Biology subjects

Chen, G.

Publications and source records attributed to Chen, G..

357 records · Page 20Linked to original sources

bioRxiv2018

Exploring protein orthogonality in immune space: a case study with AAV and Cas9 orthologs

A major hurdle in protein-based therapeutics is the interaction with the adaptive immune system, which can lead to neutralization by circulating antibodies and clearance of treated cells by cytotoxic T-lymphocytes. One method of circumventing these issues is to use human or humanized proteins which avoid the immune response by self-recognition. However, this approach limits potential protein therapeutics to those of human origin, excluding many exciting effectors and delivery vehicles such as CRISPR-Cas9 and adeno-associated viruses (AAVs). To address this issue, we propose here the sequential use of orthologous proteins whose function is constrained by natural selection, but whose structure is subject to diversification by genetic drift. This would, in principle, allow for repeated treatments by immune orthogonal orthologs without reduced efficacy due to lack of immune cross-reactivity among the proteins. To explore and validate this concept we chose 91 Type II CRISPR-Cas9 orthologs and 167 AAV capsid protein orthologs, and developed a pipeline to compare total sequence similarity as well as predicted binding to class I and class II Major Histocompatibility Complex (MHC) proteins. Interestingly, MHC binding predictions revealed wide diversity among the set of Cas9 orthologs, with 83% of pairs predicted to have non cross-reacting immune responses, while no global immune orthogonality among AAV serotypes was observed. To confirm these findings we selected two Cas9 orthologs, from S. pyogenes and S. aureus, predicted to be orthogonal in immune space, and delivered them into mice via multiple AAV serotypes. We observed cross-reacting antibodies against AAV but not Cas9 orthologs in sera from immunized mice, validating the computationally predicted immune orthogonality among these proteins. Moving forward, we anticipate this framework can be applied to prescribe sequential regimens of immune orthogonal protein therapeutics to circumvent pre-existing or induced immunity, and eventually, to rationally engineer immune orthogonality among protein orthologs.

synthetic biology↗

bioRxiv2017

Handling Multiplicity in Neuroimaging through Bayesian Lenses with Hierarchical Modeling

Here we address the current issues of inefficiency and over-penalization in the massively univariate approach followed by the correction for multiple testing, and propose a more efficient model that pools and shares information among brain regions. Using Bayesian multilevel (BML) modeling, we control two types of error that are more relevant than the conventional false positive rate (FPR): incorrect sign (type S) and incorrect magnitude (type M). BML also aims to achieve two goals: 1) improving modeling efficiency by having one integrative model and thereby dissolving the multiple testing issue, and 2) turning the focus of conventional null hypothesis significant testing (NHST) on FPR into quality control by calibrating type S errors while maintaining a reasonable level of inference efficiency The performance and validity of this approach are demonstrated through an application at the region of interest (ROI) level, with all the regions on an equal footing: unlike the current approaches under NHST, small regions are not disadvantaged simply because of their physical size. In addition, compared to the massively univariate approach, BML may simultaneously achieve increased spatial specificity and inference efficiency, and promote results reporting in totality and transparency. The benefits of BML are illustrated in performance and quality checking using an experimental dataset. The methodology also avoids the current practice of sharp and arbitrary thresholding in the p-value funnel to which the multidimensional data are reduced. The BML approach with its auxiliary tools is available as part of the AFNI suite for general use.

neuroscience↗

bioRxiv2017

Trait-level paranoia shapes inter-subject synchrony in brain activity during an ambiguous social narrative

Individuals often interpret the same event in different ways. How do personality traits modulate brain activity evoked by a complex stimulus? Here we report results from a naturalistic paradigm designed to draw out both neural and behavioral variation along a specific dimension of interest, namely paranoia. Participants listen to a narrative during functional MRI describing an ambiguous social scenario, written such that some individuals would find it highly suspicious, while others less so. Using inter-subject correlation analysis, we identify several brain areas that are differentially synchronized during listening between participants with high- and low trait-level paranoia, including theory-of-mind regions. Follow-up analyses indicate that these regions are more active to mentalizing events in high-paranoia individuals. Analyzing participants speech as they freely recall the narrative reveals semantic and syntactic features that also scale with paranoia. Results indicate that a personality trait can act as an intrinsic prime, yielding different neural and behavioral responses to the same stimulus across individuals.

