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1. ICML Topological Deep Learning Challenge 2024: Beyond the Graph Domain

Author

Bernárdez, Guillermo, Telyatnikov, Lev, Montagna, Marco, Baccini, Federica, Papillon, Mathilde, Ferriol-Galmés, Miquel, Hajij, Mustafa, Papamarkou, Theodore, Bucarelli, Maria Sofia, Zaghen, Olga, Mathe, Johan, Myers, Audun, Mahan, Scott, Lillemark, Hansen, Vadgama, Sharvaree, Bekkers, Erik, Doster, Tim, Emerson, Tegan, Kvinge, Henry, Agate, Katrina, Ahmed, Nesreen K, Bai, Pengfei, Banf, Michael, Battiloro, Claudio, Beketov, Maxim, Bogdan, Paul, Carrasco, Martin, Cavallo, Andrea, Choi, Yun Young, Dasoulas, George, Elphick, Matouš, Escalona, Giordan, Filipiak, Dominik, Fritze, Halley, Gebhart, Thomas, Gil-Sorribes, Manel, Goomanee, Salvish, Guallar, Victor, Imasheva, Liliya, Irimia, Andrei, Jin, Hongwei, Johnson, Graham, Kanakaris, Nikos, Koloski, Boshko, Kovač, Veljko, Lecha, Manuel, Lee, Minho, Leroy, Pierrick, Long, Theodore, Magai, German, Martinez, Alvaro, Masden, Marissa, Mežnar, Sebastian, Miquel-Oliver, Bertran, Molina, Alexis, Nikitin, Alexander, Nurisso, Marco, Piekenbrock, Matt, Qin, Yu, Rygiel, Patryk, Salatiello, Alessandro, Schattauer, Max, Snopov, Pavel, Suk, Julian, Sánchez, Valentina, Tec, Mauricio, Vaccarino, Francesco, Verhellen, Jonas, Wantiez, Frederic, Weers, Alexander, Zajec, Patrik, Škrlj, Blaž, and Miolane, Nina

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

This paper describes the 2nd edition of the ICML Topological Deep Learning Challenge that was hosted within the ICML 2024 ELLIS Workshop on Geometry-grounded Representation Learning and Generative Modeling (GRaM). The challenge focused on the problem of representing data in different discrete topological domains in order to bridge the gap between Topological Deep Learning (TDL) and other types of structured datasets (e.g. point clouds, graphs). Specifically, participants were asked to design and implement topological liftings, i.e. mappings between different data structures and topological domains --like hypergraphs, or simplicial/cell/combinatorial complexes. The challenge received 52 submissions satisfying all the requirements. This paper introduces the main scope of the challenge, and summarizes the main results and findings., Comment: Proceedings of the Geometry-grounded Representation Learning and Generative Modeling Workshop (GRaM) at ICML 2024

Published
2024

2. Point Cloud Classification via Deep Set Linearized Optimal Transport

Author

Mahan, Scott, Moosmüller, Caroline, and Cloninger, Alexander

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

We introduce Deep Set Linearized Optimal Transport, an algorithm designed for the efficient simultaneous embedding of point clouds into an $L^2-$space. This embedding preserves specific low-dimensional structures within the Wasserstein space while constructing a classifier to distinguish between various classes of point clouds. Our approach is motivated by the observation that $L^2-$distances between optimal transport maps for distinct point clouds, originating from a shared fixed reference distribution, provide an approximation of the Wasserstein-2 distance between these point clouds, under certain assumptions. To learn approximations of these transport maps, we employ input convex neural networks (ICNNs) and establish that, under specific conditions, Euclidean distances between samples from these ICNNs closely mirror Wasserstein-2 distances between the true distributions. Additionally, we train a discriminator network that attaches weights these samples and creates a permutation invariant classifier to differentiate between different classes of point clouds. We showcase the advantages of our algorithm over the standard deep set approach through experiments on a flow cytometry dataset with a limited number of labeled point clouds.

Published
2024

3. High fat intake sustains sorbitol intolerance after antibiotic-mediated Clostridia depletion from the gut microbiota

Author

Lee, Jee-Yon, Tiffany, Connor R, Mahan, Scott P, Kellom, Matthew, Rogers, Andrew WL, Nguyen, Henry, Stevens, Eric T, Masson, Hugo LP, Yamazaki, Kohei, Marco, Maria L, Eloe-Fadrosh, Emiley A, Turnbaugh, Peter J, and Bäumler, Andreas J

Subjects
Microbiology, Biological Sciences, Biomedical and Clinical Sciences, Nutrition, Prevention, Complementary and Integrative Health, Microbiome, Digestive Diseases, 2.1 Biological and endogenous factors, Animals, Mice, Anti-Bacterial Agents, Butyrates, Carbohydrate Metabolism, Inborn Errors, Clostridium, Escherichia coli, Gastrointestinal Microbiome, Sorbitol, Clostridia, antibiotics, carbohydrate intolerance, gut microbiota, high-fat diet, polyol, sorbitol intolerance, Medical and Health Sciences, Developmental Biology, Biological sciences, Biomedical and clinical sciences
Abstract

Carbohydrate intolerance, commonly linked to the consumption of lactose, fructose, or sorbitol, affects up to 30% of the population in high-income countries. Although sorbitol intolerance is attributed to malabsorption, the underlying mechanism remains unresolved. Here, we show that a history of antibiotic exposure combined with high fat intake triggered long-lasting sorbitol intolerance in mice by reducing Clostridia abundance, which impaired microbial sorbitol catabolism. The restoration of sorbitol catabolism by inoculation with probiotic Escherichia coli protected mice against sorbitol intolerance but did not restore Clostridia abundance. Inoculation with the butyrate producer Anaerostipes caccae restored a normal Clostridia abundance, which protected mice against sorbitol-induced diarrhea even when the probiotic was cleared. Butyrate restored Clostridia abundance by stimulating epithelial peroxisome proliferator-activated receptor-gamma (PPAR-γ) signaling to restore epithelial hypoxia in the colon. Collectively, these mechanistic insights identify microbial sorbitol catabolism as a potential target for approaches for the diagnosis, treatment, and prevention of sorbitol intolerance.

