Como a Expressão De Genes Relacionados

UNIVERSIDADE ESTADUAL DE CAMPINAS INSTITUTO DE BIOLOGIA

LEONARDO CARDOSO ALVES

RITMOS DIEL EM CANA-DE-AÇÚCAR: COMO A EXPRESSÃO DE GENES RELACIONADOS AO METABOLISMO DE CARBOIDRATOS E OS PRINCIPAIS METABÓLITOS COMPONENTES DA PAREDE CELULAR SÃO CONTROLADOS POR FOTOPERÍODOS DISTINTOS?

DIEL RHYTHMS IN SUGARCANE: HOW THE EXPRESSION OF CARBOHYDRATE METABOLISM GENES AND THE MAIN CELL WALL COMPONENT METABOLITES ARE CONTROLLED BY DISTINCT PHOTOPERIODS?

CAMPINAS 2018

LEONARDO CARDOSO ALVES

DIEL RHYTHMS IN SUGARCANE: HOW THE EXPRESSION OF CARBOHYDRATE METABOLISM GENES AND THE MAIN CELL WALL COMPONENT METABOLITES ARE CONTROLLED BY DISTINCT PHOTOPERIODS?

Tese apresentada ao Instituto de Biologia da Universidade Estadual de Campinas como parte dos requisitos para a obtenção do título de Doutor em Genética e Biologia Molecular, área de concentração Genética Vegetal e Melhoramento.

Thesis presented to the Biology Institute, University of Campinas, in partial fulfillment of the requirements for the degree of Doctor in Genetics and Molecular Biology, in Plant Genetics and Breeding.

ESTE ARQUIVO DIGITAL CORRESPONDE À VERSÃO FINAL DA TESE DEFENDIDA PELO ALUNO LEONARDO CARDOSO ALVES E ORIENTADA POR RENATO VICENTINI DOS SANTOS.

Orientador: RENATO VICENTINI DOS SANTOS

CAMPINAS 2018

Campinas, 27 de fevereiro de 2018.

COMISSÃO EXAMINADORA

Prof. Dr. Renato Vicentini dos Santos

Prof. Dr. Marcio Alves Ferreira

Prof. Dr. Américo José Carvalho Viana

Prof. Dr. Michel Georges Albert Vincentz

Prof. Dr. Igor Cesarino

Os membros da Comissão Examinadora acima assinaram a Ata de Defesa, que se encontra no processo de vida acadêmica do aluno.

A meu pai, Jorge, e irmãos, Eduardo e Lariana. Também minha querida noiva Viviane e a quem eu mais gostaria de poder dividir os momentos bons, minha mãe Veralucia.

AGRADECIMENTOS

Agradeço à minha família pelo eterno apoio em minhas escolhas e pela compreensão nos momentos em que não estive presente para desfrutar das mais diversas alegrias com vocês. Agradeço à minha grande companheira Viviane por enfrentar a dura distância durante todos esses anos, e também pela paciência em todas as vezes em que precisei pensar na tese antes de qualquer coisa. Agradeço à minha mãe – in memorian, pois sei que olha por mim. Nunca vou te esquecer. À Unicamp, Instituto de Biologia e Pós-Graduação em Genética e Biologia Molecular. Ao CNPq pela bolsa de doutorado e à Capes pela bolsa de doutorado sanduíche. À University of Cambridge, Department of Plant Sciences e ao Prof; Alex Webb pela incrível oportunidade de desenvolver o doutorado sanduíche. Ao meu orientador Prof. Dr. Renato Vicentini pela oportunidade, apoio e dedicação em me orientar. Muito obrigado por ser peça-chave em minha formação! Aos membros da banca examinadora pelas valiosas colaborações e disposição em dedicar seu tempo ao meu trabalho. Aos colegas do laboratório pelos momentos tão proveitosos de conversas e discussão dos dados. A todos amigos do CBMEG, em especial aqueles do convívio diário que acabaram sendo também a família longe de casa: Daniel, Rogério, Thiago, Victor, Laura, Natacha, Felipe, André, Natália e tantos outros. Também às técnicas Sandra, Rosângela e Célia (do IB) por sempre estenderem as mãos, mesmo sem nenhum compromisso. Aos colegas do Circadian Signal Transduction Lab pela colaboração, suporte, convivência e por proporcionarem tantos momentos bons. Também à Deusa e Leo, do departamento, por terem sido tão receptivos comigo. Ao Centro de Tecnologia Canavieira (Piracicaba) por disponibilizar as plantas utilizadas neste estudo.

RESUMO

A cana-de-açúcar (Saccharum spp.) corresponde por cerca de 75% do açúcar produzido no mundo e possui grande importância bioenergética. Os ritmos diel, ou diurnos, resultam da interação do relógio circadiano com fatores ambientais, como dia e noite, e são fundamentais na sincronia entre respostas do relógio e condições ambientais, como fotoperíodos. Carboidratos e expressão gênica são regulados pelo diel e oscilam durante o dia, em resposta a fotoperíodos. Muito se sabe sobre ritmos em Arabidopsis, porém estudos em cana-de-açúcar ainda são escassos. Neste trabalho plantas de cana foram submetidas a fotoperíodos de dia regular (RD, 12h12h luz/escuro) e curto (SD, 08h/16h) com coletas durante um dia. Foram medidas as concentrações de sacarose, açúcares redutores e totais, pectina, hemicelulose e celulose, além de análise transcriptômica via RNA-seq. SD influenciou o acúmulo de sacarose e dos componentes de parede, mas não o crescimento das plantas. A concentração de sacarose permaneceu estável durante o SD e aumentou em 44% de noite. Observou-se um mecanismo fonte- dreno irregular em SD para compensar a falta do período de luz e garantir um mínimo de sacarose para armazenamento. Em SD concentrações de pectina, hemicelulose e celulose oscilaram durante as 24h, enquanto em RD ficaram relativamente estáveis. Pectina e hemicelulose do colmo aumentaram concentrações do dia para a noite e diminuíram em folhas, enquanto celulose do colmo aumentou 38% do dia para a noite. Análise transcriptômica detectou 7.211 e 9.493 transcritos rítmicos em RD e SD. Os picos de fases em SD encontraram-se dentro da noite, ante na transição dia-noite em RD. Genes do relógio foram identificados e diferenças de fase e amplitude foram observadas para os homólogos e para ortólogos em outras espécies. Grupos funcionais relacionados a características de interesse comercial tiveram a maioria das fases na transição dia-noite em RD, porém no meio da noite em SD. Houve enriquecimento de famílias de fatores de transcrição envolvidas com metabolismo de açúcares nos picos de fases em SD. Também foram encontrados 1.198 long non-coding RNAs (lncRNAs) rítmicos e demonstrada conservação com outras gramíneas. Outros 1.453 e 2.201 genes órfãos de cana foram reportados oscilando em RD e SD. Transcritos rítmicos foram organizados em rede de co-expressão com 11.335 membros, sendo 1.176 lncRNAs, ligados a outros 3.622 transcritos, totalizando 42% da rede. Sub-redes com genes do relógio como isca; e com transcritos mais conectados e com maior amplitude dentro dos grupos funcionais foram funcionalmente similares. Este estudo trouxe dados sobre oscilação de açúcares solúveis em resposta a um dia curto e também demonstrou que componentes de parede podem ser realocados em tais condições, sugerindo influência do fotoperíodo na regulação do gerenciamento do acúmulo e consumo de carboidratos em cana-de-açúcar e demonstrando uma forma de adaptação a SD. O diel influencia na expressão gênica rítmica em cana-de-açúcar e padrões diferentes puderam ser observados. Entender a dinâmica de síntese e degradação de carboidratos, e como é o controle a nível molecular pode ser uma importante ferramenta aos programas de melhoramento visando aumentar biomassa e produção.

Palavras-chave: ritmos diel; cana-de-açúcar; fotoperíodo; expressão gênica; açúcares solúveis; parede celular

ABSTRACT

Sugarcane (Saccharum spp.) comprises around 75% of the worldwide sugar production and is an important crop for bioenergy. Diel, or diurnal, rhythms arise from the interaction within the circadian clock and environmental factors, such as day and night and are fundamental for the synchrony with circadian clock outputs and environmental conditions, such as photoperiods. Carbohydrates and gene expression are diel regulated and oscillate during the day in response to photoperiods. There are plenty of data from Arabidopsis rhythms, however little is known from sugarcane. In this study sugarcane plants were subjected to regular (RD, 12h12h light/dark) and short-day (SD, 08h16h) and leaves harvested within one day. Measurements on concentrations of sucrose, reducing and total sugars, pectin, hemicelluloses and cellulose were as well as the transcriptomic analyses via RNA-seq. SD has influenced sucrose and cell wall components accumulation, but not plant growth. Sucrose concentration kept stable during the day and increased 44% in the night in SD. An abnormal source-sink mechanism was present to alleviate the smaller light period and ensure a reasonable level of sucrose for storage. In SD concentrations of pectin, hemicelluloses and cellulose oscillated during the 24h while in RD they maintained relatively stable. Culm pectin and hemicelluloses increased from day to night and decreased in the leaves, meanwhile culm cellulose increased 38% from the day to the night. Transcriptomic analyses have detected 7.211 and 9.493 rhythmic transcripts in RD and SD. Phase peaks in SD were found inside the night while in RD in the day-night transition. Clock genes were identified, and phase and amplitude differences were found within homologs and orthologs in other species. Phases from functional groups related to commercial characteristics were mostly present at the day-night transition in RD but inside the night in SD. There was enrichment of transcription factors families involved with sugars metabolism in those peaks in SD. A total of 1.198 rhythmic long non-coding RNAs (lncRNAs) was found and conservation with other grasses was shown. Other 1.453 and 2.201 sugarcane orphan genes were reported as oscillating in RD and SD. Rhythmic transcripts were organized in a co-expression network composed by 11.335 members, from those 1.176 lncRNAs, which were connected to other 3.622 transcripts, representing 42% of the network. Sub-networks were made with clock genes as bait; and the most connected and higher amplitude transcripts from the functional groups, and both were functionally similar. This study reported data on soluble sugars oscillation in response to a short-day and also demonstrated that cell wall components can reallocate in such condition, suggesting photoperiod influence on regulation of how plants manage accumulation and consumption of carbohydrates in sugarcane meaning an adaptational response to SD. Diel influence in sugarcane rhythmic gene expression and different patterns could be reported. Understanding the dynamics of the synthesis and degradation of carbohydrates as well as the molecular control may be an interesting tool for breeding programs aiming yield and biomass increasing.

Keywords: diel rhythms; sugarcane; photoperiod; gene expression; soluble sugars; cell wall

SUMÁRIO

1. INTRODUÇÃO ...... 10 1.1 Metabolismo de sacarose ...... 11 1.2 A parede celular da cana-de-açúcar ...... 13 1.3 Ritmos diel e circadianos ...... 14 2 OBJETIVOS ...... 19 3 ARTIGOS ...... 20 3.1 Manuscrito 1 - Diel oscillations in cell wall components and soluble sugars as a response to short-day in sugarcane (Saccharum spp.) ...... 20 3.2 Manuscrito 2 - Diel transcriptome response of sugarcane (Saccharum spp.) under regular and short day reveals distinct rhythmic behavior ...... 52 4 DISCUSSÃO ...... 107 5 CONCLUSÃO ...... 114 6 REFERÊNCIAS ...... 116 7 APÊNDICES ...... 125 7.1 APÊNDICE I ...... 125 7.2 APÊNDICE II ...... 136 8 ANEXOS ...... 140

1. INTRODUÇÃO

Dentro da família das gramíneas (Poaceae) encontra-se o gênero Saccharum., e nele as espécies de cana-de-açúcar S. officinarum e S. spontaneum L. que originaram as variedades comerciais hoje concebidas (JACKSON, 2005) (Figura 1). A cana-de- açúcar destaca-se pela capacidade para acumular sacarose nos entrenós, alcançando concentrações de cerca de 540 mg/g em colmos de plantas maduras (BOTHA; BLACK, 2000). Os entrenós se apresentam em diferentes estágios de desenvolvimento, com o mais maduro na base. As células dos entrenós jovens alongam-se rapidamente enquanto sintetizam a parede celular primária. Quando param de crescer sofrem grande espessamento da parede celular e sintetizam a parede secundária. O período mais frio induz à passagem do estágio de crescimento e síntese de parede ao de acúmulo de sacarose e o entrenó amadurece. S. officinarum é a maior responsável pelo grande acúmulo de sacarose nas cultivares de “cana moderna”. A planta é formada por talos grossos, folhas largas, grande teor de açúcar, poucas fibras e número de cromossomos 2n=80. Já S. spontaneum é uma espécie robusta ,costuma conter talos mais finos, menor teor de sacarose e ser mais fibrosa. Sua ploidia prevalente parece ser 2n=8x=64, embora possa variar entre 2n=40 – 128, com 5 a 16 cromossomos (JACKSON, 2005; MANNERS, 2011).

Figura 1: Híbridos de cana-de-açúcar adultos plantados em campo. Fonte: https://www.comprerural.com/plantio-da-cana-de-acucar-tudo-o-que-e-necessario- saber-para-se-obter-sucesso-nessa-cultura/ 11

O genoma haplóide da cana varia entre 750 Mpb (S. spontaneum) a 926 Mpb (S. officinarum), correspondendo ao dobro do arroz (389 Mpb) e similar ao do sorgo (760 Mpb). O genoma de cana encontra-se parcialmente sequenciado (TOMKINS et al., 1999; VETTORE et al., 2001; CALSA-JUNIOR et al., 2004; PAGARIYA; DEVARUMATH; KAWAR, 2012; DE SETTA et al., 2014), todavia esforços tem sido feitos e estima-se que o completo sequenciamento seja disponibilizado após vencer os desafios do complexo genoma da cana que é altamente aneuplóide e poliplóide e tem tamanho próximo de 10 Gpb somando-se todos cromossomos (DE SETTA et al., 2014). Esta complexidade do genoma dificulta os programas de melhoramento via ferramentas genéticas e biotecnológicas, fazendo com que a descoberta e identificação funcional de genes ainda seja essencial para o melhoramento via marcadores moleculares e desenvolvimento de transgênicos (D’HONT; GLASZMANN, 2001; MENOSSI et al., 2008). A cana corresponde por cerca de 75% do açúcar produzido no mundo (SWAPNA, M; SRIVASTAVA, 2012), além de possuir grande papel no mercado bioenergético. Nos últimos anos a busca por fontes alternativas e renováveis de energia além do já muito difundido etanol tem sido um assunto frequentemente em pauta e, embora existam diversas estratégias de obtenção, elas ainda são caras e exigem muitos investimentos para chegarem ao nível industrial, como o etanol de segunda geração (SOCCOL et al., 2011), permitindo que o etanol obtido a partir de cana-de-açúcar ainda seja vantajoso. Atualmente a produção mundial de cana é de aproximadamente 1,7 bilhão de toneladas (FAO, 2013). O Brasil continua a liderar a produção mundial, a frente de Índia e China, tendo produzido na safra de 2017 um total de 635 milhões de toneladas, com o estado de São Paulo contribuindo com% da área plantada (CONAB, 2017).

1.1 Metabolismo de sacarose

A síntese de carboidratos nos vegetais ocorre através da fotossíntese. Após a luz ser absorvida nos cloroplastos, forma-se um gradiente de transporte de elétrons que produz ATP e reduz o NADP para disponibilizar energia às reações do ciclo de Calvin, 12

que envolve a assimilação do CO2 à ribulose-1-5-bifosfato (RuBP) pela rubisco e a síntese do 3-fosfoglicerato (3-PPG), uma molécula com três carbonos (NELSON; COX, 2002). A 3-PPG quinase catalisa a transferência outro fosfato ao 3-PPG para produzir o 1-3-bifosfoglicerato, que é reduzido a gliceraldeído-3-fosfato (G3P). Em seguida, o G3P é convertido em ribulose-5-fosfato e com a adição de outro fosfato se regenera a RuBP, que está pronta para receber outra molécula de CO2 e reiniciar o ciclo (KREIM; GIERSCH, 2007). Nos vegetais os açúcares podem atuar como componentes estruturais, reserva de carbono e como sinalizadores celulares. A disponibilidade de carbono pode regular ritmos circadianos (HAYDON et al., 2013b) e eventos como fotossíntese, mobilização e exportação de nutrientes e síntese de néctar e celulose. Já a sinalização é mais complexa, pois são modulados crescimento, desenvolvimento e respostas a estresse (ROLLAND; BAENA-GONZALEZ; SHEEN, 2006; WIND; SMEEKENS; HANSON, 2010). Mudanças na relação sacarose/hexose (glicose ou frutose) podem desencadear senescência através da inativação de quinases (THOMAS, 2013) e a degradação da parede celular gera açúcares sinalizadores para respostas a escuro (ROLLAND; BAENA-GONZALEZ; SHEEN, 2006). A sacarose proveniente da fotossíntese regula o relógio circadiano ligando-se ao PSEUDO RESPONSE REGULATOR 7 (PRR7) (HAYDON et al., 2013b) e ao PHYTOCHROME INTERACTING FACTOR que é mediados de sinais metabólicos para o relógio (SHOR et al., 2017). Sacarose, glicose e frutose são os principais açúcares encontrados na folha e caule da cana-de-açúcar (BATTA; SINGH, 1986; LINGLE et al., 2009). A sacarose é um açúcar típico de vegetais composto por glicose e frutose. A característica não-redutora da sacarose facilita que seja transportada das folhas para toda a planta e torne-se o principal produto da fotossíntese para suprir as necessidades de carbono no crescimento e armazenamento de energia (LUNN; MACRAE, 2003; NELSON; COX, 2002). A síntese de sacarose ocorre no citoplasma onde inicialmente duas unidades de G3P são condensadas para formar frutose-1-6-bifosfato, que é desfosforilada a frutose-6-fosfato. Esta é convertida em glicose-6-fosfato e depois em glicose-1-fosfato, que é substrato da UDP-glicose fosforilase na síntese de UDP-glicose. Em seguida ocorrem as duas reações chave na síntese. A condensação de UDP-glicose e frutose 13

pela sacarose fosfato sintase (SPS) gera sacarose-6-fosfato que é desfosforilada irreversivelmente pela sacarose fosfato fosfatase (SPP) resultando em sacarose (LUNN; MACRAE, 2003; LUNN, 2016). Na cana-de-açúcar a molécula é rapidamente transportada para os colmos onde ficará reservada. Nos colmos a síntese pode ocorrer a partir de açúcares livres através da atividade da sacarose sintase (SUSY), que também catalisa a síntese de sacarose através da conversão de UDP-glicose e frutose (WIND; SMEEKENS; HANSON, 2010). No entanto, genes relacionados com síntese de sacarose não são expressos em abundância nos entrenós (CASU et al., 2001 apud CASU et al., 2005), sugerindo que a síntese ocorra substancialmente nas folhas. As invertases (INV) catalisam a degradação da sacarose em glicose e frutose e suas isoformas atuam na parede celular, citosol e vacúolo. A primeira está envolvida na entrada de sacarose através da disponibilização de hexoses aos transportadores, que são novamente convertidas em sacarose no citosol pela SUSY, enquanto as outras fornecem hexoses para o metabolismo (WIND; SMEEKENS; HANSON, 2010). Diversos estímulos ambientais podem regular a expressão de INV, porém algumas isoformas podem ser reguladas pelo balanço de açúcares aos níveis transcricional e pós- transcricional (REN; ZHANG, 2013).

1.2 A parede celular da cana-de-açúcar

A composição e estrutura da parede celular das folhas e colmos da cana-de- açúcar cv SP80-3280, que possui alto teor de fibra (TIRONI et al., 2012), foi investigada através de análise de paredes intactas e fracionadas (DE SOUZA et al., 2013). Em paredes intactas a concentração de lignina foi de ~5% nas folhas e 2,5% nos colmos. Glicose e xilose nos órgãos corresponderam por 60% e 34%, respectivamente; arabinose a ~4,6% e ~2,4% em folhas e colmos, respectivamente, e traços de galactose presentes. Ácidos urônicos mantiveram-se constantes (~3%). No segundo caso foram separados componentes fortemente ligados à parede e, de fato, a proporção dos açúcares foi modificada para: 41% de glicose nas folhas e 62% nos colmos; xilose 10% nas folhas e 9% nos colmos; galactose 16% nas folhas e 15% nos colmos; arabinose 11% nas folhas e 9% nos colmos. 14

Ainda segundo (DE SOUZA et al., 2013), houve pouca diferença na estrutura da parede, com maiores variações na composição hemicelulósica. A celulose correspondeu por ~30%, hemiceluloses por ~50% e pectinas por ~10%. Seguindo o padrão das monocotiledôneas, houve pouco xiloglucano e as hemiceluloses detectadas nos tecidos ocorreram nas formas de xilano não ramificado, xilano acetilado e arabinoxilano. Em concordância, (BIAN et al., 2012) relataram que os polímeros hemicelulósicos em cana foram prioritariamente formados por arabinoxilano. A parede celular de cana-de-açúcar tem grande importância para processos bioenergéticos, como produção de etanol de segunda geração (SOCCOL et al., 2011), logo é de grande importância avaliar se sua constituição sofre modificações em condições de noite estendida, onde o balanço de carboidratos pode ser alterado em favor da economia ou melhor aproveitamento de recursos energéticos.

