Enhanced Golden Gate Assembly: evaluating overhang strength for improved ligation efficiency.

Molecular cloning, a routine yet essential technique, relies heavily on efficient ligation, which can be significantly improved using Golden Gate Assembly (GGA). A key component of GGA is the use of type IIS enzymes, which uniquely cleave downstream of their recognition sequences to generate various overhangs, including non-palindromic ones. Recent advancements in GGA include the development of newly engineered enzymes with enhanced activity. Additionally, high-throughput GGA assays, which allow for the simultaneous study of all possible overhangs, have identified optimal GGA substrates with high efficiencies and fidelities, greatly facilitating the design of complex assemblies. Interestingly, these assays reveal unexpected correlations between ligation efficiencies and overhang stabilities. One hypothesis for this observation is that newly hydrolyzed DNA fragments with strong overhangs can readily re-ligate, thereby slowing down the overall process. In this paper, we employ a combination of gel electrophoresis and numerical calculations to test this hypothesis, ultimately determining that it does not hold true under the conditions established by conventional GGA assays. Using an assembly of 10 fragments, we demonstrate that strong overhangs yield higher GGA efficiency, while weak overhangs result in lower efficiency. These findings enable us to propose optimal overhangs for efficient GGA assays, significantly increasing yield.

Robust, high-yield, rapid fabrication of DNA constructs for Magnetic Tweezers.

Single-molecule techniques are highly sensitive tools that can reveal reaction intermediates often obscured in experiments involving large ensembles of molecules. Therefore, they provide unprecedented information on the mechanisms that control biomolecular reactions. Currently, one of the most significant single-molecule assays is Magnetic Tweezers (MT), which probes enzymatic reactions at high spatio-temporal resolutions on tens, if not hundreds, of molecules simultaneously. For high-resolution MT experiments, a short double-stranded DNA molecule (less than 2000 base pairs) is typically attached between a micron-sized superparamagnetic bead and a surface. The fabrication of such a substrate is key for successful single-molecule assays, and several papers have discussed the possibility of improving the fabrication of short DNA constructs. However, reported yields are usually low and require additional time-consuming purification steps (e.g., gel purification). In this paper, we propose the use of a Golden Gate Assembly assay that allows for the production of DNA constructs within minutes (starting from PCR products). We discuss how relevant parameters may affect the yield and offer single-molecule experimentalists a simple yet robust approach to fabricate DNA constructs.

Personalized prediction of the secondary oocytes number after ovarian stimulation: A machine learning model based on clinical and genetic data.

Controlled ovarian stimulation is tailored to the patient based on clinical parameters but estimating the number of retrieved metaphase II (MII) oocytes is a challenge. Here, we have developed a model that takes advantage of the patient's genetic and clinical characteristics simultaneously for predicting the stimulation outcome. Sequence variants in reproduction-related genes identified by next-generation sequencing were matched to groups of various MII oocyte counts using ranking, correspondence analysis, and self-organizing map methods. The gradient boosting machine technique was used to train models on a clinical dataset of 8,574 or a clinical-genetic dataset of 516 ovarian stimulations. The clinical-genetic model predicted the number of MII oocytes better than that based on clinical data. Anti-Müllerian hormone level and antral follicle count were the two most important predictors while a genetic feature consisting of sequence variants in the GDF9, LHCGR, FSHB, ESR1, and ESR2 genes was the third. The combined contribution of genetic features important for the prediction was over one-third of that revealed for anti-Müllerian hormone. Predictions of our clinical-genetic model accurately matched individuals' actual outcomes preventing over- or underestimation. The genetic data upgrades the personalized prediction of ovarian stimulation outcomes, thus improving the in vitro fertilization procedure.

Influence of the modification of the cosmetic peptide Argireline on the affinity toward copper(II) ions.

