Integrative Analysis of DiseaseLand Omics Database for Disease Signatures and Treatments: A Bipolar Case Study

Genome-wide association study analysis of disease severity in Acne reveals novel biological insights

Acne vulgaris is a common skin disease that affects >85% of teenage young adults among which >8% develop severe lesions that leaves permanent scars. Genetic heritability studies of acne in twin cohorts have estimated that the heritability for acne is 80%. Previous genome-wide association studies (GWAS) have identified 50 genetic loci associated with increased risk of developing acne when compared to healthy individuals. However only a few studies have investigated genetic association with disease severity. GWAS of disease progression may provide a more effective approach to unveil potential disease modifying therapeutic targets. Here, we performed a multi-ethnic GWAS analysis to capture disease severity in acne patients by using individuals with normal acne as a control. Our cohort consists of a total of 2,956 participants, including 290 severe acne cases and 930 normal acne controls from FinnGen, and 522 cases and 1,214 controls from BioVU. We also performed mendelian randomization (MR), colocalization analyses and transcriptome-wide association study (TWAS) to identify putative causal genes. Lastly, we performed gene-set enrichment analysis using MAGMA to implicate biological pathways that drive disease severity in Acne. We identified two new loci associated with acne severity at the genome-wide significance level, six novel associated genes by MR, colocalization and TWAS analyses, including genes CDC7, SLC7A1, ADAM23, TTLL10, CDK20 and DNAJA4 , and 5 novel pathways by MAGMA analyses. Our study suggests that the etiologies of acne susceptibility and severity have limited overlap, with only 26% of known acne risk loci presenting nominal association with acne severity and none of the novel severity associated genes reported as associated with acne risk in previous GWAS.

Infections, Inflammation, and Psychiatric Illness: Review of Postmortem Evidence

While there is an abundance of epidemiological evidence implicating infectious agents in the etiology of severe mental illnesses, postmortem studies have not yet detected an increased incidence of microbial nucleic acid or proteins in the brains of people with mental illness. Nevertheless, abnormally expressed immune and inflammatory markers have consistently been found in the postmortem brain of patients with schizophrenia and mood disorders. Some of these abnormalities may be the result of an infection in utero or early in life that not only impacted the developing immune system but also the developing neurons of the brain. Some of the immune markers that are consistently found to be upregulated in schizophrenia implicate a possible viral infection and the blood brain barrier in the etiology and neuropathology of the disorder.

In silico analysis of metabolic effects of bipolar disorder on prefrontal cortex identified altered GABA, glutamate-glutamine cycle, energy metabolism and amino acid synthesis pathways

Bipolar disorder (BP) is a lifelong psychiatric condition, which often disrupts the daily life of the patients. It is characterized by unstable and periodic mood changes, which cause patients to display unusual shifts in mood, energy, activity levels, concentration and the ability to carry out day-to-day tasks. BP is a major psychiatric condition, and it is still undertreated. The causes and neural mechanisms of bipolar disorder are unclear, and diagnosis is still mostly based on psychiatric examination, furthermore the unstable character of the disorder makes diagnosis challenging. Identification of the molecular mechanisms underlying the disease may improve the diagnosis and treatment rates. Single nucleotide polymorphisms (SNP) and transcriptome profiles of patients were studied along with signalling pathways that are thought to be associated with bipolar disorder. Here, we present a computational approach that uses publicly available transcriptome data from bipolar disorder patients and healthy controls. Along with statistical analyses, data are integrated with a genome-scale metabolic model and protein-protein interaction network. Healthy individuals and bipolar disorder patients are compared based on their metabolic profiles. We hypothesize that energy metabolism alterations in bipolar disorder relate to perturbations in amino-acid metabolism and neuron-astrocyte exchange reactions. Due to changes in amino acid metabolism, neurotransmitters and their secretion from neurons and metabolic exchange pathways between neurons and astrocytes such as the glutamine-glutamate cycle are also altered. Changes in negatively charged (-1) KIV and KMV molecules are also observed, and it indicates that charge balance in the brain is highly altered in bipolar disorder. Due to this fact, we also hypothesize that positively charged lithium ions may stabilize the disturbed charge balance in neurons in addition to its effects on neurotransmission. To the best of our knowledge, our approach is unique as it is the first study using genome-scale metabolic models in neuropsychiatric research.

