Transcriptome sequencing to detect gene fusions in cancer

Evolutionary cues of gene fusion and fission in plants

KEY MESSAGE

Here, we systematically analyzed the potential fusion and fission events of neighboring genes in Arabidopsis genome and analyzed the influence on the protein targeting.

Fateful Decisions of Where to Cut the Line: Pathology Associated with Aberrant 3' End Processing and Transcription Termination

Aberrant gene expression lies at the heart of many pathologies. This review will point out how 3' end processing, the final mRNA-maturation step in the transcription cycle, is surprisingly prone to regulated as well as stochastic variations with a wide range of consequences. Whereas smaller variations contribute to the plasticity of gene expression, larger alternations to 3' end processing and coupled transcription termination can lead to pathological consequences. These can be caused by the local mutation of one gene or affect larger numbers of genes systematically, if aspects of the mechanisms of 3' end processing and transcription termination are altered.

Successive next-generation sequencing strategy for optimal fusion gene detection in non-small-cell lung cancer in clinical practice

Metastatic non-small-cell lung cancer (NSCLC) displays various molecular alterations in the RAS-MAPK pathway. In particular, NSCLCs show high rates of targetable gene fusion in ALK, RET, ROS1, NRG1 and NTRK, or MET exon 14 skipping. Rapid and accurate detection of gene fusion in EGFR/KRAS/BRAF mutations is important for treatment selection especially for first-line indications. RNA-based next-generation sequencing (NGS) panels appear to be the most appropriate as all targets are multiplexed in a single run. While comprehensive NGS panels remain costly for daily practice, optimal sequencing strategies using targeted DNA/RNA panel approaches need to be validated. Here, we describe our lung cancer screening strategy using DNA and RNA targeted approaches in a real-life cohort of 589 NSCLC patients assessed for molecular testing. Gene fusions were analysed in 174 patients negative for oncogene driver mutations or ALK immunohistochemistry in a two-step strategy. Targetable alterations were identified in 28% of contributive samples. Non-smokers had a 63.7% probability to have a targetable alteration as compared to 21.5% for smokers. Overall survival was significantly higher (p=0.03) for patients who received a molecularly matched therapy. Our study shows the feasibility in routine testing of NSCLC DNA/RNA molecular screening for all samples in a cost- and time-controlled manner. The significant high fusion detection rate in patients with wild-type RAS-MAPK tumours highlights the importance of amending testing strategies in NSCLC.

Interferon-γ inducible factor 16 (IFI16) restricts adeno-associated virus type 2 (AAV2) transduction in an immune-modulatory independent way

We determined the transcription profile of adeno-associated virus type 2 (AAV2)-infected primary human fibroblasts. Subsequent analysis revealed that cells respond to AAV infection through changes in several significantly affected pathways, including cell cycle regulation, chromatin modulation, and innate immune responses. Various assays were performed to validate selected differentially expressed genes and to confirm not only the quality but also the robustness of the raw data. One of the genes upregulated in AAV2-infected cells was interferon-γ inducible factor 16 (IFI16). IFI16 is known as a multifunctional cytosolic and nuclear innate immune sensor for double-stranded as well as single-stranded DNA, exerting its effects through various mechanisms, such as interferon response, epigenetic modifications, or transcriptional regulation. IFI16 thereby constitutes a restriction factor for many different viruses among them, as shown here, AAV2 and thereof derived vectors. Indeed, the post-transcriptional silencing of IFI16 significantly increased AAV2 transduction efficiency, independent of the structure of the virus/vector genome. We also show that IFI16 exerts its inhibitory effect on AAV2 transduction in an immune-modulatory independent way by interfering with Sp1-dependent transactivation of wild-type AAV2 and AAV2 vector promoters.

IMPORTANCE

Adeno-associated virus (AAV) vectors are among the most frequently used viral vectors for gene therapy. The lack of pathogenicity of the parental virus, the long-term persistence as episomes in non-proliferating cells, and the availability of a variety of AAV serotypes differing in their cellular tropism are advantageous features of this biological nanoparticle. To deepen our understanding of virus-host interactions, especially in terms of antiviral responses, we present here the first transcriptome analysis of AAV serotype 2 (AAV2)-infected human primary fibroblasts. Our findings indicate that interferon-γ inducible factor 16 acts as an antiviral factor in AAV2 infection and AAV2 vector-mediated cell transduction in an immune-modulatory independent way by interrupting the Sp1-dependent gene expression from viral or vector genomes.

Fusion Challenges in Solid Tumors: Shaping the Landscape of Cancer Care in Precision Medicine

Targeting actionable fusions has emerged as a promising approach to cancer treatment. Next-generation sequencing (NGS)-based techniques have unveiled the landscape of actionable fusions in cancer. However, these approaches remain insufficient to provide optimal treatment options for patients with cancer. This article provides a comprehensive overview of the actionability and clinical development of targeted agents aimed at driver fusions. It also highlights the challenges associated with fusion testing, including the evaluation of patients with cancer who could potentially benefit from testing and devising an effective strategy. The implementation of DNA NGS for all tumor types, combined with RNA sequencing, has the potential to maximize detection while considering cost effectiveness. Herein, we also present a fusion testing strategy aimed at improving outcomes in patients with cancer.

Comparative transcriptomics analysis on Senecavirus A-infected and non-infected cells

Senecavirus A (SVA) is an emerging virus that causes the vesicular disease in pigs, clinically indistinguishable from other high consequence vesicular diseases. This virus belongs to the genus Senecavirus in the family Picornaviridae. Its genome is a positive-sense, single-stranded RNA, approximately 7,300 nt in length, with a 3' poly(A) tail but without 5'-end capped structure. SVA can efficiently propagate in different cells, including some non-pig-derived cell lines. A wild-type SVA was previously rescued from its cDNA clone using reverse genetics in our laboratory. In the present study, the BSR-T7/5 cell line was inoculated with the passage-5 SVA. At 12 h post-inoculation, SVA-infected and non-infected cells were independently collected for the analysis on comparative transcriptomics. The results totally showed 628 differentially expressed genes, including 565 upregulated and 63 downregulated ones, suggesting that SVA infection significantly stimulated the transcription initiation in cells. GO and KEGG enrichment analyses demonstrated that SVA exerted multiple effects on immunity-related pathways in cells. Furthermore, the RNA sequencing data were subjected to other in-depth analyses, such as the single-nucleotide polymorphism, transcription factors, and protein-protein interactions. The present study, along with our previous proteomics and metabolomics researches, provides a multi-omics insight into the interaction between SVA and its hosts.

Cancer fusion transcripts with human non-coding RNAs

Cancer chimeric, or fusion, transcripts are thought to most frequently appear due to chromosomal aberrations that combine moieties of unrelated normal genes. When being expressed, this results in chimeric RNAs having upstream and downstream parts relatively to the breakpoint position for the 5'- and 3'-fusion components, respectively. As many other types of cancer mutations, fusion genes can be of either driver or passenger type. The driver fusions may have pivotal roles in malignisation by regulating survival, growth, and proliferation of tumor cells, whereas the passenger fusions most likely have no specific function in cancer. The majority of research on fusion gene formation events is concentrated on identifying fusion proteins through chimeric transcripts. However, contemporary studies evidence that fusion events involving non-coding RNA (ncRNA) genes may also have strong oncogenic potential. In this review we highlight most frequent classes of ncRNAs fusions and summarize current understanding of their functional roles. In many cases, cancer ncRNA fusion can result in altered concentration of the non-coding RNA itself, or it can promote protein expression from the protein-coding fusion moiety. Differential splicing, in turn, can enrich the repertoire of cancer chimeric transcripts, e.g. as observed for the fusions of circular RNAs and long non-coding RNAs. These and other ncRNA fusions are being increasingly recognized as cancer biomarkers and even potential therapeutic targets. Finally, we discuss the use of ncRNA fusion genes in the context of cancer detection and therapy.

Integrative Molecular Analyses of the MD Anderson Prostate Cancer Patient-derived Xenograft (MDA PCa PDX) Series

PURPOSE

Develop and deploy a robust discovery platform that encompasses heterogeneity, clinical annotation, and molecular characterization and overcomes the limited availability of prostate cancer models. This initiative builds on the rich MD Anderson (MDA) prostate cancer (PCa) patient-derived xenograft (PDX) resource to complement existing publicly available databases by addressing gaps in clinically annotated models reflecting the heterogeneity of potentially lethal and lethal prostate cancer.

