Identification of lipid-modifying drug targets for autoimmune diseases: insights from drug target mendelian randomization.

BACKGROUNDS: A growing body of evidence has highlighted the interactions of lipids metabolism and immune regulation. Nevertheless, there is still a lack of evidence regarding the causality between lipids and autoimmune diseases (ADs), as well as their possibility as drug targets for ADs. OBJECTIVES: This study was conducted to comprehensively understand the casual associations between lipid traits and ADs, and evaluate the therapeutic possibility of lipid-lowering drug targets on ADs. METHODS: Genetic variants for lipid traits and variants encoding targets of various lipid-lowering drugs were derived from Global Lipid Genetics Consortium (GLGC) and verified in Drug Bank. Summary data of ADs were obtained from MRC Integrative Epidemiology Unit (MER-IEU) database and FinnGen consortium, respectively. The causal inferences between lipid traits/genetic agents of lipid-lowering targets and ADs were evaluated by Mendelian randomization (MR), summary data-based MR (SMR), and multivariable MR (MVMR) analyses. Enrichment analysis and protein interaction network were employed to reveal the functional characteristics and biological relevance of potential therapeutic lipid-lowering targets. RESULTS: There was no evidence of causal effects regarding 5 lipid traits and 9 lipid-lowering drug targets on ADs. Genetically proxied 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR) inhibition was associated with a reduced risk of rheumatoid arthritis (RA) in both discovery (OR [odds ratio] = 0.45, 95%CI: 0.32, 0.63, P = 6.79 × 10- 06) and replicate datasets (OR = 0.37, 95%CI: 0.23, 0.61, P = 7.81 × 10- 05). SMR analyses supported that genetically proxied HMGCR inhibition had causal effects on RA in whole blood (OR = 0.48, 95%CI: 0.29, 0.82, P = 6.86 × 10- 03) and skeletal muscle sites (OR = 0.75, 95%CI: 0.56, 0.99, P = 4.48 × 10- 02). After controlling for blood pressure, body mass index (BMI), smoking and drinking alchohol, HMGCR suppression showed a direct causal effect on a lower risk of RA (OR = 0.33, 95%CI: 0.40, 0.96, P = 0.042). CONCLUSIONS: Our study reveals causal links of genetically proxied HMGCR inhibition (lipid-lowering drug targets) and HMGCR expression inhibition with a decreased risk of RA, suggesting that HMGCR may serve as candidate drug targets for the treatment and prevention of RA.

MicroRNAs as Key Regulators in RA and SLE: Insights into Biological Functions.

MicroRNAs (miRNAs) are non-coding RNA molecules that bind to mRNAs to regulate gene expression. Since changes in miRNA expression levels have been found in a variety of autoimmune illnesses, miRNAs are important in autoimmune diseases. MiRNAs serve not only as pathogenic factors and biomarkers for autoimmune diseases but also as important targets for disease therapeutics. Although miRNA-based treatments are still in the research stage, in-depth investigations into the biological functions of miRNAs have significantly enhanced our understanding of their mechanisms in autoimmune diseases. The purpose of this review is to summarize the biological functions of miRNAs, their roles in rheumatoid arthritis and systemic lupus erythematosus, therapeutic strategies, and challenges.

Volatile Organic Compounds of Bacillus pumilus Strain S1-10 Exhibit Fumigant Activity Against Meloidogyne incognita.

Root-knot nematodes (RKNs) are highly specialized parasites that cause significant yield losses worldwide. In this study, we isolated Bacillus pumilus strain S1-10 from the rhizosphere soil of Zingiber officinale Rosc. plants and evaluated its fumigant activity against Meloidogyne incognita. S1-10 exhibited a strong repellent effect on second-stage juveniles (J2s) of M. incognita, and in vitro assays indicated that S1-10 volatile organic compounds (VOCs) suppressed J2 activity and egg hatching. Under greenhouse conditions, 71 and 79% reductions of nematodes and eggs were detected on plants treated with S-10 VOCs compared with controls. Ten VOCs were identified through gas chromatography and mass spectrometry (GC-MS), of which 2-(methylamino)-ethanol (2-ME) had strong fumigant activity against J2s of M. incognita, with an LC50 value of 1.5 mM at 12 h. These results indicate that S1-10 represents a potential novel biocontrol agent for RKNs.

