Unveiling the enigma of acute kidney disease: predicting prognosis, exploring interventions, and embracing a multidisciplinary approach.

Acute kidney disease (AKD) is a critical transitional period between acute kidney injury and chronic kidney disease. The incidence of AKD following acute kidney injury is approximately 33.6%, and it can occur without identifiable preceding acute kidney injury. The development of AKD is associated with increased risks of chronic kidney disease, dialysis, and mortality. Biomarkers and subphenotypes are promising tools to predict prognosis in AKD. The complex clinical situations in patients with AKD necessitate a comprehensive and structured approach, termed "KAMPS" (kidney function check, advocacy, medications, pressure, sick day protocols). We introduce "MAND-MASS," an acronym devised to summarize the reconciliation of medications during episodes of acute illness, as a critical component of the sick day protocols at AKD. A multidisciplinary team care, consisting of nephrologists, pharmacists, dietitians, health educators, and nurses, is an optimal model to achieve the care bundle in KAMPS. Although the evidence for patients with AKD is still lacking, several potential pharmacological agents may improve outcomes, including but not limited to angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, mineralocorticoid receptor antagonists, sodium-glucose cotransporter 2 inhibitors, and glucagon-like peptide 1 receptor agonists. In conclusion, accurate prognosis prediction and effective treatment for AKD are critical yet unmet clinical needs. Future studies are urgently needed to improve patient care in this complex and rapidly evolving field.

Using deep learning to quantify neuronal activation from single-cell and spatial transcriptomic data.

Neuronal activity-dependent transcription directs molecular processes that regulate synaptic plasticity, brain circuit development, behavioral adaptation, and long-term memory. Single cell RNA-sequencing technologies (scRNAseq) are rapidly developing and allow for the interrogation of activity-dependent transcription at cellular resolution. Here, we present NEUROeSTIMator, a deep learning model that integrates transcriptomic signals to estimate neuronal activation in a way that we demonstrate is associated with Patch-seq electrophysiological features and that is robust against differences in species, cell type, and brain region. We demonstrate this method's ability to accurately detect neuronal activity in previously published studies of single cell activity-induced gene expression. Further, we applied our model in a spatial transcriptomic study to identify unique patterns of learning-induced activity across different brain regions in male mice. Altogether, our findings establish NEUROeSTIMator as a powerful and broadly applicable tool for measuring neuronal activation, whether as a critical covariate or a primary readout of interest.

Proenkephalin as a biomarker correlates with acute kidney injury: a systematic review with meta-analysis and trial sequential analysis.

BACKGROUND: Proenkephalin A 119-159 (PENK) is freely filtered in the glomerulus with plasma levels correlating with glomerular filtration rate. Therefore, PENK has been proposed as an early indicator of acute kidney injury (AKI) although its performance is dependent on the clinical setting. This meta-analysis aimed to investigate the correlation between PENK levels and the development of AKI. METHODS: We conducted a comprehensive search on the PubMed, Embase, Cochrane databases, the website ClinicalTrials.gov and Cnki.net until June 26, 2023. Summary receiver operating characteristic (SROC) curves were used to amalgamate the overall test performance. Diagnostic odds ratio (DOR) was employed to compare the diagnostic accuracy of PENK with other biomarkers. Quality of the evidence was assessed using the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) criteria. RESULTS: We incorporated 11 observational studies with 3969 patients with an incidence of AKI of 23.4% (929 out of 3969 patients) with the best optimal cutoff value of PENK for early detection of AKI being 57.3 pmol/L. The overall sensitivity and specificity of PENK in identifying AKI were 0.69 (95% CI 0.62-0.75) and 0.76 (95% CI 0.68-0.82), respectively. The combined positive likelihood ratio (LR) stood at 2.83 (95% CI 2.06-3.88), and the negative LR was 0.41 (95% CI 0.33-0.52). The SROC curve showcased pooled diagnostic accuracy of 0.77 (95% CI 0.73-0.81). Interestingly, patients with a history of hypertension or heart failure demonstrated a lower specificity of PENK in correlating the development of AKI. CONCLUSION: Our results indicate that PENK possesses significant potential as a biomarker for the early detection of the development of AKI, using a cutoff point of 57.3 pmol/L for PENK.

