System-level analysis of genes mutated in muscular dystrophies reveals a functional pattern associated with muscle weakness distribution.

Muscular dystrophies (MDs) are inherited genetic diseases causing weakness and degeneration of muscles. The distribution of muscle weakness differs between MDs, involving distal muscles or proximal muscles. While the mutations in most of the MD-associated genes lead to either distal or proximal onset, there are also genes whose mutations can cause both types of onsets. We hypothesized that the genes associated with different MD onsets code proteins with distinct cellular functions. To investigate this, we collected the MD-associated genes and assigned them to three onset groups: genes mutated only in distal onset dystrophies, genes mutated only in proximal onset dystrophies, and genes mutated in both types of onsets. We then systematically evaluated the cellular functions of these gene sets with computational strategies based on functional enrichment analysis and biological network analysis. Our analyses demonstrate that genes mutated in either distal or proximal onset MDs code proteins linked with two distinct sets of cellular processes. Interestingly, these two sets of cellular processes are relevant for the genes that are associated with both onsets. Moreover, the genes associated with both onsets display high centrality and connectivity in the network of muscular dystrophy genes. Our findings support the hypothesis that the proteins associated with distal or proximal onsets have distinct functional characteristics, whereas the proteins associated with both onsets are multifunctional.

PANACEA: network-based methods for pharmacotherapy prioritization in personalized oncology.

MOTIVATION: Identifying appropriate pharmacotherapy options from genomics results is a significant challenge in personalized oncology. However, computational methods for prioritizing drugs are underdeveloped. With the hypothesis that network-based approaches can improve the performance by extending the use of potential drug targets beyond direct interactions, we devised two network-based methods for personalized pharmacotherapy prioritization in cancer. RESULTS: We developed novel personalized drug prioritization approaches, PANACEA: PersonAlized Network-based Anti-Cancer therapy EvaluAtion. In PANACEA, initially, the protein interaction network is extended with drugs, and a driverness score is assigned to each altered gene. For scoring drugs, either (i) the 'distance-based' method, incorporating the shortest distance between drugs and altered genes, and driverness scores, or (ii) the 'propagation' method involving the propagation of driverness scores via a random walk with restart framework is performed. We evaluated PANACEA using multiple datasets, and demonstrated that (i) the top-ranking drugs are relevant for cancer pharmacotherapy using TCGA data; (ii) drugs that cancer cell lines are sensitive to are identified using GDSC data; and (iii) PANACEA can perform adequately in the clinical setting using cases with known drug responses. We also illustrate that the proposed methods outperform iCAGES and PanDrugs, two previous personalized drug prioritization approaches. AVAILABILITY AND IMPLEMENTATION: The corresponding R package is available on GitHub. (https://github.com/egeulgen/PANACEA.git). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

orsum: a Python package for filtering and comparing enrichment analyses using a simple principle.

BACKGROUND: Enrichment analyses are widely applied to investigate lists of genes of interest. However, such analyses often result in long lists of annotation terms with high redundancy, making the interpretation and reporting difficult. Long annotation lists and redundancy also complicate the comparison of results obtained from different enrichment analyses. An approach to overcome these issues is using down-sized annotation collections composed of non-redundant terms. However, down-sized collections are generic and the level of detail may not fit the user's study. Other available approaches include clustering and filtering tools, which are based on similarity measures and thresholds that can be complicated to comprehend and set. RESULT: We propose orsum, a Python package to filter enrichment results. orsum can filter multiple enrichment results collectively and highlight common and specific annotation terms. Filtering in orsum is based on a simple principle: a term is discarded if there is a more significant term that annotates at least the same genes; the remaining more significant term becomes the representative term for the discarded term. This principle ensures that the main biological information is preserved in the filtered results while reducing redundancy. In addition, as the representative terms are selected from the original enrichment results, orsum outputs filtered terms tailored to the study. As a use case, we applied orsum to the enrichment analyses of four lists of genes, each associated with a neurodegenerative disease. CONCLUSION: orsum provides a comprehensible and effective way of filtering and comparing enrichment results. It is available at https://anaconda.org/bioconda/orsum .

Overlap of vitamin A and vitamin D target genes with CAKUT-related processes.

