Robust rank aggregation for gene list integration and meta-analysis

Bipolar disorder: Construction and analysis of a joint diagnostic model using random forest and feedforward neural networks

Background

To construct a diagnostic model for Bipolar Disorder (BD) depressive phase using peripheral tissue RNA data from patients and combining Random Forest with Feedforward Neural Network methods.

Methods

Datasets GSE23848, GSE39653, and GSE69486 were selected, and differential gene expression analysis was conducted using the limma package in R. Key genes from the differentially expressed genes were identified using the Random Forest method. These key genes' expression levels in each sample were used to train a Feedforward Neural Network model. Techniques like L1 regularization, early stopping, and dropout layers were employed to prevent model overfitting. Model performance was then validated, followed by GO, KEGG, and protein-protein interaction network analyses.

Results

The final model was a Feedforward Neural Network with two hidden layers and two dropout layers, comprising 2345 trainable parameters. Model performance on the validation set, assessed through 1000 bootstrap resampling iterations, demonstrated a specificity of 0.769 (95 % CI 0.571-1.000), sensitivity of 0.818 (95 % CI 0.533-1.000), AUC value of 0.832 (95 % CI 0.642-0.979), and accuracy of 0.792 (95 % CI 0.625-0.958). Enrichment analysis of key genes indicated no significant enrichment in any known pathways.

Conclusion

Key genes with biological significance were identified based on the decrease in Gini coefficient within the Random Forest model. The combined use of Random Forest and Feedforward Neural Network to establish a diagnostic model showed good classification performance in Bipolar Disorder.

CRCDB: A comprehensive database for integrating and analyzing multi-omics data of early-onset and late-onset colorectal cancer

The incidence of early-onset colorectal cancer (EOCRC) has increased significantly worldwide. Uncovering biomarkers that are unique to EOCRC is of great importance to facilitate the prevention and detection of this growing cancer subtype. Although efforts have been made in the data curation about CRC, there is no integrated platform that gives access to data specifically related to young CRC patients. Here, we constructed a user-friendly open integrated resource called CRCDB (URL: http://crcdb-hust.com) which contains multi-omics data of 785 EOCRC, 4898 late-onset CRCs (LOCRC), and 1110 normal control samples from tissue, whole blood, platelets, and serum exosomes. CRCDB manages the differential analysis, survival analysis, co-expression analysis, and immune cell infiltration comparison analysis results in different CRC groups. Meta-analysis results were also provided for users for further data interpretation. Using the resource in CRCDB, we identified that genes associated with the metabolic process were less expressed in EOCRC patients, while up regulated genes most associated with the mitosis process might play an important role in the molecular pathogenesis of LOCRC. Survival-related genes were most enriched in oxidoreduction pathways in EOCRC while in immune-related pathways in LOCRC. With all the data gathered and processed, we anticipate that CRCDB could be a practical data mining platform to help explore potential applications of omics data and develop effective prevention and therapeutic strategies for the specific group of CRC patients.

Increased Expression and Prognostic Significance of BYSL in Melanoma

We evaluated the BYSL content and underlying mechanism in melanoma (SKCM) overall survival (OS). In this study, we used a comprehensive approach combining bioinformatics tools, including miRNA estimation, quantitative real-time polymerase chain reaction (qRT-PCR) of miRNAs, E3 ligase estimation, STRING analysis, TIMER analysis, examination of associated upstream modulators, protein-protein interaction (PPI) analysis, as well as retrospective and survival analyses, alongside clinical sample validation. These methods were used to investigate the content of BYSL, its methylation status, its relation to patient outcome, and its immunologic significance in tumors. Our findings revealed that BYSL expression is negatively regulated by BYSL methylation. Analysis of 468 cases of SKCM RNA sequencing samples demonstrated that enhanced BYSL expression was associated with higher tumor grade. We identified several miRNAs, namely hsa-miR-146b-3p, hsa-miR-342-3p, hsa-miR-511-5p, hsa-miR-3690, and hsa-miR-193a-5p, which showed a strong association with BYSL levels. Furthermore, we predicted the E3 ubiquitin ligase of BYSL and identified CBL, FBXW7, FZR1, KLHL3, and MARCH1 as potential modulators of BYSL. Through our investigation, we discovered that PNO1, RIOK2, TSR1, WDR3, and NOB1 proteins were strongly associated with BYSL expression. In addition, we found a close association between BYSL levels and certain immune cells, particularly dendritic cells (DCs). Notably, we observed a significant negative correlation between miR-146b-3p and BYSL mRNA expression in SKCM sera samples. Collectively, based on the previously shown evidences, BYSL can serve as a robust bioindicator of SKCM patient prognosis, and it potentially contributes to immune cell invasion in SKCM.

Five latent factors underlie response to immunotherapy

Only a subset of patients treated with immune checkpoint inhibitors (CPIs) respond to the treatment, and distinguishing responders from non-responders is a major challenge. Many proposed biomarkers of CPI response and survival probably represent alternative measurements of the same aspects of the tumor, its microenvironment or the host. Thus, we currently ignore how many truly independent biomarkers there are. With an unbiased analysis of genomics, transcriptomics and clinical data of a cohort of patients with metastatic tumors (n = 479), we discovered five orthogonal latent factors: tumor mutation burden, T cell effective infiltration, transforming growth factor-beta activity in the microenvironment, prior treatment and tumor proliferative potential. Their association with CPI response and survival was observed across all tumor types and validated across six independent cohorts (n = 1,491). These five latent factors constitute a frame of reference to organize current and future knowledge on biomarkers of CPI response and survival.

Multiple algorithms highlight key brain genes driven by multiple anesthetics

Anesthesia serves as a pivotal tool in modern medicine, creating a transient state of sensory deprivation to ensure a pain-free surgical or medical intervention. While proficient in alleviating pain, anesthesia significantly modulates brain dynamics, metabolic processes, and neural signaling, thereby impairing typical cognitive functions. Furthermore, anesthesia can induce notable impacts such as memory impairment, decreased cognitive function, and diminished intelligence, emphasizing the imperative need to explore the concealed repercussions of anesthesia on individuals. In this investigation, we aggregated gene expression profiles (GSE64617, GSE141242, GSE161322, GSE175894, and GSE178995) from public repositories following second-generation sequencing analysis of various anesthetics. Through scrutinizing post-anesthesia brain tissue gene expression utilizing Gene Set Enrichment Analysis (GSEA), Robust Rank Aggregation (RRA), and Weighted Gene Co-expression Network Analysis (WGCNA), this research aims to pinpoint pivotal genes, pathways, and regulatory networks linked to anesthesia. This undertaking not only enhances comprehension of the physiological changes brought about by anesthesia but also lays the groundwork for future investigations, cultivating new insights and innovative perspectives in medical practice.

Context-dependent roles of mitochondrial LONP1 in orchestrating the balance between airway progenitor versus progeny cells

While all eukaryotic cells are dependent on mitochondria for function, in a complex tissue, which cell type and which cell behavior are more sensitive to mitochondrial deficiency remain unpredictable. Here, we show that in the mouse airway, compromising mitochondrial function by inactivating mitochondrial protease gene Lonp1 led to reduced progenitor proliferation and differentiation during development, apoptosis of terminally differentiated ciliated cells and their replacement by basal progenitors and goblet cells during homeostasis, and failed airway progenitor migration into damaged alveoli following influenza infection. ATF4 and the integrated stress response (ISR) pathway are elevated and responsible for the airway phenotypes. Such context-dependent sensitivities are predicted by the selective expression of Bok, which is required for ISR activation. Reduced LONP1 expression is found in chronic obstructive pulmonary disease (COPD) airways with squamous metaplasia. These findings illustrate a cellular energy landscape whereby compromised mitochondrial function could favor the emergence of pathological cell types.

