Transcriptomic analysis of diabetic kidney disease and neuropathy in mouse models of type 1 and type 2 diabetes.

Diabetic kidney disease (DKD) and diabetic peripheral neuropathy (DPN) are common complications of type 1 (T1D) and type 2 (T2D) diabetes. However, the mechanisms underlying pathogenesis of these complications are unclear. In this study, we optimized a streptozotocin-induced db/+ murine model of T1D and compared it to our established db/db T2D mouse model of the same C57BLKS/J background. Glomeruli and sciatic nerve transcriptomic data from T1D and T2D mice were analyzed by self-organizing map and differential gene expression analysis. Consistent with prior literature, pathways related to immune function and inflammation were dysregulated in both complications in T1D and T2D mice. Gene-level analysis identified a high degree of concordance in shared differentially expressed genes (DEGs) in both complications and across diabetes type when using mice from the same cohort and genetic background. As we have previously shown a low concordance of shared DEGs in DPN when using mice from different cohorts and genetic backgrounds, this suggests that genetic background may influence diabetic complications. Collectively, these findings support the role of inflammation and indicate that genetic background is important in complications of both T1D and T2D.

A shotgun metagenomic analysis of the fecal microbiome in humans infected with Giardia duodenalis.

BACKGROUND: The mechanisms underlying the clinical outcome disparity during human infection with Giardia duodenalis are still unclear. In recent years, evidence has pointed to the roles of host factors as well as parasite's genetic heterogeneity as major contributing factors in the development of symptomatic human giardiasis. However, it remains contested as to how only a small fraction of individuals infected with G. duodenalis develop clinical gastrointestinal manifestations, whereas the majority of infected individuals remain asymptomatic. Here, we demonstrate that diversity in the fecal microbiome correlates with the clinical outcome of human giardiasis. METHODS: The genetic heterogeneity of G. duodenalis clinical isolates from human subjects with asymptomatic and symptomatic giardiasis was determined using a multilocus analysis approach. We also assessed the genetic proximity of G. duodenalis isolates by constructing phylogenetic trees using the maximum likelihood. Total genomic DNA (gDNA) from fecal specimens was utilized to construct DNA libraries, followed by performing paired-end sequencing using the HiSeq X platform. The Kraken2-generated, filtered FASTQ files were assigned to microbial metabolic pathways and functions using HUMAnN 3.04 and the UniRef90 diamond annotated full reference database (version 201901b). Results from HUMAnN for each sample were evaluated for differences among the biological groups using the Kruskal-Wallis non-parametric test with a post hoc Dunn test. RESULTS: We found that a total of 8/11 (72.73%) human subjects were infected with assemblage A (sub-assemblage AII) of G. duodenalis, whereas 3/11 (27.27%) human subjects in the current study were infected with assemblage B of the parasite. We also found that the parasite's genetic diversity was not associated with the clinical outcome of the infection. Further phylogenetic analysis based on the tpi and gdh loci indicated that those clinical isolates belonging to assemblage A of G. duodenalis subjects clustered compactly together in a monophyletic clade despite being isolated from human subjects with asymptomatic and symptomatic human giardiasis. Using a metagenomic shotgun sequencing approach, we observed that infected individuals with asymptomatic and symptomatic giardiasis represented distinctive microbial diversity profiles, and that both were distinguishable from the profiles of healthy volunteers. CONCLUSIONS: These findings identify a potential association between host microbiome disparity with the development of clinical disease during human giardiasis, and may provide insights into the mechanisms by which the parasite induces pathological changes in the gut. These observations may also lead to the development of novel selective therapeutic targets for preventing human enteric microbial infections.

Characterization of prostanoids response to Bordetella pertussis antigen BscF and Tdap in LPS-challenged monocytes.

