Caspase-4/11 exacerbates disease severity in SARS-CoV-2 infection by promoting inflammation and immunothrombosis.

Severe acute respiratory syndrome coronavirus 2 (SARS­CoV-2) is a worldwide health concern, and new treatment strategies are needed. Targeting inflammatory innate immunity pathways holds therapeutic promise, but effective molecular targets remain elusive. Here, we show that human caspase-4 (CASP4) and its mouse homolog, caspase-11 (CASP11), are up-regulated in SARS­CoV-2 infections and that CASP4 expression correlates with severity of SARS­CoV-2 infection in humans. SARS­CoV-2­infected Casp11−/− mice were protected from severe weight loss and lung pathology, including blood vessel damage, compared to wild-type (WT) mice and mice lacking the caspase downstream effector gasdermin-D (Gsdmd−/−). Notably, viral titers were similar regardless of CASP11 knockout. Global transcriptomics of SARS­CoV-2­infected WT, Casp11−/−, and Gsdmd−/− lungs identified restrained expression of inflammatory molecules and altered neutrophil gene signatures in Casp11−/− mice. We confirmed that protein levels of inflammatory mediators interleukin (IL)-1ß, IL-6, and CXCL1, as well as neutrophil functions, were reduced in Casp11−/− lungs. Additionally, Casp11−/− lungs accumulated less von Willebrand factor, a marker for endothelial damage, but expressed more Kruppel-Like Factor 2, a transcription factor that maintains vascular integrity. Overall, our results demonstrate that CASP4/11 promotes detrimental SARS­CoV-2­induced inflammation and coagulopathy, largely independently of GSDMD, identifying CASP4/11 as a promising drug target for treatment and prevention of severe COVID-19.

Microglia-targeted inhibition of miR-17 via mannose-coated lipid nanoparticles improves pathology and behavior in a mouse model of Alzheimer's disease.

Neuroinflammation and accumulation of Amyloid Beta (Aß) accompanied by deterioration of special memory are hallmarks of Alzheimer's disease (AD). Effective preventative and treatment options for AD are still needed. Microglia in AD brains are characterized by elevated levels of microRNA-17 (miR-17), which is accompanied by defective autophagy, Aß accumulation, and increased inflammatory cytokine production. However, the effect of targeting miR-17 on AD pathology and memory loss is not clear. To specifically inhibit miR-17 in microglia, we generated mannose-coated lipid nanoparticles (MLNPs) enclosing miR-17 antagomir (Anti-17 MLNPs), which are targeted to mannose receptors readily expressed on microglia. We used a 5XFAD mouse model (AD) that recapitulates many AD-related phenotypes observed in humans. Our results show that Anti-17 MLNPs, delivered to 5XFAD mice by intra-cisterna magna injection, specifically deliver Anti-17 to microglia. Anti-17 MLNPs downregulated miR-17 expression in microglia but not in neurons, astrocytes, and oligodendrocytes. Anti-17 MLNPs attenuated inflammation, improved autophagy, and reduced Aß burdens in the brains. Additionally, Anti-17 MLNPs reduced the deterioration in spatial memory and decreased anxiety-like behavior in 5XFAD mice. Therefore, targeting miR-17 using MLNPs is a viable strategy to prevent several AD pathologies. This selective targeting strategy delivers specific agents to microglia without the adverse off-target effects on other cell types. Additionally, this approach can be used to deliver other molecules to microglia and other immune cells in other organs.

multi-GPA-Tree: Statistical approach for pleiotropy informed and functional annotation tree guided prioritization of GWAS results.

Genome-wide association studies (GWAS) have successfully identified over two hundred thousand genotype-trait associations. Yet some challenges remain. First, complex traits are often associated with many single nucleotide polymorphisms (SNPs), most with small or moderate effect sizes, making them difficult to detect. Second, many complex traits share a common genetic basis due to 'pleiotropy' and and though few methods consider it, leveraging pleiotropy can improve statistical power to detect genotype-trait associations with weaker effect sizes. Third, currently available statistical methods are limited in explaining the functional mechanisms through which genetic variants are associated with specific or multiple traits. We propose multi-GPA-Tree to address these challenges. The multi-GPA-Tree approach can identify risk SNPs associated with single as well as multiple traits while also identifying the combinations of functional annotations that can explain the mechanisms through which risk-associated SNPs are linked with the traits. First, we implemented simulation studies to evaluate the proposed multi-GPA-Tree method and compared its performance with existing statistical approaches. The results indicate that multi-GPA-Tree outperforms existing statistical approaches in detecting risk-associated SNPs for multiple traits. Second, we applied multi-GPA-Tree to a systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA), and to a Crohn's disease (CD) and ulcertive colitis (UC) GWAS, and functional annotation data including GenoSkyline and GenoSkylinePlus. Our results demonstrate that multi-GPA-Tree can be a powerful tool that improves association mapping while facilitating understanding of the underlying genetic architecture of complex traits and potential mechanisms linking risk-associated SNPs with complex traits.

