Biology subjects

Rogers, S.

Publications and source records attributed to Rogers, S..

At least 37 records · Page 2Linked to original sources

bioRxiv2022

Tyrosine kinase inhibitor independent gene expression signature in CML offers new targets for LSPC eradication therapy

2Tyrosine kinase inhibitors (TKI) have revolutionised the treatment of CML. However, TKI do not eliminate the leukaemia stem cells (LSC), which can reinitiate the disease. Thus, finding new therapeutic targets in CML LSC is key to finding a curative treatment. Using microarray datasets, we defined a list of 227 genes which were differentially expressed in CML LSC compared to healthy controls but were not affected by TKI in vitro. Two of them, CD33 and PPIF, are targeted by gemtuzumab-ozogamicin and cyclosporin A, respectively. We treated CML and control CD34+ cells with either drug with or without imatinib to investigate the therapeutic potential of the TKI-independent gene expression programme. Cyclosporine A in combination with imatinib reduced the number of CML CFC compared with non-CML controls, but only at supra-therapeutic concentrations. Gemtuzumab-ozogamicin showed a EC50 of 146ng/mL, below the plasma peak concentration of 630ng/mL observed in AML patients and below the EC50 of 3247ng/mL observed in non-CML cells. Interestingly, gemtuzumab-ozogamicin seems to promote cell cycle progression in CML CD34+ cells and demonstrated activation of the RUNX1 pathway in a RNAseq experiment. This suggests that targeting the TKI-independent genes in CML LSC could be exploited for the development of new therapies in CML. 1 Simple summaryChronic myeloid leukaemia (CML) is initiated by a group of cancer cells called leukaemia stem cells (LSC). These LSC can survive current tyrosine kinase inhibitor (TKI) treatments and upon treatment withdrawal, are able to re-initiate the disease. Thus, eradicating the LSC would likely cure CML. In this study, we have identified a number of genes which expression is different between LSC and their healthy counterparts (haematopoietic stem cells) but are not affected by TKI treatment. We hypothesised that these genes may be potential therapeutic targets against LSC and used two different drugs, gemtuzumab-ozogamicin and cyclosporine A, to treat CML in vitro. We found that both drugs have a stronger effect on CML cells than in healthy cells. Therefore, we propose that the list of genes we identified could represent a novel source of therapeutic targets against CML.

cancer biology↗

bioRxiv2022

Replication stress tolerance and management differs between naive and primed pluripotent cells

Replication stress is an endemic threat to genome stability. For reasons unknown, replication stress response factors become essential during peri-implantation development. This coincides with a stem cell potency switch from the naive to the primed state. Using genetically matched, chimera-derived mouse naive embryonic (mESC) and primed epiblast stem cells (mEpiSC) we found that replication stress management differs between potency states. Primed mEpiSCs rely on Atr activity to prevent replication catastrophe, minimize genomic damage, avoid apoptosis, and re-enter the cell cycle. Conversely, under replications stress, mESCs readily activate Atm regardless of Atr activity, undergo replication catastrophe, and induce apoptosis. Primed pluripotent cells therefore engage Atr to counteract replication difficulties and maintain viability, whereas cells in the naive state are more readily cleared under the same conditions. We anticipate these divergent strategies enable pluripotent cells of different potency states to meet associated proliferative or developmental demands during early development.

developmental biology↗

bioRxiv2022

Triglyceride lipolysis driven by glucose restriction triggers liquid-crystalline phase transitions and proteome remodeling of lipid droplets

Lipid droplets (LDs) are reservoirs for triglycerides (TGs) and sterol-esters (SEs), but how these lipids are organized within LDs and influence its proteome remains unclear. Using in situ cryoelectron tomography, we show that glucose restriction triggers lipid phase transitions within LDs generating liquid-crystalline lattices inside them. Mechanistically this requires TG lipolysis, which decreases the LD TG:SE ratio, promoting SE transition to a liquid-crystalline phase. Molecular dynamics simulations reveal TG depletion promotes spontaneous TG and SE de-mixing in LDs, additionally altering the lipid packing of the phospholipid monolayer surface. Fluorescence imaging and proteomics further reveal that liquid-crystalline phases are associated with selective remodeling of the LD proteome. Some canonical LD proteins including Erg6 re-localize to the ER network, whereas others remain LD-associated. Model peptide LiveDrop also redistributes from LDs to the ER, suggesting liquid-crystalline-phases influence ER-LD inter organelle transport. Our data suggests glucose restriction drives TG mobilization, which alters the phase properties of LD lipids and selectively remodels the LD proteome.

