Biology subjects

Fenyo, D.

Publications and source records attributed to Fenyo, D..

At least 37 records · Page 2Linked to original sources

bioRxiv2023

Modulation of GPR133 (ADGRD1) Signaling by its Intracellular Interaction Partner Extended Synaptotagmin 1 (ESYT1)

GPR133 (ADGRD1) is an adhesion G protein-coupled receptor that signals through Gs and is required for growth of glioblastoma (GBM), an aggressive brain malignancy. The regulation of GPR133 signaling is incompletely understood. Here, we use proximity biotinylation proteomics to identify ESYT1, a Ca2+-dependent mediator of endoplasmic reticulum-plasma membrane bridge formation, as an intracellular interactor of GPR133. ESYT1 knockdown or knockout increases GPR133 signaling, while its overexpression has the opposite effect, without altering GPR133 levels in the plasma membrane. The GPR133-ESYT1 interaction requires the Ca2+-sensing C2C domain of ESYT1. Thapsigargin-mediated increases in cytosolic Ca2+ relieve signaling-suppressive effects of ESYT1 by promoting ESYT1-GPR133 dissociation. ESYT1 knockdown or knockout in GBM impairs tumor growth in vitro, suggesting functions of ESYT1 beyond the interaction with GPR133. Our findings suggest a novel mechanism for modulation of GPR133 signaling by increased cytosolic Ca2+, which reduces the signaling-suppressive interaction between GPR133 and ESYT1 to raise cAMP levels.

cell biology↗

bioRxiv2022

The MTIST platform: a microbiome time series inference standardized test simulation, dataset, and scoring systems

The human gut microbiome is promising therapeutic target, but development of interventions is hampered by limited understanding of the microbial ecosystem. Therefore, recent years have seen a surge in the engineering of inference algorithms seeking to unravel rules of ecological interactions from metagenomic data. Research groups score algorithmic performance in a variety of different ways, however, there exists no unified framework to score and rank each inference approach. The machine learning field presents a useful solution to this issue: a unified set of validation data and accompanying scoring metric. Here, we present MTIST: a platform for benchmarking microbial ecosystem inference tools. We use a generalized Lotka-Volterra framework to simulate microbial abundances over time, akin to what would be obtained by quantitative metagenomic sequencing studies or lab experiments, to generate a massive in silico training dataset (MTIST) for algorithmic validation, as well as an "ecological sign" score (ES score) to rate them. MTIST comprises 24,570 time series of microbial abundance data packaged into 648 datasets. Together, the MTIST dataset and the ES score serve as a platform to develop and compare microbiome ecosystem inference approaches.

microbiology↗

bioRxiv2022

KaryoCreate: a new CRISPR-based technology to generate chromosome-specific aneuploidy by targeting human centromeres

Aneuploidy, the presence of chromosome gains or losses, is a hallmark of cancer and congenital syndromes. Here, we describe KaryoCreate (Karyotype CRISPR Engineered Aneuploidy Technology), a system that enables generation of chromosome-specific aneuploidies by co-expression of a sgRNA targeting chromosome-specific CENPA-binding [a]-satellite repeats together with dCas9 fused to a mutant form of KNL1. We designed unique and highly specific sgRNAs for 19 out of 24 chromosomes. Expression of these sgRNAs with KNL1Mut-dCas9 leads to missegregation and induction of gains or losses of the targeted chromosome in cellular progeny with an average efficiency of 8% and 12% for gains and losses, respectively (up to 20%), tested and validated across 9 chromosomes. Using KaryoCreate in colon epithelial cells, we show that chromosome 18q loss, a frequent occurrence in gastrointestinal cancers, promotes resistance to TGF{beta}, likely due to synergistic hemizygous deletion of multiple genes. Altogether, we describe a novel technology to create and study chromosome missegregation and aneuploidy in the context of cancer and beyond. HighlightsO_LIWe designed sgRNAs targeting chromosome-specific centromeres across 19 human chromosomes C_LIO_LIKaryoCreate combines chromosome-specific centromeric sgRNAs with dCas9 fused to a mutant form of KNL1. C_LIO_LIKaryoCreate allows engineering gains and losses of specific human chromosomes. C_LIO_LIEngineered Chromosome 18q loss promotes tumor-associated phenotypes in colon-derived cells. C_LIO_LIKaryoCreate is a CRISPR-based technology to foster the study of centromere biology and aneuploidy. C_LI

