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McDonald, D.

Publications and source records attributed to McDonald, D..

At least 37 records · Page 2Linked to original sources

A stagewise response to mitochondrial dysfunction in mitochondrial DNA maintenance disorders

Mitochondrial DNA deletions clonally expand in skeletal muscle of patients with mtDNA maintenance disorders, impairing mitochondrial oxidative phosphorylation dysfunction. Previously we have shown that these mtDNA deletions originally arise and accumulate in the perinuclear mitochondria causing localised mitochondrial dysfunction before spreading through the muscle fibre. We believe that mito-nuclear signalling is a key contributor in this process. To further understand the role of mito-nuclear signalling, we use imaging mass cytometry to characterise the levels of mitochondrial respiratory complexes I-IV and ATP synthase alongside a mitochondrial mass marker, in a cohort of patients with mtDNA maintenance dosirders. We then expanded this panel to include protein markers of key signalling pathways to investigate the cellular response in fibres with different combinations of oxidative phosphorylation dysfunction and in ragged red fibres. We find CI and CIV deficiency to be most common, with a smaller proportion of cells that are also CIII and/or CV deficient. Interestingly, we also note that in cells deficient for one or more complexes, any complexes which are not deficient are commonly upregulated beyond the increase of mitochondrial mass typically observed in ragged red fibres. We further find that oxidative phosphorylation deficient fibres exhibit an increase in abundance of proteins involved in the proteostasis e.g. HSP60 and LONP1, and mitochondrial protein synthesis e.g. PHB1. Our analysis suggests that the cellular response to mitochondrial dysfunction changes depending on the combination of deficient oxidative phosphorylation complexes in each cell.

cell biology↗

Rescue of proteotoxic stress and neurodegeneration by the Zn2+ transporter ZIP7

Proteotoxic stress drives numerous degenerative diseases. In response to misfolded proteins, cells adapt by activating the unfolded protein response (UPR), including endoplasmic reticulum-associated protein degradation (ERAD). However persistent stress triggers apoptosis. Enhancing ERAD is a promising therapeutic approach for protein misfolding diseases. From plants to humans, loss of the Zn2+ transporter ZIP7 causes ER stress, however the mechanism is unknown. Here we show that ZIP7 enhances ERAD and that cytosolic Zn2+ is limiting for deubiquitination of client proteins by the Rpn11 Zn2+ metalloproteinase as they enter the proteasome in Drosophila and human cells. ZIP7 overexpression rescues defective vision caused by misfolded rhodopsin in Drosophila. Thus ZIP7 overexpression may prevent diseases caused by proteotoxic stress, and existing ZIP inhibitors may be effective against proteasome-dependent cancers. One-Sentence SummaryZn2+ transport from the ER to the cytosol promotes deubiquitination and proteasomal degradation of misfolded proteins and prevents blindness in a fly neurodegeneration model.

cell biology↗

Scaling deep phylogenetic embedding to ultra-large reference trees: a tree-aware ensemble approach

Phylogenetic placement of a query sequence on a backbone tree is increasingly used across biomedical sciences to identify the content of a sample from its DNA content. The accuracy of such analyses depends on the density of the backbone tree, making it crucial that placement methods scale to very large trees. Moreover, a new paradigm has been recently proposed to place sequences on the species tree using single-gene data. The goal is to better characterize the samples and to enable combined analyses of marker-gene (e.g., 16S rRNA gene amplicon) and genome-wide data. The recent method DEPP enables performing such analyses using metric learning. However, metric learning is hampered by a need to compute and save a quadratically growing matrix of pairwise distances during training. Thus, DEPP (or any distance-based method) does not scale to more than roughly ten thousand species, a problem that we faced when trying to use our recently released Greengenes2 (GG2) reference tree containing 331,270 species. Scalability problems can be addressed in phylogenetics using divide- and-conquer. However, applying divide- and-conquer to data-hungry machine learning methods needs nuance. This paper explores divide- and-conquer for training ensembles of DEPP models, culminating in a method called C-DEPP that uses carefully crafted techniques to enable quasi-linear scaling while maintaining accuracy. C-DEPP enables placing twenty million 16S fragments on the GG2 reference tree in 41 hours of computation.

