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RNA (New York, N.Y.) Jun 2022Mitochondria possess their own genome that encodes components of oxidative phosphorylation (OXPHOS) complexes, and mitochondrial ribosomes within the organelle translate...
Mitochondria possess their own genome that encodes components of oxidative phosphorylation (OXPHOS) complexes, and mitochondrial ribosomes within the organelle translate the mRNAs expressed from the mitochondrial genome. Given the differential OXPHOS activity observed in diverse cell types, cell growth conditions, and other circumstances, cellular heterogeneity in mitochondrial translation can be expected. Although individual protein products translated in mitochondria have been monitored, the lack of techniques that address the variation in overall mitochondrial protein synthesis in cell populations poses analytic challenges. Here, we adapted mitochondrial-specific fluorescent noncanonical amino acid tagging (FUNCAT) for use with fluorescence-activated cell sorting (FACS) and developed mito-FUNCAT-FACS. The click chemistry-compatible methionine analog L-homopropargylglycine (HPG) enabled the metabolic labeling of newly synthesized proteins. In the presence of cytosolic translation inhibitors, HPG was selectively incorporated into mitochondrial nascent proteins and conjugated to fluorophores via the click reaction (mito-FUNCAT). The application of in situ mito-FUNCAT to flow cytometry allowed us to separate changes in net mitochondrial translation activity from those of the organelle mass and detect variations in mitochondrial translation in cancer cells. Our approach provides a useful methodology for examining mitochondrial protein synthesis in individual cells.
Topics: Amino Acids; Flow Cytometry; Mitochondria; Mitochondrial Proteins; Protein Biosynthesis
PubMed: 35256452
DOI: 10.1261/rna.079097.122 -
Wiley Interdisciplinary Reviews.... May 2016Data in the biological, chemical, and clinical domains are accumulating at ever-increasing rates and have the potential to accelerate and inform drug development in new... (Review)
Review
Data in the biological, chemical, and clinical domains are accumulating at ever-increasing rates and have the potential to accelerate and inform drug development in new ways. Challenges and opportunities now lie in developing analytic tools to transform these often complex and heterogeneous data into testable hypotheses and actionable insights. This is the aim of computational pharmacology, which uses in silico techniques to better understand and predict how drugs affect biological systems, which can in turn improve clinical use, avoid unwanted side effects, and guide selection and development of better treatments. One exciting application of computational pharmacology is drug repurposing-finding new uses for existing drugs. Already yielding many promising candidates, this strategy has the potential to improve the efficiency of the drug development process and reach patient populations with previously unmet needs such as those with rare diseases. While current techniques in computational pharmacology and drug repurposing often focus on just a single data modality such as gene expression or drug-target interactions, we argue that methods such as matrix factorization that can integrate data within and across diverse data types have the potential to improve predictive performance and provide a fuller picture of a drug's pharmacological action. WIREs Syst Biol Med 2016, 8:186-210. doi: 10.1002/wsbm.1337 For further resources related to this article, please visit the WIREs website.
Topics: Animals; Databases, Factual; Drug Interactions; Drug Repositioning; Drug-Related Side Effects and Adverse Reactions; Gene Expression; Humans; Molecular Docking Simulation; Pharmaceutical Preparations; Proteins
PubMed: 27080087
DOI: 10.1002/wsbm.1337 -
Clinical Chemistry and Laboratory... May 2015The 1st Strategic Conference of the European Federation of Clinical Chemistry and Laboratory Medicine proposed a simplified hierarchy for setting analytical performance... (Review)
Review
The 1st Strategic Conference of the European Federation of Clinical Chemistry and Laboratory Medicine proposed a simplified hierarchy for setting analytical performance specifications (APS). The top two levels of the 1999 Stockholm hierarchy, i.e., evaluation of the effect of analytical performance on clinical outcomes and clinical decisions have been proposed to be replaced by one outcome-based model. This model can be supported by: (1a) direct outcome studies; and (1b) indirect outcome studies investigating the impact of analytical performance of the test on clinical classifications or decisions and thereby on the probability of patient relevant clinical outcomes. This paper reviews the need for outcome-based specifications, the most relevant types of outcomes to be considered, and the challenges and limitations faced when setting outcome-based APS. The methods of Model 1a and b are discussed and examples are provided for how outcome data can be translated to APS using the linked evidence and simulation or decision analytic techniques. Outcome-based APS should primarily reflect the clinical needs of patients; should be tailored to the purpose, role and significance of the test in a well defined clinical pathway; and should be defined at a level that achieves net health benefit for patients at reasonable costs. Whilst it is acknowledged that direct evaluations are difficult and may not be possible for all measurands, all other forms of setting APS should be weighed against that standard, and regarded as approximations. Better definition of the relationship between the analytical performance of tests and health outcomes can be used to set analytical performance criteria that aim to improve the clinical and cost-effectiveness of laboratory tests.
