Design of a self-regulating mRNA gene circuit.

Protein expression in vivo is predominately controlled via regulatory feedback mechanisms that adjust the level of mRNA transcription. However for positive sense single-stranded RNA viruses, protein expression is often controlled via secondary structural elements, such as internal ribosomal entry sites, that are encoded within the mRNA. The self-regulation of mRNA translation observed in this class of viruses suggests that it may be possible to design mRNAs that self-regulate their protein expression, enabling the creation of mRNAs for vaccines and other synthetic biology applications where protein levels in the cell can be tightly controlled without feedback to a transcriptional mechanism. As a proof of concept, I design a polycistronic mRNA based on bacteriophage MS2, where the upstream gene is capable of repressing synthesis of the downstream gene. Using a computational tool that simulates ribosome kinetics and the co-translational folding of the mRNA in response, I show that mutations to the mRNA can be identified which enhance the efficiency of the translation and the repression of the downstream gene. The results of this study open up the possibility of designing bespoke mRNA gene circuits in which the amount of protein synthesised in cells are self-regulated for therapeutic or antigenic purposes.

Gene expression and alternative splicing analysis in a large-scale Multiple Sclerosis study.

Background: Multiple Sclerosis (MS) is an autoimmune neurodegenerative disease affecting approximately 3 million people globally. Despite rigorous research on MS, aspects of its development and progression remain unclear. Understanding molecular mechanisms underlying MS is crucial to providing insights into disease pathways, identifying potential biomarkers for early diagnosis, and revealing novel therapeutic targets for improved patient outcomes. Methods: We utilized publicly available RNA-seq data (GSE138614) from post-mortem white matter tissues of five donors without any neurological disorder and ten MS patient donors. This data was interrogated for differential gene expression, alternative splicing and single nucleotide variants as well as for functional enrichments in the resulting datasets. Results: A comparison of non-MS white matter (WM) to MS samples yielded differentially expressed genes involved in adaptive immune response, cell communication, and developmental processes. Genes with expression changes positively correlated with tissue inflammation were enriched in the immune system and receptor interaction pathways. Negatively correlated genes were enriched in neurogenesis, nervous system development, and metabolic pathways. Alternatively spliced transcripts between WM and MS lesions included genes that play roles in neurogenesis, myelination, and oligodendrocyte differentiation, such as brain enriched myelin associated protein ( BCAS1 ), discs large MAGUK scaffold protein 1 ( DLG1 ), KH domain containing RNA binding ( QKI ), and myelin basic protein ( MBP ). Our approach to comparing normal appearing WM (NAWM) and active lesion (AL) from one donor and NAWM and chronic active (CA) tissues from two donors, showed that different IgH and IgK gene subfamilies were differentially expressed. We also identified pathways involved in white matter injury repair and remyelination in these tissues. Differentially spliced genes between these lesions were involved in axon and dendrite structure stability. We also identified exon skipping events and spontaneous single nucleotide polymorphisms in membrane associated ring-CH-type finger 1 ( MARCHF1 ), UDP glycosyltransferase 8 ( UGT8 ), and other genes important in autoimmunity and neurodegeneration. Conclusion: Overall, we identified unique genes, pathways, and novel splicing events affecting disease progression that can be further investigated as potential novel drug targets for MS treatment.

Complexity organization of resting-state functional-MRI networks.

