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A batalha da coevolução patógeno- hospedeiro: COVID-19 vs Humanos

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A batalha da coevolução patógeno- hospedeiro: COVID-19 vs Humanos

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Neste Webinar da Rockefeller University  experts virologistas de peso Paul Bieniasz e President Richard P. Lifton abordaram questões críticas sobre as variantes do SARS-CoV-2 e a eficácia das vacinas anti-COVID-19. Ouvi-los me deixou a princípio, ainda mais preocupada com estas questões, confirmando meus temores de que à semelhança da resistência bacteriana aos antimicrobianos, as populações de escape (mutadas) prevalecerão frente à pressão seletiva dos anticorpos.

Mas, ao final, um resultado surpreendente reacende a esperança de que, graças às maravilhas e à sofisticação da memória imunológica de nosso sistema imune - que busca melhorar sempre e maturar/refinar suas respostas, o homem parece que consegue neutralizar ou subjugar a capacidade do vírus em sofrer mutações induzidas pela pressão seletiva dos anticorpos. Na resposta humoral, a capacidade neutralizante do plasma hiperimune dos convalescentes ou vacinados -- meses após a imunização,  sofre pouco impacto (perde-se pouco) na sua potência de neutralização viral, mesmo contra as variantes virais de SARS-CoV-2 mais temidas hoje (B1.1.7 UK; B.1.351 África do Sul; P1 Brasil).

Mais um exemplo das batalhas travadas na coevolução: patógeno e hospedeiro medindo forças e uma luz no fim do túnel para nós, humanos! Viva a VIDA! Viva a Ciência!

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Tarefa B7 - Aula invertida no ensino da Imunologia Básica

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Tarefa B7 - Aula invertida no ensino da Imunologia Básica

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Achei um relato interessante de aula invertida sobre um dos temas da Imunologia Básica considerado pelos alunos como um dos mais chatos e complexos, que é a aula sobre Sistema Complemento. Trata-se de uma família de proteínas plasmáticas que circulam na forma de pró-enzimas e que são ativadas em forma de cascata, na presença de infecção, por vezes gerando fragmentos protéicos com novas atividades enzimáticas ou efetoras. É justamente a sequência de reações de ativação (a cascata) que os alunos acham o mais maçante e difícil. Nessa aula, eu já fazia uso de imagens de quadrinhos que mostravam essa sequência de eventos, com as diferentes proteínas do Complemento representadas como formas geométricas de tamanhos e cores diferentes, que íam se interconectando na sequência dos quadrinhos. Ao final, mostrava-lhes também uma animação, proveniente do CD-room do livro didático ou do acesso online das edições mais recentes desse livro-texto base da disciplina. Quando eu era profa na UNISUL, eu também incluía uma aula prática para se verificar a atividade hemolítica do Complemento (CH50), mas esta ficou inviável na UFAL por causa da necessidade de compra de um mesmo reagente relativamente barato a cada semestre, visto que o sangue de carneiro é perecível em 1 mês, e o sistema de compras da universidade não permite essa celeridade. Mas isto é uma outra história... Voltando ao foco da Metodologia Ativa, na experiência relatada nesse artigo, de Santos, Oliveira e Spalding (2018; disponível em http://intertemas.toledoprudente.edu.br/index.php/ETIC/article/view/7051), eles inovaram ao trazer para a aula também o estímulo tátil e interativo (além do visual) e em fazerem os alunos estudarem previamente sobre o assunto. Fizeram o seguinte: Com o objetivo de facilitar a assimilação do conteúdo por promoção de uma visualização mais concreta das três diferentes vias de ativação da cascata, solicitaram que os alunos pesquisassem previamente sobre o sistema e então dividiram-nos em grupos com no máximo 5 integrantes e blocos de montar foram distribuídos para que cada grupo montasse estruturas que representassem os componentes do Sistema Complemento. Usaram etiquetas para que os blocos fossem usados (ou não) nas simulações de reações das diferentes vias de ativação (clássica, alternativa, das lectinas e comum), enquanto discutiam sobre a sequência de eventos com os colegas. A seguir, os alunos deveriam anotar (em mapa conceitual ou resumo) a sequência de reações concluída pelo grupo. Por fim foi feita uma apresentação para os outros grupos com cada sequência desenvolvida fizesse a sua exposição e mostrasse os blocos interconectados, possibilitando mais discussões sobre o tema.

Embora essa abordagem ainda necessite do encontro presencial (portanto não usaria tecnologia digital nem seria possível numa aula online) ela traz uma estratégia ativa de aprendizagem, com a dinâmica da sala de aula invertida, para um dos temas considerado por alguns, dos mais chatos no escopo da disciplina.

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Tarefa B18 - Criando rubrica de avaliação de meu projeto inovador

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Tarefa B18 - Criando rubrica de avaliação de meu projeto inovador

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Chegando na reta final do curso de TDMA, construí uma rubrica para avaliação de meu projeto inovador - uma proposta de Peer instruction para aula sobre Hepatites Virais, na disciplina de VIROLOGIA.
A rubrica - que está disponível em um arquivo Google Docs - servirá para avaliar 1) a participação dos alunos na etapa de estudo prévio do assunto com uso pelos mesmos da ferramenta Milanote e 2) posterior apresentação aos colegas.
Não deixem de visitar minha atividade e comentar ;o)

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B18 - Rubrica de avaliação da atividade Peer instruction sobre Hepatites virais

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Criando site do lab de Virologia usando o Weebly

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Criando site do lab de Virologia usando o Weebly

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Hoje por dica de um colega do curso de TDMA descobri uma ferramenta que me permitiu criar o site do LAPEVI, algo que fazia tempo eu almejava fazer, e agora com o apelo que a Virologia tomou devido á pandemia de Covid, achei que era a hora certa.
Ainda está em construção, mas acho que já ficou bem legal!
Visitem lá e me deem um feedback!

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FOTOS: Algumas das equipes do LAPEVI: A Profa Dra Alessandra Abel Borges, nossa técnica de laboratório e professora Ana Rachel Vasconcelos de Lima e os alunos de 2015. Na sala de cultivo de células, duas das alunas de PIBIC de 2019, Thayna e Maria Luísa, trabalhando nas cabines de fluxo laminar e outros alunos da disciplina de Virologia do PPGCS assistindo uma prática. Na última foto, a equipe original fundadora do LAPEVI, Prof Dr. Cliton Houly, Profa Dra Laura Maria de Vaconcelos e Prof. Dr. Herman Werchau, em 1981.
Fonte: Primeira e segunda fotos, acervo pessoal profa Alessandra; terceira foto: gentilmente cedida pelo Dr. Cliton.

