Drug targeting using OX7-immunoliposomes: correlation between Thy1.1 antigen expression and tissue distribution in the rat

OX7 monoclonal antibody F((ab')2) fragments directed against Thy1.1 antigen can be used for drug targeting by coupling to the surface of drug-loaded liposomes. Such OX7-conjugated immunoliposomes (OX7-IL) were used recently for drug delivery to rat glomerular mesangial cells, which are characterized by a high level of Thy1.1 antigen expression. In the present study, the relationship between OX7-IL tissue distribution and target Thy1.1 antigen localization in different organs in rat was investigated. Western blot and immunohistofluorescence analysis revealed a very high Thy1.1 expression in brain cortex and striatum, thymus and renal glomeruli. Moderate Thy1.1 levels were observed in the collecting ducts of kidney, lung tissue and spleen. Thy1.1 was not detected in liver and heart. There was a poor correlation between Thy1.1 expression levels and organ distribution of fluorescence- or (14)C-labeled OX7-IL. The highest overall organ density of OX7-IL was observed in the spleen, followed by lung, liver and kidney. Heart and brain remained negative. With respect to intra-organ distribution, a localized and distinct signal was observed in renal glomerular mesangial cells only. As a consequence, acute pharmacological (i.e. toxic) effects of doxorubicin-loaded OX7-IL were limited to renal glomeruli. The competition with unbound OX7 monoclonal antibody F((ab')2) fragments demonstrated that the observed tissue distribution and acute pharmacological effects of OX7-IL were mediated specifically by the conjugated OX7 antibody. It is concluded that both the high target antigen density and the absence of endothelial barriers are needed to allow for tissue-specific accumulation and pharmacological effects of OX7-IL. The liposomal drug delivery strategy used is therefore specific toward renal glomeruli and can be expected to reduce the risk of unwanted side effects in other tissues.

Estimating Particle Shape and Orientation Using Volume Tensors

We develop statistical procedures for estimating shape and orientation of arbitrary three-dimensional particles. We focus on the case where particles cannot be observed directly, but only via sections. Volume tensors are used for describing particle shape and orientation, and we derive stereological estimators of the tensors. These estimators are combined to provide consistent estimators of the moments of the so-called particle cover density. The covariance structure associated with the particle cover density depends on the orientation and shape of the particles. For instance, if the distribution of the typical particle is invariant under rotations, then the covariance matrix is proportional to the identity matrix. We develop a non-parametric test for such isotropy. A flexible Lévy-based particle model is proposed, which may be analysed using a generalized method of moments in which the volume tensors enter. The developed methods are used to study the cell organization in the human brain cortex.

