Biology subjects

Lewis, C.

Publications and source records attributed to Lewis, C..

At least 37 records · Page 2Linked to original sources

bioRxiv2023

The origin, evolution and molecular diversity of the chemokine system

Chemokine signalling performs key functions in cell migration via chemoattraction, such as attracting leukocytes to the site of infection during host defence. The system consists of a ligand, the chemokine, usually secreted outside the cell, and a chemokine receptor on the surface of a target cell that recognises the ligand. Several non-canonical components interact with the system. These include a variety of molecules that usually share some degree of sequence similarity with canonical components and, in some cases, are known to bind to canonical components and/or to modulate cell migration (1, 2). While canonical components have been described in vertebrate lineages, the distribution of the non-canonical components is less clear. Uncertainty over the relationships between canonical and non-canonical components hampers our understanding of the evolution of the system. We used phylogenetic methods, including gene-tree to species-tree reconciliation, to untangle the relationships between canonical and non-canonical components, identify gene duplication events and clarify the origin of the system. We found that unrelated ligand groups independently evolved chemokine-like functions. We found non-canonical ligands outside vertebrates, such as TAFA "chemokines" found in urochordates. In contrast, all receptor groups are vertebrate-specific and all - except ACKR1 - originated from a common ancestor in early vertebrates. Both ligand and receptor copy numbers expanded through gene duplication events at the base of jawed vertebrates, with subsequent waves of innovation occurring in bony fish and mammals.

evolutionary biology↗

bioRxiv2023

Positive selection of somatically mutated clones identifies adaptive pathways in metabolic liver disease

Somatic mutations in non-malignant tissues accumulate with age and insult, but whether these mutations are adaptive on the cellular or organismal levels is unclear. To interrogate mutations found in human metabolic disease, we performed lineage tracing in mice harboring somatic mosaicism subjected to non-alcoholic steatohepatitis (NASH). Proof-of-concept studies with mosaic loss of Mboat7, a membrane lipid acyltransferase, showed that increased steatosis accelerated clonal disappearance. Next, we induced pooled mosaicism in 63 known NASH genes, allowing us to trace mutant clones side-by-side. This in vivo tracing platform, which we coined MOSAICS, selected for mutations that ameliorate lipotoxicity, including mutant genes identified in human NASH. To prioritize new genes, additional screening of 472 candidates identified 23 somatic perturbations that promoted clonal expansion. In validation studies, liver-wide deletion of Bcl6, Tbx3, or Smyd2 resulted in protection against NASH. Selection for clonal fitness in mouse and human livers identifies pathways that regulate metabolic disease. HighlightsO_LIMosaic Mboat7 mutations that increase lipotoxicity lead to clonal disappearance in NASH. C_LIO_LIIn vivo screening can identify genes that alter hepatocyte fitness in NASH. C_LIO_LIMosaic Gpam mutations are positively selected due to reduced lipogenesis. C_LIO_LIIn vivo screening of transcription factors and epifactors identified new therapeutic targets in NASH. C_LI

cell biology↗

bioRxiv2023

Distinct hypothalamus-habenula circuits govern risk preference

Appropriate risk evaluation is essential for survival in complex, uncertain environments. Confronted with choosing between certain (safe) and uncertain (risky) options, animals show strong preference for either option consistently across extended time periods. How such risk preference is encoded in the brain remains elusive. A candidate region is the lateral habenula (LHb), which is prominently involved in value-guided behavior. Here, using a balanced two-alternative choice task and longitudinal two-photon calcium imaging, we identify LHb neurons with risk-preference-selective activity reflecting individual risk preference prior to action selection. By employing whole-brain anatomical tracing, multi-fiber photometry, and projection- and cell-type-specific optogenetics, we find that glutamatergic LHb projections from the medial (MH) but not lateral (LH) hypothalamus provide behavior-relevant synaptic input before action selection. Optogenetic stimulation of MH{lozenge}LHb axons evoked excitatory and inhibitory postsynaptic responses, whereas LH{lozenge}LHb projections were excitatory. We thus reveal functionally distinct hypothalamus-habenula circuits for risk preference in habitual economic decision-making.

neuroscience↗

bioRxiv2022

IL-6 prevents Th2 cell polarization by promoting SOCS3-dependent suppression of IL-2 signaling

