We’re sorry, something doesn't seem to be working properly.

Please try refreshing the page. If that doesn't work, please contact us so we can address the problem.

Chronic binge alcohol administration dysregulates global regulatory gene networks associated with skeletal muscle wasting in simian immunodeficiency virus-infected macaques

  • Liz Simon1, 2,
  • Andrew D. Hollenbach3,
  • Jovanny Zabaleta4 and
  • Patricia E. Molina1, 2Email author
Contributed equally
BMC Genomics201516:1097

https://doi.org/10.1186/s12864-015-2329-z

Received: 27 May 2015

Accepted: 15 December 2015

Published: 23 December 2015

Abstract

Background

There are more than 1 million persons living with HIV/AIDS (PLWHA) in the United States and approximately 40 % of them have a history of alcohol use disorders (AUD). Chronic heavy alcohol consumption and HIV/AIDS both result in reduced lean body mass and muscle dysfunction, increasing the incidence of comorbid conditions. Previous studies from our laboratory using rhesus macaques infected with Simian Immunodeficiency Virus (SIV) demonstrated that chronic binge alcohol (CBA) administration in the absence of antiretroviral therapy exacerbates skeletal muscle (SKM) wasting at end-stage SIV disease. The aim of this study was to characterize how CBA alters global gene regulatory networks that lead to SKM wasting at end-stage disease. Administration of intragastric alcohol or sucrose to male rhesus macaques began 3 months prior to SIV infection and continued throughout the duration of study. High-output array analysis was used to determine CBA-dependent changes in mRNA expression, miRNA expression, and promoter methylation status of SKM at end-stage disease (~10 months post-SIV) from healthy control (control), sucrose-administered, SIV-infected (SUC/SIV), and CBA-administered/SIV-infected (CBA/SIV) macaques.

Results

In addition to previously reported effects on the extracellular matrix and the promotion of a pro-inflammatory environment, we found that CBA adversely affects gene regulatory networks that involve “universal” cellular functions, protein homeostasis, calcium and ion homeostasis, neuronal growth and signaling, and satellite cell growth and survival.

Conclusions

The results from this study provide an overview of the impact of CBA on gene regulatory networks involved in biological functions, including transcriptional and epigenetic processes, illustrating the genetic and molecular mechanisms associated with CBA-dependent SKM wasting at end-stage SIV infection.

Keywords

Chronic binge alcohol administration SIV Skeletal muscle wasting High-output array analysis Gene regulatory networks Epigenetic regulation

Background

An estimated 1.15 million persons are living with human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS) (PLWHA) in the United States [1, 2]. With the use of antiretroviral therapy (ART), HIV is now a chronic disease with higher incidence of associated non-AIDS defining illnesses. PLWHA have a higher prevalence of alcohol use disorders (AUDs) than the general population [3]. Chronic heavy alcohol consumption accelerates the progression of HIV/AIDS and contributes to comorbid pathologies seen in PLWHA [4]. Among the different pathophysiological comorbidities that enhance the progression of the disease, decreased muscle mass/function remains a strong and consistent predictor of mortality, with the frequency of low skeletal muscle (SKM) mass detected at a much younger age in PLWHA compared to that of the general population [5, 6].

Chronic heavy alcohol consumption and HIV both result in significant SKM derangements such as atrophy, weakness, and dysfunction [5, 710]. However, there are few reports that describe the comorbid effects of chronic alcohol consumption and HIV on SKM biology [1113]. Our previous studies provided evidence that chronic binge alcohol (CBA) administration accentuates metabolic derangements [11, 12, 14, 15], leading to a marked decrease in SKM mass (SAIDS wasting) and dysfunctional skeletal muscle phenotype, thereby accelerating the time to end-stage disease in SIV-infected macaques [12]. Decreased mass and dysfunctional SKM phenotypes are associated with changes in gene expression levels — changes that were functionally correlated to the generation of an inflammatory milieu, dysregulation of components of the ubiquitin proteasome system, increased proteasomal activity, depletion of SKM anti-oxidant capacity, and an increased expression of pro-fibrotic genes [13, 14]. In subsequent studies we further demonstrated the relevance of these observed gene expression changes by showing that CBA impairs the differentiation potential of myoblasts to form myotubes, suggesting that SKM regeneration is adversely affected [16]. Taken together, our studies provide strong evidence that CBA accelerates the loss of SKM mass and impairs regeneration potential, which we predict would decrease quality of life and increase morbidity and mortality among PLWHA.

In order to understand the underlying genetic and molecular mechanisms that contribute to CBA-dependent SKM wasting at end-stage SIV, we previously examined the pattern of dysregulation of several key genes involved in biological processes required for SKM homeostasis [13, 14, 16]. Although these studies were informative, they provided only part of the larger picture, since CBA/SIV produces multi-systemic and dynamic changes that disrupt finely tuned and highly integrated global gene regulatory networks. These networks include multiple molecular processes, including the initiation of transcription and epigenetic mechanisms such as methylation of promoters and the post-transcriptional regulation by microRNAs. Therefore, the global analysis of the transcriptomic and epigenomic profiles from tissues derived from CBA/SIV whole animal studies was considered to be invaluable for understanding how CBA disrupts the interaction, integration, and function of these global gene regulatory networks to contribute to SKM wasting at end-stage SIV infection.

In this study we utilized high-output microarray gene analysis using RNA and/or DNA isolated from SKM tissue derived from CBA-administered rhesus macaques at end-stage SIV infection to determine CBA-dependent changes in mRNA expression, miRNA expression, and promoter methylation status. Our results provide a model that describes the impact of CBA on global transcriptional and epigenetic networks that disrupt the complex interplay between biological functions, thereby disrupting normal SKM function. In addition to the previously described changes in expression of genes that promote a pro-inflammatory environment and impair the integrity and composition of the extracellular matrix (ECM) [13], our results showed changes in gene regulatory networks that disrupt calcium and ion homeostasis, neuromuscular junction (NMJ), satellite cell growth and survival, “universal” functions of the cell (e.g., glycolysis) and protein homeostasis. This is the first report to describe the effects of CBA on SKM gene regulatory networks, providing a more comprehensive picture of the genetic and molecular mechanisms underlying CBA-dependent SKM wasting at end-stage SIV infection.

Results

Chronic binge alcohol-dependent mRNA expression changes

A total of 431 genes were significantly increased and 404 genes were significantly decreased in a CBA-dependent manner. Functional enrichment analysis was performed using GSEA as described in methods. In the CBA/SIV group, there were 74 gene sets that were upregulated among 111 gene sets. Of these 74 gene sets 36 were significant at a false discovery rate (FDR) < 25 % and of these, 27 gene sets are significantly enriched at a p-value < 0.05. (Fig. 1a, Additional file 1: Table S1). Pathways associated with transcription, programmed cell death, response to stress, protein kinase cascade and others were enriched in the CBA/SIV group (Fig. 1b). In the SUC/SIV group, there were 37 upregulated gene sets among 111 total gene sets (Additional file 1: Table S1). However, there were no gene sets that were significantly enriched according to the FDR or p-value.
Fig. 1

Functional enrichment of CBA-altered mRNAs at end-stage SIV disease. a The enriched pathways analyzed by gene set enrichment analysis revealed significant association of genes of CBA/SIV group in fifteen main gene sets. b Three representative plots of enriched gene sets in the CBA/SIV group. Enrichment score (ES) is the degree to which a gene set is overrepresented at the top or bottom of a ranked list of genes. NES is normalized enrichment score. Nominal p-value (NOM p-val) estimates the statistical significance of the enrichment score for a single gene set. False discovery rate (FDR) is the estimated probability that a gene set with a given NES represents a false positive finding

In addition to previously described changes in genes affecting the inflammatory response, oxidative stress response, ECM reorganization [13], and enzymes involved in the ubiquitin mediated degradation of proteins and initiation of translation [14, 17], we found that CBA altered the expression of genes in five additional distinct biological categories: (1) “universal” cellular functions, (2) protein homeostasis, (3) calcium and ion homeostasis, (4) muscle and neuromuscular junction (NMJ) function, and (5) myogenesis, encompassing muscle satellite growth and survival.

