Cooperative properties of lipid bilayers : collective director fluctuations and the effects of hydrophobic mismatch in protein/lipid membrane systems

Cooperative director fluctuations in lipid bilayers have been postulated for many years. ^2H-NMR T_1^(-1), T_(1P)^(-1) , and T_2^(-1); measurements have been used identify these motions and to determine the origin of increased slow bilayer motion upon addition of unlike lipids or proteins to a pure lipid bilayer.

The contribution of cooperative director fluctuations to NMR relaxation in lipid bilayers has been expressed mathematically using the approach of Doane et al.^1 and Pace and Chan.^2 The T_2^(-1)’s of pure dimyristoyllecithin (DML) bilayers deuterated at the 2, 9 and 10, and all positions on both lipid hydrocarbon chains have been measured. Several characteristics of these measurements indicate the presence of cooperative director fluctuations. First of all, T_2^(-1) exhibits a linear dependence on S2/CD. Secondly, T_2^(-1) varies across the ^2H-NMR powder pattern as sin^2 (2, β), where , β is the angle between the average bilayer director and the external magnetic field. Furthermore, these fluctuations are restricted near the lecithin head group suggesting that the head group does not participate in these motions but, rather, anchors the hydrocarbon chains in the bilayer.

T_2^(-1)has been measured for selectively deuterated liquid crystalline DML hilayers to which a host of other lipids and proteins have been added. The T_2^(-1) of the DML bilayer is found to increase drastically when chlorophyll a (chl a) and Gramicidin A' (GA') are added to the bilayer. Both these molecules interfere with the lecithin head group spacing in the bilayer. Molecules such as myristic acid, distearoyllecithin (DSL), phytol, and cholesterol, whose hydrocarbon regions are quite different from DML but which have small,neutral polar head groups, leave cooperative fluctuations in the DML bilayer unchanged.

The effect of chl a on cooperative fluctuations in the DML bilayer has been examined in detail using ^2H-NMR T_1^(-1), T_(1P)^(-1) , and T_2^(-1); measurements. Cooperative fluctuations have been modelled using the continuum theory of the nematic state of liquid crystals. Chl a is found to decrease both the correlation length and the elastic constants in the DML bilayer.

A mismatch between the hydrophobic length of a lipid bilayer and that of an added protein has also been found to change the cooperative properties of the lecithin bilayer. Hydrophobic mismatch has been studied in a series GA' / lecithin bilayers. The dependence of 2H-NMR order parameters and relaxation rates on GA' concentration has been measured in selectively deuterated DML, dipalmitoyllecithin (DPL), and DSL systems. Order parameters, cooperative lengths, and elastic constants of the DML bilayer are most disrupted by GA', while the DSL bilayer is the least perturbed by GA'. Thus, it is concluded that the hydrophobic length of GA' best matches that of the DSL bilayer. Preliminary Raman spectroscopy and Differential Scanning Calorimetry experiments of GA' /lecithin systems support this conclusion. Accommodation of hydrophobic mismatch is used to rationalize the absence of H_(II) phase formation in GA' /DML systems and the observation of H_(II) phase in GA' /DPL and GA' /DSL systems.

1. J. W. Doane and D. L. Johnson, Chem. Phy3. Lett., 6, 291-295 (1970). 2. R. J. Pace and S. I. Chan, J. Chem. Phy3., 16, 4217-4227 (1982).

Characterization of the human SCF ubiquitin ligases - structure, function, and regulation

The SCF ubiquitin ligase complex of budding yeast triggers DNA replication by cata lyzi ng ubiquitination of the S phase CDK inhibitor SIC1. SCF is composed of several evolutionarily conserved proteins, including ySKP1, CDC53 (Cullin), and the F-box protein CDC4. We isolated hSKP1 in a two-hybrid screen with hCUL1, the human homologue of CDC53. We showed that hCUL1 associates with hSKP1 in vivo and directly interacts with hSKP1 and the human F-box protein SKP2 in vitro, forming an SCF-Iike particle. Moreover, hCUL1 complements the growth defect of yeast CDC53^(ts) mutants, associates with ubiquitination-promoting activity in human cell extracts, and can assemble into functional, chimeric ubiquitin ligase complexes with yeast SCF components. These data demonstrated that hCUL1 functions as part of an SCF ubiquitin ligase complex in human cells. However, purified human SCF complexes consisting of CUL1, SKP1, and SKP2 are inactive in vitro, suggesting that additional factors are required.

