Department of Biomedical Engineering and Informatics

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A Putative long-range RNA-RNA interaction between ORF8 and Spike of SARS-CoV-2
(Public Library of Science, 2022-09-01) Omoru, Okiemute Beatrice; Pereira, Filipe; Janga, Sarath Chandra; Manzourolajdad, Amirhossein; BioHealth Informatics, School of Informatics and Computing
SARS-CoV-2 has affected people worldwide as the causative agent of COVID-19. The virus is related to the highly lethal SARS-CoV-1 responsible for the 2002-2003 SARS outbreak in Asia. Research is ongoing to understand why both viruses have different spreading capacities and mortality rates. Like other beta coronaviruses, RNA-RNA interactions occur between different parts of the viral genomic RNA, resulting in discontinuous transcription and production of various sub-genomic RNAs. These sub-genomic RNAs are then translated into other viral proteins. In this work, we performed a comparative analysis for novel long-range RNA-RNA interactions that may involve the Spike region. Comparing in-silico fragment-based predictions between reference sequences of SARS-CoV-1 and SARS-CoV-2 revealed several predictions amongst which a thermodynamically stable long-range RNA-RNA interaction between (23660-23703 Spike) and (28025-28060 ORF8) unique to SARS-CoV-2 was observed. The patterns of sequence variation using data gathered worldwide further supported the predicted stability of the sub-interacting region (23679-23690 Spike) and (28031-28042 ORF8). Such RNA-RNA interactions can potentially impact viral life cycle including sub-genomic RNA production rates.
Abundance of Secondary Metabolites in Human Microbiome
Sarsani, Vishal; Kulkarni, Nikhil; Janga, Sarath Chandra
Human body harbors the most complicated microbial ecosystem. Bacteria that have co-evolved within a human context have barely been explored for secondary metabolites. These secondary metabolites are hypothesized to possess biological activities significant within the human host context. In our study, we studied conservation profiles of 203 secondary metabolite gene clusters across 16 human body sites and found that gastrointestinal tract and oral sites show the highest conservation for secondary metabolic gene clusters. We observed that majority of highly conserved metabolites belong to pathway type NRPS. Our phylogenetic analysis of highly conserved stool and oral samples revealed abundance of firmicutes, bacteroidetes and actinobacteria phylum.
Accurate Identification of RNA Editing Events Using Matched RNA and DNA Sequenced Samples Uncovers the Contribution of the Editing Landscape to Disease Progression in Glioblastoma Patients
Hashemikhabir, Seyedsasan; Hundley, Heather A.; Janga, Sarath Chandra
RNA editing event is increasingly appreciated as an important posttranscriptional regulatory mechanism in mammals. Adenosine deaminases that act on RNA (ADARs) are the enzymes that catalyze adenosine (A) to inosine (I) editing events. Human brain RNA is reported to have highest number of editing events. Many neurotransmitter receptors and ion channels undergo editing within exonic regions which generates a different protein than that encoded by the genome. ALU repeats in introns and untranslated regions of brain mRNAs are often targeted by editing events and result in altered splicing and post-transcriptional gene regulation.
ADGRG1 enriches for functional human hematopoietic stem cells following ex vivo expansion-induced mitochondrial oxidative stress
(The American Society for Clinical Investigation, 2021) Chen, Yandan; Fang, Shuyi; Ding, Qingwei; Jiang, Rongzhen; He, Jiefeng; Wang, Qin; Jin, Yuting; Huang, Xinxin; Liu, Sheng; Capitano, Maegan L.; Trinh, Thao; Teng, Yincheng; Meng, Qingyou; Wan, Jun; Broxmeyer, Hal E.; Guo, Bin; BioHealth Informatics, School of Informatics and Computing
The heterogeneity of human hematopoietic stem cells (HSCs) and hematopoietic progenitor cells (HPCs) under stress conditions such as ex vivo expansion is poorly understood. Here, we report that the frequencies of SCID-repopulating cells were greatly decreased in cord blood (CB) CD34+ HSCs and HPCs upon ex vivo culturing. Transcriptomic analysis and metabolic profiling demonstrated that mitochondrial oxidative stress of human CB HSCs and HPCs notably increased, along with loss of stemness. Limiting dilution analysis revealed that functional human HSCs were enriched in cell populations with low levels of mitochondrial ROS (mitoROS) during ex vivo culturing. Using single-cell RNA-Seq analysis of the mitoROS low cell population, we demonstrated that functional HSCs were substantially enriched in the adhesion GPCR G1-positive (ADGRG1+) population of CD34+CD133+ CB cells upon ex vivo expansion stress. Gene set enrichment analysis revealed that HSC signature genes including MSI2 and MLLT3 were enriched in CD34+CD133+ADGRG1+ CB HSCs. Our study reveals that ADGRG1 enriches for functional human HSCs under oxidative stress during ex vivo culturing, which can be a reliable target for drug screening of agonists of HSC expansion.
