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Akitake B, Douglas HM, LaFosse PK, Beiran M, Deveau CE, O'Rawe J, Li AJ, Ryan LN, Duffy SP, Zhou Z, Deng Y, Rajan K, Histed MH. Amplified cortical neural responses as animals learn to use novel activity patterns. Curr Biol 2023; 33:2163-2174.e4. [PMID: 37148876 DOI: 10.1016/j.cub.2023.04.032] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [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: 06/22/2022] [Revised: 02/09/2023] [Accepted: 04/14/2023] [Indexed: 05/08/2023]
Abstract
Cerebral cortex supports representations of the world in patterns of neural activity, used by the brain to make decisions and guide behavior. Past work has found diverse, or limited, changes in the primary sensory cortex in response to learning, suggesting that the key computations might occur in downstream regions. Alternatively, sensory cortical changes may be central to learning. We studied cortical learning by using controlled inputs we insert: we trained mice to recognize entirely novel, non-sensory patterns of cortical activity in the primary visual cortex (V1) created by optogenetic stimulation. As animals learned to use these novel patterns, we found that their detection abilities improved by an order of magnitude or more. The behavioral change was accompanied by large increases in V1 neural responses to fixed optogenetic input. Neural response amplification to novel optogenetic inputs had little effect on existing visual sensory responses. A recurrent cortical model shows that this amplification can be achieved by a small mean shift in recurrent network synaptic strength. Amplification would seem to be desirable to improve decision-making in a detection task; therefore, these results suggest that adult recurrent cortical plasticity plays a significant role in improving behavioral performance during learning.
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Affiliation(s)
- Bradley Akitake
- Unit on Neural Computation and Behavior, National Institute of Mental Health Intramural Program, National Institutes of Health, Bethesda, MD 20892, USA
| | - Hannah M Douglas
- Unit on Neural Computation and Behavior, National Institute of Mental Health Intramural Program, National Institutes of Health, Bethesda, MD 20892, USA
| | - Paul K LaFosse
- Unit on Neural Computation and Behavior, National Institute of Mental Health Intramural Program, National Institutes of Health, Bethesda, MD 20892, USA
| | - Manuel Beiran
- Nash Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
| | - Ciana E Deveau
- Unit on Neural Computation and Behavior, National Institute of Mental Health Intramural Program, National Institutes of Health, Bethesda, MD 20892, USA
| | - Jonathan O'Rawe
- Unit on Neural Computation and Behavior, National Institute of Mental Health Intramural Program, National Institutes of Health, Bethesda, MD 20892, USA
| | - Anna J Li
- Unit on Neural Computation and Behavior, National Institute of Mental Health Intramural Program, National Institutes of Health, Bethesda, MD 20892, USA
| | - Lauren N Ryan
- Unit on Neural Computation and Behavior, National Institute of Mental Health Intramural Program, National Institutes of Health, Bethesda, MD 20892, USA
| | - Samuel P Duffy
- Unit on Neural Computation and Behavior, National Institute of Mental Health Intramural Program, National Institutes of Health, Bethesda, MD 20892, USA
| | - Zhishang Zhou
- Unit on Neural Computation and Behavior, National Institute of Mental Health Intramural Program, National Institutes of Health, Bethesda, MD 20892, USA
| | - Yanting Deng
- Unit on Neural Computation and Behavior, National Institute of Mental Health Intramural Program, National Institutes of Health, Bethesda, MD 20892, USA
| | - Kanaka Rajan
- Nash Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Mark H Histed
- Unit on Neural Computation and Behavior, National Institute of Mental Health Intramural Program, National Institutes of Health, Bethesda, MD 20892, USA.
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Crawford BB, Adams SB, Sittler T, Van den Akker J, Chan SB, Leitner O, Ryan LN, Gil E, Van 't Veer LJ. Abstract P3-08-02: Multi-gene panel testing for hereditary cancer predisposition in unsolved high risk breast and ovarian cancer patients. Cancer Res 2017. [DOI: 10.1158/1538-7445.sabcs16-p3-08-02] [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] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Background
Among women with an elevated risk of hereditary breast and ovarian cancer who previously tested negative for pathogenic mutations in BRCA1 and BRCA2, a subset remain at increased risk of having hereditary breast, ovarian or other cancers, and should be offered multi-gene panel testing. We tested three groups of women who were enrolled in the UCSF Cancer Genetics and Prevention Program: (i) 97 women with a personal history of bilateral breast cancer, (ii) 104 women with a personal history of breast cancer and a first-degree or second-degree relative with ovarian cancer, and (iii) 99 women with a personal history of ovarian, fallopian tube, or primary peritoneal cancer. All women previously tested negative for pathogenic BRCA1 and BRCA2 mutations by either limited or comprehensive testing.
Methods
We performed comprehensive next-generation sequencing using a panel of 19 genes developed by Color Genomics (a CLIA-certified laboratory) covering ATM, BARD1, BRCA1, BRCA2, BRIP1, CDH1, CHEK2, EPCAM, MLH1, MSH2, MSH6, NBN, PALB2, PMS2, PTEN, RAD51C, RAD51D, STK11, and TP53.
Results
Across the groups tested, 9% had pathogenic mutations in one or more of the genes analyzed (8% in genes other than BRCA1 and BRCA2). Among these women, Ashkenazi Jewish and Hispanic women had elevated mutation rates compared to those of other ethnicities. In addition, we identified two women with pathogenic mutations in two cancer susceptibility genes, which has significant implications for family testing. These results demonstrate the importance of genetic testing of genes other than BRCA1 and BRCA2.
