Isolating salient variations of interest in single-cell data with contrastiveVI.

Single-cell datasets are routinely collected to investigate changes in cellular state between control cells and the corresponding cells in a treatment condition, such as exposure to a drug or infection by a pathogen. To better understand heterogeneity in treatment response, it is desirable to deconvolve variations enriched in treated cells from those shared with controls. However, standard computational models of single-cell data are not designed to explicitly separate these variations. Here, we introduce contrastive variational inference (contrastiveVI; https://github.com/suinleelab/contrastiveVI ), a framework for deconvolving variations in treatment-control single-cell RNA sequencing (scRNA-seq) datasets into shared and treatment-specific latent variables. Using three treatment-control scRNA-seq datasets, we apply contrastiveVI to perform a variety of analysis tasks, including visualization, clustering and differential expression testing. We find that contrastiveVI consistently achieves results that agree with known ground truths and often highlights subtle phenomena that may be difficult to ascertain with standard workflows. We conclude by generalizing contrastiveVI to accommodate joint transcriptome and surface protein measurements.

Genome analysis of Streptomyces recifensis SN1E1 to investigate mechanisms for inhibiting fire blight disease.

AIM: Fire blight, attributed to the bacterium Erwinia amylovora, significantly damages economically important crops, such as apples and pears. Conventional methods for managing fire blight involve the application of chemical pesticides, such as streptomycin and oxytetracycline. Nevertheless, apprehensions are increasing regarding developing antibiotic and pesticide-resistant strains, compounded by documented instances of plant toxicity. Here, we present that Streptomyces recifensis SN1E1 has exhibited remarkable efficacy in suppressing apple fire blight disease. This study aims to unravel the molecular-level antimicrobial mechanisms employed by the SN1E1 strain. METHODS AND RESULTS: We identified four antimicrobial-associated biosynthetic gene clusters within the genomics of S. recifensis SN1E1. To validate antimicrobial activity against E. amylovora, knock-out mutants of biosynthetic genes linked to antimicrobial activity were generated using the CRISPR/Cas9 mutagenesis system. Notably, the whiE4 and phzB deficient mutants displayed statistically reduced antibacterial activity against E. amylovora. CONCLUSION: This research establishes a foundation for environmental and biological control studies. The potential utilization of environmentally friendly microbial agents derived from the SN1E1 strain holds promise for the biological control of fire blight disease.

GNAQ-Regulated ZO-1 and ZO-2 Act as Tumor Suppressors by Modulating EMT Potential and Tumor-Repressive Microenvironment in Lung Cancer.

Epithelial-to-mesenchymal transition (EMT) plays a critical role in the development and progression of lung cancer by promoting its invasiveness and metastasis. Using integrative analyses of the public lung cancer database, we found that the expression levels of the tight junction proteins, zonula occluden (ZO)-1 and ZO-2, were lower in lung cancer tissues, including both lung adenocarcinoma and lung squamous cell carcinoma than in normal lung tissues analyzed using The Cancer Genome Atlas (TCGA). Although the ectopic expression or knockdown of ZO-1 and ZO-2 did not affect the growth of lung cancer cells, they significantly regulated cell migration and invasion. When M0 macrophages were co-cultured with ZO-1 or ZO-2 knockdown Calu-1 cells, M2-like polarization was efficiently induced. Conversely, co-culture of M0 THP-1 cells with A549 cells stably expressing ZO-1 or ZO-2 significantly reduced M2 differentiation. We also identified G protein subunit alpha q (GNAQ) as a potential ZO-1- and ZO-2-specific activator through analysis of correlated genes with the TCGA lung cancer database. Our results suggest that the GNAQ-ZO-1/2 axis may play a tumor-suppressive role in lung cancer development and progression and highlight ZO-1 and ZO-2 as key EMT- and tumor microenvironment-suppressive proteins. These findings provide new insights for the development of targeted therapies for lung cancer.

Adversarial deconfounding autoencoder for learning robust gene expression embeddings.

