Latent space search based multimodal optimization with personalized edge-network biomarker for multi-purpose early disease prediction.

Considering that cancer is resulting from the comutation of several essential genes of individual patients, researchers have begun to focus on identifying personalized edge-network biomarkers (PEBs) using personalized edge-network analysis for clinical practice. However, most of existing methods ignored the optimization of PEBs when multimodal biomarkers exist in multi-purpose early disease prediction (MPEDP). To solve this problem, this study proposes a novel model (MMPDENB-RBM) that combines personalized dynamic edge-network biomarkers (PDENB) theory, multimodal optimization strategy and latent space search scheme to identify biomarkers with different configurations of PDENB modules (i.e. to effectively identify multimodal PDENBs). The application to the three largest cancer omics datasets from The Cancer Genome Atlas database (i.e. breast invasive carcinoma, lung squamous cell carcinoma and lung adenocarcinoma) showed that the MMPDENB-RBM model could more effectively predict critical cancer state compared with other advanced methods. And, our model had better convergence, diversity and multimodal property as well as effective optimization ability compared with the other state-of-art methods. Particularly, multimodal PDENBs identified were more enriched with different functional biomarkers simultaneously, such as tissue-specific synthetic lethality edge-biomarkers including cancer driver genes and disease marker genes. Importantly, as our aim, these multimodal biomarkers can perform diverse biological and biomedical significances for drug target screen, survival risk assessment and novel biomedical sight as the expected multi-purpose of personalized early disease prediction. In summary, the present study provides multimodal property of PDENBs, especially the therapeutic biomarkers with more biological significances, which can help with MPEDP of individual cancer patients.

Comparison of CT-derived Plaque Characteristic Index With CMR Perfusion for Ischemia Diagnosis in Stable CAD.

BACKGROUND: Coronary computed tomography angiography (CCTA) and cardiac magnetic resonance (CMR) have been used to diagnose lesion-specific ischemia in patients with coronary artery disease. The aim of this study was to investigate the diagnostic performance of CCTA-derived plaque characteristic index compared with myocardial blood flow (MBF) and myocardial perfusion reserve (MPR) derived from CMR perfusion in the assessment of lesion-specific ischemia. METHODS: Between October 2020 and March 2022, consecutive patients with suspected or known coronary artery disease, who were clinically referred for invasive coronary angiography were prospectively enrolled. All participants sequentially underwent CCTA and CMR and invasive fractional flow reserve within 2 weeks. The diagnostic performance of CCTA-derived plaque characteristics, CMR perfusion-derived stress MBF, and MPR were compared. Lesions with fractional flow reserve ≤0.80 were considered to be hemodynamically significant stenosis. RESULTS: Nighty-two patients with 141 vessels were included in this study. Plaque length, minimum luminal area, plaque area, percent area stenosis, total atheroma volume, vessel volume, lipid-rich volume, spotty calcium, napkin-ring signs, stress MBF, and MPR in flow-limiting stenosis group were significantly different from nonflow-limiting group. The overall accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of lesion-specific ischemia diagnosis were 61.0%, 55.3%, 63.1%, 35.6%, and 79.3% for stress MBF, and 89.4%, 89.5%, 89.3%, 75.6%, 95.8% for MPR; meanwhile, 82.3%, 79.0%, 84.5%, 65.2%, and 91.6% for CCTA-derived plaque characteristic index. CONCLUSIONS: In our prospective study, CCTA-derived plaque characteristics and MPR derived from CMR performed well in diagnosing lesion-specific myocardial ischemia and were significantly better than stress MBF in stable coronary artery disease.

Efficient genome editing in cotton using the virus-mediated CRISPR/Cas9 and grafting system.

KEY MESSAGE: The extensive application of CRISPR in cotton was limited due to the labor-intensive transformation process. Thus, we here established a convenient method of CRISPR in cotton by CLCrV-mediated sgRNA delivery.

