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Background In southern Europe, the risk of cancer in patients with end-stage kidney disease receiving dialysis has not been well quantified. The aim of this study was to assess the overall pattern of risk for de novo malignancies (DNMs) among dialysis patients in the Friuli Venezia Giulia region, north-eastern Italy. Methods A population-based cohort study among 3407 dialysis patients was conducted through a record linkage between local healthcare databases and the cancer registry (1998–2013). Person-years (PYs) were calculated from 30 days after the date of first dialysis to the date of DNM diagnosis, kidney transplant, death, last follow-up or December 31, 2013, whichever came first. The risk of DNM, as compared to the general population, was estimated using standardized incidence ratios (SIRs) and 95% confidence intervals (CIs). Results During 10,798 PYs, 357 DNMs were diagnosed in 330 dialysis patients. A higher than expected risk of 1.3-fold was found for all DNMs combined (95% CI: 1.15–1.43). The risk was particularly high in younger dialysis patients (SIR = 1.88, 95% CI: 1.42–2.45 for age 40–59 years), and it decreased with age. Moreover, significantly increased DNM risks emerged during the first 3 years since dialysis initiation, especially within the first year (SIR = 8.52, 95% CI: 6.89–10.41). Elevated excess risks were observed for kidney (SIR = 3.18; 95% CI: 2.06–4.69), skin non-melanoma (SIR = 1.81, 95% CI: 1.46–2.22), oral cavity (SIR = 2.42, 95% CI: 1.36–4.00), and Kaposi’s sarcoma (SIR = 10.29, 95% CI: 1.25–37.16). Conclusions The elevated risk for DNM herein documented suggest the need to implement a targeted approach to cancer prevention and control in dialysis patients.

Background High-throughput transcriptome sequencing (RNA-seq) technology promises to discover novel protein-coding and non-coding transcripts, particularly the identification of long non-coding RNAs (lncRNAs) from de novo sequencing data. This requires tools that are not restricted by prior gene annotations, genomic sequences and high-quality sequencing. Results We present an alignment-free tool called PLEK ( p redictor of l ong non-coding RNAs and m e ssenger RNAs based on an improved k -mer scheme), which uses a computational pipeline based on an improved k -mer scheme and a support vector machine (SVM) algorithm to distinguish lncRNAs from messenger RNAs (mRNAs), in the absence of genomic sequences or annotations. The performance of PLEK was evaluated on well-annotated mRNA and lncRNA transcripts. 10-fold cross-validation tests on human RefSeq mRNAs and GENCODE lncRNAs indicated that our tool could achieve accuracy of up to 95.6%. We demonstrated the utility of PLEK on transcripts from other vertebrates using the model built from human datasets. PLEK attained >90% accuracy on most of these datasets. PLEK also performed well using a simulated dataset and two real de novo assembled transcriptome datasets (sequenced by PacBio and 454 platforms) with relatively high indel sequencing errors. In addition, PLEK is approximately eightfold faster than a newly developed alignment-free tool, named Coding-Non-Coding Index (CNCI), and 244 times faster than the most popular alignment-based tool, Coding Potential Calculator (CPC), in a single-threading running manner. Conclusions PLEK is an efficient alignment-free computational tool to distinguish lncRNAs from mRNAs in RNA-seq transcriptomes of species lacking reference genomes. PLEK is especially suitable for PacBio or 454 sequencing data and large-scale transcriptome data. Its open-source software can be freely downloaded from https://sourceforge.net/projects/plek/files/ .

A growing body of evidence now suggests that artificial intelligence and machine learning techniques can serve as an indispensable foundation for the process of drug design and discovery. In light of latest advancements in computing technologies, deep learning algorithms are being created during the development of clinically useful drugs for treatment of a number of diseases. In this review, we focus on the latest developments for three particular arenas in drug design and discovery research using deep learning approaches, such as generative adversarial network (GAN) frameworks. Firstly, we review drug design and discovery studies that leverage various GAN techniques to assess one main application such as molecular de novo design in drug design and discovery. In addition, we describe various GAN models to fulfill the dimension reduction task of single-cell data in the preclinical stage of the drug development pipeline. Furthermore, we depict several studies in de novo peptide and protein design using GAN frameworks. Moreover, we outline the limitations in regard to the previous drug design and discovery studies using GAN models. Finally, we present a discussion of directions and challenges for future research.

