Uncovering strain- and age-dependent innate immune responses to SARS-CoV-2 infection in air-liquid-interface cultured nasal epithelia.

Continuous assessment of the impact of SARS-CoV-2 on the host at the cell-type level is crucial for understanding key mechanisms involved in host defense responses to viral infection. We investigated host response to ancestral-strain and Alpha-variant SARS-CoV-2 infections within air-liquid-interface human nasal epithelial cells from younger adults (26-32 Y) and older children (12-14 Y) using single-cell RNA-sequencing. Ciliated and secretory-ciliated cells formed the majority of highly infected cell-types, with the latter derived from ciliated lineages. Strong innate immune responses were observed across lowly infected and uninfected bystander cells and heightened in Alpha-infection. Alpha highly infected cells showed increased expression of protein-refolding genes compared with ancestral-strain-infected cells in children. Furthermore, oxidative phosphorylation-related genes were down-regulated in bystander cells versus infected and mock-control cells, underscoring the importance of these biological functions for viral replication. Overall, this study highlights the complexity of cell-type-, age- and viral strain-dependent host epithelial responses to SARS-CoV-2.

RCPA: An Open-Source R Package for Data Processing, Differential Analysis, Consensus Pathway Analysis, and Visualization.

Identifying impacted pathways is important because it provides insights into the biology underlying conditions beyond the detection of differentially expressed genes. Because of the importance of such analysis, more than 100 pathway analysis methods have been developed thus far. Despite the availability of many methods, it is challenging for biomedical researchers to learn and properly perform pathway analysis. First, the sheer number of methods makes it challenging to learn and choose the correct method for a given experiment. Second, computational methods require users to be savvy with coding syntax, and comfortable with command-line environments, areas that are unfamiliar to most life scientists. Third, as learning tools and computational methods are typically implemented only for a few species (i.e., human and some model organisms), it is difficult to perform pathway analysis on other species that are not included in many of the current pathway analysis tools. Finally, existing pathway tools do not allow researchers to combine, compare, and contrast the results of different methods and experiments for both hypothesis testing and analysis purposes. To address these challenges, we developed an open-source R package for Consensus Pathway Analysis (RCPA) that allows researchers to conveniently: (1) download and process data from NCBI GEO; (2) perform differential analysis using established techniques developed for both microarray and sequencing data; (3) perform both gene set enrichment, as well as topology-based pathway analysis using different methods that seek to answer different research hypotheses; (4) combine methods and datasets to find consensus results; and (5) visualize analysis results and explore significantly impacted pathways across multiple analyses. This protocol provides many example code snippets with detailed explanations and supports the analysis of more than 1000 species, two pathway databases, three differential analysis techniques, eight pathway analysis tools, six meta-analysis methods, and two consensus analysis techniques. The package is freely available on the CRAN repository. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Processing Affymetrix microarrays Basic Protocol 2: Processing Agilent microarrays Support Protocol: Processing RNA sequencing (RNA-Seq) data Basic Protocol 3: Differential analysis of microarray data (Affymetrix and Agilent) Basic Protocol 4: Differential analysis of RNA-Seq data Basic Protocol 5: Gene set enrichment analysis Basic Protocol 6: Topology-based (TB) pathway analysis Basic Protocol 7: Data integration and visualization.

Effects of radiation dose and dose rate on alginate and the use of radiation degraded alginate for peanut.