cell biology↗

bioRxiv2022

MenDEL: automated search of BAC sets covering long DNA regions of interest

MotivationSynthetic genomics as a field seeks to synthesize large regions of genomes from the ground up. Such large-scale projects, especially in complex genomes can rely on pre-existing BAC (Bacterial Artificial Chromosome) libraries as starting material to reduce cost. However, choosing BACs that cover long DNA regions, especially those that require many BACs, is a manual, idiosyncratic, time consuming, and error prone process. Automating this work would make the assembly of large DNA constructs more efficient. ResultsWe have developed MenDEL - a web-based DNA design application, that provides efficient tools for finding BACs that cover long regions of interest and allow for sorting results based on multiple user defined criteria - total length, number of BACs, longest BAC. etc. In addition, it enables the user to find a combination of BACs from pre-existing libraries that cover a region of interest not found in any single BAC. Availability and ImplementationMenDEL application is available to registered users at https://mendel-isg.nyumc.org, Java code used in the application to find BAC sets is available at https://github.com/MendelProject/BACFinder

bioinformatics↗

bioRxiv2022

MenDEL: PCR Primer Design as Constrained Optimization Process

MotivationThe synthesis of large DNA assemblies has applications in biotechnology, and can help us better understand genome biology. These large DNA assemblies are often constructed from many smaller DNA segments, and it is critical to assess that they are correctly assembled. One low cost and rapid method to ensure that the connection between each segment is correct is to use PCR with primer pairs that span assembly junctions. However, the design of PCR primers for large assemblies consisting of multiple segments, and therefore containing multiple assembly junctions, is a challenging process. Rule-based automation of the process often results in finding primers that satisfy general criteria, but are not necessarily the best fit for every particular junction. ResultsWe have developed MenDEL - a web-based DNA design application, that provides a primer pair computation tool for multiple assembly junctions in such a way that for each junction we automatically pick the optimal pair of primers based on user specified constraints. Availability and ImplementationThe MenDEL application is available at https://mendel-isg.nyumc.org to registered users, and the code base for computing junction primers is available at https://github.com/MendelProject/PrimerOptimization.

bioinformatics↗

bioRxiv2022

Lupus disease flares are concordant with immune responses to blooms of lipoglycan-expressing Ruminococcus blautia gnavus strains arising from unstable gut microbiota communities

Whereas genetic susceptibility for Systemic Lupus Erythematosus has been well explored, the precipitants for clinical disease flares remain a mystery. To investigate for dynamic-relationships between gut-microbial communities and Lupus disease activity, we performed taxonomic surveys of fecal 16S rRNA gene amplicon-libraries from Lupus patients and healthy volunteers, obtained at serial timepoints over many months to several years. Individual Lupus patients commonly displayed imbalances in alpha and beta microbiota-diversity, which were uniquely different from healthy individuals as well as from other Lupus patients. Moreover, multivariate analysis of sequential Lupus libraries documented community-wide ecological microbiota instability overtime, most pronounced in patients with Lupus Nephritis LN), a severe form associated with worse prognosis. Lupus gut communities displayed transient spikes of pathogenic bacterial species, with by far the most prevalent being blooms of Ruminococcus blautia gnavus (RG), occurring in nearly half of LN patients often concordant with disease activity flares. RG strains isolated during disease flares, but not those isolated from healthy individuals or patients with inflammatory bowel disease, commonly expressed a novel, highly immunogenic cell wall-associated lipoglycan with conserved structural features that include a diacyl glycerol anchor. Cross-reactive antigenic determinants on these lipoglycans were recognized by murine monoclonal antibodies, and by spontaneously arising Lupus serum IgG antibodies with peak serum antibody responses also concordant with RG blooms. As SLE is frequently characterized by remitting-relapsing disease, despite appropriate treatment, we speculate that gut blooms of pathogenic bacteria, that impair gut barrier function and stoke systemic inflammation, directly contribute to immunopathogenesis.

