Machine learning for geospatial data : algorithms, software tools and case studies

Résumé Cette thèse est consacrée à l'analyse, la modélisation et la visualisation de données environnementales à référence spatiale à l'aide d'algorithmes d'apprentissage automatique (Machine Learning). L'apprentissage automatique peut être considéré au sens large comme une sous-catégorie de l'intelligence artificielle qui concerne particulièrement le développement de techniques et d'algorithmes permettant à une machine d'apprendre à partir de données. Dans cette thèse, les algorithmes d'apprentissage automatique sont adaptés pour être appliqués à des données environnementales et à la prédiction spatiale. Pourquoi l'apprentissage automatique ? Parce que la majorité des algorithmes d'apprentissage automatiques sont universels, adaptatifs, non-linéaires, robustes et efficaces pour la modélisation. Ils peuvent résoudre des problèmes de classification, de régression et de modélisation de densité de probabilités dans des espaces à haute dimension, composés de variables informatives spatialisées (« géo-features ») en plus des coordonnées géographiques. De plus, ils sont idéaux pour être implémentés en tant qu'outils d'aide à la décision pour des questions environnementales allant de la reconnaissance de pattern à la modélisation et la prédiction en passant par la cartographie automatique. Leur efficacité est comparable au modèles géostatistiques dans l'espace des coordonnées géographiques, mais ils sont indispensables pour des données à hautes dimensions incluant des géo-features. Les algorithmes d'apprentissage automatique les plus importants et les plus populaires sont présentés théoriquement et implémentés sous forme de logiciels pour les sciences environnementales. Les principaux algorithmes décrits sont le Perceptron multicouches (MultiLayer Perceptron, MLP) - l'algorithme le plus connu dans l'intelligence artificielle, le réseau de neurones de régression généralisée (General Regression Neural Networks, GRNN), le réseau de neurones probabiliste (Probabilistic Neural Networks, PNN), les cartes auto-organisées (SelfOrganized Maps, SOM), les modèles à mixture Gaussiennes (Gaussian Mixture Models, GMM), les réseaux à fonctions de base radiales (Radial Basis Functions Networks, RBF) et les réseaux à mixture de densité (Mixture Density Networks, MDN). Cette gamme d'algorithmes permet de couvrir des tâches variées telle que la classification, la régression ou l'estimation de densité de probabilité. L'analyse exploratoire des données (Exploratory Data Analysis, EDA) est le premier pas de toute analyse de données. Dans cette thèse les concepts d'analyse exploratoire de données spatiales (Exploratory Spatial Data Analysis, ESDA) sont traités selon l'approche traditionnelle de la géostatistique avec la variographie expérimentale et selon les principes de l'apprentissage automatique. La variographie expérimentale, qui étudie les relations entre pairs de points, est un outil de base pour l'analyse géostatistique de corrélations spatiales anisotropiques qui permet de détecter la présence de patterns spatiaux descriptible par une statistique. L'approche de l'apprentissage automatique pour l'ESDA est présentée à travers l'application de la méthode des k plus proches voisins qui est très simple et possède d'excellentes qualités d'interprétation et de visualisation. Une part importante de la thèse traite de sujets d'actualité comme la cartographie automatique de données spatiales. Le réseau de neurones de régression généralisée est proposé pour résoudre cette tâche efficacement. Les performances du GRNN sont démontrées par des données de Comparaison d'Interpolation Spatiale (SIC) de 2004 pour lesquelles le GRNN bat significativement toutes les autres méthodes, particulièrement lors de situations d'urgence. La thèse est composée de quatre chapitres : théorie, applications, outils logiciels et des exemples guidés. Une partie importante du travail consiste en une collection de logiciels : Machine Learning Office. Cette collection de logiciels a été développée durant les 15 dernières années et a été utilisée pour l'enseignement de nombreux cours, dont des workshops internationaux en Chine, France, Italie, Irlande et Suisse ainsi que dans des projets de recherche fondamentaux et appliqués. Les cas d'études considérés couvrent un vaste spectre de problèmes géoenvironnementaux réels à basse et haute dimensionnalité, tels que la pollution de l'air, du sol et de l'eau par des produits radioactifs et des métaux lourds, la classification de types de sols et d'unités hydrogéologiques, la cartographie des incertitudes pour l'aide à la décision et l'estimation de risques naturels (glissements de terrain, avalanches). Des outils complémentaires pour l'analyse exploratoire des données et la visualisation ont également été développés en prenant soin de créer une interface conviviale et facile à l'utilisation. Machine Learning for geospatial data: algorithms, software tools and case studies Abstract The thesis is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense machine learning can be considered as a subfield of artificial intelligence. It mainly concerns with the development of techniques and algorithms that allow computers to learn from data. In this thesis machine learning algorithms are adapted to learn from spatial environmental data and to make spatial predictions. Why machine learning? In few words most of machine learning algorithms are universal, adaptive, nonlinear, robust and efficient modeling tools. They can find solutions for the classification, regression, and probability density modeling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well-suited to be implemented as predictive engines in decision support systems, for the purposes of environmental data mining including pattern recognition, modeling and predictions as well as automatic data mapping. They have competitive efficiency to the geostatistical models in low dimensional geographical spaces but are indispensable in high-dimensional geo-feature spaces. The most important and popular machine learning algorithms and models interesting for geo- and environmental sciences are presented in details: from theoretical description of the concepts to the software implementation. The main algorithms and models considered are the following: multi-layer perceptron (a workhorse of machine learning), general regression neural networks, probabilistic neural networks, self-organising (Kohonen) maps, Gaussian mixture models, radial basis functions networks, mixture density networks. This set of models covers machine learning tasks such as classification, regression, and density estimation. Exploratory data analysis (EDA) is initial and very important part of data analysis. In this thesis the concepts of exploratory spatial data analysis (ESDA) is considered using both traditional geostatistical approach such as_experimental variography and machine learning. Experimental variography is a basic tool for geostatistical analysis of anisotropic spatial correlations which helps to understand the presence of spatial patterns, at least described by two-point statistics. A machine learning approach for ESDA is presented by applying the k-nearest neighbors (k-NN) method which is simple and has very good interpretation and visualization properties. Important part of the thesis deals with a hot topic of nowadays, namely, an automatic mapping of geospatial data. General regression neural networks (GRNN) is proposed as efficient model to solve this task. Performance of the GRNN model is demonstrated on Spatial Interpolation Comparison (SIC) 2004 data where GRNN model significantly outperformed all other approaches, especially in case of emergency conditions. The thesis consists of four chapters and has the following structure: theory, applications, software tools, and how-to-do-it examples. An important part of the work is a collection of software tools - Machine Learning Office. Machine Learning Office tools were developed during last 15 years and was used both for many teaching courses, including international workshops in China, France, Italy, Ireland, Switzerland and for realizing fundamental and applied research projects. Case studies considered cover wide spectrum of the real-life low and high-dimensional geo- and environmental problems, such as air, soil and water pollution by radionuclides and heavy metals, soil types and hydro-geological units classification, decision-oriented mapping with uncertainties, natural hazards (landslides, avalanches) assessments and susceptibility mapping. Complementary tools useful for the exploratory data analysis and visualisation were developed as well. The software is user friendly and easy to use.

