Biology subjects

Yardimci, G. G.

Publications and source records attributed to Yardimci, G. G..

13 recordsLinked to original sources

bioRxiv2026

Paired oral clinical specimens reveal the underlying ecology supporting the emergence of inflammophilic microbiome communities

BackgroundInflammatory oral diseases are associated with reproducible shifts from commensal-dominated microbiota toward pathobiont-enriched communities, yet the ecological mechanisms underlying the emergence of inflammophiles remain poorly understood. This study aims to investigate if host-derived inflammatory environments act as selective pressures that restructure microbial metabolism and community organization during disease progression. MethodsWe performed 16S rRNA gene sequencing of patient-matched pediatric dental plaque and odontogenic abscess specimens to capture microbial community transitions across an inflammatory ecological gradient. Community ecology modeling and inferred metagenomic analyses were used to identify taxa and functional programs associated with commensal and inflammophilic states. ResultsPatient-matched comparisons revealed a reproducible ecological selection gradient linking inflammatory environments to expansion of metabolically specialized inflammophiles and depletion of carbohydrate-utilizing commensals. Commensal-dominated plaque communities exhibited anabolic, carbohydrate-centered metabolic capacity, whereas abscess microbiota were enriched for catabolic metabolism, amino acid fermentation, and antimicrobial resistance, consistent with adaptation to inflammation-driven nutrient landscapes and immune pressure. ConclusionsThese findings support a model in which host inflammation drives ecological restructuring of the oral microbiome toward metabolically adapted inflammophilic communities. Defining the metabolic requirements and selective pressures governing these transitions provides a framework for microbiome-directed therapeutic strategies aimed at restoring ecological stability during inflammatory dysbiosis.

bioinformatics↗

bioRxiv2026

PU.1 inhibition sensitizes stem-monocytic AML to BCL2 blockade

Acute myeloid leukemia (AML) exhibits substantial transcriptional heterogeneity across differentiation states that influences therapeutic response to BCL2 inhibition with venetoclax. While hematopoietic stem cell (HSC)-like AMLs show high sensitivity to venetoclax and monocytic-like AMLs demonstrate resistance, the therapeutic behavior of leukemias harboring both transcriptional programs remains poorly defined. Analysis of a large AML cohort reveals a distinct patient population exhibiting concurrent HSC- and monocyte-like transcriptional signatures, which we term stem-monocytic AML. Ex vivo drug sensitivity profiling demonstrates that stem-monocytic AMLs exhibit venetoclax resistance comparable to pure monocytic disease, despite expressing HSC-like transcriptional features. Using a leukemia cell line model that recapitulates stem-monocytic AML characteristics, we show through immunophenotyping and single-cell lineage tracing that venetoclax preferentially depletes immature blasts while sparing differentiated monocytic populations. Single-cell transcriptomic and chromatin accessibility analyses identify enrichment of myeloid differentiation transcription factors, particularly PU.1, in resistant populations. A targeted CRISPR knockout screen confirms that PU.1 disruption induces differentiation arrest and enhances venetoclax sensitivity primarily in the immature immunophenotypic compartments. Pharmacologic PU.1 inhibition with the small molecule DB2313 synergizes with venetoclax in both cell line models and primary patient samples. These findings establish stem-monocytic AML as a transcriptionally and functionally distinct subtype and nominate combined PU.1 and BCL2 inhibition as a rational therapeutic strategy for improving venetoclax response in this patient population.

