Biology subjects

Song, F.

Publications and source records attributed to Song, F..

36 records · Page 2Linked to original sources

bioRxiv2022

Meta-analysis of fecal metagenomes reveals global viral signatures and its diagnostic potential for colorectal cancer and adenoma

IntroductionGut microbiome plays an important role in maintaining human health. Although mounting evidence has revealed the critical function of the gut bacteriome in the progression of CRC, the contribution of gut viral community to CRC is rarely studied. ObjectivesThe present study aimed to reveal the gut virome signatures of colorectal adenoma patients and CRC patients and decipher the potential viral markers to build clinical predictive models for diagnosis. Methods1,282 available fecal metagenomes data from 9 published CRC studies were collected. A new virus database was constructed based on a reference-independent virome approach for further analysis. Viral markers were filtered by statistical methods and used to build machine learning models such as Random Forest and Least Absolute Shrinkage and Selection Operator (LASSO) to distinguish patients from controls. New fecal samples were collected to validate the generalization of predictive model. ResultsThe gut viral composition of CRC patients was drastically altered compared with healthy, as evidenced by changes in several Siphoviridae viruses and a reduction of Microviridae, whereas the virome variation in adenoma patients was relatively low. The viral markers contained the phages of Porphyromonas, Fusobacterium, Hungatella, and Ruminococcaceae. In 9 cohorts and independent validation cohorts, a random forest (RF) classifier and LASSO model got the optimal AUC 0.830 and 0.906, respectively. While the gut virome analysis of adenoma patients identified 88 differential viruses and achieved an optimal AUC of 0.772 for discriminating patients from controls. ConclusionOur findings demonstrate the distinctly different composition of gut virome between healthy controls and CRC patients, and highlight the potential of viral markers for clinical diagnosis.

microbiology↗

bioRxiv2022

TLR4/NF-κB Signaling Contributes to the Inflammation in Ovary and Inguinal Fats of Polycystic Ovary Syndrome

BackgroundPolycystic ovary syndrome (PCOS) is a complex reproductive endocrine disorder. It has highly heterogeneous clinical manifestations which are characterized by biochemical hyperandrogenemia, obesity, insulin resistance, dyslipidemia, anovulation, and polycystic ovaries. However, the etiologies of PCOS are still unclear. Recently, studies have found that the low-grade inflammation contributed to the occurrence of PCOS, and as a critical biomarker indicated the endocrine disruptions in PCOS. ObjectiveThis study is aimed to investigate the processes and mediators of inflammation in contributing to the development of PCOS. MethodsLetrozole (LET) induced PCOS rat model was used in this study. Body weight, body temperature, inguinal fats weight, fasting glucose level, ovarian morphology, NF-{kappa}B signaling target genes in ovary, and protein expression levels of TLR4 and NF-{kappa}B in ovarian and inguinal fats were measured in rats with placebo and LET administrations for 6 and 12 weeks. ResultsPCOS rats, especially with LET intervention for 12 weeks, had higher body weight, inguinal fats weight and fasting glucose level compared to control group. The protein expression levels of TLR4 and NF-{kappa}B in cytoplasm of ovarian and inguinal fats were increased in LET-induced PCOS rats compared to control groups, while NF-{kappa}B in nucleus were reduced in PCOS rats. The expressions of ACTB, C3, CXCL3, NQO1 and SELP in ovarian were statistically different in PCOS rats induced by LET compared to control groups. ConclusionThese findings indicated that stimulating TLR4/NF-{kappa}B pathway in inguinal fats and ovary tissues contributed to the increased inflammation in LET-induced PCOS rats, which, in turn, exacerbated the phenotype of PCOS including weight gain, adipose tissue accumulation, hyperglycemia and follicular dysplasia.

