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Article ; Online: Missing data in multi-omics integration

Javier E. Flores / Daniel M. Claborne / Zachary D. Weller / Bobbie-Jo M. Webb-Robertson / Katrina M. Waters / Lisa M. Bramer

Frontiers in Artificial Intelligence, Vol

Recent advances through artificial intelligence

2023 Volume 6

Abstract: Biological systems function through complex interactions between various ‘omics (biomolecules), and a more complete understanding of these systems is only possible through an integrated, multi-omic perspective. This has presented the need for the ... ...

Abstract	Biological systems function through complex interactions between various ‘omics (biomolecules), and a more complete understanding of these systems is only possible through an integrated, multi-omic perspective. This has presented the need for the development of integration approaches that are able to capture the complex, often non-linear, interactions that define these biological systems and are adapted to the challenges of combining the heterogenous data across ‘omic views. A principal challenge to multi-omic integration is missing data because all biomolecules are not measured in all samples. Due to either cost, instrument sensitivity, or other experimental factors, data for a biological sample may be missing for one or more ‘omic techologies. Recent methodological developments in artificial intelligence and statistical learning have greatly facilitated the analyses of multi-omics data, however many of these techniques assume access to completely observed data. A subset of these methods incorporate mechanisms for handling partially observed samples, and these methods are the focus of this review. We describe recently developed approaches, noting their primary use cases and highlighting each method's approach to handling missing data. We additionally provide an overview of the more traditional missing data workflows and their limitations; and we discuss potential avenues for further developments as well as how the missing data issue and its current solutions may generalize beyond the multi-omics context.
Keywords	data integration ; missing data ; multi-omics ; multi-view ; artificial intelligence ; machine learning ; Electronic computers. Computer science ; QA75.5-76.95
Subject code	006
Language	English
Publishing date	2023-02-01T00:00:00Z
Publisher	Frontiers Media S.A.
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Decrease in multiple complement proteins associated with development of islet autoimmunity and type 1 diabetes

Bobbie-Jo M. Webb-Robertson / Ernesto S. Nakayasu / Fran Dong / Kathy C. Waugh / Javier E. Flores / Lisa M. Bramer / Athena A. Schepmoes / Yuqian Gao / Thomas L. Fillmore / Suna Onengut-Gumuscu / Ashley Frazer-Abel / Stephen S. Rich / V. Michael Holers / Thomas O. Metz / Marian J. Rewers

iScience, Vol 27, Iss 2, Pp 108769- (2024)

2024

Abstract: Summary: Type 1 diabetes (T1D) is a chronic condition caused by autoimmune destruction of the insulin-producing pancreatic β cells. While it is known that gene-environment interactions play a key role in triggering the autoimmune process leading to T1D, ... ...

Abstract	Summary: Type 1 diabetes (T1D) is a chronic condition caused by autoimmune destruction of the insulin-producing pancreatic β cells. While it is known that gene-environment interactions play a key role in triggering the autoimmune process leading to T1D, the pathogenic mechanism leading to the appearance of islet autoantibodies—biomarkers of autoimmunity—is poorly understood. Here we show that disruption of the complement system precedes the detection of islet autoantibodies and persists through disease onset. Our results suggest that children who exhibit islet autoimmunity and progress to clinical T1D have lower complement protein levels relative to those who do not progress within a similar time frame. Thus, the complement pathway, an understudied mechanistic and therapeutic target in T1D, merits increased attention for use as protein biomarkers of prediction and potentially prevention of T1D.
Keywords	Immunology ; Diabetology ; Proteomics ; Science ; Q
Language	English
Publishing date	2024-02-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Better understanding and prediction of antiviral peptides through primary and secondary structure feature importance

Abu Sayed Chowdhury / Sarah M. Reehl / Kylene Kehn-Hall / Barney Bishop / Bobbie-Jo M. Webb-Robertson

Scientific Reports, Vol 10, Iss 1, Pp 1-

2020 Volume 8

Abstract: Abstract The emergence of viral epidemics throughout the world is of concern due to the scarcity of available effective antiviral therapeutics. The discovery of new antiviral therapies is imperative to address this challenge, and antiviral peptides (AVPs) ...

