Biology subjects

Thompson, J.

Publications and source records attributed to Thompson, J..

At least 73 records · Page 4Linked to original sources

bioRxiv2020

A novel isoform of ACE2 is expressed in human nasal and bronchial respiratory epithelia and is upregulated in response to RNA respiratory virus infection

Angiotensin-converting enzyme 2 (ACE2) is the main entry point in the airways for SARS-CoV-2. ACE2 binding to SARS-CoV-2 protein Spike triggers viral fusion with the cell membrane, resulting in viral RNA genome delivery into the host. Despite ACE2s critical role in SARS-CoV-2 infection, an understanding of ACE2 expression, including in response to viral infection, remains unclear. Until now ACE2 was thought to encode five transcripts and one 805 amino acid protein. Here we identify a novel short isoform of ACE2. Short ACE2 is expressed in the airway epithelium, the main site of SARS-CoV-2 infection; it is substantially upregulated in response to interferon stimulation and RV infection, but not in response to SARS-CoV-2 infection, and it shows differential regulation in asthma patients. This short isoform lacks SARS-CoV-2 spike glycoprotein high-affinity binding sites and altogether, our data are consistent with a model where short ACE2 may influence host susceptibility to SARS-CoV-2 infection.

molecular biology↗

bioRxiv2020

A Conserved TCRβ signature dominates a highly polyclonal T-cell expansion during the acute phase of a murine malaria infection

CD4+ {beta} T-cells are key mediators of the immune response to a first Plasmodium infection, undergoing extensive activation and splenic expansion during the acute phase of an infection. However, the clonality and clonal composition of this expansion has not previously been described. Using a comparative infection model, we sequenced the splenic CD4+ T-cell receptor repertoires generated over the time-course of a Plasmodium chabaudi infection. We show through repeat replicate experiments, single-cell RNA-seq, and analyses of independent RNA-seq data, that following a first infection - within a highly polyclonal expansion - T-effector repertoires are consistently dominated by TRBV3 gene usage. Clustering by sequence similarity, we find the same dominant clonal signature is expanded across replicates in the acute phase of an infection, revealing a conserved pathogen-specific T-cell response that is consistently a hallmark of a first infection, but not expanded upon re-challenge. Determining the host or parasite factors driving this conserved response may uncover novel immune targets for malaria therapeutic purposes.

immunology↗

bioRxiv2020

Probabilistic Ranking Of Microbiomes with Taxa Selection to discover and validate microbiome function models for multiple litter decomposition studies

The overwhelming complexity of microbiomes makes it difficult to decipher functional relationships between specific microbes and ecosystem properties. While machine learning analyses have demonstrated an impressive ability to correlate microbial community composition with macroscopic functions, mechanisms that dictate model predictions are often unknown, and predictions often lack an assigned metric of uncertainty. In this study, we apply Bayesian networks to build on prior feature selection analyses and construct easy-to-interpret probabilistic models, which accurately predict levels of dissolved organic carbon (DOC) from the relative abundance of soil bacteria (16S rRNA gene profiles). In addition to standard cross-validation, we show that a Bayesian network model trained using samples from a pine litter decomposition study accurately predicts DOC of samples from an independent oak litter decomposition study, suggesting that mechanisms driving variation in soil carbon storage may be conserved across different types of decomposing plant litter. Furthermore, the structure of the resulting Bayesian network model defines a minimal set of highly informative taxa, whose abundances directly constrain the probability of high or low DOC conditions. Significant accuracy of the Bayesian network model with independent data sets supports the validity of the identified relationships between taxa abundance and DOC. SummaryUnderstanding the interplay between microbiomes and the environments they inhabit is a daunting task. While recent advances in gene sequencing technology provide a means of profiling the relative abundance of microbial species, the resulting data are noisy, sparse, and limited to small sample sizes. Despite these challenges, machine learning approaches have demonstrated a promising ability to discover patterns linking the microbiome with macroscopic behavior. However, most machine learning models applied to microbiome data do not estimate prediction uncertainty and provide little insight regarding how predictions are made. In this study, we couple machine learning approaches for feature reduction with Bayesian networks to model the relationship between the soil microbiome and dissolved organic carbon (DOC). We show that Bayesian networks are accurate and provide a transparent link between microbial abundance and DOC. To validate Bayesian networks, we demonstrate accurate predictions for held-out testing data and with data from independent decomposition experiments.

