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.

Three-dimensional sonography of placental mesenchymal dysplasia and its differential diagnosis.

Abstract: Objective—Placental mesenchymal dysplasia (PMD) is an uncommon vascular anomaly of the placenta characterized by mesenchymal stem villous hyperplasia. Its main sonographic features is a thickened placenta with hypoechoic areas, and an accurate sonographic diagnosis is challenging. The aim of this study was to report two cases of PMD, and discuss the differential diagnosis of its sonographic features. Methods—Cases of placental masses studied by two-dimensional (2D), three-dimensional (3D) and Color Doppler imaging. Results—Case 1 - a thick placenta with multiple hypoechoic areas was noted at 13 weeks of gestation. At 19 weeks, the multi-cystic area, clearly demarcated from a normal looking placenta, measured 6.5×8.5cm and enlarged gradually. The patient delivered a 625 gram female after a spontaneous labor at almost 26 weeks’ gestation. Case 2 - a first sonographic examination at 25 weeks’ gestation revealed a thickened placenta with hypoechoic areas and a fetus with a single umbilical artery and a ventricular septal defect. At 27 weeks, the abnormal area of the placenta measured 14.5×7.5cm. At 32 weeks’ gestation, a caesarean section was performed due to nonreassuring fetal heart tracing and a 1415 gram female infant was delivered. Both cases were evaluated by 2D, 3D and Color Doppler imaging and the pathologic features of both placentas were consistent with PMD. Conclusions—Placental mesenchymal dysplasia should be considered in the differential diagnosis of every placental mass, especially in cases of multi-cystic placental lesion with lack of high velocity signals inside the lesion, and a normal karyotype.

Distinct genomic signatures of adaptation in pre- and postnatal environments during human evolution

Abstract: The human genome evolution project seeks to reveal the genetic underpinnings of key phenotypic features that are distinctive of humans, such as a greatly enlarged cerebral cortex, slow development, and long life spans. This project has focused predominantly on genotypic changes during the 6-million-year descent from the last common ancestor (LCA) of humans and chimpanzees. Here, we argue that adaptive genotypic changes during earlier periods of evolutionary history also helped shape the distinctive human phenotype. Using comparative genome sequence data from 10 vertebrate species, we find a signature of human ancestry-specific adaptive evolution in 1,240 genes during their descent from the LCA with rodents. We also find that the signature of adaptive evolution is significantly different for highly expressed genes in human fetal and adult-stage tissues. Functional annotation clustering shows that on the ape stem lineage, an especially evident adaptively evolved biological pathway contains genes that function in mitochondria, are crucially involved in aerobic energy production, and are highly expressed in two energy-demanding tissues, heart and brain. Also, on this ape stem lineage, there was adaptive evolution among genes associated with human autoimmune and aging-related diseases. During more recent human descent, the adaptively evolving, highly expressed genes in fetal brain are involved in mediating neuronal connectivity. Comparing adaptively evolving genes from pre- and postnatal-stage tissues suggests that different selective pressures act on the development vs. the maintenance of the human phenotype.

Gene-Centric Genomewide Association Study via Entropy

Abstract: Genes are the functional units in most organisms. Compared to genetic variants located outside genes, genic variants are more likely to affect disease risk. The development of the human HapMap project provides an unprecedented opportunity for genetic association studies at the genomewide level for elucidating disease etiology. Currently, most association studies at the single-nucleotide polymorphism (SNP) or the haplotype level rely on the linkage information between SNP markers and disease variants, with which association findings are difficult to replicate. Moreover, variants in genes might not be sufficiently covered by currently available methods. In this article, we present a gene-centric approach via entropy statistics for a genomewide association study to identify disease genes. The new entropy-based approach considers genic variants within one gene simultaneously and is developed on the basis of a joint genotype distribution among genetic variants for an association test. A grouping algorithm based on a penalized entropy measure is proposed to reduce the dimension of the test statistic. Type I error rates and power of the entropy test are evaluated through extensive simulation studies. The results indicate that the entropy test has stable power under different disease models with a reasonable sample size. Compared to single SNP-based analysis, the gene-centric approach has greater power, especially when there is more than one disease variant in a gene. As the genomewide genic SNPs become available, our entropy-based gene-centric approach would provide a robust and computationally efficient way for gene-based genomewide association study.

A soft cervix, categorized by shear-wave elastography, in women with short or with normal cervical length at 18-24 weeks is associated with a higher prevalence of spontaneous preterm delivery.

Abstract: OBJECTIVE To determine whether a soft cervix identified by shear-wave elastography between 18 and 24 weeks of gestation is associated with increased frequency of spontaneous preterm delivery (sPTD). MATERIALS AND METHODS This prospective cohort study included 628 consecutive women with a singleton pregnancy. Cervical length (mm) and softness [shear-wave speed: (SWS) meters per second (m/s)] of the internal cervical os were measured at 18-24 weeks of gestation. Frequency of sPTD <37 (sPTD<37) and <34 (sPTD<34) weeks of gestation was compared among women with and without a short (≤25 mm) and/or a soft cervix (SWS <25th percentile). RESULTS There were 31/628 (4.9%) sPTD<37 and 12/628 (1.9%) sPTD<34 deliveries. The combination of a soft and a short cervix increased the risk of sPTD<37 by 18-fold [relative risk (RR) 18.0 (95% confidence interval [CI], 7.7-43.9); P<0.0001] and the risk of sPTD<34 by 120-fold [RR 120.0 (95% CI 12.3-1009.9); P<0.0001] compared to women with normal cervical length. A soft-only cervix increased the risk of sPTD<37 by 4.5-fold [RR 4.5 (95% CI 2.1-9.8); P=0.0002] and of sPTD<34 by 21-fold [RR 21.0 (95% CI 2.6-169.3); P=0.0003] compared to a non-soft cervix. CONCLUSIONS A soft cervix at 18-24 weeks of gestation increases the risk of sPTD <37 and <34 weeks of gestation independently of cervical length.

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.