“Universal” cellular functions category

One hundred twenty three genes encoding enzymes that can be viewed as having “universal” cellular functions were significantly different in the CBA/SIV group. Of these genes, 18 encode proteins important for the production of energy, including 5 of the upregulated genes required for the metabolism of alcohol and 10 of the downregulated genes involved in the glycolytic pathway and glycogen degradation, connecting glycolysis to the TCA cycle and the respiratory pathway. CBA also altered the expression of genes required for histone remodeling and modification (9 genes), multifunctional transcriptional regulators (27 genes), general kinases and phosphatases (22 genes), lipid biosynthesis and degradation (11 genes), mitochondrial function (12 genes), along with other individual genes important for the general functioning of the cell.

Protein homeostasis category

CBA/SIV altered the expression of 39 genes important for the synthesis of amino acids and nucleic acids, enzymes essential for the proper charging of tRNA and ribosome biogenesis, proteins utilized in the splicing and maturation of mRNA, and multifunctional transcription factors that regulate the initiation of transcription (Additional file 2: Table S2). Further, 11 genes required for the trafficking of proteins through the endoplasmic reticulum and Golgi apparatus and the glycosylation of proteins in these cellular compartments had altered expression, suggesting that the effects of CBA on protein homeostasis not only encompass the initiation of translation and ubiquitin-mediated protein degradation, but also involve all aspects from the generation of components required for synthesis to the proper post-translational modifications required for functional mature proteins.

Calcium and ion homeostasis category

CBA altered the expression of 25 genes important for calcium homeostasis and 23 genes that encode ion channels. We also found that CBA increased the expression of two calcium-dependent cadherins, which are important for muscle development, and one calcium-dependent gene necessary for neuronal development.

Muscle and NMJ function category

Among the 13 differentially expressed genes involved in muscle differentiation, one encoded a myogenic factor directly involved in promoting or inhibiting myogenic differentiation, myogenic enhancing factor 2C (MEF2C). A CBA-dependent decrease in the expression of genes required for proper contractile function, cell structure and integrity, and cellular expansion was also observed (Additional file 2: Table S2). We found an increased expression of pinin desmosome-associated protein (PNN), a gene that reverses the expression of E-cadherin, an adhesion molecule required for muscle differentiation. Consistent with increased collagen deposition and the pro-fibrotic environment in muscle wasting [13], CBA increased the expression of the gene encoding the transmembrane protein 119 (TMEM119), which drives the differentiation of myoblasts into osteoclasts, a cell type that promotes the deposition of collagen. CBA altered expression of genes important for neuronal survival and regeneration, CNS development, acetylcholine receptor expression, and neuronal organization and orientation, consistent with a CBA-mediated alteration of NMJ and normal muscle function.

Muscle satellite cell growth and survival category

Consistent with our previous findings that CBA impairs skeletal muscle regenerative capacity, the results from this study showed that CBA altered the expression of 38 genes that contribute to the growth, survival, and cell cycle regulation of cells. These include genes important for cell cycle progression, growth factor related signaling pathways, cellular survival and apoptosis, and additional genes (including kinases, transcription factors, and co-regulatory proteins) that mediate or contribute to cellular proliferation and/or survival. We previously published reports that demonstrate the functional relevance of many of these differentially expressed genes [13].

Chronic binge alcohol-dependent mirna expression changes

We identified 35 miRNAs whose expression changed ≥1.5-fold in a CBA-dependent manner. Of these, 14 were downregulated and 21 were upregulated (Fig. 2a). Functional enrichment of predicted target genes of differentially expressed miRNAs was analyzed using miRsystem as described in the methods section. Pathways associated with MAPK signaling, insulin signaling, neuronal signaling, and focal adhesions were enriched (Fig. 2b). The 100 most enriched pathways that included ≥20 differentially expressed miRNAs, ≥20 target genes, and had a score ≥1 are shown in Table 1. Target genes from additional pathways showed a higher representation of proteins important for essential enzymes or cofactors in ubiquitin-mediated degradation of proteins, genes involved in maintenance of the integrity or in the breakdown of the extracellular matrix and muscle contraction (Additional file 3: Table S3).
Fig. 2

Functional enrichment of CBA-altered miRNAs at end-stage SIV disease. a CBA-dependent differentially expressed miRNAs. b Pathways enriched by predicted target genes of differentially expressed miRNAs as determined by miRSystem. c qPCR confirmation of miRNAs and target genes: Relative miRNA expression of miR-34a, miR-10b and miR-20 in the skeletal muscle of control (white bars), SUC/SIV (grey bars), and CBA/SIV (black bars) macaques determined by qPCR. Relative mRNA expression of ESR1, BCL2, and KRAS in the skeletal muscle of control (white bars), SUC/SIV (grey bars), and CBA/SIV (black bars) macaques determined by qPCR. Values are mean ± SEM. *p < 0.05 vs. Control and SUC/SIV

Table 1

CBA-dependent alterations in microRNA expression at end-stage SIV infection

Category

Term

Total genes in term

Union targets in term

miRNAs in term

Score

Reactome

Axon_guidance

266

154

28

3.983

Reactome

Developmental_biology

494

229

28

3.716

KEGG

Pathways_in_cancer

325

173

28

2.951

Reactome

L1cam_interactions

94

61

26

2.77

KEGG

Mapk_signaling_pathway

272

135

29

2.466

KEGG

Wnt_signaling_pathway

150

91

28

2.466

Go_mf

Protein_binding_transcription_factor_activity

369

164

28

2.334

Reactome

Signalling_by_ngf

221

118

26

2.243

PID

Direct_p53_effectors

137

65

27

2.062

KEGG

Focal_adhesion

199

99

27

1.992

Reactome

Hemostasis

467

191

29

1.989

KEGG

Endocytosis

201

102

27

1.944

KEGG

Prostate_cancer

89

49

26

1.941

KEGG

Neurotrophin_signaling_pathway

127

71

27

1.909

PID

Pdgfr-beta_signaling_pathway

126

69

27

1.9

PID

Regulation_of_nuclear_smad2_3_signaling

82

52

27

1.847

Reactome

Neuronal_system

289

120

28

1.837

KEGG

Glioma

65

35

27

1.836

PID

Signaling_events_mediated_by_hepatocyte_growth_factor_receptor_(c-met)

