An expanded regulatory network temporally controls Candida albicans biofilm formation

Candida albicans biofilms are composed of highly adherent and densely arranged cells with properties distinct from those of free‐floating (planktonic) cells. These biofilms are a significant medical problem because they commonly form on implanted medical devices, are drug resistant and are difficult to remove. C. albicans biofilms are not static structures; rather they are dynamic and develop over time. Here we characterize gene expression in biofilms during their development, and by comparing them to multiple planktonic reference states, we identify patterns of gene expression relevant to biofilm formation. In particular, we document time‐dependent changes in genes involved in adhesion and metabolism, both of which are at the core of biofilm development. Additionally, we identify three new regulators of biofilm formation, Flo8, Gal4, and Rfx2, which play distinct roles during biofilm development over time. Flo8 is required for biofilm formation at all time points, and Gal4 and Rfx2 are needed for proper biofilm formation at intermediate time points.     

Substrate Trapping Proteomics Reveals Targets of the βTrCP2/FBXW11 Ubiquitin Ligase

Defining the full complement of substrates for each ubiquitin ligase remains an important challenge. Improvements in mass spectrometry instrumentation and computation and in protein biochemistry methods have resulted in several new methods for ubiquitin ligase substrate identification. Here we used the parallel adapter capture (PAC) proteomics approach to study βTrCP2/FBXW11, a substrate adaptor for the SKP1–CUL1–F-box (SCF) E3 ubiquitin ligase complex. The processivity of the ubiquitylation reaction necessitates transient physical interactions between FBXW11 and its substrates, thus making biochemical purification of FBXW11-bound substrates difficult. Using the PAC-based approach, we inhibited the proteasome to “trap” ubiquitylated substrates on the SCF^FBXW11 E3 complex. Comparative mass spectrometry analysis of immunopurified FBXW11 protein complexes before and after proteasome inhibition revealed 21 known and 23 putatively novel substrates. In focused studies, we found that SCF^FBXW11 bound, polyubiquitylated, and destabilized RAPGEF2, a guanine nucleotide exchange factor that activates the small GTPase RAP1. High RAPGEF2 protein levels promoted cell-cell fusion and, consequently, multinucleation. Surprisingly, this occurred independently of the guanine nucleotide exchange factor (GEF) catalytic activity and of the presence of RAP1. Our data establish new functions for RAPGEF2 that may contribute to aneuploidy in cancer. More broadly, this report supports the continued use of substrate trapping proteomics to comprehensively define targets for E3 ubiquitin ligases. All proteomic data are available via ProteomeXchange with identifier PXD001062.     

工业微生物酸胁迫的耐受机制及改造途径

工业微生物在发酵生产过程中会面对发酵环境和自身产生的各类酸性物质,而这些酸性物质会影响工业微生物的生长和代谢,即产生酸胁迫。微生物通过调控胞内质子浓度、保护和修复生物大分子、改变细胞膜组分以及整体水平调控等耐受机制来应对酸胁迫。结合酸胁迫的各种耐受机制,利用自然筛选和人工改造的方法提高工业微生物的抗酸胁迫能力,为构建出更能适应工业生产条件的菌株提供理论依据。     

Application of artificial intelligence technique for modelling elastic properties of 2D nanoscale material

A novel computational approach is proposed to investigate the shear modulus of graphene nanostructures. In this approach, the factors that affect the shear modulus of graphene structures are analysed using an integrated artificial intelligence (AI) cluster comprising molecular dynamics (MD) and gene expression programming. The MD-based-AI approach has the ability to formulate the explicit relationship of shear modulus graphene nanostructure with respect to aspect ratio, temperature, number of atomic planes and vacancy defects. In addition, the shear modulus of graphene predicted using an integrated MD-based-AI model is in good agreement with that of experimental results obtained from the literature. The sensitivity and parametric analysis were further conducted to find out specific influence and variation of each of the input system parameters on the shear modulus of two graphene structures. It was found that the number of defects has the most dominating influence on the shear modulus of graphene nanostructure.     

Temperature dependence of vesicular dynamics at excitatory synapses of rat hippocampus

How vesicular dynamics parameters depend on temperature and how temperature affects the parameter change during prolonged high frequency stimulation was determined by fitting a model of vesicular storage and release to the amplitudes of the excitatory post-synaptic currents (EPSC) recorded from CA1 neurons in rat hippocampal slices. The temperature ranged from low (13 °C) to higher and more physiological temperature (34 °C). Fitting the model of vesicular storage and release to the EPSC amplitudes during a single pair of brief high–low frequency stimulation trains yields the estimates of all parameters of the vesicular dynamics, and with good precision. Both fractional release and replenishment rate decrease as the temperature rises. Change of the underlying ‘basic’ parameters (release coupling, replenishment coupling and readily releasable pool size), which the model-fitting also yields is complex. The replenishment coupling between the readily releasable pool (RRP) and resting pool increases with temperature (which renders the replenishment rate higher), but this is more than counterbalanced by greater RRP size (which renders the replenishment rate lower). Finally, during long, high frequency patterned stimulation that leads to significant synaptic depression, the replenishment rate decreases markedly and rapidly at low temperatures (<22 °C), but at high temperatures (>28 °C) the replenishment rate rises with stimulation, making synapses better able to maintain synaptic efficacy.