Arq Bras Cardiol abc Arquivos Brasileiros de Cardiologia Arq. Bras. Cardiol. 0066-782X 1678-4170 Sociedade Brasileira de Cardiologia - SBC M3RSKrJv4cjhkDNqRLVQGRH S0066-782X2023001000305 00305 10.36660/abc.20220874 Artigo Original Abordagem de Bioinformática e Biologia de Sistemas para Identificar a Ligação Patogenética entre Insuficiência Cardíaca e Sarcopenia 0000-0001-6017-5927 Xu Rui Concepção e desenho da pesquisa Redação do Obtenção de dados e Análise e interpretação dos dadosmanuscrito 1 Ma Ling-ling Obtenção de dados e Análise e interpretação dos dados 1 Cui Shuai Revisão crítica do manuscrito quanto ao conteúdo intelectual importante 1 Chen Ling Revisão crítica do manuscrito quanto ao conteúdo intelectual importante 1 Xu Hong Concepção e desenho da pesquisa 1 Gerontology center People’s Hospital of Xinjiang Uygur Autonomous Region Urumqi China Gerontology center, People’s Hospital of Xinjiang Uygur Autonomous Region, Urumqi – China Correspondência: Hong Xu • N°. 91 Tianchi Road, Tianshan Disztrict, Urumqi, Xinjiang E-mail: dengta2022@126.com

Editor responsável pela revisão: Gláucia Maria Moraes de Oliveira

Potencial conflito de interesse

Não há conflito com o presente artigo.

 27 10 2023 10 2023 120 10 e20220874 13 12 2022 15 07 2023 16 08 2023 This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Figura Central

: Abordagem de Bioinformática e Biologia de Sistemas para Identificar a Ligação Patogenética entre Insuficiência Cardíaca e Sarcopenia Fluxograma do projeto de pesquisa.

Resumo Fundamento

Apesar das evidências crescentes de que pacientes com insuficiência cardíaca (IC) são suscetíveis à sarcopenia, o motivo da associação não é bem compreendido.

 Objetivo

O objetivo deste estudo é explorar ainda mais o mecanismo molecular de ocorrência desta complicação.

 Métodos

Conjuntos de dados de expressão gênica para HF (GSE57345) e Sarcopenia (GSE1428) foram obtidos do banco de dados Gene Expression Omnibus (GEO). Genes diferencialmente expressos (DEGs) foram identificados usando pacotes ‘edgeR’ e “limma” de R, e suas funções foram analisadas usando Gene Ontology (GO) e a Enciclopédia de Genes e Genomas de Kyoto (KEGG). Redes de interação proteína-proteína (PPI) foram construídas e visualizadas usando Search Tool for the Retrieval of Interacting Genes (STRING) e Cytoscape. Os genes hub foram selecionados usando o plugin cytoHubba e validados com GSE76701 para IC e GSE136344 para Sarcopenia. As vias relacionadas e os mecanismos moleculares dos genes hub foram realizados pela análise de enriquecimento de genes (GSEA). As análises estatísticas foram realizadas no software R. P < 0,05 foi considerado estatisticamente significativo.

 Resultados

Foram encontrados 114 DEGs comuns. As vias relacionadas ao fator de crescimento, secreção de insulina e cGMP-PKG estavam enriquecidas tanto na IC quanto na sarcopenia. Descobriu-se que CYP27A1, KCNJ8, PIK3R5, TIMP2, CXCL12, KIT e VCAM1 são genes hub significativos após validação com GSEA enfatizando a importância dos genes hub na regulação da resposta inflamatória.

 Conclusão

Nosso estudo revela que a IC e a Sarcopenia compartilham vias e mecanismos patogênicos comuns. Estes achados podem sugerir novas direções para pesquisas futuras sobre a patogênese subjacente.

 Sarcopenia Insuficiência Cardíaca Biologia Computacional Genes Introdução

O envelhecimento global das populações em todo o mundo aumenta a prevalência de doenças relacionadas com a idade, como a IC, o que onera significativamente os sistemas de saúde.1 A etiologia da IC é complexa e multifatorial, resultando na redução da capacidade funcional, muitas vezes com mau prognóstico. A sarcopenia foi identificada como um potencial preditor extracardíaco de pior prognóstico em pacientes com IC.2

A sarcopenia é um distúrbio progressivo em que os indivíduos afetados experimentam a perda progressiva e debilitante de massa muscular, contribuindo, em última análise, para altas taxas de fragilidade entre as populações mais idosas.3 Está associada a um risco aumentado de quedas, osteoporose, perda de independência e aumento da mortalidade.4 A perda muscular é frequentemente descrita como um tipo de sarcopenia secundária, às vezes sob o termo “caquexia” em pacientes com IC.5 No entanto, embora esta perda de massa muscular esquelética associada à idade continue a ser uma grande preocupação para pacientes idosos com IC, os mecanismos subjacentes à co-ocorrência de sarcopenia e IC são pouco compreendidos.

Uma análise de genes e vias comuns pode fornecer informações sobre a coexistência de IC e Sarcopenia. Assim, analisamos genes hub comuns a ambos os distúrbios e previmos as vias associadas a esses genes por meio de análise bioinformática quantitativa de dados disponíveis publicamente. Os achados podem fornecer uma nova visão sobre os mecanismos subjacentes à co-ocorrência destes dois distúrbios comuns.

 Métodos Desenho do estudo e coleta de dados

Gene Expression Omnibus (GEO) é um repositório público de dados genômicos funcionais que suporta envios de dados compatíveis com MIAME. Ferramentas são fornecidas para ajudar os usuários a consultarem e baixar experimentos e perfis de expressão genética selecionados. Os conjuntos de dados de expressão gênica foram obtidos do banco de dados GEO usando os termos de pesquisa “Heart Failure” e “Sarcopenia”.6,7 Para inclusão, os critérios foram a presença de matrizes independentes com grandes tamanhos de amostra e dados humanos. Isso resultou na inclusão de dois conjuntos de dados, a saber, GSE573457 e GSE1428.8 O conjunto de dados GSE57345 incluiu dados de sequenciamento de RNA de 177 pacientes com IC e 136 controles saudáveis da Filadélfia, enquanto o conjunto de dados GSE1428 continha dados de sequenciamento de RNA de amostras do músculo vasto lateral de 12 pacientes com Sarcopenia (70-80 anos) e 10 jovens controles saudáveis (19-25 anos) de Boston.

