RNA pull-down-confocal nanoscanning (RP-CONA), a novel method for studying RNA/protein interactions in cell extracts that detected potential drugs for Parkinson’s disease targeting RNA/HuR complexes

MicroRNAs (miRNAs, miRs) are a class of small non-coding RNAs that regulate gene expression through specific base-pair targeting. The functional mature miRNAs usually undergo a two-step cleavage from primary miRNAs (pri-miRs), then precursor miRNAs (pre-miRs). The biogenesis of miRNAs is tightly controlled by different RNA-binding proteins (RBPs). The dysregulation of miRNAs is closely related to a plethora of diseases. Targeting miRNA biogenesis is becoming a promising therapeutic strategy. HuR and MSI2 are both RBPs. MiR-7 is post-transcriptionally inhibited by the HuR/MSI2 complex, through a direct interaction between HuR and the conserved terminal loop (CTL) of pri-miR-7-1. Small molecules dissociating pri-miR-7/HuR interaction may induce miR-7 production. Importantly, the miR-7 levels are negatively correlated with Parkinson’s disease (PD). PD is a common, incurable neurodegenerative disease causing serious motor deficits. A hallmark of PD is the presence of Lewy bodies in the human brain, which are inclusion bodies mainly composed of an aberrantly aggregated protein named α-synuclein (α-syn). Decreasing α-syn levels or preventing α-syn aggregation are under investigation as PD treatments. Notably, α-syn is negatively regulated by several miRNAs, including miR-7, miR-153, miR-133b and others. One hypothesis is that elevating these miRNA levels can inhibit α-syn expression and ameliorate PD pathologies. In this project, we identified miR-7 as the most effective α-syn inhibitor, among the miRNAs that are downregulated in PD, and with α-syn targeting potentials. We also observed potential post-transcriptional inhibition on miR-153 biogenesis in neuroblastoma, which may help to uncover novel therapeutic targets towards PD. To identify miR-7 inducers that benefit PD treatment by repressing α-syn expression, we developed a novel technique RNA Pull-down Confocal Nanoscaning (RP-CONA) to monitor the binding events between pri-miR-7 and HuR. By attaching FITC-pri-miR-7-1-CTL-biotin to streptavidin-coated agarose beads and incubating them in human cultured cell lysates containing overexpressed mCherry-HuR, the bound RNA and protein can be visualised as quantifiable fluorescent rings in corresponding channels in a confocal high-content image system. A pri-miR-7/HuR inhibitor can decrease the relative mCherry/FITC intensity ratio in RP-CONA. With this technique, we performed several small-scale screenings and identified that a bioflavonoid, quercetin can largely dissociate the pri-miR-7/HuR interaction. Further studies proved that quercetin was an effective miR-7 inducer as well as α-syn inhibitor in HeLa cells. To understand the mechanism of quercetin mediated α-syn inhibition, we tested the effects of quercetin treatment with miR-7-1 and HuR knockout HeLa cells. We found that HuR was essential in this pathway, while miR-7 hardly contributed to the α-syn inhibition. HuR can directly bind an AU-rich element (ARE) at the 3’ untranslated region (3’-UTR) of α-syn mRNA and promote translation. We believe quercetin mainly disrupts the ARE/HuR interaction and disables the HuR-induced α-syn expression. In conclusion, we developed and optimised RP-CONA, an on-bead, lysate-based technique detecting RNA/protein interactions, as well as identifying RNA/protein modulators. With RP-CONA, we found quercetin inducing miR-7 biogenesis, and inhibiting α-syn expression. With these beneficial effects, quercetin has great potential to be applied in the clinic of PD treatment. Finally, RP-CONA can be used in many other RNA/protein interactions studies

