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2.9: Molecular Backups for Muscles - Biology

2.9: Molecular Backups for Muscles - Biology



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For plants, the needs for energy are different than for animals. How is this possible?

Creatine + ATP <=> Creatine phosphate + ADP

The ΔG°’ of this reaction is +12.6 kJ/mol, reflecting the energies noted above. In a resting muscle cell, ATP is abundant and ADP is low, driving the reaction to the right, creating creatine phosphate. When muscular contraction commences, ATP levels fall and ADP levels climb. The above reaction then reverses and proceeds to synthesize ATP immediately. Thus creatine phosphate acts like a battery, storing energy when ATP levels are high and releasing it almost instantaneously to create ATP when its levels fall.


2.9: Molecular Backups for Muscles - Biology

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Mechanisms of skeletal muscle injury

Muscle injuries are one of the most common injuries occurring in sports, their frequency varying from 10 to 55% of all the sustained injuries1,2. Almost all of those seem to involve only four muscle groups, hamstrings, adductors, quadriceps and calf muscles1. Muscle injuries can be of shearing type (caused by contusion, strain or laceration), in which the muscle fibers and their basal lamina and mysial sheaths as well as the nearby capillaries all rupture2𠄴. In the other type of injury, in situ necrosis (or rhabdomyolysis), the myofibers are partially necrotized while the basal lamina and mysial sheaths as well as the adjacent blood vessels remain intact2. Over 90% of all sports related injuries are either contusions or strains, whereas muscle lacerations are uncommon injuries in sports4. Muscle contusion occurs when a muscle is subjected to sudden, heavy extrinsic compressive force, such as a direct blow, i.e. the injury is not a consequence of the intrinsic force of the exercise itself. In strains, the myofibers are exposed to an excessive intrinsic tensile force. Their severity vary from very mild strain injury like delayed onset muscle soreness (DOMS) to “real” strains, shearing type of muscle injuries, in which myofibers and the associated connective tissue structures including blood vessels are ruptured5. “Real” muscle strains induced by exercise do not differ in their regenerative response in a significant way from those contusions or lacerations.


Introduction

The neuromuscular system is recognized as one of the physiologic systems most affected by spaceflight (Fitts et al., 2001). When exposed to the microgravity of spaceflight,muscles developed on Earth have been shown to display changes in morphology,contractile function and myosin heavy chain (MHC) gene expression(Caiozzo et al., 1994 Caiozzo et al., 1996 Criswell et al., 1996 Day et al., 1995 Edgerton et al., 1995 Fitts et al., 2000 Harrison et al., 2003). Recently, it was shown that cultured embryonic avian muscle cells are directly responsive to spaceflight, undergoing atrophy as the result of decreased protein synthesis (Vandenburgh et al.,1999). The observation that MHC levels were decreased postflight relative to control cultures suggests that muscles developing in microgravity express less MHC.

