Incomplete time-series gene expression in integrative study for islet autoimmunity prediction.

Type 1 diabetes (T1D) outcome prediction plays a vital role in identifying novel risk factors, ensuring early patient care and designing cohort studies. TEDDY is a longitudinal cohort study that collects a vast amount of multi-omics and clinical data from its participants to explore the progression and markers of T1D. However, missing data in the omics profiles make the outcome prediction a difficult task. TEDDY collected time series gene expression for less than 6% of enrolled participants. Additionally, for the participants whose gene expressions are collected, 79% time steps are missing. This study introduces an advanced bioinformatics framework for gene expression imputation and islet autoimmunity (IA) prediction. The imputation model generates synthetic data for participants with partially or entirely missing gene expression. The prediction model integrates the synthetic gene expression with other risk factors to achieve better predictive performance. Comprehensive experiments on TEDDY datasets show that: (1) Our pipeline can effectively integrate synthetic gene expression with family history, HLA genotype and SNPs to better predict IA status at 2 years (sensitivity 0.622, AUC 0.715) compared with the individual datasets and state-of-the-art results in the literature (AUC 0.682). (2) The synthetic gene expression contains predictive signals as strong as the true gene expression, reducing reliance on expensive and long-term longitudinal data collection. (3) Time series gene expression is crucial to the proposed improvement and shows significantly better predictive ability than cross-sectional gene expression. (4) Our pipeline is robust to limited data availability. Availability: Code is available at https://github.com/compbiolabucf/TEDDY.

In silico model for miRNA-mediated regulatory network in cancer.

Deregulation of gene expression is associated with the pathogenesis of numerous human diseases including cancer. Current data analyses on gene expression are mostly focused on differential gene/transcript expression in big data-driven studies. However, a poor connection to the proteome changes is a widespread problem in current data analyses. This is partly due to the complexity of gene regulatory pathways at the post-transcriptional level. In this study, we overcome these limitations and introduce a graph-based learning model, PTNet, which simulates the microRNAs (miRNAs) that regulate gene expression post-transcriptionally in silico. Our model does not require large-scale proteomics studies to measure the protein expression and can successfully predict the protein levels by considering the miRNA-mRNA interaction network, the mRNA expression, and the miRNA expression. Large-scale experiments on simulations and real cancer high-throughput datasets using PTNet validated that (i) the miRNA-mediated interaction network affects the abundance of corresponding proteins and (ii) the predicted protein expression has a higher correlation with the proteomics data (ground-truth) than the mRNA expression data. The classification performance also shows that the predicted protein expression has an improved prediction power on cancer outcomes compared to the prediction done by the mRNA expression data only or considering both mRNA and miRNA. Availability: PTNet toolbox is available at http://github.com/CompbioLabUCF/PTNet.

Multi-omics data integration by generative adversarial network.

MOTIVATION: Accurate disease phenotype prediction plays an important role in the treatment of heterogeneous diseases like cancer in the era of precision medicine. With the advent of high throughput technologies, more comprehensive multi-omics data is now available that can effectively link the genotype to phenotype. However, the interactive relation of multi-omics datasets makes it particularly challenging to incorporate different biological layers to discover the coherent biological signatures and predict phenotypic outcomes. In this study, we introduce omicsGAN, a generative adversarial network model to integrate two omics data and their interaction network. The model captures information from the interaction network as well as the two omics datasets and fuse them to generate synthetic data with better predictive signals. RESULTS: Large-scale experiments on The Cancer Genome Atlas breast cancer, lung cancer and ovarian cancer datasets validate that (i) the model can effectively integrate two omics data (e.g. mRNA and microRNA expression data) and their interaction network (e.g. microRNA-mRNA interaction network). The synthetic omics data generated by the proposed model has a better performance on cancer outcome classification and patients survival prediction compared to original omics datasets. (ii) The integrity of the interaction network plays a vital role in the generation of synthetic data with higher predictive quality. Using a random interaction network does not allow the framework to learn meaningful information from the omics datasets; therefore, results in synthetic data with weaker predictive signals. AVAILABILITY AND IMPLEMENTATION: Source code is available at: https://github.com/CompbioLabUCF/omicsGAN. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

mTOR Contributes to the Proteome Diversity through Transcriptome-Wide Alternative Splicing.