Machine Learning of Plasma Proteomics Classifies Diagnosis of Interstitial Lung Disease

Rationale: Distinguishing connective tissue disease-associated interstitial lung disease (CTD-ILD) from idiopathic pulmonary fibrosis (IPF) can be clinically challenging. Objectives: To identify proteins that separate and classify patients with CTD-ILD and those with IPF. Methods: Four registries with 1,247 patients with IPF and 352 patients with CTD-ILD were included in analyses. Plasma samples were subjected to high-throughput proteomics assays. Protein features were prioritized using recursive feature elimination to construct a proteomic classifier. Multiple machine learning models, including support vector machine, LASSO (least absolute shrinkage and selection operator) regression, random forest, and imbalanced Random Forest, were trained and tested in independent cohorts. The validated models were used to classify each case iteratively in external datasets. Measurements and Main Results: A classifier with 37 proteins (proteomic classifier 37 [PC37]) was enriched in the biological process of bronchiole development and smooth muscle proliferation and immune responses. Four machine learning models used PC37 with sex and age score to generate continuous classification values. Receiver operating characteristic curve analyses of these scores demonstrated consistent areas under the curve of 0.85-0.90 in the test cohort and 0.94-0.96 in the single-sample dataset. Binary classification demonstrated 78.6-80.4% sensitivity and 76-84.4% specificity in the test cohort and 93.5-96.1% sensitivity and 69.5-77.6% specificity in the single-sample classification dataset. Composite analysis of all machine learning models confirmed 78.2% (194 of 248) accuracy in the test cohort and 82.9% (208 of 251) in the single-sample classification dataset. Conclusions: Multiple machine learning models trained with large cohort proteomic datasets consistently distinguished CTD-ILD from IPF. Many of the identified proteins are involved in immune pathways. We further developed a novel approach for single-sample classification, which could facilitate honing the differential diagnosis of ILD in challenging cases and improve clinical decision making.

Identification and validation of Rab GTPases RAB13 as biomarkers for peritoneal metastasis and immune cell infiltration in colorectal cancer patients

Background

As one of the most common cancer, colorectal cancer (CRC) is with high morbidity and mortality. Peritoneal metastasis (PM) is a fatal state of CRC, and few patients may benefit from traditional therapies. There is a complex interaction between PM and immune cell infiltration. Therefore, we aimed to determine biomarkers associated with colorectal cancer peritoneal metastasis (CRCPM) and their relationship with immune cell infiltration.

Methods

By informatic analysis, differently expressed genes (DEGs) were selected and hub genes were screened out. RAB13, one of the hub genes, was identificated from public databases and validated in CRC tissues. The ESTIMATE, CEBERSORT and TIMER algorithms were applied to analyze the correlation between RAB13 and immune infiltration in CRC. RAB13's expression in different cells were analyzed at the single-cell level in scRNA-Seq. The Gene Set Enrichment Analysis (GSEA) was performed for RAB13 enrichment and further confirmed. Using oncoPredict algorithm, RAB13's impact on drug sensitivity was evaluated.

Results

High RAB13 expression was identified in public databases and led to a poor prognosis. RAB13 was found to be positively correlated with the macrophages and other immune cells infiltration and from scRNA-Seq, RAB13 was found to be located in CRC cells and macrophages. GSEA revealed that high RAB13 expression enriched in a various of biological signaling, and oncoPredict algorithm showed that RAB13 expression was correlated with paclitaxel sensitivity.

Conclusion

Our study indicated clinical role of RAB13 in CRC-PM, suggesting its potential as a therapeutic target in the future.

The life-saving benefit of dexamethasone in severe COVID-19 is linked to a reversal of monocyte dysregulation

Dexamethasone is a life-saving treatment for severe COVID-19, yet its mechanism of action is unknown, and many patients deteriorate or die despite timely treatment initiation. Here, we identify dexamethasone treatment-induced cellular and molecular changes associated with improved survival in COVID-19 patients. We observed a reversal of transcriptional hallmark signatures in monocytes associated with severe COVID-19 and the induction of a monocyte substate characterized by the expression of glucocorticoid-response genes. These molecular responses to dexamethasone were detected in circulating and pulmonary monocytes, and they were directly linked to survival. Monocyte single-cell RNA sequencing (scRNA-seq)-derived signatures were enriched in whole blood transcriptomes of patients with fatal outcome in two independent cohorts, highlighting the potential for identifying non-responders refractory to dexamethasone. Our findings link the effects of dexamethasone to specific immunomodulation and reversal of monocyte dysregulation, and they highlight the potential of single-cell omics for monitoring in vivo target engagement of immunomodulatory drugs and for patient stratification for precision medicine approaches.

Evaluating the significance of ECSCR in the diagnosis of ulcerative colitis and drug efficacy assessment

Background

The main challenge in diagnosing and treating ulcerative colitis (UC) has prompted this study to discover useful biomarkers and understand the underlying molecular mechanisms.

Methods

In this study, transcriptomic data from intestinal mucosal biopsies underwent Robust Rank Aggregation (RRA) analysis to identify differential genes. These genes intersected with UC key genes from Weighted Gene Co-expression Network Analysis (WGCNA). Machine learning identified UC signature genes, aiding predictive model development. Validation involved external data for diagnostic, progression, and drug efficacy assessment, along with ELISA testing of clinical serum samples.

Results

RRA integrative analysis identified 251 up-regulated and 211 down-regulated DEGs intersecting with key UC genes in WGCNA, yielding 212 key DEGs. Subsequently, five UC signature biomarkers were identified by machine learning based on the key DEGs-THY1, SLC6A14, ECSCR, FAP, and GPR109B. A logistic regression model incorporating these five genes was constructed. The AUC values for the model set and internal validation data were 0.995 and 0.959, respectively. Mechanistically, activation of the IL-17 signaling pathway, TNF signaling pathway, PI3K-Akt signaling pathway in UC was indicated by KEGG and GSVA analyses, which were positively correlated with the signature biomarkers. Additionally, the expression of the signature biomarkers was strongly correlated with various UC types and drug efficacy in different datasets. Notably, ECSCR was found to be upregulated in UC serum and exhibited a positive correlation with neutrophil levels in UC patients.

Conclusions

THY1, SLC6A14, ECSCR, FAP, and GPR109B can serve as potential biomarkers of UC and are closely related to signaling pathways associated with UC progression. The discovery of these markers provides valuable information for understanding the molecular mechanisms of UC.

Targeting tumor-associated macrophage-derived CD74 improves efficacy of neoadjuvant chemotherapy in combination with PD-1 blockade for cervical cancer

BACKGROUND

Cervical cancer has the second-highest mortality rate among malignant tumors of the female reproductive system. Immune checkpoint inhibitors such as programmed cell death protein 1 (PD-1) blockade are promising therapeutic agents, but their efficacy when combined with neoadjuvant chemotherapy (NACT) has not been fully tested, and how they alter the tumor microenvironment has not been comprehensively elucidated.

METHODS

In this study, we conducted single-cell RNA sequencing using 46,950 cells from nine human cervical cancer tissues representing sequential different stages of NACT and PD-1 blockade combination therapy. We delineated the trajectory of cervical epithelial cells and identified the crucial factors involved in combination therapy. Cell-cell communication analysis was performed between tumor and immune cells. In addition, THP-1-derived and primary monocyte-derived macrophages were cocultured with cervical cancer cells and phagocytosis was detected by flow cytometry. The antitumor activity of blocking CD74 was validated in vivo using a CD74 humanized subcutaneous tumor model.

RESULTS

Pathway enrichment analysis indicated that NACT activated cytokine and complement-related immune responses. Cell-cell communication analysis revealed that after NACT therapy, interaction strength between T cells and cancer cells decreased, but intensified between macrophages and cancer cells. We verified that macrophages were necessary for the PD-1 blockade to exert antitumor effects in vitro. Additionally, CD74-positive macrophages frequently interacted with the most immunoreactive epithelial subgroup 3 (Epi3) cancer subgroup during combination NACT. We found that CD74 upregulation limited phagocytosis and stimulated M2 polarization, whereas CD74 blockade enhanced macrophage phagocytosis, decreasing cervical cancer cell viability in vitro and in vivo.

CONCLUSIONS

Our study reveals the dynamic cell-cell interaction network in the cervical cancer microenvironment influenced by combining NACT and PD-1 blockade. Furthermore, blocking tumor-associated macrophage-derived CD74 could augment neoadjuvant therapeutic efficacy.

Combined Analysis of Multi-Study miRNA and mRNA Expression Data Shows Overlap of Selected miRNAs Involved in West Nile Virus Infections

The emerging zoonotic West Nile virus (WNV) has serious impact on public health. Thus, understanding the molecular basis of WNV infections in mammalian hosts is important to develop improved diagnostic and treatment strategies. In this context, the role of microRNAs (miRNAs) has been analyzed by several studies under different conditions and with different outcomes. A systematic comparison is therefore necessary. Furthermore, additional information from mRNA target expression data has rarely been taken into account to understand miRNA expression profiles under WNV infections. We conducted a meta-analysis of publicly available miRNA expression data from multiple independent studies, and analyzed them in a harmonized way to increase comparability. In addition, we used gene-set tests on mRNA target expression data to further gain evidence about differentially expressed miRNAs. For this purpose, we also studied the use of target information from different databases. We detected a substantial number of miRNA that emerged as differentially expressed from several miRNA datasets, and from the mRNA target data analysis as well. When using mRNA target data, we found that the targetscan databases provided the most useful information. We demonstrated improved miRNA detection through research synthesis of multiple independent miRNA datasets coupled with mRNA target set testing, leading to the discovery of multiple miRNAs which should be taken into account for further research on the molecular mechanism of WNV infections.

