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.

Longitudinal non-targeted metabolomic profiling of urine samples for monitoring of kidney transplantation patients.

The assessment of kidney function within the first year following transplantation is crucial for predicting long-term graft survival. This study aimed to develop a robust and accurate model using metabolite profiles to predict early long-term outcomes in patient groups at the highest risk of early graft loss. A group of 61 kidney transplant recipients underwent thorough monitoring during a one-year follow-up period, which included a one-week hospital stay and follow-up assessments at three and six months. Based on their 12-month follow-up serum creatinine levels: Group 2 had levels exceeding 1.5 mg/dl, while Group 1 had levels below 1.5 mg/dl. Metabolites were detected by mass spectrometer and first pre-processed. Univariate and multivariate statistical analyses were employed to identify significant differences between the two groups. Nineteen metabolites were found to differ significantly in the 1st week, and seventeen metabolites in the 3rd month (adjusted p-value < 0.05, quality control (QC) < 30, a fold change (FC) > 1.1 or a FC < 0.91, Variable Influence on Projection (VIP) > 1). However, no significant differences were observed in the 6th month. These distinctive metabolites mainly belonged to lipid, fatty acid, and amino acid categories. Ten models were constructed using a backward conditional approach, with the best performance seen in model 5 for Group 2 at the 1st-week mark (AUC 0.900) and model 3 at the 3rd-month mark (AUC 0.924). In conclusion, the models developed in the early stages may offer potential benefits in the management of kidney transplant patients.

Corneal bacterial microbiome in patients with keratoconus using next-generation sequencing-based 16S rRNA gene analysis.

PURPOSE: To investigate the corneal bacterial microbiome in patients with keratoconus using next-generation sequencing and develop a new perspective on the pathogenesis of the disease. METHODS: This prospective observational study included 10 patients with keratoconus who underwent corneal crosslinking procedure and 10 healthy controls who underwent photorefractive keratectomy. Patients included in the study were aged 18 years or older. The demographic and clinical characteristics of participants were recorded. Corneal epithelial samples were collected between March 2021 and June 2021. Isolated bacterial DNA from corneal epithelial samples was analyzed using 16 S ribosomal RNA gene analysis. The relative abundance rates at the phylum and genus levels were calculated. Alpha diversity parameters were assessed. RESULTS: Eleven phyla and 521 genera of bacteria were identified in all participants. At the phylum level, Proteobacteria, Firmicutes, Actinobacteria, and Bacteroidetes were most abundant in both groups. There were no statistical differences between the two groups except Bacteriodetes (p < 0.05). At the genus level, the relative abundance rates of twenty bacteria were significantly different between keratoconus and healthy corneas (p < 0.05). Aquabacterium was the most abundant genus in patients with keratoconus, while Shigella was the most abundant genus in healthy controls. Alpha diversity parameters were lower in patients with keratoconus, although the difference did not reach statistical significance (p > 0.05). CONCLUSIONS: Our preliminary study revealed that there are similarities and differences in the corneal microbiome between keratoconus and healthy individuals. Further research is required on the relationship between the abnormal corneal microbiome composition and the pathogenesis of keratoconus.

Metabolomic Analyses to Identify Candidate Biomarkers of Cystinosis.

