Campus de Botucatu 

Instituto de Biociências 

Biociências 

PG-BGA 
 

 

Perfil global de expressão de MicroRNAs na próstata ventral 

de ratos submetidos à restrição proteica materna: Efeitos 

perinatais e reflexos no envelhecimento 

 

 

 

MS. FLÁVIA BESSI CONSTANTINO 

 

 

 

 

Tese apresentada ao Instituto de 

Biociências, Campus de Botucatu, UNESP, para 

obtenção do título de Doutor no Programa de Pós-

Graduação em Biologia Geral e Aplicada, Área de 

concentração Biologia Estrutural e Funcional. 

 

 

 

 

BOTUCATU – SP 

2021 

 



                                                                                                                                                                               

 

 

Campus de Botucatu 

Instituto de Biociências 

Biociências 

PG-BGA 

 

UNIVERSIDADE ESTADUAL PAULISTA “Júlio de Mesquita Filho” 

Instituto de Biociências-Departamento de Biologia Estrutural e Funcional 

 

 

Perfil global de expressão de MicroRNAs na próstata ventral 

de ratos submetidos à restrição proteica materna: Efeitos 

perinatais e reflexos no envelhecimento 

 

 

MS. FLÁVIA BESSI CONSTANTINO 

PROF. DR. LUIS ANTONIO JUSTULIN JUNIOR 

PROF. DR. ROBSON FRANCISCO CARVALHO 

 

 

Tese apresentada ao Instituto de 

Biociências, Campus de Botucatu, UNESP, para 

obtenção do título de Doutor no Programa de Pós-

Graduação em Biologia Geral e Aplicada, Área de 

concentração Biologia Estrutural e Funcional. 

 

 

BOTUCATU – SP 

2021 



 

3 

 

 

Ficha catalográfica 

 

 

 

 

 

  



 

4 

 

 

 

 

 

 

 

 

 

 

 

 

A minha Família, Namorado e Amigos... 

Dedico  



 

5 

 

Agradecimentos 

Ao Dr. Luis Antonio Justulin Junior, pelo conhecimento compartilhado, pela 

confiança em meu trabalho, por sempre acreditar em meu potencial e me apoiar sempre, 

sem você não teria chegado tão longe. Obrigada por me receber de braços abertos no 

laboratório e me tornar uma amante da pesquisa. 

Ao Laboratório de Matriz Extracelular (Labmec) e agregados, pelo 

companheirismo, conhecimento compartilhado, por todo apoio em momentos difíceis e 

pelas risadas. 

Ao Miguel Antunes, por sempre acreditar em mim, me fazer buscar sempre 

mais, por todo apoio e carinho. Obrigada por além de ser meu técnico, ser meu amigo.  

A minha Família, por sempre serem meu porto seguro e me fazerem alçar voos 

mais altos. Obrigada por sempre me apoiarem e estarem sempre presentes mesmo 

longe.  

Ao Renato Colenci. por sempre estar ao meu lado, por me fazer uma pessoa 

melhor, por sempre me ouvir e dar conselhos, por fazer meus dias mais felizes. Obrigada, 

sem o seu apoio não teria conseguido.  

As agencias de fomento FAPESP (processo nº2017/08716-6) e CAPEs pelo 

suporte financeiro. 

E a todos que contribuíram de alguma forma para o meu crescimento e para 

a confecção desta tese.   



 

6 

 

 

 

 

 

 

 

 

 

 

 

 

 

"A tarefa não é tanto ver aquilo que ninguém viu, mas pensar o que 

ninguém ainda pensou sobre aquilo que todo mundo vê."  

(Arthur Schopenhauer)  



 

7 

 

Sumário 

Lista de abreviaturas ............................................................................................ 9 

Lista de Figuras .................................................................................................. 11 

Lista de tabelas .................................................................................................. 12 

Resumo .............................................................................................................. 14 

Capítulo 1: Revisão Bibliográfica ........................................................................ 15 

1.1 Programação Fetal .................................................................................... 15 

1.2 Programação Fetal e baixa ingesta de Proteína ....................................... 16 

1.3 Próstata e a programação Fetal por baixa ingesta de proteína ................. 18 

1.4 Mecanismos Epigenéticos Envolvidos na Programação Fetal .................. 22 

Referencias ..................................................................................................... 27 

Capítulo 2: MiRNAome integrative analysis reveals molecular mechanisms 

associated with development of PCa in the offspring subjected to maternal low protein 

diet. ............................................................................................................................... 28 

Abstract ........................................................................................................... 29 

1.0 Introduction ............................................................................................ 30 

2.0 Material and Methods ............................................................................. 32 

3.0 Results ................................................................................................... 39 

4.0 Discussion .............................................................................................. 49 

5.0 Referencie .............................................................................................. 54 

Capítulo 3: Prediction of non-canonical routes for SARS-CoV-2 infection in human 

placenta cells – submitted to Science Signaling. ........................................................... 67 

Abstract ........................................................................................................... 69 

1. Introduction ................................................................................................. 70 

2. Results ........................................................................................................ 72 



 

8 

 

3. Discussion ................................................................................................... 74 

4. Materials and Methods ................................................................................ 77 

References ...................................................................................................... 79 

 

 

  



 

9 

 

Lista de abreviaturas 

ACE2: angiotensin-converting enzyme 2 

BPH: Benign prostatic hyperplasia 

CaP: câncer de próstata 

CEEA: Comissão de Ética na Experimentação Animal 

COBEA: Colégio Brasileiro de Experimentação Animal 

COVID-19: coronavirus disease 2019 

CTR: Controle 

CTSL: cathepsin L 

CUG: complexo urogenital 

DAG: distância ano genital 

DEGs: differentially expressed genes 

DG: Dia gestacional 

DM2: Diabetes mellitus do tipo 2 

DNMT: DNA metil transferases 

DOHaD: Developmental Origins of Health and Disease 

DPN: dias pós-natal 

DPP4: dipeptidyl peptidase 4 

GEO: Gene Expression Omnibus 

GLLP: Gestational and lactational low protein diet 

GSEA: Gene Set Enrichment Analysis 

GSH: Glutationa Peroxidase 

HAC: histona acetilase 

HDAC: deacelilase 

HE: hematoxilina-eosina 

HPB: hiperplasia prostática benigna 

IGF-1: Fator de crescimento semelhante à insulina tipo 1 

KOBAS: KEGG Orthology Based Annotation System 

LPD: Low protein diet 



 

10 

 

LR: Likelihood Ratio 

miRNA: microRNAs 

mRNAS: RNA mensageiro 

ORF8: open reading frame 8 

PA: lobo anterior 

PCA: análise de componentes principais 

PCa: Prostate cancer 

PDL: lobo dorsolateral 

PF: Programação Fetal 

PND: Postnatal day 

PPI: protein-protein interactions 

PTHrP: parathyroid hormone-related protein 

PV: lobo ventral 

RBD: receptor-binding domain 

RTKs: receptores de tirosina-quinase  

SARS-CoV-2: severe acute respiratory syndrome coronavirus 2 

Shh: Sonic hedgehog 

SOD: Superóxido Dismutase 

Tbx3: T-box transcription factor 

TCGA: The Cancer Genome Atlas 

TMPRSS2: cellular transmembrane serine protease 2 

USG: seio urogenital 

ZIKV: Zika virus 

  



 

11 

 

Lista de Figuras 

Capítulo 1 

Figura 1. Organização anatômica da próstata. 

Figura 2. Estágios do desenvolvimento da próstata de rato e humano. 

Figura 3. Esquema da glândula prostática. Imagem de Mateus Betta de Oliveira  

Figura 4. Esquema da biogênese dos miRNAs. 

Capítulo 2 

Figure 1. The GLLP group has lower weight, late prostate development and a higher level of testosterone 

and estrogen. 

Figure 2. MiRNAs differentially expressed in the GLLP group have predicted targets related to interesting 

enriched pathways. 

Figure 3. GLLP group presents some deregulated miRNAs in common with prostate cancer patients. 

Figure 4. Expression of mir-33-5p using the RT-qPCR technique in different organs showing the lower 

expression in prostate, testicle and liver and a higher level in adrenal gland in GLLP group when compared 

to CTR. 

Figure 5. miR-33a-5p overexpression causes low levels of cell viability 

Figure 6. The predicted mir-33-5p targets are involved in all selected pathways and with prostate cancer. 

Capítulo 3 

Figure 1. The expression landscape of human placenta proteins potentially interacting with 

SARS-CoV-2.  

Figure 2. Single-cell analysis of the human placenta indicates potential non-canonical routes of 

SARS-CoV-2.  

Supplementary Figure 1. Gene expression profile of gestational trimesters and its potential to 

interact with SARS-CoV-2 proteins.  

Supplementary Figure 2. Gene expression of ACE2, TMPRSS2, CTSL, DPP4, DAAM1, and 

PAICS in scRNA-seq of fetal tissues.  

   



 

12 

 

Lista de tabelas 

Capítulo 1 

Capítulo 2 

Table 1. Rat and prostate weight and stereological results.  

Supplementary Table 1. Differentially expressed miRNAs in prostate tissue between CTR group 

and GLLP group (Log2FC >= |0.4|; FDR< 0.05). 

Supplementary Table 2. Target prediction analysis of the differentially expressed miRNAs 

generated in the mirwalk (http://mirwalk.umm.uni-heidelberg.de/) 

Supplementary Table 3. Differentially expressedm RNAs and proteins in prostate tissue between 

the CTR GLLP groups of rats on the postnatal day 21 (mRNAs-Log2FC> = |0.66|; p value <0.05. 

Proteins-Log2FC> = |1|; p value <0.05). 

Supplementary Table 4. Integrative analysis combining the prediction of the miRNAs targets 

with differentially expressed mRNAs and proteins, taking into account inverted log2 fold change 

values. 

Supplementary Table 5. Functional enrichment analysis x up-regulated targets resulting from 

the integrative analysis using the Kobas tool (http://kobas.cbi.pku.edu.cn/kobas3/?t=1) 

[DOI:10.1093/nar/gkr483] 

Supplementary Table 6. Functional enrichment analysis x down-regulated targets resulting from 

the integrative analysis using the Kobas tool (http://kobas.cbi.pku.edu.cn/kobas3/?t=1) 

[DOI:10.1093/nar/gkr483] 

Supplementary Table 7. Differentially expressed miRNAs in prostate tissue between CTR and 

cancer groups (Log2FC >= |0.4|; FDR< 0.05) from The Cancer Genome Atlas (TCGA). 

Supplementary Table 8. Deregulated miRNAs shared by rats and patients with prostate cancer. 

Capítulo 3 

Supplementary Table 1.  Differentially expressed genes in placenta tissue during the gestation 

(1º trimester x 2º trimester and 1º trimester x 2º trimester; Log2FC >= |0.5|; FDR< 0.05). 

Supplementary Table 2.  Functional enrichment analysis generated in the Metascape tool 

(https://metascape.org) [DOI: 10.1038/s41467-019-09234-6] for each cluster generate for 

placenta gene expression during gestation. 



 

13 

 

Supplementary Table 3.  Functional enrichment analysis list generated in the Metascape tool 

(https://metascape.org) [DOI: 10.1038/s41467-019-09234-6] for DEGs in the placenta tissues (1º 

trimester x 2º trimester and 1º trimester x 2º trimester. 

Supplementary Table 4. Human-SARS-CoV Interactome based on the in silico computational 

framework P-HIPSTer (http://phipster.org/). 

Supplementary Table 5. Human-ZIKA Interactome based on the in silico computational 

framework P-HIPSTer (http://phipster.org/). 

Supplementary Table6. The Human-SARS-CoV interactome obtained in the P-HIPSTer 

(http://phipster.org/) for placenta.  

Supplementary Table 7. The Human-ZIKA interactome obtained in the P-HIPSTer 

(http://phipster.org/) for placenta.  

Supplementary table 8. Human-SARs Interactome based on literature. 

 

  



 

14 

 

Resumo 

Nas últimas décadas, foi observado aumento na incidência de câncer na população. 

Estudos mostram que o câncer pode se originar de insultos sofridos por indivíduos 

durante a vida intrauterina, visto que que este período é caracterizado pela capacidade 

do embrião/feto em se adaptar às mudanças ambientais, alterando a expressão gênica 

por mecanismos pós-transcricionais. Já foi demonstrado que a restrição proteica materna 

promove a carcinogênese da próstata em ratos idosos; No entanto, há falta de 

informações sobre o mecanismo molecular envolvido nesse processo. Assim, 

objetivamos identificar os possíveis microRNAs desregulados em ratos jovens 

programados e localizar seus possíveis alvos associados à carcinogênese da próstata. 

