Adaptive T-cell immunity controls senescence-prone MyD88- or CARD11-mutant B-cell lymphomas.

Aberrant B-cell receptor/NF-κB signaling is a hallmark feature of B-cell non-Hodgkin lymphomas, especially in diffuse large B-cell lymphoma (DLBCL). Recurrent mutations in this cascade, for example, in CD79B, CARD11, or NFKBIZ, and also in the Toll-like receptor pathway transducer MyD88, all deregulate NF-κB, but their differential impact on lymphoma development and biology remains to be determined. Here, we functionally investigate primary mouse lymphomas that formed in recipient mice of Eµ-myc transgenic hematopoietic stem cells stably transduced with naturally occurring NF-κB mutants. Although most mutants supported Myc-driven lymphoma formation through repressed apoptosis, CARD11- or MyD88-mutant lymphoma cells selectively presented with a macrophage-activating secretion profile, which, in turn, strongly enforced transforming growth factor ß (TGF-ß)-mediated senescence in the lymphoma cell compartment. However, MyD88- or CARD11-mutant Eµ-myc lymphomas exhibited high-level expression of the immune-checkpoint mediator programmed cell death ligand 1 (PD-L1), thus preventing their efficient clearance by adaptive host immunity. Conversely, these mutant-specific dependencies were therapeutically exploitable by anti-programmed cell death 1 checkpoint blockade, leading to direct T-cell-mediated lysis of predominantly but not exclusively senescent lymphoma cells. Importantly, mouse-based mutant MyD88- and CARD11-derived signatures marked DLBCL subgroups exhibiting mirroring phenotypes with respect to the triad of senescence induction, macrophage attraction, and evasion of cytotoxic T-cell immunity. Complementing genomic subclassification approaches, our functional, cross-species investigation unveils pathogenic principles and therapeutic vulnerabilities applicable to and testable in human DLBCL subsets that may inform future personalized treatment strategies.

An Evaluation of Hepatitis E Virus Molecular Typing Methods.

BACKGROUND: Hepatitis E virus (HEV) is a major cause of acute viral hepatitis. Better understanding of HEV subtypes involved in hepatitis E infections is essential. Investigation of sources and routes of transmission and the identification of potential clusters/outbreaks rely upon molecular typing of viral strains. A study was carried out to evaluate the ability of laboratories to undertake molecular typing with genotype and subtype determination. METHODS: A blinded panel of 11 different Orthohepevirus A strains was distributed to 28 laboratories performing HEV sequence analysis. Laboratories used their routine HEV sequencing and genotyping methods. RESULTS: Results were returned by 25 laboratories. Overall, 93% samples were assigned to the correct genotype and 81% were assigned to the correct subtype. Fragments amplified for typing ranged in size and the sequencing assays targeted both the structural and non-structural protein-coding regions. There was good agreement between the reported sequences where methods targeted overlapping fragments. In some cases, incorrect genotypes/subtypes were reported, including those not contained in the panel, and in one case, a genotype was reported for a blinded control sample containing Zika virus; collectively these data indicate contamination problems. CONCLUSIONS: In general, identification of genotypes was good; however, in a small number of cases, there was a failure to generate sequences from some of the samples. There was generally broad agreement between the use of online typing tools such as the one provided by HEVnet and curated lists of published HEV reference sequences; however, going forward harmonization between these resources is essential.

Shine-Dalgarno Sequences Play an Essential Role in the Translation of Plastid mRNAs in Tobacco.

In prokaryotic systems, the translation initiation of many, though not all, mRNAs depends on interaction between a sequence element upstream of the start codon (the Shine-Dalgarno sequence [SD]) and a complementary sequence in the 3' end of the 16S rRNA (anti-Shine-Dalgarno sequence [aSD]). Although many chloroplast mRNAs harbor putative SDs in their 5' untranslated regions and the aSD displays strong conservation, the functional relevance of SD-aSD interactions in plastid translation is unclear. Here, by generating transplastomic tobacco (Nicotiana tabacum) mutants with point mutations in the aSD coupled with genome-wide analysis of translation by ribosome profiling, we provide a global picture of SD-dependent translation in plastids. We observed a pronounced correlation between weakened predicted SD-aSD interactions and reduced translation efficiency. However, multiple lines of evidence suggest that the strength of the SD-aSD interaction is not the only determinant of the translational output of many plastid mRNAs. Finally, the translation efficiency of mRNAs with strong secondary structures around the start codon is more dependent on the SD-aSD interaction than weakly structured mRNAs. Thus, our data reveal the importance of the aSD in plastid translation initiation, uncover chloroplast genes whose translation is influenced by SD-aSD interactions, and provide insights into determinants of translation efficiency in plastids.

