Cutibacterium recovered from deep specimens at the time of revision shoulder arthroplasty samples has increased biofilm-forming capacity and hemolytic activity compared with Cutibacterium skin isolates from normal subjects.

BACKGROUND: Biofilm formation and hemolytic activity are factors that may correlate with the virulence of Cutibacterium. We sought to compare the prevalence of these potential markers of pathogenicity between Cutibacterium recovered from deep specimens obtained at the time of surgical revision for failed shoulder arthroplasty and Cutibacterium recovered from skin samples from normal subjects. METHODS: We compared 42 deep-tissue or explant isolates with 43 control Cutibacterium samples obtained from skin isolates from normal subjects. Subtyping information was available for all isolates. Biofilm-forming capacity was measured by inoculating a normalized amount of each isolate onto a 96-well plate. Planktonic bacteria were removed, the remaining adherent bacteria were stained with crystal violet, the crystal violet was re-solubilized in ethyl alcohol, and biofilm-forming capacity was quantitated by optical density (OD). Hemolytic activity was measured by plating a normalized amount of isolate onto agar plates. The area of the colony and the surrounding area of blood lysis were measured and reported as minimal, moderate, or severe hemolysis. RESULTS: Biofilm-forming capacity was significantly higher in the tissue and explant samples compared with the control skin samples (OD of 0.34 ± 0.30 for deep tissue vs. 0.20 ± 0.28 for skin, P = .002). Hemolytic activity was also significantly higher in the tissue and explant samples than in the control skin samples (P < .0001). Samples with hemolytic activity had significantly higher biofilm-forming capacity compared with samples without hemolytic activity (OD of 0.27 ± 0.29 vs. 0.12 ± 0.15, P = .015). No difference in biofilm-forming capacity or hemolytic activity was found between subtypes. CONCLUSIONS: Cutibacterium obtained from deep specimens at the time of revision shoulder arthroplasty has higher biofilm-forming capacity and hemolytic activity than Cutibacterium recovered from the skin of normal subjects. These data add support for the view that Cutibacterium harvested from deep tissues may have clinically significant virulence characteristics. The lack of correlation between these clinically relevant phenotypes and subtypes indicates that additional study is needed to identify genotypic markers that better correlate with biofilm and hemolytic activity.

What do positive and negative Cutibacterium culture results in periprosthetic shoulder infection mean? A multi-institutional control study.

BACKGROUND: Deep tissue culture specimens obtained at the time of revision shoulder arthroplasty are commonly positive for Cutibacterium. Clinical interpretation of positive cultures can be difficult. This was a multi-institutional study evaluating the accuracy of cultures for Cutibacterium using positive control (PC) and negative control (NC) samples. The relationship between time to culture positivity and strength of culture positivity was also studied. METHODS: Eleven different institutions were each sent 12 blinded samples (10 PC and 2 NC samples). The 10 PC samples included 2 sets of 5 different dilutions of a Cutibacterium isolate from a failed total shoulder arthroplasty with a probable periprosthetic infection. At each institution, the samples were handled as if they were received from the operating room. Specimen growth, time to culture positivity, and strength of culture positivity (based on semiquantitative assessment) were reported. RESULTS: A total of 110 PC samples and 22 NC samples were tested. One hundred percent of specimens at the 4 highest dilutions were positive for Cutibacterium. At the lowest dilution, 91% of samples showed positive findings. Cutibacterium grew in 14% of NC samples. Cutibacterium grew in PC samples at an average of 4.0 ± 1.3 days, and all of these samples showed growth within 7 days. The time to positivity was significantly shorter (P < .001) and the strength of positivity was significantly higher (P < .001) in true-positive cultures compared with false-positive cultures. CONCLUSIONS: This multi-institutional study suggests that different institutions may report highly consistent rates of culture positivity for revision shoulder arthroplasty samples with higher bacterial loads. In contrast, with lower bacterial loads, the results are somewhat less consistent. Clinicians should consider using a shorter time to positivity and a higher strength of positivity as adjuncts in determining whether a tissue culture sample is a true positive.

Cutibacterium acnes Isolates from Deep Tissue Specimens Retrieved during Revision Shoulder Arthroplasty: Similar Colony Morphology Does Not Indicate Clonality.

