Publications by authors named "Yuval Kluger"

121 Publications

Integrated transcriptome and trajectory analysis of cutaneous T-cell lymphoma identifies putative precancer populations.

Blood Adv 2022 Aug 10. Epub 2022 Aug 10.

Yale University, New Haven, Connecticut, United States.

Cutaneous T-cell lymphoma (CTCL) incidence increases with age, and blood involvement portends a worse prognosis. To advance our understanding of CTCL development and identify potential therapeutic targets, we performed integrative analyses of paired single-cell RNA and TCR sequencing of peripheral blood CD4+ T-cells from CTCL patients to reveal disease unifying features. The malignant CD4+ T-cells of CTCL show highly diverse transcriptomic profiles across patients, with most displaying a mature Th2 differentiation and T-cell exhaustion phenotype. TCR-CDR3 peptide prediction analysis suggested limited diversity between CTCL samples, consistent with a role for a common antigenic stimulus. PHATE affinity-based transition analysis identified putative precancerous circulating populations characterized by an intermediate stage of gene expression and mutation level between the normal CD4+ T-cells and malignant CTCL cells. We further revealed the therapeutic potential of targeting CD82 and JAK that endow the malignant CTCL cells with survival and proliferation advantages.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1182/bloodadvances.2022008168DOI Listing
August 2022

HIV viral transcription and immune perturbations in the CNS of people with HIV despite ART.

JCI Insight 2022 07 8;7(13). Epub 2022 Jul 8.

Department of Neurology, Yale School of Medicine, New Haven, Connecticut, USA.

People with HIV (PWH) on antiretroviral therapy (ART) experience elevated rates of neurological impairment, despite controlling for demographic factors and comorbidities, suggesting viral or neuroimmune etiologies for these deficits. Here, we apply multimodal and cross-compartmental single-cell analyses of paired cerebrospinal fluid (CSF) and peripheral blood in PWH and uninfected controls. We demonstrate that a subset of central memory CD4+ T cells in the CSF produced HIV-1 RNA, despite apparent systemic viral suppression, and that HIV-1-infected cells were more frequently found in the CSF than in the blood. Using cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq), we show that the cell surface marker CD204 is a reliable marker for rare microglia-like cells in the CSF, which have been implicated in HIV neuropathogenesis, but which we did not find to contain HIV transcripts. Through a feature selection method for supervised deep learning of single-cell transcriptomes, we find that abnormal CD8+ T cell activation, rather than CD4+ T cell abnormalities, predominated in the CSF of PWH compared with controls. Overall, these findings suggest ongoing CNS viral persistence and compartmentalized CNS neuroimmune effects of HIV infection during ART and demonstrate the power of single-cell studies of CSF to better understand the CNS reservoir during HIV infection.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1172/jci.insight.160267DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9310520PMC
July 2022

Coupled fibromodulin and SOX2 signaling as a critical regulator of metastatic outgrowth in melanoma.

Cell Mol Life Sci 2022 Jun 23;79(7):377. Epub 2022 Jun 23.

Section of Medical Oncology, Department of Medicine, Yale University School of Medicine, 333 Cedar Street, SHM234E, New Haven, CT, 06520, USA.

We aimed to study mechanisms controlling metastatic outgrowth of melanoma into clinically relevant lesions, a critical process responsible for the majority of melanoma deaths. To this end, we developed novel in vivo models and identified molecular events that can be ascribed to their distinct phenotypes, indolent or highly metastatic. Induction of a proliferative state at distant sites was associated with high levels of the stem-like/progenitor marker, SOX2, and required the upregulation of FMOD, an extracellular matrix component, which modulates tumor-stroma interactions. Functional studies revealed a possible link between FMOD and SOX2; dual FMOD and SOX2 silencing nearly abolished brain metastasis and had a similar effect on distant metastasis to other sites. Our in vitro data suggests that FMOD and SOX2 cooperation plays an important role in tumor vasculogenic mimicry. Furthermore, we found that FMOD and SOX2 functional roles might converge at the activation of transcriptional co-factors YAP and TAZ, possibly via crosstalk with the tumor suppressor Hippo pathway. Finally, high expression of both genes in patient specimens predicted early development of brain metastasis. Thus, our study identifies FMOD and SOX2 cooperation as a novel regulatory mechanism that might be linked functionally to melanoma metastatic competence.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1007/s00018-022-04364-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9226089PMC
June 2022

Deep unsupervised feature selection by discarding nuisance and correlated features.

Neural Netw 2022 Aug 12;152:34-43. Epub 2022 Apr 12.

Yale University, United States of America. Electronic address:

Modern datasets often contain large subsets of correlated features and nuisance features, which are not or loosely related to the main underlying structures of the data. Nuisance features can be identified using the Laplacian score criterion, which evaluates the importance of a given feature via its consistency with the Graph Laplacians' leading eigenvectors. We demonstrate that in the presence of large numbers of nuisance features, the Laplacian must be computed on the subset of selected features rather than on the complete feature set. To do this, we propose a fully differentiable approach for unsupervised feature selection, utilizing the Laplacian score criterion to avoid the selection of nuisance features. We employ an autoencoder architecture to cope with correlated features, trained to reconstruct the data from the subset of selected features. Building on the recently proposed concrete layer that allows controlling for the number of selected features via architectural design, simplifying the optimization process. Experimenting on several real-world datasets, we demonstrate that our proposed approach outperforms similar approaches designed to avoid only correlated or nuisance features, but not both. Several state-of-the-art clustering results are reported. Our code is publically available at https://github.com/jsvir/lscae.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.neunet.2022.04.002DOI Listing
August 2022

Inflammasome activation in infected macrophages drives COVID-19 pathology.

