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 Novartis–Oxford 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 patients’ baseline age, using phase III study data (≈8000 patients). Results: NO.MS contains data of ≈35,000 patients (>200,000 brain images from ≈10,000 patients), with >10 years 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 2 years while risk is higher (25%–75%) 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

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> </p> <p><strong>This project has received funding from the European Union’s 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>  </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 – 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 – sORFs found in less than five Alphaproteobacteria other than <i>Bradyrhizobium</i> spp.; Alphaproteobacteria_Conserved – sORFs found in five or more Alphaproteobacteria other than <i>Bradyrhizobium</i> spp.; Other – sORFs found in organisms outside Alphaproteobacteria; No homologs, – 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 “Other”). Sporadic – sORFs found in less than 20 Alphaproteobacteria and less than 20 organisms outside Alphaproteobacteria; More in Alphaproteobacteria – sORFs found in at least 20 Alphaproteobacteria and less than 20 organisms outside Alphaproteobacteria; More outside Alphaproteobacteria – sORFs found in less than 20 Alphaproteobacteria and at least 20 organisms outside Alphaproteobacteria; Conserved – 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

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_cyp, blr1853 promoter; P_int, internal promoter in the sense direction; P_as, 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_as (see B). The drawing is not to scale. <b>B)</b> Reporter fusions used to measure the activity of the wild type (wt) P_as and its mutated version P_as-mut3. Shown are parts of the cloned 63 nt sequence. The TSS of asRNA AsR1-blr1853 is indicated along with the –10 and –15 boxes of the P_as 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