 Identificação de genes expressos diferencialmente com software R

Os dados de GSE57345 e GSE1428 foram normalizados, e os DEGs entre amostras de pacientes e controle foram identificados com o pacote R ‘edgeR’ e ‘limma’.9 Modulações foram determinadas para a expressão dos genes individuais, com genes mostrando modulações > 1,2 e valor p < 0,05 classificado como DEGs. Genes comuns à Sarcopenia e IC foram obtidos pela sobreposição dos dois conjuntos de DEGs. O pacote R ‘Venn Diagram’ foi usado para obter seus DEGs comuns.10 Em seguida, sobrepusemos os genes relacionados de HF e Sarcopenia para obter genes comuns para análise posterior.

 Anotação funcional e análise de enriquecimento de via

Uma análise funcional adicional dos DEGs comuns foi conduzida pela avaliação de anotações GO e vias enriquecidos em KEGG usando o pacote ‘cluster’ em R.11 As anotações GO se enquadram em três categorias, a saber, processo biológico (BP), componente celular (CC) e função molecular (MF). Valor de P < 0,05 foi utilizado como limite de significância. Construção de rede PPI e identificação de genes hub

As redes PPI para os DEGs comuns foram então criadas em STRING com visualização pelo Cytoscape 3.9.0.12 Escores de confiança> 0,4 foram definidas para valores intermediários. O plugin Cytoscape, CytoHubba, foi utilizado para filtrar os genes hub na rede PPI usando o algoritmo de Degree.13

 Análise de enriquecimento de conjunto genético

O GSEA foi usada para determinar as associações entre vias e funções dos genes hub.14 Os níveis de significância foram estabelecidos em valores nominais de p <0,05, escores de enriquecimento normalizados (NES) > 1 e valores q de taxa de falsos positivos (FDR) <0,25.

 Validação da expressão de genes hub em outros conjuntos de dados

Os níveis de mRNA dos genes hub foram então verificados para GSE7670115 e GSE136344.16 GSE76701 continha 4 indivíduos com IC e 4 controles, enquanto GSE136344 continha 19 indivíduos com Sarcopenia e 11 controles. O teste T avaliou as diferenças entre os dois conjuntos de dados com um valor p <0,05 considerado significativo.

 Análise estatística

Este estudo conduziu todas as análises estatísticas usando o software R (versão 4.1.2; https://www.r-project.org/). A distribuição normal dos diferentes parâmetros foi verificada pelo teste de Kolmogorov-Smirnov. As diferenças entre os grupos foram avaliadas pelo teste t não pareado de Student. Um valor de p < 0,05 foi considerado significativo.

 Resultados Identificação de DEGs

O fluxograma desta pesquisa foi apresentado na Figura Central. Todos os dados de dois conjuntos de dados independentes (GSE57345: HF e GSE1428: Sarcopenia) foram obtidos do GEO. Os dados do microarray foram normalizados e os DEGs foram identificados (1954 em GSE57345 e 2242 em GSE1428). Para melhor visualização, os DEGs para HF e Sarcopenia foram apresentados como volcano plots (Figuras 1A, B). 224 DEGs comuns a ambos os grupos foram identificados utilizando o diagrama de Venn (Figura 1C). Os genes que apresentaram diferentes tendências de expressão nos conjuntos de dados GSE57345 e GSE1428 foram descartados da análise, deixando 114 DEGs restantes.

– Volcano plot e diagrama de Venn. A) Volcano plot de GSE57345. B) Volcano plot de GSE1428. Os genes regulados positivamente estão marcados em vermelho claro; genes regulados negativamente estão marcados em azul claro. C) Os dois conjuntos de dados mostraram uma sobreposição de 224 DEGs.

Análises de vias GO e KEGG

As funções desses DEGs comuns foram exploradas usando análises de enriquecimento GO e KEGG no pacote ‘cluster profiler’ no software R. A análise KEGG indicou enriquecimento dos DEGs em vias relacionadas ao fator de crescimento, secreção de insulina e cGMP-PKG (Figura 2A, 2B). As análises GO mostraram que os genes foram enriquecidos principalmente na via do fator de crescimento (Figura 3A, 3B).

– A) Com base no valor adj P, o gráfico de barras mostra as principais vias KEGG entre sarcopenia e IC. B) As principais vias de enriquecimento do KEGG foram apresentadas como mapas conceituais.

– A) Com base no valor de ajuste P, o gráfico de barras mostra as principais vias GO entre sarcopenia e IC em relação à função molecular, processo biológico e componente celular. B) Os principais vias de enriquecimento do banco de dados GO foram apresentados como mapas conceituais.

Construção de rede PPI de DEGs comuns e identificação de genes hub

Os 114 DEGs comuns foram então importados para o STRING, com o arquivo STRING posteriormente importado para o Cytoscape para visualização. A Figura 4 mostra a rede PPI, na qual podem ser vistos 64 nós e 180 bordas. Os 10 principais genes hub foram encontrados usando o plugin CytoHubba e avaliados pelo grau de CYP27A1, KCNJ8, PIK3R5, TM7SF2, TIMP2, CXCL12, KIT, VCAM1, CYP46A1 e VCAM1 (Figura 5A).

– Diagrama de rede PPI. Vermelho indica genes regulados positivamente e azul claro indica genes regulados negativamente.

– A) Detecção de genes hub da rede PPIs de genes comuns. Os 10 genes hub destacados com base em seu grau. B) GSEA dos genes hub.

Resultados GSEA de genes hub

O GSEA foi então utilizado para examinar as possíveis funções dos genes hub, juntamente com a identificação de vias afetadas pela expressão diferencial dos genes, levando assim à identificação de vias associadas ao desenvolvimento de IC e sarcopenia. Os resultados mostraram que os genes hub foram significativamente associados à ativação das vias de sinalização NF-kappa B e de sinalização TNF (Figura 5B).