Nature 2022 06 28;606(7914):585-593. Epub 2022 Apr 28.

Department of Immunobiology, Yale University School of Medicine, New Haven, CT, USA.

Severe COVID-19 is characterized by persistent lung inflammation, inflammatory cytokine production, viral RNA and a sustained interferon (IFN) response, all of which are recapitulated and required for pathology in the SARS-CoV-2-infected MISTRG6-hACE2 humanized mouse model of COVID-19, which has a human immune system. Blocking either viral replication with remdesivir or the downstream IFN-stimulated cascade with anti-IFNAR2 antibodies in vivo in the chronic stages of disease attenuates the overactive immune inflammatory response, especially inflammatory macrophages. Here we show that SARS-CoV-2 infection and replication in lung-resident human macrophages is a critical driver of disease. In response to infection mediated by CD16 and ACE2 receptors, human macrophages activate inflammasomes, release interleukin 1 (IL-1) and IL-18, and undergo pyroptosis, thereby contributing to the hyperinflammatory state of the lungs. Inflammasome activation and the accompanying inflammatory response are necessary for lung inflammation, as inhibition of the NLRP3 inflammasome pathway reverses chronic lung pathology. Notably, this blockade of inflammasome activation leads to the release of infectious virus by the infected macrophages. Thus, inflammasomes oppose host infection by SARS-CoV-2 through the production of inflammatory cytokines and suicide by pyroptosis to prevent a productive viral cycle.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41586-022-04802-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9288243PMC
June 2022

Decomposing a deterministic path to mesenchymal niche formation by two intersecting morphogen gradients.

Dev Cell 2022 04 13;57(8):1053-1067.e5. Epub 2022 Apr 13.

Department of Pathology, Yale University, New Haven, CT 06520, USA; Department of Dermatology, Yale University, New Haven, CT 06520, USA; Molecular, Cellular and Developmental Biology, Yale University, New Haven, CT 06520, USA; Yale Cancer Center, New Haven, CT 06520, USA; Yale Stem Cell Center, New Haven, CT 06520, USA. Electronic address:

Organ formation requires integrating signals to coordinate proliferation, specify cell fates, and shape tissue. Tracing these events and signals remains a challenge, as intermediate states across many critical transitions are unresolvable over real time and space. Here, we designed a unique computational approach to decompose a non-linear differentiation process into key components to resolve the signals and cell behaviors that drive a rapid transition, using the hair follicle dermal condensate as a model. Combining scRNA sequencing with genetic perturbation, we reveal that proliferative Dkk1+ progenitors transiently amplify to become quiescent dermal condensate cells by the mere spatiotemporal patterning of Wnt/β-catenin and SHH signaling gradients. Together, they deterministically coordinate a rapid transition from proliferation to quiescence, cell fate specification, and morphogenesis. Moreover, genetically repatterning these gradients reproduces these events autonomously in "slow motion" across more intermediates that resolve the process. This analysis unravels two morphogen gradients that intersect to coordinate events of organogenesis.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.devcel.2022.03.011DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9050909PMC
April 2022

Cancer Relevance of Human Genes.

J Natl Cancer Inst 2022 Jul;114(7):988-995

Breast Medical Oncology, School of Medicine, Yale University, New Haven, CT, USA.

Background: We hypothesize that genes that directly or indirectly interact with core cancer genes (CCGs) in a comprehensive gene-gene interaction network may have functional importance in cancer.

Methods: We categorized 12 767 human genes into CCGs (n = 468), 1 (n = 5467), 2 (n = 5573), 3 (n = 915), and more than 3 steps (n = 416) removed from the nearest CCG in the Search Tool for the Retrieval of Interacting Genes/Proteins network. We estimated cancer-relevant functional importance in these neighborhood categories using 1) gene dependency score, which reflects the effect of a gene on cell viability after knockdown; 2) somatic mutation frequency in The Cancer Genome Atlas; 3) effect size that estimates to what extent a mutation in a gene enhances cell survival; and 4) negative selection pressure of germline protein-truncating variants in healthy populations.

Results: Cancer biology-related functional importance of genes decreases as their distance from the CCGs increases. Genes closer to cancer genes show greater connectedness in the network, have greater importance in maintaining cancer cell viability, are under greater negative germline selection pressure, and have higher somatic mutation frequency in cancer. Based on these 4 metrics, we provide cancer relevance annotation to known human genes.

Conclusions: A large number of human genes are connected to CCGs and could influence cancer biology to various extent when dysregulated; any given mutation may be functionally important in one but not in another individual depending on genomic context.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/jnci/djac068DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9275765PMC
July 2022

mA mRNA modification maintains colonic epithelial cell homeostasis via NF-κB-mediated antiapoptotic pathway.

Sci Adv 2022 03 25;8(12):eabl5723. Epub 2022 Mar 25.

Shanghai Institute of Immunology, State Key Laboratory of Oncogenes and Related Genes, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.

