BackgroundAs context is important to gene expression, so is the preprocessing of microarray to transcriptomics. Microarray data suffers from several normalization and significance problems. Arbitrary fold change (FC) cut-offs of >2 and significance p-values of <0.02 lead data collection to look only at genes which vary wildly amongst other genes. Therefore, questions arise as to whether the biology or the statistical cutoff are more important within the interpretation. In this paper, we reanalyzed a zebrafish (D. rerio) microarray data set using GeneSpring and different differential gene expression cut-offs and found the data interpretation was drastically different. Furthermore, despite the advances in microarray technology, the array captures a large portion of genes known but yet still leaving large voids in the number of genes assayed, such as leptin a pleiotropic hormone directly related to hypoxia-induced angiogenesis.ResultsThe data strongly suggests that the number of differentially expressed genes is more up-regulated than down-regulated, with many genes indicating conserved signalling to previously known functions. Recapitulated data from Marques et al. (2008) was similar but surprisingly different with some genes showing unexpected signalling which may be a product of tissue (heart) or that the intended response was transient.ConclusionsOur analyses suggest that based on the chosen statistical or fold change cut-off; microarray analysis can provide essentially more than one answer, implying data interpretation as more of an art than a science, with follow up gene expression studies a must. Furthermore, gene chip annotation and development needs to maintain pace with not only new genomes being sequenced but also novel genes that are crucial to the overall gene chips interpretation.

Aim To investigate the factors associated with the duration of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) RNA shedding in patients with coronavirus disease 2019 (COVID‐19). Methods A retrospective cohort of COVID‐19 patients admitted to a designated hospital in Beijing was analyzed to study the factors affecting the duration of viral shedding. Results The median duration of viral shedding was 11 days (IQR, 8‐14.3 days) as measured from illness onset. Univariate regression analysis showed that disease severity, corticosteroid therapy, fever (temperature>38.5℃), and time from onset to hospitalization were associated with prolonged duration of viral shedding ( p <0.05). Multivariate regression analysis showed that fever (temperature>38.5℃) (OR 5.1, 95%CI: 1.5‐18.1), corticosteroid therapy (OR 6.3, 95%CI: 1.5‐27.8), and time from onset to hospitalization (OR 1.8, 95%CI: 1.19‐2.7) were associated with increased odds of prolonged duration of viral shedding. Conclusions Corticosteroid treatment, fever (temperature>38.5℃), and longer time from onset to hospitalization were associated with prolonged viral shedding in COVID‐19 patients. This article is protected by copyright. All rights reserved.

ABSTRACT:A treecode algorithm is presented for rapid computation of the nonbonded potential energy in classical molecular systems. The algorithm treats a general form of pairwise particle interaction with the Coulomb and London dispersion potentials as special cases. The energy is computed as a sum of group-group interactions using a variant of Appel's recursive strategy. Several adaptive techniques are employed to reduce the execution time. These include an adaptive tree with nonuniform rectangular cells, variable order multipole approximation, and a run-time choice between direct summation and multipole approximation for each group-group interaction. The multipole approximation is derived by Taylor expansion in Cartesian coordinates, and the necessary coefficients are computed using a recurrence relation. An error bound is derived and used to select the order of approximation. Test results are presented for a variety of systems.

BackgroundHedgehog (HH) signaling plays a critical role in normal cellular processes, in normal mammalian gastrointestinal development and differentiation, and in oncogenesis and maintenance of the malignant phenotype in a variety of human cancers. Increasing evidence further implicates the involvement of HH signaling in oncogenesis and metastatic behavior of colon cancers. However, genomic approaches to elucidate the role of HH signaling in cancers in general are lacking, and data derived on HH signaling in colon cancer is extremely limited.Methodology/Principal FindingsTo identify unique downstream targets of the GLI genes, the transcriptional regulators of HH signaling, in the context of colon carcinoma, we employed a small molecule inhibitor of both GLI1 and GLI2, GANT61, in two human colon cancer cell lines, HT29 and GC3/c1. Cell cycle analysis demonstrated accumulation of GANT61-treated cells at the G1/S boundary. cDNA microarray gene expression profiling of 18,401 genes identified Differentially Expressed Genes (DEGs) both common and unique to HT29 and GC3/c1. Analyses using GenomeStudio (statistics), Matlab (heat map), Ingenuity (canonical pathway analysis), or by qRT-PCR, identified p21Cip1 (CDKN1A) and p15Ink4b (CDKN2B), which play a role in the G1/S checkpoint, as up-regulated genes at the G1/S boundary. Genes that determine further cell cycle progression at G1/S including E2F2, CYCLIN E2 (CCNE2), CDC25A and CDK2, and genes that regulate passage of cells through G2/M (CYCLIN A2 [CCNA2], CDC25C, CYCLIN B2 [CCNB2], CDC20 and CDC2 [CDK1], were down-regulated. In addition, novel genes involved in stress response, DNA damage response, DNA replication and DNA repair were identified following inhibition of HH signaling.Conclusions/SignificanceThis study identifies genes that are involved in HH-dependent cellular proliferation in colon cancer cells, and following its inhibition, genes that regulate cell cycle progression and events downstream of the G1/S boundary.

