Finerenone in Heart Failure with Mildly Reduced or Preserved Ejection Fraction.

BACKGROUND: Steroidal mineralocorticoid receptor antagonists reduce morbidity and mortality among patients with heart failure and reduced ejection fraction, but their efficacy in those with heart failure and mildly reduced or preserved ejection fraction has not been established. Data regarding the efficacy and safety of the nonsteroidal mineralocorticoid receptor antagonist finerenone in patients with heart failure and mildly reduced or preserved ejection fraction are needed. METHODS: In this international, double-blind trial, we randomly assigned patients with heart failure and a left ventricular ejection fraction of 40% or greater, in a 1:1 ratio, to receive finerenone (at a maximum dose of 20 mg or 40 mg once daily) or matching placebo, in addition to usual therapy. The primary outcome was a composite of total worsening heart failure events (with an event defined as a first or recurrent unplanned hospitalization or urgent visit for heart failure) and death from cardiovascular causes. The components of the primary outcome and safety were also assessed. RESULTS: Over a median follow-up of 32 months, 1083 primary-outcome events occurred in 624 of 3003 patients in the finerenone group, and 1283 primary-outcome events occurred in 719 of 2998 patients in the placebo group (rate ratio, 0.84; 95% confidence interval [CI], 0.74 to 0.95; P = 0.007). The total number of worsening heart failure events was 842 in the finerenone group and 1024 in the placebo group (rate ratio, 0.82; 95% CI, 0.71 to 0.94; P = 0.006). The percentage of patients who died from cardiovascular causes was 8.1% and 8.7%, respectively (hazard ratio, 0.93; 95% CI, 0.78 to 1.11). Finerenone was associated with an increased risk of hyperkalemia and a reduced risk of hypokalemia. CONCLUSIONS: In patients with heart failure and mildly reduced or preserved ejection fraction, finerenone resulted in a significantly lower rate of a composite of total worsening heart failure events and death from cardiovascular causes than placebo. (Funded by Bayer; FINEARTS-HF ClinicalTrials.gov number, NCT04435626.).

Unsupervised pattern identification in spatial gene expression atlas reveals mouse brain regions beyond established ontology.

The rapid growth of large-scale spatial gene expression data demands efficient and reliable computational tools to extract major trends of gene expression in their native spatial context. Here, we used stability-driven unsupervised learning (i.e., staNMF) to identify principal patterns (PPs) of 3D gene expression profiles and understand spatial gene distribution and anatomical localization at the whole mouse brain level. Our subsequent spatial correlation analysis systematically compared the PPs to known anatomical regions and ontology from the Allen Mouse Brain Atlas using spatial neighborhoods. We demonstrate that our stable and spatially coherent PPs, whose linear combinations accurately approximate the spatial gene data, are highly correlated with combinations of expert-annotated brain regions. These PPs yield a brain ontology based purely on spatial gene expression. Our PP identification approach outperforms principal component analysis and typical clustering algorithms on the same task. Moreover, we show that the stable PPs reveal marked regional imbalance of brainwide genetic architecture, leading to region-specific marker genes and gene coexpression networks. Our findings highlight the advantages of stability-driven machine learning for plausible biological discovery from dense spatial gene expression data, streamlining tasks that are infeasible by conventional manual approaches.

Staphylococcus aureus proteases trigger eosinophil-mediated skin inflammation.

Staphylococcus aureus skin colonization and eosinophil infiltration are associated with many inflammatory skin disorders, including atopic dermatitis, bullous pemphigoid, Netherton's syndrome, and prurigo nodularis. However, whether there is a relationship between S. aureus and eosinophils and how this interaction influences skin inflammation is largely undefined. We show in a preclinical mouse model that S. aureus epicutaneous exposure induced eosinophil-recruiting chemokines and eosinophil infiltration into the skin. Remarkably, we found that eosinophils had a comparable contribution to the skin inflammation as T cells, in a manner dependent on eosinophil-derived IL-17A and IL-17F production. Importantly, IL-36R signaling induced CCL7-mediated eosinophil recruitment to the inflamed skin. Last, S. aureus proteases induced IL-36α expression in keratinocytes, which promoted infiltration of IL-17-producing eosinophils. Collectively, we uncovered a mechanism for S. aureus proteases to trigger eosinophil-mediated skin inflammation, which has implications in the pathogenesis of inflammatory skin diseases.

