Thiazolidinediones Decrease the Recurrence of Intracerebral Hemorrhage in Type 2 Diabetes Mellitus Patients: A Nested Case-Control Study.

INTRODUCTION: Preclinical evidence demonstrated the therapeutic potential of TZDs for the treatment of intracerebral hemorrhage (ICH). The present study conducted an investigation of cerebrovascular and cardiovascular outcomes following ICH in patients with type 2 diabetes mellitus (T2DM) treated with or without TZDs. METHODS: This retrospective nested case-control study used data from the Taiwan National Health Insurance Research Database. A total of 62,515 T2DM patients who were hospitalized with a diagnosis of ICH were enrolled, including 7,603 TZD users. Data for TZD non-users were extracted using propensity score matching. Primary outcomes included death and major adverse cardiovascular events (MACEs), which were defined as a composite of ischemic stroke, hemorrhagic stroke (HS), acute myocardial infarction (AMI), and congestive heart failure (CHF). Patients aged < 20 years with a history of traumatic brain injury or any prior history of MACEs were excluded. RESULTS: TZD users had significantly lower MACE risks compared with TZD non-users following ICH (adjusted hazard ratio [aHR]: 0.90, 95% confidence interval [CI]: 0.85-0.94, p < 0.001). The most significant MACE difference reported for TZD users was HS, which possessed lower incidence than in TZD non-users, especially for the events that happened within 3 months following ICH (aHR: 0.74, 95% CI: 0.62-0.89 within one month, p < 0.01; aHR: 0.68, 95% CI: 0.54-0.85 between 1-3 month). CONCLUSION: The use of TZD in patients with T2DM was associated with a lower risk of subsequent HS and mortality following ICH.

A modified SELEX approach to identify DNA aptamers with binding specificity to the major histocompatibility complex presenting ovalbumin model antigen.

Aptamers have sparked significant interest in cell recognition because of their superior binding specificity and biocompatibility. Cell recognition can be mediated by targeting the major histocompatibility complex (MHC) that presents short peptides derived from intracellular antigens. Although numerous antibodies have demonstrated a specific affinity for the peptide-MHC complex, the number of aptamers that exhibit comparable characteristics is limited. Aptamers are usually selected from large libraries via the Systemic Evolution of Ligands by Exponential Enrichment (SELEX), an iterative process of selection and PCR amplification to enrich a pool of aptamers with high affinity. However, the success rate of aptamer identification is low, possibly due to the presence of complementary sequences or sequences rich in guanine and cytosine that are less accessible for primers. Here, we modified SELEX by employing systemic consecutive selections with minimal PCR amplification. We also modified the analysis by selecting aptamers that were identified in multiple selection rounds rather than those that are highly enriched. Using this approach, we were able to identify two aptamers with binding specificity to cells expressing the ovalbumin alloantigen as a proof of concept. These two aptamers were also discovered among the top 150 abundant candidates, despite not being highly enriched, by performing conventional SELEX. Additionally, we found that highly enriched aptamers tend to contain fractions of the primer sequence and have minimal target affinity. Candidate aptamers are easily missed in the conventional SELEX process. Therefore, our modification for SELEX may facilitate the identification of aptamers for more application in diverse biomedical fields. Significance: we modify the conventional method to improve the efficiency in the identification of the aptamer, a single strand of nucleic acid with binding specificity to the target molecule, showing as a proof of concept that this approach is particularly useful to select aptamers that can selectively bind to cells presenting a particular peptide by the major histocompatibility complex (MHC) on the cell surface. Given that cancer cells may express mutant peptide-MHC complexes that are distinct from those expressed by normal cells, this study sheds light on the potential application of aptamers to cancer cell targeting.

Differential expression of GABAA receptor subunits δ and α6 mediates tonic inhibition in parvalbumin and somatostatin interneurons in the mouse hippocampus.

