Structural and Dynamic Features of Acinetobacter baumannii OXA-66 ß-Lactamase Explain Its Stability and Evolution of Novel Variants.

OXA-66 is a member of the OXA-51 subfamily of class D ß-lactamases native to the Acinetobacter genus that includes Acinetobacter baumannii, one of the ESKAPE pathogens and a major cause of drug-resistant nosocomial infections. Although both wild type OXA-66 and OXA-51 have low catalytic activity, they are ubiquitous in the Acinetobacter genomes. OXA-51 is also remarkably thermostable. In addition, newly emerging, single and double amino acid variants show increased activity against carbapenems, indicating that the OXA-51 subfamily is growing and gaining clinical significance. In this study, we used molecular dynamics simulations, X-ray crystallography, and thermal denaturation data to examine and compare the dynamics of OXA-66 wt and its gain-of-function variants: I129L (OXA-83), L167V (OXA-82), P130Q (OXA-109), P130A, and W222L (OXA-234). Our data indicate that OXA-66 wt also has a high melting temperature, and its remarkable stability is due to an extensive and rigid hydrophobic bridge formed by a number of residues around the active site and harbored by the three loops, P, Ω, and ß5-ß6. Compared to the WT enzyme, the mutants exhibit higher flexibility only in the loop regions, and are more stable than other robust carbapenemases, such as OXA-23 and OXA-24/40. All the mutants show increased rotational flexibility of residues I129 and W222, which allows carbapenems to bind. Overall, our data support the hypothesis that structural features in OXA-51 and OXA-66 promote evolution of multiple highly stable variants with increased clinical relevance in A. baumannii.

Heterogeneity of glycan biomarker clusters as an indicator of recurrence in pancreatic cancer.

Introduction: Outcomes following tumor resection vary dramatically among patients with pancreatic ductal adenocarcinoma (PDAC). A challenge in defining predictive biomarkers is to discern within the complex tumor tissue the specific subpopulations and relationships that drive recurrence. Multiplexed immunofluorescence is valuable for such studies when supplied with markers of relevant subpopulations and analysis methods to sort out the intra-tumor relationships that are informative of tumor behavior. We hypothesized that the glycan biomarkers CA19-9 and STRA, which detect separate subpopulations of cancer cells, define intra-tumoral features associated with recurrence. Methods: We probed this question using automated signal thresholding and spatial cluster analysis applied to the immunofluorescence images of the STRA and CA19-9 glycan biomarkers in whole-block sections of PDAC tumors collected from curative resections. Results: The tumors (N = 22) displayed extreme diversity between them in the amounts of the glycans and in the levels of spatial clustering, but neither the amounts nor the clusters of the individual and combined glycans associated with recurrence. The combined glycans, however, marked divergent types of spatial clusters, alternatively only STRA, only CA19-9, or both. The co-occurrence of more than one cluster type within a tumor associated significantly with disease recurrence, in contrast to the independent occurrence of each type of cluster. In addition, intra-tumoral regions with heterogeneity in biomarker clusters spatially aligned with pathology-confirmed cancer cells, whereas regions with homogeneous biomarker clusters aligned with various non-cancer cells. Conclusion: Thus, the STRA and CA19-9 glycans are markers of distinct and co-occurring subpopulations of cancer cells that in combination are associated with recurrence. Furthermore, automated signal thresholding and spatial clustering provides a tool for quantifying intra-tumoral subpopulations that are informative of outcome.

Heterogeneity of Glycan Biomarker Clusters as an Indicator of Recurrence in Pancreatic Cancer.

Outcomes following tumor resection vary dramatically among patients with pancreatic cancer. A challenge in defining predictive biomarkers is to discern within the complex tumor tissue the specific subpopulations and relationships that drive recurrence. Multiplexed immunofluorescence is valuable for such studies when supplied with markers of relevant subpopulations and analysis methods to sort out the intra-tumor relationships that are informative of tumor behavior. We hypothesized that the glycan biomarkers CA19-9 and STRA, which detect separate subpopulations of cancer cells, define intra-tumoral features associated with recurrence. We probed this question using automated signal thresholding and spatial cluster analysis applied to the immunofluorescence images of the STRA and CA19-9 glycan biomarkers in whole-block tumor sections. The tumors (N = 22) displayed extreme diversity between them in the amounts of the glycans and in the levels of spatial clustering, but neither the amounts nor the clusters of the individual and combined glycans associated with recurrence. The combined glycans, however, marked divergent types of spatial clusters, alternatively only STRA, only CA19-9, or both. The co-occurrence of more than one cluster type within a tumor associated significantly with disease recurrence, in contrast to the independent occurrence of each type of cluster. In addition, intra-tumoral regions with heterogeneity in biomarker clusters spatially aligned with pathology-confirmed cancer cells, whereas regions with homogeneous biomarker clusters aligned with various non-cancer cells. Thus, the STRA and CA19-9 glycans are markers of distinct and co-occurring subpopulations of cancer cells that in combination are associated with recurrence. Furthermore, automated signal thresholding and spatial clustering provides a tool for quantifying intra-tumoral subpopulations that are informative of outcome.

