Metabolomics analysis of placental tissue obtained from patients with fetal growth restriction.

AIM: The aim of this study was to determine whether there was a difference in placental metabolite profiles between patients with fetal growth restriction (FGR) and healthy controls. METHODS: The study included 10 patients with FGR diagnosis with 14 healthy controls with both matched maternal age and body mass index. 1 H HR-MAS NMR spectroscopy data obtained from placental tissue samples of patients with FGR and healthy control group were analyzed with bioinformatics methods. The obtained results of metabolite levels were further validated with the internal standard (IS) quantification method. RESULTS: Principal component analysis (PCA) and the partial least squares discriminant analysis (PLS-DA) score plots obtained with the multivariate statistical analysis of preprocessed spectral data shows a separation between the samples from patients with FGR and healthy controls. Bioinformatics analysis results suggest that the placental levels of lactate, glutamine, glycerophosphocholine, phosphocholine, taurine, and myoinositol are increased in patients with FGR compared to the healthy controls. CONCLUSIONS: Placental metabolic dysfunctions are a common occurrence in FGR.

Metabolomic analysis of endometrial cancer by high-resolution magic angle spinning NMR spectroscopy.

PURPOSE: To analyze endometrial metabolite profiles between patients with endometrial cancer and controls. METHODS: Seventeen (17) women with endometrium cancer and 18 controls were enrolled in this study. 1H HR-MAS (High Resolution-Magic Angle Spinning) NMR (Nuclear Magnetic Resonance) spectroscopy data obtained from endometrial tissue samples of patients with endometrial cancer and control group were analyzed with bioinformatics methods. RESULTS: Principal component analysis (PCA) and the partial least squares discriminant analysis (PLS-DA) score plots obtained with the multivariate statistical analysis of pre-processed spectral data shows a separation between the samples from patients with endometrial cancer and controls. Analysis results suggest that the levels of lactate, glucose, o-phosphoethanolamine, choline, glycerophosphocholine, phosphocholine, leucine, isoleucine, valine, glutamate, glutamine, n-acetyltyrosine, methionine, taurine, alanine, aspartate and phenylalanine are increased in patients with endometrial cancer compared to the controls. CONCLUSION: The metabolomics signature of patients with endometrial cancer is different from that of benign endometrial tissue.

scMAGS: Marker gene selection from scRNA-seq data for spatial transcriptomics studies.

Single-Cell RNA sequencing (scRNA-seq) has provided unprecedented opportunities for exploring gene expression and thus uncovering regulatory relationships between genes at the single-cell level. However, scRNA-seq relies on isolating cells from tissues. Therefore, the spatial context of the regulatory processes is lost. A recent technological innovation, spatial transcriptomics, allows for the measurement of gene expression while preserving spatial information. An initial step in the spatial transcriptomic analysis is to identify the cell type, which requires a careful selection of cell-specific marker genes. For this purpose, currently, scRNA-seq data is used to select a limited number of marker genes from among all genes that distinguish cell types from each other. This study proposes scMAGS (single-cell MArker Gene Selection), a novel method for marker gene selection from scRNA-seq data for spatial transcriptomics studies. scMAGS uses a filtering step in which the candidate genes are identified before the marker gene selection step. For the selection of marker genes, cluster validity indices, the Silhouette index, or the Calinski-Harabasz index (for large datasets) are utilized. Experimental results showed that, in comparison to the existing methods, scMAGS is scalable, fast, and accurate. Even for large datasets with millions of cells, scMAGS could find the required number of marker genes in a reasonable amount of time with fewer memory requirements. scMAGS is made freely available at https://github.com/doganlab/scmags and can be downloaded from the Python Package Directory (PyPI) software repository with the command pip install scmags.

Metabolomics analysis of follicular fluid in women with ovarian endometriosis undergoing in vitro fertilization.

The purpose of this study was to investigate whether a change in the follicular fluid metabolomics profile due to endometrioma is identifiable. Twelve women with ovarian endometriosis (aged<40 years, with a body mass index [BMI] of <30 kg/m2) and 12 age- and BMI-matched controls (women with infertility purely due to a male factor) underwent ovarian stimulation for intracytoplasmic sperm injection (ICSI). Follicular fluid samples were collected from both of groups at the time of oocyte retrieval for ICSI. Next, nuclear magnetic resonance (NMR) spectroscopy was performed for the collected follicular fluids. The metabolic compositions of the follicular fluids were then compared using univariate and multivariate statistical analyses of NMR data. Univariate and multivariate statistical analyses of NMR data showed that the metabolomic profiles of the follicular fluids obtained from the women with ovarian endometriosis were distinctly different from those obtained from the control group. In comparison with the controls, the follicular fluids of the women with ovarian endometriosis had statistically significant elevated levels of lactate, ß-glucose, pyruvate, and valine. We conclude that the levels of lactate, ß-glucose, pyruvate, and valine in the follicular fluid of the women with endometrioma were higher than those of the controls. Abbreviations: ASRM: American Society for Reproductive Medicine; BMI: body mass index; CPMG: Carr-Purcell-Meiboom-Gill; E2: estradiol; ESHRE: European Society of Human Reproduction and Embryology; ERETIC: electronic to access in vivo concentration; FF: follicular fluid; FSH: follicle-stimulating hormone; hCG: human chorionic gonadotropin; HEPES: 2-hydroxyethyl-1-piperazineethanesulfonic acid; ICSI: intracytoplasmic sperm injection; IVF: in vitro fertilization; NMR: nuclear magnetic resonance spectroscopy; PCA: principal component analysis; PCOS: polycystic ovary syndrome; PLS-DA: partial least squares discriminant analysis; ppm: parts per million; PULCON: pulse length-based concentration determination; TSP: 3-(trimethylsilyl)-1-propanesulfonic acid sodium salt; VIP: variable importance in projection.

Computational Methods for Analysis of the DNA-Binding Preferences of Cys2His2 Zinc-Finger Proteins.

Cys2His2 zinc-finger proteins (C2H2-ZFPs) constitute the largest class of human transcription factors (TFs) and also the least characterized one. Determining the DNA sequence preferences of C2H2-ZFPs is an important first step toward elucidating their roles in transcriptional regulation. Among the most promising approaches for obtaining the sequence preferences of C2H2-ZFPs are those that combine machine-learning predictions with in vivo binding maps of these proteins. Here, we provide a protocol and guidelines for predicting the DNA-binding preferences of C2H2-ZFPs from their amino acid sequences using a machine learning-based recognition code. This protocol also describes the tools and steps to combine these predictions with ChIP-seq data to remove inaccuracies, identify the zinc-finger domains within each C2H2-ZFP that engage with DNA in vivo, and pinpoint the genomic binding sites of the C2H2-ZFPs.