No abstract

Localized mRNAs are transported to sites of local protein synthesis in large ribonucleoprotein (RNP) granules, but their molecular composition is incompletely understood. Insulin-like growth factor II mRNA-binding protein (IMP) zip code-binding proteins participate in mRNA localization, and in motile cells IMP-containing granules are dispersed around the nucleus and in cellular protrusions. We isolated the IMP1-containing RNP granules and found that they represent a unique RNP entity distinct from neuronal hStaufen and/or fragile X mental retardation protein granules, processing bodies, and stress granules. Granules were 100 -300 nm in diameter and consisted of IMPs, 40 S ribosomal subunits, shuttling heterologous nuclear RNPs, poly ( Moreover the exon junction complex, which is deposited during splicing, is removed during the so-called pioneering round of translation (for reviews, see Refs. 1 and 2). Finally a particular mRNA becomes embroidered with nuclear RNA-binding proteins, and the specific ensemble may determine cytoplasmic events such as RNA localization, translation, and stability (for a review, see Ref.3). Cytoplasmic mRNPs may become destined for local translation. In support of this possibility, RNAs have been found in large mRNP granules, which are transported along cytoskeletal structures and anchored at their final destination. Messenger RNA localization has mainly been examined in polarized oocytes and neurons, and it has been proposed that local postsynaptic protein synthesis is required for synaptic plasticity (4). Previous studies have identified neuronal Staufen (5) and FMRP granules (6, 7), containing mRNAs, small and large ribosomal subunits, translation initiation factors including eIF4E and eIF2␣, and RNA-binding proteins (Refs. 8 -11; for a review, see Ref. 12). The protein composition of neuronal mRNP granules is to some degree overlapping with stress granules and processing bodies (P-bodies). The hallmark of stress granules is the presence of stalled 48 S initiation complexes and stress-dependent RNA-binding factors such as G3BP (13,14), whereas P-bodies contain components of the 5Ј-3Ј mRNA decay machinery and factors involved in nonsense-mediated decay (15).The zip code-binding proteins IMP1, -2, and -3 (human), ZBP1 (chicken), Vg1-RBP/Vera (Xenopus), and coding region determinant-binding protein (mouse) are members of the From the ‡Department

a b s t r a c tMetabolic flux analysis (MFA) is widely used to estimate intracellular fluxes. Conventional MFA, however, is limited to continuous cultures and the mid-exponential growth phase of batch cultures. Dynamic MFA (DMFA) has emerged to characterize time-resolved metabolic fluxes for the entire culture period. Here, the linear DMFA approach was extended using B-spline fitting (B-DMFA) to estimate mass balanced fluxes. Smoother fits were achieved using reduced number of knots and parameters. Additionally, computation time was greatly reduced using a new heuristic algorithm for knot placement. B-DMFA revealed that Chinese hamster ovary cells shifted from 37°C to 32°C maintained a constant IgG volumespecific productivity, whereas the productivity for the controls peaked during mid-exponential growth phase and declined afterward. The observed 42% increase in product titer at 32°C was explained by a prolonged cell growth with high cell viability, a larger cell volume and a more stable volume-specific productivity.

Constraint-based analysis of genomescale models (GEMs) arose shortly after the first genome sequences became available. As numerous reviews of the field show, this approach and methodology has proven to be successful in studying a wide range of biological phenomena (McCloskey et al, 2013;Bordbar et al, 2014). However, efforts to expand the user base are impeded by hurdles in correctly formulating these problems to obtain numerical solutions. In particular, in a study entitled "An exact arithmetic toolbox for a consistent and reproducible structural analysis of metabolic network models" (Chindelevitch et al, 2014), the authors apply an exact solver to 88 genome-scale constraint-based models of metabolism. The authors claim that COBRA calculations (Orth et al, 2010) are inconsistent with their results and that many published and actively

Several studies have shown that neither the formal representation nor the functional requirements of genome-scale metabolic models (GEMs) are precisely defined. Without a consistent standard, comparability, reproducibility, and interoperability of models across groups and software tools cannot be guaranteed.Here, we present memote (https://github.com/opencobra/memote) an open-source software containing a community-maintained, standardized set of me tabolic mo del te sts. The tests cover a range of aspects from annotations to conceptual integrity and can be extended to include experimental datasets for automatic model validation. In addition to testing a model once, memote can be configured to do so automatically, i.e., while building a GEM. A comprehensive report displays the model's performance parameters, which supports informed model development and facilitates error detection.Memote provides a measure for model quality that is consistent across reconstruction platforms and analysis software and simplifies collaboration within the community by establishing workflows for publicly hosted and version controlled models.A. Richelle: Lilly Innovation Fellowship Award B. García-Jiménez and J.

Acetogen bacteria are important for maintaining biosustainability as they can recycle gaseous C 1 waste feedstocks (e.g., industrial waste gases and syngas from gasified biomass or municipal solid waste) into fuels and chemicals. Notably, the acetogen Clostridium autoethanogenum is being used as a cell factory in industrial-scale gas fermentation.

⌘ Lead Contact 20The human transcriptome is so large, diverse and dynamic that, even after a decade of investigation by RNA sequencing (RNA-Seq), we are yet to resolve its true dimensions. RNA-Seq suffers from an expression-dependent bias that impedes characterization of low-abundance transcripts. We performed targeted single-molecule and short-read RNA-Seq to survey the transcriptional landscape of a single human chromosome (Hsa21) at unprecedented resolution. Our analysis reaches the lower limits of the 25 transcriptome, identifying a fundamental distinction between protein-coding and noncoding gene content: almost every noncoding exon undergoes alternative splicing, producing a seemingly limitless variety of isoforms. Analysis of syntenic regions of the mouse genome shows that few noncoding exons are shared between human and mouse, yet human splicing profiles are recapitulated on Hsa21 in mouse cells, indicative of regulation by a deeply conserved splicing code. We propose that noncoding exons are 30 functionally modular, with alternative splicing generating an enormous repertoire of potential regulatory RNAs and a rich transcriptional reservoir for gene evolution.

Changes in microbial metabolism have been used as the main approach to assess function and elucidate environmental and host-microbiome interactions. This can be hampered by uncharacterised metagenome species and lack of metabolic annotation. To address this, we present a comprehensive computational platform for population stratification based on microbiome composition, the underlying metabolic potential and generation of metagenome species and community level metabolic models. We revisit the concepts of enterotype and microbiome richness introducing the reactobiome as a stratification method to unravel the metabolic features of the human gut microbiome. The reactobiome encapsulates resilience and microbiome dysbiosis at a functional level. We describe five reactotypes in healthy populations from 16 countries, with specific amino acid, carbohydrate and xenobiotic metabolic features. The validity of the approach was tested to unravel host-microbiome and environmental interactions by applying the reactobiome analysis on a one-year Swedish longitudinal cohort, integrating gut metagenomics, plasma metabolomics and clinical data.