Variability in estimated gene expression among commonly used RNA-seq pipelines

Identifying the stability of housekeeping genes to be used for the quantitative real-time PCR normalization in retinal tissue of streptozotocin-induced diabetic rats

AIM

To investigate the stability of the seven housekeeping genes: beta-actin (ActB), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), 18s ribosomal unit 5 (18s), cyclophilin A (CycA), hypoxanthine-guanine phosphoribosyl transferase (HPRT), ribosomal protein large P0 (36B4) and terminal uridylyl transferase 1 (U6) in the diabetic retinal tissue of rat model.

METHODS

The expression of these seven genes in rat retinal tissues was determined using real-time quantitative reverse transcription polymerase chain reaction (RT-qPCR) in two groups; normal control rats and streptozotocin-induced diabetic rats. The stability analysis of gene expression was investigated using geNorm, NormFinder, BestKeeper, and comparative delta-Ct (ΔCt) algorithms.

RESULTS

The 36B4 gene was stably expressed in the retinal tissues of normal control animals; however, it was less stable in diabetic retinas. The 18s gene was expressed consistently in both normal control and diabetic rats' retinal tissue. That this gene was the best reference for data normalisation in RT-qPCR studies that used the retinal tissue of streptozotocin-induced diabetic rats. Furthermore, there was no ideal gene stably expressed for use in all experimental settings.

CONCLUSION

Identifying relevant genes is a need for achieving RT-qPCR validity and reliability and must be appropriately achieved based on a specific experimental setting.

U-CAN-seq: A Universal Competition Assay by Nanopore Sequencing

RNA viruses quickly evolve subtle genotypic changes that can have major impacts on viral fitness and host range, with potential consequences for human health. It is therefore important to understand the evolutionary fitness of novel viral variants relative to well-studied genotypes of epidemic viruses. Competition assays are an effective and rigorous system with which to assess the relative fitness of viral genotypes. However, it is challenging to quickly and cheaply distinguish and quantify fitness differences between very similar viral genotypes. Here, we describe a protocol for using reverse transcription PCR in combination with commercial nanopore sequencing services to perform competition assays on untagged RNA viruses. Our assay, called the Universal Competition Assay by Nanopore Sequencing (U-CAN-seq), is relatively cheap and highly sensitive. We used a well-studied N24A mutation in the chikungunya virus (CHIKV) nsp3 gene to confirm that we could detect a competitive disadvantage using U-CAN-seq. We also used this approach to show that mutations to the CHIKV 5' conserved sequence element that disrupt sequence but not structure did not affect the fitness of CHIKV. However, similar mutations to an adjacent CHIKV stem loop (SL3) did cause a fitness disadvantage compared to wild-type CHIKV, suggesting that structure-independent, primary sequence determinants in this loop play an important role in CHIKV biology. Our novel findings illustrate the utility of the U-CAN-seq competition assay.

Haploinsufficiency of phosphodiesterase 10A activates PI3K/AKT signaling independent of PTEN to induce an aggressive glioma phenotype

Glioblastoma is universally fatal and characterized by frequent chromosomal copy number alterations harboring oncogenes and tumor suppressors. In this study, we analyzed exome-wide human glioblastoma copy number data and found that cytoband 6q27 is an independent poor prognostic marker in multiple data sets. We then combined CRISPR-Cas9 data, human spatial transcriptomic data, and human and mouse RNA sequencing data to nominate PDE10A as a potential haploinsufficient tumor suppressor in the 6q27 region. Mouse glioblastoma modeling using the RCAS/tv-a system confirmed that Pde10a suppression induced an aggressive glioma phenotype in vivo and resistance to temozolomide and radiation therapy in vitro. Cell culture analysis showed that decreased Pde10a expression led to increased PI3K/AKT signaling in a Pten-independent manner, a response blocked by selective PI3K inhibitors. Single-nucleus RNA sequencing from our mouse gliomas in vivo, in combination with cell culture validation, further showed that Pde10a suppression was associated with a proneural-to-mesenchymal transition that exhibited increased cell adhesion and decreased cell migration. Our results indicate that glioblastoma patients harboring PDE10A loss have worse outcomes and potentially increased sensitivity to PI3K inhibition.

In Silico Comparison of WRKY Transcription Factors in Wild and Cultivated Soybean and Their Co-expression Network Arbitrating Disease Resistance

WRKY Transcription factors (TFs) play critical roles in plant defence mechanisms that are activated in response to biotic and abiotic stresses. However, information on the Glycine soja WRKYs (GsoWRKYs) is scarce. Owing to its importance in soybean breeding, here we identified putative WRKY TFs in wild soybean, and compared the results with Glycine max WRKYs (GmaWRKYs) by phylogenetic, conserved motif, and duplication analyses. Moreover, we explored the expression trends of WRKYs in G. max (oomycete, fungi, virus, bacteria, and soybean cyst nematode) and G. soja (soybean cyst nematode), and identified commonly expressed WRKYs and their co-expressed genes. We identified, 181 and 180 putative WRKYs in G. max and G. soja, respectively. Though the number of WRKYs in both studied species is almost the same, they differ in many ways, i.e., the number of WRKYs on corresponding chromosomes, conserved domain structures, WRKYGQK motif variants, and zinc-finger motifs. WRKYs in both species grouped in three major clads, i.e., I-III, where group-II had sub-clads IIa-IIe. We found that GsoWRKYs expanded mostly through segmental duplication. A large number of WRKYs were expressed in response to biotic stresses, i.e., Phakospora pachyrhizi, Phytoplasma, Heterodera glycines, Macrophomina phaseolina, and Soybean mosaic virus; 56 GmaWRKYs were commonly expressed in soybean plants infected with these diseases. Finally, 30 and 63 GmaWRKYs and GsoWRKYs co-expressed with 205 and 123 non-WRKY genes, respectively, indicating that WRKYs play essential roles in biotic stress tolerance in Glycine species.

