Initial Mix-and-Match COVID-19 Vaccination Perceptions, Concerns, and Side Effects across Canadians.

Research indicates that mixing the first two doses of COVID-19 vaccine types (i.e., adenoviral vector and mRNA) produces potent immune responses against the coronavirus, but it is unclear how individuals may perceive these benefits, or whether there are different concerns compared to individuals who received two doses of the same vaccine. This research examines the demographic characteristics, psychological perceptions, and vaccination-related opinions and experiences of a large Canadian sample (N = 1002) who had received two initial doses of any COVID-19 vaccine combination. Participants included 791 (78.9%) who received two doses of the exact same brand and type of vaccine, 164 (16.4%) who received two doses of the same type of vaccine (i.e., either mRNA or adenoviral vector) but from different brands (e.g., Pfizer-BioNTech + Moderna), and 47 (4.7%) who received two doses from different types and brands of vaccine (e.g., Oxford-AstraZeneca + Pfizer-BioNTech). Results showed that, after the first vaccine dose, participants who received an adenoviral vector vaccine (e.g., Oxford-AstraZeneca) experienced the highest number of common side effects, and more severe levels of each side effect compared to those who received an mRNA vaccine (e.g., Pfizer-BioNTech or Moderna). After the second dose, participants who received Moderna as their second vaccine experienced the highest number of and most severe side effects, regardless of whether they received Moderna, Pfizer-BioNTech, or Oxford-AstraZeneca as their first dose. Real-world implications of these findings are discussed.

Longitudinal Changes of COVID-19 Symptoms in Social Media: Observational Study.

BACKGROUND: In December 2019, the COVID-19 outbreak started in China and rapidly spread around the world. Many studies have been conducted to understand the clinical characteristics of COVID-19, and recently postinfection sequelae of this disease have begun to be investigated. However, there is little consensus on the longitudinal changes of lasting physical or psychological symptoms from prior COVID-19 infection. OBJECTIVE: This study aims to investigate and analyze public social media data from Reddit to understand the longitudinal impact of COVID-19 symptoms before and after recovery from COVID-19. METHODS: We collected 22,890 Reddit posts that were generated by 14,401 authors from March 14 to December 16, 2020. Using active learning and intensive manual inspection, 292 (2.03%) active authors, who were infected by COVID-19 and frequently reported disease progress on Reddit, along with their 2213 (9.67%) longitudinal posts, were identified. Machine learning tools to extract biomedical information were applied to identify COVID-19 symptoms mentioned in the Reddit posts. We then examined longitudinal changes in individual physiological and psychological characteristics before and after recovery from COVID-19 infection. RESULTS: In total, 58 physiological and 3 psychological symptoms were identified in social media before and after recovery from COVID-19 infection. From the analyses, we found that symptoms of patients with COVID-19 lasted 2.5 months. On average, symptoms appeared around a month before recovery and remained for 1.5 months after recovery. Well-known COVID-19 symptoms, such as fever, cough, and chest congestion, appeared relatively earlier in patient journeys and were frequently observed before recovery from COVID-19. Meanwhile, mental discomfort or distress, such as brain fog or stress, fatigue, and manifestations on toes or fingers, were frequently mentioned after recovery and remained as intermediate- and longer-term sequelae. CONCLUSIONS: In this study, we showed the dynamic changes in COVID-19 symptoms during the infection and recovery phases of the disease. Our findings suggest the feasibility of using social media data for investigating disease states and understanding the evolution of the physiological and psychological characteristics of COVID-19 infection over time.

Identification of Risk Factors and Symptoms of COVID-19: Analysis of Biomedical Literature and Social Media Data.