EXPERIMENTAL DESIGN

We performed whole-genome, targeted, and RNA sequencing in representative samples of the same tumor from 44 PDXs derived from 38 patients linked to donor tumor metadata and corresponding organoids. The cohort includes models derived from different morphologic groups, disease states, and involved organ sites (including circulating tumor cells), as well as paired samples representing heterogeneity or stages before and after therapy.

RESULTS

The cohort recapitulates clinically reported alterations in prostate cancer genes, providing a data resource for clinical and molecular interrogation of suitable experimental models. Paired samples displayed conserved molecular alteration profiles, suggesting the relevance of other regulatory mechanisms (e.g., epigenomic) influenced by the microenvironment and/or treatment. Transcriptomically, models were grouped on the basis of morphologic classification. DNA damage response-associated mechanisms emerged as differentially regulated between adenocarcinoma and neuroendocrine prostate cancer in a cross-interrogation of PDX/patient datasets.

CONCLUSIONS

We addressed the gap in clinically relevant prostate cancer models through comprehensive molecular characterization of MDA PCa PDXs, providing a discovery platform that integrates with patient data and benchmarked to therapeutically relevant consensus clinical groupings. This unique resource supports robust hypothesis generation and testing from basic, translational, and clinical perspectives.

Discovery of a polymorphic gene fusion via bottom-up chimeric RNA prediction

Gene fusions and their chimeric products are commonly linked with cancer. However, recent studies have found chimeric transcripts in non-cancer tissues and cell lines. Large-scale efforts to annotate structural variations have identified gene fusions capable of generating chimeric transcripts even in normal tissues. In this study, we present a bottom-up approach targeting population-specific chimeric RNAs, identifying 58 such instances in the GTEx cohort, including notable cases such as SUZ12P1-CRLF3, TFG-ADGRG7 and TRPM4-PPFIA3, which possess distinct patterns across different ancestry groups. We provide direct evidence for an additional 29 polymorphic chimeric RNAs with associated structural variants, revealing 13 novel rare structural variants. Additionally, we utilize the All of Us dataset and a large cohort of clinical samples to characterize the association of the SUZ12P1-CRLF3-causing variant with patient phenotypes. Our study showcases SUZ12P1-CRLF3 as a representative example, illustrating the identification of elusive structural variants by focusing on those producing population-specific fusion transcripts.

Plausible role of INPP4A dysregulation in idiopathic pulmonary fibrosis

INPP4A has been shown to be involved in the regulation of cell proliferation and apoptosis of multiple cell types including fibroblasts. Previous reports from our group have demonstrated the role of inositol polyphosphate 4-phosphatase Type I A (INPP4A) in these functions. Though existing evidences suggest a critical role for INPP4A in the maintenance of lung homeostasis, its role in chronic lung diseases is relatively under explored. In the current study, we made an attempt to understand the regulation of INPP4A in idiopathic pulmonary fibrosis (IPF). Through integration of relevant INPP4A gene expression data from public repositories with our results from in vitro experiments and mouse models, we show that INPP4A is altered in IPF. Interestingly, the direction of the change is dependent both on the disease stage and the region of the lung used. INPP4A was found to be upregulated when analyzed in lung sample representative of the whole lung, but was downregulated in the fibrotic regions of the lung. Similarly, INPP4A was found to be high, compared to controls, only in the early stage of the disease. Though the observed increase in INPP4A was found to be negatively correlated to physiological indices, FVC, and DLCO, of lung function, treatment with anti-INPP4A antibody worsened the condition in bleomycin treated mice. These contrasting results taken together are suggestive of a nuanced regulation of INPP4A in IPF which is dependent on the disease stage, cellular state and extent of fibrosis in the lung region being analyzed.

Modification of the Hi-C Technology for Molecular Genetic Analysis of Formalin-Fixed Paraffin-Embedded Sections of Tumor Tissues

Molecular genetic analysis of tumor tissues is the most important step towards understanding the mechanisms of cancer development; it is also necessary for the choice of targeted therapy. The Hi-C (high-throughput chromatin conformation capture) technology can be used to detect various types of genomic variants, including balanced chromosomal rearrangements, such as inversions and translocations. We propose a modification of the Hi-C method for the analysis of chromatin contacts in formalin-fixed paraffin-embedded (FFPE) sections of tumor tissues. The developed protocol allows to generate high-quality Hi-C data and detect all types of chromosomal rearrangements. We have analyzed various databases to compile a comprehensive list of translocations that hold clinical importance for the targeted therapy selection. The practical value of molecular genetic testing is its ability to influence the treatment strategies and to provide prognostic insights. Detecting specific chromosomal rearrangements can guide the choice of the targeted therapies, which is a critical aspect of personalized medicine in oncology.

Short-Read RNA-Seq

RNA sequencing (RNA-Seq) has emerged as a powerful and versatile tool for the comprehensive analysis of transcriptomes and has been widely used to investigate gene expression, copy number variation, alternative splicing, and novel transcript discovery. This chapter outlines the methodology for conducting short-read RNA-Seq, starting from RNA enrichment to library preparation and sequencing. Throughout the chapter, practical tips and best practices are provided to guide researchers in order to optimize each step of the RNA-Seq workflow. Multiple quality control steps throughout the workflow that are critical to obtain high-quality RNA-Seq data are also discussed.

Toward the functional interpretation of somatic structural variations: bulk- and single-cell approaches

Structural variants (SVs) are genomic rearrangements that can take many different forms such as copy number alterations, inversions and translocations. During cell development and aging, somatic SVs accumulate in the genome with potentially neutral, deleterious or pathological effects. Generation of somatic SVs is a key mutational process in cancer development and progression. Despite their importance, the detection of somatic SVs is challenging, making them less studied than somatic single-nucleotide variants. In this review, we summarize recent advances in whole-genome sequencing (WGS)-based approaches for detecting somatic SVs at the tissue and single-cell levels and discuss their advantages and limitations. First, we describe the state-of-the-art computational algorithms for somatic SV calling using bulk WGS data and compare the performance of somatic SV detectors in the presence or absence of a matched-normal control. We then discuss the unique features of cutting-edge single-cell-based techniques for analyzing somatic SVs. The advantages and disadvantages of bulk and single-cell approaches are highlighted, along with a discussion of their sensitivity to copy-neutral SVs, usefulness for functional inferences and experimental and computational costs. Finally, computational approaches for linking somatic SVs to their functional readouts, such as those obtained from single-cell transcriptome and epigenome analyses, are illustrated, with a discussion of the promise of these approaches in health and diseases.

Uniformly shaped harmonization combines human transcriptomic data from different platforms while retaining their biological properties and differential gene expression patterns

Introduction: Co-normalization of RNA profiles obtained using different experimental platforms and protocols opens avenue for comprehensive comparison of relevant features like differentially expressed genes associated with disease. Currently, most of bioinformatic tools enable normalization in a flexible format that depends on the individual datasets under analysis. Thus, the output data of such normalizations will be poorly compatible with each other. Recently we proposed a new approach to gene expression data normalization termed Shambhala which returns harmonized data in a uniform shape, where every expression profile is transformed into a pre-defined universal format. We previously showed that following shambhalization of human RNA profiles, overall tissue-specific clustering features are strongly retained while platform-specific clustering is dramatically reduced. Methods: Here, we tested Shambhala performance in retention of fold-change gene expression features and other functional characteristics of gene clusters such as pathway activation levels and predicted cancer drug activity scores. Results: Using 6,793 cancer and 11,135 normal tissue gene expression profiles from the literature and experimental datasets, we applied twelve performance criteria for different versions of Shambhala and other methods of transcriptomic harmonization with flexible output data format. Such criteria dealt with the biological type classifiers, hierarchical clustering, correlation/regression properties, stability of drug efficiency scores, and data quality for using machine learning classifiers. Discussion: Shambhala-2 harmonizer demonstrated the best results with the close to 1 correlation and linear regression coefficients for the comparison of training vs validation datasets and more than two times lesser instability for calculation of drug efficiency scores compared to other methods.

