Immunogenicity and safety of an ORF7-deficient skin-attenuated and neuro-attenuated live vaccine for varicella: a randomised, double-blind, controlled, phase 2a trial.

BACKGROUND: The Oka varicella vaccine strain remains neurovirulent and can establish lifelong latent infection, raising safety concerns about vaccine-related herpes zoster. In this study, we aimed to evaluate the immunogenicity and safety of a skin-attenuated and neuro-attenuated varicella vaccine candidate (v7D vaccine). METHODS: We did this randomised, double-blind, controlled, phase 2a clinical trial in Jiangsu, China. Healthy children aged 3-12 years with no history of varicella infection or vaccination were enrolled and randomly assigned (1:1:1:1) to receive a single subcutaneous injection of the v7D vaccine at 3·3 log10 plaque forming units (PFU; low-dose v7D group), 3·9 log10 PFU (medium-dose v7D group), and 4·2 log10 PFU (high-dose v7D group), or the positive control varicella vaccine (vOka vaccine group). All the participants, laboratory personnel, and investigators other than the vaccine preparation and management staff were masked to the vaccine allocation. The primary outcome was assessment of the geometric mean titres (GMTs) and seroconversion rates of anti-varicella zoster virus immunoglobulin G (IgG) induced by different dose groups of v7D vaccine at 0, 42, 60, and 90 days after vaccination in the per-protocol set for humoral immune response analysis. Safety was a secondary outcome, focusing on adverse events within 42 days post-vaccination, and serious adverse events within 6 months after vaccination. This study was registered on Chinese Clinical Trial Registry, ChiCTR2000034434. FINDINGS: On Aug 18-21, 2020, 842 eligible volunteers were enrolled and randomly assigned treatment. After three participants withdrew, 839 received a low dose (n=211), middle dose (n=210), or high dose (n=210) of v7D vaccine, or the vOka vaccine (n=208). In the per-protocol set for humoral immune response analysis, the anti-varicella zoster virus IgG antibody response was highest at day 90. At day 90, the seroconversion rates of the low-dose, medium-dose, and high-dose groups of v7D vaccine and the positive control vOka vaccine group were 100·0% (95% CI 95·8-100·0; 87 of 87 participants), 98·9% (93·8-100·0; 87 of 88 participants), 97·8% (92·4-99·7; 91 of 93 participants), and 96·4% (89·8-99·2; 80 of 83 participants), respectively; the GMTs corresponded to values of 30·8 (95% CI 26·2-36·0), 31·3 (26·7-36·6), 28·2 (23·9-33·2), and 38·5 (31·7-46·7). The v7D vaccine, at low dose and medium dose, elicited a humoral immune response similar to that of the vOka vaccine. However, the high-dose v7D vaccine induced a marginally lower GMT compared with the vOka vaccine at day 90 (p=0·027). In the per-protocol set, the three dose groups of the v7D vaccine induced a similar humoral immune response at each timepoint, with no statistically significant differences. The incidence of adverse reactions in the low-dose, medium-dose, and high-dose groups of v7D vaccine was significantly lower than that in the vOka vaccine group (17% [35 of 211 participants], 20% [41 of 210 participants], and 13% [27 of 210 participants] vs 24% [50 of 208 participants], respectively; p=0·025), especially local adverse reactions (10% [22 of 211 participants], 14% [30 of 210 participants] and 9% [18 of 210 participants] vs 18% [38 of 208 participants], respectively; p=0·016). None of the serious adverse events were vaccine related. INTERPRETATION: The three dose groups of the candidate v7D vaccine exhibit similar humoral immunogenicity to the vOka vaccine and are well tolerated. These findings encourage further investigations on two-dose vaccination schedules, efficacy, and the potential safety benefit of v7D vaccine in the future. FUNDING: The National Natural Science Foundation of China, CAMS Innovation Fund for Medical Sciences, the Fundamental Research Funds for the Central Universities, and Beijing Wantai. TRANSLATION: For the Chinese translation of the abstract see Supplementary Materials section.

Single-sequence protein structure prediction by integrating protein language models.