Inhibitory γ-aminobutyric acid (GABA)-ergic interneurons mediate inhibition in neuronal circuitry and support normal brain function. Consequently, dysregulation of inhibition is implicated in various brain disorders. Parvalbumin (PV) and somatostatin (SST) interneurons, the two major types of GABAergic inhibitory interneurons in the hippocampus, exhibit distinct morpho-physiological properties and coordinate information processing and memory formation. However, the molecular mechanisms underlying the specialized properties of PV and SST interneurons remain unclear. This study aimed to compare the transcriptomic differences between these two classes of interneurons in the hippocampus using the ribosome tagging approach. The results revealed distinct expressions of genes such as voltage-gated ion channels and GABAA receptor subunits between PV and SST interneurons. Gabrd and Gabra6 were identified as contributors to the contrasting tonic GABAergic inhibition observed in PV and SST interneurons. Moreover, some of the differentially expressed genes were associated with schizophrenia and epilepsy. In conclusion, our results provide molecular insights into the distinct roles of PV and SST interneurons in health and disease.

The Network Zoo: a multilingual package for the inference and analysis of gene regulatory networks.

Inference and analysis of gene regulatory networks (GRNs) require software that integrates multi-omic data from various sources. The Network Zoo (netZoo; netzoo.github.io) is a collection of open-source methods to infer GRNs, conduct differential network analyses, estimate community structure, and explore the transitions between biological states. The netZoo builds on our ongoing development of network methods, harmonizing the implementations in various computing languages and between methods to allow better integration of these tools into analytical pipelines. We demonstrate the utility using multi-omic data from the Cancer Cell Line Encyclopedia. We will continue to expand the netZoo to incorporate additional methods.

Phylotranscriptomic patterns of network stochasticity and pathway dynamics during embryogenesis.

MOTIVATION: The hourglass model is a popular evo-devo model depicting that the developmental constraints in the middle of a developmental process are higher, and hence the phenotypes are evolutionarily more conserved, than those that occur in early and late ontogeny stages. Although this model has been supported by studies analyzing developmental gene expression data, the evolutionary explanation and molecular mechanism behind this phenomenon are not fully understood yet. To approach this problem, Raff proposed a hypothesis and claimed that higher interconnectivity among elements in an organism during organogenesis resulted in the larger constraints at the mid-developmental stage. By employing stochastic network analysis and gene-set pathway analysis, we aim to demonstrate such changes of interconnectivity claimed in Raff's hypothesis. RESULTS: We first compared the changes of network randomness among developmental processes in different species by measuring the stochasticity within the biological network in each developmental stage. By tracking the network entropy along each developmental process, we found that the network stochasticity follows an anti-hourglass trajectory, and such a pattern supports Raff's hypothesis in dynamic changes of interconnections among biological modules during development. To understand which biological functions change during the transition of network stochasticity, we sketched out the pathway dynamics along the developmental stages and found that species may activate similar groups of biological processes across different stages. Moreover, higher interspecies correlations are found at the mid-developmental stages. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

GWAS Meta-Analysis Reveals Shared Genes and Biological Pathways between Major Depressive Disorder and Insomnia.

Major depressive disorder (MDD) is one of the most prevalent and disabling mental disorders worldwide. Among the symptoms of MDD, sleep disturbance such as insomnia is prominent, and the first reason patients may seek professional help. However, the underlying pathophysiology of this comorbidity is still elusive. Recently, genome-wide association studies (GWAS) have begun to unveil the genetic background of several psychiatric disorders, including MDD and insomnia. Identifying the shared genomic risk loci between comorbid psychiatric disorders could be a valuable strategy to understanding their comorbidity. This study seeks to identify the shared genes and biological pathways between MDD and insomnia based on their shared genetic variants. First, we performed a meta-analysis based on the GWAS summary statistics of MDD and insomnia obtained from Psychiatric Genomics Consortium and UK Biobank, respectively. Next, we associated shared genetic variants to genes using two gene mapping strategies: (a) positional mapping based on genomic proximity and (b) expression quantitative trait loci (eQTL) mapping based on gene expression linkage across multiple tissues. As a result, a total of 719 shared genes were identified. Over half (51%) of them are protein-coding genes. Functional enrichment analysis shows that the most enriched biological pathways are related to epigenetic modification, sensory perception, and immunologic signatures. We also identified druggable targets using a network approach. Together, these results may provide insights into understanding the genetic predisposition and underlying biological pathways of comorbid MDD and insomnia symptoms.

Exploring the Impact of Cerebrovascular Disease and Major Depression on Non-diseased Human Tissue Transcriptomes.

