Jakob Skou Pedersen

MicroRNAs (miRNAs) are important post-transcriptional regulators, yet their expression is typically unobserved in single-cell and most bulk RNA-seq datasets. We present miDGD, a deep generative decoder model that predicts miRNA abundance directly from gene expression alone. Trained on bulk and single-cell datasets from TCGA, GTEx, and human cell lines, miDGD learned a shared latent representation of matched mRNA and miRNA profiles that organized samples into biologically meaningful clusters reflecting tissue and cancer types. The model reconstructed both tissue-specific and broadly expressed miRNAs, recapitulated known miRNA-target relationships, and showed robust performance in sparse and single-cell data. miDGD outperformed miRSCAPE and recent miRNA activity inference methods, with improved cross-dataset generalization. These results establish a deep generative model as an improved framework for predicting miRNA expression when direct measurements are unavailable.

Gene regulatory mechanisms control cell differentiation and homeostasis but are often undetectable, particularly at the single-cell level. We introduce bayesReact, which quantifies regulatory activities from bulk or single-cell omics data. It is based on an unsupervised generative model, exploiting the fact that each regulator typically targets many genes sharing a sequence motif. Using mRNA expression data, we illustrate and evaluate bayesReact on microRNAs (miRNAs). It outperforms existing methods on sparse bulk data and improves activity inference on single-cell data. Inferred miRNA activities correlate with miRNA expression across pan-cancer TCGA and healthy GTEx tissue samples. The activities capture cancer-type-specific miRNA patterns, e.g., for miR-122-5p and miR-124-3p, which also correlate more strongly with their target genes than their measured expression. This includes a strong negative correlation between miR-124-3p and the anti-neuronal REST transcription factor in nervous system cancers. Analyzing single-cell data, bayesReact detects prominent miRNAs during murine stem cell differentiation, including miR-298-5p, miR-92-2-5p, and the Sfmbt2 cluster (miR-297-669). Furthermore, spatio-temporal inference shows increasing miR-124-3p activity in differentiating neurons during embryonic spinal cord development in mice. bayesReact enables large-scale hypothesis-generating screens for novel regulatory factors and the discovery of condition-specific activities. It is implemented as a user-friendly R package (https://github.com/JakobSkouPedersenLab/bayesReact).

Motivation: The fragment length distribution of cell-free DNA (cfDNA) can reveal the presence of circulating tumor DNA (ctDNA) in a plasma sample. A previous study documented that non-negative matrix factorization (NMF) can extract relevant features from the fragment length distributions. These distributions, however, are affected by technical biases. When NMF is performed on samples from multiple datasets, some of the extracted signatures often capture these technical biases rather than the actual biological differences. Results: We present two methods for extracting biologically meaningful NMF signatures across heterogeneous datasets with varying technical biases. Using simulated data, we first demonstrate that these new methods are more effective at estimating the true proportions of the underlying processes. We then show that the methods increase the transferability of fragment length signatures to cfDNA datasets from external labs. Classification models using the two proposed NMF extensions achieve an average cross-dataset AUC of 0.842 and 0.814, compared to 0.776 for standard NMF and 0.688 for a set of previously reported manually selected fragment length features. We further show that one of the proposed methods requires only 1-5 samples to estimate the batch effect during inference. Availability and Implementation: The source code and instructions on how to run it are available at: https://github.com/BesenbacherLab/batch-NMF

Understanding the molecular landscape of nonmuscle-invasive bladder cancer (NMIBC) is essential to improve risk assessment and treatment regimens. We performed a comprehensive genomic analysis of patients with NMIBC using whole-exome sequencing (n = 438), shallow whole-genome sequencing (n = 362) and total RNA sequencing (n = 414). A large genomic variation within NMIBC was observed and correlated with different molecular subtypes. Frequent loss of heterozygosity in FGFR3 and 17p (affecting TP53) was found in tumors with mutations in FGFR3 and TP53, respectively. Whole-genome doubling (WGD) was observed in 15% of the tumors and was associated with worse outcomes. Tumors with WGD were genomically unstable, with alterations in cell-cycle-related genes and an altered immune composition. Finally, integrative clustering of multi-omics data highlighted the important role of genomic instability and immune cell exhaustion in disease aggressiveness. These findings advance our understanding of genomic differences associated with disease aggressiveness in NMIBC and may ultimately improve patient stratification.