Predicting biomarkers related to idiopathic pulmonary fibrosis: Robust ranking aggregation analysis and animal experiment verification

Idiopathic pulmonary fibrosis (IPF) is a progressive and incurable lung disease characterized by unknown etiology. This study employs robust ranking aggregation to identify consistent differential genes across multiple datasets, aiming to enhance prognostic evaluation and facilitate the development of more effective immunotherapy strategies for IPF. Using the GSE10667, GSE110147, and GSE24206 datasets, the analysis identifies 92 robust differentially expressed genes (DEGs), including SPP1, IGF1, ASPN, and KLHL13, highlighted as potential biomarkers through machine learning and experimental validation. Additionally, significant differences in immune cell types between IPF samples and controls, such as Plasma cells, Macrophages M0, Mast cells resting, T cells CD8, and NK cells resting, inform the construction of diagnostic and survival prediction models, demonstrating good applicability. These findings provide insights into IPF pathophysiology and suggest potential therapeutic targets.

Acetate drives ovarian cancer quiescence via ACSS2-mediated acetyl-CoA production

Quiescence is a reversible cell cycle exit traditionally thought to be associated with a metabolically inactive state. Recent work in muscle cells indicates that metabolic reprogramming is associated with quiescence. Whether metabolic changes occur in cancer to drive quiescence is unclear. Using a multi-omics approach, we found that the metabolic enzyme ACSS2, which converts acetate into acetyl-CoA, is both highly upregulated in quiescent ovarian cancer cells and required for their survival. Indeed, quiescent ovarian cancer cells have increased levels of acetate-derived acetyl-CoA, confirming increased ACSS2 activity in these cells. Furthermore, either inducing ACSS2 expression or supplementing cells with acetate was sufficient to induce a reversible quiescent cell cycle exit. RNA-Seq of acetate treated cells confirmed negative enrichment in multiple cell cycle pathways as well as enrichment of genes in a published G0 gene signature. Finally, analysis of patient data showed that ACSS2 expression is upregulated in tumor cells from ascites, which are thought to be more quiescent, compared to matched primary tumors. Additionally, high ACSS2 expression is associated with platinum resistance and worse outcomes. Together, this study points to a previously unrecognized ACSS2-mediated metabolic reprogramming that drives quiescence in ovarian cancer. As chemotherapies to treat ovarian cancer, such as platinum, have increased efficacy in highly proliferative cells, our data give rise to the intriguing question that metabolically-driven quiescence may affect therapeutic response.

LINC01133 promotes pancreatic ductal adenocarcinoma epithelial-mesenchymal transition mediated by SPP1 through binding to Arp3

Pancreatic ductal adenocarcinoma (PDAC) is a lethal disease with limited treatment methods. Long non-coding RNAs (lncRNAs) have been found involved in tumorigenic and progression. The present study revealed that LINC01133, a fewly reported lncRNA, was one of 16 hub genes that could predict PDAC patients' prognosis. LINC01133 was over-expressed in PDAC tumors compared to adjacent pancreas and could promote PDAC proliferation and metastasis in vitro and in vivo, as well as inhibit PDAC apoptosis. LINC01133 expression positively correlated to secreted phosphoprotein 1 (SPP1) expression, leading to an enhanced epithelial-mesenchymal transition (EMT) process. LINC01133 bound with actin-related protein 3 (Arp3), the complex reduced SPP1 mRNA degradation which increased SPP1 mRNA level, ultimately leading to PDAC proliferation. This research revealed a novel mechanism of PDAC development and provided a potential prognosis indicator that may benefit PDAC patients.

Epigenetic regulation by TET1 in gene-environmental interactions influencing susceptibility to congenital malformations

The etiology of neural tube defects (NTDs) involves complex gene-environmental interactions. Folic acid (FA) prevents NTDs, but the mechanisms remain poorly understood and at least 30% of human NTDs resist the beneficial effects of FA supplementation. Here, we identify the DNA demethylase TET1 as a nexus of folate-dependent one-carbon metabolism and genetic risk factors post-neural tube closure. We determine that cranial NTDs in Tet1 -/- embryos occur at two to three times higher penetrance in genetically heterogeneous than in homogeneous genetic backgrounds, suggesting a strong impact of genetic modifiers on phenotypic expression. Quantitative trait locus mapping identified a strong NTD risk locus in the 129S6 strain, which harbors missense and modifier variants at genes implicated in intracellular endocytic trafficking and developmental signaling. NTDs across Tet1 -/- strains are resistant to FA supplementation. However, both excess and depleted maternal FA diets modify the impact of Tet1 loss on offspring DNA methylation primarily at neurodevelopmental loci. FA deficiency reveals susceptibility to NTD and other structural brain defects due to haploinsufficiency of Tet1. In contrast, excess FA in Tet1 -/- embryos drives promoter DNA hypermethylation and reduced expression of multiple membrane solute transporters, including a FA transporter, accompanied by loss of phospholipid metabolites. Overall, our study unravels interactions between modified maternal FA status, Tet1 gene dosage and genetic backgrounds that impact neurotransmitter functions, cellular methylation and individual susceptibilities to congenital malformations, further implicating that epigenetic dysregulation may underlie NTDs resistant to FA supplementation.

A single-cell transcriptomic study of heterogeneity in human embryonic tanycytes

Disruptions in energy homeostasis can lead to diseases like obesity and diabetes, affecting millions of people each year. Tanycytes, the adult stem cells in the hypothalamus, play crucial roles in assisting hypothalamic neurons in maintaining energy balance. Although tanycytes have been extensively studied in rodents, our understanding of human tanycytes remains limited. In this study, we utilized single-cell transcriptomics data to explore the heterogeneity of human embryonic tanycytes, investigate their gene regulatory networks, analyze their intercellular communication, and examine their developmental trajectory. Our analysis revealed the presence of two clusters of β tanycytes and three clusters of α tanycytes in our dataset. Surprisingly, human embryonic tanycytes displayed significant similarities to mouse tanycytes in terms of marker gene expression and transcription factor activities. Trajectory analysis indicated that α tanycytes were the first to be generated, giving rise to β tanycytes in a dorsal-ventral direction along the third ventricle. Furthermore, our CellChat analyses demonstrated that tanycytes generated earlier along the developmental lineages exhibited increased intercellular communication compared to those generated later. In summary, we have thoroughly characterized the heterogeneity of human embryonic tanycytes from various angles. We are confident that our findings will serve as a foundation for future research on human tanycytes.

Analysis and validation of hub genes in neutrophil extracellular traps for the long-term prognosis of myocardial infarction

INTRODUCTION

The study focuses on the long-term prognosis of myocardial infarction (MI) influenced by neutrophil extracellular traps (NETs). It also aims to analyze and validate relative hub genes in this process, in order to further explore new therapeutic targets that can improve the prognosis of MI.

MATERIALS AND METHODS

We established a MI model in mice by ligating the left anterior descending branch (LAD) and conducted an 8-week continuous observation to study the dynamic changes in the structure and function of the heart in these mice. Meanwhile, we administered Apocynin, an inhibitor of NADPH Oxidase, which has also been shown to inhibit the formation of NETs, to mice undergoing MI surgery in order to compare. This study employed hematoxylin-eosin (HE) staining, echocardiography, immunofluorescence, and real-time quantitative PCR (RT-qPCR) to examine the impact of NETs on the long-term prognosis of MI. Next, datasets related to MI and NETs were downloaded from the GEO database, respectively. The Limma package of R software was used to identify differentially expressed genes (DEGs). After analyzing the "Robust Rank Aggregation (RRA)" package, we conducted a screening for robust differentially expressed genes (DEGs) and performed pathway enrichment analysis using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) to determine the functional roles of these robust DEGs. The protein-protein interaction (PPI) network was visualized and hub genes were filtered using Cytoscape.

RESULTS

Immunofluorescence and qPCR results showed an increase in the expression of Myeloperoxidase (MPO) at week 1 and week 8 in the hearts of mice after MI. HE staining reveals a series of pathological manifestations in the heart of the MI group during 8 weeks, including enlarged size, disordered arrangement of cardiomyocytes, infiltration of inflammatory cells, and excessive deposition of collagen fibers, among others. The utilization of Apocynin could significantly improve these poor performances. The echocardiography displayed the cardiac function of the heart in mice. The MI group has a reduced range of heart movement and decreased ejection ability. Moreover, the ventricular systolic movement was found to be abnormal, and its wall thickening rate decreased over time, indicating a progressive worsening of myocardial ischemia. The Apocynin group, on the contrary, showed fewer abnormal changes in the aforementioned aspects. A total of 81 DEGs and 4 hub genes (FOS, EGR1, PTGS2, and HIST1H4H) were obtained. The results of RT-qPCR demonstrated abnormal expression of these four genes in the MI group, which could be reversed by treatment of Apocynin.

