Case Growth analysis to inform Local Response to Covid-19 Epidemic in a Diverse Us Community

Early detection of new outbreak waves is critical for effective and sustained response to the COVID-19 pandemic. We conducted a growth rate analysis using local community and inpatient records from seven hospital systems to characterize distinct phases in SARS-CoV-2 outbreak waves in the Greater Houston area. We determined the transition times from rapid spread of infection in the community to surge in the number of inpatients in local hospitals. We identified 193,237 residents who tested positive for SARS-CoV-2 via molecular testing from April 8, 2020 to June 30, 2021, and 30,031 residents admitted within local healthcare institutions with a positive SARS-CoV-2 test, including emergency cases. We detected two distinct COVID-19 waves: May 12, 2020-September 6, 2020 and September 27, 2020-May 15, 2021; each encompassed four growth phases: lagging, exponential/rapid growth, deceleration, and stationary/linear. Our findings showed that, during early stages of the pandemic, the surge in the number of daily cases in the community preceded that of inpatients admitted to local hospitals by 12-36 days. Rapid decline in hospitalized cases was an early indicator of transition to deceleration in the community. Our real-time analysis informed local pandemic response in one of the largest U.S. metropolitan areas, providing an operationalized framework to support robust real-world surveillance for outbreak preparedness

Inter-observer variability of radiologists for Cambridge classification of chronic pancreatitis using CT and MRCP: results from a large multi-center study

Purpose: Determine inter-observer variability among radiologists in assigning Cambridge Classification (CC) of chronic pancreatitis (CP) based on magnetic resonance imaging (MRI)/magnetic resonance cholangiopancreatography (MRCP) and contrast-enhanced CT (CECT). Methods: Among 422 eligible subjects enrolled into the PROCEED study between 6/2017 and 8/2018, 39 were selected randomly for this study (chronic abdominal pain (n = 8; CC of 0), suspected CP (n = 22; CC of 0, 1 or 2) or definite CP (n = 9; CC of 3 or 4). Each imaging was scored by the local radiologist (LRs) and three of five central radiologists (CRs) at other consortium sites. The CRs were blinded to clinical data and site information of the participants. We compared the CC score assigned by the LR with the consensus CC score assigned by the CRs. The weighted kappa statistic (K) was used to estimate the inter-observer agreement. Results: For the majority of subjects (34/39), the group assignment by LR agreed with the consensus composite CT/MRCP score by the CRs (concordance ranging from 75 to 89% depending on cohort group). There was moderate agreement (63% and 67% agreed, respectively) between CRs and LRs in both the CT score (weighted Kappa [95% CI] = 0.56 [0.34, 0.78]; p-value = 0.57) and the MR score (weighted Kappa [95% CI] = 0.68 [0.49, 0.86]; p-value = 0.72). The composite CT/MR score showed moderate agreement (weighted Kappa [95% CI] = 0.62 [0.43, 0.81]; p-value = 0.80). Conclusion: There is a high degree of concordance among radiologists for assignment of CC using MRI and CT

Identification and Characterization of Nucleolin as a COUP-TFII Coactivator of Retinoic Acid Receptor β Transcription in Breast Cancer Cells

The orphan nuclear receptor COUP-TFII plays an undefined role in breast cancer. Previously we reported lower COUP-TFII expression in tamoxifen/endocrine-resistant versus sensitive breast cancer cell lines. The identification of COUP-TFII-interacting proteins will help to elucidate its mechanism of action as a transcriptional regulator in breast cancer.FLAG-affinity purification and multidimensional protein identification technology (MudPIT) identified nucleolin among the proteins interacting with COUP-TFII in MCF-7 tamoxifen-sensitive breast cancer cells. Interaction of COUP-TFII and nucleolin was confirmed by coimmunoprecipitation of endogenous proteins in MCF-7 and T47D breast cancer cells. In vitro studies revealed that COUP-TFII interacts with the C-terminal arginine-glycine repeat (RGG) domain of nucleolin. Functional interaction between COUP-TFII and nucleolin was indicated by studies showing that siRNA knockdown of nucleolin and an oligonucleotide aptamer that targets nucleolin, AS1411, inhibited endogenous COUP-TFII-stimulated RARB2 expression in MCF-7 and T47D cells. Chromatin immunoprecipitation revealed COUP-TFII occupancy of the RARB2 promoter was increased by all-trans retinoic acid (atRA). RARβ2 regulated gene RRIG1 was increased by atRA and COUP-TFII transfection and inhibited by siCOUP-TFII. Immunohistochemical staining of breast tumor microarrays showed nuclear COUP-TFII and nucleolin staining was correlated in invasive ductal carcinomas. COUP-TFII staining correlated with ERα, SRC-1, AIB1, Pea3, MMP2, and phospho-Src and was reduced with increased tumor grade.Our data indicate that nucleolin plays a coregulatory role in transcriptional regulation of the tumor suppressor RARB2 by COUP-TFII

