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.

Prostate cancer (PC) is a leading cause of death in men because of the high incidence and long-term inefficacy of the existing treatment options. Furthermore, it exhibits significant health disparities that affect African-American (AA) men more adversely than others do. Previously, we established CYP3A5, a highly expressed protein in AAs PC, as a positive regulator of androgen receptor (AR) signaling. We examined the impact of CYP3A5 depletion on genome-wide transcriptional output using RNA sequencing to gain deeper mechanistic insights. The data revealed that 561 genes were downregulated and 263 were upregulated upon silencing of CYP3A5 in PC cells. Furthermore, in silico pathway analyses of differentially expressed genes suggested that the cell cycle regulation pathway was most significantly affected by CYP3A5 inhibition. Cell cycle analysis of CYP3A5-silenced cells and those treated with clobetasol, a specific CYP3A5 pharmacological inhibitor, showed G1/S phase blockade. Both CYP3A5-depletion and pharmacological inhibition resulted in the downregulation of cyclin D, cyclin B, and CDK2, along with the upregulation of p27kip1 but had minimal effects on CDK4/6 levels. Combination treatment with clobetasol and the CDK4/6 inhibitor palbociclib exhibited synergy with combination index (CI) values ranging from 0.28-0.78. Our findings support the utility of CYP3A5 as a druggable therapeutic target that works more effectively in combination with CDK4/6 inhibition to limit the progression of PC, especially for AA patients with AA. This combination addresses CDK4/6 inhibitor resistance, which is often linked to CDK2 overexpression, and can potentially be useful in reducing disparities in the clinical outcomes of PC. ### Competing Interest Statement The authors have declared no competing interest.

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.

Importance Measuring drug use behaviors in individuals and across large communities presents substantial challenges, often complicated by socioeconomic and demographic variables. Objectives To detect spatial and temporal changes in community drug use by analyzing concentrations of analytes in influent wastewater and exploring their associations with area-based socioeconomic and sociodemographic metrics like the area deprivation index (ADI) and rural-urban commuting area (RUCA) codes. Design, Setting, and Participants This longitudinal, cross-sectional wastewater study was performed from May 2022 to April 2023 and included biweekly influent wastewater samples of 39 analytes from 8 sampling locations across 6 wastewater treatment plants in southern Nevada. Statistical analyses were conducted in December 2023. Main Outcomes and Measures It was hypothesized that wastewater monitoring of pharmaceuticals and personal care products (PPCPs) and high-risk substances (HRSs) could reveal true spatial and temporal drug use patterns in near-real time. Data collection of samples for PPCPs and HRSs was performed using mass spectrometry. Both ADI and RUCA scores were utilized to characterize neighborhood contexts in the analysis. The false discovery rate (FDR) method was utilized to correct for multiple comparisons (P-FDR). Results Over the 12-month wastewater monitoring period, 208 samples for PPCPs and HRSs were collected, and analysis revealed an increase in the consumption of HRSs and the seasonal variation in PPCP use in southern Nevada. There was a significant increase in levels of stimulant-associated analytes, such as cocaine (beta = 9.17 x 10(-4); SE = 1.29 x 10(-4); P-FDR = 1.40 x 10(-10)), and opioids or their metabolites, notably norfentanyl (beta = 1.48 x 10(-4); SE = 1.88 x 10(-4); P-FDR = 1.66 x 10(-12)). In contrast, DEET, an active ingredient in mosquito and tick repellents, demonstrated a seasonal use pattern (beta = -4.85 x 10(-4); SE = 2.09 x 10(-4); P-FDR = 4.87 x 10(-2)). Wastewater from more disadvantaged or rural neighborhoods, as assessed through ADI and RUCA scores, was more likely to show a significant positive correlation with HRSs, such as cocaine (beta = 0.075; SE = 0.038; P = .05) and norfentanyl (beta = 0.004; SE = 0.001; P = 1.64 x 10(-5)). Conclusions and Relevance These findings suggest that wastewater monitoring of PPCPs and HRSs offers a complementary method to existing public health tools, providing timely data for tracking substance use behaviors and use of PPCPs at a population level.

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.

