Insights into SARS-CoV-2 Surveillance among Prison Populations in Mato Grosso do Sul, Brazil, in 2022.

This study examines the epidemiological and genomic characteristics, along with the transmission dynamics, of SARS-CoV-2 within prison units I and II in Campo Grande, Mato Grosso do Sul, Brazil. Conducted between May and October 2022, it reveals how the virus spreads in the confined settings of prisons, emphasizing the roles of overcrowded cells, frequent transfers, and limited healthcare access. The research involved 1927 participants (83.93% of the total prison population) and utilized nasopharyngeal swabs and RT-qPCR testing for detection. Contact tracing monitored exposure within cells. Out of 2108 samples, 66 positive cases were identified (3.13%), mostly asymptomatic (77.27%), with the majority aged 21-29 and varying vaccination statuses. Next-generation sequencing generated 28 whole genome sequences, identifying the Omicron variant (subtypes BA.2 and BA.5) with 99% average coverage. Additionally, the study seeks to determine the relationship between immunization levels and the incidence of SARS-CoV-2 cases within this enclosed population. The findings underscore the necessity of comprehensive control strategies in prisons, including rigorous screening, isolation protocols, vaccination, epidemiological monitoring, and genomic surveillance to mitigate disease transmission and protect both the incarcerated population and the broader community.

Using discrete wavelet transform for optimizing COVID-19 new cases and deaths prediction worldwide with deep neural networks.

This work aims to compare deep learning models designed to predict daily number of cases and deaths caused by COVID-19 for 183 countries, using a daily basis time series, in addition to a feature augmentation strategy based on Discrete Wavelet Transform (DWT). The following deep learning architectures were compared using two different feature sets with and without DWT: (1) a homogeneous architecture containing multiple LSTM (Long-Short Term Memory) layers and (2) a hybrid architecture combining multiple CNN (Convolutional Neural Network) layers and multiple LSTM layers. Therefore, four deep learning models were evaluated: (1) LSTM, (2) CNN + LSTM, (3) DWT + LSTM and (4) DWT + CNN + LSTM. Their performances were quantitatively assessed using the metrics: Mean Absolute Error (MAE), Normalized Mean Squared Error (NMSE), Pearson R, and Factor of 2. The models were designed to predict the daily evolution of the two main epidemic variables up to 30 days ahead. After a fine-tuning procedure for hyperparameters optimization of each model, the results show a statistically significant difference between the models' performances both for the prediction of deaths and confirmed cases (p-value<0.001). Based on NMSE values, significant differences were observed between LSTM and CNN+LSTM, indicating that convolutional layers added to LSTM networks made the model more accurate. The use of wavelet coefficients as additional features (DWT+CNN+LSTM) achieved equivalent results to CNN+LSTM model, which demonstrates the potential of wavelets application for optimizing models, since this allows training with a smaller time series data.

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.

Human Retrovirus Codon Usage from tRNA Point of View: Therapeutic Insights.

The purpose of this study was to investigate the balance between transfer ribonucleic acid (tRNA) supply and demand in retrovirus-infected cells, seeking the best targets for antiretroviral therapy based on the hypothetical tRNA Inhibition Therapy (TRIT). Codon usage and tRNA gene data were retrieved from public databases. Based on logistic principles, a therapeutic score (T-score) was calculated for all sense codons, in each retrovirus-host system. Codons that are critical for viral protein translation, but not as critical for the host, have the highest T-score values. Theoretically, inactivating the cognate tRNA species should imply a severe reduction of the elongation rate during viral mRNA translation. We developed a method to predict tRNA species critical for retroviral protein synthesis. Four of the best TRIT targets in HIV-1 and HIV-2 encode Large Hydrophobic Residues (LHR), which have a central role in protein folding. One of them, codon CUA, is also a TRIT target in both HTLV-1 and HTLV-2. Therefore, a drug designed for inactivating or reducing the cytoplasmatic concentration of tRNA species with anticodon TAG could attenuate significantly both HIV and HTLV protein synthesis rates. Inversely, replacing codons ending in UA by synonymous codons should increase the expression, which is relevant for DNA vaccine design.

A Statistical Method without Training Step for the Classification of Coding Frame in Transcriptome Sequences.