Lenalidomide in the treatment of anti-myelin-associated glycoprotein neuropathy: A phase 1 study to identify the maximum tolerated dose.

BACKGROUND: Anti-myelin-associated glycoprotein (MAG) neuropathy is a debilitating demyelinating polyneuropathy with no approved therapies. Our primary objective was to ascertain lenalidomide safety and maximum tolerated dose (MTD) in anti-MAG neuropathy. METHODS: This phase 1b, open-label, single-arm, dose-finding trial was conducted from 2019 through 2022. The original design included a dose-escalation/extension phase followed by a dose-expansion phase. Three doses of lenalidomide were evaluated: 10, 15, and 25 mg. The main outcome was the MTD. RESULTS: Eleven patients enrolled (10 men), with a mean age of 67.6 years (SD = 6.18, range 58-77 years) and mean disease duration of 8.5 years (SD = 10.9, range 1-40 years). The study terminated early due to higher-than-expected non-dose-limiting toxicity venous thromboembolism (VTE) events. The calculated MTD was 25 mg (posterior mean of toxicity probability was 0.01 with a 95% credible interval of 0.00, 0.06), but a recommended phase 2 dose of 15 mg was advised. For secondary exploratory outcomes, only EQ-5D (-0.95, 95% CI -1.81 to -0.09) and total IgM (-162 mg/dL, 95% CI -298 to -26) showed signs of improvement by month 12. CONCLUSIONS: Lenalidomide was associated with higher-than-expected VTE events in anti-MAG neuropathy patients, despite a calculated MTD of 25 mg. A recommended phase 2 dose of 15 mg was advised. Lenalidomide did not improve disability or impairment at 12 months, although this study was not powered for efficacy. The risks of long term lenalidomide may outweigh benefit for patients with anti-MAG neuropathy. Any future efficacy study should address VTE risk, as current myeloma guidelines appear inadequate. TRIAL REGISTRATION: Lenalidomide in Anti-MAG Neuropathy: Phase 1b Study, ClinicalTrials.gov Identifier: NCT03701711, https://clinicaltrials.gov/ct2/show/NCT03701711. First submitted October 10, 2018. First patient enrolled in January 2019.

GPA-Tree: statistical approach for functional-annotation-tree-guided prioritization of GWAS results.

MOTIVATION: In spite of great success of genome-wide association studies (GWAS), multiple challenges still remain. First, complex traits are often associated with many single nucleotide polymorphisms (SNPs), each with small or moderate effect sizes. Second, our understanding of the functional mechanisms through which genetic variants are associated with complex traits is still limited. To address these challenges, we propose GPA-Tree and it simultaneously implements association mapping and identifies key combinations of functional annotations related to risk-associated SNPs by combining a decision tree algorithm with a hierarchical modeling framework. RESULTS: First, we implemented simulation studies to evaluate the proposed GPA-Tree method and compared its performance with existing statistical approaches. The results indicate that GPA-Tree outperforms existing statistical approaches in detecting risk-associated SNPs and identifying the true combinations of functional annotations with high accuracy. Second, we applied GPA-Tree to a systemic lupus erythematosus (SLE) GWAS and functional annotation data including GenoSkyline and GenoSkylinePlus. The results from GPA-Tree highlight the dysregulation of blood immune cells, including but not limited to primary B, memory helper T, regulatory T, neutrophils and CD8+ memory T cells in SLE. These results demonstrate that GPA-Tree can be a powerful tool that improves association mapping while facilitating understanding of the underlying genetic architecture of complex traits and potential mechanisms linking risk-associated SNPs with complex traits. AVAILABILITY AND IMPLEMENTATION: The GPATree software is available at https://dongjunchung.github.io/GPATree/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Fibroblast-Specific Proteotranscriptomes Reveal Distinct Fibrotic Signatures of Human Sinoatrial Node in Nonfailing and Failing Hearts.