77

50

26

1.74

KEGG

Regulation_of_actin_cytoskeleton

213

100

26

1.73

PID

E2f_transcription_factor_network

73

44

25

1.717

Reactome

Transmission_across_chemical_synapses

190

85

28

1.701

PID

C-myb_transcription_factor_network

81

46

24

1.683

Reactome

Nuclear_receptor_transcription_pathway

51

27

26

1.66

PID

Ephb_forward_signaling

36

27

22

1.619

KEGG

Erbb_signaling_pathway

87

48

27

1.614

KEGG

Melanoma

71

34

27

1.594

KEGG

Melanogenesis

101

55

28

1.582

KEGG

Small_cell_lung_cancer

84

43

26

1.563

PID

Notch_signaling_pathway

59

37

25

1.55

KEGG

Tgf-beta_signaling_pathway

84

47

27

1.545

PID

Tcr_signaling_in_naive_cd4 + _t_cells

64

36

24

1.544

Reactome

Signaling_by_egfr

109

62

25

1.521

KEGG

Pancreatic_cancer

70

41

26

1.518

KEGG

Fc_gamma_r-mediated_phagocytosis

94

47

25

1.506

PID

Coregulation_of_androgen_receptor_activity

57

35

26

1.5

KEGG

Adherens_junction

73

44

26

1.499

KEGG

Tight_junction

132

61

27

1.498

PID

Shp2_signaling

54

34

25

1.485

PID

P73_transcription_factor_network

73

34

25

1.462

Reactome

Rho_gtpase_cycle

124

58

25

1.455

Reactome

Signaling_by_fgfr

114

57

26

1.447

KEGG

Chronic_myeloid_leukemia

73

39

25

1.432

KEGG

Renal_cell_carcinoma

70

40

25

1.428

PID

Ap-1_transcription_factor_network

69

41

25

1.41

Reactome

G1_phase

38

23

22

1.387

KEGG

Dilated_cardiomyopathy

90

42

24

1.384

Reactome

Signaling_by_interleukins

106

47

25

1.371

Reactome

Downstream_signal_transduction

93

53

25

1.354

KEGG

Bacterial_invasion_of_epithelial_cells

70

39

24

1.349

Reactome

Adaptive_immune_system

482

162

28

1.347

PID

Integrins_in_angiogenesis

74

46

25

1.342

KEGG

Ubiquitin_mediated_proteolysis

135

70

24

1.337

PID

Hif-1-alpha_transcription_factor_network

65

35

26

1.337

KEGG

Oocyte_meiosis

112

52

27

1.334

PID

Cxcr4-mediated_signaling_events

102

54

25

1.323

Reactome

Cd28_co-stimulation

31

20

21

1.316

PID

Role_of_calcineurin-dependent_nfat_signaling_in_lymphocytes

55

35

23

1.31

KEGG

Cell_cycle

124

55

25

1.29

PID

Cdc42_signaling_events

70

40

25

1.29

KEGG

P53_signaling_pathway

68

38

24

1.289

PID

Posttranslational_regulation_of_adherens_junction_stability_and_dissassembly

48

29

24

1.28

Reactome

Platelet_activation_signaling_and_aggregation

205

85

27

1.278

PID

Regulation_of_retinoblastoma_protein

64

35

27

1.275

PID

Bcr_signaling_pathway

68

36

25

1.274

PID

Signaling_events_regulated_by_ret_tyrosine_kinase

38

22

22

1.266

Reactome

Circadian_clock

33

20

24

1.254

KEGG

Long-term_potentiation

70

40

25

1.252

Reactome

Cytokine_signaling_in_immune_system

220

77

27

1.236

KEGG

Shigellosis

61

36

23

1.233

KEGG

Vascular_smooth_muscle_contraction

126

49

26

1.21

KEGG

Gnrh_signaling_pathway

101

45

27

1.21

Reactome

Mitotic_g1-g1_s_phases

135

46

25

1.195

PID

Signaling_events_mediated_by_hdac_class_i

67

31

22

1.183

PID

Foxo_family_signaling

49

31

25

1.179

PID

Signaling_events_mediated_by_VEGFR

68

41

24

1.163

KEGG

Insulin_signaling_pathway

137

63

28

1.153

PID

Igf1_pathway

29

21

23

1.147

Reactome

Cell_cycle_mitotic

330

95

27

1.146

PID

Validated_targets_of_c-myc_transcriptional_repression

63

31

25

1.137

Reactome

P75_ntr_receptor-mediated_signalling

86

40

23

1.135

KEGG

Colorectal_cancer

62

38

25

1.13

Reactome

Cell_death_signalling_via_nrage_nrif_and_nade

64

32

23

1.126

Reactome

Transmembrane_transport_of_small_molecules

427

145

28

1.125

PID

Atf-2_transcription_factor_network

58

34

25

1.124

PID

Ifn-gamma_pathway

42

30

24

1.12

Reactome

Metabolism_of_liPIDs_and_lipoproteins

292

89

27

1.119

KEGG

Calcium_signaling_pathway

177

73

26

1.113

Reactome

G_alpha_(12_13)_signalling_events

77

36

22

1.111

PID

Syndecan-1-mediated_signaling_events

46

27

22

1.11

PID

Lpa_receptor_mediated_events

66

37

23

1.109

Reactome

Fatty_acid_triacylglycerol_and_ketone_body_metabolism

112

40

21

1.096

PID

E-cadherin signaling in_the_nascent_adherens_junction

38

27

22

1.094

PID

Bmp_receptor_signaling

42

28

21

1.09

KEGG

Chemokine_signaling_pathway

189

77

28

1.088

Reactome

Nrage_signals_death_through_jnk

47

25

23

1.086

Reactome

Cell-cell_communication

129

60

25

1.083

KEGG

T_cell_receptor_signaling_pathway

108

50

25

1.083

Reactome

Antigen_processing_ubiquitination_proteasome_degradation

213

86

26

1.078

PID

Fgf_signaling_pathway

59

35

24

1.061

Expression values for 3 upregulated candidate miRNAs (miR-34a, miR-10b and miR-20) in the microarray were confirmed by qPCR (Fig. 2c). We observed significant CBA/SIV-dependent changes in miRNA gene expression consistent with our microarray results. We also determined expression levels of 3 mRNAs: (1) estrogen receptor-alpha (ESR1), which demonstrated CBA/SIV-dependent changes in the microarray results and serves as a validated target of miR-34a and −20, (2) B-cell lymphoma-2 (BCl-2), a validated target of miR-34a, and (3) Kirsten Rat Sarcoma (KRAS), a validated target of miR-18. We found significant changes in ESR1 that were consistent with our microarray, and all mRNA showed significant changes with the expected inverse correlation to miRNA changes (Fig. 2c).

As described in the methods, we also analyzed biological functions of target genes of differentially regulated miRNAs. Genes targeted by downregulated miRNAs are involved in inflammatory/immune response, general cellular functions, neuronal function, etc. Frequently, a single gene was targeted by a miRNA within each individual biological function. Nearly 50 % of the genes targeted by upregulated miRNAs are primarily associated with five biological functional groups including (1) regulation of cell cycle progression, (2) activation of the innate immune response upon viral infection, (3) control of neuronal survival and plasticity, CNS development, and neuronal differentiation, (4) multifunctional receptors that mediate several cellular functions (e.g., proliferation, differentiation, glucose homeostasis, etc.); and (5) the Wnt, mitogen activated protein kinase (MAPK) and transforming growth factor (TGF) β signaling (Additional file 4: Table S4).

Chronic binge alcohol-dependent methylation changes

A total of 112 genes had alcohol-dependent changes in promoter methylation of ≥1.5-fold (12 had decreased and 100 had increased promoter methylation) (Additional file 5: Table S5). Eight of the genes with decreased methylation and 16 of the genes with increased methylation correspond to genes up- or downregulated, respectively, in the mRNA gene expression array. The major BPs that genes with differential methylation expression are involved in are transmission of nerve impulse, cell communication, ion homeostasis, glucose metabolism and cellular processes (Table 2).
Table 2

CBA-dependent alterations in promoter methylation at end-stage SIV infection

Biological process

Count

%

P-Value

Benjamini

Regulation of synaptic transmission

7

0.5

0.00027

0.3

Regulation of transmission of nerve impulse

7

0.5

0.00042

0.23

Regulation of neurological system process

7

0.5

0.00052

0.15

Regulation of system process

8

0.5

0.0043

0.6

Cellular process

81

5.6

0.0057

0.65

Regulation of biological quality

19

1.3

0.0068

0.67

Alcohol metabolic process

9

0.6

0.0073

0.65

Neurotransmitter metabolic process

3

0.2

0.0091

0.69

Vesicle-mediated transport

10

0.7

0.014

0.74

Cell communication

12

0.8

0.015

0.75

Transmembrane receptor protein tyrosine kinase signaling pathway

6

0.4

0.016

0.75

Cell-cell signaling

10

0.7

0.017

0.75

Glucose metabolic process

5

0.3

0.018

0.73

Regulation of neurogenesis

5

0.3

0.024

0.81

Establishment of localization in cell

12

0.8

0.024

0.79

Enzyme linked receptor protein signaling pathway

7

0.5

0.025

0.79

Negative regulation of biological process

20

1.4

0.026

0.78

Intracellular transport

10

0.7

0.029

0.8

Cellular component biogenesis

13

0.9

0.031

0.81

Negative regulation of programmed cell death

7

0.5

0.031

0.8

Regulation of cellular component organization

8

0.5

0.031

0.79

Regulation of phosphorylation

8

0.5

0.034

0.78

Cellular ion homeostasis

7

0.5

0.037

0.8

Regulation of phosphorus metabolic process

8

0.5

0.041

0.8

Cellular localization

12

0.8

0.041

0.79

Regulation of multicellular organismal process

12

0.8

0.044

0.8

RNA metabolic process

12

0.8

0.044

0.8

Count: number of genes involved in the term; %: percentage of involved genes/total genes; P-Value: modified fisher exact P-value, EASE Score; Benjamini: adjusted P-value using Benjamini-Hochberg procedure

