Reassortment Patterns in Swine Influenza Viruses

Three human influenza pandemics occurred in the twentieth century, in 1918, 1957, and 1968. Influenza pandemic strains are the results of emerging viruses from non-human reservoirs to which humans have little or no immunity. At least two of these pandemic strains, in 1957 and in 1968, were the results of reassortments between human and avian viruses. Also, many cases of swine influenza viruses have reportedly infected humans, in particular, the recent H1N1 influenza virus of swine origin, isolated in Mexico and the United States. Pigs are documented to allow productive replication of human, avian, and swine influenza viruses. Thus it has been conjectured that pigs are the “mixing vessel” that create the avian-human reassortant strains, causing the human pandemics. Hence, studying the process and patterns of viral reassortment, especially in pigs, is a key to better understanding of human influenza pandemics. In the last few years, databases containing sequences of influenza A viruses, including swine viruses, collected since 1918 from diverse geographical locations, have been developed and made publicly available. In this paper, we study an ensemble of swine influenza viruses to analyze the reassortment phenomena through several statistical techniques. The reassortment patterns in swine viruses prove to be similar to the previous results found in human viruses, both in vitro and in vivo, that the surface glycoprotein coding segments reassort most often. Moreover, we find that one of the polymerase segments (PB1), reassorted in the strains responsible for the last two human pandemics, also reassorts frequently

Application of affymetrix array and massively parallel signature sequencing for identification of genes involved in prostate cancer progression

BACKGROUND: Affymetrix GeneChip Array and Massively Parallel Signature Sequencing (MPSS) are two high throughput methodologies used to profile transcriptomes. Each method has certain strengths and weaknesses; however, no comparison has been made between the data derived from Affymetrix arrays and MPSS. In this study, two lineage-related prostate cancer cell lines, LNCaP and C4-2, were used for transcriptome analysis with the aim of identifying genes associated with prostate cancer progression. METHODS: Affymetrix GeneChip array and MPSS analyses were performed. Data was analyzed with GeneSpring 6.2 and in-house perl scripts. Expression array results were verified with RT-PCR. RESULTS: Comparison of the data revealed that both technologies detected genes the other did not. In LNCaP, 3,180 genes were only detected by Affymetrix and 1,169 genes were only detected by MPSS. Similarly, in C4-2, 4,121 genes were only detected by Affymetrix and 1,014 genes were only detected by MPSS. Analysis of the combined transcriptomes identified 66 genes unique to LNCaP cells and 33 genes unique to C4-2 cells. Expression analysis of these genes in prostate cancer specimens showed CA1 to be highly expressed in bone metastasis but not expressed in primary tumor and EPHA7 to be expressed in normal prostate and primary tumor but not bone metastasis. CONCLUSION: Our data indicates that transcriptome profiling with a single methodology will not fully assess the expression of all genes in a cell line. A combination of transcription profiling technologies such as DNA array and MPSS provides a more robust means to assess the expression profile of an RNA sample. Finally, genes that were differentially expressed in cell lines were also differentially expressed in primary prostate cancer and its metastases

Identification of stromally expressed molecules in the prostate by tag-profiling of cancer-associated fibroblasts, normal fibroblasts and fetal prostate.

