Classifying Alzheimer's Disease Neuropathology Using Clinical and MRI Measurements.

Background: Computer-aided machine learning models are being actively developed with clinically available biomarkers to diagnose Alzheimer's disease (AD) in living persons. Despite considerable work with cross-sectional in vivo data, many models lack validation against postmortem AD neuropathological data. Objective: Train machine learning models to classify the presence or absence of autopsy-confirmed severe AD neuropathology using clinically available features. Methods: AD neuropathological status are assessed at postmortem for participants from the National Alzheimer's Coordinating Center (NACC). Clinically available features are utilized, including demographics, Apolipoprotein E(APOE) genotype, and cortical thicknesses derived from ante-mortem MRI scans encompassing AD meta regions of interest (meta-ROI). Both logistic regression and random forest models are trained to identify linearly and nonlinearly separable features between participants with the presence (N = 91, age-at-MRI = 73.6±9.24, 38 women) or absence (N = 53, age-at-MRI = 68.93±19.69, 24 women) of severe AD neuropathology. The trained models are further validated in an external data set against in vivo amyloid biomarkers derived from PET imaging (amyloid-positive: N = 71, age-at-MRI = 74.17±6.37, 26 women; amyloid-negative: N = 73, age-at-MRI = 71.59±6.80, 41 women). Results: Our models achieve a cross-validation accuracy of 84.03% in classifying the presence or absence of severe AD neuropathology, and an external-validation accuracy of 70.14% in classifying in vivo amyloid positivity status. Conclusions: Our models show that clinically accessible features, including APOE genotype and cortical thinning encompassing AD meta-ROIs, are able to classify both postmortem confirmed AD neuropathological status and in vivo amyloid status with reasonable accuracies. These results suggest the potential utility of AD meta-ROIs in determining AD neuropathological status in living persons.

Detecting time-varying genetic effects in Alzheimer's disease using a longitudinal genome-wide association studies model.

INTRODUCTION: The development and progression of Alzheimer's disease (AD) is a complex process, during which genetic influences on phenotypes may also change. Incorporating longitudinal phenotypes in genome-wide association studies (GWAS) could unmask these genetic loci. METHODS: We conducted a longitudinal GWAS using a varying coefficient test to identify age-dependent single nucleotide polymorphisms (SNPs) in AD. Data from 1877 Alzheimer's Neuroimaging Data Initiative participants, including impairment status and amyloid positron emission tomography (PET) scan standardized uptake value ratio (SUVR) scores, were analyzed using a retrospective varying coefficient mixed model association test (RVMMAT). RESULTS: RVMMAT identified 244 SNPs with significant time-varying effects on AD impairment status, with 12 SNPs on chromosome 19 validated using National Alzheimer's Coordinating Center data. Age-stratified analyses showed these SNPs' effects peaked between 70 and 80 years. Additionally, 73 SNPs were linked to longitudinal amyloid accumulation changes. Pathway analyses implicated immune and neuroinflammation-related disruptions. DISCUSSION: Our findings demonstrate that longitudinal GWAS models can uncover time-varying genetic signals in AD. Highlights: Identify time-varying genetic effects using a longitudinal GWAS model in AD.Illustrate age-dependent genetic effects on both diagnoses and amyloid accumulation.Replicate time-varying effect of APOE in a second dataset.

Environmental Surveillance of Flood Control Infrastructure Impacted by Unsheltered Individuals Leads to the Detection of SARS-CoV-2 and Novel Mutations in the Spike Gene.