pathology↗

bioRxiv2022

A Modular Chemoenzymatic Approach to C14-Functionalized Steroids

C14-functionalized steroids belong to a unique class of steroids with important biological activities. However, the lack of efficient methods to access C14-functionalized steroids impede related steroidal drug discovery. Herein we report a modular chemoenzymatic approach to access diversified C14-functionalized steroids. We first identified a novel C14-hydroxylase (CYP14A) from Cochliobolus lunatus with high catalytic efficiency and substrate promiscuity. Protein engineering of CYP14A generated three variants I111A, M115K and V124A that greatly improved the C14-hydroxy regioselectivity. Based on this efficient biocatalytic method, a range of C14-OH steroids with C17 side chain were prepared in good yields, which was then transformed into {Delta}14 olefins through a facile elimination. The newly formed {Delta}14 olefin served as a versatile handle to install diversified functional groups (e.g. epoxide, {beta}-OH, F, Cl and N3) at C14 position through hydrofunctionalization. Furthermore, the synthetic utility of this powerful chemoenzymatic methodology was demonstrated by performing a 7-step semisynthesis of periplogenin and the diversity-oriented synthesis of cardenolide (+)-digitoxigenin and its three diastereomers in a concise manner.

biochemistry↗

bioRxiv2022

Using Mathematical Modeling to Distinguish Intrinsic and Acquired Targeted Therapeutic Resistance in Head and Neck Cancer

The promise of precision medicine has been limited by the pervasive therapeutic resistance to many targeted therapies for cancer. Inferring the timing (i.e., pre-existing or acquired) and mechanism (i.e., drug-induced) of such resistance is crucial for designing effective new therapeutics. This paper studies the mechanism and timing of cetuximab resistance in head and neck squamous cell carcinoma (HNSCC) using tumor volume data obtained from patient-derived tumor xenografts. We propose a family of mathematical models, with each member of the family assuming a different timing and mechanism of resistance. We present a method for fitting these models to individual volumetric data, and utilize model selection and parameter sensitivity analyses to ask: which member of the family of models best describes HNSCC response to cetuximab, and what does that tell us about the timing and mechanisms driving resistance? We find that along with time-course volumetric data to a single dose of cetuximab, the initial resistance fraction and, in some instances, dose escalation volumetric data are required to distinguish among the family of models and thereby infer the mechanisms of resistance. These findings can inform future experimental design so that we can best leverage the synergy of wet laboratory experimentation and mathematical modeling in the study of novel targeted cancer therapeutics.

cancer biology↗

bioRxiv2021

A simple, cost-effective and automation-friendly direct PCR approach for bacterial community analysis

Bacterial communities in water, soil, and humans play an essential role in environmental ecology and human health. PCR-based amplicon analysis, such as 16S ribosomal RNA sequencing, is a fundamental tool for quantifying and studying microbial composition, dynamics, and interactions. However, given the complexity of microbial communities, a substantial number of samples becomes necessary to analyses that parse the factors that determine microbial composition. A common bottleneck in performing these kinds of experiments is genomic DNA (gDNA) extraction, which is time-consuming, expensive, and often biased on the types of species. Direct PCR methods are a potentially simpler and more accurate alternative to gDNA extraction methods that do not require the intervening purification step. In this study, we evaluated three variations of direct PCR methods using diverse heterogeneous bacterial cultures, ZymoBIOMICS Microbial Community Standards, and groundwater. By comparing direct PCR methods with DNeasy blood and tissue kits and DNeasy Powersoil kits, we found a specific variant of the direct PCR method exhibits a comparable overall efficiency to the conventional DNeasy Powersoil protocol. We also found the method showed higher efficiency for extracting gDNA from the gram negative strains compared to DNeasy blood and tissue protocol. This direct PCR method is 1600 times cheaper ($0.34 for 96 samples), 10 times simpler (15 min hands-on time for 96 samples) than DNeasy Powersoil protocol. The direct PCR method can also be fully automated, and is compatible with small volume samples, thereby permitting scaling of samples and replicates needed to support high-throughput large-scale bacterial community analysis. IMPORTANCEUnderstanding bacterial interaction and assembling in complex microbial communities using 16S ribosomal RNA sequencing normally requires a large experimental load. However, the current DNA extraction methods including cell disruption and genomic DNA purification are normally biased, costly, time and labor consuming, and not amenable to miniaturization by droplets or 1536 well plates due to the significant DNA loss during purification step for tiny volume and low cell density samples. Direct PCR method could potentially solve these problems. In this study, we demonstrate a direct PCR method which exhibits similar efficiency as the widely used method - DNeasy Powersoil protocol, while 1600 times cheaper and 10 times faster to execute. This simple, cost-effective, and automation friendly direct PCR based 16S ribosomal RNA sequencing method allows us to study the dynamics, microbial interaction and assembly of varying microbial communities in a high throughput fashion.

