Human genomics. The human transcriptome across tissues and individuals

Systematic dissection, preservation, and multiomics in whole human and bovine hearts

OBJECTIVES

We sought to develop a rigorous, systematic protocol for the dissection and preservation of human hearts for biobanking that expands previous success in postmortem transcriptomics to multiomics from paired tissue.

BACKGROUND

Existing cardiac biobanks consist largely of biopsy tissue or explanted hearts in select diseases and are insufficient for correlating whole organ phenotype with clinical data.

METHODS

We demonstrate optimal conditions for multiomics interrogation (ribonucleic acid (RNA) sequencing, untargeted metabolomics) in hearts by evaluating the effect of technical variables (storage solution, temperature) and simulated postmortem interval (PMI) on RNA and metabolite stability. We used bovine (n=3) and human (n=2) hearts fixed in PAXgene or snap-frozen with liquid nitrogen.

RESULTS

Using a paired Wald test, only two of the genes assessed were differentially expressed between left ventricular samples from bovine hearts stored in PAXgene at 0 and 12 hours PMI (FDR q<0.05). We obtained similar findings in human left ventricular samples, suggesting stability of RNA transcripts at PMIs up to 12 hours. Different library preparation methods (mRNA poly-A capture vs. rRNA depletion) resulted in similar quality metrics with both library preparations achieving >95% of reads properly aligning to the reference genomes across all PMIs for bovine and human hearts. PMI had no effect on RNA Integrity Number or quantity of RNA recovered at the time points evaluated. Of the metabolites identified (855 total) using untargeted metabolomics of human left ventricular tissue, 503 metabolites remained stable across PMIs (0, 4, 8, 12 hours). Most metabolic pathways retained several stable metabolites.

CONCLUSIONS

Our data demonstrate a technically rigorous, reproducible protocol that will enhance cardiac biobanking practices and facilitate novel insights into human CVD.

CONDENSED ABSTRACT

Cardiovascular disease (CVD) is the leading cause of mortality worldwide. Current biobanking practices insufficiently capture both the diverse array of phenotypes present in CVDs and the spatial heterogeneity across cardiac tissue sites. We have developed a rigorous and systematic protocol for the dissection and preservation of human cardiac biospecimens to enhance the availability of whole organ tissue for multiple applications. When combined with longitudinal clinical phenotyping, our protocol will enable multiomics in hearts to deepen our understanding of CVDs.

Genetics of mitochondrial diseases: Current approaches for the molecular diagnosis

Mitochondrial diseases are a genetically and phenotypically variable set of monogenic disorders. The main characteristic of mitochondrial diseases is a defective oxidative phosphorylation. Both nuclear and mitochondrial DNA encode the approximately 1500 mitochondrial proteins. Since identification of the first mitochondrial disease gene in 1988 a total of 425 genes have been associated with mitochondrial diseases. Mitochondrial dysfunctions can be caused both by pathogenic variants in the mitochondrial DNA or the nuclear DNA. Hence, besides maternal inheritance, mitochondrial diseases can follow all modes of Mendelian inheritance. The maternal inheritance and tissue specificity distinguish molecular diagnostics of mitochondrial disorders from other rare disorders. With the advances made in the next-generation sequencing technology, whole exome sequencing and even whole-genome sequencing are now the established methods of choice for molecular diagnostics of mitochondrial diseases. They reach a diagnostic rate of more than 50% in clinically suspected mitochondrial disease patients. Moreover, next-generation sequencing is delivering a constantly growing number of novel mitochondrial disease genes. This chapter reviews mitochondrial and nuclear causes of mitochondrial diseases, molecular diagnostic methodologies, and their current challenges and perspectives.

Pan-Genomic Regulation of Gene Expression in Normal and Pathological Human Placentas

In this study, we attempted to find genetic variants affecting gene expression (eQTL = expression Quantitative Trait Loci) in the human placenta in normal and pathological situations. The analysis of gene expression in placental diseases (Pre-eclampsia and Intra-Uterine Growth Restriction) is hindered by the fact that diseased placental tissue samples are generally taken at earlier gestations compared to control samples. The difference in gestational age is considered a major confounding factor in the transcriptome regulation of the placenta. To alleviate this significant problem, we propose here a novel approach to pinpoint disease-specific cis-eQTLs. By statistical correction for gestational age at sampling as well as other confounding/surrogate variables systematically searched and identified, we found 43 e-genes for which proximal SNPs influence expression level. Then, we performed the analysis again, removing the disease status from the covariates, and we identified 54 e-genes, 16 of which are identified de novo and, thus, possibly related to placental disease. We found a highly significant overlap with previous studies for the list of 43 e-genes, validating our methodology and findings. Among the 16 disease-specific e-genes, several are intrinsic to trophoblast biology and, therefore, constitute novel targets of interest to better characterize placental pathology and its varied clinical consequences. The approach that we used may also be applied to the study of other human diseases where confounding factors have hampered a better understanding of the pathology.

SpliceVault predicts the precise nature of variant-associated mis-splicing

Even for essential splice-site variants that are almost guaranteed to alter mRNA splicing, no current method can reliably predict whether exon-skipping, cryptic activation or multiple events will result, greatly complicating clinical interpretation of pathogenicity. Strikingly, ranking the four most common unannotated splicing events across 335,663 reference RNA-sequencing (RNA-seq) samples (300K-RNA Top-4) predicts the nature of variant-associated mis-splicing with 92% sensitivity. The 300K-RNA Top-4 events correctly identify 96% of exon-skipping events and 86% of cryptic splice sites for 140 clinical cases subject to RNA testing, showing higher sensitivity and positive predictive value than SpliceAI. Notably, RNA re-analyses showed we had missed 300K-RNA Top-4 events for several clinical cases tested before the development of this empirical predictive method. Simply, mis-splicing events that happen around a splice site in RNA-seq data are those most likely to be activated by a splice-site variant. The SpliceVault web portal allows users easy access to 300K-RNA for informed splice-site variant interpretation and classification.

Preclinical species gene expression database: Development and meta-analysis

The evaluation of toxicity in preclinical species is important for identifying potential safety liabilities of experimental medicines. Toxicology studies provide translational insight into potential adverse clinical findings, but data interpretation may be limited due to our understanding of cross-species biological differences. With the recent technological advances in sequencing and analyzing omics data, gene expression data can be used to predict cross species biological differences and improve experimental design and toxicology data interpretation. However, interpreting the translational significance of toxicogenomics analyses can pose a challenge due to the lack of comprehensive preclinical gene expression datasets. In this work, we performed RNA-sequencing across four preclinical species/strains widely used for safety assessment (CD1 mouse, Sprague Dawley rat, Beagle dog, and Cynomolgus monkey) in ∼50 relevant tissues/organs to establish a comprehensive preclinical gene expression body atlas for both males and females. In addition, we performed a meta-analysis across the large dataset to highlight species and tissue differences that may be relevant for drug safety analyses. Further, we made these databases available to the scientific community. This multi-species, tissue-, and sex-specific transcriptomic database should serve as a valuable resource to enable informed safety decision-making not only during drug development, but also in a variety of disciplines that use these preclinical species.

