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Gene targeting is a commonly used method to reveal the function of genes. Although it is an attractive tool for molecular studies, it can frequently be a challenge because its efficiency can be low and it requires the screening of a large number of transformants. Generally, these problems originate from the elevated level of ectopic integration caused by non-homologous DNA end joining (NHEJ). To eliminate this problem, NHEJ-related genes are frequently deleted or disrupted. Although these manipulations can improve gene targeting, the phenotype of the mutant strains raised the question of whether mutations have side effects. The aim of this study was to disrupt the lig4 gene in the dimorphic fission yeast, S. japonicus, and investigate the phenotypic changes of the mutant strain. The mutant cells have shown various phenotypic changes, such as increased sporulation on complete medium, decreased hyphal growth, faster chronological aging, and higher sensitivity to heat shock, UV light, and caffeine. In addition, higher flocculation capacity has been observed, especially at lower sugar concentrations. These changes were supported by transcriptional profiling. Many genes belonging to metabolic and transport processes, cell division, or signaling had altered mRNA levels compared to the control strain. Although the disruption improved the gene targeting, we assume that the lig4 inactivation can cause unexpected physiological side effects, and we have to be very careful with the manipulations of the NHEJ-related genes. To reveal the exact mechanisms behind these changes, further investigations are required. [ABSTRACT FROM AUTHOR]

The cyanobacterium strain Synechococcus cedrorum SAG 88.79 stock culture has fungal contamination stated by the Sammlung von Algenkulturen der Universität Göttingen itself. In this recent work, this particular fungal strain was isolated, identified, and morphologically characterised. The fungal strain AGSC12 belongs to the species Cyphellophora olivacea, with respect to the sequence similarity, phylogeny, and morphology of the strain. Colony morphology and growth capability were examined on SMA, EMMA, PDA, MEA, YEA, and YPA plates. Growth of the colonies was the most successful on YPA plates, followed by PDA and MEA containing plates. Surprisingly, the AGSC12 strain showed extreme tolerance to NaHCO3, albeit it, is is considered a general fungistatic compound. Moreover, positive association between the AGSC12 and SAG 88.79 strains was revealed, as the SAG 88.79 strain always attained higher cell density in co-cultures with the fungus than in mono-cultures. Besides, a taxonomic note on the SAG 88.79 strain itself was also stated. [ABSTRACT FROM AUTHOR]

Simple Summary: Breast cancer is a leading cause of morbidity and mortality worldwide. The metastatic disease is largely responsible for cancer patient deaths, and its treatment implies usually different therapies. In this context, the prediction of response to treatment or depiction of treatment-resistant phenotypes is essential in clinical practice, especially in the new era of precision medicine. In this study, we used several combinations of feature selection methods and machine-learning classifiers to construct predictive models of the metabolic response to the treatment of metastatic breast cancer lesions. These models were based on clinical variables and radiomic features extracted from 2-deoxy-2-[18F]fluoro-D-glucose positron emission tomography/computed tomography ([18F]F-FDG PET/CT) images, obtained prior to the treatment. Our main goal was to know if this prediction was feasible and to identify those combinations with better predictive performance. We found that several combinations were successful to predict the metabolic response to treatment, of which the least absolute shrinkage and selection operator (Lasso) + support vector machines (SVM) had the best mean performance in terms of area under the curve, in both training and validation cohorts. Model performances depended largely on the selected combinations. Background: This study aimed to identify optimal combinations between feature selection methods and machine-learning classifiers for predicting the metabolic response of individual metastatic breast cancer lesions, based on clinical variables and radiomic features extracted from pretreatment [18F]F-FDG PET/CT images. Methods: A total of 48 patients with confirmed metastatic breast cancer, who received different treatments, were included. All patients had an [18F]F-FDG PET/CT scan before and after the treatment. From 228 metastatic lesions identified, 127 were categorized as responders (complete or partial metabolic response) and 101 as non-responders (stable or progressive metabolic response), by using the percentage changes in SULpeak (peak standardized uptake values normalized for body lean body mass). The lesion pool was divided into training (n = 182) and testing cohorts (n = 46); for each lesion, 101 image features from both PET and CT were extracted (202 features per lesion). These features, along with clinical and pathological information, allowed the prediction model's construction by using seven popular feature selection methods in cross-combination with another seven machine-learning (ML) classifiers. The performance of the different models was investigated with the receiver-operating characteristic curve (ROC) analysis, using the area under the curve (AUC) and accuracy (ACC) metrics. Results: The combinations, least absolute shrinkage and selection operator (Lasso) + support vector machines (SVM), or random forest (RF) had the highest AUC in the cross-validation, with 0.93 ± 0.06 and 0.92 ± 0.03, respectively, whereas Lasso + neural network (NN) or SVM, and mutual information (MI) + RF, had the higher AUC and ACC in the validation cohort, with 0.90/0.72, 0.86/0.76, and 87/85, respectively. On average, the models with Lasso and models with SVM had the best mean performance for both AUC and ACC in both training and validation cohorts. Conclusions: Image features obtained from a pretreatment [18F]F-FDG PET/CT along with clinical vaiables could predict the metabolic response of metastatic breast cancer lesions, by their incorporation into predictive models, whose performance depends on the selected combination between feature selection and ML classifier methods. [ABSTRACT FROM AUTHOR]

