Biology subjects

Liu, E.

Publications and source records attributed to Liu, E..

At least 37 records · Page 2Linked to original sources

bioRxiv2025

G1 and G2 ApolipoproteinL1 modulate macrophage inflammation and lipid accumulation through the polyamine pathway

The G1 and G2 variants of the gene encoding Apolipoprotein L1 (APOL1) increase risk for kidney disease and cardiometabolic traits. While previous studies have elucidated key mechanisms by which G1 and G2 APOL1 cause cellular inflammation and cytotoxicity, it remains unclear whether these mechanisms drive inflammation in G1 and G2 macrophages. In this study, we used mouse bone-marrow-derived macrophages and human induced pluripotent stem cell-derived macrophages to identify altered immune signaling and inflammatory activation caused by G1 and G2 APOL1. We demonstrated that G1 and G2 APOL1 increased lipid accumulation, pro-inflammatory cytokine expression, and inflammasome signaling; this inflammatory response was sustained when treated with anti-inflammatory cytokines IL-4 and IL-10. Additionally, in G1 and G2 macrophages we observed increased mitochondrial size and elongation, oxidative phosphorylation, and glycolysis. Finally, we used unbiased metabolite analysis to identify an accumulation of polyamine spermidine and the enrichment of the spermidine synthesis pathway in G1 and G2 macrophages. When treated with polyamine inhibitor -difluoromethylornithine (DFMO), lipid accumulation and inflammasome gene expression decreased in G1 and G2 macrophages. Together, these findings establish the pro-inflammatory effects of G1 and G2 APOL1 in macrophages and identify a novel pathway which ameliorates G1 and G2 effects on cellular inflammation.

cell biology↗

bioRxiv2025

Learning Genetic Perturbation Effects with Variational Causal Inference

Advances in sequencing technologies have enhanced the understanding of gene regulation in cells. In particular, Perturb-seq has enabled high-resolution profiling of the transcriptomic response to genetic perturbations at the single-cell level. This understanding has implications in functional genomics and potentially for identifying therapeutic targets. Various computational models have been developed to predict perturbational effects. While deep learning models excel at interpolating observed perturbational data, they tend to overfit and may not generalize well to unseen perturbations. In contrast, mechanistic models, such as linear causal models based on gene regulatory networks, hold greater potential for extrapolation, as they encapsulate regulatory information that can predict responses to unseen perturbations. However, their application has been limited to small studies due to overly simplistic assumptions, making them less effective in handling noisy, large-scale single-cell data. We propose a hybrid approach that combines a mechanistic causal model with variational deep learning, termed Single Cell Causal Variational Autoencoder (SCCVAE). The mechanistic model employs a learned regulatory network to represent perturbational changes as shift interventions that propagate through the learned network. SCCVAE integrates this mechanistic causal model into a variational autoencoder, generating rich, comprehensive transcriptomic responses. Our results indicate that SCCVAE exhibits superior performance over current state-of-the-art baselines for extrapolating to predict unseen perturbational responses. Additionally, for the observed perturbations, the latent space learned by SCCVAE allows for the identification of functional perturbation modules and simulation of single-gene knockdown experiments of varying penetrance, presenting a robust tool for interpreting and interpolating perturbational responses at the single-cell level. Author summaryUnderstanding how genes interact and respond to perturbations is crucial for uncovering the mechanisms of cells and identifying potential ways to treat diseases. Recent advances in sequencing technologies now allow us to measure how individual cells react when specific genes are altered. However, making sense of this complex data requires advanced computational tools. In our work, we address the challenge of predicting how cells respond to potentially new untested genetic perturbations. We noticed that while deep learning models perform well on data measured before, they struggle with making predictions on new cases. On the other hand, models based on biological understanding can, in theory, make better predictions, but they often rely on overly simple assumptions that do not hold with real-world data. We developed a new method that combines the strengths of both approaches. Our model, called SCCVAE, uses knowledge of gene networks together with deep learning to better predict how cells will respond to gene changes. It can simulate new experiments and help identify groups of genes that work together. This tool could be valuable for researchers studying perturbational changes, as well as gene functions and diseases.

