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Neural tube defects (NTDs), including anencephaly and spina bifida, are common major malformations of fetal development resulting from incomplete closure of the neural tube. These conditions lead to either universal death (anencephaly) or severe lifelong complications (spina bifida). Despite hundreds of genetic mouse models of neural tube defect phenotypes, the genetics of human NTDs are poorly understood. Furthermore, pharmaceuticals, such as antiseizure medications, have been found clinically to increase the risk of NTDs when administered during pregnancy. Therefore, a model that recapitulates human neurodevelopment would be of immense benefit to understand the genetics underlying NTDs and identify teratogenic mechanisms. Using our self-organizing single rosette cortical organoid (SOSR-COs) system, we have developed a high-throughput image analysis pipeline for evaluating the SOSR-CO structure for NTD-like phenotypes. Similar to small molecule inhibition of apical constriction, the antiseizure medication valproic acid (VPA), a known cause of NTDs, increases the apical lumen size and apical cell surface area in a dose-responsive manner. GSK3β and HDAC inhibitors caused similar lumen expansion; however, RNA sequencing suggests VPA does not inhibit GSK3β at these concentrations. The knockout of SHROOM3, a well-known NTD-related gene, also caused expansion of the lumen, as well as reduced f-actin polarization. The increased lumen sizes were caused by reduced cell apical constriction, suggesting that impingement of this process is a shared mechanism for VPA treatment and SHROOM3-KO, two well-known causes of NTDs. Our system allows the rapid identification of NTD-like phenotypes for both compounds and genetic variants and should prove useful for understanding specific NTD mechanisms and predicting drug teratogenicity.

Machine learning applied to digital pathology has been increasingly used to assess kidney function and diagnose the underlying cause of chronic kidney disease (CKD). We developed a novel computational framework, Clustering-based Spatial Analysis (CluSA), that leverages unsupervised learning to learn spatial relationships between local visual patterns in kidney tissue. This framework minimizes the need for time-consuming and impractical expert annotations. To incorporate spatial information over the clustered image patterns on the biopsy sample, we spatially encoded clustered patterns with colors and performed spatial analysis through graph neural network. A random forest classifier with various groups of features were used to predict CKD. For predicting high/low eGFR at the biopsy, we achieved a sensitivity of 0.97, specificity of 0.90, and accuracy of 0.95. AUC was 0.96. For predicting eGFR changes in one-year, we achieved a sensitivity of 0.83, specificity of 0.85, and accuracy of 0.84. AUC was 0.85. This study presents the first spatial analysis based on unsupervised machine learning algorithms. Without expert annotation, CluSA framework can not only accurately classify and predict the degree of kidney function at the biopsy and in one year, but also identify novel predictors of kidney function and renal prognosis.

Kidney fibrosis constitutes the shared final pathway of nearly all chronic nephropathies, but biomarkers for the non-invasive assessment of kidney fibrosis are currently not available. To address this, we characterize five candidate biomarkers of kidney fibrosis: Cadherin-11 (CDH11), Sparc-related modular calcium binding protein-2 (SMOC2), Pigment epithelium-derived factor (PEDF), Matrix-Gla protein, and Thrombospondin-2. Gene expression profiles in single-cell and single-nucleus RNA-sequencing (sc/snRNA-seq) datasets from rodent models of fibrosis and human chronic kidney disease (CKD) were explored, and Luminex-based assays for each biomarker were developed. Plasma and urine biomarker levels were measured using independent prospective cohorts of CKD: the Boston Kidney Biopsy Cohort, a cohort of individuals with biopsy-confirmed semiquantitative assessment of kidney fibrosis, and the Seattle Kidney Study, a cohort of patients with common forms of CKD. Ordinal logistic regression and Cox proportional hazards regression models were used to test associations of biomarkers with interstitial fibrosis and tubular atrophy and progression to end-stage kidney disease and death, respectively. Sc/snRNA-seq data confirmed cell-specific expression of biomarker genes in fibroblasts. After multivariable adjustment, higher levels of plasma CDH11, SMOC2, and PEDF and urinary CDH11 and PEDF were significantly associated with increasing severity of interstitial fibrosis and tubular atrophy in the Boston Kidney Biopsy Cohort. In both cohorts, higher levels of plasma and urinary SMOC2 and urinary CDH11 were independently associated with progression to end-stage kidney disease. Higher levels of urinary PEDF associated with end-stage kidney disease in the Seattle Kidney Study, with a similar signal in the Boston Kidney Biopsy Cohort, although the latter narrowly missed statistical significance. Thus, we identified CDH11, SMOC2, and PEDF as promising non-invasive biomarkers of kidney fibrosis.

