Biology subjects

Ji, H.

Publications and source records attributed to Ji, H..

At least 127 records · Page 7Linked to original sources

bioRxiv2020

Complete and haplotype-specific sequence assembly of segmental duplication-mediated genome rearrangements using targeted CRISPR-targeted ultra-long read sequencing (CTLR-Seq)

We have developed a generally applicable method based on CRISPR/Cas9-targeted ultra-long read sequencing (CTLR-Seq) to completely and haplotype-specifically resolve, at base-pair resolution, large, complex, and highly repetitive genomic regions that had been previously impenetrable to next-generation sequencing analysis such as large segmental duplication (SegDup) regions and their associated genome rearrangements that stretch hundreds of kilobases. Our method combines in vitro Cas9-mediated cutting of the genome and pulse-field gel electrophoresis to haplotype-specifically isolate intact large (200-550 kb) target regions that encompass previously unresolvable genomic sequences. These target fragments are then sequenced (amplification-free) to produce ultra-long reads at up to 40x on-target coverage using Oxford nanopore technology, allowing for the complete assembly of the complex genomic regions of interest at single base-pair resolution. We applied CTLR-Seq to resolve the exact sequence of SegDup rearrangements that constitute the boundary regions of the 22q11.2 deletion CNV and of the 16p11.2 deletion and duplication CNVs. These CNVs are among the strongest known risk factors for schizophrenia and autism. We then perform de novo assembly to resolve, for the first time, at single base-pair resolution, the sequence rearrangements of the 22q11.2 and 16p11.2 CNVs, mapping out exactly the genes and non-coding regions that are affected by the CNV for different carriers.

genomics↗

bioRxiv2020

Assessment of two DNA extraction kits for profiling poultry respiratory microbiota from multiple sample types

Characterization of poultry microbiota is becoming increasingly important due to the growing need for microbiome-based interventions to improve poultry health and production performance. However, the lack of standardized protocols for sampling, sample processing, DNA extraction, sequencing, and bioinformatic analysis can hinder data comparison between studies. Here, we investigated how the DNA extraction process affects microbial community compositions and diversity metrics in different chicken respiratory sample types including choanal and tracheal swabs, nasal cavity and tracheal washes, and lower respiratory lavage. We did a side-by-side comparison of the performances of Qiagen DNeasy blood and tissue (BT) and ZymoBIOMICS DNA Miniprep (ZB) kits. In general, samples extracted with the BT kit yielded higher concentrations of total DNA while those extracted with the ZB kit contained higher numbers of bacterial 16S rRNA gene copies per unit volume. Therefore, the samples were normalized to equal amounts of 16S rRNA gene copies prior to sequencing. For each sample type, all predominant taxa detected in samples extracted with one kit were present in replicate samples extracted with the other kit and did not show significant differences at the class level. Furthermore, between-kit differences in alpha and beta diversity metrics were statistically indistinguishable. Therefore, both kits perform similarly with regard to 16S rRNA gene-based poultry microbiome analysis.

microbiology↗

bioRxiv2020

Genome sequencing sheds light on the contribution of structural variants to Brassica oleracea diversification

Brassica oleracea includes several morphologically diverse, economically important vegetable crops. Here we present high-quality chromosome-scale genome assemblies for two B. oleracea morphotypes, cauliflower and cabbage. Direct comparison of these two assemblies identifies ~120 K high-confidence structural variants (SVs). Population analysis of 271 B. oleracea accessions using these SVs clearly separates different morphotypes, suggesting the association of SVs with B. oleracea intraspecific divergence. Genes affected by SVs selected between cauliflower and cabbage are enriched with functions related to response to stress and stimulus and meristem and flower development. Furthermore, genes affected by selected SVs and involved in the switch from vegetative to generative growth that defines curd initiation, inflorescence meristem proliferation for curd formation, maintenance and enlargement, are identified, providing insights into the regulatory network of curd development. This study reveals the important roles of SVs in diversification of different morphotypes of B. oleracea, and the newly assembled genomes and the SVs provide rich resources for future research and breeding.

plant biology↗

bioRxiv2020

Discovery and Development of Human SARS-CoV-2 Neutralizing Antibodies using an Unbiased Phage Display Library Approach