1.3 Ritmos diel e circadianos

As plantas são organismos com relativa plasticidade na adaptação de seu metabolismo para suportar condições impostas pelo ambiente em que estão. O relógio circadiano constitui-se de uma estimativa de tempo endógena que sincroniza os eventos biológicos aos ambientais, como início da noite e amanhecer, de acordo com as 24 horas do dia. O relógio continua gerando oscilação com ou sem presença de luz, temperaturas baixas/altas ou constância de açúcares e confere vantagens adaptativas (DODD et al., 2005; HAYDON et al., 2013b; DODD; WEBB, 2014). Seu funcionamento acontece através da interação de componentes centrais oscilatórios (o relógio) que ativam ou reprimem a expressão de genes e formam feedback-loops das informações ambientais que chegam à planta e das respostas dos osciladores a elas (MICALLEF, 2011). A correta sincronização do ciclo tem se mostrado benéfica, aumentando a concentração de clorofila, crescimento e fixação de carbono pela fotossíntese (DODD et al., 2005). Devido à existência de diversos mutantes para componentes do relógio circadiano, a maioria dos dados refere-se a estudos com Arabidopsis, uma dicotiledônea C3, e pouco se sabe no caso da cana-de-açúcar, uma gramínea C4 acumuladora de sacarose. Análise por microarranjo (HOTTA; NISHIYAMA JR; SOUZA, 2013) das folhas de uma variedade comercial de cana exposta a 48h de luz contínua identificou 2.354 15

sondas sob controle circadiano. As predições funcionais foram realizadas utilizando os dados de Arabidopsis e concluiu-se que há alta similaridade entre os osciladores centrais do relógio das duas espécies, todavia ainda é necessário avaliar como elas podem afetar características economicamente importantes como acúmulo de sacarose e biomassa. As isoformas do elemento PHYTOCHROME A 1 e 2 estavam mais expressas 4 h após o suposto anoitecer enquanto PHYC1 e 2 foram ativas no crepúsculo. CRYPTOCHROME1 2 e 3 foram ativas ao meio-dia e no crepúsculo, respectivamente. Uma isoforma de ZEITLUPE (ZTL) teve pico transcricional ao meio dia (ZTL1) e outra próxima ao amanhecer (ZTL2). Os elementos do oscilador central seguiram padrão conhecido de comportamento, CIRCADIAN CLOCK ASSOCIATED 1 (CCA1) e LATE ELONGATED HYPOCOTYL (LHY) ativos ao amanhecer, enquanto TIME OF CAB EXPRESSION 1 (TOC1)-1 e 2 ao anoitecer. A família PRR-3, 7 e 59 foi mais expressa 4h antes do anoitecer. GIGANTEA 2 e 3 tiveram picos de expressão no crepúsculo e 4h antes de anoitecer, respectivamente. Ainda que exista conservação interespecífica para os componentes e reguladores circadianos quando comparados com Arabidopsis (SERIKAWA et al., 2008; LIANG et al., 2010; HOTTA; NISHIYAMA JR; SOUZA, 2013; HUANG et al., 2017), podem ocorrer divergências quanto à função, número de cópias de homólogos e fase (ZDEPSKI et al., 2008; KHAN; ROWE; HARMON, 2010; FILICHKIN et al., 2011; CALIXTO; WAUGH; BROWN, 2015), sendo a última o momento em que um gene tem seu pico de expressão (MCCLUNG, 2006). Embora ritmos circadianos sejam cruciais para o desenvolvimento e adaptação das plantas ao ambiente, os experimentos circadianos são desenvolvidos sob condições contínuas de luz e temperatura, o que os distancia das reais condições a que as plantas estão sujeitas no dia-a-dia (DE MONTAIGU et al., 2015; LI et al., 2016). Por outro lado, os ritmos diurnos, também denominados como “diel” por denotarem algo que ocorra a cada 24h naturalmente, resultam da interação do relógio com condições ambientais como o dia e noite, ou ciclo diurno (LI et al., 2016). Ritmos diel são ainda pouco estudados (LI et al., 2016), mas fundamentais na sincronia entre respostas do relógio e condições ambientais (DE MONTAIGU et al., 2015), como fotoperíodos. 16

Estudos recentes têm demonstrado que relógio e diel atuam juntos na regulação de vias metabólicas envolvidas com carbono e nitrogênio (LI et al., 2016). Além dos dias com período de iluminação regular, os dias curtos também tem sido objeto de estudos visando entendimento de processos adaptativos e que possam atuar de forma rítmica. Eventos como retardamento de crescimento e novo balanço na concentração de açúcares podem ser observados em resposta às mudanças na disponibilidade de carboidratos. A taxa de crescimento de Arabidopsis diminui conforme a noite aumenta, porém é possível até 3h/21h luz/escuro. As plantas se adaptam ao diferente fotoperíodo e aceleram a síntese de amido, bem como reduzem a taxa de consumo do mesmo conforme o dia diminuiu e noite aumenta, como tentativa de suprir a disponibilidade de carbono do começo ao fim da noite (GIBON et al., 2009). Modelos têm proposto que o metabolismo de amido em Arabidopsis é controlado pelo relógio circadiano que atua respondendo à homeostase de sacarose para manter a homeostase de carbono (SEKI et al., 2017). A linearidade do consumo de amido é então uma resposta da minimização da falta de carbono (FEUGIER; SATAKE, 2013; WEBB; SATAKE, 2015). A falta de sacarose explica padrões observados para amido em Arabidopsis como: linearidade no metabolismo, consumo quase total ao fim da noite e ajuste de acordo com fotoperíodo (WEBB; SATAKE, 2015). Em sorgo, uma gramínea acumuladora de sacarose como a cana e próxima filogeneticamente (PATERSON et al., 2009), os efeitos do dia curto também foram estudados quanto ao acúmulo de carboidratos (BRITZ; HUNGERFORD; LEE, 1987). Observou-se que este processo sofre regulação circadiana, e não dependente de fotossíntese ou da demanda do dreno. A dependência rítmica da luz para o acúmulo de amido e sacarose foi diferente no escuro e no suposto segundo dia observou-se um novo padrão, reforçando esta ideia. Houve grande diminuição da exportação de açúcares da folha de acordo com a redução fotossintética, todavia, ainda sim existiu sincronia com o suposto dia. Assim como a cana-de-açúcar, o sorgo é uma planta de dias curtos e inibe sua floração em dias longos garantindo o desenvolvimento vegetativo e aumentando a quantia de biomassa. Foi demonstrado que o componente oscilatório de sorgo SbPRR37 17

é o fator central na repressão da floração em dias longos. SbPRR37 inibe a expressão do ativador de floração FLOWERING LOCUS T (FT) e de EARLY HEADING DATE 1, um ativador de FT, e induz a de CONSTANS, um repressor de FT. Plantas condicionadas a escuro total não expressaram SbPRR37, mas as mantidas em dias curtos expressaram pela manhã. Em dias longos ou completamente iluminados a expressão ocorreu pela manhã e anoitecer. Essas observações evidenciam a necessidade de luz e a coordenação pelo relógio circadiano. A expressão de SbPRR37 ao anoitecer requer a fase escura do relógio, mas os dias curtos não a alcançam, de forma que o fator é reprimido e permite a floração (MURPHY et al., 2011). Devido à importância econômica da cana, diversos trabalhos avaliando expressão e funcionalidade gênica têm sido desenvolvidos com diferentes objetivos e utilizando técnicas como microarranjo (CASU et al., 2003, 2007; BOWER et al., 2005; PAPINI-TERZI et al., 2009; RODRIGUES; DE LAIA; ZINGARETTI, 2009), avaliação de biblioteca subtrativa (PAGARIYA; DEVARUMATH; KAWAR, 2012), clonagem e qRT- PCR (LI et al., 2013), SuperSAGE (KIDO et al., 2012) e ferramentas de bioinformática (VICENTINI et al., 2012), porém estudos visando avaliar respostas rítmicas ainda são escassos. Embora existam muitos dados a respeito de expressão rítmica em plantas, eles ainda se referem extensivamente a Arabidopsis, tornando necessária a obtenção e interpretação de sua fina atuação em outras espécies (MCCLUNG, 2013) como a cana- de-açúcar. Além disso, devido ao grande número de processos regulados pelo relógio circadiano e pelo ciclo diel, há um grande potencial de utilização do conhecimento obtido para o melhoramento de plantas (MICALLEF, 2011). Ainda há muitas questões a se explorar quanto à expressão rítmica de genes envolvidos na síntese, acúmulo e destinação de carboidratos em cana-de-açúcar. Neste sentido, a utilização do RNA-seq como ferramenta demonstra-se altamente colaborativa na obtenção massiva de dados e descoberta de genes rítmicos em resposta ao fotoperíodo (MICALLEF, 2011; CALIXTO; WAUGH; BROWN, 2015; HAYDON; ROMÁN; ARSHAD, 2015). A utilização de ferramentas em larga escala também pode colaborar com a identificação de long non-coding RNAs (lncRNAs) rítmicos. Esta classe de RNAs é genericamente classificada pela ausência de ORF funcional e tamanho >= 200 18

nucleotídeos (QUINN; CHANG, 2015) e não costumava ser evidenciada em trabalhos de expressão rítmica devido ao extensivo uso de microarranjos (HUGHES et al., 2012) que priorizam a detecção de genes codantes. De fato, há poucas publicações envolvendo lncRNAs rítmicos em plantas ou mesmo em animais (HUGHES et al., 2012; ZHANG et al., 2014; FAN et al., 2017; HENRIQUES et al., 2017). Em Arabidopsis lncRNAs foram encontrados respondendo à luz (WANG et al., 2014), sendo expressos em fase com as regiões exônicas à frente (HAZEN et al., 2009) e controlando a floração atuando em conjunto com CYCLING DOF FACTOR 5, gene chave no controle da floração (HENRIQUES et al., 2017). Muitos lncRNAs têm como alvos os genes do relógio como LHY, CCA1, TOC1 e PRR3-5-7-9 (ROMANOWSKI; YANOVSKY, 2015).. LncRNAs podem estar relacionados com modificação rítmica da cromatina e regulação direta de genes (KORNFELD; BRUNING, 2014), o que sugere que possam colaborar com o controle do metabolismo de açúcares, por exemplo. No entanto, a função dos lncRNAs no relógio circadiano ainda é incerta (ROMANOWSKI; YANOVSKY, 2015).

2 OBJETIVOS

Este estudo visou avaliar o efeito dos ritmos diel no controle da expressão de genes relacionados ao metabolismo de carboidratos e na oscilação de metabólitos em resposta a regimes distintos de exposição à luz em cultivar de cana-de-açúcar.

Objetivos específicos • Identificar através de análise de expressão em larga escala os genes sob regulação do diel com base no contraste obtido devido aos regimes distintos de fotoperíodo em cana-de-açúcar. • Investigar a influência destes genes em relação ao conjunto de respostas metabólicas relacionadas a síntese, acúmulo e transporte de sacarose e de carboidratos estruturais. • Determinar a concentração de sacarose, glicose, frutose e componentes de parede celular ao longo do ciclo diel. • Investigar as oscilações de açúcares solúveis e componentes de parede nas plantas sujeitas a fotoperíodos distintos. • Integrar os dados de expressão obtidos com dados de transcriptômica já disponíveis de forma a colaborar com programas de melhoramento de cana-de-açúcar.

3 ARTIGOS

3.1 Manuscrito 1 - Diel oscillations in cell wall components and soluble sugars as a response to short-day in sugarcane (Saccharum spp.)

Leonardo Cardoso Alves1, Juan Pablo Portilla Llerena2, Paulo Mazzafera2,3; Renato Vicentini1* 1Bioinformatics and Systems Biology Laboratory, Department of Genetics and Evolution and Bioagents, University of Campinas, Campinas, SP, Brazil 2Department of Plant Biology, University of Campinas, Campinas, SP, Brazil 3Crop Science Department, College of Agriculture Luiz de Queiroz, University of São Paulo, Piracicaba, Brazil

Artigo submetido à revista Frontiers in Plant Science

ABSTRACT

Sugarcane is a tropical crop capable of accumulate in the stem high levels of sucrose as storage carbohydrate. For that reason, it accounts for about 75% of all the sugar produced in the world and became the main sugar source for the production of first generation bioethanol in Brazil. Daily rhythms imply plants to adapt and coordinate metabolism to achieve the maximum photosynthesis and carbohydrate production within one day. While circadian rhythms arise from the interaction of an internal oscillator and external stimuli, diel rhythms occur in response to a light-dark cycle. Diel collaborate with synchronizing circadian rhythms to photoperiods, and carbohydrates oscillate in a diel fashion. Under regular photoperiods, they are synthesized during the daytime and consumed through nighttime, as an energy reserve, until reach daytime levels. However, a short-day can induce higher rates of synthesis within daytime and lower rates of consumption in the dark. Cell wall carbohydrates are also diurnal regulated, and it has been shown that celluloses, hemicelluloses and pectin are deposited/degraded in different times of the day. To access the diel carbohydrate profile in young sugarcane cultivar, we measured soluble sugars and cell wall components at a time course in plants subjected either to a regular day or a short-day. Short-day influenced sucrose synthesis and cell wall 21

components but had no effect on morphological characteristics. A 44% increase in sucrose level was detected in the dark meanwhile it was stable during the day. An abnormal source-sink mechanism took place to assure a minimum level of sucrose for storage. Cellulose, hemicellulose and pectin also fluctuate in a 24h-interval when subjected to short-day. A 38% increase in culm cellulose was observed from the middle of the day to the first hour of the night. Culm pectin and hemicellulose also increased from the day to the night while decreased in the leaves. These results show unreported diurnal patterns of soluble sugars metabolism together with a temporal regulation in cell wall metabolism under short-day, suggesting diel as a player on the way sugarcane manages sugar accumulation and partition. Understanding cell wall synthesis/degradation dynamics may help to improve sugarcane yield, which is important both for sugars and ethanol industries. Keywords: sugarcane; diel; soluble sugars; cell wall; photoperiod; short-day.

INTRODUCTION

Sugarcane is a C4 tropical crop able to accumulate levels of sucrose as high as 540 mg/g in mature stalks (Botha and Black, 2000). Such characteristic made sugarcane the crop responsible for about 75% of the sugar produced in the world (Swapna, M; Srivastava, 2012), and Brazil harvests about 38% of the world’s sugarcane production (FAO, 2017). Additionally, sugarcane is also used for biofuels production in Brazil (Bottcher et al., 2013). In 2017 it was estimate a production of ~646 million/tons of sugarcane, and a production of ~40 million/tons of sugar and ~26 billion/liters of ethanol (CONAB, 2017). Apart of that, sugarcane has become interesting for the second generation (2G) ethanol industry because of the high rates of biomass production and the potential of transformation of the bagasse in bioenergy (Bottcher et al., 2013). Sucrose, glucose and fructose are the main carbohydrates found in sugarcane leaves and stalks (Batta and Singh, 1986; Lingle et al., 2009). Two key reactions control sucrose synthesis, firstly sucrose-phosphate-synthase condenses UDP-glucose and fructose into sucrose-6-phosphate (S6P) and them sucrose-phosphate-phosphatase dephosphorylates S6P resulting in sucrose (Patrick et al., 2013) In sugarcane sucrose is 22

rapidly transported from leaves to culms for storage, through a source-sink mechanism (Patrick et al., 2013; Rae et al., 2005; Rohwer and Botha, 2001). Rotation of the Earth in a 24h cycle implies plants to coordinate their metabolism to ensure photosynthesis while carbohydrate metabolism is powered to the maximum during the light period (Dodd and Webb, 2014). Circadian rhythms arise from the control of an oscillator which is able of running even in the absence of external stimuli, such as light and temperature (Dodd and Webb, 2014). However circadian data is measured by continuous conditions that do not reflect the real conditions plants are facing (de Montaigu et al., 2015). On the other hand, diel rhythms occur in response to a light- dark, or diurnal, cycle. Diel rhythms are also major players controlling the response to environmental stimuli synchronizing endogenous circadian rhythms to external stimuli (de Montaigu et al., 2015) such as photoperiods. Sucrose and reducing sugars are diel regulated and differ significantly during the day and night in different light conditions (Hoffman et al., 2010). These carbohydrates are synthesized during the day as a photosynthesis product and respired during the night as energy source. In sorghum (Sorghum bicolor) the starch synthesis pathway is diel controlled (Mutisya et al., 2009) while in Arabidopsis it has been shown the ability to coordinate the level of starch synthesis/break down accordingly to the length of the day. The shorter the daytime, the faster is starch synthesis and slower is the break down in the nighttime maintaining a minimum level of this carbohydrate at the end of each night (Gibon et al., 2004; Scialdone et al., 2013). In sugarcane, sucrose concentrations range from 6.2 – 50 mg/g and 17.1 – 400 mg/g in leaves and intermediary stalks, respectively. Reducing sugars in the leaves accounted for 0.53 – 190 mg/g while in the culms it floated between 10.8 – 210 mg/g (Batta et al., 1995, 2008; Batta and Singh, 1986; Botha and Black, 2000; Cunha et al., 2017; Ebrahim et al., 1998; Inman-Bamber et al., 2010; Mattiello et al., 2015; Mccormick et al., 2006; Sachdeva et al., 2003; Wu and Birch, 2007; Zhu et al., 1997). However, it is difficult to compare such studies because they were made with different cultivars; in growth cabinets or field; and diverge in growth stages and/or tissues. In addition, sugar concentration varies in a wide range accordingly to culm internode age (Batta et al., 2008; Botha and Black, 2000; Mccormick et al., 2006; Wu and Birch, 2007). Most important, 23

none of such studies aimed to evaluate diel carbohydrate oscillations nor data were collected in a time course manner. So, there is still a lack of studies focusing on how sugars fluctuate in a sugarcane cultivar in response to different photoperiods, in a diel fashion. Despite the complexity, cell wall is a dynamic structure composed mainly by cellulose, hemicellulose, pectin, lignin and proteins, and comprises the major terrestrial carbon reservoir (Barnes and Anderson, 2017; Cosgrove, 2005). The wall protects plant cells against pathogens and allow cell expansion by relaxing and shrinking (Vorwerk et al., 2004). Sugarcane cell walls from leaves and culms follow a grass-like pattern and are composed by complex hemicellulose, pectin and pectic arabinogalactans bound to cellulose (de Souza et al., 2013). Cellulose, a crystalline, inelastic and mechanically resistant material formed by chains of β-1,4 glucose (Cosgrove, 2005; Vorwerk et al., 2004), is the main cell wall component. It is embedded in hemicellulose and pectin. Hemicellulose is mostly formed by xyloglucan and arabinoxylan in sugarcane leaves and stalks (de Souza et al., 2013) as expected for grasses (Cosgrove, 2005). Pectin is a matrix made of glucuronic acid-rich fraction involving hemicelluloses to form a flexible layer over cellulose (Cosgrove, 2005; Vorwerk et al., 2004). Sugarcane pectin fraction comprise about 10% of the cell wall (de Souza et al., 2013). Cell wall changes its composition during plant cell growth, becoming more complex. In different situations like fruit softening (Guillon et al., 2017; Paniagua et al., 2017; Zhang et al., 2011), flower development and pollen formation (Barnes and Anderson, 2017; Chebli et al., 2012), cell wall changes its composition dynamically. However, little is known on wall components fluctuation in a smaller time window, such as one day (Bobák and Nečesaný, 1967; Hosoo et al., 2002; Mahboubi et al., 2015; Solomon et al., 2010), and in response to diverse environmental stimuli. Here we show, as far as our concern, the first diel dynamic study from soluble sugars and cell wall components under a regular day (RD, 12h12h light/dark) in comparison to a short day (SD, 08h16h light/dark) in sugarcane. Under RD, sucrose was linearly synthesized in the daytime and broken down during the night. However, in SD a much lower rate of synthesis was measured in the daytime and, surprisingly, a higher rate of degradation could be detected during the nighttime. We also demonstrated that 24

cellulose, hemicellulose and pectin are relatively stable during the diel in RD plants. Nevertheless, we highlighted that a short-day induced significant fluctuation on these cell wall components within one day in sugarcane.

MATERIAL AND METHODS

Plant material and photoperiodic conditions One hundred and twenty-four plants from the commercial variety of sugarcane SP83-2847 (Saccharum spp.) were propagated by stem cuttings and grown in small pots (160 cm3) under greenhouse conditions at Centro de Tecnologia Canavieira (CTC), Piracicaba, Brazil. After 60 days at ~40 cm high, plants were divided in two groups and exposed to different photoperiods for 30 days in a controlled chamber. Light was supplied by light-emitting diodes under a 12h light (100 µE m-2 s-1 photon flux density)/12h dark (regular day, RD) or 8h/16h light/dark (short day, SD), temperature was maintained at -1 27±2 °C and CO2 concentration inside of the chamber was 405 µM L . Light and CO2 concentration were measured using an Infra-Red Gas Analyser LCPro+ (ADC Bioscientific). Plants were watered twice a day and randomly reorganized in concern to their spatial position inside the chamber every weak. The culms and the middle section of +1 leaves with no vascular tissue were harvested from six (RD) and four (SD) plants at each of the following time points during a period of 24 hours (ZT0 means the moment – in hours – after the lights were turned on): ZT1, ZT6, ZT11, ZT13, ZT16, ZT20 and ZT23 for 12h/12h light/dark cycle; and ZT1, ZT4, ZT7, ZT9, ZT14, ZT19 and ZT23 for 08h/16h light/dark cycle. Thus, ZT1, 6 and 11 in 12h/12h (RD) and ZT1, 4 and 7 in 08h/16h (SD) corresponded to 1 hour, after starting illumination and then middle and last hour of the light period, respectively. The other ZTs periods corresponded to the first hour after darkness (ZT13 in RD and ZT9 in SD) and then two middle points and the last hour of the darkness period (Supplementary Figure 1). All the material was frozen under liquid nitrogen and stored at -80 °C until further analysis. Plant size, +1 leaf size and width (from the wider part) were measured for 27 plants at 0 and 30 days after the exposition to the different photoperiods. Three random 25

plants were harvested at days 0 and 30 and their roots, leaves and culms dry masses were also measured. Cell wall components isolation and quantification Three biological replicates of 300 mg from pools of leaves and stalks were used to determine pectin, hemicellulose and cellulose in technical triplicates in each tissue and in each harvested ZT following the method described previously (Chen et al., 2002). For the pectin fraction samples were washed and centrifuged at 13,000 RPM for 10 min with 1 mL chilled water, 1 mL acetone and 1 mL of a 1:1 (v/v) methanol- chloroform solution. Pellets were dried in room temperature and then they were incubated for 3h at 37°C in 1mL of a α-amylase solution (2 U/mL) in 0.1M sodium acetate buffer (pH 6.5). Samples were centrifuged at 13,000 RPM for 10 min and the pellets were incubated three times with 600 µL of 20 mM ammonium oxalate (pH 4) for 1h at 70°C. The supernatants were collected after centrifugation and joined in the same microtube. The pellets resulting from this extraction were used to extract the hemicellulose fraction. They were incubated with 600 µL of 0,1 M NaOH for 24h under vacuum at room temperature and in the dark. Samples were centrifuged at 13,000 RPM for 10 min and supernatants were collected. Pellets were then incubated three times with 400 µL of 17.5% (w/v) NaOH for 8h in the same previous conditions. The four supernatants obtained by centrifugation were joined in the same microtube. Finally, the cellulosic fraction was extracted. Pellets were washed with the following solutions (1 mL): water; 1 mM acetic acid; 1 mL ethanol.

After drying at room temperature, the pellets were resuspended in 1 mL 72% (v/v) H2SO4 for 1h, vortexing at each 10 min. Sugar content from each fraction was quantified in triplicates following the phenol-sulfuric method (DuBois et al., 1956) using glucose as standard.

Soluble sugars extraction and quantification Leaves and stalks were ground in liquid nitrogen in triplicate in the same conditions as described in the previous section. Total soluble sugars, sucrose and reducing sugars were each extracted from samples of 20 mg of freeze-dried material. Samples were extracted with 1.5 mL 70% (v/v) ethanol at 70°C for 1h and centrifuged at 13,000 RPM for 10 min. This procedure was repeated four times and the solutions were 26

pooled in the same microtube. Sucrose and total sugars content were quantified in technical triplicates with the phenol-sulfuric method (DuBois et al., 1956), using pure sucrose and glucose to build standard curves. Reducing sugars quantification was made according to the Somogyi-Nelson protocol (Somogyi, 1952) with glucose as reference.

Statistical analysis Significantly different means from sugars measurements were accessed through Student's two-sided unpaired t-test using the “t-test” function in R. P-values < 0.05 were considered as significant. ANOVA was used to access differences within size and dry mass measurements.