Entropy measures are increasingly being used to analyze the structure of neural activity observed by functional magnetic resonance imaging (fMRI), with resting-state networks (RSNs) being of interest for their reproducible descriptions of the brain's functional architecture. Temporal correlations have shown a dichotomy among these networks: those that engage with the environment, known as extrinsic, which include the visual and sensorimotor networks; and those associated with executive control and self-referencing, known as intrinsic, which include the default mode network and the frontoparietal control network. While these inter-voxel temporal correlations enable the assessment of synchrony among the components of individual networks, entropic measures introduce an intra-voxel assessment that quantifies signal features encoded within each blood oxygen level-dependent (BOLD) time series. As a result, this framework offers insights into comprehending the representation and processing of information within fMRI signals. Multiscale entropy (MSE) has been proposed as a useful measure for characterizing the entropy of neural activity across different temporal scales. This measure of temporal entropy in BOLD data is dependent on the length of the time series; thus, high-quality data with fine-grained temporal resolution and a sufficient number of time frames is needed to improve entropy precision. We apply MSE to the Midnight Scan Club, a highly sampled and well-characterized publicly available dataset, to analyze the entropy distribution of RSNs and evaluate its ability to distinguish between different functional networks. Entropy profiles are compared across temporal scales and RSNs. Our results have shown that the spatial distribution of entropy at infra-slow frequencies (0.005-0.1 Hz) reproduces known parcellations of RSNs. We found a complexity hierarchy between intrinsic and extrinsic RSNs, with intrinsic networks robustly exhibiting higher entropy than extrinsic networks. Finally, we found new evidence that the topography of entropy in the posterior cerebellum exhibits high levels of entropy comparable to that of intrinsic RSNs.

Reliability of MR Enterography Features for Describing Fibrostenosing Crohn Disease.

Background Clinical decision making and drug development for fibrostenosing Crohn disease is constrained by a lack of imaging definitions, scoring conventions, and validated end points. Purpose To assess the reliability of MR enterography features to describe Crohn disease strictures and determine correlation with stricture severity. Materials and Methods A retrospective study of patients with symptomatic terminal ileal Crohn disease strictures who underwent MR enterography at tertiary care centers (Cleveland Clinic: September 2013 to November 2020; Mayo Clinic: February 2008 to March 2019) was conducted by using convenience sampling. In the development phase, blinded and trained radiologists independently evaluated 26 MR enterography features from baseline and follow-up examinations performed more than 6 months apart, with no bowel resection performed between examinations. Follow-up examinations closest to 12 months after baseline were selected. Reliability was assessed using the intraclass correlation coefficient (ICC). In the validation phase, after five features were redefined, reliability was re-estimated in an independent convenience sample using baseline examinations. Multivariable linear regression analysis identified features with at least moderate interrater reliability (ICC ≥0.41) that were independently associated with stricture severity. Results Ninety-nine (mean age, 40 years ± 14 [SD]; 50 male) patients were included in the development group and 51 (mean age, 45 years ± 16 [SD]; 35 female) patients were included in the validation group. In the development group, nine features had at least moderate interrater reliability. One additional feature demonstrated moderate reliability in the validation group. Stricture length (ICC = 0.85 [95% CI: 0.75, 0.91] and 0.91 [95% CI: 0.75, 0.96] in development and validation phase, respectively) and maximal associated small bowel dilation (ICC = 0.74 [95% CI: 0.63, 0.80] and 0.73 [95% CI: 0.58, 0.87] in development and validation group, respectively) had the highest interrater reliability. Stricture length, maximal stricture wall thickness, and maximal associated small bowel dilation were independently (regression coefficients, 0.09-3.97; P < .001) associated with stricture severity. Conclusion MR enterography definitions and scoring conventions for reliably assessing features of Crohn disease strictures were developed and validated, and feature correlation with stricture severity was determined. © RSNA, 2024 Supplemental material is available for this article. See also the article by Rieder and Ma et al in this issue. See also the editorial by Galgano and Summerlin in this issue.

Attention-based CNN-BiLSTM for sleep state classification of spatiotemporal wide-field calcium imaging data.

