132 Publications

Quantifying Live Microbial Load in Human Saliva Samples over Time Reveals Stable Composition and Dynamic Load

C. Marotz, J. Morton, P. Navarro, J. Coker, P. Belda-Ferre, R. Knight, K. Zengler

Evaluating microbial community composition through next-generation sequencing has become increasingly accessible. However, metagenomic sequencing data sets provide researchers with only a snapshot of a dynamic ecosystem and do not provide information about the total microbial number, or load, of a sample. Additionally, DNA can be detected long after a microorganism is dead, making it unsafe to assume that all microbial sequences detected in a community came from living organisms. By combining relic DNA removal by propidium monoazide (PMA) with microbial quantification with flow cytometry, we present a novel workflow to quantify live microbial load in parallel with metagenomic sequencing. We applied this method to unstimulated saliva samples, which can easily be collected longitudinally and standardized by passive collection time. We found that the number of live microorganisms detected in saliva was inversely correlated with salivary flow rate and fluctuated by an order of magnitude throughout the day in healthy individuals. In an acute perturbation experiment, alcohol-free mouthwash resulted in a massive decrease in live bacteria, which would have been missed if we did not consider dead cell signal. While removing relic DNA from saliva samples did not greatly impact the microbial composition, it did increase our resolution among samples collected over time. These results provide novel insight into the dynamic nature of host-associated microbiomes and underline the importance of applying scale-invariant tools in the analysis of next-generation sequencing data sets.

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February 16, 2021

Computationally designed peptide macrocycle inhibitors of New Delhi metallo-β-lactamase 1

V. Mulligan, S. Workman, D. Renfrew, R. Bonneau, et al.

The rise of antibiotic resistance calls for new therapeutics targeting resistance factors such as the New Delhi metallo-β-lactamase 1 (NDM-1), a bacterial enzyme that degrades β-lactam antibiotics. We present structure-guided computational methods for designing peptide macrocycles built from mixtures of L- and D-amino acids that are able to bind to and inhibit targets of therapeutic interest. Our methods explicitly consider the propensity of a peptide to favor a binding-competent conformation, which we found to predict rank order of experimentally observed IC50 values across seven designed NDM-1- inhibiting peptides. We were able to determine X-ray crystal structures of three of the designed inhibitors in complex with NDM-1, and in all three the conformation of the peptide is very close to the computationally designed model. In two of the three structures, the binding mode with NDM-1 is also very similar to the design model, while in the third, we observed an alternative binding mode likely arising from internal symmetry in the shape of the design combined with flexibility of the target. Although challenges remain in robustly predicting target backbone changes, binding mode, and the effects of mutations on binding affinity, our methods for designing ordered, bindingcompetent macrocycles should have broad applicability to a wide range of therapeutic targets.

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February 10, 2021

Lower Airway Dysbiosis Affects Lung Cancer Progression

J. Tsay, B. Wu, I. Sulaiman, K. Gershner, R. Schluger , Y. Li, T. Yie, P. Meyn, E. Olsen, L. Perez, B. Franca, J. Carpenito, T. Iizumi, M. El-Ashmawy, M. Badri, J. Morton, et al

In lung cancer, enrichment of the lower airway microbiota with oral commensals commonly occurs, and ex vivo models support that some of these bacteria can trigger host transcriptomic signatures associated with carcinogenesis. Here, we show that this lower airway dysbiotic signature was more prevalent in the stage IIIB–IV tumor–node–metastasis lung cancer group and is associated with poor prognosis, as shown by decreased survival among subjects with early-stage disease (I–IIIA) and worse tumor progression as measured by RECIST scores among subjects with stage IIIB–IV disease. In addition, this lower airway microbiota signature was associated with upregulation of the IL17, PI3K, MAPK, and ERK pathways in airway transcriptome, and we identified Veillonella parvula as the most abundant taxon driving this association. In a KP lung cancer model, lower airway dysbiosis with V. parvula led to decreased survival, increased tumor burden, IL17 inflammatory phenotype, and activation of checkpoint inhibitor markers.

