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Peder Worning

Publications and source records attributed to Peder Worning.

7 recordsLinked to original sources
PubMed2026

Development of linezolid and daptomycin resistance in vancomycin resistant Enterococcus faecium during antibiotic treatment.

The increasing incidence of vancomycin-resistant enterococci (VRE) over the past decade has reduced treatment options largely to linezolid and daptomycin. However, the emergence of resistance to both agents further complicates the management of VRE infections. While the mechanisms of linezolid resistance are relatively well understood, those underlying daptomycin resistance remain less clearly defined. In this study, we analyzed genomic changes associated with the development of linezolid and daptomycin resistance in initially susceptible isolates following treatment at a Danish university hospital. Phenotypic susceptibility testing and whole-genome sequencing were performed on eight isolates obtained from the same patient. We identified two distinct Enterococcus faecium clones with different mechanisms of linezolid resistance. Linezolid resistance was associated with a G2576T mutation in the 23S rRNA gene (ST80 clone) and the presence of the poxtA gene (ST3082 clone). The ST80 clone also developed daptomycin resistance during therapy. We found that daptomycin resistance might result from either a G173R substitution in a gene annotated as an "ABC transporter ATP-binding protein (LolD)" or a nonsense mutation (Q58*) in phosphoketolase, with both alterations potentially acting synergistically, but further studies are warranted to confirm if these mutations can confer resistance. Together with these findings, the study demonstrates that a single patient may harbor multiple E. faecium clones simultaneously, highlighting the risk of treatment failure if all clones are not accurately identified.

Daptomycin↗
PubMed2004

Definition of supertypes for HLA molecules using clustering of specificity matrices.

Major histocompatibility complex (MHC) proteins are encoded by extremely polymorphic genes and play a crucial role in immunity. However, not all genetically different MHC molecules are functionally different. Sette and Sidney (1999) have defined nine HLA class I supertypes and showed that with only nine main functional binding specificities it is possible to cover the binding properties of almost all known HLA class I molecules. Here we present a comprehensive study of the functional relationship between all HLA molecules with known specificities in a uniform and automated way. We have developed a novel method for clustering sequence motifs. We construct hidden Markov models for HLA class I molecules using a Gibbs sampling procedure and use the similarities among these to define clusters of specificities. These clusters are extensions of the previously suggested ones. We suggest splitting some of the alleles in the A1 supertype into a new A26 supertype, and some of the alleles in the B27 supertype into a new B39 supertype. Furthermore the B8 alleles may define their own supertype. We also use the published specificities for a number of HLA-DR types to define clusters with similar specificities. We report that the previously observed specificities of these class II molecules can be clustered into nine classes, which only partly correspond to the serological classification. We show that classification of HLA molecules may be done in a uniform and automated way. The definition of clusters allows for selection of representative HLA molecules that can cover the HLA specificity space better. This makes it possible to target most of the known HLA alleles with known specificities using only a few peptides, and may be used in construction of vaccines. Supplementary material is available at http://www.cbs.dtu.dk/researchgroups/immunology/supertypes.html.

Amino Acid Motifs↗
PubMed2004

Improved prediction of MHC class I and class II epitopes using a novel Gibbs sampling approach.

MOTIVATION: Prediction of which peptides will bind a specific major histocompatibility complex (MHC) constitutes an important step in identifying potential T-cell epitopes suitable as vaccine candidates. MHC class II binding peptides have a broad length distribution complicating such predictions. Thus, identifying the correct alignment is a crucial part of identifying the core of an MHC class II binding motif. In this context, we wish to describe a novel Gibbs motif sampler method ideally suited for recognizing such weak sequence motifs. The method is based on the Gibbs sampling method, and it incorporates novel features optimized for the task of recognizing the binding motif of MHC classes I and II. The method locates the binding motif in a set of sequences and characterizes the motif in terms of a weight-matrix. Subsequently, the weight-matrix can be applied to identifying effectively potential MHC binding peptides and to guiding the process of rational vaccine design. RESULTS: We apply the motif sampler method to the complex problem of MHC class II binding. The input to the method is amino acid peptide sequences extracted from the public databases of SYFPEITHI and MHCPEP and known to bind to the MHC class II complex HLA-DR4(B1*0401). Prior identification of information-rich (anchor) positions in the binding motif is shown to improve the predictive performance of the Gibbs sampler. Similarly, a consensus solution obtained from an ensemble average over suboptimal solutions is shown to outperform the use of a single optimal solution. In a large-scale benchmark calculation, the performance is quantified using relative operating characteristics curve (ROC) plots and we make a detailed comparison of the performance with that of both the TEPITOPE method and a weight-matrix derived using the conventional alignment algorithm of ClustalW. The calculation demonstrates that the predictive performance of the Gibbs sampler is higher than that of ClustalW and in most cases also higher than that of the TEPITOPE method.

