Natural history of vascular calcification in dialysis and transplant patients

Nephrol Dial Transplant 2004; 19:     

Immunization with glycosylated Kb-binding peptides generates carbohydrate-specific,unrestricted cytotoxic T cells

Cytotoxic T cells (CTL) recognize target proteins as short peptides presented by major histocompatibility complex (MHC) class I restriction elements. However, there is also evidence for peptide-independent T cell receptor (TCR) recognition of target proteins and non-protein structures. How such T cell responses are generated is presently unclear. We generated carbohydrate (CHO)-specific, MHC-unrestricted CTL responses by coupling di- and trisaccharides to K^b- or D^b-binding peptides for direct immunization in mice. Four peptides and three CHO have been analyzed with the CHO either in terminal or central positions on the carrier peptide. With two of these glycopeptides, with galabiose (Galα1-4Gal; Gal₂) bound to a homocysteine (via an ethylene spacer arm) in position 4 or 6 in a vesicular stomatitis virus nucleoprotein-derived peptide (RGYVYQGL binding to K^b), CTL were generated which preferentially killed target cells treated with glycopeptide compared to those treated with the core peptide. Polyclonal CTL were also found to kill target cells expressing the same Gal₂ epitope in a glycolipid. By fractionation of CTL, preliminary data indicate that glycopeptide-specific K^b-restricted CTL and unrestricted CHO-specific CTL belong to different T cell populations with regard to TCR expression. The results demonstrate that hapten-specific unrestricted CTL responses can be generated with MHC class I-binding carrier peptides. Different models that might explain the generation of such responses are discussed.     

Inflammatory Effects of Edwardsiella ictaluri Lipopolysaccharide Modifications in Catfish Gut

Bacterial lipopolysaccharides (LPS) are structural components of the outer membranes of Gram-negative bacteria and also are potent inducers of inflammation in mammals. Higher vertebrates are extremely sensitive to LPS, but lower vertebrates, like fish, are resistant to their systemic toxic effects. However, the effects of LPS on the fish intestinal mucosa remain unknown. Edwardsiella ictaluri is a primitive member of the Enterobacteriaceae family that causes enteric septicemia in channel catfish (Ictalurus punctatus). E. ictaluri infects and colonizes deep lymphoid tissues upon oral or immersion infection. Both gut and olfactory organs are the primary sites of invasion. At the systemic level, E. ictaluri pathogenesis is relatively well characterized, but our knowledge about E. ictaluri intestinal interaction is limited. Recently, we observed that E. ictaluri oligo-polysaccharide (O-PS) LPS mutants have differential effects on the intestinal epithelia of orally inoculated catfish. Here we evaluate the effects of E. ictaluri O-PS LPS mutants by using a novel catfish intestinal loop model and compare it to the rabbit ileal loop model inoculated with Salmonella enterica serovar Typhimurium LPS. We found evident differences in rabbit ileal loop and catfish ileal loop responses to E. ictaluri and S. Typhimurium LPS. We determined that catfish respond to E. ictaluri LPS but not to S. Typhimurium LPS. We also determined that E. ictaluri inhibits cytokine production and induces disruption of the intestinal fish epithelia in an O-PS-dependent fashion. The E. ictaluri wild type and ΔwibT LPS mutant caused intestinal tissue damage and inhibited proinflammatory cytokine synthesis, in contrast to E. ictaluri Δgne and Δugd LPS mutants. We concluded that the E. ictaluri O-PS subunits play a major role during pathogenesis, since they influence the recognition of the LPS by the intestinal mucosal immune system of the catfish. The LPS structure of E. ictaluri mutants is needed to understand the mechanism of interaction.     

Next generation sequencing for HLA loci in full heritage Pima Indians of Arizona,Part II: HLA-A, -B,and -C with selected non-classical loci at 4-field resolution from whole genome sequences

While the samples and data from the Pima Indians of the Gila River Indian Community have been included in many international HLA workshops and conferences and have been the focus of numerous population reports and the source of novel alleles at the classical HLA loci, they have not been studied for the non-classical loci. In order to expand our HLA-disease association studies, we typed over 300 whole genome sequences from full Pima heritage members, controlled for first degree relationship, and employed recently developed computer algorithms to resolve HLA alleles. Both classical—HLA-A, -B, and -C— and non-classical— HLA-E, -F, -G, -J, -L, -W, -Y, -DPA2, -DPB2, -DMA, -DMB, -DOA, -DRB2, -DRB9, TAP1— loci were typed at the 4-field level of resolution. We present allele and selected haplotype frequencies, test the genotype distributions for population structure, discuss the issues that are created for tests of Hardy-Weinberg equilibrium over the four sample spaces of high resolution HLA typing, and address the implications for the evolution of non-classical pseudogenes that are no longer expressed in a phenotype subject to natural selection.     

