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1. Large-scale targeted sequencing identifies risk genes for neurodevelopmental disorders

Author

Wang T., Hoekzema K., Vecchio D., Wu H., Sulovari A., Coe B. P., Gillentine M. A., Wilfert A. B., Perez-Jurado L. A., Kvarnung M., Sleyp Y., Earl R. K., Rosenfeld J. A., Geisheker M. R., Han L., Du B., Barnett C., Thompson E., Shaw M., Carroll R., Friend K., Catford R., Palmer E. E., Zou X., Ou J., Li H., Guo H., Gerdts J., Avola E., Calabrese G., Elia M., Greco D., Lindstrand A., Nordgren A., Anderlid B. -M., Vandeweyer G., Van Dijck A., Van der Aa N., McKenna B., Hancarova M., Bendova S., Havlovicova M., Malerba G., Bernardina B. D., Muglia P., van Haeringen A., Hoffer M. J. V., Franke B., Cappuccio G., Delatycki M., Lockhart P. J., Manning M. A., Liu P., Scheffer I. E., Brunetti Pierri N., Rommelse N., Amaral D. G., Santen G. W. E., Trabetti E., Sedlacek Z., Michaelson J. J., Pierce K., Courchesne E., Kooy R. F., Acampado J., Ace A. J., Amatya A., Astrovskaya I., Bashar A., Brooks E., Butler M. E., Cartner L. A., Chin W., Chung W. K., Daniels A. M., Feliciano P., Fleisch C., Ganesan S., Jensen W., Lash A. E., Marini R., Myers V. J., O'Connor E., Rigby C., Robertson B. E., Shah N., Shah S., Singer E., Snyder L. A. G., Stephens A. N., Tjernagel J., Vernoia B. M., Volfovsky N., White L. C., Hsieh A., Shen Y., Zhou X., Turner T. N., Bahl E., Thomas T. R., Brueggeman L., Koomar T., Michael R. J., O'Roak B. J., Barnard R. A., Gibbs R. A., Muzny D., Sabo A., Ahmed K. L. B., Eichler E. E., Siegel M., Abbeduto L., Hilscher B. A., Li D., Smith K., Thompson S., Albright C., Butter E. M., Eldred S., Hanna N., Jones M., Coury D. L., Scherr J., Pifher T., Roby E., Dennis B., Higgins L., Brown M., Alessandri M., Gutierrez A., Hale M. N., Herbert L. M., Schneider H. L., David G., Annett R. D., Sarver D. E., Arriaga I., Camba A., Gulsrud A. C., Haley M., McCracken J. T., Sandhu S., Tafolla M., Yang W. S., Carpenter L. A., Bradley C. C., Gwynette F., Manning P., Shaffer R., Thomas C., Bernier R. A., Fox E. A., Gerdts J. A., Pepper M., Ho T., Cho D., Piven J., Lechniak H., Soorya L. V., Gordon R., Wainer A., Yeh L., Ochoa-Lubinoff C., Russo N., Berry-Kravis E., Booker S., Erickson C. A., Prock L. M., Pawlowski K. G., Matthews E. T., Brewster S. J., Hojlo M. A., Abada E., Lamarche E., Murali S. C., Harvey W. T., Kaplan H. E., Pierce K. L., DeMarco L., Horner S., Pandey J., Plate S., Sahin M., Riley K. D., Carmody E., Constantini J., Esler A., Fatemi A., Hutter H., Landa R. J., McKenzie A. P., Neely J., Singh V., Van Metre B., Wodka E. L., Fombonne E. J., Huang-Storms L. Y., Pacheco L. D., Mastel S. A., Coppola L. A., Francis S., Jarrett A., Jacob S., Lillie N., Gunderson J., Istephanous D., Simon L., Wasserberg O., Rachubinski A. L., Rosenberg C. R., Kanne S. M., Shocklee A. D., Takahashi N., Bridwell S. L., Klimczac R. L., Mahurin M. A., Cotrell H. E., Grant C. A., Hunter S. G., Martin C. L., Taylor C. M., Walsh L. K., Dent K. A., Mason A., Sziklay A., Smith C. J., Nordenskjold M., Romano C., Peeters H., Gecz J., Xia K., SPARK Consortium, Wang, T., Hoekzema, K., Vecchio, D., Wu, H., Sulovari, A., Coe, B. P., Gillentine, M. A., Wilfert, A. B., Perez-Jurado, L. A., Kvarnung, M., Sleyp, Y., Earl, R. K., Rosenfeld, J. A., Geisheker, M. R., Han, L., Du, B., Barnett, C., Thompson, E., Shaw, M., Carroll, R., Friend, K., Catford, R., Palmer, E. E., Zou, X., Ou, J., Li, H., Guo, H., Gerdts, J., Avola, E., Calabrese, G., Elia, M., Greco, D., Lindstrand, A., Nordgren, A., Anderlid, B. -M., Vandeweyer, G., Van Dijck, A., Van der Aa, N., Mckenna, B., Hancarova, M., Bendova, S., Havlovicova, M., Malerba, G., Bernardina, B. D., Muglia, P., van Haeringen, A., Hoffer, M. J. V., Franke, B., Cappuccio, G., Delatycki, M., Lockhart, P. J., Manning, M. A., Liu, P., Scheffer, I. E., Brunetti Pierri, N., Rommelse, N., Amaral, D. G., Santen, G. W. E., Trabetti, E., Sedlacek, Z., Michaelson, J. J., Pierce, K., Courchesne, E., Kooy, R. F., Acampado, J., Ace, A. J., Amatya, A., Astrovskaya, I., Bashar, A., Brooks, E., Butler, M. E., Cartner, L. A., Chin, W., Chung, W. K., Daniels, A. M., Feliciano, P., Fleisch, C., Ganesan, S., Jensen, W., Lash, A. E., Marini, R., Myers, V. J., O'Connor, E., Rigby, C., Robertson, B. E., Shah, N., Shah, S., Singer, E., Snyder, L. A. G., Stephens, A. N., Tjernagel, J., Vernoia, B. M., Volfovsky, N., White, L. C., Hsieh, A., Shen, Y., Zhou, X., Turner, T. N., Bahl, E., Thomas, T. R., Brueggeman, L., Koomar, T., Michael, R. J., O'Roak, B. J., Barnard, R. A., Gibbs, R. A., Muzny, D., Sabo, A., Ahmed, K. L. B., Eichler, E. E., Siegel, M., Abbeduto, L., Hilscher, B. A., Li, D., Smith, K., Thompson, S., Albright, C., Butter, E. M., Eldred, S., Hanna, N., Jones, M., Coury, D. L., Scherr, J., Pifher, T., Roby, E., Dennis, B., Higgins, L., Brown, M., Alessandri, M., Gutierrez, A., Hale, M. N., Herbert, L. M., Schneider, H. L., David, G., Annett, R. D., Sarver, D. E., Arriaga, I., Camba, A., Gulsrud, A. C., Haley, M., Mccracken, J. T., Sandhu, S., Tafolla, M., Yang, W. S., Carpenter, L. A., Bradley, C. C., Gwynette, F., Manning, P., Shaffer, R., Thomas, C., Bernier, R. A., Fox, E. A., Gerdts, J. A., Pepper, M., Ho, T., Cho, D., Piven, J., Lechniak, H., Soorya, L. V., Gordon, R., Wainer, A., Yeh, L., Ochoa-Lubinoff, C., Russo, N., Berry-Kravis, E., Booker, S., Erickson, C. A., Prock, L. M., Pawlowski, K. G., Matthews, E. T., Brewster, S. J., Hojlo, M. A., Abada, E., Lamarche, E., Murali, S. C., Harvey, W. T., Kaplan, H. E., Pierce, K. L., Demarco, L., Horner, S., Pandey, J., Plate, S., Sahin, M., Riley, K. D., Carmody, E., Constantini, J., Esler, A., Fatemi, A., Hutter, H., Landa, R. J., Mckenzie, A. P., Neely, J., Singh, V., Van Metre, B., Wodka, E. L., Fombonne, E. J., Huang-Storms, L. Y., Pacheco, L. D., Mastel, S. A., Coppola, L. A., Francis, S., Jarrett, A., Jacob, S., Lillie, N., Gunderson, J., Istephanous, D., Simon, L., Wasserberg, O., Rachubinski, A. L., Rosenberg, C. R., Kanne, S. M., Shocklee, A. D., Takahashi, N., Bridwell, S. L., Klimczac, R. L., Mahurin, M. A., Cotrell, H. E., Grant, C. A., Hunter, S. G., Martin, C. L., Taylor, C. M., Walsh, L. K., Dent, K. A., Mason, A., Sziklay, A., Smith, C. J., Nordenskjold, M., Romano, C., Peeters, H., Gecz, J., and Xia, K.

