Beclin 2 functions in autophagy, degradation of G protein-coupled receptors, and metabolism.

The molecular mechanism of autophagy and its relationship to other lysosomal degradation pathways remain incompletely understood. Here, we identified a previously uncharacterized mammalian-specific protein, Beclin 2, which, like Beclin 1, functions in autophagy and interacts with class III PI3K complex components and Bcl-2. However, Beclin 2, but not Beclin 1, functions in an additional lysosomal degradation pathway. Beclin 2 is required for ligand-induced endolysosomal degradation of several G protein-coupled receptors (GPCRs) through its interaction with GASP1. Beclin 2 homozygous knockout mice have decreased embryonic viability, and heterozygous knockout mice have defective autophagy, increased levels of brain cannabinoid 1 receptor, elevated food intake, and obesity and insulin resistance. Our findings identify Beclin 2 as a converging regulator of autophagy and GPCR turnover and highlight the functional and mechanistic diversity of Beclin family members in autophagy, endolysosomal trafficking, and metabolism.

EGFR-mediated Beclin 1 phosphorylation in autophagy suppression, tumor progression, and tumor chemoresistance.

Cell surface growth factor receptors couple environmental cues to the regulation of cytoplasmic homeostatic processes, including autophagy, and aberrant activation of such receptors is a common feature of human malignancies. Here, we defined the molecular basis by which the epidermal growth factor receptor (EGFR) tyrosine kinase regulates autophagy. Active EGFR binds the autophagy protein Beclin 1, leading to its multisite tyrosine phosphorylation, enhanced binding to inhibitors, and decreased Beclin 1-associated VPS34 kinase activity. EGFR tyrosine kinase inhibitor (TKI) therapy disrupts Beclin 1 tyrosine phosphorylation and binding to its inhibitors and restores autophagy in non-small-cell lung carcinoma (NSCLC) cells with a TKI-sensitive EGFR mutation. In NSCLC tumor xenografts, the expression of a tyrosine phosphomimetic Beclin 1 mutant leads to reduced autophagy, enhanced tumor growth, tumor dedifferentiation, and resistance to TKI therapy. Thus, oncogenic receptor tyrosine kinases directly regulate the core autophagy machinery, which may contribute to tumor progression and chemoresistance.

Mapping cellular interactions from spatially resolved transcriptomics data.

Cell-cell communication (CCC) is essential to how life forms and functions. However, accurate, high-throughput mapping of how expression of all genes in one cell affects expression of all genes in another cell is made possible only recently through the introduction of spatially resolved transcriptomics (SRT) technologies, especially those that achieve single-cell resolution. Nevertheless, substantial challenges remain to analyze such highly complex data properly. Here, we introduce a multiple-instance learning framework, Spacia, to detect CCCs from data generated by SRTs, by uniquely exploiting their spatial modality. We highlight Spacia's power to overcome fundamental limitations of popular analytical tools for inference of CCCs, including losing single-cell resolution, limited to ligand-receptor relationships and prior interaction databases, high false positive rates and, most importantly, the lack of consideration of the multiple-sender-to-one-receiver paradigm. We evaluated the fitness of Spacia for three commercialized single-cell resolution SRT technologies: MERSCOPE/Vizgen, CosMx/NanoString and Xenium/10x. Overall, Spacia represents a notable step in advancing quantitative theories of cellular communications.

Cell-free formation of RNA granules: bound RNAs identify features and components of cellular assemblies.

Cellular granules lacking boundary membranes harbor RNAs and their associated proteins and play diverse roles controlling the timing and location of protein synthesis. Formation of such granules was emulated by treatment of mouse brain extracts and human cell lysates with a biotinylated isoxazole (b-isox) chemical. Deep sequencing of the associated RNAs revealed an enrichment for mRNAs known to be recruited to neuronal granules used for dendritic transport and localized translation at synapses. Precipitated mRNAs contain extended 3' UTR sequences and an enrichment in binding sites for known granule-associated proteins. Hydrogels composed of the low complexity (LC) sequence domain of FUS recruited and retained the same mRNAs as were selectively precipitated by the b-isox chemical. Phosphorylation of the LC domain of FUS prevented hydrogel retention, offering a conceptual means of dynamic, signal-dependent control of RNA granule assembly.