Aqueous solutions of alginate (4 %) with or without hydrogen peroxide (0-2 % H2O2) were irradiated under a gamma Co-60 source. The effect of dose rate on the radiation scission yield (Gs) of resulting irradiated alginate was determined. At the dose of 20 kGy, the G(s) value of irradiated alginate decreased with the increase dose rate, suggesting that the irradiation at a suitable dose rate could further improve the radiation chemical yield of degradation. For the alginate irradiated at the same dose rate, G(s) value increased with the increase of H2O2 concentration. Average molecular weight (Mw) and polydispersity index (PI) of irradiated alginate rapidly decreased with the increase in dose and further decreased by addition of H2O2. The oligoalginate with Mw ~ 9800 g/mol was obtained by radiation degradation of 4 % alginate solution containing 2 % H2O2 at dose of 20 kGy. Radiation scission of glycoside bonds and formation of carbonyl groups (C=O) were indicated in UV and FTIR spectra of irradiated alginate. Peanut seedlings were fertilized with alginate and oligoalginate solutions, and the results showed that all growth parameters of the treated plants were better than those of the control. Furthermore, the oligoalginate prepared by gamma irradiation can be applied as a plant growth promoter for agriculture production.

Renin-Angiotensin Inhibitor, Captopril, Attenuates Growth of Patient-Derived Colorectal Liver Metastasis Organoids.

The recurrence of colorectal liver metastasis (CRLM) following liver resection is common; approximately 40% of patients will experience tumor recurrence post-surgery. Renin-angiotensin inhibitors (RASis) have been shown to attenuate the growth and progression of CRLM in pre-clinical models following liver resection. This study examined the efficacy of the RASi captopril on patient-derived colorectal liver metastasis organoids. Patient-derived organoids (PDOs) were established using fresh samples of colorectal liver metastasis from appropriately consented patients undergoing liver resection. To mimic the regenerating liver post-CRLM liver resection, PDOs were cultured under hepatocyte regeneration conditions in vitro. CRLM PDOs were established from three patients' parent tissue. CRLM PDOs and parent tissue expressed markers of colorectal cancer, CDX2 and CK20, consistently. Furthermore, CRLM PDOs treated with captopril showed a dose dependent reduction in their expansion in vitro. In conclusion, CRLM PDOs recapitulate in vivo disease and displayed a dose-dependent response to treatment with captopril. RASis may be an additional viable treatment for patients with CRLM.

Trabid patient mutations impede the axonal trafficking of adenomatous polyposis coli to disrupt neurite growth.

ZRANB1 (human Trabid) missense mutations have been identified in children diagnosed with a range of congenital disorders including reduced brain size, but how Trabid regulates neurodevelopment is not understood. We have characterized these patient mutations in cells and mice to identify a key role for Trabid in the regulation of neurite growth. One of the patient mutations flanked the catalytic cysteine of Trabid and its deubiquitylating (DUB) activity was abrogated. The second variant retained DUB activity, but failed to bind STRIPAK, a large multiprotein assembly implicated in cytoskeleton organization and neural development. Zranb1 knock-in mice harboring either of these patient mutations exhibited reduced neuronal and glial cell densities in the brain and a motor deficit consistent with fewer dopaminergic neurons and projections. Mechanistically, both DUB-impaired and STRIPAK-binding-deficient Trabid variants impeded the trafficking of adenomatous polyposis coli (APC) to microtubule plus-ends. Consequently, the formation of neuronal growth cones and the trajectory of neurite outgrowth from mutant midbrain progenitors were severely compromised. We propose that STRIPAK recruits Trabid to deubiquitylate APC, and that in cells with mutant Trabid, APC becomes hyperubiquitylated and mislocalized causing impaired organization of the cytoskeleton that underlie the neuronal and developmental phenotypes.

Assessment of hepatitis B virus infection and interhost cellular responses using intrahepatic cholangiocyte organoids.