bioinformatics↗

FLONE: fully Lorentz network embedding for inferring novel drug targets

MotivationTo predict novel drug targets, graph-based machine learning methods have been widely used to capture the relationships between drug, target, and disease entities in heterogeneous biological networks. However, most of the existing methods cannot explicitly consider disease types when predicting targets. More importantly, drug-disease-target (DDT) networks could exhibit multi-relational hierarchical sub-structures with the information of biological interactive functions, but these methods, especially those based on Euclidean space embedding cannot fully utilize such topology information, which might lead to sub-optimal prediction results. We hypothesized that, by properly importing hyperbolic space embedding specifically for modeling hierarchical DDT networks, graph-based machine learning algorithms could better capture the relationships between aforementioned entities, which ultimately improves the target prediction performance. ResultsTo test our hypothesis, we formulated the drug target prediction problem as a knowledge graph triple completion task explicitly considering disease types. To solve this problem, we proposed FLONE, a novel hyperbolic Lorentz space embedding-based method to capture the hierarchical structural information in the DDT network when inferring drug targets. We evaluated it on two different DDT networks, our experimental results showed that by introducing Lorentz space, one of the isomorphic models of hyperbolic space, FLONE generates more accurate candidate target predictions given the drug and disease than the Euclidean translation-based counterparts, which supports our hypothesis. Beyond these experiments, we also devised the hyperbolic encoders to fuse drug and target similarity information into FLONE, to make it capable to handle triples corresponding to previously unseen drugs and targets for more challenging prediction scenarios. Availability and implementationSource code and dataset information are at: https://github.com/arantir123/DDT_triple_prediction. Contacts.he@cs.bham.ac.uk

bioinformatics↗

OPTIMAL: An OPTimsed Imaging Mass cytometry AnaLysis framework for Segmentation and Data Exploration

Analysis of Imaging Mass Cytometry (IMC) data and other low-resolution multiplexed tissue imaging technologies is often confounded by poor single cell segmentation and sub-optimal approaches for data visualisation and exploration. This can lead to inaccurate identification of cell phenotypes, states or spatial relationships compared to reference data from single cell suspension technologies. To this end we have developed the "OPTIMAL" framework to benchmark any approaches for cell segmentation, parameter transformation, batch effect correction, data visualisation/clustering and spatial neighbourhood analysis. Using a panel of 27 metal-tagged antibodies recognising well characterised phenotypic and functional markers to stain the same FFPE human tonsil sample Tissue Microarray (TMA) over 12 temporally distinct batches we tested several cell segmentation models, a range of different arcsinh cofactor parameter transformation values, five different dimensionality reduction algorithms and two clustering methods. Finally we assessed the optimal approach for performing neighbourhood analysis. We found that single cell segmentation was improved by the use of an Ilastik-derived probability map but that issues with poor segmentation were only really evident after clustering and cell type/state identification and not always evident when using "classical" bi-variate data display techniques. The optimal arcsinh cofactor for parameter transformation was 1 as it maximised the statistical separation between negative and positive signal distributions and a simple Z-score normalisation step after arcsinh transformation eliminated batch effects. Of the five different dimensionality reduction approaches tested, PacMap gave the best data structure with FLOWSOM clustering out-performing Phenograph in terms of cell type identification. We also found that neighbourhood analysis was influenced by the method used for finding neighbouring cells with a "disc" pixel expansion outperforming a "bounding box" approach combined with the need for filtering objects based on size and image-edge location. Importantly OPTIMAL can be used to assess and integrate with any existing approach to IMC data analysis and, as it creates .FCS files from the segmentation output, allows for single cell exploration to be conducted using a wide variety of accessible software and algorithms familiar to conventional flow cytometrists.

