Top PDF Ribosome profiling reveals the what, when, where and how of protein synthesis

Ribosome profiling reveals the what, when, where and how of protein synthesis

Ribosome profiling reveals the what, when, where and how of protein synthesis

How much? A quantitative view of protein synthesis. The simplest and broadest application of ribosome profiling is as a quantitative proteomics tool to monitor which proteins are being synthesized, and at what levels, thus providing rich molecular insight into a given cell state. Ribosome footprint density reflects the number of ribo- somes at a given position. Assuming that the average translation elongation rate is similar for different genes, ribosome profiling provides direct, global and quantita- tive measurements of rates of protein synthesis, thereby capturing information that has been largely invisible to gene expression measurements of mRNA levels alone. Mass spectrometry can, in principle, be used to measur e rates of protein synthesis; however, this is technically difficult, as it typically requires metabolic labelling and multiple measurements per sample. Analysis of the positions of mRNAs in polysome gradients provides valuable complementary information to that obtained with ribosome profiling, but again, this method is labo- rious and typically yields only a qualitative measure of protein synthesis.
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Ribosome Profiling Reveals HSP90 Inhibitor Effects on Stage-Specific Protein Synthesis in Leishmania donovani

Ribosome Profiling Reveals HSP90 Inhibitor Effects on Stage-Specific Protein Synthesis in Leishmania donovani

Ribosome profiling is a new technique related to the DNase footprint assay (44) and able to fill the knowledge gap between RNA abundance and proteome analysis. It was developed by Ingolia et al. (45) and is based on the deep sequencing of ribosome- protected mRNA fragments. The comparison of the deep sequencing data from ribosome-protected mRNA fragments and the total mRNA from a cell allows distin- guishing mRNA that is actively translated at a given time point in the cell, but also the translation efficiency, i.e., the number of ribosome-protected RNA fragments for each given mRNA. This facilitates quantification of nascent protein synthesis and identifica- tion of new and unusual open reading frames (ORFs) or short regulatory upstream ORFs (uORFs) in the untranslated regions (46, 47). This is of high importance for the study of organisms whose gene expression control relies on posttranscriptional events only, e.g., the Trypanosomatida (29). Ribosome profiling was used to unravel the stage conversion pathways in Trypanosoma cruzi (48), T. brucei (49, 50), and Toxoplasma gondii (51). In the second study, ribosome profiling also revealed new coding sequences that had es- caped previous, algorithm-based detection and annotation. Since small proteins often escape detection by mass spectrometry due to not yielding enough peptides for identification, ribosome profiling can detect expression of short ORFs more sensitively. In this paper, we applied ribosome profiling to monitor the changes in the protein synthesis induced by HSP90 inhibition of L. donovani parasites. HSP90 activity or the lack thereof affects the synthesis of several chaperone proteins, but also of histones, amastins, proteolytic proteins, and redox enzymes. We also find evidence that the HSP90 inhibitor radicicol has significant off-target effects, i.e., non-HSP90-specific ef- fects, that modulate the synthesis of proteins. We find that the observed changes of protein synthesis do not correlate with changes in RNA abundance, confirming earlier findings obtained by RNA arrays and comparative proteomics (32).
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In Vivo Transcriptional Profiling of Yersinia pestis Reveals a Novel Bacterial Mediator of Pulmonary Inflammation

In Vivo Transcriptional Profiling of Yersinia pestis Reveals a Novel Bacterial Mediator of Pulmonary Inflammation

