BACKGROUND: Acute myeloid leukemia (AML) is the second most common type of leukemia in children. Although prognostic and diagnostic tests of AML patients have improved, there is still a great demand for new reliable clinical biomarkers for AML. Read-through fusion transcripts (RTFTs) are complex transcripts of adjacent genes whose molecular mechanisms are poorly understood. This is the first report of the presence of the PPP1R1B::STARD3 fusion transcript in an AML patient. Here, we investigated the presence of PPP1R1B::STARD3 RTFT in a case of AML using paired-end RNA sequencing (RNA-seq).
METHODS: A Persian 12-year-old male was admitted to Dr. Sheikh Hospital of Mashhad, Iran, in September 2019 with the following symptoms, including fever, convulsions, hemorrhage, and bone pain. The patient was diagnosed with AML (non-M3-FAB subtype) based on cell morphologies and immunophenotypical features. Chromosomal analysis using the G-banding technique revealed t (9;22) (q34;q13).
CONCLUSIONS: Single-cell RNA sequencing (scRNA-seq) analysis suggested that the PPP1R1B promoter may be responsible for the PPP1R1B::STARD3 expression. Alterations in the level of lipid metabolites implicate cancer development, and this fusion can play a crucial role in the cholesterol movement in cancer cells. PPP1R1B::STARD3 may be considered a candidate for targeted therapies of the cholesterol metabolic and the PI3K/AKT signaling pathways involved in cancer development and progression.

BACKGROUND: Past studies suggest that there are changes in peripheral blood cell gene expression in response to ischaemic stroke; however, the specific changes which occur during the acute phase are poorly characterised. The current study aimed to identify peripheral blood cell genes specifically associated with the early response to ischaemic stroke using whole blood samples collected from participants diagnosed with ischaemic stroke (n = 29) or stroke mimics (n = 27) following emergency presentation to hospital. Long non-coding RNA (lncRNA), mRNA and micro-RNA (miRNA) abundance was measured by RNA-seq, and the consensusDE package was used to identify genes which were differentially expressed between groups. A sensitivity analysis excluding two participants with metastatic disease was also conducted.
RESULTS: The mean time from symptom onset to blood collection was 2.6 h. Most strokes were mild (median NIH stroke scale score 2.0). Ten mRNAs (all down-regulated in samples provided by patients experiencing ischaemic stroke) and 30 miRNAs (14 over-expressed and 16 under-expressed in participants with ischaemic stroke) were significantly different between groups in the whole cohort and sensitivity analyses. No significant over-representation of gene ontology categories by the differentially expressed genes was observed. Random forest analysis suggested a panel of differentially expressed genes (ADGRG7 and miRNAs 96, 532, 6766, 6798 and 6804) as potential ischaemic stroke biomarkers, although modelling analyses demonstrated that these genes had poor diagnostic performance.
CONCLUSIONS: This study provides evidence suggesting that the early response to minor ischaemic stroke is predominantly reflected by changes in the expression of miRNAs in peripheral blood cells. Further work in independent cohorts particularly in patients with more severe stroke is needed to validate these findings and investigate their clinical relevance.

BACKGROUND: Intronic variants outside the canonical splice site are challenging to interpret and therefore likely represent an underreported cause of human disease. Autosomal recessive variants in DYNC2H1 are associated with short-rib thoracic dysplasia 3 with or without polydactyly (SRTD3), a clinically heterogeneous disease generally presenting with short ribs, shortened tubular bones, narrow thorax and acetabular roof anomalies. We describe a case of SRTD3 with compound heterozygous frameshift and intronic variants and highlight the essential role of RNA sequencing (RNA-Seq) in variant interpretation.
METHODS: Following inconclusive clinical genetic testing identifying a likely pathogenic frameshift variant and an intronic variant of uncertain significance (VUS) in DYNC2H1 in trans, the family enrolled in the Care4Rare Canada research program, where RNA-Seq studies were performed.
RESULTS: The proband presented with post-axial polydactyly of all four limbs, a significantly small chest with a pectus excavatum and anterior flaring of the ribs. RNA-Seq investigations revealed a novel splice junction as a result of the intronic VUS and significantly decreased DYNC2H1 gene expression in the proband.
CONCLUSIONS: This case demonstrates the diagnostic utility of RNA-Seq for variant interpretation following inconclusive clinical testing, which can ultimately lead to diagnosis for patients with rare disease.

