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Am J Physiol Endocrinol Metab 290: E900-E907, 2006. First published December 13, 2005; doi:10.1152/ajpendo.00444.2005
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Effects of streptozotocin-induced diabetes and physical training on gene expression of extracellular matrix proteins in mouse skeletal muscle

T. Maarit Lehti,1 Mika Silvennoinen,1,2 Riikka Kivelä,1,2 Heikki Kainulainen,2 and Jyrki Komulainen1,2

1LIKES Research Center for Sport and Health Sciences; and 2Neuromuscular Research Center, Department of Biology of Physical Activity, University of Jyväskylä, Jyväskylä, Finland

Submitted 13 September 2005 ; accepted in final form 5 December 2005


   ABSTRACT
TOP
 ABSTRACT
MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
Diabetes induces changes in the structure and function of the extracellular matrix (ECM) in many tissues. We investigated the effects of diabetes, physical training, and their combination on the gene expression of ECM proteins in skeletal muscle. Mice were divided to control (C), training (T), streptozotocin-induced diabetic (D), and diabetic training (DT) groups. Training groups (T, DT) performed 1, 3, or 5 wk of endurance training on a treadmill. Gene expression of calf muscles was analyzed using microarray and quantitative PCR. Training group samples were collected 24 h after the last training session. Diabetes affected the gene expression of several collagens (types I, III, IV, V, VI, and XV), some noncollagenous glycoproteins, and proteoglycans (e.g., elastin, thrombospondin-1, laminin-2, decorin). Reduced gene expression of collagens in diabetic skeletal muscle was partially attenuated as a result of physical training. In diabetes, mRNA expression of the basement membrane (BM) collagens decreased and that of noncollagenous glycoproteins increased. This may change the structure of the BM in a less collagenous direction and affect its properties.

collagen; exercise; glycoprotein; proteoglycan

PHYSICAL EXERCISE IS RECOMMENDED for the prevention of type 2 diabetes and for the management of both type 1 and type 2 diabetes. It has been shown to attenuate diabetes-induced energy-metabolic changes in skeletal muscle (21). Diabetic muscles are more vulnerable than healthy muscles to exercise-induced myofiber damage (6), and ability to increase muscle mass and rate of protein synthesis depends on the severity of the diabetes (10, 11). Some of the complications of diabetes are consequences of changes in the extracellular matrix (ECM). In skeletal muscle, diabetes-induced changes are observed in the structure of the basement membrane (BM) and in the activities of the enzymes of collagen biosynthesis (4, 16).

In skeletal muscle, the ECM gives mechanical support to the myofibers during contractions, provides the tissue with elastic properties, and participates in the transmission of force from the myofiber to tendon. The ECM comprises collagens, noncollagenous glycoproteins, and proteoglycans. In skeletal muscle, fibrillar collagens (types I, III, and V) are the main components of the ECM, and type IV collagen is the main component of the BM that surrounds every myofiber connecting the sarcolemma to the ECM. Together with collagens, glycoproteins and proteoglycans form structures and interactions that are significant for myofiber maintenance, structural integrity, and signaling (see reviews in Refs. 22, 24, and 31).

Both endurance and resistance training usually accelerate the biosynthesis of ECM components of skeletal muscle. Several studies have shown an exercise-induced increase in collagen turnover (24), but less is known about the training effect on glycoproteins and proteoglycans. The amount and gene expression of these macromolecules have been shown to increase in muscle and tendon following various types of exercise (15, 29).

Transforming growth factor-beta (TGF-beta) modulates ECM protein expression via Smad proteins (see revew in Ref. 33). TGF-beta is expressed as three isoforms. Their signal is mediated from the extracellular space by receptors to receptor-associated Smads in the cytosol. Receptor-associated Smads bind with common Smad on their way to the nucleus, but inhibitory Smads may interfere with this signal transduction. In addition to TGF-beta, connective tissue growth factor (CTGF) is known to regulate ECM gene expression.

The ECM of diabetic skeletal muscle has been little studied. In a study of muscle atrophy in several systemic diseases, e.g., diabetes, Lecker et al. (27) found changes in common in the transcription of some ECM-related genes regardless of the reason for atrophy. By measuring the marker enzymes of collagen biosynthesis, Han et al. (16) suggested that diabetes decreases collagen synthesis, whereas running training (12–16 wk) has an increasing effect. However, training as such was able only to a limited extent to prevent changes in the collagen metabolism of diabetic muscle.

There is no comprehensive knowledge regarding diabetes-induced changes in ECM collagens, noncollagenous glycoproteins, and proteoglycans in skeletal muscle. Moreover, it is not known how training affects these proteins in diabetes. The purpose of the present study was therefore to determine 1) whether diabetes affects gene expression of ECM proteins, 2) whether diabetes-induced changes can be restored by training, and 3) whether these changes are explained by mRNA levels of ECM-regulating proteins.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
Animal care and physical training. Male NMRI mice (n = 60; Harlan, Horst, The Netherlands) were housed in standard conditions (temperature 22°C, light from 8:00 AM to 8:00 PM) with free access to tap water and food pellets (R36; Labfor, Stockholm, Sweden). Treatment of the animals was in accordance with the European Convention for the Protection of Vertebrate Animals Used for Experimental and Other Scientific Purposes and was approved by the University of Jyväskylä Ethics Committee for Animal Care and Use.