HOXB9 Overexpression Promotes Colorectal Cancer Progression and Is Associated with Worse Survival in Liver Resection Patients for Colorectal Liver Metastases

As is known, HOXB9 is an important factor affecting disease progression and overall survival (OS) in cancer. However, its role in colorectal cancer (CRC) remains unclear. We aimed to explore the role of HOXB9 in CRC progression and its association with OS in colorectal liver metastases (CRLM). We analysed differential HOXB9 expression in CRC using the Tissue Cancer Genome Atlas database (TCGA). We modulated HOXB9 expression in vitro to assess its impact on cell proliferation and epithelial-mesenchymal transition (EMT). Lastly, we explored the association of HOXB9 protein expression with OS, using an institutional patient cohort (n = 110) who underwent liver resection for CRLM. Furthermore, HOXB9 was upregulated in TCGA-CRC (n = 644) vs. normal tissue (n = 51) and its expression levels were elevated in KRAS mutations (p < 0.0001). In vitro, HOXB9 overexpression increased cell proliferation (p < 0.001) and upregulated the mRNA expression of EMT markers (VIM, CDH2, ZEB1, ZEB2, SNAI1 and SNAI2) while downregulated CDH1, (p < 0.05 for all comparisons). Conversely, HOXB9 silencing disrupted cell growth (p < 0.0001). High HOXB9 expression (HR = 3.82, 95% CI: 1.59-9.2, p = 0.003) was independently associated with worse OS in CRLM-HOXB9-expressing patients after liver resection. In conclusion, HOXB9 may be associated with worse OS in CRLM and may promote CRC progression, whereas HOXB9 silencing may inhibit CRC growth.

Brain-immune interactions in neuropsychiatric disorders: Lessons from transcriptome studies for molecular targeting

Understanding the pathophysiological mechanisms of neuropsychiatric disorders has been a challenging quest for neurobiologists. Recent years have witnessed enormous technological advances in the field of neuroimmunology, blurring boundaries between the central nervous system and the periphery. Consequently, the discipline has expanded to cover interactions between the nervous and immune systems in health and diseases. The complex interplay between the peripheral and central immune pathways in neuropsychiatric disorders has recently been documented in various studies, but the genetic determinants remain elusive. Recent transcriptome studies have identified dysregulated genes involved in peripheral immune cell activation, blood-brain barrier integrity, glial cell activation, and synaptic plasticity in major depressive disorder, bipolar disorder, autism spectrum disorder, and schizophrenia. Herein, the key transcriptomic techniques applied in investigating differentially expressed genes and pathways responsible for altered brain-immune interactions in neuropsychiatric disorders are discussed. The application of transcriptomics that can aid in identifying molecular targets in various neuropsychiatric disorders is highlighted.

Identification of novel cell glycolysis related gene signature predicting survival in patients with breast cancer

One of the most frequently identified tumors and a contributing cause of death in women is breast cancer (BC). Many biomarkers associated with survival and prognosis were identified in previous studies through database mining. Nevertheless, the predictive capabilities of single-gene biomarkers are not accurate enough. Genetic signatures can be an enhanced prediction method. This research analyzed data from The Cancer Genome Atlas (TCGA) for the detection of a new genetic signature to predict BC prognosis. Profiling of mRNA expression was carried out in samples of patients with TCGA BC (n = 1222). Gene set enrichment research has been undertaken to classify gene sets that vary greatly between BC tissues and normal tissues. Cox models for additive hazards regression were used to classify genes that were strongly linked to overall survival. A subsequent Cox regression multivariate analysis was used to construct a predictive risk parameter model. Kaplan–Meier survival predictions and log-rank validation have been used to verify the value of risk prediction parameters. Seven genes (PGK1, CACNA1H, IL13RA1, SDC1, AK3, NUP43, SDC3) correlated with glycolysis were shown to be strongly linked to overall survival. Depending on the 7-gene-signature, 1222 BC patients were classified into subgroups of high/low-risk. Certain variables have not impaired the prognostic potential of the seven-gene signature. A seven-gene signature correlated with cellular glycolysis was developed to predict the survival of BC patients. The results include insight into cellular glycolysis mechanisms and the detection of patients with poor BC prognosis.