Scn1a haploinsufficiency in the prefrontal cortex engages to cognitive impairment and depressive phenotype.

Altered development and function of the prefrontal cortex (PFC) during adolescence is implicated in the origin of mental disorders. Deficits in the GABAergic system prominently contribute to these alterations. Nav1.1 is a voltage-gated Na+ channel critical for normal GABAergic activity. Here, we studied the role of Nav1.1 in PFC function and its potential relationship with the aetiology of mental disorders. Dysfunction of Nav1.1 activity in the medial PFC (mPFC) of adolescent mice enhanced the local excitation/inhibition ratio, resulting in epileptic activity, cognitive deficits and depressive-like behaviour in adulthood, along with a gene expression profile linked to major depressive disorder (MDD). Additionally, it reduced extracellular serotonin concentration in the dorsal raphe nucleus and brain-derived neurotrophic factor expression in the hippocampus, two MDD-related brain areas beyond the PFC. We also observed alterations in oscillatory activity and impaired hippocampal-mPFC coherence during sleep. Finally, we found reduced expression levels of SCN1A, the gene encoding Nav1.1, in post-mortem PFC samples from human MDD subjects. Collectively, our results provide a novel mechanistic framework linking adolescence-specific alterations in Nav1.1 function in the PFC to the pathogenesis of epilepsy and comorbidities such as cognitive impairment and depressive disorders.

Effects of exercise on whole-blood transcriptome profile in children with overweight/obesity.

BACKGROUND: The current knowledge about the molecular mechanisms underlying the health benefits of exercise is still limited, especially in childhood. We set out to investigate the effects of a 20-week exercise intervention on whole-blood transcriptome profile (RNA-seq) in children with overweight/obesity. METHODS: Twenty-four children (10.21 ± 1.33 years, 46% girls) with overweight/obesity, were randomized to either a 20-week exercise program (intervention group; n = 10), or to a no-exercise control group (n = 14). Whole-blood transcriptome profile was analyzed using RNA-seq by STRT technique with GlobinLock technology. RESULTS: Following the 20-week exercise intervention program, 161 genes were differentially expressed between the exercise and the control groups among boys, and 121 genes among girls (p-value <0.05), while after multiple correction, no significant difference between exercise and control groups persisted in gene expression profiles (FDR >0.05). Genes enriched in GO processes and molecular pathways showed different immune response in boys (antigen processing and presentation, infections, and T cell receptor complex) and in girls (Fc epsilon RI signaling pathway) (FDR <0.05). CONCLUSION: These results suggest that 20-week exercise intervention program alters the molecular pathways involved in immune processes in children with overweight/obesity.

What lies underneath: Precise classification of brain states using time-dependent topological structure of dynamics.

The self-organising global dynamics underlying brain states emerge from complex recursive nonlinear interactions between interconnected brain regions. Until now, most efforts of capturing the causal mechanistic generating principles have supposed underlying stationarity, being unable to describe the non-stationarity of brain dynamics, i.e. time-dependent changes. Here, we present a novel framework able to characterise brain states with high specificity, precisely by modelling the time-dependent dynamics. Through describing a topological structure associated to the brain state at each moment in time (its attractor or 'information structure'), we are able to classify different brain states by using the statistics across time of these structures hitherto hidden in the neuroimaging dynamics. Proving the strong potential of this framework, we were able to classify resting-state BOLD fMRI signals from two classes of post-comatose patients (minimally conscious state and unresponsive wakefulness syndrome) compared with healthy controls with very high precision.

Distinct whole-blood transcriptome profile of children with metabolic healthy overweight/obesity compared to metabolic unhealthy overweight/obesity.