Cultured muscle cells lack innervation, which is required for proper muscle development and to prevent muscle atrophy in vivo(Szewczyk and Jacobson, 2005). Therefore, studies of muscle development or atrophy in whole animals are required to confirm results obtained with cultured cells. Muscles of the nematode Caenorhabditis elegans have been studied extensively, and show significant similarity to vertebrate muscles. The principal muscles in C. elegans are the body wall and pharyngeal muscles(Epstein et al., 1974). Body wall muscle is analogous to vertebrate skeletal muscle and functions to allow locomotion. In body wall muscle, myogenesis appears to be controlled by the helix-loop-helix transcription factor HLH-1(Chen et al., 1994 Krause, 1995), which controls the expression of two MHC isoforms [MHC A and B encoded by myo-3 and unc-54, respectively (Dibb et al., 1985 Epstein et al.,1974 Karn et al.,1983 MacLeod et al.,1981 Miller et al.,1986)]. The pharyngeal muscles function rhythmically in feeding and possibly pseudocoelomic circulation. They are analogous to vertebrate cardiac muscle, and contain two MHC isoforms [MHC C and D, encoded by myo-2 and myo-1, respectively(Ardizzi and Epstein, 1987 Miller et al., 1986)]. In developing pharyngeal muscle, these MHCs appear to be regulated by the cooperative action of transcription factors PEB-1, PHA-4 and CEH-22(Gaudet and Mango, 2002 Kalb et al., 2002 Okkema and Fire, 1994 Okkema et al., 1997). The transcriptional regulation of C. elegans myosin genes is in many respects similar to that of vertebrates. However, both body wall and pharyngeal muscle also contain the invertebrate paramyosin core protein encoded by unc-15 (Epstein et al., 1985 Kagawa et al.,1989). The extensive similarities to mammalian muscle have allowed C. elegans to be developed as a small animal model for studies of a number of types of muscle atrophy, including muscular dystrophy(Grisoni et al., 2002),starvation (Zdinak et al.,1997), denervation (Szewczyk et al., 2000), growth factor alterations(Szewczyk and Jacobson, 2003),aging (Fisher, 2004) and altered function of myosin chaperones(Hoppe et al., 2004). C. elegans has also been developed as a model for studies of spaceflight effects on physiology (Hartman et al.,2001 Nelson et al.,1994a Nelson et al.,1994b). In this study, we therefore employed space flown C. elegans to confirm and extend the findings made with cultured embryonic avian muscle cells.

In this report we demonstrate that muscles of C. elegans that developed in space, during the European Space Agency (ESA) DELTA mission,display decreased expression of the transcription factors controlling both body wall and pharyngeal muscle myogenesis as well as decreased expression of muscle-specific MHCs. These results demonstrate that the changes previously observed in cultured embryonic avian muscle cell development in space also occur in vivo in the nematode C. elegans. Our results suggest that altered MHC expression is a highly conserved molecular response to spaceflight and can be studied in small genetic model organisms. Furthermore, decreased protein synthesis in developing muscle implies that, in space, a reduction in muscle repair and remodeling may underlie at least a portion of spaceflight-induced muscle atrophy.


DISCUSSION

Although titin molecular mass, collagen content and MHC percentages have been measured in a few muscles of several animal species, comparatively little is known about their roles and relative contributions in human skeletal muscles. This study investigated organizational themes of these parameters across the human body. We sampled 100 muscles across the entire body with the exception of head and neck muscles and intrinsics of the foot. There are very few human muscle studies with sample sizes over 500 most are descriptive (Gaudy et al., 2001) or make use of non-invasive techniques (Kawakami et al., 2006). Our sample size of 599 is the largest dissection-based human muscle study. Further, other large studies have generally been carried out on a small group of muscles across a large population our study reports data from many muscles across a few individuals. This reflects a difference in goals – whereas other studies typically attempt to obtain very accurate information about a few muscles, our goal was to gain general information about a wide population of muscles. Thus, the data reported here represent a robust human muscle study with a unique goal.

(A) Titin molecular mass, (B) collagen content and (C) percentage MHC distribution in muscles with different functions. In some cases, significant differences exist across muscles of different function but correlated trends between different molecular parameters are less clear than in Fig. 1. Abbreviations: Ab, abduction IR, internal rotation Fl, flexion Ex, extension Ad, adduction ER, external rotation.

(A) Titin molecular mass, (B) collagen content and (C) percentage MHC distribution in muscles with different functions. In some cases, significant differences exist across muscles of different function but correlated trends between different molecular parameters are less clear than in Fig. 1. Abbreviations: Ab, abduction IR, internal rotation Fl, flexion Ex, extension Ad, adduction ER, external rotation.

