Immunological Characterization and Transmembrane Topology of 5-Hydroxytryptamine3 Receptors by Functional Epitope Tagging

Abstract: 5-Hydroxytryptamine₃ (5-HT₃) receptors are the only known monoamine receptors mediating fast excitatory responses in mammalian neurons. Their primary structure as well as their electrophysiological and pharmacological properties show a phylogenetic relation to nicotinic acetylcholine, GABA_A, and glycine receptors. As a prototypical member of this gene superfamily, we investigated the membrane topology of functional homomeric 5-HT₃ receptors by using epitope tagging of the channel subunits expressed in heterologous systems. Visualization of 5-HT₃ receptors in transfected COS-7 cells, either in western blot (molecular mass 61.2 ± 0.8 kDa) or in situ, was performed with previously characterized antibodies recognizing artificial epitopes as well as with anti-fusion protein antibodies directed against a wild-type receptor intracellular domain. The extracellular location of the distal C-terminal tagged domain demonstrates the presence of a fourth transmembrane domain in 5-HT₃ serotonin-gated channels. In this region, the significant homology between members of this class of neurotransmitter-gated channels suggests strongly that they have a common transmembrane organization basically different from glutamate-gated and ATP-gated channels.     

CO2 supplementation eliminates sugar-rich media requirement for plant propagation using a simple inexpensive temporary immersion photobioreactor

Plant Cell, Tissue and Organ Culture (PCTOC) - In vitro plant propagation systems such as temporary immersion bioreactors (TIBs) are valuable tools that enable production of disease-free plants...     

A Massively Parallel Pipeline to Clone DNA Variants and Examine Molecular Phenotypes of Human Disease Mutations

Understanding the functional relevance of DNA variants is essential for all exome and genome sequencing projects. However, current mutagenesis cloning protocols require Sanger sequencing, and thus are prohibitively costly and labor-intensive. We describe a massively-parallel site-directed mutagenesis approach, “Clone-seq”, leveraging next-generation sequencing to rapidly and cost-effectively generate a large number of mutant alleles. Using Clone-seq, we further develop a comparative interactome-scanning pipeline integrating high-throughput GFP, yeast two-hybrid (Y2H), and mass spectrometry assays to systematically evaluate the functional impact of mutations on protein stability and interactions. We use this pipeline to show that disease mutations on protein-protein interaction interfaces are significantly more likely than those away from interfaces to disrupt corresponding interactions. We also find that mutation pairs with similar molecular phenotypes in terms of both protein stability and interactions are significantly more likely to cause the same disease than those with different molecular phenotypes, validating the in vivo biological relevance of our high-throughput GFP and Y2H assays, and indicating that both assays can be used to determine candidate disease mutations in the future. The general scheme of our experimental pipeline can be readily expanded to other types of interactome-mapping methods to comprehensively evaluate the functional relevance of all DNA variants, including those in non-coding regions.     

HINT: High-quality protein interactomes and their applications in understanding human disease

Background

Mathematical models of dynamical systems facilitate the computation of characteristic properties that are not accessible experimentally. In cell biology, two main properties of interest are (1) the time-period a protein is accessible to other molecules in a certain state - its half-life - and (2) the time it spends when passing through a subsystem - its transit-time. We discuss two approaches to quantify the half-life, present the novel method of in silico labeling, and introduce the label half-life and label transit-time. The developed method has been motivated by laboratory tracer experiments. To investigate the kinetic properties and behavior of a substance of interest, we computationally label this species in order to track it throughout its life cycle. The corresponding mathematical model is extended by an additional set of reactions for the labeled species, avoiding any double-counting within closed circuits, correcting for the influences of upstream fluxes, and taking into account combinatorial multiplicity for complexes or reactions with several reactants or products. A profile likelihood approach is used to estimate confidence intervals on the label half-life and transit-time.

Results

Application to the JAK-STAT signaling pathway in Epo-stimulated BaF3-EpoR cells enabled the calculation of the time-dependent label half-life and transit-time of STAT species. The results were robust against parameter uncertainties.