We observed that deletion of the ybtX gene resulted in dimin- ished disease symptoms during the proinflammatory disease phase but had no effect on bacterial burden or dissemination. Detailed examination of pulmonary infection with the ⌬ybtX strain revealed reduced inflammation in the lung as demonstrated by histopathological analysis. The ybtX open reading frame is the third gene in the ybtPQXS operon located in the 36-kb high- pathogenicity island (HPI) that is nearly identical in all pathogenic Yersinia species (6, 25). With the exception of ybtD, all genes re- quired for the regulation, synthesis, and transport of the sidero- phore yersiniabactin (Ybt) are carried within the HPI. Y. pestis mutants unable to transport or synthesize Ybt are attenuated in virulence models of bubonic and pneumonic plague (8). The products of the two genes immediately upstream of ybtX, ybtP and ybtQ, form an inner membrane ABC transporter necessary for the uptake of Ybt bound to iron (7). ybtS, the gene immediately down- stream of ybtX, encodes a salicylate synthase required for the syn- thesis of Ybt (26, 27). The ybtX open reading frame is predicted to encode a hydrophobic inner membrane protein that belongs to the major facilitator superfamily (MFS) (7). Deletion of ybtX in Yersinia species has no impact on Ybt synthesis, uptake, utilization of iron in a Ybt-dependent manner, or virulence in mice (7, 28). This is somewhat surprising, as deletion of any of the other ybt genes within the HPI results in attenuated bacterial growth in the absence of iron and during infection.
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Lin28 mediated temporal promotion of protein synthesis is crucial for neural progenitor cell maintenance and brain development in mice

Lin28 mediated temporal promotion of protein synthesis is crucial for neural progenitor cell maintenance and brain development in mice

Fig. 5. Lin28-mediated translation regulation revealed by sucrose density gradient fractionation coupled with RNA-seq analysis. (A) Fractions containing polysomes from control and Lin28a/b dKO mutant cortical tissues were illustrated through polysome profiling studies, and corresponding fractions (#11-#25) were confirmed by measuring RNA concentration. (B) RNA-seq and bioinformatics analyses of total RNAs or RNAs from polysome fractions derived from E11.5 embryonic brains. Venn diagrams show total and polysome-associated mRNAs that change in abundance in Lin28a/b dKO mutants compared with controls. Genes that were expressed differently in wild type and mutants (non-parameter test, P<0.05) were analyzed with a Log2(fold change)=1.5 cut off. (C) Volcano plots show gene expression levels relative to controls from a bioinformatic study; blue dots represent transcripts significantly decreased; red dots represent transcripts significantly increased; gray dots represent levels that were not significantly changed. Genes that were expressed differently in wild type and mutants (P<0.05) were analyzed with a − log10(0.05)=1.3 cut off. (D-G) Gene set enrichment analysis (GSEA) of polysome-associated mRNAs for gene sets involved in the cell cycle (D), in ribosome biogenesis (E), in translation (F) and in mTORC1 signaling (G). Each line represents a single gene in the gene set. (H) Analysis of RNA-Seq data from total mRNAs and polysome-associated mRNAs reveals translational regulation of genes involved in the cell cycle, neural differentiation, ribosome biogenesis and translation. (I,K,M) Western blot analyses of the expression of proteins as indicated using cortical tissues from E11.5 Lin28a/b dKO mutant embryos. (J,L,N) Quantification of western blot data from three independent experiments. The ratios between pS6K versus total S6k or pS6 versus total S6 were calculated (non-parametric Mann – Whitney test; ns, not significant). All data are presented as mean±s.e.m.
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Ribosome profiling of the retrovirus murine leukemia virus

Ribosome profiling of the retrovirus murine leukemia virus

We found that the average RiboSeq CHX density (by counting reads that are in-phase with either ORF and nor- malizing by RNASeq) in the N-terminal extension region was 6.7% of that within the standard gag ORF, consistent with previous reports [21]. Previously, it has been reported that initiation of Glyco-Gag synthesis begins at a non- canonical (CUG) initiation codon located at nt 357–359 of the MuLV genome [16]. This codon occurs within a favourable context (accCUGg) [16] (in mammalian cells, the presence of an A residue at position −  3, or G resi- dues at − 3 and + 4—relative to the first nucleotide of a potential start codon—is favourable for initiation [22, 23]). Surprisingly, however, our RiboSeq HAR library reveals very low coverage at this CUG codon (Fig. 2—light green rectangle represents the N-terminal extension of gag, as per [16]). It is possible that technical biases—for example nuclease bias, PCR bias, ligation bias, or some combina- tion of these—resulting from variations in local nucleotide composition might contribute towards the low coverage observed at this CUG. Supporting this possibility, RNASeq density was also particularly low at this position.
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Ribosome profiling reveals resemblance between long non coding RNAs and 5′ leaders of coding RNAs