SARS-CoV-2 neutralizing antibodies represent an important component of the ongoing search for effective treatment of and protection against COVID-19. We report here on the use of a naive phage display antibody library to identify a panel of fully human SARS-CoV-2 neutralizing antibodies. Following functional profiling in vitro against an early pandemic isolate as well as a recently emerged isolate bearing the D614G Spike mutation, the clinical candidate antibody, STI-1499, and the affinity-engineered variant, STI-2020, were evaluated for in vivo efficacy in the Syrian golden hamster model of COVID-19. Both antibodies demonstrated potent protection against the pathogenic effects of the disease and a dose-dependent reduction of virus load in the lungs, reaching undetectable levels following a single dose of 500 micrograms of STI-2020. These data support continued development of these antibodies as therapeutics against COVID-19 and future use of this approach to address novel emerging pandemic disease threats.

microbiology↗

bioRxiv2020

Distinct mechanisms for TMPRSS2 expression explain organ-specific inhibition of SARS-CoV-2 infection by enzalutamide

The coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has rapidly become a global public health threat due to the lack of effective drugs or vaccines against SARS-CoV-2. The efficacy of several repurposed drugs has been evaluated in clinical trials. Among these drugs, a relatively new antiandrogen agent, enzalutamide, was proposed because it reduces the expression of transmembrane serine protease 2 (TMPRSS2), a key component mediating SARS-CoV-2-driven entry into host cells, in prostate cancer cells. However, definitive evidence for the therapeutic efficacy of enzalutamide in COVID-19 is lacking. Here, we evaluated the antiviral efficacy of enzalutamide in prostate cancer cells, lung cancer cells, human lung organoids and SARS-CoV-2-infected Ad-ACE2-transduced Tmprss2 knockout (Tmprss2-KO) and wild-type (WT) mice. TMPRSS2 knockout significantly inhibited SARS-CoV-2 infection in vivo. Enzalutamide effectively inhibited SARS-CoV-2 infection in human prostate cancer cells (LNCaP) but not in human lung cancer cells or patient-derived lung organoids. Although Tmprss2 knockout effectively blocked SARS-CoV-2 infection in ACE2-transduced mice, enzalutamide showed no antiviral activity due to the AR independence of TMPRSS2 expression in mouse and human lung epithelial cells. Moreover, we observed distinct AR binding patterns between prostate cells and lung cells and a lack of direct binding of AR to TMPRSS2 in human lung cells. Thus, our findings do not support the postulated protective role of enzalutamide in treating COVID-19.

microbiology↗

bioRxiv2020

A Targeted Vaccine against COVID-19: S1-Fc Vaccine Targeting the Antigen-Presenting Cell Compartment Elicits Protection against SARS-CoV-2 Infection

Vaccination efficacy is enhanced by targeting the antigen-presenting cell compartment. Here, we show that S1-Fc antigen delivery targeting the Fc{gamma}R+ antigen-presenting cell compartment elicits anti-SARS-CoV-2 S1-antigen specific IgG production in vivo exerting biologically functional and protective activity against live virus infection, assessed in a stringent experimental virus challenge assay in vitro. The S1-domain of the SARS-CoV-2 spike protein was genetically fused to a human immunoglobulin Fc moiety, which contributes to mediate S1-Fc cellular internalization by Fc{gamma}R+ antigen-presenting cells. Immediately upon administration intramuscularly, our novel vaccine candidate recombinant rS1-Fc homes to lymph nodes in vivo where Fc{gamma}R+ antigen-presenting cells reside. Seroconversion is achieved as early as day 7, mounting considerably increased levels of anti-S1 IgGs in vivo. Interestingly, immunization at elevated doses with non-expiring S1-Fc encoding dsDNA favors the education of a desired antigen-specific adaptive T cell response. However, low-dose immunization, safeguarding patient safety, using recombinant rS1-Fc, elicits a considerably elevated protection amplitude against live SARS-CoV-2 infection. Our promising findings on rS1-Fc protein immunization prompted us to further develop an affordable and safe product for delivery to our communities in need for COVID-19 vaccinations.

microbiology↗

bioRxiv2020

Phagocytosis drives NAD+ reduction-induced dendrite degeneration in Drosophila

After injury, severed dendrites and axons expose the "eat-me" signal phosphatidylserine (PS) on their surface and degenerate by disassembly. While axon degeneration is controlled by a conserved "axon-death" pathway that is thought to activate self-destruction, how PS exposure is regulated by this pathway and whether PS-induced phagocytosis contributes to neurite breakdown in vivo remain unknown. Here we show that in Drosophila sensory dendrites, PS exposure and self-destruction are triggered by two distinct levels of NAD+ reduction downstream of Sarm activation. Surprisingly, phagocytosis is the main driver of dendrite degeneration induced by both genetic NAD+ disruptions and injury. Furthermore, the axon-death factor Axed is only partially required for self-destruction of injured dendrites, acting in parallel with PS-induced phagocytosis. Lastly, injured dendrites exhibit a unique rhythmic calcium flashing that correlates with self-destruction. Therefore, a special genetic program coordinates PS exposure and self-destruction in injury-induced dendrite degeneration in vivo.