BACKGROUND: Wide-field calcium imaging (WFCI) with genetically encoded calcium indicators allows for spatiotemporal recordings of neuronal activity in mice. When applied to the study of sleep, WFCI data are manually scored into the sleep states of wakefulness, non-REM (NREM) and REM by use of adjunct EEG and EMG recordings. However, this process is time-consuming, invasive and often suffers from low inter- and intra-rater reliability. Therefore, an automated sleep state classification method that operates on spatiotemporal WFCI data is desired. NEW METHOD: A hybrid network architecture consisting of a convolutional neural network (CNN) to extract spatial features of image frames and a bidirectional long short-term memory network (BiLSTM) with attention mechanism to identify temporal dependencies among different time points was proposed to classify WFCI data into states of wakefulness, NREM and REM sleep. RESULTS: Sleep states were classified with an accuracy of 84 % and Cohen's κ of 0.64. Gradient-weighted class activation maps revealed that the frontal region of the cortex carries more importance when classifying WFCI data into NREM sleep while posterior area contributes most to the identification of wakefulness. The attention scores indicated that the proposed network focuses on short- and long-range temporal dependency in a state-specific manner. COMPARISON WITH EXISTING METHOD: On a held out, repeated 3-hour WFCI recording, the CNN-BiLSTM achieved a κ of 0.67, comparable to a κ of 0.65 corresponding to the human EEG/EMG-based scoring. CONCLUSIONS: The CNN-BiLSTM effectively classifies sleep states from spatiotemporal WFCI data and will enable broader application of WFCI in sleep research.

Factors associated with participation in a walking intervention for veterans who smoke and have chronic pain.

This analysis was part of the Pain and Smoking Study (PASS), a randomized trial of a cognitive behavioral intervention (CBI) for Veterans with chronic pain who smoke. The objective of this study was to examine factors associated with participation in the walking component of the intervention. Demographics and clinical characteristics were obtained at baseline. Completion of two or more CBI counseling sessions was required to be included in analyses. Average daily step counts obtained via pedometer in the prior week were recorded in up to three telephone counseling sessions. Participants were then categorized as "sedentary" (≤ 4999 daily steps) or "not sedentary" (≥ 5000 daily steps). Multivariable logistic regression was used to model variance in activity categorization. Overall, 91.0% of participants were men, 70.5% were white, mean age was 58.4 years, mean BMI was 28.6, median pack years was 20.5, and 43.8% were depressed. Veterans reported moderate pain intensity (4.9/10) and pain interference (5.4/10). Pain locations included: lower extremity (67.4%), back (53.4%) and upper extremity (28.1%). Median daily steps were 2491 [IQR: 1720-3550] (sedentary) (n = 65), 7307 [IQR: 5952-8533] (not sedentary) (n = 24), and 3196 [IQR: 2237-5067] (overall) (n = 89). Veterans with older age (odds ratio (OR): 1.10, 95% confidence interval (CI): 1.04, 1.17) and presence of LE pain (OR: 5.98, 95% CI: 1.82, 19.65) had increased odds of being "sedentary." Integrated smoking cessation and chronic pain self-management interventions that include a walking component may need to consider the impact of age and pain location on participation.Trial registration: The trial is registered at www.ClinicalTrials.gov (NCT02971137). First posted on November 22, 2016.

Astrocytes and the tumor microenvironment inflammatory state dictate the killing of glioblastoma cells by Smac mimetic compounds.

Smac mimetic compounds (SMCs) are small molecule drugs that sensitize cancer cells to TNF-α-induced cell death and have multiple immunostimulatory effects through alterations in NF-κB signaling. The combination of SMCs with immunotherapies has been reported to result in durable cures of up to 40% in syngeneic, orthotopic murine glioblastoma (GBM) models. Herein, we find that SMC resistance is not due to a cell-intrinsic mechanism of resistance. We thus evaluated the contribution of GBM and brain stromal components to identify parameters leading to SMC efficacy and resistance. The common physiological features of GBM tumors, such as hypoxia, hyaluronic acid, and glucose deprivation were found not to play a significant role in SMC efficacy. SMCs induced the death of microglia and macrophages, which are the major immune infiltrates in the tumor microenvironment. This death of microglia and macrophages then enhances the ability of SMCs to induce GBM cell death. Conversely, astrocytes promoted GBM cell growth and abrogated the ability of SMCs to induce death of GBM cells. The astrocyte-mediated resistance can be overcome in the presence of exogenous TNF-α. Overall, our results highlight that SMCs can induce death of microglia and macrophages, which then provides a source of death ligands for GBM cells, and that the targeting of astrocytes is a potential mechanism for overcoming SMC resistance for the treatment of GBM.