Ribosome profiling reveals resemblance between long non coding RNAs and 5′ leaders of coding RNAs

Fig. 2. Ribosome profiles outline translated ORFs of coding genes. (A) Representative examples of ribosome- protected fragment (RPF) densities associated with protein- coding genes. Gene structures are depicted as thick bars for the coding sequence (CDS), thin bars for 5 ⬘ leaders and 3 ⬘ trailers, and dashed lines for introns. Note that the majority of RPFs map within the CDSs and are flanked by the annotated initiation (START, green) and termination codon (STOP, red). The bottom three panels show examples of uORF-containing genes. For these genes, RPF reads map to the CDSs and to short ORFs within the 5 ⬘ leaders. (B) RefSeq metagene analysis of relative phasing of ribosome P-sites (see Materials and methods). Relative phasing is defined as the number of RPFs at a given position divided by the mean of the number of RPFs at the four adjacent positions. i.e. relative phasing at position i=RPFs at position i/mean (RPFs at positions i-2, i-1, i+1 and i+2). As in previous studies (Ingolia et al., 2011), triplet phasing of ribosome profiles was observed.
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Ribosome Profiling of Synechocystis Reveals Altered Ribosome Allocation at Carbon Starvation

Ribosome Profiling of Synechocystis Reveals Altered Ribosome Allocation at Carbon Starvation

Ribosome profiling quantifies the translation step of gene expression through deep sequencing of ribosome-protected RNA fragments (RPFs, or “footprints”) (12). Aligning and counting RPFs allows estimation of protein synthesis rates throughout the genome, thus providing a measure of cellular resource allocation at the time of sampling. Additionally, the distribution of RPFs mapped within an open reading frame (ORF) indicates ribosome pausing during elongation (13). Comparative ribosome profiling can reveal time- or condition-dependent translational gene regulation. For example, anti- biotic biosynthesis pathways in Streptomyces coelicolor were found to be “translation- ally induced” as cells enter stationary phase, as evidenced by increases in translational efficiency for genes in secondary metabolism (14). Also working with Streptomyces coelicolor, Bucca et al. reported an increase in translational efficiency of several genes upon temperature shift from 30°C to 42°C (15). Ribosome profiling of Synechocystis during C i depletion might, thus, reveal new mechanisms for how carbon limitation is
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A Prediction Model for Child Development Analysis using Naive Bayes and Decision Tree Fusion Technique – NB Tree

A Prediction Model for Child Development Analysis using Naive Bayes and Decision Tree Fusion Technique – NB Tree

In this research study, a comparitive study was conducted on various datamining classifcation and prediction algorithms for child development analysis. The framework for predicting child development analysis uses a Hybrid Naive Bayes and Decision Tree technique. Both these algorithms are good classification and prediction techniques individually. By combining both these techniques, more accurate prediction techniques can be obtained. From the study it was able to conclude that the proposed framework out performs other machine learning algorithms in terms of prediction accuracy, time consumption and error rate. In practice, NB-Trees are shown to scale to large databases and, in general, outperform Decision Trees and NBCs alone. NB-Trees appears to be a viable approach for generating prediction model especially when there are
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Dual Transcriptome Profiling of Leishmania Infected Human Macrophages Reveals Distinct Reprogramming Signatures