microbiology↗

bioRxiv2021

DBPOM: a comprehensive database of pharmaco-omics for cancer precision medicine

During the course of cancer treatment, both efficacy and the adverse effects of drugs on patients should be taken into account. Although some public databases and modeling frameworks have been developed through studies on drug response, the negative effects of drugs are always neglected. Furthermore, most of them only considered the ramifications of the drug on the cell line, but the effects on the patient still require a huge amount of work to integrate data from various databases and calculations, especially in relation to precision treatment. In order to address these issues, we developed the DBPOM (http://www.dbpom.net/, a comprehensive database of pharmaco-omics for cancer precision medicine), which explores various drugs efficacy levels by calculating their potency in reverse, or enhancing cancer-associated gene expression changes. When compared with existing databases, the DBPOM could estimate the effectiveness of a drug on individual patients through the mapping of various cell lines to each person according to their genetic mutation similarities. The DBPOM is an easy-to-use and one-stop database for clinicians and drug researchers to search and analyze the overall effect of a drug or a drug combination on cancer patients as well as the biological functions that they target. We anticipate that DBPOM will become an important resource and analysis platform for drug development, drug mechanism studies and the discovery of new therapies.

bioinformatics↗

bioRxiv2021

YHP: Y-chromosome Haplogroup Predictor for predicting male lineages based on Y-STRs

Human Y chromosome reflects the evolutionary process of males. Male lineage tracing by Y chromosome is of great use in evolutionary, forensic, and anthropological studies when male samples exist or especially when the biological sample is a mixture of male and female individuals. Identifying the male lineage based on the specific distribution of Y haplogroups narrows down the investigation scope. Integrating previously published datasets with genotypes of Y chromosome short tandem repeats (Y-STRs) and high-resolution haplogroups (122 haplogroups in total), we developed YHP (Y Haplogroup Predictor), an open-access and userfriendly software package to predict haplogroups, compare the similarity, and conduct mismatch analysis of samples with Y-STR profiles. The software is available at Github (https://github.com/cissy123/YHP-Y-Haplogroup-Predictor-). Author SummaryFamilial searching has been used in forensic, anthropologic, and personalized scenarios. Software packages have been developed to assist in male familial searching, such as predicting Y-SNP haplogroups by Y-STRs. However, these software packages, in general, achieve this goal with a rough resolution. In this study, we developed a software package to conduct high-resolution haplogroup inference to help familial searching and at the same time reduce the cost, since it does not require tiresome Y-SNP sequencing.

genetics↗

bioRxiv2020

Berberine reverses multidrug resistance in Candida albicans by hijacking the drug efflux pump Mdr1p

Clinical use of antimicrobials faces great challenges from the emergence of multidrug resistant (MDR) pathogens. The overexpression of drug efflux pumps is one of the major contributors to MDR. It is considered as a promising approach to overcome MDR by reversing the function of drug efflux pumps. In the life-threatening fungal pathogen Candida albicans, the major facilitator superfamily (MFS) transporter Mdr1p can excrete many structurally unrelated antifungals, leading to multidrug resistance. Here we report a counterintuitive case of reversing multidrug resistance in C. albicans by using a natural product berberine to hijack the overexpressed Mdr1p for its own importation. Moreover, we illustrate that the imported berberine accumulates in mitochondria, and compromises the mitochondrial function by impairing mitochondrial membrane potential and mitochondrial Complex I. It results in the selective elimination of Mdr1p overexpressed C. albicans cells. Furthermore, we show that berberine treatment can prolong the mean survival time (MST) of mice with a blood-borne dissemination of Mdr1p overexpressed multidrug resistant candidiasis. This study provided a potential direction of novel anti-MDR drug discovery by screening for multidrug efflux pump converters.