RESULTS

Morphological development of the young cultivar was not affected by a short-day We measured plant size and the +1 leaves size and width at days 0 and 30 from sets of 27 plants under each diel. Also, we choose 3 plants from each diel condition to access their roots, leaves and culms dry masses at day 0 and other 3 plants at day 30 for the same measurements. At day 0 no significant differences (p-value < 0.05) from all the comparisons between RD and SD were detected, suggesting the groups of plants were similar by the beginning of the diel period. Surprisingly, by day 30, again no significant differences (p-value < 0.05) were observed when comparing data from RD against SD plants (Table 1 and Supplementary Tables 1 - 4). That suggests the different light regimes had no influence on morphological conditions of sugarcane young plants.

Table 1. The +1 leaf length and width; and plant size were measured for plants under each diel regime in days 0 and 30. The dry masses from leaves, culms and roots from plants under each diel were measured in day 0 and from other set of plants in day 30. Values are mean ± SD. n=27 for +1 leaf size and width; and plant size. n=3 dry mass day 0 and n=3 dry mass day 30.

Plant size +1 leaf Dry mass (cm) Length (cm) Width (cm) Leaves (g) Culm (g) Roots (g) Total (g) 12h12h light/dark Day 0 37.82±12.4 1.09±0.2 53.44±14.8 0.53±0.3 0.72±0.4 0.6±0.4 1.85±1.0 Day 30 38.17±12.3 1.1±0.2 85.03±25.3 1.01±0.5 0.94±0.5 0.57±0.4 2.52±1.3 08h16h light/dark Day 0 37.49±12.3 1.18±0.2 56.21±16.1 0.43±0.3 0.58±0.3 0.39±0.2 1.4±0.8 Day 30 38.79±11.6 1.2±0.2 91.68±25.3 0.9±0.4 0.85±0.2 0.46±0.2 2.21±0.8

Concentration of cell wall components fluctuate when subjected to a short-day regime We quantified the fractions of the cell wall in leaves and culms from RD and SD plants (Supplementary Table 5). Hemicellulose comprised ~22-25% of cell wall dry mass in both tissues and photoperiods while pectin corresponded for ~5%. Cellulose comprised ~50% of culm cell wall dry mass while ~37% was found in the leaf. These observations are in line to previous reports (de Souza et al., 2013; Landell et al., 2013) suggesting a level of stability in such concentrations across cultivars. However, there was a tendency for variation in concentrations during the day so the proportions of each component relative to the dry mass and in response to photoperiod were altered. Most of these variations were observed in SD plants. Nevertheless, culm pectin and culm cellulose from RD plants also have changed during the 24h period, mainly in the night. In SD plants all the components oscillated in leaves while hemicellulose and cellulose also oscillated in the culms. As an illustration, culm cellulose from SD plants increased from 438.15 mg/g DM-1 in the middle of the day (ZT4) to 563.54 mg/g DM-1 in the beginning of the night 28

(ZT9) (p-value 0.0194) and to 566.40 mg/g DM-1 in the end of the night (ZT23) (p-value 0.0151). Leaf hemicelluloses rose from 191.01 mg/g DM-1 in ZT4 to 273.86 mg/g DM-1 in the end of the day (ZT7) (p-value 0.0404); and from ZT7 to ZT9 it reduced 62 mg/g DM- 1 (p-value 0.0368). Supplementary Table 6 demonstrates significant (p-value < 0.05) fluctuation on cell wall components during the 24h period. Diverse patterns of distribution from the fractions during the 24h time course are observed in response to each photoperiod, regarding the scale differences (Figure 1). Under RD leaf pectin and hemicelluloses had a similar behavior as mean concentration decreased from ZT1 to ZT6 and increased until the beginning of the night. They then tended to fall again during the night to lower concentrations than the beginning of the day (Figure 1A). A different pattern was observed in leaves from SD plants (Figure 1B). Also, cellulose and pectin became very similarly distributed during the night peaking at ZT19 while hemicellulose was like cellulose in the day and pectin and cellulose during the night. Interestingly, in the day pectin was opposite to cellulose and hemicelluloses peaking at the middle of the day. Also, pectin average concentration rapidly increased 45% from ZT1 to ZT4 (p-value 0.0263) while cellulose declined 23% from ZT1 to ZT9 (p-value 0.0331). All culm components from RD plants peaked at ZT16 (Figure 1C) and the tendency of pectin distribution demonstrated a drastically increase from the end of the day to ZT16 (+39%, p-value 0.0267). Cellulose increased 26% from ZT1 to ZT16 (p-value 0.341). In SD plants, a different distribution of the averages for each culm component was detected in comparison to RD plants, as observed in the leaves (Figure 1D). Pectin peaked at the same proportion of the period from RD while cellulose peaked at the beginning of the night and kept stable until ZT14. Except for ZT19, cellulose maintained stable concentrations during the night and an inverse pattern of distribution could be seen in comparison to leaves in the nighttime. At ZT19 pectin and cellulose average concentrations have fallen while this was the peak time in the leaves. Hemicellulose increased during the day and decreased during the night, as in the leaves. However, they peaked in ZT23 and, alongside to cellulose, most of the significant differences were between ZT4 and the timepoints in the night. 29

Figure 1. Diel fluctuation of cell wall components in response to photoperiods. Each point represents the mean ± SD (n = 3). Daytime (white) and night (light gray).

A short-day stabilizes sucrose synthesis during the day and induces synthesis during nighttime in sugarcane leaves We quantified the total soluble sugars, sucrose and reducing sugars composition of leaves and culms during the time course (Supplementary Table 7). Our data showed that sucrose and total sugars synthesis in the leaves and the storage of all fractions were higher in RD plants (Figure 2). Supplementary Table 8 shows significant fluctuation on soluble sugars through the 24h period. Sucrose was linearly synthesized during the day and increased ~75% in concentration from the beginning to the end of the day (p-value 0.0020) (Figure 2A) while in the culms it reached the highest daytime concentration at ZT6. Sucrose is translocated from leaves to culms, and this was clear during the night in RD plants. Sucrose peaked twice in the culms during the night; right in 30

the beginning (ZT11) and in the middle-end of the night (ZT20) (Figure 2C). Culm’s sucrose rose from ~80 to ~124 mg/g DM-1 (+55%) in the two-hour interval from the day- night transition (ZTs 11-13) (p-value 0.0338) meanwhile culms from SD plants did not significantly altered their sucrose content at the same transition (ZTs 7-9; ~53 to ~67 mg/g DM-1). The most drastic differences in leaf sucrose concentrations were detected at ZT1 vs ZT11 (p-value 0.0020) and ZT1 vs ZT13 (p-value 0.0355) under RD. SD plants behaved in a different fashion in concern to sucrose concentration dynamics. They synthesized much less sucrose than RD plants during the day (Figure 2B,D). Also, while in regular illumination sucrose was maintained at similar levels in leaves and culms, in short-day culm concentrations were much lesser than leaves did. Nevertheless, at the night sucrose notably accumulate in the leaves until ZT19 and kept similar concentrations in leaves and culms (Figure 2B). Due to such dynamic a ~44% increase in sucrose concentration was detected in the comparison between ZTs 9 and 19 (p-value 0.0186), the highest difference observed in SD plants. Total sugars fraction also tended to increase during the day and decrease during the night, similarly to sucrose in all tissues and photoperiods, because sucrose is synthesized in much higher concentration than the other sugars. However, in RD plants’ leaves, a plateau was reached from the middle of the day until the beginning of the night (Figure 2A). Additionally, in culms the pattern of distribution of total sugars and sucrose was very similar. They behave quite stable during the day and then increased right from the transition between day and night to a peak at the first hour of the night. They then tended to decrease in concentration and reached another peak at the next to last timepoint (Figure 2C). SD plants fluctuated their reducing sugars load in the leaves. At the same time as they were stable in RD leaves during the period (Figure 2A), an increase was detected from ZT4 to ZT9 (+42%, p-value 0.0248) when under 08h of light (Figure 2B). During the night the concentration decreased 23% from ZT9 to ZT23. A different distribution was observed in the culms in response to each photoperiod. Under regular illumination a peak was reached at ZT6 and other at ZT20, the last accounted for double leaf’s concentration. SD plants accumulated less reducing sugars in the culm than in the leaf during the day. 31

Nevertheless, these sugars highly increased in accumulation from ZT1 to ZT23 (+156%, p-value 0.0416) when they were also higher than leaf content (p-value 0.0312).

Figure 2. Diel fluctuation of soluble sugars in response to photoperiods. Each point represents the mean ± SD (n = 3). Daytime (white) and night (light gray).

DISCUSSION

We have found no morphological differences between sugarcane under RD and SD regimes. That is in opposition to adult Arabidopsis growing under 08h16h L/D whose growth rates were lower than under 12h12h L/D (Gibon et al., 2009). However, not only the photoperiod length (in hours) can play a role in growth but the number of days of the photoperiod treatment may also be considered. A 21 short-day treatment had no effect in leaf area, petiole length and yield meanwhile 35 days resulted in lower rates in strawberry (Konsin et al., 2001). Additionally, young sugarcane grows at slower rates than 32

other crops until it reaches the stem elongation stage (Allison et al., 2007). That suggests the shortened light period had no influence on young sugarcane morphological characteristics. Next, we measured soluble sugars from sugarcane. Data agree with a number of studies reporting soluble sugars measurements, even though the conditions and organs vary largely between then (Supplementary Table 9). Sucrose, glucose and fructose are diurnally regulated and differ significantly during the day and night in different photoperiods (Hoffman et al., 2010). In most plants sucrose is the main transported carbohydrate from source to sink tissues, so its metabolism is important to the maintenance of the plant system (Patrick et al., 2013). In sugarcane, sucrose also serves as the energy storage carbohydrate which requires a fine tuning in the balance of photo assimilates between source (leaves) and sink (culm) (Papini-Terzi et al., 2009; Patrick et al., 2013; Rohwer and Botha, 2001). We reported an adjusted source-sink mechanism in plants under a regular photoperiod. Those plants synthesized sucrose along the day, due to photosynthesis, and translocated sucrose from leaves to culms during the night. During the day leaves and culms had a similar level of sucrose while in the night much more sucrose was found accumulated in the culms, an obvious consequence of the lack of carbon assimilation by the photosynthesis (Figure 3A). The shortening of light period to only 8h resulted in adjustments on that dynamics. Sucrose levels in the leaves were strongly stable through the day, nevertheless much higher than in the culms (Figure 3B). During the night leaves and culms accumulated sucrose at a similar pattern like the RD plants did. Source-sink mechanism is also supported at transcriptional level as previously reported leaf phase profile of transcripts (being phase as the time of the day when expression reaches a peak) for enzymes associated to sucrose metabolism coincide to that dynamics. Genes related with sucrose synthesis are highly expressed in the early morning while the break down during the nighttime (Hotta et al., 2013). The level of total sugars also followed the same logic of distribution from sucrose: they accumulate during the day in a balance between leaves and culms (Figure 3C) in RD but their levels were similar in SD plants (Figure 3D). The pattern from RD plants is in accordance to previous study in rice leaves (Li et al., 2016). Such responses 33

could be triggered by photosynthesis in rice, as expression of genes mostly phased right in the beginning of the day (ZT1), in order to assure sugars synthesis and, as the daytime passed, the frequency of phasing decreased. In addition, their peaking correlates to phasing of transcripts for light harvesting complexes reported in sugarcane under constant light regime (Hotta et al., 2013). In Arabidopsis, photosynthesis is due to be under circadian control (Dodd et al., 2005). However, in rice under natural conditions a diel influence together to the circadian control was reported (Li et al., 2016). In that study, the physiological photosynthetic apparatus was also regulated by diel cycle alongside to many genes for light harvesting complexes phasing at the first hour of the day or at the first hour and the mid-end of the day.

Figure 3. Diel fluctuations of soluble sugars in leaves and culms under each photoperiod. Each point represents the mean ± SD (n = 3). Daytime (white) and night (light gray). Student’s t-test was performed for the comparisons of means within each time point, where the significant levels were determined as * P < 0.05; ** P < 0.01; *** P < 0.001.

Sucrose is transported to the sink when its rate of synthesis is higher than the accumulation capacity of the source, which means less sucrose in the source leads to less sucrose available to export (Lemoine et al., 2013; Smith, 1993). Indeed, sucrose synthesis in SD plants was significantly lower than in RD plants (Figure 4A) and that could be the reason for the difference between source and sink. In accordance, it has been shown a 08h16h photoperiod resulted in lower amount of total starch accumulation than a 12h12h did in Arabidopsis (Gibon et al., 2004, 2009). In Arabidopsis it has been shown that short-days induce higher rates of starch synthesis during the day and subsequent 35

lower rates of consumption during the night (Gibon et al., 2004) in a mechanism that can be mathematically modelled (Scialdone et al., 2013) in order to maintain the necessary levels of the storage carbohydrate. That has not happened to SD sugarcane plants. In sugarcane sucrose is the storage carbohydrate and during the day sucrose synthesis ran at a stable rate. Surprisingly, during the night the rate of sucrose levels in the leaves grew from ZT9 to ZT19, even though there was no photosynthesis, clearly indicating a probable synthesis from the starch accumulated during the light period. From ZT19 to 23 the accumulation fallen to a level close to ZT1. Possible further effects of such change in behavior are that the increased nocturnal sucrose accumulation, reaching levels close to those measured in RD leaves at ZT11, played a role on activation of genes coding to sugars and cell wall metabolism and/or collaborated to protein phosphorylation, as the latter is involved to sucrose accumulation (Papini-Terzi et al., 2009). Interestingly, the level of cell wall components tended to peak at ZT19 in the leaf (Figure 1B) and notably only when under SD there were significant differences between diverse time points from the day vs night regarding to cell wall components (Supplementary Table 6). Altogether these data suggest that what regularly happened in sugarcane during the day in a regular photoperiod was shifted to the night in order to alleviate the lack of the illuminated period in SD plants. That effort, however, was still not enough to synthesize and store sucrose and total sugars at the same levels detected in RD plants because of the smaller day (Figure 4B,D). A sugar-signaling pathway could be playing role on such metabolic reorganization and influencing sugar accumulation capacity. Such question has been done after reporting that a set of genes associated to sucrose content and related to signal transduction were found to be early sugar-responsive in sugarcane cultivar (Papini-Terzi et al., 2009). More recently, signal transduction genes and a SnRK- interacting protein were enriched in high Brix sugarcane cultivar (Ferreira et al., 2016) reinforcing the hypothesis. Generally, culms and leaves from SD plants accumulated much less sucrose and total sugars during the 24h period (Figures 4A,D). These observations suggest that sucrose breakdown and transport to the culm was accentuated in SD plants to prioritize sucrose accumulation in that tissue. That makes sense as the growth pattern of sugarcane is optimized to increase sucrose content (Allison et al., 2007). 36

It is also possible that other sugars might have being reallocated to the nocturnal sucrose synthesis besides the already available glucose and fructose. Indeed, there were not differences in the levels of reducing sugars in RD leaves. On the other hand, SD leaves have accumulated reducing sugars from the middle of the day (ZT4) until the beginning of the night (ZT9) (p-value = 0.0242) (Figure 4E,F). In addition, differences in the nocturnal levels of total sugars between leaves and culms are much lower in SD than RD plants (Figure 3C,D), indicating those sugars could have been allocated to the leaf as substrates to sucrose synthesis, as there was no photosynthesis. Maltose (a reducing sugar), triose phosphate and glyceraldehyde 3-phosphate are products from starch degradation and can be utilized as substrates and regulators to the sucrose synthesis in Arabidopsis (McCormick and Kruger, 2015; Strand et al., 2000; Smith, 1993). On the other hand, it has been shown that starch concentration is lower than 1.6 mg/g DM-1 in the sugarcane cultivar reported in the present study (SP83-2847) (Figueira et al., 2011).

Figure 4. Diel fluctuations of soluble sugars between plants subjected to a 12h12h and 08h16h light regimes. Each point represents the mean ± SD (n = 3). Daytime (white) and night (light gray). Student’s t-test was performed for the comparisons of means within each time point, where the significant levels were determined as * P < 0.05; ** P < 0.01; *** P < 0.001. 38

Another explanation can rely on the regulation of nocturnal sucrose metabolism by fructose-2,6-biphosphate (Fru-2,6-P2), which regulates the interconversion of fructose- 6-phosphate and fructose-1,6-biphosphate (Fru-1,6-P2) (Nielsen et al., 2004) determining whether sucrose is going to be synthesized or break down. A higher than normal level of Fru-2,6-P2 decrease the rate of sucrose synthesis (Scott et al., 1995) while half of the regular level increase the rate of sucrose synthesis (Scott et al., 2000). In our data the light-dark transition depleted the level of sucrose in both photoperiods, however SD plants restarted sucrose synthesis right after ZT9. Reducing sugars increased during the whole diel in SD culms and increased during the day to decrease in the night in the leaves, while sucrose strongly increased during the night in the leaves. A lack of Fru-2,6-P2 stimulates the pathway to the sucrose direction (Scott et al., 2000) and we suggest hexoses from three-carbon molecules alongside a low level of Fru-2,6-P2 collaborate to regulate gene expression in response to SD photoperiod in a different fashion from Arabidopsis. Because of the importance of sucrose to sugarcane, the short-day condition influenced the synthesis of sucrose at night until ZT19 to maintain a reasonable level of sucrose storage. Sucrose then returned at ZT23 to a level similar to ZT1. The fluctuations of cell wall carbohydrates also took our attention. Because (i) cell wall components collaborate to the maintenance of the mechanical properties of the wall and (ii) cell wall depends on carbohydrate biosynthesis, we attempted to measure components concentrations at each timepoint to generate knowledge about photoperiod control on wall metabolism during the 24h period. The wall assembly/disassembly dynamics goes together to cell development, so the cell can relax and stretch without its destruction. Polysaccharides are deposited and renovated according to the needs of a plant to adapt to the environment (Barnes and Anderson, 2017; Toole et al., 2010). That mechanism requires coordination on gene expression to assure that each step would be done at their moment on the development. In wheat seeds, genes encoding for proteins responsible for synthesis/degradation of cell wall components were expressed at different stages of development, demonstrating these carbohydrates are synthesized and reallocated the whole time (Pellny et al., 2012). The transcript and chemical composition profiles of barley (Hordeum vulgare) cell wall also change as the grain develops (Wilson et al., 2012). Cell walls from grains, culms and 39

leaves of the grass Brachypodium distachyon and sugarcane cell suspension cultures contain high amounts of glycosil hydrolases (GH), oxido-reductases (OR) and invertases (INV) (Calderan-Rodrigues et al., 2014; Douché et al., 2013; Francin-Allami et al., 2015). GH and ORs (mainly peroxidases) plays roles on the maintenance of the polysaccharides networks allowing the assembly of hemicelluloses and pectin components (Douché et al., 2013) and changes in hemicelluloses composition through the time (Toole et al., 2010). INV disassembles the components to ensure the presence of precursors in the synthesis of other cell wall components (Calderan-Rodrigues et al., 2014). In general, the assembly/disassembly of cell wall seems to perform a regular and obvious behavior through the development in regular conditions. A few studies have been demonstrating the diel effect on cell wall metabolism of trees (Bobák and Nečesaný, 1967; Hosoo et al., 2002; Mahboubi et al., 2015; Solomon et al., 2010). However, young grasses have not been the focus of those studies nor the photoperiod effect. Still, the main findings are that cellulose is deposited at the end of the day and during the night and hemicelluloses could be synthesized at the night (Bobák and Nečesaný, 1967; Hosoo et al., 2002). A diurnal transcriptome of Eucaliptus sp. xylem also shed light on wall diel dynamics (Solomon et al., 2010). Under a 12h12h photoperiod, ZT16 accounted for the major phasing of differentially expressed genes. Coincidently, this ZT correspond to the major peaks in RD wall components in our study (Figure 5A,C,E). Genes for cell wall modification were induced namely GLYCOSYL TRANSFERASES, GLYCOSYL HYDROLASES AND CARBOHYDRATE ESTERASE. Morning expression was seen in PECTINASE and XYLOGLUCAN ENDO-TRANSGLUCOSYLASE. Primary wall-related CESA were not diurnal regulated. Nevertheless, a diel control was reported for wall formation genes namely SUCROSE SYNTHASE and UDP-GLUCURONIC ACID DECARBOXYLASE.

Figure 5. Diel fluctuations of cell wall components in leaves and culms under each photoperiod. Each point represents the mean ± SD (n = 3). Daytime (white) and night (light gray). Student’s t-test was performed for the comparisons of means within each time point, where the significant levels were determined as * P < 0.05; ** P < 0.01; *** P < 0.001.

Recently a diurnal pattern of wood formation was reported in Populus sp. employing tracking radioactive CO2 incorporation into all wall components (Mahboubi et al., 2015). Authors reported the highest 13C incorporation into cell walls happened in the night, and our results for RD and SD pectin and SD celluloses agree with that. A negative correlation between hemicellulosic sugars, lignin and nighttime was also reported suggesting lignin deposition takes place in the dark but not hemicelluloses. Cellulose, hemicelluloses and pectin compose around 95% of young sugarcane cell walls (Botha and Moore, 2014; de Souza et al., 2013), which encouraged us to measure those fractions. After 30 days subjected to SD light regime the level of cell wall components from the sugarcane cultivar remained relatively the same as RD, indicating that there was no effect from the photoperiod which could result in differential thickness of cell walls during the 24h period (Figure 6A-F). In RD plants there was no significant fluctuation (p-value < 0.05) of cell wall components in the leaf during the period and only punctual differences could be detected from culm pectin and cellulose (Supplementary Table 6) even though clear trends are seen in the time courses. Either, no differences in morphological characteristics could be observed after the photoperiod treatment, as reported above. Taken together those observations suggest young sugarcane SP83-2847 cultivar is not strongly affected by regular photoperiods regarding cell wall metabolism, when subjected to a regular day. Cell wall metabolism in developing sugarcane tends to behave in a linear fashion during the diel under such conditions, except for pectin. Short-day, however, influence fluctuations on the levels of wall’s components. All but culm pectin significantly fluctuated (p-value < 0.05) during the 24h period (Supplementary Table 6). Cell walls from leaves and culms are quite similar in composition, but differ in the fine structure of pectin and hemicellulose (de Souza et al., 2013). The focus of the present study was not to analyze fine structure of the walls but to find fluctuations on the general components. Still, their proportions match previous reports (de Souza et al., 2013; Landell et al., 2013). We have found culm and leaf components fluctuate similarly during the day in RD plants. Pectin significantly increased and decreased during the night in the culm and tends to decrease in the leaves as well. The only differences between those tissues were detected in the cellulose fraction. Besides early sugarcane developmental 42

stage take place before the proper stem elongation stage (Bonnett, 2014), a culm structure is already present and they accumulated significantly more cellulose than leaves in RD and SD (Figure 5E,F). Cell walls are sometimes referred as static structures, but they are composed of a large amount of sugars which can be a source of energy for growing plants (Barnes and Anderson, 2017). In addition, plants are able to recycle primary cell wall components (Barnes and Anderson, 2017) during development and during one single day. Like soluble sugars, SD induced fluctuation from cell wall components. The why sugarcane assemble/disassemble cell walls within a day can rely on two assumptions: (1) that could be a source of sucrose synthesis precursors and/or (2) that could be related to cell and tissue growth. The second assumption does not look like to be clearly explained by the present data because plant growth was not influenced by photoperiod. Still, the differential fluctuations were mostly detected in the SD plants (Supplementary Table 8). Hence, fluctuations in pectin and hemicelluloses might play a role as a source of sucrose synthesis precursors. Accordingly, while leaf sucrose increased during the night, pectin and hemicelluloses strongly decreased (Figure 6A,C). Different trends are seen for pectin in leaf and culm. In leaves pectin peaked in the day and night while in culms in the middle of the night. The major part of sugarcane pectin is water soluble and not linked to other wall components (de Souza et al., 2013) and so easier to reallocate, which could explain the rapid increasings/decreasings. Several pectin breakdown genes were up-regulated in a high biomass sugarcane genotype reinforcing the suggestion that pectin is rapidly degraded for recycling (Wai et al., 2017). Interestingly, rice transcriptome diel survey has found PECTINESTERASES (pectin-degrading enzymes) highly accumulated in the end of the night, which can suggest pectin is catabolized in the nighttime and could in part explain the decreasing in leaf and culm pectin in RD plants after the beginning and middle of the night, respectively.