Defective interleukin-6 (IL-6) signaling has been associated with Th2 bias and elevated IgE. However, the underlying mechanism by which IL-6 prevents the development of Th2-driven diseases remains unknown. Using a model of house-dust-mite (HDM)-induced Th2 differentiation and allergic airway inflammation, we show that IL-6 signaling in allergen-specific T cells was required to prevent Th2 development and subsequent IgE response and allergic inflammation. Th2 cell lineage commitment required strong sustained IL-2 signaling. Importantly, we found that IL-6 turned off IL-2 signaling during early T cell activation and thus inhibited Th2 priming. Mechanistically, we found that IL-6-driven inhibition of IL-2 signaling in responding T cells was mediated by upregulation of Suppressor Of Cytokine Signaling 3 (SOCS3). This mechanism could be mimicked by pharmacological Janus Kinase-1 (JAK1) inhibition. Collectively, our results identify an unrecognized mechanism that prevents the development of unwanted Th2 cell responses and associated diseases and outline potential preventive interventions.

immunology↗

bioRxiv2022

Prediction of Electric Fields Induced by Transcranial Magnetic Stimulation in the Brain using a Deep Encoder-Decoder Convolutional Neural Network

Transcranial magnetic stimulation (TMS) is a non-invasive, effective, and safe neuromodulation technique to diagnose and treat neurological and psychiatric disorders. However, the complexity and heterogeneity of the brain composition and structure pose a challenge in accurately determining whether critical brain regions have received the right level of induced electric field. Numerical computation methods, like finite element analysis (FEA), can be used to estimate electric field distribution. However, these methods need exceedingly high computational resources and are time-consuming. In this work, we developed a deep convolutional neural network (DCNN) encoder-decoder model to predict induced electric fields, in real-time, from T1-weighted and T2-weighted magnetic resonance imaging (MRI) based anatomical slices. We recruited 11 healthy subjects and applied TMS to the primary motor cortex to measure resting motor thresholds. Head models were developed from MRIs of the subjects using the SimNIBS pipeline. Head model overall size was scaled to 20 new size scales for each subject to form a total of 231 head models. Scaling was done to increase the number of input data representing different head model sizes. Sim4Life, a FEA software, was used to compute the induced electric fields, which served as the DCNN training data. For the trained network, the peak signal to noise ratios of the training and testing data were 32.83dB and 28.01dB, respectively. The key contribution of our model is the ability to predict the induced electric fields in real-time and thereby accurately and efficiently predict the TMS strength needed in targeted brain regions.

bioengineering↗

bioRxiv2022

Pleiotrophin drives a pro-metastatic immune niche within the breast tumor microenvironment

Metastatic cancer cells adapt to thrive in secondary organs. To investigate metastatic adaptation, we performed transcriptomic analysis of metastatic and non-metastatic murine breast cancer cells. We found that pleiotrophin (PTN), a neurotrophic cytokine, is a metastasis-associated factor that is expressed highly by aggressive breast cancers. Moreover, elevated PTN in plasma correlated significantly with metastasis and reduced survival of breast cancer patients. Mechanistically, we find that PTN activates NF-kB in cancer cells leading to altered cytokine production, subsequent neutrophil recruitment and an immune suppressive microenvironment. Consequently, inhibition of PTN, pharmacologically or genetically, reduces the accumulation of tumor associated neutrophils and reverts local immune suppression resulting in increased T cell activation and attenuated metastasis. Furthermore, inhibition of PTN significantly enhanced the efficacy of immune checkpoint blockade + chemotherapy in reducing metastatic burden in mice. These findings establish PTN as a previously unrecognized driver of a pro-metastatic immune niche and thus represents a promising therapeutic target for the treatment of metastatic breast cancer.

cancer biology↗

bioRxiv2020

Imputed Gene Expression Risk Scores: A Functionally Informed Component of Polygenic Risk

