Non-canonical pathway network modelling and ubiquitination site prediction through homology modelling of NF-κB

PrUb-EL: A hybrid framework based on deep learning for identifying ubiquitination sites in Arabidopsis thaliana using ensemble learning strategy

Identification of ubiquitination sites is central to many biological experiments. Ubiquitination is a kind of post-translational protein modification (PTM). It is a key mechanism for increasing protein diversity and plays a vital role in regulating cell function. In recent years, many models have been developed to predict ubiquitination sites in humans, mice and yeast. However, few studies have predicted ubiquitination sites in Arabidopsis thaliana. In view of this, a deep network model named PrUb-EL is proposed to predict ubiquitination sites in Arabidopsis thaliana. Firstly, six features based on the protein sequence are extracted with amino acid index database (AAindex), dipeptide deviates from the expected mean (DDE), dipeptide composition (DPC), blocks substitution matrix (BLOSUM62), enhanced amino acid composition (EAAC) and binary encoding. Secondly, the synthetic minority over-sampling technique (SMOTE) is utilized to process the imbalanced data set. Then a new classifier named DG is presented, which includes Dense block, Residual block and Gated recurrent unit (GRU) block. Finally, each of six feature extraction methods is integrated into the DG model, and the ensemble learning strategy is used to gain the final prediction result. Experimental results show that PrUb-EL has good predictive ability with the accuracy (ACC) and area under the ROC curve (auROC) values of 91.00% and 97.70% using 5-fold cross-validation, respectively. Note that the values of ACC and auROC are 88.58% and 96.09% in the independent test, respectively. Compared with previous studies, our model has significantly improved performance thus it is an excellent method for identifying ubiquitination sites in Arabidopsis thaliana. The datasets and code used for the article are available at https://github.com/Tom-Wangy/PreUb-EL.git.

IL-33 induces NF-κB activation in ILC2 that can be suppressed by in vivo and ex vivo 17β-estradiol

Asthmatic women tend to develop severe airway disease in their reproductive years, and 30%-40% of asthmatic women have peri-menstrual worsening of asthma symptoms. This indicates that fluctuations in ovarian hormones are involved in advancement of asthmatic disease and exacerbation of symptoms. Group 2 innate lymphoid cells, or ILC2, are readily detected in allergic conditions, such as rhinosinusitis, in individuals that develop nasal polyps do to allergen exposures, and in allergic asthma. ILC2 are airway localized immune cells activated by IL-33, an innate cytokine that perpetuates allergic inflammation by driving the production of IL-5 and IL-13. We have previously shown that ILC2 are highly activated in naïve and ovalbumin (OVA) challenged, female BALB/c mice in comparison to male mice following stimulation with IL-33. Here, we investigated the effect of steady-state ovarian hormones on ILC2 and the NF-κB signaling pathway following OVA sensitization and challenge. We found that estrogen-treated ovariectomized mice (OVX-E2) that had been challenged with OVA had reduced IL-5 and IL-13 production by lung ILC2 as compared to lung ILC2 isolated from intact male and female sham-operated controls that had been treated with OVA. ILC2 were isolated from untreated animals and co-cultured ex vivo with and without estrogen plus IL-33. Those estrogen-treated ILC2 similarly produced less IL-5 and IL-13 in comparison to untreated, and had reduced NF-κB activation. Single-cell RNA sequencing showed that 120 genes were differentially expressed in male and female ILC2, and Nfkb1 was found among top-ranked regulatory interactions. Together, these results provide new insight into the suppressive effect of estrogen on ILC2 which may be protective in female asthmatics. Understanding further how estrogen modulates ILC2 may provide therapeutic targets for the treatment of allergic diseases.

c-Rel Is Required for IL-33-Dependent Activation of ILC2s

Group 2 innate lymphoid cells (ILC2s) are emerging as important cellular regulators of homeostatic and disease-associated immune processes. The cytokine interleukin-33 (IL-33) promotes ILC2-dependent inflammation and immunity, with IL-33 having been shown to activate NF-κB in a wide variety of cell types. However, it is currently unclear which NF-κB members play an important role in IL-33-dependent ILC2 biology. Here, we identify the NF-κB family member c-Rel as a critical component of the IL-33-dependent activation of ILC2s. Although c-Rel is dispensable for ILC2 development, it is critical for ILC2 function in the lung, with c-Rel-deficient (c-Rel-/- ) mice present a significantly reduced response to papain- and IL-33-induced lung inflammation. We also show that the absence of c-Rel reduces the IL-33-dependent expansion of ILC2 precursors and lower levels of IL-5 and IL-13 cytokine production by mature ILC2s in the lung. Together, these results identify the IL-33-c-Rel axis as a central control point of ILC2 activation and function.

