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Wauford N, Patel A, Tordoff J, Enghuus C, Jin A, Toppen J, Kemp ML, Weiss R. Synthetic symmetry breaking and programmable multicellular structure formation. Cell Syst 2023; 14:806-818.e5. [PMID: 37689062 PMCID: PMC10919224 DOI: 10.1016/j.cels.2023.08.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 04/14/2023] [Accepted: 08/02/2023] [Indexed: 09/11/2023]
Abstract
During development, cells undergo symmetry breaking into differentiated subpopulations that self-organize into complex structures.1,2,3,4,5 However, few tools exist to recapitulate these behaviors in a controllable and coupled manner.6,7,8,9 Here, we engineer a stochastic recombinase genetic switch tunable by small molecules to induce programmable symmetry breaking, commitment to downstream cell fates, and morphological self-organization. Inducers determine commitment probabilities, generating tunable subpopulations as a function of inducer dosage. We use this switch to control the cell-cell adhesion properties of cells committed to each fate.10,11 We generate a wide variety of 3D morphologies from a monoclonal population and develop a computational model showing high concordance with experimental results, yielding new quantitative insights into the relationship between cell-cell adhesion strengths and downstream morphologies. We expect that programmable symmetry breaking, generating precise and tunable subpopulation ratios and coupled to structure formation, will serve as an integral component of the toolbox for complex tissue and organoid engineering.
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Affiliation(s)
- Noreen Wauford
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Akshay Patel
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jesse Tordoff
- Program in Computational and Systems Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Casper Enghuus
- Department of Microbiology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Andrew Jin
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Jack Toppen
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA
| | - Melissa L Kemp
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Ron Weiss
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Electrical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
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2
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Hebel S, Kahn-Woods E, Enghuus C, Koenig H, Lalley-Chareczko L, Radix A, Daughtridge G. 979. Disparities in PrEP uptake and adherence among cisgender women using a pharmacologic measure. Open Forum Infect Dis 2020. [PMCID: PMC7776913 DOI: 10.1093/ofid/ofaa439.1165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Background HIV pre-exposure prophylaxis (PrEP) is 99% effective at preventing new HIV infections if taken daily. To be successful, PrEP requires concurrent efforts to optimize uptake, persistence, and adherence. In 2018, cisgender (cis) women accounted for 19% of new HIV infections in the US but comprised only 7% of all PrEP users. Studies show poor PrEP adherence amongst cis women, but there is a paucity of real-world clinical data describing PrEP adherence among cis women and gender minority people. Methods An adherence test that measures the concentration of tenofovir in urine samples using a liquid chromatography mass spectrometry (LC-MS/MS) was used to assess recent PrEP adherence at 8 clinics. Urine samples were collected during routine visits and analyzed using the LC-MS/MS assay. Test results were retrospectively paired with gender data, when available, and sex assigned at birth (SAAB) data. Adherence data were aggregated and analyzed to assess non-adherence proportions by sub-population. Results Gender data were available from 1,461 patients at 5 clinics, 1,344 (92%) of whom were cis males (Figure 1). From the 5 clinics where gender and SAAB data were available, 3,835 tests were conducted and 517 (13.5%) indicated non-adherence (Figure 2). 3 additional clinics conduct routine adherence testing and collect SAAB data (gender data not available). At these 8 clinics, SAAB data were available for 2,773 PrEP patients, totaling 5,602 urine tests (Figure 3). Among these 5,602 adherence tests, 813 (14.5%) indicated non-adherence (Figure 4). SAAB females demonstrated significantly higher non-adherence than SAAB males (22% vs 14%, p< 0.001). Across clinics, 89%-98% of PrEP patients are SAAB male (Figure 5). Within these 8 clinics, SAAB female demonstrated consistently higher non-adherence (17%-44%, vs 12%-17% for SAAB males) (Figure 6). Figures 1 and 2 ![]()
Figures 3 and 4 ![]()
Figures 5 and 6 ![]()
Conclusion Real-world data align with nationwide trends in PrEP utilization and show that the majority of PrEP patients are cis men. When initiated on PrEP, cis women exhibit higher rates of non-adherence than cis men. These data underscore the need to collect gender-identity data to monitor PrEP disparities and suggest that greater efforts are needed to target PrEP access, utilization, and accompanying support services to cis women and gender minority groups. Disclosures All Authors: No reported disclosures
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Affiliation(s)
| | | | | | - Helen Koenig
- University of Pennsylvania, Philadelphia, Pennsylvania
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3