Protein structure prediction has been greatly improved by deep learning in the past few years. However, the most successful methods rely on multiple sequence alignment (MSA) of the sequence homologs of the protein under prediction. In nature, a protein folds in the absence of its sequence homologs and thus, a MSA-free structure prediction method is desired. Here, we develop a single-sequence-based protein structure prediction method RaptorX-Single by integrating several protein language models and a structure generation module and then study its advantage over MSA-based methods. Our experimental results indicate that in addition to running much faster than MSA-based methods such as AlphaFold2, RaptorX-Single outperforms AlphaFold2 and other MSA-free methods in predicting the structure of antibodies (after fine-tuning on antibody data), proteins of very few sequence homologs, and single mutation effects. By comparing different protein language models, our results show that not only the scale but also the training data of protein language models will impact the performance. RaptorX-Single also compares favorably to MSA-based AlphaFold2 when the protein under prediction has a large number of sequence homologs.

Liquid lens based holographic camera for real 3D scene hologram acquisition using end-to-end physical model-driven network.

With the development of artificial intelligence, neural network provides unique opportunities for holography, such as high fidelity and dynamic calculation. How to obtain real 3D scene and generate high fidelity hologram in real time is an urgent problem. Here, we propose a liquid lens based holographic camera for real 3D scene hologram acquisition using an end-to-end physical model-driven network (EEPMD-Net). As the core component of the liquid camera, the first 10 mm large aperture electrowetting-based liquid lens is proposed by using specially fabricated solution. The design of the liquid camera ensures that the multi-layers of the real 3D scene can be obtained quickly and with great imaging performance. The EEPMD-Net takes the information of real 3D scene as the input, and uses two new structures of encoder and decoder networks to realize low-noise phase generation. By comparing the intensity information between the reconstructed image after depth fusion and the target scene, the composite loss function is constructed for phase optimization, and the high-fidelity training of hologram with true depth of the 3D scene is realized for the first time. The holographic camera achieves the high-fidelity and fast generation of the hologram of the real 3D scene, and the reconstructed experiment proves that the holographic image has the advantage of low noise. The proposed holographic camera is unique and can be used in 3D display, measurement, encryption and other fields.

Dual interface trapezium liquid prism with beam steering function.

In this paper, a dual interface trapezium liquid prism with beam steering function is implemented and analyzed. The electrowetting-on-dielectric method is used to perform the desired beam steering function without mechanical moving parts. This work examines deflection angles at different applied voltages to determine the beam steering range. The deflection angle can be experimentally measured from 0° to 3.43°. The proposed liquid prism can be applied in the field of optical manipulation, solar collecting system and so on.

ABAG-docking benchmark: a non-redundant structure benchmark dataset for antibody-antigen computational docking.

Accurate prediction of antibody-antigen complex structures is pivotal in drug discovery, vaccine design and disease treatment and can facilitate the development of more effective therapies and diagnostics. In this work, we first review the antibody-antigen docking (ABAG-docking) datasets. Then, we present the creation and characterization of a comprehensive benchmark dataset of antibody-antigen complexes. We categorize the dataset based on docking difficulty, interface properties and structural characteristics, to provide a diverse set of cases for rigorous evaluation. Compared with Docking Benchmark 5.5, we have added 112 cases, including 14 single-domain antibody (sdAb) cases and 98 monoclonal antibody (mAb) cases, and also increased the proportion of Difficult cases. Our dataset contains diverse cases, including human/humanized antibodies, sdAbs, rodent antibodies and other types, opening the door to better algorithm development. Furthermore, we provide details on the process of building the benchmark dataset and introduce a pipeline for periodic updates to keep it up to date. We also utilize multiple complex prediction methods including ZDOCK, ClusPro, HDOCK and AlphaFold-Multimer for testing and analyzing this dataset. This benchmark serves as a valuable resource for evaluating and advancing docking computational methods in the analysis of antibody-antigen interaction, enabling researchers to develop more accurate and effective tools for predicting and designing antibody-antigen complexes. The non-redundant ABAG-docking structure benchmark dataset is available at https://github.com/Zhaonan99/Antibody-antigen-complex-structure-benchmark-dataset.

Protective mechanisms of quercetin in neonatal rat brain injury induced by hypoxic-ischemic brain damage (HIBD).

