Peak intensity prediction in MALDI-TOF mass spectrometry: A machine learning study to support quantitative proteomics

Timm W, Scherbart A, Boecker S, Kohlbacher O, Nattkemper TW. Peak intensity prediction in MALDI-TOF mass spectrometry: A machine learning study to support quantitative proteomics. BMC Bioinformatics. 2008;9(1):443.Background: Mass spectrometry is a key technique in proteomics and can be used to analyze complex samples quickly. One key problem with the mass spectrometric analysis of peptides and proteins, however, is the fact that absolute quantification is severely hampered by the unclear relationship between the observed peak intensity and the peptide concentration in the sample. While there are numerous approaches to circumvent this problem experimentally (e. g. labeling techniques), reliable prediction of the peak intensities from peptide sequences could provide a peptide-specific correction factor. Thus, it would be a valuable tool towards label-free absolute quantification. Results: In this work we present machine learning techniques for peak intensity prediction for MALDI mass spectra. Features encoding the peptides' physico-chemical properties as well as string-based features were extracted. A feature subset was obtained from multiple forward feature selections on the extracted features. Based on these features, two advanced machine learning methods (support vector regression and local linear maps) are shown to yield good results for this problem (Pearson correlation of 0.68 in a ten-fold cross validation). Conclusion: The techniques presented here are a useful first step going beyond the binary prediction of proteotypic peptides towards a more quantitative prediction of peak intensities. These predictions in turn will turn out to be beneficial for mass spectrometry-based quantitative proteomics

An Efficient Vector System to Modify Cells Genetically

The transfer of foreign genes into mammalian cells has been essential for understanding the functions of genes and mechanisms of genetic diseases, for the production of coding proteins and for gene therapy applications. Currently, the identification and selection of cells that have received transferred genetic material can be accomplished by methods, including drug selection, reporter enzyme detection and GFP imaging. These methods may confer antibiotic resistance, or be disruptive, or require special equipment. In this study, we labeled genetically modified cells with a cell surface biotinylation tag by co-transfecting cells with BirA, a biotin ligase. The modified cells can be quickly isolated for downstream applications using a simple streptavidin bead method. This system can also be used to screen cells expressing two sets of genes from separate vectors

Analysis of Mycobacterium tuberculosis-Specific CD8 T-Cells in Patients with Active Tuberculosis and in Individuals with Latent Infection

CD8 T-cells contribute to control of Mycobacterium tuberculosis infection, but little is known about the quality of the CD8 T-cell response in subjects with latent infection and in patients with active tuberculosis disease. CD8 T-cells recognizing epitopes from 6 different proteins of Mycobacterium tuberculosis were detected by tetramer staining. Intracellular cytokines staining for specific production of IFN-γ and IL-2 was performed, complemented by phenotyping of memory markers on antigen-specific CD8 T-cells. The ex-vivo frequencies of tetramer-specific CD8 T-cells in tuberculous patients before therapy were lower than in subjects with latent infection, but increased at four months after therapy to comparable percentages detected in subjects with latent infection. The majority of CD8 T-cells from subjects with latent infection expressed a terminally-differentiated phenotype (CD45RA+CCR7−). In contrast, tuberculous patients had only 35% of antigen-specific CD8 T-cells expressing this phenotype, while containing higher proportions of cells with an effector memory- and a central memory-like phenotype, and which did not change significantly after therapy. CD8 T-cells from subjects with latent infection showed a codominance of IL-2+/IFN-γ+ and IL-2−/IFN-γ+ T-cell populations; interestingly, only the IL-2+/IFN-γ+ population was reduced or absent in tuberculous patients, highly suggestive of a restricted functional profile of Mycobacterium tuberculosis-specific CD8 T-cells during active disease. These results suggest distinct Mycobacterium tuberculosis specific CD8 T-cell phenotypic and functional signatures between subjects which control infection (subjects with latent infection) and those who do not (patients with active disease)

Engineering the Melanocortin-4 Receptor to Control Constitutive and Ligand-Mediated Gs Signaling In Vivo

The molecular and functional diversity of G protein–coupled receptors is essential to many physiological processes. However, this diversity presents a significant challenge to understanding the G protein–mediated signaling events that underlie a specific physiological response. To increase our understanding of these processes, we sought to gain control of the timing and specificity of Gs signaling in vivo. We used naturally occurring human mutations to develop two Gs-coupled engineered receptors that respond solely to a synthetic ligand (RASSLs). Our Gs-coupled RASSLs are based on the melanocortin-4 receptor, a centrally expressed receptor that plays an important role in the regulation of body weight. These RASSLs are not activated by the endogenous hormone α-melanocyte-stimulating hormone but respond potently to a selective synthetic ligand, tetrahydroisoquinoline. The RASSL variants reported here differ in their intrinsic basal activities, allowing the separation of the effects of basal signaling from ligand-mediated activation of the Gs pathway in vivo. These RASSLs can be used to activate Gs signaling in any tissue, but would be particularly useful for analyzing downstream events that mediate body weight regulation in mice. Our study also demonstrates the use of human genetic variation for protein engineering