Structure generators are widely used in de novo design studies and their performance substantially influences an outcome. Approaches based on the deep learning models and conventional atom-based approaches may result in invalid structures and fail to address their synthetic feasibility issues. On the other hand, conventional reaction-based approaches result in synthetically feasible compounds but novelty and diversity of generated compounds may be limited. Fragment-based approaches can provide both better novelty and diversity of generated compounds but the issue of synthetic complexity of generated structure was not explicitly addressed before. Here we developed a new framework of fragment-based structure generation that, by design, results in the chemically valid structures and provides flexible control over diversity, novelty, synthetic complexity and chemotypes of generated compounds. The framework was implemented as an open-source Python module and can be used to create custom workflows for the exploration of chemical space.

Background The study of de novo variation is important for assessing biological characteristics of new variation and for studies related to human phenotypes. Software programs exist to call de novo variants and programs also exist to test the burden of these variants in genomic regions; however, I am unaware of a program that fits in between these two aspects of de novo variant assessment. This intermediate space is important for assessing the quality of de novo variants and to understand the characteristics of the callsets. For this reason, I developed an R package called acorn. Results Acorn is an R package that examines various features of de novo variants including subsetting the data by individual(s), variant type, or genomic region; calculating features including variant change counts, variant lengths, and presence/absence at CpG sites; and characteristics of parental age in relation to de novo variant counts. Conclusions Acorn is an R package that fills a critical gap in assessing de novo variants and will be of benefit to many investigators studying de novo variation.

ABSTRACT Recent studies have shown that disruptions in the nicotinamide adenine dinucleotide (NAD+) de novo synthesis pathway accelerate ovarian aging, yet its role in spermatogenesis remains largely unknown. In this study, we investigated the impact of the NAD+ de novo synthesis pathway on spermatogenesis by generating Qprt‐deficient mice using CRISPR‐Cas9 to target quinolinate phosphoribosyl transferase (Qprt), a key enzyme predominantly expressed in spermatocytes. Our results revealed that the deletion of Qprt did not affect NAD+ levels or spermatogenesis in the testes of 3‐month‐old mice. However, from 6 months of age onward, Qprt‐deficient mice exhibited significantly reduced NAD+ levels in the testes compared to wild‐type (WT) controls, along with a notable decrease in germ cell numbers and increased apoptosis. Additionally, these mice demonstrated mitochondrial dysfunction in spermatocytes, impaired progression through prophase I of meiosis, defective double‐strand break (DSB) repair, and abnormal meiotic sex chromosome inactivation. Importantly, supplementation with the NAD+ precursor nicotinamide riboside (NR) in Qprt‐deficient mice restored NAD+ levels and rescued the spermatogenic defects. These findings underscore the critical role of NAD+ de novo synthesis in maintaining NAD+ homeostasis and highlight its importance in meiotic recombination and meiotic sex chromosome inactivation in spermatogenesis. In Qprt‐deficient mice, testicular NAD+ levels decline accelerated with age, leading to mitochondrial dysfunction, disrupted DSB repair, impaired meiotic sex chromosome inactivation, and germ cell loss. Nicotinamide riboside (NR) supplementation restores NAD+ levels and alleviates these defects, highlighting the importance of NAD+ de novo synthesis in age‐related reproductive health.