cell biology↗

bioRxiv2020

Phase response analyses support a relaxation oscillator model of locomotor rhythm generation in Caenorhabditis elegans

Neural circuits coordinate with muscles and sensory feedback to generate motor behaviors appropriate to an animals environment. In C. elegans, the mechanisms by which the motor circuit generates undulations and modulates them based on the environment are largely unclear. We quantitatively analyzed C. elegans locomotion during free movement and during transient optogenetic muscle inhibition. Undulatory movements were highly asymmetrical with respect to the duration of bending and unbending during each cycle. Phase response curves induced by brief optogenetic inhibition of head muscles showed gradual increases and rapid decreases as a function of phase at which the perturbation was applied. A relaxation oscillator model based on proprioceptive thresholds that switch the active muscle moment was developed and is shown to quantitatively agree with data from free movement, phase responses, and previous results for gait adaptation to mechanical loadings. Our results suggest a neuromuscular mechanism underlying C. elegans motor pattern generation within a compact circuit.

neuroscience↗

bioRxiv2020

Unique K-mer sequences for validating cancer-related substitution, insertion and deletion mutations

The cancer genome sequencing has led to important discoveries such as identifying cancer gene. However, challenges remain in the analysis of cancer genome sequencing. One significant issue is that mutations identified by multiple variant callers are frequently discordant even when using the same genome sequencing data. For insertion and deletion mutations, oftentimes there is no agreement among different callers. Identifying somatic mutations involves read mapping and variant calling, a complicated process that uses many parameters and model tuning. To validate the identification of true mutations, we developed a method using k-mer sequences. First, we characterized the landscape of unique versus non-unique k-mers in the human genome. Second, we developed a software package, KmerVC, to validate the given somatic mutations from sequencing data. Our program validates the occurrence of a mutation based on statistically significant difference in frequency of k-mers with and without a mutation from matched normal and tumor sequences. Third, we tested our method on both simulated and cancer genome sequencing data. Counting k-mer involving mutations effectively validated true positive mutations including insertions and deletions across different individual samples in a reproducible manner. Thus, we demonstrated a straightforward approach for rapidly validating mutations from cancer genome sequencing data.

bioinformatics↗

bioRxiv2020

NTR 2.0: a rationally-engineered prodrug converting enzyme with substantially enhanced efficacy for targeted cell ablation

Heterologously-expressed bacterial nitroreductase (NTR) enzymes sensitize eukaryotic cells to prodrugs such as metronidazole (MTZ), enabling selective cell ablation paradigms that have expanded studies of cell function and regeneration in vertebrate systems. However, first-generation NTRs require confoundingly toxic prodrug treatments (e.g. 10 mM MTZ) and some cell types have proven resistant. We used rational engineering and cross-species screening to develop a NTR variant, NTR 2.0, which exhibits ~100-fold improvement in MTZ-mediated cell-specific ablation efficacy. Toxicity tests in zebrafish showed no deleterious effects of prolonged MTZ treatments of [≤]1 mM. NTR 2.0 therefore enables sustained cell loss paradigms and ablation of previously resistant cell types. These properties permit enhanced interrogations of cell function, extended challenges to the regenerative capacities of discrete stem cell niches, and enable modeling of chronic degenerative diseases. Accordingly, we have created a series of bipartite transgenic resources to facilitate dissemination of NTR 2.0 to the research community.

cell biology↗

bioRxiv2020

Predicting Molecular Fingerprint from Electron-Ionization Mass Spectrum with Deep Neural Networks

Electron-ionization mass spectrometry (EI-MS) hyphenated gas chromatography (GC) is the workhorse to analyze volatile compounds in complex samples. The spectral matching method can only identify compounds within spectral database. In response, we present a deep-learning-based approach (DeepEI) for structure elucidation of unknown compound with its EI-MS spectrum. DeepEI employs deep neural networks to predict molecular fingerprint from EI-MS spectrum, and searches molecular structure database with the predicted fingerprints. In addition, a convolutional neural network was also trained to filter the structures in database and improve the identification performance. Our method shows improvement on the competing method NEIMS in identification accuracy on both NIST test dataset and MassBank dataset. Furthermore, DeepEI (spectrum to fingerprint) and NEIMS (fingerprint to spectrum) can be combined to improve identification accuracy.

bioinformatics↗

bioRxiv2020

Large-scale Phenotypic Drug Screen Identifies Neuroprotectants in Zebrafish and Mouse Models of Retinitis Pigmentosa

