This study aims to compare the effectiveness of using discrete heartbeats versus an entire 12-lead electrocardiogram (ECG) as the input for predicting future occurrences of arrhythmia and atrial fibrillation using deep learning models. Experiments were conducted using two types of inputs: a combination of discrete heartbeats extracted from 12-lead ECG and an entire 12-lead ECG signal of 10 s. This study utilized 326,904 ECG signals from 134,447 patients and categorized them into three groups: true–normal sinus rhythm (T-NSR), atrial fibrillation–normal sinus rhythm (AF-NSR), and clinically important arrhythmia–normal sinus rhythm (CIA-NSR). The T-NSR group comprised patients with at least three normal rhythms in a year and no atrial fibrillation or arrhythmias history. Clinically important arrhythmia included atrial fibrillation, atrial flutter, atrial premature contraction, atrial tachycardia, ventricular premature contraction, ventricular tachycardia, right and left bundle branch block, and atrioventricular block over the second degree. The AF-NSR group included normal sinus rhythm paired with atrial fibrillation or atrial flutter within 14 days, and the CIA-NSR group comprised normal sinus rhythm paired with CIA occurring within 14 days. Three deep learning models, ResNet-18, LSTM, and Transformer-based models, were utilized to distinguish T-NSR from AF-NSR and T-NSR from CIA-NSR. The experiments demonstrated the potential of using discrete heartbeats in predicting future arrhythmia and atrial fibrillation incidences extracted from 12-lead electrocardiogram (ECG) signals alone, without any additional patient information. The analysis reveals that these discrete heartbeats contain subtle patterns that deep learning models can identify. Focusing on discrete heartbeats may lead to more timely and accurate diagnoses of these conditions, improving patient outcomes and enabling automated diagnosis using ECG signals as a biomarker. Cardiac arrhythmias, encompassing conditions such as atrial fibrillation, are among the leading causes of concern in cardiovascular health. The insidious nature of these conditions, often manifesting asymptomatically or with minimal symptoms, renders them particularly elusive to standard detection methods ,2,3. TheThe gravity of atrial fibrillation, a prominent subtype of arrhythmias, lies in its strong correlation with intensified risks of both stroke and heart failure ,6,7,8,9.The motivation behind our study is to bridge this detection gap. Recognizing the challenges faced in identifying these conditions early, we delved into innovative methods aimed at enhancing the screening process. By refining the current diagnostic paradigms, we believe we can bring a robust solution that aids in the proactive management of atrial fibrillation and other arrhythmias ,11. ThroElectrocardiogram (ECG) recording includes 1-dimensional time series data that measure the heart’s electrical activity, and it is a valuable tool for diagnosing and monitoring arrhythmias and atrial fibrillation. Recent studies have demonstrated the potential of deep learning techniques in predicting the future incidence of arrhythmias and atrial fibrillation using ECG signals ,14,15,16The utilization of discrete heartbeats as input data has been identified as a more optimal approach for detecting subtle abnormalities indicating future incidences of atrial fibrillation and other arrhythmias ,18, compTo further optimize the approach for identifying subtle abnormalities in ECG signals, our methodology for predicting future cardiac events from normal sinus rhythm relies exclusively on the ECG signal, without using additional patient data, such as electronic medical records, that contain potentially sensitive or private information, such as age, gender, medical history, family history of heart disease, medication use, lifestyle factors , and comorbid conditions (hypertension or diabetes). While utilizing such data could enhance the accuracy of ECG prediction algorithms, deep learning models may focus more on medical records than ECG signals, leading to biased prediction results.The primary aim of our study is to demonstrate that utilizing discrete heartbeats extracted from 10-s 12-lead sinus rhythm ECGs as inputs yields superior results compared to using an entire 12-lead heartbeat as the input for predicting future incidences of cardiac arrhythmias and atrial fibrillation. We conducted two distinct experiments: one for predicting the future incidence of atrial fibrillation, and another for predicting arrhythmias with prediction windows of 14 days. The reason for conducting separate experiments for arrhythmia and atrial fibrillation, despite atrial fibrillation being a type of arrhythmia, is to precisely analyze and understand the distinct characteristics and patterns associated with each condition. Isolating atrial fibrillation as a separate experiment allows for a more focused investigation into the unique features and predictive factors specific to it. The chosen prediction windows were aligned with the typical duration of wearing cardiac event monitors, which ranged up to 14 days. Evaluating the effectiveness of our approach in predicting clinically important arrhythmias within these windows provides insight into its potential usefulness in clinical practice. Moreover, our approach’s reliance on the ECG signal makes it a practical and feasible solution for clinical implementation, given that ECGs are routinely performed in clinical settings. Our study results indicate that using discrete heartbeats as the input yielded superior results compared to the conventional approach and could be a valuable tool for healthcare providers in predicting future cardiac arrhythmias from normal sinus rhythm and improving patient care and disease management.We included 134,447 patients with 326,904 ECGs acquired from two Ewha Womans University Hospitals in Mokdong and Seoul, Republic of Korea, between May 2017 and May 2022. Raw ECGs were obtained from Philips  and General Electric  ECG machines in XML format. Philips ECGs are standard 10-s, 12-lead ECGs with a sampling rate of 500 Hz. GE ECGs are 10-s, 8- or 12-lead ECGs with a sampling rate of 500 Hz. The 8-lead ECGs from the General Electric ECG machine were reconstructed to 12 leads using Einthoven’s law and Goldberger’s equations .We categorized 326,904 ECG datasets into three groups: true–normal sinus rhythm (“T-NSR”), atrial fibrillation–normal sinus rhythm (“AF-NSR”), and clinically important arrhythmia–normal sinus rhythm (“CIA-NSR”). We defined arrhythmias based on several criteria, which included atrial fibrillation, atrial flutter, atrial arrhythmia, premature ventricular contraction, right and left bundle branch block, and any atrioventricular block exceeding the second degree. Each of these conditions holds clinical significance and necessitates medical intervention.For the T-NSR group, we considered patients who recorded a minimum of three ECGs displaying normal sinus rhythm over 12 months and who had no documented history of atrial fibrillation or other arrhythmias. From this pool of T-NSR ECGs, we randomly selected one ECG per patient. The AF-NSR group comprised ECGs explicitly labeled as normal sinus rhythm and which had a corresponding ECG showing atrial fibrillation or atrial flutter within the subsequent 14 days. The CIA-NSR group included normal sinus rhythm paired with ECGs that showed CIA occurring within 14 days after the initial normal sinus rhythm reading, as illustrated in To ensure the integrity of our dataset, we omitted any ECGs that were flawed or had missing or inconclusive interpretations from the T-NSR, AF-NSR, and CIA-NSR groups. To uphold consistency in our findings and focus on the adult demographic, we also excluded the ECG records of individuals younger than 18.Each ECG interpretation, thus far, was determined by automatic symptom analysis reports from the Philips and GE ECG machines. To ensure the accuracy of our data, we converted all selected ECGs into waveform images. We asked trained practitioners with more than five years of experience in cardiology to manually annotate them. Any ECGs with discrepancies between the automatic diagnosis and manual annotations were excluded from the study. To ensure robust model evaluation and simulate real-world scenarios, we partitioned the dataset based on the dates of the ECG scans and the patients who underwent them. The training and validation set spanned from 23 May 2017 to 10 June 2021, while the test set covered 11 June 2021 to 23 May 2022. For convenience in the learning context, train and validation set separation was performed on the ECG scan level. Following these selection processes, we obtained each group’s final number of ECGs, as shown in We employed several preprocessing techniques on our 10-s 12-lead ECG signal data to obtain accurate and reliable data . First, Given that every architecture integrates a convolutional layer as its initial layer, we standardized the length of individual heartbeats to match the mean length across all observed discrete heartbeats, set at 700. Heartbeats exceeding this length were truncated accordingly, whereas shorter ones were zero-padded. For the 12-lead ECG signal, we established a consistent signal length of 5000. We consciously abstained from utilizing interpolation techniques to resize the signals, as this could potentially introduce undue signal distortion.ResNet-18 extracts essential features of the input using convolution operations like various convolutional neural networks. To solve the vanishing gradient problem of CNN architectures , ResNet-Combining Conv1D and LSTM layers b in a neDuring the training phase a, we useFor each architecture, we trained five different models using five different fixed seeds that control random variables for weight initialization, data shuffling, and dropout. Experimenting with five different seeds and ensembling them can be helpful in several ways. First, it can reduce the variance in the model’s performance caused by randomness in the training process. By training the model with different random seeds, we can obtain several different versions of the model, each with its own biases and strengths. Ensembling these models can help to reduce the impact of individual biases and improve the overall performance of the model. Secondly, ensembling models trained with different seeds can provide a more robust estimate of the model’s performance. By combining the outputs of several different models, we can reduce the impact of outliers and obtain a more accurate estimate of the model’s true performance .In the testing phase b, we ensThe F1 score, AUC of the ROC (AUROC), precision , recall, and negative predictive value (NPV) for each T-NSR, AF-NSR pair, and T-NSR, CIA-NSR pair were used to evaluate the performance of our model. The F1 score in Equation (3) is the harmonic mean of the precision (Equation (1)) and recall (Equation (2)). The F1 score is often used as an evaluation metric in various medical AI fields, along with the AUC of the ROC. The AUC of the ROC is a performance metric ranging from 0.5 to 1 that shows the discriminatory ability of the model. The AUC of the ROC alone is not suitable to validate a model’s performance since the AUC of the ROC is sensitive to class-imbalanced datasets, such as our datasets (6.6446 NSR to 1 AF-NSR). In other words, the AUC of the ROC will be biased towards evaluating the majority class: T-NSR ECGs. The NPV in Equation (4) measures the proportion of true negative predictions among all the negative predictions. To carefully evaluate our model to the class-imbalanced dataset, we propose F1, AUROC, precision, recall, and NPV for model evaluation.The Conv1D+LSTM model exhibited the best performance for T-NSR/AF-NSR, achieving an