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Quan, J.

Publications and source records attributed to Quan, J..

17 recordsLinked to original sources

Combination antagonism of TNF superfamily signaling for T cell immunosuppression

The tumor necrosis factor (TNF) and TNF receptor (TNFR) superfamilies comprise 47 proteins that regulate immune signaling and T cell costimulation. While TNF inhibitors are established therapies for immune-mediated inflammatory diseases (IMIDs), their efficacy is limited by primary non-response and loss of efficacy over time. Preclinical evidence suggests that TNF/TNFR members exhibit redundant and synergistic signaling, motivating combination targeting strategies. In this study, we have systematically evaluated TNF/TNFR combinations as potential immunotolerance targets using integrated computational and experimental approaches. We applied a gene prioritization framework incorporating transcriptomics, genetics, druggability, and pathway regulation data to derive disease association scores for the TNF/TNFR genes in rheumatoid arthritis and inflammatory bowel diseases. These scores, together with T cell expression profiling, were used to prioritize ten targets for combinatorial screening in mixed lymphocyte reactions using clinical-stage and preclinical pharmacological inhibitors. Four combinations of drugs inhibiting TNF+CD40L, TNF+OX40L, CD40L+OX40L, and CD40L+LT{beta}/LIGHT each in combination significantly reduced T cell production of IL-2 and IFN-{gamma}. RNA-Seq analysis revealed that these combinations downregulated genes involved in T cell activation, proliferation, differentiation, and cytokine production that were upregulated during allogeneic responses. Notably, TNF+CD40L co-inhibition (Adalimumab+Dapirolizumab) produced the most robust suppression, uniquely downregulating 337 genes enriched for T cell activation pathways including NF-{kappa}B, ERK1/2, and cytokine production. These findings demonstrate that combinatorial TNF/TNFR targeting can potently suppress allogeneic T cell responses and support further preclinical evaluation as a tolerance-inducing therapeutic strategy for refractory IMIDs.

immunology↗

Cognitive effects of placebo antipsychotics: Investigating their mechanisms using event-related potentials (ERPs) in a cognitive test and a self-referential task

Placebos modulate neurocognitive processes. We examined whether a fully deceptive antipsychotic-placebo changes cognition not only by enhancing arousal and attention but also by changing self-representations, namely, by adding that of being under a treatment related to psychosis and by weakening the binding of experience to reality. Drug-naive healthy participants (N = 83) were split into a group (N = 40) who took a pill described as an antipsychotic and a no-pill control group (N = 43). Participants performed a cognitive test (CogTest), that is, a semantic categorization task and a self-referential role judgment task (SRT). Behavioral responses and event-related potentials (ERPs) were recorded. In the CogTest, placebos showed faster response times and higher accuracy than no-pills. This cognitive improvement was associated with more negative occipito-temporal early ERPs (OTEEs), larger central P2s (CentP2s), and larger late positive potentials (LPPs), with the latter correlating with response times. As in the literature, CentP2s were maximal in the SRT, possibly because it maximally stimulates the binding of the stimulus to long-term self-representations. The larger CentP2s and LPPs for placebos than for no-pills in the CogTest were thus tentatively related to the binding of the stimulus with egocentric self-representations, temporarily enriched by the addition of the representation of being on drug. On the contrary, the more negative OTEEs were related to a weaker binding to allocentric ones, preventing a strong binding of experience with reality. The smaller N400s observed for placebos than for no-pills in both tasks were related to greater openness to new experiences.

