Site-directed mutagenesis of α-<Emphasis Type="SmallCaps">l</Emphasis>-rhamnosidase from <Emphasis Type="Italic">Alternaria</Emphasis> sp. L1 to enhance synthesis yield of reverse hydrolysis based on rational design

The α-l-rhamnosidase catalyzes the hydrolytic release of rhamnose from polysaccharides and glycosides and is widely used due to its applications in a variety of industrial processes. Our previous work reported that a wild-type α-l-rhamnosidase (RhaL1) from Alternaria sp. L1 could synthesize rhamnose-containing chemicals (RCCs) though reverse hydrolysis reaction with inexpensive rhamnose as glycosyl donor. To enhance the yield of reverse hydrolysis reaction and to determine the amino acid residues essential for the catalytic activity of RhaL1, site-directed mutagenesis of 11 residues was performed in this study. Through rationally designed mutations, the critical amino acid residues which may form direct or solvent-mediated hydrogen bonds with donor rhamnose (Asp²⁵², Asp²⁵⁷, Asp²⁶⁴, Glu⁵³⁰, Arg⁵⁴⁸, His⁵⁵³, and Trp⁵⁵⁵) and may form the hydrophobic pocket in stabilizing donor (Trp²⁶¹, Tyr³⁰², Tyr³¹⁶, and Trp³⁶⁹) in active-site of RhaL1 were analyzed, and three positive mutants (W261Y, Y302F, and Y316F) with improved product yield stood out. From the three positive variants, mutant W261Y accelerated the reverse hydrolysis with a prominent increase (43.7 %) in relative yield compared to the wild-type enzyme. Based on the 3D structural modeling, we supposed that the improved yield of mutant W261Y is due to the adjustment of the spatial position of the putative catalytic acid residue Asp²⁵⁷. Mutant W261Y also exhibited a shift in the pH-activity profile in hydrolysis reaction, indicating that introducing of a polar residue in the active site cavity may affect the catalysis behavior of the enzyme.     

PAQR3 controls autophagy by integrating AMPK signaling to enhance ATG14L‐associated PI3K activity

The Beclin1–VPS34 complex is recognized as a central node in regulating autophagy via interacting with diverse molecules such as ATG14L for autophagy initiation and UVRAG for autophagosome maturation. However, the underlying molecular mechanism that coordinates the timely activation of VPS34 complex is poorly understood. Here, we identify that PAQR3 governs the preferential formation and activation of ATG14L‐linked VPS34 complex for autophagy initiation via two levels of regulation. Firstly, PAQR3 functions as a scaffold protein that facilitates the formation of ATG14L‐ but not UVRAG‐linked VPS34 complex, leading to elevated capacity of PI(3)P generation ahead of starvation signals. Secondly, AMPK phosphorylates PAQR3 at threonine 32 and switches on PI(3)P production to initiate autophagosome formation swiftly after glucose starvation. Deletion of PAQR3 leads to reduction of exercise‐induced autophagy in mice, accompanied by a certain degree of disaggregation of ATG14L‐associated VPS34 complex. Together, this study uncovers that PAQR3 can not only enhance the capacity of pro‐autophagy class III PI3K due to its scaffold function, but also integrate AMPK signal to activation of ATG14L‐linked VPS34 complex upon glucose starvation.     

FUNDC1 regulates mitochondrial dynamics at the ER–mitochondrial contact site under hypoxic conditions

In hypoxic cells, dysfunctional mitochondria are selectively removed by a specialized autophagic process called mitophagy. The ER–mitochondrial contact site (MAM) is essential for fission of mitochondria prior to engulfment, and the outer mitochondrial membrane protein FUNDC1 interacts with LC3 to recruit autophagosomes, but the mechanisms integrating these processes are poorly understood. Here, we describe a new pathway mediating mitochondrial fission and subsequent mitophagy under hypoxic conditions. FUNDC1 accumulates at the MAM by associating with the ER membrane protein calnexin. As mitophagy proceeds, FUNDC1/calnexin association attenuates and the exposed cytosolic loop of FUNDC1 interacts with DRP1 instead. DRP1 is thereby recruited to the MAM, and mitochondrial fission then occurs. Knockdown of FUNDC1, DRP1, or calnexin prevents fission and mitophagy under hypoxic conditions. Thus, FUNDC1 integrates mitochondrial fission and mitophagy at the interface of the MAM by working in concert with DRP1 and calnexin under hypoxic conditions in mammalian cells.     

The Significance of Ras Activity in Pancreatic Cancer Initiation

The genetic landscape of pancreatic cancer shows nearly ubiquitous mutations of K-RAS. However, oncogenic K-Ras^mt alone is not sufficient to lead to pancreatic ductal adenocarcinoma (PDAC) in either human or in genetically modified adult mouse models. Many stimulants, such as high fat diet, CCK, LPS, PGE2 and others, have physiological effects at low concentrations that are mediated in part through modest increases in K-Ras activity. However, at high concentrations, they induce inflammation that, in the presence of oncogenic K-Ras expression, substantially accelerates PDAC formation. The mechanism involves increased activity of oncogenic K-Ras^mt. Unlike what has been proposed in the standard paradigm for the role of Ras in oncogenesis, oncogenic K-Ras^mt is now known to not be constitutively active. Rather, it can be activated by standard mechanisms similar to wild-type K-Ras, but its activity is sustained for a prolonged period. Furthermore, if the level of K-Ras activity exceeds a threshold at which it begins to generate its own activators, then a feed-forward loop is formed between K-Ras activity and inflammation and pathological processes including oncogenesis are initiated. Oncogenic K-Ras^mt activation, a key event in PDAC initiation and development, is subject to complex regulatory mechanisms. Reagents which inhibit inflammation, such as the Cox2 inhibitor celecoxib, block the feed-forward loop and prevent induction of PDAC in models with endogenous oncogenic K-Ras^mt. Increased understanding of the role of activating and inhibitory mechanisms on oncogenic K-Ras^mt activity is of paramount importance for the development of preventive and therapeutic strategies to fight against this lethal disease.     