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FOTOS: Algumas das equipes do LAPEVI: A Profa Dra Alessandra Abel Borges, nossa técnica de laboratório e professora Ana Rachel Vasconcelos de Lima e os alunos de 2015. Na sala de cultivo de células, duas das alunas de PIBIC de 2019, Thayna e Maria Luísa, trabalhando nas cabines de fluxo laminar e outros alunos da disciplina de Virologia do PPGCS assistindo uma prática.

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Tarefa B15 - Galeria de Projetos Inovadores

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Tarefa B15 - Galeria de Projetos Inovadores

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Para esta tarefa, revisitei minha atividade B8, tentando criar um projeto inovador na metodologia de ensino e aprendizagem do tema Hepatites Virais na disciplina de Virologia.

https://docs.google.com/presentation/d/19PlX-S_Oyq4YaaXAc6Qou8yzrz7FIt4jD-eFJTDaWV4/edit?usp=sharing

Tentei associar o uso das ferramentas que explorei durante o curso tais como Moodle, Prezi, Milanote, Padlet, Quizzis e youtube para diversificar a aprendizagem sobre o tema. Na teoria parece que ficou legal, o desafio agora é colocar o projeto na prática com uma turma de alunos e ver se vai funcionar.

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Tarefa B13 - Pratique no Laboratório de Aprendizagem Colaborativa Online: MILANOTE

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Tarefa B13 - Pratique no Laboratório de Aprendizagem Colaborativa Online: MILANOTE

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Hoje eu conheci uma ferramenta muito legal e fácil de utilizar chamada MILANOTE. Ela permite organizar ideias e projetos em painéis visuais, sendo uma ótima opção para que equipes possam reunir ideias em um único ambiente. Aplicável na Sala de Aula Invertida e na Aprendizagem Baseada em Projetos.
Na minha tarefa, optei por utilizar a ferramenta para uso dos alunos no estudo prévio na prática de Sala de Aula invertida, para uma aula de Virologia Básica, tema Morfologia Viral.
Convido-os a visitar a página e a contribuir com postagens, nas quais vc tem a liberade de escolher uma espécie de vírus de seu interesse e ali adicionar uma imagem de M.E. viral e falar um pouquinho sobre esse vírus.
Adorei a ferramenta!
Visite a minha página clicando na imagem abaixo
Te espero lá  :o)

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Testando o Milanote para MORFOLOGIA VIRAL

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Tarefa B11 - Crie uma Experiência Interativa de Aprendizagem: PREZI 

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Tarefa B11 - Crie uma Experiência Interativa de Aprendizagem: PREZI 

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A proposta desta atividade era que nós criassemos uma experiência de aprendizagem fazendo uso de um dos recursos de uma lista de aplicativos que permitem produzir conteúdos com interação e aprendizagem ativa.  Escolhi o Prezi, que é uma ferramenta muito legal para criar apresentações com inserções ativas, como zoom em textos, inclusão de imagens, pequenos vídeos e links. Cria recursos visuais que com certeza tormam a aula e a aprendizagem mais interessante.
Na minha tarefa, experimentei produzir um Slide chart ou talvez seja um Infográfico (não sei ao certo com o que mais se parece) para uso na Aprendizagem Baseada em Problemas sobre um tema da aula de Febre amarela. Tentei inserir o máximo de recursos que era possível explorar usando apenas dois slides  - era para ser um slide só, mas me empolguei :o)
Adorei !!! Se não der certo o link da figura (clique nela e depois em "ver original", acho que dá pra visitar meu Slide chart copiando e colando o endereço no seu navegador: https://prezi.com/i/iiiffjql6ge3/espectro-clinico-febre-amarela/

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espectro clínico febre amarela created by Alessandra Borges on today

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Tarefa B10 - Investigue duas ou mais ferramentas e publique no glossário

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Tarefa B10 - Investigue duas ou mais ferramentas e publique no glossário

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Diante da impressionante lista de links para aplicativos de novas ferramentas para uso em metodologias ativas, escolhi investigar sobre QUIZIZZ e POPPLET. Trata-se de plataformas para Gamificação e para criar Mapas mentais/conceituais, respectivamente. Pessoalmente, gostei muito do Quizziz, fiz um teste de montar um quizz para uma aula introdutória de Imunologia e gostei muito (curiosos podem dar uma olhada em https://quizizz.com/admin/quiz/5f84a5d16a49e20020006c11 ). Acho que os alunos realmente vaõ curtir estudar imunologia em formato de teste e competição. Por outro lado, não aprovei a ferrameta Popplet, achei-a limitada. Pretendo testar futuramente outras opções de ferramentas para criar meus mapas conceituais.

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Tarefa B9 - leitura de textos de apoio sobre a infinidade de aplicativos para metodologias ativas 

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Tarefa B9 - leitura de textos de apoio sobre a infinidade de aplicativos para metodologias ativas 

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É assombrosa a quantidade de recursos hoje disponível para se fazer qualquer coisa no mundo virtual, com aplicações à Educação. Assim como a pandemia de Covid-19 vai marcar a história da humanidade como o ano em que a "página virou" em relação ao nosso estilo de vida, hábitos, rotina, valores, etc., também essa nova onda tsunami que é a pressão por se adotar urgentemente uma nova forma de ensinar terá 2020 como seu marco incial de virada de página. Confesso que me abateu ler a todos esses textos de apoio e ver que a mensagem subliminar (ou explícita em determinados pontos dos mesmos) é que todo o nosso esforço como professor e todo o nosso conhecimento, a nossa experiência, todo o nosso dom de ensinar até aqui foram agora reduzidos a um valor igual a zero. Esta é a mensagem, e embora eu esteja conseguindo aprender algumas coisas (diante de um tsunami, quem consegue pegar um barco e navegar tranquilamente sobre ele?), confesso que o curso tem me deixado muito triste e mal. Lamentável que o mundo e a visão das pessoas tenha chegado a este ponto. Difícil demais.
Quando pequena, vendo um livro de história da arte, sempre achei horrível esta pintura de Edvard Munch e tinha medo de virar a página que a antecedia, porque a imagem me assustava. Hoje não consigo pensar em imagem melhor para representar o que estou sentindo diante de todo esse cenário e toda essa pressão para que professores-cientistas se transformem em educadores-fantáticos-tecnológicos, assim, como num passe de mágica.

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Nobel de Medicina ou Fisologia pela descoberta da Hepatite C 

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Nobel de Medicina ou Fisologia pela descoberta da Hepatite C 

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Agora já posso dizer que já tive o privilégio de assistir à apresentação e de cumprimentar com um aperto de mão a um virologista ganhador do Prêmio Nobel de Fisiologia ou Medicina de 2020, Charles Rice! A notícia dos laureados pode ser vista em:
https://canaltech.com.br/saude/premio-nobel-medicina-2020-172573/
Essa oportunidade de conhecê-lo foi num workshop de arbovírus, que teve no Instituto Carlos Chagas - FIOCRUZ-PR, em 2018.