Subjects
0301 basic medicine, Male, CCCTC-Binding Factor, Transcription Factor, Basic Helix-Loop-Helix Transcription Factor, DNA Mutational Analysis, General Physics and Astronomy, RNA-Binding Protein, Heterogeneous-Nuclear Ribonucleoprotein U, VARIANTS, medicine.disease_cause, Cohort Studies, 0302 clinical medicine, Neurodevelopmental Disorder, Basic Helix-Loop-Helix Transcription Factors, SPARK Consortium, 2.1 Biological and endogenous factors, Copy-number variation, Aetiology, lcsh:Science, GABRG2, Genetics, Mutation, Multidisciplinary, biology, Neurodevelopmental disorders, RNA-Binding Proteins, High-Throughput Nucleotide Sequencing, Autism spectrum disorders, Multidisciplinary Sciences, DNA-Binding Proteins, Science & Technology - Other Topics, Female, Case-Control Studie, Engineering sciences. Technology, Human, Science, DNA-Binding Protein, Genetic Association Studie, COPY-NUMBER VARIATION, General Biochemistry, Genetics and Molecular Biology, DNA sequencing, Article, KCNQ3 Potassium Channel, DNA Mutational Analysi, 03 medical and health sciences, medicine, Humans, Genetic Predisposition to Disease, AUTISM, Gene, Genetic Association Studies, Neurodevelopmental disorders Donders Center for Medical Neuroscience [Radboudumc 7], Science & Technology, DISABILITY, Prevention, Case-control study, General Chemistry, Repressor Protein, medicine.disease, FRAMEWORK, Repressor Proteins, DE-NOVO MUTATION, 030104 developmental biology, CTCF, Neurodevelopmental Disorders, Case-Control Studies, biology.protein, Next-generation sequencing, Autism, lcsh:Q, Cohort Studie, 030217 neurology & neurosurgery, Transcription Factors
Abstract

Most genes associated with neurodevelopmental disorders (NDDs) were identified with an excess of de novo mutations (DNMs) but the significance in case–control mutation burden analysis is unestablished. Here, we sequence 63 genes in 16,294 NDD cases and an additional 62 genes in 6,211 NDD cases. By combining these with published data, we assess a total of 125 genes in over 16,000 NDD cases and compare the mutation burden to nonpsychiatric controls from ExAC. We identify 48 genes (25 newly reported) showing significant burden of ultra-rare (MAF, For many neurodevelopmental disorder (NDD) risk genes, the significance for mutational burden is unestablished. Here, the authors sequence 125 candidate NDD genes in over 16,000 NDD cases; case-control mutational burden analysis identifies 48 genes with a significant burden of severe ultra-rare mutations.

