The transcriptional legacy of developmental stochasticity.

Genetic and environmental variation are key contributors during organism development, but the influence of minor perturbations or noise is difficult to assess. This study focuses on the stochastic variation in allele-specific expression that persists through cell divisions in the nine-banded armadillo (Dasypus novemcinctus). We investigated the blood transcriptome of five wild monozygotic quadruplets over time to explore the influence of developmental stochasticity on gene expression. We identify an enduring signal of autosomal allelic variability that distinguishes individuals within a quadruplet despite their genetic similarity. This stochastic allelic variation, akin to X-inactivation but broader, provides insight into non-genetic influences on phenotype. The presence of stochastically canalized allelic signatures represents a novel axis for characterizing organismal variability, complementing traditional approaches based on genetic and environmental factors. We also developed a model to explain the inconsistent penetrance associated with these stochastically canalized allelic expressions. By elucidating mechanisms underlying the persistence of allele-specific expression, we enhance understanding of development's role in shaping organismal diversity.

Comprehensive in virio structure probing analysis of the influenza A virus identifies functional RNA structures involved in viral genome replication.

The influenza A virus genome is segmented into eight viral RNAs (vRNA). Secondary structures of vRNA are known to be involved in the viral proliferation process. Comprehensive vRNA structures in vitro, in virio, and in cellulo have been analyzed. However, the resolution of the structure map can be improved by comparative analysis and statistical modeling. Construction of a more high-resolution and reliable RNA structure map can identify uncharacterized functional structure motifs on vRNA in virion. Here, we establish the global map of the vRNA secondary structure in virion using the combination of dimethyl sulfate (DMS)-seq and selective 2'-hydroxyl acylation analyzed by primer extension (SHAPE)-seq with a robust statistical analysis. Our high-resolution analysis identified a stem-loop structure at nucleotide positions 39 - 60 of segment 6 and further validated the structure at nucleotide positions 87 - 130 of segment 5 that was previously predicted to form a pseudoknot structure in silico. Notably, when the cells were infected with recombinant viruses which possess the mutations to disrupt the structure, the replication and packaging of the viral genome were drastically decreased. Our results provide comprehensive and high-resolution information on the influenza A virus genome structures in virion and evidence that the functional RNA structure motifs on the influenza A virus genome are associated with appropriate replication and packaging of the viral genome.

Genome-Wide RNA Secondary Structure Prediction.

The information of RNA secondary structure has been widely applied to the inference of RNA function. However, a classical prediction method is not feasible to long RNAs such as mRNA due to the problems of computational time and numerical errors. To overcome those problems, sliding window methods have been applied while their results are not directly comparable to global RNA structure prediction. In this chapter, we introduce ParasoR, a method designed for parallel computation of genome-wide RNA secondary structures. To enable genome-wide prediction, ParasoR distributes dynamic programming (DP) matrices required for structure prediction to multiple computational nodes. Using the database of not the original DP variable but the ratio of variables, ParasoR can locally compute the structure scores such as stem probability or accessibility on demand. A comprehensive analysis of local secondary structures by ParasoR is expected to be a promising way to detect the statistical constraints on long RNAs.

RNA Secondary Structure Alteration Caused by Single Nucleotide Variants.

A point mutation in coding RNA can cause not only an amino acid substitution but also a dynamic change of RNA secondary structure, leading to a dysfunctional RNA. Although in silico structure prediction has been used to detect structure-disrupting point mutations known as riboSNitches, exhaustive simulation of long RNAs is needed to detect a significant enrichment or depletion of riboSNitches in functional RNAs. Here, we have developed a novel algorithm Radiam (RNA secondary structure Analysis with Deletion, Insertion, And substitution Mutations) for a comprehensive riboSNitch analysis of long RNAs. Radiam is based on the ParasoR framework, which efficiently computes local RNA secondary structures for long RNAs. ParasoR can compute a variety of structure scores over globally consistent structures with maximal span constraints for the base pair distance. Using the reusable structure database made by ParasoR, Radiam performs an efficient recomputation of the secondary structures for mutated sequences. An exhaustive simulation of Radiam is expected to find reliable riboSNitch candidates on long RNAs by evaluating their statistical significance in terms of the change of local structure stability.

