DART-ID increases single-cell proteome coverage.

Analysis by liquid chromatography and tandem mass spectrometry (LC-MS/MS) can identify and quantify thousands of proteins in microgram-level samples, such as those comprised of thousands of cells. This process, however, remains challenging for smaller samples, such as the proteomes of single mammalian cells, because reduced protein levels reduce the number of confidently sequenced peptides. To alleviate this reduction, we developed Data-driven Alignment of Retention Times for IDentification (DART-ID). DART-ID implements principled Bayesian frameworks for global retention time (RT) alignment and for incorporating RT estimates towards improved confidence estimates of peptide-spectrum-matches. When applied to bulk or to single-cell samples, DART-ID increased the number of data points by 30-50% at 1% FDR, and thus decreased missing data. Benchmarks indicate excellent quantification of peptides upgraded by DART-ID and support their utility for quantitative analysis, such as identifying cell types and cell-type specific proteins. The additional datapoints provided by DART-ID boost the statistical power and double the number of proteins identified as differentially abundant in monocytes and T-cells. DART-ID can be applied to diverse experimental designs and is freely available at http://dart-id.slavovlab.net

Machine Learning and Integrative Analysis of Biomedical Big Data.

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues

Xenopus: An ideal system for chemical genetics

Chemical genetics, or chemical biology, has become an increasingly powerful method for studying biological processes. The main objective of chemical genetics is the identification and use of small molecules that act directly on proteins, allowing rapid and reversible control of activity. These compounds are extremely powerful tools for researchers, particularly in biological systems that are not amenable to genetic methods. In addition, identification of small molecule interactions is an important step in the drug discovery process. Increasingly, the African frog Xenopus is being used for chemical genetic approaches. Here, we highlight the advantages of Xenopus as a first-line in vivo model for chemical screening as well as for testing reverse engineering approaches. genesis 50:207–218, 2012. © 2012 Wiley Periodicals, Inc

Determination of a predictive cleavage motif for eluted major histocompatibility complex class II ligands

CD4+ T cells have a major role in regulating immune responses. They are activated by recognition of peptides mostly generated from exogenous antigens through the major histocompatibility complex (MHC) class II pathway. Identification of epitopes is important and computational prediction of epitopes is used widely to save time and resources. Although there are algorithms to predict binding affinity of peptides to MHC II molecules, no accurate methods exist to predict which ligands are generated as a result of natural antigen processing. We utilized a dataset of around 14,000 naturally processed ligands identified by mass spectrometry of peptides eluted from MHC class II expressing cells to investigate the existence of sequence signatures potentially related to the cleavage mechanisms that liberate the presented peptides from their source antigens. This analysis revealed preferred amino acids surrounding both N- and C-terminuses of ligands, indicating sequence-specific cleavage preferences. We used these cleavage motifs to develop a method for predicting naturally processed MHC II ligands, and validated that it had predictive power to identify ligands from independent studies. We further confirmed that prediction of ligands based on cleavage motifs could be combined with predictions of MHC binding, and that the combined prediction had superior performance. However, when attempting to predict CD4+ T cell epitopes, either alone or in combination with MHC binding predictions, predictions based on the cleavage motifs did not show predictive power. Given that peptides identified as epitopes based on CD4+ T cell reactivity typically do not have well-defined termini, it is possible that motifs are present but outside of the mapped epitope. Our attempts to take that into account computationally did not show any sign of an increased presence of cleavage motifs around well-characterized CD4+ T cell epitopes. While it is possible that our attempts to translate the cleavage motifs in MHC II ligand elution data into T cell epitope predictions were suboptimal, other possible explanations are that the cleavage signal is too diluted to be detected, or that elution data are enriched for ligands generated through an antigen processing and presentation pathway that is less frequently utilized for T cell epitopes.Fil: Paul, Sinu. La Jolla Institute for Allergy and Immunology; Estados UnidosFil: Karosiene, Edita. La Jolla Institute for Allergy and Immunology; Estados UnidosFil: Dhanda, Sandeep Kumar. La Jolla Institute for Allergy and Immunology; Estados UnidosFil: Jurtz, Vanessa. Technical University of Denmark; DinamarcaFil: Edwards, Lindy. La Jolla Institute for Allergy and Immunology; Estados UnidosFil: Nielsen, Morten. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas; Argentina. Technical University of Denmark; DinamarcaFil: Sette, Alessandro. University of California at San Diego; Estados Unidos. La Jolla Institute for Allergy and Immunology; Estados UnidosFil: Peters, Bjoern. La Jolla Institute for Allergy and Immunology; Estados Unidos. University of California at San Diego; Estados Unido