Mutation screening of the urocortin gene: identification of new single nucleotide polymorphisms and association studies with obesity in French Caucasians.

A linkage between obesity-related phenotypes and the 2p21-23 locus has been reported previously. The urocortin (UCN) gene resides at this interval, and its protein decreases appetite behavior, suggesting that UCN may be a candidate gene for susceptibility to obesity. We localized the UCN gene by radiation hybrid mapping, and the surrounding markers were genotyped in a collection of French families. Evidence for linkage was shown between the marker D2S165 and leptin levels (LOD score, 1.34; P = 0.006) and between D2S2247 and the z-score of body mass index (LOD score, 1.829; P = 0.0019). The gene was screened for SNPs in 96 obese patients. Four new variants were established. Two single nucleotide polymorphisms were located in the promoter (-535 A-->G, -286 G-->A), one in intron 1 (+31 C-->G), and one in the 3'-untranslated region (+34 C-->T). Association studies in cohorts of 722 unrelated obese and 381 control subjects and transmission disequilibrium tests, performed for the two frequent promoter polymorphisms, in 120 families (894 individuals) showed that no association was present between these variants and obesity, obesity-related phenotypes, and diabetes. Thus, our analyses of the genetic variations of the UCN gene suggest that, at least in French Caucasians, they do not represent a major cause of obesity.

