The association between schizophrenia and white blood cells count: a bidirectional two-sample Mendelian randomization study

The antipsychotic potential of Salix Mucronata on ketamine-induced rats

Salix mucronata is one of the herbal plants offered by the traditional health practitioners in KwaZulu-Natal, South Africa for the treatment of schizophrenia. This study aimed to investigate the effects of repeated administration of ketamine on social interaction, novelty and motivation in adult, male Sprague Dawley rats. It also aimed to investigate the potential of risperidone and the herbal extract of S. mucronata to reverse impairments that are induced by ketamine. Experimental rats (n=45) received a dose of ketamine at 30 mg/kg via intraperitoneal injection for 5 consecutive days. They were then allocated into their respective treatment groups and given risperidone (APD) and the herbal extract of S. mucronata (TM) at doses of 6 mg/kg and 5 mg/kg, respectively, for 7 consecutive days. Social behaviour was tested using the 3-chambered sociability test, and anhedonia was tested using the sucrose preference test. Ketamine induction elicited social withdrawal and reduced social novelty which were later successfully reversed by risperidone and S. mucronata. The rats showed reduced preference to sucrose post-induction and post-treatment. Ketamine and mild stress caused by scruff restraint elicited reduced weight gain for the animals. No differences were noted on brain mass between controls and experimental groups and also between risperidone and S. mucronata groups. However, reduced brain volume was noted in experimental groups. Dopamine and acetylcholine concentration levels were high in groups which received risperidone and S. mucronata. These findings highlight that the antipsychotic potential of S. mucronata is similar to risperidone.

Identification of Immune-Related Biomarkers of Schizophrenia in the Central Nervous System Using Bioinformatic Methods and Machine Learning Algorithms

Schizophrenia is a disastrous mental disorder. Identification of diagnostic biomarkers and therapeutic targets is of significant importance. In this study, five datasets of schizophrenia post-mortem prefrontal cortex samples were downloaded from the GEO database and then merged and de-batched for the analyses of differentially expressed genes (DEGs) and weighted gene co-expression network analysis (WGCNA). The WGCNA analysis showed the six schizophrenia-related modules containing 12,888 genes. The functional enrichment analyses indicated that the DEGs were highly involved in immune-related processes and functions. The immune cell infiltration analysis with the CIBERSORT algorithm revealed 12 types of immune cells that were significantly different between schizophrenia subjects and controls. Additionally, by intersecting DEGs, WGCNA module genes, and an immune gene set obtained from online databases, 151 schizophrenia-associated immune-related genes were obtained. Moreover, machine learning algorithms including LASSO and Random Forest were employed to further screen out 17 signature genes, including GRIN1, P2RX7, CYBB, PTPN4, UBR4, LTF, THBS1, PLXNB3, PLXNB1, PI15, RNF213, CXCL11, IL7, ARHGAP10, TTR, TYROBP, and EIF4A2. Then, SVM-RFE was added, and together with LASSO and Random Forest, a hub gene (EIF4A2) out of the 17 signature genes was revealed. Lastly, in a schizophrenia rat model, the EIF4A2 expression levels were reduced in the model rat brains in a brain-regional dependent manner, but can be reversed by risperidone. In conclusion, by using various bioinformatic and biological methods, this study found 17 immune-related signature genes and a hub gene of schizophrenia that might be potential diagnostic biomarkers and therapeutic targets of schizophrenia.

Acute radiation skin injury in stage III-IV head and neck cancer: scale correlates and predictive model

PURPOSE

Active radiation skin injury (ARSI) has the highest incidence of acute adverse reactions caused by radiotherapy (RT) in patients with head and neck cancer (HNC). This study aimed to screen risk factors that can facilitate the identification of HNC patients at high risk of ARSI.

METHODS

Data from 255 stage III-IV HNC patients who underwent intensity-modulated radiation therapy (IMRT) were collected. The data from our medical records, including clinical characteristics and hematological indices before RT, were retrospectively collected and arranged. The Common Terminology Criteria for Adverse Events Criteria (CTCAE), Radiation Therapy Oncology Group Criteria (RTOG), World Health Organization Criteria (WHO), Oncology Nursing Society (ONS), Acute Radiation Dermatitis Graduation Scale, Douglas & Fowler and Radiation Dermatitis Severity Scale (RDSS) were used to assess ARSI. Of these, CTCAE was used for further analysis. Binary logistic regression analyses were used to identity risk factors. To establish the correction between each risk factor and the ARSI score, the odds ratio (OR) and 95% confidence interval (CI) were computed.

RESULTS

The assessment results of the CTCAE with RTOG, WHO, ONS, Graduation Scale, Douglas & Fowler and RDSS have good consistency. After radiotherapy, 18.4% of patients had at least 3 (3 +) grade ARSI. Multivariate logistic regression analysis revealed that the KPS score, blood glucose level, white blood cell count, and plasma free thyroxine (FT4) concentration were independent risk factors for 3 + grade ARSI. A nomogram was constructed on the basis of these risk factors, which demonstrated good predictive power according to the area under the ROC curve (AUC). The satisfactory consistency and clinical efficacy of the nomogram were confirmed by calibration curves and decision curve analysis (DCA).