cell biology↗

bioRxiv2022

Cytoneme-mediated transport of active Wnt5b/Ror2 complexes in zebrafish gastrulation

Chemical signaling is the primary means by which cells communicate in the embryo. The underlying principle refers to a group of ligand-producing cells and a group of cells that respond to this signal because they express the appropriate receptors. In the zebrafish embryo, Wnt5b binds to the receptor Ror2 to trigger the Wnt/Planar Cell Polarity (Wnt/PCP) signaling pathway to regulate tissue polarity and cell migration. However, it is still unclear how this lipophilic ligand is transported from the source cells through the aqueous extracellular space to the target tissue. Here we show that Wnt5b, together with Ror2, is loaded on long protrusions. The active Wnt5b/Ror2 complexes are handed over from these cytonemes to the receiving cell to trigger Wnt/PCP signaling, regardless of whether the cell expresses functional receptors. On the tissue level, we show that cytoneme-dependent spreading of active Wnt5b/Ror2 affects convergence and extension in the zebrafish gastrula.

developmental biology↗

bioRxiv2022

Cancer-associated fibroblast-derived ROR2 induces WNT/PCP activation and polarized migration in receiving gastric cancer cells.

Bone marrow-derived mesenchymal stem cells and cancer-associated fibroblasts in the tumor-stromal environment have been linked to cancer progression in many studies. These fibroblasts provide signaling factors to the tumor cells that promote proliferation, survival, invasion, and metastasis. One signaling pathway influencing tumor cell behavior is the WNT/Planar Cell Polarity (PCP) signaling in gastric cancer. Here, we show that the gastric tumor cell line, AGS, can respond to the PCP ligand WNT5A, however, express a very low level of the bona-fide WNT/PCP receptor, ROR2. At the same time, we find that CAF display long filopodia and had significantly higher levels of ROR2 than normal gastric fibroblasts. By high-resolution imaging, we observe a direct, cytoneme-mediated transfer of a complex containing ROR2 and WNT5A from CAF to the gastric cancer cells. The amount of ROR2 transferred correlated with JNK signaling in receiving cells, showing a direct requirement for receptor transfer. Co-culture of AGS with CAF expressing a dominant-negative form of ROR2 exhibited reduced actin polarization and migration compared to wild-type CAF. Furthermore, induction of migration via paracrine ROR2 transfer was observed in a zebrafish in vivo model. These unexpected findings demonstrate a fresh role in the direct transfer of a Wnt receptor from a signal-producing cell to a receiving cell and explain the mechanism by which gastric cancer cells expressing low levels of ROR2 can respond to a WNT5A-high tumor microenvironment.

cancer biology↗

bioRxiv2022

Telomerase-independent maintenance of telomere length in a vertebrate

Telomere shortening places a key limitation on cell proliferation1. In all vertebrates explored to date, this limitation is overcome by telomerase-dependent telomere extension. Failure to maintain telomere length results in premature ageing and functional impairments in highly replicative cell populations as telomeres erode2. Alternative lengthening of telomeres (ALT), a telomerase-independent mechanism, compensates for telomere loss in a subset of human cancer cell lines 2. Here, we demonstrate that the highly regenerative newt Pleurodeles waltl lacks telomerase activity, contains telomeres distinct from all known vertebrates in both sequence and structure, and deploys ALT for physiological telomere maintenance. This constitutes the first report of telomerase-independent resolution of the end-replication problem at the whole-organism level within Chordata. One-Sentence SummaryP. waltl telomere biology is distinct amongst vertebrates and uses ALT at the whole-organism level.

molecular biology↗

bioRxiv2022

The scaffolding protein Flot2 regulates cytoneme-based transport of Wnt3 in gastric cancer

The Wnt/{beta}-catenin signalling pathway regulates multiple cellular processes during development and many diseases, including cell proliferation, migration, and differentiation. Despite their hydrophobic nature, Wnt proteins exert their function over long distances to induce paracrine signalling. Recent studies have identified several factors involved in Wnt secretion, however, our understanding of how Wnt ligands are transported between cells to interact with their cognate receptors is still debated. Here, we demonstrate that gastric cancer cells utilise cytonemes to transport Wnt3 intercellularly to promote proliferation. Furthermore, we identify the membrane-bound scaffolding protein Flotillin-2 (Flot2), frequently overexpressed in gastric cancer, as a regulator of these cytonemes. Together with the Wnt co-receptor and cytoneme initiator Ror2, Flot2 determines the number and length of Wnt3 cytonemes in gastric cancer. Finally, we show that Flot2 is necessary for Wnt8a cytonemes during zebrafish embryogenesis, suggesting a conserved mechanism for Flot2-mediated Wnt transport on cytonemes in development and disease.

