Addressing factors modulating COVID-19 is crucial since abundant clinical evidence shows that outcomes are markedly heterogeneous between patients. This requires identifying the factors and understanding how they mechanistically influence COVID-19. Here, we describe how eleven selected factors (age, sex, genetic factors, lipid disorders, heart failure, gut dysbiosis, diet, vitamin D deficiency, air pollution and exposure to chemicals) influence COVID-19 by applying the Adverse Outcome Pathway (AOP), which is well-established in regulatory toxicology. This framework aims to model the sequence of events leading to an adverse health outcome. Several linear AOPs depicting pathways from the binding of the virus to ACE2 up to clinical outcomes observed in COVID-19 have been developed and integrated into a network offering a unique overview of the mechanisms underlying the disease. As SARS-CoV-2 infectibility and ACE2 activity are the major starting points and inflammatory response is central in the development of COVID-19, we evaluated how those eleven intrinsic and extrinsic factors modulate those processes impacting clinical outcomes. Applying this AOP-aligned approach enables the identification of current knowledge gaps orientating for further research and allows to propose biomarkers to identify of high-risk patients. This approach also facilitates expertise synergy from different disciplines to address public health issues.

/pdf/factors-modulating-covid-19-a-mechanistic-understanding-22snisd0.pdf

Factors Modulating COVID-19: A Mechanistic Understanding Based on the Adverse Outcome Pathway Framework

Concomitant infection or co-infection with distinct SARS-CoV-2 genotypes has been reported as part of the epidemiological surveillance of the COVID-19 pandemic. In the context of the spread of more transmissible variants during 2021, co-infections are not only important due to the possible changes in the clinical outcome, but also the chance to generate new genotypes by recombination. However, a few approaches have developed bioinformatic pipelines to identify co-infections. Here we present a metagenomic pipeline based on the inference of multiple fragments similar to amplicon sequence variant (ASV-like) from sequencing data and a custom SARS-CoV-2 database to identify the concomitant presence of divergent SARS-CoV-2 genomes, i.e., variants of concern (VOCs). This approach was compared to another strategy based on whole-genome (metagenome) assembly. Using single or pairs of sequencing data of COVID-19 cases with distinct SARS-CoV-2 VOCs, each approach was used to predict the VOC classes (Alpha, Beta, Gamma, Delta, Omicron or non-VOC and their combinations). The performance of each pipeline was assessed using the ground-truth or expected VOC classes. Subsequently, the ASV-like pipeline was used to analyze 1021 cases of COVID-19 from Costa Rica to investigate the possible occurrence of co-infections. After the implementation of the two approaches, an accuracy of 96.2% was revealed for the ASV-like inference approach, which contrasts with the misclassification found (accuracy 46.2%) for the whole-genome assembly strategy. The custom SARS-CoV-2 database used for the ASV-like analysis can be updated according to the appearance of new VOCs to track co-infections with eventual new genotypes. In addition, the application of the ASV-like approach to all the 1021 sequenced samples from Costa Rica in the period October 12th-December 21th 2021 found that none corresponded to co-infections with VOCs. In conclusion, we developed a metagenomic pipeline based on ASV-like inference for the identification of co-infection with distinct SARS-CoV-2 VOCs, in which an outstanding accuracy was achieved. Due to the epidemiological, clinical, and molecular relevance of the concomitant infection with distinct genotypes, this work represents another piece in the process of the surveillance of the COVID-19 pandemic in Costa Rica and worldwide. 

/pdf/metagenomic-pipeline-for-identifying-co-infections-among-1nc2shjt.pdf

Metagenomic pipeline for identifying co-infections among distinct SARS-CoV-2 variants of concern: study cases from Alpha to Omicron

The human gut microbiome interacts with many diseases, with recent small studies suggesting a link with COVID-19 severity. Exploring this association at the population-level may provide novel insights and help to explain differences in COVID-19 severity between countries. We explore whether there is an association between the gut microbiome of people within different countries and the severity of COVID-19, measured as hospitalisation rate. We use data from the large (n = 3,055) open-access gut microbiome repository curatedMetagenomicData, as well as demographic and country-level metadata. Twelve countries were placed into two groups (high/low) according to COVID-19 hospitalisation rate before December 2020 (ourworldindata.com). We use an unsupervised machine learning method, Topological Data Analysis (TDA). This method analyses both the local geometry and global topology of a high-dimensional dataset, making it particularly suitable for population-level microbiome data. We report an association of distinct baseline population-level gut microbiome signatures with COVID-19 severity. This was found both with a PERMANOVA, as well as with TDA. Specifically, it suggests an association of anti-inflammatory bacteria, including Bifidobacteria species and Eubacterium rectale, with lower severity, and pro-inflammatory bacteria such as Prevotella copri with higher severity. This study also reports a significant impact of country-level confounders, specifically of the proportion of over 70-year-olds in the population, GDP, and human development index. Further interventional studies should examine whether these relationships are causal, as well as considering the contribution of other variables such as genetics, lifestyle, policy, and healthcare system. The results of this study support the value of a population-level association design in microbiome research in general and for the microbiome-COVID-19 relationship, in particular. Finally, this research underscores the potential of TDA for microbiome studies, and in identifying novel associations.