CONCLUSION

A low KPS score, high blood glucose level, high white blood cell count, and high thyroid hormone prior to radiotherapy for stage III-IV HNC are independent risk factors for grade 3 + RSI.

A novel blood-based epigenetic biosignature in first-episode schizophrenia patients through automated machine learning

Schizophrenia (SCZ) is a chronic, severe, and complex psychiatric disorder that affects all aspects of personal functioning. While SCZ has a very strong biological component, there are still no objective diagnostic tests. Lately, special attention has been given to epigenetic biomarkers in SCZ. In this study, we introduce a three-step, automated machine learning (AutoML)-based, data-driven, biomarker discovery pipeline approach, using genome-wide DNA methylation datasets and laboratory validation, to deliver a highly performing, blood-based epigenetic biosignature of diagnostic clinical value in SCZ. Publicly available blood methylomes from SCZ patients and healthy individuals were analyzed via AutoML, to identify SCZ-specific biomarkers. The methylation of the identified genes was then analyzed by targeted qMSP assays in blood gDNA of 30 first-episode drug-naïve SCZ patients and 30 healthy controls (CTRL). Finally, AutoML was used to produce an optimized disease-specific biosignature based on patient methylation data combined with demographics. AutoML identified a SCZ-specific set of novel gene methylation biomarkers including IGF2BP1, CENPI, and PSME4. Functional analysis investigated correlations with SCZ pathology. Methylation levels of IGF2BP1 and PSME4, but not CENPI were found to differ, IGF2BP1 being higher and PSME4 lower in the SCZ group as compared to the CTRL group. Additional AutoML classification analysis of our experimental patient data led to a five-feature biosignature including all three genes, as well as age and sex, that discriminated SCZ patients from healthy individuals [AUC 0.755 (0.636, 0.862) and average precision 0.758 (0.690, 0.825)]. In conclusion, this three-step pipeline enabled the discovery of three novel genes and an epigenetic biosignature bearing potential value as promising SCZ blood-based diagnostics.

Bidirectional two-sample Mendelian randomization study of differential white blood cell counts and schizophrenia

BACKGROUND

Schizophrenia and white blood cell counts (WBC) are both complex and polygenic traits. Previous evidence suggests that increased WBC are associated with higher all-cause mortality, and other studies have found elevated WBC in first-episode psychosis and chronic schizophrenia. However, these observational findings may be confounded by antipsychotic exposures and their effects on WBC. Mendelian randomization (MR) is a useful method for examining the directions of genetically-predicted relationships between schizophrenia and WBC.

METHODS

We performed a two-sample MR using summary statistics from genome-wide association studies (GWAS) conducted by the Psychiatric Genomics Consortium Schizophrenia Workgroup (N = 130,644) and the Blood Cell Consortium (N = 563,946). The MR methods included inverse variance weighted (IVW), MR Egger, weighted median, MR-PRESSO, contamination mixture, and a novel approach called mixture model reciprocal causal inference (MRCI). False discovery rate was employed to correct for multiple testing.

RESULTS

Multiple MR methods supported bidirectional genetically-predicted relationships between lymphocyte count and schizophrenia: IVW (b = 0.026; FDR p-value = 0.008), MR Egger (b = 0.026; FDR p-value = 0.008), weighted median (b = 0.013; FDR p-value = 0.049), and MR-PRESSO (b = 0.014; FDR p-value = 0.010) in the forward direction, and IVW (OR = 1.100; FDR p-value = 0.021), MR Egger (OR = 1.231; FDR p-value < 0.001), weighted median (OR = 1.136; FDR p-value = 0.006) and MRCI (OR = 1.260; FDR p-value = 0.026) in the reverse direction. MR Egger (OR = 1.171; FDR p-value < 0.001) and MRCI (OR = 1.154; FDR p-value = 0.026) both suggested genetically-predicted eosinophil count is associated with schizophrenia, but MR Egger (b = 0.060; FDR p-value = 0.010) and contamination mixture (b = -0.013; FDR p-value = 0.045) gave ambiguous results on whether genetically predicted liability to schizophrenia would be associated with eosinophil count. MR Egger (b = 0.044; FDR p-value = 0.010) and MR-PRESSO (b = 0.009; FDR p-value = 0.045) supported genetically predicted liability to schizophrenia is associated with elevated monocyte count, and the opposite direction was also indicated by MR Egger (OR = 1.231; FDR p-value = 0.045). Lastly, unidirectional genetic liability from schizophrenia to neutrophil count were proposed by MR-PRESSO (b = 0.011; FDR p-value = 0.028) and contamination mixture (b = 0.011; FDR p-value = 0.045) method.

CONCLUSION

This MR study utilised multiple MR methods to obtain results suggesting bidirectional genetic genetically-predicted relationships for elevated lymphocyte counts and schizophrenia risk. In addition, moderate evidence also showed bidirectional genetically-predicted relationships between schizophrenia and monocyte counts, and unidirectional effect from genetic liability for eosinophil count to schizophrenia and from genetic liability for schizophrenia to neutrophil count. The influence of schizophrenia to eosinophil count is less certain. Our findings support the role of WBC in schizophrenia and concur with the hypothesis of neuroinflammation in schizophrenia.