Colonic mucosal barrier dysfunction is one of the major causes of inflammatory bowel disease (IBD). However, the mechanisms underlying mucosal barrier dysfunction are poorly understood. -methyladenosine (mA) mRNA modification is an important modulator of epitranscriptional regulation of gene expression, participating in multiple physiological and pathological processes. However, the function of mA modification in colonic epithelial cells and stem cells is unknown. Here, we show that mA modification is essential for maintaining the homeostatic self-renewal in colonic stem cells. Specific deletion of the methyltransferase 14 () gene in mouse colon resulted in colonic stem cell apoptosis, causing mucosal barrier dysfunction and severe colitis. Mechanistically, we revealed that restricted colonic epithelial cell death by regulating the stability of mRNA and modulating the NF-κB pathway. Our results identified a previously unidentified role for mA modification in colonic epithelial cells and stem cells, suggesting that mA modification may be a potential therapeutic target for IBD.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1126/sciadv.abl5723DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8956260PMC
March 2022

Computation and visualization of cell-cell signaling topologies in single-cell systems data using Connectome.

Sci Rep 2022 03 9;12(1):4187. Epub 2022 Mar 9.

Department of Biomedical Engineering, Yale University, New Haven, CT, USA.

Single-cell RNA-sequencing data has revolutionized our ability to understand of the patterns of cell-cell and ligand-receptor connectivity that influence the function of tissues and organs. However, the quantification and visualization of these patterns in a way that informs tissue biology are major computational and epistemological challenges. Here, we present Connectome, a software package for R which facilitates rapid calculation and interactive exploration of cell-cell signaling network topologies contained in single-cell RNA-sequencing data. Connectome can be used with any reference set of known ligand-receptor mechanisms. It has built-in functionality to facilitate differential and comparative connectomics, in which signaling networks are compared between tissue systems. Connectome focuses on computational and graphical tools designed to analyze and explore cell-cell connectivity patterns across disparate single-cell datasets and reveal biologic insight. We present approaches to quantify focused network topologies and discuss some of the biologic theory leading to their design.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41598-022-07959-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8906120PMC
March 2022

Inhibition of renalase drives tumour rejection by promoting T cell activation.

Eur J Cancer 2022 04 24;165:81-96. Epub 2022 Feb 24.

Department of Medicine Section of Nephrology, Yale University, New Haven, CT, USA; Department of Medicine VACHS, Yale University, New Haven, CT, USA; Department of Medicine Yale School of Medicine, Yale University, New Haven, CT, USA. Electronic address:

Background: Although programmed cell death protein 1 (PD-1) inhibitors have revolutionised treatment for advanced melanoma, not all patients respond. We previously showed that inhibition of the flavoprotein renalase (RNLS) in preclinical melanoma models decreases tumour growth. We hypothesised that RNLS inhibition promotes tumour rejection by effects on the tumour microenvironment (TME).

Methods: We used two distinct murine melanoma models, studied in RNLS knockout (KO) or wild-type (WT) mice. WT mice were treated with the anti-RNLS antibody, m28, with or without anti-PD-1. 10X single-cell RNA-sequencing was used to identify transcriptional differences between treatment groups, and tumour cell content was interrogated by flow cytometry. Samples from patients treated with immunotherapy were examined for RNLS expression by quantitative immunofluorescence.

Results: RNLS KO mice injected with wild-type melanoma cells reject their tumours, supporting the importance of RNLS in cells in the TME. This effect was blunted by anti-cluster of differentiation 3. However, MØ-specific RNLS ablation was insufficient to abrogate tumour formation. Anti-RNLS antibody treatment of melanoma-bearing mice resulted in enhanced T cell infiltration and activation and resulted in immune memory on rechallenging mice with injection of melanoma cells. At the single-cell level, treatment with anti-RNLS antibodies resulted in increased tumour density of MØ, neutrophils and lymphocytes and increased expression of IFNγ and granzyme B in natural killer cells and T cells. Intratumoural Forkhead Box P3 + CD4 cells were decreased. In two distinct murine melanoma models, we showed that melanoma-bearing mice treated with anti-RNLS antibodies plus anti-PD-1 had superior tumour shrinkage and survival than with either treatment alone. Importantly, in pretreatment samples from patients treated with PD-1 inhibitors, high RNLS expression was associated with decreased survival (log-rank P = 0.006), independent of other prognostic variables.

Conclusions: RNLS KO results in melanoma tumour regression in a T-cell-dependent fashion. Anti-RNLS antibodies enhance anti-PD-1 activity in two distinct aggressive murine melanoma models resistant to PD-1 inhibitors, supporting the development of anti-RNLS antibodies with PD-1 inhibitors as a novel approach for melanomas poorly responsive to anti-PD-1.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.ejca.2022.01.002DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8940682PMC
April 2022

Zero-preserving imputation of single-cell RNA-seq data.

Nat Commun 2022 01 11;13(1):192. Epub 2022 Jan 11.

Program in Applied Mathematics, Yale University, New Haven, CT, 06511, USA.

A key challenge in analyzing single cell RNA-sequencing data is the large number of false zeros, where genes actually expressed in a given cell are incorrectly measured as unexpressed. We present a method based on low-rank matrix approximation which imputes these values while preserving biologically non-expressed genes (true biological zeros) at zero expression levels. We provide theoretical justification for this denoising approach and demonstrate its advantages relative to other methods on simulated and biological datasets.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41467-021-27729-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8752663PMC
January 2022

A humanized mouse model of chronic COVID-19.