Argireline (Ac-EEMQRR-NH2 ), a well-known neurotransmitter peptide with a potency similar to botulinum neurotoxins, reveals a proven affinity toward Cu(II) ions. We report herein Cu(II) chelating properties of three new Argireline derivatives, namely, AN4 (Ac-EAHRR-NH2 ), AN5 (Ac-EEHQRR-NH2 ), and AN6 (Ac-EAHQRK-NH2 ). Two complementary experimental techniques, i.e., potentiometric titration (PT) and isothermal titration calorimetry (ITC), have been employed to describe the acid-base properties of the investigated peptides as well as the thermodynamic parameters of the Cu(II) complex formation. Additionally, based on density functional theory (DFT) calculations, we propose the most likely structures of the resulting Cu-peptide complexes. Finally, the cytotoxicity of the free peptides and the corresponding Cu(II) complexes was estimated in human skin cells for their possible future cosmetic application. The biological results were subsequently compared with free Argireline, its Cu(II)-complexes, and the previously studied AN2 derivative (EAHQRR).

X-chromosome inactivation patterns depend on age and tissue but not conception method in humans.

Female somatic X-chromosome inactivation (XCI) balances the X-linked transcriptional dosages between the sexes, randomly silencing the maternal or paternal X chromosome in each cell of 46,XX females. Skewed XCI toward one parental X has been observed in association with ageing and in some female carriers of X-linked diseases. To address the problem of non-random XCI, we quantified the XCI skew in different biological samples of naturally conceived females of different age groups and girls conceived after in vitro fertilization (IVF). Generally, XCI skew differed between saliva, blood, and buccal swabs, while saliva and blood had the most similar XCI patterns in individual females. XCI skew increased with age in saliva, but not in other tissues. We showed no significant differences in the XCI patterns in tissues of naturally conceived and IVF females. The gene expression profile of the placenta and umbilical cord blood was determined depending on the XCI pattern. The increased XCI skewing in the placental tissue was associated with the differential expression of several genes out of 40 considered herein. Notably, skewed XCI patterns (> 80:20) were identified with significantly increased expression levels of four genes: CD44, KDM6A, PHLDA2, and ZRSR2. The differences in gene expression patterns between samples with random and non-random XCI may shed new light on factors contributing to the XCI pattern outcome and indicate new paths in future research on the phenomenon of XCI skewing.

Exploring gonadotropin dosing effects on MII oocyte retrieval in ovarian stimulation.

PURPOSE: Ovarian stimulation with gonadotropins is crucial for obtaining mature oocytes for in vitro fertilization (IVF). Determining the optimal gonadotropin dosage is essential for maximizing its effectiveness. Our study aimed to develop a machine learning (ML) model to predict oocyte counts in IVF patients and retrospectively analyze whether higher gonadotropin doses improve ovarian stimulation outcomes. METHODS: We analyzed the data from 9598 ovarian stimulations. An ML model was employed to predict the number of mature metaphase II (MII) oocytes based on clinical parameters. These predictions were compared with the actual counts of retrieved MII oocytes at different gonadotropin dosages. RESULTS: The ML model provided precise predictions of MII counts, with the AMH and AFC being the most important, and the previous stimulation outcome and age, the less important features for the prediction. Our findings revealed that increasing gonadotropin dosage did not result in a higher number of retrieved MII oocytes. Specifically, for patients predicted to produce 4-8 MII oocytes, a decline in oocyte count was observed as gonadotropin dosage increased. Patients with low (1-3) and high (9-12) MII predictions achieved the best results when administered a daily dose of 225 IU; lower and higher doses proved to be less effective. CONCLUSIONS: Our study suggests that high gonadotropin doses do not enhance MII oocyte retrieval. Our ML model can offer clinicians a novel tool for the precise prediction of MII to guide gonadotropin dosing.

Efficacy of a Combination Therapy with Laronidase and Genistein in Treating Mucopolysaccharidosis Type I in a Mouse Model.

Mucopolysaccharidosis type I (MPS I) is a lysosomal storage disorder caused by α-L-iduronidase deficiency. The standard treatment, enzyme replacement therapy with laronidase, has limited effectiveness in treating neurological symptoms due to poor blood-brain barrier penetration. An alternative is substrate reduction therapy using molecules, such as genistein, which crosses this barrier. This study evaluated the effectiveness of a combination of laronidase and genistein in a mouse model of MPS I. Over 12 weeks, MPS I and wild-type mice received laronidase, genistein, or both. Glycosaminoglycan (GAG) storage in visceral organs and the brain, its excretion in urine, and the serum level of the heparin cofactor II-thrombin (HCII-T) complex, along with behavior, were assessed. The combination therapy resulted in reduced GAG storage in the heart and liver, whereas genistein alone reduced the brain GAG storage. Laronidase and combination therapy decreased liver and spleen weights and significantly reduced GAG excretion in the urine. However, this therapy negated some laronidase benefits in the HCII-T levels. Importantly, the combination therapy improved the behavior of female mice with MPS I. These findings offer valuable insights for future research to optimize MPS I treatments.