Amyloid‐β peptide (Aβ) oligomers are pathogenic species of amyloid aggregates in Alzheimer's disease. Like certain protein toxins, Aβ oligomers permeabilize cellular membranes, presumably through a pore formation mechanism. Owing to their structural and stoichiometric heterogeneity, the structure of these pores remains to be characterized. We studied a functional Aβ42‐pore equivalent, created by fusing Aβ42 to the oligomerizing, soluble domain of the α‐hemolysin (αHL) toxin. Our data reveal Aβ42‐αHL oligomers to share major structural, functional, and biological properties with wild‐type Aβ42‐pores. Single‐particle cryo‐EM analysis of Aβ42‐αHL oligomers (with an overall 3.3 Å resolution) reveals the Aβ42‐pore region to be intrinsically flexible. The Aβ42‐αHL oligomers will allow many of the features of the wild‐type amyloid oligomers to be studied that cannot be otherwise, and may be a highly specific antigen for the development of immuno‐base diagnostics and therapies., A functional and structural Aβ42‐oligomer equivalent, created by fusing Aβ42 to the oligomerizing, soluble domain of the α‐hemolysin (αHL) toxin, shares major structural, functional, and biological properties with Alzheimer's wild‐type Aβ42 oligomers. Cryo‐EM studies reveal that the αHL‐displayed oligomers structurally remain in a well‐defined stoichiometry, providing a stable mimetic antigen for the development of conformation‐specific antibodies.

COVID-19 morbidity and mortality are increased in patients with diabetes and kidney disease via unknown mechanisms. SARS-CoV-2 uses angiotensin-converting enzyme 2 (ACE2) for entry into host cells. Since ACE2 is a susceptibility factor for infection, we investigated how diabetic kidney disease and medications alter ACE2 receptor expression in kidneys. Single cell RNA profiling of healthy living donor and kidney biopsies from patients with diabetic kidney disease revealed ACE2 expression primarily in proximal tubular epithelial cells. This cell specific localization was confirmed by in situ hybridization. ACE2 expression levels were unaltered by exposures to renin angiotensin aldosterone system inhibitors in diabetic kidney disease. Bayesian integrative analysis of a large compendium of public-omics datasets identified molecular network modules induced in ACE2-expressing proximal tubular epithelial cells in diabetic kidney disease (searchable at hb.flatironinstitute.org/covid-kidney) that were linked to viral entry, immune activation, endomembrane reorganization, and RNA processing. The diabetic kidney disease ACE2-positive proximal tubular epithelial cell module overlapped with expression patterns seen in SARS-CoV-2 infected cells. Similar cellular programs were seen in ACE2-positive proximal tubular epithelial cells obtained from urine samples of 13hospitalized patients with COVID-19, suggesting a consistent ACE2-coregulated proximal tubular epithelial cell expression program that may interact with the SARS-CoV-2 infection processes. Thus SARS-CoV-2 receptor networks can seed further research into risk stratification and therapeutic strategies for COVID-19 related kidney damage., Graphical abstract