Published
2020

2. Author Correction: Large-scale targeted sequencing identifies risk genes for neurodevelopmental disorders (Nature Communications, (2020), 11, 1, (4932), 10.1038/s41467-020-18723-y)

Author

Wang, T., Hoekzema, K., Vecchio, Davide., Wu, H., Sulovari, A., Coe, B. P., Gillentine, M. A., Wilfert, A. B., Perez-Jurado, L. A., Kvarnung, M., Sleyp, Y., Earl, R. K., Rosenfeld, J. A., Geisheker, M. R., Han, L., Du, B., Barnett, C., Thompson, E., Shaw, M., Carroll, R., Friend, K., Catford, R., Palmer, E. E., Zou, X., Ou, J., Li, H., Guo, H., Gerdts, J., Avola, E., Calabrese, Giuseppe, Elia, Maurizio., Greco, Donatella, Lindstrand, A., Nordgren, A., Anderlid, B. -M., Vandeweyer, G., Van Dijck, A., Van der Aa, N., Mckenna, B., Hancarova, M., Bendova, S., Havlovicova, M., Malerba, G., Bernardina, B. D., Muglia, P., van Haeringen, A., Hoffer, M. J. V., Franke, B., Cappuccio, G., Delatycki, M., Lockhart, P. J., Manning, M. A., Liu, P., Scheffer, I. E., Brunetti-Pierri, N., Rommelse, N., Amaral, D. G., Santen, G. W. E., Trabetti, E., Sedlacek, Z., Michaelson, J. J., Pierce, K., Courchesne, E., Kooy, R. F., Acampado, J., Ace, A. J., Amatya, A., Astrovskaya, I., Bashar, A., Brooks, E., Butler, M. E., Cartner, L. A., Chin, W., Chung, W. K., Daniels, A. M., Feliciano, P., Fleisch, C., Ganesan, S., Jensen, W., Lash, A. E., Marini, R., Myers, V. J., O'Connor, E., Rigby, C., Robertson, B. E., Shah, N., Shah, S., Singer, E., Snyder, L. A. G., Stephens, A. N., Tjernagel, J., Vernoia, B. M., Volfovsky, N., White, L. C., Hsieh, A., Shen, Y., Zhou, X., Turner, T. N., Bahl, E., Thomas, T. R., Brueggeman, L., Koomar, T., O'Roak, B. J., Barnard, R. A., Gibbs, R. A., Muzny, D., Sabo, A., Ahmed, K. L. B., Eichler, E. E., Siegel, M., Abbeduto, L., Hilscher, B. A., Li, D., Smith, K., Thompson, S., Albright, C., Butter, E. M., Eldred, S., Hanna, N., Jones, M., Coury, D. L., Scherr, J., Pifher, T., Roby, E., Dennis, B., Higgins, L., Brown, M., Alessandri, M., Gutierrez, A., Hale, M. N., Herbert, L. M., Schneider, H. L., David, G., Annett, R. D., Sarver, D. E., Arriaga, I., Camba, A., Gulsrud, A. C., Haley, M., Mccracken, J. T., Sandhu, S., Tafolla, M., Yang, W. S., Carpenter, L. A., Bradley, C. C., Gwynette, F., Manning, P., Shaffer, R., Thomas, C., Bernier, R. A., Fox, E. A., Gerdts, J. A., Pepper, M., Ho, T., Cho, D., Piven, J., Lechniak, H., Soorya, L. V., Gordon, R., Wainer, A., Yeh, L., Ochoa-Lubinoff, C., Russo, N., Berry-Kravis, E., Booker, S., Erickson, C. A., Prock, L. M., Pawlowski, K. G., Matthews, E. T., Brewster, S. J., Hojlo, M. A., Abada, E., Lamarche, E., Murali, S. C., Harvey, W. T., Kaplan, H. E., Pierce, K. L., Demarco, L., Horner, S., Pandey, J., Plate, S., Sahin, M., Riley, K. D., Carmody, E., Constantini, J., Esler, A., Fatemi, A., Hutter, H., Landa, R. J., Mckenzie, A. P., Neely, J., Singh, V., Van Metre, B., Wodka, E. L., Fombonne, E. J., Huang-Storms, L. Y., Pacheco, L. D., Mastel, S. A., Coppola, L. A., Francis, S., Jarrett, A., Jacob, S., Lillie, N., Gunderson, J., Istephanous, D., Simon, L., Wasserberg, O., Rachubinski, A. L., Rosenberg, C. R., Kanne, S. M., Shocklee, A. D., Takahashi, N., Bridwell, S. L., Klimczac, R. L., Mahurin, M. A., Cotrell, H. E., Grant, C. A., Hunter, S. G., Martin, C. L., Taylor, C. M., Walsh, L. K., Dent, K. A., Mason, A., Sziklay, A., Smith, C. J., Nordenskjold, M., Romano, Corrado, Peeters, H., Gecz, J., and Xia, K.

