Population-scale tissue transcriptomics maps long non-coding RNAs to complex disease.

Long non-coding RNA (lncRNA) genes have well-established and important impacts on molecular and cellular functions. However, among the thousands of lncRNA genes, it is still a major challenge to identify the subset with disease or trait relevance. To systematically characterize these lncRNA genes, we used Genotype Tissue Expression (GTEx) project v8 genetic and multi-tissue transcriptomic data to profile the expression, genetic regulation, cellular contexts, and trait associations of 14,100 lncRNA genes across 49 tissues for 101 distinct complex genetic traits. Using these approaches, we identified 1,432 lncRNA gene-trait associations, 800 of which were not explained by stronger effects of neighboring protein-coding genes. This included associations between lncRNA quantitative trait loci and inflammatory bowel disease, type 1 and type 2 diabetes, and coronary artery disease, as well as rare variant associations to body mass index.

The cis-Regulatory Atlas of the Mouse Immune System.

A complete chart of cis-regulatory elements and their dynamic activity is necessary to understand the transcriptional basis of differentiation and function of an organ system. We generated matched epigenome and transcriptome measurements in 86 primary cell types that span the mouse immune system and its differentiation cascades. This breadth of data enable variance components analysis that suggests that genes fall into two distinct classes, controlled by either enhancer- or promoter-driven logic, and multiple regression that connects genes to the enhancers that regulate them. Relating transcription factor (TF) expression to the genome-wide accessibility of their binding motifs classifies them as predominantly openers or closers of local chromatin accessibility, pinpointing specific cis-regulatory elements where binding of given TFs is likely functionally relevant, validated by chromatin immunoprecipitation sequencing (ChIP-seq). Overall, this cis-regulatory atlas provides a trove of information on transcriptional regulation through immune differentiation and a foundational scaffold to define key regulatory events throughout the immunological genome.

Gut CD4+ T cell phenotypes are a continuum molded by microbes, not by TH archetypes.

CD4+ effector lymphocytes (Teff) are traditionally classified by the cytokines they produce. To determine the states that Teff cells actually adopt in frontline tissues in vivo, we applied single-cell transcriptome and chromatin analyses to colonic Teff cells in germ-free or conventional mice or in mice after challenge with a range of phenotypically biasing microbes. Unexpected subsets were marked by the expression of the interferon (IFN) signature or myeloid-specific transcripts, but transcriptome or chromatin structure could not resolve discrete clusters fitting classic helper T cell (TH) subsets. At baseline or at different times of infection, transcripts encoding cytokines or proteins commonly used as TH markers were distributed in a polarized continuum, which was functionally validated. Clones derived from single progenitors gave rise to both IFN-γ- and interleukin (IL)-17-producing cells. Most of the transcriptional variance was tied to the infecting agent, independent of the cytokines produced, and chromatin variance primarily reflected activities of activator protein (AP)-1 and IFN-regulatory factor (IRF) transcription factor (TF) families, not the canonical subset master regulators T-bet, GATA3 or RORγ.

Parsing the Interferon Transcriptional Network and Its Disease Associations.

Type 1 interferon (IFN) is a key mediator of organismal responses to pathogens, eliciting prototypical "interferon signature genes" that encode antiviral and inflammatory mediators. For a global view of IFN signatures and regulatory pathways, we performed gene expression and chromatin analyses of the IFN-induced response across a range of immunocyte lineages. These distinguished ISGs by cell-type specificity, kinetics, and sensitivity to tonic IFN and revealed underlying changes in chromatin configuration. We combined 1,398 human and mouse datasets to computationally infer ISG modules and their regulators, validated by genetic analysis in both species. Some ISGs are controlled by Stat1/2 and Irf9 and the ISRE DNA motif, but others appeared dependent on non-canonical factors. This regulatory framework helped to interpret JAK1 blockade pharmacology, different clusters being affected under tonic or IFN-stimulated conditions, and the IFN signatures previously associated with human diseases, revealing unrecognized subtleties in disease footprints, as affected by human ancestry.

AIRE relies on Z-DNA to flag gene targets for thymic T cell tolerization.