UbiComb: A Hybrid Deep Learning Model for Predicting Plant-Specific Protein Ubiquitylation Sites

Protein ubiquitylation is an essential post-translational modification process that performs a critical role in a wide range of biological functions, even a degenerative role in certain diseases, and is consequently used as a promising target for the treatment of various diseases. Owing to the significant role of protein ubiquitylation, these sites can be identified by enzymatic approaches, mass spectrometry analysis, and combinations of multidimensional liquid chromatography and tandem mass spectrometry. However, these large-scale experimental screening techniques are time consuming, expensive, and laborious. To overcome the drawbacks of experimental methods, machine learning and deep learning-based predictors were considered for prediction in a timely and cost-effective manner. In the literature, several computational predictors have been published across species; however, predictors are species-specific because of the unclear patterns in different species. In this study, we proposed a novel approach for predicting plant ubiquitylation sites using a hybrid deep learning model by utilizing convolutional neural network and long short-term memory. The proposed method uses the actual protein sequence and physicochemical properties as inputs to the model and provides more robust predictions. The proposed predictor achieved the best result with accuracy values of 80% and 81% and F-scores of 79% and 82% on the 10-fold cross-validation and an independent dataset, respectively. Moreover, we also compared the testing of the independent dataset with popular ubiquitylation predictors; the results demonstrate that our model significantly outperforms the other methods in prediction classification results.

TmDorX2 positively regulates antimicrobial peptides in Tenebrio molitor gut, fat body, and hemocytes in response to bacterial and fungal infection

Dorsal, a member of the nuclear factor-kappa B (NF-κB) family of transcription factors, is a critical downstream component of the Toll pathway that regulates the expression of antimicrobial peptides (AMPs) against pathogen invasion. In this study, the full-length ORF of Dorsal was identified from the RNA-seq database of the mealworm beetle Tenebrio molitor (TmDorX2). The ORF of TmDorX2 was 1,482 bp in length, encoding a polypeptide of 493 amino acid residues. TmDorX2 contains a conserved Rel homology domain (RHD) and an immunoglobulin-like, plexins, and transcription factors (IPT) domain. TmDorX2 mRNA was detected in all developmental stages, with the highest levels observed in 3-day-old adults. TmDorX2 transcripts were highly expressed in the adult Malpighian tubules (MT) and the larval fat body and MT tissues. After challenging the larvae with Staphylococcus aureus and Escherichia coli, the TmDorX2 mRNA levels were upregulated 6 and 9 h post infection in the whole body, fat body, and hemocytes. Upon Candida albicans challenge, the TmDorX2 mRNA expression were found highest at 9 h post-infection in the fat body. In addition, TmDorX2-knockdown larvae exposed to E. coli, S. aureus, or C. albicans challenge showed a significantly increased mortality rate. Furthermore, the expression of 11 AMP genes was downregulated in the gut and fat body of dsTmDorX2-injected larvae upon E. coli challenge. After C. albicans and S. aureus challenge of dsTmDorX2-injected larvae, the expression of 11 and 10 AMPs was downregulated in the gut and fat body, respectively. Intriguingly, the expression of antifungal transcripts TmTenecin-3 and TmThaumatin-like protein-1 and -2 was greatly decreased in TmDorX2-silenced larvae in response to C. albicans challenge, suggesting that TmDorX2 regulates antifungal AMPs in the gut in response to C. albicans infection. The AMP expression profiles in the fat body, hemocytes, gut, and MTs suggest that TmDorX2 might have an important role in promoting the survival of T. molitor larvae against all mentioned pathogens.

NFκB and Kidney Injury

The global burden of chronic kidney disease will increase during the next century. As NFκB, first described more than 30 years ago, plays a major role in immune and non-immune-mediated diseases and in inflammatory and metabolic disorders, this review article summarizes current knowledge on the role of NFκB in in vivo kidney injury and describes the new and so far not completely understood crosstalk between canonical and non-canonical NFκB pathways in T-lymphocyte activation in renal disease.