The replication of a virus within its host cell involves numerous interactions between viral and cellular factors, which have to be tightly controlled in space and time. The intricate interplay between viral exploitation of cellular pathways and the intrinsic host defense mechanisms is difficult to unravel by traditional bulk approaches. In recent years, novel fluorescence microscopy techniques and single virus tracking have transformed the investigation of dynamic virus-host interactions. A prerequisite for the application of these imaging-based methods is the attachment of a fluorescent label to the structure of interest. However, their small size, limited coding capacity and multifunctional proteins render viruses particularly challenging targets for fluorescent labeling approaches. Click chemistry in conjunction with genetic code expansion provides virologists with a novel toolbox for site-specific, minimally invasive labeling of virion components, whose potential has just recently begun to be exploited. Here, we summarize recent achievements, current developments and future challenges for the labeling of viral nucleic acids, proteins, glycoproteins or lipids using click chemistry in order to study dynamic processes in virus-cell interactions.

Plants have remarkable regenerative capacity, which allows them to survive tissue damage after exposure to biotic and abiotic stresses. Some of the key transcription factors and hormone crosstalk mechanisms involved in wound-induced organ regeneration have been extensively studied in the model plant Arabidopsis thaliana. However, little is known about the role of metabolism in wound-induced organ formation. Here, we performed detailed transcriptome analysis and used a targeted metabolomics approach to study de novo organ formation in tomato hypocotyl explants and found tissue-specific metabolic differences and divergent developmental pathways. Our results indicate that successful regeneration in the apical region of the hypocotyl depends on a specific metabolic switch involving the upregulation of photorespiratory pathway components and the differential regulation of photosynthesis-related gene expression and gluconeogenesis pathway activation. These findings provide a useful resource for further investigation of the molecular mechanisms involved in wound-induced organ formation in crop species such as tomato.

The Eurasian perch (Perca fluviatilis) is the most common fish of the Percidae family and is widely distributed across Eurasia. Perch is a popular target for professional and recreational fisheries, and a promising freshwater aquaculture species in Europe. However, despite its high ecological, economical and societal importance, the available genomic resources for P. fluviatilis are rather limited. In this work, we report de novo assembly and annotation of the whole genome sequence of perch. The linked-read based technology with 10X Genomics Chromium chemistry and Supernova assembler produced a draft perch genome ∼1.0 Gbp assembly (scaffold N50 = 6.3 Mb; the longest individual scaffold of 29.3 Mb; BUSCO completeness of 88.0%), which included 281.6 Mb of putative repeated sequences. The perch genome assembly presented here, generated from small amount of starting material (0.75 ng) and a single linked-read library, is highly continuous and considerably more complete than the currently available draft of P. fluviatilis genome. A total of 23,397 protein-coding genes were predicted, 23,171 (99%) of which were annotated functionally from either sequence homology or protein signature searches. Linked-read technology enables fast, accurate and cost-effective de novo assembly of large non-model eukaryote genomes. The highly continuous assembly of the Eurasian perch genome presented in this study will be an invaluable resource for a range of genetic, ecological, physiological, ecotoxicological, functional and comparative genomic studies in perch and other fish species of the Percidae family.

Isogenic laboratory mouse strains enhance reproducibility because individual animals are genetically identical. For the most widely used isogenic strain, C57BL/6, there exists a wealth of genetic, phenotypic, and genomic data, including a high-quality reference genome (GRCm38.p6). Now 20 years after the first release of the mouse reference genome, C57BL/6J mice are at least 26 inbreeding generations removed from GRCm38 and the strain is now maintained with periodic reintroduction of cryorecovered mice derived from a single breeder pair, aptly named Adam and Eve. To provide an update to the mouse reference genome that more accurately represents the genome of today’s C57BL/6J mice, we took advantage of long read, short read, and optical mapping technologies to generate a de novo assembly of the C57BL/6J Eve genome (B6Eve). Using these data, we have addressed recurring variants observed in previous mouse genomic studies. We have also identified structural variations, closed gaps in the mouse reference assembly, and revealed previously unannotated coding sequences. This B6Eve assembly explains discrepant observations that have been associated with GRCm38-based analyses, and will inform a reference genome that is more representative of the C57BL/6J mice that are in use today.