A fluorophore-conjugated reagent enabling rapid detection, isolation and live tracking of senescent cells.

Cellular senescence is a stress-response mechanism implicated in various physiological processes, diseases, and aging. Current detection approaches have partially addressed the issue of senescent cell identification in clinical specimens. Effective methodologies enabling precise isolation or live tracking of senescent cells are still lacking. In-depth analysis of truly senescent cells is, therefore, an extremely challenging task. We report (1) the synthesis and validation of a fluorophore-conjugated, Sudan Black-B analog (GLF16), suitable for in vivo and in vitro analysis of senescence by fluorescence microscopy and flow cytometry and (2) the development and application of a GLF16-carrying micelle vector facilitating GLF16 uptake by living senescent cells in vivo and in vitro. The compound and the applied methodology render isolation of senescent cells an easy, rapid, and precise process. Straightforward nanocarrier-mediated GLF16 delivery in live senescent cells comprises a unique tool for characterization of senescence at an unprecedented depth.

Optimal information loading into working memory explains dynamic coding in the prefrontal cortex.

Working memory involves the short-term maintenance of information and is critical in many tasks. The neural circuit dynamics underlying working memory remain poorly understood, with different aspects of prefrontal cortical (PFC) responses explained by different putative mechanisms. By mathematical analysis, numerical simulations, and using recordings from monkey PFC, we investigate a critical but hitherto ignored aspect of working memory dynamics: information loading. We find that, contrary to common assumptions, optimal loading of information into working memory involves inputs that are largely orthogonal, rather than similar, to the late delay activities observed during memory maintenance, naturally leading to the widely observed phenomenon of dynamic coding in PFC. Using a theoretically principled metric, we show that PFC exhibits the hallmarks of optimal information loading. We also find that optimal information loading emerges as a general dynamical strategy in task-optimized recurrent neural networks. Our theory unifies previous, seemingly conflicting theories of memory maintenance based on attractor or purely sequential dynamics and reveals a normative principle underlying dynamic coding.

Structural basis of protein substrate processing by human mitochondrial high-temperature requirement A2 protease.

The human high-temperature requirement A2 (HtrA2) protein is a trimeric protease that cleaves misfolded proteins to protect cells from stresses caused by toxic, proteinaceous aggregates, and the aberrant function of HtrA2 is closely related to the onset of neurodegenerative disorders. Our methyl-transverse relaxation optimized spectroscopy (TROSY)­based NMR studies using small-peptide ligands have previously revealed a stepwise activation mechanism involving multiple distinct conformational states. However, very little is known about how HtrA2 binds to protein substrates and if the distinct conformational states observed in previous peptide studies might be involved in the processing of protein clients. Herein, we use solution-based NMR spectroscopy to investigate the interaction between the N-terminal Src homology 3 domain from downstream of receptor kinase (drk) with an added C-terminal HtrA2-binding motif (drkN SH3-PDZbm) that exhibits marginal folding stability and serves as a mimic of a physiological protein substrate. We show that drkN SH3-PDZbm binds to HtrA2 via a two-pronged interaction, involving both its C-terminal PDZ-domain binding motif and a central hydrophobic region, with binding occurring preferentially via an unfolded ensemble of substrate molecules. Multivalent interactions between several clients and a single HtrA2 trimer significantly stimulate the catalytic activity of HtrA2, suggesting that binding avidity plays an important role in regulating substrate processing. Our results provide a thermodynamic, kinetic, and structural description of the interaction of HtrA2 with protein substrates and highlight the importance of a trimeric architecture for function as a stress-protective protease that mitigates aggregation.

Exceptional Rate Performances of Li-Rich Mn-Based Cathodes Enabled by Boron-Based Additives-Driven Self-Optimized Interface.