Proteomic analysis of the Plasmodium male gamete reveals the key role for glycolysis in flagellar motility.

BACKGROUND: Gametogenesis and fertilization play crucial roles in malaria transmission. While male gametes are thought to be amongst the simplest eukaryotic cells and are proven targets of transmission blocking immunity, little is known about their molecular organization. For example, the pathway of energy metabolism that power motility, a feature that facilitates gamete encounter and fertilization, is unknown. METHODS: Plasmodium berghei microgametes were purified and analysed by whole-cell proteomic analysis for the first time. Data are available via ProteomeXchange with identifier PXD001163. RESULTS: 615 proteins were recovered, they included all male gamete proteins described thus far. Amongst them were the 11 enzymes of the glycolytic pathway. The hexose transporter was localized to the gamete plasma membrane and it was shown that microgamete motility can be suppressed effectively by inhibitors of this transporter and of the glycolytic pathway. CONCLUSIONS: This study describes the first whole-cell proteomic analysis of the malaria male gamete. It identifies glycolysis as the likely exclusive source of energy for flagellar beat, and provides new insights in original features of Plasmodium flagellar organization

Production of ent-kaurene from lignocellulosic hydrolysate in Rhodosporidium toruloides.

BACKGROUND:Rhodosporidium toruloides has emerged as a promising host for the production of bioproducts from lignocellulose, in part due to its ability to grow on lignocellulosic feedstocks, tolerate growth inhibitors, and co-utilize sugars and lignin-derived monomers. Ent-kaurene derivatives have a diverse range of potential applications from therapeutics to novel resin-based materials. RESULTS:The Design, Build, Test, and Learn (DBTL) approach was employed to engineer production of the non-native diterpene ent-kaurene in R. toruloides. Following expression of kaurene synthase (KS) in R. toruloides in the first DBTL cycle, a key limitation appeared to be the availability of the diterpene precursor, geranylgeranyl diphosphate (GGPP). Further DBTL cycles were carried out to select an optimal GGPP synthase and to balance its expression with KS, requiring two of the strongest promoters in R. toruloides, ANT (adenine nucleotide translocase) and TEF1 (translational elongation factor 1) to drive expression of the KS from Gibberella fujikuroi and a mutant version of an FPP synthase from Gallus gallus that produces GGPP. Scale-up of cultivation in a 2 L bioreactor using a corn stover hydrolysate resulted in an ent-kaurene titer of 1.4 g/L. CONCLUSION:This study builds upon previous work demonstrating the potential of R. toruloides as a robust and versatile host for the production of both mono- and sesquiterpenes, and is the first demonstration of the production of a non-native diterpene in this organism

TRAPID : an efficient online tool for the functional and comparative analysis of de novo RNA-Seq transcriptomes

Transcriptome analysis through next-generation sequencing technologies allows the generation of detailed gene catalogs for non-model species, at the cost of new challenges with regards to computational requirements and bioinformatics expertise. Here, we present TRAPID, an online tool for the fast and efficient processing of assembled RNA-Seq transcriptome data, developed to mitigate these challenges. TRAPID offers high-throughput open reading frame detection, frameshift correction and includes a functional, comparative and phylogenetic toolbox, making use of 175 reference proteomes. Benchmarking and comparison against state-of-the-art transcript analysis tools reveals the efficiency and unique features of the TRAPID system