Published
2024

4. A broad-spectrum synthetic antibiotic that does not evoke bacterial resistance

Author

Heithoff, Douglas M, Mahan, Scott P, Barnes, Lucien, Leyn, Semen A, George, Cyril X, Zlamal, Jaime E, Limwongyut, Jakkarin, Bazan, Guillermo C, Fried, Jeffrey C, Fitzgibbons, Lynn N, House, John K, Samuel, Charles E, Osterman, Andrei L, Low, David A, and Mahan, Michael J

Subjects
Biodefense, Antimicrobial Resistance, Infectious Diseases, Hematology, Prevention, Vaccine Related, Emerging Infectious Diseases, Development of treatments and therapeutic interventions, 5.1 Pharmaceuticals, Infection, Good Health and Well Being, Mice, Animals, Anti-Bacterial Agents, Bacterial Infections, Anti-Infective Agents, Bacteria, Sepsis, Microbial Sensitivity Tests, Drug Resistance, Multiple, Bacterial, Mammals, Antibiotics, Antimicrobial resistance, Multidrug-resistant pathogens, Conjugated oligoelectrolytes, COE, Clinical Sciences, Public Health and Health Services
Abstract

BackgroundAntimicrobial resistance (AMR) poses a critical threat to public health and disproportionately affects the health and well-being of persons in low-income and middle-income countries. Our aim was to identify synthetic antimicrobials termed conjugated oligoelectrolytes (COEs) that effectively treated AMR infections and whose structures could be readily modified to address current and anticipated patient needs.MethodsFifteen chemical variants were synthesized that contain specific alterations to the COE modular structure, and each variant was evaluated for broad-spectrum antibacterial activity and for in vitro cytotoxicity in cultured mammalian cells. Antibiotic efficacy was analyzed in murine models of sepsis; in vivo toxicity was evaluated via a blinded study of mouse clinical signs as an outcome of drug treatment.FindingsWe identified a compound, COE2-2hexyl, that displayed broad-spectrum antibacterial activity. This compound cured mice infected with clinical bacterial isolates derived from patients with refractory bacteremia and did not evoke bacterial resistance. COE2-2hexyl has specific effects on multiple membrane-associated functions (e.g., septation, motility, ATP synthesis, respiration, membrane permeability to small molecules) that may act together to negate bacterial cell viability and the evolution of drug-resistance. Disruption of these bacterial properties may occur through alteration of critical protein-protein or protein-lipid membrane interfaces-a mechanism of action distinct from many membrane disrupting antimicrobials or detergents that destabilize membranes to induce bacterial cell lysis.InterpretationThe ease of molecular design, synthesis and modular nature of COEs offer many advantages over conventional antimicrobials, making synthesis simple, scalable and affordable. These COE features enable the construction of a spectrum of compounds with the potential for development as a new versatile therapy for an imminent global health crisis.FundingU.S. Army Research Office, National Institute of Allergy and Infectious Diseases, and National Heart, Lung, and Blood Institute.

Published
2023

5. Semi-Supervised Manifold Learning with Complexity Decoupled Chart Autoencoders

Author

Schonsheck, Stefan C., Mahan, Scott, Klock, Timo, Cloninger, Alexander, and Lai, Rongjie

Subjects
Computer Science - Machine Learning, Mathematics - Numerical Analysis
Abstract

Autoencoding is a popular method in representation learning. Conventional autoencoders employ symmetric encoding-decoding procedures and a simple Euclidean latent space to detect hidden low-dimensional structures in an unsupervised way. Some modern approaches to novel data generation such as generative adversarial networks askew this symmetry, but still employ a pair of massive networks--one to generate the image and another to judge the images quality based on priors learned from a training set. This work introduces a chart autoencoder with an asymmetric encoding-decoding process that can incorporate additional semi-supervised information such as class labels. Besides enhancing the capability for handling data with complicated topological and geometric structures, the proposed model can successfully differentiate nearby but disjoint manifolds and intersecting manifolds with only a small amount of supervision. Moreover, this model only requires a low-complexity encoding operation, such as a locally defined linear projection. We discuss the approximation power of such networks and derive a bound that essentially depends on the intrinsic dimension of the data manifold rather than the dimension of ambient space. Next we incorporate bounds for the sampling rate of training data need to faithfully represent a given data manifold. We present numerical experiments that verify that the proposed model can effectively manage data with multi-class nearby but disjoint manifolds of different classes, overlapping manifolds, and manifolds with non-trivial topology. Finally, we conclude with some experiments on computer vision and molecular dynamics problems which showcase the efficacy of our methods on real-world data.

Published
2022

6. The IRE1α-XBP1 Signaling Axis Promotes Glycolytic Reprogramming in Response to Inflammatory Stimuli

Author

English, Bevin C, Savage, Hannah P, Mahan, Scott P, Diaz-Ochoa, Vladimir E, Young, Briana M, Abuaita, Basel H, Sule, Gautam, Knight, Jason S, O’Riordan, Mary X, Bäumler, Andreas J, and Tsolis, Renée M

Subjects
Biochemistry and Cell Biology, Biomedical and Clinical Sciences, Biological Sciences, Biodefense, Emerging Infectious Diseases, Infectious Diseases, 2.1 Biological and endogenous factors, Infection, Inflammatory and immune system, Protein Serine-Threonine Kinases, Toll-Like Receptor 4, Endoribonucleases, X-Box Binding Protein 1, Lipopolysaccharides, Unfolded Protein Response, Transcription Factors, Endoplasmic Reticulum Stress, Brucella, endoplasmic reticulum, immunometabolism, innate immunity, Microbiology, Biochemistry and cell biology, Medical microbiology
Abstract

