Biology subjects

Yi, C.

Publications and source records attributed to Yi, C..

45 records · Page 3Linked to original sources

bioRxiv2020

NIMBus: a Negative Binomial Regression based Integrative Method for Mutation Burden Analysis

BackgroundIdentifying frequently mutated regions is a key approach to discover DNA elements influencing cancer progression. However, it is challenging to identify these burdened regions due to mutation rate heterogeneity across the genome and across different individuals. Moreover, it is known that this heterogeneity partially stems from genomic confounding factors, such as replication timing and chromatin organization. The increasing availability of cancer whole genome sequences and functional genomics data from the Encyclopedia of DNA Elements (ENCODE) may help address these issues. ResultsWe developed a Negative binomial regression-based Integrative Method for mutation Burden analysiS (NIMBus). Our approach addresses the over-dispersion of mutation count statistics by (1) using a Gamma-Poisson mixture model to capture the mutation-rate heterogeneity across different individuals and (2) estimating regional background mutation rates by regressing the varying local mutation counts against genomic features extracted from ENCODE. We applied NIMBus to whole-genome cancer sequences from the PanCancer Analysis of Whole Genomes project (PCAWG) and other cohorts. It successfully identified well-known coding and noncoding drivers, such as TP53 and the TERT promoter. To further characterize the burdening of non-coding regions, we used NIMBus to screen transcription factor binding sites in promoter regions that intersect DNase I hypersensitive sites (DHSs). This analysis identified mutational hotspots that potentially disrupt gene regulatory networks in cancer. We also compare this method to other mutation burden analysis methods. ConclusionNIMBus is a powerful tool to identify mutational hotspots. The NIMBus software and results are available as an online resource at github.gersteinlab.org/nimbus.

bioinformatics↗

bioRxiv2020

Understanding the effects of the direct contacts and the indirect contacts on the epidemic spreading among beef cattle farms in southwest Kansas

Moving infected animals and sharing contaminated vehicles are considered as the most potent ways for between-farm disease transmission. The objective of this study is to develop a network-based simulation model to investigate the effects of direct contact, indirect contact, and their combination on a hypothetical foot-and-mouth disease spreading between beef-cattle farms in southwest Kansas, US, and explore the effect of different types of information-sharing networks on preventing the disease spreading. Based on synthetic cattle and truck movement data in southwest Kansas, we build a farm-level contact network with three layers, a cattle movement layer (direct contact), a truck movement layer (indirect contact), and an information-sharing layer. Through scenario analyses, we compare the disease transmission dynamics, the distribution of outbreak epidemic size, and the disease breakout percentage of different contact structures - only direct contact, only indirect contact, and their combination. In addition, we evaluate different types of information sharing methods by comparing the epidemic size and the estimated economic loss. Simulation results show that neither direct contact nor indirect contact individually can result in a massive outbreak of the disease, but their combination plays a significant role. Additionally, we detect different probabilities of disease outbreaks by starting the simulations at different farms; starting at some farms with high capacity increases the probability of disease outbreaks. Three different information sharing-networks are developed and found effective in preventing the disease from spreading and reducing the economic loss. The information-sharing layer based on trading records has the best performance when compared with a random network and a geographic network.

microbiology↗

bioRxiv2020

Accurate Diagnosis of Colorectal Cancer Based on Histopathology Images Using Artificial Intelligence

BackgroundAccurate and robust pathological image analysis for colorectal cancer (CRC) diagnosis is time-consuming and knowledge-intensive, but is essential for CRC patients treatment. The current heavy workload of pathologists in clinics/hospitals may easily lead to unconscious misdiagnosis of CRC based on their daily image analyses. MethodsBased on a state-of-the-art transfer-learned deep convolutional neural network in artificial intelligence (AI), we proposed a novel patch aggregation strategy for clinic CRC prediction/diagnosis using weakly labeled pathological whole slide image (WSI) patches. This approach was trained and validated using an unprecedented and enormously large number of 170,099 patches, >14,680 WSIs, from >9,631 subjects that covered diverse and representative clinical cases from multi-independent-sources across China, U.S., and Germany. ResultsOur innovative AI tool was consistently nearly perfectly agreed with (average Kappa-statistic 0.896) and even often better than most of the experienced expert pathologists when tested in diagnosing CRC WSIs from multi-centers. The average area under the receiver operating characteristics curve (AUC) of AI was greater than that of the pathologists (0.981 vs 0.970) and achieved the best performance among the application of other AI methods to CRC diagnosis. Our AI-generated heatmap highlights the image regions of cancer tissue/cells. ConclusionsThis first-ever generalizable AI system can handle large amounts of WSIs consistently and robustly without potential bias due to fatigue commonly experienced by clinical pathologists. Hence, it will drastically alleviate the heavy clinical burden of daily pathology diagnosis, and improve the treatment for CRC patients. This tool is generalizable to other cancer diagnosis based on image recognition.