BACKGROUND: In December 2019, the COVID-19 outbreak started in China and rapidly spread around the world. Lack of a vaccine or optimized intervention raised the importance of characterizing risk factors and symptoms for the early identification and successful treatment of patients with COVID-19. OBJECTIVE: This study aims to investigate and analyze biomedical literature and public social media data to understand the association of risk factors and symptoms with the various outcomes observed in patients with COVID-19. METHODS: Through semantic analysis, we collected 45 retrospective cohort studies, which evaluated 303 clinical and demographic variables across 13 different outcomes of patients with COVID-19, and 84,140 Twitter posts from 1036 COVID-19-positive users. Machine learning tools to extract biomedical information were introduced to identify mentions of uncommon or novel symptoms in tweets. We then examined and compared two data sets to expand our landscape of risk factors and symptoms related to COVID-19. RESULTS: From the biomedical literature, approximately 90% of clinical and demographic variables showed inconsistent associations with COVID-19 outcomes. Consensus analysis identified 72 risk factors that were specifically associated with individual outcomes. From the social media data, 51 symptoms were characterized and analyzed. By comparing social media data with biomedical literature, we identified 25 novel symptoms that were specifically mentioned in tweets but have been not previously well characterized. Furthermore, there were certain combinations of symptoms that were frequently mentioned together in social media. CONCLUSIONS: Identified outcome-specific risk factors, symptoms, and combinations of symptoms may serve as surrogate indicators to identify patients with COVID-19 and predict their clinical outcomes in order to provide appropriate treatments.

Predicting Glycaemia in Type 1 Diabetes Patients: Experiments in Feature Engineering and Data Imputation.

Patients with type 1 diabetes manually regulate blood glucose concentration by adjusting insulin dosage in response to factors such as carbohydrate intake and exercise intensity. Automated near-term prediction of blood glucose concentration is essential to prevent hyper- and hypoglycaemic events in type 1 diabetes patients and to improve control of blood glucose levels by physicians and patients. The imperfect nature of patient monitoring introduces missing values into all variables that play important roles to predict blood glucose level, necessitating data imputation. In this paper, we investigated the importance of variables and explored various feature engineering methods to predict blood glucose level. Next, we extended our work by developing a new empirical imputation method and investigating the predictive accuracy achieved under different methods to impute missing data. Also, we examined the influence of past signal values on the prediction of blood glucose levels. We reported the relative performance of predictive models in different testing scenarios and different imputation methods. Finally, we found an optimal combination of data imputation methods and built an ensemble model for the reliable prediction of blood glucose levels on a 30-minute horizon.

Temporal Stability and Prognostic Biomarker Potential of the Prostate Cancer Urine miRNA Transcriptome.

BACKGROUND: The development of noninvasive tests for the early detection of aggressive prostate tumors is a major unmet clinical need. miRNAs are promising noninvasive biomarkers: they play essential roles in tumorigenesis, are stable under diverse analytical conditions, and can be detected in body fluids. METHODS: We measured the longitudinal stability of 673 miRNAs by collecting serial urine samples from 10 patients with localized prostate cancer. We then measured temporally stable miRNAs in an independent training cohort (n = 99) and created a biomarker predictive of Gleason grade using machine-learning techniques. Finally, we validated this biomarker in an independent validation cohort (n = 40). RESULTS: We found that each individual has a specific urine miRNA fingerprint. These fingerprints are temporally stable and associated with specific biological functions. We identified seven miRNAs that were stable over time within individual patients and integrated them with machine-learning techniques to create a novel biomarker for prostate cancer that overcomes interindividual variability. Our urine biomarker robustly identified high-risk patients and achieved similar accuracy as tissue-based prognostic markers (area under the receiver operating characteristic = 0.72, 95% confidence interval = 0.69 to 0.76 in the training cohort, and area under the receiver operating characteristic curve = 0.74, 95% confidence interval = 0.55 to 0.92 in the validation cohort). CONCLUSIONS: These data highlight the importance of quantifying intra- and intertumoral heterogeneity in biomarker development. This noninvasive biomarker may usefully supplement invasive or expensive radiologic- and tissue-based assays.

miR-191 promotes radiation resistance of prostate cancer through interaction with RXRA.

Radiation therapy is a common treatment for prostate cancer, however recurrence remains a problem. MicroRNA expression is altered in prostate cancer and may promote therapy resistance. Through bioinformatic analyses of TCGA and CPC-GENE patient cohorts, we identified higher miR-191 expression in tumor versus normal tissue, and increased expression in higher Gleason scores. In vitro and in vivo experiments demonstrated that miR-191 overexpression promotes radiation survival, and contributes to a more aggressive phenotype. Retinoid X receptor alpha, RXRA, was discovered to be a novel target of miR-191, and knockdown recapitulated radioresistance. Furthermore, treatment of prostate cancer cells with the RXRA agonist 9-cis-retinoic acid restored radiosensitivity. Supporting this relationship, patients with high miR-191 and low RXRA abundance experienced quicker biochemical recurrence. Reduced RXRA translated to a higher risk of distant failure after radiotherapy. Notably, this miR-191/RXRA interaction was conserved in a novel primary cell line derived from radiorecurrent prostate cancer. Together, our findings demonstrate that miR-191 promotes prostate cancer survival after radiotherapy, and highlights retinoids as a potential option to improve radiotherapy response.