MOTIVATION: Increasing number of gene expression profiles has enabled the use of complex models, such as deep unsupervised neural networks, to extract a latent space from these profiles. However, expression profiles, especially when collected in large numbers, inherently contain variations introduced by technical artifacts (e.g. batch effects) and uninteresting biological variables (e.g. age) in addition to the true signals of interest. These sources of variations, called confounders, produce embeddings that fail to transfer to different domains, i.e. an embedding learned from one dataset with a specific confounder distribution does not generalize to different distributions. To remedy this problem, we attempt to disentangle confounders from true signals to generate biologically informative embeddings. RESULTS: In this article, we introduce the Adversarial Deconfounding AutoEncoder (AD-AE) approach to deconfounding gene expression latent spaces. The AD-AE model consists of two neural networks: (i) an autoencoder to generate an embedding that can reconstruct original measurements, and (ii) an adversary trained to predict the confounder from that embedding. We jointly train the networks to generate embeddings that can encode as much information as possible without encoding any confounding signal. By applying AD-AE to two distinct gene expression datasets, we show that our model can (i) generate embeddings that do not encode confounder information, (ii) conserve the biological signals present in the original space and (iii) generalize successfully across different confounder domains. We demonstrate that AD-AE outperforms standard autoencoder and other deconfounding approaches. AVAILABILITY AND IMPLEMENTATION: Our code and data are available at https://gitlab.cs.washington.edu/abdincer/ad-ae. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

AIControl: replacing matched control experiments with machine learning improves ChIP-seq peak identification.

ChIP-seq is a technique to determine binding locations of transcription factors, which remains a central challenge in molecular biology. Current practice is to use a 'control' dataset to remove background signals from a immunoprecipitation (IP) 'target' dataset. We introduce the AIControl framework, which eliminates the need to obtain a control dataset and instead identifies binding peaks by estimating the distributions of background signals from many publicly available control ChIP-seq datasets. We thereby avoid the cost of running control experiments while simultaneously increasing the accuracy of binding location identification. Specifically, AIControl can (i) estimate background signals at fine resolution, (ii) systematically weigh the most appropriate control datasets in a data-driven way, (iii) capture sources of potential biases that may be missed by one control dataset and (iv) remove the need for costly and time-consuming control experiments. We applied AIControl to 410 IP datasets in the ENCODE ChIP-seq database, using 440 control datasets from 107 cell types to impute background signal. Without using matched control datasets, AIControl identified peaks that were more enriched for putative binding sites than those identified by other popular peak callers that used a matched control dataset. We also demonstrated that our framework identifies binding sites that recover documented protein interactions more accurately.

Dopamine receptor antagonists induce differentiation of PC-3 human prostate cancer cell-derived cancer stem cell-like cells.

BACKGROUND: The objective of this study was to determine whether PC-3 human prostate cancer cell-derived cancer stem cells (CSC)-like cells grown in a regular cell culture plate not coated with a matrix molecule might be useful for finding differentiation-inducing agents that could alter properties of prostate CSC. METHODS: Monolayer cells prepared from sphere culture of PC-3 cells were characterized for the presence of pluripotency and tumorigenicity. They were then applied to screen a compound library to find compounds that could induce morphology changes of cells. Mechanisms of action of compounds selected from the chemical library that induced the loss of pluripotency of cells were also investigated. RESULTS: C5A cells prepared from PC-3 cell-derived sphere culture expressed pluripotency markers such as Oct4, Sox2, and Klf4. C5A cells were highly proliferative. They were invasive in vitro and tumorigenic in vivo. Some dopamine receptor antagonists such as thioridazine caused reduction of pluripotency markers and tumorigenicity. Thioridazine, unlike promazine, inhibited phosphorylation of AMPK in a dose dependent manner. BML-275, an AMPK inhibitor, also induced differentiation of C5A cells as seen with thioridazine whereas A769663, an AMPK activator, blocked its differentiation-inducing ability. Transfection of C5A cells with siRNAs of dopamine receptor subtypes revealed that knockdown of DRD2 or DRD4 induced morphology changes of C5A cells. CONCLUSIONS: Some dopamine receptor antagonists such as thioridazine can induce differentiation of CSC-like cells by inhibiting phosphorylation of AMPK. Binding to DRD2 or DRD4 might have mediated the action of thioridazine involved in the differentiation of CSC-like cells.