Semi-supervised subspace clustering and applications to neuroscience

Machine learning techniques are used for extracting valuable knowledge from data. Nowa¬days, these techniques are becoming even more important due to the evolution in data ac¬quisition and storage, which is leading to data with different characteristics that must be exploited. Therefore, advances in data collection must be accompanied with advances in machine learning techniques to solve new challenges that might arise, on both academic and real applications. There are several machine learning techniques depending on both data characteristics and purpose. Unsupervised classification or clustering is one of the most known techniques when data lack of supervision (unlabeled data) and the aim is to discover data groups (clusters) according to their similarity. On the other hand, supervised classification needs data with supervision (labeled data) and its aim is to make predictions about labels of new data. The presence of data labels is a very important characteristic that guides not only the learning task but also other related tasks such as validation. When only some of the available data are labeled whereas the others remain unlabeled (partially labeled data), neither clustering nor supervised classification can be used. This scenario, which is becoming common nowadays because of labeling process ignorance or cost, is tackled with semi-supervised learning techniques. This thesis focuses on the branch of semi-supervised learning closest to clustering, i.e., to discover clusters using available labels as support to guide and improve the clustering process. Another important data characteristic, different from the presence of data labels, is the relevance or not of data features. Data are characterized by features, but it is possible that not all of them are relevant, or equally relevant, for the learning process. A recent clustering tendency, related to data relevance and called subspace clustering, claims that different clusters might be described by different feature subsets. This differs from traditional solutions to data relevance problem, where a single feature subset (usually the complete set of original features) is found and used to perform the clustering process. The proximity of this work to clustering leads to the first goal of this thesis. As commented above, clustering validation is a difficult task due to the absence of data labels. Although there are many indices that can be used to assess the quality of clustering solutions, these validations depend on clustering algorithms and data characteristics. Hence, in the first goal three known clustering algorithms are used to cluster data with outliers and noise, to critically study how some of the most known validation indices behave. The main goal of this work is however to combine semi-supervised clustering with subspace clustering to obtain clustering solutions that can be correctly validated by using either known indices or expert opinions. Two different algorithms are proposed from different points of view to discover clusters characterized by different subspaces. For the first algorithm, available data labels are used for searching for subspaces firstly, before searching for clusters. This algorithm assigns each instance to only one cluster (hard clustering) and is based on mapping known labels to subspaces using supervised classification techniques. Subspaces are then used to find clusters using traditional clustering techniques. The second algorithm uses available data labels to search for subspaces and clusters at the same time in an iterative process. This algorithm assigns each instance to each cluster based on a membership probability (soft clustering) and is based on integrating known labels and the search for subspaces into a model-based clustering approach. The different proposals are tested using different real and synthetic databases, and comparisons to other methods are also included when appropriate. Finally, as an example of real and current application, different machine learning tech¬niques, including one of the proposals of this work (the most sophisticated one) are applied to a task of one of the most challenging biological problems nowadays, the human brain model¬ing. Specifically, expert neuroscientists do not agree with a neuron classification for the brain cortex, which makes impossible not only any modeling attempt but also the day-to-day work without a common way to name neurons. Therefore, machine learning techniques may help to get an accepted solution to this problem, which can be an important milestone for future research in neuroscience. Resumen Las técnicas de aprendizaje automático se usan para extraer información valiosa de datos. Hoy en día, la importancia de estas técnicas está siendo incluso mayor, debido a que la evolución en la adquisición y almacenamiento de datos está llevando a datos con diferentes características que deben ser explotadas. Por lo tanto, los avances en la recolección de datos deben ir ligados a avances en las técnicas de aprendizaje automático para resolver nuevos retos que pueden aparecer, tanto en aplicaciones académicas como reales. Existen varias técnicas de aprendizaje automático dependiendo de las características de los datos y del propósito. La clasificación no supervisada o clustering es una de las técnicas más conocidas cuando los datos carecen de supervisión (datos sin etiqueta), siendo el objetivo descubrir nuevos grupos (agrupaciones) dependiendo de la similitud de los datos. Por otra parte, la clasificación supervisada necesita datos con supervisión (datos etiquetados) y su objetivo es realizar predicciones sobre las etiquetas de nuevos datos. La presencia de las etiquetas es una característica muy importante que guía no solo el aprendizaje sino también otras tareas relacionadas como la validación. Cuando solo algunos de los datos disponibles están etiquetados, mientras que el resto permanece sin etiqueta (datos parcialmente etiquetados), ni el clustering ni la clasificación supervisada se pueden utilizar. Este escenario, que está llegando a ser común hoy en día debido a la ignorancia o el coste del proceso de etiquetado, es abordado utilizando técnicas de aprendizaje semi-supervisadas. Esta tesis trata la rama del aprendizaje semi-supervisado más cercana al clustering, es decir, descubrir agrupaciones utilizando las etiquetas disponibles como apoyo para guiar y mejorar el proceso de clustering. Otra característica importante de los datos, distinta de la presencia de etiquetas, es la relevancia o no de los atributos de los datos. Los datos se caracterizan por atributos, pero es posible que no todos ellos sean relevantes, o igualmente relevantes, para el proceso de aprendizaje. Una tendencia reciente en clustering, relacionada con la relevancia de los datos y llamada clustering en subespacios, afirma que agrupaciones diferentes pueden estar descritas por subconjuntos de atributos diferentes. Esto difiere de las soluciones tradicionales para el problema de la relevancia de los datos, en las que se busca un único subconjunto de atributos (normalmente el conjunto original de atributos) y se utiliza para realizar el proceso de clustering. La cercanía de este trabajo con el clustering lleva al primer objetivo de la tesis. Como se ha comentado previamente, la validación en clustering es una tarea difícil debido a la ausencia de etiquetas. Aunque existen muchos índices que pueden usarse para evaluar la calidad de las soluciones de clustering, estas validaciones dependen de los algoritmos de clustering utilizados y de las características de los datos. Por lo tanto, en el primer objetivo tres conocidos algoritmos se usan para agrupar datos con valores atípicos y ruido para estudiar de forma crítica cómo se comportan algunos de los índices de validación más conocidos. El objetivo principal de este trabajo sin embargo es combinar clustering semi-supervisado con clustering en subespacios para obtener soluciones de clustering que puedan ser validadas de forma correcta utilizando índices conocidos u opiniones expertas. Se proponen dos algoritmos desde dos puntos de vista diferentes para descubrir agrupaciones caracterizadas por diferentes subespacios. Para el primer algoritmo, las etiquetas disponibles se usan para bus¬car en primer lugar los subespacios antes de buscar las agrupaciones. Este algoritmo asigna cada instancia a un único cluster (hard clustering) y se basa en mapear las etiquetas cono-cidas a subespacios utilizando técnicas de clasificación supervisada. El segundo algoritmo utiliza las etiquetas disponibles para buscar de forma simultánea los subespacios y las agru¬paciones en un proceso iterativo. Este algoritmo asigna cada instancia a cada cluster con una probabilidad de pertenencia (soft clustering) y se basa en integrar las etiquetas conocidas y la búsqueda en subespacios dentro de clustering basado en modelos. Las propuestas son probadas utilizando diferentes bases de datos reales y sintéticas, incluyendo comparaciones con otros métodos cuando resulten apropiadas. Finalmente, a modo de ejemplo de una aplicación real y actual, se aplican diferentes técnicas de aprendizaje automático, incluyendo una de las propuestas de este trabajo (la más sofisticada) a una tarea de uno de los problemas biológicos más desafiantes hoy en día, el modelado del cerebro humano. Específicamente, expertos neurocientíficos no se ponen de acuerdo en una clasificación de neuronas para la corteza cerebral, lo que imposibilita no sólo cualquier intento de modelado sino también el trabajo del día a día al no tener una forma estándar de llamar a las neuronas. Por lo tanto, las técnicas de aprendizaje automático pueden ayudar a conseguir una solución aceptada para este problema, lo cual puede ser un importante hito para investigaciones futuras en neurociencia.