Critical Differential Expression Assessment for Individual Bulk RNA-Seq Projects

Finding the right balance of quality and quantity can be important, and it is essential that project quality does not drop below the level where important main conclusions are missed or misstated. We use knock-out and over-expression studies as a simplification to test recovery of a known causal gene in RNA-Seq cell line experiments. When single-end RNA-Seq reads are aligned with STAR and quantified with htseq-count, we found potential value in testing the use of the Generalized Linear Model (GLM) implementation of edgeR with robust dispersion estimation more frequently for either single-variate or multi-variate 2-group comparisons (with the possibility of defining criteria less stringent than |fold-change| > 1.5 and FDR < 0.05). When considering a limited number of patient sample comparisons with larger sample size, there might be some decreased variability between methods (except for DESeq1). However, at the same time, the ranking of the gene identified using immunohistochemistry (for ER/PR/HER2 in breast cancer samples from The Cancer Genome Atlas) showed as possible shift in performance compared to the cell line comparisons, potentially highlighting utility for standard statistical tests and/or limma-based analysis with larger sample sizes. If this continues to be true in additional studies and comparisons, then that could be consistent with the possibility that it may be important to allocate time for potential methods troubleshooting for genomics projects. Analysis of public data presented in this study does not consider all experimental designs, and presentation of downstream analysis is limited. So, any estimate from this simplification would be an underestimation of the true need for some methods testing for every project. Additionally, this set of independent cell line experiments has a limitation in being able to determine the frequency of missing a highly important gene if the problem is rare (such as 10% or lower). For example, if there was an assumption that only one method can be tested for "initial" analysis, then it is not completely clear to the extent that using edgeR-robust might perform better than DESeq2 in the cell line experiments. Importantly, we do not wish to cause undue concern, and we believe that it should often be possible to define a gene expression differential expression workflow that is suitable for some purposes for many samples. Nevertheless, at the same time, we provide a variety of measures that we believe emphasize the need to critically assess every individual project and maximize confidence in published results.

Dysregulation of ferroptosis-related genes in granulosa cells associates with impaired oocyte quality in polycystic ovary syndrome

Background

Poor oocyte quality remains one of the major challenges for polycystic ovary syndrome (PCOS) patients during in vitro fertilization (IVF) treatment. Granulosa cells (GCs) in PCOS display altered functions and could cause an unfavorable microenvironment for oocyte growth and maturation. Ferroptosis is a new form of programmed cell death, but its role in PCOS has been largely unclarified.

Methods

Ferroptosis-related differentially expressed genes (DEGs) of GCs in women with PCOS were identified by bioinformatic analyses of GSE155489 and GSE168404 datasets. Functional enrichment analyses were conducted using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes. Core ferroptosis-related genes were further screened by random forest, and evaluated for diagnostic value by receiver operating characteristic curve analyses. Gene expression was validated by real-time quantitative polymerase chain reaction of collected GC samples, and analyzed for association with oocyte quality. In addition, gene regulatory network was constructed based on predicted RNA interactions and transcription factors, while potential therapeutic compounds were screened through molecular docking with crystallographic protein structures.

Results

A total of 14 ferroptosis-related DEGs were identified. These DEGs were mainly enriched in reactive oxygen species metabolic process, mitochondrial outer membrane, antioxidant activity as well as ferroptosis and adipocytokine signaling pathways. Eight core ferroptosis-related genes (ATF3, BNIP3, DDIT4, LPIN1, NOS2, NQO1, SLC2A1 and SLC2A6) were further selected in random forest model, which showed high diagnostic performance for PCOS. Seven of them were validated in GC samples, and five were found to be significantly and positively correlated with one or more oocyte quality parameters in PCOS patients, including oocyte retrieval rate, mature oocyte rate, normal fertilization rate, and good-quality embryo rate. Gene regulatory network revealed JUN and HMGA1 as two important transcription factors, while dicoumarol and flavin adenine dinucleotide were predicted as small molecules with therapeutic potential.

Conclusions

This is the first comprehensive report to study the differential expression of ferroptosis-related genes in GCs of PCOS and their clinical relevance with oocyte quality. Our findings could provide novel insights on the potential role of GC ferroptosis in PCOS pathogenesis, diagnosis, and targeted treatment.

The target atlas for antibody-drug conjugates across solid cancers

Antibody-Drug Conjugates (ADCs) represent a rapidly advancing category of oncology therapeutics, spanning the targeted therapy for both hematologic malignancies and solid cancers. A crucial aspect of ADC research involves the identification of optimal surface antigens that can effectively differentiate target cells from most mammalian cell types. Herein, we have devised an algorithm and compiled an extensive dataset annotating cell membrane proteins. This dataset is derived from comprehensive transcriptomic, proteomic, and genomic data encompassing 19 types of solid cancer as well as normal tissues. The aim is to uncover potential therapeutic surface antigens for precise ADC targeting. The resulting target landscape comprises 165 combinations of targets and indications, along with 75 candidates of cell surface proteins. Notably, 35 of these candidates possess characteristics suitable for ADC targeting, and have not been previously reported in ADC research and development. Additionally, we have identified a total of 159 ADCs from a pool of 760 clinical trials. Of these, 72 ADCs are presently undergoing interventional evaluation for a variety of solid cancer types, targeting 36 unique antigens. We conducted an analysis of their expression in normal tissues using this comprehensive annotation dataset, revealing a diverse range of profiles for the current ADC targets. Moreover, we emphasize that the biological impacts of target antigens have the potential to enhance their clinical effectiveness. We propose a comprehensive assessment of the drugability of target antigens, considering multiple facets. This study represents a thorough exploration of pan-cancer ADC targets over the past two decades, underscoring the potential of a comprehensive solid cancer target atlas to broaden the scope of ADC therapies.