cancer biology↗

bioRxiv2023

Robust CNV detection using single-cell ATAC-seq

Copy number variation (CNV) is a widely studied type of structural variation seen in the genomes of cancerous and other dysfunctional cells. CNVs can have direct and indirect effects on gene dosage, and are thought to drive cancer progression and other disorders. Advancements in single-cell assays such as sc-ATAC-seq and sc-RNA-seq, along with their ubiquitous use, allows for the identification of CNVs at single cell resolution. While there are a variety of available tools for CNV detection in sc-RNA-seq, development of sc-ATAC-seq based accurate and reliable CNV callers is in the early stages, with only two available algorithms so far. We present RIDDLER, a single-cell ATAC-seq CNV detection algorithm based on outlier aware generalized linear modeling. By utilizing tools from robust statistics, we developed an extensible model that is able to identify single-cell CNVs from sc-ATAC-seq data in an unsupervised fashion, while providing probabilistic justification for results. Our statistical approach also allows us to estimate when loss of signal is likely caused by drop-out or a true genome deletion event, as well as predict reliable CNVs without the need for normative reference cells. We demonstrate the effectiveness of our algorithm on cancer cell line models where it achieves better agreement with bulk WGS derived CNVs than competing methods. We also compare our approach on 10x multimone data, where it shows better agreement and integration with RNA derived CNV estimates.

genomics↗

bioRxiv2022

Multi-center integrated analysis of non-coding CRISPR screens

The ENCODE Consortiums efforts to annotate non-coding, cis-regulatory elements (CREs) have advanced our understanding of gene regulatory landscapes which play a major role in health and disease. Pooled, non-coding CRISPR screens are a promising approach for systematically investigating gene regulatory mechanisms. Here, the ENCODE Functional Characterization Centers report 109 screens comprising 346,970 individual perturbations across 13.3Mb of the genome, using a variety of methods, readouts, and statistical analyses. Across 332 functionally confirmed CRE-gene links, we identify principles for screening endogenous, non-coding elements for causal regulatory mechanisms. Nearly all CREs show strong evidence of open chromatin, and targeting accessibility peak summits is a critical component of our proposed sgRNA design rules. We provide experimental guidelines to accurately detect CREs with variable, often low, transcriptional effects. We discover a previously undescribed DNA strand-bias for CRISPRi in transcribed regions with implications for screen design and analysis. Benchmarking five screen analysis tools, we find CASA produces the most conservative CRE calls and is robust to artifacts of low-specificity sgRNAs. Together, we provide an accessible data resource, predesigned sgRNAs targeting 3,275,697 ENCODE SCREEN candidate CREs, and screening guidelines to accelerate functional characterization of the non-coding genome.

genomics↗

bioRxiv2022

GoPeaks: Histone Modification Peak Calling for CUT&Tag

Genome-wide mapping of the histone modification landscape is critical to understanding tran-scriptional regulation. Cleavage Under Targets and Tagmentation (CUT&Tag) is a new method for profiling the localization of covalent histone modifications, offering improved sensitivity and decreased cost compared with Chromatin Immunoprecipitation Sequencing (ChIP-seq). Here, we present GoPeaks, a peak calling method specifically designed for histone modification CUT&Tag data. GoPeaks implements a Binomial distribution and stringent read count cut-off to nominate candidate genomic regions. We compared the performance of GoPeaks against commonly used peak calling algorithms to detect H3K4me3, H3K4me1, and H3K27Ac peaks from CUT&Tag data. These histone modifications display a range of peak profiles and are frequently used in epigenetic studies. We found GoPeaks robustly detects genome-wide histone modifications and, notably, identifies H3K27Ac with improved sensitivity compared to other standard peak calling algorithms.

bioinformatics↗

bioRxiv2021

High-content single-cell combinatorial indexing

Single-cell genomics assays have emerged as a dominant platform for interrogating complex biological systems. Methods to capture various properties at the single-cell level typically suffer a tradeoff between cell count and information content, which is defined by the number of unique and usable reads acquired per cell. We and others have described workflows that utilize single-cell combinatorial indexing (sci)1, leveraging transposase-based library construction2 to assess a variety of genomic properties in high throughput; however, these techniques often produce sparse coverage for the property of interest. Here, we describe a novel adaptor-switching strategy, s3, capable of producing one-to-two order-of-magnitude improvements in usable reads obtained per cell for chromatin accessibility (s3-ATAC), whole genome sequencing (s3-WGS), and whole genome plus chromatin conformation (s3-GCC), while retaining the same high-throughput capabilities of predecessor sci technologies. We apply s3 to produce high-coverage single-cell ATAC-seq profiles of mouse brain and human cortex tissue; and whole genome and chromatin contact maps for two low-passage patient-derived cell lines from a primary pancreatic tumor.

