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SUMMARYHuman genome sequencing studies have identified numerous loci associated with complex diseases. However, translating human genetic and genomic findings to disease pathobiology and therapeutic discovery remains a major challenge at multiscale interactome network levels. Here, we present a deep-learning-based ensemble framework, termed PIONEER (Protein-proteinInteractiOn iNtErfacEpRediction), that accurately predicts protein binding partner-specific interfaces for all protein interactions in humans and seven other common model organisms. We demonstrate that PIONEER outperforms existing state-of-the-art methods. We further systematically validated PIONEER predictions experimentally through generating 2,395 mutations and testing their impact on 6,754 mutation-interaction pairs, confirming PIONEER-predicted interfaces are comparable in accuracy as experimentally determined interfaces using PDB co-complex structures. We show that disease-associated mutations are enriched in PIONEER-predicted protein-protein interfaces after mapping mutations from ∼60,000 germline exomes and ∼36,000 somatic genomes. We identify 586 significant protein-protein interactions (PPIs) enriched with PIONEER-predicted interface somatic mutations (termed oncoPPIs) from pan-cancer analysis of ∼11,000 tumor whole-exomes across 33 cancer types. We show that PIONEER-predicted oncoPPIs are significantly associated with patient survival and drug responses from both cancer cell lines and patient-derived xenograft mouse models. We identify a landscape of PPI-perturbing tumor alleles upon ubiquitination by E3 ligases, and we experimentally validate the tumorigenic KEAP1-NRF2 interface mutation p.Thr80Lys in non-small cell lung cancer. We show that PIONEER-predicted PPI-perturbing alleles alter protein abundance and correlates with drug responses and patient survival in colon and uterine cancers as demonstrated by proteogenomic data from the National Cancer Institute’s Clinical Proteomic Tumor Analysis Consortium. PIONEER, implemented as both a web server platform and a software package, identifies functional consequences of disease-associated alleles and offers a deep learning tool for precision medicine at multiscale interactome network levels.

Microplastics (MPs) are novel pollutants, which can carry toxic contaminants and are released in biota and accumulate. The adsorption behavior of MPs and aged MPs has attracted extensive attention. In this paper, the aging process of polystyrene (PS) and poly (butyleneadipate-co-terephthalate) (PBAT) plastics under ultraviolet (UV) irradiation at a high temperature and their adsorption properties for the contaminant diclofenac sodium (DCF) before and after aging was investigated. There are many factors affecting the adsorption capacity of MPs. In this experiment, three aspects of MPs, organic pollutants, and environmental factors are explored. The Freundlich model as well as the pseudosecondary kinetic model is more applicable to the process of DCF adsorption by MPs. The main effects of adsorption of organic pollutants by MPs are electrostatic interactions, hydrogen-halogen bonds, and hydrophobic interactions. The adsorption capacity of the UV-aged MPs on DCF is significantly enhanced, and the order of adsorption capacity is Q(A-PBAT) (27.65 mg/g) > Q (A-PS) (23.91 mg/g) > Q (PBAT) (9.30 mg/g) > Q (PS) (9.21 mg/g). The results show that more active sites are generated on the surface of MPs after aging, which can enhance their adsorption capacity for organic pollutants. This adsorption mechanism will increase their role as contaminant carriers in the aquatic food chain.

Emergence of new viral agents is driven by evolution of interactions between viral proteins and host targets. For instance, increased infectivity of SARS-CoV-2 compared to SARS-CoV-1 arose in part through rapid evolution along the interface between the spike protein and its human receptor ACE2, leading to increased binding affinity. To facilitate broader exploration of how pathogen-host interactions might impact transmission and virulence in the ongoing COVID-19 pandemic, we performed state-of-the-art interface prediction followed by molecular docking to construct a three-dimensional structural interactome between SARS-CoV-2 and human. We additionally carried out downstream meta-analyses to investigate enrichment of sequence divergence between SARS-CoV-1 and SARS-CoV-2 or human population variants along viral-human protein-interaction interfaces, predict changes in binding affinity by these mutations/variants and further prioritize drug repurposing candidates predicted to competitively bind human targets. We believe this resource ( http://3D-SARS2.yulab.org ) will aid in development and testing of informed hypotheses for SARS-CoV-2 etiology and treatments.