IRIS: Discovery of cancer immunotherapy targets arising from pre-mRNA alternative splicing.

Alternative splicing (AS) is prevalent in cancer, generating an extensive but largely unexplored repertoire of novel immunotherapy targets. We describe Isoform peptides from RNA splicing for Immunotherapy target Screening (IRIS), a computational platform capable of discovering AS-derived tumor antigens (TAs) for T cell receptor (TCR) and chimeric antigen receptor T cell (CAR-T) therapies. IRIS leverages large-scale tumor and normal transcriptome data and incorporates multiple screening approaches to discover AS-derived TAs with tumor-associated or tumor-specific expression. In a proof-of-concept analysis integrating transcriptomics and immunopeptidomics data, we showed that hundreds of IRIS-predicted TCR targets are presented by human leukocyte antigen (HLA) molecules. We applied IRIS to RNA-seq data of neuroendocrine prostate cancer (NEPC). From 2,939 NEPC-associated AS events, IRIS predicted 1,651 epitopes from 808 events as potential TCR targets for two common HLA types (A*02:01 and A*03:01). A more stringent screening test prioritized 48 epitopes from 20 events with "neoantigen-like" NEPC-specific expression. Predicted epitopes are often encoded by microexons of ≤30 nucleotides. To validate the immunogenicity and T cell recognition of IRIS-predicted TCR epitopes, we performed in vitro T cell priming in combination with single-cell TCR sequencing. Seven TCRs transduced into human peripheral blood mononuclear cells (PBMCs) showed high activity against individual IRIS-predicted epitopes, providing strong evidence of isolated TCRs reactive to AS-derived peptides. One selected TCR showed efficient cytotoxicity against target cells expressing the target peptide. Our study illustrates the contribution of AS to the TA repertoire of cancer cells and demonstrates the utility of IRIS for discovering AS-derived TAs and expanding cancer immunotherapies.

Frontotemporal lobar degeneration targets brain regions linked to expression of recently evolved genes.

In frontotemporal lobar degeneration (FTLD), pathological protein aggregation in specific brain regions is associated with declines in human-specialized social-emotional and language functions. In most patients, disease protein aggregates contain either TDP-43 (FTLD-TDP) or tau (FTLD-tau). Here, we explored whether FTLD-associated regional degeneration patterns relate to regional gene expression of human accelerated regions (HARs), conserved sequences that have undergone positive selection during recent human evolution. To this end, we used structural neuroimaging from patients with FTLD and human brain regional transcriptomic data from controls to identify genes expressed in FTLD-targeted brain regions. We then integrated primate comparative genomic data to test our hypothesis that FTLD targets brain regions linked to expression levels of recently evolved genes. In addition, we asked whether genes whose expression correlates with FTLD atrophy are enriched for genes that undergo cryptic splicing when TDP-43 function is impaired. We found that FTLD-TDP and FTLD-tau subtypes target brain regions with overlapping and distinct gene expression correlates, highlighting many genes linked to neuromodulatory functions. FTLD atrophy-correlated genes were strongly enriched for HARs. Atrophy-correlated genes in FTLD-TDP showed greater overlap with TDP-43 cryptic splicing genes and genes with more numerous TDP-43 binding sites compared with atrophy-correlated genes in FTLD-tau. Cryptic splicing genes were enriched for HAR genes, and vice versa, but this effect was due to the confounding influence of gene length. Analyses performed at the individual-patient level revealed that the expression of HAR genes and cryptically spliced genes within putative regions of disease onset differed across FTLD-TDP subtypes. Overall, our findings suggest that FTLD targets brain regions that have undergone recent evolutionary specialization and provide intriguing potential leads regarding the transcriptomic basis for selective vulnerability in distinct FTLD molecular-anatomical subtypes.

