Exercise-induced gene expression changes in skeletal muscle of old mice

Abstract

Exercise is believed to be beneficial for skeletal muscle functions across all ages. Regimented exercise is often prescribed as an effective treatment/prophylaxis for age-related loss of muscle mass and functions known as sarcopenia, and plays an important role in the maintenance of mobility and functional independence in the elderly. However, response to exercise changes with aging, with a shift from a predominantly anabolic response resulting in limited gain of muscle strength and endurance. These changes likely reflect age-dependent alterations in transcriptional response underlying the muscular adaptation to exercise. The exact changes in gene expression accompanying exercise, however, are largely unknown, and elucidating them is of a great clinical interest for understanding and optimizing the exercise-based therapies for sarcopenia. In order to characterize the exercise-induced transcriptomic changes in aged muscle, a paired-end RNA sequencing was performed on the rRNA-depleted total RNA extracted from the gastrocnemius muscles of 24 months-old mice after 8 weeks of regimented exercise (exercise group) or no formal exercise program (sedentary group).

Differential gene expression analysis of aged skeletal muscle revealed upregulations in the group of genes involved in neurotransmission and neuroexcitation, as well as equally notable absence of anabolic gene upregulations in the exercised group. In particular, genes encoding the transporters and receptor components of glutaminergic transmission were significantly upregulated in exercised muscles, as exemplified by Gria 1, Gria 2 and Grin2c encoding glutamate receptor 1, 2 and 2C respectively, Grin1 and Grin2b encoding N-methyl-D-aspartate receptors (NMDARs), Nptx1 responsible for glutaminergic receptor clustering, and Slc1a2 and Slc17a7 regulating synaptic uptake of glutamate. These changes were accompanied by an increase in the post-synaptic density of NMDARs and acetylcholine receptors (AChRs), as well as their innervation at neuromuscular junctions (NMJs).

These results suggest that neural responses predominate the adaptive response of aged skeletal muscle to exercise, and indicate a possibility that glutaminergic transmission at NMJs may be present and responsible for synaptic protection and neural remodeling accompanying the exercise-induced functional enhancement in aged skeletal muscle. In addition, the absence of upregulations in the anabolic pathways highlights them as the area of potential pharmacological targeting for optimizing exercise-led sarcopenia therapy.

Introduction:

Regimented exercise is known to have many beneficial effects on strength and mobility, both of which are particularly important for the maintenance of physical functions and independence in the elderly population (1). In skeletal muscle, regimented training increases the levels of protein synthesis, pro-myogenic growth factors such as mTOR (2), insulin-like growth factor (IGF-1) (3) and androgens (4), and reduces the levels of myostatin mediated catabolism (5). Additionally, physical activity is known to promote mitochondrial biogenesis, restore insulin sensitivity and reduce inflammation within skeletal muscle (6,7).

Although physical training is beneficial across all ages, the anabolic response of skeletal muscle to exercise changes dramatically with aging (8,9). This likely reflects changes in gene expression underlying skeletal muscle adaptation to exercise (10,11,12,13). Previous transcriptome analyses of skeletal muscle from young, healthy subjects have identified an upregulation in a number of signaling pathways following exercise. In particular, gene expressions of the key regulators such as AMPK, PPARγ coactivator-1α (PGC-1α), and hexokinase II (HKII) were markedly upregulated, indicating enhanced mitochondrial biogenesis and mitochondrial oxidative responses in exercised muscles (10,14,15,16). In contrast, transcriptional response to exercise training was found to be blunted in elderly patients, with less changes in gene expression observed after treadmill training with an increasing resistance (17,18). Despite the evidence indicating that the anabolic response to exercise declines with aging (18), some preclinical studies have demonstrated distinct effects of exercise in aged muscle. For instance, fragmentation and degradation of neuromuscular junction synapses that occur with aging were protected by exercise (19). The maintenance of NMJ synapses may explain improved muscle function following exercise, even in the absence of an anabolic response in the old. The mechanisms responsible for the protective effects of exercise on NMJ synapses, however, remain largely unknown (20).

To assess the transcriptomic changes that occurs with exercise in aged skeletal muscle, RNA sequencing was performed on gastrocnemius muscle of 24-month-old mice after the completion of 8-week running regimen (exercise group) or no formal exercise program (sedentary group). The aim of the present study is two-folds: 1) To characterize the pattern of exercise-induced gene expression changes in aged skeletal muscle; 2) To explore the phenotypical implications of the significant gene upregulations in the key biological processes in aged skeletal muscle. We hypothesize that serial, aerobic exercise results in transcriptional changes in skeletal muscle of old mice, with some accompanying changes to the NMJ architecture in exercised muscle.

Materials and Methods:

Animals

All animal procedures were approved by the Institutional Animal Care and Use Committee of Brigham and Women’s Hospital, and were performed in accordance with the U.S. National Institutes of Health guidelines. 24-month old male C57BL/6 mice were obtained from the National Institution of Aging. The animals were housed following a 12 light / 12 dark cycle.

Exercise regime

Mice were subjected to a moderate regimented running exercise on a treadmill at an increasing speed of 12 m/min for 40 minutes, three times weekly over the course of 8 weeks (exercise group, n=3) or no regimented exercise training program (sedentary group, n=3) (21, 22). Animals were allowed to move freely within their cages for the remainder of the time with free access to food and water. Animals were trained at the same time each time at 8pm in the evening.

RNA sequencing and data analysis

2 days after the completion of the 8-week regimes, gastrocnemius muscle was harvested from the animals and immediately snap-frozen using liquid nitrogen for the subsequent analyses. Total RNA was extracted using TRIzole reagent. Following rRNA deletion, the sequencing library was prepared using Illumina HiSeq® at 2x150bp configuration, with the minimal read of 30 million base pairs per sample. After quality assessment and adaptor trimming, the reads were aligned to the reference genome using STAR (23). The expression level of each gene was then estimated by counting the number of reads that aligned to each exon using FeatureCounts (24), and normalized as fragments per kilobase per million reads (FPKM). Differential expression analysis was performed using GESeq2 (25) to compare the gene expression in the sedentary and exercise groups, using the sedentary group as the reference. Sample similarity was assessed by principal component analysis (PCA) using the first two components. Functional enrichment analysis was performed with WebGestalt (26) to evaluate the gene ontology of the differentially regulated genes.