Spatial transcriptomics reveals unique gene expression changes in different brain regions after sleep deprivation.

Sleep deprivation has far-reaching consequences on the brain and behavior, impacting memory, attention, and metabolism. Previous research has focused on gene expression changes in individual brain regions, such as the hippocampus or cortex. Therefore, it is unclear how uniformly or heterogeneously sleep loss affects the brain. Here, we use spatial transcriptomics to define the impact of a brief period of sleep deprivation across the brain in male mice. We find that sleep deprivation induced pronounced differences in gene expression across the brain, with the greatest changes in the hippocampus, neocortex, hypothalamus, and thalamus. Both the differentially expressed genes and the direction of regulation differed markedly across regions. Importantly, we developed bioinformatic tools to register tissue sections and gene expression data into a common anatomical space, allowing a brain-wide comparison of gene expression patterns between samples. Our results suggest that distinct molecular mechanisms acting in discrete brain regions underlie the biological effects of sleep deprivation.

Gene Expression Within a Human Choroidal Neovascular Membrane Using Spatial Transcriptomics.

Purpose: Macular neovascularization is a relatively common and potentially visually devastating complication of age-related macular degeneration. In macular neovascularization, pathologic angiogenesis can originate from either the choroid or the retina, but we have limited understanding of how different cell types become dysregulated in this dynamic process. Methods: To study how gene expression is altered in focal areas of pathology, we performed spatial RNA sequencing on a human donor eye with macular neovascularization as well as a healthy control donor. We performed differential expression to identify genes enriched within the area of macular neovascularization and used deconvolution algorithms to predict the originating cell type of these dysregulated genes. Results: Within the area of neovascularization, endothelial cells demonstrated increased expression of genes related to Rho family GTPase signaling and integrin signaling. Likewise, VEGF and TGFB1 were identified as potential upstream regulators that could drive the observed gene expression changes produced by endothelial and retinal pigment epithelium cells in the macular neovascularization donor. These spatial gene expression profiles were compared to previous single-cell gene expression experiments in human age-related macular degeneration as well as a model of laser-induced neovascularization in mice. As a secondary aim, we investigated regional gene expression patterns within the macular neural retina and between the macular and peripheral choroid. Conclusions: Overall, this study spatially analyzes gene expression across the retina, retinal pigment epithelium, and choroid in health and describes a set of candidate molecules that become dysregulated in macular neovascularization.

Activity-induced gene expression in the human brain.

Activity-induced gene expression underlies synaptic plasticity and brain function. Here, using molecular sequencing techniques, we define activity-dependent transcriptomic and epigenomic changes at the tissue and single-cell level in the human brain following direct electrical stimulation of the anterior temporal lobe in patients undergoing neurosurgery. Genes related to transcriptional regulation and microglia-specific cytokine activity displayed the greatest induction pattern, revealing a precise molecular signature of neuronal activation in the human brain.

Disease-specific selective vulnerability and neuroimmune pathways in dementia revealed by single cell genomics.