Neonatal hypoxic-ischemic brain damage (HIBD) is a leading cause of infant mortality worldwide. This study explored whether quercetin (Que) exerts neuroprotective effects in a rat model of HIBD. A total of 36 seven-day-old Sprague-Dawley rats were divided into control, Que, HI, and HI + Que groups. The Rice method was used to establish HIBD in HI and HI + Que rats, which were treated with hypoxia (oxygen concentration of 8%) for 2 h after ligation of the left common carotid artery. The rats in the HI + Que group were intraperitoneally injected with Que (30 mg/kg) 1 h before hypoxia, and the rats in the Que group were only injected with the same amount of Que. Brain tissues were harvested 24 h postoperation and assessed by hematoxylin and eosin staining, 2,3,5-triphenyltetrazolium chloride staining, and terminal deoxynucleotidyl transferase dUTP nick-end labeling assay; relative gene and protein levels were evaluated by RT-qPCR, IHC, or western blot (WB) assay. Brain tissue morphologies were characterized by transmission electron microscopy (TEM); LC3B protein levels were assessed by immunofluorescence staining. Escape latencies and platform crossing times were significantly improved (p < .05) in HI + Que groups; infarct volume significantly decreased (p < .001), whereas the numbers of autophagic bodies and apoptotic cells increased and decreased, respectively. Meanwhile, NLRX1, ATG7, and Beclin1 expressions were significantly upregulated, and mTOR and TIM23 expressions, LC3B protein level, and LC 3II/LC 3I ratio were significantly downregulated. Que exerted neuroprotective effects in a rat model of HIBD by regulating NLRX1 and autophagy.

The epidemiology of varicella and effectiveness of varicella vaccine in Ganyu, China: a long-term community surveillance study.

BACKGROUND: The real-world data of long-term protection under moderate vaccination coverage is limited. This study aimed to evaluate varicella epidemiology and the long-term effectiveness under moderate coverage levels in Ganyu District, Lianyungang City, Jiangsu Province. METHODS: This was a population-based, retrospective birth cohort study based on the immunization information system (IIS) and the National Notifiable Disease Surveillance System (NNDSS) in Ganyu District. Varicella cases reported from 2009 to 2020 were included to describe the epidemiology of varicella, and eleven-year consecutive birth cohorts (2008-2018) were included to estimate the vaccine effectiveness (VE) of varicella by Cox regression analysis. RESULTS: A total of 155,232 native children and 3,251 varicella cases were included. The vaccination coverage was moderate with 37.1%, correspondingly, the annual incidence of varicella infection increased 4.4-fold from 2009 to 2020. A shift of the varicella cases to older age groups was observed, with the peak proportion of cases shifting from 5-6 year-old to 7-8 year-old. The adjusted effectiveness of one dose of vaccine waned over time, and the adjusted VE decreased from 72.9% to 41.8% in the one-dose group. CONCLUSIONS: The insufficient vaccination coverage (37.1%) may have contributed in part to the rising annual incidence of varicella infection, and a shift of varicella cases to older age groups occurred. The effectiveness of one dose of varicella vaccine was moderate and waned over time. It is urgent to increase varicella vaccine coverage to 80% to reduce the incidence of varicella and prevent any potential shift in the age at infection in China.

Integration of pre-trained protein language models into geometric deep learning networks.

Geometric deep learning has recently achieved great success in non-Euclidean domains, and learning on 3D structures of large biomolecules is emerging as a distinct research area. However, its efficacy is largely constrained due to the limited quantity of structural data. Meanwhile, protein language models trained on substantial 1D sequences have shown burgeoning capabilities with scale in a broad range of applications. Several preceding studies consider combining these different protein modalities to promote the representation power of geometric neural networks but fail to present a comprehensive understanding of their benefits. In this work, we integrate the knowledge learned by well-trained protein language models into several state-of-the-art geometric networks and evaluate a variety of protein representation learning benchmarks, including protein-protein interface prediction, model quality assessment, protein-protein rigid-body docking, and binding affinity prediction. Our findings show an overall improvement of 20% over baselines. Strong evidence indicates that the incorporation of protein language models' knowledge enhances geometric networks' capacity by a significant margin and can be generalized to complex tasks.

Head-to-head immunogenicity comparison of an Escherichia coli-produced 9-valent human papillomavirus vaccine and Gardasil 9 in women aged 18-26 years in China: a randomised blinded clinical trial.