Quantitative RT-PCR

Total RNA from gastrocnemius muscle was extracted using RNeasy Mini Kit (Qiagen) according to the manufacturer’s instructions. cDNA was made from the extracted mRNA using Superscript III First-Strand kit (Invitrogen). Quantitative RT-PCR reactions were then carried out using SYBR Green PCR mix (Biosystems) in ABI StepOnePlus. The thermal cycles for the reactions were: 40 seconds at 95°C, followed by 40 cycles of 30 seconds at 95°C, 60°C, then 72°C. All the signals were normalized to that of β-actin. Primers sequences are summarized in Table 1.

Table 1.

Primer sequences

Gene	Forward primer	Reverse primer
Dlgap1	ATCACAGCCCAGAGTAGCA	GACTTTGGGTGGAGTTGC
SNAP25	GCAGGGTAACAAACGATGCC	CTTCCCAGCATCTTTGTTGC
Gria1	CTAGGCTGCCTGAACCTTTG	GGGAAGATTGAATGGAAGCA
Grin1	AGCAAAATGTGTCCCTGTCC	GCATCCTTGTGTCGCTTGTA
Gabbr2	GAGGACATCAACTCCCCAGA	CTGGCTGTAGGGCTGACAC
Cacna1e	TGGATGTGTCCTTCCCTCCT	GTCACCTACATGGGGTCAGC
β-actin	CCTAAGGCCAACCGTGAAAA	AGCCATACAGGGACAGCACA

Protein quantification with Immunoblotting

Whole muscle lysate was extracted by homogenizing 5mg of gastrocnemius muscle piece from each sample in RIPA buffer (ThermoFisher, 89901). A total of 20μg protein per sample was separated on a 4–12% SDS-polyacrylamide gel with MOPS SDS Running Buffer (Novex-Life Technologies) at 100V. The protein was then transferred onto Immuno-Blot PVDF Membranes using the iBlot2 Dry Blotting System (Thermo Fisher Scientific). After 1h of blocking (Life Technologies) at room temperature, the membranes were incubated with 1:1000 anti-PGC-α antibody (#2178, Cell Signaling) at 4°C overnight on slow rocking, followed by a 1h incubation with 1:1000 anti-rabbit HRP-conjugated secondary antibody (#G-20234, ThermoFisher) at room temperature. Each incubation was followed by three rounds of 5-minute-long washing with TBS-T buffer. For immunodetection, the membranes were immersed in ECL solutions per manufacturer’s specifications (Amersham Biosciences) and exposed to Hyperfilm. For GAPDH detection, the same membranes were incubated with 1:1000 anti-GAPHD (#2118, Cell Signaling) after washing. Band intensities on the films were analyzed using ImageJ software.

Histology and immunostaining

Gastrocnemius muscle was frozen in OCT® media using liquid nitrogen and stored at −80°C for subsequent sectioning. 6μm-thick tissue sections were prepared on glass slides, dried overnight at room temperature, and fixed in acetone pre-cooled at −20°C for 10 minutes. After rinsing with 10mM phosphate buffered saline (PBS), the sections were blocked with Blocker BSA (#37520, ThermoFisher), and incubated with 1:200 Alexa Fluor 488-conjugated α-Bungarotoxin (#B13422, ThermoFisher) and 1:200 anti-neurofilament antibody (#AB1987, EMD Milipore) in the blocking solution overnight at 4°C. This was followed by 1h of incubation with 1:400 Alexa Fluor 647 secondary antibody at room temperature. Immunofluorescent images were acquired using Olympus BX53 microscope with a 20x objective. 10 non-overlapping, high-power fields (20x) were chosen randomly for analysis. The post-synaptic AChRs and NMDARs were counted by two separate, double-blinded technicians. Post-synaptic densities of AChRs and NMDARs were calculated as the average number of the receptors per high power field using 10 images per sample, and the percentage innervation of the post-synaptic AChRs were calculated as the proportion of AChRs co-stained with neurofilament out of the total AChRs expressed as a percentage.

In order to visualize type I, IIa and IIb myofibers, the following protocol was followed. The cryostat muscle cross-sections were thawed at room temperature for 15-20 minutes. The slides were washed twice in phosphate-buffered saline (PBS) and then incubated for 5 min in 0.05% TX-100 in PBS for permeabilization. The washed sections were incubated in room temperature for 1h in blocking solution containing 1% BSA and 5% Goat normal serum in TBS, followed by incubation with primary antibodies diluted in blocking buffer, overnight at 4 °C. Samples were washed three times in PBS and then incubated for 1 h at room temperature with secondary antibodies. The following combinations of the primary and secondary antibodies were utilized: MHC I (#BA-F8, DSHB), Goat Anti-Mouse IgG2b Alexa Fluor 594 (#A-21145, ThermoFisher); MHC IIa (#SC-71, DSHB), Goat Anti-Mouse IgG1 Alexa Fluor 488 (#A-21121, ThermoFisher); and MHC IIb (#BF-F3, DSHB), Goat Anti-Mouse IgM (Heavy chain) Alexa Fluor 350 (#A-31552, ThermoFisher).

For visualizing the muscle capillaries, skeletal muscle sections were blocked with Blocker BSA (#37520, ThermoFisher), and stained with antibodies against CD31 (#ab7388, Abeam) and Laminin (#ab11571, Abcam). Secondary antibodies used were (#A21470, ThermoFisher) and (#A11037, ThermoFisher), respectively. Capillaries were identified as CD31 positive areas, and an individual muscle fiber as a discrete area demarcated by laminin. Capillary density was calculated as the mean ratio of capillary-to-fiber of 10 non-overlapping, randomly selected high-power fields (20x), and presented the mean ± standard error of mean.