Motivation: Regulatory constraints are crucial in maintaining tissue and cell integrity, and play important roles during developmental processes and environmental responses. Yet many regulatory mechanisms remain unobserved at the single-cell level and statistical inference may, in some cases, help elucidate their condition-specific activity and perturbation during disease progression. Results: We introduce bayesReact (BAYESian modeling of Regular Expression ACTivity), a generative model of motif occurrence across experimentally ranked sequences to infer motif-based regulatory activities. The method is evaluated for microRNAs (miRNAs), which perform post-transcriptional regulation through target mRNA destabilization and translational repression. Inferred miRNA activities positively correlate with the observed miRNA expressions in primary tumors from The Cancer Genome Atlas (TCGA) and mouse stem cells. The top miRNA activity profiles are as informative for TCGA cancer-type cluster identification as the top miRNA or mRNA expression profiles. The activity captures tissue-specific miRNA patterns observed in the matched expression, e.g., the expression of miR-122-5p in the liver and miR-124-3p in low-grade gliomas (LGG). We observe a negative association between the activity of the two miRNAs and their target gene expressions, including between the miR-124-3p activity and the anti-neuronal REST expression in LGG. bayesReact outperforms the existing method, miReact, on sparse count data, and shows a higher correlation with the miRNA expression in single-cell data. The method recovers temporal activities of prominent miRNAs during murine stem cell differentiation, including miR-298-5p, miR-92-2-5p, and the large Sfmbt2 cluster (miR-297-669). The bayesReact model is probabilistic and quantifies the uncertainty of all provided estimates. It is unsupervised and permits screens of bulk or single-cell data to identify condition-specific regulatory motif candidates. It further improves miRNA activity inference in single-cell data. Availability and implementation: bayesReact is implemented as an R-package, uses a Hamiltonian Monte-Carlo sampler for posterior approximation, and is available at https://github.com/astamr/bayesReact.

Circular RNAs represent a class of endogenous RNAs that regulate gene expression and influence cell biological decisions with implications for the pathogenesis of several diseases. Here, we disclose a novel gene-regulatory role of circHIPK3 by combining analyses of large genomics datasets and mechanistic cell biological follow-up experiments. Using time-course depletion of circHIPK3 and specific candidate RNA-binding proteins, we identify several perturbed genes by RNA sequencing analyses. Expression-coupled motif analyses identify an 11-mer motif within circHIPK3, which also becomes enriched in genes that are downregulated upon circHIPK3 depletion. By mining eCLIP datasets and combined with RNA immunoprecipitation assays, we demonstrate that the 11-mer motif constitutes a strong binding site for IGF2BP2 in bladder cancer cell lines. Our results suggest that circHIPK3 can sequester IGF2BP2 as a competing endogenous RNA (ceRNA), leading to target mRNA stabilization. As an example of a circHIPK3-regulated gene, we focus on the STAT3 mRNA as a specific substrate of IGF2BP2 and validate that manipulation of circHIPK3 regulates IGF2BP2-STAT3 mRNA binding and, thereby, STAT3 mRNA levels. Surprisingly, absolute copy number quantifications demonstrate that IGF2BP2 outnumbers circHIPK3 by orders of magnitude, which is inconsistent with a simple 1:1 ceRNA hypothesis. Instead, we show that circHIPK3 can nucleate multiple copies of IGF2BP2, potentially via phase separation, to produce IGF2BP2 condensates. Our results support a model where a few cellular circHIPK3 molecules can induce IGF2BP2 condensation, thereby regulating key factors for cell proliferation.

Circular RNAs (circRNAs) represent a class of widespread endogenous RNAs that regulate gene expression and thereby influence cell biological decisions with implications for the pathogenesis of several diseases. Here, we disclose a novel gene-regulatory role of circHIPK3 by combining analyses of large genomics datasets and mechanistic cell biological follow-up experiments. Specifically, we use temporal depletion of circHIPK3 or specific RNA binding proteins (RBPs) and identify several perturbed genes by RNA sequencing analyses. Using expression-coupled motif analyses of mRNA expression data from various knockdown experiments, we identify an 11-mer motif within circHIPK3, which is also enriched in genes that become downregulated upon circHIPK3 depletion. By mining eCLIP datasets, we find that the 11-mer motif constitutes a strong binding site for IGF2BP2 and validate this circHIPK3-IGF2BP2 interaction experimentally using RNA-immunoprecipitation and competition assays in bladder cancer cell lines. Our results suggest that circHIPK3 and IGF2BP2 mRNA targets compete for binding. Since the identified 11-mer motif found in circHIPK3 is enriched in upregulated genes following IGF2BP2 knockdown, and since IGF2BP2 depletion conversely globally antagonizes the effect of circHIPK3 knockdown on target genes, our results suggest that circHIPK3 can sequester IGF2BP2 as a competing endogenous RNA (ceRNA), leading to target mRNA stabilization. As an example of a circHIPK3-regulated gene, we focus on the STAT3 mRNA as a specific substrate of IGF2BP2 and validate that manipulation of circHIPK3 regulates IGF2BP2-STAT3 mRNA binding and thereby STAT3 mRNA levels. However, absolute copy number quantifications demonstrate that IGF2BP2 outnumbers circHIPK3 by orders of magnitude, which is inconsistent with a simple 1:1 ceRNA hypothesis. Instead, we show that circHIPK3 can nucleate multiple copies of IGF2BP2, potentially via phase separation, to produce IGF2BP2 condensates. Finally, we show that circHIPK3 expression correlates with overall survival of patients with bladder cancer. Our results are consistent with a model where relatively few cellular circHIPK3 molecules function as inducers of IGF2BP2 condensation thereby regulating STAT3 and other key factors for cell proliferation and potentially cancer progression.