Examination of the genes in which an increase in promoter methylation was observed; which would be predicted to result in a decrease in gene expression, demonstrated that nearly 70 % of the genes affected belonged to five biological functional groups and some showed overlap. The genes altered are involved in transcription, RNA processing, translation, and trafficking of proteins. Additional genes are involved in pathways including the Notch, TGFβ, Wnt, MAPK, nuclear factor kappa B (NFkB), Fibroblast growth factor (FGF) and insulin-like growth factor (IGF) signaling. Other functions of genes that were altered included cellular adhesion, cell growth and survival, energy production, myogenic function, oxidative stress response, extracellular matrix proteins, and ubiquitin-mediated degradation.

As seen with the miRNA results, most of the genes that could be affected by a decrease in methylation in their promoter are not involved in any one particular biological function (Additional file 5: Table S5). A small number (<3 per category) of genes affected by decreased methylation contribute to general cellular functioning, actin proteins, proteins of the extracellular matrix, energy metabolism, cellular adhesion, cell growth, and neural functioning. These results suggest that decreased gene promoter methylation minimally contributes to accentuated SKM loss in CBA/SIV macaques.

Discussion

We examined the SKM transcriptional and epigenetic changes, including differential expression of miRNA and promoter methylation profile, resulting from CBA administration to SIV-infected rhesus macaques. The results obtained were used to develop a gene regulatory network illustrating the principal sites of CBA-mediated alterations associated with SKM wasting at end-stage SIV infection. Our results show that CBA disrupts complex gene regulatory networks that affect the interplay between transcriptional and epigenetic factors leading to altered expression of genes whose biological functions contribute to one or more of the physiological events important for normal muscle functioning, including muscle regeneration to repair injury, muscle contraction and tensile strength, protein homeostasis, and NMJ function and development.

We and others previously focused on alcohol-mediated alterations in a single gene or class of genes involved in such biological functions of protein homeostasis [11, 18, 19], responses to increased oxidative stress [17], or immunological function [12]. Using new, more affordable genomics techniques and bioinformatics analytical methods, our previous, more limited SKM microarray analysis showed differential expression of genes-expression changes that were shown to be functionally involved in pro-inflammatory and ECM remodeling processes in SKM of CBA/SIV macaques [13]. In the present study, we expanded the analysis of this microarray data set to obtain a more comprehensive understanding of how CBA disrupts complex gene regulatory networks, thereby elucidating some additional underlying molecular mechanisms that contribute to CBA-dependent accentuation of SKM wasting at end-stage SIV infection.

Gene regulatory networks have both transcriptional and epigenetic components, including regulation of the initiation of gene expression through the action of transcription factors, the pre-transcriptional epigenetic regulation through promoter methylation, and the post-transcriptional regulation of mRNA stability or translation through the action of microRNAs (Fig. 3). Consistent with the interplay between these events, we identified several instances in which changes in methylation status (24 genes affected), miRNA expression (1 gene affected), or both (1 gene affected) are inversely proportional to our observed CBA-dependent changes in mRNA gene expression (Fig. 3). However, the majority of genes with expression changes ≥1.5-fold (as compared to expression in SUC/SIV macaques) did not correlate with alterations in miRNA or methylation. This suggests that the activation or repression of transcription initiation, that are independent of epigenetic modulation, are the most likely regulatory mechanisms responsible for these changes in gene expression. Further, a majority of genes that are affected by promoter methylation or that are validated/predicted targets of miRNA were not seen in our mRNA data, suggesting that these epigenetic changes have more subtle effects on the expression of individual genes (<1.5-fold change).
Fig. 3

Venn diagram of CBA-dependent alterations in gene regulatory networks at end-stage SIV infection. The majority of differentially expressed genes (521) in the SKM of CBA/SIV macaques (≥1.5-fold different) compared to SKM of SUC/SIV macaques) did not correlate with alterations in miRNA or methylation. However, some of the differentially expressed genes were epigenetically regulated: changes in methylation status (24 genes), miRNA expression (1 gene), or both together (1 gene) were inversely proportional to the observed CBA-dependent changes in mRNA gene expression. Further, there were CBA-dependent miRNA (163 target genes) and methylation (143 genes) alterations that were not found in the mRNA data that potentially contribute to skeletal muscle wasting

The results from these studies identify four additional distinct biological categories in which gene regulatory networks are altered in a CBA-dependent manner: (1) “universal” cellular functions, (2) protein homeostasis, (3) calcium and ion homeostasis, which affect the functioning of muscle and neuromuscular junctions, and (4) satellite cell growth and survival (Fig. 4).
Fig. 4

Schematic integrating the global effects of CBA on gene regulatory networks important for normal muscle function at end-stage SIV infection. Normal muscle function requires functional muscle fibers and the ability to activate satellite cells to proliferate and differentiate and pre-synaptic neurons to form functional neuromuscular junctions. The blue ovals indicate the cell type or physiological function necessary for normal muscle function and integrity. The orange octagons indicate the general biological functions required to maintain the function and/or integrity of the cell type or physiological function. The yellow boxes indicate the specific biological functions affected by CBA-dependent changes in gene regulatory networks. The number of mRNA (genes), microRNA (miRs), or methylation (Me) that are altered due to CBA are indicated for each specific biological function

“Universal” cellular functions

We found that 22 % of mRNAs, 18 % of target genes of altered miRNAs, and 21 % genes with promoters whose methylation status changed are involved with “universal” cellular functions. These universal functions include glycolysis and energy production, histone remodeling and modification, multifunctional transcriptional regulators, general kinases and phosphatases, lipid biosynthesis and degradation, multifunctional signaling pathways, maintaining DNA and chromosome integrity, transport of large molecules, and general mitochondrial function. Although seemingly diverse, and although each may not independently have a large biological impact, these changes combined as a whole would be expected to have a significant effect on the ability of any cell to function, regardless of the tissue of origin.

Protein homeostasis

Previous findings from our laboratory have demonstrated that CBA may disrupt protein homeostasis through altered insulin signaling and increased ubiquitin-proteasome-dependent protein degradation [20], possibly inhibiting protein synthesis by altering mTOR signaling [21], and affecting translation elongation [22]. Consistent with these previous reports, we found changes in gene regulatory networks that affect all three of these biological functions. However, we also detected effects on gene regulatory networks whose functions contribute to additional aspects of protein homeostasis, including the biosynthesis of nucleotides and amino acids, the generation of charged tRNA, the formation of functional ribosomes, transcription, processing and editing of mRNA, oligosaccharide biosynthesis, and trafficking of proteins through the endoplasmic reticulum and trans-Golgi network. Thus, CBA impacts processes preceding transcription and culminating in the production of fully modified functional proteins or glycoproteins. Interestingly, although methylation changes influence genes with varied biological functions, nearly 25 % of genes are involved with protein homeostasis, suggesting that the largest impact of changes in methylation is on finer modulation of genes important for the production of functional proteins.

