Perchlorate (ClO4−), an oxidizing agent, is a ubiquitous environmental pollutant. Several studies have investigated its thyroid hormone disrupting properties. Its associations with other biological measures are largely unknown. This study, combining 2005–2008 National Health and Nutrition Examination Surveys, investigated associations between urinary perchlorate and biomarkers of iron homeostasis, lipids, blood cell counts, and glucose metabolism. Healthy males (n = 3705), non-pregnant females (n = 2967), and pregnant females (n = 356), aged 12–59 years, were included in the linear regression models, which showed significant positive (+) and negative (−) associations for both males and non-pregnant females with serum uric acid (−), serum iron (−), RBC count (−), blood urea nitrogen (+), and lymphocyte count (+). Other significant associations were observed for either males or non-pregnant females. Among pregnant females, perchlorate was significantly associated with blood urea nitrogen (+) and serum iron (−). These associations may be indicators of perchlorate’s potential effect on several biological systems, which when considered in total, may implicate perturbation of iron homeostasis.
perchlorate; epidemiology; biomarkers; iron homeostasis
With the accelerated implementation of genomic medicine, health-care providers will depend heavily on professional guidelines and recommendations. Because genomics affects many diseases across the life span, no single professional group covers the entirety of this rapidly developing field.
To pursue a discussion of the minimal elements needed to develop evidence-based guidelines in genomics, the Centers for Disease Control and Prevention and the National Cancer Institute jointly held a workshop to engage representatives from 35 organizations with interest in genomics (13 of which make recommendations). The workshop explored methods used in evidence synthesis and guideline development and initiated a dialogue to compare these methods and to assess whether they are consistent with the Institute of Medicine report “Clinical Practice Guidelines We Can Trust.”
The participating organizations that develop guidelines or recommendations all had policies to manage guideline development and group membership, and processes to address conflicts of interests. However, there was wide variation in the reliance on external reviews, regular updating of recommendations, and use of systematic reviews to assess the strength of scientific evidence.
Ongoing efforts are required to establish criteria for guideline development in genomic medicine as proposed by the Institute of Medicine.
evidence synthesis; genomic medicine; guideline development
Successfully realizing the vision of genomic medicine will require management of large amounts of complex data. The electronic health record (EHR) is destined to play a critical role in the translation of genomic information into clinical care. The papers in this special issue explore the challenges associated with the implementation of genomics in the EHR. The proposed solutions are meant to provide guidance for those responsible for moving genomics into the clinic.
electronic health record; electronic medical record; genomics; implementation science; clinical decision support; genetics; translational medicine; eMERGE
Genome-wide association studies, DNA sequencing studies, and other genomic studies are finding an increasing number of genetic variants associated with clinical phenotypes that may be useful in developing diagnostic, preventive, and treatment strategies for individual patients. However, few common variants have been integrated into routine clinical practice. The reasons for this are several, but two of the most significant are limited evidence about the clinical implications of the variants and a lack of a comprehensive knowledge base that captures genetic variants, their phenotypic associations, and other pertinent phenotypic information that is openly accessible to clinical groups attempting to interpret sequencing data. As the field of medicine begins to incorporate genome-scale analysis into clinical care, approaches need to be developed for collecting and characterizing data on the clinical implications of variants, developing consensus on their actionability, and making this information available for clinical use. The National Human Genome Research Institute (NHGRI) and the Wellcome Trust thus convened a workshop to consider the processes and resources needed to: 1) identify clinically valid genetic variants; 2) decide whether they are actionable and what the action should be; and 3) provide this information for clinical use. This commentary outlines the key discussion points and recommendations from the workshop.
genomic medicine; clinical actionability; database; electronic health records (EHR); pharmacogenomics; DNA sequencing
To develop, operationalize, and pilot test a transparent, reproducible, and evidence informed method to qualify when to report incidental findings from next generation sequencing technologies.
Using evidence-based principles, we propose a three stage process. Stage I ‘rules out’ incidental findings below a minimal threshold of evidence and is evaluated using inter-rater agreement and comparison with an expert-based approach. Stage II documents criteria for clinical actionability using a standardized approach to allow experts to consistently consider and recommend whether results should be routinely reported (Stage III). We used expert opinion to determine the face validity of Stages II and III using three case studies. We evaluated the time and effort for Stages I and II.