Para isso, ratos machos Sprague Dawley nascidos de mães alimentadas com dieta 

padrão (17% de proteína), grupo controle (CTR), ou dieta pobre em proteínas (6% de 

proteína), grupo gestational and lactational low protein diet (GLLP), durante a gestação 

e lactação foram sacrificados no dia pós-natal 21. O sangue foi coletado para analises 

hormonais e a próstata ventral foi processada para morfologia e por sequenciamento, 

HigSeq -2500 Illumina, para determinar o perfil do microRNoma. Dados de 

sequenciamento de miRNAs de pacientes com câncer de próstata, oriundos do the 

cancer genome atlas (TCGA), foram reanalisados para comparação com os resultados 

de ratos. Além disso, foi realizada a transfecção do miR-33 em células PNT-2 para avaliar 

a expressão de seus alvos e a viabilidade celular após tratamento através da técnica de 

MTT.  Assim, foi observado atraso no desenvolvimento da próstata ventral e aumento 

dos níveis de testosterona e expressão de estrogênio em ratos GLLP. Além disso, 20 

miRNAs foram identificados como diferencialmente expressos, 6 down regulados e 14 

up regulados. Os resultados de enriquecimento para miRNAs down regulados estão 

relacionados às vias do câncer e da angiogênese, enquanto os miRNAs up regulados 

estão enriquecendo vias de retículo endoplasmático e de carcinoma. A reanálise do 

sequenciamento de miRNA de TCGA revelou 7 em comum com o grupo GLLP. Um deles 

é o miR-33 que, além de ser down regulado na próstata, também é desregulado no 

testículo, na glândula adrenal e no fígado, dados gerados através do RTq-PCR. Sua 

supra expressão nas células transfectadas causou uma diminuição na viabilidade celular 

e seu alvo, CYP1B1, é uma enzima importante no metabolismo do estrogênio, que pode 

levar à carcinogênese mediada por mudanças nos níveis hormonais e transformação do 

estradiol. Concluímos que o miR-33 é um alvo importante para explicar as alterações 

causadas pela LPD materna podendo, ser considerado um considerável biomarcador por 

ser desregulado em vários órgãos analisados. 

Palavras-Chave: Programação Fetal, Câncer de Próstata e MicroRNAs  



 

15 

 

Capítulo 1: Revisão Bibliográfica 

1.1 Programação Fetal 

A boa nutrição é fundamental para a manutenção da saúde, sendo que nenhum 

país pode alcançar a cobertura universal de saúde sem investir em ações que garantam 

a nutrição essencial de sua população.  A má alimentação é uma das maiores causas de 

comorbidade e morte no mundo, mais que o Tabaco e a hipertensão (AFSHIN et al., 

2019). Além disso, está relacionada com o aumento do risco de doenças infecciosas, 

como pneumonia, diarreia, sarampo e tuberculose, assim como o aumento de doenças 

no coração, câncer, diabetes além de mortes neonatais (BLACK et al., 2013; CAULFIELD 

et al., 2004; HOFFMAN et al., 2020; PELLETIER et al., 1995; PORTELA et al., 2021). 

Em 2017, 56% das mortes de crianças com menos de 5 anos foram atribuídas à má 

nutrição materna (INSTITUTE FOR HEALTH METRICS AND EVALUATION (IHME), 

2018). Estudos vem mostrando que a má nutrição materna pode estar relacionada com 

o aparecimento de doenças crônicas na vida adulta dos filhos, mostrando que a origem 

dessas doenças pode se dar a partir de insultos sofridos durante o período 

embrionário/fetal. Esse processo ficou conhecido como Programação Fetal (PF) 

(BARKER, 2007; MERICQ et al., 2017; PINHO et al., 2014; SANTOS et al., 2019).  

Um dos primeiros pesquisadores a estudar a PF foi Ravelli e colaboradores em 

1976 (RAVELLI; STEIN; SUSSER, 1976). Eles fizeram um estudo coorte retrospectivo 

com 300.000 pessoas que foram expostas à fome holandesa durante o período pré-natal 

e logo após o nascimento. A Fome Holandesa Aconteceu em 1944-45 durante a 

Segunda Guerra mundial que por conta da invasão alemã e pelo congelamento do mar 

impedindo a chegada de navios, a população ficou sem abastecimento de comida e 

passou a ter uma alimentação de 400-800 calorias diariamente. Ravelli e colaboradores 

(1976) observaram que se o feto foi exposto à restrição alimentar durante o último 

trimestre de gestação, ele apresentava uma menor probabilidade de obesidade por esse 

período ser um período crítico para o desenvolvimento de tecido adiposo. Porém, se a 

restrição alimentar acomete o feto durante a primeira metade da gestação o quadro 

muda, tendo maior probabilidade de obesidade pois a restrição alimentar afeta o centro 

hipotalâmico responsável pela saciedade e crescimento fetal. Após este período, a 



 

16 

 

alimentação deixou de ser restrita e levou a um excesso de gordura no organismo o que 

ocasiona um maior crescimento e aumento de células adiposas (RAVELLI; STEIN; 

SUSSER, 1976).  

Além de Ravelli, o epidemiologista inglês David Barker também encontrou 

resultados pertinentes. Em 1989, BARKER e seus colaboradores relacionaram o baixo 

peso ao nascimento com mortes por doenças cardíacas na vida adulta (BARKER et al., 

1989). Em outro estudo demonstraram que pacientes com baixo peso ao nascimento, 

associado com maior tamanho de placenta apresentam maior pressão arterial na vida 

adulta (BARKER et al., 1990). Além disso, estudos vem mostrando que a restrição 

alimentar materna pode levar a modificação do metabolismo de colesterol,  afetar níveis 

de insulina podendo iniciar um estado de resistência à insulina e levar ao Diabetes 

mellitus do tipo 2 (DM2) na vida adulta (BARKER et al., 1993; BARKER, 1997). Por 

apresentar baixo peso ao nascimento elas passam por um período de crescimento 

acelerado (catch-up growth) (FITZHARDINGE; STEVEN, 1972), o que contribui para 

aumentar o risco de sobrepeso e obesidade já na infância. Ainda não se sabe quais 

fatores determinam o crescimento acelerado no período pós-natal. No entanto, alguns 

estudos sugerem que esse tipo de crescimento pode estar associado a fatores inerentes 

ao período intrauterino e, consequentemente, as características e hábitos maternos 

durante a gravidez. 

Através destes e outros estudos se originou a Hipótese de Barker, e com o passar 

do tempo, a ideia de programação fetal se estendeu para outros períodos, além do 

período fetal, e não ficou restrita ao processo de doença, mas à saúde também, 

originando a denominada Developmental Origins of Health and Disease (DOHaD) 

(GLUCKMAN; HANSON; BUKLIJAS, 2010; SCHULZ, 2010). 

1.2 Programação Fetal e baixa ingesta de Proteína 

A organização alimentar e agrícola das nações unidas mostra que em países da 

Europa, Oceania e América do Norte as pessoas se alimentam em média de 100 gramas 

(g) de proteínas por dia. Os países do sul da Ásia, África Subsaariana e América do Sul 

tem uma média de 50 a 90 g de proteína. No entanto, a maioria dos países de baixa 

renda está dentro da faixa entre 30-60 g dia (UNITED NATIONS FOOD AND 



 

17 

 

AGRICULTURAL ORGANIZATION (FAO), 2020). A porcentagem mínima de proteína 

recomendada pela organização mundial de saúde é de no mínimo 0,8 g/dia de proteína 

por quilograma de peso para um indivíduo adulto, sendo que para gestantes e lactantes 

essa recomendação aumenta. Assim, um dos grandes fatores que levam a má nutrição 

é a ausência de proteína na alimentação, seja este um fator cultural ou socioeconômico. 

Além de que, diversos modelos tem sido utilizados para melhor conhecimento dos efeitos 

da PF, sendo o mais utilizado a dieta hipoproteica oferecida durante a gestação e 

lactação,  principalmente utilizando-se de modelos com animais de laboratório 

(COLOMBELLI et al., 2017; DE BRITO ALVES et al., 2016; OZANNE; HALES, 2004; 

PINHO et al., 2014). Segundo o Instituto Americano de Nutrição para dietas de ratos o 

indicado é que a ração tenha pelo menos 12% de proteína. A maioria dos estudos com 

programação fetal por restrição proteica utiliza valores de 6-8% de proteína para os 

animais restritos contra 20-22% para os animais controle. 

Estudos vem mostrando que a prole de mãe que se alimenta com baixa 

quantidade de proteína tem menor peso ao nascimento, seguido pelo conhecido catch-

up growth (crescimento acelerado), resistência à insulina e DM2 na vida adulta, 

diminuindo a expectativa de vida destes individuos (EMBLETON et al., 2016; 

MCMILLEN; ADAM; MÜHLHÄUSLER, 2005; PETRY et al., 2001). Quando a dieta 

hipoproteica vem seguida de um estilo de vida desfavorável como o consumo de uma 

dieta hiperlipídica, sedentarismo e consumo de alimentos processado, os quadros 

clínicos são agravados (DEARDEN; OZANNE, 2014; KWON et al., 2012; OZANNE; 

HALES, 2004).  

O consumo de uma dieta hipoproteica também exibe resistência à insulina em 

tecido adiposo (TARRY-ADKINS et al., 2015), fibrose e esteatose hepática e um 

processo inflamatório aumentado (KWON et al., 2012; TARRY-ADKINS et al., 2016). Há 

redução de crescimento de diferentes órgãos, dislipidemia, elevação da pressão sistólica 

(hipertensão), disfunção renal e vascular (BLACK et al., 2015; DE BRITO ALVES et al., 

2016; FALCÃO-TEBAS et al., 2012; LEANDRO et al., 2012). Acarreta menor número de 

néfrons no rim (HABIB et al., 2012; SENE et al., 2013), menor quantidade de células 

beta-pancreática e de ilhotas de Langerhans no pâncreas (CALZADA et al., 2016; DAHRI 



 

18 

 

et al., 1991), proporção alterada entre os tipos celulares e aumento de estresse oxidativo 

(BURNS et al., 1997; TARRY-ADKINS et al., 2016; VEGA et al., 2016), menor número 

de neurônios que controlam o apetite no hipotálamo (PLAGEMANN et al., 2000) e menor 

número de alvéolos pulmonares (ZANA-TAIEB et al., 2013). Em conjunto, estes dados 

demonstram que o status nutricional durante o desenvolvimento pré e pós-natal alteram 

o metabolismo da prole e a morfofisiologia de diferentes órgãos e sistemas, sendo que 

as consequências podem ser observadas tanto ao nascimento, como a longo prazo. 

1.3 Próstata e a programação Fetal por baixa ingesta de proteína 

A próstata é uma glândula exócrina acessória do sistema genital cujo 

desenvolvimento e homeostasia encontram-se sob controle androgênico (BIANCARDI et 

al., 2017; CUNHA et al., 1985; ZAVIACIC; ABLIN, 2000). Ela secreta um complexo 

proteolítico composto por fosfatase ácida, ácido cítrico, fibrinolisina, enzimas específicas 

e outros componentes do fluido seminal que auxiliam no sucesso reprodutivo 

(AUMÜLLER; SEITZ, 1990; MARKER et al., 2003).  

A próstata está localizada no compartimento subperitoneal, anterior ao reto e 

inferior à bexiga urinaria (SCHAUER; ROWLEY, 2011). Estudos mostram que ela possui 

três zonas morfologicamente distintas: (1) zona periférica (região com maior 

acometimento de câncer), (2) zona de transição e (3) zona central, além de uma região 

não glandular, o estroma fibromuscular anterior (Figura1.A) (LOWSLEY, 1912).  

Já a próstata de roedores apresenta quatro lobos: lobo anterior (PA), lobo ventral 

(PV), lobo dorsal e lobo lateral, sendo os dois últimos também chamados de lobo 

dorsolateral (PDL) (Figura 1B). Cada lobo é localizado em posição específica em torno 

da uretra (LOWSLEY, 1912). Se fizermos uma homologia com a próstata humana, o lobo 

anterior corresponde á zona central da glândula e o lobo dorsolateral a zona periférica. 

O lobo ventral não apresenta nenhuma homologia com a próstata humana, porém, é o 

mais responsivo a andrógeno, de rápido e fácil acesso, além de geralmente não 

desenvolver prostatite, por essas e outras razões, este é o lobo mais estudado (ROY-

BURMAN et al., 2004). 



 

19 

 

Figura 1. Organização anatômica da próstata. A. Próstata humana. B. Próstata de camundongo. Imagem A 

retirada do site https://www.urologyhealth.org/urologic-conditions/prostatitis-(infection-of-the-prostate) 

Acesso:03/08/2020 e imagem B adaptada de (AHMAD; SANSOM; LEUNG, 2008). 

 

Nos camundongos e ratos, o desenvolvimento prostático tem início por volta do 

17º dia de gestação e, no humano entre 9-10 semanas de gestação (WELSH et al., 

2008). A próstata se desenvolve através de um processo conservador, denominado de 

morfogênese de ramificação, onde pequenos brotos se projetam a partir do epitélio do 

seio urogenital (USG). Particularmente no rato, a síntese de testosterona pelos testículos 

fetais tem início entre os dias 13-14 da gestação (DG) (WILSON et al., 1983). Em 

resposta aos andrógenos, o epitélio do UGS passa a expressar Sonic hedgehog (Shh), 

que por sua vez estimula expressão do gene homeobox Nkx3.1 no DG15.5, dois dias 

antes de surgirem os primeiros brotos prostáticos (WILHELM; KOOPMAN, 2006). Depois 

do nascimento, ocorre alongamento dos brotos e início da morfogênese com a formação 

dos ductos glandulares (PRINS et al., 1992), visto que em ratos a maior parte do 

desenvolvimento acontece durante os primeiros 15 dias pós-natal (DPN) enquanto que, 

em humanos o desenvolvimento é quase todo durante a vida intrauterina (TIMMS; 

MOHS; DIDIO, 1994). Em ambos, esse processo se estende até que a maturidade sexual 

seja atingida com a puberdade (MARKER et al., 2003) (Figura 2). 

