Histone acetyltransferase Sas3 contributes to fungal development, cell wall integrity, and virulence in Aspergillus fumigatus.

Histone acetyltransferase (HAT)-mediated epigenetic modification is essential for diverse cellular processes in eukaryotes. However, the functions of HATs in the human pathogen Aspergillus fumigatus remain poorly understood. In this study, we characterized the functions of MOZ, Ybf2/Sas3, Sas2, and Tip60 (MYST)-family histone acetyltransferase something about silencing (Sas3) in A. fumigatus. Phenotypic analysis revealed that loss of Sas3 results in significant impairments in colony growth, conidiation, and virulence in the Galleria mellonella model. Subcellular localization and Western blot analysis demonstrated that Sas3 localizes to nuclei and is capable of acetylating lysine 9 and 14 of histone H3 in vivo. Importantly, we found that Sas3 is critical for the cell wall integrity (CWI) pathway in A. fumigatus as evidenced by hypersensitivity to cell wall-perturbing agents, altered cell wall thickness, and abnormal phosphorylation levels of CWI protein kinase MpkA. Furthermore, site-directed mutagenesis studies revealed that the conserved glycine residues G641 and G643 and glutamate residue E664 are crucial for the acetylation activity of Sas3. Unexpectedly, only triple mutations of Sas3 (G641A/G643A/E664A) displayed defective phenotypes similar to the Δsas3 mutant, while double or single mutations did not. This result implies that the role of Sas3 may extend beyond histone acetylation. Collectively, our findings demonstrate that MYST-family HAT Sas3 plays an important role in the fungal development, virulence, and cell wall integrity in A. fumigatus. IMPORTANCE: Epigenetic modification governed by HATs is indispensable for various cellular processes in eukaryotes. Nonetheless, the precise functions of HATs in the human pathogen Aspergillus fumigatus remain elusive. In this study, we unveil the roles of MYST-family HAT Sas3 in colony growth, conidiation, virulence, and cell wall stress response in A. fumigatus. Particularly, our findings demonstrate that Sas3 can function through mechanisms unrelated to histone acetylation, as evidenced by site-directed mutagenesis experiments. Overall, this study broadens our understanding of the regulatory mechanism of HATs in fungal pathogens.

Inverse Reinforcement Learning for Trajectory Imitation Using Static Output Feedback Control.

This article studies the trajectory imitation control problem of linear systems suffering external disturbances and develops a data-driven static output feedback (OPFB) control-based inverse reinforcement learning (RL) approach. An Expert-Learner structure is considered where the learner aims to imitate expert's trajectory. Using only measured expert's and learner's own input and output data, the learner computes the policy of the expert by reconstructing its unknown value function weights and thus, imitates its optimally operating trajectory. Three static OPFB inverse RL algorithms are proposed. The first algorithm is a model-based scheme and serves as basis. The second algorithm is a data-driven method using input-state data. The third algorithm is a data-driven method using only input-output data. The stability, convergence, optimality, and robustness are well analyzed. Finally, simulation experiments are conducted to verify the proposed algorithms.

Data-Driven H∞ Optimal Output Feedback Control for Linear Discrete-Time Systems Based on Off-Policy Q-Learning.

This article develops two novel output feedback (OPFB) Q -learning algorithms, on-policy Q -learning and off-policy Q -learning, to solve H∞ static OPFB control problem of linear discrete-time (DT) systems. The primary contribution of the proposed algorithms lies in a newly developed OPFB control algorithm form for completely unknown systems. Under the premise of satisfying disturbance attenuation conditions, the conditions for the existence of the optimal OPFB solution are given. The convergence of the proposed Q -learning methods, and the difference and equivalence of two algorithms are rigorously proven. Moreover, considering the effects brought by probing noise for the persistence of excitation (PE), the proposed off-policy Q -learning method has the advantage of being immune to probing noise and avoiding biasedness of solution. Simulation results are presented to verify the effectiveness of the proposed approaches.

Inverse Reinforcement Q-Learning Through Expert Imitation for Discrete-Time Systems.