Fully automated pixel-wise quantitative CMR-myocardial perfusion with CMR-coronary angiography to detect hemodynamically significant coronary artery disease.

OBJECTIVES: We applied a fully automated pixel-wise post-processing framework to evaluate fully quantitative cardiovascular magnetic resonance myocardial perfusion imaging (CMR-MPI). In addition, we aimed to evaluate the additive value of coronary magnetic resonance angiography (CMRA) to the diagnostic performance of fully automated pixel-wise quantitative CMR-MPI for detecting hemodynamically significant coronary artery disease (CAD). METHODS: A total of 109 patients with suspected CAD were prospectively enrolled and underwent stress and rest CMR-MPI, CMRA, invasive coronary angiography (ICA), and fractional flow reserve (FFR). CMRA was acquired between stress and rest CMR-MPI acquisition, without any additional contrast agent. Finally, CMR-MPI quantification was analyzed by a fully automated pixel-wise post-processing framework. RESULTS: Of the 109 patients, 42 patients had hemodynamically significant CAD (FFR ≤ 0.80 or luminal stenosis ≥ 90% on ICA) and 67 patients had hemodynamically non-significant CAD (FFR ˃ 0.80 or luminal stenosis < 30% on ICA) were enrolled. On the per-territory analysis, patients with hemodynamically significant CAD had higher myocardial blood flow (MBF) at rest, lower MBF under stress, and lower myocardial perfusion reserve (MPR) than patients with hemodynamically non-significant CAD (p < 0.001). The area under the receiver operating characteristic curve of MPR (0.93) was significantly larger than those of stress and rest MBF, visual assessment of CMR-MPI, and CMRA (p < 0.05), but similar to that of the integration of CMR-MPI with CMRA (0.90). CONCLUSIONS: Fully automated pixel-wise quantitative CMR-MPI can accurately detect hemodynamically significant CAD, but the integration of CMRA obtained between stress and rest CMR-MPI acquisition did not provide significantly additive value. KEY POINTS: • Full quantification of stress and rest cardiovascular magnetic resonance myocardial perfusion imaging can be postprocessed fully automatically, generating pixel-wise myocardial blood flow (MBF) and myocardial perfusion reserve (MPR) maps. • Fully quantitative MPR provided higher diagnostic performance for detecting hemodynamically significant coronary artery disease, compared with stress and rest MBF, qualitative assessment, and coronary magnetic resonance angiography (CMRA). • The integration of CMRA and MPR did not significantly improve the diagnostic performance of MPR alone.

Alterations of the gut microbiota in patients with immunoglobulin light chain amyloidosis.

Background: Emerging evidence revealed that gut microbial dysbiosis is implicated in the development of plasma cell dyscrasias and amyloid deposition diseases, but no data are available on the relationship between gut microbiota and immunoglobulin light chain (AL) amyloidosis. Methods: To characterize the gut microbiota in patients with AL amyloidosis, we collected fecal samples from patients with AL amyloidosis (n=27) and age-, gender-, and BMI-matched healthy controls (n=27), and conducted 16S rRNA MiSeq sequencing and amplicon sequence variants (ASV)-based analysis. Results: There were significant differences in gut microbial communities between the two groups. At the phylum level, the abundance of Actinobacteriota and Verrucomicrobiota was significantly higher, while Bacteroidota reduced remarkably in patients with AL amyloidosis. At the genus level, 17 genera, including Bifidobacterium, Akkermansia, and Streptococcus were enriched, while only 4 genera including Faecalibacterium, Tyzzerella, Pseudomonas, and Anaerostignum decreased evidently in patients with AL amyloidosis. Notably, 5 optimal ASV-based microbial markers were identified as the diagnostic model of AL amyloidosis and the AUC value of the train set and the test set was 0.8549 (95% CI 0.7310-0.9789) and 0.8025 (95% CI 0.5771-1), respectively. With a median follow-up of 19.0 months, further subgroup analysis also demonstrated some key gut microbial markers were related to disease severity, treatment response, and even prognosis of patients with AL amyloidosis. Conclusions: For the first time, we demonstrated the alterations of gut microbiota in AL amyloidosis and successfully established and validated the microbial-based diagnostic model, which boosted more studies about microbe-based strategies for diagnosis and treatment in patients with AL amyloidosis in the future.