BackgroundIntegration of functional genomic annotations when estimating polygenic risk scores (PRS) can provide insight into aetiology and improve risk prediction. This study explores the predictive utility of gene expression risk scores (GeRS), calculated using imputed gene expression and transcriptome-wide association study (TWAS) results. MethodsThe predictive utility of GeRS was evaluated using 12 neuropsychiatric and anthropometric outcomes measured in two target samples: UK Biobank and the Twins Early Development Study (TEDS). GeRS were calculated based on imputed gene expression levels and TWAS results, using 53 gene expression-genotype panels, termed SNP-weight sets, capturing expression across a range of tissues. We compare the predictive utility of elastic net models containing GeRS within and across SNP-weight sets, and models containing both GeRS and PRS. We estimate the proportion of SNP-based heritability attributable to cis-regulated gene expression. ResultsGeRS significantly predicted a range of outcomes, with elastic net models combining GeRS across SNP-weight sets improving prediction. GeRS were less predictive than PRS, but models combining GeRS and PRS improved prediction for several outcomes, with relative improvements ranging from 0.3% for Height (p=0.023) to 4% for Rheumatoid Arthritis (p=5.9x10-8). The proportion of SNP-based heritability attributable to cis-regulated expression was modest for most outcomes, even when restricting GeRS to colocalised genes. ConclusionGeRS represent a component of PRS and could be useful for functional stratification of genetic risk. Only in specific circumstances can GeRS substantially improve prediction over PRS alone. Future research considering functional genomic annotations when estimating genetic risk is warranted.

genomics↗

bioRxiv2020

Effects of face masks on acoustic analysis and speech perception: Implications for peri-pandemic protocols

Wearing face masks (alongside physical distancing) provides some protection against infection from COVID-19. Face masks can also change how we communicate and subsequently affect speech signal quality. Here we investigated how three face mask types (N95, surgical and cloth) affect acoustic analysis of speech and perceived intelligibility in healthy subjects. We compared speech produced with and without the different masks on acoustic measures of timing, frequency, perturbation and power spectral density. Speech clarity was also examined using a standardized intelligibility tool by blinded raters. Mask type impacted the power distribution in frequencies above 3kHz for both the N95 and surgical masks. Measures of timing and spectral tilt also differed across mask conditions. Cepstral and harmonics to noise ratios remained flat across mask type. No differences were observed across conditions for word or sentence intelligibility measures. Our data show that face masks change the speech signal, but some specific acoustic features remain largely unaffected (e.g., measures of voice quality) irrespective of mask type. Outcomes have bearing on how future speech studies are run when personal protective equipment is worn.

bioengineering↗

bioRxiv2020

Rapid Population Decline of the Pillar Coral Dendrogyra cylindrus Along the Florida Reef Tract

Coral reefs worldwide are in a state of decline, but the status of populations and stressors for rare species are generally not well documented using broad-scale monitoring protocol. We fate-tracked all known colonies of the pillar coral Dendrogyra cylindrus on the Florida Reef Tract from 2013 - 2020 to assess the population and document the impacts of chronic and acute stressors. Large average colony size, an absence of juveniles, and large geographic distances between genotypes suggest that the Florida D. cylindrus population has been reproductively extinct for decades. During the study period, low-intensity chronic stressors were balanced by regrowth, while back-to-back years of coral bleaching and thermally-exacerbated disease led to declines that the subsequent years of recovery suggest would take 11 uninterrupted years to overcome. The most recent stressor on Floridas D. cylindrus population is "stony coral tissue loss disease." Following the appearance of the disease in Florida in 2015, it resulted in unrecoverable losses to the D. cylindrus population as tissue, colonies, and whole genotypes were driven to extinction. Losses of 91% of coral tissue, 88% of colonies, and 73% of genotypes between 2014 and early 2020 have led to functional extinction of D. cylindrus on the Florida Reef Tract.

ecology↗

bioRxiv2020

Investigating an in-silico approach for prioritizing antidepressant drug prescription based on drug-induced expression profiles and predicted gene expression