Nat Biotechnol 2022 06 17;40(6):906-920. Epub 2021 Dec 17.

Department of Immunobiology, Yale University School of Medicine, New Haven, CT, USA.

Coronavirus disease 2019 (COVID-19) is an infectious disease that can present as an uncontrolled, hyperactive immune response, causing severe immunological injury. Existing rodent models do not recapitulate the sustained immunopathology of patients with severe disease. Here we describe a humanized mouse model of COVID-19 that uses adeno-associated virus to deliver human ACE2 to the lungs of humanized MISTRG6 mice. This model recapitulates innate and adaptive human immune responses to severe acute respiratory syndrome coronavirus 2 infection up to 28 days after infection, with key features of chronic COVID-19, including weight loss, persistent viral RNA, lung pathology with fibrosis, a human inflammatory macrophage response, a persistent interferon-stimulated gene signature and T cell lymphopenia. We used this model to study two therapeutics on immunopathology, patient-derived antibodies and steroids and found that the same inflammatory macrophages crucial to containing early infection later drove immunopathology. This model will enable evaluation of COVID-19 disease mechanisms and treatments.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41587-021-01155-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9203605PMC
June 2022

Design and implementation of a cohort study of persons living with HIV infection who are initiating medication treatment for opioid use disorder to evaluate HIV-1 persistence.

Contemp Clin Trials Commun 2021 Dec 11;24:100866. Epub 2021 Nov 11.

Department of Internal Medicine, Section of Infectious Diseases, AIDS Program, Yale School of Medicine, New Haven, CT, USA.

Background: Opioid use disorder (OUD) negatively impacts the HIV continuum of care for persons living with HIV (PLH). Medication treatment for OUD (MOUD) may have differential biological effects in individuals with HIV and OUD. To understand the role of MOUD - opioid agonist methadone, partial agonist buprenorphine and antagonist naltrexone - in HIV-1 persistence and reactivation, we will use molecular virology approaches to carry out the first prospective, longitudinal studies of adults living with HIV with OUD initiating MOUD. One of the major challenges to studying the impact of MOUD on HIV persistence is the low retention rate of study participants and the requirement of large-volume blood sampling to study the HIV proviral landscape and expression profiles.

Methods: A prospective cohort study is underway to study the HIV-1 expression, proviral landscape, and clonal expansion dynamics using limited blood sampling from persons with DSM-5 diagnosed OUD who are living with HIV infection and initiating treatment with methadone, buprenorphine, or extended-release naltrexone.

Results: We describe the recruitment, laboratory, and statistical methods of this study as well as the protocol details of this on-going study. Out of the 510 screened for enrollment into the study, 35 (7%) were eligible and 27 were enrolled thus far. Retention through month 3 has been high at 95%.

Conclusions: This on-going study is evaluating the impact of MOUD on HIV persistence at the molecular virology level using limited blood sampling via a prospective, longitudinal study of people living with HIV DSM-5 OUD initiating treatment with MOUD.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.conctc.2021.100866DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8605182PMC
December 2021

Inflammasome activation in infected macrophages drives COVID-19 pathology.

bioRxiv 2022 Apr 1. Epub 2022 Apr 1.

Severe COVID-19 is characterized by persistent lung inflammation, inflammatory cytokine production, viral RNA, and sustained interferon (IFN) response all of which are recapitulated and required for pathology in the SARS-CoV-2 infected MISTRG6-hACE2 humanized mouse model of COVID-19 with a human immune system . Blocking either viral replication with Remdesivir or the downstream IFN stimulated cascade with anti-IFNAR2 in the chronic stages of disease attenuated the overactive immune-inflammatory response, especially inflammatory macrophages. Here, we show SARS-CoV-2 infection and replication in lung-resident human macrophages is a critical driver of disease. In response to infection mediated by CD16 and ACE2 receptors, human macrophages activate inflammasomes, release IL-1 and IL-18 and undergo pyroptosis thereby contributing to the hyperinflammatory state of the lungs. Inflammasome activation and its accompanying inflammatory response is necessary for lung inflammation, as inhibition of the NLRP3 inflammasome pathway reverses chronic lung pathology. Remarkably, this same blockade of inflammasome activation leads to the release of infectious virus by the infected macrophages. Thus, inflammasomes oppose host infection by SARS-CoV-2 by production of inflammatory cytokines and suicide by pyroptosis to prevent a productive viral cycle.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1101/2021.09.27.461948DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8491846PMC
April 2022

The Spectral Underpinning of word2vec.

Front Appl Math Stat 2020 Dec 3;6. Epub 2020 Dec 3.

Department of Mathematics, University of Washington, Seattle, WA, United States.

Word2vec introduced by Mikolov et al. is a word embedding method that is widely used in natural language processing. Despite its success and frequent use, a strong theoretical justification is still lacking. The main contribution of our paper is to propose a rigorous analysis of the highly nonlinear functional of word2vec. Our results suggest that word2vec may be primarily driven by an underlying spectral method. This insight may open the door to obtaining provable guarantees for word2vec. We support these findings by numerical simulations. One fascinating open question is whether the nonlinear properties of word2vec that are not captured by the spectral method are beneficial and, if so, by what mechanism.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3389/fams.2020.593406DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8425479PMC
December 2020

Doubly Stochastic Normalization of the Gaussian Kernel Is Robust to Heteroskedastic Noise.

