-
Journal of Agricultural and Food... Jun 2024Omega-3 long-chain polyunsaturated fatty acids (LCPUFA) play critical roles in human development and health. Their intake is often effectively estimated solely based on...
Omega-3 Long-Chain Polyunsaturated Fatty Acids in Nonseafood and Estimated Intake in the USA: Quantitative Analysis by Covalent Adduct Chemical Ionization Mass Spectrometry.
Omega-3 long-chain polyunsaturated fatty acids (LCPUFA) play critical roles in human development and health. Their intake is often effectively estimated solely based on seafood consumption, though the high intake of terrestrial animal-based foods with minor amounts of LCPUFA may be significant. Covalent adduct chemical ionization (CACI) tandem mass spectrometry is one approach for structural and quantitative analysis of minor unsaturated fatty acids (FA), for which standards are unavailable. Here, CACI-MS and MS/MS are used to identify and quantify minor omega-3 LCPUFA of terrestrial animal foods based on the application of measured response factors (RFs) to various FA. American mean intakes of pork, beef, chicken, and eggs contribute 20, 27, 45, and 71 mg/day of docosahexaenoic acid (DHA), respectively. The estimated intake of omega-3 DHA, eicosapentaenoic acid, and docosapentaenoic acid from nonseafood sources is significant, at 164, 103, and 330 mg/day, greater than most existing estimates of omega-3 LCPUFA intake.
PubMed: 38943596
DOI: 10.1021/acs.jafc.4c03546 -
International Journal of Urology :... Jun 2024
PubMed: 38943341
DOI: 10.1111/iju.15525 -
Tissue Engineering. Part C, Methods Jun 2024The synthesis and assembly of mature, organized elastic fibers remains a limitation to the clinical use of many engineered tissue replacements. There is a critical need...
The synthesis and assembly of mature, organized elastic fibers remains a limitation to the clinical use of many engineered tissue replacements. There is a critical need for a more in-depth understanding of elastogenesis regulation for the advancement of methods to induce and guide production of elastic matrix structures in engineered tissues that meet the structural and functional requirements of native tissue. The dramatic increase in elastic fibers through normal pregnancy has led us to explore the potential role of mechanical stretch in combination with pregnancy levels of the steroid hormones 17β-estradiol and progesterone on elastic fiber production by human uterine myometrial smooth muscle cells in a 3D culture model. Opposed to a single strain regimen, we sought to better understand how the amplitude and frequency parameters of cyclic strain influence elastic fiber production in these myometrial tissue constructs (MTC). Mechanical stretch was applied to MTC at a range of strain amplitudes (5%, 10%, and 15% at 0.5 Hz frequency) and frequencies (0.1 Hz, 0.5 Hz, 1 Hz, and constant 0 Hz at 10% amplitude), with and without pregnancy-level hormones, for 6 days. MTC were assessed for cell proliferation, matrix elastin protein content, and expression of the main elastic fiber genes, elastin (ELN) and fibrillin-1 (FBN1). Significant increases in elastin protein, and ELN and FBN1 mRNA were produced from samples subjected to a 0.5 Hz, 10% strain regimen, as well as samples stretched at higher amplitude (15%, 0.5 Hz) and higher frequency (1 Hz, 10%); however, no significant effects due to third-trimester mimetic hormone treatment were determined. These results establish a minimum level of strain is required to stimulate the synthesis of elastic fiber components in our culture model, and show this response can be similarly enhanced by increasing either the amplitude or frequency parameter of applied strain. Further, our results demonstrate strain alone is sufficient to stimulate elastic fiber production and suggest hormones may not be a significant factor in regulating elastin synthesis. This 3D culture model will provide a useful tool to further investigate mechanisms underlying pregnancy-induced de novo elastic fiber synthesis and assembly by uterine smooth muscle cells.