Scalable detection of statistically significant communities and hierarchies,using message passing for modularity

Modularity is a popular measure of community structure. However, maximizing the modularity can lead to many competing partitions, with almost the same modularity, that are poorly correlated with each other. It can also produce illusory ‘‘communities’’ in random graphs where none exist. We address this problem by using the modularity as a Hamiltonian at finite temperature and using an efficient belief propagation algorithm to obtain the consensus of many partitions with high modularity, rather than looking for a single partition that maximizes it. We show analytically and numerically that the proposed algorithm works all of the way down to the detectability transition in networks generated by the stochastic block model. It also performs well on real-world networks, revealing large communities in some networks where previous work has claimed no communities exist. Finally we show that by applying our algorithm recursively, subdividing communities until no statistically significant subcommunities can be found, we can detect hierarchical structure in real-world networks more efficiently than previous methods.Community detection, or node clustering, is a key problem in network science, computer science, sociology, and biology. It aims to partition the nodes in a network into groups such that there are many edges connecting nodes within the same group and comparatively few edges connecting nodes in different groups.Many methods have been proposed for this problem. These include spectral clustering, where we classify nodes according to the eigenvectors of a linear operator such as the adjacency matrix, the random walk matrix, the graph Laplacian, or other linear operators (1–3); statistical inference, where we fit the network with a generative model such as the stochastic block model (4–7); and a wide variety of other methods, e.g., refs. 8–10. See ref. 11 for a review.We focus here on a popular measure of the quality of a partition, the modularity (e.g., refs. 8 and 12–14). We think of a partition {t} into q groups as a function t:V → {1, …, q}, where t_i is the group to which node i belongs. The modularity of a partition {t} of a network with n nodes and m edges is defined asQ({t})=1m(∑〈ij〉∈ℰδtitj−∑〈ij〉didj2mδtitj).[1]Here ? is the set of edges, d_i is the degree of node i, and δ is the Kronecker delta function. The modularity is proportional to the number of edges connecting nodes in the same community minus the expected number of such edges if the graph were random conditioned on its degree distribution, that is, the expectation in a null model where i and j are connected with probability proportional to d_id_j.However, maximizing over all possible partitions often gives a large modularity even in random graphs with no community structure (15–18). Thus, maximizing the modularity can lead to overfitting, where the “optimal” partition simply reflects random noise. Even in real-world networks, the modularity often exhibits a large amount of degeneracy, with multiple local optima that are poorly correlated with each other and are not robust to small perturbations (19).Thus, we need to add some notion of statistical significance to our algorithms. One approach is hypothesis testing, comparing various measures of community structure to the distribution we would see in a null model such as Erdős–Rényi (ER) graphs (20–22). However, even when communities really exist, the modularity of the true partition is often no higher than that of random graphs. In Fig. 1, we show partitions of two networks with the same size and degree distribution: an ER graph (Left) and a graph generated by the stochastic block model (Right), in the detectable regime where it is easy to find a partition correlated with the true one (5, 6). The true partition of the network in Fig. 1, Right has a smaller modularity than the partition found for the random graph in Fig. 1, Left. We can find a partition with higher modularity (and lower accuracy) in Fig. 1, Right, using, e.g., simulated annealing, but then the modularities we obtain for the two networks are similar. Thus, the usual approach of null distributions and P values for hypothesis testing does not appear to work.Open in a separate windowFig. 1.The adjacency matrices of two networks, partitioned to show possible community structure. Each blue point is an edge. (Left) The network is an ER graph, with no real community structure; however, a search by simulated annealing finds a partition with modularity 0.391. (Right) The network has true communities and is generated by the stochastic block model, but the true partition has modularity of just 0.333. Thus, illusory communities in random graphs can have higher modularity than true communities in structured graphs. Both networks have size n=5,000 and a Poisson degree distribution with mean c = 3; the network at Right has c_out/c_in = 0.2, in the easily detectable regime of the stochastic block model.We propose to solve this problem with the tools of statistical physics. As in ref. 16, we treat the modularity as the Hamiltonian of a spin system. We define the energy of a partition {t} as E({t}) = ?mQ({t}) and introduce a Gibbs distribution as a function of inverse temperature β, P({t}) ∝ e^?βE({t}). Rather than maximizing the modularity by searching for the ground state of this system, we focus on its Gibbs distribution at a finite temperature, looking for many high-modularity partitions rather than a single one. In analogy with previous work on the stochastic block model (5, 6), we define a partition {t^} by computing the marginals of the Gibbs distribution and assigning each node to its most likely community. Specifically, if ψti is the marginal probability that i belongs to group t, then t^i=argmaxtψti, breaking ties randomly if more than one t achieves the maximum. We call {t^} the retrieval partition and call its modularity Q({t^}) the retrieval modularity. We claim that {t^} is a far better measure of significant community structure than the maximum-modularity partition. In the language of statistics, the maximum marginal prediction is better than the maximum a posteriori prediction (e.g., ref. 23). More informally, the consensus of many good solutions is better than the ‘‘best’’ single one (24, 25).We give an efficient belief propagation (BP) algorithm to approximate these marginals, which is derived from the cavity method of statistical physics. This algorithm is highly scalable; each iteration takes linear time on sparse networks if the number of groups is fixed, and it converges rapidly in most cases. It is optimal in the sense that for synthetic graphs generated by the stochastic block model, it works all of the way down to the detectability transition. It provides a principled way to choose the number of communities, unlike other algorithms that tend to overfit. Finally, by applying this algorithm recursively, subdividing communities until no statistically significant subcommunities exist, we can uncover hierarchical structure.We validate our approach with experiments on real and synthetic networks. In particular, we find significant large communities in some large networks where previous work claimed there were none. We also compare our algorithm with several others, finding that it obtains more accurate results, both in terms of determining the number of communities and in terms of matching their ground-truth structure.     

