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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.

MotivationClassification of a mutation is important for variant prioritization and diagnostics. However, it is still a challenging task that many mutations are classified as variant of unknown significance. Therefore, in silico tools are required for classifying variants with unknown significance. Over the past decades, several computational methods have been developed but they usually have limited accuracy and high false-positive rates. To address these needs, we developed a new machine learning-based method for calculating the impact of a mutation by converting protein structures to networks and using network properties of the mutated site.ResultsHere, we propose a novel machine learning-based method, predatoR, for mutation impact prediction. The model was trained using both VariBench and ClinVar datasets and benchmarked against currently available methods using the Missense3D datasets. predatoR outperformed 32 different mutation impact prediction methods with an AUROC value of 0.941.AvailabilitypredatoR tool is available as an open-source R package at GitHub (https://github.com/berkgurdamar/predatoR).Contactberkgurdamar@gmail.com

Formation of atherosclerotic plaques, called atherogenesis, is a complex process affected by genetic and environmental factors. It was proposed that endoplasmic reticulum (ER) stress is an important factor in the pathogenesis of atherosclerosis and that vitamin E affects atherosclerotic plaque formation via its antioxidant properties. Here, we investigated ER stress-related molecular mechanisms in high-cholesterol diet (HCD, 2%)-induced atherosclerosis model and the role of vitamin E supplementation in it, beyond its antioxidant properties. The consequences of HCD and vitamin E supplementation were examined by determining protein levels of ER stress markers in aortic tissues. As vitamin E supplementation acts on several unfolded protein response (UPR) factors, it decreased ER stress induced by HCD. To elucidate the associated pathways, gene expression profiling was performed, revealing differentially expressed genes enriched in ER stress-related pathways such as the proteasome and the apoptosis pathways. We further assessed the proteasomal activity impaired by HCD in the aorta and showed that vitamin E reversed it to that of control animals. Overall, the study characterized the effects of HCD and vitamin E on ER stress-related gene expression, revealing the role of proteolytic systems during atherogenesis.

Recessive mutations in the genes encoding the four subunits of the tRNA splicing endonuclease complex (TSEN54, TSEN34, TSEN15, and TSEN2) cause various forms of pontocerebellar hypoplasia, a disorder characterized by hypoplasia of the cerebellum and the pons, microcephaly, dysmorphisms, and other variable clinical features. Here, we report an intronic recessive founder variant in the gene TSEN2 that results in abnormal splicing of the mRNA of this gene, in six individuals from four consanguineous families affected with microcephaly, multiple craniofacial malformations, radiological abnormalities of the central nervous system, and cognitive retardation of variable severity. Remarkably, unlike patients with previously described mutations in the components of the TSEN complex, all the individuals that we report developed atypical hemolytic uremic syndrome (aHUS) with thrombotic microangiopathy, microangiopathic hemolytic anemia, thrombocytopenia, proteinuria, severe hypertension, and end-stage kidney disease (ESKD) early in life. Bulk RNA sequencing of peripheral blood cells of four affected individuals revealed abnormal tRNA transcripts, indicating an alteration of the tRNA biogenesis. Morpholino-mediated skipping of exon 10 of tsen2 in zebrafish produced phenotypes similar to human patients. Thus, we have identified a novel syndrome accompanied by aHUS suggesting the existence of a link between tRNA biology and vascular endothelium homeostasis, which we propose to name with the acronym TRACK syndrome (TSEN2 Related Atypical hemolytic uremic syndrome, Craniofacial malformations, Kidney failure).