Figure 6. Diel fluctuations of cell wall components between plants subjected to a 12h12h and 08h16h light regimes. Each point represents the mean ± SD (n = 3). Daytime (white) and night (light gray). Student’s t-test was performed for the comparisons of means within each time point, where the significant levels were determined as * P < 0.05; ** P < 0.01; *** P < 0.001.

Patterns of distribution of hemicelluloses and celluloses were similar in each leaves and culms fractions, under RD and SD (Figure 1). Previous transcriptomic data showed a key enzyme in the hemicellulosic synthesis, UDP-GLUCURONIC ACID DECARBOXYLASE, shared a very similar pattern of expression with SUCROSE SYNTHASE (Solomon et al., 2010), which is linked to cellulose biosynthesis (Wai et al., 2017). Coordination of hemicellulose and cellulose might be advantageous as xylan function as a cross-linking matrix holding cellulose microfibrils (Solomon et al., 2010). Cellulosic fractions were interestingly fluctuating in the present study. Under RD a peak in ZT16 is seen in the culms, nevertheless the only significant difference within the diel was between ZTs 1 and 16 (Figure 5E; Supplementary Table 6). On the other hand, an inverse pattern for cellulose deposition was found in SD plants. Leaves oscillate from ZT1 to ZT7 than fell to ZT9 and recovered by ZT19. Meanwhile culms increased cellulose form the middle of the day to the first hour of the night, then reached a plateau until fell on ZT19 and recover to the same value as the rest of the night (Figure 5F). Curiously, at ZT19 leaf and culm celluloses presented the same concentration. The same could be seen for hemicelluloses (Figure 5D). Literature data for diel cellulose deposition comes from trees stems (Bobák and Nečesaný, 1967; Hosoo et al., 2002; Mahboubi et al., 2015), as reported already. Still our results from SD plants are like those as cellulose was deposited mainly from the afternoon to the night. A decrease in cellulose content at ZT19 could mean a sugar turnover took place in the culms to collaborate on the sucrose synthesis up in the leaf. Supporting this hypothesis lies the fact that most of the differences in cellulose concentration in the leaf during the diel were between a daytime and a nighttime (Supplementary Table 6). Also, daily ZTs were not significantly different, but ZT1 was higher than ZT9, which is the first hour in the night. Which metabolic processes are most dependent to wall dynamics are still to be confirmed. Nevertheless, unlocking links between wall synthesis and degradation and its impact on plant growth can help on improving biomass accumulation and yield (Barnes and Anderson, 2017). Knowledge on wall dynamics in an interval of a day in response to a short-day regime, can be crucial to understanding on how sugarcane manage to accumulate sucrose and cell wall itself, which are the main valuable components of sugarcane. An easy access to cellulosic fraction remain still one of the major issues to the 45

2G ethanol industry because lignin and/or hemicellulose should be treated before (Botha and Moore, 2014). Understanding how dynamic cell walls are and how they manage to respond to external stimuli in a short time-frame and which enzymes isoforms could be involved into such processes could help industry on the further development of solutions to improve ethanol production. The composition of hemicelluloses and pectin has been characterized (de Souza et al., 2013). Hence, further studies should detail diel profile of hemicelluloses and pectin subcomponents searching for temporal regulation on their metabolism. In this study we surveyed levels of soluble sugars and cell wall components in developing sugarcane under diel control. We showed the plant adapt to a short-day and metabolize the cell wall in a different fashion while maintaining a reasonable level of sucrose through a nocturnal synthesis.

ACKNOWLEDGEMENTS Authors thank National Council for Scientific and Technological Development (CNPq) for financial support to LA (Process 140976/2013-2). This work was funded by grant 08/58031-0 to RV from FAPESP (Fundação de Amparo à Pesquisa do Estado de São Paulo). RV received a research fellowship from CNPq.

AUTHOR CONTRIBUTIONS LA and RV designed the experiments. LA and JL did all the experiments and analyzed the data. LA wrote the manuscript and PM helped in manuscript editing and writing with critical questions. RV collaborated in manuscript writing and data evaluation and analysis of results with critical questions.

CONFLICTS OF INTEREST Authors declare no competing financial interest.

SUPPLEMENTARY MATERIAL O referido material suplementar pode ser encontrado no Apêndice I.

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3.2 Manuscrito 2 - Diel transcriptome response of sugarcane (Saccharum spp.) under regular and short day reveals distinct rhythmic behavior

Leonardo C Alves1; Carlos T Hotta2; Renato Vicentini1

1Bioinformatics and Systems Biology Laboratory, Department of Genetics and Evolution and Bioagents, University of Campinas, Campinas, Brazil 2Biochemistry Department, Chemistry Institute, University of Sao Paulo, Sao Paulo, Brazil

ABSTRACT

Diel rhythms occur in response to a light-dark cycle and are major players synchronizing the endogenous rhythms to external stimuli such as photoperiods. Perfect matching between photoperiod and rhythms is of great importance for plants, resulting in competitive advantages. Sugarcane comprises about 75% of the world’s sugar production and has an enormous potential for biofuel industry because of its high capacity of sucrose storage in the stems, which reaches about 670 mM. Most of the knowledge of rhythmic expression came from Arabidopsis studies, a diploid C3 plant with starch-storage, while sugarcane runs C4 photosynthesis, accumulates sucrose and is highly polyploid. Here we report a RNA-seq transcriptomic survey from sugarcane subjected to regular and short day. We report a different phase profile related to each photoperiod, with an enrichment in the middle of the night under short day. Sugarcane clock genes are discussed. We organized transcripts in functional groups related to characteristic of commercial interest for sugarcane and discussed their relationships. We also report a set of rhythmic long non-coding RNAs and emphasize their level of conservation to other crops. A gene co- expression network was created to better understand the gene networks under a diel control. Notably, many lncRNAs were present in the network.

INTRODUCTION

Earth rotation implies organisms to synchronize their metabolism with the day- night cycle. Circadian rhythms arise from interaction of core oscillatory components (the “clock”) resulting in feedback-loops accordingly to day and night (MICALLEF, 2011), temperature and sugars from photosynthesis (HAYDON et al., 2013b). The output pathways regulate many physiological processes such as photosynthesis and organ growth (HOTTA; NISHIYAMA JR; SOUZA, 2013) and increases chlorophyll concentration growth rates and carbon fixation (DODD et al., 2005). Nevertheless, circadian rhythms measurements are made under constant conditions which are not the natural conditions plants are exposed to (DE MONTAIGU et al., 2015). Diel rhythms occur in response to a light-dark cycle and are major players synchronizing the endogenous rhythms to external stimuli (DE MONTAIGU et al., 2015) such as photoperiods. Still, little is known about this regulation on plant metabolism and gene expression (LI et al., 2016). Rhythmic behaviour is suggested for a considerable part of plant genomes. Transcriptomics studies from vascular, mesophyll and whole leaf of Arabidopsis, subjected to long and short day light regimes, have shown that up to 96% of the transcripts cycled in at least one condition (ENDO et al., 2014). In poplar and rice, under regular day and temperature conditions, 28.7% and 38.7% of the transcriptome are rhythmic, respectively (FILICHKIN et al., 2011), while in sugarcane the estimations are about 32% of sense and 22% of antisense transcripts oscillate under circadian control (HOTTA; NISHIYAMA JR; SOUZA, 2013). Studies in soybean (MARCOLINO-GOMES et al., 2014), barley (KUSAKINA et al., 2015), Brachypodium (Brachypodium dystachyon), Setaria (Setaria viridis) (HUANG et al., 2017) and sugarcane (HOTTA; NISHIYAMA JR; SOUZA, 2013) have shown orthologs from Arabidopsis clock genes acting in similar ways, which suggests they are relatively conserved and could generate similar downstream events. Perfect matching between photoperiod and circadian rhythms is of great importance for plants because results in competitive advantages (DODD et al., 2005). Changes in photoperiod in hours trigger responses such as floral repression in sorghum (MURPHY et al., 2011) and so short days have been studied aiming to understand 54

adaptive processes which could happen in a rhythmic style. Arabidopsis roots grew lesser under 08h/16h light/dark photoperiod and it may be due to an inhibition of the clock genes CIRCADIAN CLOCK ASSOCIATED (CCA1) and LATE ELONGATED HYPOCOTYL (LHY) under that conditions (YAZDANBAKHSH et al., 2011). In Populus sp the transition from long to short days have altered phasing and level of expression of the clock genes GIGANTEA (GI), EARLY FLOWERING 3 (ELF3), LHY and PSEUDO-RESPONSE REGULATORS 5-7 (PRR5-7) and the sugars metabolism genes GRANULE BOUND STARCH SYNTHASE 1 (GBS1), BETA FRUCTOFUROSIDASE and BETA AMYLASES accordingly to the photoperiod (HOFFMAN et al., 2010). Commercial sugarcane (Saccharum hybrid) comprises about 75% of the world’s sugar production (SWAPNA, M; SRIVASTAVA, 2012) and is key player in the biofuel industry because of its high capacity of sucrose storage in the stems of up to 670 mM (WELBAUM, 2014). The lack of a full sequenced genome associated to its complexity are barriers for genetic studies (MENOSSI et al., 2008), and many areas still require deep studies, for instance the diel responses to the environment. Despite the economic importance of sugarcane, large scale sequencing such as RNA-seq studies became to be published just recently (CARNAVALE BOTTINO et al., 2013; WU et al., 2013; CARDOSO- SILVA et al., 2014; MATTIELLO et al., 2015; VICENTINI et al., 2015; WAI et al., 2017) and none were focused on rhythmic expression. Large scale studies are important because they allow the identification of low expressed transcripts, novel cycling transcripts and cycling long non-coding RNAs (lncRNAs) (HUGHES et al., 2012) and alternative splicing isoforms. It was shown in Arabidopsis that a spliceosome ribonucleoprotein modulates temperature effects on the circadian period and controls the alternative splicing of clock genes (SCHLAEN et al., 2015), meanwhile a light impulse during the night is enough to influence the alternative splicing of 382 genes (MANCINI et al., 2016). Thermal, drought and biotic stresses are capable of generate splicing isoforms oscillating out-of-phase relative to the main isoform (FILICHKIN et al., 2015). LncRNAs are of particular interest because there is little information about their rhythmic behavior (HUGHES et al., 2012; ZHANG et al., 2014). They can be expressed in response to light (WANG et al., 2014), control flowering (HENRIQUES et al., 2017) and 55

many of them target clock genes such as LHY, CCA1, TOC1, PRR3-5-7-9 (ROMANOWSKI; YANOVSKY, 2015). LncRNAs may be involved in rhythmic chromatin modifications and direct gene regulation (KORNFELD; BRUNING, 2014) which suggest they may collaborate in the regulation of metabolic pathways such as sugars synthesis and degradation. Most of the knowledge of rhythmic expression come from Arabidopsis studies. However, Arabidopsis is a C3, diploid and starch-storage plant while sugarcane runs C4 photosynthesis, accumulates sucrose and is highly polyploid. Despite the relative conservation between coding regions of Arabidopsis clock genes and monocot species, the regulatory regions diverge, and events of duplication and gene lost have change the number of some clock genes (CALIXTO; WAUGH; BROWN, 2015) which suggest there may be differences in downstream processes. There is interest in expanding the knowledge obtained to crop species in order to improve yields and the responses to environment changes (MCCLUNG, 2013). Altogether, these observations led to the need of more studies regarding rhythmic expression in sugarcane cultivars. In this study we performed diel gene expression analyses in a sugarcane cultivar in order to evaluate the responses to a regular day (RD, 12h12h light/dark) and a short day (SD, 08h16h light/dark). We reported 7211 oscillating transcripts in RD and 9493 in SD plants and discussed their relationships and metabolic functions related to characteristics of commercial interest for sugarcane. Sugarcane clock genes were reported, and their phasing profiles compared to other crops and Arabidopsis. Under RD, transcripts most phased around dusk while in SD the majority phased in the middle of the night. Functional groups were organized, and their phasing profile were similar to the whole dataset in RD, nevertheless photoperiod influenced phasing of transcripts from SD plants and phase profiles diverged from the dataset and those groups, mainly for transcription factors (TF). We mapped rhythmic TFs and regulators families and found enrichment of those involved with sugars metabolism in the SD night. Rhythmic long non- coding RNAs were identified in both RD and SD and an interesting conservation level was observed with other crops. We then organized the rhythmic transcripts in a co-expression network and discussed their relationships regarding metabolic functions of interest. We highlight many lncRNAs were present in the co-expression network. 56

MATERIAL AND METHODS

Plant material and photoperiodic conditions One hundred and twenty-four plants from the commercial variety of sugarcane SP83-2847 (Saccharum hybrid) were propagated by stem cuttings and grown in small pots under greenhouse conditions at Centro de Tecnologia Canavieira (CTC), Piracicaba, Brazil. After 60 days, plants were entrained in different photoperiods for 30 days in a controlled environment. Light was supplied by light-emitting diodes under a 12h light (100 µE m-2 s-1 photon flux density)/12h dark (regular day, RD) or 8h/16h light/dark (shot day, -1 SD), temperature was maintained at 27±2 °C and CO2 concentration at 405 µM L . CO2 concentration was measured using a LCPro+ gas monitor (ADC Bioscientific). Plants were watered twice a day and randomly reorganized in concern to their spatial position inside the chamber every weak. The culms and the middle section of +1 leaves with no vascular tissue were harvested from six RD and four SD plants at each of the following time points during a period of 24 hours (ZT0 means the moment – in hours – when lights were turned on): ZT1, ZT6, ZT11, ZT13, ZT16, ZT20 and ZT23 for 12h/12h light/dark cycle; and ZT1, ZT4, ZT7, ZT9, ZT14, ZT19 and ZT23 for 8h/16h light/dark cycle. Thus, ZT1, 6 and 11 in 12h/12h (RD) and ZT1, 4 and 7 in 8h/16h (SD) corresponded to 1 hour, after starting illumination and then middle and last hour of the light period, respectively. The other ZTs periods corresponded to the first hour after darkness (ZT13 in RD and ZT9 in SD) and then two middle points and the last hour of the darkness period (Supplementary Figure 1). All the material was frozen under liquid nitrogen and stored at -80 °C until further analysis.

RNA-extraction, library preparation and RNA-seq sequencing Total RNA was extracted using a RNEasy Plant Mini Kit (Qiagen) from 100 mg of ground leaves harvested at each time point following the manufacturer’s manual. RNAs were quantified using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific) and their quality was checked by electrophoresis on 1% agarose gels containing formaldehyde. Single-end Illumina mRNA libraries were generated from 3.5 µg of total RNA using TruSeq RNA Sample Preparation Kit (Illumina) according to manufacturer’s 57

protocol. Libraries were evaluated for integrity and fragment size (260 bp) using Bioanalyzer (Agilent Technologies) and quantified by qPCR with the KAPA Library Quantification for Illumina kit (KAPA Biosystems). Libraries were single-ended sequenced, two per lane, using the Illumina Hi-Seq 2500 platform with 100 bp read length.

Transcript mapping and abundance estimation The raw data generated by Illumina sequencing was trimmed and quality checked with the NGS QC Toolkit (PATEL; JAIN, 2012). High quality reads, which contained at least 70 nucleotides with phread score Q ≥ 20 (1 error at each 100 bases) were maintained. Reads will be deposited in the NCBI SRA database. rRNA and bacteria homologs reads were removed after Blast (e-val ≤ 1e-5) search against plant rRNAs (18S, 5.8S and 26S) and the Escherichia coli K12 complete genome (GenBank accession CP000948.1). Estimations of relative transcript abundances were carried out by the R package RSEM (LI; DEWEY, 2011) using a sugarcane reference transcriptome (in house assembly). RSEM estimate the abundance values of each expressed transcript and normalize them by Fragments Per Kilobase of exon per Million fragments mapped (FPKM). These values were further used as input to the rhythmic detection tools. In this case only transcripts with FPKM > 0 in at least one time point were considered.

Identification of transcripts with rhythmic expression Rhythmic expression behavior was detected within the dataset where transcripts had at least one time point with FPKM > 0 out from the 7 time points. Three different algorithms were used: Haystack (MOCKLER; MICHAEL; PRIEST, 2007), empirical-JTK_CYCLE (HUTCHISON et al., 2015), and RAIN (THABEN; WESTERMARK, 2014). Haystack uses Pearson correlation to correlate a set of diel and circadian waveform models to the time course data (MOCKLER; MICHAEL; PRIEST, 2007; MICHAEL et al., 2008) and can be supplied with external models. The best waveform alongside its p-value are given for each transcript. We set Haystack with the following parameters: -c 0.8, -f 0, -r 0.05 and -b 0. We modified the predefined models from Haystack and added models according to our time course, resulting in 537 models. Only 24 h period models were maintained (Supplementary File 1). Empirical-JTK_CYCLE (eJTK_CYCLE) searches for 58

asymmetric waveforms and provides empirical p-values to the correlations between waves and the time course (HUTCHISON et al., 2015). We set eJTK_CYCLE using a period of 24 hr, phase 00-20 by 4, asymmetries 04-20 by 4. Transcripts exhibiting an empirical p-value < 0.05 were considered significant as previously (LEONE et al., 2015). RAIN uses non-parametric methods to detect rhythms in a 24 h period. RAIN does not take in account predefined oscillating patterns being able to detect arbitrary or nonsymmetric wave forms. In summary, RAIN evaluates rising and falling patterns, which are unrelated between themselves (THABEN; WESTERMARK, 2014). We set RAIN with the following parameters: period = 24, method = independent; peak.border = c(0.1, 0.9), detlat = 1. Additionally, measure.sequence contained vectors demonstrating the distribution of the time points from each photoperiod. Lastly, we combined all the results from the three tools in a rhythmic dataset to each photoperiod condition and such datasets were used as input to further analysis. With the Diurnal tool (MOCKLER; MICHAEL; PRIEST, 2007) (http://diurnal.mocklerlab.org) we accessed the sugarcane diurnally rhythmic genes which are also rhythmic in Arabidopsis (correlation cutoff ≥ 0.8). Diurnal has data from several Arabidopsis experiments developed under diverse photoperiods, temperature and tissues. We compared our data with a circadian regulated dataset (LL12_LDHH) to filter out the genes which oscillate in response to circadian clock. Genes classified as is and present in both RD and SD were filtered out. So, we compared our data with the closest diel experiments which are: LDHC and LDHH_ST against our 12h/12h condition; and ColSD against our 08h/16h condition (for further information regarding the experiments see ftp://www.mocklerlab.org/diurnal/expression_data). We then created functional groups of genes which oscillates in both plants in both photoperiods and oscillates only in sugarcane in both photoperiods. The groups were names as: A) sugars metabolism and cell wall; B) transcription factors and regulators; C) kinases and phosphatases involved to sucrose metabolism; D) photosynthesis. For define which genes would be members of each group we sought the following steps: for A) genes assigned to carbohydrate metabolic pathways by using the “Cellular overview” tool from the Plant Metabolic Network database (http://pmn.plantcyc.org/overviewsWeb/celOv.shtml) and those ones assigned to “Carbohydrate metabolism” and its sub pathways from Kyoto Encyclopedia of Genes 59

and Genomes (KEGG) were classified into this group. Keyword search was used when necessary namely for transporters, and literature data was used to search for cell wall components (CESARINO et al., 2013). For B) we searched from all the Arabidopsis transcription factors and regulators of transcription available at PlntTFDB v.3 (PÉREZ- RODRÍGUEZ et al., 2009) (http://plntfdb.bio.uni-potsdam.de/v3.0/). For C) keyword search for “kinase” and “phosphatase” which returned know proteins related to sucrose metabolism, namely, but not only, PP2A, KINβ, SnRKs and hexokinase. For D) as in A, search in both PMN and KEGG for photosynthesis members and literature data from maize (KHAN; ROWE; HARMON, 2010).

Databases, annotation, ontology enrichment analyses and lncRNA survey We anchored our data in an in house de novo transcriptome assembly made with sugarcane data containing around 600 million reads and composed of ~141,000 transcripts. From the assembly, 95,084 transcripts are orthologs to sorghum proteins, and are composed of homologs and splicing isoforms; and 21,178 are orthologs to sorghum lncRNAs. As the sugarcane annotation is still incipient, we retrieved the sorghum- Arabidopsis orthologs and considered the Arabidopsis annotation to classify the transcripts regarding function. Gene ontology enrichment analysis were carried out in the AgriGO environment (DU et al., 2010) employing Fischer’s exact test corrected for false discovery rate (FDR < 0.05) and Venn diagrams were constructed with Venny 2.1.0 (http://bioinfogp.cnb.csic.es/tools/venny/index.html). Heatmap analyses were conducted in R using the “heatmap.2” function from the gplots package. Rhythmic lncRNAs were identified in silico in two steps: fist we detected all the transcripts mapped to the already annotated lncRNAs (n=21,718) from the transcriptome assembly; second, we kept all the unmapped transcripts and blastN (e-val 1e-10) against the no-hit to coding and non-coding sequences portion (n=36,012) from the same database where there are possible other lncRNAs. The last bulk was further also blastN (e-val 1e-10) against plant sequences from the PNRD database (http://structuralbiology.cau.edu.cn/PNRD/, accession 11/27/2017) and rRNAs (SSU and LSU) from SILVA database (https://www.arb-silva.de/, accession 60

11/27/2017) in order to identify and filter out other forms of ncRNAs. Only sequences longer than 200 nt were considered as lncRNAs. We tested the lncRNAs’ dataset for conservation between other species by using data available in GreeNC (Gallart et al., 2016, accession 11/27/2017), which annotates lncRNAs present in genomes available in the Phytozome database, and blastN (e-val 10-20) against the following data: Sbicolor_313_v3 (n=5305), Zmays_284_AGPv3 (n=18110), Sitalica_312_v2 (n=3492), Osativa_204_v7 (n=5237), Bdistachyon_314_v3 (n=5584), Taestivum_269_v2.2 (n=38820) and Athaliana_167_TAIR10 (n=3008).