Learning single-cell chromatin accessibility profiles using meta-analytic marker genes.

MOTIVATION: Single-cell assay for transposase accessible chromatin using sequencing (scATAC-seq) is a valuable resource to learn cis-regulatory elements such as cell-type specific enhancers and transcription factor binding sites. However, cell-type identification of scATAC-seq data is known to be challenging due to the heterogeneity derived from different protocols and the high dropout rate. RESULTS: In this study, we perform a systematic comparison of seven scATAC-seq datasets of mouse brain to benchmark the efficacy of neuronal cell-type annotation from gene sets. We find that redundant marker genes give a dramatic improvement for a sparse scATAC-seq annotation across the data collected from different studies. Interestingly, simple aggregation of such marker genes achieves performance comparable or higher than that of machine-learning classifiers, suggesting its potential for downstream applications. Based on our results, we reannotated all scATAC-seq data for detailed cell types using robust marker genes. Their meta scATAC-seq profiles are publicly available at https://gillisweb.cshl.edu/Meta_scATAC. Furthermore, we trained a deep neural network to predict chromatin accessibility from only DNA sequence and identified key motifs enriched for each neuronal subtype. Those predicted profiles are visualized together in our database as a valuable resource to explore cell-type specific epigenetic regulation in a sequence-dependent and -independent manner.

Prevalent and dynamic binding of the cell cycle checkpoint kinase Rad53 to gene promoters.

Replication of the genome must be coordinated with gene transcription and cellular metabolism, especially following replication stress in the presence of limiting deoxyribonucleotides. The Saccharomyces cerevisiae Rad53 (CHEK2 in mammals) checkpoint kinase plays a major role in cellular responses to DNA replication stress. Cell cycle regulated, genome-wide binding of Rad53 to chromatin was examined. Under replication stress, the kinase bound to sites of active DNA replication initiation and fork progression, but unexpectedly to the promoters of about 20% of genes encoding proteins involved in multiple cellular functions. Rad53 promoter binding correlated with changes in expression of a subset of genes. Rad53 promoter binding to certain genes was influenced by sequence-specific transcription factors and less by checkpoint signaling. However, in checkpoint mutants, untimely activation of late-replicating origins reduces the transcription of nearby genes, with concomitant localization of Rad53 to their gene bodies. We suggest that the Rad53 checkpoint kinase coordinates genome-wide replication and transcription under replication stress conditions.

Characteristics of Pediatric Nasal Foreign Body Cases That Required Multiple Removal Procedures: A Single Tertiary Medical Center Cross-Sectional Study.

OBJECTIVES: Removal of nasal foreign bodies in children should be completed as few numbers of times as possible to reduce the burden on the child. This study aimed to reveal the characteristics of pediatric nasal foreign body cases that required multiple removal procedure and to investigate the appropriate removal method for each shape of foreign body. METHODS: We retrospectively reviewed patients who visited a pediatric emergency department because of nasal foreign body from November 1, 2018, to October 31, 2020. We compared the characteristics of patients, foreign bodies, and treatment between the success and failure groups in the first removal procedure. Moreover, we described the detail of patients in the failure group. We also showed final successful removal methods according to the shape of the foreign bodies. RESULTS: We included 104 children in our analysis. Between the success and failure groups in the first removal procedure, there was no statistically significant difference in the characteristics of patients and foreign bodies but a significant difference in the removal method ( P < 0.001). In the failure groups, the most frequent number of times required for removal is 2 and the most common methods in the first removal procedure was the parent's kiss, most of which were performed on spherical foreign bodies in younger children, Finally, only 7 cases (6.7%) failed to remove the foreign body. According to the shape of the foreign bodies, the percentage of forceps and clamps used for successful removal was high in every shape. CONCLUSIONS: There was a significant difference in the removal method between the success and failure groups in the first removal procedure, but finally, most foreign bodies were removed by emergency physicians. Therefore, further research is needed to determine the appropriate removal method for each patient and foreign body.

Assessing Versatile Machine Learning Models for Glioma Radiogenomic Studies across Hospitals.