CONCLUSION

The NETs formation could be highly related to MI and the long-term prognosis of MI can be significantly influenced by the NETs formation. Four hub genes, namely FOS, EGR1, PTGS2, and HIST1H4H, have the potential to be key genes related to this process. They could also serve as biomarkers for predicting MI prognosis and as targets for gene therapy.

A machine learning and drug repurposing approach to target ferroptosis in colorectal cancer stratified by sex and KRAS

The landscape of sex differences in Colorectal Cancer (CRC) has not been well characterized with respect to the mechanisms of action for oncogenes such as KRAS. However, our recent study showed that tumors from male patients with KRAS mutations have decreased iron-dependent cell death called ferroptosis. Building on these findings, we further examined ferroptosis in CRC, considering both sex of the patient and KRAS mutations, using public databases and our in-house CRC tumor cohort. Through subsampling inference and variable importance analysis (VIMP), we identified significant differences in gene expression between KRAS mutant and wild type tumors from male patients. These genes suppress (e.g., SLC7A11 ) or drive (e.g., SLC1A5 ) ferroptosis, and these findings were further validated with Gaussian mixed models. Furthermore, we explored the prognostic value of ferroptosis regulating genes and discovered sex- and KRAS-specific differences at both the transcriptional and metabolic levels by random survival forest with backward elimination algorithm (RSF-BE). Of note, genes and metabolites involved in arginine synthesis and glutathione metabolism were uniquely associated with prognosis in tumors from males with KRAS mutations. Additionally, drug repurposing is becoming popular due to the high costs, attrition rates, and slow pace of new drug development, offering a way to treat common and rare diseases more efficiently. Furthermore, increasing evidence has shown that ferroptosis inhibition or induction can improve drug sensitivity or overcome chemotherapy drug resistance. Therefore, we investigated the correlation between gene expression, metabolite levels, and drug sensitivity across all CRC primary tumor cell lines using data from the Genomics of Drug Sensitivity in Cancer (GDSC) resource. We observed that ferroptosis suppressor genes such as DHODH , GCH1 , and AIFM2 in KRAS mutant CRC cell lines were resistant to cisplatin and paclitaxel, underscoring why these drugs are not effective for these patients. The comprehensive map generated here provides valuable biological insights for future investigations, and the findings are supported by rigorous analysis of large-scale publicly available data and our in-house cohort. The study also emphasizes the potential application of VIMP, Gaussian mixed models, and RSF-BE models in the multi-omics research community. In conclusion, this comprehensive approach opens doors for leveraging precision molecular feature analysis and drug repurposing possibilities in KRAS mutant CRC.

A T-Cell-Derived 3-Gene Signature Distinguishes SARS-CoV-2 from Common Respiratory Viruses

Research on the host responses to respiratory viruses could help develop effective interventions and therapies against the current and future pandemics from the host perspective. To explore the pathogenesis that distinguishes SARS-CoV-2 infections from other respiratory viruses, we performed a multi-cohort analysis with integrated bioinformatics and machine learning. We collected 3730 blood samples from both asymptomatic and symptomatic individuals infected with SARS-CoV-2, seasonal human coronavirus (sHCoVs), influenza virus (IFV), respiratory syncytial virus (RSV), or human rhinovirus (HRV) across 15 cohorts. First, we identified an enhanced cellular immune response but limited interferon activities in SARS-CoV-2 infection, especially in asymptomatic cases. Second, we identified a SARS-CoV-2-specific 3-gene signature (CLSPN, RBBP6, CCDC91) that was predominantly expressed by T cells, could distinguish SARS-CoV-2 infection, including Omicron, from other common respiratory viruses regardless of symptoms, and was predictive of SARS-CoV-2 infection before detectable viral RNA on RT-PCR testing in a longitude follow-up study. Thereafter, a user-friendly online tool, based on datasets collected here, was developed for querying a gene of interest across multiple viral infections. Our results not only identify a unique host response to the viral pathogenesis in SARS-CoV-2 but also provide insights into developing effective tools against viral pandemics from the host perspective.

COL: a pipeline for identifying putatively functional back-splicing

Circular RNAs (circRNAs) are a class of generally non-coding RNAs produced by back- splicing. Although the vast majority of circRNAs are likely to be products of splicing error and thereby confer no benefits to organisms, a small number of circRNAs have been found to be functional. Identifying other functional circRNAs from the sea of mostly non-functional circRNAs is an important but difficult task. Because available experimental methods for this purpose are of low throughput or versality and existing computational methods have limited reliability or applicability, new methods are needed. We hypothesize that functional back- splicing events that generate functional circRNAs (i) exhibit substantially higher back-splicing rates than expected from the total splicing amounts, (ii) have conserved splicing motifs, and (iii) show unusually high back-splicing levels. We confirm these features in back-splicing shared among human, macaque, and mouse, which should enrich functional back-splicing. Integrating the three features, we design a computational pipeline named COL for identifying putatively functional back-splicing. Different from the methods that require multiple samples, COL can predict functional back-splicing using a single sample. Under the same data requirement, COL has a lower false positive rate than that of the commonly used method that is based on the back- splicing level alone. We conclude that COL is an efficient and versatile method for rapid identification of putatively functional back-splicing and circRNAs that can be experimentally validated. COL is available at https://github.com/XuLabSJTU/COL .

Mutation Patterns Predict Drug Sensitivity in Acute Myeloid Leukemia

PURPOSE

The inherent genetic heterogeneity of acute myeloid leukemia (AML) has challenged the development of precise and effective therapies. The objective of this study was to elucidate the genomic basis of drug resistance or sensitivity, identify signatures for drug response prediction, and provide resources to the research community.

EXPERIMENTAL DESIGN

We performed targeted sequencing, high-throughput drug screening, and single-cell genomic profiling on leukemia cell samples derived from patients with AML. Statistical approaches and machine learning models were applied to identify signatures for drug response prediction. We also integrated large public datasets to understand the co-occurring mutation patterns and further investigated the mutation profiles in the single cells. The features revealed in the co-occurring or mutual exclusivity pattern were further subjected to machine learning models.

RESULTS

We detected genetic signatures associated with sensitivity or resistance to specific agents, and identified five co-occurring mutation groups. The application of single-cell genomic sequencing unveiled the co-occurrence of variants at the individual cell level, highlighting the presence of distinct subclones within patients with AML. Using the mutation pattern for drug response prediction demonstrates high accuracy in predicting sensitivity to some drug classes, such as MEK inhibitors for RAS-mutated leukemia.

CONCLUSIONS

Our study highlights the importance of considering the gene mutation patterns for the prediction of drug response in AML. It provides a framework for categorizing patients with AML by mutations that enable drug sensitivity prediction.

Integrated systems biology analysis of acute lymphoblastic leukemia: unveiling molecular signatures and drug repurposing opportunities

Acute lymphoblastic leukemia (ALL) is a hematological malignancy characterized by aberrant proliferation and accumulation of lymphoid precursor cells within the bone marrow. The tyrosine kinase inhibitor (TKI), imatinib mesylate, has played a significant role in the treatment of Philadelphia chromosome-positive ALL (Ph + ALL). However, the achievement of durable and sustained therapeutic success remains a challenge due to the development of TKI resistance during the clinical course.The primary objective of this investigation is to propose a novel and efficacious treatment approach through drug repositioning, targeting ALL and its Ph + subtype by identifying and addressing differentially expressed genes (DEGs). This study involves a comprehensive analysis of transcriptome datasets pertaining to ALL and Ph + ALL in order to identify DEGs associated with the progression of these diseases to identify possible repurposable drugs that target identified hub proteins.The outcomes of this research have unveiled 698 disease-related DEGs for ALL and 100 for Ph + ALL. Furthermore, a subset of drugs, specifically glipizide for Ph + ALL, and maytansine and isoprenaline for ALL, have been identified as potential candidates for therapeutic intervention. Subsequently, cytotoxicity assessments were performed to confirm the in vitro cytotoxic effects of these selected drugs on both ALL and Ph + ALL cell lines.In conclusion, this study offers a promising avenue for the management of ALL and Ph + ALL through drug repurposed drugs. Further investigations are necessary to elucidate the mechanisms underlying cell death, and clinical trials are recommended to validate the promising results obtained through drug repositioning strategies.

