Divergent connectomic organization delineates genetic evolutionary traits in the human brain.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
04 10 2021
04 10 2021
Historique:
received:
18
04
2021
accepted:
07
09
2021
entrez:
5
10
2021
pubmed:
6
10
2021
medline:
28
12
2021
Statut:
epublish
Résumé
The relationship between human brain connectomics and genetic evolutionary traits remains elusive due to the inherent challenges in combining complex associations within cerebral tissue. In this study, insights are provided about the relationship between connectomics, gene expression and divergent evolutionary pathways from non-human primates to humans. Using in vivo human brain resting-state data, we detected two co-existing idiosyncratic functional systems: the segregation network, in charge of module specialization, and the integration network, responsible for information flow. Their topology was approximated to whole-brain genetic expression (Allen Human Brain Atlas) and the co-localization patterns yielded that neuron communication functionalities-linked to Neuron Projection-were overrepresented cell traits. Homologue-orthologue comparisons using dN/dS-ratios bridged the gap between neurogenetic outcomes and biological data, summarizing the known evolutionary divergent pathways within the Homo Sapiens lineage. Evidence suggests that a crosstalk between functional specialization and information flow reflects putative biological qualities of brain architecture, such as neurite cellular functions like axonal or dendrite processes, hypothesized to have been selectively conserved in the species through positive selection. These findings expand our understanding of human brain function and unveil aspects of our cognitive trajectory in relation to our simian ancestors previously left unexplored.
Identifiants
pubmed: 34608211
doi: 10.1038/s41598-021-99082-6
pii: 10.1038/s41598-021-99082-6
pmc: PMC8490416
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
19692Subventions
Organisme : NIA NIH HHS
ID : R01 AG061445
Pays : United States
Organisme : NIA NIH HHS
ID : R01 AG061811
Pays : United States
Informations de copyright
© 2021. The Author(s).
Références
Van Essen, D. C., Donahue, C. J. & Glasser, M. F. Development and evolution of cerebral and cerebellar cortex. Brain Behav. Evol. 91, 158–169 (2018).
pubmed: 30099464
doi: 10.1159/000489943
Franchini, L. F. & Pollard, K. S. Human evolution: The non-coding revolution. BMC Biol. 15, 1–12 (2017).
doi: 10.1186/s12915-017-0428-9
Sousa, A. M. M., Meyer, K. A., Santpere, G., Gulden, F. O. & Sestan, N. Evolution of the human nervous system function, structure, and development. Cell 170, 226–247 (2017).
pubmed: 28708995
pmcid: 5647789
doi: 10.1016/j.cell.2017.06.036
Bae, B. I., Jayaraman, D. & Walsh, C. A. Genetic changes shaping the human brain. Dev. Cell 32, 423–434 (2015).
pubmed: 25710529
pmcid: 4429600
doi: 10.1016/j.devcel.2015.01.035
Preuss, T. M. Human brain evolution: From gene discovery to phenotype discovery. Proc. Natl. Acad. Sci. U. S. A. 109, 10709–10716 (2012).
pubmed: 22723367
pmcid: 3386880
doi: 10.1073/pnas.1201894109
Relethford, J. H. Genetic evidence and the modern human origins debate. Heredity (Edinb.) 100, 555–563 (2008).
doi: 10.1038/hdy.2008.14
Robson, S. L. & Wood, B. Hominin life history: Reconstruction and evolution. J. Anat. 212, 394–425 (2008).
pubmed: 18380863
pmcid: 2409099
doi: 10.1111/j.1469-7580.2008.00867.x
Sherwood, C. C., Subiaul, F. & Zawidzki, T. W. A natural history of the human mind: Tracing evolutionary changes in brain and cognition. J. Anat. 212, 426–454 (2008).
pubmed: 18380864
pmcid: 2409100
doi: 10.1111/j.1469-7580.2008.00868.x
Sousa, A. M. M. et al. Molecular and cellular reorganization of neural circuits in the human lineage. Science 358, 1027–1032 (2017).
