Production of angiotensin converting enzyme inhibitory peptides from β-lactoglobulin and casein derived peptides: an integrative approach

Angiotensin I-converting enzyme (ACE) inhibition is one of the mechanisms by which reduction in blood pressure is exerted. Whey proteins are a rich source of ACE inhibitory peptides and have shown a blood pressure reduction effect i.e. antihypertensive activity. The aim of this work was to develop a simplified process using a combination of adsorption and microfiltration steps for the production of hydrolysates from whey with high ACE inhibitory activity and potency; the latter was measured as the IC50, which is the peptide concentration required to reduce ACE activity by half. This process integrates the selective separation of β-lactoglobulin and casein derived peptides (CDP) from rennet whey and their hydrolysis, which results in partially pure, less complex hydrolysates with high bioactive potency. Hydrolysis was carried out with protease N ‘Amano’ in a thermostatically controlled membrane reactor operated in a batch mode. By applying the integrative approach it was possible to produce from the same feedstock two different hydrolysates that exhibited high ACE inhibition. One hydrolysate was mainly composed of casein-derived peptides with IC50= 285 μg/mL. In this hydrolysate we identified the well known potent ACE-I and anti-hypertensive tri-peptide Ile-Pro-Pro (IPP) and another novel octa-peptide Gln-Asp-Lys-Thr-Glu-Ile-Pro-Thr (QDKTEIPT). The second hydrolysate was mainly composed of β-lactoglobulin derived peptides with IC50=128 µg/mL. This hydrolysate contained a tetra-peptide (Ile-Ile-Ala-Glu) IIAE as one of the two major peptides. A further advantage to this process is that enzyme activity was substantially increased as enzyme product inhibition was reduced.

PMCRI: a parallel modular classification rule induction framework

In a world where massive amounts of data are recorded on a large scale we need data mining technologies to gain knowledge from the data in a reasonable time. The Top Down Induction of Decision Trees (TDIDT) algorithm is a very widely used technology to predict the classification of newly recorded data. However alternative technologies have been derived that often produce better rules but do not scale well on large datasets. Such an alternative to TDIDT is the PrismTCS algorithm. PrismTCS performs particularly well on noisy data but does not scale well on large datasets. In this paper we introduce Prism and investigate its scaling behaviour. We describe how we improved the scalability of the serial version of Prism and investigate its limitations. We then describe our work to overcome these limitations by developing a framework to parallelise algorithms of the Prism family and similar algorithms. We also present the scale up results of a first prototype implementation.

Parallel induction of modular classification rules

The Distributed Rule Induction (DRI) project at the University of Portsmouth is concerned with distributed data mining algorithms for automatically generating rules of all kinds. In this paper we present a system architecture and its implementation for inducing modular classification rules in parallel in a local area network using a distributed blackboard system. We present initial results of a prototype implementation based on the Prism algorithm.

Parallel rule induction with information theoretic pre-pruning

In a world where data is captured on a large scale the major challenge for data mining algorithms is to be able to scale up to large datasets. There are two main approaches to inducing classification rules, one is the divide and conquer approach, also known as the top down induction of decision trees; the other approach is called the separate and conquer approach. A considerable amount of work has been done on scaling up the divide and conquer approach. However, very little work has been conducted on scaling up the separate and conquer approach.In this work we describe a parallel framework that allows the parallelisation of a certain family of separate and conquer algorithms, the Prism family. Parallelisation helps the Prism family of algorithms to harvest additional computer resources in a network of computers in order to make the induction of classification rules scale better on large datasets. Our framework also incorporates a pre-pruning facility for parallel Prism algorithms.

P-Prism: a computationally efficient approach to scaling up classification rule induction

Top Down Induction of Decision Trees (TDIDT) is the most commonly used method of constructing a model from a dataset in the form of classification rules to classify previously unseen data. Alternative algorithms have been developed such as the Prism algorithm. Prism constructs modular rules which produce qualitatively better rules than rules induced by TDIDT. However, along with the increasing size of databases, many existing rule learning algorithms have proved to be computational expensive on large datasets. To tackle the problem of scalability, parallel classification rule induction algorithms have been introduced. As TDIDT is the most popular classifier, even though there are strongly competitive alternative algorithms, most parallel approaches to inducing classification rules are based on TDIDT. In this paper we describe work on a distributed classifier that induces classification rules in a parallel manner based on Prism.