Multi-omic approaches for host-microbiome data integration

The gut microbiome interacts with the host through complex networks that affect physiology and health outcomes. It is becoming clear that these interactions can be measured across many different omics layers, including the genome, transcriptome, epigenome, metabolome, and proteome, among others. Multi-omic studies of the microbiome can provide insight into the mechanisms underlying host-microbe interactions. As more omics layers are considered, increasingly sophisticated statistical methods are required to integrate them. In this review, we provide an overview of approaches currently used to characterize multi-omic interactions between host and microbiome data. While a large number of studies have generated a deeper understanding of host-microbiome interactions, there is still a need for standardization across approaches. Furthermore, microbiome studies would also benefit from the collection and curation of large, publicly available multi-omics datasets.

Spaceflight Induces Strength Decline in Caenorhabditis elegans

Background: Understanding and countering the well-established negative health consequences of spaceflight remains a primary challenge preventing safe deep space exploration. Targeted/personalized therapeutics are at the forefront of space medicine strategies, and cross-species molecular signatures now define the 'typical' spaceflight response. However, a lack of direct genotype-phenotype associations currently limits the robustness and, therefore, the therapeutic utility of putative mechanisms underpinning pathological changes in flight. Methods: We employed the worm Caenorhabditis elegans as a validated model of space biology, combined with 'NemaFlex-S' microfluidic devices for assessing animal strength production as one of the most reproducible physiological responses to spaceflight. Wild-type and dys-1 (BZ33) strains (a Duchenne muscular dystrophy (DMD) model for comparing predisposed muscle weak animals) were cultured on the International Space Station in chemically defined media before loading second-generation gravid adults into NemaFlex-S devices to assess individual animal strength. These same cultures were then frozen on orbit before returning to Earth for next-generation sequencing transcriptomic analysis. Results: Neuromuscular strength was lower in flight versus ground controls (16.6% decline, p < 0.05), with dys-1 significantly more (23% less strength, p < 0.01) affected than wild types. The transcriptional gene ontology signatures characterizing both strains of weaker animals in flight strongly corroborate previous results across species, enriched for upregulated stress response pathways and downregulated mitochondrial and cytoskeletal processes. Functional gene cluster analysis extended this to implicate decreased neuronal function, including abnormal calcium handling and acetylcholine signaling, in space-induced strength declines under the predicted control of UNC-89 and DAF-19 transcription factors. Finally, gene modules specifically altered in dys-1 animals in flight again cluster to neuronal/neuromuscular pathways, suggesting strength loss in DMD comprises a strong neuronal component that predisposes these animals to exacerbated strength loss in space. Conclusions: Highly reproducible gene signatures are strongly associated with space-induced neuromuscular strength loss across species and neuronal changes in calcium/acetylcholine signaling require further study. These results promote targeted medical efforts towards and provide an in vivo model for safely sending animals and people into deep space in the near future.

Variability in donor leukocyte counts confound the use of common RNA sequencing data normalization strategies in transcriptomic biomarker studies performed with whole blood

Gene expression data generated from whole blood via next generation sequencing is frequently used in studies aimed at identifying mRNA-based biomarker panels with utility for diagnosis or monitoring of human disease. These investigations often employ data normalization techniques more typically used for analysis of data originating from solid tissues, which largely operate under the general assumption that specimens have similar transcriptome composition. However, this assumption may be violated when working with data generated from whole blood, which is more cellularly dynamic, leading to potential confounds. In this study, we used next generation sequencing in combination with flow cytometry to assess the influence of donor leukocyte counts on the transcriptional composition of whole blood specimens sampled from a cohort of 138 human subjects, and then subsequently examined the effect of four frequently used data normalization approaches on our ability to detect inter-specimen biological variance, using the flow cytometry data to benchmark each specimens true cellular and molecular identity. Whole blood samples originating from donors with differing leukocyte counts exhibited dramatic differences in both genome-wide distributions of transcript abundance and gene-level expression patterns. Consequently, three of the normalization strategies we tested, including median ratio (MRN), trimmed mean of m-values (TMM), and quantile normalization, noticeably masked the true biological structure of the data and impaired our ability to detect true interspecimen differences in mRNA levels. The only strategy that improved our ability to detect true biological variance was simple scaling of read counts by sequencing depth, which unlike the aforementioned approaches, makes no assumptions regarding transcriptome composition.

Integrated microRNA-mRNA analysis reveals a possible molecular mechanism of enteritis susceptibility in Litopenaeus vannamei

Enteritis is one of the main diseases affecting Pacific whiteleg shrimp (Litopenaeus vannamei) in recent years, and it has resulted in huge losses to the aquaculture industry. Prior to this study, the molecular mechanism underlying enteritis in L. vannamei was unclear, and comprehensive multi-omics analysis had not been conducted. In this study, 1209 differentially expressed genes (DEGs) were identified from the hepatopancreas of L. vannamei with and without enteritis. Kyoto Encyclopedia of Genes and Genomes analysis showed that genes were significantly enriched in immune, metabolic, and endocrine regulatory pathways. Forty-eight significantly different microRNAs (miRNAs) were identified in the miRNA-Seq analysis. Further functional annotation analysis showed that the regulatory pathway of target gene enrichment of differentially expressed miRNAs was consistent with DEGs. Through miRNA-mRNA integration analysis, 47 meaningful miRNA-mRNA pairs were obtained, of which melanogenesis and pancreatic secretion were considered key pathways. Subsequent miRNA-mRNA interaction network analysis revealed that mja-miR-6493-3p, Mja-miR-6494, novel-198, novel-272, novel-261, novel-200, novel-183, novel-184, novel-237, and novel-192 may be key miRNAs involved in the regulation of these two signaling pathways. Finally, the RAS signaling pathway was found to inhibit the translation level of proteins in the hepatopancreas. These results suggest that target gene integration analysis of mRNA-miRNA can reveal the molecular mechanism underlying enteritis in L. vannamei and also provide valuable new insights for resisting enteritis.