Management of the In-Season Athlete with an Anterior Shoulder Dislocation.

Management of the in-season athlete presenting with an anterior shoulder dislocation is a nuanced process that continues to be refined. Options and pathways between nonoperative and operative treatment have undergone many iterations over a century of orthopedic research and advancement. It requires an understanding of sport-specific demands and the individual athlete's goals. The orthopedic surgeon must have mastery of the natural history, treatment options, and outcomes of anterior shoulder dislocations. Balance of these factors is delicate and highly individualized for each athlete; and is why management of the in-season athlete with an anterior shoulder dislocation remains an art for the orthopedic surgeon.

Clinical Prediction Models in Neurocritical Care: An Overview of the Literature and Example Application to Prediction of Hospital Mortality in Traumatic Brain Injury.

Clinical prediction models serve as valuable instruments for assessing the risk of crucial outcomes and facilitating decision-making in clinical settings. Constructing these models requires nuanced analytical decisions and expertise informed by the current statistical literature. Access and thorough understanding of such literature may be limited for neurocritical care physicians, which may hinder the interpretation of existing predictive models. The present emphasis is on narrowing this knowledge gap by providing neurocritical care specialists with methodological guidance for interpreting predictive models in neurocritical care. Presented are the statistical learning principles integral to constructing a model predicting hospital mortality (nonsurvival during hospitalization) in patients with moderate and severe blunt traumatic brain injury using components of the IMPACT-Core model. Discussion encompasses critical elements such as model flexibility, hyperparameter selection, data imbalance, cross-validation, model assessment (discrimination and calibration), prediction instability, and probability thresholds. The intricate interplay among these components, the data set, and the clincal context of neurocritical care is elaborated. Leveraging this comprehensive exploration of statistical learning can enhance comprehension of articles encompassing model generation, tailored clinical care, and, ultimately, better interpretation and clinical applicability of predictive models.

Biological Nano-Agrochemicals for Crop Production as an Emerging Way to Address Heat and Associated Stresses.

Climate change is a global problem facing all aspects of the agricultural sector. Heat stress due to increasing atmospheric temperature is one of the most common climate change impacts on agriculture. Heat stress has direct effects on crop production, along with indirect effects through associated problems such as drought, salinity, and pathogenic stresses. Approaches reported to be effective to mitigate heat stress include nano-management. Nano-agrochemicals such as nanofertilizers and nanopesticides are emerging approaches that have shown promise against heat stress, particularly biogenic nano-sources. Nanomaterials are favorable for crop production due to their low toxicity and eco-friendly action. This review focuses on the different stresses associated with heat stress and their impacts on crop production. Nano-management of crops under heat stress, including the application of biogenic nanofertilizers and nanopesticides, are discussed. The potential and limitations of these biogenic nano-agrochemicals are reviewed. Potential nanotoxicity problems need more investigation at the local, national, and global levels, as well as additional studies into biogenic nano-agrochemicals and their effects on soil, plant, and microbial properties and processes.

Ability of a multi-segment foot model to measure kinematic differences in cavus, neutrally aligned, asymptomatic planus, and symptomatic planus foot types.

