A phase 1/2 safety and efficacy study of TAK-754 gene therapy: The challenge of achieving durable factor VIII expression in haemophilia A clinical trials.

Introduction: Haemophilia A is an X-linked bleeding disorder resulting from a deficiency of factor VIII (FVIII). To date, multiple gene therapies have entered clinical trials with the goal of providing durable haemostatic protection from a single dose. TAK 754 (BAX 888) is an investigational AAV8-based gene therapy containing a FVIII transgene.

The Type 2 Diabetes Knowledge Portal: An open access genetic resource dedicated to type 2 diabetes and related traits.

Associations between human genetic variation and clinical phenotypes have become a foundation of biomedical research. Most repositories of these data seek to be disease-agnostic and therefore lack disease-focused views. The Type 2 Diabetes Knowledge Portal (T2DKP) is a public resource of genetic datasets and genomic annotations dedicated to type 2 diabetes (T2D) and related traits.

High-throughput genetic clustering of type 2 diabetes loci reveals heterogeneous mechanistic pathways of metabolic disease.

Aims/hypothesis: Type 2 diabetes is highly polygenic and influenced by multiple biological pathways. Rapid expansion in the number of type 2 diabetes loci can be leveraged to identify such pathways.

Methods: We developed a high-throughput pipeline to enable clustering of type 2 diabetes loci based on variant-trait associations.

The Lipid Droplet Knowledge Portal: A resource for systematic analyses of lipid droplet biology.

Lipid droplets (LDs) are organelles of cellular lipid storage with fundamental roles in energy metabolism and cell membrane homeostasis. There has been an explosion of research into the biology of LDs, in part due to their relevance in diseases of lipid storage, such as atherosclerosis, obesity, type 2 diabetes, and hepatic steatosis. Consequently, there is an increasing need for a resource that combines datasets from systematic analyses of LD biology.

Type 2 diabetes genetic loci informed by multi-trait associations point to disease mechanisms and subtypes: A soft clustering analysis.

Background: Type 2 diabetes (T2D) is a heterogeneous disease for which (1) disease-causing pathways are incompletely understood and (2) subclassification may improve patient management. Unlike other biomarkers, germline genetic markers do not change with disease progression or treatment. In this paper, we test whether a germline genetic approach informed by physiology can be used to deconstruct T2D heterogeneity.

Metabolomics insights into early type 2 diabetes pathogenesis and detection in individuals with normal fasting glucose.

Aims/hypothesis: Identifying the metabolite profile of individuals with normal fasting glucose (NFG [<5.55 mmol/l]) who progressed to type 2 diabetes may give novel insights into early type 2 diabetes disease interception and detection.

Methods: We conducted a population-based prospective study among 1150 Framingham Heart Study Offspring cohort participants, age 40-65 years, with NFG.

Type 2 Diabetes Variants Disrupt Function of SLC16A11 through Two Distinct Mechanisms.

Type 2 diabetes (T2D) affects Latinos at twice the rate seen in populations of European descent. We recently identified a risk haplotype spanning SLC16A11 that explains ∼20% of the increased T2D prevalence in Mexico. Here, through genetic fine-mapping, we define a set of tightly linked variants likely to contain the causal allele(s).

Structural Basis of Neurohormone Perception by the Receptor Tyrosine Kinase Torso.

In insects, brain-derived Prothoracicotropic hormone (PTTH) activates the receptor tyrosine kinase (RTK) Torso to initiate metamorphosis through the release of ecdysone. We have determined the crystal structure of silkworm PTTH in complex with the ligand-binding region of Torso. Here we show that ligand-induced Torso dimerization results from the sequential and negatively cooperative formation of asymmetric heterotetramers.

Remodelling of a homeobox gene cluster by multiple independent gene reunions in Drosophila.

Genome clustering of homeobox genes is often thought to reflect arrangements of tandem gene duplicates maintained by advantageous coordinated gene regulation. Here we analyse the chromosomal organization of the NK homeobox genes, presumed to be part of a single cluster in the Bilaterian ancestor, across 20 arthropods. We find that the ProtoNK cluster was extensively fragmented in some lineages, showing that NK clustering in Drosophila species does not reflect selectively maintained gene arrangements.

Functional divergence of the miRNA transcriptome at the onset of Drosophila metamorphosis.

MicroRNAs (miRNAs) are endogenous RNA molecules that regulate gene expression posttranscriptionally. To date, the emergence of miRNAs and their patterns of sequence evolution have been analyzed in great detail. However, the extent to which miRNA expression levels have evolved over time, the role different evolutionary forces play in shaping these changes, and whether this variation in miRNA expression can reveal the interplay between miRNAs and mRNAs remain poorly understood.

Comprehensive genomic analyses associate UGT8 variants with musical ability in a Mongolian population.

Background: Musical abilities such as recognising music and singing performance serve as means for communication and are instruments in sexual selection. Specific regions of the brain have been found to be activated by musical stimuli, but these have rarely been extended to the discovery of genes and molecules associated with musical ability.