Clinical characteristics and prognostic value of renal immune complex deposition in patients with light chain amyloidosis.

Although patients with light chain amyloidosis (AL) may present with co-deposition of amyloid and immune complexes (ICs) in renal biopsies, data on clinical characteristics and prognostic value of renal IC deposition are limited. A total of 73 patients with AL amyloidosis who were newly diagnosed by renal biopsy in Xijing Hospital (Xi'an, China) were divided into two groups (IC and non-IC groups). As a result, renal IC deposition was found in 26% of patients. Patients with IC deposition were associated with more urinary protein excretion and lower serum albumin. Notably, patients in the non-IC group achieved higher hematological overall response rate (81.5% vs. 47.4%, p = 0.007) and ≥VGPR rate (75.9% vs. 39.8%, p = 0.004) compared with those in IC group. Renal response rate was also higher in the non-IC group (63% vs. 31.6%, p = 0.031). With the median follow-up time of 19 months, a significantly worse overall survival was observed in patients with the IC group as compared with those without renal IC deposition in the Kaplan-Meier analysis (p = 0.036). Further multivariate analysis demonstrated that renal immune complex deposition was associated with worse overall survival in patients with AL amyloidosis (HR 5.927, 95% CI 2.148-16.356, p = 0.001).

Prioritization of cancer driver gene with prize-collecting steiner tree by introducing an edge weighted strategy in the personalized gene interaction network.

BACKGROUND: Cancer is a heterogeneous disease in which tumor genes cooperate as well as adapt and evolve to the changing conditions for individual patients. It is a meaningful task to discover the personalized cancer driver genes that can provide diagnosis and target drug for individual patients. However, most of existing methods mainly ranks potential personalized cancer driver genes by considering the patient-specific nodes information on the gene/protein interaction network. These methods ignore the personalized edge weight information in gene interaction network, leading to false positive results. RESULTS: In this work, we presented a novel algorithm (called PDGPCS) to predict the Personalized cancer Driver Genes based on the Prize-Collecting Steiner tree model by considering the personalized edge weight information. PDGPCS first constructs the personalized weighted gene interaction network by integrating the personalized gene expression data and prior known gene/protein interaction network knowledge. Then the gene mutation data and pathway data are integrated to quantify the impact of each mutant gene on every dysregulated pathway with the prize-collecting Steiner tree model. Finally, according to the mutant gene's aggregated impact score on all dysregulated pathways, the mutant genes are ranked for prioritizing the personalized cancer driver genes. Experimental results on four TCGA cancer datasets show that PDGPCS has better performance than other personalized driver gene prediction methods. In addition, we verified that the personalized edge weight of gene interaction network can improve the prediction performance. CONCLUSIONS: PDGPCS can more accurately identify the personalized driver genes and takes a step further toward personalized medicine and treatment. The source code of PDGPCS can be freely downloaded from https://github.com/NWPU-903PR/PDGPCS .

Multi-modal optimization to identify personalized biomarkers for disease prediction of individual patients with cancer.