BACKGROUND: Youth populations with overweight/obesity (OW/OB) exhibit heterogeneity in cardiometabolic health phenotypes. The underlying mechanisms for those differences are still unclear. This study aimed to analyze the whole-blood transcriptome profile (RNA-seq) of children with metabolic healthy overweight/obesity (MHO) and metabolic unhealthy overweight/obesity (MUO) phenotypes. METHODS: Twenty-seven children with OW/OB (10.1 ± 1.3 years, 59% boys) from the ActiveBrains project were included. MHO was defined as having none of the following criteria for metabolic syndrome: elevated fasting glucose, high serum triglycerides, low high-density lipoprotein-cholesterol, and high systolic or diastolic blood pressure, while MUO was defined as presenting one or more of these criteria. Inflammatory markers were additionally determined. Total blood RNA was analyzed by 5'-end RNA-sequencing. RESULTS: Whole-blood transcriptome analysis revealed a distinct pattern of gene expression in children with MHO compared to MUO children. Thirty-two genes differentially expressed were linked to metabolism, mitochondrial, and immune functions. CONCLUSIONS: The identified gene expression patterns related to metabolism, mitochondrial, and immune functions contribute to a better understanding of why a subset of the population remains metabolically healthy despite having overweight/obesity. IMPACT: A distinct pattern of whole-blood transcriptome profile (RNA-seq) was identified in children with metabolic healthy overweight/obesity (MHO) compared to metabolic unhealthy overweight/obesity (MUO) phenotype. The most relevant genes in understanding the molecular basis underlying the MHO/MUO phenotypes in children could be: RREB1, FAM83E, SLC44A1, NRG1, TMC5, CYP3A5, TRIM11, and ADAMTSL2. The identified whole-blood transcriptome profile related to metabolism, mitochondrial, and immune functions contribute to a better understanding of why a subset of the population remains metabolically healthy despite having overweight/obesity.

Cardiorespiratory fitness in children with overweight/obesity: Insights into the molecular mechanisms.

OBJECTIVES: High cardiorespiratory fitness (CRF) levels reduce the risk of developing cardiovascular disease (CVD) during adulthood. However, little is known about the molecular mechanisms underlying the health benefits of high CRF levels at the early stage of life. This study aimed to analyze the whole-blood transcriptome profile of fit children with overweight/obesity (OW/OB) compared to unfit children with OW/OB. DESIGN: 27 children with OW/OB (10.14 ± 1.3 years, 59% boys) from the ActiveBrains project were evaluated. VO2 peak was assessed using a gas analyzer, and participants were categorized into fit or unfit according to the CVD risk-related cut-points. Whole-blood transcriptome profile (RNA sequencing) was analyzed. Differential gene expression analysis was performed using the limma R/Bioconductor software package (analyses adjusted by sex and maturational status), and pathways' enrichment analysis was performed with DAVID. In addition, in silico validation data mining was performed using the PHENOPEDIA database. RESULTS: 256 genes were differentially expressed in fit children with OW/OB compared to unfit children with OW/OB after adjusting by sex and maturational status (FDR < 0.05). Enriched pathway analysis identified gene pathways related to inflammation (eg, dopaminergic and GABAergic synapse pathways). Interestingly, in silico validation data mining detected a set of the differentially expressed genes to be related to CVD, metabolic syndrome, hypertension, inflammation, and asthma. CONCLUSION: The distinct pattern of whole-blood gene expression in fit children with OW/OB reveals genes and gene pathways that might play a role in reducing CVD risk factors later in life.

Cross-disorder analysis of endometriosis and its comorbid diseases reveals shared genes and molecular pathways and proposes putative biomarkers of endometriosis.

RESEARCH QUESTION: Women with endometriosis are considered to be at higher risk of several chronic diseases, such as autoimmune disorders, gynaecological cancers, asthma/atopic diseases and cardiovascular and inflammatory bowel diseases. Could the study of endometriosis-associated comorbidities help to identify potential biomarkers and target pathways of endometriosis? DESIGN: A systematic review was performed to identify all possible endometriosis-associated comorbid conditions. Next, this list of disorders was coded into MeSH terms, and the gene expression profiles were downloaded from the Phenopedia database and subsequently analysed following a systems biology approach. RESULTS: The results identified a group of 127 candidate genes that were recurrently expressed in endometriosis and its closest comorbidities and that were defined as 'endometriosis sibling disorders' (ESD). The enrichment analysis showed that these candidate genes are principally involved in immune and drug responses, hormone metabolism and cell proliferation, which are well-known hallmarks of endometriosis. The expression of ESD genes was then validated on independent sample cohorts (n = 207 samples), in which the involvement of 16 genes (AGTR1, BDNF, C3, CCL2, CD40, CYP17A1, ESR1, IGF1, IGF2, IL10, MMP1, MMP7, MMP9, PGR, SERPINE1 and TIMP2) in endometriosis was confirmed. Several of these genes harbour polymorphisms that associate to either endometriosis or its comorbid conditions. CONCLUSIONS: The study results highlight the molecular processes underlying the aetiopathogenesis of endometriosis and its comorbid conditions, and identify putative endometriosis biomarkers.

