Biology subjects

Todd, S.

Publications and source records attributed to Todd, S..

8 recordsLinked to original sources

bioRxiv2025

3D Reconstruction of Dinoflagellate Chromosomes from Hi-C Data Refutes the Cholesteric Liquid Crystal Hypothesis

Dinoflagellates have permanently condensed chromosomes that are often described as liquid crystalline. Specifically, a Cholesteric Liquid Crystal (CLC) model was proposed in which DNA is organized into parallel fibers within stacked discs, such that the fiber orientation rotates by a constant angle between adjacent discs. Extrachromosomal loops extending from the discs were hypothesized to be more accessible and thus to contain transcriptionally active genes. Although the CLC model captures some features of dinoflagellate chromosome structure, its validity has not been rigorously tested against modern genomic data. Here, we use chromatin conformation capture (Hi-C) data to simulate 3D conformations of chromosome scaffolds for three dinoflagellate species: Fugacium kawagutii, Symbiodinium microadriaticum, and Breviolum minutum. Consensus and population-based modeling generate diverse polymer conformations with moderate orientational and nematic order. However, we find no evidence of cholesteric discs. Moreover, contact probability curves from empirical Hi-C data are inconsistent with the CLC model. Nevertheless, we show that introducing locus-specific boundaries into the CLC model can produce simulated Hi-C contact maps with topologically associating domains (TADs), which are observed in experimental Hi-C contact maps for these species. Finally, by mapping RNA-seq data onto our simulated conformations, we show that actively transcribed genes are present throughout entire chromosomes, and not exclusively on extrachromosomal loops or at the surface. Our results challenge the long-standing CLC model and suggest that dinoflagellate chromosomes are organized into condensed but non-crystalline structures that do not impede transcription.

biophysics↗

bioRxiv2020

Antagonism of STAT3 signalling by Ebola virus

Many viruses target signal transducers and activators of transcription (STAT) 1 and 2 to antagonise antiviral interferon (IFN) signalling, but targeting of signalling by other STATs/cytokines, including STAT3/interleukin (IL-) 6 that regulate processes important to Ebola virus (EBOV) haemorrhagic fever, is poorly defined. We report that EBOV potently inhibits STAT3 responses to IL-6 family cytokines, and that this is mediated by the IFN-antagonist VP24. Mechanistic analysis indicates that VP24 effects a unique strategy combining distinct karyopherin-dependent and karyopherin-independent mechanisms to antagonise STAT3-STAT1 heterodimers and STAT3 homodimers, respectively. This appears to reflect distinct mechanisms of nuclear trafficking of the STAT3 complexes, revealed for the first time by our analysis of VP24 function. These findings are consistent with major roles for global inhibition of STAT3 signalling in EBOV infection, and provide new insights into the molecular mechanisms of STAT3 nuclear trafficking, significant to pathogen-host interactions, cell physiology and pathologies such as cancer. Author summaryEbola virus (EBOV) continues to pose a significant risk to human health globally, causing ongoing disease outbreaks with case-fatality rates between 40 and 60%. Suppression of immune responses is a critical component of EBOV haemorrhagic fever, but understanding of EBOV impact on signalling by cytokines other than interferon is limited. We find that infectious EBOV inhibits interleukin-6 cytokine signalling via antagonism of STAT3. The antagonistic strategy uniquely combines two distinct mechanisms, which appear to reflect differing nuclear trafficking mechanisms of critical STAT3 complexes. This provides fundamental insights into the mechanisms of pathogenesis of a lethal virus, and biology of STAT3, a critical player in immunity, development, growth and cancer.

microbiology↗

bioRxiv2019

Pan-filovirus one-step reverse transcription-polymerase chain reaction screening assay