microbiology↗

bioRxiv2020

Lipid Droplet Is an Ancient and Inheritable Organelle in Bacteria

Lipid droplet (LD) is a monolayer phospholipid membrane-bound organelle found in all eukaryotes and several prokaryotes which plays key roles in cellular lipid homeostasis and human health. The origin and evolution of the organelle remains unknown. Here, we report that through screening over 660 bacteria using biophysical and biochemical methods, plus LD isolation and proteomic tool, LDs were identified in most of these microbes, affiliated with five main bacterial phyla. Moreover, LDs were also identified in E. coli overexpressing lipid synthesis enzymes, indicating that bacteria without detectable LDs possessed the ability of LD biogenesis. The similarity of isolated LDs from representative strains and evolutionary analysis of LD major protein PspA demonstrate that LDs were conserved in bacteria. Furthermore, time-lapse imaging revealed that LDs were inheritable accompanying with bacterial growth and division. Finally, a common ancestor of LD-containing bacteria was predicted to originate 3.19 billion years ago by a phylogenetic analysis. Our findings suggest that LD is a widespread and inheritable organelle from an ancient common ancestor.

cell biology↗

bioRxiv2020

A Heat-labile Enterotoxin B Subunit Mutant (LTB26) of Escherichia coli Enhances Mucosal Immune adjuvanticity via Increased BCR Activity

Although several SARS-COV-2 vaccines have been approved, no one oral live vaccine is available. Here, an oral SARS-COV-2 RBD live vaccine containing LTB26 adjuvant has been developed. BALB/c mice are oral vaccinated with attenuated Salmonella typhimurium SL7207 containing pcDNA3.1-LTB26RBD or pcDNA3.1-RBD plasmids. The result shows that the high level of RBD specific antibody is produced in pcDNA3.1- LTB26RBD treatment. The mechanism indicates that LTB26 enhances RBD antibody production by significantly upregulating the activity of MHC II+ DCs and CD19+CD45+ B cells. LTB26 mutant is derived from heat-labile enterotoxin B subunit (LTB) wild type of Escherichia coli with enhanced immune adjuvanticity. Based on the pre-experiment result that SL7207 interferes the function of LTB26, the purified LTB26 was mixed with purified human rotavirus VP8 antigen to explore the mechanism of adjuvant. The results suggests that LTB26 enhances mucosal immune responses via increased of BCR and MHC II+ expression. Furthermore, LTB26 promotes both Th1 and Th2 cell mediated immunity. Therefore, LTB26 maybe a potent adjuvant for mucosal vaccine development in view of the safety of LTB26 than LT toxin.

immunology↗

bioRxiv2020

Subtype-specific epigenomic landscape and 3D genome structure in bladder cancer

Muscle-invasive bladder cancers have recently been characterized by their distinct expression of luminal and basal genes, which could be used to predict key clinical features such as disease progression and overall survival. For example, FOXA1, GATA3, and PPARG have been shown to be essential for luminal subtype-specific regulation and subtype switching, while TP63 and STAT3 are critical for basal subtype bladder cancer. Despite these advances, the underlying epigenetic mechanism and 3D chromatin architecture for subtype-specific regulation in bladder cancers remains largely unknown. Here, we determined the genome-wide transcriptome, enhancer landscape, TF binding profiles (FOXA1 and GATA3) in luminal and basal subtypes of bladder cancers. Furthermore, we mapped genome-wide chromatin interactions by Hi-C in both bladder cancer cell lines and primary patient tumors, for the first time in bladder cancer. We showed that subtype-specific transcription is accompanied by specific open chromatin and epigenomic marks, at least partially driven by distinct TF binding at distal-enhancers of luminal and basal bladder cancers. Finally, we identified a novel clinically relevant transcriptional factor, Neuronal PAS Domain Protein 2 (NPAS2), in luminal bladder cancers that regulates other luminal-specific genes (such as FOXA1, GATA3, and PPARG) and affects cancer cell proliferation and migration. In summary, our work shows a subtype-specific epigenomic and 3D genome structure in urinary bladder cancers and suggested a novel link between the circadian TF NPAS2 and a clinical bladder cancer subtype.