Finding personalized biomarkers for disease prediction of patients with cancer remains a massive challenge in precision medicine. Most methods focus on one subnetwork or module as a network biomarker; however, this ignores the early warning capabilities of other modules with different configurations of biomarkers (i.e. multi-modal personalized biomarkers). Identifying such modules would not only predict disease but also provide effective therapeutic drug target information for individual patients. To solve this problem, we developed a novel model (denoted multi-modal personalized dynamic network biomarkers (MMPDNB)) based on a multi-modal optimization mechanism and personalized dynamic network biomarker (PDNB) theory, which can provide multiple modules of personalized biomarkers and unveil their multi-modal properties. Using the genomics data of patients with breast or lung cancer from The Cancer Genome Atlas database, we validated the effectiveness of the MMPDNB model. The experimental results showed that compared with other advanced methods, MMPDNB can more effectively predict the critical state with the highest early warning signal score during cancer development. Furthermore, MMPDNB more significantly identified PDNBs containing driver and biomarker genes specific to cancer tissues. More importantly, we validated the biological significance of multi-modal PDNBs, which could provide effective drug targets of individual patients as well as markers for predicting early warning signals of the critical disease state. In conclusion, multi-modal optimization is an effective method to identify PDNBs and offers a new perspective for understanding tumor heterogeneity in cancer precision medicine.

Network Control Models With Personalized Genomics Data for Understanding Tumor Heterogeneity in Cancer.

Due to rapid development of high-throughput sequencing and biotechnology, it has brought new opportunities and challenges in developing efficient computational methods for exploring personalized genomics data of cancer patients. Because of the high-dimension and small sample size characteristics of these personalized genomics data, it is difficult for excavating effective information by using traditional statistical methods. In the past few years, network control methods have been proposed to solve networked system with high-dimension and small sample size. Researchers have made progress in the design and optimization of network control principles. However, there are few studies comprehensively surveying network control methods to analyze the biomolecular network data of individual patients. To address this problem, here we comprehensively surveyed complex network control methods on personalized omics data for understanding tumor heterogeneity in precision medicine of individual patients with cancer.

qPTMplants: an integrative database of quantitative post-translational modifications in plants.

As a crucial molecular mechanism, post-translational modifications (PTMs) play critical roles in a wide range of biological processes in plants. Recent advances in mass spectrometry-based proteomic technologies have greatly accelerated the profiling and quantification of plant PTM events. Although several databases have been constructed to store plant PTM data, a resource including more plant species and more PTM types with quantitative dynamics still remains to be developed. In this paper, we present an integrative database of quantitative PTMs in plants named qPTMplants (http://qptmplants.omicsbio.info), which hosts 1 242 365 experimentally identified PTM events for 429 821 nonredundant sites on 123 551 proteins under 583 conditions for 23 PTM types in 43 plant species from 293 published studies, with 620 509 quantification events for 136 700 PTM sites on 55 361 proteins under 354 conditions. Moreover, the experimental details, such as conditions, samples, instruments and methods, were manually curated, while a variety of annotations, including the sequence and structural characteristics, were integrated into qPTMplants. Then, various search and browse functions were implemented to access the qPTMplants data in a user-friendly manner. Overall, we anticipate that the qPTMplants database will be a valuable resource for further research on PTMs in plants.

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.

Weighted minimum feedback vertex sets and implementation in human cancer genes detection.

BACKGROUND: Recently, many computational methods have been proposed to predict cancer genes. One typical kind of method is to find the differentially expressed genes between tumour and normal samples. However, there are also some genes, for example, 'dark' genes, that play important roles at the network level but are difficult to find by traditional differential gene expression analysis. In addition, network controllability methods, such as the minimum feedback vertex set (MFVS) method, have been used frequently in cancer gene prediction. However, the weights of vertices (or genes) are ignored in the traditional MFVS methods, leading to difficulty in finding the optimal solution because of the existence of many possible MFVSs. RESULTS: Here, we introduce a novel method, called weighted MFVS (WMFVS), which integrates the gene differential expression value with MFVS to select the maximum-weighted MFVS from all possible MFVSs in a protein interaction network. Our experimental results show that WMFVS achieves better performance than using traditional bio-data or network-data analyses alone. CONCLUSION: This method balances the advantage of differential gene expression analyses and network analyses, improves the low accuracy of differential gene expression analyses and decreases the instability of pure network analyses. Furthermore, WMFVS can be easily applied to various kinds of networks, providing a useful framework for data analysis and prediction.

