Probabilistic Fluorescence-Based Synapse Detection

Brain function results from communication between neurons connected by complex synaptic networks. Synapses are themselves highly complex and diverse signaling machines, containing protein products of hundreds of different genes, some in hundreds of copies, arranged in precise lattice at each individual synapse. Synapses are fundamental not only to synaptic network function but also to network development, adaptation, and memory. In addition, abnormalities of synapse numbers or molecular components are implicated in most mental and neurological disorders. Despite their obvious importance, mammalian synapse populations have so far resisted detailed quantitative study. In human brains and most animal nervous systems, synapses are very small and very densely packed: there are approximately 1 billion synapses per cubic millimeter of human cortex. This volumetric density poses very substantial challenges to proteometric analysis at the critical level of the individual synapse. The present work describes new probabilistic image analysis methods for single-synapse analysis of synapse populations in both animal and human brains.Comment: Current awaiting peer revie

The Importance of DNA Repair in Tumor Suppression

The transition from a normal to cancerous cell requires a number of highly specific mutations that affect cell cycle regulation, apoptosis, differentiation, and many other cell functions. One hallmark of cancerous genomes is genomic instability, with mutation rates far greater than those of normal cells. In microsatellite instability (MIN tumors), these are often caused by damage to mismatch repair genes, allowing further mutation of the genome and tumor progression. These mutation rates may lie near the error catastrophe found in the quasispecies model of adaptive RNA genomes, suggesting that further increasing mutation rates will destroy cancerous genomes. However, recent results have demonstrated that DNA genomes exhibit an error threshold at mutation rates far lower than their conservative counterparts. Furthermore, while the maximum viable mutation rate in conservative systems increases indefinitely with increasing master sequence fitness, the semiconservative threshold plateaus at a relatively low value. This implies a paradox, wherein inaccessible mutation rates are found in viable tumor cells. In this paper, we address this paradox, demonstrating an isomorphism between the conservatively replicating (RNA) quasispecies model and the semiconservative (DNA) model with post-methylation DNA repair mechanisms impaired. Thus, as DNA repair becomes inactivated, the maximum viable mutation rate increases smoothly to that of a conservatively replicating system on a transformed landscape, with an upper bound that is dependent on replication rates. We postulate that inactivation of post-methylation repair mechanisms are fundamental to the progression of a tumor cell and hence these mechanisms act as a method for prevention and destruction of cancerous genomes.Comment: 7 pages, 5 figures; Approximation replaced with exact calculation; Minor error corrected; Minor changes to model syste

STATISTICAL METHODS FOR THE ANALYSIS OF CANCER GENOME SEQUENCING DATA

The purpose of cancer genome sequencing studies is to determine the nature and types of alterations present in a typical cancer and to discover genes mutated at high frequencies. In this article we discuss statistical methods for the analysis of data generated in these studies. We place special emphasis on a two-stage study design introduced by Sjoblom et al.[1]. In this context, we describe statistical methods for constructing scores that can be used to prioritize candidate genes for further investigation and to assess the statistical signicance of the candidates thus identfied

Preserving Derivative Information while Transforming Neuronal Curves

The international neuroscience community is building the first comprehensive atlases of brain cell types to understand how the brain functions from a higher resolution, and more integrated perspective than ever before. In order to build these atlases, subsets of neurons (e.g. serotonergic neurons, prefrontal cortical neurons etc.) are traced in individual brain samples by placing points along dendrites and axons. Then, the traces are mapped to common coordinate systems by transforming the positions of their points, which neglects how the transformation bends the line segments in between. In this work, we apply the theory of jets to describe how to preserve derivatives of neuron traces up to any order. We provide a framework to compute possible error introduced by standard mapping methods, which involves the Jacobian of the mapping transformation. We show how our first order method improves mapping accuracy in both simulated and real neuron traces under random diffeomorphisms. Our method is freely available in our open-source Python package brainlit