Dual Transcriptome Profiling of Leishmania Infected Human Macrophages Reveals Distinct Reprogramming Signatures

membrane proteins that regulate vesicle docking and fusion in processes such as exocytosis (51, 52) and phagocytosis (51, 53, 54). While some synaptotagmin family members (SYT5 and SYT11) have been implicated in Leishmania infection (55–57), the in- volvement of SYT2 and SYT8 has not yet been investigated. Given the general role of synaptotagmins as regulators of membrane trafficking and fusion, it is possible that the higher levels of SYT2 and SYT8 observed during L. major infection compared to L. ama- zonensis infection may be linked to differences in parasitophorous vacuole maintenance throughout the infection—L. major divides in membrane-bound compartments, with each parasite division maintaining singular parasites in a vacuole; conversely, L. ama- zonensis may possibly require fewer fission events to maintain its communal vacuoles. Additionally, synaptotagmins are also known to play a role in SNARE activity regulation by mediating membrane fusion in a Ca 2⫹ -dependent manner (58–60). Since Leishmania have been shown to target SNARES (VAMP8 in par- ticular) in order to modulate antigen cross-presentation (61), it is possible that transcriptional upregulation of synaptotagmins upon infection by Leishmania may be related to their role in the regulation of SNAREs. CMIP, which encodes c-Maf-inducing protein, and GABRE, which encodes the gamma-aminobutyric acid (GABA) A receptor epsilon, were also expressed at higher levels in L. major-infected macrophages, though the mechanism by which these genes may differentially interact with or be influ- enced by each parasite species is unclear.
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How, When, and Where Relic DNA Affects Microbial Diversity

How, When, and Where Relic DNA Affects Microbial Diversity

In contrast, we identified at least one scenario where relic DNA can create overes- timation bias. Microscale heterogeneity in structured habitats can lead to nonuniform distributions of microorganisms and their metabolic activities. Our simulations indicate that the resulting “hot spots” can degrade relic DNA sequences in a density-dependent manner, resulting in a more even relic SAD. Although not statistically significant, sediment samples trended toward positive bias for richness and PD (Fig. 3), which could potentially reflect “hot spots” of relic DNA degradation. There are other situations where inflation bias could potentially arise, including when species in the regional pool disperse into habitats for which they are poorly adapted. For example, our simulations indicate that when poorly adapted immigrants immediately die, they can enrich the relic DNA pool with allochthonous sequences that are dissimilar to sequences found in the local community and thereby influence estimates of diversity (see Fig. S7 in the supplemental material). A similar effect could arise when dead bacteria are transported across ecosystem boundaries, a phenomenon that occurs, for example, when marine snow is exported from surface waters to marine sediments (24). More generally, bias may arise under nonequilibrium conditions where community turnover of the intact DNA pool is faster than turnover of the relic DNA pool. Any abiotic or biotic perturba- tion that removes a substantial amount of living biomass could result in transient divergence in the composition of sequences in the intact and relic DNA pools. For example, virulent phage can rapidly reduce the abundance of bacterial prey and in the process release a large pulse of dissolved DNA into the environment (25). In this way, relic DNA might distort estimates of temporal stability in microbial communities. Last, although not fully explored here, shifts in rank abundance of taxa that are independent of parameters describing the SAD could also result in biased estimates of microbial diversity. In sum, there are ways to deviate from neutral expectations about the degradation of relic DNA, which should lead to biased estimates of microbial diversity. And, of course, many of the processes described above can operate simultaneously, which may amplify or dampen the effects of relic DNA bias. Data from our study suggest that such conditions may not be prevalent, but additional sampling across an even broader range of ecosystems is needed to determine whether or not relic DNA bias is a major concern for estimating biodiversity.
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Control of Ribosome Synthesis During the Cell Division Cycles of E. coli and Synechococcus

Control of Ribosome Synthesis During the Cell Division Cycles of E. coli and Synechococcus

