Biology subjects

Dai, C.

Publications and source records attributed to Dai, C..

50 records · Page 3Linked to original sources

bioRxiv2021

Targeting circPTPN12/1 miR-21-5p/ΔNp63α pathway as a therapeutic strategy for human endometrial fibrosis

Emerging evidence demonstrates the important role of circular RNAs (circRNAs) in regulating pathological processes in various diseases including organ fibrosis. Endometrium fibrosis is the leading cause of uterine infertility, but the role of circRNAs in its pathogenesis is largely unknown. Here, we provide the evidence that upregulation of circPTPN12 in endometrial epithelial cells (EECs) of fibrotic endometrium functions as endogenous sponge of miR-21-5p to inhibit miR-21-5p expression and activity, which in turn results in upregulation of {Delta}Np63 to induce the epithelial mesenchymal transition (EMT) of EECs (EEC-EMT). In a mouse model of endometrium fibrosis, circPTPN12 appears to be a cofactor of driving EEC-EMT. Our findings reveal the novel mechanism in the pathogenesis of endometrium fibrosis and the potential therapeutic strategy for endometrium fibrosis via targeting circPTPN12/miR-21-5p/{Delta}Np63 pathway.

molecular biology↗

bioRxiv2020

Microbial Species Abundance Distributions Guide Human Population Size Estimation from Sewage Metagenomes

The metagenome embedded in urban sewage is an attractive new data source to understand urban ecology and assess human health status at scales beyond a single host. Analyzing the viral fraction of wastewater in the ongoing COVID-19 pandemic has shown the potential of wastewater as aggregated samples for early detection, prevalence monitoring, and variant identification of human diseases in large populations. However, using census-based population size instead of real-time population estimates can mislead the interpretation of data acquired from sewage, hindering assessment of representativeness, inference of prevalence, or comparisons of taxa across sites. Here, we show that taxon abundance and sub-species diversisty in gut-associated microbiomes are new feature space to utilize for human population estimation. Using a population-scale human gut microbiome sample of over 1,100 people, we found that taxon-abundance distributions of gut-associated multi-person microbiomes exhibited generalizable relationships with respect to human population size. Here and throughout this paper, the human population size is essentially the sample size from the wastewater sample. We present a new algorithm, MicrobiomeCensus, for estimating human population size from sewage samples. MicrobiomeCensus harnesses the inter-individual variability in human gut microbiomes and performs maximum likelihood estimation based on simultaneous deviation of multiple taxas relative abundances from their population means. MicrobiomeCensus outperformed generic algorithms in data-driven simulation benchmarks and detected population size differences in field data. New theorems are provided to justify our approach. This research provides a mathematical framework for inferring population sizes in real time from sewage samples, paving the way for more accurate ecological and public health studies utilizing the sewage metagenome. Author summaryWastewater-based epidemiology (WBE) is an emerging field that employs sewage as aggregated samples of human populations. This approach is particularly promising for tracking diseases that can spread asymptomatically in large populations, such as the COVID-19. As a new type of biological data, sewage has its own unique challenges to utilize. While wastewater samples are usually assumed to represent large populations, it is not guaranteed, because of stochasticity in toilet flushes; unlike epidemiological experiments collecting data from individuals, sample size, i.e., the human population size represented by a wastewater sample, is a fundamental yet difficult-to-characterize parameter for sewage samples. Researchers would need to aggregate data from large areas and week-long collection to stabilize data, during which, important spikes in small areas or short time scales may be lost. It also remains challenging to turn viral titers into case prevalences, evaluating representativeness, or comparing measurements across sites/studies. This study provides a framework to estimate human population size from sewage utilizing human gut-associated microorganisms. Through analysis, we demonstrate that variance of taxon abundances and single-nucleotide polymorphism as two variables that change with population size. We provide a new tool MicrobiomeCensus that performs population size estimation from microbial taxon abundances. MicrobiomeCensus outperforms generic algorithms in terms of computational efficiency while at comparable or better accuracy. Using MicrobiomeCensus, we detected population size differences in sewage samples taken in Cambridge, MA, under two sampling approaches, i.e., "grab" or "composite" sampling. This study provides a framework to utilize individual-level microbiomes to learn from sewage, paving the way to prevalence estimation and improved spatio-temporal resolutions in WBE..

bioinformatics↗

bioRxiv2020

SARS-CoV-2 Cell Entry Factors ACE2 and TMPRSS2 are Expressed in the Pancreas but Not in Islet Endocrine Cells

