Metabolic phenotyping in phenylketonuria reveals disease clustering independently of metabolic control.

Phenylketonuria (PKU, MIM #261600) is one of the most common inborn errors of metabolism (IEM) with an incidence of 1:10000 in the European population. PKU is caused by autosomal recessive mutations in phenylalanine hydroxylase (PAH) and manifests with elevation of phenylalanine (Phe) in plasma and urine. Untreated PKU manifests with intellectual disability including seizures, microcephaly and behavioral abnormalities. Early treatment and good compliance result in a normal intellectual outcome in many but not in all patients. This study examined plasma metabolites in patients with PKU (n = 27), hyperphenylalaninemia (HPA, n = 1) and healthy controls (n = 32) by LC- MS/MS. We hypothesized that PKU patients would exhibit a distinct "submetabolome" compared to that of healthy controls. We further hypothesized that the submetabolome of PKU patients with good metabolic control would resemble that of healthy controls. Results from this study show: (i) Distinct clustering of healthy controls and PKU patients based on polar metabolite profiling, (ii) Increased and decreased concentrations of metabolites within and afar from the Phe pathway in treated patients, and (iii) A specific PKU-submetabolome independently of metabolic control assessed by Phe in plasma. We examined the relationship between PKU metabolic control and extended metabolite profiles in plasma. The PKU submetabolome characterized in this study represents the combined effects of dietary adherence, adjustments in metabolic pathways to compensate for defective Phe processing, as well as metabolic derangements that could not be corrected with dietary management even in patients classified as having good metabolic control. New therapeutic targets may be uncovered to approximate the PKU submetabolome to that of healthy controls and prevent long-term organ damage.

Chronic disease clusters are associated with prolonged, bothersome, and multisite musculoskeletal pain: a population-based study on Northern Finns.

BACKGROUND: Chronic diseases often accumulate with musculoskeletal (MSK) pain. However, less evidence is available on idiosyncratic patterns of chronic diseases and their relationships with the severity of MSK pain in general MSK pain populations. MATERIAL AND METHODS: Questionnaire-based data on physician-diagnosed chronic diseases, MSK pain and its dimensions (frequency, intensity, bothersomeness, and the number of pain sites), and confounders were collected from the Northern Finland Birth Cohort 1966 at the age of 46. Latent Class Analysis (LCA) was used to identify chronic disease clusters among individuals who reported any MSK pain within the previous year (n = 6105). The associations between chronic disease clusters, pain dimensions, and severe MSK pain, which was defined as prolonged (over 30 d within the preceding year), bothersome (Numerical Rating Scale >5), and multisite (two or more pain sites) pain, were analyzed using logistic regression and general linear regression models, adjusted for sex and educational level (n for the full sample = 4768). RESULTS: LCA resulted in three clusters: Metabolic (10.8% of the full sample), Psychiatric (2.9%), and Relatively Healthy (86.3%). Compared to the Relatively Healthy cluster, the Metabolic and Psychiatric clusters had higher odds for daily pain and higher mean pain intensity, bothersomeness, and the number of pain sites. Similarly, the odds for severe MSK pain were up to 75% (95% confidence interval: 44%-113%) and 155% (81%-259%) higher in the Metabolic and Psychiatric clusters, respectively, after adjustments for sex and educational level. CONCLUSIONS: Distinct patterns of chronic disease accumulation can be identified in the general MSK pain population. It seems that mental and metabolic health are at interplay with severe MSK pain. These findings suggest a potential need to screen for psychiatric and metabolic entities of health when treating working-aged people with MSK pain.Key messagesThis large study on middle-aged people with musculoskeletal pain aimed to examine the idiosyncratic patterns of chronic diseases and their relationships with the severity of musculoskeletal pain. Latent class cluster analysis identified three chronic disease clusters: Psychiatric, Metabolic, and Relatively Healthy. People with accumulated mental (Psychiatric cluster) or metabolic diseases (Metabolic cluster) experienced more severe pain than people who were relatively healthy (Relatively Healthy cluster). These findings suggest a potential need to screen for psychiatric and metabolic entities of health when treating working-aged people with MSK pain.