Gene co-expression network Gene co-expression network (GCN) from diel expression was created through the Heuristic Cluster Chiseling Algorithm method (HCCA) (MUTWIL et al., 2010) that supports weighted edges and may control average cluster sizes. We applied a filter to maintain only transcripts whose expression were >= 0.5 FPKM in all time-points so we could obtain a network reliable to analyze. Rhythmic transcripts identified in 12h12h and 08h16h were used to create a GCN through the HCCA method (Heuristic Cluster Chiseling Algorithm, MUTWIL et al., 2010) in R environment. Cytoscape v3.3 (SHANNON et al., 2003) was used to visualize the networks. The enrichment analyses of ontology terms were performed using the ClueGO Cytoscape plugin v2.5 (BINDEA et al., 2009) using the Arabidopsis thaliana reference set provided by the tool and containing 8365 genes, with the following parameters: GO tree interval 1 to 10; GO term pathway = 2; min #genes = 4%; kappa score = 0.4; enrichment = right sided.

RESULTS To gain knowledge on how a short-day affects the gene expression in the SP83-2847 sugarcane hybrid we extracted RNAs within one day. We then sequenced RNAs and accessed expression values. A workflow scheme for the whole experiment is available in Supplementary figure 1. Overall read qualities were high for all samples while reads mapped to transcripts in a range of 68-78% of the libraries (Table 1).

Table 1. Read quality after trimming and filtering and RSEM alignment statistics. 61

High quality Quality score Photoperiod Timepoint Total reads Total aligned reads reads (mean) ZT1 20,917,812 99.6% 36 14,202,640 ZT6 13,011,391 99.7% 37 9,781,383 ZT11 11,604,206 99.6% 37 8,533,904 12h/12h ZT13 12,800,027 99.6% 37 9,469,788 ZT16 7,965,841 99.6% 37 5,992,007 ZT20 14,345,023 99.6% 37 10,299,105 ZT23 12,080,928 96.2% 35 8,924,644 ZT1 38,161,407 100% 36 30,015,873 ZT4 40,593,671 98.2% 36 30,578,771 ZT7 33,768,571 98.5% 36 25,867,726 08h/16h ZT9 26,465,517 98.8% 36 20,028,352 ZT14 36,714,751 98.8% 36 28,498,819 ZT19 33,915,682 98.7% 36 26,246,137 ZT23 14,622,849 97.3% 35 11,432,522 Total 317,933,465 239,871,671

Oscillating transcripts profile and phase distribution We have found 128,609 and 133,920 expressed transcripts in at least one timepoint from plants subjected to regular day (RD, 12h12h) and short day (SD, 08h16h), respectively. Rhythmic detection tools found 7.211 (5.6%) and 9.493 (7.1%) transcripts under RD and SD (Figure 1A) showing rhythmic expression (Figure 1B). Employing the three different tools has been a valuable approach because each one searches for different waveforms, including arbitraries, and finds datasets which the others did not. This approach allowed us to obtain a more comprehensive dataset. Previous study using microarray suggested a higher ratio of sugarcane transcriptome oscillates under constant light (HOTTA; NISHIYAMA JR; SOUZA, 2013), however the data queried was considerably lower due to the microarray capacity and the deepest of the RNA-seq dataset. In Arabidopsis array data, the oscillating ratios ranges between 20%, 37% and 62

40% of the transcripts under LDHC_SM, LDHC_ST and 08h/16h light/dark, respectively (MICHAEL et al., 2008). In rice and poplar arrays such ratios corresponded to 28.7% and 38.7% under 12h/12h light/dark (FILICHKIN et al., 2011). Interestingly, under field conditions only 16% of poplar genes were detected as oscillating (HOFFMAN et al., 2010). Besides the percentage of oscillating transcripts in the present dataset was lower, the number of transcripts was high, suggesting a large ratio of the sugarcane transcriptome was under a diel control in response to photoperiods. Gene ontology enrichment of all sugarcane rhythmic transcripts orthologs to sorghum genes under each photoperiod revealed that the highly enriched biological processes were similar, besides the overall dataset was quite different (FDR < 0.05) (Figure 1C, Supplementary File 2). Accordingly to (CARDOSO-SILVA et al., 2014), the high-sucrose yield sugarcane varieties differ based on their sugar transport and metabolism. They found some GO terms related to these characteristics enriched in all the six genotypes evaluated. We found 17 enriched terms (FDR < 0.05) related to sugar transport, however only 2 had higher significance in SD than RD plants. Some of these were also reported by (CARDOSO-SILVA et al., 2014) and were significant only in RD plants , for instance: “substrate-specific transporter activity”, “substrate-specific transmembrane transporter activity” and “transporter activity”. We also found enriched terms mentioned by these authors as important to sugar metabolism namely “monosaccharide metabolic process”, “glucose metabolic process”, “small molecule biosynthetic process” and “small molecule metabolic process”. In addition, we also found the terms “monosaccharide catabolic process” and “monosaccharide biosynthetic process” enriched. All those were highly significant or present only in RD plants. As that authors mentioned, sugarcane varieties can differ accordingly to their GO enrichment profile regarding their potential to accumulate sucrose and we added that response to photoperiod can play a role and influence such profile.

Figure 1. Identification of diel oscillating transcripts and enrichment of their GO terms. (A) Number of rhythmic transcripts detected by Haystack, RAIN and emp_JTK for 12h/12h (7,211 in total) and 08h/16h (9,493 in total) photoperiod regimes. (B) Heatmap of the diel expression of all rhythmic transcripts at each photoperiod. (C) Top 20 most significant enriched GO terms (FDR < 0.05) for each photoperiod from sugarcane-sorghum orthologs transcripts. Enrichment analyzes were carried out with the Singular enrichment analysis tool from AgriGO.

We next performed a phasing distribution analysis from all oscillating transcripts. Phase is the time of the day at a given rhythm occur (MCCLUNG, 2006). As an illustration, a peak of expression from a given gene. Phase is defined in Zeitgeber Time (ZT) which is German for time giver. The onset of light is a strong time giver so that moment is defined as ZT0 (MCCLUNG, 2006). As it would be reasonably expected, there was no significant Pearson correlation between the distributions from both photoperiods (R2=0.15, Figure 2A). The largest difference in phase distributions was detected in the middle points of the night (T5 and T6) when there was an enrichment in phasing of transcripts from SD plants suggesting an influence in gene expression to respond adaptatively to the adverse light period (Figure 2A). The higher proportion of genes phasing in the light-dark transition in plants under regular photoperiod is in accordance with previous report in rice and poplar and is consistent with the expression of circadian regulated genes in anticipation of that transition (FILICHKIN et al., 2011). Despite differences in phasing distribution, we found no significant differences in the relative amplitudes for both datasets (Komolgorov-Smirnov test, P > 0.05) (Figure 2B). That suggest reprograming phase profile could be more important than increasing levels of expression for the overall set of coding transcripts. On the other hand, differential amplitude has been suggested as a way to ensure non-coding RNA transcription because of their shorter half-lives compared to coding genes (FAN et al., 2017). In order to examine how the distribution of phasing from rhythmic transcripts would be comparable to the entire dataset that they came from, we have plotted the frequency of the highly expressed timepoints from each transcript either rhythmic or not. There was a positive Pearson correlation (R2=0.74) in phase distribution between all the expressed transcripts and the oscillating transcripts from RD plants (Figure 2C). Nevertheless, in SD plants these distributions did not match (R2=0.02, Figure 2D) and no phase enrichment at T5 and T6 was observable as it was for the oscillating transcripts.

Figure 2. Phase distributions of transcripts and relative amplitudes in response to 12h/12h and 08h/16h light/dark regimes according to timepoints. (A) Oscillating transcripts poorly correlate between different regimes according to coefficient of regression and (B) no difference in relative amplitude was revealed. (C) Highly positive Pearson correlation between oscillating and non-oscillating transcripts from plants under 12h/12h photoperiod and (D) poorly Pearson correlation between oscillating and non-oscillating transcripts from plants under 08h/16h photoperiod. Relative amplitude was defined as ratio between highest and lowest expression of a given transcript. Komolgorov-Smirnov test was performed to compare those distributions and P < 0.05 was considered significant. 66

Gene ontology enrichment analysis was also performed for the rhythmic transcripts classified by “day” (ZTs 1, 6, 11 or 1, 4, 7) and “night” (ZTs 13, 16, 20, 23 or 9, 14, 19, 23) to demonstrate whether any illuminated/not-illuminated period would comprise ontology terms which we could relate to the change in the photoperiod conditions. A total of 721 enriched biological processes terms (FDR < 0.05) were organized in a Venn diagram and 189 (26.3%) were shared by the four groups (Supplementary file 3). They mostly represent basal metabolism (as an illustration: “metabolic process”, “small molecule metabolic process”, “cellular process” and “primary metabolic process”). A higher number of terms were shared between the days than between the nights, beyond the already mentioned 189. Meanwhile both 12h and 08h days shared 180 terms, the 12h and 16h nights shared only 119 enriched terms. Another interesting observation is that 106 terms were enriched with a significant FDR only in the 16h night, while the 12h day, 12h night and 08h day comprised for less than 40 unique terms each one. The term “cellulose biosynthetic process” was enriched only in the 16h night along to many terms related to RNA and protein metabolism, for example “RNA catabolic process”, “mRNA processing”, “RNA interference”, “RNA splicing via transesterification”, “nuclear mRNA splicing via spliceosome”, “gene silencing by RNA”, “protein modification by small protein conjugation or removal”, “protein import to peroxisome matrix”, “histone modification”, “protein deneddylation”, “protein amino acid phosphorylation” (Supplementary file 3).

Different phases from functional sugarcane transcriptome A total of 2494 transcripts were expressed in both photoperiods, so we could examine their phase profile in RD and SD looking to phase shifting in response to SD. They were then classified in categories considering the peaking ZT and the relative position regarding the period. As an illustration, if a gene peaked at ZT6 in RD and changed to ZT4 in SD, it represented a phase shifting in ZT, however the proportion of the period remained the same: the middle of daytime. A total of 981 transcripts changed their phases, yet the relative position in the period remained the same; 103 transcripts changed phases from ZT13 to 14. Despite it was a little difference, the peak was moved from the end of the day to the middle of the night. Other 297 transcripts did not change 67

their phases and remained at the same proportion of the period; and 1113 transcripts changed phasing and the relative proportion of the period (Supplementary file 4). GO enrichment of these groups demonstrate diverse functions were enriched (Supplementary file 4). Notably, many terms related to cellular localization, photosynthesis, chlorophyll and pigment metabolism, carbohydrate, RNAs and isoprenoid metabolism were enriched from the genes which changed phases but remained at the same relative proportion of the day. Almost all the genes whose phase changed from ZT13 to ZT14 were enriched for transport terms, namely “organic acid/catabolic acid/amino acid transport” and “amine transport”. From the group of genes whose phase and position did not changed, ZT1 and 1 and ZT23 and 23, several terms related to sugars catabolism were also observed and others related to response to red, far red and blue lights were enriched. The most diverse GO enriched terms were showed within genes whose phase and position shifted during the 24h period. Interestingly, many terms related to catabolic processes, mainly carbohydrates and sugars, were enriched in this group. As an illustration, “cellular carbohydrate / carbohydrate / disaccharide / monosaccharide / hexose / glucose catabolic process”. These observations demonstrate the importance and ability of diel rhythms in controlling the expression of basal metabolism to take maximum benefit from external stimuli.

Transcript classification accordingly to functions of interest to study in sugarcane We kept the transcripts from sugarcane-sorghum orthologous genes from which we had the functional annotation regarding the Arabidopsis database (TAIR 10) and we have previously detected as diel oscillating (RD, n=2645 and SD, n=4563). We then used them as input to Diurnal tool, aiming to access the rhythmic elements in both Arabidopsis and sugarcane and only in sugarcane. We next organized the correspondent transcripts in “functional groups” related to sugar metabolism and regulation. Groups were: a) sugar metabolism and cell wall; b) transcription factors and regulators; c) kinases and phosphatases involved in sucrose metabolism; and d) photosynthesis. Table 2 contains the number of transcripts in each group from each photoperiod. A list with all the members from each group can be accessed in Supplementary file 5. 68

Table 2 – Number of rhythmic transcripts orthologs to sorghum which oscillated in sugarcane and Arabidopsis or only in sugarcane under both photoperiods.

Sugarcane and Exclusive to sugarcane Arabidopsis Functional group 12h12h 08h16h 12h12h 08h16h Sugars metabolism and cell wall 100 175 59 29 Transcription factors and regulators 204 439 172 97 Kinases and phosphatases involved 30 48 11 6 in sucrose metabolism Photosynthesis 44 59 19 4

More transcripts were rhythmic in sugarcane and Arabidopsis than only in sugarcane under both photoperiods. In RD plants the number of rhythmic transcripts only in sugarcane in each functional group were about the half of the sugarcane and Arabidopsis. On the other hand, much more transcripts detected as rhythmic in both plants were found than only in sugarcane subjected to a SD regime. The number of transcripts which phased at each ZT throughout the day in sugarcane and Arabidopsis in both RD and SD were organized in histograms, so we could access their phasing profile (Figure 3). There are differences in phase distribution from the groups under each photoperiod. Firstly, groups from RD plants, except photosynthesis, tend to behave similarly during the day and night (Figure 3 A,C,E,G) and their phase distribution was similar to that from the entire rhythmic dataset (Figure 2 C). They fall from ZT1 to the middle of the day and rise to ZT11. Then, they fall during the night. Secondly, groups from SD plants, except for kinases and phosphatases, tend to behave similarly to RD during the day (Figure 3 B,D,F,H). In the night all but transcription factors rose from ZT9 to peak at 14 and fell until the end of the night. Transcription factors rose from ZT4 and reached a plateau at ZTs 14 and 19. Only transcription factors were distributed similarly to the entire rhythmic dataset model (Figure 2 D). 69

Figure 3. Phase distribution of functional groups from rhythmic transcripts in sugarcane and Arabidopsis. Rhythmic transcripts orthologs to sorghum were classified in the following functional groups: sugars metabolism and cell wall, transcription factors and regulators, kinases and phosphatases involved in sucrose metabolism, and photosynthesis. Further they were tested in the Diurnal tool to access whether they were also rhythmic in Arabidopsis. Phasing profiles in sugarcane were accessed from that rhythmic transcripts in both plants in each photoperiod. A, C, E and G contain the phase distributions from transcripts under 12h12h while B, D, F and H contain from transcripts under 08h16h. 70

GO enrichment analyses were made for the genes phasing at the middle points of the night in both RD (ZTs 16 and 20) and SD (ZTs 14 and 19) from the set of transcripts which we used to create each functional group, because most of the transcripts phased in that ZTs. No Biological Processes were significantly enriched on the rhythmic groups only in sugarcane, probably because of the limited number of transcripts. Interestingly, on the groups shared between Arabidopsis and sugarcane a high number of child terms related to negative regulation of diverse metabolic processes were significantly enriched (FDR<0.05) only at ZT19 from SD. “Negative regulation of cell death / - programed cell death” were also enriched in ZT16 (RD), “negative regulation of defense response” was only in ZT20 (RD) and none negative term was enriched in ZT14 (SD) (Supplementary figure 2).

Oscillating orphan genes and long non-coding RNAs Apart from the sorghum orthologous transcripts that we detected and classified by function, we also listed a considerable number of rhythmic transcripts from non- orthologous genes. There were 1453 and 2201 sugarcane potentially orphan genes expressed in RD and SD plants, respectively. Recently we did show sugarcane has a set of about 12,000 transcripts homologs only to sugarcane unigenes (CARDOSO-SILVA et al., 2014) and these rhythmic transcripts may be part of a unique dataset from sugarcane. We have also made an in-silico search for sugarcane rhythmic expressed lncRNAs. Such RNAs are characteristic for lacking or having small Open Reading Frames and be longer than 200 nucleotides. After applying size and similarity filters, we found 508 and 801 transcripts from RD and SD plants, respectively. From those, only 111 were shared by both photoperiods resulting in a total of 1198 unique rhythmic lncRNAs. As there are few publications discussing the massive presence of rhythmic lncRNAs (HUGHES et al., 2012; ZHANG et al., 2014; FAN et al., 2017) and some lncRNAs may be involved with circadian rhythms (ROMANOWSKI; YANOVSKY, 2015), these are relevant data for further studies. In addition, most of these lncRNAs are shared with other grasses.

Gene co-expression network We next queried the rhythmic transcripts for possible interactions through a gene co-expression network (GCN). A filter of minimum 0.5 FPKM in each timepoint resulted in a 11335-node and 130-clusters GCN. GCNs from large-scale datasets can become highly complex and limit further analyzes. So, it is important to select input data and organize the GCN in modules, subgroups with highly-connected genes, which could better represent the relationships and allow the operator to better understand the network (MUTWIL et al., 2010; GEHAN et al., 2015). We next searched for functional relationships within the GCN using the Arabidopsis annotation information. Gene Ontology analysis with ClueGO shed light on the overall functional composition of the GCN. A total of 1639 genes were annotated for ontologies under a significance level of P < 0.05. Highly significant terms for diverse functions were assigned, such as “posttranscriptional gene silencing”, “cellular protein metabolic process”, “response to light stimulus”, “intracellular transport” and “RNA processing”. Besides the whole GCN, we also were interested in subnetworks (subGCN), so we used the transcripts for clock genes present in the GCN as bait and found the transcripts connected to them until the third level (n=712). Another subGCN was created using as bait those transcripts belonging to the functional groups and whose amplitudes were higher and their neighbors until the second level (n=1013). Enrichment analyses were carried out on those subGCNs and interestingly the significant terms found were similar, despite the size and composition of them. The terms “response to light stimulus” and “response to radiation” were highly significant in both subGCNs. Notably, both subGCNs are composed mainly by terms related to light and photosynthesis. In the clock subGCN terms related to chlorophyll and other pigments metabolism are also enriched while in the subGCN containing members of the functional groups terms related direct to light are present, namely “response to red or far red/-far red/-red light”. This subGCN is also composed by many transcription factors, genes related to sugars and cell wall metabolism and lncRNAs.

DISCUSSION

Coordination and synchronization within environmental stimuli and physiological rhythms occur due to the circadian clock, a transcription-translation feedback-loop network. Physiological processes running during the period of a day fluctuate in response to the clock control and environmental cues such as light, temperature and sucrose (HAYDON et al., 2013b). So, diel rhythmic outputs are product of endogenous circadian rhythms and external cues. The composition of the core clock is well studied in Arabidopsis (MCCLUNG, 2013) and studies focusing on phylogenetic and functional analyzes of orthologous to Arabidopsis’ clock genes in grasses have been published the last years (ZDEPSKI et al., 2008; FILICHKIN et al., 2011; MURPHY et al., 2011; CALIXTO; WAUGH; BROWN, 2015; KUSAKINA et al., 2015). Clock genes were identified in this study, however many transcripts were not classified as rhythmic by the rhythmic detection tools, even though their diel expression profile indicated rhythmicity and similarity with Arabidopsis orthologs. We manually included these transcripts in our rhythmic datasets to further analyze their expression profiles. We found transcripts expressed during the day in both photoperiods for clock genes (LHY, PpdH-1, TOC1, PRR59-98-73, ELF3a, ELF3b and GI) and for genes involved with the clock (FLOWERING TIME T (FT), CASEIN KINASE II (CKB1) and PROTEIN ARGININE METHYLTRANSFERASE 5 (PRMT5)). Figure 4 demonstrates expression profile from clock genes alongside to Arabidopsis orthologs data retrieved from Diurnal (http://diurnal.mocklerlab.org/) under regular (LDHC and LDHH_ST) and short-day conditions (ColSD) as a comparison. All the 10 transcripts for LHY consistently phased at ZTs 1 and 23 in RD and SD in accordance to Arabidopsis RD and SD. It is well stablished that the topology of clock is conserved between plants (MCCLUNG, 2013; HERNANDO; ROMANOWSKI; YANOVSKY, 2017) although these genes might not necessarily have the same function or respond to overexpression and knockdown the same way in Arabidopsis and other plants (SERIKAWA et al., 2008; HOTTA; NISHIYAMA JR; SOUZA, 2013). ELF3 together to ELF4 and LUX proteins are part of the Evening Complex which binds to the PRR9 and LUX promoters and forms a repressive complex at the circadian clock (HERNANDO; ROMANOWSKI; YANOVSKY, 2017). We detected two sugarcane 73

ELF3, in accordance to rice (ZHAO et al., 2012), and they phased during evening in both RD and SD or very before the night (ZTs 11 and 7). That is in line to Arabidopsis data and similar to rice and Brachypodium where OsELF3-1 phased at ZT0 and OsELF3-2 at ZT16 under SD (ZHAO et al., 2012) and BdELF3 at ZT0 under RD (HUANG et al., 2017). Gigantea (GI) interacts with ZTL to form a feedback loop with TOC1 in the evening (HAYDON et al., 2013a), and peaks at ZT8 under regular and short-day with no photoperiodic influence in phasing. On the other hand, in Brachypodium a photoperiodic influence was reported since GI phased in ZT12 and 8 under LD and SD, respectively (HONG et al., 2010). We observed ScGI phased at ZTs 6 and 11 in LD and ZT7 in SD. However, as clock genes are strongly regulated in terms of transcription, that does not clearly indicate a photoperiodic regulation in phasing because ZTs 6 (RD) and 7 (SD) are in fact close in daytime. Arabidopsis and soybean GI are transcribed only during the light period (Diurnal; Marcolino-Gomes et al., 2014), but sugarcane, rice and Brachypodium GIs (HONG et al., 2010; ZHAO et al., 2012) are still accumulated in the first hours of the night which could mean GI plays a major role on TOC1 accumulation in monocots than in dicots. The AtPRRs orthologous are not easily identified by homology searches in grasses because many duplication events might have happened after the divergence from their common ancestral, resulting in different copy number of these genes in each species (CALIXTO; WAUGH; BROWN, 2015) and sequence overlapping. In fact, the common ancestral between barley and Arabidopsis might have shared only 2/3 of the clock genes (CALIXTO; WAUGH; BROWN, 2015). We have found four transcripts for ScTOC1, four for ScPRR59, three for ScPRR95, five for ScPRR73 and five for ScPpd-H1, the AtPRR7 ortholog in barley (KUSAKINA et al., 2015). Not only the allele number can diverge between species, but the phasing of core clock components can also be different from that observed in Arabidopsis, as it was reported in previous studies with rice (ZDEPSKI et al., 2008; FILICHKIN et al., 2011) and maize (KHAN; ROWE; HARMON, 2010). ScTOC1 phased at the day-night transition in both RD and SD in accordance to Arabidopsis, rice and soybean orthologs (Diurnal; Marcolino-Gomes et al., 2014; Zhao et al., 2012). Amplitudes from all the ScTOC1 were higher in SD plants and a ~2-fold 74

higher amplitude could be detected for the same transcript in SD than RD. That is probably the most active TOC1 paralog in this sugarcane cultivar. Either ScPpdH-1 and ScPRR73 amplitudes were higher in RD. ScPpd-H1 phased at the middle of the day in both RD and SD in accordance to AtPRR7 and GmPRR7 on RD. Amplitude ratios ranged from ~5 to ~10-fold higher in RD. All but one transcripts for ScPRR73 phased at ZT6 in RD regime while in SD they phased from ZT4 to 9. Nevertheless, the amplitude from the transcript phasing at ZT9 was ~3 and ~7.5-fold higher than the other main expressed transcripts at SD, suggesting ScPRR73 main phase take place at the day-night transition in SD, in accordance to rice OsPRR73 (ZHAO et al., 2012). A transcript whose amplitude was higher in RD shifted phasing from ZT6 to the first hour of the night while the others remained phasing from the middle to the end of the daytime. Phasing around the middle of the day in RD was also observed for GmPRR3 but at ZT12 in Arabidopsis (Diurnal; Marcolino-Gomes et al., 2014). Phasing profiles from ScPRR59 and 95 transcripts were very similar for both. In RD they peak at ZT6 while in SD peak at the evening. Nevertheless, peaking from these PRRs vary across species, as in rice they were reported as peaking at the same time in the evening under RD (ZDEPSKI et al., 2008) and at ZT8 in SD (ZHAO et al., 2012). However, AtPRR5 peaks at late afternoon under RD and at evening under SD while AtPRR9 peaks at ZT4 under RD and SD. Lastly, GmPRR9 peaks at ZT8 under RD (MARCOLINO-GOMES et al., 2014). Besides they were highly abundant under SD conditions, amplitudes were in fact very similar under both diel regimes. The amplitudes from the highly expressed ScPRR59 reached 69 FPKM (SD) and 47 FPKM (RD) while for ScPRR95 the amplitudes were even more similar, 213 FPKM against 206 FPKM. The high number of transcripts from sugarcane orthologous to Arabidopsis clock genes found in our dataset lies in the presence of paralogs. As sugarcane has a highly polyploid genome (MENOSSI et al., 2008) it is to be expected that many copies of them are present and expressed. We also observed they diverge in terms of amplitude under the same or each diel regime as a likely dosage effect. As an illustration, the four ScTOC1 amplitudes vary from 4.5 to 90 FPKM within RD. The same paralogs’ amplitudes vary from 5.9 to 174 FPKM in SD. LHY amplitudes range from 2.4 to 284 FPKM under RD and from 0.7 to 446 FPKM. Differences in phasing from homologous may be a result of 75

the circadian-response element composition in the promoters (ZDEPSKI et al., 2008). Interspecific differences in phasing may be involved to functionality (ZDEPSKI et al., 2008), nevertheless recently it has been shown that Brachypodium and Setaria ELF3 restores Arabidopsis ELF3 function suggesting a protein-domain layer of regulation as being more important than gene sequence and phasing similarities (HENRIQUES et al., 2017).