BACKGROUND: An Escherichia coli-produced human papillomavirus (HPV) 16 and 18 bivalent vaccine (Cecolin) was prequalified by WHO in 2021. This study aimed to compare the immunogenicity of the E coli-produced HPV 9-valent vaccine Cecolin 9 (against HPV 6, 11, 16, 18, 31, 33, 45, 52, and 58) with Gardasil 9. METHODS: This was a randomised, single-blind trial conducted in China. Healthy non-pregnant women aged 18-26 years, who were not breastfeeding and with no HPV vaccination history, were enrolled in the Ganyu Centre for Disease Control and Prevention (Lianyungang City, Jiangsu Province, China). Women were stratified by age (18-22 years and 23-26 years) and randomly assigned (1:1) using a permutated block size of eight to receive three doses of Cecolin 9 or Gardasil 9 at day 0, day 45, and month 6. All participants, as well as study personnel without access to the vaccines, were masked. Neutralising antibodies were measured by a triple-colour pseudovirion-based neutralisation assay. The primary outcomes, seroconversion rates and geometric mean concentrations (GMCs) at month 7, were analysed in the per-protocol set for immunogenicity (PPS-I). Non-inferiority was identified for the lower limit of the 95% CI of the GMC ratio (Cecolin 9 vs Gardasil 9) at a margin of 0·5 and a seroconversion rate difference (Cecolin 9-Gardasil 9) at a margin of -5%. This study was registered at ClinicalTrials.gov (NCT04782895) and is completed. FINDINGS: From March 14 to 18, 2021, a total of 553 potential participants were screened, of which 244 received at least one dose of Cecolin 9 and 243 received at least one dose of Gardasil 9. The seroconversion rates for all HPV types in both groups were 100% in the PPS-I, with the values of the lower limits of 95% CIs for seroconversion rate differences ranging between -1·8% and -1·7%. The GMC ratios of five types were higher than 1·0, with the highest ratio, for HPV 58, at 1·65 (95% CI 1·38-1·97), and those of four types were lower than 1·0, with the lowest ratio, for HPV 11, at 0·79 (0·68-0·93). The incidence of adverse reactions in both groups was similar (43% [104/244] vs 47% [115/243]). INTERPRETATION: Cecolin 9 induced non-inferior HPV type-specific immune responses compared with Gardasil 9 and is a potential candidate to accelerate the elimination of cervical cancer by allowing for global accessibility to 9-valent HPV vaccinations, especially in low-income and middle-income countries. FUNDING: National Natural Science Foundation, Fujian Provincial Natural Science Foundation, Xiamen Science and Technology Plan Project, Fundamental Research Funds for the Central Universities, CAMS Innovation Fund for Medical Sciences of China, and Xiamen Innovax.

Improved the heterodimer protein complex prediction with protein language models.

AlphaFold-Multimer has greatly improved the protein complex structure prediction, but its accuracy also depends on the quality of the multiple sequence alignment (MSA) formed by the interacting homologs (i.e. interologs) of the complex under prediction. Here we propose a novel method, ESMPair, that can identify interologs of a complex using protein language models. We show that ESMPair can generate better interologs than the default MSA generation method in AlphaFold-Multimer. Our method results in better complex structure prediction than AlphaFold-Multimer by a large margin (+10.7% in terms of the Top-5 best DockQ), especially when the predicted complex structures have low confidence. We further show that by combining several MSA generation methods, we may yield even better complex structure prediction accuracy than Alphafold-Multimer (+22% in terms of the Top-5 best DockQ). By systematically analyzing the impact factors of our algorithm we find that the diversity of MSA of interologs significantly affects the prediction accuracy. Moreover, we show that ESMPair performs particularly well on complexes in eucaryotes.

An end-to-end deep learning method for protein side-chain packing and inverse folding.

Protein side-chain packing (PSCP), the task of determining amino acid side-chain conformations given only backbone atom positions, has important applications to protein structure prediction, refinement, and design. Many methods have been proposed to tackle this problem, but their speed or accuracy is still unsatisfactory. To address this, we present AttnPacker, a deep learning (DL) method for directly predicting protein side-chain coordinates. Unlike existing methods, AttnPacker directly incorporates backbone 3D geometry to simultaneously compute all side-chain coordinates without delegating to a discrete rotamer library or performing expensive conformational search and sampling steps. This enables a significant increase in computational efficiency, decreasing inference time by over 100× compared to the DL-based method DLPacker and physics-based RosettaPacker. Tested on the CASP13 and CASP14 native and nonnative protein backbones, AttnPacker computes physically realistic side-chain conformations, reducing steric clashes and improving both rmsd and dihedral accuracy compared to state-of-the-art methods SCWRL4, FASPR, RosettaPacker, and DLPacker. Different from traditional PSCP approaches, AttnPacker can also codesign sequences and side chains, producing designs with subnative Rosetta energy and high in silico consistency.

