Biology subjects

Hu, Z.

Publications and source records attributed to Hu, Z..

At least 289 records · Page 16Linked to original sources

bioRxiv2021

DeepHBV: A deep learning model to predict hepatitis B virus (HBV) integration sites.

Hepatitis B virus (HBV) is one of the main causes for viral hepatitis and liver cancer. Previous studies showed HBV can integrate into host genome and further promote malignant transformation. In this study, we developed an attention-based deep learning model DeepHBV to predict HBV integration sites by learning local genomic features automatically. We trained and tested DeepHBV using the HBV integration sites data from dsVIS database. Initially, DeepHBV showed AUROC of 0.6363 and AUPR of 0.5471 on the dataset. Adding repeat peaks and TCGA Pan Cancer peaks can significantly improve the model performance, with an AUROC of 0.8378 and 0.9430 and an AUPR of 0.7535 and 0.9310, respectively. On independent validation dataset of HBV integration sites from VISDB, DeepHBV with HBV integration sequences plus TCGA Pan Cancer (AUROC of 0.7603 and AUPR of 0.6189) performed better than HBV integration sequences plus repeat peaks (AUROC of 0.6657 and AUPR of 0.5737). Next, we found the transcriptional factor binding sites (TFBS) were significantly enriched near genomic positions that were paid attention to by convolution neural network. The binding sites of AR-halfsite, Arnt, Atf1, bHLHE40, bHLHE41, BMAL1, CLOCK, c-Myc, COUP-TFII, E2A, EBF1, Erra and Foxo3 were highlighted by DeepHBV attention mechanism in both dsVIS dataset and VISDB dataset, revealing the HBV integration preference. In summary, DeepHBV is a robust and explainable deep learning model not only for the prediction of HBV integration sites but also for further mechanism study of HBV induced cancer. Author summaryHepatitis B virus (HBV) is one of the main causes for viral hepatitis and liver cancer. Previous studies showed HBV can integrate into host genome and further promote malignant transformation. In this study, we developed an attention-based deep learning model DeepHBV to predict HBV integration sites by learning local genomic features automatically. The performance of DeepHBV model significantly improves after adding genomic features, with an AUROC of 0.9430 and an AUPR of 0.9310. Furthermore, we enriched the transcriptional factor binding sites of proteins by convolution neural network. In summary, DeepHBV is a robust and explainable deep learning model not only for the prediction of HBV integration sites but also for the further study of HBV integration mechanism.

bioinformatics↗

bioRxiv2020

A novel and effective Cre/lox-based genetic tool for repeated, targeted and markerless integration in Yarrowia lipolytica

The unconventional yeast Yarrowia lipolytica is extensively applied in bioproduction fields owing to its excellent metabolite and protein production ability. Nonetheless, utilization of this promising host is still restricted by limited availability of precise and effective gene integration tools. In this study, a novel and efficient genetic tool was developed for targeted, repeated, and markerless gene integration based on Cre/lox site-specific recombination system. The developed tool required only a single selection marker and could completely excise all of the unnecessary sequences. A total of three plasmids were created and seven rounds of marker-free gene integration were examined in Y. lipolytica. All the integration efficiencies remained above 90%, and analysis of protein production and growth characteristics of the engineered strains confirmed that genome modification via the novel genetic tool was feasible. Further work also confirmed the genetic tool was effective for integration of other genes, loci, and strains. Thus, this study significantly promotes the application of Cre/lox system and presents a powerful tool for genome engineering in Y. lipolytica. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/424803v2_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@11b3be9org.highwire.dtl.DTLVardef@fa7e2aorg.highwire.dtl.DTLVardef@170fafaorg.highwire.dtl.DTLVardef@6d4750_HPS_FORMAT_FIGEXP M_FIG C_FIG The novel genetic tool comprised three plasmids, namely, Cre-Y1, Cre-Y2, and Cre-Y3. Cre-Y1 was introduced into the Upleu locus of Po1f, and then recombination between two lox71 sites could remove unnecessary plasmid elements and produce an initial strain harboring a lox71 site. Subsequently, Cre-Y2 was integrated onto the leu2 locus, forming a transitional strain with three lox sites (lox71, lox66, and rclox66). Recombination reactions among these three lox sites could excise unnecessary DNA fragments and retain a lox72 site, an rclox66 site, and a target gene expression cassette in the genome. Similarly, integration of Cre-Y3 produced strains harboring two target gene expression cassettes. Consequently, repeated, targeted, and markerless gene integration could be efficiently realized following iterative integration of Cre-Y2 and Cre-Y3. HighlightsO_LIA powerful genetic tool was developed for targeted and markerless gene integration C_LIO_LIThe genetic tool was effective for various genes, loci, and strains C_LIO_LIOnly a single selection marker was adequate for repeated gene integration C_LIO_LIAll of the useless and redundant sequences would be excised C_LIO_LIAll integration efficiencies could remain above 90% C_LI