Calcium and ion homeostasis

The increases in intracellular calcium stores, mediated through the mobilization and influx of calcium, are essential for many aspects of proper muscle functioning, including contraction, NMJ function, and differentiation. Further, sodium and potassium channels contribute to proper muscle and NMJ functioning by regulating muscle contractions and mobilizing intracellular calcium stores. The proper functioning of muscle fibers, along with the ability of satellite cells to fuse into functional muscle, requires the tight regulation of calcium influx, efflux, and intracellular calcium mobilization [23, 24]. Further, NMJ, the critical neuromuscular link mediating contraction, requires calcium for acetylcholine release [18]. Adding complexity to this process is the interplay between calcium and ion channels in which voltage-gated Na+/K+ channels are essential to mediate signals originating in the central nervous system to induce calcium release in muscle cells, thereby facilitating contraction. Our results show that CBA/SIV alters gene regulatory networks that affect genes important for calcium and ion homeostasis, either through direct expression, post-transcriptional regulation by miRNA, or altered methylation status. These results suggest that CBA may reduce the ability of NMJ to transmit signals to SKM fibers thereby affecting the mobilization of intracellular calcium stores, which would decrease the functionality of muscle contractions and the ability of muscle satellite cells to successfully fuse to form new tissue in response to muscle injury.

Satellite cell growth and survival

Moderate everyday SKM use and injury trigger a cascade of events important for muscle repair [19]. As part of this response, the muscle stem cells (or myoblast satellite cells) proliferate, enter the myogenic program, and fuse with existing muscle fibers. Once expanded, satellite cells express and activate common myogenic factors, reorganize their cytoskeleton to mediate fusion, and interact with the ECM to not only repair the myofibers, but also to restore the interaction between myofibers and surrounding tissue environment. It was demonstrated that in the absence of satellite cells, there is excessive accumulation of ECM and this adversely affects the increase in muscle mass in response to muscle overload experiments [25]. Our results indicate that at end-stage SIV infection, CBA negatively alters gene regulatory networks that affect genes important for cell growth and survival, cytoskeleton reorganization or ECM involvement, and regulation of myogenesis. The expression of a number of genes is affected, which when combined over long periods of time, may significantly impact SKM regenerative capacity and contribute to SKM wasting.

The primary objective of the study was to identify a global network of the transcriptomic and epigenomic profiles from skeletal muscle derived from CBA/SIV whole animal studies. We were able to confirm the differential expression of 3 selected upregulated miRNAs and some of their target mRNAs by qPCR. ESR1, a target gene for 3 of the upregulated miRNAs (miR-34a, miR-18 and miR-20), was downregulated in the gene microarray, showed increased promoter methylation in the methylation array, and decreased mRNA expression as confirmed by qPCR in the SKM of CBA/SIV macaques. ESR1 is required for translocation of GLUT-4 receptors to the plasma membrane for glucose uptake. Its role in development of insulin resistance has been demonstrated in ESR1 knockout mice. ESR1 is also required for satellite cell proliferation [2629]. Thus, decreased ESR1 expression in the SKM of CBA/SIV macaques potentially contributes to impaired glucose homeostasis and satellite cell growth. There was also a significant decrease in the expression of BCL-2, a target of miR-34a, in the SKM of CBA/SIV macaques. BCL-2 promotes cellular survival [30] and may be implicated in satellite cell growth and survival.

Taken together, our results allow us to propose a model describing the CBA-dependent global alterations in gene regulatory networks that contribute to muscle wasting in end-stage SIV infection. In this model, normal muscle function requires functional contractile muscle fibers, as well as the ability to activate fusion of myoblast satellite cells to regenerate injured muscle tissue and the ability of pre-synaptic neurons to form functional neuromuscular junctions (Fig. 4). The occurrence of each of these events depends on several subcategories of biological functions. For example, the proper contractile functioning of muscle cells requires cell and tissue integrity, proper protein homeostasis, and the presence of the mechanical components that drive contraction (Fig. 4, orange octagons). Each of these individual biological functions requires the finely balanced expression of specific genes, a balance regulated through transcriptional and epigenetic gene regulatory networks (Fig. 4, yellow boxes). Adding to this finely balanced regulatory network, many specific genes and miRNA contribute to multiple biological functions, such as calcium and ion homeostasis, which work in a finely coordinated manner to facilitate muscle contraction; NMJ function and myoblast satellite cell fusion; as well as cytoskeletal integrity and ECM maintenance, which contribute to muscle regeneration and muscle tissue integrity.

CBA administration promotes a persistent inflammatory SKM environment resulting from increased expression of a myriad of genes important for immunological function, inflammatory responses, and ability to combat oxidative stress (data not shown) [12, 13]. This persistent inflammatory state promotes changes in transcriptional and epigenetic regulation of hundreds of genes, either through direct effects on the initiation of transcription to alter the expression of genes and miRNA, the post-transcriptional miRNA regulation of mRNA stability and translation, or the altered methylation status of gene promoter regions (Fig. 3). These alterations produce subtle and significant effects on the expression of genes, which individually may not produce significant changes but when combined as a whole, work to disrupt the finely tuned balance required for normal muscle function and repair, thereby eliciting a gradual process of muscle wasting.

Conclusion

Our results provide an integrated analysis of gene regulatory networks affecting SKM wasting in CBA/SIV macaques that extends our previous observations on ECM [13] and protein homeostasis [20, 31]. Alterations in calcium and ion homeostasis, NMJ functions, deficiencies in growth, survival, and regenerative capability of myoblasts, “universal” cellular functions, and gene regulatory networks expand the scope of CBA-mediated effects beyond those exclusive to maintaining protein homeostasis. Together, these alterations create a global, interconnected, and integrated network that leads to a general loss of SKM mass due to the inability of muscles to function, respond to, and repair damage and injury.

Methods

SKM used for these studies was obtained from animals used in experiments approved by the Institutional Animal Care and Use Committee at both Tulane National Primate Research Center (TNPRC) in Covington, Louisiana and Louisiana State University Health Sciences Center (LSUHSC) in New Orleans, Louisiana, and adhered to National Institutes of Health guidelines for the care and use of experimental animals. The pathophysiological course of SIV infection has been previously described in published manuscripts [1114]. A total of 28 4–6-year-old male macaques (Macaca mulatta) were studied in three experimental groups: SUC/SIV group (n = 9), CBA/SIV group (n = 11), and control group (n = 8). For the control group, skeletal muscle samples were obtained at necropsy from a group of SIV-negative, healthy control macaques and used as reference values for comparison of analyzed variables.

Experimental protocol

The gastric catheter placement for alcohol delivery, the alcohol delivery protocol, and the route of SIV infection have been described in detail elsewhere [11, 14, 32]. Animals were briefly exposed to daily intragastric administration of a mean of 2.5 g per kg body weight ethanol (30 % w/v water), beginning 3 months prior to SIV infection and continuing throughout the duration of study. This protocol of CBA administration provided an average of 15 % of the animals’ total daily caloric intake and produced blood alcohol concentrations of 50–60 mM. Control animals were infused with sucrose. Animals were provided monkey chow (Lab Fiber Plus Primate diet DT; PMI Nutrition International, St. Louis, MO) ad libitum and supplemented with fruits, vitamins, and Noyes treats (Research Diets, New Brunswick, NJ).