Diversity of skeletal muscle

Titin molecular mass ranged from 3.4 to 4.1 MDa in our experimental sample mean titin molecular mass for individual muscles ranged from 3.6 to 3.8 MDa. Prior to this study, human muscle titins were known to range in size from ∼3.0 MDa for cardiac titin to 3.70 MDa for soleus titin (Freiburg et al., 2000 Labeit and Kolmerer, 1995), and the genetic coding capacity for the largest titin is 4.2 MDa (Bang et al., 2001). The data reported here greatly expand upon the known size diversity of titin molecular mass in human skeletal muscle. In fact, 48 of the 100 muscles under study had titin molecular masses of over 3.70 MDa. When viewed in the context of average eukaryotic proteins, this size diversity seems large – approximately the size of 3.5–4 human proteins (Brocchieri and Karlin, 2005). However, this variability can be also viewed as small, as it is only ∼5% of the total mass of titin. Further, variability within a single muscle (average s.d. of each muscle=47 kDa) is on a par with the variability in the total sample population (total s.d. for all samples=69 kDa), which clearly demonstrates that titin molecular mass is tightly regulated. Similar variability patterns are seen in collagen content (average s.d. of log transformed data=0.18, total s.d.=0.22) and MHC percentage (MHC-1 average s.d.=17.4%, total s.d.=20.9% MHC-2A average s.d.=10.6%, total s.d.=13.1% MHC-2X average s.d.=9.8%, total s.d.=11.3%). Titin has the smallest average s.d.:total s.d. ratio, indicating that, of these properties, it is the most specific to each muscle. However, the similarity between average s.d. and s.d. of the entire sample population for each factor measured implies that there is likely not enough variability in any of these parameters to fully account for the larger degrees of variability observed in physiological measurements. It is likely that structural considerations such as muscle architecture are more strongly predictive of muscle function.

Relationships among titin molecular mass and (A) percentage MHC-1, (B) percentage MHC-2A, (C) percentage MHC-2X and (D) collagen content. Dashed lines are linear regression results collagen content data (μg mg –1 ) were log transformed because independent variable data should be normally distributed for linear regression analysis. Note the significant negative correlation between titin mass and percentage MHC-1, which is opposite to that reported for rabbit muscle (Prado et al., 2005).

Relationships among titin molecular mass and (A) percentage MHC-1, (B) percentage MHC-2A, (C) percentage MHC-2X and (D) collagen content. Dashed lines are linear regression results collagen content data (μg mg –1 ) were log transformed because independent variable data should be normally distributed for linear regression analysis. Note the significant negative correlation between titin mass and percentage MHC-1, which is opposite to that reported for rabbit muscle (Prado et al., 2005).

Grouping trends

When muscles were grouped by anatomical region, muscle function, antigravity versus non-antigravity function, or single versus multiple joint crossings, the most striking patterns were observed in anatomical region groupings (Fig. 1). In the upper and lower extremities, titin molecular mass and collagen content decreased from distal to proximal (except for shoulder collagen), increasing again for axial muscles.

When considered in the context of passive tension, the titin and collagen values predict opposite effects. When titin is discussed as a molecular spring responsible for development of passive tension in the myocyte (Granzier and Irving, 1995), its differing levels of stiffness are thought to arise from differential splicing of a single titin gene (Labeit and Kolmerer, 1995), resulting in longer, more compliant isoforms that give rise to lower passive tension while shorter isoforms are stiffer and yield higher passive tension (Horowits, 1992 Wang et al., 1991). The trend in titin molecular mass would therefore suggest that passive tension should be low in distal muscles and high in proximal muscles. However, increased collagen content is associated with increased passive tension our data thus suggest that passive tension would be high in distal muscles and low in proximal muscles. Our data also suggest that, physiologically, the role of titin may not be to bear the majority of passive tension as the distal muscles, based on titin molecular mass, should be more compliant. We therefore claim that titin size variability is likely to have a different physiological role, potentially acting to ‘tune’ the protein's mechanotransduction capability. A similar relationship between titin size and collagen content has been observed in cardiac muscle, suggesting that stiffening (increased in passive tension) of cardiac muscle may occur independent of titin's contributions to passive tension (Neagoe et al., 2002).

It is important to note that most previous studies of titin's role in passive muscle stiffness were carried out using single fiber or even myofibrillar preparations (Prado et al., 2005 Horowits, 1992 Gollapudi and Lin, 2009 Granzier and Irving, 1995). A recent review summarizing several studies that scaled from single fibers to fiber bundles (thereby adding extracellular matrix, ECM), reported that the modulus changed by factors ranging from less than 1 to as high as 16-fold (Gillies and Lieber, 2011). This suggests two major points: both titin and ECM contribute to passive tension, and the relative contributions differ among muscles. Steps toward resolving the latter issue could have been taken in the present study by conducting similar passive tension experiments on the biopsies but as the effect of postmortem time on passive tension is not known, these experiments were not performed.