Advanced Functions Embedded in the Second Version of Database, Global Evaluation of SARS-CoV-2/hCoV-19 Sequences 2
(Frontiers Media, 2022-04-11) Li, Kailing; Wang, Audrey K.Y.; Liu, Sheng; Fang, Shuyi; Lu, Alex Z.; Shen, Jikui; Yang, Lei; Hu, Chang-Deng; Yang, Kai; Wan, Jun; BioHealth Informatics, School of Informatics and Computing
The Global Evaluation of SARS-CoV-2/hCoV-19 Sequences 2 (GESS v2 https://shiny.ph.iu.edu/GESS_v2/) is an updated version of GESS, which has offered a handy query platform to analyze single-nucleotide variants (SNVs) on millions of high coverages and high-quality severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) complete genomes provided by the Global Initiative on Sharing Avian Influenza Data (GISAID). Including the tools in the first version, the GESS v2 is embedded with new functions, which allow users to search SNVs, given the viral nucleotide or amino acid sequence. The GESS v2 helps users to identify SNVs or SARS-CoV-2 lineages enriched in countries of user's interest and show the migration path of a selected lineage on a world map during specific time periods chosen by the users. In addition, the GESS v2 can recognize the dynamic variations of newly emerging SNVs in each month to help users monitor SNVs, which will potentially become dominant soon. More importantly, multiple sets of analyzed results about SNVs can be downloaded directly from the GESS v2 by which users can conduct their own independent research. With these significant updates, the GESS v2 will continue to serve as a public open platform for researchers to explore SARS-CoV-2 evolutionary patterns from the perspectives of the prevalence and impact of SNVs.
AI recognition of patient race in medical imaging: a modelling study
(Elsevier, 2022-06) Gichoya, Judy Wawira; Banerjee, Imon; Bhimireddy, Ananth Reddy; Burns, John L.; Celi, Leo Anthony; Chen, Li-Ching; Correa, Ramon; Dullerud, Natalie; Ghassemi, Marzyeh; Huang, Shih-Cheng; Kuo, Po-Chih; Lungren, Matthew P.; Palmer, Lyle J.; Price, Brandon J.; Purkayastha, Saptarshi; Pyrros, Ayis T.; Oakden-Rayner, Lauren; Okechukwu, Chima; Seyyed-Kalantari, Laleh; Trivedi, Hari; Wang, Ryan; Zaiman, Zachary; Zhang, Haoran; BioHealth Informatics, School of Informatics and Computing
Background Previous studies in medical imaging have shown disparate abilities of artificial intelligence (AI) to detect a person's race, yet there is no known correlation for race on medical imaging that would be obvious to human experts when interpreting the images. We aimed to conduct a comprehensive evaluation of the ability of AI to recognise a patient's racial identity from medical images. Methods Using private (Emory CXR, Emory Chest CT, Emory Cervical Spine, and Emory Mammogram) and public (MIMIC-CXR, CheXpert, National Lung Cancer Screening Trial, RSNA Pulmonary Embolism CT, and Digital Hand Atlas) datasets, we evaluated, first, performance quantification of deep learning models in detecting race from medical images, including the ability of these models to generalise to external environments and across multiple imaging modalities. Second, we assessed possible confounding of anatomic and phenotypic population features by assessing the ability of these hypothesised confounders to detect race in isolation using regression models, and by re-evaluating the deep learning models by testing them on datasets stratified by these hypothesised confounding variables. Last, by exploring the effect of image corruptions on model performance, we investigated the underlying mechanism by which AI models can recognise race. Findings In our study, we show that standard AI deep learning models can be trained to predict race from medical images with high performance across multiple imaging modalities, which was sustained under external validation conditions (x-ray imaging [area under the receiver