Assessing the diagnostic accuracy of artificial intelligence in post-endovascular aneurysm repair endoleak detection using dual-energy computed tomography angiography.

Purpose: The aim of this study was to evaluate the diagnostic accuracy of an artificial intelligence (AI) tool in detecting endoleaks in patients undergoing endovascular aneurysm repair (EVAR) using dual-energy computed tomography angiography (CTA). Material and methods: The study involved 95 patients who underwent EVAR and subsequent CTA follow-up. Dualenergy scans were performed, and images were reconstructed as linearly blended (LB) and 40 keV virtual monoenergetic (VMI) images. The AI tool PRAEVAorta®2 was used to assess arterial phase images for endoleaks. Two experienced readers independently evaluated the same images, and their consensus served as the reference standard. Key metrics, including accuracy, precision, recall, F1 score, and area under the receiver operating characteristic (ROC) curve (AUC), were calculated. Results: The final analysis included 94 patients. The AI tool demonstrated an accuracy of 78.7%, precision of 67.6%, recall of 10 71.9%, F1 score of 69.7%, and an AUC of 0.77 using LB images. However, the tool failed to process 40 keV VMI images correctly, limiting further analysis of these datasets. Conclusions: The AI tool showed moderate diagnostic accuracy in detecting endoleaks using LB images but failed to achieve the reliability needed for clinical use due to the significant number of misdiagnoses.

Evaluating the risk of endometriosis based on patients' self-assessment questionnaires.

BACKGROUND: Endometriosis is a condition that significantly affects the quality of life of about 10 % of reproductive-aged women. It is characterized by the presence of tissue similar to the uterine lining (endometrium) outside the uterus, which can lead lead scarring, adhesions, pain, and fertility issues. While numerous factors associated with endometriosis are documented, a wide range of symptoms may still be undiscovered. METHODS: In this study, we employed machine learning algorithms to predict endometriosis based on the patient symptoms extracted from 13,933 questionnaires. We compared the results of feature selection obtained from various algorithms (i.e., Boruta algorithm, Recursive Feature Selection) with experts' decisions. As a benchmark model architecture, we utilized a LightGBM algorithm, along with Multivariate Imputation by Chained Equations (MICE) and k-nearest neighbors (KNN), for missing data imputation. Our primary objective was to assess the model's performance and feature importance compared to existing studies. RESULTS: We identified the top 20 predictors of endometriosis, uncovering previously overlooked features such as Cesarean section, ovarian cysts, and hernia. Notably, the model's performance metrics were maximized when utilizing a combination of multiple feature selection methods. Specifically, the final model achieved an area under the receiver operator characteristic curve (AUC) of 0.85 on the training dataset and an AUC of 0.82 on the testing dataset. CONCLUSIONS: The application of machine learning in diagnosing endometriosis has the potential to significantly impact clinical practice, streamlining the diagnostic process and enhancing efficiency. Our questionnaire-based prediction approach empowers individuals with endometriosis to proactively identify potential symptoms, facilitating informed discussions with healthcare professionals about diagnosis and treatment options.

A Machine-Learning-Based Approach to Prediction of Biogeographic Ancestry within Europe.

Data obtained with the use of massive parallel sequencing (MPS) can be valuable in population genetics studies. In particular, such data harbor the potential for distinguishing samples from different populations, especially from those coming from adjacent populations of common origin. Machine learning (ML) techniques seem to be especially well suited for analyzing large datasets obtained using MPS. The Slavic populations constitute about a third of the population of Europe and inhabit a large area of the continent, while being relatively closely related in population genetics terms. In this proof-of-concept study, various ML techniques were used to classify DNA samples from Slavic and non-Slavic individuals. The primary objective of this study was to empirically evaluate the feasibility of discerning the genetic provenance of individuals of Slavic descent who exhibit genetic similarity, with the overarching goal of categorizing DNA specimens derived from diverse Slavic population representatives. Raw sequencing data were pre-processed, to obtain a 1200 character-long binary vector. A total of three classifiers were used-Random Forest, Support Vector Machine (SVM), and XGBoost. The most-promising results were obtained using SVM with a linear kernel, with 99.9% accuracy and F1-scores of 0.9846-1.000 for all classes.