[Analysis of HIV transmission hotspots and characteristics of cross-regional transmission in Guangxi Zhuang Autonomous Region based on molecular network].

Objective: To analyze HIV transmission hotspots and characteristics of cross-regional transmission in Guangxi Zhuang autonomous region (Guangxi) based on the molecular network analysis, and provide evidence for optimization of precise AIDS prevention and control strategies. Methods: A total of 5 996 HIV pol sequences sampled from Guangxi between 1997 and 2020 were analyzed together with 165 534 published HIV pol sequences sampled from other regions. HIV-TRACE was used to construct molecular network in a pairwise genetic distance threshold of 0.5%. Results: The proportion of HIV sequences entering the molecular network of HIV transmission hotspots in Guangxi was 31.5% (1 886/5 996). In the molecular network of HIV cross-regional transmission, the links within Guangxi accounted for 51.6% (2 613/5 062), the links between Guangxi and other provinces in China accounted for 48.0% (2 430/5 062), and the links between Guangxi and other countries accounted for 0.4% (19/5 062). The main regions which had cross-regional linked with Guangxi were Guangdong (49.5%, 1 212/2 449), Beijing (17.5%, 430/2 449), Shanghai (6.9%, 168/2 449), Sichuan (5.7%, 140/2 449), Yunnan (4.2%, 102/2 449), Shaanxi (3.8%, 93/2 449), Zhejiang (2.8%, 69/2 449), Hainan (2.0%, 49/2 449), Anhui (1.5%, 37/2 449), Jiangsu (1.3%, 33/2 449), and other regions (each one <1.0%), respectively. The risk factors of entering the molecular network of HIV transmission hotspots in Guangxi included being aged ≥50 years (compared with being aged 25-49 years, aOR=1.68,95%CI:1.46-1.95), males (compared with females, aOR=1.21,95%CI:1.05-1.40), being single (compared with being married, aOR=1.18,95%CI:1.00-1.39), having education level of high school or above (compared with having education level of junior high school or below, aOR=1.21,95%CI:1.04-1.42), acquired HIV through homosexual intercourse (compared with acquired with HIV through heterosexual intercourse, aOR=1.77, 95%CI:1.48-2.12). The risk factors of cross-regional transmission included males (compared with females, aOR=1.74,95%CI:1.13-2.75), having education level of high school or above (compared with having education level of junior high school or below, aOR=1.96,95%CI:1.43-2.69), being freelancer/unemployed/retired (compared with being farmers, aOR=1.50,95%CI:1.07-2.11), acquired HIV through homosexual intercourse (compared with acquired with HIV through heterosexual intercourse, aOR=3.28,95%CI:2.30-4.72). Conclusion: There are HIV transmission hotspots in Guangxi. Guangxi and other provinces in China form a complex cross-regional transmission network. Future studies should carry out social network surveys in high-risk populations inferred from the molecular network analysis for the timely identification of hidden transmission chains and reduction of the second-generation transmission of HIV.

Association of Multimorbidity, Disease Clusters, and Modification by Genetic Factors With Risk of Dementia.