Tetrahydrobiopterin enhances mitochondrial biogenesis and cardiac contractility via stimulation of PGC1α signaling.

Tetrahydrobiopterin (BH4) shows therapeutic potential as an endogenous target in cardiovascular diseases. Although it is involved in cardiovascular metabolism and mitochondrial biology, its mechanisms of action are unclear. We investigated how BH4 regulates cardiovascular metabolism using an unbiased multiple proteomics approach with a sepiapterin reductase knock-out (Spr-/-) mouse as a model of BH4 deficiency. Spr-/- mice exhibited a shortened life span, cardiac contractile dysfunction, and morphological changes. Multiple proteomics and systems-based data-integrative analyses showed that BH4 deficiency altered cardiac mitochondrial oxidative phosphorylation. Along with decreased transcription of major mitochondrial biogenesis regulatory genes, including Ppargc1a, Ppara, Esrra, and Tfam, Spr-/- mice exhibited lower mitochondrial mass and severe oxidative phosphorylation defects. Exogenous BH4 supplementation, but not nitric oxide supplementation or inhibition, rescued these cardiac and mitochondrial defects. BH4 supplementation also recovered mRNA and protein levels of PGC1α and its target proteins involved in mitochondrial biogenesis (mtTFA and ERRα), antioxidation (Prx3 and SOD2), and fatty acid utilization (CD36 and CPTI-M) in Spr-/- hearts. These results indicate that BH4-activated transcription of PGC1α regulates cardiac energy metabolism independently of nitric oxide and suggests that BH4 has therapeutic potential for cardiovascular diseases involving mitochondrial dysfunction.

MicroRNA­198 suppresses prostate tumorigenesis by targeting MIB1.

MicroRNAs are small non­coding RNA molecules which act as modulators of gene function, and have been identified as playing important roles in cancer as both tumor suppressors and oncogenes. The present study aimed to examine the role of miR­198 in prostate cancer aggression by analyzing how it influences several hallmarks of cancer. Abundance of miR­198 in prostate cancer and association with clinical characteristics was analyzed using a CPC­Gene prostate cancer dataset. Overexpression of miR­198 was performed using transient transfection of miR­198 mimic prior to assaying proliferation, cell cycle, and colony formation in LNCaP and DU145 cell lines using standard protocols. In vivo tumor formation in athymic nude mice was examined using LNCaP xenografts with stable overexpression conferred using lentiviral miR­198 transduction. Protein and mRNA abundance of MIB1 was determined using western blotting and RT­qPCR respectively, while miR­198 binding to MIB1 was validated using a luciferase reporter assay. miR­198 abundance was lower in high Gleason grade prostate cancer relative to intermediate and low­grade cancer. Overexpression of miR­198 diminished proliferation of prostate cancer cell lines, increased G0/G1 cell cycle arrest, and significantly impaired colony formation. Elevated miR­198 abundance was also demonstrated to impair tumor formation in vivo using LNCaP xenografts. Mindbomb E3 ubiquitin protein ligase 1 (MIB1) was demonstrated to be directly targeted by miR­198, and knockdown of MIB1 recapitulated the effects of miR­198 on proliferation and colony formation. The present evidence supports miR­198 as an important tumor suppressor in prostate cancer, and demonstrates for the first time that it acts by targeting MIB1. The present study reinforces the importance and complexity of miRNA in regulating prostate cancer aggression.

Widespread and Functional RNA Circularization in Localized Prostate Cancer.