Toward the Ultimate Limit of Analyte Detection, in Graphene-Based Field-Effect Transistors.

The ultimate sensitivity of field-effect-transistor (FET)-based devices for ionic species detection is of great interest, given that such devices are capable of monitoring single-electron-level modulations. It is shown here, from both theoretical and experimental perspectives, that for such ultimate limits to be approached the thermodynamic as well as kinetic characteristics of the (FET surface)-(linker)-(ion-receptor) ensemble must be considered. The sensitivity was probed in terms of optimal packing of the ensemble, through a minimal charge state/capacitance point of view and atomic force microscopy. Through the fine-tuning of the linker and receptor interaction with the sensing surface, a record limit of detection as well as specificity in the femtomolar range, orders of magnitude better than previously obtained and in excellent accord with prediction, was observed.

Cross-Validation of Metabolic Phenotypes in SARS-CoV-2 Infected Subpopulations Using Targeted Liquid Chromatography-Mass Spectrometry (LC-MS).

To ensure biological validity in metabolic phenotyping, findings must be replicated in independent sample sets. Targeted workflows have long been heralded as ideal platforms for such validation due to their robust quantitative capability. We evaluated the capability of liquid chromatography-mass spectrometry (LC-MS) assays targeting organic acids and bile acids to validate metabolic phenotypes of SARS-CoV-2 infection. Two independent sample sets were collected: (1) Australia: plasma, SARS-CoV-2 positive (n = 20), noninfected healthy controls (n = 22) and COVID-19 disease-like symptoms but negative for SARS-CoV-2 infection (n = 22). (2) Spain: serum, SARS-CoV-2 positive (n = 33) and noninfected healthy controls (n = 39). Multivariate modeling using orthogonal projections to latent structures discriminant analyses (OPLS-DA) classified healthy controls from SARS-CoV-2 positive (Australia; R2 = 0.17, ROC-AUC = 1; Spain R2 = 0.20, ROC-AUC = 1). Univariate analyses revealed 23 significantly different (p < 0.05) metabolites between healthy controls and SARS-CoV-2 positive individuals across both cohorts. Significant metabolites revealed consistent perturbations in cellular energy metabolism (pyruvic acid, and 2-oxoglutaric acid), oxidative stress (lactic acid, 2-hydroxybutyric acid), hypoxia (2-hydroxyglutaric acid, 5-aminolevulinic acid), liver activity (primary bile acids), and host-gut microbial cometabolism (hippuric acid, phenylpropionic acid, indole-3-propionic acid). These data support targeted LC-MS metabolic phenotyping workflows for biological validation in independent sample sets.

Network Connectivity Alterations across the MAPT Mutation Clinical Spectrum.

OBJECTIVE: Microtubule-associated protein tau (MAPT) mutations cause frontotemporal lobar degeneration, and novel biomarkers are urgently needed for early disease detection. We used task-free functional magnetic resonance imaging (fMRI) mapping, a promising biomarker, to analyze network connectivity in symptomatic and presymptomatic MAPT mutation carriers. METHODS: We compared cross-sectional fMRI data between 17 symptomatic and 39 presymptomatic carriers and 81 controls with (1) seed-based analyses to examine connectivity within networks associated with the 4 most common MAPT-associated clinical syndromes (ie, salience, corticobasal syndrome, progressive supranuclear palsy syndrome, and default mode networks) and (2) whole-brain connectivity analyses. We applied K-means clustering to explore connectivity heterogeneity in presymptomatic carriers at baseline. Neuropsychological measures, plasma neurofilament light chain, and gray matter volume were compared at baseline and longitudinally between the presymptomatic subgroups defined by their baseline whole-brain connectivity profiles. RESULTS: Symptomatic and presymptomatic carriers had connectivity disruptions within MAPT-syndromic networks. Compared to controls, presymptomatic carriers showed regions of connectivity alterations with age. Two presymptomatic subgroups were identified by clustering analysis, exhibiting predominantly either whole-brain hypoconnectivity or hyperconnectivity at baseline. At baseline, these two presymptomatic subgroups did not differ in neuropsychological measures, although the hypoconnectivity subgroup had greater plasma neurofilament light chain levels than controls. Longitudinally, both subgroups showed visual memory decline (vs controls), yet the subgroup with baseline hypoconnectivity also had worsening verbal memory and neuropsychiatric symptoms, and extensive bilateral mesial temporal gray matter decline. INTERPRETATION: Network connectivity alterations arise as early as the presymptomatic phase. Future studies will determine whether presymptomatic carriers' baseline connectivity profiles predict symptomatic conversion. ANN NEUROL 2023;94:632-646.