synthetic biology↗

bioRxiv2020

Precision mapping of the mouse brain metabolome

Metabolism is physiologically fundamental to a biological system. Understanding brain metabolism is critical for our comprehensive knowledge of brain function in health and disease. Combining a microarray collection system with targeted metabolomics analysis, here we performed precision mapping of the metabolome in the mouse brain and created maps for 79 metabolites with a resolution of 0.125mm3 per pixel (i.e., brain subregion). The metabolome atlas provides researchers with a useful resource to interpret the vulnerability of specific brain regions to various disease-relevant metabolic perturbations.

neuroscience↗

bioRxiv2020

Recombinant Fc-fusion vaccine of RBD induced protection against SARS-CoV-2 in non-human primate and mice

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) continues to infect people globally. The increased COVID-19 cases and no licensed vaccines highlight the need to develop safe and effective vaccines against SARS-CoV-2 infection. Multiple vaccines candidates are under pre-clinical or clinical trails with different strengths and weaknesses. Here we developed a pilot scale production of a recombinant subunit vaccine (RBD-Fc Vacc) with the Receptor Binding Domain of SARS-CoV-2 S protein fused with the Fc domain of human IgG1. RBD-Fc Vacc induced SARS-CoV-2 specific neutralizing antibodies in non-human primates and human ACE2 transgenic mice. The antibodies induced in macaca fascicularis neutralized three divergent SARS-CoV2 strains, suggesting a broader neutralizing ability. Three times immunizations protected Macaca fascicularis (20ug or 40ug per dose) and mice (10ug or 20ug per dose) from SARS-CoV-2 infection respectively. These data support clinical development of SARS-CoV-2 vaccines for humans. RBD-Fc Vacc is currently being assessed in randomized controlled phase 1/II human clinical trails. SummaryThis study confirms protective efficacy of a SARS-CoV-2 RBD-Fc subunit vaccine.

immunology↗

bioRxiv2020

A versatile resource of 1500 diverse wild and cultivated soybean genomes for post-genomics research

With the advance of next-generation sequencing technologies, over 15 terabytes of raw soybean genome sequencing data were generated and made available in the public. To develop a consolidated, diverse, and user-friendly genomic resource to facilitate post-genomic research, we sequenced 91 highly diverse wild soybean genomes representing the entire US collection of wild soybean accessions to increase the genetic diversity of the sequenced genomes. Having integrated and analyzed the sequencing data with the public data, we identified and annotated 32 million single nucleotide polymorphisms (32mSNPs) with a resolution of 30 SNPs/kb and 12 non-synonymous SNPs/gene in 1,556 accessions (1.5K). Population structure analysis showed that the 1.5K accessions represent the genetic diversity of the 20,087 (20K) soybean accessions in the U.S. collection. Inclusion of wild soybean genomes significantly increased the genetic diversity and shorten linkage disequilibrium distance in the panel of soybean accessions. We identified a collection of paired accessions sharing the highest genomic identity between the 1.5K and 20K accessions as genomically "equivalent" accessions to maximize the use of the genome sequences. We demonstrated that the 32mSNPs in the 1.5K accessions can be effectively used for in-silico genotyping, discovering trait QTL, gene alleles/mutations, identifying germplasms containing beneficial allele and domestication selection of trait alleles. We made the 32mSNPs and 1.5K accessions with detailed annotation available at SoyBase and Ag Data Commons. The dataset could serve as a versatile resource to release the potential of the huge amount of genome sequencing data for a variety of postgenomic research.