The development of successful therapeutics for dementias requires an understanding of their shared and distinct molecular features in the human brain. We performed single-nuclear RNAseq and ATACseq in Alzheimer disease (AD), Frontotemporal degeneration (FTD), and Progressive Supranuclear Palsy (PSP), analyzing 40 participants, yielding over 1.4M cells from three brain regions ranging in vulnerability and pathological burden. We identify 35 shared disease-associated cell types and 14 that are disease-specific, replicating those previously identified in AD. Disease - specific cell states represent molecular features of disease-specific glial-immune mechanisms and neuronal vulnerability in each disorder, layer 4/5 intra-telencephalic neurons in AD, layer 2/3 intra-telencephalic neurons in FTD, and layer 5/6 near-projection neurons in PSP. We infer intrinsic disease-associated gene regulatory networks, which we empirically validate by chromatin footprinting. We find that causal genetic risk acts in specific neuronal and glial cells that differ across disorders, primarily non-neuronal cells in AD and specific neuronal subtypes in FTD and PSP. These data illustrate the heterogeneous spectrum of glial and neuronal composition and gene expression alterations in different dementias and identify new therapeutic targets by revealing shared and disease-specific cell states.

Oxidative stress mediates the inhibitory effects of Manzamine A on uterine leiomyoma cell proliferation and extracellular matrix deposition via SOAT inhibition.

Uterine fibroids, the most common benign tumors of the myometrium in women, are characterized by abnormal extracellular matrix deposition and uterine smooth muscle cell neoplasia, with high recurrence rates. Here, we investigated the potential of the marine natural product manzamine A (Manz A), which has potent anti-cancer effects, as a treatment for uterine fibroids. Manz A inhibited leiomyoma cell proliferation in vitro and in vivo by arresting cell cycle progression and inducing caspase-mediated apoptosis. We performed target prediction analysis and identified sterol o-acyltransferases (SOATs) as potential targets of Manz A. Cholesterol esterification and lipid droplet formation were reduced by Manz A, in line with reduced SOAT expression. As a downstream target of SOAT, Manz A also prevented extracellular matrix deposition by inhibiting the ß-catenin/fibronectin/metalloproteinases axis and enhanced autophagy turnover. Excessive free fatty acid accumulation by SOAT inhibition led to reactive oxygen species to impair mitochondrial oxidative phosphorylation and trigger endoplasmic reticulum stress via PERK/eIF2α/CHOP signaling. The inhibitory effect of ManzA on cell proliferation was partially restored by PERK knockdown and eliminated by tauroursodeoxycholic acid, suggesting oxidative stress plays a critical role in the mechanism of action of Manz A. These findings suggest that targeting SOATs by Manz A may be a promising therapeutic approach for uterine fibroids.

Mapping the spatial transcriptomic signature of the hippocampus during memory consolidation.

Memory consolidation involves discrete patterns of transcriptional events in the hippocampus. Despite the emergence of single-cell transcriptomic profiling techniques, mapping the transcriptomic signature across subregions of the hippocampus has remained challenging. Here, we utilized unbiased spatial sequencing to delineate transcriptome-wide gene expression changes across subregions of the dorsal hippocampus of male mice following learning. We find that each subregion of the hippocampus exhibits distinct yet overlapping transcriptomic signatures. The CA1 region exhibited increased expression of genes related to transcriptional regulation, while the DG showed upregulation of genes associated with protein folding. Importantly, our approach enabled us to define the transcriptomic signature of learning within two less-defined hippocampal subregions, CA1 stratum radiatum, and oriens. We demonstrated that CA1 subregion-specific expression of a transcription factor subfamily has a critical functional role in the consolidation of long-term memory. This work demonstrates the power of spatial molecular approaches to reveal simultaneous transcriptional events across the hippocampus during memory consolidation.

GENE EXPRESSION WITHIN A HUMAN CHOROIDAL NEOVASCULAR MEMBRANE USING SPATIAL TRANSCRIPTOMICS.