Detecting intragenic trans-splicing events from non-co-linearly spliced junctions by hybrid sequencing

Trans-spliced RNAs (ts-RNAs) are a type of non-co-linear (NCL) transcripts that consist of exons in an order topologically inconsistent with the corresponding DNA template. Detecting ts-RNAs is often interfered by experimental artifacts, circular RNAs (circRNAs) and genetic rearrangements. Particularly, intragenic ts-RNAs, which are derived from separate precursor mRNA molecules of the same gene, are often mistaken for circRNAs through analyses of RNA-seq data. Here we developed a bioinformatics pipeline (NCLscan-hybrid), which integrated short and long RNA-seq reads to minimize false positives and proposed out-of-circle and rolling-circle long reads to distinguish between intragenic ts-RNAs and circRNAs. Combining NCLscan-hybrid screening and multiple experimental validation steps successfully confirmed that four NCL events, which were previously regarded as circRNAs in databases, originated from trans-splicing. CRISPR-based endogenous genome modification experiments further showed that flanking intronic complementary sequences can significantly contribute to ts-RNA formation, providing an efficient/specific method to deplete ts-RNAs. We also experimentally validated that one ts-RNA (ts-ARFGEF1) played an important role for p53-mediated apoptosis through affecting the PERK/eIF2a/ATF4/CHOP signaling pathway in breast cancer cells. This study thus described both bioinformatics procedures and experimental validation steps for rigorous characterization of ts-RNAs, expanding future studies for identification, biogenesis, and function of these important but understudied transcripts.

Characterization and evaluation of gene fusions as a measure of genetic instability and disease prognosis in prostate cancer

BACKGROUND

Prostate cancer (PCa) is one of the most prevalent cancers worldwide. The clinical manifestations and molecular characteristics of PCa are highly variable. Aggressive types require radical treatment, whereas indolent ones may be suitable for active surveillance or organ-preserving focal therapies. Patient stratification by clinical or pathological risk categories still lacks sufficient precision. Incorporating molecular biomarkers, such as transcriptome-wide expression signatures, improves patient stratification but so far excludes chromosomal rearrangements. In this study, we investigated gene fusions in PCa, characterized potential novel candidates, and explored their role as prognostic markers for PCa progression.

METHODS

We analyzed 630 patients in four cohorts with varying traits regarding sequencing protocols, sample conservation, and PCa risk group. The datasets included transcriptome-wide expression and matched clinical follow-up data to detect and characterize gene fusions in PCa. With the fusion calling software Arriba, we computationally predicted gene fusions. Following detection, we annotated the gene fusions using published databases for gene fusions in cancer. To relate the occurrence of gene fusions to Gleason Grading Groups and disease prognosis, we performed survival analyses using the Kaplan-Meier estimator, log-rank test, and Cox regression.

RESULTS

Our analyses identified two potential novel gene fusions, MBTTPS2,L0XNC01::SMS and AMACR::AMACR. These fusions were detected in all four studied cohorts, providing compelling evidence for the validity of these fusions and their relevance in PCa. We also found that the number of gene fusions detected in a patient sample was significantly associated with the time to biochemical recurrence in two of the four cohorts (log-rank test, p-value < 0.05 for both cohorts). This was also confirmed after adjusting the prognostic model for Gleason Grading Groups (Cox regression, p-values < 0.05).

CONCLUSIONS

Our gene fusion characterization workflow revealed two potential novel fusions specific for PCa. We found evidence that the number of gene fusions was associated with the prognosis of PCa. However, as the quantitative correlations were only moderately strong, further validation and assessment of clinical value is required before potential application.

MALAT1 functions as a transcriptional promoter of MALAT1::GLI1 fusion for truncated GLI1 protein expression in cancer

BACKGROUND

The long non-coding RNA metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) is a cancer biomarker. Furthermore, fusion of the MALAT1 gene with glioma-associated oncogene 1 (GLI1) is a diagnostic marker of plexiform fibromyxoma and gastroblastoma; however, the function of this fusion gene remains unexplored.

METHOD

In this study, we elucidate the structure and function of the MALAT1::GLI1 fusion gene. To this end, we determined a transcriptional start site (TSS) and promoter region for truncated GLI1 expression using rapid amplification of the 5' cDNA end and a luciferase reporter assay in cultured cells transfected with a plasmid harboring the MALAT1::GLI1 fusion gene.

RESULTS

We found that the TATA box, ETS1 motif, and TSS were located in MALAT1 and that MALAT1 exhibited transcriptional activity and induced expression of GLI1 from the MALAT1::GLI1 fusion gene. Truncated GLI1, lacking SUMOylation and SUFU binding sites and located in the nucleus, upregulated mRNA expression of GLI1 target genes in the hedgehog signaling pathway.

CONCLUSIONS

We demonstrate a distinct and alternative function of MALAT1 as a transcriptional promoter for expression of the MALAT1::GLI1 fusion gene. Our findings will aid future research on MALAT1 and its fusion gene partners.

125I Radioactive Particles Drive Protective Autophagy in Hepatocellular Carcinoma by Upregulating ATG9B

BACKGROUND AND AIMS

¹²⁵I radioactive particles implantation have demonstrated efficacy in eradicating hepatocellular carcinoma (HCC). However, progressive resistance of HCC to ¹²⁵I radioactive particles has limited its wide clinical application.

METHODS

We investigated the cellular responses to ¹²⁵I radioactive particles treatment and autophagy-related 9B (ATG9B) silencing in HCC cell lines and Hep3B xenografted tumor model using Cell Counting Kit-8 reagent, western blotting, immunofluorescence, flow cytometry, transmission electron microscopy and immunohistochemistry.

RESULTS

In this study, we demonstrated that ¹²⁵I radioactive particles induced cell apoptosis and protective autophagy of HCC in vitro and in vivo. Inhibition of autophagy enhanced the radiosensitivity of HCC to ¹²⁵I radioactive particles. Moreover, ¹²⁵I radioactive particles induced autophagy by upregulating ATG9B, with increased expression level of LC3B and decreased expression level of p62. Furthermore, ATG9B silencing downregulated LC3B expression and upregulated p62 expression and enhanced radiosensitivity of HCC to ¹²⁵I radioactive particles in vitro and in vivo.

CONCLUSIONS

Inhibition of ATG9B enhanced the antitumor effects of ¹²⁵I particle radiation against HCC in vitro and in vivo. Our findings suggest that ¹²⁵I particle radiation plus chloroquine or/and the ATG9B inhibitor may be a novel therapeutic strategy for HCC.

The Heterogeneous Complexity of Myeloid Neoplasm: Multi-Level Approaches to Study the Disease

Myeloid neoplasms (MNs) include a spectrum of bone marrow malignancies that result from the clonal expansion and arrest of differentiation of myeloid progenitor cells [...].

Discovery of A Polymorphic Gene Fusion via Bottom-Up Chimeric RNA Prediction

Gene fusions and their chimeric products are typically considered hallmarks of cancer. However, recent studies have found chimeric transcripts in non-cancer tissues and cell lines. In addition, efforts to annotate structural variation at large scale have found examples of gene fusions with potential to produce chimeric transcripts in normal tissues. In this report, we provide a means for targeting population-specific chimeric RNAs to enrich for those generated by gene fusion events. We identify 57 such chimeric RNAs from the GTEx cohort, including SUZ12P1-CRLF3 and TFG-ADGRG7 , whose distribution we assessed across the populations of the 1000 Genomes Project. We reveal that SUZ12P1-CRLF3 results from a common complex structural variant in populations with African heritage, and identify its likely mechanism for formation. Additionally, we utilize a large cohort of clinical samples to characterize the SUZ12P1-CRLF3 chimeric RNA, and find an association between the variant and indications of Neurofibramatosis Type I. We present this gene fusion as a case study for identifying hard-to-find and potentially functional structural variants by selecting for those which produce population-specific fusion transcripts.

KEY POINTS

- Discovery of 57 polymorphic chimeric RNAs- Characterization of SUZ12P1-CRLF3 polymorphic chimeric RNA and corresponding rearrangement- Novel bottom-up approach to identify structural variants which produce transcribed gene fusions.