Li-rich Mn-based cathode material (LRM), as a promising cathode for high energy density lithium batteries, suffers from severe side reactions in conventional lithium hexafluorophosphate (LiPF6)-based carbonate electrolytes, leading to unstable interfaces and poor rate performances. Herein, a boron-based additives-driven self-optimized interface strategy is presented to dissolve low ionic conductivity LiF nanoparticles at the outer cathode electrolyte interface, leading to the optimized interfacial components, as well as the enhanced Li ion migration rate in electrolytes. Being attributed to these superiorities, the LRM||Li battery delivers a high-capacity retention of 92.19% at 1C after 200 cycles and a low voltage decay of 1.08 mV/cycle. This work provides a new perspective on the rational selection of functional additives with an interfacial self-optimized characteristic to achieve a long lifespan LRM with exceptional rate performances.

A comparison of wavelet-based action potential detection from the NeuroAmp and the Iowa Bioengineering Nerve Traffic Analysis system.

Microneurographic recordings of muscle sympathetic nerve activity (MSNA) reflect postganglionic sympathetic axonal activity directed toward the skeletal muscle vasculature. Recordings are typically evaluated for spontaneous bursts of MSNA; however, the filtering and integration of raw neurograms to obtain multiunit bursts conceals the underlying c-fiber discharge behavior. The continuous wavelet transform with matched mother wavelet has permitted the assessment of action potential discharge patterns, but this approach uses a mother wavelet optimized for an amplifier that is no longer commercially available (University of Iowa Bioengineering Nerve Traffic Analysis System; Iowa NTA). The aim of this project was to determine the morphology and action potential detection performance of mother wavelets created from the commercially available NeuroAmp (ADinstruments), from distinct laboratories, compared with a mother wavelet generated from the Iowa NTA. Four optimized mother wavelets were generated in a two-phase iterative process from independent datasets, collected by separate laboratories (one Iowa NTA, three NeuroAmp). Action potential extraction performance of each mother wavelet was compared for each of the NeuroAmp-based datasets. The total number of detected action potentials was not significantly different across wavelets. However, the predictive value of action potential detection was reduced when the Iowa NTA wavelet was used to detect action potentials in NeuroAmp data, but not different across NeuroAmp wavelets. To standardize approaches, we recommend a NeuroAmp-optimized mother wavelet be used for the evaluation of sympathetic action potential discharge behavior when microneurographic data are collected with this system.NEW & NOTEWORTHY The morphology of custom mother wavelets produced across laboratories using the NeuroAmp was highly similar, but distinct from the University of Iowa Bioengineering Nerve Traffic Analysis System. Although the number of action potentials detected was similar between collection systems and mother wavelets, the predictive value differed. Our data suggest action potential analysis using the continuous wavelet transform requires a mother wavelet optimized for the collection system.

Transverse relaxation optimized spectroscopy of NH2 groups in glutamine and asparagine side chains of proteins.

A transverse relaxation optimized spectroscopy (TROSY) approach is described for the optimal detection of NH2 groups in asparagine and glutamine side chains of proteins. Specifically, we have developed NMR experiments for isolating the slow-relaxing 15N and 1H components of NH2 multiplets. Although even modest sensitivity gains in 2D NH2-TROSY correlation maps compared to their decoupled NH2-HSQC counterparts can be achieved only occasionally, substantial improvements in resolution of the NMR spectra are demonstrated for asparagine and glutamine NH2 sites of a buried cavity mutant, L99A, of T4 lysozyme at 5 ºC. The NH2-TROSY approach is applied to CPMG relaxation dispersion measurements at the side chain NH2 positions of the L99A T4 lysozyme mutant - a model system for studies of the role of protein dynamics in ligand binding.

Equation-of-motion orbital-optimized coupled-cluster doubles method with the density-fitting approximation: An efficient implementation.