Radiogenomics use non-invasively obtained imaging data, such as magnetic resonance imaging (MRI), to predict critical biomarkers of patients. Developing an accurate machine learning (ML) technique for MRI requires data from hundreds of patients, which cannot be gathered from any single local hospital. Hence, a model universally applicable to multiple cohorts/hospitals is required. We applied various ML and image pre-processing procedures on a glioma dataset from The Cancer Image Archive (TCIA, n = 159). The models that showed a high level of accuracy in predicting glioblastoma or WHO Grade II and III glioma using the TCIA dataset, were then tested for the data from the National Cancer Center Hospital, Japan (NCC, n = 166) whether they could maintain similar levels of high accuracy. Results: we confirmed that our ML procedure achieved a level of accuracy (AUROC = 0.904) comparable to that shown previously by the deep-learning methods using TCIA. However, when we directly applied the model to the NCC dataset, its AUROC dropped to 0.383. Introduction of standardization and dimension reduction procedures before classification without re-training improved the prediction accuracy obtained using NCC (0.804) without a loss in prediction accuracy for the TCIA dataset. Furthermore, we confirmed the same tendency in a model for IDH1/2 mutation prediction with standardization and application of dimension reduction that was also applicable to multiple hospitals. Our results demonstrated that overfitting may occur when an ML method providing the highest accuracy in a small training dataset is used for different heterogeneous data sets, and suggested a promising process for developing an ML method applicable to multiple cohorts.

Fine-Tuning Approach for Segmentation of Gliomas in Brain Magnetic Resonance Images with a Machine Learning Method to Normalize Image Differences among Facilities.

Machine learning models for automated magnetic resonance image segmentation may be useful in aiding glioma detection. However, the image differences among facilities cause performance degradation and impede detection. This study proposes a method to solve this issue. We used the data from the Multimodal Brain Tumor Image Segmentation Benchmark (BraTS) and the Japanese cohort (JC) datasets. Three models for tumor segmentation are developed. In our methodology, the BraTS and JC models are trained on the BraTS and JC datasets, respectively, whereas the fine-tuning models are developed from the BraTS model and fine-tuned using the JC dataset. Our results show that the Dice coefficient score of the JC model for the test portion of the JC dataset was 0.779 ± 0.137, whereas that of the BraTS model was lower (0.717 ± 0.207). The mean Dice coefficient score of the fine-tuning model was 0.769 ± 0.138. There was a significant difference between the BraTS and JC models (p < 0.0001) and the BraTS and fine-tuning models (p = 0.002); however, no significant difference between the JC and fine-tuning models (p = 0.673). As our fine-tuning method requires fewer than 20 cases, this method is useful even in a facility where the number of glioma cases is small.

Intraductal Transplantation Models of Human Pancreatic Ductal Adenocarcinoma Reveal Progressive Transition of Molecular Subtypes.

Pancreatic ductal adenocarcinoma (PDAC) is the most lethal common malignancy, with little improvement in patient outcomes over the past decades. Recently, subtypes of pancreatic cancer with different prognoses have been elaborated; however, the inability to model these subtypes has precluded mechanistic investigation of their origins. Here, we present a xenotransplantation model of PDAC in which neoplasms originate from patient-derived organoids injected directly into murine pancreatic ducts. Our model enables distinction of the two main PDAC subtypes: intraepithelial neoplasms from this model progress in an indolent or invasive manner representing the classical or basal-like subtypes of PDAC, respectively. Parameters that influence PDAC subtype specification in this intraductal model include cell plasticity and hyperactivation of the RAS pathway. Finally, through intratumoral dissection and the direct manipulation of RAS gene dosage, we identify a suite of RAS-regulated secreted and membrane-bound proteins that may represent potential candidates for therapeutic intervention in patients with PDAC. SIGNIFICANCE: Accurate modeling of the molecular subtypes of pancreatic cancer is crucial to facilitate the generation of effective therapies. We report the development of an intraductal organoid transplantation model of pancreatic cancer that models the progressive switching of subtypes, and identify stochastic and RAS-driven mechanisms that determine subtype specification.See related commentary by Pickering and Morton, p. 1448.This article is highlighted in the In This Issue feature, p. 1426.

Correction: Identification of lysine methylation in the core GTPase domain by GoMADScan.

[This corrects the article DOI: 10.1371/journal.pone.0219436.].