AIRE is an unconventional transcription factor that enhances the expression of thousands of genes in medullary thymic epithelial cells and promotes clonal deletion or phenotypic diversion of self-reactive T cells1-4. The biological logic of AIRE's target specificity remains largely unclear as, in contrast to many transcription factors, it does not bind to a particular DNA sequence motif. Here we implemented two orthogonal approaches to investigate AIRE's cis-regulatory mechanisms: construction of a convolutional neural network and leveraging natural genetic variation through analysis of F1 hybrid mice5. Both approaches nominated Z-DNA and NFE2-MAF as putative positive influences on AIRE's target choices. Genome-wide mapping studies revealed that Z-DNA-forming and NFE2L2-binding motifs were positively associated with the inherent ability of a gene's promoter to generate DNA double-stranded breaks, and promoters showing strong double-stranded break generation were more likely to enter a poised state with accessible chromatin and already-assembled transcriptional machinery. Consequently, AIRE preferentially targets genes with poised promoters. We propose a model in which Z-DNA anchors the AIRE-mediated transcriptional program by enhancing double-stranded break generation and promoter poising. Beyond resolving a long-standing mechanistic conundrum, these findings suggest routes for manipulating T cell tolerance.

Obtaining genetics insights from deep learning via explainable artificial intelligence.

Artificial intelligence (AI) models based on deep learning now represent the state of the art for making functional predictions in genomics research. However, the underlying basis on which predictive models make such predictions is often unknown. For genomics researchers, this missing explanatory information would frequently be of greater value than the predictions themselves, as it can enable new insights into genetic processes. We review progress in the emerging area of explainable AI (xAI), a field with the potential to empower life science researchers to gain mechanistic insights into complex deep learning models. We discuss and categorize approaches for model interpretation, including an intuitive understanding of how each approach works and their underlying assumptions and limitations in the context of typical high-throughput biological datasets.

Microheterogeneity in the Kinetics and Sex-Specific Response to Type I IFN.

The response to type I IFNs involves the rapid induction of prototypical IFN signature genes (ISGs). It is not known whether the tightly controlled ISG expression observed at the cell population level correctly represents the coherent responses of individual cells or whether it masks some heterogeneity in gene modules and/or responding cells. We performed a time-resolved single-cell analysis of the first 3 h after in vivo IFN stimulation in macrophages and CD4+ T and B lymphocytes from mice. All ISGs were generally induced in concert, with no clear cluster of faster- or slower-responding ISGs. Response kinetics differed between cell types: mostly homogeneous for macrophages, but with far more kinetic diversity among B and T lymphocytes, which included a distinct subset of nonresponsive cells. Velocity analysis confirmed the differences between macrophages in which the response progressed throughout the full 3 h, versus B and T lymphocytes in which it was rapidly curtailed by negative feedback and revealed differences in transcription rates between the lineages. In all cell types, female cells responded faster than their male counterparts. The ISG response thus seems to proceed as a homogeneous gene block, but with kinetics that vary between immune cell types and with sex differences that might underlie differential outcomes of viral infections.

Mosaic loss of Chromosome Y in aged human microglia.

Mosaic loss of Chromosome Y (LOY) is a common acquired structural mutation in the leukocytes of aging men that is correlated with several age-related diseases, including Alzheimer's disease (AD). The molecular basis of LOY in brain cells has not been systematically investigated. Here, we present a large-scale analysis of single-cell and single-nuclei RNA brain data sets, yielding 851,674 cells, to investigate the cell type-specific burden of LOY. LOY frequencies differed widely between donors and CNS cell types. Among five well-represented neural cell types, LOY was enriched in microglia and rare in neurons, astrocytes, and oligodendrocytes. In microglia, LOY was significantly enriched in AD subjects. Differential gene expression (DE) analysis in microglia found 172 autosomal genes, three X-linked genes, and 10 pseudoautosomal genes associated with LOY. To our knowledge, we provide the first evidence of LOY in the microglia and highlight its potential roles in aging and the pathogenesis of neurodegenerative disorders such as AD.