Summary/AbstractReports of new-onset diabetes and diabetic ketoacidosis in individuals with COVID-19 have led to the hypothesis that SARS-CoV-2, the virus that causes COVID-19, is directly cytotoxic to pancreatic islet {beta} cells. This would require binding and entry of SARS-CoV-2 into host {beta} cells via cell surface co-expression of ACE2 and TMPRSS2, the putative receptor and effector protease, respectively. To define ACE2 and TMPRSS2 expression in the human pancreas, we examined six transcriptional datasets from primary human islet cells and assessed protein expression by immunofluorescence in pancreata from donors with and without diabetes. ACE2 and TMPRSS2 transcripts were low or undetectable in pancreatic islet endocrine cells as determined by bulk or single cell RNA sequencing, and neither protein was detected in or {beta} cells from these donors. Instead, ACE2 protein was expressed in the islet and exocrine tissue microvasculature and also found in a subset of pancreatic ducts, whereas TMPRSS2 protein was restricted to ductal cells. The absence of significant ACE2 and TMPRSS2 co-expression in islet endocrine cells reduces the likelihood that SARS-CoV-2 directly infects pancreatic islet {beta} cells through these cell entry proteins.

cell biology↗

bioRxiv2020

Dietary Protein on Growth Performance, Serum Biochemical Index and offspring of Hu ewes in late pregnancy

The purpose of this study was to explore the effects of ewe growth performance, serum biochemical indicators and lamb growth and development at different protein levels in late pregnancy. A total of 15 pregnant ewes (46.4 {+/-} 1.38kg initial BW) were assigned to 3 groups with 5 ewes in each group in a randomized block design. P1, P2 and P3 of three groups were fed diets with different levels of crude protein at 11.25%, 12.5% and 13.75% (low, medium and high) respectively, while the dietary energy levels remained unchanged. Choose ewes with the same body condition from 90 days of pregnancy to different protein diets during delivery, and feed the same diets during lactation. New-born lambs were left to suckle their dams freely for the first three days and were kept with their dams till weaning at 60 days of age. Dietary protein levels during late gestation showed no significant effect on the weight gain of ewes and their offspring in the late pregnancy (P > 0.05) but had significant effects on blood urea nitrogen (BUN), glucose (GLU), C-reactive protein (CRPL3) and ammonia nitrogen (NH3L) before parturition, and on triglyceride (TG) in lactating ewes. Dietary protein levels during late gestation had significant effects on birth height, body length, chest depth, chest circumference, straight crown hip length and curved crown hip length of lambs (P < 0.05) as well as on weaning body length, chest circumference, head width and head length (P < 0.05). The results showed that although different dietary protein levels during late gestation have no significant effects on growth performance of ewes, singleton, twins and triplets, it affects lambs body shape and ewes metabolism. Therefore, the optimum protein concentration for the growth of ewes and subsequent lambs in this experiment is 11.25%.

physiology↗

bioRxiv2020

Multiomic Immunophenotyping of COVID-19 Patients Reveals Early Infection Trajectories

Host immune responses play central roles in controlling SARS-CoV2 infection, yet remain incompletely characterized and understood. Here, we present a comprehensive immune response map spanning 454 proteins and 847 metabolites in plasma integrated with single-cell multi-omic assays of PBMCs in which whole transcriptome, 192 surface proteins, and T and B cell receptor sequence were co-analyzed within the context of clinical measures from 50 COVID19 patient samples. Our study reveals novel cellular subpopulations, such as proliferative exhausted CD8+ and CD4+ T cells, and cytotoxic CD4+ T cells, that may be features of severe COVID-19 infection. We condensed over 1 million immune features into a single immune response axis that independently aligns with many clinical features and is also strongly associated with disease severity. Our study represents an important resource towards understanding the heterogeneous immune responses of COVID-19 patients and may provide key information for informing therapeutic development.