Recent studies have focused on the early detection of ovarian cancer (OC) using tumor materials by liquid biopsy. The mechanisms of microRNAs (miRNAs) to impact OC and signaling pathways are still unknown. This study aims to reliably perform functional analysis of previously validated circulating miRNAs' target genes by using pathfindR. Also, overall survival and pathological stage analyses were evaluated with miRNAs' target genes which are common in the The Cancer Genome Atlas and GTEx datasets. Our previous studies have validated three downregulated miRNAs (hsa-miR-885-5p, hsa-miR-1909-5p, and hsa-let7d-3p) having a diagnostic value in OC patients' sera, with high-throughput techniques. The predicted target genes of these miRNAs were retrieved from the miRDB database (v6.0). Active-subnetwork-oriented Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was conducted by pathfindR using the target genes. Enrichment of KEGG pathways assessed by the analysis of pathfindR indicated that 24 pathways were related to the target genes. Ubiquitin-mediated proteolysis, spliceosome and Notch signaling pathway were the top three pathways with the lowest p-values (p < 0.001). Ninety-three common genes were found to be differentially expressed (p < 0.05) in the datasets. No significant genes were found to be significant in the analysis of overall survival analyses, but 24 genes were found to be significant with pathological stages analysis (p < 0.05). The findings of our study provide in-silico evidence that validated circulating miRNAs' target genes and enriched pathways are related to OC and have potential roles in theranostics applications. Further experimental investigations are required to validate our results which will ultimately provide a new perspective for translational applications in OC management.

Background Multiple sclerosis (MS) has a complex pathophysiology, variable clinical presentation, and unpredictable prognosis; understanding the underlying mechanisms requires combinatorial approaches that warrant the integration of diverse molecular omics data. Methods Here, we combined genomic and proteomic data of the same individuals among a Turkish MS patient group to search for biologically important networks. We previously identified differentially-expressed proteins by cerebrospinal fluid proteome analysis of 179 MS patients and 42 non-MS controls. Among this study group, 11 unrelated MS patients and 60 independent, healthy controls were subjected to whole-genome SNP genotyping, and genome-wide associations were assessed. Pathway enrichment analyses of MS-associated SNPs and differentially-expressed proteins were conducted using the functional enrichment tool, PANOGA. Results Nine shared pathways were detected between the genomic and proteomic datasets after merging and clustering the enriched pathways. Complement and coagulation cascade was the most significantly associated pathway (hsa04610, P = 6.96 × 10−30). Other pathways involved in neurological or immunological mechanisms included adherens junctions (hsa04520, P = 6.64 × 10−25), pathogenic Escherichia coli infection (hsa05130, P = 9.03 × 10−14), prion diseases (hsa05020, P = 5.13 × 10−13). Conclusion We conclude that integrating multiple datasets of the same patients helps reducing false negative and positive results of genome-wide SNP associations and highlights the most prominent cellular players among the complex pathophysiological mechanisms.

Phenylketonuria is an autosomal recessive disorder caused by mutations in the phenylalanine hydroxylase gene. In phenylketonuria causes various symptoms including severe mental retardation. PAH gene of a classical Phenylketonuria patient was sequenced, and two novel heterozygous mutations, p.Y198N and p.Y204F, were found. This study aimed to reveal the impacts of these variants on the structural stability of the PAH enzyme. In-silico analyses using prediction tools and molecular dynamics simulations were performed. Mutations were introduced to the wild type catalytic monomer and full length tetramer crystal structures. Variant pathogenicity analyses predicted p.Y198N to be damaging, and p.Y204F to be benign by some prediction tools and damaging by others. Simulations suggested p.Y198N mutation cause significant fluctuations in the spatial organization of two catalytic residues in the temperature accelerated MD simulations with the monomer and increased root-mean-square deviations in the tetramer structure. p.Y204F causes noticeable changes in the spatial positioning of T278 suggesting a possible segregation from the catalytic site in temperature accelerated MD simulations with the monomer. This mutation also leads to increased root-mean-square fluctuations in the regulatory domain which may lead to conformational change resulting in inhibition of dimerization and enzyme activation. Our study reports two novel mutations in the PAH gene and gives insight to their effects on the PAH activity. MD simulations did not yield conclusive results that explains the phenotype but gave plausible insight to possible effects which should be investigated further with in-silico and in-vitro studies to assess the roles of these mutations in etiology of PKU. Communicated by Ramaswamy H. Sarma