Macular neovascularization is a relatively common and potentially visually devastating complication of age-related macular degeneration. In macular neovascularization, pathologic angiogenesis can originate from either the choroid or the retina, but we have limited understanding of how different cell types become dysregulated in this dynamic process. In this study, we performed spatial RNA sequencing on a human donor eye with macular neovascularization as well as a healthy control donor. We identified genes enriched within the area of macular neovascularization and used deconvolution algorithms to predict the originating cell type of these dysregulated genes. Within the area of neovascularization, endothelial cells were predicted to increase expression of genes related to Rho family GTPase signaling and integrin signaling. Likewise, VEGF and TGFB1 were identified as potential upstream regulators that could drive the observed gene expression changes produced by endothelial and retinal pigment epithelium cells in the macular neovascularization donor. These spatial gene expression profiles were compared to previous single-cell gene expression experiments in human age-related macular degeneration as well as a model of laser-induced neovascularization in mice. As a secondary aim, we also investigated spatial gene expression patterns within the macular neural retina and between the macular and peripheral choroid. We recapitulated previously described regional-specific gene expression patterns across both tissues. Overall, this study spatially analyzes gene expression across the retina, retinal pigment epithelium, and choroid in health and describes a set of candidate molecules that become dysregulated in macular neovascularization.

One-Pot Knoevenagel/Imination/6π-Azaelectrocyclization Sequence for the Synthesis of Disubstituted Nicotinonitriles.

We report on a copper-catalyzed three-component reaction for the synthesis of disubstituted nicotinonitriles using 3-bromopropenals, benzoylacetonitriles, and ammonium acetate (NH4OAc). The Knoevenagel-type condensation of 3-bromopropenals with benzoylacetonitriles gives δ-bromo-2,4-dienones that contain strategically placed functional groups that react with the ammonia generated in situ to give the corresponding azatrienes. These azatrienes can then be transformed into trisubstituted pyridines under the reaction conditions via a reaction sequence involving 6π-azaelectrocyclization and aromatization.

A Pilot Study of Ex Vivo Human Prefrontal RNA Transcriptomics in Parkinson's Disease.

Parkinson's disease (PD) can dramatically change cortical neurophysiology. The molecular basis for PD-related cortical changes is unclear because gene expression data are usually derived from postmortem tissue collected at the end of a complex disease and they profoundly change in the minutes after death. Here, we studied cortical changes in tissue from the prefrontal cortex of living PD patients undergoing deep-brain stimulation implantation surgery. We examined 780 genes using the NanoString nCounter platform and found that 40 genes were differentially expressed between PD (n = 12) and essential tremor (ET; n = 9) patients. One of these 40 genes, STAT1, correlated with intraoperative 4-Hz rhythms and intraoperative performance of an oddball reaction-time task. Using a pre-designed custom panel of 780 targets, we compared these intraoperative data with those from a separate cohort of fresh-frozen tissue from the same frontal region in postmortem human PD donors (n = 6) and age-matched neurotypical controls (n = 6). This cohort revealed 279 differentially expressed genes. Fifteen of the 40 intraoperative PD-specific genes overlapped with postmortem PD-specific genes, including CALB2 and FOXP2. Transcriptomic analyses identified pathway changes in PD that had not been previously observed in postmortem cases. These molecular signatures of cortical function and dysfunction may help us better understand cognitive and neuropsychiatric aspects of PD.

Mapping the spatial transcriptomic signature of the hippocampus during memory consolidation.

Memory consolidation involves discrete patterns of transcriptional events in the hippocampus. Despite the emergence of single-cell transcriptomic profiling techniques, defining learning-responsive gene expression across subregions of the hippocampus has remained challenging. Here, we utilized unbiased spatial sequencing to elucidate transcriptome-wide changes in gene expression in the hippocampus following learning, enabling us to define molecular signatures unique to each hippocampal subregion. We find that each subregion of the hippocampus exhibits distinct yet overlapping transcriptomic signatures. Although the CA1 region exhibited increased expression of genes related to transcriptional regulation, the DG showed upregulation of genes associated with protein folding. We demonstrate the functional relevance of subregion-specific gene expression by genetic manipulation of a transcription factor selectively in the CA1 hippocampal subregion, leading to long-term memory deficits. This work demonstrates the power of using spatial molecular approaches to reveal transcriptional events during memory consolidation.