 Validação de Genes Hub

Esses achados foram validados nos conjuntos de dados GEO GSE76701 para IC e GSE136344 para Sarcopenia. Em comparação com os controles, a interseção de 10 genes dos dois arquivos de matriz de conjuntos de dados revelou a regulação negativa significativa de 7 genes hub candidatos na IC (Figura 6A) e na Sarcopenia (Figura 6B). Esses genes hub foram CYP27A1, KCNJ8, PIK3R5, TIMP2, CXCL12, KIT e VCAM1.

– Validação de genes hub. A) Os genes Hub foram validados em GSE76701 para IC. B) Os genes Hub foram validados em GSE136344 para Sarcopenia. *p < 0,05, **p < 0,01, ***p < 0,001.

Discussão

Há evidências de que muitos pacientes com IC apresentam fadiga, deficiência nutricional, diminuição da capacidade de caminhar e redução da força muscular, conhecida como sarcopenia. A sarcopenia está associada ao envelhecimento e é caracterizada pela redução da resistência física e da massa muscular.17 A incidência de sarcopenia é maior em pacientes com IC em comparação com indivíduos controle da mesma idade, e esses pacientes frequentemente apresentam perda muscular mais rápida, o que compromete ainda mais sua função cardíaca.2 É, portanto, provável que a IC e a sarcopenia possam ter uma patogênese comum ou sobreposta. A elucidação desses mecanismos patogênicos é necessária para o desenvolvimento de tratamentos adequados.

Este estudo identificou 114 DEGs que se sobrepunham entre as duas doenças. As redes PPI e a subsequente validação destes DEGs sobrepostos identificaram 7 genes significativos, nomeadamente, CYP27A1, KCNJ8, PIK3R5, TIMP2, CXCL12, KIT e VCAM1. Como mostrado pelas análises de enriquecimento GO e KEGG, estes genes foram significativamente enriquecidos nas vias responsáveis pelo fator de crescimento, secreção de insulina e cGMP-PKG. As vias dos fatores de crescimento desempenham papéis importantes no desenvolvimento e manutenção da vasculatura, prevenindo o crescimento excessivo, a remodelação e a desestabilização por vários mecanismos de feedback.18 A via de secreção da insulina é fundamental para o metabolismo da glicose, e sua desregulação está associada ao diabetes, um conhecido fator de risco para IC e Sarcopenia.19 A via cGMP-PKG está envolvida na disfunção diastólica, associada à rigidez diastólica, relaxamento lento e redução da elasticidade dos cardiomiócitos.20

A GSEA indicou a associação de vias relacionadas à inflamação, incluindo as vias de sinalização NF-kappa B e TNF, com a patogênese da IC e da sarcopenia. Ambos os distúrbios estão associados à inflamação crônica, como observado nos níveis elevados de citocinas pró-inflamatórias, como TNF-a, IL-6 e IL-12. Estes aumentam a adiposidade visceral e reduzem a massa e a força muscular, aumentando o risco de IC.21,22 Nossos achados sugerem que os genes hub estão intimamente envolvidos com processos relacionados à inflamação mediados pelas vias de sinalização identificadas e contribuem para o desenvolvimento de IC e Sarcopenia. Considerando os genes hub, o CYP27A1 é um membro da família do citocromo P450 responsável por regular a homeostase do colesterol, convertendo o excesso de colesterol em ácido biliar.23 Ele também catalisa a 25-hidroxilação da vitamina D3, resultando na ativação funcional.24 Tanto a homeostase do colesterol quanto a vitamina D os níveis têm sido associados à patogênese e aos resultados da IC e da sarcopenia.25,26 O KCNJ8 é expresso pela maioria das células de mamíferos, onde regula os potenciais de membrana; níveis elevados são encontrados no coração, onde, juntamente com o SER2, forma um canal de potássio dependente de ATP. KCNJ8 tem sido associado a distúrbios cardiovasculares, incluindo vasomoção coronariana anormal e disfunção microvascular, doença cardíaca isquêmica e diabetes tipo 2.27-29 PIK3R5 está envolvido em muitos processos celulares, incluindo crescimento, proliferação, diferenciação, motilidade, tráfego intracelular e sobrevivência. Também foi proposto como biomarcador para hipertensão e diabetes mellitus.30,31 Foi relatado que níveis elevados de pressão arterial e glicose estão associados ao aumento da incidência de IC e sarcopenia.32,33 TIMP2, juntamente com outros membros da família de genes TIMP, inibem metaloproteinases de matriz (MMPs).34 MMPs, incluindo MMP-1, -2, -3, -9 e -19, são peptidases que degradam a matriz extracelular. TIMP2 e essas MMPs podem controlar a homeostase da matriz, modulando, especialmente, a produção e degradação de colágeno, que é conhecido por desempenhar um papel importante na patogênese da IC.35 A interrupção do equilíbrio MMP/TIMP2 no envelhecimento dos músculos esqueléticos afeta adversamente a função metabólica de a matriz extracelular e a produção excessiva de colágeno; estes, por sua vez, influenciam a massa e a função muscular e podem levar à sarcopenia.36 CXCL12 é um ligante de um receptor acoplado à proteína G e é conhecido por estar envolvido em várias atividades celulares, incluindo respostas imunológicas e inflamatórias, embriogênese, homeostase de tecido e carcinogênese e metástase.37 O CXCL12 é relatado como um elo importante entre inflamação e fibrose e foi proposto como um alvo para o tratamento da IC.38 Na sarcopenia, o CXCL12 influencia o desenvolvimento e funcionamento de osteoblastos, osteoclastos, células satélites e mioblastos, todos necessários para manter a homeostase muscular.39 O KIT codifica um receptor tirosina quinase que regula a proliferação e sobrevivência celular, bem como o desenvolvimento de mastócitos, gametogênese e melanogênese.40 O KIT é supostamente fortemente expresso no tecido cardíaco e parece ser envolvido na IC.41 O KIT promove a fosforilação de MAPK1/ERK2 durante a mitofagia.42 As interrupções na mitofagia, a degradação autofágica de mitocôndrias disfuncionais, estão associadas à atrofia das fibras musculares na sarcopenia.43 VCAM1 pertence à superfamília das imunoglobulinas e codifica uma sialoglicoproteína expressa em superfícies endoteliais após ativação de citocinas. Está envolvido na resposta imune e na promoção do direcionamento das células imunes para locais de inflamação.44 As vias imunológicas e inflamatórias estão associadas à patogênese da sarcopenia e da IC.21,22 Assim, os genes hub identificados e suas vias de sinalização associadas provavelmente serão intimamente envolvido na patogênese da IC e da sarcopenia.