pubmed: 29170230
pmcid: 5776074
doi: 10.1126/science.aan3456
Lieberman, P. The evolution of language and thought. J. Anthropol. Sci. 94, 127–146 (2016).
pubmed: 26963222
Lord, L. D., Stevner, A. B., Deco, G. & Kringelbach, M. L. Understanding principles of integration and segregation using whole-brain computational connectomics: Implications for neuropsychiatric disorders. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 375, 20160283 (2017).
doi: 10.1098/rsta.2016.0283
Tognoli, E. & Kelso, J. A. S. The metastable brain. Neuron 81, 35–48 (2014).
pubmed: 24411730
pmcid: 3997258
doi: 10.1016/j.neuron.2013.12.022
Deco, G., Tononi, G., Boly, M. & Kringelbach, M. L. Rethinking segregation and integration: Contributions of whole-brain modelling. Nat. Rev. Neurosci. 16, 430–439 (2015).
pubmed: 26081790
doi: 10.1038/nrn3963
Benito-Aragón, C. et al. Neurofilament-lysosomal genetic intersections in the cortical network of stuttering. Prog. Neurobiol. 184, 101718 (2020).
pubmed: 31669185
doi: 10.1016/j.pneurobio.2019.101718
Xin, Q. et al. Sequence alterations of cortical genes linked to individual connectivity of the human brain. Cereb. Cortex 29, 3828–3835 (2019).
pubmed: 30307489
doi: 10.1093/cercor/bhy262
Ortiz-Terán, L. et al. Brain circuit-gene expression relationships and neuroplasticity of multisensory cortices in blind children. Proc. Natl. Acad. Sci. U. S. A. 114, 6830–6835 (2017).
pubmed: 28607055
pmcid: 5495230
doi: 10.1073/pnas.1619121114
Parkes, L., Fulcher, B. D., Yücel, M. & Fornito, A. Transcriptional signatures of connectomic subregions of the human striatum. Genes Brain Behav. 16, 647–663 (2017).
pubmed: 28421658
doi: 10.1111/gbb.12386
Romme, I. A. C., de Reus, M. A., Ophoff, R. A., Kahn, R. S. & van den Heuvel, M. P. Connectome disconnectivity and cortical gene expression in patients with schizophrenia. Biol. Psychiatry 81, 495–502 (2017).
pubmed: 27720199
doi: 10.1016/j.biopsych.2016.07.012
Rittman, T. et al. Regional expression of the MAPT gene is associated with loss of hubs in brain networks and cognitive impairment in Parkinson disease and progressive supranuclear palsy. Neurobiol. Aging 48, 153–160 (2016).
pubmed: 27697694
pmcid: 5096886
doi: 10.1016/j.neurobiolaging.2016.09.001
Richiardi, J. et al. Correlated gene expression supports synchronous activity in brain networks. Science 348, 1241–1244 (2015).
pubmed: 26068849
pmcid: 4829082
doi: 10.1126/science.1255905
Wiesner, C. et al. Lasp-1 regulates podosome function. PLoS ONE 7, 1–10 (2012).
Patania, A. et al. Topological gene expression networks recapitulate brain anatomy and function. Netw. Neurosci. 3, 744–762 (2019).
pubmed: 31410377
pmcid: 6663211
doi: 10.1162/netn_a_00094
Cioli, C., Abdi, H., Beaton, D., Burnod, Y. & Mesmoudi, S. Differences in human cortical gene expression match the temporal properties of large-scale functional networks. PLoS ONE 9, e115913 (2014).
pubmed: 25546015
pmcid: 4278769
doi: 10.1371/journal.pone.0115913
Anderson, K. M. et al. Gene expression links functional networks across cortex and striatum. Nat. Commun. 9, 1–14 (2018).
doi: 10.1038/s41467-018-03811-x
Diez, I. & Sepulcre, J. Neurogenetic profiles delineate large-scale connectivity dynamics of the human brain. Nat. Commun. 9, 1–10 (2018).
doi: 10.1038/s41467-018-06346-3
McColgan, P. et al. Brain regions showing white matter loss in Huntington’s disease are enriched for synaptic and metabolic genes. Biol. Psychiatry 83, 456–465 (2018).