Cystinosis is a rare, devastating hereditary disease secondary to recessive CTNS gene mutations. The most commonly used diagnostic method is confirmation of an elevated leukocyte cystine level; however, this method is expensive and difficult to perform. This study aimed to identify candidate biomarkers for the diagnosis and follow-up of cystinosis based on multiomics studies. The study included three groups: newly-diagnosed cystinosis patients (patient group, n = 14); cystinosis patients under treatment (treatment group, n = 19); and healthy controls (control group, n = 30). Plasma metabolomics analysis identified 10 metabolites as candidate biomarkers that differed between the patient and control groups [L-serine, taurine, lyxose, 4-trimethylammoniobutanoic acid, orotic acid, glutathione, PE(O-18:1(9Z)/0:0), 2-hydroxyphenyl acetic acid, acetyl-N-formil-5-metoxikinuramine, 3-indoxyl sulphate]. As compared to the healthy control group, in the treatment group, hypotaurine, phosphatidylethanolamine, N-acetyl-d-mannosamine, 3-indolacetic acid, p-cresol, phenylethylamine, 5-aminovaleric acid, glycine, creatinine, and saccharic acid levels were significantly higher, and the metabolites quinic acid, capric acid, lenticin, xanthotoxin, glucose-6-phosphate, taurine, uric acid, glyceric acid, alpha-D-glucosamine phosphate, and serine levels were significantly lower. Urinary metabolomic analysis clearly differentiated the patient group from the control group by means of higher allo-inositol, talose, glucose, 2-hydroxybutiric acid, cystine, pyruvic acid, valine, and phenylalanine levels, and lower metabolite (N-acetyl-L-glutamic acid, 3-aminopropionitrile, ribitol, hydroquinone, glucuronic acid, 3-phosphoglycerate, xanthine, creatinine, and 5-aminovaleric acid) levels in the patient group. Urine metabolites were also found to be significantly different in the treatment group than in the control group. Thus, this study identified candidate biomarkers that could be used for the diagnosis and follow-up of cystinosis.

A splice site mutation in the TSEN2 causes a new syndrome with craniofacial and central nervous system malformations, and atypical hemolytic uremic syndrome.

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).

Thermostability of the PYL-PP2C Heterodimer Is Dependent on Magnesium: In Silico Insights into the Link between Heat Stress Response and Magnesium Deficiency in Plants.

Magnesium deficiency increases the susceptibility of plants toward heat stress. The correlation between magnesium levels and stress response has been studied at the physiological level; albeit, the molecular explanation to this relationship remains elusive. Among diverse pathways implicated in the heat stress, the abscisic acid (ABA) signal modulates the heat stress response by magnesium dependent phosphatases (PP2Cs). Exclusively, sequestration of PP2Cs by ABA receptors (PYLs) in the heterodimer form activates the stress response through ABA responsive transcription factors. In this study, the molecular interplay between magnesium levels and ABA related heat stress response was investigated. Molecular dynamics simulations have been applied to two different PYL-PP2C heterodimer systems representing normal and magnesium deficient conditions. The heterodimer conformation and stability were delineated at high temperatures mimicking heat stress. Results showed that the thermostability of the heat stress response heterodimer was significantly dependent on the magnesium. Furthermore, a conserved aromatic cluster at the dimer interface acted synergistically with the metal to confer thermostability to the heterodimer structure. These structural insights into one of the possible links between magnesium levels and stress highlight the importance of metal micronutrients for tuning the stability of the stress-related proteins and optimizing tolerance.

RACK1 Is an Interaction Partner of ATG5 and a Novel Regulator of Autophagy.

Autophagy is biological mechanism allowing recycling of long-lived proteins, abnormal protein aggregates, and damaged organelles under cellular stress conditions. Following sequestration in double- or multimembrane autophagic vesicles, the cargo is delivered to lysosomes for degradation. ATG5 is a key component of an E3-like ATG12-ATG5-ATG16 protein complex that catalyzes conjugation of the MAP1LC3 protein to lipids, thus controlling autophagic vesicle formation and expansion. Accumulating data indicate that ATG5 is a convergence point for autophagy regulation. Here, we describe the scaffold protein RACK1 (receptor activated C-kinase 1, GNB2L1) as a novel ATG5 interactor and an autophagy protein. Using several independent techniques, we showed that RACK1 interacted with ATG5. Importantly, classical autophagy inducers (starvation or mammalian target of rapamycin blockage) stimulated RACK1-ATG5 interaction. Knockdown of RACK1 or prevention of its binding to ATG5 using mutagenesis blocked autophagy activation. Therefore, the scaffold protein RACK1 is a new ATG5-interacting protein and an important and novel component of the autophagy pathways.

ZK DrugResist 2.0: A TextMiner to extract semantic relations of drug resistance from PubMed.