For Stage I, we assessed 99 conditions and found high inter-rater agreement (89%), and strong agreement with a separate expert-based method. Case studies for familial adenomatous polyposis, hereditary hemochromatosis, and α1-Antitrypsin Deficiency were all recommended for routine reporting as incidental findings. The method requires less than three days per topic.
We establish an operational definition of clinically actionable incidental findings and provide documentation and pilot testing of a feasible method that is scalable to the whole genome.
whole genome sequencing; clinical actionability; population screening; secondary findings; whole exome sequencing
Phenome-wide association studies (PheWAS) have demonstrated utility in validating genetic associations derived from traditional genetic studies as well as identifying novel genetic associations. Here we used an electronic health record (EHR)-based PheWAS to explore pleiotropy of genetic variants in the fat mass and obesity associated gene (FTO), some of which have been previously associated with obesity and type 2 diabetes (T2D). We used a population of 10,487 individuals of European ancestry with genome-wide genotyping from the Electronic Medical Records and Genomics (eMERGE) Network and another population of 13,711 individuals of European ancestry from the BioVU DNA biobank at Vanderbilt genotyped using Illumina HumanExome BeadChip. A meta-analysis of the two study populations replicated the well-described associations between FTO variants and obesity (odds ratio [OR] = 1.25, 95% Confidence Interval = 1.11–1.24, p = 2.10 × 10−9) and FTO variants and T2D (OR = 1.14, 95% CI = 1.08–1.21, p = 2.34 × 10−6). The meta-analysis also demonstrated that FTO variant rs8050136 was significantly associated with sleep apnea (OR = 1.14, 95% CI = 1.07–1.22, p = 3.33 × 10−5); however, the association was attenuated after adjustment for body mass index (BMI). Novel phenotype associations with obesity-associated FTO variants included fibrocystic breast disease (rs9941349, OR = 0.81, 95% CI = 0.74–0.91, p = 5.41 × 10−5) and trends toward associations with non-alcoholic liver disease and gram-positive bacterial infections. FTO variants not associated with obesity demonstrated other potential disease associations including non-inflammatory disorders of the cervix and chronic periodontitis. These results suggest that genetic variants in FTO may have pleiotropic associations, some of which are not mediated by obesity.
PheWAS; genetic association; pleiotropy; Exome chip; FTO; BMI
Particulate matter (PM) is an important risk factor for asthma. Generation of oxidative stress by PM is a major mechanism of its health effects. Transcription factor nuclear factor (erythroid-derived 2)-like 2 (Nrf2) mediates antioxidant and phase II enzymes and is essential in protecting against oxidative stress and lung inflammation. We have previously shown that ambient ultrafine particles (UFP) could exert a potent adjuvant effect on allergic sensitization to ovalbumin (OVA) in mice. We hypothesized that Nrf2 deficiency in dendritic cells (DC) could enhance the adjuvant potential of UFP on allergic sensitization. We show that the adjuvant effect of intranasally instilled UFP is significantly enhanced in Nrf2 knockout (Nrf2-/-) mice compared with their wild-type (Nrf2+/+) counterparts. Under resting conditions Nrf2-/- DC displayed an intrinsic predilection to a T-helper 2 (Th2)-favoring cytokine profile characterized by low level of IL-12p70 and high level of IL-6 as compared to Nrf2+/+ DC. Adoptive transfer of OVA/UFP-treated Nrf2-/- DC provoked a more severe allergic inflammation in the lung than Nrf2+/+ DC in the same treatment group. We conclude that Nrf2 deficiency in DC may promote a constitutive immune-polarizing cytokine milieu, which we propose may have contributed to the augmented adjuvant effect of UFP on allergic sensitization.
Nrf2; Dendritic cell; Adjuvant; Allergic sensitization; Lung inflammation; Ultrafine particles; IL-12p70; IL-6; T-helper 2
The electronic Medical Records and Genomics (eMERGE) (Phase I) network was established in 2007 to further genomic discovery using biorepositories linked to the electronic health record (EHR). In Phase II, which began in 2011, genomic discovery efforts continue and in addition the network is investigating best practices for implementing genomic medicine, in particular, the return of genomic results in the EHR for use by physicians at point-of-care. To develop strategies for addressing the challenges of implementing genomic medicine in the clinical setting, the eMERGE network is conducting studies that return clinically-relevant genomic results to research participants and their health care providers. These genomic medicine pilot studies include returning individual genetic variants associated with disease susceptibility or drug response, as well as genetic risk scores for common “complex” disorders. Additionally, as part of a network-wide pharmacogenomics-related project, targeted resequencing of 84 pharmacogenes is being performed and select genotypes of pharmacogenetic relevance are being placed in the EHR to guide individualized drug therapy. Individual sites within the eMERGE network are exploring mechanisms to address incidental findings generated by resequencing of the 84 pharmacogenes. In this paper, we describe studies being conducted within the eMERGE network to develop best practices for integrating genomic findings into the EHR, and the challenges associated with such work.
