Browsing by Author "Cummings, Michael P."
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Item Can RNA-Seq Resolve the Rapid Radiation of Advanced Moths and Butterflies (Hexapoda: Lepidoptera: Apoditrysia)? An Exploratory Study(PLoS One, 2013-12-04) Bazinet, Adam L.; Cummings, Michael P.; Mitter, Kim T.; Mitter, Charles W.Recent molecular phylogenetic studies of the insect order Lepidoptera have robustly resolved family-level divergences within most superfamilies, and most divergences among the relatively species-poor early-arising superfamilies. In sharp contrast, relationships among the superfamilies of more advanced moths and butterflies that comprise the mega-diverse clade Apoditrysia (ca. 145,000 spp.) remain mostly poorly supported. This uncertainty, in turn, limits our ability to discern the origins, ages and evolutionary consequences of traits hypothesized to promote the spectacular diversification of Apoditrysia. Low support along the apoditrysian “backbone” probably reflects rapid diversification. If so, it may be feasible to strengthen resolution by radically increasing the gene sample, but case studies have been few. We explored the potential of next-generation sequencing to conclusively resolve apoditrysian relationships. We used transcriptome RNA-Seq to generate 1579 putatively orthologous gene sequences across a broad sample of 40 apoditrysians plus four outgroups, to which we added two taxa from previously published data. Phylogenetic analysis of a 46-taxon, 741-gene matrix, resulting from a strict filter that eliminated ortholog groups containing any apparent paralogs, yielded dramatic overall increase in bootstrap support for deeper nodes within Apoditrysia as compared to results from previous and concurrent 19-gene analyses. High support was restricted mainly to the huge subclade Obtectomera broadly defined, in which 11 of 12 nodes subtending multiple superfamilies had bootstrap support of 100%. The strongly supported nodes showed little conflict with groupings from previous studies, and were little affected by changes in taxon sampling, suggesting that they reflect true signal rather than artifacts of massive gene sampling. In contrast, strong support was seen at only 2 of 11 deeper nodes among the “lower”, non-obtectomeran apoditrysians. These represent a much harder phylogenetic problem, for which one path to resolution might include further increase in gene sampling, together with improved orthology assignments.Item A comparative evaluation of sequence classification programs(2012-05-10) Bazinet, Adam L.; Cummings, Michael P.Background: A fundamental problem in modern genomics is to taxonomically or functionally classify DNA sequence fragments derived from environmental sampling (i.e., metagenomics). Several different methods have been proposed for doing this effectively and efficiently, and many have been implemented in software. In addition to varying their basic algorithmic approach to classification, some methods screen sequence reads for ’barcoding genes’ like 16S rRNA, or various types of protein-coding genes. Due to the sheer number and complexity of methods, it can be difficult for a researcher to choose one that is well-suited for a particular analysis. Results: We divided the very large number of programs that have been released in recent years for solving the sequence classification problem into three main categories based on the general algorithm they use to compare a query sequence against a database of sequences. We also evaluated the performance of the leading programs in each category on data sets whose taxonomic and functional composition is known. Conclusions: We found significant variability in classification accuracy, precision, and resource consumption of sequence classification programs when used to analyze various metagenomics data sets. However, we observe some general trends and patterns that will be useful to researchers who use sequence classification programs.Item Phylogeny of Cladobranchia (Gastropoda: Nudibranchia): a total evidence analysis using DNA sequence data from public databases(2015-07) Goodheart, Jessica A.; Bazinet, Adam L.; Collins, Allen G.; Cummings, Michael P.Cladobranchia is a clade of charismatic and exclusively marine slugs (Gastropoda: Nudibranchia). Though Cladobranchia and its sister taxon, Anthobranchia, have been supported by molecular data, little resolution among the higher-level groups within these two clades has emerged from previous analyses. Cladobranchia is traditionally divided into three taxa (Dendronotida, Euarminida, and Aeolidida), none of which have been supported by molecular phylogenetic studies. Reconstructions of the evolutionary relationships within Cladobranchia have resulted in poorly supported phylogenies, rife with polytomies and non-monophyletic groups contradicting previous taxonomic hypotheses. In this study, we present a working hypothesis for the evolutionary history of Cladobranchia, utilizing publicly available data that have been generated since the last attempt at a detailed phylogeny for this group (we include approximately 200 more taxa and a total of five genes). Our results resolve Cladobranchia as monophyletic and provide support for a small proportion of genera and families, but it is clear that the presently available data are insufficient to provide a robust and well-resolved phylogeny of these taxa as a whole.Item Prey preference follows phylogeny: evolutionary dietary patterns within the marine gastropod group Cladobranchia (Gastropoda: Heterobranchia: Nudibranchia)(Springer Nature, 2017-10-26) Goodheart, Jessica A.; Bazinet, Adam L.; Valdés, Ángel; Collins, Allen G.; Cummings, Michael P.The impact of predator-prey interactions on the evolution of many marine invertebrates is poorly understood. Since barriers to genetic exchange are less obvious in the marine realm than in terrestrial or freshwater