Development of growth selection system and pocket engineering of d-amino acid oxidase to enhance selective deamination activity toward d-phosphinothricin.

D-amino acid oxidase (DAAO)-catalyzed selective oxidative deamination is a very promising process for synthesizing l-amino acids including l-phosphinothricin ( l-PPT, a high-efficiency and broad-spectrum herbicide). However, the wild-type DAAO's low activity toward unnatural substrates like d-phosphinothricin ( d-PPT) hampers its application. Herein, a DAAO from Caenorhabditis elegans (CeDAAO) was screened and engineered to improve the catalytic potential on d-PPT. First, we designed a novel growth selection system, taking into account the intricate relationship between the growth of Escherichia coli (E. coli) and the catalytic mechanism of DAAO. The developed system was used for high-throughput screening of gene libraries, resulting in the discovery of a variant (M6) with significantly increased catalytic activity against d-PPT. The variant displays different catalytic properties on substrates with varying hydrophobicity and hydrophilicity. Analysis using Alphafold2 modeling and molecular dynamic simulations showed that the reason for the enhanced activity was the substrate-binding pocket with enlarged size and suitable charge distribution. Further QM/MM calculations revealed that the crucial factor for enhancing activity lies in reducing the initial energy barrier of the reductive half reaction. Finally, a comprehensive binding-model index to predict the enhanced activity of DAAO toward d-PPT, and an enzymatic deracemization approach was developed, enabling the efficient synthesis of l-PPT with remarkable efficiency.

Identification of methylation-regulated genes modulating microglial phagocytosis in hyperhomocysteinemia-exacerbated Alzheimer's disease.

BACKGROUND: Hyperhomocysteinemia (HHcy) has been linked to development of Alzheimer's disease (AD) neuropathologically characterized by the accumulation of amyloid ß (Aß). Microglia (MG) play a crucial role in uptake of Aß fibrils, and its dysfunction worsens AD. However, the effect of HHcy on MG Aß phagocytosis remains unstudied. METHODS: We isolated MG from the cerebrum of HHcy mice with genetic cystathionine-ß-synthase deficiency (Cbs-/-) and performed bulk RNA-seq. We performed meta-analysis over transcriptomes of Cbs-/- mouse MG, human and mouse AD MG, MG Aß phagocytosis model, human AD methylome, and GWAS AD genes. RESULTS: HHcy and hypomethylation conditions were identified in Cbs-/- mice. Through Cbs-/- MG transcriptome analysis, 353 MG DEGs were identified. Phagosome formation and integrin signaling pathways were found suppressed in Cbs-/- MG. By analyzing MG transcriptomes from 4 AD patient and 7 mouse AD datasets, 409 human and 777 mouse AD MG DEGs were identified, of which 37 were found common in both species. Through further combinatory analysis with transcriptome from MG Aß phagocytosis model, we identified 130 functional-validated Aß phagocytic AD MG DEGs (20 in human AD, 110 in mouse AD), which reflected a compensatory activation of Aß phagocytosis. Interestingly, we identified 14 human Aß phagocytic AD MG DEGs which represented impaired MG Aß phagocytosis in human AD. Finally, through a cascade of meta-analysis of transcriptome of AD MG, functional phagocytosis, HHcy MG, and human AD brain methylome dataset, we identified 5 HHcy-suppressed phagocytic AD MG DEGs (Flt1, Calponin 3, Igf1, Cacna2d4, and Celsr) which were reported to regulate MG/MΦ migration and Aß phagocytosis. CONCLUSIONS: We established molecular signatures for a compensatory response of Aß phagocytosis activation in human and mouse AD MG and impaired Aß phagocytosis in human AD MG. Our discoveries suggested that hypomethylation may modulate HHcy-suppressed MG Aß phagocytosis in AD.

Association between prenatal exposure to per- and polyfluoroalkyl substances and neurodevelopment in children: Evidence based on birth cohort.