BACKGROUND: Up to 50% of the adult human sinoatrial node (SAN) is composed of dense connective tissue. Cardiac diseases including heart failure (HF) may increase fibrosis within the SAN pacemaker complex, leading to impaired automaticity and conduction of electric activity to the atria. Unlike the role of cardiac fibroblasts in pathologic fibrotic remodeling and tissue repair, nothing is known about fibroblasts that maintain the inherently fibrotic SAN environment. METHODS: Intact SAN pacemaker complex was dissected from cardioplegically arrested explanted nonfailing hearts (non-HF; n=22; 48.7±3.1 years of age) and human failing hearts (n=16; 54.9±2.6 years of age). Connective tissue content was quantified from Masson trichrome-stained head-center and center-tail SAN sections. Expression of extracellular matrix proteins, including collagens 1 and 3A1, CILP1 (cartilage intermediate layer protein 1), and POSTN (periostin), and fibroblast and myofibroblast numbers were quantified by in situ and in vitro immunolabeling. Fibroblasts from the central intramural SAN pacemaker compartment (≈10×5×2 mm3) and right atria were isolated, cultured, passaged once, and treated ± transforming growth factor ß1 and subjected to comprehensive high-throughput next-generation sequencing of whole transcriptome, microRNA, and proteomic analyses. RESULTS: Intranodal fibrotic content was significantly higher in SAN pacemaker complex from HF versus non-HF hearts (57.7±2.6% versus 44.0±1.2%; P<0.0001). Proliferating phosphorylated histone 3+/vimentin+/CD31- (cluster of differentiation 31) fibroblasts were higher in HF SAN. Vimentin+/α-smooth muscle actin+/CD31- myofibroblasts along with increased interstitial POSTN expression were found only in HF SAN. RNA sequencing and proteomic analyses identified unique differences in mRNA, long noncoding RNA, microRNA, and proteomic profiles between non-HF and HF SAN and right atria fibroblasts and transforming growth factor ß1-induced myofibroblasts. Specifically, proteins and signaling pathways associated with extracellular matrix flexibility, stiffness, focal adhesion, and metabolism were altered in HF SAN fibroblasts compared with non-HF SAN. CONCLUSIONS: This study revealed increased SAN-specific fibrosis with presence of myofibroblasts, CILP1, and POSTN-positive interstitial fibrosis only in HF versus non-HF human hearts. Comprehensive proteotranscriptomic profiles of SAN fibroblasts identified upregulation of genes and proteins promoting stiffer SAN extracellular matrix in HF hearts. Fibroblast-specific profiles generated by our proteotranscriptomic analyses of the human SAN provide a comprehensive framework for future studies to investigate the role of SAN-specific fibrosis in cardiac rhythm regulation and arrhythmias.

STAT1 is regulated by TRIM24 and promotes immunosuppression in head and neck squamous carcinoma cells, but enhances T cell antitumour immunity in the tumour microenvironment.

BACKGROUND: Head and neck squamous cell carcinoma (HNSCC) is a significant problem and is frequently resistant to current treatments. STAT1 is important in anti-tumour immune responses against HNSCC. However, the role of STAT1 expression by tumour cells and its regulation during HNSCC is unclear. METHODS: We determined the effects of STAT1 inhibition on tumour development and immunity in CAL27 and UMSCC22A HNSCC cell lines in vitro and in a HNSCC carcinogen-induced model in vivo. RESULTS: STAT1 siRNA knockdown in human HNSCC cells impaired their proliferation and expression of the immunosuppressive marker PD-L1. Stat1-deficient mice displayed increased oral lesion incidence and multiplicity during tumour carcinogenesis in vivo. Immunosuppressive markers PD-1 in CD8+ T cells and PD-L1 in monocytic MDSCs and macrophages were reduced in oral tumours and draining lymph nodes of tumour-bearing Stat1-deficient mice. However, STAT1 was required for anti-tumour functions of T cells during HNSCC in vivo. Finally, we identified TRIM24 to be a negative regulator of STAT1 that plays a similar tumorigenic function to STAT1 in vitro and thus may be a potential target when treating HNSCC. CONCLUSION: Our findings indicate that STAT1 activity plays an important role in tumorigenicity and immunosuppression during HNSCC development.