Retinitis pigmentosa (RP) and associated inherited retinal diseases (IRDs) are caused by rod photoreceptor degeneration, necessitating therapeutics promoting rod photoreceptor survival. To address this, we tested compounds for neuroprotective effects in zebrafish and mouse RP models, reasoning drugs effective across species may translate better clinically. We first performed a large-scale phenotypic drug screen using a larval zebrafish model of inducible RP. 2,934 compounds, mostly human-approved drugs, were tested across six concentrations. Statistically, 113 compounds achieved "hit" status. Secondary tests of 42 high-priority hits confirmed eleven lead compounds. Nine leads were then evaluated in mouse RP models, with six exhibiting neuroprotective effects. An analysis of potential mechanisms of action suggested complementary activities. Paired lead compound assays in zebrafish showed additive neuroprotective effects for the majority. These results highlight the value of cross-species phenotypic drug discovery and suggest combinatorial drug therapies may provide enhanced therapeutic benefits for patients with RP and IRDs.

neuroscience↗

bioRxiv2020

Deep Learning Enable Untargeted Metabolite Extraction from High Throughput Coverage Data-Independent Acquisition

The sequential window acquisition of all theoretical spectra (SWATH) technique is a specific variant of data-independent acquisition (DIA), which is supposed to increase the metabolite coverage and the reproducibility compared to data-dependent acquisition (DDA). However, SWATH technique lost the direct link between the precursor ion and the fragments. Here, we propose a deep-learning-based approach (DeepSWATH) to reconstruct the association between the MS/MS spectra and their precursors. Comparing with MS-DIAL, the proposed method can extract more accurate spectra with less noise to improve the identification accuracy of metabolites. Besides, DeepSWATH can also handle severe coelution conditions.

bioinformatics↗

bioRxiv2020

A Systematic Evaluation of Single-cell RNA-sequencing Imputation Methods

The rapid development of single-cell RNA-sequencing (scRNA-seq) technology, with increased sparsity compared to bulk RNA-sequencing (RNA-seq), has led to the emergence of many methods for preprocessing, including imputation methods. Here, we systematically evaluate the performance of 18 state-of-the-art scRNA-seq imputation methods using cell line and tissue data measured across experimental protocols. Specifically, we assess the similarity of imputed cell profiles to bulk samples as well as investigate whether methods recover relevant biological signals or introduce spurious noise in three downstream analyses: differential expression, unsupervised clustering, and inferring pseudotemporal trajectories. Broadly, we found significant variability in the performance of the methods across evaluation settings. While most scRNA-seq imputation methods recover biological expression observed in bulk RNA-seq data, the majority of the methods do not improve performance in downstream analyses compared to no imputation, in particular for clustering and trajectory analysis, and thus should be used with caution. Furthermore, we find that the performance of scRNA-seq imputation methods depends on many factors including the experimental protocol, the sparsity of the data, the number of cells in the dataset, and the magnitude of the effect sizes. We summarize our results and provide a key set of recommendations for users and investigators to navigate the current space of scRNA-seq imputation methods.

genomics↗

bioRxiv2019

Overcoming high nanopore basecaller error rates for DNA storage via basecaller-decoder integration and convolutional codes

As magnetization and semiconductor based storage technologies approach their limits, bio-molecules, such as DNA, have been identified as promising media for future storage systems, due to their high storage density (petabytes/gram) and long-term durability (thousands of years). Furthermore, nanopore DNA sequencing enables high-throughput sequencing using devices as small as a USB thumb drive and thus is ideally suited for DNA storage applications. Due to the high insertion/deletion error rates associated with basecalled nanopore reads, current approaches rely heavily on consensus among multiple reads and thus incur very high reading costs. We propose a novel approach which overcomes the high error rates in basecalled sequences by integrating a Viterbi error correction decoder with the basecaller, enabling the decoder to exploit the soft information available in the deep learning based basecaller pipeline. Using convolutional codes for error correction, we experimentally observed 3x lower reading costs than the state-of-the-art techniques at comparable writing costs. The code, data and Supplementary Material is available at https://github.com/shubhamchandak94/nanopore_dna_storage.

bioengineering↗

bioRxiv2019

Whole genome analysis identifies the association of TP53 genomic deletions with lower survival in Stage III colorectal cancer

