Network-Based Approach to Identify Potential Targets and Drugs that Promote Neuroprotection and Neurorepair in Acute Ischemic Stroke

Analysis of Chinese Medical Syndrome Features of Ischemic Stroke Based on Similarity of Symptoms Subgroup

OBJECTIVE

To derive the Chinese medicine (CM) syndrome classification and subgroup syndrome characteristics of ischemic stroke patients.

METHODS

By extracting the CM clinical electronic medical records (EMRs) of 7,170 hospitalized patients with ischemic stroke from 2016 to 2018 at Weifang Hospital of Traditional Chinese Medicine, Shandong Province, China, a patient similarity network (PSN) was constructed based on the symptomatic phenotype of the patients. Thereafter the efficient community detection method BGLL was used to identify subgroups of patients. Finally, subgroups with a large number of cases were selected to analyze the specific manifestations of clinical symptoms and CM syndromes in each subgroup.

RESULTS

Seven main subgroups of patients with specific symptom characteristics were identified, including M3, M2, M1, M5, M0, M29 and M4. M3 and M0 subgroups had prominent posterior circulatory symptoms, while M3 was associated with autonomic disorders, and M4 manifested as anxiety; M2 and M4 had motor and motor coordination disorders; M1 had sensory disorders; M5 had more obvious lung infections; M29 had a disorder of consciousness. The specificity of CM syndromes of each subgroup was as follows. M3, M2, M1, M0, M29 and M4 all had the same syndrome as wind phlegm pattern; M3 and M0 both showed hyperactivity of Gan (Liver) yang pattern; M2 and M29 had similar syndromes, which corresponded to intertwined phlegm and blood stasis pattern and phlegm-stasis obstructing meridians pattern, respectively. The manifestations of CM syndromes often appeared in a combination of 2 or more syndrome elements. The most common combination of these 7 subgroups was wind-phlegm. The 7 subgroups of CM syndrome elements were specifically manifested as pathogenic wind, pathogenic phlegm, and deficiency pathogens.

CONCLUSIONS

There were 7 main symptom similarity-based subgroups in ischemic stroke patients, and their specific characteristics were obvious. The main syndromes were wind phlegm pattern and hyperactivity of Gan yang pattern.

Deregulated Protein Kinases: Friend and Foe in Ischemic Stroke

Ischemic stroke is the third leading cause of mortality worldwide, but its medical management is still limited to the use of thrombolytics as a lifesaving option. Multiple molecular deregulations of the protein kinase family occur during the period of ischemia/reperfusion. However, experimental studies have shown that alterations in the expression of essential protein kinases and their pharmacological modulation can modify the neuropathological milieu and hasten neurophysiological recovery. This review highlights the role of key protein kinase members and their implications in the evolution of stroke pathophysiology. Activation of ROCK-, MAPK-, and GSK-3β-mediated pathways following neuronal ischemia/reperfusion injury in experimental conditions aggravate the neuropathology and delays recovery. Targeting ROCK, MAPK, and GSK-3β will potentially enhance myelin regeneration, improve blood-brain barrier (BBB) function, and suppress inflammation, which ameliorates neuronal survival. Conversely, protein kinases such as PKA, Akt, PKCα, PKCε, Trk, and PERK salvage neurons post-ischemia by mechanisms including enhanced toxin metabolism, restoring BBB integrity, neurotrophic effects, and apoptosis suppression. Certain protein kinases such as ERK1/2, JNK, and AMPK have favourable and unfavourable effects in salvaging ischemia-injured neurons. Targeting multiple protein kinase-mediated pathways simultaneously may improve neuronal recovery post-ischemia.

