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The zoonotic pathogen Campylobacter jejuni is among the leading causes of foodborne diseases worldwide. While C. jejuni colonises many wild animals and livestock, persistence mechanisms enabling the bacterium to adapt to host species' guts are not fully understood. In order to identify putative determinants influencing host preferences of distinct lineages, bootstrapping based on stratified random sampling combined with a k-mer-based genome-wide association was conducted on 490 genomes from diverse origins in Germany and Canada. We show a strong association of both the core and the accessory genome characteristics with distinct host animal species, indicating multiple adaptive trajectories defining the evolution of C. jejuni lifestyle preferences in different ecosystems. Here, we demonstrate that adaptation towards a specific host niche ecology is most likely a long evolutionary and multifactorial process, expressed by gene absence or presence and allele variations of core genes. Several host-specific allelic variants from different phylogenetic backgrounds, including dnaE, rpoB, ftsX or pycB play important roles for genome maintenance and metabolic pathways. Thus, variants of genes important for C. jejuni to cope with specific ecological niches or hosts may be useful markers for both surveillance and future pathogen intervention strategies.

BACKGROUND: List prices of tumor necrosis factor (TNF) inhibitors drastically increased during the last decade, but previous research has shown that half of these increases were offset by rising manufacturer discounts. It remains unclear to what extent manufacturers' discounts have offset increases in list prices of each self-administered injectable TNF inhibitor. Evaluating trends in net prices and discounts at the product level will be paramount in understanding the role of competition in the biologic market. OBJECTIVES: To (a) describe product-level changes in net prices of each self-administered injectable TNF inhibitor available in 2007-2019 and (b) quantify to what extent manufacturer discounts have offset increases in list prices. METHODS: We obtained 2007-2019 pricing data for etanercept, adalimumab, certolizumab, and golimumab from the investment firm SSR Health, which uses company-reported sales to estimate net prices and discounts for brand products manufactured by publicly traded companies. For each drug and year, we calculated annual costs of treatment for patients with rheumatoid arthritis based on list and net prices and discounts in Medicaid and other payers. RESULTS: From 2007-2019, list prices of etanercept and adalimumab increased by 293% and 295%, respectively; however, discounts offset 47% and 45% of these increases, leading to net price increases of 171% and 203%. List prices of golimumab and certolizumab increased by 183% and 182%, respectively, but with discounts offsetting 58% and 59% of these increases, net prices increased by 103% and 109%. Net prices of golimumab started to decrease after 2016, while net prices of adalimumab and certolizumab experienced their first drop in 2019. Across the study period, discounts in Medicaid and in other payers increased, respectively, from 21% to 85% and 6% to 32% for etanercept; from 26% to 88% and 19% to 35% for adalimumab; from 28% to 63% and 22% to 46% for golimumab; and from 29% to 83% and 27% to 47% for certolizumab. CONCLUSIONS: Despite growing manufacturer discounts, net prices of self-administered injectable TNF inhibitors still increased at a mean annual rate of 9.6% in 2007-2019. This led to net prices tripling for adalimumab and more than doubling for etanercept, golimumab, and certolizumab. DISCLOSURES: This study was funded by the Myers Family Foundation. Hernandez is funded by the National Heart, Lung and Blood Institute (grant number K01HL142847). Funding sources had no role in design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication. Hernandez has served on Pfizer's scientific advisory board. The other authors have nothing to disclose.

Patterns of sleep behaviors appear to differ across students with special needs including those classified with developmental disorders, attention-deficit/hyperactivity disorder, and autism. One understudied segment of the special needs population is that of gifted children, students who bring their own unique set of behavioral characteristics. In the present study, it was hypothesized that because of unique cognitive and socio-emotional intensities, gifted children would experience elevated levels of sleep disturbance in comparison to their non-gifted peers. Handedness was also surveyed for additional insight into cerebral organization. The study's participants were 35 gifted and 23 non-gifted students between the ages of 8-12. To measure sleep behaviors, data from the Child Sleep Habits Questionnaire (Abbreviated), Sleep Self-Report, and a sleep diary were collected. To assess handedness, the Edinburgh Handedness Inventory was administered. Results indicated that although gifted students did trend toward experiencing more sleep disturbance, when compared to non-gifted students, no significant differences were found in mean bedtime, hours slept, sleeping problems, or use of electronic devices before bedtime. In terms of handedness, gifted participants displayed more left-hand bias. Although no differences were found between sleep patterns of gifted vs. non-gifted children, this study adds to the limited evidential base on gifted children, handedness, and sleep. Additional research on sleep behaviors within this special needs cohort may be warranted.