The landscape of expression and alternative splicing variation across human traits

Understanding the consequences of individual transcriptome variation is fundamental to deciphering human biology and disease. We implement a statistical framework to quantify the contributions of 21 individual traits as drivers of gene expression and alternative splicing variation across 46 human tissues and 781 individuals from the Genotype-Tissue Expression project. We demonstrate that ancestry, sex, age, and BMI make additive and tissue-specific contributions to expression variability, whereas interactions are rare. Variation in splicing is dominated by ancestry and is under genetic control in most tissues, with ribosomal proteins showing a strong enrichment of tissue-shared splicing events. Our analyses reveal a systemic contribution of types 1 and 2 diabetes to tissue transcriptome variation with the strongest signal in the nerve, where histopathology image analysis identifies novel genes related to diabetic neuropathy. Our multi-tissue and multi-trait approach provides an extensive characterization of the main drivers of human transcriptome variation in health and disease.

Retrotransposon insertions associated with risk of neurologic and psychiatric diseases

Transposable elements (TEs) are active in neuronal cells raising the question whether TE insertions contribute to risk of neuropsychiatric disease. While genome-wide association studies (GWAS) serve as a tool to discover genetic loci associated with neuropsychiatric diseases, unfortunately GWAS do not directly detect structural variants such as TEs. To examine the role of TEs in psychiatric and neurologic disease, we evaluated 17,000 polymorphic TEs and find 76 are in linkage disequilibrium with disease haplotypes (P < 10-6 ) defined by GWAS. From these 76 polymorphic TEs, we identify potentially causal candidates based on having insertions in genomic regions of regulatory chromatin and on having associations with altered gene expression in brain tissues. We show that lead candidate insertions have regulatory effects on gene expression in human neural stem cells altering the activity of a minimal promoter. Taken together, we identify 10 polymorphic TE insertions that are potential candidates on par with other variants for having a causal role in neurologic and psychiatric disorders.

Age-dependent expression of ion channel genes in rat

Ion channels regulate a large number of cellular functions and their functional role in many diseases makes them potential therapeutic targets. Given their diverse distribution across multiple organs, the roles of ion channels, particularly in age-associated transcriptomic changes in specific organs, are yet to be fully revealed. Using RNA-seq data, we investigated the rat transcriptomic profiles of ion channel genes across 11 organs/tissues and 4 developmental stages in both sexes of Fischer 344 rats and identify tissue-specific and age-dependent changes in ion channel gene expression. Organ-enriched ion channel genes were identified. In particular, the brain showed higher tissue-specificity of ion channel genes, including Gabrd, Gabra6, Gabrg2, Grin2a, and Grin2b. Notably, age-dependent changes in ion channel gene expression were prominently observed in the thymus, including in Aqp1, Clcn4, Hvcn1, Itpr1, Kcng2, Kcnj11, Kcnn3, and Trpm2. Our comprehensive study of ion channel gene expression will serve as a primary resource for biological studies of aging-related diseases caused by abnormal ion channel functions.

Combined population transcriptomic and genomic analysis reveals cis-regulatory differentiation of non-coding RNAs in maize

Long intergenic non-coding RNA (lincRNA), cis-acting expression quantitative trait locus (cis-eQTL), maize, regulatory evolution. The law of genetic variation during domestication explains the evolutionary mechanism and provides a theoretical basis for improving existing varieties of maize. Previous studies focused on exploiting regulatory variations controlling the expression of protein-coding genes rather than of non-protein-coding genes. Here, we examined the genetic and evolutionary features of long non-coding RNAs from intergenic regions (long intergenic non-coding RNAs, lincRNAs) using population-scale transcriptome data and identified 1168 lincRNAs with cis-acting expression quantitative trait loci (cis-eQTLs). We found that lincRNAs are more likely to be regulated by cis-eQTLs, which exert stronger effects than the protein-coding genes. During maize domestication and improvement, upregulated alleles of lincRNAs, which originated from both standing variation and new mutation, accumulate more frequently and show larger effect sizes than the coding genes. A stronger signature of genetic differentiation was observed in their regulatory regions compared to those of randomly sampled lincRNAs. In addition, we found that cis-regulatory differentiation of lincRNAs is related to the sequence conservation of lincRNA transcripts. Non-conserved lincRNAs more tend to gain upregulated alleles and show a stronger relationship with selected traits than conserved lincRNAs between maize and its wild relatives. Our findings in maize improve the understanding of cis-regulatory variation in lincRNA genes during domestication and improvement and provide an effective approach for prioritizing candidates for further investigation.

Circulating Proteins Influencing Psychiatric Disease: A Mendelian Randomization Study

BACKGROUND

There is a pressing need for novel drug targets for psychiatric disorders. Circulating proteins are potential candidates because they are relatively easy to measure and modulate and play important roles in signaling.

METHODS

We performed two-sample Mendelian randomization analyses to estimate the associations between circulating protein abundances and risk of 10 psychiatric disorders. Genetic variants associated with 1611 circulating protein abundances identified in 6 large-scale proteomic studies were used as genetic instruments. Effects of the circulating proteins on psychiatric disorders were estimated by Wald ratio or inverse variance-weighted ratio tests. Horizontal pleiotropy, colocalization, and protein-altering effects were examined to validate the assumptions of Mendelian randomization.

RESULTS

Nine circulating protein-to-disease associations withstood multiple sensitivity analyses. Among them, 2 circulating proteins had associations replicated in 3 proteomic studies. A 1 standard deviation increase in the genetically predicted circulating TIMP4 level was associated with a reduced risk of anorexia nervosa (minimum odds ratio [OR] = 0.83; 95% CI, 0.76-0.91) and bipolar disorder (minimum OR = 0.88; 95% CI, 0.82-0.94). A 1 standard deviation increase in the genetically predicted circulating ESAM level was associated with an increased risk of schizophrenia (maximum OR = 1.32; 95% CI, 1.22-1.43). In addition, 58 suggestive protein-to-disease associations warrant validation with observational or experimental evidence. For instance, a 1 standard deviation increase in the ERLEC1-201-to-ERLEC1-202 splice variant ratio was associated with a reduced risk of schizophrenia (OR = 0.94; 95% CI, 0.90-0.97).

CONCLUSIONS

Prioritized circulating proteins appear to influence the risk of psychiatric disease and may be explored as intervention targets.

LAFITE Reveals the Complexity of Transcript Isoforms in Subcellular Fractions

Characterization of the subcellular distribution of RNA is essential for understanding the molecular basis of biological processes. Here, the subcellular nanopore direct RNA-sequencing (DRS) of four lung cancer cell lines (A549, H1975, H358, and HCC4006) is performed, coupled with a computational pipeline, Low-abundance Aware Full-length Isoform clusTEr (LAFITE), to comprehensively analyze the full-length cytoplasmic and nuclear transcriptome. Using additional DRS and orthogonal data sets, it is shown that LAFITE outperforms current methods for detecting full-length transcripts, particularly for low-abundance isoforms that are usually overlooked due to poor read coverage. Experimental validation of six novel isoforms exclusively identified by LAFITE further confirms the reliability of this pipeline. By applying LAFITE to subcellular DRS data, the complexity of the nuclear transcriptome is revealed in terms of isoform diversity, 3'-UTR usage, m6A modification patterns, and intron retention. Overall, LAFITE provides enhanced full-length isoform identification and enables a high-resolution view of the RNA landscape at the isoform level.