Nine undescribed oxidized ergosterols from the endophytic fungus Penicillium herquei and their cytotoxic activity.

A chemical investigation of the EtOAc extract of the endophytic fungus Penicillium herquei led to the isolation of nine undescribed oxidized ergosterols, penicisterols A-I (1-9), along with ten known analogs (10-19). Their structures and absolute configurations were elucidated by a combination of spectroscopic data analysis, quantum-chemical electronic circular dichroism (ECD) calculations and comparisons, [Rh2(OCOCF3)4]-induced ECD experiments, DFT-calculated 13C chemical shifts and DP4+ probability analysis. Compound 1 was a rare example of ergosterol in which the bond between C-8 and C-9 was cleaved to form an enol ether. Moreover, compound 2 possessed a rare (2,5-dioxo-4-imidazolidinyl)-carbamic acid ester group substituted at C-3. All undescribed oxidized ergosterols (1-9) were evaluated for their cytotoxic activity against five cancer cell lines including 4T1 (mouse breast carcinoma), A549 (human pulmonary carcinoma), HCT-116 (human colorectal carcinoma), HeLa (human cervical carcinoma) and Hepg2 (human hepatoma carcinoma) cells. Compounds 2 and 3 displayed moderate cytotoxic activity against 4T1, A549 and HeLa cells, with IC50 values ranging from 17.22 to 31.35 µM.

[Efficacy and mechanism of scalp acupuncture for spastic cerebral palsy].

OBJECTIVE: To observe the clinical efficacy of scalp acupuncture for spastic cerebral palsy (CP), and to explore its possible mechanism based on brain white matter fiber bundles, nerve growth related proteins and inflammatory cytokines. METHODS: A total of 90 children with spastic CP were randomly divided into a scalp acupuncture group and a sham scalp acupuncture group, 45 cases in each group. The children in the two groups were treated with conventional comprehensive rehabilitation treatment. The children in the scalp acupuncture group were treated with scalp acupuncture at the parietal temporal anterior oblique line, parietal temporal posterior oblique line on the affected side, and parietal midline. The children in the sham scalp acupuncture group were treated with scalp acupuncture at 1 cun next to the above point lines. The needles were kept for 30 min, once a day, 5 days a week, for 12 weeks. Before and after treatment, the diffusion tensor imaging (DTI) indexes of magnetic resonance (FA values of corticospinal tract [CST], anterior limb of internal capsule [ICAL], posterior limb of internal capsule [ICPL], genu of internal capsule [ICGL], genu of corpus callosum [GCC], body of corpus callosum [BCC] and splenium of corpus callosum [SCC]), serum levels of nerve growth related proteins (neuron-specific enolase [NSE], glial fibrillary acidic protein [GFAP], myelin basic protein [MBP], ubiquitin carboxy terminal hydrolase-L1 [UCH-L1]) and inflammatory cytokines (interleukin 33 [IL-33], tumor necrosis factor α [TNF-α]), cerebral hemodynamic indexes (mean blood flow velocity [Vm], systolic peak flow velocity [Vs] and resistance index [RI], pulsatility index [PI] of cerebral artery), surface electromyography (SEMG) signal indexes (root mean square [RMS] values of rectus femoris, hamstring muscles, gastrocnemius muscles, tibialis anterior muscles), gross motor function measure-88 (GMFM-88) score, modified Ashworth scale (MAS) score, ability of daily living (ADL) score were observed in the two groups. The clinical effect of the two groups was compared. RESULTS: After treatment, the FA value of each fiber bundle, Vm, Vs, GMFM-88 scores and ADL scores in the two groups were higher than those before treatment (P<0.05), and the above indexes in the scalp acupuncture group were higher than those in the sham scalp acupuncture group (P<0.05). After treatment, the serum levels of NSE, GFAP, MBP, UCH-L1, IL-33, TNF-α as well as RI, PI, MAS scores and RMS values of each muscle were lower than those before treatment (P<0.05), and the above indexes in the scalp acupuncture group were lower than those in the sham scalp acupuncture group (P<0.05). The total effective rate was 95.6% (43/45) in the scalp acupuncture group, which was higher than 82.2% (37/45) in the sham scalp acupuncture group (P<0.05). CONCLUSION: Scalp acupuncture could effectively treat spastic CP, improve the cerebral hemodynamics and gross motor function, reduce muscle tension and spasticity, and improve the ability of daily life. The mechanism may be related to repairing the white matter fiber bundles and regulating the levels of nerve growth related proteins and inflammatory cytokines.