SIAM J Math Data Sci 2021 23;3(1):388-413. Epub 2021 Mar 23.

Program in Applied Mathematics, Yale University.

A fundamental step in many data-analysis techniques is the construction of an affinity matrix describing similarities between data points. When the data points reside in Euclidean space, a widespread approach is to from an affinity matrix by the Gaussian kernel with pairwise distances, and to follow with a certain normalization (e.g. the row-stochastic normalization or its symmetric variant). We demonstrate that the doubly-stochastic normalization of the Gaussian kernel with zero main diagonal (i.e., no self loops) is robust to heteroskedastic noise. That is, the doubly-stochastic normalization is advantageous in that it automatically accounts for observations with different noise variances. Specifically, we prove that in a suitable high-dimensional setting where heteroskedastic noise does not concentrate too much in any particular direction in space, the resulting (doubly-stochastic) noisy affinity matrix converges to its clean counterpart with rate , where is the ambient dimension. We demonstrate this result numerically, and show that in contrast, the popular row-stochastic and symmetric normalizations behave unfavorably under heteroskedastic noise. Furthermore, we provide examples of simulated and experimental single-cell RNA sequence data with intrinsic heteroskedasticity, where the advantage of the doubly-stochastic normalization for exploratory analysis is evident.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1137/20M1342124DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8194191PMC
March 2021

Spectral neighbor joining for reconstruction of latent tree Models.

SIAM J Math Data Sci 2021 1;3(1):113-141. Epub 2021 Feb 1.

Program in Applied Mathematics, Yale University, New Haven, CT 06511.

A common assumption in multiple scientific applications is that the distribution of observed data can be modeled by a latent tree graphical model. An important example is phylogenetics, where the tree models the evolutionary lineages of a set of observed organisms. Given a set of independent realizations of the random variables at the leaves of the tree, a key challenge is to infer the underlying tree topology. In this work we develop Spectral Neighbor Joining (SNJ), a novel method to recover the structure of latent tree graphical models. Given a matrix that contains a measure of similarity between all pairs of observed variables, SNJ computes a spectral measure of cohesion between groups of observed variables. We prove that SNJ is consistent, and derive a sufficient condition for correct tree recovery from an estimated similarity matrix. Combining this condition with a concentration of measure result on the similarity matrix, we bound the number of samples required to recover the tree with high probability. We illustrate via extensive simulations that in comparison to several other reconstruction methods, SNJ requires fewer samples to accurately recover trees with a large number of leaves or long edges.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1137/20m1365715DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8194222PMC
February 2021

Histopathologic and Machine Deep Learning Criteria to Predict Lymphoma Transformation in Bone Marrow Biopsies.

Arch Pathol Lab Med 2022 01;146(2):182-193

From the Departments of Pathology (Irshaid, Garritano, Patsenker, Kluger, Katz, Xu).

Context.—: Large cell transformation (LCT) of indolent B-cell lymphomas, such as follicular lymphoma (FL) and chronic lymphocytic leukemia (CLL), signals a worse prognosis, at which point aggressive chemotherapy is initiated. Although LCT is relatively straightforward to diagnose in lymph nodes, a marrow biopsy is often obtained first given its ease of procedure, low cost, and low morbidity. However, consensus criteria for LCT in bone marrow have not been established.

Objective.—: To study the accuracy and reproducibility of a trained convolutional neural network in identifying LCT, in light of promising machine learning tools that may introduce greater objectivity to morphologic analysis.

Design.—: We retrospectively identified patients who had a diagnosis of FL or CLL who had undergone bone marrow biopsy for the clinical question of LCT. We scored morphologic criteria and correlated results with clinical disease progression. In addition, whole slide scans were annotated into patches to train convolutional neural networks to discriminate between small and large tumor cells and to predict the patient's probability of transformation.

Results.—: Using morphologic examination, the proportion of large lymphoma cells (≥10% in FL and ≥30% in CLL), chromatin pattern, distinct nucleoli, and proliferation index were significantly correlated with LCT in FL and CLL. Compared to pathologist-derived estimates, machine-generated quantification demonstrated better reproducibility and stronger correlation with final outcome data.

Conclusions.—: These histologic findings may serve as indications of LCT in bone marrow biopsies. The pathologist-augmented with machine system appeared to be the most predictive, arguing for greater efforts to validate and implement these tools to further enhance physician practice.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.5858/arpa.2020-0510-OADOI Listing
January 2022

Detection of differentially abundant cell subpopulations in scRNA-seq data.

Proc Natl Acad Sci U S A 2021 06;118(22)

Department of Pathology, Yale University, New Haven, CT 06511.

Comprehensive and accurate comparisons of transcriptomic distributions of cells from samples taken from two different biological states, such as healthy versus diseased individuals, are an emerging challenge in single-cell RNA sequencing (scRNA-seq) analysis. Current methods for detecting differentially abundant (DA) subpopulations between samples rely heavily on initial clustering of all cells in both samples. Often, this clustering step is inadequate since the DA subpopulations may not align with a clear cluster structure, and important differences between the two biological states can be missed. Here, we introduce DA-seq, a targeted approach for identifying DA subpopulations not restricted to clusters. DA-seq is a multiscale method that quantifies a local DA measure for each cell, which is computed from its nearest neighboring cells across a range of values. Based on this measure, DA-seq delineates contiguous significant DA subpopulations in the transcriptomic space. We apply DA-seq to several scRNA-seq datasets and highlight its improved ability to detect differences between distinct phenotypes in severe versus mildly ill COVID-19 patients, melanomas subjected to immune checkpoint therapy comparing responders to nonresponders, embryonic development at two time points, and young versus aging brain tissue. DA-seq enabled us to detect differences between these phenotypes. Importantly, we find that DA-seq not only recovers the DA cell types as discovered in the original studies but also reveals additional DA subpopulations that were not described before. Analysis of these subpopulations yields biological insights that would otherwise be undetected using conventional computational approaches.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1073/pnas.2100293118DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8179149PMC
June 2021

Pazopanib ameliorates acute lung injuries via inhibition of MAP3K2 and MAP3K3.