 

https://www.urologyhealth.org/urologic-conditions/prostatitis-(infection-of-the-prostate)


 

20 

 

        Figura 2. Estágios do desenvolvimento da próstata de rato e humano. Imagem de (PRINS; PUTZ, 2008). 

A próstata apresenta uma complexa rede de ducto, que nada mais é do que uma 

região de lúmen revestido por células epiteliais secretoras, que possuem polaridade 

apical-basal e são responsáveis pela síntese e secreção de proteínas e fluidos para o 

lúmen prostático (MARKER et al., 2003).  As células epiteliais se organizam de forma 

a dar a característica histológica de glândula túbulo-alveolar composta, o seu epitélio 

usualmente é colunar alto, porém pode haver alteração dependendo do estado 

funcional da glândula (DERMER, 1978) (Figura 3). O componente estromal engloba 

todos os elementos celulares e extracelulares que estão fora da lâmina basal epitelial 

como a camada fibromuscular que cerca a glândula. Além da camada muscular, o 

estroma também apresenta fibroblastos, vasos sanguíneos e os pericitos associados, 

células imunitárias, terminações nervosas e vasos linfáticos, os quais são incorporados 

a uma matriz extracelular colagenosa (AUMÜLLER; SEITZ, 1990). 

 

                       

 

 

 



 

21 

 

Figura 3. Esquema da glândula prostática. Imagem de Mateus Betta de Oliveira. 

Portanto, além de sua importância para a fertilidade, nos últimos anos a próstata 

tem despertado grande interesse médico-científico pela alta incidência de distúrbios, 

principalmente a hiperplasia prostática benigna (HPB) e o câncer de próstata (CaP) 

(DASGUPTA; SRINIDHI; VISHWANATHA, 2012). O CaP é a terceira causa de óbitos 

por câncer entre homens no mundo e é o segundo mais incidente (Tabela 1)(“Global 

Cancer Observatory” acess:26/05/2021).  

Frente a isso e considerando que o período intrauterino é caracterizado pela 

capacidade do embrião/feto em se adaptar às mudanças ambientais, alterando a 

expressão gênica por mecanismos pós-transcricionais, vários autores demonstraram o 

impacto negativo de alterações do ambiente intrauterino sobre o desenvolvimento e 

fisiologia prostática. Ratos do DPN 21 submetidos a restrição proteica materna 

apresentaram menor peso prostático, com diminuição do tamanho nos ácinos da PDL 

(RAMOS et al., 2010). Pinho et al. (2014) também observaram atraso no 

desenvolvimento prostático associado com diminuição de proliferação celular na prole 

de animais submetidos ao mesmo protocolo e analisados no DPN 1.  



 

22 

 

Tabela 1. Distribuição proporcional dos dez tipos de câncer mais incidentes estimados para 2020 em homens, 

exceto pele não melanoma, (https://gco.iarc.fr/) acesso: 26/05/2021. 

Nosso grupo de pesquisa demonstrou recentemente que a restrição proteica 

perinatal reduziu o processo de angiogênese prostática, levando a menor troca de 

moléculas entre os vasos e os compartimentos epitelial e estromal, impactando 

negativamente o desenvolvimento glandular. Estes resultados foram relacionados à 

menor concentração de testosterona, insulina e fator de crescimento semelhante à 

insulina tipo 1 (IGF-1) circulante, com efeitos negativos sobre os índices de proliferação 

e diferenciação do epitélio glandular (COLOMBELLI et al., 2017). Mais recentemente, 

observamos que ratos com 540 dias de idade que sofreram restrição proteica na 

gestação e lactação apresentaram maior incidência de lesões prostáticas, com aumento 

na incidência de neoplasia intraepitelial (SANTOS et al., 2019). Estes dados demonstram 

que a restrição proteica materna é suficiente para desencadear um aumento de 

incidência de lesões prostáticas na prole durante o envelhecimento. Entretanto, os 

mecanismos moleculares envolvidos neste processo ainda não foram totalmente 

esclarecidos. 

1.4 Mecanismos Epigenéticos Envolvidos na Programação Fetal 

O período de desenvolvimento intrauterino é caracterizado pela grande 

plasticidade e habilidade do embrião/feto em responder a alterações ambientais (dieta, 

https://gco.iarc.fr/


 

23 

 

estresse e hormônios), modulando a expressão de genes envolvidos com o controle da 

proliferação e diferenciação celular em uma fase de morfogênese de órgãos e sistemas 

vitais (BURTON; FOWDEN; THORNBURG, 2016). Entretanto, pouco se sabe sobre os 

agentes envolvidos neste processo e as consequências para a prole. Nos últimos anos, 

mecanismos epigenéticos tem sido apontado como os principais moduladores da 

expressão gênica envolvidos em várias formas de programação fetal (JOUBERT et al., 

2016), uma vez que mudanças ambientais podem alterar de forma persistente 

marcadores epigenéticos e desencadear respostas fenotípicas que afetam a estrutura e 

o funcionamento de órgãos de maneira permanente (SINCLAIR et al., 2007; 

WATERLAND; JIRTLE, 2003). 

O termo "epigenética" foi utilizado pela primeira vez por Waddington 

(WADDINGTON, 2012) para definir as interações dos genes e o ambiente que levam ao 

desenvolvimento de um fenótipo. A partir de então, outros autores referem-se à 

epigenética como mudanças reversíveis e herdáveis no genoma capazes de afetar a 

expressão gênica e o fenótipo celular, sem, entretanto, alterar a sequência primária de 

nucleotídeos do DNA (DEANS; MAGGERT, 2015; FERNANDEZ-TWINN; 

CONSTÂNCIA; OZANNE, 2015). Estudos em modelo de programação fetal por restrição 

proteica tem demonstrado a participação de mecanismos epigenéticos e, apesar destes 

mecanismos terem sido bastante estudados no contexto do desenvolvimento 

embrionário e da biologia do câncer, o conhecimento sobre como a epigenética contribui 

para a programação fetal ainda é escasso. Isto se torna particularmente importante, uma 

vez que alterações em fatores ambientais, como a nutrição materna impacta fortemente 

os níveis de expressão gênica na prole (HEERWAGEN et al., 2010). Os principais 

mecanismos de regulação epigenética incluem metilação de DNA, por enzimas DNA 

metil transferases (DNMT), modificação pós-transcricional de histonas pelas enzimas 

histona acetilase (HAC) ou deacelilase (HDAC), além da participação de RNAs não 

codificantes, dentre estes, os microRNAs (miRNA) que também tem sido descritos no 

mecanismo epigenético de regulação da expressão gênica (ALLIS; JENUWEIN, 2016; 

HEERWAGEN et al., 2010).  



 

24 

 

miRNAs são uma classe de pequenos RNAs não codificantes com 

aproximadamente 22 nucleotídeos que têm como função a regulação pós-transcricional 

da expressão de genes. Os miRNAs se originam de uma longa molécula primária 

transcrita de um precursor pela RNA polimerase II em seu determinado gene loci com 

formato hairpin chamado de miRNA primário (pri-miRNA). A transcrição do pri-miRNAs 

pode ser controlado por fatores transcricionais da RNA Pol II (p53, Proteína de 

determinação de MYC, ZEB1 e ZEB2 e MYOD1) e por regulação epigenética (metilação 

de DNA e modificações de histonas)(CAI; HAGEDORN; CULLEN, 2004; DAVIS-

DUSENBERY; HATA, 2010; KIM; HAN; SIOMI, 2009; KROL; LOEDIGE; FILIPOWICZ, 

2010; LEE et al., 2004).  Os pri-miRNAs passam por passos de maturação, ainda no 

núcleo. A Drosha, que possui dois domínios o RNase III (RIIIDs) e o dsRNA-binding 

(dsRBD), juntamente com seu cofator essencial a proteína DiGeorge Syndrome Critical 

Region Gene 8 (DGCR8) formam um complexo chamado Microprocessador (DENLI et 

al., 2004; GREGORY et al., 2004; HAN et al., 2004; LANDTHALER; YALCIN; TUSCHL, 

2004) que inicia a maturação cortando a stem–loop para liberar um pequeno RNA em 

forma de hairpin com ~ 65 nucleotídeos de comprimento (pre-miRNA) (LEE et al., 2003). 

Após o processamento da Drosha, o pre-miRNA é exportado no citoplasma através de 

um complexo formado pela proteína Exportin 5 (EXP5), proteína nuclear de ligação GTP-

RAN e o pre-miRNAs (BOHNSACK; CZAPLINSKI; GORLICH, 2004; LUND et al., 2004; 

YI et al., 2003). Após a passagem pelo complexo de poros nucleares, o GTP é hidrolisado 

sendo o complexo desmontado e ocorrendo a liberação do pre-miRNA no citoplasma, 

onde a maturação pode ser completada. No citoplasma o pre-miRNA é processado pela 

Dicer, uma outra enzima RNase III, que juntamente com a TAR RNA-binding protein 

(TRBP) reduz o pre-miRNA para uma fita dupla de aproximadamente 20 nucleotídeos 

que agora é denominado de miRNA maduro. Uma das fitas do pequeno duplex de RNA 

gerado pelo Dicer se liga com uma proteína AGO para formar um complexo efetor 

chamado complexo de silenciamento induzido por RNA (RISC) (HAMMOND et al., 2001; 

MOURELATOS et al., 2002; TABARA et al., 1999). Assim, os miRNAs funcionam como 

um guia, unindo-se ao seu alvo, enquanto a proteína AGO funciona como um recrutador 

de fatores que induzem a repressão translacional dos mRNA (Figura 3). 



 

25 

 

 

Figura 4. Esquema da biogênese dos miRNAs. (WINTER et al., 2009) 

 As primeiras publicações referentes a miRNAs foram publicadas em 1993 por 

Victor Ambros e Gary Ruvkun. Foram dois artigos publicados na mesma edição da Cell, 

cada um descrevendo o seu trabalho. Victor descreveu o primeiro miRNA, mesmo essa 

terminologia vindo a ser usada oito anos depois (LEE; FEINBAUM; AMBROS, 1993) e 

Gary descreveu o mecanismo regulatório do mRNA (lin-4 regulando lin-14) (WIGHTMAN; 

HA; RUVKUN, 1993). Desde então houve uma crescente no número de publicações, 

sendo que nos últimos 10 anos foram publicados mais de 100 mil estudos relacionados 

com miRNAs. Esses estudos são variados, porém uma grande parcela é relacionando a 

atuação dos miRNAs nos diversos tipos de canceres (CHAKRABORTY et al., 2020; MIN 

et al., 2014; PARASRAMKA et al., 2012; PIAN et al., 2020). Um dos pontos estudados é 

a expressão dos pri-miRNAs. Os genes dos miRNAs, muitas vezes, são localizados em 

regiões que possuem uma maior probabilidade de serem deletadas, amplificadas ou 

translocados (CALIN et al., 2004). Essa característica pode causar a desregulação da 

expressão dos pri-miRNAs  e promover a iniciação ou progressão do câncer (ZHANG et 



 

26 

 

al., 2006). Esta desregulação pode ser causada pelo próprio câncer, que aumenta a 

expressão de fatores transcricionais e com isso desregular a expressão dos pri-miRNAs 

(BOMMER et al., 2007; LIN; GREGORY, 2015; RAVER-SHAPIRA et al., 2007). Outro 

ponto importante é o complexo Microprocessador que frequentemente está desregulado 

no câncer (MURALIDHAR et al., 2011; SUGITO et al., 2006). Mas as alterações podem  

ocorrer também no momento da exportação para o citoplasma (MELO et al., 2010) e na 

proteína Dicer (KUMAR et al., 2009). Assim, as alterações nos níveis de expressão dos 

miRNAs veem sendo muito estudas e correlacionadas com o surgimento ou progressão 

de diversos tipos de câncer.  

Fatores dietéticos também estão sendo relacionados com a desregulação dos 

miRNAs induzidas pela nutrição materna durante a gestação e ou lactação que afeta a 

prole. A alta ingestão materna de gordura durante a gestação e lactação modulou o 

metabolismo lipídico hepático e a expressão de microRNA-122 (miR-122) e microRNA-

370 (miR-370) na prole (BENATTI et al., 2014) além de alterar a expressão hepática da 

insulina como o fator de crescimento 2 e microRNAs importantes na prole adulta (miR-

503, miR-379, miR-770-3p, miR-369-3p, miR-197, miR-21, miR-328, miR-471, miR-207, 

miR-667 up regulados e miR-410, miR-804, miR-323-5p, let-7c, miR-302a, miR-711, 

miR-26a, miR-122, miR-216b, miR-294, miR-185, miR-192, miR-29a, miR-194, miR-145, 

miR-126-3p, miR-762, miR-16, miR-1224, miR-22, miR-30c-2, miR-494, miR-483 down 

regulados)(ZHANG et al., 2009). Já a baixa ingestão de proteína na última semana de 

gravidez é crítica e suficiente para induzir efeitos na homeostase da glicose, causando 

alterações permanente no conjunto específico de miRNAs que podem contribuir para a 

vulnerabilidade geral de descendência para a obesidade, resistência à insulina e 

diabetes tipo 2 (ALEJANDRO et al., 2020). Alguns estudos mostram que a LPD maternal 

causa desregulação de miRNAs que causam mudanças metabólicas, inflamação 

crônica, aumento da pressão sanguínea e alterações na morfologia do coração 

(ASSALIN; GONTIJO; BOER, 2019; ZHENG et al., 2017). Além disso, um estudo 

publicado no Journal of Hepatology mostra que a dieta rica em gordura fornecida 

multigeracional aumenta a incidência de carcinoma hepatocelular por indução via a 

regulação do eixo miR-27a-3p-Acsl1/Aldh2 (SUN et al., 2020). 