Cutibacterium acnes is the most common bacterium associated with periprosthetic shoulder infections. Sequencing of C. acnes has been proposed as a potential rapid diagnostic tool and a method of determining subtypes associated with pathogenicity and antibiotic resistance patterns. When multiple deep samples from the same surgery are culture positive for the same species and the isolates show the same culture phenotype, it is typically assumed that these isolates are clonal. However, it is well-known that C. acnes is not clonal on the skin of most individuals. We hypothesized that the C. acnes bacteria recovered at the time of revision shoulder arthroplasty would often represent more than one subtype, and we tested this hypothesis in this work. For patients undergoing revision shoulder arthroplasty, multiple samples from the surgical field were taken. For those patients with multiple samples that were culture positive for C. acnes, isolates from each sample were subjected to full genome sequencing. Of 11 patients, 5 (45%) had different subtypes of C. acnes within the deep tissues even though the colony morphology was similar. One patient had four subtypes in the deep tissues, while four patients had two different subtypes. Up to four different subtypes of C. acnes were observed in the deep tissues of a single patient. Clonality of C. acnes isolates from deep specimens from a potential periprosthetic shoulder infection cannot be assumed. Sequence-based characterization of virulence and antibiotic resistance may require testing of multiple deep specimens.

Cutibacterium subtype distribution on the skin of primary and revision shoulder arthroplasty patients.

BACKGROUND: The skin of healthy shoulders is known to harbor multiple different subtypes of Cutibacterium (formerly Propionibacterium) acnes at the same time. C acnes can often be isolated from deep tissue and explant samples obtained during revision of a failed shoulder arthroplasty, presumably because the shoulder was inoculated with organisms from the patient's skin at the time of the index arthroplasty. It is possible that specific subtypes or distributions of subtypes may be associated with an increased pathogenic potential and that the skin of patients undergoing revision arthroplasty contains different distributions of the subtypes than in patients undergoing primary arthroplasty. We analyzed the subtype distribution of Cutibacterium from the skin of shoulders undergoing revision arthroplasty vs. primary arthroplasty. METHODS: Preoperative skin swabs were collected from 25 patients who underwent primary shoulder arthroplasty and 27 patients who underwent revision shoulder arthroplasty. The results of semiquantitative cultures of the skin and deep tissues were reported as specimen Cutibacterium values, and scores from all deep tissue samples were added to report the total shoulder Cutibacterium score. Single-locus sequence typing (SLST) of C acnes from the skin swabs was used to determine the subtype distribution for each patient. The percentage of each subtype for each patient was averaged in patients undergoing revision arthroplasty and then compared with that in patients undergoing primary arthroplasty. RESULTS: The C acnes subtype distribution on the skin of revision arthroplasty patients was different from that of primary shoulder arthroplasty patients, with a significantly higher percentage of SLST subtype A (36.9% vs. 16.0%, P = .0018). The distribution of SLST subtypes was similar between revision arthroplasty patients with strongly positive culture findings vs. those with weakly positive or negative culture findings. CONCLUSIONS: Significant differences in the skin Cutibacterium subtype distributions were found between shoulders undergoing revision shoulder arthroplasty and those undergoing primary shoulder arthroplasty. Future studies are needed to determine whether certain Cutibacterium subtype distributions are associated with an increased risk of arthroplasty revision.

Stitching together multiple data dimensions reveals interacting metabolomic and transcriptomic networks that modulate cell regulation.

Cells employ multiple levels of regulation, including transcriptional and translational regulation, that drive core biological processes and enable cells to respond to genetic and environmental changes. Small-molecule metabolites are one category of critical cellular intermediates that can influence as well as be a target of cellular regulations. Because metabolites represent the direct output of protein-mediated cellular processes, endogenous metabolite concentrations can closely reflect cellular physiological states, especially when integrated with other molecular-profiling data. Here we develop and apply a network reconstruction approach that simultaneously integrates six different types of data: endogenous metabolite concentration, RNA expression, DNA variation, DNA-protein binding, protein-metabolite interaction, and protein-protein interaction data, to construct probabilistic causal networks that elucidate the complexity of cell regulation in a segregating yeast population. Because many of the metabolites are found to be under strong genetic control, we were able to employ a causal regulator detection algorithm to identify causal regulators of the resulting network that elucidated the mechanisms by which variations in their sequence affect gene expression and metabolite concentrations. We examined all four expression quantitative trait loci (eQTL) hot spots with colocalized metabolite QTLs, two of which recapitulated known biological processes, while the other two elucidated novel putative biological mechanisms for the eQTL hot spots.