At the age of 10 wk, the animals were randomly assigned to diabetic and control groups. Experimental diabetes, similar to type 1, was induced by a peritoneal injection of streptozotocin (STZ, 180 mg/kg; Sigma-Aldrich, Lyon, France) dissolved in sodium citrate buffer solution (0.1 mol/l, pH 4.5). An equal volume of buffer was injected into the control mice. Mice were characterized as diabetic when their urine glucose values were greater than 200 mg/dl 72 h after the injection of STZ (Glukotest; Roche, Basel, Switzerland). Diabetic mice were not treated with insulin during the study, and they showed symptoms of type 1 diabetes, such as polyuria and weight loss.

Diabetic and healthy animals were randomly assigned to 12 groups (n = 5 per group), which were either sedentary or trained for 1, 3, or 5 wk. Groups were named as follows: sedentary healthy mice (C1, C3, C5), trained healthy mice (T1, T3, T5), sedentary diabetic mice (D1, D3, D5), and trained diabetic mice (DT1, DT3, DT5). Training groups performed treadmill running (21 m/min, 2.5° incline, 1 h/day) for 5 days a week. Animals were weighed once a week during the experiment as well as at the beginning and at the end of the study.

Tissue preparation. Trained mice were euthanized by cervical dislocation 24 h after the last training bout together with their respective sedentary controls. Blood and muscle samples were taken immediately. Calf muscles (soleus, gastrocnemius, and plantaris) were removed, weighed, and frozen in liquid nitrogen. Serum glucose was analyzed with a HemoCue B-Glucose analyzer (HemoCue, Ängleholm, Sweden).

The proximal part (~30 mg) of the right calf muscle complex was homogenized in cold 0.2 M NaCl Tris-buffered solution (pH 7.5). The supernatants were used for the assay of citrate synthase activity as previously described (20). Dissolved muscle protein concentration was measured using the Bio-Rad Protein Assay according to the manufacturer’s instructions (Bio-Rad, Hercules, CA). Enzyme activities were expressed as units per milligram of dissolved protein.

Total RNA was extracted from the left calf muscle complex with TRIzol Reagent (Invitrogen, Carlsbad, CA) and further purified with RNeasy columns (Qiagen, Valencia, CA) according to the manufacturer’s protocols. Concentration and purity of RNA were determined by spectrophotometry at wavelengths of 260 and 280 nm. RNA integrity was tested by agarose gel electrophoresis. For the microarray analysis, an equal amount of RNA from each sample was pooled within each group, resulting in 12 arrays. Individual RNA samples were used for the real-time PCR.

Analyses of mRNA expression. Pooled RNA samples were analyzed with an Affymetrix Gene Chip MG U74Av2 (Affymetrix, Santa Clara, CA) representing 6,000 known genes and 6,000 ESTs. The microarray analyses were performed by the Finnish DNA Microarray Center at Turku Center for Biotechnology according to the manufacturer’s instructions. Arrays were scanned using a GeneArray Scanner G2500A (Agilent, Palo Alto, CA), and images were analyzed with Microarray Suite 5.0 software (Affymetrix). All chips were scaled (global scaling) to the average target intensity of 50 to minimize differences between chips caused by physical differences, hybridization efficiencies, and manual laboratory work. The data were subjected to robust normalization, which reduces errors caused by binding capacity and linearity differences between probe sets. The quality checks recommended by Affymetrix were made to all samples before the comparison analysis to determine differentially expressed genes. The following comparisons were performed using Microarray Suite 5.0: trained (T) vs. control (C), diabetic (D) vs. C, trained diabetic (DT) vs. C, and DT vs. D. The comparisons were done at each time point. Transcripts had to meet multiple criteria before they were regarded as differentially expressed. Changes in expression had to be significant (increased or decreased) according to the Microarray Suite 5.0 algorithms and the two based logarithmic ratio of expressions >0,3 or <–0,3. Transcripts had to be at least marginally present in one of the two compared samples, and they also had to be present in at least three samples. GeneSpring 6.1 software (Silicon Genetics, Redwood City, CA) was used in applying the last-mentioned filter and in drawing up the gene lists and the preliminary result tables. The complete data set is publicly available in the NCBI Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo/; acc. no. GSE1659).