In the United States, the growing number of people experiencing homelessness has become a socioeconomic crisis with public health ramifications, recently exacerbated by the COVID-19 pandemic. We hypothesized that the environmental surveillance of flood control infrastructure may be an effective approach to understand the prevalence of infectious disease. From December 2021 through July 2022, we tested for SARS-CoV-2 RNA from two flood control channels known to be impacted by unsheltered individuals residing in upstream tunnels. Using qPCR, we detected SARS-CoV-2 RNA in these environmental water samples when significant COVID-19 outbreaks were occurring in the surrounding community. We also performed whole genome sequencing to identify SARS-CoV-2 lineages. Variant compositions were consistent with those of geographically and temporally matched municipal wastewater samples and clinical specimens. However, we also detected 10 of 22 mutations specific to the Alpha variant in the environmental water samples collected during January 2022-one year after the Alpha infection peak. We also identified mutations in the spike gene that have never been identified in published reports. Our findings demonstrate that environmental surveillance of flood control infrastructure may be an effective tool to understand public health conditions among unsheltered individuals-a vulnerable population that is underrepresented in clinical surveillance data.

Early Detection of Novel SARS-CoV-2 Variants from Urban and Rural Wastewater through Genome Sequencing and Machine Learning.

Genome sequencing from wastewater has emerged as an accurate and cost-effective tool for identifying SARS-CoV-2 variants. However, existing methods for analyzing wastewater sequencing data are not designed to detect novel variants that have not been characterized in humans. Here, we present an unsupervised learning approach that clusters co-varying and time-evolving mutation patterns leading to the identification of SARS-CoV-2 variants. To build our model, we sequenced 3,659 wastewater samples collected over a span of more than two years from urban and rural locations in Southern Nevada. We then developed a multivariate independent component analysis (ICA)-based pipeline to transform mutation frequencies into independent sources with co-varying and time-evolving patterns and compared variant predictions to >5,000 SARS-CoV-2 clinical genomes isolated from Nevadans. Using the source patterns as data-driven reference "barcodes", we demonstrated the model's accuracy by successfully detecting the Delta variant in late 2021, Omicron variants in 2022, and emerging recombinant XBB variants in 2023. Our approach revealed the spatial and temporal dynamics of variants in both urban and rural regions; achieved earlier detection of most variants compared to other computational tools; and uncovered unique co-varying mutation patterns not associated with any known variant. The multivariate nature of our pipeline boosts statistical power and can support accurate and early detection of SARS-CoV-2 variants. This feature offers a unique opportunity for novel variant and pathogen detection, even in the absence of clinical testing.

RETROSPECTIVE VARYING COEFFICIENT ASSOCIATION ANALYSIS OF LONGITUDINAL BINARY TRAITS: APPLICATION TO THE IDENTIFICATION OF GENETIC LOCI ASSOCIATED WITH HYPERTENSION.

Many genetic studies contain rich information on longitudinal phenotypes that require powerful analytical tools for optimal analysis. Genetic analysis of longitudinal data that incorporates temporal variation is important for understanding the genetic architecture and biological variation of complex diseases. Most of the existing methods assume that the contribution of genetic variants is constant over time and fail to capture the dynamic pattern of disease progression. However, the relative influence of genetic variants on complex traits fluctuates over time. In this study, we propose a retrospective varying coefficient mixed model association test, RVMMAT, to detect time-varying genetic effect on longitudinal binary traits. We model dynamic genetic effect using smoothing splines, estimate model parameters by maximizing a double penalized quasi-likelihood function, design a joint test using a Cauchy combination method, and evaluate statistical significance via a retrospective approach to achieve robustness to model misspecification. Through simulations we illustrated that the retrospective varying-coefficient test was robust to model misspecification under different ascertainment schemes and gained power over the association methods assuming constant genetic effect. We applied RVMMAT to a genome-wide association analysis of longitudinal measure of hypertension in the Multi-Ethnic Study of Atherosclerosis. Pathway analysis identified two important pathways related to G-protein signaling and DNA damage. Our results demonstrated that RVMMAT could detect biologically relevant loci and pathways in a genome scan and provided insight into the genetic architecture of hypertension.

Science Education for the Youth (SEFTY): A Neuroscience Outreach Program for High School Students in Southern Nevada during the COVID-19 Pandemic.

