GXYLT2: an emerging therapeutic target and predictive biomarker for anti-PD-1 efficacy in clear cell renal cell carcinoma
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A first-in-human study of quantitative ultrasound to assess transplant kidney fibrosis
Kidney transplantation is the optimal treatment for renal failure. In the United States, a biopsy at the time of organ procurement is often used to assess kidney quality to decide whether it should be used for transplant. This assessment is focused on renal fibrotic burden, because fibrosis is an important measure of irreversible kidney injury. Unfortunately, biopsy at the time of transplant is plagued by problems, including bleeding risk, inaccuracies introduced by sampling bias and rapid sample preparation, and the need for round-the-clock pathology expertise. We developed a quantitative algorithm, called renal H-scan, that can be added to standard ultrasound workflows to quickly and noninvasively measure renal fibrotic burden in preclinical animal models and human transplant kidneys. Furthermore, we provide evidence that biopsy-based fibrosis estimates, because of their highly localized nature, are inaccurate measures of whole-kidney fibrotic burden and do not associate with kidney function post-transplant. In contrast, we show that whole-kidney H-scan fibrosis estimates associate closely with post-transplant renal function. Taken together, our data suggest that the addition of H-scan to standard ultrasound workflows could provide a safe, rapid and easy-to-perform method for accurate quantification of transplant kidney fibrotic burden, and thus better prediction of post-transplant renal outcomes.
Neddylation of RhoA impairs its protein degradation and promotes renal interstitial fibrosis progression in diabetic nephropathy
Diabetic nephropathy (DN) is a common and serious complication of diabetes, characterized by chronic fibro-inflammatory processes with an unclear pathogenesis. Renal fibrosis plays a significant role in the development and progression of DN. While recent research suggests that the neddylation pathway may influence fibrotic processes, its specific dysregulation in DN and the underlying mechanisms remain largely unexplored. This study identified the neddylation of RhoA as a novel post-translational modification that regulates its expression and promotes renal fibrosis in DN. We here demonstrated that two key components of the neddylation pathway—NEDD8-activating enzyme E1 subunit 1 (NAE1) and NEDD8—are significantly upregulated in human chronic kidney disease (CKD) specimens compared to healthy kidneys, implicating neddylation in CKD-associated fibrosis. Our findings further revealed that both pharmacological inhibition of neddylation using MLN4924 and genetic knockdown of NAE1 mitigate renal fibrosis in mouse models of streptozotocin-induced diabetes and unilateral ureteral obstruction (UUO). Immunoprecipitation-mass spectrometry (IP-MS) and subsequent function assays demonstrated a direct interaction between RhoA and NEDD8. Importantly, neddylation inhibition reduced RhoA protein expression, highlighting a potential therapeutic target. Additionally, a positive correlation was noted between elevated NEDD8 mRNA levels and RhoA mRNA expression in human CKD specimens. RhoA overexpression counteracted the antifibrotic effects of neddylation inhibition, underscoring its critical role in fibrosis progression. Mechanistically, we unveiled that neddylation enhances RhoA protein stability by inhibiting its ubiquitination-mediated degradation, which subsequently activates the ERK1/2 pathway. Collectively, this study provides novel insights into NAE1-dependent RhoA neddylation as a key contributor to renal fibrosis in DN.
Predictive learning as the basis of the testing effect
A prominent learning phenomenon is the testing effect, meaning that testing enhances retention more than studying. Emergent frameworks propose fundamental (Hebbian and predictive) learning principles as its basis. Predictive learning posits that learning occurs based on the contrast (error) between a prediction and the feedback on that prediction (prediction error). Here, we propose that in testing (but not studying) scenarios, participants predict potential answers, and its contrast with the subsequent feedback yields a prediction error, which facilitates testing-based learning. To investigate this, we developed an associative memory network incorporating Hebbian and/or predictive learning, together with an experimental design where human participants studied or tested English-Swahili word pairs followed by recognition. Three behavioral experiments (N = 80, 81, 62) showed robust testing effects when feedback was provided. Model fitting (of 10 different models) suggested that only models incorporating predictive learning can account for the breadth of data associated with the testing effect. Our data and model suggest that predictive learning underlies the testing effect.
New mouse models for exploring renal tumor extension into the inferior vena cava
Renal tumors with inferior vena cava tumor thrombus (IVCTT) remain a challenge in urology. However, in vivo models remain unavailable, which hampers the elucidation of its pathogenesis, identification of therapeutic targets, and screening for effective drugs. In this study, we initially develop two IVCTT models in BALB/c and BALB/c-nu/nu mice using the mouse Renca cell line. The pathological features and immune microenvironment of IVCTT in immunocompetent mice closely resembles those observed in humans. Single-cell transcriptome sequencing, immunohistochemistry and multiplex immunohistochemistry reveal a predominance of monocytes, macrophages, and neutrophils within IVCTT, mirroring the cellular composition of the human IVCTT; however, fewer lymphocytes are observed. The IVCTT in immunodeficient mice progresses much faster than in immunocompetent mice. More importantly, we successfully use the human tumor cell line on the BALB/c nu/nu mice to create an IVCTT model. The proposed in vivo models mimic the progression of renal tumors with IVCTT, clarify that the immune system can inhibit tumor thrombus progression, and provide tools for subsequent mechanistic research and translational preclinical studies.
Neuroinflammatory fluid biomarkers in patients with Alzheimer’s disease: a systematic literature review
Neuroinflammation is associated with both early and late stages of the pathophysiology of Alzheimer’s disease (AD). Fluid biomarkers are gaining significance in clinical practice for diagnosis in presymptomatic stages, monitoring, and disease prognosis. This systematic literature review (SLR) aimed to identify fluid biomarkers for neuroinflammation related to clinical stages across the AD continuum and examined long-term outcomes associated with changes in biomarkers.
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