Changes in gene expression induced by Micro-Immunotherapy

Background: Metabolic syndrome (MS) is a metabolic disorder associated with obesity, type-II diabetes, and “low grade inflammation”, with the concomitant increased risk of cardiovascular events. As a chronic inflammatory process, MS results in a dysregulation of the cytokine profile. 2L®INFLAM, a Micro-immunotherapy (MI) medication formulated with highly diluted cytokines, is currently prescribed in Belgium for inflammatory diseases and potentially may be helpful for MS patients. Aims: To investigate the impact of 2L®INFLAM on selected gene expression markers (mRNA) in patients suffering from MS, in addition to biological and clinical parameters. Methodology: Four well characterized MS adult patients with stabilized body-weight were advised to take one capsule of 2L®INFLAM per day (by sublingual-oral route) for 6 months (composition in table 1). Concomitantly to biological and clinical examination, genes expression status was assessed by a DNA microarray technology (Oxygen™) comprising 200 genes involved mainly in oxidative stress and inflammation. Whole blood collection was performed before and after treatment (3-6 months) and mRNA levels measured. Gene expression was classified in 3 series (normally expressed, up or down-regulated) and genes related to diabetes predisposition were scored by using a proprietary Diascore (Probiox). Results: Before MI medication, a significant percentage of dysregulated genes (median: 16.3%) as well as a positive Diascore (median: 1.6) were noticed. Impressive correction of dysregulated genes (reaching 90% for one patient) was observed after 3 months of treatment (median: 2.3%) in addition to an improvement of Diascore in 3 MS patients out of 4 (median: 0.5). During the same period, both clinical and biological parameters remained unchanged. Conclusions: MS patients showing a high level of gene dysregulation efficiently normalized after 3 months of 2L®INFLAM (64%-90%), suggesting a biological regulatory effect of MI and a potential benefit of this medication for diabetic patients. Up and down-deregulated gene profiles were specific for each patient and not related to cytokine components of the formula. These preliminary data support the “domino effect” of MI sequential formula to restore in depth the immune homeostasis. DNA microarray technology may represent a promising tool for new provings as well as for biochemical comprehension of the “in vivo” effectiveness of highly diluted immune messengers. Table 1: 2L®INFLAM composition Compounds Dilutions Interleukin-1 (IL-1): 17 CH* Interleukin-1 Ra (IL-1 Ra): 3 CH Interleukin-2 (IL-2): 9 CH Interleukin-4 (IL-4): 7 CH Interleukin-6 (IL-6): 9 CH Interleukin-8 (IL-8): 9 CH Interleukin-10 (IL-10): 4 CH Interleukin-13 (IL-13): 9 CH Ciliary Neuro Trophic Factor (CNTF): 17 CH Leukemia Inhibitory Factor (LIF): 17 CH Oncostatine M (OSM): 9 CH Platelet Derived Growth Factor (PDGF): 5 CH Prostaglandine E2 (PgE2): 200 K** Rantes (Rantes): 17 CH Transforming Growth Factor beta(TGFβ): 5 CH Tumor Necrosis Factor α (TNFα): 17 CH SNA INFLAMa-01 18 C SNA INFLAMb-01 18 CH * CH: Centesimal Hahnemannian (1/100) ** K: Centesimal Korsakovian (1/100)

Mapping Molecular Recognition of β1,3-1,4-Glucans by a Surface Glycan-Binding Protein from the Human Gut Symbiont Bacteroides ovatus

With the knowledge of bacterial gene systems encoding proteins that target dietary carbohydrates as a source of nutrients and their importance for human health, major efforts are being made to understand carbohydrate recognition by various commensal bacteria. Here, we describe an integrative strategy that combines carbohydrate microarray technology with structural studies to further elucidate the molecular determinants of carbohydrate recognition by BoSGBP MLG -A, a key protein expressed at the surface of Bacteroides ovatus for utilization of mixed-linkage β1,3-1,4-glucans.

Determining the Maximum States of the Ensemble Distribution of Boolean Networks

Inference of the gene regulation mechanism from gene expression patterns has become increasingly popular, in recent years, with the advent of microarray technology. Obtaining the states of genes and their regulatory relationships would greatly enable the scientists to investigate and understand the mechanisms of the diseases. However, it is still a big challenge to determine relationships from several thousands of genes. Here, we simplify the above complex gene state determination problem as an inference of the distribution of the ensemble Boolean networks (BNs). In order to investigate and calculate the distribution of the BNs’ states, we first compute the probabilities of the different BNs’ states and obtain the number of states Ω. Then, we find the maximum possible distribution of the number of the BNs’ states and calculate the fluctuation of the distribution. Finally, two representative experiments are conducted, and the efficiency of the obtained results is verified. The proposed algorithm is conceptually concise and easily applicable to many other realistic models; furthermore, it is highly extensible for various situations.