Five species within the genera Ebolavirus and Marburgvirus of the family Filoviridae are known to cause severe hemorrhagic fever with high mortality rates in humans and non-human primates. Recent large outbreaks of Ebola virus disease in West Africa (2014 - 2016) and the Democratic Republic of the Congo (2018 - ongoing) have demonstrated the epidemic potential with devastating public health consequences. Several known and novel filovirus species have been found in bats in recent years. However, the role of each virus species in the disease ecology of human disease is still unclear. In particular, the transmission mechanism from potential animal hosts to humans is not known. Therefore, a simple, flexible, cost-effective screening tool for detecting the presence of any (putative) member of the filovirus family in animal samples is needed. In this study, a one-step conventional pan-filovirus RT-PCR assay was developed. The designed universal consensus primers of this screening test target two highly conserved regions of the nucleoprotein (NP) of all currently known filoviruses. The assay was capable of specific amplification of viral RNA of all six primate-pathogenic (human and non-human) filovirus species and resulted in 317 bp long RT-PCR products. This amplicon length renders the assay suitable for flexible application as conventional reverse transcription polymerase chain reaction (RT-PCR) as well as for future use as rapid real-time quantitative reverse transcription polymerase chain reaction (RT-qPCR).

molecular biology↗

bioRxiv2019

A practical computerized decision support system for predicting the severity of Alzheimer’s disease of an individual

Computerized clinical decision support systems can help to provide objective, standardized, and timely dementia diagnosis. However, current computerized systems are mainly based on the group analysis, discrete classification of disease stages, or expensive and not readily accessible biomarkers, while current clinical practice relies relatively heavily on cognitive and functional assessments (CFA). In this study, we developed a computational framework using a suite of machine learning tools for identifying key markers in predicting the severity of Alzheimers disease (AD) from a large set of biological and clinical measures. Six machine learning approaches, namely Kernel Ridge Regression (KRR), Support Vector Regression (SVR), and k-Nearest Neighbor (kNNreg) for regression and Support Vector Machine (SVM), Random Forest (RF), and k-Nearest Neighbor (kNNclass) for classification, were used for the development of predictive models. We demonstrated high predictive power of CFA. Predictive performance of models incorporating CFA was shown to be consistently higher accuracy than those based solely on biomarker modalities. We found that KRR and SVM were the best performing regression and classification methods respectively. The optimal SVM performance was observed for a set of four CFA test scores (FAQ, ADAS13, MoCA, MMSE) with multi-class classification accuracy of 83.0%, 95%CI = (72.1%, 93.8%) while the best performance of the KRR model was reported with combined CFA and MRI neuroimaging data, i.e., R2 = 0.874, 95%CI = (0.827, 0.922). Given the high predictive power of CFA and their widespread use in clinical practice, we then designed a data-driven and self-adaptive computerized clinical decision support system (CDSS) prototype for evaluating the severity of AD of an individual on a continuous spectrum. The system implemented an automated computational approach for data pre-processing, modelling, and validation and used exclusively the scores of selected cognitive measures as data entries. Taken together, we have developed an objective and practical CDSS to aid AD diagnosis.

neuroscience↗

bioRxiv2019

CSynth: A Dynamic Modelling and Visualisation Tool for 3D Chromatin Structure

The 3D structure of chromatin in the nucleus is important for gene expression and regulation. Chromosomal conformation capture techniques, such as Hi-C, generate large amounts of data showing interaction points on the genome but these are hard to interpret using standard tools. We have developed CSynth, a high performance 3D genome browser and real time chromatin restraint-based modeller to visualise dynamic and interactive models of chromatin capture data. CSynth does its calculations in the GPU hence is much faster than existing modelling software to infer and visualise the chromatin structure which also allow real-time interaction with the modelling parameters. It also allows straightforward comparison of interaction data and the results of third party 3D modelling outputs. In addition we include an option to view and manipulate these complicated structures using Virtual Reality (VR) allowing scientists to immerse themselves in the models for further understanding. This VR component has also proven to be a valuable teaching and public engagement tool. CSynth is web based and available to use at http://csynth.org.