systems, non-allopatric divergence may play a fundamental role in the generation of biodiversity. In this context, shifts between major prey types could constitute important factors explaining the biodiversity of marine taxa, particularly in groups with highly specialized diets. However, the scarcity of marine specialized consumers for which reliable phylogenies exist hampers attempts to test the role of trophic specialization in evolution. In this study, RNA-Seq data is used to produce a phylogeny of Cladobranchia, a group of marine invertebrates that feed on a diverse array of prey taxa but mostly specialize on cnidarians. The broad range of prey type preferences allegedly present in two major groups within Cladobranchia suggest that prey type shifts are relatively common over evolutionary timescales. In the present study, we generated a well-supported phylogeny of the major lineages within Cladobranchia using RNA-Seq data, and used ancestral state reconstruction analyses to better understand the evolution of prey preference. These analyses answered several fundamental questions regarding the evolutionary relationships within Cladobranchia, including support for a clade of species from Arminidae as sister to Tritoniidae (which both preferentially prey on Octocorallia). Ancestral state reconstruction analyses supported a cladobranchian ancestor with a preference for Hydrozoa and show that the few transitions identified only occur from lineages that prey on Hydrozoa to those that feed on other types of prey. There is strong phylogenetic correlation with prey preference within Cladobranchia, suggesting that prey type specialization within this group has inertia. Shifts between different types of prey have occurred rarely throughout the evolution of Cladobranchia, indicating that this may not have been an important driver of the diversity within this group.Item Supplementary material for machine learning analysis of data from a simplified mobility testing procedure with a single sensor and single task accurately differentiates Parkinson's disease from controls(2023) Khalil, Rana M.; Shulman, Lisa M.; Gruber-Baldini, Ann L.; Shakya, Sunita; von Coelln, Rainer; Cummings, Michael P.; Fenderson, Rebecca; van Hoven, Maxwell; Hausdorff, Jeffrey M.; Cummings, Michael P.Quantitative mobility analysis using wearable sensors, while promising as a diagnostic tool for Parkinson's disease (PD), is not commonly applied in clinical settings. Major obstacles include uncertainty regarding the best protocol for instrumented mobility testing and subsequent data processing, as well as the added workload and complexity of this multi-step process. To simplify sensor-based mobility testing in diagnosing PD, we analyzed data from 262 PD participants and 50 controls performing several motor tasks wearing a sensor on the lower back containing a triaxial accelerometer and a triaxial gyroscope. Using ensembles of heterogeneous machine learning models incorporating a range of classifiers trained on a large set of sensor features, we show that our models effectively differentiate between participants with PD and controls, both for mixed-stage PD (92.6% accuracy) and a group selected for mild PD only (89.4% accuracy). Omitting algorithmic segmentation of complex mobility tasks decreased the diagnostic accuracy of our models, as did the inclusion of kinesiological features. Feature importance analysis revealed Timed Up & Go (TUG) tasks to contribute highest-yield predictive features, with only minor decrease in accuracy for models based on cognitive TUG as a single mobility task. Our machine learning approach facilitates major simplification of instrumented mobility testing without compromising predictive performance.Item Supplementary material for Machine learning analysis of wearable sensor data from mobility testing distinguishes Parkinson's disease from other forms of parkinsonism(2024-03-13) Khalil, Rana M.; Shulman, Lisa M.; Gruber-Baldini, Ann L.; Hausdorff, Jeffrey M.; von Coelln, Rainer; Cummings, Michael P.; Cummings, Michael P.Parkinson's Disease (PD) and other forms of parkinsonism share characteristic motor symptoms, including tremor, bradykinesia, and rigidity. This overlap in the clinical presentation creates a diagnostic challenge, underscoring the need for objective differentiation tools. In this study, we analyzed wearable sensor data collected during mobility testing from 260 PD participants and 18 participants with etiologically diverse forms of parkinsonism. Our findings illustrate that machine learning-based analysis of data from a single wearable sensor can effectively distinguish idiopathic PD from non-PD parkinsonism with a balanced accuracy of 83.5%, comparable to expert diagnosis. Moreover, we found that diagnostic performance can be improved through severity-based partitioning of participants, achieving a balanced accuracy of 95.9%, 91.2% and 100% for mild, moderate and severe cases, respectively. Beyond its diagnostic implications, our results suggest the possibility of streamlining the testing protocol by using the Timed Up and Go test as a single mobility task. Furthermore, we present a detailed analysis of several case studies of challenging scenarios commonly encountered in clinical practice, including diagnostic uncertainty at the initial visit, and changes in clinical diagnosis at a subsequent visit. Together, these findings demonstrate the potential of applying machine learning on sensor-based measures of mobility to distinguish between PD and other forms of parkinsonism.Item Supplementary materials for machine learning-driven multifunctional peptide engineering for sustained ocular drug delivery(2023) Chou, Renee Ti; Hsueh, Henry T.; Rai, Usha; Liyanage, Wathsala; Kim, Yoo Chun; Appell, Matthew B.; Pejavar, Jahnavi; Leo, Kirby T.; Davison, Charlotte; Kolodziejski, Patricia; Mozzer, Ann; Kwon, HyeYoung; Sista, Maanasa; Anders, Nicole