ERRγ enhances cardiac maturation with T-tubule formation in human iPSC-derived cardiomyocytes

ヒトのiPS細胞から新生児レベルまで成熟した心筋細胞を作製する. 京都大学プレスリリース. 2021-06-21.Lowering the cost of heart cell therapies. 京都大学プレスリリース. 2021-06-21.One of the earliest maturation steps in cardiomyocytes (CMs) is the sarcomere protein isoform switch between TNNI1 and TNNI3 (fetal and neonatal/adult troponin I). Here, we generate human induced pluripotent stem cells (hiPSCs) carrying a TNNI1[EmGFP] and TNNI3[mCherry] double reporter to monitor and isolate mature sub-populations during cardiac differentiation. Extensive drug screening identifies two compounds, an estrogen-related receptor gamma (ERRγ) agonist and an S-phase kinase-associated protein 2 inhibitor, that enhances cardiac maturation and a significant change to TNNI3 expression. Expression, morphological, functional, and molecular analyses indicate that hiPSC-CMs treated with the ERRγ agonist show a larger cell size, longer sarcomere length, the presence of transverse tubules, and enhanced metabolic function and contractile and electrical properties. Here, we show that ERRγ-treated hiPSC-CMs have a mature cellular property consistent with neonatal CMs and are useful for disease modeling and regenerative medicine

Evolving Robust Gene Regulatory Networks

<div><p>Design and implementation of robust network modules is essential for construction of complex biological systems through hierarchical assembly of ‘parts’ and ‘devices’. The robustness of gene regulatory networks (GRNs) is ascribed chiefly to the underlying topology. The automatic designing capability of GRN topology that can exhibit robust behavior can dramatically change the current practice in synthetic biology. A recent study shows that Darwinian evolution can gradually develop higher topological robustness. Subsequently, this work presents an evolutionary algorithm that simulates natural evolution in silico, for identifying network topologies that are robust to perturbations. We present a Monte Carlo based method for quantifying topological robustness and designed a fitness approximation approach for efficient calculation of topological robustness which is computationally very intensive. The proposed framework was verified using two classic GRN behaviors: oscillation and bistability, although the framework is generalized for evolving other types of responses. The algorithm identified robust GRN architectures which were verified using different analysis and comparison. Analysis of the results also shed light on the relationship among robustness, cooperativity and complexity. This study also shows that nature has already evolved very robust architectures for its crucial systems; hence simulation of this natural process can be very valuable for designing robust biological systems.</p></div

Verification of robustness measure for the evolved network topologies.

<p>Verification of robustness measure for the evolved network topologies.</p

Level of robustness of different evolved network topologies.

<p>Level of robustness of different evolved network topologies.</p

Phase Response curves for different oscillating circuits. The X axis represents the phase (<i>θ</i>) and Y axis represents the phase shifts (Δ<i>θ</i>).

<p>(a) n = 2, N = 2 (b) n = 2, N = 3 (c) n = 3, N = 3 (d) n = 4, N = 3 (e) n = 3, N = 4 (f) n = 4, N = 4.</p

Evolved robust oscillatory network topologies with low (n = 2) cooperativity and N = 3 using the quantitative measure of robustness in (5).

<p>(a) Evolved in 40% run (b) Evolved in 35% run (c) Evolved in 25% run.</p

Robustness of bistable networks with different component numbers (N) and cooperativity levels (n).

<p>Robustness of bistable networks with different component numbers (N) and cooperativity levels (n).</p

Evolved robust oscillatory network topologies with low cooperativity (n = 2) and different number of genes (N).

<p>(a) n = 2, N = 2 (b) n = 2, N = 3, (c) n = 2, N = 4.</p

Algorithm for calculating the robustness using fitness approximation.

<p>Algorithm for calculating the robustness using fitness approximation.</p

Evolved bistable network topologies.

<p>(a) n = 2, 3, 4 and N = 2 (b) n = 2, N = 3 (Net01) (c) n = 3, N = 3 (Net02).</p