Allosteric collaboration between elongation factor G and the ribosomal L1 stalk directs tRNA movements during translation

Determining the mechanism by which transfer RNAs (tRNAs) rapidly and precisely transit through the ribosomal A, P and E sites during translation remains a major goal in the study of protein synthesis. Here, we report the real-time dynamics of the L1 stalk, a structural element of the large ribosomal subunit that is implicated in directing tRNA movements during translation. Within pre-translocation ribosomal complexes, the L1 stalk exists in a dynamic equilibrium between open and closed conformations. Binding of elongation factor G (EF-G) shifts this equilibrium towards the closed conformation through one of at least two distinct kinetic mechanisms, where the identity of the P-site tRNA dictates the kinetic route that is taken. Within post-translocation complexes, L1 stalk dynamics are dependent on the presence and identity of the E-site tRNA. Collectively, our data demonstrate that EF-G and the L1 stalk allosterically collaborate to direct tRNA translocation from the P to the E sites, and suggest a model for the release of E-site tRNA

Hierarchically-coupled hidden Markov models for learning kinetic rates from single-molecule data

We address the problem of analyzing sets of noisy time-varying signals that all report on the same process but confound straightforward analyses due to complex inter-signal heterogeneities and measurement artifacts. In particular we consider single-molecule experiments which indirectly measure the distinct steps in a biomolecular process via observations of noisy time-dependent signals such as a fluorescence intensity or bead position. Straightforward hidden Markov model (HMM) analyses attempt to characterize such processes in terms of a set of conformational states, the transitions that can occur between these states, and the associated rates at which those transitions occur; but require ad-hoc post-processing steps to combine multiple signals. Here we develop a hierarchically coupled HMM that allows experimentalists to deal with inter-signal variability in a principled and automatic way. Our approach is a generalized expectation maximization hyperparameter point estimation procedure with variational Bayes at the level of individual time series that learns an single interpretable representation of the overall data generating process.Comment: 9 pages, 5 figure

Graphical models for inferring single molecule dynamics

The recent explosion of experimental techniques in single molecule biophysics has generated a variety of novel time series data requiring equally novel computational tools for analysis and inference. This article describes in general terms how graphical modeling may be used to learn from biophysical time series data using the variational Bayesian expectation maximization algorithm (VBEM). The discussion is illustrated by the example of single-molecule fluorescence resonance energy transfer (smFRET) versus time data, where the smFRET time series is modeled as a hidden Markov model (HMM) with Gaussian observables. A detailed description of smFRET is provided as well. The VBEM algorithm returns the model’s evidence and an approximating posterior parameter distribution given the data. The former provides a metric for model selection via maximum evidence (ME), and the latter a description of the model’s parameters learned from the data. ME/VBEM provide several advantages over the more commonly used approach of maximum likelihood (ML) optimized by the expectation maximization (EM) algorithm, the most important being a natural form of model selection and a well-posed (non-divergent) optimization problem. The results demonstrate the utility of graphical modeling for inference of dynamic processes in single molecule biophysics

Low Phytoestrogen Levels in Feed Increase Fetal Serum Estradiol Resulting in the “Fetal Estrogenization Syndrome” and Obesity in CD-1 Mice

doi:10.1289/ehp.10448Although estrogenic chemicals can disrupt development of the reproductive system, there is debate about whether phytoestrogens in soy are beneficial, benign, or harmful. We compared reproductive and metabolic characteristics in male and female mice reared and maintained on non-soy low-phytoestrogen feed or soy-based high-phytoestrogen feed. Removing phytoestrogens from mouse feed produces an obese phenotype consistent with metabolic syndrome, and the associated reproductive system abnormalities are consistent with FES due to elevated endogenous fetal estradiol. Laboratory rodents may have become adapted to high-phytoestrogen intake over many generations of being fed soy-based commercial feed; removing all phytoestrogens from feed leads to alterations that could disrupt many types of biomedical research

DOK2 inhibits EGFR-mutated lung adenocarcinoma

Somatic mutations in the EGFR proto-oncogene occur in ~15% of human lung adenocarcinomas and the importance of EGFR mutations for the initiation and maintenance of lung cancer is well established from mouse models and cancer therapy trials in human lung cancer patients. Recently, we identified DOK2 as a lung adenocarcinoma tumor suppressor gene. Here we show that genomic loss of DOK2 is associated with EGFR mutations in human lung adenocarcinoma, and we hypothesized that loss of DOK2 might therefore cooperate with EGFR mutations to promote lung tumorigenesis. We tested this hypothesis using genetically engineered mouse models and find that loss of Dok2 in the mouse accelerates lung tumorigenesis initiated by oncogenic EGFR, but not that initiated by mutated Kras. Moreover, we find that DOK2 participates in a negative feedback loop that opposes mutated EGFR; EGFR mutation leads to recruitment of DOK2 to EGFR and DOK2-mediated inhibition of downstream activation of RAS. These data identify DOK2 as a tumor suppressor in EGFR-mutant lung adenocarcinoma

STAT4 deficiency reduces obesity-induced insulin resistance and adipose tissue inflammation

Signal transducer and activator of transcription (STAT) 4 is one of the seven members of the STAT family. STAT4 has a prominent role in mediating interleukin-12-induced T-helper cell type 1 lineage differentiation. T cells are key players in the maintenance of adipose tissue (AT) inflammation. The role of STAT4 in obesity and AT inflammation is unknown. We sought to determine the role of STAT4 in AT inflammation in obesity-induced insulin resistance. We studied STAT4-null mice on the C57Bl6/J background. We have found that STAT4(-/-)C57Bl6/J mice develop high-fat diet-induced obesity (DIO) similar to wild-type controls, but that they have significantly improved insulin sensitivity and better glucose tolerance. Using flow cytometry and real-time PCR, we show that STAT4(-/-) mice with DIO produce significantly reduced numbers of inflammatory cytokines and chemokines in adipocytes, have reduced numbers of CD8(+) cells, and display increased alternative (M2) macrophage polarization. CD8(+) cells, but not CD4(+) cells, from STAT4(-/-) mice displayed reduced in vitro migration. Also, we found that adipocyte inflammation is reduced and insulin signaling is improved in STAT4(-/-) mice with DIO. We have identified STAT4 as a key contributor to insulin resistance and AT inflammation in DIO. Targeting STAT4 activation could be a novel approach to reducing AT inflammation and insulin resistance in obesity

Interaction Analysis between HLA-DRB1 Shared Epitope Alleles and MHC Class II Transactivator CIITA Gene with Regard to Risk of Rheumatoid Arthritis

HLA-DRB1 shared epitope (SE) alleles are the strongest genetic determinants for autoantibody positive rheumatoid arthritis (RA). One of the key regulators in expression of HLA class II receptors is MHC class II transactivator (CIITA). A variant of the CIITA gene has been found to associate with inflammatory diseases