Modulating the activity of human soluble guanylate cyclase (hsGC) through allosteric regulation of the βH-NOX domain has been considered as an immediate treatment for cardiovascular disorder (CVDs). Currently available βH-NOX domain-specific agonists including cinaciguat are unable to deal with the conundrum raised due to oxidative stress in the case of CVDs and their associated comorbidities. Therefore, the idea of investigating novel compounds for allosteric regulation of hsGC activation has been rekindled to circumvent CVDs. Current study aims to identify novel βH-NOX domain-specific compounds that can selectively turn on sGC functions by modulating the conformational dynamics of the target protein. Through a comprehensive computational drug-discovery approach, we first executed a target-based performance assessment of multiple docking (PLANTS, QVina, LeDock, Vinardo, Smina) scoring functions based on multiple performance metrices. QVina showed the highest capability of selecting true-positive ligands over false positives thus, used to screen 4.8 million ZINC15 compounds against βH-NOX domain. The docked ligands were further probed in terms of contact footprint and pose reassessment through clustering analysis and PLANTS docking, respectively. Subsequently, energy-based AMBER rescoring of top 100 low-energy complexes, per-residue energy decomposition analysis, and ADME-Tox analysis yielded the top three compounds i.e. ZINC000098973660, ZINC001354120371, and ZINC000096022607. The impact of three selected ligands on the internal structural dynamics of the βH-NOX domain was also investigated through molecular dynamics simulations. The study revealed potential electrostatic interactions for better conformational dialogue between βH-NOX domain and allosteric ligands that are critical for the activation of hsGC as compared to the reference compound. HighlightsScoring capability of QVina, Vinardo, Smina, PLANTS, and LeDock were assessed against the hsGC βH-NOX specific active and decoy dataset.Target biasness was achieved whilst screening by retaining two out of three substructures in screened compound that are critical for heme-independent hsGC βH-NOX activation.Target-specific biasness was further ensured by preserving the interactions of functionally important residues in the top three hits.MM-GB/PBSA yields energetically favored docked complexes.Principal Component Analysis (PCA) exhibited noticeable fluctuations in ligand-binding regions of the active-site that are responsible for heme and NO- independent activation. Scoring capability of QVina, Vinardo, Smina, PLANTS, and LeDock were assessed against the hsGC βH-NOX specific active and decoy dataset. Target biasness was achieved whilst screening by retaining two out of three substructures in screened compound that are critical for heme-independent hsGC βH-NOX activation. Target-specific biasness was further ensured by preserving the interactions of functionally important residues in the top three hits. MM-GB/PBSA yields energetically favored docked complexes. Principal Component Analysis (PCA) exhibited noticeable fluctuations in ligand-binding regions of the active-site that are responsible for heme and NO- independent activation.

Objective: Benign ovarian cysts (BOC) are the most common tumors in women of reproductive age. Usually, these cysts are harmless, but, a small number of them occasionally progress to malignancy. Among ovarian malignancies, epithelial ovarian cancer (EOC) comprises 90\% and is the most important cause of gynecologic cancerrelated deaths. We aimed to identify dysregulated miRNAs in patients with benign ovarian cysts (n = 11) compared to EOC (n = 10) and to healthy individuals (HI) (n = 15). Methods: The serum samples from EOC and BOC patients were collected before operation. We studied three different sample groups (serum of EOC (n = 8), HI (n = 8), and BOC (n = 8) patients) that contained the highest-quality of RNA. Microarray data were analyzed according to expression of miRNAs and target genes by bioinformatics tools. Results: When compared to EOC samples, 75 miRNAs were dysregulated in BOC samples. Sixty-six miRNAs from BOC were dysregulated when compared to HI samples. Bioinformatics analysis of BOC vs. EOC and BOC vs. HI showed that 46 miRNAs were congruent and their expression alterations were similar (up- or down-regulated). Further analysis showed that these 46 miRNAs are associated to one of three pathways involved in cancer pathogenesis. Conclusion: Several miRNAs might playa role in BOC formation and/or malignant transformation. These dysregulated miRNAs could potentially be a biomarker to distinguish between a completely BOC and one that is malignant or has potential for malignant transformation.

