Kinetic characterization of brush border myosin-I ATPase

Brush border myosin-I (BBM-I) is a single-headed unconventional myosin found in the microvilli of intestinal epithelial cells. We used stopped-flow kinetic analysis to measure the rate and equilibrium constants for several steps in the BBM-I ATPase cycle. We determined the rates for ATP binding to BBM-I and brush border actomyosin-I (actoBBM-I), the rate of actoBBM-I dissociation by ATP, and the rates for the steps in ADP dissociation from actoBBM-I. The rate and equilibrium constants for several of the steps in the actoBBM-I ATPase are significantly different from those of other members of the myosin superfamily. Most notably, dissociation of the actoBBM-I complex by ATP and release of ADP from actoBBM-I are both very slow. The slow rates of these steps may play a role in lengthening the time spent in force-generating states and in limiting the maximal rate of BBM-I motility. In addition, release of ADP from the actoBBM-I complex occurs in at least two steps. This study provides evidence for a member of the myosin superfamily with markedly divergent kinetic behavior.

Galectin-4 and Small Intestinal Brush Border Enzymes Form Clusters

Detergent-insoluble complexes prepared from pig small intestine are highly enriched in several transmembrane brush border enzymes including aminopeptidase N and sucrase-isomaltase, indicating that they reside in a glycolipid-rich environment in vivo. In the present work galectin-4, an animal lectin lacking a N-terminal signal peptide for membrane translocation, was discovered in these complexes as well, and in gradient centrifugation brush border enzymes and galectin-4 formed distinct soluble high molecular weight clusters. Immunoperoxidase cytochemistry and immunogold electron microscopy showed that galectin-4 is indeed an intestinal brush border protein; we also localized galectin-4 throughout the cell, mainly associated with membraneous structures, including small vesicles, and to the rootlets of microvillar actin filaments. This was confirmed by subcellular fractionation, showing about half the amount of galectin-4 to be in the microvillar fraction, the rest being associated with insoluble intracellular structures. A direct association between the lectin and aminopeptidase N was evidenced by a colocalization along microvilli in double immunogold labeling and by the ability of an antibody to galectin-4 to coimmunoprecipitate aminopeptidase N and sucrase-isomaltase. Furthermore, galectin-4 was released from microvillar, right-side-out vesicles as well as from mucosal explants by a brief wash with 100 mM lactose, confirming its extracellular localization. Galectin-4 is therefore secreted by a nonclassical pathway, and the brush border enzymes represent a novel class of natural ligands for a member of the galectin family. Newly synthesized galectin-4 is rapidly “trapped” by association with intracellular structures prior to its apical secretion, but once externalized, association with brush border enzymes prevents it from being released from the enterocyte into the intestinal lumen.

Brush border myosin-I truncated in the motor domain impairs the distribution and the function of endocytic compartments in an hepatoma cell line.

Myosins I, a ubiquitous monomeric class of myosins that exhibits actin-based motor properties, are associated with plasma and/or vesicular membranes and have been suggested as players for trafficking events between cell surface and intracellular membranous structures. To investigate the function of myosins 1, we have transfected a mouse hepatoma cell line (BWTG3) with cDNAs encoding the chicken brush border myosin-I (BBMI) and two variants truncated in the motor domain. One variant is deleted of the first 446 amino acids and thereby lacks the ATP binding site, whereas the other is deleted of the entire motor domain and lacks the ATP and actin binding sites. We have observed (i) that significant amounts of the truncated variants are recovered with membrane fractions after cell fractionation, (ii) that they codistribute with a compartment containing alpha2-macroglobulin internalized for 30 min as determined by fluorescent microscopy, (iii) that the production of BBMI-truncated variants impairs the distribution of the acidic compartment and ligands internalized for 30 min, and (iv) that the production of the truncated variant containing the actin binding site decreases the rate of alpha2-macroglobulin degradation whereas the production of the variant lacking the ATP binding site and the actin binding site increases the rate of a2-macroglobulin degradation. These observations indicate that the two truncated variants have a dominant negative effect on the distribution and the function of the endocytic compartments. We propose that an unidentified myosin-I might contribute to the distribution of endocytic compartments in a juxtanuclear position and/or to the regulation of the delivery of ligands to the degradative compartment in BWTG3 cells.