Congenital Anomalies of the Kidney and Urinary Tract (CAKUT) are a group of abnormalities affecting the kidneys and their outflow tracts, which include the ureters, the bladder, and the urethra. CAKUT patients display a large clinical variability as well as a complex aetiology, as only 5% to 20% of the cases have a monogenic origin. It is thereby suspected that interactions of both genetic and environmental factors contribute to the disease. Vitamins are among the environmental factors that are considered for CAKUT aetiology. In this study, we collected vitamin A and vitamin D target genes and computed their overlap with CAKUT-related gene sets. We observed significant overlaps between vitamin A target genes and CAKUT causal genes, or with genes involved in renal system development, which indicates that an excess or deficiency of vitamin A might be relevant to a broad range of urogenital abnormalities.

Comparison of MRI, PSMA PET/CT, and Fusion PSMA PET/MRI for Detection of Clinically Significant Prostate Cancer.

PURPOSE: The aim of our study is to compare the efficacy of positron emission tomography (PET) and magnetic resonance imaging (MRI) for detecting intraprostatic lesions in patients with clinically significant prostate cancer who underwent radical prostatectomy; additionally, investigate the benefits of rostate-specific membrane antigen (PSMA) PET-MR software fusion images to the diagnosis. METHODS: Thirty patients, who underwent radical prostatectomy between June 2015 and April 2018, were included in the study. Subjects with gallium PSMA PET-CT and multiparametric prostate MRI performed according to Prostate Imaging Reporting and Data System v2 criteria in our clinic were included in the study. 68Ga-PSMA PET-CT images were fused with MR sequences for analysis. RESULTS: The mean age of cases was 63.2 years (ranged from 45 to 79 years). Index lesions of 29 cases were detected by MRI and 22 of them by PET CT. Both modalities were found to be less sensitive for detection of bilaterality and multifocality (42.85% and 20% for MRI, 28.57% and 20% for PET CT, respectively). There was no statistically significant difference between modalities. It was observed that if a clinically significant tumor focus was not detected by MRI, it was small (6 mm or less) in diameter or had a low Gleason score. CONCLUSIONS: Software fusion PSMA PET-MRI increased the sensitivity of the index lesion identification compared with PSMA PET-CT and also increased the sensitivity of real lesion size identification compared with multiparametric prostate MRI.

Impact of Refractive Errors on Da Vinci SI Robotic System.

OBJECTIVE: To investigate the impact of refractive errors on binocular visual acuity while using the Da Vinci SI robotic system console. METHODS: Eighty volunteers were examined on the Da Vinci SI robotic system console by using a near vision chart. Refractive errors, anisometropia status, and Fly Stereo Acuity Test scores were recorded. Spherical equivalent (SE) were calculated for all volunteers' right and left eyes. Visual acuity was assessed by the logarithm of the minimal angle of resolution (LogMAR) method. Binocular uncorrected and best corrected (with proper contact lens or glasses) LogMAR values of the subjects were recorded. The difference between these values (DiffLogMAR) are affected by different refractive errors. RESULTS: In the myopia and/or astigmatism group, uncorrected SE was found to have significant impact on the DiffLogMAR (p < 0.001) and myopia greater than 1.75 diopter had significantly higher DiffLogMAR values (p < 0.05). Subjects with presbyopia had significantly higher DiffLogMAR values (p < 0.01), and we observed positive correlation between presbyopia and DiffLogMAR values (p = 0.33, p < 0.01). The cut off value of presbyopia that correlated the most with DiffLogMAR differences was found to be 1.25 diopter (p < 0.001). In 13 hypermetropic volunteers, we found significant correlation between hypermetropia value and DiffLogMAR (p > 0.7, p < 0.01). The statistical analysis between Fly test and SE revealed a significant impact of presbyopia and hypermetropia to the stereotactic view of the subject (p = -0.734, p < 0.05). CONCLUSION: Surgeons suffering from myopia greater than 1.75 diopter, presbyopia greater than 1.25 diopter (D), and hypermetropia regardless of grade must always perform robotic surgeries with the proper correction.

Progesterone at high doses reduces the growth of U87 and A172 glioblastoma cells: Proteomic changes regarding metabolism and immunity.

