Consistent Multi-Omic Relationships Uncover Molecular Basis of Pediatric Asthma IgE Regulation.

Serum total immunoglobulin E levels (total IgE) capture the state of the immune system in relation to allergic sensitization. High levels are associated with airway obstruction and poor clinical outcomes in pediatric asthma. Inconsistent patient response to anti-IgE therapies motivates discovery of molecular mechanisms underlying serum IgE level differences in children with asthma. To uncover these mechanisms using complementary metabolomic and transcriptomic data, abundance levels of 529 named metabolites and expression levels of 22,772 genes were measured among children with asthma in the Childhood Asthma Management Program (CAMP, N=564) and the Genetic Epidemiology of Asthma in Costa Rica Study (GACRS, N=309) via the TOPMed initiative. Gene-metabolite associations dependent on IgE were identified within each cohort using multivariate linear models and were interpreted in a biochemical context using network topology, pathway and chemical enrichment, and representation within reactions. A total of 1,617 total IgE-dependent gene-metabolite associations from GACRS and 29,885 from CAMP met significance cutoffs. Of these, glycine and guanidinoacetic acid (GAA) were associated with the most genes in both cohorts, and the associations represented reactions central to glycine, serine, and threonine metabolism and arginine and proline metabolism. Pathway and chemical enrichment analysis further highlighted additional related pathways of interest. The results of this study suggest that GAA may modulate total IgE levels in two independent pediatric asthma cohorts with different characteristics, supporting the use of L-Arginine as a potential therapeutic for asthma exacerbation. Other potentially new targetable pathways are also uncovered.

IntLIM 2.0: identifying multi-omic relationships dependent on discrete or continuous phenotypic measurements.

Motivation: IntLIM uncovers phenotype-dependent linear associations between two types of analytes (e.g. genes and metabolites) in a multi-omic dataset, which may reflect chemically or biologically relevant relationships. Results: The new IntLIM R package includes newly added support for generalized data types, covariate correction, continuous phenotypic measurements, model validation and unit testing. IntLIM analysis uncovered biologically relevant gene-metabolite associations in two separate datasets, and the run time is improved over baseline R functions by multiple orders of magnitude. Availability and implementation: IntLIM is available as an R package with a detailed vignette (https://github.com/ncats/IntLIM) and as an R Shiny app (see Supplementary Figs S1-S6) (https://intlim.ncats.io/). Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Slit2/Robo1 signaling inhibits small-cell lung cancer by targeting ß-catenin signaling in tumor cells and macrophages.

Small-cell lung cancer (SCLC) is an aggressive neuroendocrine subtype of lung cancer with poor patient prognosis. However, the mechanisms that regulate SCLC progression and metastasis remain undefined. Here, we show that the expression of the slit guidance ligand 2 (SLIT2) tumor suppressor gene is reduced in SCLC tumors relative to adjacent normal tissue. In addition, the expression of the SLIT2 receptor, roundabout guidance receptor 1 (ROBO1), is upregulated. We find a positive association between SLIT2 expression and the Yes1 associated transcriptional regulator (YAP1)-expressing SCLC subtype (SCLC-Y), which shows a better prognosis. Using genetically engineered SCLC cells, adenovirus gene therapy, and preclinical xenograft models, we show that SLIT2 overexpression or the deletion of ROBO1 restricts tumor growth in vitro and in vivo. Mechanistic studies revealed significant inhibition of myeloid-derived suppressor cells (MDSCs) and M2-like tumor-associated macrophages (TAMs) in the SCLC tumors. In addition, SLIT2 enhances M1-like and phagocytic macrophages. Molecular analysis showed that ROBO1 knockout or SLIT2 overexpression suppresses the transforming growth factor beta 1 (TGF-ß1)/ß-catenin signaling pathway in both tumor cells and macrophages. Overall, we find that SLIT2 and ROBO1 have contrasting effects on SCLC tumors. SLIT2 suppresses, whereas ROBO1 promotes, SCLC growth by regulating the Tgf-ß1/glycogen synthase kinase-3 beta (GSK3)/ß-catenin signaling pathway in tumor cells and TAMs. These studies indicate that SLIT2 could be used as a novel therapeutic agent against aggressive SCLC.