Importance: Individual conditions have been identified as risk factors for dementia; however, it is important to consider the role of multimorbidity, as conditions often co-occur. Objective: To investigate whether multimorbidity is associated with incident dementia and whether associations vary by different clusters of disease and genetic risk for dementia. Design, Setting, and Participants: This population-based prospective cohort study used data from the UK Biobank cohort, with baseline data collected between 2006 and 2010 and with up to 15 years of follow-up. Participants included women and men without dementia and aged at least 60 years at baseline. Medical conditions were captured as part of nurse-led verbal interviews conducted at baseline assessment centers. Data were analyzed from October 2020 to July 2022. Exposures: The presence of at least 2 long-term conditions from a preselected list of 42 conditions was used to define multimorbidity. High genetic risk for dementia was based on presence of 1 or 2 apolipoprotein (APOE) ε4 alleles. Main Outcomes and Measures: The main outcome, incident dementia, was derived from hospital inpatient and death registry records. Associations of multimorbidity with dementia were assessed with Cox proportional hazards models. Results: A total of 206 960 participants (mean [SD] age, 64.1 [2.9] years, 108 982 [52.7%] women) were included in the final sample, of whom 89 201 participants (43.1%) had multimorbidity. Over a mean (SD) of 11.8 (2.2) years of follow-up, 6182 participants (3.0%) developed dementia. The incidence rate was 1.87 (95% CI, 1.80-1.94) per 1000 person-years for those without multimorbidity and 3.41 (95% CI, 3.30-3.53) per 1000 person-years for those with multimorbidity. In Cox proportional hazards models adjusted for age, sex, ethnicity, education, socioeconomic status, and APOE-ε4 carrier status, multimorbidity was associated with an increased risk of incident dementia (hazard ratio [HR], 1.63 [95% CI, 1.55-1.71]). The highest dementia risk was observed for the hypertension, diabetes, and coronary heart disease cluster (HR, 2.20 [95% CI, 1.98-2.46]) and pain, osteoporosis, and dyspepsia cluster (HR, 2.00 [95% CI, 1.68-2.37]) in women and in the diabetes and hypertension cluster (HR, 2.24 [95% CI, 1.97-2.55]) and coronary heart disease, hypertension, and stroke cluster (HR, 1.94 [95% CI, 1.71-2.20]) in men, compared with no multimorbidity. The associations between multimorbidity and dementia were greater in those with a lower genetic risk of dementia (HR, 1.96 [95% CI, 1.81-2.11]) than in those with a higher genetic risk of dementia (HR, 1.39 [95% CI, 1.30-1.49]). Similar findings were observed when stratifying diseases clusters by genetic risk for dementia. Conclusions and Relevance: These findings suggest that multimorbidity was associated with an increased risk of dementia. The associations varied by clusters of disease and genetic risk for dementia. These findings could help with the identification of individuals at high risk of dementia as well as the development of targeted interventions to reduce or delay dementia incidence.

Phylogenetic analysis of 17271 Indian SARS-CoV-2 genomes to identify temporal and spatial hotspot mutations.

The second wave of SARS-CoV-2 has hit India hard and though the vaccination drive has started, moderate number of COVID affected patients is still present in the country, thereby leading to the analysis of the evolving virus strains. In this regard, multiple sequence alignment of 17271 Indian SARS-CoV-2 sequences is performed using MAFFT followed by their phylogenetic analysis using Nextstrain. Subsequently, mutation points as SNPs are identified by Nextstrain. Thereafter, from the aligned sequences temporal and spatial analysis are carried out to identify top 10 hotspot mutations in the coding regions based on entropy. Finally, to judge the functional characteristics of all the non-synonymous hotspot mutations, their changes in proteins are evaluated as biological functions considering the sequences by using PolyPhen-2 while I-Mutant 2.0 evaluates their structural stability. For both temporal and spatial analysis, there are 21 non-synonymous hotspot mutations which are unstable and damaging.

Geospatial Transmission Hotspots of Recent HIV Infection - Malawi, October 2019-March 2020.

Persons infected with HIV are more likely to transmit the virus during the early stages (acute and recent) of infection, when viral load is elevated and opportunities to implement risk reduction are limited because persons are typically unaware of their status (1,2). Identifying recent HIV infections (acquired within the preceding 12 months)* is critical to understanding the factors and geographic areas associated with transmission to strengthen program intervention, including treatment and prevention (2). During June 2019, a novel recent infection surveillance initiative was integrated into routine HIV testing services in Malawi, a landlocked country in southeastern Africa with one of the world's highest prevalences of HIV infection.† The objectives of this initiative were to collect data on new HIV diagnoses, characterize the epidemic, and guide public health response (2). New HIV diagnoses were classified as recent infections based on a testing algorithm that included results from the rapid test for recent infection (RTRI)§ and HIV viral load testing (3,4). Among 9,168 persons aged ≥15 years with a new HIV diagnosis who received testing across 103 facilities during October 2019-March 2020, a total of 304 (3.3%) were classified as having a recent infection. Higher proportions of recent infections were detected among females, persons aged <30 years, and clients at maternal and child health and youth clinics. Using a software application that analyzes clustering in spatially referenced data, transmission hotspots were identified with rates of recent infection that were significantly higher than expected. These near real-time HIV surveillance data highlighted locations across Malawi, allowing HIV program stakeholders to assess program gaps and improve access to HIV testing, prevention, and treatment services. Hotspot investigation information could be used to tailor HIV testing, prevention, and treatment to ultimately interrupt transmission.

