Peripapillary atrophy area predicts the decrease of macular choroidal thickness in young adults during myopia progression.

OBJECTIVE: This study aimed to investigate the influence of peripapillary atrophy (PPA) area and axial elongation on the longitudinal changes in macular choroidal thickness (ChT) in young individuals with myopia. METHODS AND ANALYSIS: In this longitudinal investigation, 431 eyes-342 categorised as non-high myopia (non-HM) and 89 as HM-were examined for 2 years. Participants were examined with swept-source optical coherence tomography. The macular ChT, PPA area and axial length (AL) were measured at baseline and follow-up visits. Multiple regression analysis was performed to identify factors associated with ChT changes. The areas under the receiver operating characteristic curves were analysed to ascertain the predictive capacity of the PPA area and axial elongation for the reduction in macular ChT. RESULTS: Initial measurements revealed that the average macular ChT was 240.35±56.15 µm in the non-HM group and 198.43±50.27 µm in the HM group (p<0.001). It was observed that the HM group experienced a significantly greater reduction in average macular ChT (-7.35±11.70 µm) than the non-HM group (-1.85±16.95 µm, p=0.004). Multivariate regression analysis showed that a greater reduction of ChT was associated with baseline PPA area (ß=-26.646, p<0.001) and the change in AL (ß=-35.230, p<0.001). The combination of the baseline PPA area with the change in AL was found to be effective in predicting the decrease in macular ChT, with an area under the curve of 0.741 (95% CI 0.694 to 0.787). CONCLUSION: Over 2 years, eyes with HM exhibit a more significant decrease in ChT than those without HM. Combining the baseline PPA area with the change in AL could be used to predict the decrease of macular ChT.

Image-based artificial intelligence for the prediction of pathological complete response to neoadjuvant chemoradiotherapy in patients with rectal cancer: a systematic review and meta-analysis.

OBJECTIVE: Artificial intelligence (AI) holds enormous potential for noninvasively identifying patients with rectal cancer who could achieve pathological complete response (pCR) following neoadjuvant chemoradiotherapy (nCRT). We aimed to conduct a meta-analysis to summarize the diagnostic performance of image-based AI models for predicting pCR to nCRT in patients with rectal cancer. METHODS: This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A literature search of PubMed, Embase, Cochrane Library, and Web of Science was performed from inception to July 29, 2023. Studies that developed or utilized AI models for predicting pCR to nCRT in rectal cancer from medical images were included. The Quality Assessment of Diagnostic Accuracy Studies-AI was used to appraise the methodological quality of the studies. The bivariate random-effects model was used to summarize the individual sensitivities, specificities, and areas-under-the-curve (AUCs). Subgroup and meta-regression analyses were conducted to identify potential sources of heterogeneity. Protocol for this study was registered with PROSPERO (CRD42022382374). RESULTS: Thirty-four studies (9933 patients) were identified. Pooled estimates of sensitivity, specificity, and AUC of AI models for pCR prediction were 82% (95% CI: 76-87%), 84% (95% CI: 79-88%), and 90% (95% CI: 87-92%), respectively. Higher specificity was seen for the Asian population, low risk of bias, and deep-learning, compared with the non-Asian population, high risk of bias, and radiomics (all P < 0.05). Single-center had a higher sensitivity than multi-center (P = 0.001). The retrospective design had lower sensitivity (P = 0.012) but higher specificity (P < 0.001) than the prospective design. MRI showed higher sensitivity (P = 0.001) but lower specificity (P = 0.044) than non-MRI. The sensitivity and specificity of internal validation were higher than those of external validation (both P = 0.005). CONCLUSIONS: Image-based AI models exhibited favorable performance for predicting pCR to nCRT in rectal cancer. However, further clinical trials are warranted to verify the findings.

Automated and reusable deep learning (AutoRDL) framework for predicting response to neoadjuvant chemotherapy and axillary lymph node metastasis in breast cancer using ultrasound images: a retrospective, multicentre study.

