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PubMed2026

Applicability of Nanopore-only whole-genome sequencing for Pseudomonas aeruginosa outbreak investigation in the ICU setting: a multicentric study.

UNLABELLED: Pseudomonas aeruginosa outbreaks frequently occur in intensive care units (ICUs). In particular, ICU patients requiring mechanical ventilation are vulnerable to P. aeruginosa ventilator-associated pneumonia, which is associated with high morbidity and mortality. Fast and accurate genotyping during the early stage is crucial to document and manage P. aeruginosa outbreaks at the ICU. In this study, we have evaluated the applicability of Oxford Nanopore whole-genome sequencing (WGS) for outbreak investigation and antimicrobial resistance (AMR) prediction. To evaluate whether a Nanopore-only WGS workflow was able to reproduce Illumina-confirmed transmission clusters, 19 P. aeruginosa isolates from ICUs at UZ Brussels (Belgium) that were previously sequenced with Illumina were sequenced using a Nanopore-only workflow based on the latest V14 chemistry, followed by bioinformatic analysis via BugSeq and MBioSEQ Ridom Typer. Although both bioinformatic platforms showed high concordance between Illumina and Nanopore data, MBioSEQ Ridom Typer yielded the lowest allelic distance (maximum one cgMLST allele), confirming all outbreak clusters. When applying the Nanopore-only workflow to longitudinally collected isolates, low genetic heterogeneity (maximum three cgMLST alleles) was observed between isolates from the same patient. WGS and subsequent outbreak analysis of 65 respiratory P. aeruginosa isolates collected from 38 different ICU patients across six Belgian hospitals during a 9-month period showed no intra- or inter-hospital transmission. When the Nanopore-only WGS data were used to predict AMR, there was high categorical agreement (95%) between AMR genotype and phenotype. These findings highlight the potential of Nanopore WGS as a rapid and accurate tool for outbreak investigation of P. aeruginosa. IMPORTANCE: In recent years, Nanopore sequencing has found its way to clinical laboratories because of its affordability, scalability, and, most importantly, its ability to obtain sequencing results in near-real time. However, despite improved raw read accuracies with the latest generation R10.4.1 flow cells, the question remains whether the achieved accuracy is sufficient for accurate bacterial outbreak investigation, particularly in high-risk settings such as intensive care units (ICUs). In this study, we show that Nanopore-only whole-genome sequencing (WGS) is able to match Illumina-only WGS in terms of accuracy for Pseudomonas aeruginosa outbreak investigation in the ICU setting, although important sequence type-dependent and even strain-specific methylation issues need to be resolved in order to guarantee this accuracy. By providing a fast and accurate workflow for reliable P. aeruginosa outbreak investigation, this study could pave the way for large-scale implementation of Nanopore-only WGS, leading to faster outbreak response times.

Humans↗

PubMed2006

Delayed biosynthesis of varicella-zoster virus glycoprotein C: upregulation by hexamethylene bisacetamide and retinoic acid treatment of infected cells.

In the course of examining the trafficking pathways of varicella-zoster virus (VZV) glycoproteins gE, gI, gH, and gB, we discovered that all four are synthesized within 4 to 6 h postinfection (hpi) in cultured cells. Thereafter, they travel via the trans-Golgi network to the outer cell membrane. When we carried out a similar analysis on VZV gC, we observed little gC biosynthesis in the first 72 hpi. Further examination disclosed that gC was present in the inocula of infected cells, but no new gC biosynthesis occurred during the first 24 to 48 h thereafter, during which time new synthesis of gE, gH, and major capsid protein was easily detectable. Similarly, delayed gC biosynthesis was confirmed with three different VZV strains and two different cell lines. Bioinformatics analyses disclosed the presence of PBX/HOX consensus binding domains in the promoter/enhancer regions of the genes for VZV gC and ORF4 protein (whose orthologs transactivate gC in other herpesviruses). Bioinformatics analysis also identified two HOXA9 activation regions on ORF4 protein. Treatment of infected cultures with chemicals known to induce the production of PBX/HOX transcription proteins, namely, hexamethylene bisacetamide (HMBA) and retinoic acid, led to more rapid gC biosynthesis. Immunoblotting demonstrated a fivefold increase in the HOXA9 protein after HMBA treatment. In summary, these results documented that gC was not produced during early VZV replication cycles, presumably related to a deficiency in the PBX/HOX transcription factors. Furthermore, these results explain the apparent spontaneous loss of VZV gC in some passaged viruses, as well as other anomalous gC results.