Influence of augmentation on the performance of the double ResNet-based model for chest X-ray classification.

Purpose: A pandemic disease elicited by the SARS-CoV-2 virus has become a serious health issue due to infecting millions of people all over the world. Recent publications prove that artificial intelligence (AI) can be used for medical diagnosis purposes, including interpretation of X-ray images. X-ray scanning is relatively cheap, and scan processing is not computationally demanding. Material and methods: In our experiment a baseline transfer learning schema of processing of lung X-ray images, including augmentation, in order to detect COVID-19 symptoms was implemented. Seven different scenarios of augmentation were proposed. The model was trained on a dataset consisting of more than 30,000 X-ray images. Results: The obtained model was evaluated using real images from a Polish hospital, with the use of standard metrics, and it achieved accuracy = 0.9839, precision = 0.9697, recall = 1.0000, and F1-score = 0.9846. Conclusions: Our experiment proved that augmentations and masking could be important steps of data pre-processing and could contribute to improvement of the evaluation metrics. Because medical professionals often tend to lack confidence in AI-based tools, we have designed the proposed model so that its results would be explainable and could play a supporting role for radiology specialists in their work.

Murine Fibroblasts and Primary Hepatocytes as Tools When Studying the Efficacy of Potential Therapies for Mucopolysaccharidosis Type I.

Mucopolysaccharidosis type I (MPS I) is a metabolic genetic disease caused by the deficiency of a lysosomal enzyme involved in glycosaminoglycans (GAGs) degradation. MPS I cells have a constant level of GAG synthesis, but disturbed degradation means that GAGs accumulate progressively, impairing cell metabolism. GAG metabolism can be modulated by flavonoids, and these are being studied as therapeutics for MPS. We have optimised the protocol for obtaining fibroblasts and hepatocytes from the MPS I murine model and characterised the cells for their suitability as an in vitro model for testing compounds with therapeutic potential. Methods: Murine primary hepatocytes and fibroblasts were used as a cellular model to study the effect of genistein, biochanin A, and kaempferol on the modulation of the GAG synthesis process. Flavonoids were used individually as well as in two-component mixtures. There were no statistically significant differences in GAG synthesis levels from cell types obtained from either wild-type or MPS I mice. We also showed that MPS I fibroblasts and hepatocytes store GAGs, which makes them useful in vitro models for testing the effectiveness of substrate reduction therapies. Furthermore, tested flavonoids had a different impact on GAG synthesis depending on cell type and whether they were used alone or in a mixture. The tested flavonoids reduce GAG synthesis more effectively in fibroblasts than in hepatocytes, regardless of whether they are used individually or in a mixture. Flavonoids modulate the level of GAG synthesis differently depending on cell types, therefore in vitro experiments performed to assess the effectiveness of potential therapies for metabolic diseases should be carried out using more than one cell model, and only such an approach will allow for full answering scientific questions.

Cellular and Gene Expression Response to the Combination of Genistein and Kaempferol in the Treatment of Mucopolysaccharidosis Type I.

Flavonoids are investigated as therapeutics for mucopolysaccharidosis, a metabolic disorder with impaired glycosaminoglycan degradation. Here we determined the effects of genistein and kaempferol, used alone or in combination, on cellular response and gene expression in a mucopolysaccharidosis type I model. We assessed the cell cycle, viability, proliferation, subcellular localization of the translocation factor EB (TFEB), number and distribution of lysosomes, and glycosaminoglycan synthesis after exposure to flavonoids. Global gene expression was analysed using DNA microarray and quantitative PCR. The type and degree of flavonoid interaction were determined based on the combination and dose reduction indexes. The combination of both flavonoids synergistically inhibits glycosaminoglycan synthesis, modulates TFEB localization, lysosomal number, and distribution. Genistein and kaempferol in a 1:1 ratio regulate the expression of 52% of glycosaminoglycan metabolism genes. Flavonoids show synergy, additivity, or slight antagonism in all analysed parameters, and the type of interaction depends on the concentration and component ratios. With the simultaneous use of genistein and kaempferol in a ratio of 4:1, even a 10-fold reduction in the concentration of kaempferol is possible. Flavonoid mixtures, used as the treatment of mucopolysaccharidosis, are effective in reducing glycosaminoglycan production and storage and show a slight cytotoxic effect compared to single-flavonoid usage.