Prostanoids are potent inflammatory mediators that play a regulatory role in the innate immune activation of the adaptive immune response to determine the duration of protection against infection. We aim to quantify the modulation of prostanoids profiles in lipopolysaccharide (LPS)-stimulated THP-1 cells treated with the novel pertussis antigen BscF. We compared the effect with pertussis antigens present in the current Tdap vaccine to understand the immunomodulatory effect that might contribute to the diminished Tdap vaccine effectiveness. The inflammatory challenge with LPS induced a robust elevation of most prostanoid family members compared to the control treatment. Treatment with BscF and Tdap significantly reduced the LPS-stimulated elevation of prostaglandins (PGs) D2, E2, and F2α, as well as thromboxane (TX) A2 levels. An opposite trend was observed for PGI2, as both antigens accelerated the LPS-stimulated upregulation. Further, we quantified cyclooxygenases (COXs) that catalyze the biosynthesis of prostanoids and found that both antigens significantly reduced LPS-stimulated COX-1 and COX-2, demonstrating that the waning of acellular pertussis vaccines' protective immunity may be due to other downstream enzymes not related to COXs. Our present study validates the potential role of BscF as an adjuvant, resulting in the next-generation pertussis vaccine discovery.

Modulation of Inflammatory Signaling Molecules in Bordetella pertussis Antigen-Challenged Human Monocytes in Presence of Adrenergic Agonists.

BscF is a type III secretion system (T3SS) needle protein from Bordetella pertussis and has previously been shown to induce a sufficient Th1 and Th17 response in human monocytes and mice as a prerequisite for long-lasting protective immunity against pertussis infection. In our current study, we aim to compare the modulation of inflammatory signaling molecules as a direct measure of the immune response to the B. pertussis antigens BscF and Tdap in the presence or absence of the adrenergic receptor agonists phenylephrine (PE) or isoproterenol (ISO) to observe differences that may contribute to the diminished protective immunity of the current acellular pertussis (aP) vaccine, Tdap. Stimulation of human monocyte THP-1 cells with LPS, BscF, and Tdap induced a robust elevation of CCL20, CXCL10, PGE2, and PGF2α among most chemokine and prostanoid members when compared with the control treatment. Treatment with the adrenergic agonist PE or ISO significantly enhanced the BscF- and Tdap-stimulated modulation of CCL20 and CXCL10 but not PGE2 and PGF2α, suggesting that adrenergic modulation of pertussis antigen responses might be a new therapeutic strategy to improve the longevity of pertussis immunity. Stimulation of THP-1 cells with BscF alone initiated significant expression of CXCL10 and PGF2α but not when Tdap was used, suggesting that BscF might be an important pertussis antigen for next-generation pertussis vaccines or when combined with the current aP vaccine. Our data offer opportunities for designing new therapeutic approaches against pertussis infection.

Method of Microglial DNA-RNA Purification from a Single Brain of an Adult Mouse.

Microglia, the resident brain immune effectors cells, show dynamic activation level changes for most neuropsychiatric diseases, reflecting their complex regulatory function and potential as a therapeutic target. Emerging single-cell molecular biology studies are used to investigate the genetic modification of individual cells to better understand complex gene regulatory pathways. Although multiple protocols for microglia isolation from adult mice are available, it is always challenging to get sufficient purified microglia from a single brain for simultaneous DNA and RNA extraction for subsequent downstream analysis. Moreover, for data comparison between treated and untreated groups, standardized cell isolation techniques are essential to decrease variability. Here, we present a combined method of microglia isolation from a single adult mouse brain, using a magnetic bead-based column separation technique, and a column-based extraction of purified DNA-RNA from the isolated microglia for downstream application. Our current method provides step-by-step instructions accompanied by visual explanations of important steps for isolating DNA-RNA simultaneously from a highly purified microglia population.

Predicting Drug-Induced Liver Injury Using Machine Learning on a Diverse Set of Predictors.

