Activity

Toxin-antitoxin (TA) systems, abundant in prokaryotes, are composed of a toxin gene and its cognate antitoxin. Several toxins are implied to affect the physiological state and stress tolerance of bacteria in a population. We previously identified a chromosomally encoded hok-sok type I TA system in Erwinia amylovora, the causative agent of fire blight disease on pome fruit trees. A high-level induction of the hok gene was lethal to E. amylovora cells through unknown mechanisms. The molecular targets or regulatory roles of Hok were unknown.

Here, we examined the physiological and transcriptomic changes of Erwinia amylovora cells expressing hok at subtoxic levels that were confirmed to confer no cell death, and at toxic levels that resulted in killing of cells. In both conditions, hok caused membrane rupture and collapse of the proton motive force in a subpopulation of E. amylovora cells. We demonstrated that induction of hok resulted in upregulation of ATP biosynthesis genes, and caused leakage of ATP from cells only at toxic levels. We showed that overexpression of the phage shock protein gene pspA largely reversed the cell death phenotype caused by high levels of hok induction. We also showed that induction of hok at a subtoxic level rendered a greater proportion of stationary phase E. amylovora cells tolerant to the antibiotic streptomycin.

We characterized the molecular mechanism of toxicity by high-level of hok induction and demonstrated that low-level expression of hok primes the stress responses of E. amylovora against further membrane and antibiotic stressors.

We characterized the molecular mechanism of toxicity by high-level of hok induction and demonstrated that low-level expression of hok primes the stress responses of E. amylovora against further membrane and antibiotic stressors.

Currently, large-scale gene expression profiling has been successfully applied to the discovery of functional connections among diseases, genetic perturbation, and drug action. To address the cost of an ever-expanding gene expression profile, a new, low-cost, high-throughput reduced representation expression profiling method called L1000 was proposed, with which one million profiles were produced. Although a set of ~ 1000 carefully chosen landmark genes that can capture ~ 80% of information from the whole genome has been identified for use in L1000, the robustness of using these landmark genes to infer target genes is not satisfactory. Therefore, more efficient computational methods are still needed to deep mine the influential genes in the genome.

Here, we propose a computational framework based on deep learning to mine a subset of genes that can cover more genomic information. Specifically, an AutoEncoder framework is first constructed to learn the non-linear relationship between genes, and then DeepLIFnctional connections.

Freezing injury, which is an important abiotic stress in horticultural crops, influences the growth and development and the production area of kiwifruit (Actinidia Lind1). Among Actinidia species, Actinidia arguta has excellent cold resistance, but knowledge relevant to molecular mechanisms is still limited. Understanding the mechanism underlying cold resistance in kiwifruit is important for breeding cold resistance.

In our study, a population resulting from the cross of A. arguta ‘Ruby-3’ × ’Kuilv’ male was generated for kiwifruit hardiness study, and 20 cold-tolerant and 20 cold-sensitive populations were selected from 492 populations according to their LT50. Then, we performed bulked segregant RNA-seq combined with single-molecule real-time sequencing to identify differentially expressed genes that provide cold hardiness. We found that the content of soluble sucrose and the activity of β-amylase were higher in the cold-tolerant population than in the cold-sensitive population. Upon - 30 °C low-temperattinidia and identified potential genes that are important for cold resistance in kiwifruit.

Brain Computer Interfaces (BCIs) translate the activity of the nervous system to a control signal which is interpretable for an external device. Using continuous motor BCIs, the user will be able to control a robotic arm or a disabled limb continuously. In addition to decoding the target position, accurate decoding of force amplitude is essential for designing BCI systems capable of performing fine movements like grasping. In this study, we proposed a stack Long Short-Term Memory (LSTM) neural network which was able to accurately predict the force amplitude applied by three freely moving rats using their Local Field Potential (LFP) signal.

The performance of the network was compared with the Partial Least Square (PLS) method. The average coefficient of correlation (r) for three rats were 0.67 in PLS and 0.73 in LSTM based network and the coefficient of determination ([Formula see text]) were 0.45 and 0.54 for PLS and LSTM based network, respectively. The network was able to accurately decode the force values without explicitly using time lags in the input features. Additionally, the proposed method was able to predict zero-force values very accurately due to benefiting from an output nonlinearity.

The proposed stack LSTM structure was able to predict applied force from the LFP signal accurately. In addition to higher accuracy, these results were achieved without explicitly using time lags in input features which can lead to more accurate and faster BCI systems.

The proposed stack LSTM structure was able to predict applied force from the LFP signal accurately. In addition to higher accuracy, these results were achieved without explicitly using time lags in input features which can lead to more accurate and faster BCI systems.

One striking feature of the large KRAB domain-containing zinc finger protein (KZFP) family is its rapid evolution, leading to hundreds of member genes with various origination time in a certain mammalian genome. However, a comprehensive genome-wide and across-taxa analysis of the structural and expressional features of KZFPs with different origination time is lacking. This type of analysis will provide valuable clues about the functional characteristics of this special family.

In this study, we found several conserved paradoxical phenomena about this issue. selleck 1) Ordinary young domains/proteins tend to be disordered, but most of KRAB domains are completely structured in 64 representative species across the superclass of Sarcopterygii and most of KZFPs are also highly structured, indicating their rigid and unique structural and functional characteristics; as exceptions, old-zinc-finger-containing KZFPs have relatively disordered KRAB domains and linker regions, contributing to diverse interacting partners and functions.