immunology↗

bioRxiv2022

Cell type-specific prediction of 3D chromatin architecture

The mammalian genome is spatially organized in the nucleus to enable cell type-specific gene expression. Investigating how chromatin organization determines this specificity remains a challenge. Methods for measuring the 3D chromatin organization, such as Hi-C, are costly and bear strong technical limitations, restricting their broad application particularly in high-throughput genetic perturbations. In this study, we present C.Origami, a deep neural network model that performs de novo prediction of cell type-specific chromatin organization. The C.Origami model enables in silico experiments to examine the impact of genetic perturbations on chromatin interactions in cancer genomes and beyond. In addition, we propose an in silico genetic screening framework that enables high-throughput identification of impactful genomic regions on 3D chromatin organization. We demonstrate that cell type-specific in silico genetic perturbation and screening, enabled by C.Origami, can be used to systematically discover novel chromatin regulatory mechanisms in both normal and disease-related biological systems.

genomics↗

bioRxiv2021

Proteogenomic analysis of aneuploidy reveals divergent types of gene expression regulation across cellular pathways

How cells control gene expression is a fundamental question. The relative contribution of protein-level and transcript-level regulation to this process remains unclear. Here we perform a proteogenomic analysis of tumors and untransformed cells containing somatic copy number alterations (SCNAs). By revealing how cells regulate transcript and protein abundances of SCNA-containing genes, we provide insights into the rules of gene regulation. While gene compensation mainly occurs at the protein level across tumor types, genes gained or lost show surprisingly low protein compensation in lung and high RNA compensation in colon cancer. Protein complex genes have a strong protein-level regulation while non-complex genes have a strong transcript-level regulation. Exceptions are plasma membrane protein complexes showing a very low protein-level regulation. Strikingly, we find a strong negative association between the degree of transcript-level and protein-level regulation across genes and pathways. Moreover, genes participating in the same pathway show similar degree of transcript- and protein-level regulation. Pathways including translation, splicing and mitochondrial function show a stronger protein-level regulation while cell adhesion and migration pathways show a stronger transcript-level regulation. These results suggest that the evolution of gene regulation is shaped by functional constraints and that many cellular pathways tend to evolve a predominant mechanism of gene regulation, possibly due to energetic constraints. HighlightsO_LIProteogenomic analyses of cancer SCNAs reveal tissue specificity in gene compensation. C_LIO_LIGenes gained or lost show surprisingly low protein compensation in lung cancer and unexpected RNA compensation in colon cancer. C_LIO_LIWe use DNA-RNA and RNA-protein correlations to infer the degree of RNA-level and protein-level regulation. C_LIO_LIProtein complex genes and non-complex genes show high protein-level and RNA-level regulation, respectively. C_LIO_LIPlasma membrane complexes are an exception showing more RNA-level than protein-level regulation than other complex genes. C_LIO_LIGenes participating in the same pathway show similar degree of RNA-level and protein-level regulation. C_LIO_LIThere is a strong negative relationship between the RNA- and protein-level regulation among pathways, suggesting that they are regulated either at the protein or at the RNA level. C_LIO_LIGenes involved in RNA processing and protein synthesis are upregulated in highly aneuploid tumors, especially at the protein level. C_LI

cell biology↗

bioRxiv2021

nucGEMs probe the biophysical properties of the nucleoplasm

The cell interior is highly crowded and far from thermodynamic equilibrium. This environment can dramatically impact molecular motion and assembly, and therefore influence subcellular organization and biochemical reaction rates. These effects depend strongly on length-scale, with the least information available at the important mesoscale (10-100 nanometers), which corresponds to the size of crucial regulatory molecules such as RNA polymerase II. It has been challenging to study the mesoscale physical properties of the nucleoplasm because previous methods were labor-intensive and perturbative. Here, we report nuclear Genetically Encoded Multimeric nanoparticles (nucGEMs). Introduction of a single gene leads to continuous production and assembly of protein-based bright fluorescent nanoparticles of 40 nm diameter. We implemented nucGEMs in budding and fission yeast and in mammalian cell lines. We found differences in particle motility between the nucleus and the cytosol at the mesoscale, that mitotic chromosome condensation ejects nucGEMs from the nucleus, and that nucGEMs are excluded from heterochromatin and the nucleolus. nucGEMs enable hundreds of nuclear rheology experiments per hour, and allow evolutionary comparison of the physical properties of the cytosol and nucleoplasm.