genetics↗

bioRxiv2019

Inferring diploid 3D chromatin structures from Hi-C data

The 3D organization of the genome plays a key role in many cellular processes, such as gene regulation, differentiation, and replication. Assays like Hi-C measure DNA-DNA contacts in a high-throughput fashion, and inferring accurate 3D models of chromosomes can yield insights hidden in the raw data. For example, structural inference can account for noise in the data, disambiguate the distinct structures of homologous chromosomes, orient genomic regions relative to nuclear landmarks, and serve as a framework for integrating other data types. Although many methods exist to infer the 3D structure of haploid genomes, inferring a diploid structure from Hi-C data is still an open problem. Indeed, the diploid case is very challenging, because Hi-C data typically does not distinguish between homologous chromosomes. We propose a method to infer 3D diploid genomes from Hi-C data. We demonstrate the accuarcy of the method on simulated data, and we also use the method to infer 3D structures for mouse chromosome X, confirming that the active homolog exhibits a bipartite structure, whereas the active homolog does not.

bioinformatics↗

bioRxiv2019

Jointly embedding multiple single-cell omics measurements

Many single-cell sequencing technologies are now available, but it is still difficult to apply multiple sequencing technologies to the same single cell. In this paper, we propose an unsupervised manifold alignment algorithm, MMD-MA, for integrating multiple measurements carried out on disjoint aliquots of a given population of cells. Effectively, MMD-MA performs an in silico co-assay by embedding cells measured in different ways into a learned latent space. In the MMD-MA algorithm, single-cell data points from multiple domains are aligned by optimizing an objective function with three components: (1) a maximum mean discrepancy (MMD) term to encourage the differently measured points to have similar distributions in the latent space, (2) a distortion term to preserve the structure of the data between the input space and the latent space, and (3) a penalty term to avoid collapse to a trivial solution. Notably, MMD-MA does not require any correspondence information across data modalities, either between the cells or between the features. Furthermore, MMD-MAs weak distributional requirements for the domains to be aligned allow the algorithm to integrate heterogeneous types of single cell measures, such as gene expression, DNA accessibility, chromatin organization, methylation, and imaging data. We demonstrate the utility of MMD-MA in simulation experiments and using a real data set involving single-cell gene expression and methylation data.

bioinformatics↗

bioRxiv2017

Dynamic reorganization of nuclear architecture during human cardiogenesis

While chromosomal architecture varies among cell types, little is known about how this organization is established or its role in development. We integrated Hi-C, RNA-seq and ATAC-seq during cardiac differentiation from human pluripotent stem cells to generate a comprehensive profile of chromosomal architecture. We identified active and repressive domains that are dynamic during cardiogenesis and recapitulate in vivo cardiomyocytes. During differentiation, heterochromatic regions condense in cis. In contrast, many cardiac-specific genes, such as TTN (titin), decompact and transition to an active compartment coincident with upregulation. Moreover, we identify a network of genes, including TTN, that share the heart-specific splicing factor, RBM20, and become associated in trans during differentiation, suggesting the existence of a 3D nuclear splicing factory. Our results demonstrate both the dynamic nature in nuclear architecture and provide insights into how developmental genes are coordinately regulated.\n\nOne Sentence SummaryThe three-dimensional structure of the human genome is dynamically regulated both globally and locally during cardiogenesis.

genomics↗

bioRxiv2017

GenomeDISCO: A concordance score for chromosome conformation capture experiments using random walks on contact map graphs