In clinical practice, antidepressant prescription is a trial and error approach, which is time consuming and discomforting for patients. This study investigated an in-silico approach for ranking antidepressants based on their hypothetical likelihood of efficacy. We determined the transcriptomic profile of citalopram remitters by performing a transcriptomic-wide association study on STAR*D data (N =1163). The transcriptional profile of remitters was compared with 21 antidepressant-induced gene expression profiles in five human cell lines available in the connectivity map database. Spearman correlation, Pearson correlation, and the Kolmogorov Smirnov test were used to determine the similarity between antidepressant-induced profiles and remitter profiles, subsequently calculating the average rank of antidepressants across the three methods and a p-value for each rank by using a permutation procedure. The drugs with the top ranks were those having high positive correlation with the expression profiles of remitters and they may have higher chances of efficacy in the tested patients. In MCF7 (breast cancer cell line), escitalopram had the highest average rank, with an average rank higher than expected by chance (p=0.0014). In A375 (human melanoma) and PC3 (prostate cancer) cell lines, escitalopram and citalopram emerged as the second highest ranked antidepressants, respectively (p=0.0310 and 0.0276, respectively). In HA1E (kidney) and HT29 (colon cancer) cell types, citalopram and escitalopram did not fall among top antidepressants. The correlation between citalopram remitters and (es)citalopram-induced expression profiles in three cell lines suggests that our approach may be useful and with future improvements it can be applicable at the individual level to tailor treatment prescription.

bioinformatics↗

bioRxiv2020

Delineating the Genetic Component of Gene Expression in Major Depression

BackgroundMajor Depression (MD) is determined by a multitude of factors including genetic risk variants which regulate gene expression (GE). Here, we examined the genetic component of GE in MD by performing a Transcriptome-Wide Association Study (TWAS), inferring GE-trait relationships from genetic, transcriptomic and phenotypic information. MethodGenes differentially expressed in depression were identified with the TWAS FUSION method, based on summary statistics from the largest genome-wide association analysis of MD (Ncases = 135,458) and GE levels from 20 tissue datasets. Follow-up analyses were performed to extensively characterize the identified associations: colocalization, conditional, and fine-mapping analyses together with functionally-enriched pathway investigations. ResultsTranscriptome-wide significant GE differences between cases and controls were found at 91 genes, 50 of which were not found in previous MD TWASs. Of the 91 significant genes, eight represented strong, colocalized, and potentially causal associations with depression, which were independent from the effect of nearby genes. Such "high-confidence associations" include NEGR1, CTC-467M3.3, TMEM106B, CTD-2298J14.2, CCDC175, ESR2, PROX2, ZC3H7B. Lastly, TWAS-based enrichment analysis highlighted dysregulation of gene sets for long term potentiation, dendritic shaft, and memory processes in MD. ConclusionThis study has shed light on the genetic component of GE in depression by characterizing the identified associations, unravelling novel risk genes, and determining which associations are congruent with a causal model. These findings can be used as a resource for prioritizing and designing subsequent functional studies of MD.

genomics↗

bioRxiv2020

Value-guided remapping of sensory circuits by lateral orbitofrontal cortex in reversal learning

Flexible decision-making is crucial for adaptive behaviour. Such behaviour in mammals largely relies on the frontal cortex, and specifically, the orbitofrontal cortex (OFC). How OFC neurons encode decision variables and instruct sensory areas to guide adaptive behaviour is a key open question. Here we developed a reversal learning task for head-fixed mice together with two-photon calcium imaging to monitor the activity of lateral OFC neuronal populations and investigated their dynamic interaction with primary somatosensory cortex (S1). Mice trained on this task learned to discriminate go/no-go tactile stimuli and adapt their behaviour upon changes in stimulus-reward contingencies ( rule-switch). Longitudinal imaging at cellular resolution across weeks during all behavioural phases revealed a distinct engagement of S1 and lateral OFC neurons: S1 neural activity reflected task learning-related responses, while neurons in the lateral OFC saliently and transiently responded to the rule-switch. A subset of OFC neurons conveyed a value prediction error signal via feedback projections to S1, as direct anatomical long-range projections were revealed by retrograde tracing combined with whole-brain light-sheet microscopy. Top-down signals implemented an update of sensory representations and functionally reconfigured a small subpopulation of S1 neurons that were differentially modulated by reward-history. Functional remapping of these neurons crucially depended on top-down inputs, as chemogenetic silencing of lateral OFC neurons disrupted reversal learning and impaired plastic changes in these outcome-sensitive S1 neurons. Our results reveal the presence of long-range cortical interactions between cellular ensembles in higher and lower-order brain areas specifically recruited during context-dependent learning and task-switching. Such interactions crucially implement history-dependent reward-value computations and error heuristics, which, in turn, help guide adaptive behaviour.