CarboGrove: a resource of glycan-binding specificities through analyzed glycan-array datasets from all platforms.

Glycan arrays continue to be the primary resource for determining the glycan-binding specificity of proteins. The volume and diversity of glycan-array data are increasing, but no common method and resource exist to analyze, integrate, and use the available data. To meet this need, we developed a resource of analyzed glycan-array data called CarboGrove. Using the ability to process and interpret data from any type of glycan array, we populated the database with the results from 35 types of glycan arrays, 13 glycan families, 5 experimental methods, and 19 laboratories or companies. In meta-analyses of glycan-binding proteins, we observed glycan-binding specificities that were not uncovered from single sources. In addition, we confirmed the ability to efficiently optimize selections of glycan-binding proteins to be used in experiments for discriminating between closely related motifs. Through descriptive reports and a programmatically accessible Application Programming Interface, CarboGrove yields unprecedented access to the wealth of glycan-array data being produced and powerful capabilities for both experimentalists and bioinformaticians.

Combined Analysis of Multiple Glycan-Array Datasets: New Explorations of Protein-Glycan Interactions.

Glycan arrays are indispensable for learning about the specificities of glycan-binding proteins. Despite the abundance of available data, the current analysis methods do not have the ability to interpret and use the variety of data types and to integrate information across datasets. Here, we evaluated whether a novel, automated algorithm for glycan-array analysis could meet that need. We developed a regression-tree algorithm with simultaneous motif optimization and packaged it in software called MotifFinder. We applied the software to analyze data from eight different glycan-array platforms with widely divergent characteristics and observed an accurate analysis of each dataset. We then evaluated the feasibility and value of the combined analyses of multiple datasets. In an integrated analysis of datasets covering multiple lectin concentrations, the software determined approximate binding constants for distinct motifs and identified major differences between the motifs that were not apparent from single-concentration analyses. Furthermore, an integrated analysis of data sources with complementary sets of glycans produced broader views of lectin specificity than produced by the analysis of just one data source. MotifFinder, therefore, enables the optimal use of the expanding resource of the glycan-array data and promises to advance the studies of protein-glycan interactions.

Imaging Mass Spectrometry and Lectin Analysis of N-Linked Glycans in Carbohydrate Antigen-Defined Pancreatic Cancer Tissues.

The early detection of pancreatic ductal adenocarcinoma (PDAC) is a complex clinical obstacle yet is key to improving the overall likelihood of patient survival. Current and prospective carbohydrate biomarkers carbohydrate antigen 19-9 (CA19-9) and sialylated tumor-related antigen (sTRA) are sufficient for surveilling disease progression yet are not approved for delineating PDAC from other abdominal cancers and noncancerous pancreatic pathologies. To further understand these glycan epitopes, an imaging mass spectrometry (IMS) approach was used to assess the N-glycome of the human pancreas and pancreatic cancer in a cohort of patients with PDAC represented by tissue microarrays and whole-tissue sections. Orthogonally, these same tissues were characterized by multiround immunofluorescence that defined expression of CA19-9 and sTRA as well as other lectins toward carbohydrate epitopes with the potential to improve PDAC diagnosis. These analyses revealed distinct differences not only in N-glycan spatial localization across both healthy and diseased tissues but importantly between different biomarker-categorized tissue samples. Unique sulfated biantennary N-glycans were detected specifically in normal pancreatic islets. N-glycans from CA19-9-expressing tissues tended to be biantennary, triantennary, and tetra-antennary structures with both core and terminal fucose residues and bisecting GlcNAc. These N-glycans were detected in less abundance in sTRA-expressing tumor tissues, which favored triantennary and tetra-antennary structures with polylactosamine extensions. Increased sialylation of N-glycans was detected in all tumor tissues. A candidate new biomarker derived from IMS was further explored by fluorescence staining with selected lectins on the same tissues. The lectins confirmed the expression of the epitopes in cancer cells and revealed different tumor-associated staining patterns between glycans with bisecting GlcNAc and those with terminal GlcNAc. Thus, the combination of lectin-immunohistochemistry and lectin-IMS techniques produces more complete information for tumor classification than the individual analyses alone. These findings potentiate the development of early assessment technologies to rapidly and specifically identify PDAC in the clinic that may directly impact patient outcomes.

Advances in Tools to Determine the Glycan-Binding Specificities of Lectins and Antibodies.

Proteins that bind carbohydrate structures can serve as tools to quantify or localize specific glycans in biological specimens. Such proteins, including lectins and glycan-binding antibodies, are particularly valuable if accurate information is available about the glycans that a protein binds. Glycan arrays have been transformational for uncovering rich information about the nuances and complexities of glycan-binding specificity. A challenge, however, has been the analysis of the data. Because protein-glycan interactions are so complex, simplistic modes of analyzing the data and describing glycan-binding specificities have proven inadequate in many cases. This review surveys the methods for handling high-content data on protein-glycan interactions. We contrast the approaches that have been demonstrated and provide an overview of the resources that are available. We also give an outlook on the promising experimental technologies for generating new insights into protein-glycan interactions, as well as a perspective on the limitations that currently face the field.