Immune cells must be able to adjust their metabolic programs to effectively carry out their effector functions. Here, we show that the endoplasmic reticulum (ER) stress sensor Inositol-requiring enzyme 1 alpha (IRE1α) and its downstream transcription factor X box binding protein 1 (XBP1) enhance the upregulation of glycolysis in classically activated macrophages (CAMs). The IRE1α-XBP1 signaling axis supports this glycolytic switch in macrophages when activated by lipopolysaccharide (LPS) stimulation or infection with the intracellular bacterial pathogen Brucella abortus. Importantly, these different inflammatory stimuli have distinct mechanisms of IRE1α activation; while Toll-like receptor 4 (TLR4) supports glycolysis under both conditions, TLR4 is required for activation of IRE1α in response to LPS treatment but not B. abortus infection. Though IRE1α and XBP1 are necessary for maximal induction of glycolysis in CAMs, activation of this pathway is not sufficient to increase the glycolytic rate of macrophages, indicating that the cellular context in which this pathway is activated ultimately dictates the cell's metabolic response and that IRE1α activation may be a way to fine-tune metabolic reprogramming. IMPORTANCE The immune system must be able to tailor its response to different types of pathogens in order to eliminate them and protect the host. When confronted with bacterial pathogens, macrophages, frontline defenders in the immune system, switch to a glycolysis-driven metabolism to carry out their antibacterial functions. Here, we show that IRE1α, a sensor of ER stress, and its downstream transcription factor XBP1 support glycolysis in macrophages during infection with Brucella abortus or challenge with Salmonella LPS. Interestingly, these stimuli activate IRE1α by independent mechanisms. While the IRE1α-XBP1 signaling axis promotes the glycolytic switch, activation of this pathway is not sufficient to increase glycolysis in macrophages. This study furthers our understanding of the pathways that drive macrophage immunometabolism and highlights a new role for IRE1α and XBP1 in innate immunity.

Published
2023

7. Salmonella re-engineers the intestinal environment to break colonization resistance in the presence of a compositionally intact microbiota

Author

Rogers, Andrew W.L., Radlinski, Lauren C., Nguyen, Henry, Tiffany, Connor R., Carvalho, Thaynara Parente, Masson, Hugo L.P., Goodson, Michael L., Bechtold, Lalita, Yamazaki, Kohei, Liou, Megan J., Miller, Brittany M., Mahan, Scott P., Young, Briana M., Demars, Aurore M., Gretler, Sophie R., Larabi, Anaïs B., Lee, Jee-Yon, Bays, Derek J., Tsolis, Renee M., and Bäumler, Andreas J.

Published
2024
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8. DNA: Dynamic Network Augmentation

Author

Mahan, Scott, Doster, Tim, and Kvinge, Henry

Subjects
Computer Science - Machine Learning
Abstract

In many classification problems, we want a classifier that is robust to a range of non-semantic transformations. For example, a human can identify a dog in a picture regardless of the orientation and pose in which it appears. There is substantial evidence that this kind of invariance can significantly improve the accuracy and generalization of machine learning models. A common technique to teach a model geometric invariances is to augment training data with transformed inputs. However, which invariances are desired for a given classification task is not always known. Determining an effective data augmentation policy can require domain expertise or extensive data pre-processing. Recent efforts like AutoAugment optimize over a parameterized search space of data augmentation policies to automate the augmentation process. While AutoAugment and similar methods achieve state-of-the-art classification accuracy on several common datasets, they are limited to learning one data augmentation policy. Often times different classes or features call for different geometric invariances. We introduce Dynamic Network Augmentation (DNA), which learns input-conditional augmentation policies. Augmentation parameters in our model are outputs of a neural network and are implicitly learned as the network weights are updated. Our model allows for dynamic augmentation policies and performs well on data with geometric transformations conditional on input features.

Published
2021

9. Host cells subdivide nutrient niches into discrete biogeographical microhabitats for gut microbes

Author

Liou, Megan J, Miller, Brittany M, Litvak, Yael, Nguyen, Henry, Natwick, Dean E, Savage, Hannah P, Rixon, Jordan A, Mahan, Scott P, Hiyoshi, Hirotaka, Rogers, Andrew WL, Velazquez, Eric M, Butler, Brian P, Collins, Sean R, McSorley, Stephen J, Harshey, Rasika M, Byndloss, Mariana X, Simon, Scott I, and Bäumler, Andreas J

Subjects
Microbiology, Medical Microbiology, Biomedical and Clinical Sciences, Biological Sciences, Biodefense, Digestive Diseases, Microbiome, Foodborne Illness, Infectious Diseases, Emerging Infectious Diseases, 2.2 Factors relating to the physical environment, 2.1 Biological and endogenous factors, Infection, Escherichia coli, Gastrointestinal Microbiome, Humans, Nitrates, Nutrients, Salmonella typhimurium, Enterobacterales, Salmonella, biogeography, chemotaxis, gut microbiota, nitrate, nutrient niches, Immunology, Biochemistry and cell biology, Medical microbiology
Abstract

Changes in the microbiota composition are associated with many human diseases, but factors that govern strain abundance remain poorly defined. We show that a commensal Escherichia coli strain and a pathogenic Salmonella enterica serovar Typhimurium isolate both utilize nitrate for intestinal growth, but each accesses this resource in a distinct biogeographical niche. Commensal E. coli utilizes epithelial-derived nitrate, whereas nitrate in the niche occupied by S. Typhimurium is derived from phagocytic infiltrates. Surprisingly, avirulent S. Typhimurium was shown to be unable to utilize epithelial-derived nitrate because its chemotaxis receptors McpB and McpC exclude the pathogen from the niche occupied by E. coli. In contrast, E. coli invades the niche constructed by S. Typhimurium virulence factors and confers colonization resistance by competing for nitrate. Thus, nutrient niches are not defined solely by critical resources, but they can be further subdivided biogeographically within the host into distinct microhabitats, thereby generating new niche opportunities for distinct bacterial species.

