Biology subjects

Richards, A.

Publications and source records attributed to Richards, A..

29 records · Page 2Linked to original sources

bioRxiv2022

Leveraging CD39 To Identify Tumor-Reactive CD8 T cells In Human Lung Cancer

The repertoire of tumor-infiltrating lymphocytes (TILs) can be vast, and many of these TILs are not endowed with tumor reactivity. While a number of reports have shown that tumor-reactive TILs express CD39, few reports have demonstrated that conversely, CD39 can be leveraged to serve as a proxy of tumor-reactive CD8 T cells. Using single-cell CITE/RNA/TCRseq, we show that CD39+ CD8 T cells in human lung cancers demonstrate transcriptional and proteomic features of exhaustion, tumor reactivity, and clonal expansion. Moreover, TCR cloning revealed that CD39 enriched for tumor-reactive CD8 T cell clones. Flow cytometry of 440 lung cancer specimens revealed that CD39 level on CD8 T cells is only weakly correlated with tumoral features that currently guide lung cancer therapy, such as histology, driver mutation, PD-L1 and tumor mutation burden. PD-1 axis blockade, but not cytotoxic chemotherapy, increased intratumoral CD39+ CD8 T cells. CD39 correlated with PD-1 expression on CD8 T cells and high pre-treatment/early-on-treatment levels were associated with improved clinical outcomes, but not immune-related adverse events, from immune checkpoint blockade therapy. This comprehensive profiling of the clinical, pathological and molecular features highlights the utility of CD39 as a proxy for tumor-reactive CD8 T cells in human lung cancer.

immunology↗

bioRxiv2021

Site Pattern Probabilities Under the Multispecies Coalescent and a Relaxed Molecular Clock: Theory and Applications

1The first step in statistical inference of the evolutionary histories of species is developing a probability model that describes the mutation process as accurately and realistically as possible. A major complication of this inference is that different loci on the genome can have histories that diverge from the common species history and each other. The multispecies coalescent process is commonly used to model one source of this divergence, incomplete lineage sorting, or ILS. Chifman and Kubatko (2015) computed the site pattern probabilities for four taxa under a full probability model based on the Jukes-Cantor substitution model when the molecular clock holds. This paper generalizes that work to a relaxed clock model, allowing for mutation rates to differ among species. This will enable better phylogentic inference in cases where the molecular clock does not hold.

evolutionary biology↗

bioRxiv2021

Proteomic Organellar Mapping Reveals Modulation of Peroxisomes by HIV-1

As systems biology approaches to virology have become more tractable, highly studied viruses such as HIV can now be analyzed in new, unbiased ways, including spatial proteomics. We employed here a differential centrifugation protocol to fractionate Jurkat T cells for proteomic analysis by mass spectrometry; these cells contain inducible HIV-1 genomes, enabling us to look for changes in the spatial proteome induced by viral gene expression. Using these proteomics data, we evaluated the merits of several reported machine learning pipelines for classification of the spatial proteome and identification of protein translocations. From these analyses we found that classifier performance in this system was organelle-dependent, with Bayesian t-augmented Gaussian mixture modeling outperforming support vector machine (SVM) learning for mitochondrial and ER proteins, but underperforming on cytosolic, nuclear, and plasma membrane proteins by QSep analysis. We also observed a generally higher performance for protein translocation identification using a Bayesian model, BANDLE, on SVM-classified data. Comparative BANDLE analysis of cells induced to express the wild-type viral genome vs. cells induced to express a genome unable to express the accessory protein Nef identified known Nef-dependent interactors such as TCR signaling components and coatomer complex. Lastly, we found that SVM classification showed higher consistency and was less sensitive to HIV-dependent noise. These findings illustrate important considerations for studies of the spatial proteome following viral infection or viral gene expression and provide a reference for future studies of HIV-gene-dropout viruses.