biophysics↗

bioRxiv2021

Modeling transcriptional profiles of gene perturbation with deep neural network

BackgroundCell line perturbation data could be utilized as a reference for inferring underlying molecular processes in new gene expression profiles. It is important to develop accurate and computationally efficient algorithms to exploit biological knowledge in the growing compendium of existing perturbation data and harness these for new predictions. ResultsWe reframed the problem of inferring possible gene perturbation based on a reference perturbation database into a classification task and evaluated the application of deep neural network models to address this problem. Our results showed that a fully-connected multi-layer neural network was able to achieve up to 74.9% accuracy in a holdout test set, but the model generalizability was limited by consistency between training and testing data. ConclusionCapacity and flexibility enables neural network models to efficiently represent transcriptomic features associated with single gene knockdown perturbations. With consistent signals between training and testing sets, neural networks may be trained to classify new samples to experimentally confirmed molecular phenotypes.

bioinformatics↗

bioRxiv2021

Spatially Interacting Phosphorylation Sites and Mutations in Cancer

Advances in mass-spectrometry have generated increasingly large-scale proteomics datasets containing tens of thousands of phosphorylation sites (phosphosites) that require prioritization. We develop a bioinformatics tool called HotPho and systematically discover 3D co-clustering of phosphosites and cancer mutations on protein structures. HotPho identifies 474 such hybrid clusters containing 1,255 co-clustering phosphosites, including RET p.S904/Y928, the conserved HRAS/KRAS p.Y96, and IDH1 p.Y139/IDH2 p.Y179 that are adjacent to recurrent mutations on protein structures not found by linear proximity approaches. Hybrid clusters, enriched in histone and kinase domains, frequently include expression-associated mutations experimentally shown as activating and conferring genetic dependency. Approximately 300 co-clustering phosphosites are verified in patient samples of 5 cancer types or previously implicated in cancer, including CTNNB1 p.S29/Y30, EGFR p.S720, MAPK1 p.S142, and PTPN12 p.S275. In summary, systematic 3D clustering analysis highlights nearly 3,000 likely functional mutations and over 1,000 cancer phosphosites for downstream investigation and evaluation of potential clinical relevance.

bioinformatics↗

bioRxiv2020

DreamAI: algorithm for the imputation of proteomics data

Deep proteomics profiling using labeled LC-MS/MS experiments has been proven to be powerful to study complex diseases. However, due to the dynamic nature of the discovery mass spectrometry, the generated data contain a substantial fraction of missing values. This poses great challenges for data analyses, as many tools, especially those for high dimensional data, cannot deal with missing values directly. To address this problem, the NCI-CPTAC Proteogenomics DREAM Challenge was carried out to develop effective imputation algorithms for labeled LC-MS/MS proteomics data through crowd learning. The final resulting algorithm, DreamAI, is based on an ensemble of six different imputation methods. The imputation accuracy of DreamAI, as measured by Pearson correlation, is about 15%-50% greater than existing tools among less abundant proteins, which are more vulnerable to be missed in proteomics data sets. This new tool notably enhances data analysis capabilities in proteomics research.

bioinformatics↗

bioRxiv2020

Discovery of protein modifications using high resolution differential mass spectrometry proteomics

Recent studies have revealed diverse amino acid, post-translational and non-canonical modifications of proteins in diverse organisms and tissues. However, their unbiased detection and analysis remain hindered by technical limitations. Here, we present a spectral alignment method for the identification of protein modifications using high-resolution mass spectrometry proteomics. Termed SAMPEI for Spectral Alignment-based Modified PEptide Identification, this open-source algorithm is designed for the discovery of functional protein and peptide signaling modifications, without prior knowledge of their identities. Using synthetic standards and controlled chemical labeling experiments, we demonstrate its high specificity and sensitivity for the discovery of sub-stoichiometric protein modifications in complex cellular extracts. SAMPEI mapping of mouse macrophage differentiation revealed diverse post-translational protein modifications, including distinct forms of cysteine itaconatylation. SAMPEIs robust parameterization and versatility are expected to facilitate the discovery of biological modifications of diverse macromolecules. SAMPEI is implemented as a Python package, and is available open-source from BioConda and GitHub (https://github.com/FenyoLab/SAMPEI).