RNA-targeted Therapies and High-throughput Screening Methods

RNA-binding proteins (RBPs) are involved in regulating all aspects of RNA metabolism, including processing, transport, translation, and degradation. Dysregulation of RNA metabolism is linked to a plethora of diseases, such as cancer, neurodegenerative diseases, and neuromuscular disorders. Recent years have seen a dramatic shift in the knowledge base, with RNA increasingly being recognised as an attractive target for precision medicine therapies. In this article, we are going to review current RNA-targeted therapies. Furthermore, we will scrutinise a range of drug discoveries targeting protein-RNA interactions. In particular, we will focus on the interplay between Lin28 and let-7, splicing regulatory proteins and survival motor neuron (SMN) pre-mRNA, as well as HuR, Musashi, proteins and their RNA targets. We will highlight the mechanisms RBPs utilise to modulate RNA metabolism and discuss current high-throughput screening strategies. This review provides evidence that we are entering a new era of RNA-targeted medicine

Rec4Ad: A Free Lunch to Mitigate Sample Selection Bias for Ads CTR Prediction in Taobao

Click-Through Rate (CTR) prediction serves as a fundamental component in online advertising. A common practice is to train a CTR model on advertisement (ad) impressions with user feedback. Since ad impressions are purposely selected by the model itself, their distribution differs from the inference distribution and thus exhibits sample selection bias (SSB) that affects model performance. Existing studies on SSB mainly employ sample re-weighting techniques which suffer from high variance and poor model calibration. Another line of work relies on costly uniform data that is inadequate to train industrial models. Thus mitigating SSB in industrial models with a uniform-data-free framework is worth exploring. Fortunately, many platforms display mixed results of organic items (i.e., recommendations) and sponsored items (i.e., ads) to users, where impressions of ads and recommendations are selected by different systems but share the same user decision rationales. Based on the above characteristics, we propose to leverage recommendations samples as a free lunch to mitigate SSB for ads CTR model (Rec4Ad). After elaborating data augmentation, Rec4Ad learns disentangled representations with alignment and decorrelation modules for enhancement. When deployed in Taobao display advertising system, Rec4Ad achieves substantial gains in key business metrics, with a lift of up to +6.6\% CTR and +2.9\% RPM

Integrating clinical and cross-cohort metagenomic features: a stable and non-invasive colorectal cancer and adenoma diagnostic model

Background: Dysbiosis is associated with colorectal cancer (CRC) and adenomas (CRA). However, the robustness of diagnostic models based on microbial signatures in multiple cohorts remains unsatisfactory.Materials and Methods: In this study, we used machine learning models to screen metagenomic signatures from the respective cross-cohort datasets of CRC and CRA (selected from CuratedMetagenomicData, each disease included 4 datasets). Then select a CRC and CRA data set from the CuratedMetagenomicData database and meet the requirements of having both metagenomic data and clinical data. This data set will be used to verify the inference that integrating clinical features can improve the performance of microbial disease prediction models.Results: After repeated verification, we selected 20 metagenomic features that performed well and were stably expressed within cross-cohorts to represent the diagnostic role of bacterial communities in CRC/CRA. The performance of the selected cross-cohort metagenomic features was stable for multi-regional and multi-ethnic populations (CRC, AUC: 0.817–0.867; CRA, AUC: 0.766–0.833). After clinical feature combination, AUC of our integrated CRC diagnostic model reached 0.939 (95% CI: 0.932–0.947, NRI=30%), and that of the CRA integrated model reached 0.925 (95%CI: 0.917–0.935, NRI=18%).Conclusion: In conclusion, the integrated model performed significantly better than single microbiome or clinical feature models in all cohorts. Integrating cross-cohort common discriminative microbial features with clinical features could help construct stable diagnostic models for early non-invasive screening for CRC and CRA

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

Treatment of Synthetic Ammonium Sulfate Wastewater by Mixed Culture of Chlorella pyrenoidosa and Enriched Nitrobacteria