Combined transcriptomic and proteomic studies reveal non-canonical signaling functions in the jasmonate pathway

Abstract Lipid derived signals mediate many stress and defense responses in multicellular eukaryotes. Among these are the jasmonates, potently active signaling compounds in plants. Jasmonic acid (JA) and 12-oxo-phytodienoic acid (OPDA) are the two best known members of the large jasmonate family. This thesis further investigates their roles as signals using genomic and proteomic approaches. The study is based on a simple genetic model involving two key genes. The first is ALLENE OXIDE SYNTHASE (AOS), encoding the most important enzyme in generating jasmonates. The second is CORONATINE INSENSITIVE 1 (COI1), a gene involved in all currently documented canonical signaling responses. We asked the simple question: do null mutations in AOS and COI1 have analogous effects on the transcriptome ? We found that they do not. If most COI1-dependent genes were also AOS-dependent, the expression of a zinc-finger protein was AOS-dependent but was unaffected by the coi1-1 mutation. We thus supposed that a jasmonate member, most probably OPDA, can alter gene expression partially independently of COI1. Conversely, the expression of at least three genes, one of these is a protein kinase, was shown to be COI1-dependent but did not require a functional AOS protein. We conclude that a non-jasmonate signal might alter gene expression through COIL Proteomic comparison of coi1-1 and aos plants confirmed these observations and highlighted probable protein degradation processes controlled by jasmonates and COI1 in the wounded leaf. This thesis revealed new functions for COI1 and for AOS-generated oxylipins in the jasmonate signaling pathway. Résumé Les signaux dérivés d'acides gras sont des médiateurs de réponses aux stress et de la défense des eucaryotes multicellulaires. Parmi eux, les jasmonates sont de puissants composés de sig¬nalisation chez les plantes. L'acide jasmonique (JA) et l'acide 12-oxo-phytodienoïc (OPDA) sont les deux membres les mieux caractérisés de la grande famille des jasmonates. Cette thèse étudie plus profondément leurs rôles de signalisation en utilisant des approches génomique et protéomique. Cette étude est basée sur un modèle génétique simple n'impliquant que deux gènes. Le premier est PALLENE OXYDE SYNTHASE (AOS) qui encode l'enzyme la plus importante pour la fabrication des jasmonates. Le deuxième est CORONATINE INSENSITIVE 1 (COI1) qui est impliqué dans la totalité des réponses aux jasmonates connues à ce jour. Nous avons posé la question suivante : est-ce que les mutations nulles dans les gènes AOS et COI1 ont des effets analogues sur le transcriptome ? Nous avons trouvé que ce n'était pas le cas. Si la majorité des gènes dépendants de COI1 sont également dépendants d'AOS, l'expression d'un gène codant pour une protéine formée de doigts de zinc n'est pas affectée par la mutation de COI1 tout en étant dépendante d'AOS. Nous avons donc supposé qu'un membre de la famille des jasmonates, probablement OPDA, pouvait modifier l'expression de certains gènes indépendamment de COI1. Inversement, nous avons montré que, tout en étant dépendante de COI1, l'expression d'au moins trois gènes, dont un codant pour une protéine kinase, n'était pas affectée par l'absence d'une protéine AOS fonctionnelle. Nous en avons conclu qu'un signal autre qu'un jasmonate devait modifier l'expression de certains gènes à travers COI1. La comparaison par protéomique de plantes aos et coi1-1 a confirmé ces observations et a mis en évidence un probable processus de dégradation de protéines contrôlé par les jasmonates et COU_ Cette thèse a mis en avant de nouvelles fonctions pour COI1 et pour des oxylipines générées par AOS dans le cadre de la signalisation par les jasmonates.

Glutathione deficit in schizophrenia: strategies to increase glutathione levels in " in vitro " and clinical studies