Background: Previous deep learning models have been proposed to predict the pathological complete response (pCR) and axillary lymph node metastasis (ALNM) in breast cancer. Yet, the models often leveraged multiple frameworks, required manual annotation, and discarded low-quality images. We aimed to develop an automated and reusable deep learning (AutoRDL) framework for tumor detection and prediction of pCR and ALNM using ultrasound images with diverse qualities. Methods: The AutoRDL framework includes a You Only Look Once version 5 (YOLOv5) network for tumor detection and a progressive multi-granularity (PMG) network for pCR and ALNM prediction. The training cohort and the internal validation cohort were recruited from Guangdong Provincial People's Hospital (GPPH) between November 2012 and May 2021. The two external validation cohorts were recruited from the First Affiliated Hospital of Kunming Medical University (KMUH), between January 2016 and December 2019, and Shunde Hospital of Southern Medical University (SHSMU) between January 2014 and July 2015. Prior to model training, super-resolution via iterative refinement (SR3) was employed to improve the spatial resolution of low-quality images from the KMUH. We developed three models for predicting pCR and ALNM: a clinical model using multivariable logistic regression analysis, an image model utilizing the PMG network, and a combined model that integrates both clinical and image data using the PMG network. Findings: The YOLOv5 network demonstrated excellent accuracy in tumor detection, achieving average precisions of 0.880-0.921 during validation. In terms of pCR prediction, the combined modelpost-SR3 outperformed the combined modelpre-SR3, image modelpost-SR3, image modelpre-SR3, and clinical model (AUC: 0.833 vs 0.822 vs 0.806 vs 0.790 vs 0.712, all p < 0.05) in the external validation cohort (KMUH). Consistently, the combined modelpost-SR3 exhibited the highest accuracy in ALNM prediction, surpassing the combined modelpre-SR3, image modelpost-SR3, image modelpre-SR3, and clinical model (AUC: 0.825 vs 0.806 vs 0.802 vs 0.787 vs 0.703, all p < 0.05) in the external validation cohort 1 (KMUH). In the external validation cohort 2 (SHSMU), the combined model also showed superiority over the clinical and image models (0.819 vs 0.712 vs 0.806, both p < 0.05). Interpretation: Our proposed AutoRDL framework is feasible in automatically predicting pCR and ALNM in real-world settings, which has the potential to assist clinicians in optimizing individualized treatment options for patients. Funding: National Key Research and Development Program of China (2023YFF1204600); National Natural Science Foundation of China (82227802, 82302306); Clinical Frontier Technology Program of the First Affiliated Hospital of Jinan University, China (JNU1AF-CFTP-2022-a01201); Science and Technology Projects in Guangzhou (202201020022, 2023A03J1036, 2023A03J1038); Science and Technology Youth Talent Nurturing Program of Jinan University (21623209); and Postdoctoral Science Foundation of China (2022M721349).

Ocular trauma from Lawn mower accidents: Clinical insights, visual outcomes and microbial profiles.

AIMS: To characterize the clinical features of ocular trauma resulting from lawn mower, identify determinants of unfavorable final visual acuity (FVA), and assess the spectrum of microbial in posttraumatic endophthalmitis. METHODS: This retrospective study enrolled patients who experienced ocular trauma due to lawn mower at Zhongshan Ophthalmic Center from January 2013 to August 2021. Demographics, clinical features, isolated microorganisms, risk factors influencing reduced visual acuity, treatment regimens, and utilization of eyewear were collected. RESULTS: The study included 140 participants (140 eyes) (49.47 ± 12.03 years, 95% male). The predominant injury manifestations were penetrating globe injuries (75.7%) and intraocular foreign bodies (51.4%). Endophthalmitis occurred in 35 cases (25%) and Bacillus cereus (23.5%) was the primary pathogen, followed by Staphylococcus epidermidis (11.8%) and Streptococcus species (11.8%). Following the initial assessment, where 77.9% of patients had initial visual acuity (IVA) at grade IV (ranging from light perception to 4/200) and only 0.7% attained grade I (better than 20/40), post-treatment results revealed that 5.7% achieved FVA at grade I, with a concurrent decrease in patients with grade IV vision to 64.3%. Multivariate logistic regression revealed that injury protection (p < 0.001, OR = 0.237, 95% CI = 0.126-0.446), IVA (p = 0.001, OR = 4.102, 95% CI = 1.730-9.729), and retinal detachment (p = 0.042, OR = 8.105, 95% CI = 1.075-61.111) were significant independent risk factors impacting FVA. CONCLUSION: Lawn mower often cause severe ocular injuries, with high-velocity metal foreign bodies that can lead to infections, most commonly caused by Bacillus cereus. Correct use of protective gear, initial vision assessment, and detecting retinal detachment are crucial for visual prognosis.