We present a method for evaluating Coulomb interactions in periodic molecular systems. The real space term in Ewald summation is accelerated using a tree code in which interactions between clusters and distant particles are approximated by multipole expansions. The performance is reported for water systems.

Characterizing the alterations of protein expression in cancer cells can be very useful in providing insight into the changes in the functional pathways and thus the fundamental mechanisms of cancer development at the molecular level. In this study, we profiled protein expressions in eleven pairs of primary cell cultures derived from renal-cell carcinoma (RCC) tissues and patient-matched normal kidney tissues utilizing two-dimensional polyacrylamide gel electrophoresis (2-D PAGE). Together with the immunoblot analysis of proteins from the RCC tissues, the study also demonstrated that the alterations of protein expression observed in RCC primary cell cultures reflected those observed in the original RCC tissues. We analyzed the expression profiles and identified proteins differentially expressed in RCC primary cell cultures by 2-D PAGE and mass spectrometry (MS). We found sixteen proteins were overexpressed and seven proteins underexpressed in RCC. The deregulated expressions of proteins include those involved in metabolism, cellular morphology, heat shock response, cell growth, etc. Overexpression of three proteins, alphabeta-crystallin, manganese superoxide dismutase (MnSOD), and annexin IV, most commonly observed in primary RCC cell cultures, were also observed by immunoblot analysis of proteins from the RCC tissues from which the primary cell cultures were derived. Semi-quantitative reverse transcription (RT)-polymerase chain reaction (PCR) analysis revealed the direct correlation between deregulated gene expression and the corresponding protein abundance in two of the three most commonly upregulated proteins we found in RCC.

Background: Renal cell carcinoma (RCC) is the most common cancer in adult kidney. The accuracy of current diagnosis and prognosis of the disease and the effectiveness of the treatment for the disease are limited by the poor understanding of the disease at the molecular level. To better understand the genetics and biology of RCC, we profiled the expression of 7,129 genes in both clear cell RCC tissue and cell lines using oligonucleotide arrays.

High fidelity genome-wide expression analysis has strengthened the idea that microRNA (miRNA) signatures in peripheral blood mononuclear cells (PBMCs) can be potentially used to predict the pathology when anatomical samples are inaccessible like heart. PBMCs from 48 non-failing controls and 44 patients with relatively stable chronic heart failure (ejection fraction of ≤ 40%) associated with dilated cardiomyopathy (DCM) were used for miRNA analysis. Genome-wide miRNA-microarray on PBMCs from chronic heart failure patients identified miRNA signature uniquely characterized by the downregulation of miRNA-548 family members. We have also independently validated downregulation of miRNA-548 family members (miRNA-548c & 548i) using real time-PCR in a large cohort of independent patient samples. Independent in silico Ingenuity Pathway Analysis (IPA) of miRNA-548 targets shows unique enrichment of signaling molecules and pathways associated with cardiovascular disease and hypertrophy. Consistent with specificity of miRNA changes with pathology, PBMCs from breast cancer patients showed no alterations in miRNA-548c expression compared to healthy controls. These studies suggest that miRNA-548 family signature in PBMCs can therefore be used as to detect early heart failure. Our studies show that cognate networking of predicted miRNA-548 targets in heart failure can be used as a powerful ancillary tool to predict the ongoing pathology.