Nadine Levin | University of California, Los Angeles (original) (raw)

Papers by Nadine Levin

Society, Biology, and Human Affairs

Journal of Biosocial Science, Jan 1, 2010

oxfordleadershipprize.org

Journal of Biosocial Science, Jan 1, 2011

Cell host & microbe, Jan 1, 2008

Educating dendritic cells (DC) to become tolerogenic DC, which promote regulatory IL-10 immune re... more Educating dendritic cells (DC) to become tolerogenic DC, which promote regulatory IL-10 immune responses, represents an effective immune evasion strategy for pathogens. Yersinia pestis virulence factor LcrV is reported to induce IL-10 production via interaction with Toll-like receptor (TLR) 2. However, TLR2 À/À mice are not protected against subcutaneous plague infection. Using complementary in vitro and in vivo approaches and LcrV as a model, we show that TLR6 associates with TLR2 to induce tolerogenic DC and regulatory type-1 T cells selectively secreting IL-10. In contrast, TLR1 heterodimerizes with TLR2 to promote proinflammatory IL-12p40 cytokine, producing DC and inflammatory T cell differentiation. LcrV specifically hijacks the TLR2/6 pathway to stimulate IL-10 production, which blocks host protective inflammatory responses. These results explain why TLR2 can mediate both pro-and anti-inflammatory responses and identify TLR6 as a distinct receptor driving regulatory IL-10 responses.

Talks by Nadine Levin

An SMA and STM sponsored panel. Post-genomic research agendas, though mired in problems of pro... more An SMA and STM sponsored panel.

Post-genomic research agendas, though mired in problems of productivity and output, have important consequences for the ways that bodies are conceptualized and evaluated. In this paper, I draw on one year of ethnographic research in a laboratory investigating “metabonomics” (the study of metabolism) to explore how the boundaries between bodily health and disease are shaped by various practices, technologies, and values. Overall, I examine how statistical practices are becoming increasingly commonplace into laboratory and clinical research methods, and consequently how they inhibit borderline cases of disease from crossing the boundary at which medical treatment is determined as necessary.

To begin, I show how statistical practices—algorithms, programs, visualizations—render illness into a series of biochemical signals, such that health and disease are expressed as mathematical patterns of similarity and difference. Moving to a case study of research on liver disease in the United Kingdom, I show how statistical methods struggle to delineate the boundaries for clinical symptoms and grades of disease. This is particularly important in the case of liver disease, as the need for transplants is determined by statistics-based scores and measures of bodily health. Ultimately, I argue that by measuring and quantifying only particular types of biological information, statistical methods remove the uncertainty and ambiguity contained within borderline cases of liver disease. These statistical methods, in reducing the body to mathematical information, prevent patients with medically significant—but not statistically significant—cases of disease from qualifying for liver transplants.

Many STS scholars have documented the ways that genomic knowledge and technologies have shaped cl... more Many STS scholars have documented the ways that genomic knowledge and technologies have shaped clinical research. Comparatively little research, however, has explored the ways that statistical practices pervade the technologies and experimental configurations that more broadly constitute the diverse forms of clinical research. In this paper, I draw on one year of ethnographic research in a laboratory investigating “metabonomics” (the “omics” study of metabolism), in order to explore how statistical practices are increasingly used to explain and define disease processes in laboratory and clinical settings.

To begin, I focus on a case study of an attempt to combine and contrast molecular metabolic (bioinformatics) data with histological (pathology-based) images of the brain. I show that as statistical practices—algorithms, models, visualizations—render tissues into a series of biochemical signals, health and disease are expressed as mathematical patterns of similarity and difference. Consequently, I argue that the role of statistics in clinical settings is changing: while statistics has traditionally been used to support clinical measurements, it is now intrinsic to the molecular practices that researchers use to draw the boundaries between health and disease. Situating my case study within the broader movement of molecular technologies into clinical settings, I show how statistics is being “redesigned,” such that pathological models of health and disease are displaced by mathematical, molecular enactments of patterns and structures. Ultimately, I argue that statistical practices, in only capturing certain types of biological information, struggle to delineate the boundaries for clinical symptoms and grades of disease.

"Personalized medicine, founded on the search for molecular markers of health and disease, has co... more "Personalized medicine, founded on the search for molecular markers of health and disease, has come to dominate the funding cycles, media attention, and research agenda of modern biomedical science. Social science research on personalized medicine has tended to focus on the impacts of the fields of genetics and genomics, examining how scientific knowledge of gene sequences and variants is affecting the development of biomarkers for disease diagnosis and treatment. This body of research has generated a monolithic picture of personalized medicine in which each bodily condition corresponds to a single biomarker, and in which genetic material forms the singular currency for talking about health and disease.

