Gut microbiota differs between children with inflammatory bowel disease and healthy siblings in taxonomic and functional composition: a metagenomic analysis

Current treatment for pediatric inflammatory bowel disease (IBD) patients is often ineffective, with serious side effects. Manipulating the gut microbiota via fecal microbiota transplantation (FMT) is an emerging treatment approach but remains controversial. We aimed to assess the composition of the fecal microbiome through a comparison of pediatric IBD patients to their healthy siblings, evaluating risks and prospects for FMT in this setting. A case-control (sibling) study was conducted analyzing fecal samples of six children with Crohn's disease (CD), six children with ulcerative colitis (UC) and 12 healthy siblings by metagenomic sequencing. In addition, lifetime antibiotic intake was retrospectively determined. Species richness and diversity were significantly reduced in UC patients compared with control [Mann-Whitney U-test false discovery rate (MWU FDR) = 0.011]. In UC, bacteria positively influencing gut homeostasis, e.g., Eubacterium rectale and Faecalibacterium prausnitzii, were significantly reduced in abundance (MWU FDR = 0.05). Known pathobionts like Escherichia coli were enriched in UC patients (MWU FDR = 0.084). Moreover, E. coli abundance correlated positively with that of several virulence genes (SCC > 0.65, FDR < 0.1). A shift toward antibiotic-resistant taxa in both IBD groups distinguished them from controls [MWU Benjamini-Hochberg-Yekutieli procedure (BY) FDR = 0.062 in UC, MWU BY FDR = 0.019 in CD). The collected results confirm a microbial dysbiosis in pediatric UC, and to a lesser extent in CD patients, replicating associations found previously using different methods. Taken together, these observations suggest microbiotal remodeling therapy from family donors, at least for children with UC, as a viable option.NEW & NOTEWORTHY In this sibling study, prior reports of microbial dysbiosis in IBD patients from 16S rRNA sequencing was verified using deep shotgun sequencing and augmented with insights into the abundance of bacterial virulence genes and bacterial antibiotic resistance determinants, seen against the background of data on the specific antibiotic intake of each of the study participants. The observed dysbiosis, which distinguishes patients from siblings, highlights such siblings as potential donors for microbiotal remodeling therapy in IBD

MOCAT2: a metagenomic assembly, annotation and profiling framework

MOCAT2 is a software pipeline for metagenomic sequence assembly and gene prediction with novel features for taxonomic and functional abundance profiling. The automated generation and efficient annotation of non-redundant reference catalogs by propagating pre-computed assignments from 18 databases covering various functional categories allows for fast and comprehensive functional characterization of metagenomes. Availability and Implementation: MOCAT2 is implemented in Perl 5 and Python 2.7, designed for 64-bit UNIX systems and offers support for high-performance computer usage via LSF, PBS or SGE queuing systems; source code is freely available under the GPL3 license at http://mocat.embl.de. Contact: [email protected]

Improved Differential Diagnosis of Alzheimer’s Disease by Integrating ELISA and Mass Spectrometry-Based Cerebrospinal Fluid Biomarkers

BACKGROUND: Alzheimer’s disease (AD) is diagnosed based on a clinical evaluation as well as analyses of classical biomarkers: Aβ₄₂, total tau (t-tau), and phosphorylated tau (p-tau) in cerebrospinal fluid (CSF). Although the sensitivities and specificities of the classical biomarkers are fairly good for detection of AD, there is still a need to develop novel biochemical markers for early detection of AD. OBJECTIVE: We explored if integration of novel proteins with classical biomarkers in CSF can better discriminate AD from non-AD subjects. METHODS: We applied ELISA, mass spectrometry, and multivariate modeling to investigate classical biomarkers and the CSF proteome in subjects (n = 206) with 76 AD patients, 74 mild cognitive impairment (MCI) patients, 11 frontotemporal dementia (FTD) patients, and 45 non-dementia controls. The MCI patients were followed for 4–9 years and 21 of these converted to AD, whereas 53 remained stable. RESULTS: By combining classical CSF biomarkers with twelve novel markers, the area of the ROC curves (AUROCS) of distinguishing AD and MCI/AD converters from non-AD were 93% and 96%, respectively. The FTDs and non-dementia controls were identified versus all other groups with AUROCS of 96% and 87%, respectively. CONCLUSIONS: Integration of new and classical CSF biomarkers in a model-based approach can improve the identification of AD, FTD, and non-dementia control subjects

Interoperable and scalable data analysis with microservices: applications in metabolomics.

