Content Disputes in Wikipedia Reflect Geopolitical Instability

Indicators that rank countries according socioeconomic measurements are important tools for regional development and political reform. Those currently in widespread use are sometimes criticized for a lack of reproducibility or the inability to compare values over time, necessitating simple, fast and systematic measures. Here, we applied the ‘guilt by association’ principle often used in biological networks to the information network within the online encyclopedia Wikipedia to create an indicator quantifying the degree to which pages linked to a country are disputed by contributors. The indicator correlates with metrics of governance, political or economic stability about as well as they correlate with each other, and though faster and simpler, it is remarkably stable over time despite constant changes in the underlying disputes. For some countries, changes over a four year period appear to correlate with world events related to conflicts or economic problems

Focal structural variants revealed by whole genome sequencing disrupt the histone demethylase KDM4C in B cell lymphomas

Histone methylation-modifiers, like EZH2 and KMT2D, are recurrently altered in B-cell lymphomas. To comprehensively describe the landscape of alterations affecting genes encoding histone methylation-modifiers in lymphomagenesis we investigated whole genome and transcriptome data of 186 mature B-cell lymphomas sequenced in the ICGC MMML-Seq project. Besides confirming common alterations of KMT2D (47% of cases), EZH2 (17%), SETD1B (5%), PRDM9 (4%), KMT2C (4%), and SETD2 (4%) also identified by prior exome or RNAseq studies, we here unravel KDM4C in chromosome 9p24, encoding a histone demethylase, to be recurrently altered. Focal structural variation was the main mechanism of KDM4C alterations, which was independent from 9p24 amplification. We identified KDM4C alterations also in lymphoma cell lines including a focal homozygous deletion in a classical Hodgkin lymphoma cell line. By integrating RNAseq and genome sequencing data we predict KDM4C structural variants to result in loss-of-function. By functional reconstitution studies in cell lines, we provide evidence that KDM4C can act as tumor suppressor. Thus, we show that identification of structural variants in whole genome sequencing data adds to the comprehensive description of the mutational landscape of lymphomas and, moreover, establish KDM4C as putative tumor suppressive gene recurrently altered in subsets of B-cell derived lymphomas

An organelle-specific protein landscape identifies novel diseases and molecular mechanisms

Contains fulltext : 158967.pdf (publisher's version ) (Open Access)Cellular organelles provide opportunities to relate biological mechanisms to disease. Here we use affinity proteomics, genetics and cell biology to interrogate cilia: poorly understood organelles, where defects cause genetic diseases. Two hundred and seventeen tagged human ciliary proteins create a final landscape of 1,319 proteins, 4,905 interactions and 52 complexes. Reverse tagging, repetition of purifications and statistical analyses, produce a high-resolution network that reveals organelle-specific interactions and complexes not apparent in larger studies, and links vesicle transport, the cytoskeleton, signalling and ubiquitination to ciliary signalling and proteostasis. We observe sub-complexes in exocyst and intraflagellar transport complexes, which we validate biochemically, and by probing structurally predicted, disruptive, genetic variants from ciliary disease patients. The landscape suggests other genetic diseases could be ciliary including 3M syndrome. We show that 3M genes are involved in ciliogenesis, and that patient fibroblasts lack cilia. Overall, this organelle-specific targeting strategy shows considerable promise for Systems Medicine

Content disputes in Wikipedia reflect geopolitical instability.

Indicators that rank countries according socioeconomic measurements are important tools for regional development and political reform. Those currently in widespread use are sometimes criticized for a lack of reproducibility or the inability to compare values over time, necessitating simple, fast and systematic measures. Here, we applied the 'guilt by association' principle often used in biological networks to the information network within the online encyclopedia Wikipedia to create an indicator quantifying the degree to which pages linked to a country are disputed by contributors. The indicator correlates with metrics of governance, political or economic stability about as well as they correlate with each other, and though faster and simpler, it is remarkably stable over time despite constant changes in the underlying disputes. For some countries, changes over a four year period appear to correlate with world events related to conflicts or economic problems

Combinations of protein-chemical complex structures reveal new targets for established drugs.