Laboratory outreach programs for K-12 students in the United States from 2020 to 2022 were suspended or delayed due to COVID-19 restrictions. While Southern Nevada also observed similar closures for onsite programs, we and others hypothesized that in-person laboratory activities could be prioritized after increasing vaccine doses were available to the public and masking was encouraged. Here, we describe how the Laboratory of Neurogenetics and Precision Medicine at the University of Nevada Las Vegas (UNLV) collaborated with administrators from a local school district to conduct training activities for high school students during the COVID-19 pandemic. The Science Education for the Youth (SEFTY) program's curriculum was constructed to incorporate experiential learning, fostering collaboration and peer-to-peer knowledge exchange. Leveraging neuroscience tools from our UNLV laboratory, we engaged with 117 high school applicants from 2021 to 2022. Our recruitment efforts yielded a diverse cohort, with >41% Pacific Islander and Asian students, >9% African American students, and >12% multiracial students. We assessed the impact of the SEFTY program through pre- and postassessment student evaluations, revealing a significant improvement of 20.3% in science proficiency (p < 0.001) after participating in the program. Collectively, our laboratory curriculum offers valuable insights into the capacity of an outreach program to actively foster diversity and cultivate opportunities for academic excellence, even in the challenging context of a global pandemic.

Spatial and Temporal Drug Usage Patterns in Wastewater Correlate with Socioeconomic and Demographic Indicators in Southern Nevada.

Evaluating drug use within populations in the United States poses significant challenges due to various social, ethical, and legal constraints, often impeding the collection of accurate and timely data. Here, we aimed to overcome these barriers by conducting a comprehensive analysis of drug consumption trends and measuring their association with socioeconomic and demographic factors. From May 2022 to April 2023, we analyzed 208 wastewater samples from eight sampling locations across six wastewater treatment plants in Southern Nevada, covering a population of 2.4 million residents with 50 million annual tourists. Using bi-weekly influent wastewater samples, we employed mass spectrometry to detect 39 analytes, including pharmaceuticals and personal care products (PPCPs) and high risk substances (HRS). Our results revealed a significant increase over time in the level of stimulants such as cocaine (pFDR=1.40×10-10) and opioids, particularly norfentanyl (pFDR =1.66×10-12), while PPCPs exhibited seasonal variation such as peak usage of DEET, an active ingredient in insect repellents, during the summer (pFDR =0.05). Wastewater from socioeconomically disadvantaged or rural areas, as determined by Area Deprivation Index (ADI) and Rural-Urban Commuting Area Codes (RUCA) scores, demonstrated distinct overall usage patterns, such as higher usage/concentration of HRS, including cocaine (p=0.05) and norfentanyl (p=1.64×10-5). Our approach offers a near real-time, comprehensive tool to assess drug consumption and personal care product usage at a community level, linking wastewater patterns to socioeconomic and demographic factors. This approach has the potential to significantly enhance public health monitoring strategies in the United States.

Science Education for the Youth (SEFTY): A Neuroscience Outreach Program for High School Students in Southern Nevada During the COVID-19 Pandemic.

Laboratory outreach programs for K-12 students in the United States from 2020-2022 were suspended or delayed due to COVID-19 restrictions. While Southern Nevada also observed similar closures for onsite programs, we and others hypothesized that in-person laboratory activities could be prioritized after increasing vaccine doses were available to the public and masking was encouraged. Here, we describe how the Laboratory of Neurogenetics and Precision Medicine at the University of Nevada Las Vegas (UNLV) collaborated with administrators from a local school district to conduct training activities for high school students during the COVID-19 pandemic. The Science Education for the Youth (SEFTY) program's curriculum was constructed to incorporate experiential learning, fostering collaboration and peer-to-peer knowledge exchange. Leveraging neuroscience tools from our UNLV laboratory, we engaged with 117 high school applicants from 2021-2022. Our recruitment efforts yielded a diverse cohort, with >41% Pacific Islander and Asian students, >9% African American students, and >12% multiracial students. We assessed the impact of the SEFTY program through pre- and post-assessment student evaluations, revealing a significant improvement of 20.3% in science proficiency ( p <0.001) after participating in the program. Collectively, our laboratory curriculum offers valuable insights into the capacity of an outreach program to actively foster diversity and cultivate opportunities for academic excellence, even in the challenging context of a global pandemic. Significance Statement: The Science Education for the Youth (SEFTY) program at UNLV successfully engaged 117 diverse high school students in neuroscience-based experiential learning, demonstrating the viability of in-person education during a pandemic. Significant improvements in science proficiency (20.3% increase) underscore the program's effectiveness in fostering academic excellence and diversity. This initiative potentially serves as a model for maintaining high-quality, inclusive science education in challenging times.