Sci Transl Med 2021 04;13(591)

Vascular Biology and Therapeutics Program, Yale University School of Medicine, New Haven, CT 06520, USA.

Acute lung injury (ALI) causes high mortality and lacks any pharmacological intervention. Here, we found that pazopanib ameliorated ALI manifestations and reduced mortality in mouse ALI models and reduced edema in human lung transplantation recipients. Pazopanib inhibits mitogen-activated protein kinase kinase kinase 2 (MAP3K2)- and MAP3K3-mediated phosphorylation of NADPH oxidase 2 subunit p47 at Ser to increase reactive oxygen species (ROS) formation in myeloid cells. Genetic inactivation of MAP3K2 and MAP3K3 in myeloid cells or hematopoietic mutation of p47 Ser to alanine attenuated ALI manifestations and abrogates anti-ALI effects of pazopanib. This myeloid MAP3K2/MAP3K3-p47 pathway acted via paracrine HO to enhance pulmonary vasculature integrity and promote lung epithelial cell survival and proliferation, leading to increased pulmonary barrier function and resistance to ALI. Thus, pazopanib has the potential to be effective for treating ALI.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1126/scitranslmed.abc2499DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8466683PMC
April 2021

Graph of graphs analysis for multiplexed data with application to imaging mass cytometry.

PLoS Comput Biol 2021 03 29;17(3):e1008741. Epub 2021 Mar 29.

Department of Pathology, School of Medicine, Yale University, New Haven, Connecticut, United States of America.

Imaging Mass Cytometry (IMC) combines laser ablation and mass spectrometry to quantitate metal-conjugated primary antibodies incubated in intact tumor tissue slides. This strategy allows spatially-resolved multiplexing of dozens of simultaneous protein targets with 1μm resolution. Each slide is a spatial assay consisting of high-dimensional multivariate observations (m-dimensional feature space) collected at different spatial positions and capturing data from a single biological sample or even representative spots from multiple samples when using tissue microarrays. Often, each of these spatial assays could be characterized by several regions of interest (ROIs). To extract meaningful information from the multi-dimensional observations recorded at different ROIs across different assays, we propose to analyze such datasets using a two-step graph-based approach. We first construct for each ROI a graph representing the interactions between the m covariates and compute an m dimensional vector characterizing the steady state distribution among features. We then use all these m-dimensional vectors to construct a graph between the ROIs from all assays. This second graph is subjected to a nonlinear dimension reduction analysis, retrieving the intrinsic geometric representation of the ROIs. Such a representation provides the foundation for efficient and accurate organization of the different ROIs that correlates with their phenotypes. Theoretically, we show that when the ROIs have a particular bi-modal distribution, the new representation gives rise to a better distinction between the two modalities compared to the maximum a posteriori (MAP) estimator. We applied our method to predict the sensitivity to PD-1 axis blockers treatment of lung cancer subjects based on IMC data, achieving 97.3% average accuracy on two IMC datasets. This serves as empirical evidence that the graph of graphs approach enables us to integrate multiple ROIs and the intra-relationships between the features at each ROI, giving rise to an informative representation that is strongly associated with the phenotypic state of the entire image.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1371/journal.pcbi.1008741DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8032202PMC
March 2021

A humanized mouse model of chronic COVID-19 to evaluate disease mechanisms and treatment options.

Res Sq 2021 Mar 17. Epub 2021 Mar 17.

Coronavirus-associated acute respiratory disease, called coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). More than 90 million people have been infected with SARS-CoV-2 and more than 2 million people have died of complications due to COVID-19 worldwide. COVID-19, in its severe form, presents with an uncontrolled, hyperactive immune response and severe immunological injury or organ damage that accounts for morbidity and mortality. Even in the absence of complications, COVID-19 can last for several months with lingering effects of an overactive immune system. Dysregulated myeloid and lymphocyte compartments have been implicated in lung immunopathology. Currently, there are limited clinically-tested treatments of COVID-19 with disparities in the apparent efficacy in patients. Accurate model systems are essential to rapidly evaluate promising discoveries but most currently available in mice, ferrets and hamsters do not recapitulate sustained immunopathology described in COVID19 patients. Here, we present a comprehensively humanized mouse COVID-19 model that faithfully recapitulates the innate and adaptive human immune responses during infection with SARS-CoV-2 by adapting recombinant adeno-associated virus (AAV)-driven gene therapy to deliver human ACE2 to the lungs of MISTRG6 mice. Our unique model allows for the first time the study of chronic disease due to infection with SARS-CoV-2 in the context of patient-derived antibodies to characterize in real time the potential culprits of the observed human driving immunopathology; most importantly this model provides a live view into the aberrant macrophage response that is thought to be the effector of disease morbidity and ARDS in patients. Application of therapeutics such as patient-derived antibodies and steroids to our model allowed separation of the two aspects of the immune response, infectious viral clearance and immunopathology. Inflammatory cells seeded early in infection drove immune-patholgy later, but this very same early anti-viral response was also crucial to contain infection.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.21203/rs.3.rs-279341/v1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7987100PMC
March 2021

An in vivo screen of noncoding loci reveals that is a gatekeeper of an Ikaros-dependent checkpoint during haematopoiesis.

