Biology subjects

Fernando, R.

Publications and source records attributed to Fernando, R..

5 recordsLinked to original sources

bioRxiv2026

Genetic Characterization of the TAPBP and Its Haplotypic Association with BF2 in the Chicken Major Histocompatibility Complex

TAPBP is a key chaperone of the peptide-loading complex that facilitates peptide loading onto major histocompatibility complex class I (MHC I) molecules. This study characterized TAPBP alleles in Korean Native Chickens (KNCs), identified novel variants, and evaluated haplotypic associations with BF2. Thirty-six samples representing six KNC lines were genotyped using LEI0258 and the MHC-B SNP panel, and individuals homozygous at both markers were classified into 16 groups. The same samples were subjected to Sanger sequencing of TAPBP exons 3-8. Sequences were assembled and aligned against MHC-B reference haplotypes and the Red Junglefowl reference. Additional comparisons with "tapasin allele" datasets enabled the identification of novel variants. Six novel nucleotide variants were detected across exons 3-6, including one nonsynonymous substitution in exon 4 (D251H). This residue corresponds to position Q265 in human TAPBP and lies adjacent to residues involved in MHC I interaction, suggesting potential functional relevance. Furthermore, TAPBP exhibited high haplotype diversity (Hd = 0.93) and moderate nucleotide diversity ({pi} = 0.00892), with exon 5 showing the highest diversity ({pi} = 0.01). B9 was the most frequent haplotype at the nucleotide level, whereas B6/B24 predominated at the amino acid level. Comparison with BF2 data revealed haplotype-dependent pairing patterns: BF2-B9 consistently matched TAPBP-B9, whereas BF2-B6 was associated with distinct TAPBP nucleotide variants, indicating allelic diversification within a shared haplotypic background. Homozygosity at LEI0258 and the SNP panel corresponded with TAPBP homozygosity, supporting marker-based prediction. These findings highlight potential BF2-TAPBP associations and provide a foundation for understanding variation in MHC I peptide loading.

genetics↗

bioRxiv2023

A human urothelial microtissue model reveals shared colonization and survival strategies between uropathogens and asymptomatic bacteria

Urinary tract infection is among the most common infections worldwide, and is typically studied in animals and cell lines with limited uropathogenic strains. Here, we assessed diverse bacterial pathogens and asymptomatic bacteria (ASB) in a human urothelial microtissue model including full stratification/differentiation and urine tolerance. Several uropathogens and ASB-like E. coli invaded intracellularly, suggesting invasion is a shared survival strategy, instead of a virulence hallmark. The E. coli adhesin FimH was required for intracellular community formation, but not for invasion. Other shared lifestyles included filamentation (Gram-negatives), chaining (Gram-positives) and hijacking of exfoliating cells, while biofilm-like aggregates formed mainly with Pseudomonas and Proteus. Urothelial cells expelled invasive bacteria in Rab-/LC3-decorated structures, while highly cytotoxic/invasive uropathogens, but not ASB, disrupted host barrier function and strongly induced exfoliation and cytokine production. Overall, this work highlights diverse species-/strain-specific infection strategies and corresponding host responses in a human urothelial microenvironment, providing insights at the tissue, cell and molecular level. One-Sentence SummaryA human urothelial model revealed shared colonization strategies between uropathogens and asymptomatic bacteria, and pathogen-specific innate immune responses

microbiology↗

bioRxiv2023

Successful placentation in human pregnancy is regulated by reciprocal interactions between maternal uterine NK cells and fetal placental trophoblast

Fetal growth and development during human pregnancy depends on delivery of adequate maternal oxygen and nutrients to the fetus via the placenta. In humans, the balanced invasion of fetal placental trophoblast cells into the maternal uterine lining, where they interact with uterine natural killer cells (uNK), is thought to be critical for a successful pregnancy but exactly how this influences reproductive outcomes remains undefined. Here, we used our trophoblast organoid model and primary tissue samples to determine how uNK affect placentation. By locating potential interaction axes between primary trophoblast cells and uNK using single cell transcriptomics, and in vitro modelling of these interactions in trophoblast organoids, we identify a uNK-derived cytokine signal that promotes trophoblast differentiation by enhancing epithelial-mesenchymal transition and increasing trophoblast cells at the late stage of the invasive pathway. Moreover, it affects transcriptional programs involved in increasing blood flow, placental access to nutrients, and dampening inflammatory and adaptive immune responses, as well as gene signatures associated with disorders of pregnancy such as pre-eclampsia. Our findings shed new light on how optimal immunological interactions between maternal uNK cells and fetal trophoblast enhance reproductive success.

developmental biology↗

bioRxiv2017

Parallel Computing to Speed up Whole-Genome Bayesian Regression Analyses Using Orthogonal Data Augmentation

1 AbstractBayesian multiple regression methods are widely used in whole-genome analyses to solve the problem that the number p of marker covariates is usually larger than the number n of observations. Inferences from most Bayesian methods are based on Markov chain Monte Carlo methods, where statistics are computed from a Markov chain constructed to have a stationary distribution equal to the posterior distribution of the unknown parameters. In practice, chains of about fifty thousand steps are typically used in whole-genome Bayesian regression analyses, which is computationally intensive. In this paper, we have shown how the sampling of marker effects can be made independent within each step of the chain. This is done by augmenting the marker covariate matrix by adding p new rows to it such that columns of the augmented marker covariate matrix are orthogonal. The phenotypes corresponding to the augmented rows of marker covariate matrix are considered missing. Ideally, the computations at each step of the MCMC chain, can be speeded up by the number k of computer processors up to the number p of markers. Addressing the heavy computational burden associated with Bayesian methods by parallel computing will lead to greater use of these methods.

genetics↗

bioRxiv2017

Multiple-trait Bayesian Regression Methods with Mixture Priors for Genomic Prediction

Bayesian multiple-regression methods incorporating different mixture priors for marker effects are widely used in genomic prediction. Improvement in prediction accuracies from using those methods, such as BayesB, BayesC and BayesC{pi}, have been shown in single-trait analyses with both simulated data and real data. These methods have been extended to multi-trait analyses, but only under a specific limited circumstance that assumes a locus affects all the traits or none of them. In this paper, we develop and implement the most general multi-trait BayesC{Pi} and BayesB methods allowing a broader range of mixture priors. Further, we compare them to single-trait methods and the \"restricted\" multi-trait formulation using real data. In those data analyses, significant higher prediction accuracies were sometimes observed from these new broad-based multi-trait Bayesian multiple-regression methods. The software tool JWAS offers routines to perform the analyses.

genetics↗