The mammalian target of rapamycin (mTOR) pathway is crucial in energy metabolism and cell proliferation. Previously, we reported transcriptome-wide 3'-untranslated region (UTR) shortening by alternative polyadenylation upon mTOR activation and its impact on the proteome. Here, we further interrogated the mTOR-activated transcriptome and found that hyperactivation of mTOR promotes transcriptome-wide exon skipping/exclusion, producing short isoform transcripts from genes. This widespread exon skipping confers multifarious regulations in the mTOR-controlled functional proteomics: AS in coding regions widely affects the protein length and functional domains. They also alter the half-life of proteins and affect the regulatory post-translational modifications. Among the RNA processing factors differentially regulated by mTOR signaling, we found that SRSF3 mechanistically facilitates exon skipping in the mTOR-activated transcriptome. This study reveals a role of mTOR in AS regulation and demonstrates that widespread AS is a multifaceted modulator of the mTOR-regulated functional proteome.

mTOR-regulated U2af1 tandem exon splicing specifies transcriptome features for translational control.

U2 auxiliary factor 1 (U2AF1) functions in 3'-splice site selection during pre-mRNA processing. Alternative usage of duplicated tandem exons in U2AF1 produces two isoforms, U2AF1a and U2AF1b, but their functional differences are unappreciated due to their homology. Through integrative approaches of genome editing, customized-transcriptome profiling and crosslinking-mediated interactome analyses, we discovered that the expression of U2AF1 isoforms is controlled by mTOR and they exhibit a distinctive molecular profile for the splice site and protein interactomes. Mechanistic dissection of mutually exclusive alternative splicing events revealed that U2AF1 isoforms' inherent differential preferences of nucleotide sequences and their stoichiometry determine the 3'-splice site. Importantly, U2AF1a-driven transcriptomes feature alternative splicing events in the 5'-untranslated region (5'-UTR) that are favorable for translation. These findings unveil distinct roles of duplicated tandem exon-derived U2AF1 isoforms in the regulation of the transcriptome and suggest U2AF1a-driven 5'-UTR alternative splicing as a molecular mechanism of mTOR-regulated translational control.

Computational Methods to Study Human Transcript Variants in COVID-19 Infected Lung Cancer Cells.

Microbes and viruses are known to alter host transcriptomes by means of infection. In light of recent challenges posed by the COVID-19 pandemic, a deeper understanding of the disease at the transcriptome level is needed. However, research about transcriptome reprogramming by post-transcriptional regulation is very limited. In this study, computational methods developed by our lab were applied to RNA-seq data to detect transcript variants (i.e., alternative splicing (AS) and alternative polyadenylation (APA) events). The RNA-seq data were obtained from a publicly available source, and they consist of mock-treated and SARS-CoV-2 infected (COVID-19) lung alveolar (A549) cells. Data analysis results show that more AS events are found in SARS-CoV-2 infected cells than in mock-treated cells, whereas fewer APA events are detected in SARS-CoV-2 infected cells. A combination of conventional differential gene expression analysis and transcript variants analysis revealed that most of the genes with transcript variants are not differentially expressed. This indicates that no strong correlation exists between differential gene expression and the AS/APA events in the mock-treated or SARS-CoV-2 infected samples. These genes with transcript variants can be applied as another layer of molecular signatures for COVID-19 studies. In addition, the transcript variants are enriched in important biological pathways that were not detected in the studies that only focused on differential gene expression analysis. Therefore, the pathways may lead to new molecular mechanisms of SARS-CoV-2 pathogenesis.

Sustainable landfill leachate treatment: Optimize use of guar gum as natural coagulant and floc characterization.

Sustainable wastewater treatment necessitates the application of natural and green material in the approach. Thus, selecting a natural coagulant in leachate treatment is a crucial step in landfill operation to prevent secondary environmental pollution due to residual inorganic coagulant in treated effluent. Current study investigated the application of guar gum in landfill leachate treatment. Central composite design in response surface methodology was used to optimize the performance of Chemical Oxygen Demand (COD) removal. Quadratic model developed indicated the optimum COD removal 22.57% at guar gum dosage of 44.39 mg/L, pH 8.56 (natural pH of leachate) and mixing speed 79.27 rpm. Scanning electron microscopy showed that floc was compact and energy-dispersive-x-ray analysis showed that guar gum was capable to adsorb multiple ions from the leachate. Structural characterization using Fourier Transform Infrared analysis demonstrated that hydrogen bonding between guar and pollutant particles was involved in coagulation and flocculation process. Therefore, guar gum coagulant present potential to be an alternative in leachate treatment where pH requirement is not required during treatment. Simultaneously, adsorption by guar gum offers added pollutant removal advantage.

RNA and Protein Interactomes of an RNA-Binding Protein Tagged with FLAG Epitopes Using Combinatory Approaches of Genome Engineering and Stable Transfection.