Patterns of somatic single nucleotide variants observed in human cancers vary widely between different tumor types. They depend not only on the activity of diverse mutational processes, such as exposure to ultraviolet light and the deamination of methylated cytosines, but largely also on the sequence content of different genomic regions on which these processes act. With MutViz (http://gmql.eu/mutviz/), we have presented a user-friendly web tool for the identification of mutation enrichments that offers preloaded mutations from public datasets for a variety of cancer types, well organized within an effective database architecture. Somatic mutation patterns can be visually and statistically analyzed within arbitrary sets of small, user-provided genomic regions, such as promoters or collections of transcription factor binding sites. Here, we present MutViz 2.0, a largely extended and consolidated version of the tool: we took into account the immediate (trinucleotide) sequence context of mutations, improved the representation of clinical annotation of tumor samples and devised a method for signature refitting on limited genomic regions to infer the contribution of individual mutational processes to the mutation patterns observed in these regions. We described both the features of MutViz 2.0, concentrating on the novelties, and the substantial re-engineering of the cloud-based architecture., Graphical Abstract Graphical AbstractSimplified workflow of data analysis and visualization with MutViz: Analysis of somatic mutations within user-defined genomic regions for identification and visualization of region-specific mutation characteristics.

Background Introns are generally removed from primary transcripts to form mature RNA molecules in a post-transcriptional process called splicing. An efficient splicing of primary transcripts is an essential step in gene expression and its misregulation is related to numerous human diseases. Thus, to better understand the dynamics of this process and the perturbations that might be caused by aberrant transcript processing it is important to quantify splicing efficiency. Results Here, we introduce SPLICE-q, a fast and user-friendly Python tool for genome-wide SPLICing Efficiency quantification. It supports studies focusing on the implications of splicing efficiency in transcript processing dynamics. SPLICE-q uses aligned reads from strand-specific RNA-seq to quantify splicing efficiency for each intron individually and allows the user to select different levels of restrictiveness concerning the introns’ overlap with other genomic elements such as exons of other genes. We applied SPLICE-q to globally assess the dynamics of intron excision in yeast and human nascent RNA-seq. We also show its application using total RNA-seq from a patient-matched prostate cancer sample. Conclusions Our analyses illustrate that SPLICE-q is suitable to detect a progressive increase of splicing efficiency throughout a time course of nascent RNA-seq and it might be useful when it comes to understanding cancer progression beyond mere gene expression levels. SPLICE-q is available at: https://github.com/vrmelo/SPLICE-q

Point of-care ultrasound (POCUS) has become a mainstream bedside tool for clinicians in several specialties and is gaining recognition in hospital medicine. There are many clinical applications in which the inpatient practitioner can use POCUS to improve his or her diagnosis, monitoring, and treatment of patients. POCUS is valuable in many clinical scenarios, including acute renal failure, increasing lower extremity edema, change in inpatient clinical status, and acute dyspnea. The medical literature has demonstrated the ability of nonradiologists to accurately detect conditions, including hydronephrosis; extremes of central venous pressure; deep venous thrombosis; pericardial effusion with tamponade; and several pulmonary pathologic states, including pulmonary edema, pleural effusion, consolidation, and pneumothorax. Further development of POCUS in hospital medicine is highly likely given increased awareness and exposure among medical trainees, a developing literature base, and growing engagement from specialty societies.

Atypical teratoid/rhabdoid tumor (AT/RT) of the central nervous system is a highly malignant, pediatric brain tumor typically arising de novo. Inactivation of SMARCB1 is a defining molecular event. We present here a rare case of an adult (35 years) low-grade SMARCB1-deleted brain tumor with transition into prototypical AT/RT over 14 years. Molecular analysis was performed for 3 tumor presentations including copy number analysis, DNA methylation analysis (450k), and whole exome sequencing. We detected the identical somatic SMARCB1 deletion at all 3 time-points. In an unsupervised hierarchical clustering of methylation data together with 127 reference cases comprising 9 brain tumor classes all 3 manifestations clustered with AT/RT. Exome sequencing revealed an increase of mutational burden over time. The acquired mutations and additional copy number changes did not affect known cancer genes. In conclusion, we demonstrate molecular changes associated with histological and clinical transition of a low-grade brain tumor to an adult AT/RT. Our observation of a stable disease course for nearly 10 years in a tumor with SMARCB1 loss and an AT/RT-like DNA methylation profile indicates that caution may be required in the diagnostic interpretation of such findings in adult patients.