An integrative methodology based on protein-protein interaction networks for identification and functional annotation of disease-relevant genes applied to channelopathies.

BACKGROUND: Biologically data-driven networks have become powerful analytical tools that handle massive, heterogeneous datasets generated from biomedical fields. Protein-protein interaction networks can identify the most relevant structures directly tied to biological functions. Functional enrichments can then be performed based on these structural aspects of gene relationships for the study of channelopathies. Channelopathies refer to a complex group of disorders resulting from dysfunctional ion channels with distinct polygenic manifestations. This study presents a semi-automatic workflow using protein-protein interaction networks that can identify the most relevant genes and their biological processes and pathways in channelopathies to better understand their etiopathogenesis. In addition, the clinical manifestations that are strongly associated with these genes are also identified as the most characteristic in this complex group of diseases. RESULTS: In particular, a set of nine representative disease-related genes was detected, these being the most significant genes in relation to their roles in channelopathies. In this way we attested the implication of some voltage-gated sodium (SCN1A, SCN2A, SCN4A, SCN4B, SCN5A, SCN9A) and potassium (KCNQ2, KCNH2) channels in cardiovascular diseases, epilepsies, febrile seizures, headache disorders, neuromuscular, neurodegenerative diseases or neurobehavioral manifestations. We also revealed the role of Ankyrin-G (ANK3) in the neurodegenerative and neurobehavioral disorders as well as the implication of these genes in other systems, such as the immunological or endocrine systems. CONCLUSIONS: This research provides a systems biology approach to extract information from interaction networks of gene expression. We show how large-scale computational integration of heterogeneous datasets, PPI network analyses, functional databases and published literature may support the detection and assessment of possible potential therapeutic targets in the disease. Applying our workflow makes it feasible to spot the most relevant genes and unknown relationships in channelopathies and shows its potential as a first-step approach to identify both genes and functional interactions in clinical-knowledge scenarios of target diseases. METHODS: An initial gene pool is previously defined by searching general databases under a specific semantic framework. From the resulting interaction network, a subset of genes are identified as the most relevant through the workflow that includes centrality measures and other filtering and enrichment databases.

Evaluation of the 3D fractal dimension as a marker of structural brain complexity in multiple-acquisition MRI.

Fractal analysis represents a promising new approach to structural neuroimaging data, yet systematic evaluation of the fractal dimension (FD) as a marker of structural brain complexity is scarce. Here we present in-depth methodological assessment of FD estimation in structural brain MRI. On the computational side, we show that spatial scale optimization can significantly improve FD estimation accuracy, as suggested by simulation studies with known FD values. For empirical evaluation, we analyzed two recent open-access neuroimaging data sets (MASSIVE and Midnight Scan Club), stratified by fundamental image characteristics including registration, sequence weighting, spatial resolution, segmentation procedures, tissue type, and image complexity. Deviation analyses showed high repeated-acquisition stability of the FD estimates across both data sets, with differential deviation susceptibility according to image characteristics. While less frequently studied in the literature, FD estimation in T2-weighted images yielded robust outcomes. Importantly, we observed a significant impact of image registration on absolute FD estimates. Applying different registration schemes, we found that unbalanced registration induced (a) repeated-measurement deviation clusters around the registration target, (b) strong bidirectional correlations among image analysis groups, and (c) spurious associations between the FD and an index of structural similarity, and these effects were strongly attenuated by reregistration in both data sets. Indeed, differences in FD between scans did not simply track differences in structure per se, suggesting that structural complexity and structural similarity represent distinct aspects of structural brain MRI. In conclusion, scale optimization can improve FD estimation accuracy, and empirical FD estimates are reliable yet sensitive to image characteristics.

