A multi-mechanism approach reduces length of stay in the ICU for severe COVID-19 patients.

PURPOSE: COVID-19 pandemic has multifaceted presentations with rising evidence of immune-mediated mechanisms underplay. We sought to explore the outcomes of severe COVID-19 patients treated with a multi-mechanism approach (MMA) in addition to standard-of-care (SC) versus patients who only received SC treatment. MATERIALS AND METHODS: Data were collected retrospectively for patients admitted to the intensive care unit (ICU). This observational cohort study was performed at five institutions, 3 in the United States and 2 in Honduras. Patients were stratified for MMA vs. SC treatment during ICU stay. MMA treatment consists of widely available medications started immediately upon hospitalization. These interventions target immunomodulation, anticoagulation, viral suppression, and oxygenation. Primary outcomes included in-hospital mortality and length of stay (LOS) for the index hospitalization and were measured using logistic regression. RESULTS: Of 86 patients admitted, 65 (76%) who had severe COVID-19 were included in the study; 30 (46%) patients were in SC group, compared with 35 (54%) patients treated with MMA group. Twelve (40%) patients in the SC group died, compared with 5 (14%) in the MMA group (p-value = 0.01, Chi squared test). After adjustment for gender, age, treatment group, Q-SOFA score, the MMA group had a mean length of stay 8.15 days, when compared with SC group with 13.55 days. ICU length of stay was reduced by a mean of 5.4 days (adjusted for a mean age of 54 years, p-value 0.03) and up to 9 days (unadjusted for mean age), with no significant reduction in overall adjusted mortality rate, where the strongest predictor of mortality was the use of mechanical ventilation. CONCLUSION: The finding that MMA decreases the average ICU length of stay by 5.4 days and up to 9 days in older patients suggests that implementation of this treatment protocol could allow a healthcare system to manage 60% more COVID-19 patients with the same number of ICU beds.

Challenges and Opportunities on Health Sciences Regional Campuses: A Multidiscipline, Multicampus Survey.

AIM: We explored faculty and staff perceptions of the challenges and opportunities of working on regional campuses of a large academic health science center. BACKGROUND: The growth of multicampus academic institutions presents numerous issues for intercampus planning and for organizational/professional relationships. We were interested in learning how regional campus faculty and staff experienced these issues, with the practical goal of making recommendations to both central and regional campus administrations. METHOD: A cross-sectional, online survey was distributed to faculty and staff who worked at regional campuses of a large health sciences university. RESULTS: Regional faculty and staff felt more valued by local colleagues and administrators than by their central campus counterparts. Top challenges were central administration's lack of communication and understanding of regionals' unique circumstances and needs. CONCLUSION: Regional campuses' workplace experience is significantly different from that of central campus. More timely communication and active solicitation of regional campus input are needed.

Essential role of Cdc42 in cardiomyocyte proliferation and cell-cell adhesion during heart development.

Cdc42 is a member of the Rho GTPase family and functions as a molecular switch in regulating cell migration, proliferation, differentiation and survival. However, the role of Cdc42 in heart development remains largely unknown. To determine the function of Cdc42 in heart formation, we have generated a Cdc42 cardiomyocyte knockout (CCKO) mouse line by crossing Cdc42 flox mice with myosin light chain (MLC) 2a-Cre mice. The inactivation of Cdc42 in embryonic cardiomyocytes induced lethality after embryonic day 12.5. Histological analysis of CCKO embryos showed cardiac developmental defects that included thin ventricular walls and ventricular septum defects. Microarray and real-time PCR data also revealed that the expression level of p21 was significantly increased and cyclin B1 was dramatically decreased, suggesting that Cdc42 is required for cardiomyocyte proliferation. Phosphorylated Histone H3 staining confirmed that the inactivation of Cdc42 inhibited cardiomyocytes proliferation. In addition, transmission electron microscope studies showed disorganized sarcomere structure and disruption of cell-cell contact among cardiomyocytes in CCKO hearts. Accordingly, we found that the distribution of N-cadherin/ß-Catenin in CCKO cardiomyocytes was impaired. Taken together, our data indicate that Cdc42 is essential for cardiomyocyte proliferation, sarcomere organization and cell-cell adhesion during heart development.

A provisional gene regulatory atlas for mouse heart development.