Impact on splicing in Saccharomyces cerevisiae of random 50-base sequences inserted into an intron.

Intron splicing is a key regulatory step in gene expression in eukaryotes. Three sequence elements required for splicing-5' and 3' splice sites and a branchpoint-are especially well-characterized in Saccharomyces cerevisiae, but our understanding of additional intron features that impact splicing in this organism is incomplete, due largely to its small number of introns. To overcome this limitation, we constructed a library in S. cerevisiae of random 50-nt (N50) elements individually inserted into the intron of a reporter gene and quantified canonical splicing and the use of cryptic splice sites by sequencing analysis. More than 70% of approximately 140,000 N50 elements reduced splicing by at least 20%. N50 features, including higher GC content, presence of GU repeats, and stronger predicted secondary structure of its pre-mRNA, correlated with reduced splicing efficiency. A likely basis for the reduced splicing of such a large proportion of variants is the formation of RNA structures that pair N50 bases-such as the GU repeats-with other bases specifically within the reporter pre-mRNA analyzed. However, multiple models were unable to explain more than a small fraction of the variance in splicing efficiency across the library, suggesting that complex nonlinear interactions in RNA structures are not accurately captured by RNA structure prediction methods. Our results imply that the specific context of a pre-mRNA may determine the bases allowable in an intron to prevent secondary structures that reduce splicing. This large data set can serve as a resource for further exploration of splicing mechanisms.

Gemini: memory-efficient integration of hundreds of gene networks with high-order pooling.

MOTIVATION: The exponential growth of genomic sequencing data has created ever-expanding repositories of gene networks. Unsupervised network integration methods are critical to learn informative representations for each gene, which are later used as features for downstream applications. However, these network integration methods must be scalable to account for the increasing number of networks and robust to an uneven distribution of network types within hundreds of gene networks. RESULTS: To address these needs, we present Gemini, a novel network integration method that uses memory-efficient high-order pooling to represent and weight each network according to its uniqueness. Gemini then mitigates the uneven network distribution through mixing up existing networks to create many new networks. We find that Gemini leads to more than a 10% improvement in F1 score, 15% improvement in micro-AUPRC, and 63% improvement in macro-AUPRC for human protein function prediction by integrating hundreds of networks from BioGRID, and that Gemini's performance significantly improves when more networks are added to the input network collection, while Mashup and BIONIC embeddings' performance deteriorates. Gemini thereby enables memory-efficient and informative network integration for large gene networks and can be used to massively integrate and analyze networks in other domains. AVAILABILITY AND IMPLEMENTATION: Gemini can be accessed at: https://github.com/MinxZ/Gemini.

AAV5-miHTT-mediated huntingtin lowering improves brain health in a Huntington's disease mouse model.

Huntingtin (HTT)-lowering therapies show great promise in treating Huntington's disease. We have developed a microRNA targeting human HTT that is delivered in an adeno-associated serotype 5 viral vector (AAV5-miHTT), and here use animal behaviour, MRI, non-invasive proton magnetic resonance spectroscopy and striatal RNA sequencing as outcome measures in preclinical mouse studies of AAV5-miHTT. The effects of AAV5-miHTT treatment were evaluated in homozygous Q175FDN mice, a mouse model of Huntington's disease with severe neuropathological and behavioural phenotypes. Homozygous mice were used instead of the more commonly used heterozygous strain, which exhibit milder phenotypes. Three-month-old homozygous Q175FDN mice, which had developed acute phenotypes by the time of treatment, were injected bilaterally into the striatum with either formulation buffer (phosphate-buffered saline + 5% sucrose), low dose (5.2 × 109 genome copies/mouse) or high dose (1.3 × 1011 genome copies/mouse) AAV5-miHTT. Wild-type mice injected with formulation buffer served as controls. Behavioural assessments of cognition, T1-weighted structural MRI and striatal proton magnetic resonance spectroscopy were performed 3 months after injection, and shortly afterwards the animals were sacrificed to collect brain tissue for protein and RNA analysis. Motor coordination was assessed at 1-month intervals beginning at 2 months of age until sacrifice. Dose-dependent changes in AAV5 vector DNA level, miHTT expression and mutant HTT were observed in striatum and cortex of AAV5-miHTT-treated Huntington's disease model mice. This pattern of microRNA expression and mutant HTT lowering rescued weight loss in homozygous Q175FDN mice but did not affect motor or cognitive phenotypes. MRI volumetric analysis detected atrophy in four brain regions in homozygous Q175FDN mice, and treatment with high dose AAV5-miHTT rescued this effect in the hippocampus. Like previous magnetic resonance spectroscopy studies in Huntington's disease patients, decreased total N-acetyl aspartate and increased myo-inositol levels were found in the striatum of homozygous Q175FDN mice. These neurochemical findings were partially reversed with AAV5-miHTT treatment. Striatal transcriptional analysis using RNA sequencing revealed mutant HTT-induced changes that were partially reversed by HTT lowering with AAV5-miHTT. Striatal proton magnetic resonance spectroscopy analysis suggests a restoration of neuronal function, and striatal RNA sequencing analysis shows a reversal of transcriptional dysregulation following AAV5-miHTT in a homozygous Huntington's disease mouse model with severe pathology. The results of this study support the use of magnetic resonance spectroscopy in HTT-lowering clinical trials and strengthen the therapeutic potential of AAV5-miHTT in reversing severe striatal dysfunction in Huntington's disease.

