17 research outputs found
Characterisation of MS phenotypes across the age span using a novel data set integrating 34 clinical trials (NO.MS cohort): age is a key contributor to presentation
Background:
The Oxford Big Data Institute, multiple sclerosis (MS) physicians and Novartis aim to address unresolved questions in MS with a novel comprehensive clinical trial data set.
Objective:
The objective of this study is to describe the NovartisâOxford MS (NO.MS) data set and to explore the relationships between age, disease activity and disease worsening across MS phenotypes.
Methods:
We report key characteristics of NO.MS. We modelled MS lesion formation, relapse frequency, brain volume change and disability worsening cross-sectionally, as a function of patientsâ baseline age, using phase III study data (â8000 patients).
Results:
NO.MS contains data of â35,000 patients (>200,000 brain images from â10,000 patients), with >10âyears follow-up. (1) Focal disease activity is highest in paediatric patients and decreases with age, (2) brain volume loss is similar across age and phenotypes and (3) the youngest patients have the lowest likelihood (<25%) of disability worsening over 2âyears while risk is higher (25%â75%) in older, disabled or progressive MS patients. Young patients benefit most from treatment.
Conclusion:
NO.MS will illuminate questions related to MS characterisation, progression and prognosis. Age modulates relapse frequency and, thus, the phenotypic presentation of MS. Disease worsening across all phenotypes is mediated by age and appears to some extent be independent from new focal inflammatory activity
Diagnostics and correction of batch effects in large-scale proteomic studies: a tutorial.
Advancements in mass spectrometry-based proteomics have enabled experiments encompassing hundreds of samples. While these large sample sets deliver much-needed statistical power, handling them introduces technical variability known as batch effects. Here, we present a step-by-step protocol for the assessment, normalization, and batch correction of proteomic data. We review established methodologies from related fields and describe solutions specific to proteomic challenges, such as ion intensity drift and missing values in quantitative feature matrices. Finally, we compile a set of techniques that enable control of batch effect adjustment quality. We provide an R package, proBatch , containing functions required for each step of the protocol. We demonstrate the utility of this methodology on five proteomic datasets each encompassing hundreds of samples and consisting of multiple experimental designs. In conclusion, we provide guidelines and tools to make the extraction of true biological signal from large proteomic studies more robust and transparent, ultimately facilitating reliable and reproducible research in clinical proteomics and systems biology
Genome-wide transcription start site mapping of Bradyrhizobium japonicum grown free-living or in symbiosis â a rich resource to identify new transcripts, proteins and to study gene regulation
Nitrogen fixation and molecular oxygen: comparative genomic reconstruction of transcription regulation in Alphaproteobacteria
Biological nitrogen fixation plays a crucial role in the nitrogen cycle. An ability to fix atmospheric nitrogen, reducing it to ammonium, was described for multiple species of Bacteria and Archaea. Being a complex and sensitive process, nitrogen fixation requires a complicated regulatory system, also, on the level of transcription. The transcriptional regulatory network for nitrogen fixation was extensively studied in several representatives of the class Alphaproteobacteria. This regulatory network includes the activator of nitrogen fixation NifA, working in tandem with the alternative sigma-factor RpoN as well as oxygen-responsive regulatory systems, one-component regulators FnrN/FixK and two-component system FixLJ. Here we used a comparative genomics analysis for in silico study of the transcriptional regulatory network in 50 genomes of Alphaproteobacteria. We extended the known regulons and proposed the scenario for the evolution of the nitrogen fixation transcriptional network. The reconstructed network substantially expands the existing knowledge of transcriptional regulation in nitrogen-fixing microorganisms and can be used for genetic experiments, metabolic reconstruction, and evolutionary analysis
Integration of Multi-omics Data for Prediction of Metabolic Traits
<p>In biomarker research, the goal is to construct an prediction rule on the basis of a small number of predictors. Formally, this means representing a macro-level response as a function of molecular features (DNA variants, transcript or protein abundancies) with minimal error. The aim is to develop a framework for selection of a composite biomarker: an ensemble of small number of predictors, that is able to predict the macro-level response.</p>
<p>To benchmark the process of construction of the composite biomarker, we use a mouse model. Mouse model has an advantage<br>
over human samples, as many confounding factors are controlled. Here we use measurements of 35 murine strains<br>
from the BXD recombinant inbred strain panel exposed to high-fat and chow diets. As explanatory variable set we use molecular profile of liver, and as response variables, we have selected 7 phenotypic traits related to metabolism.</p>
<p>Â </p>
<p><strong>This project has received funding from the European Unionâs Horizon 2020 research and innovation programme under grant agreement No 668858. This work was supported (in part) by the Swiss State Secretariat for Education, Research and Innovation (SERI) under contract number 15.0324-2. The opinions expressed and arguments employed therein do not necessarily reflect the official views of the Swiss Government.</strong><br>
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Transcription interference in blr1853.