Congenital Heart Disease (CHD) is one of the most common birth defects. Elucidating the molecular mechanisms underlying normal cardiac development is an important step towards early identification of abnormalities during the developmental program and towards the creation of early intervention strategies. We developed a novel computational strategy for leveraging high-content data sets, including a large selection of microarray data associated with mouse cardiac development, mouse genome sequence, ChIP-seq data of selected mouse transcription factors and Y2H data of mouse protein-protein interactions, to infer the active transcriptional regulatory network of mouse cardiac development. We identified phase-specific expression activity for 765 overlapping gene co-expression modules that were defined for obtained cardiac lineage microarray data. For each co-expression module, we identified the phase of cardiac development where gene expression for that module was higher than other phases. Co-expression modules were found to be consistent with biological pathway knowledge in Wikipathways, and met expectations for enrichment of pathways involved in heart lineage development. Over 359,000 transcription factor-target relationships were inferred by analyzing the promoter sequences within each gene module for overrepresentation against the JASPAR database of Transcription Factor Binding Site (TFBS) motifs. The provisional regulatory network will provide a framework of studying the genetic basis of CHD.

Managing biological complexity across orthologs with a visual knowledgebase of documented biomolecular interactions.

The complexity of biomolecular interactions and influences is a major obstacle to their comprehension and elucidation. Visualizing knowledge of biomolecular interactions increases comprehension and facilitates the development of new hypotheses. The rapidly changing landscape of high-content experimental results also presents a challenge for the maintenance of comprehensive knowledgebases. Distributing the responsibility for maintenance of a knowledgebase to a community of subject matter experts is an effective strategy for large, complex and rapidly changing knowledgebases. Cognoscente serves these needs by building visualizations for queries of biomolecular interactions on demand, by managing the complexity of those visualizations, and by crowdsourcing to promote the incorporation of current knowledge from the literature.

Visual data mining of coexpression data to set research priorities in cardiac development research.

Over the past decade, an immense amount of biomedical data have become available in the public domain due to the development of ever-more efficient screening tools such as expression microarrays. To fully leverage this important new resource, it has become imperative to develop new methodologies for mining and visualizing data to make inferences beyond the scope of the original experiments. This need motivated the development of a new freely available web-based application called StarNet ( http://vanburenlab.medicine.tamhsc.edu/starnet2.html ). Here we describe the use of StarNet, which functions primarily as a query tool that draws correlation networks centered about a gene of interest. To support inferences and the development of new hypotheses using the resulting correlation network, StarNet queries all genes in the correlation network against a database of known interactions and displays the results in a second graph and provides a statistical test of Gene Ontology term enrichment (keyword enrichment) to provide tentative summary functional annotations for the correlation network. Finally, StarNet provides additional tools for comparing networks drawn from two different selected data sets, thus providing methods for making inferences and developing new hypotheses about differential wiring for different regulatory domains.

A review of integration strategies to support gene regulatory network construction.

Gene regulatory network (GRN) construction is a central task of systems biology. Integration of different data sources to infer and construct GRNs is an important consideration for the success of this effort. In this paper, we will discuss distinctive strategies of data integration for GRN construction. Basically, the process of integration of different data sources is divided into two phases: the first phase is collection of the required data and the second phase is data processing with advanced algorithms to infer the GRNs. In this paper these two phases are called "structural integration" and "analytic integration," respectively. Compared with the nonintegration strategies, the integration strategies perform quite well and have better agreement with the experimental evidence.

Alcohol induced epigenetic perturbations during the inflammatory stage of fracture healing.

It is well recognized by orthopedic surgeons that fractures of alcoholics are more difficult to heal successfully and have a higher incidence of non-union, but the mechanism of alcohol's effect on fracture healing is unknown. In order to give direction for the study of the effects of alcohol on fracture healing, we propose to identify gene expression and microRNA changes during the early stages of fracture healing that might be attributable to alcohol consumption. As the inflammatory stage appears to be the most critical for successful fracture healing, this paper focuses on the events at day three following fracture or the stage of inflammation. Sprague-Dawley rats were placed on an ethanol-containing or pair-fed Lieber and DeCarli diet for four weeks prior to surgical fracture. Following insertion of a medullary pin, a closed mid-diaphyseal fracture was induced using a Bonnarens and Einhorn fracture device. At three days' post-fracture, the region of the fracture calluses was harvested from the right hind-limb. RNA was extracted and microarray analysis was conducted against the entire rat genome. There were 35 genes that demonstrated significant increased expression due to alcohol consumption and 20 that decreased due to alcohol. In addition, the expression of 20 microRNAs was increased and six decreased. In summary, while it is recognized that mRNA levels may or may not represent protein levels successfully produced by the cell, these studies reveal changes in gene expression that support the hypothesis that alcohol consumption affects events involved with inflammation. MicroRNAs are known to modulate mRNA and these findings were consistent with much of what was seen with mRNA microarray analysis, especially the involvement of smad4 which was demonstrated by mRNA microarray, microRNA and polymerase chain reaction.

