Biology subjects

Hao, L.

Publications and source records attributed to Hao, L..

At least 55 records · Page 3Linked to original sources

bioRxiv2022

Nirmatrelvir, an orally active Mpro inhibitor, is a potent inhibitor of SARS-CoV-2 Variants of Concern

New variants of SARS-CoV-2 with potential for enhanced transmission, replication, and immune evasion capabilities continue to emerge causing reduced vaccine efficacy and/or treatment failure. As of January 2021, the WHO has defined five variants of concern (VOC): B.1.1.7 (Alpha, ), B.1.351 (Beta, {beta}), P.1 (Gamma, {gamma}), B.1.617.2 (Delta, {delta}), and B.1.1.529 (Omicron, o). To provide a therapeutic option for the treatment of COVID-19 and variants, Nirmatrelvir, the antiviral component of PAXLOVID, an oral outpatient treatment recently authorized for conditional or emergency use treatment of COVID-19, was developed to inhibit SARS-CoV-2 replication. Nirmatrelvir (PF-07321332) is a specific inhibitor of coronavirus main protease (Mpro, also referred to as 3CLpro), with potent antiviral activity against several human coronaviruses, including SARS-CoV-2, SARS-CoV, and MERS (Owen et al, Science 2021. doi: 10.1126/science.abl4784). Here, we evaluated PF-07321332 against the five SARS-CoV-2 VOC (, {beta}, {gamma}, {delta},, o) and two Variants of Interest or VOI, C.37 ({lambda}) and B.1.621 (), using qRT-PCR in VeroE6 cells lacking the P-glycoprotein (Pgp) multidrug transporter gene (VeroE6 P-gp knockout cells). Nirmatrelvir potently inhibited USA-WA1/2020 strain, and , {beta}, {gamma}, {lambda}, {delta}, , and o variants in VeroE6 P-gp knockout cells with mean EC50 values 38.0 nM, 41.0 nM, 127.2 nM, 24.9 nM, 21.2 nM, 15.9 nM, 25.7 nM and 16.2 nM, respectively. Sequence analysis of the Mpro encoded by the variants showed ~100% identity of active site amino acid sequences, reflecting the essential role of Mpro during viral replication leading to ability of Nirmatrelvir to exhibit potent activity across all the variants.

microbiology↗

bioRxiv2021

Integrated Proteomic and Metabolomic Analyses of the Mitochondrial Neurodegenerative Disease MELAS

MELAS (mitochondrial encephalomyopathy, lactic acidosis, stroke-like episodes) is a progressive neurodegenerative disease caused by pathogenic mitochondrial DNA variants. The pathogenic mechanism of MELAS remains enigmatic due to the exceptional clinical heterogeneity and the obscure genotype-phenotype correlation among MELAS patients. To gain insights into the pathogenic signature of MELAS, we designed a comprehensive strategy integrating proteomics and metabolomics in patient-derived dermal fibroblasts harboring the ultra-rare MELAS pathogenic variant m.14453G>A, specifically affecting the mitochondrial respiratory Complex I. Global proteomics was achieved by data-dependent acquisition (DDA) and verified by data-independent acquisition (DIA) using both Spectronaut and the recently launched MaxDIA platforms. Comprehensive metabolite coverage was achieved for both polar and nonpolar metabolites in both reverse phase and HILIC LC-MS/MS analyses. Our proof-of-principle MELAS study with multi-omics integration revealed OXPHOS dysregulation with a predominant deficiency of Complex I subunits, as well as alterations in key bioenergetic pathways, glycolysis, tricarboxylic acid cycle, and fatty acid {beta}-oxidation. The most clinically relevant discovery is the downregulation of the arginine biosynthesis pathway, likely due to blocked argininosuccinate synthase, which is congruent with the MELAS cardinal symptom of stroke-like episodes and its current treatment by arginine infusion. In conclusion, we demonstrated an integrated proteomic and metabolomic strategy for patient-derived fibroblasts, which has great clinical potential to discover therapeutic targets and design personalized interventions after validation with a larger patient cohort in the future. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=98 SRC="FIGDIR/small/473301v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@1f59135org.highwire.dtl.DTLVardef@110451forg.highwire.dtl.DTLVardef@129f9d8org.highwire.dtl.DTLVardef@d1f347_HPS_FORMAT_FIGEXP M_FIG Graphic Abstract: Integrated proteomics and metabolomics of patient fibroblasts revealed dysregulations in arginine biosynthesis, OXPHOS complexes, and bioenergetic pathways in MELAS, a mitochondrial neurodegenerative disease caused by mitochondrial DNA mutations. C_FIG

systems biology↗

bioRxiv2021

Dihydroartemisinin broke immune evasion through YAP1/JAK1/STAT1, 3 pathways to enhance anti-PD-1 therapy in hepatocellular carcinoma