cancer biology↗

bioRxiv2021

Alignment of multiple metabolomics LC-MS datasets from disparate diseases to reveal fever-associated metabolites

Acute febrile illnesses are still a major cause of mortality and morbidity globally, particularly in low to middle income countries. The aim of this study was to determine any possible metabolic commonalities of patients infected with disparate pathogens that cause fever. Three liquid chromatography-mass spectrometry (LC-MS) datasets investigating the metabolic effects of malaria, leishmaniasis and Zika virus infection were used. The retention time (RT) drift between the datasets was determined using landmarks obtained from the standard reference mixtures generally used in the quality control of the LC-MS experiments. We used fitted Gaussian Process models (GPs) to perform a high level correction of the RT drift between the experiments, followed by standard peakset alignment between the samples with corrected RTs of the three LC-MS datasets. Statistical analysis, annotation and pathway analysis of the integrated peaksets were subsequently performed. Metabolic dysregulation patterns common across the datasets were identified, with kynurenine pathway being the most affected pathway between all three fever-associated datasets.

bioinformatics↗

bioRxiv2021

Liquid-crystalline lipid phase transitions in lipid droplets selectively remodel the LD proteome

Lipid droplets (LDs) are reservoirs for triglycerides (TGs) and sterol-esters (SEs). How lipids are organized within LDs and influence the LD proteome remains unclear. Using in situ cryo-electron tomography, we show that glucose restriction triggers lipid phase transitions within LDs generating liquid-crystalline lattices inside them. Mechanistically, this requires TG lipolysis, which alters LD neutral lipid composition and promotes SE transition to a liquid-crystalline phase. Fluorescence imaging and proteomics further reveal that LD liquid-crystalline lattices selectively remodel the LD proteome. Some canonical LD proteins including Erg6 re-localize to the ER network, whereas others remain on LDs. Model peptide LiveDrop also redistributes from LDs to the ER, suggesting liquid-crystalline-phases influence ER-LD inter-organelle transport. Proteomics also indicates glucose restriction elevates peroxisome lipid oxidation, suggesting TG mobilization provides fatty acids for cellular energetics. This suggests glucose restriction drives TG mobilization, which alters the phase properties of LD lipids and selectively remodels the LD proteome.

cell biology↗

bioRxiv2021

Using topic modeling to detect cellular crosstalk in scRNA-seq

Cell-cell interactions are vital for numerous biological processes including development, differentiation, and response to inflammation. Currently most methods for studying interactions on scRNA-seq level are based on curated databases of ligands and receptors. While those methods are useful, they are limited to our current biological knowledge. Recent advances in single cell protocols have allowed for physically interacting cells to be captured and as such we have the potential to study interactions in a complimentary way without relying on prior knowledge. We introduce a new method for detecting genes that change as a result of interaction based on Latent Dirichlet Allocation (LDA). We apply our method to synthetic datasets to demonstrate its ability to detect genes that change in an interacting population compared to a reference population. Next, we apply our approach to two datasets of physically interacting cells and identify genes that change as a result of interaction, examples include adhesion and co-stimulatory molecules which confirm physical interaction between cells. For each dataset we produce a ranking of genes that are changing in subpopulations of the interacting cells. In addition to genes, discussed in the original publications we highlight further candidates for interaction in the top 100 and 300 ranked genes. Lastly, we apply our method to a dataset generated by a standard droplet based protocol, not designed to capture interacting cells and discuss its suitability for analysing interactions. We present a method that streamlines the detection of interactions and does not require prior clustering and generation of synthetic reference profiles to detect changes in expression. Author summaryWhile scRNA-seq research is a dynamic area, progress is lacking when it comes to developing methods that allow analysis of interaction that is independent of curated resources of known interacting pairs. Recent advances of sequencing protocols have allowed for interacting cells to be captured. We propose a novel method based on LDA that captures changes in gene expression as a result of interaction. Our method does not require prior information in the form of clustering or generation of synthetic reference profiles. We demonstrate the suitability of our approach by applying it to synthetic and real datasets and manage to capture biologically interesting candidates of interaction.

bioinformatics↗

bioRxiv2020

Rapid Development of Improved Data-dependent Acquisition Strategies.