Sorting nexin 5 mediates virus-induced autophagy and immunity.

Autophagy, a process of degradation that occurs via the lysosomal pathway, has an essential role in multiple aspects of immunity, including immune system development, regulation of innate and adaptive immune and inflammatory responses, selective degradation of intracellular microorganisms, and host protection against infectious diseases1,2. Autophagy is known to be induced by stimuli such as nutrient deprivation and suppression of mTOR, but little is known about how autophagosomal biogenesis is initiated in mammalian cells in response to viral infection. Here, using genome-wide short interfering RNA screens, we find that the endosomal protein sorting nexin 5 (SNX5)3,4 is essential for virus-induced, but not for basal, stress- or endosome-induced, autophagy. We show that SNX5 deletion increases cellular susceptibility to viral infection in vitro, and that Snx5 knockout in mice enhances lethality after infection with several human viruses. Mechanistically, SNX5 interacts with beclin 1 and ATG14-containing class III phosphatidylinositol-3-kinase (PI3KC3) complex 1 (PI3KC3-C1), increases the lipid kinase activity of purified PI3KC3-C1, and is required for endosomal generation of phosphatidylinositol-3-phosphate (PtdIns(3)P) and recruitment of the PtdIns(3)P-binding protein WIPI2 to virion-containing endosomes. These findings identify a context- and organelle-specific mechanism-SNX5-dependent PI3KC3-C1 activation at endosomes-for initiation of autophagy during viral infection.

Metabolic Diversity in Human Non-Small Cell Lung Cancer Cells.

Intermediary metabolism in cancer cells is regulated by diverse cell-autonomous processes, including signal transduction and gene expression patterns, arising from specific oncogenotypes and cell lineages. Although it is well established that metabolic reprogramming is a hallmark of cancer, we lack a full view of the diversity of metabolic programs in cancer cells and an unbiased assessment of the associations between metabolic pathway preferences and other cell-autonomous processes. Here, we quantified metabolic features, mostly from the 13C enrichment of molecules from central carbon metabolism, in over 80 non-small cell lung cancer (NSCLC) cell lines cultured under identical conditions. Because these cell lines were extensively annotated for oncogenotype, gene expression, protein expression, and therapeutic sensitivity, the resulting database enables the user to uncover new relationships between metabolism and these orthogonal processes.

TLR9 and beclin 1 crosstalk regulates muscle AMPK activation in exercise.

The activation of adenosine monophosphate-activated protein kinase (AMPK) in skeletal muscle coordinates systemic metabolic responses to exercise1. Autophagy-a lysosomal degradation pathway that maintains cellular homeostasis2-is upregulated during exercise, and a core autophagy protein, beclin 1, is required for AMPK activation in skeletal muscle3. Here we describe a role for the innate immune-sensing molecule Toll-like receptor 9 (TLR9)4, and its interaction with beclin 1, in exercise-induced activation of AMPK in skeletal muscle. Mice that lack TLR9 are deficient in both exercise-induced activation of AMPK and plasma membrane localization of the GLUT4 glucose transporter in skeletal muscle, but are not deficient in autophagy. TLR9 binds beclin 1, and this interaction is increased by energy stress (glucose starvation and endurance exercise) and decreased by a BCL2 mutation3,5 that blocks the disruption of BCL2-beclin 1 binding. TLR9 regulates the assembly of the endolysosomal phosphatidylinositol 3-kinase complex (PI3KC3-C2)-which contains beclin 1 and UVRAG-in skeletal muscle during exercise, and knockout of beclin 1 or UVRAG inhibits the cellular AMPK activation induced by glucose starvation. Moreover, TLR9 functions in a muscle-autonomous fashion in ex vivo contraction-induced AMPK activation, glucose uptake and beclin 1-UVRAG complex assembly. These findings reveal a heretofore undescribed role for a Toll-like receptor in skeletal-muscle AMPK activation and glucose metabolism during exercise, as well as unexpected crosstalk between this innate immune sensor and autophagy proteins.