Different land use practices may improve soil quality or lead to soil deterioration. Recently, environmental problems, such as heavy pollution and soil erosion, have led to serious land degradation in the Taojia River basin. In this study, we explored the soil fertility characteristics (mechanical composition; pH; soil organic matter (SOM); soil total nitrogen (TN); and the activity of four enzymes, i.e., urease, hydrogen peroxide, alkaline phosphatase, and sucrose enzymes) under different types of land use in the Taojia River basin. Soil samples were taken from 0–10 cm, 10–20 cm, and 20–40 cm depths from four different land use types that were widely used in the Taojia river basin, including cultivated land, vegetable fields, woodlands, and wastelands. The results showed that the soil enzyme activity and the constituents of the soil were closely related and significantly affected each other (p < 0.05). Woodland soil exhibited the highest content of SOM in all soil depths. Soil total nitrogen mainly depended on the accumulation of biomass and the decomposition intensity of organic matter, so the changes in TN followed the trends of the changes in SOM. Woodland soil showed an improved mechanical composition. We were also able to observe an increased clay content in woodland soil. Woodland soil also exhibited the reversal of soil desertification and an increase in nutrient/water retention capacity. Therefore, an increase in woodland areas would be an appropriate goal in terms of land use in order to improve the eco-environmental quality of the Taojia River basin.

Automated performance metrics (APMs) objectively measure surgeon performance during a robot-assisted radical prostatectomy (RARP). Machine learning (ML) has shown that APMs, especially during the vesico-urethral anastomosis (VUA) of the RARP, are predictive of long-term outcomes such as continence recovery time. This study focuses on APMs during the VUA, specifically on stitch versus sub-stitch levels, to distinguish surgeon experience. During the VUA, APMs, recorded by a systems data recorder (Intuitive Surgical), were reported for each overall stitch (C(total)) and its individual components: needle handling/targeting (C(1)), needle driving (C(2)), and suture cinching (C(3)) (Figure 1A). These metrics were organized into three datasets (GlobalSet [whole stitch], RowSet [independent sub-stitches], and ColumnSet [associated sub-stitches] (Figure 1B) and applied to three ML models (AdaBoost, Gradient Boosting, and Random Forest) in order to solve two classifications tasks: experts (≥ 100 cases) vs. novices (

Purpose To assess differences in ocular biometric measurements between primary angle closure suspect (PACS) eyes and primary angle closure (PAC) and primary angle closure glaucoma (PACG) eyes. Design Cross-sectional study. Participants Patients with primary angle closure disease (PACD) were identified from the Chinese American Eye Study, a population-based study in Los Angeles, California. Methods Patients previously underwent complete ocular examinations including gonioscopy and anterior segment (AS)-OCT imaging with the Tomey CASIA SS-1000 (Tomey Corporation). Four AS-OCT images were analyzed per eye. Averaged and sectoral measurements of biometric parameters, including angle recess area (ARA), trabecular iris space area (TISA), iris area, iris curvature, lens vault, anterior chamber depth, and anterior chamber area, were compared between early PACD (PACS) and late PACD (PAC and PACG) groups. Machine learning classifiers that attempt to differentiate between early and late PACD eyes were developed by applying different regression algorithms to a training dataset of sectoral parameter measurements. Classifier performance was assessed using an independent test dataset. Main Outcome Measures Averaged and sectoral measurements of biometric parameters. Results Two hundred ninety-eight eyes (231 PACS, 67 PAC or PACG) of 298 patients were analyzed. No difference was found in averaged biometric measurements between the 2 groups before (P > 0.09) or after (P > 0.14) adjusting for age and gender. Differences (P Conclusions Differences in biometric measurements between subtypes of PACD eyes were small in a population-based cohort of Chinese Americans. The poor performance of classifiers based on these measurements highlights potential challenges of developing quantitative methods to detect late PACD.