No entanto, este estudo tem várias limitações. O estudo retrospectivo concentrou-se em um conjunto de dados de expressão gênica com um tamanho de amostra relativamente pequeno, levando potencialmente a um viés de seleção. Também é possível que genes significativos tenham sido negligenciados durante as diferentes etapas do processo de seleção. Investigações futuras devem utilizar amostras maiores e avaliar modelos celulares e animais para verificação.

 Conclusões

Foram identificados DEGs comuns associados à IC e à sarcopenia, e suas funções e interações foram analisadas por redes de enriquecimento e PPI. Os resultados indicaram que ambas as doenças tinham muitas vias patogénicas comuns, possivelmente sob o controle dos genes hub identificados, ilustraram o possível mecanismo da sarcopenia secundária à IC e identificaram novos genes candidatos que poderiam ser utilizados como biomarcadores ou como potenciais alvos terapêuticos.

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Jitrapakdee S Wutthisathapornchai A Wallace JC MacDonald MJ Regulation of Insulin Secretion: Role of Mitochondrial Signalling Diabetologia 2010 53 6 1019 1032 10.1007/s00125-010-1685-0 20. Krüger M, Kötter S, Grützner A, Lang P, Andresen C, Redfield MM, et al. Protein Kinase G Modulates Human Myocardial Passive Stiffness by Phosphorylation of the Titin Springs. Circ Res. 2009;104(1):87-94. doi: 10.1161/CIRCRESAHA.108.184408. Krüger M Kötter S Grützner A Lang P Andresen C Redfield MM et al Protein Kinase G Modulates Human Myocardial Passive Stiffness by Phosphorylation of the Titin Springs Circ Res 2009 104 1 87 94 10.1161/CIRCRESAHA.108.184408 21. Livshits G, Kalinkovich A. Inflammaging as a Common Ground for the Development and Maintenance of Sarcopenia, Obesity, Cardiomyopathy and Dysbiosis. Ageing Res Rev. 2019;56:100980. doi: 10.1016/j.arr.2019.100980. Livshits G Kalinkovich A Inflammaging as a Common Ground for the Development and Maintenance of Sarcopenia, Obesity, Cardiomyopathy and Dysbiosis Ageing Res Rev 2019 56 100980 10.1016/j.arr.2019.100980 22. Koshikawa M, Harada M, Noyama S, Kiyono K, Motoike Y, Nomura Y, et al. Association between Inflammation and Skeletal Muscle Proteolysis, Skeletal Mass and Strength in Elderly Heart Failure Patients and their Prognostic Implications. BMC Cardiovasc Disord. 2020;20(1):228. doi: 10.1186/s12872-020-01514-0. Koshikawa M Harada M Noyama S Kiyono K Motoike Y Nomura Y et al Association between Inflammation and Skeletal Muscle Proteolysis, Skeletal Mass and Strength in Elderly Heart Failure Patients and their Prognostic Implications BMC Cardiovasc Disord 2020 20 1 228 10.1186/s12872-020-01514-0 23. Tang W, Norlin M, Wikvall K. Glucocorticoid Receptor-Mediated Upregulation of Human CYP27A1, a Potential Anti-Atherogenic Enzyme. 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Ter Borg S Luiking YC van Helvoort A Boirie Y Schols JMGA de Groot CPGM Low Levels of Branched Chain Amino Acids, Eicosapentaenoic Acid and Micronutrients are Associated with Low Muscle Mass, Strength and Function in Community-Dwelling Older Adults J Nutr Health Aging 2019 23 1 27 34 10.1007/s12603-018-1108-3 27. Lang V, Youssef N, Light PE. The Molecular Genetics of Sulfonylurea Receptors in the Pathogenesis and Treatment of Insulin Secretory Disorders and Type 2 Diabetes. Curr Diab Rep. 2011;11(6):543-51. doi: 10.1007/s11892-011-0233-8. Lang V Youssef N Light PE The Molecular Genetics of Sulfonylurea Receptors in the Pathogenesis and Treatment of Insulin Secretory Disorders and Type 2 Diabetes Curr Diab Rep 2011 11 6 543 551 10.1007/s11892-011-0233-8 28. Fedele F, Mancone M, Chilian WM, Severino P, Canali E, Logan S, et al. Role of Genetic Polymorphisms of ion Channels in the Pathophysiology of Coronary Microvascular Dysfunction and Ischemic Heart Disease. Basic Res Cardiol. 2013;108(6):387. doi: 10.1007/s00395-013-0387-4. Fedele F Mancone M Chilian WM Severino P Canali E Logan S et al Role of Genetic Polymorphisms of ion Channels in the Pathophysiology of Coronary Microvascular Dysfunction and Ischemic Heart Disease Basic Res Cardiol 2013 108 6 387 10.1007/s00395-013-0387-4 29. Emanuele E, Falcone C, Carabela M, Minoretti P, D’Angelo A, Montagna L, et al. Absence of Kir6.1/KCNJ8 Mutations in Italian Patients with Abnormal Coronary Vasomotion. Int J Mol Med. 2003;12(4):509-12. Emanuele E Falcone C Carabela M Minoretti P D’Angelo A Montagna L et al Absence of Kir6.1/KCNJ8 Mutations in Italian Patients with Abnormal Coronary Vasomotion Int J Mol Med 2003 12 4 509 512 30. Yang F, Chen Y, Xue Z, Lv Y, Shen L, Li K, et al. High-Throughput Sequencing and Exploration of the lncRNA-circRNA-miRNA-mRNA Network in Type 2 Diabetes Mellitus. Biomed Res Int. 2020;2020:8162524. doi: 10.1155/2020/8162524. Yang F Chen Y Xue Z Lv Y Shen L Li K et al High-Throughput Sequencing and Exploration of the lncRNA-circRNA-miRNA-mRNA Network in Type 2 Diabetes Mellitus Biomed Res Int 2020 2020 8162524 10.1155/2020/8162524 31. Quintanilha JCF, Racioppi A, Wang J, Etheridge AS, Denning S, Peña CE, et al. PIK3R5 Genetic Predictors of Hypertension Induced by VEGF-Pathway Inhibitors. Pharmacogenomics J. 2022;22(1):82-8. doi: 10.1038/s41397-021-00261-5. Quintanilha JCF Racioppi A Wang J Etheridge AS Denning S Peña CE et al PIK3R5 Genetic Predictors of Hypertension Induced by VEGF-Pathway Inhibitors Pharmacogenomics J 2022 22 1 82 88 10.1038/s41397-021-00261-5 32. Yano T, Katano S, Kouzu H, Nagaoka R, Inoue T, Takamura Y, et al. Distinct Determinants of Muscle Wasting in Nonobese Heart Failure Patients with and Without Type 2 Diabetes Mellitus. J Diabetes. 