BACKGROUND: Although numerous studies have examined the effect of prenatal per- and polyfluoroalkyl substances (PFAS) exposure on neurodevelopment in children, findings have been inconsistent. OBJECTIVE: To better understand the effects of PFAS exposure during pregnancy on offspring neurodevelopment, we conducted a systematic review of prenatal exposure to different types of PFAS and neurodevelopment in children. METHODS: A comprehensive search was conducted in the PubMed, Web of Science, and EMBASE electronic databases up to March 2023. Only birth cohort studies that report a specific association between PFAS exposure during pregnancy and neurodevelopment were included in this review. RESULTS: 31 birth cohort studies that met the inclusion criteria were qualitatively integrated. Among these, 14 studies investigated the impact of PFAS exposure during pregnancy on cognition, 13 on neurobehavior, and 4 on both cognition and neurobehavior. Additionally, 4 studies explored the influence of PFAS on children's comprehensive development. CONCLUSION: Prenatal PFAS exposure was associated with poor neurodevelopment in children, including psychomotor development, externalizing behavior, and comprehensive development. However, conclusive evidence regarding its effects on other neurological outcomes remains limited. In addition, sex-specific effects on social behavior and sleep problems were identified.

Switching the Cofactor Preference of Formate Dehydrogenase to Develop an NADPH-Dependent Biocatalytic System for Synthesizing Chiral Amino Acids.

Efficient formate dehydrogenase (FDH)-based cofactor regeneration systems are widely used for biocatalytic processes due to their ready availability, low reduction potential, and production of only benign byproducts. However, FDHs are usually specific to NAD+, and NADPH regeneration with formate is challenging. Herein, an FDH with a preference for NAD+ from Azospirillum palustre (ApFDH) was selected owing to its high activity. By static and dynamic structural analyses, a beneficial substitution, D222Q, was identified for cofactor-preference switching. However, its total activity was substantially decreased by 90% owing to the activity-specificity trade-off. Subsequently, a semirational library was designed and screened, which yielded a variant ApFDHD222Q+A199G+H380S with satisfactory activity and NADP+ specificity. Our analysis of dynamical cross-correlations revealed a substitution combination that brought balance to the dynamical correlation network. This combination successfully overcame the activity-specificity-stability trade-off and resulted in a beneficial outcome. The substitution combination (D222Q-A199G/H380S-C256A/C146S) enabled the simultaneous improvement of activity, specificity, and stability and was successfully applied to other 17 FDHs. Finally, by employing engineered ApFDH, an NADPH regeneration system was developed, optimized, and utilized for the asymmetric biosynthesis of l-phosphinothricin.

Association between pyrethroid exposure and risk of depressive symptoms in the general US adults.

This study aimed to investigate the association between pyrethroid exposure and the risk of depressive symptoms in adults in the USA. Data of participants aged ≥20 years (n = 6455) from the National Health and Nutrition Examination Survey (NHANES, 2007-2014) were included. 3-Phenoxybenzoic acid (3-PBA), an adequately detected pyrethroid metabolite, was used as a biomarker to assess pyrethroid exposure. Depressive symptoms were defined as the Patient's Health Questionnaire (PHQ-9) total score ≥10 or use of antidepressant. Multivariable logistic regression analyses were performed to examine the association between urinary 3-PBA levels and the risk of depressive symptoms. In this study, 1150 participants (weighted frequency, 18.45%) developed depressive symptoms. Participants in the highest tertile have a higher risk of depressive symptoms than those in the lowest tertile of urinary 3-PBA and weighted OR of 1.28 (95% CI, 1.00-1.63, P=0.019). There was a nonlinear association between urinary 3-PBA and depressive symptoms (P for nonlinearity = 0.034). Mediation analysis showed the mediating effect of trouble sleeping on the association of urinary 3-PBA with depressive symptoms was 28.8% (P = 0.006). Our findings indicate that pyrethroid exposure is associated with the increased risk of depressive symptoms, and trouble sleeping may mediated this association. Further studies should be conducted to validate our findings and elucidate their underlying mechanisms.