Although Crepis was the first model plant group in which chromosomal changes were considered to play an important role in speciation, their chromosome structure and evolution have been barely investigated using molecular cytogenetic methods. The aim of the study was to provide a better understanding of the patterns and directions of Crepis chromosome evolution, using comparative analyses of rDNA loci number and localisation. The chromosome base number and chromosomal organisation of 5S and 35S rDNA loci were analysed in the phylogenetic background for 39 species of Crepis, which represent the evolutionary lineages of Crepis sensu stricto and Lagoseris, including Lapsana communis. The phylogenetic relationships among all the species were inferred from nrITS and newly obtained 5S rDNA NTS sequences. Despite high variations in rDNA loci chromosomal organisation, most species had a chromosome with both rDNA loci within the same (usually short) chromosomal arm. The comparative analyses revealed several independent rDNA loci number gains and loci repositioning that accompanied diversification and speciation in Crepis. Some of the changes in rDNA loci patterns were reconstructed for the same evolutionary lineages as descending dysploidy. [ABSTRACT FROM AUTHOR]

Fission yeasts have a unique life history and exhibit distinct evolutionary patterns from other yeasts. Besides, the species demonstrate stable genome structures despite the relatively fast evolution of their genomic sequences. To reveal what could be the reason for that, comparative genomic analyses were carried out. Our results provided evidence that the structural and sequence evolution of the fission yeasts were correlated. Moreover, we revealed ancestral locally collinear blocks (aLCBs), which could have been inherited from their last common ancestor. These aLCBs proved to be the most conserved regions of the genomes as the aLCBs contain almost eight genes/blocks on average in the same orientation and order across the species. Gene order of the aLCBs is mainly fission-yeast-specific but supports the idea of filamentous ancestors. Nevertheless, the sequences and gene structures within the aLCBs are as mutable as any sequences in other parts of the genomes. Although genes of certain Gene Ontology (GO) categories tend to cluster at the aLCBs, those GO enrichments are not related to biological functions or high co-expression rates, they are, rather, determined by the density of essential genes and Rec12 cleavage sites. These data and our simulations indicated that aLCBs might not only be remnants of ancestral gene order but are also maintained by natural selection. [ABSTRACT FROM AUTHOR]

Transcatheter aortic valve replacement (TAVR) has rapidly become a viable alternative to the conventional isolated surgical aortic valve replacement (iSAVR) for treating severe symptomatic aortic stenosis. However, data on younger patients is scarce and a gap exists between data-based recommendations and the clinical use of TAVR. In our study, we utilized a machine learning (ML) driven approach to model the complex decision-making process of Heart Teams when treating young patients with severe symptomatic aortic stenosis with either TAVR or iSAVR and to identify the relevant considerations. Out of the considered factors, the variables most prominently featured in our ML model were congestive heart failure, established risk assessment scores, previous cardiac surgeries, a reduced left ventricular ejection fraction and peripheral vascular disease. Our study demonstrates a viable application of ML-based approaches for studying and understanding complex clinical decision-making processes. [ABSTRACT FROM AUTHOR]

The evolution of the karyotype and genome size was examined in species of Crepis sensu lato. The phylogenetic relationships, inferred from the plastid and nrITS DNA sequences, were used as a framework to infer the patterns of karyotype evolution. Five different base chromosome numbers (x = 3, 4, 5, 6, and 11) were observed. A phylogenetic analysis of the evolution of the chromosome numbers allowed the inference of x = 6 as the ancestral state and the descending dysploidy as the major direction of the chromosome base number evolution. The derived base chromosome numbers (x = 5, 4, and 3) were found to have originated independently and recurrently in the different lineages of the genus. A few independent events of increases in karyotype asymmetry were inferred to have accompanied the karyotype evolution in Crepis. The genome sizes of 33 Crepis species differed seven-fold and the ancestral genome size was reconstructed to be 1C = 3.44 pg. Both decreases and increases in the genome size were inferred to have occurred within and between the lineages. The data suggest that, in addition to dysploidy, the amplification/elimination of various repetitive DNAs was likely involved in the genome and taxa differentiation in the genus. [ABSTRACT FROM AUTHOR]