Deciphering Protein Glycosylation by Computational Integration of On-chip Profiling, Glycan-array Data, and Mass Spectrometry.

The difficulty in uncovering detailed information about protein glycosylation stems from the complexity of glycans and the large amount of material needed for the experiments. Here we report a method that gives information on the isomeric variants of glycans in a format compatible with analyzing low-abundance proteins. On-chip glycan modification and probing (on-chip gmap) uses sequential and parallel rounds of exoglycosidase cleavage and lectin profiling of microspots of proteins, together with algorithms that incorporate glycan-array analyses and information from mass spectrometry, when available, to computationally interpret the data. In tests on control proteins with simple or complex glycosylation, on-chip gmap accurately characterized the relative proportions of core types and terminal features of glycans. Subterminal features (monosaccharides and linkages under a terminal monosaccharide) were accurately probed using a rationally designed sequence of lectin and exoglycosidase incubations. The integration of mass information further improved accuracy in each case. An alternative use of on-chip gmap was to complement the mass spectrometry analysis of detached glycans by specifying the isomers that comprise the glycans identified by mass spectrometry. On-chip gmap provides the potential for detailed studies of glycosylation in a format compatible with clinical specimens or other low-abundance sources.

Mining High-Complexity Motifs in Glycans: A New Language To Uncover the Fine Specificities of Lectins and Glycosidases.

Knowledge of lectin and glycosidase specificities is fundamental to the study of glycobiology. The primary specificities of such molecules can be uncovered using well-established tools, but the complex details of their specificities are difficult to determine and describe. Here we present a language and algorithm for the analysis and description of glycan motifs with high complexity. The language uses human-readable notation and wildcards, modifiers, and logical operators to define motifs of nearly any complexity. By applying the syntax to the analysis of glycan-array data, we found that the lectin AAL had higher binding where fucose groups are displayed on separate branches. The lectin SNA showed gradations in binding based on the length of the extension displaying sialic acid and on characteristics of the opposing branches. A new algorithm to evaluate changes in lectin binding upon treatment with exoglycosidases identified the primary specificities and potential fine specificities of an α1-2-fucosidase and an α2-3,6,8-neuraminidase. The fucosidase had significantly lower action where sialic acid neighbors the fucose, and the neuraminidase showed statistically lower action where α1-2 fucose neighbors the sialic acid or is on the opposing branch. The complex features identified here would have been inaccessible to analysis using previous methods. The new language and algorithms promise to facilitate the precise determination and description of lectin and glycosidase specificities.

The structure of a doripenem-bound OXA-51 class D ß-lactamase variant with enhanced carbapenemase activity.

OXA-51 is a class D ß-lactamase that is thought to be the native carbapenemase of Acinetobacter baumannii. Many variants of OXA-51 containing active site substitutions have been identified from A. baumannii isolates, and some of these substitutions increase hydrolytic activity toward carbapenem antibiotics. We have determined the high-resolution structures of apo OXA-51 and OXA-51 with one such substitution (I129L) with the carbapenem doripenem trapped in the active site as an acyl-intermediate. The structure shows that acyl-doripenem adopts an orientation very similar to carbapenem ligands observed in the active site of OXA-24/40 (doripenem) and OXA-23 (meropenem). In the OXA-51 variant/doripenem complex, the indole ring of W222 is oriented away from the doripenem binding site, thereby eliminating a clash that is predicted to occur in wildtype OXA-51. Similarly, in the OXA-51 variant complex, L129 adopts a different rotamer compared to I129 in wildtype OXA-51. This alternative position moves its side chain away from the hydroxyethyl moiety of doripenem and relieves another potential clash between the enzyme and carbapenem substrates. Molecular dynamics simulations of OXA-51 and OXA-51 I129L demonstrate that compared to isoleucine, a leucine at this position greatly favors a rotamer that accommodates the ligand. These results provide a molecular justification for how this substitution generates enhanced binding affinity for carbapenems, and therefore helps explain the prevalence of this substitution in clinical OXA-51 variants.

Clinical Variants of the Native Class D ß-Lactamase of Acinetobacter baumannii Pose an Emerging Threat through Increased Hydrolytic Activity against Carbapenems.

The threat posed by the chromosomally encoded class D ß-lactamase of Acinetobacter baumannii (OXA-51/66) has been unclear, in part because of its relatively low affinity and turnover rate for carbapenems. Several hundred clinical variants of OXA-51/66 have been reported, many with substitutions of active-site residues. We determined the kinetic properties of OXA-66 and five clinical variants with respect to a wide variety of ß-lactam substrates. The five variants displayed enhanced activity against carbapenems and in some cases against penicillins, late-generation cephalosporins, and the monobactam aztreonam. Molecular dynamics simulations show that in OXA-66, P130 inhibits the side-chain rotation of I129 and thereby prevents doripenem binding because of steric clash. A single amino acid substitution at this position (P130Q) in the variant OXA-109 greatly enhances the mobility of both I129 and a key active-site tryptophan (W222), thereby facilitating carbapenem binding. This expansion of substrate specificity represents a very worrisome development for the efficacy of ß-lactams against this troublesome pathogen.