microbiology↗

bioRxiv2020

A spinoparabrachial circuit defined by Tacr1 expression drives pain

Painful stimuli evoke a mixture of sensations, negative emotions and behaviors. These myriad effects are thought to be produced by parallel ascending circuits working in combination. Here we describe a pathway from spinal cord to brain for ongoing pain. Activation of a defined subset of spinal projection neurons expressing Tacr1 evokes a full repertoire of somatotopically-directed coping behaviors in the absence of noxious input. These cells project to a tiny cluster of Tacr1-positive neurons in the superior lateral parabrachial nucleus (PBN-SL) that themselves are responsive to sustained but not acute noxious stimuli. Activation of these PBN-SLTacr1 neurons alone does not trigger pain responses but instead serves to dramatically heighten nocifensive behaviors and suppress itch. Remarkably, mice with silenced PBN-SLTacr1 neurons ignore long-lasting noxious stimuli. These data reveal a spinoparabrachial pathway that plays a key role in the sensation of ongoing pain.

neuroscience↗

bioRxiv2020

The use of two-sample methods for Mendelian randomization analyses on single large datasets

BackgroundWith genome-wide association data for many exposures and outcomes now available from large biobanks, one-sample Mendelian randomization (MR) is increasingly used to investigate causal relationships. Many robust MR methods are available to address pleiotropy, but these assume independence between the gene-exposure and gene-outcome association estimates. Unlike in two-sample MR, in one-sample MR the two estimates are obtained from the same individuals, and the assumption of independence does not hold in the presence of confounding. MethodsWith simulations mimicking a typical study in UK Biobank we assessed the performance, in terms of bias and precision of the MR estimate, of the fixed-effect and (multiplicative) random-effects meta-analysis method, weighted median estimator, weighted mode estimator and MR-Egger regression, used in both one-sample and two-sample data. We considered scenarios differing for: presence/absence of a true causal effect; amount of confounding; presence and type of pleiotropy (none, balanced or directional). ResultsEven in the presence of substantial correlation due to confounding, all methods performed well when used in one-sample MR except for MR-Egger, which resulted in bias reflecting direction and magnitude of the confounding. Such bias was much reduced in the presence of very high variability in instrumental strength across variants (I2GX of 97%). ConclusionsTwo-sample MR methods can be safely used for one-sample MR performed within large biobanks, expect for MR-Egger. MR-Egger is not recommended for one-sample MR unless the correlation between the gene-exposure and gene-outcome estimates due to confounding can be kept low, or the variability in instrumental strength is very high. Key MessagesO_LICurrent availability of phenotypic and genetic data from large biobanks, such as UK Biobank, has led to increasing use of one-sample Mendelian randomization (MR) to investigate causal relationships in epidemiological research C_LIO_LIRobust MR methods have been developed to address pleiotropy, but they assume independence between the gene-exposure and gene-outcome association estimates; this holds in two-sample MR but not in one-sample MR C_LIO_LIWe illustrate the practical implications, in terms of bias and precision of the MR causal effect estimate, of using robust two-sample methods in one-sample MR studies performed within large biobanks C_LIO_LITwo-sample MR methods can be safely used for one-sample MR performed within large biobanks, expect for MR-Egger regression C_LIO_LIMR-Egger is not recommended for one-sample MR unless the correlation between the gene-exposure and gene-outcome estimates due to confounding can be kept low, or the variability in instrumental strength is very high C_LI