neuroscience↗

bioRxiv2017

GABAergic deficits and schizophrenia-like behaviors in a mouse model carrying patient-derived neuroligin-2 R215H mutation

Schizophrenia (SCZ) is a severe mental disorder characterized by delusion, hallucination, and cognitive deficits. We have previously identified from schizophrenia patients a loss-of-function mutation Arg215 [->] His215 (R215H) of neuroligin 2 (NLGN2) gene, which encodes a cell adhesion molecule critical for GABAergic synapse formation and function. Here, we generated a novel transgenic mouse line with neuroligin-2 (NL2) R215H mutation, which showed a significant loss of NL2 protein, reduced GABAergic transmission, and impaired hippocampal activation. Importantly, R215H KI mice displayed anxiety-like behaviors, impaired pre-pulse inhibition (PPI), cognition deficits and abnormal stress responses, recapitulating several key aspects of schizophrenia-like behavior. Our results demonstrate a significant impact of a single point mutation NL2 R215H on brain functions, providing a novel animal model for the study of schizophrenia and neuropsychiatric disorders.

neuroscience↗

bioRxiv2017

Preliminary results of models to predict areas in the Americas with increased likelihood of Zika virus transmission in 2017.

Numerous Zika virus vaccines are being developed. However, identifying sites to evaluate the efficacy of a Zika virus vaccine is challenging due to the general decrease in Zika virus activity. We compare results from three different modeling approaches to estimate areas that may have increased relative risk of Zika virus transmission during 2017. The analysis focused on eight priority countries (i.e., Brazil, Colombia, Costa Rica, Dominican Republic, Ecuador, Mexico, Panama, and Peru). The models projected low incidence rates during 2017 for all locations in the priority countries but identified several subnational areas that may have increased relative risk of Zika virus transmission in 2017. Given the projected low incidence of disease, the total number of participants, number of study sites, or duration of study follow-up may need to be increased to meet the efficacy study endpoints.

epidemiology↗

bioRxiv2017

The High-resolution Timeline of Expression of Ribosomal Protein Genes in Yeast

The yeast ribosome is a complex molecular machine built from four rRNAs and over 70 r-proteins. Ribosome biogenesis involves ordered incorporation of ribosomal proteins, accompanied by and association and dissociation of other proteins specific to different stages of the process. By model-based analysis of temporal profiles of gene expression in a metabolically regulated system, we obtained an accurate, high-resolution estimation of the time of expression of genes coding for proteins involved in ribosome biogenesis. The ribosomal proteins are expressed in a sequence that spans approximately 25-minutes under metabolically regulated conditions. The genes coding for proteins incorporated into the mature ribosome are expressed significantly later than those that are not incorporated, but are otherwise involved in ribosome biogenesis, localization and assembly, rRNA processing and translational initiation. The relative expression time of proteins localized within specified neighborhood is significantly correlated with the distance to the centroid of the mature ribosome: protein localized closer to the center of mass of the entire complex tend to be expressed earlier than the protein localized further from the center. The timeline of gene expression also agrees with the known dependencies between recruitment of specific proteins into the mature ribosome. These findings are consistent in two independent experiments. We have further identified regulatory elements correlated with the time of regulation, including a possible dependence of expression time on the position of the RAP1 binding site within the 5UTR.

biochemistry↗

bioRxiv2017

Intraclass correlation: improved modeling approaches and applications for neuroimaging