PubMed: 38943281
DOI: 10.1089/ten.TEC.2024.0038 -
Progress in Molecular Biology and... 2024Female cancers, which include breast and gynaecological cancers, represent a significant global health burden for women. Despite advancements in research pertinent to... (Review)
Review
Female cancers, which include breast and gynaecological cancers, represent a significant global health burden for women. Despite advancements in research pertinent to unearthing crucial pathological characteristics of these cancers, challenges persist in discovering potential therapeutic strategies. This is further exacerbated by economic burdens associated with de novo drug discovery and clinical intricacies such as development of drug resistance and metastasis. Drug repurposing, an innovative approach leveraging existing FDA-approved drugs for new indications, presents a promising avenue to expedite therapeutic development. Computational techniques, including virtual screening and analysis of drug-target-disease relationships, enable the identification of potential candidate drugs. Integration of diverse data types, such as omics and clinical information, enhances the precision and efficacy of drug repurposing strategies. Experimental approaches, including high-throughput screening assays, in vitro, and in vivo models, complement computational methods, facilitating the validation of repurposed drugs. This review highlights various target mining strategies based on analysis of differential gene expression, weighted gene co-expression, protein-protein interaction network, and host-pathogen interaction, among others. To unearth drug candidates, the technicalities of leveraging information from databases such as DrugBank, STITCH, LINCS, and ChEMBL, among others are discussed. Further in silico validation techniques encompassing molecular docking, pharmacophore modelling, molecular dynamic simulations, and ADMET analysis are elaborated. Overall, this review delves into the exploration of individual case studies to offer a wide perspective of the ever-evolving field of drug repurposing, emphasizing the multifaceted approaches and methodologies employed for the same to confront female cancers.
Topics: Drug Repositioning; Humans; Female; Antineoplastic Agents; Neoplasms
PubMed: 38942544
DOI: 10.1016/bs.pmbts.2024.05.002 -
Progress in Molecular Biology and... 2024Protozoan parasites are major hazards to human health, society, and the economy, especially in equatorial regions of the globe. Parasitic diseases, including... (Review)
Review
Protozoan parasites are major hazards to human health, society, and the economy, especially in equatorial regions of the globe. Parasitic diseases, including leishmaniasis, malaria, and others, contribute towards majority of morbidity and mortality. Around 1.1 million people die from these diseases annually. The lack of licensed vaccinations worsens the worldwide impact of these diseases, highlighting the importance of safe and effective medications for their prevention and treatment. However, the appearance of drug resistance in parasites continuously affects the availability of medications. The demand for novel drugs motivates global antiparasitic drug discovery research, necessitating the implementation of many innovative ways to maintain a continuous supply of promising molecules. Drug repurposing has come out as a compelling tool for drug development, offering a cost-effective and efficient alternative to standard de novo approaches. A thorough examination of drug repositioning candidates revealed that certain drugs may not benefit significantly from their original indications. Still, they may exhibit more pronounced effects in other disorders. Furthermore, certain medications can produce a synergistic effect, resulting in enhanced therapeutic effectiveness when given together. In this chapter, we outline the approaches employed in drug repurposing (sometimes referred to as drug repositioning), propose novel strategies to overcome these hurdles and fully exploit the promise of drug repurposing. We highlight a few major human protozoan diseases and a range of exemplary drugs repurposed for various protozoan infections, providing excellent outcomes for each disease.
Topics: Drug Repositioning; Humans; Animals; Protozoan Infections; Antiprotozoal Agents
PubMed: 38942539
DOI: 10.1016/bs.pmbts.2024.05.001 -
Methods in Enzymology 2024Structural biology research of terpene synthases (TSs) has provided a useful basis to understand their catalytic mechanisms in producing diverse terpene products with...
Structural biology research of terpene synthases (TSs) has provided a useful basis to understand their catalytic mechanisms in producing diverse terpene products with polycyclic ring systems and multiple chiral centers. However, compared to the large numbers of>95,000 terpenoids discovered to date, few structures of TSs have been solved and the understanding of their catalytic mechanisms is lagging. We here (i) introduce the basic catalytic logic, the structural architectures, and the metal-binding conserved motifs of TSs; (ii) provide detailed experimental procedures, in gene cloning and plasmid construction, protein purification, crystallization, X-ray diffraction data collection and structural elucidation, for structural biology research of TSs; and (iii) discuss the prospects of structure-based engineering and de novo design of TSs in generating valuable terpene molecules, which cannot be easily achieved by chemical synthesis.