genetics↗

bioRxiv2020

Potential role of the X circular code in the regulation of gene expression

The X circular code is a set of 20 trinucleotides (codons) that has been identified in the protein-coding genes of most organisms (bacteria, archaea, eukaryotes, plasmids, viruses). It has been shown previously that the X circular code has the important mathematical property of being an error-correcting code. Thus, motifs of the X circular code, i.e. a series of codons belonging to X, which are significantly enriched in the genes, allow identification and maintenance of the reading frame in genes. X motifs have also been identified in many transfer RNA (tRNA) genes and in important functional regions of the ribosomal RNA (rRNA), notably in the peptidyl transferase center and the decoding center. Here, we investigate the potential role of X motifs as functional elements in the regulation of gene expression. Surprisingly, the definition of a simple parameter identifies several relations between the X circular code and gene expression. First, we identify a correlation between the 20 codons of the X circular code and the optimal codons/dicodons that have been shown to influence translation efficiency. Using previously published experimental data, we then demonstrate that the presence of X motifs in genes can be used to predict the level of gene expression. Based on these observations, we propose the hypothesis that the X motifs represent a new genetic signal, contributing to the maintenance of the correct reading frame and the optimization and regulation of gene expression. Author SummaryThe standard genetic code is used by (quasi-) all organisms to translate information in genes into proteins. Recently, other codes have been identified in genomes that increase the versatility of gene decoding. Here, we focus on the circular codes, an important class of genome codes, that have the ability to detect and maintain the reading frame during translation. Motifs of the X circular code are enriched in protein-coding genes from most organisms from bacteria to eukaryotes, as well as in important molecules in the gene translation machinery, including transfer RNA (tRNA) and ribosomal RNA (rRNA). Based on these observations, it has been proposed that the X circular code represents an ancestor of the standard genetic code, that was used in primordial systems to simultaneously decode a smaller set of amino acids and synchronize the reading frame. Using previously published experimental data, we highlight several links between the presence of X motifs in genes and more efficient gene expression, supporting the hypothesis that the X circular code still contributes to the complex dynamics of gene regulation in extant genomes.

bioinformatics↗

bioRxiv2020

Regulation of human development by ubiquitin chain editing of chromatin remodelers

Embryonic development occurs through commitment of pluripotent stem cells to differentiation programs that require highly coordinated changes in gene expression. Chromatin remodeling of gene regulatory elements is a critical component of how such changes are achieved. While many factors controlling chromatin dynamics are known, mechanisms of how different chromatin regulators are orchestrated during development are not well understood. Here, we describe LINKED (LINKage-specific-deubiquitylation-deficiency-induced Embryonic Defects) syndrome, a novel multiple congenital anomaly disorder caused by hypomorphic hemizygous missense variants in the deubiquitylase OTUD5/DUBA. Studying LINKED mutations in vitro, in mouse, and in models of neuroectodermal differentiation of human pluripotent stem cells, we uncover a novel regulatory circuit that coordinates chromatin remodeling pathways during early differentiation. We show that the K48-linkage-specific deubiquitylation activity of OTUD5 is essential for murine and human development and, if reduced, leads to aberrant cell-fate specification. OTUD5 controls differentiation through preventing the degradation of multiple chromatin regulators including ARID1A/B and HDAC2, mutation of which underlie developmental syndromes that exhibit phenotypic overlap with LINKED patients. Accordingly, loss of OTUD5 during early differentiation leads to less accessible chromatin at neural and neural crest enhancers and thus aberrant rewiring of gene expression networks. Our work identifies a novel mechanistic link between phenotypically related developmental disorders and an essential function for linkagespecific ubiquitin editing of substrate groups (i.e. chromatin remodeling complexes) during early cellfate decisions - a regulatory concept, we predict to be a general feature of embryonic development.

genetics↗

bioRxiv2019

Enhanced Cancer Subtyping via Pan-Transcriptomics Data Fusion, Monte-Carlo Consensus Clustering, and Auto Classifier Creation

Subtyping of tumor transcriptome expression profiles is a routine method used to distinguish tumor heterogeneity. Unsupervised clustering techniques are often combined with survival analysis to decipher the relationship between genes and the survival times of patients. However, the reproducibility of these subtyping based studies is poor. There are multiple reports which have conflicting subtype and gene-survival time relationship results. In this study, we introduce the issues underlying the lack of reproducibility in transcriptomic subtyping studies. This problem arises from the routine analysis of small cohorts (< 100 individuals) and use of biased traditional consensus clustering techniques. Our approach carefully combines multiple RNA-sequencing and microarray datasets, followed by subtyping via Monte-Carlo Consensus Clustering and creation of deep subtyping classifiers. This paper demonstrates an improved subtyping methodology by investigating pancreatic ductal adenocarcinoma. Importantly, our methodology identifies six biologically novel pancreatic ductal adenocarcinoma subtypes. Our approach also enables a degree of reproducibility, via our pancreatic ductal adenocarcinoma classifier PDACNet, which classical subtyping studies have failed to establish.

