Prevalence and trends in mono- and co-infection of COVID-19, influenza A/B, and respiratory syncytial virus, January 2018–June 2023

ObjectivesThis study aimed to determine the impact of the COVID-19 pandemic on the overall prevalence and co-infection rates for COVID-19, influenza A/B, and respiratory syncytial virus in a large national population.MethodsWe conducted a retrospective review of 1,318,118 multi-component nucleic acid amplification tests for COVID-19, influenza A/B, and RSV performed at Labcorp® sites from January 2018 to June 2023, comparing positivity rates and co-infection rates by age, sex, and seasonality.ResultsIn 2021–2023, 1,232 (0.10%) tested positive for COVID-19 and influenza A/B, 366 (0.03%) tested positive for COVID-19 and RSV, 874 (0.07%) tested for influenza A/B and RSV, and 13 (0.001%) tested positive for COVID-19, influenza A/B, and RSV. RSV positivity rates were particularly higher in Q2 and Q3 of 2021 and in Q3 of 2022. Higher influenza A positivity proportions were found in Q4 of 2021 and again in Q2 and Q4 of 2022. Influenza B positivity had been minimal since the start of the pandemic, with a slight increase observed in Q2 of 2023.ConclusionOur findings highlight the need for adaptability in preparation for upper respiratory infection occurrences throughout the year as we adjust to the COVID-19 pandemic due to the observed changes in the seasonality of influenza and RSV. Our results highlight low co-infection rates and suggest heightened concerns for co-infections during peaks of COVID-19, influenza, and RSV, which may perhaps be reduced

Characterization and Modeling of Metabolic Stress Responses in Cellular Aging

Cellular aging describes the buildup of changes over time that affect normal mechanisms of cells, tissues and organisms throughout their lifespan, which can lead to any number of potential health risks, diseases or other disorders. One of the major causes of these changes is declining mitochondrial function, though the cause of this energy stress is still debated. The prevailing experimental model for aging studies examines cells in a senescent state as the hallmark of aging. Yet this permanent, post-mitotic phase is more commonly observed in vitro. Aged cells in vivo often retain their mitotic potential, indicative of a paused, quiescent state. This thesis proposes a new platform to study aging through perturbations of mitochondrial function via an experimental energy restriction in quiescence (ERiQ) model that may be more relevant to aging in tissues. This model causes adaptive changes in major stress response pathways for AKT, NF-κB, p53 and mTOR as a reaction to reduced ATP, NAD+ and NADP levels. The construction of a theoretical computational model, complementary to the experimental model, is based on feedback motifs that investigate the interplay between those key stress response pathways. The in silico model demonstrates adaptations to sudden energetic perturbations, promoting pro-survival phenotypes and recovery. This thesis hypothesizes that the very same survival mechanisms are chronically activated during aging, but also cause conflicting responses that actively suppress mitochondrial function to contribute to a lockstep progression of decline. The model makes predictions consistent with inhibitory and gain-of-function experiments in aging. The relevance of ERiQ as a model to study aging is further emphasized by a transcription factor (TF) meta-analysis of gene expression datasets accrued from 18 tissues from individuals at different biological ages, which were compared to 7 different experimental platforms. Experimental datasets included replicative senescence and ERiQ, in which ATP was transiently reduced. TF motifs in promoter regions of trimmed sets of target genes were scanned using JASPAR and TRANSFAC motifs and TF signatures established a global mapping of agglomerating motifs with distinct clusters when ranked hierarchically. Remarkably, the majority of in vivo aged tissues correlated with the ERiQ profile instead of senescence, confirming its relevance as a new experimental model. Fitting motifs in a minimalistic protein-protein interaction (PPI) network model allowed us to probe for connectivity to distinct stress sensors, as well as identify novel targets of study in transcription factors that significantly switch enrichment between ERiQ and senescence. In the PPI, DNA damage sensors ATM and ATR linked to one subnetwork associated with senescence. By contrast, energy sensors PTEN and AMPK connected to the nodes in the ERiQ subnetwork. These data suggest that energy deprivation may be linked to transcriptional patterns characteristic of many aged tissues distinct from cumulative DNA damage associated with senescence. Finally, this thesis exemplifies the combined use of the predictive power of the computational model with experimental investigation in vitro. Preliminary experiments show how the model can be refined to reflect how certain conditions may alter metabolic output and offer intriguing insights into the future of cellular aging studies.Ph.D., Biomedical Engineering -- Drexel University, 201

Simulation of Cellular Energy Restriction in Quiescence (ERiQ)—A Theoretical Model for Aging

Cellular responses to energy stress involve activation of pro-survival signaling nodes, compensation in regulatory pathways and adaptations in organelle function. Specifically, energy restriction in quiescent cells (ERiQ) through energetic perturbations causes adaptive changes in response to reduced ATP, NAD+ and NADP levels in a regulatory network spanned by AKT, NF-κB, p53 and mTOR. Based on the experimental ERiQ platform, we have constructed a minimalistic theoretical model consisting of feedback motifs that enable investigation of stress-signaling pathways. The computer simulations reveal responses to acute energetic perturbations, promoting cellular survival and recovery to homeostasis. We speculated that the very same stress mechanisms are activated during aging in post-mitotic cells. To test this hypothesis, we modified the model to be deficient in protein damage clearance and demonstrate the formation of energy stress. Contrasting the network’s pro-survival role in acute energetic challenges, conflicting responses in aging disrupt mitochondrial maintenance and contribute to a lockstep progression of decline when chronically activated. The model was analyzed by a local sensitivity analysis with respect to lifespan and makes predictions consistent with inhibitory and gain-of-function experiments in aging

Global mapping of transcription factor motifs in human aging.

