Optimization of carbon and energy utilization through differential translational efficiency.

Control of translation is vital to all species. Here we employ a multi-omics approach to decipher condition-dependent translational regulation in the model acetogen Clostridium ljungdahlii. Integration of data from cells grown autotrophically or heterotrophically revealed that pathways critical to carbon and energy metabolism are under strong translational regulation. Major pathways involved in carbon and energy metabolism are not only differentially transcribed and translated, but their translational efficiencies are differentially elevated in response to resource availability under different growth conditions. We show that translational efficiency is not static and that it changes dynamically in response to mRNA expression levels. mRNAs harboring optimized 5'-untranslated region and coding region features, have higher translational efficiencies and are significantly enriched in genes encoding carbon and energy metabolism. In contrast, mRNAs enriched in housekeeping functions harbor sub-optimal features and have lower translational efficiencies. We propose that regulation of translational efficiency is crucial for effectively controlling resource allocation in energy-deprived microorganisms

Distinct cardiovascular phenotypes are associated with prognosis in systemic sclerosis: a cardiovascular magnetic resonance study

AIMS: Cardiovascular involvement in systemic sclerosis (SSc) is heterogeneous and ill-defined. This study aimed to: (i) discover cardiac phenotypes in SSc by cardiovascular magnetic resonance (CMR); (ii) provide a CMR-based algorithm for phenotypic classification; and (iii) examine for associations between phenotypes and mortality. METHODS AND RESULTS: A retrospective, single-centre, observational study of 260 SSc patients who underwent clinically indicated CMR including native myocardial T1 and T2 mapping from 2016 to 2019 was performed. Agglomerative hierarchical clustering using only CMR variables revealed five clusters of SSc patients with shared CMR characteristics: dilated right hearts with right ventricular failure (RVF); biventricular failure dilatation and dysfunction (BVF); and normal function with average cavity (NF-AC), normal function with small cavity (NF-SC), and normal function with large cavity (NF-LC) sizes. Phenotypes did not co-segregate with clinical or antibody classifications. A CMR-based decision tree for phenotype classification was created. Sixty-three (24%) patients died during a median follow-up period of 3.4 years. After adjustment for age and presence of pulmonary hypertension (PH), independent CMR predictors of all-cause mortality were native T1 (P  0.14). Hazard ratios (HR) were statistically significant for RVF (HR = 8.9, P < 0.001), BVF (HR = 5.2, P = 0.006), and NF-LC (HR = 4.9, P = 0.002) groups. The NF-LC group remained significantly predictive of mortality after adjusting for RVEF, native T1, and PH diagnosis (P = 0.0046). CONCLUSION: We identified five CMR-defined cardiac SSc phenotypes that did not co-segregate with clinical data and had distinct outcomes, offering opportunities for a more precision-medicine based management approach

Combating viral contaminants in CHO cells by engineering innate immunity

Viral contamination in biopharmaceutical manufacturing can lead to shortages in the supply of critical therapeutics. To facilitate the protection of bioprocesses, we explored the basis for the susceptibility of CHO cells to RNA virus infection. Upon infection with certain ssRNA and dsRNA viruses, CHO cells fail to generate a significant interferon (IFN) response. Nonetheless, the downstream machinery for generating IFN responses and its antiviral activity is intact in these cells: treatment of cells with exogenously-added type I IFN or poly I:C prior to infection limited the cytopathic effect from Vesicular stomatitis virus (VSV), Encephalomyocarditis virus (EMCV), and Reovirus-3 virus (Reo-3) in a STAT1-dependent manner. To harness the intrinsic antiviral mechanism, we used RNA-Seq to identify two upstream repressors of STAT1: Gfi1 and Trim24. By knocking out these genes, the engineered CHO cells exhibited activation of cellular immune responses and increased resistance to the RNA viruses tested. Thus, omics-guided engineering of mammalian cell culture can be deployed to increase safety in biotherapeutic protein production among many other biomedical applications

Model-based assessment of mammalian cell metabolic functionalities using omics data.

