CUREing biochemistry lab monotony.

Undergraduate research experience is critical to success in post-graduate research settings. The recent movement away from "cookbook" style labs to course-based undergraduate research experiences (CUREs) in undergraduate laboratories has allowed universities to provide inclusive research experience while bypassing the limitations of extracurricular apprenticeships. This paper describes an upper-level biochemistry CURE designed to provide students with an introductory experience to graduate-level research by studying a suspected DNA helicase. This CURE is designed to span multiple semesters, where each student cohort builds upon the work of previous semesters. Pre- and post-course surveys were employed to assess student confidence in bench skills, perceptions of the course, and project ownership. The results show that the incorporation of lab meeting-style recitations and poster presentations led to higher project ownership, while overcoming troubleshooting was a significant challenge. Furthermore, confidence in every experimental technique increased significantly in all but one instance. Based on these results, this CURE is providing students with a realistic experience in graduate-level research.

Shared activity patterns arising at genetic susceptibility loci reveal underlying genomic and cellular architecture of human disease.

Genetic variants underlying complex traits, including disease susceptibility, are enriched within the transcriptional regulatory elements, promoters and enhancers. There is emerging evidence that regulatory elements associated with particular traits or diseases share similar patterns of transcriptional activity. Accordingly, shared transcriptional activity (coexpression) may help prioritise loci associated with a given trait, and help to identify underlying biological processes. Using cap analysis of gene expression (CAGE) profiles of promoter- and enhancer-derived RNAs across 1824 human samples, we have analysed coexpression of RNAs originating from trait-associated regulatory regions using a novel quantitative method (network density analysis; NDA). For most traits studied, phenotype-associated variants in regulatory regions were linked to tightly-coexpressed networks that are likely to share important functional characteristics. Coexpression provides a new signal, independent of phenotype association, to enable fine mapping of causative variants. The NDA coexpression approach identifies new genetic variants associated with specific traits, including an association between the regulation of the OCT1 cation transporter and genetic variants underlying circulating cholesterol levels. NDA strongly implicates particular cell types and tissues in disease pathogenesis. For example, distinct groupings of disease-associated regulatory regions implicate two distinct biological processes in the pathogenesis of ulcerative colitis; a further two separate processes are implicated in Crohn's disease. Thus, our functional analysis of genetic predisposition to disease defines new distinct disease endotypes. We predict that patients with a preponderance of susceptibility variants in each group are likely to respond differently to pharmacological therapy. Together, these findings enable a deeper biological understanding of the causal basis of complex traits.

CIB1: a small protein with big ambitions.

Calcium- and integrin-binding protein 1 (CIB1) is a small, ubiquitously expressed protein that was first identified as an intracellular binding partner of a platelet-specific α-integrin cytoplasmic tail. Although early studies revealed a role for CIB1 in regulating platelet integrin activity, recent studies have indicated a more diverse role for CIB1 in many different cell types and processes, including calcium signaling, migration, adhesion, proliferation, and survival. Increasing evidence also points to a novel role for CIB1 in cancer and cardiovascular disease. In addition, an array of CIB1 binding partners has been identified that provide important insight into how CIB1 may regulate these processes. Some of these binding partners include the serine/threonine kinases, p21-activated kinase 1 (PAK1), apoptosis signal-regulating kinase 1 (ASK1), and polo-like kinase 3 (PLK3). Structural and mutational studies indicate that CIB1 binds most or all of its partners via a well-defined hydrophobic cleft. Although CIB1 itself lacks known enzymatic activity, it supports the PI3K/AKT and MEK/ERK oncogenic signaling pathways, in part, by directly modulating enzymes in these pathways. In this review, we discuss our current understanding of CIB1 and key questions regarding structure and function and how this seemingly diminutive protein impacts important signaling pathways and cellular processes in human health and disease.-Leisner, T. M., Freeman, T. C., Black, J. L., Parise, L. V. CIB1: a small protein with big ambitions.

Identification of novel integrin binding partners for calcium and integrin binding protein 1 (CIB1): structural and thermodynamic basis of CIB1 promiscuity.

The short cytoplasmic tails of the α- and ß-chains of integrin adhesion receptors regulate integrin activation and cell signaling. Significantly less is known about proteins that bind to α-integrin cytoplasmic tails (CTs) as opposed to ß-CTs to regulate integrins. Calcium and integrin binding protein 1 (CIB1) was previously identified as an αIIb binding partner that inhibits agonist-induced activation of the platelet-specific integrin, αIIbß3. A sequence alignment of all α-integrin CTs revealed that key residues in the CIB1 binding site of αIIb are well-conserved, and was used to delineate a consensus binding site (I/L-x-x-x-L/M-W/Y-K-x-G-F-F). Because the CIB1 binding site of αIIb is conserved in all α-integrins and CIB1 expression is ubiquitous, we asked if CIB1 could interact with other α-integrin CTs. We predicted that multiple α-integrin CTs were capable of binding to the same hydrophobic binding pocket on CIB1 with docking models generated by all-atom replica exchange discrete molecular dynamics. After demonstrating novel in vivo interactions between CIB1 and other whole integrin complexes with co-immunoprecipitations, we validated the modeled predictions with solid-phase competitive binding assays, which showed that other α-integrin CTs compete with the αIIb CT for binding to CIB1 in vitro. Isothermal titration calorimetry measurements indicated that this binding is driven by hydrophobic interactions and depends on residues in the CIB1 consensus binding site. These new mechanistic details of CIB1-integrin binding imply that CIB1 could bind to all integrin complexes and act as a broad regulator of integrin function.