Total RNA (5 µg) was reverse transcribed using a High-Capacity cDNA Archive kit (Applied Biosystems, Foster City, CA) according to the manufacturer’s instructions. On the basis of the microarray results, four genes were chosen for closer analyses by real-time PCR. Primers were designed for fibulin-2 (X75285 [GenBank] ), {alpha}2-chain of laminin-2 (U12147 [GenBank] ), {alpha}1-chain of collagen type IV (J04694 [GenBank] ), and {alpha}1-chain of type III collagen (BC043089 [GenBank] ). Oligo Exporer software (http://www.uku.fi/~kuulasma/OligoSoftware/index.htm) was used for the primer design (Table 1). A sample of cDNA (5 ng of RNA equivalent) was analyzed with a QuantiTect SYBR Green PCR kit (Qiagen, Hilden, Germany) and ABI Prism 7700 Sequence Detection System (Applied Biosystems). All samples were run at least in duplicate along with the standards. Amplification was performed at the following temperatures: 95°C for 15 min, followed by 40 cycles at 94°C for 15 s, followed by 30 s at annealing temperature, 30 s at 72°C, and finally 15 s at the detection temperature. The annealing and detection temperatures are shown in Table 1. For GAPDH, TaqMan probe-based (Mm99999915_g1) analysis was used. The primer and probe set was purchased from Applied Biosystems, and the conditions they recommended were used for the real-time PCR. Specific mRNAs in the sample were quantified according to the corresponding gene-specific standard curve. To compensate for variations in mRNA quantity and reverse transcription efficiency, the results were normalized to GAPDH. In the microarray data, GAPDH showed the steadiest expression in all conditions when normally used housekeeping genes were compared. The specificity of the amplified target sequence was confirmed on observing a single reaction product of the right size on an agarose gel and a single peak on the DNA melting temperature curve determined at the end of the reaction.


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  		Table 1. Primers for quantitative real-time PCR

 
Statistical methods. Student’s t-test was used in determining statistically significant differences between healthy and diabetic or sedentary and trained animals in body weight, serum glucose concentration, and muscle citrate synthase activity. Nonparametric Kruskall-Wallis with Mann-Whitney U-tests were used to analyze differences in the quantitative PCR measurements. For the microarray, one-sided Wilcoxon’s signed rank test (WSR) was used to determine which genes were expressed above the background from the raw probe cell intensities in the expression arrays. This nonparametric test is robust, insensitive to outliers, and does not assume a normal data distribution (28). Genes were deemed significantly expressed at P ≤ 0.04 and marginally expressed at 0.04 < P ≤ 0.06. Statistical algorithms based on the WSR test were also used to determine significant differential expression in the comparative analyses between treatment groups. Gene expression was deemed significantly increased at P ≤ 0.0025 and significantly decreased at P ≥ 0.9975. Calculation of the magnitude of the change in expression was based on differences between corresponding probe pair intensities across the two arrays and one-step Tukey’s biweight estimate statistics. The aforesaid nonparametric test is suitable for expression analysis due to its insensitivity to the intensities of outlier probe pair (18).


   RESULTS
TOP
 ABSTRACT
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 GRANTS
 REFERENCES
 
Body weight, serum glucose, and citrate synthase. The body weight of the mice in groups D and DT decreased during the experiment (P < 0.001). At the beginning of the study, there was no significant difference in weight among the study groups. Serum glucose concentrations in trained (DT) and untrained diabetic (D) mice were five times higher than in healthy control mice (P < 0.001). Serum glucose tended to be lower in DT than in D mice (P = 0.07). T and DT mice had higher citrate synthase activities than the respective untrained mice (P < 0.05). Citrate synthase activity was lower in the skeletal muscles of diabetic mice compared with healthy mice (P < 0.05). The results for body weight, glucose concentration, and citrate synthase activity are presented in Table 2.


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  		Table 2. Effects of diabetes and endurance training on physiological variables in C, D, DT, and T groups

 
Collagens. Streptozotocin-induced diabetes affected the expression of several ECM protein genes in the mouse skeletal muscle. From the complete data, genes of particular interest were selected on the basis of the literature and prior experience. The microarray results are presented in three tables. Tables 3 (collagens) and 4 (noncollagenous glycoproteins and proteoglycans) show whether there were probes for the gene on the array, a detectable amount of mRNA, and change in gene expression compared with controls. In Table 5, significant changes are shown in more detail.


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  		Table 3. Observations of muscle collagens on the microarray

 

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  		Table 4. Observations of noncollagenous glycoproteins and proteoglycans on the microarray

 

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  		Table 5. Significant changes in mRNA levels of ECM genes

 
Changes were observed in all the major (I, III, IV, V) as well as some minor (VI, XV) collagen types of the skeletal muscle (Table 3). Generally, the transcription of collagens was decreased in D and DT at all time points (Table 5). However, training attenuated inhibition of mRNA synthesis in DT1 and DT5, although training of the healthy mice tended to decrease the transcription in T1. The gene expression of many collagens tended to increase in T3 and T5, but the change was significant only in the type I collagen {alpha}2-chain. The results for the collagen type III {alpha}1-chain (Col3a1) and collagen type IV {alpha}1-chain (Col4a1) were further verified by the real-time PCR (Fig. 1). The results were well in line with the results of the microarray analyses.


Figure 1
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  		Fig. 1. Quantitative PCR measurements of Col3a1 (A), Col4a1 (B), Fbln2 (C), and Lama2 (D) mRNA presented as fold change from respective control values. Values are means (SD). **P < 0.01, *P < 0.05 compared with controls; ++P < 0.01, +P < 0.05 compared with D; ##P < 0.01 compared with DT.

