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Identifying germline BRCA1/2 mutation carriers is vital for reducing their risk of breast and ovarian cancer. To derive a serum miRNA-based diagnostic test we used samples from 653 healthy women from six international cohorts, including 350 (53.6%) with BRCA1/2 mutations and 303 (46.4%) BRCA1/2 wild-type. All individuals were cancer-free before and at least 12 months after sampling. RNA-sequencing followed by differential expression analysis identified 19 miRNAs significantly associated with BRCA mutations, 10 of which were ultimately used for classification: hsa-miR-20b-5p, hsa-miR-19b-3p, hsa-let-7b-5p, hsa-miR-320b, hsa-miR-139-3p, hsa-miR-30d-5p, hsa-miR-17-5p, hsa-miR-182-5p, hsa-miR-421, hsa-miR-375-3p. The final logistic regression model achieved area under the receiver operating characteristic curve 0.89 (95% CI: 0.87–0.93), 93.88% sensitivity and 80.72% specificity in an independent validation cohort. Mutated gene, menopausal status or having preemptive oophorectomy did not affect classification performance. Circulating microRNAs may be used to identify BRCA1/2 mutations in patients of high risk of cancer, offering an opportunity to reduce screening costs. BRCA1/2 mutations are known to increase risk of breast and ovarian cancer but carrier status in healthy individuals is unknown without genetic testing. Here, the authors created a circulating miRNA signature to predict BRCA1/2 carrier status in healthy individuals to aid the decision process on genetic testing. [ABSTRACT FROM AUTHOR]

Alström syndrome (ALMS) and Bardet–Biedl syndrome (BBS) are rare genetic diseases with a number of common clinical features ranging from early-childhood obesity and retinal degeneration. ALMS and BBS belong to the ciliopathies, which are known to have the expression products of genes, encoding them as cilia-localized proteins in multiple target organs. The aim of this study was to perform transcriptomic and proteomic analysis on cellular models of ALMS and BBS syndromes to identify common and distinct pathological mechanisms present in both syndromes. For this purpose, epithelial cells were isolated from the urine of patients and healthy subjects, which were then cultured and reprogrammed into induced pluripotent stem (iPS) cells. The pathways of genes associated with the metabolism of lipids and glycosaminoglycan and the transport of small molecules were found to be concomitantly downregulated in both diseases, while transcripts related to signal transduction, the immune system, cell cycle control and DNA replication and repair were upregulated. Furthermore, protein pathways associated with autophagy, apoptosis, cilium assembly and Gli1 protein were upregulated in both ciliopathies. These results provide new insights into the common and divergent pathogenic pathways between two similar genetic syndromes, particularly in relation to primary cilium function and abnormalities in cell differentiation. [ABSTRACT FROM AUTHOR]

Simple Summary: Pancreatic ductal adenocarcinoma (PDAC) is one of the most deadly cancers worldwide. The occurrence of oncogenic KRAS mutations is considered a signature event in PDAC, leading to genomic instability. The aim of our study was to evaluate the impact of the oncogenic KRAS G12D mutation on the activity of the error-prone alt-EJ repair mechanism, and to investigate the potential role of Polθ in the development of pancreatic cancer. We found that oncogenic KRAS increases the expression of key alt-EJ proteins in a mouse and human PDAC model. Using TLR assay, we also found increased alt-EJ activity in mouse and human cell lines upon the expression of KRAS D12D. The inactivation/impairment of alt-EJ by polymerase theta (Polθ) depletion delays the development of pancreatic cancer and prolongs the survival of experimental mice, though it does not prevent the PDAC development, which leads to full-blown PDAC with disseminated metastasis. Our studies provide a high-value target as a novel therapeutic candidate for the treatment of pancreatic and other cancers. Pancreatic ductal adenocarcinoma (PDAC), due to its genomic heterogeneity and lack of effective treatment, despite decades of intensive research, will become the second leading cause of cancer-related deaths by 2030. Step-wise acquisition of mutations, due to genomic instability, is considered to drive the development of PDAC; the KRAS mutation occurs in 95 to 100% of human PDAC, and is already detectable in early premalignant lesions designated as pancreatic intraepithelial neoplasia (PanIN). This mutation is possibly the key event leading to genomic instability and PDAC development. Our study aimed to investigate the role of the error-prone DNA double-strand breaks (DSBs) repair pathway, alt-EJ, in the presence of the KRAS G12D mutation in pancreatic cancer development. Our findings show that oncogenic KRAS contributes to increasing the expression of Polθ, Lig3, and Mre11, key components of alt-EJ in both mouse and human PDAC models. We further confirm increased catalytic activity of alt-EJ in a mouse and human model of PDAC bearing the KRAS G12D mutation. Subsequently, we focused on estimating the impact of alt-EJ inactivation by polymerase theta (Polθ) deletion on pancreatic cancer development, and survival in genetically engineered mouse models (GEMMs) and cancer patients. Here, we show that even though Polθ deficiency does not fully prevent the development of pancreatic cancer, it significantly delays the onset of PanIN formation, prolongs the overall survival of experimental mice, and correlates with the overall survival of pancreatic cancer patients in the TCGA database. Our study clearly demonstrates the role of alt-EJ in the development of PDAC, and alt-EJ may be an attractive therapeutic target for pancreatic cancer patients. [ABSTRACT FROM AUTHOR]