neuroscience↗

bioRxiv2019

Cross-validation of technologies for genotyping CYP2D6 and CYP2C19

BackgroundCYP2D6 and CYP2C19 are cytochrome P450 enzymes involved in the metabolism of many medications from multiple therapeutic classes. Associations between patterns of variants (known as haplotypes) in the genes encoding them (CYP2D6 and CYP2C19) and enzyme activities are well described. The genes in fact comprise 21% of biomarkers in drug labels. Despite this, genotyping is not common, partly attributable to its challenging nature (CYP2D6 having >100 haplotypes, including those with sequence from an adjacent pseudogene, and gene duplications). We cross-validated different methodologies for identifying haplotypes in these genes against each other. MethodsNinety-two samples with a variety of CYP2D6 and CYP2C19 genotypes according to prior AmpliChip CYP450 and TaqMan CYP2C19*17 data were selected from the Genome-based therapeutic drugs for depression (GENDEP) study. Genotyping was performed with TaqMan copy number variant (CNV) and single nucleotide variant (SNV) analysis, the next generation sequencing-based Ion S5 AmpliSeq Pharmacogenomics Panel, PharmacoScan, long-range polymerase chain reaction (L-PCR) followed by amplicon analysis, and Agena for CYP2C19. Variant pattern to haplotype translation was automated. ResultsThe inter-platform concordance for CYP2C19 was high (up to 100% for available data). For CYP2D6, the IonS5-PharmacoScan concordance was 94% for a range of variants tested apart from those with at least one extra copy of a CYP2D6 gene (occurring at a frequency of 3.8%, 33/853), or those with substantial sequence derived from pseudogene, known as hybrids (3%, 26/853). ConclusionsInter-platform concordance for CYP2C19 was high, and, moreover, the Ion S5 and PharmacoScan data were 100% concordant with that from a TaqMan CYP2C19*2 assay. We have also demonstrated feasibility of using an NGS platform for genotyping CYP2D6 and CYP2C19, with automated data interpretation methodology. This points the way to a method of making CYP2D6 and CYP2C19 genotyping more readily accessible.

genetics↗

bioRxiv2019

The independent and combined influence of schizophrenia polygenic risk score and heavy cannabis use on risk for psychotic disorder: A case-control analysis from the EUGEI study.

Background: Some recent studies have challenged the direction of causality for the association between cannabis use and psychotic disorder, suggesting that cannabis use initiation is explained by common genetic variants associated with risk of schizophrenia. We used data from the European Union Gene-Environment Interaction consortium (EUGEI) case-control study to test for the independent and combined effect of heavy cannabis use, and of Schizophrenia Polygenic risk score (SZ PRS), on risk for psychotic disorder. Methods: Genome-wide data were obtained from 492 first episode psychosis patients (FEPp) and from 787 controls of European Ancestry, and used to generate SZ PRS from the summary results of an independent meta-analysis. Information on pattern of cannabis use was used to build a 7-level frequency-type composite cannabis use measure that we previously found was a strong predictor of psychotic disorder. Results: SZ PRS did not predict cannabis initiation (b=0.027; p=0.51) or how frequently controls (b=0.027; p=0.06) or FEPp (b=0.006; p=0.91) used it, or the type of cannabis they used (Controls: b = 0.032; p=0.31); FEPp: b= 0.005; p=0.89). The frequency-type composite cannabis use measure (OR=1.32; 95% CI 1.22-1.44) and SZ PRS (OR=2.29; 95%CI 1.71-3.05) showed independent effects from each other on the OR for psychotic disorder. Conclusion: SZ PRS does not predict an individual s propensity to try cannabis, frequency of use, or the potency of the cannabis used. Our findings provide the first evidence that SZ PRS and heavy cannabis use exert effects independent from each other on the risk for psychotic disorder.

genetics↗

bioRxiv2019

Early life stress impairs social function through AVP-dependent mechanisms

Impaired social function is a core feature of many psychiatric illnesses. Adverse experiences during childhood increase risk for mental illness, however it is currently unclear whether stress early in life plays a direct role in the development of social difficulties. Using an animal model of pre-pubertal stress (PPS), we investigated effects on social behaviour, oxytocin and arginine vasopressin (AVP). We also explored social performance and AVP expression in participants with borderline personality disorder (BPD) who experienced a high incidence of childhood stress. Social behaviour was impaired and AVP expression increased in animals experiencing PPS and participants with BPD. Behavioural deficits in animals were rescued through administration of the AVP receptor 1a antagonist Relcovaptan (SR49059). AVP levels and recognition of negative emotions were significantly correlated in BPD participants only. In conclusion, early life stress plays a profound role in the precipitation of social dysfunction, and AVP mediates at least part of this effect.