Network controllability-based algorithm to target personalized driver genes for discovering combinatorial drugs of individual patients.

Multiple driver genes in individual patient samples may cause resistance to individual drugs in precision medicine. However, current computational methods have not studied how to fill the gap between personalized driver gene identification and combinatorial drug discovery for individual patients. Here, we developed a novel structural network controllability-based personalized driver genes and combinatorial drug identification algorithm (CPGD), aiming to identify combinatorial drugs for an individual patient by targeting personalized driver genes from network controllability perspective. On two benchmark disease datasets (i.e. breast cancer and lung cancer datasets), performance of CPGD is superior to that of other state-of-the-art driver gene-focus methods in terms of discovery rate among prior-known clinical efficacious combinatorial drugs. Especially on breast cancer dataset, CPGD evaluated synergistic effect of pairwise drug combinations by measuring synergistic effect of their corresponding personalized driver gene modules, which are affected by a given targeting personalized driver gene set of drugs. The results showed that CPGD performs better than existing synergistic combinatorial strategies in identifying clinical efficacious paired combinatorial drugs. Furthermore, CPGD enhanced cancer subtyping by computationally providing personalized side effect signatures for individual patients. In addition, CPGD identified 90 drug combinations candidates from SARS-COV2 dataset as potential drug repurposing candidates for recently spreading COVID-19.

Diagnostic value of bronchoalveolar lavage fluid cryptococcal antigen-lateral flow immunochromatographic assay for pulmonary cryptococcosis in non-HIV patients.

BACKGROUND: The aim of this study was to investigate the diagnostic value of cryptococcal antigen-lateral flow immunochromatographic assay (CrAg-LFA) in bronchoalveolar lavage fluid (BALF) of patients with pulmonary cryptococcosis (PC). METHODS: A total of 308 patients were divided into the PC group (n = 72) and the non-PC group (n = 236). The clinical data, pathogen detection, radiological imaging, and the detection of the cryptococcal antigen in blood and BALF samples were analyzed. RESULTS: The sensitivity, specificity, positive, and negative predicted values of CrAg-LFA in the serum were 75.0%, 99.6%, 98.2%, and 92.9%, respectively, while those in the BALF were 93.1%, 100.0%, 100.0%, and 97.9%, respectively. The sensitivity of the CrAg-LFA in BALF was significantly higher than that in the serum of the patients in the PC group (P < 0.05). CONCLUSION: CrAg-LFA has a higher diagnostic value for PC when analyzing BALF samples compared to serum samples.

Skin Ultrastructure and the Changes of HIF-2α, H-FABP Expression in the Myocardium of Electric Shock Death Rats / 法医学杂志

Objective To observe the skin ultrastructure change of electric shock death rats and to test the expression changes of hypoxia-inducible factor-2α （HIF-2α） and heart type-fatty acid-binding protein （H-FABP） of myocardial cells, in order to provide basis for forensic identification of electric shock death. Methods The electric shock model of rats was established. The 72 rats were randomly divided into control group, electric shock death group and postmortem electric shock group. Each group was divided into three subgroups, immediate （0 min）, 30 min and 60 min after death. The skin changes of rats were observed by HE staining, the changes of skin ultrastructure were observed by scanning electron microscopy, and the expression of HIF-2α and H-FABP in rats myocardium was tested by immunohistochemical staining. Results The skin in the electric shock death group and postmortem electric shock group had no significant difference through the naked eye or by HE staining. Under the scanning electron microscope, a large number of cellular debris, cells with unclear boundaries, withered cracks, circular or elliptical holes scattered on the cell surface and irregular edges were observed. A large number of spherical foreign body particles were observed. Compared with the control group, the expression of HIF-2α in all electric shock death subgroups increased, reaching the peak immediately after death. In the postmortem electric shock group, HIF-2α expression only increased immediately after death, but was lower than that of electric shock death group （P<0.05）. Compared with the control group, the expression of H-FABP in all subgroups of electric shock death group and postmortem electric shock group significantly decreased. The expression of H-FABP in all subgroups of electric shock death group was lower than that of the postmortem electric shock group （P<0.05）. Conclusion Electric shock can increase HIF-2α expression and decrease H-FABP expression in the myocardium, which may be of forensic significance for the determination of electric shock death and identification of antemortem and postmortem electric shock.