Structure-activity relationships derived by machine learning: the use of atoms and their bond connectivities to predict mutagenicity by inductive logic programming.

We present a general approach to forming structure-activity relationships (SARs). This approach is based on representing chemical structure by atoms and their bond connectivities in combination with the inductive logic programming (ILP) algorithm PROGOL. Existing SAR methods describe chemical structure by using attributes which are general properties of an object. It is not possible to map chemical structure directly to attribute-based descriptions, as such descriptions have no internal organization. A more natural and general way to describe chemical structure is to use a relational description, where the internal construction of the description maps that of the object described. Our atom and bond connectivities representation is a relational description. ILP algorithms can form SARs with relational descriptions. We have tested the relational approach by investigating the SARs of 230 aromatic and heteroaromatic nitro compounds. These compounds had been split previously into two subsets, 188 compounds that were amenable to regression and 42 that were not. For the 188 compounds, a SAR was found that was as accurate as the best statistical or neural network-generated SARs. The PROGOL SAR has the advantages that it did not need the use of any indicator variables handcrafted by an expert, and the generated rules were easily comprehensible. For the 42 compounds, PROGOL formed a SAR that was significantly (P < 0.025) more accurate than linear regression, quadratic regression, and back-propagation. This SAR is based on an automatically generated structural alert for mutagenicity.

Scientific bases of human-machine communication by voice.

The scientific bases for human-machine communication by voice are in the fields of psychology, linguistics, acoustics, signal processing, computer science, and integrated circuit technology. The purpose of this paper is to highlight the basic scientific and technological issues in human-machine communication by voice and to point out areas of future research opportunity. The discussion is organized around the following major issues in implementing human-machine voice communication systems: (i) hardware/software implementation of the system, (ii) speech synthesis for voice output, (iii) speech recognition and understanding for voice input, and (iv) usability factors related to how humans interact with machines.

The role of voice input for human-machine communication.

Optimism is growing that the near future will witness rapid growth in human-computer interaction using voice. System prototypes have recently been built that demonstrate speaker-independent real-time speech recognition, and understanding of naturally spoken utterances with vocabularies of 1000 to 2000 words, and larger. Already, computer manufacturers are building speech recognition subsystems into their new product lines. However, before this technology can be broadly useful, a substantial knowledge base is needed about human spoken language and performance during computer-based spoken interaction. This paper reviews application areas in which spoken interaction can play a significant role, assesses potential benefits of spoken interaction with machines, and compares voice with other modalities of human-computer interaction. It also discusses information that will be needed to build a firm empirical foundation for the design of future spoken and multimodal interfaces. Finally, it argues for a more systematic and scientific approach to investigating spoken input and performance with future language technology.

Military and government applications of human-machine communication by voice.

This paper describes a range of opportunities for military and government applications of human-machine communication by voice, based on visits and contacts with numerous user organizations in the United States. The applications include some that appear to be feasible by careful integration of current state-of-the-art technology and others that will require a varying mix of advances in speech technology and in integration of the technology into applications environments. Applications that are described include (1) speech recognition and synthesis for mobile command and control; (2) speech processing for a portable multifunction soldier's computer; (3) speech- and language-based technology for naval combat team tactical training; (4) speech technology for command and control on a carrier flight deck; (5) control of auxiliary systems, and alert and warning generation, in fighter aircraft and helicopters; and (6) voice check-in, report entry, and communication for law enforcement agents or special forces. A phased approach for transfer of the technology into applications is advocated, where integration of applications systems is pursued in parallel with advanced research to meet future needs.

Deployment of human-machine dialogue systems.

The deployment of systems for human-to-machine communication by voice requires overcoming a variety of obstacles that affect the speech-processing technologies. Problems encountered in the field might include variation in speaking style, acoustic noise, ambiguity of language, or confusion on the part of the speaker. The diversity of these practical problems encountered in the "real world" leads to the perceived gap between laboratory and "real-world" performance. To answer the question "What applications can speech technology support today?" the concept of the "degree of difficulty" of an application is introduced. The degree of difficulty depends not only on the demands placed on the speech recognition and speech synthesis technologies but also on the expectations of the user of the system. Experience has shown that deployment of effective speech communication systems requires an iterative process. This paper discusses general deployment principles, which are illustrated by several examples of human-machine communication systems.