Providing early indication of regional anomalies in COVID-19 case counts in England using search engine queries.

Prior work has shown the utility of using Internet searches to track the incidence of different respiratory illnesses. Similarly, people who suffer from COVID-19 may query for their symptoms prior to accessing the medical system (or in lieu of it). To assist in the UK government's response to the COVID-19 pandemic we analyzed searches for relevant symptoms on the Bing web search engine from users in England to identify areas of the country where unexpected rises in relevant symptom searches occurred. These were reported weekly to the UK Health Security Agency to assist in their monitoring of the pandemic. Our analysis shows that searches for "fever" and "cough" were the most correlated with future case counts during the initial stages of the pandemic, with searches preceding case counts by up to 21 days. Unexpected rises in search patterns were predictive of anomalous rises in future case counts within a week, reaching an Area Under Curve of 0.82 during the initial phase of the pandemic, and later reducing due to changes in symptom presentation. Thus, analysis of regional searches for symptoms can provide an early indicator (of more than one week) of increases in COVID-19 case counts.

A hierarchical study for urban statistical indicators on the prevalence of COVID-19 in Chinese city clusters based on multiple linear regression (MLR) and polynomial best subset regression (PBSR) analysis.

With evidence-based measures, COVID-19 can be effectively controlled by advanced data analysis and prediction. However, while valuable insights are available, there is a shortage of robust and rigorous research on what factors shape COVID-19 transmissions at the city cluster level. Therefore, to bridge the research gap, we adopted a data-driven hierarchical modeling approach to identify the most influential factors in shaping COVID-19 transmissions across different Chinese cities and clusters. The data used in this study are from Chinese officials, and hierarchical modeling conclusions drawn from the analysis are systematic, multifaceted, and comprehensive. To further improve research rigor, the study utilizes SPSS, Python and RStudio to conduct multiple linear regression and polynomial best subset regression (PBSR) analysis for the hierarchical modeling. The regression model utilizes the magnitude of various relative factors in nine Chinese city clusters, including 45 cities at a different level of clusters, to examine these aspects from the city cluster scale, exploring the correlation between various factors of the cities. These initial 12 factors are comprised of 'Urban population ratio', 'Retail sales of consumer goods', 'Number of tourists', 'Tourism Income', 'Ratio of the elderly population (> 60 year old) in this city', 'population density', 'Mobility scale (move in/inbound) during the spring festival', 'Ratio of Population and Health facilities', 'Jobless rate (%)', 'The straight-line distance from original epicenter Wuhan to this city', 'urban per capita GDP', and 'the prevalence of the COVID-19'. The study's results provide rigorously-tested and evidence-based insights on most instrumental factors that shape COVID-19 transmissions across cities and regions in China. Overall, the study findings found that per capita GDP and population mobility rates were the most affected factors in the prevalence of COVID-19 in a city, which could inform health experts and government officials to design and develop evidence-based and effective public health policies that could curb the spread of the COVID-19 pandemic.

Assessing the impact of SARS-CoV-2 prevention measures in Austrian schools using agent-based simulations and cluster tracing data.

We aim to identify those measures that effectively control the spread of SARS-CoV-2 in Austrian schools. Using cluster tracing data we calibrate an agent-based epidemiological model and consider situations where the B1.617.2 (delta) virus strain is dominant and parts of the population are vaccinated to quantify the impact of non-pharmaceutical interventions (NPIs) such as room ventilation, reduction of class size, wearing of masks during lessons, vaccinations, and school entry testing by SARS-CoV2-antigen tests. In the data we find that 40% of all clusters involved no more than two cases, and 3% of the clusters only had more than 20 cases. The model shows that combinations of NPIs together with vaccinations are necessary to allow for a controlled opening of schools under sustained community transmission of the SARS-CoV-2 delta variant. For plausible vaccination rates, primary (secondary) schools require a combination of at least two (three) of the above NPIs.

