Using emerging data analysis technics to improve pediatric disease diagnosis

Many data types are used in bioinformatics research, including genomics, transcriptomics, proteomics, pathway data, disease network, and gene ontology (GO) data, which are heavily studied in disease diagnosis or biomarker detection. The use of newer data types, such as glycomics, fMRI, and facial behavior data, is also growing and can provide unique perspectives for disease cell biology. These new data types have unique properties that require newly adapted algorithms for precise and granular characterization, which is essential before machine learning or statistical models can be confidently used to study disease mechanisms or identify biomarkers from large-scale datasets. The newly developed tools can then allow sophisticated evaluations and yield high-quality results. The first part of my thesis introduced GlyCompare, a powerful glycomics analysis pipeline. The pipeline corrects for the sparsity and non-independence in glycomics data by accounting for the shared biosynthetic network in the data. This new approach makes the downstream analyses more interpretable and better powered.Then in the second part, a generalizable machine learning platform was developed with 42,840 models composed of 3570 gene expression feature sets and 12 classification methods. A gene expression ASD diagnostic classifier built with this platform had AUC-ROC ≥ 0.8 on both Training and Test sets. Our classifier is diagnostically predictive and replicable across different toddler ages, races, and ethnicities; outperforms the risk gene mutation classifier; and has potential for clinical translation. In the last section, I developed a pipeline to evaluate facial behavior data from toddlers using state-of-the-art expression analysis software. In certain situations, emotional response is overly intense in ASD compared to other toddlers. Our action unit classifier had a sensitivity of 83.3% and a specificity of 67.5% in the test dataset (90.1% and 75% in the training dataset). We verified that our classifier was unbiased against common confounding factors (age, race, and ethnicity). By combining the action unit classifier and Geo-Pref non-social score, we achieved a specificity of 100% and sensitivity of 50% on the training and test datasets. The ensemble classifier maintained the high specificity while considerably increasing the sensitivity, which provides the potential for screening applications

Using emerging data analysis technics to improve pediatric disease diagnosis

Many data types are used in bioinformatics research, including genomics, transcriptomics, proteomics, pathway data, disease network, and gene ontology (GO) data, which are heavily studied in disease diagnosis or biomarker detection. The use of newer data types, such as glycomics, fMRI, and facial behavior data, is also growing and can provide unique perspectives for disease cell biology. These new data types have unique properties that require newly adapted algorithms for precise and granular characterization, which is essential before machine learning or statistical models can be confidently used to study disease mechanisms or identify biomarkers from large-scale datasets. The newly developed tools can then allow sophisticated evaluations and yield high-quality results. The first part of my thesis introduced GlyCompare, a powerful glycomics analysis pipeline. The pipeline corrects for the sparsity and non-independence in glycomics data by accounting for the shared biosynthetic network in the data. This new approach makes the downstream analyses more interpretable and better powered.Then in the second part, a generalizable machine learning platform was developed with 42,840 models composed of 3570 gene expression feature sets and 12 classification methods. A gene expression ASD diagnostic classifier built with this platform had AUC-ROC ≥ 0.8 on both Training and Test sets. Our classifier is diagnostically predictive and replicable across different toddler ages, races, and ethnicities; outperforms the risk gene mutation classifier; and has potential for clinical translation. In the last section, I developed a pipeline to evaluate facial behavior data from toddlers using state-of-the-art expression analysis software. In certain situations, emotional response is overly intense in ASD compared to other toddlers. Our action unit classifier had a sensitivity of 83.3% and a specificity of 67.5% in the test dataset (90.1% and 75% in the training dataset). We verified that our classifier was unbiased against common confounding factors (age, race, and ethnicity). By combining the action unit classifier and Geo-Pref non-social score, we achieved a specificity of 100% and sensitivity of 50% on the training and test datasets. The ensemble classifier maintained the high specificity while considerably increasing the sensitivity, which provides the potential for screening applications

Large scale validation of an early-age eye-tracking biomarker of an autism spectrum disorder subtype.