Detecting time-varying genetic effects in Alzheimer's disease using a longitudinal GWAS model.

Background: The development and progression of Alzheimer's disease (AD) is a complex process that can change over time, during which genetic influences on phenotypes may also fluctuate. Incorporating longitudinal phenotypes in genome wide association studies (GWAS) could help unmask genetic loci with time-varying effects. In this study, we incorporated a varying coefficient test in a longitudinal GWAS model to identify single nucleotide polymorphisms (SNPs) that may have time- or age-dependent effects in AD. Methods: Genotype data from 1,877 participants in the Alzheimer's Neuroimaging Data Initiative (ADNI) were imputed using the Haplotype Reference Consortium (HRC) panel, resulting in 9,573,130 SNPs. Subjects' longitudinal impairment status at each visit was considered as a binary and clinical phenotype. Participants' composite standardized uptake value ratio (SUVR) derived from each longitudinal amyloid PET scan was considered as a continuous and biological phenotype. The retrospective varying coefficient mixed model association test (RVMMAT) was used in longitudinal GWAS to detect time-varying genetic effects on the impairment status and SUVR measures. Post-hoc analyses were performed on genome-wide significant SNPs, including 1) pathway analyses; 2) age-stratified genotypic comparisons and regression analyses; and 3) replication analyses using data from the National Alzheimer's Coordinating Center (NACC). Results: Our model identified 244 genome-wide significant SNPs that revealed time-varying genetic effects on the clinical impairment status in AD; among which, 12 SNPs on chromosome 19 were successfully replicated using data from NACC. Post-hoc age-stratified analyses indicated that for most of these 244 SNPs, the maximum genotypic effect on impairment status occurred between 70 to 80 years old, and then declined with age. Our model further identified 73 genome-wide significant SNPs associated with the temporal variation of amyloid accumulation. For these SNPs, an increasing genotypic effect on PET-SUVR was observed as participants' age increased. Functional pathway analyses on significant SNPs for both phenotypes highlighted the involvement and disruption of immune responses- and neuroinflammation-related pathways in AD. Conclusion: We demonstrate that longitudinal GWAS models with time-varying coefficients can boost the statistical power in AD-GWAS. In addition, our analyses uncovered potential time-varying genetic variants on repeated measurements of clinical and biological phenotypes in AD.

Detection of interactions between genetic marker sets and environment in a genome-wide study of hypertension.

As human complex diseases are influenced by the interplay of genes and environment, detecting gene-environment interactions (G×E) can shed light on biological mechanisms of diseases and play an important role in disease risk prediction. Development of powerful quantitative tools to incorporate G×E in complex diseases has potential to facilitate the accurate curation and analysis of large genetic epidemiological studies. However, most of existing methods that interrogate G×E focus on the interaction effects of an environmental factor and genetic variants, exclusively for common or rare variants. In this study, we proposed two tests, MAGEIT_RAN and MAGEIT_FIX, to detect interaction effects of an environmental factor and a set of genetic markers containing both rare and common variants, based on the MinQue for Summary statistics. The genetic main effects in MAGEIT_RAN and MAGEIT_FIX are modeled as random or fixed, respectively. Through simulation studies, we illustrated that both tests had type I error under control and MAGEIT_RAN was overall the most powerful test. We applied MAGEIT to a genome-wide analysis of gene-alcohol interactions on hypertension in the Multi-Ethnic Study of Atherosclerosis. We detected two genes, CCNDBP1 and EPB42, that interact with alcohol usage to influence blood pressure. Pathway analysis identified sixteen significant pathways related to signal transduction and development that were associated with hypertension, and several of them were reported to have an interactive effect with alcohol intake. Our results demonstrated that MAGEIT can detect biologically relevant genes that interact with environmental factors to influence complex traits.