Polycystic ovary syndrome: clinical and laboratory variables related to new phenotypes using machine-learning models.

PURPOSE: Polycystic Ovary Syndrome (PCOS) is the most frequent endocrinopathy in women of reproductive age. Machine learning (ML) is the area of artificial intelligence with a focus on predictive computing algorithms. We aimed to define the most relevant clinical and laboratory variables related to PCOS diagnosis, and to stratify patients into different phenotypic groups (clusters) using ML algorithms. METHODS: Variables from a database comparing 72 patients with PCOS and 73 healthy women were included. The BorutaShap method, followed by the Random Forest algorithm, was applied to prediction and clustering of PCOS. RESULTS: Among the 58 variables investigated, the algorithm selected in decreasing order of importance: lipid accumulation product (LAP); abdominal circumference; thrombin activatable fibrinolysis inhibitor (TAFI) levels; body mass index (BMI); C-reactive protein (CRP), high-density lipoprotein cholesterol (HDL-c), follicle-stimulating hormone (FSH) and insulin levels; HOMA-IR value; age; prolactin, 17-OH progesterone and triglycerides levels; and family history of diabetes mellitus in first-degree relative as the variables associated to PCOS diagnosis. The combined use of these variables by the algorithm showed an accuracy of 86% and area under the ROC curve of 97%. Next, PCOS patients were gathered into two clusters in the first, the patients had higher BMI, abdominal circumference, LAP and HOMA-IR index, as well as CRP and insulin levels compared to the other cluster. CONCLUSION: The developed algorithm could be applied to select more important clinical and biochemical variables related to PCOS and to classify into phenotypically different clusters. These results could guide more personalized and effective approaches to the treatment of PCOS.

The Health Impact of MAFLD, a Novel Disease Cluster of NAFLD, Is Amplified by the Integrated Effect of Fatty Liver Disease-Related Genetic Variants.

BACKGROUND & AIMS: Metabolic dysfunction-associated fatty liver disease (MAFLD) is a newly proposed disease category that derived from non-alcoholic fatty liver disease. The impact of MAFLD on health events has not been investigated. METHODS: UK Biobank participants were diagnosed for whether MAFLD presented at baseline. Five genetic variants (PNPLA3 rs738409 C/G, TM6SF2 rs58542926 C/T, GCKR rs1260326 T/C, MBOAT7 rs641738 C/T, and HSD17B13 rs72613567 T/TA) were integrated into a genetic risk score (GRS). Cox proportional hazard model was used to examine the association of MAFLD with incident diseases. RESULTS: A total of 160 979 (38.0%, 95% confidence interval [CI] 37.9%, 38.2%) participants out of 423 252 were diagnosed as MAFLD. Compared with participants without MAFLD, MAFLD cases had multivariate adjusted hazard ratio (HR) for liver cancer of 1.59 (95% CI, 1.28, 1.98), cirrhosis of 2.77 (2.29, 3.36), other liver diseases of 2.09 (1.95, 2.24), cardiovascular diseases of 1.39 (1.34, 1.44), renal diseases of 1.56 (1.48, 1.65), and cancers of 1.07 (1.05, 1.10). The impact of MAFLD, especially on hepatic events, was amplified by high GRS, of which the genetic variations in PNPLA3, TM6SF2, and MBOAT7 play the principal roles. MAFLD case with normal body weight is also associated with an increased risk of hepatic outcomes, but the genetic factor seems do not influence the risk in this subpopulation. CONCLUSIONS: MAFLD is independently associated with an increased risk of both intrahepatic and extrahepatic events. Fatty liver disease related genetic variants amplify the effect of MAFLD on disease outcomes.

