RCL1 copy number variants are associated with a range of neuropsychiatric phenotypes

A systematic scoping review of rodent models of catatonia: Clinical correlations, translation and future approaches

Catatonia is a psychiatric disorder, which subsumes a plethora of affective, motor and behavioral symptoms. In the last two decades, the number of behavioral and neuroimaging studies on catatonia has steadily increased. The majority of behavioral and neuroimaging studies in psychiatric patients suggested aberrant higher-order frontoparietal networks which, on the biochemical level, are insufficiently modulated by gamma-aminobutyric acid (GABA)-ergic and glutamatergic transmission. However, the pathomechanisms of catatonic symptoms have rarely been studied using rodent models. Here, we performed a scoping review of literature available on PubMed for studies on rodent models of catatonia. We sought to identify what we could learn from pre-clinical animal models of catatonia-like symptoms, their underlying neuronal correlates, and the complex molecular (i.e. genes and neurotransmitter) mechanisms by which its modulation exerts its effects. What becomes evident is that although many transgenic models present catatonia-like symptoms, they have not been used to better understand the pathophysiological mechanisms underlying catatonia so far. However, the identified neuronal correlates of catatonia-like symptoms correlate to a great extent with findings from neuroscience research in psychiatric patients. This points us towards fundamental cortical-striatal-thalamocortical and associated networks modulated by white matter inflammation as well as aberrant dopaminergic, GABAergic, and glutamatergic neurotransmission that is involved in catatonia. Therefore, this scoping review opens up the possibility of finally using transgenic models to help with identifying novel target mechanisms for the development of new drugs for the treatment of catatonia.

Prognosis-Related Molecular Subtypes and Immune Features Associated with Hepatocellular Carcinoma

Bioinformatics tools were used to identify prognosis-related molecular subtypes and biomarkers of hepatocellular carcinoma (HCC). Differential expression analysis of four datasets identified 3330 overlapping differentially expressed genes (DEGs) in the same direction in all four datasets. Those genes were involved in the cell cycle, FOXO signaling pathway, as well as complement and coagulation cascades. Based on non-negative matrix decomposition, two molecular subtypes of HCC with different prognoses were identified, with subtype C2 showing better overall survival than subtype C1. Cox regression and Kaplan-Meier analysis showed that 217 of the overlapping DEGs were closely associated with HCC prognosis. The subset of those genes showing an area under the curve >0.80 was used to construct random survival forest and least absolute shrinkage and selection operator models, which identified seven feature genes (SORBS2, DHRS1, SLC16A2, RCL1, IGFALS, GNA14, and FANCI) that may be involved in HCC occurrence and prognosis. Based on the feature genes, risk score and recurrence models were constructed, while a univariate Cox model identified FANCI as a key gene involved mainly in the cell cycle, DNA replication, and mismatch repair. Further analysis showed that FANCI had two mutation sites and that its gene may undergo methylation. Single-sample gene set enrichment analysis showed that Th2 and T helper cells are significantly upregulated in HCC patients compared to controls. Our results identify FANCI as a potential prognostic biomarker for HCC.

Multiclass Cancer Prediction Based on Copy Number Variation Using Deep Learning

DNA copy number variation (CNV) is the type of DNA variation which is associated with various human diseases. CNV ranges in size from 1 kilobase to several megabases on a chromosome. Most of the computational research for cancer classification is traditional machine learning based, which relies on handcrafted extraction and selection of features. To the best of our knowledge, the deep learning-based research also uses the step of feature extraction and selection. To understand the difference between multiple human cancers, we developed three end-to-end deep learning models, i.e., DNN (fully connected), CNN (convolution neural network), and RNN (recurrent neural network), to classify six cancer types using the CNV data of 24,174 genes. The strength of an end-to-end deep learning model lies in representation learning (automatic feature extraction). The purpose of proposing more than one model is to find which architecture among them performs better for CNV data. Our best model achieved 92% accuracy with an ROC of 0.99, and we compared the performances of our proposed models with state-of-the-art techniques. Our models have outperformed the state-of-the-art techniques in terms of accuracy, precision, and ROC. In the future, we aim to work on other types of cancers as well.

Maize RNA 3'-terminal phosphate cyclase-like protein promotes 18S pre-rRNA cleavage and is important for kernel development

Plant ribosomes contain four specialized ribonucleic acids, the 5S, 5.8S, 18S, and 25S ribosomal RNAs (rRNAs). Maturation of the latter three rRNAs requires cooperative processing of a single transcript by several endonucleases and exonucleases at specific sites. In maize (Zea mays), the exact nucleases and components required for rRNA processing remain poorly understood. Here, we characterized a conserved RNA 3'-terminal phosphate cyclase (RCL)-like protein, RCL1, that functions in 18S rRNA maturation. RCL1 is highly expressed in the embryo and endosperm during early seed development. Loss of RCL1 function resulted in lethality due to aborted embryo cell differentiation. We also observed pleiotropic defects in the rcl1 endosperm, including abnormal basal transfer cell layer growth and aleurone cell identity, and reduced storage reserve accumulation. The rcl1 seeds had lower levels of mature 18S rRNA and the related precursors were altered in abundance compared with wild type. Analysis of transcript levels and protein accumulation in rcl1 revealed that the observed lower levels of zein and starch synthesis enzymes mainly resulted from effects at the transcriptional and translational levels, respectively. These results demonstrate that RCL1-mediated 18S pre-rRNA processing is essential for ribosome function and messenger RNA translation during maize seed development.