Microplastics (MPs) and the typical hydrophilic organic pollutant Malachite green (MG) are frequently detected in sewage treatment plants. Potassium permanganate (KMnO4) pre-oxidation is an economical and effective technology in wastewater treatment. It is important to study the surface physicochemical characteristics of MPs and understand their fate in wastewater treatment plants after pre-oxidation. In this study, Polyvinyl chloride (PVC) MPs were treated by single and composite KMnO4 pre-oxidation with different pH values. After the pre-oxidation treatment, the appearance of Osingle bondMn spectra and surface nanoparticles indicated the oxides (MnO2) were produced on the MPs surface. Moreover, the adhesion of MnO2 is helpful to improve the hydrophilicity and adsorption capacity of MG. The adsorption capacity of pristine PVC for MG was 2.6 mg/g. But the adsorption capacity increased to 7.0 mg/g for single oxidation and 140.7 mg/g for composite oxidation, respectively. The desorption experiment results indicate the pre-oxidation process could reduce the release efficiency of MG from the PVC MPs due to the better binding of surface MnO2 nanoparticles to MG. However, the total desorption capacity is still high. which illustrates that there is a high potential risk of MG which can transfer from the surface of the PVC MPs to the gastrointestinal fluids. publishedVersion

Background Amniotic membrane extract enhances the rate of epithelialisation after corneal ulceration in several species but has not been studied in the equine cornea. Objectives To evaluate the effect of amniotic membrane extract on re-epithelialisation of equine corneal ulcers compared to ulcers treated with antibiotic, antifungal, and mydriatic medical therapy alone; to evaluate equine corneal healing after experimentally induced superficial ulceration. Study design Masked, randomised, controlled experimental trial. Methods Superficial, 8 mm corneal ulcers were created bilaterally in each horse. One eye was treated with amniotic membrane extract; the opposite was control. Both eyes were treated with medical therapy. Treatment eyes received amniotic membrane extract, and control eyes received the amniotic membrane extract vehicle. Ulcers were stained with fluorescein and photographed in 12-hour increments until completely healed. Ulcer surface area was determined by analysing photographs with ImageJ. A mixed linear model was used to compare ulcer surface area and hours until healing between treatment groups. A regression model was also used to calculate corneal re-epithelialisation rate over time. Results Regardless of therapy, healing occurred in two phases: an initial rapid phase of 0.88 mm2 /hr (95% CI: 0.81 - 0.94 mm2 /hr) for approximately 48-54 hours followed by a second, slow phase of 0.07 mm2 /hr (95% CI: 0.04 - 0.09 mm2 /hr). Most eyes healed within 135.5 ± 48.5 hours. Treatment (amniotic membrane extract vs. control) was not significantly associated with size of ulcers over time (P=0.984). Discomfort was minimal to absent in all horses. Main limitations Results achieved experimental studies may differ from outcomes in the clinical setting. Conclusions There was no significant difference in healing rate with addition of amniotic membrane extract to medical therapy for equine superficial corneal ulcers. A biphasic corneal healing process was observed, with an initial rapid phase followed by a slow phase. Further study will be needed to determine if amniotic membrane extract will be helpful for infected or malacic equine corneal ulcers.

In this article, a distributed and time-delayed k-winner-take-all (DT-kWTA) network is established and analyzed for competitively coordinated task assignment of a multirobot system. It is considered and designed from the following three aspects. First, a network is built based on a k-winner-take-all (kWTA) competitive algorithm that selects k maximum values from the inputs. Second, a distributed control strategy is used to improve the network in terms of communication load and computational burden. Third, the time-delayed problem prevalent in arbitrary causal systems (especially, in networks) is taken into account in the proposed network. This work combines distributed kWTA competition network with time delay for the first time, thus enabling it to better handle realistic applications than previous work. In addition, it theoretically derives the maximum delay allowed by the network and proves the convergence and robustness of the network. The results are applied to a multirobot system to conduct its robots' competitive coordination to complete the given task.

Motivation In silico drug target prediction provides valuable information for drug repurposing, understanding of side effects as well as expansion of the druggable genome. In particular, discovery of actionable drug targets is critical to developing targeted therapies for diseases. Results Here, we develop a robust method for drug target prediction by leveraging a class imbalance-tolerant machine learning framework with a novel training scheme. We incorporate novel features, including drug–gene phenotype similarity and gene expression profile similarity that capture information orthogonal to other features. We show that our classifier achieves robust performance and is able to predict gene targets for new drugs as well as drugs that potentially target unexplored genes. By providing newly predicted drug–target associations, we uncover novel opportunities of drug repurposing that may benefit cancer treatment through action on either known drug targets or currently undrugged genes. Supplementary information Supplementary data are available at Bioinformatics online.