bioinformatics↗

bioRxiv2025

Supra-resonant wingbeats in insects

Powering small-scale flapping flight is challenging, yet insects sustain exceptionally fast wingbeats with ease. Since insects act as tiny biomechanical resonators, tuning their wingbeat frequency to the resonant frequency of their springy thorax and wings could make them more efficient fliers. But operating at resonance poses control problems and potentially constrains wingbeat frequencies within and across species. Resonance may be particularly limiting for the many orders of insects that power flight with specialized muscles that activate in response to mechanical stretch. Here, we test whether insects operate at their resonant frequency. First, we extensively characterize bumblebees and find that they surprisingly flap well above their resonant frequency via interactions between stretch-activation and mechanical resonance. Modeling and robophysical experiments then show that resonance is actually a lower bound for rapid wingbeats in most insects because muscles only pull, not push. Supra-resonance emerges as a general principle of high-frequency flight across five orders of insects from moths to flies.

zoology↗

bioRxiv2025

Improved Prediction of Bacterial Type VI Secretion Effector Proteins Using an Integrated Convolutional Neural Network Model Combining N-terminal Signal Sequences, Evolutionary Information and Pre-Trained Protein Language Features

Type VI secretion system effectors (T6SEs) are crucial for bacterial pathogenicity, making their accurate identification essential for understanding bacterial virulence mechanisms. This study analyzed the differences in amino acid composition of N-terminal signal sequences between T6SEs and non-T6SEs, uncovering distinct positional amino acid preferences in T6SEs. Using a combination of unsupervised and supervised analysis, we evaluated feature encoding methods and developed T6CNN, an ensemble model that integrates N-terminal signal sequences, evolutionary information, and pre-trained protein language features for T6SE prediction. T6CNN demonstrated outstanding performance in independent testing, outperforming existing tools with a 7.9% accuracy increase (to 0.953), a 13.2% sensitivity improvement (to 0.964), and a 6.6% specificity enhancement (to 0.951). The T6CNN model offers a reliable and accurate solution for T6SE prediction, with significant potential to advance research on bacterial pathogenicity. ImportanceThis study introduces T6CNN, a new computational model for identifying Type VI secretion system effectors used by harmful bacteria. By analyzing early protein sequences, T6CNN uncovers unique features that reliably distinguish effector proteins. Integrating evolutionary data and pre-trained protein language features, the model outperforms existing methods in accuracy, sensitivity, and specificity. This enhanced prediction tool deepens our understanding of bacterial infection mechanisms and offers researchers a valuable resource for pinpointing key virulence factors. Ultimately, T6CNN may help drive the development of more targeted antibacterial treatments and strategies to combat bacterial diseases.

bioinformatics↗

bioRxiv2025

Tarloxotinib targets tumor hypoxia to improve therapeutic efficacy of immune checkpoint inhibition in a TLR9 dependent manner