2021;13(1):7-18. doi: 10.1111/1753-0407.13090. Yano T Katano S Kouzu H Nagaoka R Inoue T Takamura Y et al Distinct Determinants of Muscle Wasting in Nonobese Heart Failure Patients with and Without Type 2 Diabetes Mellitus J Diabetes 2021 13 1 7 18 10.1111/1753-0407.13090 33. Medvedev NV, Gorshunova NK. Age-Related Sarcopenia as the Risk Factor of Development of Myocardial Dysfunction and Chronic Heart Failure in Elderly Patients with Arterial Hypertension. Adv Gerontol. 2012;25(3):456-60. Medvedev NV Gorshunova NK Age-Related Sarcopenia as the Risk Factor of Development of Myocardial Dysfunction and Chronic Heart Failure in Elderly Patients with Arterial Hypertension Adv Gerontol 2012 25 3 456 460 34. Kim LB, Russkih GS, Putyatina AN, Tsypysheva OB. Age-Related Dynamics of the Contents of Matrix Metalloproteinases (MMP-1, -2, -3, -9) and Tissue Inhibitors of Matrix Metalloproteinases (TIMP-1, -2, -4) in Blood Plasma of Residents of the European Part of the Arctic Zone of the Russian Federation. Adv Gerontol. 2018;31(2):223-30. Kim LB Russkih GS Putyatina AN Tsypysheva OB Age-Related Dynamics of the Contents of Matrix Metalloproteinases (MMP-1, -2, -3, -9) and Tissue Inhibitors of Matrix Metalloproteinases (TIMP-1, -2, -4) in Blood Plasma of Residents of the European Part of the Arctic Zone of the Russian Federation Adv Gerontol 2018 31 2 223 230 35. Zile MR, O’Meara E, Claggett B, Prescott MF, Solomon SD, Swedberg K, et al. Effects of Sacubitril/Valsartan on Biomarkers of Extracellular Matrix Regulation in Patients with HFrEF. J Am Coll Cardiol. 2019;73(7):795-806. doi: 10.1016/j.jacc.2018.11.042. Zile MR O’Meara E Claggett B Prescott MF Solomon SD Swedberg K et al Effects of Sacubitril/Valsartan on Biomarkers of Extracellular Matrix Regulation in Patients with HFrEF J Am Coll Cardiol 2019 73 7 795 806 10.1016/j.jacc.2018.11.042 36. Lluri G, Langlois GD, McClellan B, Soloway PD, Jaworski DM. Tissue Inhibitor of Metalloproteinase-2 (TIMP-2) Regulates Neuromuscular Junction Development Via a Beta1 Integrin-Mediated Mechanism. J Neurobiol. 2006;66(12):1365-77. doi: 10.1002/neu.20315. Lluri G Langlois GD McClellan B Soloway PD Jaworski DM Tissue Inhibitor of Metalloproteinase-2 (TIMP-2) Regulates Neuromuscular Junction Development Via a Beta1 Integrin-Mediated Mechanism J Neurobiol 2006 66 12 1365 1377 10.1002/neu.20315 37. García-Cuesta EM, Santiago CA, Vallejo-Díaz J, Juarranz Y, Rodríguez-Frade JM, Mellado M. The Role of the CXCL12/CXCR4/ACKR3 Axis in Autoimmune Diseases. Front Endocrinol (Lausanne). 2019;10:585. doi: 10.3389/fendo.2019.00585. García-Cuesta EM Santiago CA Vallejo-Díaz J Juarranz Y Rodríguez-Frade JM Mellado M The Role of the CXCL12/CXCR4/ACKR3 Axis in Autoimmune Diseases Front Endocrinol 2019 10 10.3389/fendo.2019.00585 38. Li R, Frangogiannis NG. Chemokines in Cardiac Fibrosis. Curr Opin Physiol. 2021;19:80-91. doi: 10.1016/j.cophys.2020.10.004. Li R Frangogiannis NG Chemokines in Cardiac Fibrosis Curr Opin Physiol 2021 19 80 91 10.1016/j.cophys.2020.10.004 39. Gilbert W, Bragg R, Elmansi AM, McGee-Lawrence ME, Isales CM, Hamrick MW. Stromal Cell-Derived Factor-1 (CXCL12) and its Role in Bone and Muscle Biology. Cytokine. 2019;123:154783. doi: 10.1016/j.cyto.2019.154783. Gilbert W Bragg R Elmansi AM McGee-Lawrence ME Isales CM Hamrick MW Stromal Cell-Derived Factor-1 (CXCL12) and its Role in Bone and Muscle Biology Cytokine 2019 123 154783 10.1016/j.cyto.2019.154783 40. Martinez-Anton A, Gras D, Bourdin A, Dubreuil P, Chanez P. KIT as a Therapeutic Target for Non-Oncological Diseases. Pharmacol Ther. 2019;197:11-37. doi: 10.1016/j.pharmthera.2018.12.008. Martinez-Anton A Gras D Bourdin A Dubreuil P Chanez P KIT as a Therapeutic Target for Non-Oncological Diseases Pharmacol Ther 2019 197 11 37 10.1016/j.pharmthera.2018.12.008 41. Basuray A, French B, Ky B, Vorovich E, Olt C, Sweitzer NK, et al. Heart Failure with Recovered Ejection Fraction: Clinical Description, Biomarkers, and Outcomes. Circulation. 2014;129(23):2380-7. doi: 10.1161/CIRCULATIONAHA.113.006855. Basuray A French B Ky B Vorovich E Olt C Sweitzer NK et al Heart Failure with Recovered Ejection Fraction: Clinical Description, Biomarkers, and Outcomes Circulation 2014 129 23 2380 2387 10.1161/CIRCULATIONAHA.113.006855 42. Sun S, Shen Y, Wang J, Li J, Cao J, Zhang J. Identification and Validation of Autophagy-Related Genes in Chronic Obstructive Pulmonary Disease. Int J Chron Obstruct Pulmon Dis. 2021;16:67-78. doi: 10.2147/COPD.S288428. Sun S Shen Y Wang J Li J Cao J Zhang J Identification and Validation of Autophagy-Related Genes in Chronic Obstructive Pulmonary Disease Int J Chron Obstruct Pulmon Dis 2021 16 67 78 10.2147/COPD.S288428 43. Del Campo A. Mitophagy as a New Therapeutic Target for Sarcopenia. Acta Physiol. 2019;225(2):e13219. doi: 10.1111/apha.13219. Del Campo A Mitophagy as a New Therapeutic Target for Sarcopenia Acta Physiol 2019 225 2 e13219 10.1111/apha.13219 44. Lukacs NW, Strieter RM, Evanoff HL, Burdick MD, Kunkel SL. VCAM-1 Influences Lymphocyte Proliferation and Cytokine Production During Mixed Lymphocyte Responses. Cell Immunol. 1994;154(1):88-98. doi: 10.1006/cimm.1994.1059. Lukacs NW Strieter RM Evanoff HL Burdick MD Kunkel SL VCAM-1 Influences Lymphocyte Proliferation and Cytokine Production During Mixed Lymphocyte Responses Cell Immunol 1994 154 1 88 98 10.1006/cimm.1994.1059