biochemistry↗

bioRxiv2020

Affinity proteomic dissection of the human nuclear cap-binding-complex interactome

A 5, 7-methylguanosine cap is a quintessential feature of RNA polymerase II-transcribed RNAs, and a textbook aspect of co-transcriptional RNA processing. The cap is bound by the cap-binding complex (CBC), canonically consisting of nuclear cap-binding proteins 1 and 2 (NCBP1/2). The CBC has come under renewed investigative interest in recent years due to its participation in RNA-fate decisions via interactions with RNA productive factors as well as with adapters of the degradative RNA exosome - including the proteins SRRT (a.k.a. ARS2) and ZC3H18, and macromolecular assemblies such as the nuclear exosome targeting (NEXT) complex and the poly(A) exosome targeting (PAXT) connection. A novel cap-binding protein, NCBP3, was recently proposed to form an alternative, non-canonical CBC together with NCBP1, and to interact with the canonical CBC along with the protein SRRT. The theme of post-transcriptional RNA fate, and how it relates to co-transcriptional ribonucleoprotein assembly is abundant with complicated, ambiguous, and likely incomplete models. In an effort to clarify the compositions of NCBP1-, 2-, and 3-related macromolecular assemblies, including their intersections and differences, we have applied an affinity capture-based interactome screening approach, where the experimental design and data processing have been modified and updated to identify interactome differences between targets under a range of experimental conditions, in the context of label-free quantitative mass spectrometry. This study generated a comprehensive view of NCBP-protein interactions in the ribonucleoprotein context and demonstrates the potential of our approach to benefit the interpretation of complex biological pathways.

biochemistry↗

bioRxiv2019

BlackSheep: A Bioconductor and Bioconda package for differential extreme value analysis

Unbiased assays such as shotgun proteomics and RNA-seq provide high-resolution molecular characterization of tumors. These assays measure molecules with highly varied distributions, making interpretation and hypothesis testing challenging. Samples with the most extreme measurements for a molecule can reveal the most interesting biological insights, yet are often excluded from analysis. Furthermore, rare disease subtypes are, by definition, underrepresented in cancer cohorts. To provide a strategy for identifying molecules aberrantly enriched in small sample cohorts, we present BlackSheep--a package for non-parametric description and differential analysis of genome-wide data, available at https://github.com/ruggleslab/blackSheep. BlackSheep is a complementary tool to other differential expression analysis methods that may be underpowered when analyzing small subgroups in a larger cohort.

bioinformatics↗

bioRxiv2019

Distinct features of human myeloid cell cytokine response profiles identify neutrophil activation by cytokines as a prognostic feature during tuberculosis and cancer

Myeloid cells are a vital component of innate immunity and comprise of monocytes, macrophages, dendritic cells and granulocytes. How myeloid cell lineage affects activation states in response to cytokines remains poorly understood. The cytokine environment and cellular infiltrate during an inflammatory response may contain prognostic features that could predict disease outcome. Here we analyzed the transcriptional responses of human monocytes, macrophages, dendritic cells and neutrophils in response to stimulation by IFN-{gamma}, IFN-{beta} IFN-{lambda}, IL-4, IL-13 and IL-10 cytokines, to better understand the heterogeneity of activation states in inflammatory conditions. This generated a myeloid cell cytokine specific response matrix that can infer representation of myeloid cells and the cytokine environment they encounter during infection and in tumors. Neutrophils were highly responsive to type 1 and type 2 cytokine stimulation but did not respond to IL-10. We identified transcripts specific to IFN-{beta} stimulation, whereas other IFN signature genes were upregulated by both IFN-{gamma} and IFN-{beta}. When we used our matrix to deconvolute blood profiles from tuberculosis patients, the IFN-{beta} specific neutrophil signature was reduced in TB patients with active disease whereas the shared response to IFN-{gamma} and IFN-{beta} in neutrophils was increased. When applied to glioma patients, transcripts of neutrophils exposed to IL-4 or IL-13 and monocyte responses to IFN-{gamma} or IFN-{beta} emerged as opposing predictors of patient survival. Hence, by dissecting how different myeloid cells respond to cytokine activation, we can delineate biological roles for myeloid cells in different cytokine environments during disease processes, especially during infection and tumor progression.

