Association between prognostic factors and the outcomes of patients infected with SARS-CoV-2 harboring multiple spike protein mutations

Sequence analysis of the Spike, RNA-dependent RNA polymerase, and protease genes reveals a distinct evolutionary pattern of SARS-CoV-2 variants circulating in Yogyakarta and Central Java provinces, Indonesia

During the Covid-19 pandemic, the resurgence of SARS-CoV-2 was due to the development of novel variants of concern (VOC). Thus, genomic surveillance is essential to monitor continuing evolution of SARS-CoV-2 and to track the emergence of novel variants. In this study, we performed phylogenetic, mutation, and selection pressure analyses of the Spike, nsp12, nsp3, and nsp5 genes of SARS-CoV-2 isolates circulating in Yogyakarta and Central Java provinces, Indonesia from May 2021 to February 2022. Various bioinformatics tools were employed to investigate the evolutionary dynamics of distinct SARS-CoV-2 isolates. During the study period, 213 and 139 isolates of Omicron and Delta variants were identified, respectively. Particularly in the Spike gene, mutations were significantly more abundant in Omicron than in Delta variants. Consistently, in all of four genes studied, the substitution rates of Omicron were higher than that of Delta variants, especially in the Spike and nsp12 genes. In addition, selective pressure analysis revealed several sites that were positively selected in particular genes, implying that these sites were functionally essential for virus evolution. In conclusion, our study demonstrated a distinct evolutionary pattern of SARS-CoV-2 variants circulating in Yogyakarta and Central Java provinces, Indonesia.

Prognostic factors for the outcomes of COVID-19 patients infected with SARS-CoV-2 Omicron and Delta variants

BACKGROUND

The SARS-CoV-2 Omicron variant has replaced the previously dominant Delta variant because of high transmissibility. However, studies on the impact of the Omicron variant on the severity of COVID-19 are still limited in developing countries. Our study aimed to determine the prognostic factors for the outcomes of patients infected with SARS-CoV-2 Omicron and Delta variants, including age, sex, comorbidities, and smoking.

METHODS

In this retrospective cross-sectional study, we involved 352 patients with COVID-19 from Yogyakarta and Central Java provinces, Indonesia, from May 2021 to February 2022, consisting of 164 males and 188 females. We included all patients with the PCR's Ct value of less than 30 for further whole-genome sequencing.

RESULTS

Ct value and mean age of COVID-19 patients were not significantly different between both groups (p = 0.146 and 0.273, respectively). Patients infected with Omicron (n = 139) and Delta (n = 213) variants showed similar hospitalization (p = 0.396) and mortality rates (p = 0.565). Multivariate analysis of both groups showed that older age (≥ 65 years) had a higher risk for hospitalization (OR = 3.86 [95% CI = 1.29-11.5]; p = 0.015) and fatalities (OR = 3.91 [95% CI = 1.35-11.42]; p = 0.012). In both groups, patients with cardiovascular disease had a higher risk for hospitalization (OR = 5.36 [95% CI = 1.08-26.52]; p = 0.039), whereas patients with diabetes revealed a higher risk for fatalities (OR = 9.47 [95% CI = 3.23-27.01]; p = < 0.001).

CONCLUSIONS

Our study shows that patients infected with Omicron and Delta variants reveal similar clinical outcomes, including hospitalization and mortality. Our findings further confirm that older age, cardiovascular disease, and diabetes are substantial prognostic factors for the outcomes of COVID-19 patients. Our findings imply that COVID-19 patients with older age, cardiovascular disease, or diabetes should be treated comprehensively and cautiously to prevent further morbidity and mortality. Furthermore, incomplete data on vaccination status hampered us from analyzing further its impact on hospitalization and mortality in our patients.

Interpretable and Predictive Deep Neural Network Modeling of the SARS-CoV-2 Spike Protein Sequence to Predict COVID-19 Disease Severity

Through the COVID-19 pandemic, SARS-CoV-2 has gained and lost multiple mutations in novel or unexpected combinations. Predicting how complex mutations affect COVID-19 disease severity is critical in planning public health responses as the virus continues to evolve. This paper presents a novel computational framework to complement conventional lineage classification and applies it to predict the severe disease potential of viral genetic variation. The transformer-based neural network model architecture has additional layers that provide sample embeddings and sequence-wide attention for interpretation and visualization. First, training a model to predict SARS-CoV-2 taxonomy validates the architecture's interpretability. Second, an interpretable predictive model of disease severity is trained on spike protein sequence and patient metadata from GISAID. Confounding effects of changing patient demographics, increasing vaccination rates, and improving treatment over time are addressed by including demographics and case date as independent input to the neural network model. The resulting model can be interpreted to identify potentially significant virus mutations and proves to be a robust predctive tool. Although trained on sequence data obtained entirely before the availability of empirical data for Omicron, the model can predict the Omicron's reduced risk of severe disease, in accord with epidemiological and experimental data.

Tracking the molecular evolution and transmission patterns of SARS-CoV-2 lineage B.1.466.2 in Indonesia based on genomic surveillance data

Background

As a new epi-center of COVID-19 in Asia and a densely populated developing country, Indonesia is facing unprecedented challenges in public health. SARS-CoV-2 lineage B.1.466.2 was reported to be an indigenous dominant strain in Indonesia (once second only to the Delta variant). However, it remains unclear how this variant evolved and spread within such an archipelagic nation.