plant biology↗

bioRxiv2020

Interpretable Segmentation of Amyloid-βStained Whole Slide Images of Brain Tissue

Neurodegenerative disease pathologies have been reported in both grey matter (GM) and white matter (WM) with different density distributions, an automated separation of GM/WM would be extremely advantageous for aiding in neuropathologic deep phenotyping. Standard segmentation methods typically involve manual annotations, where a trained researcher traces the delineation of GM/WM in ultra-high-resolution Whole Slide Images (WSIs). This method can be time-consuming and subjective, preventing the analysis of large amounts of WSIs at scale. This paper proposes an automated segmentation pipeline combining a Convolutional Neural Network (CNN) module for segmenting GM/WM regions and a post-processing module to remove artifacts/residues of tissues as well as generate XML annotations that can be visualized via Aperio ImageScope. First, we investigate two baseline models for medical image segmentation: FCN, and U-Net. Then we propose a patch-based approach, ResNet-Patch, to classify the GM/WM/background regions. In addition, we integrate a Neural Conditional Random Field (NCRF) module, ResNet-NCRF, to model and incorporate the spatial correlations among neighboring patches. Although their mechanisms are greatly different, both U-Net and ResNet-Patch/ResNet-NCRF achieve Intersection over Union (IoU) of more than 90% in GM and more than 80% in WM, while ResNet-Patch achieves 1% superior to U-Net with lower variance among various WSIs. ResNet-NCRF further improves the IoU by 3% for WM compared to ResNet-Patch before post-processing. We also apply gradient-weighted class activation mapping (Grad-CAM) to interpret the segmentation masks and provide relevant explanations and insights.

neuroscience↗

bioRxiv2020

High-throughput imaging of mRNA at the single-cell level in human primary immune cells

Measurement of gene expression at the single-cell level has led to important advances in the study of transcriptional regulation programs in healthy and disease states. In particular, single-cell gene expression approaches have shed light on the high level of transcriptional heterogeneity of individual cells, both at baseline and in response to experimental or environmental perturbations. We have developed a method for High-Content Imaging (HCI)-based quantification of transcript abundance at the single-cell level in primary human immune cells and have validated its performance under multiple experimental conditions to demonstrate its general applicability. This method, which we abbreviate as hcHCR, combines the high sensitivity of the hybridization chain reaction (HCR) for the visualization of mRNA molecules in single cells, with the speed, scalability, and technical reproducibility of HCI. We first tested eight microscopy-compatible attachment substrates for short-term culture of primary human B cells, T cells, monocytes, or neutrophils. We then miniaturized HCR in a 384-well format and documented the ability of the method to detect increased or decreased transcript abundance at the single-cell level in thousands of cells for each experimental condition by HCI. Furthermore, we demonstrated the feasibility of multiplexing gene expression measurements by simultaneously assaying the abundance of two transcripts per cell, both at baseline and in response to an experimental stimulus. Finally, we tested the robustness of the assay to technical and biological variation. We anticipate that hcHCR will be a suitable and cost-effective assay for low- to medium-throughput chemical, genetic or functional genomic screens in primary human cells, with the possibility of performing personalized screens or screens on cells obtained from patients with a specific disease.

genomics↗

bioRxiv2020

Single-PanIN-seq Unveils that ARID1A Deficiency Promotes Pancreatic Tumorigenesis by Attenuating KRAS Induced Senescence