Identifying Network Perturbation in Cancer.

We present a computational framework, called DISCERN (DIfferential SparsE Regulatory Network), to identify informative topological changes in gene-regulator dependence networks inferred on the basis of mRNA expression datasets within distinct biological states. DISCERN takes two expression datasets as input: an expression dataset of diseased tissues from patients with a disease of interest and another expression dataset from matching normal tissues. DISCERN estimates the extent to which each gene is perturbed-having distinct regulator connectivity in the inferred gene-regulator dependencies between the disease and normal conditions. This approach has distinct advantages over existing methods. First, DISCERN infers conditional dependencies between candidate regulators and genes, where conditional dependence relationships discriminate the evidence for direct interactions from indirect interactions more precisely than pairwise correlation. Second, DISCERN uses a new likelihood-based scoring function to alleviate concerns about accuracy of the specific edges inferred in a particular network. DISCERN identifies perturbed genes more accurately in synthetic data than existing methods to identify perturbed genes between distinct states. In expression datasets from patients with acute myeloid leukemia (AML), breast cancer and lung cancer, genes with high DISCERN scores in each cancer are enriched for known tumor drivers, genes associated with the biological processes known to be important in the disease, and genes associated with patient prognosis, in the respective cancer. Finally, we show that DISCERN can uncover potential mechanisms underlying network perturbation by explaining observed epigenomic activity patterns in cancer and normal tissue types more accurately than alternative methods, based on the available epigenomic data from the ENCODE project.

Sparse expression bases in cancer reveal tumor drivers.

We define a new category of candidate tumor drivers in cancer genome evolution: 'selected expression regulators' (SERs)-genes driving dysregulated transcriptional programs in cancer evolution. The SERs are identified from genome-wide tumor expression data with a novel method, namely SPARROW ( SPAR: se selected exp R: essi O: n regulators identified W: ith penalized regression). SPARROW uncovers a previously unknown connection between cancer expression variation and driver events, by using a novel sparse regression technique. Our results indicate that SPARROW is a powerful complementary approach to identify candidate genes containing driver events that are hard to detect from sequence data, due to a large number of passenger mutations and lack of comprehensive sequence information from a sufficiently large number of samples. SERs identified by SPARROW reveal known driver mutations in multiple human cancers, along with known cancer-associated processes and survival-associated genes, better than popular methods for inferring gene expression networks. We demonstrate that when applied to acute myeloid leukemia expression data, SPARROW identifies an apoptotic biomarker (PYCARD) for an investigational drug obatoclax. The PYCARD and obatoclax association is validated in 30 AML patient samples.

A Distributed Network for Intensive Longitudinal Monitoring in Metastatic Triple-Negative Breast Cancer.

Accelerating cancer research is expected to require new types of clinical trials. This report describes the Intensive Trial of OMics in Cancer (ITOMIC) and a participant with triple-negative breast cancer metastatic to bone, who had markedly elevated circulating tumor cells (CTCs) that were monitored 48 times over 9 months. A total of 32 researchers from 14 institutions were engaged in the patient's evaluation; 20 researchers had no prior involvement in patient care and 18 were recruited specifically for this patient. Whole-exome sequencing of 3 bone marrow samples demonstrated a novel ROS1 variant that was estimated to be present in most or all tumor cells. After an initial response to cisplatin, a hypothesis of crizotinib sensitivity was disproven. Leukapheresis followed by partial CTC enrichment allowed for the development of a differential high-throughput drug screen and demonstrated sensitivity to investigational BH3-mimetic inhibitors of BCL-2 that could not be tested in the patient because requests to the pharmaceutical sponsors were denied. The number and size of CTC clusters correlated with clinical status and eventually death. Focusing the expertise of a distributed network of investigators on an intensively monitored patient with cancer can generate high-resolution views of the natural history of cancer and suggest new opportunities for therapy. Optimization requires access to investigational drugs.

NgR1 Expressed in P19 Embryonal Carcinoma Cells Differentiated by Retinoic Acid Can Activate STAT3.