BACKGROUND: The development of complex diseases is contributed by the combination of multiple factors and complicated interactions between them. Inflammation has recently been associated with many complex diseases and may cause long-term damage to the human body. In this study, we examined whether two types of complex disease, cerebrovascular disease (CVD) or major depression (MD), systematically altered the transcriptomes of non-diseased human tissues and whether inflammation is linked to identifiable molecular signatures, using post-mortem samples from the Genotype-Tissue Expression (GTEx) project. RESULTS: Following a series of differential expression analyses, dozens to hundreds of differentially expressed genes (DEGs) were identified in multiple tissues between subjects with and without a history of CVD or MD. DEGs from these disease-associated tissues-the visceral adipose, tibial artery, caudate, and spinal cord for CVD; and the hypothalamus, putamen, and spinal cord for MD-were further analyzed for functional enrichment. Many pathways associated with immunological events were enriched in the upregulated DEGs of the CVD-associated tissues, as were the neurological and metabolic pathways in DEGs of the MD-associated tissues. Eight gene-tissue pairs were found to overlap with those prioritized by our transcriptome-wide association studies, indicating a potential genetic effect on gene expression for circulating cytokine phenotypes. CONCLUSION: Cerebrovascular disease and major depression cause detectable changes in the gene expression of non-diseased tissues, suggesting that a possible long-term impact of diseases, lifestyles and environmental factors may together contribute to the appearance of "transcriptomic scars" on the human body. Furthermore, inflammation is probably one of the systemic and long-lasting effects of cerebrovascular events.

New COVID-19 saliva-based test: How good is it compared with the current nasopharyngeal or throat swab test?

As of April 15, 2020, the US Food and Drug Administration has granted emergency use authorization to a first saliva test for diagnosis of severe acute respiratory syndrome coronavirus 2 infection, the device developed by RUCDR Infinite Biologics laboratory, Rutgers University. A key feature that distinguishes the saliva-based test from nasopharyngeal or oropharyngeal (throat) swabs is that this kit allows self-collection and can spare healthcare professionals to be at risk during collecting nasopharyngeal or oropharyngeal samples, thereby preserving personal protective equipment for use in patient care rather than sampling and testing. Consequently, broader testing than the current methods of nasal or throat swabs will significantly increase the number of people screening, leading to more effective control of the spread of COVID-19. Nonetheless, a comparison of saliva-based assay with current swab test is needed to understand what and how we can benefit from this newly developed assay. Therefore, in this mini-review article, we aimed to summarize the current and emerging tools, focusing on diagnostic power of different clinical sampling and specimens.

Sex Differences in Gene Expression and Regulatory Networks across 29 Human Tissues.

Sex differences manifest in many diseases and may drive sex-specific therapeutic responses. To understand the molecular basis of sex differences, we evaluated sex-biased gene regulation by constructing sample-specific gene regulatory networks in 29 human healthy tissues using 8,279 whole-genome expression profiles from the Genotype-Tissue Expression (GTEx) project. We find sex-biased regulatory network structures in each tissue. Even though most transcription factors (TFs) are not differentially expressed between males and females, many have sex-biased regulatory targeting patterns. In each tissue, genes that are differentially targeted by TFs between the sexes are enriched for tissue-related functions and diseases. In brain tissue, for example, genes associated with Parkinson's disease and Alzheimer's disease are targeted by different sets of TFs in each sex. Our systems-based analysis identifies a repertoire of TFs that play important roles in sex-specific architecture of gene regulatory networks, and it underlines sex-specific regulatory processes in both health and disease.

Engineering Stable Pseudomonas putida S12 by CRISPR for 2,5-Furandicarboxylic Acid (FDCA) Production.

FDCA (2,5-furandicarboxylic acid) can be enzymatically converted from HMF (5-hydroxymethylfurfural). Pseudomonas putida S12 is promising for FDCA production, but generating stable P. putida S12 is difficult due to its polyploidy and lack of genome engineering tools. Here we showed that coupling CRISPR and λ-Red recombineering enabled one-step gene integration with high efficiency and frequency, and simultaneously replaced endogenous genes in all chromosomes. Using this approach, we generated two stable P. putida S12 strains expressing HMF/furfural oxidoreductase (HMFH) and HMF oxidase (HMFO), both being able to convert 50 mM HMF to ≈42-43 mM FDCA in 24 h. Cosupplementation of MnO2 and CaCO3 to the medium drastically improved the cell tolerance to HMF and enhanced FDCA production. Cointegrating HMFH and HMFT1 (HMF transporter) genes further improved FDCA production, enabling the cells to convert 250 mM HMF to 196 mM (30.6 g/L) FDCA in 24 h. This study implicates the potentials of CRISPR for generating stable P. putida S12 strains for FDCA production.