In this paper I draw on one year of ethnographic fieldwork in a laboratory to provide an alternate view of personalized medicine, in which biomarkers are made up of combinations of biochemical compounds known as “biomarker panels”. Arising from research in the emerging field of “metabonomics,” these biomarker panels reflect changes in metabolism, and take the form of multivariate statistical models of metabolic processes. Focusing on daily research practices, I show how biomarker panels emerge as the natural and obvious way of understanding health and disease, representing a worldview in which biology is highly statisticalized. By generating dynamic, multifactorial combinations of biological information, metabonomics researchers claim that they can account—in ways that genetics and genomics fail to do—for the changes in health that arise from gene-environment interactions.

Ultimately, I argue that metabonomics research highlights how our definitions and views of biomarkers are highly dependent upon local configurations of research practice. By exploring the fields that are related to but distinct from genetics and genomics, we can account for the multiple ways in which personalized medicine research is reconfiguring our understandings of health and disease in the body. "

This presentation combines a literature review of the topics presented so far in the Medical Anth... more This presentation combines a literature review of the topics presented so far in the Medical Anthropology Research Seminars, but in the context of: what do we learn by looking at how genomics fits within the post-genomics sciences more broadly, and furthermore withiin a personalized medicine agenda? I will discuss how we need to pay more attention to the role that statistical practices increasingly play in biomedical research, contrasting the kinds of meaning/value that emerge from statistical vs. more standard "biological" (as is the case with histopathology) perspectives on health and disease.

Since the inception of the Human Genome Project in the 1990s, it has been argued that genomics ha... more Since the inception of the Human Genome Project in the 1990s, it has been argued that genomics has shaped our knowledge of health and our methods for treating disease. Given recent advances in genetic testing, pharmacogenomics, and personalized medicine, this seminar series examines how developments in genomic are affecting patients, clinicians, pharmaceutical companies, and medical institutions. Drawing upon multidisciplinary research, our speakers discuss the impacts of genomics of medical practice, and highlight the main questions, challenges, and opportunities surrounding genomic ideas and technologies.

Society, Biology, and Human Affairs

Journal of Biosocial Science, Jan 1, 2010

oxfordleadershipprize.org

Journal of Biosocial Science, Jan 1, 2011

Cell host & microbe, Jan 1, 2008

Educating dendritic cells (DC) to become tolerogenic DC, which promote regulatory IL-10 immune re... more Educating dendritic cells (DC) to become tolerogenic DC, which promote regulatory IL-10 immune responses, represents an effective immune evasion strategy for pathogens. Yersinia pestis virulence factor LcrV is reported to induce IL-10 production via interaction with Toll-like receptor (TLR) 2. However, TLR2 À/À mice are not protected against subcutaneous plague infection. Using complementary in vitro and in vivo approaches and LcrV as a model, we show that TLR6 associates with TLR2 to induce tolerogenic DC and regulatory type-1 T cells selectively secreting IL-10. In contrast, TLR1 heterodimerizes with TLR2 to promote proinflammatory IL-12p40 cytokine, producing DC and inflammatory T cell differentiation. LcrV specifically hijacks the TLR2/6 pathway to stimulate IL-10 production, which blocks host protective inflammatory responses. These results explain why TLR2 can mediate both pro-and anti-inflammatory responses and identify TLR6 as a distinct receptor driving regulatory IL-10 responses.

An SMA and STM sponsored panel. Post-genomic research agendas, though mired in problems of pro... more An SMA and STM sponsored panel.

Post-genomic research agendas, though mired in problems of productivity and output, have important consequences for the ways that bodies are conceptualized and evaluated. In this paper, I draw on one year of ethnographic research in a laboratory investigating “metabonomics” (the study of metabolism) to explore how the boundaries between bodily health and disease are shaped by various practices, technologies, and values. Overall, I examine how statistical practices are becoming increasingly commonplace into laboratory and clinical research methods, and consequently how they inhibit borderline cases of disease from crossing the boundary at which medical treatment is determined as necessary.

