COVID-19 Healthcare Demand Projections: Beaumont-Port Arthur MSA, Texas

To support healthcare planning, we analyzed the Beaumont-Port Arthur MSA module of our US COVID-19 Pandemic Model to project the number of cases, healthcare requirements and deaths under different scenarios. Note that the results presented herein are based on multiple assumptions about the transmission rate and age-specific severity of COVID-19. There is still much we do not understand about the transmission dynamics of this virus, including the extent of asymptomatic infection and transmission. These results do not represent the full range of uncertainty. Rather, they are meant to serve as plausible scenarios for gauging the likely impacts of control measures in the Beaumont-Port Arthur MSA. We have updated our model inputs based on the daily number of COVID-19 hospitalizations in Beaumont-Port Arthur between April 2, 2020 and April 20, 2020, provided by the Southeast Texas Regional Advisory Council (SETRAC). The projections assume that schools were closed on March 19, 2020 (start of state mandated school closures) and extensive social distancing began on March 28, 2020 with the Jefferson County Stay at Home order [1]. The data suggest that recent social distancing has reduced transmission by anywhere between 70% and 100% relative to the period prior to March 19th. We make projections for six different scenarios. The first four-70%, 80%, 95% and 100% reductions in transmission fall within this range of current estimates; the other two-0% and 50% reductions in transmission provide more pessimistic projections that could occur with extreme relaxation of social distancing measures. For each of the scenarios, the graphs project COVID-19 cases, hospitalizations, patients requiring ICU care, patients requiring ventilation and deaths. We are posting these results prior to peer review to provide intuition for both policy makers and the public regarding both the immediate threat of COVID-19 and the extent to which continued social distancing, transmission-reducing precautions such as keeping physical distance and wearing cloth face coverings, can mitigate that threat.Integrative Biolog

Genomic distribution of H3K9me2 and DNA methylation in a maize genome

DNA methylation and dimethylation of lysine 9 of histone H3 (H3K9me2) are two chromatin modifications that can be associated with gene expression or recombination rate. The maize genome provides a complex landscape of interspersed genes and transposons. The genome-wide distribution of DNA methylation and H3K9me2 were investigated in seedling tissue for the maize inbred B73 and compared to patterns of these modifications observed in Arabidopsis thaliana. Most maize transposons are highly enriched for DNA methylation in CG and CHG contexts and for H3K9me2. In contrast to findings in Arabidopsis, maize CHH levels in transposons are generally low but some sub-families of transposons are enriched for CHH methylation and these families exhibit low levels of H3K9me2. The profile of modifications over genes reveals that DNA methylation and H3K9me2 is quite low near the beginning and end of genes. Although elevated CG and CHG methylation are found within gene bodies, CHH and H3K9me2 remain low. Maize has much higher levels of CHG methylation within gene bodies than observed in Arabidopsis and this is partially attributable to the presence of transposons within introns for some maize genes. These transposons are associated with high levels of CHG methylation and H3K9me2 but do not appear to prevent transcriptional elongation. Although the general trend is for a strong depletion of H3K9me2 and CHG near the transcription start site there are some putative genes that have high levels of these chromatin modifications. This study provides a clear view of the relationship between DNA methylation and H3K9me2 in the maize genome and how the distribution of these modifications is shaped by the interplay of genes and transposons.The research was supported by a grant from the National Science Foundation (IOS-1237931) to MWV and NMS. This work also used resources or cyberinfrastructure provided by iPlant Collaborative. The iPlant Collaborative is funded by a grant from the National Science Foundation (DBI-0735191; www. iplantcollaborative.org). Start-up funds from the University of Georgia and a research grant from the National Science Foundation (IOS-1339194) to RJS supported aspects of this study

COVID-19 Healthcare Demand Projections: Houston-The Woodlands-Sugar Land MSA, Texas