Abstract Prostate cancer (PC) is the second leading cause of cancer-related death in American men, which disproportionately affects Black/African American (AA) men. The androgen receptor (AR) signaling plays a pivotal role in PC development and is a primary target for intervention in patients with advanced disease. Aberrant activation of AR is also suggested to play a significant role in castration-resistant prostate cancer (CRPC). Despite advancements in anti-androgen therapies with new targeting agents, such as enzalutamide, metastatic CRPC (mCRPC) remains incurable. CYP3A5, a monooxygenase expressed in the prostate, liver and intestine, is involved in drug metabolism and steroid biosynthesis. We earlier reported that intratumoral CYP3A5 activated AR signaling by facilitating its nuclear translocation of AR. More notably, we found a higher CYP3A5 expression in AAs, attributed to the *1 CYP3A5 variant, as compared to non-Hispanic White Americans (NHWA) possessing the *3 variant. Analysis of RNA-seq data using patient tumor samples revealed that elevated CYP3A5 in AA PC patients was associated with Wnt-β catenin signaling activation via TCF4 overexpression. Here, we investigated the role of CYP3A5 in enzalutamide resistance and Wnt signaling by generating enzalutamide-resistant PC cell lines of NHWA (low CYP3A5-LNCaP) and AA-origin (high CYP3A5-MDAPCa2b). RT-qPCR assay was used to examine the changes in AR and CYP3A5 expression between the parental and the enzalutamide-resistant cell lines. The data show heightened CYP3A5 expression in enzalutamide-resistant cells of AA origin (MDAPCa2b/EnzR) but not of NHWA origin (LNCaP/EnzR). Interestingly, MDAPCa2b/EnzR exhibited no change in AR levels whereas it was increased in LNCaP/EnzR cells relative to parental lines. A loss of function assay was performed for siRNA-mediated silencing of CYP3A5 to study its effect on Wnt signaling. RNA sequencing results show that CYP3A5 inhibition in MDAPCa2b downregulated Wnt pathway genes (Wnt5A, Wnt10B, Wnt11, Fzd2, and Dvl3). These observations align with our previous RNA seq results using patient samples where we observed upregulated Wnt-β catenin signaling in high CYP3A5 expressing patient tumor samples. Western analysis post-CYP3A5 inhibition in LNCaP and MDAPCa2b cells revealed upregulated Axin and downregulated LRP6 and Dvl-3, signifying CYP3A5's regulation of the canonical Wnt-β catenin pathway. Additionally, MDAPCa2b saw downregulated Wnt 5A and Wnt 10B, known to influence the Wnt non-canonical pathway. Altogether, our data suggest that *1 CYP3A5 variant in AAs likely contributes to aggressive behavior and therapeutic resistance of PC and serves as a molecular determinant of disparate clinical outcomes. Citation Format: Jake McLean, Ajay P. Singh, Edwin Oh, Ranjana Mitra. CYP3A5 promotes aggressive and therapeutically resistant prostate cancer by modulating AR and Wnt signaling: Implications for racially disparate clinical outcomes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4369.

The COVID-19 pandemic highlighted the value of wastewater surveillance in providing unbiased assessments of incidence/prevalence for infectious disease targets, ultimately leading to the development of local, state, and national programs across the United States. To address the growing epidemic of drug abuse, there have been calls to extend these programs to high risk substances (HRS) and metabolites, while leveraging the experience gained during the pandemic and from ongoing efforts in other countries. This study further advances the science of wastewater surveillance for HRS by (1) highlighting analytical and sewer transport considerations, (2) proposing sucralose normalization to adjust for varying human urine/fecal load and confounded population estimates (e.g., high tourism areas), and (3) characterizing temporal and geographic trends in HRS use. This one-year study across eight sewersheds in Southern Nevada (208 total samples) monitored concentrations of 17 pharmaceuticals and personal care products (PPCPs) and 22 HRS and metabolites, including natural, semi-synthetic, and synthetic opioids. The data indicated a ∼200 % increase in heroin and methamphetamine use since 2010, a stark increase in fentanyl consumption beginning in October 2022, and statistically significant differences in HRS consumption patterns between sewersheds and on certain dates. Notably, the latter outcome highlights the potential for wastewater surveillance data to be strategically translated into public health action to reduce and/or more rapidly respond to overdoses.

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.

As human complex diseases are influenced by the interaction between genetics and the environment, identifying gene-environment interactions (GxE) is crucial for understanding disease mechanisms and predicting risk. Developing robust quantitative tools for GxE analysis can enhance the study of complex diseases. However, many existing methods that explore GxE focus on the interplay between an environmental factor and genetic variants, exclusively for common or rare variants. In this study, we developed MAGEIT_RAN and MAGEIT_FIX to identify interactions between an environmental factor and a set of genetic markers, including 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 and fixed effects, respectively. Simulation studies showed that both tests had type I error under control, with MAGEIT_RAN being the most powerful test. Applying MAGEIT to a genome-wide analysis of gene-alcohol interactions on hypertension and seated systolic blood pressure in the Multiethnic Study of Atherosclerosis revealed genes like EIF2AK2, CCNDBP1, and EPB42 influencing blood pressure through alcohol interaction. Pathway analysis identified 1 apoptosis and survival pathway involving PKR and 2 signal transduction pathways associated with hypertension and alcohol intake, demonstrating MAGEIT_RAN's ability to detect biologically relevant gene-environment interactions.

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.

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.

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.

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.