Methods: A total of 1008 individuals from 73 families were enrolled and a pitch-production accuracy test was applied to determine musical ability.

HarmonyDOCK: the structural analysis of poses in protein-ligand docking.

Molecular docking is a widely used method for lead optimization. However, docking tools often fail to predict how a ligand (the smaller molecule, such as a substrate or drug candidate) binds to a receptor (the accepting part of a protein). We present here the HarmonyDOCK, a novel method for assessing the docking software accuracy, and creating the scoring function which would determine consensus protein-ligand pose among those generated by available docking programs.

VoteDock: consensus docking method for prediction of protein-ligand interactions.

Molecular recognition plays a fundamental role in all biological processes, and that is why great efforts have been made to understand and predict protein-ligand interactions. Finding a molecule that can potentially bind to a target protein is particularly essential in drug discovery and still remains an expensive and time-consuming task. In silico, tools are frequently used to screen molecular libraries to identify new lead compounds, and if protein structure is known, various protein-ligand docking programs can be used.

Fragile regions and not functional constraints predominate in shaping gene organization in the genus Drosophila.

During evolution, gene repatterning across eukaryotic genomes is not uniform. Some genomic regions exhibit a gene organization conserved phylogenetically, while others are recurrently involved in chromosomal rearrangement, resulting in breakpoint reuse. Both gene order conservation and breakpoint reuse can result from the existence of functional constraints on where chromosomal breakpoints occur or from the existence of regions that are susceptible to breakage.

Virtual high throughput screening using combined random forest and flexible docking.

We present here the random forest supervised machine learning algorithm applied to flexible docking results from five typical virtual high throughput screening (HTS) studies. Our approach is aimed at: i) reducing the number of compounds to be tested experimentally against the given protein target and ii) extending results of flexible docking experiments performed only on a subset of a chemical library in order to select promising inhibitors from the whole dataset. The random forest (RF) method is applied and tested here on compounds from the MDL drug data report (MDDR).

3D-Fun: predicting enzyme function from structure.

The 'omics' revolution is causing a flurry of data that all needs to be annotated for it to become useful. Sequences of proteins of unknown function can be annotated with a putative function by comparing them with proteins of known function. This form of annotation is typically performed with BLAST or similar software.

Principles of genome evolution in the Drosophila melanogaster species group.

That closely related species often differ by chromosomal inversions was discovered by Sturtevant and Plunkett in 1926. Our knowledge of how these inversions originate is still very limited, although a prevailing view is that they are facilitated by ectopic recombination events between inverted repetitive sequences. The availability of genome sequences of related species now allows us to study in detail the mechanisms that generate interspecific inversions.

In silico prediction of SARS protease inhibitors by virtual high throughput screening.

A structure-based in silico virtual drug discovery procedure was assessed with severe acute respiratory syndrome coronavirus main protease serving as a case study. First, potential compounds were extracted from protein-ligand complexes selected from Protein Data Bank database based on structural similarity to the target protein. Later, the set of compounds was ranked by docking scores using a Electronic High-Throughput Screening flexible docking procedure to select the most promising molecules.

Target specific compound identification using a support vector machine.

In many cases at the beginning of an HTS-campaign, some information about active molecules is already available. Often known active compounds (such as substrate analogues, natural products, inhibitors of a related protein or ligands published by a pharmaceutical company) are identified in low-throughput validation studies of the biochemical target. In this study we evaluate the effectiveness of a support vector machine applied for those compounds and used to classify a collection with unknown activity.

Three dimensional model of severe acute respiratory syndrome coronavirus helicase ATPase catalytic domain and molecular design of severe acute respiratory syndrome coronavirus helicase inhibitors.

The modeling of the severe acute respiratory syndrome coronavirus helicase ATPase catalytic domain was performed using the protein structure prediction Meta Server and the 3D Jury method for model selection, which resulted in the identification of 1JPR, 1UAA and 1W36 PDB structures as suitable templates for creating a full atom 3D model. This model was further utilized to design small molecules that are expected to block an ATPase catalytic pocket thus inhibit the enzymatic activity. Binding sites for various functional groups were identified in a series of molecular dynamics calculation.

PDB-UF: database of predicted enzymatic functions for unannotated protein structures from structural genomics.

Background: The number of protein structures from structural genomics centers dramatically increases in the Protein Data Bank (PDB). Many of these structures are functionally unannotated because they have no sequence similarity to proteins of known function. However, it is possible to successfully infer function using only structural similarity.

Ligand.Info small-molecule Meta-Database.

Ligand.Info is a compilation of various publicly available databases of small molecules. The total size of the Meta-Database is over 1 million entries.

Structure prediction, evolution and ligand interaction of CHASE domain.

Cytokinins are plant hormones involved in the essential processes of plant growth and development. They bind with receptors known as CRE1/WOL/AHK4, AHK2, and AHK3, which possess histidine kinase activity. Recently, the sensor domain cyclases/histidine kinases associated sensory extracellular (CHASE) was identified in those proteins but little is known about its structure and interaction with ligands.