pubmed: 29174593
pmcid: 5803509
doi: 10.1016/j.biopsych.2017.10.019
Hawrylycz, M. J. et al. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature 489, 391–399 (2012).
pubmed: 22996553
pmcid: 4243026
doi: 10.1038/nature11405
Sepulcre, J., Sabuncu, M. R., Yeo, T. B., Liu, H. & Johnson, K. A. Stepwise connectivity of the modal cortex reveals the multimodal organization of the human brain. J. Neurosci. 32, 10649–10661 (2012).
pubmed: 22855814
pmcid: 3483645
doi: 10.1523/JNEUROSCI.0759-12.2012
Sepulcre, J. et al. Neurogenetic contributions to amyloid beta and tau spreading in the human cortex. Nat. Med. 24, 1910–1918 (2018).
pubmed: 30374196
pmcid: 6518398
doi: 10.1038/s41591-018-0206-4
Romero-Garcia, R. et al. Structural covariance networks are coupled to expression of genes enriched in supragranular layers of the human cortex. Neuroimage 171, 256–267 (2018).
pubmed: 29274746
doi: 10.1016/j.neuroimage.2017.12.060
Forest, M. et al. Gene networks show associations with seed region connectivity. Hum. Brain Mapp. 38, 3126–3140 (2017).
pubmed: 28321948
pmcid: 6866840
doi: 10.1002/hbm.23579
Bassett, D. S. & Bullmore, E. Small-world brain networks. Neuroscientist 12, 512–523 (2006).
pubmed: 17079517
doi: 10.1177/1073858406293182
Watts, D. J. & Strogatz, S. H. Collective dynamics of "small-world" networks. Nature 393, 440–442 (1998).
doi: 10.1038/30918
pubmed: 9623998
Sporns, O., Tononi, G. & Edelman, G. M. Theoretical neuroanatomy and the connectivity of the cerebral cortex. Behav. Brain Res. 135 69–74 (2002).
pubmed: 12356436
doi: 10.1016/S0166-4328(02)00157-2
Bassett, D. S. et al. Hierarchical organization of human cortical networks in health and Schizophrenia. J. Neurosci. 28, 9239–9248 (2008).
pubmed: 18784304
pmcid: 2878961
doi: 10.1523/JNEUROSCI.1929-08.2008
Bullmore, E. T. & Bassett, D. S. Brain graphs: Graphical models of the human brain connectome. Annu. Rev. Clin. Psychol. 7, 113–140 (2011).
pubmed: 21128784
doi: 10.1146/annurev-clinpsy-040510-143934
Sepulcre, J. et al. The organization of local and distant functional connectivity in the human brain. PLoS Comput. Biol. 6, e1000808 (2010).
pubmed: 20548945
pmcid: 2883589
doi: 10.1371/journal.pcbi.1000808
Sporns, O., Chialvo, D. R., Kaiser, M. & Hilgetag, C. C. Organization, development and function of complex brain networks. Trends Cogn. Sci. 8, 418–425 (2004).
pubmed: 15350243
doi: 10.1016/j.tics.2004.07.008
Bassett, D. S. & Bullmore, E. T. Human brain networks in health. Curr Opin Neurol. 10, 324–336 (2009).
Bassett, D. S. & Bullmore, E. T. Small-world brain networks revisited. Neuroscientist 23, 499–516 (2017).
doi: 10.1177/1073858416667720
pubmed: 27655008
Sporns, O. Graph theory methods: Applications in brain networks. Dialogues Clin. Neurosci. 20, 111–120 (2018).
pubmed: 30250388
pmcid: 6136126
doi: 10.31887/DCNS.2018.20.2/osporns
Shen, E. H., Overly, C. C. & Jones, A. R. The Allen human brain atlas. Trends Neurosci. 35, 711–714 (2012).
pubmed: 23041053
doi: 10.1016/j.tins.2012.09.005
Bueichekú, E. et al. Central neurogenetic signatures of the visuomotor integration system. Proc. Natl. Acad. Sci. U. S. A. 117, 6836–6843 (2020).