Probabilistic fluorescence-based synapse detection

Deeper exploration of the brain’s vast synaptic networks will require new tools for high-throughput structural and molecular profiling of the diverse populations of synapses that compose those networks. Fluorescence microscopy (FM) and electron microscopy (EM) offer complementary advantages and disadvantages for single-synapse analysis. FM combines exquisite molecular discrimination capacities with high speed and low cost, but rigorous discrimination between synaptic and non-synaptic fluorescence signals is challenging. In contrast, EM remains the gold standard for reliable identification of a synapse, but offers only limited molecular discrimination and is slow and costly. To develop and test single-synapse image analysis methods, we have used datasets from conjugate array tomography (cAT), which provides voxel-conjugate FM and EM (annotated) images of the same individual synapses. We report a novel unsupervised probabilistic method for detection of synapses from multiplex FM (muxFM) image data, and evaluate this method both by comparison to EM gold standard annotated data and by examining its capacity to reproduce known important features of cortical synapse distributions. The proposed probabilistic model-based synapse detector accepts molecular-morphological synapse models as user queries, and delivers a volumetric map of the probability that each voxel represents part of a synapse. Taking human annotation of cAT EM data as ground truth, we show that our algorithm detects synapses from muxFM data alone as successfully as human annotators seeing only the muxFM data, and accurately reproduces known architectural features of cortical synapse distributions. This approach opens the door to data-driven discovery of new synapse types and their density. We suggest that our probabilistic synapse detector will also be useful for analysis of standard confocal and super-resolution FM images, where EM cross-validation is not practical

Characterization and prognostic value of mutations in exons 5 and 6 of the p53 gene in patients with colorectal cancers in central Iran

Background/Aims: We aimed to investigate the relation-ships among various mutations of the p53 gene and their protein products, histological characteristics, and disease prognosis of primary colorectal cancer in Isfahan, central Iran. Methods: Sixty-one patients with colorectal adenocarcinoma were enrolled in the study. Mutations of the p53 gene were detected by single-stranded conformation polymorphism and DNA sequencing. The protein stability was evaluated by immunohistochemistry. Patients were followed up to 48 months. Results: Twenty-one point mutations in exons 5 and 6 were detected in the tumor specimens of 14 patients (23%). Of those, 81% and 9.5% were missense and nonsense mutations, respectively. There were also two novel mutations in the intronic region between exons 5 and 6. In 11 mutated specimens, protein stability and protein accumulation were identified. There was a relationship between the type of mutation and protein accumulation in exons 5 and 6 of the p53 gene. The presence of the mutation was associated with an advanced stage of cancer (trend, p<0.009). Patients with mutated p53 genes had significantly lower survival rates than those with wild type p53 genes (p<0.01). Conclusions: Mutations in exons 5 and 6 of the p53 gene are common genetic alterations in colorectal adenocarcinoma in central Iran and are associated with a poor prognosis of the disease

SILAC-based phosphoproteomics reveals an inhibitory role of KSR1 in p53 transcriptional activity via modulation of DBC1

BACKGROUND We have previously identified kinase suppressor of ras-1 (KSR1) as a potential regulatory gene in breast cancer. KSR1, originally described as a novel protein kinase, has a role in activation of mitogen-activated protein kinases. Emerging evidence has shown that KSR1 may have dual functions as an active kinase as well as a scaffold facilitating multiprotein complex assembly. Although efforts have been made to study the role of KSR1 in certain tumour types, its involvement in breast cancer remains unknown. METHODS A quantitative mass spectrometry analysis using stable isotope labelling of amino acids in cell culture (SILAC) was implemented to identify KSR1-regulated phosphoproteins in breast cancer. In vitro luciferase assays, co-immunoprecipitation as well as western blotting experiments were performed to further study the function of KSR1 in breast cancer. RESULTS Of significance, proteomic analysis reveals that KSR1 overexpression decreases deleted in breast cancer-1 (DBC1) phosphorylation. Furthermore, we show that KSR1 decreases the transcriptional activity of p53 by reducing the phosphorylation of DBC1, which leads to a reduced interaction of DBC1 with sirtuin-1 (SIRT1); this in turn enables SIRT1 to deacetylate p53. CONCLUSION Our findings integrate KSR1 into a network involving DBC1 and SIRT1, which results in the regulation of p53 acetylation and its transcriptional activity