Ab-Initio Calculation of Molecular Aggregation Effects: a Coumarin-343 Case Study

We present time-dependent density functional theory (TDDFT) calculations for single and dimerized Coumarin-343 molecules in order to investigate the quantum mechanical effects of chromophore aggregation in extended systems designed to function as a new generation of sensors and light-harvesting devices. Using the single-chromophore results, we describe the construction of effective Hamiltonians to predict the excitonic properties of aggregate systems. We compare the electronic coupling properties predicted by such effective Hamiltonians to those obtained from TDDFT calculations of dimers, and to the coupling predicted by the transition density cube (TDC) method. We determine the accuracy of the dipole-dipole approximation and TDC with respect to the separation distance and orientation of the dimers. In particular, we investigate the effects of including Coulomb coupling terms ignored in the typical tight-binding effective Hamiltonian. We also examine effects of orbital relaxation which cannot be captured by either of these models

Lemur tyrosine kinase-2 signalling regulates kinesin-1 light chain-2 phosphorylation and binding of Smad2 cargo.

A recent genome-wide association study identified the gene encoding lemur tyrosine kinase-2 (LMTK2) as a susceptibility gene for prostate cancer. The identified genetic alteration is within intron 9, but the mechanisms by which LMTK2 may impact upon prostate cancer are not clear because the functions of LMTK2 are poorly understood. Here, we show that LMTK2 regulates a known pathway that controls phosphorylation of kinesin-1 light chain-2 (KLC2) by glycogen synthase kinase-3β (GSK3β). KLC2 phosphorylation by GSK3β induces the release of cargo from KLC2. LMTK2 signals via protein phosphatase-1C (PP1C) to increase inhibitory phosphorylation of GSK3β on serine-9 that reduces KLC2 phosphorylation and promotes binding of the known KLC2 cargo Smad2. Smad2 signals to the nucleus in response to transforming growth factor-β (TGFβ) receptor stimulation and transport of Smad2 by kinesin-1 is required for this signalling. We show that small interfering RNA loss of LMTK2 not only reduces binding of Smad2 to KLC2, but also inhibits TGFβ-induced Smad2 signalling. Thus, LMTK2 may regulate the activity of kinesin-1 motor function and Smad2 signalling

Does targeting manual therapy and/or exercise improve patient outcomes in nonspecific low back pain? A systematic review

<p>Abstract</p> <p>Background</p> <p>A central element in the current debate about best practice management of non-specific low back pain (NSLBP) is the efficacy of targeted versus generic (non-targeted) treatment. Many clinicians and researchers believe that tailoring treatment to NSLBP subgroups positively impacts on patient outcomes. Despite this, there are no systematic reviews comparing the efficacy of targeted versus non-targeted manual therapy and/or exercise. This systematic review was undertaken in order to determine the efficacy of such targeted treatment in adults with NSLBP.</p> <p>Method</p> <p>MEDLINE, EMBASE, Current Contents, AMED and the Cochrane Central Register of Controlled Trials were electronically searched, reference lists were examined and citation tracking performed. Inclusion criteria were randomized controlled trials of targeted manual therapy and/or exercise for NSLPB that used trial designs capable of providing robust information on targeted treatment (treatment effect modification) for the outcomes of activity limitation and pain. Included trials needed to be hypothesis-testing studies published in English, Danish or Norwegian. Method quality was assessed using the criteria recommended by the Cochrane Back Review Group.</p> <p>Results</p> <p>Four high-quality randomized controlled trials of targeted manual therapy and/or exercise for NSLBP met the inclusion criteria. One study showed statistically significant effects for short-term outcomes using McKenzie directional preference-based exercise. Research into subgroups requires much larger sample sizes than traditional two-group trials and other included studies showed effects that might be clinically important in size but were not statistically significant with their samples sizes.</p> <p>Conclusions</p> <p>The clinical implications of these results are that they provide very cautious evidence supporting the notion that treatment targeted to subgroups of patients with NSLBP may improve patient outcomes. The results of the studies included in this review are too patchy, inconsistent and the samples investigated are too small for any recommendation of any treatment in routine clinical practice to be based on these findings. The research shows that adequately powered controlled trials using designs capable of providing robust information on treatment effect modification are uncommon. Considering how central the notion of targeted treatment is to manual therapy principles, further studies using this research method should be a priority for the clinical and research communities.</p

Analytical methods for inferring functional effects of single base pair substitutions in human cancers

Cancer is a genetic disease that results from a variety of genomic alterations. Identification of some of these causal genetic events has enabled the development of targeted therapeutics and spurred efforts to discover the key genes that drive cancer formation. Rapidly improving sequencing and genotyping technology continues to generate increasingly large datasets that require analytical methods to identify functional alterations that deserve additional investigation. This review examines statistical and computational approaches for the identification of functional changes among sets of single-nucleotide substitutions. Frequency-based methods identify the most highly mutated genes in large-scale cancer sequencing efforts while bioinformatics approaches are effective for independent evaluation of both non-synonymous mutations and polymorphisms. We also review current knowledge and tools that can be utilized for analysis of alterations in non-protein-coding genomic sequence

Network-Guided Analysis of Genes with Altered Somatic Copy Number and Gene Expression Reveals Pathways Commonly Perturbed in Metastatic Melanoma