bioinformatics↗

Associative white matter tracts selectively predict sensorimotor learning

Human learning is a complex phenomenon that varies greatly among individuals and is related to the microstructure of major white matter tracts in several learning domains, yet the impact of the existing myelination of white matter tracts on future learning outcomes remains unclear. We employed a machine-learning model selection framework to evaluate whether existing microstructure might predict individual differences in the potential for learning a sensorimotor task, and further, if the mapping between the microstructure of major white matter tracts and learning was selective for learning outcomes. We used diffusion tractography to measure the mean fractional anisotropy (FA) of white matter tracts in 60 adult participants who then underwent training and subsequent testing to evaluate learning. During training, participants practiced drawing a set of 40 novel symbols repeatedly using a digital writing tablet. We measured drawing learning as the slope of draw duration over the practice session and visual recognition learning as the performance accuracy in an old/new 2-AFC recognition task. Results demonstrated that the microstructure of major white matter tracts selectively predicted learning outcomes, with left hemisphere pArc and SLF 3 tracts predicting drawing learning and the left hemisphere MDLFspl predicting visual recognition learning. These results were replicated in a repeat, held-out data set and supported with complementary analyses. Overall, results suggest that individual differences in the microstructure of human white matter tracts may be selectively related to future learning outcomes and open avenues of inquiry concerning the impact of existing tract myelination in the potential for learning. Significance statementA selective mapping between tract microstructure and future learning has been demonstrated in the murine model and, to our knowledge, has not yet been demonstrated in humans. We employed a data-driven approach that identified only two tracts, the two most posterior segments of the arcuate fasciculus in the left hemisphere, to predict learning a sensorimotor task (drawing symbols) and this prediction model did not transfer to other learning outcomes (visual symbol recognition). Results suggest that individual differences in learning may be selectively related to the tissue properties of major white matter tracts in the human brain.

neuroscience↗

Greengenes2 enables a shared data universe for microbiome studies

16S rRNA and shotgun metagenomics studies typically yield different results, usually attributed to biases in PCR amplification of 16S rRNA genes. Here, we introduce Greengenes2 and show that differences in reference phylogeny are more important. By inserting sequences into a whole-genome phylogeny, we show that 16S rRNA and shotgun metagenomic data generated from the same samples agree in principal coordinates space, taxonomy, and in phenotype effect size when analyzed with the same tree.

bioinformatics↗

GBMdeconvoluteR accurately infers proportions of neoplastic and immune cell populations from bulk glioblastoma transcriptomics data

The biological and clinical impact of neoplastic and immune cell type ratios in the glioblastoma (GBM) tumour microenvironment is being realised. Characterising and quantifying cell types within GBMs at scale will facilitate a better understanding of the association between the cellular landscape and tumour phenotypes or clinical correlates. This study aimed to develop a tool that can deconvolute immune and neoplastic cells within the GBM tumour microenvironment from bulk RNA sequencing data. We developed an IDH wild-type (IDHwt) GBM specific single immune cell reference dataset, from four independent studies, consisting of B cells, T cells, NK cells, microglia, tumour associated macrophages, monocytes, mast and DC cells. We used this alongside an existing neoplastic single cell-type dataset consisting of astrocyte-like, oligodendrocyte- and neuronal-progenitor like and mesenchymal GBM cancer cells to create both marker and gene signature matrix-based deconvolution tools. We then applied single-cell resolution imaging mass cytometry (IMC) to ten IDHwt GBM samples, five paired primary and recurrent tumours, in parallel with these tools to determine which performed best. Marker based gene expression deconvolution using GBM tissue specific markers, which we have packaged as GBMdeconvoluteR, gave the most accurate results. The correlation between immune cell quantification by IMC and by GBMdeconvoluteR for primary IDHwt GBM samples was 0.52 (Pearsons P=7.8x10-3) and between neoplastic cell quantification by IMC and by GBMdeconvoluteR was 0.75 (Pearsons P=1.2x10-3). We applied GBMdeconvoluteR to bulk GBM RNAseq data from The Cancer Genome Atlas (TCGA) and were able to recapitulate recent findings from multi-omics single cell studies with regards associations between mesenchymal GBM cancer cells and both lymphoid and myeloid cells. Furthermore, we were able to expand upon this to show that these associations are stronger in patients with worse prognosis. GBMdeconvoluteR is accessible online at https://gbmdeconvoluter.leeds.ac.uk. Key pointsGBMdeconvoluteR is a glioblastoma-specific cellular deconvolution tool. When applied to bulk GBM RNAseq data, it accurately quantifies the neoplastic and immune cells in that tumour. It is available online at https://gbmdeconvoluter.leeds.ac.uk

cancer biology↗

A condensate forming tether for lariat debranching enzyme is defective in non-photosensitive trichiothiodystrophy