Validation of an oligonucleotide ligation assay for quantification of human immunodeficiency virus type 1 drug-resistant mutants by use of massively parallel sequencing.

Global HIV treatment programs need sensitive and affordable tests to monitor HIV drug resistance. We compared mutant detection by the oligonucleotide ligation assay (OLA), an economical and simple test, to massively parallel sequencing. Nonnucleoside reverse transcriptase inhibitor (K103N, V106M, Y181C, and G190A) and lamivudine (M184V) resistance mutations were quantified in blood-derived plasma RNA and cell DNA specimens by OLA and 454 pyrosequencing. A median of 1,000 HIV DNA or RNA templates (range, 163 to 1,874 templates) from blood specimens collected in Mozambique (n = 60) and Kenya (n = 51) were analyzed at 4 codons in each sample (n = 441 codons assessed). Mutations were detected at 75 (17%) codons by OLA sensitive to 2.0%, at 71 codons (16%; P = 0.78) by pyrosequencing using a cutoff value of ≥ 2.0%, and at 125 codons (28%; P < 0.0001) by pyrosequencing sensitive to 0.1%. Discrepancies between the assays included 15 codons with mutant concentrations of ∼2%, one at 8.8% by pyrosequencing and not detected by OLA, and one at 69% by OLA and not detected by pyrosequencing. The latter two cases were associated with genetic polymorphisms in the regions critical for ligation of the OLA probes and pyrosequencing primers, respectively. Overall, mutant concentrations quantified by the two methods correlated well across the codons tested (R(2) > 0.8). Repeat pyrosequencing of 13 specimens showed reproducible detection of 5/24 mutations at <2% and 6/6 at ≥ 2%. In conclusion, the OLA and pyrosequencing performed similarly in the quantification of nonnucleoside reverse transcriptase inhibitor and lamivudine mutations present at >2% of the viral population in clinical specimens. While pyrosequencing was more sensitive, detection of mutants below 2% was not reproducible.

Construction of regulatory networks using expression time-series data of a genotyped population.

The inference of regulatory and biochemical networks from large-scale genomics data is a basic problem in molecular biology. The goal is to generate testable hypotheses of gene-to-gene influences and subsequently to design bench experiments to confirm these network predictions. Coexpression of genes in large-scale gene-expression data implies coregulation and potential gene-gene interactions, but provide little information about the direction of influences. Here, we use both time-series data and genetics data to infer directionality of edges in regulatory networks: time-series data contain information about the chronological order of regulatory events and genetics data allow us to map DNA variations to variations at the RNA level. We generate microarray data measuring time-dependent gene-expression levels in 95 genotyped yeast segregants subjected to a drug perturbation. We develop a Bayesian model averaging regression algorithm that incorporates external information from diverse data types to infer regulatory networks from the time-series and genetics data. Our algorithm is capable of generating feedback loops. We show that our inferred network recovers existing and novel regulatory relationships. Following network construction, we generate independent microarray data on selected deletion mutants to prospectively test network predictions. We demonstrate the potential of our network to discover de novo transcription-factor binding sites. Applying our construction method to previously published data demonstrates that our method is competitive with leading network construction algorithms in the literature.

Phylobook: a tool for display, clade annotation and extraction of sequences from molecular phylogenies.

As the volume of sequence data from variable pathogens increases, means of analyzing, annotating and extracting specific taxa for study becomes more difficult. To meet these challenges for datasets with hundreds to thousands of taxa, 'Phylobook' was developed. Starting with a sequence alignment file, Phylobook generates and displays phylogenetic trees adjacent to highlighter plots showing the position of mutations, and allows the user to identify lineages and recombinants, annotate and export selected subsets of sequences for downstream analysis. Accurate lineage assignment, which is difficult to automate, is aided using annotations created by different clustering methods. Phylobook provides web-based display combined with automated clustering and manual editing to allow for expert assessment and correction of lineage assignments and extraction for downstream analysis.