Three Microbial Musketeers of the Seas: Shewanella baltica, Aliivibrio fischeri and Vibrio harveyi, and Their Adaptation to Different Salinity Probed by a Proteomic Approach.

Osmotic changes are common challenges for marine microorganisms. Bacteria have developed numerous ways of dealing with this stress, including reprogramming of global cellular processes. However, specific molecular adaptation mechanisms to osmotic stress have mainly been investigated in terrestrial model bacteria. In this work, we aimed to elucidate the basis of adjustment to prolonged salinity challenges at the proteome level in marine bacteria. The objects of our studies were three representatives of bacteria inhabiting various marine environments, Shewanella baltica, Vibrio harveyi and Aliivibrio fischeri. The proteomic studies were performed with bacteria cultivated in increased and decreased salinity, followed by proteolytic digestion of samples which were then subjected to liquid chromatography with tandem mass spectrometry analysis. We show that bacteria adjust at all levels of their biological processes, from DNA topology through gene expression regulation and proteasome assembly, to transport and cellular metabolism. The finding that many similar adaptation strategies were observed for both low- and high-salinity conditions is particularly striking. The results show that adaptation to salinity challenge involves the accumulation of DNA-binding proteins and increased polyamine uptake. We hypothesize that their function is to coat and protect the nucleoid to counteract adverse changes in DNA topology due to ionic shifts.

Virus-Host Interaction Gets Curiouser and Curiouser. PART II: Functional Transcriptomics of the E. coli DksA-Deficient Cell upon Phage P1vir Infection.

The virus-host interaction requires a complex interplay between the phage strategy of reprogramming the host machinery to produce and release progeny virions, and the host defense against infection. Using RNA sequencing, we investigated the phage-host interaction to resolve the phenomenon of improved lytic development of P1vir phage in a DksA-deficient E. coli host. Expression of the ant1 and kilA P1vir genes in the wild-type host was the highest among all and most probably leads to phage virulence. Interestingly, in a DksA-deficient host, P1vir genes encoding lysozyme and holin are downregulated, while antiholins are upregulated. Gene expression of RepA, a protein necessary for replication initiating at the phage oriR region, is increased in the dksA mutant; this is also true for phage genes responsible for viral morphogenesis and architecture. Still, it seems that P1vir is taking control of the bacterial protein, sugar, and lipid metabolism in both, the wild type and dksA- hosts. Generally, bacterial hosts are reacting by activating their SOS response or upregulating the heat shock proteins. However, only DksA-deficient cells upregulate their sulfur metabolism and downregulate proteolysis upon P1vir infection. We conclude that P1vir development is enhanced in the dksA mutant due to several improvements, including replication and virion assembly, as well as a less efficient lysis.

Virus-Host Interaction Gets Curiouser and Curiouser. PART I: Phage P1vir Enhanced Development in an E. coli DksA-Deficient Cell.

Bacteriophage P1 is among the best described bacterial viruses used in molecular biology. Here, we report that deficiency in the host cell DksA protein, an E. coli global transcription regulator, improves P1 lytic development. Using genetic and microbiological approaches, we investigated several aspects of P1vir biology in an attempt to understand the basis of this phenomenon. We found several minor improvements in phage development in the dksA mutant host, including more efficient adsorption to bacterial cell and phage DNA replication. In addition, gene expression of the main repressor of lysogeny C1, the late promoter activator Lpa, and lysozyme are downregulated in the dksA mutant. We also found nucleotide substitutions located in the phage immunity region immI, which may be responsible for permanent virulence of phage P1vir. We suggest that downregulation of C1 may lead to a less effective repression of lysogeny maintaining genes and that P1vir may be balancing between lysis and lysogeny, although finally it is able to enter the lytic pathway only. The mentioned improvements, such as more efficient replication and more "gentle" cell lysis, while considered minor individually, together may account for the phenomenon of a more efficient P1 phage development in a DksA-deficient host.

