Biology subjects

Su, E.

Publications and source records attributed to Su, E..

7 recordsLinked to original sources

bioRxiv2026

Multi-domain analysis of the fecal microbiome of dairy heifers, beef heifers, bred beef heifers, and adult beef cows in the central United States using paired 16S rRNA and 18S rRNA metabarcoding

The growth and performance of cattle is dependent on efficient feed conversion, facilitated by the gut microbiome, including both prokaryotic and eukaryotic organisms. Extrinsic factors such as management practices and intrinsic factors like age and genetics can shape these communities. To characterize the relative effects of such factors on the prokaryotic and eukaryotic microbiomes, fresh fecal samples from dairy heifers (n = 8), beef heifers (n = 8), bred beef heifers (n = 8), and adult beef cows (n = 8) were subjected to paired 16S rRNA (V4) and a novel 18S rRNA (V4-V5) metabarcoding assay, and 18S rRNA amplicons were annotated using two databases in parallel. Taxonomic resolution of 18S rRNA amplicons was excellent, with SILVA and EUKARYOME databases each providing better resolution of different groups of organisms. While no differences in eukaryotic richness or alpha diversity were detected, beta diversity differed in all pairwise comparisons except between beef heifers and bred beef heifers. Prokaryotic richness was lower in adult beef cows than the other groups, and beta diversity differed in all pairwise comparisons. Adult beef cows were the most distinct, characterized by replacement of fungal organisms with protozoa and loss of thermophilic bacteria in the eukaryotic and prokaryotic compartments, respectively. Among numerous differentially abundant features, dairy heifers harbored greater abundance of Cooperia oncophora while beef heifers contained greater abundance of Buxtonella sulcata and Mucor. Correlation network analysis revealed multiple significant positive and negative associations between bacterial, fungal, and protozoal features during maturation of the fecal microbiome. Collectively, these data highlight the richness and diversity of both the prokaryotic and eukaryotic compartments of the bovine fecal microbiome, and the influence of management practices and age on both communities. From a technical standpoint, these data demonstrate the ability to resolve fungal, protozoal, and metazoan organisms, including potential parasites and pathogens, with a single metabarcoding assay. They also highlight the nuanced differences between databases and the value of parallel analyses using more than one database.

systems biology↗

bioRxiv2026

Metastatic Osteosarcoma is Characterized by Loss of Osteoblastic Lineage Fidelity

Osteosarcoma (OS) is a rare and aggressive bone cancer with limited therapeutic progress in several decades. To characterize the transcriptional diversity of malignant cell states in OS, we analyzed single-nucleus RNA sequencing data from 24 tumors, including both primary and metastatic lesions. We identified a canonical osteoblastic cell state that predominated in primary tumor cells, but was dampened in metastases. Integrating tumor data with data from embryonic skeletal development and an experimental osteoblast differentiation model revealed that metastatic OS cells resemble poorly specified mesenchyme enriched for non-osseous programs. Clonal populations in metastatic samples acquired mesenchymal cell states distinct from paired primary tumors. Similar cell state shifts were evident in post-treatment primary tumors relative to paired pre-treatment samples. Our results suggest that localized post-treatment and metastatic OS malignant cells lose osteoblastic lineage fidelity, which may represent a unifying axis of disease evolution and reveal new therapeutic vulnerabilities.

Cancer Biology↗

bioRxiv2024

Synovial Sarcoma Chromatin Dynamics Reveal a Continuum in SS18:SSX Reprograming

Synovial sarcoma (SyS) is an aggressive soft-tissue malignancy characterized by a pathognomonic chromosomal translocation leading to the formation of the SS18::SSX fusion oncoprotein. SS18::SSX associates with mammalian BAF complexes suggesting deregulation of chromatin architecture as the oncogenic driver in this tumour type. To examine the epigenomic state of SyS we performed comprehensive multi-omics analysis on 52 primary pre-treatment human SyS tumours. Our analysis revealed a continuum of epigenomic states across the cohort at fusion target genes independent of rare somatic genetic lesions. We identify cell-of-origin signatures defined by enhancer states and reveal unexpected relationships between H2AK119Ub1 and active marks. The number of bivalent promoters, dually marked by the repressive H3K27me3 and activating H3K4me3 marks, has strong prognostic value and outperforms tumor grade in predicting patient outcome. Finally, we identify SyS defining epigenomic features including H3K4me3 expansion associated with striking promoter DNA hypomethylation in which SyS displays the lowest mean methylation level of any sarcoma subtype. We explore these distinctive features as potential vulnerabilities in SyS and identify H3K4me3 inhibition as a promising therapeutic strategy.