Clinical and molecular relevance of genetic variants in the non-coding transcriptome of patients with cytogenetically normal acute myeloid leukemia.

Expression levels of long non-coding RNA (lncRNA) have been shown to associate with clinical outcome of patients with cytogenetically normal acute myeloid leukemia (CN-AML). However, the frequency and clinical significance of genetic variants in the nucleotide sequences of lncRNA in AML patients is unknown. Herein, we analyzed total RNA sequencing data of 377 younger adults (aged <60 years) with CN-AML, who were comprehensively characterized with regard to clinical outcome. We used available genomic databases and stringent filters to annotate genetic variants unequivocally located in the non-coding transcriptome of AML patients. We detected 981 variants, which are recurrently present in lncRNA that are expressed in leukemic blasts. Among these variants, we identified a cytosine-to-thymidine variant in the lncRNA RP5-1074L1.4 and a cytosine-to-thymidine variant in the lncRNA SNHG15, which independently associated with longer survival of CN-AML patients. The presence of the SNHG15 cytosine-to-thymidine variant was also found to associate with better outcome in an independent dataset of CN-AML patients, despite differences in treatment protocols and RNA sequencing techniques. In order to gain biological insights, we cloned and overexpressed both wild-type and variant versions of the SNHG15 lncRNA. In keeping with its negative prognostic impact, overexpression of the wild-type SNHG15 associated with higher proliferation rate of leukemic blasts when compared with the cytosine-to-thymidine variant. We conclude that recurrent genetic variants of lncRNA that are expressed in the leukemic blasts of CN-AML patients have prognostic and potential biological significance.

Genetic analysis of a malignant meningioma and associated metastases.

To identify genes altered in a highly aggressive metastatic meningioma primary as well as its metastases. Exome sequencing of a primary anaplastic meningioma and metastatic lesions in which DNA could be extracted and compared to germline DNA. Genetic analysis of the metastatic sites found 31 common mutations among the primary tumor and two metastatic sites. Additionally, genetic mutations were identified which were either infrequently (MUC3A, ALDH1A3, HOXA1) or not at all previously described in meningiomas (CASS4, CMKLR1). Exome sequencing of a metastatic meningioma and its distant metastases outside the CNS identified mutations that were not previously well described.

High dimensional model representation of log-likelihood ratio: binary classification with expression data.

BACKGROUND: Binary classification rules based on a small-sample of high-dimensional data (for instance, gene expression data) are ubiquitous in modern bioinformatics. Constructing such classifiers is challenging due to (a) the complex nature of underlying biological traits, such as gene interactions, and (b) the need for highly interpretable glass-box models. We use the theory of high dimensional model representation (HDMR) to build interpretable low dimensional approximations of the log-likelihood ratio accounting for the effects of each individual gene as well as gene-gene interactions. We propose two algorithms approximating the second order HDMR expansion, and a hypothesis test based on the HDMR formulation to identify significantly dysregulated pairwise interactions. The theory is seen as flexible and requiring only a mild set of assumptions. RESULTS: We apply our approach to gene expression data from both synthetic and real (breast and lung cancer) datasets comparing it also against several popular state-of-the-art methods. The analyses suggest the proposed algorithms can be used to obtain interpretable prediction rules with high prediction accuracies and to successfully extract significantly dysregulated gene-gene interactions from the data. They also compare favorably against their competitors across multiple synthetic data scenarios. CONCLUSION: The proposed HDMR-based approach appears to produce a reliable classifier that additionally allows one to describe how individual genes or gene-gene interactions affect classification decisions. Both real and synthetic data analyses suggest that our methods can be used to identify gene networks with dysregulated pairwise interactions, and are therefore appropriate for differential networks analysis.

BISR-RNAseq: an efficient and scalable RNAseq analysis workflow with interactive report generation.