PUMILIO competes with AUF1 to control DICER1 RNA levels and miRNA processing.

DICER1 syndrome is a cancer pre-disposition disorder caused by mutations that disrupt the function of DICER1 in miRNA processing. Studying the molecular, cellular and oncogenic effects of these mutations can reveal novel mechanisms that control cell homeostasis and tumor biology. Here, we conduct the first analysis of pathogenic DICER1 syndrome allele from the DICER1 3'UTR. We find that the DICER1 syndrome allele, rs1252940486, abolishes interaction with the PUMILIO RNA binding protein with the DICER1 3'UTR, resulting in the degradation of the DICER1 mRNA by AUF1. This single mutational event leads to diminished DICER1 mRNA and protein levels, and widespread reprogramming of miRNA networks. The in-depth characterization of the rs1252940486 DICER1 allele, reveals important post-transcriptional regulatory events that control DICER1 levels.

Mass COVID-19 patient screening using UvsX and UvsY mediated DNA recombination and high throughput parallel sequencing.

The Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), also known as 2019 novel coronavirus (2019-nCoV), is a highly infectious RNA virus. A percentage of patients develop coronavirus disease 2019 (COVID-19) after infection, whose symptoms include fever, cough, shortness of breath and fatigue. Acute and life-threatening respiratory symptoms are experienced by 10-20% of symptomatic patients, particularly those with underlying medical conditions. One of the main challenges in the containment of COVID-19 is the identification and isolation of asymptomatic/pre-symptomatic individuals. A number of molecular assays are currently used to detect SARS-CoV-2. Many of them can accurately test hundreds or even thousands of patients every day. However, there are presently no testing platforms that enable more than 10,000 tests per day. Here, we describe the foundation for the REcombinase Mediated BaRcoding and AmplificatioN Diagnostic Tool (REMBRANDT), a high-throughput Next Generation Sequencing-based approach for the simultaneous screening of over 100,000 samples per day. The REMBRANDT protocol includes direct two-barcoded amplification of SARS-CoV-2 and control amplicons using an isothermal reaction, and the downstream library preparation for Illumina sequencing and bioinformatics analysis. This protocol represents a potentially powerful approach for community screening of COVID-19 that may be modified for application to any infectious or non-infectious genome.

cPLA2 blockade attenuates S100A7-mediated breast tumorigenicity by inhibiting the immunosuppressive tumor microenvironment.

BACKGROUND: Molecular mechanisms underlying inflammation-associated breast tumor growth are poorly studied. S100A7, a pro-inflammatory molecule has been shown to enhance breast cancer growth and metastasis. However, the S100A7-mediated molecular mechanisms in enhancing tumor growth and metastasis are unclear. METHODS: Human breast cancer tissue and plasma samples were used to analyze the expression of S100A7, cPLA2, and PGE2. S100A7-overexpressing or downregulated human metastatic breast cancer cells were used to evaluate the S100A7-mediated downstream signaling mechanisms. Bi-transgenic mS100a7a15 overexpression, TNBC C3 (1)/Tag transgenic, and humanized patient-derived xenograft mouse models and cPLA2 inhibitor (AACOCF3) were used to investigate the role of S100A7/cPLA2/PGE2 signaling in tumor growth and metastasis. Additionally, CODEX, a highly advanced multiplexed imaging was employed to delineate the effects of S100A7/cPLA2 inhibition on the recruitment of various immune cells. RESULTS: In this study, we found that S100A7 and cPLA2 are highly expressed and correlate with decreased overall survival in breast cancer patients. Further mechanistic studies revealed that S100A7/RAGE signaling promotes the expression of cPLA2 to mediate its oncogenic effects. Pharmacological inhibition of cPLA2 suppressed S100A7-mediated tumor growth and metastasis in multiple pre-clinical models including transgenic and humanized patient-derived xenograft (PDX) mouse models. The attenuation of cPLA2 signaling reduced S100A7-mediated recruitment of immune-suppressive myeloid cells in the tumor microenvironment (TME). Interestingly, we discovered that the S100A7/cPLA2 axis enhances the immunosuppressive microenvironment by increasing prostaglandin E2 (PGE2). Furthermore, CO-Detection by indEXing (CODEX) imaging-based analyses revealed that cPLA2 inhibition increased the infiltration of activated and proliferating CD4+ and CD8+ T cells in the TME. In addition, CD163+ tumor associated-macrophages were positively associated with S100A7 and cPLA2 expression in malignant breast cancer patients. CONCLUSIONS: Our study provides new mechanistic insights on the cross-talk between S100A7/cPLA2 in enhancing breast tumor growth and metastasis by generating an immunosuppressive TME that inhibits the infiltration of cytotoxic T cells. Furthermore, our studies indicate that S100A7/cPLA2 could be used as novel prognostic marker and cPLA2 inhibitors as promising drugs against S100A7-overexpressing aggressive breast cancer.