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Tarefa B8 - Análise de uma prática com uso ou possibilidades de uso de metodologias ativas - A vez das Hepatites virais

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Tarefa B8 - Análise de uma prática com uso ou possibilidades de uso de metodologias ativas - A vez das Hepatites virais

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https://docs.google.com/document/d/1xEv5hZy3IRl9Tk9Bl6zgZLLpcCcaf3UvdKNMNgKqqcM/edit?usp=sharing

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Create a new document and edit with others at the same time -- from your computer, phone or tablet. Get stuff done with or without an internet connection. Use Docs to edit Word files. Free from Google.

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Hoje passei o dia todo em função de cumprir três das tarefas da semana do curso de TDMA que estavam atrasadas. A atividade de leitura de textos (5 textos com média de umas 15 páginas cada) foi a parte mais difícil, por ser muito longa, abordar assuntos muitas teóricos em demasia e ser de outra área do conhecimento, a pedagógica. Enfim, fecho o dia com 3 das 4 tarefas que estavam pro fazer, e já fiquei contente. Não está sendo nada fácil ter de dedicar tanto tempo a este curso, que demanda na realidade muito, mas muito mais do que as 4h semanais propostas no plano de ensino. Mas está valendo porque estou aprendendo e porque é necessário. Hoje vi que uma aula de um tema um tantinho enfadonho (meus alunos, pela imagem, imaginam qual é?) pode se tornar mais interessante com a aula invertida. Legal!
03 de out 2020

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Esta semana vi um paper muito legal que descreveu as frequências alélicas de HLA nas populações da América do Sul, um estudo enorme, com resultados inéditos e que serão de enorme ajuda a quem trabalha com polimorfismo genético envolvendo resposta imune e suscetibilidade ou resistência à infecções. Neste paper, esses genes (das moléculas envolvidas na apresentação de antígenos aos linfócitos) foram avaliados quanto para se verificar quais seriam os melhores alvos (epítopos) das proteínas do coronavírus seriam as mais adequadas para o desenvolvimento de vacinas. Gosto do assunto HLA porque além de eu ser professora de Imunologia, tb desenvolvi minha tese de doutorado envolvendo esse tema, só que relacionado a outro tipo de vírus, o Hantavírus.

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Laboratory of Cellular Biophysics, The Rockefeller University, New York, NY, United States

Laboratorio de Bioinformática, Biología Molecular y Desarrollos Tecnológicos, Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Peru

Departamento de Patologia, Faculdade de Medicina Veterinária e Zootecnia, Programa Interunidades em Biotecnologia, Universidade de São Paulo, São Paulo, Brazil

Unidad de Bioinformática, Centro de Investigaciones Tecnológicas, Biomédicas y Medioambientales, Lima, Peru

Departamento Académico de Microbiología Médica, Facultad de Medicina, Universidad Nacional Mayor de San Marcos, Lima, Peru

Coronavirus disease (COVID-19), caused by the virus SARS-CoV-2, is already responsible for more than 4.3 million confirmed cases and 295,000 deaths worldwide as of May 15, 2020. Ongoing efforts to control the pandemic include the development of peptide-based vaccines and diagnostic tests. In these approaches, HLA allelic diversity plays a crucial role. Despite its importance, current knowledge of HLA allele frequencies in South America is very limited. In this study, we have performed a literature review of datasets reporting HLA frequencies of South American populations, available in scientific literature and/or in the Allele Frequency Net Database. This allowed us to enrich the current scenario with more than 12.8 million data points. As a result, we are presenting updated HLA allelic frequencies based on country, including 91 alleles that were previously thought to have frequencies either under 5% or of an unknown value. Using alleles with an updated frequency of at least ≥5% in any South American country, we predicted epitopes in SARS-CoV-2 proteins using NetMHCpan (I and II) and MHC flurry. Then, the best predicted epitopes (class-I and -II) were selected based on their binding to South American alleles (Coverage Score). Class II predicted epitopes were also filtered based on their three-dimensional exposure. We obtained 14 class-I and four class-II candidate epitopes with experimental evidence (reported in the Immune Epitope Database and Analysis Resource), having good coverage scores for South America. Additionally, we are presenting 13 HLA-I and 30 HLA-II novel candidate epitopes without experimental evidence, including 16 class-II candidates in highly exposed conserved areas of the NTD and RBD regions of the Spike protein. These novel candidates have even better coverage scores for South America than those with experimental evidence. Finally, we show that recent similar studies presenting candidate epitopes also predicted some of our candidates but discarded them in the selection process, resulting in candidates with suboptimal coverage for South America. In conclusion, the candidate epitopes presented provide valuable information for the development of epitope-based strategies against SARS-CoV-2, such as peptide vaccines and diagnostic tests. Additionally, the updated HLA allelic frequencies provide a better representation of South America and may impact different immunogenetic studies.

Introduction

The novel virus Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) ( 1, 2) is the first member of the Betacoronavirus genus to reach pandemic status ( 3). This virus probably originated in bats and infects humans with the participation of an intermediate host ( 4- 7), like its two highly human-infective relatives, SARS-CoV ( 8, 9) and MERS-CoV ( 10). It causes Coronavirus Disease 2019 (COVID-19), whose clinical symptoms include fever, cough, fatigue, sputum production, and difficulty breathing ( 11, 12). Transmission is mostly human-to-human through respiratory droplets and direct contact, carrying infectious virions to the nose, mouth, and eyes ( 13, 14). It has spread to 216 countries, resulting in more than 4.3 million confirmed cases and 295,000 deaths worldwide as of May 15, 2020 ( 15). It has an estimated basic reproductive number ( R ₀) of 2.24-3.58 ( 16).

SARS-CoV-2, as with other Coronaviruses, is characterized by a high recombination frequency and mutation rate, in addition to a relatively large and sophisticated genetic machinery compared to other RNA viruses. At the 5′ end of the genome, the cleavable polyprotein ab (ORF1ab) is processed into 16 non-structural proteins (NSPs). They are involved in the viral replication and assembly process, as well as in immune evasion ( 17, 18). At the 3′ end, the structural proteins Spike (S), envelope (E), membrane (M), and nucleocapsid (N) are interspersed by the accessory proteins ORF 3a, 6, 7a, 7b, 8, and 10 ( 19, 20). A key factor in viral attachment and entry is the receptor-binding domain (RBD), located in the subunit 1 (S1) of the S protein. This binds strongly to the angiotensin-converting enzyme 2 (ACE2) receptors ( 21, 22). Other possible receptors, like CD209L ( 23), CD147 ( 24), and the protease TMPRSS2 ( 25), could also participate in the viral entry and processing.