Intraclass correlation (ICC) is a reliability metric that gauges similarity when, for example, entities are measured under similar, or even the same, well-controlled conditions, which in MRI applications include runs/sessions, twins, parent/child, scanners, sites, etc. The popular definition and interpretation of ICC are usually framed statistically under the conventional ANOVA platform. Here, we show that the ANOVA frame-work is often limited, rigid, and inflexible in modeling capabilities. Additionally we provide a comprehensive overview of ICC analysis in its prior usage in neuroimaging. These intrinsic limitations motivate several novel improvements. Specifically, we start with the conventional ICC model under the ANOVA platform, and extend it along two dimensions: first, fixing the failure in ICC estimation when negative values occur under degen-erative circumstance, and second, incorporating reliability information of effect estimates into the ICC model. These endeavors lead to four modeling strategies: linear mixed-effects (LME), Bayesian mixed-effects (BME), multilevel mixed-effects (MME), and Bayesian multilevel mixed-effects (BMME). Compared to ANOVA, each of these four models directly provides estimates for fixed effects as well as their statistical significances, in addition to the ICC estimate. These new modeling approaches can also accommodate missing data as well as fixed effects for confounding variables. More importantly, we show that the novel MME and BMME approaches offer more accurate characterization and decomposition among the variance components, leading to more robust ICC computation. Based on these theoretical considerations and model performance comparisons with a real experimental dataset, we offer the following general-purpose recommendations. First, ICC estimation through MME or BMME is preferable when precision information is available for the effect estimate; precision information provides weights that more accurately allocate the variances in the data. When precision information is unavailable, ICC estimation through LME or the novel BME is the preferred option. Second, even though the absolute agreement version, ICC(2,1), is presently more popular in the field, the consistency version, ICC(3,1), is a practical and informative choice for whole-brain ICC analysis that achieves a well-balanced compromise when all potential fixed effects are accounted for. Third, approaches for clear, meaningful, and useful result reporting in ICC analysis are discussed. All models, ICC formulations, and related statistical testing methods have been implemented in an open source program 3dICC, which is publicly available as part of the AFNI suite. Even though our work here focuses on the whole brain level, the modeling strategy and recommendations can be equivalently applied to other situations such as voxel, region, and network levels.

neuroscience↗

bioRxiv2017

DNA 5-Hydroxymethylcytosines from Cell-free Circulating DNA as Diagnostic Biomarkers for Human Cancers

DNA modifications such as 5-methylcytosines (5mC) and 5-hydroxymethylcytosines (5hmC) are epigenetic marks known to affect global gene expression in mammals(1, 2). Given their prevalence in the human genome, close correlation with gene expression, and high chemical stability, these DNA epigenetic marks could serve as ideal biomarkers for cancer diagnosis. Taking advantage of a highly sensitive and selective chemical labeling technology(3), we report here genome-wide 5hmC profiling in circulating cell-free DNA (cfDNA) and in genomic DNA of paired tumor/adjacent tissues collected from a cohort of 90 healthy individuals and 260 patients recently diagnosed with colorectal, gastric, pancreatic, liver, or thyroid cancer. 5hmC was mainly distributed in transcriptionally active regions coincident with open chromatin and permissive histone modifications. Robust cancer-associated 5hmC signatures in cfDNA were identified with specificity for different cancers. 5hmC-based biomarkers of circulating cfDNA demonstrated highly accurate predictive value for patients with colorectal and gastric cancers versus healthy controls, superior to conventional biomarkers, and comparable to 5hmC biomarkers from tissue biopsies. This new strategy could lead to the development of effective blood-based, minimally-invasive cancer diagnosis and prognosis approaches.

cancer biology↗

bioRxiv2017

Musical Imagery Involves the Wernicke’s Area in Bilateral and Anti-Correlated Network Interactions in Musicians

Musical imagery is a human experience of imagining music without actually hearing it. The neural basis of such a mental ability is unclear, especially for musicians capable of accurate and vivid musical imagery due to their musical training. Here, we visualized an 8-min symphony as a silent movie, and used it as real-time cues for musicians to continuously imagine the music for multiple synchronized sessions during functional magnetic resonance imaging. The activations and networks evoked by musical imagery were compared with those when the subjects directly listened to the same music. The musical imagery and perception shared similar responses at bilateral secondary auditory areas and Wernickes area for encoding the musical feature. But the Wernickes area was involved in highly distinct network interactions during musical imagery vs. perception. The former involved positive correlations with a subset of the auditory network and the attention network, but negative correlations with the default mode network; the latter was confined to the intrinsic auditory network in the resting state. Our results highlight the important role of the Wernickes area in forming vivid musical imagery through bilateral and anti-correlated network interactions, challenging the conventional view of segregated and lateralized processing of music vs. language.

neuroscience↗

bioRxiv2017

Parsing Of Compositions And Microstructure Characteristics For Rust-Spots Of Pear Pericarp