Acetamides↗

PubMed2005

cis-regulatory logic of short-range transcriptional repression in Drosophila melanogaster.

Bioinformatics analysis of transcriptional control is guided by knowledge of the characteristics of cis-regulatory regions or enhancers. Features such as clustering of binding sites and co-occurrence of binding sites have aided enhancer identification, but quantitative predictions of enhancer function are not yet generally feasible. To facilitate the analysis of regulatory sequences in Drosophila melanogaster, we identified quantitative parameters that affect the activity of short-range transcriptional repressors, proteins that play key roles in development. In addition to the previously noted distance dependence, repression is strongly influenced by the stoichiometry, affinity, spacing, and arrangement of activator binding sites. Repression is insensitive to the type of activation domain, suggesting that short-range repression may primarily affect activators at the level of DNA binding. The activity of several short-range, but not long-range, repressors is circumscribed by the same quantitative parameters. This cis-regulatory "grammar" may aid the identification of enhancers regulated by short-range repressors and facilitate bioinformatic prediction of the functional output of transcriptional regulatory sequences.

Animals↗

PubMed2026

Metagenomic polymorphic toxin effector and immunity profiling predicts microbiome development and disease-related dysbiosis.

Bacteria use antagonistic interbacterial weapons, such as polymorphic toxin secretion systems (TSS), to compete for niches in the human gut microbiome. We hypothesized that TSS influence gut microbiome development and disease-related dysbiosis. We developed a bioinformatic marker gene approach (PolyProf) to quantify TSS including ~200 effector and immunity genes and applied it to ~15,000 publicly available human metagenomes. PolyProf alpha and beta diversity readily distinguished 12 different human disease states and enabled the construction of highly accurate linear regression classifier machine learning models. Elastic net machine learning models integrating bacterial taxonomy with PolyProf had strong predictive value for 12 disease states, outperforming models utilizing taxonomy alone. During microbiome development in the first year of life, PolyProf alpha diversity increases, and beta diversity becomes increasingly like the maternal microbiome, influenced by vertical transfer, delivery mode, and breastfeeding. PolyProf is related to strain sharing among adults through social interactions. In summary, TSS genes strongly correlate with microbiome development and interpersonal strain sharing, suggesting roles for interbacterial antagonism. Since PolyProf distinguishes diverse adult disease statuses, these dynamics may contribute to non-genetic inheritance.IMPORTANCEPrevious research has demonstrated that bacteria compete within the gut microbiome using toxin secretion systems (TSS). How TSS contribute to human microbiome development and the microbiome alterations observed in human diseases is not known. This study develops a new bioinformatic tool for profiling TSS-related genes in metagenomic data. Application of this approach to large-scale human fecal metagenomic data demonstrates the dynamic association of TSS during microbiome development, including the exchange of strains among social contacts. TSS gene abundance patterns are highly predictive of 12 disease states. This study advances the field by enabling TSS profiling in metagenomes and by identifying disease and microbiome development biomarkers that provide hypotheses for future mechanistic studies and may be useful for disease diagnosis.

Dysbiosis↗

PubMed2025

Molecular insights into the persistence and co-occurrence of two different carbapenem-resistant Pseudomonas aeruginosa lineages within a hospital setting.

UNLABELLED: Carbapenem-resistant Pseudomonas aeruginosa (CRPA) represents a critical-priority pathogen capable of causing life-threatening, multidrug-resistant infections. We performed susceptibility testing, whole-genome sequencing, and bioinformatic analyses on 137 CRPA isolates from a Guangdong hospital. We found that the major specimen types were respiratory specimens (57/137, 41.6%) and bronchoalveolar lavage (42/137, 30.7%). All isolates were carbapenem-resistant but had low resistance to polymyxin B (0.7%, 1/137). IncP-6-positive isolates exhibited ≥2- to 32-fold higher resistance to 9/12 antibiotics (P < 0.05), with no difference to imipenem and meropenem. Fifty-four sequence types and 11 O-serogroups were identified, with ST1971 (6.6%) and O11 (29.9%) being predominant. Temporal and spatial patterns suggest persistent co-occurrence of clade 1 and clade 2 isolates, indicating potential nosocomial outbreak and clonal transmission. IMPORTANCE: The prevalence of carbapenem-resistant Pseudomonas aeruginosa (CRPA) has increased rapidly in recent years, yet few genetic and epidemiological studies on CRPA isolates have been performed. We performed susceptibility testing, whole-genome sequencing, and bioinformatic analyses on hospital isolates to investigate their resistance profiles and molecular epidemiology. These findings may offer new insights for developing effective global strategies to control CRPA and reduce untreatable infections in clinical settings.