Analysis of HIV transmission hotspots and characteristics of cross-regional transmission in Guangxi Zhuang Autonomous Region based on molecular network / 中华流行病学杂志

Objective: To analyze HIV transmission hotspots and characteristics of cross-regional transmission in Guangxi Zhuang autonomous region (Guangxi) based on the molecular network analysis, and provide evidence for optimization of precise AIDS prevention and control strategies. Methods: A total of 5 996 HIV pol sequences sampled from Guangxi between 1997 and 2020 were analyzed together with 165 534 published HIV pol sequences sampled from other regions. HIV-TRACE was used to construct molecular network in a pairwise genetic distance threshold of 0.5%. Results: The proportion of HIV sequences entering the molecular network of HIV transmission hotspots in Guangxi was 31.5% (1 886/5 996). In the molecular network of HIV cross-regional transmission, the links within Guangxi accounted for 51.6% (2 613/5 062), the links between Guangxi and other provinces in China accounted for 48.0% (2 430/5 062), and the links between Guangxi and other countries accounted for 0.4% (19/5 062). The main regions which had cross-regional linked with Guangxi were Guangdong (49.5%, 1 212/2 449), Beijing (17.5%, 430/2 449), Shanghai (6.9%, 168/2 449), Sichuan (5.7%, 140/2 449), Yunnan (4.2%, 102/2 449), Shaanxi (3.8%, 93/2 449), Zhejiang (2.8%, 69/2 449), Hainan (2.0%, 49/2 449), Anhui (1.5%, 37/2 449), Jiangsu (1.3%, 33/2 449), and other regions (each one <1.0%), respectively. The risk factors of entering the molecular network of HIV transmission hotspots in Guangxi included being aged ≥50 years (compared with being aged 25-49 years, aOR=1.68,95%CI:1.46-1.95), males (compared with females, aOR=1.21,95%CI:1.05-1.40), being single (compared with being married, aOR=1.18,95%CI:1.00-1.39), having education level of high school or above (compared with having education level of junior high school or below, aOR=1.21,95%CI:1.04-1.42), acquired HIV through homosexual intercourse (compared with acquired with HIV through heterosexual intercourse, aOR=1.77, 95%CI:1.48-2.12). The risk factors of cross-regional transmission included males (compared with females, aOR=1.74,95%CI:1.13-2.75), having education level of high school or above (compared with having education level of junior high school or below, aOR=1.96,95%CI:1.43-2.69), being freelancer/unemployed/retired (compared with being farmers, aOR=1.50,95%CI:1.07-2.11), acquired HIV through homosexual intercourse (compared with acquired with HIV through heterosexual intercourse, aOR=3.28,95%CI:2.30-4.72). Conclusion: There are HIV transmission hotspots in Guangxi. Guangxi and other provinces in China form a complex cross-regional transmission network. Future studies should carry out social network surveys in high-risk populations inferred from the molecular network analysis for the timely identification of hidden transmission chains and reduction of the second-generation transmission of HIV.

Epidemiological characteristics of COVID-19 clusters in Hainan, China.

ABSTRACT: To analyze the epidemiological characteristics of coronavirus disease 2019 (COVID-19) clusters in Hainan, and to provide a basis for the prevention and control of disease clusters.Descriptive epidemiology was used to retrospectively analyze the characteristics of disease clusters in 168 cases of COVID-19.Of the 168 COVID-19 cases, 99 (58.93%) comprised 29 clusters, 22 (75.86%) of which were imported and included 63 cases (63.64%), while 7 clusters (24.14%) were local and included 36 cases (36.36%). Of the cluster cases, 49 were men (49.49%) and 50 were women (50.50%), the median age was 52 years, and the maximum number of cases from 41 to 60 was at 37 years (37.37%). There were 67 first generation cases (67.68%), 28 (28.28%) second generation, and 4 (4.04%) third generation. Of the clusters, 68.97% occurred from January 31 to February 7, with the highest peak on February 6. The local disease clusters occurred with a time lag. The 2 cities with the most reported incidents were Sanya (10 cases, 34.48%) and Haikou (5 cases, 17.24%). Family clusters were most frequent, with 18 clusters (62.07%) involving 62 cases (62.63%), followed by social clusters, with 3 clusters (10.34%). The most complex clusters involved 3 cluster types (family, travel, and community). There was a statistically significant difference in the infectivity of the imported clusters versus the local clusters, with imported clusters being lower (Z = -2.851, P = .004). The infectivity of all cases or family members was highest in Haikou and lowest in Sanya. The infectivity of all cases with an incubation period of ≤7 days was 1.53 ±â€Š1.01, in which the infectivity of family members was 1.29 ±â€Š1.10. The infectivity of all cases with an incubation period of ≤14 days was 1.89 ±â€Š1.23, in which the infectivity of family members was 1.43 ±â€Š1.37.COVID-19 clusters in Hainan mainly occurred in families, and local clusters had high infectivity. Therefore, key populations and regions should be monitored, and targeted preventive measures should be carried out to provide a reference for the prevention and control of disease clusters.