Immune dysregulation is associated with symptom dimensions and cognitive deficits in schizophrenia: accessible evidence from complete blood count

BACKGROUND

Schizophrenia (SCZ) is a psychotic disorder with an unknown pathogenesis accompanied by varying degrees of cognitive deficits. Recent studies have shown that immune dysregulation plays an important role in developing symptoms and cognitive deficits in SCZ. This study aimed to determine the complete blood count (CBC), including white blood cells, neutrophils, monocytes, lymphocytes, platelets, neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), and monocyte-lymphocyte ratio (MLR), in patients with SCZ and explore their correlations with SCZ symptom dimensions and cognitive function.

METHODS

Seventy-four patients with SCZ and 57 age- and sex-matched healthy controls with available demographic and clinical information were recruited for this study. Blood samples were collected, and symptom dimensions and cognitive function were evaluated using the Positive and Negative Syndrome Scale (PANSS) and MATRICS Consensus Cognitive Battery (MCCB) separately.

RESULTS

Our results demonstrate that SCZ patients showed higher monocyte counts, PLR, MLR, and worse performance in the total MCCB than healthy controls. Neutrophil and lymphocyte counts and NLR were positively related to symptom severity and negatively related to depressive symptoms. White blood cell (WBC) count, monocyte count, and MLR were positively correlated with cognitive performance in patients with SCZ.

CONCLUSION

In summary, this study suggests that cognitive deficits and symptom severity in patients were associated with dysregulation of immunity. Moreover, we found that WBC could be used as a marker for symptom severity and cognitive deficits in SCZ and that neutrophils are more closely related to the former and monocytes to the latter. We hope that clinicians will pay more attention to dysregulated immunity in patients with SCZ in the future.

Causal relationship between psoriasis vulgaris and dementia: Insights from Mendelian randomization analysis

Many clinical studies have demonstrated a correlation between psoriasis vulgaris and dementia, yet this correlation remains controversial. Our study employed the Mendelian randomization (MR) method to investigate the causal relationship between psoriasis vulgaris and dementia. Data were obtained from the summary statistics of the genome-wide association studies from IEU-OpenGWAS project database. In univariate Mendelian randomization (UVMR) analysis, psoriasis vulgaris was used as exposure. Alzheimer disease (AD), vascular dementia (VaD), dementia with Lewy bodies (DLB), Parkinson's disease with dementia (PDD) and frontotemporal dementia (FTD) served as the outcomes. In multivariate Mendelian randomization (MVMR) analysis, VaD served as the outcome. The first MVMR analysis used psoriasis vulgaris, mean platelet volume (MPV), platelet distribution width (PDW) and platelet count (PLT) as exposures. The second MVMR analysis used psoriasis vulgaris, vitamin D level and 25 hydroxyvitamin D level as exposures. The main analysis employed the inverse variance weighted method, and the outcomes were evaluated by odds ratio (OR) and 95% confidence interval (95% CI). In UVMR analysis, the results depicted that psoriasis vulgaris was associated with VaD (OR: 0.903, 95% CI: 0.818-0.996, p = 0.041). The results revealed insignificant associations between psoriasis vulgaris and other dementia types. After adjusting the effects of MPV, PDW and PLT in MVMR analysis, the association between psoriasis vulgaris and VaD was no longer significant (p = 0.164). Similarly, after adjusting the effects of vitamin D level and 25 hydroxyvitamin D level in MVMR analysis, the association between psoriasis vulgaris and VaD was also no longer significant (p = 0.533). Our study suggests that psoriasis vulgaris may potentially decrease VaD incidence. However, the causal association between psoriasis vulgaris and VaD may be impeded by platelet-related indices, vitamin D level and 25 hydroxyvitamin D level.

Mapping the landscape of lineage-specific dynamic regulation of gene expression using single-cell transcriptomics and application to genetics of complex disease

Single-cell transcriptome data can provide insights into how genetic variation influences biological processes involved in human biology and disease. However, the identification of gene-level associations in distinct cell types faces several challenges, including the limited reference resource from population scale studies, data sparsity in single-cell RNA sequencing, and the complex cell-state pattern of expression within individual cell types. Here we develop genetic models of cell type specific and cell state adjusted gene expression in mid-brain neurons in the process of specializing from induced pluripotent stem cells. The resulting framework quantifies the dynamics of the genetic regulation of gene expression and estimates its cell type specificity. As an application, we show that the approach detects known and new genes associated with schizophrenia and enables insights into context-dependent disease mechanisms. We provide a genomic resource from a phenome-wide application of our models to more than 1500 phenotypes from the UK Biobank. Using longitudinal genetically determined expression, we implement a predictive causality framework, evaluating the prediction of future values of a target gene expression using prior values of a putative regulatory gene. Collectively, this work demonstrates the insights that can be gained into the molecular underpinnings of diseases by quantifying the genetic control of gene expression at single-cell resolution.