Towards a computationally efficient approach to modular classification rule induction

Induction of classification rules is one of the most important technologies in data mining. Most of the work in this field has concentrated on the Top Down Induction of Decision Trees (TDIDT) approach. However, alternative approaches have been developed such as the Prism algorithm for inducing modular rules. Prism often produces qualitatively better rules than TDIDT but suffers from higher computational requirements. We investigate approaches that have been developed to minimize the computational requirements of TDIDT, in order to find analogous approaches that could reduce the computational requirements of Prism.

Induction of modular classification rules: using Jmax-pruning

The Prism family of algorithms induces modular classification rules which, in contrast to decision tree induction algorithms, do not necessarily fit together into a decision tree structure. Classifiers induced by Prism algorithms achieve a comparable accuracy compared with decision trees and in some cases even outperform decision trees. Both kinds of algorithms tend to overfit on large and noisy datasets and this has led to the development of pruning methods. Pruning methods use various metrics to truncate decision trees or to eliminate whole rules or single rule terms from a Prism rule set. For decision trees many pre-pruning and postpruning methods exist, however for Prism algorithms only one pre-pruning method has been developed, J-pruning. Recent work with Prism algorithms examined J-pruning in the context of very large datasets and found that the current method does not use its full potential. This paper revisits the J-pruning method for the Prism family of algorithms and develops a new pruning method Jmax-pruning, discusses it in theoretical terms and evaluates it empirically.

Scaling up classification rule induction through parallel processing

The fast increase in the size and number of databases demands data mining approaches that are scalable to large amounts of data. This has led to the exploration of parallel computing technologies in order to perform data mining tasks concurrently using several processors. Parallelization seems to be a natural and cost-effective way to scale up data mining technologies. One of the most important of these data mining technologies is the classification of newly recorded data. This paper surveys advances in parallelization in the field of classification rule induction.

Computationally efficient induction of classification rules with the PMCRI and J-PMCRI frameworks

In order to gain knowledge from large databases, scalable data mining technologies are needed. Data are captured on a large scale and thus databases are increasing at a fast pace. This leads to the utilisation of parallel computing technologies in order to cope with large amounts of data. In the area of classification rule induction, parallelisation of classification rules has focused on the divide and conquer approach, also known as the Top Down Induction of Decision Trees (TDIDT). An alternative approach to classification rule induction is separate and conquer which has only recently been in the focus of parallelisation. This work introduces and evaluates empirically a framework for the parallel induction of classification rules, generated by members of the Prism family of algorithms. All members of the Prism family of algorithms follow the separate and conquer approach.

Endothelin-converting enzyme-1 regulates trafficking and signalling of the neurokinin 1 receptor in endosomes of myenteric neurones

Neuropeptide signalling at the plasma membrane is terminated by neuropeptide degradation by cell-surface peptidases, and by beta-arrestin-dependent receptor desensitization and endocytosis. However, receptors continue to signal from endosomes by beta-arrestin-dependent processes, and endosomal sorting mediates recycling and resensitization of plasma membrane signalling. The mechanisms that control signalling and trafficking of receptors in endosomes are poorly defined. We report a major role for endothelin-converting enzyme-1 (ECE-1) in controlling substance P (SP) and the neurokinin 1 receptor (NK(1)R) in endosomes of myenteric neurones. ECE-1 mRNA and protein were expressed by myenteric neurones of rat and mouse intestine. SP (10 nM, 10 min) induced interaction of NK(1)R and beta-arrestin at the plasma membrane, and the SP-NK(1)R-beta-arrestin signalosome complex trafficked by a dynamin-mediated mechanism to ECE-1-containing early endosomes, where ECE-1 can degrade SP. After 120 min, NK(1)R recycled from endosomes to the plasma membrane. ECE-1 inhibitors (SM-19712, PD-069185) and the vacuolar H(+)ATPase inhibitor bafilomycin A(1), which prevent endosomal SP degradation, suppressed NK(1)R recycling by >50%. Preincubation of neurones with SP (10 nM, 5 min) desensitized Ca(2+) transients to a second SP challenge after 10 min, and SP signals resensitized after 60 min. SM-19712 inhibited NK(1)R resensitization by >90%. ECE-1 inhibitors also caused sustained SP-induced activation of extracellular signal-regulated kinases, consistent with stabilization of the SP-NK(1)R-beta-arrestin signalosome. By degrading SP and destabilizing endosomal signalosomes, ECE-1 has a dual role in controlling endocytic signalling and trafficking of the NK(1)R: promoting resensitization of G protein-mediated plasma membrane signalling, and terminating beta-arrestin-mediated endosomal signalling.