Methods

For statistical description, the spatiotemporal distributions of the B.1.466.2 variant were plotted using the publicly accessible metadata in GISAID. A total of 1302 complete genome sequences of Indonesian B.1.466.2 strains with high coverage were downloaded from the GISAID’s EpiCoV database on 28 August 2021. To determine the molecular evolutionary characteristics, we performed a time-scaled phylogenetic analysis using the maximum likelihood algorithm and called the single nucleotide variants taking the Wuhan-Hu-1 sequence as reference. To investigate the spatiotemporal transmission patterns, we estimated two dynamic parameters (effective population size and effective reproduction number) and reconstructed the phylogeography among different islands.

Results

As of the end of August 2021, nearly 85% of the global SARS-CoV-2 lineage B.1.466.2 sequences (including the first one) were obtained from Indonesia. This variant was estimated to account for over 50% of Indonesia’s daily infections during the period of March–May 2021. The time-scaled phylogeny suggested that SARS-CoV-2 lineage B.1.466.2 circulating in Indonesia might have originated from Java Island in mid-June 2020 and had evolved into two disproportional and distinct sub-lineages. High-frequency non-synonymous mutations were mostly found in the spike and NSP3; the S-D614G/N439K/P681R co-mutations were identified in its larger sub-lineage. The demographic history was inferred to have experienced four phases, with an exponential growth from October 2020 to February 2021. The effective reproduction number was estimated to have reached its peak (11.18) in late December 2020 and dropped to be less than one after early May 2021. The relevant phylogeography showed that Java and Sumatra might successively act as epi-centers and form a stable transmission loop. Additionally, several long-distance transmission links across seas were revealed.

Conclusions

SARS-CoV-2 variants circulating in the tropical archipelago may follow unique patterns of evolution and transmission. Continuous, extensive and targeted genomic surveillance is essential.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12985-022-01830-1.

SARS-CoV-2 Variants and Clinical Outcomes: A Systematic Review

Background: From the start of the COVID-19 pandemic, new SARS-CoV-2 variants have emerged that potentially affect transmissibility, severity, and immune evasion in infected individuals. In the present systematic review, the impact of different SARS-CoV-2 variants on clinical outcomes is analyzed. Methods: A systematic review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020. Two databases (PubMed and ScienceDirect) were searched for original articles published from 1 January 2020 to 23 November 2021. The articles that met the selection criteria were appraised according to the Newcastle–Ottawa Quality Assessment Scale. Results: Thirty-three articles were included, involving a total of 253,209 patients and 188,944 partial or complete SARS-CoV-2 sequences. The most reported SARS-CoV-2 variants showed changes in the spike protein, N protein, RdRp and NSP3. In 28 scenarios, SARS-CoV-2 variants were found to be associated with a mild to severe or even fatal clinical outcome, 15 articles reported such association to be statistically significant. Adjustments in eight of them were made for age, sex and other covariates. Conclusions: SARS-CoV-2 variants can potentially have an impact on clinical outcomes; future studies focused on this topic should consider several covariates that influence the clinical course of the disease.

Is the Infection of the SARS-CoV-2 Delta Variant Associated With the Outcomes of COVID-19 Patients?

Background: Severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) Delta variant (B.1.617.2) has been responsible for the current increase in Coronavirus disease 2019 (COVID-19) infectivity rate worldwide. We compared the impact of the Delta variant and non-Delta variant on the COVID-19 outcomes in patients from Yogyakarta and Central Java provinces, Indonesia. Methods: In this cross-sectional study, we ascertained 161 patients, 69 with the Delta variant and 92 with the non-Delta variant. The Illumina MiSeq next-generation sequencer was used to perform the whole-genome sequences of SARS-CoV-2. Results: The mean age of patients with the Delta variant and the non-Delta variant was 27.3 ± 20.0 and 43.0 ± 20.9 (p = 3 × 10-6). The patients with Delta variant consisted of 23 males and 46 females, while the patients with the non-Delta variant involved 56 males and 36 females (p = 0.001). The Ct value of the Delta variant (18.4 ± 2.9) was significantly lower than that of the non-Delta variant (19.5 ± 3.8) (p = 0.043). There was no significant difference in the hospitalization and mortality of patients with Delta and non-Delta variants (p = 0.80 and 0.29, respectively). None of the prognostic factors were associated with the hospitalization, except diabetes with an OR of 3.6 (95% CI = 1.02-12.5; p = 0.036). Moreover, the patients with the following factors have been associated with higher mortality rate than the patients without the factors: age ≥65 years, obesity, diabetes, hypertension, and cardiovascular disease with the OR of 11 (95% CI = 3.4-36; p = 8 × 10-5), 27 (95% CI = 6.1-118; p = 1 × 10-5), 15.6 (95% CI = 5.3-46; p = 6 × 10-7), 12 (95% CI = 4-35.3; p = 1.2 × 10-5), and 6.8 (95% CI = 2.1-22.1; p = 0.003), respectively. Multivariate analysis showed that age ≥65 years, obesity, diabetes, and hypertension were the strong prognostic factors for the mortality of COVID-19 patients with the OR of 3.6 (95% CI = 0.58-21.9; p = 0.028), 16.6 (95% CI = 2.5-107.1; p = 0.003), 5.5 (95% CI = 1.3-23.7; p = 0.021), and 5.8 (95% CI = 1.02-32.8; p = 0.047), respectively. Conclusions: We show that the patients infected by the SARS-CoV-2 Delta variant have a lower Ct value than the patients infected by the non-Delta variant, implying that the Delta variant has a higher viral load, which might cause a more transmissible virus among humans. However, the Delta variant does not affect the COVID-19 outcomes in our patients. Our study also confirms that older age and comorbidity increase the mortality rate of patients with COVID-19.