Omics and Remote Homology Integration to Decipher Protein Functionality

In the recent years, several "omics" technologies based on specific biomolecules (from DNA, RNA, proteins, or metabolites) have won growing importance in the scientific field. Despite each omics possess their own laboratorial protocols, they share a background of bioinformatic tools for data integration and analysis. A recent subset of bioinformatic tools, based on available templates or remote homology protocols, allow computational fast and high-accuracy prediction of protein structures. The quickly predict of actually unsolved protein structures, together with late omics findings allow a boost of scientific advances in multiple fields such as cancer, longevity, immunity, mitochondrial function, toxicology, drug design, biosensors, and recombinant protein engineering. In this chapter, we assessed methodological approaches for the integration of omics and remote homology inferences to decipher protein functionality, opening the door to the next era of biological knowledge.

A systems biology approach to better understand human tick-borne diseases

Tick-borne diseases (TBDs) are a growing global health concern. Despite extensive studies, ill-defined tick-associated pathologies remain with unknown aetiologies. Human immunological responses after tick bite, and inter-individual variations of immune-response phenotypes, are not well characterised. Current reductive experimental methodologies limit our understanding of more complex tick-associated illness, which results from the interactions between the host, tick, and microbes. An unbiased, systems-level integration of clinical metadata and biological host data - obtained via transcriptomics, proteomics, and metabolomics - offers to drive the data-informed generation of testable hypotheses in TBDs. Advanced computational tools have rendered meaningful analysis of such large data sets feasible. This review highlights the advantages of integrative system biology approaches as essential for understanding the complex pathobiology of TBDs.

Generation of Induced Pluripotent Stem Cells from Lymphoblastoid Cell Lines by Electroporation of Episomal Vectors

Background and Objectives

Lymphoblastoid cell lines (LCLs) deposited from disease-affected individuals could be a valuable donor cell source for generating disease-specific induced pluripotent stem cells (iPSCs). However, generation of iPSCs from the LCLs is still challenging, as yet no effective gene delivery strategy has been developed.

Methods and Results

Here, we reveal an effective gene delivery method specifically for LCLs. We found that LCLs appear to be refractory toward retroviral and lentiviral transduction. Consequently, lentiviral and retroviral transduction of OCT4, SOX2, KFL4 and c-MYC into LCLs does not elicit iPSC colony formation. Interestingly, however we found that transfection of oriP/EBNA-1-based episomal vectors by electroporation is an efficient gene delivery system into LCLs, enabling iPSC generation from LCLs. These iPSCs expressed pluripotency makers (OCT4, NANOG, SSEA4, SALL4) and could form embryoid bodies.

Conclusions

Our data show that electroporation is an effective gene delivery method with which LCLs can be efficiently reprogrammed into iPSCs.

Quantitative PCR from human genomic DNA: The determination of gene copy numbers for congenital adrenal hyperplasia and RCCX copy number variation

Quantitative PCR (qPCR) is used for the determination of gene copy number (GCN). GCNs contribute to human disorders, and characterize copy number variation (CNV). The single laboratory method validations of duplex qPCR assays with hydrolysis probes on CYP21A1P and CYP21A2 genes, residing a CNV (RCCX CNV) and related to congenital adrenal hyperplasia, were performed using 46 human genomic DNA samples. We also performed the verifications on 5 qPCR assays for the genetic elements of RCCX CNV; C4A, C4B, CNV breakpoint, HERV-K(C4) CNV deletion and insertion alleles. Precision of each qPCR assay was under 1.01 CV%. Accuracy (relative error) ranged from 4.96±4.08% to 9.91±8.93%. Accuracy was not tightly linked to precision, but was significantly correlated with the efficiency of normalization using the RPPH1 internal reference gene (Spearman's ρ: 0.793-0.940, p>0.0001), ambiguity (ρ = 0.671, p = 0.029) and misclassification (ρ = 0.769, p = 0.009). A strong genomic matrix effect was observed, and target-singleplex (one target gene in one assay) qPCR was able to appropriately differentiate 2 GCN from 3 GCN at best. The analysis of all GCNs from the 7 qPCR assays using a multiplex approach increased the resolution of differentiation, and produced 98% of GCNs unambiguously, and all of which were in 100% concordance with GCNs measured by Southern blot, MLPA and aCGH. We conclude that the use of an internal (in one assay with the target gene) reference gene, the use of allele-specific primers or probes, and the multiplex approach (in one assay or different assays) are crucial for GCN determination using qPCR or other methods.

The impact of paternal age on new mutations and disease in the next generation

Advanced paternal age is associated with an increased risk of fathering children with genetic disorders and other adverse reproductive consequences. However, the mechanisms underlying this phenomenon remain largely unexplored. In this review, we focus on the impact of paternal age on de novo mutations that are an important contributor to genetic disease and can be studied both indirectly through large-scale sequencing studies and directly in the tissue in which they predominantly arise-the aging testis. We discuss the recent data that have helped establish the origins and frequency of de novo mutations, and highlight experimental evidence about the close link between new mutations, parental age, and genetic disease. We then focus on a small group of rare genetic conditions, the so-called "paternal age effect" disorders that show a strong association between paternal age and disease prevalence, and discuss the underlying mechanism ("selfish selection") and implications of this process in more detail. More broadly, understanding the causes and consequences of paternal age on genetic risk has important implications both for individual couples and for public health advice given that the average age of fatherhood is steadily increasing in many developed nations.

Where protein structure and cell diversity meet

Protein-protein interaction networks - interactomes - are charted with the hope to understand how phenotypes emerge and how they are altered in disease states. Early efforts to map interactomes have focused on the assembly of context agnostic, reference networks. However, recent studies have mapped interactomes across different cell lines and tissues, finding highly variable interactomes due to the rewiring of protein-protein interactions in different contexts. Increasing evidence points to significant links between protein structure and interactome diversity seen across cell types and tissues. We discuss how recent findings support the key role of alternative splicing and phosphorylation, two well-established regulators of protein structural and functional diversity, in defining cell type- and tissue-specific interactomes. Moreover, we show that intrinsically disordered protein regions are most favorably equipped to support interactome rewiring by acting as hubs of protein structure and function regulation.

Aging is associated with a systemic length-associated transcriptome imbalance

Aging is among the most important risk factors for morbidity and mortality. To contribute toward a molecular understanding of aging, we analyzed age-resolved transcriptomic data from multiple studies. Here, we show that transcript length alone explains most transcriptional changes observed with aging in mice and humans. We present three lines of evidence supporting the biological importance of the uncovered transcriptome imbalance. First, in vertebrates the length association primarily displays a lower relative abundance of long transcripts in aging. Second, eight antiaging interventions of the Interventions Testing Program of the National Institute on Aging can counter this length association. Third, we find that in humans and mice the genes with the longest transcripts enrich for genes reported to extend lifespan, whereas those with the shortest transcripts enrich for genes reported to shorten lifespan. Our study opens fundamental questions on aging and the organization of transcriptomes.

