SAGD: a comprehensive sex-associated gene database from transcriptomes

Sex differences in metabolic adaptation in infants with cyanotic congenital heart disease

BACKGROUND

Female infants with congenital heart disease (CHD) face significantly higher postoperative mortality rates after adjusting for cardiac complexity. Sex differences in metabolic adaptation to cardiac stressors may be an early contributor to cardiac dysfunction. In adult diseases, hypoxic/ischemic cardiomyocytes undergo a cardioprotective metabolic shift from oxidative phosphorylation to glycolysis which appears to be regulated in a sexually dimorphic manner. We hypothesize sex differences in cardiac metabolism are present in cyanotic CHD and detectable as early as the infant period.

METHODS

RNA sequencing was performed on blood samples (cyanotic CHD cases, n = 11; controls, n = 11) and analyzed using gene set enrichment analysis (GSEA). Global plasma metabolite profiling (UPLC-MS/MS) was performed using a larger representative cohort (cyanotic CHD, n = 27; non-cyanotic CHD, n = 11; unaffected controls, n = 12).

RESULTS

Hallmark gene sets in glycolysis, fatty acid metabolism, and oxidative phosphorylation were significantly enriched in cyanotic CHD females compared to male counterparts, which was consistent with metabolomic differences between sexes. Minimal sex differences in metabolic pathways were observed in normoxic patients (both controls and non-cyanotic CHD cases).

CONCLUSION

These observations suggest underlying differences in metabolic adaptation to chronic hypoxia between males and females with cyanotic CHD.

IMPACT

Children with cyanotic CHD exhibit sex differences in utilization of glycolysis vs. fatty acid oxidation pathways to meet the high-energy demands of the heart in the neonatal period. Transcriptomic and metabolomic results suggest that under hypoxic conditions, males and females undergo metabolic shifts that are sexually dimorphic. These sex differences were not observed in neonates in normoxic conditions (i.e., non-cyanotic CHD and unaffected controls). The involved metabolic pathways are similar to those observed in advanced heart failure, suggesting metabolic adaptations beginning in the neonatal period may contribute to sex differences in infant survival.

hnRNP A1 dysfunction alters RNA splicing and drives neurodegeneration in multiple sclerosis (MS)

Neurodegeneration is the primary driver of disease progression in multiple sclerosis (MS) resulting in permanent disability, creating an urgent need to discover its underlying mechanisms. Herein, we establish that dysfunction of the RNA binding protein heterogeneous nuclear ribonucleoprotein A1 (hnRNP A1) results in differential of binding to RNA targets causing alternative RNA splicing, which contributes to neurodegeneration in MS and its models. Using RNAseq of MS brains, we discovered differential expression and aberrant splicing of hnRNP A1 target RNAs involved in neuronal function and RNA homeostasis. We confirmed this in vivo in experimental autoimmune encephalomyelitis employing CLIPseq specific for hnRNP A1, where hnRNP A1 differentially binds and regulates RNA, including aberrantly spliced targets identified in human samples. Additionally, dysfunctional hnRNP A1 expression in neurons caused neurite loss and identical changes in splicing, corroborating hnRNP A1 dysfunction as a cause of neurodegeneration. Collectively, these data indicate hnRNP A1 dysfunction causes altered neuronal RNA splicing, resulting in neurodegeneration in MS.

Integrating massive RNA-seq data to elucidate transcriptome dynamics in Drosophila melanogaster