Reduced RBPMS Levels Promote Cell Proliferation and Decrease Cisplatin Sensitivity in Ovarian Cancer Cells.

Worldwide, the number of cancer-related deaths continues to increase due to the ability of cancer cells to become chemotherapy-resistant and metastasize. For women with ovarian cancer, a staggering 70% will become resistant to the front-line therapy, cisplatin. Although many mechanisms of cisplatin resistance have been proposed, the key mechanisms of such resistance remain elusive. The RNA binding protein with multiple splicing (RBPMS) binds to nascent RNA transcripts and regulates splicing, transport, localization, and stability. Evidence indicates that RBPMS also binds to protein members of the AP-1 transcription factor complex repressing its activity. Until now, little has been known about the biological function of RBPMS in ovarian cancer. Accordingly, we interrogated available Internet databases and found that ovarian cancer patients with high RBPMS levels live longer compared to patients with low RBPMS levels. Similarly, immunohistochemical (IHC) analysis in a tissue array of ovarian cancer patient samples showed that serous ovarian cancer tissues showed weaker RBPMS staining when compared with normal ovarian tissues. We generated clustered regularly interspaced short palindromic repeats (CRISPR)-mediated RBPMS knockout vectors that were stably transfected in the high-grade serous ovarian cancer cell line, OVCAR3. The knockout of RBPMS in these cells was confirmed via bioinformatics analysis, real-time PCR, and Western blot analysis. We found that the RBPMS knockout clones grew faster and had increased invasiveness than the control CRISPR clones. RBPMS knockout also reduced the sensitivity of the OVCAR3 cells to cisplatin treatment. Moreover, ß-galactosidase (ß-Gal) measurements showed that RBPMS knockdown induced senescence in ovarian cancer cells. We performed RNAseq in the RBPMS knockout clones and identified several downstream-RBPMS transcripts, including non-coding RNAs (ncRNAs) and protein-coding genes associated with alteration of the tumor microenvironment as well as those with oncogenic or tumor suppressor capabilities. Moreover, proteomic studies confirmed that RBPMS regulates the expression of proteins involved in cell detoxification, RNA processing, and cytoskeleton network and cell integrity. Interrogation of the Kaplan-Meier (KM) plotter database identified multiple downstream-RBPMS effectors that could be used as prognostic and response-to-therapy biomarkers in ovarian cancer. These studies suggest that RBPMS acts as a tumor suppressor gene and that lower levels of RBPMS promote the cisplatin resistance of ovarian cancer cells.

Integrated multi-omics analysis of RB-loss identifies widespread cellular programming and synthetic weaknesses.

Inactivation of RB is one of the hallmarks of cancer, however gaps remain in our understanding of how RB-loss changes human cells. Here we show that pRB-depletion results in cellular reprogramming, we quantitatively measured how RB-depletion altered the transcriptional, proteomic and metabolic output of non-tumorigenic RPE1 human cells. These profiles identified widespread changes in metabolic and cell stress response factors previously linked to E2F function. In addition, we find a number of additional pathways that are sensitive to RB-depletion that are not E2F-regulated that may represent compensatory mechanisms to support the growth of RB-depleted cells. To determine whether these molecular changes are also present in RB1-/- tumors, we compared these results to Retinoblastoma and Small Cell Lung Cancer data, and identified widespread conservation of alterations found in RPE1 cells. To define which of these changes contribute to the growth of cells with de-regulated E2F activity, we assayed how inhibiting or depleting these proteins affected the growth of RB1-/- cells and of Drosophila E2f1-RNAi models in vivo. From this analysis, we identify key metabolic pathways that are essential for the growth of pRB-deleted human cells.