Use of multiple time points to model parotid differentiation

In order to understand the process of terminal differentiation in salivary acinar cells, mRNA and microRNA expression was measured across the month long process of differentiation in the parotid gland of the rat. Acinar cells were isolated at either nine time points (mRNA) or four time points (microRNA) in triplicate using laser capture microdissection (LCM). One of the values of this dataset comes from the high quality RNA (RIN > 7) that was used in this study, which can be prohibitively difficult to obtain from such an RNaseI-rich tissue. Global mRNA expression was measured by rat genome microarray hybridization (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE65586), and expression of microRNAs by qPCR array (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE65324). Comparing expression at different ages, 2656 mRNAs and 64 microRNAs were identified as differentially expressed. Because mRNA expression was sampled at many time points, clustering and regression analysis were able to identify dynamic expression patterns that had not been implicated in acinar differentiation before. Integration of the two datasets allowed the identification of microRNA target genes, and a gene regulatory network. Bioinformatics R code and additional details of experimental methods and data analysis are provided

Inter-observer variability of radiologists for Cambridge classification of chronic pancreatitis using CT and MRCP: results from a large multi-center study

Purpose: Determine inter-observer variability among radiologists in assigning Cambridge Classification (CC) of chronic pancreatitis (CP) based on magnetic resonance imaging (MRI)/magnetic resonance cholangiopancreatography (MRCP) and contrast-enhanced CT (CECT). Methods: Among 422 eligible subjects enrolled into the PROCEED study between 6/2017 and 8/2018, 39 were selected randomly for this study (chronic abdominal pain (n = 8; CC of 0), suspected CP (n = 22; CC of 0, 1 or 2) or definite CP (n = 9; CC of 3 or 4). Each imaging was scored by the local radiologist (LRs) and three of five central radiologists (CRs) at other consortium sites. The CRs were blinded to clinical data and site information of the participants. We compared the CC score assigned by the LR with the consensus CC score assigned by the CRs. The weighted kappa statistic (K) was used to estimate the inter-observer agreement. Results: For the majority of subjects (34/39), the group assignment by LR agreed with the consensus composite CT/MRCP score by the CRs (concordance ranging from 75 to 89% depending on cohort group). There was moderate agreement (63% and 67% agreed, respectively) between CRs and LRs in both the CT score (weighted Kappa [95% CI] = 0.56 [0.34, 0.78]; p-value = 0.57) and the MR score (weighted Kappa [95% CI] = 0.68 [0.49, 0.86]; p-value = 0.72). The composite CT/MR score showed moderate agreement (weighted Kappa [95% CI] = 0.62 [0.43, 0.81]; p-value = 0.80). Conclusion: There is a high degree of concordance among radiologists for assignment of CC using MRI and CT

A Systems Biology Approach Identifies a Regulatory Network in Parotid Acinar Cell Terminal Differentiation