Decoding the Spermatogenesis Program: New Insights from Transcriptomic Analyses

Spermatogenesis is a complex differentiation process coordinated spatiotemporally across and along seminiferous tubules. Cellular heterogeneity has made it challenging to obtain stage-specific molecular profiles of germ and somatic cells using bulk transcriptomic analyses. This has limited our ability to understand regulation of spermatogenesis and to integrate knowledge from model organisms to humans. The recent advancement of single-cell RNA-sequencing (scRNA-seq) technologies provides insights into the cell type diversity and molecular signatures in the testis. Fine-grained cell atlases of the testis contain both known and novel cell types and define the functional states along the germ cell developmental trajectory in many species. These atlases provide a reference system for integrated interspecies comparisons to discover mechanistic parallels and to enable future studies. Despite recent advances, we currently lack high-resolution data to probe germ cell-somatic cell interactions in the tissue environment, but the use of highly multiplexed spatial analysis technologies has begun to resolve this problem. Taken together, recent single-cell studies provide an improvedunderstanding of gametogenesis to examine underlying causes of infertility and enable the development of new therapeutic interventions.

Modeling sex differences in humans using isogenic induced pluripotent stem cells

Biological sex is a fundamental trait influencing development, reproduction, pathogenesis, and medical treatment outcomes. Modeling sex differences is challenging because of the masking effect of genetic variability and the hurdle of differentiating chromosomal versus hormonal effects. In this work we developed a cellular model to study sex differences in humans. Somatic cells from a mosaic Klinefelter syndrome patient were reprogrammed to generate isogenic induced pluripotent stem cell (iPSC) lines with different sex chromosome complements: 47,XXY/46,XX/46,XY/45,X0. Transcriptional analysis of the hiPSCs revealed novel and known genes and pathways that are sexually dimorphic in the pluripotent state and during early neural development. Female hiPSCs more closely resembled the naive pluripotent state than their male counterparts. Moreover, the system enabled differentiation between the contributions of X versus Y chromosome to these differences. Taken together, isogenic hiPSCs present a novel platform for studying sex differences in humans and bear potential to promote gender-specific medicine in the future.

The alternative proteome in neurobiology

Translation involves the biosynthesis of a protein sequence following the decoding of the genetic information embedded in a messenger RNA (mRNA). Typically, the eukaryotic mRNA was considered to be inherently monocistronic, but this paradigm is not in agreement with the translational landscape of cells, tissues, and organs. Recent ribosome sequencing (Ribo-seq) and proteomics studies show that, in addition to currently annotated reference proteins (RefProt), other proteins termed alternative proteins (AltProts), and microproteins are encoded in regions of mRNAs thought to be untranslated or in transcripts annotated as non-coding. This experimental evidence expands the repertoire of functional proteins within a cell and potentially provides important information on biological processes. This review explores the hitherto overlooked alternative proteome in neurobiology and considers the role of AltProts in pathological and healthy neuromolecular processes.

Multiplexed high-throughput immune cell imaging reveals molecular health-associated phenotypes

Phenotypic plasticity is essential to the immune system, yet the factors that shape it are not fully understood. Here, we comprehensively analyze immune cell phenotypes including morphology across human cohorts by single-round multiplexed immunofluorescence, automated microscopy, and deep learning. Using the uncertainty of convolutional neural networks to cluster the phenotypes of eight distinct immune cell subsets, we find that the resulting maps are influenced by donor age, gender, and blood pressure, revealing distinct polarization and activation-associated phenotypes across immune cell classes. We further associate T cell morphology to transcriptional state based on their joint donor variability and validate an inflammation-associated polarized T cell morphology and an age-associated loss of mitochondria in CD4⁺ T cells. Together, we show that immune cell phenotypes reflect both molecular and personal health information, opening new perspectives into the deep immune phenotyping of individual people in health and disease.

The landscape of hervRNAs transcribed from human endogenous retroviruses across human body sites

BACKGROUND

Human endogenous retroviruses (HERVs), the remnants of ancient retroviruses, account for 8% of the human genome, but most have lost their transcriptional abilities under physiological conditions. However, mounting evidence shows that several expressed HERVs do exert biological functions. Here, we systematically characterize physiologically expressed HERVs and examine whether they may give insight into the molecular fundamentals of human development and disease.

RESULTS

We systematically identify 13,889 expressed HERVs across normal body sites and demonstrate that they are expressed in body site-specific patterns and also by sex, ethnicity, and age. Analyzing cis-ERV-related quantitative trait loci, we find that 5435 hervRNAs are regulated by genetic variants. Combining this with a genome-wide association study, we elucidate that the dysregulation of expressed HERVs might be associated with various complex diseases, particularly neurodegenerative and psychiatric diseases. We further find that physiologically activated hervRNAs are associated with histone modifications rather than DNA demethylation.

CONCLUSIONS

Our results present a locus-specific landscape of physiologically expressed hervRNAs, which represent a hidden layer of genetic architecture in development and disease.

Targeted resequencing of the 13q13 spondyloarthritis-linked locus identifies a rare variant in FREM2 possibly associated with familial spondyloarthritis

OBJECTIVES

The strong heritability of spondyloarthritis remains poorly explained, despite several large-scale association studies. A recent linkage analysis identified a new region linked to SpA on 13q13. Here we searched for variants potentially explaining this linkage signal by deep-sequencing of the region.

METHODS

Re-sequencing of the 1.4 Mb target interval was performed in 92 subjects from the 43 best-linked multicases families (71 spondyloarthritis and 21 unaffected relatives), using hybridization capture-based protocol (Illumina Nextera®). Variants of interest were then genotyped by TaqMan and high resolution melting to check their co-segregation with disease in the same families and to test their association with spondyloarthritis in an independent cohort of 1,091 unrelated cases and 399 controls. Expression of FREM2 was assessed by immunostaining.

RESULTS

Of the 7,563 variants identified, 24 were non-synonymous coding single-nucleotide variants. Two of them were located in the FREM2 gene on a haplotype co-segregating with the disease, including one common variant (R1840W, minor allele frequency=0.11) and one rare variant (R727H, minor allele frequency=0.0001). In the case-control analysis, there was no significant association between R1840W and spondyloarthritis (P-value=0.21), whereas R727H was not detected in any of the genotyped individuals. Immunostaining experiments revealed that FREM2 is expressed in synovial membrane, cartilage and colon.

CONCLUSIONS

Targeted re-sequencing of a spondyloarthritis-linked region allowed us to identify a rare non-synonymous coding variant in FREM2, co-segregating with spondyloarthritis in a large family. This gene is expressed in several tissues relevant to spondyloarthritis pathogenesis, supporting its putative implication in spondyloarthritis.

LipGene: Lipschitz Continuity Guided Adaptive Learning Rates for Fast Convergence on Microarray Expression Data Sets

Hyperparameter tuning, specifically tuning of learning rate, can often be a time-consuming process, especially when dealing with large data sets. A mathematical foundation in the choice of learning rate can minimize tuning efforts. We propose the application of a novel adaptive learning rate paradigm, guided by Lipschitz continuity of the loss functions (LipGene), to the task of Gene Expression Inference using shallow neural networks. We utilize Mean Absolute Error and Quantile loss separately for training. Our adaptive learning rate, which is dynamically computed for each epoch, is based on the principle of Lipschitz constant and requires no tuning. Experimentally, we prove that our proposed approach greatly surpasses conventional choices of learning rates in terms of both speed of convergence and generalizability. Advocating the principle of Parsimonious Computing, our method can reduce compute infrastructure required for training by using smaller networks with a minimal compromise on the prediction error.