De Novo Transcriptome Profiling of Naegleria fowleri Trophozoites and Cysts via RNA Sequencing

Naegleria fowleri is a pathogenic free-living amoeba, commonly found around the world in warm, fresh water and soil. N. fowleri trophozoites can infect humans by entering the brain through the nose and causing usually fatal primary amebic meningoencephalitis (PAM). Trophozoites can encyst to survive under unfavorable conditions such as cold temperature, starvation, and desiccation. Recent technological advances in genomics and bioinformatics have provided unique opportunities for the identification and pre-validation of pathogen-related and environmental resistance through improved understanding of the biology of pathogenic N. fowleri trophozoites and cysts at a molecular level. However, genomic and transcriptomic data on differential expression genes (DEGs) between trophozoites and cysts of N. fowleri are very limited. Here, we report transcriptome Illumina RNA sequencing (RNA-seq) for N. fowleri trophozoites and cysts and de novo transcriptome assembly. RNA-seq libraries were generated from RNA extracted from N. fowleri sampled from cysts, and a reference transcriptome was generated through the assembly of trophozoite data. In the database, the assembly procedure resulted in 42,220 contigs with a mean length of 11,254 nucleotides and a C+G content of 37.21%. RNA sequencing showed that 146 genes in cysts of N. fowleri indicated 2-fold upregulation in comparison with trophozoites of N. fowleri, and 163 genes were downregulated; these genes were found to participate in the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. The KEGG pathway included metabolic (131 sequences) and genetic information processing (66 sequences), cellular processing (43 sequences), environmental information processing (22 sequences), and organismal system (20 sequences) pathways. On the other hand, an analysis of 11,254 sequences via the Gene Ontology database showed that their annotations contained 1069 biological processes including the cellular process (228 sequences) and metabolic process (214 sequences); 923 cellular components including cells (240 sequences) and cell parts (225 sequences); and 415 molecular functions including catalytic activities (195 sequences) and binding processes (186 sequences). Differential expression levels increased in cysts of N. fowleri compared to trophozoites of N. fowleri, which were mainly categorized as serine/threonine protease, kinase, and lipid metabolism-related proteins. These results may provide new insights into pathogen-related genes or environment-resistant genes in the pathogenesis of N. fowleri.

Neoplasia-associated Chromosome Translocations Resulting in Gene Truncation

Chromosomal translocations in cancer as well as benign neoplasias typically lead to the formation of fusion genes. Such genes may encode chimeric proteins when two protein-coding regions fuse in-frame, or they may result in deregulation of genes via promoter swapping or translocation of the gene into the vicinity of a highly active regulatory element. A less studied consequence of chromosomal translocations is the fusion of two breakpoint genes resulting in an out-of-frame chimera. The breaks then occur in one or both protein-coding regions forming a stop codon in the chimeric transcript shortly after the fusion point. Though the latter genetic events and mechanisms at first awoke little research interest, careful investigations have established them as neither rare nor inconsequential. In the present work, we review and discuss the truncation of genes in neoplastic cells resulting from chromosomal rearrangements, especially from seemingly balanced translocations.

Pan-Cancer Analysis Reveals Recurrent BCAR4 Gene Fusions across Solid Tumors

Chromosomal rearrangements often result in active regulatory regions juxtaposed upstream of an oncogene to generate an expressed gene fusion. Repeated activation of a common downstream partner-with differing upstream regions across a patient cohort-suggests a conserved oncogenic role. Analysis of 9,638 patients across 32 solid tumor types revealed an annotated long noncoding RNA (lncRNA), Breast Cancer Anti-Estrogen Resistance 4 (BCAR4), was the most prevalent, uncharacterized, downstream gene fusion partner occurring in 11 cancers. Its oncogenic role was confirmed using multiple cell lines with endogenous BCAR4 gene fusions. Furthermore, overexpressing clinically prevalent BCAR4 gene fusions in untransformed cell lines was sufficient to induce an oncogenic phenotype. We show that the minimum common region to all gene fusions harbors an open reading frame that is necessary to drive proliferation.

IMPLICATIONS

BCAR4 gene fusions represent an underappreciated class of gene fusions that may have biological and clinical implications across solid tumors.

Transcriptomic Harmonization as the Way for Suppressing Cross-Platform Bias and Batch Effect

(1) Background: Emergence of methods interrogating gene expression at high throughput gave birth to quantitative transcriptomics, but also posed a question of inter-comparison of expression profiles obtained using different equipment and protocols and/or in different series of experiments. Addressing this issue is challenging, because all of the above variables can dramatically influence gene expression signals and, therefore, cause a plethora of peculiar features in the transcriptomic profiles. Millions of transcriptomic profiles were obtained and deposited in public databases of which the usefulness is however strongly limited due to the inter-comparison issues; (2) Methods: Dozens of methods and software packages that can be generally classified as either flexible or predefined format harmonizers have been proposed, but none has become to the date the gold standard for unification of this type of Big Data; (3) Results: However, recent developments evidence that platform/protocol/batch bias can be efficiently reduced not only for the comparisons of limited transcriptomic datasets. Instead, instruments were proposed for transforming gene expression profiles into the universal, uniformly shaped format that can support multiple inter-comparisons for reasonable calculation costs. This forms a basement for universal indexing of all or most of all types of RNA sequencing and microarray hybridization profiles; (4) Conclusions: In this paper, we attempted to overview the landscape of modern approaches and methods in transcriptomic harmonization and focused on the practical aspects of their application.

Analysis of gene expression profiles in two spinal cord injury models

Objectives

To analyze the changes of gene expression at different timepoints after spinal cord injury (SCI) with tenth segment thoracic injury.

Methods

Two SCI models, the complete paraplegia (H) and Allen’s strike (D) methods were applied to induce SCI in rats, and transcriptome sequencing was performed 1, 3, 7, 14, 56, and 70 days after SCI, respectively. Principal component analysis, differentially expressed gene analysis, and hierarchical clustering analysis were applied to analyze the differentially expressed genes (DEGs). Gene Ontology GO enrichment analysis, Kyoto Encyclopedia of Genes and Genomes enrichment analysis, and Gene Set Enrichment Analysis revealed the pathway of gene enrichment.

Results

There were 1,907, 3,120, 3,728, 978, 2,319, and 3,798 DEGs in the complete paraplegia group and 2,380, 878, 1,543, 6,040, 1,945, and 3,850 DEGs in the Allen’s strike method group and after SCI at 1, 3, 7, 14, 56, and 70 days, respectively. The transcriptome contours of D1, H1, D3, and H14 were clustered with C; the H56, D56, H70, and D70 transcriptome contours were similar and clustered together. H3, D7, and H7 were clustered together, and D14 was clustered separately. The transcriptome differences of the two SCI models were mainly concentrated during the first 2 weeks after SCI. The DEGs after SCI in the complete paraplegia group were more concentrated. Most of the early transcriptional regulation stabilized within 2 weeks after injury.

Conclusions

There were DEGs between the two SCI models. Through the gene changes and pathway enrichment of the entire time period after SCI, the molecular mechanism of SCI repair was revealed in depth, which provided a reference for SCI treatment in the future.

Fusion Gene Detection and Quantification by Asymmetric Capture Sequencing (aCAP-Seq)

Several fusion genes such as BCR::ABL1, FIP1L1::PDGFRA, and PML::RARA are now efficiently targeted by specific therapies in patients with leukemia. Although these therapies have significantly improved patient outcomes, leukemia relapse and progression remain clinical concerns. Most myeloid next-generation sequencing (NGS) panels do not detect or quantify these fusions. It therefore remains difficult to decipher the clonal architecture and dynamics of myeloid malignancy patients, although these factors can affect clinical decisions and provide pathophysiologic insights. An asymmetric capture sequencing strategy (aCAP-Seq) and a bioinformatics algorithm (HmnFusion) were developed to detect and quantify MBCR::ABL1, μBCR::ABL1, PML::RARA, and FIP1L1::PDGFRA fusion genes in an NGS panel targeting 41 genes. One-hundred nineteen DNA samples derived from 106 patients were analyzed by conventional methods at diagnosis or on follow-up and were sequenced with this NGS myeloid panel. The specificity and sensitivity of fusion detection by aCAP-Seq were 100% and 98.1%, respectively, with a limit of detection estimated at 0.1%. Fusion quantifications were linear from 0.1% to 50%. Breakpoint locations and sequences identified by NGS were concordant with results obtained by Sanger sequencing. Finally, this new sensitive and cost-efficient NGS method allowed integrated analysis of resistant chronic myeloid leukemia patients and thus will be of interest to elucidate the mutational landscape and clonal architecture of myeloid malignancies driven by these fusion genes at diagnosis, relapse, or progression.