MotivationThe three-dimensional organization of chromatin plays a critical role in gene regulation and disease. High-throughput chromosome conformation capture experiments such as Hi-C are used to obtain genome-wide maps of 3D chromatin contacts. However, robust estimation of data quality and systematic comparison of these contact maps is challenging due to the multi-scale, hierarchical structure of chromatin contacts and the resulting properties of experimental noise in the data. Measuring concordance of contact maps is important for assessing reproducibility of replicate experiments and for modeling variation between different cellular contexts.\n\nResultsWe introduce a concordance measure called GenomeDISCO (DIfferences between Smoothed COntact maps) for assessing the similarity of a pair of contact maps obtained from chromosome conformation capture experiments. The key idea is to smooth contact maps using random walks on the contact map graph, before estimating concordance. We use simulated datasets to benchmark GenomeDISCOs sensitivity to different types of noise that affect chromatin contact maps. When applied to a large collection of Hi-C datasets, GenomeDISCO accurately distinguishes biological replicates from samples obtained from different cell types. GenomeDISCO also generalizes to other chromosome conformation capture assays, such as HiChIP.\n\nAvailabilitySoftware implementing GenomeDISCO is available at https://github.com/kundajelab/genomedisco.\n\nContactakundaje@stanford.edu\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2017

An Integrative Framework For Detecting Structural Variations In Cancer Genomes

Structural variants can contribute to oncogenesis through a variety of mechanisms, yet, despite their importance, the identification of structural variants in cancer genomes remains challenging. Here, we present an integrative framework for comprehensively identifying structural variation in cancer genomes. For the first time, we apply next-generation optical mapping, high-throughput chromosome conformation capture (Hi-C), and whole genome sequencing to systematically detect SVs in a variety of cancer cells.\n\nUsing this approach, we identify and characterize structural variants in up to 29 commonly used normal and cancer cell lines. We find that each method has unique strengths in identifying different classes of structural variants and at different scales, suggesting that integrative approaches are likely the only way to comprehensively identify structural variants in the genome. Studying the impact of the structural variants in cancer cell lines, we identify widespread structural variation events affecting the functions of non-coding sequences in the genome, including the deletion of distal regulatory sequences, alteration of DNA replication timing, and the creation of novel 3D chromatin structural domains.\n\nThese results underscore the importance of comprehensive structural variant identification and indicate that non-coding structural variation may be an underappreciated mutational process in cancer genomes.

genomics↗

bioRxiv2017

HiCRep: assessing the reproducibility of Hi-C data using a stratum-adjusted correlation coefficient

Hi-C is a powerful technology for studying genome-wide chromatin interactions. However, current methods for assessing Hi-C data reproducibility can produce misleading results because they ignore spatial features in Hi-C data, such as domain structure and distance dependence. We present HiCRep, a framework for assessing the reproducibility of Hi-C data that systematically accounts for these features. In particular, we introduce a novel similarity measure, the stratum adjusted correlation coefficient (SCC), for quantifying the similarity between Hi-C interaction matrices. Not only does it provide a statistically sound and reliable evaluation of reproducibility, SCC can also be used to quantify differences between Hi-C contact matrices and to determine the optimal sequencing depth for a desired resolution. The measure consistently shows higher accuracy than existing approaches in distinguishing subtle differences in reproducibility and depicting interrelationships of cell lineages. The proposed measure is straightforward to interpret and easy to compute, making it well-suited for providing standardized, interpretable, automatable, and scalable quality control. The freely available R package HiCRep implements our approach.

bioinformatics↗

bioRxiv2016

Software tools for visualizing Hi-C data

Recently developed, high-throughput assays for measuring the three-dimensional configuration of DNA in the nucleus have provided unprecedented insights into the relationship between DNA 3D configuration and function. However, accurate interpretation of data from assays such as ChIA-PET and Hi-C is challenging because the data is large and cannot be easily rendered using a standard genome browser. In particular, an effective Hi-C visualization tool must provide a variety of visualization modes and be capable of viewing the data in conjunction with existing, complementary data. We review a number of such software tools that have been described recently in the literature, focusing on tools that do not require programming expertise on the part of the user. In particular, we describe HiBrowse, Juicebox, my5C, the 3D Genome Browser, and the Epigenome Browser, outlining their complementary functionalities and highlighting which types of visualization tasks each tool is best designed to handle.

genomics↗