Extracting useful knowledge from an unstructured textual data is a challenging task for biologists, since biomedical literature is growing exponentially on a daily basis. Building an automated method for such tasks is gaining much attention of researchers. ZK DrugResist is an online tool that automatically extracts mutations and expression changes associated with drug resistance from PubMed. In this study we have extended our tool to include semantic relations extracted from biomedical text covering drug resistance and established a server including both of these features. Our system was tested for three relations, Resistance (R), Intermediate (I) and Susceptible (S) by applying hybrid feature set. From the last few decades the focus has changed to hybrid approaches as it provides better results. In our case this approach combines rule-based methods with machine learning techniques. The results showed 97.67% accuracy with 96% precision, recall and F-measure. The results have outperformed the previously existing relation extraction systems thus can facilitate computational analysis of drug resistance against complex diseases and further can be implemented on other areas of biomedicine.

Probing the roles of two tryptophans surrounding the unique zinc coordination site in lipase family I.5.

A unique zinc domain found in all of the identified members of the lipase family I.5 is surrounded by two conserved tryptophans (W61 and W212). In this study, we investigated the role of these hydrophobic residues in thermostability and thermoactivity of the lipase from Bacillus thermocatenulatus (BTL2) taken as the representative of the family. Circular dichroism spectroscopy revealed that the secondary structure of BTL2 is conserved by the tryptophan mutations (W61A, W212A, and W61A/W212A), and that W61 is located in a more rigid and less solvent exposed region than is W212. Thermal denaturation and optimal activity analyses pointed out that zinc induces thermostability and thermoactivity of BTL2, in which both tryptophans W61 and W212 play contributing roles. Molecular explanations describing the roles of these tryptophans were pursued by X-ray crystallography of the open form of the W61A mutant and molecular dynamics simulations which highlighted a critical function for W212 in zinc binding to the coordination site. This study reflects the potential use of hydrophobic amino acids in vicinity of metal coordination sites in lipase biocatalysts design.

Prediction of neddylation sites from protein sequences and sequence-derived properties.

BACKGROUND: Neddylation is a reversible post-translational modification that plays a vital role in maintaining cellular machinery. It is shown to affect localization, binding partners and structure of target proteins. Disruption of protein neddylation was observed in various diseases such as Alzheimer's and cancer. Therefore, understanding the neddylation mechanism and determining neddylation targets possibly bears a huge importance in further understanding the cellular processes. This study is the first attempt to predict neddylated sites from protein sequences by using several sequence and sequence-based structural features. RESULTS: We have developed a neddylation site prediction method using a support vector machine based on various sequence properties, position-specific scoring matrices, and disorder. Using 21 amino acid long lysine-centred windows, our model was able to predict neddylation sites successfully, with an average 5-fold stratified cross validation performance of 0.91, 0.91, 0.75, 0.44, 0.95 for accuracy, specificity, sensitivity, Matthew's correlation coefficient and area under curve, respectively. Independent test set results validated the robustness of reported new method. Additionally, we observed that neddylation sites are commonly flexible and there is a significant positively charged amino acid presence in neddylation sites. CONCLUSIONS: In this study, a neddylation site prediction method was developed for the first time in literature. Common characteristics of neddylation sites and their discriminative properties were explored for further in silico studies on neddylation. Lastly, up-to-date neddylation dataset was provided for researchers working on post-translational modifications in the accompanying supplementary material of this article.

A novel analysis strategy for integrating methylation and expression data reveals core pathways for thyroid cancer aetiology.

BACKGROUND: Recently, a wide range of diseases have been associated with changes in DNA methylation levels, which play a vital role in gene expression regulation. With ongoing developments in technology, attempts to understand disease mechanism have benefited greatly from epigenetics and transcriptomics studies. In this work, we have used expression and methylation data of thyroid carcinoma as a case study and explored how to optimally incorporate expression and methylation information into the disease study when both data are available. Moreover, we have also investigated whether there are important post-translational modifiers which could drive critical insights on thyroid cancer genetics. RESULTS: In this study, we have conducted a threshold analysis for varying methylation levels to identify whether setting a methylation level threshold increases the performance of functional enrichment. Moreover, in order to decide on best-performing analysis strategy, we have performed data integration analysis including comparison of 10 different analysis strategies. As a result, combining methylation with expression and using genes with more than 15% methylation change led to optimal detection rate of thyroid-cancer associated pathways in top 20 functional enrichment results. Furthermore, pooling the data from different experiments increased analysis confidence by improving the data range. Consequently, we have identified 207 transcription factors and 245 post-translational modifiers with more than 15% methylation change which may be important in understanding underlying mechanisms of thyroid cancer. CONCLUSION: While only expression or only methylation information would not reveal both primary and secondary mechanisms involved in disease state, combining expression and methylation led to a better detection of thyroid cancer-related genes and pathways that are found in the recent literature. Moreover, focusing on genes that have certain level of methylation change improved the functional enrichment results, revealing the core pathways involved in disease development such as; endocytosis, apoptosis, glutamatergic synapse, MAPK, ErbB, TGF-beta and Toll-like receptor pathways. Overall, in addition to novel analysis framework, our study reveals important thyroid-cancer related mechanisms, secondary molecular alterations and contributes to better knowledge of thyroid cancer aetiology.