Topics: Clinical Laboratory Techniques; Consensus; Evidence-Based Medicine; Quality Assurance, Health Care
PubMed: 25996384
DOI: 10.1515/cclm-2015-0214 -
Molecules (Basel, Switzerland) Nov 2023This review discusses the significance of natural deep eutectic solvents (NaDESs) as a promising green extraction technology. It employs the consolidated meta-analytic... (Review)
Review
This review discusses the significance of natural deep eutectic solvents (NaDESs) as a promising green extraction technology. It employs the consolidated meta-analytic approach theory methodology, using the Web of Science and Scopus databases to analyze 2091 articles as the basis of the review. This review explores NaDESs by examining their properties, challenges, and limitations. It underscores the broad applications of NaDESs, some of which remain unexplored, with a focus on their roles as solvents and preservatives. NaDESs' connections with nanocarriers and their use in the food, cosmetics, and pharmaceutical sectors are highlighted. This article suggests that biomimicry could inspire researchers to develop technologies that are less harmful to the human body by emulating natural processes. This approach challenges the notion that green science is inferior. This review presents numerous successful studies and applications of NaDESs, concluding that they represent a viable and promising avenue for research in the field of green chemistry.
PubMed: 38005377
DOI: 10.3390/molecules28227653 -
Journal of the National Cancer Institute Sep 2016The subpopulation treatment effect pattern plot (STEPP) is an appealing method for assessing the clinical impact of a predictive marker on patient outcomes and...
BACKGROUND
The subpopulation treatment effect pattern plot (STEPP) is an appealing method for assessing the clinical impact of a predictive marker on patient outcomes and identifying a promising subgroup for further study. However, its original formulation lacked a decision analytic justification and applied only to a single marker.
METHODS
We derive a decision-analytic result that motivates STEPP. We discuss the incorporation of multiple predictive markers into STEPP using risk difference, cadit, and responders-only benefit functions.
RESULTS
Applying STEPP to data from a breast cancer treatment trial with multiple markers, we found that none of the three benefit functions identified a promising subgroup for further study. Applying STEPP to hypothetical data from a trial with 100 markers, we found that all three benefit functions identified promising subgroups as evidenced by the large statistically significant treatment effect in these subgroups.
CONCLUSIONS
Because the method has desirable decision-analytic properties and yields an informative plot, it is worth applying to randomized trials on the chance there is a large treatment effect in a subgroup determined by the predictive markers.
Topics: Age Factors; Antineoplastic Agents, Hormonal; Biomarkers, Tumor; Body Mass Index; Breast Neoplasms; Clinical Decision-Making; Decision Support Techniques; Disease-Free Survival; Female; Humans; Ki-67 Antigen; Letrozole; Logistic Models; Lymphatic Metastasis; Nitriles; Predictive Value of Tests; Randomized Controlled Trials as Topic; Receptors, Estrogen; Receptors, Progesterone; Tamoxifen; Triazoles; Tumor Burden
PubMed: 27193772
DOI: 10.1093/jnci/djw101 -
Cell Reports Methods Dec 2023Glycomics, the comprehensive profiling of all glycan structures in samples, is rapidly expanding to enable insights into physiology and disease mechanisms. However,...
Glycomics, the comprehensive profiling of all glycan structures in samples, is rapidly expanding to enable insights into physiology and disease mechanisms. However, glycan structure complexity and glycomics data interpretation present challenges, especially for differential expression analysis. Here, we present a framework for differential glycomics expression analysis. Our methodology encompasses specialized and domain-informed methods for data normalization and imputation, glycan motif extraction and quantification, differential expression analysis, motif enrichment analysis, time series analysis, and meta-analytic capabilities, synthesizing results across multiple studies. All methods are integrated into our open-source glycowork package, facilitating performant workflows and user-friendly access. We demonstrate these methods using dedicated simulations and glycomics datasets of N-, O-, lipid-linked, and free glycans. Differential expression tests here focus on human datasets and cancer vs. healthy tissue comparisons. Our rigorous approach allows for robust, reliable, and comprehensive differential expression analyses in glycomics, contributing to advancing glycomics research and its translation to clinical and diagnostic applications.
Topics: Humans; Glycomics; Polysaccharides
PubMed: 37992708
DOI: 10.1016/j.crmeth.2023.100652 -
Frontiers in Genetics 2020Time-series can provide critical insights into the structure and function of microbial communities. The analysis of temporal data warrants statistical considerations,...