Fidelity Mechanics: Analogues of the Four Thermodynamic Laws and Landauer's Principle.

Fidelity mechanics is formalized as a framework for investigating critical phenomena in quantum many-body systems. Fidelity temperature is introduced for quantifying quantum fluctuations, which, together with fidelity entropy and fidelity internal energy, constitute three basic state functions in fidelity mechanics, thus enabling us to formulate analogues of the four thermodynamic laws and Landauer's principle at zero temperature. Fidelity flows, which are irreversible, are defined and may be interpreted as an alternative form of renormalization group flows. Thus, fidelity mechanics offers a means to characterize both stable and unstable fixed points: divergent fidelity temperature for unstable fixed points and zero-fidelity temperature and (locally) maximal fidelity entropy for stable fixed points. In addition, fidelity entropy behaves differently at an unstable fixed point for topological phase transitions and at a stable fixed point for topological quantum states of matter. A detailed analysis of fidelity mechanical-state functions is presented for six fundamental models-the quantum spin-1/2 XY model, the transverse-field quantum Ising model in a longitudinal field, the quantum spin-1/2 XYZ model, the quantum spin-1/2 XXZ model in a magnetic field, the quantum spin-1 XYZ model, and the spin-1/2 Kitaev model on a honeycomb lattice for illustrative purposes. We also present an argument to justify why the thermodynamic, psychological/computational, and cosmological arrows of time should align with each other, with the psychological/computational arrow of time being singled out as a master arrow of time.

Diagnostic value of spiral CT energy spectrum imaging in lymph node metastasis of colorectal cancer.

OBJECTIVES: To evaluate the value of preoperative CT energy spectrum imaging in detecting lymph node metastasis of colorectal cancer. METHODS: From September 2019 to November 2021, a retrospective study was performed for the eighty-two patients with colorectal cancer through preoperative colonoscopy or surgical pathology confirmed in our hospital. Based on the lymph node metastasis status, these cases were divided into the metastasis and non-metastasis groups. GE Revolution CT scanner was used to scan the patients with energy spectrum imaging, it measured and recorded the single-energy CT values from 40 to 140 keV and various energy spectrum parameters of lymph nodes around the lesions in the arterial and venous phases, and statistically analyze the above indices. RESULTS: In the arterial and venous phases: the single-energy CT values of 40-140 keV in the non-metastatic group were higher than those in the metastatic group (all P < 0.05); the parameter values of IC (iodine concentration), NIC (normalized iodine concentration), λ (the slope of the energy spectrum curve), and Eff-Z (effective-Z) in the non-metastatic group were higher than those in the metastatic group (all P < 0.05). Further evaluation of ROC curve showed that the higher AUC (area under curve) of the single-energy CT value of 50 keV in the arterial phase was 0.889, among the energy spectrum parameters of IC, NIC, λ, and Eff-Z, the NIC had the better diagnostic efficiency and the AUC of the NIC was 0.873, the highest AUC of the combination of NIC and λ was 0.885 when the energy spectrum parameters were combined. The higher AUC of the single-energy CT value of 60 keV in the venous phase was 0.853, among the energy spectrum parameters of IC, NIC, λ, and Eff-Z, the λ had the better diagnostic efficiency and the AUC of the λ was 0.822, the higher AUC of the combination of NIC, λ, and Eff-Z was 0.840 when the energy spectra were combined. CONCLUSIONS: Parameters of energy spectrum CT imaging can effectively evaluate whether lymph nodes have metastases, and provide favorable imaging diagnosis basis for the range and the number of lymph nodes to be cleaned during clinical operation and can evaluate the prognosis of patients. It is worthy of clinical recommendation.

Performance assessment of sample-specific network control methods for bulk and single-cell biological data analysis.