steady state growth conditions (Cashel and Gallant, 1969). In cells subjected to amino acid starvation or cells that are entering or are in a stationary phase of growth, ppGpp concentration increased significantly above basal level while rRNA transcription is decreased. The role of ppGpp as an effector molecule that controls the rRNA synthesis is a complex one. In any case, the control of the rRNA synthesis has been found to be associated with the rrn promoter strength, the regulatory molecules, Fis, H- NS, and feedback regulators, ppGpp and iNTP (Gourse et al., 1996; Gaal et al., 1997; Barker et al., 2001; Schneider et al., 2002). The promoter strength of rrn operons is increased by the UP element (Ross et al., 1993). Fis proteins enhance the binding of RNAP to promoters by direct interaction with the C-terminal domain of the alpha subunit of RNAP (Bokal et al., 1997; Alyar et al., 1998). The UP elements and trans-acting Fis protein (Ross et al., 1990) are able to increase transcription at least 300- fold (Rao et al., 1994). On the other hand, H-NS proteins inhibit the promoter activity at P1 (Afferbach et al., 1998). However, how does ppGpp decrease rrn expression and how does iNTP increase rrn expression? Experimental results show that ppGpp binds to β , β ' of RNAP and s 70 (Chatterji et al., 1998; Toulokhonov et al., 2001;
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Proliferation/Quiescence: When to start? Where to stop? What to stock?

Proliferation/Quiescence: When to start? Where to stop? What to stock?

The molecular nature of Start has been extensively investigated and a fair amount of key proteins, including cyclins and cyclin-dependent kinases, together with cru- cial signalling networks have been discovered. However, in yeast, as in metazoa, how these regulators integrate external and internal signals to trigger either the re- entry into the cell cycle or the transition to non-dividing cellular states remains largely mysterious.

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Phenotypic Profiling Reveals that Candida albicans Opaque Cells Represent a Metabolically Specialized Cell State Compared to Default White Cells

Phenotypic Profiling Reveals that Candida albicans Opaque Cells Represent a Metabolically Specialized Cell State Compared to Default White Cells

Hierarchical clustering. Hierarchical clustering analysis (HCA) was performed using an algorithm that iteratively compares the output between two substrate conditions in order to find those that are the most similar. Each condition represents the unique phenotypic output defined by three parameters—metabolic activity, phenotypic switch- ing (given by the percent opaque cells in the final population), and induction of filamentation (given by the percent filamentous cells at the end of the experiment). Phenotype similarity was assessed using a Pearson product-moment correlation coefficient ( ␳ ), which indicates how the two conditions are related (positive or negative correlation), as well as the strength of the respective relationship (with 0 denoting the absence of a correlation and ⫹ 1 or ⫺ 1 denoting total correlations). The Pearson correlation between each two conditions represents the covariance of the two phenotypic outputs divided by the product of their standard deviations. The phenotypic output of any condition can be represented on a 3D plot using the coordinates given by the three phenotypes (metabolic activity, percent opaque cells, and percent fila- mentation), similar to the plots shown in Fig. S4A in the supplemental material. HCA groups the points on these graphs based on the distance between them.
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Evidence review of what works for health systems strengthening, where and when?

Evidence review of what works for health systems strengthening, where and when?

of essential medicines at the primary health care level. They identified one randomised multi-centre trial conducted in Cameroon, Nigeria, and Uganda on “community-directed interventions” (defined as programmes where communities establish their own, locally-appropriate measures to ensure the supply of medicines, and local leaders take responsibility for the on-going facilitation of the system). These interventions resulted in significantly increased coverage of vitamin A, anti-parasite drugs (Ivermectin), and appropriate malaria treatment. Another observational study from Tanzania assessing community-directed interventions also found increased availability of anti-parasite drugs, but not vitamin A. Supervisory programmes aimed at improving stock management practices at health facilities in Zimbabwe were found to result in better stock management indicators and improved drug availability in a randomised controlled trial, although this latter finding was not statistically significant (Nunan and Duke, 2011). There is some evidence from observational studies in Nepal and India that training pharmaceutical staff results in fewer drug stock-outs (Nunan and Duke, 2011).
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Episodic-like Memory in Dogs: Solving What-Where-When Tasks