Figure 4: Expression profile from sugarcane clock genes identified in the present study according to the photoperiod. Black lines represent the Arabidopsis ortholog profile according to data retrieved from Diurnal (LDHC and LDHH_ST and ColSD datasets) and coloured lines represent sugarcane orthologs. 76

Apart from the clock components, we also mapped phasing from the 2494 shared rhythmic transcripts between RD and SD. These results shed light on how groups of genes are coordinate in sugarcane in response to those photoperiods. The clock is fitted with external stimuli such as temperature, light and sugars to ensure convenient adaptation and higher yields (HAYDON et al., 2013b). Such synchrony between gene expression and environment can be explained by the internal/external coincidence models, for example in the well-known relationship between photoperiod and flowering (SAWA; KAY; IMAIZUMI, 2008; MURPHY et al., 2011). A change in photoperiod imply in a new clock calibration regarding the light reference (YEANG, 2013) and so result in a new synchrony within plants and environment. Interestingly, from the 981 genes whose shifted their phases in response to the photoperiod but still maintained it as the same proportion of the day, we found many highly significant enriched GO terms related to localization, as an illustration “cellular localization”, “protein localization” and “plastid localization” (Supplementary file 4A). Processes within eukaryotic cells are very spatiotemporally organized, in which protein function is also controlled by the availability of their molecular partners (SCOTT et al., 2005). So, the maintenance of phasing at the same proportion of the day could suggest cells also coordinate localization in order to assure that the correct reactions will happen by the correct moment. We also found out many terms related to photosynthesis and metabolism of pigments, carbohydrates, RNA and lipids. That suggest genes encoding for proteins from the basal metabolism also were shifting in phase accordingly to photoperiod conditions in young sugarcane cultivar. A smaller group with 103 genes whose phase changed by 1h (ZT 13 and ZT14) but changed from the end of the day to the middle of the night was also analyzed. Only six terms were significant but interestingly four were associated to transport, namely “organic acid/-carboxylic acid”/-amino acid”/-amine transport” (Supplementary file 4B). Genes phasing at ZTs 23 and 1, which did not change their phases neither the relative position, were enriched for diverse GO classes. Many were associated to light response, namely “cellular response to radiation”, “response to light stimulus”, “response to red light”/-far red light”/-blue light” (Supplementary file 4C). Since those ZTs are around 77

the night-day transition, that is in line with the facts that the circadian mechanism collaborates to control photosynthesis (DODD et al., 2005) and anticipate further events. A group with 1113 genes was formed by those whose phases and positions within the day have changed. This group was largely diverse as seen by many enriched GO terms from diverse processes, such as “response to chemical”/-temperature stimulus”, “response to metallic ions”, “response to light”, “reproductive processes”, “amino acid metabolism”. Nevertheless, many child terms from “catabolic process” were also high significantly enriched, as an illustration “macromolecule/-small molecule”/- cellular carbohydrate”/-glucose catabolic process” and “disaccharide/-maltose metabolic process” (Supplementary file 4D). Sugarcane photosynthesis and protection against photooxidation are regulated by circadian and diel rhythms and photosynthesis genes maintained their phasing around dawn or shifted phasing to match the middle of the day. Transcripts for antioxidant enzymes such as catalases and stromal-ascorbate peroxidase coordinated their phasing by the end of the day and by the day-night transition in accordance to protect the photosynthetic apparatus. Such observations on phase shifting profile could collaborate to understand how sugarcane circadian oscillator manage to coordinate the metabolism regarding external conditions, mainly the available light, to anticipate the upcoming events. They also demonstrate that even a plant with a highly complex genome is still under strong control of circadian and diel mechanisms.

Functional classification of sugarcane transcripts of interest We sought to find classes of genes of interest to sugarcane studies under diel regulation. We then tested the transcripts orthologs to sorghum, and from whose we had the functional annotation came from Arabidopsis, in order to define whether they were under direct control of the circadian cycle or under diel regulation. We fed the Diurnal tool with 3271 (RD) and 3753 (SD) unique aliases and tested them against the circadian clock database LL12_LDHH. The output showed which genes were under circadian control and the 442 entities shared by both RD and SD were filtered out to maintain only the diel- regulated genes in that analysis. The 1470 sugarcane and Arabidopsis genes (2506 78

transcripts) for RD, 2380 sugarcane and Arabidopsis genes (4295 transcripts) for SD and 1359 genes (2192 transcripts) and 930 genes (1716 transcripts) rhythmic in sugarcane but not in Arabidopsis were further classified in groups regarding carbohydrate metabolism and transcription factors (Table 2). Functional classification of rhythmic transcripts allowed us to access the oscillating profile from transcripts involved to characteristics of interest to commercial sugarcane. Such approach collaborated to a better understanding on the dynamics of expression under free running conditions from genes involved to sucrose metabolism in sugarcane (HOTTA; NISHIYAMA JR; SOUZA, 2013), carbohydrate and photosynthesis metabolism in maize (KHAN; ROWE; HARMON, 2010) and revealed that many transcription factors oscillated through the day in Arabidopsis, rice and poplar (FILICHKIN et al., 2011). We have also found many transcription factors and regulators oscillating. There were 94 families represented in RD and 164 in SD plants, from a total of 204 and 440 transcripts. As members from some TFs and regulators families were previously described as responding to sugars (PRICE et al., 2004; MORIKAMI et al., 2005; MATIOLLI et al., 2011) or associated to the brix value in sugarcane (PAPINI-TERZI et al., 2009) we looked for them and have found many of these sugar-related families were under diel regulation in sugarcane (Figure 5).

Figure 5. Phasing of members from diverse TF families involved to sugars in response to photoperiod. Some of these families were enriched during the night in SD plants. Numbers inside squares indicate how many transcripts have phased at that ZT. Grey boxes indicate night-time.

A different ratio of transcripts from each family was observed in RD and SD regarding the number of transcripts from each functional group, suggesting some families were induced/repressed in response to the photoperiod. NAC, MYB and AP2/ERF were substantially induced in the ZTs 14 and 19 (middle-night) from SD plants. Six NAC transcripts, out from 12, phased at ZT14 in SD while only 2 phased at ZT16 in RD. Three and 4 MYB members phased at ZTs 14 and 19 (SD), out from 19 while only 2 phased at ZT16 (RD), out from 16. Out from 14, 2 and 10 AP2/ERF transcripts phased at ZTs 14 and 19 while just 2 phased at ZT 16, out from 8. The enrichment of these families at ZTs 14 and 19 correlate to the entire group of transcription factors. Notably 60 and 68 families, out from the 168 found in SD, phased at ZTs 14 and 19, respectively. Additionally, one RAP2.2 gene (AP2/ERF member), was strongly induced in SD plants with an amplitude of ~12500 FPKM against ~1600 in RD and shifted its phase from the middle of the day in RD to ZT 14 in SD. Such hormone-related TFs families phase deviations and enrichment by the end of the night may be involved to a cross-talk between signaling pathways via hormones, namely ethylene, ABA and auxin, and sucrose content and sensing which has 80

been suggested in young sugarcane internodes (PAPINI-TERZI et al., 2009). It has been demonstrated that sugars and auxin together to PIF proteins can control growth in response to environmental responses (SAIRANEN et al., 2012), while it was suggested that ABI4, a AP2 domain-containing TF, is involved to sugar insensitivity as long as it is also known to interact with ethylene (VAN DER MERWE; BOTHA, 2014). Indeed, AP2/ERF was reported as the most glucose-responsive TF family in Arabidopsis (PRICE et al., 2004). The long night has implied in a phasing deviation from these families of sugar-responsive TFs to well integrate the information that a short-day is coming on the next hours. Evidences from soybean drought responsive genes also demonstrated an interaction between clock and environmental cues resulting in diel oscillations (MARCOLINO-GOMES et al., 2014). Photosynthesis is under circadian control in Arabidopsis (DODD et al., 2014). In maize under L/L most of photosynthesis related genes phased at ZT4 under a clock regulation on C4 components to ensure the maximum light absorption through the day (KHAN; ROWE; HARMON, 2010). We have observed in sugarcane under diel conditions most of phasing right at the beginning of the day (ZT1) and that was also reported from sugarcane under L/L (HOTTA; NISHIYAMA JR; SOUZA, 2013). Interestingly, in SD, photosynthesis components phased at ZT1 and at the end of the day (ZT7). Further, light harvesting complexes peaked at ZT6 in accordance to circadian control (HOTTA; NISHIYAMA JR; SOUZA, 2013), however a phase shifting from the middle of the day to ZT1 was observed for many LHCs. Taken together, that suggests sugarcane also endeavor to ensure the maximum light absorption, nevertheless shifting the phase from C4 photosynthesis and LHC genes also under the influence of a shortened light period. In rice it was reported that natural light/dark cycles regulated together to the clock the photosynthesis and transpiration rates, stomatal conductance and intracellular CO2 concentration (LI et al., 2016). We have demonstrated sugarcane regulates the phasing of genes encoding to sugars metabolism and cell wall; and kinases and phosphatases related to sucrose in response to a short-day (Figure 3). Members from both groups phased similarly under RD and then changed to a different pattern under SD where a peak is present in the middle of the night. These groups are in fact very connected. A model suggested sucrose 81

phosphate synthase (SPS) is maintained inactive by SnRK1 and 14-3-3 proteins. SPS is activated after a de-phosphorylation by PP2A (DE MARIA FELIX et al., 2009). The new phase peak in the middle of the night correlates to TFs’ peak suggesting a possible interaction between sugars and TFs. Additionally, as already mentioned, a phase enrichment from sugar-related TFs was detected at such ZTs. From the group shared between Arabidopsis and sugarcane, genes for sucrose synthesis namely SPS phased around night-day transition (ZTs 23-1) while SPP phased at the end of the day (ZT13) and SUSY at ZTs 11 and 13 in RD plants. At the breakdown side, INVERTASES (INV) phased at ZT13 and HEXOKINASE at ZTs 1, 6 and 11. Both phasing profiles are in accordance with sugarcane under free running conditions, however for degradation our data have shown a tendency for phasing at ZT13, the end of the day, while in L/L genes phased from ZT8 to 16 (HOTTA; NISHIYAMA JR; SOUZA, 2013). When under SD these genes deviated their phases from the middle of the night in case of SPS and SPP (ZT14) or maintained the phasing accordingly to the proportion of the day observed for RD plants, as for SUSY whose phases were at ZTs 7 and 9. INV phased at the middle of the day and fructokinase mainly at ZT19, similarly to L/L data.

Sugarcane long non-coding RNAs and orphan genes Non-coding RNAs are of interest to study because they may influence gene- expression through the maintenance of an open chromatin configuration, forming molecular frames and scaffolds for assembly of complexes, so the machinery of transcription can access the DNA (CROSTHWAITE, 2004; CHEKANOVA, 2015). LncRNAs were shown to be responding to light in Arabidopsis (WANG et al., 2014) and also being expressed in phase to upstream exonic regions (HAZEN et al., 2009). More recently, characterization of FLORE, a new Arabidopsis lncRNA, described involvement to flowering time control through reciprocal inhibition with CDF5, a key gene in delay flowering. Authors have found FLORE-ox plants anticipate flowering and increased FT levels. Concomitantly, an early flowering phenotype was reverted to late flowering when CDF5 was driven by FLORE promoter and showed reduced FT levels (HENRIQUES et al., 2017). LncRNAs may be related to rhythmic chromatin modification and direct gene regulation (KORNFELD; BRUNING, 2014), which suggest they can collaborate to the 82

control of sugars metabolism, for an example. However, the function of lncRNAs in the circadian clock is still not clear (ROMANOWSKI; YANOVSKY, 2015). Until recently, long non-coding RNAs have not been reported in the circadian/diel rhythms context because most of such studies use to be anchored on microarrays, which generally comprise coding sequences. Fortunately, in case of sugarcane, microarray study reported evidence of the expression from 164 probes to Natural Antisense Transcripts (NATs) under circadian control (HOTTA; NISHIYAMA JR; SOUZA, 2013). NATs are a class of antisense non-protein coding RNAs fully or partially complementary to mRNas and seem important for controlling circadian genes (CROSTHWAITE, 2004). We have already shown the expression of sugarcane lncRNAs and demonstrate they have a level of conservancy with a potential to indicate the breeding program they came from (CARDOSO-SILVA et al., 2014). We now demonstrate by using RNA-seq that 1198 sugarcane lncRNAs were rhythmic expressed in response to photoperiods. Only 111 were shared between both photoperiods and 508 (7%) and 801 (8.4%) were expressed in response to RD and SD, respectively, suggesting they could be involved in regulation of gene-expression. Indeed, RNA-seq technology has been collaborating to the discovery of rhythmic lncRNAs. In Droshopila melanogaster brains’ authors reported a frequency of about 10% from the transcriptome as ncRNAs which were not reported in previous studies (HUGHES et al., 2012). In mice a set of ~1000 know and new ncRNAs distributed along the organs were described (ZHANG et al., 2014). Recently it was shown that large sets of mice lncRNAs may be regulated by circadian genes and that lncRNAs may enhance other circadian genes expression because of the cis- regulation between ncRNAs and protein-coding genes (FAN et al., 2017). Sugarcane lncRNAs were expressed at relatively lower levels than mRNAs, consistent with Arabidopsis, Populus sp. and mouse (WANG et al., 2014; TIAN et al., 2016; FAN et al., 2017). Amplitude interval from lncRNAs ranged mainly from 2.5 to 99.9 FPKM (median 18.7 and 10.8 for RD and SD) meanwhile the mRNAs comprised FPKMs from 2.5 to 2499 with the majority being expressed with 50 to 99.9 amplitude (FPKM) (median 7.3 and 5.9 for RD and SD) (Figure 6 A). We further examined conservation between other species. No lncRNA was conserved with Arabidopsis thaliana. That makes sense, as lncRNAs fixation tend to be 83

instable in the genomes and the phylogenetic distance between monocots and eudicots may play a major role in that point. The levels of conservation between lncRNAs from Populus tomentosa, rice and maize; and from Arabidopsis and rice were lower than 1% (HAUDRY et al., 2013; TIAN et al., 2016). When examining conservation with other grasses, mainly of agricultural and bioethanol interest, a level of conservation was present. Sugarcane has 202 lncRNAs conserved with sorghum, 87 with maize and 20 with Setaria (Figure 6 B), supporting the suggestion that many ncRNAs are conserved among the grasses (VICENTINI et al., 2012). Most of that conserved rhythmic lncRNAs between sugarcane and other grasses reported here are also conserved between that grasses themselves (Figura 6 C) suggesting that such RNAs may play roles on diel regulation or may be under diel regulation, in a wider scenario comprising the grasses group instead of only the sugarcane genome. Further rhythmic surveys will collaborate to identify whether those lncRNAs are also rhythmic on that species.

Figure 6 - Expression and conservation of sugarcane rhythmic lncRNAs. (A) Amplitudes from lncRNAs expression ranged mainly from 2.5 to 49.9 FPKM meanwhile amplitudes from rhythmic transcripts comprised the interval from 2.5 to 2499 FPKM at most. (B) Numbers of conserved sugarcane lncRNAs with S. bicolor, Z. mays, S. italica, T. aestivum, O. sativa, B. distachyon and A. thaliana. (C) There is also conservation between the species who share lncRNAs with sugarcane, mainly sorghum and maize (B). “Orphan” genes are named as is because they are present only in a single genome, still they can be differentially expressed, encode proteins or not, and face 84

evolutionary pressure (PRABH; RÖDELSPERGER, 2016). Orphan genes act as a normal gene, as it was demonstrated to QQS, an Arabidopsis orphan gene which collaborate to carbon and nitrogen allocation among carbohydrate and protein content, whose maintained its function active after being expressed in soybean, rice and maize (LI et al., 2015). So, understanding the expression dynamics of these genes become of interest. Recently, it was demonstrated sugarcane has about 12,000 unique transcripts probably from orphan genes (CARDOSO-SILVA et al., 2014) and we reported here 1453 and 2201 possibly orphan genes classified as rhythmic expressed under SD and RD photoperiods in sugarcane. Further studies may shed light on whether they are being expressed in response to some environmental stimuli or being carried by other expressed genes.

Gene co-expression network GCNs are powerful tools to provide insights on how plants respond to external stimulus such as desiccation (COSTA et al., 2015). Recently, GCNs have been successfully used to predict, detect and analyze diel rhythmic genes alongside functional clustering within the GCN in Daphnia pulex (RUND et al., 2016) and to compare rhythmic profiles within Arabidopsis and algae together to functional clustering enrichment (DE LOS REYES et al., 2017). In Brachypodium GCN analysis revealed key modules responding to four different stresses. A specific module comprising 35% of the genes annotated to GO “photosynthesis” was differentially expressed in stress (PRIEST et al., 2014). The present GCN is composed by diel regulated transcripts in both RD and SD organized in 130 clusters where transcripts correlate accordingly to their expression profiles. Interestingly, the lncRNAs are highly connected in the network. Using the 1176 lncRNAs as prey we found they are directly co-expressed to other 3622 transcripts, meaning ~42% of the entire GCN, and supporting the suggestion that they are involved with responses to photoperiod. The whole GCN is enriched (p < 0.05) for terms related to general metabolic pathways such as those involved to light stimulus, protein and organonitrogen metabolic processes, peptide metabolism, transcription regulation, and response to diverse compounds as reactive oxygen species, cytokinin, nitrogen. Also, chloroplast organization and pre/post-metabolism of RNA (Figure 7). Nevertheless, terms involved with stomatal movement, plastid and chloroplast organization, response to 85

radiation and light stimulus are related to photosynthesis and were enriched in sugarcane subjected to different photoperiods, similarly to Brachypodium genes under regular conditions when compared to stressed plants (PRIEST et al., 2014).

Figure 7. Significant enriched gene ontology terms (p < 0.05) for members of the co- expression network. The annotated genes present in the network were subjected to a enrichment analyze using ClueGO. A total of 1639 genes were associated to terms and the most represented are showed here. Aiming to improve the GCN analysis we subdivided it in subGCNs related to characteristics of interest, such as clock genes (clock-subGCN) or members of the 86

functional groups (functional-subGCN). Clock-subGCN did support reports that clock is strongly involved to metabolism control and photosynthesis (DODD et al., 2005) as seen by its members functions. Many lncRNAs are present (n=51) alongside transcription factors and regulators (n=46). Indeed, a number of transcripts involved to sugars and cell wall metabolism such as 2 SUSY, 2 TREHALOSE-6-PHOSPHATE SYNTHASES (TPS), 2 ALPHA-AMYLASE-LIKE and 5 ALDOLASE-TYPE TIM BARREL DOMAIN. This domain is highly conserved in Arabidopsis Fructose-1,6-biphosphate aldolase (LU et al., 2012). Two PHOSPHATASES 2C (PP2C). PP2 family members has been associated to sucrose synthesis regulation in sugarcane (DE MARIA FELIX et al., 2009), and 2 CIPK10. CIPK10 are SnRK3 family members and mediate calcium sensing (COELLO; HEY; HALFORD, 2011) meanwhile oscillations in cytosolic calcium have been reported as involved with daylength (LOVE; DODD; WEBB, 2004). Later, PHOTOSYSTEM SUBUNIT and CHLOROPHYLL BINDING PROTEINS also compose this subGCN. Enrichment analysis of the clock-subGCN supported the involvement with response to light and metabolism of chlorophyll (Supplementary Figure 3). Functional characterization of genes through transgenic evaluation is a laborious task, especially for sugarcane because of its genome complexity (MARTINS et al., 2015). We selected as bait the ten transcripts from each functional group with higher amplitude and number of connections (degree) (Table 3) and found their neighbors until the second level. We then accessed the top expressed and connected transcripts together with their correlated entities. It is worth to note that enrichment of ontology terms from members demonstrate the function-subGCN is similar to the clock-subGCN in terms of biological processes (Supplementary Figure 4). Indeed, TOC1, PRR95 and 2 PpdH1 are also within the function-subGCN. That could support the notion that the clock plays roles together to diel regulation to ensure plants adapt and extract the maximum power from the environment (LI et al., 2016).