Improving protein structure prediction using templates and sequence embedding.

MOTIVATION: Protein structure prediction has been greatly improved by deep learning, but the contribution of different information is yet to be fully understood. This article studies the impacts of two kinds of information for structure prediction: template and multiple sequence alignment (MSA) embedding. Templates have been used by some methods before, such as AlphaFold2, RoseTTAFold and RaptorX. AlphaFold2 and RosetTTAFold only used templates detected by HHsearch, which may not perform very well on some targets. In addition, sequence embedding generated by pre-trained protein language models has not been fully explored for structure prediction. In this article, we study the impact of templates (including the number of templates, the template quality and how the templates are generated) on protein structure prediction accuracy, especially when the templates are detected by methods other than HHsearch. We also study the impact of sequence embedding (generated by MSATransformer and ESM-1b) on structure prediction. RESULTS: We have implemented a deep learning method for protein structure prediction that may take templates and MSA embedding as extra inputs. We study the contribution of templates and MSA embedding to structure prediction accuracy. Our experimental results show that templates can improve structure prediction on 71 of 110 CASP13 (13th Critical Assessment of Structure Prediction) targets and 47 of 91 CASP14 targets, and templates are particularly useful for targets with similar templates. MSA embedding can improve structure prediction on 63 of 91 CASP14 (14th Critical Assessment of Structure Prediction) targets and 87 of 183 CAMEO targets and is particularly useful for proteins with shallow MSAs. When both templates and MSA embedding are used, our method can predict correct folds (TMscore > 0.5) for 16 of 23 CASP14 FM targets and 14 of 18 Continuous Automated Model Evaluation (CAMEO) targets, outperforming RoseTTAFold by 5% and 7%, respectively. AVAILABILITY AND IMPLEMENTATION: Available at https://github.com/xluo233/RaptorXFold. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Three-phase electrowetting liquid lens with deformable liquid iris.

Inspired by the arrangement of iris and crystalline lens in human eyes, we propose a three-phase electrowetting liquid lens with a deformable liquid iris (TELL-DLI). The proposed electrowetting liquid lens has three-phase fluid: air, conductive liquid, and dyed insulating liquid. The insulating liquid is distributed on the inner wall of the chamber in a ring shape. By applying voltage, the contact angle is changed, so that the dyed insulating liquid contracts towards the center, which is similar to the contraction of iris and the function of crystalline lens muscle in human eyes. The variation range of focal length is from -451.9 mm to -107.9 mm. The variation range of the aperture is from 4.89 mm to 0.6 mm. Under the step voltage of 200 V, the TELL-DLI can be switched between the maximum aperture state and the zero aperture state, and the switching time is ∼150/200 ms. Because of the discrete electrodes, TELL-DLI can regionally control the shape and position of the iris, and switch between circle, ellipse, sector, and strip. The TELL-DLI has a wide application prospect in imaging systems, such as microscopic imaging system, and has the potential to be applied in the field of complex beam navigation.

Metabolic profiling of petroleum-degrading microbial communities incubated under high-pressure conditions.