Anti-depression mechanism of Zuojin Pills:based on UHPLC-TOF-MS, network pharmacology, and experimental verification / 中国中药杂志

This study aims to explore the anti-depression mechanism of Zuojin Pills based on the plasma constituents, network pharmacology, and experimental verification. UHPLC-TOF-MS was used for qualitative analysis of Zuojin Pills-containing serum. Targets of the plasma constituents and the disease were retrieved from PharmMapper and GeneCards. Then the protein-protein interaction(PPI) network was constructed and core targets were screened for GO term enrichment and KEGG pathway enrichment. Cytoscape 3.7.2 was employed construct the "compound-target-pathway" network and the targets and signaling pathways of Zuojin Pills against depression were predicted. CUMS-induced depression mouse model was established to verify the key targets. The results showed that a total of 21 constituents migrating to blood of Zuojin Pills were identified, which were mainly alkaloids. A total of 155 common targets of the constituents and the disease and 67 core targets were screened out. KEGG enrichment and PPI network analysis showed that Zuojin Pills may play a role in the treatment of depression through AMPK/SIRT1, NLRP3, insulin and other targets and pathways. Furthermore, the results of animal experiments showed that Zuojin Pills could significantly improve the depression behaviors of depression, reduce the levels of IL-1β, IL-6 and TNF-α in hippocampus and serum, activate AMPK/SIRT1 signaling, and reduce the protein expression of NLRP3. In conclusion, Zuojin Pills may play a role in the treatment of depression by activating AMPK/SIRT1 signaling pathway, and inhibiting NLRP3 activation and neuroinflammation in the hippocampus of mice.

[Three-Dimensional Nonlinear Finite Element Modeling and Analysis of Concomitant Atlanto-Occipital Fusion and Atlantoaxial Joint Dislocation].

OBJECTIVE: To establish, with finite element technology, a three-dimensional nonlinear finite element model of the normal occipital bone, atlas and axis and a three-dimensional nonlinear finite element model of concomitant atlanto-occipital fusion and atlantoaxial dislocation, providing a biomechanical method for clinical research on the upper cervical spine. METHODS: Finite element analysis was conducted with the CT data of a 27-year-old male volunteer, and a three-dimensional nonlinear finite element model, i.e., the normal model, of the normal occipital bone, atlas and axis was established accordingly. Finite element analysis was conducted with the CT data of a 35-year-old male patient with concomitant atlanto-occipital fusion and atlantoaxial dislocation. Then, the ideal state of a simple ligament rupture under high load was generated by computer simulation, and a three-dimensional nonlinear finite element model of concomitant atlanto-occipital fusion and atlantoaxial dislocation was established, i.e., the atlanto-occipital fusion with atlantoaxial dislocation model. For both models, a vertical upward torque of 1.5 N·m was applied on the upper surface of the occipital bone. Through comparative analysis of the two models under stress, the data of the range of motion (ROM) for flexion, extension, lateral bending, and rotation were examined. In addition, stress and deformation analysis with 1.5 N·m torque load was conducted to validate the effectiveness of the two three-dimensional nonlinear finite element models established in the study. RESULTS: When the normal model established in the study was under 1.5 N·m torque load, it exhibited a maximum ROM for each unit of flexion, extension, and the ROM approximated the experimental measurement results of human mechanics, confirming the validity of the simulation. The stress and deformation results of the model were consistent with the basic principles of mechanics. The moment-angular displacement of the model showed obvious nonlinear characteristics. Compared with the normal model, the atlanto-occipital fusion with atlantoaxial dislocation model showed reduced ROM of the atlanto-occipital joint under a torque of 1.5 N·m, while the ROM of the C1-C2 joint for the four conditions of flexion, posterior extention, lateral bending, and rotation under load, with the exception of rotating motion, was greatly increased compared with that of the normal model, which was in line with the actual clinical performance of the patient. CONCLUSION: The atlanto-occipital fusion with atlantoaxial dislocation model and the three-dimensional nonlinear finite element model of the normal occipital bone, atlas and axis were successfully established by finite element technology. The models had valid simulation and reliable kinematic characteristics, and could be used as a reliable tool to simulate clinical diseases.

Novel Knowledge-Based Transcriptomic Profiling of Lipid Lysophosphatidylinositol-Induced Endothelial Cell Activation.