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Wu MR, Nissim L, Stupp D, Pery E, Binder-Nissim A, Weisinger K, Enghuus C, Palacios SR, Humphrey M, Zhang Z, Maria Novoa E, Kellis M, Weiss R, Rabkin SD, Tabach Y, Lu TK. A high-throughput screening and computation platform for identifying synthetic promoters with enhanced cell-state specificity (SPECS). Nat Commun 2019; 10:2880. [PMID: 31253799 PMCID: PMC6599391 DOI: 10.1038/s41467-019-10912-8] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2019] [Accepted: 05/28/2019] [Indexed: 01/26/2023] Open
Abstract
Cell state-specific promoters constitute essential tools for basic research and biotechnology because they activate gene expression only under certain biological conditions. Synthetic Promoters with Enhanced Cell-State Specificity (SPECS) can be superior to native ones, but the design of such promoters is challenging and frequently requires gene regulation or transcriptome knowledge that is not readily available. Here, to overcome this challenge, we use a next-generation sequencing approach combined with machine learning to screen a synthetic promoter library with 6107 designs for high-performance SPECS for potentially any cell state. We demonstrate the identification of multiple SPECS that exhibit distinct spatiotemporal activity during the programmed differentiation of induced pluripotent stem cells (iPSCs), as well as SPECS for breast cancer and glioblastoma stem-like cells. We anticipate that this approach could be used to create SPECS for gene therapies that are activated in specific cell states, as well as to study natural transcriptional regulatory networks.
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Affiliation(s)
- Ming-Ru Wu
- Synthetic Biology Group, Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Lior Nissim
- Department of Biochemistry and Molecular Biology, The Institute for Medical Research Israel-Canada, Hadassah Medical School, The Hebrew University of Jerusalem, 91120, Jerusalem, Israel
| | - Doron Stupp
- Department of Developmental Biology and Cancer Research, The Institute for Medical Research Israel-Canada, Hadassah Medical School, The Hebrew University of Jerusalem, 91120, Jerusalem, Israel
| | - Erez Pery
- Synthetic Biology Group, Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.,Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.,Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Adina Binder-Nissim
- Synthetic Biology Group, Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Karen Weisinger
- Synthetic Biology Group, Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Casper Enghuus
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.,Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Sebastian R Palacios
- Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.,Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Melissa Humphrey
- Brain Tumor Research Center, Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, 02144, USA
| | - Zhizhuo Zhang
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.,Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Eva Maria Novoa
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.,Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.,Center for Genomic Regulation (CRG), 08003, Barcelona, Spain
| | - Manolis Kellis
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.,Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Ron Weiss
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.,Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.,Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Samuel D Rabkin
- Brain Tumor Research Center, Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, 02144, USA.,Department of Neurosurgery (Microbiology & Immunobiology), Harvard Medical School, Boston, MA, 02115, USA
| | - Yuval Tabach
- Department of Developmental Biology and Cancer Research, The Institute for Medical Research Israel-Canada, Hadassah Medical School, The Hebrew University of Jerusalem, 91120, Jerusalem, Israel.
| | - Timothy K Lu
- Synthetic Biology Group, Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA. .,Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA. .,Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA. .,Biophysics Program, Harvard University, Boston, MA, 02115, USA. .,Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
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Der BS, Glassey E, Bartley BA, Enghuus C, Goodman DB, Gordon DB, Voigt CA, Gorochowski TE. DNAplotlib: Programmable Visualization of Genetic Designs and Associated Data. ACS Synth Biol 2017; 6:1115-1119. [PMID: 27744689 DOI: 10.1021/acssynbio.6b00252] [Citation(s) in RCA: 43] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
DNAplotlib ( www.dnaplotlib.org ) is a computational toolkit for the programmable visualization of highly customizable, standards-compliant genetic designs. Functions are provided to aid with both visualization tasks and to extract and overlay associated experimental data. High-quality output is produced in the form of vector-based PDFs, rasterized images, and animated movies. All aspects of the rendering process can be easily customized or extended by the user to cover new forms of genetic part or regulation. DNAplotlib supports improved communication of genetic design information and offers new avenues for static, interactive and dynamic visualizations that map and explore the links between the structure and function of genetic parts, devices and systems; including metabolic pathways and genetic circuits. DNAplotlib is cross-platform software developed using Python.