The efficacy of anti-PD-1 therapy is not as expected in patients with hepatocellular carcinoma (HCC). Yes-associated protein 1 (YAP1) was overexpressed and activated in HCC. This study aimed to investigate the potential mechanism and inhibitor of YAP1 on immune evasion, and promote anti-PD-1 therapy in HCC. Here, we showed that dihydroartemisinin (DHA), an FDA approved drug, directly suppressed YAP1 expression, leading to break immune evasion in liver tumor niche, characterized by decreased PD-L1 in liver tumor cells and increased CD8+ T cell infiltration. Mechanismly, YAP1 is not only directly related to PD-L1, but also involved in activating the JAK1/STAT1, 3 pathways. Moreover, Yap1 knockout elevated CD4+ and CD8+ T cells in liver tumor niche of Yap1LKO mice. Consistently, verteporfin, YAP1 inhibitor, decreased TGF-{beta} in liver tumor niche and exhausted CD8+ T cells in spleen. Furthermore, DHA combined with anti-PD-1 treatment promoted CD4+ T cell infiltration in the spleen and CD8+ T cells in tumor tissues. Thus, we provide a new combined therapeutic strategy for anti-PD-1 with DHA, a potent YAP1 inhibitor, in HCC.

cancer biology↗

bioRxiv2021

Macroscopic dynamics of gene regulatory networks revealed by individual entropy

Complex systems are usually high-dimensional with intricate interactions among internal components, and may display complicated dynamics under different conditions. While it is difficult to measure detail dynamics of each component, proper macroscopic description of a complex system is crucial for quantitative studies. In biological systems, each cell is a complex system containing a huge number of molecular components that are interconnected with each other through intricate molecular interaction networks. Here, we consider gene regulatory networks in a cell, and introduce individual entropy as a macroscopic variable to quantify the transcriptional dynamics in response to changes in random perturbations and/or network structures. The proposed individual entropy measures the information entropy of a system at each instant with respect to a basal reference state, and may provide temporal dynamics to characterize switches of system states. Individual entropy provides a method to quantify the stationary macroscopic dynamics of a gene set that is dependent on the gene regulation connections, and can be served as an indicator for the evolution of network structure variation. Moreover, the individual entropy with reference to a preceding state enable us to characterize different dynamic patterns generated from varying network structures. Our results show that the proposed individual entropy can be a valuable macroscopic variable of complex systems in characterizing the transition processes from order to disorder dynamics, and to identify the critical events during the transition process.

systems biology↗

bioRxiv2021

Neuropilin-1 Mediates SARS-CoV-2 Infection in Bone Marrow-derived Macrophages

SARS-CoV-2 infection in human can cause medical complications across various tissues and organs. Despite of the advances to understanding the pathogenesis of SARS-CoV-2, its tissue tropism and interactions with host cells have not been fully understood. Existing clinical data have suggested possible SARS-CoV-2 infection in human skeleton system. In the present study, we found that authentic SARS-CoV-2 could efficiently infect human and mouse bone marrow-derived macrophages (BMMs) and alter the expression of macrophage chemotaxis and osteoclast-related genes. Importantly, in a mouse SARS-CoV-2 infection model that was enabled by the intranasal adenoviral (AdV) delivery of human angiotensin converting enzyme 2 (hACE2), SARS-CoV-2 was found to be present in femoral BMMs as determined by in situ immunofluorescence analysis. Using single-cell RNA sequencing (scRNA-Seq), we characterized SARS-CoV-2 infection in BMMs. Importantly, SARS-CoV-2 entry on BMMs appeared to be dependent on the expression of neuropilin-1 (NRP1) rather than the widely recognized receptor ACE2. It was also noted that unlike brain macrophages which displayed aging-dependent NRP1 expression, BMMs from neonatal and aged mice had constant NRP1 expression, making BMMs constantly vulnerable target cells for SARS-CoV-2. Furthermore, it was found that the abolished SARS-CoV-2 entry in BMM-derived osteoclasts was associated with the loss of NRP1 expression during BMM-to-osteoclast differentiation. Collectively, our study has suggested that NRP1 can mediate SARS-CoV-2 infection in BMMs, which precautions the potential impact of SARS-CoV-2 infection on human skeleton system.