Laser Doppler

Laser Doppler perfusion imaging (LDPI) was performed using an LDPI analyzer (Moor Instruments, Inc. DE). Briefly, mice were kept under isoflurane anesthesia and the mean LDPI flux intensity of the left hindlimb was measured at a rate of 4ms/pixel. Data are presented as mean flux intensity across the hindlimb area.

Statistical analysis

Statistical analysis was performed using an unpaired t-test to compare the muscle capillary densities, hind limb blood flow, PCR results and protein levels of the sedentary and exercised groups on GraphPad Prism version 8.01 (GraphPad Software, USA). Normality and equal variance were verified prior to selection of appropriate tests. Data are expressed as mean ± standard error of mean (SEM), and statistical significance was set at p-value < 0.05.

Results:

Differential gene expression in aged muscles of exercised and sedentary mice

In order to characterize the exercise-induced gene expression changes in aged muscle, a paired-end RNA sequencing was performed on the rRNA-depleted total RNA extracted from the gastrocnemius muscles of 24 months-old mice after 8 weeks of regimented exercise (exercise group) or no formal exercise program (sedentary group). The average read depth were 31,081,656 reads per sample, and the mean quality score per base pair as evaluated by FastQC was 36.79 (Table 2).

Table 2: The read number, yields and mean quality score of each sample;

The mean quality score distribution per base pair was evaluated with Fast QC.

# Reads	Yields (Mbases)		Mean Quality Score	% Bases >=30
Old sed 1	33,169,552	9,951	35.76	92.91
Old sed 2	32,102,665	9,631	35.87	93.48
Old sed 3	29,507,900	8,853	35.73	92.79
Old ex 1	30,009,600	9,003	35.83	93.22
Old ex 2	31,117,675	9,335	35.78	93.01
Old ex 3	30,582,541	9,174	35.78	92.97

A comparison of gene expression between the sedentary and exercise groups were drawn using DESeq2, and p-value and log2 fold change were generated for each gene using the Wald test. Where genes with adjusted p-value (padj) less than 0.01 and the log2 fold changes greater than 1 or less than −1 are considered differentially expressed, a total of 146 genes were identified as such, with 124 upregulated genes and 22 downregulated genes (Figure 2A). A full list of the differentially regulated genes with their respective padj values and fold changes can be found in Table 3. Top 30 differentially expressed genes in terms of their padj values were: Mobp, Kcnq2, Nell2, Rasgrp, Hpcal4, Gfap, Gria1, Gria2, Slc17a7, Hpca, Syt1, Ddn, Snap25, Aplp1m, Adcy1, Kif1a, Cplx2, Slc1a2, Kif5a, Ndrg4, Ttr, Atp1a3, Anhg11, Kcnab1, Meg3, Gm4841, Gdf11, Sms, Lgals1, and H19.

Figure 2: Transcriptional changes following exercise in aged muscle.

The global transcriptional change across the groups compared was visualized by a volcano plot (1A). The log2 fold change of each gene is represented on the x-axis and the log10 of its adjusted p-value is on the y-axis. Genes with an adjusted p-value less than 0.05 and a log2 fold change greater than 1 are indicated by red dots. Genes with an adjusted p-value less than 0.05 and a log2 fold change less than −1 are indicated by green dots. A bi-clustering heatmap was used to visualize the expression profile of the top 30 differentially expressed genes sorted by their adjusted p-value by plotting their log2 transformed expression values in samples (1B).

Table 3:

Log2 fold changes and (adjusted) p-values of significantly differentially expressed genes