Published
2022

10. Rotating spiders and reflecting dogs: a class conditional approach to learning data augmentation distributions

Author

Mahan, Scott, Kvinge, Henry, and Doster, Tim

Subjects
Computer Science - Machine Learning
Abstract

Building invariance to non-meaningful transformations is essential to building efficient and generalizable machine learning models. In practice, the most common way to learn invariance is through data augmentation. There has been recent interest in the development of methods that learn distributions on augmentation transformations from the training data itself. While such approaches are beneficial since they are responsive to the data, they ignore the fact that in many situations the range of transformations to which a model needs to be invariant changes depending on the particular class input belongs to. For example, if a model needs to be able to predict whether an image contains a starfish or a dog, we may want to apply random rotations to starfish images during training (since these do not have a preferred orientation), but we would not want to do this to images of dogs. In this work we introduce a method by which we can learn class conditional distributions on augmentation transformations. We give a number of examples where our methods learn different non-meaningful transformations depending on class and further show how our method can be used as a tool to probe the symmetries intrinsic to a potentially complex dataset., Comment: 10 pages, 6 figures, submitted to NeurIPS 2021

Published
2021

11. Nonclosedness of Sets of Neural Networks in Sobolev Spaces

Author

Mahan, Scott, King, Emily, and Cloninger, Alex

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning
Abstract

We examine the closedness of sets of realized neural networks of a fixed architecture in Sobolev spaces. For an exactly $m$-times differentiable activation function $\rho$, we construct a sequence of neural networks $(\Phi_n)_{n \in \mathbb{N}}$ whose realizations converge in order-$(m-1)$ Sobolev norm to a function that cannot be realized exactly by a neural network. Thus, sets of realized neural networks are not closed in order-$(m-1)$ Sobolev spaces $W^{m-1,p}$ for $p \in [1,\infty]$. We further show that these sets are not closed in $W^{m,p}$ under slightly stronger conditions on the $m$-th derivative of $\rho$. For a real analytic activation function, we show that sets of realized neural networks are not closed in $W^{k,p}$ for any $k \in \mathbb{N}$. The nonclosedness allows for approximation of non-network target functions with unbounded parameter growth. We partially characterize the rate of parameter growth for most activation functions by showing that a specific sequence of realized neural networks can approximate the activation function's derivative with weights increasing inversely proportional to the $L^p$ approximation error. Finally, we present experimental results showing that networks are capable of closely approximating non-network target functions with increasing parameters via training.

Published
2020
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12. Smartphone-based pathogen diagnosis in urinary sepsis patients

Author

Barnes, Lucien, Heithoff, Douglas M, Mahan, Scott P, Fox, Gary N, Zambrano, Andrea, Choe, Jane, Fitzgibbons, Lynn N, Marth, Jamey D, Fried, Jeffrey C, Soh, H Tom, and Mahan, Michael J

Subjects
Medical Microbiology, Biomedical and Clinical Sciences, Clinical Sciences, Sepsis, Clinical Research, Bioengineering, Prevention, Hematology, Infectious Diseases, Urologic Diseases, 4.1 Discovery and preclinical testing of markers and technologies, Detection, screening and diagnosis, Infection, Inflammatory and immune system, Good Health and Well Being, Animals, Disease Models, Animal, Humans, Mice, Nucleic Acid Amplification Techniques, Point-of-Care Systems, Reproducibility of Results, Sensitivity and Specificity, Smartphone, Urinalysis, Urinary Tract Infections, Smartphone-based pathogen diagnosis, Urinary sepsis, Urinary tract infection, Urinary diagnostic test, Public Health and Health Services, Clinical sciences, Epidemiology
Abstract

BackgroundThere is an urgent need for rapid, sensitive, and affordable diagnostics for microbial infections at the point-of-care. Although a number of innovative systems have been reported that transform mobile phones into potential diagnostic tools, the translational challenge to clinical diagnostics remains a significant hurdle to overcome.MethodsA smartphone-based real-time loop-mediated isothermal amplification (smaRT-LAMP) system was developed for pathogen ID in urinary sepsis patients. The free, custom-built mobile phone app allows the phone to serve as a stand-alone device for quantitative diagnostics, allowing the determination of genome copy-number of bacterial pathogens in real time.FindingsA head-to-head comparative bacterial analysis of urine from sepsis patients revealed that the performance of smaRT-LAMP matched that of clinical diagnostics at the admitting hospital in a fraction of the time (~1 h vs. 18-28 h). Among patients with bacteremic complications of their urinary sepsis, pathogen ID from the urine matched that from the blood - potentially allowing pathogen diagnosis shortly after hospital admission. Additionally, smaRT-LAMP did not exhibit false positives in sepsis patients with clinically negative urine cultures.InterpretationThe smaRT-LAMP system is effective against diverse Gram-negative and -positive pathogens and biological specimens, costs less than $100 US to fabricate (in addition to the smartphone), and is configurable for the simultaneous detection of multiple pathogens. SmaRT-LAMP thus offers the potential to deliver rapid diagnosis and treatment of urinary tract infections and urinary sepsis with a simple test that can be performed at low cost at the point-of-care. FUND: National Institutes of Health, Chan-Zuckerberg Biohub, Bill and Melinda Gates Foundation.

Published
2018

13. Implications of Geometry and Topology on Deep Learning Capabilities

Author

Mahan, Scott

Subjects
Mathematics
Abstract

In this dissertation, we explore the impact of geometry and topology on the capabilities of deep learning models. Learning theory requires fitting a model to data while generalizing to unseen data as accurately possible. Naturally, properties of the underlying data distribution affect the ability of models to learn the risk-minimizing function. In addition, the sampling size and number of parameters required are quantities that can be improved by leveraging geometric and topological information. In Chapter 2, we show that under certain assumptions on a network activation function, sets of networks with fixed architecture are not closed in Sobolev spaces. This means that we can often approximate some functions and their derivatives to arbitrary accuracy, even if that function cannot be realized exactly by a neural network. However, doing so requires parameters to explode, which provides further insight on the approximation capabilities of neural networks.Chapter 3 analyzes the generalization benefits of data augmentation. Though data augmentation is widely believed to improve model generalization, we establish novel results with provably tighter bounds on generalization error under algorithmic and distributional assumptions. In particular, algorithms with strong stability criteria have improved generalization under data augmentation. Moreover, invariance properties of the data distribution can ensure that we learn the risk minimizing function with better generalization.We turn to semi-supervised learning in Chapter 4, where we demonstrate how autoencoders applied to separate charts on manifolds can decrease the model complexity required for reconstruction. Under assumptions on the geometry and topology of a manifold, we characterize how many charts are needed for encoding via linear projections. We also show that this approach has a mild impact on the decoder complexity, which depends only weakly on the ambient data dimension.Finally, Chapter 5 studies point cloud classification via a linear optimal transport embedding. We provide sufficient conditions for being able to nearly isometrically embed distributions into Euclidean space via input-convex neural networks trained on optimal transport maps. We can then linearly separate classes based on point cloud data sampled from the target distributions. Again, we leverage the underlying geometry of the data to improve model capabilities.

