ORCID Profile
0000-0001-7531-7246
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Publisher: Oxford University Press (OUP)
Date: 08-11-2022
Abstract: Understanding how variations in the plasma and brain proteome contribute to multiple sclerosis susceptibility can provide important insights to guide drug repurposing and therapeutic development for multiple sclerosis. However, the role of genetically predicted protein abundance in multiple sclerosis remains largely unknown. Integrating plasma proteomics (n = 3,301) and brain proteomics (n = 376 discovery n = 152 replication) into multiple sclerosis genome-wide association studies (n = 14,802 cases and 26,703 controls), we employed summary-based methods to identify candidate proteins involved in multiple sclerosis susceptibility. Next, we evaluated associations of the corresponding genes with multiple sclerosis at tissue-level using large gene expression quantitative trait data from whole-blood (n = 31,684) and brain (n = 1,194) tissue. Further, to assess transcriptional profiles for candidate proteins at cell-level, we examined gene expression patterns in immune cell types (dataset 1: n = 73 cases and 97 controls dataset 2: n = 31 cases and 31 controls) for identified plasma proteins, and in brain cell types (dataset 1: n = 4 cases and 5 controls dataset 2: n = 5 cases and 3 controls) for identified brain proteins. In a longitudinal multiple sclerosis cohort (n = 203 cases followed up to 15 years), we also assessed the corresponding gene-level associations with the outcome of disability worsening. We identified 39 novel proteins associated with multiple sclerosis risk. Based on five identified plasma proteins, four available corresponding gene candidates showed consistent associations with multiple sclerosis risk in whole-blood, and we found TAPBPL upregulation in multiple sclerosis B cells, CD8+ T cells and natural killer cells compared to controls. Among the 34 candidate brain proteins, 18 were replicated in a smaller cohort and 14 of 21 available corresponding gene candidates also showed consistent associations with multiple sclerosis risk in brain tissue. In cell-specific analysis, six identified brain candidates showed consistent differential gene expression in neuron and oligodendrocyte cell clusters. Based on the 39 protein-coding genes, we found 23 genes that were associated with disability worsening in multiple sclerosis cases. The findings present a set of candidate protein biomarkers for multiple sclerosis, reinforced by high concordance in downstream transcriptomics findings at tissue-level. This study also highlights the heterogeneity of cell-specific transcriptional profiles for the identified proteins, and that numerous candidates were also implicated in disease progression. Together, these findings can serve as an important anchor for future studies of disease mechanisms and therapeutic development.
Publisher: Springer Science and Business Media LLC
Date: 18-07-2023
DOI: 10.1038/S41598-023-38415-Z
Abstract: The indirect contribution of multiple sclerosis (MS) relapses to disability worsening outcomes, and vice-versa, remains unclear. Disease modifying therapies (DMTs) are potential modulators of this association. Understanding how these endo-phenotypes interact may provide insights into disease pathogenesis and treatment practice in relapse-onset MS (ROMS). Utilising a unique, prospectively collected clinical data from a longitudinal cohort of 279 first demyelinating event cases followed for up to 15 years post-onset, we examined indirect associations between relapses and treatment and the risk of disability worsening, and vice-versa. Indirect association parameters were estimated using joint models for longitudinal and survival data. Early relapses within 2.5 years of MS onset predicted early disability worsening outcomes (HR = 3.45, C.I 2.29–3.61) per relapse, but did not contribute to long-term disability worsening thereinafter (HR = 0.21, C.I 0.15–0.28). Conversely, disability worsening outcomes significantly contributed to relapse risk each year (HR = 2.96, C.I 2.91–3.02), and persisted over time (HR = 3.34, C.I 2.90–3.86), regardless of DMT treatments. The duration of DMTs significantly reduced the hazards of relapses (1st-line DMTs: HR = 0.68, C.I 0.58–0.79 3rd-line DMTs: HR = 0.37, C.I 0.32–0.44) and disability worsening events ( 1st-line DMTs: HR = 0.74, C.I 0.69–0.79 3rd-line DMTs: HR = 0.90, C.I 0.85–0.95), respectively. Results from time-dynamic survival probabilities further revealed in iduals having higher risk of future relapses and disability worsening outcomes, respectively. The study provided evidence that in ROMS, relapses accrued within 2.5 years of MS onset are strong indicators of disability worsening outcomes, but late relapses accrued 2.5 years post onset are not overt risk factors for further disability worsening. In contrast, disability worsening outcomes are strong positive predictors of current and subsequent relapse risk. Long-term DMT use and older age strongly influence the in idual outcomes and their associations.