bioinformatics↗

bioRxiv2019

Co-designed land-use scenarios and their implications for storm runoff and streamflow in New England

Future changes in both landscape and climate have the potential to create or exacerbate problems with stormwater management, high flows, and flooding. In New England, four plausible land-use scenarios were co-developed with stakeholders to give insight to the effects on ecosystem services of different trajectories of socio-economic connectedness and natural resource innovation. To assess the effects of these land-use scenarios on water-related ecosystem services, we applied the Soil and Water Assessment Tool to two watersheds under two climates. Differences in land use had minimal effects on the overall water balance but did affect high flows and the relative contribution of storm runoff to streamflow. For most of the scenarios, the effect was small and less than the effect due to climate change. For one scenario - envisioned to have global socio-economic connectedness and low levels of natural-resource innovation - the effects of land-use changes were comparable to the effects due to climate. For that scenario, changes to the landscape increased the annual maximum daily flow by 10%, similar to the 5-15% increase attributable to climate change. These results, which were consistent across both watersheds, can help inform planning and policies regarding land use, development, and maintenance of hydrologic ecosystem services. Research highlightsO_LIStakeholder-engaged scenarios provide meaningful and plausible futures for the New England landscape and assessment of effects of land-use change on storm runoff and streamflow C_LIO_LIEffects of land use on the overall water balance are small across the landscape scenarios C_LIO_LIFuture land-use change has the potential to affect storm runoff and high flows to a degree that is comparable to the effects due to changes in climate in 2060 C_LIO_LIThe degree of natural resource innovation affects storm runoff and high flows when population growth is large and has a negligible effect when population growth is low C_LI

ecology↗

bioRxiv2019

Ongoing repair of migration-coupled DNA damage allows stem cells to reach wound sites

The impact of mechanical stress during cell migration may be a previously unappreciated source of genome instability [1-3], but to what extent this happens in vivo remains unknown. Here we consider an in vivo system where the adult stem cells of planarian flatworms are required to migrate to a distal wound site [4]. We observe a relationship between adult stem cell migration and ongoing DNA damage and repair during tissue regeneration. Migrating planarian stem cells undergo changes in nuclear shape and increased levels of DNA damage. Increased DNA damage levels resolve once stem cells reach the wound site and stop migrating. Stem cells in which DNA damage is induced prior to wounding take longer to initiate migration suggesting migration activity is sensitive to DNA damage. Migrating stem cells populations are more sensitive to further DNA damage than stationary stem cells, providing evidence that levels of migration-coupled-DNA-damage (MCDD) are significant. RNAi mediated knockdown of DNA repair pathway components blocks normal stem cell migration, confirming that DNA repair pathways are required to allow successful migration to a distal wound site. Together these lines of evidence demonstrate that migration leans to DNA damage in vivo and requires DNA repair mechanisms. Our findings reveal that migration of stem cells represents an unappreciated source of damage, that could be a significant source of mutations in animals during development or during long term tissue homeostasis.

developmental biology↗

bioRxiv2019

Spatial simulation of co-designed land-cover change scenarios in New England: Alternative futures and their consequences for conservation priorities