The volume of ribonucleic acid (RNA)-seq data has increased exponentially, providing numerous new insights into various biological processes. However, due to significant practical challenges, such as data heterogeneity, it is still difficult to ensure the quality of these data when integrated. Although some quality control methods have been developed, sample consistency is rarely considered and these methods are susceptible to artificial factors. Here, we developed MassiveQC, an unsupervised machine learning-based approach, to automatically download and filter large-scale high-throughput data. In addition to the read quality used in other tools, MassiveQC also uses the alignment and expression quality as model features. Meanwhile, it is user-friendly since the cutoff is generated from self-reporting and is applicable to multimodal data. To explore its value, we applied MassiveQC to Drosophila RNA-seq data and generated a comprehensive transcriptome atlas across 28 tissues from embryogenesis to adulthood. We systematically characterized fly gene expression dynamics and found that genes with high expression dynamics were likely to be evolutionarily young and expressed at late developmental stages, exhibiting high nonsynonymous substitution rates and low phenotypic severity, and they were involved in simple regulatory programs. We also discovered that human and Drosophila had strong positive correlations in gene expression in orthologous organs, revealing the great potential of the Drosophila system for studying human development and disease.

COVIDanno, COVID-19 annotation in human

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the etiologic agent of coronavirus disease 19 (COVID-19), has caused a global health crisis. Despite ongoing efforts to treat patients, there is no universal prevention or cure available. One of the feasible approaches will be identifying the key genes from SARS-CoV-2-infected cells. SARS-CoV-2-infected in vitro model, allows easy control of the experimental conditions, obtaining reproducible results, and monitoring of infection progression. Currently, accumulating RNA-seq data from SARS-CoV-2 in vitro models urgently needs systematic translation and interpretation. To fill this gap, we built COVIDanno, COVID-19 annotation in humans, available at http://biomedbdc.wchscu.cn/COVIDanno/. The aim of this resource is to provide a reference resource of intensive functional annotations of differentially expressed genes (DEGs) among different time points of COVID-19 infection in human in vitro models. To do this, we performed differential expression analysis for 136 individual datasets across 13 tissue types. In total, we identified 4,935 DEGs. We performed multiple bioinformatics/computational biology studies for these DEGs. Furthermore, we developed a novel tool to help users predict the status of SARS-CoV-2 infection for a given sample. COVIDanno will be a valuable resource for identifying SARS-CoV-2-related genes and understanding their potential functional roles in different time points and multiple tissue types.

Integrated analysis of robust sex-biased gene signatures in human brain

BACKGROUND

Sexual dimorphism is highly prominent in mammals with many physiological and behavioral differences between male and female form of the species. Accordingly, the fundamental social and cultural stratification factors for humans is sex. The sex differences are thought to emerge from a combination of genetic and environmental factors. It distinguishes individuals most prominently on the reproductive traits, but also affects many of the other related traits and manifest in different disease susceptibilities and treatment responses across sexes. Sex differences in brain have raised a lot of controversy due to small and sometimes contradictory sex-specific effects. Many studies have been published to identify sex-biased genes in one or several brain regions, but the assessment of the robustness of these studies is missing. We therefore collected huge amount of publicly available transcriptomic data to first estimate whether consistent sex differences exist and further explore their likely origin and functional significance.

RESULTS AND CONCLUSION

In order to systematically characterise sex-specific differences across human brain regions, we collected transcription profiles for more than 16,000 samples from 46 datasets across 11 brain regions. By systematic integration of the data from multiple studies, we identified robust transcription level differences in human brain across to identify male-biased and female-biased genes in each brain region. Firstly, both male and female-biased genes were highly conserved across primates and showed a high overlap with sex-biased genes in other species. Female-biased genes were enriched for neuron-associated processes while male-biased genes were enriched for membranes and nuclear structures. Male-biased genes were enriched on the Y chromosome while female-biased genes were enriched on the X chromosome, which included X chromosome inactivation escapees explaining the origins of some sex differences. Male-biased genes were enriched for mitotic processes while female-biased genes were enriched for synaptic membrane and lumen. Finally, sex-biased genes were enriched for drug-targets and more female-biased genes were affected by adverse drug reactions than male-biased genes. In summary, by building a comprehensive resource of sex differences across human brain regions at gene expression level, we explored their likely origin and functional significance. We have also developed a web resource to make the entire analysis available for the scientific community for further exploration, available at https://joshiapps.cbu.uib.no/SRB_app/.