Immune checkpoint inhibitors can elicit deep and durable immune responses although most cancer patients fail to experience long-term remission. There is consensus that tumor hypoxia coordinates a multitude of underlying resistance mechanisms that contribute to treatment failure. Here we show that the clinical-stage hypoxia-activated prodrug tarloxotinib lowers tumor hypoxia 8-fold in the EGFR-dependent MB49 syngeneic tumor model, attracting a effector CD8+ T cell infiltrate into an oxygen enriched tumor microenvironment, leading to potentiation of checkpoint inhibitor activity. Various methodologies, including CD8+ T cell depletion and exogenous T cell priming confirmed that tarloxotinib has a positive impact on the function of activated CD8+ T cells. Whilst anti-PD-L1, anti-PD-1 and anti-CTLA-4 treatments all benefited from the remodeled tumor microenvironment, anti-PD-L1 was most responsive to tarloxotinib coadministration, providing a 100% complete response rate and >360% improvement in tumor growth delay (day 19; p<0.0001). This robust interaction was associated with MB49 tumor enrichment of CD8+ T cell infiltrate from 8% to 43% of the total CD45+ population. To further understand these promising observations, comparative RNA transcript analysis of 770 cancer/immune related genes in naive and tarloxotinib treated tumors highlighted the strong induction of gene clusters related to co-stimulatory signaling, cytokine/chemokine signaling, interferon signaling, immune cell adhesion/migration, antigen presentation and the lymphoid/myeloid compartments. Serum protein analysis confirmed upregulation of a mixture of cytokines/chemokines, including IL-6, IL-12p40, IFN{gamma}, G-CSF and MIP-1{beta}, amongst others. The source of this broad immunogenic response was identified as toll-like receptor 9 (TLR9) dependent, with the positive interaction between anti-PD-L1 and tarloxotinib blocked in MyD88 or TLR9 knockout mice. Further, the observed therapeutic interaction was still evident in MC38 and EG7.OVA syngeneic tumor models, both refractory to tarloxotinib by virtue of EGFR-signal independence. Consistent with the clinical experience of minimal systemic toxicities related to EGFR inhibition (e.g. diarrhea), mouse body weight loss was minimal across all in vivo studies and histopathology screening for evidence of lung fibrosis proved negative. Tarloxotinib therefore represents a systemically administered small molecule with an established clinical safety profile that is capable of activating TLR9 signaling within tumors whilst remodeling the microenvironment to facilitate efficacy of checkpoint blockade. What is already known on this topicSolid tumor resistance to immune checkpoint inhibitors (ICI) is orchestrated through a diverse collection of hypoxia-driven mechanisms. In preclinical models modifying the tumor microenvironment (TME) to lessen hypoxia typically improves responses to ICI. What this study addsTarloxotinib is a hypoxia-activated prodrug (HAP) of an irreversible EGFR/HER2 TKI that profoundly remodels the TME, both eliminating tumor hypoxia and elevating cytokine/chemokine production via a TLR9 dependent effect. Together, this results in a marked tumor influx of activated CD8+ T cells (and other TILs), that results in major improvements in the efficacy of ICIs, particularly anti-PD-L1. How this study might affect research, practice or policyTarloxotinib is the first example of a tumor-targeted small molecule TLR9 stimulant that restores ICI sensitivity in a range of syngeneic tumor models. Phase I/II data has demonstrated that tarloxotinib is well tolerated, with few EGFR-dependent (on-target) side-effects, reflecting minimization of normal tissue exposure with the tumor-targeted HAP approach. Importantly, it also optimizes tumor selective exposure, offering a compelling clinical rationale for evaluation of this combination in cancer patients experiencing ICI relapse or resistance.

immunology↗

bioRxiv2025

Sequencing-based Spatial Transcriptomics with scRNA-seq Sensitivity

The advent of spatial transcriptomics has dramatically expanded our ability to study the vast network of cell-cell interactions at the molecular level in tissue. Among current methods, sequencing-based approaches have great potential in discovering because of its unbiased capture. In the last couple of years, the spatial resolution for the capture addresses has been significantly improved from 100 m to <1 m, well below the size of a mammalian cell. However, the capture efficiency has always been a pain point, [~]one order of magnitude lower than that of single cell RNA sequencing (scRNA-seq). The low capture efficiency limits the depth and breadth of its applications in the study of complex biological systems and diseases. Here, we introduce Salus Spatial transcriptomic system (Salus-STS), which provides [~]1 m capture resolution and a capture efficiency [~]1 order of magnitude higher than other current methods. Analysis with sub-cellular resolution becomes practical for sequencing-based spatial transcriptomics.

molecular biology↗

bioRxiv2023

The expression profile and tumorigenic mechanisms of CD97 (ADGRE5) in glioblastoma render it a targetable vulnerability

