PhyloSift: Phylogenetic analysis of genomes and metagenomes

Like all organisms on the planet, environmental microbes are subject to the forces of molecular evolution. Metagenomic sequencing provides a means to access the DNA sequence of uncultured microbes. By combining DNA sequencing of microbial communities with evolutionary modeling and phylogenetic analysis we might obtain new insights into microbiology and also provide a basis for practical tools such as forensic pathogen detection. In this work we present an approach to leverage phylogenetic analysis of metagenomic sequence data to conduct several types of analysis. First, we present a method to conduct phylogeny-driven Bayesian hypothesis tests for the presence of an organism in a sample. Second, we present a means to compare community structure across a collection of many samples and develop direct associations between the abundance of certain organisms and sample metadata. Third, we apply new tools to analyze the phylogenetic diversity of microbial communities and again demonstrate how this can be associated to sample metadata. These analyses are implemented in an open source software pipeline called PhyloSift. As a pipeline, PhyloSift incorporates several other programs including LAST, HMMER, and pplacer to automate phylogenetic analysis of protein coding and RNA sequences in metagenomic datasets generated by modern sequencing platforms (e.g., Illumina, 454). © 2014 Darling et al

Two Years Later: Journals Are Not Yet Enforcing the ARRIVE Guidelines on Reporting Standards for Pre-Clinical Animal Studies

There is growing concern that poor experimental design and lack of transparent reporting contribute to the frequent failure of pre-clinical animal studies to translate into treatments for human disease. In 2010, the Animal Research: Reporting of In Vivo Experiments (ARRIVE) guidelines were introduced to help improve reporting standards. They were published in PLOS Biology and endorsed by funding agencies and publishers and their journals, including PLOS, Nature research journals, and other top-tier journals. Yet our analysis of papers published in PLOS and Nature journals indicates that there has been very little improvement in reporting standards since then. This suggests that authors, referees, and editors generally are ignoring guidelines, and the editorial endorsement is yet to be effectively implemented

Expression of Drug Targets in Patients Treated with Sorafenib, Carboplatin and Paclitaxel

Introduction: Sorafenib, a multitarget kinase inhibitor, targets members of the mitogen-activated protein kinase (MAPK) pathway and VEGFR kinases. Here we assessed the association between expression of sorafenib targets and biomarkers of taxane sensitivity and response to therapy in pre-treatment tumors from patients enrolled in ECOG 2603, a phase III comparing sorafenib, carboplatin and paclitaxel (SCP) to carboplatin, paclitaxel and placebo (CP). Methods: Using a method of automated quantitative analysis (AQUA) of in situ protein expression, we quantified expression of VEGF-R2, VEGF-R1, VEGF-R3, FGF-R1, PDGF-Rβ, c-Kit, B-Raf, C-Raf, MEK1, ERK1/2, STMN1, MAP2, EB1 and Bcl-2 in pretreatment specimens from 263 patients. Results: An association was found between high FGF-R1 and VEGF-R1 and increased progression-free survival (PFS) and overall survival (OS) in our combined cohort (SCP and CP arms). Expression of FGF-R1 and VEGF-R1 was higher in patients who responded to therapy ((CR+PR) vs. (SD+PD+ un-evaluable)). Conclusions: In light of the absence of treatment effect associated with sorafenib, the association found between FGF-R1 and VEGF-R1 expression and OS, PFS and response might reflect a predictive biomarker signature for carboplatin/paclitaxel-based therapy. Seeing that carboplatin and pacitaxel are now widely used for this disease, corroboration in another cohort might enable us to improve the therapeutic ratio of this regimen. © 2013 Jilaveanu et al

The carcinogenic potential of tacrolimus ointment beyond immune suppression: a hypothesis creating case report

BACKGROUND: Since tacrolimus ointment was approved by the U.S. Food and Drug Administration (FDA) as a promising treatment for atopic dermatitis, it has been approved in more than 30 additional countries, including numerous European Union member nations. Moreover, in the current clinical routine the use of this drug is no longer restricted to the approved indication, but has been extended to a wide variety of inflammatory skin diseases including some with the potential of malignant transformation. So far, the side-effects reported from the topical use of tacrolimus have been relatively minor (e.g. burning, pruritus, erythema). Recently, however, the FDA reviewed the safety of topical tacrolimus, which resulted in a warning that the use of calcineurin inhibitors may be associated with an increased risk of cancer. CASE PRESENTATION: Oral lichen planus (OLP) was diagnosed in a 56-year-old women in February 1999. After several ineffective local and systemic therapeutic measures an off-label treatment of this recalcitrant condition using Tacrolimus 0.1% ointment was initiated in May 2002. After a few weeks of treatment most of the lesions ameliorated, with the exception of the plaques on the sides of the tongue. Nevertheless, the patient became free of symptoms which, however, reoccurred once tacrolimus was weaned, as a consequence treatment was maintained. In April 2005, the plaques on the left side of the tongue appeared increasingly compact and a biopsy specimen confirmed the suspected diagnosis of an oral squamous cell carcinoma. CONCLUSION: The suspected causal relationship between topical use of tacrolimus and the development of a squamous cell carcinoma prompted us to test the notion that the carcinogenicity of tacrolimus may go beyond mere immune suppression. To this end, tacrolimus has been shown to have an impact on cancer signalling pathways such as the MAPK and the p53 pathway. In the given case, we were able to demonstrate that these pathways had also been altered subsequent to tacrolimus therapy