genomics↗

bioRxiv2020

Interleukin-35 pretreatment attenuates lipopolysaccharide-induced heart injury by inhibition of inflammation, apoptosis and fibrosis

Previous studies have demonstrated that targeting inflammation is a promising strategy for treating lipopolysaccharide (LPS)-induced sepsis and related heart injury. Interleukin-35 (IL-35), which consists of two subunits, Epstein-Barr virus-induced gene 3 (EBI3) and p35, is an immunosuppressive cytokine of the IL-12 family and exhibits strong anti-inflammatory activity. However, the role of IL-35 in LPS-induced heart injury remains obscure. In this study, we explored the role of IL-35 in heart injury induced by LPS and its potential mechanisms. Mice were treated with a plasmid encoding IL-35 (pIL-35) and then injected intraperitoneally (ip) with LPS (10 mg/kg). Cardiac function was assessed by echocardiography 12 h later. LPS apparently decreased the expression of EBI3 and p35 and caused cardiac dysfunction and pathological changes, which were significantly improved by pIL-35 pretreatment. Moreover, pIL-35 pretreatment significantly decreased the levels of cardiac proinflammatory cytokines including TNF-, IL-6, and IL-1{beta}, and the NLRP3 inflammasome. Furthermore, increased BCL-2 levels and decreased BAX levels inhibited apoptosis, and LPS-induced upregulation of the expression of pro-fibrotic genes (MMP2 and MMP9) was inhibited. Further investigation indicated that pIL-35 pretreatment suppressed the activation of the cardiac NF-{kappa}Bp65 and TGF-{beta}1/Smad2/3 signaling pathways in LPS-treated mice. Similar cardioprotective effects of IL-35 pretreatment were observed in mouse myocardial fibroblasts challenged with LPS in vitro. In summary, IL-35 pretreatment can attenuate cardiac inflammation, apoptosis, and fibrosis induced by LPS, implicating IL-35 as a promising therapeutic target in sepsis-related cardiac injury.

molecular biology↗

bioRxiv2019

A supervised learning framework for chromatin loop detection in genome-wide contact maps

Accurately predicting chromatin loops from genome-wide interaction matrices such as Hi-C data is critical to deepen our understanding of proper gene regulation events. Current approaches are mainly focused on searching for statistically enriched dots on a genome-wide map. However, given the availability of a wide variety of orthogonal data types such as ChIA-PET, GAM, SPRITE, and high-throughput imaging, a supervised learning approach could facilitate the discovery of a comprehensive set of chromatin interactions. Here we present Peakachu, a Random Forest classification framework that predicts chromatin loops from genome-wide contact maps. Compared with current enrichment-based approaches, Peakachu identified more meaningful short-range interactions. We show that our models perform well in different platforms such as Hi-C, Micro-C, and DNA SPRITE, across different sequencing depths, and across different species. We applied this framework to systematically predict chromatin loops in 56 Hi-C datasets, and the results are available at the 3D Genome Browser (www.3dgenome.org).

bioinformatics↗

bioRxiv2019

An Integrated Framework for Genome Analysis Reveals Numerous Previously Unrecognizable Structural Variants in Leukemia Patients' Samples

While genomic analysis of tumors has stimulated major advances in cancer diagnosis, prognosis and treatment, current methods fail to identify a large fraction of somatic structural variants in tumors. We have applied a combination of whole genome sequencing and optical genome mapping to a number of adult and pediatric leukemia samples, which revealed in each of these samples a large number of structural variants not recognizable by current tools of genomic analyses. We developed computational methods to determine which of those variants likely arose as somatic mutations. The method identified 97% of the structural variants previously reported by karyotype analysis of these samples and revealed an additional fivefold more such somatic rearrangements. The method identified on average tens of previously unrecognizable inversions and duplications and hundreds of previously unrecognizable insertions and deletions. These structural variants recurrently affected a number of leukemia associated genes as well as cancer driver genes not previously associated with leukemia and genes not previously associated with cancer. A number of variants only affected intergenic regions but caused cis-acting alterations in expression of neighboring genes. Analysis of TCGA data indicates that the status of several of the recurrently mutated genes identified in this study significantly affect survival of AML patients. Our results suggest that current genomic analysis methods fail to identify a majority of structural variants in leukemia samples and this lacunae may hamper diagnostic and prognostic efforts.

