Functional validation of CHMP7 as an ADHD risk gene

Charged multivesicular body protein 7 was identified as a prognostic biomarker correlated with metastasis in colorectal cancer

BACKGROUND

Metastasis is the main reason leading to death in colorectal cancer (CRC) and about 25% of CRC patients developed metastasis when first diagnosed. Thus, unveiling biomarkers of CRC metastasis is of great significance.

AIM

To reveal biomarkers of CRC metastasis.

METHODS

Weighted gene co-expression network analysis was conducted to identify metastatic biomarkers in CRC through a systematic analysis of the GSE29621 dataset. Comprehensive validation was performed subsequently using publicly available datasets from The Cancer Genome Atlas and Gene Expression Omnibus and supplemented with experimental verification in CRC cell lines. Moreover, the identified hub gene charged multivesicular body protein 7 (CHMP7) was further subjected to clinical correlation analysis via Kaplan-Meier survival curves and Gene Set Enrichment Analysis to assess its prognostic significance and potential mechanistic involvement in CRC progression.

RESULTS

CHMP7 was identified as a key metastatic biomarker of CRC which displayed lower expression in CRC tissues, especially in CRC patients with metastasis and CRC cell lines with high metastasis potential. The expression of CHMP7 was significantly correlated with normal, metastatic tumor, pathologic stage, and lymphatic invasion (P < 0.05). CRC patients with higher expression of CHMP7 exhibited better overall survival. Besides, Gene Set Enrichment Analysis results showed that CHMP7 might be involved in metastatic related pathways.

CONCLUSION

Our results indicate that CHMP7 might be a prognostic biomarker correlated with CRC metastasis.

Identification of gene co-expression modules from zebrafish brain data: Applications in psychiatry illustrated through alcohol-related traits

Cumulative evidence suggests that zebrafish is a useful model in psychiatric research. Weighted Gene Co-expression Network Analysis (WGCNA) enables the reduction of genome-wide expression data to modules of highly co-expressed genes, which are hypothesized to interact within molecular networks. In this study, we first applied WGCNA to zebrafish brain expression data across different experimental conditions. Then, we characterized the different co-expression modules by gene-set enrichment analysis and hub gene-phenotype association. Finally, we analyzed association of polygenic risk scores (PRSs) based on genes of some interesting co-expression modules with alcohol dependence in 524 patients and 729 controls from Galicia, using competitive tests. Our approach revealed 34 co-expression modules in the zebrafish brain, with some showing enrichment in human synaptic genes, brain tissues, or brain developmental stages. Moreover, certain co-expression modules were enriched in psychiatry-related GWAS and comprised hub genes associated with psychiatry-related traits in both human GWAS and zebrafish models. Expression patterns of some co-expression modules were associated with the tested experimental conditions, mainly with substance withdrawal and cold stress. Notably, a PRS based on genes from co-expression modules exclusively associated with substance withdrawal in zebrafish showed a stronger association with human alcohol dependence than PRSs based on randomly selected brain-expressed genes. In conclusion, our analysis led to the identification of co-expressed gene modules that may model human brain gene networks involved in psychiatry-related traits. Specifically, we detected a cluster of co-expressed genes whose expression was exclusively associated with substance withdrawal in zebrafish, which significantly contributed to alcohol dependence susceptibility in humans.

Evaluating the regulatory function of non-coding autism-associated single nucleotide polymorphisms on gene expression in human brain tissue