ID	log2FoldChange	pvalue	padj	Gene.name
ENSMUSG00000000031	1.39560670621462	1.71703554846636E-13	2.48060125686935E-10	H19
ENSMUSG00000002489	−1.10659260192092	2.4272445650038E-06	0.000539483111240153	Tiam1
ENSMUSG00000003279	2.49217537330478	3.12601315774994E-06	0.000674052419253931	Dlgap1
ENSMUSG00000003469	2.3059449172462	1.02880273746067E-05	0.00183235600961698	Phyhip
ENSMUSG00000004110	2.81460491749238	1.10868075838915E-07	4.71091497542589E-05	Cacna1e
ENSMUSG00000004267	1.38401868346461	1.2315388837186E-05	0.00211810026822411	Eno2
ENSMUSG00000005089	2.94302257233517	5.99590065549362E-19	5.0937198693998E-15	Slc1a2
ENSMUSG00000006651	1.87312416861693	3.67224043810237E-09	2.30664598301152E-06	Aplp1
ENSMUSG00000009394	1.85182867260335	1.34455431639108E-06	0.000351060227950278	Syn2
ENSMUSG00000009633	1.40242632579043	5.80215256590034E-07	0.000178348293871409	G0s2
ENSMUSG00000010086	2.71039572730934	1.88130381076096E-05	0.00305384226450152	Rnf112
ENSMUSG00000011751	2.91200226839815	0.000248262198867955	0.0223969013106936	Sptbn4
ENSMUSG00000014602	2.20849475579516	2.88242345567949E-10	2.6026482290126E-07	Kif1a
ENSMUSG00000015090	3.35989489772161	1.24006704679683E-07	4.97645795140938E-05	Ptgds
ENSMUSG00000015222	1.45090064049702	5.35373271215662E-08	2.41704301539146E-05	Map2
ENSMUSG00000016346	3.50899930751989	7.81093035761755E-11	8.06032220546434E-08	Kcnq2
ENSMUSG00000017390	2.31360427289172	1.79393403135223E-07	6.4792412377364E-05	Aldoc
ENSMUSG00000017639	2.36314439279978	0.000228186381070176	0.0212684428859409	Rab11fip4
ENSMUSG00000017740	1.53492496238611	7.87857169463401E-07	0.000227643450544755	Slc12a5
ENSMUSG00000018865	3.76507829230199	6.62046794493746E-06	0.00124215455065599	Sult4a1
ENSMUSG00000020297	2.69359366529093	2.52804570905911E-06	0.000553373884223893	Nsg2
ENSMUSG00000020312	−1.00540614314908	0.000391996943360625	0.0305686317569126	Shc2
ENSMUSG00000020431	1.96796606985924	6.46758705319593E-10	4.91774895565903E-07	Adcy1
ENSMUSG00000020524	2.84377884163274	4.66088568988906E-10	3.74087864232374E-07	Gria1
ENSMUSG00000020598	1.8733624585735	2.54599463217856E-05	0.00391297706926422	Nrcam
ENSMUSG00000020734	3.80091147094916	0.000314538785304201	0.0263844304144133	Grin2c
ENSMUSG00000020886	1.08901701712808	4.53517449081226E-06	0.000897529669435132	Dlg4
ENSMUSG00000020932	2.80885910294522	1.87495993140264E-09	1.23125209677154E-06	Gfap
ENSMUSG00000021087	1.24561723369848	7.87005087512102E-05	0.0088149803563991	Rtn1
ENSMUSG00000021268	1.0063524022921	1.29897824273573E-09	9.20940872370846E-07	Meg3
ENSMUSG00000021313	1.91135237099055	0.000155901512002559	0.0152183050263579	Ryr2
ENSMUSG00000021700	3.68810770485233	2.58370509206527E-07	9.10409450367486E-05	Rab3c
ENSMUSG00000021815	−1.74012839735835	6.87605953787393E-05	0.00794707457149317	Mss51
ENSMUSG00000022055	3.13745634045097	1.10065266602502E-06	0.000311786844432617	Nefl
ENSMUSG00000022208	2.23648740305708	6.49550181064979E-05	0.00778567093142505	Jph4
ENSMUSG00000022212	2.44014658455657	4.57493187138975E-07	0.00014687564610215	Cpne6
ENSMUSG00000022421	1.26544739021926	7.87106296099871E-05	0.0088149803563991	Nptxr
ENSMUSG00000022454	3.04211213788244	4.27707642999427E-10	3.6347601873016E-07	Nell2
ENSMUSG00000022456	1.68860921641444	0.000349399856073764	0.0278882857497109	Sept3
ENSMUSG00000022619	2.0045208246076	0.000110668861440662	0.0113392414271861	Mapk8ip2
ENSMUSG00000023011	2.55245445635137	1.10532717195524E-05	0.00192393513894426	Faim2
ENSMUSG00000023033	2.73466201981119	4.0227254223627E-05	0.00543143123148355	Scn8a
ENSMUSG00000023236	2.94357986341824	4.7098756615728E-05	0.00607531907881626	Scg5
ENSMUSG00000024109	2.64409515626596	1.39570917105191E-07	5.4496784849154E-05	Nrxn1
ENSMUSG00000024268	1.9158738534581	6.57285385819634E-05	0.00778567093142505	Celf4
ENSMUSG00000024803	1.03721417192182	4.70237001968261E-05	0.00607531907881626	Ankrd1
ENSMUSG00000025318	3.5949322999362	3.39532119852713E-05	0.00495476821768904	Jph3
ENSMUSG00000025352	−1.99744162149255	3.04942396190925E-16	8.81100559554058E-13	Gdf11
ENSMUSG00000025461	−3.85327046103513	0.000559388883150561	0.0386674219850534	Cd163l1
ENSMUSG00000025582	2.58220488371184	2.12215070592863E-06	0.00048664621029446	Nptx1
ENSMUSG00000025777	−1.01605287400455	8.6078683858452E-06	0.00157415031101653	Gdap1
ENSMUSG00000025867	1.82756865150178	4.91789585684914E-11	5.71349002592036E-08	Cplx2
ENSMUSG00000025887	−1.23654011704177	7.60317394801406E-05	0.00864905937220151	Casp12
ENSMUSG00000026204	1.6778269485563	0.000285571276648247	0.0247044804415403	Ptprn
ENSMUSG00000026442	1.66672892556274	5.65430661268849E-05	0.00699347248419322	Nfasc
ENSMUSG00000026459	1.04427975636359	8.83376348716179E-05	0.00952398366410644	Myog
ENSMUSG00000026959	3.50816215432866	1.96391401463164E-05	0.00315251841882037	Grin1
ENSMUSG00000027016	−1.92356958566623	1.1775363819023E-06	0.000327151309795048	Zfp385b
ENSMUSG00000027273	3.55124413200266	2.22337832445036E-17	1.07070488844448E-13	Snap25
ENSMUSG00000027347	2.41671348931434	4.80732697125943E-08	2.31267416027797E-05	Rasgrp1
ENSMUSG00000027350	2.85099538685297	4.48772839482161E-07	0.00014687564610215	Chgb
ENSMUSG00000027581	2.58571801745361	0.000348010647578194	0.0278882857497109	Stmn3
ENSMUSG00000027827	−1.51746784520151	4.96247656735773E-08	2.31267416027797E-05	Kcnab1
ENSMUSG00000028197	−1.31460639929624	3.20242056175674E-05	0.00476962575831955	Col24a1
ENSMUSG00000028782	2.33267278791182	1.26230879077349E-06	0.000338106008878108	Adgrb2
ENSMUSG00000028785	2.94136432507872	9.37631705435349E-14	1.50510724982494E-10	Hpca
ENSMUSG00000028931	1.28801562241816	0.00022725255191114	0.0212684428859409	Kcnab2
ENSMUSG00000029053	1.18344682183387	0.000235277298824065	0.0216791991772812	Prkcz
ENSMUSG00000029120	1.6484681050789	2.19049466783897E-05	0.00347759082046919	Ppp2r2c
ENSMUSG00000029608	3.50795433818651	9.26969672647432E-05	0.00984700798583637	Rph3a
ENSMUSG00000030000	4.29157119686422	1.52313377643959E-06	0.000379391614969359	Add2
ENSMUSG00000030209	3.56968633833958	3.33487324634411E-07	0.000114711699499841	Grin2b
ENSMUSG00000030428	1.67100390104071	1.99487764699904E-06	0.000464838667196696	Ttyh1