 

27 

 

Referências Bibliográficas 

As referências da revisão bibliográfica estão em conjunto com as do capítulo 2. 

  



 

28 

 

Capítulo 2: MiRNAome integrative analysis reveals molecular 

mechanisms associated with development of PCa in the offspring 

subjected to maternal low protein diet. 

 

Constantino F.B.1, Protela L.F.1, Camargo A.C.L.1, Colombelli K.T1, Santos S.A1, Fioretto M.N1, 

Freire P.P.4, Minatel B.C.3, Barros-Filho M.C.2,3, Kislinger T.5, Moreno C.S.6. Lam W.L.2, 

Carvalho R.F.1, Justulin L.A1* 

 

1. Department of Morphology, Institute of Biosciences, Sao Paulo State University (UNESP), Botucatu, SP, Brazil 

2. Department of Integrative Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada 

3. International Research Center, A.C. Camargo Cancer Center, Sao Paulo, SP, Brazil 

4. Department of Immunology - University of São Paulo (USP), São Paulo, SP, Brazil 

5. Medical Biophisics, University of Toronto, Toronto, ON, Canada 

6. Departments of Pathology & Laboratory Medicine, and Biomedical Informatics, Emory University School of 

Medicine, Atlanta, GA, United State 

 

 

*Corresponding author: 

Prof. Dr. Luis A Justulin 

Sao Paulo State University (UNESP), Institute of Biosciences of Botucatu, SP, Brazil.  

Zip Code: 18618-689 

Phone number: +551438800481 

email: l.justulin@unesp.br 

  

mailto:l.justulin@unesp.br


 

29 

 

Abstract 

In the last decades, there has been an increase in the incidence of cancer in the 

population. Studies show that cancer can originate through insults suffered by individuals 

during intrauterine life, a condition known as Fetal Programming. (PF). The perinatal 

period is characterized by the ability of the embryo / fetus to adapt to environmental 

changes, altering gene expression by post-transcriptional mechanisms. Recently, we 

demonstrated that maternal protein restriction promotes prostate carcinogenesis in 

elderly rats; However, information about the molecular mechanism involved in this 

process is lacking. Thus, we aimed at identifying possible deregulated microRNAs in 

young programmed rats and locate their possible targets associated with prostate 

carcinogenesis. For this, male Sprague Dawley rats born to mothers fed a standard diet 

(17% protein) - a control group (CTR) or a low protein diet (6% protein) - a gestational 

and lactational low protein diet (GLLP), during pregnancy and lactation were sacrificed 

on the postnatal day 21. We observed a delay in the development of the ventral prostate 

and increased levels of testosterone and estrogen expression in GLLP rats. In addition, 

20 miRNAs were identified as differentially expressed, 6 downregulated and 14 

upregulated. The enrichment results for downregulated miRNAs are related to cancer and 

angiogenesis pathways, while upregulated miRNAs are enriching endoplasmic reticulum 

and carcinoma pathways. The reanalysis of miRNA sequencing from TCGA revealed 7 

in common with the GLLP group. One of them is miR-33 which, in addition to being down-

regulated in the prostate, is also unregulated in the testis, adrenal gland and liver. Their 

overexpression in transfected cells caused a decrease in cell viability and your target, 

CYP1B1, which is an important enzyme in estrogen metabolism, leading to 

carcinogenesis mediated by changes in hormone levels. We concluded that miR-33 is an 

important target to explain the changes caused by maternal LPD and can also be 

considered the biomarker because it is unregulated in several organs. 

 

 

 



 

30 

 

1.0 Introduction 

Inadequate diet is one of the main causes of comorbidities in the world, being 

related to diseases such as cancer, diabetes and neonatal deaths (AFSHIN et al., 2019; 

BLACK et al., 2013; CAULFIELD et al., 2004; PELLETIER et al., 1995). Studies show 

that maternal malnutrition in early life may be related to an increased likelihood of chronic 

diseases emergence in adulthood, showing that the origin of these diseases may be due 

to aggressions suffered during the embryo/fetal period. This is one of the themes studied 

by the international society named Developmental Origins of Health and Disease 

(DOHaD) (BARKER, 2007; MERICQ et al., 2017; PINHO et al., 2014; SANTOS et al., 

2019; SCHULZ, 2010). Maternal exposure to low protein diet (LPD) during pregnancy 

and lactation has been widely performed in laboratory models, and it is an example of a 

metabolic programming that follows during the critical periods of development 

(COLOMBELLI et al., 2017; OZANNE; HALES, 2004; PINHO et al., 2014; SENE et al., 

2013; VEGA et al., 2016). Studies show that offspring whose dams received LPD  have 

lower birth weight, followed by accelerated growth (catch-up growth), insulin resistance 

and type 2 diabetes in adulthood and  decreasing life expectancy (ALEJANDRO et al., 

2020; EMBLETON et al., 2016; MCMILLEN; ADAM; MÜHLHÄUSLER, 2005; PETRY et 

al., 2001). 

In addition, studies have aroused interest in the maternal LPD on the prostate 

(PINHO et al., 2014; PORTELA et al., 2021; RAMOS et al., 2010; SANTOS et al., 2020). 

Prostate is an exocrine accessory gland to the genital system whose development and 

homeostasis are under androgenic control (BIANCARDI et al., 2017; CUNHA et al., 

1985). It is an important gland to the fertility (VERZE; CAI; LORENZETTI, 2016), and  has 



 

31 

 

aroused, in recent years, medical-scientific interest, as it has a high incidence of benign 

prostatic hyperplasia (BPH) and prostate cancer (PCa), which is the third cancer that most 

kills the men in the world (DASGUPTA; SRINIDHI; VISHWANATHA, 2012; “Global 

Cancer Observatory”). In a previous study, Santos et al. related maternal LPD to the onset 

of prostate cancer (SANTOS et al., 2019). However, little is known about the epigenetic 

mechanisms involved in these changes, and studies show that epigenetic factors are the 

main modulators of gene expression involved in fetal programming-related risks of 

diseases at adulthood (JOUBERT et al., 2016). 

Environmental changes can persistently alter epigenetic markers and trigger 

phenotypic responses that permanently affect the structure and functioning of the organs 

(SINCLAIR et al., 2007; WATERLAND; JIRTLE, 2003). The main mechanisms of 

epigenetic regulation include DNA methylation, by DNA methyltransferase enzymes 

(DNMT), post-transcriptional modification of histones by histone acetylase (HAC) or 

deacetylase (HDAC), in addition to the participation of non-coding RNAs. It is also 

included miRNA, which have been also been described as an epigenetic mechanism for 

the regulation of gene expression (ALLIS; JENUWEIN, 2016; HEERWAGEN et al., 2010). 

MiRNAs are small, single-stranded, evolutionarily conserved molecules (~ 17-25 

nucleotides) which suppress the expression of genes that encode proteins, aiming at 

translational repression, mRNA degradation or both (SHYU; WILKINSON; VAN HOOF, 

2008). Maternal LPD during gestation and lactation deregulated six miRNAs from 

offspring mice on postnatal day (PND) 21. Inflammatory pathways (including MAPK, TGF-

beta and Toll-like receptor) have been associated with the six differentially expressed 

miRNAs, as well as increased levels of IL-6 and TNF-α in the liver (ZHENG et al., 2017). 



 

32 

 

Another study showed that maternal LPD leads to the dysregulation of two important 

miRNAs for the control of PPAR-γ and C/EBP-β expression, transcription factors involved 

in the differentiation of adipocytes and fat deposition, affecting lipid metabolism in the 

offspring in weaning age (PAN et al., 2013). Thus, maternal LPD have been linked to 

miRNA dysregulation, so our objective was to assess the profile of unregulated miRNAs 

after maternal LPD and relate them to PCa. 

2.0 Material and Methods 

 2.1 Animals 

Male and female Sprague Dawley rats were purchased at the State University of 

Campinas (Campinas, SP, Brazil) and kept at the Department of Structural and Functional 

Biology at the São Paulo State University (UNESP), Brazil. The animals were maintained 

under controlled temperature (22-25°C), photoperiod (12h), and relative humidity (55%) 

and received water and feed ad libitum. The animal design was performed according to 

SANTOS et al. (2019). Briefly, when 3 months old, weighing about 250-300g, the animals 

were placed to mate in a harem model (3 female rats for 1 male rat per box). The 

pregnancy confirmation was detected through vaginal washing.The pregnant rats were 

distributed into two groups: 1: Control (CTR) group, which received a standard diet (17% 

protein) during gestation and lactation; 2: Gestational and lactational low protein diet 

(GLLP) group, which received hypoprotein diet (6% protein) during gestation and lactation 

(Supplementary Figure 1). At birth, litters were adjusted to eight pups (4 males and 4 

females). Male rat pups were weighed and euthanized in postnatal day (PND) 21 through 

anesthesia (ketamine and xilazin) followed by decapitation. The blood was collected for 

hormonal analysis and the ventral prostate lobe (VP) was collected, fixed for 



 

33 

 

morphological and stereological analysis, and frozen in liquid nitrogen for the extraction 

of total RNA to perform the techniques of miRNAs sequencing (microRNAome) and RT-

qPCR. Testicle, adrenal gland, skin, and liver were collected to RT-qPCR.    

The experimental procedures are according to the Ethical Principles on Animal 

Experimentation adopted by the Brazilian College of Animal Experimentation (COBEA) 

and were approved by the Ethics Committee on Animal Experimentation of the Institute 

of Biosciences of Botucatu (CEEA-949). 

2.2 Morphological Analyses  

The ventral prostates (VP) of six animals per litter/group from PND 21 were 

removed, and immediately fixed in the methacarn solution during approximately 4 hours 

(Puchtler et al 1970). Then, the samples were dehydrated by ethanol followed by 

diaphanization in xylene, and later the sample were embedded in Paraplast® Plus (Sigma-

Aldrich Chemicals, St Louis, MO, USA). Consecutive 5 μm serial sections were obtained 

and the slides were stained with hematoxylin-eosin for morphological and stereological 

analysis. Stereological analysis was performed to analyze the relative proportions of the 

VP components (epithelium, lumen, and stroma). Results were expressed as a 

percentage of each component and a proportion of the total area analyzed (WEIBEL; 

KISTLER; SCHERLE, 1966). 

2.3 Hormonal Analysis 

The blood sample of 12 animals per litter/group from the PND 21 were collected, 

centrifuged and serum was stored until use. Analysis of hormone levels was performed 

by the colorimetric Elisa method following the manufacturers’ protocol. The measured 

hormones were: Estrogen (17β-estradiol, Monobind®, 4925-300 CA, USA Sensitivity: 6.5 



 

34 

 

pg/mL) and Testosterone (17β-hydroxy-4-androsten-3-one, Monobind®, 3725-300A, 

USA. Sensitivity: 0.038 ng/mL). 

 2.4 Total RNA extraction and miRNA sequencing 

Total RNA was extracted from prostate by Invitrogen™ Trizol® Reagent according 

to manufacturer’s protocols (Thermo Fisher Scientific, MA, USA). The RNA samples were 

quantified by spectrophotometry using the Thermo Scientific™ NanoDrop (Thermo Fisher 

Scientific, MA, USA) and treated with Invitrogen™ DNase I (Thermo Fisher Scientific, MA, 

USA) to remove contamination with genomic DNA. The quality of the RNA was confirmed 

by Bioanalyzer 2100 Eukaryote Total RNA Pico (Agilent Tech, CA, USA) through the RNA 

integrity number (RIN), which must be greater than 8.  

The libraries were prepared by TruSeq® Small RNA Library Prep Kit (Illumina, CA, 

USA). Small non-coding RNA sequencing were performed on the Illumina HiSeq2500 at 

Macrogen inc. After sequencing, FASTQ files were pre-processed by adapter trimming 

and quality filtering, based on the Phred quality score (≥20). Sequence reads were 

aligned to the current version of the Rattus Novegicus genome (Rnor_6.0) using the 

Spliced Transcripts Alignment to a Reference (STAR) aligner and annotated miRNA 

species quantified based on genomic loci. All these steps were performed at OASIS 

Platform (RAHMAN et al., 2018). Differential expression and exploratory analysis were 

performed using the DESeq package (Log2FC |0.4|; FDR< 0.05) 

(https://yanli.shinyapps.io/DEApp/). 