To help prepare for an uncertain future, planners and scientists often engage with stakeholders to co-design alternative scenarios of land-use change. Methods to translate the resulting qualitative scenarios into quantitative simulations that characterize the future landscape condition are needed to understand consequences of the scenarios while maintaining the legitimacy of the process. We use the New England Landscape Futures (NELF) project as a case study to demonstrate a transparent method for translating participatory scenarios to simulations of Land-Use and Land-Cover (LULC) change and for understanding the major drivers of land-use change and diversity of plausible scenarios and the consequences of alternative land-use pathways for conservation priorities. The NELF project co-designed four narrative scenarios that contrast with a Recent Trends scenario that projects a continuation of observed changes across the 18-million-hectare region during the past 20 years. Here, we (1) describe the process and utility of translating qualitative scenarios into spatial simulations using a dynamic cellular land change model; (2) evaluate the outcomes of the scenarios in terms of the differences in the LULC configuration relative to the Recent Trends scenario and to each other; (3) compare the fate of forests within key areas of concern to the stakeholders; and (4) describe how a user-inspired outreach tool was developed to make the simulations and analyses accessible to diverse users. The four alternative scenarios populate a quadrant of future conditions that crosses high to low natural resource planning and innovation with local to global socio-economic connectedness. The associated simulations are strongly divergent in terms of the amount of LULC change and the spatial pattern of change. Features of the simulations can be linked back to the original storylines. Among the scenarios there is a fivefold difference in the amount of high-density development, and a twofold difference in the amount of protected land. Overall, the rate of LULC change has a greater influence on forestlands of concern to the stakeholders than does the spatial configuration. The simulated scenarios have been integrated into an online mapping tool that was designed via a user-engagement process to meet the needs of diverse stakeholders who are interested the future of the land and in using future scenarios to guide land use planning and conservation priorities.

ecology↗

bioRxiv2019

Site-directed M2 proton channel inhibitors enable synergistic combination therapy for rimantadine-resistant pandemic influenza

Pandemic influenza A virus (IAV) remains a significant threat to global health. Preparedness relies primarily upon a single class of neuraminidase (NA) targeted antivirals, against which resistance is steadily growing. The M2 proton channel is an alternative clinically proven antiviral target, yet a near-ubiquitous S31N polymorphism in M2 evokes resistance to licensed adamantane drugs. Hence, inhibitors capable of targeting N31 containing M2 (M2-N31) are highly desirable.\n\nRational in silico design and in vitro screens delineated compounds favouring either lumenal or peripheral M2 binding, yielding effective M2-N31 inhibitors in both cases. Hits included adamantanes as well as novel compounds, with some showing low micromolar potency versus pandemic \"swine\" H1N1 influenza (Eng195) in culture. Interestingly, a published adamantane-based M2-N31 inhibitor rapidly selected a resistant V27A polymorphism (M2-A27/N31), whereas this was not the case for non-adamantane compounds. Nevertheless, combinations of adamantanes and novel compounds achieved synergistic antiviral effects, and the latter synergised with the neuraminidase inhibitor (NAi), Zanamivir. Thus, site-directed drug combinations show potential to rejuvenate M2 as an antiviral target whilst reducing the risk of drug resistance.

microbiology↗

bioRxiv2019

Machine learning to predict microbial community functions: An analysis of dissolved organic carbon from litter decomposition.

Microbial communities are ubiquitous and often influence macroscopic properties of the ecosystems they inhabit. However, deciphering the functional relationship between specific microbes and ecosystem properties is an ongoing challenge owing to the complexity of the communities. This challenge can be addressed, in part, by integrating the advances in DNA sequencing technology with computational approaches like machine learning. Although machine learning techniques have been applied to microbiome data, use of these techniques remains rare, and user-friendly platforms to implement such techniques are not widely available. We developed a tool that implements neural network and random forest models to perform regression and feature selection tasks on microbiome data. In this study, we applied the tool to analyze soil microbiome (16S rRNA gene profiles) and dissolved organic carbon (DOC) data from a 44-day plant litter decomposition experiment. The microbiome data includes 1709 total bacterial operational taxonomic units (OTU) from 300+ microcosms. Regression analysis of predicted and actual DOC for a held-out test set of 51 samples yield Pearsons correlation coefficients of .636 and .676 for neural network and random forest approaches, respectively. Important taxa identified by the machine learning techniques are compared to results from a standard tool (indicator species analysis) widely used by microbial ecologists. Of 1709 bacterial taxa, indicator species analysis identified 285 taxa as significant determinants of DOC concentration. Of the top 285 ranked features determined by machine learning methods, a subset of 86 taxa are common to all feature selection techniques. Using this subset of features, prediction results for random permutations of the data set are at least equally accurate compared to predictions determined using the entire feature set. Our results suggest that integration of multiple methods can aid identification of a robust subset of taxa within complex communities that may drive specific functional outcomes of interest.