Pasteurization of donated human milk preserves it for storage and makes it safe for feeding, but at the expense of its composition, nutritional values and functions. Here, we aimed to investigate the impact of Holder Pasteurization (HoP) and High Pressure Processing (HPP) methods on miRNA in human milk and to evaluate impact of these changes on miRNA functions. Milk samples obtained from women in 50th day of lactation (n = 3) were subjected either to HoP, HPP or remained unpasteurized as a control. Subsequently, miRNA was isolated from whole material and exosomal fraction and sequenced with Illumina NextSeq 500. Sequencing data were processed, read counts were mapped to miRNA and analyzed both quantitatively with DESeq2 and functionally with DIANA mirPath v.3. While HPP caused statistically insignificant decrease in number of miRNA reads compared to unprocessed material, HoP led to 82-fold decrease in whole material (p = 0.0288) and 302-fold decrease in exosomes (p = 0.0021) not leaving enough reads for further analysis. Changes in composition of miRNA fraction before and after HPP indicated uneven stability of individual miRNAs under high pressure conditions, with miR-30d-5p identified as relatively stable and miR-29 family as sensitive to HPP. Interestingly, about 2/3 of unprocessed milk miRNA content consists of only 10 distinct miRNAs with miR-148a-3p at the top. Functional analysis of most abundant human milk miRNAs showed their involvement in signaling pathways, cell communication, proliferation and metabolism that are obviously important in rapidly growing infants. Functions of miRNAs which suffered the greatest depletion during HPP were similar to roles of the majority of unprocessed human milk's miRNA, which indicates that those functions may be weakened although not completely lost. Our findings indicate that HPP is less detrimental to human milk miRNAs than HoP and should be considered in further research on recommended processing procedures for human milk banks. [ABSTRACT FROM AUTHOR]

Simple Summary: Radiation-induced hypothyroidism (RIHT) commonly develops in cancer survivors that receive radiation therapy for cancers in the head and neck region. The state-of-art normal tissue complication probability (NTCP) models perform satisfactorily; however, they do not use the whole spectrum of information that can be obtained from imaging techniques. The radiomic approach offers the ability to efficiently mine features, which are imperceptible to the human eye, but may provide crucial data about the patient's condition. We gathered CT images and clinical data from 98 patients undergoing radiotherapy for head and neck cancers, 27 of whom later developed RIHT. For them, we created machine-learning models to predict RIHT using automatically extracted radiomic features and appropriate clinical and dosimetric parameters. We also validated the well-established external state-of-art NTCP models on our datasets and observed that our radiomic-based models performed very similarly to them. This shows that automated tools may perform as well as the current standard but can be theoretically applied faster and be implemented into existing imaging software used when planning radiotherapy. State-of-art normal tissue complication probability (NTCP) models do not take into account more complex individual anatomical variations, which can be objectively quantitated and compared in radiomic analysis. The goal of this project was development of radiomic NTCP model for radiation-induced hypothyroidism (RIHT) using imaging biomarkers (radiomics). We gathered CT images and clinical data from 98 patients, who underwent intensity-modulated radiation therapy (IMRT) for head and neck cancers with a planned total dose of 70.0 Gy (33–35 fractions). During the 28-month (median) follow-up 27 patients (28%) developed RIHT. For each patient, we extracted 1316 radiomic features from original and transformed images using manually contoured thyroid masks. Creating models based on clinical, radiomic features or a combination thereof, we considered 3 variants of data preprocessing. Based on their performance metrics (sensitivity, specificity), we picked best models for each variant ((0.8, 0.96), (0.9, 0.93), (0.9, 0.89) variant-wise) and compared them with external NTCP models ((0.82, 0.88), (0.82, 0.88), (0.76, 0.91)). We showed that radiomic-based models did not outperform state-of-art NTCP models (p > 0.05). The potential benefit of radiomic-based approach is that it is dose-independent, and models can be used prior to treatment planning allowing faster selection of susceptible population. [ABSTRACT FROM AUTHOR]