average AUC of 0.9419, as illustrated in The findings from our study indicate that utilizing discrete heartbeats from normal sinus rhythm ECG signals as the input in deep learning models demonstrated higher efficacy in predicting future occurrences of arrhythmia and atrial fibrillation, as evident from the outcomes presented in t-test. A paired t-test is suitable in this context because it evaluates if there’s a significant difference between two paired groups. The “pairing” in our case came from evaluating the two input methods on the same dataset across five different seed models. The null hypothesis (H0) for our test was set as: “There is no significant difference between the performance metrics of the two input methods”. Conversely, the alternative hypothesis (H1) was set as: “There is a significant difference between the performance metrics of the two input methods.” The metrics of interest in our study were the F1 score and AUROC. The results consistently indicate p-values less than the significance level of 0.05 for both the F1 and AUROC across all models, as shown in We sought to statistically compare the performance of two modeling methods using a paired p-value below the 0.05 threshold is typically interpreted as strong evidence against the null hypothesis in many scientific disciplines. It suggests that the observed data  would be unlikely if the null hypothesis were true. Therefore, we reject the null hypothesis in favor of the alternative hypothesis, suggesting that there was a significant difference between the performance metrics of the two input methods.A This study presents evidence of the effectiveness of using discrete heartbeats extracted from normal sinus rhythm ECGs in predicting future arrhythmia and atrial fibrillation incidences with deep learning methods. The results of the study also suggest that a specific biomarker for future incidences of arrhythmia and atrial fibrillation may be present in the normal sinus rhythm ECG signal.It is worth noting that the study only utilized signals from ECG recordings and did not incorporate additional patient information, such as electronic medical records, which may raise concerns about privacy and the potential compromise of patient confidentiality. Despite relying on limited data, the study still demonstrates high performance in predicting future arrhythmia and atrial fibrillation incidences from normal sinus rhythm ECGs, suggesting that ECG signals alone may be sufficient for accurate prediction. This finding is promising, as it indicates that analyzing discrete heartbeats extracted from normal sinus rhythm ECGs may facilitate efficient and precise diagnosis and treatment without requiring extensive patient information.Our dataset observed a pronounced proportion of CIA-NSR to T-NSR, with a ratio of 11,929 to 35,455, equating to approximately 33.6%. This stands in contrast to general population statistics, where the prevalence of arrhythmias is around 5% . People In artificial intelligence research using ECGs, there have been studies that predict the clinical data of patients. Several studies have successfully indicated patients’ clinical data, such as gender classification, age prediction, and heart failure prognosis ,33,34. TClinicalTrials.gov under NCT05725187. This forward-looking approach aims to validate and potentially refine our model in real-time scenarios, enhancing its reliability and adaptability across a broader range of healthcare contexts.There are several limitations to the study that should be taken into consideration. Firstly, the research relied on data from only two hospitals, an aspect that inherently needs broader external validation. Broadening our data sources to encompass more hospitals or diverse patient groups would enhance the robustness and generalizability of our conclusions. Secondly, it is essential to acknowledge that the T-NSR ECGs examined in this study might inadvertently encompass instances of AF-NSR or CIA-NSR due to the absence of continuous data for labeling. This potential overlap exists despite our rigorous data collection from patients who had three or more T-NSR ECGs within a year and exhibited no clinical symptoms of AF or CIA during medical evaluations by physicians. Such challenges persist in intermittent electrocardiogram research unless continuous monitoring is employed, like long-term implantable loop recorders . To overFor future research, it would be beneficial to investigate the specific heartbeats within individual electrocardiogram signals that predict the future incidence of atrial fibrillation and arrhythmia. By identifying these heartbeats, we can determine which components or features of discrete heartbeats act as potential biomarkers. Additionally, we could group study participants based on relevant demographic and medical factors and assign distinct threshold values to each subgroup to gain more nuanced insights into the predictive value of specific discrete heartbeat features for arrhythmia and atrial fibrillation. These approaches could provide greater insight into the underlying mechanisms and physiological factors contributing to the development of arrhythmia and atrial fibrillation and potentially develop more personalized diagnostics.Based on the study presented in this paper, we obtained approval  from the Ministry of Food and Drug Safety in South Korea for our exploratory clinical trials. Leveraging the deep learning-based cardiac arrhythmia prediction, we have developed SYN-MAC, a software-as-a-service (SaaS) product in This study’s results suggest that using discrete heartbeats extracted from normal sinus rhythm ECG signals to predict future clinically important arrhythmia and atrial fibrillation incidences rather than using entire 12-lead ECG signals with deep learning models is a promising approach. The LSTM models for both atrial fibrillation and clinically important arrhythmia prediction using discrete heartbeat showed strong performance compared to using entire 12-lead ECG signals. The study demonstrated that ECG signals alone were sufficient for accurate prediction, and a potential biomarker may be present in the normal sinus rhythm ECG signal. This suggests that using discrete heartbeats with deep learning models may enable the detection of subtle patterns in ECG signals, which could lead to a more accurate and earlier diagnosis of clinically important arrhythmia and atrial fibrillation.


Disulfidptosis is a newly-identified non-programmed cell death mode with tight associations with glucose metabolism. Elevated glycolysis is an important metabolic feature of tumor cells, which fulfills the energy requirement for their rapid growth and progression. Our present study determined to develop a disulfidptosis and glycolysis related gene (DGRG) risk score signature to predict the prognosis and ICI therapeutic responsiveness for CRC patients. First, the gene expression and clinical profiles for CRC patients were obtained from TCGA and GEO database. Using weighted gene co-expression network analysis, we identified hub genes showing the strongest correlations with both disulfidptosis and glycolysis activities. Next, a DGRG risk score signature was successfully developed through univariate and least absolute shrinkage and selection operator method Cox regression method. A DGRG risk score-based nomogram could further enhance the predictive performance. In addition, an array of systemic analysis was performed to unravel the correlation of DGRG risk score with tumor microenvironment. The results showed that CRC patients with low DGRG risk level had up-regulated immune cell infiltrations, enhanced metabolic activities and heightened gene mutation frequencies, while high risk patients was the opposite. Moreover, our present study identified low risk CRC patients as potential beneficiaries from immune checkpoint inhibitor (ICI) therapies. Our present work highlighted the potential utility of DGRG risk score signature in prognosis prediction and ICI responsiveness determination for CRC patients, which demonstrated promising clinical application value. In recent years, immunotherapy represents an emerging paradigm for the comprehensive management of cancer and have achieved effective therapeutic responsiveness in many types of malignant tumors5. For CRC patients, the potential utility of immune checkpoint inhibitors (ICI) as a novel therapeutic strategy is gaining widespread attention. So far, nivolumab monotherapy and the combined utility of nivolumab with ipilimumab was confirmed to induce optimistic and durable treatment responses by Checkmate-142 phase II clinical trials7. The two drugs were approved by FDA for the treatment of metastatic CRC patients with microsatellite instability status in 20188. Of note, efficient and durable disease control in response to ICI therapies is impacted by multiple external and internal factors, among which the heterogeneity in tumor microenvironment (TME) is regarded as a key determinant9. Patients with abundant immune cell infiltration and sufficiently-activated anti-tumor immune response encompassed a “hot tumor” TME and were associated with favorable therapeutic sensitivity towards ICI treatment. On the contrary, an immune-desert or “cold tumor” TME often represents impaired therapeutic responses9. Therefore, a deeper understanding about the potential contributors to TME heterogeneity is of great importance to find out the potential beneficiaries of ICI treatment and improve the survival outcomes for CRC patients.Colorectal cancer (CRC) is one of the most common malignancies in digestive system with high incidence and mortality rates. Globally, CRC accounts for the third and second positions for newly-emergent cases and cancer-related deaths, respectively10. Mechanistically, SLC7A11 high expression coupled with glucose depletion causes the excessive disulfide bond formation among cytoskeleton proteins, which further leads to the collapse of actin network and cell death10. Of note, the pharmacological blockade of glucose uptake by GLUT inhibitors was proved to exert cancer killing effects by the promoting disulfidptosis in SLC7A11-high tumor cells, highlighting the therapeutic utility of disulfidptosis-induction strategies in cancer treatment10. However, since the conceptualization of disulfidptosis is in its infancy, the potential regulatory role of this PCD pattern in tumorigenesis remains largely unknown and deserves further investigation.Recently, a group of scientists identify a novel programmed cell death (PCD) mode induced by aberrant accumulation of intracellular disulfides and define it as disulfidptosis12. In terms of glucose metabolism, cancer cells rely heavily on glycolysis to meet the energy requirements and elevated glycolysis could be observed even in oxygen-replete conditions. The unique aerobic oxidation mode, known as Warburg effect, engenders cancer cells with enhanced proliferative and metastasis abilities, and is tightly associated with recurrence and multidrug resistance14.Metabolism reprogramming, defined as the significant alterations in metabolic pathways in tumors in comparison to normal tissues, represents one of the hallmarks of cancerConsidering the intimate association between disulfidptosis and glucose metabolism, as well as the essential role of glycolysis in tumor cells’ metabolic process, we determined to explore the potential interaction between disulfidptosis—and glycolysis-related genes (DGRG) in the pathogenesis of CRC. In the present study, we first screened out hub genes in association with disulfidptosis and glycolysis through weighted gene co-expression network analysis (WGCNA) in two CRC patients cohorts. Then, using univariate and Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression analyses, we developed DGRG risk score