Transcriptomic Profiling of Rectus Abdominis Muscle in Women with Gestational Diabetes-Induced Myopathy: Characterization of Pathophysiology and Potential Muscle Biomarkers of Pregnancy-Specific Urinary Incontinence

Gestational diabetes mellitus (GDM) is recognized as a "window of opportunity" for the future prediction of such complications as type 2 diabetes mellitus and pelvic floor muscle disorders, including urinary incontinence and genitourinary dysfunction. Translational studies have reported that pelvic floor muscle disorders are due to a GDM-induced-myopathy (GDiM) of the pelvic floor muscle and rectus abdominis muscle (RAM). We now describe the transcriptome profiling of the RAM obtained by Cesarean section from GDM and non-GDM women with and without pregnancy-specific urinary incontinence (PSUI). We identified 650 genes in total, and the differentially expressed genes were defined by comparing three control groups to the GDM with PSUI group (GDiM). Enrichment analysis showed that GDM with PSUI was associated with decreased gene expression related to muscle structure and muscle protein synthesis, the reduced ability of muscle fibers to ameliorate muscle damage, and the altered the maintenance and generation of energy through glycogenesis. Potential genetic muscle biomarkers were validated by RT-PCR, and their relationship to the pathophysiology of the disease was verified. These findings help elucidate the molecular mechanisms of GDiM and will promote the development of innovative interventions to prevent and treat complications such as post-GDM urinary incontinence.

Dendritic Cells or Macrophages? The Microenvironment of Human Clear Cell Renal Cell Carcinoma Imprints a Mosaic Myeloid Subtype Associated with Patient Survival

Since their initial description by Elie Metchnikoff, phagocytes have sparked interest in a variety of biologic disciplines. These important cells perform central functions in tissue repair and immune activation as well as tolerance. Myeloid cells can be immunoinhibitory, particularly in the tumor microenvironment, where their presence is generally associated with poor patient prognosis. These cells are highly adaptable and plastic, and can be modulated to perform desired functions such as antitumor activity, if key programming molecules can be identified. Human clear cell renal cell carcinoma (ccRCC) is considered immunogenic; yet checkpoint blockades that target T cell dysfunction have shown limited clinical efficacy, suggesting additional layers of immunoinhibition. We previously described "enriched-in-renal cell carcinoma" (erc) DCs that were often found in tight contact with dysfunctional T cells. Using transcriptional profiling and flow cytometry, we describe here that ercDCs represent a mosaic cell type within the macrophage continuum co-expressing M1 and M2 markers. The polarization state reflects tissue-specific signals that are characteristic of RCC and renal tissue homeostasis. ErcDCs are tissue-resident with increasing prevalence related to tumor grade. Accordingly, a high ercDC score predicted poor patient survival. Within the profile, therapeutic targets (VSIG4, NRP1, GPNMB) were identified with promise to improve immunotherapy.

Druggable transcriptomic pathways revealed in Parkinson's patient-derived midbrain neurons

Complex genetic predispositions accelerate the chronic degeneration of midbrain substantia nigra neurons in Parkinson’s disease (PD). Deciphering the human molecular makeup of PD pathophysiology can guide the discovery of therapeutics to slow the disease progression. However, insights from human postmortem brain studies only portray the latter stages of PD, and there is a lack of data surrounding molecular events preceding the neuronal loss in patients. We address this gap by identifying the gene dysregulation of live midbrain neurons reprogrammed in vitro from the skin cells of 42 individuals, including sporadic and familial PD patients and matched healthy controls. To minimize bias resulting from neuronal reprogramming and RNA-seq methods, we developed an analysis pipeline integrating PD transcriptomes from different RNA-seq datasets (unsorted and sorted bulk vs. single-cell and Patch-seq) and reprogramming strategies (induced pluripotency vs. direct conversion). This PD cohort’s transcriptome is enriched for human genes associated with known clinical phenotypes of PD, regulation of locomotion, bradykinesia and rigidity. Dysregulated gene expression emerges strongest in pathways underlying synaptic transmission, metabolism, intracellular trafficking, neural morphogenesis and cellular stress/immune responses. We confirmed a synaptic impairment with patch-clamping and identified pesticides and endoplasmic reticulum stressors as the most significant gene-chemical interactions in PD. Subsequently, we associated the PD transcriptomic profile with candidate pharmaceuticals in a large database and a registry of current clinical trials. This study highlights human transcriptomic pathways that can be targeted therapeutically before the irreversible neuronal loss. Furthermore, it demonstrates the preclinical relevance of unbiased large transcriptomic assays of reprogrammed patient neurons.

Clinical variant interpretation and biologically relevant reference transcripts

Clinical variant interpretation is highly dependent on the choice of reference transcript. Although the longest transcript has traditionally been chosen as the reference, APPRIS principal and MANE Select transcripts, biologically supported reference sequences, are now available. In this study, we show that MANE Select and APPRIS principal transcripts are the best reference transcripts for clinical variation. APPRIS principal and MANE Select transcripts capture almost all ClinVar pathogenic variants, and they are particularly powerful over the 94% of coding genes in which they agree. We find that a vanishingly small number of ClinVar pathogenic variants affect alternative protein products. Alternative isoforms that are likely to be clinically relevant can be predicted using TRIFID scores, the highest scoring alternative transcripts are almost 700 times more likely to house pathogenic variants. We believe that APPRIS, MANE and TRIFID are essential tools for clinical variant interpretation.

Prediction of transcript isoforms in 19 chicken tissues by Oxford Nanopore long-read sequencing

To identify and annotate transcript isoforms in the chicken genome, we generated Nanopore long-read sequencing data from 68 samples that encompassed 19 diverse tissues collected from experimental adult male and female White Leghorn chickens. More than 23.8 million reads with mean read length of 790 bases and average quality of 18.2 were generated. The annotation and subsequent filtering resulted in the identification of 55,382 transcripts at 40,547 loci with mean length of 1,700 bases. We predicted 30,967 coding transcripts at 19,461 loci, and 16,495 lncRNA transcripts at 15,512 loci. Compared to existing reference annotations, we found ∼52% of annotated transcripts could be partially or fully matched while ∼47% were novel. Seventy percent of novel transcripts were potentially transcribed from lncRNA loci. Based on our annotation, we quantified transcript expression across tissues and found two brain tissues (i.e., cerebellum and cortex) expressed the highest number of transcripts and loci. Furthermore, ∼22% of the transcripts displayed tissue specificity with the reproductive tissues (i.e., testis and ovary) exhibiting the most tissue-specific transcripts. Despite our wide sampling, ∼20% of Ensembl reference loci were not detected. This suggests that deeper sequencing and additional samples that include different breeds, cell types, developmental stages, and physiological conditions, are needed to fully annotate the chicken genome. The application of Nanopore sequencing in this study demonstrates the usefulness of long-read data in discovering additional novel loci (e.g., lncRNA loci) and resolving complex transcripts (e.g., the longest transcript for the TTN locus).