Water and Carbon Dioxide Capillary Bridges in Nanoscale Slit Pores: Effects of Temperature, Pressure, and Salt Concentration on the Water Contact Angle.

We perform molecular dynamics (MD) simulations of a nanoscale water capillary bridge (WCB) surrounded by carbon dioxide over a wide range of temperatures and pressures (T = 280-400 K and carbon dioxide pressures PCO2 ≈ 0-80 MPa). The water-carbon dioxide system is confined by two parallel silica-based surfaces (hydroxylated ß-cristobalite) separated by h = 5 nm. The aim of this work is to study the WCB contact angle (θc) as a function of T and PCO2. Our simulations indicate that θc varies weakly with temperature and pressure: Δθc ≈ 10-20° for PCO2 increasing from ≈0 to 80 MPa (T = 320 K); Δθc ≈ -10° for T increasing from 320 to 360 K (with a fixed amount of carbon dioxide). Interestingly, at all conditions studied, a thin film of water (1-2 water layers-thick) forms under the carbon dioxide volume. Our MD simulations suggest that this is due to the enhanced ability of water, relative to carbon dioxide, to form hydrogen-bonds with the walls. We also study the effects of adding salt (NaCl) to the WCB and corresponding θc. It is found that at the salt concentrations studied (mole fractions xNa = xCl = 3.50, 9.81%), the NaCl forms a large crystallite within the WCB with the ions avoiding the water-carbon dioxide interface and the walls surface. This results in θc being insensitive to the presence of NaCl.

Therapeutic efficacy of RAS inhibitor trametinib using a juvenile myelomonocytic leukemia patient-derived xenograft model.

Juvenile myelomonocytic leukemia (JMML) is an aggressive pediatric leukemia with few effective treatments and poor outcomes even after stem cell transplantation, the only current curative treatment. We developed a JMML patient-derived xenograft (PDX) mouse model and demonstrated the in vivo therapeutic efficacy and confirmed the target of trametinib, a RAS-RAF-MEK-ERK pathway inhibitor, in this model. A PDX model was created through transplantation of patient JMML cells into mice, up to the second generation, and successful engraftment was confirmed using flow cytometry. JMML PDX mice were treated with trametinib versus vehicle control, with a median survival of 194 days in the treatment group versus 124 days in the control group (p = 0.02). Trametinib's target as a RAS pathway inhibitor was verified by showing inhibition of ERK phosphorylation using immunoblot assays. In conclusion, trametinib monotherapy significantly prolongs survival in our JMML PDX model by inhibiting the RAS pathway. Our model can be effectively used for assessment of novel targeted treatments, including potential combination therapies, to improve JMML outcomes.

Validation of a prediction system for risk of kidney allograft failure in pediatric kidney transplant recipients: An international observational study.