ARID1A is one of the most frequently mutated epigenetic regulators in a wide spectrum of cancers. Recent studies have shown that ARID1A deficiency induces global changes in the epigenetic landscape of enhancers and promoters. These broad and complex effects make it challenging to identify the driving mechanisms of ARID1A deficiency in promoting cancer progression. Here we identified the anti-senescence effect of ARID1A deficiency in the progression of pancreatic intraepithelial neoplasia (PanIN) by profiling the transcriptome of individual PanINs in the mouse model. Interestingly, we found that ARID1A deficiency upregulates the expression of Aldehyde dehydrogenase ALDH1A1, which plays an essential role in attenuating the senescence induced by oncogenic KRAS. Despite that ALDH proteins have been commonly used as cancer stem cell markers, their effect in promoting attenuation of senescence is not known before. Therefore, ALDH proteins could be considered a potential adjuvant drug target in treating pancreatic cancer.

cancer biology↗

bioRxiv2020

IFN-{lambda}4 may contribute to HCV persistence by increasing ER stress and enhancing IRF1 signaling

Chronic hepatitis C virus (HCV) infection and cirrhosis are major risk factors for developing hepatocellular carcinoma (HCC). Genetic polymorphisms in the IFNL3/IFNL4 locus have been associated both with impaired clearance of HCV and protection from liver fibrosis, an early stage of cirrhosis. Here, we aimed to address the genetic and functional relationships between IFNL3/IFNL4 polymorphisms, HCV-related cirrhosis, and HCC risk. We evaluated associations between IFNL4 genotype, defined as the presence of rs368234815-dG or rs12979860-T alleles, with cirrhosis and HCC risk in patients with chronic HCV - 2,931 from Taiwan and 3,566 from Japan. We detected associations between IFNL4 genotype and decreased risk of cirrhosis (OR=0.66, 95%CI=0.46-0.93, P=0.018, in Taiwan), but increased risk of HCC (OR=1.28, 95%CI=1.07-1.52, P=0.0058, in Japan). In-vitro, IFN-{lambda}4 expression increased ER stress, and enhanced positive regulation of IFN responses via IRF1 induction, which mediated antiproliferative effects in hepatic cells. Our data present novel IFN-{lambda}4-associated pathways that may be contributing to HCV persistence and development of HCC.

cancer biology↗

bioRxiv2020

Methyltransferase-like 3 modulates severe acute respiratory syndrome coronavirus-2 RNA N6-methyladenosine modification and replication

The coronavirus disease 2019 pandemic caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is an ongoing global public crisis. Although viral RNA modification has been reported based on the transcriptome architecture, the types and functions of RNA modification are still unknown. In this study, we evaluated the roles of RNA N6-methyladenosine (m6A) modification in SARS-CoV-2. Our methylated RNA immunoprecipitation sequencing (MeRIP-Seq) analysis showed that SARS-CoV-2 RNA contained m6A modification. Moreover, SARS-CoV-2 infection not only increased the expression of methyltransferase-like 3 (METTL3) but also altered its distribution. Modification of METTL3 expression by short hairpin RNA or plasmid transfection for knockdown or overexpression, respectively, affected viral replication. Furthermore, the viral key protein RdRp interacted with METTL3, and METTL3 was distributed in both the nucleus and cytoplasm in the presence of RdRp. RdRp appeared to modulate the sumoylation and ubiquitination of METTL3 via an unknown mechanism. Taken together, our findings demonstrated that the host m6A modification complex interacted with viral proteins to modulate SARS-CoV-2 replication.

molecular biology↗

bioRxiv2020

Targeted Degradation of Transcription Factors by TRAFTACs: Transcription Factor Targeting Chimeras

Many diseases, including cancer, stem from aberrant activation and overexpression of oncoproteins that are associated with multiple signaling pathways. Although proteins with catalytic activity are able to be successfully drugged, the majority of other protein families, such as transcription factors, remain intractable due to their lack of ligandable sites. In this study, we report the development of TRAnscription Factor TArgeting Chimeras (TRAFTACs) as a generalizable strategy for targeted transcription-factor degradation. Herein, we show that TRAFTACs, which consist of a chimeric oligonucleotide that simultaneously binds to the transcription-factor of interest (TOI) and to HaloTag fused dCas9 protein, can induce degradation of the former via the proteasomal pathway. Application of TRAFTACs to two oncogenic TOIs, NF-{kappa}B and brachyury, suggests that TRAFTACs can be successfully employed for the targeted degradation of other DNA-binding proteins with minor changes to the chimeric oligonucleotide.