signature, and later confirmed its favorable performance in prognosis prediction. In addition, the association of DGRG risk score with TME landscape and the potential therapeutic utility of it in guiding ICI therapies were further investigated.We first investigated the expression landscape and clinical relevance of 29 disulfidptosis-related genes. In TCGA cohort, 22 of the 29 genes were differentially expressed between CRC tumor and normal tissues Fig. A. Accord15, which is supposed to be conducive for induction of disulfidptosis.We estimated the enrichment scores of disulfidptosis and glycolysis using ssGSEA method and investigated their associations via Spearman correlation analysis. As shown in Fig. In view of the intimate associations between disulfidptosis and glycolysis activities, we aimed to find out hub genes co-correlated with the two processes (DGRGs) using WGCNA. To construct a scale-free topology network, β was set as 6 and 5 for TCGA Fig. E,F and GTo assess the prognostic value of hub genes and develop a quantitative measurement for patients’ risk level, we first performed univariate Cox regression analysis for TCGA patients and identified five genes closely related to OS Fig. , followeOf note, the risk score was inversely correlated with the enrichment scores of dusulfidptosis and glycolysis , distant metastasis (M1 status) and advanced tumor stages (stage III-IV) were more frequently happened in high risk patients compared with the low risk counterpart 43 and stromal activities44 all had elevated enrichment levels in high risk patients, which could also undermined the anti-cancer immunity and ICI responsiveness.In the present DGRG risk score model, patients with different risk level were correlated with distinct survival outcomes and responsiveness to ICI therapies, which, to some extent, could be attributed to disparities in TME landscapes and biological function. First, low risk patients encompassed a hot tumor phenotype, which were supposed to have advantages in survival and ICI sensitivityFinally, we used the “pRRophetic” to screen out suitable medication strategies for high and low risk patients respectively. It was revealed that three commonly-used drugs for malignancies in digestive system: 5-flurouracil, cisplatin and sorafenib all had lower IC50s in low risk patients, whereas erlotinib treatment might be more effective for high risk patients.Admittedly, the present study had certain limitations. First, we only observed the potential positive associations between disulfidptosis and glycolysis from gene expression level and explain the potential reason by literature review, which requires to be supported by more experimental evidence. Second, as an in silico study conducted by retrospectively analyzing public datasets, the prediction for survival and ICI therapeutic responsiveness needs to be validated in real-world prospective CRC patient populations. Third, since the conceptualization of disulfidpotosis is in its infancy, future newly-emergent marker genes are still needed to be enrolled to increase the predictive performance of the current risk score model. Nevertheless, this to our best knowledge was the first study to explore the crosstalk of disulfidptosis- and glycolysis- related genes in the pathogenesis of CRC, establish a relevant prognostic model and analyze the impact of it on the TME landscape and therapeutic responsiveness.In summary, our present work used WGCNA method to identify disulfidptosis- and glycolysis-related genes and developed a DGRG risk score signature to quantify CRC patients’ risk level. There were significant differences in clinical features, immune infiltrations, functional enrichment patterns and gene mutation landscapes for patients with high and low risk levels. More importantly, the DGRG risk score signature had favorable performance in predicting the survival outcomes and determining the ICI therapeutic sensitivity for CRC patients, which will provide novel insights into the development of personalized treatment strategies and promote the future research and development of novel medications.The analytical process of the study was depicted in Fig. To make sure the gene expression data was comparable, the FPKM format of TCGA RNA-seq data was transformed into TPM format, which was believed to resemble GEO microarray data. Besides, the “Combat” algorithm of R package “sva” was used to eliminate the batch effect within the two cohorts.A disulfidptosis gene set containing 22 genes were curated from related articles. Two Glycolysis-related gene sets: HALLMARK_GLYCOLYSIS and REACTOME_GLYCOLYSIS were downloaded from MSigDb website Table . We esti45, we conducted WGCNA analysis to identify hub genes showing the strongest correlations with both disulfidptosis and glycolysis activities. First, we selected genes with top 5000 of median absolute deviation (MAD) for TCGA and GEO cohort respectively for hierarchical clustering analysis. Pearson correlation coefficients were used to evaluate the distance between each gene. Then, we identified the soft thresholding power (β) value based on the scale-free topology network criterion and converted the expression matrix into a adjacency matrix. Next, according to topological overlap measure (TOM) and dissimilarity (1-TOM), the adjacency matrix was further clustered and formed into a topology matrix. A dynamic tree cut algorithm was employed for gene module determination. The minimum gene number in each module was set as 30. Finally, the correlations of the eigengenes in each module with the disulfidptosis and glycolysis activities were estimated by Pearson correlation analysis. The most correlated module was selected as the hub gene modules and the composing genes were defined as hub genes. Kyoto Encyclopedia of Genes and Genomes (KEGG) database contains high-level function profiles for target genes46. Gene otology (GO) analysis was also a commonly-used bioinformatic tool that could provide biological function information for genes of interests based on three dimensions: molecular function (MF), biological process (BP), and cellular component (CC). Herein, we conducted KEGG and GO pathway enrichment analysis using R package “clusterprofiler”47.Based on previously described protocolsIn an attempt to quantify CRC patients’ risk level, we developed a DGRG risk score signature. In brief, DGRGs were first subject to univariate Cox regression analysis to evaluate the prognostic value. Then we conducted least absolute and selection operator (LASSO) regression analysis using R package “glmnet” to further reduce overfitting. The formula for DGRG risk score calculation was: To augment the clinical application value of DGRG risk score signature, we first conducted univariate and multivariate Cox regression analysis integrating risk score and other clinical factors. Elements with statistical significance were further used to construct a nomogram, which was visualized by “rms” package in R. Calibration curves were drawn to evaluate the consistency between actual survival and that predicted by nomogram. Besides, we performed receiver operating curve (ROC) analysis and used the area under curve (AUC) values to assess the efficacy of nomogram in predicting patients’ OS at different time points.41  analysis based on KEGG gene set and gene set enrichment analysis (GSVA) analysis based on Hallmark gene set. Besides, using ssGSEA method, we quantified the enrichment scores of immune and stromal-related gene signatures developed by Mariathasan et al.41 Table , as well49. A previous research conceptualized the anti-cancer immune response into a seven-step cyclic event50. Herein, regulatory genes involved in each step were obtained from the tracking tumor immunophenotype (TIP) website for ssGSEA analysis53. Differences in immune cell infiltration levels and anti-tumor immunity activities between high and low risk patients were then compared.We used TIMER, MCPcounter and QUANTISEQ algorithms to estimate the abundance of various immune cells infiltrated in TME. The calculation process was conducted by R package “IOBR”56 and MSI-high (MSI-H) status58 demonstrating elevated sensitivity to ICI treatment. Herein, we estimated TMB values in Perl software using annotation files59. Besides, MSI profiles for CRC patients were obtained from TCGA website. Disparities of TMB and MSI between the two risk groups were subsequently compared. Moreover, the DGRG risk score was applied to two external ICI cohorts to verify the predictive efficacy. The cohorts include IMvigor210  and David Liu’s cohort (melanoma patients receiving anti-PD1 treatment)60.To assess the potential utility of DGRG risk score in guiding ICI therapy, we compared the expression differences of PD1, PDL1 and CTLA4 between high and low risk patients, which are three commonly-used targets of ICI therapies. In addition, tumor mutation burden (TMB) and the microsatellite instability (MSI) status were recognized as two metrices to assess ICI therapeutic responses, with higher TMB value61.Gene mutation data for CRC patients was downloaded from TCGA database. The top 20 genes with the highest mutation frequencies were identified for two risk groups respectively and visualized in the form of waterfall plot via “maftools” packages in R62. Herein, we utilized this package to estimate the half inhibitory concentration (IC50) values for 5-flurouracil, cisplatin, sorafenib and erlotinib, in order to select the suitable anti-tumor medication for CRC patients at different risk levels.The “pRRophetic” R package used empirical Bayesian approach and ridge regression methodology with tenfold cross-validation to fit transcriptome data of tissue samples with the experiment-based drug sensitivity profiles of multiple cancer cell lines from GDSC database63. Herein, we utilized this database to unearth the expression patterns of DGRGs in different cell subtypes in the TME of CRC patients.Tumor Immune Single Cell Hub (TISCH) was a large-scale online database that integrates single-cell transcriptomic profiles of nearly 2 million cells from 76 high-quality tumor datasets across 27 cancer types64. The primary antibodies used for IHC experiment included anti-ORC1  and anti-PD-L1  antibodies. IHC profiler plugin of Image J software was used to estimate the optical density score of each sample based on previously-described methods65.We conducted this study according to the Declaration of Helsinki. A total of 10 fresh CRC tumor tissues were obtained during surgical removal in Renmin hospital of Wuhan University to prepare into formalin-fixed, paraffin-embedded (FFPE) specimens. The whole study design was approved by the Medical Ethics Committee of Renmin Hospital of Wuhan University (WDRY2021-K126), and all participants have signed written informed consent. Immunohistochemical (IHC) staining of FFPE specimens were performed following standardized procedures as previously describedComparisons of quantitative and qualitative data used Wilcoxon rank sum test and Chi-square test, respectively. For survival data, log-rank test was chosen and the result was displayed in the form of Kaplan–Meier plots. Besides, univariate and multivariate Cox regression analysis were used to assess the independent prognostic value of factors. The results were demonstrated in the form of forest plots. All statistical analyses were performed in R software (version 4.1.2) and SPSS Statistics 25 software. Statistical significance was set at p < 0.05.Research involving human research participants must have been performed in accordance with the Declaration of Helsinki. The study design was approved by the Medical Ethics Committee of Renmin Hospital of Wuhan university (WDRY2021-K126). Written informed consent has been provided by all participants.Supplementary Figures.Supplementary Tables.