Visualizing genomic characteristics across an RNA-Seq based reference landscape of normal and neoplastic brain

In order to better understand the relationship between normal and neoplastic brain, we combined five publicly available large-scale datasets, correcting for batch effects and applying Uniform Manifold Approximation and Projection (UMAP) to RNA-Seq data. We assembled a reference Brain-UMAP including 702 adult gliomas, 802 pediatric tumors and 1409 healthy normal brain samples, which can be utilized to investigate the wealth of information obtained from combining several publicly available datasets to study a single organ site. Normal brain regions and tumor types create distinct clusters and because the landscape is generated by RNA-Seq, comparative gene expression profiles and gene ontology patterns are readily evident. To our knowledge, this is the first meta-analysis that allows for comparison of gene expression and pathways of interest across adult gliomas, pediatric brain tumors, and normal brain regions. We provide access to this resource via the open source, interactive online tool Oncoscape, where the scientific community can readily visualize clinical metadata, gene expression patterns, gene fusions, mutations, and copy number patterns for individual genes and pathway over this reference landscape.

An RNA seq-based reference landscape of human normal and neoplastic brain

In order to better understand the relationship between normal and neoplastic brain, we combined five publicly available large-scale datasets, correcting for batch effects and applying Uniform Manifold Approximation and Projection (UMAP) to RNA-seq data. We assembled a reference Brain-UMAP including 702 adult gliomas, 802 pediatric tumors and 1409 healthy normal brain samples, which can be utilized to investigate the wealth of information obtained from combining several publicly available datasets to study a single organ site. Normal brain regions and tumor types create distinct clusters and because the landscape is generated by RNA seq, comparative gene expression profiles and gene ontology patterns are readily evident. To our knowledge, this is the first meta-analysis that allows for comparison of gene expression and pathways of interest across adult gliomas, pediatric brain tumors, and normal brain regions. We provide access to this resource via the open source, interactive online tool Oncoscape, where the scientific community can readily visualize clinical metadata, gene expression patterns, gene fusions, mutations, and copy number patterns for individual genes and pathway over this reference landscape.

An RNA seq-based reference landscape of human normal and neoplastic brain

In order to better understand the relationship between normal and neoplastic brain, we combined five publicly available large-scale datasets, correcting for batch effects and applying Uniform Manifold Approximation and Projection (UMAP) to RNA-seq data. We assembled a reference Brain-UMAP including 702 adult gliomas, 802 pediatric tumors and 1409 healthy normal brain samples, which can be utilized to investigate the wealth of information obtained from combining several publicly available datasets to study a single organ site. Normal brain regions and tumor types create distinct clusters and because the landscape is generated by RNA seq, comparative gene expression profiles and gene ontology patterns are readily evident. To our knowledge, this is the first meta-analysis that allows for comparison of gene expression and pathways of interest across adult gliomas, pediatric brain tumors, and normal brain regions. We provide access to this resource via the open source, interactive online tool Oncoscape, where the scientific community can readily visualize clinical metadata, gene expression patterns, gene fusions, mutations, and copy number patterns for individual genes and pathway over this reference landscape.

Genomic Basis for Individual Differences in Susceptibility to the Neurotoxic Effects of Diesel Exhaust

Air pollution is a known environmental health hazard. A major source of air pollution includes diesel exhaust (DE). Initially, research on DE focused on respiratory morbidities; however, more recently, exposures to DE have been associated with neurological developmental disorders and neurodegeneration. In this study, we investigated the effects of sub-chronic inhalation exposure to DE on neuroinflammatory markers in two inbred mouse strains and both sexes, including whole transcriptome examination of the medial prefrontal cortex. We exposed aged male and female C57BL/6J (B6) and DBA/2J (D2) mice to DE, which was cooled and diluted with HEPA-filtered compressed air for 2 h per day, 5 days a week, for 4 weeks. Control animals were exposed to HEPA-filtered air on the same schedule as DE-exposed animals. The prefrontal cortex was harvested and analyzed for proinflammatory cytokine gene expression (Il1β, Il6, Tnfα) and transcriptome-wide response by RNA-seq. We observed differential cytokine gene expression between strains and sexes in the DE-exposed vs. control-exposed groups for Il1β, Tnfα, and Il6. For RNA-seq, we identified 150 differentially expressed genes between air and DE treatment related to natural killer cell-mediated cytotoxicity per Kyoto Encyclopedia of Genes and Genomes pathways. Overall, our data show differential strain-related effects of DE on neuroinflammation and neurotoxicity and demonstrate that B6 are more susceptible than D2 to gene expression changes due to DE exposures than D2. These results are important because B6 mice are often used as the default mouse model for DE studies and strain-related effects of DE neurotoxicity warrant expanded studies.

Reprisal of Schima superba to Mn stress and exploration of its defense mechanism through transcriptomic analysis

One of the most diverse protein families, ATP-binding cassette (ABC) transporters, play a role in disease resistance, heavy metal tolerance, and food absorption.Differentially expressed genes contribute in the investigation of plant defense mechanisms under varying stress conditions. To elucidate the molecular mechanisms involved in Mn metal stress, we performed a transcriptomic analysis to explore the differential gene expression in Schima superba with the comparison of control. A total of 79.84 G clean data was generated and 6558 DEGs were identified in response to Mn metal stress. Differentially expressed genes were found to be involved in defense, signaling pathways, oxidative burst, transcription factors and stress responses. Genes important in metal transport were more expressive in Mn stress than control plants. The investigation of cis-acting regions in the ABC family indicated that these genes might be targeted by a large variety of trans-acting elements to control a variety of stress circumstances. Moreover, genes involved in defense responses, the mitogen-activated protein kinase (MAPK) signaling and signal transduction in S. superba were highly induced in Mn stress. Twenty ABC transporters were variably expressed on 1st, 5th, and 10th day of Mn treatment, according to the qRT PCR data. Inclusively, our findings provide an indispensable foundation for an advanced understanding of the metal resistance mechanisms. Our study will enrich the sequence information of S. superba in a public database and would provide a new understanding of the molecular mechanisms of heavy metal tolerance and detoxification.