pathology↗

bioRxiv2020

Transposase assisted tagmentation of RNA/DNA hybrid duplexes

Tn5-mediated transposition of double-strand DNA has been widely utilized in various high-throughput sequencing applications. Here, we report that the Tn5 transposase is also capable of direct tagmentation of RNA/DNA hybrids in vitro. As a proof-of-concept application, we utilized this activity to replace the traditional library construction procedure of RNA sequencing, which contains many laborious and time-consuming processes. Results of activity of transposase assisted RNA/DNA hybrids co-tagmentation (termed "ATRAC-seq") are comparable to traditional RNA-seq methods in terms of gene number, gene body coverage and gene expression analysis; at the meantime, ATRAC-seq enables a one-tube library construction protocol and hence is more rapid (within 8 h) and convenient. We expect this tagmentation activity on RNA/DNA hybrids to have broad potentials on RNA biology and chromatin research.

genomics↗

bioRxiv2020

Inactive variants of death receptor p75NTR reduce Alzheimer's neuropathology by interfering with APP internalization

A prevalent model of Alzheimers disease (AD) pathogenesis postulates the generation of neurotoxic fragments derived from the amyloid precursor protein (APP) after its internalization to endocytic compartments. However, the molecular pathways that regulate APP internalization and intracellular trafficking in neurons are unknown. Here we report that 5xFAD mice, an animal model of AD, expressing signaling-deficient variants of the p75 neurotrophin receptor (p75NTR) show greater neuroprotection from AD neuropathology than animals lacking this receptor. p75NTR knock-in mice lacking the death domain or transmembrane Cys259 showed lower levels of A{beta} species, amyloid plaque burden, gliosis, mitochondrial stress and neurite dystrophy than global knock-outs. Strikingly, long-term synaptic plasticity and memory, which are completely disrupted in 5xFAD mice, were fully recovered in the knock-in mice. Mechanistically, we found that p75NTR interacts with APP and regulates its internalization in hippocampal neurons. Inactive p75NTR variants internalized much slower and to lower levels than wild type p75NTR, favoring non-amyloidogenic APP cleavage by reducing APP internalization and colocalization with BACE1, the critical protease for generation of neurotoxic APP fragments. These results reveal a novel pathway that directly and specifically regulates APP internalization, amyloidogenic processing and disease progression, and suggest that inhibitors targeting the p75NTR transmembrane domain may be an effective therapeutic strategy in AD.

neuroscience↗

bioRxiv2019

STAT3 localizes in mitochondria-associated ER membranes instead of in mitochondria

Signal Transducer and Activator of Transcription 3 (STAT3) is a transcription factor (TF) that regulates a variety of biological processes, including a key role in mediating mitochondrial metabolism. It has been shown that STAT3 performs this function by translocating in minute amounts into mitochondria and interacting with mitochondrial proteins and genome. However, whether STAT3 localizes in mitochondria is still up for debate. To decipher the role of mitochondrial STAT3 requires a detailed understanding of its cellular localization. Using Percoll density gradient centrifugation, we surprisingly found that STAT3 is not located in the mitochondrial fraction, but instead, in the mitochondria-associated endoplasmic reticulum membrane (MAM) fraction. This was confirmed by sub-diffraction image analysis of labeled mitochondria in embryonic astrocytes. Also, we find that other TFs that have been previously found to localize in mitochondria are also found instead in the MAM fraction. Our results suggest that STAT3 and other transcriptional factors are, contrary to prior studies, consolidated specifically at MAMs, and further efforts to understand mitochondrial STAT3 function must take into consideration this localization, as the associated functional consequences offer a different interpretation to the questions of STAT3 trafficking and signaling in the mitochondria.

cell biology↗

bioRxiv2019

Prognostic significances of interleukin-17-producing cells and Th17 cells in malignant cancers: a meta-analysis of the literatures