Subsequently, mammalian SCF ubiquitin ligases were shown to regulate various physiological processes by targeting important cellular regulators, like lĸBα, β-catenin, and p27, for ubiquitin-dependent proteolysis by the 26S proteasome. Little, however, is known about the regulation of various SCF complexes. By using sequential immunoaffinity purification and mass spectrometry, we identified proteins that interact with human SCF components SKP2 and CUL1 in vivo. Among them we identified two additional SCF subunits: HRT1, present in all SCF complexes, and CKS1, that binds to SKP2 and is likely to be a subunit of SCF5^(SKP2) complexes. Subsequent work by others demonstrated that these proteins are essential for SCF activity. We also discovered that COP9 Signalosome (CSN), previously described in plants as a suppressor of photomorphogenesis, associates with CUL1 and other SCF subunits in vivo. This interaction is evolutionarily conserved and is also observed with other Cullins, suggesting that all Cullin based ubiquitin ligases are regulated by CSN. CSN regulates Cullin Neddylation presumably through CSNS/JAB1, a stochiometric Signalosome subunit and a putative deneddylating enzyme. This work sheds light onto an intricate connection that exists between signal transduction pathways and protein degradation machinery inside the cell and sets stage for gaining further insights into regulation of protein degradation.

Phospholipids and a protein-conducting channel regulate cotranslational protein targeting

Signal recognition particle (SRP) and signal recognition particle receptor (SR) are evolutionarily conserved GTPases that deliver secretory and membrane proteins to the protein-conducting channel Sec61 complex in the lipid bilayer of the endoplasmic reticulum in eukaryotes or the SecYEG complex in the inner membrane of bacteria. Unlike the canonical Ras-type GTPases, SRP and SR are activated via nucleotide-dependent heterodimerization. Upon formation of the SR•SRP targeting complex, SRP and SR undergo a series of discrete conformational changes that culminate in their reciprocal activation and hydrolysis of GTP. How the SR•SRP GTPase cycle is regulated and coupled to the delivery of the cargo protein to the protein-conducting channel at the target membrane is not well-understood. Here we examine the role of the lipid bilayer and SecYEG in regulation of the SRP-mediated protein targeting pathway and show that they serve as important biological cues that spatially control the targeting reaction.

In the first chapter, we show that anionic phospholipids of the inner membrane activate the bacterial SR, FtsY, and favor the late conformational states of the targeting complex conducive to efficient unloading of the cargo. The results of our studies suggest that the lipid bilayer acts as a spatial cue that weakens the interaction of the cargo protein with SRP and primes the complex for unloading its cargo onto SecYEG.

In the second chapter, we focus on the effect of SecYEG on the conformational states and activity of the targeting complex. While phospholipids prime the complex for unloading its cargo, they are insufficient to trigger hydrolysis of GTP and the release of the cargo from the complex. SecYEG modulates the conformation of the targeting complex and triggers the GTP hydrolysis from the complex, thus driving the targeting reaction to completion. The results of this study suggest that SecYEG is not a passive recipient of the cargo protein; rather, it actively releases the cargo from the targeting complex. Together, anionic phospholipids and SecYEG serve distinct yet complementary roles. They spatially control the targeting reaction in a sequential manner, ensuring efficient delivery and unloading of the cargo protein.

In the third chapter, we reconstitute the transfer reaction in vitro and visualize it in real time. We show that the ribosome-nascent chain complex is transferred to SecYEG via a stepwise mechanism with gradual dissolution and formation of the contacts with SRP and SecYEG, respectively, explaining how the cargo is kept tethered to the membrane during the transfer and how its loss to the cytosol is avoided.

In the fourth chapter, we examine interaction of SecYEG with secretory and membrane proteins and attempt to address the role of a novel insertase YidC in this interaction. We show that detergent-solubilized SecYEG is capable of discriminating between the nascent chains of various lengths and engages a signal sequence in a well-defined conformation in the absence of accessory factors. Further, YidC alters the conformation of the signal peptide bound to SecYEG. The results described in this chapter show that YidC affects the SecYEG-nascent chain interaction at early stages of translocation/insertion and suggest a YidC-facilitated mechanism for lateral exit of transmembrane domains from SecYEG into the lipid bilayer.