PitViper: a software for comparative meta-analysis and annotation of functional screening data

Recent advancements in shRNA and Cas protein technologies have enabled functional screening methods targeting genes or non-coding regions using single or pooled shRNA and sgRNA. CRISPR-based systems have also been developed for modulating DNA accessibility, resulting in CRISPR-mediated interference (CRISPRi) or activation (CRISPRa) of targeted genes or genomic DNA elements. However, there is still a lack of software tools for integrating diverse array of functional genomics screening outputs that could offer a cohesive framework for comprehensive data integration. Here, we developed PitViper, a flexible and interactive open-source software designed to fill this gap, providing reliable results for the type of elements being screened. It is an end-to-end automated and reproducible bioinformatics pipeline integrating gold-standard methods for functional screening analysis. Our sensitivity analyses demonstrate that PitViper is a useful tool for identifying potential super-enhancer liabilities in a leukemia cell line through genome-wide CRISPRi-based screening. It offers a robust, flexible, and interactive solution for integrating data analysis and reanalysis from functional screening methods, making it a valuable resource for researchers in the field.

Identification of potential tissue-specific biomarkers involved in pig fat deposition through integrated bioinformatics analysis and machine learning

Backfat thickness (BT) and intramuscular fat (IMF) content are closely appertained to meat production and quality in pig production. Deposition in subcutaneous adipose (SA) and IMF concerns different genes and regulatory mechanisms. And larger studies with rigorous design should be carried to explore the molecular regulation of fat deposition in different tissues. The purpose of this study is to gain a better understanding of the molecular mechanisms underlying differences in fat deposition among different tissues and identify tissue-specific genes involved in regulating fat deposition. The SA-associated datasets (GSE122349 and GSE145956) and IMF-associated datasets (GSE165613 and GSE207279) were downloaded from the Gene Expression Omnibus (GEO) as the BT and IMF group, respectively. Subsequently, the Robust Rank Aggregation (RRA) algorithm identified 27 down- and 29 up-regulated differentially expressed genes (DEGs) in the BT group. Based on bioinformatics and three machine learning algorithms, four SA deposition-related potential biomarkers, namely ACLY, FASN, ME1, and ARVCF were selected. FASN was evaluated as the most valuable biomarker for the SA mechanism. The 18 down- and 34 up-regulated DEGs in the IMF group were identified, and ACTA2 and HMGCL were screened as the IMF deposition-related candidate core genes, especially the ACTA2 may play the critical role in IMF deposition regulation. Moreover, based on the constructed ceRNA network, we postulated that the role of predicted ceRNA interaction network of XIST, NEAT1/miR-15a-5p, miR-16-5p, miR-424-5p, miR-497-5p/FASN were vital in the SA metabolism, XIST, NEAT1/miR-27a/b-3p, 181a/c-5p/ACTA2 might contribute to the regulation to IMF metabolism, which all gave suggestions in molecular mechanism for regulation of fat deposition. These findings may facilitate advancements in porcine quality at the genetic and molecular levels and assist with human obesity-associated diseases.

Modeling type 1 diabetes progression using machine learning and single-cell transcriptomic measurements in human islets

Type 1 diabetes (T1D) is a chronic condition in which beta cells are destroyed by immune cells. Despite progress in immunotherapies that could delay T1D onset, early detection of autoimmunity remains challenging. Here, we evaluate the utility of machine learning for early prediction of T1D using single-cell analysis of islets. Using gradient-boosting algorithms, we model changes in gene expression of single cells from pancreatic tissues in T1D and non-diabetic organ donors. We assess if mathematical modeling could predict the likelihood of T1D development in non-diabetic autoantibody-positive donors. While most autoantibody-positive donors are predicted to be non-diabetic, select donors with unique gene signatures are classified as T1D. Our strategy also reveals a shared gene signature in distinct T1D-associated models across cell types, suggesting a common effect of the disease on transcriptional outputs of these cells. Our study establishes a precedent for using machine learning in early detection of T1D.

Low copy CRISPR-Cas13d mitigates collateral RNA cleavage

While CRISPR-Cas13 systems excel in accurately targeting RNA, the potential for collateral RNA degradation poses a concern for therapeutic applications and limits broader adoption for transcriptome perturbations. We evaluate the extent to which collateral RNA cleavage occurs when Rfx Cas13d is delivered via plasmid transfection or lentiviral transduction and find that collateral activity only occurs with high levels of Rfx Cas13d expression. Using transcriptome-scale and combinatorial CRISPR pooled screens in cell lines with low-copy Rfx Cas13d, we find high on-target knockdown, without extensive collateral activity regardless of the expression level of the target gene. In contrast, transfection of Rfx Cas13d, which yields higher nuclease expression, results in collateral RNA degradation. Further, our analysis of a high-fidelity Cas13 variant uncovers a marked decrease in on-target efficiency, suggesting that its reduced collateral activity may be due to an overall diminished nuclease capability.

Application of Multiomics Approach to Investigate the Therapeutic Potentials of Stem Cell-derived Extracellular Vesicle Subpopulations for Alzheimer's Disease

Alzheimer's disease (AD) presents a complex interplay of molecular alterations, yet understanding its pathogenesis remains a challenge. In this study, we delved into the intricate landscape of proteome and transcriptome changes in AD brains compared to healthy controls, examining 788 brain samples revealing common alterations at both protein and mRNA levels. Moreover, our analysis revealed distinct protein-level changes in aberrant energy metabolism pathways in AD brains that were not evident at the mRNA level. This suggests that the changes in protein expression could provide a deeper molecular representation of AD pathogenesis. Subsequently, using a comparative proteomic approach, we explored the therapeutic potential of mesenchymal stem cell-derived extracellular vehicles (EVs), isolated through various methods, in mitigating AD-associated changes at the protein level. Our analysis revealed a particular EV-subtype that can be utilized for compensating dysregulated mitochondrial proteostasis in the AD brain. By using network biology approaches, we further revealed the potential regulators of key therapeutic proteins. Overall, our study illuminates the significance of proteome alterations in AD pathogenesis and identifies the therapeutic promise of a specific EV subpopulation with reduced pro-inflammatory protein cargo and enriched proteins to target mitochondrial proteostasis.

A Systematic Review and Meta-Analysis of microRNA Profiling Studies in Chronic Kidney Diseases

Chronic kidney disease (CKD) represents an increasing health burden. Evidence suggests the importance of miRNA in diagnosing CKD, yet the reports are inconsistent. This study aimed to determine novel miRNA biomarkers and potential therapeutic targets from hypothesis-free miRNA profiling studies in human and murine CKDs. Comprehensive literature searches were conducted on five databases. Subgroup analyses of kidney diseases, sample types, disease stages, and species were conducted. A total of 38 human and 12 murine eligible studies were analyzed using Robust Rank Aggregation (RRA) and vote-counting analyses. Gene set enrichment analyses of miRNA signatures in each kidney disease were conducted using DIANA-miRPath v4.0 and MIENTURNET. As a result, top target genes, Gene Ontology terms, the interaction network between miRNA and target genes, and molecular pathways in each kidney disease were identified. According to vote-counting analysis, 145 miRNAs were dysregulated in human kidney diseases, and 32 were dysregulated in murine CKD models. By RRA, miR-26a-5p was significantly reduced in the kidney tissue of Lupus nephritis (LN), while miR-107 was decreased in LN patients' blood samples. In both species, epithelial-mesenchymal transition, Notch, mTOR signaling, apoptosis, G2/M checkpoint, and hypoxia were the most enriched pathways. These miRNA signatures and their target genes must be validated in large patient cohort studies.

De Novo Purine Metabolism is a Metabolic Vulnerability of Cancers with Low p16 Expression

p16 is a tumor suppressor encoded by the CDKN2A gene whose expression is lost in approximately 50% of all human cancers. In its canonical role, p16 inhibits the G1-S-phase cell cycle progression through suppression of cyclin-dependent kinases. Interestingly, p16 also has roles in metabolic reprogramming, and we previously published that loss of p16 promotes nucleotide synthesis via the pentose phosphate pathway. However, the broader impact of p16/CDKN2A loss on other nucleotide metabolic pathways and potential therapeutic targets remains unexplored. Using CRISPR knockout libraries in isogenic human and mouse melanoma cell lines, we determined several nucleotide metabolism genes essential for the survival of cells with loss of p16/CDKN2A. Consistently, many of these genes are upregulated in melanoma cells with p16 knockdown or endogenously low CDKN2A expression. We determined that cells with low p16/CDKN2A expression are sensitive to multiple inhibitors of de novo purine synthesis, including antifolates. Finally, tumors with p16 knockdown were more sensitive to the antifolate methotrexate in vivo than control tumors. Together, our data provide evidence to reevaluate the utility of these drugs in patients with p16/CDKN2Alow tumors as loss of p16/CDKN2A may provide a therapeutic window for these agents.