Sprod for de-noising spatially resolved transcriptomics data based on position and image information.

Spatially resolved transcriptomics (SRT) provide gene expression close to, or even superior to, single-cell resolution while retaining the physical locations of sequencing and often also providing matched pathology images. However, SRT expression data suffer from high noise levels, due to the shallow coverage in each sequencing unit and the extra experimental steps required to preserve the locations of sequencing. Fortunately, such noise can be removed by leveraging information from the physical locations of sequencing, and the tissue organization reflected in corresponding pathology images. In this work, we developed Sprod, based on latent graph learning of matched location and imaging data, to impute accurate SRT gene expression. We validated Sprod comprehensively and demonstrated its advantages over previous methods for removing drop-outs in single-cell RNA-sequencing data. We showed that, after imputation by Sprod, differential expression analyses, pathway enrichment and cell-to-cell interaction inferences are more accurate. Overall, we envision de-noising by Sprod to become a key first step towards empowering SRT technologies for biomedical discoveries.

MetaNorm: incorporating meta-analytic priors into normalization of NanoString nCounter data.

MOTIVATION: Non-informative or diffuse prior distributions are widely employed in Bayesian data analysis to maintain objectivity. However, when meaningful prior information exists and can be identified, using an informative prior distribution to accurately reflect current knowledge may lead to superior outcomes and great efficiency. RESULTS: We propose MetaNorm, a Bayesian algorithm for normalizing NanoString nCounter gene expression data. MetaNorm is based on RCRnorm, a powerful method designed under an integrated series of hierarchical models that allow various sources of error to be explained by different types of probes in the nCounter system. However, a lack of accurate prior information, weak computational efficiency, and instability of estimates that sometimes occur weakens the approach despite its impressive performance. MetaNorm employs priors carefully constructed from a rigorous meta-analysis to leverage information from large public data. Combined with additional algorithmic enhancements, MetaNorm improves RCRnorm by yielding more stable estimation of normalized values, better convergence diagnostics and superior computational efficiency. AVAILABILITY AND IMPLEMENTATION: R Code for replicating the meta-analysis and the normalization function can be found at github.com/jbarth216/MetaNorm.

Deep Learning-Based H-Score Quantification of Immunohistochemistry-Stained Images.

Immunohistochemistry (IHC) is a well-established and commonly used staining method for clinical diagnosis and biomedical research. In most IHC images, the target protein is conjugated with a specific antibody and stained using diaminobenzidine (DAB), resulting in a brown coloration, whereas hematoxylin serves as a blue counterstain for cell nuclei. The protein expression level is quantified through the H-score, calculated from DAB staining intensity within the target cell region. Traditionally, this process requires evaluation by 2 expert pathologists, which is both time consuming and subjective. To enhance the efficiency and accuracy of this process, we have developed an automatic algorithm for quantifying the H-score of IHC images. To characterize protein expression in specific cell regions, a deep learning model for region recognition was trained based on hematoxylin staining only, achieving pixel accuracy for each class ranging from 0.92 to 0.99. Within the desired area, the algorithm categorizes DAB intensity of each pixel as negative, weak, moderate, or strong staining and calculates the final H-score based on the percentage of each intensity category. Overall, this algorithm takes an IHC image as input and directly outputs the H-score within a few seconds, significantly enhancing the speed of IHC image analysis. This automated tool provides H-score quantification with precision and consistency comparable to experienced pathologists but at a significantly reduced cost during IHC diagnostic workups. It holds significant potential to advance biomedical research reliant on IHC staining for protein expression quantification.