NgR1, a Nogo receptor, is involved in inhibition of neurite outgrowth and axonal regeneration and regulation of synaptic plasticity. P19 embryonal carcinoma cells were induced to differentiate into neuron-like cells using all trans-retinoic acid and the presence and/or function of cellular molecules, such as NgR1, NMDA receptors and STAT3, were examined. Neuronally differentiated P19 cells expressed the mRNA and protein of NgR1, which could stimulate the phosphorylation of STAT3 when activated by Nogo-P4 peptide, an active segment of Nogo-66. During the whole period of differentiation, mRNAs of all of the NMDA receptor subtypes tested (NR1, NR2A-2D) were consistently expressed, which meant that neuronally differentiated P19 cells maintained some characteristics of neurons, especially central nervous system neurons. Our results suggests that neuronally differentiated P19 cells expressing NgR1 may be an efficient and convenient in vitro model for studying the molecular mechanism of cellular events that involve NgR1 and its binding partners, and for screening compounds that activate or inhibit NgR1.

Genetic Variation of Monilinia fructicola Population in Korea.

Brown rot disease, caused by Monilinia spp., poses a significant threat to pome and stone fruit crops globally, resulting in substantial economic losses during pre- and post-harvest stages. Monilinia fructigena, M. laxa, and M. fructicola are identified as the key agents responsible for brown rot disease. In this study, we employed the amplified fragment length polymorphism (AFLP) method to assess the genetic diversity of 86 strains of Monilinia spp. isolated from major stone fruit cultivation regions in South Korea. Specifically, strains were collected from Chungcheong, Gangwon, Gyeonggi, Gyeongsang, and Jeolla provinces (-do). A comparative analysis of strain characteristics, such as isolation locations, host plants, and responses to chemical fungicides, was conducted. AFLP phylogenetic classification using 20 primer pairs revealed the presence of three distinct groups, with strains from Jeolla province consistently forming a separate group at a high frequency. Furthermore, M. fructicola was divided into three groups by the AFLP pattern. Principal coordinate analysis and PERMANOVA were applied to compare strain information, such as origin, host, and fungicide sensitivity, revealing significant partition patterns for AFLP according to geographic origin and host plants. This study represents the utilization of AFLP methodology to investigate the genetic variability among M. fructicol isolates, highlighting the importance of continuous monitoring and management of variations in the brown rot pathogen.

A deep profile of gene expression across 18 human cancers.

Clinically and biologically valuable information may reside untapped in large cancer gene expression data sets. Deep unsupervised learning has the potential to extract this information with unprecedented efficacy but has thus far been hampered by a lack of biological interpretability and robustness. Here, we present DeepProfile, a comprehensive framework that addresses current challenges in applying unsupervised deep learning to gene expression profiles. We use DeepProfile to learn low-dimensional latent spaces for 18 human cancers from 50,211 transcriptomes. DeepProfile outperforms existing dimensionality reduction methods with respect to biological interpretability. Using DeepProfile interpretability methods, we show that genes that are universally important in defining the latent spaces across all cancer types control immune cell activation, while cancer type-specific genes and pathways define molecular disease subtypes. By linking DeepProfile latent variables to secondary tumor characteristics, we discover that tumor mutation burden is closely associated with the expression of cell cycle-related genes. DNA mismatch repair and MHC class II antigen presentation pathway expression, on the other hand, are consistently associated with patient survival. We validate these results through Kaplan-Meier analyses and nominate tumor-associated macrophages as an important source of survival-correlated MHC class II transcripts. Our results illustrate the power of unsupervised deep learning for discovery of novel cancer biology from existing gene expression data.

Transparent medical image AI via an image-text foundation model grounded in medical literature.

