Yanping Zhang

The importance of connexins (Cxs) in cochlear functions has been demonstrated by the finding that mutations in Cx genes cause a large proportion of sensorineural hearing loss cases. However, it is still unclear how Cxs contribute to the cochlear function. Recent data (33) obtained from Cx30 knockout mice showing that a reduction of Cx diversity in assembling gap junctions is sufficient to cause deafness suggest that functional interactions of different subtypes of Cxs may be essential in normal hearing. In this work we show that the two major forms of Cxs (Cx26 and Cx30) in the cochlea have overlapping expression patterns beginning at early embryonic stages. Cx26 and Cx30 were colocalized in most gap junction plaques in the cochlea, and their coassembly was tested by coimmunoprecipitation. To compare functional differences of gap junctions with different molecular configurations, homo- and heteromeric gap junctions composed of Cx26 and/or Cx30 were reconstituted by transfections in human embryonic kidney-293 cells. The ratio imaging technique and fluorescent tracer diffusion assays were used to assess the function of reconstituted gap junctions. Our results revealed that gap junctions with different molecular configurations show differences in biochemical coupling, and that intercellular Ca(2+) signaling across heteromeric gap junctions consisting of Cx26 and Cx30 was at least twice as fast as their homomerically assembled counterparts. Our data suggest that biochemical permeability and the dynamics of intercellular signaling through gap junction channels, in addition to gap junction-mediated intercellular ionic coupling, may be important factors to consider for studying functional roles of gap junctions in the cochlea.

Isothermal autocatalytic DNA circuits have been proven to be versatile and powerful biocomputing platforms by virtue of their self-sustainable and self-accelerating reaction profiles, yet they are currently constrained by their complicated designs, severe signal leakages, and unclear reaction mechanisms. Herein, we developed a simpler-yet-efficient autocatalytic assembly circuit (AAC) for highly robust bioimaging in live cells and mice. The scalable and sustainable AAC system was composed of a mere catalytic DNA assembly reaction with minimal strand complexity and, upon specific stimulation, could reproduce numerous new triggers to expedite the whole reaction. Through in-depth theoretical simulations and systematic experimental demonstrations, the catalytic efficiency of these reproduced triggers was found to play a vital role in the autocatalytic profile and thus could be facilely improved to achieve more efficient and characteristic autocatalytic signal amplification. Due to its exponentially high signal amplification and minimal reaction components, our self-stacking AAC facilitated the efficient detection of trace biomolecules with low signal leakage, thus providing great clinical diagnosis and therapeutic assessment potential.

The analyses of multi-omics data have revealed candidate genes for objective traits. However, they are integrated poorly, especially in non-model organisms, and they pose a great challenge for prioritizing candidate genes for follow-up experimental verification. Here, we present a general convolutional neural network model that integrates multi-omics information to prioritize the candidate genes of objective traits. By applying this model to Sus scrofa, which is a non-model organism, but one of the most important livestock animals, the model precision was 72.9%, recall 73.5%, and F1-Measure 73.4%, demonstrating a good prediction performance compared with previous studies in Arabidopsis thaliana and Oryza sativa. Additionally, to facilitate the use of the model, we present ISwine ( http://iswine.iomics.pro/ ), which is an online comprehensive knowledgebase in which we incorporated almost all the published swine multi-omics data. Overall, the results suggest that the deep learning strategy will greatly facilitate analyses of multi-omics integration in the future.