Each human genome carries tens of thousands of coding variants. The extent to which this variation is functional and the mechanisms by which they exert their influence remains largely unexplored. To address this gap, we leverage the ExAC database of 60,706 human exomes to investigate experimentally the impact of 2009 missense single nucleotide variants (SNVs) across 2185 protein-protein interactions, generating interaction profiles for 4797 SNV-interaction pairs, of which 421 SNVs segregate at > 1% allele frequency in human populations. We find that interaction-disruptive SNVs are prevalent at both rare and common allele frequencies. Furthermore, these results suggest that 10.5% of missense variants carried per individual are disruptive, a higher proportion than previously reported; this indicates that each individual’s genetic makeup may be significantly more complex than expected. Finally, we demonstrate that candidate disease-associated mutations can be identified through shared interaction perturbations between variants of interest and known disease mutations., Low frequency coding single-nucleotide variants (SNVs) are predicted to disproportionately affect protein function. Here, the authors evaluate 2,009 missense SNVs across 2,185 protein-protein interactions using yeast two-hybrid and protein complementation assays and find that disruptive SNVs often occur in disease-associated genes.

Background: Mineralocorticoid receptor antagonists (MRA) improve outcomes in chronic kidney disease (CKD) and acute myocardial infarction (AMI) patients. However, the lack of evidence regarding long-term clinical outcomes in the use of MRA, including spironolactone, in patients with AMI combined with CKD.Objectives: This study aimed to investigate whether spironolactone could significantly reduce the risk of all-cause mortality and re-admission in patients with AMI and CKD.Methods: In this single center, observational, retrospective, registry based clinical study, a total of 2,465 AMI patients were initially screened; after excluding patients with estimated glomerular filtration rate more than 60 ml/min/1.73 m2, 360 patients in the standard treatment group and 200 patients in the spironolactone group met the criteria. All enrolled patients follow-up for 30 months. The primary outcomes were all-cause mortality and re-admission. The key safety outcome was hyperkalemia rates during the 30 months follow-up period.Results: 160 (44.4%) and 41 (20.5%) patients in the standard treatment and spironolactone groups died, respectively [hazard ratio (HR): 0.389; 95% confidence interval (CI): 0.276–0.548; p < 0.001]. Re-admission occurred in 217 (60.3%) and 95 (47.5%) patients in the standard treatment and spironolactone groups, respectively (HR: 0.664; 95% CI: 0.522–0.846; p = 0.004). The spironolactone group was divided into two based on the daily dose, low dose group (no more than 40 mg) and high dose group (more than 40 mg); the differences in the mortality rate between low dose group (16.7%) and the standard treatment group (44.4%) (HR: 0.309; 95% CI: 0.228–0.418; p < 0.001) and high dose group (34.1%) (HR: 0.429; 95% CI: 0.199–0.925; p = 0.007) were significant. The differences in re-hospitalization rate between low dose group (43.6%) and the standard treatment group (60.3%) (HR: 0.583; 95% CI: 0.457–0.744; p < 0.001) and high dose group (61.4%) (HR: 0.551; 95% CI: 0.326–0.930; p = 0.007) was significant. Hyperkalemia occurred in 18 (9.0%) and 18 (5.0%) patients in the spironolactone group and standard treatment group, respectively (HR: 1.879; 95% CI: 0.954–3.700; p = 0.068). Whereas, Hyperkalemia occurred in high dose group (20.5%) significantly more often than in the standard treatment group (p < 0.001) and low dose group (5.8%) (p = 0.003).Conclusion: Using MRA, such as spironolactone, may substantially reduce the risk of both all-cause mortality and re-admission in patients with AMI and CKD; the use of low-dose spironolactone has the best efficacy and safety. However, this was a relatively small sample size, single center, observational, retrospective, registry based clinical study and further prospective evaluation in adequately powered randomized trials were needed before further use of spironolactone in AMI with CKD population.

Bolstered by recent methodological and hardware advances, deep learning has increasingly been applied to biological problems and structural proteomics. Such approaches have achieved remarkable improvements over traditional machine learning methods in tasks ranging from protein contact map prediction to protein folding, prediction of protein-protein interaction interfaces, and characterization of protein-drug binding pockets. In particular, emergence of ab initio protein structure prediction methods including AlphaFold2 has revolutionized protein structural modeling. From a protein function perspective, numerous deep learning methods have facilitated deconvolution of the exact amino acid residues and protein surface regions responsible for binding other proteins or small molecule drugs. In this review, we provide a comprehensive overview of recent deep learning methods applied in structural proteomics.