The pre-mRNA life cycle requires intron processing; yet, how intron processing defects influence splicing and gene expression is unclear. Here, we find TTDN1, which is frequently mutated in non-photosensitive trichothiodystrophy (NP-TTD), functionally links intron lariat processing to the spliceosome. The conserved TTDN1 C-terminal region directly binds lariat debranching enzyme DBR1, while its N-terminal intrinsically disordered region (IDR) binds the intron binding complex (IBC). The IDR forms condensates in vitro and is needed for IBC interaction. TTDN1 loss causes significant intron lariat accumulation, as well as splicing and gene expression defects, mirroring phenotypes observed in NP-TTD patient cells. Ttdn1{Delta}/{Delta} mice recapitulate intron processing defects and neurodevelopmental phenotypes seen in NP-TTD. A DBR1-IDR fusion recruits DBR1 to the IBC and circumvents the requirement for TTDN1, indicating this tethering role as its major molecular function. Collectively, our findings unveil key functional connections between lariat processing, splicing outcomes, and NP-TTD molecular pathology.

biochemistry↗

Programmed cell death-1 receptor mediated regulation of Tbet+ NK1.1- Innate Lymphoid Cells within the Tumor Microenvironment

Innate Lymphoid Cells (ILCs) play a key role in tissue mediated immunity and can be controlled by co-receptor signaling. Here we define a subset of ILCs that are Tbet+NK1.1- and are present within the tumor microenvironment (TME). We show programmed death-1 receptor (PD-1) expression on ILCs within TME is found in Tbet+NK1.1-ILCs. PD-1 significantly controlled the proliferation and function of Tbet+NK1.1-ILCs in multiple murine and human tumors. We found tumor derived lactate enhanced PD-1 expression on Tbet+NK1.1-ILCs within the TME, which resulted in dampened mTOR signaling along with increased fatty acid uptake. In line with these metabolic changes, PD-1 deficient Tbet+NK1.1-ILCs expressed significantly increased IFN{gamma}, granzyme B and K. Furthermore, PD1 deficient Tbet+NK1.1- ILCs contributed towards diminished tumor growth in an experimental murine model of melanoma. These data demonstrate that PD-1 can regulate anti-tumor responses of Tbet+NK1.1-ILCs within the tumor microenvironment. HighlightsO_LITbet+NK1.1- ILCs are found in WT and PD1 ko mice C_LIO_LIPD-1 is expressed on Tbet+NK1.1- ILC1s within multiple TME C_LIO_LIPD-1 controls the proliferation and function of Tbet+NK1.1- ILCs within the tumor microenvironment by modulating fatty acid metabolism. C_LIO_LIPD-1 regulates the proliferation of human Tbet+ ILC1s in human cutaneous squamous cell carcinoma (cSCC) and melanoma tumor microenvironment. C_LI

immunology↗

Effects of Variation in Urine Sample Storage Conditions on 16S Urogenital Microbiome Analyses

Replicability is a well-established challenge in microbiome research with a variety of contributing factors at all stages, from sample collection to code execution. Here, we focus on voided urine sample storage conditions for urogenital microbiome analysis. Using urine samples collected from 10 healthy adult women, we investigated the microbiome preservation efficacy of AssayAssure(R) Genelock (as opposed to no preservative) under different temperature conditions. We varied temperature over 48 hours in order to examine the impact of conditions samples may experience with home voided urine collection and shipping to a central biorepository. The following common lab and shipping conditions were investigated: -20C, ambient temperature, 4C, a freeze-thaw cycle, and a heat cycle. At 48 hours, all samples were stored at -80C until processing. After generating 16S rRNA gene amplicon sequencing data using the highly sensitive KatharoSeq protocol, we observed individual variation in both alpha and beta diversity metrics below interhuman differences, corroborating reports of individual microbiome variability in other specimen types. While there was no significant difference in beta diversity when comparing AssayAssure(R) Genelock vs. no preservative, we did observe a higher concordance with AssayAssure samples shipped at colder temperatures (-20C and 4C) when compared to the samples shipped at -20C without preservative. Our results indicate that AssayAssure does not introduce a significant amount of microbial bias when used on a range of temperatures but is most effective at colder temperatures. ImportanceThe urogenital microbiome is an understudied yet important human microbiome niche. Research has been stimulated by the relatively recent discovery that urine is not sterile: urinary tract microbes have been linked to health problems including urinary infections, incontinence, and cancer. The quality of life and economic impact of UTIs and urgency incontinence alone are enormous, with $3.5 billion and $82.6 billion respectively spent in the U.S. annually. Given the low biomass of urine, novelty of the field, and well-established replicability bias in microbiome studies, it is critical to study storage conditions on urine samples to minimize microbial biases. Efficient and reliable preservation methods permit home self-sample collection and shipping, increasing the accessibility of larger-scale studies. Here, we examined both buffer and temperature variation effects on 16S rRNA gene amplicon sequencing results from urogenital samples, providing data on the consequences of common storage methods on urogenital microbiome results.