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Aprovação ética e consentimento informado

Este estudo foi aprovado pelo Comitê de Ética do People’s Hospital of Xinjiang Uygur Autonomous Region sob o número de protocolo KY2022031398. Todos os procedimentos envolvidos nesse estudo estão de acordo com a Declaração de Helsinki de 1975, atualizada em 2013. O consentimento informado foi obtido de todos os participantes incluídos no estudo.

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 Original Article Bioinformatics and Systems Biology Approach to Identify the Pathogenetic Link between Heart Failure and Sarcopenia 0000-0001-6017-5927 Xu Rui Conception and design of the research Writing of the manuscript Acquisition of data and Analysis and interpretation of the data 1 Ma Ling-ling Acquisition of data and Analysis and interpretation of the data 1 Cui Shuai Critical revision of the manuscript for important intellectual content 1 Chen Ling Critical revision of the manuscript for important intellectual content 1 Xu Hong Conception and design of the research 1 China Gerontology center, People’s Hospital of Xinjiang Uygur Autonomous Region, Urumqi – China Mailing Address: Hong Xu • N°. 91 Tianchi Road, Tianshan Disztrict, Urumqi, Xinjiang E-mail: dengta2022@126.com

Potential conflict of interest

No potential conflict of interest relevant to this article was reported.

Editor responsible for the review: Gláucia Maria Moraes de Oliveira

 Central Illustration

: Bioinformatics and Systems Biology Approach to Identify the Pathogenetic Link between Heart Failure and Sarcopenia Research design flow chart.

Abstract Background

Despite increasing evidence that patients with heart failure (HF) are susceptible to sarcopenia, the reason for the association is not well understood.

 Objective

The purpose of this study is to explore further the molecular mechanism of the occurrence of this complication.

 Methods

Gene expression datasets for HF (GSE57345) and Sarcopenia (GSE1428) were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified using ‘edgeR’ and “limma” packages of R, and their functions were analyzed using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Protein-protein interaction (PPI) networks were constructed and visualized using Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape. Hub genes were selected using the plugin cytoHubba and validation with GSE76701 for HF and GSE136344 for Sarcopenia. The related pathways and molecular mechanisms of the hub genes were performed by Gene set enrichment analysis (GSEA). The statistical analyses were performed using R software. P < 0.05 was considered statistically significant.

 Results

A total of 114 common DEGs were found. Pathways related to growth factor, Insulin secretion and cGMP-PKG were enriched in both HF and Sarcopenia. CYP27A1, KCNJ8, PIK3R5, TIMP2, CXCL12, KIT, and VCAM1 were found to be significant hub genes after validation, with GSEA emphasizing the importance of the hub genes in the regulation of the inflammatory response.

 Conclusion

Our study reveals that HF and Sarcopenia share common pathways and pathogenic mechanisms. These findings may suggest new directions for future research into the underlying pathogenesis.

 Sarcopenia Heart Failure Computational Biology Genes Introduction

The overall aging of populations worldwide is leading to an increased prevalence of age-related disorders such as HF, which burdens healthcare systems significantly.1 The etiology of HF is complex and multifactorial, resulting in reduced functional capacity, often with poor prognosis. Sarcopenia has been identified as a potential extracardiac predictor of a poorer prognosis in HF patients.2

Sarcopenia is a progressive disorder wherein affected individuals experience the progressive, debilitating loss of muscle mass, ultimately contributing to high rates of frailty among older populations.3 It is associated with an increased risk of falling, osteoporosis, loss of independence, and increased mortality.4 Muscle wasting is frequently described as a type of secondary sarcopenia, sometimes under the term “cachexia” in patients with HF.5 Nevertheless, while this age-associated loss of skeletal muscle mass remains a major concern for elderly patients with HF, the mechanisms underlying the co-occurrence of sarcopenia and HF are poorly understood.

An analysis of common genes and pathways may provide insight into the coexistence of HF and Sarcopenia. Thus, we analyzed hub genes common to both disorders and predicted the pathways associated with these genes by quantitative bioinformatic analysis of publicly available data. The findings may provide fresh insight into the mechanisms underlying the co-occurrence of these two common disorders.

