A equipe de pesquisa identificou ORF3d, um novo gene de sobreposia§a£o no SARS-CoV-2 que tem o potencial de codificar uma proteana que émais longa do que o esperado apenas pelo acaso.
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)
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Os pesquisadores descobriram um novo gene "oculto" no SARS-CoV-2 - o varus que causa o COVID-19 - que pode ter contribuado para sua biologia única e potencial pandaªmico. Em um varus que tem apenas cerca de 15 genes no total, saber mais sobre esse e outros genes sobrepostos - ou "genes dentro dos genes" - pode ter um impacto significativo em como combatemos o varus. O novo gene édescrito hoje na revista eLife .
"A sobreposição de genes pode ser uma das maneiras pelas quais os coronavarus evoluaram para se replicar com eficiência, impedir a imunidade do hospedeiro ou se transmitirem", disse o autor Chase Nelson, pesquisador de pa³s-doutorado da Academia Sinica em Taiwan e cientista visitante no Museu americano de história natural. "Saber que genes sobrepostos existem e como eles funcionam pode revelar novos caminhos para o controle do coronavarus , por exemplo, por meio de drogas antivirais."
A equipe de pesquisa identificou ORF3d, um novo gene de sobreposição no SARS-CoV-2 que tem o potencial de codificar uma proteana que émais longa do que o esperado apenas pelo acaso. Eles descobriram que esse gene também estãopresente em um coronavarus pangolim previamente descoberto, talvez refletindo perdas ou ganhos repetidos desse gene durante a evolução do SARS-CoV-2 e varus relacionados. Além disso, o ORF3d foi identificado de forma independente e demonstrou elicitar uma forte resposta de anticorpos em pacientes com COVID-19, demonstrando que a proteana do novo gene éproduzida durante a infecção humana.
"Ainda não sabemos sua função ou se hásignificado clanico", disse Nelson. "Mas prevemos que érelativamente improva¡vel que esse gene seja detectado por uma resposta de células T, em contraste com a resposta de anticorpos. E talvez isso tenha algo a ver com como o gene foi capaz de surgir."
Aprimeira vista, os genes podem parecer linguagem escrita no sentido de que são feitos de cadeias de letras (nos varus de RNA, os nucleotadeos A, U, G e C) que transmitem informações. Mas, embora as unidades da linguagem (palavras) sejam discretas e não se sobreponham, os genes podem ser sobrepostos e multifuncionais, com informações criptografadas, dependendo de onde vocêcomea§a a "ler". Genes sobrepostos são difaceis de detectar, e a maioria dos programas de computador cientaficos não foi projetada para localiza¡-los. No entanto, eles são comuns em varus. Isso ocorre em parte porque os varus de RNA tem uma alta taxa de mutação, então eles tendem a manter sua contagem de genes baixa para prevenir um grande número de mutações. Como resultado, os varus desenvolveram uma espanãcie de sistema de compressão de dados em que uma letra em seu genoma pode contribuir para dois ou atétrês genes diferentes.
"A falta de genes sobrepostos nos coloca em risco de ignorar aspectos importantes da biologia viral", disse Nelson. "Em termos de tamanho do genoma, o SARS-CoV-2 e seus parentes estãoentre os varus de RNA mais longos que existem. Portanto, eles são talvez mais propensos a 'truques gena´micos' do que outros varus de RNA."
Antes da pandemia, enquanto trabalhava no Museu como Gerstner Scholar em Bioinforma¡tica e Biologia Computacional, Nelson desenvolveu um programa de computador que rastreia genomas em busca de padraµes de mudança genanãtica que são exclusivos de genes sobrepostos. Para este estudo, Nelson juntou-se a colegas de instituições como a Technical University of Munich e a University of California, Berkeley, para aplicar este software e outros manãtodos a riqueza de novos dados de sequaªncia disponíveis para SARS-CoV-2. O grupo tem esperana§a de que outros cientistas investiguem o gene que descobriram no laboratório para definir sua função e possivelmente determinar qual papel ele pode ter desempenhado no surgimento do varus pandaªmico.