Adaptation to Endoplasmic Reticulum Stress Enhances Resistance of Oral Cancer Cells to Cisplatin by Up-Regulating Polymerase η and Increasing DNA Repair Efficiency.

Endoplasmic reticulum (ER) stress response is an adaptive program to cope with cellular stress that disturbs the function and homeostasis of ER, which commonly occurs during cancer progression to late stage. Late-stage cancers, mostly requiring chemotherapy, often develop treatment resistance. Chemoresistance has been linked to ER stress response; however, most of the evidence has come from studies that correlate the expression of stress markers with poor prognosis or demonstrate proapoptosis by the knockdown of stress-responsive genes. Since ER stress in cancers usually persists and is essentially not induced by genetic manipulations, we used low doses of ER stress inducers at levels that allowed cell adaptation to occur in order to investigate the effect of stress response on chemoresistance. We found that prolonged tolerable ER stress promotes mesenchymal-epithelial transition, slows cell-cycle progression, and delays the S-phase exit. Consequently, cisplatin-induced apoptosis was significantly decreased in stress-adapted cells, implying their acquisition of cisplatin resistance. Molecularly, we found that proliferating cell nuclear antigen (PCNA) ubiquitination and the expression of polymerase η, the main polymerase responsible for translesion synthesis across cisplatin-DNA damage, were up-regulated in ER stress-adaptive cells, and their enhanced cisplatin resistance was abrogated by the knockout of polymerase η. We also found that a fraction of p53 in stress-adapted cells was translocated to the nucleus, and that these cells exhibited a significant decline in the level of cisplatin-DNA damage. Consistently, we showed that the nuclear p53 coincided with strong positivity of glucose-related protein 78 (GRP78) on immunostaining of clinical biopsies, and the cisplatin-based chemotherapy was less effective for patients with high levels of ER stress. Taken together, this study uncovers that adaptation to ER stress enhances DNA repair and damage tolerance, with which stressed cells gain resistance to chemotherapeutics.

Tissue-aware RNA-Seq processing and normalization for heterogeneous and sparse data.

BACKGROUND: Although ultrahigh-throughput RNA-Sequencing has become the dominant technology for genome-wide transcriptional profiling, the vast majority of RNA-Seq studies typically profile only tens of samples, and most analytical pipelines are optimized for these smaller studies. However, projects are generating ever-larger data sets comprising RNA-Seq data from hundreds or thousands of samples, often collected at multiple centers and from diverse tissues. These complex data sets present significant analytical challenges due to batch and tissue effects, but provide the opportunity to revisit the assumptions and methods that we use to preprocess, normalize, and filter RNA-Seq data - critical first steps for any subsequent analysis. RESULTS: We find that analysis of large RNA-Seq data sets requires both careful quality control and the need to account for sparsity due to the heterogeneity intrinsic in multi-group studies. We developed Yet Another RNA Normalization software pipeline (YARN), that includes quality control and preprocessing, gene filtering, and normalization steps designed to facilitate downstream analysis of large, heterogeneous RNA-Seq data sets and we demonstrate its use with data from the Genotype-Tissue Expression (GTEx) project. CONCLUSIONS: An R package instantiating YARN is available at http://bioconductor.org/packages/yarn .

Understanding Tissue-Specific Gene Regulation.

Although all human tissues carry out common processes, tissues are distinguished by gene expression patterns, implying that distinct regulatory programs control tissue specificity. In this study, we investigate gene expression and regulation across 38 tissues profiled in the Genotype-Tissue Expression project. We find that network edges (transcription factor to target gene connections) have higher tissue specificity than network nodes (genes) and that regulating nodes (transcription factors) are less likely to be expressed in a tissue-specific manner as compared to their targets (genes). Gene set enrichment analysis of network targeting also indicates that the regulation of tissue-specific function is largely independent of transcription factor expression. In addition, tissue-specific genes are not highly targeted in their corresponding tissue network. However, they do assume bottleneck positions due to variability in transcription factor targeting and the influence of non-canonical regulatory interactions. These results suggest that tissue specificity is driven by context-dependent regulatory paths, providing transcriptional control of tissue-specific processes.