While pressure is a significant characteristic of petroleum reservoirs, it is often overlooked in laboratory studies. To clarify the composition and metabolic properties of microbial communities under high-pressure conditions, we established methanogenic and sulfate-reducing enrichment cultures under high-pressure conditions using production water from the Jilin Oilfield in China. We utilized a metagenomics approach to analyze the microbial community after a 90-day incubation period. Under methanogenic conditions, Firmicutes, Deferribacteres, Ignavibacteriae, Thermotogae, and Nitrospirae, in association with the hydrogenotrophic methanogen Archaeoglobaceae and acetoclastic Methanosaeta, were highly represented. Genomes for Ca. Odinarchaeota and the hydrogen-dependent methylotrophic Ca. Methanosuratus were also recovered from the methanogenic culture. The sulfate-reducing community was dominated by Firmicutes, Thermotogae, Nitrospirae, Archaeoglobus, and several candidate taxa including Ca. Bipolaricaulota, Ca. Aminicenantes, and Candidate division WOR-3. These candidate taxa were key pantothenate producers for other community members. The study expands present knowledge of the metabolic roles of petroleum-degrading microbial communities under high-pressure conditions. Our results also indicate that microbial community interactions were shaped by syntrophic metabolism and the exchange of amino acids and cofactors among members. Furthermore, incubation under in situ pressure conditions has the potential to reveal the roles of microbial dark matter.

Integrated analysis of multiple microarray studies to establish differential diagnostic models of Crohn's disease and ulcerative colitis based on a metalloproteinase-associated module.

Background: The ulcerative colitis (UC) and Crohn's disease (CD) subtypes of inflammatory bowel disease (IBD) are autoimmune diseases influenced by multiple complex factors. The clinical treatment strategies for UC and CD often differ, indicating the importance of improving their discrimination. Methods: Two methods, robust rank aggregation (RRA) analysis and merging and intersection, were applied to integrate data from multiple IBD cohorts, and the identified differentially expressed genes (DEGs) were used to establish a protein-protein interaction (PPI) network. Molecular complex detection (MCODE) was used to identify important gene sets. Two differential diagnostic models to distinguish CD and UC were established via a least absolute shrinkage and selection operator (LASSO) logistic regression, and model evaluation was performed in both the training and testing groups, including receiver operating characteristic (ROC) curves, calibration plots and decision curve analysis (DCA). The potential value of MMP-associated genes was further verified using different IBD cohorts and clinical samples. Results: Four datasets (GSE75214, GSE10616, GSE36807, and GSE9686) were included in the analysis. Both data integration methods indicated that the activation of the MMP-associated module was significantly elevated in UC. Two LASSO models based on continuous variable (Model_1) and binary variable (Model_2) MMP-associated genes were established to discriminate CD and UC. The results showed that Model_1 exhibited good discrimination in the training and testing groups. The calibration analysis and DCA showed that Model_1 exhibited good performance in the training group but failed in the testing group. Model_2 exhibited good discrimination, calibration and DCA results in the training and testing groups and exhibited greater diagnostic value. The effects of Model_1 and Model_2 were further verified in a new IBD cohort of GSE179285. The MMP genes exhibited high value as biomarkers for the discrimination of IBD patients using published cohort and immunohistochemistry (IHC) staining data. The MMP-associated gene levels were statistically significantly positively correlated with the levels of the differentially expressed cell types, indicating their potential value in differential diagnosis. The single-cell analysis confirmed that the expression of ANXA1 in UC was higher than that in CD. Conclusion: MMP-associated modules are the main differential gene sets between CD and UC. The established Model_2 overcomes batch differences and has good clinical applicability. Subsequent in-depth research investigating how MMPs are involved in the development of different IBD subtypes is necessary.

Leaching Characteristics of Potentially Toxic Metals from Tailings at Lujiang Alum Mine, China.