Turnover of SARS-CoV-2 Lineages Shaped the Pandemic and Enabled the Emergence of New Variants in the State of Rio de Janeiro, Brazil.

In the present study, we provide a retrospective genomic epidemiology analysis of the SARS-CoV-2 pandemic in the state of Rio de Janeiro, Brazil. We gathered publicly available data from GISAID and sequenced 1927 new genomes sampled periodically from March 2021 to June 2021 from 91 out of the 92 cities of the state. Our results showed that the pandemic was characterized by three different phases driven by a successive replacement of lineages. Interestingly, we noticed that viral supercarriers accounted for the overwhelming majority of the circulating virus (>90%) among symptomatic individuals in the state. Moreover, SARS-CoV-2 genomic surveillance also revealed the emergence and spread of two new variants (P.5 and P.1.2), firstly reported in this study. Our findings provided important lessons learned from the different epidemiological aspects of the SARS-CoV-2 dynamic in Rio de Janeiro. Altogether, this might have a strong potential to shape future decisions aiming to improve public health management and understanding mechanisms underlying virus dispersion.

An Opportunistic Survey Reveals an Unexpected Coronavirus Diversity Hotspot in North America.

In summer 2020, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) was detected on mink farms in Utah. An interagency One Health response was initiated to assess the extent of the outbreak and included sampling animals from on or near affected mink farms and testing them for SARS-CoV-2 and non-SARS coronaviruses. Among the 365 animals sampled, including domestic cats, mink, rodents, raccoons, and skunks, 261 (72%) of the animals harbored at least one coronavirus. Among the samples that could be further characterized, 127 alphacoronaviruses and 88 betacoronaviruses (including 74 detections of SARS-CoV-2 in mink) were identified. Moreover, at least 10% (n = 27) of the coronavirus-positive animals were found to be co-infected with more than one coronavirus. Our findings indicate an unexpectedly high prevalence of coronavirus among the domestic and wild free-roaming animals tested on mink farms. These results raise the possibility that mink farms could be potential hot spots for future trans-species viral spillover and the emergence of new pandemic coronaviruses.

Analysis of SARS-CoV-2 Genomes from West Java, Indonesia.

West Java Health Laboratory (WJHL) is one of the many institutions in Indonesia that have sequenced SARS-CoV-2 genome. Although having submitted a large number of sequences since September 2020, however, these submitted data lack advanced analyses. Therefore, in this study, we analyze the variant distribution, hotspot mutation, and its impact on protein structure and function of SARS-CoV-2 from the collected samples from WJHL. As many as one hundred sixty-three SARS-CoV-2 genome sequences submitted by West Java Health Laboratory (WJHL), with collection dates between September 2020 and June 2021, were retrieved from GISAID. Subsequently, the frequency and distribution of non-synonymous mutations across different cities and regencies from these samples were analyzed. The effect of the most prevalent mutations from dominant variants on the stability of their corresponding proteins was examined. The samples mostly consisted of people of working-age, and were distributed between female and male equally. All of the sample sequences showed varying levels of diversity, especially samples from West Bandung which carried the highest diversity. Dominant variants are the VOC B.1.617.2 (Delta) variant, B.1.466.2 variant, and B.1.470 variant. The genomic regions with the highest number of mutations are the spike, NSP3, nucleocapsid, NSP12, and ORF3a protein. Mutation analysis showed that mutations in structural protein might increase the stability of the protein. Oppositely, mutations in non-structural protein might lead to a decrease in protein stability. However, further research to study the impact of mutations on the function of SARS-CoV-2 proteins are required.