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To investigate the clinical significance of the interferon (IFN) score, especially the IFN-I score, in patients with anti-melanoma differentiation-associated gene 5 (MDA5) antibody-positive dermatomyositis (anti-MDA5 DM). We enrolled 262 patients with different autoimmune diseases, including idiopathic inflammatory myopathy, systemic lupus erythematosus, rheumatoid arthritis, adult-onset Still's disease, and Sjögren's syndrome, as well as 58 healthy controls. Multiplex quantitative real-time polymerase chain reaction (RT-qPCR) using four TaqMan probes was used to evaluate type I IFN-stimulated genes (IFI44 and MX1), one type II IFN-stimulated gene (IRF1), and one internal control gene (HRPT1), which were used to determine the IFN-I score. The clinical features and disease activity index were compared between the high and low IFN-I score groups in 61 patients with anti-MDA5+ DM. The associations between laboratory findings and the predictive value of the baseline IFN-I score for mortality were analyzed. The IFN score was significantly higher in patients with anti-MDA5+ DM than in healthy controls. The IFN-I score was positively correlated with the serum IFN-α concentration, ferritin concentration, and Myositis Disease Activity Assessment Visual Analogue Scale (MYOACT) score. Compared with patients with a low IFN-I score, patients with a high IFN-I score showed a higher MYOACT score, C-reactive protein concentration, aspartate transaminase concentration, ferritin concentration, plasma cell percentage, and CD3+ T-cell percentage, as well as lower lymphocyte, natural killer cell, and monocyte counts. The 3-month survival rate was significantly lower in patients with an IFN-I score of >4.9 than in those with an IFN-I score of ≤4.9 (72.9% . 100%, respectively; P = 0.044). The IFN score, especially the IFN-I score, measured by multiplex RT-qPCR is a valuable tool to monitor disease activity and predict mortality in patients with anti-MDA5+ DM.

Background Batch effects are notoriously common technical variations in multiomics data and may result in misleading outcomes if uncorrected or over-corrected. A plethora of batch-effect correction algorithms are proposed to facilitate data integration. However, their respective advantages and limitations are not adequately assessed in terms of omics types, the performance metrics, and the application scenarios. Results As part of the Quartet Project for quality control and data integration of multiomics profiling, we comprehensively assess the performance of seven batch effect correction algorithms based on different performance metrics of clinical relevance, i.e., the accuracy of identifying differentially expressed features, the robustness of predictive models, and the ability of accurately clustering cross-batch samples into their own donors. The ratio-based method, i.e., by scaling absolute feature values of study samples relative to those of concurrently profiled reference material(s), is found to be much more effective and broadly applicable than others, especially when batch effects are completely confounded with biological factors of study interests. We further provide practical guidelines for implementing the ratio based approach in increasingly large-scale multiomics studies. Conclusions Multiomics measurements are prone to batch effects, which can be effectively corrected using ratio-based scaling of the multiomics data. Our study lays the foundation for eliminating batch effects at a ratio scale.

It is theorized that multivalent interaction can result in better affinity and selectivity than monovalent interaction in the design of high-performance ligands. Accordingly, biomolecular engineers are increasingly taking advantage of multivalent interactions to fabricate novel molecular assemblies, resulting in new functions for ligands or enhanced performance of existing ligands. Substantial efforts have been expended in using small molecules or epitopes of antibodies for designing multifunctional or better-performing ligands. However, few attempts to use nucleic acid aptamers as functional domains have been reported. In this study, we explore the design of bivalent nucleic acid ligands by using thrombin and its aptamers as the model by which to evaluate its functions. By assembling two thrombin-binding aptamers with optimized design parameters, this assembly has resulted in the successful development of a nucleic acid-based high-performance bivalent protein inhibitor. Our experimentation proved (i) that the simultaneous binding of two aptamers after linkage achieved 16.6-fold better inhibition efficiency than binding of the monovalent ligand and (ii) that such an improvement originated from changes in the kinetics of the binding interactions, with a koff rate [almost equal to]1/50 as fast. In addition, the newly generated aptamer assembly is an excellent anticoagulant reagent when tested with different samples. Because this optimized ligand design offers a simple and noninvasive means of accomplishing higher performance from known functional aptamers, it holds promise as a potent antithrombin agent in the treatment of various diseases related to abnormal thrombin activities.

Batch effects, defined as unwanted technical variations caused by differences in labs, pipelines, or batches, are notorious in MS-based proteomics data, wherein protein quantities are inferred from precursor- and peptide-level intensities. However, the optimal stage for batch-effect correction remains elusive and crucial. Leveraging real-world multi-batch data from the Quartet protein reference materials and simulated data, we benchmark batch-effect correction at precursor, peptide, and protein levels combined across two designed scenarios (balanced and confounded), three quantification methods (MaxLFQ, TopPep3, and iBAQ), and seven batch-effect correction algorithms (Combat, Median centering, Ratio, RUV-III-C, Harmony, WaveICA2.0, and NormAE). Our findings reveal that protein-level correction is the most robust strategy, and the quantification process interacts with batch-effect correction algorithms. Furthermore, we extend our analysis to large-scale data from 1431 plasma samples of type 2 diabetes patients in Phase 3 clinical trials, demonstrating the superior prediction performance of the MaxLFQ-Ratio combination. These findings support that batch-effect correction at the protein level enhances multi-batch data integration in large proteomics cohort studies. Batch effects in MS-based proteomics pose important challenges in protein quantification, and the optimal stage for batch-effect correction remains elusive and crucial. Here, the authors, through benchmarking using reference standards and simulated data, demonstrate that protein-level batch correction is most robust in MS-based proteomics.