Enhanced Pathology Image Quality with Restore-Generative Adversarial Network.

Whole slide imaging is becoming a routine procedure in clinical diagnosis. Advanced image analysis techniques have been developed to assist pathologists in disease diagnosis, staging, subtype classification, and risk stratification. Recently, deep learning algorithms have achieved state-of-the-art performances in various imaging analysis tasks, including tumor region segmentation, nuclei detection, and disease classification. However, widespread clinical use of these algorithms is hampered by their performances often degrading due to image quality issues commonly seen in real-world pathology imaging data such as low resolution, blurring regions, and staining variation. Restore-Generative Adversarial Network (GAN), a deep learning model, was developed to improve the imaging qualities by restoring blurred regions, enhancing low resolution, and normalizing staining colors. The results demonstrate that Restore-GAN can significantly improve image quality, which leads to improved model robustness and performance for existing deep learning algorithms in pathology image analysis. Restore-GAN has the potential to be used to facilitate the applications of deep learning models in digital pathology analyses.

Tumor-suppressor function of Beclin 1 in breast cancer cells requires E-cadherin.

Beclin 1, an autophagy and haploinsufficient tumor-suppressor protein, is frequently monoallelically deleted in breast and ovarian cancers. However, the precise mechanisms by which Beclin 1 inhibits tumor growth remain largely unknown. To address this question, we performed a genome-wide CRISPR/Cas9 screen in MCF7 breast cancer cells to identify genes whose loss of function reverse Beclin 1-dependent inhibition of cellular proliferation. Small guide RNAs targeting CDH1 and CTNNA1, tumor-suppressor genes that encode cadherin/catenin complex members E-cadherin and alpha-catenin, respectively, were highly enriched in the screen. CRISPR/Cas9-mediated knockout of CDH1 or CTNNA1 reversed Beclin 1-dependent suppression of breast cancer cell proliferation and anchorage-independent growth. Moreover, deletion of CDH1 or CTNNA1 inhibited the tumor-suppressor effects of Beclin 1 in breast cancer xenografts. Enforced Beclin 1 expression in MCF7 cells and tumor xenografts increased cell surface localization of E-cadherin and decreased expression of mesenchymal markers and beta-catenin/Wnt target genes. Furthermore, CRISPR/Cas9-mediated knockout of BECN1 and the autophagy class III phosphatidylinositol kinase complex 2 (PI3KC3-C2) gene, UVRAG, but not PI3KC3-C1-specific ATG14 or other autophagy genes ATG13, ATG5, or ATG7, resulted in decreased E-cadherin plasma membrane and increased cytoplasmic E-cadherin localization. Taken together, these data reveal previously unrecognized cooperation between Beclin 1 and E-cadherin-mediated tumor suppression in breast cancer cells.

A Deep Learning Approach for Histology-Based Nucleus Segmentation and Tumor Microenvironment Characterization.

Microscopic examination of pathology slides is essential to disease diagnosis and biomedical research. However, traditional manual examination of tissue slides is laborious and subjective. Tumor whole-slide image (WSI) scanning is becoming part of routine clinical procedures and produces massive data that capture tumor histologic details at high resolution. Furthermore, the rapid development of deep learning algorithms has significantly increased the efficiency and accuracy of pathology image analysis. In light of this progress, digital pathology is fast becoming a powerful tool to assist pathologists. Studying tumor tissue and its surrounding microenvironment provides critical insight into tumor initiation, progression, metastasis, and potential therapeutic targets. Nucleus segmentation and classification are critical to pathology image analysis, especially in characterizing and quantifying the tumor microenvironment (TME). Computational algorithms have been developed for nucleus segmentation and TME quantification within image patches. However, existing algorithms are computationally intensive and time consuming for WSI analysis. This study presents Histology-based Detection using Yolo (HD-Yolo), a new method that significantly accelerates nucleus segmentation and TME quantification. We demonstrate that HD-Yolo outperforms existing WSI analysis methods in nucleus detection, classification accuracy, and computation time. We validated the advantages of the system on 3 different tissue types: lung cancer, liver cancer, and breast cancer. For breast cancer, nucleus features by HD-Yolo were more prognostically significant than both the estrogen receptor status by immunohistochemistry and the progesterone receptor status by immunohistochemistry. The WSI analysis pipeline and a real-time nucleus segmentation viewer are available at https://github.com/impromptuRong/hd_wsi.