Topics: Alkyl and Aryl Transferases; Crystallography, X-Ray; Terpenes; Cloning, Molecular; Models, Molecular; Protein Conformation
PubMed: 38942516
DOI: 10.1016/bs.mie.2024.03.012 -
Briefings in Bioinformatics May 2024This study describes the development of a resource module that is part of a learning platform named "NIGMS Sandbox for Cloud-based Learning"...
This study describes the development of a resource module that is part of a learning platform named "NIGMS Sandbox for Cloud-based Learning" (https://github.com/NIGMS/NIGMS-Sandbox). The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox at the beginning of this Supplement. This module delivers learning materials on de novo transcriptome assembly using Nextflow in an interactive format that uses appropriate cloud resources for data access and analysis. Cloud computing is a powerful new means by which biomedical researchers can access resources and capacity that were previously either unattainable or prohibitively expensive. To take advantage of these resources, however, the biomedical research community needs new skills and knowledge. We present here a cloud-based training module, developed in conjunction with Google Cloud, Deloitte Consulting, and the NIH STRIDES Program, that uses the biological problem of de novo transcriptome assembly to demonstrate and teach the concepts of computational workflows (using Nextflow) and cost- and resource-efficient use of Cloud services (using Google Cloud Platform). Our work highlights the reduced necessity of on-site computing resources and the accessibility of cloud-based infrastructure for bioinformatics applications.
Topics: Cloud Computing; Transcriptome; Computational Biology; Software; Humans; Gene Expression Profiling; Internet
PubMed: 38941113
DOI: 10.1093/bib/bbae313 -
Pediatric Cardiology Jun 2024Transcatheter stent implantation is a widely performed procedure for treating native coarctation of the aorta (CoA) in pediatric patients. However, data on mid- to...
Transcatheter stent implantation is a widely performed procedure for treating native coarctation of the aorta (CoA) in pediatric patients. However, data on mid- to long-term outcomes are limited. The aim of this study was to evaluate the mid-term safety and efficacy of transcatheter CoA stenting based on centrally adjudicated outcomes. This retrospective cohort study included patients aged 15 years or younger undergoing de novo stenting for CoA or recoarctation (reCoA) between 2006 and 2017. Immediate and 5-year outcomes were assessed. Immediate outcomes (procedural and in-hospital) were retrieved from electronic records. Rates of 5-year reCoA, stent fractures, aneurysmal/pseudoaneurysmal formation, and all-cause mortality were mid-term outcomes. The study included 274 patients (64% male and 36% female) with a median (interquartile range) age of 9 (6-12) years. Procedural success was achieved in 251 patients (91.6%). Procedural complications occurred in 4 patients (1.4%), consisting of stent migration in 1 (0.3%) and small non-expanding non-flow-limiting aortic wall injuries in 3 (1.1%). Major vascular access complications were observed in 18 patients (6.6%), acute limb ischemia in 8 (2.9%). In-hospital mortality occurred in 4 patients (1.4%). Five-year cumulative incidence rates of stent fractures, reCoA, and aortic aneurysmal/pseudoaneurysmal formation were 17/100 (17%), 73/154 (48%), and 8/101 (7.92%), respectively. Of 73 reCoAs, 47 were treated with balloon angioplasty, and 15 underwent a second stent implantation. Five-year all-cause mortality occurred in 4/251 (1.6%) patients. Coarctoplasty with stents was safe and effective in our pediatric population during a 5-year follow-up despite a high rate of reCoA.
PubMed: 38940826
DOI: 10.1007/s00246-024-03551-4 -
Bioinformatics (Oxford, England) Jun 2024World Health Organization estimates that there were over 10 million cases of tuberculosis (TB) worldwide in 2019, resulting in over 1.4 million deaths, with a worrisome...