Regulatory network changes between cell lines and their tissues of origin.

BACKGROUND: Cell lines are an indispensable tool in biomedical research and often used as surrogates for tissues. Although there are recognized important cellular and transcriptomic differences between cell lines and tissues, a systematic overview of the differences between the regulatory processes of a cell line and those of its tissue of origin has not been conducted. The RNA-Seq data generated by the GTEx project is the first available data resource in which it is possible to perform a large-scale transcriptional and regulatory network analysis comparing cell lines with their tissues of origin. RESULTS: We compared 127 paired Epstein-Barr virus transformed lymphoblastoid cell lines (LCLs) and whole blood samples, and 244 paired primary fibroblast cell lines and skin samples. While gene expression analysis confirms that these cell lines carry the expression signatures of their primary tissues, albeit at reduced levels, network analysis indicates that expression changes are the cumulative result of many previously unreported alterations in transcription factor (TF) regulation. More specifically, cell cycle genes are over-expressed in cell lines compared to primary tissues, and this alteration in expression is a result of less repressive TF targeting. We confirmed these regulatory changes for four TFs, including SMAD5, using independent ChIP-seq data from ENCODE. CONCLUSIONS: Our results provide novel insights into the regulatory mechanisms controlling the expression differences between cell lines and tissues. The strong changes in TF regulation that we observe suggest that network changes, in addition to transcriptional levels, should be considered when using cell lines as models for tissues.

Exploring regulation in tissues with eQTL networks.

Characterizing the collective regulatory impact of genetic variants on complex phenotypes is a major challenge in developing a genotype to phenotype map. Using expression quantitative trait locus (eQTL) analyses, we constructed bipartite networks in which edges represent significant associations between genetic variants and gene expression levels and found that the network structure informs regulatory function. We show, in 13 tissues, that these eQTL networks are organized into dense, highly modular communities grouping genes often involved in coherent biological processes. We find communities representing shared processes across tissues, as well as communities associated with tissue-specific processes that coalesce around variants in tissue-specific active chromatin regions. Node centrality is also highly informative, with the global and community hubs differing in regulatory potential and likelihood of being disease associated.

MetaDCN: meta-analysis framework for differential co-expression network detection with an application in breast cancer.

MOTIVATION: Gene co-expression network analysis from transcriptomic studies can elucidate gene-gene interactions and regulatory mechanisms. Differential co-expression analysis helps further detect alterations of regulatory activities in case/control comparison. Co-expression networks estimated from single transcriptomic study is often unstable and not generalizable due to cohort bias and limited sample size. With the rapid accumulation of publicly available transcriptomic studies, co-expression analysis combining multiple transcriptomic studies can provide more accurate and robust results. RESULTS: In this paper, we propose a meta-analytic framework for detecting differentially co-expressed networks (MetaDCN). Differentially co-expressed seed modules are first detected by optimizing an energy function via simulated annealing. Basic modules sharing common pathways are merged into pathway-centric supermodules and a Cytoscape plug-in (MetaDCNExplorer) is developed to visualize and explore the findings. We applied MetaDCN to two breast cancer applications: ER+/ER- comparison using five training and three testing studies, and ILC/IDC comparison with two training and two testing studies. We identified 20 and 4 supermodules for ER+/ER- and ILC/IDC comparisons, respectively. Ranking atop are 'immune response pathway' and 'complement cascades pathway' for ER comparison, and 'extracellular matrix pathway' for ILC/IDC comparison. Without the need for prior information, the results from MetaDCN confirm existing as well as discover novel disease mechanisms in a systems manner. AVAILABILITY AND IMPLEMENTATION: R package 'MetaDCN' and Cytoscape App 'MetaDCNExplorer' are available at http://tsenglab.biostat.pitt.edu/software.htm . CONTACT: ctseng@pitt.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Effects of aging on circadian patterns of gene expression in the human prefrontal cortex.

