Mechanisms of robustness in gene regulatory networks involved in neural development

Abstract

The functions of living organisms are affected by different kinds of perturbation, both internal and external, which in many cases have functional effects and phenotypic impact. The effects of these perturbations become particularly relevant for multicellular organisms with complex body patterns and cell type heterogeneity, where transcriptional programs controlled by gene regulatory networks determine, for example, the cell fate during embryonic development. Therefore, an essential aspect of development in these organisms is the ability to maintain the functionality of their genetic developmental programs even in the presence of genetic variation, changing environmental conditions and biochemical noise, a property commonly termed robustness. We discuss the implication of different molecular mechanisms of robustness involved in neurodevelopment, which is characterized by the interplay of many developmental programs at a molecular, cellular and systemic level. We specifically focus on processes affecting the function of gene regulatory networks, encompassing transcriptional regulatory elements and post-transcriptional processes such as miRNA-based regulation, but also higher order regulatory organization, such as gene network topology. We also present cases where impairment of robustness mechanisms can be associated with neurodevelopmental disorders, as well as reasons why understanding these mechanisms should represent an important part of the study of gene regulatory networks driving neural development.

Genomic characteristics of adipose-derived stromal cells induced into neurons based on single-cell RNA sequencing

“…The basis of cell heterogeneity is the difference in cell transcription state, and the heterogeneity of transcription state is determined and maintained by GNRs dominated by transcription factors [ 56 ]. Therefore, the analysis of GRNs in single cells can help to explore the biological significance behind cellular heterogeneity and provide valuable clues for the study of disease diagnosis, treatment, and developmental differentiation.…”

Oscillatory network spontaneously recovers both activity and robustness after prolonged removal of neuromodulators

“…The factors that determine the robustness of complex biological systems (neuronal, ecological, molecular) have been the source of much research and debate in the past halfcentury, some of which being the size and heterogeneity of the networks, type and strength of connections, morphology of the components, and neuromodulation (May, 1972;Achard and De Schutter, 2006;Allesina and Tang, 2012;Chen et al, 2021;Arcuschin et al, 2023;Hutt et al, 2023;Zang and Marder, 2023). Small networks seem to defy some of the requirements for the expression of robust activity (Goldman et al, 2001;Tang et al, 2012;Doloc-Mihu and Calabrese, 2016;Haddad and Marder, 2018) due to their small size, and strong and homogeneous synaptic types, for example (Marder and Calabrese, 1996;Marder and Bucher, 2007).…”

“…Animals maintain robust behaviors such as heartbeat, respiration, locomotion, and digestion throughout their lives while facing ongoing challenges imposed by their external environment (Tang et al, 2010;Dethier et al, 2015;Doloc-Mihu and Calabrese, 2016;Ratliff et al, 2021;Arcuschin et al, 2023;Hutt et al, 2023;Zang and Marder, 2023). A global challenge all living beings currently face is changes in weather patterns and large fluctuations in local temperatures.…”

Neuronal network complexity strengthens activity robustness

“…What factors contribute to this apparent robustness and stability in such systems? There is a rich history of various approaches to address this question ( 2 – 8 ). The latest, by Hutt, Rich, Valiante, and Lefebvre in this issue of PNAS ( 9 ), uses analytical and computational tools partly borrowed from those heavily used in macroecology to elegantly address this question in semirealistic neuronal networks.…”

“…On the other hand, weak interactions can also be destabilizing in food webs, but stabilizing in mutualistic and competitive networks ( 2 , 4 ). In other cases, network structure, strength, and density of interactions can be strong determinants of the stability of network stability and robustness ( 5 – 8 , 10 ).…”