Published
2020

3. Author Correction: Large-scale targeted sequencing identifies risk genes for neurodevelopmental disorders (Nature Communications, (2020), 11, 1, (4932), 10.1038/s41467-020-18723-y)

Author

Wang, T, Hoekzema, K, Vecchio, D, Wu, H, Sulovari, A, Coe, BP, Gillentine, MA, Wilfert, AB, Perez-Jurado, LA, Kvarnung, M, Sleyp, Y, Earl, RK, Rosenfeld, JA, Geisheker, MR, Han, L, Du, B, Barnett, C, Thompson, E, Shaw, M, Carroll, R, Friend, K, Catford, R, Palmer, EE, Zou, X, Ou, J, Li, H, Guo, H, Gerdts, J, Avola, E, Calabrese, G, Elia, M, Greco, D, Lindstrand, A, Nordgren, A, Anderlid, BM, Vandeweyer, G, Van Dijck, A, Van der Aa, N, McKenna, B, Hancarova, M, Bendova, S, Havlovicova, M, Malerba, G, Bernardina, BD, Muglia, P, van Haeringen, A, Hoffer, MJV, Franke, B, Cappuccio, G, Delatycki, M, Lockhart, PJ, Manning, MA, Liu, P, Scheffer, IE, Brunetti-Pierri, N, Rommelse, N, Amaral, DG, Santen, GWE, Trabetti, E, Sedláček, Z, Michaelson, JJ, Pierce, K, Courchesne, E, Kooy, RF, Acampado, J, Ace, AJ, Amatya, A, Astrovskaya, I, Bashar, A, Brooks, E, Butler, ME, Cartner, LA, Chin, W, Chung, WK, Daniels, AM, Feliciano, P, Fleisch, C, Ganesan, S, Jensen, W, Lash, AE, Marini, R, Myers, VJ, O’Connor, E, Rigby, C, Robertson, BE, Shah, N, Shah, S, Singer, E, Snyder, LAG, Stephens, AN, Tjernagel, J, Vernoia, BM, Volfovsky, N, White, LC, Hsieh, A, Shen, Y, Zhou, X, Turner, TN, Bahl, E, Thomas, TR, Wang, T, Hoekzema, K, Vecchio, D, Wu, H, Sulovari, A, Coe, BP, Gillentine, MA, Wilfert, AB, Perez-Jurado, LA, Kvarnung, M, Sleyp, Y, Earl, RK, Rosenfeld, JA, Geisheker, MR, Han, L, Du, B, Barnett, C, Thompson, E, Shaw, M, Carroll, R, Friend, K, Catford, R, Palmer, EE, Zou, X, Ou, J, Li, H, Guo, H, Gerdts, J, Avola, E, Calabrese, G, Elia, M, Greco, D, Lindstrand, A, Nordgren, A, Anderlid, BM, Vandeweyer, G, Van Dijck, A, Van der Aa, N, McKenna, B, Hancarova, M, Bendova, S, Havlovicova, M, Malerba, G, Bernardina, BD, Muglia, P, van Haeringen, A, Hoffer, MJV, Franke, B, Cappuccio, G, Delatycki, M, Lockhart, PJ, Manning, MA, Liu, P, Scheffer, IE, Brunetti-Pierri, N, Rommelse, N, Amaral, DG, Santen, GWE, Trabetti, E, Sedláček, Z, Michaelson, JJ, Pierce, K, Courchesne, E, Kooy, RF, Acampado, J, Ace, AJ, Amatya, A, Astrovskaya, I, Bashar, A, Brooks, E, Butler, ME, Cartner, LA, Chin, W, Chung, WK, Daniels, AM, Feliciano, P, Fleisch, C, Ganesan, S, Jensen, W, Lash, AE, Marini, R, Myers, VJ, O’Connor, E, Rigby, C, Robertson, BE, Shah, N, Shah, S, Singer, E, Snyder, LAG, Stephens, AN, Tjernagel, J, Vernoia, BM, Volfovsky, N, White, LC, Hsieh, A, Shen, Y, Zhou, X, Turner, TN, Bahl, E, and Thomas, TR

Abstract

The original version of this Article contained an error on page 5 of the Results section, which incorrectly read ‘They are characterized by craniofacial dysmorphisms (9/10), thin vermillion border and lips (4/7), and feeding difficulties (6/11), and exhibit neonatal hypotonia (10/7)’. The correct version states ‘They are characterized by craniofacial dysmorphisms (9/10), thin vermillion border and lips (4/7), and feeding difficulties (6/11), and exhibit neonatal hypotonia (7/10)’.