A major challenge in drug development is safety and toxicity concerns due to drug side effects. One such side effect, drug-induced liver injury (DILI), is considered a primary factor in regulatory clearance. The Critical Assessment of Massive Data Analysis (CAMDA) 2020 CMap Drug Safety Challenge goal was to develop prediction models based on gene perturbation of six preselected cell-lines (CMap L1000), extended structural information (MOLD2), toxicity data (TOX21), and FDA reporting of adverse events (FAERS). Four types of DILI classes were targeted, including two clinically relevant scores and two control classifications, designed by the CAMDA organizers. The L1000 gene expression data had variable drug coverage across cell lines with only 247 out of 617 drugs in the study measured in all six cell types. We addressed this coverage issue by using Kru-Bor ranked merging to generate a singular drug expression signature across all six cell lines. These merged signatures were then narrowed down to the top and bottom 100, 250, 500, or 1,000 genes most perturbed by drug treatment. These signatures were subject to feature selection using Fisher's exact test to identify genes predictive of DILI status. Models based solely on expression signatures had varying results for clinical DILI subtypes with an accuracy ranging from 0.49 to 0.67 and Matthews Correlation Coefficient (MCC) values ranging from -0.03 to 0.1. Models built using FAERS, MOLD2, and TOX21 also had similar results in predicting clinical DILI scores with accuracy ranging from 0.56 to 0.67 with MCC scores ranging from 0.12 to 0.36. To incorporate these various data types with expression-based models, we utilized soft, hard, and weighted ensemble voting methods using the top three performing models for each DILI classification. These voting models achieved a balanced accuracy up to 0.54 and 0.60 for the clinically relevant DILI subtypes. Overall, from our experiment, traditional machine learning approaches may not be optimal as a classification method for the current data.

Alpha-Synuclein-induced DNA Methylation and Gene Expression in Microglia.

Synucleinopathy disorders are characterized by aggregates of α-synuclein (α-syn), which engage microglia to elicit a neuroinflammatory response. Here, we determined the gene expression and DNA methylation changes in microglia induced by aggregate α-syn. Transgenic murine Thy-1 promoter (mThy1)-Asyn mice overexpressing human α-syn are a model of synucleinopathy. Microglia from 3 and 13-month-old mice were used to isolate nucleic acids for methylated DNA and RNA-sequencing. α-Syn-regulated changes in gene expression and genomic methylation were determined and examined for functional enrichment followed by network analysis to further elucidate possible connections within the data. Microglial DNA isolated from our 3-month cohort had 5315 differentially methylated gene (DMG) changes, while RNA levels demonstrated a change in 119 differentially expressed genes (DEGs) between mThy1-Asyn mice and wild-type littermate controls. The 3-month DEGs and DMGs were highly associated with adhesion and migration signaling, suggesting a phenotypic transition from resting to active microglia. We observed 3742 DMGs and 3766 DEGs in 13-month mThy1-Asyn mice. These genes were often related to adhesion, migration, cell cycle, cellular metabolism, and immune response. Network analysis also showed increased cell mobility and inflammatory functions at 3 months, shifting to cell cycle, immune response, and metabolism changes at 13 months. We observed significant α-syn-induced methylation and gene expression changes in microglia. Our data suggest that α-syn overexpression initiates microglial activation leading to neuroinflammation and cellular metabolic stresses, which is associated with disease progression.

Correction to: Pathway crosstalk perturbation network modeling for identification of connectivity changes induced by diabetic neuropathy and pioglitazone.

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A Network Pharmacology Approach for the Identification of Common Mechanisms of Drug-Induced Peripheral Neuropathy.

Drug-induced peripheral neuropathy is a side effect of a variety of therapeutic agents that can affect therapeutic adherence and lead to regimen modifications, impacting patient quality of life. The molecular mechanisms involved in the development of this condition have yet to be completely described in the literature. We used a computational network pharmacology approach to explore the Connectivity Map, a large collection of transcriptional profiles from drug perturbation experiments to identify common genes affected by peripheral neuropathy-inducing drugs. Consensus profiles for 98 of these drugs were used to construct a drug-gene perturbation network. We identified 27 genes significantly associated with neuropathy-inducing drugs. These genes may have a potential role in the action of neuropathy-inducing drugs. Our results suggest that molecular mechanisms, including alterations in mitochondrial function, microtubule and cytoskeleton function, ion channels, transcriptional regulation including epigenetic mechanisms, signal transduction, and wound healing, may play a critical role in drug-induced peripheral neuropathy.

Pathway crosstalk perturbation network modeling for identification of connectivity changes induced by diabetic neuropathy and pioglitazone.