Episodic-like Memory in Dogs: Solving What-Where-When Tasks

Further evidence for episodic-like memory in non-human animals was shown in rats (Rattus norvegicus) in Babb and Crystal (2005). Taking advantage of rats’ robust spatial memory on radial mazes, the authors tested rats to see if they could remember what food items they ate, where they ate these food items, and, depending on how much time passed after eating these food items, where to go to retrieve the most food items. First, rats were placed on an eight-arm radial maze, in which four of the arms were inaccessible. At the end of three accessible arms was a piece of standard rat chow, while at the end of the one remaining arm was a piece of highly preferred chocolate. Rats were then removed from the maze and returned after a period of time, in which now all eight arms were accessible. Some days rats were returned after a short interval (30 min), whereas other days rats were returned after a long interval (4 h). If rats were returned after 30 min, the previously inaccessible arms would be baited with standard rat chow and the previously visited arms would all be non-baited. If the rats were returned after 4 h, in addition to the previously inaccessible arms being baited with standard rat chow, the arm that was previously baited with chocolate was re-baited with chocolate. Optimal performance should be revisiting the chocolate arm after 4 h but not after 30 min. Rats revisited the chocolate arm more often when 4 h passed than when 30 min passed. Thus, rats, like scrub jays, can encode WWW memory by remembering what food items they visited, where they visited these items, and can make optimal decisions based on how much time has elapsed since food retrieval.
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Massively Parallel Fitness Profiling Reveals Multiple Novel Enzymes in Pseudomonas putida Lysine Metabolism

Massively Parallel Fitness Profiling Reveals Multiple Novel Enzymes in Pseudomonas putida Lysine Metabolism

Random barcode transposon sequencing (RB-TnSeq) is a genome-wide approach that measures the importance of each gene to growth (or fitness) in a massively parallel assay (14). RB-TnSeq can identify phenotypes for thousands of previously uncharacter- ized genes (14, 15), including the levulinic acid degradation pathway in P. putida KT2440 (16). In this study, we applied RB-TnSeq to uncover multiple novel genes implicated in L - and D -lysine metabolism in P. putida. We first describe a three-enzyme route connecting L -2AA to 2-ketoglutarate (2KG) (Fig. S1B). Within this pathway, D -lysine metabolism connects to central metabolism through a 2-hydroxyglutarate (2HG) inter- mediate, which is directly produced from 2-oxoadipate (2OA) in a reaction catalyzed by a DUF1338-containing protein. The function of this protein family, widely distributed across many domains of life, was previously unknown. Subsequently, we further characterize the glutarate hydroxylase CsiD by demonstrating its 2-oxoacid promiscuity during the hydroxylation of glutarate. Finally, we show that the expression levels of all of the newly discovered enzymes change significantly in response to specific metab- olites within the two catabolic pathways.
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The Molecular Basis For Post And Co-Translational N-Terminal Acetylation

The Molecular Basis For Post And Co-Translational N-Terminal Acetylation

The three major NATs are NatA, B, and C, which are responsible for acetylating the vast majority of N-termini in the cell (Figure 1.5A). The major NATs are comprised of a catalytic subunit, and a larger auxiliary subunit that is necessary for catalysis. Some have additional binding partners. These can be other NATs, like Naa50, or regulatory proteins like HYPK, both of which bind to NatA. NatA, B, and C all bind to the ribosome and act to co-translationally modify nascent N-termini as they emerge from the ribosome exit tunnel (discussed more in section 1.5). Each has hundred of substrates and recognizes N-termini based on the first two residues. NatA recognizes N-termini with small residues (Ala, Cys, Gly, Ser, Thr, Val) at their N- terminus after the initial methionine (iMet) has been cleaved by methionine aminopeptidase. NatB acetylates substrates with an iMet followed by an acidic residue or their corresponding amides, and NatC acetylates N-termini with an iMet followed by large hydrophobic residues. It has been reported that in addition to their promiscuity in the number of N-terminal substrates they have, that the catalytic subunits of some NATs target lysine residues as well, without interacting with their auxiliary binding partners (Dorfel and Lyon 2015). If true, this would significantly expand the substrate scope of the NATs. This is a controversial claim in the field, and has been challenged by many groups (Arnesen et al. 2005; Murray-Rust et al. 2006).
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Synthesis, in vitro evaluation, and radiolabeling of fluorinated puromycin analogues: potential candidates for PET imaging of protein synthesis