Published
2020

10. Nanomaterials for intelligent CRISPR-Cas tools: improving environment sustainability.

Author

Bahl E, Jyoti A, Singh A, Siddqui A, Upadhyay SK, Jain D, Shah MP, and Saxena J

Abstract

Clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein (Cas) is a desirable gene modification tool covering a wide area in various sectors of medicine, agriculture, and microbial biotechnology. The role of this incredible genetic engineering technology has been extensively investigated; however, it remains formidable with cargo choices, nonspecific delivery, and insertional mutagenesis. Various nanomaterials including lipid, polymeric, and inorganic are being used to deliver the CRISPR-Cas system. Progress in nanomaterials could potentially address these challenges by accelerating precision targeting, cost-effectiveness, and one-step delivery. In this review, we highlighted the advances in nanotechnology and nanomaterials as smart delivery systems for CRISPR-Cas so as to ameliorate applications for environmental remediation including biomedical research and healthcare, strategies for mitigating antimicrobial resistance, and to be used as nanofertilizers for enhancing crop growth, and reducing the environmental impact of traditional fertilizers. The timely co-evolution of nanotechnology and CRISPR technologies has contributed to smart novel nanostructure hybrids for improving the onerous tasks of environmental remediation and biological sustainability., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Published
2024
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11. Using deep learning to quantify neuronal activation from single-cell and spatial transcriptomic data.

Author

Bahl E, Chatterjee S, Mukherjee U, Elsadany M, Vanrobaeys Y, Lin LC, McDonough M, Resch J, Giese KP, Abel T, and Michaelson JJ

Subjects
Male, Mice, Animals, Neuronal Plasticity physiology, Neurons metabolism, Brain physiology, Gene Expression Profiling, Deep Learning
Abstract

Neuronal activity-dependent transcription directs molecular processes that regulate synaptic plasticity, brain circuit development, behavioral adaptation, and long-term memory. Single cell RNA-sequencing technologies (scRNAseq) are rapidly developing and allow for the interrogation of activity-dependent transcription at cellular resolution. Here, we present NEUROeSTIMator, a deep learning model that integrates transcriptomic signals to estimate neuronal activation in a way that we demonstrate is associated with Patch-seq electrophysiological features and that is robust against differences in species, cell type, and brain region. We demonstrate this method's ability to accurately detect neuronal activity in previously published studies of single cell activity-induced gene expression. Further, we applied our model in a spatial transcriptomic study to identify unique patterns of learning-induced activity across different brain regions in male mice. Altogether, our findings establish NEUROeSTIMator as a powerful and broadly applicable tool for measuring neuronal activation, whether as a critical covariate or a primary readout of interest., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Published
2024
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12. Mapping the spatial transcriptomic signature of the hippocampus during memory consolidation.

Author

Vanrobaeys Y, Mukherjee U, Langmack L, Beyer SE, Bahl E, Lin LC, Michaelson JJ, Abel T, and Chatterjee S

Subjects
Male, Mice, Animals, Hippocampus physiology, CA1 Region, Hippocampal metabolism, Learning, Transcriptome genetics, Memory Consolidation
Abstract

Memory consolidation involves discrete patterns of transcriptional events in the hippocampus. Despite the emergence of single-cell transcriptomic profiling techniques, mapping the transcriptomic signature across subregions of the hippocampus has remained challenging. Here, we utilized unbiased spatial sequencing to delineate transcriptome-wide gene expression changes across subregions of the dorsal hippocampus of male mice following learning. We find that each subregion of the hippocampus exhibits distinct yet overlapping transcriptomic signatures. The CA1 region exhibited increased expression of genes related to transcriptional regulation, while the DG showed upregulation of genes associated with protein folding. Importantly, our approach enabled us to define the transcriptomic signature of learning within two less-defined hippocampal subregions, CA1 stratum radiatum, and oriens. We demonstrated that CA1 subregion-specific expression of a transcription factor subfamily has a critical functional role in the consolidation of long-term memory. This work demonstrates the power of spatial molecular approaches to reveal simultaneous transcriptional events across the hippocampus during memory consolidation., (© 2023. Springer Nature Limited.)

Published
2023
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13. Activity-induced gene expression in the human brain.

Author

Chatterjee S, Park BJ, Vanrobaeys Y, Heiney SA, Rhone AE, Nourski KV, Langmack L, Mukherjee U, Kovach CK, Kocsis Z, Kikuchi Y, Petkov CI, Hefti MM, Bahl E, Michaelson JJ, Kawasaki H, Oya H, Howard MA 3rd, Nickl-Jockschat T, Lin LC, and Abel T

Abstract

Activity-induced gene expression underlies synaptic plasticity and brain function. Here, using molecular sequencing techniques, we define activity-dependent transcriptomic and epigenomic changes at the tissue and single-cell level in the human brain following direct electrical stimulation of the anterior temporal lobe in patients undergoing neurosurgery. Genes related to transcriptional regulation and microglia-specific cytokine activity displayed the greatest induction pattern, revealing a precise molecular signature of neuronal activation in the human brain., Competing Interests: Competing interests T.A. is a scientific advisor to Aditum Bio and Radius Health and he serves on the scientific advisory board of Embark Neuro. T.A serves on the Scientific Advisory Board of EmbarkNeuro and is a scientific advisor to Aditum Bio and Radius Health. The other authors declare no conflicting interests.

Published
2023
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14. Mapping the spatial transcriptomic signature of the hippocampus during memory consolidation.