2.5 Integrative analysis 

The integrative analysis was carried out using the results of the transcriptome and 

proteome from another study of our group (data not published). These results were 

https://yanli.shinyapps.io/DEApp/


 

35 

 

obtained from the same animal model mentioned in this study. For the results of the 

transcriptome, mRNAs were considered differently expressed with p-value<0.05 and log2 

Fold Change |0.66|. For proteome results, differential expressed proteins were 

considered with p-value<0.05 and log2 Fold Change |1|.  

To perform the integrative analysis, we first find the target prediction of unregulated 

miRNAs using miRWalk 3.0 (http://mirwalk.umm.uni-heidelberg.de/) (DWEEP; GRETZ, 

2015). Then, the correlation of miRNAs/mRNAs and miRNAs/proteins were observed 

considering values of inverted Fold Change (down/up or up/down). 

2.6 Enrichment analysis  

The enrichment analysis was done using the results of the Integrative analysis and 

was separately performed separately for targets upregulated and downregulated. For this 

we use the platform “KEGG Orthology Based Annotation System” (KOBAS) 

(http://kobas.cbi.pku.edu.cn/kobas3/?t=1) (XIE et al., 2011) was used. This platform 

generates an enrichment from several databases such as Gene Ontology, Panther, 

KEGG and Reactome. The pathways were considered enriched with p-adjusted <0.05.  

2.7 Quantitative polymerase chain reaction (RT-qPCR) 

RT-qPCR for miRNAs were performed according to the relevant literature 

(VARKONYI-GASIC et al., 2007). 

The cDNA was synthesized using the Applied Biosystems™ High-Capacity cDNA 

Reverse Transcription Kit according to the manufacturer. For miRNAs, the stem-loop RT 

primer, designed according to (CHEN, 2005), was hybridized with the selected miRNA 

molecule giving rise to the reverse primer (Table 2). This reverse primer was used to 

http://mirwalk.umm.uni-heidelberg.de/
http://kobas.cbi.pku.edu.cn/kobas3/?t=1


 

36 

 

perform the miRNA cDNA instead of the Rondon primer used for mRNA cDNA. The cDNA 

reaction product was amplified using specific primers (Table 2) and Applied Biosystems™ 

SYBR™ Green PCR Master Mix system (Thermo Fisher Scientific, MA, USA). For 

miRNAs reaction was used the primer Forward and the primer universal described in table 

2. The reactions were performed in duplicates for each target gene in the Applied 

Biosystems™ QuantStudio™ 12K Flex Real-Time PCR System (Thermo Fisher 

Scientific, MA, USA) in 96-well plates according to the manufacturer's instructions. The 

relative quantification of each gene was performed using the 2-∆∆CT method (LIVAK; 

SCHMITTGEN, 2001). The values obtained for all samples were normalized by the 

reference miRNA (U6) and mRNA (actin). The values were calculated using the 

expression ratio of the GLLP/CTR groups. Student's “t” test was applied and considered 

statistically significant when p <0.05. The sequences of the primers pair used are 

described in table 2. 

Table 2. Sequences of primers. 

2.8 TCGA data: a translational analysis 

Raw prostate miRNA sequencing data from “The Cancer Genome Atlas” (TCGA) 

repository (https://portal.gdc.cancer.gov/repository), which contains 498 samples from 

patients with prostate cancer and 52 samples from a solid tissue (as a control group) were 

downloaded.  Oasis platform (https://oasis.dzne.de/) was used to reanalyze this data with 

the same protocol used for rat data, but aligned with the most current construction of the 

Foward Reverse

miR-33-5p CGGCGGGTGCATTGTAGTTGCATTGCA GTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACTGCAAT

U6 GCAAATTCGTGAAGCGTTCC GTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACAAAAATAT

Universal GTGCAGGGTCCGAGGT

CYP1B1 (rat) GGGCTGGATTTGGAGGATGT TCCTGGCTGGCTCCAAAG

SEMA6C (rat) TCTGCAGTCTGCGCCTTCT CCTTGAACTTGCCCTCAAAGC

CYP1B1 (Human) TCCTCCTCTTCACCAGGTATCC CTGGTCACCCATACAAGGCA

SEMA6C (Human) CTCCGAGAGCCACACTTTGT CGAGCATCCTCCACAGAGAC

https://portal.gdc.cancer.gov/repository
https://oasis.dzne.de/


 

37 

 

human genome (hg38) (RAHMAN et al., 2018). miRNAs were considered differentially 

expressed with FDR>0.05 and log2 fold change |0.4|. The differentially expressed 

miRNAs of patients with prostate cancer were compared with unregulated miRNAs of the 

GLLP group. 

Also, mRNAs results from GEPIA were also used to compare with target prediction 

of the miR-33a-5p. GEPIA is a Web Site that analyzed RNA sequencing expression data 

of 9,736 tumors (TCGA) and 8,587 normal samples (GTEx) using a standard processing 

pipeline (Cite GEPIA). mRNAs were considered differentially expressed with FDR>0.05 

and log2 fold change |0.4|. 

2.9 Cellular Culture 

Non-cancerous prostate cells, PNT2 cell line (Rio de Janeiro Cell Bank - BCRJ, 

Rio de Janeiro, Brazil), were maintained in Gibco™ RPMI 1640 Medium (Thermo Fisher 

Scientific, MA, USA). The cells were expanded in 25 cm2 and 75 cm2 polystyrene plates 

and kept in an oven with 5% CO2 in a humid atmosphere at 37ºC. When the cells reached 

80% confluence, they were subjected to 0.25% Gibco™ Trypsin-EDTA (0.5%), no phenol 

red (Thermo Fisher Scientific, MA, USA) and transferred to 12-well plates to compose the 

study groups. Each experimental group (explained in the next topic) was performed in 

triplicate and three biological repetitions. 

2.9.1 Cellular Transfection 

To form the different study groups, hsa-miR-33a-5p mimics were transfected into 

the cell. For this, two complexes were formed to be used in the final transfection solution. 

First, Invitrogen™ Lipofectamine™ RNAiMAX Transfection Reagent (Thermo Fisher 

Scientific, MA, USA) was diluted in Gibco™ Opti-MEM Reduced Serum Medium (Thermo 



 

38 

 

Fisher Scientific, MA, USA) to form the first complex. Then, the oligonucleotides were 

diluted in Opti-MEM® to form the second complex. The Lipofectamine+Opti-MEM 

complex was mixed with the oligos+Opti-MEM complex and incubated for 5 minutes at 

room temperature. After this period, 150 μl of the final transfection solution was added to 

each well containing PNT2 cells (80% confluence) and normal culture medium. The cells 

were incubated for a period of 15 hours and the medium with the treatment were 

exchanged by the specific medium. After 24 hours, the cells were collected and 

processed for extraction of total RNA using Invitrogen™ Trizol® Reagent according to 

manufacturer’s protocols (Thermo Fisher Scientific, MA, USA) or added Invitrogen™ MTT 

(3-(4,5-Dimethylthiazol-2-yl)-2,5-Diphenyltetrazolium Bromide) (Thermo Fisher Scientific, 

MA, USA) solution to analyze cell viability after treatment. 

2.9.2 Cell viability assay 

PNT2 cells were seed in a 12-weel plates and treated with mimic as previously 

described. After 24, 48 or 72 hours of the treatment, the cell medium was removed and 

200 µl of a 10% MTT solution (Invitrogen™ MTT (3-(4,5-Dimethylthiazol-2-yl)-2,5-

Diphenyltetrazolium Bromide) (Thermo Fisher Scientific, MA, USA)) was added. 4 hours 

later, the medium containing MTT solution was removed and 300 μl DMSO was added 

directly to each well. We replaced the solution in a 96-well plate (each well in the 12-well 

plate becoming 3 in the 96-well plate). The optical density of each well was measured by 

spectrophotometer at the wavelength of 550 nm. The experiments were performed in 

triplicate. 

2.10. Statistical Analysis 



 

39 

 

For global analyzes, specific statistical packages were used, as previously 

described. For the other parameters, the Shapiro-Wilk normality test was performed. For 

the data that passed the test, the comparison between the experimental groups was 

perform using the Student's T test. For data that did not pass the normality test, the Mann-

Whitney test was performed. A statistically significant difference was considered when p 

<0.05. 

2.11. Making the images  

The PCA graph was generated using the Clustvis tool (https://biit.cs.ut.ee/clustvis/) 

(METSALU; VILO, 2015) and the volcano graph on the VolcaNoseR website 

(https://huygens.science.uva.nl/VolcaNoseR/). Heatmap graphic was generated on the 

Morpheus platform (https://software.broadinstitute.org/morpheus/) and Sankey diagram 

using SankeyMATIC Web site (http://sankeymatic.com/). 

 3.0 Results 

 3.1 Animals of the GLLP group have lower body and prostate weight in 

addition to high levels of estrogen and testosterone. 

The animals subjected to maternal low protein diet (LPD)during gestation and 

lactation had a lower birth weight that remained until PND 21 (Fig. 1A and Table 1). In 

addition, the prostate weights, normalized by the weight of the animal (relative) or not, 

were also lower in the GLLP group (Table 1). 

The prostatic tissue morphometry of the CTR experimental animals present dilated 

acini, with ample light and full of secretion and with a cylindrical secretory epithelium 

associated with fibromuscular stroma (Fig. 1B). The GLLP animals, the animals in the 

GLLP group had smaller acini, with reduced light and more evident stroma when 

https://huygens.science.uva.nl/VolcaNoseR/
https://software.broadinstitute.org/morpheus/
http://sankeymatic.com/


 

40 

 

compared with the CTR group (Fig. 1B). Stereological analyses were corroborated by 

histological data, where we observed a decrease in light and an increase in stroma in the 

GLLP group in relation to CTR (Table 1). The estrogen and testosterone levels were 

increased in the GLLP group (Fig. 1C). 

 



 

41 

 

Figure 1. The GLLP group has lower weight, late prostate development and a higher level of testosterone and 

estrogen. A. Representative images of male rat pups from experimental groups at different ages. We can observe the 

difference between the groups: GLLP group is lower in both ages. B. Histological sections of the ventral prostate lobes 

(VP) stained with hematoxylin-eosin (HE) showing the delayed prostate development marked by smaller acinus and 

more evident stroma in GLLP group. C. Bar graph of blood hormone measurement (Estrogen and Testosterone) 

showing the high levels in GLLP group of both hormones. CTR: Control group; GLLP: Gestation and lactation low 

protein diet; PND: Postnatal day VP: Ventral prostate. ep: epithelium; st: stroma; L: lumen. * means statistical difference. 

Scale bar: 200 μm.  

Table 1. Rat and prostate weight and stereological results.  

CTR: Control, GLLP: Gestation and lactation low protein diet, PND: Postnatal day, VP: Ventral prostate, 

g: grams. * means statistical difference 

3.2 Performance of miRNAs differentially expressed in the GLLP group: 

Target prediction, integration analysis (transcriptome and proteome) and pathways 

enrichment analysis. 

The 605 readings mapped for each sample were found after analysis on the 

Oasis platform and the raw data were sent to the database (unpublished data). We 

identified a differential expression profile between CTR and GLLP group by principal 

component analysis (PCA) (Figure 2A). Six miRNAs (rno-miR-33-5p, rno-miR-871-3p, 

rno-miR-3553, rno-miR-144-5p, rno-miR-99a-5p and rno-miR-7a-5p) have been identified 

that are at least 1.3 times less expressed in the GLLP group compared to CTR and 

fourteen miRNAs (rno-miR-708-5p, rno-miR-487b-3p, rno-miR-337-3p, rno-miR-411-3p, 

rno-miR-410-3p, rno-miR-496-3p, rno-miR-323-3p, rno-miR-299a-5p, rno-miR-412-3p, 

CTR GLLP

PND1 Rat weight (g) 6.62 ± 0.4 6.02 ± 0.5 *

PND21 Rat weight (g) 39.25 ± 6.2 20.02 ± 2.9 *

VP weight (g) 0.03 ± 0.006 0.01 ± 0.003 *

VP relative weight (g) 0.0009 ± 0.0001 0.0007 ± 0.0001 *

Epithelium(%) 35.71 ± 5.8 48.05 ± 6.1 *

Lumen (%) 45.47 ± 9.8 30.50 ± 4.9 *

Stroma (%) 19.79± 5.4 29.63 ± 4.9 *

Glandular fraction 82.04 ± 5.2 67.58 ± 14.3 *



 

42 

 

rno-miR-493-5p, rno-miR-483-5p, rno-miR-6331, rno-miR-184, rno-miR-483-3p) are 

expressed at least 1.3 times more in the GLLP group as compared to the CTR (Figure 

2B).  

Target prediction for the 20 miRNAs resulted in 8172 potential targets 

(Supplementary Table 2). These predicted targets were compared with transcriptome and 

proteomic data (unpublished), which identified 708 differentially expressed mRNAs (525 

regulated up and 183 down regulated) and 289 deregulated proteins (163 regulated up 

and 126 regulated down) in the GLLP group when compared with the CTR group 

(Supplementary Table 3). Thus, after integrative analysis of these data considering 

inverted fold changes levels, 80 targets with increased expression were identified (52 

mRNAs, 28 proteins and 0 common) (Figure 2C) and 119 targets with decreased 

expression (64 mRNA, 53 proteins and 2 in common) (Figure 2D), totaling 199 possible 

targets of miRNAs (Supplementary Table 4). 