immunology↗

bioRxiv2020

A neural m6A/YTHDF pathway is required for learning and memory in Drosophila

The roles of epitranscriptomic modifications in mRNA regulation have recently received substantial attention, with appreciation growing for their phenotypically selective impacts within the animal. We adopted Drosophila melanogaster as a model system to study m6A, the most abundant internal modification of mRNA. Here, we report proteomic and functional analyses of fly m6A-binding proteins, confirming nuclear (YTHDC) and cytoplasmic (YTHDF) YTH domain proteins as the major m6A binders. Since all core m6A pathway mutants are viable, we assessed in vivo requirements of the m6A pathway in cognitive processes. Assays of short term memory revealed an age-dependent requirement of m6A writers working via YTHDF, but not YTHDC, comprising the first phenotypes assigned to Drosophila mutants of the cytoplasmic m6A reader. These factors promote memory via neural-autonomous activities, and are required in the mushroom body, the center for associative learning. To inform their basis, we mapped m6A from wild-type and mettl3 null mutant heads, allowing robust discrimination of Mettl3-dependent m6A sites. In contrast to mammalian m6A, which is predominant in 3 UTRs, Drosophila m6A is highly enriched in 5 UTRs and occurs in an adenosine-rich context. Genomic analyses demonstrate that Drosophila m6A does not directionally affect RNA stability, but is preferentially deposited on genes with low translational efficiency. However, functional tests indicate a role for m6A in translational activation, since we observe reduced nascent protein synthesis in mettl3-KO cells. Finally, we show that ectopic YTHDF can increase m6A target reporter output in an m6A-binding dependent manner, and that this activity is required for in vivo neural function of YTHDF in memory. Altogether, we provide the first tissue-specific m6A maps in this model organism and reveal selective behavioral and translational defects for m6A/YTHDF mutants.

animal behavior and cognition↗

bioRxiv2019

Longitudinal wastewater sampling in buildings reveals temporal dynamics of metabolites

Direct sampling of building wastewater has the potential to enable "precision public health" observations and interventions. Temporal sampling offers additional dynamic information that can be used to increase the informational content of individual metabolic "features", but few studies have focused on high-resolution sampling. Here, we sampled three spatially close buildings, revealing individual metabolomics features, retention time (rt) and mass-to-charge ratio (mz) pairs, that often possess similar stationary statistical properties, as expected from aggregate sampling. However, the temporal profiles of features--providing orthogonal information to physicochemical properties--illustrate that many possess significantly different feature temporal dynamics (fTDs) across buildings, with rapid and unpredictable deviations from the mean. Internal to a building, numerous and seemingly unrelated features, with mz and rt differences up to hundreds of Daltons and seconds, display highly correlated fTDs, suggesting non-obvious feature relationships. Data-driven building classification achieves high sensitivity and specificity, and extracts building-identifying features with unique dynamics. Analysis of fTDs from many short-duration samples allows for tailored community monitoring with applicability in public health studies.

bioinformatics↗

bioRxiv2019

Individualized ctDNA Fingerprints to Monitor Treatment Response and Recurrence in Multiple Cancer Types

Circulating tumor DNA (ctDNA) panels hold high promise of accurately predicting the therapeutic response of tumors while being minimally invasive and cost-efficient. However, their use has been limited to a small number of tumor types and patients. Here, we developed individualized ctDNA fingerprints suitable for most patients with multiple cancer types. The panels were designed based on individual whole-exome sequencing data in 521 Chinese patients and targeting high clonal population clusters of somatic mutations. Together, these patients represent 12 types of cancers and seven different treatments. The customized ctDNA panels have a median somatic mutation number of 19, most of which are patient-specific rather than cancer hotspot mutations; 66.8% of the patients were ctDNA-positive. We further evaluated the ctDNA content fraction (CCF) of the mutations, and analyzed the association between the change of ctDNA concentration and therapeutic response. We followed up 106 patients for clinical evaluation, demonstrating a significant correlation of changes in ctDNA with clinical outcomes, with a consistency rate of 93.4%. In particular, the median CCF increased by 204.6% in patients with progressive disease, decreased by 82.5% in patients with remission, and was relatively stable in patients with stable disease. Overall, 85% of the patients with a ctDNA-positive status experienced metastasis or relapse long before imaging detection, except for two patients who developed recurrence and metastasis almost simultaneously. The average lead time between the first ctDNA-positive finding and radiological diagnosis was 76 days in three patients that changed from a ctDNA-negative to -positive status. Our individualized ctDNA analysis can effectively monitor the treatment response, metastasis, and recurrence in multiple cancer types in patients with multiple treatment options, therefore offering great clinical applicability for improving personalized treatment in cancer.\n\nOne Sentence SummaryctDNA fingerprint panels were customized to predict the treatment response for multiple cancer types from individual whole-exome sequencing data.

cancer biology↗

bioRxiv2019

Multi-site sampling and risk prioritization reveals the public health relevance of antibiotic resistance genes found in wastewater environments