<p><b>A)</b> Blr1853 locus and the structure of <i>lacZYA</i> reporter fusions used to analyze transcription interference in blr1853. Plasmid names are indicated. Blue straight line, DNA of the blr1853 locus; blue wave lines, asRNA AsR1-blr1853 (an abundant 65 nt form detected in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0165429#pone.0165429.g005" target="_blank">Fig 5C</a> and a long form previously detected by RT-PCR, [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0165429#pone.0165429.ref015" target="_blank">15</a>]); orange line, <i>lacZYA</i> genes; thin black flexed arrows, active TSSs; thin gray flexed arrows, inactive TSSs; open boxes with promoter designations, promoters upstream of the TSSs; P<sub>cyp</sub>, blr1853 promoter; P<sub>int</sub>, internal promoter in the sense direction; P<sub>as</sub>, internal promoter in the antisense direction. The genomic coordinates of the TSSs are given on top [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0165429#pone.0165429.ref015" target="_blank">15</a>]. Red stars indicate three mutations introduced in P<sub>as</sub> (see B). The drawing is not to scale. <b>B)</b> Reporter fusions used to measure the activity of the wild type (wt) P<sub>as</sub> and its mutated version P<sub>as-mut3</sub>. Shown are parts of the cloned 63 nt sequence. The TSS of asRNA AsR1-blr1853 is indicated along with the â10 and â15 boxes of the P<sub>as</sub> promoter. The mutated bases are in red. For other descriptions, see A). <b>C)</b> Beta-galactosidase activities of <i>B</i>. <i>japonicum</i> cells harboring the reporter constructs shown in A) and B). Measurements were performed with aerobic exponentially growing cultures. Shown are the results from three independent experiments with technical duplicates. Error bars indicate the standard deviation.</p
TBLASTX analysis of <i>B</i>. <i>japonicum</i> sORFs showing the distribution of homologs among bacteria.
<p><b>A)</b> Analysis of 39 sORFs with proteomic evidence. Indicated is their presence only in <i>B</i>. <i>japonicum</i> strains (<i>B</i>. <i>japonicum</i>), in the genus <i>Bradyrhizobium</i>, or in Alphaproteobacteria (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0165429#pone.0165429.s021" target="_blank">S4 Table</a>). Other â the sORF blr0566_ISGA present in Alphaproteobacteria and outside Alphaproteobacteria. <b>B)</b> Analysis of all 1080 sORFs (with and without proteomic evidence) (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0165429#pone.0165429.s023" target="_blank">S6 Table</a>). Alphaproteobacteria_Rare â sORFs found in less than five Alphaproteobacteria other than <i>Bradyrhizobium</i> spp.; Alphaproteobacteria_Conserved â sORFs found in five or more Alphaproteobacteria other than <i>Bradyrhizobium</i> spp.; Other â sORFs found in organisms outside Alphaproteobacteria; No homologs, â sORFs found only in <i>B</i>. <i>japonicum</i> USDA 110. <b>C)</b> Analysis of 47 sORFs with homologs outside Alphaproteobacteria (belonging to the category âOtherâ). Sporadic â sORFs found in less than 20 Alphaproteobacteria and less than 20 organisms outside Alphaproteobacteria; More in Alphaproteobacteria â sORFs found in at least 20 Alphaproteobacteria and less than 20 organisms outside Alphaproteobacteria; More outside Alphaproteobacteria â sORFs found in less than 20 Alphaproteobacteria and at least 20 organisms outside Alphaproteobacteria; Conserved â sORFs found in at least 20 Alphaproteobacteria and at least 20 organisms outside Alphaproteobacteria (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0165429#pone.0165429.s026" target="_blank">S9 Table</a>).</p
Down-regulation of BjsR4 under tellurite and iron stresses, and impact of this down-regulation on survival at high iron concentration.
<p><b>A)</b> The steady-state level of BjsR4 is decreased upon addition of tellurite. Total RNA of three independent cultures was isolated before (control) and after the following stresses: 5 min exposure to 2 mM H<sub>2</sub>O<sub>2</sub> or 30 min exposure to 50 mM NaCl (salt), 1 mM tellurite (K<sub>2</sub>TeO<sub>2</sub>), 39°C (heat) or 20°C (cold). After hybridization with the BjsR4-specific probe (top panel) the membrane was re-hybridized with the probe specific for 5S rRNA as loading control (bottom panel). <b>B)</b> The level of BjsR4 is decreased upon treatment with 1 mM tellurite and 10 mM Fe<sub>2</sub>SO<sub>4</sub> but not 5 mM Zn<sub>2</sub>SO<sub>4</sub> or 5mM Mn<sub>2</sub>SO<sub>4</sub>. Cultures were exposed for 30 min to the indicated metal ion solutions and then Northern blot analysis of total RNA was performed with probes specific to BjsR4 and 5S rRNA. <b>C)</b> Overexpression of the sRNA BjsR4 in <i>B</i>. <i>japonicum</i>. A representative Northern blot hybridization of the empty vector control (EVC) and the overexpressing strain (OE) is shown. The detected RNAs are indicated. The fold increase in the BjsR4 amount (Fold OE) given below the panel was calculated from four independent biological replicates. <b>D)</b> Increased sensitivity of the BjsR4 overexpressing strain to iron. Cells of the EVC and OE strains were grown to an OD<sub>600</sub> of 0.2 and 10 mM Mn<sub>2</sub>SO<sub>4,</sub> 5 mM K<sub>2</sub>TeO<sub>2</sub> or 10 mM Fe<sub>2</sub>SO<sub>4</sub> was added. Two hours later the cells were washed, serially diluted, spotted onto PSY plates with spectinomycine and tetracycline and grown for seven days at 30°C Shown is a representative example of six biologically independent experiments with similar results. <b>E)</b> Quantitative analysis of percentage colony forming units (c.f.u.) of the EVC and OE strains that survived after exposure to 10 mM Fe<sub>2</sub>SO<sub>4</sub>. Shown are the means and standard deviations from six biologically independent replicates.</p