The intrahepatic cholangiocyte organoids (ICOs) model was evaluated for host differences in hepatitis B virus (HBV) infection, cellular responses, antiviral and immunomodulator responses. Twelve ICOs generated from liver resections and biopsies were assessed for metabolic markers and functional HBV entry receptor expression throughout differentiation. Structural changes relevant to HBV infection were characterized using histology, confocal, and electron microscopy examinations. Optimal ICO culture conditions for HBV infection using HepAD38 (genotype D) and plasma-derived HBV (genotype B and C) were described. HBV infection was confirmed using HBcAg immunostaining, qRT-PCR (RNA, covalently closed circular DNA [cccDNA], extracellular DNA) and ELISA (HBsAg and HBeAg). Drug response to antiviral and immunosuppressive agent, and cellular responses (interferon-stimulated genes [ISG]) to interferon-α and viral mimic (PolyI:C) were assessed. ICOs underwent metabolic and structural remodeling following differentiation. Optimal HBV infection was achieved in well-differentiated ICOs using spinoculation, with time and donor-dependent increase in HBV RNA, cccDNA, extracellular DNA, HBeAg and HBsAg. Donor-dependent drug responsiveness to entry inhibitor and JAK inhibitor was observed. Despite having a robust ISG response to interferon-α and PolyI:C, HBV infection in ICOs did not upregulate ISGs. Human ICOs support HBV infection and replication with donor-dependent variation in viral dynamics and cellular responses. These features can be utilized for the development of personalized drug testing platform for antivirals.

Assessment of HBV infection and inter-host cellular responses using intrahepatic cholangiocyte organoids.

Intrahepatic cholangiocyte organoids (ICOs) model was evaluated for host differences in hepatitis B virus (HBV) infection, cellular responses, antiviral, and immunomodulator responses. Twelve ICOs generated from liver resections and biopsies were assessed for metabolic markers and functional HBV entry receptor expression throughout differentiation. Structural changes relevant to HBV infection were characterized using histology, confocal, and electron microscopy examinations. Optimal ICO culture conditions for HBV infection using HepAD38 (genotype D) and plasma derived HBV (genotype B & C) were described. HBV infection was confirmed using HBcAg immunostaining, qRT-PCR (RNA, cccDNA, extracellular DNA), and ELISA (HBsAg and HBeAg). Drug response to antiviral and immunosuppressive agent, and cellular responses (interferon-stimulated genes [ISG]) to interferon-α and viral mimic (PolyI:C) were assessed. ICOs underwent metabolic and structural remodeling following differentiation. Optimal HBV infection was achieved in well-differentiated ICOs using spinoculation, with time and donor dependent increase in HBV RNA, cccDNA, extracellular DNA, HBeAg, and HBsAg. Donor dependent drug-responsiveness to entry inhibitor and JAK inhibitor was observed. Despite having a robust ISG response to interferon-α and PolyI:C, HBV infection in ICOs did not upregulate ISGs. Human ICOs support HBV infection and replication with donor dependent variation in viral dynamics and cellular responses. These features can be utilized for development of personalized drug testing platform for antivirals.

Assessment of Pre-Clinical Liver Models Based on Their Ability to Predict the Liver-Tropism of Adeno-Associated Virus Vectors.

The liver is a prime target for in vivo gene therapies using recombinant adeno-associated viral vectors. Multiple clinical trials have been undertaken for this target in the past 15 years; however, we are still to see market approval of the first liver-targeted adeno-associated virus (AAV)-based gene therapy. Inefficient expression of the therapeutic transgene, vector-induced liver toxicity and capsid, and/or transgene-mediated immune responses reported at high vector doses are the main challenges to date. One of the contributing factors to the insufficient clinical outcomes, despite highly encouraging preclinical data, is the lack of robust, biologically and clinically predictive preclinical models. To this end, this study reports findings of a functional evaluation of 6 AAV vectors in 12 preclinical models of the human liver, with the aim to uncover which combination of models is the most relevant for the identification of AAV capsid variant for safe and efficient transgene delivery to primary human hepatocytes. The results, generated by studies in models ranging from immortalized cells, iPSC-derived and primary hepatocytes, and primary human hepatic organoids to in vivo models, increased our understanding of the strengths and weaknesses of each system. This should allow the development of novel gene therapies targeting the human liver.

Epigenetic Silencing of RIPK3 in Hepatocytes Prevents MLKL-mediated Necroptosis From Contributing to Liver Pathologies.