microbiology↗

bioRxiv2021

Genome Warehouse: A Public Repository Housing Genome-scale Data

The Genome Warehouse (GWH) is a public repository housing genome assembly data for a wide range of species and delivering a series of web services for genome data submission, storage, release, and sharing. As one of the core resources in the National Genomics Data Center (NGDC), part of the China National Center for Bioinformation (CNCB, https://bigd.big.ac.cn/), GWH accepts both full genome and partial genome (chloroplast, mitochondrion, and plasmid) sequences with different assembly levels, as well as an update of existing genome assemblies. For each assembly, GWH collects detailed genome-related metadata including biological project and sample, and genome assembly information, in addition to genome sequence and annotation. To archive high-quality genome sequences and annotations, GWH is equipped with a uniform and standardized procedure for quality control. Besides basic browse and search functionalities, all released genome sequences and annotations can be visualized with JBrowse. By December 2020, GWH has received 17,264 direct submissions covering a diversity of 949 species, and has released 3370 of them. Collectively, GWH serves as an important resource for genome-scale data management and provides free and publicly accessible data to support research activities throughout the world. GWH is publicly accessible at https://bigd.big.ac.cn/gwh/.

bioinformatics↗

bioRxiv2020

Development and Comparative Evaluation of Endolysosomal Proximity Labeling-based Proteomic Methods in Human iPSC-derived Neurons

Proximity-based in situ labeling techniques offer a unique way to capture both stable and transient protein-protein and protein-organelle interactions. Combining this technology with mass spectrometry (MS)-based proteomics allows us to obtain snapshots of molecular microenvironments with nanometer resolution, facilitating the discovery of complex and dynamic protein networks. However, a number of technical challenges still exist, such as interferences from endogenously biotinylated proteins and other highly abundant bystanders, how to select the proper controls to minimize false discoveries, and experimental variations among biological/technical replicates. Here, we developed a new method to capture the proteomic microenvironment of the neuronal endolysosomal network, by knocking in (KI) an engineered ascorbate peroxidase (APEX) gene to the endogenous locus of lysosome-associated membrane protein 1 (LAMP1). We found that normalizing proximity labeling proteomics data to the endogenously biotinylated protein (PCCA) can greatly reduce variations and enable fair comparisons among different batch of APEX labeling and different APEX probes. We conducted comparative evaluation between this KI-LAMP1-APEX method and our two overexpression LAMP1-APEX probes, achieving complementary coverage of both known and new lysosomal membrane and lysosomal-interacting proteins in human iPSC-derived neurons. To summarize, this study demonstrated new analytical tools to characterize lysosomal functions and microenvironment in human neurons and filled critical gaps in the field for designing and optimizing proximity labeling proteomic experiments.

systems biology↗

bioRxiv2020

The global landscape of SARS-CoV-2 genomes, variants, and haplotypes in 2019nCoVR

On 22 January 2020, the National Genomics Data Center (NGDC), part of the China National Center for Bioinformation (CNCB), created the 2019 Novel Coronavirus Resource (2019nCoVR), an open-access SARS-CoV-2 information resource. 2019nCoVR features a comprehensive integration of sequence and clinical information for all publicly available SARS-CoV-2 isolates, which are manually curated with value-added annotations and quality evaluated by our in-house automated pipeline. Of particular note, 2019nCoVR performs systematic analyses to generate a dynamic landscape of SARS-CoV-2 genomic variations at a global scale. It provides all identified variants and detailed statistics for each virus isolate, and congregates the quality score, functional annotation, and population frequency for each variant. It also generates visualization of the spatiotemporal change for each variant and yields historical viral haplotype network maps for the course of the outbreak from all complete and high-quality genomes. Moreover, 2019nCoVR provides a full collection of SARS-CoV-2 relevant literature on COVID-19 (Coronavirus Disease 2019), including published papers from PubMed as well as preprints from services such as bioRxiv and medRxiv through Europe PMC. Furthermore, by linking with relevant databases in CNCB-NGDC, 2019nCoVR offers data submission services for raw sequence reads and assembled genomes, and data sharing with National Center for Biotechnology Information. Collectively, all SARS-CoV-2 genome sequences, variants, haplotypes and literature are updated daily to provide timely information, making 2019nCoVR a valuable resource for the global research community. 2019nCoVR is accessible at https://bigd.big.ac.cn/ncov/.

