Biology subjects

Heinonen, M.

Publications and source records attributed to Heinonen, M..

5 recordsLinked to original sources

bioRxiv2025

Impact of gestational antibiotics on maternal and offspring gut microbiota and growth in pigs

Maternal microbiota modulates the development of the microbiota in the offspring. Effects of gestational antibiotics are not well understood, as most studies have focused on the perinatal period. We treated sows with penicillin, tetracycline or saline on days 78-80 of the 114-119-day gestation, a critical period in the fetal immune system development. Microbiotas were analyzed by 16S rRNA gene amplicon sequencing in sow feces and vagina at days 77 and 113, in colostrum, and in piglet feces at three days, three weeks and ten weeks of age. Sow fecal microbiota changed during pregnancy, but less in antibiotic groups. No significant effects on sow microbiota remained on day 113. The piglets of the antibiotic-treated sows had lower Firmicutes+Actinobacteriota to Bacteroidota+Proteobacteria ratio, lower alpha diversity and higher relative abundance of Escherichia at three days. At ten weeks, they exhibited higher alpha diversity, had higher of Prevotella and Clostridium sp. CAG-127, and smaller increase of Limosilactobacillus than the control. Increased alpha diversity at 10 weeks was associated with lower weight gain during nursing. Oliverpabstia and Mitsuokella were positively associated with growth, while CAG-127 and Campylobacter B were negatively associated. Sow antibiotic treatment decreased the positive effect of Oliverpabstia and Mitsuokella and increased the negative effects of CAG-127 and Campylobacter B. Gestational antibiotics may have had adverse effects on microbiota and growth of the offspring, even if their effects on maternal microbiota were undetectable by parturition.

microbiology↗

bioRxiv2024

Bacterial profiles of the oral, vaginal, and rectal mucosa and colostrum of periparturient sows

The commensal microbiota influences the health, feeding efficiency, and reproductive performance of sows. The microbiota composition in the alimentary and genitourinary tracts and in colostrum/milk during pregnancy and lactation also impacts the developing microbiota and immune system of the piglets and subsequently their growth and health. Knowledge of the microbial compositions is important for evaluation of these effects and for discovering ways to improve the health and productivity of the sows. Oral, vaginal, and rectal mucosa were sampled from 32 sows of variable parity in late pregnancy on four commercial piglet-producing farms in Finland. Colostrum samples were taken within 6 hours of delivery of the first piglet. Microbial compositions were analyzed by 16S rRNA gene amplicon sequencing. Moderate differences in diversity and composition were observed between farms. The most abundant genera of the oral microbiota were Rothia, Moraxella, and Streptococcus. The rectal microbiota was dominated by Clostridium sensu stricto 1. Streptococcus was the most abundant genus in the vagina and colostrum. Differences in relative abundances of genera were detected between primiparous and multiparous sows. Some of these differences were detected across all the farms; in the multiparous sows, the relative abundances of Fusobacterium and Neisseria were lower in oral samples. Clostridium sensu stricto 1, Romboutsia, and Lachnospiraceae_UCG_007 were higher and Prevotella lower in rectal samples, and Streptococcus higher in colostrum samples. In vaginal samples, approximately half of the multiparous sows had significantly higher relative abundances of the genera Fusobacterium and Streptococcus than the primiparous sows. Among the differentially abundant taxa, F. necrophorum and F. nucleatum were identified in oral samples, Fusobacterium gastrosuis and Fusobacterium necrophorum in vaginal samples, and Streptococcus dysgalactiae in colostrum samples. Most of the differences were due to unidentified species within the respective genus. This study provides a comprehensive overview of the mucosal and colostrum microbiota of periparturient sows during normal production conditions on Finnish commercial farms, including potentially interesting differences in the relative abundances of several genera at different mucosal sites between sows of low and high parity.

microbiology↗

bioRxiv2022

Predicting recognition between T cell receptors and epitopes using contextualized motifs

We introduce TCRconv, a deep learning model for predicting recognition between T-cell receptors and epitopes. TCRconv uses a deep protein language model and convolutions to extract contextualized motifs and provides state-of-the-art TCR-epitope prediction accuracy. Using TCR repertoires from COVID-19 patients, we demonstrate that TCRconv can provide insight into T-cell dynamics and phenotypes during the disease.

bioinformatics↗

bioRxiv2019

TCRGP: Determining epitope specificity of T cell receptors

T cell receptors (TCRs) can recognize various pathogens and consequently start immune responses. TCRs can be sequenced from individuals and methods analyzing the specificity of the TCRs can help us better understand individuals immune status in different diseases. We have developed TCRGP, a novel Gaussian process method to predict if TCRs recognize certain epitopes. This method can utilize CDR sequences from TCR and TCR{beta} chains and learn which CDRs are important in recognizing different epitopes. We have experimented with with epitope-specific data against 29 epitopes and performed a comprehensive evaluation with existing prediction methods. On this data, TCRGP outperforms other state-of-the-art methods in epitope-specificity predictions. We also propose a novel analysis approach for combined single-cell RNA and TCR{beta} (scRNA+TCR{beta}) sequencing data by quantifying epitope-specific TCRs with TCRGP in phenotypes identified from scRNA-seq data. With this approach, we find HBV-epitope specific T cells and their transcriptomic states in hepatocellular carcinoma patients.

bioinformatics↗

bioRxiv2017

Flex ddG: Rosetta ensemble-based estimation of changes in protein-protein binding affinity upon mutation

Computationally modeling changes in binding free energies upon mutation (interface {Delta}{Delta}G) allows large-scale prediction and perturbation of protein-protein interactions. Additionally, methods that consider and sample relevant conformational plasticity should be able to achieve higher prediction accuracy over methods that do not. To test this hypothesis, we developed a method within the Rosetta macromolecular modeling suite (flex ddG) that samples conformational diversity using \"backrub\" to generate an ensemble of models, then applying torsion minimization, side chain repacking and averaging across this ensemble to estimate interface {Delta}{Delta}G values. We tested our method on a curated benchmark set of 1240 mutants, and found the method outperformed existing methods that sampled conformational space to a lesser degree. We observed considerable improvements with flex ddG over existing methods on the subset of small side chain to large side chain mutations, as well as for multiple simultaneous non-alanine mutations, stabilizing mutations, and mutations in antibody-antigen interfaces. Finally, we applied a generalized additive model (GAM) approach to the Rosetta energy function; the resulting non-linear reweighting model improved agreement with experimentally determined interface DDG values, but also highlights the necessity of future energy function improvements.

biophysics↗