Untargeted Pixel-by-Pixel Imaging of Metabolite Ratio Pairs as a Novel Tool for Biomedical Discovery in Mass Spectrometry Imaging.

Mass spectrometry imaging (MSI) is a powerful technology used to define the spatial distribution and relative abundance of structurally identified and yet-undefined metabolites across tissue cryosections. While numerous software packages enable pixel-by-pixel imaging of individual metabolites, the research community lacks a discovery tool that images all metabolite abundance ratio pairs. Importantly, recognition of correlated metabolite pairs informs discovery of unanticipated molecules contributing to shared metabolic pathways, uncovers hidden metabolic heterogeneity across cells and tissue subregions, and indicates single-timepoint flux through pathways of interest. Here, we describe the development and implementation of an untargeted R package workflow for pixel-by-pixel ratio imaging of all metabolites detected in an MSI experiment. Considering untargeted MSI studies of murine brain and embryogenesis, we demonstrate that ratio imaging minimizes systematic data variation introduced by sample handling and instrument drift, markedly enhances spatial image resolution, and reveals previously unrecognized metabotype-distinct tissue regions. Furthermore, ratio imaging facilitates identification of novel regional biomarkers and provides anatomical information regarding spatial distribution of metabolite-linked biochemical pathways. The algorithm described herein is applicable to any MSI dataset containing spatial information for metabolites, peptides or proteins, offering a potent tool to enhance knowledge obtained from current spatial metabolite profiling technologies.

An interpretable machine learning model based on contrast-enhanced CT parameters for predicting treatment response to conventional transarterial chemoembolization in patients with hepatocellular carcinoma.

OBJECTIVE: To explore the potential of pre-therapy computed tomography (CT) parameters in predicting the treatment response to initial conventional TACE (cTACE) in intermediate-stage hepatocellular carcinoma (HCC) and develop an interpretable machine learning model. METHODS: This retrospective study included 367 patients with intermediate-stage HCC who received cTACE as first-line therapy from three centers. We measured the mean attenuation values of target lesions on multi-phase contrast-enhanced CT and further calculated three CT parameters, including arterial (AER), portal venous (PER), and arterial portal venous (APR) enhancement ratios. We used logistic regression analysis to select discriminative features and trained three machine learning models via 5-fold cross-validation. The performance in predicting treatment response was evaluated in terms of discrimination, calibration, and clinical utility. Afterward, a Shapley additive explanation (SHAP) algorithm was leveraged to interpret the outputs of the best-performing model. RESULTS: The mean diameter, ECOG performance status, and cirrhosis were the important clinical predictors of cTACE treatment response, by multiple logistic regression. Adding the CT parameters to clinical variables showed significant improvement in performance (net reclassification index, 0.318, P < 0.001). The Random Forest model (hereafter, RF-combined model) integrating CT parameters and clinical variables demonstrated the highest performance on external validation dataset (AUC of 0.800). The decision curve analysis illustrated the optimal clinical benefits of RF-combined model. This model could successfully stratify patients into responders and non-responders with distinct survival (P = 0.001). CONCLUSION: The RF-combined model can serve as a robust and interpretable tool to identify the appropriate crowd for cTACE sessions, sparing patients from receiving ineffective and unnecessary treatments.

An immobilized composite microbial material combined with slow release agents enhances oil-contaminated groundwater remediation.

Microbial remediation of oil-contaminated groundwater is often limited by the low temperature and lack of nutrients in the groundwater environment, resulting in low degradation efficiency and a short duration of effectiveness. In order to overcome this problem, an immobilized composite microbial material and two types of slow release agents (SRA) were creatively prepared. Three oil-degrading bacteria, Serratia marcescens X, Serratia sp. BZ-L I1 and Klebsiella pneumoniae M3, were isolated from oil-contaminated groundwater, enriched and compounded, after which the biodegradation rate of the Venezuelan crude oil and diesel in groundwater at 15 °C reached 63 % and 79 %, respectively. The composite microbial agent was immobilized on a mixed material of silver nitrate-modified zeolite and activated carbon with a mass ratio of 1:5, which achieved excellent oil adsorption and water permeability performance. The slow release processes of spherical and tablet SRAs (SSRA, TSRA) all fit well with the Korsmeyer-Peppas kinetic model, and the nitrogen release mechanism of SSRA N2 followed Fick's law of diffusion. The highest oil removal rates by the immobilized microbial material combined with SSRA N2 and oxygen SRA reached 94.9 % (sand column experiment) and 75.1 % (sand tank experiment) during the 45 days of remediation. Moreover, the addition of SRAs promoted the growth of oil-degrading bacteria based on microbial community analysis. This study demonstrates the effectiveness of using immobilized microbial material combined with SRAs to achieve a high efficiency and long-term microbial remediation of oil contaminated shallow groundwater.