Spatial molecular profiling: platforms, applications and analysis tools.

Molecular profiling technologies, such as genome sequencing and proteomics, have transformed biomedical research, but most such technologies require tissue dissociation, which leads to loss of tissue morphology and spatial information. Recent developments in spatial molecular profiling technologies have enabled the comprehensive molecular characterization of cells while keeping their spatial and morphological contexts intact. Molecular profiling data generate deep characterizations of the genetic, transcriptional and proteomic events of cells, while tissue images capture the spatial locations, organizations and interactions of the cells together with their morphology features. These data, together with cell and tissue imaging data, provide unprecedented opportunities to study tissue heterogeneity and cell spatial organization. This review aims to provide an overview of these recent developments in spatial molecular profiling technologies and the corresponding computational methods developed for analyzing such data.

Deep Learning of Rhabdomyosarcoma Pathology Images for Classification and Survival Outcome Prediction.

Rhabdomyosarcoma (RMS), the most common malignant soft tissue tumor in children, has several histologic subtypes that influence treatment and predict patient outcomes. Assistance with histologic classification for pathologists as well as discovery of optimized predictive biomarkers is needed. A convolutional neural network for RMS histology subtype classification was developed using digitized pathology images from 80 patients collected at time of diagnosis. A subsequent embryonal rhabdomyosarcoma (eRMS) prognostic model was also developed in a cohort of 60 eRMS patients. The RMS classification model reached a performance of an area under the receiver operating curve of 0.94 for alveolar rhabdomyosarcoma and an area under the receiver operating curve of 0.92 for eRMS at slide level in the test data set (n = 192). The eRMS prognosis model separated the patients into predicted high- and low-risk groups with significantly different event-free survival outcome (likelihood ratio test; P = 0.02) in the test data set (n = 136). The predicted risk group is significantly associated with patient event-free survival outcome after adjusting for patient age and sex (predicted high- versus low-risk group hazard ratio, 4.64; 95% CI, 1.05-20.57; P = 0.04). This is the first comprehensive study to develop computational algorithms for subtype classification and prognosis prediction for RMS histopathology images. Such models can aid pathology evaluation and provide additional parameters for risk stratification.

Deep learning in digital pathology for personalized treatment plans of cancer patients.

Over the past decade, many new cancer treatments have been developed and made available to patients. However, in most cases, these treatments only benefit a specific subgroup of patients, making the selection of treatment for a specific patient an essential but challenging task for oncologists. Although some biomarkers were found to associate with treatment response, manual assessment is time-consuming and subjective. With the rapid developments and expanded implementation of artificial intelligence (AI) in digital pathology, many biomarkers can be quantified automatically from histopathology images. This approach allows for a more efficient and objective assessment of biomarkers, aiding oncologists in formulating personalized treatment plans for cancer patients. This review presents an overview and summary of the recent studies on biomarker quantification and treatment response prediction using hematoxylin-eosin (H&E) stained pathology images. These studies have shown that an AI-based digital pathology approach can be practical and will become increasingly important in improving the selection of cancer treatments for patients.

A Bayesian zero-inflated negative binomial regression model for the integrative analysis of microbiome data.