Published
2023

14. Sequence variants in SLC16A11 are a common risk factor for type 2 diabetes in Mexico

Author

Williams, Amy L, Jacobs, Suzanne BR, Moreno-Macías, Hortensia, Huerta-Chagoya, Alicia, Churchhouse, Claire, Márquez-Luna, Carla, García-Ortíz, Humberto, José Gómez-Vázquez, María, Burtt, Noël P, Aguilar-Salinas, Carlos A, González-Villalpando, Clicerio, Florez, Jose C, Orozco, Lorena, Haiman, Christopher A, Tusié-Luna, Teresa, Altshuler, David, Ripke, Stephan, Manning, Alisa K, Neale, Benjamin, Reich, David, Stram, Daniel O, Fernández-López, Juan Carlos, Romero-Hidalgo, Sandra, Patterson, Nick, Aguilar-Delfín, Irma, Martínez-Hernández, Angélica, Centeno-Cruz, Federico, Mendoza-Caamal, Elvia, Revilla-Monsalve, Cristina, Islas-Andrade, Sergio, Córdova, Emilio, Rodríguez-Arellano, Eunice, Soberón, Xavier, González-Villalpando, María Elena, Henderson, Brian E, Monroe, Kristine, Wilkens, Lynne, Kolonel, Laurence N, Le Marchand, Loic, Riba, Laura, Ordóñez-Sánchez, María Luisa, Rodríguez-Guillén, Rosario, Cruz-Bautista, Ivette, Rodríguez-Torres, Maribel, Muñoz-Hernández, Linda Liliana, Sáenz, Tamara, Gómez, Donají, Alvirde, Ulices, Onofrio, Robert C, Brodeur, Wendy M, Gage, Diane, Murphy, Jacquelyn, Franklin, Jennifer, Mahan, Scott, Ardlie, Kristin, Crenshaw, Andrew T, Winckler, Wendy, Prüfer, Kay, Shunkov, Michael V, Sawyer, Susanna, Stenzel, Udo, Kelso, Janet, Lek, Monkol, Sankararaman, Sriram, MacArthur, Daniel G, Derevianko, Anatoli P, Pääbo, Svante, Gopal, Shuba, Grammatikos, James A, Smith, Ian C, Bullock, Kevin H, Deik, Amy A, Souza, Amanda L, Pierce, Kerry A, Clish, Clary B, Fennell, Timothy, and Farjoun, Yossi

Subjects
Nutrition, Diabetes, Prevention, Genetics, 2.1 Biological and endogenous factors, Aetiology, Metabolic and endocrine, Alleles, Animals, Asian People, Black People, Cohort Studies, Diabetes Mellitus, Type 2, Endoplasmic Reticulum, Female, Genetic Predisposition to Disease, Genome-Wide Association Study, Haplotypes, HeLa Cells, Humans, Indians, North American, Lipid Metabolism, Liver, Male, Mexico, Monocarboxylic Acid Transporters, Neanderthals, Polymorphism, Single Nucleotide, RNA, Messenger, Triglycerides, White People, SIGMA Type 2 Diabetes Consortium, Hela Cells, General Science & Technology
Abstract

Performing genetic studies in multiple human populations can identify disease risk alleles that are common in one population but rare in others, with the potential to illuminate pathophysiology, health disparities, and the population genetic origins of disease alleles. Here we analysed 9.2 million single nucleotide polymorphisms (SNPs) in each of 8,214 Mexicans and other Latin Americans: 3,848 with type 2 diabetes and 4,366 non-diabetic controls. In addition to replicating previous findings, we identified a novel locus associated with type 2 diabetes at genome-wide significance spanning the solute carriers SLC16A11 and SLC16A13 (P = 3.9 × 10(-13); odds ratio (OR) = 1.29). The association was stronger in younger, leaner people with type 2 diabetes, and replicated in independent samples (P = 1.1 × 10(-4); OR = 1.20). The risk haplotype carries four amino acid substitutions, all in SLC16A11; it is present at ~50% frequency in Native American samples and ~10% in east Asian, but is rare in European and African samples. Analysis of an archaic genome sequence indicated that the risk haplotype introgressed into modern humans via admixture with Neanderthals. The SLC16A11 messenger RNA is expressed in liver, and V5-tagged SLC16A11 protein localizes to the endoplasmic reticulum. Expression of SLC16A11 in heterologous cells alters lipid metabolism, most notably causing an increase in intracellular triacylglycerol levels. Despite type 2 diabetes having been well studied by genome-wide association studies in other populations, analysis in Mexican and Latin American individuals identified SLC16A11 as a novel candidate gene for type 2 diabetes with a possible role in triacylglycerol metabolism.

