Biology subjects

Ting, S.

Publications and source records attributed to Ting, S..

5 recordsLinked to original sources

bioRxiv2025

Brawn before bite in endemic Asian mammals after the end-Cretaceous extinction

The first 10 million years (Myr) following the Cretaceous-Paleogene (K-Pg) mass extinction marked a period of global greenhouse conditions and dramatic rise of placental mammals. Because [~]80% of known terrestrial sections capturing post-K-Pg mammal recovery come from North America, a substantial knowledge gap exists in the tempo and mode of recovery in Asia, where only 3% of sites are located and most contain species found nowhere else. We show that isolated Paleocene placental assemblages from China (1) reached high tooth size disparity early in the Paleocene, (2) tracked regional and global environmental changes in their dental shape later in the Paleocene, and (3) achieved maximum dental shape-performance integration near the end of the first 10 Myr post-K-Pg. This brawn before bite transformation, coupled with prolonged dental shape versus performance variability, favors a scenario whereby many living orders of placental mammals were borne out of phenotypically and functionally plastic ancestral assemblages, including those in tropical south China, during the Paleocene.

paleontology↗

bioRxiv2023

CytoCellDB: A Resource Database For Classification and Analysis of Extrachromosomal DNA in Cancer

Extrachromosomal DNA (ecDNA), or double minute chromosomes, are established cytogenetic markers for malignancy and genome instability. More recently, the cancer community has gained a heightened awareness of the roles of ecDNA in cancer proliferation, drug resistance and epigenetic remodeling. A current hindrance to understanding the biological roles of ecDNA is the lack of available cell line model systems with experimental cytogenetic data that confirm ecDNA status. Although several recent landmark studies have identified common cell lines and tumor models with ecDNA, the current sample size limits our ability to detect ecDNA-driven molecular differences due to limitations in power. Increasing the number of model systems known to express ecDNA would provide new avenues for understanding the fundamental underpinnings of ecDNA biology and would unlock a wealth of potential targeting strategies for ecDNA-driven cancers. To bridge this gap, we created CytoCellDB, a resource that provides karyotype annotations and leverages publicly available global cell line data from the Cancer Dependency Map (DepMap) and the Cancer Cell Line Encyclopedia (CCLE). Here, we identify 139 cell lines that express ecDNA, which is a 200% increase from the current sample size. We expanded the total number of cancer cell lines with ecDNA annotations to 577, which is a 400% increase or 31% of cell lines in CCLE/ DepMap. We demonstrate that a strength of CytoCellDB is the ability to interrogate ecDNA, and a compendium of other chromosomal aberrations, in the context of cancer-specific vulnerabilities, drug sensitivities, and molecular data (genomics, transcriptomics, methylation, proteomics). We anticipate that CytoCellDB will advance cytogenomics research and population-scale discoveries related to ecDNA as well as provide insights into strategies and best practices for determining novel therapeutics that overcome ecDNA-driven drug resistance.

genomics↗

bioRxiv2023

Deciphering the Functional Roles of Individual Cancer Alleles Across Comprehensive Cancer Genomic Studies

Cancer genome data has been growing in both size and complexity, primarily driven by advances in next-generation sequencing technologies, such as Pan-cancer data from TCGA, ICGC, and single-cell sequencing. Yet, discerning the functional role of individual genomic lesions remains a substantial challenge due to the complexity and scale of the data. Previously, we introduced REVEALER, which identifies groups of genomic alterations that significantly associate with target functional profiles or phenotypes, such as pathway activation, gene dependency, or drug response. In this paper, we present a new mathematical formulation of the algorithm. This version (REVEALER 2.0) is considerably more powerful than the original, allowing for rapid processing and analysis of much larger datasets and facilitating higher-resolution discoveries at the level of individual alleles. REVEALER 2.0 employs the Conditional Information Coefficient (CIC) to pinpoint features that are either complementary or mutually exclusive but still correlate with the target functional profile. The aggregation of these features provides a better explanation for the target functional profile than any single alteration on its own. This is indicative of scenarios where several activating genomic lesions can initiate or stimulate a key pathway or process. We replaced the initial three-dimensional kernel estimation with multiple precomputed one-dimensional kernel estimations, resulting in an approximate 150x increase in speed and efficiency. This improvement, combined with its efficient execution, makes REVEALER 2.0 suitable for much larger datasets and a more extensive range of genomic challenges.

bioinformatics↗

bioRxiv2023

Machine learning of three-dimensional protein structures to predict the functional impacts of genome variation

Research in the human genome sciences generates a substantial amount of genetic data for hundreds of thousands of individuals, which concomitantly increases the number of variants with unknown significance (VUS). Bioinformatic analyses can successfully reveal rare variants and variants with clear associations to disease-related phenotypes. These studies have made a significant impact on how clinical genetic screens are interpreted and how patients are stratified for treatment. There are few, if any, comparable computational methods for variants to biological activity predictions. To address this gap, we developed a machine learning method that uses protein three-dimensional structures from AlphaFold to predict how a variant will influence changes to a genes downstream biological pathways. We trained state-of-the-art machine learning classifiers to predict which protein regions will most likely impact transcriptional activities of two proto-oncogenes, nuclear factor erythroid 2 (NFE2)-related factor 2 (Nrf2) and c-MYC. We have identified classifiers that attain accuracies higher than 80%, which have allowed us to identify a set of key protein regions that lead to significant perturbations in c-MYC or Nrf2 transcriptional pathway activities. SignificanceThe vast majority of mutations are either unspecified and/or their downstream biological implications are poorly understood. We have created a method that utilizes protein structure to cluster mutations from population-scale repositories to predict downstream functional impacts. The broader impacts of this approach include advanced filtering of mutations that are likely to impact genome function.

bioinformatics↗

bioRxiv2020

Status-dependent aging rates in long-lived, social mole-rats are shaped by HPA stress axis

Sexual activity and/or reproduction doubles life expectancy in the long-lived rodent genus Fukomys. To investigate the molecular mechanisms underlying this phenomenon, we analyzed a total of 636 RNA-seq samples across 15 tissues. This analysis suggests that the differences in life expectancy between reproductive and non-reproductive mole-rats are mainly caused by critical changes in the regulation of the hypothalamic-pituitary-adrenal stress axis, which we further substantiate with a series of independent evidence. In accordance with previous studies, the up-regulation of the proteasome and several so-called "anti-aging molecules", such as DHEA, is also linked with enhanced life expectancy. On the other hand, several our results oppose crucial findings in short-lived model organisms. For example, we found the up-regulation of the IGF1/GH axis and several other anabolic processes to be compatible with a considerable lifespan prolongation. These contradictions question the extent to which findings from short-lived species can be transferred to longer-lived ones.

genomics↗