A network-based machine-learning framework to identify both functional modules and disease genes

Disease gene identification is a critical step towards uncovering the molecular mechanisms of diseases and systematically investigating complex disease phenotypes. Despite considerable efforts to develop powerful computing methods, candidate gene identification remains a severe challenge owing to the connectivity of an incomplete interactome network, which hampers the discovery of true novel candidate genes. We developed a network-based machine-learning framework to identify both functional modules and disease candidate genes. In this framework, we designed a semi-supervised non-negative matrix factorization model to obtain the functional modules related to the diseases and genes. Of note, we proposed a disease gene-prioritizing method called MapGene that integrates the correlations from both functional modules and network closeness. Our framework identified a set of functional modules with highly functional homogeneity and close gene interactions. Experiments on a large-scale benchmark dataset showed that MapGene performs significantly better than the state-of-the-art algorithms. Further analysis demonstrates MapGene can effectively relieve the impact of the incompleteness of interactome networks and obtain highly reliable rankings of candidate genes. In addition, disease cases on Parkinson's disease and diabetes mellitus confirmed the generalization of MapGene for novel candidate gene identification. This work proposed, for the first time, an integrated computing framework to predict both functional modules and disease candidate genes. The methodology and results support that our framework has the potential to help discover underlying functional modules and reliable candidate genes in human disease.

Citicoline for treating people with acute ischemic stroke

BACKGROUND

Stroke is one of the leading causes of long-lasting disability and mortality and its global burden has increased in the past two decades. Several therapies have been proposed for the recovery from, and treatment of, ischemic stroke. One of them is citicoline. This review assessed the benefits and harms of citicoline for treating patients with acute ischemic stroke.

OBJECTIVES

To assess the clinical benefits and harms of citicoline compared with placebo or any other control for treating people with acute ischemic stroke.

SEARCH METHODS

We searched in the Cochrane Stroke Group Trials Register, CENTRAL, MEDLINE Ovid, Embase Ovid, LILACS until 29 January 2020. We searched the World Health Organization Clinical Trials Search Portal and ClinicalTrials.gov. Additionally, we also reviewed reference lists of the retrieved publications and review articles, and searched the websites of the US Food and Drug Administration (FDA) and European Medicines Agency (EMA).

SELECTION CRITERIA

We included randomized controlled trials (RCTs) in any setting including participants with acute ischemic stroke. Trials were eligible for inclusion if they compared citicoline versus placebo or no intervention.

DATA COLLECTION AND ANALYSIS

We selected RCTs, assessed the risk of bias in seven domains, and extracted data by duplicate. Our primary outcomes of interest were all-cause mortality and the degree of disability or dependence in daily activities at 90 days. We estimated risk ratios (RRs) for dichotomous outcomes. We measured statistical heterogeneity using the I² statistic. We conducted our analyses using the fixed-effect and random-effects model meta-analyses. We assessed the overall quality of evidence for six pre-specified outcomes using the GRADE approach.

MAIN RESULTS

We identified 10 RCTs including 4281 participants. In all these trials, citicoline was given either orally, intravenously, or a combination of both compared with placebo or standard care therapy. Citicoline doses ranged between 500 mg and 2000 mg per day. We assessed all the included trials as having high risk of bias. Drug companies sponsored six trials. A pooled analysis of eight trials indicates there may be little or no difference in all-cause mortality comparing citicoline with placebo (17.3% versus 18.5%; RR 0.94, 95% CI 0.83 to 1.07; I² = 0%; low-quality evidence due to risk of bias). Four trials found no difference in the proportion of patients with disability or dependence in daily activities according to the Rankin scale comparing citicoline with placebo (21.72% versus 19.23%; RR 1.11, 95% CI 0.97 to 1.26; I² = 1%; low-quality evidence due to risk of bias). Meta-analysis of three trials indicates there may be little or no difference in serious cardiovascular adverse events comparing citicoline with placebo (8.83% versus 7.77%; RR 1.04, 95% CI 0.84 to 1.29; I² = 0%; low-quality evidence due to risk of bias). Overall, either serious or non-serious adverse events - central nervous system, gastrointestinal, musculoskeletal, etc. - were poorly reported and harms may have been underestimated. Four trials assessing functional recovery with the Barthel Index at a cut-off point of 95 points or more did not find differences comparing citicoline with placebo (32.78% versus 30.70%; RR 1.03, 95% CI 0.94 to 1.13; I² = 24%; low-quality evidence due to risk of bias). There were no differences in neurological function (National Institutes of Health Stroke Scale at a cut-off point of ≤ 1 points) comparing citicoline with placebo according to five trials (24.31% versus 22.44%; RR 1.08, 95% CI 0.96 to 1.21; I² = 27%, low-quality evidence due to risk of bias). A pre-planned Trial Sequential Analysis suggested that no more trials may be needed for the primary outcomes but no trial provided information on quality of life.