ENSMUSG00000030683	1.86829578341036	0.000379820480291644	0.0298604218940448	Sez6l2
ENSMUSG00000031028	1.74588305800165	0.000636342833065882	0.0433013687647912	Tub
ENSMUSG00000031144	1.00961562774746	0.000632016934736772	0.0432736903134699	Syp
ENSMUSG00000031428	2.30797648869033	8.31357685433235E-05	0.00916841563469767	Zcchc18
ENSMUSG00000032093	−1.87704124571394	7.20433920538098E-08	3.15397237879209E-05	Cd3e
ENSMUSG00000032181	1.92481296798847	0.000435601753870794	0.0331217817798493	Scg3
ENSMUSG00000032315	1.35246696597093	0.000638415695085521	0.0433013687647912	Cyp1a1
ENSMUSG00000032356	2.36659842171692	0.000237206178059465	0.0216791991772812	Rasgrf1
ENSMUSG00000032482	2.82020845305947	8.64118470948204E-05	0.00945751481044598	Cspg5
ENSMUSG00000032517	3.0529974001845	3.1609113314385E-08	1.69132170389971E-05	Mobp
ENSMUSG00000032532	3.95544149707296	3.30655579507199E-05	0.00487447056851072	Cck
ENSMUSG00000032883	−1.06045913609506	3.99232096967968E-05	0.00543143123148355	Acsl3
ENSMUSG00000032936	1.55706624888584	0.000727788505743047	0.0482310116627055	Camkv
ENSMUSG00000033419	2.43620382211611	0.000238595740928061	0.0216791991772812	Snap91
ENSMUSG00000033615	2.77350090688328	1.21433040102407E-07	4.97645795140938E-05	Cplx1
ENSMUSG00000033676	2.630730749922	0.000172028936029793	0.0165686802588161	Gabrb3
ENSMUSG00000033981	2.81269429002697	3.01594077785117E-11	3.96102694705598E-08	Gria2
ENSMUSG00000034156	2.55910861592633	0.0003205335257535	0.0266134933710392	Tspoap1
ENSMUSG00000034730	2.25551452161989	0.000235872076680839	0.0216791991772812	Adgrb1
ENSMUSG00000034818	2.85884349707036	0.000282889354885479	0.024619894638738	Celf5
ENSMUSG00000035864	3.04350332002376	7.05159530615325E-19	5.0937198693998E-15	Syt1
ENSMUSG00000035963	−1.6134976836155	3.66485000217388E-06	0.000735362333075084	Odf3l2
ENSMUSG00000036564	1.77692946521505	2.60780297647572E-08	1.44903575389018E-05	Ndrg4
ENSMUSG00000036902	−1.04509014812749	0.000217505667669637	0.020673055136995	Neto2
ENSMUSG00000037217	2.55218286998815	5.35568506003525E-05	0.00672813757063733	Syn1
ENSMUSG00000037406	−1.78723525592021	0.000251145428970192	0.0223969013106936	Htra4
ENSMUSG00000038486	2.12709882089011	4.70493133986161E-05	0.00607531907881626	Sv2a
ENSMUSG00000038526	1.1434271371564	3.24809037705737E-06	0.000690075907019822	Car14
ENSMUSG00000038738	1.64228042760882	0.000155136489405606	0.0152183050263579	Shank1
ENSMUSG00000039809	2.90780358664025	1.42008132922078E-05	0.00238557150735495	Gabbr2
ENSMUSG00000040035	2.76978447672105	3.89133638245575E-05	0.00543143123148355	Disp2
ENSMUSG00000040907	2.32228897322867	1.59723808781127E-14	3.29647123637278E-11	Atp1a3
ENSMUSG00000041220	2.11589779511973	0.000115301773225769	0.0117307374492443	Elovl6
ENSMUSG00000041773	1.43325637417727	6.77023249592749E-07	0.000199611324221764	Enc1
ENSMUSG00000041986	2.76018412900659	6.75822930741176E-05	0.00787388216162723	Elmod1
ENSMUSG00000043531	−1.43008112190141	0.000301515135697811	0.0258301448488518	Sorcs1
ENSMUSG00000043557	−1.55705528384814	3.57859822226316E-06	0.000735362333075084	Mdga1
ENSMUSG00000044349	1.68226286038953	1.67645675531775E-08	9.68790829763019E-06	Snhg11
ENSMUSG00000044468	1.03339882100358	0.000403741342523784	0.0310258041246867	Fam46c
ENSMUSG00000044667	2.88288223917943	2.66519235068019E-05	0.00405305619897649	Plppr4
ENSMUSG00000045589	2.556001998243	1.7908762138936E-05	0.00297388375426677	Frrs1l
ENSMUSG00000045763	1.02842668186396	0.00051651426228938	0.0369757934140852	Basp1
ENSMUSG00000046093	3.08156245334489	5.14123142084617E-11	5.71349002592036E-08	Hpcal4
ENSMUSG00000047842	2.55145899344982	8.1425899349318E-05	0.0090489228299969	Diras2
ENSMUSG00000048939	−1.15336106403371	5.2827669671715E-07	0.000165913335597232	Atp13a5
ENSMUSG00000048978	3.60524654287299	9.01354936434219E-05	0.00964583316049272	Nrsn1
ENSMUSG00000049583	4.20074825953766	8.56930857534841E-06	0.00157415031101653	Grm5
ENSMUSG00000053963	1.51743464380402	3.62988667518395E-06	0.000735362333075084	Stum
ENSMUSG00000054459	3.13482491576958	1.753637940113E-07	6.4792412377364E-05	Vsnl1
ENSMUSG00000055022	2.88171341164836	1.8254218602354E-06	0.000432325731390506	Cntn1
ENSMUSG00000055567	2.76006460174016	3.62311976697637E-06	0.000735362333075084	Unc80
ENSMUSG00000056553	2.98394907931859	6.70713507220985E-05	0.00787388216162723	Ptprn2
ENSMUSG00000058153	2.69048409820676	0.00051110466137187	0.0369502421688461	Sez6l
ENSMUSG00000058589	2.94785748377086	4.84239488384578E-05	0.00613809497844098	Anks1b
ENSMUSG00000059003	3.2571600731408	1.36079274349108E-06	0.000351060227950278	Grin2a
ENSMUSG00000059213	2.72408712461625	8.31055562111919E-17	3.00156492645772E-13	Ddn
ENSMUSG00000059456	1.29303661485755	0.000393560289803125	0.0305686317569126	Ptk2b
ENSMUSG00000060371	2.81707908791413	5.66371067107778E-05	0.00699347248419322	Caln1
ENSMUSG00000061080	1.33988674848738	0.000303574381216027	0.0258301448488518	Lsamp
ENSMUSG00000061808	1.78437054783808	1.3386695036885E-09	9.20940872370846E-07	Ttr
ENSMUSG00000061911	2.55028086848528	0.000303947160261979	0.0258301448488518	Myt1l
ENSMUSG00000062044	3.02527506219179	0.00022744174374227	0.0212684428859409	Lmtk3
ENSMUSG00000062591	2.37224418377499	4.77837174418781E-08	2.31267416027797E-05	Tubb4a
ENSMUSG00000067889	1.30939574370528	2.47351595753271E-05	0.00384246075682528	Sptbn2
ENSMUSG00000068606	−1.31903129697675	3.8591115896758E-08	1.9911637548588E-05	Gm4841
ENSMUSG00000070570	3.06083385041898	2.72764786545795E-14	4.92579108903387E-11	Slc17a7
ENSMUSG00000070880	3.5522888359932	2.94435058495066E-05	0.00443094092716481	Gad1
ENSMUSG00000074657	2.15888519200472	8.03766471888117E-16	1.93533570322794E-12	Kif5a
ENSMUSG00000090877	1.34052076498087	0.000519246824454012	0.0369757934140852	Hspa1b
ENSMUSG00000097545	4.15524763795485	5.35853641770728E-06	0.00104614561657591	Mir124a-1hg
ENSMUSG00000102437	−2.42817391161126	6.43848896681571E-07	0.000193785104382472	Gm38048
ENSMUSG00000102752	−1.54992315400369	0.000487776668618442	0.0359536200588297	Gm7694
ENSMUSG00000111203	−1.83843059483812	1.26377271955547E-06	0.000338106008878108	Gm48719

