Biology subjects

Mitchell, C.

Publications and source records attributed to Mitchell, C..

28 records · Page 2Linked to original sources

bioRxiv2021

Safety and efficacy of a new vaginal gel, Feminilove, for the treatment of symptoms associated with vaginal dryness and vulvovaginal atrophy in women: an in vitro and in vivo study

Vaginal dryness is a common symptom associated with vulvovaginal atrophy of menopause. The impact of vaginal dryness is very significant as it negatively affects quality of life, daily activities, sexual satisfaction as well as on interpersonal relationships. Symptoms of vaginal dryness is often underreported and undertreated. Recently, vaginal lubricants and moisturizers have been applied as one of the alternative and safe approaches to relieve vaginal dryness for women with mild to moderate vaginal dryness. We evaluated the safety and beneficial effects of a new type of estrogen-free vaginal gel, Feminilove BIO-FRESH moisturizing vaginal gel, using in vitro and in vivo experimental tools. Our results suggest that; 1) Feminilove vaginal gel exhibits minimal cell cytotoxicity on various human vaginal cells; 2) Feminilove vaginal gel exhibits minimal side-effects on the structure of vaginal mucosa stratum of experimental animals; 3) Feminiove vaginal gel inhibits the growth of pathogenic vaginal bacteria (E. coli) while promotes the growth of beneficial vaginal bacteria (Lactobacillus spp); 4) Feminilove vaginal gel elicits an anti-inflammatory response on vaginal epithelial cells; and 5) Feminilove vaginal gel promotes the production of tropoelastin and collagen on cultural vaginal smooth muscle and may restore loose vaginal wall (i.e., tightening effects). In summary, our results indicate that Feminilove BIO-FRESH moisturizing vaginal gel is a safe and effective remedy for vaginal dryness and vulvovaginal atrophy in women.

pharmacology and toxicology↗

bioRxiv2021

Proximity-dependent biotinylation to elucidate the interactome of TNK2 non-receptor tyrosine kinase

Non-receptor tyrosine kinases represent an important class of signaling molecules which are involved in driving diverse cellular pathways. Although, the large majority have been well-studied in terms of their protein-binding partners, the interactomes of some of the key non-receptor tyrosine kinases such as TNK2 (also known as activated Cdc42-associated kinase 1 or ACK1) have not been systematically investigated. Aberrant expression and hyperphosphorylation of TNK2 has been implicated in a number of cancers. However, the exact proteins and cellular events that mediate phenotypic changes downstream of TNK2 are unclear. Biological systems that employ proximity-dependent biotinylation methods, such as BioID, are being increasingly used to map protein-protein interactions as they provide increased sensitivity in discovering interaction partners. In this study, we employed BioID coupled to the biotinylation site identification technology (BioSITe) method that we recently developed to perform molecular mapping of intracellular proteins associated with TNK2. We also employed stable isotope labeling with amino acids in cell culture (SILAC) to quantitatively explore the interactome of TNK2. By performing a controlled comparative analysis between full-length TNK2 and its truncated counterpart, we were not only able to confidently identify site-level biotinylation of previously well-established TNK2 binders and substrates such as NCK1, NCK2, CTTN, STAT3, but also discover several novel TNK2 interacting partners. We validated TNK2 interaction with one of the novel TNK2 interacting protein, clathrin interactor 1 (CLINT1), using immunoblot analysis. Overall, this work reveals the power of the BioSITe method coupled to BioID and highlights several molecules that warrant further exploration to assess their functional significance in TNK2-mediated signaling.

cancer biology↗

bioRxiv2021

Differentially methylated regions and methylation QTLs for teen depression and early puberty in the Fragile Families Child Wellbeing Study

AO_SCPLOWBSTRACTC_SCPLOWThe Fragile Families Child Wellbeing Study (FFCWS) is a longitudinal cohort of ethnically diverse and primarily low socioeconomic status children and their families in the U.S. Here, we analyze DNA methylation data collected from 748 FFCWS participants in two waves of this study, corresponding to participant ages 9 and 15. Our primary goal is to leverage the DNA methylation data from these two time points to study methylation associated with two key traits in adolescent health that are over-represented in these data: Early puberty and teen depression. We first identify differentially methylated regions (DMRs) for depression and early puberty. We then identify DMRs for the interaction effects between these two conditions and age by including interaction terms in our regression models to understand how age-related changes in methylation are influenced by depression or early puberty. Next, we identify methylation quantitative trait loci (meQTLs) using genotype data from the participants. We also identify meQTLs with epistatic effects with depression and early puberty. We find enrichment of our interaction meQTLs with functional categories of the genome that contribute to the heritability of co-morbid complex diseases. We replicate our meQTLs in data from the GoDMC study. This work leverages the important focus of the FFCWS data on disadvantaged children to shed light on the methylation states associated with teen depression and early puberty, and on how genetic regulation of methylation is affected in adolescents with these two conditions.