SoFIA: a data integration framework for annotating high-throughput datasets.

MOTIVATION: Integrating heterogeneous datasets from several sources is a common bioinformatics task that often requires implementing a complex workflow intermixing database access, data filtering, format conversions, identifier mapping, among further diverse operations. Data integration is especially important when annotating next generation sequencing data, where a multitude of diverse tools and heterogeneous databases can be used to provide a large variety of annotation for genomic locations, such a single nucleotide variants or genes. Each tool and data source is potentially useful for a given project and often more than one are used in parallel for the same purpose. However, software that always produces all available data is difficult to maintain and quickly leads to an excess of data, creating an information overload rather than the desired goal-oriented and integrated result. RESULTS: We present SoFIA, a framework for workflow-driven data integration with a focus on genomic annotation. SoFIA conceptualizes workflow templates as comprehensive workflows that cover as many data integration operations as possible in a given domain. However, these templates are not intended to be executed as a whole; instead, when given an integration task consisting of a set of input data and a set of desired output data, SoFIA derives a minimal workflow that completes the task. These workflows are typically fast and create exactly the information a user wants without requiring them to do any implementation work. Using a comprehensive genome annotation template, we highlight the flexibility, extensibility and power of the framework using real-life case studies. AVAILABILITY AND IMPLEMENTATION: https://github.com/childsish/sofia/releases/latest under the GNU General Public License CONTACT: liam.childs@hu-berlin.de SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Local absence of secondary structure permits translation of mRNAs that lack ribosome-binding sites.

The initiation of translation is a fundamental and highly regulated process in gene expression. Translation initiation in prokaryotic systems usually requires interaction between the ribosome and an mRNA sequence upstream of the initiation codon, the so-called ribosome-binding site (Shine-Dalgarno sequence). However, a large number of genes do not possess Shine-Dalgarno sequences, and it is unknown how start codon recognition occurs in these mRNAs. We have performed genome-wide searches in various groups of prokaryotes in order to identify sequence elements and/or RNA secondary structural motifs that could mediate translation initiation in mRNAs lacking Shine-Dalgarno sequences. We find that mRNAs without a Shine-Dalgarno sequence are generally less structured in their translation initiation region and show a minimum of mRNA folding at the start codon. Using reporter gene constructs in bacteria, we also provide experimental support for local RNA unfoldedness determining start codon recognition in Shine-Dalgarno--independent translation. Consistent with this, we show that AUG start codons reside in single-stranded regions, whereas internal AUG codons are usually in structured regions of the mRNA. Taken together, our bioinformatics analyses and experimental data suggest that local absence of RNA secondary structure is necessary and sufficient to initiate Shine-Dalgarno--independent translation. Thus, our results provide a plausible mechanism for how the correct translation initiation site is recognized in the absence of a ribosome-binding site.

Artificial intelligence and neoantigens: paving the path for precision cancer immunotherapy.

Cancer immunotherapy has witnessed rapid advancement in recent years, with a particular focus on neoantigens as promising targets for personalized treatments. The convergence of immunogenomics, bioinformatics, and artificial intelligence (AI) has propelled the development of innovative neoantigen discovery tools and pipelines. These tools have revolutionized our ability to identify tumor-specific antigens, providing the foundation for precision cancer immunotherapy. AI-driven algorithms can process extensive amounts of data, identify patterns, and make predictions that were once challenging to achieve. However, the integration of AI comes with its own set of challenges, leaving space for further research. With particular focus on the computational approaches, in this article we have explored the current landscape of neoantigen prediction, the fundamental concepts behind, the challenges and their potential solutions providing a comprehensive overview of this rapidly evolving field.

Identification of cis-elements conferring high levels of gene expression in non-green plastids.