Peptidyl arginine deiminase-4 exacerbates ischemic AKI by finding NEMO

Peptidyl arginine deiminase-4 (PAD4) catalyzes the conversion of peptidylarginine residues to peptidylcitrulline. We have previously shown that kidney ischemia-reperfusion (I/R) injury increases renal proximal tubular PAD4 expression and activity. Furthermore, kidney PAD4 plays a critical role in ischemic acute kidney injury (AKI) by promoting renal tubular inflammation, neutrophil infiltration, and NF-κB activation. However, the mechanisms of PAD4-mediated renal tubular inflammation and NF-κB activation after I/R remain unclear. Here, we show that recombinant PAD4 preferentially citrullinates recombinant IKKγ [also called NF-κB essential modulator (NEMO)] over recombinant IKKα or IKKβ. Consistent with this finding, PAD4 citrullinated renal proximal tubular cell IKKγ and promoted NF-κB activation via IκBα phosphorylation in vitro. NEMO inhibition with a selective NEMO-binding peptide attenuated PAD4-mediated proinflammatory cytokine mRNA induction in HK-2 cells. Moreover, NEMO inhibition did not affect proximal tubular cell survival, proliferation, or apoptosis, unlike global NF-κB inhibition. In vivo, NEMO-binding peptide treatment protected against ischemic AKI. Finally, NEMO-binding peptide attenuated recombinant PAD4-mediated exacerbation of ischemic AKI, renal tubular inflammation, and apoptosis. Taken together, our results show that PAD4 exacerbates ischemic AKI and inflammation by promoting renal tubular NF-κB activity and inflammation via NEMO citrullination. Targeting NEMO activation may serve as a potential therapy for this devastating clinical problem.

DeepUbi: a deep learning framework for prediction of ubiquitination sites in proteins

Background

Protein ubiquitination occurs when the ubiquitin protein binds to a target protein residue of lysine (K), and it is an important regulator of many cellular functions, such as signal transduction, cell division, and immune reactions, in eukaryotes. Experimental and clinical studies have shown that ubiquitination plays a key role in several human diseases, and recent advances in proteomic technology have spurred interest in identifying ubiquitination sites. However, most current computing tools for predicting target sites are based on small-scale data and shallow machine learning algorithms.

Results

As more experimentally validated ubiquitination sites emerge, we need to design a predictor that can identify lysine ubiquitination sites in large-scale proteome data. In this work, we propose a deep learning predictor, DeepUbi, based on convolutional neural networks. Four different features are adopted from the sequences and physicochemical properties. In a 10-fold cross validation, DeepUbi obtains an AUC (area under the Receiver Operating Characteristic curve) of 0.9, and the accuracy, sensitivity and specificity exceeded 85%. The more comprehensive indicator, MCC, reaches 0.78. We also develop a software package that can be freely downloaded from https://github.com/Sunmile/DeepUbi.

Conclusion

Our results show that DeepUbi has excellent performance in predicting ubiquitination based on large data.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2677-9) contains supplementary material, which is available to authorized users.

Santamarine Inhibits NF-кB and STAT3 Activation and Induces Apoptosis in HepG2 Liver Cancer Cells via Oxidative Stress

Sesquiterpene lactones have long been used in traditional Chinese medicines to treat inflammatory diseases. Recently, sesquiterpene lactone family compounds have been recognized as potential anticancer agents. Thus, it is necessary to explore new sesquiterpene lactones and their antitumor mechanism for cancer treatments. In the present study, we have explored the potential anti-cancer activity of a novel sesquiterpene lactone compound “santamarine” (STM) in HepG2 cells. It inhibited proliferation and induced apoptosis dose-dependently with IC₅₀ ~ 70 μM. Induction of apoptosis was found to be linked with increased reactive oxygen species (ROS) generation, decreased activity of thioredoxin reductase (TrxR), glutathione (GSH) depletion, mitochondrial membrane potential (ΔΨm) dissipation, Bcl-2 family proteins modulation, cytochrome c release, caspases-9, -8 and -3 activation and PARP cleavage. Further mechanistic study demonstrated that STM inhibited the constitutive and TNF-α-induced translocation of NF-кB into nucleus by decreasing phosphorylation of IkB-α. Moreover, STM inhibited STAT3 activation by decreasing phosphorylation at tyrosine705. NAC pretreatment reversed the effect of STM-mediated cell death, NF-кB inhibition and blockage of STAT3 activity, indicating the involvement of oxidative stress in STM-mediated anticancer activity. Further studies are needed to explore the exact molecular mechanism of STM-induced apoptosis to develop it into a lead for treatment of liver cancer in future.