Heterogeneity in the physiological states and pharmacological responses of differentiating 3T3-L1 preadipocytes

A systems biology–based analysis shows that differentiating adipocytes look very different at the single-cell level and form distinct cellular subpopulations

emerging technologies for food and drug safety

Abstract Emerging technologies are playing a major role in the generation of new approaches to assess the safety of both foods and drugs. However, the integration of emerging technologies in the regulatory decision-making process requires rigorous assessment and consensus amongst international partners and research communities. To that end, the Global Coalition for Regulatory Science Research (GCRSR) in partnership with the Brazilian Health Surveillance Agency (ANVISA) hosted the seventh Global Summit on Regulatory Science (GSRS17) in Brasilia, Brazil on September 18–20, 2017 to discuss the role of new approaches in regulatory science with a specific emphasis on applications in food and medical product safety. The global regulatory landscape concerning the application of new technologies was assessed in several countries worldwide. Challenges and issues were discussed in the context of developing an international consensus for objective criteria in the development, application and review of emerging technologies. The need for advanced approaches to allow for faster, less expensive and more predictive methodologies was elaborated. In addition, the strengths and weaknesses of each new approach was discussed. And finally, the need for standards and reproducible approaches was reviewed to enhance the application of the emerging technologies to improve food and drug safety. The overarching goal of GSRS17 was to provide a venue where regulators and researchers meet to develop collaborations addressing the most pressing scientific challenges and facilitate the adoption of novel technical innovations to advance the field of regulatory science

Acknowledgements

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Supervised prediction of drug-induced nephrotoxicity based on interleukin-6 and -8 expression levels

10.1186/1471-2105-15-S16-S16BMC Bioinformatics1516S1

New criteria for selecting differentially expressed genes: Filter-based feature selection techniques for better detection of changes in the distributions of expression levels

IEEE Engineering in Medicine and Biology Magazine, 26(2): pp. 17-26

Quantitative Protein Localization Signatures Reveal an Association between Spatial and Functional Divergences of Proteins

<div><p>Protein subcellular localization is a major determinant of protein function. However, this important protein feature is often described in terms of discrete and qualitative categories of subcellular compartments, and therefore it has limited applications in quantitative protein function analyses. Here, we present Protein Localization Analysis and Search Tools (PLAST), an automated analysis framework for constructing and comparing quantitative signatures of protein subcellular localization patterns based on microscopy images. PLAST produces human-interpretable protein localization maps that quantitatively describe the similarities in the localization patterns of proteins and major subcellular compartments, without requiring manual assignment or supervised learning of these compartments. Using the budding yeast <i>Saccharomyces cerevisiae</i> as a model system, we show that PLAST is more accurate than existing, qualitative protein localization annotations in identifying known co-localized proteins. Furthermore, we demonstrate that PLAST can reveal protein localization-function relationships that are not obvious from these annotations. First, we identified proteins that have similar localization patterns and participate in closely-related biological processes, but do not necessarily form stable complexes with each other or localize at the same organelles. Second, we found an association between spatial and functional divergences of proteins during evolution. Surprisingly, as proteins with common ancestors evolve, they tend to develop more diverged subcellular localization patterns, but still occupy similar numbers of compartments. This suggests that divergence of protein localization might be more frequently due to the development of more specific localization patterns over ancestral compartments than the occupation of new compartments. PLAST enables systematic and quantitative analyses of protein localization-function relationships, and will be useful to elucidate protein functions and how these functions were acquired in cells from different organisms or species. A public web interface of PLAST is available at <a href="http://plast.bii.a-star.edu.sg" target="_blank">http://plast.bii.a-star.edu.sg</a>.</p></div

Construction of quantitative protein subcellular localization profiles.