bioinformatics↗

bioRxiv2018

Characterisation of deubiquitylating enzymes in the cellular response to high-LET ionising radiation and complex DNA damage

PurposeIonising radiation, particular high linear energy transfer (LET) radiation, can induce complex DNA damage (CDD) where two or more DNA lesions are induced in close proximity which contributes significantly to the cell killing effects. However knowledge of the enzymes and mechanisms involved in co-ordinating the recognition and processing of CDD in cellular DNA are currently lacking.\n\nMethods and MaterialsAn siRNA screen of deubiquitylation enzymes was conducted in HeLa cells irradiated with high-LET -particles or protons, versus low-LET protons and x-rays, and cell survival monitored by clonogenic assays. Candidates whose depletion led to decreased cell survival specifically in response to high-LET radiation were validated in both HeLa and oropharyngeal squamous cell carcinoma (UMSCC74A) cells, and the association with CDD repair was confirmed by using an enzyme modified neutral comet assay.\n\nResultsDepletion of USP6 decreased cell survival specifically following high-LET -particles and protons, but not by low-LET protons or x-rays. USP6 depletion caused cell cycle arrest and a deficiency in CDD repair mediated through instability of poly(ADP-ribose) polymerase-1 (PARP-1). This phenotype was mimicked using the PARP inhibitor olaparib.\n\nConclusionUSP6 controls cell survival in response to high-LET radiation by stabilising PARP-1 protein levels which is essential for CDD repair. We also describe synergy between CDD induced by high-LET protons and PARP inhibition in effective cancer cell killing.

cancer biology↗

bioRxiv2018

Molecular measurable residual disease testing of blood during AML cytotoxic therapy for early prediction of clinical response

AbstractO_ST_ABSPURPOSEC_ST_ABSMeasurable residual disease (MRD) testing after initial chemotherapy treatment can predict relapse and survival in AML. However, it has not been established if repeat molecular or genetic testing during chemotherapy can offer information regarding the chemotherapy sensitivity of the leukemic clone.\n\nPATIENTS AND METHODSBlood from 45 adult AML patients at day 1 and 4 of induction (n = 35) or salvage (n = 10) cytotoxic chemotherapy was collected for both quantitative real-time PCR (qPCR) assessment (WT1) and next generation sequencing (NGS, >500x depth) of 49 gene regions recurrently mutated in MDS/AML.\n\nRESULTSThe median age was 62 (23-78); 42% achieved a complete response. WT1 was overexpressed in most patients tested but was uninformative for very early MRD assessment. A median of 4 non-synonymous variants (range 0-7) were detected by DNA sequencing of blood on day 1 of therapy (median VAF: 29%). Only two patients had no variants detectable. All mutations remained detectable in blood on day 4 of intensive chemotherapy and remarkably the ratio of mutated to wild-type sequence was often maintained. This phenomenon was not limited to variants in DNMT3A, TET2 and ASXL1. The kinetics of NPM1 and TP53 variant burden early during chemotherapy appeared to be exceptions and exhibited consistent trends in this cohort.\n\nCONCLUSIONSMolecular testing of blood on day 4 of chemotherapy is not predictive of clinical response in AML. The observed stability in variant allele frequency suggests that cytotoxic therapy may have a limited therapeutic index for clones circulating in blood containing these mutations. Further validation is required to confirm the utility of monitoring NPM1 and TP53 kinetics in blood during cytotoxic therapy.

cancer biology↗

bioRxiv2018

Interspecies interactions mediated by conductive minerals in the sediments of the ferruginous Lake La Cruz, Spain