Nuclear localization of prostaglandin E2 receptors

Prostaglandin E2 receptors (EP) were detected by radioligand binding in nuclear fractions isolated from porcine brain and myometrium. Intracellular localization by immunocytofluorescence revealed perinuclear localization of EPs in porcine cerebral microvascular endothelial cells. Nuclear association of EP1 was also found in fibroblast Swiss 3T3 cells stably overexpressing EP1 and in human embryonic kidney 293 (Epstein–Barr virus-encoded nuclear antigen) cells expressing EP1 fused to green fluorescent protein. High-resolution immunostaining of EP1 revealed their presence in the nuclear envelope of isolated (cultured) endothelial cells and in situ in brain (cortex) endothelial cells and neurons. Stimulation of these nuclear receptors modulate nuclear calcium and gene transcription.

Pharmacological evaluation of an [I-123] labelled imidazopyridine-3-acetamide for the study of benzodiazepine receptors

In vitro binding of the iodinated imidazopyri dine, N',N'-dimethyl-6-methyl-(4'-[I-123]iodophenyl)imidazo[1,2-a]pyridine-3-acetamide [I-123]IZOL to benzodiazepine binding sites on brain cortex, adrenal and kidney membranes is reported. Saturation experiments showed that [I-123]IZOL, bound to a single class of binding site (n(H)=0.99) on adrenal and kidney mitochondrial membranes with a moderate affinity (K-d=30 nM). The density of binding sites was 22 +/- 6 and 1.2 +/- 0.4 pmol/mg protein on adrenal and kidney membranes, respectively. No specific binding was observed in mitochondrial-synaptosomal membranes of brain cortex. In biodistribution studies in rats, the highest uptake of [I-123]IZOL was found 30 min post injection in adrenals (7.5% ID/g), followed by heart, kidney, lung (1% ID/g) and brain (0.12% ID/g), consistent with the distribution of peripheral benzodiazepine binding sites. Pre-administration of unlabelled IZOL and the specific PBBS drugs, PK 11195 and Ro 5-4864 significantly reduced the uptake of [I-123]IZOL by 30% (p < 0.05) in olfactory bulbs and by 51-86% (p < 0.01) in kidney, lungs, heart and adrenals, while it increased by 30% to 50% (p < 0.01) in the rest of the brain and the blood. Diazepam, a mixed CBR-PBBS drug, inhibited the uptake in kidney, lungs, heart, adrenals and olfactory bulbs by 32% to 44% (p < 0.01) but with no effect on brain uptake and in blood concentration. Flumazenil, a central benzodiazepine drug and haloperidol (dopamine antagonist/sigma receptor drug) displayed no effect in [I-123]IZOL in peripheral organs and in the brain. [I-123]IZOL may deserve further development for imaging selectively peripheral benzodiazepine binding sites. (c) 2006 Elsevier Inc. All rights reserved.

