Decrease in multiple complement proteins associated with development of islet autoimmunity and type 1 diabetes

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.

Characterizing Families of Spectral Similarity Scores and Their Use Cases for Gas Chromatography-Mass Spectrometry Small Molecule Identification

Metabolomics provides a unique snapshot into the world of small molecules and the complex biological processes that govern the human, animal, plant, and environmental ecosystems encapsulated by the One Health modeling framework. However, this "molecular snapshot" is only as informative as the number of metabolites confidently identified within it. The spectral similarity (SS) score is traditionally used to identify compound(s) in mass spectrometry approaches to metabolomics, where spectra are matched to reference libraries of candidate spectra. Unfortunately, there is little consensus on which of the dozens of available SS metrics should be used. This lack of standard SS score creates analytic uncertainty and potentially leads to issues in reproducibility, especially as these data are integrated across other domains. In this work, we use metabolomic spectral similarity as a case study to showcase the challenges in consistency within just one piece of the One Health framework that must be addressed to enable data science approaches for One Health problems. Here, using a large cohort of datasets comprising both standard and complex datasets with expert-verified truth annotations, we evaluated the effectiveness of 66 similarity metrics to delineate between correct matches (true positives) and incorrect matches (true negatives). We additionally characterize the families of these metrics to make informed recommendations for their use. Our results indicate that specific families of metrics (the Inner Product, Correlative, and Intersection families of scores) tend to perform better than others, with no single similarity metric performing optimally for all queried spectra. This work and its findings provide an empirically-based resource for researchers to use in their selection of similarity metrics for GC-MS identification, increasing scientific reproducibility through taking steps towards standardizing identification workflows.

Engineering transcriptional regulation of pentose metabolism in Rhodosporidium toruloides for improved conversion of xylose to bioproducts

Efficient conversion of pentose sugars remains a significant barrier to the replacement of petroleum-derived chemicals with plant biomass-derived bioproducts. While the oleaginous yeast Rhodosporidium toruloides (also known as Rhodotorula toruloides) has a relatively robust native metabolism of pentose sugars compared to other wild yeasts, faster assimilation of those sugars will be required for industrial utilization of pentoses. To increase the rate of pentose assimilation in R. toruloides, we leveraged previously reported high-throughput fitness data to identify potential regulators of pentose catabolism. Two genes were selected for further investigation, a putative transcription factor (RTO4_12978, Pnt1) and a homolog of a glucose transceptor involved in carbon catabolite repression (RTO4_11990). Overexpression of Pnt1 increased the specific growth rate approximately twofold early in cultures on xylose and increased the maximum specific growth by 18% while decreasing accumulation of arabitol and xylitol in fast-growing cultures. Improved growth dynamics on xylose translated to a 120% increase in the overall rate of xylose conversion to fatty alcohols in batch culture. Proteomic analysis confirmed that Pnt1 is a major regulator of pentose catabolism in R. toruloides. Deletion of RTO4_11990 increased the growth rate on xylose, but did not relieve carbon catabolite repression in the presence of glucose. Carbon catabolite repression signaling networks remain poorly characterized in R. toruloides and likely comprise a different set of proteins than those mainly characterized in ascomycete fungi.

Missing data in multi-omics integration: Recent advances through artificial intelligence

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.

Interrogating the role of the milk microbiome in mastitis in the multi-omics era

There is growing interest in a functional understanding of milk-associated microbiota as there is ample evidence that host-associated microbial communities play an active role in host health and phenotype. Mastitis, characterized by painful inflammation of the mammary gland, is prevalent among lactating humans and agricultural animals and is associated with significant clinical and economic consequences. The etiology of mastitis is complex and polymicrobial and correlative studies have indicated alterations in milk microbial community composition. Recent evidence is beginning to suggest that a causal relationship may exist between the milk microbiota and host phenotype in mastitis. Multi-omic approaches can be leveraged to gain a mechanistic, molecular level understanding of how the milk microbiome might modulate host physiology, thereby informing strategies to prevent and ameliorate mastitis. In this paper, we review existing studies that have utilized omics approaches to investigate the role of the milk microbiome in mastitis. We also summarize the strengths and challenges associated with the different omics techniques including metagenomics, metatranscriptomics, metaproteomics, metabolomics and lipidomics and provide perspective on the integration of multiple omics technologies for a better functional understanding of the milk microbiome.

Putting Humpty Dumpty Back Together Again: What Does Protein Quantification Mean in Bottom-Up Proteomics?