genomics↗

bioRxiv2020

Reading frame restoration at the EYS locus, and allele-specific chromosome removal after Cas9 cleavage in human embryos

The correction of disease-causing mutations in human embryos could reduce the burden of inherited genetic disorders in the fetus and newborn, and improve the efficiency of fertility treatments for couples with disease-causing mutations in lieu of embryo selection. Here we evaluate the repair outcomes of a Cas9-induced double-strand break (DSB) introduced on the paternal chromosome at the EYS locus, which carries a frame-shift mutation causing blindness. We show that the most common repair outcome is microhomology-mediated end joining, which occurs during the first cell cycle in the zygote, leading to embryos with non-mosaic restoration of the reading frame. However, about half of the breaks remain unrepaired, resulting in an undetectable paternal allele and, upon entry into mitosis, loss of one or both chromosomal arms. Thus, Cas9 allows for the modification of chromosomal content in human embryos in a targeted manner, which may be useful for the prevention of trisomies. HighlightsO_LICas9-mediated DSB induction and repair by end joining occurs within hours C_LIO_LIEnd joining provides an efficient way to restore reading frames without mosaicism C_LIO_LIUnrepaired DSBs persist through mitosis and result in frequent chromosome loss C_LI

genetics↗

bioRxiv2020

Analyzing matched sets of microbiome data using the LDM and PERMANOVA

BackgroundMatched-set data arise frequently in microbiome studies. For example, we may collect pre- and post-treatment samples from a set of individuals, or use important confounding variables to match data from case participants to one or more control participants. Thus, there is a need for statistical methods for data comprised of matched sets, to test hypotheses against traits of interest (e.g., clinical outcomes or environmental factors) at the community level and/or the OTU (operational taxonomic unit) level. Optimally, these methods should accommodate complex data such as those with unequal sample sizes cross sets, confounders varying within sets, as well as continuous traits of interest. MethodsPERMANOVA is a commonly used distance-based method for testing hypotheses at the community level. We have also developed the linear decomposition model (LDM) that unifies the community-level and OTU-level tests into one framework. Here we present a strategy that can be used with both PERMANOVA and the LDM for analyzing matched-set data. We propose to include an indicator variable for each set as covariates, so as to constrain comparisons between samples within a set, and also permute traits within each set, which can account for exchangeable sample correlations. The flexible nature of PERMANOVA and the LDM allows discrete or continuous traits or interactions to be tested, within-set confounders to be adjusted, and unbalanced data to be fully exploited. ResultsOur simulations indicate that our proposed strategy outperformed alternative strategies in a wide range of scenarios. Using simulation, we also explored optimal designs for matched-set studies. The flexibility of PERMANOVA and the LDM for a variety of matched-set microbiome data is illustrated by the analysis of data from two real studies. ConclusionsIncluding set indicator variables and permuting within sets when analyzing matched-set data with PERMANOVA or the LDM is a strategy that performs well and is capable of handling the complex data structures that frequently occur in microbiome studies.

bioinformatics↗

bioRxiv2020

Delivery mode impacts newborn gut colonization efficiency

Delivery mode is the variable with the greatest influence on the infant gut microbiome composition in the first few months of life. Children born by Cesarean section (C-section) lack species from the Bacteroides genus in their gut microbial community, and this difference can be detectable until 6-18 months of age. One hypothesis is that these differences stem from lack of exposure to the maternal vaginal microbiome, as children born by C-section do not pass through the birth canal; however, Bacteroides species are not common members of the vaginal microbiome, thus this explanation seems inadequate. Here, we set out to re-evaluate this hypothesis by collecting rectal and vaginal samples before delivery from 73 mothers with paired stool from their infants in the first two weeks of life. We compared microbial profiles of infants born by planned, pre-labor C-section to those born by emergent, post-labor surgery (where the child was in the birth canal, but eventually delivered through an abdominal incision), and found no significant differences in the microbiome between these two groups. Both groups showed the characteristic signature lack of Bacteroides species, despite their difference in exposure to the birth canal. Surprisingly, this signature was only evident in samples from week two of life, but not in the first week. Children born by C-section often had high abundance of Bacteroides in their first few days of life, but these were not stable colonizers of the infant gut, as they were not detectable by week two. Finally, we used metagenomic sequencing to compare microbial strains in maternal vaginal and rectal samples and samples from their infants; we found evidence for mother-to-child transmission of rectal rather than vaginal strains. These results challenge birth canal exposure as the dominant factor in infant gut microbiome establishment and implicate colonization efficiency rather than exposure as a dictating factor of the newborn gut microbiome composition.