On taming the effect of transcript level intra-condition count variation during differential expression analysis: A story of dogs, foxes and wolves

The evolution of RNA-seq technologies has yielded datasets of scientific value that are often generated as condition associated biological replicates within expression studies. With expanding data archives opportunity arises to augment replicate numbers when conditions of interest overlap. Despite correction procedures for estimating transcript abundance, a source of ambiguity is transcript level intra-condition count variation; as indicated by disjointed results between analysis tools. We present TVscript, a tool that removes reference-based transcripts associated with intra-condition count variation above specified thresholds and we explore the effects of such variation on differential expression analysis. Initially iterative differential expression analysis involving simulated counts, where levels of intra-condition variation and sets of over represented transcripts are explicitly specified, was performed. Then counts derived from inter- and intra-study data representing brain samples of dogs, wolves and foxes (wolves vs. dogs and aggressive vs. tame foxes) were used. For simulations, the sensitivity in detecting differentially expressed transcripts increased after removing hyper-variable transcripts, although at levels of intra-condition variation above 5% detection became unreliable. For real data, prior to applying TVscript, ≈20% of the transcripts identified as being differentially expressed were associated with high levels of intra-condition variation, an over representation relative to the reference set. As transcripts harbouring such variation were removed pre-analysis, a discordance from 26 to 40% in the lists of differentially expressed transcripts is observed when compared to those obtained using the non-filtered reference. The removal of transcripts possessing intra-condition variation values within (and above) the 97^th and 95^th percentiles, for wolves vs. dogs and aggressive vs. tame foxes, maximized the sensitivity in detecting differentially expressed transcripts as a result of alterations within gene-wise dispersion estimates. Through analysis of our real data the support for seven genes with potential for being involved with selection for tameness is provided. TVscript is available at: https://sourceforge.net/projects/tvscript/.

The role of antigen expression in shaping the repertoire of HLA presented ligands

Human leukocyte antigen (HLA) presentation of peptides is a prerequisite of T cell immune activation. The understanding of the rules defining this event has large implications for our knowledge of basic immunology and for the rational design of immuno-therapeutics and vaccines. Historically, most of the available prediction methods have been solely focused on the information related to antigen processing and presentation. Recent work has, however, demonstrated that method performance can be boosted by integrating information related to antigen abundance. Here we expand on these later findings and develop an extended version of NetMHCpan, called NetMHCpanExp, integrating information on antigen abundance from RNA-Seq experiments. In line with earlier works, the model demonstrates improved performance for both HLA ligand and cancer neoantigen epitope prediction. Optimal results are obtained by use of sample-specific abundance information but also reference datasets can be applied with a limited performance drop. The developed tool is available at https://services.healthtech.dtu.dk/service.php?NetMHCpanExp-1.0.

CPDR: An R Package of Recommending Personalized Drugs for Cancer Patients by Reversing the Individual’s Disease-Related Signature

Due to cancer heterogeneity, only some patients can benefit from drug therapy. The personalized drug usage is important for improving the treatment response rate of cancer patients. The value of the transcriptome of patients has been recently demonstrated in guiding personalized drug use, and the Connectivity Map (CMAP) is a reliable computational approach for drug recommendation. However, there is still no personalized drug recommendation tool based on transcriptomic profiles of patients and CMAP. To fill this gap, here, we proposed such a feasible workflow and a user-friendly R package—Cancer-Personalized Drug Recommendation (CPDR). CPDR has three features. 1) It identifies the individual disease signature by using the patient subgroup with transcriptomic profiles similar to those of the input patient. 2) Transcriptomic profile purification is supported for the subgroup with high infiltration of non-cancerous cells. 3) It supports in silico drug efficacy assessment using drug sensitivity data on cancer cell lines. We demonstrated the workflow of CPDR with the aid of a colorectal cancer dataset from GEO and performed the in silico validation of drug efficacy. We further assessed the performance of CPDR by a pancreatic cancer dataset with clinical response to gemcitabine. The results showed that CPDR can recommend promising therapeutic agents for the individual patient. The CPDR R package is available at https://github.com/AllenSpike/CPDR.

Transcriptome analysis of infected Crandell Rees Feline Kidney (CRFK) cells by canine parvovirus type 2c Laotian isolates

The advent of RNA sequencing technology provides insight into the dynamic nature of tremendous transcripts within Crandell-Reese feline kidney (CRFK) cells in response to canine parvovirus (CPV-2c) infection. A total of 1,603 genes displayed differentially expressed genes (DEGs), including 789 up-regulated genes and 814 downregulated genes in the infected cells. Gene expression profiles have shown a subtle pattern of defense mechanism and immune response to CPV through significant DEGs when extensively examined via Gene Ontology (GO) and pathway analysis. Prospective GO analysis was performed and identified several enriched GO biological process terms with significant participating roles in the immune system process and defense response to virus pathway. A Gene network was constructed using the 22 most significantly enriched genes of particular interests in defense response to virus pathways to illustrate the key pathways. Eleven genes (C1QBP, CD40, HYAL2, IFNB1, IFNG, IL12B, IL6, IRF3, LSM14A, MAVS, NLRC5) were identified, which are directly related to the defense response to the virus. Results of transcriptome profiling permit us to understand the heterogeneity of DEGs during in vitro experimental study of CPV infection, reflecting a unique transcriptome signature for the CPV virus. Our findings also demonstrate a distinct scenario of enhanced CPV responses in CRFK cells for viral clearance that involved multistep and perplexity of biological processes. Collectively, our data have given a fundamental role in anti-viral immunity as our highlights of this study, thus providing outlooks on future research priorities to be important in studying CPV.