Glioblastoma (GBM) is the most common and aggressive primary brain malignancy. Adhesion G protein-coupled receptors (aGPCRs) have attracted interest for their functional role in gliomagenesis and their potential as treatment targets. To identify therapeutically targetable opportunities among aGPCR family members in unbiased fashion, we analyzed expression levels of all aGPCRs in GBM and non-neoplastic brain tissue. Using bulk and single cell transcriptomic and proteomic data, we show that CD97 (ADGRE5), an aGPCR previously implicated in GBM pathogenesis, is the most promising aGPCR target in GBM, by virtue of its abundance in all GBM tumors and its de novo expression profile in GBM compared to normal brain tissue and neural progenitors. CD97 knockdown or knockout significantly reduces the tumor initiation capacity of patient-derived GBM cultures (PDGC) in vitro and in vivo. Transcriptomic and metabolomic data from PDGCs suggest that CD97 promotes glycolytic metabolism. The oncogenic and metabolic effects of CD97 are mediated by the MAPK pathway. Activation of MAPK signaling depends on phosphorylation of the cytosolic C-terminus of CD97 and recruitment of {beta}-arrestin. Using single-cell RNA-sequencing and biochemical assays, we demonstrate that THY1/CD90 is the most likely CD97 ligand in GBM. Lastly, we show that targeting of PDGCs with an anti-CD97 antibody-drug conjugate in vitro selectively kills tumor cells but not human astrocytes or neural stem cells. Our studies identify CD97 as an important regulator of tumor metabolism in GBM, elucidate mechanisms of receptor activation and signaling, and provide strong scientific rationale for developing biologics to target it for therapeutic purposes.

cancer biology↗

bioRxiv2023

Discovery of a unique pathway for glutathione utilization in Francisella

Glutathione (GSH) is an abundant metabolite that can act as a signal, a nutrient source, or serve in a redox capacity for intracellular pathogens. For Francisella, GSH is thought to be a critical in vivo source of cysteine; however, the cellular pathways permitting GSH utilization by Francisella differ between strains and have remained poorly understood. Using genetic screening, we discovered a unique pathway for GSH utilization in Francisella spp. Whereas prior work suggested GSH catabolism initiates in the periplasm, the pathway we define consists of a major facilitator superfamily member that transports intact GSH and a previously unrecognized bacterial cytoplasmic enzyme that catalyzes the first step of GSH degradation. Interestingly, we find that the transporter gene for this pathway is pseudogenized in pathogenic Francisella, explaining phenotypic discrepancies in GSH utilization among Francisella spp. and revealing a critical role for GSH in the environmental niche of these bacteria.

microbiology↗

bioRxiv2021

DSCN: double-target selection guided by CRISPR screening and network

Cancer is a complex disease with usually multiple disease mechanisms. Target combination is a better strategy than a single target in developing cancer therapies. However, target combinations are generally more difficult to be predicted. Current CRISPR-cas9 technology enables genome-wide screening for potential targets, but only a handful of genes have been screend as target combinations. Thus, an effective computational approach for selecting candidate target combinations is highly desirable. Selected target combinations also need to be translational between cell lines and cancer patients. We have therefore developed DSCN (double-target selection guided by CRISPR screening and network), a method that matches expression levels in patients and gene essentialities in cell lines through spectral-clustered protein-protein interaction (PPI) network. In DSCN, a sub-sampling approach is developed to model first-target knockdown and its impact on the PPI network, and it also facilitates the selection of a second target. Our analysis first demonstrated high correlation of the DSCN sub-sampling-based gene knockdown model and its predicted differential gene expressions using observed gene expression in 22 pancreatic cell lines before and after MAP2K1 and MAP2K2 inhibition (R2 = 0.75). In our DSCN algorithm, various scoring schemes were evaluated. The diffusion-path method showed the most significant statistical power of differentialting known synthetic lethal (SL) versus non-SL gene pairs (P = 0.001) in pancreatic cancer. The superior performance of DSCN over existing network-based algorithms, such as OptiCon[1] and VIPER[2], in the selection of target combinations is attributable to its ability to calculate combinations for any gene pairs, whereas other approaches focus on the combinations among optimized regulators in the network. DSCNs computational speed is also at least ten times faster than that of other methods. Finally, in applying DSCN to predict target combinations and drug combinations for individual samples (DSCNi), we showed high correlation of DSCNi predicted target combinations with synergistic drug combinations (P = 1e-5) in pancreatic cell lines. In summary, DSCN is a highly effective computational method for the selection of target combinations. Author SummaryCancer therapies require targets to function. Compared to single target, target combination is a better strategy for developing cancer therapies. However, predicting target combination is much complicated than predicting single target. Current CRISPR technology enables whole genome screening of potential targets. But most of the experiments have been conducted on single target (gene) level. To facilitate the prediction of target combinations, we developed DSCN (double-target selection guided by CRISPR screening and network) that utilize single target-level CRISPR screening data and expression profiles for predicting target combinations by connecting cell-line omics-data with tissue omics-data. DSCN showed great accuracy on different cancer types and superior performance compared to existing network-based prediction tools. We also introduced DSCNi derived from DSCN and designed specific for predicting target combinations for single-paitent. We showed synergistic target combinations predicted by DSCNi accurately reflected synergies on drug combination levels. Thus, DSCN and DSCNi have the potential be further applied in personalized medicine field.