A large number of subjects are generally required for the genome wide association studies (GWAS) in complex traits such as Alzheimer’s disease (AD) 1,2 . Here we incorporated longitudinal data in AD-GWAS to improve the statistical power for the identification of AD-associated genetic variants with a limited sample-size. Time-varying genetic contributions towards AD could also be modeled and captured in this longitudinal GWAS. 1,877 subjects from the ADNI database 3 with genotyping data available were included in this analysis. Subjects’ genotype data were imputed to the Haplotype Reference Consortium using the Michigan Imputation Server 4 , with 9,573,130 single nucleotide polymorphisms (SNPs) remaining after the quality control step. Subjects’ longitudinal diagnosis at each visit were obtained from ADNI, providing 10,832 phenotypes for 1,877 subjects. Subjects’ diagnoses were further binarized into the normal and diseased groups. We applied the retrospective varying coefficient mixed model association test (RVMMAT) to detect time-varying genetic effect on this longitudinal binary phenotype 5 . Briefly, dynamic genetic effect was modeled using smoothing splines and estimated by maximizing a double penalized quasi-likelihood function via a retrospective approach. Subjects’ sex, age at each diagnosis, and the first 5 principal components of whole genome were included as covariates. A categorical variable for genotyping platforms was included as an additional covariate. RVMMAT showed no evidence of inflation in the quantile-quantile (Q-Q) plot, with an inflation factor (lambda) of 0.99 (Fig. 1A). 45 SNPs reached genome-wide significance after false discovery rate correction for multiple comparisons (Fig. 1(B)). Among these genetic variants, 35 SNPs were clustered at the APOE, APOC1, TOMM40, and NECTIN2 genes on chromosome 19, and 6 SNPs were associated with SLAIN2 gene on chromosome 4. We demonstrate that RVMMAT could boost the statistical power in AD-GWAS with a limited sample-size. We expect this method to benefit the identification of genetic variants associated with pathological or clinical biomarker-based longitudinal phenotypes in AD.

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, wastewa-ter 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 waste-water 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 elemen-tary 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.

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.

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.

Background: African American men (AAs) have the highest incidence of prostate cancer (PC) and often develop therapeutic resistance leading to high mortality. The androgen receptor (AR) is paramount in the growth and progression of PC, and insufficient blockade of AR leads to therapeutic resistance. Our previous work shows that CYP3A5 facilitates the activation of AR, promoting the transcription of genes supporting PC growth. CYP3A5 is polymorphic, and the wild-type (wt; *1) variant encoding the full-length active protein is expressed in 73% of AAs and only in 5% of non-Hispanic White Americans (NHWA). The race-linked expression of wt-CYP3A5*1 in AAs can lead to hyperactive AR contributing to therapeutic resistance. We performed an RNA sequencing study utilizing samples from AAs and NWHA patients to understand the allelic influence of CYP3A5 on PC severity. Methods: Illumina RNA sequencing was performed using RNA from 13 AA and 12 NHWA prostatic adenocarcinomas. CYP3A5 genotyping separated them into two groups: a) carrying wt-CYP3A5 (*1/*1 and *1/*3; N=14); and b) mutant CYP3A5 (*3/*3; N=11) variants. The samples’ ancestry was confirmed using markers previously described. Differentially expressed genes (DEGs) between wt-*1 and mutant-*3 CYP3A5 carrying groups were identified using DESeq2, at a false discovery rate of adjusted p < 0.05. Alternately we also used the Kallisto method for quantifying the abundances of the transcript from RNA seq data. Results: Aligned RNA reads with the human genome using DESeq2 resulted in the identification of 30 DEGs between the two groups, of these, 14 genes are known to be dysregulated in PC and promote aggressive disease (SEMG2, ZNRF3, CCL18, U3, MT-ND6, SCARNA9, NRCAM, LINC00853, TCF4, SLC38A6, HHLA3, DECR1, THTPA, and NEFH). Aberrant TCF4 along with ZNRF3 is known to promote PC growth via Wnt-beta catenin signaling. SLC38A6 is a glutamine transporter and is often upregulated in PC to promote glutamine uptake in an AR-dependent manner. NEFH is an intermediate filament protein that is downregulated in PC metastasis. Kallisto method identified 84 DEGs, 11 of those genes overlap with the previous DFGs identified using the transcriptome alignment method. The additional DFGs identified with the Kallisto method include aberrant expression of multiple genes (MT-CO1/2, MT-ND1/6) involved in mitochondrial dysfunction known to contribute towards cell growth and tumorigenesis beyond the Warburg effect. The wt-*1 CYP3A5 expressing group also shows increased expression of several pseudogenes, dysregulation of pseudogenes has been observed in several cancers and can act as a master regulator for gene expression, promoting tumorigenesis. Conclusion: The presence of wt-*1 CYP3A5 may be one of the factors leading to aggressive PC in AAs as it shows differential expression of several genes known to accelerate PC growth. Citation Format: Jeetesh Sharma, Richard Tillett, Shirley Shen, Jabril Johnson, Rick A. Kittles, Mohammad Saleem Bhat, Oscar Goodman Jr, Edwin C. Oh, Ranjana Mitra. RNA sequencing reveals that African Americans carrying wild-type CYP3A5 differentially express genes known to promote aggressive prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1409.

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.

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.