 The enrichment analysis for 80 upregulated targets enriched terms related 

to: Proteoglycans in cancer, positive regulation of angiogenesis, cellular response to 

tumor necrosis factor, Hepatocellular carcinoma, regulation of vascular endothelial 

growth factor production, response to estradiol, Pathways in cancer, Wnt signaling 

pathway (Figure 2.E and Supplementary Table 5). For the 119 down regulated targets, 

the enriched pathways are related to: Endoplasmic reticulum, Hepatocellular carcinoma, 

positive regulation of ERAD pathway (Figure 2.A and Supplementary Table 6). 

 

 



 

43 

 

Figure 2. MiRNAs differentially expressed in the GLLP group have predicted targets related to interesting 

enriched pathways. A. Principal component analysis (PCA) show similarities between samples within each group. Red 

circle delimits the set of samples from the CTR group, and blue circle delimits the set of samples from the GLLP group. 

B. Heatmap showing the expression in log2 of Fold change (red up regulated, blue down regulated) and FDR (larger 



 

44 

 

circle size represents larger FDR value) of the differentially expressed miRNAs. C. Venn diagram showing the 

integration of predicted targets of downregulated miRNAs with upregulated mRNAs and proteins. D Venn diagram 

showing the integration of predicted targets of upregulated miRNAs with downregulated mRNAs and proteins. E. Bar 

graphs showing the ontological pathways and terms enriched of the miRNAs integrated predicted targets. Red bars 

refer to upregulated miRNAs integrated predicted targets; blue bars refer to downregulated miRNAs integrated 

predicted targets. The numbers below de bars represent the adjusted p-value shown in –log10. CTR: Control; GLLP: 

Gestational and lactational low protein intake. 

3.5 Translational Analysis: Results of prostate cancer (TCGA) and 

correlation with rat data. 

Using TCGA small-RNA sequencing, the expression of 1010 miRNAs was 

detected. The Differential expression profile was identified between the solid tissue group 

(CTR) and the Cancer group by principal component analysis (PCA) (Figure 3.A). The 

volcano graph (Fig. 3.B) identified 243 miRNAs that are downregulated in the Cancer 

group, and 248 miRNAs upregulated (Supplementary Table 7). Values of FDR> 0.05 and 

Log2 Fold Change of ± 0.4 were considered. 

The correlation between the Rat and Human differentially expressed miRNAs 

resulted in 7 miRNAs in common (Figure 3.C and Supplementary Table 8). From this, five 

are upregulated in GLLP group but downregulated in cancer samples. The miR-708-5, 

are upregulated in both groups and the miR-33-5p, are downregulated in both groups.  

Considering everything that has already been mentioned, from fold change values, 

enriched pathways and relationship with prostate cancer, mir-33-5p was evident in all 

criteria, proving to be a good miRNA to be studied. 



 

45 

 

Figure 3. GLLP group presents some deregulated miRNAs in common with prostate cancer patients. A. 

Principal component analysis (PCA) shows similarities between samples within each group. Blue circle delimits the set 

of samples from the CTR group (n=52), and red circle delimits the set of samples from the cancer group (n=498). B. 

Volcano graph with the differential expression between the two experimental conditions. The vertical axis corresponds 

to log2 Fold Change <0.4> and the horizontal axis represents values of FDR <0.05 expressed in -log10. C. Heatmap 

showing the expression in log2 of Fold change (red upregulated, blue downregulated) and FDR (larger circle size 

represents larger FDR value) of the differentially expressed miRNAs in common between prostate cancer patients and 

GLLP group. 

3.6 An exploratory analysis of miR-33a-5p 

The first analysis performed with miR-33 was to prove its downregulation in the 

prostate of animals in the GLLP group using the RT-qPCR method (Fig. 4). In addition, 

the expression of this miRNA is also downregulated in the testis and liver. Furthermore, 

the expression of miR-33 was increased in the adrenal gland, on the GLLP group, and 

the expression levels of miR-33 were not different in the skin (Fig. 4). 



 

46 

 

Figure 4. Expression of mir-33-5p using the RT-qPCR technique in different organs showing the lower 

expression in prostate, testicle and liver and a higher level in adrenal gland in GLLP group when compared to CTR. 

Prostate statistics was Student T test (bar graph) and the other organs Mann-Whitney test (box plot graph) CTR: Control 

group; GLLP: Gestation and lactation low protein diet. * means statistical difference. 

 The second analysis was to transfect the PNT2 cell line with miR-33, 

thereby increasing its expression. The technique was successfully performed, where the 

expression levels of cells treated with miR-33 MIMIC showed high expression levels while 

cells that were only treated with lipofectamine did not have their levels increased (Fig. 

5A). This overexpression of miR-33 persisted significantly until at least 72 hours after the 

end of treatment, although expression levels have decreased (Fig. 5B).  

  To see the effect of mir-33a-5p overexpression on the culture cell line, we 

performed the MTT test to analyze cell viability. The result for this assay shows that the 

overexpression of mir-33a-5p decrease the cell viability levels (Fig. 5C).  



 

47 

 

 

Figure 5. miR-33a-5p overexpression causes low levels of cell viability A. miR-33a-5p expression in PNT2 

cell after treatment with Mimic (higher levels) or Lipofectamine alone (normal levels) represented by a Box plot. B. Line 

graph showing miR-33-5p expression levels after 24, 48 and 72 hours of treatment with Mimic. Expression levels in 24 

hours are higher, but the difference remains significant until 72 hours, which was the last hour analyzed. C. Cell viability 

levels performed by MTT 24, 48 and 72 hours after the end of the treatment with mimic. The line graph shows the low 

levels of cell viability when miR-33a-5p is overexpressed. CTR: Control group; GLLP: Gestation and lactation low 

protein diet. * means statistical difference. 

When we look at the enriched pathways of the downregulated miRNAs, we see 

that the targets of miR-33 are present in all of them, showing the importance of miR-33 

for the regulation of the organism (Fig. 6A). In addition, we used RNA sequencing data 

results analyzed by Gepia from 9,736 tumors patientes (TCGA) and 8,587 normal 



 

48 

 

samples (GTEx) to compare with the miR-33 predicted targets and we observed 6 targets 

that is deregulated in both (Fig. 6B). From this results we choose two miR-33 targets 

predicted to validated, the SEMA6C and CYP1B.  

The expression of both targets presented upregulated in GLLP animals (Fig. 6C) 

but there was no difference in the transfected cell line (Fig 6D).  

 

Figure 6. The predicted mir-33-5p targets are involved in all selected pathways and with prostate cancer. A. 

Sankey diagram showing the pathways enriched by predicted integrated targets of the upregulated miRNAs and the 

targets that are enriching the pathways. Red circle highlights the miR-33-5p targets. B. Venn diagram showing shared 

between deregulated mRNAs in prostate cancer with the predicted and integrated miR-33 targets. C. Expression levels 

of two predicted mir-33 targets (SEMAC and CYp1B1) in the prostate of rats in the CTR and GLLP group. The results 



 

49 

 

show that the downregulation of miR-33 leads to increased expression of the targets. D. Expression levels of two 

predicted mir-33 targets (SEMAC and CYp1B1) in PNT2 cells transfected with mimic miR-33a-5p. 

 4.0 Discussion 

The term malnutrition is associated with the inadequate or insufficient intake of any 

food component, such as proteins. The minimum percentage of protein recommended by 

the American Institute of Nutrition for rat diets is 12% (NUTRITION, 1995). In our study, 

only 6% of the protein was made available to rats, that is, half of the recommended. 

Several authors have shown that feed intake with hypoproteins is associated with low 

birth weight and reduced growth of different organs in rodent models. (DE BRITO ALVES 

et al., 2016; FALCÃO-TEBAS et al., 2012; LEANDRO et al., 2012; LIMA et al., 2015; 

OZANNE; HALES, 2004). In our animals it was not different, the GLLP animals had a 

lower body weight at birth, in addition to a significant decrease weight in the prostate, 

which may be related to the delay in the development of this organ, which had smaller 

acini, with less light and increased stroma. These data corroborate the results of Santos 

et al. (2019) demonstrated a reduction in the prostate luminal fraction , as well as a 

decreased glandular secretion, produced by the quantification of the prostatein protein in 

LPD animals in PND 21. In addition, Ramos et al. (RAMOS et al., 2010) identified smaller 

amount and reduced size of acini in the dorsolateral prostate of rats exposed to 

intrauterine protein restriction and Pinho et al. (2014) also observed a delay in prostate 

development in the offspring of animals following the same protocol but with PND 1. 

In addition to the morphological alterations, we found important hormonal changes, 

such as an increase in serum testosterone and estrogen in this PND 21 animal model. 

Studies show that prostate exposure to high testosterone levels is a potential carcinogen 

in the prostate, where 30% of animals with low doses but with chronic testosterone have 



 

50 

 

prostate cancer. However, estrogen has a synergistic effect with testosterone, 100% of 

animals exposed to testosterone doses associated with estrogen developed prostate 

cancer. (BOSLAND, 2005). Zambrano el al. (ZAMBRANO et al., 2014) observed that 

mothers who have low protein intake have high levels of estrogen in the gestational 

period. Thus, this may be one of the mechanisms that assist in the appearance of prostate 

cancer in  aging animals, since studies show that the development of the prostate is 

sensitive to hormonal changes and exposure in the early stages of life can play a 

significant role in the appearance of precancerous lesions and carcinogenesis in 

adulthood through epigenetic changes (PRINS et al., 2008). 

Epigenetics are inherited changes in gene expression without undergoing changes 

in DNA sequences. Interestingly, miRNAs are also considered an important epigenetic 

component, as they are targets for methylation of their DNA and for regulating epigenetic 

modifiers such as DNMTs and deacetylated histones (SALIMINEJAD et al., 2019). Some 

studies have already shown that maternal LPD causes the dysregulation of miRNAs that 

can lead to metabolic changes, chronic inflammation, increased blood pressure and 

morphological changes in the heart (ASSALIN; GONTIJO; BOER, 2019; ZHENG et al., 

2017). In our study, maternal LPD caused alteration of 20 miRNAs and after integrating 

predicted targets with protein and mRNA data, we observed that miRNAs are enriching 

many cancer-related pathways. When we compared the 20 unregulated miRNAs with the 

491 unregulated miRNAs of prostate cancer patients (TCGA data) we find 7 miRNAs in 

common. Our data suggest that miRNAs deregulated by fetal programming due to LPD 

may be influencing the microenvironment in order to predispose to the onset of cancer.  



 

51 

 

Among miRNAs, miR-33 has been widely studied both for its tumor suppressive 

effect, considering that its positive regulation decreases proliferation inhibiting tumor 

growth, and  reducing the chance of metastasis by affecting cell migration, which can be 

a powerful strategy for the treatment of cancer (CIRERA-SALINAS et al., 2012; 

FRIEDMAN et al., 2012; KUO et al., 2013; RICE et al., 2013; THOMAS et al., 2012; ZHOU 

et al., 2015), and for its role in regulating cholesterol / lipid metabolism, being an important 

therapeutic target for atherosclerosis (HORIE et al., 2010; MARQUART et al., 2010; 

NAJAFI-SHOUSHTARI et al., 2010; RAYNER et al., 2010, 2011a, 2011b; ROTLLAN 

NOEMI et al., 2013; ROTTIERS et al., 2013). In addition, studies have shown that miR-

33 inhibition leads to the development of obesity, insulin resistance and increased food 

intake, in organs such as liver, white adipose tissue and skeletal muscle, suggesting that 

the metabolic regulation by miR-33 is more complex than was known (NÄÄR, 2018; 

PRICE et al., 2018). The low-protein maternal diet offered at the end of pregnancy affects 

the fraction of offspring pancreatic β cells at birth, increasing their susceptibility to 

metabolic disorders and type 2 diabetes in adulthood. In addition to increasing glucose 

intolerance and reducing insulin sensitivity in aging male offspring (ALEJANDRO et al., 

2020; BERENDS et al., 2018). Putting all information about miR-33 together, we can 

suggest that it is an important miRNA for fetal programming. In addition, miR-33 is 

deregulated not only in the prostate, but also in the testicle, adrenal and liver shown that 

it can be a biological marker for programming low protein intake. 

Among the targets of miR-33, we chose 2 to perform the validation, SEMA6C and 

CYP1B1. Semaphorins are a family of proteins that, in addition to being important for the 

immune response because they have receptors in most defense cells, also play an 



 

52 

 

important role in the tumor microenvironment (FRANZOLIN; TAMAGNONE, 2019). For 

example, SEMA6D has been described as being associated with tumor progression and 

angiogenesis in gastric cancer (LU et al., 2016; ZHAO, 2006). SEMA7A expression is 

positively regulated in breast cancers and is associated with shorter survival (BLACK et 

al., 2016). In certain tumors, SEMA3E has been associated with progression due to the 

promotion of cell invasion, metastatic spread and tumor angiogenesis. (CASAZZA et al., 

2010, 2012; LUCHINO et al., 2013; TAMAGNONE; MAZZONE, 2011).  