pubmed: 32144139
pmcid: 7104395
doi: 10.1073/pnas.1912429117
Kryazhimskiy, S. & Plotkin, J. B. The population genetics of dN/dS. PLoS Genet. 4, e1000304 (2008).
pubmed: 19081788
pmcid: 2596312
doi: 10.1371/journal.pgen.1000304
Benjamini, Y., Drai, D., Elmer, G., Kafkafi, N. & Golani, I. Controlling the false discovery rate in behavior genetics research. Behav. Brain Res. 125, 279–284 (2001).
pubmed: 11682119
doi: 10.1016/S0166-4328(01)00297-2
Buckner, R. L., Krienen, F. M. & Yeo, B. T. T. Opportunities and limitations of intrinsic functional connectivity MRI. Nat. Neurosci. 16, 832–837 (2013).
pubmed: 23799476
doi: 10.1038/nn.3423
Preuss, T. M. The human brain: Rewired and running hot. Ann. N. Y. Acad. Sci. 1225, 182–191 (2011).
doi: 10.1111/j.1749-6632.2011.06001.x
Murphy, K., Birn, R. M. & Bandettini, P. A. Resting-state fMRI confounds and cleanup. Neuroimage 80, 349–359 (2013).
pubmed: 23571418
doi: 10.1016/j.neuroimage.2013.04.001
Craddock, R. C., Milham, M. P. & LaConte, S. M. Predicting intrinsic brain activity. Neuroimage 82, 127–136 (2013).
pubmed: 23707580
doi: 10.1016/j.neuroimage.2013.05.072
Jones, A. R., Overly, C. C. & Sunkin, S. M. The allen brain atlas: 5 years and beyond. Nat. Rev. Neurosci. 10, 821–828 (2009).
pubmed: 19826436
doi: 10.1038/nrn2722
Goel, P., Kuceyeski, A., Locastro, E. & Raj, A. Spatial patterns of genome-wide expression profiles reflect anatomic and fiber connectivity architecture of healthy human brain. Hum. Brain Mapp. 35, 4204–4218 (2014).
pubmed: 24677576
pmcid: 4283562
doi: 10.1002/hbm.22471
Vértes, P. E. et al. Gene transcription profiles associated with inter-modular hubs and connection distance in human functional magnetic resonance imaging networks. Philos. Trans. R. Soc. B Biol. Sci. 371, 20150362 (2016).
doi: 10.1098/rstb.2015.0362
Yates, A. D. et al. Ensembl 2020. Nucleic Acids Res. 48, D682–D688 (2020).
pubmed: 31691826
Buckner, R. L. & Krienen, F. M. The evolution of distributed association networks in the human brain. Trends Cogn. Sci. 17, 648–665 (2013).
pubmed: 24210963
doi: 10.1016/j.tics.2013.09.017
Mendoza, G. & Merchant, H. Motor system evolution and the emergence of high cognitive functions. Prog. Neurobiol. 122, 73–93 (2014).
pubmed: 25224031
doi: 10.1016/j.pneurobio.2014.09.001
Enard, W. The molecular basis of human brain evolution. Curr. Biol. 26, R1109–R1117 (2016).
pubmed: 27780052
doi: 10.1016/j.cub.2016.09.030
Holloway, R. L., Broadfield, D. C. & Yuan, M. S. The Human Fossil Record. The Human Fossil Record Vol. 3 (Wiley, 2004).
doi: 10.1002/0471663573
Holloway, R. Brain size, allometry, and reorganization: Toward a synthesis. In Development and evolution of brain size: Behavioral implications (eds Hahn, M. E. et al.) 59–88 (Academic Press, 1979).
doi: 10.1016/B978-0-12-314650-2.50010-0
Sherwood, C. C., Bauernfeind, A. L., Bianchi, S., Raghanti, M. A. & Hof, P. R. Human Brain Evolution Writ Large and Small. Progress in Brain Research Vol. 195 (Elsevier B.V., Berlin, 2012).