Comparison of MHC percentages for each functional muscle group in the lower extremity. Note that at each joint of the lower extremity there are differences between flexors and extensors opposite to those seen in the rat hindlimb (Eng et al., 2008).

Comparison of MHC percentages for each functional muscle group in the lower extremity. Note that at each joint of the lower extremity there are differences between flexors and extensors opposite to those seen in the rat hindlimb (Eng et al., 2008).

In addition to the trends observed for titin and collagen content, differences were seen in MHC content across different areas of the body. There are many possible organizing principles that could account for the trends observed we present one such explanation here but it is important to note that this is merely one of many potentially viable interpretations of the data. These trends may reflect a combination of the importance of motor control and fatigue resistance. Muscles with a higher percentage MHC-1 are generally more fatigue resistant because of the reliance on oxidative phosphorylation for energy production, rather than glycolysis. In addition, slow motor units are generally composed of small axons that innervate few fibers, while the reverse is true for fast motor units. The high MHC-1 content of muscles of the hand may thereby reflect the importance of precise manual dexterity. However, dexterity must be augmented by a good range of motion and force production capability to maximize its utility. These parameters are related to muscle fiber length and physiological cross-sectional area (PCSA), respectively. The size of the antebrachium allows placement of muscles with fiber lengths and PCSAs large enough to provide sufficient excursion and force at the digital and carpal joints, enhancing the intrinsic dexterity of the hand. The lower amounts of slow myosin found in these muscles implies that force production is prioritized over fatigue resistance and that motor control of forearm muscles does not need to be as precisely regulated as those of the hand. This trend of lower percentage MHC-1 in muscles with large PCSA and fiber lengths is amplified in the brachium and then reversed in muscles of the shoulder. This may be because the shoulder is responsible for accurately positioning the hand in three-dimensional space while also supporting the weight of the brachium and antebrachium, for which fatigue resistance and good motor control are both paramount. The percentage MHC-1 of the muscles of the axial skeleton and lower extremity is higher than that of most of the upper extremity regions. Many of these muscles are frequently activated because of their postural and support function so the high percentage MHC-1 is likely a reflection of the importance of fatigue resistance of these muscle groups.

Titin as a predictive factor

When grouped by anatomical region or function, DFA demonstrated that titin was the factor that best discriminated between different groups. DFA also revealed that percentage MHC-2X and collagen content were the factors that best predicted antigravity versus non-antigravity function and single versus multiple joint crossings, respectively. However, use of these two parameters correctly classified 54.8% of muscles, barely better than the 50% correct classification that would be expected based upon random chance. So, we do not feel that conclusions based on these results are very powerful or predictive of function. The very high discriminating ability of titin is partly due to its low variability within a specific muscle [average titin molecular mass coefficient of variation (CV) for individual muscles=1%], which is dramatically less than either the MHC or collagen content data (average CV of 28% for MHC-1 and 44% for collagen content). The finding that titin molecular mass is a good predictor of anatomical location or muscle function suggests that titin's mechanotransduction capability (to the extent that it depends on molecular mass) is muscle specific. There is no question that titin can function as a mechanotransducer (Lange et al., 2005) but the precise molecular nature or even the protein subdomains responsible for mechanotransduction are not yet well defined.

Comparison with other mammals

Animals are frequently used as models to better understand human physiology. Thus, it was relevant to examine trends observed in animal systems and determine whether similar trends exist in humans. Understanding how physiology differs between humans and other mammals is important in terms of knowing how to properly interpret animal studies.