Simple Summary: The pituitary gland governs the function of nearly all endocrine glands and pituitary oncogenesis often distorts the hormonal balance. For optimal surgical cure it is crucial to discriminate pathological tissue from intact pituitary gland. Our multimodal imaging approach allows for morpho-molecular metabolic analysis and discrimination of pituitary gland and adenomas combining different complementary techniques such as optical coherence tomography (OCT), multiphoton microscopy (MPM) and line scan Raman microspectroscopy (LSRM). Radiomics as well as analysis of spectroscopic features allows enhanced discrimination of pituitary gland and adenomas and, furthermore, classification of pituitary adenoma subtypes. Pituitary adenomas count among the most common intracranial tumors. During pituitary oncogenesis structural, textural, metabolic and molecular changes occur which can be revealed with our integrated ultrahigh-resolution multimodal imaging approach including optical coherence tomography (OCT), multiphoton microscopy (MPM) and line scan Raman microspectroscopy (LSRM) on an unprecedented cellular level in a label-free manner. We investigated 5 pituitary gland and 25 adenoma biopsies, including lactotroph, null cell, gonadotroph, somatotroph and mammosomatotroph as well as corticotroph. First-level binary classification for discrimination of pituitary gland and adenomas was performed by feature extraction via radiomic analysis on OCT and MPM images and achieved an accuracy of 88%. Second-level multi-class classification was performed based on molecular analysis of the specimen via LSRM to discriminate pituitary adenomas subtypes with accuracies of up to 99%. Chemical compounds such as lipids, proteins, collagen, DNA and carotenoids and their relation could be identified as relevant biomarkers, and their spatial distribution visualized to provide deeper insight into the chemical properties of pituitary adenomas. Thereby, the aim of the current work was to assess a unique label-free and non-invasive multimodal optical imaging platform for pituitary tissue imaging and to perform a multiparametric morpho-molecular metabolic analysis and classification. [ABSTRACT FROM AUTHOR]

Simple Summary: Breast cancer is the second most common diagnosed malignancy in women worldwide. In this study, we examine the feasibility of breast tumor characterization based on [18F]FDG-PET/CT images using machine learning (ML) approaches in combination with data-preprocessing techniques. ML prediction models for breast cancer detection and the identification of breast cancer receptor status, proliferation rate, and molecular subtypes were established and evaluated. Furthermore, the importance of most repeatable features was investigated. Results displayed high performance of malignant/benign tumor differentiation and triple negative tumor subtype ML models. We observed high repeatability of radiomic features for both high performing predictive models. Background: This study investigated the performance of ensemble learning holomic models for the detection of breast cancer, receptor status, proliferation rate, and molecular subtypes from [18F]FDG-PET/CT images with and without incorporating data pre-processing algorithms. Additionally, machine learning (ML) models were compared with conventional data analysis using standard uptake value lesion classification. Methods: A cohort of 170 patients with 173 breast cancer tumors (132 malignant, 38 benign) was examined with [18F]FDG-PET/CT. Breast tumors were segmented and radiomic features were extracted following the imaging biomarker standardization initiative (IBSI) guidelines combined with optimized feature extraction. Ensemble learning including five supervised ML algorithms was utilized in a 100-fold Monte Carlo (MC) cross-validation scheme. Data pre-processing methods were incorporated prior to machine learning, including outlier and borderline noisy sample detection, feature selection, and class imbalance correction. Feature importance in each model was assessed by calculating feature occurrence by the R-squared method across MC folds. Results: Cross validation demonstrated high performance of the cancer detection model (80% sensitivity, 78% specificity, 80% accuracy, 0.81 area under the curve (AUC)), and of the triple negative tumor identification model (85% sensitivity, 78% specificity, 82% accuracy, 0.82 AUC). The individual receptor status and luminal A/B subtype models yielded low performance (0.46–0.68 AUC). SUVmax model yielded 0.76 AUC in cancer detection and 0.70 AUC in predicting triple negative subtype. Conclusions: Predictive models based on [18F]FDG-PET/CT images in combination with advanced data pre-processing steps aid in breast cancer diagnosis and in ML-based prediction of the aggressive triple negative breast cancer subtype. [ABSTRACT FROM AUTHOR]