In this study, we investigated the modalities of coding open reading frame (cORF) classification of expressed sequence tags (EST) by using the universal feature method (UFM). The UFM algorithm is based on the scoring of purine bias (Rrr) and stop codon frequencies. UFM classifies ORFs as coding or non-coding through a score based on 5 factors: (i) stop codon frequency; (ii) the product of the probabilities of purines occurring in the three positions of nucleotide triplets; (iii) the product of the probabilities of Cytosine (C), Guanine (G), and Adenine (A) occurring in the 1st, 2nd, and 3rd positions of triplets, respectively; (iv) the probabilities of a G occurring in the 1st and 2nd positions of triplets; and (v) the probabilities of a T occurring in the 1st and an A in the 2nd position of triplets. Because UFM is based on primary determinants of coding sequences that are conserved throughout the biosphere, it is suitable for cORF classification of any sequence in eukaryote transcriptomes without prior knowledge. Considering the protein sequences of the Protein Data Bank (RCSB PDB or more simply PDB) as a reference, we found that UFM classifies cORFs of ≥200 bp (if the coding strand is known) and cORFs of ≥300 bp (if the coding strand is unknown), and releases them in their coding strand and coding frame, which allows their automatic translation into protein sequences with a success rate equal to or higher than 95%. We first established the statistical parameters of UFM using ESTs from Plasmodium falciparum, Arabidopsis thaliana, Oryza sativa, Zea mays, Drosophila melanogaster, Homo sapiens and Chlamydomonas reinhardtii in reference to the protein sequences of PDB. Second, we showed that the success rate of cORF classification using UFM is expected to apply to approximately 95% of higher eukaryote genes that encode for proteins. Third, we used UFM in combination with CAP3 to assemble large EST samples into cORFs that we used to analyze transcriptome phenotypes in rice, maize, and humans. We discuss the error rate and the interference of noisy sequences such as pseudogenes, transposons, and retrotransposons. This method is suitable for rapid cORF extraction from transcriptome data and allows correct description of the genome phenotypes of plant genomes without prior knowledge. Additional care is necessary when addressing the human transcriptome due to the interference caused by large amounts of noisy sequences. UFM can be regarded as a low complexity tool for prior knowledge extraction concerning the coding fraction of the transcriptome of any eukaryote. Due to its low level of complexity, UFM is also very robust to variations of codon usage.

Classifying coding DNA with nucleotide statistics.

In this report, we compared the success rate of classification of coding sequences (CDS) vs. introns by Codon Structure Factor (CSF) and by a method that we called Universal Feature Method (UFM). UFM is based on the scoring of purine bias (Rrr) and stop codon frequency. We show that the success rate of CDS/intron classification by UFM is higher than by CSF. UFM classifies ORFs as coding or non-coding through a score based on (i) the stop codon distribution, (ii) the product of purine probabilities in the three positions of nucleotide triplets, (iii) the product of Cytosine (C), Guanine (G), and Adenine (A) probabilities in the 1st, 2nd, and 3rd positions of triplets, respectively, (iv) the probabilities of G in 1st and 2nd position of triplets and (v) the distance of their GC3 vs. GC2 levels to the regression line of the universal correlation. More than 80% of CDSs (true positives) of Homo sapiens (>250 bp), Drosophila melanogaster (>250 bp) and Arabidopsis thaliana (>200 bp) are successfully classified with a false positive rate lower or equal to 5%. The method releases coding sequences in their coding strand and coding frame, which allows their automatic translation into protein sequences with 95% confidence. The method is a natural consequence of the compositional bias of nucleotides in coding sequences.

Universal Features for the Classification of Coding and Non-coding DNA Sequences.

In this report, we revisited simple features that allow the classification of coding sequences (CDS) from non-coding DNA. The spectrum of codon usage of our sequence sample is large and suggests that these features are universal. The features that we investigated combine (i) the stop codon distribution, (ii) the product of purine probabilities in the three positions of nucleotide triplets, (iii) the product of Cytosine, Guanine, Adenine probabilities in 1st, 2nd, 3rd position of triplets, respectively, (iv) the product of G and C probabilities in 1st and 2nd position of triplets. These features are a natural consequence of the physico-chemical properties of proteins and their combination is successful in classifying CDS and non-coding DNA (introns) with a success rate >95% above 350 bp. The coding strand and coding frame are implicitly deduced when the sequences are classified as coding.

Swine and poultry pathogens: the complete genome sequences of two strains of Mycoplasma hyopneumoniae and a strain of Mycoplasma synoviae.

This work reports the results of analyses of three complete mycoplasma genomes, a pathogenic (7448) and a nonpathogenic (J) strain of the swine pathogen Mycoplasma hyopneumoniae and a strain of the avian pathogen Mycoplasma synoviae; the genome sizes of the three strains were 920,079 bp, 897,405 bp, and 799,476 bp, respectively. These genomes were compared with other sequenced mycoplasma genomes reported in the literature to examine several aspects of mycoplasma evolution. Strain-specific regions, including integrative and conjugal elements, and genome rearrangements and alterations in adhesin sequences were observed in the M. hyopneumoniae strains, and all of these were potentially related to pathogenicity. Genomic comparisons revealed that reduction in genome size implied loss of redundant metabolic pathways, with maintenance of alternative routes in different species. Horizontal gene transfer was consistently observed between M. synoviae and Mycoplasma gallisepticum. Our analyses indicated a likely transfer event of hemagglutinin-coding DNA sequences from M. gallisepticum to M. synoviae.

The mutual information theory for the certification of rice coding sequences.

We report here the use of the mutual information theory for the certification of annotated rice coding sequences of both GenBank and TIGR databases. Considering coding sequences larger than 600 bp, we successfully screened out genes with aberrant compositional features. We found that they represent about 10% of both datasets after cleaning for gene redundancy. Most of the rejected accessions showed a different trend in GC3% vs GC2% plot compared to the set of accessions that have been published in international journals. This suggests the existence of a bias in the pattern recognition algorithms used by gene prediction programs.