RNA-sequencing (RNA-seq) technology has led to a surge of neuroscience research using animal models to probe the complex molecular mechanisms underlying brain function and behavior, including substance use disorders. However, findings from rodent studies often fail to be translated into clinical treatments. Here, we developed a novel pipeline for narrowing candidate genes from preclinical studies by translational potential and demonstrated its utility in 2 RNA-seq studies of rodent self-administration. This pipeline uses evolutionary conservation and preferential expression of genes across brain tissues to prioritize candidate genes, increasing the translational utility of RNA-seq in model organisms. Initially, we demonstrate the utility of our prioritization pipeline using an uncorrected P-value. However, we found no differentially expressed genes in either dataset after correcting for multiple testing with false discovery rate (FDR < 0.05 or <0.1). This is likely due to low statistical power that is common across rodent behavioral studies, and, therefore, we additionally illustrate the use of our pipeline on a third dataset with differentially expressed genes corrected for multiple testing (FDR < 0.05). We also advocate for improved RNA-seq data collection, statistical testing, and metadata reporting that will bolster the field\'s ability to identify reliable candidate genes and improve the translational value of bioinformatics in rodent research.

In differential gene expression data analysis, one objective is to identify groups of co-expressed genes from a large dataset in order to detect the association between such a group of genes and an experimental condition. This is often done through a clustering approach, such as k-means or bipartition hierarchical clustering, based on particular similarity measures in the grouping process. In such a dataset, the gene differential expression itself is an innate attribute that can be used in the feature extraction process. For example, in a dataset consisting of multiple treatments versus their controls, the expression of a gene in each treatment would have three possible behaviors, upregulated, downregulated, or unchanged. We present in this chapter, a differential expression feature extraction (DEFE) method by using a string consisting of three numerical values at each character to denote such behavior, i.e., 1 = up, 2 = down, and 0 = unchanged, which results in up to 3B differential expression patterns across all B comparisons. This approach has been successfully applied in many research projects, and among these, we demonstrate the strength of DEFE in a case study on RNA-sequencing (RNA-seq) data analysis of wheat challenged with the phytopathogenic fungus, Fusarium graminearum. Combinations of multiple schemes of DEFE patterns revealed groups of genes putatively associated with resistance or susceptibility to FHB.

In RNA-seq data processing, short reads are usually aligned from one species against its own genome sequence; however, in plant-pathogen interaction systems, reads from both host and pathogen samples are blended together. In contrast with single-genome analyses, both pathogen and host reference genomes are involved in the alignment process. In such circumstances, the order in which the alignment is carried out, whether the host or pathogen is aligned first, or if both genomes are aligned simultaneously, influences the read counts of certain genes. This is a problem, especially at advanced infection stages. It is crucial to have an appropriate strategy for aligning the reads to their respective genomes, yet the existing strategies of either sequential or parallel alignment become problematic when mapping mixed reads to their corresponding reference genomes. The challenge lies in the determination of which reads belong to which species, especially when homology exists between the host and pathogen genomes. This chapter proposes a combo-genome alignment strategy, which was compared with existing alignment scenarios. Simulation results demonstrated that the degree of discrepancy in the results is correlated with phylogenetic distance of the two species in the mixture which was attributable to the extent of homology between the two genomes involved. This correlation was also found in the analysis using two real RNA-seq datasets of Fusarium-challenged wheat plants. Comparisons of the three RNA-seq processing strategies on three simulation datasets and two real Fusarium-infected wheat datasets showed that an alignment to a combo-genome, consisting of both host and pathogen genomes, improves mapping quality as compared to sequential alignment procedures.

Formalin-fixed paraffin-embedded (FFPE) samples are the only remaining biological archive for many toxicological and clinical studies, yet their use in genomics has been limited due to nucleic acid damage from formalin fixation. Older FFPE samples with highly degraded RNA pose a particularly difficult technical challenge. Probe-based targeted sequencing technologies show promise in addressing this issue but have not been directly compared to standard whole-genome RNA-Sequencing (RNA-Seq) methods. In this study, we evaluated dose-dependent transcriptional changes from paired frozen (FROZ) and FFPE liver samples stored for over 20 years using targeted resequencing (TempO-Seq) and whole-genome RNA-Seq methods. Samples were originally collected from male mice exposed to a reference chemical (dichloroacetic acid, DCA) at 0, 198, 313, and 427 mg/kg-day (n = 6/dose) by drinking water for 6 days. TempO-Seq showed high overlap in differentially expressed genes (DEGs) between matched FFPE and FROZ samples and high concordance in fold-change values across the two highest dose levels of DCA vs. control (R2 ≥ 0.94). Similarly, high concordance in fold-change values was observed between TempO-Seq FFPE and RNA-Seq FROZ results (R2 ≥ 0.92). In contrast, RNA-Seq FFPE samples showed few overlapping DEGs compared to FROZ RNA-Seq (≤5 for all dose groups). Modeling of DCA-dependent changes in gene sets identified benchmark doses from TempO-Seq FROZ and FFPE samples within 1.4-fold of RNA-Seq FROZ samples (93.9 mg/kg-d), whereas RNA-Seq FFPE samples were 3.3-fold higher (310.3 mg/kg-d). This work demonstrates that targeted sequencing may provide a more robust method for quantifying gene expression profiles from aged archival FFPE samples.

