Lipidic cubic phase serial millisecond crystallography using synchrotron radiation.

Lipidic cubic phases (LCPs) have emerged as successful matrixes for the crystallization of membrane proteins.Moreover, the viscous LCP also provides a highly effective delivery medium for serial femtosecond crystallography (SFX) at X-ray free-electron lasers (XFELs). Here, the adaptation of this technology to perform serial millisecond crystallography (SMX) at more widely available synchrotron microfocus beamlines is described. Compared with conventional microcrystallography, LCP-SMX eliminates the need for difficult handling of individual crystals and allows for data collection at room temperature. The technology is demonstrated by solving a structure of the light-driven protonpump bacteriorhodopsin (bR) at a resolution of 2.4 A ° . The room-temperature structure of bR is very similar to previous cryogenic structures but shows small yet distinct differences in the retinal ligand and proton-transfer pathway

k-Nearest neighbor models for microarray gene expression analysis and clinical outcome prediction

In the clinical application of genomic data analysis and modeling, a number of factors contribute to the performance of disease classification and clinical outcome prediction. This study focuses on the k-nearest neighbor (KNN) modeling strategy and its clinical use. Although KNN is simple and clinically appealing, large performance variations were found among experienced data analysis teams in the MicroArray Quality Control Phase II (MAQC-II) project. For clinical end points and controls from breast cancer, neuroblastoma and multiple myeloma, we systematically generated 463 320 KNN models by varying feature ranking method, number of features, distance metric, number of neighbors, vote weighting and decision threshold. We identified factors that contribute to the MAQC-II project performance variation, and validated a KNN data analysis protocol using a newly generated clinical data set with 478 neuroblastoma patients. We interpreted the biological and practical significance of the derived KNN models, and compared their performance with existing clinical factors

Atomic structure of granulin determined from native nanocrystalline granulovirus using an X-ray free-electron laser

To understand how molecules function in biological systems, new methods are required to obtain atomic resolution structures from biological material under physiological conditions. Intense femtosecond-duration pulses from X-ray free-electron lasers (XFELs) can outrun most damage processes, vastly increasing the tolerable dose before the specimen is destroyed. This in turn allows structure determination from crystals much smaller and more radiation sensitive than previously considered possible, allowing data collection from room temperature structures and avoiding structural changes due to cooling. Regardless, high-resolution structures obtained from XFEL data mostly use crystals far larger than 1 μm3 in volume, whereas the X-ray beam is often attenuated to protect the detector from damage caused by intense Bragg spots. Here, we describe the 2 Å resolution structure of native nanocrystalline granulovirus occlusion bodies (OBs) that are less than 0.016 μm3 in volume using the full power of the Linac Coherent Light Source (LCLS) and a dose up to 1.3 GGy per crystal. The crystalline shell of granulovirus OBs consists, on average, of about 9,000 unit cells, representing the smallest protein crystals to yield a high-resolution structure by X-ray crystallography to date. The XFEL structure shows little to no evidence of radiation damage and is more complete than a model determined using synchrotron data from recombinantly produced, much larger, cryocooled granulovirus granulin microcrystals. Our measurements suggest that it should be possible, under ideal experimental conditions, to obtain data from protein crystals with only 100 unit cells in volume using currently available XFELs and suggest that single-molecule imaging of individual biomolecules could almost be within reach

Flow-aligned, single-shot fiber diffraction using a femtosecond X-ray free-electron laser

A major goal for X-ray free-electron laser (XFEL) based science is to elucidate structures of biological molecules without the need for crystals. Filament systems may provide some of the first single macromolecular structures elucidated by XFEL radiation, since they contain one-dimensional translational symmetry and thereby occupy the diffraction intensity region between the extremes of crystals and single molecules. Here, we demonstrate flow alignment of as few as 100 filaments (Escherichia coli pili, F-actin, and amyloid fibrils), which when intersected by femtosecond X-ray pulses result in diffraction patterns similar to those obtained from classical fiber diffraction studies. We also determine that F-actin can be flow-aligned to a disorientation of approximately 5 degrees. Using this XFEL-based technique, we determine that gelsolin amyloids are comprised of stacked β-strands running perpendicular to the filament axis, and that a range of order from fibrillar to crystalline is discernable for individual α-synuclein amyloids

Does Applicability Domain Exist in Microarray-Based Genomic Research?