Proc Natl Acad Sci U S A 2021 01;118(3)

Howard Hughes Medical Institute, New Haven, CT 06520;

Haematopoiesis relies on tightly controlled gene expression patterns as development proceeds through a series of progenitors. While the regulation of hematopoietic development has been well studied, the role of noncoding elements in this critical process is a developing field. In particular, the discovery of new regulators of lymphopoiesis could have important implications for our understanding of the adaptive immune system and disease. Here we elucidate how a noncoding element is capable of regulating a broadly expressed transcription factor, Ikaros, in a lymphoid lineage-specific manner, such that it imbues Ikaros with the ability to specify the lymphoid lineage over alternate fates. Deletion of the locus, which is proximal to Ikaros, led to a severe reduction in early lymphoid progenitors, exerting control over the earliest fate decisions during lymphoid lineage commitment. locus deletion led to alterations in Ikaros isoform expression and a significant reduction in Ikaros protein. The locus may function through direct DNA interaction as Hi-C analysis demonstrated an interaction between the two loci. Finally, we identify an Ikaros-regulated erythroid-lymphoid checkpoint that is governed by in a lymphoid-lineage-specific manner. appears to act as a gatekeeper of Ikaros's broad lineage-specifying functions, selectively stabilizing Ikaros activity in the lymphoid lineage and permitting diversion to the erythroid fate in its absence. These findings represent a key illustration of how a transcription factor with broad lineage expression must work in concert with noncoding elements to orchestrate hematopoietic lineage commitment.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1073/pnas.1918062118DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7826330PMC
January 2021

Alignment free identification of clones in B cell receptor repertoires.

Nucleic Acids Res 2021 02;49(4):e21

Department of Pathology, Yale School of Medicine, New Haven, CT, USA.

Following antigenic challenge, activated B cells rapidly expand and undergo somatic hypermutation, yielding groups of clonally related B cells with diversified immunoglobulin receptors. Inference of clonal relationships based on the receptor sequence is an essential step in many adaptive immune receptor repertoire sequencing studies. These relationships are typically identified by a multi-step process that involves: (i) grouping sequences based on shared V and J gene assignments, and junction lengths and (ii) clustering these sequences using a junction-based distance. However, this approach is sensitive to the initial gene assignments, which are error-prone, and fails to identify clonal relatives whose junction length has changed through accumulation of indels. Through defining a translation-invariant feature space in which we cluster the sequences, we develop an alignment free clonal identification method that does not require gene assignments and is not restricted to a fixed junction length. This alignment free approach has higher sensitivity compared to a typical junction-based distance method without loss of specificity and PPV. While the alignment free procedure identifies clones that are broadly consistent with the junction-based distance method, it also identifies clones with characteristics (multiple V or J gene assignments or junction lengths) that are not detectable with the junction-based distance method.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/nar/gkaa1160DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7913774PMC
February 2021

Heavy-tailed kernels reveal a finer cluster structure in t-SNE visualisations.

Mach Learn Knowl Discov Databases 2020 30;11906:124-139. Epub 2020 Apr 30.

Institute for Ophthalmic Research, University of Tübingen, Germany.

T-distributed stochastic neighbour embedding (t-SNE) is a widely used data visualisation technique. It differs from its predecessor SNE by the low-dimensional similarity kernel: the Gaussian kernel was replaced by the heavy-tailed Cauchy kernel, solving the 'crowding problem' of SNE. Here, we develop an efficient implementation of t-SNE for a t-distribution kernel with an arbitrary degree of freedom , with → ∞ corresponding to SNE and = 1 corresponding to the standard t-SNE. Using theoretical analysis and toy examples, we show that < 1 can further reduce the crowding problem and reveal finer cluster structure that is invisible in standard t-SNE. We further demonstrate the striking effect of heavier-tailed kernels on large real-life data sets such as MNIST, single-cell RNA-sequencing data, and the HathiTrust library. We use domain knowledge to confirm that the revealed clusters are meaningful. Overall, we argue that modifying the tail heaviness of the t-SNE kernel can yield additional insight into the cluster structure of the data.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1007/978-3-030-46150-8_8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7582035PMC
April 2020

Cyclin-Dependent Kinase 1 Activity Is a Driver of Cyst Growth in Polycystic Kidney Disease.

J Am Soc Nephrol 2021 01 12;32(1):41-51. Epub 2020 Oct 12.

Department of Internal Medicine, Yale University, New Haven, Connecticut

Background: Mutations in and , which encode the transmembrane proteins polycystin-1 and polycystin-2, respectively, cause autosomal dominant polycystic kidney disease (ADPKD). Polycystins are expressed in the primary cilium, and disrupting cilia structure significantly slows ADPKD progression following inactivation of polycystins. The cellular mechanisms of polycystin- and cilia-dependent cyst progression in ADPKD remain incompletely understood.