The recent COVID-19 pandemic has sparked a global public health crisis. Vital to the development of informed treatments for this disease is a comprehensive understanding of the molecular interactions involved in disease pathology. One lens through which we can better understand this pathology is through the network of protein-protein interactions between its viral agent, SARS-CoV-2, and its human host. For instance, increased infectivity of SARS-CoV-2 compared to SARS-CoV can be explained by rapid evolution along the interface between the Spike protein and its human receptor (ACE2) leading to increased binding affinity. Sequence divergences that modulate other protein-protein interactions may further explain differences in transmission and virulence in this novel coronavirus. To facilitate these comparisons, we combined homology-based structural modeling with the ECLAIR pipeline for interface prediction at residue resolution, and molecular docking with PyRosetta. This enabled us to compile a novel 3D structural interactome meta-analysis for the published interactome network between SARS-CoV-2 and human. This resource includes docked structures for all interactions with protein structures, enrichment analysis of variation along interfaces, predicted ΔΔG between SARS-CoV and SARS-CoV-2 variants for each interaction, predicted impact of natural human population variation on binding affinity, and a further prioritized set of drug repurposing candidates predicted to overlap with protein interfaces†. All predictions are available online†for easy access and are continually updated when new interactions are published.† Some sections of this pre-print have been redacted to comply with current bioRxiv policy restricting the dissemination of purely in silico results predicting potential therapies for SARS-CoV-2 that have not undergone thorough peer-review. The results section titled “Prioritization of Candidate Inhibitors of SARS-CoV-2-Human Interactions Through Binding Site Comparison,” Figure 4, Supplemental Table 9, and all links to our web resource have been removed. Blank headers left in place to preserve structure and item numbering. Our full manuscript will be published in an appropriate journal following peer-review.

Acoustic-resolution photoacoustic microscopy (AR-PAM) is a promising tool for microvascular imaging. In AR-PAM, a focused transducer is typically used. Limited by acoustic diffraction, in-focus lateral resolution is dependent on the center frequency and numerical aperture of the transducer. On the other hand, out-of-focus lateral resolution will deteriorate, which can be restored to in-focus lateral resolution by synthetic aperture focusing technique (SAFT). Previously, we demonstrated that with prior knowledge of the point-spread function of the AR-PAM imaging system, combined SAFT and Richard-Lucy deconvolution can be applied to achieve super resolution (SR) beyond acoustic diffraction limit and to enhance signal-to-noise ratio (SNR) in both focal and out-of-focus regions. However, SNR of the original AR-PAM image highly affects the performance. Moreover, discontinuities arise in the line pattern that is originally continuous. In this study, we propose a novel algorithm, which combines a novel SAFT method and a directional model-based (D-M) deconvolution method, to break the acoustic diffraction limit. By using our algorithm, FWHM of 20 ~µm for AR-PAM system over DOF of ~1.8 mm is experimentally achieved. Compared with our previous work using Richard-Lucy deconvolution, the D-M deconvolution demonstrates the advantages in high SNR, and good line continuity. Compared with the directional SAFT method, our algorithm achieves SR and higher SNR.

Emergence of new viral agents is driven by evolution of interactions between viral proteins and host targets. For instance, increased infectivity of SARS-CoV-2 compared to SARS-CoV-1 arose in part through rapid evolution along the interface between the spike protein and its human receptor ACE2, leading to increased binding affinity. To facilitate broader exploration of how pathogen-host interactions might impact transmission and virulence in the ongoing COVID-19 pandemic, we performed state-of-the-art interface prediction followed by molecular docking to construct a three-dimensional structural interactome between SARS-CoV-2 and human. We additionally carried out downstream meta-analyses to investigate enrichment of sequence divergence between SARS-CoV-1 and SARS-CoV-2 or human population variants along viral-human protein-interaction interfaces, predict changes in binding affinity by these mutations/variants and further prioritize drug repurposing candidates predicted to competitively bind human targets. We believe this resource ( http://3D-SARS2.yulab.org ) will aid in development and testing of informed hypotheses for SARS-CoV-2 etiology and treatments.