<div><p>Objective</p><p>The transcription factor networks that drive parotid salivary gland progenitor cells to terminally differentiate, remain largely unknown and are vital to understanding the regeneration process.</p><p>Methodology</p><p>A systems biology approach was taken to measure mRNA and microRNA expression in vivo across acinar cell terminal differentiation in the rat parotid salivary gland. Laser capture microdissection (LCM) was used to specifically isolate acinar cell RNA at times spanning the month-long period of parotid differentiation.</p><p>Results</p><p>Clustering of microarray measurements suggests that expression occurs in four stages. mRNA expression patterns suggest a novel role for <i>Pparg</i> which is transiently increased during mid postnatal differentiation in concert with several target gene mRNAs. 79 microRNAs are significantly differentially expressed across time. Profiles of statistically significant changes of mRNA expression, combined with reciprocal correlations of microRNAs and their target mRNAs, suggest a putative network involving <i>Klf4</i>, a differentiation inhibiting transcription factor, which decreases as several targeting microRNAs increase late in differentiation. The network suggests a molecular switch (involving <i>Prdm1</i>, <i>Sox11</i>, <i>Pax5</i>, miR-200a, and miR-30a) progressively decreases repression of <i>Xbp1</i> gene transcription, in concert with decreased translational repression by miR-214. <i>The transcription factor Xbp1</i> mRNA is initially low, increases progressively, and may be maintained by a positive feedback loop with <i>Atf6</i>. Transfection studies show that <i>Xbp1Mist1</i> promoter. In addition, <i>Xbp1</i> and <i>Mist1</i> each activate the parotid secretory protein (<i>Psp</i>) gene, which encodes an abundant salivary protein, and is a marker of terminal differentiation.</p><p>Conclusion</p><p>This study identifies novel expression patterns of <i>Pparg</i>, <i>Klf4</i>, and <i>Sox11</i> during parotid acinar cell differentiation, as well as numerous differentially expressed microRNAs. Network analysis identifies a novel stemness arm, a genetic switch involving transcription factors and microRNAs, and transition to an <i>Xbp1</i> driven differentiation network. This proposed network suggests key regulatory interactions in parotid gland terminal differentiation.</p></div

mRNAs with a Quadratic Expression Pattern.

<p>Quadratic regression reveals late activation of acinar cell specific genes. (A) Quadratic regression analysis identified 430 genes having a significant quadratic trend (FDR < 0.05), which were clustered into eight patterns. For visualization, the expression data for each gene was scaled to a mean of zero and standard deviation of 1 before plotting. The red line traces the average expression for the cluster. (B) Log2 plot of Quadratic Cluster 6 members with at least a 4-fold expression difference between P25 and E18. This shows late up-regulation of several genes known to produce salivary proteins (i.e. <i>DNase I</i>, <i>Chitinase</i>, <i>Prp15</i>, <i>Sgp158/Prr21</i>).</p

<i>Xbp1</i> Regulates <i>Mist1</i> Expression during Parotid Differentiation.

<p>(A) Log2 plot of microarray data for <i>Xbp1</i> and <i>Mist1</i>. (B) Expression of <i>Xbp1</i> and <i>Mist1</i> is highly correlated across parotid differentiation. Plot of Log2 <i>Xbp1</i> vs. Log2 <i>Mist1</i> shows a linear trend with R² = 0.9538. (C) Luciferase assay shows activation of <i>Mist1</i> promoter by <i>Xbp1</i> in ParC5 cells. Increasing amount of <i>Xbp1</i>-S (spliced <i>Xbp1</i>) cDNA/well (0.25 μg, 0.5 μg, and 1 μg) were co-transfected with a luciferase expression plasmid driven by a <i>Mist1</i> promoter. Significant increase in luciferase expression was observed for all concentrations of <i>Xbp1</i>-S (p = 0.017, p = 0.01, and p = 0.05 respectively) (n = 3).</p

MicroRNA Expression Changes between Time Points.

<p>MicroRNA Expression Changes between Time Points.</p

Transient Activation of <i>Pparg</i> during Parotid Acinar Cell Differentiation.

<p>(A) Network showing transcription factor <i>Pparg</i> and known downstream target genes found in DE Cluster 4 (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0125153#pone.0125153.g002" target="_blank">Fig 2</a>). DE Cluster 4 contains 106 genes (including <i>Pparg</i>) with a unique expression pattern; higher expression only in stages 2 and 3. The Metacore knowledge-base identifies 18 of these as <i>Pparg</i> target genes. A green arrow indicates activation of transcription while red arrow indicates inhibition. A grey line means the interaction is uncharacterized. Although a red arrow connects <i>Pparg</i> and <i>ACP5</i>, some publications list the interaction as activating [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0125153#pone.0125153.ref067" target="_blank">67</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0125153#pone.0125153.ref068" target="_blank">68</a>] indicating it could be context dependent. (B) Log2 expression of <i>Pparg</i> from microarray data. (C) qPCR data confirming the expression profile of <i>Pparg</i>. RNA samples from independent animals were collected at three time points (E20, P5, and P25). Expression was normalized to <i>Arbp</i>, and data showed significant change in expression by ANOVA. n = 3.</p