Publisher: MDPI AG
Date: 28-03-2023
DOI: 10.3390/MICROORGANISMS11040866
Abstract: Feeding practice is essential to growth and development of preterm toddlers. However, the relationship of feeding mode with gut microbiota and neurodevelopment outcomes of preterm toddlers has not been characterized fully. We conducted this cohort study to assess neurodevelopment outcomes and gut microbiota community structures of preterm toddlers who received either breast milk, formula or mixed feeding. Fifty-five preterm toddlers born weeks and 24 term toddlers were recruited in the study. Bayley III mental and physical index scores were measured among preterm toddlers at 12 ± 2 and 18 ± 2 months corrected age (CA). Gut microbiome composition was analyzed by 16S rRNA gene sequencing in fecal s les collected from all participants at 12 months, 16 months and 20 months after birth. We found exclusive breast milk feeding for over three months in the first six months after birth was associated with significant increase in language composite score at 12 months CA (86 (79,97) vs. 77 (71.75,79), p = 0.008) and both language (106.05 ± 14.68 vs. 90.58 ± 12.25, p = 0.000) and cognitive composite score at 18 months CA (107.17 ± 10.85 vs. 99.00 ± 9.24, p = 0.007). The alpha ersity, beta ersity and composition of gut microbiota from those breastfed preterm toddlers not only resembled healthy term toddlers but also followed similar structure of preterm toddlers with enhanced language and cognitive performance. Our results suggest exclusive breast milk feeding for over three months in preterm toddlers leads to optimal cognitive and language development and well-balanced microbiota.
Publisher: Springer Science and Business Media LLC
Date: 11-11-2022
DOI: 10.1038/S41598-022-23685-W
Abstract: Limited studies have been conducted to identify and validate multiple sclerosis (MS) genetic loci associated with disability progression. We aimed to identify MS genetic loci associated with worsening of disability over time, and to develop and validate ensemble genetic learning model(s) to identify people with MS (PwMS) at risk of future worsening. We examined associations of 208 previously established MS genetic loci with the risk of worsening of disability we learned ensemble genetic decision rules and validated the predictions in an external dataset. We found 7 genetic loci ( rs7731626 : HR 0.92, P = 2.4 × 10 –5 rs12211604 : HR 1.16, P = 3.2 × 10 –7 rs55858457 : HR 0.93, P = 3.7 × 10 –7 rs10271373 : HR 0.90, P = 1.1 × 10 –7 rs11256593 : HR 1.13, P = 5.1 × 10 –57 rs12588969 : HR = 1.10, P = 2.1 × 10 –10 rs1465697 : HR 1.09, P = 1.7 × 10 –128 ) associated with risk worsening of disability most of which were located near or tagged to 13 genomic regions enriched in peptide hormones and steroids biosynthesis pathways by positional and eQTL mapping. The derived ensembles produced a set of genetic decision rules that can be translated to provide additional prognostic values to existing clinical predictions, with the additional benefit of incorporating relevant genetic information into clinical decision making for PwMS. The present study extends our knowledge of MS progression genetics and provides the basis of future studies regarding the functional significance of the identified loci.
Publisher: Oxford University Press (OUP)
Date: 10-2021
DOI: 10.1093/BRAINCOMMS/FCAB288
Abstract: Our inability to reliably predict disease outcomes in multiple sclerosis remains an issue for clinicians and clinical trialists. This study aims to create, from available clinical, genetic and environmental factors a clinical–environmental–genotypic prognostic index to predict the probability of new relapses and disability worsening. The analyses cohort included prospectively assessed multiple sclerosis cases (N = 253) with 2858 repeated observations measured over 10 years. N = 219 had been diagnosed as relapsing-onset, while N = 34 remained as clinically isolated syndrome by the 10th-year review. Genotype data were available for 199 genetic variants associated with multiple sclerosis risk. Penalized Cox regression models were used to select potential genetic variants and predict risk for relapses and/or worsening of disability. Multivariable Cox regression models with backward elimination were then used to construct clinical–environmental, genetic and clinical–environmental–genotypic prognostic index, respectively. Robust time-course predictions were obtained by Landmarking. To validate our models, Weibull calibration models were used, and the Chi-square statistics, Harrell’s C-index and pseudo-R2 were used to compare models. The predictive performance at diagnosis was evaluated using the Kullback–Leibler and Brier (dynamic) prediction error (reduction) curves. The combined index (clinical–environmental–genotypic) predicted a quadratic time-dynamic disease course in terms of worsening (HR = 2.74, CI: 2.00–3.76 pseudo-R2=0.64 C-index = 0.76), relapses (HR = 2.16, CI: 1.74–2.68 pseudo-R2 = 0.91 C-index = 0.85), or both (HR = 3.32, CI: 1.88–5.86 pseudo-R2 = 0.72 C-index = 0.77). The Kullback–Leibler and Brier curves suggested that for short-term prognosis (≤5 years from diagnosis), the clinical–environmental components of disease were more relevant, whereas the genetic components reduced the prediction errors only in the long-term (≥5 years from diagnosis). The combined components performed slightly better than the in idual ones, although their prognostic sensitivities were largely modulated by the clinical–environmental components. We have created a clinical–environmental–genotypic prognostic index using relevant clinical, environmental, and genetic predictors, and obtained robust dynamic predictions for the probability of developing new relapses and worsening of symptoms in multiple sclerosis. Our prognostic index provides reliable information that is relevant for long-term prognostication and may be used as a selection criterion and risk stratification tool for clinical trials. Further work to investigate component interactions is required and to validate the index in independent data sets.