A previous study that examined similar parameters in the rat hindlimb found that plantarflexors were ‘slower’ than dorsiflexors, ascribing this to the greater antigravity function of the plantarflexors (Eng et al., 2008). However, we found that, on average, human dorsiflexors were slower, although this relationship was not statistically significant (P=0.10, 1–β=0.37). Further, we found opposite relationships at each joint in the lower extremity [Fig. 4 cf. fig. 4 from Eng et al. (Eng et al., 2008)]. The differences across the ankle are likely due to the fact that rodents are digigrade while humans are plantargrade. Differences across the knee may be due to the fact that rat knee extensor activity is relatively higher than that of humans as their gait pattern relies on a much more flexed knee compared with humans.

In addition, previous work in rabbits has suggested that there may be a link between the active and passive tension development systems, as observed in the relationship between titin molecular mass and percentage MHC-1 (Prado et al., 2005). However, across our human study population, we observed a relationship opposite to that reported for rabbit muscle [Fig. 3A cf. figs 7a and 7d from Prado et al. (Prado et al., 2005)]. Therefore, it appears that if any interaction between active and passive tension production systems exists, the nature of this interaction is fundamentally different in human versus rabbit muscle systems.

Limitations

Despite the greatest of care being taken during study design and experimental execution, this study has several limitations. We understand that the values obtained in our assays do not represent the general human population. Rather, they are most representative of an elderly Caucasian population living in Minnesota in the area surrounding Minneapolis.

It is exceedingly difficult to obtain biopsies of multiple muscles from donors who are not deceased. In addition, most young, healthy deceased tissue donors are justifiably prioritized to organ and tissue donation for transplant. Further, the time constraints created by the rapid post-mortem degradation of titin meant that the great majority of donors eligible for our study were of advanced age. The effects of aging on MHC isoform distribution have been studied in humans (Lexell et al., 1983 Lexell, 1995 Lexell et al., 1988 Larsson, 1983) and rodents (Caccia et al., 1979 Kanda and Hashizume, 1989 Kadhiresan et al., 1996). It is understood that aging brings about a loss in fibers (Lexell et al., 1988) and a decrease in type II/type I fiber number (Caccia et al., 1979 Larsson, 1983 Kanda and Hashizume, 1989) and fiber area ratio (Larsson, 1983 Arbanas et al., 2010) this age-related remodeling of motor units appears to involve denervation of fast fibers with reinnervation from nerves that innervate slow fibers (Kadhiresan et al., 1996). We thereby expect the MHC percentages in our study population to be skewed towards higher percentage MHC-1 amounts. A recent study using the same MHC determination method reported values of ∼30% MHC-1 for both gracilis and semitendinosus muscles in a population of normal children with an average age of 16 (Smith et al., 2011). For comparison, the percentage MHC-1 in this study for gracilis and semitendinosus was 65.5±8.5% and 60.5±9.3%, respectively. However, the focus of our study places a higher priority on relative MHC values between muscles, not between people. While it might be possible that the ‘slowing’ of muscles with age happens differentially between muscles (thereby limiting the application of our conclusions), the lack of studies investigating multiple muscle regions makes it impossible to adequately address this concern.

MHC-1, -2A and -2X proteins are all encoded by different genes in the human genome. This is different from titin, which is encoded by a single gene different isoforms are generally thought to arise as products of alternative splicing (Labeit and Kolmerer, 1995). Because of this difference, upregulation or downregulation of a single gene can modify MHC isoform distribution within a muscle but will not affect the titin molecular mass. Factors controlling the degree of splicing (and thereby titin molecular mass) are not yet known and are likely to be more complex than changes in levels of genetic expression. Therefore, the effects of age on titin molecular mass would be expected to be less dramatic than percentage MHC changes unless titin molecular mass differences are sensitive to epigenetic changes. The study of Smith et al. (Smith et al., 2011) reported titin molecular masses that were not significantly different from those reported in our study (gracilis: P=0.20, 1–β=0.52 semitendinosus: P=0.07, 1–β=0.52), implying that the effects of age on titin molecular mass are small.

Another important limitation is our sample size. Although six donors may be considered a small sample population, the study focus was to find trends in overall human muscle organization, not necessarily to obtain data that were completely representative of the average human population. We were therefore more concerned with relative values between muscles and believe our study population was large enough to accomplish our goals of finding organizational trends.