Ribosomal profiling is an emerging experimental technology to measure protein synthesis by sequencing short mRNA fragments undergoing translation in ribosomes. Applied on the genome wide scale, this is a powerful tool to profile global protein synthesis within cell populations of interest. Such information can be utilized for biomarker discovery and detection of treatment-responsive genes. However, analysis of ribosomal profiling data requires careful preprocessing to reduce the impact of artifacts and dedicated statistical methods for visualizing and modeling the high-dimensional discrete read count data. Here we present Ribosomal Profiling Reports (RP-REP), a new open-source cloud-enabled software that allows users to execute start-to-end gene-level ribosomal profiling and RNA-Seq analysis on a pre-configured Amazon Virtual Machine Image (AMI) hosted on AWS or on the user\'s own Ubuntu Linux server. The software works with FASTQ files stored locally, on AWS S3, or at the Sequence Read Archive (SRA). RP-REP automatically executes a series of customizable steps including filtering of contaminant RNA, enrichment of true ribosomal footprints, reference alignment and gene translation quantification, gene body coverage, CRAM compression, reference alignment QC, data normalization, multivariate data visualization, identification of differentially translated genes, and generation of heatmaps, co-translated gene clusters, enriched pathways, and other custom visualizations. RP-REP provides functionality to contrast RNA-SEQ and ribosomal profiling results, and calculates translational efficiency per gene. The software outputs a PDF report and publication-ready table and figure files. As a use case, we provide RP-REP results for a dengue virus study that tested cytosol and endoplasmic reticulum cellular fractions of human Huh7 cells pre-infection and at 6 h, 12 h, 24 h, and 40 h post-infection. Case study results, Ubuntu installation scripts, and the most recent RP-REP source code are accessible at GitHub. The cloud-ready AMI is available at AWS (AMI ID: RPREP RSEQREP (Ribosome Profiling and RNA-Seq Reports) v2.1 (ami-00b92f52d763145d3)).

DHDPS is a key enzyme in the aspartate-derived lysine biosynthesis pathway and an evident object of study for biofortification strategies in plants. DHDPS isoforms with novel regulatory properties in Medicago truncatula were demonstrated earlier and hypothesized to be involved in abiotic and biotic stress responses. Here, we present a phylogenetic analysis of the DHPDS gene family in land plants which establishes the existence of a legume-specific class of DHDPS, termed DHDPS B-type, distinguishable from the DHDPS A-type commonly present in all land plants. The G. max genome comprises two A-type DHDPS genes (Gm.DHDPS-A1; Glyma.09G268200, Gm.DHDPS-A2; Glyma.18G221700) and one B-type (Gm.DHDPS-B; Glyma.03G022300). To further investigate the expression pattern of the G. max DHDPS isozymes in different plant tissues and under various stress conditions, 461 RNA-seq experiments were exploited and re-analyzed covering two expression atlases, 13 abiotic and 5 biotic stress studies. Gm.DHDPS-B is seen almost exclusively expressed in roots and nodules in addition to old cotyledons or senescent leaves while both DHDPS A-types are expressed constitutively in all tissues analyzed with the highest expression in mature seeds. Furthermore, Gm.DHDPS-B expression is significantly upregulated in some but not all stress responses including salt stress, flooding, ethylene or infection with Phytophthora sojae and coincides with downregulation of DHDPS A-types. In conclusion, we demonstrate the potential of an in-depth RNA-seq re-analysis for the guidance of future experiments and to expand on current knowledge.

Au-Kline syndrome is a severe multisystemic syndrome characterized by several congenital defects, including intellectual disability. Loss-of-function and missense variants in the HNRNPK gene are associated with a range of dysmorphic features. This report describes an eleven-year-old Chinese boy with intellectual disability and developmental delays. Family-based whole-exome and Sanger sequencing identified a de novo missense variant in HNRNPK (NM_002140.3: c.143T > A, p. Leu48Val). In silico analysis predicted that this variant would be damaged in a highly conserved residue in the K homology 1 (KH1) domain. Bioinformatic analysis showed that the affinity change (ΔΔG) caused by this variant was -0.033 kcal/mol, indicating that it would have reduced affinity for RNA binding. Transcript analysis of the peripheral blood from this case found 42 aberrantly expressed and 86 aberrantly spliced genes (p-value <0.01). Functional enrichment analysis confirmed that the biological functions of these genes, including protein binding and transcriptional regulation, are associated with HNRNPK. In summary, this study identifies the first Chinese patient with a novel de novo heterozygous HNRNPK gene variant that contributes to Au-Kline syndrome and expands current knowledge of the clinical spectrum of HNRNPK variants.