microbiology↗

Scalable power analysis and effect size exploration of microbiome community differences with Evident

Differentiating microbial communities among samples is a major objective in biomedicine. Quantifying the effect size of these differences allows researchers to understand the factors most associated with communities and to optimize the design and clinical resources required to address particular research questions. Here, we present Evident, a package for effect size calculations and power analysis on microbiome data and show that Evident scales to large datasets with numerous metadata covariates.

bioinformatics↗

Fish microbiomes 101: disentangling the rules governing marine fish mucosal microbiomes across 101 species

Fish are the most diverse and widely distributed vertebrates, yet little is known about the microbial ecology of fishes nor the biological and environmental factors that influence the fish microbiome. The microbiota from 101 species of Southern California marine fishes, spanning 22 orders, 55 families, and 83 genera representing ~25% of local marine fish diversity, was analyzed to identify patterns that explain microbial diversity patterns in a geographical subset of marine fish biodiversity. We compared fish microbiomes (gill, skin, midgut, and hindgut) using alpha, beta, and gamma diversity along with establishing a novel method to estimate microbial biomass (Qiime2 plugin katharoseq). For oceanic fishes from the neritic zone, host size and distance from shore were negatively associated with microbial biomass densities and diversity in the gills. Body site was the strongest driver for beta diversity with strong evidence of phylosymbiosis observed across the gill, skin, and hindgut, but not midgut. The majority of microbes from all fish body sites were of unknown origin but overall sea water generally contributes more microbes to fish mucus compared to marine sediment. In a meta-analysis of vertebrate hindguts (569 species), mammals had the highest gamma diversity when controlling for host species number while fishes had the highest percent of unique microbial taxa (92%). In fishes, the midgut, gill, and skin contains the majority of microbial diversity which collectively can be 5.5 times higher than the hindgut. The composite dataset will be useful to vertebrate microbiome researchers and fish biologists interested in microbial ecology with applications in aquaculture and fisheries management.

