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.

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.

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.

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.

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.

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.

DNA methylation changes in endometrium and correlation with gene expression during the transition from pre-receptive to receptive phase.

The inner uterine lining (endometrium) is a unique tissue going through remarkable changes each menstrual cycle. Endometrium has its characteristic DNA methylation profile, although not much is known about the endometrial methylome changes throughout the menstrual cycle. The impact of methylome changes on gene expression and thereby on the function of the tissue, including establishing receptivity to implanting embryo, is also unclear. Therefore, this study used genome-wide technologies to characterize the methylome and the correlation between DNA methylation and gene expression in endometrial biopsies collected from 17 healthy fertile-aged women from pre-receptive and receptive phase within one menstrual cycle. Our study showed that the overall methylome remains relatively stable during this stage of the menstrual cycle, with small-scale changes affecting 5% of the studied CpG sites (22,272 out of studied 437,022 CpGs, FDR < 0.05). Of differentially methylated CpG sites with the largest absolute changes in methylation level, approximately 30% correlated with gene expression measured by RNA sequencing, with negative correlations being more common in 5' UTR and positive correlations in the gene 'Body' region. According to our results, extracellular matrix organization and immune response are the pathways most affected by methylation changes during the transition from pre-receptive to receptive phase.

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.

Maternal Pre-Pregnancy Obesity Is Associated with Altered Placental Transcriptome.

Maternal obesity has a major impact on pregnancy outcomes. There is growing evidence that maternal obesity has a negative influence on placental development and function, thereby adversely influencing offspring programming and health outcomes. However, the molecular mechanisms underlying these processes are poorly understood. We analysed ten term placenta's whole transcriptomes in obese (n = 5) and normal weight women (n = 5), using the Affymetrix microarray platform. Analyses of expression data were carried out using non-parametric methods. Hierarchical clustering and principal component analysis showed a clear distinction in placental transcriptome between obese and normal weight women. We identified 72 differentially regulated genes, with most being down-regulated in obesity (n = 61). Functional analyses of the targets using DAVID and IPA confirm the dysregulation of previously identified processes and pathways in the placenta from obese women, including inflammation and immune responses, lipid metabolism, cancer pathways, and angiogenesis. In addition, we detected new molecular aspects of obesity-derived effects on the placenta, involving the glucocorticoid receptor signalling pathway and dysregulation of several genes including CCL2, FSTL3, IGFBP1, MMP12, PRG2, PRL, QSOX1, SERPINE2 and TAC3. Our global gene expression profiling approach demonstrates that maternal obesity creates a unique in utero environment that impairs the placental transcriptome.

Speeding-up Bioinformatics Algorithms with Heterogeneous Architectures: Highly Heterogeneous Smith-Waterman (HHeterSW).

The Smith-Waterman algorithm has a great sensitivity when used for biological sequence-database searches, but at the expense of high computing-power requirements. To overcome this problem, there are implementations in literature that exploit the different hardware-architectures available in a standard PC, such as GPU, CPU, and coprocessors. We introduce an application that splits the original database-search problem into smaller parts, resolves each of them by executing the most efficient implementations of the Smith-Waterman algorithms in different hardware architectures, and finally unifies the generated results. Using non-overlapping hardware allows simultaneous execution, and up to 2.58-fold performance gain, when compared with any other algorithm to search sequence databases. Even the performance of the popular BLAST heuristic is exceeded in 78% of the tests. The application has been tested with standard hardware: Intel i7-4820K CPU, Intel Xeon Phi 31S1P coprocessors, and nVidia GeForce GTX 960 graphics cards. An important increase in performance has been obtained in a wide range of situations, effectively exploiting the available hardware.

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.

Analysis of microRNA Microarrays in Cardiogenesis.

microRNAs are a subclass of noncoding RNAs which have been demonstrated to play pivotal roles in multiple cellular mechanisms. microRNAs are small RNA molecules of 22-24 nt in length capable of modulating protein translation and/or RNA stability by base-priming with complementary sequences of the mRNAs, normally at the 3'untranslated region. To date, over 2,000 microRNAs have been already identified in humans, and orthologous microRNAs have been also identified in distinct animals and plants ranging a wide vast of species. High-throughput analyses by microarrays have become a gold standard to analyze the changes on microRNA expression in normal and pathological cellular or tissue conditions. In this chapter, we provide insights into the usage of this uprising technology in the context of cardiac development and disease.

A Pitx2-MicroRNA Pathway Modulates Cell Proliferation in Myoblasts and Skeletal-Muscle Satellite Cells and Promotes Their Commitment to a Myogenic Cell Fate.

The acquisition of a proliferating-cell status from a quiescent state as well as the shift between proliferation and differentiation are key developmental steps in skeletal-muscle stem cells (satellite cells) to provide proper muscle regeneration. However, how satellite cell proliferation is regulated is not fully understood. Here, we report that the c-isoform of the transcription factor Pitx2 increases cell proliferation in myoblasts by downregulating microRNA 15b (miR-15b), miR-23b, miR-106b, and miR-503. This Pitx2c-microRNA (miRNA) pathway also regulates cell proliferation in early-activated satellite cells, enhancing Myf5(+) satellite cells and thereby promoting their commitment to a myogenic cell fate. This study reveals unknown functions of several miRNAs in myoblast and satellite cell behavior and thus may have future applications in regenerative medicine.

Fractals in the neurosciences, Part II: clinical applications and future perspectives.

It has been ascertained that the human brain is a complex system studied at multiple scales, from neurons and microcircuits to macronetworks. The brain is characterized by a hierarchical organization that gives rise to its highly topological and functional complexity. Over the last decades, fractal geometry has been shown as a universal tool for the analysis and quantification of the geometric complexity of natural objects, including the brain. The fractal dimension has been identified as a quantitative parameter for the evaluation of the roughness of neural structures, the estimation of time series, and the description of patterns, thus able to discriminate different states of the brain in its entire physiopathological spectrum. Fractal-based computational analyses have been applied to the neurosciences, particularly in the field of clinical neurosciences including neuroimaging and neuroradiology, neurology and neurosurgery, psychiatry and psychology, and neuro-oncology and neuropathology. After a review of the basic concepts of fractal analysis and its main applications to the basic neurosciences in part I of this series, here, we review the main applications of fractals to the clinical neurosciences for a holistic approach towards a fractal geometry model of the brain.