Accurate detection of tumor-specific gene fusions reveals strongly immunogenic personal neo-antigens

Cancer-associated gene fusions are a potential source for highly immunogenic neoantigens, but the lack of computational tools for accurate, sensitive identification of personal gene fusions has limited their targeting in personalized cancer immunotherapy. Here we present EasyFuse, a machine learning computational pipeline for detecting cancer-specific gene fusions in transcriptome data obtained from human cancer samples. EasyFuse predicts personal gene fusions with high precision and sensitivity, outperforming previously described tools. By testing immunogenicity with autologous blood lymphocytes from patients with cancer, we detected pre-established CD4⁺ and CD8⁺ T cell responses for 10 of 21 (48%) and for 1 of 30 (3%) identified gene fusions, respectively. The high frequency of T cell responses detected in patients with cancer supports the relevance of individual gene fusions as neoantigens that might be targeted in personalized immunotherapies, especially for tumors with low mutation burden.

The evolving landscape of prostate cancer somatic mutations

BACKGROUND

The landscape of somatic mutations in prostate cancer (PCa) has quickly evolved over the past years.

RESULTS

This evolution was in part due to the improved quality and lower cost of genomic sequencing platforms available to an ever-larger group of clinicians and researchers. The result of these efforts is a better understanding of early and late mutations that are enriched or nearly exclusive to treated PCa. There are, however, some important limitations to the current knowledge. The expanding variety of next-generation sequencing (NGS) assays either capture a wide spectrum of mutations but at low coverage or are focused panels that cover a select number of genes, most often cancer-related, at a deep coverage. Both of these approaches have their advantages, but ultimately miss low-frequency mutations or fail to cover the spectrum of potential mutations. Additionally, some alterations, such as the common ETS gene fusions, require a mixture of DNA and RNA analysis to capture the true frequency. Finally, almost all studies rely on bulk PCa tumor samples, which fail to consider tumor heterogeneity. Given all these caveats, the true picture of the somatic landscape of PCa continues to develop.

SUMMARY

In this review, the focus will be on how the landscape of mutations evolves during disease progression considering therapy. It will focus on a select group of early and late mutations and utilize SPOP mutations to illustrate recurrent alterations that may have clinical implications.

Shambhala-2: A Protocol for Uniformly Shaped Harmonization of Gene Expression Profiles of Various Formats

Uniformly shaped harmonization of gene expression profiles is central for the simultaneous comparison of multiple gene expression datasets. It is expected to operate with the gene expression data obtained using various experimental methods and equipment, and to return harmonized profiles in a uniform shape. Such uniformly shaped expression profiles from different initial datasets can be further compared directly. However, current harmonization techniques have strong limitations that prevent their broad use for bioinformatic applications. They can either operate with only up to two datasets/platforms or return data in a dynamic format that will be different for every comparison under analysis. This also does not allow for adding new data to the previously harmonized dataset(s), which complicates the analysis and increases calculation costs. We propose here a new method termed Shambhala-2 that can transform multi-platform expression data into a universal format that is identical for all harmonizations made using this technique. Shambhala-2 is based on sample-by-sample cubic conversion of the initial expression dataset into a preselected shape of the reference definitive dataset. Using 8390 samples of 12 healthy human tissue types and 4086 samples of colorectal, kidney, and lung cancer tissues, we verified Shambhala-2's capacity in restoring tissue-specific expression patterns for seven microarray and three RNA sequencing platforms. Shambhala-2 performed well for all tested combinations of RNAseq and microarray profiles, and retained gene-expression ranks, as evidenced by high correlations between different single- or aggregated gene expression metrics in pre- and post-Shambhalized samples, including preserving cancer-specific gene expression and pathway activation features. © 2022 Wiley Periodicals LLC. Basic Protocol: Shambhala-2 harmonizer Alternate Protocol 1: Linear Shambhala/Shambhala-1 Alternate Protocol 2: Alternative (flexible-format and uniformly shaped) normalization methods Support Protocol 1: Watermelon multisection (WM) Support Protocol 2: Calculation of cancer-to-normal log-fold-change (LFC) and pathway activation level (PAL).

Preliminary evaluation of the mechanism underlying vulnerability/resistance to methylmercury toxicity by comparative gene expression profiling of rat primary cultured cerebrocortical and hippocampal neurons

Methylmercury (MeHg), an environmentally toxic substance, causes site-specific neuronal cell death; while MeHg exposure causes death in cerebrocortical neurons, interestingly, it does not in hippocampal neurons, which are generally considered to be vulnerable to toxic substances. This phenomenon of site-specific neuronal cell death can be reproduced in animal experiments; however, the mechanism underlying the resistance of hippocampal neurons to MeHg toxicity has not been clarified. In this study, we comparatively analyzed the response to MeHg exposure in terms of viability and the expression characteristics of primary cultured cerebrocortical neurons and hippocampal neurons derived from fetal rat brain. Neuronal differentiated hippocampal neurons were more resistant to MeHg toxicity than cerebrocortical neurons, as indicated by a 2‒3 fold higher half-maximal inhibitory concentration (IC₅₀; 3.3 μM vs. 1.2 μM), despite similar intracellular mercury concentrations in both neuronal cell types. Comprehensive RNA sequencing-based gene expression analysis of non-MeHg-exposed cells revealed that 80 out of 15,208 genes showed at least 10-fold higher expression in hippocampal neurons than in cerebrocortical neurons, whereas six genes showed at least 10-fold higher expression in cerebrocortical neurons than in hippocampal neurons. In particular, genes related to neuronal function, including those encoding transthyretin and brain-derived neurotrophic factor, showed approximately 50-fold higher expression in hippocampal neurons than in cerebrocortical neurons. In conclusion, the resistance of hippocampal neurons to MeHg toxicity may be related to the high expression of neuronal function-related proteins.

Limitations and opportunities of technologies for the analysis of cell-free DNA in cancer diagnostics

Cell-free DNA (cfDNA) in the circulating blood plasma of patients with cancer contains tumour-derived DNA sequences that can serve as biomarkers for guiding therapy, for the monitoring of drug resistance, and for the early detection of cancers. However, the analysis of cfDNA for clinical diagnostic applications remains challenging because of the low concentrations of cfDNA, and because cfDNA is fragmented into short lengths and is susceptible to chemical damage. Barcodes of unique molecular identifiers have been implemented to overcome the intrinsic errors of next-generation sequencing, which is the prevailing method for highly multiplexed cfDNA analysis. However, a number of methodological and pre-analytical factors limit the clinical sensitivity of the cfDNA-based detection of cancers from liquid biopsies. In this Review, we describe the state-of-the-art technologies for cfDNA analysis, with emphasis on multiplexing strategies, and discuss outstanding biological and technical challenges that, if addressed, would substantially improve cancer diagnostics and patient care.