Predicting long-term kidney allograft failure is an unmet need for clinical care and clinical trial optimization in children. We aimed to validate a kidney allograft failure risk prediction system in a large international cohort of pediatric kidney transplant recipients. Patients from 20 centers in Europe and the United States, transplanted between 2004 and 2017, were included. Allograft assessment included estimated glomerular filtration rate, urine protein-to-creatinine ratio, circulating antihuman leukocyte antigen donor-specific antibody, and kidney allograft histology. Individual predictions of allograft failure were calculated using the integrative box (iBox) system. Prediction performances were assessed using discrimination and calibration. The allograft evaluations were performed in 706 kidney transplant recipients at a median time of 9.1 (interquartile range, 3.3-19.2) months posttransplant; mean estimated glomerular filtration rate was 68.7 ± 28.1 mL/min/1.73 m2, and median urine protein-to-creatinine ratio was 0.1 (0.0-0.4) g/g, and 134 (19.0%) patients had antihuman leukocyte antigen donor-specific antibodies. The iBox exhibited accurate calibration and discrimination for predicting the outcomes up to 10 years after evaluation, with a C-index of 0.81 (95% confidence interval, 0.75-0.87). This study confirms the generalizability of the iBox to predict long-term kidney allograft failure in children, with performances similar to those reported in adults. These results support the use of the iBox to improve patient monitoring and facilitate clinical trials in children.

Feasibility and Safety of a "Shared Care" Model in Complex Hepatopancreatobiliary Surgery: A 5-year Observational Study of Outcomes in Pancreaticoduodenectomy.

OBJECTIVE: To determine the safety of a fully functioning shared care model (SCM) in hepatopancreatobiliary surgery through evaluating outcomes in pancreaticoduodenectomy. BACKGROUND: SCMs, where a team of surgeons share in care delivery and resource utilization, represent a surgeon-level opportunity to improve system efficiency and peer support, but concerns around clinical safety remain, especially in complex elective surgery. METHODS: Patients who underwent pancreaticoduodenectomy between 2016 and 2020 were included. Adoption of shared care was demonstrated by analyzing shared care measures, including the number of surgeons encountered by patients during their care cycle, the proportion of patients with different consenting versus primary operating surgeon (POS), and the proportion of patients who met their POS on the day of surgery. Outcomes, including 30-day mortality, readmission, unplanned reoperation, sepsis, and length of stay, were collected from the institution's National Surgical Quality Improvement Program (NSQIP) database and compared with peer hospitals contributing to the pancreatectomy-specific NSQIP collaborative. RESULTS: Of the 174 patients included, a median of 3 surgeons was involved throughout the patients' care cycle, 69.0% of patients had different consenting versus POS and 57.5% met their POS on the day of surgery. Major outcomes, including mortality (1.1%), sepsis (5.2%), and reoperation (7.5%), were comparable between the study group and NSQIP peer hospitals. Length of stay (10 day) was higher in place of lower readmission (13.2%) in the study group compared with peer hospitals. CONCLUSIONS: SCMs are feasible in complex elective surgery without compromising patient outcomes, and wider adoption may be encouraged.

Dynamic autonomic nervous system states arise during emotions and manifest in basal physiology.

The outflow of the autonomic nervous system (ANS) is continuous and dynamic, but its functional organization is not well understood. Whether ANS patterns accompany emotions, or arise in basal physiology, remain unsettled questions in the field. Here, we searched for brief ANS patterns amidst continuous, multichannel physiological recordings in 45 healthy older adults. Participants completed an emotional reactivity task in which they viewed video clips that elicited a target emotion (awe, sadness, amusement, disgust, or nurturant love); each video clip was preceded by a pre-trial baseline period and followed by a post-trial recovery period. Participants also sat quietly for a separate 2-min resting period to assess basal physiology. Using principal components analysis and unsupervised clustering algorithms to reduce the second-by-second physiological data during the emotional reactivity task, we uncovered five ANS states. Each ANS state was characterized by a unique constellation of patterned physiological changes that differentiated among the trials of the emotional reactivity task. These ANS states emerged and dissipated over time, with each instance lasting several seconds on average. ANS states with similar structures were also detectable in the resting period but were intermittent and of smaller magnitude. Our results offer new insights into the functional organization of the ANS. By assembling short-lived, patterned changes, the ANS is equipped to generate a wide range of physiological states that accompany emotions and that contribute to the architecture of basal physiology.