genomics↗

bioRxiv2019

Flexible Experimental Designs for Valid Single-cell RNA-sequencing Experiments Allowing Batch Effects Correction

Despite their widespread applications, single-cell RNA-sequencing (scRNA-seq) experiments are still plagued by batch effects and dropout events. Although the completely randomized experimental design has frequently been advocated to control for batch effects, it is rarely implemented in real applications due to time and budget constraints. Here, we mathematically prove that under two more flexible and realistic experimental designs--the "reference panel" and the "chain-type" designs--true biological variability can also be separated from batch effects. We develop Batch effects correction with Unknown Subtypes for scRNA-seq data (BUSseq), which is an interpretable Bayesian hierarchical model that closely follows the data-generating mechanism of scRNA-seq experiments. BUSseq can simultaneously correct batch effects, cluster cell types, impute missing data caused by dropout events, and detect differentially expressed genes without requiring a preliminary normalization step. We demonstrate that BUSseq outperforms existing methods with simulated and real data.

bioinformatics↗

bioRxiv2017

Genome-wide and high-density CRISPR-Cas9 screens identify point mutations in PARP1 causing PARP inhibitor resistance

PARP inhibitors (PARPi) target homologous recombination defective tumour cells via synthetic lethality. Genome-wide and high-density CRISPR-Cas9 \"tag, mutate and enrich\" mutagenesis screens identified single amino acid mutations in PARP1 that cause profound PARPi-resistance. These included PARP1 mutations outside of the DNA interacting regions of the protein, such as mutations in solvent exposed regions of the catalytic domain and clusters of mutations around points of contact between ZnF, WGR and HD domains. These mutations altered PARP1 trapping, as did a mutation found in a clinical case of PARPi resistance. These genetic studies reinforce the importance of trapped PARP1 as a key cytotoxic DNA lesion and suggest that interactions between non-DNA binding domains of PARP1 influence cytotoxicity. Finally, different mechanisms of PARPi resistance (BRCA1 reversion, PARP1, 53BP1, REV7 mutation) had differing effects on chemotherapy sensitivity, suggesting that the underlying mechanism of PARPi resistance likely influences the success of subsequent therapies.

cancer biology↗

bioRxiv2017

Measuring the reproducibility and quality of Hi-C data

Hi-C is currently the most widely used assay to investigate the 3D organization of the genome and to study its role in gene regulation, DNA replication, and disease. However, Hi-C experiments are costly to perform and involve multiple complex experimental steps; thus, accurate methods for measuring the quality and reproducibility of Hi-C data are essential to determine whether the output should be used further in a study. Using real and simulated data, we profile the performance of several recently proposed methods for assessing reproducibility of population Hi-C data, including HiCRep, GenomeDISCO, HiC-Spector and QuASAR-Rep. By explicitly controlling noise and sparsity through simulations, we demonstrate the deficiencies of performing simple correlation analysis on pairs of matrices, and we show that methods developed specifically for Hi-C data produce better measures of reproducibility. We also show how to use established (e.g., ratio of intra to interchromosomal interactions) and novel (e.g., QuASAR-QC) measures to identify low quality experiments. In this work, we assess reproducibility and quality measures by varying sequencing depth, resolution and noise levels in Hi-C data from 13 cell lines, with two biological replicates each, as well as 176 simulated matrices. Through this extensive validation and benchmarking of Hi-C data, we describe best practices for reproducibility and quality assessment of Hi-C experiments. We make all software publicly available at http://github.com/kundajelab/3DChromatin_ReplicateQC to facilitate adoption in the community.

genomics↗

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↗