Network control principles for identifying personalized driver genes in cancer.

To understand tumor heterogeneity in cancer, personalized driver genes (PDGs) need to be identified for unraveling the genotype-phenotype associations corresponding to particular patients. However, most of the existing driver-focus methods mainly pay attention on the cohort information rather than on individual information. Recent developing computational approaches based on network control principles are opening a new way to discover driver genes in cancer, particularly at an individual level. To provide comprehensive perspectives of network control methods on this timely topic, we first considered the cancer progression as a network control problem, in which the expected PDGs are altered genes by oncogene activation signals that can change the individual molecular network from one health state to the other disease state. Then, we reviewed the network reconstruction methods on single samples and introduced novel network control methods on single-sample networks to identify PDGs in cancer. Particularly, we gave a performance assessment of the network structure control-based PDGs identification methods on multiple cancer datasets from TCGA, for which the data and evaluation package also are publicly available. Finally, we discussed future directions for the application of network control methods to identify PDGs in cancer and diverse biological processes.

A novel network control model for identifying personalized driver genes in cancer.

Although existing computational models have identified many common driver genes, it remains challenging to identify the personalized driver genes by using samples of an individual patient. Recently, the methods of exploiting the structure-based control principles of complex networks provide new clues for identifying minimum number of driver nodes to drive the state transition of large-scale complex networks from an initial state to the desired state. However, the structure-based network control methods cannot be directly applied to identify the personalized driver genes due to the unknown network dynamics of the personalized system. Here we proposed the personalized network control model (PNC) to identify the personalized driver genes by employing the structure-based network control principle on genetic data of individual patients. In PNC model, we firstly presented a paired single sample network construction method to construct the personalized state transition network for capturing the phenotype transitions between healthy and disease states. Then, we designed a novel structure-based network control method from the Feedback Vertex Sets-based control perspective to identify the personalized driver genes. The wide experimental results on 13 cancer datasets from The Cancer Genome Atlas firstly showed that PNC model outperforms current state-of-the-art methods, in terms of F-measures for identifying cancer driver genes enriched in the gold-standard cancer driver gene lists. Furthermore, these results showed that personalized driver genes can be explored by their network characteristics even when they are hidden factors in transcription and mutation profiles. Our PNC gives novel insights and useful tools into understanding the tumor heterogeneity in cancer. The PNC package and data resources used in this work can be freely downloaded from https://github.com/NWPU-903PR/PNC.

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.

Catalytic Asymmetric [4+2] Cycloaddition of in Situ Generated o-Quinone Methide Imines with o-Hydroxystyrenes: Diastereo- and Enantioselective Construction of Tetrahydroquinoline Frameworks.

A catalytic asymmetric [4+2] cycloaddition of ortho-quinone methide imines in situ generated from o-aminobenzyl alcohols with o-hydroxystyrenes has been established under the catalysis of chiral phosphoramide, which afforded chiral tetrahydroquinolines in moderate to good yields, good enantioselectivities, and excellent diastereoselectivities (up to 82% yield, 93:7 er, all >95:5 dr). In this catalytic asymmetric [4+2] cycloaddition, the hydrogen-bonding interaction between chiral phosphoramide and two substrates was proposed to play a crucial role in controlling the enantioselectivity. This reaction not only provides a useful approach for constructing chiral tetrahydroquinoline frameworks, but also demonstrates the great practicability of ortho-quinone methide imines in catalytic asymmetric cycloadditions.