Wrapping of DNA into nucleosomes restricts accessibility to the DNA and may affect the recognition of binding motifs by transcription factors. A certain class of transcription factors, the pioneer transcription factors, can specifically recognize their DNA binding sites on nucleosomes, may initiate local chromatin opening and facilitate the binding of co-factors in a cell-type-specific manner. For the majority of human pioneer transcription factors, the locations of their binding sites, mechanisms of binding and regulation remain unknown. We have developed a computational method to predict the cell-type-specific ability of transcription factors to bind nucleosomes by integrating ChIP-seq, MNase-seq and DNase-seq data with details of nucleosome structure. We have demonstrated the ability of our approach in discriminating pioneer from canonical transcription factors and predicted new potential pioneer transcription factors in H1, K562, HepG2 and HeLa cell lines. Lastly, we systemically analyzed the interaction modes between various pioneer transcription factors and detected several clusters of distinctive binding sites on nucleosomal DNA. Open chromatin is thought to comprise actively transcribed genes, while compact chromatin contains repressed genes. However, many recent observations point to a limited association between DNA accessibility, chromatin compaction and gene transcription at the global genomic scale. Indeed, rapid transcription activation may occur by relatively small changes of DNA solvent exposure, and localized nucleosomal array structures can be dynamically exposed without the large-scale chromatin rearrangements5. Nucleosomes represent the basic subunits of chromatin structure and function. They comprise a histone octamer of four types of core histones of two copies each, and ~147 base pairs of DNA wrapped around them6. Wrapping of DNA into nucleosomes inherently restricts DNA accessibility and the recognition of binding motifs by transcription factors. Intrinsically disordered histone tails flank histone core domains and may also modulate DNA accessibility by forming transient interactions with the nucleosomal and linker DNA7. The control of the DNA accessibility at nucleosomal and subnucleosomal scales is of major importance in understanding of how certain transcription factors can target compact chromatin to induce transcription activation or repression10.In eukaryotic cell, DNA is packaged in the form of chromatin yet, it should be dynamically accessed during transcription and replication processes at high spatiotemporal precision12. Although nucleosomes generally hinder the binding of transcription factors (TFs), a certain class, so-called pioneer transcription factors (PTF), can specifically recognize their binding sites on nucleosomal DNA, in some cases initiating local chromatin opening and facilitating subsequent binding of other co-factors in a cell-type specific manner12. Several studies have revealed the critical roles of pioneer transcription factors in mediating the cell-type specific gene expression and establishment of cell lineage reprogramming13.The differentiation of cells into different lineages occurs through chromatin reprogramming, involving the cooperative behavior of various transcription factors18. Using the NCAP–SELEX approach, a recent study has characterized the interaction modes between nucleosomes and 220 transcription factors14. Another high-throughput protein microarray study of 593 human TFs systematically identified the structural features of TFs binding with nucleosomes 19. It has been revealed that the vast majority of TFs preferably bind naked DNA instead of nucleosomal DNA at physiological concentrations, whereas certain transcription factors specifically target nucleosomes at different locations and orientations16. Moreover, several structures of pioneer transacription factors in complex with nucleosomes have recently been resolved23. Despite significant advances in recent experimental approaches, the interaction modes of most transcription factors with nucleosomes remain obscure and their cell-type-specific pioneer activities are largely unknown. The development of new computational approaches can improve our understanding of binding properties of various TFs with nucleosomes, thereby helping to identify novel pioneer transcription factors.Significant efforts have been made to characterize the interaction landscape between various TFs and nucleosomese.g. ChIP–seq, ATAC-seq, Dnase-seq and MNase-seq data24), which allows us to gain insights into the chromatin structure and details of epigenetic binding events. The rapid growth of such data sets has further stimulated the development of computational methods to characterize cell-type specific transcription factors 28. Several machine learning models have been proposed to predict transcription factor binding sites and identify sequence context features critical for TF binding29. In addition, gene regulatory network-based approaches have helped identify the key transcription factors in cell fate determination31. Despite the success of computational methods in genome-wide prediction of binding sites of canonical transcription factors, the locations of pioneer transcription factors’ binding sites, mechanisms of binding and regulation have not been systematically explored.Through the advances in high-throughput sequencing techniques, a large volume of data has been generated  with the data on DNA binding events, available through ChIP-seq and other methods, can reveal the interplay between transcription factor binding and nucleosome positioning, providing insights into the mechanisms of pioneer transcription factor interactions with nucleosomes and their ability to modulate chromatin accessibility36. Then, the downloaded fastq files were processed using the mnaseseq pipeline from nf-core (https://github.com/nf-core/mnaseseq)37, a recently developed bioinformatics pipeline for MNase seq data analysis. Adapter trimming of sequencing reads was performed with Trim Galore. Then, adapter-trimmed reads were mapped to the reference genome using Burrows-Wheeler Aligner (BWA)38. Human genome GRCh37 was used as a reference genome for reads mapping and the maximum number of mismatches in alignment was set to 4. The minimum and maximum insert sizes for filtering of mono-nucleosome paired-end reads were set to 120 and 180 base pairs. Duplicate reads were marked using Picard MarkDuplicates command (http://broadinstitute.github.io/picard/) and discarded from the analysis to avoid PCR duplication artifacts (less than 10% of reads were duplicated). Read libraries of replicates from the same experiment condition were merged into the analysis. Next, the BAM sequence alignment files were converted into BED format using bedtools v2.30.039. The aligned reads with fragment sizes from 146 to 148 base pairs were selected for mapping of the representative dyad positions .The overall workflow of our computational framework is shown in 40 and dyad positions were determined as midpoints of mapped MNase-seq reads41. Namely, we used a triweight kernel 40.To determine the representative dyad position of nucleosomes, we implemented and modified a previously developed nucleosome mapping protocolfunction  and the Here 42 and the minimum distance between the neighboring local maxima was set to 150 base pairs with “find local-extrema-maxima-min-sep=150”. Then, for every 60-bp window centered at each local maxima, one representative dyad location was determined as the dyad location with the highest number of dyad counts in this interval. Other dyad positions within the same 60-bp interval were discarded. If two or more dyad positions had the same dyad counts within the same interval, the dyad position located closest to the local maximum of the smoothed counts was selected as the representative dyad. Last, nucleosome regions (NRs) were determined as genomic regions centered at the representative dyad positions and flanked by 73 bp segments on each side.Next, we identified the genomic locations with the local maximum values of the smoothed dyad counts using bwtool44. The open chromatin regions were identified as genomic regions centered at narrow peaks and flanked by 1000 bp segments on each side. The active enhancer regions were defined as the open chromatin regions overlapped with both H3K27ac and H3K4me1 ChIP-seq narrow peaks. The nucleosome-depleted regions (NDRs) represent genomic regions free of nucleosomes and were identified as genomic regions not covered by any mono-nucleosome fragments (120 to 180 base pairs length) in MNase-seq data in all replicate experiments.To map the open chromatin and enhancer regions, we used DNase-seq and H3K27ac and H3K4me1 ChIP-seq data from the following five human cell lines  are defined as those that have less than 20% overlap between open regions in H1 embryonic cell line and open regions in one of differentiated cell lines so that these regions are closed in one cell line type and open in another45  and YY/RR  dinucleotides on NRs. Then, we aligned NRs by superimposing their dyad positions and computed the frequency of observed dinucleotides at each location of nucleosomal DNA (as a function of distance in base pairs from the nucleosome dyad). As a result, we observed pronounced dinucleotide patterns at specific nucleosomal DNA positions for all cell lines which are indicative of the high qulity of the data and nucleosome mapping procedure .43 with the MNase-seq data for the same human cell lines   . In case“motif profiles”. To do that, we first mapped ChIP-seq TF motifs for a given TF to the closest nucleosome dyad position and then aligned all dyad positions and corresponding ChIP-seq TF motifs. For each TF binding motif, we considered sequences in both strands of DNA. Then, we counted the number of base pairs from different TF binding motifs that mapped at each location of nucleosomal DNA and flanking DNA regions (+/− 1000 bp) around the dyad.Next, we calculated the transcription factor binding motif profiles or briefly Pioneer transcription factors (PTFs) can engage nucleosomal DNA, while truly canonical TFs cannot bind to nucleosomes. To predict PTFs, we calculated the binding motif enrichment of different TFs on NRs compared to NDRs. Namely, we counted a number of base pairs of ChIP-seq TF motifs overlapped with the NR and NDR regions and constructed the following contingency table . We thenhttps://egg2.wustl.edu/roadmap/data/byDataType/rna/expression/) and identified TFs which are highly expressed in each cell line .Here Receiver operating curves (ROC) and precision-recall (PR) curve analyses were further performed to evaluate the power of enrichment scores in discriminating pioneer transcription factors from other transcription factors. 