Antidepressant-Like Effects of Medial Prefrontal Cortex Deep Brain Stimulation in Rats

Background: Subcallosal cingulate gyrus (SCG) deep brain stimulation (DBS) is being investigated as a treatment for major depression. We report on the effects of ventromedial prefrontal cortex (vmPFC) DBS in rats, focusing on possible mechanisms involved in an antidepressant-like response in the forced swim test (FST). Methods: The outcome of vmPFC stimulation alone or combined with different types of lesions, including serotonin (5-HT) or nore-pineprhine (NE) depletion, was characterized in the FST. We also explored the effects of DBS on novelty-suppressed feeding, learned helplessness, and sucrose consumption in animals predisposed to helplessness. Results: Stimulation at parameters approximating those used in clinical practice induced a significant antidepressant-like response in the FST. Ventromedial PFC lesions or local muscimol injections did not lead to a similar outcome. However, animals treated with vmPFC ibotenic acid lesions still responded to DBS, suggesting that the modulation of fiber near the electrodes could play a role in the antidepressant-like effects of stimulation. Also important was the integrity of the serotonergic system, as the effects of DBS in the FST were completely abolished in animals bearing 5-HT, but not NE, depleting lesions. In addition, vmPFC stimulation induced a sustained increase in hippocampal 5-HT levels. Preliminary work with other models showed that DBS was also able to influence specific aspects of depressive-like states in rodents, including anxiety and anhedonia, but not helplessness. Conclusions: Our study suggests that vmPFC DES in rats maybe useful to investigate mechanisms involved in the antidepressant effects of SCG DBS.

Expression of hNP22 is altered in the frontal cortex and hippocampus of the alcoholic human brain

Background: Human neuronal protein (hNP22) is a gene with elevated messenger RNA expression in the prefrontal cortex of the human alcoholic brain. hNP22 has high homology with a rat protein (rNP22). These proteins also share homology with a number of cytoskeleton-interacting proteins. Methods: A rabbit polyclonal antibody to an 18-amino acid epitope was produced for use in Western and immunohistochemical analysis. Samples from the human frontal and motor cortices were used for Western blots (n = 10), whereas a different group of frontal cortex and hippocampal samples were obtained for immunohistochemistry (n = 12). Results: The hNP22 antibody detected a single protein in both rat and human brain. Western blots revealed a significant increase in hNP22 protein levels in the frontal cortex but not the motor cortex of alcoholic cases. Immunohistochemical studies confirmed the increased hNP22 protein expression in all cortical layers. This is consistent with results previously obtained using Northern analysis. Immunohistochemical analysis also revealed a significant increase of hNP22 immunoreactivity in the CA3 and CA4 but not other regions of the hippocampus. Conclusions: It is possible that this protein may play a role in the morphological or plastic changes observed after chronic alcohol exposure and withdrawal, either as a cytoskeleton-interacting protein or as a signaling molecule.

Doublecortin-positive cells in the adult primate cerebral cortex and possible role in brain plasticity and development.

We have demonstrated that cortical cell autografts might be a useful therapy in two monkey models of neurological disease: motor cortex lesion and Parkinson's disease. However, the origin of the useful transplanted cells obtained from cortical biopsies is not clear. In this report we describe the expression of doublecortin (DCX) in these cells based on reverse-transcription polymerase chain reaction (RT-PCR) and immunodetection in the adult primate cortex and cell cultures. The results showed that DCX-positive cells were present in the whole primate cerebral cortex and also expressed glial and/or neuronal markers such as glial fibrillary protein (GFAP) or neuronal nuclei (NeuN). We also demonstrated that only DCX/GFAP positive cells were able to proliferate and originate progenitor cells in vitro. We hypothesize that these DCX-positive cells in vivo have a role in cortical plasticity and brain reaction to injury. Moreover, in vitro these DCX-positive cells have the potential to reacquire progenitor characteristics that confirm their potential for brain repair.