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A New Era for Space Life Science: International Standards for Space Omics Processing

L. Rutter, R. Barker, D. Bezdan, H. Cope, S. Costes, L. Degoricija, K. Fisch, M. Gabitto, S. Gebre, S. Giacomello, S. Gilroy, S. Green, C. Mason, S. Reinsch, N. Szewczyk, D. Taylor, J. Galazka, R. Herranz, M. Muratani

With the rise of commercial spaceflight and prospective human missions to Mars, a wider health range of humans will enter space for longer spans and at higher exposure to environmental stressors than ever before. Numerous adverse health effects have been observed in space, including bone demineralization and skeletal muscle atrophy, among others. Scientists across the world are conducting space omics studies to develop countermeasures for safe and effective crewed space missions. However, optimal extraction of scientific insight from such data is contingent on improved standardization. In response, we founded ISSOP (International Standards for Space Omics Processing), an international consortium of scientists who aim to enhance guidelines between space biologists globally. This paper informs scientists and data scientists from many fields about the challenges and future avenues of space omics and can serve as an introductory reference for new members in the space biology discipline.

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December 16, 2020

CHD8 haploinsufficiency alters the developmental trajectories of human excitatory and inhibitory neurons linking autism phenotypes with transient cellular defects

C. Villa, C. Cheroni, A. López-Tóbon, C. Dotter, B. Oliveira, R. Sacco, A. Yahya, J. Morandell, M. Gabriele, C. Sommer, M. Gabitto, G. Testa, G. Novarino

Chromodomain helicase DNA-binding 8 (CHD8) is one of the most frequently mutated genes causative of autism spectrum disorder (ASD). While its phenotypic spectrum often encompasses macrocephaly and hence implicates cortical abnormalities in this form of ASD, the neurodevelopmental impact of human CHD8 haploinsufficiency remains unexplored. Here we combined human cerebral organoids and single cell transcriptomics to define the effect of ASD-linked CHD8 mutations on human cortical development. We found that CHD8 haploinsufficiency causes a major disruption of neurodevelopmental trajectories with an accelerated generation of inhibitory neurons and a delayed production of excitatory neurons alongside the ensuing protraction of the proliferation phase. This imbalance leads to a significant enlargement of cerebral organoids aligned to the macrocephaly observed in patients with CHD8 mutations. By adopting an isogenic design of patient-specific mutations and mosaic cerebral organoids, we define genotype-phenotype relationships and uncover their cell-autonomous nature. Finally, our results assign different CHD8-dependent molecular defects to particular cell types, pointing to an abnormal and extended program of proliferation and alternative splicing specifically affected in, respectively, the radial glial and immature neuronal compartments. By identifying temporally restricted cell-type specific effects of human CHD8 mutations, our study uncovers developmental alterations as reproducible endophenotypes for neurodevelopmental disease modelling.

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November 26, 2020

Deep metagenomics examines the oral microbiome during dental caries, revealing novel taxa and co-occurrences with host molecules

J. Baker, J. Morton, M. Dinis, R. Alvarez, N. Tran, R. Knight, A. Edlund

Dental caries, the most common chronic infectious disease worldwide, has a complex etiology involving the interplay of microbial and host factors that are not completely understood. In this study, the oral microbiome and 38 host cytokines and chemokines were analyzed across 23 children with caries and 24 children with healthy dentition. De novo assembly of metagenomic sequencing obtained 527 metagenome-assembled genomes (MAGs), representing 150 bacterial species. Forty-two of these species had no genomes in public repositories, thereby representing novel taxa. These new genomes greatly expanded the known pangenomes of many oral clades, including the enigmatic Saccharibacteria clades G3 and G6, which had distinct functional repertoires compared to other oral Saccharibacteria. Saccharibacteria are understood to be obligate epibionts, which are dependent on host bacteria. These data suggest that the various Saccharibacteria clades may rely on their hosts for highly distinct metabolic requirements, which would have significant evolutionary and ecological implications. Across the study group, Rothia, Neisseria, and Haemophilus spp. were associated with good dental health, whereas Prevotella spp., Streptococcus mutans, and Human herpesvirus 4 (Epstein-Barr virus [EBV]) were more prevalent in children with caries. Finally, 10 of the host immunological markers were significantly elevated in the caries group, and co-occurrence analysis provided an atlas of potential relationships between microbes and host immunological molecules. Overall, this study illustrated the oral microbiome at an unprecedented resolution and contributed several leads for further study that will increase the understanding of caries pathogenesis and guide therapeutic development.