BACKGROUND & AIMS: Necroptosis is a highly inflammatory mode of cell death that has been implicated in causing hepatic injury including steatohepatitis/ nonalcoholic steatohepatitis (NASH); however, the evidence supporting these claims has been controversial. A comprehensive, fundamental understanding of cell death pathways involved in liver disease critically underpins rational strategies for therapeutic intervention. We sought to define the role and relevance of necroptosis in liver pathology. METHODS: Several animal models of human liver pathology, including diet-induced steatohepatitis in male mice and diverse infections in both male and female mice, were used to dissect the relevance of necroptosis in liver pathobiology. We applied necroptotic stimuli to primary mouse and human hepatocytes to measure their susceptibility to necroptosis. Paired liver biospecimens from patients with NASH, before and after intervention, were analyzed. DNA methylation sequencing was also performed to investigate the epigenetic regulation of RIPK3 expression in primary human and mouse hepatocytes. RESULTS: Identical infection kinetics and pathologic outcomes were observed in mice deficient in an essential necroptotic effector protein, MLKL, compared with control animals. Mice lacking MLKL were indistinguishable from wild-type mice when fed a high-fat diet to induce NASH. Under all conditions tested, we were unable to induce necroptosis in hepatocytes. We confirmed that a critical activator of necroptosis, RIPK3, was epigenetically silenced in mouse and human primary hepatocytes and rendered them unable to undergo necroptosis. CONCLUSIONS: We have provided compelling evidence that necroptosis is disabled in hepatocytes during homeostasis and in the pathologic conditions tested in this study.

scCAN: single-cell clustering using autoencoder and network fusion.

Unsupervised clustering of single-cell RNA sequencing data (scRNA-seq) is important because it allows us to identify putative cell types. However, the large number of cells (up to millions), the high-dimensionality of the data (tens of thousands of genes), and the high dropout rates all present substantial challenges in single-cell analysis. Here we introduce a new method, named single-cell Clustering using Autoencoder and Network fusion (scCAN), that can overcome these challenges to accurately segregate different cell types in large and sparse scRNA-seq data. In an extensive analysis using 28 real scRNA-seq datasets (more than three million cells) and 243 simulated datasets, we validate that scCAN: (1) correctly estimates the number of true cell types, (2) accurately segregates cells of different types, (3) is robust against dropouts, and (4) is fast and memory efficient. We also compare scCAN with CIDR, SEURAT3, Monocle3, SHARP, and SCANPY. scCAN outperforms these state-of-the-art methods in terms of both accuracy and scalability. The scCAN package is available at https://cran.r-project.org/package=scCAN . Data and R scripts are available at http://sccan.tinnguyen-lab.com/.

Captopril, a Renin-Angiotensin System Inhibitor, Attenuates Tumour Progression in the Regenerating Liver Following Partial Hepatectomy.

(1) Liver regeneration following partial hepatectomy for colorectal liver metastasis (CRLM) has been linked to tumour recurrence. Inhibition of the renin−angiotensin system (RASi) attenuates CRLM growth in the non-regenerating liver. This study investigates whether RASi exerts an antitumour effect within the regenerating liver following partial hepatectomy for CRLM and examines RASi-induced changes in the tumour immune microenvironment; (2) CRLM in mice was induced via intrasplenic injection of mouse colorectal tumour cells, followed by splenectomy on Day 0. Mice were treated with RASi captopril (250 mg/kg/day), or saline (control) from Day 4 to Day 16 (endpoint) and underwent 70% partial hepatectomy on Day 7. Liver and tumour samples were characterised by flow cytometry and immunofluorescence; (3) captopril treatment reduced tumour burden in mice following partial hepatectomy (p < 0.01). Captopril treatment reduced populations of myeloid-derived suppressor cells (MDSCs) (CD11b+Ly6CHi p < 0.05, CD11b+Ly6CLo p < 0.01) and increased PD-1 expression on infiltrating hepatic tissue-resident memory (TRM)-like CD8+ (p < 0.001) and double-negative (CD4-CD8-; p < 0.001) T cells; (4) RASi reduced CRLM growth in the regenerating liver and altered immune cell composition by reducing populations of immunosuppressive MDSCs and boosting populations of PD-1+ hepatic TRMs. Thus, RASi should be explored as an adjunct therapy for patients undergoing partial hepatectomy for CRLM.