Tandem mass spectrometry (LC-MS/MS) is widely used to identify unknown ions in untargeted metabolomics. Data Dependent Acquisition (DDA) chooses which ions to fragment based upon intensity observed in MS1 survey scans and typically only fragment a small subset of the ions present. Despite this inefficiency, relatively little work has addressed the development of new DDA methods, partly due to the high overhead associated with running the many extracts necessary to optimise approaches in busy MS facilities. In this work, we firstly provide theoretical results that show how much improvement is possible over current DDA strategies. We then describe an in silico framework for fast and cost efficient development of new DDA acquisition strategies using a previously developed Virtual Metabolomics Mass Spectrometer (ViMMS). Additional functionality is added to ViMMS to allow methods to be used both in simulation and on real samples via an instrument application programming interface (API). We demonstrate this framework through the development and optimisation of two new DDA methods which introduce new advanced ion prioritisation strategies. Upon application of the here developed methods to two complex metabolite mixtures, our results show that they are able to fragment more unique ions than standard DDA acquisition strategies.

bioinformatics↗

bioRxiv2020

Glucose restriction drives spatial re-organization of mevalonate metabolism and liquid-crystalline lipid droplet biogenesis

Eukaryotes compartmentalize metabolic pathways into sub-cellular domains, but the role of inter-organelle contacts in organizing metabolic reactions remains poorly understood. Here, we show that in response to acute glucose restriction (AGR) yeast undergo metabolic remodeling of their mevalonate pathway that is spatially coordinated at nucleus-vacuole junctions (NVJs). The NVJ serves as a metabolic platform by selectively retaining HMG-CoA Reductases (HMGCRs), driving mevalonate pathway flux in an Upc2-dependent manner. AGR-induced HMGCR compartmentalization enhances mevalonate metabolism and sterol-ester biosynthesis that generates lipid droplets (LDs) with liquid-crystalline sub-architecture. Loss of NVJ-dependent HMGCR partitioning affects yeast growth, but can be bypassed by artificially multimerizing HMGCRs, indicating NVJ compartmentalization enhances mevalonate pathway flux by promoting HMGCR inter-enzyme associations. We propose a non-canonical mechanism regulating mevalonate metabolism via the spatial compartmentalization of rate-limiting HMGCR enzymes, and reveal that AGR creates LDs with remarkable phase transition properties. One Sentence SummarySpatial compartmentalization of HMG-CoA Reductases at ER-lysosome contacts modulates mevalonate pathway flux

cell biology↗

bioRxiv2020

Probabilistic Framework for Integration of Mass Spectrum and Retention Time Information in Small Molecule Identification

MotivationIdentification of small molecules in a biological sample remains a major bottleneck in molecular biology, despite a decade of rapid development of computational approaches for predicting molecular structures using mass spectrometry (MS) data. Recently, there has been increasing interest in utilizing other information sources, such as liquid chromatography (LC) retention time (RT), to improve the MS based identifications. ResultsWe put forward a probabilistic modelling framework to integrate MS and RT data of multiple features in an LC-MS experiment. We model the MS measurements and all pairwise retention order information as a Markov random field and use efficient approximate inference for scoring and ranking potential molecular structures. Our experiments show improved identification accuracy by combining tandem mass spectrometry data (MS2) and retention orders using our approach, thereby outperforming state-of-the-art methods. Furthermore, we demonstrate the benefit of our model when only a subset of LC-MS features have MS2 measurements available besides MS1. Availability and implementationSoftware and data is freely available at https://github.com/aalto-ics-kepaco/msms_rt_score_integration. Contacteric.bach@aalto.fi

systems biology↗

bioRxiv2020

Spec2Vec: Improved mass spectral similarity scoring through learning of structural relationships

Spectral similarity is used as a proxy for structural similarity in many tandem mass spectrometry (MS/MS) based metabolomics analyses such as library matching and molecular networking. Although weaknesses in the relationship between spectral similarity scores and the true structural similarities have been described, little development of alternative scores has been undertaken. Here, we introduce Spec2Vec, a novel spectral similarity score inspired by a natural language processing algorithm -- Word2Vec. Spec2Vec learns fragmental relationships within a large set of spectral data to derive abstract spectral embeddings that can be used to assess spectral similarities. Using data derived from GNPS MS/MS libraries including spectra for nearly 13,000 unique molecules, we show how Spec2Vec scores correlate better with structural similarity than cosine-based scores. We demonstrate the advantages of Spec2Vec in library matching and molecular networking. Spec2Vec is computationally more scalable allowing structural analogue searches in large databases within seconds.

bioinformatics↗

bioRxiv2020

matchms - processing and similarity evaluation of mass spectrometry data

Mass spectrometry data is at the heart of numerable applications in the biomedical and life sciences. With growing use of high throughput techniques researchers need to analyse larger and more complex datasets. In particular through joint effort in the research community, fragmentation mass spectrometry datasets are growing in size and number. Platforms such as MassBank (Horai et al. 2010), GNPS (Wang et al. 2016) or MetaboLights (Haug et al. 2020) serve as an open-access hub for sharing of raw, processed, or annotated fragmentation mass spectrometry data (MS/MS). Without suitable tools, however, exploitation of such datasets remains overly challenging. In particular, large collected datasets contain data aquired using different instruments and measurement conditions, and can further contain a significant fraction of inconsistent, wrongly labeled, or incorrect metadata (annotations).