bioinformatics↗

bioRxiv2021

Severity of SARS-CoV-2 infection as a function of the interferon landscape across the respiratory tract of COVID-19 patients.

The COVID-19 outbreak driven by SARS-CoV-2 has caused more than 2.5 million deaths globally, with the most severe cases characterized by over-exuberant production of immune-mediators, the nature of which is not fully understood. Interferons of the type I (IFN-I) or type III (IFN-III) families are potent antivirals, but their role in COVID-19 remains debated. Our analysis of gene and protein expression along the respiratory tract shows that IFNs, especially IFN-III, are over-represented in the lower airways of patients with severe COVID-19, while high levels of IFN-III, and to a lesser extent IFN-I, characterize the upper airways of patients with high viral burden but reduced disease risk or severity; also, IFN expression varies with abundance of the cell types that produce them. Our data point to a dynamic process of inter- and intra-family production of IFNs in COVID-19, and suggest that IFNs play opposing roles at distinct anatomical sites.

immunology↗

bioRxiv2021

Morphological principles of neuronal mitochondria

In the highly dynamic metabolic landscape of a neuron, mitochondrial membrane architectures can provide critical insight into the unique energy balance of the cell. Current theoretical calculations of functional outputs like ATP and heat often represent mitochondria as idealized geometries and therefore can miscalculate the metabolic fluxes. To analyze mitochondrial morphology in neurons of mouse cerebellum neuropil, 3D tracings of complete synaptic and axonal mitochondria were constructed using a database of serial TEM tomography images and converted to watertight meshes with minimal distortion of the original microscopy volumes with a granularity of 1.6 nanometer isotropic voxels. The resulting in silico representations were subsequently quantified by differential geometry methods in terms of the mean and Gaussian curvatures, surface areas, volumes, and membrane motifs, all of which can alter the metabolic output of the organelle. Finally, we identify structural motifs that are present across this population of mitochondria; observations which may contribute to future modeling studies of mitochondrial physiology and metabolism in neurons.

neuroscience↗

bioRxiv2021

The Amyloid Precursor Protein regulates human cortical neurogenesis

The approximately 16 billion neurons of the human neocortex are derived from a relatively limited number of developmental neural stem cells (NSCs). During embryogenesis, human cortical NSCs initially generate neurons at a particularly slow rate while preserving their progenitor state for a relatively long time. How this balance between the progenitor state and neurogenic state is regulated, and whether it contributes to species-specific brain patterning, is poorly understood. Here we show that the characteristic potential of human NSCs to remain in a progenitor state as they generate neurons for a prolonged amount of time requires the Amyloid Precursor Protein (APP). In contrast, APP is dispensable in mouse NSCs, which undergo neurogenesis at a much faster rate. Mechanistically, loss of APP cell-autonomously accelerates neurogenesis through activation of the AP1 transcription factor and repression of WNT signaling. We propose that the fine balance between self-renewal and differentiation is homeostatically regulated by APP, which may contribute to human-specific temporal patterns of neurogenesis.

developmental biology↗

bioRxiv2020

Efficient and cost-effective bacterial mRNA sequencing from low input samples through ribosomal RNA depletion