In inverse reinforcement learning (RL), there are two agents. An expert target agent has a performance cost function and exhibits control and state behaviors to a learner. The learner agent does not know the expert's performance cost function but seeks to reconstruct it by observing the expert's behaviors and tries to imitate these behaviors optimally by its own response. In this article, we formulate an imitation problem where the optimal performance intent of a discrete-time (DT) expert target agent is unknown to a DT Learner agent. Using only the observed expert's behavior trajectory, the learner seeks to determine a cost function that yields the same optimal feedback gain as the expert's, and thus, imitates the optimal response of the expert. We develop an inverse RL approach with a new scheme to solve the behavior imitation problem. The approach consists of a cost function update based on an extension of RL policy iteration and inverse optimal control, and a control policy update based on optimal control. Then, under this scheme, we develop an inverse reinforcement Q-learning algorithm, which is an extension of RL Q-learning. This algorithm does not require any knowledge of agent dynamics. Proofs of stability, convergence, and optimality are given. A key property about the nonunique solution is also shown. Finally, simulation experiments are presented to show the effectiveness of the new approach.

Aspergillus fumigatus Elongator complex subunit 3 affects hyphal growth, adhesion and virulence through wobble uridine tRNA modification.

The eukaryotic multisubunit Elongator complex has been shown to perform multiple functions in transcriptional elongation, histone acetylation and tRNA modification. However, the Elongator complex plays different roles in different organisms, and the underlying mechanisms remain unexplored. Moreover, the biological functions of the Elongator complex in human fungal pathogens remain unknown. In this study, we verified that the Elongator complex of the opportunistic fungal pathogen Aspergillus fumigatus consists of six subunits (Elp1-6), and the loss of any subunit results in similarly defective colony phenotypes with impaired hyphal growth and reduced conidiation. The catalytic subunit-Elp3 of the Elongator complex includes a S-adenosyl methionine binding (rSAM) domain and a lysine acetyltransferase (KAT) domain, and it plays key roles in the hyphal growth, biofilm-associated exopolysaccharide galactosaminogalactan (GAG) production, adhesion and virulence of A. fumigatus; however, Elp3 does not affect H3K14 acetylation levels in vivo. LC-MS/MS chromatograms revealed that loss of Elp3 abolished the 5-methoxycarbonylmethyl-2-thiouridine (mcm5s2U) modification of tRNA wobble uridine (U34), and the overexpression of tRNAGlnUUG and tRNAGluUUC, which normally harbor mcm5s2U modifications, mainly rescues the defects of the Δelp3 mutant, suggesting that tRNA modification rather than lysine acetyltransferase is responsible for the primary function of Elp3 in A. fumigatus. Strikingly, global proteomic comparison analyses showed significantly upregulated expression of genes related to amino acid metabolism in the Δelp3 mutant strain compared to the wild-type strain. Western blotting showed that deletion of elp3 resulted in overexpression of the amino acid starvation-responsive transcription factor CpcA, and deletion of CpcA markedly reversed the defective phenotypes of the Δelp3 mutant, including attenuated virulence. Therefore, the findings of this study demonstrate that A. fumigatus Elp3 functions as a tRNA-modifying enzyme in the regulation of growth, GAG production, adhesion and virulence by maintaining intracellular amino acid homeostasis. More broadly, our study highlights the importance of U34 tRNA modification in regulating cellular metabolic states and virulence traits of fungal pathogens.

Inverse Reinforcement Learning in Tracking Control Based on Inverse Optimal Control.

This article provides a novel inverse reinforcement learning (RL) algorithm that learns an unknown performance objective function for tracking control. The algorithm combines three steps: 1) an optimal control update; 2) a gradient descent correction step; and 3) an inverse optimal control (IOC) update. The new algorithm clarifies the relation between inverse RL and IOC. It is shown that the reward weight of an unknown performance objective that generates a target control policy may not be unique. We characterize the set of all weights that generate the same target control policy. We develop a model-based algorithm and, further, two model-free algorithms for systems with unknown model information. Finally, simulation experiments are presented to show the effectiveness of the proposed algorithms.

Two Lysine Sites That Can Be Malonylated Are Important for LuxS Regulatory Roles in Bacillus velezensis.

