Neuropeptidomic Components Generated by Proteomic Functions in Secretory Vesicles for Cell–Cell Communication

Diverse neuropeptides participate in cell–cell communication to coordinate neuronal and endocrine regulation of physiological processes in health and disease. Neuropeptides are short peptides ranging in length from ~3 to 40 amino acid residues that are involved in biological functions of pain, stress, obesity, hypertension, mental disorders, cancer, and numerous health conditions. The unique neuropeptide sequences define their specific biological actions. Significantly, this review article discusses how the neuropeptide field is at the crest of expanding knowledge gained from mass-spectrometry-based neuropeptidomic studies, combined with proteomic analyses for understanding the biosynthesis of neuropeptidomes. The ongoing expansion in neuropeptide diversity lies in the unbiased and global mass-spectrometry-based approaches for identification and quantitation of peptides. Current mass spectrometry technology allows definition of neuropeptide amino acid sequence structures, profiling of multiple neuropeptides in normal and disease conditions, and quantitative peptide measures in biomarker applications to monitor therapeutic drug efficacies. Complementary proteomic studies of neuropeptide secretory vesicles provide valuable insight into the protein processes utilized for neuropeptide production, storage, and secretion. Furthermore, ongoing research in developing new computational tools will facilitate advancements in mass-spectrometry-based identification of small peptides. Knowledge of the entire repertoire of neuropeptides that regulate physiological systems will provide novel insight into regulatory mechanisms in health, disease, and therapeutics

The processing proteases prohormone thiol protease, PC1/3 and PC2, and 70-kDa aspartic proteinase show preferences among proenkephalin, proneuropeptide Y, and proopiomelanocortin substrates.

Proteases of cysteine, aspartic, and subtilisin classes have been indicated as candidate prohormone processing enzymes. The chromaffin granule proenkephalin processing proteases have been characterized as the novel cysteine protease prohormone thiol protease (PTP), a 70-kDa aspartic proteinase, and the subtilisin-like PC1/3 and PC2 enzymes. The goal of this study was to assess whether these processing proteases possess preference(s) for prohormone substrates. The recombinant prohormones proenkephalin, proneuropeptide Y (pro-NPY), and proopiomelanocortin (POMC) were expressed in Escherichia coli using the T7 expression system and purified for in vitro processing studies. Results indicated that the chromaffin granule processing proteases possess selectivity for particular prohormones. PTP preferred proenkephalin, with good cleavage of pro-NPY and slow processing of POMC. In contrast, the 70-kDa aspartic proteinase cleaved POMC most readily, with cleavage of proenkephalin and some processing of pro-NPY. PC1/3 and PC2 preferred POMC among the prohormones tested. Importantly, these results indicate that prohormone selectivity of processing proteases may be an important factor in predicting the primary and rate-limiting protease(s) required for processing a particular prohormone

In-Process Monitoring of Hobbing Process Using an Acoustic Emission Sensor and Supervised Machine Learning

The complexity of products increases considerably, and key functions can often only be realized by using high-precision components. Microgears have a particularly complex geometry and thus the manufacturing requirements often reach technological limits. Their geometric deviations are relatively large in comparison to the small component size and thus have a major impact on the functionality in terms of generating unwanted noise and vibrations in the final product. There are still no readily available production-integrated measuring methods that enable quality control of all produced microgears. Consequently, many manufacturers are not able to measure any geometric gear parameters according to standards such as DIN ISO 21771. If at all, only samples are measured, as this is only possible by means of specialized, sensitive, and cost-intensive tactile or optical measuring technologies. In a novel approach, this paper examines the integration of an acoustic emission sensor into the hobbing process of microgears in order to predict process parameters as well as geometric and functional features of the produced gears. In terms of process parameters, radial feed and tool tumble are investigated, whereas the total profile deviation is used as a representative geometric variable and the overall transmission error as a functional variable. The approach is experimentally validated by means of the design of experiments. Furthermore, different approaches for feature extraction from time-continuous sensor data and different machine-learning approaches for predicting process and geometry parameters are compared with each other and tested for suitability. It is shown that structure-borne sound, in combination with supervised machine learning and data analysis, is suitable for inprocess monitoring of microgear hobbing processes

Chromaffin Granule Aspartic Proteinase Processes Recombinant Proopiomelanocortin (POMC)

Our search for proteases responsible for proenkephalin (PE) processing in adrenal medulla led to the isolation of a 70 kDa aspartic proteinase that cleaves PE between the basic residues of the Lys-Arg processing site (1). Studies in pituitary have also identified a similar aspartic proteinase that processes POMC (2,3). To compare the chromaffin granule (CG) 70 kDa aspartic proteinase with that in pituitary, processing of recombinant POMC by the CG enzyme was examined. POMC was expressed in the T7 expression system in E. coli, and purified to homogeneity. The CG 70 kDa aspartic proteinase converted POMC to 27 and 22 kDa bands that were detected by anti-N-POMC immunoblots, and to 26, 22, and 14 kDa bands that were immunoreactive with anti-β-lipotropin. POMC products represented by these bands indicate appropriate POMC processing by the CG 70 kDa aspartic proteinase. These results, combined with the similar biochemical properties of these two enzymes, suggest that the CG 70 kDa aspartic proteinase resembles the POMC-converting enzyme (PCE), an aspartic proteinase in pituitary (2,3)

Development and analysis of a holistic function-driven adaptive assembly strategy applied to micro gears

