The COVID-19 pandemic has been a source of ongoing challenges and presents an increased risk of illness in group environments, including jails, long-term care facilities, schools, and residential college campuses. Early reports that the SARS-CoV-2 virus was detectable in wastewater in advance of confirmed cases sparked widespread interest in wastewater-based epidemiology (WBE) as a tool for mitigation of COVID-19 outbreaks. One hypothesis was that wastewater surveillance might provide a cost-effective alternative to other more expensive approaches such as pooled and random testing of groups. In this paper, we report the outcomes of a wastewater surveillance pilot program at the University of North Carolina at Charlotte, a large urban university with a substantial population of students living in on-campus dormitories. Surveillance was conducted at the building level on a thrice-weekly schedule throughout the university's fall residential semester. In multiple cases, wastewater surveillance enabled the identification of asymptomatic COVID-19 cases that were not detected by other components of the campus monitoring program, which also included in-house contact tracing, symptomatic testing, scheduled testing of student athletes, and daily symptom reporting. In the context of all cluster events reported to the University community during the fall semester, wastewater-based testing events resulted in the identification of smaller clusters than were reported in other types of cluster events. Wastewater surveillance was able to detect single asymptomatic individuals in dorms with resident populations of 150–200. While the strategy described was developed for COVID-19, it is likely to be applicable to mitigation of future pandemics in universities and other group-living environments.

Vibrio vulnificus is a natural inhabitant of estuarine waters worldwide and is of medical relevance due to its ability to cause grievous wound infections and/or fatal septicemia. Genetic polymorphisms within the virulence-correlated gene (vcg) serve as a primary feature to distinguish clinical (C-) genotypes from environmental (E-) genotypes. C-genotypes demonstrate superior survival in human serum relative to E-genotypes, and genome comparisons have allowed for the identification of several putative virulence factors that could potentially aid C-genotypes in disease progression. We used RNA sequencing to analyze the transcriptome of C-genotypes exposed to human serum relative to seawater, which revealed two divergent genetic programs under these two conditions. In human serum, cells displayed a distinct “virulence profile” in which a number of putative virulence factors were upregulated, including genes involved in intracellular signaling, substrate binding and transport, toxin and exoenzyme production, and the heat shock response. Conversely, the “environmental profile” exhibited by cells in seawater revealed upregulation of transcription factors such as rpoS, rpoN, and iscR, as well as genes involved in intracellular signaling, chemotaxis, adherence, and biofilm formation. This dichotomous genetic switch appears to be largely governed by cyclic-di-GMP signaling, and remarkably resembles the dual life-style of V. cholerae as it transitions from host to environment. Furthermore, we found a “general stress response” module, known as the stressosome, to be upregulated in seawater. This signaling system has been well characterized in Gram-positive bacteria, however its role in V. vulnificus is not clear. We examined temporal gene expression patterns of the stressosome and found it to be upregulated in natural estuarine waters indicating that this system plays a role in sensing and responding to the environment. This study advances our understanding of gene regulation in V. vulnificus, and brings to the forefront a number of previously overlooked genetic networks.

Hybridization of nucleic acids on solid surfaces is a key process involved in high-throughput technologies such as microarrays and, in some cases, next-generation sequencing (NGS). A physical understanding of the hybridization process helps to determine the accuracy of these technologies. The goal of a widespread research program is to develop reliable transformations between the raw signals reported by the technologies and individual molecular concentrations from an ensemble of nucleic acids. This research has inputs from many areas, from bioinformatics and biostatistics, to theoretical and experimental biochemistry and biophysics, to computer simulations. A group of leading researchers met in Ploen Germany in 2011 to discuss present knowledge and limitations of our physico-chemical understanding of high-throughput nucleic acid technologies. This meeting inspired us to write this summary, which provides an overview of the state-of-the-art approaches based on physico-chemical foundation to modeling of the nucleic acids hybridization process on solid surfaces. In addition, practical application of current knowledge is emphasized.

Between 1996 and 2006, the US Centers for Disease Control reported that the only category of food-borne infections increasing in frequency were those caused by members of the genus Vibrio. The Gram-negative bacterium Vibrio vulnificus is a ubiquitous inhabitant of estuarine waters, and is the number one cause of seafood-related deaths in the US. Many V. vulnificus isolates have been studied, and it has been shown that two genetically distinct subtypes, distinguished by 16S rDNA and other gene polymorphisms, are associated predominantly with either environmental or clinical isolation. While local genetic differences between the subtypes have been probed, only the genomes of clinical isolates have so far been completely sequenced. In order to better understand V. vulnificus as an agent of disease and to identify the molecular components of its virulence mechanisms, we have completed whole genome shotgun sequencing of three diverse environmental genotypes using a pyrosequencing approach. V. vulnificus strain JY1305 was sequenced to a depth of 33×, and strains E64MW and JY1701 were sequenced to lesser depth, covering approximately 99.9% of each genome. We have performed a comparative analysis of these sequences against the previously published sequences of three V. vulnificus clinical isolates. We find that the genome of V. vulnificus is dynamic, with 1.27% of genes in the C-genotype genomes not found in the E- genotype genomes. We identified key genes that differentiate between the genomes of the clinical and environmental genotypes. 167 genes were found to be specifically associated with environmental genotypes and 278 genes with clinical genotypes. Genes specific to the clinical strains include components of sialic acid catabolism, mannitol fermentation, and a component of a Type IV secretory pathway VirB4, as well as several other genes with potential significance for human virulence. Genes specific to environmental strains included several that may have implications for the balance between self-preservation under stress and nutritional competence.

Background: High-density short oligonucleotide microarrays are a primary research tool for assessing global gene expression. Background noise on microarrays comprises a significant portion of the measured raw data, which can have serious implications for the interpretation of the generated data if not estimated correctly. Results: We introduce an approach to calculate probe affinity based on sequence composition, incorporating nearest-neighbor (NN) information. Our model uses position-specific dinucleotide information, instead of the original single nucleotide approach, and adds up to 10% to the total variance explained (R²) when compared to the previously published model. We demonstrate that correcting for background noise using this approach enhances the performance of the GCRMA preprocessing algorithm when applied to control datasets, especially for detecting low intensity targets. Conclusion: Modifying the previously published position-dependent affinity model to incorporate dinucleotide information significantly improves the performance of the model. The dinucleotide affinity model enhances the detection of differentially expressed genes when implemented as a background correction procedure in GeneChip preprocessing algorithms. This is conceptually consistent with physical models of binding affinity, which depend on the nearest-neighbor stacking interactions in addition to base-pairing.