neuroscience↗

bioRxiv2019

Association of extent of cannabis use and psychotic like intoxication experiences in a multi-national sample of First Episode Psychosis patients and controls

AimsFirst Episode Psychosis (FEP) patients who use cannabis experience more frequent psychotic and euphoric intoxication experiences compared to controls. It is not clear whether this is consequent to patients being more vulnerable to the effects of cannabis use or to their heavier pattern of use. We aimed to determine whether extent of use predicted psychotic-like and euphoric intoxication experiences in FEP patients and controls and whether this differs between groups. MethodsWe analysed data on lifetime cannabis using patients (n=655) and controls (n=654) across 15 sites from six countries in the EU-GEI study (2010-2015). We used multiple regression to model predictors of cannabis-induced experiences and to determine if there was an interaction between caseness and extent of use. ResultsCaseness, frequency of cannabis use and money spent on cannabis predicted psychotic-like and euphoric experiences, independent of other experiences (p[≤]0.001). For psychotic-like experiences there was a significant interaction for caseness x frequency of use (p<0.001) and caseness x money spent on cannabis (p=0.001) such that FEP patients had increased experiences at increased levels of use compared to controls. There was no similar significant interaction for euphoric experiences (p>0.5). Conclusions and RelevanceFEP patients are particularly sensitive to increased psychotic-like, but not euphoric experiences, at higher frequency and amount of cannabis use compared to controls. This suggests a specific psychotomimetic response in patients related particularly to heavy cannabis use.

neuroscience↗

bioRxiv2018

Visual exposure optimizes stimulus encoding in primary visual cortex

The brain adapts to the sensory environment. For example, simple sensory exposure can modify the response properties of early sensory neurons. How these changes affect the overall encoding and maintenance of stimulus information across neuronal populations remains unclear. We perform parallel recordings in the primary visual cortex of anesthetized cats and find that brief, repetitive exposure to structured visual stimuli enhances stimulus encoding by decreasing the selectivity and increasing the range of the neuronal responses that persist after stimulus presentation. Low-dimensional projection methods and simple classifiers demonstrate that visual exposure increases the segregation of persistent neuronal population responses into stimulus-specific clusters. These observed refinements preserve the representational details required for stimulus reconstruction and are detectable in post-exposure spontaneous activity. Assuming response facilitation and recurrent network interactions as the core mechanisms underlying stimulus persistence, we show that the exposure-driven segregation of stimulus responses can arise through strictly local plasticity mechanisms, also in the absence of firing rate changes. Our findings provide evidence for the existence of an automatic, unguided optimization process that enhances the encoding power of neuronal populations in early visual cortex, thus potentially benefiting simple readouts at higher stages of visual processing.

neuroscience↗

bioRxiv2018

Cortical excitation:inhibition imbalance causes network specific functional hypoconnectivity: a DREADD-fMRI study

Abnormal brain development manifests itself at different spatial scales. However, whether abnormalities at the cellular level can be diagnosed from network activity measured withfunctional magnetic resonance imaging (fMRI) is largely unknown, yet of high clinical relevance. Here we applied fMRI while using chemogenetics to increase the excitation-to-inhibition ratio (E:I) within cortical microcircuits of the mouse brain, thereby mimicking a putative mechanism of neurodevelopmental disorders including autism. Increased E:I caused a significant reduction of long-range connectivity, irrespective of whether excitatory neurons were facilitated or inhibitory Parvalbumin interneurons were suppressed. Training a classifier on fMRI signals, we were able to accurately classify cortical areas exhibiting increased E:I. This classifier was validated in an independent cohort of Fmr1y/- knock-out mice, a model for autism with well-documented loss of Parvalbumin neurons and chronic alterations of E:I. Our findings demonstrate a promising novel approach towards inferring microcircuit abnormalities from macroscopic fMRI measurements.

neuroscience↗