genomics↗

bioRxiv2020

CRISPR-Cas-amplified urine biomarkers for multiplexed and portable cancer diagnostics

Synthetic biomarkers, exogenous probes that generate molecular reporters, represent an emerging paradigm in precision diagnostics with applications across infectious and noncommunicable diseases. In order to achieve their promise, these methods reply on multiplexing strategies to provide tools that are both sensitive and specific. However, the field of synthetic biomarkers has not benefited from molecular strategies such as DNA-barcoding due to the susceptibility of nucleic acids in vivo. Herein, we exploit chemically-stabilized DNAs to tag synthetic biomarkers and produce diagnostic signals via CRISPR nucleases. Our strategy capitalizes on disease-associated, protease-activated release of nucleic acid barcodes and polymerase-amplification-free, CRISPR-Cas-mediated barcode detection in unprocessed biofluids. In murine cancer models, we show that the DNA-encoded urine biomarkers can noninvasively detect and monitor disease progression, and demonstrate that nuclease amplification can be harnessed to convert the readout to a point-of-care tool. This technique combines specificity with ease of use to offer a new platform to study human disease and guide therapeutic decisions.

bioengineering↗

bioRxiv2020

Personalized vagino-cervical microbiome dynamics after oral probiotics

The vaginal microbiota is presumably much simpler than the gut microbiome, and oral probiotics appear as a promising means to modulate its homeostasis in the general population. Here, 60 women were followed for over a year before, during and after a probiotic containing Lactobacillus rhamnosus GR-1 and L. reuteri RC-14. Shotgun metagenomic data of 1334 samples from multiple body sites did not support colonization of the probiotics to the vagino-cervical microbiome, yet the microbiome was stable in those dominated by Lactobacilli and some individuals have likely benefited from this medication-free intervention.

microbiology↗

bioRxiv2020

Characterizing the growing microorganisms at species level in 46 anaerobic digesters at Danish wastewater treatment plants: A six-year survey on microbiome structure and key drivers

Anaerobic digestion (AD) is a key technology at many wastewater treatment plants (WWTPs) for converting surplus activated sludge to methane-rich biogas. However, the limited number of surveys and the lack of comprehensive data sets have hindered a deeper understanding of the characteristics and associations between key variables and the microbiome composition. Here, we present a six-year survey of 46 anaerobic digesters, located at 22 WWTPs in Denmark, which is the largest known study of the microbial ecology of AD at WWTPs at a regional scale. For three types of AD (mesophilic, mesophilic with thermal hydrolysis pretreatment, and thermophilic), we present the typical value range of 12 key parameters including operational variables and performance parameters. The bacterial and archaeal microbiomes were analyzed at species-level resolution using amplicon sequencing in >1,000 samples and the new ecosystem-specific MiDAS 3 reference database. We detected 42 phyla, 1,600 genera and 3,584 species in the bacterial microbiome, where 70% of the genera and 93% of the species represented uncultivated taxa that were only classified based on MiDAS 3 denovo placeholder taxonomy. More than 40% of the 100 most abundant bacterial species did not grow in the digesters and were only present due to immigration with the feed sludge. Temperature, ammonium concentration, and pH were the main drivers shaping the microbiome clusters of the three types of ADs for both bacteria and for archaea. Within mesophilic digesters, feed sludge composition and other key parameters (organic loading rate, biogas yield, and ammonium concentration) correlated with the growing bacterial microbiome. Furthermore, correlation analysis revealed the main drivers for specific species among growing bacteria and archaea, and revealed the potential ecological function of many novel taxa. Our study highlights the influence of immigration on bacterial AD microbiome. Subsetting the growing microbes improves the understanding of the diversity and main drivers of microbiome assembly, and elucidates functionality of specific species-level microorganisms. This six-year survey provides a comprehensive insight into microbiome structure at species level, engineering and ecological performance, and a foundation for future studies of the ecological significance/characteristics and function of the novel taxa.

microbiology↗

bioRxiv2020

Rapid Neural Reorganization during Retrieval Practice Predicts Subsequent Long-term Retention and False Memory