Informational structures: A dynamical system approach for integrated information.

Integrated Information Theory (IIT) has become nowadays the most sensible general theory of consciousness. In addition to very important statements, it opens the door for an abstract (mathematical) formulation of the theory. Given a mechanism in a particular state, IIT identifies a conscious experience with a conceptual structure, an informational object which exists, is composed of identified parts, is informative, integrated and maximally irreducible. This paper introduces a space-time continuous version of the concept of integrated information. To this aim, a graph and a dynamical systems treatment is used to define, for a given mechanism in a state for which a dynamics is settled, an Informational Structure, which is associated to the global attractor at each time of the system. By definition, the informational structure determines all the past and future behavior of the system, possesses an informational nature and, moreover, enriches all the points of the phase space with cause-effect power by means of its associated Informational Field. A detailed description of its inner structure by invariants and connections between them allows to associate a transition probability matrix to each informational structure and to develop a measure for the level of integrated information of the system.

Cross-disorder comparative analysis of comorbid conditions reveals novel autism candidate genes.

BACKGROUND: Numerous studies have highlighted the elevated degree of comorbidity associated with autism spectrum disorder (ASD). These comorbid conditions may add further impairments to individuals with autism and are substantially more prevalent compared to neurotypical populations. These high rates of comorbidity are not surprising taking into account the overlap of symptoms that ASD shares with other pathologies. From a research perspective, this suggests common molecular mechanisms involved in these conditions. Therefore, identifying crucial genes in the overlap between ASD and these comorbid disorders may help unravel the common biological processes involved and, ultimately, shed some light in the understanding of autism etiology. RESULTS: In this work, we used a two-fold systems biology approach specially focused on biological processes and gene networks to conduct a comparative analysis of autism with 31 frequently comorbid disorders in order to define a multi-disorder subcomponent of ASD and predict new genes of potential relevance to ASD etiology. We validated our predictions by determining the significance of our candidate genes in high throughput transcriptome expression profiling studies. Using prior knowledge of disease-related biological processes and the interaction networks of the disorders related to autism, we identified a set of 19 genes not previously linked to ASD that were significantly differentially regulated in individuals with autism. In addition, these genes were of potential etiologic relevance to autism, given their enriched roles in neurological processes crucial for optimal brain development and function, learning and memory, cognition and social behavior. CONCLUSIONS: Taken together, our approach represents a novel perspective of autism from the point of view of related comorbid disorders and proposes a model by which prior knowledge of interaction networks may enlighten and focus the genome-wide search for autism candidate genes to better define the genetic heterogeneity of ASD.

Endometrial transcriptome analysis indicates superiority of natural over artificial cycles in recurrent implantation failure patients undergoing frozen embryo transfer.

Little consensus has been reached on the best protocol for endometrial preparation for frozen embryo transfer (FET). It is not known how, and to what extent, hormone supplementation in artificial cycles influences endometrial preparation for embryo implantation at a molecular level, especially in patients who have experienced recurrent implantation failure. Transcriptome analysis of 15 endometrial biopsy samples at the time of embryo implantation was used to compare two different endometrial preparation protocols, natural versus artificial cycles, for FET in women who have experienced recurrent implantation failure compared with fertile women. IPA and DAVID were used for functional analyses of differentially expressed genes. The TRANSFAC database was used to identify oestrogen and progesterone response elements upstream of differentially expressed genes. Cluster analysis demonstrated that natural cycles are associated with a better endometrial receptivity transcriptome than artificial cycles. Artificial cycles seemed to have a stronger negative effect on expression of genes and pathways crucial for endometrial receptivity, including ESR2, FSHR, LEP, and several interleukins and matrix metalloproteinases. Significant overrepresentation of oestrogen response elements among the genes with deteriorated expression in artificial cycles (P < 0.001) was found; progesterone response elements predominated in genes with amended expression with artificial cycles (P = 0.0052).

Gene expression profiling to investigate tyrosol-induced lifespan extension in Caenorhabditis elegans.