BACKGROUND: RNA sequencing has become an increasingly affordable way to profile gene expression patterns. Here we introduce a workflow implementing several open-source softwares that can be run on a high performance computing environment. RESULTS: Developed as a tool by the Bioinformatics Shared Resource Group (BISR) at the Ohio State University, we have applied the pipeline to a few publicly available RNAseq datasets downloaded from GEO in order to demonstrate the feasibility of this workflow. Source code is available here: workflow: https://code.bmi.osumc.edu/gadepalli.3/BISR-RNAseq-ICIBM2019 and shiny: https://code.bmi.osumc.edu/gadepalli.3/BISR_RNASeq_ICIBM19. Example dataset is demonstrated here: https://dataportal.bmi.osumc.edu/RNA_Seq/. CONCLUSION: The workflow allows for the analysis (alignment, QC, gene-wise counts generation) of raw RNAseq data and seamless integration of quality analysis and differential expression results into a configurable R shiny web application.

Requirement of Neuronal Ribosome Synthesis for Growth and Maintenance of the Dendritic Tree.

The nucleolus serves as a principal site of ribosome biogenesis but is also implicated in various non-ribosomal functions, including negative regulation of the pro-apoptotic transcription factor p53. Although disruption of the nucleolus may trigger the p53-dependent neuronal death, neurotoxic consequences of a selective impairment of ribosome production are unclear. Here, we report that in rat forebrain neuronal maturation is associated with a remarkable expansion of ribosomes despite postnatal down-regulation of ribosomal biogenesis. In cultured rat hippocampal neurons, inhibition of the latter process by knockdowns of ribosomal proteins S6, S14, or L4 reduced ribosome content without disrupting nucleolar integrity, cell survival, and signaling responses to the neurotrophin brain-derived neurotrophic factor. Moreover, reduced general protein synthesis and/or formation of RNA stress granules suggested diminished ribosome recruitment to at least some mRNAs. Such a translational insufficiency was accompanied by impairment of brain-derived neurotrophic factor-mediated dendritic growth. Finally, RNA stress granules and smaller dendritic trees were also observed when ribosomal proteins were depleted from neurons with established dendrites. Thus, a robust ribosomal apparatus is required to carry out protein synthesis that supports dendritic growth and maintenance. Consequently, deficits of ribosomal biogenesis may disturb neurodevelopment by reducing neuronal connectivity. Finally, as stress granule formation and dendritic loss occur early in neurodegenerative diseases, disrupted homeostasis of ribosomes may initiate and/or amplify neurodegeneration-associated disconnection of neuronal circuitries.

Gene expression profiling of brain samples from patients with Lewy body dementia.

Dementia with Lewy Bodies (DLB) is the second most common neurodegenerative disorder in the elderly. The development and progression of DLB remain unclear. In this study we used next generation sequencing to assess RNA expression profiles and cellular processes associated with DLB in the anterior cingulate cortex, a brain region affected by DLB pathology. The expression measurements were made in autopsy brain tissues from 8 DLB subjects and 10 age-matched controls using AmpliSeq technology with ion torrent sequencing. The analysis of RNA expression profiles revealed 490 differentially expressed genes, among which 367 genes were down-regulated and 123 were up-regulated. Functional enrichment analysis of genes differentially expressed in DLB indicated downregulation of genes associated with myelination, neurogenesis, and regulation of nervous system development. miRNA binding sites enriched in these mRNAs yielded a list of candidate miRNAs participating in DLB pathophysiology. Our study provides a comprehensive picture of gene expression landscape in DLB, identifying key cellular processes associated with DLB pathology.

Neurodegeneration-associated instability of ribosomal DNA.

Homologous recombination (HR)-mediated instability of the repetitively organized ribosomal DNA (rDNA) has been proposed as a mediator of cell senescence in yeast triggering the DNA damage response. High individual variability in the content of human rDNA suggests that this genomic region remained relatively unstable throughout evolution. Therefore, quantitative real-time polymerase chain reaction was used to determine the genomic content of rDNA in post mortem samples of parietal cortex from 14 young and 9 elderly individuals with no diagnosis of a chronic neurodegenerative/neurological disease. In addition, rDNA content in that brain region was compared between 10 age-matched control individuals and 10 patients with dementia with Lewy bodies (DLB) which involves neurodegeneration of the cerebral cortex. Probing rRNA-coding regions of rDNA revealed no effects of aging on the rDNA content. Elevated rDNA content was observed in DLB. Conversely, in the DLB pathology-free cerebellum, lower genomic content of rDNA was present in the DLB group. In the parietal cortex, such a DLB-associated instability of rDNA was not accompanied by any major changes of cytosine-phosphate-guanine methylation of the rDNA promoter. As increased cerebro-cortical rDNA content was previously reported in Alzheimer's disease, neurodegeneration appears to be associated with instability of rDNA. The hypothetical origins and consequences of this phenomenon are discussed including possibilities that the DNA damage-induced recombination destabilizes rDNA and that differential content of rDNA affects heterochromatin formation, gene expression and/or DNA damage response. This article is part of a Special Issue entitled: Role of the Nucleolus in Human Disease.