IMP dehydrogenase-2 drives aberrant nucleolar activity and promotes tumorigenesis in glioblastoma.

In many cancers, high proliferation rates correlate with elevation of rRNA and tRNA levels, and nucleolar hypertrophy. However, the underlying mechanisms linking increased nucleolar transcription and tumorigenesis are only minimally understood. Here we show that IMP dehydrogenase-2 (IMPDH2), the rate-limiting enzyme for de novo guanine nucleotide biosynthesis, is overexpressed in the highly lethal brain cancer glioblastoma. This leads to increased rRNA and tRNA synthesis, stabilization of the nucleolar GTP-binding protein nucleostemin, and enlarged, malformed nucleoli. Pharmacological or genetic inactivation of IMPDH2 in glioblastoma reverses these effects and inhibits cell proliferation, whereas untransformed glia cells are unaffected by similar IMPDH2 perturbations. Impairment of IMPDH2 activity triggers nucleolar stress and growth arrest of glioblastoma cells even in the absence of functional p53. Our results reveal that upregulation of IMPDH2 is a prerequisite for the occurance of aberrant nucleolar function and increased anabolic processes in glioblastoma, which constitutes a primary event in gliomagenesis.

Identification of lysine methylation in the core GTPase domain by GoMADScan.

RAS is the founding member of a superfamily of GTPases and regulates signaling pathways involved in cellular growth control. While recent studies have shown that the activation state of RAS can be controlled by lysine ubiquitylation and acetylation, the existence of lysine methylation of the RAS superfamily GTPases remains unexplored. In contrast to acetylation, methylation does not alter the side chain charge and it has been challenging to deduce its impact on protein structure by conventional amino acid substitutions. Herein, we investigate lysine methylation on RAS and RAS-related GTPases. We developed GoMADScan (Go language-based Modification Associated Database Scanner), a new user-friendly application that scans and extracts posttranslationally modified peptides from databases. The GoMADScan search on PhosphoSitePlus databases identified methylation of conserved lysine residues in the core GTPase domain of RAS superfamily GTPases, including residues corresponding to RAS Lys-5, Lys-16, and Lys-117. To follow up on these observations, we immunoprecipitated endogenous RAS from HEK293T cells, conducted mass spectrometric analysis and found that RAS residues, Lys-5 and Lys-147, undergo dimethylation and monomethylation, respectively. Since mutations of Lys-5 have been found in cancers and RASopathies, we set up molecular dynamics (MD) simulations to assess the putative impact of Lys-5 dimethylation on RAS structure. Results from our MD analyses predict that dimethylation of Lys-5 does not significantly alter RAS conformation, suggesting that Lys-5 methylation may alter existing protein interactions or create a docking site to foster new interactions. Taken together, our findings uncover the existence of lysine methylation as a novel posttranslational modification associated with RAS and the RAS superfamily GTPases, and putative impact of Lys-5 dimethylation on RAS structure.

reactIDR: evaluation of the statistical reproducibility of high-throughput structural analyses towards a robust RNA structure prediction.

BACKGROUND: Recently, next-generation sequencing techniques have been applied for the detection of RNA secondary structures, which is referred to as high-throughput RNA structural (HTS) analyses, and many different protocols have been used to detect comprehensive RNA structures at single-nucleotide resolution. However, the existing computational analyses heavily depend on the experimental methodology to generate data, which results in difficulties associated with statistically sound comparisons or combining the results obtained using different HTS methods. RESULTS: Here, we introduced a statistical framework, reactIDR, which can be applied to the experimental data obtained using multiple HTS methodologies. Using this approach, nucleotides are classified into three structural categories, loop, stem/background, and unmapped. reactIDR uses the irreproducible discovery rate (IDR) with a hidden Markov model to discriminate between the true and spurious signals obtained in the replicated HTS experiments accurately, and it is able to incorporate an expectation-maximization algorithm and supervised learning for efficient parameter optimization. The results of our analyses of the real-life HTS data showed that reactIDR had the highest accuracy in the classification of ribosomal RNA stem/loop structures when using both individual and integrated HTS datasets, and its results corresponded the best to the three-dimensional structures. CONCLUSIONS: We have developed a novel software, reactIDR, for the prediction of stem/loop regions from the HTS analysis datasets. For the rRNA structure analyses, reactIDR was shown to have robust accuracy across different datasets by using the reproducibility criterion, suggesting its potential for increasing the value of existing HTS datasets. reactIDR is publicly available at https://github.com/carushi/reactIDR .