To determine whether pro-inflammatory lipid lysophosphatidylinositols (LPIs) upregulate the expressions of membrane proteins for adhesion/signaling and secretory proteins in human aortic endothelial cell (HAEC) activation, we developed an EC biology knowledge-based transcriptomic formula to profile RNA-Seq data panoramically. We made the following primary findings: first, G protein-coupled receptor 55 (GPR55), the LPI receptor, is expressed in the endothelium of both human and mouse aortas, and is significantly upregulated in hyperlipidemia; second, LPIs upregulate 43 clusters of differentiation (CD) in HAECs, promoting EC activation, innate immune trans-differentiation, and immune/inflammatory responses; 72.1% of LPI-upregulated CDs are not induced in influenza virus-, MERS-CoV virus- and herpes virus-infected human endothelial cells, which hinted the specificity of LPIs in HAEC activation; third, LPIs upregulate six types of 640 secretomic genes (SGs), namely, 216 canonical SGs, 60 caspase-1-gasdermin D (GSDMD) SGs, 117 caspase-4/11-GSDMD SGs, 40 exosome SGs, 179 Human Protein Atlas (HPA)-cytokines, and 28 HPA-chemokines, which make HAECs a large secretory organ for inflammation/immune responses and other functions; fourth, LPIs activate transcriptomic remodeling by upregulating 172 transcription factors (TFs), namely, pro-inflammatory factors NR4A3, FOS, KLF3, and HIF1A; fifth, LPIs upregulate 152 nuclear DNA-encoded mitochondrial (mitoCarta) genes, which alter mitochondrial mechanisms and functions, such as mitochondrial organization, respiration, translation, and transport; sixth, LPIs activate reactive oxygen species (ROS) mechanism by upregulating 18 ROS regulators; finally, utilizing the Cytoscape software, we found that three mechanisms, namely, LPI-upregulated TFs, mitoCarta genes, and ROS regulators, are integrated to promote HAEC activation. Our results provide novel insights into aortic EC activation, formulate an EC biology knowledge-based transcriptomic profile strategy, and identify new targets for the development of therapeutics for cardiovascular diseases, inflammatory conditions, immune diseases, organ transplantation, aging, and cancers.

Influence of medical domain knowledge on deep learning for Alzheimer's disease prediction.

BACKGROUND AND OBJECTIVE: Alzheimer's disease (AD) is the most common type of dementia that can seriously affect a person's ability to perform daily activities. Estimates indicate that AD may rank third as a cause of death for older people, after heart disease and cancer. Identification of individuals at risk for developing AD is imperative for testing therapeutic interventions. The objective of the study was to determine could diagnostics of AD from EMR data alone (without relying on diagnostic imaging) be significantly improved by applying clinical domain knowledge in data preprocessing and positive dataset selection rather than setting naïve filters. METHODS: Data were extracted from the repository of heterogeneous ambulatory EMR data, collected from primary care medical offices all over the U.S. Medical domain knowledge was applied to build a positive dataset from data relevant to AD. Selected Clinically Relevant Positive (SCRP) datasets were used as inputs to a Long-Short-Term Memory (LSTM) Recurrent Neural Network (RNN) deep learning model to predict will the patient develop AD. RESULTS: Risk scores prediction of AD using the drugs domain information in an SCRP AD dataset of 2,324 patients achieved high out-of-sample score - 0.98-0.99 Area Under the Precision-Recall Curve (AUPRC) when using 90% of SCRP dataset for training. AUPRC dropped to 0.89 when training the model using less than 1,500 cases from the SCRP dataset. The model was still significantly better than when using naïve dataset selection. CONCLUSION: The LSTM RNN method that used data relevant to AD performed significantly better when learning from the SCRP dataset than when datasets were selected naïvely. The integration of qualitative medical knowledge for dataset selection and deep learning technology provided a mechanism for significant improvement of AD prediction. Accurate and early prediction of AD is significant in the identification of patients for clinical trials, which can possibly result in the discovery of new drugs for treatments of AD. Also, the contribution of the proposed predictions of AD is a better selection of patients who need imaging diagnostics for differential diagnosis of AD from other degenerative brain disorders.

Time-to-event estimation by re-defining time.