The precision and functionality of an assembly heavily depend on the dimensions of its components, which can often lead to quality issues. However, increasing precision can be expensive and impractical. Alternative methods, such as adaptive assembly and optimization, can help achieve high-precision assemblies using less precise components. Adaptive assembly involves adjusting the assembly process to account for component variations, which can improve accuracy and reduce errors. Optimization techniques can be used to identify the most efficient and effective assembly strategy for a given set of components, taking into consideration factors such as complexity, volume, cost, and quality. This paper proposes an exclusive adaptive assembly strategy for micro gear pairing by evaluating and comparing different assembly strategies. Manufacturers can determine the best fit for their specific needs and enhance the precision and functionality of their assemblies

Characteristics of the chromaffin granule aspartic proteinase involved in proenkephalin processing

Proteolytic processing of neuropeptide precursors is required for production of active neurotransmitters and hormones. In this study, a chromaffin granule (CG) aspartic proteinase of 70 kDa was found to contribute to enkephalin precursor cleaving activity, as assayed with recombinant ([35S]Met) preproenkephalin. The 70-kDa CG aspartic proteinase was purified by concanavalin A-Sepharose, Sephacryl S-200, and pepstatin A agarose affinity chromatography. The proteinase showed optimal activity at pH 5.5. It was potently inhibited by pepstatin A, a selective aspartic proteinase inhibitor, but not by inhibitors of serine, cysteine, or metalloproteinases. Lack of inhibition by Val-D-Leu-Pro-Phe-Val-D-Leu--an inhibitor of pepsin, cathepsin D, and cathepsin E--distinguishes the CG aspartic proteinases from classical members of the aspartic proteinase family. The CG aspartic proteinase cleaved recombinant proenkephalin between the Lys172-Arg173 pair located at the COOH-terminus of (Met)enkephalin-Arg6-Gly7-Leu8, as assessed by peptide microsequencing. The importance of full-length prohormone as substrate was demonstrated by the enzyme\u27s ability to hydrolyze 35S-labeled proenkephalin and proopiomelanocortin and its inability to cleave tri- and tetrapeptide substrates containing dibasic or monobasic cleavage sites. In this study, results provide evidence for the role of an aspartic proteinase in proenkephalin and prohormone processing

Prohormone thiol protease\u27 (PTP) a novel cysteine protein for proenkephalin and prohormone processing in Proteolytic and cellular mechanisms in prohormone and proprotein processing

Production of peptide hormones and neurotransmitters requires several steps which involves transcription of the pro-hormone gene, translation of the corresponding mRNA, packaging of the prohormone into secretory vesicles, processing by proteolytic mechanisms, storage of mature neuropeptides in secretory vesicles, and regulated secretion of bioactive peptides. Among these steps, posttranslational processing is required for converting the inactive protein precursor into biologically active neuropeptides. Clearly, limited proteolysis is crticical for generating neuropeptides. Endoproteases and extoproteases are required for prohormone processing, which occurs in the regulated secretory pathway of neuroendocrine cells. These potent neuropeptides are stored and secreted from secretory vesicles. The released peptide hormones and neurotransmitters mediate cell-cell communication in neuroendocrine systems

Prohormone thiol protease (PTP) processing of recombinant proenkephalin.

The prohormone thiol protease (PTP) from adrenal medullary chromaffin granules has been demonstrated as a novel cysteine protease that converts the model enkephalin precursor, ([35S]Met)-preproenkephalin, to appropriate enkephalin related peptide products [Krieger, T. J., & Hook, V. Y. H. (1991) J. Biol. Chem. 266, 8376-8383; Kreiger, T. J., Mende-Mueller, L., & Hook, V. Y. H. (1992) J. Neurochem. 59, 26-31; Azaryan, A. V., & Hook, V. Y. H. (1994) FEBS Lett. 341, 197-202]. In this report, PTP processing of authentic proenkephalin (PE) was examined with respect to production of appropriate intermediate products, and kinetics of PE processing were assessed. Recombinant PE was obtained by high level expression in Escherichia coli, with the pET3c expression vector; PE was then purified from E. coli by DEAE-Sepharose chromatography, preparative gel electrophoresis, and reverse-phase HPLC. Authentic purified PE was confirmed by amino acid composition analyses and peptide microsequencing. In time course studies, PTP converted PE (12 microM) to intermediates of 22.5, 21.7, 12.5, and 11.0 kDa that represented NH2-terminal fragments of PE, as assessed by peptide microsequencing. Differences in molecular masses of the 22.5, 21.7, 12.5, and 11.0 kDa products reflect PTP processing of PE within the COOH-terminal region of PE, which resembles PE processing in vivo [Liston, D. L., Patey, G., Rossier, J., Verbanck, P., & Vanderhaeghen, J. (1983) Science 225, 734-737; Udenfriend, S., & Kilpatrick, D. L. (1983) Arch. Biochem. Biophys. 221, 309-314]. Products of 12.5, 11.0, and 8.5 kDa were generated by PTP cleavage between Lys-Arg at the COOH-terminus of (Met)enkephalin-Arg6-Gly7-Leu8

Evolutionary Cost-Tolerance Optimization for Complex Assembly Mechanisms Via Simulation and Surrogate Modeling Approaches: Application on Micro Gears

With the introduction of new technologies, the scope of miniaturization has broadened. The conditions under which complicated products are designed, manufactured, and assembled ultimately influence how well they perform. The intricacy and crucial functionality of products are frequently only fulfilled through the use of high-precision components such as micro gears. In power transmission systems, gears are used in a variety of industries. Micro gears or gears with micro features, with tolerances of less than 5 m, are pushing manufacturing processes to their technological limits. Monte-Carlo simulation methods enable an accurate forecast of inaccuracies in compliance. The complexity of the micro gear's design, on the other hand, increases the simulation computation and runtime. An alternative method for simulation is to create a surrogate model to predict the behavior. This paper proposes a statistical surrogate model to predict the conformity of a pair of micro gears. Afterward, the advantage of the surrogate model enables the optimal tolerances assignment while taking gear functionality, and production cost into account