operating characteristics curve (AUC) range 0·91-0·99], CT chest imaging [0·87-0·96], and mammography [0·81]). We also showed that this detection is not due to proxies or imaging-related surrogate covariates for race (eg, performance of possible confounders: body-mass index [AUC 0·55], disease distribution [0·61], and breast density [0·61]). Finally, we provide evidence to show that the ability of AI deep learning models persisted over all anatomical regions and frequency spectrums of the images, suggesting the efforts to control this behaviour when it is undesirable will be challenging and demand further study. Interpretation The results from our study emphasise that the ability of AI deep learning models to predict self-reported race is itself not the issue of importance. However, our finding that AI can accurately predict self-reported race, even from corrupted, cropped, and noised medical images, often when clinical experts cannot, creates an enormous risk for all model deployments in medical imaging. Funding National Institute of Biomedical Imaging and Bioengineering, MIDRC grant of National Institutes of Health, US National Science Foundation, National Library of Medicine of the National Institutes of Health, and Taiwan Ministry of Science and Technology.
Altered Caveolin-1 Dynamics Result in Divergent Mineralization Responses in Bone and Vascular Calcification
(Springer, 2023-08-19) Bakhshian Nik, Amirala; Kaiser, Katherine; Sun, Patrick; Khomtchouk, Bohdan B.; Hutcheson, Joshua D.; Biomedical Engineering and Informatics, Luddy School of Informatics, Computing, and Engineering
Introduction: Though vascular smooth muscle cells adopt an osteogenic phenotype during pathological vascular calcification, clinical studies note an inverse correlation between bone mineral density and arterial mineral-also known as the calcification paradox. Both processes are mediated by extracellular vesicles (EVs) that sequester calcium and phosphate. Calcifying EV formation in the vasculature requires caveolin-1 (CAV1), a membrane scaffolding protein that resides in membrane invaginations (caveolae). Of note, caveolin-1-deficient mice, however, have increased bone mineral density. We hypothesized that caveolin-1 may play divergent roles in calcifying EV formation from vascular smooth muscle cells (VSMCs) and osteoblasts (HOBs). Methods: Primary human coronary artery VSMCs and osteoblasts were cultured for up to 28 days in an osteogenic media. CAV1 expression was knocked down using siRNA. Methyl β-cyclodextrin (MβCD) and a calpain inhibitor were used, respectively, to disrupt and stabilize the caveolar domains in VSMCs and HOBs. Results: CAV1 genetic variation demonstrates significant inverse relationships between bone-mineral density (BMD) and coronary artery calcification (CAC) across two independent epidemiological cohorts. Culture in osteogenic (OS) media increased calcification in HOBs and VSMCs. siRNA knockdown of CAV1 abrogated VSMC calcification with no effect on osteoblast mineralization. MβCD-mediated caveolae disruption led to a 3-fold increase of calcification in VSMCs treated with osteogenic media (p < 0.05) but hindered osteoblast mineralization (p < 0.01). Conversely, stabilizing caveolae by calpain inhibition prevented VSMC calcification (p < 0.05) without affecting osteoblast mineralization. There was no significant difference in CAV1 content between lipid domains from HOBs cultured in OS and control media. Conclusion: Our data indicate fundamental cellular-level differences in physiological and pathophysiological mineralization mediated by CAV1 dynamics. This is the first study to suggest that divergent mechanisms in calcifying EV formation may play a role in the calcification paradox. Supplementary information: The online version contains supplementary material available at 10.1007/s12195-023-00779-7.