While pregnancy may accelerate glioblastoma multiforme (GBM) growth, parity and progesterone (P4) containing treatments (ie, hormone replacement therapy) reduce the risk of GBM development. In parallel, low and high doses of P4 exert stimulating and inhibitory actions on GBM growth, respectively. The mechanisms behind the high-dose P4-suppression of GBM growth is unknown. In the present study, we assessed the changes in growth and proteomic profiles when high-dose P4 (100 and 300 µM) was administered in human U87 and A172 GBM cell lines. The xCELLigence system was used to examine cell growth when different concentrations of P4 (20, 50, 100, and 300 µM) was administered. The protein profiles were determined by two-dimensional gel electrophoresis in both cell lines when 100 and 300 µM P4 were administered. Finally, the pathways enriched by the differentially expressed proteins were assessed using bioinformatic tools. Increasing doses of P4 blocked the growth of both GBM cells. We identified 26 and 51 differentially expressed proteins (fc > 2) in A172 and U87 cell lines treated with P4, respectively. Only the pro-tumorigenic mitochondrial ornithine aminotransferase and anti-apoptotic mitochondrial 60 kDa heat shock protein were downregulated in A172 cell line and U87 cell line when treated with P4, respectively. Detoxification of reactive oxygen species, cellular response to stress, glucose metabolism, and immunity-related proteins were altered in P4-treated GBM cell lines. The paradox on the effect of low and high doses of P4 on GBM growth is gaining attention. The mechanism related to the high dose of P4 on GBM growth can be explained by the alterations in detoxification mechanisms, stress, and immune response and glucose metabolism. P4 suppresses GBM growth and as it is nontoxic in comparison to classical chemotherapeutics, it can be used as a new strategy in GBM treatment in the future.

pathfindR: An R Package for Comprehensive Identification of Enriched Pathways in Omics Data Through Active Subnetworks.

Pathway analysis is often the first choice for studying the mechanisms underlying a phenotype. However, conventional methods for pathway analysis do not take into account complex protein-protein interaction information, resulting in incomplete conclusions. Previously, numerous approaches that utilize protein-protein interaction information to enhance pathway analysis yielded superior results compared to conventional methods. Hereby, we present pathfindR, another approach exploiting protein-protein interaction information and the first R package for active-subnetwork-oriented pathway enrichment analyses for class comparison omics experiments. Using the list of genes obtained from an omics experiment comparing two groups of samples, pathfindR identifies active subnetworks in a protein-protein interaction network. It then performs pathway enrichment analyses on these identified subnetworks. To further reduce the complexity, it provides functionality for clustering the resulting pathways. Moreover, through a scoring function, the overall activity of each pathway in each sample can be estimated. We illustrate the capabilities of our pathway analysis method on three gene expression datasets and compare our results with those obtained from three popular pathway analysis tools. The results demonstrate that literature-supported disease-related pathways ranked higher in our approach compared to the others. Moreover, pathfindR identified additional pathways relevant to the conditions that were not identified by other tools, including pathways named after the conditions.

Supervised Machine Learning Algorithms for Evaluation of Solid Lipid Nanoparticles and Particle Size.

AIMS AND OBJECTIVES: Solid Lipid Nanoparticles (SLNs) are pharmaceutical delivery systems that have advantages such as controlled drug release, long-term stability etc. Particle Size (PS) is one of the important criteria of SLNs. These factors affect drug release rate, bio-distribution etc. In this study, the formulation of SLNs using high-speed homogenization technique has been evaluated. The main emphasis of the work is to study whether the effect of mixing time and formulation ingredients on PS can be modeled. For this purpose, different machine learning algorithms have been applied and evaluated using the mean absolute error metric. MATERIALS AND METHODS: SLNs were prepared by high-speed homogenizaton. PS, size distribution and zeta potential measurements were performed on freshly prepared samples. In order to model the formulation of the particles in terms of mixing time and formulation ingredients and evaluate the predictability of PS depending on these parameters, different machine learning algorithms were applied on the prepared dataset and the performances of the algorithms were also evaluated. RESULTS: PS of SLNs obtained was in the range of 263-498nm. The results present that PS of SLNs can be best estimated by decision tree based methods, among which Random Forest has the least mean absolute error value with 0.028. As a result, the estimation of machine learning algorithms demonstrates that particle size can be estimated by both decision rule-based machine learning methods and function fitting machine learning methods. CONCLUSION: Our findings present that machine learning methods can be highly useful for determining formulation parameters for further research.