Although our knowledge about the mechanisms of gene expression in chloroplasts has increased substantially over the past decades, next to nothing is known about the signals and factors that govern expression of the plastid genome in non-green tissues. Here we report the development of a quantitative method suitable for determining the activity of cis-acting elements for gene expression in non-green plastids. The in vivo assay is based on stable transformation of the plastid genome and the discovery that root length upon seedling growth in the presence of the plastid translational inhibitor kanamycin is directly proportional to the expression strength of the resistance gene nptII in transgenic tobacco plastids. By testing various combinations of promoters and translation initiation signals, we have used this experimental system to identify cis-elements that are highly active in non-green plastids. Surprisingly, heterologous expression elements from maize plastids were significantly more efficient in conferring high expression levels in root plastids than homologous expression elements from tobacco. Our work has established a quantitative method for characterization of gene expression in non-green plastid types, and has led to identification of cis-elements for efficient plastid transgene expression in non-green tissues, which are valuable tools for future transplastomic studies in basic and applied research.

Matapax: an online high-throughput genome-wide association study pipeline.

High-throughput sequencing and genotyping methods are dramatically increasing the number of observable genetic intraspecies differences that can be exploited as genetic markers. In addition, automated phenotyping platforms and "omics" profiling technologies further enlarge the set of quantifiable macroscopic and molecular traits at an ever-increasing pace. Combined, both lines of technological advances create unparalleled opportunities to identify candidate gene regions and, ideally, even single genes responsible for observed variations in a particular trait via association studies. However, as of yet, this new potential is not sufficiently matched by enabling software solutions to easily exploit this wealth of genotype/phenotype information. We have developed Matapax, a Web-based platform to address this need. Initially, we built the infrastructure to support association studies in Arabidopsis (Arabidopsis thaliana) based on several genotyping efforts covering up to 1,375 Arabidopsis accessions. Based on the user-supplied trait information, associated single-nucleotide polymorphism markers and single-nucleotide polymorphism-harboring or -neighboring genes are identified using both the GAPIT and EMMA libraries developed for R. Additional interrogation is facilitated by displaying candidate regions and genes in a genome browser and by providing relevant annotation information. In the future, we plan to broaden the scope of organisms to other plant species as more genotype/phenotype information becomes available. Matapax is freely available at http://matapax.mpimp-golm.mpg.de and can be accessed using any internet browser.

CAR Gene Delivery by T-cell Targeted Lentiviral Vectors is Enhanced by Rapamycin Induced Reduction of Antiviral Mechanisms.

Lentiviral vectors (LV) have become the dominant tool for stable gene transfer into lymphocytes including chimeric antigen receptor (CAR) gene delivery to T cells, a major breakthrough in cancer therapy. Yet, room for improvement remains, especially for the latest LV generations delivering genes selectively into T cell subtypes, a key requirement for in vivo CAR T cell generation. Toward improving gene transfer rates with these vectors, whole transcriptome analyses on human T lymphocytes are conducted after exposure to CAR-encoding conventional vectors (VSV-LV) and vectors targeted to CD8+ (CD8-LV) or CD4+ T cells (CD4-LV). Genes related to quiescence and antiviral restriction are found to be upregulated in CAR-negative cells exposed to all types of LVs. Down-modulation of various antiviral restriction factors, including the interferon-induced transmembrane proteins (IFITMs) is achieved with rapamycin as verified by mass spectrometry (LC-MS). Strikingly, rapamycin enhances transduction by up to 7-fold for CD8-LV and CD4-LV without compromising CAR T cell activities but does not improve VSV-LV. When administered to humanized mice, CD8-LV results in higher rates of green fluorescent protein (GFP) gene delivery. Also in vivo CAR T cell generation is improved in kinetics and tumor control, however to a moderate extent, leaving room for improvement by optimizing the rapamycin administration schedule. The data favor multi-omics approaches for improvements in gene delivery.

Clinal variation in the non-acclimated and cold-acclimated freezing tolerance of Arabidopsis thaliana accessions.