Prevalence of podiatric medical problems in veterans versus nonveterans.

BACKGROUND: Lower-extremity pathologic abnormalities have been common in military recruits for many years. Many of these conditions can become chronic and persist even after retiring from military service. We hypothesized that certain foot abnormalities are more prevalent in veterans versus nonveterans. The purpose of this study was to evaluate what foot and ankle disorders are associated with veteran status while controlling for other demographic factors. METHODS: The National Health Interview Survey (Podiatry Supplement) from 1990 was used for this secondary data analysis. The data were divided into veterans and nonveterans, and the prevalence of podiatric medical problems, including callus, flatfoot deformity, bunion deformity, hammer toe deformity, arthritis, and sprain, was evaluated for each group. RESULTS: Flatfoot deformity and arthritis were significantly more prevalent in veterans versus nonveterans in the United States. Bunion deformity was significantly more prevalent in male veterans than in male nonveterans. Male veterans were less likely than male nonveterans to have sprains, and female veterans were more likely than their nonveteran counterparts to have sprains. CONCLUSIONS: These results may help us understand the potential risk factors for podiatric medical problems and may be used for formulating prevention programs.

Characteristics of adult flatfoot in the United States.

Many factors have been suggested to cause flatfoot deformity. The purpose of this study was to identify risk factors for flatfoot deformity, which itself can be a causative factor for other foot and ankle pathologies. The National Health Interview Survey (Podiatry Supplement) from 1990 was analyzed to determine associations of various demographic factors and other foot and ankle pathologies with self-reported flatfoot deformity. We found statistically significant (P

Discovering disease-specific biomarker genes for cancer diagnosis and prognosis.

The large amounts of microarray data provide us a great opportunity to identify gene expression profiles (GEPs) in different tissues or disease states. Disease-specific biomarker genes likely share GEPs that are distinct in disease samples as compared with normal samples. The similarity of the GEPs may be evaluated by Pearson Correlation Coefficient (PCC) and the distinctness of GEPs may be assessed by Kolmogorov-Smirnov distance (KSD). In this study, we used the PCC and KSD metrics for GEPs to identify disease-specific (cancer-specific) biomarkers. We first analyzed and compared GEPs using microarray datasets for smoking and lung cancer. We found that the number of genes with highly different GEPs between comparing groups in smoking dataset was much larger than that in lung cancer dataset; this observation was further verified when we compared GEPs in smoking dataset with prostate cancer datasets. Moreover, our Gene Ontology analysis revealed that the top ranked biomarker candidate genes for prostate cancer were highly enriched in molecular function categories such as 'cytoskeletal protein binding' and biological process categories such as 'muscle contraction'. Finally, we used two genes, ACTC1 (encoding an actin subunit) and HPN (encoding hepsin), to demonstrate the feasibility of diagnosing and monitoring prostate cancer using the expression intensity histograms of marker genes. In summary, our results suggested that this approach might prove promising and powerful for diagnosing and monitoring the patients who come to the clinic for screening or evaluation of a disease state including cancer.

Classification of genes and putative biomarker identification using distribution metrics on expression profiles.

BACKGROUND: Identification of genes with switch-like properties will facilitate discovery of regulatory mechanisms that underlie these properties, and will provide knowledge for the appropriate application of Boolean networks in gene regulatory models. As switch-like behavior is likely associated with tissue-specific expression, these gene products are expected to be plausible candidates as tissue-specific biomarkers. METHODOLOGY/PRINCIPAL FINDINGS: In a systematic classification of genes and search for biomarkers, gene expression profiles (GEPs) of more than 16,000 genes from 2,145 mouse array samples were analyzed. Four distribution metrics (mean, standard deviation, kurtosis and skewness) were used to classify GEPs into four categories: predominantly-off, predominantly-on, graded (rheostatic), and switch-like genes. The arrays under study were also grouped and examined by tissue type. For example, arrays were categorized as 'brain group' and 'non-brain group'; the Kolmogorov-Smirnov distance and Pearson correlation coefficient were then used to compare GEPs between brain and non-brain for each gene. We were thus able to identify tissue-specific biomarker candidate genes. CONCLUSIONS/SIGNIFICANCE: The methodology employed here may be used to facilitate disease-specific biomarker discovery.