Phylobook generates and displays phylogenetic trees adjacent to highlighter plots showing the position of mutations, allows the user to identify lineages and recombinants, annotate and export selected subsets of sequences for downstream analysis.

Genomic Islands Shape the Genetic Background of Both JP2 and Non-JP2 Aggregatibacter actinomycetemcomitans.

Aggregatibacter actinomycetemcomitans is a periodontal pathogen associated with periodontitis. This species exhibits substantial variations in gene content among different isolates and has different virulence potentials. This study examined the distribution of genomic islands and their insert sites among genetically diverse A. actinomycetemcomitans strains by comparative genomic analysis. The results showed that some islands, presumably more ancient, were found across all genetic clades of A. actinomycetemcomitans. In contrast, other islands were specific to individual clades or a subset of clades and may have been acquired more recently. The islands for the biogenesis of serotype-specific antigens comprise distinct genes located in different loci for serotype a and serotype b-f strains. Islands that encode the same cytolethal distending toxins appear to have been acquired via distinct mechanisms in different loci for clade b/c and for clade a/d/e/f strains. The functions of numerous other islands remain to be elucidated. JP2 strains represent a small branch within clade b, one of the five major genetic clades of A. actinomycetemcomitans. In conclusion, the complex process of genomic island acquisition, deletion, and modification is a significant force in the genetic divergence of A. actinomycetemcomitans. Assessing the genetic distinctions between JP2 and non-JP2 strains must consider the landscape of genetic variations shaped by evolution.

Genome sequence of a novel species, Propionibacterium humerusii.

As part of a larger project to sequence multiple clinical isolates of Propionibacterium acnes, we have produced a draft genome sequence of a novel Propionibacterium species that is closely related to, yet distinct (by sequence) from P. acnes. We have tentatively named this new species Propionibacterium humerusii.

The derivation of diagnostic markers of chronic myeloid leukemia progression from microarray data.

Currently, limited molecular markers exist that can determine where in the spectrum of chronic myeloid leukemia (CML) progression an individual patient falls at diagnosis. Gene expression profiles can predict disease and prognosis, but most widely used microarray analytical methods yield lengthy gene candidate lists that are difficult to apply clinically. Consequently, we applied a probabilistic method called Bayesian model averaging (BMA) to a large CML microarray dataset. BMA, a supervised method, considers multiple genes simultaneously and identifies small gene sets. BMA identified 6 genes (NOB1, DDX47, IGSF2, LTB4R, SCARB1, and SLC25A3) that discriminated chronic phase (CP) from blast crisis (BC) CML. In CML, phase labels divide disease progression into discrete states. BMA, however, produces posterior probabilities between 0 and 1 and predicts patients in "intermediate" stages. In validation studies of 88 patients, the 6-gene signature discriminated early CP from late CP, accelerated phase, and BC. This distinction between early and late CP is not possible with current classifications, which are based on known duration of disease. BMA is a powerful tool for developing diagnostic tests from microarray data. Because therapeutic outcomes are so closely tied to disease phase, these probabilities can be used to determine a risk-based treatment strategy at diagnosis.

Protinfo PPC: a web server for atomic level prediction of protein complexes.

'Protinfo PPC' (Prediction of Protein Complex) is a web server that predicts atomic level structures of interacting proteins from their amino-acid sequences. It uses the interolog method to search for experimental protein complex structures that are homologous to the input sequences submitted by a user. These structures are then used as starting templates to generate protein complex models, which are returned to the user in Protein Data Bank format via email. The server supports modeling of both homo and hetero multimers and generally produces full atomic level models (including insertion/deletion regions) of protein complexes as long as at least one putative homologous template for the query sequences is found. The modeling pipeline behind Protinfo PPC has been rigorously benchmarked and proven to produce highly accurate protein complex models. The fully automated all atom comparative modeling service for protein complexes provided by Protinfo PPC server offers wide capabilities ranging from prediction of protein complex interactions to identification of possible interaction sites, which will be useful for researchers studying these topics. The Protinfo PPC web server is available at http://protinfo.compbio.washington.edu/ppc/.

Bacterial cyclic diguanylate signaling networks sense temperature.