M.; Hemingway, Avelina; Rompicharla, Sri Vishnu Kiran; Edwards, Malia; Pitha, Ian; Hanes, Justin; Cummings, Michael P.; Ensign, Laura M.; Cummings, Michael P.; Ensign, Laura M.Sustained drug delivery strategies have many potential benefits for treating a range of diseases, particularly chronic diseases that require treatment for years. For many chronic ocular diseases, patient adherence to eye drop dosing regimens and the need for frequent intraocular injections are significant barriers to effective disease management. Here, we utilize peptide engineering to impart melanin binding properties to peptide-drug conjugates to act as a sustained-release depot in the eye. We developed a super learning-based methodology to engineer multifunctional peptides that efficiently enter cells, bind to melanin, and have low cytotoxicity. When the lead multifunctional peptide (HR97) was conjugated to brimonidine, an intraocular pressure (IOP)-lowering drug that is prescribed for three times per day topical dosing, IOP reduction was observed for up to 18 days after a single intracameral HR97-brimonidine injection in rabbits. Further, the cumulative IOP-lowering effect was increased ~17-fold compared to free brimonidine injection. Engineered multifunctional peptide-drug conjugates are a promising approach for providing sustained therapeutic delivery in the eye and beyond.Item Supplementary materials for Plasmodium vivax antigen candidate prediction improves with the addition of Plasmodium falciparum data(2023) Chou, Renee Ti; Ouattara, Amed; Takala-Harrison, Shannon; Cummings, Michael P.Intensive malaria control and elimination efforts have led to substantial reductions in malaria incidence over the past two decades. However, the reduction in Plasmodium falciparum malaria cases has led to a species shift in some geographic areas, with P. vivax predominating in many areas outside of Africa. Despite its wide geographic distribution, P. vivax vaccine development has lagged far behind that for P. falciparum, in part due to the inability to cultivate P. vivax in vitro, hindering traditional approaches for antigen identification. In a prior study, we have used a positive-unlabeled random forest (PURF) machine learning approach to identify P. falciparum antigens for consideration in vaccine development efforts. Here we integrate systems data from P. falciparum (the better-studied species) to improve PURF models to predict potential P. vivax vaccine antigen candidates. We further show that inclusion of known antigens from the other species is critical for model performance, but the inclusion of unlabeled proteins the other species can result in misdirection of the model toward predictors of species classification, rather than antigen identification. Beyond malaria, incorporating antigens from a closely related species may aid in vaccine development for emerging pathogens having few or no known antigens.Item Supplementary materials for positive-unlabeled learning identifies vaccine candidate antigens in the malaria parasite Plasmodium falciparum(2023) Chou, Renee Ti; Ouattara, Amed; Adams, Matthew; Berry, Andrea A.; Takala-Harrison, Shannon; Cummings, Michael P.Malaria vaccine development is hampered by extensive antigenic variation and complex life stages of Plasmodium species. Vaccine development has focused on a small number of antigens identified prior to availability of the P. falciparum genome. In this study, we implement a machine learning-based reverse vaccinology approach to predict potential new malaria vaccine candidate antigens. We assemble and analyze P. falciparum proteomic, structural, functional, immunological, genomic, and transcriptomic data, and use positive-unlabeled learning to predict potential antigens based on the properties of known antigens and remaining proteins. We prioritize candidate antigens based on model performance on reference antigens with different genetic diversity and quantify the protein properties that contribute the most to identifying top candidates. Candidate antigens are characterized by gene essentiality, gene ontology, and gene expression in different life stages to inform future vaccine development. This approach provides a framework for identifying and prioritizing candidate vaccine antigens for a broad range of pathogens.Item Supplementary materials for statistical and machine learning analyses demonstrate test-retest reliability assessment is misled by focusing on total duration of mobility tasks in Parkinson's disease(2023) Khalil, Rana M.; Shulman, Lisa M.; Gruber-Baldini, Ann L.; Shakya, Sunita; Hausdorff, Jeffrey M.; von Coelln, Rainer; Cummings, Michael P.; Cummings, Michael P.Mobility tasks like the Timed Up and Go test (TUG), cognitive TUG (cogTUG), and walking with turns provide insight into dynamic motor control, balance, and cognitive functions affected by Parkinson’s disease (PD). We evaluate the test-retest reliability of these tasks by assessing the performance of machine learning models based on quantitative sensor-derived measures, and statistical measures to examine total duration, subtask duration, and other quantitative measures across both trials. We show that the diagnostic accuracy of differentiating between PD and control participants decreases from the first to the second trial of our mobility tasks, suggesting that mobility testing can be simplified by not repeating tasks without losing relevant information. Although the total duration remains relatively consistent between trials, there is more variability in subtask duration and sensor-derived measures, evident in the differences in machine learning model performance and statistical metrics. Relying solely on total task duration and conventional statistical metrics to gauge the reliability of mobility tasks overlooks the nuanced variations in movement captured by other quantitative measures.