cell biology↗

bioRxiv2020

SARS-CoV-2 RNA reverse-transcribed and integrated into the human genome

Prolonged SARS-CoV-2 RNA shedding and recurrence of PCR-positive tests have been widely reported in patients after recovery, yet these patients most commonly are non-infectious1-14. Here we investigated the possibility that SARS-CoV-2 RNAs can be reverse-transcribed and integrated into the human genome and that transcription of the integrated sequences might account for PCR-positive tests. In support of this hypothesis, we found chimeric transcripts consisting of viral fused to cellular sequences in published data sets of SARS-CoV-2 infected cultured cells and primary cells of patients, consistent with the transcription of viral sequences integrated into the genome. To experimentally corroborate the possibility of viral retro-integration, we describe evidence that SARS-CoV-2 RNAs can be reverse transcribed in human cells by reverse transcriptase (RT) from LINE-1 elements or by HIV-1 RT, and that these DNA sequences can be integrated into the cell genome and subsequently be transcribed. Human endogenous LINE-1 expression was induced upon SARS-CoV-2 infection or by cytokine exposure in cultured cells, suggesting a molecular mechanism for SARS-CoV-2 retro-integration in patients. This novel feature of SARS-CoV-2 infection may explain why patients can continue to produce viral RNA after recovery and suggests a new aspect of RNA virus replication.

genomics↗

bioRxiv2020

Large-scale analysis of DNA methylation identifies cellular alterations in blood from psychosis patients and molecular biomarkers of treatment-resistant schizophrenia.

ObjectivePsychosis - a complex and heterogeneous neuropsychiatric condition characterized by hallucinations and delusions - is a common feature of schizophrenia. There is evidence for altered DNA methylation (DNAm) associated with schizophrenia in both brain and peripheral tissues. We aimed to undertake a systematic analysis of variable DNAm associated with psychosis, schizophrenia, and treatment-resistant schizophrenia, also exploring measures of biological ageing, smoking, and blood cell composition derived from DNAm data to identify molecular biomarkers of disease. MethodsWe quantified DNAm across the genome in blood samples from 4,483 participants from seven case-control cohorts including patients with schizophrenia or first-episode psychosis. Measures of biological age, cellular composition and smoking status were derived from DNAm data using established algorithms. DNAm and derived measures were analyzed within each cohort and the results combined by meta-analysis. ResultsPsychosis cases were characterized by significant differences in measures of blood cell proportions and elevated smoking exposure derived from the DNAm data, with the largest differences seen in treatment-resistant schizophrenia patients. DNAm at 95 CpG sites was significantly different between psychosis cases and controls, with 1,048 differentially methylated positions (DMPs) identified between schizophrenia cases and controls. Schizophrenia-associated DMPs colocalize to regions identified in genetic association studies, with genes annotated to these sites enriched for pathways relevant to disease. Finally, a number of the schizophrenia associated differences were only present in the treatment-resistant schizophrenia subgroup. ConclusionsWe show that DNAm data can be leveraged to derive measures of blood cell counts and smoking that are strongly associated with psychosis. Our DNAm meta-analysis identified multiple DMPs associated with both psychosis and a more refined diagnosis of schizophrenia, with evidence for differential methylation associated with treatment-resistant schizophrenia that potentially reflects exposure to clozapine.

genomics↗

bioRxiv2019

Reinforcement learning as an intermediate phenotype in psychosis? Deficits sensitive to illness stage but not associated with polygenic risk of schizophrenia in the general population

BackgroundSchizophrenia is a complex disorder in which the causal relations between risk genes and observed clinical symptoms are not well understood and the explanatory gap is too wide to be clarified without considering an intermediary level. Thus, we aimed to test the hypothesis of a pathway from molecular polygenic influence to clinical presentation occurring via deficits in reinforcement learning. MethodsWe administered a reinforcement learning task (Go/NoGo) that measures reinforcement learning and the effect of Pavlovian bias on decision making. We modelled the behavioural data with a hierarchical Bayesian approach (hBayesDM) to decompose task performance into its underlying learning mechanisms. Study 1 included controls (n= 29, F|M=0.81), At Risk Mental State for psychosis (ARMS, n= 23, F|M=0.35) and FEP (First-episode psychosis, n= 26, F|M=0.18). Study 2 included healthy adolescents (n= 735, F|M= 1.06), 390 of whom had their polygenic risk scores for schizophrenia (PRSs) calculated. ResultsPatients with FEP showed significant impairments in overriding Pavlovian conflict, a lower learning rate and a lower sensitivity to both reward and punishment. Less widespread deficits were observed in ARMS. PRSs did not significantly predict performance on the task in the general population, which only partially correlated with measures of psychopathology. ConclusionsReinforcement learning deficits are observed in first episode psychosis and, to some extent, in those at clinical risk for psychosis, and were not predicted by molecular genetic risk for schizophrenia in healthy individuals. The study does not support the role of reinforcement learning as an intermediate phenotype in psychosis.