genomics; electronic health records; incidental findings; implementation; genetic counseling; next generation sequencing; pharmacogenetics
There is tremendous potential for genome sequencing to improve clinical diagnosis and care once it becomes routinely accessible, but this will require formalizing research methods into clinical best practices in the areas of sequence data generation, analysis, interpretation and reporting. The CLARITY Challenge was designed to spur convergence in methods for diagnosing genetic disease starting from clinical case history and genome sequencing data. DNA samples were obtained from three families with heritable genetic disorders and genomic sequence data were donated by sequencing platform vendors. The challenge was to analyze and interpret these data with the goals of identifying disease-causing variants and reporting the findings in a clinically useful format. Participating contestant groups were solicited broadly, and an independent panel of judges evaluated their performance.
A total of 30 international groups were engaged. The entries reveal a general convergence of practices on most elements of the analysis and interpretation process. However, even given this commonality of approach, only two groups identified the consensus candidate variants in all disease cases, demonstrating a need for consistent fine-tuning of the generally accepted methods. There was greater diversity of the final clinical report content and in the patient consenting process, demonstrating that these areas require additional exploration and standardization.
The CLARITY Challenge provides a comprehensive assessment of current practices for using genome sequencing to diagnose and report genetic diseases. There is remarkable convergence in bioinformatic techniques, but medical interpretation and reporting are areas that require further development by many groups.
Background and objectives
Worry is predominantly a verbal-linguistic process with relatively little imagery. This study investigated whether the verbal nature of worry contributes to the maintenance of worry by enhancing attention to threat. It was hypothesised that verbal worry would lead to greater attentional bias to threat than imagery-based worry.
Fifty high-worriers were randomly assigned to one of two groups, one in which they were instructed to worry in a verbal way and one in which they worried in an imagery-based way, before completing a dot probe task as a measure of attention to threat-related words.
Those who worried in verbal form demonstrated greater attentional bias to threat than did those who worried in imagery-based form. These findings could not be accounted for by group differences in personal relevance of or distress associated with worry topics, state mood following worry, levels of the relatedness of participants' worries to stimuli on the dot probe task, trait anxiety, general propensity to worry, nor adherence to the worry training.
The present study only included word stimuli in the dot probe task; inclusion of images would allow for firmly rejecting the hypothesis that the attention effects observed following verbal worry were merely a result of priming verbal threat representations. Also, future studies could include a further control group that does not engage in any form of worry to ascertain that verbal worry increased attentional bias rather than imagery decreasing pre-existing attentional bias.
Possible mechanisms underlying this effect of verbal worry on attention to threat are discussed, together with clinical implications of the current findings.
•Worry is predominantly a verbal-linguistic process with relatively little imagery.•High-worriers worried in verbal or imagery form before an emotional dot probe task.•Verbal worry is associated with attentional bias for threat words.•Imagery-based worry is not associated with an attentional bias for threat words.•The verbal nature of worry has a causal role in its maintenance.
Worry; Attention; Imagery; Verbal-linguistic processing; Dot probe
Whole genome sequencing (WGS) is being used for evaluation of individuals with undiagnosed disease of suspected genetic origin. Implementing WGS into clinical practice will place an increased burden upon care teams with regard to pre-test patient education and counseling about results. To quantitate the time needed for appropriate pre-test evaluation of participants in WGS testing, we documented the time spent by our clinical research group on various activities related to program preparation, participant screening, and consent prior to WGS. Participants were children or young adults with autism, intellectual or developmental disability, and/or congenital anomalies, who have remained undiagnosed despite previous evaluation, and their biologic parents. Results showed that significant time was spent in securing allocation of clinical research space to counsel participants and families, and in acquisition and review of participant’s medical records. Pre-enrollment chart review identified two individuals with existing diagnoses resulting in savings of $30,000 for the genome sequencing alone, as well as saving hours of personnel time for genome interpretation and communication of WGS results. New WGS programs should plan for costs associated with additional pre-test administrative planning and patient evaluation time that will be required to provide high quality care.
