Citations

Mutations in Leucine-rich repeat kinase 2 (LRRK2) and PTEN-induced kinase 1 (PINK1) are associated with familial Parkinson’s disease (PD). LRRK2 phosphorylates Rab guanosine triphosphatase (GTPases) within the Switch II domain while PINK1 directly phosphorylates Parkin and ubiquitin (Ub) and indirectly induces phosphorylation of a subset of Rab GTPases. Herein we have crossed LRRK2 [R1441C] mutant knock-in mice with PINK1 knock-out (KO) mice and report that loss of PINK1 does not impact endogenous LRRK2-mediated Rab phosphorylation nor do we see significant effect of mutant LRRK2 on PINK1-mediated Rab and Ub phosphorylation. In addition, we observe that a pool of the Rab-specific, protein phosphatase family member 1H phosphatase, is transcriptionally up-regulated and recruited to damaged mitochondria, independent of PINK1 or LRRK2 activity. Parallel signaling of LRRK2 and PINK1 pathways is supported by assessment of motor behavioral studies that show no evidence of genetic interaction in crossed mouse lines. Previously we showed loss of cilia in LRRK2 R1441C mice and herein we show that PINK1 KO mice exhibit a ciliogenesis defect in striatal cholinergic interneurons and astrocytes that interferes with Hedgehog induction of glial derived-neurotrophic factor transcription. This is not exacerbated in double-mutant LRRK2 and PINK1 mice. Overall, our analysis indicates that LRRK2 activation and/or loss of PINK1 function along parallel pathways to impair ciliogenesis, suggesting a convergent mechanism toward PD. Our data suggest that reversal of defects downstream of ciliogenesis offers a common therapeutic strategy for LRRK2 or PINK1 PD patients, whereas LRRK2 inhibitors that are currently in clinical trials are unlikely to benefit PINK1 PD patients.

ABSTRACT Symmetry refers to properties that remain invariant upon mathematical transformations. The principles of symmetry have guided numerous important discoveries in physics and chemistry but not in biology and medicine. Here, we aim to explore the presence of symmetry relationships at the gene expression level as a mean to distinguish between healthy and disease states. We deployed Learning-Based Invariant Feature Engineering - LIFE, a hybrid machine learning approach implemented with two symmetric invariant feature functions (IFFs) to identify Invariant Feature Genes (IFGs), which are gene pairs whose IFF single-value outputs remain invariant across individual samples in a given biological phenotype. Our multiclass classification results across the transcriptomes of 25 normal organs, 25 cancer types, and blood samples obtained from 4 different types of neurodegenerative diseases revealed the presence of unique phenotype-specific IFGs. We constructed networks using these IFGs (IF-Nets) and intriguingly, we demonstrated that the hubs could serve as information encoders, capable of reconstructing sample-wise expression values in relation to their counterpart genes. More importantly, we found that hubs of cancer IF-Nets were enriched with both approved and clinical trial drugs, highlighting “symmetry breaking” as a novel approach for treating diseases.

Key Points Studies on GFR assessment in people with cancer are needed. In this study, the creatinine–cystatin C equations performed better than other equations, and this was observed in solid and hematological cancers. Our findings give support to the preferential use of creatinine and cystatin C–based equations in people with cancer. Background GFR assessment is important in clinical practice with implications for diagnosis, prognostication, and drug dosing. People with cancer are at risk of imprecision in GFR estimation. This cross-sectional study evaluated the performance of various creatinine and cystatin C–based equations in comparison with measured GFR (mGFR) in people with cancer. Methods We retrieved data for all adult patients who had mGFR by urinary iothalamate clearance between 2011 and 2023 at Mayo Clinic and use of an electronic health record diagnosis code for cancer within 2 years before mGFR. The CKD Epidemiology Collaboration (CKD-EPI), European Kidney Function Consortium, and Cockcroft–Gault equations were computed, along with performance metrics (bias, precision, and root mean square error [RMSE]. Confidence intervals were generated by bootstrapping, and analysis were stratified by solid and hematological cancers. Results From all adults with cancer and mGFR, 1145 had both creatinine and cystatin C available within 7 days of mGFR. Among all equations, the creatinine–cystatin C CKD-EPI equation provided the best performance, with small bias (median, 3.0; 95% confidence interval, 2.3 to 3.8) and higher precision (RMSE, 14.5) compared with creatinine-only or cystatin C–only equations (RMSE varying from 16.6 to 20), and this was also true in solid and hematological cancers. The creatinine–cystatin European Kidney Function Consortium equation had a similar performance to CKD-EPI, Cockcroft–Gault showed worst precision (30% of people with errors above 30%), and cystatin C CKD-EPI equation was the most biased, prone to underestimation of mGFR. Conclusions In our cohort of patients with mGFR and cancer, the CKD-EPI creatinine–cystatin C equation performed best for GFR assessment, and this was true for both solid and hematological cancers. Our findings give support for the preferential use of creatinine and cystatin C–based equations instead of creatinine-only or cystatin C–only equations in people with cancer. Podcast This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2025_03_25_CJASNMar20.3.3252025.mp3