Glycoprotein M6A, a stress related gene, plays an important role in synapse and filopodia formation. Filopodia formation is vital for development, immunity, angiogenesis, wound healing and metastasis. In this study, structural and functional analysis of high-risk SNPs associated with Glycoprotein M6-A were evaluated using six different bioinformatics tools. Results classified T210I, T134I, Y153H, I215T, F156L, T160I, I226T, R247W, R178C, W159R, N157S and P151L as deleterious mutants that are crucial for the structure and function of the protein causing malfunction of M6-a and ultimately leads to disease development. The three-dimensional structure of wild-type M6-a and mutant M6-a were also predicted. Furthermore, the effects of high risk substitutions were also analyzed with interaction with valproic acid. Based on structural models obtained, the binding pocket of ligand bound glycoprotein M6-A structure showed few core interacting residues which are different in the mutant models. Among all substitutions, F156L showed complete loss of binding pocket when interacting with valproic acid as compared to the wild type model. Up to the best of our knowledge this is the first comprehensive study where GPM6A mutations were analyzed. The mechanism of action of GPM6A is still not fully defined which limits the understanding of functional details encoding M6-A. Our results may help enlighten some molecular aspects underlying glycoprotein M6-A. Communicated by Ramaswamy H. Sarma.

Although studies have shown that urban environments and mass-transit systems have distinct genetic profiles, there are no systematic worldwide studies of these dense, human microbial ecosystems. To address this gap in knowledge, we created a global metagenomic and antimicrobial resistance (AMR) atlas of urban mass transit systems from 60 cities, spanning 4,728 samples and 4,424 taxonomically-defined microorganisms collected for three years. This atlas provides an annotated, geospatial profile of microbial strains, functional characteristics, antimicrobial resistance markers, and novel genetic elements, including 10,928 novel predicted viral species, 1302 novel bacteria, and 2 novel archaea. Urban microbiomes often resemble human commensal microbiomes from the skin and airways, but also contain a consistent “core” of 31 species which are predominantly not human commensal species. Samples show distinct microbial signatures which may be used to accurately predict properties of their city of origin including population, proximity to the coast, and taxonomic profile. These data also show that AMR density across cities varies by several orders of magnitude, including many AMRs present on plasmids with cosmopolitan distributions. Together, these results constitute a high-resolution, global metagenomic atlas, which enables the discovery of new genetic components of the built human environment, highlights potential forensic applications, and provides an essential first draft of the global AMR burden of the world’s cities.

Purpose Blink reflex (BR) under general anesthesia as an intraoperative neuromonitoring method was used to monitor facial nerves in few studies. This study aimed to test the utility of intraoperative BR during cerebellopontine angle and skull base surgeries, assess its prognostic value for facial nerve functions, and compare it with facial corticobulbar motor evoked potentials (CoMEPs). Methods Blink reflex and facial CoMEPs were recorded from 40 patients undergoing skull base surgeries. Subdermal needles were placed in the supraorbital notch for stimulation and in the orbicularis oculi muscle for recording the BR. A double train of 20 to 40 V intensity with an intertrain interval of 40 to 60 milliseconds, an interstimulus interval of 2.5 milliseconds, and a stimulus duration of 0.5 milliseconds were applied. Facial nerve functions were assessed with the House-Brackmann grading system in the postoperative day 1 and third-month period and correlated with intraoperative BR and CoMEPs measurements. Results Of 40 patients, BR was recordable on the affected side in 32 (80%) and contralateral side in 35 (87.5%) patients. According to our statistical results, BR had a slightly better sensitivity than facial CoMEPs in predicting impairment of facial nerve functions for both postoperative and third-month time points. Blink reflex showed better accuracy for predicting postoperative nerve functions, whereas CoMEPs correlated better in predicting third-month outcome. Conclusions We suggest that BR is a valuable intraoperative neuromonitoring method that can be used in addition to facial CoMEPs during skull base surgeries to assess real-time facial nerve integrity and predict prognosis.