S-ribosylhomocysteine lyase (LuxS) has been shown to regulate bacterial multicellular behaviors, typically biofilm formation. However, the mechanisms for the regulation are still mysterious. We previously identified a malonylation modification on K124 and K130 of the LuxS in the plant growth-promoting rhizobacterium B. velezensis (FZB42). In this work, we investigated the effects of the two malonylation sites on biofilm formation and other biological characteristics of FZB42. The results showed that the K124R mutation could severely impair biofilm formation, swarming, and sporulation but promote AI-2 production, suggesting inhibitory effects of high-level AI-2 on the features. All mutations (K124R, K124E, K130R, and K130E) suppressed FZB42 sporulation but increased its antibiotic production. The double mutations generally had a synergistic effect or at least equal to the effects of the single mutations. The mutation of K130 but not of K124 decreased the in vitro enzymatic activity of LuxS, corresponding to the conservation of K130 among various Bacillus LuxS proteins. From the results, we deduce that an alternative regulatory circuit may exist to compensate for the roles of LuxS upon its disruption. This study broadens the understanding of the biological function of LuxS in bacilli and underlines the importance of the two post-translational modification sites.

The fungal-specific histone acetyltransferase Rtt109 regulates development, DNA damage response, and virulence in Aspergillus fumigatus.

In eukaryotes, histone acetylation catalyzed by histone acetyltransferase (HAT) has been demonstrated to be critical for various physiological processes. However, the biological functions of HAT and the underlying mechanism by which HAT-regulated processes are involved in fungal development and virulence in the human opportunistic pathogen Aspergillus fumigatus remain largely unexplored. Here, we functionally characterized the roles of Rtt109 in A. fumigatus, an ortholog of Saccharomyces cerevisiae histone acetyltransferase Rtt109. In vivo and in vitro HAT assays revealed that AfRtt109 functions as a canonical histone acetyltransferase, acetylating lysines 9 and 56 of histone H3. Deletion of Afrtt109 leads to severe defects in vegetative growth, conidiation, and causes reduced virulence in the Galleria mellonella model, as well as hypersensitivity to genotoxic agents. Moreover, site-directed mutagenesis revealed that the conserved arginine residues R265 and R306 of Rtt109 are required for the H3K9 and H3K56 acetylation and virulence of A. fumigatus. Unexpectedly, R265E and R306E mutants did not exhibit any detectable phenotypic defects, implying that A. fumigatus Rtt109 regulates fungal development via histone acetylation-independent mechanisms. Together, our results revealed the critical role of fungal-specific HAT Rtt109 in regulating fungal development and virulence, and suggested that it may serve as a unique target for antifungal therapies.

Off-Policy Reinforcement Learning for Tracking in Continuous-Time Systems on Two Time Scales.

This article applies a singular perturbation theory to solve an optimal linear quadratic tracker problem for a continuous-time two-time-scale process. Previously, singular perturbation was applied for system regulation. It is shown that the two-time-scale tracking problem can be separated into a linear-quadratic tracker (LQT) problem for the slow system and a linear-quadratic regulator (LQR) problem for the fast system. We prove that the solutions to these two reduced-order control problems can approximate the LQT solution of the original control problem. The reduced-order slow LQT and fast LQR control problems are solved by off-policy integral reinforcement learning (IRL) using only measured data from the system. To test the effectiveness of the proposed method, we use an industrial thickening process as a simulation example and compare our method to a method with the known system model and a method without time-scale separation.

Aspergillus fumigatus Mitochondrial Acetyl Coenzyme A Acetyltransferase as an Antifungal Target.