In the past few years, a wealth of sample-specific network construction methods and structural network control methods has been proposed to identify sample-specific driver nodes for supporting the Sample-Specific network Control (SSC) analysis of biological networked systems. However, there is no comprehensive evaluation for these state-of-the-art methods. Here, we conducted a performance assessment for 16 SSC analysis workflows by using the combination of 4 sample-specific network reconstruction methods and 4 representative structural control methods. This study includes simulation evaluation of representative biological networks, personalized driver genes prioritization on multiple cancer bulk expression datasets with matched patient samples from TCGA, and cell marker genes and key time point identification related to cell differentiation on single-cell RNA-seq datasets. By widely comparing analysis of existing SSC analysis workflows, we provided the following recommendations and banchmarking workflows. (i) The performance of a network control method is strongly dependent on the up-stream sample-specific network method, and Cell-Specific Network construction (CSN) method and Single-Sample Network (SSN) method are the preferred sample-specific network construction methods. (ii) After constructing the sample-specific networks, the undirected network-based control methods are more effective than the directed network-based control methods. In addition, these data and evaluation pipeline are freely available on https://github.com/WilfongGuo/Benchmark_control.

New Insights into the Impact of Ecological Factor on Bioactivities and Phytochemical Composition of Paeonia veitchii.

Paeonia veitchii has been widely distributed in China under different ecological types. Its roots contain diverse phytochemical constituents, which possess very high bioactivities. However, the influence of ecological factors on activities and ingredients of P. veitchii roots still remains unknown. The purpose of this research was to analyze the variation in bioactivities and phytochemical composition of P. veitchii roots upon exposure to various ecological factors. Seven P. veitchii populations collected from different regions in China were evaluated. The results of correlation analysis suggested that four major ecological factors, including average annual temperature, elevation, total potassium, and organic matter, had a strong correlation with the bioactivities of P. veitchii roots. Further, the major ecological factors were also highly correlated with the contents of naringin, gallic acid, benzoylpaeoniflorin, and paeoniflorin. The principal component analysis results supported four major metabolites as the main contributing ingredients. All populations were classified into three groups, G1, G2, and G3, through hierarchical cluster analysis. G1 showed more significant advantages in the above-mentioned four ecological factors, four active ingredients, and bioactivities compared to the other two groups. P. veitchii roots growing at lower average annual temperature, high elevation, rich total potassium and organic matter in the soils were presumed to have relatively higher bioactivities. These data expand the study on the bioactivities and phytochemical composition of P. veitchii roots and have a guiding significance for the ecological factor selection during the cultivation process of this herbaceous peony species.

Selenoprotein M stimulates the proliferative and metastatic capacities of renal cell carcinoma through activating the PI3K/AKT/mTOR pathway.

High-throughput sequencing methods have facilitated the identification of novel selenoproteins, which exert a vital role in the development and progression of tumor diseases. Recently, Selenoprotein M (SELM) is upregulated in several types of cancer. However, the biological roles of SELM in renal cell carcinoma (RCC) remain unclear. In this paper, quantitative reverse transcription PCR (qRT-PCR) and Western blot were used to measure relative levels of SELM in a cohort of RCC tissues with matched normal tissues as well as human RCC cell lines. SELM expression was found to be upregulated in RCC. High level of SELM was related to poor prognosis of RCC. Furthermore, silence of SELM could inhibit the in vitro proliferative, migratory, and invasive capacities of RCC. In addition, downregulated SELM could impede in vivo tumorigenesis of RCC. SELM could activate the PI3K/Akt/mTOR pathway and mediate expressions of matrix metallopeptidase 2 and 9 (MMP2, MMP9). In conclusion, our study reveals the oncogenic function of SELM in RCC, and SELM may be a therapeutic and prognostic target for RCC.

Genetic Association Between NGFR, ADAM17 Gene Polymorphism, and Parkinson's Disease in the Chinese Han Population.