Ammonium sulfate wastewater can cause eutrophication and black odor of water body. Although ammonia nitrogen can be used as nutrient of microalgae, high ammonia nitrogen levels could inhibit the growth of microalgae. Nitrobacteria can transform ammonia nitrogen into nitrate nitrogen. In this study, mono Chlorella pyrenoidosa culture (mono-C.py), synchronous mixed culture (mixed-a), and asynchronous mixed culture (mixed-b) systems were examined for their ability to treat ammonium sulfate wastewater. Nitrogen removal rate of mixed-b at the end of culture (52.96%) was higher than that of the mono-C.py (46.37%) and the mixed-a (39.11%). Higher total suspended solid concentration (2.40 g/L), crude protein yield (0.76 g/L), and heating value yield (35.73 kJ/L) were obtained in mixed-b, meanwhile with excellent settlement performance (91.43 +/- 0.51%). Mechanism analysis of settlement showed that the relative abundance of floc-forming-related bacteria Sphingopyxis and Acidovorax were increased generally, while nitrification/denitrifying members were decreased in mixed-b along with the culture proceeding

The joint effect of ammonium and pH on the growth of Chlorella vulgaris and ammonium removal in artificial liquid digestate

Although ammonium containing digestate is an ideal alternative medium for microalgae cultivation, high ammonium or unfavorable pH may inhibit microalgal growth. In this study, the joint effect of ammonium and pH on the growth of C. vulgaris and nutrient removal in artificial digestate was investigated. Our results show that ammonium and pH both affected algal growth, but free ammonia (FA) was the main actual inhibitory factor. Algal specific growth rate presented a negative correlation with FA and their relationship was well fitted by a linear regression model. Microalgal growth was little affected below 36.8 mg L-1 FA, while the obvious inhibition occurred at 184 mg L-1 FA (EC50), indicating a high tolerance to FA. Ammonium removal was well described by a first-order kinetics model. FA stress stimulated the production of extracellular organic matters (EOMs), which was good for micmalgae adaptation but adverse to pollutant removal

Performance of a microalgal-bacterial consortium system for the treatment of dairy-derived liquid digestate and biomass production

To enhance the treatment performance of dairy-derived liquid digestate (DLD) using microalgal-bacterial consortium system composed of Chlorella vulgaris and indigenous bacteria (CV), activated sludge was introduced to form a new microalgal-bacterial consortium system (Co-culture). The activated sludge shortened the lag phase and increased the specific growth rate of C. vulgaris (0.56 d(-1)). The biomass yield in the Co-culture was 2.72 g L-1, which was lower than that in the CV (3.24 g L-1), but the Co-culture had an improved COD (chemical oxygen demand) removal (25.26%) compared to the CV (13.59%). Quantitative PCR and metagenomic analyses demonstrated that microalgae also promoted bacterial growth, but influenced differently on the bacterial communities of indigenous bacteria and activated sludge. Compared with indigenous bacteria, activated sludge was more prone to forming a favorable symbiosis with C. vulgaris. These findings contribute to the construction of efficient microalgal-bacterial consortium system in wastewater treatment

Cultivation of Chlorella vulgaris on unsterilized dairy-derived liquid digestate for simultaneous biofuels feedstock production and pollutant removal

In order to assess viability of microalgae cultivation using unsterilized dairy-derived liquid digestate (DLD) for simultaneous biofuels feedstock production and contaminant removal, four DLD concentrations (25%, 50%, 75% and 100%) were used to grow Chlorella vulgaris in batch photobioreactors (PBRs). The 25% DLD was an ideal alternative medium in that high growth rate (0.69 d(-1)), high lipid productivity (112.9 mg L-1 d(-1)) as well as high nutrient removal were attained. The high DLD concentration caused inhibition of microalgal growth, where COD was more inhibitive than ammonium. The presence of bacteria did not influence microalgae production because of limited growth. Microalgal growth reduced the richness and diversity of bacterial community. Furthermore, the species of Bacteroidetes, Candidatus Saccharibacteria, and Chlamydiae rather than Proteobacteria benefited microalgal-bacterial symbiosis. These findings contribute to better application of microalgal-bacterial system for large-scale microalgae cultivation as well as environmental sustainability