RNA sequencing is a powerful approach to quantify the genome-wide distribution of mRNA molecules in a population to gain deeper understanding of cellular functions and phenotypes. However, unlike eukaryotic cells, mRNA sequencing of bacterial samples is more challenging due to the absence of a poly-A tail that typically enables efficient capture and enrichment of mRNA from the abundant rRNA molecules in a cell. Moreover, bacterial cells frequently contain 100-fold lower quantities of RNA compared to mammalian cells, which further complicates mRNA sequencing from non-cultivable and non-model bacterial species. To overcome these limitations, we report EMBR-seq (Enrichment of mRNA by Blocked rRNA), a method that efficiently depletes 5S, 16S and 23S rRNA using blocking primers to prevent their amplification, resulting in greater than 80% of the sequenced RNA molecules from an E. coli culture deriving from mRNA. We demonstrate that this increased efficiency provides a deeper view of the transcriptome without introducing technical amplification-induced biases. Moreover, compared to recent methods that employ a large array of oligonucleotides to deplete rRNA, EMBR-seq uses a single oligonucleotide per rRNA, thereby making this new technology significantly more cost-effective, especially when applied to varied bacterial species. Finally, compared to existing commercial kits for bacterial rRNA depletion, we show that EMBR-seq can be used to successfully quantify the transcriptome from more than 500-fold lower starting total RNA. Thus, EMBR-seq provides an efficient and cost-effective approach to quantify global gene expression profiles from low input bacterial samples.

genomics↗

bioRxiv2020

Label-free optical detection of bioelectric potentials using electrochromic thin films

Understanding how a network of interconnected neurons receives, stores, and processes information in the human brain is one of the outstanding scientific challenges of our time. The ability to reliably detect neuroelectric activities is essential to addressing this challenge. Optical recording using voltage-sensitive fluorescent probes has provided unprecedented flexibility for choosing regions of interest in recording neuronal activities. However, when recording at a high frame rate such as 500-1000 Hz, fluorescence-based voltage sensors often suffer from photobleaching and phototoxicity, which limit the recording duration. Here, we report a new approach, Electro-Chromic Optical REcording (ECORE), that achieves label-free optical recording of spontaneous neuroelectrical activities. ECORE utilizes the electrochromism of PEDOT:PSS thin films, whose optical absorption can be modulated by an applied voltage. Being based on optical reflection instead of fluorescence, ECORE offers the flexibility of an optical probe without suffering from photobleaching or phototoxicity. Using ECORE, we optically recorded spontaneous action potentials in cardiomyocytes, cultured hippocampal and dorsal root ganglion neurons, and brain slices. With minimal perturbation to cells, ECORE allows long-term optical recording over multiple days.

neuroscience↗

bioRxiv2020

Inhibition of the av integrin-TGF-b axis improves natural killer cell function against glioblastoma stem cells

Glioblastoma, the most aggressive brain cancer, often recurs because glioblastoma stem cells (GSCs) are resistant to all standard therapies. Here, we show that patient-derived GSCs, but not normal astrocytes, are highly sensitive to lysis by healthy allogeneic natural killer (NK) cells in vitro. In contrast, single cell analysis of autologous, tissue infiltrating NK cells isolated from surgical samples of high-grade glioblastoma patient tumors using mass cytometry and single cell RNA sequencing revealed an abnormal phenotype associated with impaired lytic function compared with peripheral blood NK cells from GBM patients or healthy donors. This immunosuppression was attributed to an integrin-TGF-{beta} mechanism, activated by direct cell-cell contact between GSCs and NK cells. Treatment of GSC-engrafted mice with allogeneic NK cells in combination with inhibitors of integrin or TGF-{beta} signaling, or with TGF-{beta} receptor 2 gene-edited NK cells prevented GSC-induced NK cell dysfunction and tumor growth. Collectively, our findings reveal a novel mechanism of NK cell immune evasion by GSCs and implicate the integrin-TGF-{beta} axis as a useful therapeutic target to eliminate GSCs in this devastating tumor.

immunology↗

bioRxiv2020

CIS checkpoint deletion enhances the fitness of cord blood derived natural killer cells transduced with a chimeric antigen receptor