immunology↗

bioRxiv2019

LINE-1 ORF2P EXPRESSION IS NEARLY IMPERCEPTIBLE IN HUMAN CANCERS

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSBackgroundC_ST_ABSLong interspersed element-1 (LINE-1, L1) is the major driver of mobile DNA activity in modern humans. When expressed, LINE-1 loci produce bicistronic transcripts encoding two proteins essential for retrotransposition, ORF1p and ORF2p. Many types of human cancers are characterized by L1 promoter hypomethylation, L1 transcription, L1 ORF1p protein expression, and somatic L1 retrotransposition. ORF2p encodes the endonuclease and reverse transcriptase activities required for L1 retrotransposition. Its expression is poorly characterized in human tissues and cell lines.\n\nResultsWe report mass spectrometry based tumor proteome profiling studies wherein ORF2p eludes detection. To test whether ORF2p could be detected with specific reagents, we developed and validated five rabbit monoclonal antibodies with immunoreactivity for specific epitopes on the protein. These reagents readily detect ectopic ORF2p expressed from bicistronic L1 constructs. However, endogenous ORF2p is not detected in human tumor samples or cell lines by western blot, immunoprecipitation, or immunohistochemistry despite high levels of ORF1p expression. Moreover, we report endogenous ORF1p-associated interactomes, affinity isolated from colorectal cancers, wherein we similarly fail to detect ORF2p. These samples include primary tumors harboring hundreds of somatically-acquired L1 insertions. The new data are available via ProteomeXchange with identifier PXD013743.\n\nConclusionsAlthough somatic retrotransposition provides unequivocal genetic evidence for the expression of ORF2p in human cancers, we are unable to directly measure its presence using several standard methods. Experimental systems have previously indicated an unequal stoichiometry between ORF1p and ORF2p, but in vivo, the expression of these two proteins may be more strikingly uncoupled. These findings are consistent with observations that ORF2p is not tolerable for cell growth.

cancer biology↗

bioRxiv2019

Complex+: Aided Decision-Making for the Study of Protein Complexes

Proteins are the chief effectors of cell biology and their functions are typically carried out in the context of multi-protein assemblies; large collections of such interacting protein assemblies are often referred to as interactomes. Knowing the constituents of protein complexes is therefore important for investigating their molecular biology. Many experimental methods are capable of producing data of use for detecting and inferring the existence of physiological protein complexes. Each method has associated pros and cons, affecting the potential quality and utility of the data. Numerous informatic resources exist for the curation, integration, retrieval, and processing of protein interactions data. While each resource may possess different merits, none are definitive and few are wieldy, potentially limiting their effective use by non-experts. In addition, contemporary analyses suggest that we may still be decades away from a comprehensive map of a human protein interactome. Taken together, we are currently unable to maximally impact and improve biomedicine from a protein interactome perspective - motivating the development of experimental and computational techniques that help investigators to address these limitations. Here, we present a resource intended to assist investigators in (i) navigating the cumulative knowledge concerning protein complexes and (ii) forming hypotheses concerning protein interactions that may yet lack conclusive evidence, thus (iii) directing future experiments to address knowledge gaps. To achieve this, we integrated multiple data-types/different properties of protein interactions from multiple sources and after applying various methods of regularization, compared the protein interaction networks computed to those available in the EMBL-EBI Complex Portal, a manually curated, gold-standard catalog of macromolecular complexes. As a result, our resource provides investigators with reliable curation of bona fide and candidate physical interactors of their protein or complex of interest, prompting due scrutiny and further validation when needed. We believe this information will empower a wider range of experimentalists to conduct focused protein interaction studies and to better select research strategies that explicitly target missing information.

bioinformatics↗