Author

Vanrobeys Y, Mukherjee U, Langmack L, Bahl E, Lin LC, Michaelson JJ, Abel T, and Chatterjee S

Abstract

Memory consolidation involves discrete patterns of transcriptional events in the hippocampus. Despite the emergence of single-cell transcriptomic profiling techniques, defining learning-responsive gene expression across subregions of the hippocampus has remained challenging. Here, we utilized unbiased spatial sequencing to elucidate transcriptome-wide changes in gene expression in the hippocampus following learning, enabling us to define molecular signatures unique to each hippocampal subregion. We find that each subregion of the hippocampus exhibits distinct yet overlapping transcriptomic signatures. Although the CA1 region exhibited increased expression of genes related to transcriptional regulation, the DG showed upregulation of genes associated with protein folding. We demonstrate the functional relevance of subregion-specific gene expression by genetic manipulation of a transcription factor selectively in the CA1 hippocampal subregion, leading to long-term memory deficits. This work demonstrates the power of using spatial molecular approaches to reveal transcriptional events during memory consolidation.

Published
2023
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15. Clinical autism subscales have common genetic liabilities that are heritable, pleiotropic, and generalizable to the general population.

Author

Thomas TR, Koomar T, Casten LG, Tener AJ, Bahl E, and Michaelson JJ

Subjects
Child, Communication, Female, Humans, Male, Multifactorial Inheritance, Phenotype, Autism Spectrum Disorder psychology, Autistic Disorder epidemiology, Autistic Disorder genetics
Abstract

The complexity of autism's phenotypic spectra is well-known, yet most genetic research uses case-control status as the target trait. It is undetermined if autistic symptom domain severity underlying this heterogeneity is heritable and pleiotropic with other psychiatric and behavior traits in the same manner as autism case-control status. In N = 6064 autistic children in the SPARK cohort, we investigated the common genetic properties of twelve subscales from three clinical autism instruments measuring autistic traits: the Social Communication Questionnaire (SCQ), the Repetitive Behavior Scale-Revised (RBS-R), and the Developmental Coordination Disorder Questionnaire (DCDQ). Educational attainment polygenic scores (PGS) were significantly negatively correlated with eleven subscales, while ADHD and major depression PGS were positively correlated with ten and eight of the autism subscales, respectively. Loneliness and neuroticism PGS were also positively correlated with many subscales. Significant PGS by sex interactions were found-surprisingly, the autism case-control PGS was negatively correlated in females and had no strong correlation in males. SNP-heritability of the DCDQ subscales ranged from 0.04 to 0.08, RBS-R subscales ranged from 0.09 to 0.24, and SCQ subscales ranged from 0 to 0.12. GWAS in SPARK followed by estimation of polygenic scores (PGS) in the typically-developing ABCD cohort (N = 5285), revealed significant associations of RBS-R subscale PGS with autism-related behavioral traits, with several subscale PGS more strongly correlated than the autism case-control PGS. Overall, our analyses suggest that the clinical autism subscale traits show variability in SNP-heritability, PGS associations, and significant PGS by sex interactions, underscoring the heterogeneity in autistic traits at a genetic level. Furthermore, of the three instruments investigated, the RBS-R shows the greatest evidence of genetic signal in both (1) autistic samples (greater heritability) and (2) general population samples (strongest PGS associations)., (© 2022. The Author(s).)

Published
2022
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16. Endoplasmic reticulum chaperone genes encode effectors of long-term memory.

Author

Chatterjee S, Bahl E, Mukherjee U, Walsh EN, Shetty MS, Yan AL, Vanrobaeys Y, Lederman JD, Giese KP, Michaelson J, and Abel T

Abstract

The mechanisms underlying memory loss associated with Alzheimer's disease and related dementias (ADRD) remain unclear, and no effective treatments exist. Fundamental studies have shown that a set of transcriptional regulatory proteins of the nuclear receptor 4a (Nr4a) family serve as molecular switches for long-term memory. Here, we show that Nr4a proteins regulate the transcription of genes encoding chaperones that localize to the endoplasmic reticulum (ER). These chaperones fold and traffic plasticity-related proteins to the cell surface during long-lasting forms of synaptic plasticity and memory. Dysregulation of Nr4a transcription factors and ER chaperones is linked to ADRD, and overexpressing Nr4a1 or the chaperone Hspa5 ameliorates long-term memory deficits in a tau-based mouse model of ADRD, pointing toward innovative therapeutic approaches for treating memory loss. Our findings establish a unique molecular concept underlying long-term memory and provide insights into the mechanistic basis of cognitive deficits in dementia.

Published
2022
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17. p53-mediated neurodegeneration in the absence of the nuclear protein Akirin2.

Author

Peek SL, Bosch PJ, Bahl E, Iverson BJ, Parida M, Bais P, Manak JR, Michaelson JJ, Burgess RW, and Weiner JA

Abstract

Proper gene regulation is critical for both neuronal development and maintenance as the brain matures. We previously demonstrated that Akirin2, an essential nuclear protein that interacts with transcription factors and chromatin remodeling complexes, is required for the embryonic formation of the cerebral cortex. Here we show that Akirin2 plays a mechanistically distinct role in maintaining healthy neurons during cortical maturation. Restricting Akirin2 loss to excitatory cortical neurons resulted in progressive neurodegeneration via necroptosis and severe cortical atrophy with age. Comparing transcriptomes from Akirin2-null postnatal neurons and cortical progenitors revealed that targets of the tumor suppressor p53, a regulator of both proliferation and cell death encoded by Trp53 , were consistently upregulated. Reduction of Trp53 rescued neurodegeneration in Akirin2-null neurons. These data: (1) implicate Akirin2 as a critical neuronal maintenance protein, (2) identify p53 pathways as mediators of Akirin2 functions, and (3) suggest Akirin2 dysfunction may be relevant to neurodegenerative diseases., Competing Interests: The authors declare no competing interests, (© 2022 The Author(s).)