Table 3. Genes used as bait for retrieving the function-subGCN. The top genes regarding amplitude and degree from each functional group were used as guides to obtain a network containing their neighbors.

Amplitude Amplitude Functional Gene Degree 12h12h 08h16h group (FPKM) (FPKM) ADH1 (Alcohol dehydrogenase 1) - AT1G7720 11 96.46 594.84 CYT1 (Cytokinesis defective 1) AT2G39770 4 713.84 293.42 ENO2 (Enolase 2) – AT2G36530 9 163.02 552.69 PS3 (Phosphate starvation induced gene3) – AT3G47420 10 1602.47 646.06 RPE (Ribulose-5-phosphate-3- epimerase) – AT5G61410 8 620.3 761.41 Sugars RPE (Ribulose-5-phosphate-3- metabolism epimerase) – AT5G61410 4 276.01 170.8 and cell wall SUSY3 (Sucrose synthase 3) – AT4G02280 10 277.81 469.03 UGE2 (UDP-glucose 4-epimerase 2) – AT4G23920 7 265.53 528.69 VTC5 (Vitamin C defective 5) – AT5G55120 11 1813.68 798.06 VTC5 (Vitamin C defective 5) – AT5G55120 11 1669.77 772 VTC5 (Vitamin C defective 5) – AT5G55120 10 3905.81 2638.52 RL1 (RAD-LIKE 1) – AT4G39250 10 744.46 264.82 RL1 (RAD-LIKE 1) – AT4G39250 10 595.76 107.08 88

KNAT3 (Knotted1-like homeobox gene3) – AT5G25220 8 441.53 591.61 MYB111 – AT3G46130 8 - 669.63 MYB111 – AT3G46130 6 600.16 470.19 MYB111 – AT3G46130 6 - 515.44 RAP2.2 (Related to AP2 2) – Transcription AT3G14230 11 157.44 964.36 factors and RAP2.2 (Related to AP2 2) – regulators AT3G14230 11 - 664.76 RAP2.2 (Related to AP2 2) – AT3G14230 10 621.14 4012.03 RAP2.2 (Related to AP2 2) – AT3G14230 7 1575.33 12700.22 RAP2.2 (Related to AP2 2) – AT3G14230 5 694.74 320.79 CIPK10 (CBL-interacting protein kinase 10) – AT5G58380 10 17.85 86.24 GRF7 (General regulatory factor 7) – AT3G02520 10 117.45 149.18 GRF7 (General regulatory factor 7) – Kinases and AT3G02520 7 147.26 157.99 phosphatases GRF7 (General regulatory factor 7) – involved in AT3G02520 5 1030.63 - sucrose OST1 (SNRK2.6) – AT4G33950 11 96.45 84.41 metabolism pfkB-like carbohydrate kinase family – AT4G10260 7 90.87 86.99 PP2C family – AT4G31860 11 177.34 224.41 PP2C family – AT5G24940 11 654.9 569.21 PP2C family – AT4G33500 11 145.62 66.91 PP2C family – AT4G33500 4 - 140.8 89

BCA4 (Carbonic anhydrase) – AT1G70410 11 20.57 - BCA4 (Carbonic anhydrase) – AT1G70410 11 22.87 9.94 FD3 (Ferredoxin 3) – AT2G27510 9 - 97.38 FD3 (Ferredoxin 3) – AT2G27510 7 81.7 - FNR1 (Ferredoxin-NADP(+)- Oxidoreductase 1) – AT5G66190 5 299.7 180.05 NADP-ME3 (NADP-malic enzyme 3) – Photosynthesis AT5G25880 11 82.01 - PPC3 (Phosphoenolpyruvate carboxylase 3) – AT3G14940 7 893.94 361.84 PPC3 (Phosphoenolpyruvate carboxylase 3) – AT3G14940 5 832.73 628.4 LHB1B2 – AT2G34420 11 - 8.13 LHB1B2 – AT2G34420 7 19.12 - LHB1B2 – AT2G34420 6 - 187.28 PSBO2 (Photosystem II subunit O-2) – AT3G50820 3 1289.31 762.87

We have found many other genes involved with sugars and cell wall metabolism, lncRNAs and TFs in the function-subGCN (Supplementary file 6). Apart of those ones chosen as bait, the latter group is comprised by 7 RELATED TO AP2 (RAP2.x), 3 MYB-LIKE and 4 MYB111, 3 bHLH and TFs involved to hormone signaling such as ABA REPRESSOR 1, AUXINE RESPONSE FACTOR 6 and AUXINE INDUCIBLE 2-11 alongside members of many families. Recently, several sugarcane TFs targets expected to regulate cell wall metabolism were reported after GCN analysis, mainly NAC and MYB family members (FERREIRA et al., 2016). MYB TFs from wheat, maize and Arabidopsis have been shown to be associated to control lignin biosynthesis (SONBOL et al., 2009; ZHOU et al., 2009; FORNALÉ et al., 2010; MA; WANG; ZHU, 2011) and were upregulated in a high-lignin sugarcane genotype (FERREIRA et al., 2016) 90

and in basal portions of sugarcane leaves (MATTIELLO et al., 2015). Similarly, bHLH members were upregulated in sugarcane basal leaves’ segments, as well as AUX/IAA TFs (MATTIELLO et al., 2015) and were upregulated in response to sugars (PRICE et al., 2004). The latter were suggested to be under circadian control in sugarcane under constant light (HOTTA; NISHIYAMA JR; SOUZA, 2013). On the sugars and cell wall related, 3 UDP-GLUCOSE 4-EPIMERASE 2 (UGE2), 1 SUSY3, 5 TPS and transporters, namely SUGAR TRANSPORTER 4 and MAJOR FACILITATOR family members, were most represented. Transporters are involved with the long distance sucrose transport collaborating to the source-sink mechanism and to load/unload sucrose into the vacuoles (WATT; MCCORMICK; CRAMER, 2014). It is likely those key genes are required to activate/inactivate other large networks during the diel, due to their higher amplitude and degree, for example the PS3, GRF7, PSBO2 and the transcription factors, mainly RAP2.2. PHOSPHATE STARVATION INDUCED 3 (PS3) play a role on the basal metabolism as a glycerol-3-phosphate (G3P) permease. Functional characterization from members of the family has suggested it is involved with early development in Arabidopsis collaborating on G3P reallocation to photosynthetic tissues (RAMAIAH et al., 2011). In Arabidopsis PS3 is highly expressed at dawn and we found a similar pattern from those sugarcane PS3 transcripts. That could mean PS3 contribute to the G3P supply not only in the early developmental but at every end of nights as a support for the upcoming photosynthesis. RAP2.2 is induced in the night by ethylene and intermediates an ethylene-controlled signal transduction pathway (HINZ et al., 2010). It was shown RAP2.2 induces the expression of SUSY 1 and 4 and other fermentation-related enzymes as Lactate dehydrogenase and Alcohol dehydrogenase in the dark (HINZ et al., 2010). Overall, those relationships shed light on the complex sugarcane gene networks responding to photoperiods in a diel fashion. Therefore, understanding these networks between the gene classes could collaborate with the breeding programs focusing in yield.

CONCLUSION Diel rhythmic expression is still poorly understood (LI et al., 2016), regarding its importance. In our study we reported 7,211 rhythmic transcripts under RD and 9,493 under SD. From these, we could identify diel rhythmic long ncRNAs, reinforcing suggestions that sugarcane non-coding RNAs may collaborate to regulate or are regulated by rhythms (HOTTA; NISHIYAMA JR; SOUZA, 2013). Our classification regarding functions of interest, eg. “sugars and cell wall metabolism”, “kinases and phosphatases related to sucrose”, “transcription factors and regulators” and “photosynthesis” has abled us to explore classes of genes which can be targeted in future studies aiming sugarcane improvement. Those classes were then evaluated within a co- expression network and their most correlated genes were retrieved and demonstrated they are enriched for functions similar to the clock and their corelated genes. A notably high level of lncRNAs in the networks was also present suggesting they may play roles in diel regulation. Overall our results can collaborate to sugarcane improving programs, because understanding diel regulation on expression of genes related to carbohydrate metabolism (mainly sucrose) in plants subjected to RD and SD can result in production of cultivars better adapted to the diverse field conditions which are characteristic of equatorial countries.

SUPPLEMENTARY MATERIAL O referido material suplementar pode ser encontrado no Apêndice II.

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4 DISCUSSÃO

A coordenação e sincronia dos estímulos ambientais e ritmos fisiológicos é regulada pelo relógio circadiano através da interação com luz, temperatura e açúcares (HAYDON et al., 2013b). Porém ritmos circadianos só podem ser medidos em condições experimentais de luz contínua ou ausência desta (DE MONTAIGU et al., 2015; LI et al., 2016). Os ritmos diurnos, ou diel, são então resultados da interação entre o relógio e ambiente e ocorrem em situações naturais em resposta aos ciclos de dia-noite e colaboram na sincronia dos ritmos endógenos e inputs externos (DE MONTAIGU et al., 2015), como fotoperíodos. O presente estudo investigou a influência de diferentes fotoperíodos na oscilação de carboidratos componentes de parede e expressão gênica durante o período de um dia. As amostras de folhas foram coletadas em pontos-chave de forma que as mesmas proporções do diel fossem amostradas considerando-se o tamanho – em horas – do dia e noite, sendo logo no começo da iluminação, final da iluminação e meio deste período. Em seguida, começo do escuro, pontos intermediários e final do período escuro. Para parede celular, foram avaliados pectina, hemiceluloses e celulose em colmos e folhas e discutiu-se o padrão de oscilação dessas frações bem como inter- relações no balanço de carboidratos que podem estar associadas a elas. A constituição da parede celular de cana foi objeto de estudo (DE SOUZA et al., 2013; LANDELL; SCARPARI; XAVIER, 2013) e suas estruturas macro e micro foram quantificadas. Segundo Souza et al. (2013), paredes de folhas e colmos são similares em termos de composição das hemiceluloses e pectinas. Nosso estudo demonstrou que, embora similares, suas concentrações variam durante o dia, especialmente quando em short-day (SD). As concentrações médias aqui demonstradas ficaram próximas do que foi demonstrado previamente. Nossos dados reportam ~5% pectina e ~22-25% hemicelulose em ambos tecidos e fotoperíodos; ~37% celulose em folhas e ~50% em colmos, ante ~10% pectina, hemicelulose até ~50% e celulose ~30% (DE SOUZA et al., 2013); ~50% celulose e ~24% hemicelulose (LANDELL; SCARPARI; XAVIER, 2013). 108

Ainda que o primeiro trabalho seja da década de 60, ainda há poucos estudos a respeito do controle diurno no metabolismo de parede celular e seus objetos de estudo têm sido espécies arbóreas adultas (BOBÁK; NEČESANÝ, 1967; HOSOO et al., 2002; SOLOMON; BERGER; MYBURG, 2010; MAHBOUBI et al., 2015), embora o consenso seja que em condições normais celulose é depositada no final do dia e durante noite (BOBÁK; NEČESANÝ, 1967; HOSOO et al., 2002). Em Populus sp. foi demonstrado com auxílio de 13C que a maior parte da parede é depositada durante a noite, mas que hemiceluloses não são depositadas neste período. Nossos resultados estão de acordo com essas observações para pectina em regular-day (RD) e SD e celulose em SD. Análise transcriptômica em eucalipto em fotoperíodo regular também demonstrou expressão diferencial de genes no meio da noite (SOLOMON; BERGER; MYBURG, 2010), em acordo com nossos dados para componentes em RD. Nós reportamos aqui que em plantas de cana-de-açúcar também há oscilação de componentes de parede. Todavia isso foi mais evidente para pectina e hemicelulose em colmos de plantas RD e pectina de folhas em SD, hemiceluloses de folhas e colmos SD e celulose de colmos SD. Interessantemente, celulose encontrava-se em concentrações diferentes nos colmos de SD também dentro da noite. As oscilações de açúcares solúveis foram mais evidentes que de componentes de parede, já que não são carboidratos estruturais. Neste trabalho nós também reportamos um balanceado sistema fonte-dreno nas plantas RD, como esperado, porém alterações nesta relação foram perceptíveis em plantas SD. Em RD sacarose foi sintetizada durante o dia, seu excedente transportado ao colmo e consumida na noite, de acordo com o proposto por (ROHWER; BOTHA, 2001; PATRICK; BOTHA; BIRCH, 2013). Já em SD, a sacarose presente nas folhas durante o dia encontrava-se em concentrações próximas à no colmo e sua síntese ocorreu em taxa menor que RD. Porém, durante a noite houve uma crescente síntese até o ZT19, ainda que sem fotossíntese. Sacarose é sintetizada e transportada ao colmo quando a síntese é maior que o potencial de acúmulo (LEMOINE et al., 2013). A presença de sacarose na folha e colmo pode ser observado em RD e SD, porém em SD a síntese foi menor do que em RD. A falta de um valor limiar de sacarose pode ter sido a causa do desbalanço na regulação fonte-dreno, já que a concentração da mesma se encontrava muito próxima entre folha e 109

colmo. Este novo padrão e a síntese noturna resultantes do dia curto podem ser uma resposta de cana-de-açúcar a um fotoperíodo com poucas horas de luz. Considerando- se que sacarose funciona principalmente como reserva (PAPINI-TERZI et al., 2009) e a planta otimiza seu crescimento para acumulá-la (ALLISON; PAMMENTER; HASLAM, 2007), pode-se inferir que a reprogramação metabólica em cana não segue o padrão observado em Arabidopsis. Esta, quando submetida a dias curtos, acelera a síntese de amido de dia e reduz a velocidade de consumo de noite, de forma que termine a noite em níveis mínimos à sobrevivência (GIBON et al., 2004; SCIALDONE et al., 2013). A análise transcriptômica em resposta aos fotoperíodos também foi reportada neste estudo. Foi desenvolvida uma metodologia para busca de expressão rítmica em cana de açúcar a partir de dados de RNA-seq ancorados em transcriptoma de novo e o perfil de resposta em cada condição aliado ao agrupamento de funções de interesse foram analisados. Após a combinação de três ferramentas de identificação de ritmos que nos permitiram expandir a busca por formas consideradas rítmicas além das padrões, como seno e cosseno, foram identificados 7.211 e 9.493 transcritos rítmicos em RD e SD, respectivamente. A distribuição de fases dos datasets demonstrou padrões diferentes entre RD e SD, com uma baixa correlação entre elas (R2 = 0,15). Em RD houve enriquecimento de fases ao redor da transição dia-noite, de acordo com estudos prévios (FILICHKIN et al., 2011) e o padrão de distribuição destas teve alta correlação (R2 = 0,74 e p < 0,05) com o dataset de todos transcritos, rítmicos e não rítmicos. Em SD os maiores números de fases encontraram-se dentro na noite, nos ZTs 14 e 19, sugerindo um desvio destas para dentro da noite ao invés de transição dia-noite. Interessantemente, nestes ZTs também foram detectados picos de celulose, pectina e sacarose nas folhas SD. Não houve correlação entre as fases dos transcritos rítmicos e do dataset total de transcritos expressos em SD. Uma análise de distribuição de fases dos transcritos comuns entre RD e SD seguida de enriquecimento de termos de ontologia (GOs) resultou em um panorama das alterações de fases em resposta ao fotoperíodo. Mudanças de fase implicam em nova calibração do relógio (YEANG, 2013) e podem resultar em nova sincronia de grupos de genes com o ambiente. 110

Nós demonstramos que transcritos cujas fases mudaram, mas a proporção do dia continuou a mesma, estavam envolvidos com localização celular. Isso pode ter ocorrido em reflexo da característica das células eucariotas em compartimentalizar e controlar o momento em que ocorrem processos celulares (SCOTT et al., 2005). Desta forma estes transcritos estariam relacionados à sincronização destes processos com o momento correto do dia. Da mesma forma, transcritos cujas fases não mudaram e mantiveram-se nos ZTs 1 e 23 estavam relacionados à luz, de forma a antecipar a captação da mesma e a fotossíntese, uma característica do relógio circadiano (DODD et al., 2005) permitindo que estes eventos ocorram com a máxima eficiência possível. O maior grupo conteve os transcritos que mudaram de fase e posição no dia, e este foi composto por diversas funções como resposta a estímulos químicos e temperatura, resposta à luz, reprodução e catabolismo de carboidratos. Os transcritos para genes com funções de interesse ao estudo e melhoramento de cana-de-açúcar foram classificados em grupos funcionais, de forma a facilitar sua interpretação. Uma abordagem similar foi importante para colaborar com o entendimento da dinâmica de expressão em cana em L/L (HOTTA; NISHIYAMA; SOUZA, 2013) e demonstrou que muitos fatores de transcrição oscilam em Arabidopsis, arroz e Populus sp. (FILICHKIN et al., 2011). Foram agrupados 1.740 e 2.380 transcritos rítmicos em cana e Arabidopsis em RD e SD, respectivamente. Os grupos de genes para “metabolismo de açúcares e parede” e “quinases e fosfatases envolvidas com sacarose” tiveram suas fases reguladas em resposta ao dia curto. Enquanto suas fases tinham pico ao redor dos ZTs 11 e 13 em RD, em SD estes picos ocorreram no meio da noite no ZT 14. De fato, há uma estreita relação entre estes grupos, demonstrada por exemplo na regulação da SPS por SnRK1 e proteína 14-3-3 (DE MARIA FELIX et al., 2009). Muitos fatores de transcrição e reguladores estão envolvidos com metabolismo de açúcares (PRICE et al., 2004; MORIKAMI et al., 2005; MATIOLLI et al., 2011) e nós identificamos famílias enriquecidas durante a noite em plantas SD, em sua maioria também nos ZTs 14 e 19. A família AP2/ERF continha 10 transcritos com fase em ZT14. Este enriquecimento pode estar relacionado ao cross-talk entre vias de hormônios e açúcares, que já foi sugerido em cana (PAPINI-TERZI et al., 2009; VAN DER MERWE; BOTHA, 2014) e Arabidopsis (PRICE et al., 2004; SAIRANEN et al., 2012). 111

Transcritos para genes de fotossíntese tiveram fases majoritamente no começo do dia em RD, da mesma forma que em cana em L/L (HOTTA; NISHIYAMA; SOUZA, 2013). A fotossíntese é circadianamente regulada, de forma que fases já no começo do dia asseguram a melhor absorção de luz (KHAN; ROWE; HARMON, 2010; DODD et al., 2014). Todavia, em SD houve também pico de fases no ZT 7 e complexos de captação de luz (LHC) mudaram de fase do ZT 6 (RD) para ZT 1 em SD. Isto sugere um esforço de cana-de-açúcar em realizar o máximo de fotossíntese possível, mesmo que para isso as fases de seus LHC sejam antecipadas e está de acordo com a proposição de que o diel atua junto do relógio para regular parâmetros fisiológicos como fotossíntese, transpiração, condutância estomatal e concentração de CO2 (LI et al., 2016). Ainda sim, este esforço não foi suficiente para sintetizar sacarose ao mesmo nível das plantas RD. Genes do relógio foram identificados neste trabalho e seus perfis de expressão examinados. As fases dos genes do relógio também foram comparadas às em arroz (ZHAO et al., 2012), Brachypodium (HONG et al., 2010; HUANG et al., 2017), Arabidopsis (Diurnal tool) e soja (MARCOLINO-GOMES et al., 2014) e diferenças em fase foram observadas para todos genes. Embora suas sequências sejam conservadas com Arabidopsis (MCCLUNG, 2013; CALIXTO; WAUGH; BROWN, 2015; HERNANDO; ROMANOWSKI; YANOVSKY, 2017), suas fases e números de homólogos podem variar em cana, um reflexo da poliploidia desta planta. A composição dos promotores pode resultar na diferença de fases entre os homólogos e estas podem estar envolvidas com funções (ZDEPSKI et al., 2008). A grande aneuploidia e poliploidia de cana (MENOSSI et al., 2008) também acarreta efeitos de dosagem que podem ser o motivo das diferenças em amplitude observadas entre os genes do relógio. Por outro lado, foi demonstrado que ELF3 de Setaria e Brachypodium foram capazes de restaurar a função deste gene em Arabidopsis (HENRIQUES et al., 2017). Recentemente têm sido demonstrada a existência de non-coding RNAs rítmicos (HUGHES et al., 2012; ZHANG et al., 2014; FAN et al., 2017), principalmente graças ao uso do RNA-seq nos novos estudos. Ainda sim, non-coding RNAs foram detectados oscilando em L/L em cana-de-açúcar através de análise por microarranjo (HOTTA; NISHIYAMA; SOUZA, 2013) e um grupo de lncRNAs foi recentemente descrito 112

em cana (CARDOSO-SILVA et al., 2014). Nós então buscamos por lncRNAs rítmicos e encontramos 1.198 transcritos, sendo 508 e 801 expressos em RD e SD, respectivamente. LncRNAs são importantes porque podem colaborar com a regulação da expressão gênica (CROSTHWAITE, 2004; CHEKANOVA, 2015). Muitos lncRNAs têm genes do relógio como alvo e podem estar relacionados com a manutenção da cromatina aberta (ROMANOWSKI; YANOVSKY, 2015). Recentemente, um lncRNA foi caracterizado como atuando no controle da floração em Arabidopsis através da inibição de CDF5, gene-chave deste mecanismo (HENRIQUES et al., 2017). Os níveis de expressão dos lncRNAs aqui reportados foram menores do que dos mRNAs, como observado em Arabidopsis, Populus sp. e rato (WANG et al., 2014; TIAN et al., 2016; FAN et al., 2017). Os lncRNAs também foram testados quanto à conservação e foi-se observado que há conservação entre cana, sorgo, milho e Setaria, todas gramíneas importantes para alimentação e biocombustíveis. Vinte e quatro lncRNAs também estavam conservados em ao menos mais de uma destas espécies. lncRNAs enfrentam taxas evolutivas rápidas o que implica em pequena conservação entre espécies distantes filogeneticamente. Isto explica a diminuição do número de lncRNAs conservados entre cana e outras gramíneas, conforme a distância aumenta, e a não conservação entre cana e Arabidopsis observada. O nível de conservação de lncRNAs entre Populus tomentosa, arroz e milho; e entre Arabidopsis e arroz é menor que 1% (HAUDRY et al., 2013; TIAN et al., 2016). A presença de lncRNAs em cana pode significar uma colaboração no controle dos ritmos diel ou facilitação na expressão de genes rítmicos. É possível que lncRNAs conservados atuem de forma semelhante em espécies diferentes que são importantes para a agricultura, o que torna futuros estudos importantes para o melhor entendimento desta classe de RNAs. Finalmente, nós organizamos os transcritos rítmicos em uma rede de co- expressão. O enriquecimento de GOs para processos metabólicos desta rede demonstrou uma composição abrangente de funções, mas com destaque para termos relacionados a “light stimulus”, “transcription regulation” e “pre/post-metabolism of RNA”. Foi também interessante a grande presença de lncRNAs na rede. Os 1.176 lncRNAs se conectam diretamente a 3.622 transcritos, abrangendo ~42% de toda a rede. Isto reforça 113

a sugestão de que lncRNAs estão envolvidos com regulação da expressão rítmica em resposta ao fotoperíodo. Em seguida, duas sub-redes foram criadas: na primeira, chamada de clock-subGCN, foram usados como isca os genes do relógio e na segunda, a function-subGCN, os 10 genes com maiores amplitude e número de conexões (degree) em cada grupo funcional foram as iscas. O enriquecimento destas redes mostrou que suas funções são similares, com termos relacionados a respostas à estímulos de luz e fotossíntese. Isto suporta a sugestão de que relógio e diel atuam conjuntamente para permitir que as plantas aproveitem ao máximo os recursos do ambiente (LI et al., 2016). A function-subGCN também é composta por diversos outros genes envolvidos com metabolismo de açúcares, TFs e lncRNAs. Recentemente MYB TFs classificados como reguladores de parede celular foram encontrados co-expressos em cana (FERREIRA et al., 2016) e 3 Myb-like e 4 MYB111 foram detectados nessa rede, além dos selecionados como isca. Também transcritos para UDP-GLICOSE EPIMERASE 2, SUSY 3, TREALOSE FOSFATO SINTASE e transportadores de carboidratos estavam bem representados. Os transportadores estão envolvidos com o transporte de sacarose em longa distância colaborando com o sistema fonte-dreno (WATT; MCCORMICK; CRAMER, 2014).