A novel method for single-cell data imputation using subspace regression.

Recent advances in biochemistry and single-cell RNA sequencing (scRNA-seq) have allowed us to monitor the biological systems at the single-cell resolution. However, the low capture of mRNA material within individual cells often leads to inaccurate quantification of genetic material. Consequently, a significant amount of expression values are reported as missing, which are often referred to as dropouts. To overcome this challenge, we develop a novel imputation method, named single-cell Imputation via Subspace Regression (scISR), that can reliably recover the dropout values of scRNA-seq data. The scISR method first uses a hypothesis-testing technique to identify zero-valued entries that are most likely affected by dropout events and then estimates the dropout values using a subspace regression model. Our comprehensive evaluation using 25 publicly available scRNA-seq datasets and various simulation scenarios against five state-of-the-art methods demonstrates that scISR is better than other imputation methods in recovering scRNA-seq expression profiles via imputation. scISR consistently improves the quality of cluster analysis regardless of dropout rates, normalization techniques, and quantification schemes. The source code of scISR can be found on GitHub at https://github.com/duct317/scISR .

Air-Liquid-Interface Differentiated Human Nose Epithelium: A Robust Primary Tissue Culture Model of SARS-CoV-2 Infection.

The global urgency to uncover medical countermeasures to combat the COVID-19 pandemic caused by the severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) has revealed an unmet need for robust tissue culture models that faithfully recapitulate key features of human tissues and disease. Infection of the nose is considered the dominant initial site for SARS-CoV-2 infection and models that replicate this entry portal offer the greatest potential for examining and demonstrating the effectiveness of countermeasures designed to prevent or manage this highly communicable disease. Here, we test an air-liquid-interface (ALI) differentiated human nasal epithelium (HNE) culture system as a model of authentic SARS-CoV-2 infection. Progenitor cells (basal cells) were isolated from nasal turbinate brushings, expanded under conditionally reprogrammed cell (CRC) culture conditions and differentiated at ALI. Differentiated cells were inoculated with different SARS-CoV-2 clinical isolates. Infectious virus release into apical washes was determined by TCID50, while infected cells were visualized by immunofluorescence and confocal microscopy. We demonstrate robust, reproducible SARS-CoV-2 infection of ALI-HNE established from different donors. Viral entry and release occurred from the apical surface, and infection was primarily observed in ciliated cells. In contrast to the ancestral clinical isolate, the Delta variant caused considerable cell damage. Successful establishment of ALI-HNE is donor dependent. ALI-HNE recapitulate key features of human SARS-CoV-2 infection of the nose and can serve as a pre-clinical model without the need for invasive collection of human respiratory tissue samples.

SPEECH TASKS RELEVANT TO SLEEPINESS DETERMINED WITH DEEP TRANSFER LEARNING.

Excessive sleepiness in attention-critical contexts can lead to adverse events, such as car crashes. Detecting and monitoring sleepiness can help prevent these adverse events from happening. In this paper, we use the Voiceome dataset to extract speech from 1,828 participants to develop a deep transfer learning model using Hidden-Unit BERT (HuBERT) speech representations to detect sleepiness from individuals. Speech is an under-utilized source of data in sleep detection, but as speech collection is easy, cost-effective, and non-invasive, it provides a promising resource for sleepiness detection. Two complementary techniques were conducted in order to seek converging evidence regarding the importance of individual speech tasks. Our first technique, masking, evaluated task importance by combining all speech tasks, masking selected responses in the speech, and observing systematic changes in model accuracy. Our second technique, separate training, compared the accuracy of multiple models, each of which used the same architecture, but was trained on a different subset of speech tasks. Our evaluation shows that the best-performing model utilizes the memory recall task and categorical naming task from the Boston Naming Test, which achieved an accuracy of 80.07% (F1-score of 0.85) and 81.13% (F1-score of 0.89), respectively.

