Linking genotypic and phenotypic changes in the E. coli long-term evolution experiment using metabolomics.

Changes in an organism's environment, genome, or gene expression patterns can lead to changes in its metabolism. The metabolic phenotype can be under selection and contributes to adaptation. However, the networked and convoluted nature of an organism's metabolism makes relating mutations, metabolic changes, and effects on fitness challenging. To overcome this challenge, we use the long-term evolution experiment (LTEE) with E. coli as a model to understand how mutations can eventually affect metabolism and perhaps fitness. We used mass spectrometry to broadly survey the metabolomes of the ancestral strains and all 12 evolved lines. We combined this metabolic data with mutation and expression data to suggest how mutations that alter specific reaction pathways, such as the biosynthesis of nicotinamide adenine dinucleotide, might increase fitness in the system. Our work provides a better understanding of how mutations might affect fitness through the metabolic changes in the LTEE and thus provides a major step in developing a complete genotype-phenotype map for this experimental system.

Linking genotypic and phenotypic changes in the E. coli Long-Term Evolution Experiment using metabolomics.

Changes in an organism's environment, genome, or gene expression patterns can lead to changes in its metabolism. The metabolic phenotype can be under selection and contributes to adaptation. However, the networked and convoluted nature of an organism's metabolism makes relating mutations, metabolic changes, and effects on fitness challenging. To overcome this challenge, we use the Long-Term Evolution Experiment (LTEE) with E. coli as a model to understand how mutations can eventually affect metabolism and perhaps fitness. We used mass-spectrometry to broadly survey the metabolomes of the ancestral strains and all 12 evolved lines. We combined this metabolic data with mutation and expression data to suggest how mutations that alter specific reaction pathways, such as the biosynthesis of nicotinamide adenine dinucleotide, might increase fitness in the system. Our work provides a better understanding of how mutations might affect fitness through the metabolic changes in the LTEE and thus provides a major step in developing a complete genotype-phenotype map for this experimental system.

The landscape of transcriptional and translational changes over 22 years of bacterial adaptation.

Organisms can adapt to an environment by taking multiple mutational paths. This redundancy at the genetic level, where many mutations have similar phenotypic and fitness effects, can make untangling the molecular mechanisms of complex adaptations difficult. Here, we use the Escherichia coli long-term evolution experiment (LTEE) as a model to address this challenge. To understand how different genomic changes could lead to parallel fitness gains, we characterize the landscape of transcriptional and translational changes across 12 replicate populations evolving in parallel for 50,000 generations. By quantifying absolute changes in mRNA abundances, we show that not only do all evolved lines have more mRNAs but that this increase in mRNA abundance scales with cell size. We also find that despite few shared mutations at the genetic level, clones from replicate populations in the LTEE are remarkably similar in their gene expression patterns at both the transcriptional and translational levels. Furthermore, we show that the majority of the expression changes are due to changes at the transcriptional level with very few translational changes. Finally, we show how mutations in transcriptional regulators lead to consistent and parallel changes in the expression levels of downstream genes. These results deepen our understanding of the molecular mechanisms underlying complex adaptations and provide insights into the repeatability of evolution.

The reason we look like our parents is because we inherit their genes. Genes carry the instructions for our cells to make messenger RNAs (mRNAs), which our cells then translate into proteins. Proteins, in turn, determine many of our features. This is true for all living organisms. Any changes ­ or mutations ­ in an organism's genes can lead to variations in its proteins, which can alter the organism's traits. This is the basis for evolution: mutations can lead to changes that allow an organism to better adapt to a new environment. This increases the organism's chances of survival and reproduction ­ its evolutionary 'fitness' ­ and makes it more likely that the mutation that generated the new trait in the first place will be passed on to the organism's descendants. However, just because two organisms have evolved similar traits to adapt to similar environments, it does not mean that the genetic basis for the adaptation is the same. For example, many animals share similar coloring to warn off predators, but the way that coloring is coded genetically is completely different. In species that are related (which share many of the same genes), this type of evolution is called 'parallel evolution', and it can make it difficult for scientists to understand how an organism evolved and pinpoint exactly what mutations are linked to which features. In 1988, scientists established the 'long-term evolution experiment' to tackle questions about how evolution works. The experiment, which has been running for over 30 years, consisted on tracking the evolution of 12 populations of Escherichia coli bacteria grown in separate flasks containing the same low-nutrient medium. The initial 12 populations were genetically identical, making this an ideal system to study parallel evolution, since all the populations had to evolve to adapt to the same environment, whilst isolated from each other. In previous experiments, scientists had already noted that while the different bacterial populations grew in similar ways, they had mostly different mutations. To better understand parallel evolution, Favate et al. analyzed the synthesis rates of RNA and proteins in the E. coli populations used in the long-term evolution experiment. They found that 22 years after the start of the experiment, all 12 populations produced more RNA, grew faster and were bigger. Additionally, while the different populations had accumulated few shared mutations after 22 years, they all shared similar patterns of RNA levels and protein synthesis rates. Further probing revealed that parallel evolution may be linked to how genes are regulated: mutations in regulators of related groups of genes involved in the same processes inside the cell can amplify the degree of parallel changes in organisms. This means that mutations in these genes may lead to similar traits. These findings provide insight into how parallel evolution arises in the long-term evolution experiment, and provides clues as to how the same traits can evolve several times.

Exploring Ribosome-Positioning on Translating Transcripts with Ribosome Profiling.