The spread of bacterial antibiotic resistance across human and environmental habitats is a global public health challenge. Wastewater has been implicated as a major source of antibiotic resistance in the environment, as it carries resistant bacteria and resistance genes from humans into natural ecosystems. However, different wastewater environments and antibiotic resistance genes in wastewater do not all present the same level of risk to human health. In this study, we investigate the public health relevance of antibiotic resistance found in wastewater by combining metagenomic sequencing with risk prioritization of resistance genes, analyzing samples across urban sewage system environments in multiple countries. We find that many of the resistance genes commonly found in wastewater are not readily present in humans. Ranking antibiotic resistance genes based on their potential pathogenicity and mobility reveals that most of the resistance genes in wastewater are not clinically relevant. Additionally, we show that residential wastewater resistomes pose greater risk to human health than those in wastewater treatment plant samples, and that residential wastewater can be as risky as hospital effluent. Across countries, differences in antibiotic resistance in residential wastewater can, in some cases, reflect differences in antibiotic drug consumption. Finally, we find that the flow of antibiotic resistance genes is influenced by geographical distance and environmental selection. Taken together, we demonstrate how different analytical approaches can provide greater insights into the public health relevance of antibiotic resistance in wastewater.

microbiology↗

bioRxiv2018

Genes with high network connectivity are enriched for disease heritability

Recent studies have highlighted the role of gene networks in disease biology. To formally assess this, we constructed a broad set of pathway, network, and pathway+network annotations and applied stratified LD score regression to 42 independent diseases and complex traits (average N=323K) to identify enriched annotations. First, we constructed annotations from 18,119 biological pathways, including 100kb windows around each gene. We identified 156 pathway-trait pairs whose disease enrichment was statistically significant (FDR < 5%) after conditioning on all genes and on annotations from the baseline-LD model, a stringent step that greatly reduced the number of pathways detected; most of the significant pathway-trait pairs were previously unreported. Next, for each of four published gene networks, we constructed probabilistic annotations based on network connectivity using closeness centrality, a measure of how close a gene is to other genes in the network. For each gene network, the network connectivity annotation was strongly significantly enriched. Surprisingly, the enrichments were fully explained by excess overlap between network annotations and regulatory annotations from the baseline-LD model, validating the informativeness of the baseline-LD model and emphasizing the importance of accounting for regulatory annotations in gene network analyses. Finally, for each of the 156 enriched pathway-trait pairs, for each of the four gene networks, we constructed pathway+network annotations by annotating genes with high network connectivity to the input pathway. For each gene network, these pathway+network annotations were strongly significantly enriched for the corresponding traits. Once again, the enrichments were largely explained by the baseline-LD model. In conclusion, gene network connectivity is highly informative for disease architectures, but the information in gene networks may be subsumed by regulatory annotations, such that accounting for known annotations is critical to robust inference of biological mechanisms.

genetics↗

bioRxiv2018

Application of clinical genomic sequencing among Chinese advanced cancer patients to guide precision medicine decisions

PurposeA number of studies have suggested that high-throughput genomic analyses might improve the outcomes of cancer patients. However, whether integrative information about genomic sequencing and related clinical interpretation may benefit Chinese cancer patients with stage IV disease to date has not investigated.\n\nMethodsTargeted gene panel and whole exome of tumor/blood samples in > 1,000 Chinese cancer patients were sequenced. Then we provided patients and their oncologists with the sequencing results and a clinical recommendation roadmap based on evidence-based medicine, defined as CWES. Only patients with stage IV disease who failed the previous treatment upon receiving the CWES reports were included for analyzing the impact of CWES on clinical outcomes in 1-year follow-ups.\n\nResultsWe identified the mutational signatures of 953 Chinese cancer patients, with some being unique. Approximately 88.6% of patients had clinically actionable somatic genomic alterations. We successfully followed up 22 stage IV patients. Of these, 11 patients treatment followed the CWES reports defined as group A. Eleven patients received the next treatment, but did not follow the CWES suggestions, and are defined as group B. The types of therapies before CWES were similar in the two groups. The median PFS of group A was 12 months and 45% patients failed this round of therapy. The median PFS of group B was 4 months and 91% of patients failed the treatment.\n\nConclusionThe current study suggested that CWES has the potential to help explore the clinical benefits in multiple line therapies among advanced stage tumor patients.