ecology↗

CXCR2 inhibition enables NASH-HCC immunotherapy

ObjectiveHepatocellular carcinoma (HCC) is increasingly associated with non-alcoholic steatohepatitis (NASH). HCC immunotherapy offers great promise; however, recent data suggests NASH-HCC may be less sensitive to conventional immune checkpoint inhibition (ICI). We hypothesised that targeting neutrophils using a CXCR2 small molecule inhibitor may sensitise NASH-HCC to ICI therapy. DesignNeutrophil infiltration was characterised in human HCC and mouse models of HCC. Late-stage intervention with anti-PD1 and/or a CXCR2 inhibitor was performed in murine models of NASH-HCC. The tumour immune microenvironment was characterised by imaging mass cytometry, RNA-seq and flow cytometry. ResultsNeutrophils expressing CXCR2, a receptor crucial to neutrophil recruitment in acute-injury, are highly represented in human NASH-HCC. In models of NASH-HCC lacking response to ICI, the combination of a CXCR2 antagonist with anti-PD1 suppressed tumour burden and extended survival. Combination therapy increased intratumoral XCR1+ dendritic cell activation and CD8+ T cell numbers which are associated with anti-tumoral immunity, this was confirmed by loss of therapeutic effect upon genetic impairment of myeloid cell recruitment, neutralisation of the XCR1-ligand XCL1 or depletion of CD8+ T cells. Therapeutic benefit was accompanied by an unexpected increase in tumour-associated neutrophils (TANs) which switched from a pro-tumor to anti-tumour progenitor-like neutrophil phenotype. Reprogrammed TANs were found in direct contact with CD8+ T cells in clusters that were enriched for the cytotoxic anti-tumoural protease granzyme B. Neutrophil reprogramming was not observed in the circulation indicative of the combination therapy selectively influencing TANs. ConclusionCXCR2-inhibition induces reprogramming of the tumour immune microenvironment that promotes ICI in NASH-HCC. Significance of this studyWhat is already known on this subject? O_LIImmune checkpoint inhibition therapy is emerging as a promising new therapy for the treatment of advanced hepatocellular carcinoma (HCC). C_LIO_LIOnly a minority of HCC patients will respond to immune checkpoint inhibition (ICI) therapy and recent data suggest that HCC on the background of NASH may have reduced sensitivity to this treatment strategy. C_LIO_LINeutrophils are a typical myeloid component of the liver in NASH and are found either within the HCC tumour microenvironment or in a peritumoural location. C_LIO_LINeutrophils have considerable phenotypic plasticity and can exist in both tumour promoting and tumour suppressing states. C_LIO_LINeutrophils may have the ability to influence ICI therapy. C_LI What are the new findings? O_LICXCR2+ neutrophils are found in human NASH and within the tumour of both human and mouse models of NASH-HCC. C_LIO_LIThe resistance of NASH-HCC to anti-PD1 therapy is overcome by co-treatment with a CXCR2 small molecule inhibitor, with evidence of reduced tumour burden and extended survival. C_LIO_LIAnti-PD1 and CXCR2 inhibitor combine to selectively reprogramme tumour-associated neutrophils (TANs) from a pro- to an anti-tumour phenotype. C_LIO_LIReprogrammed TANs proliferate locally within Granzyme B+ immune clusters that contain physically associating CD8+ T cells and antigen presenting cells. C_LIO_LIConventional XCR1+ dendritic cells (cDC1s) are found to be elevated in anti-PD1 and CXCR2 inhibitor treated HCCs and together with CD8+ T cells are required for therapeutic benefit. C_LI How might it impact on clinical practice in the foreseeable future? O_LITANs can be selectively manipulated to adopt an anti-tumour phenotype which unlocks their potential for cancer therapy. The ability of CXCR2 antagonism to combine with ICI therapy to bring about enhanced therapeutic benefit in NASH-HCC (and potentially in HCC of other aetiologies) warrents clinical investigation. C_LI

cancer biology↗

Multi-omics profiling of Earth's biomes reveals that microbial and metabolite composition are shaped by the environment

As our understanding of the structure and diversity of the microbial world grows, interpreting its function is of critical interest for understanding and managing the many systems microbes influence. Despite advances in sequencing, lack of standardization challenges comparisons among studies that could provide insight into the structure and function of microbial communities across multiple habitats on a planetary scale. Technical variation among distinct studies without proper standardization of approaches prevents robust meta-analysis. Here, we present a multi-omics, meta-analysis of a novel, diverse set of microbial community samples collected for the Earth Microbiome Project. We include amplicon (16S, 18S, ITS) and shotgun metagenomic sequence data, and untargeted metabolomics data (liquid chromatography-tandem mass spectrometry and gas chromatography mass spectrometry), centering our description on relationships and co-occurrences of microbially-related metabolites and microbial taxa across environments. Standardized protocols and analytical methods for characterizing microbial communities, including assessment of molecular diversity using untargeted metabolomics, facilitate identification of shared microbial and metabolite features, permitting us to explore diversity at extraordinary scale. In addition to a reference database for metagenomic and metabolomic data, we provide a framework for incorporating additional studies, enabling the expansion of existing knowledge in the form of a community resource that will become more valuable with time. To provide examples of applying this database, we outline important ecological questions that can be addressed, and test the hypotheses that every microbe and metabolite is everywhere, but the environment selects. Our results show that metabolite diversity exhibits turnover and nestedness related to both microbial communities and the environment. The relative abundances of microbially-related metabolites vary and co-occur with specific microbial consortia in a habitat-specific manner, and highlight the power of certain chemistry - in particular terpenoids - in distinguishing Earths environments.

ecology↗

OGUs enable effective, phylogeny-aware analysis of even shallow metagenome community structures