AUTHORS' CONCLUSIONS

This review assessed the clinical benefits and harms of citicoline compared with placebo or any other standard treatment for people with acute ischemic stroke. The findings of the review suggest there may be little to no difference between citicoline and its controls regarding all-cause mortality, disability or dependence in daily activities, severe adverse events, functional recovery and the assessment of the neurological function, based on low-certainty evidence. None of the included trials assessed quality of life and the safety profile of citicoline remains unknown. The available evidence is of low quality due to either limitations in the design or execution of the trials.

Identification of herbal categories active in pain disorder subtypes by machine learning help reveal novel molecular mechanisms of algesia

Chronic pain is highly prevalent and poorly controlled, of which the accurate underlying mechanisms need be further elucidated. Herbal drugs have been widely used for controlling various pain disorders. The systematic integration of pain herbal data resources might be promising to help investigate the molecular mechanisms of pain phenotypes. Here, we integrated large-scale bibliographic literatures and well-established data sources to obtain high-quality pain relevant herbal data (i.e. 426 pain related herbs with their targets). We used machine learning method to identify three distinct herb categories with their specific indications of symptoms, targets and enriched pathways, which were characterized by the efficacy of treatment to the chronic cough related neuropathic pain, the reproduction and autoimmune related pain, and the cancer pain, respectively. We further detected the novel pathophysiological mechanisms of the pain subtypes by network medicine approach to evaluate the interactions between herb targets and the pain disease modules. This work increased the understanding of the underlying molecular mechanisms of pain subtypes that herbal drugs are participating and with the ultimate aim of developing novel personalized drugs for pain disorders.

Symptom-based network classification identifies distinct clinical subgroups of liver diseases with common molecular pathways

BACKGROUND AND OBJECTIVE

Liver disease is a multifactorial complex disease with high global prevalence and poor long-term clinical efficacy and liver disease patients with different comorbidities often incorporate multiple phenotypes in the clinic. Thus, there is a pressing need to improve understanding of the complexity of clinical liver population to help gain more accurate disease subtypes for personalized treatment.

METHODS

Individualized treatment of the traditional Chinese medicine (TCM) provides a theoretical basis to the study of personalized classification of complex diseases. Utilizing the TCM clinical electronic medical records (EMRs) of 6475 liver inpatient cases, we built a liver disease comorbidity network (LDCN) to show the complicated associations between liver diseases and their comorbidities, and then constructed a patient similarity network with shared symptoms (PSN). Finally, we identified liver patient subgroups using community detection methods and performed enrichment analyses to find both distinct clinical and molecular characteristics (with the phenotype-genotype associations and interactome networks) of these patient subgroups.