TOWARDS INTERPRETABILITY OF SPEECH PAUSE IN DEMENTIA DETECTION USING ADVERSARIAL LEARNING.

Speech pause is an effective biomarker in dementia detection. Recent deep learning models have exploited speech pauses to achieve highly accurate dementia detection, but have not exploited the interpretability of speech pauses, i.e., what and how positions and lengths of speech pauses affect the result of dementia detection. In this paper, we will study the positions and lengths of dementia-sensitive pauses using adversarial learning approaches. Specifically, we first utilize an adversarial attack approach by adding the perturbation to the speech pauses of the testing samples, aiming to reduce the confidence levels of the detection model. Then, we apply an adversarial training approach to evaluate the impact of the perturbation in training samples on the detection model. We examine the interpretability from the perspectives of model accuracy, pause context, and pause length. We found that some pauses are more sensitive to dementia than other pauses from the model's perspective, e.g., speech pauses near to the verb "is". Increasing lengths of sensitive pauses or adding sensitive pauses leads the model inference to Alzheimer's Disease (AD), while decreasing the lengths of sensitive pauses or deleting sensitive pauses leads to non-AD.

SMRT: Randomized Data Transformation for Cancer Subtyping and Big Data Analysis.

Cancer is an umbrella term that includes a range of disorders, from those that are fast-growing and lethal to indolent lesions with low or delayed potential for progression to death. The treatment options, as well as treatment success, are highly dependent on the correct subtyping of individual patients. With the advancement of high-throughput platforms, we have the opportunity to differentiate among cancer subtypes from a holistic perspective that takes into consideration phenomena at different molecular levels (mRNA, methylation, etc.). This demands powerful integrative methods to leverage large multi-omics datasets for a better subtyping. Here we introduce Subtyping Multi-omics using a Randomized Transformation (SMRT), a new method for multi-omics integration and cancer subtyping. SMRT offers the following advantages over existing approaches: (i) the scalable analysis pipeline allows researchers to integrate multi-omics data and analyze hundreds of thousands of samples in minutes, (ii) the ability to integrate data types with different numbers of patients, (iii) the ability to analyze un-matched data of different types, and (iv) the ability to offer users a convenient data analysis pipeline through a web application. We also improve the efficiency of our ensemble-based, perturbation clustering to support analysis on machines with memory constraints. In an extensive analysis, we compare SMRT with eight state-of-the-art subtyping methods using 37 TCGA and two METABRIC datasets comprising a total of almost 12,000 patient samples from 28 different types of cancer. We also performed a number of simulation studies. We demonstrate that SMRT outperforms other methods in identifying subtypes with significantly different survival profiles. In addition, SMRT is extremely fast, being able to analyze hundreds of thousands of samples in minutes. The web application is available at http://SMRT.tinnguyen-lab.com. The R package will be deposited to CRAN as part of our PINSPlus software suite.

Frizzled7 Activates ß-Catenin-Dependent and ß-Catenin-Independent Wnt Signalling Pathways During Developmental Morphogenesis: Implications for Therapeutic Targeting in Colorectal Cancer.

Frizzled7 activates ß-catenin-dependent and ß-catenin-independent Wnt signalling pathways, is highly conserved through evolution from the ancient phylum hydra to man, plays essential roles in stem cells, tissue homeostasis and regeneration in the adult, and is upregulated in diverse cancers. Much of what is known about the core components of the Wnt signalling pathways was derived from studying the function of Frizzled7 orthologues in the development of lower organism. As we interrogate Frizzled7 signalling and function for therapeutic targeting in cancer, it is timely to revisit lower organisms to gain insight into the context dependent and dynamic nature of Wnt signalling for effective drug design.