Complex regional pain syndrome type I affects brain structure in prefrontal and motor cortex.

The complex regional pain syndrome (CRPS) is a rare but debilitating pain disorder that mostly occurs after injuries to the upper limb. A number of studies indicated altered brain function in CRPS, whereas possible influences on brain structure remain poorly investigated. We acquired structural magnetic resonance imaging data from CRPS type I patients and applied voxel-by-voxel statistics to compare white and gray matter brain segments of CRPS patients with matched controls. Patients and controls were statistically compared in two different ways: First, we applied a 2-sample ttest to compare whole brain white and gray matter structure between patients and controls. Second, we aimed to assess structural alterations specifically of the primary somatosensory (S1) and motor cortex (M1) contralateral to the CRPS affected side. To this end, MRI scans of patients with left-sided CRPS (and matched controls) were horizontally flipped before preprocessing and region-of-interest-based group comparison. The unpaired ttest of the "non-flipped" data revealed that CRPS patients presented increased gray matter density in the dorsomedial prefrontal cortex. The same test applied to the "flipped" data showed further increases in gray matter density, not in the S1, but in the M1 contralateral to the CRPS-affected limb which were inversely related to decreased white matter density of the internal capsule within the ipsilateral brain hemisphere. The gray-white matter interaction between motor cortex and internal capsule suggests compensatory mechanisms within the central motor system possibly due to motor dysfunction. Altered gray matter structure in dorsomedial prefrontal cortex may occur in response to emotional processes such as pain-related suffering or elevated analgesic top-down control.

Visual maps in the adult primate cerebral cortex: some implications for brain development and evolution

In this paper, the topology of cortical visuotopic maps in adult primates is reviewed, with emphasis on recent studies. The observed visuotopic organisation can be summarised with reference to two basic rules. First, adjacent radial columns in the cortex represent partially overlapping regions of the visual field, irrespective of whether these columns are part of the same or different cortical areas. This primary rule is seldom, if ever, violated. Second, adjacent regions of the visual field tend to be represented in adjacent radial columns of a same area. This rule is not as rigid as the first, as many cortical areas form discontinuous, second-order representations of the visual field. A developmental model based on these physiological observations, and on comparative studies of cortical organisation, is then proposed, in order to explain how a combination of molecular specification steps and activity-driven processes can generate the variety of visuotopic organisations observed in adult cortex.

Decreased levels of pNR1 S897 protein in the cortex of neonatal Sprague Dawley rats with hypoxic-ischemic or NMDA-induced brain damage

Our objective was to investigate the protein level of phosphorylated N-methyl-D-aspartate (NMDA) receptor-1 at serine 897 (pNR1 S897) in both NMDA-induced brain damage and hypoxic-ischemic brain damage (HIBD), and to obtain further evidence that HIBD in the cortex is related to NMDA toxicity due to a change of the pNR1 S897 protein level. At postnatal day 7, male and female Sprague Dawley rats (13.12 ± 0.34 g) were randomly divided into normal control, phosphate-buffered saline (PBS) cerebral microinjection, HIBD, and NMDA cerebral microinjection groups. Immunofluorescence and Western blot (N = 10 rats per group) were used to examine the protein level of pNR1 S897. Immunofluorescence showed that control and PBS groups exhibited significant neuronal cytoplasmic staining for pNR1 S897 in the cortex. Both HIBD and NMDA-induced brain damage markedly decreased pNR1 S897 staining in the ipsilateral cortex, but not in the contralateral cortex. Western blot analysis showed that at 2 and 24 h after HIBD, the protein level of pNR1 S897 was not affected in the contralateral cortex (P > 0.05), whereas it was reduced in the ipsilateral cortex (P < 0.05). At 2 h after NMDA injection, the protein level of pNR1 S897 in the contralateral cortex was also not affected (P > 0.05). The levels in the ipsilateral cortex were decreased, but the change was not significant (P > 0.05). The similar reduction in the protein level of pNR1 S897 following both HIBD and NMDA-induced brain damage suggests that HIBD is to some extent related to NMDA toxicity possibly through NR1 phosphorylation of serine 897.