Pseudomonas aeruginosa↗

PubMed2026

Hypertrophic cardiomyopathy: a genome-wide association meta-analysis and polygenic risk score.

BACKGROUND: Hypertrophic cardiomyopathy (HCM) is a heritable trait with marked variability in expression and outcomes. Our aims were to discover new genetic loci associated with HCM and to test the effect of a new polygenic risk score (PRS) on incidence, phenotype and outcomes stratified by genotype status. METHODS: A discovery genome-wide association study (GWAS) was performed on 2284 HCM cases and 4525 controls. Two fixed-effects meta-analyses combined our discovery GWAS with single-trait and multi-trait results from a published study. Discovered loci underwent comprehensive bioinformatic analysis including functional and druggability annotations. A PRS using loci from the two meta-analyses was evaluated for association with HCM diagnosis in 411 213 individuals from UK Biobank (UKBB); imaging phenotypes in individuals without HCM; a composite endpoint (including all-cause mortality and transplantation); and sudden cardiac death (SCD) in 1756 HCM cases. PRS analyses were stratified by genotype status. RESULTS: Three loci were found in the discovery GWAS (BAG3, FHOD3 and novel locus PPP1R3A). In the meta-analyses, 70 unique loci were identified, four novel (MYPN, YWHAE, NOS1AP and OBSCN). Bioinformatic analyses identified NOS1AP as a candidate HCM gene. A new PRS was significantly associated with HCM diagnosis (HR=3.19, 95% CI 2.46 to 4.14 for top 5% vs lower 95%; HR=1.88, 95% CI 1.72 to 2.06 per SD increase). Significant associations were found between PRS and greater left ventricular (LV) wall thickness and higher LV ejection fraction in UKBB participants without HCM. Genotype-negative HCM cases in the top 20% of the PRS distribution had an increased risk of SCD (HR=2.72, 95% CI 1.03 to 7.17). CONCLUSIONS: We report novel HCM loci. A new PRS predicted the risk of HCM development and associated imaging characteristics in the UKBB and outcomes in an HCM cohort.

Cardiomyopathies↗

PubMed2005

Genetic, functional, and histopathological evaluation of two C-terminal BRCA1 missense variants.

BACKGROUND: The vast majority of BRCA1 missense sequence variants remain uncharacterized for their possible effect on protein expression and function, and therefore are unclassified in terms of their pathogenicity. BRCA1 plays diverse cellular roles and it is unlikely that any single functional assay will accurately reflect the total cellular implications of missense mutations in this gene. OBJECTIVE: To elucidate the effect of two BRCA1 variants, 5236G>C (G1706A) and 5242C>A (A1708E) on BRCA1 function, and to survey the relative usefulness of several assays to direct the characterisation of other unclassified variants in BRCA genes. METHODS AND RESULTS: Data from a range of bioinformatic, genetic, and histopathological analyses, and in vitro functional assays indicated that the 1708E variant was associated with the disruption of different cellular functions of BRCA1. In transient transfection experiments in T47D and 293T cells, the 1708E product was mislocalised to the cytoplasm and induced centrosome amplification in 293T cells. The 1708E variant also failed to transactivate transcription of reporter constructs in mammalian transcriptional transactivation assays. In contrast, the 1706A variant displayed a phenotype comparable to wildtype BRCA1 in these assays. Consistent with functional data, tumours from 1708E carriers showed typical BRCA1 pathology, while tumour material from 1706A carriers displayed few histopathological features associated with BRCA1 related tumours. CONCLUSIONS: A comprehensive range of genetic, bioinformatic, and functional analyses have been combined for the characterisation of BRCA1 unclassified sequence variants. Consistent with the functional analyses, the combined odds of causality calculated for the 1706A variant after multifactorial likelihood analysis (1:142) indicates a definitive classification of this variant as "benign". In contrast, functional assays of the 1708E variant indicate that it is pathogenic, possibly through subcellular mislocalisation. However, the combined odds of 262:1 in favour of causality of this variant does not meet the minimal ratio of 1000:1 for classification as pathogenic, and A1708E remains formally designated as unclassified. Our findings highlight the importance of comprehensive genetic information, together with detailed functional analysis for the definitive categorisation of unclassified sequence variants. This combination of analyses may have direct application to the characterisation of other unclassified variants in BRCA1 and BRCA2.

