Cis-regulatory variation: significance in biomedicine and evolution

Cis-regulatory regions (CRR) control gene expression and chromatin modifications. Genetic variation at CRR in individuals across a population contributes to phenotypic differences of biomedical relevance. This standing variation is important for personalized genomic medicine as well as for adaptive evolution and speciation. This review focuses on genetic variation at CRR, its influence on chromatin, gene expression, and ultimately disease phenotypes. In addition, we summarize our understanding of how this variation may contribute to evolution. Recent technological and computational advances have accelerated research in the direction of personalized medicine, combining strengths of molecular biology and genomics. This will pave new ways to understand how CRR variation affects phenotypes and chart out possible avenues of intervention

Motherlogues

Motherlogues, the dramatic reading that follows, is drawn from some 200 tape recorded interviews of mothers by women\u27s studies students at Jersey City State College. In 1979, with a generous grant from The New Jersey Department of Higher Education, Office of Separately Budgeted Research, we launched a two year project entitled Mothers and Daughters: The Changing Lives of Ethnic Women. Using students as paid researchers, we began exploring the responses of their mothers (ages 40-60) to questions about education, marriage, motherhood, homemaking and employment. We wanted to find out how a generation of working class and lower middle class women from a wide range of ethnic backgrounds had changed—in attitudes, values and expectations—over the past twenty years. We were curious about our subjects\u27 mothers\u27 lives. Did they regard their mothers as models? For what notion of womanhood had their mothers prepared them? We were also interested in learning how these mothers viewed their daughters\u27 lives and whether the experiences of the younger generation had had an impact on the outlook and behavior of the older. The interview, our basic research instrument, grew out of a unit in a Women\u27s Lives syllabus ( Women\u27s Lives is the introductory course in women\u27s studies) which has been in effect since 1974. It is reprinted in full at the end

Correlations of antibody response phenotype to genotype revealed by molecular amplification fingerprinting

It has long been possible to measure the phenotype of antibody responses (antigen-specific titers) through conventional serological assays (e.g., ELISA). In contrast, the ability to measure the genotype of antibody responses has only recently become possible through the advent of high-throughput antibody repertoire sequencing (Ig-seq), which provides quantitative molecular information on clonal expansion, diversity and somatic hypermutation. However, Ig-seq is compromised by the presence of bias and errors introduced during library preparation and sequencing and thus prevent reliable immunological conclusions from being made. By using synthetic antibody spike-in genes, we determined that Ig-seq data overestimated antibody diversity measurements by up to 5000-fold and was less than 60% accurate in clonal frequency measurements. Please click Additional Files below to see the full abstract

Cis -regulatory variation: significance in biomedicine and evolution

Cis-regulatory regions (CRR) control gene expression and chromatin modifications. Genetic variation at CRR in individuals across a population contributes to phenotypic differences of biomedical relevance. This standing variation is important for personalized genomic medicine as well as for adaptive evolution and speciation. This review focuses on genetic variation at CRR, its influence on chromatin, gene expression, and ultimately disease phenotypes. In addition, we summarize our understanding of how this variation may contribute to evolution. Recent technological and computational advances have accelerated research in the direction of personalized medicine, combining strengths of molecular biology and genomics. This will pave new ways to understand how CRR variation affects phenotypes and chart out possible avenues of intervention

Calcium antagonists and mortality in patients with coronary artery disease: A Cohort study of 11,575 patients

AbstractObjectives. This study sought to establish the risk ratio for mortality associated with calcium antagonists in a large population of patients with chronic coronary artery disease.Background. Recent reports have suggested that the use of short-acting nifedipine may cause an increase in overall mortality in patients with coronary artery disease and that a similar effect may be produced by other calcium antagonists, in particular those of the dihydropyridine type.Methods. Mortality data were obtained for 11,575 patients screened for the Bezafibrate Infarction Prevention study (5,843 with and 5,732 without calcium antagonists) after a mean follow-up period of 3.2 years.Results. There were 495 deaths (8.5%) in the calcium antagonist group compared with 410 in the control group (7.2%). The age-adjusted risk ratio for mortality was 1.08 (95% confidence interval [CI] 0.95 to 1.24). After adjustment for the differences between the groups in age and gender and the prevalence of previous myocardial infarction, angina pectoris, hypertension, New York Heart Association functional class, peripheral vascular disease, chronic obstructive pulmonary disease, diabetes and current smoking, the adjusted risk ratio declined to 0.97 (95% CI 0.84 to 1.11). After further adjustment for concomintant medication, the risk ratio was estimated at 0.94 (95% CI 0.82 to 1.08).Conclusions. The current analysis does not support the claim that calcium antagonist therapy in patients with chronic coronary artery disease, whether myocardial infarction survivors or others, harbors an increased risk of mortality

Synthetic Standards Combined With Error and Bias Correction Improve the Accuracy and Quantitative Resolution of Antibody Repertoire Sequencing in Human Naïve and Memory B Cells