bioengineering↗

bioRxiv2020

Discovery and engineering of small SlugCas9 with broad targeting range and high specificity and activity

The compact CRISPR/Cas9 system, which can be delivered by adeno-associated virus (AAV), is a promising platform for therapeutic applications. However, current compact Cas9 nucleases have limited activity, targeting scope and specificity. Here, we identified three compact SaCas9 orthologs, Staphylococcus lugdunensis Cas9 (SlugCas9), Staphylococcus lutrae Cas9 (SlutrCas9) and Staphylococcus haemolyticus Cas9 (ShaCas9), for mammalian genome editing. Interestingly, SlugCas9 recognizes a simple NNGG PAM and displays comparable activity to SaCas9. We further generated a SlugCas9-SaCas9 chimeric nuclease, which has both high specificity and high activity. We lastly engineered SlugCas9 with mutations to generate a high fidelity variant that maintains high specificity without compromising on-target editing efficiency. Our study offers important minimal Cas9 tools that are ideal for both basic research and clinical applications.

genetics↗

bioRxiv2020

Characterization of a clinical Enterobacter hormaechei strain belonging to epidemic clone ST418 co-carrying blaNDM-1, blaIMP-4 and mcr-9.1

An Enterobacter hormaechei isolate (ECL-90) simultaneously harboring blaNDM-1, blaIMP-4 and mcr-9.1 was recovered from the secretion specimen of a 24-year-old male patient in a tertiary hospital in China. The whole genome sequencing of this isolate was complete, and 4 circular plasmids with variable sizes were detected. MLST analysis assigned the isolate to ST418, known as a carbapenemase-producing epidemic clone in China. blaIMP-4 and mcr-9.1 genes were co-carried on an IncHI2/2A plasmid (pECL-90-2) and blaNDM-1 was harbored by an IncX3 plasmid (pECL-90-3). The genetic context of mcr-9.1 was identified as a prevalent structure, "rcnR-rcnA-pcoE-pcoS-IS903-mcr-9-wbuC", which is a relatively unitary model involved in the mobilization of mcr-9. Meanwhile, blaNDM-1 gene was detected within a globally widespread structure known as NDM-GE-U.S ("ISAba125- blaNDM-1-blaMBL"). Our study warrants that the convergence of genes mediating resistance to last-resort antibiotics in epidemic clones would largely facilitate their widespread in clinical settings, thus representing a potential challenge to clinical treatment and public health. ImportanceCarbapenemase-producing Enterobacteriaceae (CPE) spread at a high rate and colistin is the last-resort therapeutic for the infection caused by CPE. However, the emergence of plasmid-borne mcr genes highly facilitates the wide dissemination of colistin resistance, thus largely threatens the clinical use of colistin. Here, we for the first time characterized a clinical Enterobacter hormaechei strain co-producing blaNDM-1, blaIMP-4 and mcr-9.1 belonging to an epidemic clone (ST418). The accumulation of genes mediating resistance to last-resort antibiotics in epidemic clones would largely facilitate their widespread in clinical settings, which may cause disastrous consequence with respect to antimicrobial resistance. Understanding how resistance genes were accumulated in a single strain could help us to track the evolutionary trajectory of drug resistance. Our finding highlights the importance of surveillance on the epidemic potential of colistin-resistant CPE, and effective infection control measures to prevent the resistance dissemination.

microbiology↗

bioRxiv2020

Inferring microbial co-occurrence networks from amplicon data: a systematic evaluation