<p>(<b>A</b>) Schematic showing the major components of Protein Localization Analysis and Search Tools (PLAST). (<b>B</b>) Example images of GFP-tagged <i>Saccharomyces cerevisiae</i> strains from the UCSF dataset <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003504#pcbi.1003504-Huh1" target="_blank">[1]</a>. The intensity of each image has been scaled to the same range. (<b>C</b>) Multi-dimensional scaling plot based on the dissimilarity scores (<i>d_p</i>) among all the P-profiles_SVM constructed for the UCSF dataset. ORFs manually assigned to “nucleus”, “cytoplasm”, or “mitochondrion” categories by UCSF are shown in purple, red, or green dots, respectively. (<b>D</b>) Multidimensional scaling plot of 20 representative protein localization patterns (dots) or “exemplars” identified using an affinity-propagation clustering algorithm. The radius of the circle around each dot is proportional to the number of ORFs assigned to the exemplar. Each exemplar is colored and named according to the most enriched UCSF category among its assigned ORFs (<b>Supplementary <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003504#pcbi.1003504.s007" target="_blank">Fig. S4A</a></b>). The exemplars of MC2 (Cox8), CP3 (Rbg1), and NC3 (Hda2) are shown in B. (<b>E</b>) Comparison of the performances of P-profiles and quantitative features extracted using two other previous analysis frameworks (“Chen07” and “Huh09”) <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003504#pcbi.1003504-Chen1" target="_blank">[11]</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003504#pcbi.1003504-Huh2" target="_blank">[13]</a> in classifying ORFs according to UCSF categories. The accuracies shown were estimated using a multi-class SVM classifier and 5-fold cross validation, and averaged over all UCSF categories.</p

Large portion of WGD duplicates now have diverged localization patterns.

<p>Distributions of (<b>A</b>) P-profile dissimilarity scores (<i>d_p</i>), (<b>B</b>) ratios of shared compartments, and (<b>C</b>) numbers of compartments assigned to WGD duplicate (red) and random non-duplicate (black) pairs (M = medians, μ = means of the distributions; two-sided permutation tests for differences in medians or means.) Protein pairs with <i>d_p</i>≥10th-percentile of non-duplicate pairs are referred to as “dissimilarly localized” (DL) pairs, or otherwise as “similarly localized” (SL) pairs. (<b>D</b>) Ratios of DL duplicate pairs in the ten biological processes with the highest numbers of duplicate pairs (parentheses = numbers of duplicate pairs with P-profiles, red dashed line = DL-duplicate ratio for all duplicate pairs.) (<b>E</b>) Example images from the UCSF dataset <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003504#pcbi.1003504-Huh1" target="_blank">[1]</a> showing DL duplicate pairs with different <i>d_p</i> values. The intensity of each image has been scaled to the same range. The molecular functions of the duplicates are also shown if they are known.</p

A subcellular localization map for the yeast proteome.

<p>(<b>A</b>) An example of how PLAST assigns compartments to an ORF, YDR110W (black curve = estimated probability distribution of the <i>d_p</i> scores between the ORF and a catalog of 73 major subcellular compartments; dashed red vertical line = local maxima of the distribution with the highest <i>d_p</i> value; red curve = estimated “null” distribution of the <i>d_p</i> scores between the ORF and non-specifically localized compartments; blue vertical line = a threshold for compartments with <i>d_p</i> significantly less than the null distribution at Bonferroni-adjusted P˜<2.5×10⁻⁴.) The estimated mean and standard deviation of the null distribution are used to standardize the <i>d_p</i> scores between the ORF and all compartments. (<b>B</b>) A subcellular localization map showing the standardized P-profile dissimilariy scores () between 4066 ORFs (x-axis) and the 73 major subcellular compartments (y-axis) in a budding yeast cell. The compartments (rows) were ordered using a hierarchical clustering algorithm with cosine dissimilarity scores, and labeled with color codes according to their known functions or localizations (“common” compartments = compartments assigned to large numbers of ORFs.) A fully annotated map is shown in <b>Supplementary <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003504#pcbi.1003504.s010" target="_blank">Fig. S7</a></b>. (<b>C</b>) Using a Bonferroni-adjusted threshold of P˜<1.0×10⁻¹², we assigned compartments to each and every ORF. Among the 73 compartments, we found 22 compartments whose known components and “non-components” assigned by PLAST share at least one common, significantly-enriched GO biological process (P˜<0.05 with false-discovery-rate adjustment, hypergeometric test). Shown are the percentages of known- and non-components in all the ORFs assigned with these compartments by PLAST. The list of (up to three) common enriched GO biological processes for each compartment is also shown (pol. = polymerase, reg. = regulation, RNP = ribonucleoprotein).</p