Published
2022
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18. Altered hippocampal transcriptome dynamics following sleep deprivation.

Author

Gaine ME, Bahl E, Chatterjee S, Michaelson JJ, Abel T, and Lyons LC

Subjects
Animals, Base Sequence, Cell Nucleus metabolism, Cytoskeletal Proteins genetics, Dendrites metabolism, Gene Ontology, Male, Mice, Mice, Inbred C57BL, Nerve Tissue Proteins genetics, Neurons metabolism, Protein Biosynthesis, RNA Splicing, RNA, Messenger biosynthesis, RNA, Messenger genetics, Sleep Deprivation rehabilitation, Gene Expression Regulation, Hippocampus metabolism, Sleep Deprivation genetics, Transcriptome
Abstract

Widespread sleep deprivation is a continuing public health problem in the United States and worldwide affecting adolescents and adults. Acute sleep deprivation results in decrements in spatial memory and cognitive impairments. The hippocampus is vulnerable to acute sleep deprivation with changes in gene expression, cell signaling, and protein synthesis. Sleep deprivation also has long lasting effects on memory and performance that persist after recovery sleep, as seen in behavioral studies from invertebrates to humans. Although previous research has shown that acute sleep deprivation impacts gene expression, the extent to which sleep deprivation affects gene regulation remains unknown. Using an unbiased deep RNA sequencing approach, we investigated the effects of acute sleep deprivation on gene expression in the hippocampus. We identified 1,146 genes that were significantly dysregulated following sleep deprivation with 507 genes upregulated and 639 genes downregulated, including protein coding genes and long non-coding RNAs not previously identified as impacted by sleep deprivation. Notably, genes significantly upregulated after sleep deprivation were associated with RNA splicing and the nucleus. In contrast, downregulated genes were associated with cell adhesion, dendritic localization, the synapse, and postsynaptic membrane. Furthermore, we found through independent experiments analyzing a subset of genes that three hours of recovery sleep following acute sleep deprivation was sufficient to normalize mRNA abundance for most genes, although exceptions occurred for some genes that may affect RNA splicing or transcription. These results clearly demonstrate that sleep deprivation differentially regulates gene expression on multiple transcriptomic levels to impact hippocampal function., (© 2021. The Author(s).)

Published
2021
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19. Genetic and morphological estimates of androgen exposure predict social deficits in multiple neurodevelopmental disorder cohorts.

Author

McKenna BG, Huang Y, Vervier K, Hofammann D, Cafferata M, Al-Momani S, Lowenthal F, Zhang A, Koh JY, Thenuwara S, Brueggeman L, Bahl E, Koomar T, Pottschmidt N, Kalmus T, Casten L, Thomas TR, and Michaelson JJ

Subjects
Cohort Studies, Female, Humans, Male, Multifactorial Inheritance, Testosterone, Androgens, Autism Spectrum Disorder
Abstract

Background: Neurodevelopmental disorders (NDDs) such as autism spectrum disorder (ASD) display a strong male bias. Androgen exposure is profoundly increased in typical male development, but it also varies within the sexes, and previous work has sought to connect morphological proxies of androgen exposure, including digit ratio and facial morphology, to neurodevelopmental outcomes. The results of these studies have been mixed, and the relationships between androgen exposure and behavior remain unclear., Methods: Here, we measured both digit ratio masculinity (DRM) and facial landmark masculinity (FLM) in the same neurodevelopmental cohort (N = 763) and compared these proxies of androgen exposure to clinical and parent-reported features as well as polygenic risk scores., Results: We found that FLM was significantly associated with NDD diagnosis (ASD, ADHD, ID; all [Formula: see text]), while DRM was not. When testing for association with parent-reported problems, we found that both FLM and DRM were positively associated with concerns about social behavior ([Formula: see text], [Formula: see text]; [Formula: see text], [Formula: see text], respectively). Furthermore, we found evidence via polygenic risk scores (PRS) that DRM indexes masculinity via testosterone levels ([Formula: see text], [Formula: see text]), while FLM indexes masculinity through a negative relationship with sex hormone binding globulin (SHBG) levels ([Formula: see text], [Formula: see text]). Finally, using the SPARK cohort (N = 9419) we replicated the observed relationship between polygenic estimates of testosterone, SHBG, and social functioning ([Formula: see text], [Formula: see text], and [Formula: see text], [Formula: see text] for testosterone and SHBG, respectively). Remarkably, when considered over the extremes of each variable, these quantitative sex effects on social functioning were comparable to the effect of binary sex itself (binary male: [Formula: see text]; testosterone: [Formula: see text] from 0.1%-ile to 99.9%-ile; SHBG: [Formula: see text] from 0.1%-ile to 99.9%-ile)., Limitations: In the devGenes and SPARK cohorts, our analyses rely on indirect, rather than direct measurement of androgens and related molecules., Conclusions: These findings and their replication in the large SPARK cohort lend support to the hypothesis that increasing net androgen exposure diminishes capacity for social functioning in both males and females.

Published
2021
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20. The CBP KIX domain regulates long-term memory and circadian activity.

