Betacoronaviruses genome analysis reveals evolution toward specific codons usage: Implications for SARS-CoV-2 mitigation strategies.

Since the start of the coronavirus disease 2019 (COVID-19) pandemic, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly widespread worldwide becoming one of the major global public health issues of the last centuries. Currently, COVID-19 vaccine rollouts are finally upon us carrying the hope of herd immunity once a sufficient proportion of the population has been vaccinated or infected, as a new horizon. However, the emergence of SARS-CoV-2 variants brought concerns since, as the virus is exposed to environmental selection pressures, it can mutate and evolve, generating variants that may possess enhanced virulence. Codon usage analysis is a strategy to elucidate the evolutionary pressure of the viral genome suffered by different hosts, as possible cause of the emergence of new variants. Therefore, to get a better picture of the SARS-CoV-2 codon bias, we first identified the relative codon usage rate of all Betacoronaviruses lineages. Subsequently, we correlated putative cognate transfer ribonucleic acid (tRNAs) to reveal how those viruses adapt to hosts in relation to their preferred codon usage. Our analysis revealed seven preferred codons located in three different open reading frame which appear preferentially used by SARS-CoV-2. In addition, the tRNA adaptation analysis indicates a wide strategy of competition between the virus and mammalian as principal hosts highlighting the importance to reinforce the genomic monitoring to prompt identify any potential adaptation of the virus into new potential hosts which appear to be crucial to prevent and mitigate the pandemic.

Machine learning models exploring characteristic single-nucleotide signatures in yellow fever virus.

Yellow fever virus (YFV) is the agent of the most severe mosquito-borne disease in the tropics. Recently, Brazil suffered major YFV outbreaks with a high fatality rate affecting areas where the virus has not been reported for decades, consisting of urban areas where a large number of unvaccinated people live. We developed a machine learning framework combining three different algorithms (XGBoost, random forest and regularized logistic regression) to analyze YFV genomic sequences. This method was applied to 56 YFV sequences from human infections and 27 from non-human primate (NHPs) infections to investigate the presence of genetic signatures possibly related to disease severity (in human related sequences) and differences in PCR cycle threshold (Ct) values (in NHP related sequences). Our analyses reveal four non-synonymous single nucleotide variations (SNVs) on sequences from human infections, in proteins NS3 (E614D), NS4a (I69V), NS5 (R727G, V643A) and six non-synonymous SNVs on NHP sequences, in proteins E (L385F), NS1 (A171V), NS3 (I184V) and NS5 (N11S, I374V, E641D). We performed comparative protein structural analysis on these SNVs, describing possible impacts on protein function. Despite the fact that the dataset is limited in size and that this study does not consider virus-host interactions, our work highlights the use of machine learning as a versatile and fast initial approach to genomic data exploration.

Multi-epitope based vaccine against yellow fever virus applying immunoinformatics approaches.

Yellow fever disease is considered a re-emerging major health issue which has caused recent outbreaks with a high number of deaths. Tropical countries, mainly African and South American, are the most affected by Yellow fever outbreaks. Despite the availability of an attenuated vaccine, its use is limited for some groups such as pregnant and nursing women, immunocompromised and immunosuppressed patients, elderly people >65 years, infants <6 months and patients with biological disorders like thymus disorders. In order to achieve new preventive measures, we applied immunoinformatics approaches to develop a multi-epitope-based subunit vaccine for Yellow fever virus. Different epitopes, related to humoral and cell-mediated immunity, were predicted for complete polyproteins of two Yellow fever strains (Asibi and 17 D vaccine). Those epitopes common for both strains were mapped into a set of 137 sequences of Yellow fever virus, including 77 sequences from a recent outbreak at the state of Minas Gerais, southeast Brazil. Therefore, the present work uses robust bioinformatics approaches for the identification of a multi-epitope vaccine against the Yellow fever virus. Our results indicate that the identified multi-epitope vaccine might stimulate humoral and cellular immune responses and could be a potential vaccine candidate against Yellow fever virus infection. Hence, it should be subjected to further experimental validations. Communicated by Ramaswamy H. Sarma.

Untargeted Metabolomics Insights into Newborns with Congenital Zika Infection.

Zika virus (ZIKV), an emerging virus belonging to the Flaviviridae family, causes severe neurological clinical complications and has been associated with Guillain-Barré syndrome, fetal abnormalities known collectively as congenital Zika syndrome, and microcephaly. Studies have shown that ZIKV infection can alter cellular metabolism, directly affecting neural development. Brain growth requires controlled cellular metabolism, which is essential for cell proliferation and maturation. However, little is known regarding the metabolic profile of ZIKV-infected newborns and possible associations related to microcephaly. Furthering the understanding surrounding underlying mechanisms is essential to developing personalized treatments for affected individuals. Thus, metabolomics, the study of the metabolites produced by or modified in an organism, constitutes a valuable approach in the study of complex diseases. Here, 26 serum samples from ZIKV-positive newborns with or without microcephaly, as well as controls, were analyzed using an untargeted metabolomics approach involving gas chromatography-mass spectrometry (GC-MS). Significant alterations in essential and non-essential amino acids, as well as carbohydrates (including aldohexoses, such as glucose or mannose) and their derivatives (urea and pyruvic acid), were observed in the metabolic profiles analyzed. Our results provide insight into relevant metabolic processes in patients with ZIKV and microcephaly.

The ongoing COVID-19 epidemic in Minas Gerais, Brazil: insights from epidemiological data and SARS-CoV-2 whole genome sequencing.

The recent emergence of a coronavirus (SARS-CoV-2), first identified in the Chinese city of Wuhan in December 2019, has had major public health and economic consequences. Although 61,888 confirmed cases were reported in Brazil by 28 April 2020, little is known about the SARS-CoV-2 epidemic in this country. To better understand the recent epidemic in the second most populous state in southeast Brazil - Minas Gerais (MG) - we sequenced 40 complete SARS-CoV-2 genomes from MG cases and examined epidemiological data from three Brazilian states. Both the genome analyses and the geographical distribution of reported cases indicate for multiple independent introductions into MG. Epidemiological estimates of the reproductive number (R) using different data sources and theoretical assumptions suggest the potential for sustained virus transmission despite a reduction in R from the first reported case to the end of April 2020. The estimated date of SARS-CoV-2 introduction into Brazil was consistent with epidemiological data from the first case of a returned traveller from Lombardy, Italy. These findings highlight the nature of the COVID-19 epidemic in MG and reinforce the need for real-time and continued genomic surveillance strategies to better understand and prepare for the epidemic spread of emerging viral pathogens..