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Affiliation(s)
- Bryan S. Der
- Synthetic
Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Emerson Glassey
- Synthetic
Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Bryan A. Bartley
- Department
of Bioengineering, University of Washington, Seattle, Washington 98195, United States
| | - Casper Enghuus
- MIT Microbiology Program, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Daniel B. Goodman
- Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, United States
- Wyss Institute for Biologically Inspired
Engineering, Harvard Medical School, Boston, Massachusetts 02115, United States
| | - D. Benjamin Gordon
- Synthetic
Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Christopher A. Voigt
- Synthetic
Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Thomas E. Gorochowski
- Synthetic
Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
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Ringsted TK, Enghuus C, Petersen MA, Werner MU. Demarcation of secondary hyperalgesia zones: Punctate stimulation pressure matters. J Neurosci Methods 2015; 256:74-81. [PMID: 26310180 DOI: 10.1016/j.jneumeth.2015.08.018] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2015] [Revised: 08/16/2015] [Accepted: 08/18/2015] [Indexed: 01/30/2023]
Abstract
BACKGROUND Secondary hyperalgesia is increased sensitivity in normal tissue near an injury, and it is a measure of central sensitization reflecting injury-related effects on the CNS. Secondary hyperalgesia areas (SHAs), usually assessed by polyamide monofilaments, are important outcomes in studies of analgesic drug effects in humans. However, since the methods applied in demarcating the secondary hyperalgesia zone seem inconsistent across studies, we examined the effect of a standardized approach upon the measurement of SHA following a first degree burn injury (BI). NEW METHOD The study was a two-observer, test-retest study with the two sessions separated by 6wk. An observer-blinded design adjusted to examine day-to-day and observer-to-observer variability in SHA was used. In 23 healthy volunteers (12 females/11 males) a BI was induced by a contact thermode (47.0°C, 420s, 2.5×5.0cm(2)). The SHA, demarcated by polyamide monofilaments (bending force: 0.2, 69 and 2569mN) and a "weighted-pin" stimulator (512mN), were assessed 45 to 75min after each BI. RESULTS A random effect, linear mixed model demonstrated a logarithmic correlation between elicited skin pressures (mN/mm(2)) and the SHAs (P<0.0001). No day-to-day or observer-to-observer differences in SHAs were observed. Intraclass correlation coefficients, in the range of 0.51 to 0.84, indicated a moderate to almost perfect reliability between observers. COMPARISON WITH EXISTING METHODS No standardized approach in SHA-assessment has hitherto been presented. CONCLUSIONS This is the first study to demonstrate that demarcation of secondary hyperalgesia zones depends on the developed pressure of the punctate stimulator used.
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Affiliation(s)
- Thomas K Ringsted
- Neuroscience Center, Rigshospitalet, Copenhagen University Hospitals, Multidisciplinary Pain Center 7612, Rigshospitalet, Blegdamsvej 9, 2100 Copenhagen, Denmark.
| | - Casper Enghuus
- Neuroscience Center, Rigshospitalet, Copenhagen University Hospitals, Multidisciplinary Pain Center 7612, Rigshospitalet, Blegdamsvej 9, 2100 Copenhagen, Denmark
| | - Morten A Petersen
- Research Unit, Department of Palliative Care, Bispebjerg Hospital, Copenhagen University Hospitals, Copenhagen, Denmark
| | - Mads U Werner
- Neuroscience Center, Rigshospitalet, Copenhagen University Hospitals, Multidisciplinary Pain Center 7612, Rigshospitalet, Blegdamsvej 9, 2100 Copenhagen, Denmark
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