Alternative Splicing Profile Comparison of Differentiating I-helper Cells to Dissect the Splicing Signatures of Th1, Th2, 1h17 and Treg Cells
Lakshmipati, Deepak Kumar; Quoseena, Mir; Ulrich, Benjamin; Kaplan, Mark; Janga, Sarath Chandra
This study focuses on the contribution of Alternative Splicing (AS) events in the differentiation and post-differentiation functions of T-helper cells, specifically in Thl, Th2, Th9, Th17 and Treg cells. T cell RNA-seq data from 72hr and 2week post differentiation time points was analyzed using (r-MATS) for alternative splicing events. We observed majority of the significant events are Skipped Exon (SE) events originating from a total of 1,556 genes and lntron Retention (RI) events were the second most abundant event occurring in 1,254 genes at 72 hours post differentiation. These numbers were significantly lower at 2 weeks post differentiation. PCR and qPCR validations confirmed scores of novel splicing event predictions. Results showed several skipped exon (SE) events in KTNl, IL4RA IL27, Hnrmpd, CREM and Arid4b showing different mRNA isoforms across multiple naïve vs differentiated T cell combinations. Overall, RI event associated genes were more prevalent (3,239 genes) than those exhibiting SE (2810 genes). SE events were associated with 10.8% (Th17), 11.2% (Treg), 12.1% (Th2) and 13.9% (Thl) of the genes, a similar trend was observed with RI events with a prevalence of 12.2% (Th17), 12.5% (Treg), 14.2% (Thl) and 14.4% (Th2) of the genes. Gene ontology results showed most of the genes showing SE and RI events are involved in processes like 'mRNA Processing', 'RNA Processing' and 'RNA Binding' and ontology results for retained introns also showed p53 suppression proteins, regulated exocytosis of neurotransmitters and hormones. It was also observed that Introns consistently favored retention at the 3' end of the gene than the 5', with 430 genes showing intron retention events at the 3' end and 21 genes exhibiting them at the 5' end, for the 72 hour time point. Enriched functional ontologies were consistently seen across all cell types to be exclusive for the genes showing RI in the 5' end vs the 3' end.
An answer recommendation framework for an online cancer community forum
(Springer Nature, 2023-05-15) Athira, B.; Idicula, Sumam Mary; Jones, Josette; Kulanthaivel, Anand; BioHealth Informatics, School of Informatics and Computing
Health community forums are a kind of online platform to discuss various matters related to management of illness. People are increasingly searching for answers online, particularly when they are diagnosed with cancer like life-threatening diseases. People seek suggestions or advice through these platforms to make decisions during their treatments. However, locating the correct information or similar people is often a great challenge for them. In this scenario, this paper proposes an answer recommendation system in an online breast cancer community forum that provide guidance and valuable references to users while making decisions. The answer is the summary of already discussed topic in the forum, so that they do not need to go through all the answer posts which spans over multiple pages or initiate a thread once again. There are three phases for the answer recommendation system, including query similarity model to retrieve the past similar query, query-answer pair generation and answer recommendation. Query similarity model is employed by a Siamese network with Bi-LSTM architecture which could achieve an F1-score of 85.5%. Also, the paper shows the efficacy of transfer learning technique to generalize the model well in our breast cancer query-query pair data set. The query-answer pairs are generated by an extractive summarization technique that is based on an optimization algorithm. The effectiveness of the generated summary is evaluated based on a manually generated summary, and the result shows a ROUGE-1 score of 49%.
Analysis of Co-Indicators and Counter-Indicators Among Patients Using Coding Algorithms: Learning Phenotype study
Reddy, Nagarjuna; Jones, Josette; Kanakasabai, Saravanan; Klapper, Gregory
Chronic complications associated with the diabetes are responsible for increase in mortality and morbidity rate. The main aim of the project is to analyze the co-indicators and counter-indicators among the patients by mapping the conditions with ICD codes and developing an algorithm. A positive and strong correlation is identified with respect to BMI, Poverty, Education, Age and T2DM cohorts and it's comorbidities.

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