Common variants account for most of the estimated heritability associated with autism spectrum disorder (autism). Although several replicable single nucleotide polymorphisms (SNPs) for the condition have been detected using genome-wide association study (GWAS) methodologies, their pathophysiological relevance remains elusive. Examining this is complicated, however, as all detected loci are situated within non-coding regions of the genome. It is therefore likely that they possess roles of regulatory function as opposed to directly affecting gene coding sequences. To bridge the gap between SNP discovery and mechanistic insight, we applied a comprehensive bioinformatic pipeline to functionally annotate autism-associated polymorphisms and their non-coding linkage disequilibrium (i.e., non-randomly associated) partners. We identified 82 DNA variants of probable regulatory function that may contribute to autism pathogenesis. To validate these predictions, we measured the impact of 11 high-confidence candidates and their GWAS linkage disequilibrium partners on gene expression in human brain tissue from Autistic and non-Autistic donors. Although a small number of the surveyed variants exhibited measurable influence on gene expression as determined via quantitative polymerase chain reaction, these did not survive correction for multiple comparisons. Additionally, no significant genotype-by-diagnosis effects were observed for any of the SNP-gene associations. We contend that this may reflect an inability to effectively capture the modest, neurodevelopmental-specific impact of individual variants on biological dysregulation in available post-mortem tissue samples, as well as limitations in the existing autism GWAS data.

Genetic model of UBA5 deficiency highlights the involvement of both peripheral and central nervous systems and identifies widespread mitochondrial abnormalities

Variants in UBA5 have been reported to cause neurological disease with impaired motor function, developmental delay, intellectual disability and brain pathology as recurrent clinical manifestations. UBA5 encodes a ubiquitin-activating-like enzyme that activates ufmylation, a post-translational ubiquitin-like modification pathway, which has been implicated in neurodevelopment and neuronal survival. The reason behind the variation in severity and clinical manifestations in affected individuals and the signal transduction pathways regulated by ufmylation that compromise the nervous system remains unknown. Zebrafish have emerged as a powerful model to study neurodegenerative disease due to its amenability for in vivo analysis of muscle and neuronal tissues, high-throughput examination of motor function and rapid embryonic development allowing an examination of disease progression. Using clustered regularly interspaced short palindromic repeats-associated protein 9 genome editing, we developed and characterized zebrafish mutant models to investigate disease pathophysiology. uba5 mutant zebrafish showed a significantly impaired motor function accompanied by delayed growth and reduced lifespan, reproducing key phenotypes observed in affected individuals. Our study demonstrates the suitability of zebrafish to study the pathophysiology of UBA5-related disease and as a powerful tool to identify pathways that could reduce disease progression. Furthermore, uba5 mutants exhibited widespread mitochondrial damage in both the nervous system and the skeletal muscle, suggesting that a perturbation of mitochondrial function may contribute to disease pathology.

Predicting Disease-Associated N7-Methylguanosine (m7G) Sites via Random Walk on Heterogeneous Network

Recent studies revealed that the modification of N7-methylguanosine (m7G) has associations with many human diseases. Effectively identifying disease-associated m7G methylation sites would provide crucial clues for disease diagnosis and treatment. Previous studies have developed computational methods to predict disease-associated m7G sites based on similarities among m7G sites and diseases. However, few have focused on the influence of the known m7G-disease association information on calculating similarity measures of m7G site and disease, which potentially promotes the identification of the disease-associated m7G sites. In this work, we propose а computational method called m7GDP-RW to predict m7G-disease associations by random walk algorithm. m7GDP-RW first incorporates the feature information of m7G site and disease with the known m7G-disease associations to compute m7G site similarity and disease similarity. Then m7GDP-RW combines the known m7G-disease associations with the computed similarity of m7G site and disease to construct a m7G-disease heterogeneous network. Finally, m7GDP-RW utilizes a two-pass random walk with restart algorithm to find novel m7G-disease associations on the heterogeneous network. The experimental results show that our method achieves higher prediction accuracy compared to the existing methods. The study case also demonstrates the effectiveness of m7GDP-RW in discovering potential m7G-disease associations.