Conditional entropy in variation-adjusted windows detects selection signatures associated with expression quantitative trait loci (eQTLs).

BACKGROUND: Over the past 50,000 years, shifts in human-environmental or human-human interactions shaped genetic differences within and among human populations, including variants under positive selection. Shaped by environmental factors, such variants influence the genetics of modern health, disease, and treatment outcome. Because evolutionary processes tend to act on gene regulation, we test whether regulatory variants are under positive selection. We introduce a new approach to enhance detection of genetic markers undergoing positive selection, using conditional entropy to capture recent local selection signals. RESULTS: We use conditional logistic regression to compare our Adjusted Haplotype Conditional Entropy (H|H) measure of positive selection to existing positive selection measures. H|H and existing measures were applied to published regulatory variants acting in cis (cis-eQTLs), with conditional logistic regression testing whether regulatory variants undergo stronger positive selection than the surrounding gene. These cis-eQTLs were drawn from six independent studies of genotype and RNA expression. The conditional logistic regression shows that, overall, H|H is substantially more powerful than existing positive-selection methods in identifying cis-eQTLs against other Single Nucleotide Polymorphisms (SNPs) in the same genes. When broken down by Gene Ontology, H|H predictions are particularly strong in some biological process categories, where regulatory variants are under strong positive selection compared to the bulk of the gene, distinct from those GO categories under overall positive selection. . However, cis-eQTLs in a second group of genes lack positive selection signatures detectable by H|H, consistent with ancient short haplotypes compared to the surrounding gene (for example, in innate immunity GO:0042742); under such other modes of selection, H|H would not be expected to be a strong predictor.. These conditional logistic regression models are adjusted for Minor allele frequency(MAF); otherwise, ascertainment bias is a huge factor in all eQTL data sets. Relationships between Gene Ontology categories, positive selection and eQTL specificity were replicated with H|H in a single larger data set. Our measure, Adjusted Haplotype Conditional Entropy (H|H), was essential in generating all of the results above because it: 1) is a stronger overall predictor for eQTLs than comparable existing approaches, and 2) shows low sequential auto-correlation, overcoming problems with convergence of these conditional regression statistical models. CONCLUSIONS: Our new method, H|H, provides a consistently more robust signal associated with cis-eQTLs compared to existing methods. We interpret this to indicate that some cis-eQTLs are under positive selection compared to their surrounding genes. Conditional entropy indicative of a selective sweep is an especially strong predictor of eQTLs for genes in several biological processes of medical interest. Where conditional entropy is a weak or negative predictor of eQTLs, such as innate immune genes, this would be consistent with balancing selection acting on such eQTLs over long time periods. Different measures of selection may be needed for variant prioritization under other modes of evolutionary selection.

RNA sequencing of transcriptomes in human brain regions: protein-coding and non-coding RNAs, isoforms and alleles.

BACKGROUND: We used RNA sequencing to analyze transcript profiles of ten autopsy brain regions from ten subjects. RNA sequencing techniques were designed to detect both coding and non-coding RNA, splice isoform composition, and allelic expression. Brain regions were selected from five subjects with a documented history of smoking and five non-smokers. Paired-end RNA sequencing was performed on SOLiD instruments to a depth of >40 million reads, using linearly amplified, ribosomally depleted RNA. Sequencing libraries were prepared with both poly-dT and random hexamer primers to detect all RNA classes, including long non-coding (lncRNA), intronic and intergenic transcripts, and transcripts lacking poly-A tails, providing additional data not previously available. The study was designed to generate a database of the complete transcriptomes in brain region for gene network analyses and discovery of regulatory variants. RESULTS: Of 20,318 protein coding and 18,080 lncRNA genes annotated from GENCODE and lncipedia, 12 thousand protein coding and 2 thousand lncRNA transcripts were detectable at a conservative threshold. Of the aligned reads, 52 % were exonic, 34 % intronic and 14 % intergenic. A majority of protein coding genes (65 %) was expressed in all regions, whereas ncRNAs displayed a more restricted distribution. Profiles of RNA isoforms varied across brain regions and subjects at multiple gene loci, with neurexin 3 (NRXN3) a prominent example. Allelic RNA ratios deviating from unity were identified in > 400 genes, detectable in both protein-coding and non-coding genes, indicating the presence of cis-acting regulatory variants. Mathematical modeling was used to identify RNAs stably expressed in all brain regions (serving as potential markers for normalizing expression levels), linked to basic cellular functions. An initial analysis of differential expression analysis between smokers and nonsmokers implicated a number of genes, several previously associated with nicotine exposure. CONCLUSIONS: RNA sequencing identifies distinct and consistent differences in gene expression between brain regions, with non-coding RNA displaying greater diversity between brain regions than mRNAs. Numerous RNAs exhibit robust allele selective expression, proving a means for discovery of cis-acting regulatory factors with potential clinical relevance.