Few clinically validated biomarkers of ASD exist which can rapidly, accurately, and objectively identify autism during the first years of life and be used to support optimized treatment outcomes and advances in precision medicine. As such, the goal of the present study was to leverage both simple and computationally-advanced approaches to validate an eye-tracking measure of social attention preference, the GeoPref Test, among 1,863 ASD, delayed, or typical toddlers (12-48 months) referred from the community or general population via a primary care universal screening program. Toddlers participated in diagnostic and psychometric evaluations and the GeoPref Test: a 1-min movie containing side-by-side dynamic social and geometric images. Following testing, diagnosis was denoted as ASD, ASD features, LD, GDD, Other, typical sibling of ASD proband, or typical. Relative to other diagnostic groups, ASD toddlers exhibited the highest levels of visual attention towards geometric images and those with especially high fixation levels exhibited poor clinical profiles. Using the 69% fixation threshold, the GeoPref Test had 98% specificity, 17% sensitivity, 81% PPV, and 65% NPV. Sensitivity increased to 33% when saccades were included, with comparable validity across sex, ethnicity, or race. The GeoPref Test was also highly reliable up to 24 months following the initial test. Finally, fixation levels among twins concordant for ASD were significantly correlated, indicating that GeoPref Test performance may be genetically driven. As the GeoPref Test yields few false positives (~ 2%) and is equally valid across demographic categories, the current findings highlight the ability of the GeoPref Test to rapidly and accurately detect autism before the 2nd birthday in a subset of children and serve as a biomarker for a unique ASD subtype in clinical trials

Preparing glycomics data for robust statistical analysis with GlyCompareCT

GlyCompareCT is a portable command-line tool to facilitate downstream glycomic data analyses, by addressing data inherent sparsity and non-independence. Inputting glycan abundances, users can run GlyCompareCT with one line of code to obtain the abundances of a minimal substructure set, named glycomotif, thereby quantifying hidden biosynthetic relationships between measured glycans. Optional parameters tuning and annotation are supported for personal preference. For complete details on the use and execution of this protocol, please refer to Bao et al. (2021).1

Systems glycobiology for discovering drug targets, biomarkers, and rational designs for glyco-immunotherapy

Abstract Cancer immunotherapy has revolutionized treatment and led to an unprecedented wave of immuno-oncology research during the past two decades. In 2018, two pioneer immunotherapy innovators, Tasuku Honjo and James P. Allison, were awarded the Nobel Prize for their landmark cancer immunotherapy work regarding “cancer therapy by inhibition of negative immune regulation” –CTLA4 and PD-1 immune checkpoints. However, the challenge in the coming decade is to develop cancer immunotherapies that can more consistently treat various patients and cancer types. Overcoming this challenge requires a systemic understanding of the underlying interactions between immune cells, tumor cells, and immunotherapeutics. The role of aberrant glycosylation in this process, and how it influences tumor immunity and immunotherapy is beginning to emerge. Herein, we review current knowledge of miRNA-mediated regulatory mechanisms of glycosylation machinery, and how these carbohydrate moieties impact immune cell and tumor cell interactions. We discuss these insights in the context of clinical findings and provide an outlook on modulating the regulation of glycosylation to offer new therapeutic opportunities. Finally, in the coming age of systems glycobiology, we highlight how emerging technologies in systems glycobiology are enabling deeper insights into cancer immuno-oncology, helping identify novel drug targets and key biomarkers of cancer, and facilitating the rational design of glyco-immunotherapies. These hold great promise clinically in the immuno-oncology field

Identification and characterization of two novel bla(KLUC) resistance genes through large-scale resistance plasmids sequencing.

Plasmids are important antibiotic resistance determinant carriers that can disseminate various drug resistance genes among species or genera. By using a high throughput sequencing approach, two groups of plasmids of Escherichia coli (named E1 and E2, each consisting of 160 clinical E. coli strains isolated from different periods of time) were sequenced and analyzed. A total of 20 million reads were obtained and mapped onto the known resistance gene sequences. As a result, a total of 9 classes, including 36 types of antibiotic resistant genes, were identified. Among these genes, 25 and 27 single nucleotide polymorphisms (SNPs) appeared, of which 9 and 12 SNPs are nonsynonymous substitutions in the E1 and E2 samples. It is interesting to find that a novel genotype of bla(KLUC), whose close relatives, bla(KLUC-1) and bla(KLUC-2), have been previously reported as carried on the Kluyvera cryocrescens chromosome and Enterobacter cloacae plasmid, was identified. It shares 99% and 98% amino acid identities with Kluc-1 and Kluc-2, respectively. Further PCR screening of 608 Enterobacteriaceae family isolates yielded a second variant (named bla(KLUC-4)). It was interesting to find that Kluc-3 showed resistance to several cephalosporins including cefotaxime, whereas bla(KLUC-4) did not show any resistance to the antibiotics tested. This may be due to a positively charged residue, Arg, replaced by a neutral residue, Leu, at position 167, which is located within an omega-loop. This work represents large-scale studies on resistance gene distribution, diversification and genetic variation in pooled multi-drug resistance plasmids, and provides insight into the use of high throughput sequencing technology for microbial resistance gene detection