Adult↗

PubMed2002

Screening for microsatellite instability target genes in colorectal cancers.

BACKGROUND: Defects in the DNA repair system lead to genetic instability because replication errors are not corrected. This type of genetic instability is a key event in the malignant progression of HNPCC and a subset of sporadic colon cancers and mutation rates are particularly high at short repetitive sequences. Somatic deletions of coding mononucleotide repeats have been detected, for example, in the TGFbetaRII and BAX genes, and recently many novel target genes for microsatellite instability (MSI) have been proposed. Novel target genes are likely to be discovered in the future. More data should be created on background mutation rates in MSI tumours to evaluate mutation rates observed in the candidate target genes. METHODS: Mutation rates in 14 neutral intronic repeats were evaluated in MSI tumours. Bioinformatic searches combined with keywords related to cancer and tumour suppressor or CRC related gene homology were used to find new candidate MSI target genes. By comparison of mutation frequencies observed in intronic mononucleotide repeats versus exonic coding repeats of potential MSI target genes, the significance of the exonic mutations was estimated. RESULTS: As expected, the length of an intronic mononucleotide repeat correlated positively with the number of slippages for both G/C and A/T repeats (p=0.0020 and p=0.0012, respectively). BRCA1, CtBP1, and Rb1 associated CtIP and other candidates were found in a bioinformatic search combined with keywords related to cancer. Sequencing showed a significantly increased mutation rate in the exonic A9 repeat of CtIP (25/109=22.9%) as compared with similar intronic repeats (p< or =0.001). CONCLUSIONS: We propose a new candidate MSI target gene CtIP to be evaluated in further studies.

Colorectal Neoplasms↗

PubMed2005

Ontology driven dynamic linking of biology resources.

Biologists were early adopters of the Web and continue to use it as the primary means of delivering data, tools and knowledge to their community. The Web is made by the links between pages, yet these links have many limitations: they are static and maintained by hand; they can only link one lexical item to another single resource; ownership is necessary for the placement of link anchors and the link mechanism is essentially inflexible. Dynamic linking services, supported by ontologies, offer a mechanism to overcome such restrictions. The Conceptual Open Hypermedia Service (COHSE) system enhances web resources through the dynamic addition of hypertext links. These links are derived through the use of an ontology and associated lexicon along with a mapping from concepts to possible link targets. We describe an application of COHSE to Bioinformatics, using the Gene Ontology (GO) as an ontology and associated keyword mappings and GO associations as link targets. The resulting demonstrator (referred to here as GOHSE) provides both glossary functionality and the possibility of building knowledge based hypertext structures linking bioinformatics resources.

Animals↗

PubMed2003

Computational strategies for analyzing data in gene expression microarray experiments.

Microarray analysis has become a widely used method for generating gene expression data on a genomic scale. Microarrays have been enthusiastically applied in many fields of biological research, even though several open questions remain about the analysis of such data. A wide range of approaches are available for computational analysis, but no general consensus exists as to standard for microarray data analysis protocol. Consequently, the choice of data analysis technique is a crucial element depending both on the data and on the goals of the experiment. Therefore, basic understanding of bioinformatics is required for optimal experimental design and meaningful interpretation of the results. This review summarizes some of the common themes in DNA microarray data analysis, including data normalization and detection of differential expression. Algorithms are demonstrated by analyzing cDNA microarray data from an experiment monitoring gene expression in T helper cells. Several computational biology strategies, along with their relative merits, are overviewed and potential areas for additional research discussed. The goal of the review is to provide a computational framework for applying and evaluating such bioinformatics strategies. Solid knowledge of microarray informatics contributes to the implementation of more efficient computational protocols for the given data obtained through microarray experiments.

Algorithms↗

PubMed2004

Identification of trace element-containing proteins in genomic databases.

Development of bioinformatics tools provided researchers with the ability to identify full sets of trace element-containing proteins in organisms for which complete genomic sequences are available. Recently, independent bioinformatics methods were used to identify all, or almost all, genes encoding selenocysteine-containing proteins in human, mouse, and Drosophila genomes, characterizing entire selenoproteomes in these organisms. It also should be possible to search for entire sets of other trace element-associated proteins, such as metal-containing proteins, although methods for their identification are still in development.

Animals↗

PubMed2000

Sequencing the entire genomes of free-living organisms: the foundation of pharmacology in the new millennium.