PURPOSE: We have previously reported that tyrosol (TYR) promotes lifespan extension in the nematode Caenorhabditis elegans, also inducing a stronger resistance to thermal and oxidative stress in vivo. In this study, we performed a whole-genome DNA microarray in order to narrow down the search for candidate genes or signaling pathways potentially involved in TYR effects on C. elegans longevity. METHODS: Nematodes were treated with 0 or 250 µM TYR, total RNA was isolated at the adult stage, and derived cDNA probes were hybridized to Affymetrix C. elegans expression arrays. Microarray data analysis was performed, and relative mRNA expression of selected genes was validated using qPCR. RESULTS: Microarray analysis identified 208 differentially expressed genes (206 over-expressed and two under-expressed) when comparing TYR-treated nematodes with vehicle-treated controls. Many of these genes are linked to processes such as regulation of growth, transcription, reproduction, lipid metabolism and body morphogenesis. Moreover, we detected an interesting overlap between the expression pattern elicited by TYR and those induced by other dietary polyphenols known to extend lifespan in C. elegans, such as quercetin and tannic acid. CONCLUSIONS: Our results suggest that important cellular mechanisms directly related to longevity are influenced by TYR treatment in C. elegans, supporting our previous notion that this phenol might act on conserved genetic pathways to increase lifespan in a whole organism.

Comparative Analysis of Shapley Values Enhances Transcriptomics Insights across Some Common Uterine Pathologies.

Uterine pathologies pose a challenge to women's health on a global scale. Despite extensive research, the causes and origin of some of these common disorders are not well defined yet. This study presents a comprehensive analysis of transcriptome data from diverse datasets encompassing relevant uterine pathologies such as endometriosis, endometrial cancer and uterine leiomyomas. Leveraging the Comparative Analysis of Shapley values (CASh) technique, we demonstrate its efficacy in improving the outcomes of the classical differential expression analysis on transcriptomic data derived from microarray experiments. CASh integrates the microarray game algorithm with Bootstrap resampling, offering a robust statistical framework to mitigate the impact of potential outliers in the expression data. Our findings unveil novel insights into the molecular signatures underlying these gynecological disorders, highlighting CASh as a valuable tool for enhancing the precision of transcriptomics analyses in complex biological contexts. This research contributes to a deeper understanding of gene expression patterns and potential biomarkers associated with these pathologies, offering implications for future diagnostic and therapeutic strategies.

Fractal Dimension Analysis in Neurological Disorders: An Overview.

Fractal analysis has emerged as a powerful tool for characterizing irregular and complex patterns found in the nervous system. This characterization is typically applied by estimating the fractal dimension (FD), a scalar index that describes the topological complexity of the irregular components of the nervous system, both at the macroscopic and microscopic levels, that may be viewed as geometric fractals. Moreover, temporal properties of neurophysiological signals can also be interpreted as dynamic fractals. Given its sensitivity for detecting changes in brain morphology, FD has been explored as a clinically relevant marker of brain damage in several neuropsychiatric conditions as well as in normal and pathological cerebral aging. In this sense, evidence is accumulating for decreases in FD in Alzheimer's disease, frontotemporal dementia, Parkinson's disease, multiple sclerosis, and many other neurological disorders. In addition, it is becoming increasingly clear that fractal analysis in the field of clinical neurology opens the possibility of detecting structural alterations in the early stages of the disease, which highlights FD as a potential diagnostic and prognostic tool in clinical practice.

Hormonal and molecular characterization of follicular fluid, cumulus cells and oocytes from pre-ovulatory follicles in stimulated and unstimulated cycles.