The tight binding of Cu and Zn ions to superoxide dismutase 1 (SOD1) maintains the protein stability, associated with amyotrophic lateral sclerosis (ALS). Yet, the quantitative studies remain to be explored for the metal-binding affinity of wild-type SOD1 and its mutants. We have investigated the demetallation of Cu,Zn-SOD1 and its ALS-related G93A mutant in the presence of different standard metal ion chelators at varying temperatures by using an LC-ICP MS-based approach and fast size-exclusion chromatography. Our results showed that from the slow first-order kinetics both metal ions Zn

α-synuclein protein aggregates are the major constituent of Lewy bodies, which is a main pathogenic hallmark of Parkinson's disease. Both lipid membranes and Cu

Misfolding of the human protein α-synuclein results in toxic fibrils and the aggregation of Lewy bodies, which are a hallmark of Parkinson's disease in brain tissue. Here we disclose a supramolecular approach where peptidomimetics are rationally designed and pre-organised to recognize the surface of native helical α-Syn by forming complementary contacts with key patches of protein surface composed of charged and hydrophobic residues. Under lipid-catalyzed conditions the mimetics slow the rate of aggregation (thioflavin-T assay) and disrupt the misfolding pathway (electron microscopy of aggregates). This hypothesis is supported by comparison with a series of negative control compounds and with circular dichroism spectroscopy. Given the approach relies on selective recognition of both amino acid sequence and conformation (helical secondary structure) there is potential to develop these compounds as tools to unravel the currently intractable structure-function relationships of (i) missense mutation, and (ii) amyloid polymorphism with disease pathogenesis.

Mimics of natural antimicrobial peptides are promising compounds to fight the rising threat of multi-drug resistant bacteria. Here we report the design, synthesis and conformational analysis of a new class of antimicrobial peptide mimetics incorporating a diphenylacetylene scaffold. Within a small set of compounds, we observe a correlation between amphiphilicity, the efficiency of partitioning into negatively charged membranes and antibacterial activity. The most amphiphilic compound, which contains four isoleucine residues and four lysine residues, displays species-selective antibacterial activity (most active against Bacillus subtills) and low haemolytic activity. Solution-phase conformational analysis of this compound indicates that a defined structure is adopted in the presence of negatively charged phospholipid membranes and aqueous 2,2,2-trifluoroethanol but not in water. A conformation model indicates that the cationic and hydrophobic functional groups are segregated. These results may inform the development of highly selective antimicrobial peptide mimetics for therapeutic applications.

Prion disease is a fatal neurodegenerative disorder, in which the cellular prion protein PrPC is converted to a misfolded prion which in turn is hypothesized to permeabilize cellular membranes. The pathways leading to toxicity in prion disease are not yet completely elucidated and whether it also includes formation of membrane pores remains to be answered. Prion protein consists of two domains: a globular domain (GD) and a flexible N-terminus (FT) domain. Although a proximal nine polybasic amino acid (FT(23-31)) sequence of FT is a prerequisite for cellular membrane permeabilization, other functional domain regions may influence FT(23-31) and its permeabilization. By using single-channel electrical recordings, we reveal that FT(23-50) dominates the membrane permeabilization within the full-length mouse PrP (mPrP(23-230)). The other domain of FT(51-110) or C-terminal domain down-regulates the channel activity of FT(23-50) and the full-length mouse PrP (mPrP(23-230)). The addition of prion mimetic antibody, POM1 significantly enhances mPrP(23-230) membrane permeabilization, whereas POM1-Y104A, a POM1 mutant that binds to PrP but cannot elicit toxicity has negligible effect on membrane permeabilization. Additionally, anti-N-terminal antibody POM2 or Cu2+ stabilizes FT domain, thus provoking FT(23-110) channel activity. Furthermore, our setup provides a more direct method without an external fused protein to study the channel activity of truncated PrP in the lipid membranes. We therefore hypothesize that the primary N-terminal residues are essential for membranes permeabilization and other functional segments play a vital role to modulate the pathological effects of PrP-medicated neurotoxicity. This may yield essential insights into molecular mechanisms of prion neurotoxicity to cellular membranes in prion disease.