A bi-clustering heatmap of those top 30 differentially expressed genes sorted by their padj values demonstrated a clear clustering according to the exercise treatment (Figure 2B). It further visualized co-upregulations of genes involved in neural functions and structures, particularly those in synaptic and neurotransmitter receptor components, as well as neurotransmitter metabolism in the exercise group. The absence of expected upregulation in the metabolic gene cluster is also notable.

Analysis of biological processes

In order to further characterize the effect of exercise on biological processes in aged skeletal muscle, gene ontology enrichment analysis was performed. The genes identified as significantly differentially expressed were clustered by their gene ontology, and Fisher exact test (GeneSCF v1.1-p2) was performed to identify the gene ontologies with a significant enrichment, defined by having an adjusted p-value less than 0.05 (Figure 3, top 40 enriched GO terms). As anticipated by the gene co-upregulations shown in the heatmap, there were significant enrichments in the processes involved in neural signaling, particularly in the regulation of synaptic potential, chemical synaptic transmission, neurotransmitter secretion and components of neurotransmitter receptors such as glutamate receptors. In order to validate this finding, the following genes described by the most enriched GO terms were selected for further gene expression analysis by quantitative RT-qPCR: Dlgap1, SNAP25, Gria1, Grin1, Gabbr2, Cacna1e. The expression levels of those genes were found to be significantly increased in the exercise group by 2-7 folds compared to the sedentary group (Figure 4), consistent with the results of DESeq2 analysis. Other enriched GO terms of particular note were locomotion, locomotor behavior and regulation of synaptic plasticity. These may describe broader biological implications of the observed gene upregulations in the neurotransmission and excitation. Interestingly, some enrichments were also observed in the GO terms describing central nervous system functions, such as visual learning, memory, and social behavior. These enrichments likely reflect the transcriptional upregulations in the receptor and transporters of glutamate, a known neurotransmitter and neuromodulator responsible for synaptic plasticity in the CNS, rather than novel functions of those genes in skeletal muscle.

Figure 3:

Gene expression levels of of Dlgap1 (2A), Snap25 (2B), Gria1 (2C), Grin1 (2D), Gabbr2 (2E), and Cacna1e (2F) analyzed by RT-qPCR

Figure 4: Top 40 enriched GO terms.

The genes identified as significantly differentially expressed were clustered by their gene ontology, and Fisher exact test (GeneSCF v1.1-p2) was performed to identify the gene ontologies with a significant enrichment, defined by having an adjusted p-value less than 0.05.

Histological evaluation of Neuromuscular Junctions

The results of the differential gene expression and gene enrichment analyses demonstrated enhanced expression of genes in the components and regulation of neurotransmission. For example, components of neurotransmitter receptors were significantly upregulated, particularly in those that make up glutaminergic receptors (Gria1 and Gria2 encoding glutamate receptor 1 and 2 respectively, and Grin1 and Grin2a encoding NMDA receptor). The significant enrichment in the corresponding GO terms such as positive regulation of excitatory postsynaptic potential, ionotropic glutamate receptor pathway, and neurotransmitter secretion likely represent some functional or/and structural alteration at synapses following exercise. In order to examine the effects of these gene expression changes on neuromuscular synapses, neuromuscular junction (NMJ)s were histologically assessed by staining neurofilaments and the end-plate postsynaptic receptors for acetylcholine (ACh), the principal neurotransmitter at skeletal NMJs in mouse and human. There was a 2-fold increase in the number of acetylcholine receptor per field (p<0.001) (Figure 5A). In addition, changes were observed in the degree of innervation, with the exercise group exhibiting a significantly higher percentage of complete and partially innervated NMJs as indicated by AChRs associated with neurofilaments compared to the sedentary group (46% and 72%, respectively, p=0.007). The post-synaptic density of NMDARs was also 2.5 folds higher in the exercise group compared to that of the sedentary group (Sedentary:125.4±17.2 per field, Exercise: 316.7±61.7 per field, p<0.0001) (Figure 5C). Figure 4D and E are representatives images showing the post-synaptic densities of AChRs with neurofilament and NMDARs, respectively.

