Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

Article Types

Countries / Regions

Search Results (6)

Search Parameters:
Authors = Matthew Sims

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
14 pages, 3344 KiB  
Article
SARS-CoV-2 Genotyping Highlights the Challenges in Spike Protein Drift Independent of Other Essential Proteins
by Jeremy W. Prokop, Sheryl Alberta, Martin Witteveen-Lane, Samantha Pell, Hosam A. Farag, Disha Bhargava, Robert M. Vaughan, Austin Frisch, Jacob Bauss, Humza Bhatti, Sanjana Arora, Charitha Subrahmanya, David Pearson, Austin Goodyke, Mason Westgate, Taylor W. Cook, Jackson T. Mitchell, Jacob Zieba, Matthew D. Sims, Adam Underwood, Habiba Hassouna, Surender Rajasekaran, Maximiliano A. Tamae Kakazu, Dave Chesla, Rosemary Olivero and Adam J. Caulfieldadd Show full author list remove Hide full author list
Microorganisms 2024, 12(9), 1863; https://doi.org/10.3390/microorganisms12091863 - 9 Sep 2024
Viewed by 2012
Abstract
As of 2024, SARS-CoV-2 continues to propagate and drift as an endemic virus, impacting healthcare for years. The largest sequencing initiative for any species was initiated to combat the virus, tracking changes over time at a full virus base-pair resolution. The SARS-CoV-2 sequencing [...] Read more.
As of 2024, SARS-CoV-2 continues to propagate and drift as an endemic virus, impacting healthcare for years. The largest sequencing initiative for any species was initiated to combat the virus, tracking changes over time at a full virus base-pair resolution. The SARS-CoV-2 sequencing represents a unique opportunity to understand selective pressures and viral evolution but requires cross-disciplinary approaches from epidemiology to functional protein biology. Within this work, we integrate a two-year genotyping window with structural biology to explore the selective pressures of SARS-CoV-2 on protein insights. Although genotype and the Spike (Surface Glycoprotein) protein continue to drift, most SARS-CoV-2 proteins have had few amino acid alterations. Within Spike, the high drift rate of amino acids involved in antibody evasion also corresponds to changes within the ACE2 binding pocket that have undergone multiple changes that maintain functional binding. The genotyping suggests selective pressure for receptor specificity that could also confer changes in viral risk. Mapping of amino acid changes to the structures of the SARS-CoV-2 co-transcriptional complex (nsp7-nsp14), nsp3 (papain-like protease), and nsp5 (cysteine protease) proteins suggest they remain critical factors for drug development that will be sustainable, unlike those strategies targeting Spike. Full article
Show Figures

Figure 1

12 pages, 1185 KiB  
Article
Association of Metabolomic Biomarkers with Sleeve Gastrectomy Weight Loss Outcomes
by Wendy M. Miller, Kathryn M. Ziegler, Ali Yilmaz, Nazia Saiyed, Ilyas Ustun, Sumeyya Akyol, Jay Idler, Matthew D. Sims, Michael E. Maddens and Stewart F. Graham
Metabolites 2023, 13(4), 506; https://doi.org/10.3390/metabo13040506 - 31 Mar 2023
Cited by 7 | Viewed by 2242
Abstract
This prospective observational study aimed to evaluate the association of metabolomic alterations with weight loss outcomes following sleeve gastrectomy (SG). We evaluated the metabolomic profile of serum and feces prior to SG and three months post-SG, along with weight loss outcomes in 45 [...] Read more.
This prospective observational study aimed to evaluate the association of metabolomic alterations with weight loss outcomes following sleeve gastrectomy (SG). We evaluated the metabolomic profile of serum and feces prior to SG and three months post-SG, along with weight loss outcomes in 45 adults with obesity. The percent total weight loss for the highest versus the lowest weight loss tertiles (T3 vs. T1) was 17.0 ± 1.3% and 11.1 ± 0.8%, p < 0.001. Serum metabolite alterations specific to T3 at three months included a decrease in methionine sulfoxide concentration as well as alterations to tryptophan and methionine metabolism (p < 0.03). Fecal metabolite changes specific to T3 included a decrease in taurine concentration and perturbations to arachidonic acid metabolism, and taurine and hypotaurine metabolism (p < 0.002). Preoperative metabolites were found to be highly predictive of weight loss outcomes in machine learning algorithms, with an average area under the curve of 94.6% for serum and 93.4% for feces. This comprehensive metabolomics analysis of weight loss outcome differences post-SG highlights specific metabolic alterations as well as machine learning algorithms predictive of weight loss. These findings could contribute to the development of novel therapeutic targets to enhance weight loss outcomes after SG. Full article
Show Figures