Bottom-up proteomics provides peptide measurements and has been invaluable for moving proteomics into large-scale analyses. Commonly, a single quantitative value is reported for each protein-coding gene by aggregating peptide quantities into protein groups following protein inference or parsimony. However, given the complexity of both RNA splicing and post-translational protein modification, it is overly simplistic to assume that all peptides that map to a singular protein-coding gene will demonstrate the same quantitative response. By assuming that all peptides from a protein-coding sequence are representative of the same protein, we may miss the discovery of important biological differences. To capture the contributions of existing proteoforms, we need to reconsider the practice of aggregating protein values to a single quantity per protein-coding gene.

Statistically Driven Metabolite and Lipid Profiling of Patients from the Undiagnosed Diseases Network

Advancements in molecular separations coupled with mass spectrometry have enabled metabolome analyses for clinical cohorts. A population of interest for metabolome profiling is patients with rare disease for which abnormal metabolic signatures may yield clues into the genetic basis, as well as mechanistic drivers of the disease and possible treatment options. We undertook the metabolome profiling of a large cohort of patients with mysterious conditions characterized through the Undiagnosed Diseases Network (UDN). Due to the size and enrollment procedures, collection of the metabolomes for UDN patients took place over 2 years. We describe the study designed to adjust for measurements collected over a long time scale and how this enabled statistical analyses to summarize the metabolome of individual patients. We demonstrate the removal of time-based batch effects, overall statistical characteristics of the UDN population, and two case studies of interest that demonstrate the utility of metabolome profiling for rare diseases.

Proximity-dependent proteomics of the Chlamydia trachomatis inclusion membrane reveals functional interactions with endoplasmic reticulum exit sites

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 functional role for ERES in promoting chlamydial developmental growth.

Abstract 284: Integrated proteogenomic analysis of laser microdissected primary breast tumors define proteome clusters

Introduction: Breast tumors have 4 well-established intrinsic subtypes based on transcriptome profiling. However, clusters defined by proteomics are often in disagreement with those defined by transcriptomics. Here, we report the findings of proteogenomic profiling of 118 laser microdissected (LMD) breast tumors using RNA-Seq and mass-spectrometry (MS)-based proteomic technologies.

Methods: Cases used in this study were drawn from the Clinical Breast Care Project, with patients consented using an IRB-approved protocol. A total of 118 primary breast tumors embedded in OCT were selected and processed by LMD. Total RNA and protein were extracted using the Illustra triplePrep kit. Paired-end RNA sequencing of 118 cases was performed using the Illumina HiSeq platform, and the reads were preprocessed using a PERL-based pipeline involving the preprocessing tool PRINSEQ, splice-aligner GSNAP and HTSeq for quantifying expression. Quantitative global proteomics analyses were performed on 113 cases using isobaric TMT 6-plex labeling with the “universal reference” strategy. MS data were acquired using a Q-Exactive instrument and analyzed using Proteome Discoverer with Byonic node. Sample-to-sample normalization was conducted to remove pipetting errors and ComBat was used to remove batch effect. K-means clustering was done using Bioconductor package Consensusclustering.

Results: The number of preprocessed RNA sequencing reads for the 118 cases ranged from over 43 to 295 million. An average of 83% of reads was mapped, and 24,518 genes with a mean expression of ≥ 10 counts across 118 tumor samples were identified. The PAM50 algorithm was used for intrinsic subtyping, yielding 37 Basal-like, 16 HER2-enriched, 39 Luminal A and 26 Luminal B calls. Unsupervised clustering of 3,000 highly varying genes reflected 4 intrinsic subtypes. In the global proteomics data, 840 proteins were identified across all 113 cases. Unsupervised K-means consensus clustering on all 840 or just using the top 210 highly varying proteins indicated the optimal number of clusters to be 3. These 3 clusters were identified as Basal-enriched, Luminal A-enriched and Luminal B-enriched. HER2-enriched cases were distributed among these clusters.

We did not observe a stromal-enriched cluster in this analysis of LMD-prepared samples that selected against stromal components of the tumor.

Conclusion: Analysis of LMD breast tumors using proteogenomic technologies resulted in 3 clusters for proteome data: basal-enriched, luminal A-enriched and luminal B-enriched. Unlike a recent report on proteomics clustering using bulk processing of tumors, a stromal-enriched cluster was not observed in this analysis which excluded stromal components of the samples.