There are some aspects of the experimental execution that also merit discussion. Each muscle was sampled at one location, and this biopsy was considered representative of the entire muscle, thereby assuming that regional variation within a muscle is smaller than variation between muscles. This assumption is supported by past work in rhesus monkeys, where fiber-type percentage variability between subjects was demonstrated to be much greater than between biopsies within a muscle in a single subject (Roy et al., 1991), and in human paraspinal muscles, where fiber-type distribution was independent of biopsy depth (Regev et al., 2010).

However, even if a single biopsy is representative of the entire muscle, each muscle was considered equivalent in our analysis. This may have a confounding effect, as small muscles may be over-represented in our analysis. This effect could have been mitigated by normalizing to muscle mass or PCSA, the only known muscle parameter to be directly related to maximal force production (Powell et al., 1984). However, PCSA and muscle mass vary much more across different regions of the body than do titin molecular mass, collagen content or MHC distribution. The coefficients of variance across different anatomical regions are 71% for PCSA and 87% for muscle mass [84 muscles considered (Jacobson et al., 1992 Delp et al., 2001 Brown et al., 2011 Peterson and Rayan, 2011 Lieber, 2010)]. When compared against CV of 1% for titin molecular mass, 18% for collagen content and 14% for percentage MHC-1, it becomes apparent that the results of normalizing data from biopsies to PCSA (or muscle mass) primarily reflect the differences in average PCSA (or average muscle mass) between different body regions rather than providing further insight with regards to the relative titin molecular mass, collagen content and MHC distribution throughout the body.

A final limitation is the matter of categorization. By necessity of the assumptions that underlie our statistical analyses, each muscle had to be placed in a single category despite the physiological reality that many muscles have multiple functions or could be considered to exist in multiple locations. Muscles were categorized according to their primary function and location to the best of our ability (a full list is available in supplementary material Table S1). We recognize that this is an imperfect system but feel that it is a necessity for reduction of data into manageable groups.


Biophysics lab classes take typically place only during the first weeks of the summer semester break. The are organized in a consecutive way and build up hand-on skills step by step:

BP lab Class 1 (some examples):

  • molecular structures and vibrationalspectra with quantum-chemical methods (Tavan)
  • the aptamer-thrombin bond measured thermophoretically (Braun)
  • FCS (Rädler)

BP Lab Class 2 (some examples):

  • Single Molecule FRET and Holiday Junction (Tinnefeld)
  • IR Spectroscopy (Zinth)
  • DNA Origami (Liedl)

BP Lab Class 3 (some examples):


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Review history

The review history is available as Additional file 3.

Peer review information

Anahita Bishop was the primary editor of this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.


Discussion

This integrated study of 131 invasive urothelial bladder carcinomas provides numerous novel insights into disease biology and delineates multiple potential opportunities for therapeutic intervention. Treatment for muscle-invasive bladder cancer has not advanced beyond cisplatin-based combination chemotherapy and surgery in the past 30 years 36 , and no new drugs for the disease have been approved in that time. Median survival for patients with recurrent or metastatic bladder cancer remains 14–15 months with cisplatin-based chemotherapy, and there is no widely recognized second-line therapy 37 . With the exception of a single case report, there is also no known benefit from treatment with newer, targeted agents 38 . Several of the genomic alterations identified in this study, particularly those involving the PI(3)K/AKT/mTOR, CDKN2A/CDK4/CCND1 and RTK/RAS pathways, including ERBB2 (Her-2), ERBB3 and FGFR3, are amenable in principle to therapeutic targeting. Clinical trials based on patients with relevant druggable genomic alterations are warranted.

FGFR3 mutation is a common feature of low-grade non-invasive papillary urothelial bladder cancer, but it occurs at a much lower frequency in high-grade invasive bladder cancer. The cluster analysis in Fig. 3 highlights multiple mechanisms of FGFR3 activation, and its strong association with papillary morphology. The data presented here suggest a subset of muscle-invasive cancers that can potentially be targeted through FGFR3. Similarly, ERBB2 amplification may be targetable by strategies used in breast cancer, by small-molecule tyrosine kinase inhibitors or by novel immunotherapeutic approaches (NCT01353222) 34 . The data here provide further support for several on-going ERBB2-targeted trials in bladder cancer and further define the subpopulation of cancers suited to that approach. Finally, cluster III of the integrated expression profiling analysis reveals the existence of a urothelial carcinoma subtype with cancer stem-cell expression features (including KRT14 and KRT5), perhaps providing another avenue for therapeutic targeting.