32 known pioneer transcription factors from the literature (which were included in our list of 225 TFs) were used as positives  and otheTo identify the prevalent interaction modes of various TFs with nucleosomes, we have performed clustering of TF binding motif profiles. Prior to clustering, we filtered out potentially low-quality binding profiles using the following criteria. First, we removed under-represented TFs with the total genome-wide cumulative sum of ChiP-seq TF motifs on nucleosome regions of less than 500 base pairs. Second, due to the two-fold symmetry of DNA in nucleosome structures, ChiP-seq TF motifs on DNA complementary plus and minus strands should be structurally superimposed if the nucleosome structure is rotated by 180 degrees. If we are analyzing relative large number of binding motifs on both DNA strands, binding motif profiles should be symmetrical with respect to the nucleosome dyad. Therefore, we calculated the Pearson correlation coefficient of motif profiles between two symmetrical nucleosomal halves  for each TF and removed those with Pearson correlation coefficient values less than 0.4.48. Then, the projected data were subjected to k-medoids clustering using the pam function from the R package with the optimal number of clusters equal to six  and projected all profiles onto two dimensions using the Rtsne function from the R packagel to six . The silSince Micrococcal Nuclease has a sequence bias and cleaves DNA upstream of A or T more efficiently than of G or C nucleotides, a certain nucleotide preference exists around the ends of nucleosomal DNA reads produced by the MNase-seq experiments. It may potentially bias the TF binding profiles near the nucleosomal DNA ends. Therefore, in our analysis, we excluded regions near the nucleosomal DNA ends. To identify binding modes between TFs and nucleosomes, we clustered binding motif profiles of different TFs centered at the nucleosomal dyad locations (+/− 60 base pair from dyad) using k-medoids.49. Therefore, we hypothesized that DNA binding sites of pioneer transcription factors should not be depleted and, in some cases, should be enriched on nucleosome regions (NRs). At the same time, those canonical transcription factors that can preferentially bind to the naked DNA should exhibit the depletion of binding sites on nucleosome regions and enrichment in nucleosome-free regions. To quantitively assess these trends, we have performed binding motif enrichment analysis for each of the 225 transcription factors and calculated the motif enrichment scores  as positives . As can be seen in 53 were strongly depleted at nucleosomes  showed significantly lower enrichment scores compared to other TFs . Yamanakleosomes . It has entity56 .However, when we repeated our analysis by redefining differentially open regions as those closed in differentiated cell lines and open in H1 embryonic cell line, then ESSRB and Yamanaka pioneer transcription factor POU5F1 (OCT4) showed significantly higher enrichment scores . This codifferentially open and conserved open chromatin regions but the performance in classifications of PTF was not significantly affected by the threshold choice  and weak binders, which bind weakly to both naked DNA and nucleosomal DNA (cluster 2). Cluster 3 consists of strong binders which bind strongly to naked DNA but weakly to nucleosomal DNA. We found that TFs from their cluster 3 had the lowest enrichment scores although this trend was not statistically significant because of a lack of the data in this cluster. As to TFs in cluster 1 and 2, half of them had binding sites enriched on nucleosome regions  which correspond to strong nucleosome binders, and another half had binding sites enriched on nucleosome depleted regions  experiments on human TFs which systematically assessed transcription factor binding preferences to nucleosomal DNA versus naked DNA regions .57 and enrichment analysis on all open chromatin regions, we have identified 35 TFs in H1, K562, HepG2 and HeLa cell lines (no expression data was available for MCF-7) that ranked highly in the enrichment analysis and were also highly expressed in the corresponding cell lines  box protein that could bind to nucleosomes 60 and CUX1 could specifically interact with its recognition motif in a nucleosomal context 61.Next, using the enrichment score calculated based on NRs located in differentially open chromatin regions and NDRs located in conserved open chromatin regions, we identified 40 transcription factors that could act as PTFs with essential roles in cell differentiation in K562, HepG2 and HeLa cell lines. These identified TFs had their DNA binding sites significantly enriched on nucleosomal DNA of differentially open chromatin regions and were highly expressed in the corresponding cell lines . Among tThe enrichment analysis described above is the first step to estimate the propensity of transcription factor binding sites to be located on nucleosomal footprints. The enrichment analysis might have high specificity but a compromised sensititivity as it can misclassify those pioneer transcription factors that can bind to both naked DNA and nucleosomes at similar concentrations. Therefore, the next step would be to evaluate the actual locations of TF binding sites with respect to the nucleosomal dyad. To this end, we tested if there is a significant association between the binding motif profiles see  and nucl58. With the exception of a few cases, none of transcription factors was identified as being pioneer in all cell types because pioneer activity is often cell-type specific. For example, CTCF was identified as having a significant correlation  between binding motif profile and nucleosome occupancy for the human embryonic stem cell line , but not for other somatic cell lines  14, binding signals were compared by calculating the mutual information (MI) content between 3-mer distributions at two non-overlapping positions of the ligand, aimed at finding if SELEX ligand may contact these positions at the same time. Since nucleosomes can form on most DNA sequences, whereas TFs bind to only a few specific sequences, the NCAP-SELEX study calculated the enriched MI (E-MI) score to separate the TF signals from nucleosome signals by limiting the MI measure to the top ten most enriched 3-mer pairs. E-MI penetration score corresponds to E-MI drop by half compared to the E-MI maximum and larger values pointed to the favorable binding to the dyad regions14. As our In NCAP-SELEX experimental analyses, to quantify the preference of PTF binding to nucleosomesnificant .65. We attempt to characterize the details of PTF binding motifs at near-single nucleotide resolution on nucleosomal DNA and performed K-medoids clustering of binding motif profiles for all 225 transcription factors  leads to the highest classification accuracy in discriminating pioneer from canonical transcription factors. This finding supports the view that TF binding to nucleosomes leads to DNA and chromatin opening and might correlate with the reprogramming potential19. Our study has also verified the known and predicted several dozens of new PTFs as nucleosome binders (The modulation of chromatin accessibility with high spatiotemporal precision is a subject of ongoing debate as it is a crucial factor in regulation of transcription, replication and DNA repair processes. The question of how chromatin accessibility is precisely modulated involves multiple aspects, including epigenetic modifications, PTF infiltration, ATP-dependent remodeling and spontaneous chromatin dynamics. There are about two thousand transcription factors in the human genome and a few hundred of them may have PTF properties. Yet, for the vast majority of human PTFs, the locations of their binding sites and mechanisms of binding and regulation remain unknown binders . These p73. Pioneer transcription factors can exploit DNA unwrapping and trap nucleosomes in a partially unwrapped states74. Transcription factor binding motifs close to the nucleosome entry-exit sites may have increased exposure but would be error-prone to capture using MNase-seq data as a nucleotide bias exists around the ends of nucleosomal DNA reads produced by the MNase-seq experiments. Moreover, as we showed recently, the degree of spontaneous DNA unwrapping from nucleosomes depends on the incorporation of histone variants, like H2A.Z75, which is, in turn, might be needed for pioneer transcription factor recruitment to promote embryonic stem cell differentiation76. The third group of limitations pertains to the nucleosome positioning in a