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5 CONCLUSÃO

Ainda há muito a se explorar quanto aos ritmos diel, mas também muito a se explorar quanto à expressão rítmica em cana-de-açúcar, ainda que esta seja uma das mais importantes culturas do Brasil. Todavia, com o presente estudo pode-se concluir: • As concentrações de pectina, hemicelulose e celulose oscilaram significativamente dentro de 24h quando as plantas são sujeitas ao dia curto; • Os intervalos de concentração de pectina, hemicelulose e celulose não foram influenciados pelo fotoperíodo, permanecendo os mesmos nas plantas RD e SD. • As concentrações de açúcares totais, sacarose e açúcares redutores foram menores nas plantas em dia curto; • O dia curto implicou em desbalanço do sistema fonte-dreno e em síntese noturna de sacarose; • O dia curto implicou em enriquecimento de fases de transcritos rítmicos durante a noite, ao invés de próximos ao ZT 12, como em condições normais; • A distribuição de fases dos grupos funcionais mudou de acordo com o fotoperíodo; • Transcritos de genes relacionados com metabolismo de açúcares e fatores de transcrição tiveram distribuições de fases similares em RD e SD. • Os picos de fases destes grupos em SD ocorreram nos ZTs 14 e 19, similares aos de pectina e celulose em folha e colmo em SD e sacarose em folha SD; • Existem diversos homólogos para genes do relógio em cana e eles apresentaram diferentes fases e amplitudes. 115

• Long non-coding RNAs são expressos de forma rítmica em cana e são conservados com outras espécies de interesse econômico. Há também um grupo de possíveis genes órfãos rítmicos de cana; • A rede de co-expressão colaborou para inferir correlações funcionais entre os transcritos rítmicos aqui detectados. A divisão em sub-redes demonstrou que genes do relógio e membros dos grupos funcionais estão conectados a grupos de genes com funções similares;

116

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7 APÊNDICES

7.1 APÊNDICE I

Material suplementar referente ao Manuscrito 1.

Supplementary figure 1. Schematic overview of the time points accordingly to each photoperiod.

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Supplementary Table 1: Physiological measurements from plants under 12h/12h and 08h/16h light/dark regimes. Twenty-seven random plants from each population were chosen for accessing +1 leaves length and width; and total plant sizes in days 0 and 30.

Day 0 12h/12h light/dark 08h/16h light/dark +1 leaf Plant +1 leaf Plant size size Plant id Length (cm) Width (cm) (cm) Plant id Length (cm) Width (cm) (cm) 1 33.0 1.0 41.2 1 40.1 1.0 70.2 3 21.3 0.9 46.0 2 47.0 1.2 58.5 4 16.9 1.2 38.5 3 23.0 1.1 32.0 5 27.2 1.0 34.3 5 17.1 1.4 39.2 6 30.2 0.6 40.0 6 39.5 1.2 58.5 7 26.7 1.1 45.6 7 34.0 0.8 47.0 8 40.1 1.0 53.5 8 18.5 1.0 33.0 9 50.3 1.2 62.5 9 51.0 1.4 64.0 10 43.8 1.2 57.5 10 41.0 1.1 63.0 12 46.0 1.3 67.0 12 52.0 1.7 64.0 13 37.2 1.0 64.0 13 57.0 1.4 97.0 14 29.8 1.1 39.0 14 29.5 1.4 69.8 15 57.3 1.3 81.9 15 40.0 1.3 59.5 16 43.6 0.6 56.0 16 32.8 1.4 49.5 17 38.2 0.7 60.4 17 17.1 0.8 33.5 18 64.0 1.5 80.2 18 22.8 1.1 38.9 20 54.5 1.3 67.9 19 48.5 1.4 75.0 21 24.0 1.2 33.0 20 38.9 1.2 72.6 22 42.2 1.1 65.4 21 36.1 1.2 51.2 23 20.5 0.9 32.4 23 54.2 1.3 70.0 24 18.9 0.9 29.3 24 30.2 0.9 44.2 25 44.2 1.4 44.2 25 29.8 1.4 43.9 26 53.0 1.4 77.2 26 26.9 0.9 55.4 27 32.1 1.4 47.8 27 44.8 1.2 54.8 28 43.1 1.0 55.0 28 60.2 1.2 78.0 29 51.2 1.1 68.1 29 51.0 1.0 65.7 30 31.8 1.0 55.1 30 29.2 0.9 29.2 Average 37.8 1.1 53.4 Average 37.5 1.2 56.2

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Supplementary Table 1 cont.:

Day 30 12h/12h light/dark 08h/16h light/dark +1 leaf Plant +1 leaf Plant size size Plant id Length (cm) Width (cm) (cm) Plant id Length (cm) Width (cm) (cm) 1 33.1 0.9 49.6 1 39.8 1.0 110.4 3 21.0 0.9 79.4 2 46.3 1.3 102.0 4 17.1 1.2 49.0 3 23.1 1.1 51.4 5 27.5 1.0 41.9 5 16.9 1.1 49.5 6 30.8 0.6 75.0 6 39.2 1.2 82.1 7 27.9 1.1 49.1 7 34.0 0.8 83.2 8 41.2 1.0 77.5 8 50.0 1.4 125.0 9 50.4 1.2 109.6 9 51.2 1.3 106.0 10 44.1 1.1 103.5 10 41.6 1.1 101.0 12 46.1 1.2 104.2 12 52.1 1.8 104.2 13 38.9 1.1 105.6 13 57.2 1.4 134.0 14 29.8 1.1 67.4 14 40.0 1.5 116.7 15 57.6 1.2 121.0 15 39.0 1.3 87.0 16 44.2 0.9 91.2 16 33.9 1.4 70.2 17 38.8 0.9 98.0 17 17.8 0.8 38.9 18 63.9 1.5 121.0 18 22.0 1.1 60.0 20 54.6 1.3 105.5 19 48.1 1.4 114.0 21 26.3 1.2 47.0 20 39.1 1.2 114.6 22 42.5 1.1 116.6 21 35.6 1.2 90.2 23 20.6 0.9 57.1 23 54.2 1.4 126.0 24 18.6 0.9 48.5 24 30.1 1.0 67.6 25 44.5 1.4 100.3 25 30.0 1.3 71.6 26 53.0 1.3 111.0 26 26.5 1.0 84.2 27 31.8 1.4 74.2 27 45.1 1.2 97.0 28 43.2 1.0 92.9 28 60.5 1.2 128.0 29 50.7 1.1 106.8 29 45.0 1.1 87.5 30 32.5 1.1 93.0 30 29.1 0.9 73.1 Average 38.2 1.1 85.0 Average 38.8 1.2 91.7

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Supplementary Table 2: ANOVA tables from comparisons between +1 leaf size, width and plant size in days 0 and 30.

+1 leaf length day 0 Anova: Single Factor SUMMARY Groups Count Sum Average Variance 12h/12h 27 1021.1 37.8185 159.553 08h/16h 27 1012.2 37.4889 156.105 ANOVA Source of Variation SS df MS F P-value F crit Between Groups 1.46685 1 1.46685 0.00929 0.92357 4.02663 Within Groups 8207.11 52 157.829

Total 8208.57 53 +1 leaf width day 0 Anova: Single Factor SUMMARY Groups Count Sum Average Variance 12h/12h 27 29.4 1.08889 0.05487 08h/16h 27 31.9 1.18148 0.04926 ANOVA Source of Variation SS df MS F P-value F crit Between Groups 0.11574 1 0.11574 2.22298 0.14201 4.02663 Within Groups 2.70741 52 0.05207

Total 2.82315 53 Total plant size day 0 Anova: Single Factor SUMMARY Groups Count Sum Average Variance 12h/12h 27 1443 53.4444 227.597 08h/16h 27 1517.6 56.2074 270.085 ANOVA Source of Variation SS df MS F P-value F crit Between Groups 103.059 1 103.059 0.41415 0.5227 4.02663 Within Groups 12939.7 52 248.841

Total 13042.8 53 129

Supplementary Table 2 cont.:

+1 leaf length day 30 Anova: Single Factor SUMMARY Groups Count Sum Average Variance 12h/12h 27 1030.7 38.1741 156.85 08h/16h 27 1047.4 38.7926 139.972 ANOVA Source of Variation SS df MS F P-value F crit Between Groups 5.16463 1 5.16463 0.0348 0.85274 4.02663 Within Groups 7717.39 52 148.411

Total 7722.56 53 +1 leaf width day 30 Anova: Single Factor SUMMARY Groups Count Sum Average Variance 12h/12h 27 29.6 1.0963 0.03806 08h/16h 27 32.5 1.2037 0.04883 ANOVA Source of Variation SS df MS F P-value F crit Between Groups 0.15574 1 0.15574 3.58459 0.06389 4.02663 Within Groups 2.25926 52 0.04345

Total 2.415 53 Total plant size day 30 Anova: Single Factor SUMMARY Groups Count Sum Average Variance 12h/12h 27 2295.9 85.0333 666.716 08h/16h 27 2475.4 91.6815 662.442 ANOVA Source of Variation SS df MS F P-value F crit Between Groups 596.671 1 596.671 0.89782 0.34775 4.02663 Within Groups 34558.1 52 664.579

Total 35154.8 53 130

Supplementary Table 3: Dry mass measurements made at days 0 and 30. We defined intervals regarding plant size and choose one plant from each of those intervals to harvest and measure their organs dry masses.

12h/12h light/dark 08h/16h light/dark Dry mass Dry mass Leaves Culm Roots Total Leaves Culm Roots Total Plant Plant (g) (g) (g) (g) (g) (g) (g) (g) Day 0 Day 0 1 0.58 0.73 0.42 1.73 1 0.35 0.51 0.25 1.11 2 0.18 0.24 0.25 0.67 2 0.84 0.97 0.71 2.52 3 0.84 1.17 1.14 3.15 3 0.10 0.25 0.22 0.57 Day 30 Day 30 1 0.40 0.21 0.07 0.68 1 0.32 0.57 0.25 1.14 2 1.10 1.16 0.78 3.04 2 1.04 0.98 0.69 2.70 3 1.52 1.46 0.86 3.84 3 1.33 1.02 0.44 2.79

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Supplementary Table 4: ANOVA tables for data from dry mass measurements made at days 0 and 30.

Leaves day 0 Anova: Single Factor SUMMARY Groups Count Sum Average Variance Leaves 12h/12h 3 1.6 0.532 0.109459 Leaves 08h/16h 3 1.3 0.43166667 0.14227233 ANOVA Source of Variation SS df MS F P-value F crit Between Groups 0.01510017 1 0.01510017 0.1199705 0.74652 7.70865 Within Groups 0.50346267 4 0.12586567

Total 0.51856283 5 Culms day 0 Anova: Single Factor SUMMARY Groups Count Sum Average Variance Culms 12h/12h 3 2.15 0.71533333 0.21643333 Culms 08h/16h 3 1.73 0.577 0.132303 ANOVA Source of Variation SS df MS F P-value F crit Between Groups 0.02870417 1 0.02870417 0.16461816 0.70571 7.70865 Within Groups 0.69747267 4 0.17436817

Total 0.72617683 5 Roots day 0 Anova: Single Factor SUMMARY Groups Count Sum Average Variance Roots 12h/12h 3 1.81 0.60366667 0.22504933 Roots 08h/16h 3 1.18 0.39166667 0.07584933 ANOVA Source of Variation SS df MS F P-value F crit Between Groups 0.067416 1 0.067416 0.4480977 0.5399 7.70865 Within Groups 0.60179733 4 0.15044933

Total 0.66921333 5 132

Supplementary Table 4 cont.:

Total dry weight day 0 Anova: Single Factor Total day 0 SUMMARY Groups Count Sum Average Variance total 12h 3 5.55 1.851 1.545921 total 08h 3 4.2 1.40033333 1.01710933 ANOVA Source of Variation SS df MS F P-value F crit Between Groups 0.30465067 1 0.30465067 0.23772693 0.65137 7.70865 Within Groups 5.12606067 4 1.28151517

Total 5.43071133 5 Leaves day 30 Anova: Single Factor SUMMARY Groups Count Sum Average Variance Leaves 12h/12h 3 3.02 1.00633333 0.31850233 Leaves 08h/16h 3 2.69 0.89633333 0.27218633 ANOVA Source of Variation SS df MS F P-value F crit Between Groups 0.01815 1 0.01815 0.06145369 0.81642 7.70865 Within Groups 1.18137733 4 0.29534433

Total 1.19952733 5

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Supplementary Table 5: Cell wall components measurements accordingly to each ZT and photoperiod. Values are mean ± SD, n=3.

Cell wall components (mg/g Dry mass-1) Pectin Hemicelluloses Celluloses Photoperiod Timepoint Leaf Culm Leaf Culm Leaf Culm ZT1 4.94 ± 0.14 4.84 ± 1.10 269.40 ± 24.88 251.76 ± 16.16 394.06 ± 17.32 456.56 ± 24.68 ZT6 4.66 ± 0.75 5.06 ± 1.72 207.97 ± 51.93 252.11 ± 4.54 398.06 ± 76.66 522.45 ± 41.87 ZT11 5.48 ± 1.20 3.91 ± 0.35 211.72 ± 51.50 244.89 ± 9.78 373.58 ± 73.90 522.42 ± 26.84 12h12h ZT13 5.67 ± 0.56 4.21 ± 0.36 255.43 ± 46.63 259.75 ± 10.33 343.83 ± 44.67 499.35 ± 37.74 ZT16 4.71 ± 0.24 5.45 ± 0.49 261.87 ± 5.75 284.19 ± 33.25 370.45 ± 27.53 575.92 ± 41.23 ZT20 4.53 ± 0.91 3.67 ± 0.38 222.28 ± 54.89 218.93 ± 22.15 382.21 ± 105.89 508.79 ± 9.62 ZT23 4.01 ± 0.70 3.39 ± 0.54 197.58 ± 47.08 235.71 ± 15.88 343.93 ± 110.34 528.39 ± 53.3 ZT1 4.04 ± 0.20 4.32 ± 0.80 260.37 ± 25.66 235.91 ± 29.00 418.19 ± 33.60 444.20 ± 56.18 ZT4 5.86 ± 0.52 4.98 ± 0.74 191.01 ± 30.11 220.98 ± 12.79 335.48 ± 68.40 438.15 ± 20.54 ZT7 5.17 ± 1.04 4.57 ± 0.39 273.86 ± 16.01 266.36 ± 14.51 407.26 ± 50.9 512.73 ± 49.33 08h16h ZT9 4.02 ± 0.29 5.10 ± 0.46 211.67 ± 22.12 271.94 ± 13.46 320.33 ± 23.77 563.54 ± 35.24 ZT14 4.15 ± 1.30 6.07 ± 0.64 178.18 ± 24.04 261.47 ± 21.88 311.22 ± 82.82 563.09 ± 62.97 ZT19 5.91 ± 0.44 5.01 ± 0.05 244.44 ± 33.80 232.54 ± 5.41 454.04 ± 55.90 416.85 ± 36.67 ZT23 5.20 ± 1.18 4.51 ± 0.22 217.71 ± 23.43 289.81 ± 7.54 351.19 ± 79.05 566.40 ± 33.37

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Supplementary Table 6: Soluble sugars measurements accordingly to each ZT and photoperiod. Values are mean ± SD, n=3.

Soluble sugars (mg/g Dry mass-1) Total sugars Sucrose Reducing sugars Photoperiod Timepoint Leaf Culm Leaf Culm Leaf Culm ZT1 72.51 ± 14.64 100.84 ± 33.42 55.83 ± 4.83 67.65 ± 14.25 9.30 ± 1.48 10.88 ± 2.14 ZT6 99.83 ± 13.69 113.96 ± 14.41 73.79 ± 16.03 82.45 ± 5.42 9.39 ± 3.42 15.23 ± 2.51 ZT11 101.76 ± 9.98 108.51 ± 33.07 97.45 ± 6.10 80.01 ± 14.16 8.35 ± 0.48 11.56 ± 1.48 12h12h ZT13 102.50 ± 13.68 170.12 ± 13.55 80.07 ± 8.46 124.27 ± 5.62 10.25 ± 1.22 11.57 ± 0.93 ZT16 80.64 ± 1.87 157.16 ± 8.99 72.41 ± 2.19 88.87 ± 1.69 8.71 ± 1.56 10.87 ± 0.45 ZT20 77.56 ± 1.38 186.19 ± 8.79 73.54 ± 3.29 103.34 ± 6.34 8.92 ± 0.34 18.13 ± 0.62 ZT23 65.22 ± 4.27 113.35 ± 15.45 70.24 ± 8.18 61.27 ± 19.39 9.90 ± 2.40 8.47 ± 1.79 ZT1 69.35 ± 4.71 66.84 ± 13.17 68.66 ± 3.89 43.23 ± 4.00 8.76 ± 1.89 5.54 ± 0.31 ZT4 61.55 ± 2.67 75.62 ± 9.72 69.91 ± 5.21 46.61 ± 2.51 8.49 ± 0.37 7.47 ± 0.76 ZT7 82.30 ± 8.33 76.59 ± 8.48 71.56 ± 5.27 53.09 ± 5.79 11.52 ± 1.13 8.55 ± 2.16 08h16h ZT9 76.46 ± 4.54 97.82 ± 4.58 62.32 ± 3.06 67.50 ± 2.67 12.04 ± 0.98 11.84 ± 2.39 ZT14 88.56 ± 6.12 80.26 ± 11.99 79.65 ± 9.61 66.16 ± 12.98 9.89 ± 0.74 9.83 ± 1.67 ZT19 85.19 ± 3.99 103.96 ± 1.52 90.11 ± 7.11 78.00 ± 3.99 11.35 ± 1.99 13.05 ± 0.95 ZT23 57.19 ± 2.81 68.11 ± 4.13 64.45 ± 2.26 51.42 ± 3.85 9.23 ± 0.28 14.21 ± 2.64

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Supplementary Table 7: Sugars quantification data from sugarcane leaves and culms in mg/g*. Data shown stands for untreated samples only.

Sugar (mg/g) Organ Additional information Reference Sucrose Reducing sugars** 11.70 6.10 Young leaves Batta and Singh, 1986 28.70 19.10 3-month-old; mg/g FW*** Batta et al., 1995 10.27 1.35 4-month-old; 27°C; mg/g FW Ebrahim et al., 1998 6.20 4.00 3-month-old; mg/g FW Sachdeva et al., 2003 Leaf 13.69 2.79 +3 leaf; 9-12-month-old; field; mg/g FW McCormick et al., 2006 14.02 0.09 3-month-old; mg/g FW Mattiello et al., 2015 33.40 5.9 9-month-old; +1 leaf; field Ferreira et al., 2016 50.00 110.00 32 days old; Cunha et al., 2017 29.20 50.40 Young; mid culm Batta and Singh, 1986 134.00 26.00 6-month-old; mid culm; mg/g FW Batta et al., 1995 102.69 n/a 9-month-old; range from 8.6 - 102.7 Zhu et al., 1997 85.75 10.81 4-month-old; mid culm; 27°C; mg/g FW Ebrahim et al., 1998 400.00 n/a adult; field; mid culm Botha and Black, 2000 25.00 22.00 3-month-old; mid culm; mg/g FW Sachdeva et al., 2003 Culm 17.11 20.71 9-12-month-old; internode 6; field; mg/g FW McCormick et al., 2006 177.99 14.41 16-month-old; internode 7 Wu and Birch, 2007 150.00 20.00 7.5-month-old; upper culm; mg/g FW Batta et al., 2008 38.00 n/a 10-month-old; young internodes; hot chamber Imman-Bamber et al., 2010 34.50 147.9 9-month-old; mid internodes; field Ferreira et al., 2016 190.00 210.00 32 days old; upper culm Cunha et al., 2017 *data presented in µmol/g was converted to mg/g. **data presented as glucose and fructose in the literature are shown as reducing sugars in a joined column. ***FW stands for fresh weight. 136

7.2 APÊNDICE II

Material suplementar referente ao Manuscrito 2.

Supplementary figure 1 – Workflow for data collection and bioinformatic analyses. 137

Supplementary figure 2 – Enrichment analyses from ZTs in the middle of the night demonstrate many terms involved to negative regulation of diverse metabolic processes were significantly enriched (FDR < 0.05) only in ZT19 from SD plants. Blank squares represent none or non-significant enrichment.

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Supplementary figure 3 – A sub-network created with clock genes as bait (A) and further enrichment analyse of the present GO terms using the ClueGo plugin (B). Colors in A represent members of the functional groups, namely sugars metabolism and cell wall (yellow); transcription factors and regulators (pink); photosynthesis (red); kinases and phosphatases involved in sucrose metabolism (light-blue). Clock genes (dark blue) and ncRNAs (gray) are also colored.

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Supplementary figure 4 – A sub-network created with the higher amplitude and most connected genes within the functional groups (A) and further enrichment analyse of the present GO terms using the ClueGo plugin (B). Colors in A represent members of the functional groups, namely sugars metabolism and cell wall (yellow); transcription factors and regulators (pink); photosynthesis (red); kinases and phosphatases involved in sucrose metabolism (light-blue). Clock genes (dark blue) and ncRNAs (gray) are also colored. 140

8 ANEXOS

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