Parkinson's disease (PD) is a common neurodegenerative disease characterized by neuronal loss in the substantia nigra. The p75 neurotrophin receptor (p75NTR, encoded by NGFR) was found to play an important role in the selective neuronal death of dopamine neurons in the substantia nigra, as well as the pathogenesis and development of PD. To assess the association between NGFR gene polymorphism and the susceptibility of PD, this case-control study consisting of 414 PD patients and 623 age- and sex-matched controls in a Chinese Han cohort was conducted. Twelve tag-single nucleotide polymorphisms (tag-SNPs) were selected from the NGFR gene through the construction of linkage disequilibrium blocks. One tag-SNP from the ADAM17 gene was also selected owing to its function of encoding tumor necrosis factor α-converting enzyme, which is responsible for the shedding of the extracellular domain of p75NTR. A multiplex polymerase chain reaction-ligase detection reaction (PCR-LDR) method was applied for genotyping. The associations between tag-SNPs and the risk of PD with the adjustment for age and sex were analyzed by unconditional logistic regression, and five genetic models including codominant, dominant, recessive, over-dominant, and additive models were applied. The results showed that among the 13 tag-SNPs, rs741073 was associated with a reduced risk of PD in the codominant (OR = 0.71, 95% CI = 0.54-0.93, P = 0.037), dominant (OR = 0.76, 95% CI = 0.58-0.98, P = 0.033), and over-dominant models (OR = 0.71, 95% CI = 0.54-0.92, P = 0.010), and rs1804011 was also associated with a reduced risk of PD in the codominant (OR = 0.69, 95% CI = 0.50-0.95, P = 0.049), dominant (OR = 0.69, 95% CI = 0.50-0.93, P = 0.014), over-dominant (OR = 0.70, 95% CI = 0.51-0.96, P = 0.025), and additive models (OR = 0.72, 95% CI = 0.54-0.94, P = 0.016). However, these associations did not retain after Bonferroni correction. Conclusively, our study failed to reveal the association between the selected tag-SNPs within NGFR, ADAM17, and the susceptibility of PD. The role of p75NTR and its gene polymorphisms in the pathogenesis of PD needs to be further studied.

Discovering personalized driver mutation profiles of single samples in cancer by network control strategy.

Motivation: It is a challenging task to discover personalized driver genes that provide crucial information on disease risk and drug sensitivity for individual patients. However, few methods have been proposed to identify the personalized-sample driver genes from the cancer omics data due to the lack of samples for each individual. To circumvent this problem, here we present a novel single-sample controller strategy (SCS) to identify personalized driver mutation profiles from network controllability perspective. Results: SCS integrates mutation data and expression data into a reference molecular network for each patient to obtain the driver mutation profiles in a personalized-sample manner. This is the first such a computational framework, to bridge the personalized driver mutation discovery problem and the structural network controllability problem. The key idea of SCS is to detect those mutated genes which can achieve the transition from the normal state to the disease state based on each individual omics data from network controllability perspective. We widely validate the driver mutation profiles of our SCS from three aspects: (i) the improved precision for the predicted driver genes in the population compared with other driver-focus methods; (ii) the effectiveness for discovering the personalized driver genes and (iii) the application to the risk assessment through the integration of the driver mutation signature and expression data, respectively, across the five distinct benchmarks from The Cancer Genome Atlas. In conclusion, our SCS makes efficient and robust personalized driver mutation profiles predictions, opening new avenues in personalized medicine and targeted cancer therapy. Availability and implementation: The MATLAB-package for our SCS is freely available from http://sysbio.sibcb.ac.cn/cb/chenlab/software.htm. Contact: zhangsw@nwpu.edu.cn or zengtao@sibs.ac.cn or lnchen@sibs.ac.cn. Supplementary information: Supplementary data are available at Bioinformatics online.

A novel algorithm for finding optimal driver nodes to target control complex networks and its applications for drug targets identification.