MicroRNA-132 is a physiological regulator of hematopoietic stem cell function and B-cell development

MicroRNAs are a class of small non-coding RNAs that negatively regulate gene expression. Several microRNAs have been implicated in altering hematopoietic cell fate decisions. Importantly, deregulation of many microRNAs can lead to deleterious consequences in the hematopoietic system, including the onset of cancer, autoimmunity, or a failure to respond effectively to infection. As such, microRNAs fine-tune the balance between normal hematopoietic output and pathologic consequences. In this work, we explore the role of two microRNAs, miR-132 and miR-125b, in regulating hematopoietic stem cell (HSC) function and B cell development. In particular, we uncover the role of miR-132 in maintaining the appropriate balance between self-renewal, differentiation, and survival in aging HSCs by buffering the expression of a critical transcription factor, FOXO3. By maintain this balance, miR-132 may play a critical role in preventing aging-associated hematopoietic conditions such as autoimmune disease and cancer. We also find that miR-132 plays a critical role in B cell development by targeting a key transcription factor, Sox4, that is responsible for the differentiation of pro-B cells into pre-B cells. We find that miR-132 regulates B cell apoptosis, and by delivering miR-132 to mice that are predisposed to developing B cell cancers, we can inhibit the formation of these cancers and improve the survival of these mice. In addition to miR-132, we uncovered the role of another critical microRNA, miR-125b, that potentiates hematopoietic stem cell function. We found that enforced expression of miR-125b causes an aggressive myeloid leukemia by downregulation of its target Lin28a. Importantly, miR-125b also plays a critical role in inhibiting the formation of pro-B cells. Thus, we have discovered two microRNAs with important roles in regulating normal hematopoiesis, and whose dregulation can lead to deleterious consequences such as cancer in the aging hematopoietic system. Both miR-132 and miR-125b may therefore be targeted for therapeutics to inhibit age-related immune diseases associated with the loss of HSC function and cancer progression.

Technology for Single Cell Protein Analysis in Immunology and Cancer Prognostics

The first chapter of this thesis deals with automating data gathering for single cell microfluidic tests. The programs developed saved significant amounts of time with no loss in accuracy. The technology from this chapter was applied to experiments in both Chapters 4 and 5.

The second chapter describes the use of statistical learning to prognose if an anti-angiogenic drug (Bevacizumab) would successfully treat a glioblastoma multiforme tumor. This was conducted by first measuring protein levels from 92 blood samples using the DNA-encoded antibody library platform. This allowed the measure of 35 different proteins per sample, with comparable sensitivity to ELISA. Two statistical learning models were developed in order to predict whether the treatment would succeed. The first, logistic regression, predicted with 85% accuracy and an AUC of 0.901 using a five protein panel. These five proteins were statistically significant predictors and gave insight into the mechanism behind anti-angiogenic success/failure. The second model, an ensemble model of logistic regression, kNN, and random forest, predicted with a slightly higher accuracy of 87%.

The third chapter details the development of a photocleavable conjugate that multiplexed cell surface detection in microfluidic devices. The method successfully detected streptavidin on coated beads with 92% positive predictive rate. Furthermore, chambers with 0, 1, 2, and 3+ beads were statistically distinguishable. The method was then used to detect CD3 on Jurkat T cells, yielding a positive predictive rate of 49% and false positive rate of 0%.

The fourth chapter talks about the use of measuring T cell polyfunctionality in order to predict whether a patient will succeed an adoptive T cells transfer therapy. In 15 patients, we measured 10 proteins from individual T cells (~300 cells per patient). The polyfunctional strength index was calculated, which was then correlated with the patient's progress free survival (PFS) time. 52 other parameters measured in the single cell test were correlated with the PFS. No statistical correlator has been determined, however, and more data is necessary to reach a conclusion.

Finally, the fifth chapter talks about the interactions between T cells and how that affects their protein secretion. It was observed that T cells in direct contact selectively enhance their protein secretion, in some cases by over 5 fold. This occurred for Granzyme B, Perforin, CCL4, TNFa, and IFNg. IL- 10 was shown to decrease slightly upon contact. This phenomenon held true for T cells from all patients tested (n=8). Using single cell data, the theoretical protein secretion frequency was calculated for two cells and then compared to the observed rate of secretion for both two cells not in contact, and two cells in contact. In over 90% of cases, the theoretical protein secretion rate matched that of two cells not in contact.