Biology subjects

Leiz, J.

Publications and source records attributed to Leiz, J..

4 recordsLinked to original sources

bioRxiv2026

Targeting the Kidneys Osmotic Gradient with AVPR2 Antagonism Limits Inflammation and Fibrosis after Injury

Background: The renal medulla is characterized by a steep osmotic gradient that is essential for urinary concentration but may also influence cellular stress and injury responses. While vasopressin-dependent medullary hyperosmolarity is well established in water homeostasis, its role in shaping kidney injury and repair remains poorly understood. Methods: We pharmacologically reduced vasopressin signaling using the Avpr2 antagonist Tolvaptan and examined the consequences for renal medullary osmolality, tubular stress responses, inflammation, and fibrotic remodeling. Single-nucleus RNA sequencing, bulk transcriptomics, quantitative PCR, and histological analyses were performed in healthy kidneys and following unilateral ischemia-reperfusion injury (IRI) or cisplatin-induced nephropathy. Results: Tolvaptan treatment lowered renal medullary osmolality and suppressed osmotic stress-associated transcriptional programs in medullary collecting duct cells. Following IRI, Tolvaptan-treated mice exhibited attenuated expression of inflammatory and monocyte recruitment genes, reduced macrophage accumulation in the renal medulla, and diminished fibrotic remodeling at later stages. In cisplatin-induced nephropathy, Tolvaptan reduced tubular injury markers and inflammatory activation in the outer stripe of the outer medulla. Conclusions: These findings identify vasopressin-dependent renal medullary hyperosmolarity as a modulator of tubular stress, inflammation, and fibrotic remodeling after kidney injury. Modulation of the medullary osmotic environment influences early injury trajectories and downstream repair responses across distinct models of acute kidney injury.

pathology↗

bioRxiv2025

Interleukin enhancer binding factor 2 (Ilf2) and kidney epithelial stress resilience

BackgroundEpithelial cells in the renal medulla are continuously exposed to hyperosmolality, hypoxia, and oxidative stress, yet they display remarkable resilience. The transcriptional programs that endow this intrinsic stress tolerance remain incompletely defined. MethodsWe integrated single-nucleus RNA sequencing of mouse kidneys, computational transcription factor (TF) prioritization, and single-cell CRISPR interference screening (Perturb-seq) in inner medullary collecting duct (IMCD3) cells to systematically identify TFs mediating epithelial stress adaptation. The role of Ilf2 (Interleukin Enhancer Binding Factor 2) was further evaluated in IMCD3 cells by bulk RNA-seq, splicing analysis, functional assays under hyperosmotic stress, as well as in a mouse model of kidney ischemia-reperfusion injury (IRI) and kidney tissues from patients with early and advanced chronic kidney disease (CKD). ResultsComputational prediction and Perturb-seq identified known and novel TFs, that regulate gene expression programs in kidney medullary tubules. Among them, Ilf2 (also called NF45) emerged as a previously unrecognized regulator: Ilf2 knockdown in IMCD3 cells disrupted transcriptional and splicing programs linked to cell proliferation, cytoskeletal organization, and stress adaptation. Ilf2-deficient cells exhibited reduced proliferation, impaired nuclear integrity, and increased sensitivity to hyperosmotic stress. In mouse kidneys, Ilf2 expression increased during tubular repair after IRI, accompanied by induction of Ilf2-dependent transcripts and splicing events. Human kidneys with advanced CKD displayed elevated expression and cytoplasmic translocation of ILF2, suggesting a conserved stress-adaptive response. ConclusionsILF2 orchestrates both transcriptional and post-transcriptional regulatory mechanisms to sustain kidney epithelial stress resilience. Our findings highlight ILF2 as a potential tubular stress biomarker and therapeutic target for enhancing renoprotection. Key PointsO_LIIlf2 has significant and profound impact on gene expression programs in cultured kidney medullary epithelial cells (IMCD3). C_LIO_LIIlf2 confers cellular resilience, nuclear integrity, RNA splicing, proliferative capacity, and osmotic resistance in IMCD3 cells. C_LIO_LIInjured kidneys display increased levels of Ilf2 and up-regulation of genes and splicing events related to Ilf2 function. C_LI

cell biology↗

bioRxiv2022

Discovery of a non-canonical GRHL1 binding motif using deep convolutional and recurrent neural networks

Transcription factors regulate gene expression by binding to transcription factor binding sites (TFBSs). Most models for predicting TFBSs are based on position weight matrices (PWMs), which require a specific motif to be present in the DNA sequence and do not consider interdependencies of nucleotides. Novel approaches such as Transcription Factor Flexible Models or recurrent neural networks consequently provide higher accuracies. However, it is unclear whether such approaches can uncover novel non-canonical, hitherto unexpected TFBSs relevant to human transcriptional regulation. In this study, we trained a convolutional recurrent neural network with HT-SELEX data for GRHL1 binding and applied it to a set of GRHL1 binding sites obtained from ChIP-Seq experiments from human cells. We identified 46 non-canonical GRHL1 binding sites, which were not found by a conventional PWM approach. Unexpectedly, some of the newly predicted binding sequences lacked the CNNG core motif, so far considered obligatory for GRHL1 binding. Using isothermal titration calorimetry, we experimentally confirmed binding between the GRHL1-DNA binding domain and predicted GRHL1 binding sites, including a non-canonical GRHL1 binding site. Mutagenesis of individual nucleotides revealed a correlation between predicted binding strength and experimentally validated binding affinity across representative sequences. This correlation was neither observed with a PWM-based nor another deep learning approach. Our results show that convolutional recurrent neural networks may uncover unanticipated binding sites and facilitate quantitative transcription factor binding predictions.

bioinformatics↗

bioRxiv2021

Transcriptomic responses of the human kidney to acute injury at single cell resolution

BackgroundAcute kidney injury (AKI) occurs frequently in critically ill patients and is associated with adverse outcomes. Cellular mechanisms underlying AKI and kidney cell responses to injury remain incompletely understood. MethodsWe performed single-nuclei transcriptomics, bulk transcriptomics, molecular imaging studies, and conventional histology on kidney tissues from 8 individuals with severe AKI (stage 2 or 3 according to Kidney Disease: Improving Global Outcomes (KDIGO) criteria). Specimens were obtained within 1-2 hours after individuals had succumbed to critical illness associated with respiratory infections, with 4 of 8 individuals diagnosed with COVID-19. Control kidney tissues were obtained post-mortem or after nephrectomy from individuals without AKI. ResultsHigh-depth single cell-resolved gene expression data of human kidneys affected by AKI revealed enrichment of novel injury-associated cell states within the major cell types of the tubular epithelium, in particular in proximal tubules, thick ascending limbs and distal convoluted tubules. Four distinct, hierarchically interconnected injured cell states were distinguishable and characterized by transcriptome patterns associated with oxidative stress, hypoxia, interferon response, and epithelial-to-mesenchymal transition, respectively. Transcriptome differences between individuals with AKI were driven primarily by the cell type-specific abundance of these four injury subtypes rather than by private molecular responses. AKI-associated changes in gene expression between individuals with and without COVID-19 were similar. ConclusionThe study provides an extensive resource of the cell type-specific transcriptomic responses associated with critical illness-associated AKI in humans, highlighting recurrent disease-associated signatures and inter-individual heterogeneity. Personalized molecular disease assessment in human AKI may foster the development of tailored therapies.

molecular biology↗