Roberto Romero

Bio: Roberto Romero is an academic researcher from National Institutes of Health. The author has contributed to research in topics: Amniotic fluid & Chorioamnionitis. The author has an hindex of 151, co-authored 1516 publications receiving 108321 citations. Previous affiliations of Roberto Romero include University of Michigan & Weizmann Institute of Science.

Soluble endoglin and other circulating antiangiogenic factors in preeclampsia

Abstract: The circulating antiangiogenic protein soluble fms-like tyrosine kinase 1 (sFltl), also known as soluble vascular endothelial growth factor (VEGF) receptor 1, sequesters the proangiogenic proteins placental growth factor (PlGF) and VEGF. Its circulating level correlates with the severity of preclampsia and with the onset of hypertension or proteinuria. Increased expression of sFltl in pregnant rats creates a state resembling preeclampsia. Soluble endoglin, a coreceptor for transforming growth factors, is another antiangiogenic protein that acts with sFltl to produce a severe preeclampsia-like syndrome in pregnant rats. This nested case-control study, enrolling healthy nulliparous women taking part in the Calcium for Preeclampsia Prevention (CPEP) trial, was designed to show whether endoglin is associated with preeclampsia in humans. Seventy-two women having preeclampsia before 37 weeks' gestation were compared with four groups, each comprising 120 women, who had preeclampsia at term; had gestational hypertension; were normotensive but had an small-for-gestational-age infant; or were normotensive and delivered a normal-sized infant. Severe preeclampsia developed in 61% of women with preterm preeclampsia and 25% of those with preeclampsia at term (after 37 weeks' gestation). Symptomatic women with preterm preeclampsia had significantly higher serum levels of soluble endoglin than control women. Term preeclampsia also was associated with elevated circulating levels of endoglin. At 17-20 weeks' gestation, endoglin levels were significantly higher in women who later developed preterm preeclampsia than in control women. The same was the case at gestational weeks 25 through 28 for women who developed term preeclampsia. Elevated endoglin levels usually were accompanied by an increased sFltl:P1GF ratio. The risk of preeclampsia was greatest for women in the highest quartile of the control distributions for both biomarkers but not for either one alone. On multivariable analysis, large increases in the risk of preeclampsia with a small-for-gestational-age infant were associated with the highest quartile of soluble endoglin or sFltl:P1GF ratio. These findings, combined with those of experimental rodent studies, suggest that circulating soluble endoglin and spil-which cause endothelial dysfunction by different mechanisms-may contribute to the development of preeclampsia. Whether levels of these biomarkers will be useful in predicting the onset of clinical preeclampsia remains to be determined by longitudinal prospective studies.

limma powers differential expression analyses for RNA-sequencing and microarray studies

Abstract: limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.

STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets.

Abstract: Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, but the available information on protein-protein associations is incomplete and exhibits varying levels of annotation granularity and reliability. The STRING database aims to collect, score and integrate all publicly available sources of protein-protein interaction information, and to complement these with computational predictions. Its goal is to achieve a comprehensive and objective global network, including direct (physical) as well as indirect (functional) interactions. The latest version of STRING (11.0) more than doubles the number of organisms it covers, to 5090. The most important new feature is an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input. For the enrichment analysis, STRING implements well-known classification systems such as Gene Ontology and KEGG, but also offers additional, new classification systems based on high-throughput text-mining as well as on a hierarchical clustering of the association network itself. The STRING resource is available online at https://string-db.org/.

SPAdes, a new genome assembly algorithm and its applications to single-cell sequencing ( 7th Annual SFAF Meeting, 2012)

Abstract: The lion's share of bacteria in various environments cannot be cloned in the laboratory and thus cannot be sequenced using existing technologies. A major goal of single-cell genomics is to complement gene-centric metagenomic data with whole-genome assemblies of uncultivated organisms. Assembly of single-cell data is challenging because of highly non-uniform read coverage as well as elevated levels of sequencing errors and chimeric reads. We describe SPAdes, a new assembler for both single-cell and standard (multicell) assembly, and demonstrate that it improves on the recently released E+V-SC assembler (specialized for single-cell data) and on popular assemblers Velvet and SoapDeNovo (for multicell data). SPAdes generates single-cell assemblies, providing information about genomes of uncultivatable bacteria that vastly exceeds what may be obtained via traditional metagenomics studies. SPAdes is available online ( http://bioinf.spbau.ru/spades ). It is distributed as open source software.

Standards of Medical Care in Diabetes

Abstract: XI. STRATEGIES FOR IMPROVING DIABETES CARE D iabetes is a chronic illness that requires continuing medical care and patient self-management education to prevent acute complications and to reduce the risk of long-term complications. Diabetes care is complex and requires that many issues, beyond glycemic control, be addressed. A large body of evidence exists that supports a range of interventions to improve diabetes outcomes. These standards of care are intended to provide clinicians, patients, researchers, payors, and other interested individuals with the components of diabetes care, treatment goals, and tools to evaluate the quality of care. While individual preferences, comorbidities, and other patient factors may require modification of goals, targets that are desirable for most patients with diabetes are provided. These standards are not intended to preclude more extensive evaluation and management of the patient by other specialists as needed. For more detailed information, refer to Bode (Ed.): Medical Management of Type 1 Diabetes (1), Burant (Ed): Medical Management of Type 2 Diabetes (2), and Klingensmith (Ed): Intensive Diabetes Management (3). The recommendations included are diagnostic and therapeutic actions that are known or believed to favorably affect health outcomes of patients with diabetes. A grading system (Table 1), developed by the American Diabetes Association (ADA) and modeled after existing methods, was utilized to clarify and codify the evidence that forms the basis for the recommendations. The level of evidence that supports each recommendation is listed after each recommendation using the letters A, B, C, or E.