Conclusions
Among women with an elevated risk of hereditary breast and ovarian cancer who have previously tested negative for BRCA1 and BRCA2 mutations, we propose that women with characteristics of any of the three groups above be considered for subsequent multi-gene panel testing. Additionally, ethnicity and the possibility of multiple mutations may be indications for additional testing in these women and in family members of carriers.
Citation Format: Crawford BB, Adams SB, Sittler T, Van den Akker J, Chan SB, Leitner O, Ryan LN, Gil E, Van 't Veer LJ. Multi-gene panel testing for hereditary cancer predisposition in unsolved high risk breast and ovarian cancer patients [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P3-08-02.
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Affiliation(s)
- BB Crawford
- UCSF, San Francisco, CA; Color Genomics, Burlingame, CA; Skypax, LLC, Chapel Hill, NC
| | - SB Adams
- UCSF, San Francisco, CA; Color Genomics, Burlingame, CA; Skypax, LLC, Chapel Hill, NC
| | - T Sittler
- UCSF, San Francisco, CA; Color Genomics, Burlingame, CA; Skypax, LLC, Chapel Hill, NC
| | - J Van den Akker
- UCSF, San Francisco, CA; Color Genomics, Burlingame, CA; Skypax, LLC, Chapel Hill, NC
| | - SB Chan
- UCSF, San Francisco, CA; Color Genomics, Burlingame, CA; Skypax, LLC, Chapel Hill, NC
| | - O Leitner
- UCSF, San Francisco, CA; Color Genomics, Burlingame, CA; Skypax, LLC, Chapel Hill, NC
| | - LN Ryan
- UCSF, San Francisco, CA; Color Genomics, Burlingame, CA; Skypax, LLC, Chapel Hill, NC
| | - E Gil
- UCSF, San Francisco, CA; Color Genomics, Burlingame, CA; Skypax, LLC, Chapel Hill, NC
| | - LJ Van 't Veer
- UCSF, San Francisco, CA; Color Genomics, Burlingame, CA; Skypax, LLC, Chapel Hill, NC
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Wilkins HM, Koppel SJ, Weidling IW, Roy N, Ryan LN, Stanford JA, Swerdlow RH. Extracellular Mitochondria and Mitochondrial Components Act as Damage-Associated Molecular Pattern Molecules in the Mouse Brain. J Neuroimmune Pharmacol 2016; 11:622-628. [PMID: 27562848 DOI: 10.1007/s11481-016-9704-7] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [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: 03/22/2016] [Accepted: 08/17/2016] [Indexed: 11/26/2022]
Abstract
Mitochondria and mitochondrial debris are found in the brain's extracellular space, and extracellular mitochondrial components can act as damage associated molecular pattern (DAMP) molecules. To characterize the effects of potential mitochondrial DAMP molecules on neuroinflammation, we injected either isolated mitochondria or mitochondrial DNA (mtDNA) into hippocampi of C57BL/6 mice and seven days later measured markers of inflammation. Brains injected with whole mitochondria showed increased Tnfα and decreased Trem2 mRNA, increased GFAP protein, and increased NFκB phosphorylation. Some of these effects were also observed in brains injected with mtDNA (decreased Trem2 mRNA, increased GFAP protein, and increased NFκB phosphorylation), and mtDNA injection also caused several unique changes including increased CSF1R protein and AKT phosphorylation. To further establish the potential relevance of this response to Alzheimer's disease (AD), a brain disorder characterized by neurodegeneration, mitochondrial dysfunction, and neuroinflammation we also measured App mRNA, APP protein, and Aβ1-42 levels. We found mitochondria (but not mtDNA) injections increased these parameters. Our data show that in the mouse brain extracellular mitochondria and its components can induce neuroinflammation, extracellular mtDNA or mtDNA-associated proteins can contribute to this effect, and mitochondria derived-DAMP molecules can influence AD-associated biomarkers.
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Affiliation(s)
- Heather M Wilkins
- Department of Neurology, University of Kansas Medical Center, Kansas City, KS, USA
- University of Kansas Alzheimer's Disease Center, Kansas City, KS, USA
| | - Scott J Koppel
- Department of Neurology, University of Kansas Medical Center, Kansas City, KS, USA
- University of Kansas Alzheimer's Disease Center, Kansas City, KS, USA
| | - Ian W Weidling
- University of Kansas Alzheimer's Disease Center, Kansas City, KS, USA
- Department of Molecular and Integrative Physiology, University of Kansas Medical Center, Kansas City, KS, USA
| | - Nairita Roy
- Department of Molecular and Integrative Physiology, University of Kansas Medical Center, Kansas City, KS, USA
| | - Lauren N Ryan
- University of Kansas Alzheimer's Disease Center, Kansas City, KS, USA
| | - John A Stanford
- Department of Molecular and Integrative Physiology, University of Kansas Medical Center, Kansas City, KS, USA
| | - Russell H Swerdlow
- Department of Neurology, University of Kansas Medical Center, Kansas City, KS, USA.
- University of Kansas Alzheimer's Disease Center, Kansas City, KS, USA.
- Department of Molecular and Integrative Physiology, University of Kansas Medical Center, Kansas City, KS, USA.
- Department of Biochemistry and Molecular Biology, University of Kansas Medical Center, Kansas City, KS, USA.
- University of Kansas School of Medicine, MS 2012, Landon Center on Aging, 3901 Rainbow Blvd, Kansas City, KS, 66160, USA.
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