BACKGROUND: Aggregation of high-throughput biological data using pathway-based approaches is useful to associate molecular results to functional features related to the studied phenomenon. Biological pathways communicate with one another through the crosstalk phenomenon, forming large networks of interacting processes. RESULTS: In this work, we present the pathway crosstalk perturbation network (PXPN) model, a novel model used to analyze and integrate pathway perturbation data based on graph theory. With this model, the changes in activity and communication between pathways observed in transitions between physiological states are represented as networks. The model presented here is agnostic to the type of biological data and pathway definition used and can be implemented to analyze any type of high-throughput perturbation experiments. We present a case study in which we use our proposed model to analyze a gene expression dataset derived from experiments in a BKS-db/db mouse model of type 2 diabetes mellitus-associated neuropathy (DN) and the effects of the drug pioglitazone in this condition. The networks generated describe the profile of pathway perturbation involved in the transitions between the healthy and the pathological state and the pharmacologically treated pathology. We identify changes in the connectivity of perturbed pathways associated to each biological transition, such as rewiring between extracellular matrix, neuronal system, and G-protein coupled receptor signaling pathways. CONCLUSION: The PXPN model is a novel, flexible method used to integrate high-throughput data derived from perturbation experiments; it is agnostic to the type of data and enrichment function used, and it is applicable to a wide range of biological phenomena of interest.

Conserved Transcriptional Signatures in Human and Murine Diabetic Peripheral Neuropathy.

Diabetic peripheral neuropathy (DPN) is one of the most common complications of diabetes. In this study, we employed a systems biology approach to identify DPN-related transcriptional pathways conserved across human and various murine models. Eight microarray datasets on peripheral nerve samples from murine models of type 1 (streptozotocin-treated) and type 2 (db/db and ob/ob) diabetes of various ages and human subjects with non-progressive and progressive DPN were collected. Differentially expressed genes (DEGs) were identified between non-diabetic and diabetic samples in murine models, and non-progressive and progressive human samples using a unified analysis pipeline. A transcriptional network for each DEG set was constructed based on literature-derived gene-gene interaction information. Seven pairwise human-vs-murine comparisons using a network-comparison program resulted in shared sub-networks including 46 to 396 genes, which were further merged into a single network of 688 genes. Pathway and centrality analyses revealed highly connected genes and pathways including LXR/RXR activation, adipogenesis, glucocorticoid receptor signalling, and multiple cytokine and chemokine pathways. Our systems biology approach identified highly conserved pathways across human and murine models that are likely to play a role in DPN pathogenesis and provide new possible mechanism-based targets for DPN therapy.

Exploration of the Anti-Inflammatory Drug Space Through Network Pharmacology: Applications for Drug Repurposing.

The quintessential biological response to disease is inflammation. It is a driver and an important element in a wide range of pathological states. Pharmacological management of inflammation is therefore central in the clinical setting. Anti-inflammatory drugs modulate specific molecules involved in the inflammatory response; these drugs are traditionally classified as steroidal and non-steroidal drugs. However, the effects of these drugs are rarely limited to their canonical targets, affecting other molecules and altering biological functions with system-wide effects that can lead to the emergence of secondary therapeutic applications or adverse drug reactions (ADRs). In this study, relationships among anti-inflammatory drugs, functional pathways, and ADRs were explored through network models. We integrated structural drug information, experimental anti-inflammatory drug perturbation gene expression profiles obtained from the Connectivity Map and Library of Integrated Network-Based Cellular Signatures, functional pathways in the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome databases, as well as adverse reaction information from the U.S. Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS). The network models comprise nodes representing anti-inflammatory drugs, functional pathways, and adverse effects. We identified structural and gene perturbation similarities linking anti-inflammatory drugs. Functional pathways were connected to drugs by implementing Gene Set Enrichment Analysis (GSEA). Drugs and adverse effects were connected based on the proportional reporting ratio (PRR) of an adverse effect in response to a given drug. Through these network models, relationships among anti-inflammatory drugs, their functional effects at the pathway level, and their adverse effects were explored. These networks comprise 70 different anti-inflammatory drugs, 462 functional pathways, and 1,175 ADRs. Network-based properties, such as degree, clustering coefficient, and node strength, were used to identify new therapeutic applications within and beyond the anti-inflammatory context, as well as ADR risk for these drugs, helping to select better repurposing candidates. Based on these parameters, we identified naproxen, meloxicam, etodolac, tenoxicam, flufenamic acid, fenoprofen, and nabumetone as candidates for drug repurposing with lower ADR risk. This network-based analysis pipeline provides a novel way to explore the effects of drugs in a therapeutic space.