Electronic supplementary material
The online version of this article (doi:10.1007/s10897-014-9697-4) contains supplementary material, which is available to authorized users.
Whole genome sequencing; Time study; Electronic health record; Genetic counseling; Intellectual disability
The inclusion of genomic data in the electronic health record raises important ethical, legal, and social issues. In this article, we highlight these challenges and discuss potential solutions. We provide a brief background on the current state of electronic health records in the context of genomic medicine, discuss the importance of equitable access to genome-enabled electronic health records, and consider the potential use of electronic health records for improving genomic literacy in patients and providers. We highlight the importance of privacy, access, and security, and of determining which genomic information is included in the electronic health record. Finally, we discuss the challenges of reporting incidental findings, storing and reinterpreting genomic data, and nondocumentation and duty to warn family members at potential genetic risk.
clinical decision support, electronic health records; ethical, legal, and social implications; genomics; personalized medicine
Next Generation Sequencing studies generate a large quantity of genetic data in a relatively cost and time efficient manner and provide an unprecedented opportunity to identify candidate causative variants that lead to disease phenotypes. A challenge to these studies is the generation of sequencing artifacts by current technologies. To identify and characterize the properties that distinguish false positive variants from true variants, we sequenced a child and both parents (one trio) using DNA isolated from three sources (blood, buccal cells, and saliva). The trio strategy allowed us to identify variants in the proband that could not have been inherited from the parents (Mendelian errors) and would most likely indicate sequencing artifacts. Quality control measurements were examined and three measurements were found to identify the greatest number of Mendelian errors. These included read depth, genotype quality score, and alternate allele ratio. Filtering the variants on these measurements removed ~95% of the Mendelian errors while retaining 80% of the called variants. These filters were applied independently. After filtering, the concordance between identical samples isolated from different sources was 99.99% as compared to 87% before filtering. This high concordance suggests that different sources of DNA can be used in trio studies without affecting the ability to identify causative polymorphisms. To facilitate analysis of next generation sequencing data, we developed the Cincinnati Analytical Suite for Sequencing Informatics (CASSI) to store sequencing files, metadata (eg. relatedness information), file versioning, data filtering, variant annotation, and identify candidate causative polymorphisms that follow either de novo, rare recessive homozygous or compound heterozygous inheritance models. We conclude the data cleaning process improves the signal to noise ratio in terms of variants and facilitates the identification of candidate disease causative polymorphisms.
whole exome sequencing; variant filtering; next-generation sequencing; disease causative polymorphisms; Mendelian errors; Mendel errors; CASSI
Integrating genomic information into clinical care and the electronic health record can facilitate personalized medicine through genetically guided clinical decision support. Stakeholder involvement is critical to the success of these implementation efforts. Prior work on implementation of clinical information systems provides broad guidance to inform effective engagement strategies. We add to this evidence-based recommendations that are specific to issues at the intersection of genomics and the electronic health record. We describe stakeholder engagement strategies employed by the Electronic Medical Records and Genomics Network, a national consortium of US research institutions funded by the National Human Genome Research Institute to develop, disseminate, and apply approaches that combine genomic and electronic health record data. Through select examples drawn from sites of the Electronic Medical Records and Genomics Network, we illustrate a continuum of engagement strategies to inform genomic integration into commercial and homegrown electronic health records across a range of health-care settings. We frame engagement as activities to consult, involve, and partner with key stakeholder groups throughout specific phases of health information technology implementation. Our aim is to provide insights into engagement strategies to guide genomic integration based on our unique network experiences and lessons learned within the broader context of implementation research in biomedical informatics. On the basis of our collective experience, we describe key stakeholder practices, challenges, and considerations for successful genomic integration to support personalized medicine.
electronic health records; genomics; health information technology; personalized medicine; stakeholder engagement; translational medical research
In clinical exome and genome sequencing, there is potential for the recognition and reporting of incidental or secondary findings unrelated to the indication for ordering the sequencing but of medical value for patient care. The American College of Medical Genetics and Genomics (ACMG) recently published a policy statement on clinical sequencing, which emphasized the importance of disclosing the possibility of such results in pretest patient discussions, clinical testing, and reporting of results. The ACMG appointed a Working Group on Incidental Findings in Clinical Exome and Genome Sequencing to make recommendations about responsible management of incidental findings when patients undergo exome or genome sequencing. This Working Group conducted a year-long consensus process, including review by outside experts, and produced recommendations that have been approved by the ACMG Board. Specific and detailed recommendations, and the background and rationale for these recommendations, are described herein. We recommend that laboratories performing clinical sequencing seek and report mutations of the specified classes or types in the genes listed here. This evaluation and reporting should be performed for all clinical germline (constitutional) exome and genome sequencing, including the ‘normal’ of tumor-normal subtractive analyses in all subjects, irrespective of age, but excluding fetal samples. We recognize that there are insufficient data on clinical utility to fully support these recommendations and we encourage the creation of an ongoing process for updating these recommendations at least annually as further data are collected.