SIGNIFICANCE

Antimetabolites were the first chemotherapies, yet many have failed in the clinic due to toxicity and poor patient selection. Our data suggest that p16 loss provides a therapeutic window to kill cancer cells with widely-used antifolates with relatively little toxicity.

Identification of MACC1 as a potential biomarker for pulmonary arterial hypertension based on bioinformatics and machine learning

BACKGROUND

Pulmonary arterial hypertension (PAH) is a life-threatening disease characterized by abnormal early activation of pulmonary arterial smooth muscle cells (PASMCs), yet the underlying mechanisms remain to be elucidated.

METHODS

Normal and PAH gene expression profiles were obtained from the Gene Expression Omnibus (GEO) database and analyzed using gene set enrichment analysis (GSEA) to uncover the underlying mechanisms. Weighted gene co-expression network analysis (WGCNA) and machine learning methods were deployed to further filter hub genes. A number of immune infiltration analysis methods were applied to explore the immune landscape of PAH. Enzyme-linked immunosorbent assay (ELISA) was employed to compare MACC1 levels between PAH and normal subjects. The important role of MACC1 in the progression of PAH was verified through Western blot and real-time qPCR, among others.

RESULTS

39 up-regulated and 7 down-regulated genes were identified by 'limma' and 'RRA' packages. WGCNA and machine learning further narrowed down the list to 4 hub genes, with MACC1 showing strong diagnostic capacity. In vivo and in vitro experiments revealed that MACC1 was highsly associated with malignant features of PASMCs in PAH.

CONCLUSIONS

These findings suggest that targeting MACC1 may offer a promising therapeutic strategy for treating PAH, and further clinical studies are warranted to evaluate its efficacy.

RCPA: An Open-Source R Package for Data Processing, Differential Analysis, Consensus Pathway Analysis, and Visualization

Identifying impacted pathways is important because it provides insights into the biology underlying conditions beyond the detection of differentially expressed genes. Because of the importance of such analysis, more than 100 pathway analysis methods have been developed thus far. Despite the availability of many methods, it is challenging for biomedical researchers to learn and properly perform pathway analysis. First, the sheer number of methods makes it challenging to learn and choose the correct method for a given experiment. Second, computational methods require users to be savvy with coding syntax, and comfortable with command-line environments, areas that are unfamiliar to most life scientists. Third, as learning tools and computational methods are typically implemented only for a few species (i.e., human and some model organisms), it is difficult to perform pathway analysis on other species that are not included in many of the current pathway analysis tools. Finally, existing pathway tools do not allow researchers to combine, compare, and contrast the results of different methods and experiments for both hypothesis testing and analysis purposes. To address these challenges, we developed an open-source R package for Consensus Pathway Analysis (RCPA) that allows researchers to conveniently: (1) download and process data from NCBI GEO; (2) perform differential analysis using established techniques developed for both microarray and sequencing data; (3) perform both gene set enrichment, as well as topology-based pathway analysis using different methods that seek to answer different research hypotheses; (4) combine methods and datasets to find consensus results; and (5) visualize analysis results and explore significantly impacted pathways across multiple analyses. This protocol provides many example code snippets with detailed explanations and supports the analysis of more than 1000 species, two pathway databases, three differential analysis techniques, eight pathway analysis tools, six meta-analysis methods, and two consensus analysis techniques. The package is freely available on the CRAN repository. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Processing Affymetrix microarrays Basic Protocol 2: Processing Agilent microarrays Support Protocol: Processing RNA sequencing (RNA-Seq) data Basic Protocol 3: Differential analysis of microarray data (Affymetrix and Agilent) Basic Protocol 4: Differential analysis of RNA-Seq data Basic Protocol 5: Gene set enrichment analysis Basic Protocol 6: Topology-based (TB) pathway analysis Basic Protocol 7: Data integration and visualization.

Pan-cancer prognostic model and immune microenvironment analysis of natural killer cell-related genes

Background

Natural killer (NK) cells play a significant role in antitumor immunity and are closely related to tumor prognosis and recurrence. NK cell-based tumor immunotherapy, including immune checkpoint inhibition and CAR-engineered NK cells, is a promising area of research. However, there is a need for better NK cell-related models and associated biomarkers.

Methods

The sequences of NK cell-related genes were obtained from the published NK cell CRISPR/Cas9 library data, and the common genes were selected as NK cell-related genes. The RNA sequencing (RNA-seq) and clinical data of 32 solid tumors from The Cancer Genome Atlas (TCGA) were downloaded from the UCSC Xena database, and the RNA-seq data of normal samples were downloaded from the Genotype-Tissue Expression (GTEx) database. The differentially expressed NK cell-related genes (DENKGs) between the tumor and normal samples were analyzed. The DENKGs related to the prognosis of solid tumors were selected via univariate Cox analysis, and 32 kinds of solid tumor prognostic models were constructed using least absolute shrinkage and selection operator (LASSO) and multivariate Cox analysis. Survival, receiver operating characteristic (ROC), and independent prognostic analyses were employed to test the effectiveness of the model, along with a nomogram model and prediction curve. Differences in the immune pathways and microenvironment cells were analyzed between the high- and low-risk groups identified by the model.

Results

We constructed a pan-cancer prognostic model with 63 NK cell-related genes and further identified DEPDC1 and ASPM as potentially offering new directions in tumor research by literature screening.

Conclusions

In this study, 63 prognostic solid tumor markers were investigated using NK cell-related genes, and for the first time, a pan-cancer prognostic model was constructed to analyze their role in the immune microenvironment, which may contribute new insights into tumor research.

Investigation of key miRNAs and Target-mRNA in Kaposi's sarcoma using bioinformatic methods

Kaposi's sarcoma (KS) is the second most common tumor in human immunodeficiency virus (HIV) infected patients worldwide. While many miRNAs have been confirmed to be involved in KS biological processes, no relevant studies have combined miRNA and mRNA expression profiles using KS patient tissue biopsies. In this study, we performed transcriptome sequencing on tumor and normal tissues from four KS patients and identified differentially expressed mRNA and miRNA, further performed target gene prediction and enrichment analysis. 19,551 target-mRNAs were identified by predicting 106 miRNAs, with 553 overlapping with 571 significantly differentially expressed mRNAs. Enrichment analysis showed significant involvement of the Ubiquitin-mediated proteolysis pathway. Additionally, the miRNA-mRNA interaction network was established, and the topological score of Cytohubba's algorithm was calculated for comparison with three other datasets. The Mutual Clustering Coefficient (MCC) scoring ranking placed ZBTB34, NFIB, and RORA as the top three mRNAs, while hsa-miR-16-5p, hsa-miR-27a-3p, hsa-miR-340-5p, hsa-miR-182-5p, and hsa-miR-186-5p ranked as the top five miRNAs. Hsa-miR-101-3p is the only miRNA that appears both in the top 10 MCC scores and at the intersection of the other two datasets. Finally, qRT-PCR was used to validate the findings at the cellular level. In summary, the miRNA analysis results indicated that hsa-miR-101-3p could be used as a potential diagnostic or therapeutic marker in future studies. Moreover, the mRNA analysis results suggested that the histone binding pathways involved in mRNAs and ubiquitin-related biological processes were closely associated with KS and could serve as promising biomarkers for the diagnosis and treatment of this disease.

Integrated analysis of transcriptional changes in major depressive disorder: Insights from blood and anterior cingulate cortex

Background

Major depressive disorder (MDD) was involved in widely transcriptional changes in central and peripheral tissues. While, previous studies focused on single tissues, making it difficult to represent systemic molecular changes throughout the body. Thus, there is an urgent need to explore the central and peripheral biomarkers with intrinsic correlation.

Methods

We systematically retrieved gene expression profiles of blood and anterior cingulate cortex (ACC). 3 blood datatsets (84 MDD and 88 controls) and 6 ACC datasets (100 MDD and 100 controls) were obtained. Differential expression analysis, RobustRankAggreg (RRA) analysis, functional enrichment analysis, immune associated analysis and protein-protein interaction networks (PPI) were integrated. Furthermore, the key genes were validated in an independent ACC dataset (12 MDD and 15 controls) and a cohort with 120 MDD and 117 controls.

