Thousands of Rab GTPases for the Cell Biologist

Rab proteins are small GTPases that act as essential regulators of vesicular trafficking. 44 subfamilies are known in humans, performing specific sets of functions at distinct subcellular localisations and tissues. Rab function is conserved even amongst distant orthologs. Hence, the annotation of Rabs yields functional predictions about the cell biology of trafficking. So far, annotating Rabs has been a laborious manual task not feasible for current and future genomic output of deep sequencing technologies. We developed, validated and benchmarked the Rabifier, an automated bioinformatic pipeline for the identification and classification of Rabs, which achieves up to 90% classification accuracy. We cataloged roughly 8.000 Rabs from 247 genomes covering the entire eukaryotic tree. The full Rab database and a web tool implementing the pipeline are publicly available at www.RabDB.org. For the first time, we describe and analyse the evolution of Rabs in a dataset covering the whole eukaryotic phylogeny. We found a highly dynamic family undergoing frequent taxon-specific expansions and losses. We dated the origin of human subfamilies using phylogenetic profiling, which enlarged the Rab repertoire of the Last Eukaryotic Common Ancestor with Rab14, 32 and RabL4. Furthermore, a detailed analysis of the Choanoflagellate Monosiga brevicollis Rab family pinpointed the changes that accompanied the emergence of Metazoan multicellularity, mainly an important expansion and specialisation of the secretory pathway. Lastly, we experimentally establish tissue specificity in expression of mouse Rabs and show that neo-functionalisation best explains the emergence of new human Rab subfamilies. With the Rabifier and RabDB, we provide tools that easily allows non-bioinformaticians to integrate thousands of Rabs in their analyses. RabDB is designed to enable the cell biology community to keep pace with the increasing number of fully-sequenced genomes and change the scale at which we perform comparative analysis in cell biology

Proteins In Grapevine

International audienceAlcohol dehydrogenase (ADH; alcohol: NAD oxidoreductase; EC 1.1.1.1) is a dimeric-zinc enzyme which catalyses the inter-conversion of acetaldehyde to ethanol, using NAD/NADH as a cofactor. This is the terminal step of glycolysis, leading to fermentative metabolism in anaerobic conditions. In this context, the evolution of this enzyme’s activity, and gene expression have been widely investigated in response to anaerobiosis in the plant kingdom, especially in organs such as roots, tubers, seeds and fruit (Kadowaki et al. 1988, Matton et al. 1990, Sun Chen and Chase 1993, Millar et al. 1994, Ke et al. 1994, Chung and Ferl 1999). ADH activity is generally low under normal oxygen tension, but strongly increases when the absence of oxygen leads to ethanolic fermentation. It appears to play an important role in the plant response to anaerobiosis, as ADH induction was generally detected in organs adapted to this stress. In maize, Gerlach et al. (1983) showed that ADH is fundamental to anaerobic survival for ADH null mutants. Paul and Ferl (1991) described the enhanced transcription of two genes responsible for an increased ADH synthesis. During oxygen deprivation, the assumed role of ADH is to reduce toxic acetaldehyde to ethanol and to recycle NADH to NAD, thereby allowing anaerobic glycolysis to be used as an energy-generating pathway. It could also be involved in cytoplasmic homeostasis, as the level of ADH activity is tightly regulated by pH, and as uncontrolled cytoplasmic acidosis is lethal (Roberts et al. 1984). ADH has also been implicated in the response to a wide range of other stresses, elicitors and also to abscisic acid (Christie et al. 1991, Constabel et al. 1990, Jarillo et al. 1993, Matton et al. 1990, de Bruxelles et al. 1996, Peters and Frenkel 2004)

Sampling size for determination of adenine nucleotide contents in mature grape berries: application to reference samples analysis

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A Comparison of Outcomes in the First-Line Treatment of Multiple Myeloma Presenting with Single Versus Multiple Monoclonal Paraproteins