The lipid-α-Synuclein (α-Syn) interaction plays a crucial role in the pathogenesis of Parkinson’s disease. Here, we trap α-Syn at a conjunction of an α-hemolysin (αHL) single nanopore-lipid to investigate the folding and unfolding kinetics of α-Syn in a lipidic environment. The hybridized α-Syn is generated through a reaction between a 5’-thiol-modified nucleotide oligo (dC30) and the α-Syn mutant (A140C). Owing to an applied voltage, single-molecule hybridized α-Syn can be trapped at the single nanopore. The trapping events are associated with dielectrophoretic force. The folding and unfolding events of α-Syn can be observed at the pore-membrane junction through interpretation of blockade current amplitudes and dwell time. This can be related to the protein quaternary structure influenced by the α-Syn-membrane interaction, allowing further analysis of α-Syn conformational dynamics. We studied how disease associated metal ions (Cu2+, Zn2+) modulate folding and unfolding of α-Syn at the interface of the membranes and pore, and how α-helical peptidomimetics stabilize the helical conformation of α-Syn in the presence of a membrane. These studies aid our understanding of the complexity of the interaction of α-Syn, lipid membranes and metal ions, and in using peptidomimetics, a new strategy against α-Syn toxicity and aggregation is advanced.

Increased podocyte detachment begins immediately after kidney transplantation and is associated with long-term allograft failure. We hypothesized that cell-specific transcriptional changes in podocytes and glomerular endothelial cells after transplantation would offer mechanistic insights into the podocyte detachment process. To test this, we evaluated cell-specific transcriptional profiles of glomerular endothelial cells and podocytes from 14 patients of their first-year surveillance biopsies with normal histology from low immune risk recipients with no post-transplant complications and compared these to biopsies of 20 healthy living donor controls. Glomerular endothelial cells from these surveillance biopsies were enriched for genes related to fluid shear stress, angiogenesis, and interferon signaling. In podocytes, pathways were enriched for genes in response to growth factor signaling and actin cytoskeletal reorganization but also showed evidence of podocyte stress as indicated by reduced nephrin (adhesion protein) gene expression. In parallel, transcripts coding for proteins required to maintain podocyte adherence to the underlying glomerular basement membrane were downregulated, including the major glomerular podocyte integrin α3 and the actin cytoskeleton-related gene synaptopodin. The reduction in integrin α3 protein expression in surveillance biopsies was confirmed by immunoperoxidase staining. The combined growth and stress response of patient allografts post-transplantation paralleled similar changes in a rodent model of nephrectomy-induced glomerular hypertrophic stress that progress to develop proteinuria and glomerulosclerosis with shortened kidney life span. Thus, even among patients with apparently healthy allografts with no detectable histologic abnormality including alloimmune injury, transcriptomic changes reflecting cell stresses are already set in motion that could drive hypertrophy-associated glomerular disease progression.

Pathologists use visual classification to assess patient kidney biopsy samples when diagnosing the underlying cause of kidney disease. However, the assessment is qualitative, or semi-quantitative at best, and reproducibility is challenging. To discover previously unknown features which predict patient outcomes and overcome substantial interobserver variability, we developed an unsupervised bag-of-words model. Our study applied to the C-PROBE cohort of patients with chronic kidney disease (CKD). 107,471 histopathology images were obtained from 161 biopsy cores and identified important morphological features in biopsy tissue that are highly predictive of the presence of CKD both at the time of biopsy and in one year. To evaluate the performance of our model, we estimated the AUC and its 95% confidence interval. We show that this method is reliable and reproducible and can achieve 0.93 AUC at predicting glomerular filtration rate at the time of biopsy as well as predicting a loss of function at one year. Additionally, with this method, we ranked the identified morphological features according to their importance as diagnostic markers for chronic kidney disease. In this study, we have demonstrated the feasibility of using an unsupervised machine learning method without human input in order to predict the level of kidney function in CKD. The results from our study indicate that the visual dictionary, or visual image pattern, obtained from unsupervised machine learning can predict outcomes using machine-derived values that correspond to both known and unknown clinically relevant features.