MOTIVATION
World Health Organization estimates that there were over 10 million cases of tuberculosis (TB) worldwide in 2019, resulting in over 1.4 million deaths, with a worrisome increasing trend yearly. The disease is caused by Mycobacterium tuberculosis (MTB) through airborne transmission. Treatment of TB is estimated to be 85% successful, however, this drops to 57% if MTB exhibits multiple antimicrobial resistance (AMR), for which fewer treatment options are available.
RESULTS
We develop a robust machine-learning classifier using both linear and nonlinear models (i.e. LASSO logistic regression (LR) and random forests (RF)) to predict the phenotypic resistance of Mycobacterium tuberculosis (MTB) for a broad range of antibiotic drugs. We use data from the CRyPTIC consortium to train our classifier, which consists of whole genome sequencing and antibiotic susceptibility testing (AST) phenotypic data for 13 different antibiotics. To train our model, we assemble the sequence data into genomic contigs, identify all unique 31-mers in the set of contigs, and build a feature matrix M, where M[i, j] is equal to the number of times the ith 31-mer occurs in the jth genome. Due to the size of this feature matrix (over 350 million unique 31-mers), we build and use a sparse matrix representation. Our method, which we refer to as MTB++, leverages compact data structures and iterative methods to allow for the screening of all the 31-mers in the development of both LASSO LR and RF. MTB++ is able to achieve high discrimination (F-1 >80%) for the first-line antibiotics. Moreover, MTB++ had the highest F-1 score in all but three classes and was the most comprehensive since it had an F-1 score >75% in all but four (rare) antibiotic drugs. We use our feature selection to contextualize the 31-mers that are used for the prediction of phenotypic resistance, leading to some insights about sequence similarity to genes in MEGARes. Lastly, we give an estimate of the amount of data that is needed in order to provide accurate predictions.
AVAILABILITY
The models and source code are publicly available on Github at https://github.com/M-Serajian/MTB-Pipeline.
Topics: Mycobacterium tuberculosis; Machine Learning; Drug Resistance, Bacterial; Microbial Sensitivity Tests; Anti-Bacterial Agents; Whole Genome Sequencing; Genome, Bacterial; Humans
PubMed: 38940175
DOI: 10.1093/bioinformatics/btae243 -
Bioinformatics (Oxford, England) Jun 2024One of the core problems in the analysis of protein tandem mass spectrometry data is the peptide assignment problem: determining, for each observed spectrum, the peptide...
MOTIVATION
One of the core problems in the analysis of protein tandem mass spectrometry data is the peptide assignment problem: determining, for each observed spectrum, the peptide sequence that was responsible for generating the spectrum. Two primary classes of methods are used to solve this problem: database search and de novo peptide sequencing. State-of-the-art methods for de novo sequencing use machine learning methods, whereas most database search engines use hand-designed score functions to evaluate the quality of a match between an observed spectrum and a candidate peptide from the database. We hypothesized that machine learning models for de novo sequencing implicitly learn a score function that captures the relationship between peptides and spectra, and thus may be re-purposed as a score function for database search. Because this score function is trained from massive amounts of mass spectrometry data, it could potentially outperform existing, hand-designed database search tools.
RESULTS
To test this hypothesis, we re-engineered Casanovo, which has been shown to provide state-of-the-art de novo sequencing capabilities, to assign scores to given peptide-spectrum pairs. We then evaluated the statistical power of this Casanovo score function, Casanovo-DB, to detect peptides on a benchmark of three mass spectrometry runs from three different species. In addition, we show that re-scoring with the Percolator post-processor benefits Casanovo-DB more than other score functions, further increasing the number of detected peptides.
Topics: Databases, Protein; Peptides; Machine Learning; Mass Spectrometry; Algorithms; Sequence Analysis, Protein; Tandem Mass Spectrometry
PubMed: 38940129
DOI: 10.1093/bioinformatics/btae218