The cancer transcriptome is remarkably complex, including low-abundance transcripts, many not polyadenylated. To fully characterize the transcriptome of localized prostate cancer, we performed ultra-deep total RNA-seq on 144 tumors with rich clinical annotation. This revealed a linear transcriptomic subtype associated with the aggressive intraductal carcinoma sub-histology and a fusion profile that differentiates localized from metastatic disease. Analysis of back-splicing events showed widespread RNA circularization, with the average tumor expressing 7,232 circular RNAs (circRNAs). The degree of circRNA production was correlated to disease progression in multiple patient cohorts. Loss-of-function screening identified 11.3% of highly abundant circRNAs as essential for cell proliferation; for ∼90% of these, their parental linear transcripts were not essential. Individual circRNAs can have distinct functions, with circCSNK1G3 promoting cell growth by interacting with miR-181. These data advocate for adoption of ultra-deep RNA-seq without poly-A selection to interrogate both linear and circular transcriptomes.

The mechanistic insight of a specific interaction between 15d-Prostaglandin-J2 and eIF4A suggests an evolutionary conserved role across species.

15-deoxy-delta 12,14-prostaglandin J2 (15d-PGJ2) is an anti-inflammatory/anti-neoplastic prostaglandin that functions through covalent binding to cysteine residues of various target proteins. We previously showed that 15d-PGJ2 mediated anti-inflammatory responses are dependent on the translational inhibition through its interaction with eIF4A (Kim et al., 2007). Binding of 15d-PGJ2 to eIF4A specifically blocks the interaction between eIF4G and eIF4A, which leads to the formation of stress granules (SGs), which then cluster mRNAs with inhibited translation. Here, we show that the binding between 15d-PGJ2 and eIF4A specifically blocks the interaction between the MIF4G domain of eIF4G and eIF4A. To reveal the mechanism of this interaction, we used computational simulation-based docking studies and identified that the carboxyl tail of 15d-PGJ2 could stabilize the binding of 15d-PGJ2 to eIF4A through arginine 295 of eIF4A, which is the first suggestion that the 15d-PGJ2 tail plays a physiological role. Interestingly, the putative 15d-PGJ2 binding site on eiF4A is conserved across many species, suggesting a biological role. Our data propose that studying 15d-PGJ2 and its targets may uncover new therapeutic approaches in anti-inflammatory drug discovery.

miRNA-106a and prostate cancer radioresistance: a novel role for LITAF in ATM regulation.

Recurrence of high-grade prostate cancer after radiotherapy is a significant clinical problem, resulting in increased morbidity and reduced patient survival. The molecular mechanisms of radiation resistance are being elucidated through the study of microRNA (miR) that negatively regulate gene expression. We performed bioinformatics analyses of The Cancer Genome Atlas (TCGA) dataset to evaluate the association between miR-106a and its putative target lipopolysaccharide-induced TNF-α factor (LITAF) in prostate cancer. We characterized the function of miR-106a through in vitro and in vivo experiments and employed transcriptomic analysis, western blotting, and 3'UTR luciferase assays to establish LITAF as a bona fide target of miR-106a. Using our well-characterized radiation-resistant cell lines, we identified that miR-106a was overexpressed in radiation-resistant cells compared to parental cells. In the TCGA, miR-106a was significantly elevated in high-grade human prostate tumors relative to intermediate- and low-grade specimens. An inverse correlation was seen with its target, LITAF. Furthermore, high miR-106a and low LITAF expression predict for biochemical recurrence at 5 years after radical prostatectomy. miR-106a overexpression conferred radioresistance by increasing proliferation and reducing senescence, and this was phenocopied by knockdown of LITAF. For the first time, we describe a role for miRNA in upregulating ATM expression. LITAF, not previously attributed to radiation response, mediates this interaction. This route of cancer radioresistance can be overcome using the specific ATM kinase inhibitor, KU-55933. Our research provides the first report of miR-106a and LITAF in prostate cancer radiation resistance and high-grade disease, and presents a viable therapeutic strategy that may ultimately improve patient outcomes.

Discovery of short linear motif-mediated interactions through phage display of intrinsically disordered regions of the human proteome.