bioinformatics↗

bioRxiv2018

Behavioral evolution drives hindbrain diversification among Lake Malawi cichlid fish

The evolutionary diversification of animal behavior is often associated with changes in the structure and function of nervous systems. Such evolutionary changes arise either through alterations of individual neural components (\"mosaically\") or through scaling of the whole brain (\"conceitedly\"). Here we show that the evolution of a specific courtship behavior in Malawi cichlid fish, the construction of mating nests known as bowers, is associated with rapid, extensive, and specific diversification of orosensory, gustatory centers in the hindbrain. We find that hindbrain volume varies significantly between species that build pit (depression) compared to castle (mound) type bowers and that hindbrain features evolve rapidly and independently of phylogeny among castle-building species. Using immediate early gene expression, we confirmed a functional role for hindbrain structures during bower building. Comparisons of bower building species in neighboring Lake Tanganyika show patterns of neural diversification parallel to those in Lake Malawi. Our results suggest that mosaic brain evolution via alterations to individual brain structures is more extensive and predictable than previously appreciated.

evolutionary biology↗

bioRxiv2017

Non-annual seasonality of influenza-like illness in a tropical urban setting

In temperate countries, influenza and other viral respiratory diseases often have distinct seasonal peaks occurring during colder, wintertime months. However, little is known about the dynamics of influenza and viral respiratory disease dynamics in the tropics, despite high morbidity and a clear epidemiological link between tropical and temperate countries. In temperate countries, the dynamics of influenza and other respiratory diseases are often analyzed using syndromic surveillance data describing influenza-like illness (ILI) as ILI is highly correlated with virological surveillance for influenza. To obtain a detailed picture of respiratory disease incidence patterns in a large tropical city, we established an mHealth study in community outpatient clinics in Ho Chi Minh City, Vietnam (11N latitude). From August 2009 through December 2015, clinics reported daily case numbers of ILI using standard mobile-phone SMS messaging. A subset of these clinics performed molecular diagnostics for influenza A and B viruses. Unlike the annual patterns seen in temperate countries, ILI activity in Ho Chi Minh City exhibited strong non-annual periodicity and was not correlated with PCR-confirmed influenza. The dominant periodicity in the data was approximately 200 days. This was confirmed by a time series decomposition, a step-wise regression analysis on annual and non-annual covariates, and a forecasting exercise showing that forecasting was 30% to 40% more accurate when a 200-day non-annual cycle was included in the forecast. This suggests, for the first-time, that a non-annual cycle may be an essential driver of ILI dynamics in the tropics. This raises new questions about the seasonality and drivers of respiratory disease transmission in tropical countries.

epidemiology↗

bioRxiv2016

Structure of general-population antibody titer distributions to influenza A virus

Seroepidemiological studies aim to understand population-level exposure and immunity to infectious diseases. Results from serological assays are normally presented as binary outcomes describing the presence or absence of pathogen-specific antibody, despite the fact that many assays measure continuous quantities. A populations natural distribution of antibody titers to an endemic infectious disease may in fact include information on multiple serological states - e.g. naivete, recent infection, non-recent infection - depending on the disease in question and the acquisition and waning patterns of host immunity. In this study, we investigate a collection of 20,152 general-population serum samples from southern Vietnam collected between 2009 and 2013 from which we report antibody titers to the influenza virus HA1 protein using a continuous titer measurement from a protein microarray assay. We describe the distributions of antibody titers to subtypes 2009 H1N1 and H3N2. Using a model selection approach to fit mixture distributions, we show that 2009 H1N1 antibody titers fall into four titer subgroups and that H3N2 titers fall into three subgroups. For H1N1, our interpretation is that the two highest-titer subgroups correspond to recent infection and historical infection, which is consistent with 2009 pandemic attack rates. For H3N2, observations censored at the highest titer dilutions make similar interpretations difficult to validate.

epidemiology↗