Foliar carbon dot amendment modulates carbohydrate metabolism, rhizospheric properties and drought tolerance in maize seedling

Improving maize drought tolerance is of great importance for scaling up production due to food security and population growth. Carbon dots (CDs) were synthesized by hydrothermal method with citric acid and ethylenediamine as carbon sources. Then, CDs (5 ml, 5 mg‧L^-1) were sprayed on 25th day-old maize (Zea mays L., drought-stress, 35% soil moisture) for seven consecutive days (spraying ultra-pure water as control), after which the physiological parameters and rhizospheric properties of maize under drought were evaluated. Foliar sprayed CDs (5 mg‧L^-1) could increase root exudates (e.g., succinic acid (14.5 folds), pyruvic acid (10.0 folds), and betaine (11.8 folds)), and modify microbial community. Particularly, the relative abundance of Pseudomonas, Sphingomonas, Nitrospira, and Conocybe were significantly increased by 344.4%, 233.3%, 126.2%, and 122.6%, respectively. The altered microbial abundance could improve soil available nitrogen and phosphorus by 33.5% and 16.8%, respectively, and increase plant water uptake by 37.2%. The change of exudate synthesis and microbial abundance could be driven by the significantly increased in net photosynthesis rate by 122.9%, and carbohydrate content by 35.4% in shoots and 113.6% in roots, respectively upon foliar application of CDs. Meanwhile, fresh weight of shoots and roots were increased by 62.1% and 50.6%, and dry weight of shoots and roots were increased by 29.2% and 37.5%, respectively. These results demonstrated that foliar application of CDs could improve the rhizosphere environment to enhance maize drought tolerance and even growth. Therefore, foliar application of CDs would be a promising strategy for sustainable nano-agriculture in response to drought stress.

Gene expression signature of exercise and change of diet on non-alcoholic fatty liver disease in mice

Non-alcoholic fatty liver diseases (NAFLD) are particularly prevalent in the general Western adult population, with around one third of the population suffering from the disease. Evidence shows that NAFLD is associated with metabolic syndromes such as obesity, insulin resistance, and hypertension. Currently, the sole therapy for NAFLD involves exercise intervention. Studies showed that, with and without weight loss, exercise interventions produced a significant cutback in intrahepatic lipid content in humans, but better controlled studies that can investigate the cellular and molecular mechanisms are still lacking. In the current study we perform RNA sequencing analysis on liver samples from C57BL/6 mice submitted to aerobic exercise and diet interventions that are human-translatable and determine the genetic expression signature of exercise in the NAFLD onset. We show that aerobic exercise affects genes and pathways related to liver metabolism, muscle contraction and relaxation, immune response and inflammation, and development of liver cancer, counteracting non-alcoholic steatohepatitis and hepatocellular carcinoma development. While genes and pathways implicating immune response are activated by aerobic exercise in all interventions, the most effective intervention in terms of improvement of NASH is the combination of aerobic exercise with change of diet.

Protective Effects of Natural Polysaccharides on Intestinal Barrier Injury: A Review

Owing to their minimal side effects and effective protection from oxidative stress, inflammation, and malignant growth, natural polysaccharides (NPs) are a potential adjuvant therapy for several diseases caused by intestinal barrier injury (IBI). More studies are accumulating on the protective effects of NPs with respect to IBI, but the underlying mechanisms remain unclear. Thus, this review aims to represent current studies that investigate the protective effects of NPs on IBI by directly maintaining intestinal epithelial barrier integrity (inhibiting oxidative stress, regulating inflammatory cytokine expression, and increasing tight junction protein expression) and indirectly regulating intestinal immunity and microbiota. Furthermore, the mechanisms underlying IBI development are briefly introduced, and the structure-activity relationships of polysaccharides with intestinal barrier protection effects are discussed. Potential developments and challenges associated with NPs exhibiting protective effects against IBI have also been highlighted to guide the application of NPs in the treatment of intestinal diseases caused by IBI.

RNA-Seq for the detection of gene fusions in solid tumors: development and validation of the JAX FusionSeq™ 2.0 assay

Whole transcriptome sequencing (RNA-Seq) has gained prominence for the detection of fusions in solid tumors. Here, we describe the development and validation of an in-house RNA-Seq-based test system (FusionSeq™ 2.0) for the detection of clinically actionable gene fusions, in formalin-fixed paraffin-embedded (FFPE) specimens, using seventy tumor samples with varying fusion status. Conditions were optimized for RNA input of 50 ng, shown to be adequate to call known fusions at as low as 20% neoplastic content. Evaluation of assay performance between FFPE and fresh-frozen (FF) tissues exhibited little to no difference in fusion calling capability. Performance analysis of the assay validation data determined 100% accuracy, sensitivity, specificity, and reproducibility. This clinically developed and validated RNA-Seq-based approach for fusion detection in FPPE samples was shown to be on par if not superior to off-the-shelf commercially offered assays. With gene fusions implicated in a variety of cancer types, offering high-quality, low-cost molecular testing services for FFPE specimens will serve to best benefit the patient and the advancement of precision medicine in molecular oncology. KEY MESSAGES: A custom RNA-Seq-based test system (FusionSeq™ 2.0) for the detection of clinically actionable gene fusions, Evaluation of assay performance between FFPE and fresh-frozen (FF) tissues exhibited little to no difference in fusion calling capability. The assay can be performed with low RNA input and neoplastic content. Performance characteristics of the assay validation data determined 100% accuracy, sensitivity, specificity, and reproducibility.

JAFFAL: detecting fusion genes with long-read transcriptome sequencing

In cancer, fusions are important diagnostic markers and targets for therapy. Long-read transcriptome sequencing allows the discovery of fusions with their full-length isoform structure. However, due to higher sequencing error rates, fusion finding algorithms designed for short reads do not work. Here we present JAFFAL, to identify fusions from long-read transcriptome sequencing. We validate JAFFAL using simulations, cell lines, and patient data from Nanopore and PacBio. We apply JAFFAL to single-cell data and find fusions spanning three genes demonstrating transcripts detected from complex rearrangements. JAFFAL is available at https://github.com/Oshlack/JAFFA/wiki .

Integrating pathway knowledge with deep neural networks to reduce the dimensionality in single-cell RNA-seq data

Background

Single-cell RNA sequencing (scRNA-seq) data provide valuable insights into cellular heterogeneity which is significantly improving the current knowledge on biology and human disease. One of the main applications of scRNA-seq data analysis is the identification of new cell types and cell states. Deep neural networks (DNNs) are among the best methods to address this problem. However, this performance comes with the trade-off for a lack of interpretability in the results. In this work we propose an intelligible pathway-driven neural network to correctly solve cell-type related problems at single-cell resolution while providing a biologically meaningful representation of the data.

Results

In this study, we explored the deep neural networks constrained by several types of prior biological information, e.g. signaling pathway information, as a way to reduce the dimensionality of the scRNA-seq data. We have tested the proposed biologically-based architectures on thousands of cells of human and mouse origin across a collection of public datasets in order to check the performance of the model. Specifically, we tested the architecture across different validation scenarios that try to mimic how unknown cell types are clustered by the DNN and how it correctly annotates cell types by querying a database in a retrieval problem. Moreover, our approach demonstrated to be comparable to other less interpretable DNN approaches constrained by using protein-protein interactions gene regulation data. Finally, we show how the latent structure learned by the network could be used to visualize and to interpret the composition of human single cell datasets.

Conclusions

Here we demonstrate how the integration of pathways, which convey fundamental information on functional relationships between genes, with DNNs, that provide an excellent classification framework, results in an excellent alternative to learn a biologically meaningful representation of scRNA-seq data. In addition, the introduction of prior biological knowledge in the DNN reduces the size of the network architecture. Comparative results demonstrate a superior performance of this approach with respect to other similar approaches. As an additional advantage, the use of pathways within the DNN structure enables easy interpretability of the results by connecting features to cell functionalities by means of the pathway nodes, as demonstrated with an example with human melanoma tumor cells.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13040-021-00285-4.

Sensitive detection of fusion transcripts with padlock probe-based continuous cascade amplification (P-CCA)

A padlock probe-based continuous cascade amplification (P-CCA) is proposed for assaying fusion transcripts with high sensitivity and specificity.

Improved Gene Fusion Detection in Childhood Cancer Diagnostics Using RNA Sequencing

PURPOSE

Gene fusions play a significant role in cancer etiology, making their detection crucial for accurate diagnosis, prognosis, and determining therapeutic targets. Current diagnostic methods largely focus on either targeted or low-resolution genome-wide techniques, which may be unable to capture rare events or both fusion partners. We investigate if RNA sequencing can overcome current limitations with traditional diagnostic techniques to identify gene fusion events.

METHODS

We first performed RNA sequencing on a validation cohort of 24 samples with a known gene fusion event, after which a prospective pan-pediatric cancer cohort (n = 244) was tested by RNA sequencing in parallel to existing diagnostic procedures. This cohort included hematologic malignancies, tumors of the CNS, solid tumors, and suspected neoplastic samples. All samples were processed in the routine diagnostic workflow and analyzed for gene fusions using standard-of-care methods and RNA sequencing.