Orbital-optimized coupled-cluster methods are very helpful for theoretical predictions of the molecular properties of challenging chemical systems, such as excited states. In this research, an efficient implementation of the equation-of-motion orbital-optimized coupled-cluster doubles method with the density-fitting (DF) approach, denoted by DF-EOM-OCCD, is presented. The computational cost of the DF-EOM-OCCD method for excitation energies is compared with that of the conventional EOM-OCCD method. Our results demonstrate that DF-EOM-OCCD excitation energies are dramatically accelerated compared to EOM-OCCD. There are almost 17-fold reductions for the C 5 H 12 $$ {\mathrm{C}}_5{\mathrm{H}}_{12} $$ molecule in an aug-cc-pVTZ basis set with the RHF reference. This dramatic performance improvement comes from the reduced cost of integral transformation with the DF approach and the efficient evaluation of the particle-particle ladder (PPL) term, which is the most expensive term to evaluate. Further, our results show that the DF-EOM-OCCD approach is very helpful for the computation of excitation energies in open-shell molecular systems. Overall, we conclude that our new DF-EOM-OCCD implementation is very promising for the study of excited states in large-sized challenging chemical systems.

Escherichia coli as a versatile cell factory: Advances and challenges in recombinant protein production.

E. coli plays a substantial role in recombinant protein production. Its importance increased with the discovery of recombinant DNA technology and the subsequent production of the first recombinant insulin in E. coli. E. coli is a widely used and cost-effective host to produce recombinant proteins. It is also noteworthy that a significant portion of the approved therapeutic proteins have been produced in this organism. Despite these advantages, it has some disadvantages, such as toxicity and lack of eukaryotic post-translational modifications that can lead to the production of misfolded, insoluble, or dysfunctional proteins. This study focused on the challenges and engineering approaches for improved expression and solubility in recombinant protein production in E. coli. In this context, solution strategies such as strain and vector selection, codon usage, mRNA stability, expression conditions, translocation to the periplasmic region and addition of fusion tags in E. coli were discussed.

Prediction of C-reactive protein dynamics during meropenem treatment in neonates and infants.

AIMS: C-reactive protein (CRP) is used to determine the effect of antibiotic treatment on sepsis in neonates/infants. We aimed to develop pharmacokinetic-pharmacodynamic (PKPD) model of meropenem and CRP in neonates/infants and evaluate its predictive performance of CRP dynamics. METHODS: Data from neonates/infants treated with meropenem in 3 previous studies were analysed. To the previously developed meropenem PK models, the addition of turnover, transit or effect compartment, delay differential equation PD models of CRP as a function of meropenem concentration or its cumulative area under the curve (AUC) were evaluated. The percentage of neonates/infants (P0.1 , P0.2 ) in whom the ratio of the fifth day CRP to its peak value was predicted with an error of <0.1 (<0.2) was calculated. RESULTS: A total of 60 meropenem treatment episodes (median [range] gestational age 27.6 [22.6-40.9] weeks, postnatal age 13 [2-89] days) with a total of 351 CRP concentrations (maximum value 65.5 [13-358.4] mg/L) were included. Turnover model of CRP as a function of meropenem cumulative AUC provided the best fit and included CRP at the start of treatment, use of prior antibiotics, study and causative agent Staphylococcus aureus or enterococci as covariates. Using meropenem population predictions and data available at 0, 24, 48, 72 h after the start of treatment, P0.1 (P0.2 ) was 36.4, 36.4, 60.6 and 66.7% (70.0, 66.7, 72.7 and 78.7%), respectively. CONCLUSION: The developed PKPD model of meropenem and CRP as a function of meropenem cumulative AUC incorporating several patient characteristics predicts CRP dynamics with an error of <0.2 in most neonates/infants.

Evoked EEG Responses to TMS Targeting Regions Outside the Primary Motor Cortex and Their Test-Retest Reliability.