Results

Differential expression analysis identified 2211 and 2021 differential expressed genes (DEGs) in blood and ACC, respectively. RRA identified 45 and 25 robust DEGs in blood and ACC based on DEGs, and all of them were closely associated with immune cells. Functional enrichment results showed both the robust DEGs in blood and ACC were enriched in humoral immune response. Furthermore, PPI identified 8 hub DEGs (CD79A, CD79B, CD19, MS4A1, PLP1, CLDN11, MOG, MAG) in blood and ACC. Independent ACC dataset showed the area under the curve (AUC) based on these hub DEGs was 0.77. Meanwhile, these hub DEGs were validated in the serum of MDD patients, and also showed a promising diagnostic power.

Conclusions

The biomarker panel based on hub DEGs yield a promising diagnostic efficacy, and all of these hub DEGs were strongly correlated with immunity. Humoral immune response may be the key link between the brain and blood in MDD, and our results may provide further understanding for MDD.

Combining LIANA and Tensor-cell2cell to decipher cell-cell communication across multiple samples

In recent years, data-driven inference of cell-cell communication has helped reveal coordinated biological processes across cell types. Here, we integrate two tools, LIANA and Tensor-cell2cell, which, when combined, can deploy multiple existing methods and resources to enable the robust and flexible identification of cell-cell communication programs across multiple samples. In this work, we show how the integration of our tools facilitates the choice of method to infer cell-cell communication and subsequently perform an unsupervised deconvolution to obtain and summarize biological insights. We explain how to perform the analysis step by step in both Python and R and provide online tutorials with detailed instructions available at https://ccc-protocols.readthedocs.io/. This workflow typically takes ∼1.5 h to complete from installation to downstream visualizations on a graphics processing unit-enabled computer for a dataset of ∼63,000 cells, 10 cell types, and 12 samples.

Whole-Transcriptome Sequencing of Knee Joint Cartilage from Kashin-Beck Disease and Osteoarthritis Patients

The aim of this study was to provide a comprehensive understanding of similarities and differences in mRNAs, lncRNAs, and circRNAs within cartilage for Kashin-Beck disease (KBD) compared to osteoarthritis (OA). We conducted a comparison of the expression profiles of mRNAs, lncRNAs, and circRNAs via whole-transcriptome sequencing in eight KBD and ten OA individuals. To facilitate functional annotation-enriched analysis for differentially expressed (DE) genes, DE lncRNAs, and DE circRNAs, we employed bioinformatic analysis utilizing Gene Ontology (GO) and KEGG. Additionally, using quantitative reverse transcriptase polymerase chain reaction (qRT-PCR), we validated the expression levels of four cartilage-related genes in chondrocytes. We identified a total of 43 DE mRNAs, 1451 DE lncRNAs, and 305 DE circRNAs in KBD cartilage tissue compared to OA (q value < 0.05; |log2FC| > 1). We also performed competing endogenous RNA network analysis, which identified a total of 65 lncRNA-mRNA interactions and 4714 miRNA-circRNA interactions. In particular, we observed that circRNA12218 had binding sites for three miRNAs targeting ACAN, while circRNA12487 had binding sites for seven miRNAs targeting COL2A1. Our results add a novel set of genes and non-coding RNAs that could potentially serve as candidate diagnostic biomarkers or therapeutic targets for KBD patients.

CCL20 and CD8A as potential diagnostic biomarkers for HBV-induced liver fibrosis in chronic hepatitis B

Background

The main cause of the liver fibrosis (LF) remains hepatitis B virus (HBV) infection, especially in China. Histologically, liver fibrosis still occurs progressively in chronic hepatitis B (CHB) patients, even if HBV-DNA is negative or undetectable. The diagnosis of LF is beneficial to control the development of it, also it may promote the reversal of LF. Although liver biopsy is the gold standard of diagnosis in LF at present, it isa traumatic diagnosis. There are no diagnostic biomarkers as yet for the condition. It is badly in need of biomarkers clinically, which is simple to test, minimally invasive, highly specific, and sensitive. Early detection of HBV-LF development is crucial in the prevention, treatment, and prognosis prediction of HBV-LF. Cytokines are closely associated with both immune regulation and inflammation in the progression of hepatitis B virus associated-liver fibrosis (HBV-LF). In this bioinformatic study, we not only analyzed the relationship between HBV-LF and immune infiltration, but also identified key genes to uncover new therapeutic targets.

Objectives

To find potential biomarkers for liver fibrosis in the development of chronic hepatic B patients.

Materials and methods

We obtained two sets of data including CHB/healthy control and CHB/HBV-LF from the Integrated Gene Expression (GEO) database to select for differential expression analysis. Protein-protein interaction (PPI) network was also generated, while key genes and important gene modules involved in the occurrence and development of HBV-LF were identified. These key genes were analyzed by functional enrichment analysis, module analysis, and survival analysis. Furthermore, the relationship between these two diseases and immune infiltration was explored.

Results

Among the identified genes, 150 were individually associated with CHB and healthy control in the differential gene expression (DGE) analysis. While 14 with CHB and HBV-LF. It was also analyzed in the Robust rank aggregation (RRA) analysis, 34 differential genes were further identified by Cytohubba. Among 34 differential genes, two core genes were determined: CCL20 and CD8A. CCL20 was able to predict CHB positivity (area under the receiver operating characteristic curve [AUC-ROC] = 0.883, 95% confidence interval [CI] 0.786-0.963), while HBV-LF positivity ([AUC-ROC] = 0.687, 95% confidence interval [CI] 0.592-0.779). And CD8A was able to predict CHB positivity ([AUC-ROC] = 0.960, 95% confidence interval [CI] 0.915-0.992), while HBV-LF positivity ([AUC-ROC] = 0.773, 95% confidence interval [CI] 0.680-0.856). Relationship between CCL20 gene expression and LF grades was P < 0.05, as well as CD8A.

Conclusion

CCL20 and CD8A were found to be potential biomarkers and therapeutic targets for HBV-LF. It is instructive for research on the progression of LF in HBV patients, suppression of chronic inflammation, and development of molecularly targeted-therapy for HBV-LF.

bootGSEA: a bootstrap and rank aggregation pipeline for multi-study and multi-omics enrichment analyses

Introduction: Gene set enrichment analysis (GSEA) subsequent to differential expression analysis is a standard step in transcriptomics and proteomics data analysis. Although many tools for this step are available, the results are often difficult to reproduce because set annotations can change in the databases, that is, new features can be added or existing features can be removed. Finally, such changes in set compositions can have an impact on biological interpretation. Methods: We present bootGSEA, a novel computational pipeline, to study the robustness of GSEA. By repeating GSEA based on bootstrap samples, the variability and robustness of results can be studied. In our pipeline, not all genes or proteins are involved in the different bootstrap replicates of the analyses. Finally, we aggregate the ranks from the bootstrap replicates to obtain a score per gene set that shows whether it gains or loses evidence compared to the ranking of the standard GSEA. Rank aggregation is also used to combine GSEA results from different omics levels or from multiple independent studies at the same omics level. Results: By applying our approach to six independent cancer transcriptomics datasets, we showed that bootstrap GSEA can aid in the selection of more robust enriched gene sets. Additionally, we applied our approach to paired transcriptomics and proteomics data obtained from a mouse model of spinal muscular atrophy (SMA), a neurodegenerative and neurodevelopmental disease associated with multi-system involvement. After obtaining a robust ranking at both omics levels, both ranking lists were combined to aggregate the findings from the transcriptomics and proteomics results. Furthermore, we constructed the new R-package "bootGSEA," which implements the proposed methods and provides graphical views of the findings. Bootstrap-based GSEA was able in the example datasets to identify gene or protein sets that were less robust when the set composition changed during bootstrap analysis. Discussion: The rank aggregation step was useful for combining bootstrap results and making them comparable to the original findings on the single-omics level or for combining findings from multiple different omics levels.

Identification of hub genes in calcific aortic valve disease

Calcific aortic valve disease (CAVD) is a heart valve disorder characterized primarily by calcification of the aortic valve, resulting in stiffness and dysfunction of the valve. CAVD is prevalent among aging populations and is linked to factors such as hypertension, dyslipidemia, tobacco use, and genetic predisposition, and can result in becoming a growing economic and health burden. Once aortic valve calcification occurs, it will inevitably progress to aortic stenosis. At present, there are no medications available that have demonstrated effectiveness in managing or delaying the progression of the disease. In this study, we mined four publicly available microarray datasets (GSE12644 GSE51472, GSE77287, GSE233819) associated with CAVD from the GEO database with the aim of identifying hub genes associated with the occurrence of CAVD and searching for possible biological targets for the early prevention and diagnosis of CAVD. This study provides preliminary evidence for therapeutic and preventive targets for CAVD and may provide a solid foundation for subsequent biological studies.