BACKGROUND: Multi-segment foot models (MFMs) provide a better understanding of the intricate biomechanics of the foot, yet it is unclear if they accurately differentiate foot type function during locomotion. RESEARCH QUESTION: We employed an MFM to detect subtle kinematic differences between foot types, including: pes cavus, neutrally aligned, and asymptomatic and symptomatic pes planus. The study investigates how variable the results of this MFM are and if it can detect kinematic differences between pathologic and non-pathologic foot types during the stance phase of gait. METHODS: Independently, three raters instrumented three subjects on three days to assess variability. In a separate cohort, each foot type was statically quantified for ten subjects per group. Each subject walked while instrumented with a four-segment foot model to assess static alignment and foot motion during the stance phase of gait. Statistical analysis performed with a linear mixed effects regression. RESULTS: Model variability was highest for between-day and lowest for between-rater, with all variability measures being within the true sample variance. Almost all static measures (radiographic, digital scan, and kinematic markers) differed significantly by foot type. Sagittal hindfoot to leg and forefoot to leg kinematics differed between foot types during late stance, as well as coronal hallux to forefoot range of motion. The MFM had low between-rater variability and may be suitable for multiple raters to apply to a single study sample without introducing significant error. The model, however, only detected a few dynamic differences, with the most dramatic being the hallux to forefoot coronal plane range of motion. SIGNIFICANCE: Results only somewhat aligned with previous work. It remains unclear if the MFM is sensitive enough to accurately detect different motion between foot types (pathologic and non-pathologic). A more accurate method of tracking foot bone motion (e.g., biplane fluoroscopy) may be needed to address this question.

Insulin Resistance in Women Correlates with Chromatin Histone Lysine Acetylation, Inflammatory Signaling, and Accelerated Aging.

BACKGROUND: Epigenetic changes link medical, social, and environmental factors with cardiovascular and kidney disease and, more recently, with cancer. The mechanistic link between metabolic health and epigenetic changes is only starting to be investigated. In our in vitro and in vivo studies, we performed a broad analysis of the link between hyperinsulinemia and chromatin acetylation; our top "hit" was chromatin opening at H3K9ac. METHODS: Building on our published preclinical studies, here, we performed a detailed analysis of the link between insulin resistance, chromatin acetylation, and inflammation using an initial test set of 28 women and validation sets of 245, 22, and 53 women. RESULTS: ChIP-seq identified chromatin acetylation and opening at the genes coding for TNFα and IL6 in insulin-resistant women. Pathway analysis identified inflammatory response genes, NFκB/TNFα-signaling, reactome cytokine signaling, innate immunity, and senescence. Consistent with this finding, flow cytometry identified increased senescent circulating peripheral T-cells. DNA methylation analysis identified evidence of accelerated aging in insulin-resistant vs. metabolically healthy women. CONCLUSIONS: This study shows that insulin-resistant women have increased chromatin acetylation/opening, inflammation, and, perhaps, accelerated aging. Given the role that inflammation plays in cancer initiation and progression, these studies provide a potential mechanistic link between insulin resistance and cancer.

Augmenting patient monitoring during intravenous moderate sedation with artificial intelligence: A pilot study.

PURPOSE/OBJECTIVES: A precordial stethoscope (PS) is essential for ensuring clear breath sounds during open airway sedations. However, a traditional PS limits the ability of new users to simultaneously listen to heart and lung sounds alongside experienced practitioners, hindering their learning and development. Bluetooth speaker systems allow for multiple providers but amplify all noise. An artificial intelligence (AI) PS has the potential to selectively reduce ambient noise, allowing multiple providers to monitor concurrently and provides a visual representation of the sound waves. The study looks at the benefits of AI PS in teaching in the dental setting. METHODS: A questionnaire was created to compare the new AI PS to a conventional PS during intravenous moderate sedation in a dental clinic setting. RESULTS: Sixteen individuals involved in sedations (four dental attendings, seven dental residents, and five dental assistants), were polled with a 100% response rate. 75% of participants agreed that clarity was improved using the AI PS and 81.25% of participants agreed that breath sounds and loudness were improved using the AI PS. 93.75% reported the AI PS was beneficial in allowing attendees to concurrently monitor a sedation case alongside dental residents. 100% of assistants reported that the AI PS benefited from their participation in sedation cases. CONCLUSION: As technology advances, it is important that we as providers continue to evolve and consider implementing AI to improve our ability to monitor patients and enhance educational experiences for dental resident trainees.