Background and purposeAs a proinflammatory factor, interleukin-17 (IL-17) can play a role in both tumor promotion and suppression. IL-17 is traditionally regarded as secreting mainly by CD4+ T helper cells (Th17 cells), while other immune subsets have been proved to produce IL-17, called IL-17+ cells. Considerable studies have drawn controversial conclusions about association between IL-17+/Th17 cells and prognosis of cancer patients. This meta-analysis was performed to systematically and quantitatively analyze prognostic values of IL-17+ cells and Th17 cells in cancer patients. MethodsA comprehensive retrieval was conducted in Pubmed (MEDLINE) and EMBASE databases. Pooled risk ratios (RRs) or hazard ratios (HRs) and corresponding 95% confidence intervals (CI) were calculated to evaluate the prognostic values of IL-17+ cells and Th17 cells in cancer patients. ResultsA total of 42 studies with 5039 patitents were included. High IL-17+ cells was significantly associated with tumor recurrence (RR = 4.23, 95% CI [1.58, 11.35]), worse disease free survival (DFS) (HR = 1.84, 95% CI [1.22, 2.77]) and overall survival (OS) (HR = 1.39, 95% CI [1.04, 1.87]), especially in cancers of digestive system. Besides, no significant difference was observed between high IL-17+ cells and histological grade, lymph node metastasis, tumor volume, clinical stages or distant metastasis. Moreover, there was no significant difference in OS between high and low Th17 cells in cancer patients (HR = 0.93, 95% CI [0.58, 1.49]). ConclusionsThis meta-analysis suggests high IL-17+ cells could be an indicator for worse survival in patients with malignant cancers, especially with cancers of digestive system. Although high Th17 cells appears to have non-statistically significance on prognosis, more clinical studies should be implemented to investigate the underlying function of Th17 cells within tumor microenvironment. This study put forward a new insight for potential application of anti-IL-17 target therapy in cancer therapeutics.

cancer biology↗

bioRxiv2019

Landscape and regulation of m6A and m6Am methylome across human and mouse tissues

N6-methyladenosine (m6A), the most abundant internal mRNA modification, and N6,2-O-dimethyladenosine (m6Am), found at the first-transcribed nucleotide, are two examples of dynamic and reversible epitranscriptomic marks. However, the profiles and distribution patterns of m6A and m6Am across different human and mouse tissues are poorly characterized. Here we report the m6A and m6Am methylome through an extensive profiling of 42 human tissues and 16 mouse tissue samples. Globally, the m6A and m6Am peaks in non-brain tissues demonstrates mild tissue-specificity but are correlated in general, whereas the m6A and m6Am methylomes of brain tissues are clearly resolved from the non-brain tissues. Nevertheless, we identified a small subset of tissue-specific m6A peaks that can readily classify the tissue types. The number of m6A and m6Am peaks are partially correlated with the expression levels of their writers and erasers. In addition, the m6A- and m6Am-containing regions are enriched for single nucleotide polymorphisms. Furthermore, cross-species analysis of m6A and m6Am methylomes revealed that species, rather than tissue types, is the primary determinant of methylation. Collectively, our study provides an in-depth resource for dissecting the landscape and regulation of the m6A and m6Am epitranscriptomic marks across mammalian tissues.

molecular biology↗

bioRxiv2017

Base-resolution mapping reveals distinct m1A methylome in nuclear- and mitochondrial-encoded transcripts

Gene expression can be post-transcriptionally regulated via dynamic and reversible RNA modifications. N1-methyladenosine (m1A) is a recently identified mRNA modification; however, little is known about its precise location, regulation and function. Here, we develop a base-resolution m1A profiling method, based on m1A-induced misincorporation during reverse transcription, and report distinct classes of m1A methylome in the human transcriptome. m1A in 5-UTR, particularly those at the first nucleotide of mRNA, associate with increased translation efficiency. A different subset of m1A exhibit a GUUCRA tRNA-like motif, are evenly distributed in the transcriptome and are dependent on the methyltransferase TRMT6/61A. Additionally, we show for the first time that m1A is prevalent in the mitochondrial-encoded transcripts. Manipulation of m1A level via TRMT61B, a mitochondria-localizing m1A methyltransferase, demonstrates that m1A in mitochondrial mRNA interferes with translation. Collectively, our approaches reveal distinct classes of m1A methylome and provide a resource for functional studies of m1A-mediated epitranscriptomic regulation.

biochemistry↗