DNA copy number aberrations (CNA) were frequently observed in colorectal cancers (CRC). There is an urgent call for CNA-based biomarkers in clinics, in particular for Stage III CRC, if combined with imaging or pathologic evidence, promise more precise care at the timing. We conducted this Stage III specific biomarker discovery with a cohort of 134 CRCs, and with a newly developed high-efficiency CNA profiling protocol. Specifically, we developed the profiling protocol for tumor-normal matched tissue samples based on low-coverage clinical whole-genome sequencing (WGS). We demonstrated the protocols accuracy and robustness by a systematic benchmark with microarray, high-coverage whole-exome and -genome approaches, where the low-coverage WGS-derived CNA segments were highly accordant (PCC>0.95) with those derived from microarray, and they were substantially less variable if compared to exome-derived segments. A lasso-based model and multivariate cox regression analysis identified a chromosome 17p loss, containing the TP53 tumor suppressor gene, that was significantly associated with reduced survival (P=0.0139, HR=1.688, 95% CI = [1.112-2.562]), which was validated by an independent cohort of 187 Stage III CRCs. In summary, the new low-coverage WGS protocol has high sensitivity, high resolution and low cost and the identified 17p-loss is an effective poor prognosis marker for Stage III patients.

genomics↗

bioRxiv2019

Single-cell ATAC-seq Signal Extraction and Enhancement with SCATE

Single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq) is the state-of-the-art technology for analyzing genome-wide regulatory landscape in single cells. Single-cell ATAC-seq data are sparse and noisy. Analyzing such data is challenging. Existing computational methods cannot accurately reconstruct activities of individual cis-regulatory elements (CREs) in individual cells or rare cell subpopulations. We present a new statistical framework, SCATE, that adaptively integrates information from co-activated CREs, similar cells, and publicly available regulome data to substantially increase the accuracy for estimating activities of individual CREs. We show that using SCATE, one can better reconstruct the regulatory landscape of a heterogeneous sample.

genomics↗

bioRxiv2019

Single cell genomic characterization reveals the cellular reprogramming of the gastric tumor microenvironment

PurposeThe tumor microenvironment (TME) consists of a heterogenous cellular milieu that can influence cancer cell behavior. The characteristics of the cellular TME have a dramatic impact on treatments such as immunotherapy. These features can be revealed with single-cell RNA sequencing (scRNA-seq). We hypothesized that single cell gene expression studies of gastric cancer (GC) together with paired normal tissue and peripheral blood mononuclear cells (PBMCs) would identify critical elements of cellular dysregulation not apparent with other approaches.\n\nMethodsSingle cell gene expression studies were conducted on seven patients with GC and one patient with intestinal metaplasia. We sequenced 56,167 cells comprising GC (32,407 cells), paired normal tissue (18,657 cells) and PBMCs (5,103 cells). Protein expression of genes of interest was validated by multiplex immunofluorescence.\n\nResultsTumor epithelium had copy number alterations and a distinct gene expression program compared to normal with intra-tumor heterogeneity. The GC TME was significantly enriched for stromal cells, macrophages, dendritic cells (DCs) and Tregs. TME-exclusive stromal cells expressed extracellular matrix components distinct from normal tissue. Macrophages were transcriptionally heterogenous and did not conform to a binary M1/M2 paradigm. Gene expression program of tumor DCs was unique from PBMC DCs. TME-specific cytotoxic T cells comprised of two exhausted heterogenous subsets. Helper, cytotoxic T, Treg and NK cells expressed multiple immune checkpoint or costimulatory molecules. Receptor-ligand analysis revealed TME-exclusive inter-cellular communication.\n\nConclusionsSingle cell gene expression studies revealed widespread reprogramming across multiple cellular elements in the milieu of the GC TME. Cellular remodeling was delineated by changes in cell numbers, transcriptional states and inter-cellular interactions. This characterization facilitates understanding of tumor biology and enables the identification of novel molecular targets including for cancer immunotherapy.\n\nSTATEMENT OF TRANSLATIONAL RELEVANCEWe leveraged the power of single-cell genomics to characterize the heterogenous cell types and states in the tumor microenvironment (TME). By profiling thousands of single cells from surgical resections of gastric cancer together with paired normal mucosa and peripheral blood mononuclear cells (PBMCs), we determined the deviations in the TME from physiological conditions. Our analysis revealed a cellular reprogramming of the TME compared to normal mucosa in immune and stromal lineages. We detected transcriptional heterogeneity within macrophages and a TME-specific gene expression program in dendritic cells. Cytotoxic T cells in the TME had heterogenous profiles of exhaustion and expression of multiple immune checkpoint and costimulatory molecules. We constructed a receptor-ligand based inter-cellular communications network that was exclusive to tumor tissue. These discoveries provide information at a highly granular cellular resolution enabling advances in cancer biology, biomarker discovery and identification of treatment targets such as for immunotherapy.

cancer biology↗