Molecular diversity of LysM carbohydrate-binding motifs in fungi.

LysM motifs are carbohydrate-binding modules found in prokaryotes and eukaryotes. They bind to N-acetylglucosamine-containing carbohydrates, such as chitin, chitio-oligosaccharides and peptidoglycan. In this review, we summarize the features of the protein architecture of LysM-containing proteins in fungi and discuss their so far known biochemical properties, transcriptional profiles and biological functions. Further, based on data from evolutionary analyses and consensus pattern profiling of fungal LysM motifs, we show that they can be classified into a fungal-specific group and a fungal/bacterial group. This facilitates the classification and selection of further LysM proteins for detailed analyses and will contribute to widening our understanding of the functional spectrum of this protein family in fungi. Fungal LysM motifs are predominantly found in subgroup C chitinases and in LysM effector proteins, which are secreted proteins with LysM motifs but no catalytic domains. In enzymes, LysM motifs mediate the attachment to insoluble carbon sources. In plants, receptors containing LysM motifs are responsible for the perception of chitin-oligosaccharides and are involved in beneficial symbiotic interactions between plants and bacteria or fungi, as well as plant defence responses. In plant pathogenic fungi, LysM effector proteins have already been shown to have important functions in the dampening of host defence responses as well as protective functions of fungal hyphae against chitinases. However, the large number and diversity of proteins with LysM motifs that are being unravelled in fungal genome sequencing projects suggest that the functional repertoire of LysM effector proteins in fungi is only partially discovered so far.

PANOGA: a web server for identification of SNP-targeted pathways from genome-wide association study data.

Genome-wide association studies (GWAS) have revolutionized the search for the variants underlying human complex diseases. However, in a typical GWAS, only a minority of the single-nucleotide polymorphisms (SNPs) with the strongest evidence of association is explained. One possible reason of complex diseases is the alterations in the activity of several biological pathways. Here we present a web server called Pathway and Network-Oriented GWAS Analysis to devise functionally important pathways through the identification of SNP-targeted genes within these pathways. The strength of our methodology stems from its multidimensional perspective, where we combine evidence from the following five resources: (i) genetic association information obtained through GWAS, (ii) SNP functional information, (iii) protein-protein interaction network, (iv) linkage disequilibrium and (v) biochemical pathways.

Assessment of global and gene-specific DNA methylation in rat liver and kidney in response to non-genotoxic carcinogen exposure.

Altered expression of tumor suppressor genes and oncogenes, which is regulated in part at the level of DNA methylation, is an important event involved in non-genotoxic carcinogenesis. This may serve as a marker for early detection of non-genotoxic carcinogens. Therefore, we evaluated the effects of non-genotoxic hepatocarcinogens, 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), hexachlorobenzene (HCB), methapyrilene (MPY) and male rat kidney carcinogens, d-limonene, p-dichlorobenzene (DCB), chloroform and ochratoxin A (OTA) on global and CpG island promoter methylation in their respective target tissues in rats. No significant dose-related effects on global DNA hypomethylation were observed in tissues of rats compared to vehicle controls using LC-MS/MS in response to short-term non-genotoxic carcinogen exposure. Initial experiments investigating gene-specific methylation using methylation-specific PCR and bisulfite sequencing, revealed partial methylation of p16 in the liver of rats treated with HCB and TCDD. However, no treatment related effects on the methylation status of Cx32, e-cadherin, VHL, c-myc, Igfbp2, and p15 were observed. We therefore applied genome-wide DNA methylation analysis using methylated DNA immunoprecipitation combined with microarrays to identify alterations in gene-specific methylation. Under the conditions of our study, some genes were differentially methylated in response to MPY and TCDD, whereas d-limonene, DCB and chloroform did not induce any methylation changes. 90-day OTA treatment revealed enrichment of several categories of genes important in protein kinase activity and mTOR cell signaling process which are related to OTA nephrocarcinogenicity.