The primary goal of a time-to-event estimation model is to accurately infer the occurrence time of a target event. Most existing studies focus on developing new models to effectively utilize the information in the censored observations. In this paper, we propose a model to tackle the time-to-event estimation problem from a completely different perspective. Our model relaxes a fundamental constraint that the target variable, time, is a univariate number which satisfies a partial order. Instead, the proposed model interprets each event occurrence time as a time concept with a vector representation. We hypothesize that the model will be more accurate and interpretable by capturing (1) the relationships between features and time concept vectors and (2) the relationships among time concept vectors. We also propose a scalable framework to simultaneously learn the model parameters and time concept vectors. Rigorous experiments and analysis have been conducted in medical event prediction task on seven gene expression datasets. The results demonstrate the efficiency and effectiveness of the proposed model. Furthermore, similarity information among time concept vectors helped in identifying time regimes, thus leading to a potential knowledge discovery related to the human cancer considered in our experiments.

HDL subclass proteomic analysis and functional implication of protein dynamic change during HDL maturation.

Recent clinical trials reported that increasing high-density lipoprotein-cholesterol (HDL-C) levels does not improve cardiovascular outcomes. We hypothesize that HDL proteome dynamics determine HDL cardioprotective functions. In this study, we characterized proteome profiles in HDL subclasses and established their functional connection. Mouse plasma was fractionized by fast protein liquid chromatography, examined for protein, cholesterial, phospholipid and trigliceride content. Small, medium and large (S/M/L)-HDL subclasseses were collected for proteomic analysis by mass spectrometry. Fifty-one HDL proteins (39 in S-HDL, 27 in M-HDL and 29 in L-HDL) were identified and grouped into 4 functional categories (lipid metabolism, immune response, coagulation, and others). Eleven HDL common proteins were identified in all HDL subclasses. Sixteen, 3 and 7 proteins were found only in S-HDL, M-HDL and L-HDL, respectively. We established HDL protein dynamic distribution in S/M/L-HDL and developed a model of protein composition change during HDL maturation. We found that cholesterol efflux and immune response are essential functions for all HDL particles, and amino acid metabolism is a special function of S-HDL, whereas anti-coagulation is special for M-HDL. Pon1 is recruited into M/L-HDL to provide its antioxidative function. ApoE is incorporated into L-HDL to optimize its cholesterial clearance function. Next, we acquired HDL proteome data from Pubmed and identified 12 replicated proteins in human and mouse HDL particle. Finally, we extracted 3 shared top moleccular pathways (LXR/RXR, FXR/RXR and acute phase response) for all HDL particles and 5 top disease/bio-functions differentially related to S/M/L-HDL subclasses, and presented one top net works for each HDL subclass. We conclude that beside their essencial functions of cholesterol efflux and immune response, HDL aquired antioxidative and cholesterol clearance functions by recruiting Pon1 and ApoE during HDL maturation.

The Natural History of Labyrinthine Hemorrhage in Patients With Sudden Sensorineural Hearing Loss.

To investigate the application of inner ear 3-dimensional fluid-attenuated inversion recovery (3D-FLAIR) magnetic resonance imaging (MRI) in patients with sudden sensorineural hearing loss (SSNHL) accompanied by inner ear hemorrhage. A total of 1252 SSNHL patients who were admitted from January 2010 to April 2018 were included in the study. The patients' clinical features, complete blood counts, coagulation profiles, audiometry data, and MRI scans were retrospectively reviewed. Twenty-four patients had high labyrinth signals on inner ear 3D-FLAIR MRI (24/1252, 1.9%) that were diagnosed as inner ear hemorrhage. One patient had endolymphatic hydrops on the contralesional side. In the 24 patients, pure tone audiometry curves revealed profound deafness (19/24) and flat moderate hearing loss (5/24); most patients had associated vertigo (23/24) and tinnitus (19/24). Patients with SSNHL (N = 24) were treated. Sixteen patients had invalid improvement, 3 patients were markedly improved, 4 patients had effective treatment, and only 1 patient was cured, for a therapeutic efficacy of 33.3% (8/24). Follow-up 3D-FLAIR MRI in patients showed absorbance of labyrinthine hemorrhage and disappearance of the high signal intensity in the inner ear within 2 weeks to 4 months. Inner ear 3D-FLAIR MRI indicate that most cases of inner ear hemorrhage are spontaneous and that high labyrinth signals are absorbed within 4 months. The site of labyrinth hemorrhage is irregular and independent of hearing loss. Conventional treatment is not very effective, and an appropriate therapy for SSNHL requires further investigation.