 Methods Study design and data collection

Gene Expression Omnibus (GEO) is a public functional genomics data repository supporting MIAME-compliant data submissions. Tools are provided to help users query and download experiments and curated gene expression profiles. Gene expression datasets were obtained from the GEO database using the search terms “Heart Failure” and “Sarcopenia.”6 For inclusion, the criteria were the presence of independent arrays with large sample sizes and human data. This resulted in the inclusion of two datasets, namely, GSE573457 and GSE1428.8 The GSE57345 dataset included RNA-sequencing data from 177 patients with HF and 136 healthy controls from Philadelphia, while the GSE1428 dataset contained RNA-sequencing data of vastus lateralis muscle samples from 12 patients with Sarcopenia (70-80 years old) and 10 young healthy controls (19-25 years old) from Boston.

 Identification of differentially expressed genes with R software

The data from GSE57345 and GSE1428 were normalized, and DEGs between patient and control samples were identified with the R package ‘edgeR’ and ‘limma’..9 Fold changes were determined for the expression of the individual genes, with genes showing Fold changes > 1.2 and P-value < 0.05 classified as DEGs. Genes common to Sarcopenia and HF were obtained by overlapping the two sets of DEGs. R package ‘VennDiagram’ was used to obtain their common DEGs.10 We then overlapped the related genes of HF and Sarcopenia to obtain common genes for further analysis.

 Functional annotation and pathway enrichment analysis

Further functional analysis of the common DEGs was conducted by the assessment of GO annotations and KEGG enriched pathways using the ‘cluster’ package in R.11 GO annotations fall into three categories, namely, biological process (BP), cellular component (CC) and molecular function (MF). P value < 0.05 was used as the threshold of significance.

 PPI network construction and identification of hub genes

The PPI networks for the common DEGs were then created in STRING with visualization by Cytoscape 3.9.0.12 Confidence scores > 0.4 were set to intermediate values. The Cytoscape plugin, CytoHubba, was used for filtering the hub genes in the PPI network using the algorithm of Degree.13

 Gene set enrichment analysis

GSEA was used to determine the associations between pathways and functions of the hub genes.14 Significance levels were set at nominal p values of < 0.05, normalized enrichment scores (NES) > 1, and false positive rate (FDR) q values of < 0.25.

 Validation of hub genes expression in other data sets

The mRNA levels of the hub genes were then verified for GSE7670115 and GSE136344.16 GSE76701 contained 4 HF subjects and 4 controls, while GSE136344 contained 19 Sarcopenia subjects and 11 controls. T-test assessed differences between the two data sets with a p-value < 0.05 considered significant.

 Statistical analysis

This study conducted all statistical analyses using R software (version 4.1.2; https://www.r-project.org/). The normal distribution of different parameters was verified with the Kolmogorov-Smirnov test. Differences between the groups were evaluated using Student’s unpaired t-test. A value of p < 0.05 was considered significant.

 Results Identification of DEGs

The research flowchart of this research was shown in the Central Figure. All data from two independent datasets (GSE57345: HF and GSE1428: Sarcopenia) were obtained from the GEO. The microarray data were normalized, and the DEGs were identified (1954 in GSE57345 and 2242 in GSE1428). For better visualization, the DEGs for HF and Sarcopenia were presented as volcano plots (Figures 1A, B). 224 DEGs common to both groups were identified using the Venn diagram (Figure 1C). Genes that showed different trends in expression in the GSE57345 and GSE1428 datasets were discarded from the analysis, leaving 114 DEGs remaining.

– Volcano diagram and Venn diagram. A) Volcano map of GSE57345. B) Volcano map of GSE1428. Upregulated genes are marked in light red; downregulated genes are marked in light blue. C) The two datasets showed an overlap of 224 DEGs.

GO and KEGG Pathway Analyses

The functions of these common DEGs were explored using GO and KEGG enrichment analyses in the ‘cluster profiler’ package in R software. The KEGG analysis indicated enrichment of the DEGs in pathways related to growth factor, Insulin secretion, and cGMP-PKG (Figure 2A, 2B). GO analyses showed that the genes were mainly enriched in the growth factor pathway (Figure 3A, 3B).

– A) Based on the adj p value, the bar plot shows the Top KEGG pathways between sarcopenia and HF. B) The top enrichment pathways from KEGG were presented as bubble maps.

– A) Based on the adj P value, The bar plot shows the top GO pathways between sarcopenia and HF regarding molecular function, biological process, and cellular component. B) The top enrichment pathways from GO database were presented as bubble maps.

PPI Network Construction of Common DEGs and Identification of Hub Genes

The 114 common DEGs were then imported into STRING, with the STRING file subsequently imported into Cytoscape for visualization. Figure 4 shows the PPI network, in which 64 nodes and 180 edges can be seen. The top 10 hub genes were found using the CytoHubba plugin and assessed by the degree to be CYP27A1, KCNJ8, PIK3R5, TM7SF2, TIMP2, CXCL12, KIT, VCAM1, CYP46A1, and VCAM1 (Figure 5A).

– PPI network diagram. Red indicates up-regulated genes and light blue indicates down-regulated genes.

– A) Detection of hub genes from the PPIs network of common genes. The highlighted 10 hub genes based on their degree. B) GSEA of the hub genes.

GSEA Results of Hub Genes

GSEA was then used to examine the possible functions of the hub genes, together with identifying pathways affected by the differential expression of the genes, thus leading to the identification of pathways associated with the development of HF and Sarcopenia. Results showed that the hub genes were significantly associated with activating the NF-kappa B signaling and TNF-signaling pathways (Figure 5B).

 Validation of Hub Genes

These findings were validated in the GEO datasets GSE76701 for HF and GSE136344 for Sarcopenia. Compared with controls, the intersection of 10 genes from the two matrix files of datasets revealed the significant downregulation of 7 candidate hub genes in HF (Figure 6A) and Sarcopenia (Figure 6B). These hub genes were CYP27A1, KCNJ8, PIK3R5, TIMP2, CXCL12, KIT, and VCAM1.

