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ENCODE comprises thousands of functional genomics datasets, and the encyclopedia covers hundreds of cell types, providing a universal annotation for genome interpretation. However, for particular applications, it may be advantageous to use a customized annotation. Here, we develop such a custom annotation by leveraging advanced assays, such as eCLIP, Hi-C, and whole-genome STARR-seq on a number of data-rich ENCODE cell types. A key aspect of this annotation is comprehensive and experimentally derived networks of both transcription factors and RNA-binding proteins (TFs and RBPs). Cancer, a disease of system-wide dysregulation, is an ideal application for such a network-based annotation. Specifically, for cancer-associated cell types, we put regulators into hierarchies and measure their network change (rewiring) during oncogenesis. We also extensively survey TF-RBP crosstalk, highlighting how SUB1, a previously uncharacterized RBP, drives aberrant tumor expression and amplifies the effect of MYC, a well-known oncogenic TF. Furthermore, we show how our annotation allows us to place oncogenic transformations in the context of a broad cell space; here, many normal-to-tumor transitions move towards a stem-like state, while oncogene knockdowns show an opposing trend. Finally, we organize the resource into a coherent workflow to prioritize key elements and variants, in addition to regulators. We showcase the application of this prioritization to somatic burdening, cancer differential expression and GWAS. Targeted validations of the prioritized regulators, elements and variants using siRNA knockdowns, CRISPR-based editing, and luciferase assays demonstrate the value of the ENCODE resource.

MOTIVATION: Recently, many chromatin immunoprecipitation sequencing experiments have been carried out for a diverse group of transcription factors (TFs) in many different types of human cells. These experiments manifest large-scale and dynamic changes in regulatory network connectivity (i.e. network rewiring), highlighting the different regulatory programs operating in disparate cellular states. However, due to the dense and noisy nature of current regulatory networks, directly comparing the gains and losses of targets of key TFs across cell states is often not informative. Thus, here, we seek an abstracted, low-dimensional representation to understand the main features of network change. RESULTS: We propose a method called TopicNet that applies latent Dirichlet allocation to extract functional topics for a collection of genes regulated by a given TF. We then define a rewiring score to quantify regulatory-network changes in terms of the topic changes for this TF. Using this framework, we can pinpoint particular TFs that change greatly in network connectivity between different cellular states (such as observed in oncogenesis). Also, incorporating gene expression data, we define a topic activity score that measures the degree to which a given topic is active in a particular cellular state. And we show how activity differences can indicate differential survival in various cancers. AVAILABILITY AND IMPLEMENTATION: The TopicNet framework and related analysis were implemented using R and all codes are available at https://github.com/gersteinlab/topicnet. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Alternative RNA splicing provides an important means to expand metazoan transcriptome diversity. Contrary to what was accepted previously, splicing is now thought to predominantly take place during transcription. Motivated by emerging data showing the physical proximity of the spliceosome to Pol II, we surveyed the effect of epigenetic context on co-transcriptional splicing. In particular, we observed that splicing factors were not necessarily enriched at exon junctions and that most epigenetic signatures had a distinctly asymmetric profile around known splice sites. Given this, we tried to build an interpretable model that mimics the physical layout of splicing regulation where the chromatin context progressively changes as the Pol II moves along the guide DNA. We used a recurrent-neural-network architecture to predict the inclusion of a spliced exon based on adjacent epigenetic signals, and we showed that distinct spatio-temporal features of these signals were key determinants of model outcome, in addition to the actual nucleotide sequence of the guide DNA strand. After the model had been trained and tested (with >80% precision-recall curve metric), we explored the derived weights of the latent factors, finding they highlight the importance of the asymmetric time-direction of chromatin context during transcription.

There has been much effort to prioritize genomic variants with respect to their impact on function. However, function is often not precisely defined: sometimes it is the disease association of a variant; on other occasions, it reflects a molecular effect on transcription or epigenetics. Here, we coupled multiple genomic predictors to build GRAM, a GeneRAlized Model, to predict a well-defined experimental target: the expression-modulating effect of a non-coding variant on its associated gene, in a transferable, cell-specific manner. Firstly, we performed feature engineering: using LASSO, a regularized linear model, we found transcription factor (TF) binding most predictive, especially for TFs that are hubs in the regulatory network; in contrast, evolutionary conservation, a popular feature in many other variant-impact predictors, has almost no contribution. Moreover, TF binding inferred from in vitro SELEX is as effective as that from in vivo ChIP-Seq. Second, we implemented GRAM integrating only SELEX features and expression profiles; thus, the program combines a universal regulatory score with an easily obtainable modifier reflecting the particular cell type. We benchmarked GRAM on large-scale MPRA datasets, achieving AUROC scores of 0.72 in GM12878 and 0.66 in a multi-cell line dataset. We then evaluated the performance of GRAM on targeted regions using luciferase assays in the MCF7 and K562 cell lines. We noted that changing the insertion position of the construct relative to the reporter gene gave very different results, highlighting the importance of carefully defining the exact prediction target of the model. Finally, we illustrated the utility of GRAM in fine-mapping causal variants and developed a practical software pipeline to carry this out. In particular, we demonstrated in specific examples how the pipeline could pinpoint variants that directly modulate gene expression within a larger linkage-disequilibrium block associated with a phenotype of interest (e.g., for an eQTL).