Figure 1

7 pages, 323 KiB  
Article
High Prevalence of Iron Deficiency Exhibited in Internationally Competitive, Non-Professional Female Endurance Athletes—A Case Study
by Stacy T. Sims, Kelsi Mackay, Alana Leabeater, Anthea Clarke, Katherine Schofield and Matthew Driller
Int. J. Environ. Res. Public Health 2022, 19(24), 16606; https://doi.org/10.3390/ijerph192416606 - 10 Dec 2022
Cited by 10 | Viewed by 3527
Abstract
Background: While iron deficiency is commonly discussed in populations of professional female athletes, less is known about highly trained, sub-elite female athletes (e.g., those winning international age-group competitions) who generally have less access to medical and allied health support. Methods: Thirteen non-professional highly [...] Read more.
Background: While iron deficiency is commonly discussed in populations of professional female athletes, less is known about highly trained, sub-elite female athletes (e.g., those winning international age-group competitions) who generally have less access to medical and allied health support. Methods: Thirteen non-professional highly trained female endurance athletes provided training diaries and completed a blood test, where iron markers of haemoglobin (Hb), haematocrit (Hct), C-reactive protein (Crp), serum iron, serum ferritin, and transferrin were assessed. Resting metabolic rate (RMR) and body composition using dual-energy X-ray absorptiometry (DXA) were also obtained. Participants were classified as iron deficient (ID) if serum ferritin was <30 ug/L serum ferritin. Results: Six of the 13 females were classified as ID. Serum iron, ferritin, Hb, Hct, and ferrin were greater in the ID group (p < 0.05). Crp resulted in large to very large correlations with serum iron (r = −0.72), serum ferritin (r = −0.66), and transferrin (r = 0.70). Conclusions: In this population of highly trained female athletes, 46% were diagnosed with sub-optimal iron levels, which could have lasting health effects and impair athletic performance. The need for more education and support in non-professional athletes regarding iron deficiency is strongly advised. Full article
(This article belongs to the Special Issue Female Athlete Health in Training and Sports Performance)
11 pages, 1289 KiB  
Review
SER-109: An Oral Investigational Microbiome Therapeutic for Patients with Recurrent Clostridioides difficile Infection (rCDI)
by Sahil Khanna, Matthew Sims, Thomas J. Louie, Monika Fischer, Kerry LaPlante, Jessica Allegretti, Brooke R. Hasson, Allyson T. Fonte, Christopher McChalicher, David S. Ege, Jessica A. Bryant, Timothy J. Straub, Christopher B. Ford, Matthew R. Henn, Elaine E. L. Wang, Lisa von Moltke and Mark H. Wilcox
Antibiotics 2022, 11(9), 1234; https://doi.org/10.3390/antibiotics11091234 - 10 Sep 2022
Cited by 39 | Viewed by 11461
Abstract
Clostridioides difficile infection (CDI) is classified as an urgent health threat by the Centers for Disease Control and Prevention (CDC), and affects nearly 500,000 Americans annually. Approximately 20–25% of patients with a primary infection experience a recurrence, and the risk of recurrence [...] Read more.
Clostridioides difficile infection (CDI) is classified as an urgent health threat by the Centers for Disease Control and Prevention (CDC), and affects nearly 500,000 Americans annually. Approximately 20–25% of patients with a primary infection experience a recurrence, and the risk of recurrence increases with subsequent episodes to greater than 40%. The leading risk factor for CDI is broad-spectrum antibiotics, which leads to a loss of microbial diversity and impaired colonization resistance. Current FDA-approved CDI treatment strategies target toxin or toxin-producing bacteria, but do not address microbiome disruption, which is key to the pathogenesis of recurrent CDI. Fecal microbiota transplantation (FMT) reduces the risk of recurrent CDI through the restoration of microbial diversity. However, FDA safety alerts describing hospitalizations and deaths related to pathogen transmission have raised safety concerns with the use of unregulated and unstandardized donor-derived products. SER-109 is an investigational oral microbiome therapeutic composed of purified spore-forming Firmicutes. SER-109 was superior to a placebo in reducing CDI recurrence at Week 8 (12% vs. 40%, respectively; p < 0.001) in adults with a history of recurrent CDI with a favorable observed safety profile. Here, we discuss the role of the microbiome in CDI pathogenesis and the clinical development of SER-109, including its rigorous manufacturing process, which mitigates the risk of pathogen transmission. Additionally, we discuss compositional and functional changes in the gastrointestinal microbiome in patients with recurrent CDI following treatment with SER-109 that are critical to a sustained clinical response. Full article
(This article belongs to the Special Issue Antibiotic Therapy for Clostridioides difficile Infections)
Show Figures