Most of the traditional gene selection approaches are borrowed from other fields such as statistics and computer science, However, they do not prioritize biologically relevant genes since the ultimate goal is to determine features that optimize model performance metrics not to build a biologically meaningful model. Therefore, there is an imminent need for new computational tools that integrate the biological knowledge about the data in the process of gene selection and machine learning. Integrative gene selection enables incorporation of biological domain knowledge from external biological resources. In this study, we propose a new computational approach named CogNet that is an integrative gene selection tool that exploits biological knowledge for grouping the genes for the computational modeling tasks of ranking and classification. In CogNet, the pathfindR serves as the biological grouping tool to allow the main algorithm to rank active-subnetwork-oriented KEGG pathway enrichment analysis results to build a biologically relevant model. CogNet provides a list of significant KEGG pathways that can classify the data with a very high accuracy. The list also provides the genes belonging to these pathways that are differentially expressed that are used as features in the classification problem. The list facilitates deep analysis and better interpretability of the role of KEGG pathways in classification of the data thus better establishing the biological relevance of these differentially expressed genes. Even though the main aim of our study is not to improve the accuracy of any existing tool, the performance of the CogNet outperforms a similar approach called maTE while obtaining similar performance compared to other similar tools including SVM-RCE. CogNet was tested on 13 gene expression datasets concerning a variety of diseases.

Bacillus thermocatenulatus lipase (BTL2), a member of the isolated lipase family known as thermoalkalophilic lipases, carries potential for industrial applications owing to its ability to catalyze versatile reactions under extreme conditions. This study investigates the molecular effects of distinct solvents on the stability of BTL2 at different temperatures, aiming to contribute to lipase use in industrial applications. Initially, molecular dynamic (MD) simulations were carried out to address for the molecular impacts of distinct solvents on the structural stability of BTL2 at different temperatures. Two lipase conformations representing the active and inactive forms were simulated in 5 solvents including water, ethanol, methanol, cyclohexane, and toluene. Low temperature simulations showed that polar solvents led to enhanced lid fluctuations compared with non-polar solvents reflecting a more dynamic equilibrium between active and inactive lipase conformations in polar solvents including water, while the overall structure of the lipase in both forms became more rigid in non-polar solvents than they were in polar solvent. Notably, the native lipase fold was maintained in non-polar solvents even at high temperatures, indicating an enhancement of lipase's thermostability in non-polar organic solvents. Next, we conducted experiments for which BTL2 was expressed in a heterologous host and purified to homogeneity, and its thermostability in different solvents was assessed. Parallel to the computational findings, experimental results suggested that non-polar organic solvents contributed to BTL2's thermostability at concentrations as high as 70% (v/v). Altogether, this study provides beneficial insights to the lipase use under extreme conditions. Graphical Abstract.

Tyrosine kinase inhibitor (TKI) resistance is a major problem in chronic myeloid leukemia (CML). We generated a TKI-resistant K562 sub-population, K562-IR, under selective imatinib-mesylate pressure. K562-IR cells are CD34(-)/CD38(-), BCR-Abl-independent, proliferate slowly, highly adherent and form intact tumor spheroids. Loss of CD45 and other hematopoietic markers reveal these cells have diverged from their hematopoietic origin. CD34 negativity, high expression of E-cadherin and CD44; decreased levels of CD45 and beta-catenin do not fully confer with the leukemic stem cell (LSC) phenotype. Expression analyses reveal that K562-IR cells differentially express tissue/ organ development and differentiation genes. Our data suggest that the observed phenotypic shift is an adaptive process rendering cells under TKI stress to become oncogene independent. Cells develop transcriptional instability in search for a gene expression framework suitable for new environmental stresses, resulting in an adaptive phenotypic shift in which some cells partially display LSC-like properties. With leukemic/cancer stem cell targeted therapies underway, the difference between treating an entity and a spectrum of dynamic cellular states will have conclusive effects on the outcome., Dokuz Eylul University Scientific Research ProjectsDokuz Eylul University [:2012KBSAG092, 2009KBSAG029], Dokuz Eylul University Scientific Research Projects, grants no:2012KBSAG092 and 2009KBSAG029. the funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