neuroscience↗

Zyxin Restricts Viral Fusion and Entry across Multiple Virus Families

The entry of enveloped viruses into host cells requires fusion of the viral envelope with a host cell membrane. The composition and dynamics of cellular membranes are impacted by the underlying cytoskeleton. Zyxin, a cytosolic protein that bridges the actin cytoskeleton with adhesion receptor proteins, was recently identified as an antiviral factor that antagonizes HSV-1 entry. To determine if zyxin specifically interferes with membrane fusion, we examined its impact in a quantitative cell-cell fusion assay. HSV-1 entry proteins exhibited enhanced fusion activity with retinal pigment epithelial (RPE) cells when the cells were knocked out (KO) for zyxin. Zyxin-KO cells also showed enhanced fusion activity with pseudorabies virus (PRV), paramyxovirus and rhabdovirus fusion proteins. Additionally, the size of plaques formed following infection with members of each viral family were increased in the absence of zyxin. Bulk RNA sequencing of wild-type (WT) and zyxin-KO RPE cells identified 18 genes that enrich into several ontology groups of interest, including regulation of membrane potential, herpes simplex virus 1 infection (CCL2 and ZNF14), NABA core matrisome (CCL2 and ZNF14), extracellular matrix organization (CTSK, SERPINE1 and TGFB2), cell-cell adhesion (RAC2 and TGFB2), anchoring fibril formation, and regulation of the MAPK cascade (ACKR3 and TGFB2). These results highlight zyxin as a broadly active antiviral factor and suggest potential therapeutic implications for viral infections. IMPORTANCEEnveloped viruses enter host cells by fusing their envelope with a cellular membrane. This process is triggered when a viral glycoprotein(s) engages with a cellular membrane receptor. Less well understood are the cytoplasmic factors that indirectly govern viral fusion and entry. We report that zyxin, a cellular protein that bridges cell-adhesion receptors with the underlying actin cytoskeleton, antagonizes the fusion and entry of several enveloped viruses.

microbiology↗

Multi-omic characterization of allele-specific regulatory variation in hybrid pigs

Genetic variation in the regulation of gene expression contributes substantially to phenotypic variation. Understanding how variation in DNA sequences and epigentic modifications leads to gene expression variation remains a challenging task. In hybrid animals where cellular environments are homogeneous, differences between the expression of paternal and maternal alleles must be due to cis sequence or epigenic differences. Therefore, hybrid mapping is a powerful approach to efficiently identify and characterize genes under regulation through mechanisms in cis. In this study, using reciprocal crosses of the phenotypically divergent Duroc and Lulai pig breeds, we performed a comprehensive multi-omic characterization of regulatory variation across brain, liver, muscle, and placenta in four developmental stages. We produced one of the largest multi-omic datasets to date in pigs, including 16 whole genome sequenced genomes, 48 whole genome bisulfite sequencing, 168 ATAC-Seq and 168 RNA-Seq samples. We developed a novel read count-based method to reliably assess allele-specific methylation, chromatin accessibility, and RNA expression. We showed that tissue specificity was much stronger than developmental stage specificity in all of DNA methylation, chromatin accessibility, and gene expression. We identified 573 genes showing allele specific expression, including those influenced by parent-of-origin as well as allele genotype effects. By integrating methylation, chromatin accessibility, and gene expression data, many of these allele specific expression can be explained by allele specific methylation and/or chromatin accessibility. This study provides one of the most comprehensive characterizations of regulatory variation across multiple tissues and developmental stages in pigs and offers new opportunity of genetic improvement for this important food animal species.

developmental biology↗

Accelerated social representational drift in the nucleus accumbens in a model of autism

Impaired social interaction is one of the core deficits of autism spectrum disorder (ASD) and may result from social interactions being less rewarding. How the nucleus accumbens (NAc), as a key hub of reward circuitry, encodes social interaction and whether these representations are altered in ASD remain poorly understood. We identified NAc ensembles encoding social interactions by calcium imaging using miniaturized microscopy. NAc population activity, specifically D1 receptor-expressing medium spiny neurons (D1-MSNs) activity, predicted social interaction epochs. Despite a high turnover of NAc neurons modulated by social interaction, we found a stable population code for social interaction in NAc which was dramatically degraded in Cntnap2-/- mouse model of ASD. Surprisingly, non-specific optogenetic inhibition of NAc core neurons increased social interaction time and significantly improved sociability in Cntnap2-/- mice. Inhibition of D1- or D2-MSNs showed reciprocal effects, with D1 inhibition decreasing social interaction and D2 inhibition increasing interaction. Therefore, social interactions are preferentially, specifically and dynamically encoded by NAc neurons and social representations are degraded in this autism model.