Active retrieval can induce changes to the strength and content of a memory, yielding enhanced or distorted subsequent recall. But how consolidation influences these retrieval-induced seemingly contradictory outcomes remains unknown. Here we show rapid neural reorganization over eight runs of retrieval practice predicted subsequent recall. Behaviorally, retrieval practice boosted memory following a 24-hour (long-term) but not a 30-minute delay, and increased false memory at both delays. Long-term retention gains were predicted by multi-voxel representation distinctiveness in the posterior parietal cortex that increased progressively over retrieval practice. False memory was predicted by unstable representation distinctiveness in the medial temporal lobe during retrieval practice. Memory-related neural networks gradually reconfigured over retrieval practice, with the ventrolateral and medial prefrontal cortex acting as hubs for functional connections that predicted long-term retention gains and false memory outcomes respectively. Our findings demonstrate dynamic neural reorganization during retrieval practice, through which memories are arranged into discrete yet malleable representations for subsequent consolidation.

neuroscience↗

bioRxiv2019

Comprehensive molecular characterization of PRKCG for glioma diagnosis, prognosis and treatment prediction

Glioma is one of the most common malignant brain tumors and exhibits low resection rate and high recurrence risk. Although a large number of glioma studies powered by high-throughput sequencing technologies have led to massive multi-omics datasets, there lacks of comprehensive integration of glioma datasets for uncovering candidate biomarker genes. In this study, we collected a large-scale assemble of multi-omics multi-cohort datasets from worldwide public resources, involving a total of 16,939 samples across 19 independent studies. Through comprehensive multi-omics molecular profiling across different datasets, we revealed that PRKCG (Protein Kinase C Gamma), a brain-specific gene detectable in cerebrospinal fluid, is closely associated with glioma. Specifically, it presents lower expression and higher methylation in glioma samples compared with normal samples. PRKCG expression/methylation change from high to low is indicative of glioma progression from low-grade to high-grade and high RNA expression is suggestive of good survival. Importantly, PRKCG in combination with MGMT is effective to predict survival outcomes after TMZ chemotherapy in a more precise manner. Collectively, PRKCG bears the great potential for glioma diagnosis, prognosis and therapy, and PRKCG-like genes may represent a set of important genes associated with different molecular mechanisms in glioma tumorigenesis. Accordingly, our study indicates the importance of computational integrative multi-omics data analysis and represents a data-driven scheme toward precision tumor subtyping and accurate personalized healthcare. Author SummaryGlioma is a type of brain tumors that represents one of the most lethal human malignancies with little chance for recovery. Nowadays, more and more studies have adopted high-throughput sequencing technologies to decode the molecular profiles of glioma from different omics levels, accordingly resulting in massive glioma datasets generated from different projects and laboratories throughout the world. Therefore, it has become crucially important on how to make full use of these valuable datasets for computational identification of glioma candidate biomarker genes in aid of precision tumor subtyping and accurate personalized treatment. In this study, we comprehensively integrated glioma datasets from all over the world and performed multi-omics molecular data mining. We revealed that PRKCG, a brain-specific gene abundantly expressed in cerebrospinal fluid, bears the great potential for glioma diagnosis, prognosis and treatment prediction, which has been consistently observed on multiple independent datasets. In the era of big data, our study highlights the significance of computational integrative data mining toward precision medicine in cancer research.

bioinformatics↗

bioRxiv2019

The Urinary Microbiota in Relation to the Reproductive Tract Microbiota

Human urine is traditionally considered to be sterile, and whether the urine harbours distinct microbial communities has been a matter of debate. The potential link between female urine and reproductive tract microbial communities is currently not clear. Here we collected the urine samples from 147 Chinese women of reproductive age, and explored the nature of colonization by 16S rRNA gene amplicon sequencing, real-time qPCR and live bacteria culture. To demonstrate utility intra-individual Spearmans correlation was used to explore the relationship between urine and multi-sites of the reproductive tract. PERMANOVA was also performed to explore potential correlations between the lifestyle and various clinical factors and urinary bacterial communities. Our data demonstrated distinct bacterial communities in urine, indicative of a non-sterile environment. Types of diverse, Streptococcus-dominated, and Lactobacillus-dominated were the three most common types in the cohort. Detailed comparison of the urinary microbiota to the multi-sites of reproductive tract microbiota demonstrated the urinary microbiota was more similar to the microbiota in the cervix and uterine cavity instead of vagina in the same women. Our data demonstrates the potential connectivity of the microbiota in the female urogenital system and provided insight into the exploration of urethra and genital tract diseases.