Verbal Communication of an Orally Intubated Patient in the Intensive Care Unit: A Case Report.

BACKGROUND: Effective interaction with orally intubated patients is important for critical care rehabilitation. An electrolarynx (EL) has reportedly proven useful for facilitating verbal communication during oral intubation. The EL allows patients to express their wishes instantly. Nevertheless, this method is not commonly applied, probably because articulation is often unsatisfactory. Here, we report a case of successful EL-based communication during early mobilization and describe the key factors involved in this success. CASE: An 82-year-old man, who was intubated and undergoing mechanical ventilation for the treatment of acute respiratory failure caused by severe pneumonia, was referred to the rehabilitation department for early mobilization. The patient tried to speak during the spontaneous awakening trials and breathing trials for weaning off mechanical ventilation. However, he was frustrated by communication difficulties and consequently exhibited negative behavior toward physical therapy. We attempted to use an EL to facilitate communication, but initially the patient failed to achieve intelligible articulation. We eventually established that the intubation tube should be located at the corner of the mouth to minimize the restriction of tongue movement. Intelligible sounds were recognized and successful communication between the patient and staff was subsequently achieved. DISCUSSION: The use of an EL is worthy of consideration during early mobilization of orally intubated patients. To achieve successful communication with an EL, both patient selection (Richmond Agitation-Sedation Scale score of 0 or -1) and the proper placement of the intubation tube are necessary to ensure intelligible articulation.

Parallel computation of genome-scale RNA secondary structure to detect structural constraints on human genome.

BACKGROUND: RNA secondary structure around splice sites is known to assist normal splicing by promoting spliceosome recognition. However, analyzing the structural properties of entire intronic regions or pre-mRNA sequences has been difficult hitherto, owing to serious experimental and computational limitations, such as low read coverage and numerical problems. RESULTS: Our novel software, "ParasoR", is designed to run on a computer cluster and enables the exact computation of various structural features of long RNA sequences under the constraint of maximal base-pairing distance. ParasoR divides dynamic programming (DP) matrices into smaller pieces, such that each piece can be computed by a separate computer node without losing the connectivity information between the pieces. ParasoR directly computes the ratios of DP variables to avoid the reduction of numerical precision caused by the cancellation of a large number of Boltzmann factors. The structural preferences of mRNAs computed by ParasoR shows a high concordance with those determined by high-throughput sequencing analyses. Using ParasoR, we investigated the global structural preferences of transcribed regions in the human genome. A genome-wide folding simulation indicated that transcribed regions are significantly more structural than intergenic regions after removing repeat sequences and k-mer frequency bias. In particular, we observed a highly significant preference for base pairing over entire intronic regions as compared to their antisense sequences, as well as to intergenic regions. A comparison between pre-mRNAs and mRNAs showed that coding regions become more accessible after splicing, indicating constraints for translational efficiency. Such changes are correlated with gene expression levels, as well as GC content, and are enriched among genes associated with cytoskeleton and kinase functions. CONCLUSIONS: We have shown that ParasoR is very useful for analyzing the structural properties of long RNA sequences such as mRNAs, pre-mRNAs, and long non-coding RNAs whose lengths can be more than a million bases in the human genome. In our analyses, transcribed regions including introns are indicated to be subject to various types of structural constraints that cannot be explained from simple sequence composition biases. ParasoR is freely available at https://github.com/carushi/ParasoR .

Split-alignment of genomes finds orthologies more accurately.

We present a new pair-wise genome alignment method, based on a simple concept of finding an optimal set of local alignments. It gains accuracy by not masking repeats, and by using a statistical model to quantify the (un)ambiguity of each alignment part. Compared to previous animal genome alignments, it aligns thousands of locations differently and with much higher similarity, strongly suggesting that the previous alignments are non-orthologous. The previous methods suffer from an overly-strong assumption of long un-rearranged blocks. The new alignments should help find interesting and unusual features, such as fast-evolving elements and micro-rearrangements, which are confounded by alignment errors.