Ergosterol plays an important role in maintaining cell membrane sterol homeostasis in fungi, and as such, it is considered an effective target in antifungal chemotherapy. In yeast, the enzyme acetyl-coenzyme A (CoA) acetyltransferase (ERG10) catalyzes the Claisen condensation of two acetyl-CoA molecules to acetoacetyl-CoA in the ergosterol biosynthesis pathway and is reported as being critical for cell viability. Using yeast ERG10 for alignment, two orthologues, AfERG10A (AFUB_000550) and AfERG10B (AFUB_083570), were discovered in the opportunistic fungal pathogen Aspergillus fumigatus Despite the essentiality of AfERG10B having been previously validated, the biological function of AfERG10A remains unclear. In this study, we have characterized recombinant AfERG10A as a functional acetyl-CoA acetyltransferase catalyzing both synthetic and degradative reactions. Unexpectedly, AfERG10A localizes to the mitochondria in A. fumigatus, as shown by C-terminal green fluorescent protein (GFP) tag fusion. Both knockout and inducible promoter strategies demonstrate that Aferg10A is essential for the survival of A. fumigatus The reduced expression of Aferg10A leads to severe morphological defects and increased susceptibility to oxidative and cell wall stresses. Although the catalytic mechanism of acetyl-CoA acetyltransferase family is highly conserved, the crystal structure of AfERG10A and its complex with CoA are solved, revealing four substitutions within the CoA binding site that are different from human orthologues. Taken together, our combination of genetic and structural studies demonstrates that mitochondrial AfERG10A is essential for A. fumigatus cell viability and could be a potential drug target to feed the antifungal drug development pipeline.IMPORTANCE A growing number of people worldwide are suffering from invasive aspergillosis caused by the human opportunistic fungal pathogen A. fumigatus Current therapeutic options rely on a limited repertoire of antifungals. Ergosterol is an essential component of the fungal cell membrane as well as a target of current antifungals. Approximately 20 enzymes are involved in ergosterol biosynthesis, of which acetyl-CoA acetyltransferase (ACAT) is the first enzyme. Two ACATs in A. fumigatus are AfErg10A and AfErg10B. However, the biological function of AfErg10A is yet to be investigated. In this study, we showed that AfErg10A is localized in the mitochondria and is essential for A. fumigatus survival and morphological development. In combination with structural studies, we validated AfErg10A as a potential drug target that will facilitate the development of novel antifungals and improve the efficiency of existing drugs.

Optimal Output Regulation of Linear Discrete-Time Systems With Unknown Dynamics Using Reinforcement Learning.

This paper presents a model-free optimal approach based on reinforcement learning for solving the output regulation problem for discrete-time systems under disturbances. This problem is first broken down into two optimization problems: 1) a constrained static optimization problem is established to find the solution to the output regulator equations (i.e., the feedforward control input) and 2) a dynamic optimization problem is established to find the optimal feedback control input. Solving these optimization problems requires the knowledge of the system dynamics. To obviate this requirement, a model-free off-policy algorithm is presented to find the solution to the dynamic optimization problem using only measured data. Then, based on the solution to the dynamic optimization problem, a model-free approach is provided for the static optimization problem. It is shown that the proposed algorithm is insensitive to the probing noise added to the control input for satisfying the persistence of excitation condition. Simulation results are provided to verify the effectiveness of the proposed approach.

Off-Policy Reinforcement Learning: Optimal Operational Control for Two-Time-Scale Industrial Processes.

Industrial flow lines are composed of unit processes operating on a fast time scale and performance measurements known as operational indices measured at a slower time scale. This paper presents a model-free optimal solution to a class of two time-scale industrial processes using off-policy reinforcement learning (RL). First, the lower-layer unit process control loop with a fast sampling period and the upper-layer operational index dynamics at a slow time scale are modeled. Second, a general optimal operational control problem is formulated to optimally prescribe the set-points for the unit industrial process. Then, a zero-sum game off-policy RL algorithm is developed to find the optimal set-points by using data measured in real-time. Finally, a simulation experiment is employed for an industrial flotation process to show the effectiveness of the proposed method.

Performance-Based Adaptive Fuzzy Tracking Control for Networked Industrial Processes.

In this paper, the performance-based control design problem for double-layer networked industrial processes is investigated. At the device layer, the prescribed performance functions are first given to describe the output tracking performance, and then by using backstepping technique, new adaptive fuzzy controllers are designed to guarantee the tracking performance under the effects of input dead-zone and the constraint of prescribed tracking performance functions. At operation layer, by considering the stochastic disturbance, actual index value, target index value, and index prediction simultaneously, an adaptive inverse optimal controller in discrete-time form is designed to optimize the overall performance and stabilize the overall nonlinear system. Finally, a simulation example of continuous stirred tank reactor system is presented to show the effectiveness of the proposed control method.