RESULTS

We identified a clinically relevant gene fusion in 83 of 244 cases in the prospective cohort. Sixty fusions were detected by both routine diagnostic techniques and RNA sequencing, and one fusion was detected only in routine diagnostics, but an additional 24 fusions were detected solely by RNA sequencing. RNA sequencing, therefore, increased the diagnostic yield by 38%-39%. In addition, RNA sequencing identified both gene partners involved in the gene fusion, in contrast to most routine techniques. For two patients, the newly identified fusion by RNA sequencing resulted in treatment with targeted agents.

CONCLUSION

We show that RNA sequencing is sufficiently robust for gene fusion detection in routine diagnostics of childhood cancers and can make a difference in treatment decisions.

Susceptibility of cytoskeletal-associated proteins for tumor progression

The traditional functions of cytoskeletal-associated proteins (CAPs) in line with polymerization and stabilization of the cytoskeleton have evolved and are currently underrated in oncology. Although therapeutic drugs have been developed to target the cytoskeletal components directly in cancer treatment, several recently established therapeutic agents designed for new targets block the proliferation of cancer cells and suppress resistance to existing target agents. It would seem like these targets only work toward inhibiting the polymerization of cytoskeletal components or hindering mitotic spindle formation in cancer cells, but a large body of literature points to CAPs and their culpability in cell signaling, molecular conformation, organelle trafficking, cellular metabolism, and genomic modifications. Here, we review those underappreciated functions of CAPs, and we delineate the implications of cellular signaling instigated by evasive properties induced by aberrant expression of CAPs in response to stress or failure to exert normal functions. We present an analogy establishing CAPs as vulnerable targets for cancer systems and credible oncotargets. This review establishes a paradigm in which the cancer machinery may commandeer the conventional functions of CAPs for survival, drug resistance, and energy generation; an interesting feature overdue for attention.

Transcriptomics integrated with metabolomics reveals the effect of Lycium barbarum polysaccharide on apoptosis in Nile tilapia (Oreochromis niloticus)

Lycium barbarum polysaccharide (LBP) is one of the main active ingredients in the fruit of L. barbarum L. It has been used as herbal medicine for thousands of years in China. In this study, Nile tilapia (Oreochromis niloticus) was taken as the research object. After feeding tilapia with 5 different doses of LBP (0 mg/kg, 500 mg/kg, 1000 mg/kg, 1500 mg/kg, 2000 mg/kg) for 55 d, it was found that LBP could promote the growth of tilapia, and this effect was the strongest at Group 1500 mg/kg. Apoptosis analysis in the liver and spleen showed that dietary supplementation with 1000 mg/kg LBP had the best protective effect on the spleen and liver in tilapia. Combined transcriptomics and metabolomics of the spleen in tilapia at Group 0 mg/kg and 1000 mg/kg showed that the differentially expressed genes (DEGs) such as NT5C2L1, pmm1, FasL and the differentially metabolites such as xanthine, dGMP, guanine and glutamate were mainly concentrated in signaling pathways such as Purine metabolism and FoxO signaling pathway. In conclusion, LBP regulates the metabolic waste levels of tilapia mainly through Purine metabolism and the FoxO signaling pathway, thereby inhibiting cell apoptosis, improving the utilization of nutrients, and promoting the growth of tilapia. This study not only provides a theoretical basis for the application of LBP in aquatic animals but also provides useful information for the healthy development of the aquaculture.

Detection of gene mutations and gene-gene fusions in circulating cell-free DNA of glioblastoma patients: an avenue for clinically relevant diagnostic analysis

Glioblastoma (GBM) is the most common type of glioma and is uniformly fatal. Currently, tumour heterogeneity and mutation acquisition are major impedances for tailoring personalized therapy. We collected blood and tumour tissue samples from 25 GBM patients and 25 blood samples from healthy controls. Cell‐free DNA (cfDNA) was extracted from the plasma of GBM patients and from healthy controls. Tumour DNA was extracted from fresh tumour samples. Extracted DNA was sequenced using a whole‐genome sequencing procedure. We also collected 180 tumour DNA datasets from GBM patients publicly available at the TCGA/PANCANCER project. These data were analysed for mutations and gene–gene fusions that could be potential druggable targets. We found that plasma cfDNA concentrations in GBM patients were significantly elevated (22.6 ± 5 ng·mL⁻¹), as compared to healthy controls (1.4 ± 0.4 ng·mL⁻¹) of the same average age. We identified unique mutations in the cfDNA and tumour DNA of each GBM patient, including some of the most frequently mutated genes in GBM according to the COSMIC database (TP53, 18.75%; EGFR, 37.5%; NF1, 12.5%; LRP1B, 25%; IRS4, 25%). Using our gene–gene fusion database, ChiTaRS 5.0, we identified gene–gene fusions in cfDNA and tumour DNA, such as KDR–PDGFRA and NCDN–PDGFRA, which correspond to previously reported alterations of PDGFRA in GBM (44% of all samples). Interestingly, the PDGFRA protein fusions can be targeted by tyrosine kinase inhibitors such as imatinib, sunitinib, and sorafenib. Moreover, we identified BCR–ABL1 (in 8% of patients), COL1A1–PDGFB (8%), NIN–PDGFRB (8%), and FGFR1–BCR (4%) in cfDNA of patients, which can be targeted by analogues of imatinib. ROS1 fusions (CEP85L–ROS1 and GOPC–ROS1), identified in 8% of patient cfDNA, might be targeted by crizotinib, entrectinib, or larotrectinib. Thus, our study suggests that integrated analysis of cfDNA plasma concentration, gene mutations, and gene–gene fusions can serve as a diagnostic modality for distinguishing GBM patients who may benefit from targeted therapy. These results open new avenues for precision medicine in GBM, using noninvasive liquid biopsy diagnostics to assess personalized patient profiles. Moreover, repeated detection of druggable targets over the course of the disease may provide real‐time information on the evolving molecular landscape of the tumour.

ChiTaH: a fast and accurate tool for identifying known human chimeric sequences from high-throughput sequencing data

Fusion genes or chimeras typically comprise sequences from two different genes. The chimeric RNAs of such joined sequences often serve as cancer drivers. Identifying such driver fusions in a given cancer or complex disease is important for diagnosis and treatment. The advent of next-generation sequencing technologies, such as DNA-Seq or RNA-Seq, together with the development of suitable computational tools, has made the global identification of chimeras in tumors possible. However, the testing of over 20 computational methods showed these to be limited in terms of chimera prediction sensitivity, specificity, and accurate quantification of junction reads. These shortcomings motivated us to develop the first ‘reference-based’ approach termed ChiTaH (Chimeric Transcripts from High–throughput sequencing data). ChiTaH uses 43,466 non–redundant known human chimeras as a reference database to map sequencing reads and to accurately identify chimeric reads. We benchmarked ChiTaH and four other methods to identify human chimeras, leveraging both simulated and real sequencing datasets. ChiTaH was found to be the most accurate and fastest method for identifying known human chimeras from simulated and sequencing datasets. Moreover, especially ChiTaH uncovered heterogeneity of the BCR-ABL1 chimera in both bulk and single-cells of the K-562 cell line, which was confirmed experimentally.

Discovery of clinically relevant fusions in pediatric cancer

Background

Pediatric cancers typically have a distinct genomic landscape when compared to adult cancers and frequently carry somatic gene fusion events that alter gene expression and drive tumorigenesis. Sensitive and specific detection of gene fusions through the analysis of next-generation-based RNA sequencing (RNA-Seq) data is computationally challenging and may be confounded by low tumor cellularity or underlying genomic complexity. Furthermore, numerous computational tools are available to identify fusions from supporting RNA-Seq reads, yet each algorithm demonstrates unique variability in sensitivity and precision, and no clearly superior approach currently exists. To overcome these challenges, we have developed an ensemble fusion calling approach to increase the accuracy of identifying fusions.