Biological aging is a complex process dependent on the interplay of cell autonomous and tissue contextual changes which occur in response to cumulative molecular stress and manifest through adaptive transcriptional reprogramming. Here we describe a transcription factor (TF) meta-analysis of gene expression datasets accrued from 18 tissue sites collected at different biological ages and from 7 different in-vitro aging models. In-vitro aging platforms included replicative senescence and an energy restriction model in quiescence (ERiQ), in which ATP was transiently reduced. TF motifs in promoter regions of trimmed sets of target genes were scanned using JASPAR and TRANSFAC. TF signatures established a global mapping of agglomerating motifs with distinct clusters when ranked hierarchically. Remarkably, the ERiQ profile was shared with the majority of in-vivo aged tissues. Fitting motifs in a minimalistic protein-protein network allowed to probe for connectivity to distinct stress sensors. The DNA damage sensors ATM and ATR linked to the subnetwork associated with senescence. By contrast, the energy sensors PTEN and AMPK connected to the nodes in the ERiQ subnetwork. These data suggest that metabolic dysfunction may be linked to transcriptional patterns characteristic of many aged tissues and distinct from cumulative DNA damage associated with senescence

Switching transcription factor motifs.

<p>Switching transcription factor motifs.</p

Sample distance map and classification.

<p>The distance map indicates similarities of samples based on ranked transcription factor enrichment scores, using 125 TRANSFAC motifs. Dissimilarity increases from yellow to blue. The distance map is overlaid by a result from K-Means Classification (KMC), discriminating three distinct groups marked by white lines. Most samples included in this study aggregate with an experimental energy restriction model and also include brain samples from Parkinson’s patients. In contrast, tissues including kidney, liver, female skin and ischemic heart aggregate with experimental models of senescence.</p

Global mapping of transcriptional regulation in human aging.

<p>Enrichment scores of prioritized transcription factor motifs, using TRANSFAC database, are visualized as a dendrogram. The heat map represents unique gene regulatory signatures of 18 human tissues and 7 cell aging experiments. Motifs enriched in aging (p<0.05) are indicated in red, and avoided motifs in green. Motifs are ranked and sorted by hierarchical clustering using Spearman rank correlation, complete linkage. Tissue samples agglomerate with either an experimental energy restriction cell model in quiescence (ERiQ, sample #4), or anti-correlate as a group of nine senescence related samples. The corresponding analysis using JASPAR motifs is provided in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0190457#pone.0190457.s001" target="_blank">S1 Fig</a>.</p

Image_1_Prevalence and trends in mono- and co-infection of COVID-19, influenza A/B, and respiratory syncytial virus, January 2018–June 2023.JPEG

ObjectivesThis study aimed to determine the impact of the COVID-19 pandemic on the overall prevalence and co-infection rates for COVID-19, influenza A/B, and respiratory syncytial virus in a large national population.MethodsWe conducted a retrospective review of 1,318,118 multi-component nucleic acid amplification tests for COVID-19, influenza A/B, and RSV performed at Labcorp® sites from January 2018 to June 2023, comparing positivity rates and co-infection rates by age, sex, and seasonality.ResultsIn 2021–2023, 1,232 (0.10%) tested positive for COVID-19 and influenza A/B, 366 (0.03%) tested positive for COVID-19 and RSV, 874 (0.07%) tested for influenza A/B and RSV, and 13 (0.001%) tested positive for COVID-19, influenza A/B, and RSV. RSV positivity rates were particularly higher in Q2 and Q3 of 2021 and in Q3 of 2022. Higher influenza A positivity proportions were found in Q4 of 2021 and again in Q2 and Q4 of 2022. Influenza B positivity had been minimal since the start of the pandemic, with a slight increase observed in Q2 of 2023.ConclusionOur findings highlight the need for adaptability in preparation for upper respiratory infection occurrences throughout the year as we adjust to the COVID-19 pandemic due to the observed changes in the seasonality of influenza and RSV. Our results highlight low co-infection rates and suggest heightened concerns for co-infections during peaks of COVID-19, influenza, and RSV, which may perhaps be reduced.</p

Regulatory protein-protein-interaction (PPI) network.

<p>The PPI network was seeded by 14 enriched transcription factor proteins (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0190457#pone.0190457.t001" target="_blank">Table 1</a>), which switch between enrichment in senescence (light yellow nodes and edges) and enrichment in energy restriction (red nodes and edges). Connectivity was predicted by STRING, and visualized with NetworkAnalyst. All nodes were probed for their connectivity to DNA stress sensors ATM (ataxia-telangiectasia mutated) and ATR (ATM- and Rad3-Related) in yellow, and multiple connections were found to the subnetwork associated with senescence, but not to the energy restriction nodes. In contrast, nodes enriched in energy restriction revealed strong connectivity to the energy sensors PRKAB1 (AMPK) and phosphatase and tensin homolog (PTEN) in orange, while these proteins did not connect to the senescence nodes. The combined network shown here suggests involvement of intermediate proteins (gray nodes), some of which may also change activity as indicated by different colored network edges converging onto these nodes such as histone deacetylases HDAC1, HDAC3, SIRT1 and EP300 and polyubiquitin-C precursor (UBC). The network provides a flexible functionality allowing cells to adapt to different stressors.</p

Gene regulatory phenotypes.

<p>Gene regulatory phenotypes.</p