Abstract	Abstract The emergence of viral epidemics throughout the world is of concern due to the scarcity of available effective antiviral therapeutics. The discovery of new antiviral therapies is imperative to address this challenge, and antiviral peptides (AVPs) represent a valuable resource for the development of novel therapies to combat viral infection. We present a new machine learning model to distinguish AVPs from non-AVPs using the most informative features derived from the physicochemical and structural properties of their amino acid sequences. To focus on those features that are most likely to contribute to antiviral performance, we filter potential features based on their importance for classification. These feature selection analyses suggest that secondary structure is the most important peptide sequence feature for predicting AVPs. Our Feature-Informed Reduced Machine Learning for Antiviral Peptide Prediction (FIRM-AVP) approach achieves a higher accuracy than either the model with all features or current state-of-the-art single classifiers. Understanding the features that are associated with AVP activity is a core need to identify and design new AVPs in novel systems. The FIRM-AVP code and standalone software package are available at https://github.com/pmartR/FIRM-AVP with an accompanying web application at https://msc-viz.emsl.pnnl.gov/AVPR .
Keywords	Medicine ; R ; Science ; Q
Subject code	004
Language	English
Publishing date	2020-11-01T00:00:00Z
Publisher	Nature Publishing Group
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A resource of lipidomics and metabolomics data from individuals with undiagnosed diseases

Jennifer E. Kyle / Kelly G. Stratton / Erika M. Zink / Young-Mo Kim / Kent J. Bloodsworth / Matthew E. Monroe / Undiagnosed Diseases Network / Katrina M. Waters / Bobbie-Jo M. Webb-Robertson / David M. Koeller / Thomas O. Metz

Scientific Data, Vol 8, Iss 1, Pp 1-

2021 Volume 12

Abstract: Measurement(s) Metabolomics • Lipidomics Technology Type(s) gas chromatography-mass spectrometry • Ultra High-performance Liquid Chromatography/Tandem Mass Spectrometry Factor Type(s) age group • sex Sample Characteristic - Organism Homo sapiens Sample ... ...

Abstract	Measurement(s) Metabolomics • Lipidomics Technology Type(s) gas chromatography-mass spectrometry • Ultra High-performance Liquid Chromatography/Tandem Mass Spectrometry Factor Type(s) age group • sex Sample Characteristic - Organism Homo sapiens Sample Characteristic - Environment blood plasma material • urine material • cerebrospinal fluid material Sample Characteristic - Location United States of America Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.13656581
Keywords	Science ; Q
Language	English
Publishing date	2021-04-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Itaconic acid production is regulated by LaeA in Aspergillus pseudoterreus

Kyle R. Pomraning / Ziyu Dai / Nathalie Munoz / Young-Mo Kim / Yuqian Gao / Shuang Deng / Teresa Lemmon / Marie S. Swita / Jeremy D. Zucker / Joonhoon Kim / Stephen J. Mondo / Ellen Panisko / Meagan C. Burnet / Bobbie-Jo M. Webb-Robertson / Beth Hofstad / Scott E. Baker / Kristin E. Burnum-Johnson / Jon K. Magnuson

Metabolic Engineering Communications, Vol 15, Iss , Pp e00203- (2022)

2022

Abstract: The global regulator LaeA controls secondary metabolism in diverse Aspergillus species. Here we explored its role in regulation of itaconic acid production in Aspergillus pseudoterreus. To understand its role in regulating metabolism, we deleted and ... ...

Abstract	The global regulator LaeA controls secondary metabolism in diverse Aspergillus species. Here we explored its role in regulation of itaconic acid production in Aspergillus pseudoterreus. To understand its role in regulating metabolism, we deleted and overexpressed laeA, and assessed the transcriptome, proteome, and secreted metabolome prior to and during initiation of phosphate limitation induced itaconic acid production. We found that secondary metabolite clusters, including the itaconic acid biosynthetic gene cluster, are regulated by laeA and that laeA is required for high yield production of itaconic acid. Overexpression of LaeA improves itaconic acid yield at the expense of biomass by increasing the expression of key biosynthetic pathway enzymes and attenuating the expression of genes involved in phosphate acquisition and scavenging. Increased yield was observed in optimized conditions as well as conditions containing excess nutrients that may be present in inexpensive sugar containing feedstocks such as excess phosphate or complex nutrient sources. This suggests that global regulators of metabolism may be useful targets for engineering metabolic flux that is robust to environmental heterogeneity.
Keywords	Aspergillus pseudoterreus ; Itaconic acid ; laeA ; Process robustness ; Multi-omics ; Phosphate ; Biotechnology ; TP248.13-248.65 ; Biology (General) ; QH301-705.5
Language	English
Publishing date	2022-12-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A genomic data archive from the Network for Pancreatic Organ donors with Diabetes