genome, as binding dissociation constants for PTFs depend on where nucleosomes are located. Indeed, nucleosome positioning and stability is not uniform throughout the genome and depends on local nucleosomal DNA sequence, histone variant deposition and epigenetic chromatin modifications. Finally, pioneer transcription factors may exhibit multivalent binding recognizing not only DNA binding sites but also some parts of the histone core or histone tail regions. Deducing such dependencies from the experimental assays utilized in this study is very challenging, if not impossible.We should mention that our classification method and the data used in this study have certain limitations. First, interactions of PTFs with nucleosomes may depend on binding of other factors. Second, PTF binding relies on specific recognition of DNA binding sites and can be governed by nucleosome dynamics. Indeed, initial stages of binding can happen via the DNA site exposure through thermal fluctuations, leading to DNA unwrapping and the formation of DNA looping or twist defectsNucleosomes represent hub points in epigenetic signaling pathways and identifying complex epigenetic relationships at the level of single nucleosomes may yield functional insights into the mechanisms of binding and infiltration of pioneer transcription factors into chromatin during differentiation and reprogramming. Transcription factors regulate a large number of signaling pathways and their dysregulation contributes to a plethora of human diseases, including diabetes, cardiovascular diseases and many cancers. Conventional transcription factors have been used for a long time as biomarkers and drug targets, however, the targeted therapeutic potential of pioneer transcription factors is lagging. To fill this gap, integrative approaches using large-scale low- or medium-resolution data with precise molecular modeling or protein-protein docking may provide the required detailed characterization of many predicted PTFs in the future.Supplement 1


MicroRNAs (miRNAs) are post-transcriptional regulators that finetune gene expression via translational repression or degradation of their target mRNAs. Despite their functional relevance, frameworks for the scalable and accurate detection of miRNA orthologs are missing. Consequently, there is still no comprehensive picture of how miRNAs and their associated regulatory networks have evolved. Here we present ncOrtho, a synteny informed pipeline for the targeted search of miRNA orthologs in unannotated genome sequences. ncOrtho matches miRNA annotations from multi-tissue transcriptomes in precision, while scaling to the analysis of hundreds of custom-selected species. The presence-absence pattern of orthologs to 266 human miRNA families across 402 vertebrate species reveals four bursts of miRNA acquisition, of which the most recent event occurred in the last common ancestor of higher primates. miRNA families are rarely modified or lost, but notable exceptions for both events exist. miRNA co-ortholog numbers faithfully indicate lineage-specific whole genome duplications, and miRNAs are powerful markers for phylogenomic analyses. Their exceptionally low genetic diversity makes them suitable to resolve clades where the phylogenetic signal is blurred by incomplete lineage sorting of ancestral alleles. In summary, ncOrtho allows to routinely consider miRNAs in evolutionary analyses that were thus far reserved to protein-coding genes.  MicroRNAs (miRNAs) are single-stranded, short non-coding RNAs of ∼22 nucleotides (nt) in length that have essential roles in fine-tuning the expression network of eukaryotic cells . Canonichttps://cites.org/eng/prog/Permit_system).miRNA-dependent gene regulation in animals dates at least back to the last common ancestor of the Eumetazoa that lived more than 800 million years ago ,10. The Scanning genome sequences for the presence of orthologs to miRNAs in MirGeneDB offers a powerful alternative. It allows to propagate miRNA annotation across species independent from the availability of transcriptome data. Genome sequences abound in public databases and can be obtained even with non-invasive sampling e.g. ,25. ThusTo close this methodological gap, we have developed ncOrtho, the first software that facilitates a targeted search for miRNA orthologs in individual genome sequences. ncOrtho scales linearly in time with both the number of miRNAs and the number of taxa included in the ortholog search, and therefore enables miRNA ortholog searches in large and customizable genome collections. ncOrtho identifies orthologs with high sensitivity and specificity irrespective of the genome annotation status. The resulting phylogenetic profiles provide the basis for studying miRNA evolution at an unprecedented scale and represent the first step towards projecting regulatory networks of miRNAs from model- to non-model organisms.Genome assemblies of 161 mammalian species, 241 non-mammalian vertebrates, and 16 invertebrate species were downloaded from NCBI Refseq Genomes release 207 . Locatiocmsearch algorithm provided in the Infernal v1.1 package, and search results were filtered for sequences that achieve at least 50% of the maximally achievable bit score (i.e. the score of the reference miRNA given the CM).Pairwise orthology assignments between protein-coding genes were identified with OMA standalone v2.4.1  and servF1-score as (2 × TP)/(2 * TP + FP + FN).MirGeneDB provided the gold-standard to evaluate orthology assignments by ncOrtho . For indhttp://hgdownload.soe.ucsc.edu/goldenPath/hg38/multiz100way/maf), and extracted the longest alignment block covering each human pre-miRNA locus annotated in MirGeneDB  data was retrieved from dbSNP build 153 . We consThe presence/absence patterns of human miRNAs were visualized using PhyloProfile  once on miRNAs are not yet routinely considered in large-scale evolutionary analyses because scalable approaches for the reliable identification of miRNA orthologs across large and phylogenetically diverse taxon collections were missing. We therefore developed ncOrtho to identify orthologs of miRNA genes in genome assemblies irrespective of their annotation status. Key to this approach is the use of Covariance Models (CMs), which are probabilistic models that integrate the consensus nucleotide sequence with the secondary structure of a functional RNA and facilitate a sensitive and discriminative homolog identification ,53. The ncOrtho accepts an individual pre-miRNA (the ‘reference miRNA’) together with its genomic position in the reference species as input. Additionally, two lists of taxa together with the corresponding genome sequences are required. The first list defines a set of ‘core species’ that are considered in the training phase of the covariance model Figure . Core spk protein-coding genes separate the orthologs of the anchor genes . To account for gene losses or gene annotation artefacts, we provide the option to consider up to n flanking genes as alternative syntenic anchors   to identify positional miRNA orthologs in the core species. We distinguish two cases: If the miRNA is positioned between two protein-coding genes, we use the flanking genes as syntenic anchors and consider a region in the core species’ genome as shared syntenic if no more than ) Figure . If the ) Figure . This prThe genome sequence of each target species is scanned for the presence of a miRNA using the corresponding CM Figure . CandidaHomo sapiens served as the reference species, and Macaca mulatta, Gorilla gorilla, Pongo abelii and Nomascus leucogenys were used as core species. 242 miRNA genes were located within, and 314 between protein-coding genes, and there was no difference in the core ortholog set sizes between the two groups . Consulting the 100 vertebrate whole genome alignment . ncOrtho evaluated 8490 genomic regions resulting in 1222 ortholog candidates that entered the final validation. The same search using mouse as target species finished after 53 min  about three quarters of the represented human miRNA genes have two co-orthologs. The ray-finned fish (Actinopterygii) are substantially more diverse. Co-orthologs are rare in the early branching lineages, except for the Acipenseriformes represented by the sterlet (Acipenser ruthenus) and the paddle fish (Polyodon spathula). Throughout the teleosts, the fraction of human miRNAs represented by two or more co-orthologs are up to 15 times higher compared to the tetrapods. On most lineages, human miRNA genes are represented by two co-orthologs, but three or four co-orthologs are common (>10% for either class) in both the Salmoniformes and the Cypriniformes. Reconciling the lineage-specific increase of co-ortholog numbers with existing evidence for polyploidization or whole genome duplications (WGD) results in a perfect match  was excluded because this species could not be stably placed in the tree . While this resembles the Atlantogenata hypothesis  events in transcriptomic data from comparatively small taxon sets ,93. HeremiRNA sequences have been previously proposed as good phylogenetic markers ,20, and In summary, miRNAs are essential regulators of gene expression and their tracing across a comprehensive taxon collection is bound to shed light on the emergence and evolution of the underlying regulatory networks. At the same time, miRNAs are highly informative with respect to the evolution of the species they reside in. With the help of ncOrtho the potential of miRNAs for addressing both questions can now be tapped on a comprehensive scale independent of pre-compiled databases, and thus throughout the eukaryotic tree of life.gkad467_Supplemental_FilesClick here for additional data file.


