Carlo Maj

Peripheral whole blood microRNA alterations in major depression and bipolar disorder

Major depression (MD) and bipolar disorder (BD) are severe and potentially life-threating mood disorders whose etiology is to date not completely understood. MicroRNAs (miRNAs) are small non-coding RNAs that regulate protein synthesis post-transcriptionally by base-pairing to target gene mRNAs. Growing evidence indicated that miRNAs might play a key role in the pathogenesis of neuropsychiatric disorders and in the action of psychotropic drugs. On these bases, in this study we evaluated the expression levels of 1733 mature miRNAs annotated in miRBase v.17, through a microarray technique, in the blood of 20 MD and 20 BD patients and 20 healthy controls, in order to identify putative miRNA signatures associated with mood disorders. We found that 5 miRNAs (hsa-let-7a-5p, hsa-let-7d-5p, hsa-let-7f-5p, hsa-miR-24-3p and hsa-miR-425-3p) were specifically altered in MD patients and 5 (hsa-miR-140-3p, hsa-miR-30d-5p, hsa-miR-330-5p, hsa-miR-378a-5p and hsa-miR-21-3p) in BD patients, whereas 2 miRNAs (hsa-miR-330-3p and hsa-miR-345-5p) were dysregulated in both the diseases. The bioinformatic prediction of the genes targeted by the altered miRNAs revealed the possible involvement of neural pathways relevant for psychiatric disorders. In conclusion, the observed results indicate a dysregulation of miRNA blood expression in mood disorders and could indicate new avenues for a better understanding of their pathogenetic mechanisms. The identified alterations may represent potential peripheral biomarkers to be complemented with other clinical and biological features for the improvement of diagnostic accuracy.

Altered Gene Expression in Schizophrenia: Findings from Transcriptional Signatures in Fibroblasts and Blood

Background Whole-genome expression studies in the peripheral tissues of patients affected by schizophrenia (SCZ) can provide new insight into the molecular basis of the disorder and innovative biomarkers that may be of great utility in clinical practice. Recent evidence suggests that skin fibroblasts could represent a non-neural peripheral model useful for investigating molecular alterations in psychiatric disorders. Methods A microarray expression study was conducted comparing skin fibroblast transcriptomic profiles from 20 SCZ patients and 20 controls. All genes strongly differentially expressed were validated by real-time quantitative PCR (RT-qPCR) in fibroblasts and analyzed in a sample of peripheral blood cell (PBC) RNA from patients (n = 25) and controls (n = 22). To evaluate the specificity for SCZ, alterations in gene expression were tested in additional samples of fibroblasts and PBCs RNA from Major Depressive Disorder (MDD) (n = 16; n = 21, respectively) and Bipolar Disorder (BD) patients (n = 15; n = 20, respectively). Results Six genes (JUN, HIST2H2BE, FOSB, FOS, EGR1, TCF4) were significantly upregulated in SCZ compared to control fibroblasts. In blood, an increase in expression levels was confirmed only for EGR1, whereas JUN was downregulated; no significant differences were observed for the other genes. EGR1 upregulation was specific for SCZ compared to MDD and BD. Conclusions Our study reports the upregulation of JUN, HIST2H2BE, FOSB, FOS, EGR1 and TCF4 in the fibroblasts of SCZ patients. A significant alteration in EGR1 expression is also present in SCZ PBCs compared to controls and to MDD and BD patients, suggesting that this gene could be a specific biomarker helpful in the differential diagnosis of major psychoses.

Computational Modeling of the Metabolic States Regulated by the Kinase Akt

Signal transduction and gene regulation determine a major reorganization of metabolic activities in order to support cell proliferation. Protein Kinase B (PKB), also known as Akt, participates in the PI3K/Akt/mTOR pathway, a master regulator of aerobic glycolysis and cellular biosynthesis, two activities shown by both normal and cancer proliferating cells. Not surprisingly considering its relevance for cellular metabolism, Akt/PKB is often found hyperactive in cancer cells. In the last decade, many efforts have been made to improve the understanding of the control of glucose metabolism and the identification of a therapeutic window between proliferating cancer cells and proliferating normal cells. In this context, we have modeled the link between the PI3K/Akt/mTOR pathway, glycolysis, lactic acid production, and nucleotide biosynthesis. We used a computational model to compare two metabolic states generated by two different levels of signaling through the PI3K/Akt/mTOR pathway: one of the two states represents the metabolism of a growing cancer cell characterized by aerobic glycolysis and cellular biosynthesis, while the other state represents the same metabolic network with a reduced glycolytic rate and a higher mitochondrial pyruvate metabolism. Biochemical reactions that link glycolysis and pentose phosphate pathway revealed their importance for controlling the dynamics of cancer glucose metabolism.