Summary: Decrease in glutathione (GSH) levels was observed in cerebrospinal fluid, prefrontal cortex and post-mortem striatum of schizophrenia patients. Evidences suggest a defect in GSH synthesis at the levels of the rate-limiting synthesizing enzyme, glutamate cysteine ligase (GCL). Indeed, polymorphisms in the gene of the modifier subunit of GCL (GCLM) was shown to be associated with the disease in three different populations, GCLM gene expression is decreaséd in fibroblasts from patients and the increase in GCL activity induced by an oxidative stress is lower in patients' fibroblasts compared to controls. GSH being a major antioxydant and redox regulator, its presence is of high importance for protecting cells against oxidative stress. The aim of the present work was to use various substances to increase GSH levels by diverse strategies. Since the synthesizing enzyme GCL is defective, bypassing this enzyme was the first strategy we used. GSH ethyl ester (GSHEE), a membrane permeable analog of GSH, succeeded in replenishing GSH levels in cultured neurons and astrocytes previously depleted in GSH by L-buthionine-(S,R)-sulfoximine (BSO), an inhibitor of GCL. GSHEE also abolished dopamine-induced decrease of NMDA-mediated calcium response observed in BSO-treated neurons. y-Glutamylcysteine ethyl ester (GCSE), a membrane permeable analog of the product of GCL, increased GSH levels only in astrocytes. The second strategy was to boost the defective enzyme GCL. While quercetin (flavonoid) could increase GSH levels only in astrocytes, curcumin (polyphenol) and tertbutylhydroquinone (quinone) were successful in both neurons and astrocytes, via an increase in the gene expression of the two subunits of GCL and, consequently, an increase in the activity of the enzyme. However, FK506, an immunosupressant, was unefficient. Treating astrocytes from GCLM KO mice showed that the modulatory subunit is necessary for the action of the substances. Finally, since cysteine is the limiting precursor in the synthesis of GSH, we hypothesized that we could increase GSH levels by providing more of this precursor. N-acetyl-cysteine (NAC), a cysteine donor, was administered to schizophrenia patients, using adouble-blind and cross-over protocol. NAC significantly improved the mismatch negativity (MMN), a component of the auditory evoked potentials, thought to reflect selective current flowing through open, unblocked NMDA channels. Considering that NMDA function is reduced when GSH levels are low, increasing these levels with NAC could improve NMDA function as reflected by the improvement in the generation of the MMN. Résumé: Les taux de glutathion (GSH) dans le liquide céphalo-rachidien, le cortex préfrontal ainsi que le striatum post-mortem de patients schizophrènes, sont diminués. L'enzyme limitante dans la synthèse du GSH, la glutamyl-cysteine ligase (GCL), est défectueuse. En effet, des polymorphismes dans le gène de la sous-unité modulatrice de GCL (GCLM) sont associés à la maladie, l'expression du gène GCLM est diminuée dans les fibroblastes de patients et, lors d'un stress oxidative, l'augmentation de l'activité de GCL est plus faible chez les patients que chez les contrôles. Le GSH étant un important antioxydant et régulateur du status redox, sa présence est primordiale afin de protéger les cellules contre les stress oxydatifs. Au cours du présent travail, une variété de substances ont été utilisées dans le but d'augmenter les taux de GSH. Passer outre l'enzyme de synthèse GCL qui est défectueuse fut la première stratégie utilisée. L'éthylester de GSH (GSHEE), un analogue du GSH qui pénètre la membrane cellulaire, a augmenté les taux de GSH dans des neurones et des astrocytes déficitaires en GSH dû au L-buthionine-(S,R)-sulfoximine (BSO), un inhibiteur du GCL. Dans ces neurones, le GSHEE a aussi aboli la diminution de la réponse NMDA, induite parla dopamine. L'éthyl-ester de y-glutamylcysteine (GCEE), un analogue du produit de la GCL qui pénètre la membrane cellulaire, a augmenté les taux de GSH seulement dans les astrocytes. La seconde stratégie était d'augmenter l'activité de l'enzyme GCL. Tandis que la quercétine (flavonoïde) n'a pu augmenter les taux de GSH que dans les astrocytes, la curcumin (polyphénol) et le tert-butylhydroquinone (quinone) furent efficaces dans les deux types de cellules, via une augmentation de l'expression des gènes des deux sous-unités de GCL et de l'activité de l'enzyme. Le FK506 (immunosupresseur) n' a démontré aucune efficacité. Traiter des astrocytes provenant de souris GCLM KO a permis d'observer que la sous-unité modulatoire est nécessaire à l'action des substances. Enfin, puisque la cysteine est le substrat limitant dans la synthèse du GSH, fournir plus de ce présurseur pourrait augmenter les taux de GSH. Nacétyl-cystéine (NAC), un donneur de cystéine, a été administrée à des schizophrènes, lors d'une étude en double-aveugle et cross-over. NAC a amélioré le mismatch negativity (MMN), un composant des potentials évoqués auditifs, qui reflète le courant circulant via les canaux NMDA. Puisque la fonctionnalité des R-NMDA est diminuée lorsque les taux de GSH sont bas, augmenter ces taux avec NAC pourrait améliorer la fonction des R-NMDA, réflété par une augmentation de l'amplitude du MMN.