To investigate the leaching characteristics and potential environmental effects of potentially toxic metals (PTMs) from alum mine tailings in Lujiang, Anhui Province, soaking tests and simulated rainfall leaching experiments were conducted for two types of slag. PTMs comprising Cd, Cr, Cu, Mn, and Ni were detected in the slag. Cu and Cd contents exceeded the national soil risk screening values (GB 15618-2018). pH values of the two slag soaking solutions were negatively correlated with the solid:liquid ratio. pH values of the sintered slag soaking solutions with different solid:liquid ratios finally stabilized between 4.4 and 4.59, and those of the waste slag soaking solutions finally stabilized between 2.7 and 3.4. The concentrations of Cd, Cr, Cu, Mn, and Ni leached from waste slag were higher than those from sintered slag, and the dissolved concentrations of these PTMs in sintered slag were higher under rainfall leaching conditions than soaking conditions (the difference in Cr concentration was the smallest, 5.6%). The cumulative release of Cd, Cr, Cu, Mn, and Ni increased as the leaching liquid volume increased. The kinetic characteristics of the cumulative release of the five PTMs were best fitted by a double constant equation (R2 > 0.98 for all fits). Single factor index evaluations showed that Mn and Ni were the PTMs with high pollution degrees (Pi for Mn and Ni exceed 1) in the leaching solutions. However, considering the biotoxicity of PTMs, the water quality index evaluations showed that the water quality of the sintered slag soaking solution, the waste slag soaking solution, and the sintered slag leachate was good, poor, and undrinkable, respectively. The health risk assessment showed that the total non-carcinogenic risk (HI) values in adults for both the sintered slag leachate and waste slag soaking solution exceeded the safe level of 1, with HI values of 3.965 and 2.342, respectively. The hazard quotient (HQ) for Cd was 1.994 for the sintered slag leachate, and Cd and Cr make up 50.29% and 15.93% of the total risk, respectively. Cr makes up 28.38% of the total risk for the waste slag soaking solution. These results indicate a high non-carcinogenic risk of exposure to Cd and Cr in the leaching solution used for drinking purposes. These findings may provide a reference for the evaluation and ecological control of PTM pollution in alum mining areas.

Annotating functional effects of non-coding variants in neuropsychiatric cell types by deep transfer learning.

Genomewide association studies (GWAS) have identified a large number of loci associated with neuropsychiatric traits, however, understanding the molecular mechanisms underlying these loci remains difficult. To help prioritize causal variants and interpret their functions, computational methods have been developed to predict regulatory effects of non-coding variants. An emerging approach to variant annotation is deep learning models that predict regulatory functions from DNA sequences alone. While such models have been trained on large publicly available dataset such as ENCODE, neuropsychiatric trait-related cell types are under-represented in these datasets, thus there is an urgent need of better tools and resources to annotate variant functions in such cellular contexts. To fill this gap, we collected a large collection of neurodevelopment-related cell/tissue types, and trained deep Convolutional Neural Networks (ResNet) using such data. Furthermore, our model, called MetaChrom, borrows information from public epigenomic consortium to improve the accuracy via transfer learning. We show that MetaChrom is substantially better in predicting experimentally determined chromatin accessibility variants than popular variant annotation tools such as CADD and delta-SVM. By combining GWAS data with MetaChrom predictions, we prioritized 31 SNPs for Schizophrenia, suggesting potential risk genes and the biological contexts where they act. In summary, MetaChrom provides functional annotations of any DNA variants in the neuro-development context and the general method of MetaChrom can also be extended to other disease-related cell or tissue types.

ProALIGN: Directly Learning Alignments for Protein Structure Prediction via Exploiting Context-Specific Alignment Motifs.

Template-based modeling (TBM), including homology modeling and protein threading, is one of the most reliable techniques for protein structure prediction. It predicts protein structure by building an alignment between the query sequence under prediction and the templates with solved structures. However, it is still very challenging to build the optimal sequence-template alignment, especially when only distantly related templates are available. Here we report a novel deep learning approach ProALIGN that can predict much more accurate sequence-template alignment. Like protein sequences consisting of sequence motifs, protein alignments are also composed of frequently occurring alignment motifs with characteristic patterns. Alignment motifs are context-specific as their characteristic patterns are tightly related to sequence contexts of the aligned regions. Inspired by this observation, we represent a protein alignment as a binary matrix (in which 1 denotes an aligned residue pair) and then use a deep convolutional neural network to predict the optimal alignment from the query protein and its template. The trained neural network implicitly but effectively encodes an alignment scoring function, which reduces inaccuracies in the handcrafted scoring functions widely used by the current threading approaches. For a query protein and a template, we apply the neural network to directly infer likelihoods of all possible residue pairs in their entirety, which could effectively consider the correlations among multiple residues. We further construct the alignment with maximum likelihood, and finally build a structure model according to the alignment. Tested on three independent data sets with a total of 6688 protein alignment targets and 80 CASP13 TBM targets, our method achieved much better alignments and 3D structure models than the existing methods, including HHpred, CNFpred, CEthreader, and DeepThreader. These results clearly demonstrate the effectiveness of exploiting the context-specific alignment motifs by deep learning for protein threading.