Figure 5. Histological evaluation of NMJs.

Histological evaluation of NMJs in gastrocnemius muscle revealed increased post-synaptic density of AChRs and their innervation in the exercised gastrocnemius muscle (4A, 4B and 4D). There was also an increase in the density of NMDARs following exercise (4C and 4E). (AChRs: red, neurofilament: green, NMDARs: blue. Error bar = 100 μm)

Confirmation of the efficacy of the exercise regime

As a marker for response to exercise, capillary-to-fiber ratio in the hind limb skeletal musculature and blood flow to the leg were evaluated (27). Following a training regimen, old mice exhibited significant increases in both capillary-to-fiber ratio (Sedentary: 1.56±0.14, Exercise: 2.60±0.13, p=0.01, Figure 6) and blood flow measured as mean flux intensity by doppler ultrasound (Sedentary: 548.8±63.1, Exercise: 900.5±43.5, p=0.02) at the level of the thigh, suggesting a response to the training protocol.

Figure 6: Capillary density in aged gastrocnemius muscle following regimented exercise or sedentary activities.

Following a training regimen, old mice exhibited significant increases in both capillary-to-fiber ratio. Capillaries are shown yellow, laminin demarcating individual myofibers are shown red. Error bar = 100 μm

Metabolic responses to exercise in the aged skeletal muscle

As mentioned above, there was a notable absence of gene upregulation in the energy metabolism pathways in aged skeletal muscle following exercise. For instance, this was true for the genes encoding PPARγ coactivator-1α (PGC-1α), and hexokinase II (HKII), which are known regulators of mitochondrial biogenesis and oxidative metabolism. Consistent with these results, immunoblotting demonstrated that there was no significant difference in the protein levels of PGC-1α in gastrocnemius muscle between the exercise and sedentary groups (Figure 7). In addition, there was no significant differences between the two groups in the gastrocnemius fiber type composition (type I: 4.03 0.36 vs 3.10 0.57, p = 0.32; type IIa: 28.45 0.58 vs 27.17, p = 0.63; type IIb: 51.23 1.37 vs 48.77 1.80, p = 0.50) after the completion of the 8-week regime (Figure 8). As shown in Table 4, the animal weight (g), lean % of body weight, fat % of body weight, and gastrocnemius muscle were similar between the sedentary and exercised groups both before and after the 8-week regime.

Figure 7: The protein levels of PGC-1 alpha in aged gastrocnemius muscle following regimented exercise or sedentary activities quantified by immunoblotting.

There were no significant differences in the PGC-1 alpha levels between the two groups.

Figure 8: Fiber types of aged gastrocnemius muscle following regimented exercise or sedentary activities.

Gastrocnemius muscle sections from the sedentary and exercised groups were stained for tyoe I and type II fibers (8A). Type I fiber type is shown red, type IIa green and type IIb blue. Yellow shows laminin demarcating the boarders of individual myofibers. As shown in the graphs, there were no significant differences in the fiber type compositions between the two groups (8B). Error bar = 200 μm

Table 4.

The summary of animal body compositions

		Sedentary	Exercised	P-value
Body weight (g)	Before (Mean ± SEM)	35.00 2.44	36.67 0.72	0.62
	After Mean ± SEM	33.67 2.33	35.33 0.27	0.59
Fat (%)	Before Mean ± SEM	17.07 1.15	17.48 1.18	0.85
	After Mean ± SEM	19.20 1.43	16.38 1.86	0.27
Lean mass (%)	Before Mean ± SEM	78.93 0.53	77.10 1.75	0.46
	After Mean ± SEM	80.08 1.37	81.34 1.80	0.66
Gastrocnemius weight (mg)	Before (Mean ± SEM)	-	-	-
	After Mean ± SEM	140.67 11.60	125.67 4.25	0.38

Discussion

Exercise can have positive effects on one’s physical functions across all ages. However, response to exercise seem to change with aging, and this may be due to altered transcriptional response underlying muscular adaptation to exercise. In young, exercise is known to induce various metabolic responses in skeletal muscle (2, 3, 5), and the corresponding transcriptional upregulations in the anabolic pathways have been widely observed in both healthy human and rodent models (11,13,14,16). In particular, mitochondrial biogenesis and mitochondrial oxidative responses are a part of both immediate and long-term adaptations to exercise (28, 29). Therefore, gene expression, protein levels and activities of the key regulators such as AMPK, PPARγ coactivator-1α (PGC-1α), and hexokinase II (HKII) are regarded as reliable markers of metabolic adaptation to exercise. In aged muscle, however, there was an absence of transcriptional upregulation in those pathways, indicating metabolic resistance to exercise in aged skeletal muscle. This was further confirmed by the protein levels of PGC-1α that remained unchanged following exercise, as demonstrated by immunoblotting. Metabolic resistance likely results in restricted physiological adaptations that rely on those pathways, such as increase in muscle mass and endurance. Evidently, a number of previous studies have consistently demonstrated limited or no improvements in the overall exercise endurance after various exercise regimes in the elderly (18, 30, 31). In consistent with the results above, there was no significant difference between the two groups in the muscle fiber type composition, indicating the absence of exercise-induced fiber type switching controlled by the transcriptional regulators of oxidative and glycolytic metabolism including PGC-1α (32).