secondary findings; incidental findings; genome; genomic medicine; personalized medicine; whole-exome; whole-genome; sequencing
Applying an age cutoff to a Lynch syndrome screening program has considerable potential for decreasing total screening costs and increasing efficiency, but at a loss of effectiveness.
To determine the impact of applying an age cutoff to tumor-based Lynch syndrome (LS) screening, specifically focusing on changes in relative effectiveness, efficiency, and cost. The project was undertaken to answer questions about implementation of the LS screening program in an integrated health care delivery system.
Patients and Methods:
Clinical data extracted from an internal cancer registry, previous modeling efforts, published literature, and gray data were used to populate decision models designed to answer questions about the impact of age cutoffs in LS screening. Patients with colorectal cancer (CRC) were stratified at 10-year intervals from ages 50 to 80 years and compared with no age cutoff. Outcomes are reported for a cohort of 325 patients screened and includes total cost to screen, LS cases present in the cutoff category, number of LS cases expected to be identified by screening, cost per LS case detected, and total number and percentage of LS cases missed.
Applying an age cutoff to an LS screening program has considerable potential for decreasing total screening costs and increasing efficiency, but at a loss of effectiveness. Imposing an age cutoff of 50 years reduces the cost of the screening program to 16% of a program with no age cutoff, but at the expense of missing more than half of the cases. Failure to identify LS cases is magnified by a cascade effect in family members. The results of this analysis influenced the final policy in our system.
The Electronic Medical Records and Genomics (eMERGE) Network is a National Human Genome Research Institute (NHGRI)-funded consortium engaged in the development of methods and best-practices for utilizing the Electronic Medical Record (EMR) as a tool for genomic research. Now in its sixth year, its second funding cycle and comprising nine research groups and a coordinating center, the network has played a major role in validating the concept that clinical data derived from EMRs can be used successfully for genomic research. Current work is advancing knowledge in multiple disciplines at the intersection of genomics and healthcare informatics, particularly electronic phenotyping, genome-wide association studies, genomic medicine implementation and the ethical and regulatory issues associated with genomics research and returning results to study participants. Here we describe the evolution, accomplishments, opportunities and challenges of the network since its inception as a five-group consortium focused on genotype-phenotype associations for genomic discovery to its current form as a nine-group consortium pivoting towards implementation of genomic medicine.
electronic medical records; personalized medicine; genome-wide association studies; genetics and genomics; collaborative research
Advances in genomics and related fields promise a new era of personalized medicine in the cancer care continuum. Nevertheless, there are fundamental challenges in integrating genomic medicine into cancer practice. We explore how multilevel research can contribute to implementation of genomic medicine. We first review the rapidly developing scientific discoveries in this field and the paucity of current applications that are ready for implementation in clinical and public health programs. We then define a multidisciplinary translational research agenda for successful integration of genomic medicine into policy and practice and consider challenges for successful implementation. We illustrate the agenda using the example of Lynch syndrome testing in newly diagnosed cases of colorectal cancer and cascade testing in relatives. We synthesize existing information in a framework for future multilevel research for integrating genomic medicine into the cancer care continuum.
Colonoscopy is one of the most effective methods of cancer prevention and detection, particularly for individuals with familial risk. Recruitment of family members to behavioral intervention trials remains uniquely challenging, owing to the intensive process required to identify and contact them. Recruiting at-risk family members involves contacting the original cancer cases and asking them to provide information about their at-risk relatives, who must then be contacted for study enrollment. Though this recruitment strategy is common in family trials, few studies have compared influences of patient and relative participation to nonparticipation. Furthermore, although use of cancer registries to identify initial cases has increased, to our knowledge no study has examined the relationship between registries and family recruitment outcomes.