Spatial transcriptomics reveals unique gene expression changes in different brain regions after sleep deprivation.

Sleep deprivation has far-reaching consequences on the brain and behavior, impacting memory, attention, and metabolism. Previous research has focused on gene expression changes in individual brain regions, such as the hippocampus or cortex. Therefore, it is unclear how uniformly or heterogeneously sleep loss affects the brain. Here, we use spatial transcriptomics to define the impact of a brief period of sleep deprivation across the brain. We find that sleep deprivation induced pronounced differences in gene expression across the brain, with the greatest changes in the hippocampus, neocortex, hypothalamus, and thalamus. Both the differentially expressed genes and the direction of regulation differed markedly across regions. Importantly, we developed bioinformatic tools to register tissue sections and gene expression data into a common anatomical space, allowing a brain-wide comparison of gene expression patterns between samples. Our results suggest that distinct molecular mechanisms acting in discrete brain regions underlie the biological effects of sleep deprivation.

Visualizing Neurons Under Tension In Vivo with Optogenetic Molecular Force Sensors.

The visualization of mechanical stress distribution in specific molecular networks within a living and physiologically active cell or animal remains a formidable challenge in mechanobiology. The advent of fluorescence-resonance energy transfer (FRET)-based molecular tension sensors overcame a significant hurdle that now enables us to address previously technically limited questions. Here, we describe a method that uses genetically encoded FRET tension sensors to visualize the mechanics of cytoskeletal networks in neurons of living animals with sensitized emission FRET and confocal scanning light microscopy. This method uses noninvasive immobilization of living animals to image neuronal ß-spectrin cytoskeleton at the diffraction limit, and leverages multiple imaging controls to verify and underline the quality of the measurements. In combination with a semiautomated machine-vision algorithm to identify and trace individual neurites, our analysis performs simultaneous calculation of FRET efficiencies and visualizes statistical uncertainty on a pixel by pixel basis. Our approach is not limited to genetically encoded spectrin tension sensors, but can also be used for any kind of ratiometric imaging in neuronal cells both in vivo and in vitro.

Volumetric imaging of fast cellular dynamics with deep learning enhanced bioluminescence microscopy.

Bioluminescence microscopy is an appealing alternative to fluorescence microscopy, because it does not depend on external illumination, and consequently does neither produce spurious background autofluorescence, nor perturb intrinsically photosensitive processes in living cells and animals. The low photon emission of known luciferases, however, demands long exposure times that are prohibitive for imaging fast biological dynamics. To increase the versatility of bioluminescence microscopy, we present an improved low-light microscope in combination with deep learning methods to image extremely photon-starved samples enabling subsecond exposures for timelapse and volumetric imaging. We apply our method to image subcellular dynamics in mouse embryonic stem cells, epithelial morphology during zebrafish development, and DAF-16 FoxO transcription factor shuttling from the cytoplasm to the nucleus under external stress. Finally, we concatenate neural networks for denoising and light-field deconvolution to resolve intracellular calcium dynamics in three dimensions of freely moving Caenorhabditis elegans.

Structure-based virtual screening discovers novel kidney-type glutaminase inhibitors.

Glutaminase (GLS) serves a critical bioenergetic role for malignant tumor growth and has become a valuable therapeutic target for cancer treatment. Herein, we performed a structure-based virtual screening to discover novel GLS inhibitors and provide information for developing new GLS inhibitors. We identified critical pharmacological interactions in the GLS1 binding site by analyzing the known GLS1 inhibitors and selected potential inhibitors based on their docking score and pharmacological interactions. The inhibitory effects of compounds were further confirmed by enzymatic and cell viability assays. We treated colorectal cancer and triple-negative breast cancer cells with the selected candidates and measured the inhibitory efficacy of hit compounds on cell viability. In total, we identified three GLS1 inhibitors. The compounds identified from our structure-based virtual screening methodology exhibited great anticancer potential as a lead targeting glutamine metabolism.