Circadian transcriptional pathway atlas highlights a proteasome switch in intermittent fasting

While intermittent fasting is a safe strategy to benefit health, it remains unclear whether a “timer” exists in vivo to record fasting duration and trigger a transcriptional switch. Here, we map a circadian transcriptional pathway atlas from 600 samples across four metabolic tissues of mice under five feeding regimens. Results show that 95.6% of detected canonical pathways are rhythmic in a tissue-specific and feeding-regimen-specific manner, while only less than 25% of them induce changes in transcriptional function. Fasting for 16 h initiates a circadian resonance of 43 pathways in the liver, and the resonance punctually switches following refeeding. The hepatic proteasome coordinates the resonance, and most genes encoding proteasome subunits display a 16-h fasting-dependent transcriptional switch. These findings indicate that the hepatic proteasome may serve as a fasting timer and a coordinator of pathway transcriptional resonance, which provide a target for revealing the underlying mechanism of intermittent fasting.

While intermittent fasting benefits health, the optimal duration of each fasting remains an open question. Wei et al. map an atlas of canonical pathways in intermittent fasting, find that fasting for 16 h initiates circadian resonance of pathways in the liver, and identify the proteasome as a liver-specific fasting “timer”.

Multiancestry exome sequencing reveals INHBE mutations associated with favorable fat distribution and protection from diabetes

Body fat distribution is a major, heritable risk factor for cardiometabolic disease, independent of overall adiposity. Using exome-sequencing in 618,375 individuals (including 160,058 non-Europeans) from the UK, Sweden and Mexico, we identify 16 genes associated with fat distribution at exome-wide significance. We show 6-fold larger effect for fat-distribution associated rare coding variants compared with fine-mapped common alleles, enrichment for genes expressed in adipose tissue and causal genes for partial lipodystrophies, and evidence of sex-dimorphism. We describe an association with favorable fat distribution (p = 1.8 × 10-09), favorable metabolic profile and protection from type 2 diabetes (~28% lower odds; p = 0.004) for heterozygous protein-truncating mutations in INHBE, which encodes a circulating growth factor of the activin family, highly and specifically expressed in hepatocytes. Our results suggest that inhibin βE is a liver-expressed negative regulator of adipose storage whose blockade may be beneficial in fat distribution-associated metabolic disease.

Phase 2 of extracellular RNA communication consortium charts next-generation approaches for extracellular RNA research

The extracellular RNA communication consortium (ERCC) is an NIH-funded program aiming to promote the development of new technologies, resources, and knowledge about exRNAs and their carriers. After Phase 1 (2013-2018), Phase 2 of the program (ERCC2, 2019-2023) aims to fill critical gaps in knowledge and technology to enable rigorous and reproducible methods for separation and characterization of both bulk populations of exRNA carriers and single EVs. ERCC2 investigators are also developing new bioinformatic pipelines to promote data integration through the exRNA atlas database. ERCC2 has established several Working Groups (Resource Sharing, Reagent Development, Data Analysis and Coordination, Technology Development, nomenclature, and Scientific Outreach) to promote collaboration between ERCC2 members and the broader scientific community. We expect that ERCC2's current and future achievements will significantly improve our understanding of exRNA biology and the development of accurate and efficient exRNA-based diagnostic, prognostic, and theranostic biomarker assays.

Data Incompleteness May form a Hard-to-Overcome Barrier to Decoding Life's Mechanism

In this brief review, we attempt to demonstrate that the incompleteness of data, as well as the intrinsic heterogeneity of biological systems, may form very strong and possibly insurmountable barriers for researchers trying to decipher the mechanisms of the functioning of live systems. We illustrate this challenge using the two most studied organisms: E. coli, with 34.6% genes lacking experimental evidence of function, and C. elegans, with identified proteins for approximately 50% of its genes. Another striking example is an artificial unicellular entity named JCVI-syn3.0, with a minimal set of genes. A total of 31.5% of the genes of JCVI-syn3.0 cannot be ascribed a specific biological function. The human interactome mapping project identified only 5-10% of all protein interactions in humans. In addition, most of the available data are static snapshots, and it is barely possible to generate realistic models of the dynamic processes within cells. Moreover, the existing interactomes reflect the de facto interaction but not its functional result, which is an unpredictable emerging property. Perhaps the completeness of molecular data on any living organism is beyond our reach and represents an unsolvable problem in biology.

Neuromuscular disorders: finding the missing genetic diagnoses

Neuromuscular disorders (NMDs) are a wide-ranging group of diseases that seriously affect the quality of life of affected individuals. The development of next-generation sequencing revolutionized the diagnosis of NMD, enabling the discovery of hundreds of NMD genes and many more pathogenic variants. However, the diagnostic yield of genetic testing in NMD cohorts remains incomplete, indicating a large number of genetic diagnoses are not identified through current methods. Fortunately, recent advancements in sequencing technologies, analytical tools, and high-throughput functional screening provide an opportunity to circumvent current challenges. Here, we discuss reasons for missing genetic diagnoses in NMD, how emerging technologies and tools can overcome these hurdles, and examine future approaches to improving diagnostic yields in NMD.

Using Global t-SNE to Preserve Intercluster Data Structure

The t-distributed stochastic neighbor embedding (t-SNE) method is one of the leading techniques for data visualization and clustering. This method finds lower-dimensional embedding of data points while minimizing distortions in distances between neighboring data points. By construction, t-SNE discards information about large-scale structure of the data. We show that adding a global cost function to the t-SNE cost function makes it possible to cluster the data while preserving global intercluster data structure. We test the new global t-SNE (g-SNE) method on one synthetic and two real data sets on flower shapes and human brain cells. We find that significant and meaningful global structure exists in both the plant and human brain data sets. In all cases, g-SNE outperforms t-SNE and UMAP in preserving the global structure. Topological analysis of the clustering result makes it possible to find an appropriate trade-off of data distribution across scales. We find differences in how data are distributed across scales between the two subjects that were part of the human brain data set. Thus, by striving to produce both accurate clustering and positioning between clusters, the g-SNE method can identify new aspects of data organization across scales.

Low carnitine palmitoyltransferase 1 activity is a risk factor for narcolepsy type 1 and other hypersomnia

Study Objectives

Narcolepsy type 1 (NT1) is associated with metabolic abnormalities but their etiology remains largely unknown. The gene for carnitine palmitoyltransferase 1B (CPT1B) and abnormally low serum acylcarnitine levels have been linked to NT1. To elucidate the details of altered fatty acid metabolism, we determined levels of individual acylcarnitines and evaluated CPT1 activity in patients with NT1 and other hypersomnia.

Methods

Blood samples from 57 NT1, 51 other hypersomnia patients, and 61 healthy controls were analyzed. The levels of 25 major individual acylcarnitines were determined and the C0/(t[C16] + t[C18]) ratio was used as a CPT1 activity marker. We further performed transcriptome analysis using independent blood samples from 42 NT1 and 42 healthy controls to study the relevance of fatty acid metabolism. NT1-specific changes in CPT1 activity and in expression of related genes were investigated.