This study assessed predictors of case participation and relative enrollment in a recruitment process that utilized state cancer registries. Participation characteristics were analyzed with separate multivariable logistic regressions in three stages: (1) cancer registry-contacted colorectal cancer (CRC) cases who agreed to study contact; (2) study-contacted CRC cases who provided at-risk relative information; and (3) at-risk relatives contacted for intervention participation.
Cancer registry source was predictive of participation for both CRC cases and relatives, though relative associations (odds ratios) varied across registries. Cases were less likely to participate if they were Hispanic or nonwhite, and were more likely to participate if they were female or younger than 50 at cancer diagnosis. At-risk relatives were more likely to participate if they were from Utah, if another family member was also participating in the study, or if they had previously had a colonoscopy. The number of eligible cases who had to be contacted to enroll one eligible relative varied widely by registry, from 7 to 81.
Family recruitment utilizing cancer registry-identified cancer cases is feasible, but highly dependent on both the strategies and protocols of those who are recruiting and on participant characteristics such as sex, race, or geography. Devising comprehensive recruitment protocols that specifically target those less likely to enroll may help future research meet recruitment goals.
Family Colorectal Cancer Awareness and Risk Education Project NCT01274143.
Cancer; Colorectal; Patient selection; Recruitment
The rapid advance of gene sequencing technologies has produced an unprecedented rate of discovery of genome variation in humans. A growing number of authoritative clinical repositories archive gene variants and disease phenotypes, yet there are currently many more gene variants that lack clear annotation or disease association. To date, there has been very limited coverage of gene-specific predictors in the literature. Here the evaluation is presented of “gene-specific” predictor models based on a naïve Bayesian classifier for 20 gene–disease datasets, containing 3986 variants with clinically characterized patient conditions. The utility of gene-specific prediction is then compared with “all-gene” generalized prediction and also with existing popular predictors. Gene-specific computational prediction models derived from clinically curated gene variant disease datasets often outperform established generalized algorithms for novel and uncertain gene variants.
Amino acid properties; gene variant classification; machine learning; phenotype prediction; bioinformatics; gene variants classification; gene disease database; developing/using computerized provider order entry; designing usable (responsive) resources and systems; methods for integration of information from disparate sources; high-performance and large-scale computing; distributed systems; agents; software engineering: architecture; data exchange; communication; integration across care settings (inter- and intra-enterprise); system implementation and management issues; languages; computational methods; statistical analysis of large datasets; advanced algorithms; identifying genome and protein structure and function; detecting disease outbreaks and biological threats; visualization of data and knowledge
Rationale: The differentiation of fibroblasts into myofibroblasts is a cardinal feature of idiopathic pulmonary fibrosis (IPF). The transcription factor Yin Yang 1 (YY1) plays a role in the proliferation and differentiation of diverse cell types, but its role in fibrotic lung diseases is not known.
Objectives: To elucidate the mechanism by which YY1 regulates fibroblast differentiation and lung fibrosis.
Methods: Lung fibroblasts were cultured with transforming growth factor (TGF)-β or tumor necrosis factor-α. Nuclear factor (NF)-κB, YY1, and α-smooth muscle actin (SMA) were determined in protein, mRNA, and promoter reporter level. Lung fibroblasts and lung fibrosis were assessed in a partial YY1-deficient mouse and a YY1f/f conditional knockout mouse after being exposed to silica or bleomycin.
Measurements and Main Results: TGF-β and tumor necrosis factor-α up-regulated YY1 expression in lung fibroblasts. TGF-β–induced YY1 expression was dramatically decreased by an inhibitor of NF-κB, which blocked I-κB degradation. YY1 is significantly overexpressed in both human IPF and murine models of lung fibrosis, including in the aggregated pulmonary fibroblasts of fibrotic foci. Furthermore, the mechanism of fibrogenesis is that YY1 can up-regulate α-SMA expression in pulmonary fibroblasts. YY1-deficient (YY1+/−) mice were significantly protected from lung fibrosis, which was associated with attenuated α-SMA and collagen expression. Finally, decreasing YY1 expression through instilled adenovirus-cre in floxed-YY1f/f mice reduced lung fibrosis.
Conclusions: YY1 is overexpressed in fibroblasts in both human IPF and murine models in a NF-κB–dependent manner, and YY1 regulates fibrogenesis at least in part by increasing α-SMA and collagen expression. Decreasing YY1 expression may provide a new therapeutic strategy for pulmonary fibrosis.
nuclear factor-κB; α-smooth muscle actin; idiopathic pulmonary fibrosis