Post-translational modifications (PTMs) are covalent modifications that regulate protein functions. In coronaviruses, they are required for a successful viral cycle. Glycosylation and palmitoylation of S and E proteins are fundamental in terms of stability, enzymatic activity, subcellular localization, and protein interaction ( 26- 29). Similarly, glycosylation of the M protein ( 30, 31), phosphorylation, and ribosylation of the N protein ( 32, 33), as well as other PTMs in non-structural and accessory proteins, can play a determinant role in the viral cycle ( 34, 35). Considering the relevant role of PTMs and the complex composition of N-glycans, it was proposed that inhibition with N-butyl-deoxynojirimycin (NB-DNJ) ( 35) or the addition of carbohydrate-binding agents (CBAs) could be considered as therapeutic strategies against SARS-CoV infections ( 36). Nitric Oxide (NO) and its derivatives have been shown to inhibit SARS-CoV replication by reducing the palmitoylation on the nascent Spike protein, affecting the receptor binding. It also affects viral RNA production in the early steps of replication, potentially due to an effect on the ORF1a-encoded cysteine proteases ( 37).

Vaccine development efforts are undergoing worldwide. There are 110 prophylactic vaccine candidates as of May 15, 2020. Three of them are based on live-attenuated virus, seven on inactivated virus, 27 on viral vectors (12 replicating and 15 non-replicating), 26 on nucleic acids (10 using DNA and 16 RNA), 38 on recombinant proteins, six on Virus-Like Particles (VLP), and three unknown ( 46, 47). Some vaccines have already moved to a clinical phase. There are six in Phase I: Pathogen-specific aAPC (NCT04299724, Shenzhen Geno-Immune Medical Institute, China), Recombinant Novel Coronavirus Vaccine (Adenovirus Type 5 Vector) (NCT04313127, CanSino Biologics Inc.), bacTRL-Spike (NCT04334980, Symvivo Corporation, Canada), INO-4800 (NCT04336410, Inovio Pharmaceuticals, USA), mRNA-1273 (NCT04283461, National Institute of Allergy and Infectious Diseases, USA), and SARS-CoV-2 rS (NCT04368988, Novavax). There are four vaccines on simultaneous Phase I-II: SARS-CoV-2 inactivated vaccine (NCT04352608, Sinovac Research and Development Co., Ltd.), LV-SMENP-DC (NCT04276896, Shenzhen Geno-Immune Medical Institute, China), ChAdOx1 (NCT04324606, University of Oxford, UK), and BNT162 (NCT04368728, Biontech SE, Pfizer). There is also one vaccine in Phase II: Ad5-nCoV (NCT04341389, Institute of Biotechnology, Academy of Military Medical Sciences, PLA of China) ( 47, 48). Of note, six peptide-based vaccines are currently under development: FlowVax™ by Flow Pharma Inc. ( 49), EPV-CoV19 by EpiVax ( 50), DPX-COVID-19 by IMV inc. ( 51), Vaxil Bio (US patent: 62/987,310) ( 52), OncoGen ( 53), and USask VIDO-InterVac ( 54).

Two recent studies have extrapolated epitopes with experimental evidence in SARS-CoV to SARS-CoV-2, and selected candidates based on different criteria. Grifoni et al. ( 64) intersected these extrapolated epitopes with a set of predicted epitopes using 12 HLA-I A and B supertypes ( 65). They present 12 candidates in the S, M, and N proteins of SARS-CoV-2. Ahmed et al. ( 66) subselected the extrapolated epitopes using the Population Tool of the Immune Epitope Database and Analysis Resource (IEDB) ( 67), resulting in candidates in the S and N proteins that may potentially cover the global population. This tool is based on the HLA frequencies reported in the Allele Frequency Net Database (AFNDB), which is an important reference source for immunological studies worldwide ( 68). Nevertheless, this database has little information about South America, missing large studies published in recent years comprising millions of people (see Table S3).

Here, we performed a literature review of HLA allele frequencies of South American populations reported in scientific articles available in PubMed and datasets available in the AFNDB. These datasets were integrated by country, calculating weighted allele frequencies (WAFs). Thus, we are presenting updated WAFs for most South American countries. Then, HLA class I and II epitopes were predicted using only alleles with WAF ≥ 0.05. Finally, we selected candidate epitopes covering all of these South American HLA alleles, reporting both candidates with existing experimental evidence in the IEDB database for other coronaviruses as well as novel candidates. These candidates complement those proposed in recent articles, which in most cases scarcely cover South America. Our findings may result in a better representation of South America, enriching current development efforts of vaccines and diagnostic tests.

Methods

Alignment, Entropy, and Selection Pressure of the SARS-CoV-2 Proteins

South American HLA Alleles and Weighted Allele Frequencies (WAFs)

A review was performed considering first only datasets available in the AFNDB containing allelic frequencies of HLA-A, -B, -C, -DPA, -DPB, -DQA, -DQB, and -DRB1 in South American populations. This was called the "current scenario." We selected studies using the following inclusion criteria: (i) we collected all the studies characterizing HLA alleles with at least 4-digit resolution in 100 or more individuals (N ≥ 100); and (ii) an auxiliary rule was exceptionally applied in low-information cases only, i.e., if just two or fewer articles were obtained, both barely passing the filter (100 ≤ N ≤ 200). This exception consisted of relaxing the lower cut-off to N ≥ 40, allowing smaller studies to pass the filter to rescue additional data.

To expand the "current scenario," in addition to the previously described datasets, we collected articles in PubMed reporting Class I and Class II HLA alleles of South American populations using the same inclusion criteria. This was called the "updated scenario." The selection criteria were applied to both published datasets in scientific articles and datasets available only in the AFNDB. All alleles were matched to the current HLA nomenclature ( 76).

Then, for both current and updated scenarios, we calculated Weighted Allele Frequencies (WAFs) by country, using as approximation the weighted average of the allele frequencies in all the studies selected by country. This led to a few exceptions due to evident discrepancies in technology and resolution, resulting in the exclusion of some studies (see Figure 1 and Supplementary Methods for full detail). Only alleles with WAF ≥ 5% in the updated scenario were considered for further analysis.

Figure 1. PRISMA flowchart of the literature review. Flow diagram of the datasets processed. (A) Current Scenario, using only the datasets available in the Allele Frequency Net Database. (B) Updated Scenario, adding databases from scientific literature until April 10, 2020. Countries are represented by the first three letters of their names. PRISMA checklist included in the Supplementary Material ( 77).