cancer biology↗

bioRxiv2023

Genome-wide CRISPR screen identifies KEAP1 as a genetic dependency of ARID1A in an ovarian clear cell carcinoma model

ARID1A is the core DNA binding subunit of the BAF chromatin remodeling complex and is mutated in about [~]8% of all cancers. The frequency of ARID1A loss varies between cancer subtypes, with clear cell ovarian carcinoma (CCOC) presenting the highest incidence at >50% of cases. Despite a growing understanding of the consequences of ARID1A-loss in cancer, there remains limited targeted therapeutic options for ARID1A-deficient cancers. Using a genome-wide CRISPR screening approach, we identify KEAP1 as a genetic dependency of ARID1A in CCOC. Depletion or chemical perturbation of KEAP1 results in selective growth inhibition of ARID1A-KO cell lines and edited primary endometrial epithelial cells. While we confirm that KEAP1-NRF2 signalling is dysregulated in ARID1A-KO cells, we suggest that this synthetic lethality is not due to aberrant NRF2 signalling. Rather, we find that KEAP1 perturbation exacerbates genome instability phenotypes associated with ARID1A-deficiency. Together, our findings identify a potentially novel synthetic lethal interaction of wARID1A-deficient cells.

cancer biology↗

bioRxiv2021

Global prediction of candidate R-loop binding and R-loop regulatory proteins

In the past decade there has been a growing appreciation for R-loop structures as important regulators of the epigenome, telomere maintenance, DNA repair and replication. Given these numerous functions, dozens, or potentially hundreds, of proteins could serve as direct or indirect regulators of R-loop writing, reading, and erasing. In order to understand common properties shared amongst potential R-loop binding proteins (RLBPs) we mined published proteomic studies and distilled 10 features that were enriched in RLBPs compared to the rest of the proteome. We used these RLBP-specific features along with their amino acid composition to create a random forest classifier which predicts the likelihood of a protein to bind to R-loops. In parallel, we employed a whole-genome CRISPR screen coupled with flow-cytometry using the S9.6 monoclonal antibody to sort guide RNAs associated with induction of high S9.6 staining. Known R-loop regulating pathways such as splicing and DNA damage repair are highly enriched in our datasets, and we validate two new R-loop modulating proteins. Together these resources provide a reference to pursue analyses of novel R-loop regulatory proteins.

cell biology↗

bioRxiv2021

Auditory attention detection with EEG channel attention

Auditory attention detection (AAD) seeks to detect the attended speech from EEG signals in a multi-talker scenario, i.e. cocktail party. As the EEG channels reflect the activities of different brain areas, a task-oriented channel selection technique improves the performance of brain-computer interface applications. In this study, we propose a soft channel attention mechanism, instead of hard channel selection, that derives an EEG channel mask by optimizing the auditory attention detection task. The neural AAD system consists of a neural channel attention mechanism and a convolutional neural network (CNN) classifier. We evaluate the proposed framework on a publicly available database. We achieve 88.3% and 77.2% for 2-second and 0.1-second decision windows with 64-channel EEG; and 86.1% and 83.9% for 2-second decision windows with 32-channel and 16-channel EEG, respectively. The proposed framework outperforms other competitive models by a large margin in all test cases.

neuroscience↗

bioRxiv2018

Epiclomal: probabilistic clustering of sparse single-cell DNA methylation data

We present Epiclomal, a probabilistic clustering method arising from a hierarchical mixture model to simultaneously cluster sparse single-cell DNA methylation data and impute missing values. Using synthetic and published single-cell CpG datasets we show that Epiclomal outperforms non-probabilistic methods and is able to handle the inherent missing data feature which dominates single-cell CpG genome sequences. Using a recently published single-cell 5mCpG sequencing method (PBAL), we show that Epiclomal discovers sub-clonal patterns of methylation in aneuploid tumour genomes, thus defining epiclones. We show that epiclones may transcend copy number determined clonal lineages, thus opening this important form of clonal analysis in cancer. Epiclomal is written in R and Python and is available at https://github.com/shahcompbio/Epiclomal.

genomics↗