RNA editing signatures identify melanoma patients who respond to Pembrolizumab or Nivolumab treatment.

Immunotherapy has improved the prognosis for many melanoma patients; however, our capacity to predict patient responses and to understand the biological differences between patients who will or will not respond is limited. Gene expression profiling of tumors from patients who respond to immunotherapy has focused on deriving primarily immune-related signatures; however, these have shown limited predictive power. Recent studies have highlighted the role of RNA editing in modulating resistance to immunotherapy. To evaluate the utility of RNA editing activity as a discriminative tool in predicting immunotherapy response, we conducted a retrospective analysis of RNA-sequencing data from melanoma patients treated with Pembrolizumab or Nivolumab. Here, we developed RNA editing signatures that can identify patients who will respond to immunotherapy with very high accuracy and confidence. Our analysis demonstrates that RNA editing is a strong discriminative tool for examining sensitivity of melanoma patients to immunotherapy.

Integration of Metabolomic and Other Omics Data in Population-Based Study Designs: An Epidemiological Perspective.

It is not controversial that study design considerations and challenges must be addressed when investigating the linkage between single omic measurements and human phenotypes. It follows that such considerations are just as critical, if not more so, in the context of multi-omic studies. In this review, we discuss (1) epidemiologic principles of study design, including selection of biospecimen source(s) and the implications of the timing of sample collection, in the context of a multi-omic investigation, and (2) the strengths and limitations of various techniques of data integration across multi-omic data types that may arise in population-based studies utilizing metabolomic data.

Integration of Metabolomics and Transcriptomics to Identify Gene-Metabolite Relationships Specific to Phenotype.

Metabolomics plays an increasingly large role in translational research, with metabolomics data being generated in large cohorts, alongside other omics data such as gene expression. With this in mind, we provide a review of current approaches that integrate metabolomic and transcriptomic data. Furthermore, we provide a detailed framework for integrating metabolomic and transcriptomic data using a two-step approach: (1) numerical integration of gene and metabolite levels to identify phenotype (e.g., cancer)-specific gene-metabolite relationships using IntLIM and (2) knowledge-based integration, using pathway overrepresentation analysis through RaMP, a comprehensive database of biological pathways. Each step makes use of publicly available R packages ( https://github.com/mathelab/IntLIM and https://github.com/mathelab/RaMP-DB ), and provides a user-friendly web interface for analysis. These interfaces can be run locally through the package or can be accessed through our servers ( https://intlim.bmi.osumc.edu and https://ramp-db.bmi.osumc.edu ). The goal of this chapter is to provide step-by-step instructions on how to install the software and use the commands within the R framework, without the user interface (which is slower than running the commands through command line). Both packages are in continuous development so please refer to the GitHub sites to check for updates.

IntLIM: integration using linear models of metabolomics and gene expression data.

BACKGROUND: Integration of transcriptomic and metabolomic data improves functional interpretation of disease-related metabolomic phenotypes, and facilitates discovery of putative metabolite biomarkers and gene targets. For this reason, these data are increasingly collected in large (> 100 participants) cohorts, thereby driving a need for the development of user-friendly and open-source methods/tools for their integration. Of note, clinical/translational studies typically provide snapshot (e.g. one time point) gene and metabolite profiles and, oftentimes, most metabolites measured are not identified. Thus, in these types of studies, pathway/network approaches that take into account the complexity of transcript-metabolite relationships may neither be applicable nor readily uncover novel relationships. With this in mind, we propose a simple linear modeling approach to capture disease-(or other phenotype) specific gene-metabolite associations, with the assumption that co-regulation patterns reflect functionally related genes and metabolites. RESULTS: The proposed linear model, metabolite ~ gene + phenotype + gene:phenotype, specifically evaluates whether gene-metabolite relationships differ by phenotype, by testing whether the relationship in one phenotype is significantly different from the relationship in another phenotype (via a statistical interaction gene:phenotype p-value). Statistical interaction p-values for all possible gene-metabolite pairs are computed and significant pairs are then clustered by the directionality of associations (e.g. strong positive association in one phenotype, strong negative association in another phenotype). We implemented our approach as an R package, IntLIM, which includes a user-friendly R Shiny web interface, thereby making the integrative analyses accessible to non-computational experts. We applied IntLIM to two previously published datasets, collected in the NCI-60 cancer cell lines and in human breast tumor and non-tumor tissue, for which transcriptomic and metabolomic data are available. We demonstrate that IntLIM captures relevant tumor-specific gene-metabolite associations involved in known cancer-related pathways, including glutamine metabolism. Using IntLIM, we also uncover biologically relevant novel relationships that could be further tested experimentally. CONCLUSIONS: IntLIM provides a user-friendly, reproducible framework to integrate transcriptomic and metabolomic data and help interpret metabolomic data and uncover novel gene-metabolite relationships. The IntLIM R package is publicly available in GitHub ( https://github.com/mathelab/IntLIM ) and includes a user-friendly web application, vignettes, sample data and data/code to reproduce results.