The power and effectiveness of clinical pharmacology are about to be transformed with a speed that earlier in this decade could not have been foreseen even by the most astute visionaries. In the very near future, we will have at our disposal the reference DNA sequence for the entire human genome, estimated to contain approximately 3.5 billion bp. At the same time, the science of whole genome sequencing is fostering the computational science of bioinformatics needed to develop practical applications for pharmacology and toxicology. Indeed, it is likely that pharmacology, toxicology, bioinformatics, and genomics will merge into a new branch of medical science for studying and developing pharmaceuticals from molecule to bedside.

Animals↗

PubMed2006

Computational approaches for the prediction of protein function in the mitochondrion.

Understanding a complex biological system, such as the mitochondrion, requires the identification of the complete repertoire of proteins targeted to the organelle, the characterization of these, and finally, the elucidation of the functional and physical interactions that occur within the mitochondrion. In the last decade, significant developments have contributed to increase our understanding of the mitochondrion, and among these, computational research has played a significant role. Not only general bioinformatics tools have been applied in the context of the mitochondrion, but also some computational techniques have been specifically developed to address problems that arose from within the mitochondrial research field. In this review the contribution of bioinformatics to mitochondrial biology is addressed through a survey of current computational methods that can be applied to predict which proteins will be localized to the mitochondrion and to unravel their functional interactions.

Computational Biology↗

PubMed2005

Genetic and pharmacological inactivation of adenosine A2A receptor reveals an Egr-2-mediated transcriptional regulatory network in the mouse striatum.

The adenosine A2A receptor (A2AR) is highly expressed in the striatum, where it modulates motor and emotional behaviors. We used both microarray and bioinformatics analyses to compare gene expression profiles by genetic and pharmacological inactivation of A2AR and inferred an A2AR-controlled transcription network in the mouse striatum. A comparison between vehicle (VEH)-treated A2AR knockout (KO) mice (A2AR KO-VEH) and wild-type (WT) mice (WT-VEH) revealed 36 upregulated genes that were partially mimicked by treatment with SCH-58261 (SCH; an A2AR antagonist) and 54 downregulated genes that were not mimicked by SCH treatment. We validated the A2AR as a specific drug target for SCH by comparing A2AR KO-SCH and A2AR KO-VEH groups. The unique downregulation effect of A2AR KO was confirmed by comparing A2AR KO-SCH with WT-SCH gene groups. The distinct striatal gene expression profiles induced by A2AR KO and SCH should provide clues to the molecular mechanisms underlying the different phenotypes observed after genetic and pharmacological inactivation of A2AR. Furthermore, bioinformatics analysis discovered that Egr-2 binding sites were statistically overrepresented in the proximal promoters of A2AR KO-affected genes relative to the unaffected genes. This finding was further substantiated by the demonstration that the Egr-2 mRNA level increased in the striatum of both A2AR KO and SCH-treated mice and that striatal Egr-2 binding activity in the promoters of two A2AR KO-affected genes was enhanced in A2AR KO mice as assayed by chromatin immunoprecipitation. Taken together, these results strongly support the existence of an Egr-2-directed transcriptional regulatory network controlled by striatal A2ARs.

Animals↗

PubMedDate not supplied

The use of urine proteomic and metabonomic patterns for the diagnosis of interstitial cystitis and bacterial cystitis.

The advent of systems biology approaches that have stemmed from the sequencing of the human genome has led to the search for new methods to diagnose diseases. While much effort has been focused on the identification of disease-specific biomarkers, recent efforts are underway toward the use of proteomic and metabonomic patterns to indicate disease. We have developed and contrasted the use of both proteomic and metabonomic patterns in urine for the detection of interstitial cystitis (IC). The methodology relies on advanced bioinformatics to scrutinize information contained within mass spectrometry (MS) and high-resolution proton nuclear magnetic resonance (1H-NMR) spectral patterns to distinguish IC-affected from non-affected individuals as well as those suffering from bacterial cystitis (BC). We have applied a novel pattern recognition tool that employs an unsupervised system (self-organizing-type cluster mapping) as a fitness test for a supervised system (a genetic algorithm). With this approach, a training set comprised of mass spectra and 1H-NMR spectra from urine derived from either unaffected individuals or patients with IC is employed so that the most fit combination of relative, normalized intensity features defined at precise m/z or chemical shift values plotted in n-space can reliably distinguish the cohorts used in training. Using this bioinformatic approach, we were able to discriminate spectral patterns associated with IC-affected, BC-affected, and unaffected patients with a success rate of approximately 84%.

Bacterial Infections↗

PubMed2006

Rare germ line CHEK2 variants identified in breast cancer families encode proteins that show impaired activation.