APP Regulates Microglial Phenotype in a Mouse Model of Alzheimer's Disease.

UNLABELLED: Prior work suggests that amyloid precursor protein (APP) can function as a proinflammatory receptor on immune cells, such as monocytes and microglia. Therefore, we hypothesized that APP serves this function in microglia during Alzheimer's disease. Although fibrillar amyloid ß (Aß)-stimulated cytokine secretion from both wild-type and APP knock-out (mAPP(-/-)) microglial cultures, oligomeric Aß was unable to stimulate increased secretion from mAPP(-/-) cells. This was consistent with an ability of oligomeric Aß to bind APP. Similarly, intracerebroventricular infusions of oligomeric Aß produced less microgliosis in mAPP(-/-) mice compared with wild-type mice. The mAPP(-/-) mice crossed to an APP/PS1 transgenic mouse line demonstrated reduced microgliosis and cytokine levels and improved memory compared with wild-type mice despite robust fibrillar Aß plaque deposition. These data define a novel function for microglial APP in regulating their ability to acquire a proinflammatory phenotype during disease. SIGNIFICANCE STATEMENT: A hallmark of Alzheimer's disease (AD) brains is the accumulation of amyloid ß (Aß) peptide within plaques robustly invested with reactive microglia. This supports the notion that Aß stimulation of microglial activation is one source of brain inflammatory changes during disease. Aß is a cleavage product of the ubiquitously expressed amyloid precursor protein (APP) and is able to self-associate into a wide variety of differently sized and structurally distinct multimers. In this study, we demonstrate both in vitro and in vivo that nonfibrillar, oligomeric forms of Aß are able to interact with the parent APP protein to stimulate microglial activation. This provides a mechanism by which metabolism of APP results in possible autocrine or paracrine Aß production to drive the microgliosis associated with AD brains.

Transcriptional networks of murine diabetic peripheral neuropathy and nephropathy: common and distinct gene expression patterns.

AIMS/HYPOTHESIS: Diabetic peripheral neuropathy (DPN) and diabetic nephropathy (DN) are two common microvascular complications of type 1 and type 2 diabetes mellitus that are associated with a high degree of morbidity. In this study, using a variety of systems biology approaches, our aim was to identify common and distinct mechanisms underlying the pathogenesis of these two complications. METHODS: Our previously published transcriptomic datasets of peripheral nerve and kidney tissue, derived from murine models of type 1 diabetes (streptozotocin-injected mice) and type 2 diabetes (BKS-db/db mice) and their respective controls, were collected and processed using a unified analysis pipeline so that comparisons could be made. In addition to looking at genes and pathways dysregulated in individual datasets, pairwise comparisons across diabetes type and tissue type were performed at both gene and transcriptional network levels to complete our proposed objective. RESULTS: Gene-level analysis identified exceptionally high levels of concordant gene expression in DN (94% of 2,433 genes), but not in DPN (54% of 1,558 genes), between type 1 diabetes and type 2 diabetes. These results suggest that common pathogenic mechanisms exist in DN across diabetes type, while in DPN the mechanisms are more distinct. When these dysregulated genes were examined at the transcriptional network level, we found that the Janus kinase (JAK)-signal transducer and activator of transcription (STAT) pathway was significantly dysregulated in both complications, irrespective of diabetes type. CONCLUSIONS/INTERPRETATION: Using a systems biology approach, our findings suggest that common pathogenic mechanisms exist in DN across diabetes type, while in DPN the mechanisms are more distinct. We also found that JAK-STAT signalling is commonly dysregulated among all datasets. Using such approaches, further investigation is warranted to determine whether the same changes are observed in patients with diabetic complications.