RaMP: A Comprehensive Relational Database of Metabolomics Pathways for Pathway Enrichment Analysis of Genes and Metabolites.

The value of metabolomics in translational research is undeniable, and metabolomics data are increasingly generated in large cohorts. The functional interpretation of disease-associated metabolites though is difficult, and the biological mechanisms that underlie cell type or disease-specific metabolomics profiles are oftentimes unknown. To help fully exploit metabolomics data and to aid in its interpretation, analysis of metabolomics data with other complementary omics data, including transcriptomics, is helpful. To facilitate such analyses at a pathway level, we have developed RaMP (Relational database of Metabolomics Pathways), which combines biological pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome, WikiPathways, and the Human Metabolome DataBase (HMDB). To the best of our knowledge, an off-the-shelf, public database that maps genes and metabolites to biochemical/disease pathways and can readily be integrated into other existing software is currently lacking. For consistent and comprehensive analysis, RaMP enables batch and complex queries (e.g., list all metabolites involved in glycolysis and lung cancer), can readily be integrated into pathway analysis tools, and supports pathway overrepresentation analysis given a list of genes and/or metabolites of interest. For usability, we have developed a RaMP R package (https://github.com/Mathelab/RaMP-DB), including a user-friendly RShiny web application, that supports basic simple and batch queries, pathway overrepresentation analysis given a list of genes or metabolites of interest, and network visualization of gene-metabolite relationships. The package also includes the raw database file (mysql dump), thereby providing a stand-alone downloadable framework for public use and integration with other tools. In addition, the Python code needed to recreate the database on another system is also publicly available (https://github.com/Mathelab/RaMP-BackEnd). Updates for databases in RaMP will be checked multiple times a year and RaMP will be updated accordingly.

Divergent Soybean Calmodulins Respond Similarly to Calcium Transients: Insight into Differential Target Regulation.

Plants commonly respond to stressors by modulating the expression of a large family of calcium binding proteins including isoforms of the ubiquitous signaling protein calmodulin (CaM). The various plant CaM isoforms are thought to differentially regulate the activity of specific target proteins to modulate cellular stress responses. The mechanism(s) behind differential target activation by the plant CaMs is unknown. In this study, we used steady-state and stopped-flow fluorescence spectroscopy to investigate the strategy by which two soybean CaMs (sCaM1 and sCaM4) have evolved to differentially regulate NAD kinase (NADK), which is activated by sCaM1 but inhibited by sCaM4. Although the isolated proteins have different cation binding properties, in the presence of Mg2+ and the CaM binding domains from proteins that are differentially regulated, the two plant CaMs respond nearly identically to rapid and slow Ca2+ transients. Our data suggest that the plant CaMs have evolved to bind certain targets with comparable affinities, respond similarly to a particular Ca2+ signature, but achieve different structural states, only one of which can activate the enzyme. Understanding the basis for differential enzyme regulation by the plant CaMs is the first step to engineering a vertebrate CaM that will selectively alter the CaM signaling network.

Myofilament Calcium Sensitivity: Mechanistic Insight into TnI Ser-23/24 and Ser-150 Phosphorylation Integration.