Transcranial magnetic stimulation (TMS)-evoked electroencephalography (EEG) potentials (TEPs) provide unique insights into cortical excitability and connectivity. However, confounding EEG signals from auditory and somatosensory co-stimulation complicate TEP interpretation. Our optimized sham procedure established with TMS of primary motor cortex (Gordon in JAMA 245:118708, 2021) differentiates direct cortical EEG responses to TMS from those caused by peripheral sensory inputs. Using this approach, this study aimed to investigate TEPs and their test-retest reliability when targeting regions outside the primary motor cortex, specifically the left angular gyrus, supplementary motor area, and medial prefrontal cortex. We conducted three identical TMS-EEG sessions one week apart involving 24 healthy participants. In each session, we targeted the three areas separately using a figure-of-eight TMS coil for active TMS, while a second coil away from the head produced auditory input for sham TMS. Masking noise and electric scalp stimulation were applied in both conditions to achieve matched EEG responses to peripheral sensory inputs. High test-retest reliability was observed in both conditions. However, reliability declined for the 'cleaned' TEPs, resulting from the subtraction of evoked EEG response to the sham TMS from those to the active, particularly for latencies > 100 ms following the TMS pulse. Significant EEG differences were found between active and sham TMS at latencies < 90 ms for all targeted areas, exhibiting distinct spatiotemporal characteristics specific to each target. In conclusion, our optimized sham procedure effectively reveals EEG responses to direct cortical activation by TMS in brain areas outside primary motor cortex. Moreover, we demonstrate the impact of peripheral sensory inputs on test-retest reliability of TMS-EEG responses.

Successful direct pacing of the left bundle branch area with ICD lead using steerable delivery sheath.

Recently, conduction system pacing has been performed in patients with impaired cardiac function. We report a case in which a DF4 implantable cardioverter defibrillator lead was screwed directly into the left bundle branch area with the support of a steerable delivery sheath.

Enhancing groundwater vulnerability assessment: Comparative study of three machine learning models and five classification schemes for Cuddalore district.

Most of the groundwater vulnerability assessment methods using machine learning are binary classification. This study attempts multi-class classification models to map the groundwater vulnerability against Nitrate contamination. Further, the significance of the number of classes used in the multi-class classification is studied by considering three and five classes. Three machine learning models, namely Random Forest, Extreme Gradient Boosting and CART, with two classification schemes, were developed for the present study. The parameters used in the conventional DRASTIC method and with an additional parameter, Landuse, have been employed for the study. Evaluation metrics such as Accuracy, Kappa, Positive Predictive Value, Negative Predictive Value, and Area Under the Curve of the Receiver Operating Characteristic (AUC-ROC) were compared among all six models to select the optimal one. Based on the model evaluation metrics and consistent distribution of area among the classes Random Forest model with a three-class classification with an AUC of 0.95 is considered optimum for the selected objective. This study highlights the importance of the data classification process and the selection of the number of classes for ML model prediction in assessing groundwater vulnerability. Leveraging the effectiveness of the Geographic Information system and advanced machine learning techniques, the proposed approach offers valuable insights for enhanced groundwater management and contamination mitigation strategies.

A fourfold-objective-based cloud privacy preservation model with proposed association rule hiding and deep learning assisted optimal key generation.

Numerous studies have been conducted in an attempt to preserve cloud privacy, yet the majority of cutting-edge solutions fall short when it comes to handling sensitive data. This research proposes a "privacy preservation model in the cloud environment". The four stages of recommended security preservation methodology are "identification of sensitive data, generation of an optimal tuned key, suggested data sanitization, and data restoration". Initially, owner's data enters the Sensitive data identification process. The sensitive information in the input (owner's data) is identified via Augmented Dynamic Itemset Counting (ADIC) based Associative Rule Mining Model. Subsequently, the identified sensitive data are sanitized via the newly created tuned key. The generated tuned key is formulated with new fourfold objective-hybrid optimization approach-based deep learning approach. The optimally tuned key is generated with LSTM on the basis of fourfold objectives and the new hybrid MUAOA. The created keys, as well as generated sensitive rules, are fed into the deep learning model. The MUAOA technique is a conceptual blend of standard AOA and CMBO, respectively. As a result, unauthorized people will be unable to access information. Finally, comparative evaluation is undergone and proposed LSTM+MUAOA has achieved higher values on privacy about 5.21 compared to other existing models.