Arabidopsis thaliana is a geographically widely spread species consisting of local accessions differing both genetically and phenotypically. These differences may constitute environmental adaptations and a latitudinal cline in freezing tolerance has been shown previously. Many plants, including Arabidopsis, exhibit increased freezing tolerance after cold exposure (cold acclimation). Here we present evidence for geographical clines (both latitudinal and longitudinal) in acclimated (ACC) and non-acclimated (NA) freezing tolerance, estimated from electrolyte leakage measurements on 54 accessions. Leaf Pro contents were not correlated with freezing tolerance, while sugar contents (Glc, Fru, Suc, Raf) were in the ACC, but not the NA state. Expression levels of 14 cold-induced genes were investigated before and after 2 weeks of cold acclimation by quantitative RT-PCR. Expression of the CBF1, 2 and 3 genes was not correlated with freezing tolerance. The expression of some CBF-regulated (COR) genes, however, was correlated specifically with ACC freezing tolerance. A tight correlation between CBF and COR gene expression was only observed under non-acclimating conditions, where CBF and COR expression were also correlated with the expression of PRR5, a component of the circadian clock. Collectively, this study sheds new light on the molecular determinants of plant-freezing tolerance and cold acclimation and their geographical dependence.

Regulatory Considerations for Clinical Trial Applications with CRISPR-Based Medicinal Products.

Since first proposed as a new tool for gene targeting and genome editing, CRISPR technology has quickly advanced into the clinical stage. Initial studies highlight the potential for CRISPR-Cas9-mediated therapeutic approaches in human medicine to correct incurable genetic diseases and enhance cell-based therapeutic approaches. While acknowledging the opportunities this technology brings for the treatment of patients with severe diseases, timely development of these innovative medicinal products requires regulatory oversight and adaptation of regulatory requirements to ensure the safety and efficacy of medicinal products based on CRISPR technology. We briefly present the current regulatory framework applicable for CRISPR-Cas-based developments as advanced therapy medicinal products. Moreover, scientific- and regulatory-driven considerations relevant for advancing product development toward clinical trial applications in Germany are highlighted by discussing the key aspects of quality and nonclinical and clinical development requirements.

SAMHD1 in cancer: curse or cure?

Human sterile α motif and HD domain-containing protein 1 (SAMHD1), originally described as the major cellular deoxyribonucleoside triphosphate triphosphohydrolase (dNTPase) balancing the intracellular deoxynucleotide (dNTP) pool, has come recently into focus of cancer research. As outlined in this review, SAMHD1 has been reported to be mutated in a variety of cancer types and the expression of SAMHD1 is dysregulated in many cancers. Therefore, SAMHD1 is regarded as a tumor suppressor in certain tumors. Moreover, it has been proposed that SAMHD1 might fulfill the requirements of a driver gene in tumor development or might promote a so-called mutator phenotype. Besides its role as a dNTPase, several novel cellular functions of SAMHD1 have come to light only recently, including a role as negative regulator of innate immune responses and as facilitator of DNA end resection during DNA replication and repair. Therefore, SAMHD1 can be placed at the crossroads of various cellular processes. The present review summarizes the negative role of SAMHD1 in chemotherapy sensitivity, highlights reported SAMHD1 mutations found in various cancer types, and aims to discuss functional consequences as well as underlying mechanisms of SAMHD1 dysregulation potentially involved in cancer development.

DNA methylation reveals distinct cells of origin for pancreatic neuroendocrine carcinomas and pancreatic neuroendocrine tumors.

BACKGROUND: Pancreatic neuroendocrine neoplasms (PanNENs) fall into two subclasses: the well-differentiated, low- to high-grade pancreatic neuroendocrine tumors (PanNETs), and the poorly-differentiated, high-grade pancreatic neuroendocrine carcinomas (PanNECs). While recent studies suggest an endocrine descent of PanNETs, the origin of PanNECs remains unknown. METHODS: We performed DNA methylation analysis for 57 PanNEN samples and found that distinct methylation profiles separated PanNENs into two major groups, clearly distinguishing high-grade PanNECs from other PanNETs including high-grade NETG3. DNA alterations and immunohistochemistry of cell-type markers PDX1, ARX, and SOX9 were utilized to further characterize PanNECs and their cell of origin in the pancreas. RESULTS: Phylo-epigenetic and cell-type signature features derived from alpha, beta, acinar, and ductal adult cells suggest an exocrine cell of origin for PanNECs, thus separating them in cell lineage from other PanNENs of endocrine origin. CONCLUSIONS: Our study provides a robust and clinically applicable method to clearly distinguish PanNECs from G3 PanNETs, improving patient stratification.