Genetic correlations between Alzheimer's disease and gut microbiome genera.

A growing body of evidence suggests that dysbiosis of the human gut microbiota is associated with neurodegenerative diseases like Alzheimer's disease (AD) via neuroinflammatory processes across the microbiota-gut-brain axis. The gut microbiota affects brain health through the secretion of toxins and short-chain fatty acids, which modulates gut permeability and numerous immune functions. Observational studies indicate that AD patients have reduced microbiome diversity, which could contribute to the pathogenesis of the disease. Uncovering the genetic basis of microbial abundance and its effect on AD could suggest lifestyle changes that may reduce an individual's risk for the disease. Using the largest genome-wide association study of gut microbiota genera from the MiBioGen consortium, we used polygenic risk score (PRS) analyses with the "best-fit" model implemented in PRSice-2 and determined the genetic correlation between 119 genera and AD in a discovery sample (ADc12 case/control: 1278/1293). To confirm the results from the discovery sample, we next repeated the PRS analysis in a replication sample (GenADA case/control: 799/778) and then performed a meta-analysis with the PRS results from both samples. Finally, we conducted a linear regression analysis to assess the correlation between the PRSs for the significant genera and the APOE genotypes. In the discovery sample, 20 gut microbiota genera were initially identified as genetically associated with AD case/control status. Of these 20, three genera (Eubacterium fissicatena as a protective factor, Collinsella, and Veillonella as a risk factor) were independently significant in the replication sample. Meta-analysis with discovery and replication samples confirmed that ten genera had a significant correlation with AD, four of which were significantly associated with the APOE rs429358 risk allele in a direction consistent with their protective/risk designation in AD association. Notably, the proinflammatory genus Collinsella, identified as a risk factor for AD, was positively correlated with the APOE rs429358 risk allele in both samples. Overall, the host genetic factors influencing the abundance of ten genera are significantly associated with AD, suggesting that these genera may serve as biomarkers and targets for AD treatment and intervention. Our results highlight that proinflammatory gut microbiota might promote AD development through interaction with APOE. Larger datasets and functional studies are required to understand their causal relationships.

Identification and genome sequencing of an influenza H3N2 variant in wastewater from elementary schools during a surge of influenza A cases in Las Vegas, Nevada.

Real-time surveillance of infectious diseases at schools or in communities is often hampered by delays in reporting due to resource limitations and infrastructure issues. By incorporating quantitative PCR and genome sequencing, wastewater surveillance has been an effective complement to public health surveillance at the community and building-scale for pathogens such as poliovirus, SARS-CoV-2, and even the monkeypox virus. In this study, we asked whether wastewater surveillance programs at elementary schools could be leveraged to detect RNA from influenza viruses shed in wastewater. We monitored for influenza A and B viral RNA in wastewater from six elementary schools from January to May 2022. Quantitative PCR led to the identification of influenza A viral RNA at three schools, which coincided with the lifting of COVID-19 restrictions and a surge in influenza A infections in Las Vegas, Nevada, USA. We performed genome sequencing of wastewater RNA, leading to the identification of a 2021-2022 vaccine-resistant influenza A (H3N2) 3C.2a1b.2a.2 subclade. We next tested wastewater samples from a treatment plant that serviced the elementary schools, but we were unable to detect the presence of influenza A/B RNA. Together, our results demonstrate the utility of near-source wastewater surveillance for the detection of local influenza transmission in schools, which has the potential to be investigated further with paired school-level influenza incidence data.