COVID-19 severity is associated with population-level gut microbiome variations

Insights into the mutation T1117I in the spike and the lineage B.1.1.389 of SARS-CoV-2 circulating in Costa Rica

Latin America is one of the regions in which the COVID-19 pandemic has had a stronger impact, with more than 72 million reported infections and 1.6 million deaths until June 2022. Since this region is ecologically diverse and is affected by enormous social inequalities, efforts to identify genomic patterns of the circulating SARS-CoV-2 genotypes are necessary for the suitable management of the pandemic. To contribute to the genomic surveillance of the SARS-CoV-2 in Latin America, we extended the number of SARS-CoV-2 genomes available from the region by sequencing and analyzing the viral genome from COVID-19 patients from seven countries (Argentina, Brazil, Costa Rica, Colombia, Mexico, Bolivia and Peru). Subsequently, we analyzed the genomes circulating mainly during 2021 including records from GISAID database from Latin America. A total of 1534 genome sequences were generated from seven countries, demonstrating the laboratory and bioinformatics capabilities for genomic surveillance of pathogens that have been developed locally. For Latin America, patterns regarding several variants associated with multiple re-introductions, a relatively low percentage of sequenced samples, as well as an increment in the mutation frequency since the beginning of the pandemic, are in line with worldwide data. Besides, some variants of concern (VOC) and variants of interest (VOI) such as Gamma, Mu and Lambda, and at least 83 other lineages have predominated locally with a country-specific enrichments. This work has contributed to the understanding of the dynamics of the pandemic in Latin America as part of the local and international efforts to achieve timely genomic surveillance of SARS-CoV-2.

/pdf/overview-of-the-sars-cov-2-genotypes-circulating-in-latin-27hbe6as.pdf

Overview of the SARS-CoV-2 genotypes circulating in Latin America during 2021

The clinical manifestations of COVID-19, caused by the SARS-CoV-2, define a large spectrum of symptoms that are mainly dependent on the human host conditions. In Costa Rica, more than 169,000 cases and 2185 deaths were reported during the year 2020, the pre-vaccination period. To describe the clinical presentations at the time of diagnosis of SARS-CoV-2 infection in Costa Rica during the pre-vaccination period, we implemented a symptom-based clustering using machine learning to identify clusters or clinical profiles at the population level among 18,974 records of positive cases. Profiles were compared based on symptoms, risk factors, viral load, and genomic features of the SARS-CoV-2 sequence. A total of 18 symptoms at time of diagnosis of SARS-CoV-2 infection were reported with a frequency > 1%, and those were used to identify seven clinical profiles with a specific composition of clinical manifestations. In the comparison between clusters, a lower viral load was found for the asymptomatic group, while the risk factors and the SARS-CoV-2 genomic features were distributed among all the clusters. No other distribution patterns were found for age, sex, vital status, and hospitalization. In conclusion, during the pre-vaccination time in Costa Rica, the symptoms at the time of diagnosis of SARS-CoV-2 infection were described in clinical profiles. The host co-morbidities and the SARS-CoV-2 genotypes are not specific of a particular profile, rather they are present in all the groups, including asymptomatic cases. In addition, this information can be used for decision-making by the local healthcare institutions (first point of contact with health professionals, case definition, or infrastructure). In further analyses, these results will be compared against the profiles of cases during the vaccination period.The online version contains supplementary material available at 10.1007/s43657-022-00058-x. 

https://link.springer.com/content/pdf/10.1007/s43657-022-00058-x.pdf

Clinical Profiles at the Time of Diagnosis of SARS-CoV-2 Infection in Costa Rica During the Pre-vaccination Period Using a Machine Learning Approach

2 Methods: Across-sectional study that describes the epidemiologic behavior of the cases of suspected leptospirosis using the data of the case investigation forms of the surveillance system for this event. Laboratory data was obtained from the INCIENSAs National Leptospirosis Reference Center. The period of study was between 2004 and 2008 in the Brunca, Central Este, Huetar Atlantica, Huetar Norte and Pacifico Central regions, according to the structural organization of the Costa Rican Ministry of Health. Results: 498 case investigation forms were analyzed. The most frequent occupations are those described as "without risk" (37.3%), students (23.2%) and agricultural (22.1%). The exposure to domestic animals prevails (67.6%), of this 92% were dogs. The presence of rodents is reported in 52.6% of the cases. A 29.5% had contact with ponds or swimming pools and 28% with stagnant waters, both with a prevalence of 1, 88 and 1, 71(p<0,05). Conclusion: The frequency of cases in groups "without occupational risk" indicates that the case investigation form must be redesigned to incorporate other variables that include recreational ones.

/pdf/epidemiological-behavior-of-cases-of-suspected-leptospirosis-4maa4bdmxi.pdf

Epidemiological behavior of cases of suspected leptospirosis in five health regions of Costa Rica

Background and aim: The prevention and control of leptospirosis depends on the knowledge about the exposure of people to possible risk factors, which are rep...

Comportamiento epidemiológico de casos sospechosos por leptospirosis en cinco regiones de salud de Costa Rica

Background and aim: The prevention and control of leptospirosis depends on the knowledge about the exposure of people to possible risk factors, which are reported in the national surveillance system for this event. This study describes the epidemiologic behavior of the cases of suspected leptospirosis, which were researched using the protocol in force in the country. Methods: Across-sectional study that describes the epidemiologic behavior of the cases of suspected leptospirosisusing the data of the case investigation forms of the surveillance system for this event.

Comportamiento epidemiológico de casos sospechosos por leptospirosis en cinco regiones de salud de Costa Rica. Epidemiological behavior of cases of suspected leptospirosis in five health regions of Costa Rica

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Clinical Profiles at the Time of Diagnosis of SARS-CoV-2 Infection in Costa Rica During the Pre-vaccination Period Using a Machine Learning Approach

Epidemiological behavior of cases of suspected leptospirosis in five health regions of Costa Rica

Comportamiento epidemiológico de casos sospechosos por leptospirosis en cinco regiones de salud de Costa Rica

Comportamiento epidemiológico de casos sospechosos por leptospirosis en cinco regiones de salud de Costa Rica. Epidemiological behavior of cases of suspected leptospirosis in five health regions of Costa Rica