Cliques of Neurons Bound into Cavities Provide a Missing Link between Structure and Function

A recent publication provides the network graph for a neocortical microcircuit comprising 8 million connections between 31,000 neurons (H. Markram, et al., Reconstruction and simulation of neocortical microcircuitry, Cell, 163 (2015) no. 2, 456-492). Since traditional graph-theoretical methods may not be sufficient to understand the immense complexity of such a biological network, we explored whether methods from algebraic topology could provide a new perspective on its structural and functional organization. Structural topological analysis revealed that directed graphs representing connectivity among neurons in the microcircuit deviated significantly from different varieties of randomized graph. In particular, the directed graphs contained in the order of $10^7$ simplices {\DH} groups of neurons with all-to-all directed connectivity. Some of these simplices contained up to 8 neurons, making them the most extreme neuronal clustering motif ever reported. Functional topological analysis of simulated neuronal activity in the microcircuit revealed novel spatio-temporal metrics that provide an effective classification of functional responses to qualitatively different stimuli. This study represents the first algebraic topological analysis of structural connectomics and connectomics-based spatio-temporal activity in a biologically realistic neural microcircuit. The methods used in the study show promise for more general applications in network science

Effects of alpha-mangostin on viability, growth and cohesion of multicellular spheroids derived from human breast cancer cell lines

Background: alpha-Mangostin (a-MG) is extracted from Garcinia mangostana Linn and exerts antiproliferative activities. Although several researches on a-MG were performed using cell monolayers, the in vitro pharmacological effects on 3D cancer models have never been investigated. Aim of the present study was to find new anticancer properties of a-MG by evaluating the changes that this compound provokes in multicellular tumour spheroids (MCTSs). Methods: MCTSs were generated from MDA-MB-231 and MCF-7 breast tumour cell lines and then treated with 0.1-30 µg/ml a-MG for 24 and 48 h. MCTS size, density, and cell migration were determined by software elaboration of phase contrast images captured by a digital camera. Cell viability was evaluated by resazurin and acid phosphatase assays, while cell apoptosis was assessed by a fluorescent assay of caspase activity. The distribution of living cells inside MCTSs was shown by live/dead fluorescence staining. Results: A dose-dependent decrease in cell viability was obtained by treating MDA-MB-231 spheroids with a-MG for 48 h (IC50 = 0.70-1.25 µg/ml). A significant reduction in spheroid volume, paralleled by its increased compactness, was observed only at concentration of 30 µg/ml, but not with lower doses of a-MG. By contrast, a-MG in the range of 5-15 µg/ml increased the size of MCTSs due to a parallel reduction in cell aggregation. The same window of concentrations was also able to stimulate cell apoptosis in a dose-dependent manner. Bimodal volumetric effects were also obtained by treating the spheroids generated from the MCF-7 cells with 0.1·30 µg/ml a-MG for 48 h. Finally, doses higher than 5 µg/ml caused a progressive impairment in cell migration from the edge of MDA-MB-231 MCTSs. Conclusion: After exposure at doses of a-MG just above IC50, MDA-MB-231 spheroids showed a significant reduction in cell adhesion that did not stimulate cell migration but, on the contrary, blunted cell motility. These findings suggest a novel anticancer feature of a-MG that could be taken into consideration to improve conventional drug penetration into the tumour bulk

Enhanced interpretation of newborn screening results without analyte cutoff values

A collaboration among 157 newborn screening programs in 47 countries has lead to the creation of a database of 705,333 discrete analyte concentrations from 11,462 cases affected with 57 metabolic disorders, and from 631 heterozygotes for 12 conditions. This evidence was first applied to establish disease ranges for amino acids and acylcarnitines, and clinically validate 114 cutoff target ranges. Objective: To improve quality and performance with an evidence-based approach, multivariate pattern recognition software has been developed to aid in the interpretation of complex analyte profiles. The software generates tools that convert multiple clinically significant results into a single numerical score based on overlap between normal and disease ranges, penetration within the disease range, differences between specific conditions, and weighted correction factors. Design: Eighty-five on-line tools target either a single condition or the differential diagnosis between two or more conditions. Scores are expressed as a numerical value and as the percentile rank among all cases with the condition chosen as primary target, and are compared to interpretation guidelines. Tools are updated automatically after any new data submission (2009- 2011: 5.2 new cases added per day on average). Main outcome measures: Retrospective evaluation of past cases suggest that these tools could have avoided at least half of 277 false positive outcomes caused by carrier status for fatty acid oxidation disorders, and could have prevented 88% of false negative events caused by cutoff 7 values set inappropriately. In Minnesota, their prospective application has been a major contributing factor to the sustained achievement of a false positive rate below 0.1% and a positive predictive value above 60%. Conclusions: Application of this computational approach to raw data could make cutoff values for single analytes effectively obsolete. This paradigm is not limited to newborn screening and is applicable to the interpretation of diverse multi-analyte profiles utilized in laboratory medicine. Abstract wor