Troponin I (TnI) is a major regulator of cardiac muscle contraction and relaxation. During physiological and pathological stress, TnI is differentially phosphorylated at multiple residues through different signaling pathways to match cardiac function to demand. The combination of these TnI phosphorylations can exhibit an expected or unexpected functional integration, whereby the function of two phosphorylations are different than that predicted from the combined function of each individual phosphorylation alone. We have shown that TnI Ser-23/24 and Ser-150 phosphorylation exhibit functional integration and are simultaneously increased in response to cardiac stress. In the current study, we investigated the functional integration of TnI Ser-23/24 and Ser-150 to alter cardiac contraction. We hypothesized that Ser-23/24 and Ser-150 phosphorylation each utilize distinct molecular mechanisms to alter the TnI binding affinity within the thin filament. Mathematical modeling predicts that Ser-23/24 and Ser-150 phosphorylation affect different TnI affinities within the thin filament to distinctly alter the Ca2+-binding properties of troponin. Protein binding experiments validate this assertion by demonstrating pseudo-phosphorylated Ser-150 decreases the affinity of isolated TnI for actin, whereas Ser-23/24 pseudo-phosphorylation is not different from unphosphorylated. Thus, our data supports that TnI Ser-23/24 affects TnI-TnC binding, while Ser-150 phosphorylation alters TnI-actin binding. By measuring force development in troponin-exchanged skinned myocytes, we demonstrate that the Ca2+ sensitivity of force is directly related to the amount of phosphate present on TnI. Furthermore, we demonstrate that Ser-150 pseudo-phosphorylation blunts Ser-23/24-mediated decreased Ca2+-sensitive force development whether on the same or different TnI molecule. Therefore, TnI phosphorylations can integrate across troponins along the myofilament. These data demonstrate that TnI Ser-23/24 and Ser-150 phosphorylation regulates muscle contraction in part by modulating different TnI interactions in the thin filament and it is the combination of these differential mechanisms that provides understanding of their functional integration.

Effects of Modified Parvalbumin EF-Hand Motifs on Cardiac Myocyte Contractile Function.

Cardiac gene delivery of parvalbumin (Parv), an EF-hand Ca(2+) buffer, has been studied as a therapeutic strategy for diastolic heart failure, in which slow Ca(2+) reuptake is an important contributor. A limitation of wild-type (WT) Parv is the significant trade-off between faster relaxation and blunted contraction amplitude, occurring because WT-Parv sequesters Ca(2+) too early in the cardiac cycle and prematurely truncates sarcomere shortening in the facilitation of rapid relaxation. We recently demonstrated that an E → Q substitution (ParvE101Q) at amino acid 12 of the EF-hand Ca(2+)/Mg(2+) binding loop disrupts bidentate Ca(2+) binding, reducing Ca(2+) affinity by 99-fold and increasing Mg(2+) affinity twofold. ParvE101Q caused faster relaxation and not only preserved contractility, but unexpectedly increased it above untreated myocytes. To gain mechanistic insight into the increased contractility, we focused here on amino acid 12 of the EF-hand motif. We introduced an E → D substitution (ParvE101D) at this site, which converts bidentate Ca(2+) coordination to monodentate coordination. ParvE101D decreased Ca(2+) affinity by 114-fold and increased Mg(2+) affinity 28-fold compared to WT-Parv. ParvE101D increased contraction amplitude compared to both untreated myocytes and myocytes with ParvE101Q, with limited improvement in relaxation. Additionally, ParvE101D increased spontaneous contractions after pacing stress. ParvE101D also increased Ca(2+) transient peak height and was diffusely localized around the Z-line of the sarcomere, suggesting a Ca(2+)-dependent mechanism of enhanced contractility. Sarcoplasmic reticulum Ca(2+) load was not changed with ParvE101D, but postpacing Ca(2+) waves were increased. Together, these data show that inverted Ca(2+)/Mg(2+) binding affinities of ParvE101D increase myocyte contractility through a Ca(2+)-dependent mechanism without altering sarcoplasmic reticulum Ca(2+) load and by increasing unstimulated contractions and Ca(2+) waves. ParvE101D provides mechanistic insight into how changes in the Ca(2+)/Mg(2+) binding affinities of parvalbumin's EF-hand motif alter function of cardiac myocytes. These data are informative in developing new Ca(2+) buffering strategies for the failing heart.