Constitutively Active Androgen Receptor in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the predominant type of liver cancer and a leading cause of cancer-related death globally. It is also a sexually dimorphic disease with a male predominance both in HCC and in its precursors, non-alcoholic fatty liver disease (NAFLD)/non-alcoholic steatohepatitis (NASH). The role of the androgen receptor (AR) in HCC has been well documented; however, AR-targeted therapies have failed to demonstrate efficacy in HCC. Building upon understandings of AR in prostate cancer (PCa), this review examines the role of AR in HCC, non-androgen-mediated mechanisms of induced AR expression, the existence of AR splice variants (AR-SV) in HCC and concludes by surveying current AR-targeted therapeutic approaches in PCa that show potential for efficacy in HCC in light of AR-SV expression.

Pan-cancer analyses of synonymous mutations based on tissue-specific codon optimality

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Developed tissue-specific codon optimality in 29 human tissues.

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Applied these to analyze synonymous mutations in ∼10,000 tumor and normal samples.

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Synonymous mutations frequently increase optimal codons in most cancer types.

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Synonymous mutations frequently increase optimal codons cell cycle-related genes.

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Frequency of optimal codon gain relates to proliferation, DDR deficiency, and survival.

Codon optimality has been demonstrated to be an important determinant of mRNA stability and expression levels in multiple model organisms and human cell lines. However, tissue-specific codon optimality has not been developed to investigate how codon optimality is usually perturbed by somatic synonymous mutations in human cancers. Here, we determined tissue-specific codon optimality in 29 human tissues based on mRNA expression data from the Genotype-Tissue Expression project. We found that optimal codons were associated with differentiation, whereas non-optimal codons were correlated with proliferation. Furthermore, codons biased toward differentiation displayed greater tissue specificity in codon optimality, and the tissue specificity of codon optimality was primarily present in amino acids with high degeneracy of the genetic code. By applying tissue-specific codon optimality to somatic synonymous mutations in 8532 tumor samples across 24 cancer types and to those in 416 normal cells across six human tissues, we found that synonymous mutations frequently increased optimal codons in tumor cells and cancer-related genes (e.g., genes involved in cell cycle). Furthermore, an elevated frequency of optimal codon gain was found to promote tumor cell proliferation in three cancer types characterized by DNA damage repair deficiency and could act as a prognostic biomarker for patients with triple-negative breast cancer. In summary, this study profiled tissue-specific codon optimality in human tissues, revealed alterations in codon optimality caused by synonymous mutations in human cancers, and highlighted the non-negligible role of optimal codon gain in tumorigenesis and therapeutics.

Cancer type classification using plasma cell-free RNAs derived from human and microbes

The utility of cell-free nucleic acids in monitoring cancer has been recognized by both scientists and clinicians. In addition to human transcripts, a fraction of cell-free nucleic acids in human plasma were proven to be derived from microbes and reported to have relevance to cancer. To obtain a better understanding of plasma cell-free RNAs (cfRNAs) in cancer patients, we profiled cfRNAs in ~300 plasma samples of 5 cancer types (colorectal cancer, stomach cancer, liver cancer, lung cancer, and esophageal cancer) and healthy donors (HDs) with RNA-seq. Microbe-derived cfRNAs were consistently detected by different computational methods when potential contaminations were carefully filtered. Clinically relevant signals were identified from human and microbial reads, and enriched Kyoto Encyclopedia of Genes and Genomes pathways of downregulated human genes and higher prevalence torque teno viruses both suggest that a fraction of cancer patients were immunosuppressed. Our data support the diagnostic value of human and microbe-derived plasma cfRNAs for cancer detection, as an area under the ROC curve of approximately 0.9 for distinguishing cancer patients from HDs was achieved. Moreover, human and microbial cfRNAs both have cancer type specificity, and combining two types of features could distinguish tumors of five different primary locations with an average recall of 60.4%. Compared to using human features alone, adding microbial features improved the average recall by approximately 8%. In summary, this work provides evidence for the clinical relevance of human and microbe-derived plasma cfRNAs and their potential utilities in cancer detection as well as the determination of tumor sites.