 
Glycoproteins. An increase was observed in the transcription laminin-{alpha}2 chain, fibulin-2, and trombospondin-1 in D and DT. mRNA levels of fibrillin-1 and elastin decreased in D (Tables 4 and 5). The gene expression of fibulin-2 (Fbn1), and laminin-{alpha}2 chain (Lama2) was further studied by quantitative PCR (Fig. 1). These results were in line with the results of the microarrays, although the changes in fibulin-2 and laminin-2 were not statistically significant due to rather high variation.

Proteoglycans. The transcription of decorin and lumican was changed. These two proteins belong to the small leucine-rich proteoglycan (SLRP) family (Table 4). Decorin expression increased in D5 and DT5. Expression of lumican decreased after 1 wk in all groups and increased only in group D5 (Table 5).

TGF-beta and CTGF. The transcription rate of TGF-beta isoforms did not change (Table 5). Gene expression of TGF-beta2 and TGF-beta1 was below the detection limit. Smad proteins mediate TGF-beta signaling. The transcription of two receptor-associated Smads (Smad1 and Smad2) as well as of common Smad4, which may form a complex with receptor-associated Smads, increased in diabetic muscle. Exercise was observed to have an attenuating effect in the transcription of Smad2 and Smad4. In addition, the transcription of inhibitory Smad7 tended to increase in D and DT. Expression of receptor-associated Smad5 and inhibitory Smad6 was very low and that of receptor-associated Smad3 was below the detection limit. Diabetes increased the level of CTGF mRNA, but exercise attenuated the increase to the control level in DT1 and DT3.


   DISCUSSION
TOP
 ABSTRACT
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
GRANTS
 REFERENCES
 
The aim of this study was to investigate effects of diabetes, physical training, and their combination on the gene expression of ECM proteins in mouse skeletal muscle. The results show that diabetes decreases the gene expression of several collagens, noncollagenous glycoproteins, and SLRP proteoglycans. In addition, diabetes induced changes in the mRNA level of CTGF and proteins of TGF-beta-Smad pathway. Some of these effects of diabetes were partially attenuated with training.

Training attenuates diabetes-induced decreases in gene expression of collagens. It is well known that the ECM of many tissues is vulnerable to the changes induced by diabetes. In connection with nephropathy and retinopathy, an increase in collagen concentration was observed in the mesangial matrix as well as in the BM of the glomerulus and retina (see reviews in Refs. 2 and 39).

A microarray study revealed that tissues adapt to diabetes by modifying their transcriptome differently (23). In a review of recent studies, the transcription of Col1a1, Col1a2, Col3a1, Col4a1, Col5a1, and Col5a2 was downregulated in skeletal muscles but upregulated in the renal cortex in diabetic rats (23, 27). In the study by Han et al. (16), reduced activity of the collagen biosynthesis enzymes prolyl-4-hydroxylase and galactosylhydroxylysyl glucosyltransferase was found in diabetic skeletal muscles. In their study, physical exercise reduced the changes. An attenuating effect of endurance training was also seen in an ultrastructural study of diabetes-induced increase in collagen fiber cross-sectional area (34).

Endurance training is known to increase collagen synthesis and accumulation in skeletal muscle (25, 36). Transcription of types I and III collagens increases after endurance training (30) and acute physical exercise (17). Postexercise, gene expression of type IV collagen was found to increase even earlier than that of fibrillar collagens (17). In the case of an acute bout of exercise, no effect on the protein levels of types I and III collagens was detected (17). Observations after endurance training continue to be lacking. Endurance training increased concentration of type IV collagen in slow (soleus) but not in fast (rectus femoris) muscle (25). In this study, training had an increasing effect on transcription of few collagen genes in healthy mice but was able to alleviate diabetes-induced decrease of mRNA level of several collagen genes in the diabetic training group.

Our findings of reduced collagen gene expression support previous observations of decreased collagen metabolism in diabetic skeletal muscle (16). Partial attenuation of the diabetic effect was seen in the collagen transcriptions after physical training. Interestingly, in addition to diabetes, transcription of collagen type I and type III is decreased in old age (43).

Diabetes modifies gene expression of several ECM glycoproteins. Laminin-2, thrombospondin-1, and fibulin-2 mediate various cell-to-cell, cell-to-matrix, and matrix-to-matrix interactions, contributing thereby to cell and ECM characteristics (5, 26, 37). Elastin provides flexible properties for the ECM, and microfibrils containing fibrillin-1 help to resist mechanical forces (46).

The amount of laminin (45) and elastin (38) as well as gene expression of thrombospondin-1 (35) increase in diabetic kidneys, whereas gene expression of fibulin-2 is reduced (42). In diabetic muscle, transcription of fibrillin-1 is downregulated (27).

The laminin concentration in the BM is higher in slow than in fast muscles. It has been shown that training has no effect on laminin concentration (25). As far as we know, no studies exist on the effects of exercise on fibulin-2 and fibrillin-1 in skeletal muscle.