Exposure to cooking fumes is associated with perturbations in nasal microbiota composition: A pilot study.

Air pollution is one of the leading causes of overall mortality globally. Cooking emissions are a major source of fine particulate matter (PM2.5). However, studies on their potential perturbations on the nasal microbiota as well as their association with respiratory health are lacking. This pilot study aims to assess the environmental air quality among occupational cooks and its associations with nasal microbiota and respiratory symptoms. A total of 20 cooks (exposed) and 20 unexposed controls (mainly office workers), were recruited in Singapore from 2019 to 2021. Information on sociodemographic factors, cooking methods, and self-reported respiratory symptoms were collected using a questionnaire. Personal PM2.5 concentrations and reactive oxygen species (ROS) levels were measured using portable sensors and filter samplers. DNA was extracted from nasal swabs and sequenced using 16s sequencing. Alpha-diversity and beta-diversity were calculated, and between-group variation analysis of species was performed. Multivariable logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for associations between exposure groups and self-reported respiratory symptoms. Higher daily mean PM2.5 (P = 2 × 10-7) and environmental ROS exposure (P = 3.25 × 10-7) were observed in the exposed group. Alpha diversity of the nasal microbiota between the two groups was not significantly different. However, beta diversity was significantly different (unweighted UniFrac P = 1.11 × 10-5, weighted UniFrac P = 5.42 × 10-6) between the two exposure groups. In addition, certain taxa of bacteria were slightly more abundant in the exposed group compared to unexposed controls. There were no significant associations between the exposure groups and self-reported respiratory symptoms. In summary, the exposed group had higher PM2.5 and ROS exposure levels and altered nasal microbiotas as compared to unexposed controls, though further studies are required to replicate these findings in a larger population.

Accurate Hellmann-Feynman forces from density functional calculations with augmented Gaussian basis sets.

The Hellmann-Feynman (HF) theorem provides a way to compute forces directly from the electron density, enabling efficient force calculations for large systems through machine learning (ML) models for the electron density. The main issue holding back the general acceptance of the HF approach for atom-centered basis sets is the well-known Pulay force which, if naively discarded, typically constitutes an error upward of 10 eV/Å in forces. In this work, we demonstrate that if a suitably augmented Gaussian basis set is used for density functional calculations, the Pulay force can be suppressed, and HF forces can be computed as accurately as analytical forces with state-of-the-art basis sets, allowing geometry optimization and molecular dynamics to be reliably performed with HF forces. Our results pave a clear path forward for the accurate and efficient simulation of large systems using ML densities and the HF theorem.

Insights into the toxicological effects of nanomaterials on atherosclerosis: mechanisms involved and influence factors.

Atherosclerosis is one of the most common types of cardiovascular disease and is driven by lipid accumulation and chronic inflammation in the arteries, which leads to stenosis and thrombosis. Researchers have been working to design multifunctional nanomedicines with the ability to target, diagnose, and treat atherosclerosis, but recent studies have also identified that nanomaterials can cause atherosclerosis. Therefore, this review aims to outline the molecular mechanisms and physicochemical properties of nanomaterials that promote atherosclerosis. By analyzing the toxicological effects of nanomaterials on cells involved in the pathogenesis of atherosclerosis such as vascular endothelial cells, vascular smooth muscle cells and immune cells, we aim to provide new perspectives for the prevention and treatment of atherosclerosis, and raise awareness of nanotoxicology to advance the clinical translation and sustainable development of nanomaterials.

Mapping a shared care model in complex gastrointestinal surgery: A qualitative study of queues and stakeholders within a Canadian general surgery practice.

Shared Care Models (SCMs), in which a team of clinicians share in patient care and resource utilization, represent an opportunity for surgeon-level system change. We aimed to identify the queues and stakeholders within a complex gastrointestinal surgical care pathway to demonstrate the implications of a SCM on system efficiency. A multidisciplinary group of surgeons and care navigators working in SCMs were asked to develop a patient encounter map through consensus to illustrate relevant queues and stakeholders within a SCM. Fifteen surgeon-related queues were identified, each representing a point of potential delay to care in the patient's journey that could be addressed by shared care. A final patient encounter map was created, and advantages and challenges of SCMs were also described from multidisciplinary group discussions. The numerous queues identified in this map ultimately reflected opportunities for more efficient care navigation under a SCM through increased surgeon availability and shared resource utilization.