Published
2014

16. Comprehensive genomic characterization defines human glioblastoma genes and core pathways

Author

McLendon, Roger, Friedman, Allan, Bigner, Darrell, Van Meir, Erwin G, Brat, Daniel J, M. Mastrogianakis, Gena, Olson, Jeffrey J, Mikkelsen, Tom, Lehman, Norman, Aldape, Ken, Alfred Yung, WK, Bogler, Oliver, VandenBerg, Scott, Berger, Mitchel, Prados, Michael, Muzny, Donna, Morgan, Margaret, Scherer, Steve, Sabo, Aniko, Nazareth, Lynn, Lewis, Lora, Hall, Otis, Zhu, Yiming, Ren, Yanru, Alvi, Omar, Yao, Jiqiang, Hawes, Alicia, Jhangiani, Shalini, Fowler, Gerald, San Lucas, Anthony, Kovar, Christie, Cree, Andrew, Dinh, Huyen, Santibanez, Jireh, Joshi, Vandita, Gonzalez-Garay, Manuel L, Miller, Christopher A, Milosavljevic, Aleksandar, Donehower, Larry, Wheeler, David A, Gibbs, Richard A, Cibulskis, Kristian, Sougnez, Carrie, Fennell, Tim, Mahan, Scott, Wilkinson, Jane, Ziaugra, Liuda, Onofrio, Robert, Bloom, Toby, Nicol, Rob, Ardlie, Kristin, Baldwin, Jennifer, Gabriel, Stacey, Lander, Eric S, Ding, Li, Fulton, Robert S, McLellan, Michael D, Wallis, John, Larson, David E, Shi, Xiaoqi, Abbott, Rachel, Fulton, Lucinda, Chen, Ken, Koboldt, Daniel C, Wendl, Michael C, Meyer, Rick, Tang, Yuzhu, Lin, Ling, Osborne, John R, Dunford-Shore, Brian H, Miner, Tracie L, Delehaunty, Kim, Markovic, Chris, Swift, Gary, Courtney, William, Pohl, Craig, Abbott, Scott, Hawkins, Amy, Leong, Shin, Haipek, Carrie, Schmidt, Heather, Wiechert, Maddy, Vickery, Tammi, Scott, Sacha, Dooling, David J, Chinwalla, Asif, Weinstock, George M, Mardis, Elaine R, Wilson, Richard K, Getz, Gad, Winckler, Wendy, Verhaak, Roel GW, Lawrence, Michael S, O’Kelly, Michael, Robinson, Jim, Alexe, Gabriele, Beroukhim, Rameen, Carter, Scott, Chiang, Derek, and Gould, Josh

Subjects
Cancer, Human Genome, Genetic Testing, Brain Cancer, Genetics, Brain Disorders, Biotechnology, Rare Diseases, Aetiology, 2.1 Biological and endogenous factors, Adolescent, Adult, Aged, Aged, 80 and over, Brain Neoplasms, DNA Methylation, DNA Modification Methylases, DNA Repair, DNA Repair Enzymes, Female, Gene Dosage, Gene Expression Regulation, Neoplastic, Genes, Tumor Suppressor, Genes, erbB-1, Genome, Human, Genomics, Glioblastoma, Humans, Male, Middle Aged, Models, Molecular, Mutation, Neurofibromin 1, Phosphatidylinositol 3-Kinases, Protein Structure, Tertiary, Retrospective Studies, Signal Transduction, Tumor Suppressor Proteins, Cancer Genome Atlas Research Network, General Science & Technology
Abstract

Human cancer cells typically harbour multiple chromosomal aberrations, nucleotide substitutions and epigenetic modifications that drive malignant transformation. The Cancer Genome Atlas (TCGA) pilot project aims to assess the value of large-scale multi-dimensional analysis of these molecular characteristics in human cancer and to provide the data rapidly to the research community. Here we report the interim integrative analysis of DNA copy number, gene expression and DNA methylation aberrations in 206 glioblastomas--the most common type of adult brain cancer--and nucleotide sequence aberrations in 91 of the 206 glioblastomas. This analysis provides new insights into the roles of ERBB2, NF1 and TP53, uncovers frequent mutations of the phosphatidylinositol-3-OH kinase regulatory subunit gene PIK3R1, and provides a network view of the pathways altered in the development of glioblastoma. Furthermore, integration of mutation, DNA methylation and clinical treatment data reveals a link between MGMT promoter methylation and a hypermutator phenotype consequent to mismatch repair deficiency in treated glioblastomas, an observation with potential clinical implications. Together, these findings establish the feasibility and power of TCGA, demonstrating that it can rapidly expand knowledge of the molecular basis of cancer.

Published
2008

17. Author Correction: Characterization of Nigerian breast cancer reveals prevalent homologous recombination deficiency and aggressive molecular features

Author

Pitt, Jason J., Riester, Markus, Zheng, Yonglan, Yoshimatsu, Toshio F., Sanni, Ayodele, Oluwasola, Olayiwola, Veloso, Artur, Labrot, Emma, Wang, Shengfeng, Odetunde, Abayomi, Ademola, Adeyinka, Okedere, Babajide, Mahan, Scott, Leary, Rebecca, Macomber, Maura, Ajani, Mustapha, Johnson, Ryan S., Fitzgerald, Dominic, Grundstad, A. Jason, Tuteja, Jigyasa H., Khramtsova, Galina, Zhang, Jing, Sveen, Elisabeth, Hwang, Bryce, Clayton, Wendy, Nkwodimmah, Chibuzor, Famooto, Bisola, Obasi, Esther, Aderoju, Victor, Oludara, Mobolaji, Omodele, Folusho, Akinyele, Odunayo, Adeoye, Adewunmi, Ogundiran, Temidayo, Babalola, Chinedum, MacIsaac, Kenzie, Popoola, Abiodun, Morrissey, Michael P., Chen, Lin S., Wang, Jiebiao, Olopade, Christopher O., Falusi, Adeyinka G., Winckler, Wendy, Haase, Kerstin, Van Loo, Peter, Obafunwa, John, Papoutsakis, Dimitris, Ojengbede, Oladosu, Weber, Barbara, Ibrahim, Nasiru, White, Kevin P., Huo, Dezheng, Olopade, Olufunmilayo I., and Barretina, Jordi

Published
2019
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19. A broad-spectrum synthetic antibiotic that does not evoke bacterial resistance