Microbes tend to organize into communities consisting of hundreds of species involved in complex interactions with each other. 16S ribosomal RNA (16S rRNA) amplicon profiling provides snapshots that reveal the phylogenies and abundance profiles of these microbial communities. These snapshots, when collected from multiple samples, have the potential to reveal which microbes co-occur, providing a glimpse into the network of associations in these communities. The inference of networks from 16S data is prone to statistical artifacts. There are many tools for performing each step of the 16S analysis workflow, but the extent to which these steps affect the final network is still unclear. In this study, we perform a meticulous analysis of each step of a pipeline that can convert 16S sequencing data into a network of microbial associations. Through this process, we map how different choices of algorithms and parameters affect the co-occurrence network and estimate steps that contribute most significantly to the variance. We further determine the tools and parameters that generate the most accurate and robust co-occurrence networks based on comparison with mock and synthetic datasets. Ultimately, we develop a standardized pipeline (available at https://github.com/segrelab/MiCoNE) that follows these default tools and parameters, but that can also help explore the outcome of any other combination of choices. We envisage that this pipeline could be used for integrating multiple data-sets, and for generating comparative analyses and consensus networks that can help understand and control microbial community assembly in different biomes. ImportanceTo understand and control the mechanisms that determine the structure and function of microbial communities, it is important to map the interrelationships between its constituent microbial species. The surge in the high-throughput sequencing of microbial communities has led to the creation of thousands of datasets containing information about microbial abundances. These abundances can be transformed into networks of co-occurrences across multiple samples, providing a glimpse into the structure of microbiomes. However, processing these datasets to obtain co-occurrence information relies on several complex steps, each of which involves multiple choices of tools and corresponding parameters. These multiple options pose questions about the accuracy and uniqueness of the inferred networks. In this study, we address this workflow and provide a systematic analysis of how these choices of tools and parameters affect the final network, and on how to select those that are most appropriate for a particular dataset.

microbiology↗

bioRxiv2020

Dysregulation of hippocampal adult-born immature neurons disrupts a brain-wide network for spatial memory

Mounting evidence suggests that cognitive deficits associated with various neurological disorders may arise in part from a small population of dysregulated adult-born neurons in the dentate gyrus (DG). How these dysregulated adult-born neurons contribute to brain-wide network maladaptation and subsequent cognitive deficits remains unknown. Using an established mouse model with a small number of time-stamped dysregulated adult-born immature neurons and spatial memory deficits, we performed resting state functional magnetic resonance imaging and found that approximately 500 deficient immature neurons (<0.1% of total DG granule neurons) are sufficient to induce a significant decrease in the functional connectivity between DG and insular cortex (IC), two brain regions without direct anatomical connections. Furthermore, using a combination of rabies-based retrograde tracing and in vivo fiber photometry recording, we demonstrated that dysregulated adult-born neurons induce aberrant activity and synchrony in local hippocampal CA3 and CA1 regions, as well as distal medial-dorsal thalamus and IC regions during a spatial memory process. These results suggest that a few hundred dysregulated adult-born immature neurons can impact brain-wide network dynamics across several anatomically discrete regions and collectively contribute to impaired cognitive functions.

neuroscience↗

bioRxiv2020

Decoding Multivoxel Representations of Affective Scenes in Retinotopic Visual Cortex

The perception of opportunities and threats in complex scenes represents one of the main functions of the human visual system. In the laboratory, its neurophysiological basis is often studied by having observers view pictures varying in affective content. This body of work has consistently shown that viewing emotionally engaging, compared to neutral, pictures (1) heightens blood flow in limbic structures and frontoparietal cortex, as well as in anterior ventral and dorsal visual cortex, and (2) prompts an increase in the late positive event-related potential (LPP), a scalp-recorded and time-sensitive index of engagement within the network of aforementioned neural structures. The role of retinotopic visual cortex in this process has, however, been contentious, with competing theoretical notions predicting the presence versus absence of emotion-specific signals in retinotopic visual areas. The present study used multimodal neuroimaging and machine learning to address this question by examining the large-scale neural representations of affective pictures. Recording EEG and fMRI simultaneously while observers viewed pleasant, unpleasant, and neutral affective pictures, and applying multivariate pattern analysis to single-trial BOLD activities in retinotopic visual cortex, we identified three robust findings: First, unpleasant-versus-neutral decoding accuracy, as well as pleasant-versus-neutral decoding accuracy, were well above chance level in all retinotopic visual areas, including primary visual cortex. Second, the decoding accuracy in ventral visual cortex, but not in early visual cortex or dorsal visual cortex, was significantly correlated with LPP amplitude. Third, effective connectivity from amygdala to ventral visual cortex predicted unpleasant-versus-neutral decoding accuracy, and effective connectivity from ventral frontal cortex to ventral visual cortex predicted pleasant-versus-neutral decoding accuracy. These results suggest that affective pictures evoked valence-specific multivoxel neural representations in retinotopic visual cortex and that these multivoxel representations were influenced by reentry signals from limbic and frontal brain regions.

