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Aging is associated with a number of physiologic changes including perturbed circadian rhythms; however, mechanisms by which rhythms are altered remain unknown. To test the idea that circulating factors mediate age-dependent changes in peripheral rhythms, we compared the ability of human serum from young and old individuals to synchronize circadian rhythms in culture. We collected blood from apparently healthy young (age 25–30) and old (age 70–76) individuals at 14:00 and used the serum to synchronize cultured fibroblasts. We found that young and old sera are equally competent at initiating robust ~24 hr oscillations of a luciferase reporter driven by clock gene promoter. However, cyclic gene expression is affected, such that young and old sera promote cycling of different sets of genes. Genes that lose rhythmicity with old serum entrainment are associated with oxidative phosphorylation and Alzheimer’s Disease as identified by STRING and IPA analyses. Conversely, the expression of cycling genes associated with cholesterol biosynthesis increased in the cells entrained with old serum. Genes involved in the cell cycle and transcription/translation remain rhythmic in both conditions. We did not observe a global difference in the distribution of phase between groups, but found that peak expression of several clock-controlled genes (PER3, NR1D1, NR1D2, CRY1, CRY2, and TEF) lagged in the cells synchronized ex vivo with old serum. Taken together, these findings demonstrate that age-dependent blood-borne factors affect circadian rhythms in peripheral cells and have the potential to impact health and disease via maintaining or disrupting rhythms respectively.

Abstract Background Lipids may influence cellular penetrance by viral pathogens and the immune response that they evoke. We deeply phenotyped the lipidomic response to SARs‐CoV‐2 and compared that with infection with other pathogens in patients admitted with acute respiratory distress syndrome to an intensive care unit (ICU). Methods Mass spectrometry was used to characterise lipids and relate them to proteins, peripheral cell immunotypes and disease severity. Results Circulating phospholipases (sPLA2, cPLA2 (PLA2G4A) and PLA2G2D) were elevated on admission in all ICU groups. Cyclooxygenase, lipoxygenase and epoxygenase products of arachidonic acid (AA) were elevated in all ICU groups compared with controls. sPLA2 predicted severity in COVID‐19 and correlated with TxA2, LTE4 and the isoprostane, iPF2α‐III, while PLA2G2D correlated with LTE4. The elevation in PGD2, like PGI2 and 12‐HETE, exhibited relative specificity for COVID‐19 and correlated with sPLA2 and the interleukin‐13 receptor to drive lymphopenia, a marker of disease severity. Pro‐inflammatory eicosanoids remained correlated with severity in COVID‐19 28 days after admission. Amongst non‐COVID ICU patients, elevations in 5‐ and 15‐HETE and 9‐ and 13‐HODE reflected viral rather than bacterial disease. Linoleic acid (LA) binds directly to SARS‐CoV‐2 and both LA and its di‐HOME products reflected disease severity in COVID‐19. In healthy marines, these lipids rose with seroconversion. Eicosanoids linked variably to the peripheral cellular immune response. PGE2, TxA2 and LTE4 correlated with T cell activation, as did PGD2 with non‐B non‐T cell activation. In COVID‐19, LPS stimulated peripheral blood mononuclear cell PGF2α correlated with memory T cells, dendritic and NK cells while LA and DiHOMEs correlated with exhausted T cells. Three high abundance lipids – ChoE 18:3, LPC‐O‐16:0 and PC‐O‐30:0 – were altered specifically in COVID. LPC‐O‐16:0 was strongly correlated with T helper follicular cell activation and all three negatively correlated with multi‐omic inflammatory pathways and disease severity. Conclusions A broad based lipidomic storm is a predictor of poor prognosis in ARDS. Alterations in sPLA2, PGD2 and 12‐HETE and the high abundance lipids, ChoE 18:3, LPC‐O‐16:0 and PC‐O‐30:0 exhibit relative specificity for COVID‐19 amongst such patients and correlate with the inflammatory response to link to disease severity.

Abstract Background The accurate interpretation of RNA-Seq data presents a moving target as scientists continue to introduce new experimental techniques and analysis algorithms. Simulated datasets are an invaluable tool to accurately assess the performance of RNA-Seq analysis methods. However, existing RNA-Seq simulators focus on modeling the technical biases and artifacts of sequencing, rather than on simulating the original RNA samples. A first step in simulating RNA-Seq is to simulate RNA. Results To fill this need, we developed the Configurable And Modular Program Allowing RNA Expression Emulation (CAMPAREE), a simulator using empirical data to simulate diploid RNA samples at the level of individual molecules. We demonstrated CAMPAREE’s use for generating idealized coverage plots from real data, and for adding the ability to generate allele-specific data to existing RNA-Seq simulators that do not natively support this feature. Conclusions Separating input sample modeling from library preparation/sequencing offers added flexibility for both users and developers to mix-and-match different sample and sequencing simulators to suit their specific needs. Furthermore, the ability to maintain sample and sequencing simulators independently provides greater agility to incorporate new biological findings about transcriptomics and new developments in sequencing technologies. Additionally, by simulating at the level of individual molecules, CAMPAREE has the potential to model molecules transcribed from the same genes as a heterogeneous population of transcripts with different states of degradation and processing (splicing, editing, etc.). CAMPAREE was developed in Python, is open source, and freely available at https://github.com/itmat/CAMPAREE .

Abstract Background Full-length isoform quantification from RNA-Seq is a key goal in transcriptomics analyses and has been an area of active development since the beginning. The fundamental difficulty stems from the fact that RNA transcripts are long, while RNA-Seq reads are short. Results Here we use simulated benchmarking data that reflects many properties of real data, including polymorphisms, intron signal and non-uniform coverage, allowing for systematic comparative analyses of isoform quantification accuracy and its impact on differential expression analysis. Genome, transcriptome and pseudo alignment-based methods are included; and a simple approach is included as a baseline control. Conclusions Salmon, kallisto, RSEM, and Cufflinks exhibit the highest accuracy on idealized data, while on more realistic data they do not perform dramatically better than the simple approach. We determine the structural parameters with the greatest impact on quantification accuracy to be length and sequence compression complexity and not so much the number of isoforms. The effect of incomplete annotation on performance is also investigated. Overall, the tested methods show sufficient divergence from the truth to suggest that full-length isoform quantification and isoform level DE should still be employed selectively.

During the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, providing safe in-person schooling has been a dynamic process balancing evolving community disease burden, scientific information, and local regulatory requirements with the mandate for education. Considerations include the health risks of SARS-CoV-2 infection and its post-acute sequelae, the impact of remote learning or periods of quarantine on education and well-being of children, and the contribution of schools to viral circulation in the community. The risk for infections that may occur within schools is related to the incidence of SARS-CoV-2 infections within the local community. Thus, persistent suppression of viral circulation in the community through effective public health measures including vaccination is critical to in-person schooling. Evidence suggests that the likelihood of transmission of SARS-CoV-2 within schools can be minimized if mitigation strategies are rationally combined. This article reviews evidence-based approaches and practices for the continual operation of in-person schooling.