bioinformatics↗

bioRxiv2020

Ranking microbial metabolomic and genomic links using correlation-based and feature-based scoring functions

Specialised metabolites from microbial sources are well-known for their wide range of biomedical applications, particularly as antibiotics. When mining paired genomic and metabolomic data sets for novel specialised metabolites, establishing links between Biosynthetic Gene Clusters (BGCs) and metabolites represents a promising way of finding such novel chemistry. However, due to the lack of detailed biosynthetic knowledge for the majority of predicted BGCs, and the large number of possible combinations, this is not a simple task. This problem is becoming ever more pressing with the increased availability of paired omics data sets. Current tools are not effective at identifying valid links automatically, and manual verification is a considerable bottleneck in natural product research. We demonstrate that using multiple link-scoring functions together makes it easier to prioritise true links relative to others. Based on standardising a commonly used score, we introduce a new, more effective score, and introduce a novel score using an Input-Output Kernel Regression approach. Finally, we present NPLinker, a software framework to link genomic and metabolomic data. Results are verified using publicly available data sets that include validated links. Author summaryIn this article, we introduce NPLinker, a software framework to link genomic and metabolomic data, to link microbial secondary metabolites to their producing genomic regions. Two of the major approaches for such linking are analysis of the correlation between sets of strains, and analysis of predicted features of the molecules. While these methods are usually used separately, we demonstrate that they are in fact complementary, and show a way to combine them to improve their performance. We begin by demonstrating a weakness in the most common method of strain correlation analysis, and suggest an improvement. We then introduce a new feature-based analysis method which, unlike most such methods, does not directly depend on the natural prodcut compound class. Finally, we demonstrate that the two are complementary and proceed to combine them into a single scoring function for genomic and metabolomic links, which shows improved performance over either of the individual approaches. Verification is done using curated databases of genomic and metabolomic data, as well as public data sets of microbial data including verified links.

systems biology↗

bioRxiv2020

Decomposing metabolite set activity levels with PALS

MotivationRelated metabolites can be grouped into metabolite sets in many ways. Examples of these include the grouping of metabolites through their participation in a series of chemical reactions (forming metabolic pathways); or based on fragmentation spectral similarities and shared chemical substructures. Understanding how such metabolite sets change across samples can be incredibly useful in the interpretation and understanding of complex metabolomics data. However many of the available tools suitable for the enrichment analysis of metabolite sets are based on simple methods that badly handle the missing features inherent in untargeted metabolomics measurements and can be difficult to integrate into existing applications. ResultsWe present PALS (Pathway Activity Level Scoring), a Python library, command-line tool and Web application that performs the ranking of significantly-changing metabolite sets over different experimental conditions. As example applications, PALS is used to analyse metabolites grouped as pathways and by common MS-MS fragmentation structures. A comparison of PALS with two other commonly used methods (ORA and GSEA) is also given, and reveals that PALS is more robust to missing peaks and noisy data than the alternatives. We report results from using PALS to analyse pathways from a study of Human African Trypanosomiasis. Finally, we also report how PALS used tandem MS fragmentation structures to reveal enriched metabolite sets between clades in Rhamnaceae plant data, and on American Gut Project data. AvailabilityPALS is freely available from our project Web site at https://pals.glasgowcompbio.org/. It can be imported as a Python library, run as a stand-alone tool or used as a web application.

bioinformatics↗

bioRxiv2020

Interactive MS/MS Visualization with the Metabolomics Spectrum Resolver Web Service

The growth of online mass spectrometry metabolomics resources, including data repositories, spectral library databases, and online analysis platforms has created an environment of online/web accessibility. Here, we introduce the Metabolomics Spectrum Resolver (https://metabolomics-usi.ucsd.edu/), a tool that builds upon these exciting developments to allow for consistent data export (in human and machine-readable forms) and publication-ready visualisations of tandem mass spectrometry spectra. This tool supports the Human Proteome Organization - Proteomics Standards Initiatives Universal Spectrum Identifier (USI) specification, which has been extended to deal with the metabolomics use cases. To date, this resource already supports data formats from GNPS, MassBank, MS2LDA, MassIVE, MetaboLights, and Metabolomics Workbench and is integrated into several of these resources.

bioinformatics↗