Author

Heithoff, Douglas M., primary, Mahan, Scott P., additional, Barnes V, Lucien, additional, Leyn, Semen A., additional, George, Cyril X., additional, Zlamal, Jaime E., additional, Limwongyut, Jakkarin, additional, Bazan, Guillermo C., additional, Fried, Jeffrey C., additional, Fitzgibbons, Lynn N., additional, House, John K., additional, Samuel, Charles E., additional, Osterman, Andrei L., additional, Low, David A., additional, and Mahan, Michael J., additional

Published
2023
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20. Characterization of Nigerian breast cancer reveals prevalent homologous recombination deficiency and aggressive molecular features

Author

Pitt, Jason J., Riester, Markus, Zheng, Yonglan, Yoshimatsu, Toshio F., Sanni, Ayodele, Oluwasola, Olayiwola, Veloso, Artur, Labrot, Emma, Wang, Shengfeng, Odetunde, Abayomi, Ademola, Adeyinka, Okedere, Babajide, Mahan, Scott, Leary, Rebecca, Macomber, Maura, Ajani, Mustapha, Johnson, Ryan S., Fitzgerald, Dominic, Grundstad, A. Jason, Tuteja, Jigyasa H., Khramtsova, Galina, Zhang, Jing, Sveen, Elisabeth, Hwang, Bryce, Clayton, Wendy, Nkwodimmah, Chibuzor, Famooto, Bisola, Obasi, Esther, Aderoju, Victor, Oludara, Mobolaji, Omodele, Folusho, Akinyele, Odunayo, Adeoye, Adewunmi, Ogundiran, Temidayo, Babalola, Chinedum, MacIsaac, Kenzie, Popoola, Abiodun, Morrissey, Michael P., Chen, Lin S., Wang, Jiebiao, Olopade, Christopher O., Falusi, Adeyinka G., Winckler, Wendy, Haase, Kerstin, Van Loo, Peter, Obafunwa, John, Papoutsakis, Dimitris, Ojengbede, Oladosu, Weber, Barbara, Ibrahim, Nasiru, White, Kevin P., Huo, Dezheng, Olopade, Olufunmilayo I., and Barretina, Jordi

Published
2018
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22. Addendum: The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity

Author

Barretina, Jordi, Caponigro, Giordano, Stransky, Nicolas, Venkatesan, Kavitha, Margolin, Adam A., Kim, Sungjoon, Wilson, Christopher J., Lehár, Joseph, Kryukov, Gregory V., Sonkin, Dmitriy, Reddy, Anupama, Liu, Manway, Murray, Lauren, Berger, Michael F., Monahan, John E., Morais, Paula, Meltzer, Jodi, Korejwa, Adam, Jané-Valbuena, Judit, Mapa, Felipa A., Thibault, Joseph, Bric-Furlong, Eva, Raman, Pichai, Shipway, Aaron, Engels, Ingo H., Cheng, Jill, Yu, Guoying K., Yu, Jianjun, Aspesi, Jr, Peter, de Silva, Melanie, Jagtap, Kalpana, Jones, Michael D., Wang, Li, Hatton, Charles, Palescandolo, Emanuele, Gupta, Supriya, Mahan, Scott, Sougnez, Carrie, Onofrio, Robert C., Liefeld, Ted, MacConaill, Laura, Winckler, Wendy, Reich, Michael, Li, Nanxin, Mesirov, Jill P., Gabriel, Stacey B., Getz, Gad, Ardlie, Kristin, Chan, Vivien, Myer, Vic E., Weber, Barbara L., Porter, Jeff, Warmuth, Markus, Finan, Peter, Harris, Jennifer L., Meyerson, Matthew, Golub, Todd R., Morrissey, Michael P., Sellers, William R., Schlegel, Robert, and Garraway, Levi A.

Published
2019
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24. Assessment of a Smartphone-Based Loop-Mediated Isothermal Amplification Assay for Detection of SARS-CoV-2 and Influenza Viruses

Author

Heithoff, Douglas M., primary, Barnes, Lucien, additional, Mahan, Scott P., additional, Fox, Gary N., additional, Arn, Katherine E., additional, Ettinger, Sarah J., additional, Bishop, Andrew M., additional, Fitzgibbons, Lynn N., additional, Fried, Jeffrey C., additional, Low, David A., additional, Samuel, Charles E., additional, and Mahan, Michael J., additional

Published
2022
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25. Smartphone-based pathogen diagnosis in urinary sepsis patients

Author

Barnes, Lucien, primary, Heithoff, Douglas M., additional, Mahan, Scott P., additional, Fox, Gary N., additional, Zambrano, Andrea, additional, Choe, Jane, additional, Fitzgibbons, Lynn N., additional, Marth, Jamey D., additional, Fried, Jeffrey C., additional, Soh, H. Tom, additional, and Mahan, Michael J., additional

Published
2018
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26. Association of MTOR Mutations With Developmental Brain Disorders, Including Megalencephaly, Focal Cortical Dysplasia, and Pigmentary Mosaicism

Author

Mirzaa, Ghayda M., Campbell, Catarina D., Solovieff, Nadia, Goold, Carleton P., Jansen, Laura A., Menon, Suchithra, Timms, Andrew E., Conti, Valerio, Biag, Jonathan D., Olds, Carissa, Boyle, Evan August, Collins, Sarah, Ishak, Gisele, Poliachik, Sandra L., Girisha, Katta M., Yeung, Kit-San, Chung, Brian Hon Yin, Rahikkala, Elisa, Gunter, Sonya A., McDaniel, Sharon S., Macmurdo, Colleen Forsyth, Bernstein, Jonathan A., Martin, Beth, Leary, Rebecca J., Mahan, Scott, Liu, Shanming, Weaver, Molly, Dorschner, Michael O., Jhangiani, Shalini, Muzny, Donna M., Boerwinkle, Eric, Gibbs, Richard A., Lupski, James R., Shendure, Jay, Saneto, Russell P., Novotny, Edward J., Wilson, Christopher J., Sellers, William R., Morrissey, Michael P., Hevner, Robert F., Ojemann, Jeffrey G., Guerrini, Renzo, Murphy, Leon O., Winckler, Wendy, and Dobyns, William B.