neuroscience↗

bioRxiv2019

Jumping To Conclusions, General Intelligence, And Psychosis Liability: Findings From The Multicentric EU-GEI Case-Control Study

BackgroundThe \"jumping to conclusions\" (JTC) bias is associated with both psychosis and general cognition but their relationship is unclear. In this study, we set out to clarify the relationship between the JTC bias, IQ, psychosis and polygenic liability to schizophrenia and IQ.\n\nMethods817 FEP patients and 1294 population-based controls completed assessments of general intelligence (IQ), and JTC (assessed by the number of beads drawn on the probabilistic reasoning \"beads\" task) and provided blood or saliva samples from which we extracted DNA and computed polygenic risk scores for IQ and schizophrenia.\n\nResultsThe estimated proportion of the total effect of case/control differences on JTC mediated by IQ was 79%. Schizophrenia Polygenic Risk Score (SZ PRS) was non-significantly associated with a higher number of beads drawn (B= 0.47, 95% CI -0.21 to 1.16, p=0.17); whereas IQ PRS (B=0.51, 95% CI 0.25 to 0.76, p<0.001) significantly predicted the number of beads drawn, and was thus associated with reduced JTC bias. The JTC was more strongly associated with higher level of psychotic-like experiences (PLE) in controls, including after controlling for IQ (B= -1.7, 95% CI -2.8 to -0.5, p=0.006), but did not relate to delusions in patients.\n\nConclusionsthe JTC reasoning bias in psychosis is not a specific cognitive deficit but is rather a manifestation or consequence, of general cognitive impairment. Whereas, in the general population, the JTC bias is related to psychotic-like experiences, independent of IQ. The work has potential to inform interventions targeting cognitive biases in early psychosis.

neuroscience↗

bioRxiv2019

Differences in Genetic Liability for Insomnia and Hypersomnia in Bipolar Disorder Subtypes

BackgroundInsomnia and hypersomnia are common in bipolar disorder (BD) but it is unclear whether genetic factors influence this association. Stratifying by bipolar subtypes could elucidate the nature of this association and inform research on sleep and BD. We therefore aimed to determine whether polygenic risk scores (PRS) for insomnia, daytime sleepiness and sleep duration differ according to bipolar subtypes (bipolar I disorder, BD-I or bipolar II disorder, BD-II).\n\nMethodsIn this case-control study, we used multinomial regression to determine whether PRS for insomnia, daytime sleepiness, and sleep duration were associated with risk of BD-I or BD-II when compared to each other and to controls. Cases (n=4672) were recruited within the United Kingdom from the Bipolar Disorder Research Network. Controls (n=5714) were recruited from the 1958 British Birth Cohort and UK Blood Service. All participants were of European ancestry. Clinical subtypes of BD were determined by semi-structured psychiatric interview (the Schedules for Clinical Assessment in Neuropsychiatry) and case notes.\n\nResultsWithin cases, 3404 and 1268 met DSM-IV criteria for BD-I and BD-II, respectively. Insomnia PRS was associated with increased risk of BD-II (RR = 1.14, 95% CI = 1.07-1.21, P = 8.26 x 10-5) but not BD-I (RR = 0.98, 95% CI = 0.94-1.03, P = .409) relative to controls. In contrast, sleep duration PRS was associated with increased relative risk of BD-I (RR = 1.10, 95% CI = 1.06-1.15, P = 1.13 x 10-5), but not BD-II (RR = 0.99, 95% CI = 0.93-1.06, P = .818). Daytime sleepiness PRS was associated with an increased risk of BD-I (RR = 1.07, 95% CI = 1.02-1.11, P = .005) and BD-II (RR = 1.14, 95% CI = 1.07-1.22, P = 3.22 x 10-5) compared to controls, but did not distinguish subtypes from each other.\n\nConclusionsBipolar subtypes differ in genetic liability to insomnia and sleep duration. Our findings provide further evidence that the distinction between BD-I and BD-II has biological and genetic validity. This distinction will be critical in the selection of participants for future research on the role of sleep disturbance in BD.