Three months after initiating CBA administration, animals were inoculated intravenously with 10,000 times the 50 % infective dose (ID50) of SIVmac251. SIV disease progression was monitored throughout the study period through clinical, biochemical, and immunological parameters (CD4/CD8 lymphocyte ratios) in addition to plasma viral kinetics (SIV gag RNA levels) as described elsewhere [12, 32]. SKM (gastrocnemius) samples were obtained at necropsy when animals met any one of the criteria for euthanasia based on the following: (1) loss of 25 % of body weight from maximum body weight since assignment to protocol, (2) major organ failure or medical conditions unresponsive to treatment, (3) surgical complications unresponsive to immediate intervention, or (4) complete anorexia for 4 days. SKM tissue samples were dissected, snap frozen, and stored at −80 °C until analyses. SKM samples from all animals in the three treatment groups were used for the gene microarray, microRNA microarray, methylation array and qPCR. SKM samples used for the analysis in this study were used in two previously published studies [13, 14].

mRNA Microarray analysis

To determine how CBA affects the expression of mRNA at end-stage SIV infection, total RNA was isolated from SKM at necropsy from a total of 28 animals (SUC/SIV (n = 9), CBA/SIV (n = 11), and control (n = 8)) and microarray analysis was performed as previously described on all samples [13]. Briefly, the microarray hybridization was performed at the Stanley S. Scott Cancer Center’s Translational Genomics Core at LSUHSC in New Orleans, Louisiana. Total RNA was extracted using the RNeasy Mini Kit (Qiagen, Valencia, CA) according to the manufacturer's instructions. The RNA was hybridized to Illumina HumanWG6_v3 chips (San Diego, CA) following manufacturer’s instructions. For data analysis the samples were normalized using the cubic spline algorithm, assuming that the distribution of transcripts is similar [33]. Differential expression was determined by comparing treatment and control groups using the Illumina Custom algorithm that assumes that target signal intensity is normally distributed among replicates corresponding to some biological condition.

The fold change in gene expression of SUC/SIV and CBA/SIV was obtained by dividing the expression level of each gene by that of the control. The fold change of CBA/SIV/SUC/SIV was obtained by dividing the expression level of each gene between CBA/SIV and SUC/SIV. Comparisons between the gene expression levels of CBA/SIV with SUC/SIV animals reflect the impact of CBA on SIV-mediated changes in gene expression. Genes whose expression was altered in an alcohol-dependent manner by ≥1.5-fold were examined. The GEO accession number is GSE59111.

Gene Set Enrichment Analysis (GSEA) was run on the normalized, unfiltered microarray dataset as suggested in the tools implementation (http://software.broadinstitute.org/gsea/index.jsp) version 2.2.1) [34, 35] using the c5.all.v5.symbols. (Gene ontology), running 1000 permutations and excluding gene sets with fewer than 5 genes or more than 500. Using these parameters, 111 gene sets were selected for the analysis. The GSEA statistics are detailed in http://www.broadinstitute.org/gsea/doc/GSEAUserGuideFrame.html [36].

MicroRNA microarray analysis

To determine the impact of CBA on miRNA expression at end-stage SIV infection, small RNA (<30 bp) were purified. The microRNA microarray hybridization was performed at the microarray core at LSUHSC. Briefly, 500 μg of total RNA was biotin-labeled using the FlashTag Biotin HSR kit (Genisphere, Hatfield, PA) according to the manufacturer’s instructions. All samples showed expected labeling and the resulting targets were hybridized to miR 2.0 arrays (Affymetrix) containing 15,644 microRNA probe sets from the miRBASE v15 (http://microrna.sanger.ac.uk), which recognize microRNAs from a number of organisms including human and rhesus macaques. The arrays were washed and processed on a Fluidics Station 450 and scanned with a confocal laser scanner (GeneChip Scanner 3000, Affymetrix) according to the manufacturer's instructions. Data from the microarrays were analyzed with Affymetrix microRNA QC Tool according to the manufacturer’s instructions. Differential miR expression was performed using the One-Way ANOVA workflow with multiple test correction (FDR = 0.05) and selected from volcano plot using p-value <0.05 and exhibiting a ≥ 1.5-fold change cutoff.

All differentially regulated miRs were analyzed using miRSystem (http://mirsystem.cgm.ntu.edu.tw/) [37]. miRSystem is a database which integrates seven miRNA target gene prediction programs: DIANA, miRanda, miRBridge, PicTar, PITA, rna22, and TargetScan. The database also contains validated data from TarBase and miRecords. To balance the reliability of the predictions, three algorithm hits were used: 1) hypergeometric p-value determination; 2) empirical p-value is determined by ranking the enriched hypergeometric probability as compared with null baseline probabilities. The null baseline probability was established by randomly selecting a group of miRNAs, between 1 and 100, and using the default values in mirsystem to calculate the raw p-value for each pathway [37]; and 3) a weighted pathway-ranking method is also calculated from the expression ratios of the differentially regulated miRNAs to rank the enriched pathways. For each functional category, the ranking score was obtained by summation of the weight of its miRNA times its enrichment -log (p-value) from the predicted target genes \( \left\{\mathrm{Score}={\displaystyle \sum \mathrm{AmiRNAwi}\left[\hbox{-} \log 10\left(\mathrm{pi}\right)\right]}\right\} \). The target genes, pathway ranking, and functional annotation summaries are included in the results. The parameters set for analysis were: a) validated genes equal to or more than 3, b) observed to expected ratio (O/E) greater or equal to 2 and c) total genes in the pathway ≥ 25 and ≤ 500.

We also analyzed individual differentially expressed miRNAs using miRTarBase and TargetScan. A search for target genes using miRTarBase [38] determined that 4 of the downregulated miRNA target 12 independent genes; targeting is validated in the literature by at least two independent experimental methods [38]. For the remaining miRNAs that have no experimentally validated gene targets, we used the TargetScan database, which identifies genes statistically predicted to be targets, using Homo sapiens as the reference. This search identified 8 individual downregulated miRNAs predicted to target 18 independent genes based on their predicted efficacy of targeting (context score) [39, 40] or probability of conserved targeting (PCT) [41] [context score ≥ 85 %; PCT ≥ 0.8] [42].

DNA methylation microarray analysis

To determine how CBA affects promoter methylation in SIV-infection, the Infinium HumanMethylation27 array was utilized. The Infinium HumanMethylation 27 examines more than 27,000 CpG islands in more than 14,000 genes’ promoters. SKM samples were bisulphite-converted with Zymo EZ DNA Methylation kit (Zymo Research, Irvine, CA, USA). GenomeStudio v2011.1 (Illumina, San Diego, CA, USA) with Methylation module 1.9.0 software was used in the methylation analysis. The Infinium platform assays covers 96 % of CpG islands with multiple sites in the island, the shores (within 2 kb from CpG islands), and the shelves (>2 kb from CpG islands). All the Illumina quality controls were acceptable, including sample-independent and dependent controls, staining controls, extension controls, target removal controls, hybridization controls, bisulphite conversion I and II controls, specificity controls, non-polymorphic controls and negative controls. Probes were considered to be differentially methylated if the resulting adjusted p-value was <0.05. The Benjamini-Hochberg method [43] was used to adjust the p-values and ensure that the false discovery rate was <0.05. The corresponding gene list was derived from the gene annotations associated with the probes. The GEO accession number is GSE75729. Functional enrichment analyses of genes with differential methylation of promoter regions were performed using DAVID Bioinformatics Resources. The gene ontology Biological Processes (BP_all) is represented for genes. The count: number of genes involved in the term; %: percentage of involved genes/total genes; p-value: modified fisher exact p-value, EASE Score (p ≤ 0.05); Benjamini: adjusted p-value using Benjamini-Hochberg procedure is presented in the results [44, 45].

