MOESM1 of Social networks help to infer causality in the tumor microenvironment

Additional file 1: Table S1. xlsx. Networks

MOESM2 of Social networks help to infer causality in the tumor microenvironment

Additional file 2: Table S2. Results influence-based network vs. random networks

Comparison of PRUNET with similar available software.

<p>Scores represent the match between predicted and real phenotypes; a score of 1 represents a full agreement between training set and model predictions, while a score of 0.5 reflects a correct description of 50% of the training set.</p><p>Comparison of PRUNET with similar available software.</p

Validation of PRUNET using four biological examples.

<p>The scores obtained after the application of PRUNET to four biological examples, namely epithelial to mesenchymal transition (EMT), Th1-Th2 transdifferentiation (Th1-Th2), induced pluripotent stem cells (iPSC) and cardiomyocyte differention of human embryonic stem cells (hESC-cardiomyocyte), were compared with the scores obtained by a population of randomly generated subnetworks from the prior knowledge network. Green, orange and red lines represent the cumulative frequencies of scores obtained with training sets of a half, an intermediate number (different for each case) and all of the genes respectively, whereas the blue line represents the scores obtained from the population of subnetworks randomly generated from the prior knowledge network. The separation between blue and the other lines represents the contribution of PRUNET to the prior knowledge network in terms of describing the known phenotypes of different training set sizes.</p

PRUNET interface.

<p>The graphical user interface is organized in tabs, namely a welcome tab (<b>A</b>), input tab (<b>B</b>), options tab (<b>C</b>) and results tab (D). Both the input and results tab include a network visualizer to facilitate the interaction of the user with the program.</p

Additional file 1: of Boolean regulatory network reconstruction using literature based knowledge with a genetic algorithm optimization method

Methods. Detailed description of the methods. (PDF 1329 kb

EMT core network integrating the miR203/SNAI1 and miR200/ZEB double negative feedback loops.

<p>A) The top panel corresponds to the core network integrating described interactions between miR-203, miR-200s (miR-200), SNAI1, ZEB1, ZEB2 and E-cadherin (CDH1). The bottom panels show the stable states “E” and “M” obtained after dynamic analyses. B) <i>In silico</i> upregulation of SNAI1 in a continuous dynamic system of the EMT core network. The state of SNAI1 is changed from “0” to “1” at time point 2 (arbitrary units of time), during two units of time. Diamonds represent miRNAs, squares transcription factors, and circles coding-genes other than transcription factors. Red and green colours stand for upregulated and downregulated expression levels, respectively. Edges represent an interaction between two actors, either activation (arrow) or inhibition (blunt arrow). The “lightning” indicates a SNAI1 upregulation triggering the transition from state “E” to “M” (red arrow).</p

Large-scale analysis of miRNA expression signatures, and miR-203 expression during SNAI1 induction in MCF7-SNAI1 cells.

<p>A) List of miRNAs found downregulated, in at least three studies, in the large-scale analysis. B) qRT-PCR analyses of miR-203 expression levels normalized to U44 expression and expression levels in non-induced cells. C) Relative luciferase activity of miR-203 and miR-200b promoter constructs in non-induced (NI) and SNAI1-induced cells. Data are normalized to “NI” (*, p<0.05; **, p<0.01).</p

MiR-203 promotes epithelial-like features and represses endogenous SNAI1.

<p>HTB129-ctrl and HTB129-miR203 cells were subjected to A) qRT-PCR analyses of SNAI1 mRNA expression in HTB129-ctrl and HTB129-miR203 cells, B) fluorescent staining with DAPI (blue) and phalloidin (red) (scale bar, 20 µm), C) migration and D) invasion assays. E) Predicted miR-203 target sites within SNAI1 3′UTR. F, G) Relative luciferase activity of SNAI1-3′UTR wild type (wt) or mutant (mut) in MDA231 cells transfected with control (F, G), miR-203 (F) or miR-200a/c (G) precursors. Co-transfection with GAPDH 3′UTR vector served as additional negative control (*, p<0.05; **, p<0.01).</p

Higher Frequency and Complexity of Sleep Disturbances in Dementia with Lewy Bodies as Compared to Alzheimer's Disease