Molecular classification to refine surgical and radiotherapeutic decision-making in meningioma.

Treatment of the tumor and dural margin with surgery and sometimes radiation are cornerstones of therapy for meningioma. Molecular classifications have provided insights into the biology of disease; however, response to treatment remains heterogeneous. In this study, we used retrospective data on 2,824 meningiomas, including molecular data on 1,686 tumors and 100 prospective meningiomas, from the RTOG-0539 phase 2 trial to define molecular biomarkers of treatment response. Using propensity score matching, we found that gross tumor resection was associated with longer progression-free survival (PFS) across all molecular groups and longer overall survival in proliferative meningiomas. Dural margin treatment (Simpson grade 1/2) prolonged PFS compared to no treatment (Simpson grade 3). Molecular group classification predicted response to radiotherapy, including in the RTOG-0539 cohort. We subsequently developed a molecular model to predict response to radiotherapy that discriminates outcome better than standard-of-care classification. This study highlights the potential for molecular profiling to refine surgical and radiotherapy decision-making.

Effects of Resource Sharing Networks on Community Anti-Drug Coalitions' Outcomes: A Social Network Analysis.

Substance use-related problems continue to be a national public health crisis despite years of prevention efforts. Community anti-drug coalitions are well positioned to address substance use at local levels. Coalitions often rely on their members to connect to resources they need to address community issues and plan for sustainability over time. Such capacity building occurs through voluntary cooperation among members, making it essential to understand the role network connections play. This study sought to determine whether structural characteristics of coalitions' resource sharing networks impact members' perceptions of community improvement and coalition sustainability. Surveys at two timepoints collected data from 68 coalitions in Pennsylvania and Missouri on members' connections or ties to share information, personnel, money, or other types of collaboration. Analyses examined how coalition-level measurements of sectoral diversity, density, and resource sharing centralization, respectively, were associated with members' perceptions of community improvement, sustainability planning, and coalition sustainability. Sectoral diversity and centralization were unrelated to study outcomes. Density was also unrelated with perceived community improvement and sustainability planning. However, two facets of cooperative density were positively associated with perceived coalition sustainability: the density of ties to share information and the density of multiple types of collaborative ties. This study suggests that both information and other collaborative ties foster perceived coalition sustainability, although not community improvement.

Recommendations for Deprescribing of Medication in the Last Phase of Life: An International Delphi Study.

CONTEXT: Medications may become inappropriate for patients in the last phase of life and may even compromise their quality of life. OBJECTIVE: To find consensus on recommendations regarding deprescribing of medications for adult patients with a life expectancy of six months or less. METHODS: Experts working in palliative care or other relevant disciplines were asked to participate in this international Delphi study. Existing tools for deprescribing of medication in the last phase of life were integrated in a list of 42 recommendations regarding potential deprescription of various medication types. In two Delphi rounds, experts were asked to rate their agreement with each recommendation on a 5-point Likert-scale (strongly agree-strongly disagree). Recommendations were accepted, if at least 70% of the experts (strongly) agreed, the interquartile range (IQR) was one or less, and less than 10% strongly disagreed. RESULTS: About 47 experts from 10 countries participated (response rate 53%). In most cases (76%), consensus was reached on deprescribing recommendations for patients with a life expectancy of six months or less. The highest level of consensus was reached for recommendations on the deprescription of diuretics in case of decreasing fluid intake or increasing fluid loss, lipid modifying agents if prescribed for primary prevention, and vitamin K antagonists and direct oral anticoagulants in case of high bleeding risk. CONCLUSION: A high level of consensus was reached on recommendations on potential deprescription of several medications for patients with a life expectancy of six months or less.