In this study, diabetes induced an increase in the gene expression of laminin-2, thrombospondin-1, and fibulin-2 and a decrease in the transcription of elastin and fibrillin-1 (Table 5). Exercise attenuated changes in the mRNA levels of elastin and fibrillin-1. Our laminin, thrombospondin-1, and fibrillin-1 findings correlate well with those of previous studies of diabetic kidneys and skeletal muscle. As in the results for collagen mRNA content, our observations of the changes in elastin and fibulin-2 gene expression in diabetic skeletal muscle were contrary to those obtained from diabetic kidney. Reduced transcription of elastin and fibrillin-1 may, in turn, lead to decreased concentrations of these proteins and thus change the elastic and force-bearing features of the ECM. The novel finding observed in this study was that this diabetes-induced reduction was prevented by physical activity.

Diabetes decreases collagen and increases gene expression of other glycoproteins in the BM. The BM is an active interface between the muscle cell membrane and the ECM. In the present diabetic groups, transcription of collagen types IV and XV decreased and that of laminin-2 and fibulin-2 increased. Previously, type IV collagen content relative to laminin-2 content was shown to be higher in slow (soleus) than in fast (rectus femoris) skeletal muscles (25). These results are in line with the metabolic observations for the same data set studied here. We detected downregulation of genes typical of oxidative muscle cells and of the calcineurin-dependent signaling pathway that upregulates the formation of slow muscle fibers (Silvennoinen M, Kivelä R, Lehti TM, Komulainen J, Kontro H, Vihko V, and Kainulainen H, unpublished observation). The decrease in gene expression of BM collagens and increase in noncollagenous glycoproteins might change the structure of the BM in a less collagenous direction, thus affecting its properties.

Diabetes increases gene expression of small leucine-rich proteoglycans. The proteoglycans decorin and lumican, whose transcription was affected in diabetic skeletal muscle, are both members of the SLRP family (Table 4). Members of this family are known to interact with TGF-beta growth factors (44) and take part in collagen fibril formation (3). Knockout studies have shown that SLRPs regulate collagen fibril formation and prevent lateral fusion of the fibrils (3). In a recent study, an increased level of decorin gene expression was observed in many diabetic tissues (23). Training increased the amount of decorin in tendon (15), whereas the contribution of decorin to skeletal muscle ECM remodeling after physical activity is not well known. In the present study, an increase in the gene expression of decorin and lumican was observed in both the D5 and DT5 groups (Table 5). The increased gene expression of SLRPs may be connected to diabetes-induced changes in the collagen metabolism or TGF-beta signaling.

Training attenuates some of the diabetes-induced changes in gene expression of ECM-regulating proteins. TGF-beta and CTGF are growth factors that modulate the ECM. The TGF-beta pathway activates collagen expression via Smad proteins (22). Both TGF-beta and CTGF are necessary in the activation of fibroblasts and collagen synthesis induced by mechanical loading (12, 13). The synthesis of CTGF itself is activated by mechanical load (32), TGF-beta, and advanced glycation end products (AGEs) (40) of which mechanical load has a more prominent effect than TGF-beta (22). In this study, the transcription of some TGF-beta isoforms was not detected, whereas the rest remained constant in all groups (Table 5). Gene expression of three TGF-beta pathway proteins, Smad1, Smad2, and Smad4, increased in the diabetic groups. Diabetes also increased the CTGF mRNA level. An attenuated effect was observed in DT1 and DT3.

The TGF-beta pathway may be inhibited by decorin binding to TGF-beta (44) or by increased phosphorylation of Smad proteins (1). TGF-beta is activated by a tissue-specific activator, thrombospondin-1 (7), which functions in many tissues, e.g., lungs, but not in smooth muscle cells (14) and probably not in skeletal muscle either. The transcription of decorin was increased in D5 as well as in DT5, and thrombospondin-1 at all time points, in D and DT. The mRNA results for CTGF and thrombospondin-1, verified by quantitative PCR, were well in line with the array results (Kivelä R, Silvennoinen M, Touvra A-M, Lehti TM, Kainulainen H, and Vihko V, unpublished observation).

Verrecchia et al. (41) studied which ECM-related genes are activated by TGF-beta in human dermal fibroblasts. According to their criteria they named seven genes that were activated through TGF-beta Smad3 pathway: Col1a2, Col3a1, Col6a1, Col6a3, and tissue inhibitor of metalloproteases-1 (Timp1) and two additional genes, Col1a1 and Col5a2. These genes, except Timp1 (transcription below detection limit on the microarray), were all downregulated in D and DT in the present study. It seems obvious that the TGFbeta-Smad3 pathway is downregulated in type 1 diabetic skeletal muscle. The transcription patterns of Eln, Col4a1, Col4a2, Col15a1, and Col26a1 were markedly similar to the previously mentioned collagen genes. Transcription of both Col4a1 and Col1a1 is stimulated by TGF-beta but mediated through different pathways (19). Therefore, it would be interesting to study further whether the expression of Eln, Col4a2, Col15a1, and Col26a1 is regulated in the same way as that of Col1a1 or Col4a1.