Surface glycoprotein of Borna disease virus mediates virus spread from cell to cell

Borna disease virus (BDV) is a non‐segmented negative‐stranded RNA virus that maintains a strictly neurotropic and persistent infection in affected end hosts. The primary target cells for BDV infection are brain cells, e.g. neurons and astrocytes. The exact mechanism of how infection is propagated between these cells and especially the role of the viral glycoprotein (GP) for cell–cell transmission, however, are still incompletely understood. Here, we use different cell culture systems, including rat primary astrocytes and mixed cultures of rat brain cells, to show that BDV primarily spreads through cell–cell contacts. We employ a highly stable and efficient peptidomimetic inhibitor to inhibit the furin‐mediated processing of GP and demonstrate that cleaved and fusion‐active GP is strictly necessary for the cell‐to‐cell spread of BDV. Together, our quantitative observations clarify the role of Borna disease virus‐glycoprotein for viral dissemination and highlight the regulation of GP expression as a potential mechanism to limit viral spread and maintain persistence. These findings furthermore indicate that targeting host cell proteases might be a promising approach to inhibit viral GP activation and spread of infection.     

Phospholipase Dε enhances Braasca napus growth and seed production in response to nitrogen availability

Phospholipase D (PLD), which hydrolyses phospholipids to produce phosphatidic acid, has been implicated in plant response to macronutrient availability in Arabidopsis. This study investigated the effect of increased PLDε expression on nitrogen utilization in Brassica napus to explore the application of PLDε manipulation to crop improvement. In addition, changes in membrane lipid species in response to nitrogen availability were determined in the oil seed crop. Multiple PLDε over expression (PLDε‐OE) lines displayed enhanced biomass accumulation under nitrogen‐deficient and nitrogen‐replete conditions. PLDε‐OE plants in the field produced more seeds than wild‐type plants but have no impact on seed oil content. Compared with wild‐type plants, PLDε‐OE plants were enhanced in nitrate transporter expression, uptake and reduction, whereas the activity of nitrite reductase was higher under nitrogen‐depleted, but not at nitrogen‐replete conditions. The level of nitrogen altered membrane glycerolipid metabolism, with greater impacts on young than mature leaves. The data indicate increased expression of PLDε has the potential to improve crop plant growth and production under nitrogen‐depleted and nitrogen‐replete conditions.     

Sonic hedgehog is a regulator of extracellular glutamate levels and epilepsy

Sonic hedgehog (Shh), both as a mitogen and as a morphogen, plays an important role in cell proliferation and differentiation during early development. Here, we show that Shh inhibits glutamate transporter activities in neurons, rapidly enhances extracellular glutamate levels, and affects the development of epilepsy. Shh is quickly released in response to epileptic, but not physiological, stimuli. Inhibition of neuronal glutamate transporters by Shh depends on heterotrimeric G protein subunit Gα_i and enhances extracellular glutamate levels. Inhibiting Shh signaling greatly reduces epileptiform activities in both cell cultures and hippocampal slices. Moreover, pharmacological or genetic inhibition of Shh signaling markedly suppresses epileptic phenotypes in kindling or pilocarpine models. Our results suggest that Shh contributes to the development of epilepsy and suppression of its signaling prevents the development of the disease. Thus, Shh can act as a modulator of neuronal activity, rapidly regulating glutamate levels and promoting epilepsy.     

Predicting existing targets for new drugs base on strategies for missing interactions

Background

There has been paid more and more attention to supervised classification models in the area of predicting drug-target interactions (DTIs). However, in terms of classification, unavoidable missing DTIs in data would cause three issues which have not yet been addressed appropriately by former approaches. Directly labeled as negatives (non-DTIs), missing DTIs increase the confusion of positives (DTIs) and negatives, aggravate the imbalance between few positives and many negatives, and are usually discriminated as highly-scored false positives, which influence the existing measures sharply.

Results

Under the framework of local classification model (LCM), this work focuses on the scenario of predicting how possibly a new drug interacts with known targets. To address the first two issues, two strategies, Spy and Super-target, are introduced accordingly and further integrated to form a two-layer LCM. In the bottom layer, Spy-based local classifiers for protein targets are built by positives, as well as reliable negatives identified among unlabeled drug-target pairs. In the top layer, regular local classifiers specific to super-targets are built with more positives generated by grouping similar targets and their interactions. Furthermore, to handle the third issue, an additional performance measure, Coverage, is presented for assessing DTI prediction. The experiments based on benchmark datasets are finally performed under five-fold cross validation of drugs to evaluate this approach. The main findings are concluded as follows. (1) Both two individual strategies and their combination are effective to missing DTIs, and the combination wins the best. (2) Having the advantages of less confusing decision boundary at the bottom layer and less biased decision boundary at the top layer, our two-layer LCM outperforms two former approaches. (3) Coverage is more robust to missing interactions than other measures and is able to evaluate how far one needs to go down the list of targets to cover all the proper targets of a drug.

Conclusions

Proposing two strategies and one performance measure, this work has addressed the issues derived from missing interactions, which cause confusing and biased decision boundaries in classifiers, as well as the inappropriate measure of predicting performance, in the scenario of predicting interactions between new drugs and known targets.