A generalized signal model for dual-module velocity-selective arterial spin labeling.

PURPOSE: To develop a generalized signal model for dual-module velocity-selective arterial spin labeling (dm-VSASL) that can integrate arbitrary saturation and inversion profiles. THEORY AND METHODS: A recently developed mathematical framework for single-module VSASL is extended to address the increased complexity of dm-VSASL and to model the use of realistic velocity-selective profiles in the label-control and vascular crushing modules. Expressions for magnetization difference, arterial delivery functions, labeling efficiency, and cerebral blood flow (CBF) estimation error are presented. Sources of error are examined and timing requirements to minimize quantification errors are derived. RESULTS: For ideal velocity-selective profiles, the predicted signals match those of prior work. With realistic profiles, a CBF-dependent estimation error can occur when velocity-selective inversion (VSI) is used for the labeling modules and velocity-selective saturation (VSS) is used for the vascular crushing module. The error reflects a mismatch between the leading and trailing edges of the delivery function for the second bolus and can be minimized by choosing a nominal labeling cutoff velocity that is lower than the nominal saturation cutoff velocity. In the presence of B 0 $$ {\mathrm{B}}_0 $$ and B 1 $$ {\mathrm{B}}_1 $$ inhomogeneities, the labeling efficiency of dual-module VSI is more attenuated than that of dual-module VSS. CONCLUSION: The proposed signal model will enable researchers to more accurately assess and compare the performance of realistic dm-VSASL implementations and improve the quantification of dm-VSASL CBF measures.

A genetic basis for sex differences in Xp11 translocation renal cell carcinoma.

Xp11 translocation renal cell carcinoma (tRCC) is a rare, female-predominant cancer driven by a fusion between the transcription factor binding to IGHM enhancer 3 (TFE3) gene on chromosome Xp11.2 and a partner gene on either chromosome X (chrX) or an autosome. It remains unknown what types of rearrangements underlie TFE3 fusions, whether fusions can arise from both the active (chrXa) and inactive X (chrXi) chromosomes, and whether TFE3 fusions from chrXi translocations account for the female predominance of tRCC. To address these questions, we performed haplotype-specific analyses of chrX rearrangements in tRCC whole genomes. We show that TFE3 fusions universally arise as reciprocal translocations and that oncogenic TFE3 fusions can arise from chrXi:autosomal translocations. Female-specific chrXi:autosomal translocations result in a 2:1 female-to-male ratio of TFE3 fusions involving autosomal partner genes and account for the female predominance of tRCC. Our results highlight how X chromosome genetics constrains somatic chrX alterations and underlies cancer sex differences.

Functional analysis of ESRP1/2 gene variants and CTNND1 isoforms in orofacial cleft pathogenesis.

Orofacial cleft (OFC) is a common human congenital anomaly. Epithelial-specific RNA splicing regulators ESRP1 and ESRP2 regulate craniofacial morphogenesis and their disruption result in OFC in zebrafish, mouse and humans. Using esrp1/2 mutant zebrafish and murine Py2T cell line models, we functionally tested the pathogenicity of human ESRP1/2 gene variants. We found that many variants predicted by in silico methods to be pathogenic were functionally benign. Esrp1 also regulates the alternative splicing of Ctnnd1 and these genes are co-expressed in the embryonic and oral epithelium. In fact, over-expression of ctnnd1 is sufficient to rescue morphogenesis of epithelial-derived structures in esrp1/2 zebrafish mutants. Additionally, we identified 13 CTNND1 variants from genome sequencing of OFC cohorts, confirming CTNND1 as a key gene in human OFC. This work highlights the importance of functional assessment of human gene variants and demonstrates the critical requirement of Esrp-Ctnnd1 acting in the embryonic epithelium to regulate palatogenesis.