Biomolecular condensates create phospholipid-enriched microenvironments.

Proteins and RNA can phase separate from the aqueous cellular environment to form subcellular compartments called condensates. This process results in a protein-RNA mixture that is chemically different from the surrounding aqueous phase. Here, we use mass spectrometry to characterize the metabolomes of condensates. To test this, we prepared mixtures of phase-separated proteins and extracts of cellular metabolites and identified metabolites enriched in the condensate phase. Among the most condensate-enriched metabolites were phospholipids, due primarily to the hydrophobicity of their fatty acyl moieties. We found that phospholipids can alter the number and size of phase-separated condensates and in some cases alter their morphology. Finally, we found that phospholipids partition into a diverse set of endogenous condensates as well as artificial condensates expressed in cells. Overall, these data show that many condensates are protein-RNA-lipid mixtures with chemical microenvironments that are ideally suited to facilitate phospholipid biology and signaling.

Association between adjuvant chemotherapy and survival in stage I gastric cancer patients after curative resection.

Background: The efficacy of adjuvant chemotherapy (AC) on survival outcomes of patients with stage I gastric cancer (GC) after curative resection remains controversial. We aimed to determine whether these patients would benefit from AC. Methods: This retrospective study included patients with pathologically confirmed stage I GC who underwent curative resection between November 2010 and December 2020. Patients were divided into AC and non-AC groups, then a 1:1 propensity score matching (PSM) analysis was performed to minimize the selection bias. Potential risk factors including age, pN stage, pT stage, lymphovascular invasion, perineural invasion, tumor size, histological type, and carcinoembryonic antigen level were used as matching covariates. The recurrence-free survival (RFS) and disease-specific survival (DSS) were compared between groups using the Kaplan-Meier method. Results: A total of 902 consecutive patients were enrolled and 174 (19.3%) patients were treated with AC. PSM created 123 pairs of patients. Before PSM, patients receiving AC had lower 10-year RFS rates (90% vs 94.6%, P = 0.035) than those who did not receive AC; the two groups had similar 10-year DSS rates (93.8% vs 95.0%, P = 0.240). After PSM, there were no statistical differences in the 10-year RFS (90.9% vs 93.0%, P = 0.507) or DSS rates (93.5% vs 93.6%, P = 0.811) between the two groups. Similar results were found in the stage IA and IB subgroups. Moreover, these findings were not affected by AC cycles. Conclusions: The addition of AC could not provide survival benefits for patients with stage I GC after surgery and follow-up is thus recommended. However, large-scale randomized clinical trials are required.

Progesterone induces meiosis through two obligate co-receptors with PLA2 activity.

The steroid hormone progesterone (P4) regulates multiple aspects of reproductive and metabolic physiology. Classical P4 signaling operates through nuclear receptors that regulate transcription. In addition, P4 signals through membrane P4 receptors (mPRs) in a rapid nongenomic modality. Despite the established physiological importance of P4 nongenomic signaling, its detailed signal transduction remains elusive. Here, using Xenopus oocyte maturation as a well-established physiological readout of nongenomic P4 signaling, we identify the lipid hydrolase ABHD2 (α/ß hydrolase domain-containing protein 2) as an essential mPRß co-receptor to trigger meiosis. We show using functional assays coupled to unbiased and targeted cell-based lipidomics that ABHD2 possesses a phospholipase A2 (PLA2) activity that requires both P4 and mPRß. This PLA2 activity bifurcates P4 signaling by inducing mPRß clathrin-dependent endocytosis and producing lipid messengers that are G-protein coupled receptors agonists. Therefore, P4 drives meiosis by inducing the ABHD2 PLA2 activity that requires both mPRß and ABHD2 as obligate co-receptors.

Storage reliability prediction of electromechanical components based on virtual manufacturing and testing.