Exome Sequencing Identifies TENM4 as a Novel Candidate Gene for Schizophrenia in the SCZD2 Locus at 11q14-21.

Schizophrenia is a complex psychiatric disorder with high genetic heterogeneity, however, the contribution of rare mutations to the disease etiology remains to be further elucidated. We herein performed exome sequencing in a Han Chinese schizophrenia family and identified a missense mutation (c.6724C>T, p.R2242C) in the teneurin transmembrane protein 4 (TENM4) gene in the SCZD2 locus, a region previously linked to schizophrenia at 11q14-21. The mutation was confirmed to co-segregate with the schizophrenia phenotype in the family. Subsequent investigation of TENM4 exons 31, 32, and 33 adjacent to the p.R2242C mutation revealed two additional missense mutations in 120 sporadic schizophrenic patients. Residues mutated in these mutations, which are predicted to be deleterious to protein function, were highly conserved among vertebrates. These rare mutations were not detected in 1000 Genomes, NHLBI Exome Sequencing Project databases, or our in-house 1136 non-schizophrenic control exomes. Analysis of RNA-Seq data showed that TENM4 is expressed in the brain with high abundance and specificity. In line with the important role of TENM4 in central nervous system development, our findings suggested that increased rare variants in TENM4 could be associated with schizophrenia, and thus TENM4 could be a novel candidate gene for schizophrenia in the SCZD2 locus.

A comprehensive data mining study shows that most nuclear receptors act as newly proposed homeostasis-associated molecular pattern receptors.

BACKGROUND: Nuclear receptors (NRs) can regulate gene expression; therefore, they are classified as transcription factors. Despite the extensive research carried out on NRs, still several issues including (1) the expression profile of NRs in human tissues, (2) how the NR expression is modulated during atherosclerosis and metabolic diseases, and (3) the overview of the role of NRs in inflammatory conditions are not fully understood. METHODS: To determine whether and how the expression of NRs are regulated in physiological/pathological conditions, we took an experimental database analysis to determine expression of all 48 known NRs in 21 human and 17 murine tissues as well as in pathological conditions. RESULTS: We made the following significant findings: (1) NRs are differentially expressed in tissues, which may be under regulation by oxygen sensors, angiogenesis pathway, stem cell master regulators, inflammasomes, and tissue hypo-/hypermethylation indexes; (2) NR sequence mutations are associated with increased risks for development of cancers and metabolic, cardiovascular, and autoimmune diseases; (3) NRs have less tendency to be upregulated than downregulated in cancers, and autoimmune and metabolic diseases, which may be regulated by inflammation pathways and mitochondrial energy enzymes; and (4) the innate immune sensor inflammasome/caspase-1 pathway regulates the expression of most NRs. CONCLUSIONS: Based on our findings, we propose a new paradigm that most nuclear receptors are anti-inflammatory homeostasis-associated molecular pattern receptors (HAMPRs). Our results have provided a novel insight on NRs as therapeutic targets in metabolic diseases, inflammations, and malignancies.

Design and numerical evaluation of an axial partial-assist blood pump for Chinese and other heart failure patients.