Synthesis, in vitro evaluation, and radiolabeling of fluorinated puromycin analogues: potential candidates for PET imaging of protein synthesis

There is currently no ideal radiotracer for imaging protein synthesis rate (PSR) by positron emission tomography (PET). Existing fluorine-18 labelled amino acid-based radiotracers predominantly visualize amino acid transporter processes, and in many cases they are not incorporated into nascent proteins at all. Others are radiolabelled with the short half-life positron emitter carbon-11 which is rather impractical for many PET centers. Based on the puromycin (6) structural manifold, a series of 10 novel derivatives of 6 was prepared via Williamson ether synthesis from a common intermediate. A bioluminescence assay was employed to study their inhibitory action on protein synthesis which identified fluoroethyl analogue (7b) as a lead compound. The fluorine-18 analogue was prepared via nucleophilic substitution of the corresponding tosylate precursor in modest radiochemical yield 2±0.6% and excellent radiochemical purity (>99%) and showed complete stability over 3 h at ambient temperature.
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Interprofessional education in rural practice: how, when and where?

Interprofessional education in rural practice: how, when and where?

Interprofessional education (IPE) has been suggested as an answer to improving the effectiveness of health professional teamwork, which in turn is regarded as a key strategy for improving the delivery and outcomes of increasingly complex healthcare approaches. There is a strong theoretical base to support the implementation of IPE for all health professionals, and in response many training programs now do this, although in a wide variety of ways. There is, however, little evidence so far that IPE has the desired effect, and one reason for this may be the design of the IPE learning activities. This article presents some theory-based but practical advice for how to develop effective IPE activities. The focus is on rural practice, which is an ideal location for IPE because small teams must work together in small communities to provide optimal health care.
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Accounting for Experimental Noise Reveals That mRNA Levels, Amplified by Post-Transcriptional Processes, Largely Determine Steady-State Protein Levels in Yeast

Accounting for Experimental Noise Reveals That mRNA Levels, Amplified by Post-Transcriptional Processes, Largely Determine Steady-State Protein Levels in Yeast

Fig 6. Transcriptional and translational regulation act coherently to set protein levels. A, Left, the correlation of mRNA (orange) and ribosome footprint density (green) with protein levels [40] as originally reported [18]. Results of four subsequent ribosome footprint density datasets (gray) from other groups are shown for comparison. Right, the same correlations employing SCM-estimated protein levels. The SCM mRNA – protein correlation is shown for comparison (blue). All bars show Pearson correlations between log-transformed values. B, The exponent relating protein and mRNA levels, or equivalently the slope relating log-transformed values, estimated by noise-blind (ordinary least-squares, OLS) and noise-aware (ranged major-axis, RMA) regression analyses. Gray points, all pairs of datasets; black points, pairs of datasets covering at least half the detected transcriptome ( > 2927 genes). Dotted line shows perfect agreement; dashed line marks integrated SCM estimate (1.69). C, Cumulative distributions of slopes computed by OLS and RMA regression (solid lines), with medians indicated by dotted lines and the SCM slope estimate indicated by a dashed line, cf. S2 Fig. D, Relative translational activity (TA) measured by ribosome density (normalized, median over five datasets, N = 4435) correlates strongly and nonlinearly with mRNA level (SCM estimate). Dotted gray line shows linear (slope = 1) fit. Solid gray line shows RMA regression fit (slope = 1.68). E, Relative translational efficiency (TE) (ribosome density divided by mRNA level) increases with SCM mRNA level (Spearman r = 0.65). F, RMA-estimated slopes for translational activity (ribosome density) and protein level versus SCM-estimated mRNA level (left) and recent RNA-seq mRNA level (right). Dashed line shows SCM estimate of protein vs. mRNA slope. G, Distributions of per-gene steady-state levels of mRNAs (blue; SCM estimates [solid] and independent recent RNA-seq estimates [dotted]), ribosomes on steady-state mRNAs (dotted yellow), and proteins (magenta).
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