Results

CPT1 activity was lower in patients with NT1 (p = 0.00064) and other hypersomnia (p = 0.0014) than in controls. Regression analysis revealed that CPT1 activity was an independent risk factor for NT1 (OR: 1.68; p = 0.0031) and for other hypersomnia (OR: 1.64; p = 0.0042). There was a significant interaction between obesity (BMI &lt;25, ≥25) and the SNP rs5770917 status such that nonobese NT1 patients without risk allele had better CPT1 activity (p = 0.0089). The expression levels of carnitine-acylcarnitine translocase (CACT) and CPT2 in carnitine shuttle were lower in NT1 (p = 0.000051 and p = 0.00014, respectively).

Conclusions

These results provide evidences that abnormal fatty acid metabolism is involved in the pathophysiology of NT1 and other hypersomnia.

Acyl-CoA:diacylglycerol acyltransferase: Properties, physiological roles, metabolic engineering and intentional control

Acyl-CoA:diacylglycerol acyltransferase (DGAT, EC 2.3.1.20) catalyzes the last reaction in the acyl-CoA-dependent biosynthesis of triacylglycerol (TAG). DGAT activity resides mainly in membrane-bound DGAT1 and DGAT2 in eukaryotes and bifunctional wax ester synthase-diacylglycerol acyltransferase (WSD) in bacteria, which are all membrane-bound proteins but exhibit no sequence homology to each other. Recent studies also identified other DGAT enzymes such as the soluble DGAT3 and diacylglycerol acetyltransferase (EaDAcT), as well as enzymes with DGAT activities including defective in cuticular ridges (DCR) and steryl and phytyl ester synthases (PESs). This review comprehensively discusses research advances on DGATs in prokaryotes and eukaryotes with a focus on their biochemical properties, physiological roles, and biotechnological and therapeutic applications. The review begins with a discussion of DGAT assay methods, followed by a systematic discussion of TAG biosynthesis and the properties and physiological role of DGATs. Thereafter, the review discusses the three-dimensional structure and insights into mechanism of action of human DGAT1, and the modeled DGAT1 from Brassica napus. The review then examines metabolic engineering strategies involving manipulation of DGAT, followed by a discussion of its therapeutic applications. DGAT in relation to improvement of livestock traits is also discussed along with DGATs in various other eukaryotic organisms.

Network Embedding Across Multiple Tissues and Data Modalities Elucidates the Context of Host Factors Important for COVID-19 Infection

COVID-19 is a heterogeneous disease caused by SARS-CoV-2. Aside from infections of the lungs, the disease can spread throughout the body and damage many other tissues, leading to multiorgan failure in severe cases. The highly variable symptom severity is influenced by genetic predispositions and preexisting diseases which have not been investigated in a large-scale multimodal manner. We present a holistic analysis framework, setting previously reported COVID-19 genes in context with prepandemic data, such as gene expression patterns across multiple tissues, polygenetic predispositions, and patient diseases, which are putative comorbidities of COVID-19. First, we generate a multimodal network using the prior-based network inference method KiMONo. We then embed the network to generate a meaningful lower-dimensional representation of the data. The input data are obtained via the Genotype-Tissue Expression project (GTEx), containing expression data from a range of tissues with genomic and phenotypic information of over 900 patients and 50 tissues. The generated network consists of nodes, that is, genes and polygenic risk scores (PRS) for several diseases/phenotypes, as well as for COVID-19 severity and hospitalization, and links between them if they are statistically associated in a regularized linear model by feature selection. Applying network embedding on the generated multimodal network allows us to perform efficient network analysis by identifying nodes close by in a lower-dimensional space that correspond to entities which are statistically linked. By determining the similarity between COVID-19 genes and other nodes through embedding, we identify disease associations to tissues, like the brain and gut. We also find strong associations between COVID-19 genes and various diseases such as ischemic heart disease, cerebrovascular disease, and hypertension. Moreover, we find evidence linking PTPN6 to a range of comorbidities along with the genetic predisposition of COVID-19, suggesting that this kinase is a central player in severe cases of COVID-19. In conclusion, our holistic network inference coupled with network embedding of multimodal data enables the contextualization of COVID-19-associated genes with respect to tissues, disease states, and genetic risk factors. Such contextualization can be exploited to further elucidate the biological importance of known and novel genes for severity of the disease in patients.

Large-Scale Multi-Omics Studies Provide New Insights into Blood Pressure Regulation

Recent genome-wide association studies uncovered part of blood pressure’s heritability. However, there is still a vast gap between genetics and biology that needs to be bridged. Here, we followed up blood pressure genome-wide summary statistics of over 750,000 individuals, leveraging comprehensive epigenomic and transcriptomic data from blood with a follow-up in cardiovascular tissues to prioritise likely causal genes and underlying blood pressure mechanisms. We first prioritised genes based on coding consequences, multilayer molecular associations, blood pressure-associated expression levels, and coregulation evidence. Next, we followed up the prioritised genes in multilayer studies of genomics, epigenomics, and transcriptomics, functional enrichment, and their potential suitability as drug targets. Our analyses yielded 1880 likely causal genes for blood pressure, tens of which are targets of the available licensed drugs. We identified 34 novel genes for blood pressure, supported by more than one source of biological evidence. Twenty-eight (82%) of these new genes were successfully replicated by transcriptome-wide association analyses in a large independent cohort (n = ~220,000). We also found a substantial mediating role for epigenetic regulation of the prioritised genes. Our results provide new insights into genetic regulation of blood pressure in terms of likely causal genes and involved biological pathways offering opportunities for future translation into clinical practice.

TissueSpace: a web tool for rank-based transcriptome representation and its applications in molecular medicine

BACKGROUND

Cross-platform or cross-experiment transcriptome data is hard to compare as the original gene expression values from different platforms cannot be compared directly. The inherent gene expression ranking information is rarely utilized.

OBJECTIVE

Use of reduced vector to represent transcriptome data independent of platforms.

METHODS

Thus, we turned the expression profile into a rank vector, where a higher expression has a higher rank value, then applied Latent semantic analysis (LSA) to get compact and continuous 100-dimensional vector representations for samples.

RESULTS

Results showed that the reconstructed vector has a precision of 96.7% in recovering tissue labels from an independent dataset. A user-friendly tool TissueSpace was developed, which provides users the following functionalities: (1) convert different gene ID types to Ensembl gene IDs; (2) project any human transcriptome profile to get vector representation for downstream analysis; (3) functional enrichment for each of the 100-dimensional vector features. Case studies for its applications in human common diseases indicate its usefulness.

CONCLUSIONS

TissueSpace could be used to generate testable hypotheses for translational medicine. The TissueSpace tool is available at http://bioinformatics.fafu.edu.cn/tissuespace/ .

Suppression of the Testis-Specific Transcription of the ZBTB32 and ZNF473 Genes in Germ Cell Tumors

The family of genes containing C2H2 zinc finger domains, which has more than 700 members, is one of the largest in the genome. Of particular interest are C2H2 genes with potential tissue-specific transcription, which determine the functional properties of individual cell types, including those associated with pathological processes. The aim of this work was to identify C2H2 family genes with tissue-specific transcription and analyze changes in their activity during tumor progression. To search for these genes, we used four databases containing data on gene transcription in human tissues obtained by RNA-Seq analysis. The analysis showed that, although the major part of the C2H2 family genes is transcribed in virtually all tissues, a group of genes has tissue-specific transcription, with most of the transcripts being found in the testis. After having compared all four databases, we identified nine such genes. The testis-specific transcription was confirmed for two of them, namely ZBTB32 and ZNF473, using quantitative PCR of cDNA samples from different organs. A decrease in ZBTB32 and ZNF473 transcription levels was demonstrated in germ cell tumors. The studied genes can serve as candidate markers in germ cell tumors.