DNA methylation signature aberration as potential biomarkers in treatment-resistant schizophrenia: Constructing a methylation risk score using a machine learning method

Treatment-resistant schizophrenia (TRS) is defined as a non-response to at least two trials of antipsychotic medication with an adequate dose and duration. We aimed to evaluate the discriminant abilities of DNA methylation probes and methylation risk score between treatment-resistant schizophrenia and non-treatment-resistant schizophrenia. This study recruited 96 schizophrenia patients (TRS and non-TRS) and 56 healthy controls (HC). Participants were divided into a discovery set and a validation set. In the discovery set, we conducted genome-wide methylation analysis (human MethylationEPIC 850K BeadChip) on the subject's blood DNA and discriminated significant methylation signatures, then verified these methylation signatures in the validation set. Based on genome-wide scans of TRS versus non-TRS, thirteen differentially methylated probes were identified at FDR <0.05 and >20% differences in DNA methylation β-values. Next, we selected six probes within gene coding regions (LOC404266, LOXL2, CERK, CHMP7, and SLC17A9) to conduct verification in the validation set using quantitative methylation-specific PCR (qMSP). These six methylation probes showed satisfactory discrimination between TRS patients and non-TRS patients, with an AUC ranging from 0.83 to 0.92, accuracy ranging from 77.8% to 87.3%, sensitivity ranging from 80% to 90%, and specificity ranging from 65.6% to 85%. This methylation risk score model showed satisfactory discrimination between TRS patients and non-TRS patients, with an accuracy of 88.3%. These findings support that methylation signatures may be used as an indicator of TRS vulnerability and provide a model for the clinical use of methylation to identify TRS.

Predicting the prognosis in patients with sepsis by a pyroptosis-related gene signature

Background

Sepsis remains a life-threatening disease with a high mortality rate that causes millions of deaths worldwide every year. Many studies have suggested that pyroptosis plays an important role in the development and progression of sepsis. However, the potential prognostic and diagnostic value of pyroptosis-related genes in sepsis remains unknown.

Methods

The GSE65682 and GSE95233 datasets were obtained from Gene Expression Omnibus (GEO) database and pyroptosis-related genes were obtained from previous literature and Molecular Signature Database. Univariate cox analysis and least absolute shrinkage and selection operator (LASSO) cox regression analysis were used to select prognostic differentially expressed pyroptosis-related genes and constructed a prognostic risk score. Functional analysis and immune infiltration analysis were used to investigate the biological characteristics and immune cell enrichment in sepsis patients who were classified as low- or high-risk based on their risk score. Then the correlation between pyroptosis-related genes and immune cells was analyzed and the diagnostic value of the selected genes was assessed using the receiver operating characteristic curve.

Results

A total of 16 pyroptosis-related differentially expressed genes were identified between sepsis patients and healthy individuals. A six-gene-based (GZMB, CHMP7, NLRP1, MYD88, ELANE, and AIM2) prognostic risk score was developed. Based on the risk score, sepsis patients were divided into low- and high-risk groups, and patients in the low-risk group had a better prognosis. Functional enrichment analysis found that NOD-like receptor signaling pathway, hematopoietic cell lineage, and other immune-related pathways were enriched. Immune infiltration analysis showed that some innate and adaptive immune cells were significantly different between low- and high-risk groups, and correlation analysis revealed that all six genes were significantly correlated with neutrophils. Four out of six genes (GZMB, CHMP7, NLRP1, and AIM2) also have potential diagnostic value in sepsis diagnosis.

Conclusion

We developed and validated a novel prognostic predictive risk score for sepsis based on six pyroptosis-related genes. Four out of the six genes also have potential diagnostic value in sepsis diagnosis.