Predicting sumoylation sites using support vector machines based on various sequence features, conformational flexibility and disorder.

BACKGROUND: Sumoylation, which is a reversible and dynamic post-translational modification, is one of the vital processes in a cell. Before a protein matures to perform its function, sumoylation may alter its localization, interactions, and possibly structural conformation. Abberations in protein sumoylation has been linked with a variety of disorders and developmental anomalies. Experimental approaches to identification of sumoylation sites may not be effective due to the dynamic nature of sumoylation, laborsome experiments and their cost. Therefore, computational approaches may guide experimental identification of sumoylation sites and provide insights for further understanding sumoylation mechanism. RESULTS: In this paper, the effectiveness of using various sequence properties in predicting sumoylation sites was investigated with statistical analyses and machine learning approach employing support vector machines. These sequence properties were derived from windows of size 7 including position-specific amino acid composition, hydrophobicity, estimated sub-window volumes, predicted disorder, and conformational flexibility. 5-fold cross-validation results on experimentally identified sumoylation sites revealed that our method successfully predicts sumoylation sites with a Matthew's correlation coefficient, sensitivity, specificity, and accuracy equal to 0.66, 73%, 98%, and 97%, respectively. Additionally, we have showed that our method compares favorably to the existing prediction methods and basic regular expressions scanner. CONCLUSIONS: By using support vector machines, a new, robust method for sumoylation site prediction was introduced. Besides, the possible effects of predicted conformational flexibility and disorder on sumoylation site recognition were explored computationally for the first time to our knowledge as an additional parameter that could aid in sumoylation site prediction.

A microdeletion event at 19q13.43 in IDH-mutant astrocytomas is strongly correlated with MYC overexpression.

MYC dysregulation is pivotal in the onset and progression of IDH-mutant gliomas, mostly driven by copy-number alterations, regulatory element alterations, or epigenetic changes. Our pilot analysis uncovered instances of relative MYC overexpression without alterations in the proximal MYC network (PMN), prompting a deeper investigation into potential novel oncogenic mechanisms. Analysing comprehensive genomics profiles of 236 "IDH-mutant 1p/19q non-co-deleted" lower-grade gliomas from The Cancer Genome Atlas, we identified somatic genomic alterations within the PMN. In tumours without PMN-alterations but with MYC-overexpression, genes correlated with MYC-overexpression were identified. Our analyses yielded that 86/236 of astrocytomas exhibited no PMN-alterations, a subset of 21/86 displaying relative MYC overexpression. Within this subset, we discovered 42 genes inversely correlated with relative MYC expression, all on 19q. Further analysis pinpointed a minimal common region at 19q13.43, encompassing 15 genes. The inverse correlations of these 15 genes with relative MYC overexpression were re-confirmed using independent scRNAseq data. Further, the micro-deleted astrocytoma subset displayed significantly higher genomic instability compared to WT cases, but lower instability compared to PMN-hit cases. This newly identified 19q micro-deletion represents a potential novel mechanism underlying MYC dysregulation in astrocytomas. Given the prominence of 19q loss in IDH-mutant gliomas, our findings bear significant implications for understanding gliomagenesis.

Prediction of peptides binding to MHC class I and II alleles by temporal motif mining.