To study the function of RNA-binding proteins (RBPs), an overexpression or knockout approach is generally used. However, as many RBPs are essential to cellular functions, the complete knockout of these proteins may be lethal to the cell. Overexpression of RBPs, on the other hand, may create an altered transcriptome and aberrant phenotypes that can mask their physiological function. Additionally, biochemical characterization of RBP often requires highly specific antibodies for efficient immunoprecipitation for downstream mass spectrometry or RNA footprinting profiling. To overcome these hurdles, we have developed a strategy to generate cellular systems either using a CRISPR-Cas9-mediated epitope tag knock-in approach or a two-step workflow to first stably express an exogenous Flag-tagged RBP and subsequently knockout the endogenous RBP using CRISPR-Cas9 gene editing. The generation of these cell lines will be beneficial for downstream RNA footprinting studies and mass spectrometry-mediated interactome studies.

Real-World Evidence on the Efficacy and Tolerability of Tramadol/Dexketoprofen (TRAM/DKP) Fixed-Dose Combination for the Management of Acute Non-surgical Pain in Asian Patients: A Multicentre Retrospective Case Series.

INTRODUCTION: Multimodal analgesia is key in the effective management of acute pain. Previous clinical trials have demonstrated good results with the use of a fixed-dose combination (FDC) of tramadol 75 mg and dexketoprofen 25 mg (TRAM/DKP) in acute pain management. However, there is a dearth of real-world evidence on the efficacy and safety of this combination in the management of acute non-surgical pain, especially among Asian patients. The case series reported herein investigates the real-world experiences of physicians and Asian patients with the use of TRAM/DKP FDC in the management of acute non-surgical pain. METHODS: Data were collected retrospectively on 11 Asian patients across multiple hospitals who had received a short course of TRAM/DKP FDC for acute non-surgical orthopaedic and non-orthopaedic pain. Data on baseline characteristics, medical history, treatment regimen, clinical outcomes, and patient satisfaction were compiled and shared at a peer-to-peer expert meeting in October 2022. RESULTS: All patients experienced a reduction in pain intensity and were very satisfied with pain management, with a mean satisfaction score of 4.3/5. Five patients (range: 63-74 years) experienced mild adverse events, including nausea, vomiting, and dizziness, which resolved with no need for additional treatment in the majority of cases. No serious adverse events were recorded. CONCLUSION: Asian patients with acute non-surgical orthopaedic and non-orthopaedic pain achieved good pain control with TRAM/DKP FDC. The regimen was well tolerated, and patients reported high levels of satisfaction with the outcomes, indicating that TRAM/DKP FDC is an effective choice for the control of acute non-surgical pain in Asian patients.

Landfill leachate wastewater treatment to facilitate resource recovery by a coagulation-flocculation process via hydrogen bond.

Recent trend to recover value-added products from wastewater calls for more effective pre-treatment technology. Conventional landfill leachate treatment is often complex and thus causes negative environmental impacts and financial burden. In order to facilitate downstream processing of leachate wastewater for production of energy or value-added products, it is pertinent to maximize leachate treatment performance by using simple yet effective technology that removes pollutants with minimum chemical added into the wastewater that could potentially affect downstream processing. Hence, the optimization of coagulation-flocculation leachate treatment using multivariate approach is crucial. Central composite design was applied to optimize operating parameters viz. Alum dosage, pH and mixing speed. Quadratic model indicated that the optimum COD removal of 54% is achieved with low alum dosage, pH and mixing speed of 750 mgL-1, 8.5 and 100 rpm, respectively. Optimization result showed that natural pH of the mature landfill leachate sample is optimum for alum coagulation process. Hence, the cost of pH adjustment could be reduced for industrial application by adopting optimized parameters. The inherent mechanism of pollutant removal was elucidated by FTIR peaks at 3853 cm-1 which indicated that hydrogen bonds play a major role in leachate removal by forming well aggregated flocs. This is concordance with SEM image that the floc was well aggregated with the porous linkages and amorphous surface structure. The optimization of leachate treatment has been achieved by minimizing the usage of alum under optimized condition.

New Prospects for Modified Algae in Heavy Metal Adsorption.

Heavy metal pollution is one of the most pervasive environmental problems globally. Novel finely tuned algae have been proposed as a means to improve the efficacy and selectivity of heavy metal biosorption. This article reviews current research on selective algal heavy metal adsorption and critically discusses the performance of novel biosorbents. We emphasize emerging state-of-the-art techniques that customize algae for enhanced performance and selectivity, particularly molecular and chemical extraction techniques as well as nanoparticle (NP) synthesis approaches. The mechanisms and processes for developing novel algal biosorbents are also presented. Finally, we discuss the applications, challenges, and future prospects for modified algae in heavy metal biosorption.

Sensitive method for the determination of 1,3-dichloropropan-2-ol and 3-chloropropane-1,2-diol in soy sauce by capillary gas chromatography with mass spectrometric detection.