The amyloid-β (Aβ) peptides are key molecules in Alzheimer’s disease (AD) pathology. They interact with cellular membranes, and can bind metal ions outside the membrane. Certain oligomeric Aβ aggregates are known to induce membrane perturbations and the structure of these oligomers—and their membrane-perturbing effects—can be modulated by metal ion binding. If the bound metal ions are redox active, as e.g., Cu and Fe ions are, they will generate harmful reactive oxygen species (ROS) just outside the membrane surface. Thus, the membrane damage incurred by toxic Aβ oligomers is likely aggravated when redox-active metal ions are present. The combined interactions between Aβ oligomers, metal ions, and biomembranes may be responsible for at least some of the neuronal death in AD patients.

Leaf area index (LAI) is a key parameter of vegetation structure in the fields of agriculture, forestry, and ecology. Optical indirect methods based on the Beer-Lambert law are widely adopted in numerous fields given their high efficiency and feasibility for LAI estimation. These methods have undergone considerable progress in the past decades, thereby making them operational in ground-based LAI measurement and even in airborne estimation. However, several challenges remain, given the requirement of increasing accuracy and new applications. Clumping effect correction attained significant progress for continuous canopies with non-randomly disturbed leaves while non-continuous canopies are rarely studied. Convenient and operational measurement of leaf angle distribution and woody components is lacked. Accurate and comprehensive validations are still very difficult due to the limitations of direct measurement. The introduction of active laser scanning technology is a driving force for addressing several challenges, but its three-dimensional information has not been fully explored and utilized. In order to update the general knowledge and identify the possible error source, this study comprehensively reviews the temporal development, theoretical framework, and issues of indirect LAI measurement, followed by current methods, instruments, and platforms. Latest methods and instruments are introduced and compared to traditional ones. Current challenges, recent advances, and future perspectives are discussed to provide recommendations for further research.

Sphingosine-1-phosphate lyase insufficiency syndrome (SPLIS) is a rare metabolic disorder caused by inactivating mutations in sphingosine-1-phosphate lyase 1 (SGPL1), which is required for the final step of sphingolipid metabolism. SPLIS features include steroid-resistant nephrotic syndrome and impairment of neurological, endocrine, and hematopoietic systems. Many affected individuals die within the first 2 years. No targeted therapy for SPLIS is available. We hypothesized that SGPL1 gene replacement would address the root cause of SPLIS, thereby serving as a universal treatment for the condition. As proof of concept, we evaluated the efficacy of adeno-associated virus 9-mediated transfer of human SGPL1 (AAV-SPL) given to newborn Sgpl1-KO mice that model SPLIS and die in the first weeks of life. Treatment dramatically prolonged survival and prevented nephrosis, neurodevelopmental delay, anemia, and hypercholesterolemia. STAT3 pathway activation and elevated proinflammatory and profibrogenic cytokines observed in KO kidneys were attenuated by treatment. Plasma and tissue sphingolipids were reduced in treated compared with untreated KO pups. SGPL1 expression and activity were measurable for at least 40 weeks. In summary, early AAV-SPL treatment prevents nephrosis, lipidosis, and neurological impairment in a mouse model of SPLIS. Our results suggest that SGPL1 gene replacement holds promise as a durable and universal targeted treatment for SPLIS.