Many bacteria use the second messenger cyclic diguanylate (c-di-GMP) to control motility, biofilm production and virulence. Here, we identify a thermosensory diguanylate cyclase (TdcA) that modulates temperature-dependent motility, biofilm development and virulence in the opportunistic pathogen Pseudomonas aeruginosa. TdcA synthesizes c-di-GMP with catalytic rates that increase more than a hundred-fold over a ten-degree Celsius change. Analyses using protein chimeras indicate that heat-sensing is mediated by a thermosensitive Per-Arnt-SIM (PAS) domain. TdcA homologs are widespread in sequence databases, and a distantly related, heterologously expressed homolog from the Betaproteobacteria order Gallionellales also displayed thermosensitive diguanylate cyclase activity. We propose, therefore, that thermotransduction is a conserved function of c-di-GMP signaling networks, and that thermosensitive catalysis of a second messenger constitutes a mechanism for thermal sensing in bacteria.

Genome sequences of Mannheimia haemolytica serotype A2: ovine and bovine isolates.

This report describes the genome sequences of Mannheimia haemolytica serotype A2 isolated from pneumonic lungs of two different ruminant species, one from Ovis aries, designated ovine (O), and the other from Bos taurus, designated bovine (B).

A three-way comparative genomic analysis of Mannheimia haemolytica isolates.

BACKGROUND: Mannhemia haemolytica is a Gram-negative bacterium and the principal etiological agent associated with bovine respiratory disease complex. They transform from a benign commensal to a deadly pathogen, during stress such as viral infection and transportation to feedlots and cause acute pleuropneumonia commonly known as shipping fever. The U.S beef industry alone loses more than one billion dollars annually due to shipping fever. Despite its enormous economic importance there are no specific and accurate genetic markers, which will aid in understanding the pathogenesis and epidemiology of M. haemolytica at molecular level and assist in devising an effective control strategy. DESCRIPTION: During our comparative genomic sequence analysis of three Mannheimia haemolytica isolates, we identified a number of genes that are unique to each strain. These genes are "high value targets" for future studies that attempt to correlate the variable gene pool with phenotype. We also identified a number of high confidence single nucleotide polymorphisms (hcSNPs) spread throughout the genome and focused on non-synonymous SNPs in known virulence genes. These SNPs will be used to design new hcSNP arrays to study variation across strains, and will potentially aid in understanding gene regulation and the mode of action of various virulence factors. CONCLUSIONS: During our analysis we identified previously unknown possible type III secretion effector proteins, clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated sequences (Cas). The presence of CRISPR regions is indicative of likely co-evolution with an associated phage. If proven functional, the presence of a type III secretion system in M. haemolytica will help us re-evaluate our approach to study host-pathogen interactions. We also identified various adhesins containing immuno-dominant domains, which may interfere with host-innate immunity and which could potentially serve as effective vaccine candidates.

Relationships between deficits in tissue mass and transcriptional programs after partial hepatectomy in mice.

Liver regeneration after two-thirds partial hepatectomy (2/3 PH) results in synchronized proliferation of hepatocytes and rapid restoration of liver mass. Understanding the mechanisms that regulate this process has both biological and clinical importance. Using cDNA microarray analysis, we investigated whether gene activation after 2/3 PH is specifically related to liver growth and hepatocyte proliferation. We generated gene expression profiles at 4, 12, 20, and 30 hours after 2/3 PH and compared them with profiles obtained at the same time points after 1/3 PH, a procedure that causes minimal DNA replication. Surprisingly, a significant number of genes whose expression is altered after 2/3 PH are similarly up- or down-regulated after 1/3 PH, particularly at 4 hours. We identified a number of genes and transcription factors that are more highly expressed ("preferential expression") after 2/3 PH and show that a shift in transcriptional programs in the regenerating liver occurs between 4 and 12 hours after 2/3 PH, a time at which the decision to replicate appears to be made. These results show that the liver responds to PH with massive changes of gene expression, even in the absence of DNA replication. We suggest that the changes in gene expression during the first 4 to 6 hours after 2/3 PH may induce chromatin remodeling and facilitate the binding of new sets of transcription factors required for DNA replication.

Iterative Bayesian Model Averaging: a method for the application of survival analysis to high-dimensional microarray data.