To support healthcare planning, we analyzed the Houston-The Woodlands-Sugar Land MSA module of our US COVID-19 Pandemic Model to project the number of cases, healthcare requirements and deaths under different scenarios. Note that the results presented herein are based on multiple assumptions about the transmission rate and age-specific severity of COVID-19. There is still much we do not understand about the transmission dynamics of this virus, including the extent of asymptomatic infection and transmission. These results do not represent the full range of uncertainty. Rather, they are meant to serve as plausible scenarios for gauging the likely impacts of control measures in the Houston-The Woodlands-Sugar Land MSA. We have updated our model inputs based on the daily number of COVID-19 hospitalizations in Houston-The Woodlands-Sugar Land between April 2, 2020 and April 20, 2020, provided by the Southeast Texas Regional Advisory Council (SETRAC). The projections assume that schools were closed on March 19, 2020 (start of state mandated school closures) and extensive social distancing began on March 24, 2020 with Houston and Harris County's Stay Home Work Safe order [1]. The data suggest that recent social distancing has reduced transmission by anywhere between 80% and 100% relative to the period prior to March 19th. We make projections for five different scenarios. The first three 80%, 95% and 100% reductions in transmission fall within this range of current estimates; the other two-0% and 50% reductions in transmission-provide more pessimistic projections that could occur with extreme relaxation of social distancing measures. For each of the scenarios, the graphs project COVID-19 cases, hospitalizations, patients requiring ICU care, patients requiring ventilation and deaths. We are posting these results prior to peer review to provide intuition for both policy makers and the public regarding both the immediate threat of COVID-19 and the extent to which continued social distancing, transmission-reducing precautions such as keeping physical distance and wearing cloth face coverings, can mitigate that threat.Integrative Biolog

Early COVID-19 Pandemic Modeling: Three Compartmental Model Case Studies From Texas, USA

The novel coronavirus (SARS-CoV-2) emerged in late 2019 and spread globally in early 2020. Initial reports suggested the associated disease, COVID-19, produced rapid epidemic growth and caused high mortality. As the virus sparked local epidemics in new communities, health systems and policy makers were forced to make decisions with limited information about the spread of the disease. We developed a compartmental model to project COVID-19 healthcare demands that combined information regarding SARS-CoV-2 transmission dynamics from international reports with local COVID-19 hospital census data to support response efforts in three metropolitan statistical areas in Texas, USA: Austin-Round Rock, Houston-The Woodlands-Sugar Land, and Beaumont-Port Arthur. Our model projects that strict stay-home orders and other social distancing measures could suppress the spread of the pandemic. Our capacity to provide rapid decision-support in response to emerging threats depends on access to data, validated modeling approaches, careful uncertainty quantification, and adequate computational resources.This work was supported by CDC Contract CDC contract 75D-301-19-C-05930, NIH Grant 3R01AI151176-01S1, and Tito's Handmade Vodka.Texas Advanced Computing Center (TACC)Integrative BiologyOperations Research and Industrial EngineeringCenter for Space Researc

Epigenetic and Genetic Influences on DNA Methylation Variation in Maize Populations

DNA methylation is a chromatin modification that is frequently associated with epigenetic regulation in plants and mammals. However, genetic changes such as transposon insertions can also lead to changes in DNA methylation. Genome-wide profiles of DNA methylation for 20 maize (Zea mays) inbred lines were used to discover differentially methylated regions (DMRs). The methylation level for each of these DMRs was also assayed in 31 additional maize or teosinte genotypes, resulting in the discovery of 1966 common DMRs and 1754 rare DMRs. Analysis of recombinant inbred lines provides evidence that the majority of DMRs are heritable. A local association scan found that nearly half of the DMRs with common variation are significantly associated with single nucleotide polymorphisms found within or near the DMR. Many of the DMRs that are significantly associated with local genetic variation are found near transposable elements that may contribute to the variation in DNA methylation. Analysis of gene expression in the same samples used for DNA methylation profiling identified over 300 genes with expression patterns that are significantly associated with DNA methylation variation. Collectively, our results suggest that DNA methylation variation is influenced by genetic and epigenetic changes that are often stably inherited and can influence the expression of nearby genes

Comparing DNA replication programs reveals large timing shifts at centromeres of endocycling cells in maize roots.