To begin, I show how statistical practices—algorithms, programs, visualizations—render illness into a series of biochemical signals, such that health and disease are expressed as mathematical patterns of similarity and difference. Moving to a case study of research on liver disease in the United Kingdom, I show how statistical methods struggle to delineate the boundaries for clinical symptoms and grades of disease. This is particularly important in the case of liver disease, as the need for transplants is determined by statistics-based scores and measures of bodily health. Ultimately, I argue that by measuring and quantifying only particular types of biological information, statistical methods remove the uncertainty and ambiguity contained within borderline cases of liver disease. These statistical methods, in reducing the body to mathematical information, prevent patients with medically significant—but not statistically significant—cases of disease from qualifying for liver transplants.

Many STS scholars have documented the ways that genomic knowledge and technologies have shaped cl... more Many STS scholars have documented the ways that genomic knowledge and technologies have shaped clinical research. Comparatively little research, however, has explored the ways that statistical practices pervade the technologies and experimental configurations that more broadly constitute the diverse forms of clinical research. In this paper, I draw on one year of ethnographic research in a laboratory investigating “metabonomics” (the “omics” study of metabolism), in order to explore how statistical practices are increasingly used to explain and define disease processes in laboratory and clinical settings.

To begin, I focus on a case study of an attempt to combine and contrast molecular metabolic (bioinformatics) data with histological (pathology-based) images of the brain. I show that as statistical practices—algorithms, models, visualizations—render tissues into a series of biochemical signals, health and disease are expressed as mathematical patterns of similarity and difference. Consequently, I argue that the role of statistics in clinical settings is changing: while statistics has traditionally been used to support clinical measurements, it is now intrinsic to the molecular practices that researchers use to draw the boundaries between health and disease. Situating my case study within the broader movement of molecular technologies into clinical settings, I show how statistics is being “redesigned,” such that pathological models of health and disease are displaced by mathematical, molecular enactments of patterns and structures. Ultimately, I argue that statistical practices, in only capturing certain types of biological information, struggle to delineate the boundaries for clinical symptoms and grades of disease.

"Personalized medicine, founded on the search for molecular markers of health and disease, has co... more "Personalized medicine, founded on the search for molecular markers of health and disease, has come to dominate the funding cycles, media attention, and research agenda of modern biomedical science. Social science research on personalized medicine has tended to focus on the impacts of the fields of genetics and genomics, examining how scientific knowledge of gene sequences and variants is affecting the development of biomarkers for disease diagnosis and treatment. This body of research has generated a monolithic picture of personalized medicine in which each bodily condition corresponds to a single biomarker, and in which genetic material forms the singular currency for talking about health and disease.

In this paper I draw on one year of ethnographic fieldwork in a laboratory to provide an alternate view of personalized medicine, in which biomarkers are made up of combinations of biochemical compounds known as “biomarker panels”. Arising from research in the emerging field of “metabonomics,” these biomarker panels reflect changes in metabolism, and take the form of multivariate statistical models of metabolic processes. Focusing on daily research practices, I show how biomarker panels emerge as the natural and obvious way of understanding health and disease, representing a worldview in which biology is highly statisticalized. By generating dynamic, multifactorial combinations of biological information, metabonomics researchers claim that they can account—in ways that genetics and genomics fail to do—for the changes in health that arise from gene-environment interactions.

Ultimately, I argue that metabonomics research highlights how our definitions and views of biomarkers are highly dependent upon local configurations of research practice. By exploring the fields that are related to but distinct from genetics and genomics, we can account for the multiple ways in which personalized medicine research is reconfiguring our understandings of health and disease in the body. "

This presentation combines a literature review of the topics presented so far in the Medical Anth... more This presentation combines a literature review of the topics presented so far in the Medical Anthropology Research Seminars, but in the context of: what do we learn by looking at how genomics fits within the post-genomics sciences more broadly, and furthermore withiin a personalized medicine agenda? I will discuss how we need to pay more attention to the role that statistical practices increasingly play in biomedical research, contrasting the kinds of meaning/value that emerge from statistical vs. more standard "biological" (as is the case with histopathology) perspectives on health and disease.

Since the inception of the Human Genome Project in the 1990s, it has been argued that genomics ha... more Since the inception of the Human Genome Project in the 1990s, it has been argued that genomics has shaped our knowledge of health and our methods for treating disease. Given recent advances in genetic testing, pharmacogenomics, and personalized medicine, this seminar series examines how developments in genomic are affecting patients, clinicians, pharmaceutical companies, and medical institutions. Drawing upon multidisciplinary research, our speakers discuss the impacts of genomics of medical practice, and highlight the main questions, challenges, and opportunities surrounding genomic ideas and technologies.