The views expressed in this abstract are those of the author and do not reflect the official policy of the Department of Army/Navy/Air Force, Department of Defense, or U.S. Government.

Citation Format: Praveen-Kumar Raj-Kumar, Tao Liu, Lori A. Sturtz, Albert J. Kovatich, Marina A. Gritsenko, Vladislav A. Petyuk, Brenda Deyarmin, Viswanadham Sridhara, James Craig, Jason E. McDermott, Anil K. Shukla, Ronald J. Moore, Matthew E. Monroe, Bobbie-Jo M. Webb-Robertson, Jeffrey A. Hooke, J.Leigh Fantacone-Campbell, Leonid Kvecher, Jianfang Liu, Jennifer Kane, Jennifer Melley, Stella Somiari, Stephen C. Benz, Justin Golovato, Shahrooz Rabizadeh, Patrick Soon-Shiong, Richard D. Smith, Richard J. Mural, Karin D. Rodland, Craig D. Shriver, Hai Hu. Integrated proteogenomic analysis of laser microdissected primary breast tumors define proteome clusters [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 284.

Abstract 213: Integrated proteogenomic analysis of laser capture microdissected breast tumors

Introduction: Molecular characteristics of breast tumors play an important role in determining patients’ survival outcome. Here, we report preliminary findings of proteogenomic profiling of 50 breast tumors using RNA-Seq and mass-spectrometry (MS) based proteomic technologies. An additional 60 tumors are being analyzed, including WGS for all samples. We are also collecting patient survival outcome data.

Methods: Cases used in this study were drawn from the Clinical Breast Care Project, where patients were consented using an IRB-approved protocol. A total of 50 breast tumors were selected and processed by laser capture microdissection (LCM). This cohort includes 36 Caucasian Americans (CA) and 8 African Americans (AA), and the age of the patients is 57 ± 13 years. Protein and RNA were extracted using the Illustra triplePrep kit, which isolates DNA from the same cells as well. Quantitative global proteomics and phosphoproteomics analyses were performed using isobaric TMT 6-plex labeling with the “universal reference” strategy and IMAC enrichment of phosphopeptides. Mass spectrometry data were acquired using a Q-Exactive instrument and analyzed using Proteome Discoverer with Byonic node. Phosphopeptide abundance was normalized to abundance measurements of the parent protein for all of the phosphorylation analyses. Phosphoproteomic data was also searched for the presence of O-GlcNAc modifications. RNA-Seq analyses were done on Illumina HiSeq and the data were analyzed using GSNAP.

Results: There were 19 Luminal A, 7 Luminal B, 8 HER2-enriched, and 16 basal-like subtypes based on the PAM50 algorithm. In the global proteomics data, we were able to quantitate >8600 proteins. Unsupervised clustering on the highly varying proteins across the samples resulted in two primary clusters, with one being luminal-enriched. The other cluster contains a basal-like tumor sub-cluster and a sub-cluster of mixed subtypes. Differential protein expression analyses between the two primary clusters confirmed known markers (e.g., overexpression of KRT8/KRT18 in luminal-enriched cluster). The luminal-enriched cluster is primarily CA with post-menopausal status.

A similar search of the phosphoproteomic data yielded quantitation of >12500 phosphopeptides. Unsupervised clustering of the phosphoproteins resulted in four primary groups, with one being basal-enriched and another being luminal-enriched. We also observed >50 overexpressed phosphopeptides. While some of these phosphosites have been previously reported (e.g., on RANBP2), other phosphosites appeared to be novel (e.g., on IRF2BP2).

Conclusion: Analysis of LCM breast tumors using proteogenomic technologies resulted in basal- and luminal-enriched clusters, thus enabling us to study protein and phosphopeptide markers across multiple platforms.

The views expressed in this article are those of the author and do not reflect the official policy of the Department of Defense, or U.S. Government.

Citation Format: Viswanadham Sridhara, Tao Liu, Marina A. Gritsenko, Lori A. Sturtz, Albert J. Kovatich, Vladislav A. Petyuk, Brenda Deyarmin, Jason E. McDermott, Anil K. Shukla, Ronald J. Moore, Matthew E. Monroe, Bobbie-Jo M. Webb-Robertson, Jeffrey A. Hooke, Leigh Fantacone-Campbell, Praveen Kumar Raj Kumar, Leonid Kvecher, Jianfang Liu, Jennifer Kane, Jennifer Melley, Stella Somiari, Joji Iida, Stephen C. Benz, Justin Golovato, Shahrooz Rabizadeh, Patrick Soon-Shiong, Richard D. Smith, Richard J. Mural, Craig D. Shriver, Hai Hu, Karin D. Rodland. Integrated proteogenomic analysis of laser capture microdissected breast tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 213. doi:10.1158/1538-7445.AM2017-213

