Genomic Variation, Population History, and Long-Term Genetic Adaptation to High Altitudes in Tibetan Partridge (Perdix hodgsoniae).

Species residing across elevational gradients display adaptations in response to environmental changes such as oxygen availability, ultraviolet radiation, and temperature. Here, we study genomic variation, gene expression, and long-term adaptation in Tibetan Partridge (Perdix hodgsoniae) populations residing across the elevational gradient of the Tibetan Plateau. We generated a high-quality draft genome and used it to carry out downstream population genomic and transcriptomic analysis. The P. hodgsoniae populations residing across various elevations were genetically distinct, and their phylogenetic clustering was consistent with their geographic distribution. We identified possible evidence of gene flow between populations residing in <3,000 and >4,200 m elevation that is consistent with known habitat expansion of high-altitude populations of P. hodgsoniae to a lower elevation. We identified a 60 kb haplotype encompassing the Estrogen Receptor 1 (ESR1) gene, showing strong genetic divergence between populations of P. hodgsoniae. We identified six single nucleotide polymorphisms within the ESR1 gene fixed for derived alleles in high-altitude populations that are strongly conserved across vertebrates. We also compared blood transcriptome profiles and identified differentially expressed genes (such as GAPDH, LDHA, and ALDOC) that correlated with differences in altitude among populations of P. hodgsoniae. These candidate genes from population genomics and transcriptomics analysis were enriched for neutrophil degranulation and glycolysis pathways, which are known to respond to hypoxia and hence may contribute to long-term adaptation to high altitudes in P. hodgsoniae. Our results highlight Tibetan Partridges as a useful model to study molecular mechanisms underlying long-term adaptation to high altitudes.

SARS-CoV-2 RNA concentrations in wastewater foreshadow dynamics and clinical presentation of new COVID-19 cases.

Current estimates of COVID-19 prevalence are largely based on symptomatic, clinically diagnosed cases. The existence of a large number of undiagnosed infections hampers population-wide investigation of viral circulation. Here, we quantify the SARS-CoV-2 concentration and track its dynamics in wastewater at a major urban wastewater treatment facility in Massachusetts, between early January and May 2020. SARS-CoV-2 was first detected in wastewater on March 3. SARS-CoV-2 RNA concentrations in wastewater correlated with clinically diagnosed new COVID-19 cases, with the trends appearing 4-10 days earlier in wastewater than in clinical data. We inferred viral shedding dynamics by modeling wastewater viral load as a convolution of back-dated new clinical cases with the average population-level viral shedding function. The inferred viral shedding function showed an early peak, likely before symptom onset and clinical diagnosis, consistent with emerging clinical and experimental evidence. This finding suggests that SARS-CoV-2 concentrations in wastewater may be primarily driven by viral shedding early in infection. This work shows that longitudinal wastewater analysis can be used to identify trends in disease transmission in advance of clinical case reporting, and infer early viral shedding dynamics for newly infected individuals, which are difficult to capture in clinical investigations.

SARS-CoV-2 titers in wastewater foreshadow dynamics and clinical presentation of new COVID-19 cases.

Current estimates of COVID-19 prevalence are largely based on symptomatic, clinically diagnosed cases. The existence of a large number of undiagnosed infections hampers population-wide investigation of viral circulation. Here, we use longitudinal wastewater analysis to track SARS-CoV-2 dynamics in wastewater at a major urban wastewater treatment facility in Massachusetts, between early January and May 2020. SARS-CoV-2 was first detected in wastewater on March 3. Viral titers in wastewater increased exponentially from mid-March to mid-April, after which they began to decline. Viral titers in wastewater correlated with clinically diagnosed new COVID-19 cases, with the trends appearing 4-10 days earlier in wastewater than in clinical data. We inferred viral shedding dynamics by modeling wastewater viral titers as a convolution of back-dated new clinical cases with the viral shedding function of an individual. The inferred viral shedding function showed an early peak, likely before symptom onset and clinical diagnosis, consistent with emerging clinical and experimental evidence. Finally, we found that wastewater viral titers at the neighborhood level correlate better with demographic variables than with population size. This work suggests that longitudinal wastewater analysis can be used to identify trends in disease transmission in advance of clinical case reporting, and may shed light on infection characteristics that are difficult to capture in clinical investigations, such as early viral shedding dynamics.