Genetic and hormonal mechanisms underlying sex-specific immune responses in tuberculosis

Tuberculosis (TB), the world's deadliest bacterial infection, afflicts more human males than females, with a male/female (M/F) ratio of 1.7. Sex disparities in TB prevalence, pathophysiology, and clinical manifestations are widely reported, but the underlying biological mechanisms remain largely undefined. This review assesses epidemiological data on sex disparity in TB, as well as possible underlying hormonal and genetic mechanisms that might differentially modulate innate and adaptive immune responses in males and females, leading to sex differences in disease susceptibility. We consider whether this sex disparity can be extended to the efficacy of vaccines and discuss novel animal models which may offer mechanistic insights. A better understanding of the biological factors underpinning sex-related immune responses in TB may enable sex-specific personalized therapies for TB.

OpenPIP: An Open-source Platform for Hosting, Visualizing and Analyzing Protein Interaction Data

Knowing which proteins interact with each other is essential information for understanding how most biological processes at the cellular and organismal level operate and how their perturbation can cause disease. Continuous technical and methodological advances over the last two decades have led to many genome-wide systematically-generated protein-protein interaction (PPI) maps. To help store, visualize, analyze and disseminate these specialized experimental datasets via the web, we developed the freely-available Open-source Protein Interaction Platform (openPIP) as a customizable web portal designed to host experimental PPI maps. Such a portal is often required to accompany a paper describing the experimental data set, in addition to depositing the data in a standard repository. No coding skills are required to set up and customize the database and web portal. OpenPIP has been used to build the databases and web portals of two major protein interactome maps, the Human and Yeast Reference Protein Interactome maps (HuRI and YeRI, respectively). OpenPIP is freely available as a ready-to-use Docker container for hosting and sharing PPI data with the scientific community at http://openpip.baderlab.org/ and the source code can be downloaded from https://github.com/BaderLab/openPIP/.

Identification of Potential Biomarkers for Pan-Cancer Diagnosis and Prognosis Through the Integration of Large-Scale Transcriptomic Data

Cancer is one of the leading causes of death worldwide, bringing a significant burden to human health and society. Accurate cancer diagnosis and biomarkers that can be used as robust therapeutic targets are of great importance as they facilitate early and effective therapies. Shared etiology among cancers suggests the existence of pan-cancer biomarkers, performance of which could benefit from the large sample size and the heterogeneity of the studied patients. In this study, we conducted a systematic RNA-seq study of 9,213 tumors and 723 para-cancerous tissue samples of 28 solid tumors from the Cancer Genome Atlas (TCGA) database, and 7,008 normal tissue samples from the Genotype-Tissue Expression (GTEx) database. By differential gene expression analysis, we identified 214 up-regulated and 186 downregulated differentially expressed genes (DEGs) in more than 80% of the studied tumors, respectively, and obtained 20 highly linked up- and downregulated hub genes from them. These markers have rarely been reported in multiple tumors simultaneously. We further constructed pan-cancer diagnostic models to classify tumors and para-cancerous tissues using 10 up-regulated hub genes with an AUC of 0.894. Survival analysis revealed that these hub genes were significantly associated with the overall survival of cancer patients. In addition, drug sensitivity predictions for these hub genes in a variety of tumors obtained several broad-spectrum anti-cancer drugs targeting pan-cancer. Furthermore, we predicted immunotherapy sensitivity for cancers based on tumor mutational burden (TMB) and the expression of immune checkpoint genes (ICGs), providing a theoretical basis for the treatment of tumors. In summary, we identified a set of biomarkers that were differentially expressed in multiple types of cancers, and these biomarkers can be potentially used for diagnosis and used as therapeutic targets.

βIII-Tubulin Gene Regulation in Health and Disease

Microtubule proteins form a dynamic component of the cytoskeleton, and play key roles in cellular processes, such as vesicular transport, cell motility and mitosis. Expression of microtubule proteins are often dysregulated in cancer. In particular, the microtubule protein βIII-tubulin, encoded by the TUBB3 gene, is aberrantly expressed in a range of epithelial tumours and is associated with drug resistance and aggressive disease. In normal cells, TUBB3 expression is tightly restricted, and is found almost exclusively in neuronal and testicular tissues. Understanding the mechanisms that control TUBB3 expression, both in cancer, mature and developing tissues will help to unravel the basic biology of the protein, its role in cancer, and may ultimately lead to the development of new therapeutic approaches to target this protein. This review is devoted to the transcriptional and posttranscriptional regulation of TUBB3 in normal and cancerous tissue.

Dominant transcript expression profiles of human protein-coding genes interrogated with GTEx dataset

The discovery and quantification of mRNA transcripts using short-read next-generation sequencing (NGS) data is a complicated task. There are far more alternative mRNA transcripts expressed by human genes than can be identified from NGS transcriptome data and various bioinformatic pipelines, while the numbers of annotated human protein-coding genes has gradually declined in recent years. It is essential to learn more about the thorough tissue expression profiles of alternative transcripts in order to obtain their molecular modulations and actual functional significance. In this report, we present a bioinformatic database for interrogating the representative tissue of human protein-coding transcripts. The database allows researchers to visually explore the top-ranked transcript expression profiles in particular tissue types. Most transcripts of protein-coding genes were found to have certain tissue expression patterns. This observation demonstrated that many alternative transcripts were particularly modulated in different cell types. This user-friendly tool visually represents transcript expression profiles in a tissue-specific manner. Identification of tissue specific protein-coding genes and transcripts is a substantial advance towards interpreting their biological functions and further functional genomics studies.

Integrative Multi-Omics database (iMOMdb) of Asian Pregnant Women

Asians are underrepresented across many omics databases, thereby limiting the potential of precision medicine in nearly 60% of the global population. As such, there is a pressing need for multi-omics derived quantitative trait loci (QTLs) to fill the knowledge gap of complex traits in populations of Asian ancestry. Here, we provide the first blood-based multi-omics analysis of Asian pregnant women, constituting high-resolution genotyping (N = 1079), DNA methylation (N = 915) and transcriptome profiling (N = 238). Integrative omics analysis identified 219 154 CpGs associated with cis-DNA methylation QTLs (meQTLs) and 3703 RNAs associated with cis-RNA expression QTLs (eQTLs). Ethnicity was the largest contributor of inter-individual variation across all omics datasets, with 2561 genes identified as hotspots of this variation; 395 of these hotspot genes also contained both ethnicity-specific eQTLs and meQTLs. Gene set enrichment analysis of these ethnicity QTL hotspots showed pathways involved in lipid metabolism, adaptive immune system and carbohydrate metabolism. Pathway validation by profiling the lipidome (~480 lipids) of antenatal plasma (N = 752) and placenta (N = 1042) in the same cohort showed significant lipid differences among Chinese, Malay and Indian women, validating ethnicity-QTL gene effects across different tissue types. To develop deeper insights into the complex traits and benefit future precision medicine research in Asian pregnant women, we developed iMOMdb, an open-access database.