Missing heritability of common diseases and treatments outside the protein-coding exome.

Genetic factors strongly influence risk of common human diseases and treatment outcomes but the causative variants remain largely unknown; this gap has been called the 'missing heritability'. We propose several hypotheses that in combination have the potential to narrow the gap. First, given a multi-stage path from wellness to disease, we propose that common variants under positive evolutionary selection represent normal variation and gate the transition between wellness and an 'off-well' state, revealing adaptations to changing environmental conditions. In contrast, genome-wide association studies (GWAS) focus on deleterious variants conveying disease risk, accelerating the path from off-well to illness and finally specific diseases, while common 'normal' variants remain hidden in the noise. Second, epistasis (dynamic gene-gene interactions) likely assumes a central role in adaptations and evolution; yet, GWAS analyses currently are poorly designed to reveal epistasis. As gene regulation is germane to adaptation, we propose that epistasis among common normal regulatory variants, or between common variants and less frequent deleterious variants, can have strong protective or deleterious phenotypic effects. These gene-gene interactions can be highly sensitive to environmental stimuli and could account for large differences in drug response between individuals. Residing largely outside the protein-coding exome, common regulatory variants affect either transcription of coding and non-coding RNAs (regulatory SNPs, or rSNPs) or RNA functions and processing (structural RNA SNPs, or srSNPs). Third, with the vast majority of causative variants yet to be discovered, GWAS rely on surrogate markers, a confounding factor aggravated by the presence of more than one causative variant per gene and by epistasis. We propose that the confluence of these factors may be responsible to large extent for the observed heritability gap.

Reduced Expression of Oligodendrocyte Linage-Enriched Transcripts During the Endoplasmic Reticulum Stress/Integrated Stress Response.

Endoplasmic reticulum (ER) stress in oligodendrocyte (OL) linage cells contributes to several CNS pathologies including traumatic spinal cord injury (SCI) and multiple sclerosis. Therefore, primary rat OL precursor cell (OPC) transcriptomes were analyzed using RNASeq after treatments with two ER stress-inducing drugs, thapsigargin (TG) or tunicamycin (TM). Gene ontology term (GO) enrichment showed that both drugs upregulated mRNAs associated with the general stress response. The GOs related to ER stress were only enriched for TM-upregulated mRNAs, suggesting greater ER stress selectivity of TM. Both TG and TM downregulated cell cycle/cell proliferation-associated transcripts, indicating the anti-proliferative effects of ER stress. Interestingly, many OL lineage-enriched mRNAs were downregulated, including those for transcription factors that drive OL identity such as Olig2. Moreover, ER stress-associated decreases of OL-specific gene expression were found in mature OLs from mouse models of white matter pathologies including contusive SCI, toxin-induced demyelination, and Alzheimer's disease-like neurodegeneration. Taken together, the disrupted transcriptomic fingerprint of OL lineage cells may facilitate myelin degeneration and/or dysfunction when pathological ER stress persists in OL lineage cells.

The ER stress response compromises the transcriptomic identity of the OL lineage. Therefore, persistent, pathological ER stress may have a negative impact on structural and/or functional integrity of the white matter.

Transcriptional profiling of human cartilage endplate cells identifies novel genes and cell clusters underlying degenerated and non-degenerated phenotypes.