TMBB-DB: a transmembrane ß-barrel proteome database.

MOTIVATION: We previously reported the development of a highly accurate statistical algorithm for identifying ß-barrel outer membrane proteins or transmembrane ß-barrels (TMBBs), from genomic sequence data of Gram-negative bacteria (Freeman,T.C. and Wimley,W.C. (2010) Bioinformatics, 26, 1965-1974). We have now applied this identification algorithm to all available Gram-negative bacterial genomes (over 600 chromosomes) and have constructed a publicly available, searchable, up-to-date, database of all proteins in these genomes. RESULTS: For each protein in the database, there is information on (i) ß-barrel membrane protein probability for identification of ß-barrels, (ii) ß-strand and ß-hairpin propensity for structure and topology prediction, (iii) signal sequence score because most TMBBs are secreted through the inner membrane translocon and, thus, have a signal sequence, and (iv) transmembrane α-helix predictions, for reducing false positive predictions. This information is sufficient for the accurate identification of most ß-barrel membrane proteins in these genomes. In the database there are nearly 50 000 predicted TMBBs (out of 1.9 million total putative proteins). Of those, more than 15 000 are 'hypothetical' or 'putative' proteins, not previously identified as TMBBs. This wealth of genomic information is not available anywhere else. AVAILABILITY: The TMBB genomic database is available at http://beta-barrel.tulane.edu/. CONTACT: wwimley@tulane.edu.

Somatic retrotransposition alters the genetic landscape of the human brain.

Retrotransposons are mobile genetic elements that use a germline 'copy-and-paste' mechanism to spread throughout metazoan genomes. At least 50 per cent of the human genome is derived from retrotransposons, with three active families (L1, Alu and SVA) associated with insertional mutagenesis and disease. Epigenetic and post-transcriptional suppression block retrotransposition in somatic cells, excluding early embryo development and some malignancies. Recent reports of L1 expression and copy number variation in the human brain suggest that L1 mobilization may also occur during later development. However, the corresponding integration sites have not been mapped. Here we apply a high-throughput method to identify numerous L1, Alu and SVA germline mutations, as well as 7,743 putative somatic L1 insertions, in the hippocampus and caudate nucleus of three individuals. Surprisingly, we also found 13,692 somatic Alu insertions and 1,350 SVA insertions. Our results demonstrate that retrotransposons mobilize to protein-coding genes differentially expressed and active in the brain. Thus, somatic genome mosaicism driven by retrotransposition may reshape the genetic circuitry that underpins normal and abnormal neurobiological processes.

Oligonucleotide microarray expression profiling of contrasting invasive phenotypes in colorectal cancer.

This chapter refers to the application of laser-capture microdissection with oligonucleotide microarray analysis. The protocol described has been successfully used to identify differential transcript expression between contrasting colorectal cancer invasive phenotypes. Tissue processing, RNA extraction, quality control, amplification, fluorescent labelling, purification, hybridisation, and elements of data analysis are covered.

The prediction and characterization of YshA, an unknown outer-membrane protein from Salmonella typhimurium.

We have developed an effective pathway for the prediction and characterization of novel transmembrane ß-barrel proteins. The Freeman-Wimley algorithm, which is a highly accurate prediction method based on the physicochemical properties of experimentally characterized transmembrane ß barrel (TMBB) structures, was used to predict TMBBs in the genome of Salmonella typhimurium LT2. The previously uncharacterized product of gene yshA was tested as a model for validating the algorithm. YshA is a highly conserved 230-residue protein that is predicted to have 10 transmembrane ß-strands and an N-terminal signal sequence. All of the physicochemical and spectroscopic properties exhibited by YshA are consistent with the prediction that it is a TMBB. Specifically, recombinant YshA localizes to the outer membrane when expressed in Escherichia coli; YshA has a ß-sheet-rich secondary structure with stable tertiary contacts in the presence of detergent micelles or when reconstituted into a lipid bilayer. When in a lipid bilayer, YshA forms a membrane-spanning pore with an effective radius of ~0.7nm. Taken together, these data substantiate the predictions made by the Freeman-Wimley algorithm by showing that YshA is a TMBB protein.