Developing a robust and performant data analysis workflow that integrates all necessary components whilst still being able to scale over multiple compute nodes is a challenging task. We introduce a generic method based on the microservice architecture, where software tools are encapsulated as Docker containers that can be connected into scientific workflows and executed using the Kubernetes container orchestrator. We developed a Virtual Research Environment (VRE) which facilitates rapid integration of new tools and developing scalable and interoperable workflows for performing metabolomics data analysis. The environment can be launched on-demand on cloud resources and desktop computers. IT-expertise requirements on the user side are kept to a minimum, and workflows can be re-used effortlessly by any novice user. We validate our method in the field of metabolomics on two mass spectrometry, one nuclear magnetic resonance spectroscopy and one fluxomics study. We showed that the method scales dynamically with increasing availability of computational resources. We demonstrated that the method facilitates interoperability using integration of the major software suites resulting in a turn-key workflow encompassing all steps for mass-spectrometry-based metabolomics including preprocessing, statistics and identification. Microservices is a generic methodology that can serve any scientific discipline and opens up for new types of large-scale integrative science. The PhenoMeNal consortium maintains a web portal (https://portal.phenomenal-h2020.eu) providing a GUI for launching the Virtual Research Environment. The GitHub repository https://github.com/phnmnl/ hosts the source code of all projects. Supplementary data are available at Bioinformatics online

Current challenges in software solutions for mass spectrometry-based quantitative proteomics

This work was in part supported by the PRIME-XS project, grant agreement number 262067, funded by the European Union seventh Framework Programme; The Netherlands Proteomics Centre, embedded in The Netherlands Genomics Initiative; The Netherlands Bioinformatics Centre; and the Centre for Biomedical Genetics (to S.C., B.B. and A.J.R.H); by NIH grants NCRR RR001614 and RR019934 (to the UCSF Mass Spectrometry Facility, director: A.L. Burlingame, P.B.); and by grants from the MRC, CR-UK, BBSRC and Barts and the London Charity (to P.C.

Targeted resequencing of candidate genes using selector probes

Targeted genome enrichment is a powerful tool for making use of the massive throughput of novel DNA-sequencing instruments. We herein present a simple and scalable protocol for multiplex amplification of target regions based on the Selector technique. The updated version exhibits improved coverage and compatibility with next-generation-sequencing (NGS) library-construction procedures for shotgun sequencing with NGS platforms. To demonstrate the performance of the technique, all 501 exons from 28 genes frequently involved in cancer were enriched for and sequenced in specimens derived from cell lines and tumor biopsies. DNA from both fresh frozen and formalin-fixed paraffin-embedded biopsies were analyzed and 94% specificity and 98% coverage of the targeted region was achieved. Reproducibility between replicates was high (R2 = 0, 98) and readily enabled detection of copy-number variations. The procedure can be carried out in <24 h and does not require any dedicated instrumentation

Striatal Proteomic Analysis Suggests that First L-Dopa Dose Equates to Chronic Exposure

L-3,4-dihydroxypheylalanine (L-dopa)-induced dyskinesia represent a debilitating complication of therapy for Parkinson's disease (PD) that result from a progressive sensitization through repeated L-dopa exposures. The MPTP macaque model was used to study the proteome in dopamine-depleted striatum with and without subsequent acute and chronic L-dopa treatment using two-dimensional difference in-gel electrophoresis (2D-DIGE) and mass spectrometry. The present data suggest that the dopamine-depleted striatum is so sensitive to de novo L-dopa treatment that the first ever administration alone would be able (i) to induce rapid post-translational modification-based proteomic changes that are specific to this first exposure and (ii), possibly, lead to irreversible protein level changes that would be not further modified by chronic L-dopa treatment. The apparent equivalence between first and chronic L-dopa administration suggests that priming would be the direct consequence of dopamine loss, the first L-dopa administrations only exacerbating the sensitization process but not inducing it

Effects of lithium and valproic acid on gene expression and phenotypic markers in an NT2 neurosphere model of neural development

Mood stabilising drugs such as lithium (LiCl) and valproic acid (VPA) are the first line agents for treating conditions such as Bipolar disorder and Epilepsy. However, these drugs have potential developmental effects that are not fully understood. This study explores the use of a simple human neurosphere-based in vitro model to characterise the pharmacological and toxicological effects of LiCl and VPA using gene expression changes linked to phenotypic alterations in cells. Treatment with VPA and LiCl resulted in the differential expression of 331 and 164 genes respectively. In the subset of VPA targeted genes, 114 were downregulated whilst 217 genes were upregulated. In the subset of LiCl targeted genes, 73 were downregulated and 91 were upregulated. Gene ontology (GO) term enrichment analysis was used to highlight the most relevant GO terms associated with a given gene list following toxin exposure. In addition, in order to phenotypically anchor the gene expression data, changes in the heterogeneity of cell subtype populations and cell cycle phase were monitored using flow cytometry. Whilst LiCl exposure did not significantly alter the proportion of cells expressing markers for stem cells/undifferentiated cells (Oct4, SSEA4), neurons (Neurofilament M), astrocytes (GFAP) or cell cycle phase, the drug caused a 1.4-fold increase in total cell number. In contrast, exposure to VPA resulted in significant upregulation of Oct4, SSEA, Neurofilament M and GFAP with significant decreases in both G2/M phase cells and cell number. This neurosphere model might provide the basis of a human-based cellular approach for the regulatory exploration of developmental impact of potential toxic chemicals