Translational signatures and mRNA levels are highly correlated in human stably expressed genes

Folding Stability of Pax9 Intronic G-Quadruplex Correlates with Relative Molar Size in Eutherians

Eutherian dentition has been the focus of a great deal of studies in the areas of evolution, development, and genomics. The development of molar teeth is regulated by an antero-to-posterior cascade mechanism of activators and inhibitors molecules, where the relative sizes of the second (M2) and third (M3) molars are dependent of the inhibitory influence of the first molar (M1). Higher activator/inhibitor ratios will result in higher M2/M1 or M3/M1. Pax9 has been shown to play a key role in tooth development. We have previously shown that a G-quadruplex in the first intron of Pax9 can modulate the splicing efficiency. Using a sliding window approach with we analyzed the association of the folding energy (Mfe) of the Pax9 first intron with the relative molar sizes in 42 mammalian species, representing 9 orders. The Mfe of two regions located in the first intron of Pax9 were shown to be significantly associated with the M2/M1 and M3/M1 areas and mesiodistal lengths. The first region is located at the intron beginning and can fold into a stable G4 structure, whereas the second is downstream the G4 and 265 bp from intron start. Across species, the first intron of Pax9 varied in G-quadruplex structural stability. The correlations were further increased when the Mfe of the two sequences were added. Our results indicate that this region has a role in the evolution of the mammalian dental pattern by influencing the relative size of the molars.

Predictive biophysical modeling and understanding of the dynamics of mRNA translation and its evolution

mRNA translation is the fundamental process of decoding the information encoded in mRNA molecules by the ribosome for the synthesis of proteins. The centrality of this process in various biomedical disciplines such as cell biology, evolution and biotechnology, encouraged the development of dozens of mathematical and computational models of translation in recent years. These models aimed at capturing various biophysical aspects of the process. The objective of this review is to survey these models, focusing on those based and/or validated on real large-scale genomic data. We consider aspects such as the complexity of the models, the biophysical aspects they regard and the predictions they may provide. Furthermore, we survey the central systems biology discoveries reported on their basis. This review demonstrates the fundamental advantages of employing computational biophysical translation models in general, and discusses the relative advantages of the different approaches and the challenges in the field.

Quantitative dynamic modelling of the gene regulatory network controlling adipogenesis

Gene regulatory networks (GRNs) coherently coordinate the expressions of genes and control the behaviors of cellular systems. The complexity in modeling a quantitative GRN usually results from inaccurate parameter estimation, which is mostly due to small sample sizes. For better modeling of GRNs, we have designed a small-sample iterative optimization algorithm (SSIO) to quantitatively model GRNs with nonlinear regulatory relationships. The algorithm utilizes gene expression data as the primary input and it can be applied in case of small-sized samples. Using SSIO, we have quantitatively constructed the dynamic models for the GRNs controlling human and mouse adipogenesis. Compared with two other commonly-used methods, SSIO shows better performance with relatively lower residual errors, and it generates rational predictions on the adipocyte responses to external signals and steady-states. Sensitivity analysis further indicates the validity of our method. Several differences are observed between the GRNs of human and mouse adipocyte differentiations, suggesting the differences in regulatory efficiencies of the transcription factors between the two species. In addition, we use SSIO to quantitatively determine the strengths of the regulatory interactions as well as to optimize regulatory models. The results indicate that SSIO facilitates better investigation and understanding of gene regulatory processes.

Multiple correlations of mRNA expression and protein abundance in human cytokine profile

With the development of genomic study, researchers found that it is insufficient to predict protein expression from quantitative mRNA data in large scale, which is contrary to the traditional opinion that mRNA expression correlates with protein abundance at the single gene level. To try to solve the apparent conflicting views, here we set up a series of research models and chose soluble cytokines as targets. First, human peripheral blood mononuclear cell (PBMC) from one health donor was treated with 16 continuously changing conditions, the protein and mRNA profile were analyzed by multiplex Luminex and genomic microarray, respectively. Among the tested genes, around half mRNA correlated well with their corresponding proteins (ρ > 0.8), however if we put all the genes together, the correlation coefficient for the 16 conditions varied from 0.29 to 0.71. Second, PBMC from 14 healthy donors were stimulated with the same condition and it was found that the correlation coefficient went down (ρ < 0.6). Third, 28 rheumatoid arthritis (RA) patients were tested for their response to the same external stimuli and it turned out different individual displayed different protein expression pattern as expect. Lastly, autoimmune disease cohorts (8 diseases including RA, 103 patients in total) were assayed on the whole view. It was observed that there was still some similarity in the protein profile among patients from the single disease type although completely different patterns were displayed across different disease categories. This study built a good bridge between single gene analysis and the whole genome study and may give a reasonable explanation for the two conflicting views in current biological science.

Exposing synonymous mutations

Synonymous codon changes, which do not alter protein sequence, were previously thought to have no functional consequence. Although this concept has been overturned in recent years, there is no unique mechanism by which these changes exert biological effects. A large repertoire of both experimental and bioinformatic methods has been developed to understand the effects of synonymous variants. Results from this body of work have provided global insights into how biological systems exploit the degeneracy of the genetic code to control gene expression, protein folding efficiency, and the coordinated expression of functionally related gene families. Although it is now clear that synonymous variants are important in a variety of contexts, from human disease to the safety and efficacy of therapeutic proteins, there is no clear consensus on the approaches to identify and validate these changes. Here, we review the diverse methods to understand the effects of synonymous mutations.