Dominant negative variants in IKZF2 cause ICHAD syndrome, a new disorder characterised by immunodysregulation, craniofacial anomalies, hearing impairment, athelia and developmental delay.

BACKGROUND: Helios (encoded by IKZF2), a member of the Ikaros family of transcription factors, is a zinc finger protein involved in embryogenesis and immune function. Although predominantly recognised for its role in the development and function of T lymphocytes, particularly the CD4+ regulatory T cells (Tregs), the expression and function of Helios extends beyond the immune system. During embryogenesis, Helios is expressed in a wide range of tissues, making genetic variants that disrupt the function of Helios strong candidates for causing widespread immune-related and developmental abnormalities in humans. METHODS: We performed detailed phenotypic, genomic and functional investigations on two unrelated individuals with a phenotype of immune dysregulation combined with syndromic features including craniofacial differences, sensorineural hearing loss and congenital abnormalities. RESULTS: Genome sequencing revealed de novo heterozygous variants that alter the critical DNA-binding zinc fingers (ZFs) of Helios. Proband 1 had a tandem duplication of ZFs 2 and 3 in the DNA-binding domain of Helios (p.Gly136_Ser191dup) and Proband 2 had a missense variant impacting one of the key residues for specific base recognition and DNA interaction in ZF2 of Helios (p.Gly153Arg). Functional studies confirmed that both these variant proteins are expressed and that they interfere with the ability of the wild-type Helios protein to perform its canonical function-repressing IL2 transcription activity-in a dominant negative manner. CONCLUSION: This study is the first to describe dominant negative IKZF2 variants. These variants cause a novel genetic syndrome characterised by immunodysregulation, craniofacial anomalies, hearing impairment, athelia and developmental delay.

Cascading epigenomic analysis for identifying disease genes from the regulatory landscape of GWAS variants.

The majority of genetic variants detected in genome wide association studies (GWAS) exert their effects on phenotypes through gene regulation. Motivated by this observation, we propose a multi-omic integration method that models the cascading effects of genetic variants from epigenome to transcriptome and eventually to the phenome in identifying target genes influenced by risk alleles. This cascading epigenomic analysis for GWAS, which we refer to as CEWAS, comprises two types of models: one for linking cis genetic effects to epigenomic variation and another for linking cis epigenomic variation to gene expression. Applying these models in cascade to GWAS summary statistics generates gene level statistics that reflect genetically-driven epigenomic effects. We show on sixteen brain-related GWAS that CEWAS provides higher gene detection rate than related methods, and finds disease relevant genes and gene sets that point toward less explored biological processes. CEWAS thus presents a novel means for exploring the regulatory landscape of GWAS variants in uncovering disease mechanisms.

The effects of transcranial direct-current stimulation (tDCS) on pain intensity of patients with fibromyalgia: a systematic review and meta-analysis.