BACKGROUND: The use of ovarian stimulation, to stimulate a multi-follicular response for assisted reproduction treatments, may force the production of oocytes from follicles that do not reach optimal maturation, possibly yielding oocytes that are not fully competent. The present study aimed to define the follicular environment and oocyte competence of unstimulated pre-ovulatory follicles, to compare it with that of similar-sized stimulated follicles. For this purpose, we analyzed the follicular hormonal milieu, the oocyte meiotic spindle, the embryo development and the cumulus cells gene expression (GE) profiles. METHODS AND RESULTS: The study population was divided in two groups: (i) 42 oocyte donors undergoing unstimulated cycles and (ii) 18 oocyte donors undergoing controlled ovarian stimulation cycles (COS). Follicular fluid was analyzed to quantify the concentrations of estradiol (E2), progesterone (P), FSH, LH, testosterone (T) and androstendione (Δ4). T was higher in the COS group, while Δ4, E2 and LH were significantly higher in unstimulated cycles. The cumulus oophorus cells (CC) surrounding the oocyte were removed and their GE profiles were analyzed with microarrays. There were 18 differentially expressed genes in CC: 7 were up-regulated and 11 were down-regulated in the COS cycles. The microarray was validated by qRT-PCR. The analysis of spindle structure revealed no significant differences between the groups, except for the parameter of length which presented differences. The fertilization ability and embryo morphology on Days 2, 3 and 4 did not show any significant differences between groups. CONCLUSIONS: The use of ovarian stimulation induces changes in the follicular fluid and in CC GE that may affect immune processes, meiosis and ovulation pathways. Although these differences do not seem to relate to early-stage embryo morphology, the implications of some of the molecules, especially ALDH1A2, CTSL and ZNF33B at the CC level, deserve to be addressed in future studies.

Transcriptomic signature of trophoblast differentiation in a human embryonic stem cell model.

Identification of genes involved in trophoblast differentiation is of great interest in understanding cellular and molecular mechanisms involved in placental development and is relevant clinically to fetal development, fertility, and maternal health. Herein, we investigated differentiation of human embryonic stem cells (hESCs) down the trophoblast lineage by culture with bone morphogenetic protein 4 (BMP4) over a 10-day period. Within 2 days, the stemness markers POU5F1 and NANOG were markedly down-regulated, followed temporally by up-regulation of the CDX2, KRT7, HLA-G, ID2, CGA, and CGB trophoblast markers. To understand, on a global scale, changes in the transcriptome during the differentiation of hESCs down the trophoblast lineage, a large-scale microarray analysis was performed. Through whole-genome analysis, more than 3800 genes displayed statistically significant and 2-fold or greater changes in expression during the time course. Of those genes that showed the largest increases, many were involved in processes associated with trophoblast biology; however, novel genes were also identified. Some of them are hypothesized to be associated mainly with extracellular matrix remodeling (e.g., NID2) and cell migration and invasion (e.g., RAB25). Using Ingenuity pathways analysis software to identify signaling pathways involved in trophoblast differentiation or function, we discovered that many genes are involved in WNT/beta-catenin, ERK/MAPK, NFKB, and calcium signaling pathways, suggesting potential roles for these families in trophoblast development. This work provides an in vitro functional genomic model with which to identify genes involved in trophoblast development.

Next-generation bioinformatics: using many-core processor architecture to develop a web service for sequence alignment.

MOTIVATION: Bioinformatics algorithms and computing power are the main bottlenecks for analyzing huge amount of data generated by the current technologies, such as the 'next-generation' sequencing methodologies. At the same time, most powerful microprocessors are based on many-core chips, yet most applications cannot exploit such power, requiring parallelized algorithms. As an example of next-generation bioinformatics, we have developed from scratch a new parallelization of the Needleman-Wunsch (NW) sequence alignment algorithm for the 64-core Tile64 microprocessor. The unprecedented performance it offers for a standalone personal computer (PC) is discussed, optimally aligning sequences up to 20 times faster than the non-parallelized version, thus saving valuable time. AVAILABILITY: This algorithm is available as a free web service for the scientific community at http://www.sicuma.uma.es/multicore. The open source code is also available on such site.

Fractal-dimension analysis detects cerebral changes in preterm infants with and without intrauterine growth restriction.

