Bayesian inference for biomarker discovery in proteomics: an analytic solution.

This paper addresses the question of biomarker discovery in proteomics. Given clinical data regarding a list of proteins for a set of individuals, the tackled problem is to extract a short subset of proteins the concentrations of which are an indicator of the biological status (healthy or pathological). In this paper, it is formulated as a specific instance of variable selection. The originality is that the proteins are not investigated one after the other but the best partition between discriminant and non-discriminant proteins is directly sought. In this way, correlations between the proteins are intrinsically taken into account in the decision. The developed strategy is derived in a Bayesian setting, and the decision is optimal in the sense that it minimizes a global mean error. It is finally based on the posterior probabilities of the partitions. The main difficulty is to calculate these probabilities since they are based on the so-called evidence that require marginalization of all the unknown model parameters. Two models are presented that relate the status to the protein concentrations, depending whether the latter are biomarkers or not. The first model accounts for biological variabilities by assuming that the concentrations are Gaussian distributed with a mean and a covariance matrix that depend on the status only for the biomarkers. The second one is an extension that also takes into account the technical variabilities that may significantly impact the observed concentrations. The main contributions of the paper are: (1) a new Bayesian formulation of the biomarker selection problem, (2) the closed-form expression of the posterior probabilities in the noiseless case, and (3) a suitable approximated solution in the noisy case. The methods are numerically assessed and compared to the state-of-the-art methods (t test, LASSO, Battacharyya distance, FOHSIC) on synthetic and real data from proteins quantified in human serum by mass spectrometry in selected reaction monitoring mode.

Automatic identification of mixed bacterial species fingerprints in a MALDI-TOF mass-spectrum.

MOTIVATION: Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry has been broadly adopted by routine clinical microbiology laboratories for bacterial species identification. An isolated colony of the targeted microorganism is the single prerequisite. Currently, MS-based microbial identification directly from clinical specimens can not be routinely performed, as it raises two main challenges: (i) the nature of the sample itself may increase the level of technical variability and bring heterogeneity with respect to the reference database and (ii) the possibility of encountering polymicrobial samples that will yield a 'mixed' MS fingerprint. In this article, we introduce a new method to infer the composition of polymicrobial samples on the basis of a single mass spectrum. Our approach relies on a penalized non-negative linear regression framework making use of species-specific prototypes, which can be derived directly from the routine reference database of pure spectra. RESULTS: A large spectral dataset obtained from in vitro mono- and bi-microbial samples allowed us to evaluate the performance of the method in a comprehensive way. Provided that the reference matrix-assisted laser desorption/ionization time-of-flight mass spectrometry fingerprints were sufficiently distinct for the individual species, the method automatically predicted which bacterial species were present in the sample. Only few samples (5.3%) were misidentified, and bi-microbial samples were correctly identified in up to 61.2% of the cases. This method could be used in routine clinical microbiology practice.

DNA probe array for the simultaneous identification of herpesviruses, enteroviruses, and flaviviruses.

Viral infections of the central nervous system (CNS) are caused by a variety of viruses, namely, herpesviruses, enteroviruses, and flaviviruses. The similar clinical signs provoked by these viruses make the diagnosis difficult. We report on the simultaneous detection of these major CNS pathogens using amplification by PCR and detection of amplified products using DNA microarray technology. Consensus primers were used for the amplification of all members of each genus. Sequences specific for the identification of each virus species were selected from the sequence alignments of each target gene and were synthesized on a high-density microarray. The amplified products were pooled, labeled, and cleaved, followed by hybridization on a single array. This method was successfully used to identify herpesviruses, namely, herpes simplex virus type 1 (HSV-1), HSV-2, and cytomegalovirus; all serotypes of human enteroviruses; and five flaviviruses (West Nile virus, dengue viruses, and Langat virus). This approach, which used highly conserved consensus primers for amplification and specific sequences for identification, would be extremely useful for the detection of variants and would probably help solve some unexplained cases of encephalitis. The analytical sensitivity of the method was shown to be 500 genome equivalents ml(-1) for HSV-1, 0.3 50% tissue culture infectious doses (TCID50s) ml(-1) for the enterovirus coxsackievirus A9, and 200 TCID50s ml(-1) for West Nile virus. The clinical sensitivity of this method must now be evaluated.

Genome-wide analysis of gene expression in neuroblastomas detected by mass screening.

Neuroblastoma (NB) is the most common extracranial solid tumor of childhood and the third most common pediatric cancer. Although numerous factors including patient age, disease stage and genetic abnormalities have been shown to be predictive of outcome, the mechanisms responsible for the highly variable clinical behavior of this tumor remain largely unknown. In order to gain new insights into the biology of this tumor, we performed microarray analysis and compared the gene expression patterns of NB detected by mass screening, characterized by highly probable spontaneous regression, versus stage 4 NB with poor prognosis. The bioinformatics analysis revealed a set of 19 discriminatory genes that may play a significant role in the natural progression of the disease. Validation of these results and further mechanistic studies would shed new light on the biology of tumor progression, and provide new tools to predict clinical outcome in children with NB.

Evaluation of a high-density oligonucleotide array for characterization of grlA, grlB, gyrA and gyrB mutations in fluoroquinolone resistant Staphylococcus aureus isolates.

Using high-density oligonucleotide array technology, 30 Staphylococcus aureus strains were studied for the presence of mutations in genes involved in fluoroquinolone resistance: grlA, gyrA, grlB and gyrB. For the two most important genes, gyrA and grlA, correlation with sequencing reached 95.1%. If all genes were considered, correlation was 88.8%.

Multilocus sequence typing of Staphylococcus aureus with DNA array technology.

A newly developed oligonucleotide array suited for multilocus sequence typing (MLST) of Staphylococcus aureus strains was analyzed with two strain collections in a two-center study. MLST allele identification for the first strain collection fully agreed with conventional strain typing. Analysis of strains from the second collection revealed that chip-defined MLST was concordant with conventional MLST. Array-mediated MLST data were reproducible, exchangeable, and epidemiologically concordant.