Novel CAPN1 missense variants in complex hereditary spastic paraplegia with early-onset psychosis

CAPN1-associated hereditary spastic paraplegia (SPG76) is a rare and clinically heterogenous syndrome due to loss of calpain-1 function. Here we illustrate a translational approach to the case of an 18-year-old patient who first presented with psychiatric symptoms followed by spastic gait, intention tremor, and neurogenic bladder dysfunction, consistent with a complex form of HSP. Exome sequencing showed compound-heterozygous missense variants in CAPN1 (NM_001198868.2: c.1712A>G (p.Asn571Ser)/c.1991C>T (p.Ser664Leu)) and a previously reported heterozygous stop-gain variant in RCL1. In silico analyses of the CAPN1 variants predicted a deleterious effect and in vitro functional studies confirmed reduced calpain-1 activity and dysregulated downstream signaling. These findings support a diagnosis of SPG76 and highlight that the psychiatric symptoms can precede the motor symptoms in HSP. Our results also suggest that multiple genes can potentially contribute to complex neuropsychiatric diseases.

Rcl1 suppresses tumor progression of hepatocellular carcinoma: a comprehensive analysis of bioinformatics and in vitro experiments

Background

RNA 3’-terminal phosphate cyclase-like protein (Rcl1) is involved in pre-rRNA processing, but its implication in cancers remains unclear.

Methods

RCL1 expressions in 21 malignancies was examinated through GEPIA website portal. Clinical implication data related to RCL1 level in Hepatocellular Carcinoma (HCC) samples were downloaded through TCGA, ICGC, GEO databases. Survival analysis and gene function enrichment analyses were performed through R software. The correlation between RCL1 expression and tumor immune infiltration was assessed via the TIMER2.0 database. The effects of Rcl1 overexpression or knockdown on cell growth and metastasis was evaluated by CCK8, transwell, and cell cycle assays.

Results

RCL1 expression is commonly down-regulated in HCC. The lower expression of RCL1 is associated with higher tumor stage, higher AFP level, vascular invasion, and poor prognosis. RCL1 expression has a significant correlation with immune cells infiltration in HCC, especially myeloid-derived suppressor cell (MDSC). Moreover, it was further identified that Rcl1 expression was reduced in HCC cell lines and negatively correlated with invasion of HCC cell lines. Immunofluorescence (IF) analysis revealed that the level of Rcl1 expression in the cytoplasm of HCC cells is significantly lower than that in the cytoplasm of L-02 cell. Moreover, both gain- and loss-of-function studies demonstrated that Rcl1 inhibited the growth and metastasis of HCC cells and regulated cell cycle progression in vitro.

Conclusions

Rcl1 may serve as a novel tumor suppressor in HCC, and its biological effect needs further study.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-022-02533-x.

Rcl1 mRNA expression is down-regulated within HCC tissues and associated with poor prognosis and disease progression. Anti-cancer effects of Rcl1 on HCC were confirmed in vitro. Rcl1 may be a potential tumor suppressor in HCC.

Identifying Prognostic Significance of RCL1 and Four-Gene Signature as Novel Potential Biomarkers in HCC Patients

BACKGROUND

Hepatocellular carcinoma (HCC) is a highly malignant disease, and it is characterized by rapid progression and low five-year survival rate. At present, there are no effective methods for monitoring the treatment and prognosis of HCC.

METHODS

The transcriptome and gene expression profiles of HCC were obtained from the Cancer Genome Atlas (TCGA) program, International Cancer Genome Consortium (ICGC), and Gene Expression Omnibus (GEO) databases. The random forest method was applied to construct a four-gene prognostic model based on RNA terminal phosphate cyclase like 1 (RCL1) expression. The Kaplan-Meier method was performed to evaluate the prognostic value of RCL1, long noncoding RNAs (AC079061, AL354872, and LINC01093), and four-gene signature (SPP1, MYBL2, TRNP1, and FTCD). We examined the relationship between RCL1 expression and immune cells infiltration, tumor mutation burden (TMB), and microsatellite instability (MSI).

RESULTS

The results of multiple databases indicated that the aberrant expression of RCL1 was associated with clinical outcome, immune cells infiltration, TMB, and MSI in HCC patients. Meanwhile, we found that long noncoding RNAs (AC079061, AL354872, and LINC01093) and RCL1 were significantly coexpressed in HCC patients. We also confirmed that the four-gene signature was an independent prognostic factor for HCC patients. Ferroptosis potential index, immune checkpoint molecules, and clinical feature were found to have obvious correlations with risk score. The area under the receiver operating characteristic curve values for the model were 0.7-0.8 in the training set and the validation set, suggesting high robustness of the four-gene signature. We then built a nomogram for facilitating the use in clinical practice.

CONCLUSION

Our study demonstrated that RCL1 and a novel four-gene signature can be used as prognostic biomarkers for predicting clinical outcome in HCC patients; and this model may assist in individualized treatment monitoring of HCC patients in clinical practice.