Voluntary exercise prior to traumatic brain injury alters miRNA expression in the injured mouse cerebral cortex

MicroRNAs (miRNAs) may be important mediators of the profound molecular and cellular changes that occur after traumatic brain injury (TBI). However, the changes and possible roles of miRNAs induced by voluntary exercise prior to TBI are still not known. In this report, the microarray method was used to demonstrate alterations in miRNA expression levels in the cerebral cortex of TBI mice that were pretrained on a running wheel (RW). Voluntary RW exercise prior to TBI: i) significantly decreased the mortality rate and improved the recovery of the righting reflex in TBI mice, and ii) differentially changed the levels of several miRNAs, upregulating some and downregulating others. Furthermore, we revealed global upregulation of miR-21, miR-92a, and miR-874 and downregulation of miR-138, let-7c, and miR-124 expression among the sham-non-runner, TBI-non-runner, and TBI-runner groups. Quantitative reverse transcription polymerase chain reaction data (RT-qPCR) indicated good consistency with the microarray results. Our microarray-based analysis of miRNA expression in mice cerebral cortex after TBI revealed that some miRNAs such as miR-21, miR-92a, miR-874, miR-138, let-7c, and miR-124 could be involved in the prevention and protection afforded by voluntary exercise in a TBI model.

Effect of hyperbaric oxygenation on mitochondrial function of neuronal cells in the cortex of neonatal rats after hypoxic-ischemic brain damage

The timing and mechanisms of protection by hyperbaric oxygenation (HBO) in hypoxic-ischemic brain damage (HIBD) have only been partially elucidated. We monitored the effect of HBO on the mitochondrial function of neuronal cells in the cerebral cortex of neonatal rats after HIBD. Neonatal Sprague-Dawley rats (total of 360 of both genders) were randomly divided into normal control, HIBD, and HIBD+HBO groups. The HBO treatment began immediately after hypoxia-ischemia (HI) and continued once a day for 7 consecutive days. Animals were euthanized 0, 2, 4, 6, and 12 h post-HI to monitor the changes in mitochondrial membrane potential (ΔΨm) occurring soon after a single dose of HBO treatment, as well as 2, 3, 4, 5, 6, and 7 days post-HI to study ΔΨm changes after a series of HBO treatments. Fluctuations in ΔΨm were observed in the ipsilateral cortex in both HIBD and HIBD+HBO groups. Within 2 to 12 h after HI insult, the ΔΨm of the HIBD and HIBD+HBO groups recovered to some extent. A secondary drop in ΔΨm was observed in both groups during the 1-4 days post-HI period, but was more severe in the HIBD+HBO group. There was a secondary recovery of ΔΨm observed in the HIBD+HBO group, but not in the HIBD group, during the 5-7 days period after HI insult. HBO therapy may not lead to improvement of neural cell mitochondrial function in the cerebral cortex in the early stage post-HI, but may improve it in the sub-acute stage post-HI.

Decreased 5-HT2C receptor binding in the cerebral cortex and brain stem during pancreatic regeneration in rats

The purpose of this study was to investigate the role of central 5-HT2C receptor binding in rat model of pancreatic regeneration using 60-70% pancreatectomy. The 5-HT and 5-HT2c receptor kinetics were studied in cerebral cortex and brain stem of sham operated, 72 h pancreatectomised and 7 days pancreatectomised rats. Scatchard analysis with [3H] mesulergine in cerebral cortex showed a significant decrease (p < 0.05) in maximal binding (B^,ax) without any change in Kd in 72 h pancreatectomised rats compared with sham. The decreased Bmax reversed to sham level by 7 days after pancreatectomy. In brain stem , Scatchard analysis showed a significant decrease (p < 0.01) in Bax with a significant increase (p < 0.01) in Kd. Competition analysis in brain stem showed a shift in affinity towards a low affinity. These parameters were reversed to sham level by 7 days after pancreatectomy. Thus the results suggest that 5-HT through the 5-HT2C receptor in the brain has a functional regulatory role in the pancreatic regeneration. (Mol Cell Biochem 272: 165-170, 2005)