neuroscience↗

bioRxiv2020

A chromosome level genome of Astyanax mexicanus surface fish for comparing population-specific genetic differences contributing to trait evolution.

Identifying the genetic factors that underlie complex traits is central to understanding the mechanistic underpinnings of evolution. In nature, adaptation to severe environmental change, such as encountered following colonization of caves, has dramatically altered genomes of species over varied time spans. Genomic sequencing approaches have identified mutations associated with troglomorphic trait evolution, but the functional impacts of these mutations remain poorly understood. The Mexican Tetra, Astyanax mexicanus, is abundant in the surface waters of northeastern Mexico, and also inhabits at least 30 different caves in the region. Cave-dwelling A. mexicanus morphs are well adapted to subterranean life and many populations appear to have evolved troglomorphic traits independently, while the surface-dwelling populations can be used as a proxy for the ancestral form. Here we present a high-resolution, chromosome-level surface fish genome, enabling the first genome-wide comparison between surface fish and cavefish populations. Using this resource, we performed quantitative trait locus (QTL) mapping analyses for pigmentation and eye size and found new candidate genes for eye loss such as dusp26. We used CRISPR gene editing in A. mexicanus to confirm the essential role of a gene within an eye size QTL, rx3, in eye formation. We also generated the first genome-wide evaluation of deletion variability that includes an analysis of the impact on protein-coding genes across cavefish populations to gain insight into this potential source of cave adaptation. The new surface fish genome reference now provides a more complete resource for comparative, functional, developmental and genetic studies of drastic trait differences within a species.Competing Interest StatementThe authors have declared no competing interest.View Full Text

genomics↗

bioRxiv2020

Transcriptome analysis of alternative splicing-coupled nonsense-mediated mRNA decay in human cells reveals broad regulatory potential

To explore the regulatory potential of nonsense-mediated mRNA decay (NMD) coupled with alternative splicing, we globally surveyed the transcripts targeted by this pathway via RNA-Seq analysis of HeLa cells in which NMD had been inhibited. We identified putative NMD-targeted transcripts as those with a termination codon more than 50 nucleotides upstream of an exon-exon junction (premature termination as defined by the ‘50nt rule’) and that significantly increased in abundance upon NMD inhibition. We additionally controlled for potential transcriptional up-regulation by requiring the putative NMD targets to increase in abundance substantially more than the isoforms from the same gene that do not contain a premature termination codon. This resulted in a conservative set of 2,793 transcripts derived from 2,116 genes as physiological NMD targets (9.2% of expressed transcripts and >20% of alternatively spliced genes). Our analysis identified previously inferred unproductive isoforms and numerous heretofore-uncharacterized ones. NMD-targeted transcripts were derived from genes involved in many functional categories, and are particularly enriched for RNA splicing genes as well as for those harboring ultraconserved elements. By investigating the features of all transcripts impacted by NMD, we find that the 50nt rule is a strong predictor of NMD degradation while 3’ UTR length on its own generally has only a small effect in this human cell line. Additionally, thousands more transcripts without a premature termination codon in the main coding sequence contain a uORF and display significantly increased abundance upon NMD inhibition indicating potentially widespread regulation through decay coupled with uORF translation. Our results support that alternative splicing coupled with NMD is a prevalent post-transcriptional mechanism in human cells with broad potential for biological regulation.Competing Interest StatementThe authors have declared no competing interest.View Full Text

molecular biology↗