Abstract Background: Multiple myeloma (MM) is characterized by the neoplastic proliferation of plasma cells that most often produce a single monoclonal immunoglobulin (M-protein). In approximately 2% of cases however, two or more distinct M-proteins of different immunoglobulin isotype are noted at diagnosis. The International Myeloma Working Group (IMWG) does not provide specific guidelines for determination of treatment response for MM with multiple M-proteins (MMP) and evidence comparing response and treatment outcomes of patients (pts) with MMP to those with a single M-protein (SMP) is lacking. The current tacit convention for assessing treatment response is to sum all M-proteins to get an aggregate M-protein value to compare to subsequent aggregate M-proteins over time. This approach has not been formally validated; it is unknown whether pts with MMP have a different natural history of the disease or response to treatment. We therefore conducted a retrospective analysis comparing clinical outcomes of 1st-line therapy of patients with MMP vs SMP. Methods: A retrospective cohort study of MM pts undergoing first-line therapy was performed by interrogation of the clinical database at the Weill Cornell Medical College / New York Presbyterian Hospital. Subjects diagnosed with active MM who received first-line treatment were included. MMP status was assigned to any patient (pt) with two or more distinct bands appearing on a serum protein electrophoresis with different isotypes on serum immunofixation prior to receiving any therapy. Baseline pt characteristics, therapy received, treatment, and survival outcomes were collected and compared. For pts with MMP, response was determined by following the change in sum of all M-proteins and following the sum as a SMP when applying IMWG criteria. Results: 170 pts were identified in the period of 2005-2014 and included in the analysis: 159 pts (93.5%) with SMP and 11 (6.5%) with MMP. Pts with MMP were older (median age 72 vs 62, P = 0.01) with a lower degree of bone marrow plasmacytosis (median 19% vs 45%, P = .003), with a trend towards a higher rate of extramedullary presentation (18.2% vs 4.5%, P = 0.051). Staging by ISS and Durie-Salmon, presence of adverse cytogenetics, sex, lactate dehydrogenase, C-reactive protein, baseline hemoglobin, and serum creatinine were similar in the two groups. The M-protein isotype distribution for SMP and MMP are shown below in Table 1, with no clear pattern emerging for the SMP group. Pts with SMP were more likely to have been treated with thalidomide (32% vs 0 pts, P = .025) and less with alkylating agents (8.8 vs 27.2%, p = 0.49), however were equally likely to have received treatment with lenalidomide, bortezomib, or autologous stem cell transplant. Overall response to therapy appeared deeper in SMP vs MMP (Table 2) but was not statistically different (P = 0.053). Median PFS was similar for SMP and MMP at 148 vs 135 weeks, respectively (log rank P = 0.87). Overall survival was also unaffected by the presence of MMP: median OS for SMP and MMP was 411 and 423 weeks, (log rank P = .42). A logistic regression model showed higher age (OR 1.15, 95% CI 1.04,1.28) and lower percent plasmacytosis (OR 0.92, 95% CI 0.87, 0.98) to be associated with the presence of MMP at diagnosis. Discussion: The presence of more than one distinct M-spike isotype at diagnosis of active MM was not associated with adverse treatment or survival outcomes. The current convention of following the sum of all M-proteins in these pts is valid. The IMWG criteria should be amended to formally clarify this method of determination of response in pts with MMP. Table 1: Multiple M-protein isotypes M-Protein Isotypes N = 170 (%) IgG-kappa 60 (35.3) IgG-lambda 33 (19.4) IgA-kappa 16 (9.4) IgA-lambda 16 (9.4) Free kappa 24 (14.1) Free lambda 9 (5.3) IgD-lambda 1 (0.6) IgG lambda and IgG kappa 2 (1.1%) IgG kappa and IgA kappa 2 (1.1%) IgA kappa and IgG lambda 2 (1.1%) IgG kappa and free mu heavy chains 1 (0.6%) IgG lambda and IgA lambda 1 (0.6%) IgG kappa and IgG lambda and IgM kappa 1 (0.6%) Biclonal IgG kappa, monoclonal IgA kappa 1 (0.6%) IgG kappa, IgM kappa, and IgA kappa 1 (0.6%) Table 2: Overall response for SMP vs MMP IMWG Response SG(N = 159) PG(N = 11) P Overall Response Rate N = 155 N = 11 .053 PD 3 2 SD 14 0 PR 38 5 VGPR 63 3 CR 3 0 SCR 38 1 Disclosures Mark: Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Millennium: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Onyx: Research Funding, Speakers Bureau. Niesvizky:Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Millennium: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Onyx: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau. </jats:sec

Interleukin-4 induces functional cell-surface expression of CXCR4 on human T cells

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