BACKGROUND: MHC (Major Histocompatibility Complex) is a key player in the immune response of most vertebrates. The computational prediction of whether a given antigenic peptide will bind to a specific MHC allele is important in the development of vaccines for emerging pathogens, the creation of possibilities for controlling immune response, and for the applications of immunotherapy. One of the problems that make this computational prediction difficult is the detection of the binding core region in peptides, coupled with the presence of bulges and loops causing variations in the total sequence length. Most machine learning methods require the sequences to be of the same length to successfully discover the binding motifs, ignoring the length variance in both motif mining and prediction steps. In order to overcome this limitation, we propose the use of time-based motif mining methods that work position-independently. RESULTS: The prediction method was tested on a benchmark set of 28 different alleles for MHC class I and 27 different alleles for MHC class II. The obtained results are comparable to the state of the art methods for both MHC classes, surpassing the published results for some alleles. The average prediction AUC values are 0.897 for class I, and 0.858 for class II. CONCLUSIONS: Temporal motif mining using partial periodic patterns can capture information about the sequences well enough to predict the binding of the peptides and is comparable to state of the art methods in the literature. Unlike neural networks or matrix based predictors, our proposed method does not depend on peptide length and can work with both short and long fragments. This advantage allows better use of the available training data and the prediction of peptides of uncommon lengths.

Effect of the switch status of Helicobacter pylori outer inflammatory protein A on gastric diseases.

Helicobacter pylori OipA (Outer Inflammatory Protein A) is an outer membrane protein that takes role in the adherence and colonization to the stomach. oipA gene expression is regulated by the slipped-strand mispairing mechanism through a hypermutable CT dinucleotide repeat motif in the 5΄ region. Alterations in the CT number repeats cause frame-shift mutations to result in phase variation of oipA expression. While a functional "On" status has been recognized as a risk factor for peptic ulcer diseases and gastric cancer in many studies, some controversial findings still exist. To this end, this study compiled the sequence data of oipA from 10 different studies between 2000-2019 and 50 oipA DNA sequences from our own research that examined the relationship between the phase On/Off status of oipA and gastric diseases based on CT repeat number. Overall, we have reached 536 oipA DNA sequences from patients. This large collection of oipA sequences first clarified the absolute conservation of the peptide-pentamer of FWLHA for phase ''On'' status, suggesting this pentamer as a superior marker for the determination of oipA status than counting the number of CT repeats. Combining the sequence and patient data, we have re-analyzed the association between the ''On'' status of oipA and gastric diseases. Our results showed a strong association between oipA ''On'' status and gastric cancer supporting previous findings. We also investigated the AlphaFold2 computed structure of OipA that adopts a beta-barrel fold closely resembling to the autotransporter family of H. pylori. Altogether, this study confirms a strong association between oipA ''On'' statuses and severe gastrointestinal diseases like cancer and provides useful insights into the FWLHA pentamer as an indicator of "On" status of oipA putative autotransporter function rather than CT repeats number.

Phenotype screens of murine pancreatic cancer identify a Tgf-α-Ccl2-paxillin axis driving human-like neural invasion.

Solid cancers like pancreatic ductal adenocarcinoma (PDAC), a type of pancreatic cancer, frequently exploit nerves for rapid dissemination. This neural invasion (NI) is an independent prognostic factor in PDAC, but insufficiently modeled in genetically engineered mouse models (GEMM) of PDAC. Here, we systematically screened for human-like NI in Europe's largest repository of GEMM of PDAC, comprising 295 different genotypes. This phenotype screen uncovered 2 GEMMs of PDAC with human-like NI, which are both characterized by pancreas-specific overexpression of transforming growth factor α (TGF-α) and conditional depletion of p53. Mechanistically, cancer-cell-derived TGF-α upregulated CCL2 secretion from sensory neurons, which induced hyperphosphorylation of the cytoskeletal protein paxillin via CCR4 on cancer cells. This activated the cancer migration machinery and filopodia formation toward neurons. Disrupting CCR4 or paxillin activity limited NI and dampened tumor size and tumor innervation. In human PDAC, phospho-paxillin and TGF-α-expression constituted strong prognostic factors. Therefore, we believe that the TGF-α-CCL2-CCR4-p-paxillin axis is a clinically actionable target for constraining NI and tumor progression in PDAC.

Integrated bioinformatics analysis of validated and circulating miRNAs in ovarian cancer.

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 hsalet7d-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.