Autophagy is an evolutionarily conserved, intracellular self-defense mechanism in which organelles and proteins are sequestered into autophagic vesicles that are subsequently degraded through fusion with lysosomes. Cells, thereby, prevent the toxic accumulation of damaged or unnecessary components, but also recycle these components to sustain metabolic homoeostasis. Heightened autophagy is a mechanism of resistance for cancer cells faced with metabolic and therapeutic stress, revealing opportunities for exploitation as a therapeutic target in cancer. We summarize recent developments in the field of autophagy and cancer and build upon the results presented at the Cancer Therapy Evaluation Program (CTEP) Early Drug Development meeting in March 2010. Herein, we describe our current understanding of the core components of the autophagy machinery and the functional relevance of autophagy within the tumor microenvironment, and we outline how this knowledge has informed preclinical investigations combining the autophagy inhibitor hydroxychloroquine (HCQ) with chemotherapy, targeted therapy, and immunotherapy. Finally, we describe ongoing clinical trials involving HCQ as a first generation autophagy inhibitor, as well as strategies for the development of novel, more potent, and specific inhibitors of autophagy.

Photoactivated localization microscopy (PALM) is a powerful approach for investigating protein organization, yet tools for quantitative, spatial analysis of PALM datasets are largely missing. Combining pair-correlation analysis with PALM (PC-PALM), we provide a method to analyze complex patterns of protein organization across the plasma membrane without determination of absolute protein numbers. The approach uses an algorithm to distinguish a single protein with multiple appearances from clusters of proteins. This enables quantification of different parameters of spatial organization, including the presence of protein clusters, their size, density and abundance in the plasma membrane. Using this method, we demonstrate distinct nanoscale organization of plasma-membrane proteins with different membrane anchoring and lipid partitioning characteristics in COS-7 cells, and show dramatic changes in glycosylphosphatidylinositol (GPI)-anchored protein arrangement under varying perturbations. PC-PALM is thus an effective tool with broad applicability for analysis of protein heterogeneity and function, adaptable to other single-molecule strategies.

BACKGROUND/PURPOSE: To automatically assess hair growth during cosmetic trials, incorporating parameters such as anagen-to-telogen rate, growth rate, and especially hair diameter.

METHODS: We designed and qualified a new and automatic phototrichogram system based on a high-resolution DSLR camera system (theoretical resolution of 2.557 μm/pixel) and modular macrolens system with fixed focus, combined with a trainable pattern recognition software for automated analysis.

RESULTS: We improved the standard routine for dermatological phototrichogram technique to overcome inaccuracy in thickness measurements due to hair swelling by using an alternative immersion fluid, and increased the effective resolution for hair size and thickness measurement to <4 μm. After having qualified manual measurements as gold standard for the determination of hair diameters, we established a new trainable automatic picture analysis software able to locate and measure individual hairs in length and thickness even in picture series taken from the same skin area at different time points. Comparisons between manual and automatic measurements of the same hairs showed a >90% correlation, and by comparing the automatic results with manual measurements of the same images without individual hair annotation, we could find a correlation of at least 80%.

CONCLUSION: According to the results and findings generated in this qualification study, we have a reliable tool now that enables us to test cosmetic products for hair treatment in a highly automated way with a sufficient degree of precision and accuracy to detect even small changes in hair diameter during cosmetic trials.

The formation of amyloid fibrils, protofibrils and oligomers from the β-amyloid (Aβ) peptide represents a hallmark of Alzheimer’s disease. Aβ-peptide-derived assemblies might be crucial for disease onset, but determining their atomic structures has proven to be a major challenge. Progress over the past 5 years has yielded substantial new data obtained with improved methodologies including electron cryo-microscopy and NMR. It is now possible to resolve the global fibril topology and the cross-β sheet organization within protofilaments, and to identify residues that are crucial for stabilizing secondary structural elements and peptide conformations within specific assemblies. These data have significantly enhanced our understanding of the mechanism of Aβ aggregation and have illuminated the possible relevance of specific conformers for neurodegenerative pathologies.

Secretins form megadalton bacterial-membrane channels in at least four sophisticated multiprotein systems that are crucial for translocation of proteins and assembled fibers across the outer membrane of many species of bacteria. Secretin subunits contain multiple domains, which interact with numerous other proteins, including pilotins, secretion-system partner proteins, and exoproteins. Our understanding of the structure of secretins is rapidly progressing, and it is now recognized that features common to all secretins include a cylindrical arrangement of 12-15 subunits, a large periplasmic vestibule with a wide opening at one end and a periplasmic gate at the other. Secretins might also play a key role in the biogenesis of their cognate secretion systems.