Omics experiments are ubiquitous in biological studies, leading to a deluge of data. However, it is still challenging to connect changes in these data to changes in cell functions because of complex interdependencies between genes, proteins, and metabolites. Here, we present a framework allowing researchers to infer how metabolic functions change on the basis of omics data. To enable this, we curated and standardized lists of metabolic tasks that mammalian cells can accomplish. Genome-scale metabolic networks were used to define gene sets associated with each metabolic task. We further developed a framework to overlay omics data on these sets and predict pathway usage for each metabolic task. We demonstrated how this approach can be used to quantify metabolic functions of diverse biological samples from the single cell to whole tissues and organs by using multiple transcriptomic datasets. To facilitate its adoption, we integrated the approach into GenePattern (www.genepattern.org-CellFie)

NCBI's Virus Discovery Hackathon:Engaging Research Communities to Identify Cloud Infrastructure Requirements

A wealth of viral data sits untapped in publicly available metagenomic data sets when it might be extracted to create a usable index for the virological research community. We hypothesized that work of this complexity and scale could be done in a hackathon setting. Ten teams comprised of over 40 participants from six countries, assembled to create a crowd-sourced set of analysis and processing pipelines for a complex biological data set in a three-day event on the San Diego State University campus starting 9 January 2019. Prior to the hackathon, 141,676 metagenomic data sets from the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) were pre-assembled into contiguous assemblies (contigs) by NCBI staff. During the hackathon, a subset consisting of 2953 SRA data sets (approximately 55 million contigs) was selected, which were further filtered for a minimal length of 1 kb. This resulted in 4.2 million (Mio) contigs, which were aligned using BLAST against all known virus genomes, phylogenetically clustered and assigned metadata. Out of the 4.2 Mio contigs, 360,000 contigs were labeled with domains and an additional subset containing 4400 contigs was screened for virus or virus-like genes. The work yielded valuable insights into both SRA data and the cloud infrastructure required to support such efforts, revealing analysis bottlenecks and possible workarounds thereof. Mainly: (i) Conservative assemblies of SRA data improves initial analysis steps; (ii) existing bioinformatic software with weak multithreading/multicore support can be elevated by wrapper scripts to use all cores within a computing node; (iii) redesigning existing bioinformatic algorithms for a cloud infrastructure to facilitate its use for a wider audience; and (iv) a cloud infrastructure allows a diverse group of researchers to collaborate effectively. The scientific findings will be extended during a follow-up event. Here, we present the applied workflows, initial results, and lessons learned from the hackathon

Essentiality, Biosynthesis, and Prediction of Glycosylation: A genetic and metabolic encoding of intercellular communication augmentation

Glycans, polysaccharides generalizable to a colored graph with colored edges, decorate proteins and cell membranes at the cell-environment interface, and modulate intercellular communication, from development to pathogenesis. Providing further challenges, glycan biosynthesis and cellular behavior are co-regulating systems. We begin with a discussion of how glycosylation contributes to extracellular responses and signaling. We further organize approaches for disentangling the roles of glycans in multicellular interactions using newly available datasets and tools, including glycan biosynthesis models, omics datasets, and systems-level analyses. Thus, emerging tools in big data analytics and systems biology are facilitating novel insights on glycans and their relationship with multicellular behavior.With mechanistic and abstract models of glycan biosynthesis, glycosylation remains unpredictable. While DNA, RNA, and proteins undergo templated synthesis, wherein request (i.e., base-pairing or codon) is made and fulfillment (i.e., nucleotide or amino acid addition), glycan biosynthesis is principally non-templated; metabolically constrained. The premise of the final chapter is that glycoconjugates and glycan-biosynthetic enzymes exist in physical space, have physical and chemical attributes, and therefore interact, attract, and repel each other. Because the glycoconjugate is the point of reference for a glycan and it displays the most variation in the system of glycan biosynthesis, we hypothesize that the glycoconjugate surface is the template-request of the glycan biosynthesis while metabolite availability is the context-dependent, and therefore confounding, fulfillment of that request. Towards expanding our understanding of glycan biosynthesis, we explore site-specific glycosylation databases, amino-acid substitution matrixes, pathogenic mutations and empirical observations of sequence-dependent glycosylation to support the separation of templated biosynthesis into two parts: the request (e.g., codon, or nucleotide) and the request fulfillment (e.g., addition of the corresponding amino acid or paired nucleotide).Here we explore multiple modes of glycomic analysis including glycan-level, flux-level, and substructure-level analysis. Each resolution provides varying degrees of accuracy and insight but none provided clear and global generalizability. In the final chapter, we establish a genetic encoding for glycosylation providing support for genetic and metabolic encoding. A templated encoding for glycan biosynthesis will make glycans predictable and broadly accessible beyond direct empirical observation