BACKGROUND: The advances in target control of complex networks not only can offer new insights into the general control dynamics of complex systems, but also be useful for the practical application in systems biology, such as discovering new therapeutic targets for disease intervention. In many cases, e.g. drug target identification in biological networks, we usually require a target control on a subset of nodes (i.e., disease-associated genes) with minimum cost, and we further expect that more driver nodes consistent with a certain well-selected network nodes (i.e., prior-known drug-target genes). RESULTS: Therefore, motivated by this fact, we pose and address a new and practical problem called as target control problem with objectives-guided optimization (TCO): how could we control the interested variables (or targets) of a system with the optional driver nodes by minimizing the total quantity of drivers and meantime maximizing the quantity of constrained nodes among those drivers. Here, we design an efficient algorithm (TCOA) to find the optional driver nodes for controlling targets in complex networks. We apply our TCOA to several real-world networks, and the results support that our TCOA can identify more precise driver nodes than the existing control-fucus approaches. Furthermore, we have applied TCOA to two bimolecular expert-curate networks. Source code for our TCOA is freely available from http://sysbio.sibcb.ac.cn/cb/chenlab/software.htm or https://github.com/WilfongGuo/guoweifeng . CONCLUSIONS: In the previous theoretical research for the full control, there exists an observation and conclusion that the driver nodes tend to be low-degree nodes. However, for target control the biological networks, we find interestingly that the driver nodes tend to be high-degree nodes, which is more consistent with the biological experimental observations. Furthermore, our results supply the novel insights into how we can efficiently target control a complex system, and especially many evidences on the practical strategic utility of TCOA to incorporate prior drug information into potential drug-target forecasts. Thus applicably, our method paves a novel and efficient way to identify the drug targets for leading the phenotype transitions of underlying biological networks.

Adverse effects produced by different drugs used in the treatment of Parkinson's disease: A mixed treatment comparison.

OBJECTIVE: This mixed treatment comparison is used to compare the adverse effects of eleven different drugs used to treat Parkinson's disease (PD). The drugs that we compare include the following: ropinirole, rasagiline, rotigotine, entacapone, apomorphine, pramipexole, sumanirole, bromocriptine, piribedil, pergolide, and levodopa. METHODS: PubMed, EMBASE, and Cochrane Library were searched from the inception to December 2015. Our analysis combines the evidences of direct comparison and indirect comparison between various literatures. We evaluated the merging odds ratios (OR) value and surface under the cumulative ranking curves (SUCRA) of each of the drugs and used this as a mode of comparison. RESULTS: Twenty-four randomized controlled trials (RCTs) were included in this study. Our results demonstrated that the incidence of adverse reactions of ropinirole, rotigotine, entacapone, and sumanirole were obviously higher in terms of nausea compared to the placebo. Ropinirole produced the highest incidence rates of dyskinesia side effects, whereas pramipexole was significantly higher in terms of patients' hallucination. In addition, the SUCRA values of all the drugs showed that the incidence of adverse reaction of pergolide was relatively high (nausea: 83.5%; hallucination: 79.8%); for dyskinesia and somnolence, the incidence of ropinirole was higher (dyskinesia: 80.5%; somnolence: 69.4%); the incidence of adverse reaction of piribedil was higher on PD in terms of dizziness (67.0%); and the incidence of bromocriptine was relatively high in terms of constipation (62.3%). CONCLUSIONS: This mixed treatment comparison showed that the drugs ropinirole, bromocriptine, and piribedil produced the highest incidence rates of nausea, dyskinesia, hallucination, dizziness, constipation, and somnolence symptoms. Thus, we conclude that as these three drugs produced the most frequent symptoms, they are not recommended for the treatment of patients with Parkinson's disease.

Determination of the phenolic content, profile, and antioxidant activity of seeds from nine tree peony (Paeonia section Moutan DC.) species native to China.

As an important resource of functional food, the seeds of tree peony are rich in phenolic compounds, which are associated with antioxidant activity. However, so far there has not been systematic study on phenolic compositions and antioxidant activity of the seeds from wild tree peony species. The aim of this study was to determine the phenolic content, antioxidant compounds and antioxidant activity of seeds from nine tree peony species native to China. Among the seed samples, Paeonia rockii had the highest total flavonoid content, strongest DPPH and ABTS radical scavenging activities, and strongest cupric reducing capacity; P. decomposita subsp. rotundiloba had the highest total phenolic and flavanol contents, as well as the strongest hydroxyl radical scavenging activity. Sixteen individual phenolic compounds were quantitatively measured, with (+)-catechin being the most abundant component. The content of the phenolic compounds luteolin, paeonol, and the total flavonoid content were significantly correlated with four antioxidant activities. Hierarchical cluster analysis showed that P. rockii and P. decomposita subsp. rotundiloba could be clustered in a group having a high phenolic content and strong antioxidant activity. These results suggest P. rockii and P. decomposita subsp. rotundiloba are the most promising candidates as useful sources of natural antioxidants.