Transcriptome diversity is a systematic source of variation in RNA-sequencing data

RNA sequencing has been widely used as an essential tool to probe gene expression. While standard practices have been established to analyze RNA-seq data, it is still challenging to interpret and remove artifactual signals. Several biological and technical factors such as sex, age, batches, and sequencing technology have been found to bias these estimates. Probabilistic estimation of expression residuals (PEER), which infers broad variance components in gene expression measurements, has been used to account for some systematic effects, but it has remained challenging to interpret these PEER factors. Here we show that transcriptome diversity-a simple metric based on Shannon entropy-explains a large portion of variability in gene expression and is the strongest known factor encoded in PEER factors. We then show that transcriptome diversity has significant associations with multiple technical and biological variables across diverse organisms and datasets. In sum, transcriptome diversity provides a simple explanation for a major source of variation in both gene expression estimates and PEER covariates.

What genes are differentially expressed in individuals with schizophrenia? A systematic review

Schizophrenia is a severe, complex mental disorder characterized by a combination of positive symptoms, negative symptoms, and impaired cognitive function. Schizophrenia is highly heritable (~80%) with multifactorial etiology and complex polygenic genetic architecture. Despite the large number of genetic variants associated with schizophrenia, few causal variants have been established. Gaining insight into the mechanistic influences of these genetic variants may facilitate our ability to apply these findings to prevention and treatment. Though there have been more than 300 studies of gene expression in schizophrenia over the past 15 years, none of the studies have yielded consistent evidence for specific genes that contribute to schizophrenia risk. The aim of this work is to conduct a systematic review and synthesis of case-control studies of genome-wide gene expression in schizophrenia. Comprehensive literature searches were completed in PubMed, EmBase, and Web of Science, and after a systematic review of the studies, data were extracted from those that met the following inclusion criteria: human case-control studies comparing the genome-wide transcriptome of individuals diagnosed with schizophrenia to healthy controls published between January 1, 2000 and June 30, 2020 in the English language. Genes differentially expressed in cases were extracted from these studies, and overlapping genes were compared to previous research findings from the genome-wide association, structural variation, and tissue-expression studies. The transcriptome-wide analysis identified different genes than those previously reported in genome-wide association, exome sequencing, and structural variation studies of schizophrenia. Only one gene, GBP2, was replicated in five studies. Previous work has shown that this gene may play a role in immune function in the etiology of schizophrenia, which in turn could have implications for risk profiling, prevention, and treatment. This review highlights the methodological inconsistencies that impede valid meta-analyses and synthesis across studies. Standardization of the use of covariates, gene nomenclature, and methods for reporting results could enhance our understanding of the potential mechanisms through which genes exert their influence on the etiology of schizophrenia. Although these results are promising, collaborative efforts with harmonization of methodology will facilitate the identification of the role of genes underlying schizophrenia.

Bioinformatics Analysis Using ATAC-seq and RNA-seq for the Identification of 15 Gene Signatures Associated With the Prediction of Prognosis in Hepatocellular Carcinoma

Background

Gene expression (RNA-seq) and overall survival (OS) in TCGA were combined using chromosome accessibility (ATAC-seq) to search for key molecules affecting liver cancer prognosis.

Methods

We used the assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) to analyse chromatin accessibility in the promoter regions of whole genes in liver hepatocellular carcinoma (LIHC) and then screened differentially expressed genes (DEGs) at the mRNA level by transcriptome sequencing technology (RNA-seq). We obtained genes significantly associated with overall survival (OS) by a one-way Cox analysis. The three were screened by taking intersection and further using a Kaplan–Meier (KM) for validation. A prognostic model was constructed using the obtained genes by LASSO regression analysis.The expression of these genes in hepatocellular carcinomas was then analysed. The protein expression of these genes was verified using the Human Protein Atlas(HPA) online datasets and immunohistochemistry.

Results

ATAC-seq, RNA-seq and survival analysis, combined with a LASSO prediction model, identified signatures of 15 genes (PRDX6, GCLM, HTATIP2, SEMA3F, UCK2, NOL10, KIF18A, RAP2A, BOD1, GDI2, ZIC2, GTF3C6 SLC1A5, ERI3 and SAC3D1), all of which were highly expressed in hepatocellular carcinoma. The LASSO prognostic model showed that this risk score had high predictive accuracy for the survival prognosis at 1, 3 and 5 years. A KM curve analysis showed that high expression of all 15 gene signatures was significantly associated with a poor prognosis in LIHC patients. HPA analysis of protein expression showed that PRDX6, GCLM, HTATIP2, NOL10, KIF18A, RAP2A and GDI2 were highly expressed in the hepatocellular carcinoma tissues compared with normal control tissues.

Conclusions

PRDX6, GCLM, HTATIP2, SEMA3F, UCK2, NOL10, KIF18A, RAP2A, BOD1, GDI2, ZIC2, GTF3C6, SLC1A5, ERI3 and SAC3D1 may affect the prognosis of LIHC.

Comparability of reference-based and reference-free transcriptome analysis approaches at the gene expression level

Background

Lately, high-throughput RNA sequencing has been extensively used to elucidate the transcriptome landscape and dynamics of cell types of different species. In particular, for most non-model organisms lacking complete reference genomes with high-quality annotation of genetic information, reference-free (RF) de novo transcriptome analyses, rather than reference-based (RB) approaches, are widely used, and RF analyses have substantially contributed toward understanding the mechanisms regulating key biological processes and functions. To date, numerous bioinformatics studies have been conducted for assessing the workflow, production rate, and completeness of transcriptome assemblies within and between RF and RB datasets. However, the degree of consistency and variability of results obtained by analyzing gene expression levels through these two different approaches have not been adequately documented.