Publisher: Springer Science and Business Media LLC
Date: 21-10-2014
Publisher: Oxford University Press (OUP)
Date: 09-2021
Abstract: Disease course in multiple sclerosis (MS) is characterised by relapses and worsening of disability. This study aims to create, from available clinical, genetic, and environmental factors a multifactorial prognostic index (MPI) to predict disease course in MS. We analysed prospectively assessed MS cases (N = 253) with 2858 repeated measurements over 10-years. Of the 253 cases, N = 219 were diagnosed as relapsing-onset, while N = 34 remained as clinically isolated syndrome by the 10th-year review. Cox regression models with Least Absolute Shrinkage and Selection Operator were used to select potential genetic, clinical, and environmental factors that are predictive of relapses and/or worsening of disability. Multivariate Cox regression models with leave-one-out cross-validation were used to construct a MPI, from which robust dynamic predictions were obtained by landmarking. The predictive performance at diagnosis was evaluated using the Kullback-Leibler and Brier prediction error curves. The MPI predicted a quadratic time-dynamic disease course in terms of relapses (HR = 2.16, CI: 1.74-2.68 C-index=0.85) and worsening of disability (HR = 2.74, CI: 2.00-3.76 C-index=0.76). The Kullback-Leibler and Brier dynamic prediction error curves showed reasonable performance for both short- (≤5-years from diagnosis) and long-term (& -years from diagnosis) prognostications, respectively. The MPI provided reliable information that is relevant for long-term prognostication and may be used as a selection criterion or risk stratification tool for clinical trials. Using relevant clinical, environmental, and genotype data, we have created a MPI for people living with MS and clinically isolated syndrome.
Publisher: Informa UK Limited
Date: 07-2016
DOI: 10.2147/DDDT.S108118
Publisher: American Chemical Society (ACS)
Date: 25-08-2014
DOI: 10.1021/CI5003697
Abstract: Naturally occurring anticancer compounds represent about half of the chemotherapeutic drugs which have been put in the market against cancer until date. Computer-based or in silico virtual screening methods are often used in lead/hit discovery protocols. In this study, the "drug-likeness" of ~400 compounds from African medicinal plants that have shown in vitro and/or in vivo anticancer, cytotoxic, and antiproliferative activities has been explored. To verify potential binding to anticancer drug targets, the interactions between the compounds and 14 selected targets have been analyzed by in silico modeling. Docking and binding affinity calculations were carried out, in comparison with known anticancer agents comprising ~1,500 published naturally occurring plant-based compounds from around the world. The results reveal that African medicinal plants could represent a good starting point for the discovery of anticancer drugs. The small data set generated (named AfroCancer) has been made available for research groups working on virtual screening.
Publisher: Research Square Platform LLC
Date: 27-04-2022
DOI: 10.21203/RS.3.RS-1547958/V1
Abstract: The contribution of multiple sclerosis relapses to worsening of disability, and vice-versa, remains unclear. Vitamin D supplementation (VitD) and disease modifying therapies (DMTs) are potential modulators of this association. Understanding how these endo-phenotypes interact may provide insights to disease pathogenesis and treatment practice. Here, we examined independent associations between relapses and treatment and the risk of worsening of disability, and vice-versa. We find suggestive evidence that early relapses predict early worsening of disability but do not contribute to long-term worsening. Conversely, the effects of worsening of disability on relapse risk was pronounced and persisted. VitD and DMTs interacted significantly to markedly reduce the risk of future relapses and worsening of disability, particularly when commenced early. Personalised real-time survival probabilities revealed in iduals having higher risk of future worsening. Worsening of disability in ROMS occurs in ways not clearly tied to relapses and is strongly linked to an increased risk of future relapses.
No related grants have been discovered for Valery Fuh Ngwa.