A highly accurate statistical approach for the prediction of transmembrane beta-barrels.

MOTIVATION: Transmembrane beta-barrels (TMBBs) belong to a special structural class of proteins predominately found in the outer membranes of Gram-negative bacteria, mitochondria and chloroplasts. TMBBs are surface-exposed proteins that perform a variety of functions ranging from nutrient acquisition to osmotic regulation. These properties suggest that TMBBs have great potential for use in vaccine or drug therapy development. However, membrane proteins, such as TMBBs, are notoriously difficult to identify and characterize using traditional experimental approaches and current prediction methods are still unreliable. RESULTS: A prediction method based on the physicochemical properties of experimentally characterized TMBB structures was developed to predict TMBB-encoding genes from genomic databases. The Freeman-Wimley prediction algorithm developed in this study has an accuracy of 99% and MCC of 0.748 when using the most efficient prediction criteria, which is better than any previously published algorithm. AVAILABILITY: The MS Windows-compatible application is available for download at http://www.tulane.edu/~biochem/WW/apps.html.

Co-expression of FBN1 with mesenchyme-specific genes in mouse cell lines: implications for phenotypic variability in Marfan syndrome.

Mutations in the human FBN1 gene cause Marfan syndrome, a complex disease affecting connective tissues but with a highly variable phenotype. To identify genes that might participate in epistatic interactions with FBN1, and could therefore explain the observed phenotypic variability, we have looked for genes that are co-expressed with Fbn1 in the mouse. Microarray expression data derived from a range of primary mouse cells and cell lines were analysed using the network analysis tool BioLayout Express(3D). A cluster of 205 genes, including Fbn1, were selectively expressed by mouse cell lines of different mesenchymal lineages and by mouse primary mesenchymal cells (preadipocytes, myoblasts, fibroblasts, osteoblasts). Promoter analysis of this gene set identified several candidate transcriptional regulators. Genes within this co-expressed cluster are candidate genetic modifiers for Marfan syndrome and for other connective tissue diseases.

Functional clustering and lineage markers: insights into cellular differentiation and gene function from large-scale microarray studies of purified primary cell populations.

Very large microarray datasets showing gene expression across multiple tissues and cell populations provide a window on the transcriptional networks that underpin the differences in functional activity between biological systems. Clusters of co-expressed genes provide lineage markers, candidate regulators of cell function and, by applying the principle of guilt by association, candidate functions for genes of currently unknown function. We have analysed a dataset comprising pure cell populations from hemopoietic and non-hemopoietic cell types (http://biogps.gnf.org). Using a novel network visualisation and clustering approach, we demonstrate that it is possible to identify very tight expression signatures associated specifically with embryonic stem cells, mesenchymal cells and hematopoietic lineages. Selected examples validate the prediction that gene function can be inferred by co-expression. One expression cluster was enriched in phagocytes, which, alongside endosome-lysosome constituents, contains genes that may make up a 'pathway' for phagocyte differentiation. Promoters of these genes are enriched for binding sites for the ETS/PU.1 and MITF families. Another cluster was associated with the production of a specific extracellular matrix, with high levels of gene expression shared by cells of mesenchymal origin (fibroblasts, adipocytes, osteoblasts and myoblasts). We discuss the limitations placed upon such data by the presence of alternative promoters with distinct tissue specificity within many protein-coding genes.

Novel expression signatures identified by transcriptional analysis of separated leucocyte subsets in systemic lupus erythematosus and vasculitis.

OBJECTIVE: To optimise a strategy for identifying gene expression signatures differentiating systemic lupus erythematosus (SLE) and antineutrophil cytoplasmic antibody-associated vasculitis that provide insight into disease pathogenesis and identify biomarkers. METHODS: 44 vasculitis patients, 13 SLE patients and 25 age and sex-matched controls were enrolled. CD4 and CD8 T cells, B cells, monocytes and neutrophils were isolated from each patient and, together with unseparated peripheral blood mononuclear cells (PBMC), were hybridised to spotted oligonucleotide microarrays. RESULTS: Using expression data obtained from purified cells a substantial number of differentially expressed genes were identified that were not detectable in the analysis of PBMC. Analysis of purified T cells identified a SLE-associated, CD4 T-cell signature consistent with type 1 interferon signalling driving the generation and survival of tissue homing T cells and thereby contributing to disease pathogenesis. Moreover, hierarchical clustering using expression data from purified monocytes provided significantly improved discrimination between the patient groups than that obtained using PBMC data, presumably because the differentially expressed genes reflect genuine differences in processes underlying disease pathogenesis. CONCLUSION: Analysis of leucocyte subsets enabled the identification of gene signatures of both pathogenic relevance and with better disease discrimination than those identified in PBMC. This approach thus provides substantial advantages in the search for diagnostic and prognostic biomarkers in autoimmune disease.