Designing proteins to combat disease: Cardiac troponin C as an example.

Throughout history, muscle research has led to numerous scientific breakthroughs that have brought insight to a more general understanding of all biological processes. Potentially one of the most influential discoveries was the role of the second messenger calcium and its myriad of handling and sensing systems that mechanistically control muscle contraction. In this review we will briefly discuss the significance of calcium as a universal second messenger along with some of the most common calcium binding motifs in proteins, focusing on the EF-hand. We will also describe some of our approaches to rationally design calcium binding proteins to palliate, or potentially even cure cardiovascular disease. Considering not all failing hearts have the same etiology, genetic background and co-morbidities, personalized therapies will need to be developed. We predict designer proteins will open doors for unprecedented personalized, and potentially, even generalized medicines as gene therapy or protein delivery techniques come to fruition.

Myofilament Calcium Sensitivity: Consequences of the Effective Concentration of Troponin I.

Control of calcium binding to and dissociation from cardiac troponin C (TnC) is essential to healthy cardiac muscle contraction/relaxation. There are numerous aberrant post-translational modifications and mutations within a plethora of contractile, and even non-contractile, proteins that appear to imbalance this delicate relationship. The direction and extent of the resulting change in calcium sensitivity is thought to drive the heart toward one type of disease or another. There are a number of molecular mechanisms that may be responsible for the altered calcium binding properties of TnC, potentially the most significant being the ability of the regulatory domain of TnC to bind the switch peptide region of TnI. Considering TnI is essentially tethered to TnC and cannot diffuse away in the absence of calcium, we suggest that the apparent calcium binding properties of TnC are highly dependent upon an "effective concentration" of TnI available to bind TnC. Based on our previous work, TnI peptide binding studies and the calcium binding properties of chimeric TnC-TnI fusion constructs, and building upon the concept of effective concentration, we have developed a mathematical model that can simulate the steady-state and kinetic calcium binding properties of a wide assortment of disease-related and post-translational protein modifications in the isolated troponin complex and reconstituted thin filament. We predict that several TnI and TnT modifications do not alter any of the intrinsic calcium or TnI binding constants of TnC, but rather alter the ability of TnC to "find" TnI in the presence of calcium. These studies demonstrate the apparent consequences of the effective TnI concentration in modulating the calcium binding properties of TnC.

Disease-related cardiac troponins alter thin filament Ca2+ association and dissociation rates.

The contractile response of the heart can be altered by disease-related protein modifications to numerous contractile proteins. By utilizing an IAANS labeled fluorescent troponin C, [Formula: see text], we examined the effects of ten disease-related troponin modifications on the Ca(2+) binding properties of the troponin complex and the reconstituted thin filament. The selected modifications are associated with a broad range of cardiac diseases: three subtypes of familial cardiomyopathies (dilated, hypertrophic and restrictive) and ischemia-reperfusion injury. Consistent with previous studies, the majority of the protein modifications had no effect on the Ca(2+) binding properties of the isolated troponin complex. However, when incorporated into the thin filament, dilated cardiomyopathy mutations desensitized (up to 3.3-fold), while hypertrophic and restrictive cardiomyopathy mutations, and ischemia-induced truncation of troponin I, sensitized the thin filament to Ca(2+) (up to 6.3-fold). Kinetically, the dilated cardiomyopathy mutations increased the rate of Ca(2+) dissociation from the thin filament (up to 2.5-fold), while the hypertrophic and restrictive cardiomyopathy mutations, and the ischemia-induced truncation of troponin I decreased the rate (up to 2-fold). The protein modifications also increased (up to 5.4-fold) or decreased (up to 2.5-fold) the apparent rate of Ca(2+) association to the thin filament. Thus, the disease-related protein modifications alter Ca(2+) binding by influencing both the association and dissociation rates of thin filament Ca(2+) exchange. These alterations in Ca(2+) exchange kinetics influenced the response of the thin filament to artificial Ca(2+) transients generated in a stopped-flow apparatus. Troponin C may act as a hub, sensing physiological and pathological stimuli to modulate the Ca(2+)-binding properties of the thin filament and influence the contractile performance of the heart.