Clinical stage drugs targeting inhibitor of apoptosis proteins purge episomal Hepatitis B viral genome in preclinical models.

A major unmet clinical need is a therapeutic capable of removing hepatitis B virus (HBV) genome from the liver of infected individuals to reduce their risk of developing liver cancer. A strategy to deliver such a therapy could utilize the ability to target and promote apoptosis of infected hepatocytes. Presently there is no clinically relevant strategy that has been shown to effectively remove persistent episomal covalently closed circular HBV DNA (cccDNA) from the nucleus of hepatocytes. We used linearized single genome length HBV DNA of various genotypes to establish a cccDNA-like reservoir in immunocompetent mice and showed that clinical-stage orally administered drugs that antagonize the function of cellular inhibitor of apoptosis proteins can eliminate HBV replication and episomal HBV genome in the liver. Primary human liver organoid models were used to confirm the clinical relevance of these results. This study underscores a clinically tenable strategy for the potential elimination of chronic HBV reservoirs in patients.

Captopril, a Renin-Angiotensin System Inhibitor, Attenuates Features of Tumor Invasion and Down-Regulates C-Myc Expression in a Mouse Model of Colorectal Cancer Liver Metastasis.

(1) Background: Recent clinical and experimental data suggests that the liver's regenerative response following partial hepatectomy can stimulate tumor recurrence in the liver remnant. The Wnt/ß-catenin pathway plays important roles in both colorectal cancer carcinogenesis and liver regeneration. Studies have shown that the Wnt/ß-catenin pathway regulates multiple renin-angiotensin system (RAS) genes, whilst RAS inhibition (RASi) reduces tumor burden and progression. This study explores whether RASi attenuates features of tumor progression in the regenerating liver post-hepatectomy by modulating Wnt/ß-catenin signaling. (2) Methods: Male CBA mice underwent CRLM induction, followed one week later by 70% partial hepatectomy. Mice were treated daily with captopril, a RASi, at 250 mg/kg/day or vehicle control from experimental Day 4. Tumor and liver samples were analyzed for RAS and Wnt signaling markers using qRT-PCR and immunohistochemistry. (3) Results: Treatment with captopril reduced the expression of down-stream Wnt target genes, including a significant reduction in both c-myc and cyclin-D1, despite activating Wnt signaling. This was a tumor-specific response that was not elicited in corresponding liver samples. (4) Conclusions: We report for the first time decreased c-myc expression in colorectal tumors following RASi treatment in vivo. Decreased c-myc expression was accompanied by an attenuated invasive phenotype, despite increased Wnt signaling.

A comprehensive survey of regulatory network inference methods using single cell RNA sequencing data.

Gene regulatory network is a complicated set of interactions between genetic materials, which dictates how cells develop in living organisms and react to their surrounding environment. Robust comprehension of these interactions would help explain how cells function as well as predict their reactions to external factors. This knowledge can benefit both developmental biology and clinical research such as drug development or epidemiology research. Recently, the rapid advance of single-cell sequencing technologies, which pushed the limit of transcriptomic profiling to the individual cell level, opens up an entirely new area for regulatory network research. To exploit this new abundant source of data and take advantage of data in single-cell resolution, a number of computational methods have been proposed to uncover the interactions hidden by the averaging process in standard bulk sequencing. In this article, we review 15 such network inference methods developed for single-cell data. We discuss their underlying assumptions, inference techniques, usability, and pros and cons. In an extensive analysis using simulation, we also assess the methods' performance, sensitivity to dropout and time complexity. The main objective of this survey is to assist not only life scientists in selecting suitable methods for their data and analysis purposes but also computational scientists in developing new methods by highlighting outstanding challenges in the field that remain to be addressed in the future development.