Microbiome omics approaches can reveal intriguing relationships between the human microbiome and certain disease states. Along with identification of specific bacteria taxa associated with diseases, recent scientific advancements provide mounting evidence that metabolism, genetics, and environmental factors can all modulate these microbial effects. However, the current methods for integrating microbiome data and other covariates are severely lacking. Hence, we present an integrative Bayesian zero-inflated negative binomial regression model that can both distinguish differentially abundant taxa with distinct phenotypes and quantify covariate-taxa effects. Our model demonstrates good performance using simulated data. Furthermore, we successfully integrated microbiome taxonomies and metabolomics in two real microbiome datasets to provide biologically interpretable findings. In all, we proposed a novel integrative Bayesian regression model that features bacterial differential abundance analysis and microbiome-covariate effects quantifications, which makes it suitable for general microbiome studies.

Bayesian modeling of spatial molecular profiling data via Gaussian process.

MOTIVATION: The location, timing and abundance of gene expression (both mRNA and proteins) within a tissue define the molecular mechanisms of cell functions. Recent technology breakthroughs in spatial molecular profiling, including imaging-based technologies and sequencing-based technologies, have enabled the comprehensive molecular characterization of single cells while preserving their spatial and morphological contexts. This new bioinformatics scenario calls for effective and robust computational methods to identify genes with spatial patterns. RESULTS: We represent a novel Bayesian hierarchical model to analyze spatial transcriptomics data, with several unique characteristics. It models the zero-inflated and over-dispersed counts by deploying a zero-inflated negative binomial model that greatly increases model stability and robustness. Besides, the Bayesian inference framework allows us to borrow strength in parameter estimation in a de novo fashion. As a result, the proposed model shows competitive performances in accuracy and robustness over existing methods in both simulation studies and two real data applications. AVAILABILITY AND IMPLEMENTATION: The related R/C++ source code is available at https://github.com/Minzhe/BOOST-GP. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Assessing consistency across functional screening datasets in cancer cells.

MOTIVATION: Many high-throughput screening studies have been carried out in cancer cell lines to identify therapeutic agents and targets. Existing consistency assessment studies only examined two datasets at a time, with conclusions based on a subset of carefully selected features rather than considering global consistency of all the data. However, poor concordance can still be observed for a large part of the data even when selected features are highly consistent. RESULTS: In this study, we assembled nine compound screening datasets and three functional genomics datasets. We derived direct measures of consistency as well as indirect measures of consistency based on association between functional data and copy number-adjusted gene expression data. These results have been integrated into a web application-the Functional Data Consistency Explorer (FDCE), to allow users to make queries and generate interactive visualizations so that functional data consistency can be assessed for individual features of interest. AVAILABILITY AND IMPLEMENTATION: The FDCE web tool and we have developed and the functional data consistency measures we have generated are available at https://lccl.shinyapps.io/FDCE/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A Bayesian modified Ising model for identifying spatially variable genes from spatial transcriptomics data.

A recent technology breakthrough in spatial molecular profiling (SMP) has enabled the comprehensive molecular characterizations of single cells while preserving spatial information. It provides new opportunities to delineate how cells from different origins form tissues with distinctive structures and functions. One immediate question in SMP data analysis is to identify genes whose expressions exhibit spatially correlated patterns, called spatially variable (SV) genes. Most current methods to identify SV genes are built upon the geostatistical model with Gaussian process to capture the spatial patterns. However, the Gaussian process models rely on ad hoc kernels that could limit the models' ability to identify complex spatial patterns. In order to overcome this challenge and capture more types of spatial patterns, we introduce a Bayesian approach to identify SV genes via a modified Ising model. The key idea is to use the energy interaction parameter of the Ising model to characterize spatial expression patterns. We use auxiliary variable Markov chain Monte Carlo algorithms to sample from the posterior distribution with an intractable normalizing constant in the model. Simulation studies using both simulated and synthetic data showed that the energy-based modeling approach led to higher accuracy in detecting SV genes than those kernel-based methods. When applied to two real spatial transcriptomics (ST) datasets, the proposed method discovered novel spatial patterns that shed light on the biological mechanisms. In summary, the proposed method presents a new perspective for analyzing ST data.