The intrinsically disordered regions of eukaryotic proteomes are enriched in short linear motifs (SLiMs), which are of crucial relevance for cellular signaling and protein regulation; many mediate interactions by providing binding sites for peptide-binding domains. The vast majority of SLiMs remain to be discovered highlighting the need for experimental methods for their large-scale identification. We present a novel proteomic peptide phage display (ProP-PD) library that displays peptides representing the disordered regions of the human proteome, allowing direct large-scale interrogation of most potential binding SLiMs in the proteome. The performance of the ProP-PD library was validated through selections against SLiM-binding bait domains with distinct folds and binding preferences. The vast majority of identified binding peptides contained sequences that matched the known SLiM-binding specificities of the bait proteins. For SHANK1 PDZ, we establish a novel consensus TxF motif for its non-C-terminal ligands. The binding peptides mostly represented novel target proteins, however, several previously validated protein-protein interactions (PPIs) were also discovered. We determined the affinities between the VHS domain of GGA1 and three identified ligands to 40-130 µm through isothermal titration calorimetry, and confirmed interactions through coimmunoprecipitation using full-length proteins. Taken together, we outline a general pipeline for the design and construction of ProP-PD libraries and the analysis of ProP-PD-derived, SLiM-based PPIs. We demonstrated the methods potential to identify low affinity motif-mediated interactions for modular domains with distinct binding preferences. The approach is a highly useful complement to the current toolbox of methods for PPI discovery.

Large-Scale Interaction Profiling of Protein Domains Through Proteomic Peptide-Phage Display Using Custom Peptidomes.

Protein-protein interactions are essential to cellular functions and signaling pathways. We recently combined bioinformatics and custom oligonucleotide arrays to construct custom-made peptide-phage libraries for screening peptide-protein interactions, an approach we call proteomic peptide-phage display (ProP-PD). In this chapter, we describe protocols for phage display for the identification of natural peptide binders for a given protein. We finally describe deep sequencing for the analysis of the proteomic peptide-phage display.

Targeted proteomics identifies liquid-biopsy signatures for extracapsular prostate cancer.

Biomarkers are rapidly gaining importance in personalized medicine. Although numerous molecular signatures have been developed over the past decade, there is a lack of overlap and many biomarkers fail to validate in independent patient cohorts and hence are not useful for clinical application. For these reasons, identification of novel and robust biomarkers remains a formidable challenge. We combine targeted proteomics with computational biology to discover robust proteomic signatures for prostate cancer. Quantitative proteomics conducted in expressed prostatic secretions from men with extraprostatic and organ-confined prostate cancers identified 133 differentially expressed proteins. Using synthetic peptides, we evaluate them by targeted proteomics in a 74-patient cohort of expressed prostatic secretions in urine. We quantify a panel of 34 candidates in an independent 207-patient cohort. We apply machine-learning approaches to develop clinical predictive models for prostate cancer diagnosis and prognosis. Our results demonstrate that computationally guided proteomics can discover highly accurate non-invasive biomarkers.

Interactions between Transmembrane Helices within Monomers of the Aquaporin AtPIP2;1 Play a Crucial Role in Tetramer Formation.

Aquaporin (AQP) is a water channel protein found in various subcellular membranes of both prokaryotic and eukaryotic cells. The physiological functions of AQPs have been elucidated in many organisms. However, understanding their biogenesis remains elusive, particularly regarding how they assemble into tetramers. Here, we investigated the amino acid residues involved in the tetramer formation of the Arabidopsis plasma membrane AQP AtPIP2;1 using extensive amino acid substitution mutagenesis. The mutant proteins V41A/E44A, F51A/L52A, F87A/I91A, F92A/I93A, V95A/Y96A, and H216A/L217A, harboring alanine substitutions in the transmembrane (TM) helices of AtPIP2;1 polymerized into multiple oligomeric complexes with a variable number of subunits greater than four. Moreover, these mutant proteins failed to traffic to the plasma membrane, instead of accumulating in the endoplasmic reticulum (ER). Structure-based modeling revealed that these residues are largely involved in interactions between TM helices within monomers. These results suggest that inter-TM interactions occurring both within and between monomers play crucial roles in tetramer formation in the AtPIP2;1 complex. Moreover, the assembly of AtPIP2;1 tetramers is critical for their trafficking from the ER to the plasma membrane, as well as water permeability.

PAT: predictor for structured units and its application for the optimization of target molecules for the generation of synthetic antibodies.