The aim was to investigate the diagnostic efficacy of urinary protein/creatinine ratio (UPCR) and factors influencing its substitutability of 24-h urine protein (24hUP) in children with proteinuria. A total of 356 children were recruited, including 149 with non-nephrotic-range proteinuria and 207 with nephrotic-range proteinuria which were further divided into Henoch–Schonlein purpura nephritis (HSPN), lupus nephritis (LN), and primary nephrotic syndrome (PNS). The urine protein and creatinine were measured by routine methods. Bland–Altman analysis was used to test the agreement. Spearman correlation was performed to evaluate the relevance. The receiver operating characteristic curve was used to analyze the diagnostic efficacy of UPCR. Bland–Altman analysis showed there was an excellent agreement between UPCR and 24hUP in each group. Correlations between UPCR and 24hUP were strong in 356 children (r = 0.869) and in the non-nephrotic-range proteinuria group (r = 0.806), but moderate in nephrotic-range proteinuria group (r = 0.586). With the increase of nephrotic-range proteinuria, the correlations between UPCR and 24hUP were decreased further, however, after UPCR was adjusted by 24-h urine creatinine (24hUCr), the correlation coefficient was improved (r = 0.682). In three subgroups with nephrotic-range proteinuria, high correlation coefficient (r = 0.731) was observed in HSPN, but not in LN (r = 0.552) and PNS (r = 0.563). The sensitivity and specificity of UPCR for diagnosing nephrotic-range proteinuria were 89.9 % and 92.2%. UPCR is competent in evaluating proteinuria. The degree of proteinuria, 24hUCr and the underlying pathological types of renal disease may be the important influencing factors in the correlation between UPCR and 24hUP in children with nephrotic-range proteinuria.

Both leaf inclination angle distribution (LAD) and leaf area index (LAI) dominate optical remote sensing signals. The G-function, which is a function of LAD and remote sensing geometry, is often set to 0.5 in the LAI retrieval of coniferous canopies even though this assumption is only valid for spherical LAD. Large uncertainties are thus introduced. However, because numerous tiny leaves grow on conifers, it is nearly impossible to quantitatively evaluate such uncertainties in LAI retrieval. In this study, we proposed a method to characterize the possible change of G-function of coniferous canopies as well as its effect on LAI retrieval. Specifically, a Multi-Directional Imager (MDI) was developed to capture stereo images of the branches, and the needles were reconstructed. The accuracy of the inclination angles calculated from the reconstructed needles was high. Moreover, we analyzed whether a spherical distribution is a valid assumption for coniferous canopies by calculating the possible range of the G-function from the measured LADs of branches of Larch and Spruce and the true G-functions of other species from some existing inventory data and three-dimensional (3D) tree models. Results show that the constant G assumption introduces large errors in LAI retrieval, which could be as large as 53% in the zenithal viewing direction used by spaceborne LiDAR. As a result, accurate LAD estimation is recommended. In the absence of such data, our results show that a viewing zenith angle between 45 and 65 degrees is a good choice, at which the errors of LAI retrieval caused by the spherical assumption will be less than 10% for coniferous canopies.

Comprehensive and spatially mapped molecular atlases of organs at a cellular level are a critical resource to gain insights into pathogenic mechanisms and personalized therapies for diseases. The Kidney Precision Medicine Project (KPMP) is an endeavor to generate three-dimensional (3-D) molecular atlases of healthy and diseased kidney biopsies by using multiple state-of-the-art omics and imaging technologies across several institutions. Obtaining rigorous and reproducible results from disparate methods and at different sites to interrogate biomolecules at a single-cell level or in 3-D space is a significant challenge that can be a futile exercise if not well controlled. We describe a “follow the tissue” pipeline for generating a reliable and authentic single-cell/region 3-D molecular atlas of human adult kidney. Our approach emphasizes quality assurance, quality control, validation, and harmonization across different omics and imaging technologies from sample procurement, processing, storage, shipping to data generation, analysis, and sharing. We established benchmarks for quality control, rigor, reproducibility, and feasibility across multiple technologies through a pilot experiment using common source tissue that was processed and analyzed at different institutions and different technologies. A peer review system was established to critically review quality control measures and the reproducibility of data generated by each technology before their being approved to interrogate clinical biopsy specimens. The process established economizes the use of valuable biopsy tissue for multiomics and imaging analysis with stringent quality control to ensure rigor and reproducibility of results and serves as a model for precision medicine projects across laboratories, institutions and consortia.