Results

In the present study, we evaluated the differences in expression profiles obtained with RF and RB approaches and revealed that the former tends to be satisfactorily replaced by the latter with respect to transcriptome repertoires, as well as from a gene expression quantification perspective. In addition, we urge cautious interpretation of these findings. Several genes that are lowly expressed, have long coding sequences, or belong to large gene families must be validated carefully, whenever gene expression levels are calculated using the RF method.

Conclusions

Our empirical results indicate important contributions toward addressing transcriptome-related biological questions in non-model organisms.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04226-0.

Bias-invariant RNA-sequencing metadata annotation

BACKGROUND

Recent technological advances have resulted in an unprecedented increase in publicly available biomedical data, yet the reuse of the data is often precluded by experimental bias and a lack of annotation depth and consistency. Missing annotations makes it impossible for researchers to find datasets specific to their needs.

FINDINGS

Here, we investigate RNA-sequencing metadata prediction based on gene expression values. We present a deep-learning-based domain adaptation algorithm for the automatic annotation of RNA-sequencing metadata. We show, in multiple experiments, that our model is better at integrating heterogeneous training data compared with existing linear regression-based approaches, resulting in improved tissue type classification. By using a model architecture similar to Siamese networks, the algorithm can learn biases from datasets with few samples.

CONCLUSION

Using our novel domain adaptation approach, we achieved metadata annotation accuracies up to 15.7% better than a previously published method. Using the best model, we provide a list of >10,000 novel tissue and sex label annotations for 8,495 unique SRA samples. Our approach has the potential to revive idle datasets by automated annotation making them more searchable.

Transcriptome Analysis Identifies GATA3-AS1 as a Long Noncoding RNA Associated with Resistance to Neoadjuvant Chemotherapy in Locally Advanced Breast Cancer Patients

Breast cancer is one of the leading causes of mortality in women worldwide, and neoadjuvant chemotherapy has emerged as an option for the management of locally advanced breast cancer. Extensive efforts have been made to identify new molecular markers to predict the response to neoadjuvant chemotherapy. Transcripts that do not encode proteins, termed long noncoding RNAs (lncRNAs), have been shown to display abnormal expression profiles in different types of cancer, but their role as biomarkers in response to neoadjuvant chemotherapy has not been extensively studied. Herein, lncRNA expression was profiled using RNA sequencing in biopsies from patients who subsequently showed either response or no response to treatment. The GATA3-AS1 transcript was overexpressed in the nonresponder group and was the most stable feature when performing selection in multiple random forest models. GATA3-AS1 was experimentally validated by RT-qPCR in an extended group of 68 patients. Expression analysis confirmed that GATA3-AS1 is overexpressed primarily in patients who were nonresponsive to neoadjuvant chemotherapy, with a sensitivity of 92.9%, a specificity of 75.0%, and an area under the curve of approximately 0.90, as measured by receiver operating characteristic curve analysis. The statistical model was based on luminal B-like patients and adjusted by menopausal status and phenotype (odds ratio, 37.49; 95% CI, 6.74-208.42; P = 0.001); GATA3-AS1 was established as an independent predictor of response. Thus, lncRNA GATA3-AS1 is proposed as a potential predictive biomarker of nonresponse to neoadjuvant chemotherapy.

Incorporating Machine Learning into Established Bioinformatics Frameworks

The exponential growth of biomedical data in recent years has urged the application of numerous machine learning techniques to address emerging problems in biology and clinical research. By enabling the automatic feature extraction, selection, and generation of predictive models, these methods can be used to efficiently study complex biological systems. Machine learning techniques are frequently integrated with bioinformatic methods, as well as curated databases and biological networks, to enhance training and validation, identify the best interpretable features, and enable feature and model investigation. Here, we review recently developed methods that incorporate machine learning within the same framework with techniques from molecular evolution, protein structure analysis, systems biology, and disease genomics. We outline the challenges posed for machine learning, and, in particular, deep learning in biomedicine, and suggest unique opportunities for machine learning techniques integrated with established bioinformatics approaches to overcome some of these challenges.

A critical approach for successful use of circulating microRNAs as biomarkers in cardiovascular diseases: the case of hypertrophic cardiomyopathy

MicroRNAs (miRNAs) are small noncoding RNA molecules that act as major regulators of gene expression at the post-transcriptional level. As the potential applications of miRNAs in the diagnosis and treatment of human diseases have become more evident, many studies of hypertrophic cardiomyopathy (HCM) have focused on the systemic identification and quantification of miRNAs in biofluids and myocardial tissues. HCM is a hereditary cardiomyopathy caused by mutations in genes encoding proteins of the sarcomere. Despite overall improvements in survival, progression to heart failure, stroke, and sudden cardiac death remain prominent features of living with HCM. Several miRNAs have been shown to be promising biomarkers of HCM; however, there are many challenges to ensuring the validity, consistency, and reproducibility of these biomarkers for clinical use. In particular, miRNA testing may be limited by pre-analytical and analytical caveats, making our interpretation of results challenging. Such factors that may affect miRNA testing include sample type selection, hemolysis, platelet activation, and renal dysfunction. Therefore, researchers should be careful when developing appropriate standards for the design of miRNA profiling studies in order to ensure that all results provided are both accurate and reliable. In this review, we discuss the application of miRNAs as biomarkers for HCM.