Grid computing for sensitivity analysis of stochastic biological models

Systems biology is a multidisciplinary research area aimed at investigating biological systems by developing mathematical models that approach the study and the analysis of both the structure and behaviour of a biological phenomenon from a system perspective. The dynamics described by such mathematical models can be deeply affected by many parameters, and an extensive exploration of the parameters space in order to find crucial factors is most of the time prohibitive since it requires the execution of a huge number of computer simulations. Sensitivity analysis techniques can help in understanding how much the uncertainty in the model outcome is determined by the uncertainties, or by the variations, of the model input factors (components, reactions and respective parameters). In this work we exploit the European Grid Infrastructure to manage the calculations required to perform the SA on a stochastic model of bacterial chemotaxis, using an improved version of the first order screening method of Morris. According to the results achieved in our exploratory analysis, the European Grid Infrastructure is a useful solution for distributing the stochastic simulations required to carry out the SA of a stochastic model. Considering that the more intensive the computation the more scalable the infrastructure, grid computing can be a suitable technology for large scale biological models analysis.

The role of the potassium channel gene KCNK2 in major depressive disorder

Six single nucleotide polymorphisms (SNPs) of the KCNK2 gene were investigated for their association with major depressive disorder (MDD) and treatment efficacy in 590 MDD patients and 441 controls. The A homozygotes of rs10779646 were significantly more frequent in patients than controls whereas G allele of rs7549184 was associated with the presence of psychotic symptoms and the severity of disease. Evaluating the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) dataset, we confirmed our findings.

Parallel solutions for voxel-based simulations of reaction-diffusion systems.

There is an increasing awareness of the pivotal role of noise in biochemical processes and of the effect of molecular crowding on the dynamics of biochemical systems. This necessity has given rise to a strong need for suitable and sophisticated algorithms for the simulation of biological phenomena taking into account both spatial effects and noise. However, the high computational effort characterizing simulation approaches, coupled with the necessity to simulate the models several times to achieve statistically relevant information on the model behaviours, makes such kind of algorithms very time-consuming for studying real systems. So far, different parallelization approaches have been deployed to reduce the computational time required to simulate the temporal dynamics of biochemical systems using stochastic algorithms. In this work we discuss these aspects for the spatial TAU-leaping in crowded compartments (STAUCC) simulator, a voxel-based method for the stochastic simulation of reaction-diffusion processes which relies on the Sτ-DPP algorithm. In particular we present how the characteristics of the algorithm can be exploited for an effective parallelization on the present heterogeneous HPC architectures.

SENSITIVITY ANALYSIS FOR STUDYING THE RELATION BETWEEN BIOCHEMICAL REACTIONS AND METABOLIC PHENOTYPES

Metabolic models are the most widespread type of models in systems biology and are currently used in several applications, including metabolic engineering and investigations of pathological states in which metabolic disorders play a relevant role. Once a model has been defined and corroborated, sensitivity analysis techniques can be used to study the model behavior in relation to perturbations of the model parameters. Here, we describe how it is possible to combine regionalized sensitivity analysis and response surface methodology to screen and quantitatively characterize the relation between metabolic phenotypes and biochemical reactions rates. By means of this approach, we identified the most important reactions for the citric acid efflux from mitochondria, one of the key metabolic traits of cancer cells.

A CUDA Implementation of the Spatial TAU-Leaping in Crowded Compartments (STAUCC) Simulator

The increasing awareness of the pivotal role of noise in biochemical systems has given rise to a strong need for suitable stochastic algorithms for the description and the simulation of biological phenomena. However, the high computational demand that characterizes stochastic simulation approaches coupled with the necessity to simulate the models several times to achieve statistically relevant information on the model behaviors makes the application of such kind of algorithms often unfeasible. So far, different parallelization approaches have been employed to reduce the computational time required for the analysis of biochemical systems modeled using stochastic algorithms. Most of the proposed solutions use an embarrassingly parallel approach to run in parallel several simulations using the cores of a workstation and/or the nodes of a cluster. In this work we present the Spatial TAU-leaping in Crowded Compartments (STAUCC) simulator, a software that relies on an efficient CUDA implementation of the Stau-DPP algorithm, a voxel-based method for the stochastic simulation of Reaction-Diffusion processes. We evaluate its application and performance for the modeling of diffusion processes simultaneously occurring within a space represented considering different levels of granularity.

Nanomedicine in Psychiatry: New Therapeutic Opportunities from Research on Small RNAs

Preclinical Research

The Role of Metabotropic Glutamate Receptor Genes in Schizophrenia.

Genomic studies revealed two main components in the genetic architecture of schizophrenia, one constituted by common variants determining a distributed polygenic effect and one represented by a large number of heterogeneous rare and highly disruptive mutations. These gene modifications often affect neural transmission and different studies proved an involvement of metabotropic glutamate receptors in schizophrenia phenotype. Through the combination of literature information with genomic data from public repositories, we analyzed the current knowledge on the involvement of genetic variations of the human metabotropic glutamate receptors in schizophrenia and related endophenotypes. Despite the analysis did not reveal a definitive connection, different suggestive associations have been identified and in particular a relevant role has emerged for GRM3 in affecting specific schizophrenia endophenotypes. This supports the hypothesis that these receptors are directly involved in schizophrenia disorder.