The emergence of ribosome profiling as a tool for measuring the translatome has provided researchers with valuable insights into the post-transcriptional regulation of gene expression. Despite the biological insights and technical improvements made since the technique was initially described by Ingolia et al. (Science 324(5924):218-223, 2009), ribosome profiling measurements and subsequent data analysis remain challenging. Here, we describe our lab's protocol for performing ribosome profiling in bacteria, yeast, and mammalian cells. This protocol has integrated elements from three published ribosome profiling methods. In addition, we describe a tool called RiboViz (Carja et al., BMC Bioinformatics 18:461, 2017) ( https://github.com/riboviz/riboviz ) for the analysis and visualization of ribosome profiling data. Given raw sequencing reads and transcriptome information (e.g., FASTA, GFF) for a species, RiboViz performs the necessary pre-processing and mapping of the raw sequencing reads. RiboViz also provides the user with various quality control visualizations.

O-GlcNAcylation alters the selection of mRNAs for translation and promotes 4E-BP1-dependent mitochondrial dysfunction in the retina.

Diabetes promotes the posttranslational modification of proteins by O-linked addition of GlcNAc (O-GlcNAcylation) to Ser/Thr residues of proteins and thereby contributes to diabetic complications. In the retina of diabetic mice, the repressor of mRNA translation, eIF4E-binding protein 1 (4E-BP1), is O-GlcNAcylated, and sequestration of the cap-binding protein eukaryotic translation initiation factor (eIF4E) is enhanced. O-GlcNAcylation has also been detected on several eukaryotic translation initiation factors and ribosomal proteins. However, the functional consequence of this modification is unknown. Here, using ribosome profiling, we evaluated the effect of enhanced O-GlcNAcylation on retinal gene expression. Mice receiving thiamet G (TMG), an inhibitor of the O-GlcNAc hydrolase O-GlcNAcase, exhibited enhanced retinal protein O-GlcNAcylation. The principal effect of TMG on retinal gene expression was observed in ribosome-associated mRNAs (i.e. mRNAs undergoing translation), as less than 1% of mRNAs exhibited changes in abundance. Remarkably, ∼19% of the transcriptome exhibited TMG-induced changes in ribosome occupancy, with 1912 mRNAs having reduced and 1683 mRNAs having increased translational rates. In the retina, the effect of O-GlcNAcase inhibition on translation of specific mitochondrial proteins, including superoxide dismutase 2 (SOD2), depended on 4E-BP1/2. O-GlcNAcylation enhanced cellular respiration and promoted mitochondrial superoxide levels in WT cells, and 4E-BP1/2 deletion prevented O-GlcNAcylation-induced mitochondrial superoxide in cells in culture and in the retina. The retina of diabetic WT mice exhibited increased reactive oxygen species levels, an effect not observed in diabetic 4E-BP1/2-deficient mice. These findings provide evidence for a mechanism whereby diabetes-induced O-GlcNAcylation promotes oxidative stress in the retina by altering the selection of mRNAs for translation.

Transforming growth factor-ß signaling induced during prostate cancer cell death and neuroendocrine differentiation is mediated by bone marrow stromal cells.

INTRODUCTION: Prostate cancer that has metastasized to bone undergoes critical interactions with bone marrow stromal cells (BMSCs), ultimately promoting tumor survival. Previous studies have shown that BMSCs secrete factors that promote prostate cancer apoptosis or neuroendocrine differentiation. Because of the significance of transforming growth factor-ß (TGF-ß) family cytokines in cytostasis and bone metastasis, the role of TGF-ß signaling in the context of prostate cancer-BMSC interactions was investigated. METHODS: The role of TGF-ß family signaling in BMSC-induced apoptosis of lineage-related prostate cancer cells was investigated in live/dead assays. SMAD phosphorylation or activity during apoptosis and neuroendocrine differentiation was investigated using immunofluorescence, Western blotting, and luciferase reporter assays, along with the ALK-4, -5, -7 kinase inhibitor, SB-431542. RESULTS: Treatment of castration-resistant prostate cancer cells with SB-431542 resulted in significant reduction of apoptosis mediated by HS-5 BMSCs, supporting the involvement of TGF-ß/SMAD signaling during this event. Interestingly, however, pre-treatment of BMSCs with TGF-ß1 (5 ng/mL) yielded a conditioned medium that elicited a marked reduction in prostate cancer death. Phosphorylated-SMAD2 (P-SMAD2) was activated in BMSC-triggered transdifferentiated prostate cancer cells, as demonstrated through immunoblotting and luciferase reporter assays. However, SB-431542 did not restore androgen receptor and prostate specific antigen levels down-regulated by BMSC-secreted factors. Prostate cancer cells induced to undergo neuroendocrine differentiation in a BMSC-independent mechanism also showed elevated levels of P-SMAD2. DISCUSSION: Collectively, our findings indicate that: (1) TGF-ß family cytokines or regulated factors secreted from BMSCs are involved in prostate cancer apoptosis; (2) TGF-ß signaling in prostate cancer cells is induced during neuroendocrine differentiation; and (3) TGF-ß1 stimulation of BMSCs alters paracrine signaling to create a permissive environment for prostate cancer survival, suggesting a mechanism for prostate cancer-mediated colonization of bone. CONCLUSIONS: TGF-ß signaling resulting in activation of SMAD2 in prostate cancer may be an indicator of cellular stress in the presence of toxic paracrine factors released from the bone marrow stroma, ultimately fostering prostate cancer colonization of bone.