STARNET 2: a web-based tool for accelerating discovery of gene regulatory networks using microarray co-expression data.

BACKGROUND: Although expression microarrays have become a standard tool used by biologists, analysis of data produced by microarray experiments may still present challenges. Comparison of data from different platforms, organisms, and labs may involve complicated data processing, and inferring relationships between genes remains difficult. RESULTS: STARNET 2 is a new web-based tool that allows post hoc visual analysis of correlations that are derived from expression microarray data. STARNET 2 facilitates user discovery of putative gene regulatory networks in a variety of species (human, rat, mouse, chicken, zebrafish, Drosophila, C. elegans, S. cerevisiae, Arabidopsis and rice) by graphing networks of genes that are closely co-expressed across a large heterogeneous set of preselected microarray experiments. For each of the represented organisms, raw microarray data were retrieved from NCBI's Gene Expression Omnibus for a selected Affymetrix platform. All pairwise Pearson correlation coefficients were computed for expression profiles measured on each platform, respectively. These precompiled results were stored in a MySQL database, and supplemented by additional data retrieved from NCBI. A web-based tool allows user-specified queries of the database, centered at a gene of interest. The result of a query includes graphs of correlation networks, graphs of known interactions involving genes and gene products that are present in the correlation networks, and initial statistical analyses. Two analyses may be performed in parallel to compare networks, which is facilitated by the new HEATSEEKER module. CONCLUSION: STARNET 2 is a useful tool for developing new hypotheses about regulatory relationships between genes and gene products, and has coverage for 10 species. Interpretation of the correlation networks is supported with a database of previously documented interactions, a test for enrichment of Gene Ontology terms, and heat maps of correlation distances that may be used to compare two networks. The list of genes in a STARNET network may be useful in developing a list of candidate genes to use for the inference of causal networks. The tool is freely available at http://vanburenlab.medicine.tamhsc.edu/starnet2.html, and does not require user registration.

A visual data mining tool that facilitates reconstruction of transcription regulatory networks.

BACKGROUND: Although the use of microarray technology has seen exponential growth, analysis of microarray data remains a challenge to many investigators. One difficulty lies in the interpretation of a list of differentially expressed genes, or in how to plan new experiments given that knowledge. Clustering methods can be used to identify groups of genes with similar expression patterns, and genes with unknown function can be provisionally annotated based on the concept of "guilt by association", where function is tentatively inferred from the known functions of genes with similar expression patterns. These methods frequently suffer from two limitations: (1) visualization usually only gives access to group membership, rather than specific information about nearest neighbors, and (2) the resolution or quality of the relationships are not easily inferred. METHODOLOGY/PRINCIPAL FINDINGS: We have addressed these issues by improving the precision of similarity detection over that of a single experiment and by creating a tool to visualize tractable association networks: we (1) performed meta-analysis computation of correlation coefficients for all gene pairs in a heterogeneous data set collected from 2,145 publicly available micorarray samples in mouse, (2) filtered the resulting distribution of over 130 million correlation coefficients to build new, more tractable distributions from the strongest correlations, and (3) designed and implemented a new Web based tool (StarNet, http://vanburenlab.medicine.tamhsc.edu/starnet.html) for visualization of sub-networks of the correlation coefficients built according to user specified parameters. CONCLUSIONS/SIGNIFICANCE: Correlations were calculated across a heterogeneous collection of publicly available microarray data. Users can access this analysis using a new freely available Web-based application for visualizing tractable correlation networks that are flexibly specified by the user. This new resource enables rapid hypothesis development for transcription regulatory relationships.

Cluster analysis of hydration waters around the active sites of bacterial alanine racemase using a 2-ns MD simulation.

Structural data produced by a 2-ns molecular dynamics (MD) simulation on Geobacillus alanine racemase (AlaR; PDB: 1SFT) was used to study hydration around the two AlaR active sites. AlaR is a crucial enzyme for bacterial cell wall biosynthesis. It has been shown previously that the potency of an inhibitor can be increased by incorporating a functional group or atom that displaces hydration sites close to the substrate binding pocket of its target enzyme. The complete linkage algorithm was used for cluster analysis of the active site water positions from 126 solvent configurations sampled at regular intervals from the 2-ns MD simulation. Crystal waters in the 1SFT X-ray structure occupy most of the tightly bound water sites that were discovered. We show here that tightly bound water sites can be identified by cluster analysis of MD-generated coordinates starting with data supplied by a single X-ray structure, and we predict a highly conserved hydration site close to the carboxyl oxygen of L-Ala substrate. This approach holds promise for accelerating the drug design process. We also discuss an analysis of the well-known notion of residence time and introduce a new measure called retention time.