Dosage Compensation in Females with X-Linked Metabolic Disorders.

Through the use of new genomic and metabolomic technologies, our comprehension of the molecular and biochemical etiologies of genetic disorders is rapidly expanding, and so are insights into their varying phenotypes. Dosage compensation (lyonization) is an epigenetic mechanism that balances the expression of genes on heteromorphic sex chromosomes. Many studies in the literature have suggested a profound influence of this phenomenon on the manifestation of X-linked disorders in females. In this review, we summarize the clinical and genetic findings in female heterozygotic carriers of a pathogenic variant in one of ten selected X-linked genes whose defects result in metabolic disorders.

Global Changes of 5-mC/5h-mC Ratio and Methylation of Adiponectin and Leptin Gene in Placenta Depending on Mode of Delivery.

It was suggested that the epigenetic alterations of the placenta are associated with obesity, as well as the delivery mode. This study aimed to assess the effect of maternal outcome and delivery procedure on global placental DNA methylation status, as well as selected 5'-Cytosine-phosphate-Guanine-3' (CpG) sites in ADIPOQ and LEP genes. Global DNA methylation profile in the placenta was assessed using the 5-methylcytosine (5-mC) and 5-hydroxymethylcytosine (5-hmC) ratio evaluated with the ELISA, followed by target gene methylation patterns at selected gene regions which were determined using methylation-specific qPCR in 70 placentas from healthy, pregnant women with single pregnancy. We found no statistically significant differences in 5-mC/5-hmC ratio between intrapartum cesarean sections (CS) and vaginal deliveries (p = 0.214), as well as between elective cesarean sections and vaginal deliveries (p = 0.221). In intrapartum cesarean sections, the ADIPOQ demethylation index was significantly higher (the average: 1.75) compared to elective cesarean section (the average: 1.23, p = 0.010) and vaginal deliveries (the average: 1.23, p = 0.011). The LEP demethylation index did not significantly differ among elective CS, intrapartum CS, and vaginal delivery groups. The demethylation index of ADIPOQ correlated negatively with LEP in the placenta in the vaginal delivery group (r = -0.456, p = 0.017), but not with the global methylation. The methylation of a singular locus might be different depending on the mode of delivery and uterine contractions. Further studies should be conducted with locus-specific analysis of the whole genome to detect the methylation index of specific genes involved in metabolism.

Lipophagy and Lipolysis Status in Lipid Storage and Lipid Metabolism Diseases.

This review discusses how lipophagy and cytosolic lipolysis degrade cellular lipids, as well as how these pathway ys communicate, how they affect lipid metabolism and energy homeostasis in cells and how their dysfunction affects the pathogenesis of lipid storage and lipid metabolism diseases. Answers to these questions will likely uncover novel strategies for the treatment of aforementioned human diseases, but, above all, will avoid destructive effects of high concentrations of lipids-referred to as lipotoxicity-resulting in cellular dysfunction and cell death.

Lipids and Lipid Mediators Associated with the Risk and Pathology of Ischemic Stroke.

Stroke is a severe neurological disorder in humans that results from an interruption of the blood supply to the brain. Worldwide, stoke affects over 100 million people each year and is the second largest contributor to disability. Dyslipidemia is a modifiable risk factor for stroke that is associated with an increased risk of the disease. Traditional and non-traditional lipid measures are proposed as biomarkers for the better detection of subclinical disease. In the central nervous system, lipids and lipid mediators are essential to sustain the normal brain tissue structure and function. Pathways leading to post-stroke brain deterioration include the metabolism of polyunsaturated fatty acids. A variety of lipid mediators are generated from fatty acids and these molecules may have either neuroprotective or neurodegenerative effects on the post-stroke brain tissue; therefore, they largely contribute to the outcome and recovery from stroke. In this review, we provide an overview of serum lipids associated with the risk of ischemic stroke. We also discuss the role of lipid mediators, with particular emphasis on eicosanoids, in the pathology of ischemic stroke. Finally, we summarize the latest research on potential targets in lipid metabolic pathways for ischemic stroke treatment and on the development of new stroke risk biomarkers for use in clinical practice.