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Lower airway dysbiosis affects lung cancer progression

J Tsay,, B Wu, I Sulaiman, K Gershner , R Schluger, Y Li, T Yie, P Meyn, E Olsen, L Perez, B Franca, J Carpenito, T Iizumi, M El-Ashmawy, M Badri, J. Morton, N Shen, L He, G Michaud, S Rafeq, J Bessich, R L Smith, H Sauthoff, K Felner, R Pillai, A-M Zavitsanou, S B Koralov, V Mezzano, C A Loomis, A L Moreira, W Moore, A Tsirigos, A Heguy, W N Rom, D H Sterman, H I Pass , J C Clemente, H Li, R. Bonneau, K-K Wong, T Papagiannakopoulos, L N Degal

In lung cancer, enrichment of the lower airway microbiota with oral commensals commonly occurs and ex vivo models support that some of these bacteria can trigger host transcriptomic signatures associated with carcinogenesis. Here, we show that this lower airway dysbiotic signature was more prevalent in group IIIB-IV TNM stage lung cancer and is associated with poor prognosis, as shown by decreased survival among subjects with early stage disease (I-IIIA) and worse tumor progression as measured by RECIST scores among subjects with IIIB-IV stage disease. In addition, this lower airway microbiota signature was associated with upregulation of IL-17, PI3K, MAPK and ERK pathways in airway transcriptome, and we identified Veillonella parvula as the most abundant taxon driving this association. In a KP lung cancer model, lower airway dysbiosis with V. parvula led to decreased survival, increased tumor burden, IL-17 inflammatory phenotype and activation of checkpoint inhibitor markers.

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Auto-deconvolution and molecular networking of gas chromatography–mass spectrometry data

A. Aksenov, I. Laponogov, Z. Zhang, ..., J. Morton, et. al.

We engineered a machine learning approach, MSHub, to enable auto-deconvolution of gas chromatography–mass spectrometry (GC–MS) data. We then designed workflows to enable the community to store, process, share, annotate, compare and perform molecular networking of GC–MS data within the Global Natural Product Social (GNPS) Molecular Networking analysis platform. MSHub/GNPS performs auto-deconvolution of compound fragmentation patterns via unsupervised non-negative matrix factorization and quantifies the reproducibility of fragmentation patterns across samples.

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Protein Structural Alignments From Sequence

J. Morton, C. E.M. Strauss, R. Blackwell, D. Berenberg, V. Gligorijevic, R. Bonneau

Computing sequence similarity is a fundamental task in biology, with alignment forming the basis for the annotation of genes and genomes and providing the core data structures for evolutionary analysis. Standard approaches are a mainstay of modern molecular biology and rely on variations of edit distance to obtain explicit alignments between pairs of biological sequences. However, sequence alignment algorithms struggle with remote homology tasks and cannot identify similarities between many pairs of proteins with similar structures and likely homology. Recent work suggests that using machine learning language models can improve remote homology detection. To this end, we introduce DeepBLAST, that obtains explicit alignments from residue embeddings learned from a protein language model integrated into an end-to-end differentiable alignment framework. This approach can be accelerated on the GPU architectures and outperforms conventional sequence alignment techniques in terms of both speed and accuracy when identifying structurally similar proteins.

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November 4, 2020

Specificities of modeling membrane proteins using multi-template homology modeling

J. Koehler, R. Bonneau

Structures of membrane proteins are challenging to determine experimentally and currently represent only about 2% of the structures in the ProteinDataBank. Because of this disparity, methods for modeling membrane proteins are fewer and of lower quality than those for modeling soluble proteins. However, better expression, crystallization, and cryo-EM techniques have prompted a recent increase in experimental structures of membrane proteins, which can act as templates to predict the structure of closely related proteins through homology modeling. Because homology modeling relies on a structural template, it is easier and more accurate than fold recognition methods or de novo modeling, which are used when the sequence similarity between the query sequence and the sequence of related proteins in structural databases is below 25%. In homology modeling, a query sequence is mapped onto the coordinates of a single template and refined. With the increase in available templates, several templates often cover overlapping segments of the query sequence. Multi-template modeling can be used to identify the best template for local segments and join them into a single model. Here we provide a protocol for modeling membrane proteins from multiple templates in the Rosetta software suite. This approach takes advantage of several integrated frameworks, namely RosettaScripts, RosettaCM, and RosettaMP with the membrane scoring function.

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October 23, 2020
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