Background Sleep disorders are frequent in patients with dementia. Sleep disturbances can negatively influence the disease burden of dementia and accelerate the rate of cognitive decline. It is hypothesized that sleep pathology, such as REM sleep behavior disorder (RBD), can occur many years before symptomatic disease and that it can be one of the early manifestation of neurodegeneration in alpha-synucleinopathies, such as dementia with Lewy bodies (DLB). DLB is the third most frequent type of dementia, after Alzheimer&rsquo;s dementia (AD) and vascular dementia (VaD). However, there are few validation studies of the DLB consensus criteria from 2005, and the criteria appear to still have low sensitivity. Objectives The main goals of this thesis were: 1) to investigate the frequency of sleep disturbances in dementia and compare sleep disturbance profiles in a group of patients with AD and DLB; 2) to assess the potential influence of probable RBD (pRBD) on the rate of dementia progression; and 3) to compare neuropathological diagnoses with clinical diagnoses based on DLB consensus criteria from 2005. In the first publication, we investigated the prevalence of different sleep disturbances (SD) in mild AD and mild DLB dementia, in order to characterize the profile of sleep disorders in these two dementias. We studied the relationship between SD, RBD, and neuropsychological symptoms to find out if they could be associated with a higher incidence of psychopathological symptoms, i.e. depression, anxiety, hallucinations. In the second publication, we tested the hypothesis that RBD could be a factor for a more aggressive dementia course in light of previous reports that idiopathic RBD (iRBD) is a risk factor for mild cognitive impairment (MCI) and dementia. RBD seems also to worsen the course of Parkinson&rsquo;s disease (PD) and accelerate conversion from MCI to Parkinson&rsquo;s disease dementia (PDD). We examined the association of pRBD with global cognitive decline and decline in the cognitive subdomains in a 4-years longitudinal study. In the third publication, we analyzed reliability between clinical and neuropathological diagnoses in the selection of 56 autopsied patients to characterize sensitivity and specificity of consensus criteria from 2005. Moreover, we discussed potential reasons for misdiagnosis. Subjects and methods The thesis was based on data from the prospective DEMVEST study which started in 2005 and was conducted until 2013. Patients with dementia and their caregivers were recruited from clinics of old age psychiatry and geriatrics in Western Norway. The inclusion criterion was mild dementia with Mini-Mental State Examination (MMSE) &ge;20. Dementia diagnoses were made according to established criteria, and pathologically confirmed in a subsample of 56 cases. Patients with acute cognitive disturbances and known chronic psychiatric illness were excluded. MMSE was used for cognitive screening. A battery of neuropsychological tests was used for measuring cognition: Stroop test, Controlled Oral Word Associations Test, semantic fluency (COWAT), Boston Naming Test 15 items (BNT), Trail Making Test A and B (TMT A, TMT B), the California Verbal Learning Test-II (CVLT-II), Silhouettes and Cubes on the Visual Object and Space Perception Battery (VOSP). Standard statistical analyses were performed for demographical and clinical comparisons. Longitudinal analyses were conducted with generalized linear mixed models (GLMM). Results A total of 266 patients were included in the DEMVEST study, and our cohort consisted of 139 with AD, 82 with DLB, 17 with PDD, 13 with VaD and 5 with FTD, six with other diagnoses (normal ageing, mixed AD and VaD, alcoholic dementia, unspecified dementia). Numbers of participants varied in the three papers according to purposes of the studies. Paper 1: 56% of patients with dementia, represented by AD and DLB, had at least one sleep disorder, with predominance of insomni (34.8%). Sleep problems were significantly more frequent in the group of patients with DLB than with AD (73.2% vs. 45.7%, p&lt;0,001). DLB diagnoses also increased the risk of having three or more sleep disorders. Having at least one sleep-related problem was associated with significantly higher rates of psychopathological symptoms compared to patients without any sleep disorders. The second most common sleep problem in the DLB group after insomni was pRBD, which was present in 40% of patients. Paper 2: Of the total 246 patients included at baseline we identified 47 (19.1%) with pRBD. Patients with pRBD had the same progression rate of dementia as patients without RBD, both in global cognition measured by MMSE and in different cognitive domains, during 4 years of follow-up. However, the results showed that patients with RBD had significantly lower performance in figure copying in MMSE, VOSP Cube, TMT A and Stroop Words compared to RBD-negative patients throughout the observation period. Paper 3: In a cohort of 56 patients who came to autopsy we found 31 cases with neuropathologically verified AD, 20 with DLB and PDD and five with other diagnoses. Results showed that for overall DLB/ PDD diagnosis there was a relative good consistency between the clinical and pathological diagnoses, with a sensitivity (SN) and specificity (SP) of 80% and 92% and that the positive predictive value (PPV) and negative predictive value (NPV) were 84% and 89% respectively. For possible and probable DLB alone (without PDD), the values of 73% (SN), 93% (SP), 79% (PPV) and 90% (NPV) were found. Of the 56 patients, seven were misdiagnosed; three patients with false positive DLB diagnosis and four with false negative DLB diagnosis. For AD diagnosis SN, SP, as well as PPV and NPV were 81%, 88%, 89% and 79%, respectively. We also discussed factors that could affect the accuracy of clinical diagnosis. Conclusions Sleep disturbances are common in dementia, but patients with DLB have more sleep problems and a more complicated pattern of sleep pathology than those with AD. Occurrence of at least one sleep disturbance seems to be related to a more severe course of psychiatric symptoms. We could not demonstrate that pRBD is a risk factor for faster progression of global cognition and cognition in various cognitive domains of dementia. Sensitivity, specificity and accuracy of clinical diagnoses were similar to results from previous studies which applied DLB consensus criteria from 2005 and showed that there is still a need to improve sensitivity.</p