Algorithms↗
PubMed2003

Reliable prediction of T-cell epitopes using neural networks with novel sequence representations.

In this paper we describe an improved neural network method to predict T-cell class I epitopes. A novel input representation has been developed consisting of a combination of sparse encoding, Blosum encoding, and input derived from hidden Markov models. We demonstrate that the combination of several neural networks derived using different sequence-encoding schemes has a performance superior to neural networks derived using a single sequence-encoding scheme. The new method is shown to have a performance that is substantially higher than that of other methods. By use of mutual information calculations we show that peptides that bind to the HLA A*0204 complex display signal of higher order sequence correlations. Neural networks are ideally suited to integrate such higher order correlations when predicting the binding affinity. It is this feature combined with the use of several neural networks derived from different and novel sequence-encoding schemes and the ability of the neural network to be trained on data consisting of continuous binding affinities that gives the new method an improved performance. The difference in predictive performance between the neural network methods and that of the matrix-driven methods is found to be most significant for peptides that bind strongly to the HLA molecule, confirming that the signal of higher order sequence correlation is most strongly present in high-binding peptides. Finally, we use the method to predict T-cell epitopes for the genome of hepatitis C virus and discuss possible applications of the prediction method to guide the process of rational vaccine design.

Amino Acid Sequence↗
PubMed2003

Selecting informative data for developing peptide-MHC binding predictors using a query by committee approach.

Strategies for selecting informative data points for training prediction algorithms are important, particularly when data points are difficult and costly to obtain. A Query by Committee (QBC) training strategy for selecting new data points uses the disagreement between a committee of different algorithms to suggest new data points, which most rationally complement existing data, that is, they are the most informative data points. In order to evaluate this QBC approach on a real-world problem, we compared strategies for selecting new data points. We trained neural network algorithms to obtain methods to predict the binding affinity of peptides binding to the MHC class I molecule, HLA-A2. We show that the QBC strategy leads to a higher performance than a baseline strategy where new data points are selected at random from a pool of available data. Most peptides bind HLA-A2 with a low affinity, and as expected using a strategy of selecting peptides that are predicted to have high binding affinities also lead to more accurate predictors than the base line strategy. The QBC value is shown to correlate with the measured binding affinity. This demonstrates that the different predictors can easily learn if a peptide will fail to bind, but often conflict in predicting if a peptide binds. Using a carefully constructed computational setup, we demonstrate that selecting peptides with a high QBC performs better than low QBC peptides independently from binding affinity. When predictors are trained on a very limited set of data they cannot be expected to disagree in a meaningful way and we find a data limit below which the QBC strategy fails. Finally, it should be noted that data selection strategies similar to those used here might be of use in other settings in which generation of more data is a costly process.

Algorithms↗
PubMed2002

Bias of purine stretches in sequenced chromosomes.

We examined more than 700 DNA sequences (full length chromosomes and plasmids) for stretches of purines (R) or pyrimidines (Y) and alternating YR stretches; such regions will likely adopt structures which are different from the canonical B-form. Since one turn of the DNA helix is roughly 10 bp, we measured the fraction of each genome which contains purine (or pyrimidine) tracts of lengths of 10 bp or longer (hereafter referred to as 'purine tracts'), as well as stretches of alternating pyrimidines/purine (pyr/pur tracts') of the same length. Using this criteria, a random sequence would be expected to contain 1.0% of purine tracts and also 1.0% of the alternating pyr/pur tracts. In the vast majority of cases, there are more purine tracts than would be expected from a random sequence, with an average of 3.5%, significantly larger than the expectation value. The fraction of the chromosomes containing pyr/pur tracts was slightly less than expected, with an average of 0.8%. One of the most surprising findings is a clear difference in the length distributions of the regions studied between prokaryotes and eukaryotes. Whereas short-range correlations can explain the length distributions in prokaryotes, in eukaryotes there is an abundance of long stretches of purines or alternating purine/pyrimidine tracts, which cannot be explained in this way; these sequences are likely to play an important role in eukaryotic chromosome organisation.

Animals↗