Recent synergistic multi-modal approaches have begun to link micro-scale information to systems level dynamics, transcending organisational hierarchies and offering novel perspectives into the brain’s function and dysfunction. Specifically, the distribution of micro-scale properties (such as receptor density or gene expression) can be mapped onto macro-scale measures from functional MRI to provide novel neurobiological insights. Methodological approaches to enrich functional imaging analyses with molecular information are rapidly evolving, with several streams of research having developed relatively independently, each offering unique potential to explore the trans-hierarchical functioning of the brain. Here, we address the three principal streams of research – spatial correlation, molecular-enriched network, and  •The brain exhibits micro-, meso-, and macro-scale organisational principles.•The BOLD signal is blind to the molecular mechanisms that give rise to it.•fMRI data can be mapped onto molecular systems derived in normative cohorts.•Approaches to link fMRI and molecular data each offer distinct pros and cons.•Molecular-functional relationships provide biological interpretability to fMRI analyses. Here, we describe these as a hierarchy with interactions across micro-, meso-, and macro-scale levels. The definitions of these levels are somewhat arbitrary, but this loose demarcation has proven conceptually useful . FirstlyImage 1.Neuropsychiatric disorders present a formidable healthcare challenge for which we remain largely bereft of targeted and mechanistically informed treatments. Despite substantial progress in recent decades, this largely reflects our current inability to delineate the elusive fundamental principles governing brain function. This is due in part to the challenges presented by the hierarchical organisation of the brain, which exhibits complex non-linear interactions across micro-, meso- and macro-scale systems . Herein,in silico modelling of whole brain dynamics have been used to explore the putative contribution of receptor subsystems to cortical dynamics -based analyses, although recent attempts have been made to create vertex/voxel-wise maps of gene expression using machine learning ). Furtherrelated . Similarrrelated . The higrrelated . Whilst pression , further and MOR). As such and MOR). However and MOR), and thu and MOR). Altoget and MOR). In the  and MOR). In the 2.2A general assumption made when utilising molecular information from PET and SPECT imaging and the AHBA is that the spatial distribution of receptors derived in normative, independent cohorts is applicable to fMRI data from new samples . In gene3If one assumes that a particular pattern of changes in fMRI data is driven by a certain biological process, then it is reasonable that the extent to which each region of the brain exhibits a change might be at least partially explicable by the relative abundance of the molecular machinery underlying this process across regions. The most common approach to derive molecular insights from fMRI data is to undertake conventional fMRI analyses and then examine how the results do or do not overlap with the spatial distribution of different receptors or genes of interest . This ap3.1After parcellating whole brain un-thresholded statistical maps for a contrast of interest  as well as molecular information from potentially explanatory systems  upon which the drug acts) using an anatomical or functional atlas , these The field has also moved beyond simple spatial correlation analyses and started to more broadly link multiple molecular systems to aspects of functional imaging and behaviour in a data-driven manner. When utilising multiple receptors or genes as explanatory variables to test their link with the functional measure of interest within a multiple linear regression framework to estimate the relative contribution of each variable to the total variance, one must consider that these systems frequently demonstrate strong collinearity. A commonly used technique to overcome this challenge is partial least squares regression (PLS), which reduces the number of explanatory variables to a smaller set of uncorrelated components . In this3.2The human brain is exquisitely complex, with billions of neurones interacting through trillions of shared connections. Understanding this complexity necessitates a wide range of tools to provide distinct but interrelated perspectives. For example, a substantial body of work has examined how the structural connectome shapes the network dynamics observed with fMRI . Analogo3.31, ɑ2a, M1, and 5HT2a) and inhibitory  neuromodulatory influence on cognition. These groups of receptors showed differential relationships with the first two principal components: the first was broadly engaged across all tasks and showed positive and negative relationships with the faciliatory and inhibitory groups respectively, whilst the second, which was most strongly linked to social and language processing, showed positive relationships with the catecholamine (dopaminergic and noradrenergic) receptors and negative relationships with serotonergic and cholinergic receptors. Altogether, this highlights the nuance within neuromodulatory control of cognition; whilst there seem to be some domain-general network mechanisms engaged across tasks, other systems engage with greater specificity and these each associate with different subsets of receptor systems. Moreover, how these receptor mechanisms interact remains largely unclear at a systems level, with the methodologies discussed in subsequent sections offering additional tools to explore these complex interrelationships.The BOLD signal fluctuations within anatomically distinct cortical and subcortical regions exhibit spatiotemporal relationships with each other, constituting large-scale patterns of correlated and anticorrelated activity , and dopaminergic (D1 and D2) receptor systems. Similar studies have followed. For example, changes in rCBF induced by three antipsychotic drugs were shown to be significantly correlated with D2 receptor density and DRD2 gene expression agonist activity at a set of target receptors, the relative contribution of each of these receptor systems to the resultant large scale pharmacodynamic response of the brain remains challenging to disentangle. For example, the affinity of different ligands for receptor sub-systems can be unintuitive with noradrenaline able to act on dopamine D1 and D4 receptors under some circumstances . The molpression . The fiepression . Crucialpression . Similarpression . Future 1a, 5-HT1b, 5-HT2a, 5-HT4, 5-HT6, 5-HTT), acetylcholine , glutamate , GABA (GABA-A), histamine (H3), cannabinoid (CB1), and opioid (MOR). As would be expected, regions with similar chemoarchitecture demonstrated similar changes in FC across different pharmacological challenges spanning psychedelics, anaesthetics, and cognitive enhancers. In a subsequent data-driven PLS regression analysis, psychedelics, and anaesthetics were found to relate to divergent transmitter systems. Moreover, despite these differences, the effects of all drugs mapped onto several hierarchical gradients across multiple domains of anatomy and physiology. These findings highlight the potential key relevance of high CBF, connectivity, and receptor density of parts of transmodal association cortex in mediating the powerful effects of these compounds on cognition and subjective experience. Finally, brain regions which demonstrated susceptibility to pharmacological manipulation also related to patterns of structural pathology associated with a range of neuropsychiatric disorders, including 22q11.2 deletion syndrome, attention-deficit/hyperactivity disorder, autism spectrum disorder, idiopathic generalized epilepsy, right temporal lobe epilepsy, left temporal lobe epilepsy, depression, obsessive-compulsive disorder, schizophrenia, bipolar disorder, and Parkinson’s disease. This study constitutes a significant extension of the basic spatial correlational analyses that preceded it and offers a new perspective on the longstanding challenge of understanding how multiple receptor systems interact to mediate the profound and diverse effects of psychoactive substances on various facets of consciousness and cognition.Taking an opposing but analogous approach to large-scale molecular informed investigation of psychopharmacology, Luppi and colleagues endeavoured to map the effects of a wide array of psychoactive compounds onto the multi-receptor landscape of the brain , includiThe molecular targets of pharmacological interventions can also offer cellular-level insights. For example, propofol is known to act primarily through GABA-A receptors in the mammalian brain . In doin3.5A key challenge in neuropsychiatric disorders is the multiplicity of implicated mechanisms. As such, linking mechanisms with theorised causal roles in driving symptomatology through to specific measurable perturbations of brain function would be hugely valuable. For instance, several neurodegenerative disorders are associated with hyperphosphorylation, misfolding, and aggregation of the tau protein, with variations in the gene encoding it (MAPT) conferring susceptibility to regional brain dysfunction . Rittman1e receptor, for which there are currently no available ligands. Data-driven decomposition methods applied to symptomatology trans-nosologically and systematically mapped onto underlying molecular systems may constitute a high throughput means by which to facilitate the move towards biologically-informed targeted treatments.Ji and colleagues took an alternative approach and examined a heterogeneous cohort of patients with psychosis-spectrum disorders (PSD) with the aim of mapping cognition and psychopathology to imaging data trans-nosologically . In theiRecently, we have taken a conceptually similar approach to investigate the role of immunomodulatory drugs in mood and fatigue . Inflamm3.6p-values, as two significantly autocorrelated spatial maps are more likely to show strong spatial correlation than maps with random values  is the inherent spatial autocorrelation that dominates these data, in which functional connectivity, receptor density, and gene expression are more strongly correlated within contiguous brain regions than with more anatomically distant regions . This cam values . The ubim values ), we brim values . Anotherm values . Howeverm values . Widespr4Despite offering initial insights through ease of use, the spatial correlation analyses described above are limited in their ability to exploit the rich spatiotemporal dynamics of brain-wide BOLD fluctuations. In order to overcome this, novel analytical approaches have been developed to enrich fMRI data with the molecular information provided by PET and SPECT imaging , as well4.1This employs the same two-step multiple linear regression approach used within conventional resting state network (RSN) analyses to estimate subject-specific RSNs from group-level probabilistic maps derived within independent components analysis . HoweverThe vast majority of molecular-enriched fMRI analyses to date have focussed upon resting state data, largely due to its relative simplicity, abundance, and possibility to explore across a range of basic neural mechanisms u without being restricted to a specific cognitive phenomenon. However, there are limits to which insights can be gleaned from rest alone, with dynamic neural activity associated with task-based data seemingly better placed to capture behaviourally relevant variability . Moreove4.22a receptor agonist activity . HoweverThirdly, the applicability of PET and AHBA data derived in normative cohorts to those with substantial differences remains contentious. Many molecular systems, such as noradrenergic modulatory projections from the locus coeruleus , change Finally, there is a certain degree of spatial overlap between different molecular systems . To acco5Advances in computational neuroscience have increasingly allowed for the modelling of whole-brain dynamics in silico , offerin5.1The full details of generating whole brain models are beyond the scope of this review, though Cofre and colleagues provide a detailed overview of the key methodological aspects . In shorIn the context of molecular-enriched analyses, PET receptor density information can be incorporated into these models  to explo5.22a receptor density information from PET to modulate neural gain by selectively altering the recursive excitatory and inhibitory local microcircuits of different brain regions based on its local density. The logic of this approach is analogous to the aforementioned spatial correlation and molecular-enriched network analyses; the pharmacodynamic effects of the drug are at least partially explicable by the distribution of its primary targets. Incorporating the 5-HT2a receptor distribution produced a better fit between the simulated and experimental data under the LSD condition than the other serotonergic receptors . However, the 5-HT1a receptor model performed significantly better than the others and nearly as well as that using 5-HT2a, highlighting the need to consider the effects of multiple receptor systems.To date, whole brain models that incorporate molecular information have largely been applied in the context of pharmacological challenges, often with psychedelic compounds. This has primarily been undertaken with an emphasis on demonstrating the utility of these methods as well as advancing the biophysical complexity. In an early example of this, Deco and colleagues explored the pharmacodynamics of LSD . First, 2a modulation of cortical pyramidal neurones, although many of the other serotonergic and dopaminergic targets also resulted in moderate concordance between simulated and experimental data. Another interesting approach diverges from whole brain models but applies the same fundamental logic to network control analysis , they demonstrate that each is important for fitting to the empirical data. This more complex recurrent modelling approach provides an exciting opportunity to bring these models towards biological plausibility. A crucial next step will be to explore how multiple interacting neuromodulatory systems work in concert to shape network dynamics under drugs with rich pharmacology, and disease states in which a multiplicity of receptor systems are implicated. One option may be to utilise the novel multiscale dynamic mean-field models with synaptic gating equations linked to neurotransmitter concentrations, but with heterogeneity across regions included as a function of receptor density or gene expression , result from shared or divergent mechanisms. Utilising a similar framework to the aforementioned psychedelic studies, Luppi and colleagues explored whether the characteristic network changes seen in the anaesthetic state and DOC result from inhibition and connectome perturbation . They fi5.4Beyond the pharmacological studies attempting to model psychopharmacological effects through receptor densities of the drug's primary targets, whole brain models have also incorporated transcriptomic data from the AHBA to study the functional relevance of excitatory and inhibitory receptor expression . By comp5.5In the longer term, these models may provide an important theoretical link between the network dysfunction increasingly characterised in clinical populations to the underlying driving cellular and molecular mechanisms to which we can target interventions . Indeed,5.6To date, whole brain models have considered only one receptor system or examined different receptor systems in isolation. Future models incorporating multiple receptors and accounting for their complex non-linear interactions will be important to meaningfully capture the full set of mechanisms through which the effects of drugs and disease are enacted. However, this poses additional computational and inferential challenges, such as overfitting models to noise when applied to empirical data. The existing work has also only utilised parcellated data whose spatial resolution may overlook more fine-grained pharmacodynamic effects. However, recent analytic and numerical advances have already reduced the computational expense of dynamic mean field models, with good concordance between parcellations of 100 and 1000 regions, making these methods more accessible to the broader neuroscientific community . Similar6The novel methodologies outlined here bring together micro-scale molecular level information with macro-scale systems dynamics, offering important insights into the trans-hierarchical functional organisation of the brain in both health and disease. Despite offering initial opportunities through ease of use, spatial correlation analyses are increasingly being used for large scale mapping of multiple molecular systems to multivariate fMRI measures. Receptor-enriched network analyses allow for the characterisation of spatiotemporal relationships between the BOLD signal and the distribution of molecular systems, with maps of receptor-enriched connectivity amenable to conventional inter-subject analyses, including voxel-wise regression with behavioural and clinical measures of interest. Finally, whole brain modelling allows for the systematic and causal manipulation of micro- and meso-scale facets of brain structure and function, such as the contribution of different receptor systems, to examine the counterfactual consequences on resultant simulated network dynamics. Moreover, in linking system-level dynamics to their molecular substrates, we can begin to transcend the fragmentation of neuroscience as a field by bringing together findings across multiple levels of analysis. We hope that this may ultimately help catalyse the formation of all-important ‘big-picture’ theories of brain function and dysfunction, offering novel testable hypotheses which can be examined with the full suite of tools available to modern neuroscience, both within and across micro- and macro-scales. Additionally, the enticing ability to link clinical symptomatology, through characterising trans-nosological molecular dysfunction, to mechanistically informed pharmacotherapy may help finally bring functional imaging closer to clinical reality.All authors have no formal conflicts of interest to declare.