neuroscience↗

Bovine milk glycoproteins inhibit SARS-CoV-2 and influenza virus co-infection

The attachment of S1 subunit of spike (S) protein to angiotensin-converting enzyme 2 (ACE2) is the first and crucial step of SARS-CoV-2 infection. Although S protein and ACE2 are heavily glycosylated, the precise roles of glycans in their interactions are still unclear. Here, we profiled the glycopatterns of S1 subunit of SARS-CoV-2 and ACE2, and found that the galactosylated glycoforms were dominant in both S1 subunit and ACE2. Interestingly, S1 subunit exhibited the property of glycan-binding protein (GBP) and adhered to the ACE2 via binding to the galactosylated glycans on the ACE2. Our earlier findings demonstrated that the sialylated glycoproteins isolated from bovine milk potently inhibit and neutralize viral activity against influenza A virus (IAV). Importantly, we proved further that the galactosylated glycans on isolated glycoproteins bind to the glycan recognition domains of S1 subunit and competitively inhibit binding of S1 subunit to ACE2 and ultimately impede the entry of SARS-CoV-2 pseudovirus into host cells. We provided a potential protein drug that could be multiple simultaneous inhibitor for coronavirus and IAV co-infection.

microbiology↗

Nucleotide resolution genetic mapping in pigs by publicly accessible whole genome imputation

Genetic mapping to identify genes and alleles associated with or causing economically important quantitative trait variation in livestock animals such as pigs is a major goal in the genetic improvement animals. Despite recent advances in high throughput genotyping technologies, resolution of genetic mapping in pigs remains poor due in part to the low density of genotyped variant sites. In this study, we overcame this limitation by developing a reference haplotype panel for pigs based on 2,259 whole genome sequenced animals representing 44 pig breeds. We optimized the imputation procedure to achieve an average concordance rate in excess of 97%, non-reference concordance rate 91%, and r2 0.89. We demonstrated that genotype imputation using this resource can dramatically improve resolution of genetic mapping. Finally, we developed a public web server (swimgeno.org) to allow the pig genetics community to fully utilize this resource. We expect the resource and server to significantly facilitate genetic mapping and accelerate genetic improvement in pigs.

genomics↗

Candidate Explorer: a tool for discovery, evaluation, and display of mutations causing significant immune phenotypes

When applied to immunity, forward genetic studies use meiotic mapping to provide strong statistical evidence that a particular mutation is causative of a particular immune phenotype. Notwithstanding this, co-segregation of multiple mutations, occasional unawareness of mutations, and paucity of homozygotes may lead to erroneous declarations of cause and effect. We sought to improve the selection of authentic causative mutations using a machine learning software tool, Candidate Explorer (CE), which integrates 65 data features into a single numeric score, mathematically convertible to the likelihood of verification of any putative mutation-phenotype association. CE has identified most genes within which mutations can be causative of flow cytometric phenovariation in Mus musculus. The majority of these genes were not previously known to support immune function or homeostasis. Mouse geneticists will find CE data informative in identifying causative mutations within quantitative trait loci, while clinical geneticists may use CE to help connect causative variants with rare heritable diseases of immunity, even in the absence of linkage information. CE displays integrated mutation, phenotype, and linkage data, and is freely available for query online.