In addition to TGFbeta-pathways, formation of AGEs is one of the potential regulating factors of the ECM metabolism in diabetic skeletal muscle. Accumulation of AGEs inhibits collagen synthesis, reduces the collagen turnover rate (8), and increases cross-linking of proteins, thus making the ECM stiffer and less adaptable to the changing conditions. Interestingly, in a high-glucose environment, the glycation of collagen in a tendon proceeds faster than in a normal glucose concentration environment and is discernible within a week (9).

Taken together, it is possible that in diabetic skeletal muscle low levels of TGF-beta, incipient AGE formation, and later also decorin cause downregulation of collagen transcription. Physical training, in turn, may decrease AGE formation and thus diminish the inhibition of collagen gene expression.

In conclusion, The present study showed that experimental type 1 diabetes results in changes in the transcription of collagens, noncollagenous glycoproteins, and proteoglycans in skeletal muscle. These changes may cause modifications to the structure and function of the ECM. However, exercise has a beneficial effect on the gene expression of some of these proteins in diabetic skeletal muscle. We suggest that inhibition of the TGF-beta-Smad3 pathway is involved in this process. Furthermore, an accumulation of AGEs in diabetic muscle may enforce this downregulation effect. However, our hypotheses and the involvement of other regulatory mechanisms need further investigation.


   GRANTS
TOP
 ABSTRACT
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 GRANTS
REFERENCES
 
This study was supported by LIKES-Foundation and the Ministry of Education, Finland.


Figure 2
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  		Fig. 2. Gene expression is expressed in relation to controls at the corresponding time point. Statistically significant changes are expressed with colors: orange, upregulated; green, downregulated; yellow, diabetes-induced change in expression attenuated by exercise. Group abbreviations: D, diabetic; DT, diabetic trained; T, healthy trained; nos. refer to time of treatment (wk). Signal, mean of the scaled signal intensities of control groups.

 

   ACKNOWLEDGMENTS
 
We thank Dr. S. Koskinen for critically reading the manuscript.


   FOOTNOTES
 

Address for reprint requests and other correspondence: M. Lehti, LIKES Research Center, Rautpohjankatu 8, Viveca, FIN-40700 Jyväskylä, Finland (e-mail: maarit.lehti{at}likes.fi)

The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.