Lake La Cruz is considered a biogeochemical analogue to early Earth marine environments because its water column is depleted in sulfate, but rich in methane and iron, similar to conditions envisaged for much of the Precambrian. In this early Earth analogue environment, we show that conductive particles establish a tight metabolic coupling between electroactive microbial clades. We propose that mineral-based syntrophy is of potential relevance for the evolution of Earths earliest complex life forms. We show that the anoxic sediment of Lake La Cruz, which is rich in biogeochemically reactive iron minerals, harbors known electroactive species such as Geobacter and Methanothrix, in addition to other groups which have not been previously associated with an electroactive lifestyle. Slurry incubations on various substrates in the presence of conductive particles showed significant methanogenic activity, whereas incubations with non-conductive glass beads resulted in low methanogenic rates similar to slurries without added particles. In the absence of conductive particles, all tested substrates were metabolized to acetate, which accumulated to [~]10 mM. Similar to a previous study on iron-rich Baltic Sea sediments, we observed that conductive mineral additions to La Cruz slurries enabled acetate oxidation, thus preventing acetate accumulation. Acetate oxidation coupled to high methanogenic activity was only maintained in successive mud-free enrichments when these were amended with conductive minerals. In serial mud-free transfers, conductive particles conserved a consortium of Youngiibacter-Methanothrix, whereas Youngiibacter spp. died off in the absence of conductive particles. In contrast, mud-free enrichments without conductive particles ceased any metabolic activity during the second transfers. Syntrophic consortia from this early Earth analogue environment only survived in the presence of conductive particles. Mineral-mediated syntrophy could be one of the earliest evolutionary interspecies associations. Conductive minerals might have fueled metabolic exchange between cells via intercellular electron transfer prompting tight cell-to-cell associations and possibly eukaryogenesis.

microbiology↗

bioRxiv2018

HIF1-alpha expressing cells induce a hypoxic-like response in neighbouring cancer cells

Hypoxia stimulates metastasis in cancer and is linked to poor patient prognosis. In tumours, oxygen levels vary and hypoxic regions exist within a generally well-oxygenated tumour. However, whilst the heterogeneous environment is known to contribute to metastatic progression, little is known about the mechanism by which heterogeneic hypoxia contributes to cancer progression. This is largely because existing experimental models do not recapitulate the heterogeneous nature of hypoxia. The primary effector of the hypoxic response is the transcription factor Hypoxia inducible factor 1-alpha (HIF1-alpha). HIF1-alpha is stabilised in response to low oxygen levels in the cellular environment and its expression is seen in hypoxic regions throughout the tumour.\n\nWe have developed a model system in which HIF1-alpha can be induced within a sub-population of cancer cells, thus enabling us to mimic the effects of heterogeneic HIF1-alpha expression.\n\nWe show that induction of HIF1-alpha not only recapitulates elements of the hypoxic response in the induced cells but also results in significant changes in proliferation, gene expression and mammosphere formation within the HIF1-alpha negative population.\n\nThese findings suggest that the HIF1-alpha expressing cells found within hypoxic regions are likely to contribute to the subsequent progression of a tumour by modifying the behaviour of cells in the non-hypoxic regions of the local micro-environment.

cancer biology↗

bioRxiv2018

Super-multiplexed fluorescence microscopy via photostability contrast

Many areas of biological research rely heavily on fluorescence microscopy to observe and quantify the inner workings of the cell. Traditionally, multiple types of cellular structures or biomolecules are visualized simultaneously with spectrally distinct fluorescent labels. A high degree of multiplexing is desirable as it affords the experiment greater information content, speeding up research timelines. Multiplexing can be increased by imaging a larger number of spectral channels, however, the wide emission spectra of most fluorophores limits multiplexing to four or five labels in standard fluorescence microscopes. Further multiplexing requires another dimension of contrast. Here, we show that photostability differences can be used to distinguish between fluorescent labels. By combining photobleaching characteristics with a novel unmixing algorithm, we resolve up to three fluorescent labels in a single spectral channel and unmix fluorescent labels with nearly identical emission spectra. We apply our technique to organic dyes, autofluorescent biomolecules and fluorescent proteins, and show that the latter are particularly well suited to our method as their bleaching is often reversible. Our approach has the potential to triple the multiplexing capabilities of any digital widefield or confocal fluorescence microscope with no additional hardware, making it readily accessible to a wide range of researchers.

bioengineering↗