Alzheimer's disease, Huntington's disease and cancer

NP-TCMtarget: a network pharmacology platform for exploring mechanisms of action of traditional Chinese medicine

The biological targets of traditional Chinese medicine (TCM) are the core effectors mediating the interaction between TCM and the human body. Identification of TCM targets is essential to elucidate the chemical basis and mechanisms of TCM for treating diseases. Given the chemical complexity of TCM, both in silico high-throughput compound-target interaction predicting models and biological profile-based methods have been commonly applied for identifying TCM targets based on the structural information of TCM chemical components and biological information, respectively. However, the existing methods lack the integration of TCM chemical and biological information, resulting in difficulty in the systematic discovery of TCM action pathways. To solve this problem, we propose a novel target identification model NP-TCMtarget to explore the TCM target path by combining the overall chemical and biological profiles. First, NP-TCMtarget infers TCM effect targets by calculating associations between herb/disease inducible gene expression profiles and specific gene signatures for 8233 targets. Then, NP-TCMtarget utilizes a constructed binary classification model to predict binding targets of herbal ingredients. Finally, we can distinguish TCM direct and indirect targets by comparing the effect targets and binding targets to establish the action pathways of herbal component-direct target-indirect target by mapping TCM targets in the biological molecular network. We apply NP-TCMtarget to the formula XiaoKeAn to demonstrate the power of revealing the action pathways of herbal formula. We expect that this novel model could provide a systematic framework for exploring the molecular mechanisms of TCM at the target level. NP-TCMtarget is available at http://www.bcxnfz.top/NP-TCMtarget.

Mutant Huntingtin Drives Development of an Advantageous Brain Early in Life: Evidence in Support of Antagonistic Pleiotropy

OBJECTIVE

Huntington's disease (HD) is a neurodegenerative disease caused by a triplet repeat expansion within the gene huntingtin (HTT). Antagonistic pleiotropy is a theory of aging that posits that some genes, facilitating individual fitness early in life through adaptive evolutionary changes, also augment detrimental aging-related processes. Antagonistic pleiotropy theory may explain a positive evolutionary pressure toward functionally advantageous brain development that is vulnerable to rapid degeneration. The current study investigated antagonistic pleiotropy in HD using a years-to-onset paradigm in a unique sample of children and young adults at risk for HD.

METHODS

Cognitive, behavioral, motor, and brain structural measures from premanifest gene-expanded (n = 79) and gene nonexpanded (n = 112) participants (6-21 years) in the Kids-HD study were examined. All measures in the gene-expanded group were modeled using a mixed-effects regression approach to assess years-to-onset-based changes while controlling for normal growth. Simultaneously, structure-function associations were also examined.

RESULTS

Decades from motor onset, gene-expanded participants showed significantly better cognitive, behavioral, and motor scores versus gene nonexpanded controls, along with larger cerebral volumes and cortical features. After this initial peak, a prolonged deterioration was observed in both functional and structural measures. Far from onset, brain measures were positively correlated with functional measures, supporting the view that functional advantages were mediated by structural differences.

INTERPRETATION

Mutant HTT may drive the development of a larger than normal brain that subserves superior early-life function. These findings support the antagonistic pleiotropy theory of HTT in HD, where this gene drives early advantage followed by accelerated aging processes. ANN NEUROL 2024;96:1006-1019.

Causal association between colorectal cancer and Alzheimer's disease: a bidirectional two-sample mendelian randomization study

Background: Colorectal cancer and Alzheimer's disease are both common life-threatening diseases in the elderly population. Some studies suggest a possible inverse relationship between colorectal cancer and Alzheimer's disease, but real-world research is subject to many biases. We hope to clarify the causal relationship between the two through a bidirectional two-sample Mendelian randomization study. Methods: In our study, we used genetic summary data from large-scale genome-wide association studies to investigate the relationship between colorectal cancer and Alzheimer's disease. Our primary analysis employed the inverse-variance weighted method and we also used complementary techniques, including MR-Egger, weighted median estimator, and Maximum likelihood. We applied simex adjustment to the MR-Egger results. We also utilized the MRlap package to detect potential sample overlap and its impact on the bias of the results. In addition, we performed several sensitivity and heterogeneity analyses, to ensure the reliability of our results. Results: The combined effect size results of the inverse-variance weighted method indicate that colorectal cancer may decrease the incidence of Alzheimer's disease, with an odds ratio (OR) of 0.846 (95% CI: 0.762-0.929). Similar results were observed using other methods such as MR-Egger, weighted median estimator, and Maximum likelihood. On the other hand, Alzheimer's disease may slightly increase the incidence of colorectal cancer, with an OR of 1.014 (95% CI: 1.001-1.027). However, the results of one subgroup were not significant, and the results from MRlap indicated that sample overlap introduced bias into the results. Therefore, the results of the reverse validation are not reliable. The F-statistic for all SNPs was greater than 20. Four SNPs related to the outcome were excluded using Phenoscanner website but the adjustment did not affect the overall direction of the results. The results of these statistics were further validated by MR-PRESSO, funnel plots, leave-one-out analyses, Cochran's Q, demonstrating the reliability of the findings. Conclusion: According to the findings of this Mendelian randomization study, there appears to be a causal association between colorectal cancer and Alzheimer's disease. These results could have important implications for clinical practice in terms of how colorectal cancer and Alzheimer's disease are treated. To better understand the relationship between these two diseases, more research and screening are needed in clinical settings.

Identification of a novel cellular senescence-related signature for the prediction of prognosis and immunotherapy response in colon cancer

The study was conducted to construct a cellular senescence-related risk score signature to predict prognosis and immunotherapy response in colon cancer. Colon cancer data were acquired from the Gene Expression Omnibus and The Cancer Genome Atlas databases. And cellular senescence-related genes were obtained from the CellAge database. The colon cancer data were classified into different clusters based on cellular senescence-related gene expression. Next, prognostic differential genes among clusters were identified with survival analysis. A cellular senescence-related risk score signature was developed by performing the LASSO regression analysis. Finally, PCA analysis, t-SNE analysis, Kaplan-Meier survival analysis, ROC analysis, univariate Cox regression analysis, multivariate Cox regression analysis, C-index analysis, meta-analysis, immune infiltration analysis, and IPS score analysis were used to evaluate the significance of the risk signature for predicting prognosis and immunotherapy response in colon cancer. The colon cancer data were classified into three clusters. The patients in cluster A and cluster B had longer survival. A cellular senescence-related risk score signature was developed. Patients in the low-risk score group showed a better prognosis. The risk score signature could predict colon cancer patients’ prognosis independently of other clinical characteristics. The risk score signature predicted the prognosis of colon cancer patients more accurately than other signatures. Patients in the low-risk score group showed a better response to immunotherapy. The opposite was true for the high-risk score group. In conclusion, the cellular senescence-related risk score signature could be used for the prediction of prognosis and immunotherapy response in colon cancer.