Building trustworthy and transparent image-based medical artificial intelligence (AI) systems requires the ability to interrogate data and models at all stages of the development pipeline, from training models to post-deployment monitoring. Ideally, the data and associated AI systems could be described using terms already familiar to physicians, but this requires medical datasets densely annotated with semantically meaningful concepts. In the present study, we present a foundation model approach, named MONET (medical concept retriever), which learns how to connect medical images with text and densely scores images on concept presence to enable important tasks in medical AI development and deployment such as data auditing, model auditing and model interpretation. Dermatology provides a demanding use case for the versatility of MONET, due to the heterogeneity in diseases, skin tones and imaging modalities. We trained MONET based on 105,550 dermatological images paired with natural language descriptions from a large collection of medical literature. MONET can accurately annotate concepts across dermatology images as verified by board-certified dermatologists, competitively with supervised models built on previously concept-annotated dermatology datasets of clinical images. We demonstrate how MONET enables AI transparency across the entire AI system development pipeline, from building inherently interpretable models to dataset and model auditing, including a case study dissecting the results of an AI clinical trial.

Engagement of CD300c by a novel monoclonal antibody induces the differentiation of monocytes to M1 macrophages.

Human CD300c is expressed on various immune or cancer cells and is a novel B7 family member, functioning as an activity modulator on immune cells. To elucidate the function of CD300c, we developed CL7, a human CD300c-specific monoclonal antibody, and assessed its biological activity. The specific binding of CL7 monoclonal antibody against recombinant CD300c antigen was confirmed using enzyme-linked immunosorbent assay and surface plasmon resonance analysis. The binding affinity of CL7 was strong at the sub-nanomolar level. Furthermore, CL7 effectively bound to exogenously expressed CD300c on 293T cells. CL7 antibody differentiated monocytes to M1 macrophages, as evidenced by the upregulated expression of M1-specific cell surface markers and increased secretion of M1-specific cytokines in vitro in THP-1 cells and primary macrophages, as well as the increased population size of M1 macrophages in tumors grafted into mice. Additionally, CL7 treatment upregulated PD-L1 expression on THP-1 cells. We confirmed that the mechanism of M1 macrophage differentiation was through the mitogen-activated protein kinase and NF-κB signaling pathways. CD300c expression on various immune and cancer cells was similar to that of the well-known immune checkpoint PD-L1, suggesting the possibility of CD300c as a novel tumor biomarker. We also confirmed that the tumor size was substantially reduced by CL7 antibody treatment in the CT26 mouse model. Our study supports that CD300c is a potential therapeutic target in immuno-oncology. Overall, the CD300c-specific monoclonal antibody, CL7, is a promising immunotherapeutic agent, and it induces enhanced differentiation of M1 macrophages and/or their infiltration into the tumor microenvironment.

A systematic approach to multifactorial cardiovascular disease: causal analysis.

The combination of systems biology and large data sets offers new approaches to the study of cardiovascular diseases. These new approaches are especially important for the common cardiovascular diseases that have long been described as multifactorial. This promise is undermined by biologists' skepticism of the spider web-like network diagrams required to analyze these large data sets. Although these spider webs resemble composites of the familiar biochemical pathway diagrams, the complexity of the webs is overwhelming. As a result, biologists collaborate with data analysts whose mathematical methods seem much like those of experts using Ouija boards. To make matters worse, it is not evident how to design experiments when the network implies that many molecules must be part of the disease process. Our goal is to remove some of this mystery and suggest a simple experimental approach to the design of experiments appropriate for such analysis. We will attempt to explain how combinations of data sets that include all possible variables, graphical diagrams, complementation of different data sets, and Bayesian analyses now make it possible to determine the causes of multifactorial cardiovascular disease. We will describe this approach using the term causal analysis. Finally, we will describe how causal analysis is already being used to decipher the interactions among cytokines as causes of cardiovascular disease.

ExplaiNAble BioLogical Age (ENABL Age): an artificial intelligence framework for interpretable biological age.