The biological functions of individual differentially expressed genes in the mRNA microarray, validated target genes of differentially expressed miRNAs and genes altered in the DNA methylation array were also categorized by searching each individual gene in the GeneCards® Human Genome Database (http://www.genecards.org). This is an integrative database that provides descriptions of gene functions as extracted from multiple public databases, including Entrez Gene, UniProtKB, Tocris, Bioscience, and PharmGKB. Additional information was derived from searching each individual gene in the NCBI Gene Database (http://www.ncbi.nlm.nih.gov/gene/); the remaining genes with unclear biological function were analyzed using the DAVID Bioinformatics Database (https://david.ncifcrf.gov). This allowed for analysis of genes with specific functions relevant to skeletal muscle function. Those genes that did not fall within any obvious biological function after these analyses were classified as “Miscellaneous.” This analysis is included as supplementary tables.

q PCR (qPCR) for miR expression

To validate the deep sequencing data, the relative expression of 3 differentially expressed miRs (miR-34a, miR-10b, miR-20) was further determined by individual Taqman miR assays (Thermo Fisher Scientific). Approximately 200–250 ng of total RNA was reverse-transcribed using the stem loop primers provided in the predesigned kit and ~1.3 μl of cDNA was subjected to 40 cycles of PCR on the CFX96 Bio-Rad PCR cycler (Bio-Rad) using the following thermal cycling conditions: 50 °C for 2 min, 95 °C for 10 min followed by 40 repetitive cycles of 95 °C for 15 s and 60 °C for 1 min. As a normalization control for RNA loading, SNOU6 were amplified in duplicate wells on the same multi-well plate.

qPCR for target genes of differentially regulated miRNAs

Total RNA isolated for miR sequencing studies was used for gene expression analysis as well. cDNA was synthesized from 1000 ng of the resulting total RNA using the Quantitect Reverse Transcriptase Kit (Qiagen), in accordance with the manufacturer’s instructions. Primers were designed to span exon-exon junctions (IDT, Coralville, IA) and used at a concentration of 500 nmol. The final reactions were made to a total volume of 20 μl with Quantitect SyBr Green PCR kit (Qiagen). All reactions were carried out in duplicate on a CFX96 system (Bio-Rad Laboratories, Hercules, CA) for qPCR detection. qPCR data were analyzed using the comparative Ct (delta-delta-Ct, ΔΔCT) method. Target genes were compared with the endogenous control (ribosomal protein S13 (RPS13)) and CBA/SIV and SUC/SIV values were normalized to controls.

Availability of supporting data

The data sets supporting the results of this article are available in Gene Expression Omnibus (GEO). The GEO accession numbers for the microarray data are GSE59111 and GSE75729. The links to the data are http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE75729 and http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE59111.

Notes

Abbreviations

PLWHA: 

people living with HIV/AIDS

AUD: 

alcohol use disorders

SIV: 

simian immunodeficiency virus

CBA: 

chronic binge alcohol

SKM: 

skeletal muscle

SUC: 

sucrose

HIV: 

human immunodeficiency virus

AIDS: 

acquired immunodeficiency syndrome

ART: 

antiretroviral therapy

FDR: 

false discovery rate

ECM: 

extracellular matrix

NMJ: 

neuromuscular junction

MEF2C: 

myogenic enhancing factor 2C

PNN: 

pinin desmosome associated protein

TMEM119: 

transmembrane protein 119

ESR1: 

estrogen receptor-alpha

BCl-2: 

b-cell Lymphoma-2

KRAS: 

kirsten rat sarcoma

MAPK: 

mitogen activated protein kinase

TGF β: 

transforming growth factor

ID50: 

50 % infective dose ()

NFkB: 

nuclear factor kappa B

FGF: 

fibroblast growth factor

IGF: 

insulin-like growth factor

GSEA: 

gene set enrichment analysis

ANOVA: 

analysis of variance

O/E: 

observed to expected ratio

PCT: 

probability of conserved targeting

BP: 

biological processes

ΔΔCT: 

delta-delta-Ct

RPS13: 

Ribosomal protein S13

Declarations

Acknowledgments

We acknowledge the scientific expertise and scientific discussions with Drs. Nicole LeCapitaine, Gregory Bagby and Jason Dufour. We thank Dr. Michael Levitzky for critical review and editing of the manuscript. We also thank Curtis Vande Stouwe, Jean Carnal, Amy Weinberg, Jane Schexnayder and Rhonda Martinez for their technical assistance. In addition, we thank Larissa Devlin, Wayne A. Cyprian, and Nancy Dillman from the Tulane National Research Primate Center (TNPRC, Covington, LA) for excellent care of the study animals. The work was supported by National Institutes of Health (NIH) grants: T32 AA07577, P60 AA09803, P51 RR000164, and P20 GM103501 subproject # 2 (to JZ).

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Physiology, Louisiana State University Health Sciences Center
(2)
LSUHSC-NO Comprehensive Alcohol-HIV/AIDS Research Center (CARC)
(3)
Department of Genetics
(4)
Department of Pediatrics, Stanley S. Scott Cancer Center