Electromechanical components (EMCs) such as relays and contactors have been used extensively in industrial and military areas. The storage reliability of these EMCs has a direct impact on the reliability of the system that contains them. However, during the design phase, it is difficult to predict the storage reliability of EMCs because of few failure rate data of parts, as well as limited testing time and budgets. To address these problems, a virtual Manufacturing and testing method is proposed in this paper, so as to simulate the storage degradation process of batch EMCs. By considering the influence of the quality screening process in the manufacturing process, as well as the unit-to-unit variability of EMCs on the storage degradation paths and the overall life distribution of batch products, the storage failure distribution function is obtained, based on Wiener process. At the same time, the distribution of the diffusion coefficient in the degradation model and the failure distribution model is quantified by introducing testing data of related products as priori information, so as to reflect the uncertainty of the storage degradation process of EMCs. A case study of an electromagnetic relay is carried out to illustrate the effectiveness of the proposed approach.

The future of green transportation: evaluating the impact of innovation in hybrid electric vehicles relating technologies on carbon dioxide emissions in Asia's top knowledge-based economies.

The significant contribution of the transportation sector to carbon dioxide emissions (CO2e) has become a developing concern for legislators and environmental experts. Innovation in hybrid electric vehicle-related technologies (IHVRTs) has been identified as a possible strategy for reducing CO2e in the transportation industry. Even though IHVRTs have the potential to reduce CO2e, there are insufficient studies on their impact in the top three Asian knowledge-based economies (Japan, South Korea, and Japan). This study attempts to address this gap in the literature by investigating the association between innovation in IHVRTs and CO2e in the top three Asian knowledge-based economies, with independent variables gross domestic product per capita (GDPPC), economic complexity (ECC), renewable energy consumption (RNEC), and financial development (FD). The model's coefficients are estimated using the augmented mean group, which considers cross-country dependencies and country-specific effects. The empirical findings indicate that IHVRTs have a substantial negative effect on CO2e. In addition, FD has a favorable relationship with CO2e, whereas ECC has a negative relationship with CO2e. The results also demonstrated that RNEC reduces CO2e, whereas the GDPPC reduces CO2e. The policy implications of the results imply an urgent need for additional investment in IHVRTs and a transition towards more environmentally conscious and less ecologically damaging economic activity.

Fetal metabolic adaptations to cardiovascular stress in twin-twin transfusion syndrome.

Monochorionic-diamniotic twin pregnancies are susceptible to unique complications arising from a single placenta shared by two fetuses. Twin-twin transfusion syndrome (TTTS) is a constellation of disturbances caused by unequal blood flow within the shared placenta giving rise to a major hemodynamic imbalance between the twins. Here, we applied TTTS as a model to uncover fetal metabolic adaptations to cardiovascular stress. We compared untargeted metabolomic analyses of amniotic fluid samples from severe TTTS cases vs. singleton controls. Amniotic fluid metabolites demonstrated alterations in fatty acid, glucose, and steroid hormone metabolism in TTTS. Among TTTS cases, unsupervised principal component analysis revealed two distinct clusters of disease defined by levels of glucose metabolites, amino acids, urea, and redox status. Our results suggest that the human fetal heart can adapt to hemodynamic stress by modulating its glucose metabolism and identify potential differences in the ability of individual fetuses to respond to cardiovascular stress.

Radiation therapy promotes unsaturated fatty acids to maintain survival of glioblastoma.

Radiation therapy (RT) is essential for the management of glioblastoma (GBM). However, GBM frequently relapses within the irradiated margins, thus suggesting that RT might stimulate mechanisms of resistance that limits its efficacy. GBM is recognized for its metabolic plasticity, but whether RT-induced resistance relies on metabolic adaptation remains unclear. Here, we show in vitro and in vivo that irradiated GBM tumors switch their metabolic program to accumulate lipids, especially unsaturated fatty acids. This resulted in an increased formation of lipid droplets to prevent endoplasmic reticulum (ER) stress. The reduction of lipid accumulation with genetic suppression and pharmacological inhibition of the fatty acid synthase (FASN), one of the main lipogenic enzymes, leads to mitochondrial dysfunction and increased apoptosis of irradiated GBM cells. Combination of FASN inhibition with focal RT improved the median survival of GBM-bearing mice. Supporting the translational value of these findings, retrospective analysis of the GLASS consortium dataset of matched GBM patients revealed an enrichment in lipid metabolism signature in recurrent GBM compared to primary. Overall, these results demonstrate that RT drives GBM resistance by generating a lipogenic environment permissive to GBM survival. Targeting lipid metabolism might be required to develop more effective anti-GBM strategies.