The commercial value of Kurles pears pericarp, a popular and favored fruit for its unique aroma and refreshingly crisp texture, had sharply decreased because of a rust breakout of the beloved pear in China during the atmosphere-controlled storage. High performance liquid chromatography (HPLC) and liquid chromatography-mass spectrometry (LC-MS) were used to analyze rust spots on the pericarp of Kurle pears. Therefore, the chemical compounds of four various eluates, originating from the rust-colored substance collected from the pears pericarp effected, were identified successfully for the first time, which were just rhein, aloe-emodin, chrysophanol and emodin, respectively. Taken together with microstructure characteristics for rust-spots of Kurle pear pericarp, it was no doubt that these eluates were the main factors affecting the rust spots on the pericarp of the Kurle pears during the atmosphere-controlled storage, which was a sign and consequence resisting the undesirable stress of the external environment.

plant biology↗

bioRxiv2017

Genome-wide Association Study Of Plasma Proteins Identifies Putatively Causal Genes, Proteins, And Pathways For Cardiovascular Disease

Identifying genetic variants associated with circulating protein concentrations (pQTLs) and integrating them with variants from genome-wide association studies (GWAS) may illuminate the proteomes causal role in disease and bridge a GWAS knowledge gap for hitherto unexplained SNP-disease associations. We conducted GWAS of 71 high-value proteins for cardiovascular disease in 6,861 Framingham Heart Study participants followed by external replication. We comprehensively mapped thousands of pQTLs, including functional annotations and clinical-trait associations, and created an integrated plasma-protein-QTL searchable database. We next identified 15 proteins with pQTLs coinciding with coronary heart disease (CHD)-related variants from GWAS or tested causal for CHD by Mendelian randomization; most of these proteins were associated with new-onset cardiovascular disease events in Framingham participants with long-term follow-up. Identifying pQTLs and integrating them with GWAS results yields insights into genes, proteins, and pathways that may be causally associated with disease and can serve as therapeutic targets for treatment and prevention.

epidemiology↗

bioRxiv2017

Widespread dysregulation of the circadian clock in human cancer

The mammalian circadian clock is a critical regulator of metabolism and cell division. Although multiple lines of evidence indicate that systemic disruption of the circadian clock can promote cancer, whether the clock is disrupted in primary human tumors is unknown. Here we used transcriptome data from mice to define a signature of the mammalian circadian clock based on the co-expression of 12 genes that form the core clock or are directly controlled by the clock. Our approach can be applied to samples that are not labeled with time of day and were not acquired over the entire circadian (24-h) cycle. We validated the clock signature in transcriptome data from healthy human tissues, then developed a metric we call the delta clock correlation distance ({Delta}CCD) to describe the extent to which the signature is perturbed in samples from one condition relative to another. We calculated the {Delta}CCD comparing human tumor and non-tumor samples from The Cancer Genome Atlas and eight independent datasets, discovering widespread dysregulation of clock gene co-expression in tumor samples. Subsequent analysis of data from clock gene knockouts in mice suggested that clock dysregulation in human cancer is not caused solely by loss of activity of clock genes. Furthermore, by analyzing a large set of genes previously inferred to be rhythmic in healthy human lung, we found that dysregulation of the clock in human lung cancer is accompanied by dysregulation of broader patterns of circadian co-expression. Our findings suggest that clock dysregulation is a common means by which human cancers achieve unrestrained growth and division, and that restoring clock function could be a viable therapeutic strategy in multiple cancer types. In addition, our approach opens the door to using publicly available transcriptome data to quantify clock disruption in a multitude of human phenotypes. Our method is available as a web application at https://hugheylab.shinyapps.io/deltaccd.