Imaging of intrathoracic metastases of nonseminomatous germ cell tumors

Radiologic imaging is crucial in the evaluation of intrathoracic metastatic nonseminomatous germ cell tumors. Helical CT is the workhorse of radiologic staging and is sensitive in the detection of parenchymal nodules and mediastinal lymphadenopathy. CT may also demonstrate other less common sites of metastatic disease. Although, currently, no radiologic procedure is effective in distinguishing viable tumor or teratoma from residual fibrosis and necrosis, cross-sectional imaging remains essential in the presurgical evaluation of potential metastatic disease. FDG PET and CT-guided needle biopsy may be useful in select, high-risk patients.     

Anomalous coronary arteries in adults: depiction at multi-detector row CT angiography

PURPOSE: To retrospectively determine the imaging features of anomalous coronary arteries depicted at multi-detector row computed tomographic (CT) angiography in 18 patients seen at four institutions. MATERIALS AND METHODS: Eighteen patients underwent imaging with a four- or 16-section multi-detector row CT unit by using retrospective electrocardiographic (ECG) gating after infusion of 120-150 mL of intravenous contrast material. Section thicknesses of 0.8-3.0 mm were achieved during breath holding, and images were reconstructed with a 50% overlap. Volumetric reconstructions were obtained for each patient. Each study was assessed retrospectively for the origin and course of the anomalous coronary artery by two thoracic radiologists; decisions were made in consensus. Institutional review board exemption and informed consent waiver was granted at each institution. The study was compliant with the Health Insurance Portability and Accountability Act. RESULTS: Seventeen patients were referred because of equivocal findings at cardiac catheterization or echocardiography; in one, the anomalous coronary artery was incidental. A total of 20 anomalous vessels were found. Twelve patients with 14 variant vessels had an anomalous origin of a left coronary artery (right cusp, 13; noncoronary cusp, one). In four patients, an anomalous right coronary artery originated from the left side; one patient had a single coronary artery arising from the right cusp. In one patient, a left coronary artery-to-vein fistula was observed. In 10 patients, the anomalous vessel passed between the aorta and the main pulmonary artery or right ventricular outflow track. In each case, the origin of the anomalous coronary artery and its course in relationship to the great vessels were unequivocally demonstrated. Volumetric images were useful for showing the three-dimensional orientation of the anomalous coronary artery with respect to the great vessels and cardiac chambers. CONCLUSION: Multi-detector row CT angiography provided accurate depiction of vessel origin and course in this review of 20 anomalous coronary arteries. The results of this study suggest that CT is a viable noninvasive modality for delineating coronary arterial anomalies, particularly if findings at coronary angiography are equivocal.