A Syndromic Neurodevelopmental Disorder Caused by De Novo Variants in EBF3

Early B cell factor 3 (EBF3) is a member of the highly evolutionarily conserved Collier/Olf/EBF (COE) family of transcription factors. Prior studies on invertebrate and vertebrate animals have shown that EBF3 homologs are essential for survival and that loss-of-function mutations are associated with a range of nervous system developmental defects, including perturbation of neuronal development and migration. Interestingly, aristaless-related homeobox (ARX), a homeobox-containing transcription factor critical for the regulation of nervous system development, transcriptionally represses EBF3 expression. However, human neurodevelopmental disorders related to EBF3 have not been reported. Here, we describe three individuals who are affected by global developmental delay, intellectual disability, and expressive speech disorder and carry de novo variants in EBF3. Associated features seen in these individuals include congenital hypotonia, structural CNS malformations, ataxia, and genitourinary abnormalities. The de novo variants affect a single conserved residue in a zinc finger motif crucial for DNA binding and are deleterious in a fly model. Our findings indicate that mutations in EBF3 cause a genetic neurodevelopmental syndrome and suggest that loss of EBF3 function might mediate a subset of neurologic phenotypes shared by ARX-related disorders, including intellectual disability, abnormal genitalia, and structural CNS malformations.

A semiautomated framework for integrating expert knowledge into disease marker identification

BACKGROUND

The availability of large complex data sets generated by high throughput technologies has enabled the recent proliferation of disease biomarker studies. However, a recurring problem in deriving biological information from large data sets is how to best incorporate expert knowledge into the biomarker selection process.

OBJECTIVE

To develop a generalizable framework that can incorporate expert knowledge into data-driven processes in a semiautomated way while providing a metric for optimization in a biomarker selection scheme.

METHODS

The framework was implemented as a pipeline consisting of five components for the identification of signatures from integrated clustering (ISIC). Expert knowledge was integrated into the biomarker identification process using the combination of two distinct approaches; a distance-based clustering approach and an expert knowledge-driven functional selection.

RESULTS

The utility of the developed framework ISIC was demonstrated on proteomics data from a study of chronic obstructive pulmonary disease (COPD). Biomarker candidates were identified in a mouse model using ISIC and validated in a study of a human cohort.

CONCLUSIONS

Expert knowledge can be introduced into a biomarker discovery process in different ways to enhance the robustness of selected marker candidates. Developing strategies for extracting orthogonal and robust features from large data sets increases the chances of success in biomarker identification.

Impaired transcriptional response of the murine heart to cigarette smoke in the setting of high fat diet and obesity

Smoking and obesity are each well-established risk factors for cardiovascular heart disease, which together impose earlier onset and greater severity of disease. To identify early signaling events in the response of the heart to cigarette smoke exposure within the setting of obesity, we exposed normal weight and high fat diet-induced obese (DIO) C57BL/6 mice to repeated inhaled doses of mainstream (MS) or sidestream (SS) cigarette smoke administered over a two week period, monitoring effects on both cardiac and pulmonary transcriptomes. MS smoke (250 μg wet total particulate matter (WTPM)/L, 5 h/day) exposures elicited robust cellular and molecular inflammatory responses in the lung with 1466 differentially expressed pulmonary genes (p < 0.01) in normal weight animals and a much-attenuated response (463 genes) in the hearts of the same animals. In contrast, exposures to SS smoke (85 μg WTPM/L) with a CO concentration equivalent to that of MS smoke (~250 CO ppm) induced a weak pulmonary response (328 genes) but an extensive cardiac response (1590 genes). SS smoke and to a lesser extent MS smoke preferentially elicited hypoxia- and stress-responsive genes as well as genes predicting early changes of vascular smooth muscle and endothelium, precursors of cardiovascular disease. The most sensitive smoke-induced cardiac transcriptional changes of normal weight mice were largely absent in DIO mice after smoke exposure, while genes involved in fatty acid utilization were unaffected. At the same time, smoke exposure suppressed multiple proteome maintenance genes induced in the hearts of DIO mice. Together, these results underscore the sensitivity of the heart to SS smoke and reveal adaptive responses in healthy individuals that are absent in the setting of high fat diet and obesity.