A fully implantable axial left ventricular assist device LAP31 was developed for Chinese or other heart failure patients who need partial support. Based on the 5-Lpm total cardiac blood output of Chinese without heart failure disease, the design point of LAP31 was set to a flow rate of 3 Lpm with 100-mmHg pressure head. To achieve the required pressure head and good hemolytic performance, a structure that includes a spindly rotor hub and a diffuser with splitter and cantilevered main blades was developed. Computational fluid dynamics (CFD) was used to analyze the hydraulic and hemodynamic performance of LAP31. Then in vitro hydraulics experiments were conducted. The numerical simulation results show that LAP31 could generate a 1 to 8 Lpm flow rate with a 60.9 to 182.7 mmHg pressure head when the pump was rotating between 9,000 and 12,000 rpm. The average scalar shear stress of the blood pump was 21.7 Pa, and the average exposure time was 71.0 milliseconds. The mean hemolysis index of LAP31 obtained using Heuser's hemolysis model and Giersiepen's model was 0.220% and 3.89 × 10-5% respectively. After adding the splitter blades, the flow separation at the suction surface of the diffuser was reduced. The cantilever structure reduced the tangential velocity from 6.1 to 4.7-1.4 m/s within the blade gap by changing the blade gap from shroud to hub. Subsequently, the blood damage caused by shear stress was reduced. In conclusion, the hydraulic and hemolytic characteristics of the LAP31 are acceptable for partial support.

Metabolism-associated danger signal-induced immune response and reverse immune checkpoint-activated CD40+ monocyte differentiation.

Adaptive immunity is critical for disease progression and modulates T cell (TC) and antigen-presenting cell (APC) functions. Three signals were initially proposed for adaptive immune activation: signal 1 antigen recognition, signal 2 co-stimulation or co-inhibition, and signal 3 cytokine stimulation. In this article, we propose to term signal 2 as an immune checkpoint, which describes interactions of paired molecules leading to stimulation (stimulatory immune checkpoint) or inhibition (inhibitory immune checkpoint) of an immune response. We classify immune checkpoint into two categories: one-way immune checkpoint for forward signaling towards TC only, and two-way immune checkpoint for both forward and reverse signaling towards TC and APC, respectively. Recently, we and others provided evidence suggesting that metabolic risk factors (RF) activate innate and adaptive immunity, involving the induction of immune checkpoint molecules. We summarize these findings and suggest a novel theory, metabolism-associated danger signal (MADS) recognition, by which metabolic RF activate innate and adaptive immunity. We emphasize that MADS activates the reverse immune checkpoint which leads to APC inflammation in innate and adaptive immunity. Our recent evidence is shown that metabolic RF, such as uremic toxin or hyperhomocysteinemia, induced immune checkpoint molecule CD40 expression in monocytes (MC) and elevated serum soluble CD40 ligand (sCD40L) resulting in CD40+ MC differentiation. We propose that CD40+ MC is a novel pro-inflammatory MC subset and a reliable biomarker for chronic kidney disease severity. We summarize that CD40:CD40L immune checkpoint can induce TC and APC activation via forward stimulatory, reverse stimulatory, and TC contact-independent immune checkpoints. Finally, we modeled metabolic RF-induced two-way stimulatory immune checkpoint amplification and discussed potential signaling pathways including AP-1, NF-κB, NFAT, STAT, and DNA methylation and their contribution to systemic and tissue inflammation.

Endocytosis and membrane receptor internalization: implication of F-BAR protein Carom.

Endocytosis is a cellular process mostly responsible for membrane receptor internalization. Cell membrane receptors bind to their ligands and form a complex which can be internalized. We previously proposed that F-BAR protein initiates membrane curvature and mediates endocytosis via its binding partners. However, F-BAR protein partners involved in membrane receptor endocytosis and the regulatory mechanism remain unknown. In this study, we established database mining strategies to explore mechanisms underlying receptor-related endocytosis. We identified 34 endocytic membrane receptors and 10 regulating proteins in clathrin-dependent endocytosis (CDE), a major process of membrane receptor internalization. We found that F-BAR protein FCHSD2 (Carom) may facilitate endocytosis via 9 endocytic partners. Carom is highly expressed, along with highly expressed endocytic membrane receptors and partners, in endothelial cells and macrophages. We established 3 models of Carom-receptor complexes and their intracellular trafficking based on protein interaction and subcellular localization. We conclude that Carom may mediate receptor endocytosis and transport endocytic receptors to the cytoplasm for receptor signaling and lysosome/proteasome degradation, or to the nucleus for RNA processing, gene transcription and DNA repair.

Novel extracellular and nuclear caspase-1 and inflammasomes propagate inflammation and regulate gene expression: a comprehensive database mining study.