microbiology↗

bioRxiv2020

Investigating the Genetic Architecture of Non-Cognitive Skills Using GWAS-by-Subtraction

Educational attainment (EA) is influenced by cognitive abilities and by other characteristics and traits. However little is known about the genetic architecture of these "non-cognitive" contributions to EA. Here, we use Genomic Structural Equation Modelling and results of prior genome-wide association studies (GWASs) of EA (N = 1,131,881) and cognitive test performance (N = 257,841) to estimate SNP associations with variation in EA that is independent of cognitive ability. We identified 157 genome-wide significant loci and a polygenic architecture accounting for 57% of genetic variance in EA. Phenotypic and biological annotation revealed that (1) both cognitive and non-cognitive contributions to EA were genetically correlated with socioeconomic success and longevity; and (2) non-cognitive contributions to EA were related to personality, decision making, risk-behavior, and increased risk for psychiatric disorders; (3) non-cognitive and cognitive contributions to EA were enriched in the same tissues and cell types, but (4) showed different associations with gray-matter neuroimaging phenotypes.

genetics↗

bioRxiv2019

Cell Tracking Profiler: a user-driven analysis framework for evaluating 4D live cell imaging data

Accurate measurements of cell morphology and behaviour are fundamentally important for understanding how disease, molecules and drugs affect cell function in vivo. Using muscle stem cell (muSC) responses to injury in zebrafish as our biological paradigm we have established a ground truth for muSC cell behaviour. This revealed that variability in segmentation and tracking algorithms from commonly used programs are error-prone, leading us to develop a fast semi-automated image analysis pipeline that allows user defined parameters for segmentation and correction of cell tracking. Cell Tracking Profiler (CTP) operates through the freely available Icy platform, and allows user-managed cell tracking from 3D time-lapsed datasets to provide measures of cell shape and movement. Using dimensionality reduction methods, multiple correlation and regression analyses we identify myosin II-dependent parameters of muSC behaviour during regeneration. CTP and the associated statistical tools we have developed thus provide a powerful framework for analysing complex cell behaviour in vivo from 4D datasets. SummaryAnalysis of cell shape and movement from 3D time-lapsed datasets is currently very challenging. We therefore designed Cell Tracking Profiler for analysing cell behaviour from complex datasets and demonstrate its effectiveness by analysing stem cell behaviour during muscle regeneration in zebrafish.

cell biology↗

bioRxiv2019

Assessing the Structure and Specificity of Polygenic Scores for Psychiatric Disorders in a Population-based Cohort of Older Adults

Although psychiatric phenotypes are hypothesized to organize into a two-factor internalizing - externalizing structure, few studies have evaluated the structure of psychopathology in older adults, nor explored whether genome-wide polygenic scores (PGSs) are associated with psychopathology in a domain-specific manner. We used data from 6,216 individuals of European ancestry from the Health and Retirement Study, a large population-based sample of older adults in the United States. Confirmatory factor analyses were applied to validated measures of psychopathology and PGSs were derived from well-powered GWAS. Genomic SEM was implemented to construct latent PGSs for internalizing, externalizing, and general psychopathology. Phenotypically, the data were best characterized by a single general factor of psychopathology, a factor structure that was replicated across genders and age groups. Although externalizing PGSs (cannabis use, antisocial behavior, alcohol dependence, ADHD) were not associated with any phenotypes, PGSs for MDD, neuroticism, and anxiety disorders were associated with both internalizing and externalizing phenotypes. Moreover, the latent internalizing PGS and the latent one-factor PGS, derived using weights from Genomic SEM, explained 1% more variance in the general factor of psychopathology than any of the individual PGSs. Results support the following conclusions: genetic risk factors for and phenotypic markers of psychiatric disorders are transdiagnostic in European ancestries, GWAS-derived PGSs fail to capture genetic variation associated with disease specificity in European ancestries, and blunt phenotypic measurement in GWAS may preclude our ability to evaluate the structure and specificity of genetic contributions to psychiatric disorders.

genomics↗

bioRxiv2017

Heterogeneity in polygenic scores for common human traits

Introduction Introduction Results Discussion Materials and Methods Conflict of Interest References Genome-wide association studies (GWAS) on a wide range of important human traits have identified hundreds of variants in multi-cohort meta-analyses that have highly significant associations (p<5 x10-8) that replicate across studies. However, the identified genetic variants, or single nucleotide polymorphisms (SNPs), generally explain a very small fraction of variability in the trait of interest. Moreover, for most behavioral and health scientists, the use of such massive and complex genetic data is unwieldy. In response to the desire to harness genome-wide information, use of polygenic scores (PGSs), also known as \"genetic risk scores\", has grown rapidly in the social, behavioral, and health sciences rec ...

genetics↗