RESULTS

From the comorbidity network, we found that clinical liver patients have a wide range of disease comorbidities, in which the basic liver diseases (e.g. hepatitis b, decompensated liver cirrhosis), and the common chronic diseases (e.g. hypertension, type 2 diabetes), have high degree of disease comorbidities. In addition, we identified 303 patient modules (representing the liver patient subgroups) from the PSN, in which the top 6 modules with large number of cases include 51.68% of the whole cases and 251 modules contain only 10 or fewer cases, which indicates the manifestation diversity of liver diseases. Finally, we found that the patient subgroups actually have distinct symptom phenotypes, disease comorbidity characteristics and their underlying molecular pathways, which could be used for understanding the novel disease subtypes of liver conditions. For example, three patient subgroups, namely Module 6 (M6, n = 638), M2 (n = 623) and M1 (n = 488) were associated to common chronic liver disease conditions (hepatitis, cirrhosis, hepatocellular carcinoma). Meanwhile, patient subgroups of M30 (n = 36) and M36 (n = 37) were mostly related to acute gastroenteritis and upper respiratory infection, respectively, which reflected the individual comorbidity characteristics of liver subgroups. Furthermore, we identified the distinct genes and pathways of patient subgroups and the basic liver diseases (hepatitis b and cirrhosis), respectively. The high degree of overlapping pathways between them (e.g. M36 with 93.33% shared enriched pathways) indicates the underlying molecular network mechanisms of each patient subgroup.

CONCLUSIONS

Our results demonstrate the utility and comprehensiveness of disease classification study based on community detection of patient network using shared TCM symptom phenotypes and it can be used to other more complex diseases.

Reperfusion Activates AP-1 and Heat Shock Response in Donor Kidney Parenchyma after Warm Ischemia

Utilization of kidneys from extended criteria donors leads to an increase in average warm ischemia time (WIT), which is associated with larger degrees of ischemia-reperfusion injury (IRI). Kidney resuscitation by extracorporeal perfusion in situ allows up to 60 minutes of asystole after the circulatory death. Molecular studies of kidney grafts from human donors with critically expanded WIT are warranted. Transcriptomes of two human kidneys from two different donors were profiled after 35-45 minutes of WIT and after 120 minutes of normothermic perfusion and compared. Baseline gene expression patterns in ischemic grafts display substantial intrinsic differences. IRI does not lead to substantial change in overall transcription landscape but activates a highly connected protein network with hubs centered on Jun/Fos/ATF transcription factors and HSP1A/HSPA5 heat shock proteins. This response is regulated by positive feedback. IRI networks are enriched in soluble proteins and biofluids assayable substances, thus, indicating feasibility of the longitudinal, minimally invasive assessment in vivo. Mapping of IRI related molecules in ischemic and reperfused kidneys provides a rationale for possible organ conditioning during machine assisted ex vivo normothermic perfusion. A study of natural diversity of the transcriptional landscapes in presumably normal, transplantation-suitable human organs is warranted.

Metabolic engineering to increase crop yield: From concept to execution

Although the return on investment over the last 20 years for mass screening of individual plant genes to improve crop performance has been low, the investment in these activities was essential to establish the infrastructure and tools of modern plant genomics. Complex traits such as crop yield are likely multigenic, and the exhaustive screening of random gene combinations to achieve yield gains is not realistic. Clearly, smart approaches must be developed. In silico analyses of plant metabolism and gene networks can move a trait discovery program beyond trial-and-error approaches and towards rational design strategies. Metabolic models employing flux-balance analysis are useful to determine the contribution of individual genes to a trait, or to compare, optimize, or even design metabolic pathways. Regulatory association networks provide a transcriptome-based view of the plant and can lead to the identification of transcription factors that control expression of multiple genes affecting a trait. In this review, the use of these models from the perspective of an Ag innovation company's trait discovery and development program will be discussed. Important decisions that can have significant impacts on the cost and timeline to develop a commercial trait will also be presented.

Prediction of Molecular Mechanisms for LianXia NingXin Formula: A Network Pharmacology Study

Objectives: Network pharmacological methods were used to investigate the underlying molecular mechanisms of LianXia NingXin (LXNX) formula, a Chinese prescription, to treat coronary heart disease (CHD) and disease phenotypes (CHD related diseases and symptoms).