Epitope Prediction and Selection

Linear epitopes for HLA class I (HLA-A, -B, and -C) and HLA class II (HLA-DRB1) were predicted in the SARS-CoV-2 proteins using the alleles obtained in the previous step and the cut-offs recommended by each software. For HLA class I, we predicted epitopes of 8-11aa using NetMHCpan v4.0 ( 59) with rank ≤ 2 and MHCflurry v1.6.0 ( 60) with an affinity (IC50) ≤ 500 nM. As these programs do not have the same collection of HLA alleles available for prediction, we used the consensus verdict whenever possible. Otherwise, we only used the prediction of the software with the allele available. For HLA class II, we predicted epitopes of 15aa using NetMHCIIpan v4.0 ( 61) with rank ≤ 10. Then, we annotated which predicted epitopes have previous experimental evidence for other coronaviruses (identical match, either in full length from end to end or contained inside) in the IEDB Database ( www.iedb.org) ( 67).

Each predicted epitope obtained corresponds to one or more HLA alleles, which have different WAFs by country. To combine these WAFs into a single value, we defined a Coverage Score (CS), which reflects how good a candidate epitope is for South America. This was calculated for each predicted epitope, by adding the proportion by country of alleles with WAF ≥ 5% that bind this predicted epitope. Therefore, this CS varies in a range from 0 to P, where P is the number of countries. Then, we selected candidate epitopes with the highest Coverage Scores, with and without prior experimental evidence. Additionally, for HLA-II only, we prioritized candidates based on their three-dimensional exposure. Sequence logos were generated using WebLogo 3 ( 78), showing the chemistry and frequency per amino acid of our best candidates.

Prediction of Post-translational Modifications Events

Structural Modeling and Graphical Representation

Results

Alignment, Entropy, and Selection Pressure of the SARS-CoV-2 Proteins

Diversity at each amino acid position revealed high entropy values in the proteins NSP2 (T85I, score = 0.36), NSP5 (L37F, score = 0.42), NSP12 (P323L, score = 0.69), NSP13 (P504L, score = 0.46 and Y541C, score = 0.47), Spike (D614G, score = 0.69), ORF3a (Q57H, score = 0.41 and G251V, score = 0.31), ORF8 (L84S, score = 0.57), and Nucleocapsid (R203K, score = 0.41, and G204R, score = 0.41).

Sites with the highest probability to be under positive selective pressure are located in NSP2 (T85, P568), NSP3 (K384, N444, P822, V1768, V1795), NSP6 (L75), NSP7 (S25), Spike (S943, G1124), ORF3a (A99, T14, L147), NSP12 (A97, L323, A449), NSP13 (V49), NSP14 (A482), and NSP16 (K160). See Figure 3 and Table S8.

South American HLA Alleles and Weighted Allele Frequencies (WAFs)

The IEDB population coverage tool ( http://tools.iedb.org/population/) uses information provided by the AFNDB at 4-digit resolution, which in the case of South America comprises just a small number of populations: two from Argentina, one from Bolivia, five from Brazil, two from Chile, three from Colombia, two from Ecuador, one from Paraguay, two from Peru, and four from Venezuela. For the current scenario, we collected a similar number of datasets: five from Argentina, two from Bolivia, 13 from Brazil, four from Chile, four from Colombia, five from Ecuador, one from Paraguay, four from Peru, and two from Venezuela. In both cases, there was no data for Uruguay, Guyana, French Guiana, or Suriname.

Our literature review to update the allele frequencies included six datasets for Argentina, three for Bolivia, 10 for Brazil, seven for Chile, seven for Colombia, eight for Ecuador, two for Paraguay, five for Peru, one for Uruguay, and nine for Venezuela. In both scenarios (current and updated), we only obtained HLA-II and not HLA-I data from Bolivia. All of the allele frequencies and sample sizes by study are provided in Table S4. The addition of new studies resulted in updated HLA allele frequencies.

Figure 2. Allele frequencies of HLA-I A, B, C, and HLA-II DRB1 in South America. The pie charts represent the distribution of allele frequencies in genes HLA-I A, B, C, and HLA-II DRB1. Alleles with Weighted Allele Frequency (WAF) ≥ 5% in four or more countries are represented in scales of red (A), blue (B), brown (C), and purple (DRB1), respectively. Alleles with WAF ≥ 5% in three or fewer countries are shown in white. Alleles with WAF < 5%, are shown in gray. The green gradient represents the extent of people with HLA alleles genotyped. Detailed data is available in Tables S5, S6.

Epitope Prediction

We obtained 11,644 predicted T cell epitopes in SARS-CoV-2 proteins: 7,517 for HLA-I and 4,127 for HLA-II. We found that 1400 have previous experimental evidence in the IEDB: 25 in positive T cell assays, 1327 in positive MHC-ligand binding assays, and 48 in both (see Table S7).

Table 1. Best HLA-I candidate epitopes for South America in SARS-CoV-2 proteins.

We predicted 6556 HLA-I epitopes in other SARS-CoV-2 proteins. Experimental evidence was reported for 1122, and the 10 with the highest CS (2.520-3.346) cover at least one allele with WAF ≥ 5% by country. These predicted epitopes are located in the ORF6 and NSP proteins, and represent novel candidates. The highest CS (3.346) corresponds to YADVFHLYL, located in the NSP12 protein. We also found 5434 predicted epitopes without experimental evidence. The top seven with the highest CS (3.148-4.013) are novel candidates, located in NSP proteins, and cover at least one allele with WAF ≥ 5% by country. The candidate with the highest CS (FAQDGNAAI, 4.013) is also located in the NSP12 protein (see Table 1).

Table 2. Best HLA-II candidate epitopes for South America in SARS-CoV-2 proteins.

Figure 3. Candidate epitopes in the sequence of the Membrane (M) and Spike (S) proteins of SARS-CoV-2. (A,E,G) Show the entropy per amino acid for the S, M, and E proteins, respectively, calculated by aligning 2123 SARS-CoV-2 genomes. In (B,F,H), post-translational modifications are represented as sticks with colored circles: beige (N-linked glycosylations), pink (O-GalNAc glycosylations), and lemon (palmitoylations). (B) Regions of the S protein, indicating the subunits 1 (S1), 2 (S2), and cleavage points (scissors). Positive selection pressure is represented with (+). SP, Signal peptide; NTD, N-terminal domain; RBD, Receptor Binding Domain; RBM, Receptor Binding Motif; SD1, Sub-Domain 1; SD2, Sub-Domain 2; FP, Fusion Peptide; CR, Connecting Region; HR1, Heptad Repeat 1; CH, Central Helix; BH, B-Hairpin; SD3, Sub-Domain 3; HR2, Heptad Repeat 2; TM, Transmembrane domain; CT, Cytoplasmic tail. (C) HLA-I epitopes predicted for South American alleles with WAF ≥5%. The gradient of green represents the coverage scores. The rectangles below represent the predicted epitopes with experimental evidence in the IEDB (blue), and those without experimental evidence with CS ≥2 (light blue). Overlapping predicted epitopes are represented by a single rectangle with the number of epitopes contained (underneath). The inverted triangle on top highlights our best candidates. (D) Analogously, for HLA-II. Predicted epitopes with experimental evidence are shown in red. Without experimental evidence and CS ≥6, in yellow. Those in the RBD are highlighted in orange. (F,H) Represent the topology of the M and E proteins, respectively. Exposed (E), transmembrane (TM), and intra-virion (I) regions were extracted from annotated proteins (UniProtKB IDs: P0DTC4 and P0DTC5). The best HLA-II epitopes predicted in their exposed regions (and their CS) are shown in yellow.