INTRODUCTION: Fibromyalgia (FM) is a chronic pain condition that affects millions of people worldwide. Transcranial Direct Current Stimulation (tDCS) is a non-invasive brain stimulation technique that has shown promise as a potential treatment for FM by modulating pain perception and reducing symptoms, such as fatigue and depression. We aimed to systematically review studies that assess the effect of tDCS on pain reduction in FM patients. METHODS: Seven electronic databases (PubMed, Scopus, Embase, PsycINFO, Web of Science, Cochrane, and CINAHL Complete) were searched for records in English. Studies that measured the effect of tDCS on pain intensity in FM patients were included. The Cochrane Collaboration's tool was used to assess the quality of the included studies. A random-effect model was preferred, and statistical analysis was performed by Stata software version 17. RESULTS: Twenty studies were included for qualitative, and eleven for quantitative analysis. Out of 664 patients included in the study, 443 were in the stimulation group. The left M1 area was the most common stimulation target (n = 12), and 2 mA was the most common stimulation amplitude (n = 19). The analysis showed that active tDCS significantly reduced pain intensity in FM patients in comparison to the sham group (SMD= -1.55; 95% CI -2.10, -0.99); also, no publication bias was noted. CONCLUSION: Our systematic review highlights the potential effect of tDCS on the reduction of pain intensity in FM patients. Additionally, this current evidence could suggest that tDCS applied at an intensity of 2mA to the left M1 is the most effective strategy.

Maternal psychological stress during pregnancy and newborn telomere length: a systematic review and meta-analysis.

INTRODUCTION: Telomeres protect the ends of chromosomes, and shorter leukocyte telomeres are associated with major group diseases. Maternal psychological stress may be related to the shortening of telomeres in infants. This systematic review and meta-analysis set out to consolidate the varying effect sizes found in studies of maternal psychological stress and telomere length (TL) in newborns and identify moderators of the relationship between stress during pregnancy and newborn TL. METHODS: Our systematic review was registered in Prospero. Six databases (PubMed, Scopus, Embase, PsycINFO, Web of Science, and CINAHL Complete) were searched for records in English from inception to February 10, 2023. Observational studies were included that measured the relationship of psychological stress of the mother during pregnancy on the TL of the newborn. The Newcastle-Ottawa quality assessment scale was used to assess the quality of the included studies. A random-effect model was selected. Statistical analysis performed by Stata software version 17. RESULTS: Eight studies were included for qualitative and four for quantitative analysis. There was an inverse statistically significant relationship between maternal stress and newborn TL; A one score increase in maternal psychological stress resulted in a 0.04 decrease in the TL of the newborn (B = -0.04, 95% CI = [-0.08, 0.00], p = 0.05). Selectivity analysis showed that the pooled effect size was sensitive to one study; After removing this study, the pooled effect size remained significant (B = -0.06, 95% CI = [-0. 10, -0.02], p < 0.001). CONCLUSION: Physiological and environmental factors can significantly affect the TL of newborns. Our results support a significant impact of maternal psychological stress on the TL of a newborn. This association demonstrates the significance of stress in influencing the telomere length, which can be a contributing factor in the infant's future. Therefore, recognizing this association is crucial for understanding and addressing potential health risks and necessitates the need for additional future studies to validate our findings.

Deep learning of immune cell differentiation.

Although we know many sequence-specific transcription factors (TFs), how the DNA sequence of cis-regulatory elements is decoded and orchestrated on the genome scale to determine immune cell differentiation is beyond our grasp. Leveraging a granular atlas of chromatin accessibility across 81 immune cell types, we asked if a convolutional neural network (CNN) could learn to infer cell type-specific chromatin accessibility solely from regulatory DNA sequences. With a tailored architecture and an ensemble approach to CNN parameter interpretation, we show that our trained network ("AI-TAC") does so by rediscovering ab initio the binding motifs for known regulators and some unknown ones. Motifs whose importance is learned virtually as functionally important overlap strikingly well with positions determined by chromatin immunoprecipitation for several TFs. AI-TAC establishes a hierarchy of TFs and their interactions that drives lineage specification and also identifies stage-specific interactions, like Pax5/Ebf1 vs. Pax5/Prdm1, or the role of different NF-κB dimers in different cell types. AI-TAC assigns Spi1/Cebp and Pax5/Ebf1 as the drivers necessary for myeloid and B lineage fates, respectively, but no factors seemed as dominantly required for T cell differentiation, which may represent a fall-back pathway. Mouse-trained AI-TAC can parse human DNA, revealing a strikingly similar ranking of influential TFs and providing additional support that AI-TAC is a generalizable regulatory sequence decoder. Thus, deep learning can reveal the regulatory syntax predictive of the full differentiative complexity of the immune system.