Detection of the Omicron BA.1 Variant of SARS-CoV-2 in Wastewater From a Las Vegas Tourist Area.

Importance: Interpretation of wastewater surveillance data is potentially confounded in communities with mobile populations, so it is important to account for this issue when conducting wastewater-based epidemiology (WBE). Objectives: To leverage spatial and temporal differences in wastewater whole-genome sequencing (WGS) data to quantify relative SARS-CoV-2 contributions from visitors to southern Nevada. Design, Setting, and Participants: This cross-sectional wastewater surveillance study was performed during the COVID-19 pandemic (March 2020 to February 2022) and included weekly influent wastewater samples that were analyzed by reverse transcription-quantitative polymerase chain reaction to quantify SARS-CoV-2 RNA and WGS for identification of variants of concern. This study was conducted in the Las Vegas, Nevada, metropolitan area, which is a semi-urban area with approximately 2.3 million residents and nearly 1 million weekly visitors. Samples were collected from 7 wastewater treatment plant (WWTP) locations that collectively serve the vast majority of southern Nevada (excluding the small number of septic systems) and 1 manhole serving the southern portion of the Las Vegas Strip. With Las Vegas tourism returning to prepandemic levels in 2021, it was hypothesized that visitors were contributing a disproportionate fraction of SARS-CoV-2 RNA to the largest WWTP in southern Nevada, potentially confounding efforts to estimate COVID-19 incidence in the local community through WBE. Main Outcomes and Measures: Relative SARS-CoV-2 load and variants from visitors vs the local population. Results: The Omicron BA.1 VOC was detected in the Las Vegas Strip manhole approximately 1 week before its detection at the WWTP locations (December 13, 2021) and by clinical testing (December 14, 2021). On December 13, Omicron-specific mutations represented a mean (SD) of 48.0% (4.2%) of all genomes from the Las Vegas Strip manhole and 4.1% (1.4%) of all genomes at facilities 2 and 3; by December 20, Omicron-specific mutations represented means (SD) of 82.0% (3.0%) of all genomes at the Las Vegas Strip manhole and 48.0% (2.8%) of all genomes at facilities 2 and 3, respectively. During this time, it was estimated that visitors contributed more than 60% of the SARS-CoV-2 load to the sewershed serving the Las Vegas Strip and that Omicron prevalence among visitors was 40% to 60% on December 13 and 80% to 100% on December 20th. Conclusions and Relevance: Wastewater surveillance is a valuable complement to clinical tools and can provide time-sensitive data for decision-makers and policy makers. This study represents a novel approach for quantifying the confounding effects of mobile populations on wastewater surveillance data, thereby allowing for modification of an existing WBE framework for estimating COVID-19 incidence in southern Nevada.

Identification of a rare SARS-CoV-2 XL hybrid variant in wastewater and the subsequent discovery of two infected individuals in Nevada.

The identification of novel SARS-CoV-2 variants can predict new patterns of COVID-19 community transmission and lead to the deployment of public health resources. However, increased access to at-home antigen tests and reduced free PCR tests have recently led to data gaps for the surveillance of evolving SARS-CoV-2 variants. To overcome such limitations, we asked whether wastewater surveillance could be leveraged to detect rare variants circulating in a community before local detection in human cases. Here, we performed whole genome sequencing (WGS) of SARS-CoV-2 from a wastewater treatment plant serving Las Vegas, Nevada in April 2022. Using metrics that exceeded 100× depth at a coverage of >90 % of the viral genome, we identified a variant profile similar to the XL recombinant lineage containing 26 mutations found in BA.1 and BA.2 and three private mutations. Prompted by the discovery of this rare lineage in wastewater, we analyzed clinical COVID-19 sequencing data from Southern Nevada and identified two cases infected with the XL lineage. Taken together, our data highlight how wastewater genome sequencing data can be used to discover rare SARS-CoV-2 lineages in a community and complement local public health surveillance.