The alterations identified in epigenetic pathways also suggest new possibilities for bladder cancer treatment. Ninety-nine (76%) of the tumours analysed here had an inactivating mutation in one or more of the chromatin regulatory genes, and 53 (41%) had at least two such mutations. Overall, the bladder cancers showed a mutational spectrum highly enriched with mutations in chromatin regulatory genes (Supplementary Table 2.10). Furthermore, integrated network analyses revealed a profound impact of those mutations on the activity levels of various transcription factors and pathways implicated in cancer. Drugs that target chromatin modifications—for example, recently developed agents that bind acetyl-lysine binding motifs (bromodomains)—might prove useful for treatment of the subset of bladder tumours that exhibit abnormalities in chromatin-modifying enzymes 39 . Our findings overall indicate bladder cancer as a prime candidate for exploration of that approach to therapy.


Introduction

Skeletal muscle is composed of linear arrays of multinucleated muscle fibres, each with a complex internal structure dedicated to the conversion of chemical to physical energy. These fibres are ‘end cells’, meaning that they cannot proliferate to expand or restore the population after damage. Instead, they are formed or repaired by fusion of a proliferation-capable population of precursor cells called myoblasts (see Glossary, Box 1). The sequence of transcription factor expression leading to differentiation in the precursor cell population of the main mammalian body musculature is a close reflection of that observed during initial muscle formation in the embryo and the enlargement of muscle fibres in the postnatal and juvenile stages of muscle growth, as well as that observed in muscle repair. However, the behaviour of the myogenic (Box 1) cells differs radically between these situations.

Asymmetric division: a cell division that produces daughter cells that have different fates (e.g. one stem cell and one differentiated cell).

CTG expansion: a mutation in which repeats of three nucleotides (trinucleotide repeats) increase in copy number until they cross a threshold above which they become unstable.

DBA2/J, C57Bl/10 and 129/SVemst/J: inbred mouse strains that differ in their genetic backgrounds.

Dy/dy mouse: a model of laminin alpha-2 deficiency (MDC1A) that has a mutation in the LAMA2 gene. This mouse has a moderate fibrotic and dystrophic phenotype (reviewed in Ng et al., 2012).

Dysferlin: a protein that is highly expressed at the sarcolemma of muscle fibres and is involved in repair of the sarcolemma.

Dystroglycanopathy: a muscular dystrophy caused by aberrant glycosylation of dystroglycan.

Gamma-sarcoglycan (Sgcg)-null mouse model: a model of Limb-girdle muscular dystrophy type 2C (LGMD2C).

Genetic modifier: a gene that affects the phenotypic and/or molecular expression of other genes.

Large myd mouse: a model of congenital muscular dystrophy type 1D (MDC1D). Dystroglycan glycosylation is defective in these mice as a result of a mutation in like-acetylglucosaminyltransferase (LARGE), a glycosyltransferase.

Mdx mouse: X-linked muscular dystrophy mouse model of DMD. Has a mutation in exon 23 of the Dmd gene.

Muscle precursor cell: any cell that is predetermined to differentiate into skeletal muscle.

Myoblast: the progeny of satellite cells.

MyoD: myoblast determination protein 1, a myogenic regulatory factor.

Myogenic: originating in, or produced by, muscle cells.

Niche: a stem cell niche is an interactive structural unit, organized to facilitate cell-fate decisions in a proper spatiotemporal manner (Moore and Lemischka, 2006).

Satellite cell: skeletal muscle stem cell, located between the basal lamina (the internal layer of the basement membrane) and sarcolemma (cell membrane) of a muscle fibre. A satellite cell expresses PAX7 and is quiescent in normal adult muscle.