genetics↗

Mechanism of negative regulation of NF-κB by N4BP1

Many immune responses depend upon activation of NF-{kappa}B, a key transcription factor in the elicitation of a cytokine response. Here we demonstrate that N4BP1 inhibited TLR-dependent activation of NF-{kappa}B by interacting with the NF-{kappa}B signaling essential modulator (NEMO, also known as I{kappa}B kinase {gamma}) to attenuate NEMO-NEMO dimerization or oligomerization. The UBA-like (ubiquitin associated-like) and CUE-like (ubiquitin conjugation to ER degradation) domains in N4BP1 mediated the interaction with the NEMO COZI domain. Both in vitro and in mice, N4bp1 deficiency specifically enhanced TRIF-independent (TLR2, TLR7, or TLR9-mediated), but not TRIF-dependent (TLR3 or TLR4-mediated), NF-{kappa}B activation leading to increased production of proinflammatory cytokines. In response to TLR4 or TLR3 activation, TRIF caused activation of caspase 8, which cleaved N4BP1 distal to residues D424 and D490 and abolished its inhibitory effect. N4bp1-/- mice also exhibited diminished numbers of T cells in the peripheral blood. Our work identifies N4BP1 as an inhibitory checkpoint protein that must be overcome to activate NF-{kappa}B, and a TRIF-initiated caspase 8-dependent mechanism by which this is accomplished.

immunology↗

Designing and implementing programmable depletion in sequencing libraries with DASHit

Since Next-Generation Sequencing produces reads uniformly subsampled from an input library, highly abundant sequences may mask interesting low abundance sequences. The DASH (Depleting Abundant Sequences by Hybridization) technique takes advantage of the programmability of CRISPR/Cas9 to deplete unwanted high-abundance sequences. Because desired depletion targets vary by sample type, here we describe DASHit, software that outputs an optimal DASH target set given a sequencing dataset, an updated DASH protocol, and show depletion results with DASHit-designed targets for three different species.

microbiology↗

Cell type specific genetic regulation of gene expression across human tissues

The Genotype-Tissue Expression (GTEx) project has identified expression and splicing quantitative trait loci (cis-QTLs) for the majority of genes across a wide range of human tissues. However, the interpretation of these QTLs has been limited by the heterogeneous cellular composition of GTEx tissue samples. Here, we map interactions between computational estimates of cell type abundance and genotype to identify cell type interaction QTLs for seven cell types and show that cell type interaction eQTLs provide finer resolution to tissue specificity than bulk tissue cis-eQTLs. Analyses of genetic associations to 87 complex traits show a contribution from cell type interaction QTLs and enables the discovery of hundreds of previously unidentified colocalized loci that are masked in bulk tissue.\n\nOne Sentence SummaryEstimated cell type abundances from bulk RNA-seq across tissues reveal the cellular specificity of quantitative trait loci.

genomics↗

Investigating Transfusion-Related Sepsis using Culture-Independent Metagenomic Sequencing

BackgroundTransfusion-related sepsis remains an important hospital infection control challenge. Investigating septic transfusion events is often restricted by the limitations of bacterial culture in terms of time requirements and low yield in the setting of prior antibiotic administration.\n\nMethodsIn three Gram-negative septic transfusion cases, we performed mNGS of direct clinical blood specimens in addition to standard culture-based approaches utilized for infection control investigations. Pathogen detection leveraged IDSeq, a new open-access microbial bioinformatics portal. Phylogenetic analysis was performed to assess microbial genetic relatedness and understand transmission events.\n\nResultsmNGS of direct clinical blood specimens afforded precision detection of pathogens responsible for each case of transfusion-related sepsis, and enabled discovery of a novel Acinetobacter species in a platelet product that had become contaminated despite photochemical pathogen reduction. In each case, longitudinal assessment of pathogen burden elucidated the temporal sequence of events associated with each transfusion-transmitted infection. We found that informative data could be obtained from culture-independent mNGS of residual platelet products and leftover blood specimens that were either unsuitable or unavailable for culture, or that failed to grow due to prior antibiotic administration. We additionally developed methods to enhance accuracy for detecting transfusion-associated pathogens sharing taxonomic similarity to contaminants commonly found in mNGS library preparations.\n\nConclusionsCulture-independent mNGS of blood products afforded rapid and precise assessment of pathogen identity, abundance and genetic relatedness. Together, these challenging cases demonstrated the potential for metagenomics to advance existing methods for investigating transfusion-transmitted infections.