In the nitric oxide (NO) signaling pathway, human soluble guanylate cyclase (hsGC) synthesizes cyclic guanosine monophosphate (cGMP); responsible for the regulation of cGMP-specific protein kinases (PKGs) and phosphodiesterases (PDEs). The crystal structure of the inactive hsGC cyclase dimer is known, but there is still a lack of information regarding the substrate-specific internal motions that are essential for the catalytic mechanism of the hsGC. In the current study, the hsGC cyclase heterodimer complexed with guanosine triphosphate (GTP) and cGMP was subjected to molecular dynamics simulations, to investigate the conformational dynamics that have functional implications on the catalytic activity of hsGC. Results revealed that in the GTP-bound complex of the hsGC heterodimer, helix 1 of subunit α (α:h1) moves slightly inwards and comes close to helix 4 of subunit β (β:h4). This conformational change brings loop 2 of subunit β (β:L2) closer to helix 2 of subunit α (α:h2). Likewise, loop 2 of subunit α (α:L2) comes closer to helix 2 of subunit β (β:h2). These structural events stabilize and lock GTP within the closed pocket for cyclization. In the cGMP-bound complex, α:L2 detaches from β:h2 and establishes interactions with β:L2, which results in the loss of global structure compactness. Furthermore, with the release of pyrophosphate, the interaction between α:h1 and β:L2 weakens, abolishing the tight packing of the binding pocket. This study discusses the conformational changes induced by the binding of GTP and cGMP to the hsGC catalytic domain, valuable in designing new therapeutic strategies for the treatment of cardiovascular diseases.

The identification of the variants that explain familial risk of a specific disease is important since it enables the development of genetic risk prediction tests, diagnosis tools and therapeutical applications. One possible reason of multifactorial diseases is the alterations in the activity of biological pathways, where a series of mutations occur in distinct genes. While each of these variations extends slightly the likelihood of having the disease, they work together to give birth to the perturbations in normal biological processes. We provide a protocol \(termed PANOGA, Pathway and Network Oriented GWAS \(Genome-wide association study) Analysis) to devise functionally important pathways through the identification of genes within these pathways, where these genes are targeted by single nucleotide polymorphisms \(SNPs) obtained from the GWAS analysis. Additionally, PANOGA helps to identify other disease related genes, not targeted by the SNPs, which are also located within these affected pathways. The program accepts tab delimited or excel file containing SNP rsIDs vs. genotypic p-values and is available at: http://akademik.bahcesehir.edu.tr/~bbgungor/panoga_protocol.zip

Utilizing plant-based materials as a biofuel source is an increasingly popular attempt to redesign the global energy cycle. This endeavour underlines the potential of cellulase enzymes for green energy production and requires the structural and functional engineering of natural enzymes to enhance their utilization. In this work, we aimed to engineer enzymatic and functional properties of Endoglucanase I (EGI) by swapping the Ala43-Gly83 region of Cellobiohydrolase I (CBHI) from Trichoderma reesei. Herein, we report the enhanced enzymatic activity and improved thermal stability of the engineered enzyme, called EGI_swapped, compared to EGI. The difference in the enzymatic activity profile of EGI_swapped and the EGI enzymes became more pronounced upon increasing metal-ion concentrations in the reaction media. Notably, the engineered enzyme retained a considerable level of enzymatic activity after thermal incubation for 90 min at 70 &deg, C while EGI completely lost its enzymatic activity. Circular Dichroism spectroscopy studies revealed distinctive conformational and thermal susceptibility differences between EGI_swapped and EGI enzymes, confirming the improved structural integrity of the swapped enzyme. This study highlights the importance of swapping the metal-ion coordination region in the engineering of EGI enzyme for enhanced structural and thermal stability.