Results

Our Ensemble Fusion (EnFusion) approach utilizes seven fusion calling algorithms: Arriba, CICERO, FusionMap, FusionCatcher, JAFFA, MapSplice, and STAR-Fusion, which are packaged as a fully automated pipeline using Docker and Amazon Web Services (AWS) serverless technology. This method uses paired end RNA-Seq sequence reads as input, and the output from each algorithm is examined to identify fusions detected by a consensus of at least three algorithms. These consensus fusion results are filtered by comparison to an internal database to remove likely artifactual fusions occurring at high frequencies in our internal cohort, while a “known fusion list” prevents failure to report known pathogenic events. We have employed the EnFusion pipeline on RNA-Seq data from 229 patients with pediatric cancer or blood disorders studied under an IRB-approved protocol. The samples consist of 138 central nervous system tumors, 73 solid tumors, and 18 hematologic malignancies or disorders. The combination of an ensemble fusion-calling pipeline and a knowledge-based filtering strategy identified 67 clinically relevant fusions among our cohort (diagnostic yield of 29.3%), including RBPMS-MET, BCAN-NTRK1, and TRIM22-BRAF fusions. Following clinical confirmation and reporting in the patient’s medical record, both known and novel fusions provided medically meaningful information.

Conclusions

The EnFusion pipeline offers a streamlined approach to discover fusions in cancer, at higher levels of sensitivity and accuracy than single algorithm methods. Furthermore, this method accurately identifies driver fusions in pediatric cancer, providing clinical impact by contributing evidence to diagnosis and, when appropriate, indicating targeted therapies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-08094-z.

Gene expression profiles and pathway enrichment analysis to identification of differentially expressed gene and signaling pathways in epithelial ovarian cancer based on high-throughput RNA-seq data

Epithelial ovarian cancer (EOC) can be considered as a stressful and challenging disease among all women in the world, which has been associated with a poor prognosis and its molecular pathogenesis has remained unclear. In recent years, RNA Sequencing (RNA-seq) has become a functional and amazing technology for profiling gene expression. In the present study, RNA-seq raw data from Sequence Read Archive (SRA) of six tumor and normal ovarian sample was extracted, and then analysis and statistical interpretation was done with Linux and R Packages from the open-source Bioconductor. Gene Ontology (GO) term enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were applied for the identification of key genes and pathways involved in EOC. We identified 1091 Differential Expression Genes (DEGs) which have been reported in various studies of ovarian cancer as well as other types of cancer. Among them, 333 genes were up-regulated and 273 genes were down-regulated. In addition, Differentially Expressed Genes (DEGs) including RPL41, ALDH3A2, ERBB2, MIEN1, RBM25, ATF4, UPF2, DDIT3, HOXB8 and IL17D as well as Ribosome and Glycolysis/Gluconeogenesis pathway have had the potentiality to be used as targets for EOC diagnosis and treatment. In this study, unlike that of any other studies on various cancers, ALDH3A2 was most down-regulated gene in most KEGG pathways, and ATF4 was most up-regulated gene in leucine zipper domain binding term. In the other hand, RPL41 as a regulatory of cellular ATF4 level was up-regulated in many term and pathways and augmentation of ATF4 could justify the increase of RPL41 in the EOC. Pivotal pathways and significant genes, which were identified in the present study, can be used for adaptation of different EOC study. However, further molecular biological experiments and computational processes are required to confirm the function of the identified genes associated with EOC.

PLZF and its fusion proteins are pomalidomide-dependent CRBN neosubstrates

Pomalidomide and lenalidomide are immunomodulatory agents that were derived from thalidomide. Cereblon (CRBN) is a common direct target of thalidomide and related compounds and works as a Cullin Ring 4 E3 ubiquitin ligase (CRL4) with DDB1, CUL4, and ROC1. The substrate specificity of CRL4^CRBN is modulated by thalidomide-related compounds. While lenalidomide is approved for the treatment of several diseases including multiple myeloma, 5q- syndrome, mantle cell lymphoma, and follicular lymphoma, pomalidomide is approved only for the treatment of lenalidomide-resistant multiple myeloma. Here we show that PLZF/ZBTB16 and its fusion proteins are pomalidomide-dependent neosubstrates of CRL4^CRBN. PLZF joins to RARα or potentially other partner genes, and the translocation causes leukemias, such as acute promyelocytic leukemia and T-cell acute lymphoblastic leukemia. We demonstrate that pomalidomide treatment induces PLZF-RARα degradation, resulting in antiproliferation of leukemic cells expressing PLZF-RARα. This study highlights a potential therapeutic role of pomalidomide as a degrader of leukemogenic fusion proteins.

TwinCons: Conservation score for uncovering deep sequence similarity and divergence

We have developed the program TwinCons, to detect noisy signals of deep ancestry of proteins or nucleic acids. As input, the program uses a composite alignment containing pre-defined groups, and mathematically determines a 'cost' of transforming one group to the other at each position of the alignment. The output distinguishes conserved, variable and signature positions. A signature is conserved within groups but differs between groups. The method automatically detects continuous characteristic stretches (segments) within alignments. TwinCons provides a convenient representation of conserved, variable and signature positions as a single score, enabling the structural mapping and visualization of these characteristics. Structure is more conserved than sequence. TwinCons highlights alternative sequences of conserved structures. Using TwinCons, we detected highly similar segments between proteins from the translation and transcription systems. TwinCons detects conserved residues within regions of high functional importance for the ribosomal RNA (rRNA) and demonstrates that signatures are not confined to specific regions but are distributed across the rRNA structure. The ability to evaluate both nucleic acid and protein alignments allows TwinCons to be used in combined sequence and structural analysis of signatures and conservation in rRNA and in ribosomal proteins (rProteins). TwinCons detects a strong sequence conservation signal between bacterial and archaeal rProteins related by circular permutation. This conserved sequence is structurally colocalized with conserved rRNA, indicated by TwinCons scores of rRNA alignments of bacterial and archaeal groups. This combined analysis revealed deep co-evolution of rRNA and rProtein buried within the deepest branching points in the tree of life.

Insights into Mechanisms of Pheochromocytomas and Paragangliomas Driven by Known or New Genetic Drivers

Simple Summary

Pheochromocytomas and paragangliomas are rare neuroendocrine tumors that are often hereditary. Although research has advanced considerably, significant gaps still persist in understanding risk factors, predicting metastatic potential and treating aggressive tumors. The study of rare mutations can provide new insights into how pheochromocytomas and paragangliomas develop. In this review, we provide examples of such rare events and how they can inform our understanding of the spectrum of mutations that can lead to these tumors and improve our ability to provide a genetic diagnosis.

Abstract

Pheochromocytomas and paragangliomas are rare tumors of neural crest origin. Their remarkable genetic diversity and high heritability have enabled discoveries of bona fide cancer driver genes with an impact on diagnosis and clinical management and have consistently shed light on new paradigms in cancer. In this review, we explore unique mechanisms of pheochromocytoma and paraganglioma initiation and management by drawing from recent examples involving rare mutations of hypoxia-related genes VHL, EPAS1 and SDHB, and of a poorly known susceptibility gene, TMEM127. These models expand our ability to predict variant pathogenicity, inform new functional domains, recognize environmental-gene connections, and highlight persistent therapeutic challenges for tumors with aggressive behavior.

Emerging Role of Chimeric RNAs in Cell Plasticity and Adaptive Evolution of Cancer Cells

Gene fusions can give rise to somatic alterations in cancers. Fusion genes have the potential to create chimeric RNAs, which can generate the phenotypic diversity of cancer cells, and could be associated with novel molecular functions related to cancer cell survival and proliferation. The expression of chimeric RNAs in cancer cells might impact diverse cancer-related functions, including loss of apoptosis and cancer cell plasticity, and promote oncogenesis. Due to their recurrence in cancers and functional association with oncogenic processes, chimeric RNAs are considered biomarkers for cancer diagnosis. Several recent studies demonstrated that chimeric RNAs could lead to the generation of new functionality for the resistance of cancer cells against drug therapy. Therefore, targeting chimeric RNAs in drug resistance cancer could be useful for developing precision medicine. So, understanding the functional impact of chimeric RNAs in cancer cells from an evolutionary perspective will be helpful to elucidate cancer evolution, which could provide a new insight to design more effective therapies for cancer patients in a personalized manner.