Background Polyclonal autologous T cells that are epigenetically reprogrammed through mTOR inhibition and IFN-α polarization (RAPA-201) represent a novel approach to the adoptive T cell therapy of cancer. Ex vivo inhibition of mTOR results causes a shift towards T central memory (TCM) whereas ex vivo IFN-α promotes type I cytokines, with each of these functions known to enhance the adoptive T cell therapy of cancer. Rapamycin-resistant T cells polarized for a type II cytokine phenotype were previously evaluated in the allogeneic transplantation context. Methods The clinical trial (NCT04176380) evaluated RAPA-201 therapy in combination with fludarabine-sparing low-dose host conditioning for the treatment of patients with relapsed, refractory multiple myeloma (RRMM). Results From December 2020 to December 2022, 14 patients with RRMM received a median of three RAPA-201 infusions (median dose, 80×106 cells). RAPA-201 drug products (DPs) were: polyclonal; enriched for TCM cells; reduced for immune checkpoint expression, including PD1, CD73, and LAIR1; and preferentially secreted Th1 cytokines. The median chemotherapy dose administered per cycle was 1,817 mg total for cyclophosphamide (range, 1,100–2,200) and 2.35 mg/M2 for pentostatin (range, 0–16). Nine of 14 patients (64%) achieved disease remission, with eight partial responses and one stringent complete response. Median progression-free survival was 6.0 months (range, 2.1 to>16.8 months). There were no toxicities of any grade attributable to RAPA-201, including no cytokine release syndrome and no immune effector cell-associated neurotoxicity syndrome. Only 4 of 14 patients (29%) had a serious adverse event (≥ grade 3) of any attribution. Conclusions Consistent with our hypothesis, ex vivo manufacturing using mTOR inhibition and IFN-α polarization consistently yielded a novel RAPA-201 DP that possessed a desirable phenotype relative to cytokine phenotype, memory status, and checkpoint expression. RAPA-201 recipients had preservation of T cell counts and Th1 cytokine secretion yet had increased T cell receptor clonality that associates with antitumor responses in the setting of monoclonal antibody checkpoint therapy. RAPA-201 therapy overcomes previous barriers to effective autologous polyclonal T-cell therapy, as it is feasible to manufacture, exquisitely safe to administer, and mediates remission in patients with RRMM. Trial registration number ClinicalTrials.gov: NCT04176380.

Purpose: This study compares the performance of small, fine-tuned LLMs with the large original models on two radiation oncology clinical tasks: treatment modality selection and ICD-10 code prediction. Methods: We used data of 403 treatment modality selection cases and 377 ICD-10 code prediction cases. The models used were fine-tuned LLaMA 2 7B and Mistral 7B, as well as the larger LLaMA 3.1 70B and 405B models for comparison. We evaluated the accuracy of all models on these two tasks. Results: Both fine-tuned LLaMA 2 7B and Mistral 7B outperformed LLaMA 3.1 70B and 405B in treatment modality selection task. The fine-tuned LLaMA 2 7B and Mistral 7B models achieved accuracy comparable to the larger models in ICD-10 code prediction. Conclusions: Small models, fine-tuned with radiation oncology data, can achieve performance comparable to-or even surpass-that of larger models.

An in silico model of the stomach is presented to study the phenomenon of duodenogastric reflux. We use the model to investigate the role of pyloric incompetence, food properties, and gastroparesis on reflux. This first-ever in silico study of duodenogastric reflux provides new insights into the mechanisms and factors implicated in this reflux and the sequelae of conditions that result from the exposure of the stomach lumen to bile.