Validation of Candidate Sleep Disorder Risk Genes Using Zebrafish

Sleep disorders and chronic sleep disturbances are common and are associated with cardio-metabolic diseases and neuropsychiatric disorders. Several genetic pathways and neuronal mechanisms that regulate sleep have been described in animal models, but the genes underlying human sleep variation and sleep disorders are largely unknown. Identifying these genes is essential in order to develop effective therapies for sleep disorders and their associated comorbidities. To address this unmet health problem, genome-wide association studies (GWAS) have identified numerous genetic variants associated with human sleep traits and sleep disorders. However, in most cases, it is unclear which gene is responsible for a sleep phenotype that is associated with a genetic variant. As a result, it is necessary to experimentally validate candidate genes identified by GWAS using an animal model. Rodents are ill-suited for this endeavor due to their poor amenability to high-throughput sleep assays and the high costs associated with generating, maintaining, and testing large numbers of mutant lines. Zebrafish (Danio rerio), an alternative vertebrate model for studying sleep, allows for the rapid and cost-effective generation of mutant lines using the CRISPR/Cas9 system. Numerous zebrafish mutant lines can then be tested in parallel using high-throughput behavioral assays to identify genes whose loss affects sleep. This process identifies a gene associated with each GWAS hit that is likely responsible for the human sleep phenotype. This strategy is a powerful complement to GWAS approaches and holds great promise to identify the genetic basis for common human sleep disorders.

Zebrafish, Medaka and Turquoise Killifish for Understanding Human Neurodegenerative/Neurodevelopmental Disorders

In recent years, small fishes such as zebrafish and medaka have been widely recognized as model animals. They have high homology in genetics and tissue structure with humans and unique features that mammalian model animals do not have, such as transparency of embryos and larvae, a small body size and ease of experiments, including genetic manipulation. Zebrafish and medaka have been used extensively in the field of neurology, especially to unveil the mechanisms of neurodegenerative diseases such as Parkinson's and Alzheimer's disease, and recently, these fishes have also been utilized to understand neurodevelopmental disorders such as autism spectrum disorder. The turquoise killifish has emerged as a new and unique model animal, especially for ageing research due to its unique life cycle, and this fish also seems to be useful for age-related neurological diseases. These small fishes are excellent animal models for the analysis of human neurological disorders and are expected to play increasing roles in this field. Here, we introduce various applications of these model fishes to improve our understanding of human neurological disorders.

Novel pre-clinical model for CDKL5 Deficiency Disorder

Cyclin-dependent kinase-like-5 (CDKL5) deficiency disorder (CDD) is a severe X-linked neurodegenerative disease characterised by early-onset epileptic seizures, low muscle tone, progressive intellectual disability and severe motor function. CDD affects ∼1 in 60,000 live births, with many patients experiencing a reduced quality of life due to the severity of their neurological symptoms and functional impairment. There are no effective therapies for CDD, with current treatments focusing on improving symptoms rather than addressing the underlying causes of the disorder. Zebrafish offer many unique advantages for high-throughput preclinical evaluation of potential therapies for neurological diseases, including CDD. In particular, the large number of offspring produced, together with the possibilities for in vivo imaging and genetic manipulation, allows for the detailed assessment of disease pathogenesis and therapeutic discovery. We have characterised a loss-of-function zebrafish model for CDD, containing a nonsense mutation in cdkl5. cdkl5 mutant zebrafish display defects in neuronal patterning, seizures, microcephaly, and reduced muscle function caused by impaired muscle innervation. This study provides a powerful vertebrate model for investigating CDD disease pathophysiology and allowing high-throughput screening for effective therapies.

This article has an associated First Person interview with the first author of the paper.

Summary: Characterisation of a novel loss-of-function zebrafish model for CDKL5 deficiency disorder, containing a nonsense mutation, demonstrates its utility for investigating disease aetiology and allowing high-throughput screening for potentially effective therapies.

Zebrafish as an Animal Model for Testing Agents with Antidepressant Potential

Depression is a serious mental disease that, according to statistics, affects 320 million people worldwide. Additionally, a current situation related to the COVID-19 pandemic has led to a significant deterioration of mental health in people around the world. So far, rodents have been treated as basic animal models used in studies on this disease, but in recent years, Danio rerio has emerged as a new organism that might serve well in preclinical experiments. Zebrafish have a lot of advantages, such as a quick reproductive cycle, transparent body during the early developmental stages, high genetic and physiological homology to humans, and low costs of maintenance. Here, we discuss the potential of the zebrafish model to be used in behavioral studies focused on testing agents with antidepressant potential.