High-throughput sequencing of immunoglobulin (Ig) repertoires (Ig-seq) is a powerful method for quantitatively interrogating B cell receptor sequence diversity. When applied to human repertoires, Ig-seq provides insight into fundamental immunological questions, and can be implemented in diagnostic and drug discovery projects. However, a major challenge in Ig-seq is ensuring accuracy, as library preparation protocols and sequencing platforms can introduce substantial errors and bias that compromise immunological interpretation. Here, we have established an approach for performing highly accurate human Ig-seq by combining synthetic standards with a comprehensive error and bias correction pipeline. First, we designed a set of 85 synthetic antibody heavy-chain standards (in vitro transcribed RNA) to assess correction workflow fidelity. Next, we adapted a library preparation protocol that incorporates unique molecular identifiers (UIDs) for error and bias correction which, when applied to the synthetic standards, resulted in highly accurate data. Finally, we performed Ig-seq on purified human circulating B cell subsets (naïve and memory), combined with a cellular replicate sampling strategy. This strategy enabled robust and reliable estimation of key repertoire features such as clonotype diversity, germline segment, and isotype subclass usage, and somatic hypermutation. We anticipate that our standards and error and bias correction pipeline will become a valuable tool for researchers to validate and improve accuracy in human Ig-seq studies, thus leading to potentially new insights and applications in human antibody repertoire profiling

Computational strategies for dissecting the high-dimensional complexity of adaptive immune repertoires

The adaptive immune system recognizes antigens via an immense array of antigen-binding antibodies and T-cell receptors, the immune repertoire. The interrogation of immune repertoires is of high relevance for understanding the adaptive immune response in disease and infection (e.g., autoimmunity, cancer, HIV). Adaptive immune receptor repertoire sequencing (AIRR-seq) has driven the quantitative and molecular-level profiling of immune repertoires thereby revealing the high-dimensional complexity of the immune receptor sequence landscape. Several methods for the computational and statistical analysis of large-scale AIRR-seq data have been developed to resolve immune repertoire complexity in order to understand the dynamics of adaptive immunity. Here, we review the current research on (i) diversity, (ii) clustering and network, (iii) phylogenetic and (iv) machine learning methods applied to dissect, quantify and compare the architecture, evolution, and specificity of immune repertoires. We summarize outstanding questions in computational immunology and propose future directions for systems immunology towards coupling AIRR-seq with the computational discovery of immunotherapeutics, vaccines, and immunodiagnostics.Comment: 27 pages, 2 figure

Whose Food Revolution? Perspectives from a Food Service Training Academy

Article is on the Food Service Academy of the Community Foodbank of NJ where she lectures. Article was written by Doris Friedensohn initially for RT panel at Left Forum. There are also 4 photos

Mining the sequence space of antibody repertoires to predict and design antigen-specific antibodies

The mammalian adaptive immune system is able to identify specific molecular structures on foreign pathogens. Specificity to these epitopes is achieved through a group of receptors belonging to the immunoglobulin superfamily: B cell receptors (BCR), their secreted version (Antibodies) and T cell receptors (TCR). Each of these receptors carries highly variable regions, which facilitate antigen recognition and which are generated during progenitor cell development (and thus are thought to be unique clones or clonal lineages). The current estimate for the theoretical diversity of unique naïve BCR sequences is around 5x1013 clonal combinations for humans and at least 1012 for mice. The diverse population of BCRs, antibodies or TCRs in a given individual is referred to as the immune repertoire. Immune repertoire sequencing (AIRR-Seq, Ig-Seq) utilizes deep sequencing to access and analyze this vast diversity in different immunological compartments and immune cell subsets. This massive wealth of information has generated novel insights in the fields of antibody engineering, immunodiagnostics, vaccine design, as well as basic immunology. In Chapter 1 of this thesis, I review the current trends in immune repertoire sequencing and the efforts taken to improve existing protocols in relation to accuracy and quality of the sequencing data. I highlight several of the most major challenges in the field, such as obtaining paired variable region (e.g., variable heavy and variable light) sequencing and a lack of accuracy. For example, since sequencing library preparation and platforms for deep sequencing can introduce errors and biases, it can compromise immunological interpretations. This is especially confounding in the context of B cells that undergo somatic hypermutation, a natural process that introduces mutations in antibody variable regions. In Chapter 2, I describe an experimental and computational method we have developed based on synthetic standards and molecular barcoding, which has been implemented to achieve highly accurate antibody repertoire sequencing. We show how this conceptually simple procedure allows us to significantly reduce error rates across the whole sequencing region. By applying this technique to human B cell samples, we demonstrate that it can improve the measurements of antibody repertoires across various dimensions. Although it is now possible to produce high quality Ig-Seq datasets, linking sequence to antigen-specificity is an immensely challenging task. In Chapter 3, I provide an introduction to the concept of modeling the large sequence space of immune repertoires in order to extract deterministic sequence motifs that correlate with antigen exposure and specificity. I review various classes of statistical and machine learning algorithms that can be used to model sequence generation. In chapter 4 I develop a novel approach to identify antigen-specific sequence patterns in antibody repertoires based on generative deep models. To model the underlying process of BCR generation, variational autoencoders (VAE)s were used, where it was assumed that data generation follows a Gaussian mixture model (GMM) in latent space. This provided both a latent embedding and also cluster labels that group similar sequences together, which revealed a multitude of convergent, antigen-associated sequence patterns. These antigen-associated sequence patterns were predictive of immunological history and represent antigen-binding antibodies. Finally, I demonstrate how these sequence patterns can be used to generate further antigen-specific antibodies in silico, that are experimentally verified to retain antigen-specificity