Incorporating medical interventions into carrier probability estimation for genetic counseling

BACKGROUND: Mendelian models for predicting who may carry an inherited deleterious mutation of known disease genes based on family history are used in a variety of clinical and research activities. People presenting for genetic counseling are increasingly reporting risk-reducing medical interventions in their family histories because, recently, a slew of prophylactic interventions have become available for certain diseases. For example, oophorectomy reduces risk of breast and ovarian cancers, and is now increasingly being offered to women with family histories of breast and ovarian cancer. Mendelian models should account for medical interventions because interventions modify mutation penetrances and thus affect the carrier probability estimate. METHODS: We extend Mendelian models to account for medical interventions by accounting for post-intervention disease history through an extra factor that can be estimated from published studies of the effects of interventions. We apply our methods to incorporate oophorectomy into the BRCAPRO model, which predicts a woman's risk of carrying mutations in BRCA1 and BRCA2 based on her family history of breast and ovarian cancer. This new BRCAPRO is available for clinical use. RESULTS: We show that accounting for interventions undergone by family members can seriously affect the mutation carrier probability estimate, especially if the family member has lived many years post-intervention. We show that interventions have more impact on the carrier probability as the benefits of intervention differ more between carriers and non-carriers. CONCLUSION: These findings imply that carrier probability estimates that do not account for medical interventions may be seriously misleading and could affect a clinician's recommendation about offering genetic testing. The BayesMendel software, which allows one to implement any Mendelian carrier probability model, has been extended to allow medical interventions, so future Mendelian models can easily account for interventions

Gene encoder: a feature selection technique through unsupervised deep learning-based clustering for large gene expression data

© 2020, Springer-Verlag London Ltd., part of Springer Nature. Cancer is a severe condition of uncontrolled cell division that results in a tumor formation that spreads to other tissues of the body. Therefore, the development of new medication and treatment methods for this is in demand. Classification of microarray data plays a vital role in handling such situations. The relevant gene selection is an important step for the classification of microarray data. This work presents gene encoder, an unsupervised two-stage feature selection technique for the cancer samples’ classification. The first stage aggregates three filter methods, namely principal component analysis, correlation, and spectral-based feature selection techniques. Next, the genetic algorithm is used, which evaluates the chromosome utilizing the autoencoder-based clustering. The resultant feature subset is used for the classification task. Three classifiers, namely support vector machine, k-nearest neighbors, and random forest, are used in this work to avoid the dependency on any one classifier. Six benchmark gene expression datasets are used for the performance evaluation, and a comparison is made with four state-of-the-art related algorithms. Three sets of experiments are carried out to evaluate the proposed method. These experiments are for the evaluation of the selected features based on sample-based clustering, adjusting optimal parameters, and for selecting better performing classifier. The comparison is based on accuracy, recall, false positive rate, precision, F-measure, and entropy. The obtained results suggest better performance of the current proposal

Loss of ATRX in Chondrocytes Has Minimal Effects on Skeletal Development

BACKGROUND:Mutations in the human ATRX gene cause developmental defects, including skeletal deformities and dwarfism. ATRX encodes a chromatin remodeling protein, however the role of ATRX in skeletal development is currently unknown. METHODOLOGY/PRINCIPAL FINDINGS:We induced Atrx deletion in mouse cartilage using the Cre-loxP system, with Cre expression driven by the collagen II (Col2a1) promoter. Growth rate, body size and weight, and long bone length did not differ in Atrx(Col2cre) mice compared to control littermates. Histological analyses of the growth plate did not reveal any differences between control and mutant mice. Expression patterns of Sox9, a transcription factor required for cartilage morphogenesis, and p57, a marker of cell cycle arrest and hypertrophic chondrocyte differentiation, was unaffected. However, loss of ATRX in cartilage led to a delay in the ossification of the hips in some mice. We also observed hindlimb polydactily in one out of 61 mutants. CONCLUSIONS/SIGNIFICANCE:These findings indicate that ATRX is not directly required for development or growth of cartilage in the mouse, suggesting that the short stature in ATR-X patients is caused by defects in cartilage-extrinsic mechanisms

Systematic Identification of Combinatorial Drivers and Targets in Cancer Cell Lines

There is an urgent need to elicit and validate highly efficacious targets for combinatorial intervention from large scale ongoing molecular characterization efforts of tumors. We established an in silico bioinformatic platform in concert with a high throughput screening platform evaluating 37 novel targeted agents in 669 extensively characterized cancer cell lines reflecting the genomic and tissue-type diversity of human cancers, to systematically identify combinatorial biomarkers of response and co-actionable targets in cancer. Genomic biomarkers discovered in a 141 cell line training set were validated in an independent 359 cell line test set. We identified co-occurring and mutually exclusive genomic events that represent potential drivers and combinatorial targets in cancer. We demonstrate multiple cooperating genomic events that predict sensitivity to drug intervention independent of tumor lineage. The coupling of scalable in silico and biologic high throughput cancer cell line platforms for the identification of co-events in cancer delivers rational combinatorial targets for synthetic lethal approaches with a high potential to pre-empt the emergence of resistance