGGC-3′ reverse, 5′-TGTGAAGCCGGTAGCCAAC-3′) were amplified using High Fidelity Platinum Taq DNA polymerase (Invitrogen), cloned into pGL3-Basic (Promega), and verified by sequencing. pRL-CMV renilla expression construct was from Promega. pCMV-AC-mKate (mKate2) was from OriGene. Both mPer2::luc (423-bp mPer2 promoter fragment) and mBmal1::luc (530-bp mBmal1 promoter fragment) reporter plasmids were generous gifts from Kazuhiro Yagita and described in [154]. Mouse full-length Bmal1 and Clock pcDNA3-HA constructs were generous gifts from Marina Antoch (Roswell Park Cancer Institute) and described in [155]. Mouse full-length Rev-erbα ORF (forward, 5′-ATGACGACCCTGGACTCCAA-3′ reverse, 5′-TCACTGGGCGTCCACCCGGA-3′) was amplified using AccuPrime GC-Rich DNA Polymerase (Invitrogen) and shuttled into Gateway pDONR-221 (Invitrogen) with Gateway BP Clonase Enzyme (Invitrogen). Mouse Rev-erbα ORF was then shuttled into Gateway pcDNA-Dest47 expression vector with Gateway LR Clonase Enzyme (Invitrogen). The −1 kb and −5 kb MuRF-1 promoter luciferase reporters and c.a.FOXO3A plasmids were generous gifts from Marco Sandri. The mouse GR construct was generated by PCR amplifying a Kpn1/BamH1 PCR fragment from full-length mouse GR ORF (BioScience IMAGE40111802) using forward 5′-GGGGTACCATGGACTCCAAAGAATC-3′ and reverse 5′- CGGGATCCTCATTTCTGATGAAAC-3′ primers and cloning into Kpn1/BamH1-digested pcDNA3.

In vitro transfection and luciferase assays

HEK-293T cells were cultured in DMEM with 10% FBS and penicillin-streptomycin. Cells were seeded in 96-well plates at 100,000 cell/mL for transfection and luciferase measurements. For BMAL1 target validation, cells were cotransfected with 25 ng promoter reporter construct, 25 ng pRL-CMV, and either 25 ng BMAL1-HA + 25 ng CLOCK-HA or 50 ng empty pcDNA3. For REV-ERBα sensor validation, cells were cotransfected with 25 ng mBmal1::luc, 25 ng pRL-CMV, and either 200 ng REV-ERBα or 200 ng empty pcDNA-Dest47. For MuRF-1 reporter experiments, cells were cotransfected with 25 ng MuRF-1 reporter, 25 ng pRL-CMV, and 25 ng each of GR, c.a.FOXO3A, and REV-ERBα. To maintain consistent DNA transfection concentrations across experiments, 50 ng or 25 ng empty pcDNA-Dest47 was supplemented, respectively, when transfecting only one or two transcription factors. Transfection was performed with FuGene HD (Promega) and transfection medium replaced with Phenol Red free DMEM. The next day, cells were lysed, and firefly and renilla luciferase were sequentially measured using Dual-Glo Luciferase assay system (Promega). Firefly raw values were normalized to Renilla raw values for each replicate. Data from 2 independent experiments are expressed as mean fold-change of the test condition normalized to the empty vector ± SEM.

In vivo transfection of adult skeletal muscle

Six-mo-old adult male Bmal1 mKO mice and WT littermates were anesthetized by i.p. injection of a mixture of Zoletil 100 (a combination of Zolazapam and Tiletamine, 1:1, 10 mg/kg, Laboratoire Virbac) and Rompun (Xilazine 2%, 0.06 ml/kg, Bayer). Gene transfer of TA muscles was induced by intramuscular injection of plasmid DNA (40 μg, consisting of 15 μg mBmal1::luc reporter plasmid, 5 μg mKate2 exogenous spike-in control, and 20 μg either REV-ERBα or empty pcDNA-Dest47) followed by electroporation using stainless steel electrodes connected to a ECM830 BTX porator (Genetronics, San Diego, CA).

Optical bioluminescence imaging

In vivo bioluminescence images were acquired with the IVIS 100 system (Perkin-Elmer) under general anesthesia by i.p. injection of a mixture of Zoletil 100 (a combination of Zolazapam and Tiletamine, 1:1, 10 mg/kg, Laboratoire Virbac) and Rompun (Xilazine 2%, 0.06 ml/kg, Bayer) and analysis performed according to [156] with the following parameters: field of view 25 cm, binning factor 8, exposure time 1 min. Living Image software (version 4.3) was used for image capture and analysis.

RNA isolation and qPCR

Total RNA was isolated, purified, and reverse transcribed to cDNA, and RT-qPCR was performed as described in [7]. Analysis was performed using the standard curve method, and all data were normalized relative to 36B4 expression.

Quantification and statistical analysis

All data are expressed as means ± SEM unless otherwise stated. Statistical analysis was performed using unpaired Student’s t test or 2-way ANOVA. When ANOVA revealed significant genotype differences, further analysis was performed using Bonferroni’s multiple comparison test. Differences between groups were considered statistically significant for p < 0.05.


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