microbiology↗

FLASH: A next-generation CRISPR diagnostic for multiplexed detection of antimicrobial resistance sequences

The growing prevalence of deadly microbes with resistance to previously life-saving drug therapies is a dire threat to human health. Detection of low abundance pathogen sequences remains a challenge for metagenomic Next Generation Sequencing (NGS). We introduce FLASH (Finding Low Abundance Sequences by Hybridization), a next-generation CRISPR/Cas9 diagnostic method that takes advantage of the efficiency, specificity and flexibility of Cas9 to enrich for a programmed set of sequences. FLASH-NGS achieves up to 5 orders of magnitude of enrichment and sub-attomolar gene detection with minimal background. We provide an open-source software tool (FLASHit) for guide RNA design. Here we applied it to detection of antimicrobial resistance genes in respiratory fluid and dried blood spots, but FLASH-NGS is applicable to all areas that rely on multiplex PCR.

microbiology↗

Integrating Host Response and Unbiased Microbe Detection for Lower Respiratory Tract Infection Diagnosis in Critically Ill Adults

Lower respiratory tract infections (LRTI) lead to more deaths each year than any other infectious disease category(1). Despite this, etiologic LRTI pathogens are infrequently identified due to limitations of existing microbiologic tests(2). In critically ill patients, non-infectious inflammatory syndromes resembling LRTI further complicate diagnosis. To address the need for improved LRTI diagnostics, we performed metagenomic next-generation sequencing (mNGS) on tracheal aspirates from 92 adults with acute respiratory failure and simultaneously assessed pathogens, the lung microbiome and the host transcriptome. To differentiate pathogens from respiratory commensals, we developed rules-based and logistic regression models (RBM, LRM) in a derivation cohort of 20 patients with LRTI or non-infectious acute respiratory illnesses. When tested in an independent validation cohort of 24 patients, both models achieved accuracies of 95.5%. We next developed pathogen, microbiome diversity, and host gene expression metrics to identify LRTI-positive patients and differentiate them from critically ill controls with non-infectious acute respiratory illnesses. When tested in the validation cohort, the pathogen metric performed with an AUC of 0.96 (95% CI = 0.86 - 1.00), the diversity metric with an AUC of 0.80 (95% CI = 0.63 - 0.98), and the host transcriptional classifier with an AUC of 0.91 (95% CI = 0.80 - 1.00). Combining all three achieved an AUC of 0.99 (95% CI = 0.97 - 1.00) and negative predictive value of 100%. This study suggests that a single streamlined protocol offering an integrated genomic portrait of pathogen, microbiome and host transcriptome may hold promise as a novel tool for LRTI diagnosis.\n\nSIGNIFICANCE STATEMENTLower respiratory tract infections (LRTI) are the leading cause of infectious disease-related death worldwide yet remain challenging to diagnose because of limitations in existing microbiologic tests. In critically ill patients, non-infectious respiratory syndromes that resemble LRTI further complicate diagnosis and confound targeted treatment. To address this, we developed a novel metagenomic sequencing-based approach that simultaneously interrogates three core elements of acute airway infections: the pathogen, lung microbiome and host response. We studied this approach in a prospective cohort of critically ill patients with acute respiratory failure and found that combining pathogen, microbiome and host gene expression metrics achieved accurate LRTI diagnosis and identified etiologic pathogens in patients with clinically identified infections but otherwise negative testing.\n\nFundingNHLBI K12HL119997 (Langelier C), NHLBI K23HL123778 (Christensen S), NIAID P01AI091575 and the Chan Zuckerberg Biohub (DeRisi JL), NHLBI K23 HL136844 (Moazed F), NHLBI R01HL110969, K24HL133390, R35HL140026 (Calfee C), Gladstone Institutes (Pollard KS).