Cancer genomes frequently contain somatic copy number alterations (SCNA) that can significantly perturb the expression level of affected genes and thus disrupt pathways controlling normal growth. In melanoma, many studies have focussed on the copy number and gene expression levels of the BRAF, PTEN and MITF genes, but little has been done to identify new genes using these parameters at the genome-wide scale. Using karyotyping, SNP and CGH arrays, and RNA-seq, we have identified SCNA affecting gene expression (‘SCNA-genes’) in seven human metastatic melanoma cell lines. We showed that the combination of these techniques is useful to identify candidate genes potentially involved in tumorigenesis. Since few of these alterations were recurrent across our samples, we used a protein network-guided approach to determine whether any pathways were enriched in SCNA-genes in one or more samples. From this unbiased genome-wide analysis, we identified 28 significantly enriched pathway modules. Comparison with two large, independent melanoma SCNA datasets showed less than 10% overlap at the individual gene level, but network-guided analysis revealed 66% shared pathways, including all but three of the pathways identified in our data. Frequently altered pathways included WNT, cadherin signalling, angiogenesis and melanogenesis. Additionally, our results emphasize the potential of the EPHA3 and FRS2 gene products, involved in angiogenesis and migration, as possible therapeutic targets in melanoma. Our study demonstrates the utility of network-guided approaches, for both large and small datasets, to identify pathways recurrently perturbed in cancer

Sequence and Structure Signatures of Cancer Mutation Hotspots in Protein Kinases

Protein kinases are the most common protein domains implicated in cancer, where somatically acquired mutations are known to be functionally linked to a variety of cancers. Resequencing studies of protein kinase coding regions have emphasized the importance of sequence and structure determinants of cancer-causing kinase mutations in understanding of the mutation-dependent activation process. We have developed an integrated bioinformatics resource, which consolidated and mapped all currently available information on genetic modifications in protein kinase genes with sequence, structure and functional data. The integration of diverse data types provided a convenient framework for kinome-wide study of sequence-based and structure-based signatures of cancer mutations. The database-driven analysis has revealed a differential enrichment of SNPs categories in functional regions of the kinase domain, demonstrating that a significant number of cancer mutations could fall at structurally equivalent positions (mutational hotspots) within the catalytic core. We have also found that structurally conserved mutational hotspots can be shared by multiple kinase genes and are often enriched by cancer driver mutations with high oncogenic activity. Structural modeling and energetic analysis of the mutational hotspots have suggested a common molecular mechanism of kinase activation by cancer mutations, and have allowed to reconcile the experimental data. According to a proposed mechanism, structural effect of kinase mutations with a high oncogenic potential may manifest in a significant destabilization of the autoinhibited kinase form, which is likely to drive tumorigenesis at some level. Structure-based functional annotation and prediction of cancer mutation effects in protein kinases can facilitate an understanding of the mutation-dependent activation process and inform experimental studies exploring molecular pathology of tumorigenesis

Lawson criterion for ignition exceeded in an inertial fusion experiment

For more than half a century, researchers around the world have been engaged in attempts to achieve fusion ignition as a proof of principle of various fusion concepts. Following the Lawson criterion, an ignited plasma is one where the fusion heating power is high enough to overcome all the physical processes that cool the fusion plasma, creating a positive thermodynamic feedback loop with rapidly increasing temperature. In inertially confined fusion, ignition is a state where the fusion plasma can begin "burn propagation" into surrounding cold fuel, enabling the possibility of high energy gain. While "scientific breakeven" (i.e., unity target gain) has not yet been achieved (here target gain is 0.72, 1.37 MJ of fusion for 1.92 MJ of laser energy), this Letter reports the first controlled fusion experiment, using laser indirect drive, on the National Ignition Facility to produce capsule gain (here 5.8) and reach ignition by nine different formulations of the Lawson criterion

A framework for evolutionary systems biology

<p>Abstract</p> <p>Background</p> <p>Many difficult problems in evolutionary genomics are related to mutations that have weak effects on fitness, as the consequences of mutations with large effects are often simple to predict. Current systems biology has accumulated much data on mutations with large effects and can predict the properties of knockout mutants in some systems. However experimental methods are too insensitive to observe small effects.</p> <p>Results</p> <p>Here I propose a novel framework that brings together evolutionary theory and current systems biology approaches in order to quantify small effects of mutations and their epistatic interactions <it>in silico</it>. Central to this approach is the definition of fitness correlates that can be computed in some current systems biology models employing the rigorous algorithms that are at the core of much work in computational systems biology. The framework exploits synergies between the realism of such models and the need to understand real systems in evolutionary theory. This framework can address many longstanding topics in evolutionary biology by defining various 'levels' of the adaptive landscape. Addressed topics include the distribution of mutational effects on fitness, as well as the nature of advantageous mutations, epistasis and robustness. Combining corresponding parameter estimates with population genetics models raises the possibility of testing evolutionary hypotheses at a new level of realism.</p> <p>Conclusion</p> <p>EvoSysBio is expected to lead to a more detailed understanding of the fundamental principles of life by combining knowledge about well-known biological systems from several disciplines. This will benefit both evolutionary theory and current systems biology. Understanding robustness by analysing distributions of mutational effects and epistasis is pivotal for drug design, cancer research, responsible genetic engineering in synthetic biology and many other practical applications.</p

Characterising chromosome rearrangements: recent technical advances in molecular cytogenetics

Genomic rearrangements can result in losses, amplifications, translocations and inversions of DNA fragments thereby modifying genome architecture, and potentially having clinical consequences. Many genomic disorders caused by structural variation have initially been uncovered by early cytogenetic methods. The last decade has seen significant progression in molecular cytogenetic techniques, allowing rapid and precise detection of structural rearrangements on a whole-genome scale. The high resolution attainable with these recently developed techniques has also uncovered the role of structural variants in normal genetic variation alongside single-nucleotide polymorphisms (SNPs). We describe how array-based comparative genomic hybridisation, SNP arrays, array painting and next-generation sequencing analytical methods (read depth, read pair and split read) allow the extensive characterisation of chromosome rearrangements in human genomes