This paper reports the development of a highly selective and sensitive method for the determination of parts-per-billion level of 1,3-dichloropropan-2-ol (1,3-DCP) and 3-chloropropane-1,2-diol (3-MCPD) in soy sauce using capillary gas chromatography with mass spectrometric detection. Samples were homogenised, mixed with sodium chloride solution and then adsorbed on silica gel. The loaded silica gel was packed into a chromatographic column, from which chloropropanols were extracted by elution with ethyl acetate. Heptafluorobutyric acid anhydride was added to the concentrated eluant to derivatise the chloropropanols and the derivatised analytes were separated by gas chromatography, identified and quantified by mass spectrometry. A linear relationship between the concentration of the two chloropropanols and the detector response was obtained over the concentration range of 10-1000 microg/kg. Precision of the method was satisfactory at about 5%, and recoveries of 1,3-DCP and 3-MCPD from soy sauce samples spiked at 25 microg/kg were 77 and 98%, respectively. The limit of quantitation of the method was found to be about 5 microg/kg for 1,3-DCP and 3-MCPD, respectively meeting the requirements of tolerance limits adopted by different international institutions and governments around the world. This paper is the first of its kind in reporting an analytical procedure for the simultaneous separation and determination of 3-MCPD and 1,3-DCP, a more potent contaminant, at low microg/kg level.

Femoral tunnel widening after quadrupled hamstring anterior cruciate ligament reconstruction.

PURPOSE: To evaluate the extent of femoral tunnel widening after quadrupled hamstring anterior cruciate ligament reconstruction using femoral cross pin fixation in contrast to bioabsorbable screw fixation augmented with Endopearl. METHODS: 16 patients underwent cross pin femoral fixation and the next 15 underwent bioabsorbable screw fixation augmented with Endopearl. The patients were evaluated radiographically for femoral tunnel widening. Standard posteroanterior radiographs of the knee in full extension were taken at postoperative year 2 and 5. The tunnel width was measured at the opening, the widest and the most proximal parts of the femoral tunnel. RESULTS: In the femoral cross pin and bioabsorbable screw fixations, the mean femoral tunnel sizes were 7.6 and 8.0 mm, respectively. The overall mean tunnel widening at all measured sites were 2.7 and 1.8 mm at the 2-year follow-up and were 2.5 and 1.8 mm at the 5-year follow-up, respectively. The femoral tunnel widening was greater in the cross pin than the bioabsorbable screw group, but only the difference in the most proximal part was significant (p=0.01 at year 2 and p<0.001 at year 5). CONCLUSION: Femoral tunnel widening was greater in femoral cross pin fixation, probably related to the windshield-wiper and bungee-cord effects. It usually occurred in the first 2 years after the operation and remained static thereafter.

Anterior spinal fusion versus laminoplasty for cervical spondylotic myelopathy: a retrospective review.

PURPOSE: To compare outcomes of anterior spinal fusion (ASF) versus laminoplasty for cervical spondylotic myelopathy (CSM). METHODS: Records of 26 women and 37 men aged 34 to 83 (mean, 67) years who underwent ASF or laminoplasty for CSM were reviewed. Patient demographics, duration of symptoms, pre- and post-operative Japanese Orthopaedic Association (JOA) scores, Hirabayashi recovery rate, and radiographic features (including anteroposterior canal diameter, Pavlov ratio, and number of levels compressed) were compared. RESULTS: The mean duration of symptoms was 9 months. The mean follow-up period was 40 months. Patients who underwent laminoplasty were older (p=0.015) and had more levels compressed (p<0.001) than those who underwent ASF. Patients with C3/4 compression were older than those without it (p=0.044). Younger patients had higher Hirabayashi recovery rate (p=0.043). CONCLUSION: The surgical decision for ASF or laminoplasty mainly depends on the number of levels compressed and patient age. Laminoplasty is usually reserved for older patients with multiple level involvement. Age is the main predictive factor for surgical outcome.

Rotary subluxation of the metacarpophalangeal thumb joint: a case report.

Irreducible rotary subluxation of the metacarpophalangeal joint (MCPJ) of the thumb is a rare entity. Open reduction is indicated when signs of irreducibility are seen on the radiographs. We present one such case caused by displacement of the sesamoid bone into the intercondylar notch of the first metacarpal bone. A 35-year-old woman sustained a twisting injury to her right thumb by pronating and hyperextending it while attempting to retrieve things that she had dropped into a basin conduit. True lateral radiographs showed rotary subluxation of the thumb MCPJ, a sesamoid bone overlapping with the metacarpal head, and loss of the subsesamoid joint space and an incongruent MCPJ on full flexion. A high level of clinical suspicion is needed to diagnose this rare entity.