The UlaG protein familly defines novel structural and functional motifs grafted on an ancient RNase fold

Background: Bacterial populations are highly successful at colonizing new habitats and adapting to changing environmental conditions, partly due to their capacity to evolve novel virulence and metabolic pathways in response to stress conditions and to shuffle them by horizontal gene transfer (HGT). A common theme in the evolution of new functions consists of gene duplication followed by functional divergence. UlaG, a unique manganese-dependent metallo-b-lactamase (MBL) enzyme involved in L-ascorbate metabolism by commensal and symbiotic enterobacteria, provides a model for the study of the emergence of new catalytic activities from the modification of an ancient fold. Furthermore, UlaG is the founding member of the so-called UlaG-like (UlaGL) protein family, a recently established and poorly characterized family comprising divalent (and perhaps trivalent)metal-binding MBLs that catalyze transformations on phosphorylated sugars and nucleotides. Results: Here we combined protein structure-guided and sequence-only molecular phylogenetic analyses to dissect the molecular evolution of UlaG and to study its phylogenomic distribution, its relatedness with present-day UlaGL protein sequences and functional conservation. Phylogenetic analyses indicate that UlaGL sequences are present in Bacteria and Archaea, with bona fide orthologs found mainly in mammalian and plant-associated Gramnegative and Gram-positive bacteria. The incongruence between the UlaGL tree and known species trees indicates exchange by HGT and suggests that the UlaGL-encoding genes provided a growth advantage under changing conditions. Our search for more distantly related protein sequences aided by structural homology has uncovered that UlaGL sequences have a common evolutionary origin with present-day RNA processing and metabolizing MBL enzymes widespread in Bacteria, Archaea, and Eukarya. This observation suggests an ancient origin for the UlaGL family within the broader trunk of the MBL superfamily by duplication, neofunctionalization and fixation. Conclusions: Our results suggest that the forerunner of UlaG was present as an RNA metabolizing enzyme in the last common ancestor, and that the modern descendants of that ancestral gene have a wide phylogenetic distribution and functional roles. We propose that the UlaGL family evolved new metabolic roles among bacterial and possibly archeal phyla in the setting of a close association with metazoans, such as in the mammalian gastrointestinal tract or in animal and plant pathogens, as well as in environmental settings. Accordingly, the major evolutionary forces shaping the UlaGL family include vertical inheritance and lineage-specific duplication and acquisition of novel metabolic functions, followed by HGT and numerous lineage-specific gene loss events.

REGSTATTOOLS: freeware statistical tools for the analysis of disease population databases used in health and social studies

Background: The repertoire of statistical methods dealing with the descriptive analysis of the burden of a disease has been expanded and implemented in statistical software packages during the last years. The purpose of this paper is to present a web-based tool, REGSTATTOOLS http://regstattools.net intended to provide analysis for the burden of cancer, or other group of disease registry data. Three software applications are included in REGSTATTOOLS: SART (analysis of disease"s rates and its time trends), RiskDiff (analysis of percent changes in the rates due to demographic factors and risk of developing or dying from a disease) and WAERS (relative survival analysis). Results: We show a real-data application through the assessment of the burden of tobacco-related cancer incidence in two Spanish regions in the period 1995-2004. Making use of SART we show that lung cancer is the most common cancer among those cancers, with rising trends in incidence among women. We compared 2000-2004 data with that of 1995-1999 to assess percent changes in the number of cases as well as relative survival using RiskDiff and WAERS, respectively. We show that the net change increase in lung cancer cases among women was mainly attributable to an increased risk of developing lung cancer, whereas in men it is attributable to the increase in population size. Among men, lung cancer relative survival was higher in 2000-2004 than in 1995-1999, whereas it was similar among women when these time periods were compared. Conclusions: Unlike other similar applications, REGSTATTOOLS does not require local software installation and it is simple to use, fast and easy to interpret. It is a set of web-based statistical tools intended for automated calculation of population indicators that any professional in health or social sciences may require.

Structural and optical properties of high quality zinc-blende/wurtzite GaAs nanowire heterostructures

The structural and optical properties of three different kinds of GaAs nanowires with 100% zinc-blende structure and with an average of 30% and 70% wurtzite are presented. A variety of shorter and longer segments of zinc-blende or wurtzite crystal phases are observed by transmission electron microscopy in the nanowires. Sharp photoluminescence lines are observed with emission energies tuned from 1.515 eV down to 1.43 eV when the percentage of wurtzite is increased. The downward shift of the emission peaks can be understood by carrier confinement at the interfaces, in quantum wells and in random short period superlattices existent in these nanowires, assuming a staggered band offset between wurtzite and zinc-blende GaAs. The latter is confirmed also by time-resolved measurements. The extremely local nature of these optical transitions is evidenced also by cathodoluminescence measurements. Raman spectroscopy on single wires shows different strain conditions, depending on the wurtzite content which affects also the band alignments. Finally, the occurrence of the two crystallographic phases is discussed in thermodynamic terms.