Cytochrome P450 1B1 (CYP1B1) is an important enzyme in estrogen metabolism 

(DAWLING et al., 2004; HAYES et al., 1996; LI; ZHU; GONZALEZ, 2017). Studies have 

shown that high levels of CYP1B1 can lead to carcinogenesis mediated by changes in 

hormone levels and the transformation of estradiol into semiquinones and quinones, 

which can form DNA adducts resulting in oncogenic mutations (GAJJAR; MARTIN-

HIRSCH; MARTIN, 2012; HAYES et al., 1996; SPINK et al., 1997). In addition, quinones 

/ semiquinones also undergo a redox cycle and generate reactive oxygen species, 

resulting in oxidative damage (PARL et al., 2009). Thus, high levels of estrogen, 

associated with low levels of miR-33 (suppressor CIP1B1) and high levels of CIP1B1 and 

SEMA6C may be combined, leading to a carcinogenic microenvironment that associated 

with other factors may be the origin of the cancer found in these animals at age adult. 

CYP1B1 is an important fatty acid modulator that suppresses the expression of the 

target genes PPARγ and PPARα, receptors for fatty acid metabolism, which play 

important roles in energy maintenance homeostasis. Cancer and obesity are closely 

related to disruption of fatty acid metabolism. Santos et al. showed that animals 

programmed by maternal LPD during pregnancy and lactation have low levels of 



 

53 

 

triglycerides, total protein, albumin, and glucose in the DPN21 (SANTOS et al., 2019). 

These results show that in addition to the action of CYP1B1 on hormone levels, it may 

also be regulating fatty acid metabolism. 

Thus, we conclude that the effect of maternal LPD during pregnancy and lactation 

cause changes in miRNAs important for the tumor microenvironment been one of them 

the miR-33. We also proved the regulation of this miRNA in two targets, SEMA6C and 

CYP1B1 in rat samples. We also observed that miR-33 can be an important biomarker 

for fetal programming due to maternal protein restriction, being deregulated in other 

organs such as the liver, testis, and adrenal gland.   

  



 

54 

 

 

5.0 Reference 

AFSHIN, A. et al. Health effects of dietary risks in 195 countries, 1990–2017: a systematic 
analysis for the Global Burden of Disease Study 2017. The Lancet, v. 393, n. 10184, p. 
1958–1972, maio 2019.  

AHMAD, I.; SANSOM, O. J.; LEUNG, H. Y. Advances in mouse models of prostate 
cancer. Expert Reviews in Molecular Medicine, v. 10, p. e16, 9 jun. 2008.  

ALEJANDRO, E. U. et al. Maternal low-protein diet on the last week of pregnancy 
contributes to insulin resistance and β-cell dysfunction in the mouse offspring. American 
Journal of Physiology-Regulatory, Integrative and Comparative Physiology, v. 319, 
n. 4, p. R485–R496, 1 out. 2020.  

ALLIS, C. D.; JENUWEIN, T. The molecular hallmarks of epigenetic control. Nature 
Reviews Genetics, v. 17, n. 8, p. 487–500, ago. 2016.  

ASSALIN, H. B.; GONTIJO, J. A. R.; BOER, P. A. miRNAs, target genes expression and 
morphological analysis on the heart in gestational protein-restricted offspring. PLOS 
ONE, v. 14, n. 4, p. e0210454, 29 abr. 2019.  

AUMÜLLER, G.; SEITZ, J. Protein secretion and secretory processes in male accessory 
sex glands. International Review of Cytology, v. 121, p. 127–231, 1990.  

BARKER, D. J. et al. Weight in infancy and death from ischaemic heart disease. Lancet 
(London, England), v. 2, n. 8663, p. 577–580, 9 set. 1989.  

BARKER, D. J. et al. Fetal and placental size and risk of hypertension in adult life. BMJ : 
British Medical Journal, v. 301, n. 6746, p. 259–262, 4 ago. 1990.  

BARKER, D. J. et al. Fetal nutrition and cardiovascular disease in adult life. Lancet 
(London, England), v. 341, n. 8850, p. 938–941, 10 abr. 1993.  

BARKER, D. J. P. Maternal nutrition, fetal nutrition, and disease in later life. Nutrition, v. 
13, n. 9, p. 807–813, set. 1997.  

BARKER, D. J. P. The origins of the developmental origins theory. Journal of Internal 
Medicine, v. 261, n. 5, p. 412–417, maio 2007.  

BENATTI, R. O. et al. Maternal high-fat diet consumption modulates hepatic lipid 
metabolism and microRNA-122 (miR-122) and microRNA-370 (miR-370) expression in 
offspring. British Journal of Nutrition, v. 111, n. 12, p. 2112–2122, jun. 2014.  



 

55 

 

BERENDS, L. M. et al. Programming of central and peripheral insulin resistance by low 
birthweight and postnatal catch-up growth in male mice. Diabetologia, v. 61, n. 10, p. 
2225–2234, 2018.  

BIANCARDI, M. F. et al. Female prostate: historical, developmental, and morphological 
perspectives. Cell Biology International, v. 41, n. 11, p. 1174–1183, nov. 2017.  

BLACK, M. J. et al. Accelerated age-related decline in renal and vascular function in 
female rats following early-life growth restriction. American Journal of Physiology. 
Regulatory, Integrative and Comparative Physiology, v. 309, n. 9, p. R1153-1161, 1 
nov. 2015.  

BLACK, R. E. et al. Maternal and child undernutrition and overweight in low-income and 
middle-income countries. The Lancet, v. 382, n. 9890, p. 427–451, ago. 2013.  

BLACK, S. A. et al. Semaphorin 7a exerts pleiotropic effects to promote breast tumor 
progression. Oncogene, v. 35, n. 39, p. 5170–5178, 29 set. 2016.  

BOHNSACK, M. T.; CZAPLINSKI, K.; GORLICH, D. Exportin 5 is a RanGTP-dependent 
dsRNA-binding protein that mediates nuclear export of pre-miRNAs. RNA (New York, 
N.Y.), v. 10, n. 2, p. 185–191, fev. 2004.  

BOMMER, G. T. et al. p53-mediated activation of miRNA34 candidate tumor-suppressor 
genes. Current biology: CB, v. 17, n. 15, p. 1298–1307, 7 ago. 2007.  

BOSLAND, M. C. The Role of Estrogens in Prostate Carcinogenesis: A Rationale for 
Chemoprevention. Reviews in Urology, v. 7, n. Suppl 3, p. S4–S10, 2005.  

BURNS, S. P. et al. Gluconeogenesis, glucose handling, and structural changes in livers 
of the adult offspring of rats partially deprived of protein during pregnancy and lactation. 
The Journal of Clinical Investigation, v. 100, n. 7, p. 1768–1774, 1 out. 1997.  

BURTON, G. J.; FOWDEN, A. L.; THORNBURG, K. L. Placental Origins of Chronic 
Disease. Physiological Reviews, v. 96, n. 4, p. 1509–1565, 2016.  

CAI, X.; HAGEDORN, C. H.; CULLEN, B. R. Human microRNAs are processed from 
capped, polyadenylated transcripts that can also function as mRNAs. RNA (New York, 
N.Y.), v. 10, n. 12, p. 1957–1966, dez. 2004.  

CALIN, G. A. et al. Human microRNA genes are frequently located at fragile sites and 
genomic regions involved in cancers. Proceedings of the National Academy of 
Sciences of the United States of America, v. 101, n. 9, p. 2999–3004, 2 mar. 2004.  

CALZADA, L. et al. Maternal protein restriction during gestation impairs female offspring 
pancreas development in the rat. Nutrition Research (New York, N.Y.), v. 36, n. 8, p. 
855–862, 2016.  



 

56 

 

CASAZZA, A. et al. Sema3E–Plexin D1 signaling drives human cancer cell invasiveness 
and metastatic spreading in mice. Journal of Clinical Investigation, v. 120, n. 8, p. 
2684–2698, 2 ago. 2010.  

CASAZZA, A. et al. Tumour growth inhibition and anti‐metastatic activity of a mutated 
furin‐resistant Semaphorin 3E isoform. EMBO Molecular Medicine, v. 4, n. 3, p. 234–
250, mar. 2012.  

CAULFIELD, L. E. et al. Undernutrition as an underlying cause of child deaths associated 
with diarrhea, pneumonia, malaria, and measles. The American Journal of Clinical 
Nutrition, v. 80, n. 1, p. 193–198, jul. 2004.  

CHAKRABORTY, C. et al. The Interplay among miRNAs, Major Cytokines, and Cancer-
Related Inflammation. Molecular Therapy - Nucleic Acids, v. 20, p. 606–620, 5 jun. 
2020.  

CHEN, C. Real-time quantification of microRNAs by stem-loop RT-PCR. Nucleic Acids 
Research, v. 33, n. 20, p. e179–e179, 27 nov. 2005.  

CIRERA-SALINAS, D. et al. Mir-33 regulates cell proliferation and cell cycle progression. 
Cell Cycle, v. 11, n. 5, p. 922–933, mar. 2012.  

COLOMBELLI, K. T. et al. Impairment of microvascular angiogenesis is associated with 
delay in prostatic development in rat offspring of maternal protein malnutrition. General 
and Comparative Endocrinology, v. 246, p. 258–269, maio 2017.  

CUNHA, G. R. et al. Stromal-epithelial interactions in adult organs. Cell Differentiation, 
v. 17, n. 3, p. 137–148, set. 1985.  

DAHRI, S. et al. Islet function in offspring of mothers on low-protein diet during gestation. 
Diabetes, v. 40 Suppl 2, p. 115–120, dez. 1991.  

DASGUPTA, S.; SRINIDHI, S.; VISHWANATHA, J. K. Oncogenic activation in prostate 
cancer progression and metastasis: Molecular insights and future challenges. Journal of 
Carcinogenesis, v. 11, p. 4, 2012.  

DAVIS-DUSENBERY, B. N.; HATA, A. Mechanisms of control of microRNA biogenesis. 
Journal of Biochemistry, v. 148, n. 4, p. 381–392, out. 2010.  

DAWLING, S. et al. In Vitro Model of Mammary Estrogen Metabolism: Structural and 
Kinetic Differences between Catechol Estrogens 2- and 4-Hydroxyestradiol. Chemical 
Research in Toxicology, v. 17, n. 9, p. 1258–1264, set. 2004.  

DE BRITO ALVES, J. L. et al. Maternal protein restriction induced-hypertension is 
associated to oxidative disruption at transcriptional and functional levels in the medulla 
oblongata. Clinical and Experimental Pharmacology and Physiology, v. 43, n. 12, p. 
1177–1184, dez. 2016.  



 

57 

 

DEANS, C.; MAGGERT, K. A. What Do You Mean, “Epigenetic”? Genetics, v. 199, n. 4, 
p. 887–896, abr. 2015.  

DEARDEN, L.; OZANNE, S. E. The road between early growth and obesity: new twists 
and turns. The American Journal of Clinical Nutrition, v. 100, n. 1, p. 6–7, jul. 2014.  

DENLI, A. M. et al. Processing of primary microRNAs by the Microprocessor complex. 
Nature, v. 432, n. 7014, p. 231–235, 11 nov. 2004.  

DERMER, G. B. Basal cell proliferation in benign prostatic hyperplasia. Cancer, v. 41, n. 
5, p. 1857–1862, maio 1978.  

DWEEP, H.; GRETZ, N. miRWalk2.0: a comprehensive atlas of microRNA-target 
interactions. Nature Methods, v. 12, n. 8, p. 697, ago. 2015.  

EMBLETON, N. D. et al. Catch-up growth and metabolic outcomes in adolescents born 
preterm. Archives of Disease in Childhood, v. 101, n. 11, p. 1026–1031, 2016.  

FALCÃO-TEBAS, F. et al. Maternal low-protein diet-induced delayed reflex ontogeny is 
attenuated by moderate physical training during gestation in rats. British Journal of 
Nutrition, v. 107, n. 3, p. 372–377, 14 fev. 2012.  

FERNANDEZ-TWINN, D. S.; CONSTÂNCIA, M.; OZANNE, S. E. Intergenerational 
epigenetic inheritance in models of developmental programming of adult disease. 
Seminars in Cell & Developmental Biology, v. 43, p. 85–95, jul. 2015.  

FITZHARDINGE, P. M.; STEVEN, E. M. The Small-for-Date Infant I. Later Growth 
Patterns. Pediatrics, v. 49, n. 5, p. 671–681, 1 maio 1972.  

FRANZOLIN, G.; TAMAGNONE, L. Semaphorin Signaling in Cancer-Associated 
Inflammation. International Journal of Molecular Sciences, v. 20, n. 2, p. 377, 17 jan. 
2019.  

FRIEDMAN, E. B. et al. Serum microRNAs as biomarkers for recurrence in melanoma. 
Journal of Translational Medicine, v. 10, n. 1, p. 155, 2012.  

GAJJAR, K.; MARTIN-HIRSCH, P. L.; MARTIN, F. L. CYP1B1 and hormone-induced 
cancer. Cancer Letters, v. 324, n. 1, p. 13–30, nov. 2012.  

Global Cancer Observatory. Disponível em: <https://gco.iarc.fr/>. Acesso em: 26 maio. 
2021.  