We also predicted 601 epitopes without experimental evidence in the S protein, where 41 had 6.017 ≤ CS ≤ 9.000 ( Figure 3D). Some of these are located in notoriously exposed regions (see Figure 4). All of these are novel candidates, including EKGIYQTSNFRVQPT (CS = 8.490), QTSNFRVQPTESIVR (CS = 8.714), and TSNFRVQPTESIVRF (CS = 9.000, the maximum possible score), which overlap in the RBD and have a remarkably high CS.

Figure 4. Candidate epitopes in the 3D structure of the Spike (S) protein of SARS-CoV-2. This figure shows our HLA-II candidate epitopes with experimental evidence (red), without experimental evidence and CS ≥ 6 (orange: located in RBD, yellow: located in other regions), and the binding site of the Ab CR3022 (violet). Candidate epitopes were mapped to the Spike monomer (A), and the Spike trimer ( B and C). The monomer is represented in beige, and the other 2 subunits in light purple. (C) Rotation of the trimer showing the RBD region. A pyramid representing the trimer is shown as visual aid to represent the rotation.

We obtained 81 HLA-II predicted epitopes in the M protein. Only one (PKEITVATSRTLSYY, CS = 6.110) has a reported experiment (IEDB-ID: 48051), an MHC-ligand assay presenting the peptide to the HLA-DRB1*01:01, measuring the affinity. It was already selected as a candidate in another study ( 66). Nevertheless, it is located in a predicted intra-virion region. Of those without experimental evidence and located outside the virion, TITVEELKKLLEQWN has the best CS (3.326) (see Figure 3F). In the E protein, we predicted 15 HLA-II epitopes. Similarly, only LVTLAILTALRLCAY has previous experimental evidence, but a very low CS (0.143), and it is located in a predicted non-external region. However, the candidate MYSFVSEETGTLIVN (CS = 1.764) is located outside the virion ( Figure 3H). Sequence logos of our class-I and class-II candidate epitopes are presented in Figures S1, S2, respectively.

To cover all the HLA alleles with WAF ≥ 5% in South America, we included five additional candidates. We selected predicted epitopes that bind to HLA alleles not covered by any of the candidates already selected, choosing those with the highest CS. Two have experimental evidence: MPASWVMRI (in NSP6, CS: 2.292, IEDB ID: 42260 and 42261) and SEFDRDAAM (in NSP12, CS:1.832, IEDB ID: 57419). The remaining three have no experimental evidence: GEYSHVVAF (in NSP4, CS: 2.270), KLFDRYFKY (in NSP12, CS: 2.169) and GLNDNLLEI (in NSP2, CS: 1.705).

Prediction of Post-translational Modifications Events

Twenty two potential N-linked glycosylation sites were predicted along the S protein in three clusters: (i) inside the NTD and RBD regions (N17, N61, N74, N122, N149, N165, N234, N282, N331, and N343); (ii) in the proximity of the S1/S2 and S2' cleavage sites (N603, N616, N657, N709, N717, and N801); and (iii) near the C-terminus (N1074, N1098, N1134, N1158, N1173, and N1194). In the M protein, one predicted N-linked glycosylation site (N5) is located in the exposed region, and it has been associated with antigenicity and transport in some coronaviruses ( 88). The E protein presents two potential N-linked glycosylation sites: N48, probably non-functional ( 89), and N66, suggested in SARS-CoV to be potentially associated with monomeric forms ( 90). ORF8 also presents a potential N-linked glycosylation site in N78, which could stabilize and protect the protein from proteasomal degradation, as occurring in SARS-CoV ( 91). O-linked glycosylations were predicted in residues S673, T678, and S686 of the S protein and residues T32 and T34 of the ORF3a. These events were experimentally detected in SARS-CoV ( 92). However, glycosylation in residues 686 (O-linked) and 1158 (N-linked) were not confirmed by mass spectrometry in a recent study ( 93).

Then, the predicted glycosylation sites were contrasted with the best HLA-II candidate epitopes for South America ( Table 2). Sites N61, N122, N801, N1074, N1098, and N1158 are located in nine HLA-II candidate epitopes. Additionally, sites N234, N331, N709, and the O-linked 686 fall near eight candidate epitopes.

In the N protein, a potential sumoylation site was predicted in K338. Li et al. ( 97) explored sumoylation events experimentally in SARS-CoV, not finding K338 but detecting the site K62. This site was also detected in our prediction using a less restrictive threshold. This corresponds to K61 in SARS-CoV-2, having an Asp instead of Glu in the canonical consensus motif. This site has been associated with self homo-oligomerization and host cell division interference.

Structural Modeling of the S Protein

The monomer model generated is composed of the first 1147 residues of the SARS-CoV-2 Spike protein. It fills the gaps of the crystal structure PDB:6VXX at positions 1-26, 70-79, 144-164, 173-185, 246-262, 445-446, 455-461, 469-488, 502, 621-640, 677-688, and 828-853.

Discussion

There is an urgent need to develop vaccines and better diagnostic tests for COVID-19, targeting specific immunogenic regions and epitopes with protective potential and population representativeness. Our research presents an updated report of HLA genotypes of South American populations, which led to a selection of candidate epitopes for HLA class I and II supported by experimental evidence as well as novel candidates, predicted to cover all South American countries.

The AFNDB and the IEDB population coverage tools are frequently used by the scientific community worldwide as reference sources of HLA frequencies ( 68), meaning it is crucial to have them updated. However, their collection and curation of new data rely on the scientific community users ( 68, 98). For South America, these databases contain mostly small datasets coming from ethnic groups, not representative of the countries' diversity, resulting in a current inaccurate distribution of HLA frequencies (see Table S6). We have found 30 large datasets from 10 South American countries which were not included in the AFNDB. Our literature review represents a large update from a scenario of 20,124 to now 12,857,200 datapoints among all the alleles collected of HLA-A, -B, -C, -DPA1, -DPB1, -DQA1, -DQB1, and -DRB1. This is reflected in 86 HLA alleles with WAF ≥ 5% in the updated scenario, which were previously considered with frequencies under 5% or missing in the current scenario. This issue results in a misrepresentation of South America that could be affecting multiple immunological studies using these sources, like Ahmed et al. ( 66) and other SARS-CoV-2 recent studies in the pre-print stage. To encourage and facilitate using the information collected, we are providing the datasets by country in their full extent ( Tables S3, S4) and averaged ( Tables S5, S6), as well as the selection and matching process in full detail ( Supplementary Methods).