cancer biology↗

bioRxiv2018

Comprehensive analysis of potential immunotherapy genomic biomarkers in 1,000 Chinese patients with cancer

BackgroundTumor mutation burden (TMB), DNA mismatch repair deficiency (dMMR), microsatellite instability (MSI), and PD-L1 amplification (PD-L1 AMP) may predict the efficacy of PD-1/PD-L1 blockade. In this study, we aimed to characterize the distributions of these biomarkers in over 1,000 Chinese patients with cancer.\n\nMethodsTMB, MSI, dMMR, and PD-L1 AMP were determined based on whole-exome sequencing of tumor/blood samples from > 1,000 Chinese patients with cancer.\n\nResultsIncidence rates among 953 Chinese patients with cancer showing high TMB (TMB-H), high MSI (MSI-H), dMMR and PD-L1 AMP were 35%, 4%, 0.53% and 3.79%, respectively. We found higher rates of TMB-H among hepatocellular carcinoma, breast cancer, and esophageal cancer patients than was reported for The Cancer Genome Atlas data. Lung cancer patients with EGFR mutations had significantly lower TMB values than those with wild-type EGFR, and increased TMB was significantly associated with dMMR in colorectal cancer (CRC). The frequency of tumors with MSI-H was highest in CRC (14%) and gastric cancer (4%). PD-L1 AMP occurred most frequently in lung squamous cell carcinoma (14.3%) and HER2-positive breast cancer (8.8%). Most MSI-H and dMMR cases exhibited TMB-H, but the overlap among the other biomarkers was low.\n\nConclusionWhile MSI and dMMR are associated with higher mutational loads, correlations between TMB-H and other biomarkers, between MSI-H and dMMR, and between PD-L1 AMP and other biomarkers were low, indicating different underlying causes of the four biomarkers. Thus, it is recommended that all four biomarkers be assessed for certain cancers before administration of PD-1/PD-L1 blockade treatment.

cancer biology↗

bioRxiv2017

Enhancer connectome in primary human cells reveals target genes of disease-associated DNA elements

The challenge of linking intergenic mutations to target genes has limited molecular understanding of diverse human diseases. Here, we show H3K27ac HiChIP generates high-resolution contact maps of active enhancers and target genes in rare primary human T cell subtypes and coronary artery smooth muscle cells. Differentiation of naive T cells to either T helper 17 cells or regulatory T cells create subtype-specific enhancer-promoter interactions, specifically at regions of shared DNA accessibility. These data provide a principled means of assigning molecular functions to autoimmune and cardiovascular disease risk variants, linking hundreds of noncoding variants to putative gene targets. Target genes identified with HiChIP are further supported by CRISPR interference and activation at linked enhancers, by the presence of expression quantitative trait loci, and by allele-specific enhancer loops in patient-derived primary cells. The majority of disease-associated enhancers contact genes beyond the nearest gene in the linear genome, leading to a four-fold increase of potential target genes for autoimmune and cardiovascular diseases.

genomics↗

bioRxiv2017

A functional role for the epigenetic regulator ING1 in activity-induced gene expression in primary cortical neurons

Epigenetic regulation of activity-induced gene expression involves multiple levels of molecular interaction, including histone and DNA modifications, as well as mechanisms of DNA repair. Here we demonstrate that the genome-wide deposition of Inhibitor of growth family member 1 (ING1), which is a central epigenetic regulatory protein, is dynamically regulated in response to activity in primary cortical neurons. ING1 knockdown leads to decreased expression of genes related to synaptic plasticity, including the regulatory subunit of calcineurin, Ppp3r1. In addition, ING1 binding at a site upstream of the transcription start site (TSS) of Ppp3r1 depends on yet another group of neuroepigenetic regulatory proteins, the Piwi-like family, which are also involved in DNA repair. These findings provide new insight into a novel mode of activity-induced gene expression, which involves the interaction between different epigenetic regulatory mechanisms traditionally associated with gene repression and DNA repair.\n\nAuthor contributionsL.J.L., Q.Z., T.W.B and W.W. designed the experiments. N.K., A.K., X.L., C.D., S.L. and W.W. designed and assembled shRNA constructs. L.J.L., W.W., X.L., C.D., P.R.M., E.Z., and S.L. conducted experiments. Q.Z. and Y.W. analysed ChIP-seq data. L.J.L., Q.Z., and W.W. wrote the paper. All authors reviewed and edited the manuscript.\n\nConflicts of interestNone.

neuroscience↗