   REFERENCES
TOP
 ABSTRACT
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 

  1. Abdel-Wahab N, Wicks SJ, Mason RM, and Chantry A. Decorin suppresses transforming growth factor-beta-induced expression of plasminogen activator inhibitor-1 in human mesangial cells through a mechanism that involves Ca2+-dependent phosphorylation of Smad2 at serine-240. Biochem J 362: 643–649, 2002.[CrossRef][Web of Science][Medline]
  2. Cai J and Boulton M. The pathogenesis of diabetic retinopathy: old concepts and new questions. Eye 16: 242–260, 2002.[CrossRef][Web of Science][Medline]
  3. Canty EG and Kadler KE. Collagen fibril biosynthesis in tendon: a review and recent insights. Comp Biochem Physiol A Mol Integr Physiol 133: 979–985, 2002.[CrossRef][Medline]
  4. Cohn RA, Mauer SM, Barbosa J, and Michael AF. Immunofluorescence studies of skeletal muscle extracellular membranes in diabetes mellitus. Lab Invest 39: 13–16, 1978.[Web of Science][Medline]
  5. Colognato H and Yurchenco PD. Form and function: the laminin family of heterotrimers. Dev Dyn 218: 213–234, 2000.[CrossRef][Web of Science][Medline]
  6. Copray S, Liem R, Brouwer N, Greenhaff P, Habens F, and Fernyhough P. Contraction-induced muscle fiber damage is increased in soleus muscle of streptozotocin-diabetic rats and is associated with elevated expression of brain-derived neurotrophic factor mRNA in muscle fibers and activated satellite cells. Exp Neurol 161: 597–608, 2000.[CrossRef][Web of Science][Medline]
  7. Crawford SE, Stellmach V, Murphy-Ullrich JE, Ribeiro SM, Lawler J, Hynes RO, Boivin GP, and Bouck N. Thrombospondin-1 is a major activator of TGF-beta1 in vivo. Cell 93: 1159–1170, 1998.[CrossRef][Web of Science][Medline]
  8. DeGroot J, Verzijl N, Budde M, Bijlsma JW, Lafeber FP, and TeKoppele JM. Accumulation of advanced glycation end products decreases collagen turnover by bovine chondrocytes. Exp Cell Res 266: 303–310, 2001.[CrossRef][Web of Science][Medline]
  9. Elgawish A, Glomb M, Friedlander M, and Monnier VM. Involvement of hydrogen peroxide in collagen cross-linking by high glucose in vitro and in vivo. J Biol Chem 271: 12964–12971, 1996.[Abstract/Free Full Text]
  10. Farrell PA, Fedele MJ, Hernandez J, Fluckey JD, Miller JL III, Lang CH, Vary TC, Kimball SR, and Jefferson LS. Hypertrophy of skeletal muscle in diabetic rats in response to chronic resistance exercise. J Appl Physiol 87: 1075–1082, 1999.[Abstract/Free Full Text]
  11. Fedele MJ, Hernandez JM, Lang CH, Vary TC, Kimball SR, Jefferson LS, and Farrell PA. Severe diabetes prohibits elevations in muscle protein synthesis after acute resistance exercise in rats. J Appl Physiol 88: 102–108, 2000.[Abstract/Free Full Text]
  12. Garrett Q, Khaw PT, Blalock TD, Schultz GS, Grotendorst GR, and Daniels JT. Involvement of CTGF in TGF-beta1-stimulation of myofibroblast differentiation and collagen matrix contraction in the presence of mechanical stress. Invest Ophthalmol Vis Sci 45: 1109–1116, 2004.[Abstract/Free Full Text]
  13. Gore-Hyer E, Shegogue D, Markiewicz M, Lo S, Hazen-Martin D, Greene EL, Grotendorst G, and Trojanowska M. TGF-beta and CTGF have overlapping and distinct fibrogenic effects on human renal cells. Am J Physiol Renal Physiol 283: F707–F716, 2002.[Abstract/Free Full Text]
  14. Grainger DJ and Frow EK. Thrombospondin 1 does not activate transforming growth factor beta1 in a chemically defined system or in smooth-muscle-cell cultures. Biochem J 350: 291–298, 2000.[CrossRef][Medline]
  15. Hae Yoon J, Brooks R, Hwan Kim Y, Terada M, and Halper J. Proteoglycans in chicken gastrocnemius tendons change with exercise. Arch Biochem Biophys 412: 279–286, 2003.[CrossRef][Web of Science][Medline]
  16. Han X, Karpakka J, Kainulainen H, and Takala TE. Effects of streptozotocin-induced diabetes, physical training and their combination on collagen biosynthesis in rat skeletal muscle. Acta Physiol Scand 155: 9–16, 1995.[Web of Science][Medline]
  17. Han XY, Wang W, Komulainen J, Koskinen SO, Kovanen V, Vihko V, Trackman PC, and Takala TE. Increased mRNAs for procollagens and key regulating enzymes in rat skeletal muscle following downhill running. Pflügers Arch 437: 857–864, 1999.[CrossRef][Web of Science][Medline]
  18. Hubbell E, Liu WM, and Mei R. Robust estimators for expression analysis. Bioinformatics 18: 1585–1592, 2002.[Abstract/Free Full Text]
  19. Hubchak SC, Runyan CE, Kreisberg JI, and Schnaper HW. Cytoskeletal rearrangement and signal transduction in TGF-beta1-stimulated mesangial cell collagen accumulation. J Am Soc Nephrol 14: 1969–1980, 2003.[Abstract/Free Full Text]
  20. Kainulainen H, Ahomaki E, and Vihko V. Selected enzyme activities in mouse cardiac muscle during training and terminated training. Basic Res Cardiol 79: 110–123, 1984.[CrossRef][Web of Science][Medline]
  21. Kainulainen H, Komulainen J, Joost HG, and Vihko V. Dissociation of the effects of training on oxidative metabolism, glucose utilisation and GLUT4 levels in skeletal muscle of streptozotocin-diabetic rats. Pflügers Arch 427: 444–449, 1994.[CrossRef][Web of Science][Medline]
  22. Kjaer M. Role of extracellular matrix in adaptation of tendon and skeletal muscle to mechanical loading. Physiol Rev 84: 649–698, 2004.[Abstract/Free Full Text]
  23. Knoll KE, Pietrusz JL, and Liang M. Tissue-specific transcriptome responses in rats with early streptozotocin-induced diabetes. Physiol Genomics 21: 222–229, 2005.[Abstract/Free Full Text]