Epitopes were predicted using MHCflurry v1.6.0 ( 60), NetMHCpan v4.0 ( 59), and NetMHCIIpan v4.0 ( 61). These state-of-the-art software are based on neural networks and use binding, stability, and eluted MHC-ligand mass spectrometry data ( 58). The recent update of NetMHCIIpan from v3.2 to v4.0 represents an evident improvement in prediction accuracy, being necessary to use the last version. We enriched our predictions, adding the experimental evidence reported in the IEDB for other coronaviruses. Nevertheless, certain class-II potential epitopes found in binding experiments (i.e., measuring the affinity of epitopes presented in-vitro to the MHC molecule) were located in transmembrane or internal regions. As the conformational dynamics of SARS-CoV-2 proteins remains unknown, we opted for being extra cautious, selecting only class-II candidate epitopes in known exposed regions of the protein structure.

We then define a Coverage Score (CS), a metric representing to what extent a candidate epitope covers the HLA alleles of South America. As this score is based on alleles and not on haplotypes, it might be overestimating the actual coverage. However, it represents a reasonable approximation for the current available data, mostly reported as allelic frequencies. Based on the CS, we are presenting the best predicted epitopes, with and without experimental evidence, as potential candidates. Remarkably, we found novel candidates with very high CS, some located in immunologically relevant regions like the RBD of the Spike protein. And exposed regions of the M and E proteins ( Figures 3, 4).

Viral clearance of SARS-CoV-2 infection requires activating subsets of CD4⁺ and CD8⁺ T cells ( 100). Whereas, HLA-I epitopes are derived from both structural and non-structural proteins due to their endogenous processing, HLA-II epitopes have exogenous processing, being structural proteins (like S, M, or E) of particular interest ( 64). In SARS-CoV, CD8⁺ T cell response was previously shown to be greater than CD4⁺, and it is widely elicited by different proteins including the replicase (ORF1ab) and NSPs ( 101). Even though structural proteins are associated with stronger T-cell responses ( 102- 104), non-structural proteins can also induce an immune response and provide additional epitopes ( 101). Similar experimental studies are needed to corroborate the same behavior in SARS-CoV-2. In fact, we found several HLA-I candidate epitopes in NSP proteins with a high coverage of South American HLA alleles, with and without experimental evidence ( Table 1).

Several studies had demonstrated the immunogenicity of protein S in beta-coronaviruses, being the main target for vaccine development ( 22, 105- 109). Its receptor binding domain (RBD) interacts with the human receptor ACE2, playing a crucial role during the viral entry process ( 110). Therefore, antibodies binding this region could potentially impede viral recognition. We are presenting 32 HLA-II candidate epitopes in the S protein, including seven novel candidates located in the RBD and exposed in the 3D structure. This includes the candidate epitope TSNFRVQPTESIVRF, which covers all the HLA-II alleles of South America. Some of our candidates are located near the binding site of the monoclonal antibody CR3022 in the RBD ( 111, 112) ( Figure 4C). This antibody neutralizes SARS-CoV ( 113), opening the possibility of finding neutralizing antibodies against our HLA-II candidate epitopes. Additionally, neutralizing monoclonal antibodies has been generated against S1 and S2 for SARS ( 104, 114), and against the RBD for SARS ( 115, 116) and MERS ( 117, 118).

Our HLA-I and HLA-II candidate epitopes cover 100% of the alleles with WAF ≥ 5% in South America. Remarkably, we obtained two HLA-II candidate epitopes with the maximum CS possible (covering all the alleles), one with experimental evidence.

Grifoni et al. ( 64) selected candidate epitopes in SARS-CoV-2 through: (i) sequence homology with epitopes with experimental evidence in SARS-CoV, and by (ii) epitope prediction, using 12 supertype representatives (the six most frequent HLA-I A and B alleles worldwide). Intersecting these two sources, they selected 12 HLA-I candidate epitopes in common, having an identity ≥ 90% with SARS-CoV. Their selection does not agree with any of our candidates. However, during their prediction, they obtained 12 HLA-I (CS: 0.095-3.739) and two HLA-II (CS: 6.474-6.593) predicted epitopes that have better scores for South America and match with some of our candidates. Nevertheless, further steps in their selection criteria made them drop these candidates. This is attributable to their strict filtering, due to the fact that they relied on experimental evidence from SARS-CoV only, as well as the small number of supertype alleles and epitopes chosen.

We predicted PTMs, including glycosylation, palmitoylation, sumoylation, and ADP-ribosylation events, in order to find relevant sites for the viral cycle. Sites affected by these events are expected to be conserved as they fulfill critical functions in viruses ( 33, 95- 97). N-linked glycosylation is one of the most frequents PTMs with potential effects over the folding, tropism, interactions with host proteases, antibody recognition, and antigenicity of the Spike protein ( 126- 131). N-linked glycosylations have been predicted in our candidate epitopes, including those with experimental evidence ( Table S8). SARS-CoV-2 Spike protein possesses 22 potential N-linked glycosylation sites ( Figure 3), mainly distributed in S1 and the C-terminal region of the S2 ( 119, 132). Some of them are located in the NTD and near the S1/S2 cleavage region (N122, N165, N234, N603, and N717). These sites surround the ACE2-binding domain and were shown to be critical for viral entry mediated by DC-SIGN (dendritic cell-specific ICAM-3-grabbing non-integrin) and L-SIGN (liver/lymph node-specific ICAM-3-grabbing non-integrin) ( 133), which are two C-type lectins that recognize high-mannose glycans ( 134). In contrast to SARS-CoV, SARS-CoV-2 presents an additional site (N657, near the S2 cleavage) and misses the glycosylation site N370 (in the RBD region), due to the absence of Ser/Thr to complete the sequon. Although this does not alter the affinity to the ACE2 receptor ( 135), it can significantly reduce DC-SIGN binding capacity ( 136). Thus, availability of N-linked glycosylations sites and differential affinities to ACE2, DC-SIGN, or L-SIGN may act as either enhancer forces or alternative mechanisms for viral entry ( 23, 137). Additionally, sites N227 and N699 in SARS-CoV (equivalent to N234 and N717 in SARS-CoV-2) have been hypothesized to facilitate the zoonotic transmission of this virus ( 133). A recent study based on liquid-chromatography-mass spectrometry (LC-MS) ( 132) confirmed the occurrence of N-linked glycosylation in all of our predicted sites. However, another study using LC-MS/MS ( 93) found only 17 out of 22 of the predicted sites. These differences could be due to the different experimental design and procedures used. Moreover, the composition diversity (oligomannose, complex, or hybrid-type), and frequency of these glycosylation events were dissimilar in both studies.