In the search for a useful parameter to detect and quantify subtle brain abnormalities in infants with intrauterine growth restriction (IUGR), we hypothesised that the analysis of the structural complexity of grey matter (GM) and white matter (WM) using the fractal dimension (FD), a measurement of the topological complexity of an object, could be established as a useful tool for quantitative studies of infant brain morphology. We studied a sample of 18 singleton IUGR premature infants, (12.72 months corrected age (CA), range: 12 months-14 months), 15 preterm infants matched one-to-one for gestational age (GA) at delivery (12.6 months; range: 12 months-14 months), and 15 neonates born at term (12.4 months; range: 11 months-14 months). The neurodevelopmental outcome was assessed in all subjects at 18 months CA according to the Bayley Scale for Infant and Toddler Development - Third edition (BSID-III). For MRI acquisition and processing, the infants were scanned at 12 months CA, in a TIM TRIO 3T scanner, sleeping naturally. Images were pre-processed using the SPM5 toolbox, the GM and WM segmented under the VBM5 toolbox, and the box-counting method was applied for FD calculation of normal and skeletonized segmented images. The results showed a significant decrease of the FD of the brain GM and WM in the IUGR group when compared to the preterm or at-term controls. We also identified a significant linear tendency of both GM and WM FD from IUGR to preterm and term groups. Finally, multiple linear analyses between the FD of the GM or WM and the neurodevelopmental scales showed a significant regression of the language and motor scales with the FD of the GM. In conclusion, a decreased FD of the GM and WM in IUGR infants could be a sensitive indicator for the investigation of structural brain abnormalities in the IUGR population at 12 months of age, which can also be related to functional disorders.

The bone marrow-derived human mesenchymal stem cell: potential progenitor of the endometrial stromal fibroblast.

The cellular sources that contribute to the renewal of human endometrium are largely unknown. It has been suggested that endometrial stem cells originate from bone marrow-derived mesenchymal stem cells (MSC), with subsequent development into endometrial stromal fibroblasts (hESF). We hypothesized that if bone marrow-derived MSC contribute to endometrial regeneration and are progenitors of hESF, their treatment with agents known to regulate hESF differentiation could promote their differentiation down the stromal fibroblast lineage. To this end, we treated bone marrow-derived MSC with estradiol and progesterone, bone morphogenetic protein 2 (BMP2), and activators of the protein kinase A (PKA) pathway and investigated specific markers of hESF differentiation (decidualization). Furthermore, we investigated the transcriptome of these cells in response to cAMP and compared this to the transcriptome of hESF decidualized in response to activation of the PKA pathway. The data support the idea that MSC can be differentiated down the hESF pathway, as evidenced by changes in cell shape and common expression of decidual markers and other genes important in hESF differentiation and function, and that bone marrow-derived MSC may be a source of endometrial stem/progenitor cells. In addition, we identified MSC-specific markers that distinguish them from other fibroblasts and, in particular, from hESF, which is of biologic relevance and practical value to the field of endometrial stem cell research.

Involvement of reactive nitrogen and oxygen species (RNS and ROS) in sunflower-mildew interaction.

Nitric oxide (.NO) has been shown to participate in plant response against pathogen infection; however, less is known of the participation of other NO-derived molecules designated as reactive nitrogen species (RNS). Using two sunflower (Helianthus annuus L.) cultivars with different sensitivity to infection by the pathogen Plasmopara halstedii, we studied key components involved in RNS and ROS metabolism. We analyzed the superoxide radical production, hydrogen peroxide content, l-arginine-dependent nitric oxide synthase (NOS) and S-nitrosoglutathione reductase (GSNOR) activities. Furthermore, we examined the location and contents of .NO, S-nitrosothiols (RSNOs), S-nitrosoglutathione (GSNO) and protein 3-nitrotyrosine (NO(2)-Tyr) by confocal laser scanning microscopy (CLSM) and biochemical analyses. In the susceptible cultivar, the pathogen induces an increase in proteins that undergo tyrosine nitration accompanied by an augmentation in RSNOs. This rise of RSNOs seems to be independent of the enzymatic generation of .NO because the l-arginine-dependent NOS activity is reduced after infection. These results suggest that pathogens induce nitrosative stress in susceptible cultivars. In contrast, in the resistant cultivar, no increase of RSNOs or tyrosine nitration of proteins was observed, implying an absence of nitrosative stress. Therefore, it is proposed that the increase of tyrosine nitration of proteins can be considered a general biological marker of nitrosative stress in plants under biotic conditions.