Figure 1

34 pages, 2569 KiB  
Article
Functional Transcription Factor Target Networks Illuminate Control of Epithelial Remodelling
by Ian M. Overton, Andrew H. Sims, Jeremy A. Owen, Bret S. E. Heale, Matthew J. Ford, Alexander L. R. Lubbock, Erola Pairo-Castineira and Abdelkader Essafi
Cancers 2020, 12(10), 2823; https://doi.org/10.3390/cancers12102823 - 30 Sep 2020
Cited by 5 | Viewed by 5205
Abstract
Cell identity is governed by gene expression, regulated by transcription factor (TF) binding at cis-regulatory modules. Decoding the relationship between TF binding patterns and gene regulation is nontrivial, remaining a fundamental limitation in understanding cell decision-making. We developed the NetNC software to predict [...] Read more.
Cell identity is governed by gene expression, regulated by transcription factor (TF) binding at cis-regulatory modules. Decoding the relationship between TF binding patterns and gene regulation is nontrivial, remaining a fundamental limitation in understanding cell decision-making. We developed the NetNC software to predict functionally active regulation of TF targets; demonstrated on nine datasets for the TFs Snail, Twist, and modENCODE Highly Occupied Target (HOT) regions. Snail and Twist are canonical drivers of epithelial to mesenchymal transition (EMT), a cell programme important in development, tumour progression and fibrosis. Predicted “neutral” (non-functional) TF binding always accounted for the majority (50% to 95%) of candidate target genes from statistically significant peaks and HOT regions had higher functional binding than most of the Snail and Twist datasets examined. Our results illuminated conserved gene networks that control epithelial plasticity in development and disease. We identified new gene functions and network modules including crosstalk with notch signalling and regulation of chromatin organisation, evidencing networks that reshape Waddington’s epigenetic landscape during epithelial remodelling. Expression of orthologous functional TF targets discriminated breast cancer molecular subtypes and predicted novel tumour biology, with implications for precision medicine. Predicted invasion roles were validated using a tractable cell model, supporting our approach. Full article
(This article belongs to the Special Issue Cancer Modeling and Network Biology)
Show Figures

Graphical abstract

11 pages, 9689 KiB  
Article
APELA Expression in Glioma, and Its Association with Patient Survival and Tumor Grade
by Debolina Ganguly, Chun Cai, Michelle M. Sims, Chuan He Yang, Matthew Thomas, Jinjun Cheng, Ali G. Saad and Lawrence M. Pfeffer
Pharmaceuticals 2019, 12(1), 45; https://doi.org/10.3390/ph12010045 - 26 Mar 2019
Cited by 21 | Viewed by 4997
Abstract
Glioblastoma (GBM) is the most common and deadliest primary adult brain tumor. Invasion, resistance to therapy, and tumor recurrence in GBM can be attributed in part to brain tumor-initiating cells (BTICs). BTICs isolated from various patient-derived xenografts showed high expression of the poorly [...] Read more.
Glioblastoma (GBM) is the most common and deadliest primary adult brain tumor. Invasion, resistance to therapy, and tumor recurrence in GBM can be attributed in part to brain tumor-initiating cells (BTICs). BTICs isolated from various patient-derived xenografts showed high expression of the poorly characterized Apelin early ligand A (APELA) gene. Although originally considered to be a non-coding gene, the APELA gene encodes a protein that binds to the Apelin receptor and promotes the growth of human embryonic stem cells and the formation of the embryonic vasculature. We found that both APELA mRNA and protein are expressed at high levels in a subset of brain tumor patients, and that APELA is also expressed in putative stem cell niche in GBM tumor tissue. Analysis of APELA and the Apelin receptor gene expression in brain tumor datasets showed that high APELA expression was associated with poor patient survival in both glioma and glioblastoma, and APELA expression correlated with glioma grade. In contrast, gene expression of the Apelin receptor or Apelin was not found to be associated with patient survival, or glioma grade. Consequently, APELA may play an important role in glioblastoma tumorigenesis and may be a future therapeutic target. Full article
(This article belongs to the Special Issue Choices of the Journal)
Show Figures

Figure 1

Back to TopTop