Jingchuvirales order were detected in a wide range of insect genomes covering several distantly related families. Moreover, Jingchuvirales-derived glycoproteins were recently associated by our group with the origin of a putative new retrovirus based on a glycoprotein captured by a mosquito retrotransposon. But, except for mosquitoes, there is a lack of a more detailed understanding of the endogenization mechanism, timing, and frequency per Jingchuvirales viral lineages. Here we screened Jingchuvirales glycoprotein-derived EVEs (Jg-EVEs) in eukaryotic genomes. We found six distinct endogenization events of Jg-EVEs, that belong to two out of five known Jingchuvirales families (Chuviridae and Natareviridae). For seven arthropod families bearing Jg-EVEs there is no register of bona fide circulating chuvirus infection. Hence, our results show that Jingchuvirales viruses infected or still infect these host families. Although we found abundant evidence of LTR-Gypsy retrotransposons fragments associated with the glycoprotein in Hymenoptera and other insect orders, our results show that the widespread distribution of Jingchuvirales glycoproteins in extant Arhtropods is a result of multiple ancient endogenization events and that these virus fossils are being vertically inherited in Arthropods genomes for millions of years. Endogenous virus elements (EVEs) are viral-derived sequences integrated into their host genomes. EVEs of the  Viruses are the most abundant and diverse nucleic acid-based replicating units on Earth . These pin trans for viral cDNA synthesis and integration (Rhabdoviridae and Chuviridae) out of 49 known viral families  with a negative-sense single-stranded RNA (ssRNA (-)) genome with distinct genomic structure conformations such as unsegmented, segmented, linear or circular genomes , host taxa and endogenous retrotransposons that explain the Jg-EVEs emergence and maintenance through evolutionary time? What was the timing of endogenization events in the evolutionary history of arthropods? Here, we performed an extensive literature review to catalog all complete genomes of Jingchuvirales order available, screened Jingchuvirales glycoprotein in eukaryotic genomes and reconstructed the phylogenetic history of complete genomes and endogenized glycoproteins. We detected that two out of five viral families of the order Jingchuvirales were involved in six ancient endogenization events and that all extant and widespread Jg-EVEs found in this study are derived from these events.The new Jingchuvirales” and the Boolean operator “OR” for the combination of these three terms. With the results from this search, we performed a screening based on reading the title and abstract. The papers that corresponded with the manuscript goal were selected for a full reading, while those that did not, were removed from the study.We performed a literature review using the database PubMed Central® (PMC). Initially, the identification of the published papers was carried out using the keywords “Chuvirus”, “Chuviridae” and “Only papers with the description of new chuvirus genomes published up to May 2021 were selected. The exclusion criteria were: i - papers describing only genomes from other viral families or with chuvirus genomes already available in previous papers; ii - published before 2015, the year of publication of the first original chuvirus genomes; iii - review articles, notes, and letters to the editor.Jingchuvirales glycoprotein-derived EVEs (Jg-EVEs) we used a BLASTp online approach. In this step, all Jingchuvirales glycoproteins identified in this study through literature mining were used as queries against the non-redundant (nr) protein database updated in May 2021 excluding all viruses from the subjects. The results were clustered to remove redundant hits using cd-hit with a sequence identity threshold and an alignment coverage threshold of 100% (-c 1 and -s 1) (To identify putative nd -s 1) . The matnd -s 1) .The flanking regions were used in three analyses to understand the genomic context of each Jg-EVEs. The repeat content was evaluated using the RepeatMasker  online tJingchuvirales order and the glycoprotein was used to investigate the endogenization process across different eukaryotic groups. To reconstruct the RdRp phylogeny, RdRp from viruses belonging to Mononegavirales order according to ICTV  were recovered and clusterized (ictv_ncbi.py) from NCBI  and were aligned separately by family. Each family alignment was automatically edited with CIAlign  or with genome length equal to or greater than 9 kb are aligned with the Mononegavirales reference alignment. Phylogenetic analysis of glycoproteins was performed with alignments encompassing all reference chuvirus glycoproteins retrieved from the literature, glycoproteins recovered from a previous study , circular bi-segmented genomes (Boscovirus), and a diversity genomes with linear or circular genomes and having 1 to 3 segments (other 10 genera). The Aliusviridae, Myriaviridae, Crepuscuviridae and Natareviridae are represented by viruses with linear segmented genomes.The RdRp phylogenetic tree confirmed the clustering of the new taxonomy of  by ICTV , where tJingchuvirales and putative Jg-EVEs showed the existence of 6 distinct clades associated with endogenization events  and the Event-4 to an unknown taxon of Jingchuvirales order.We found 158 EVEs  representing 939 copies (screening against respective genome) in 38 species . The glyn events . From thn events , Event-1n events , Event-3Bemisia tabaci (Unclassified element) and one Anakin  and from different genera of the same family  suggesting that the endogenization event involving these Jg-EVEs occurred in the ancestral species of the genus Bombus around 36~2 MyA and in the ancestor of the Pteromalidae family around 155~54.8 MyA  (Viruses leave traces of past infection in their host genomes as EVEs . These eroteins) , raisingroteins) . But theroteins) . Based oBombus species (LCA 36~2 MYA) and Cyphomyrmex (Myrmicinae subfamily) and Odontomachus (Ponerinae subfamily) (LCA 155~54.8 MyA) add further evidence for ancient integration events and long term vertical transmission since the Eocene and lower Cretaceous (Chuviridae and Netaviridae). Therefore, there is no specific association between EVE and host taxa and the higher number of endogenization events derived from the Chuviridae family may be simply a result of its larger host range (Our results showed four Jingchuvirales glycoprotein endogenization events in 38 eukaryote genomes investigated. We were able to characterize four endogenization events that took place in the ancestors of several Insecta taxa. Jg-EVEs widespread distribution in extant insects may be a consequence of long-term vertical transmission since ancestral endogenizations. Ortholog copies of Jg-EVEs found between etaceous . But stust range . Transpost range  suggestist range  and thatst range . Our anast range .EVEs are equivalent to genetic fossils, and as such, they store information about past or extant viral infections as well as providing additional information about viral host range . Based oChuviridae family cover circular, linear, segmented and non-segmented genomes, which is particularly unusual for RNA viruses (Jingchuvirales order. Our RdRp phylogeny  shows a different clade organization with clades exclusively with linear genomes and others with circular genomes. The new families Aliusviridae, Myriaviridae, Crepuscuviridae and Natareviridae proposed by ICTV are composed only of linear genomes arranged in specific clades of the Jingrchuvirales order (The diversity of genomic structures of the  viruses . These ahylogeny  shows aes order .Jingchuvirales glycoproteins EVEs in insect genomes.In this study, we identified several Jg-EVEs across eukaryote genomes. These elements originated in the ancient past through six distinct integration events, the majority occurring in insects. Despite the presence of TEs on EVEs boundaries, we found no evidence of glycoprotein capture by retrotransposons in other insect species except by the already characterized event in Culicidae. Therefore, new studies are warranted to better understand the deep relationships and long-term maintenance of