Robin: an intuitive wizard application for R-based expression microarray quality assessment and analysis.

The wide application of high-throughput transcriptomics using microarrays has generated a plethora of technical platforms, data repositories, and sophisticated statistical analysis methods, leaving the individual scientist with the problem of choosing the appropriate approach to address a biological question. Several software applications that provide a rich environment for microarray analysis and data storage are available (e.g. GeneSpring, EMMA2), but these are mostly commercial or require an advanced informatics infrastructure. There is a need for a noncommercial, easy-to-use graphical application that aids the lab researcher to find the proper method to analyze microarray data, without this requiring expert understanding of the complex underlying statistics, or programming skills. We have developed Robin, a Java-based graphical wizard application that harnesses the advanced statistical analysis functions of the R/BioConductor project. Robin implements streamlined workflows that guide the user through all steps of two-color, single-color, or Affymetrix microarray analysis. It provides functions for thorough quality assessment of the data and automatically generates warnings to notify the user of potential outliers, low-quality chips, or low statistical power. The results are generated in a standard format that allows ready use with both specialized analysis tools like MapMan and PageMan and generic spreadsheet applications. To further improve user friendliness, Robin includes both integrated help and comprehensive external documentation. To demonstrate the statistical power and ease of use of the workflows in Robin, we present a case study in which we apply Robin to analyze a two-color microarray experiment comparing gene expression in tomato (Solanum lycopersicum) leaves, flowers, and roots.

Identification and classification of ncRNA molecules using graph properties.

The study of non-coding RNA genes has received increased attention in recent years fuelled by accumulating evidence that larger portions of genomes than previously acknowledged are transcribed into RNA molecules of mostly unknown function, as well as the discovery of novel non-coding RNA types and functional RNA elements. Here, we demonstrate that specific properties of graphs that represent the predicted RNA secondary structure reflect functional information. We introduce a computational algorithm and an associated web-based tool (GraPPLE) for classifying non-coding RNA molecules as functional and, furthermore, into Rfam families based on their graph properties. Unlike sequence-similarity-based methods and covariance models, GraPPLE is demonstrated to be more robust with regard to increasing sequence divergence, and when combined with existing methods, leads to a significant improvement of prediction accuracy. Furthermore, graph properties identified as most informative are shown to provide an understanding as to what particular structural features render RNA molecules functional. Thus, GraPPLE may offer a valuable computational filtering tool to identify potentially interesting RNA molecules among large candidate datasets.

Single feature polymorphism (SFP)-based selective sweep identification and association mapping of growth-related metabolic traits in Arabidopsis thaliana.

BACKGROUND: Natural accessions of Arabidopsis thaliana are characterized by a high level of phenotypic variation that can be used to investigate the extent and mode of selection on the primary metabolic traits. A collection of 54 A. thaliana natural accession-derived lines were subjected to deep genotyping through Single Feature Polymorphism (SFP) detection via genomic DNA hybridization to Arabidopsis Tiling 1.0 Arrays for the detection of selective sweeps, and identification of associations between sweep regions and growth-related metabolic traits. RESULTS: A total of 1,072,557 high-quality SFPs were detected and indications for 3,943 deletions and 1,007 duplications were obtained. A significantly lower than expected SFP frequency was observed in protein-, rRNA-, and tRNA-coding regions and in non-repetitive intergenic regions, while pseudogenes, transposons, and non-coding RNA genes are enriched with SFPs. Gene families involved in plant defence or in signalling were identified as highly polymorphic, while several other families including transcription factors are depleted of SFPs. 198 significant associations between metabolic genes and 9 metabolic and growth-related phenotypic traits were detected with annotation hinting at the nature of the relationship. Five significant selective sweep regions were also detected of which one associated significantly with a metabolic trait. CONCLUSIONS: We generated a high density polymorphism map for 54 A. thaliana accessions that highlights the variability of resistance genes across geographic ranges and used it to identify selective sweeps and associations between metabolic genes and metabolic phenotypes. Several associations show a clear biological relationship, while many remain requiring further investigation.

