Gap genes mediate the division of the anterior-posterior axis of insects into different fates through regulating downstream hox genes. Decades of tinkering the segmentation gene network of Drosophila melanogaster led to the conclusion that gap genes are regulated (at least initially) through a threshold-based mechanism, guided by both anteriorly- and posteriorly-localized morphogen gradients. In this paper, we show that the response of the gap gene network in the beetle Tribolium castaneum upon perturbation is consistent with a threshold-free 'Speed Regulation' mechanism, in which the speed of a genetic cascade of gap genes is regulated by a posterior morphogen gradient. We show this by re-inducing the leading gap gene (namely, hunchback) resulting in the re-induction of the gap gene cascade at arbitrary points in time. This demonstrates that the gap gene network is self-regulatory and is primarily under the control of a posterior regulator in Tribolium and possibly other short/intermediate-germ insects.
Numerical data and sample sizes are all documented in Figure 4-source data 1, Figure 5-source data 1, and Figure 5-source data 2
- Ezzat El-Sherif
- Martin Klingler
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
- Naama Barkai, Weizmann Institute of Science, Israel
© 2018, Boos et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Splicing is highly regulated and is modulated by numerous factors. Quantitative predictions for how a mutation will affect precursor mRNA (pre-mRNA) structure and downstream function are particularly challenging. Here, we use a novel chemical probing strategy to visualize endogenous precursor and mature MAPT mRNA structures in cells. We used these data to estimate Boltzmann suboptimal structural ensembles, which were then analyzed to predict consequences of mutations on pre-mRNA structure. Further analysis of recent cryo-EM structures of the spliceosome at different stages of the splicing cycle revealed that the footprint of the Bact complex with pre-mRNA best predicted alternative splicing outcomes for exon 10 inclusion of the alternatively spliced MAPT gene, achieving 74% accuracy. We further developed a β-regression weighting framework that incorporates splice site strength, RNA structure, and exonic/intronic splicing regulatory elements capable of predicting, with 90% accuracy, the effects of 47 known and 6 newly discovered mutations on inclusion of exon 10 of MAPT. This combined experimental and computational framework represents a path forward for accurate prediction of splicing-related disease-causing variants.
Predicting the dynamics and functions of microbiomes constructed from the bottom-up is a key challenge in exploiting them to our benefit. Current models based on ecological theory fail to capture complex community behaviors due to higher order interactions, do not scale well with increasing complexity and in considering multiple functions. We develop and apply a long short-term memory (LSTM) framework to advance our understanding of community assembly and health-relevant metabolite production using a synthetic human gut community. A mainstay of recurrent neural networks, the LSTM learns a high dimensional data-driven non-linear dynamical system model. We show that the LSTM model can outperform the widely used generalized Lotka-Volterra model based on ecological theory. We build methods to decipher microbe-microbe and microbe-metabolite interactions from an otherwise black-box model. These methods highlight that Actinobacteria, Firmicutes and Proteobacteria are significant drivers of metabolite production whereas Bacteroides shape community dynamics. We use the LSTM model to navigate a large multidimensional functional landscape to design communities with unique health-relevant metabolite profiles and temporal behaviors. In sum, the accuracy of the LSTM model can be exploited for experimental planning and to guide the design of synthetic microbiomes with target dynamic functions.