genomics↗

bioRxiv2018

The contribution of psychiatric risk alleles to a general liability to psychopathology in early life

BackgroundPsychiatric disorders show phenotypic as well as genetic overlaps. Factor analyses of child and adult psychopathology have found that phenotypic overlaps largely can be explained by a latent general \"p\" factor that reflects general liability to psychopathology. We investigated whether shared genetic liability across disorders would be reflected in associations between multiple different psychiatric polygenic risk scores (PRS) and a general psychopathology factor in childhood.\n\nMethodsThe sample was a UK, prospective, population-based cohort (ALSPAC), including data on psychopathology at age 7 (N=8161) years. PRS were generated from large published genome-wide association studies.\n\nOutcomesThe general psychopathology factor was associated with both schizophrenia PRS and attention-deficit/hyperactivity disorder (ADHD) PRS, whereas there was no strong evidence of association with major depressive disorder and autism spectrum disorder PRS. Schizophrenia PRS was also associated with a specific \"emotional\" problems factor.\n\nInterpretationOur findings suggest that genetic liability to schizophrenia and ADHD may contribute to shared genetic risks across childhood psychiatric diagnoses at least partly via the general psychopathology factor. However, the pattern of observations could not be explained by a general \"p\" factor on its own.\n\nFundingThis work was supported by the Wellcome Trust (204895/Z/16/Z).Introduction

genetics↗

bioRxiv2017

Knockdown of Liver-Derived C1 Inhibitor Results in Brain Pathology in mice

Plasma proteins and activated immune cells are known contributors of vascular brain disorders. However, the mechanisms and routes involved are still unclear. In order to understand the cross-talk between plasma proteins and the brain, we knocked down circulating C1 inhibitor (C1INH) in wild-type (WT) mice using antisense-oligonucleotide (ASO) technique and examined the brain. C1INH is a plasma protein inhibitor of vascular inflammation induced by activation of the kallikrein-kinin system (KKS) and the complement system. This knockdown induced the activation of the KKS but spared the activation of the classical complement system. Activation of the KKS induced an upregulation of the bradykinin pathway in the periphery and the brain, resulting in hypotension. Blood-brain barrier (BBB) permeability, plasma protein extravasations, activated glial cells and elevated levels of IL-1beta, IL-6, TNF-alpha, and iNOS were detected in brains of C1INH ASO treated mice. Infiltrating innate immune cells were evident, entering the brain through the lateral ventricle walls and the neurovascular units. The mice showed normal motor functions, however, cognition was impaired. Altogether, our results highlight the important role of regulated plasma-C1INH as a gatekeeper of the neurovascular system. Thus, manipulation of C1INH in neurovascular disorders might be therapeutically beneficial.

neuroscience↗

bioRxiv2017

Variation in gut microbiota composition impacts host gene expression

Variation in gut microbiome is associated with wellness and disease in humans, yet the molecular mechanisms by which this variation affects the host are not well understood. A likely mechanism is through changing gene regulation in interfacing host epithelial cells. Here, we treated colonic epithelial cells with live microbiota from five healthy individuals and quantified induced changes in transcriptional regulation and chromatin accessibility in host cells. We identified over 5,000 host genes that change expression, including 588 distinct associations between specific taxa and host genes. The taxa with the strongest influence on gene expression alter the response of genes associated with complex traits. Using ATAC-seq, we show that a subset of these changes in gene expression are likely the result of changes in host chromatin accessibility and transcription factor binding induced by exposure to gut microbiota. We then created a manipulated microbial community with titrated doses of Collinsella, demonstrating that both natural and controlled microbiome composition leads to distinct, and predictable, gene expression profiles in host cells. Together, our results suggest that specific microbes play an important role in regulating expression of individual host genes involved in human complex traits. The ability to fine tune the expression of host genes by manipulating the microbiome suggests future therapeutic routes.

genomics↗