O-linked glycosylations were predicted at three previously proposed sites flanking the S1/S2 cleavage site ( 138). These events were not found in two recent mass spectrometry studies ( 93, 132). Thus, occurrence of these events could be very rare or affected by intra-host conditions. Interestingly, T323 and S325 in RBD were detected as O-glycosylated ( 93), which may be related to increasing affinity with the human receptor ACE2 ( 110, 139). N- and O-linked glycosylation events can influence not only their position but also the surrounding area under the glycan shield. In fact, both N- and O-linked glycosylations could be associated with masking epitopes or important amino acids, resulting in immune evasion ( 129, 140, 141). Further in-vivo studies should be performed to determine the real complexity and heterogeneity of these events.

Meanwhile, palmitoylation predicted sites in the cytoplasmic tail of the S protein ( Table S8) have been reported in other Coronaviruses ( 34). The presence of these sites supports the importance of membrane-proximal cysteine-rich clusters in processes like the Spike-mediated cell fusion ( 27, 95, 142), infectivity ( 143), and viral assembly ( 144, 145).

Conservation of PTMs in amino acids can support the selection of candidate epitopes. In contrast, their emergence or disappearance resulting in better fitness should be considered important evolutive events and must be particularly monitored. In sum, the emergence/disappearance of alternative codons affecting PTMs in the evolutive course of the current pandemic is intriguing, and it has already been noticed in some SARS-CoV-2 genomes ( 73). Additional studies may unravel the impact of these events and could help in the development of strategies to control the infectious viral cycle.

In summary, our study provides updated HLA allele frequencies for South America, rectifying previously misrepresented alleles. This led to the identification of potential Class-I and -II epitopes in SARS-CoV-2 with high regional coverage. Some are supported by existing experimental evidence, while the rest represent novel candidates. These could represent targets for neutralizing antibodies or could be used for the development of vaccines and diagnostics tests, which needs to be further studied. Our study highlights the advantage of a regionally-focused design. Approaches based on the global population have the advantage of broad coverage. However, this may result in leaving aside the best regional candidates and reducing the regional population coverage, as shown in this study. Furthermore, incorrect HLA frequencies could result in misleading results and misrepresentation of certain populations. We hope our findings may promote regional efforts with potentially better specificity.

Lack of knowledge of the HLA allele frequencies' distribution in South America also limits regional scientific studies in the field. This impacts the study of infectious and autoimmune diseases, cancer immunotherapy, and transplantations. Our results will serve as an immediate source of information for multiple ongoing studies based on HLA allele frequencies.

In conclusion, the candidate epitopes presented may help in the fight against SARS-CoV-2, providing valuable information for the development of peptide vaccines and diagnostic tests. And updated HLA allele frequencies will impact on the study of many human diseases. We hope this literature review may result in a better representation of South America in future immunogenetic studies.

Data Availability Statement

All datasets generated for this study are included in the article/ Supplementary Material.

Author Contributions

DR and RC devised the research project. DR, AM, and OM-S performed the literature review. DR, MR, and RC selected and analyzed the viral genomes. DR, AM, and MR performed the epitope predictions. DR, AM, MR, and OM-S matched the predicted epitopes with previous experimental data and published predictions. AM and MR modeled the protein structures. AM, DR, and RC performed the prediction of selection pressures and post-translational modifications. AM and DR prepared the figures. All authors contributed to analyzing the results and manuscript preparation.

Funding

The publication fee of this study was sponsored by the Vice-rectorate for Research and Postgraduate Studies of the National University of San Marcos (Vicerrectorado de Investigación y Posgrado de la Universidad Nacional Mayor de San Marcos).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We gratefully acknowledge the authors and laboratories that generated and submitted genome sequences to the GISAID's EpiCov™ Database, on which this research is based. The detailed list of genomes sequenced used, authors, and submitting laboratories is provided in Table S1. All submitters of data may be contacted directly via www.gisaid.org. We also thank Rydberg Supo-Escalante and Eduardo Gushiken for their critical observations and recommendations during the course of this research.

Finally, we warmly thank the Professional School of Genetics and Biotechnology at the National University of San Marcos (Escuela Profesional de Genética y Biotecnología de la Universidad Nacional Mayor de San Marcos) for the invaluable scientific and humanistic formation provided to all authors.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2020.02008/full#supplementary-material

Abbreviations

AFNDB, Allele Frequency Net Database; COVID-19, Coronavirus Disease 2019; CS, Coverage Score; GISAID, Global Initiative on Sharing All Influenza Data database; HLA, Human Leukocyte Antigen; IEDB, Immune Epitope Database and Analysis Resource; PDB, Protein Data Bank; PTM, Post-translational modifications; SARS-CoV-2, Severe Acute Respiratory Syndrome Coronavirus 2; WAF, Weighted Allele Frequency.

References

39. Corman VM, Landt O, Kaiser M, Molenkamp R, Meijer A, Chu DK, et al. Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCR. Euro Surveill. (2020) 25:45. doi: 10.2807/1560-7917.ES.2020.25.3.2000045
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49. Flow Pharma FlowVaxTM Peptide Vaccine for COVID-19. (2020). Available online at: https://www.flowpharma.com/ (accessed April 11, 2020).

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Identification of Novel Candidate Epitopes on SARS-CoV-2 Proteins for South America: A Review of HLA Frequencies by Country

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Coronavirus disease (COVID-19), caused by the virus SARS-CoV-2, is already responsible for more than 4.3 million confirmed cases and 295,000 deaths worldwide as of May 15, 2020. Ongoing efforts to control the pandemic include the development of peptide-based vaccines and diagnostic tests. In these approaches, HLA allelic diversity plays a crucial role.

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No Café do curso de TDMA desta semana foram apresentadas pela Profa Dênia diversas possibilidades e exemplos de uso de uma infinidade de aplicativos e ferramentas que podem tornar nosso trabalho docente mais interativo e interessante. Fiquei atordoada com a quantidade de informação, quantos recursos temos disponível hoje! Aprender aos pouquinhos a usar pelo menos parte disso tudo é meu desejo e meu desafio hoje!

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Meu nome é Alessandra Abel Borges, sou professora de Imunologia e Virologia no Instituto de Ciências Biológicas e da Saúde, da UFAL. Os vírus sempre me intrigaram, desde criança. Comecei a estudá-los num estágio ainda na faculdade...O tempo passou e hoje sou Virologista há 21 anos. Neste ano de 2020 a pandemia de coronavírus pôs em evidência minha área de estudo e virou o mundo (e a Educação) de cabeça para baixo.

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Este é meu e-portfólio, minha primeira tarefa no curso de TDMA. Reaprender a ensinar e dominar tecnologias novas é meu maior desafio desde que pisei pela primeira vez numa sala de aula como professora, no longínquo ano de 2001 em Tubarão/SC...