GLUCKMAN, P. D.; HANSON, M. A.; BUKLIJAS, T. A conceptual framework for the 
developmental origins of health and disease. Journal of Developmental Origins of 
Health and Disease, v. 1, n. 1, p. 6–18, fev. 2010.  

GREGORY, R. I. et al. The Microprocessor complex mediates the genesis of microRNAs. 
Nature, v. 432, n. 7014, p. 235–240, nov. 2004.  



 

58 

 

HABIB, S. L. et al. Diabetes and risk of renal cell carcinoma. Journal of Cancer, v. 3, p. 
42–48, 2012.  

HAMMOND, S. M. et al. Argonaute2, a link between genetic and biochemical analyses of 
RNAi. Science (New York, N.Y.), v. 293, n. 5532, p. 1146–1150, 10 ago. 2001.  

HAN, J. et al. The Drosha-DGCR8 complex in primary microRNA processing. Genes & 
Development, v. 18, n. 24, p. 3016–3027, 15 dez. 2004.  

HAYES, C. L. et al. 17 beta-estradiol hydroxylation catalyzed by human cytochrome P450 
1B1. Proceedings of the National Academy of Sciences, v. 93, n. 18, p. 9776–9781, 
3 set. 1996.  

HEERWAGEN, M. J. R. et al. Maternal obesity and fetal metabolic programming: a fertile 
epigenetic soil. American Journal of Physiology-Regulatory, Integrative and 
Comparative Physiology, v. 299, n. 3, p. R711–R722, set. 2010.  

HOFFMAN, D. J. et al. Developmental Origins of Metabolic Disease. Physiological 
Reviews, 3 dez. 2020.  

HORIE, T. et al. MicroRNA-33 encoded by an intron of sterol regulatory element-binding 
protein 2 (Srebp2) regulates HDL in vivo. Proceedings of the National Academy of 
Sciences, v. 107, n. 40, p. 17321–17326, 5 out. 2010.  

INSTITUTE FOR HEALTH METRICS AND EVALUATION (IHME). Findings from the 
Global Burden of Disease Study 2017. Seattle: [s.n.].  

JOUBERT, B. R. et al. DNA Methylation in Newborns and Maternal Smoking in 
Pregnancy: Genome-wide Consortium Meta-analysis. American Journal of Human 
Genetics, v. 98, n. 4, p. 680–696, 7 abr. 2016.  

KIM, V. N.; HAN, J.; SIOMI, M. C. Biogenesis of small RNAs in animals. Nature Reviews 
Molecular Cell Biology, v. 10, n. 2, p. 126–139, fev. 2009.  

KROL, J.; LOEDIGE, I.; FILIPOWICZ, W. The widespread regulation of microRNA 
biogenesis, function and decay. Nature Reviews. Genetics, v. 11, n. 9, p. 597–610, set. 
2010.  

KUMAR, M. S. et al. Dicer1 functions as a haploinsufficient tumor suppressor. Genes & 
Development, v. 23, n. 23, p. 2700–2704, 1 dez. 2009.  

KUO, P.-L. et al. MicroRNA-33a functions as a bone metastasis suppressor in lung cancer 
by targeting parathyroid hormone related protein. Biochimica et Biophysica Acta (BBA) 
- General Subjects, v. 1830, n. 6, p. 3756–3766, 1 jun. 2013.  

KWON, D.-H. et al. Dietary protein restriction induces steatohepatitis and alters 
leptin/signal transducers and activators of transcription 3 signaling in lactating rats. The 
Journal of Nutritional Biochemistry, v. 23, n. 7, p. 791–799, jul. 2012.  



 

59 

 

LANDTHALER, M.; YALCIN, A.; TUSCHL, T. The human DiGeorge syndrome critical 
region gene 8 and Its D. melanogaster homolog are required for miRNA biogenesis. 
Current biology: CB, v. 14, n. 23, p. 2162–2167, 14 dez. 2004.  

LEANDRO, C. G. et al. Maternal Moderate Physical Training during Pregnancy 
Attenuates the Effects of a Low-Protein Diet on the Impaired Secretion of Insulin in Rats: 
Potential Role for Compensation of Insulin Resistance and Preventing Gestational 
Diabetes Mellitus. Journal of Biomedicine and Biotechnology, v. 2012, p. 1–7, 2012.  

LEE, R. C.; FEINBAUM, R. L.; AMBROS, V. The C. elegans heterochronic gene lin-4 
encodes small RNAs with antisense complementarity to lin-14. Cell, v. 75, n. 5, p. 843–
854, 3 dez. 1993.  

LEE, Y. et al. The nuclear RNase III Drosha initiates microRNA processing. Nature, v. 
425, n. 6956, p. 415–419, 25 set. 2003.  

LEE, Y. et al. MicroRNA genes are transcribed by RNA polymerase II. The EMBO 
journal, v. 23, n. 20, p. 4051–4060, 13 out. 2004.  

LI, F.; ZHU, W.; GONZALEZ, F. J. Potential role of CYP1B1 in the development and 
treatment of metabolic diseases. Pharmacology & Therapeutics, v. 178, p. 18–30, out. 
2017.  

LIMA, R. F. DE et al. Bisphenol-A promotes antiproliferative effects during neonatal 
prostate development in male and female gerbils. Reproductive Toxicology, v. 58, p. 
238–245, dez. 2015.  

LIN, S.; GREGORY, R. I. MicroRNA biogenesis pathways in cancer. Nature Reviews 
Cancer, v. 15, n. 6, p. 321–333, jun. 2015.  

LIVAK, K.; SCHMITTGEN, T. Analysis of relative gene expression data using real-
time quantitative PCR and the 2(-Delta Delta C(T)) Method. Disponível em: 
<https://pubmed.ncbi.nlm.nih.gov/11846609/>. Acesso em: 14 ago. 2020.  

LOWSLEY, O. S. The development of the human prostate gland with reference to the 
development of other structures at the neck of the urinary bladder. American Journal of 
Anatomy, v. 13, n. 3, p. 299–349, 1912.  

LU, Y. et al. Expression of semaphorin 6D and its receptor plexin-A1 in gastric cancer 
and their association with tumor angiogenesis. Oncology Letters, v. 12, n. 5, p. 3967–
3974, nov. 2016.  

LUCHINO, J. et al. Semaphorin 3E Suppresses Tumor Cell Death Triggered by the Plexin 
D1 Dependence Receptor in Metastatic Breast Cancers. Cancer Cell, v. 24, n. 5, p. 673–
685, nov. 2013.  

LUND, E. et al. Nuclear export of microRNA precursors. Science (New York, N.Y.), v. 
303, n. 5654, p. 95–98, 2 jan. 2004.  



 

60 

 

MARKER, P. C. et al. Hormonal, cellular, and molecular control of prostatic development. 
Developmental Biology, v. 253, n. 2, p. 165–174, 15 jan. 2003.  

MARQUART, T. J. et al. miR-33 links SREBP-2 induction to repression of sterol 
transporters. Proceedings of the National Academy of Sciences, v. 107, n. 27, p. 
12228–12232, 6 jul. 2010.  

MCMILLEN, I. C.; ADAM, C. L.; MÜHLHÄUSLER, B. S. Early origins of obesity: 
programming the appetite regulatory system. The Journal of Physiology, v. 565, n. Pt 
1, p. 9–17, 15 maio 2005.  

MELO, S. A. et al. A genetic defect in exportin-5 traps precursor microRNAs in the nucleus 
of cancer cells. Cancer Cell, v. 18, n. 4, p. 303–315, 19 out. 2010.  

MERICQ, V. et al. Long-term metabolic risk among children born premature or small for 
gestational age. Nature Reviews Endocrinology, v. 13, n. 1, p. 50–62, jan. 2017.  

MIN, W. et al. The expression and significance of five types of miRNAs in breast cancer. 
Medical Science Monitor Basic Research, v. 20, p. 97–104, 21 jul. 2014.  

MOURELATOS, Z. et al. miRNPs: a novel class of ribonucleoproteins containing 
numerous microRNAs. Genes & Development, v. 16, n. 6, p. 720–728, 15 mar. 2002.  

MURALIDHAR, B. et al. Functional evidence that Drosha overexpression in cervical 
squamous cell carcinoma affects cell phenotype and microRNA profiles. The Journal of 
Pathology, v. 224, n. 4, p. 496–507, ago. 2011.  

NÄÄR, A. M. miR-33: A Metabolic Conundrum. Trends in Endocrinology & 
Metabolism, v. 29, n. 10, p. 667–668, out. 2018.  

NAJAFI-SHOUSHTARI, S. H. et al. MicroRNA-33 and the SREBP Host Genes Cooperate 
to Control Cholesterol Homeostasis. Science, v. 328, n. 5985, p. 1566–1569, 18 jun. 
2010.  

NUTRITION, N. R. C. (US) S. ON L. A. Nutrient Requirements of the Laboratory Rat. 
[s.l.] National Academies Press (US), 1995.  

OZANNE, S. E.; HALES, C. N. Catch-up growth and obesity in male mice. Nature, v. 427, 
n. 6973, p. 411–412, jan. 2004.  

PAN, S. et al. MicroRNA-130b and microRNA-374b mediate the effect of maternal dietary 
protein on offspring lipid metabolism in Meishan pigs. British Journal of Nutrition, v. 
109, n. 10, p. 1731–1738, 28 maio 2013.  

PARASRAMKA, M. A. et al. A role for low-abundance miRNAs in colon cancer: the miR-
206/Krüppel-like factor 4 (KLF4) axis. Clinical Epigenetics, v. 4, n. 1, p. 16, 24 set. 2012.  



 

61 

 

PARL, F. F. et al. Estrogen Exposure, Metabolism, and Enzyme Variants in a Model for 
Breast Cancer Risk Prediction. Cancer Informatics, v. 7, p. CIN.S2262, jan. 2009.  

PELLETIER, D. L. et al. The Effects of malnutrition on child mortality in developing 
countries. Bulletin of the World Health Organization 1995 ; 73(4) : 443-448, 1995.  

PETRY, C. et al. Diabetes in Old Male Offspring of Rat Dams Fed a Reduced Protein 
Diet. International journal of experimental diabetes research, v. 2, p. 139–43, 1 fev. 
2001.  

PIAN, C. et al. Discovering Cancer-Related miRNAs from miRNA-Target Interactions by 
Support Vector Machines. Molecular Therapy - Nucleic Acids, v. 19, p. 1423–1433, 6 
mar. 2020.  

PINHO, C. F. et al. Gestational protein restriction delays prostate morphogenesis in male 
rats. Reproduction, Fertility and Development, v. 26, n. 7, p. 967, 2014.  

PLAGEMANN, A. et al. Hypothalamic neuropeptide Y levels in weaning offspring of low-
protein malnourished mother rats. Neuropeptides, v. 34, n. 1, p. 1–6, fev. 2000.  

PORTELA, L. MF. et al. Increased oxidative stress and cancer biomarkers in the ventral 
prostate of older rats submitted to maternal malnutrition. Molecular and Cellular 
Endocrinology, v. 523, p. 111148, mar. 2021.  

PRICE, N. L. et al. Genetic Ablation of miR-33 Increases Food Intake, Enhances Adipose 
Tissue Expansion, and Promotes Obesity and Insulin Resistance. Cell Reports, v. 22, n. 
8, p. 2133–2145, 20 2018.  

PRINS, G. S. et al. Androgen receptor expression and 5 alpha-reductase activity along 
the proximal-distal axis of the rat prostatic duct. Endocrinology, v. 130, n. 5, p. 3066–
3073, maio 1992.  

PRINS, G. S. et al. Perinatal Exposure to Oestradiol and Bisphenol A Alters the Prostate 
Epigenome and Increases Susceptibility to Carcinogenesis. Basic & clinical 
pharmacology & toxicology, v. 102, n. 2, p. 134, fev. 2008.  

PRINS, G. S.; PUTZ, O. Molecular signaling pathways that regulate prostate gland 
development. Differentiation; Research in Biological Diversity, v. 76, n. 6, p. 641–
659, jul. 2008.  

RAHMAN, R.-U. et al. Oasis 2: improved online analysis of small RNA-seq data. BMC 
Bioinformatics, v. 19, n. 1, p. 54, 14 fev. 2018.  

RAMOS, C. DA F. et al. The prostate of weaned pups is altered by maternal malnutrition 
during lactation in rats. Asian Journal of Andrology, v. 12, n. 2, p. 180–185, mar. 2010.  



 

62 

 

RAVELLI, G. P.; STEIN, Z. A.; SUSSER, M. W. Obesity in young men after famine 
exposure in utero and early infancy. The New England Journal of Medicine, v. 295, n. 
7, p. 349–353, 12 ago. 1976.  

RAVER-SHAPIRA, N. et al. Transcriptional activation of miR-34a contributes to p53-
mediated apoptosis. Molecular Cell, v. 26, n. 5, p. 731–743, 8 jun. 2007.  

RAYNER, K. J. et al. MiR-33 Contributes to the Regulation of Cholesterol Homeostasis. 
Science, v. 328, n. 5985, p. 1570–1573, 18 jun. 2010.  

RAYNER, K. J. et al. Antagonism of miR-33 in mice promotes reverse cholesterol