DNA copy number changes define spatial patterns of heterogeneity in colorectal cancer.

Genetic heterogeneity between and within tumours is a major factor determining cancer progression and therapy response. Here we examined DNA sequence and DNA copy-number heterogeneity in colorectal cancer (CRC) by targeted high-depth sequencing of 100 most frequently altered genes. In 97 samples, with primary tumours and matched metastases from 27 patients, we observe inter-tumour concordance for coding mutations; in contrast, gene copy numbers are highly discordant between primary tumours and metastases as validated by fluorescent in situ hybridization. To further investigate intra-tumour heterogeneity, we dissected a single tumour into 68 spatially defined samples and sequenced them separately. We identify evenly distributed coding mutations in APC and TP53 in all tumour areas, yet highly variable gene copy numbers in numerous genes. 3D morpho-molecular reconstruction reveals two clusters with divergent copy number aberrations along the proximal-distal axis indicating that DNA copy number variations are a major source of tumour heterogeneity in CRC.

Assembly of a comprehensive regulatory network for the mammalian circadian clock: a bioinformatics approach.

By regulating the timing of cellular processes, the circadian clock provides a way to adapt physiology and behaviour to the geophysical time. In mammals, a light-entrainable master clock located in the suprachiasmatic nucleus (SCN) controls peripheral clocks that are present in virtually every body cell. Defective circadian timing is associated with several pathologies such as cancer and metabolic and sleep disorders. To better understand the circadian regulation of cellular processes, we developed a bioinformatics pipeline encompassing the analysis of high-throughput data sets and the exploitation of published knowledge by text-mining. We identified 118 novel potential clock-regulated genes and integrated them into an existing high-quality circadian network, generating the to-date most comprehensive network of circadian regulated genes (NCRG). To validate particular elements in our network, we assessed publicly available ChIP-seq data for BMAL1, REV-ERBα/ß and RORα/γ proteins and found strong evidence for circadian regulation of Elavl1, Nme1, Dhx6, Med1 and Rbbp7 all of which are involved in the regulation of tumourigenesis. Furthermore, we identified Ncl and Ddx6, as targets of RORγ and REV-ERBα, ß, respectively. Most interestingly, these genes were also reported to be involved in miRNA regulation; in particular, NCL regulates several miRNAs, all involved in cancer aggressiveness. Thus, NCL represents a novel potential link via which the circadian clock, and specifically RORγ, regulates the expression of miRNAs, with particular consequences in breast cancer progression. Our findings bring us one step forward towards a mechanistic understanding of mammalian circadian regulation, and provide further evidence of the influence of circadian deregulation in cancer.

SLocX: Predicting Subcellular Localization of Arabidopsis Proteins Leveraging Gene Expression Data.

Despite the growing volume of experimentally validated knowledge about the subcellular localization of plant proteins, a well performing in silico prediction tool is still a necessity. Existing tools, which employ information derived from protein sequence alone, offer limited accuracy and/or rely on full sequence availability. We explored whether gene expression profiling data can be harnessed to enhance prediction performance. To achieve this, we trained several support vector machines to predict the subcellular localization of Arabidopsis thaliana proteins using sequence derived information, expression behavior, or a combination of these data and compared their predictive performance through a cross-validation test. We show that gene expression carries information about the subcellular localization not available in sequence information, yielding dramatic benefits for plastid localization prediction, and some notable improvements for other compartments such as the mitochondrion, the Golgi, and the plasma membrane. Based on these results, we constructed a novel subcellular localization prediction engine, SLocX, combining gene expression profiling data with protein sequence-based information. We then validated the results of this engine using an independent test set of annotated proteins and a transient expression of GFP fusion proteins. Here, we present the prediction framework and a website of predicted localizations for Arabidopsis. The relatively good accuracy of our prediction engine, even in cases where only partial protein sequence is available (e.g., in sequences lacking the N-terminal region), offers a promising opportunity for similar application to non-sequenced or poorly annotated plant species. Although the prediction scope of our method is currently limited by the availability of expression information on the ATH1 array, we believe that the advances in measuring gene expression technology will make our method applicable for all Arabidopsis proteins.