Methods: Unbiased transcriptional profiling in an adult-onset mouse model before cysts formed revealed significant differentially expressed genes (DEGs) in single-knockout kidneys, which were used to identify candidate pathways dysregulated in kidneys destined to form cysts. studies validated the role of the candidate pathway in the progression of ADPKD. Wild-type and double-knockout mice that are protected from cyst growth served as controls.

Results: The RNASeq data identified cell proliferation as the most dysregulated pathway, with 15 of 241 DEGs related to cell cycle functions. appeared as a central component in this analysis. expression was similarly dysregulated in models of ADPKD, and conditional inactivation of with markedly improved the cystic phenotype and kidney function compared with inactivation of alone. The / double knockout blocked cyst cell proliferation that otherwise accompanied inactivation alone.

Conclusions: Dysregulation of is an early driver of cyst cell proliferation in ADPKD due to inactivation. Selective targeting of cyst cell proliferation is an effective means of slowing ADPKD progression caused by inactivation of .
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1681/ASN.2020040511DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7894654PMC
January 2021

Randomized near-neighbor graphs, giant components and applications in data science.

J Appl Probab 2020 Jun 16;57(2):458-476. Epub 2020 Jul 16.

Dept. of Mathematics, Yale University, New Haven, CT 06511.

If we pick random points uniformly in [0, 1] and connect each point to its log -nearest neighbors, where ≥ 2 is the dimension and is a constant depending on the dimension, then it is well known that the graph is connected with high probability. We prove that it suffices to connect every point to log log points chosen randomly among its log -nearest neighbors to ensure a giant component of size - () with high probability. This construction yields a much sparser random graph with ~ log log instead of ~ log edges that has comparable connectivity properties. This result has nontrivial implications for problems in data science where an affinity matrix is constructed: instead of connecting each point to its nearest neighbors, one can often pick ' ≪ random points out of the nearest neighbors and only connect to those without sacrificing quality of results. This approach can simplify and accelerate computation; we illustrate this with experimental results in spectral clustering of large-scale datasets.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1017/jpr.2020.21DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7480951PMC
June 2020

Multi-Omics Investigation of Innate Navitoclax Resistance in Triple-Negative Breast Cancer Cells.

Cancers (Basel) 2020 Sep 8;12(9). Epub 2020 Sep 8.

Yale Cancer Center, Yale School of Medicine, New Haven, CT 06511, USA.

Cancer cells employ various defense mechanisms against drug-induced cell death. Investigating multi-omics landscapes of cancer cells before and after treatment can reveal resistance mechanisms and inform new therapeutic strategies. We assessed the effects of navitoclax, a BCL2 family inhibitor, on the transcriptome, methylome, chromatin structure, and copy number variations of MDA-MB-231 triple-negative breast cancer (TNBC) cells. Cells were sampled before treatment, at 72 h of exposure, and after 10-day drug-free recovery from treatment. We observed transient alterations in the expression of stress response genes that were accompanied by corresponding changes in chromatin accessibility. Most of these changes returned to baseline after the recovery period. We also detected lasting alterations in methylation states and genome structure that suggest permanent changes in cell population composition. Using single-cell analyses, we identified 2350 genes significantly upregulated in navitoclax-resistant cells and derived an 18-gene navitoclax resistance signature. We assessed the navitoclax-response-predictive function of this signature in four additional TNBC cell lines in vitro and in silico in 619 cell lines treated with 251 different drugs. We observed a drug-specific predictive value in both experiments, suggesting that this signature could help guiding clinical biomarker studies involving navitoclax.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3390/cancers12092551DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7563413PMC
September 2020

Impact of healthcare worker shift scheduling on workforce preservation during the COVID-19 pandemic.

Infect Control Hosp Epidemiol 2020 12 20;41(12):1443-1445. Epub 2020 Jul 20.

Program of Applied Mathematics, Yale University, New Haven, Connecticut.

Reducing severe acute respiratory coronavirus virus 2 (SARS-CoV-2) infections among healthcare workers is critical. We ran Monte Carlo simulations modeling the spread of SARS-CoV-2 in non-COVID-19 wards, and we found that longer nursing shifts and scheduling designs in which teams of nurses and doctors co-rotate no more frequently than every 3 days can lead to fewer infections.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1017/ice.2020.337DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7403749PMC
December 2020

Germline variant burden in cancer genes correlates with age at diagnosis and somatic mutation burden.

Nat Commun 2020 05 15;11(1):2438. Epub 2020 May 15.

Breast Medical Oncology, School of Medicine, Yale University, New Haven, CT, USA.

Cancers harbor many somatic mutations and germline variants, we hypothesized that the combined effect of germline variants that alter the structure, expression, or function of protein-coding regions of cancer-biology related genes (gHFI) determines which and how many somatic mutations (sM) must occur for malignant transformation. We show that gHFI and sM affect overlapping genes and the average number of gHFI in cancer hallmark genes is higher in patients who develop cancer at a younger age (r = -0.77, P = 0.0051), while the average number of sM increases in increasing age groups (r = 0.92, P = 0.000073). A strong negative correlation exists between average gHFI and average sM burden in increasing age groups (r = -0.70, P = 0.017). In early-onset cancers, the larger gHFI burden in cancer genes suggests a greater contribution of germline alterations to the transformation process while late-onset cancers are more driven by somatic mutations.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41467-020-16293-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7228928PMC
May 2020
-->