cancer biology↗

bioRxiv2017

Components Of Alcohol Use And All-Cause Mortality

ImportanceCurrent recommendations for low-risk drinking are based on drinking quantity: up to one drink daily for women and two drinks daily for men. Drinking frequency has not been independently examined for its contribution to mortality.\n\nObjectiveTo evaluate the impact of drinking frequency on all-cause mortality after adjusting for drinks per day and binge drinking behavior.\n\nDesignTwo independent observational studies with self-reported alcohol use and subsequent all-cause mortality: the National Health Interview Survey (NHIS), and data from Veterans Health Administration clinics (VA).\n\nSettingEpidemiological sample (NHIS) and VA outpatient database (VA Corporate Data Warehouse).\n\nParticipants208,661 individuals from the NHIS interviewed between 1997 and 2009 at the age of 30 to 70 with mortality follow-up in the last quarter of 2011; 75,515 VA outpatients born between 1948 and 1968 who completed an alcohol survey in 2008 with mortality follow-up in June 2016.\n\nExposuresQuantity of alcohol use when not binging (1-2 drinks on typical day, 3-4 drinks on typical day), frequency of non-binge drinking (never, weekly or less, 2-3 times weekly, 4 or more times weekly), and frequency of binge drinking (never, less than weekly, 1-3 times weekly, 4 or more times weekly). Covariates included age, sex, race, and comorbidity.\n\nMain Outcomes and MeasuresAll-cause mortality.\n\nResultsAfter adjusting for binge drinking behavior, survival analysis showed an increased risk for all-cause mortality among people who typically drink 1-2 drinks four or more times weekly, relative to people who typically drink 1-2 drinks at a time weekly or less (NHIS dataset HR=1.15, 95% CI 1.06-1.26; VA dataset HR=1.31, 95% CI 1.15-1.49).\n\nConclusions and RelevanceDrinking four or more times weekly increased risk of all-cause mortality, even among those who drank only 1 or 2 drinks daily. This was seen in both a large epidemiological database and a large hospital-based database, suggesting that the results can be generalized.

epidemiology↗

bioRxiv2017

HadoopCNV: A Dynamic Programming Imputation Algorithm To Detect Copy Number Variants From Sequencing Data

BACKGROUNDWhole-genome sequencing (WGS) data may be used to identify copy number variations (CNVs). Existing CNV detection methods mostly rely on read depth or alignment characteristics (paired-end distance and split reads) to infer gains/losses, while neglecting allelic intensity ratios and cannot quantify copy numbers. Additionally, most CNV callers are not scalable to handle a large number of WGS samples.\n\nMETHODSTo facilitate large-scale and rapid CNV detection from WGS data, we developed a Dynamic Programming Imputation (DPI) based algorithm called HadoopCNV, which infers copy number changes through both allelic frequency and read depth information. Our implementation is built on the Hadoop framework, enabling multiple compute nodes to work in parallel.\n\nRESULTSCompared to two widely used tools - CNVnator and LUMPY, HadoopCNV has similar or better performance on both simulated data sets and real data on the NA12878 individual. Additionally, analysis on a 10-member pedigree showed that HadoopCNV has a Mendelian precision that is similar or better than other tools. Furthermore, HadoopCNV can accurately infer loss of heterozygosity (LOH), while other tools cannot. HadoopCNV requires only 1.6 hours for a human genome with 30X coverage, on a 32-node cluster, with a linear relationship between speed improvement and the number of nodes. We further developed a method to combine HadoopCNV and LUMPY result, and demonstrated that the combination resulted in better performance than any individual tools.\n\nCONCLUSIONSThe combination of high-resolution, allele-specific read depth from WGS data and Hadoop framework can result in efficient and accurate detection of CNVs.

bioinformatics↗

bioRxiv2016

Premature termination codons signaled targeted repair of frameshift mutation by endogenous RNA-directed gene editing

The molecular mechanisms for repairing DNA damages and point mutations have been well understood but it remains unclear how a frameshift mutation is repaired. Here we report that frameshift reversion occurs in E. coli more frequently than expected and appears to be a targeted gene repair signaled by premature termination codons (PTCs), producing high-level variations in the repaired genes. Genome resequencing shows that the revertant genome is highly stable, and the single-molecule variations in the repaired genes are derived from RNA editing. A multi-omics analysis shows that the expression levels change greatly in most the DNA and RNA manipulating genes. DNA replication, transcription, RNA editing, RNA degradation, nucleotide excision repair, mismatch repair, and homologous recombination were upregulated in the frameshift or revertant, but the base excision repair was not. Moreover, genes and transposons in a duplicate region silenced in wild type E. coli were activated in the frameshift. Finally, we propose a nonsense-mediated gene revising (NMGR) model for frame repair, which also acts as a driving force for molecular evolution. In essence, nonsense mRNAs are recognized, edited, and transported to template the repair of the coding gene by RNA-directed DNA repair, nucleotide excision, mismatch repair, and homologous recombination. Thanks to NMGR, the mutation rate temporarily rises in a frameshift gene, bringing genetic diversity while repairing the frameshift mutation and accelerating the evolution process without a high mutation rate in the genome.

genetics↗