Enhancement of cyber security in IoT based on ant colony optimized artificial neural adaptive Tensor flow.

The Internet of Things (IoT) is a network that connects various hardware, software, data storage, and applications. These interconnected devices provide services to businesses and can potentially serve as entry points for cyber-attacks. The privacy of IoT devices is increasingly vulnerable, particularly to threats like viruses and illegal software distribution lead to the theft of critical information. Ant Colony-Optimized Artificial Neural-Adaptive Tensorflow (ACO-ANT) technique is proposed to detect malicious software illicitly disseminated through the IoT. To emphasize the significance of each token in source duplicate data, the noise data undergoes processing using tokenization and weighted attribute techniques. Deep learning (DL) methods are then employed to identify source code duplication. Also the Multi-Objective Recurrent Neural Network (M-RNN) is used to identify suspicious activities within an IoT environment. The performance of proposed technique is examined using Loss, accuracy, F measure, precision to identify its efficiency. The experimental outcomes demonstrate that the proposed method ACO-ANT on Malimg dataset provides 12.35%, 14.75%, 11.84% higher precision and 10.95%, 15.78%, 13.89% higher f-measure compared to the existing methods. Further, leveraging block chain for malware detection is a promising direction for future research the fact that could enhance the security of IoT and identify malware threats.

Optimized expression of Peptidyl-prolyl cis/transisomerase cyclophilinB with prokaryotic toxicity from Sporothrix globosa.

Cyclophilin B (CypB), a significant member of immunophilins family with peptidyl-prolyl cis-trans isomerase (PPIase) activity, is crucial for the growth and metabolism of prokaryotes and eukaryotes. Sporothrix globosa (S. globosa), a principal pathogen in the Sporothrix complex, causes sporotrichosis. Transcriptomic analysis identified the cypB gene as highly expressed in S. globosa. Our previous study demonstrated that the recombinant Escherichia coli strain containing SgcypB gene failed to produce sufficient product when it was induced to express the protein, implying the potential toxicity of recombinant protein to the bacterial host. Bioinformatics analysis revealed that SgCypB contains transmembrane peptides within the 52 amino acid residues at the N-terminus and 21 amino acids near the C-terminus, and 18 amino acid residues within the cytoplasm. AlphaFold2 predicted a SgCypB 3D structure in which there is an independent PPIase domain consisting of a spherical extracellular part. Hence, we chose to express the extracellular domain to yield high-level recombinant protein with PPIase activity. Finally, we successfully produced high-yield, truncated recombinant CypB protein from S. globosa (SgtrCypB) that retained characteristic PPIase activity without host bacterium toxicity. This study presents an alternative expression strategy for proteins toxic to prokaryotes, such as SgCypB. ONE-SENTENCE SUMMARY: The recombinant cyclophilin B protein of Sporothrix globosa was expressed successfully by retaining extracellular domain with peptidyl-prolyl cis-trans isomerase activity to avoid toxicity to the host bacterium.

Clinical feasibility of post-contrast accelerated 3D T1-Sampling Perfection with Application-optimized Contrasts using different flip angle Evolutions (SPACE) with iterative denoising for intracranial enhancing lesions: a retrospective study.