BACKGROUND: Biological age is a measure of health that offers insights into ageing. The existing age clocks, although valuable, often trade off accuracy and interpretability. We introduce ExplaiNAble BioLogical Age (ENABL Age), a computational framework that combines machine-learning models with explainable artificial intelligence (XAI) methods to accurately estimate biological age with individualised explanations. METHODS: To construct the ENABL Age clock, we first predicted an age-related outcome (eg, all-cause or cause-specific mortality), and then rescaled these predictions to estimate biological age, using UK Biobank and National Health and Nutrition Examination Survey (NHANES) datasets. We adapted existing XAI methods to decompose individual ENABL Ages into contributing risk factors. For broad accessibility, we developed two versions: ENABL Age-L, based on blood tests, and ENABL Age-Q, based on questionnaire characteristics. Finally, we validated diverse ageing mechanisms captured by each ENABL Age clock through genome-wide association studies (GWAS) association analyses. FINDINGS: Our ENABL Age clock was significantly correlated with chronological age (r=0·7867, p<0·0001 for UK Biobank; r=0·7126, p<0·0001 for NHANES). These clocks distinguish individuals who are healthy (ie, their ENABL Age is lower than their chronological age) from those who are unhealthy (ie, their ENABL Age is higher than their chronological age), predicting mortality more effectively than existing clocks. Groups of individuals who were unhealthy showed approximately three to 12 times higher log hazard ratio than healthy groups, as per ENABL Age. The clocks achieved high mortality prediction power with an area under the receiver operating characteristic curve of 0·8179 for 5-year mortality and 0·8115 for 10-year mortality on the UK Biobank dataset, and 0·8935 for 5-year mortality and 0·9107 for 10-year mortality on the NHANES dataset. The individualised explanations that revealed the contribution of specific characteristics to ENABL Age provided insights into the important characteristics for ageing. An association analysis with risk factors and ageing-related morbidities and GWAS results on ENABL Age clocks trained on different mortality causes showed that each clock captures distinct ageing mechanisms. INTERPRETATION: ENABL Age brings an important leap forward in the application of XAI for interpreting biological age clocks. ENABL Age also carries substantial potential in practical settings, assisting medical professionals in untangling the complexity of ageing mechanisms, and potentially becoming a valuable tool in informed clinical decision-making processes. FUNDING: National Science Foundation and National Institutes of Health.

Unraveling the Role of Cytochrome P450 as a Key Regulator Lantipeptide Production in Streptomyces globisporus.

The aim of this study was to investigate the regulation of lantipeptide production in Streptomyces globisporus SP6C4, which produces the novel antifungal lantipeptides conprimycin and grisin, and to identify the role of cytochrome P450 (P450) in tis regulation. To investigate the regulation of lantipeptide production, we created gene deletion mutants, including ΔP450, ΔtsrD, ΔlanM, ΔP450ΔtsrD, and ΔP450ΔlanM. These mutants were characterized in terms of their morphology, sporulation, attachment, and antifungal activity against Fusarium oxysporum. The gene deletion mutants showed distinct characteristics compared to the wild-type strain. Among them, the ΔP450ΔlanM double mutant exhibited a recovery of antifungal activity against F. oxysporum, indicating that P450 plays a significant role in regulating lantipeptide production in S. globisporus SP6C4. Our findings highlight the significant role of P450 in the regulation of lantipeptide production and morphological processes in S. globisporus. The results suggest a potential link between P450-mediated metabolic pathways and the regulation of growth and secondary metabolism in SP6C4, thereby highlighting P450 as a putative target for the development of new antifungal agents.

Fostering transparent medical image AI via an image-text foundation model grounded in medical literature.

Building trustworthy and transparent image-based medical AI systems requires the ability to interrogate data and models at all stages of the development pipeline: from training models to post-deployment monitoring. Ideally, the data and associated AI systems could be described using terms already familiar to physicians, but this requires medical datasets densely annotated with semantically meaningful concepts. Here, we present a foundation model approach, named MONET (Medical cONcept rETriever), which learns how to connect medical images with text and generates dense concept annotations to enable tasks in AI transparency from model auditing to model interpretation. Dermatology provides a demanding use case for the versatility of MONET, due to the heterogeneity in diseases, skin tones, and imaging modalities. We trained MONET on the basis of 105,550 dermatological images paired with natural language descriptions from a large collection of medical literature. MONET can accurately annotate concepts across dermatology images as verified by board-certified dermatologists, outperforming supervised models built on previously concept-annotated dermatology datasets. We demonstrate how MONET enables AI transparency across the entire AI development pipeline from dataset auditing to model auditing to building inherently interpretable models.