Biological networks are powerful tools for predicting undocumented relationships between molecules. The underlying principle is that existing interactions between molecules can be used to predict new interactions. Here we use this principle to suggest new protein-chemical interactions via the network derived from three-dimensional structures. For pairs of proteins sharing a common ligand, we use protein and chemical superimpositions combined with fast structural compatibility screens to predict whether additional compounds bound by one protein would bind the other. The method reproduces 84% of complexes in a benchmark, and we make many predictions that would not be possible using conventional modeling techniques. Within 19,578 novel predicted interactions are 7,793 involving 718 drugs, including filaminast, coumarin, alitretonin and erlotinib. The growth rate of confident predictions is twice that of experimental complexes, meaning that a complete structural drug-protein repertoire will be available at least ten years earlier than by X-ray and NMR techniques alone

Insights into cancer severity from biomolecular interaction mechanisms

To attain a deeper understanding of diseases like cancer, it is critical to couple genetics with biomolecular mechanisms. High-throughput sequencing has identified thousands of somatic mutations across dozens of cancers, and there is a pressing need to identify the few that are pathologically relevant. Here we use protein structure and interaction data to interrogate nonsynonymous somatic cancer mutations, identifying a set of 213 molecular interfaces (protein-protein, -small molecule or -nucleic acid) most often perturbed in cancer, highlighting several potentially novel cancer genes. Over half of these interfaces involve protein-small-molecule interactions highlighting their overall importance in cancer. We found distinct differences in the predominance of perturbed interfaces between cancers and histological subtypes and presence or absence of certain interfaces appears to correlate with cancer severity

Mechismo: predicting the mechanistic impact of mutations and modifications on molecular interactions

Systematic interrogation of mutation or protein modification data is important to identify sites with functional consequences and to deduce global consequences from large data sets. Mechismo (mechismo.russellab.org) enables simultaneous consideration of thousands of 3D structures and biomolecular interactions to predict rapidly mechanistic consequences for mutations and modifications. As useful functional information often only comes from homologous proteins, we benchmarked the accuracy of predictions as a function of protein/structure sequence similarity, which permits the use of relatively weak sequence similarities with an appropriate confidence measure. For protein–protein, protein–nucleic acid and a subset of protein–chemical interactions, we also developed and benchmarked a measure of whether modifications are likely to enhance or diminish the interactions, which can assist the detection of modifications with specific effects. Analysis of high-throughput sequencing data shows that the approach can identify interesting differences between cancers, and application to proteomics data finds potential mechanistic insights for how post-translational modifications can alter biomolecular interactions

Emerging Trends in the Field of Inflammation and Proteinopathy in ALS/FTD Spectrum Disorder

: Proteinopathy and neuroinflammation are two main hallmarks of neurodegenerative diseases. They also represent rare common events in an exceptionally broad landscape of genetic, environmental, neuropathologic, and clinical heterogeneity present in patients. Here, we aim to recount the emerging trends in amyotrophic lateral sclerosis (ALS) and frontotemporal degeneration (FTD) spectrum disorder. Our review will predominantly focus on neuroinflammation and systemic immune imbalance in ALS and FTD, which have recently been highlighted as novel therapeutic targets. A common mechanism of most ALS and ~50% of FTD patients is dysregulation of TAR DNA-binding protein 43 (TDP-43), an RNA/DNA-binding protein, which becomes depleted from the nucleus and forms cytoplasmic aggregates in neurons and glia. This, in turn, via both gain and loss of function events, alters a variety of TDP-43-mediated cellular events. Experimental attempts to target TDP-43 aggregates or manipulate crosstalk in the context of inflammation will be discussed. Targeting inflammation, and the immune system in general, is of particular interest because of the high plasticity of immune cells compared to neurons