The fate of SARS-CoV-2 viral RNA in coastal New England wastewater treatment plants

Municipal sewage carries SARS-CoV-2 viruses shed in the human stool by infected individuals to wastewater treatment plants (WWTPs). It is well-established that increasing prevalence of COVID-19 in a community increases the viral load in its WWTPs. Despite the fact that wastewater treatment facilities serve a critical role in protecting downstream human and environmental health through removal or inactivation of the virus, little is known about the fate of the virus along the treatment train. To assess the efficacy of differing WWTP size and treatment processes in viral RNA removal we quantified two SARS-CoV-2 nucleocapsid (N) biomarkers (N1 and N2) in both liquid and solids phases for multiple treatment train locations from seven coastal New England WWTPs. SARS-CoV-2 biomarkers were commonly detected in the influent, primary treated, and sludge samples (returned activated sludge, waste activated sludge, and digested sludge), and not detected after secondary clarification processes or disinfection. Solid fractions had 470 to 3,700-fold higher concentrations of viral biomarkers than liquid fractions, suggesting considerably higher affinity of the virus for the solid phase. Our findings indicate that a variety of wastewater treatment designs are efficient at achieving high removal of SARS CoV-2 from effluent; however, quantifiable viral RNA was commonly detected in wastewater solids at various points in the facility. This study supports the important role municipal wastewater treatment facilities serve in reducing the discharge of SARS-CoV-2 viral fragments to the environment and highlights the need to better understand the fate of this virus in wastewater solids

Global analysis of X-chromosome dosage compensation

BACKGROUND: Drosophila melanogaster females have two X chromosomes and two autosome sets (XX;AA), while males have a single X chromosome and two autosome sets (X;AA). Drosophila male somatic cells compensate for a single copy of the X chromosome by deploying male-specific-lethal (MSL) complexes that increase transcription from the X chromosome. Male germ cells lack MSL complexes, indicating that either germline X-chromosome dosage compensation is MSL-independent, or that germ cells do not carry out dosage compensation. RESULTS: To investigate whether dosage compensation occurs in germ cells, we directly assayed X-chromosome transcripts using DNA microarrays and show equivalent expression in XX;AA and X;AA germline tissues. In X;AA germ cells, expression from the single X chromosome is about twice that of a single autosome. This mechanism ensures balanced X-chromosome expression between the sexes and, more importantly, it ensures balanced expression between the single X chromosome and the autosome set. Oddly, the inactivation of an X chromosome in mammalian females reduces the effective X-chromosome dose and means that females face the same X-chromosome transcript deficiency as males. Contrary to most current dosage-compensation models, we also show increased X-chromosome expression in X;AA and XX;AA somatic cells of Caenorhabditis elegans and mice. CONCLUSION: Drosophila germ cells compensate for X-chromosome dose. This occurs by equilibrating X-chromosome and autosome expression in X;AA cells. Increased expression of the X chromosome in X;AA individuals appears to be phylogenetically conserved

Evaluation of random forests performance for genome-wide association studies in the presence of interaction effects

Random forests (RF) is one of a broad class of machine learning methods that are able to deal with large-scale data without model specification, which makes it an attractive method for genome-wide association studies (GWAS). The performance of RF and other association methods in the presence of interactions was evaluated using the simulated data from Genetic Analysis Workshop 16 Problem 3, with knowledge of the major causative markers, risk factors, and their interactions in the simulated traits. There was good power to detect the environmental risk factors using RF, trend tests, or regression analyses but the power to detect the effects of the causal markers was poor for all methods. The causal marker that had an interactive effect with smoking did show moderate evidence of association in the RF and regression analyses, suggesting that RF may perform well at detecting such interactions in larger, more highly powered datasets

Importance sampling method of correction for multiple testing in affected sib-pair linkage analysis

Using the Genetic Analysis Workshop 13 simulated data set, we compared the technique of importance sampling to several other methods designed to adjust p-values for multiple testing: the Bonferroni correction, the method proposed by Feingold et al., and naïve Monte Carlo simulation. We performed affected sib-pair linkage analysis for each of the 100 replicates for each of five binary traits and adjusted the derived p-values using each of the correction methods. The type I error rates for each correction method and the ability of each of the methods to detect loci known to influence trait values were compared. All of the methods considered were conservative with respect to type I error, especially the Bonferroni method. The ability of these methods to detect trait loci was also low. However, this may be partially due to a limitation inherent in our binary trait definitions