Abstract

IMPORTANCE: Focal cortical dysplasia (FCD), hemimegalencephaly, and megalencephaly constitute a spectrum of malformations of cortical development with shared neuropathologic features. These disorders are associated with significant childhood morbidity and mortality. OBJECTIVE: To identify the underlying molecular cause of FCD, hemimegalencephaly, and diffuse megalencephaly. DESIGN, SETTING, AND PARTICIPANTS: Patients with FCD, hemimegalencephaly, or megalencephaly (mean age, 11.7 years; range, 2-32 years) were recruited from Pediatric Hospital A. Meyer, the University of Hong Kong, and Seattle Children’s Research Institute from June 2012 to June 2014. Whole-exome sequencing (WES) was performed on 8 children with FCD or hemimegalencephaly using standard-depth (50-60X) sequencing in peripheral samples (blood, saliva, or skin) from the affected child and their parents and deep (150-180X) sequencing in affected brain tissue. Targeted sequencing and WES were used to screen 93 children with molecularly unexplained diffuse or focal brain overgrowth. Histopathologic and functional assays of phosphatidylinositol 3-kinase–AKT (serine/threonine kinase)–mammalian target of rapamycin (mTOR) pathway activity in resected brain tissue and cultured neurons were performed to validate mutations. MAIN OUTCOMES AND MEASURES: Whole-exome sequencing and targeted sequencing identified variants associated with this spectrum of developmental brain disorders. RESULTS: Low-level mosaic mutations of MTOR were identified in brain tissue in 4 children with FCD type 2a with alternative allele fractions ranging from 0.012 to 0.086. Intermediate-level mosaic mutation of MTOR (p.Thr1977Ile) was also identified in 3 unrelated children with diffuse megalencephaly and pigmentary mosaicism in skin. Finally, a constitutional de novo mutation of MTOR (p.Glu1799Lys) was identified in 3 unrelated children with diffuse megalencephaly and intellectual disability. Molecular and functional analysis in 2 children with FCD2a from whom multiple affected brain tissue samples were available revealed a mutation gradient with an epicenter in the most epileptogenic area. When expressed in cultured neurons, all MTOR mutations identified here drive constitutive activation of mTOR complex 1 and enlarged neuronal size. CONCLUSIONS AND RELEVANCE: In this study, mutations of MTOR were associated with a spectrum of brain overgrowth phenotypes extending from FCD type 2a to diffuse megalencephaly, distinguished by different mutations and levels of mosaicism. These mutations may be sufficient to cause cellular hypertrophy in cultured neurons and may provide a demonstration of the pattern of mosaicism in brain and substantiate the link between mosaic mutations of MTOR and pigmentary mosaicism in skin.

Published
2016
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27. Collaborative Design for Automated DNA Storage That Allows for Rapid, Accurate, Large-Scale Studies

Author

Mahan, Scott, Ardlie, Kristin G., Krenitsky, Kevin F., Walsh, Gary, and Clough, Graham

Abstract

Genomics Collaborative, Inc., a division of Sera Care Life Sciences, Inc. (Cambridge, MA), is among the first commercial entities in the world to enable genetic research on an industrial scale via its Large Scale Global Repository™, a biobank of human specimens collected for research purposes. With the demand for large-scale DNA studies increasing, decisions about the strategic direction of sample storage and collection must be made to create a sound plan to support continued demands for drug discovery. Reported here is the approach used by Genomics Collaborative to automate its DNA processing, storage, and retrieval.

Published
2004
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28. Melanoma genome sequencing reveals frequent PREX2 mutations

Author

Berger, Michael F., Hodis, Eran, Heffernan, Timothy P., Deribe, Yonathan Lissanu, Lawrence, Michael S., Protopopov, Alexei, Ivanova, Elena, Watson, Ian R., Nickerson, Elizabeth, Ghosh, Papia, Zhang, Hailei, Zeid, Rhamy, Ren, Xiaojia, Cibulskis, Kristian, Sivachenko, Andrey Y., Wagle, Nikhil, Sucker, Antje, Sougnez, Carrie, Onofrio, Robert, Ambrogio, Lauren, Auclair, Daniel, Fennell, Timothy, Carter, Scott L., Drier, Yotam, Stojanov, Petar, Singer, Meredith A., Voet, Douglas, Jing, Rui, Saksena, Gordon, Barretina, Jordi, Ramos, Alex H., Pugh, Trevor J., Stransky, Nicolas, Parkin, Melissa, Winckler, Wendy, Mahan, Scott, Ardlie, Kristin, Baldwin, Jennifer, Wargo, Jennifer, Schadendorf, Dirk, Meyerson, Matthew, Gabriel, Stacey B., Golub, Todd R., Wagner, Stephan N., Lander, Eric S., Getz, Gad, Chin, Lynda, and Garraway, Levi A.

Abstract

Melanoma is notable for its metastatic propensity, lethality in the advanced setting, and association with ultraviolet (UV) exposure early in life1. To obtain a comprehensive genomic view of melanoma, we sequenced the genomes of 25 metastatic melanomas and matched germline DNA. A wide range of point mutation rates was observed: lowest in melanomas whose primaries arose on non-UV exposed hairless skin of the extremities (3 and 14 per Mb genome), intermediate in those originating from hair-bearing skin of the trunk (range = 5 to 55 per Mb), and highest in a patient with a documented history of chronic sun exposure (111 per Mb). Analysis of whole-genome sequence data identified PREX2 - a PTEN-interacting protein and negative regulator of PTEN in breast cancer2 - as a significantly mutated gene with a mutation frequency of approximately 14% in an independent extension cohort of 107 human melanomas. PREX2 mutations are biologically relevant, as ectopic expression of mutant PREX2 accelerated tumor formation of immortalized human melanocytes in vivo. Thus, whole-genome sequencing of human melanoma tumors revealed genomic evidence of UV pathogenesis and discovered a new recurrently mutated gene in melanoma.

Published
2012
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