BACKGROUND: Caspase-1 is present in the cytosol as an inactive zymogen and requires the protein complexes named "inflammasomes" for proteolytic activation. However, it remains unclear whether the proteolytic activity of caspase-1 is confined only to the cytosol where inflammasomes are assembled to convert inactive pro-caspase-1 to active caspase-1. METHODS: We conducted meticulous data analysis methods on proteomic, protein interaction, protein intracellular localization, and gene expressions of 114 experimentally identified caspase-1 substrates and 38 caspase-1 interaction proteins in normal physiological conditions and in various pathologies. RESULTS: We made the following important findings: (1) Caspase-1 substrates and interaction proteins are localized in various intracellular organelles including nucleus and secreted extracellularly; (2) Caspase-1 may get activated in situ in the nucleus in response to intra-nuclear danger signals; (3) Caspase-1 cleaves its substrates in exocytotic secretory pathways including exosomes to propagate inflammation to neighboring and remote cells; (4) Most of caspase-1 substrates are upregulated in coronary artery disease regardless of their subcellular localization but the majority of metabolic diseases cause no significant expression changes in caspase-1 nuclear substrates; and (5) In coronary artery disease, majority of upregulated caspase-1 extracellular substrate-related pathways are involved in induction of inflammation; and in contrast, upregulated caspase-1 nuclear substrate-related pathways are more involved in regulating cell death and chromatin regulation. CONCLUSIONS: Our identification of novel caspase-1 trafficking sites, nuclear and extracellular inflammasomes, and extracellular caspase-1-based inflammation propagation model provides a list of targets for the future development of new therapeutics to treat cardiovascular diseases, inflammatory diseases, and inflammatory cancers.

DPDR-CPI, a server that predicts Drug Positioning and Drug Repositioning via Chemical-Protein Interactome.

The cost of developing a new drug has increased sharply over the past years. To ensure a reasonable return-on-investment, it is useful for drug discovery researchers in both industry and academia to identify all the possible indications for early pipeline molecules. For the first time, we propose the term computational "drug candidate positioning" or "drug positioning", to describe the above process. It is distinct from drug repositioning, which identifies new uses for existing drugs and maximizes their value. Since many therapeutic effects are mediated by unexpected drug-protein interactions, it is reasonable to analyze the chemical-protein interactome (CPI) profiles to predict indications. Here we introduce the server DPDR-CPI, which can make real-time predictions based only on the structure of the small molecule. When a user submits a molecule, the server will dock it across 611 human proteins, generating a CPI profile of features that can be used for predictions. It can suggest the likelihood of relevance of the input molecule towards ~1,000 human diseases with top predictions listed. DPDR-CPI achieved an overall AUROC of 0.78 during 10-fold cross-validations and AUROC of 0.76 for the independent validation. The server is freely accessible via http://cpi.bio-x.cn/dpdr/.

A robust data scaling algorithm to improve classification accuracies in biomedical data.

BACKGROUND: Machine learning models have been adapted in biomedical research and practice for knowledge discovery and decision support. While mainstream biomedical informatics research focuses on developing more accurate models, the importance of data preprocessing draws less attention. We propose the Generalized Logistic (GL) algorithm that scales data uniformly to an appropriate interval by learning a generalized logistic function to fit the empirical cumulative distribution function of the data. The GL algorithm is simple yet effective; it is intrinsically robust to outliers, so it is particularly suitable for diagnostic/classification models in clinical/medical applications where the number of samples is usually small; it scales the data in a nonlinear fashion, which leads to potential improvement in accuracy. RESULTS: To evaluate the effectiveness of the proposed algorithm, we conducted experiments on 16 binary classification tasks with different variable types and cover a wide range of applications. The resultant performance in terms of area under the receiver operation characteristic curve (AUROC) and percentage of correct classification showed that models learned using data scaled by the GL algorithm outperform the ones using data scaled by the Min-max and the Z-score algorithm, which are the most commonly used data scaling algorithms. CONCLUSION: The proposed GL algorithm is simple and effective. It is robust to outliers, so no additional denoising or outlier detection step is needed in data preprocessing. Empirical results also show models learned from data scaled by the GL algorithm have higher accuracy compared to the commonly used data scaling algorithms.