Circulating tumor DNA (ctDNA) is a promising biomarker, reflecting the presence of tumor cells. Sequencing-based detection of ctDNA at low tumor fractions is challenging due to the crude error rate of sequencing. To mitigate this challenge, we developed ultra-deep mutation-integrated sequencing (UMIseq), a fixed-panel deep targeted sequencing approach, which is universally applicable to all colorectal cancer (CRC) patients. UMIseq features UMI-mediated error correction, the exclusion of mutations related to clonal hematopoiesis, a panel of normal samples for error modeling, and signal integration from single-nucleotide variations, insertions, deletions, and phased mutations. UMIseq was trained and independently validated on pre-operative (pre-OP) plasma from CRC patients (n = 364) and healthy individuals (n = 61). UMIseq displayed an area under the curve surpassing 0.95 for allele frequencies (AFs) down to 0.05%. In the training cohort, the pre-OP detection rate reached 80% at 95% specificity, while it was 70% in the validation cohort. UMIseq enabled the detection of AFs down to 0.004%. To assess the potential for detection of residual disease, 26 post-operative plasma samples from stage III CRC patients were analyzed. From this we found that the detection of ctDNA was associated with recurrence. In conclusion, UMIseq demonstrated robust performance with high sensitivity and specificity, enabling the detection of ctDNA at low allele frequencies.

Introduction: Circulating tumor DNA (ctDNA) is a promising biomarker, reflecting the presence of tumor cells. Sequencing-based detection of ctDNA at low tumor fractions is challenging due to the crude error rate of sequencing. To mitigate this challenge, we developed UMIseq, a fixed-panel deep-targeted sequencing approach, which is universally applicable to all colorectal cancer (CRC) patients. Methods: UMIseq features UMI-mediated error correction, exclusion of mutations related to clonal hematopoiesis, a panel of normals for error modeling, and signal integration from single-nucleotide variations, insertions, deletions, and phased mutations. UMIseq was trained and independently validated on pre-operative (pre-OP) plasma from CRC patients (n=364) and healthy individuals (n=61). Results: UMIseq displayed an area under the curve surpassing 0.95 for allele frequencies (AF) down to 0.05%. In the training cohort, the pre-OP detection rate reached 80% at 95% specificity, while in the validation cohort, it was 70%. UMIseq enabled the detection of AFs down to 0.004%. To assess the potential for detection of residual disease, 26 post-operative plasma samples from stage III CRC patients were analyzed. Detection of ctDNA was associated with recurrence (p =0.08). Conclusion: UMIseq demonstrated robust performance with high sensitivity and specificity, enabling the detection of ctDNA at low allele frequencies.

Circular RNAs (circRNAs) represent a class of widespread endogenous RNAs that regulate gene expression and thereby influence cell biological decisions with implications for the pathogenesis of several diseases. Here, we disclose a novel gene-regulatory role of circHIPK3 by combining analyses of large genomics datasets and mechanistic cell biological follow-up experiments. Specifically, we use temporal depletion of circHIPK3 or specific RNA binding proteins (RBPs) and identify several perturbed genes by RNA sequencing analyses. Using expression-coupled motif analyses of mRNA expression data from various knockdown experiments, we identify an 11-mer motif within circHIPK3, which is also enriched in genes that become downregulated upon circHIPK3 depletion. By mining eCLIP datasets, we find that the 11-mer motif constitutes a strong binding site for IGF2BP2 and validate this circHIPK3-IGF2BP2 interaction experimentally using RNA-immunoprecipitation and competition assays in bladder cancer cell lines. Our results suggest that circHIPK3 and IGF2BP2 mRNA targets compete for binding. Since the identified 11-mer motif found in circHIPK3 is enriched in upregulated genes following IGF2BP2 knockdown, and since IGF2BP2 depletion conversely globally antagonizes the effect of circHIPK3 knockdown on target genes, our results suggest that circHIPK3 can sequester IGF2BP2 as a competing endogenous RNA (ceRNA), leading to target mRNA stabilization. As an example of a circHIPK3-regulated gene, we focus on the STAT3 mRNA as a specific substrate of IGF2BP2 and validate that manipulation of circHIPK3 regulates IGF2BP2- STAT3 mRNA binding and thereby STAT3 mRNA levels. However, absolute copy number quantifications demonstrate that IGF2BP2 outnumbers circHIPK3 by orders of magnitude, which is inconsistent with a simple 1:1 ceRNA hypothesis. Instead, we show that circHIPK3 can nucleate multiple copies of IGF2BP2, potentially via phase separation, to produce IGF2BP2 condensates. Finally, we show that circHIPK3 expression correlates with overall survival of patients with bladder cancer. Our results are consistent with a model where relatively few cellular circHIPK3 molecules function as inducers of IGF2BP2 condensation thereby regulating STAT3 and other key factors for cell proliferation and potentially cancer progression.