genomics↗

A systematic survey of human tissue-specific gene expression and splicing reveals new opportunities for therapeutic target identification and evaluation

Differences in the expression of genes and their splice isoforms across human tissues are fundamental factors to consider for therapeutic target evaluation. To this end, we conducted a transcriptome-wide survey of tissue-specific gene expression and splicing events in the unprecedented collection of 8527 high-quality RNA-seq samples from the GTEx project, covering 36 human peripheral tissues and 13 brain subregions. We derived a weighted tissue-specificity scoring scheme accounting for the similarity of related tissues and inherent variability across individual samples. We showed that ~50.6% of all annotated human genes show tissue-specific expression, including many low abundance transcripts vastly underestimated by previous array-based expression atlases. As utilities for drug discovery, we demonstrated that tissue-specificity is a highly desirable attribute of validated drug targets and tissue-specificity can be used to prioritize disease-associated genes from genome-wide association studies (GWAS). Using brain striatum-specific gene expression as an example, we provided a template to leverage tissue-specific gene expression to identify novel therapeutic targets. Mining of tissue-specific splicing further reveals new opportunities for tissue-specific targeting. Thus, the high quality transcriptome atlas provided by the GTEx is an invaluable resource for drug discovery and systematic analysis anchored on the human tissue specific gene expression provides a promising avenue to identify novel therapeutic target hypotheses.

genomics↗

Genetic and real-world clinical data, combined with empirical validation, nominate JAK-STAT signalling as a target for Alzheimer’s Disease therapeutic development

As Genome Wide Association Studies (GWAS) have grown in size, the number of genetic variants that have been nominated for an increasing number of diseases has correspondingly increased. Despite this increase in the number of associated SNPs per disease, their biological interpretation has in many cases remained elusive. To address this, we have combined GWAS results with an orthogonal source of evidence, namely real-world, routinely collected clinical data from more than 6 million patients in order to drive target nomination. First we show that when examined at a pathway level, analysis of all GWAS studies groups Alzheimers disease (AD) in a cluster with disorders of immunity and inflammation. Using clinical data we show that the degree of comorbidity of these diseases with AD correlates with the strength of their genetic association with molecular participants in the JAK-STAT pathway. Using four independent open-science datasets we then find evidence for altered regulation of JAK-STAT pathway genes in AD. Finally, we use both in vitro and in vivo rodent models to demonstrate that A{beta} induces gene expression of key drivers of this pathway, providing experimental evidence validating these data-driven observations. These results therefore nominate JAK-STAT anomalies as a prominent aetiopathological event in AD and hence potential target for therapeutic development, and moreover demonstrate a de-novo multi-modal approach to derive information from rapidly increasing genomic datasets.\n\nOne Sentence SummaryCombining evidence from genome wide association studies, real-world clinical and cohort molecular data together with experimental studies in rodent model systems nominates JAK-STAT signaling as an aetiopathological event in Alzheimers disease

bioinformatics↗

Developmental And Genetic Regulation Of The Human Cortex Transcriptome In Schizophrenia

GWAS have identified 108 loci that confer risk for schizophrenia, but risk mechanisms for individual loci are largely unknown. Using developmental, genetic, and illness-based RNA sequencing expression analysis, we characterized the human brain transcriptome around these loci and found enrichment for developmentally regulated genes with novel examples of shifting isoform usage across pre- and post-natal life. We found widespread expression quantitative trait loci (eQTLs), including many with transcript specificity and previously unannotated sequence that were independently replicated. We leveraged this eQTL database to show that 48.1% of risk variants for schizophrenia associated with nearby expression. Within patients and controls, we implemented a novel algorithm for RNA quality adjustment, and identified 237 genes significantly associated with diagnosis that replicated in an independent case-control dataset. These genes implicated synaptic processes and were strongly regulated in early development (p < 10-20). These data offer new targets for modeling schizophrenia risk in cellular systems.

neuroscience↗