Certified RNA reference materials are indispensable for assessing the reliability of RNA sequencing to detect intrinsically small biological differences in clinical settings, such as molecular subtyping of diseases. As part of the Quartet Project for quality control and data integration of multi-omics profiling, we established four RNA reference materials derived from immortalized B-lymphoblastoid cell lines from four members of a monozygotic twin family. Additionally, we constructed ratio-based transcriptome-wide reference datasets between two samples, providing cross-platform and cross-laboratory ‘ground truth’. Investigation of the intrinsically subtle biological differences among the Quartet samples enables sensitive assessment of cross-batch integration of transcriptomic measurements at the ratio level. The Quartet RNA reference materials, combined with the ratio-based reference datasets, can serve as unique resources for assessing and improving the quality of transcriptomic data in clinical and biological settings. A new RNA reference set improves detection of differential expression in clinical settings.

Apart from the canonical Watson-Crick duplex, nucleic acids can often form other structures, e.g. G-quadruplex and triplex. These structures give nucleic acid additional functions besides coding for genetic information. Aptamers are one type of functional nucleic acids that bind to specific targets with high selectivity and affinity by folding into special tertiary structures. Despite the fact that numerous aptamers have been reported, only a few different types of aptamer structures are identified. Here we report a novel triplex-quadruplex hybrid scaffold formed by a codeine binding aptamer (CBA). CBA and its derivatives are G-rich DNA sequences. Codeine binding can induce the formation of a complex structure for this aptamer containing a G-quadruplex and a G·GC triplex, while codeine is located at the junction of the triplex and quadruplex. When split CBA into two moieties, codeine does not bind either moieties individually, but can bind them together by inducing the formation of the triplex-quadruplex scaffold. This structure formation induced by codeine binding is shown to inhibit polymerase reaction, which shows a potential application of the aptamer sequence in gene regulations.

Accurate indel calling plays an important role in precision medicine. A benchmarking indel set is essential for thoroughly evaluating the indel calling performance of bioinformatics pipelines. A reference sample with a set of known-positive variants was developed in the FDA-led Sequencing Quality Control Phase 2 (SEQC2) project, but the known indels in the known-positive set were limited. This project sought to provide an enriched set of known indels that would be more translationally relevant by focusing on additional cancer related regions. A thorough manual review process completed by 42 reviewers, two advisors, and a judging panel of three researchers significantly enriched the known indel set by an additional 516 indels. The extended benchmarking indel set has a large range of variant allele frequencies (VAFs), with 87% of them having a VAF below 20% in reference Sample A. The reference Sample A and the indel set can be used for comprehensive benchmarking of indel calling across a wider range of VAF values in the lower range. Indel length was also variable, but the majority were under 10 base pairs (bps). Most of the indels were within coding regions, with the remainder in the gene regulatory regions. Although high confidence can be derived from the robust study design and meticulous human review, this extensive indel set has not undergone orthogonal validation. The extended benchmarking indel set, along with the indels in the previously published known-positive set, was the truth set used to benchmark indel calling pipelines in a community challenge hosted on the precisionFDA platform. This benchmarking indel set and reference samples can be utilized for a comprehensive evaluation of indel calling pipelines. Additionally, the insights and solutions obtained during the manual review process can aid in improving the performance of these pipelines.

Using cell-based aptamer selection, we have developed a strategy to use the differences at the molecular level between any two types of cells for the identification of molecular signatures on the surface of targeted cells. A group of aptamers have been generated for the specific recognition of leukemia cells. The selected aptamers can bind to target cells with an equilibrium dissociation constant ($K_d$) in the nanomolar-to-picomolar range. The cell-based selection process is simple, fast, straightforward, and reproducible, and, most importantly, can be done without prior knowledge of target molecules. The selected aptamers can specifically recognize target leukemia cells mixed with normal human bone marrow aspirates and can also identify cancer cells closely related to the target cell line in real clinical specimens. The cell-based aptamer selection holds a great promise in developing specific molecular probes for cancer diagnosis and cancer biomarker discovery.

Molecular subtyping of triple-negative breast cancer (TNBC) is essential for understanding the mechanisms and discovering actionable targets of this highly heterogeneous type of breast cancer. We previously performed a large single-center and multiomics study consisting of genomics, transcriptomics, and clinical information from 465 patients with primary TNBC. To facilitate reusing this unique dataset, we provided a detailed description of the dataset with special attention to data quality in this study. The multiomics data were generally of high quality, but a few sequencing data had quality issues and should be noted in subsequent data reuse. Furthermore, we reconduct data analyses with updated pipelines and the updated version of the human reference genome from hg19 to hg38. The updated profiles were in good concordance with those previously published in terms of gene quantification, variant calling, and copy number alteration. Additionally, we developed a user-friendly web-based database for convenient access and interactive exploration of the dataset. Our work will facilitate reusing the dataset, maximize the values of data and further accelerate cancer research.Measurement(s)RNA expression profiling • whole-exome sequencing (WES) • somatic mutations • copy number alterations (CNAs)Technology Type(s)RNA sequencing • DNA sequencing • OncoScan CNV assayFactor Type(s)Intervention or procedureSample Characteristic - OrganismHomo sapiensSample Characteristic - LocationChina