Conclusions

Our approach renders possible the estimation of species and label half-lives and transit-times. It is applicable to large non-linear systems and an implementation is provided within the PottersWheel modeling framework (http://www.potterswheel.de).     

Three-dimensional reconstruction of protein networks provides insight into human genetic disease

To better understand the molecular mechanisms and genetic basis of human disease, we systematically examine relationships between 3,949 genes, 62,663 mutations and 3,453 associated disorders by generating a three-dimensional, structurally resolved human interactome. This network consists of 4,222 high-quality binary protein-protein interactions with their atomic-resolution interfaces. We find that in-frame mutations (missense point mutations and in-frame insertions and deletions) are enriched on the interaction interfaces of proteins associated with the corresponding disorders, and that the disease specificity for different mutations of the same gene can be explained by their location within an interface. We also predict 292 candidate genes for 694 unknown disease-to-gene associations with proposed molecular mechanism hypotheses. This work indicates that knowledge of how in-frame disease mutations alter specific interactions is critical to understanding pathogenesis. Structurally resolved interaction networks should be valuable tools for interpreting the wealth of data being generated by large-scale structural genomics and disease association studies.     

ENCAPP: elastic-net-based prognosis prediction and biomarker discovery for human cancers

Background

With the explosion of genomic data over the last decade, there has been a tremendous amount of effort to understand the molecular basis of cancer using informatics approaches. However, this has proven to be extremely difficult primarily because of the varied etiology and vast genetic heterogeneity of different cancers and even within the same cancer. One particularly challenging problem is to predict prognostic outcome of the disease for different patients.

Results

Here, we present ENCAPP, an elastic-net-based approach that combines the reference human protein interactome network with gene expression data to accurately predict prognosis for different human cancers. Our method identifies functional modules that are differentially expressed between patients with good and bad prognosis and uses these to fit a regression model that can be used to predict prognosis for breast, colon, rectal, and ovarian cancers. Using this model, ENCAPP can also identify prognostic biomarkers with a high degree of confidence, which can be used to generate downstream mechanistic and therapeutic insights.

Conclusion

ENCAPP is a robust method that can accurately predict prognostic outcome and identify biomarkers for different human cancers.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1465-9) contains supplementary material, which is available to authorized users.     

Dissecting Disease Inheritance Modes in a Three-Dimensional Protein Network Challenges the “Guilt-by-Association” Principle

To better understand different molecular mechanisms by which mutations lead to various human diseases, we classified 82,833 disease-associated mutations according to their inheritance modes (recessive versus dominant) and molecular types (in-frame [missense point mutations and in-frame indels] versus truncating [nonsense mutations and frameshift indels]) and systematically examined the effects of different classes of disease mutations in a three-dimensional protein interactome network with the atomic-resolution interface resolved for each interaction. We found that although recessive mutations affecting the interaction interface of two interacting proteins tend to cause the same disease, this widely accepted “guilt-by-association” principle does not apply to dominant mutations. Furthermore, recessive truncating mutations in regions encoding the same interface are much more likely to cause the same disease, even for interfaces close to the N terminus of the protein. Conversely, dominant truncating mutations tend to be enriched in regions encoding areas between interfaces. These results suggest that a significant fraction of truncating mutations can generate functional protein products. For example, TRIM27, a known cancer-associated protein, interacts with three proteins (MID2, TRIM42, and SIRPA) through two different interfaces. A dominant truncating mutation (c.1024delT [p.Tyr342Thrfs^∗30]) associated with ovarian carcinoma is located between the regions encoding the two interfaces; the altered protein retains its interaction with MID2 and TRIM42 through the first interface but loses its interaction with SIRPA through the second interface. Our findings will help clarify the molecular mechanisms of thousands of disease-associated genes and their tens of thousands of mutations, especially for those carrying truncating mutations, often erroneously considered “knockout” alleles.