  24. Kovanen VM. Extracellular matrix and exercise. In: Molecular and Cellular Exercise Physiology, edited by Mooren FC and Völker KV. Champaign, IL: Human Kinetics, 2005, chapt. 5, p. 71–93.
  25. Kovanen V. Effects of ageing and physical training on rat skeletal muscle. An experimental study on the properties of collagen, laminin, and fibre types in muscles serving different functions. Acta Physiol Scand Suppl 577: 1–56, 1989.[Medline]
  26. Lawler J, Duquette M, Ferro P, Copeland NG, Gilbert DJ, and Jenkins NA. Characterization of the murine thrombospondin gene. Genomics 11: 587–600, 1991.[CrossRef][Web of Science][Medline]
  27. Lecker SH, Jagoe RT, Gilbert A, Gomes M, Baracos V, Bailey J, Price SR, Mitch WE, and Goldberg AL. Multiple types of skeletal muscle atrophy involve a common program of changes in gene expression. FASEB J 18: 39–51, 2004.[Abstract/Free Full Text]
  28. Liu WM, Mei R, Di X, Ryder TB, Hubbell E, Dee S, Webster TA, Harrington CA, Ho MH, Baid J, and Smeekens SP. Analysis of high density expression microarrays with signed-rank call algorithms. Bioinformatics 18: 1593–1599, 2002.[Abstract/Free Full Text]
  29. Matsuda M, Nosaka T, Sato M, and Ohshima N. Effects of physical exercise on the elasticity and elastic components of the rat aorta. Eur J Appl Physiol Occup Physiol 66: 122–126, 1993.[CrossRef][Web of Science][Medline]
  30. Perhonen M, Han X, Wang W, Karpakka J, and Takala TE. Skeletal muscle collagen type I and III mRNA, [corrected] prolyl 4-hydroxylase, and collagen in hypobaric trained rats. J Appl Physiol 80: 2226–2233, 1996.[Abstract/Free Full Text]
  31. Sanes JR. The basement membrane/basal lamina of skeletal muscle. J Biol Chem 278: 12601–12604, 2003.[Free Full Text]
  32. Schild C and Trueb B. Mechanical stress is required for high-level expression of connective tissue growth factor. Exp Cell Res 274: 83–91, 2002.[CrossRef][Web of Science][Medline]
  33. Schiller M, Javelaud D, and Mauviel A. TGF-beta-induced SMAD signaling and gene regulation: consequences for extracellular matrix remodeling and wound healing. J Dermatol Sci 35: 83–92, 2004.[CrossRef][Web of Science][Medline]
  34. Searls YM, Smirnova IV, Fegley BR, and Stehno-Bittel L. Exercise attenuates diabetes-induced ultrastructural changes in rat cardiac tissue. Med Sci Sports Exerc 36: 1863–1870, 2004.
  35. Tada H and Isogai S. The fibronectin production is increased by thrombospondin via activation of TGF-beta in cultured human mesangial cells. Nephron 79: 38–43, 1998.[CrossRef][Web of Science][Medline]
  36. Takala TE, Myllyla R, Salminen A, Anttinen H, and Vihko V. Increased activities of prolyl 4-hydroxylase and galactosylhydroxylysyl glucosyltransferase, enzymes of collagen biosynthesis, in skeletal muscle of endurance-trained mice. Pflügers Arch 399: 271–274, 1983.[CrossRef][Web of Science][Medline]
  37. Talts JF, Andac Z, Gohring W, Brancaccio A, and Timpl R. Binding of the G domains of laminin alpha1 and alpha2 chains and perlecan to heparin, sulfatides, alpha-dystroglycan and several extracellular matrix proteins. EMBO J 18: 863–870, 1999.[CrossRef][Web of Science][Medline]
  38. Thongboonkerd V, Barati MT, McLeish KR, Benarafa C, Remold-O’Donnell E, Zheng S, Rovin BH, Pierce WM, Epstein PN, and Klein JB. Alterations in the renal elastin-elastase system in type 1 diabetic nephropathy identified by proteomic analysis. J Am Soc Nephrol 15: 650–662, 2004.[Abstract/Free Full Text]
  39. Tsilibary EC. Microvascular basement membranes in diabetes mellitus. J Pathol 200: 537–546, 2003.[CrossRef][Web of Science][Medline]
  40. Twigg SM, Chen MM, Joly AH, Chakrapani SD, Tsubaki J, Kim HS, Oh Y, and Rosenfeld RG. Advanced glycosylation end products up-regulate connective tissue growth factor (insulin-like growth factor-binding protein-related protein 2) in human fibroblasts: a potential mechanism for expansion of extracellular matrix in diabetes mellitus. Endocrinology 142: 1760–1769, 2001.[Abstract/Free Full Text]
  41. Verrecchia F, Chu ML, and Mauviel A. Identification of novel TGF-beta/Smad gene targets in dermal fibroblasts using a combined cDNA microarray/promoter transactivation approach. J Biol Chem 276: 17058–17062, 2001.[Abstract/Free Full Text]
  42. Wada J, Zhang H, Tsuchiyama Y, Hiragushi K, Hida K, Shikata K, Kanwar YS, and Makino H. Gene expression profile in streptozotocin-induced diabetic mice kidneys undergoing glomerulosclerosis. Kidney Int 59: 1363–1373, 2001.[CrossRef][Web of Science][Medline]
  43. Welle S, Brooks A, and Thornton CA. Senescence-related changes in gene expression in muscle: similarities and differences between mice and men. Physiol Genomics 5: 67–73, 2001.[Abstract/Free Full Text]
  44. Yamaguchi Y, Mann DM, and Ruoslahti E. Negative regulation of transforming growth factor-beta by the proteoglycan decorin. Nature 346: 281–284, 1990.[CrossRef][Medline]
  45. Yang CW, Hattori M, Vlassara H, He CJ, Carome MA, Yamato E, Elliot S, Striker GE, and Striker LJ. Overexpression of transforming growth factor-beta 1 mRNA is associated with up-regulation of glomerular tenascin and laminin gene expression in nonobese diabetic mice. J Am Soc Nephrol 5: 1610–1617, 1995.[Abstract]
  46. Yin W, Smiley E, Germiller J, Sanguineti C, Lawton T, Pereira L, Ramirez F, and Bonadio J. Primary structure and developmental expression of Fbn-1, the mouse fibrillin gene. J Biol Chem 270: 1798–1806, 1995.[Abstract/Free Full Text]



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