With aging, significant changes in circadian rhythms occur, including a shift in phase toward a "morning" chronotype and a loss of rhythmicity in circulating hormones. However, the effects of aging on molecular rhythms in the human brain have remained elusive. Here, we used a previously described time-of-death analysis to identify transcripts throughout the genome that have a significant circadian rhythm in expression in the human prefrontal cortex [Brodmann's area 11 (BA11) and BA47]. Expression levels were determined by microarray analysis in 146 individuals. Rhythmicity in expression was found in ∼ 10% of detected transcripts (P < 0.05). Using a metaanalysis across the two brain areas, we identified a core set of 235 genes (q < 0.05) with significant circadian rhythms of expression. These 235 genes showed 92% concordance in the phase of expression between the two areas. In addition to the canonical core circadian genes, a number of other genes were found to exhibit rhythmic expression in the brain. Notably, we identified more than 1,000 genes (1,186 in BA11; 1,591 in BA47) that exhibited age-dependent rhythmicity or alterations in rhythmicity patterns with aging. Interestingly, a set of transcripts gained rhythmicity in older individuals, which may represent a compensatory mechanism due to a loss of canonical clock function. Thus, we confirm that rhythmic gene expression can be reliably measured in human brain and identified for the first time (to our knowledge) significant changes in molecular rhythms with aging that may contribute to altered cognition, sleep, and mood in later life.

Dissecting the human protein-protein interaction network via phylogenetic decomposition.

The protein-protein interaction (PPI) network offers a conceptual framework for better understanding the functional organization of the proteome. However, the intricacy of network complexity complicates comprehensive analysis. Here, we adopted a phylogenic grouping method combined with force-directed graph simulation to decompose the human PPI network in a multi-dimensional manner. This network model enabled us to associate the network topological properties with evolutionary and biological implications. First, we found that ancient proteins occupy the core of the network, whereas young proteins tend to reside on the periphery. Second, the presence of age homophily suggests a possible selection pressure may have acted on the duplication and divergence process during the PPI network evolution. Lastly, functional analysis revealed that each age group possesses high specificity of enriched biological processes and pathway engagements, which could correspond to their evolutionary roles in eukaryotic cells. More interestingly, the network landscape closely coincides with the subcellular localization of proteins. Together, these findings suggest the potential of using conceptual frameworks to mimic the true functional organization in a living cell.

Lengthening of 3'UTR increases with morphological complexity in animal evolution.

MOTIVATION: Evolutionary expansion of gene regulatory circuits seems to boost morphological complexity. However, the expansion patterns and the quantification relationships have not yet been identified. In this study, we focus on the regulatory circuits at the post-transcriptional level, investigating whether and how this principle may apply. RESULTS: By analysing the structure of mRNA transcripts in multiple metazoan species, we observed a striking exponential correlation between the length of 3' untranslated regions (3'UTR) and morphological complexity as measured by the number of cell types in each organism. Cellular diversity was similarly associated with the accumulation of microRNA genes and their putative targets. We propose that the lengthening of 3'UTRs together with a commensurate exponential expansion in post-transcriptional regulatory circuits can contribute to the emergence of new cell types during animal evolution.

Crosstalk between transcription factors and microRNAs in human protein interaction network.

BACKGROUND: Gene regulatory networks control the global gene expression and the dynamics of protein output in living cells. In multicellular organisms, transcription factors and microRNAs are the major families of gene regulators. Recent studies have suggested that these two kinds of regulators share similar regulatory logics and participate in cooperative activities in the gene regulatory network; however, their combinational regulatory effects and preferences on the protein interaction network remain unclear. METHODS: In this study, we constructed a global human gene regulatory network comprising both transcriptional and post-transcriptional regulatory relationships, and integrated the protein interactome into this network. We then screened the integrated network for four types of regulatory motifs: single-regulation, co-regulation, crosstalk, and independent, and investigated their topological properties in the protein interaction network. RESULTS: Among the four types of network motifs, the crosstalk was found to have the most enriched protein-protein interactions in their downstream regulatory targets. The topological properties of these motifs also revealed that they target crucial proteins in the protein interaction network and may serve important roles of biological functions. CONCLUSIONS: Altogether, these results reveal the combinatorial regulatory patterns of transcription factors and microRNAs on the protein interactome, and provide further evidence to suggest the connection between gene regulatory network and protein interaction network.