Urban monitoring of antimicrobial resistance during a COVID-19 surge through wastewater surveillance.

During the early phase of the COVID-19 pandemic, infected patients presented with symptoms similar to bacterial pneumonias and were treated with antibiotics before confirmation of a bacterial or fungal co-infection. We reasoned that wastewater surveillance could reveal potential relationships between reduced antimicrobial stewardship, specifically misprescribing antibiotics to treat viral infections, and the occurrence of antimicrobial resistance (AMR) in an urban community. Here, we analyzed microbial communities and AMR profiles in sewage samples from a wastewater treatment plant (WWTP) and a community shelter in Las Vegas, Nevada during a COVID-19 surge in December 2020. Using a respiratory pathogen and AMR enrichment next-generation sequencing panel, we identified four major phyla in the wastewater, including Actinobacteria, Firmicutes, Bacteroidetes and Proteobacteria. Consistent with antibiotics that were reportedly used to treat COVID-19 infections (e.g., fluoroquinolones and beta-lactams), we also measured a significant spike in corresponding AMR genes in the wastewater samples. AMR genes associated with colistin resistance (mcr genes) were also identified exclusively at the WWTP, suggesting that multidrug resistant bacterial infections were being treated during this time. We next compared the Las Vegas sewage data to local 2018-2019 antibiograms, which are antimicrobial susceptibility profile reports about common clinical pathogens. Similar to the discovery of higher levels of beta-lactamase resistance genes in sewage during 2020, beta-lactam antibiotics accounted for 51 ± 3 % of reported antibiotics used in antimicrobial susceptibility tests of 2018-2019 clinical isolates. Our data highlight how wastewater-based epidemiology (WBE) can be leveraged to complement more traditional surveillance efforts by providing community-level data to help identify current and emerging AMR threats.

Use of wastewater surveillance for early detection of Alpha and Epsilon SARS-CoV-2 variants of concern and estimation of overall COVID-19 infection burden.

A decline in diagnostic testing for SARS-CoV-2 is expected to delay the tracking of COVID-19 variants of concern and interest in the United States. We hypothesize that wastewater surveillance programs provide an effective alternative for detecting emerging variants and assessing COVID-19 incidence, particularly when clinical surveillance is limited. Here, we analyzed SARS-CoV-2 RNA in wastewater from eight locations across Southern Nevada between March 2020 and April 2021. Trends in SARS-CoV-2 RNA concentrations (ranging from 4.3 log10 gc/L to 8.7 log10 gc/L) matched trends in confirmed COVID-19 incidence, but wastewater surveillance also highlighted several limitations with the clinical data. Amplicon-based whole genome sequencing (WGS) of 86 wastewater samples identified the B.1.1.7 (Alpha) and B.1.429 (Epsilon) lineages in December 2020, but clinical sequencing failed to identify the variants until January 2021, thereby demonstrating that 'pooled' wastewater samples can sometimes expedite variant detection. Also, by calibrating fecal shedding (11.4 log10 gc/infection) and wastewater surveillance data to reported seroprevalence, we estimate that ~38% of individuals in Southern Nevada had been infected by SARS-CoV-2 as of April 2021, which is significantly higher than the 10% of individuals confirmed through clinical testing. Sewershed-specific ascertainment ratios (i.e., X-fold infection undercounts) ranged from 1.0 to 7.7, potentially due to demographic differences. Our data underscore the growing application of wastewater surveillance in not only the identification and quantification of infectious agents, but also the detection of variants of concern that may be missed when diagnostic testing is limited or unavailable.

SARS-CoV-2 variant detection at a university dormitory using wastewater genomic tools.