Methods: The different seed gene lists associated with the herbs of LXNX formula, the CHD co-morbid diseases and symptoms which were relieved by the LXNX formula (co-morbid diseases and symptoms) were curated manually from biomedical databases and published biomedical literatures. Module enrichment analysis was used to identify CHD-related disease modules in the protein–protein interaction (PPI) network which were also associated to the targets of LXNX formula (LXNX formula’s CHD modules). The molecular characteristics of LXNX formula’s CHD modules were investigated via functional enrichment analysis in terms of gene ontology and pathways. We performed shortest path analysis to explore the interactions between the drug targets of LXNX formula and CHD related disease phenotypes (e.g., co-morbid diseases and symptoms).

Results: We identified two significant CHD related disease modules (i.e., M146 and M203), which were targeted by the herbs of LXNX formula. Pathway and GO term functional analysis results indicated that G-protein coupled receptor signaling pathways (GPCR) of M146 and cellular protein metabolic process of M203 are important functional pathways for the respective module. This is further confirmed by the shortest path analysis between the drug targets of LXNX formula and the aforementioned disease modules. In addition, corticotropin releasing hormone (CRH) and natriuretic peptide precursor A (NPPA) are the only two LXNX formula target proteins with the low shortest path length (on average shorter than 3) to their respective CHD module and co-morbid disease and symptom gene groups.

Conclusion: G-protein coupled receptor signaling pathway and cellular protein metabolic process are the key LXNX formula’s pathways to treat CHD disease phenotypes, in which CRH and NPPA are the two key drug targets of LXNX formula. Further evidences from Chinese herb pharmacological databases indicate that Pinellia ternata (Banxia) has relatively strong adjustive functions on the two key targets.

Integrated Modules Analysis to Explore the Molecular Mechanisms of Phlegm-Stasis Cementation Syndrome with Ischemic Heart Disease

Background: Ischemic heart disease (IHD) has been the leading cause of death for several decades globally, IHD patients usually hold the symptoms of phlegm-stasis cementation syndrome (PSCS) as significant complications. However, the underlying molecular mechanisms of PSCS complicated with IHD have not yet been fully elucidated. Materials and Methods: Network medicine methods were utilized to elucidate the underlying molecular mechanisms of IHD phenotypes. Firstly, high-quality IHD-associated genes from both human curated disease-gene association database and biomedical literatures were integrated. Secondly, the IHD disease modules were obtained by dissecting the protein-protein interaction (PPI) topological modules in the String V9.1 database and the mapping of IHD-associated genes to the PPI topological modules. After that, molecular functional analyses (e.g., Gene Ontology and pathway enrichment analyses) for these IHD disease modules were conducted. Finally, the PSCS syndrome modules were identified by mapping the PSCS related symptom-genes to the IHD disease modules, which were further validated by both pharmacological and physiological evidences derived from published literatures. Results: The total of 1,056 high-quality IHD-associated genes were integrated and evaluated. In addition, eight IHD disease modules (the PPI sub-networks significantly relevant to IHD) were identified, in which two disease modules were relevant to PSCS syndrome (i.e., two PSCS syndrome modules). These two modules had enriched pathways on Toll-like receptor signaling pathway (hsa04620) and Renin-angiotensin system (hsa04614), with the molecular functions of angiotensin maturation (GO:0002003) and response to bacterium (GO:0009617), which had been validated by classical Chinese herbal formulas-related targets, IHD-related drug targets, and the phenotype features derived from human phenotype ontology (HPO) and published biomedical literatures. Conclusion: A network medicine-based approach was proposed to identify the underlying molecular modules of PSCS complicated with IHD, which could be used for interpreting the pharmacological mechanisms of well-established Chinese herbal formulas (e.g., Tao Hong Si Wu Tang, Dan Shen Yin, Hunag Lian Wen Dan Tang and Gua Lou Xie Bai Ban Xia Tang). In addition, these results delivered novel understandings of the molecular network mechanisms of IHD phenotype subtypes with PSCS complications, which would be both insightful for IHD precision medicine and the integration of disease and TCM syndrome diagnoses.