The stromal microenvironment has key roles in prostate development and cancer, and cancer-associated fibroblasts (CAFs) stimulate tumourigenesis via several mechanisms including the expression of pro-tumourigenic factors. Mesenchyme (embryonic stroma) controls prostate organogenesis, and in some circumstances can re-differentiate prostate tumours. We have applied next-generation Tag profiling to fetal human prostate, normal human prostate fibroblasts (NPFs) and CAFs to identify molecules expressed in prostatic stroma. Comparison of gene expression profiles of a patient-matched pair of NPFs vs CAFs identified 671 transcripts that were enriched in CAFs and 356 transcripts whose levels were decreased, relative to NPFs. Gene ontology analysis revealed that CAF-enriched transcripts were associated with prostate morphogenesis and CAF-depleted transcripts were associated with cell cycle. We selected mRNAs to follow-up by comparison of our data sets with published prostate cancer fibroblast microarray profiles as well as by focusing on transcripts encoding secreted and peripheral membrane proteins, as well as mesenchymal transcripts identified in a previous study from our group. We confirmed differential transcript expression between CAFs and NPFs using QrtPCR, and defined protein localization using immunohistochemistry in fetal prostate, adult prostate and prostate cancer. We demonstrated that ASPN, CAV1, CFH, CTSK, DCN, FBLN1, FHL1, FN, NKTR, OGN, PARVA, S100A6, SPARC, STC1 and ZEB1 proteins showed specific and varied expression patterns in fetal human prostate and in prostate cancer. Colocalization studies suggested that some stromally expressed molecules were also expressed in subsets of tumour epithelia, indicating that they may be novel markers of EMT. Additionally, two molecules (ASPN and STC1) marked overlapping and distinct subregions of stroma associated with tumour epithelia and may represent new CAF markers

Summary of the DREAM8 Parameter Estimation Challenge: Toward Parameter Identification for Whole-Cell Models

Whole-cell models that explicitly represent all cellular components at the molecular level have the potential to predict phenotype from genotype. However, even for simple bacteria, whole-cell models will contain thousands of parameters, many of which are poorly characterized or unknown. New algorithms are needed to estimate these parameters and enable researchers to build increasingly comprehensive models. We organized the Dialogue for Reverse Engineering Assessments and Methods (DREAM) 8 Whole-Cell Parameter Estimation Challenge to develop new parameter estimation algorithms for whole-cell models. We asked participants to identify a subset of parameters of a whole-cell model given the model’s structure and in silico “experimental” data. Here we describe the challenge, the best performing methods, and new insights into the identifiability of whole-cell models. We also describe several valuable lessons we learned toward improving future challenges. Going forward, we believe that collaborative efforts supported by inexpensive cloud computing have the potential to solve whole-cell model parameter estimation

International Consensus Statement on Rhinology and Allergy: Rhinosinusitis

Background: The 5 years since the publication of the first International Consensus Statement on Allergy and Rhinology: Rhinosinusitis (ICAR‐RS) has witnessed foundational progress in our understanding and treatment of rhinologic disease. These advances are reflected within the more than 40 new topics covered within the ICAR‐RS‐2021 as well as updates to the original 140 topics. This executive summary consolidates the evidence‐based findings of the document. Methods: ICAR‐RS presents over 180 topics in the forms of evidence‐based reviews with recommendations (EBRRs), evidence‐based reviews, and literature reviews. The highest grade structured recommendations of the EBRR sections are summarized in this executive summary. Results: ICAR‐RS‐2021 covers 22 topics regarding the medical management of RS, which are grade A/B and are presented in the executive summary. Additionally, 4 topics regarding the surgical management of RS are grade A/B and are presented in the executive summary. Finally, a comprehensive evidence‐based management algorithm is provided. Conclusion: This ICAR‐RS‐2021 executive summary provides a compilation of the evidence‐based recommendations for medical and surgical treatment of the most common forms of RS

Quantitative modeling of the terminal differentiation of B cells and mechanisms of lymphomagenesis.

Mature B-cell exit from germinal centers is controlled by a transcriptional regulatory module that integrates antigen and T-cell signals and, ultimately, leads to terminal differentiation into memory B cells or plasma cells. Despite a compact structure, the module dynamics are highly complex because of the presence of several feedback loops and self-regulatory interactions, and understanding its dysregulation, frequently associated with lymphomagenesis, requires robust dynamical modeling techniques. We present a quantitative kinetic model of three key gene regulators, BCL6, IRF4, and BLIMP, and use gene expression profile data from mature human B cells to determine appropriate model parameters. The model predicts the existence of two different hysteresis cycles that direct B cells through an irreversible transition toward a differentiated cellular state. By synthetically perturbing the interactions in this network, we can elucidate known mechanisms of lymphomagenesis and suggest candidate tumorigenic alterations, indicating that the model is a valuable quantitative tool to simulate B-cell exit from the germinal center under a variety of physiological and pathological conditions