Heterogeneous expression of CFTR in insulin-secreting β-cells of the normal human islet

Cystic fibrosis (CF) is due to mutations in the CF-transmembrane conductance regulator (CFTR) and CF-related diabetes (CFRD) is its most common co-morbidity, affecting ~50% of all CF patients, significantly influencing pulmonary function and longevity. Yet, the complex pathogenesis of CFRD remains unclear. Two non-mutually exclusive underlying mechanisms have been proposed in CFRD: i) damage of the endocrine cells secondary to the severe exocrine pancreatic pathology and ii) intrinsic β-cell impairment of the secretory response in combination with other factors. The later has proven difficult to determine due to low expression of CFTR in β-cells, which results in the general perception that this Cl⁻channel does not participate in the modulation of insulin secretion or the development of CFRD. The objective of the present work is to demonstrate CFTR expression at the molecular and functional levels in insulin-secreting β-cells in normal human islets, where it seems to play a role. Towards this end, we have used immunofluorescence confocal and immunofluorescence microscopy, immunohistochemistry, RT-qPCR, Western blotting, pharmacology, electrophysiology and insulin secretory studies in normal human, rat and mouse islets. Our results demonstrate heterogeneous CFTR expression in human, mouse and rat β-cells and provide evidence that pharmacological inhibition of CFTR influences basal and stimulated insulin secretion in normal mouse islets but not in islets lacking this channel, despite being detected by electrophysiological means in ~30% of β-cells. Therefore, our results demonstrate a potential role for CFTR in the pancreatic β-cell secretory response suggesting that intrinsic β-cell dysfunction may also participate in the pathogenesis of CFRD.

Increased biological relevance of transcriptome analyses in human skeletal muscle using a model-specific pipeline

Background

Human skeletal muscle responds to weight-bearing exercise with significant inter-individual differences. Investigation of transcriptome responses could improve our understanding of this variation. However, this requires bioinformatic pipelines to be established and evaluated in study-specific contexts. Skeletal muscle subjected to mechanical stress, such as through resistance training (RT), accumulates RNA due to increased ribosomal biogenesis. When a fixed amount of total-RNA is used for RNA-seq library preparations, mRNA counts are thus assessed in different amounts of tissue, potentially invalidating subsequent conclusions. The purpose of this study was to establish a bioinformatic pipeline specific for analysis of RNA-seq data from skeletal muscles, to explore the effects of different normalization strategies and to identify genes responding to RT in a volume-dependent manner (moderate vs. low volume). To this end, we analyzed RNA-seq data derived from a twelve-week RT intervention, wherein 25 participants performed both low- and moderate-volume leg RT, allocated to the two legs in a randomized manner. Bilateral muscle biopsies were sampled from m. vastus lateralis before and after the intervention, as well as before and after the fifth training session (Week 2).

Result

Bioinformatic tools were selected based on read quality, observed gene counts, methodological variation between paired observations, and correlations between mRNA abundance and protein expression of myosin heavy chain family proteins. Different normalization strategies were compared to account for global changes in RNA to tissue ratio. After accounting for the amounts of muscle tissue used in library preparation, global mRNA expression increased by 43–53%. At Week 2, this was accompanied by dose-dependent increases for 21 genes in rested-state muscle, most of which were related to the extracellular matrix. In contrast, at Week 12, no readily explainable dose-dependencies were observed. Instead, traditional normalization and non-normalized models resulted in counterintuitive reverse dose-dependency for many genes. Overall, training led to robust transcriptome changes, with the number of differentially expressed genes ranging from 603 to 5110, varying with time point and normalization strategy.

Conclusion

Optimized selection of bioinformatic tools increases the biological relevance of transcriptome analyses from resistance-trained skeletal muscle. Moreover, normalization procedures need to account for global changes in rRNA and mRNA abundance.

The tumor immune microenvironmental analysis of 2,033 transcriptomes across 7 cancer types

Understanding the tumor microenvironment is important to efficiently identify appropriate patients for immunotherapies in a variety of cancers. Here, we presented the tumor microenvironmental analysis of 2,033 cancer samples across 7 cancer types: colon adenocarcinoma, skin cutaneous melanoma, kidney renal papillary cell carcinoma, sarcoma, pancreatic adenocarcinoma, glioblastoma multiforme, and pheochromocytoma / paraganglioma from The Cancer Genome Atlas cohort. Unsupervised hierarchical clustering based on the gene expression profiles separated the cancer samples into two distinct clusters, and characterized those into immune-competent and immune-deficient subtypes using the estimated abundances of infiltrated immune and stromal cells. We demonstrated differential tumor microenvironmental characteristics of immune-competent subtypes across 7 cancer types, particularly immunosuppressive tumor microenvironment features in kidney renal papillary cell carcinoma with significant poorer survival rates and immune-supportive features in sarcoma and skin cutaneous melanoma. Additionally, differential genomic instability patterns between the subtypes were found across the cancer types, and discovered that immune-competent subtypes in most of cancer types had significantly higher immune checkpoint gene expressions. Overall, this study suggests that our subtyping approach based on transcriptomic data could contribute to precise prediction of immune checkpoint inhibitor responses in a wide range of cancer types.

A kinase-deficient NTRK2 splice variant predominates in glioma and amplifies several oncogenic signaling pathways

Independent scientific achievements have led to the discovery of aberrant splicing patterns in oncogenesis, while more recent advances have uncovered novel gene fusions involving neurotrophic tyrosine receptor kinases (NTRKs) in gliomas. The exploration of NTRK splice variants in normal and neoplastic brain provides an intersection of these two rapidly evolving fields. Tropomyosin receptor kinase B (TrkB), encoded NTRK2, is known for critical roles in neuronal survival, differentiation, molecular properties associated with memory, and exhibits intricate splicing patterns and post-translational modifications. Here, we show a role for a truncated NTRK2 splice variant, TrkB.T1, in human glioma. TrkB.T1 enhances PDGF-driven gliomas in vivo, augments PDGF-induced Akt and STAT3 signaling in vitro, while next generation sequencing broadly implicates TrkB.T1 in the PI3K signaling cascades in a ligand-independent fashion. These TrkB.T1 findings highlight the importance of expanding upon whole gene and gene fusion analyses to include splice variants in basic and translational neuro-oncology research.

Tropomyosin receptor kinase B (TrkB), encoded by the neurotrophic tyrosine receptor kinase 2 (NTRK2) gene, exhibits intricate splicing patterns and post-translational modifications. Here, the authors perform whole gene and transcript-level analyses and report the TrkB.T1 splice variant enhances PDGF-driven gliomas in vivo and augments PI3K signaling cascades in vitro.