BACKGROUND: The identification of structured units in a protein sequence is an important first step for most biochemical studies. Importantly for this study, the identification of stable structured region is a crucial first step to generate novel synthetic antibodies. While many approaches to find domains or predict structured regions exist, important limitations remain, such as the optimization of domain boundaries and the lack of identification of non-domain structured units. Moreover, no integrated tool exists to find and optimize structural domains within protein sequences. RESULTS: Here, we describe a new tool, PAT ( http://www.kimlab.org/software/pat ) that can efficiently identify both domains (with optimized boundaries) and non-domain putative structured units. PAT automatically analyzes various structural properties, evaluates the folding stability, and reports possible structural domains in a given protein sequence. For reliability evaluation of PAT, we applied PAT to identify antibody target molecules based on the notion that soluble and well-defined protein secondary and tertiary structures are appropriate target molecules for synthetic antibodies. CONCLUSION: PAT is an efficient and sensitive tool to identify structured units. A performance analysis shows that PAT can characterize structurally well-defined regions in a given sequence and outperforms other efforts to define reliable boundaries of domains. Specially, PAT successfully identifies experimentally confirmed target molecules for antibody generation. PAT also offers the pre-calculated results of 20,210 human proteins to accelerate common queries. PAT can therefore help to investigate large-scale structured domains and improve the success rate for synthetic antibody generation.

A high-throughput pipeline for the production of synthetic antibodies for analysis of ribonucleoprotein complexes.

Post-transcriptional regulation of mRNAs plays an essential role in the control of gene expression. mRNAs are regulated in ribonucleoprotein (RNP) complexes by RNA-binding proteins (RBPs) along with associated protein and noncoding RNA (ncRNA) cofactors. A global understanding of post-transcriptional control in any cell type requires identification of the components of all of its RNP complexes. We have previously shown that these complexes can be purified by immunoprecipitation using anti-RBP synthetic antibodies produced by phage display. To develop the large number of synthetic antibodies required for a global analysis of RNP complex composition, we have established a pipeline that combines (i) a computationally aided strategy for design of antigens located outside of annotated domains, (ii) high-throughput antigen expression and purification in Escherichia coli, and (iii) high-throughput antibody selection and screening. Using this pipeline, we have produced 279 antibodies against 61 different protein components of Drosophila melanogaster RNPs. Together with those produced in our low-throughput efforts, we have a panel of 311 antibodies for 67 RNP complex proteins. Tests of a subset of our antibodies demonstrated that 89% immunoprecipitate their endogenous target from embryo lysate. This panel of antibodies will serve as a resource for global studies of RNP complexes in Drosophila. Furthermore, our high-throughput pipeline permits efficient production of synthetic antibodies against any large set of proteins.

Pooled screening for antiproliferative inhibitors of protein-protein interactions.

Protein-protein interactions (PPIs) are emerging as a promising new class of drug targets. Here, we present a novel high-throughput approach to screen inhibitors of PPIs in cells. We designed a library of 50,000 human peptide-binding motifs and used a pooled lentiviral system to express them intracellularly and screen for their effects on cell proliferation. We thereby identified inhibitors that drastically reduced the viability of a pancreatic cancer line (RWP1) while leaving a control line virtually unaffected. We identified their target interactions computationally, and validated a subset in experiments. We also discovered their potential mechanisms of action, including apoptosis and cell cycle arrest. Finally, we confirmed that synthetic lipopeptide versions of our inhibitors have similarly specific and dosage-dependent effects on cancer cell growth. Our screen reveals new drug targets and peptide drug leads, and it provides a rich data set covering phenotypes for the inhibition of thousands of interactions.

A systematic approach to identify novel cancer drug targets using machine learning, inhibitor design and high-throughput screening.

We present an integrated approach that predicts and validates novel anti-cancer drug targets. We first built a classifier that integrates a variety of genomic and systematic datasets to prioritize drug targets specific for breast, pancreatic and ovarian cancer. We then devised strategies to inhibit these anti-cancer drug targets and selected a set of targets that are amenable to inhibition by small molecules, antibodies and synthetic peptides. We validated the predicted drug targets by showing strong anti-proliferative effects of both synthetic peptide and small molecule inhibitors against our predicted targets.