Plant cells undergo two types of cell cycles-the mitotic cycle in which DNA replication is coupled to mitosis, and the endocycle in which DNA replication occurs in the absence of cell division. To investigate DNA replication programs in these two types of cell cycles, we pulse labeled intact root tips of maize (Zea mays) with 5-ethynyl-2'-deoxyuridine (EdU) and used flow sorting of nuclei to examine DNA replication timing (RT) during the transition from a mitotic cycle to an endocycle. Comparison of the sequence-based RT profiles showed that most regions of the maize genome replicate at the same time during S phase in mitotic and endocycling cells, despite the need to replicate twice as much DNA in the endocycle and the fact that endocycling is typically associated with cell differentiation. However, regions collectively corresponding to 2% of the genome displayed significant changes in timing between the two types of cell cycles. The majority of these regions are small with a median size of 135 kb, shift to a later RT in the endocycle, and are enriched for genes expressed in the root tip. We found larger regions that shifted RT in centromeres of seven of the ten maize chromosomes. These regions covered the majority of the previously defined functional centromere, which ranged between 1 and 2 Mb in size in the reference genome. They replicate mainly during mid S phase in mitotic cells but primarily in late S phase of the endocycle. In contrast, the immediately adjacent pericentromere sequences are primarily late replicating in both cell cycles. Analysis of CENH3 enrichment levels in 8C vs 2C nuclei suggested that there is only a partial replacement of CENH3 nucleosomes after endocycle replication is complete. The shift to later replication of centromeres and possible reduction in CENH3 enrichment after endocycle replication is consistent with a hypothesis that centromeres are inactivated when their function is no longer needed

Subtle Perturbations of the Maize Methylome Reveal Genes and Transposons Silenced by Chromomethylase or RNA-Directed DNA Methylation Pathways

DNA methylation is a chromatin modification that can provide epigenetic regulation of gene and transposon expression. Plants utilize several pathways to establish and maintain DNA methylation in specific sequence contexts. The chromomethylase (CMT) genes maintain CHG (where H = A, C or T) methylation. The RNA-directed DNA methylation (RdDM) pathway is important for CHH methylation. Transcriptome analysis was performed in a collection of Zea mays lines carrying mutant alleles for CMT or RdDM-associated genes. While the majority of the transcriptome was not affected, we identified sets of genes and transposon families sensitive to context-specific decreases in DNA methylation in mutant lines. Many of the genes that are up-regulated in CMT mutant lines have high levels of CHG methylation, while genes that are differentially expressed in RdDM mutants are enriched for having nearby mCHH islands, implicating context-specific DNA methylation in the regulation of expression for a small number of genes. Many genes regulated by CMTs exhibit natural variation for DNA methylation and transcript abundance in a panel of diverse inbred lines. Transposon families with differential expression in the mutant genotypes show few defining features, though several families up-regulated in RdDM mutants show enriched expression in endosperm tissue, highlighting the potential importance for this pathway during reproduction. Taken together, our findings suggest that while the number of genes and transposon families whose expression is reproducibly affected by mild perturbations in context-specific methylation is small, there are distinct patterns for loci impacted by RdDM and CMT mutants

Repliscan: a tool for classifying replication timing regions

Abstract Background Replication timing experiments that use label incorporation and high throughput sequencing produce peaked data similar to ChIP-Seq experiments. However, the differences in experimental design, coverage density, and possible results make traditional ChIP-Seq analysis methods inappropriate for use with replication timing. Results To accurately detect and classify regions of replication across the genome, we present Repliscan. Repliscan robustly normalizes, automatically removes outlying and uninformative data points, and classifies Repli-seq signals into discrete combinations of replication signatures. The quality control steps and self-fitting methods make Repliscan generally applicable and more robust than previous methods that classify regions based on thresholds. Conclusions Repliscan is simple and effective to use on organisms with different genome sizes. Even with analysis window sizes as small as 1 kilobase, reliable profiles can be generated with as little as 2.4x coverage