Endosomal endothelin-converting enzyme-1: a regulator of beta-arrestin-dependent ERK signaling

Neuropeptide signaling at the cell surface is regulated by metalloendopeptidases, which degrade peptides in the extracellular fluid, and beta-arrestins, which interact with G protein-coupled receptors (GPCRs) to mediate desensitization. beta-Arrestins also recruit GPCRs and mitogen-activated protein kinases to endosomes to allow internalized receptors to continue signaling, but the mechanisms regulating endosomal signaling are unknown. We report that endothelin-converting enzyme-1 (ECE-1) degrades substance P (SP) in early endosomes of epithelial cells and neurons to destabilize the endosomal mitogen-activated protein kinase signalosome and terminate signaling. ECE-1 inhibition caused endosomal retention of the SP neurokinin 1 receptor, beta-arrestins, and Src, resulting in markedly sustained ERK2 activation in the cytosol and nucleus, whereas ECE-1 overexpression attenuated ERK2 activation. ECE-1 inhibition also enhanced SP-induced expression and phosphorylation of the nuclear death receptor Nur77, resulting in cell death. Thus, endosomal ECE-1 attenuates ERK2-mediated SP signaling in the nucleus to prevent cell death. We propose that agonist availability in endosomes, here regulated by ECE-1, controls beta-arrestin-dependent signaling of endocytosed GPCRs.

Endothelin-converting enzyme 1 promotes re-sensitization of neurokinin 1 receptor-dependent neurogenic inflammation

BACKGROUND AND PURPOSE: The metalloendopeptidase endothelin-converting enzyme 1 (ECE-1) is prominently expressed in the endothelium where it converts big endothelin to endothelin-1, a vasoconstrictor peptide. Although ECE-1 is found in endosomes in endothelial cells, the role of endosomal ECE-1 is unclear. ECE-1 degrades the pro-inflammatory neuropeptide substance P (SP) in endosomes to promote recycling and re-sensitization of its neurokinin 1 (NK(1)) receptor. We investigated whether ECE-1 regulates NK(1) receptor re-sensitization and the pro-inflammatory effects of SP in the endothelium. EXPERIMENTAL APPROACH: We examined ECE-1 expression, SP trafficking and NK(1) receptor re-sensitization in human microvascular endothelial cells (HMEC-1), and investigated re-sensitization of SP-induced plasma extravasation in rats. KEY RESULTS: HMEC-1 expressed all four ECE-1 isoforms (a-d), and fluorescent SP trafficked to early endosomes containing ECE-1b/d. The ECE-1 inhibitor SM-19712 prevented re-sensitization of SP-induced Ca2+ signals in HMEC-1 cells. Immunoreactive ECE-1 and NK(1) receptors co-localized in microvascular endothelial cells in the rat. SP-induced extravasation of Evans blue in the urinary bladder, skin and ears of the rat desensitized when the interval between two SP injections was 10 min, and re-sensitized after 480 min. SM-19712 inhibited this re-sensitization. CONCLUSIONS AND IMPLICATIONS: By degrading endocytosed SP, ECE-1 promotes the recycling and re-sensitization of NK(1) receptors in endothelial cells, and thereby induces re-sensitization of the pro-inflammatory effects of SP. Thus, ECE-1 inhibitors may ameliorate the pro-inflammatory actions of SP.