Suppressed Phase Separation of Mixed-Halide Perovskite Quantum Dots Confined in Mesoporous Metal Organic Frameworks.

Mixed-halide perovskite quantum dots (PeQDs) are the most competitive candidates in designing solar cells and light-emitting devices (LEDs) due to their tunable bandgap and high-efficiency quantum yield. However, phase separation in mixed-halide perovskites under illumination can form rich iodine and bromine regions, which change its optical responses. Herein, we synthesize PeQDs combined with mesoporous zinc-based metal organic framework (MOF) crystals, which can greatly improve the stability of anti-anion exchange, including photo-, thermal, and long-term stabilities under illumination. This unique structure provides a solution for improving the performance of perovskite optoelectronic devices and stabilizing mixed-halide perovskite devices.

Comparative Study of C-TIRADS, ACR-TIRADS, and EU-TIRADS for Diagnosis and Management of Thyroid Nodules.

RATIONALE AND OBJECTIVES: Chinese Thyroid Imaging Reporting and Data Systems (C-TIRADS) was developed to provide a more simplified tool for stratifying thyroid nodules. Here we aimed to validate the efficacy of C-TIRADS in distinguishing benign from malignant and in guiding fine-needle aspiration biopsies in comparison with the American College of Radiology TIRADS (ACR-TIRADS) and European TIRADS (EU-TIRADS). MATERIALS AND METHODS: This study retrospectively included 3438 thyroid nodules (≥10 mm) in 3013 patients (mean age, 47.1 years ± 12.9) diagnosed between January 2013 and November 2019. Ultrasound features of the nodules were evaluated and categorized according to the lexicons of the three TIRADS. We compared these TIRADS by using the area under the receiver operating characteristic curve (AUROC), the area under the precision-recall curve (AUPRC), sensitivity, specificity, net reclassification improvement (NRI), and unnecessary fine-needle aspiration biopsy (FNAB) rate. RESULTS: Of the 3438 thyroid nodules, 707 (20.6%) were malignant. C-TIRADS showed higher discrimination performance (AUROC, 0.857; AUPRC, 0.605) than ACR-TIRADS (AUROC, 0.844; AUPRC, 0.567) and EU-TIRADS (AUROC, 0.802; AUPRC, 0.455). The sensitivity of C-TIRADS (85.3%) was lower than that of ACR-TIRADS (89.1%) but higher than that of EU-TIRADS (78.4%). The specificity of C-TIRADS (76.9%) was similar to that of EU-TIRADS (78.9%) and higher than that of ACR-TIRADS (69.5%). The unnecessary FNAB rate was lowest with C-TIRADS (21.2%), followed by ACR-TIRADS (41.7%) and EU-TIRADS (58.3%). C-TIRADS obtained significant NRI for recommending FNAB over ACR-TIRADS (19.0%, P < 0.001) and EU-TIRADS (25.5%, P < 0.001). CONCLUSION: C-TIRADS may be a clinically applicable tool to manage thyroid nodules, which warrants thorough tests in other geographic settings.

Distinct cAMP Signaling Microdomains Differentially Regulate Melanosomal pH and Pigmentation.

cAMP signaling is a well-established regulator of melanin synthesis. Two distinct cAMP signaling pathways-the transmembrane adenylyl cyclase pathway, activated primarily by the MC1R, and the soluble adenylyl cyclase (sAC) pathway-affect melanin synthesis. The sAC pathway affects melanin synthesis by regulating melanosomal pH, and the MC1R pathway affects melanin synthesis by regulating gene expression and post-translational modifications. However, whether MC1R genotype affects melanosomal pH is poorly understood. We now report that loss of function MC1R does not affect melanosomal pH. Thus, sAC signaling appears to be the only cAMP signaling pathway that regulates melanosomal pH. We also addressed whether MC1R genotype affects sAC-dependent regulation of melanin synthesis. Although sAC loss of function in wild-type human melanocytes stimulates melanin synthesis, sAC loss of function has no effect on melanin synthesis in MC1R nonfunctional human and mouse melanocytes or skin and hair melanin in e/e mice. Interestingly, activation of transmembrane adenylyl cyclases, which increases epidermal eumelanin synthesis in e/e mice, leads to enhanced production of eumelanin in sAC-knockout mice relative to that in sAC wild-type mice. Thus, MC1R- and sAC-dependent cAMP signaling pathways define distinct mechanisms that regulate melanosomal pH and pigmentation.