Background: Patients with the rare syndromic forms of monogenic diabetes: Alström syndrome (ALMS) and Bardet-Biedl syndrome (BBS) have multiple metabolic abnormalities, including early-onset obesity, insulin resistance, lipid disorders and type 2 diabetes mellitus. The aim of this study was to determine if the expression of circulating miRNAs in patients with ALMS and BBS differs from that in healthy and obese individuals and determine if miRNA levels correlate with metabolic tests, BMI-SDS and patient age., Methods: We quantified miRNA expression (Qiagen, Germany) in four groups of patients: with ALMS (n=13), with BBS (n=7), patients with obesity (n=19) and controls (n=23). Clinical parameters including lipids profile, serum creatinine, cystatin C, fasting glucose, insulin and C-peptide levels, HbA1c values and insulin resistance (HOMA-IR) were assessed in patients with ALMS and BBS., Results: We observed multiple up- or downregulated miRNAs in both ALMS and BBS patients compared to obese patients and controls, but only 1 miRNA (miR-301a-3p) differed significantly and in the same direction in ALMS and BBS relative to the other groups. Similarly, 1 miRNA (miR-92b-3p) was dysregulated in the opposite directions in ALMS and BBS patients, but diverged from 2 other groups. We found eight miRNAs (miR-30a-5p, miR-92b-3p, miR-99a-5p, miR-122-5p, miR-192-5p, miR-193a-5p, miR-199a-3p and miR-205-5p) that significantly correlated with at least of the analyzed clinical variables representing an association with the course of the diseases., Conclusions: Our results show for the first time that serum miRNAs can be used as available indicators of disease course in patients with ALMS and BBS syndromes., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Zmyslowska, Smyczynska, Stanczak, Jeziorny, Szadkowska, Fendler and Borowiec.)

Chromosome-scale genome assembly of the yeast Saprochaete ingens CBS 517.90 was determined by a combination of technologies producing short (HiSeq X; Illumina) and long (MinION; Oxford Nanopore Technologies) reads. The 21.2-Mbp genome sequence has a GC content of 36.9% and codes for 6,475 predicted proteins., (Copyright © 2019 Hodorová et al.)

Saprochaete suaveolens is an ascomycetous yeast that produces a range of fruity flavors and fragrances. Here, we report the high-contiguity genome sequence of the ex-holotype strain, NRRL Y-17571 (CBS 152.25). The nuclear genome sequence contains 24.4 Mbp and codes for 8,119 predicted proteins.

Introduction: The leading method for prediction of growth hormone (GH) therapy effectiveness are multiple linear regression (MLR) models. Best of our knowledge, we are the first to apply artificial neural networks (ANN) to solve this problem. For ANN there is no necessity to assume the functions linking independent and dependent variables. The aim of study is to compare ANN and MLR models of GH therapy effectiveness., Material and Methods: Analysis comprised the data of 245 GH-deficient children (170 boys) treated with GH up to final height (FH). Independent variables included: patients' height, pre-treatment height velocity, chronological age, bone age, gender, pubertal status, parental heights, GH peak in 2 stimulation tests, IGF-I concentration. The output variable was FH., Results: For testing dataset, MLR model predicted FH SDS with average error (RMSE) 0.64 SD, explaining 34.3% of its variability; ANN model derived on the same pre-processed data predicted FH SDS with RMSE 0.60 SD, explaining 42.0% of its variability; ANN model derived on raw data predicted FH with RMSE 3.9 cm (0.63 SD), explaining 78.7% of its variability., Conclusion: ANN seem to be valuable tool in prediction of GH treatment effectiveness, especially since they can be applied to raw clinical data.