Transcript copy number estimation using a mouse whole-genome oligonucleotide microarray.

The ability to quantitatively measure the expression of all genes in a given tissue or cell with a single assay is an exciting promise of gene-expression profiling technology. An in situ-synthesized 60-mer oligonucleotide microarray designed to detect transcripts from all mouse genes was validated, as well as a set of exogenous RNA controls derived from the yeast genome (made freely available without restriction), which allow quantitative estimation of absolute endogenous transcript abundance.

Mechanochemical model of microtubule structure and self-assembly kinetics.

Microtubule self-assembly is largely governed by the chemical kinetics and thermodynamics of tubulin-tubulin interactions. An important aspect of microtubule assembly is that hydrolysis of the beta-tubulin-associated GTP promotes protofilament curling. Protofilament curling presumably drives the transition from tip structures associated with growth (sheetlike projections and blunt ends) to those associated with shortening (rams' horns and frayed ends), and transitions between these structures have been proposed to be important for growth-shortening transitions. However, previous models for microtubule dynamic instability have not considered such structures or mechanics explicitly. Here we present a three-dimensional model that explicitly incorporates mechanical stress and strain within the microtubule lattice. First, we found that the model recapitulates three-dimensional tip structures and rates of assembly and disassembly for microtubules grown under standard conditions, and we propose that taxol may stabilize microtubule growth by reducing flexural rigidity. Second, in contrast to recent suggestions, it was determined that sheetlike tips are more likely to undergo catastrophe than blunt tips. Third, partial uncapping of the tubulin-GTP cap provides a possible mechanism for microtubule pause events. Finally, simulations of the binding and structural effects of XMAP215 produced the experimentally observed growth and shortening rates, and tip structure.

Age-associated alteration of gene expression patterns in mouse oocytes.

Decreasing oocyte competence with maternal aging is a major factor in human infertility. To investigate the age-dependent molecular changes in a mouse model, we compared the expression profiles of metaphase II oocytes collected from 5- to 6-week-old mice with those collected from 42- to 45-week-old mice using the NIA 22K 60-mer oligo microarray. Among approximately 11,000 genes whose transcripts were detected in oocytes, about 5% (530) showed statistically significant expression changes, excluding the possibility of global decline in transcript abundance. Consistent with the generally accepted view of aging, the differentially expressed genes included ones involved in mitochondrial function and oxidative stress. However, the expression of other genes involved in chromatin structure, DNA methylation, genome stability and RNA helicases was also altered, suggesting the existence of additional mechanisms for aging. Among the transcripts decreased with aging, we identified and characterized a group of new oocyte-specific genes, members of the human NACHT, leucine-rich repeat and PYD-containing (NALP) gene family. These results have implications for aging research as well as for clinical ooplasmic donation to rejuvenate aging oocytes.

Transcriptome analysis of mouse stem cells and early embryos.

Understanding and harnessing cellular potency are fundamental in biology and are also critical to the future therapeutic use of stem cells. Transcriptome analysis of these pluripotent cells is a first step towards such goals. Starting with sources that include oocytes, blastocysts, and embryonic and adult stem cells, we obtained 249,200 high-quality EST sequences and clustered them with public sequences to produce an index of approximately 30,000 total mouse genes that includes 977 previously unidentified genes. Analysis of gene expression levels by EST frequency identifies genes that characterize preimplantation embryos, embryonic stem cells, and adult stem cells, thus providing potential markers as well as clues to the functional features of these cells. Principal component analysis identified a set of 88 genes whose average expression levels decrease from oocytes to blastocysts, stem cells, postimplantation embryos, and finally to newborn tissues. This can be a first step towards a possible definition of a molecular scale of cellular potency. The sequences and cDNA clones recovered in this work provide a comprehensive resource for genes functioning in early mouse embryos and stem cells. The nonrestricted community access to the resource can accelerate a wide range of research, particularly in reproductive and regenerative medicine.

The NIA cDNA project in mouse stem cells and early embryos.

A catalog of mouse genes expressed in early embryos, embryonic and adult stem cells was assembled, including 250000 ESTs, representing approximately 39000 unique transcripts. The cDNA libraries, enriched in full-length clones, were condensed into the NIA 15 and 7.4K clone sets, freely distributed to the research community, providing a standard platform for expression studies using microarrays. They are essential tools for studying mammalian development and stem cell biology, and to provide hints about the differential nature of embryonic and adult stem cells.