Pompe disease diagnosis and management guideline

ACMG standards and guidelines are designed primarily as an educational resource for physicians and other health care providers to help them provide quality medical genetic services. Adherence to these standards and guidelines does not necessarily ensure a successful medical outcome. These standards and guidelines should not be considered inclusive of all proper procedures and tests or exclusive of other procedures and tests that are reasonably directed to obtaining the same results. in determining the propriety of any specific procedure or test, the geneticist should apply his or her own professional judgment to the specific clinical circumstances presented by the individual patient or specimen. It may be prudent, however, to document in the patient's record the rationale for any significant deviation from these standards and guidelines.Duke Univ, Med Ctr, Durham, NC 27706 USAOregon Hlth Sci Univ, Portland, OR 97201 USANYU, Sch Med, New York, NY USAUniv Florida, Coll Med, Powell Gene Therapy Ctr, Gainesville, FL 32611 USAIndiana Univ, Bloomington, in 47405 USAUniv Miami, Miller Sch Med, Coral Gables, FL 33124 USAHarvard Univ, Childrens Hosp, Sch Med, Cambridge, MA 02138 USAUniversidade Federal de São Paulo, São Paulo, BrazilColumbia Univ, New York, NY 10027 USANYU, Bellevue Hosp, Sch Med, New York, NY USAColumbia Univ, Med Ctr, New York, NY 10027 USAUniversidade Federal de São Paulo, São Paulo, BrazilWeb of Scienc

Classifying the evolutionary and ecological features of neoplasms

The consensus conference was supported by Wellcome Genome Campus Advanced Courses and Scientific Conferences. C.C.M. is supported in part by US NIH grants P01 CA91955, R01 CA149566, R01 CA170595, R01 CA185138 and R01 CA140657 as well as CDMRP Breast Cancer Research Program Award BC132057. M.J. is supported by NIH grant K99CA201606. K.S.A. is supported by NCI 5R21 CA196460. K. Polyak is supported by R35 CA197623, U01 CA195469, U54 CA193461, and the Breast Cancer Research Foundation. K.J.P. is supported by NIH grants CA143803, CA163124, CA093900 and CA143055. D.P. is supported by the European Research Council (ERC-617457- PHYLOCANCER), the Spanish Ministry of Economy and Competitiveness (BFU2015-63774-P) and the Education, Culture and University Development Department of the Galician Government. K.S.A. is supported in part by the Breast Cancer Research Foundation and NCI R21CA196460. C.S. is supported by the Royal Society, Cancer Research UK (FC001169), the UK Medical Research Council (FC001169), and the Wellcome Trust (FC001169), NovoNordisk Foundation (ID 16584), the Breast Cancer Research Foundation (BCRF), the European Research Council (THESEUS) and Marie Curie Network PloidyNet. T.A.G. is a Cancer Research UK fellow and a Wellcome Trust funded Investigator. E.S.H. is supported by R01 CA185138-01 and W81XWH-14-1-0473. M.Gerlinger is supported by Cancer Research UK and The Royal Marsden/ICR National Institute of Health Research Biomedical Research Centre. M.Ge., M.Gr., Y.Y., and A.So. were also supported in part by the Wellcome Trust [105104/Z/14/Z]. J.D.S. holds the Edward B. Clark, MD Chair in Pediatric Research, and is supported by the Primary Children's Hospital (PCH) Pediatric Cancer Research Program, funded by the Intermountain Healthcare Foundation and the PCH Foundation. A.S. is supported by the Chris Rokos Fellowship in Evolution and Cancer. Y.Y. is a Cancer Research UK fellow and supported by The Royal Marsden/ICR National Institute of Health Research Biomedical Research Centre. E.S.H. was supported in part by PCORI grants 1505–30497 and 1503–29572, NIH grants R01 CA185138, T32 CA093245, and U10 CA180857, CDMRP Breast Cancer Research Program Award BC132057, a CRUK Grand Challenge grant, and the Breast Cancer Research Foundation. A.R.A.A. was funded in part by NIH grant U01CA151924. A.R.A.A., R.G. and J.S.B. were funded in part by NIH grant U54CA193489