BACKGROUND: Post-contrast T1-Sampling Perfection with Application-optimized Contrasts using different flip angle Evolutions (SPACE) is the preferred 3D T1 spin-echo sequence for evaluating brain metastases, regardless of the prolonged scan time. PURPOSE: To evaluate the application of accelerated post-contrast T1-SPACE with iterative denoising (ID) for intracranial enhancing lesions in oncologic patients. MATERIAL AND METHODS: For evaluation of intracranial lesions, 108 patients underwent standard and accelerated T1-SPACE during the same imaging session. Two neuroradiologists evaluated the overall image quality, artifacts, degree of enhancement, mean contrast-to-noise ratiolesion/parenchyma, and number of enhancing lesions for standard and accelerated T1-SPACE without ID. RESULTS: Although there was a significant difference in the overall image quality and mean contrast-to-noise ratiolesion/parenchyma between standard and accelerated T1-SPACE without ID and accelerated SPACE with and without ID, there was no significant difference between standard and accelerated T1-SPACE with ID. Accelerated T1-SPACE showed more artifacts than standard T1-SPACE; however, accelerated T1-SPACE with ID showed significantly fewer artifacts than accelerated T1-SPACE without ID. Accelerated T1-SPACE without ID showed a significantly lower number of enhancing lesions than standard- and accelerated T1-SPACE with ID; however, there was no significant difference between standard and accelerated T1-SPACE with ID, regardless of lesion size. CONCLUSION: Although accelerated T1-SPACE markedly decreased the scan time, it showed lower overall image quality and lesion detectability than the standard T1-SPACE. Application of ID to accelerated T1-SPACE resulted in comparable overall image quality and detection of enhancing lesions in brain parenchyma as standard T1-SPACE. Accelerated T1-SPACE with ID may be a promising replacement for standard T1-SPACE.

The number of drones to inseminate a queen with has little potential for optimization of honeybee breeding programs.

BACKGROUND: Mating control is a crucial aspect of honeybee breeding. Instrumental insemination of queens gives the breeder maximum control over the genetic origin of the involved drones. However, in addition to the drones' descent, the breeder's control also extends over the number of drones to use for inseminations. Thus far, this aspect has largely been ignored in attempts to optimize honeybee breeding schemes. The literature provides some comparisons between single drone inseminations (SDI) and multi drone inseminations (MDI) but it is unclear whether the number of drones used in MDI is a relevant parameter for the optimization of honeybee breeding programs. METHODS: By computer simulations, we investigated the effect of the number of drones per inseminated queen in breeding programs that relied on best linear unbiased prediction (BLUP) breeding values. We covered a range of 1 to 50 drones per queen and observed the developments of genetic gain and inbreeding over a period of 20 years. Hereby, we focused on insemination schemes that take the drones for one queen from a single colony. RESULTS: SDI strategies led to 5.46% to 14.19% higher genetic gain than MDI at the cost of 6.1% to 30.2% higher inbreeding rates. The number of drones used in MDI settings had only a negligible impact on the results. There was a slight tendency that more drones lead to lower genetic gain and lower inbreeding rates but whenever more than five drones were used for inseminations, no significant differences could be observed. CONCLUSION: The opportunities to optimize breeding schemes via the number of drones used in inseminations are very limited. SDI can be a viable strategy in situations where breeders are interested in genetically homogeneous offspring or precise pedigree information. However, such strategies have to account for the fact that the semen from a single drone is insufficient to fill a queen's spermatheca, whence SDI queens will not build full-strength colonies. When deciding for MDI, breeders should focus on collecting enough semen for a succesful insemination, regardless of how many drones they need for this purpose.

Single-molecule nanopore sequencing reveals extreme target copy number heterogeneity in arylomycin-resistant mutants.