Single-Hit Inactivation Drove Tumor Suppressor Genes Out of the X Chromosome during Evolution

Cancer-related genes are under intense evolutionary pressure. In this study, we conjecture that X-linked tumor suppressor genes (TSG) are not protected by the Knudson's two-hit mechanism and are therefore subject to negative selection. Accordingly, nearly all mammalian species exhibited lower TSG-to-noncancer gene ratios on their X chromosomes compared with nonmammalian species. Synteny analysis revealed that mammalian X-linked TSGs were depleted shortly after the emergence of the XY sex-determination system. A phylogeny-based model unveiled a higher X chromosome-to-autosome relocation flux for human TSGs. This was verified in other mammals by assessing the concordance/discordance of chromosomal locations of mammalian TSGs and their orthologs in Xenopus tropicalis. In humans, X-linked TSGs are younger or larger in size. Consistently, pan-cancer analysis revealed more frequent nonsynonymous somatic mutations of X-linked TSGs. These findings suggest that relocation of TSGs out of the X chromosome could confer a survival advantage by facilitating evasion of single-hit inactivation.

SIGNIFICANCE

This work unveils extensive trafficking of TSGs from the X chromosome to autosomes during evolution, thus identifying X-linked TSGs as a genetic Achilles' heel in tumor suppression.

Sexual dimorphism of the immune system predicts clinical outcomes in glioblastoma immunotherapy: A systematic review and meta-analysis

Background

Biological differences based on sex have been documented throughout the scientific literature. Glioblastoma (GBM), the most common primary malignant brain tumor in adults, has a male sex incidence bias, however, no clinical trial data examining differential effects of treatment between sexes currently exists.

Method

We analyzed genomic data, as well as clinical trials, to delineate the effect of sex on the immune system and GBM outcome following immunotherapy.

Results

We found that in general females possess enriched immunological signatures on gene set enrichment analysis, which also stratified patient survival when delineated by sex. Female GBM patients treated with immunotherapy had a statistically significant survival advantage at the 1-year compared to males (relative risk [RR] = 1.15; P = .0241). This effect was even more pronounced in vaccine-based immunotherapy (RR = 1.29; P = .0158).

Conclusions

Our study shows a meaningful difference in the immunobiology between males and females that also influences the overall response to immunotherapy in the setting of GBM.

Selection of suitable internal controls for gene expression normalization in rats with spinal cord injury

There is a lack of systematic research on the expression of internal control genes used for gene expression normalization in real-time reverse transcription polymerase chain reaction in spinal cord injury research. In this study, we used rat models of spinal cord hemisection to analyze the expression stability of 13 commonly applied reference genes: Actb, Ankrd27, CypA, Gapdh, Hprt1, Mrpl10, Pgk1, Rictor, Rn18s, Tbp, Ubc, Ubxn11, and Ywhaz. Our results show that the expression of Ankrd27, Ubc, and Tbp were stable after spinal cord injury, while Actb was the most unstable internal control gene. Ankrd27, Ubc, Tbp, and Actb were consequently used to investigate the effects of internal control genes with differing stabilities on the normalization of target gene expression. Target gene expression levels and changes over time were similar when Ankrd27, Ubc, and Tbp were used as internal controls but different when Actb was used as an internal control. We recommend that Ankrd27, Ubc, and Tbp are used as internal control genes for real-time reverse transcription polymerase chain reaction in spinal cord injury research. This study was approved by the Administration Committee of Experimental Animals, Jiangsu Province, China (approval No. 20180304-008) on March 4, 2018.