Germ line mutations in CHEK2, the gene that encodes the Chk2 serine/threonine kinase activated in response to DNA damage, have been found to confer an increased risk of some cancers. We have previously reported the presence of the common deleterious 1100delC and four rare CHEK2 mutations in inherited breast cancer. Here, we report that predictions made by bioinformatic analysis on the rare mutations indicate that two of these, delE161 (483-485delAGA) and R117G, are likely to be deleterious. We show that the proteins encoded by 1100delC and delE161 are both unstable and inefficiently phosphorylated at Thr68 in response to DNA damage, a step necessary for the oligomerization of Chk2. Oligomerization is in turn necessary for additional phosphorylation and full activation of the protein. A second rare mutation, R117G, is phosphorylated at Thr68 but fails to show a mobility shift on DNA damage, suggesting that it fails to become further phosphorylated and hence fully activated. Our results indicate that delE161 and R117G encode nonfunctional proteins and are therefore likely to be pathogenic. The findings from the biochemical analysis correlate well with predictions made by bioinformatics analysis. In addition, the results imply that these mutations, as well as 1100delC, cannot act in a dominant-negative manner to cause cancer, and tumorigenesis in association with these mutations may be due to haploinsufficiency.

Amino Acid Sequence↗

PubMed2006

Mass spectrometric identification of proteins in complex post-genomic projects. Soluble proteins of the metabolically versatile, denitrifying 'Aromatoleum' sp. strain EbN1.

The rapidly developing proteomics technologies help to advance the global understanding of physiological and cellular processes. The lifestyle of a study organism determines the type and complexity of a given proteomic project. The complexity of this study is characterized by a broad collection of pathway-specific subproteomes, reflecting the metabolic versatility as well as the regulatory potential of the aromatic-degrading, denitrifying bacterium 'Aromatoleum' sp. strain EbN1. Differences in protein profiles were determined using a gel-based approach. Protein identification was based on a progressive application of MALDI-TOF-MS, MALDI-TOF-MS/MS and LC-ESI-MS/MS. This progression was result-driven and automated by software control. The identification rate was increased by the assembly of a project-specific list of background signals that was used for internal calibration of the MS spectra, and by the combination of two search engines using a dedicated MetaScoring algorithm. In total, intelligent bioinformatics could increase the identification yield from 53 to 70% of the analyzed 5,050 gel spots; a total of 556 different proteins were identified. MS identification was highly reproducible: most proteins were identified more than twice from parallel 2DE gels with an average sequence coverage of >50% and rather restrictive score thresholds (Mascot >or=95, ProFound >or=2.2, MetaScore >or=97). The MS technologies and bioinformatics tools that were implemented and integrated to handle this complex proteomic project are presented. In addition, we describe the basic principles and current developments of the applied technologies and provide an overview over the current state of microbial proteome research.

Amino Acid Sequence↗

PubMed2006

Urinary proteome of steroid-sensitive and steroid-resistant idiopathic nephrotic syndrome of childhood.

The response to steroid therapy is used to characterize the idiopathic nephrotic syndrome (INS) of childhood as either steroid-sensitive (SSNS) or steroid-resistant (SRNS), a classification with a better prognostic capability than renal biopsy. The majority (approximately 80%) of INS is due to minimal change disease but the percentage of focal and segmental glomerulosclerosis is increasing. We applied a new technological platform to examine the urine proteome to determine if different urinary protein excretion profiles could differentiate patients with SSNS from those with SRNS. Twenty-five patients with INS and 17 control patients were studied. Mid-stream urines were analyzed using surface enhanced laser desorption and ionization mass spectrometry(SELDI-MS). Data were analyzed using multiple bioinformatic techniques. Patient classification was performed using Biomarker Pattern Software and a generalized form of Adaboost and predictive models were generated using a supervised algorithm with cross-validation. Urinary proteomic data distinguished INS patients from control patients, irrespective of steroid response, with a sensitivity of 92.3%, specificity of 93.7%, positive predictive value of 96% and a negative predictive value of 88.2%. Classification of patients as SSNS or SRNS was 100%. A protein of mass 4,144 daltons was identified as the single most important classifier in distinguishing SSNS from SRNS. SELDI-MS combined with bioinformatics can identify different proteomic patterns in INS. Characterization of the proteins of interest identified by this proteomic approach with prospective clinical validation may yield a valuable clinical tool for the non-invasive prediction of treatment response and prognosis.

Adolescent↗