A coordinated multiorgan metabolic response contributes to human mitochondrial myopathy.

Mitochondrial diseases are a heterogeneous group of monogenic disorders that result from impaired oxidative phosphorylation (OXPHOS). As neuromuscular tissues are highly energy-dependent, mitochondrial diseases often affect skeletal muscle. Although genetic and bioenergetic causes of OXPHOS impairment in human mitochondrial myopathies are well established, there is a limited understanding of metabolic drivers of muscle degeneration. This knowledge gap contributes to the lack of effective treatments for these disorders. Here, we discovered fundamental muscle metabolic remodeling mechanisms shared by mitochondrial disease patients and a mouse model of mitochondrial myopathy. This metabolic remodeling is triggered by a starvation-like response that evokes accelerated oxidation of amino acids through a truncated Krebs cycle. While initially adaptive, this response evolves in an integrated multiorgan catabolic signaling, lipid store mobilization, and intramuscular lipid accumulation. We show that this multiorgan feed-forward metabolic response involves leptin and glucocorticoid signaling. This study elucidates systemic metabolic dyshomeostasis mechanisms that underlie human mitochondrial myopathies and identifies potential new targets for metabolic intervention.

Novel genetically engineered mouse models for clear cell renal cell carcinoma.

Genetically engineered mouse models (GEMMs) are important immunocompetent models for research into the roles of individual genes in cancer and the development of novel therapies. Here we use inducible CRISPR-Cas9 systems to develop two GEMMs which aim to model the extensive chromosome p3 deletion frequently observed in clear cell renal cell carcinoma (ccRCC). We cloned paired guide RNAs targeting early exons of Bap1, Pbrm1, and Setd2 in a construct containing a Cas9D10A (nickase, hSpCsn1n) driven by tetracycline (tet)-responsive elements (TRE3G) to develop our first GEMM. The founder mouse was crossed with two previously established transgenic lines, one carrying the tet-transactivator (tTA, Tet-Off) and one with a triple-mutant stabilized HIF1A-M3 (TRAnsgenic Cancer of the Kidney, TRACK), both driven by a truncated, proximal tubule-specific γ-glutamyltransferase 1 (ggt or γGT) promoter, to create triple-transgenic animals. Our results indicate that this model (BPS-TA) induces low numbers of somatic mutations in Bap1 and Pbrm1 (but not in Setd2), known tumor suppressor genes in human ccRCC. These mutations, largely restricted to kidneys and testis, induced no detectable tissue transformation in a cohort of 13 month old mice (N = 10). To gain insights into the low frequencies of insertions and deletions (indels) in BPS-TA mice we analyzed wild type (WT, N = 7) and BPS-TA (N = 4) kidneys by RNAseq. This showed activation of both DNA damage and immune response, suggesting activation of tumor suppressive mechanisms in response to genome editing. We then modified our approach by generating a second model in which a ggt-driven, cre-regulated Cas9WT(hSpCsn1) was employed to introduce Bap1, Pbrm1, and Setd2 genome edits in the TRACK line (BPS-Cre). The BPS-TA and BPS-Cre lines are both tightly controlled in a spatiotemporal manner with doxycycline (dox) and tamoxifen (tam), respectively. In addition, whereas the BPS-TA line relies on paired guide RNAs (gRNAs), the BPS-Cre line requires only single gRNAs for gene perturbation. In the BPS-Cre we identified increased Pbrm1 gene-editing frequencies compared to the BPS-TA model. Whereas we did not detect Setd2 edits in the BPS-TA kidneys, we found extensive editing of Setd2 in the BPS-Cre model. Bap1 editing efficiencies were comparable between the two models. Although no gross malignancies were observed in our study, this is the first reported GEMM which models the extensive chromosome 3p deletion frequently observed in kidney cancer patients. Further studies are required (1) to model more extensive 3p deletions, e.g. impacting additional genes, and (2) to increase the cellular resolution, e.g. by employing single-cell RNAseq to ascertain the effects of specific combinatorial gene inactivation.