Endothelin-converting enzyme-1 degrades internalized somatostatin-14

Agonist-induced internalization of somatostatin receptors (ssts) determines subsequent cellular responsiveness to peptide agonists and influences sst receptor scintigraphy. To investigate sst2A trafficking, rat sst2A tagged with epitope was expressed in human embryonic kidney cells and tracked by antibody labeling. Confocal microscopical analysis revealed that stimulation with sst and octreotide induced internalization of sst2A. Internalized sst2A remained sequestrated within early endosomes, and 60 min after stimulation, internalized sst2A still colocalized with beta-arrestin1-enhanced green fluorescence protein (EGFP), endothelin-converting enzyme-1 (ECE-1), and rab5a. Internalized (125)I-Tyr(11)-SST-14 was rapidly hydrolyzed by endosomal endopeptidases, with radioactive metabolites being released from the cell. Internalized (125)I-Tyr(1)-octreotide accumulated as an intact peptide and was released from the cell as an intact peptide ligand. We have identified ECE-1 as one of the endopeptidases responsible for inactivation of internalized SST-14. ECE-1-mediated cleavage of SST-14 was inhibited by the specific ECE-1 inhibitor, SM-19712, and by preventing acidification of endosomes using bafilomycin A(1). ECE-1 cleaved SST-14 but not octreotide in an acidic environment. The metallopeptidases angiotensin-1 converting enzyme and ECE-2 did not hydrolyze SST-14 or octreotide. Our results show for the first time that stimulation with SST-14 and octreotide induced sequestration of sst2A into early endosomes and that endocytosed SST-14 is degraded by endopeptidases located in early endosomes. Furthermore, octreotide was not degraded by endosomal peptidases and was released as an intact peptide. This mechanism may explain functional differences between octreotide and SST-14 after sst2A stimulation. Moreover, further investigation of endopeptidase-regulated trafficking of neuropeptides may result in novel concepts of neuropeptide receptor inactivation in cancer diagnosis.

Endothelin-converting enzyme 1 degrades neuropeptides in endosomes to control receptor recycling

Neuropeptide signaling requires the presence of G protein-coupled receptors (GPCRs) at the cell surface. Activated GPCRs interact with beta-arrestins, which mediate receptor desensitization, endocytosis, and mitogenic signaling, and the peptide-receptor-arrestin complex is sequestered into endosomes. Although dissociation of beta-arrestins is required for receptor recycling and resensitization, the critical event that initiates this process is unknown. Here we report that the agonist availability in the endosomes, controlled by the membrane metalloendopeptidase endothelin-converting enzyme 1 (ECE-1), determines stability of the peptide-receptor-arrestin complex and regulates receptor recycling and resensitization. Substance P (SP) binding to the tachykinin neurokinin 1 receptor (NK1R) induced membrane translocation of beta-arrestins followed by trafficking of the SP-NK1R-beta-arrestin complex to early endosomes containing ECE-1a-d. ECE-1 degraded SP in acidified endosomes, disrupting the complex; beta-arrestins returned to the cytosol, and the NK1R, freed from beta-arrestins, recycled and resensitized. An ECE-1 inhibitor, by preventing NK1R recycling in endothelial cells, inhibited resensitization of SP-induced inflammation. This mechanism is a general one because ECE-1 similarly regulated NK3R resensitization. Thus, peptide availability in endosomes, here regulated by ECE-1, determines the stability of the peptide-receptor-arrestin complex. This mechanism regulates receptor recycling, which is necessary for sustained signaling, and it may also control beta-arrestin-dependent mitogenic signaling of endocytosed receptors. We propose that other endosomal enzymes and transporters may similarly control the availability of transmitters in endosomes to regulate trafficking and signaling of GPCRs. Antagonism of these endosomal processes represents a strategy for inhibiting sustained signaling of receptors, and defects may explain the tachyphylaxis of drugs that are receptor agonists.

Endothelin-converting enzyme-1 regulates endosomal sorting of calcitonin receptor-like receptor and beta-arrestins

Although cell surface metalloendopeptidases degrade neuropeptides in the extracellular fluid to terminate signaling, the function of peptidases in endosomes is unclear. We report that isoforms of endothelin-converting enzyme-1 (ECE-1a-d) are present in early endosomes, where they degrade neuropeptides and regulate post-endocytic sorting of receptors. Calcitonin gene-related peptide (CGRP) co-internalizes with calcitonin receptor-like receptor (CLR), receptor activity-modifying protein 1 (RAMP1), beta-arrestin2, and ECE-1 to early endosomes, where ECE-1 degrades CGRP. CGRP degradation promotes CLR/RAMP1 recycling and beta-arrestin2 redistribution to the cytosol. ECE-1 inhibition or knockdown traps CLR/RAMP1 and beta-arrestin2 in endosomes and inhibits CLR/RAMP1 recycling and resensitization, whereas ECE-1 overexpression has the opposite effect. ECE-1 does not regulate either the resensitization of receptors for peptides that are not ECE-1 substrates (e.g., angiotensin II), or the recycling of the bradykinin B(2) receptor, which transiently interacts with beta-arrestins. We propose a mechanism by which endosomal ECE-1 degrades neuropeptides in endosomes to disrupt the peptide/receptor/beta-arrestin complex, freeing internalized receptors from beta-arrestins and promoting recycling and resensitization.