Tandem gene amplification is a frequent and dynamic source of antibiotic resistance in bacteria. Ongoing expansions and contractions of repeat arrays during population growth are expected to manifest as cell-to-cell differences in copy number (CN). As a result, a clonal bacterial culture could comprise subpopulations of cells with different levels of antibiotic sensitivity that result from variable gene dosage. Despite the high potential for misclassification of heterogenous cell populations as either antibiotic-susceptible or fully resistant in clinical settings, and the concomitant risk of inappropriate treatment, CN distribution among cells has defied analysis. Here, we use the MinION single-molecule nanopore sequencer to uncover CN heterogeneity in clonal populations of Escherichia coli and Acinetobacter baumannii grown from single cells isolated while selecting for resistance to an optimized arylomycin, a member of a recently discovered class of Gram-negative antibiotic. We found that gene amplification of the arylomycin target, bacterial type I signal peptidase LepB, is a mechanism of unstable arylomycin resistance and demonstrate in E. coli that amplification instability is independent of RecA. This instability drives the emergence of a nonuniform distribution of lepB CN among cells with a range of 1 to at least 50 copies of lepB identified in a single clonal population. In sum, this remarkable heterogeneity, and the evolutionary plasticity it fuels, illustrates how gene amplification can enable bacterial populations to respond rapidly to novel antibiotics. This study establishes a rationale for further nanopore-sequencing studies of heterogeneous cell populations to uncover CN variability at single-molecule resolution.

Dissecting the role of interprotomer cooperativity in the activation of oligomeric high-temperature requirement A2 protein.

The human high-temperature requirement A2 (HtrA2) mitochondrial protease is critical for cellular proteostasis, with mutations in this enzyme closely associated with the onset of neurodegenerative disorders. HtrA2 forms a homotrimeric structure, with each subunit composed of protease and PDZ (PSD-95, DLG, ZO-1) domains. Although we had previously shown that successive ligand binding occurs with increasing affinity, and it has been suggested that allostery plays a role in regulating catalysis, the molecular details of how this occurs have not been established. Here, we use cysteine-based chemistry to generate subunits in different conformational states along with a protomer mixing strategy, biochemical assays, and methyl-transverse relaxation optimized spectroscopy-based NMR studies to understand the role of interprotomer allostery in regulating HtrA2 function. We show that substrate binding to a PDZ domain of one protomer increases millisecond-to-microsecond timescale dynamics in neighboring subunits that prime them for binding substrate molecules. Only when all three PDZ-binding sites are substrate bound can the enzyme transition into an active conformation that involves significant structural rearrangements of the protease domains. Our results thus explain why when one (or more) of the protomers is fixed in a ligand-binding-incompetent conformation or contains the inactivating S276C mutation that is causative for a neurodegenerative phenotype in mouse models of Parkinson's disease, transition to an active state cannot be formed. In this manner, wild-type HtrA2 is only active when substrate concentrations are high and therefore toxic and unregulated proteolysis of nonsubstrate proteins can be suppressed.

Oligomeric assembly regulating mitochondrial HtrA2 function as examined by methyl-TROSY NMR.

Human High temperature requirement A2 (HtrA2) is a mitochondrial protease chaperone that plays an important role in cellular proteostasis and in regulating cell-signaling events, with aberrant HtrA2 function leading to neurodegeneration and parkinsonian phenotypes. Structural studies of the enzyme have established a trimeric architecture, comprising three identical protomers in which the active sites of each protease domain are sequestered to form a catalytically inactive complex. The mechanism by which enzyme function is regulated is not well understood. Using methyl transverse relaxation optimized spectroscopy (TROSY)-based solution NMR in concert with biochemical assays, a functional HtrA2 oligomerization/binding cycle has been established. In the absence of substrates, HtrA2 exchanges between a heretofore unobserved hexameric conformation and the canonical trimeric structure, with the hexamer showing much weaker affinity toward substrates. Both structures are substrate inaccessible, explaining their low basal activity in the absence of the binding of activator peptide. The binding of the activator peptide to each of the protomers of the trimer occurs with positive cooperativity and induces intrasubunit domain reorientations to expose the catalytic center, leading to increased proteolytic activity. Our data paint a picture of HtrA2 as a finely tuned, stress-protective enzyme whose activity can be modulated both by oligomerization and domain reorientation, with basal levels of catalysis kept low to avoid proteolysis of nontarget proteins.