The effect of sex on the mouse lens transcriptome

The transcriptome of mammalian tissues differs between males and females, and these differences can change across the lifespan, likely regulating known sexual dimorphisms in disease prevalence and severity. Cataract, the most prevalent disease of the ocular lens, occurs at similar rates in young individuals, but its incidence is elevated in older women compared to men of the same age. However, the influence of sex on the lens transcriptome was unknown. RNAseq based transcriptomic profiling of young adult C57BL/6J mouse lens epithelial and fiber cells revealed that few genes are differentially expressed between the sexes. In contrast, lens cells from aged (24 month old) male and female C57BL/6J mice differentially expressed many genes, including several whose expression is lens preferred. Like cataracts, posterior capsular opacification (PCO), a major sequela of cataract surgery, may also be more prevalent in women. Lens epithelial cells isolated from mouse eyes 24 h after lens fiber cell removal exhibited numerous transcriptomic differences between the sexes, including genes implicated in complement cascades and extracellular matrix regulation, and these differences are much more pronounced in aged mice than in young mice. These results provide an unbiased basis for future studies on how sex affects the lens response to aging, cataract development, and cataract surgery.

Sex-specific responses to cold in a very cold-tolerant, northern Drosophila species

Organisms can plastically alter resource allocation in response to changing environmental factors. For example, in harsh conditions, organisms are expected to shift investment from reproduction toward survival; however, the factors and mechanisms that govern the magnitude of such shifts are relatively poorly studied. Here we compared the impact of cold on males and females of the highly cold-tolerant species Drosophila montana at the phenotypic and transcriptomic levels. Although both sexes showed similar changes in cold tolerance and gene expression in response to cold treatment, indicating that the majority of changes are concordant between the sexes, we identified a clear reduction in sexually dimorphic gene expression, suggesting that preparing for the colder season involves reducing investment in sex-specific traits. This reduction was larger in males than females, as expected if male sexual traits are more condition-dependent than female traits, as predicted by theory. Gene expression changes were primarily associated with shifts in metabolic profile, which likely play a role in increasing cold tolerance. Finally, we found that the expression of immune genes was reduced following cold treatment, suggesting that reduced investment in costly immune function may be important in helping flies survive colder periods.

InSexBase: an annotated genomic resource of sex chromosomes and sex-biased genes in insects

Sex determination and the regulation of sexual dimorphism are among the most fascinating topics in modern biology. As the most species-rich group of sexually reproducing organisms on Earth, insects have multiple sex determination systems. Though sex chromosomes and sex-biased genes are well-studied in dozens of insects, their gene sequences are scattered in various databases. Moreover, a shortage of annotation hinders the deep mining of these data. Here, we collected the chromosome-level sex chromosome data of 49 insect species, including 34 X chromosomes, 15 Z chromosomes, 5 W chromosomes and 2 Y chromosomes. We also obtained Y-linked contigs of four insects species—Anopheles gambiae, Drosophila innubila, Drosophila yakuba and Tribolium castaneum. The unannotated chromosome-level sex chromosomes were annotated using a standard pipeline, yielding a total of 123 030 protein-coding genes, 2 159 427 repeat sequences, 894 miRNAs, 1574 rRNAs, 5105 tRNAs, 395 snoRNAs (small nucleolar RNA), 54 snRNAs (small nuclear RNA) and 5959 other ncRNAs (non-coding RNA). In addition, 36 781 sex-biased genes were identified by analyzing 62 RNA-seq (RNA sequencing) datasets. Together with 5707 sex-biased genes from the Drosophila genus collected from the Sex-Associated Gene Database, we obtained a total of 42 488 sex-biased genes from 13 insect species. All these data were deposited into InSexBase, a new user-friendly database of insect sex chromosomes and sex-biased genes.

Database URL:http://www.insect-genome.com/Sexdb/.