microbiology↗

bioRxiv2019

The vagino-cervical microbiome as a woman's life history

The gut microbiome has been the center of attention for human commensal microbiome studies. The vaginal microbiome is also densely populated with bacteria, viruses and fungi, and the presence of microorganisms beyond the cervix is increasingly reported in non-infectious conditions1-3. Due to the over 90% of human sequences in female reproductive tract samples3,4, metagenomic information has been very limited. 16S rRNA gene amplicon sequencing studies have identified community types in the vaginal microbiota, and observed its dynamic changes due to menstrual cycles and sexual behaviors in small cohorts5,6. Here we perform metagenomic shotgun sequencing on cervical samples from 516 women of reproductive age (more than 10-fold of the Human Microbiome Project (HMP)4), and dissect major factors, especially pregnancy and delivery histories and contraception methods on the microbiome composition. Features of other body sites, such as mood fluctuations and facial speckles could potentially be deduced from the vagino-cervical microbiome. Our results offer an unprecedented glimpse into the microbiota in the female reproductive tract and imply disease susceptibilities that may be relieved by behavioural changes.

microbiology↗

bioRxiv2018

Low-dose cadmium potentiates lung inflammatory response to 2009 pandemic H1N1 influenza virus in mice

BACKGROUNDCadmium (Cd) is a toxic, pro-inflammatory metal ubiquitous in the diet that accumulates in body organs due to inefficient elimination. Many individuals exposed to dietary Cd are also infected by seasonal influenza virus. The H1N1 strain causes mild to severe pneumonia which can be fatal.\n\nOBJECTIVESTo determine the influence of low-dose Cd exposure on inflammatory responses to H1N1 influenza A virus.\n\nMETHODSWe exposed mice to low-dose (1 mg CdCl2/l) Cd or vehicle (water) for 16 weeks prior to infection with a sub-lethal dose of H1N1. Lung inflammation was assessed by histopathology and flow cytometry. We used a combination of transcriptomics, metabolomics and bioinformatics to determine the molecular associations of inflammatory cells important in Cd-exacerbated responses.\n\nRESULTSCd-treated mice had increased lung tissue inflammatory cells, including neutrophils, monocytes, T lymphocytes and dendritic cells, following H1N1 infection. Lung genetic responses to infection (increasing TNF-a, interferon and complement, and decreasing myogenesis) were also exacerbated. Global correlations with immune cell counts, leading edge gene transcripts and metabolites revealed that Cd increased correlation of myeloid immune cells with pro-inflammatory genes, particularly interferon-{gamma}, and metabolites in amino acid, nucleobase, glycerophospholipid and vitamin B3 pathways.\n\nDISCUSSIONCd burden in mice increased inflammation in response to sub-lethal H1N1 challenge, which was coordinated by genetic and metabolic responses, and could provide new targets for intervention against lethal inflammatory pathology of clinical H1N1 infection.

pharmacology and toxicology↗

bioRxiv2018

Time-scale separation and stochasticity conspire to impact phenotypic dynamics in the canonical and inverted Bacillus subtilis core genetic regulation circuits.

In this work, we study two seemingly unrelated aspects of core genetic nonlinear dynamical control of the competence phenotype in Bacillus subtilis, a common Gram-positive bacterium living in the soil. We focus on hitherto unchartered aspects of the dynamics by exploring the effect of time scale separation between transcription and translation and, as well, the effect of intrinsic molecular stochasticity. We consider these aspects of regulatory control as two possible evolutionary handles. Hence, using theory and computations, we study how the onset of oscillations breaks the excitability-based competence phenotype in two topologically close evolutionary competing circuits: the canonical \"wild-type\" regulation circuit selected by Evolution and, the indirect-feedback inverted circuit that failed to be selected by Evolution, as was shown elsewhere, due to dynamical reasons.

systems biology↗

bioRxiv2017

GVC: A superfast and universal genomic variant caller

Germline and somatic variant detection from human and cancer whole-genome sequencing data is a challenge task for genome-wide association study and cancer genomics in precision medicine. Many confounding factors contribute the difficulties including complexity of variant, sequencing and alignment error, tumor clonality and sample purity etc. Current genomic variant callers are too time-consuming to meet the requirement of clinical application in precision medicine. We developed superfast and universal Genomic Variant Caller (GVC), which can simultaneously detect various genomic variants including SNV, sINDEL and SV from personal and normal-cancer paired whole-genome/exome sequencing data within fifteen minutes. Whats more, it achieved higher sensitivity and precision than popular variant callers including GATK4, Mutect, NovoBreak in germline and somatic variant detection from NA12878 and ICGC-TCGA Dream Challenge Datasets respectuvely. It is worth mentioning that GVC achieved comparable performance in variant detection from NA12878 sequenced by three different high-throughput sequencing platforms including Illumina HiSeq2000, NovaSeq and BGISEQ-500.

bioinformatics↗