BACKGROUND: Low back pain is a leading cause of disability worldwide and is frequently attributed to intervertebral disc (IVD) degeneration. Though the contributions of the adjacent cartilage endplates (CEP) to IVD degeneration are well documented, the phenotype and functions of the resident CEP cells are critically understudied. To better characterize CEP cell phenotype and possible mechanisms of CEP degeneration, bulk and single-cell RNA sequencing of non-degenerated and degenerated CEP cells were performed. METHODS: Human lumbar CEP cells from degenerated (Thompson grade ≥ 4) and non-degenerated (Thompson grade ≤ 2) discs were expanded for bulk (N=4 non-degenerated, N=4 degenerated) and single-cell (N=1 non-degenerated, N=1 degenerated) RNA sequencing. Genes identified from bulk RNA sequencing were categorized by function and their expression in non-degenerated and degenerated CEP cells were compared. A PubMed literature review was also performed to determine which genes were previously identified and studied in the CEP, IVD, and other cartilaginous tissues. For single-cell RNA sequencing, different cell clusters were resolved using unsupervised clustering and functional annotation. Differential gene expression analysis and Gene Ontology, respectively, were used to compare gene expression and functional enrichment between cell clusters, as well as between non-degenerated and degenerated CEP samples. RESULTS: Bulk RNA sequencing revealed 38 genes were significantly upregulated and 15 genes were significantly downregulated in degenerated CEP cells relative to non-degenerated cells (|fold change| ≥ 1.5). Of these, only 2 genes were previously studied in CEP cells, and 31 were previously studied in the IVD and other cartilaginous tissues. Single-cell RNA sequencing revealed 11 unique cell clusters, including multiple chondrocyte and progenitor subpopulations with distinct gene expression and functional profiles. Analysis of genes in the bulk RNA sequencing dataset showed that progenitor cell clusters from both samples were enriched in "non-degenerated" genes but not "degenerated" genes. For both bulk- and single-cell analyses, gene expression and pathway enrichment analyses highlighted several pathways that may regulate CEP degeneration, including transcriptional regulation, translational regulation, intracellular transport, and mitochondrial dysfunction. CONCLUSIONS: This thorough analysis using RNA sequencing methods highlighted numerous differences between non-degenerated and degenerated CEP cells, the phenotypic heterogeneity of CEP cells, and several pathways of interest that may be relevant in CEP degeneration.

Bariatric Surgery Is Associated with Lower Concentrations of Fecal Secondary Bile Acids and Their Metabolizing Microbial Enzymes: A Pilot Study.

INTRODUCTION: Excess body fat elevates colorectal cancer risk. While bariatric surgery (BRS) induces significant weight loss, its effects on the fecal stream and colon biology are poorly understood. Specifically, limited data exist on the impact of bariatric surgery (BRS) on fecal secondary bile acids (BA), including lithocholic acid (LCA), a putative promotor of colorectal carcinogenesis. METHODS: This cross-sectional case-control study included 44 patients with obesity; 15 pre-BRS (controls) vs. 29 at a median of 24.1 months post-BRS. We examined the fecal concentrations of 11 BA by liquid chromatography and gene abundance of BA-metabolizing bacterial enzymes through fecal metagenomic sequencing. Differences were quantified using non-parametric tests for BA levels and linear discriminant analysis (LDA) effect size (LEfSe) for genes encoding BA-metabolizing enzymes. RESULTS: Total fecal secondary BA concentrations trended towards lower levels post- vs. pre-BRS controls (p = 0.07). Individually, fecal LCA concentrations were significantly lower post- vs. pre-BRS (8477.0 vs. 11,914.0 uM/mg, p < 0.008). Consistent with this finding, fecal bacterial genes encoding BA-metabolizing enzymes, specifically 3-betahydroxycholanate-3-dehydrogenase (EC 1.1.1.391) and 3-alpha-hydroxycholanate dehydrogenase (EC 1.1.1.52), were also lower post- vs. pre-BRS controls (LDA of - 3.32 and - 2.64, respectively, adjusted p < 0.0001). Post-BRS fecal BA concentrations showed significant inverse correlations with weight loss, a healthy diet quality, and increased physical activity. CONCLUSIONS: Concentrations of LCA, a secondary BA, and bacterial genes needed for BA metabolism are lower post-BRS. These changes can impact health and modulate the colorectal cancer cascade. Further research is warranted to examine how surgical alterations and the associated dietary changes impact bile acid metabolism.