References

  1. CDC. HIV Surveillance Report. 2011. p. 23.Google Scholar
  2. Broz D, Wejnert C, Pham HT, DiNenno E, Heffelfinger JD, Cribbin M, et al. HIV Infection and risk, prevention, and testing behaviors among injecting drug Users - National HIV Behavioral Surveillance System, 20 U.S. Cities. Morb Mortal Wkly Rep Surveill Summ. 2014;63(6):1–51.Google Scholar
  3. Hasin DS, Stinson FS, Ogburn E, Grant BF. Prevalence, correlates, disability, and comorbidity of DSM-IV alcohol abuse and dependence in the United States: results from the National Epidemiologic Survey on Alcohol and Related Conditions. Arch Gen Psychiatry. 2007;64(7):830–42.PubMedView ArticleGoogle Scholar
  4. Bryant KJ, Nelson S, Braithwaite RS, Roach D. Integrating HIV/AIDS and alcohol research. Alcohol Res Health. 2010;33(3):167–78.PubMedPubMed CentralGoogle Scholar
  5. Richert L, Dehail P, Mercie P, Dauchy FA, Bruyand M, Greib C, et al. High frequency of poor locomotor performance in HIV-infected patients. Aids. 2011;25(6):797–805.PubMedView ArticleGoogle Scholar
  6. Tang AM, Jacobson DL, Spiegelman D, Knox TA, Wanke C. Increasing risk of 5 % or greater unintentional weight loss in a cohort of HIV-infected patients, 1995 to 2003. J Acquir Immune Defic Syndr. 2005;40(1):70–6.PubMedView ArticleGoogle Scholar
  7. Clary CR, Guidot DM, Bratina MA, Otis JS. Chronic alcohol ingestion exacerbates skeletal muscle myopathy in HIV-1 transgenic rats. AIDS Res Ther. 2011;8:30.PubMedPubMed CentralView ArticleGoogle Scholar
  8. Vary TC, Lang CH. Assessing effects of alcohol consumption on protein synthesis in striated muscles. Methods Mol Biol. 2008;447:343–55.PubMedView ArticleGoogle Scholar
  9. Scott WB, Oursler KK, Katzel LI, Ryan AS, Russ DW. Central activation, muscle performance, and physical function in men infected with human immunodeficiency virus. Muscle Nerve. 2007;36(3):374–83.PubMedPubMed CentralView ArticleGoogle Scholar
  10. Preedy VR, Peters TJ. Alcohol and muscle disease. J R Soc Med. 1994;87(4):188–90.PubMedPubMed CentralGoogle Scholar
  11. Molina PE, Lang CH, McNurlan M, Bagby GJ, Nelson S. Chronic alcohol accentuates simian acquired immunodeficiency syndrome-associated wasting. Alcohol Clin Exp Res. 2008;32(1):138–47.PubMedPubMed CentralView ArticleGoogle Scholar
  12. Molina PE, McNurlan M, Rathmacher J, Lang CH, Zambell KL, Purcell J, et al. Chronic alcohol accentuates nutritional, metabolic, and immune alterations during asymptomatic simian immunodeficiency virus infection. Alcohol Clin Exp Res. 2006;30(12):2065–78.PubMedView ArticleGoogle Scholar
  13. Dodd T, Simon L, LeCapitaine NJ, Zabaleta J, Mussell J, Berner P, et al. Chronic binge alcohol administration accentuates expression of pro-fibrotic and inflammatory genes in the skeletal muscle of simian immunodeficiency virus-infected macaques. Alcohol Clin Exp Res. 2014;38(11):2697–706.PubMedPubMed CentralView ArticleGoogle Scholar
  14. LeCapitaine NJ, Wang ZQ, Dufour JP, Potter BJ, Bagby GJ, Nelson S, et al. Disrupted anabolic and catabolic processes may contribute to alcohol-accentuated SAIDS-associated wasting. J Infect Dis. 2011;204(8):1246–55.PubMedPubMed CentralView ArticleGoogle Scholar
  15. Lang CH, Pruznak AM, Nystrom GJ, Vary TC. Alcohol-induced decrease in muscle protein synthesis associated with increased binding of mTOR and raptor: comparable effects in young and mature rats. Nutr Metab. 2009;6:4.View ArticleGoogle Scholar
  16. Simon LLN, Berner P, Stouwe CV, Mussell JC, Allerton TD, Primeaux SD, et al. Chronic binge alcohol consumption alters myogenic gene expression and reduces in vitro myogenic differentiation potential of myoblasts from rhesus macaques. Am J Physiol Regul Integr Comp Physiol. 2014.Google Scholar
  17. Fernandez-Sola J, Preedy VR, Lang CH, Gonzalez-Reimers E, Arno M, Lin JC, et al. Molecular and cellular events in alcohol-induced muscle disease. Alcohol Clin Exp Res. 2007;31(12):1953–62.PubMedView ArticleGoogle Scholar
  18. Csillik B. Calcium channels in the neuromuscular junction. Int Rev Cytol. 1993;147:193–232.PubMedView ArticleGoogle Scholar
  19. Tidball JG. Mechanisms of muscle injury, repair, and regeneration. Comprehensive Physiol. 2011;1(4):2029–62.Google Scholar
  20. Lecapitaine NJ, Wang ZQ, Dufour JP, Potter BJ, Bagby GJ, Nelson S, et al. Disrupted anabolic and catabolic processes may contribute to alcohol-accentuated SAIDS-associated wasting. J Infect Dis. 2011;204(8):1246–55.PubMedPubMed CentralView ArticleGoogle Scholar
  21. Steiner JL, Lang CH. Alcohol impairs skeletal muscle protein synthesis and mTOR signaling in a time-dependent manner following electrically stimulated muscle contraction. J Appl Physiol. 2014;117(10):1170–9.PubMedPubMed CentralView ArticleGoogle Scholar
  22. Steiner JL, Lang CH. Alcohol intoxication following muscle contraction in mice decreases muscle protein synthesis but not mTOR signal transduction. Alcohol Clin Exp Res. 2015;39(1):1–10.PubMedView ArticleGoogle Scholar
  23. Berchtold MW, Brinkmeier H, Muntener M. Calcium ion in skeletal muscle: its crucial role for muscle function, plasticity, and disease. Physiol Rev. 2000;80(3):1215–65.PubMedGoogle Scholar
  24. Hindi SM, Tajrishi MM, Kumar A. Signaling mechanisms in mammalian myoblast fusion. Sci Signal. 2013;6(272):re2.PubMedPubMed CentralView ArticleGoogle Scholar
  25. Fry CS, Lee JD, Jackson JR, Kirby TJ, Stasko SA, Liu H, et al. Regulation of the muscle fiber microenvironment by activated satellite cells during hypertrophy. FASEB J. 2014;28(4):1654–65.PubMedPubMed CentralView ArticleGoogle Scholar
  26. Kamanga-Sollo E, Pampusch MS, Xi G, White ME, Hathaway MR, Dayton WR. IGF-I mRNA levels in bovine satellite cell cultures: effects of fusion and anabolic steroid treatment. J Cell Physiol. 2004;201(2):181–9.PubMedView ArticleGoogle Scholar
  27. Thomas A, Bunyan K, Tiidus PM. Oestrogen receptor-alpha activation augments post-exercise myoblast proliferation. Acta Physiol (Oxf). 2010;198(1):81–9.View ArticleGoogle Scholar
  28. Barros RP, Gustafsson JA. Estrogen receptors and the metabolic network. Cell Metab. 2011;14(3):289–99.PubMedView ArticleGoogle Scholar
  29. Barros RP, Machado UF, Warner M, Gustafsson JA. Muscle GLUT4 regulation by estrogen receptors ERbeta and ERalpha. Proc Natl Acad Sci U S A. 2006;103(5):1605–8.PubMedPubMed CentralView ArticleGoogle Scholar
  30. Vaux DL, Cory S, Adams JM. Bcl-2 gene promotes haemopoietic cell survival and cooperates with c-myc to immortalize pre-B cells. Nature. 1988;335(6189):440–2.PubMedView ArticleGoogle Scholar
  31. Steiner JL, Lang CH. Dysregulation of skeletal muscle protein metabolism by alcohol. Am J Physiol Endocrinol Metab. 2015;308(9):E699–712.PubMedView ArticleGoogle Scholar
  32. Bagby GJ, Stoltz DA, Zhang P, Kolls JK, Brown J, Bohm Jr RP, et al. The effect of chronic binge ethanol consumption on the primary stage of SIV infection in rhesus macaques. Alcohol Clin Exp Res. 2003;27(3):495–502.PubMedView ArticleGoogle Scholar
  33. Workman C, Jensen LJ, Jarmer H, Berka R, Gautier L, Nielser HB, et al. A new non-linear normalization method for reducing variability in DNA microarray experiments. Genome Biol. 2002;3(9):research0048.PubMedPubMed CentralView ArticleGoogle Scholar
  34. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005;102(43):15545–50.PubMedPubMed CentralView ArticleGoogle Scholar
  35. Mootha VK, Lindgren CM, Eriksson KF, Subramanian A, Sihag S, Lehar J, et al. PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet. 2003;34(3):267–73.PubMedView ArticleGoogle Scholar
  36. Subramaniana A, Tamayoa P. Moothaa VK, Mukherjeed S, Eberta BL, Gillettea MA, et al. Gene setenrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. ProcNatl Acad Sci U S A. 2005;102(43): 15545–50.Google Scholar
  37. Lu TP, Lee CY, Tsai MH, Chiu YC, Hsiao CK, Lai LC, et al. miRSystem: an integrated system for characterizing enriched functions and pathways of microRNA targets. PLoS One. 2012;7(8), e42390.PubMedPubMed CentralView ArticleGoogle Scholar
  38. Hsu SD, Tseng YT, Shrestha S, Lin YL, Khaleel A, Chou CH, et al. miRTarBase update 2014: an information resource for experimentally validated miRNA-target interactions. Nucleic Acids Res. 2014;42(Database issue):D78–85.PubMedPubMed CentralView ArticleGoogle Scholar
  39. Garcia DM, Baek D, Shin C, Bell GW, Grimson A, Bartel DP. Weak seed-pairing stability and high target-site abundance decrease the proficiency of lsy-6 and other microRNAs. Nat Struct Mol Biol. 2011;18(10):1139–46.PubMedPubMed CentralView ArticleGoogle Scholar
  40. Grimson A, Farh KK, Johnston WK, Garrett-Engele P, Lim LP, Bartel DP. MicroRNA targeting specificity in mammals: determinants beyond seed pairing. Mol Cell. 2007;27(1):91–105.PubMedPubMed CentralView ArticleGoogle Scholar
  41. Friedman RC, Farh KK, Burge CB, Bartel DP. Most mammalian mRNAs are conserved targets of microRNAs. Genome Res. 2009;19(1):92–105.PubMedPubMed CentralView ArticleGoogle Scholar
  42. Lu J, Clark AG. Impact of microRNA regulation on variation in human gene expression. Genome Res. 2012;22(7):1243–54.PubMedPubMed CentralView ArticleGoogle Scholar
  43. Benjamini YHY. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B (Methodological). 1995;57:289–300.Google Scholar
  44. Dennis Jr G, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC, et al. DAVID: database for annotation, visualization, and integrated discovery. Genome Biol. 2003;4(5):3.View ArticleGoogle Scholar
  45. da Huang W, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009;4(1):44–57.View ArticleGoogle Scholar

Copyright

© Simon et al. 2015

Advertisement