Structural and optical properties of InAs/GaAs quantum structures

The goal of the thesis was to study fundamental structural and optical properties of InAs islands and In(Ga)As quantum rings. The research was carried out at the Department of Micro and Nanosciences of Helsinki University of Technology. A good surface quality can be essential for the potential applications in optoelectronic devices. For such device applications it is usually necessary to control size, density and arrangement of the islands. In order to study the dependence of the structural properties of the islands and the quantum rings on growth conditions, atomic force microscope was used. Obtained results reveal that the size and the density of the In(Ga)As quantum rings strongly depend on the growth temperature, the annealing time and the thickness of the partial capping layer. From obtained results it is possible to conclude that to get round shape islands and high density one has to use growth temperature of 500 ̊C. In the case of formation of In(Ga)As quantum rings the effect of mobility anisotropy is observed that so the shape of the rings is not symmetric. To exclude this effect it is preferable to use a higher annealing temperature of 570 ̊C. Optical properties were characterized by PL spectroscopy. PL emission was observed from buried InAs quantum dots and In(Ga)As quantum rings grown with different annealing time and temperature and covered with a various thickness of the partial capping layer.

Development, physical-chemical stability and release studies of four alcohol-free spironolactone suspensions for use in pediatrics

Dissolution studies have become of great significance because, in most cases, drug dissolution is the rate-limiting step in the absorption process. As occurs with solid oral dosage forms, heterogeneous disperse systems (suspensions) could also have some problems with their in vitro dissolution. The objective of this study was to evaluate influence of the excipients on the release of spironolactone from four alcohol free suspensions (pharmaceutical compounding) of spironolactone 5 mg/mL suitable for pediatric use. Also the comparison of the physical and chemical stability of the suspensions stored at 4, 25 and 40 ºC over a 60- day period has been studied. Rheological behavior, particle size, a prediction of long-term physical stability, pH and assay of spironolactone by HPLC were assessed at prefixed times. The dissolution profile of each suspension was determined and compared with that of the commercial tablets. A microbiological study of the best formula was also performed. Chemically, the four spironolactone suspensions were stable for 60 days stored at three temperatures; Suspension IV had optimum pH values and the highest recovery percentage. In terms of physical stability, sedimentation occurred in Suspension IV and flotation of spironolactone in Suspensions I, II and III. Suspension III had the highest viscosity and the slowest drug release. Suspension IV was also microbiologically stable for 60 days. In conclusion, Suspension IV had the best properties and the least suitable form was Suspension III, as its high viscosity made it difficult to achieve homogeneous redispersion, and it had the slowest dissolution profile.

Structural and functional properties of two human FXYD3 (Mat-8) isoforms.

Six of 7 FXYD proteins have been shown to be tissue-specific modulators of Na,K-ATPase. In this study, we have identified two splice variants of human FXYD3, or Mat-8, in CaCo-2 cells. Short human FXYD3 has 72% sequence identity with mouse FXYD3, whereas long human FXYD3 is identical to short human FXYD3 but has a 26-amino acid insertion after the transmembrane domain. Short and long human FXYD3 RNAs and proteins are differentially expressed during differentiation of CaCo-2 cells. Long human FXYD3 is mainly expressed in nondifferentiated cells and short human FXYD3 in differentiated cells and both FXYD3 variants can be co-immunoprecipitated with a Na,K-ATPase antibody. In contrast to mouse FXYD3, which has two transmembrane domains for lack of cleavage of the signal peptide, human FXYD3 has a cleavable signal peptide and adopts a type I topology. After co-expression in Xenopus oocytes, both human FXYD3 variants associate stably only with Na,K-ATPase isozymes but not with H,K-ATPase or Ca-ATPase. Similar to mouse FXYD3, short human FXYD3 decreases the apparent K(+) and Na(+) affinity of Na,K-ATPase over a large range of membrane potentials. On the other hand, long human FXYD3 decreases the apparent K(+) affinity only at slightly negative and positive membrane potentials and increases the apparent Na(+) affinity of Na,K-ATPase. Finally, both short and long human FXYD3 induce a hyperpolarization activated current, similar to that induced by mouse FXYD3. Thus, we have characterized two human FXYD3 isoforms that are differentially expressed in differentiated and non-differentiated cells and show different functional properties.