BACKGROUND: Microarray technology is increasingly used to identify potential biomarkers for cancer prognostics and diagnostics. Previously, we have developed the iterative Bayesian Model Averaging (BMA) algorithm for use in classification. Here, we extend the iterative BMA algorithm for application to survival analysis on high-dimensional microarray data. The main goal in applying survival analysis to microarray data is to determine a highly predictive model of patients' time to event (such as death, relapse, or metastasis) using a small number of selected genes. Our multivariate procedure combines the effectiveness of multiple contending models by calculating the weighted average of their posterior probability distributions. Our results demonstrate that our iterative BMA algorithm for survival analysis achieves high prediction accuracy while consistently selecting a small and cost-effective number of predictor genes. RESULTS: We applied the iterative BMA algorithm to two cancer datasets: breast cancer and diffuse large B-cell lymphoma (DLBCL) data. On the breast cancer data, the algorithm selected a total of 15 predictor genes across 84 contending models from the training data. The maximum likelihood estimates of the selected genes and the posterior probabilities of the selected models from the training data were used to divide patients in the test (or validation) dataset into high- and low-risk categories. Using the genes and models determined from the training data, we assigned patients from the test data into highly distinct risk groups (as indicated by a p-value of 7.26e-05 from the log-rank test). Moreover, we achieved comparable results using only the 5 top selected genes with 100% posterior probabilities. On the DLBCL data, our iterative BMA procedure selected a total of 25 genes across 3 contending models from the training data. Once again, we assigned the patients in the validation set to significantly distinct risk groups (p-value = 0.00139). CONCLUSION: The strength of the iterative BMA algorithm for survival analysis lies in its ability to account for model uncertainty. The results from this study demonstrate that our procedure selects a small number of genes while eclipsing other methods in predictive performance, making it a highly accurate and cost-effective prognostic tool in the clinical setting.

Independent and cooperative antiviral actions of beta interferon and gamma interferon against herpes simplex virus replication in primary human fibroblasts.

Type I and type II interferons (IFNs) act in synergy to inhibit the replication of a variety of viruses, including herpes simplex virus (HSV). To understand the mechanism of this effect, we have analyzed the transcriptional profiles of primary human fibroblast cells that were first treated with IFN-beta1, IFN-gamma, or a combination of both and then subsequently infected with HSV-1. We have identified two types of synergistic activities in the gene expression patterns induced by IFN-beta1 and IFN-gamma that may contribute to inhibition of HSV-1 replication. The first is defined as "synergy by independent action," in which IFN-beta1 and IFN-gamma induce distinct gene categories. The second, "synergy by cooperative action," is a term that describes the positive interaction between IFN-beta1 and IFN-gamma as defined by a two-way analysis of variance. This form of synergy leads to a much higher level of expression for a subset of genes than is seen with either interferon alone. The cooperatively induced genes by IFN-beta1 and IFN-gamma include those involved in apoptosis, RNA degradation, and the inflammatory response. Furthermore, the combination of IFN-beta1 and IFN-gamma induces significantly more apoptosis and inhibits HSV-1 gene expression and DNA replication significantly more than treatment with either interferon alone. Taken together, these data suggest that IFN-beta1 and IFN-gamma work both independently and cooperatively to create an antiviral state that synergistically inhibits HSV-1 replication in primary human fibroblasts and that cooperatively induced apoptosis may play a role in the synergistic effect on viral replication.

Methods for the inference of biological pathways and networks.

In this chapter, we discuss a number of approaches to network inference from large-scale functional genomics data. Our goal is to describe current methods that can be used to infer predictive networks. At present, one of the most effective methods to produce networks with predictive value is the Bayesian network approach. This approach was initially instantiated by Friedman et al. and further refined by Eric Schadt and his research group. The Bayesian network approach has the virtue of identifying predictive relationships between genes from a combination of expression and eQTL data. However, the approach does not provide a mechanistic bases for predictive relationships and is ultimately hampered by an inability to model feedback. A challenge for the future is to produce networks that are both predictive and provide mechanistic understanding. To do so, the methods described in several chapters of this book will need to be integrated. Other chapters of this book describe a number of methods to identify or predict network components such as physical interactions. At the end of this chapter, we speculate that some of the approaches from other chapters could be integrated and used to "annotate" the edges of the Bayesian networks. This would take the Bayesian networks one step closer to providing mechanistic "explanations" for the relationships between the network nodes.