Author

Chatterjee S, Angelakos CC, Bahl E, Hawk JD, Gaine ME, Poplawski SG, Schneider-Anthony A, Yadav M, Porcari GS, Cassel JC, Giese KP, Michaelson JJ, Lyons LC, Boutillier AL, and Abel T

Subjects
Animals, CREB-Binding Protein chemistry, CREB-Binding Protein metabolism, Female, Male, Mice, CREB-Binding Protein genetics, Circadian Rhythm genetics, Memory, Long-Term, Protein Domains
Abstract

Background: CREB-dependent transcription necessary for long-term memory is driven by interactions with CREB-binding protein (CBP), a multi-domain protein that binds numerous transcription factors potentially affecting expression of thousands of genes. Identifying specific domain functions for multi-domain proteins is essential to understand processes such as cognitive function and circadian clocks. We investigated the function of the CBP KIX domain in hippocampal memory and gene expression using CBP KIX/KIX mice with mutations that prevent phospho-CREB (Ser133) binding., Results: We found that CBP KIX/KIX mice were impaired in long-term memory, but not learning acquisition or short-term memory for the Morris water maze. Using an unbiased analysis of gene expression in the dorsal hippocampus after training in the Morris water maze or contextual fear conditioning, we discovered dysregulation of CREB, CLOCK, and BMAL1 target genes and downregulation of circadian genes in CBP KIX/KIX mice. Given our finding that the CBP KIX domain was important for transcription of circadian genes, we profiled circadian activity and phase resetting in CBP KIX/KIX mice. CBP KIX/KIX mice exhibited delayed activity peaks after light offset and longer free-running periods in constant dark. Interestingly, CBP KIX/KIX mice displayed phase delays and advances in response to photic stimulation comparable to wildtype littermates. Thus, this work delineates site-specific regulation of the circadian clock by a multi-domain protein., Conclusions: These studies provide insight into the significance of the CBP KIX domain by defining targets of CBP transcriptional co-activation in memory and the role of the CBP KIX domain in vivo on circadian rhythms.

Published
2020
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21. Novel and ultra-rare damaging variants in neuropeptide signaling are associated with disordered eating behaviors.

Author

Lutter M, Bahl E, Hannah C, Hofammann D, Acevedo S, Cui H, McAdams CJ, and Michaelson JJ

Subjects
Adolescent, Adult, Computational Biology, Exome, Feeding and Eating Disorders metabolism, Female, Genetic Predisposition to Disease, Humans, Male, Middle Aged, Neuropeptides metabolism, Young Adult, Appetite Regulation genetics, Feeding and Eating Disorders genetics, Gene Regulatory Networks, Neuropeptides genetics, Signal Transduction physiology
Abstract

Objective: Eating disorders develop through a combination of genetic vulnerability and environmental stress, however the genetic basis of this risk is unknown., Methods: To understand the genetic basis of this risk, we performed whole exome sequencing on 93 unrelated individuals with eating disorders (38 restricted-eating and 55 binge-eating) to identify novel damaging variants. Candidate genes with an excessive burden of predicted damaging variants were then prioritized based upon an unbiased, data-driven bioinformatic analysis. One top candidate pathway was empirically tested for therapeutic potential in a mouse model of binge-like eating., Results: An excessive burden of novel damaging variants was identified in 186 genes in the restricted-eating group and 245 genes in the binge-eating group. This list is significantly enriched (OR = 4.6, p<0.0001) for genes involved in neuropeptide/neurotrophic pathways implicated in appetite regulation, including neurotensin-, glucagon-like peptide 1- and BDNF-signaling. Administration of the glucagon-like peptide 1 receptor agonist exendin-4 significantly reduced food intake in a mouse model of 'binge-like' eating., Conclusions: These findings implicate ultra-rare and novel damaging variants in neuropeptide/neurotropic factor signaling pathways in the development of eating disorder behaviors and identify glucagon-like peptide 1-receptor agonists as a potential treatment for binge eating.

Published
2017
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22. cerebroViz: an R package for anatomical visualization of spatiotemporal brain data.

Author

Bahl E, Koomar T, and Michaelson JJ

Subjects
Brain anatomy & histology, Brain growth & development, Databases, Factual, Gene Expression, Humans, Brain metabolism, Gene Expression Profiling methods, Software, Spatio-Temporal Analysis
Abstract

Summary: Spatiotemporal transcriptomic profiling has provided valuable insight into the patterning of gene expression throughout the human brain from early fetal development to adulthood. When combined with prior knowledge of a disease's age at onset and region-specificity, these expression profiles have provided the necessary context to both strengthen putative gene-disease associations and infer new associations. While a wealth of spatiotemporal expression data exists, there are currently no tools available to visualize this data within the anatomical context of the brain, thus limiting the intuitive interpretation of many such findings. We present cerebroViz, an R package to map spatiotemporal brain data to vector graphic diagrams of the human brain. Our tool allows rapid generation of publication-quality figures that highlight spatiotemporal trends in the input data, while striking a balance between usability and customization. cerebroViz is generalizable to any data quantifiable at a brain-regional resolution and currently supports visualization of up to thirty regions of the brain found in databases such as BrainSpan, GTEx and Roadmap Epigenomics., Availability and Implementation: cerebroViz is freely available through GitHub ( https://github.com/ethanbahl/cerebroViz ). The tutorial is available at http://ethanbahl.github.io/cerebroViz/., Contacts: ethan-bahl@uiowa.edu or jacob-michaelson@uiowa.edu., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author 2016. Published by Oxford University Press.)

Published
2017
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