Uncovering hub genes and immunological characteristics for heart failure utilizing RRA, WGCNA and Machine learning

Background

Heart failure (HF) is a major public health issue with high mortality and morbidity. This study aimed to find potential diagnostic markers for HF by the combination of bioinformatics analysis and machine learning, as well as analyze the role of immune infiltration in the pathological process of HF.

Methods

The gene expression profiles of 124 HF patients and 135 nonfailing donors (NFDs) were obtained from six datasets in the NCBI Gene Expression Omnibus (GEO) public database. We applied robust rank aggregation (RRA) and weighted gene co-expression network analysis (WGCNA) method to identify critical genes in HF. To discover novel diagnostic markers in HF, three machine learning methods were employed, including best subset regression, regularization technique, and support vector machine-recursive feature elimination (SVM-RFE). Besides, immune infiltration was investigated in HF by single-sample gene set enrichment analysis (ssGSEA).

Results

Combining RRA with WGCNA method, we recognized 39 critical genes associated with HF. Through integrating three machine learning methods, FCN3 and SMOC2 were determined as novel diagnostic markers in HF. Differences in immune infiltration signature were also found between HF patients and NFDs. Moreover, we explored the potential associations between two diagnostic markers and immune response in the pathogenesis of HF.

Conclusions

In summary, FCN3 and SMOC2 can be used as diagnostic markers of HF, and immune infiltration plays an important role in the initiation and progression of HF.

Prediction of on-target and off-target activity of CRISPR-Cas13d guide RNAs using deep learning

Transcriptome engineering applications in living cells with RNA-targeting CRISPR effectors depend on accurate prediction of on-target activity and off-target avoidance. Here we design and test ~200,000 RfxCas13d guide RNAs targeting essential genes in human cells with systematically designed mismatches and insertions and deletions (indels). We find that mismatches and indels have a position- and context-dependent impact on Cas13d activity, and mismatches that result in G-U wobble pairings are better tolerated than other single-base mismatches. Using this large-scale dataset, we train a convolutional neural network that we term targeted inhibition of gene expression via gRNA design (TIGER) to predict efficacy from guide sequence and context. TIGER outperforms the existing models at predicting on-target and off-target activity on our dataset and published datasets. We show that TIGER scoring combined with specific mismatches yields the first general framework to modulate transcript expression, enabling the use of RNA-targeting CRISPRs to precisely control gene dosage.

Framework for in vivo T cell screens

In vivo T cell screens are a powerful tool for elucidating complex mechanisms of immunity, yet there is a lack of consensus on the screen design parameters required for robust in vivo screens: gene library size, cell transfer quantity, and number of mice. Here, we describe the Framework for In vivo T cell Screens (FITS) to provide experimental and analytical guidelines to determine optimal parameters for diverse in vivo contexts. As a proof-of-concept, we used FITS to optimize the parameters for a CD8+ T cell screen in the B16-OVA tumor model. We also included unique molecular identifiers (UMIs) in our screens to (1) improve statistical power and (2) track T cell clonal dynamics for distinct gene knockouts (KOs) across multiple tissues. These findings provide an experimental and analytical framework for performing in vivo screens in immune cells and illustrate a case study for in vivo T cell screens with UMIs.

Modulator of TMB-associated immune infiltration (MOTIF) predicts immunotherapy response and guides combination therapy

Patients with high tumor mutational burden (TMB) levels do not consistently respond to immune checkpoint inhibitors (ICIs), possibly because a high TMB level does not necessarily result in adequate infiltration of CD8+ T cells. Using bulk ribonucleic acid sequencing (RNA-seq) data from 9311 tumor samples across 30 cancer types, we developed a novel tool called the modulator of TMB-associated immune infiltration (MOTIF), which comprises genes that can determine the extent of CD8+ T cell infiltration prompted by a certain TMB level. We confirmed that MOTIF can accurately reflect the integrity and defects of the cancer-immunity cycle. By analyzing 84 human single-cell RNA-seq datasets from 32 types of solid tumors, we revealed that MOTIF can provide insights into the diverse roles of various cell types in the modulation of CD8+ T cell infiltration. Using pretreatment RNA-seq data from 13 ICI-treated cohorts, we validated the use of MOTIF in predicting CD8+ T cell infiltration and ICI efficacy. Among the components of MOTIF, we identified EMC3 as a negative regulator of CD8+ T cell infiltration, which was validated via in vivo studies. Additionally, MOTIF provided guidance for the potential combinations of programmed death 1 blockade with certain immunostimulatory drugs to facilitate CD8+ T cell infiltration and improve ICI efficacy.

Single-nucleus RNA and multiomics in situ pairwise sequencing reveals cellular heterogeneity of the abnormal ligamentum teres in patients with developmental dysplasia of the hip

Developmental dysplasia of the hip (DDH) is the most common hip deformity in pediatric orthopedics. One of the common pathological changes in DDH is the thickening and hypertrophy of the ligamentum teres. However, the underlying pathogenic mechanism responsible for these changes remains unclear. This study represents the first time that the heterogeneity of cell subsets in the abnormal ligamentum teres of patients with DDH has been resolved at the single-cell and spatial levels by snRNA-Seq and MiP-Seq. Through gene set enrichment and intercellular communication network analyses, we found that receptor-like cells and ligament stem cells may play an essential role in the pathological changes resulting in ligamentum teres thickening and hypertrophy. Eight ligand-receptor pairs related to the ECM-receptor pathway were observed to be closely associated with DDH. Further, using the Monocle R package, we predicted a differentiation trajectory of pericytes into two branches, leading to junctional ligament stem cells or fibroblasts. The expression of extracellular matrix-related genes along pseudotemporal trajectories was also investigated. Using MiP-Seq, we determined the expression distribution of marker genes specific to different cell types within the ligamentum teres, as well as differentially expressed DDH-associated genes at the spatial level.

Building flexible and robust analysis frameworks for molecular subtyping of cancers

Molecular subtyping is essential to infer tumor aggressiveness and predict prognosis. In practice, tumor profiling requires in-depth knowledge of bioinformatics tools involved in the processing and analysis of the generated data. Additionally, data incompatibility (e.g., microarray versus RNA sequencing data) and technical and uncharacterized biological variance between training and test data can pose challenges in classifying individual samples. In this article, we provide a roadmap for implementing bioinformatics frameworks for molecular profiling of human cancers in a clinical diagnostic setting. We describe a framework for integrating several methods for quality control, normalization, batch correction, classification and reporting, and develop a use case of the framework in breast cancer.

Comprehensive scRNA-seq Model Reveals Artery Endothelial Cell Heterogeneity and Metabolic Preference in Human Vascular Disease

Vascular disease is one of the major causes of death worldwide. Endothelial cells are important components of the vascular structure. A better understanding of the endothelial cell changes in the development of vascular disease may provide new targets for clinical treatment strategies. Single-cell RNA sequencing can serve as a powerful tool to explore transcription patterns, as well as cell type identity. Our current study is based on comprehensive scRNA-seq data of several types of human vascular disease datasets with deep-learning-based algorithm. A gene set scoring system, created based on cell clustering, may help to identify the relative stage of the development of vascular disease. Metabolic preference patterns were estimated using a graphic neural network model. Overall, our study may provide potential treatment targets for retaining normal endothelial function under pathological situations.

Integrating single-cell and bulk expression data to identify and analyze cancer prognosis-related genes

Compared with traditional evaluation methods of cancer prognosis based on tissue samples, single-cell sequencing technology can provide information on cell type heterogeneity for predicting biomarkers related to cancer prognosis. Therefore, the bulk and single-cell expression profiles of breast cancer and normal cells were comprehensively analyzed to identify malignant and non-malignant markers and construct a reliable prognosis model. We first screened highly reliable differentially expressed genes from bulk expression profiles of multiple breast cancer tissues and normal tissues, and inferred genes related to cell malignancy from single-cell data. Then we identified eight critical genes related to breast cancer to conduct Cox regression analysis, calculate polygenic risk score (PRS), and verify the predictive ability of PRS in two data groups. The results show that PRS can divide breast cancer patients into high-risk group and low-risk group. PRS is related to the overall survival time and relapse-free interval and is a prognosis factor independent of conventional clinicopathological characteristics. Breast cancer is usually regarded as a cancer with a relatively good prognosis. In order to further explore whether this workflow can be applied to cancer with poor prognosis, we selected lung cancer for a comparative study. The results show that this workflow can also build a reasonable prognosis model for lung cancer. This study provides new insight and practical source code for further research on cancer biomarkers and drug targets. It also provides basis for survival prediction, treatment response prediction, and personalized treatment.