Instead, there seem to be an array of transcriptional responses that suggest enhanced neuronal functions or activities within the exercised muscle of old mice. In particular, there were significant upregulations in genes involved in the regulation of synaptic transmission. For example, the transcriptional levels of Dlgap1 encoding a postsynaptic scaffold protein, SNAP25 encoding t-SNARE regulating neurotransmitter release, and Cacna1e encoding a voltage-dependent calcium channel regulating neurotransmitter release and muscle contraction were more than two folds higher in the exercise group compared to the sedentary group. Cholinergic neurotransmission is considered to be the main mode of synaptic transmission at NMJs in vertebrates including mice. While there was no significant upregulation in the genes encoding AChRs themselves, histological analysis of NMJs revealed that there was a significant increase in the density of post-synaptic AChRs and their innervation at the motor end-plates in the exercised muscle. This is consistent with the previous study demonstrating that free physical activities protected NMJs from degradation and fragmentation in old mice (19). On the other hand, evidence for enhanced glutaminergic transmission following exercise is compelling, with significant upregulations in glutaminergic receptors (Gria 1, Gria 2 and Grin2c encoding glutamate receptor 1, 2 and 2C respectively, and Grin1 and Grin2b encoding NMD A receptor), glutaminergic receptor clustering (Nptx1) and glutamate uptake (Slc1a2 and Slc17a7). While glutamate is considered to be predominantly a neurotransmitter of the central nervous system in vertebrates, the possibly that glutamate is also released at NMJs is highly suggested by the presence of the relevant receptors, transporters as well as molecules involved in the downstream signaling of glutamate at NMJs (33, 34, 35, 36). The role of glutamate signaling at NMJs in adult is unclear. However, considering its known role as a positive regulator of cholinergic synapses and synaptic plasticity in skeletal muscle during development, it is possible that similar effects may be observed in aged muscle following exercise. In particular, increase in the post-synaptic density of NMDARs and AChRs at the motor end-plate following exercise may mirror the developmental process whereby selective stabilization of synapses occurs through the coactivation of post-synaptic AChRs and NMDRs in skeletal muscle (37). Indeed, recent study in adult zebrafish demonstrated that glutamate is co-released with acetylcholine at NMJs, and its gene expression is specifically upregulated in NMJs of fast muscles following exercise and during regeneration after injuries (34). Taken together, these findings indicate that glutaminergic transmission may be present in NMJs of mice and other vertebrates, and exercise-induced upregulation in this process may be involved in protection of cholinergic NMJs and/or remodeling of neuronal input to skeletal muscle in aged mice. The exact phenotypical implications of these changes are unclear. However, the clues may lie in the GO terms with significant enrichment, such as “synaptic plasticity” and “locomotion”; these gene expression changes may underlie the observed improvements in skeletal muscle function and locomotion of aged organisms, even in the absence of an accompanying increase in anabolism. This study utilized 24-month old mice, which are physiologically similar to humans approximately 70 years of age. This is an important time in humans that marks the beginning of an accelerated loss of skeletal muscle mass and decreased physiologic response to exercise (31), making the results of this study clinically relevant to exercise training for elderly patients. Evidently, reduction in falls and fall-related injuries are one of the recognized benefits of exercise in the elderly (38), and the maintenance of functional independence is one of the most valuable benefits elderly patients can derive from exercise.

Although the primary aim of the present study to describe exercise-induced transcriptional changes that occur in skeletal muscle of old mice has been addressed, there are some limitations. Firstly, the lack of young groups limits the scope of this study. Comparison to the exercise-induced changes that occur in young mice would further outline some of the age-specific factors that underlie the aging-related changes in the physiological response to exercise. In particular, the causes and implications of the shift in adaptive response to exercise from metabolic to neural with aging remains to be elucidated. Further studies in future that considers this difference will be able to answer this related yet distinct question. Secondly, characterization of glutaminergic transmissions at vertebral NMJs, particularly in relation to exercise, deserves further investigations. In addition, the “snapshot” at the 8-week timepoint offered by this study does not describe the dynamic changes in gene expression profile over the period of exercise. Characterizing how these changes in transcriptional activities correspond to the presumed neuronal remodeling during and after exercise will better illustrate the mechanisms underlying the process. Thirdly, we must consider the possibility that the absence of some of the expected changes, especially in the metabolic pathways may reflect decreased sensitivity to exercise stimuli. A moderate exercise regime was chosen to best reflect the exercise regime that is prescribed to elderly human patients, who are generally unable to perform exercise of greater intensities. Although there are no direct human-mouse comparisons in terms of exercise intensity, this regimen reflected the upper ranger of the exercise tolerable by 24-month-old mice. This, however, prevents us from examining the effects of higher intensity of exercise using this model. On the other hand, the observed increase in blood flow to the hind limbs as well as the capillary density in gastrocnemius muscle indicate that exercise of this intensity was indeed efficacious in inducing physiological responses in the mice. Finally, although a statistical significance for the pathways and genes of our interest using both RNA sequencing and PCR have been reached, we acknowledge the small sample size (n = 3 per group) to be one of the limitations of this study.

In summary, the results of the present study described a distinct pattern of gene expression changes following exercise, characterized by upregulations in glutaminergic and other synaptic transmissions in aged muscle. In addition, lack of transcriptional upregulations in the anabolic pathways was equally notable, and may underlie anabolic resistance observed in the aged muscle. In older patients, functional benefits derived from exercise may depend largely on synaptic protection and/or neural remodeling in skeletal muscle, and effective targeting of its anabolic resistance may be required for optimizing exercise-led therapies.

Figure 1.

Method summary

Figure 9.

Summary of the study

Highlights.

• Response to exercise changes with aging, with a shift from a predominantly anabolic response resulting in limited gain of muscle strength and endurance

• These changes likely reflect age-dependent alterations in transcriptional response underlying the muscular adaptation to exercise.

• Differential gene expression analysis of aged skeletal muscle revealed upregulations in the group of genes involved in neurotransmission and neuroexcitation

• These changes were accompanied by an increase in the post-synaptic density of NMDARs and acetylcholine receptors (AChRs), as well as their innervation at neuromuscular junctions (NMJs)