Immune checkpoint therapy has produced remarkable improvements in the outcome for certain cancers. To broaden the clinical impact of checkpoint targeting, we devised a strategy that couples targeting of the cytokine-inducible SH2-containing (CIS) protein, a key negative regulator of interleukin (IL)-15 signaling, with chimeric antigen receptor (CAR) engineering of natural killer (NK) cells. This combined strategy boosted NK cell effector function through enhancing the Akt/mTORC1 axis and c-MYC signaling, resulting in increased aerobic glycolysis. When tested in a lymphoma mouse model, this combined approach improved NK cell anti-tumor activity more than either alteration alone, eradicating lymphoma xenografts without signs of any measurable toxicity. We conclude that combining CIS checkpoint deletion with CAR engineering promotes the metabolic fitness of NK cells in an otherwise suppressive tumor microenvironment. This approach, together with the prolonged survival afforded by CAR modification, represents a promising milestone in the development of the next generation of NK cells for cancer immunotherapy.

immunology↗

bioRxiv2019

Profiling APOL1 Nephropathy Risk Variants in Genome-Edited Kidney Organoids with Single-Cell Transcriptomics

BackgroundDNA variants in APOL1 associate with kidney disease, but the pathophysiological mechanisms remain incompletely understood. Model organisms lack the APOL1 gene, limiting the degree to which disease states can be recapitulated. Here we present single-cell RNA sequencing (scRNA-seq) of genome-edited human kidney organoids as a platform for profiling effects of APOL1 risk variants in diverse nephron cell types.\n\nMethodsWe performed footprint-free CRISPR-Cas9 genome editing of human induced pluripotent stem cells (iPSCs) to knock in APOL1 high-risk G1 variants at the native genomic locus. iPSCs were differentiated into kidney organoids, treated with vehicle, IFN-{gamma}, or the combination of IFN-{gamma} and tunicamycin, and analyzed with scRNA-seq to profile cell-specific changes in differential gene expression patterns, compared to isogenic G0 controls.\n\nResultsBoth G0 and G1 iPSCs differentiated into kidney organoids containing nephron-like structures with glomerular epithelial cells, proximal tubules, distal tubules, and endothelial cells. Organoids expressed detectable APOL1 only after exposure to IFN-{gamma}. scRNA-seq revealed cell type-specific differences in G1 organoid response to APOL1 induction. Additional stress of tunicamycin exposure led to increased glomerular epithelial cell dedifferentiation in G1 organoids.\n\nConclusionsSingle-cell transcriptomic profiling of human genome-edited kidney organoids expressing APOL1 risk variants provides a novel platform for studying the pathophysiology of APOL1-mediated kidney disease.\n\nSIGNIFICANCE STATEMENTGaps persist in our mechanistic understanding of APOL1-mediated kidney disease. The authors apply genome-edited human kidney organoids, combined with single-cell transcriptomics, to profile APOL1 risk variants at the native genomic locus in different cell types. This approach captures interferon-mediated induction of APOL1 gene expression and reveals cellular dedifferentiation after a secondary insult of endoplasmic reticulum stress. This system provides a human cellular platform to interrogate complex mechanisms and human-specific regulators underlying APOL1-mediated kidney disease.

genomics↗

bioRxiv2019

Multiple Cross Displacement Amplification Coupled with Gold Nanoparticles-Based Lateral Flow Biosensor for Detection of the Mobilized Colistin Resistance Gene mcr-1

Fast dissemination of the mobilized colistin resistance (mcr) gene mcr-1 in Enterobacteriaceae causes a huge threat to the treatment of severe infection. In the current report, a multiple cross displacement amplification (MCDA) coupled with the detection of amplified products by gold nanoparticles-based lateral flow biosensor (LFB) assay (MCDA-LFB) was established to identify the mcr-1 gene with simpleness, rapidity, specificity and sensitivity. The MCDA-LFB assay was performed at a isothermal temperature (63{degrees}C) for only 30 min during the amplification stage, and the reaction products were directly identified by using LFB which obtained the result with 2 min. The entire process of experiments, from templates extraction to result judging, was accomplished less than 60 min. For the analytical specificity of this method, all of the 16 mcr-1-producing strains were positive, and all of the non-mcr-1 isolates got the negative results. The sensitivity of mcr-1-MCDA-LFB assay was as little as 600 fg of plasmid DNA per reaction in pure culture, and approximately 4.5x103 CFU/mL (~4.5 CFU/reaction) in fecal samples spiked with 100 l of strains. Therefore, this technique established in the present study is suitable for the surveillance of mcr-1 gene in clinic and livestock industry.

molecular biology↗