– Validation of hub genes. A) Hub genes were validated in GSE76701 for HF. B) Hub genes were validated in GSE136344 for Sarcopenia. *p < 0.05, **p < 0.01, ***p < 0.001.

Discussion

There is evidence that many patients with HF experience fatigue, nutritional deficiency, decreased ability to walk, and reduced muscle strength, known as Sarcopenia. Sarcopenia is associated with aging and is characterized by reduced physical stamina and muscle mass.17 The incidence of Sarcopenia is higher in HF patients compared with age-matched control subjects, and these patients often show more rapid muscle loss, which further compromises their cardiac function.2 It is thus likely that HF and Sarcopenia may have a common or overlapping pathogenesis. The elucidation of these pathogenic mechanisms is necessary to develop suitable treatments.

This study identified 114 DEGs that overlapped between the two diseases. PPI networks and subsequent validation of these overlapping DEGs identified 7 significant genes, namely, CYP27A1, KCNJ8, PIK3R5, TIMP2, CXCL12, KIT, and VCAM1. As shown by GO and KEGG enrichment analyses, these genes were significantly enriched in pathways responsible for growth factor, Insulin secretion, and cGMP-PKG. Growth factor pathways play major roles in developing and maintaining the vasculature, preventing excess growth, remodeling, and destabilization by various feedback mechanisms.18 The insulin secretion pathway is key to glucose metabolism, and its dysregulation is associated with diabetes, a known risk factor for both HF and Sarcopenia.19 The cGMP-PKG pathway is involved in diastolic dysfunction, associated with diastolic stiffness, slow relaxation, and reduced elasticity of the cardiomyocytes.20

GSEA indicated the association of inflammation-related pathways, including the NF-kappa B and TNF-signaling pathways, with HF and Sarcopenia pathogenesis. Both disorders are associated with chronic inflammation, as seen in the raised levels of pro-inflammatory cytokines, such as TNF-a, IL-6, and IL-12. These enhance visceral adiposity and reduce muscle mass and strength, increasing the risk of HF.21,22 Our findings suggest that the hub genes are closely involved with inflammation-related processes mediated by the identified signaling pathways and contribute to the development of HF and Sarcopenia.

Considering the hub genes, CYP27A1 is a member of the cytochrome P450 family responsible for regulating cholesterol homeostasis by converting excess cholesterol to bile acid.23 It also catalyzes 25-hydroxylation of vitamin D3, resulting in functional activation.24 Both cholesterol homeostasis and vitamin D levels have been linked to the pathogenesis and outcomes of HF and Sarcopenia.25,26 KCNJ8 is expressed by most mammalian cells, where it regulates membrane potentials; high levels are found in the heart, where it, together with SER2, forms an ATP-dependent potassium channel. KCNJ8 has been linked with cardiovascular disorders, including abnormal coronary vasomotion and microvascular dysfunction, ischemic heart disease, and type 2 diabetes.27-29 PIK3R5 is involved in many cellular processes, including growth, proliferation, differentiation, motility, intracellular trafficking, and survival. It has also been proposed as a biomarker for hypertension and diabetes mellitus.30,31 Raised blood pressure and glucose levels are reported to be associated with increased incidence of HF and Sarcopenia.32,33 TIMP2, together with other members of the TIMP gene family, inhibit matrix metalloproteinases (MMPs).34 MMPs, including MMP-1, -2, -3, -9, and -19, are peptidases that degrade the extracellular matrix. TIMP2 and these MMPs can control homeostasis of the matrix, modulating, especially, collagen production and degradation, which is known to play an important role in HF pathogenesis.35 Disruption of the MMP/TIMP2 balance in aging skeletal muscles adversely affects the metabolic function of the extracellular matrix and excess collagen production; these, in turn, influence both muscle mass and function and can lead to Sarcopenia.36 CXCL12 is a ligand of a G-protein-coupled receptor and is known to be involved in various cellular activities, including immune and inflammatory responses, embryogenesis, tissue homeostasis, and carcinogenesis and metastasis.37 CXCL12 is reported to be an important link between inflammation and fibrosis and has been proposed as a target for the treatment of HF.38 In sarcopenia, CXCL12 influences the development and functioning of osteoblasts, osteoclasts, satellite cells, and myoblasts, all necessary for maintaining muscle homeostasis.39 KIT encodes a receptor tyrosine kinase that regulates cellular proliferation and survival, as well as mast cell development, gametogenesis, and melanogenesis.40 KIT is reportedly strongly expressed in heart tissue and appears to be involved in HF.41 KIT promotes the phosphorylation of MAPK1/ERK2 during mitophagy.42 Disruptions in mitophagy, the autophagic degradation of dysfunctional mitochondria, are associated with muscle fiber atrophy in sarcopenia.43 VCAM1 belongs to the immunoglobulin superfamily and encodes a sialoglycoprotein expressed on endothelial surfaces following cytokine activation. It is involved in the immune response and promoting immune cell targeting to inflammation sites.44 Immune and inflammatory pathways are associated with the pathogenesis of both sarcopenia and HF.21,22 Thus, the identified hub genes and their associated signaling pathways are likely to be closely involved in the pathogenesis of both HF and Sarcopenia.

However, this study has several limitations. The retrospective study focused on a gene expression dataset with a relatively small sample size, potentially leading to selection bias. It is also possible that significant genes might have been overlooked during the different steps of the selection process. Future investigations should use larger samples and assess both cellular and animal models for verification.

 Conclusions

To summarize, common DEGs associated with HF and sarcopenia were identified, and their functions and interactions were analyzed by enrichment and PPI networks. The findings indicated that both diseases had many common pathogenic pathways, possibly under the control of the identified hub genes, illustrated the possible mechanism of sarcopenia secondary to HF, and identified novel gene candidates who could be used as biomarkers or as potential therapeutic targets.

Study association

This study is not associated with any thesis or dissertation work.

Ethics approval and consent to participate

This study was approved by the Ethics Committee of the People’s Hospital of Xinjiang Uygur Autonomous Region under the protocol number KY2022031398. All the procedures in this study were in accordance with the 1975 Helsinki Declaration, updated in 2013. Informed consent was obtained from all participants included in the study.

Sources of funding: There were no external funding sources for this study.