Biological embedding of experience: A primer on epigenetics.

Biological embedding occurs when life experience alters biological processes to affect later life health and well-being. Although extensive correlative data exist supporting the notion that epigenetic mechanisms such as DNA methylation underlie biological embedding, causal data are lacking. We describe specific epigenetic mechanisms and their potential roles in the biological embedding of experience. We also consider the nuanced relationships between the genome, the epigenome, and gene expression. Our ability to connect biological embedding to the epigenetic landscape in its complexity is challenging and complicated by the influence of multiple factors. These include cell type, age, the timing of experience, sex, and DNA sequence. Recent advances in molecular profiling and epigenome editing, combined with the use of comparative animal and human longitudinal studies, should enable this field to transition from correlative to causal analyses.

Gene expression profiles complement the analysis of genomic modifiers of the clinical onset of Huntington disease.

Huntington disease (HD) is a neurodegenerative disorder that is caused by a CAG repeat expansion in HTT. The length of this repeat, however, only explains a proportion of the variability in age of onset in patients. Genome-wide association studies have identified modifiers that contribute toward a proportion of the observed variance. By incorporating tissue-specific transcriptomic information with these results, additional modifiers can be identified. We performed a transcriptome-wide association study assessing heritable differences in genetically determined expression in diverse tissues, with genome-wide data from over 4000 patients. Functional validation of prioritized genes was undertaken in isogenic HD stem cells and patient brains. Enrichment analyses were performed with biologically relevant gene sets to identify the core pathways. HD-associated gene coexpression modules were assessed for associations with neurological phenotypes in an independent cohort and to guide drug repurposing analyses. Transcriptomic analyses identified genes that were associated with age of HD onset and displayed colocalization with gene expression signals in brain tissue (FAN1, GPR161, PMS2, SUMF2), with supporting evidence from functional experiments. This included genes involved in DNA repair, as well as novel-candidate modifier genes that have been associated with other neurological conditions. Further, cortical coexpression modules were also associated with cognitive decline and HD-related traits in a longitudinal cohort. In summary, the combination of population-scale gene expression information with HD patient genomic data identified novel modifier genes for the disorder. Further, these analyses expanded the pathways potentially involved in modifying HD onset and prioritized candidate therapeutics for future study.

Using Transcriptomic Hidden Variables to Infer Context-Specific Genotype Effects in the Brain.

Deciphering the environmental contexts at which genetic effects are most prominent is central for making full use of GWAS results in follow-up experiment design and treatment development. However, measuring a large number of environmental factors at high granularity might not always be feasible. Instead, here we propose extracting cellular embedding of environmental factors from gene expression data by using latent variable (LV) analysis and taking these LVs as environmental proxies in detecting gene-by-environment (GxE) interaction effects on gene expression, i.e., GxE expression quantitative trait loci (eQTLs). Applying this approach to two largest brain eQTL datasets (n = 1,100), we show that LVs and GxE eQTLs in one dataset replicate well in the other dataset. Combining the two samples via meta-analysis, 895 GxE eQTLs are identified. On average, GxE effect explains an additional ∼4% variation in expression of each gene that displays a GxE effect. Ten of these 52 genes are associated with cell-type-specific eQTLs, and the remaining genes are multi-functional. Furthermore, after substituting LVs with expression of transcription factors (TF), we found 91 TF-specific eQTLs, which demonstrates an important use of our brain GxE eQTLs.