Messenger RNA decay measurements are typically performed on a population of cells. However, this approach cannot reveal sufficient complexity to provide information on mechanisms that may regulate mRNA degradation, possibly on short timescales. To address this deficiency, we measured cell cycle-regulated decay in single yeast cells using single-molecule FISH. We found that two genes responsible for mitotic progression, SWI5 and CLB2, exhibit a mitosis-dependent mRNA stability switch. Their transcripts are stable until mitosis, when a precipitous decay eliminates the mRNA complement, preventing carryover into the next cycle. Remarkably, the specificity and timing of decay is entirely regulated by their promoter, independent of specific cis mRNA sequences. The mitotic exit network protein Dbf2p binds to SWI5 and CLB2 mRNAs cotranscriptionally and regulates their decay. This work reveals the promoter-dependent control of mRNA stability, a regulatory mechanism that could be employed by a variety of mRNAs and organisms.

The conventional view of neurons is that synaptic inputs are integrated on a timescale of milliseconds to seconds in the dendrites, with action potential initiation occurring in the axon initial segment. We found a much slower form of integration that leads to action potential initiation in the distal axon, well beyond the initial segment. In a subset of rodent hippocampal and neocortical interneurons, hundreds of spikes, evoked over minutes, resulted in persistent firing that lasted for a similar duration. Although axonal action potential firing was required to trigger persistent firing, somatic depolarization was not. In paired recordings, persistent firing was not restricted to the stimulated neuron; it could also be produced in the unstimulated cell. Thus, these interneurons can slowly integrate spiking, share the output across a coupled network of axons and respond with persistent firing even in the absence of input to the soma or dendrites.

The molecular structure of amyloid fibrils and the mechanism of their formation are of substantial medical and biological importance, but present an ongoing experimental and computational challenge. An early high-resolution view of amyloid-like structure was obtained on amyloid-like crystals of a small fragment of the yeast prion protein Sup35p: the peptide GNNQQNY. As GNNQQNY also forms amyloid-like fibrils under similar conditions, it has been theorized that the crystal’s structural features are shared by the fibrils. Here we apply magic-angle-spinning (MAS) NMR to examine the structure and dynamics of these fibrils. Previously multiple NMR signals were observed for such samples, seemingly consistent with the presence of polymorphic fibrils. Here we demonstrate that peptides with these three distinct conformations instead assemble together into composite protofilaments. Electron microscopy (EM) of the ribbon-like fibrils indicates that these protofilaments combine in differing ways to form striations of variable widths, presenting another level of structural complexity. Structural and dynamic NMR data reveal the presence of highly restricted side-chain conformations involved in interfaces between differently structured peptides, likely comprising interdigitated steric zippers. We outline molecular interfaces that are consistent with the observed EM and NMR data. The rigid and uniform structure of the GNNQQNY crystals is found to contrast distinctly with the more complex structural and dynamic nature of these "composite" amyloid fibrils. These results provide insight into the fibril-crystal distinction and also indicate a necessary caution with respect to the extrapolation of crystal structures to the study of fibril structure and formation.

We address the problem of labeling individual datapoints given some knowledge about (small) subsets or groups of them. The knowledge we have for a group is the likelihood value for each group member to satisfy a certain model. This problem is equivalent to hypergraph labeling problem where each datapoint corresponds to a node and the each subset correspond to a hyperedge with likelihood value as its weight. We propose a novel method to model the label dependence using an Undirected Graphical Model and reduce the problem of hypergraph labeling into an inference problem. This paper describes the structure and necessary components of such model and proposes useful cost functions. We discuss the behavior of proposed algorithm with different forms of the cost functions, identify suitable algorithms for inference and analyze required properties when it is theoretically guaranteed to have exact solution. Examples of several real world problems are shown as applications of the proposed method.

The estimation of visual motion has long been studied as a paradigmatic neural computation, and multiple models have been advanced to explain behavioral and neural responses to motion signals. A broad class of models, originating with the Reichardt correlator model, proposes that animals estimate motion by computing a temporal cross-correlation of light intensities from two neighboring points in visual space. These models provide a good description of experimental data in specific contexts but cannot explain motion percepts in stimuli lacking pairwise correlations. Here, we develop a theoretical formalism that can accommodate diverse stimuli and behavioral goals. To achieve this, we treat motion estimation as a problem of Bayesian inference. Pairwise models emerge as one component of the generalized strategy for motion estimation. However, correlation functions beyond second order enable more accurate motion estimation. Prior expectations that are asymmetric with respect to bright and dark contrast use correlations of both even and odd orders, and we show that psychophysical experiments using visual stimuli with symmetric probability distributions for contrast cannot reveal whether the subject uses odd-order correlators for motion estimation. This result highlights a gap in previous experiments, which have largely relied on symmetric contrast distributions. Our theoretical treatment provides a natural interpretation of many visual motion percepts, indicates that motion estimation should be revisited using a broader class of stimuli, demonstrates how correlation-based motion estimation is related to stimulus statistics, and provides multiple experimentally testable predictions.