Constructing an accurate predictive model for clinical decision-making on the basis of a relatively small number of tumor samples with high-dimensional microarray data remains a very challenging problem. The validity of such models has been seriously questioned due to their failure in clinical validation using independent samples. Besides the statistical issues such as selection bias, some studies further implied the probable reason was improper sample selection that did not resemble the genomic space defined by the training population. Assuming that predictions would be more reliable for interpolation than extrapolation, we set to investigate the impact of applicability domain (AD) on model performance in microarray-based genomic research by evaluating and comparing model performance for samples with different extrapolation degrees. We found that the issue of applicability domain may not exist in microarray-based genomic research for clinical applications. Therefore, it is not practicable to improve model validity based on applicability domain

Catalytic cleavage of HEAT and subsequent covalent binding of the tetralone moiety by the SARS-CoV-2 main protease

Here we present the crystal structure of SARS-CoV-2 main protease (Mpro) covalently bound to 2-methyl-1-tetralone. This complex was obtained by co-crystallization of Mpro with HEAT (2-(((4-hydroxyphenethyl)amino)methyl)-3,4-dihydronaphthalen-1(2H)-one) in the framework of a large X-ray crystallographic screening project of Mpro against a drug repurposing library, consisting of 5632 approved drugs or compounds in clinical phase trials. Further investigations showed that HEAT is cleaved by Mpro in an E1cB-like reaction mechanism into 2-methylene-1-tetralone and tyramine. The catalytic Cys145 subsequently binds covalently in a Michael addition to the methylene carbon atom of 2-methylene-1-tetralone. According to this postulated model HEAT is acting in a pro-drug-like fashion. It is metabolized by Mpro, followed by covalent binding of one metabolite to the active site. The structure of the covalent adduct elucidated in this study opens up a new path for developing non-peptidic inhibitors

Functional analysis of multiple genomic signatures demonstrates that classification algorithms choose phenotype-related genes

Gene expression signatures of toxicity and clinical response benefit both safety assessment and clinical practice; however, difficulties in connecting signature genes with the predicted end points have limited their application. The Microarray Quality Control Consortium II (MAQCII) project generated 262 signatures for ten clinical and three toxicological end points from six gene expression data sets, an unprecedented collection of diverse signatures that has permitted a wide-ranging analysis on the nature of such predictive models. A comprehensive analysis of the genes of these signatures and their nonredundant unions using ontology enrichment, biological network building and interactome connectivity analyses demonstrated the link between gene signatures and the biological basis of their predictive power. Different signatures for a given end point were more similar at the level of biological properties and transcriptional control than at the gene level. Signatures tended to be enriched in function and pathway in an end point and model-specific manner, and showed a topological bias for incoming interactions. Importantly, the level of biological similarity between different signatures for a given end point correlated positively with the accuracy of the signature predictions. These findings will aid the understanding, and application of predictive genomic signatures, and support their broader application in predictive medicine

CADM1 is a strong neuroblastoma candidate gene that maps within a 3.72 Mb critical region of loss on 11q23

<p>Abstract</p> <p>Background</p> <p>Recurrent loss of part of the long arm of chromosome 11 is a well established hallmark of a subtype of aggressive neuroblastomas. Despite intensive mapping efforts to localize the culprit 11q tumour suppressor gene, this search has been unsuccessful thus far as no sufficiently small critical region could be delineated for selection of candidate genes.</p> <p>Methods</p> <p>To refine the critical region of 11q loss, the chromosome 11 status of 100 primary neuroblastoma tumours and 29 cell lines was analyzed using a BAC array containing a chromosome 11 tiling path. For the genes mapping within our refined region of loss, meta-analysis on published neuroblastoma mRNA gene expression datasets was performed for candidate gene selection. The DNA methylation status of the resulting candidate gene was determined using re-expression experiments by treatment of neuroblastoma cells with the demethylating agent 5-aza-2'-deoxycytidine and bisulphite sequencing.</p> <p>Results</p> <p>Two small critical regions of loss within 11q23 at chromosomal band 11q23.1-q23.2 (1.79 Mb) and 11q23.2-q23.3 (3.72 Mb) were identified. In a first step towards further selection of candidate neuroblastoma tumour suppressor genes, we performed a meta-analysis on published expression profiles of 692 neuroblastoma tumours. Integration of the resulting candidate gene list with expression data of neuroblastoma progenitor cells pinpointed <it>CADM1 </it>as a compelling candidate gene. Meta-analysis indicated that <it>CADM1 </it>expression has prognostic significance and differential expression for the gene was noted in unfavourable neuroblastoma versus normal neuroblasts. Methylation analysis provided no evidence for a two-hit mechanism in 11q deleted cell lines.</p> <p>Conclusion</p> <p>Our study puts <it>CADM1 </it>forward as a strong candidate neuroblastoma suppressor gene. Further functional studies are warranted to elucidate the role of <it>CADM1 </it>in neuroblastoma development and to investigate the possibility of <it>CADM1 </it>haploinsufficiency in neuroblastoma.</p