Time-series can provide critical insights into the structure and function of microbial communities. The analysis of temporal data warrants statistical considerations, distinct from comparative microbiome studies, to address ecological questions. This primer identifies unique challenges and approaches for analyzing microbiome time-series. In doing so, we focus on (1) identifying compositionally similar samples, (2) inferring putative interactions among populations, and (3) detecting periodic signals. We connect theory, code and data via a series of hands-on modules with a motivating biological question centered on marine microbial ecology. The topics of the modules include characterizing shifts in community structure and activity, identifying expression levels with a diel periodic signal, and identifying putative interactions within a complex community. Modules are presented as self-contained, open-access, interactive tutorials in R and Matlab. Throughout, we highlight statistical considerations for dealing with autocorrelated and compositional data, with an eye to improving the robustness of inferences from microbiome time-series. In doing so, we hope that this primer helps to broaden the use of time-series analytic methods within the microbial ecology research community.
PubMed: 32373155
DOI: 10.3389/fgene.2020.00310 -
Current Opinion in Structural Biology Aug 2014In this review we discuss the current advances relating to structure determination from protein microcrystals with special emphasis on the newly developed method called... (Review)
Review
In this review we discuss the current advances relating to structure determination from protein microcrystals with special emphasis on the newly developed method called MicroED. This method uses a transmission electron cryo-microscope to collect electron diffraction data from extremely small 3-dimensional (3D) crystals. MicroED has been used to solve the 3D structure of the model protein lysozyme to 2.9Å resolution. As the method further matures, MicroED promises to offer a unique and widely applicable approach to protein crystallography using nanocrystals.
Topics: Analytic Sample Preparation Methods; Cryoelectron Microscopy; Crystallography, X-Ray; Proteins
PubMed: 24709395
DOI: 10.1016/j.sbi.2014.03.004 -
PLoS Computational Biology Dec 2021There is a growing realization that multi-way chromatin contacts formed in chromosome structures are fundamental units of gene regulation. However, due to the paucity...
There is a growing realization that multi-way chromatin contacts formed in chromosome structures are fundamental units of gene regulation. However, due to the paucity and complexity of such contacts, it is challenging to detect and identify them using experiments. Based on an assumption that chromosome structures can be mapped onto a network of Gaussian polymer, here we derive analytic expressions for n-body contact probabilities (n > 2) among chromatin loci based on pairwise genomic contact frequencies available in Hi-C, and show that multi-way contact probability maps can in principle be extracted from Hi-C. The three-body (triplet) contact probabilities, calculated from our theory, are in good correlation with those from measurements including Tri-C, MC-4C and SPRITE. Maps of multi-way chromatin contacts calculated from our analytic expressions can not only complement experimental measurements, but also can offer better understanding of the related issues, such as cell-line dependent assemblies of multiple genes and enhancers to chromatin hubs, competition between long-range and short-range multi-way contacts, and condensates of multiple CTCF anchors.
Topics: Chromatin; Chromosome Mapping; DNA; Enhancer Elements, Genetic; Gene Expression Regulation; Genes; Genomics; High-Throughput Nucleotide Sequencing; Humans
PubMed: 34871311
DOI: 10.1371/journal.pcbi.1009669 -
Briefings in Bioinformatics May 2022The human major histocompatibility complex (MHC), also known as human leukocyte antigen (HLA), plays an important role in the adaptive immune system by presenting...
MOTIVATION
The human major histocompatibility complex (MHC), also known as human leukocyte antigen (HLA), plays an important role in the adaptive immune system by presenting non-self-peptides to T cell receptors. The MHC region has been shown to be associated with a variety of diseases, including autoimmune diseases, organ transplantation and tumours. However, structural analytic tools of HLA are still sparse compared to the number of identified HLA alleles, which hinders the disclosure of its pathogenic mechanism.
RESULT
To provide an integrative analysis of HLA, we first collected 1296 amino acid sequences, 256 protein data bank structures, 120 000 frequency data of HLA alleles in different populations, 73 000 publications and 39 000 disease-associated single nucleotide polymorphism sites, as well as 212 modelled HLA heterodimer structures. Then, we put forward two new strategies for building up a toolkit for transplantation and tumour immunotherapy, designing risk alignment pipeline and antigenic peptide prediction pipeline by integrating different resources and bioinformatic tools. By integrating 100 000 calculated HLA conformation difference and online tools, risk alignment pipeline provides users with the functions of structural alignment, sequence alignment, residue visualization and risk report generation of mismatched HLA molecules. For tumour antigen prediction, we first predicted 370 000 immunogenic peptides based on the affinity between peptides and MHC to generate the neoantigen catalogue for 11 common tumours. We then designed an antigenic peptide prediction pipeline to provide the functions of mutation prediction, peptide prediction, immunogenicity assessment and docking simulation. We also present a case study of hepatitis B virus mutations associated with liver cancer that demonstrates the high legitimacy of our antigenic peptide prediction process. HLA3D, including different HLA analytic tools and the prediction pipelines, is available at http://www.hla3d.cn/.
Topics: Computational Biology; HLA Antigens; Histocompatibility Antigens Class I; Humans; Immunotherapy; Neoplasms; Peptides; Protein Binding
PubMed: 35289353
DOI: 10.1093/bib/bbac076