Daniel J. Perry / Melanie R. Shapiro / Sonya W. Chamberlain / Irina Kusmartseva / Srikar Chamala / Leandro Balzano-Nogueira / Mingder Yang / Jason O. Brant / Maigan Brusko / MacKenzie D. Williams / Kieran M. McGrail / James McNichols / Leeana D. Peters / Amanda L. Posgai / John S. Kaddis / Clayton E. Mathews / Clive H. Wasserfall / Bobbie-Jo M. Webb-Robertson / Martha Campbell-Thompson /

Desmond Schatz / Carmella Evans-Molina / Alberto Pugliese / Patrick Concannon / Mark S. Anderson / Michael S. German / Chester E. Chamberlain / Mark A. Atkinson / Todd M. Brusko

Scientific Data, Vol 10, Iss 1, Pp 1-

2023 Volume 16

Abstract: Abstract The Network for Pancreatic Organ donors with Diabetes (nPOD) is the largest biorepository of human pancreata and associated immune organs from donors with type 1 diabetes (T1D), maturity-onset diabetes of the young (MODY), cystic fibrosis- ... ...

Abstract	Abstract The Network for Pancreatic Organ donors with Diabetes (nPOD) is the largest biorepository of human pancreata and associated immune organs from donors with type 1 diabetes (T1D), maturity-onset diabetes of the young (MODY), cystic fibrosis-related diabetes (CFRD), type 2 diabetes (T2D), gestational diabetes, islet autoantibody positivity (AAb+), and without diabetes. nPOD recovers, processes, analyzes, and distributes high-quality biospecimens, collected using optimized standard operating procedures, and associated de-identified data/metadata to researchers around the world. Herein describes the release of high-parameter genotyping data from this collection. 372 donors were genotyped using a custom precision medicine single nucleotide polymorphism (SNP) microarray. Data were technically validated using published algorithms to evaluate donor relatedness, ancestry, imputed HLA, and T1D genetic risk score. Additionally, 207 donors were assessed for rare known and novel coding region variants via whole exome sequencing (WES). These data are publicly-available to enable genotype-specific sample requests and the study of novel genotype:phenotype associations, aiding in the mission of nPOD to enhance understanding of diabetes pathogenesis to promote the development of novel therapies.
Keywords	Science ; Q
Subject code	571
Language	English
Publishing date	2023-05-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Proximity-dependent proteomics of the Chlamydia trachomatis inclusion membrane reveals functional interactions with endoplasmic reticulum exit sites.

Mary S Dickinson / Lindsey N Anderson / Bobbie-Jo M Webb-Robertson / Joshua R Hansen / Richard D Smith / Aaron T Wright / Kevin Hybiske

PLoS Pathogens, Vol 15, Iss 4, p e

2019 Volume 1007698

Abstract: Chlamydia trachomatis is the most common cause of bacterial sexually transmitted infection, responsible for millions of infections each year. Despite this high prevalence, the elucidation of the molecular mechanisms of Chlamydia pathogenesis has been ... ...

Abstract	Chlamydia trachomatis is the most common cause of bacterial sexually transmitted infection, responsible for millions of infections each year. Despite this high prevalence, the elucidation of the molecular mechanisms of Chlamydia pathogenesis has been difficult due to limitations in genetic tools and its intracellular developmental cycle. Within a host epithelial cell, chlamydiae replicate within a vacuole called the inclusion. Many Chlamydia-host interactions are thought to be mediated by the Inc family of type III secreted proteins that are anchored in the inclusion membrane, but their array of host targets are largely unknown. To investigate how the inclusion membrane proteome changes over the course of an infected cell, we have adapted the APEX2 system of proximity-dependent biotinylation. APEX2 is capable of specifically labeling proteins within a 20 nm radius in living cells. We transformed C. trachomatis to express the enzyme APEX2 fused to known inclusion membrane proteins, allowing biotinylation and purification of inclusion-associated proteins. Using quantitative mass spectrometry against APEX2 labeled samples, we identified over 400 proteins associated with the inclusion membrane at early, middle, and late stages of epithelial cell infection. This system was sensitive enough to detect inclusion interacting proteins early in the developmental cycle, at 8 hours post infection, a previously intractable time point. Mass spectrometry analysis revealed a novel, early association between C. trachomatis inclusions and endoplasmic reticulum exit sites (ERES), functional regions of the ER where COPII-coated vesicles originate. Pharmacological and genetic disruption of ERES function severely restricted early chlamydial growth and the development of infectious progeny. APEX2 is therefore a powerful in situ approach for identifying critical protein interactions on the membranes of pathogen-containing vacuoles. Furthermore, the data derived from proteomic mapping of Chlamydia inclusions has illuminated an important ...
Keywords	Immunologic diseases. Allergy ; RC581-607 ; Biology (General) ; QH301-705.5
Subject code	572 ; 570
Language	English
Publishing date	2019-04-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Automated mass spectrometry imaging of over 2000 proteins from tissue sections at 100-μm spatial resolution