The morphology and orientation of polymer crystals are important factors which determine the performance of thin-film, polymer-based technologies such as organic electronic devices and gas separation membranes. Here, we utilize polymer-substrate epitaxy to achieve a highly oriented crystalline morphology during thin-film processing. To accomplish this, we employ matrixassisted pulsed laser evaporation (MAPLE), a slow physical vapor deposition process, to deposit linear polyethylene epitaxially atop a graphene substrate. Via MAPLE, we demonstrate the ability to achieve a film morphology comprised of well-aligned, edge-on crystalline lamellae. Furthermore, we show that MAPLE can be exploited to grow crystalline lamellae composed entirely of extended polymer chains which exhibit a near-equilibrium melting temperature. Our study demonstrates that MAPLE, as a bottom-up approach, can deposit polymer thin films with improved control over crystalline morphology. KEYWORDS epitaxial polymer crystallization, graphene, grazing-incidence x-ray diffraction, matrixassisted pulsed laser evaporation, melting, 1 | INTRODUCTION Enforcing the crystallization of polymers into a desired thin-film morphology remains a major scientific challenge. [4] Yet, the morphology is a critical factor determining the performance of [...]

Objective: To investigate the initial set of patient-reported outcomes (PROs) in the American College of Surgeons National Surgical Quality Improvement Program (NSQIP) and their associations with 30-day surgical outcomes. Background: PROs provide important information that can be used to improve routine care and facilitate quality improvement. The American College of Surgeons conducted a demonstration project to capture PROs into the NSQIP to complement clinical data. Methods: From February 2020 to March 2023, 65 hospitals collected Patient-Reported Outcomes Measurement Information System measures assessing global health, pain interference, fatigue, and physical function from patients accrued into the NSQIP. Using multivariable mixed regression, we compared the scores of patients with and without 30-day complications and further analyzed scores exceeding 1-SD worse than national benchmarks. Results: Overall, 33842 patients completed the Patient-Reported Outcomes Measurement Information System measures with a median of 58 days (IQR: 47-72) postoperatively. Among patients without complications (n = 31210), 33.9% had PRO scores 1-SD worse than national benchmarks. Patients with complications were 1.7 times more likely to report worse PROs (95% CI: 1.6-1.8). Patients with complications had lower scores for global physical health [adjusted mean difference (AMD): 2.6, 95% CI: 2.2-3.0], lower for global mental health (AMD: 1.8, 95% CI: 1.4-2.2), higher for pain interference (AMD: 2.4, 95% CI: 2.0-2.8), higher fatigue (AMD: 2.7, 95% CI: 2.3-3.1), and lower physical function (AMD: 3.2, 95% CI: 2.8-3.5). Conclusions: Postoperative complications negatively affect multiple key dimensions of patients' health-related quality of life. PROs were well below national benchmarks for many patients, even among those without complications. Identifying solutions to improve PROs after surgery remains a tremendous quality improvement opportunity. [ABSTRACT FROM AUTHOR]

Context Active surveillance for papillary thyroid cancer (PTC) meeting criteria for surgical resection is uncommon. Which patients may prove reasonable candidates for this approach is not well defined. Objective This work aimed to examine the feasibility and safety of active surveillance for patients with known or suspected intrathyroidal PTC up to 4 cm in diameter. Methods A retrospective review was conducted of all consecutive patients who underwent nonoperative active surveillance of suspicious or malignant thyroid nodules over a 20-year period from 2001 to 2021. We included patients with an initial ultrasound–fine-needle aspiration confirming either (a) Bethesda 5 or 6 cytology or (b) a "suspicious" Afirma molecular test. The primary outcomes and measures included the rate of adverse oncologic outcomes (mortality and recurrence), as well as the cumulative incidence of size/volume growth. Results Sixty-nine patients were followed with active surveillance for 1 year or longer (average 55 months), with 26 patients (38%) having nodules 2 cm or larger. No patients were found to develop new-incident occurrence of lymph node or distant metastasis. One patient, however, demonstrated concern for progression to a dedifferentiated cancer on repeat core biopsy 17 years after initial start of nonoperative selection. A total of 21% of patients had an increase in maximum diameter more than 3 mm, while volume increase of 50% or greater was noted in 25% of patients. Thirteen patients ultimately underwent delayed (rescue) surgery, and no disease recurrence was noted after such treatment. Age and initial nodule size were not predictors of nodule growth. Conclusion These data expand consideration of active surveillance of PTC in select patients with intrathyroidal suspected malignancy greater than 1 cm in diameter. Rescue surgery, if required at a later time point, appears effective. [ABSTRACT FROM AUTHOR]