Predicting accurate ab initio DNA electron densities with equivariant neural networks.

One of the fundamental limitations of accurately modeling biomolecules like DNA is the inability to perform quantum chemistry calculations on large molecular structures. We present a machine learning model based on an equivariant Euclidean neural network framework to obtain accurate ab initio electron densities for arbitrary DNA structures that are much too large for conventional quantum methods. The model is trained on representative B-DNA basepair steps that capture both base pairing and base stacking interactions. The model produces accurate electron densities for arbitrary B-DNA structures with typical errors of less than 1%. Crucially, the error does not increase with system size, which suggests that the model can extrapolate to large DNA structures with negligible loss of accuracy. The model also generalizes reasonably to other DNA structural motifs such as the A- and Z-DNA forms, despite being trained on only B-DNA configurations. The model is used to calculate electron densities of several large-scale DNA structures, and we show that the computational scaling for this model is essentially linear. We also show that this machine learning electron density model can be used to calculate accurate electrostatic potentials for DNA. These electrostatic potentials produce more accurate results compared with classical force fields and do not show the usual deficiencies at short range.

RAS oncogene signal strength regulates matrisomal gene expression and tumorigenicity of mouse keratinocytes.

Environmental and molecular carcinogenesis are linked by the discovery that chemical carcinogen induced-mutations in the Hras or Kras genes drives tumor development in mouse skin. Importantly, enhanced expression or allele amplification of the mutant Ras gene contributes to selection of initiated cells, tumor persistence, and progression. To explore the consequences of Ras oncogene signal strength, primary keratinocytes were isolated and cultured from the LSL-HrasG12D and LSL-KrasG12D C57BL/6J mouse models and the mutant allele was activated by adeno-Cre recombinase. Keratinocytes expressing one (H) or two (HH) mutant alleles of HrasG12D, one KrasG12D allele (K), or one of each (HK) were studied. All combinations of activated Ras alleles stimulated proliferation and drove transformation marker expression, but only HH and HK formed tumors. HH, HK, and K sustained long-term keratinocyte growth in vitro, while H and WT could not. RNA-Seq yielded two distinct gene expression profiles; HH, HK, and K formed one cluster while H clustered with WT. Weak MAPK activation was seen in H keratinocytes but treatment with a BRAF inhibitor enhanced MAPK signaling and facilitated tumor formation. K keratinocytes became tumorigenic when they were isolated from mice where the LSL-KrasG12D allele was backcrossed from the C57BL/6 onto the FVB/N background. All tumorigenic keratinocytes but not the non-tumorigenic precursors shared a common remodeling of matrisomal gene expression that is associated with tumor formation. Thus, RAS oncogene signal strength determines cell-autonomous changes in initiated cells that are critical for their tumor-forming potential.

A dynamic gradient architecture generates brain activity states.

The human brain exhibits a diverse yet constrained range of activity states. While these states can be faithfully represented in a low-dimensional latent space, our understanding of the constitutive functional anatomy is still evolving. Here we applied dimensionality reduction to task-free and task fMRI data to address whether latent dimensions reflect intrinsic systems and if so, how these systems may interact to generate different activity states. We find that each dimension represents a dynamic activity gradient, including a primary unipolar sensory-association gradient underlying the global signal. The gradients appear stable across individuals and cognitive states, while recapitulating key functional connectivity properties including anticorrelation, modularity, and regional hubness. We then use dynamical systems modeling to show that gradients causally interact via state-specific coupling parameters to create distinct brain activity patterns. Together, these findings indicate that a set of dynamic, intrinsic spatial gradients interact to determine the repertoire of possible brain activity states.