Paul D. Piehowski / Ying Zhu / Lisa M. Bramer / Kelly G. Stratton / Rui Zhao / Daniel J. Orton / Ronald J. Moore / Jia Yuan / Hugh D. Mitchell / Yuqian Gao / Bobbie-Jo M. Webb-Robertson / Sudhansu K. Dey / Ryan T. Kelly / Kristin E. Burnum-Johnson

Nature Communications, Vol 11, Iss 1, Pp 1-

2020 Volume 12

Abstract: Imaging mass spectrometry is a powerful emerging tool for mapping the spatial distribution of biomolecules across tissue surfaces. Here the authors showcase an automated technology for deep proteome imaging that utilizes ultrasensitive microfluidics and ... ...

Abstract	Imaging mass spectrometry is a powerful emerging tool for mapping the spatial distribution of biomolecules across tissue surfaces. Here the authors showcase an automated technology for deep proteome imaging that utilizes ultrasensitive microfluidics and a mass spectrometry workflow to analyze tissue voxels, generating quantitative cell-type-specific images.
Keywords	Science ; Q
Language	English
Publishing date	2020-01-01T00:00:00Z
Publisher	Nature Publishing Group
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Novel genetic risk factors influence progression of islet autoimmunity to type 1 diabetes

Suna Onengut-Gumuscu / Umadevi Paila / Wei-Min Chen / Aakrosh Ratan / Zhennan Zhu / Andrea K. Steck / Brigitte I. Frohnert / Kathleen C. Waugh / Bobbie-Jo M. Webb-Robertson / Jill M. Norris / Leslie A. Lange / Marian J. Rewers / Stephen S. Rich

Scientific Reports, Vol 10, Iss 1, Pp 1-

2020 Volume 7

Abstract: Abstract Type 1 diabetes arises from the autoimmune destruction of insulin-producing beta-cells of the pancreas, resulting in dependence on exogenously administered insulin to maintain glucose homeostasis. In this study, our aim was to identify genetic ... ...

Abstract	Abstract Type 1 diabetes arises from the autoimmune destruction of insulin-producing beta-cells of the pancreas, resulting in dependence on exogenously administered insulin to maintain glucose homeostasis. In this study, our aim was to identify genetic risk factors that contribute to progression from islet autoimmunity to clinical type 1 diabetes. We analyzed 6.8 million variants derived from whole genome sequencing of 160 islet autoantibody positive subjects, including 87 who had progressed to type 1 diabetes. The Cox proportional-hazard model for survival analysis was used to identify genetic variants associated with progression. We identified one novel region, 20p12.1 (TASP1; genome-wide P < 5 × 10–8) and three regions, 1q21.3 (MRPS21–PRPF3), 2p25.2 (NRIR), 3q22.1 (COL6A6), with suggestive evidence of association (P < 8.5 × 10–8) with progression from islet autoimmunity to type 1 diabetes. Once islet autoimmunity is initiated, functional mapping identified two critical pathways, response to viral infections and interferon signaling, as contributing to disease progression. These results provide evidence that genetic pathways involved in progression from islet autoimmunity differ from those pathways identified once disease has been established. These results support the need for further investigation of genetic risk factors that modulate initiation and progression of subclinical disease to inform efforts in development of novel strategies for prediction and intervention of type 1 diabetes.
Keywords	Medicine ; R ; Science ; Q
Language	English
Publishing date	2020-11-01T00:00:00Z
Publisher	Nature Publishing Group
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Automated mass spectrometry imaging of over 2000 proteins from tissue sections at 100-μm spatial resolution

Nature Communications, Vol 11, Iss 1, Pp 1-

2020 Volume 12

Abstract	Imaging mass spectrometry is a powerful emerging tool for mapping the spatial distribution of biomolecules across tissue surfaces. Here the authors showcase an automated technology for deep proteome imaging that utilizes ultrasensitive microfluidics and a mass spectrometry workflow to analyze tissue voxels, generating quantitative cell-type-specific images.
Keywords	Science ; Q
Language	English
Publishing date	2020-01-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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