Symmetric cell division: a cell division that produces daughter cells that have the same fate (e.g. two stem cells, or two differentiated cells).

Utrophin: a cytoskeletal protein that has some structural and functional similarities to dystrophin.

Here, we briefly discuss skeletal muscle formation, growth and repair, with particular reference to muscular dystrophies. Most of the data behind these descriptions are derived from studies in animal models, mainly rodents, or from in vitro models of myogenesis. The relationship between the human condition of interest and the animal models requires careful consideration (Partridge, 2013). Likewise, while in vitro or ex vivo models of myogenesis are the source of much of the molecular biological data on myogenesis, they do not reproduce the interactions with the cellular, matrix and systemic features of the in vivo environment that tune the process of myogenesis to the physiological needs of the animal as a whole. Thus, the applicability of knowledge for disease treatment gained from the above models should be treated with reserve.


G Protein Pathways, Part B: G Proteins and their Regulators

Mary J. Cismowski , . Emir Duzic , in Methods in Enzymology , 2002

Genetic Alterations of Yeast Pheromone Response Pathway

Haploid Saccharomyces cerevisiae exists in two mating types, designated a and α (see J. Kurjan 32 and L. Bardwell et al. 33 for general reviews of the yeast pheromone response pathway). Each haploid constitutively secretes mating pheromone, a-factor, or α-factor respectively, that activates G-protein-coupled receptors on the surface of haploids of the opposite mating type. On receptor activation by pheromone, its associated heterotrimeric G-protein undergoes subunit dissociation into GTP-bound activated Gα and Gβγ dimer ( Fig. 1 ). Free Gβγ then transduces a signal through a p21-activated kinase to a mitogen-activated protein (MAP) kinase cascade, leading to activation of the transcription factor Ste12 as well as Far1-mediated growth arrest in the G1 phase of the cell cycle. Activated Ste 12 then binds to specific pheromone-responsive promoters to induce of a variety of genes required for the cytoskeletal rearrangements associated with mating.

Fig. 1 . Engineering of the yeast pheromone response pathway for functional screening. (A) Schematic of the yeast pheromone response pathway. Major signaling components, determined either by functional analysis or by homology to signaling components of higher eukaryotes, are indicated. α, Gpa1 β, Ste4 γ, Ste18. (B) Modifications to the pheromone response pathway in the screening strains. Screening for G-protein activators is performed in strains carrying the FUS1p-HIS3 construct screening for G-protein repressors is performed in strains carrying the FUS1p-CAN1 construct. See text for details.

In screening yeast for cDNAs that express G-protein pathway activators, the cell cycle arrest normally associated with pheromone pathway activation can be circumvented by deletion of FAR1, 34 thereby uncoupling pathway activation from growth arrest. The introduction of an essential biosynthetic gene whose expression is driven by a pheromone-responsive promoter provides the means for identifying pheromone pathway activators through a growth-based screen. For the screens described here, the promoter for the pheromone-responsive gene FUS1 (designated FUS1p) was ligated to HIS3, a gene encoding imidazoleglycerolphosphate dehydratase and required for histidine biosynthesis in yeast. Growth of far1his3 yeast strains carrying an integrated FUS1p-HIS3 construct is therefore inhibited in medium lacking histidine unless the pheromone response pathway is activated.

In searching for intracellular modulators of this G-protein-coupled signaling pathway it can also be advantageous to delete the native GPCR. This eliminates possible interference from modulators acting at the receptor–G-protein interface or acting directly on the GPCR. Finally, to target the mammalian cDNA screens toward different components of the signaling pathway, individual yeast genes can be replaced by their mammalian counterparts. For example, to target the heterotrimeric G-protein we replaced the native yeast Gα, Gpa1, with a human Gαi2 chimera containing the first 41 amino acids of Gpa1 and introduced it into a his3 far1 FUS1p-HIS3 strain (CY1316). 27,30 This chimeric Gα functionally couples to the yeast Gβγ as evidenced by its ability to suppress pheromone pathway activity in the absence of Gpa1 (see Table I , below).


Watch the video: Class 11 Biology Muscle Contraction (August 2022).