FifBase: a comprehensive fertility-associated indicators factor database for domestic animals

Fertility refers to the ability of animals to maintain reproductive function and give birth to offspring, which is an important indicator to measure the productivity of animals. Fertility is affected by many factors, among which environmental factors may also play key roles. During the past years, substantial research studies have been conducted to detect the factors related to fecundity, including genetic factors and environmental factors. However, the identified genes associated with fertility from countless previous studies are randomly dispersed in the literature, whereas some other novel fertility-related genes are needed to detect from omics-based datasets. Here, we constructed a fertility index factor database FifBase based on manually curated published literature and RNA-Seq datasets. During the construction of the literature group, we obtained 3301 articles related to fecundity for 13 species from PubMed, involving 2823 genes, which are related to 75 fecundity indicators or 47 environmental factors. Eventually, 1558 genes associated with fertility were filtered in 10 species, of which 1088 and 470 were from RNA-Seq datasets and text mining data, respectively, involving 2910 fertility-gene pairs and 58 fertility-environmental factors. All these data were cataloged into FifBase (http://www.nwsuaflmz.com/FifBase/), where the fertility-related factor information, including gene annotation and environmental factors, can be browsed, retrieved and downloaded with the user-friendly interface.

Mutations in G Protein-Coupled Receptors: Mechanisms, Pathophysiology and Potential Therapeutic Approaches

There are approximately 800 annotated G protein-coupled receptor (GPCR) genes, making these membrane receptors members of the most abundant gene family in the human genome. Besides being involved in manifold physiologic functions and serving as important pharmacotherapeutic targets, mutations in 55 GPCR genes cause about 66 inherited monogenic diseases in humans. Alterations of nine GPCR genes are causatively involved in inherited digenic diseases. In addition to classic gain- and loss-of-function variants, other aspects, such as biased signaling, trans-signaling, ectopic expression, allele variants of GPCRs, pseudogenes, gene fusion, and gene dosage, contribute to the repertoire of GPCR dysfunctions. However, the spectrum of alterations and GPCR involvement is probably much larger because an additional 91 GPCR genes contain homozygous or hemizygous loss-of-function mutations in human individuals with currently unidentified phenotypes. This review highlights the complexity of genomic alteration of GPCR genes as well as their functional consequences and discusses derived therapeutic approaches. SIGNIFICANCE STATEMENT: With the advent of new transgenic and sequencing technologies, the number of monogenic diseases related to G protein-coupled receptor (GPCR) mutants has significantly increased, and our understanding of the functional impact of certain kinds of mutations has substantially improved. Besides the classical gain- and loss-of-function alterations, additional aspects, such as biased signaling, trans-signaling, ectopic expression, allele variants of GPCRs, uniparental disomy, pseudogenes, gene fusion, and gene dosage, need to be elaborated in light of GPCR dysfunctions and possible therapeutic strategies.

Compressing gene expression data using multiple latent space dimensionalities learns complementary biological representations

BACKGROUND

Unsupervised compression algorithms applied to gene expression data extract latent or hidden signals representing technical and biological sources of variation. However, these algorithms require a user to select a biologically appropriate latent space dimensionality. In practice, most researchers fit a single algorithm and latent dimensionality. We sought to determine the extent by which selecting only one fit limits the biological features captured in the latent representations and, consequently, limits what can be discovered with subsequent analyses.

RESULTS

We compress gene expression data from three large datasets consisting of adult normal tissue, adult cancer tissue, and pediatric cancer tissue. We train many different models across a large range of latent space dimensionalities and observe various performance differences. We identify more curated pathway gene sets significantly associated with individual dimensions in denoising autoencoder and variational autoencoder models trained using an intermediate number of latent dimensionalities. Combining compressed features across algorithms and dimensionalities captures the most pathway-associated representations. When trained with different latent dimensionalities, models learn strongly associated and generalizable biological representations including sex, neuroblastoma MYCN amplification, and cell types. Stronger signals, such as tumor type, are best captured in models trained at lower dimensionalities, while more subtle signals such as pathway activity are best identified in models trained with more latent dimensionalities.

CONCLUSIONS

There is no single best latent dimensionality or compression algorithm for analyzing gene expression data. Instead, using features derived from different compression models across multiple latent space dimensionalities enhances biological representations.