Changizi, M. A. Principles underlying mammalian neocortical scaling. Biol. Cybern. 84, 207–215 (2001).
pubmed: 11252638
doi: 10.1007/s004220000205
Somel, M., Liu, X. & Khaitovich, P. Human brain evolution: Transcripts, metabolites and their regulators. Nat. Rev. Neurosci. 14, 112–127 (2013).
pubmed: 23324662
doi: 10.1038/nrn3372
Sholtis, S. J. & Noonan, J. P. Gene regulation and the origins of human biological uniqueness. Trends Genet. 26, 110–118 (2010).
pubmed: 20106546
doi: 10.1016/j.tig.2009.12.009
Laland, K. N., Odling-Smee, J. & Myles, S. How culture shaped the human genome: Bringing genetics and the human sciences together. Nat. Rev. Genet. 11, 137–148 (2010).
pubmed: 20084086
doi: 10.1038/nrg2734
Vallender, E. J., Mekel-Bobrov, N. & Lahn, B. T. Genetic basis of human brain evolution. Trends Neurosci. 31, 637–644 (2008).
pubmed: 18848363
pmcid: 2715140
doi: 10.1016/j.tins.2008.08.010
Sabeti, P. C. et al. Positive natural selection in the human lineage. Science 312, 1614–1620 (2006).
pubmed: 16778047
doi: 10.1126/science.1124309
Holmes, A. J. et al. Brain Genomics Superstruct Project initial data release with structural, functional, and behavioral measures. Sci. Data 2, 150031 (2015).
pubmed: 26175908
pmcid: 4493828
doi: 10.1038/sdata.2015.31
Smith, S. M. et al. Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 23 Suppl 1, S208–S219 (2004).
pubmed: 15501092
doi: 10.1016/j.neuroimage.2004.07.051
Jenkinson, M., Bannister, P., Brady, M. & Smith, S. Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 17, 825–841 (2002).
pubmed: 12377157
doi: 10.1006/nimg.2002.1132
Van Dijk, K. R. A., Sabuncu, M. R. & Buckner, R. L. The influence of head motion on intrinsic functional connectivity MRI. Neuroimage 59, 431–438 (2012).
pubmed: 21810475
doi: 10.1016/j.neuroimage.2011.07.044
Fulcher, B. D., Arnatkeviciute, A. & Fornito, A. Overcoming false-positive gene-category enrichment in the analysis of spatially resolved transcriptomic brain atlas data. Nat. Commun. 12, 1–13 (2021).
doi: 10.1038/s41467-021-22862-1
French, L. & Paus, T. A FreeSurfer view of the cortical transcriptome generated from the Allen Human Brain Atlas. Front. Neurosci. 9, 323 (2015).
pubmed: 26441498
pmcid: 4584957
doi: 10.3389/fnins.2015.00323
Desikan, R. S. et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31, 968–980 (2006).
pubmed: 16530430
doi: 10.1016/j.neuroimage.2006.01.021
Sherman, B. T. et al. The DAVID gene functional classification tool: a novel biological module-centric algorithm to functionally analyze large gene lists. Genome Biol. 8, 1–16 (2007).
doi: 10.1186/gb-2007-8-1-r1
Burt, J. B., Helmer, M., Shinn, M., Anticevic, A. & Murray, J. D. Generative modeling of brain maps with spatial autocorrelation. Neuroimage 220, 117038 (2020).
pubmed: 32585343
doi: 10.1016/j.neuroimage.2020.117038
Jeffares, D. C., Tomiczek, B., Sojo, V. & dos Reis, M. A beginners guide to estimating the non-synonymous to synonymous rate ratio of all protein-coding genes in a genome. Methods Mol. Biol. 1201, 65–90 (2015).
pubmed: 25388108
doi: 10.1007/978-1-4939-1438-8_4
Wilson, D. J. et al. GenomegaMap: Within-Species Genome-Wide dN/dS Estimation from over 10,000 Genomes. Mol. Biol. Evol. 37, 2450–2460 (2020).
pubmed: 32167543
pmcid: 7403622
doi: 10.1093/molbev/msaa069
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing (2021).