Universal order parameters and quantum phase transitions: a finite-size approach.

We propose a method to construct universal order parameters for quantum phase transitions in many-body lattice systems. The method exploits the H-orthogonality of a few near-degenerate lowest states of the Hamiltonian describing a given finite-size system, which makes it possible to perform finite-size scaling and take full advantage of currently available numerical algorithms. An explicit connection is established between the fidelity per site between two H-orthogonal states and the energy gap between the ground state and low-lying excited states in the finite-size system. The physical information encoded in this gap arising from finite-size fluctuations clarifies the origin of the universal order parameter. We demonstrate the procedure for the one-dimensional quantum formulation of the q-state Potts model, for q = 2, 3, 4 and 5, as prototypical examples, using finite-size data obtained from the density matrix renormalization group algorithm.

Synthesis of micheliolide derivatives and their activities against AML progenitor cells.

Micheliolide (MCL) derivatives with etherification or esterification of the hydroxyl group at the C4 position were synthesized and evaluated for their activities against different acute myelogenous leukemia (AML) cell lines. These derivatives demonstrated comparable activities against AML cell lines HL-60 and doxorubicin resistant cell line HL-60/A. As to multi-drug resistant AML progenitor cells KG-1a, MCL and some of its derivatives maintained significant activities, and only 1.1-2.7 fold activity reductions were observed when compared with the activities against HL-60, while doxorubicin showed 20-fold activity reduction. Our study demonstrated that the C4 hydroxyl group of MCL might not only be a suitable position for structural modifications, but also be a starting point for the design of appropriate molecular probes to explore the specific targets in the progenitor cell line KG-1a.

Universal construction of order parameters for translation-invariant quantum lattice systems with symmetry-breaking order.

For any translation-invariant quantum lattice system with a symmetry group G, we propose a practical and universal construction of order parameters which identify quantum phase transitions with symmetry-breaking order. They are defined in terms of the fidelity between a ground state and its symmetry-transformed counterpart, and are computed through tensor network representations of the ground-state wave function. To illustrate our scheme, we consider three quantum systems on an infinite lattice in one spatial dimension, namely, the quantum Ising model in a transverse magnetic field, the quantum spin-1/2XYX model in an external magnetic field, and the quantum spin-1 XXZ model with single-ion anisotropy. All these models have symmetry group Z(2) and exhibit broken-symmetry phases. We also discuss the role of the order parameters in identifying factorized states.

Catalytic performance of corn stover hydrolysis by a new isolate Penicillium sp. ECU0913 producing both cellulase and xylanase.

A fungal strain, marked as ECU0913, producing high activities of both cellulase and xylanase was newly isolated from soil sample collected near decaying straw and identified as Penicillium sp. based on internal transcribed spacer sequence homology. The cultivation of this fungus produced both cellulase (2.40 FPU/ml) and xylanase (241 IU/ml) on a stepwisely optimized medium at 30 °C for 144 h. The cellulase and xylanase from Penicillium sp. ECU0913 was stable at an ambient temperature with half-lives of 28 and 12 days, respectively. Addition of 3 M sorbitol greatly improved the thermostability of the two enzymes, with half-lives increased by 2.3 and 188-folds, respectively. Catalytic performance of the Penicillium cellulase and xylanase was evaluated by the hydrolysis of corn stover pretreated by steam explosion. With an enzyme dosage of 50 FPU/g dry substrate, the conversions of cellulose and hemicellulose reached 77.2% and 47.5%, respectively, without adding any accessory enzyme.