In the Fall of 2020, university campuses in the United States resumed on-campus instruction and implemented wastewater monitoring for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). While quantitative polymerase chain reaction (qPCR) tests were deployed successfully to detect viral RNA in wastewater across campuses, the feasibility of detecting viral variants from a residential building like a dormitory was unclear. Here, we demonstrate that wastewater surveillance from a dormitory with at least three infected students could lead to the identification of viral genomes with more than 95% coverage. Our results indicate that viral variant detection from wastewater is achievable at a dormitory and that coronavirus disease 2019 (COVID-19) wastewater surveillance programs will benefit from the implementation of viral whole genome sequencing at universities.

Dietary berries, insulin resistance and type 2 diabetes: an overview of human feeding trials.

Dietary berries are a rich source of several nutrients and phytochemicals and in recent years, accumulating evidence suggests they can reduce risks of several chronic diseases, including type 2 diabetes (T2D). The objective of this review is to summarize and discuss the role of dietary berries (taken as fresh, frozen, or other processed forms) on insulin resistance and biomarkers of T2D in human feeding studies. Reported feeding trials involve different berries taken in different forms, and consequently differences in nutritional or polyphenol composition must be considered in their interpretation. Commonly consumed berries, especially cranberries, blueberries, raspberries and strawberries, ameliorate postprandial hyperglycemia and hyperinsulinemia in overweight or obese adults with insulin resistance, and in adults with the metabolic syndrome (MetS). In non-acute long-term studies, these berries either alone, or in combination with other functional foods or dietary interventions, can improve glycemic and lipid profiles, blood pressure and surrogate markers of atherosclerosis. Studies specifically in people with T2D are few, and more knowledge is needed. Nevertheless, existing evidence, although sparse, suggests that berries have an emerging role in dietary strategies for the prevention of diabetes and its complications in adults. Despite the beneficial effects of berries on diabetes prevention and management, they must be consumed as part of a healthy and balanced diet.

Minimotifs dysfunction is pervasive in neurodegenerative disorders.

Minimotifs are modular contiguous peptide sequences in proteins that are important for posttranslational modifications, binding to other molecules, and trafficking to specific subcellular compartments. Some molecular functions of proteins in cellular pathways can be predicted from minimotif consensus sequences identified through experimentation. While a role for minimotifs in regulating signal transduction and gene regulation during disease pathogenesis (such as infectious diseases and cancer) is established, the therapeutic use of minimotif mimetic drugs is limited. In this review, we discuss a general theme identifying a pervasive role of minimotifs in the pathomechanism of neurodegenerative diseases. Beyond their longstanding history in the genetics of familial neurodegeneration, minimotifs are also major players in neurotoxic protein aggregation, aberrant protein trafficking, and epigenetic regulation. Generalizing the importance of minimotifs in neurodegenerative diseases offers a new perspective for the future study of neurodegenerative mechanisms and the investigation of new therapeutics.

Gene expression elucidates functional impact of polygenic risk for schizophrenia.

Over 100 genetic loci harbor schizophrenia-associated variants, yet how these variants confer liability is uncertain. The CommonMind Consortium sequenced RNA from dorsolateral prefrontal cortex of people with schizophrenia (N = 258) and control subjects (N = 279), creating a resource of gene expression and its genetic regulation. Using this resource, ∼20% of schizophrenia loci have variants that could contribute to altered gene expression and liability. In five loci, only a single gene was involved: FURIN, TSNARE1, CNTN4, CLCN3 or SNAP91. Altering expression of FURIN, TSNARE1 or CNTN4 changed neurodevelopment in zebrafish; knockdown of FURIN in human neural progenitor cells yielded abnormal migration. Of 693 genes showing significant case-versus-control differential expression, their fold changes were ≤ 1.33, and an independent cohort yielded similar results. Gene co-expression implicates a network relevant for schizophrenia. Our findings show that schizophrenia is polygenic and highlight the utility of this resource for mechanistic interpretations of genetic liability for brain diseases.