Recent advances in sequencing technologies, namely next-generation and third-generation sequencing, have had a major impact on biomedical research in many different fields, including epigenetics and immunological research. Although these technologies have a common goal, the experimental approaches and techniques used by the different sequencing platforms may differ significantly, leading to both specific advantages and characteristic shortcomings. To harness their full potential by selecting the appropriate technology for a biological problem at hand, and to perform an appropriate computational analysis of the generated sequencing data, it is imperative to have a basic understanding of how the technologies work and what individual strengths and weaknesses they have. This chapter provides an introductory, necessarily incomplete overview of these sequencing technologies, and the computational analysis of the data that they produce. A particular focus will lie on their application to study the epigenetic regulation of cellular functions in the context of the immune system, but most considerations hold for other research fields as well.

PKR-like kinase (PERK) plays a significant role in inducing angiogenesis in various cancer types including glioblastoma. By proteomics analysis of the conditioned medium from a glioblastoma cell line treated with a PERK inhibitor, we showed that peptidylglycine α-amidating monooxygenase (PAM) expression is regulated by PERK under hypoxic conditions. Moreover, PERK activation via CCT020312 (a PERK selective activator) increased the cleavage and thus the generation of PAM cleaved cytosolic domain (PAM sfCD) that acts as a signaling molecule from the cytoplasm to the nuclei. PERK was also found to interact with PAM, suggesting a possible involvement in the generation of PAM sfCD. Knockdown of PERK or PAM reduced the formation of tubes by HUVECs in vitro. Furthermore, in vivo data highlighted the importance of PAM in the growth of glioblastoma with reduction of PAM expression in engrafted tumor significantly increasing the survival in mice. In summary, our data revealed PAM as a potential target for antiangiogenic therapy in glioblastoma.

Skin cancer is the most common type of cancer worldwide. Ozone depletion and climate changes might cause a further increase in the incidence rate in the future. Although the early detection of skin cancer enables it to be treated successfully, some tumours can evolve and become more aggressive, especially in the case of melanoma. Therefore, good diagnostic and prognostic markers are needed to ensure correct detection and treatment. Transcription factor p63, a member of the p53 family of proteins, plays an essential role in the development of stratified epithelia such as skin. In this paper, we conduct a comprehensive review of p63 expression in different types of skin cancer and discuss its possible use in the diagnosis and prognosis of cutaneous tumours.

The discovery that a considerable portion of eukaryotic genomes is transcribed and gives rise to long noncoding RNAs (lncRNAs) provides an important new perspective on the transcriptome and raises questions about the centrality of these lncRNAs in gene-regulatory processes and diseases. The rapidly increasing number of mechanistically investigated lncRNAs has provided evidence for distinct functional classes, such as enhancer-like lncRNAs, which modulate gene expression via chromatin looping, and noncoding competing endogenous RNAs (ceRNAs), which act as microRNA decoys. Despite great progress in the last years, the majority of lncRNAs are functionally uncharacterized and their implication for disease biogenesis and progression is unknown. Here, we summarize recent developments in lncRNA function prediction in general and lncRNA-disease associations in particular, with emphasis on in silico methods based on network analysis and on ceRNA function prediction. We believe that such computational techniques provide a valuable aid to prioritize functional lncRNAs or disease-relevant lncRNAs for targeted, experimental follow-up studies.

In this Article, author Benedikt Brors was erroneously associated with affiliation number '8' (Department of Developmental Neurobiology, St Jude Children's Research Hospital, Memphis, Tennessee, USA); the author's two other affiliations (affiliations '3' and '7', both at the German Cancer Research Center (DKFZ)) were correct. This has been corrected online.

The mutational processes responsible for the somatic mutations observed in tumor samples can significantly vary not only between tumor types but also among the individual cancers within a tumor class. Mutational processes can be represented by so called “mutational signatures” which reflect the occurrences of base changes within their sequence contexts (i.e., in dependence on their flanking bases). We present a user-friendly R package, called decompTumor2Sig, that can be used to evaluate the contribution of Shiraishi signatures to the somatic mutations found in an individual tumor.