We introduce Operational Genomic Unit (OGU), a metagenome analysis strategy that directly exploits sequence alignment hits to individual reference genomes as the minimum unit for assessing the diversity of microbial communities and their relevance to environmental factors. This approach is independent from taxonomic classification, granting the possibility of maximal resolution of community composition, and organizes features into an accurate hierarchy using a phylogenomic tree. The outputs are suitable for contemporary analytical protocols for community ecology, differential abundance and supervised learning while supporting phylogenetic methods, such as UniFrac and phylofactorization, that are seldomly applied to shotgun metagenomics despite being prevalent in 16S rRNA gene amplicon studies. As demonstrated in one synthetic and two real-world case studies, the OGU method produces biologically meaningful patterns from microbiome datasets. Such patterns further remain detectable at very low metagenomic sequencing depths. Compared with taxonomic unit-based analyses implemented in currently adopted metagenomics tools, and the analysis of 16S rRNA gene amplicon sequence variants, this method shows superiority in informing biologically relevant insights, including stronger correlation with body environment and host sex on the Human Microbiome Project dataset, and more accurate prediction of human age by the gut microbiomes in the Finnish population. We provide Woltka, a bioinformatics tool to implement this method, with full integration with the QIIME 2 package and the Qiita web platform, to facilitate OGU adoption in future metagenomics studies. ImportanceShotgun metagenomics is a powerful, yet computationally challenging, technique compared to 16S rRNA gene amplicon sequencing for decoding the composition and structure of microbial communities. However, current analyses of metagenomic data are primarily based on taxonomic classification, which is limited in feature resolution compared to 16S rRNA amplicon sequence variant analysis. To solve these challenges, we introduce Operational Genomic Units (OGUs), which are the individual reference genomes derived from sequence alignment results, without further assigning them taxonomy. The OGU method advances current read-based metagenomics in two dimensions: (i) providing maximal resolution of community composition while (ii) permitting use of phylogeny-aware tools. Our analysis of real-world datasets shows several advantages over currently adopted metagenomic analysis methods and the finest-grained 16S rRNA analysis methods in predicting biological traits. We thus propose the adoption of OGU as standard practice in metagenomic studies.

bioinformatics↗

Charting oncogenicity of genes and variants across lineages via multiplexed screens in teratomas

Deconstructing tissue-specific effects of genes and variants on proliferative advantage is critical to understanding cellular transformation and to systematic selection of cancer therapeutics. Dissecting these specificities at scale requires integrated methods for multiplexed genetic screens tracking fitness across time, across human cell types, and in a suitable cellular niche since functional differences also depend on physiological cues. Towards this, we present a novel approach, harnessing single-cell cancer driver screens in teratomas coupled with hit enrichment by serial teratoma reinjection, to simultaneously screen drivers across multiple lineages in vivo. Using this system, we analyzed population shifts and lineage-specific enrichment for 51 cancer associated genes and gene variants, profiling over 100,000 cells spanning over 20 lineages, across two rounds of serially injected teratomas. We confirmed that c-MYC alone or combined with myristoylated AKT1 potently drives proliferation in progenitor neural lineages, demonstrating signatures of malignancy. These drivers directed teratoma development to lineages representative of pediatric tumors such as medulloblastoma and rhabdomyosarcoma. Additionally, mutant MEK1S218D/S222D provides a proliferative advantage in mesenchymal lineages like fibroblasts. Our method provides a powerful new platform for multi-lineage longitudinal study of oncogenesis.

systems biology↗

High resolution profiling of pathways of escape for SARS-CoV-2 spike-binding antibodies

Defining long-term protective immunity to SARS-CoV-2 is one of the most pressing questions of our time and will require a detailed understanding of potential ways this virus can evolve to escape immune protection. Immune protection will most likely be mediated by antibodies that bind to the viral entry protein, Spike (S). Here we used Phage-DMS, an approach that comprehensively interrogates the effect of all possible mutations on binding to a protein of interest, to define the profile of antibody escape to the SARS-CoV-2 S protein using COVID-19 convalescent plasma. Antibody binding was common in two regions: the fusion peptide and linker region upstream of the heptad repeat region 2. However, escape mutations were variable within these immunodominant regions. There was also individual variation in less commonly targeted epitopes. This study provides a granular view of potential antibody escape pathways and suggests there will be individual variation in antibody-mediated virus evolution.

immunology↗