Human m6A-mRNA and lncRNA epitranscriptomic microarray reveal function of RNA methylation in hemoglobin H-constant spring disease

The thalassemia of Hemoglobin H-Constant Spring disease (HbH-CS) is the most common type of Thalassemia in non-transfusion thalassemia. Interestingly, the clinical manifestations of the same genotype of thalassemia can be vastly different, likely due to epigenetic regulation. Here, we used microarray technology to reveal the epigenetic regulation of m6A in modifiable diseases and demonstrated a role of BCL2A1 in disease regulation. In this study, we revealed that methylating enzyme writers including METTL16, WTAP, CBLL1, RBM15B, and ZC3H13 displayed low expression and the demethylating enzyme ALKBH5, along with reader proteins including IGF2BP2 and YTHDF3 exhibited high expression. In addition, BCL2A1 was hypo-methylated and showed low expression. We also revealed that the BCL2A1 methylation level and IGF2BP2 expression were negatively correlated. Additionally, the mRNAs expression between ALKBH5 and IGF2BP2 were positively correlated. In HbH-CS, most genes were hypo-methylated. This included BCL2A1, which may play an important role in the process of red blood cell differentiation and development of HbH-CS. Moreover, the mRNA-M6A methylation status may be regulated by the demethylating enzyme ALKBH5 via IGF2BP2.

Dissection The Practical Soybean Breeding Pipeline By Developing High Throughput Functional Array ZDX1

Microarray technology facilitates rapid, accurate, and economical genotyping. Here, using resequencing data from 2,214 representative soybean accessions, we developed the ZDX1 high-throughput functional soybean array, containing 158,959 SNPs, covering 90.92% of soybean genes and sites related to agronomically important traits. We genotyped 817 soybean accessions using ZDX1, including parental lines, non-parental lines, and progeny from a practical breeding pipeline. It was clarified that non-parental lines had highest genetic diversity, and 235 SNPs were identified to be fixed in the progeny. The unknown soybean cyst nematode-resistant and early maturity accessions were identified by using allele combinations. Notably, we found that breeding index was a good indicator for progeny selection, in which the superior progeny were derived from the crossing more distantly related parents with at least one parent having a higher breeding index. Based on this rule, two varieties were directionally developed. Meanwhile, redundant parents were screened out and potential combinations were formulated. GBLUP analysis displayed that the markers in genic regions had priority to be higher accuracy on predicting four agronomic traits compared with either whole genome or intergenic markers. Then we used progeny to expand the training population to increase the prediction accuracy of breeding selection by 32.1%. Collectively, our work provided a versatile array for high accuracy selecting and predicting both parents and progeny that can greatly accelerate soybean breeding.

Applications of Protein Microarrays in Biomarker Discovery for Autoimmune Diseases

Dysregulated autoantibodies and cytokines were deemed to provide important cues for potential illnesses, such as various carcinomas and autoimmune diseases. Increasing biotechnological approaches have been applied to screen and identify the specific alterations of these biomolecules as distinctive biomarkers in diseases, especially autoimmune diseases. As a versatile and robust platform, protein microarray technology allows researchers to easily profile dysregulated autoantibodies and cytokines associated with autoimmune diseases using various biological specimens, mainly serum samples. Here, we summarize the applications of protein microarrays in biomarker discovery for autoimmune diseases. In addition, the key issues in the process of using this approach are presented for improving future studies.

A Preliminary Investigation on Plasma Cell Adhesion Molecules Levels by Protein Microarray Technology in Major Depressive Disorder

Objectives: Major depressive disorder (MDD) is a serious mental disorder, and there is a great difficulty to diagnose and treat. Hitherto, relatively few studies have explored the correlation between the levels of plasma cell adhesion molecules and MDD.Methods: Thirty outpatients with acute episodes of MDD in Shanghai Mental Health Center and 34 healthy volunteers from the community were recruited as subjects. Protein microarray technology was applied to compared the differences in plasma levels of 17 kinds of adhesion molecular proteins between the two groups. Meanwhile, the diagnostic value of different proteins in depression was discussed by using the receiver operating characteristic curve.Results: The levels of Carcinoembryonic Antigen Related Cell Adhesion Molecule-1(CEACAM-1) and Neural Cell Adhesion Molecule (NrCAM) in MDD patients were significantly higher than those in healthy controls (P < 0.05). The area under ROC curve of CEACAM-1 combined with NrCAM was 0.723, with the sensitivity 0.800 and the specificity 0.676.Conclusion: The plasma levels of CEACAM-1 and NrCAM were significantly up-regulated in MDD, and their combined application was of potential diagnostic value, deserving to expand the sample size for further verification.

Understanding microbial infections using microarray technology

Human microbial infections are symbiotic processes between pathogens and humans that often lead to human disease and death. Microbial infections involve the attachment, growth, and survival of microorganisms on human skin, inside the body, or inside specific cells. Microbial infections can be localized to one body region or migrate to secondary body locations utilizing various transport mechanisms. Understanding host-pathogen interactions related to the expression of essential genes during and after infection can lead to valuable information for biologists and clinicians. Microarray technologies allow researchers to perform genomic characterization experiments rapidly and efficiently. Microarray experiments support the resolution of underlying molecular events that play a role in normal and aberrant physiologic activities in living systems. Microarray technology, coupled with bioinformatics analysis, generates comprehensive insights into relevant genes, proteins, and protein-protein interactions. This review article explores recent microarray research studies from select protozoan and bacterial pathogens to illustrate how researchers utilize microarray technology to examine microbial infection aspects. Microarray studies of pathogen and host genomes at various stages of the infection process will generate a more precise understanding of pathogenic life cycles and pathogen survival strategies. Detailed knowledge of the genes involved in the microbial infection process will lead to discovering disease biomarkers and potent therapeutic solutions.

Paper review: An overview on microarray technologies

Bioinformatics is a branch in Statistics which is still unpopular among statistics students in Indonesia. Bioinformatics research used microarray technology, because data is available through to microarray experiment on tissue sample at hand. Microarray technology has been widely used to provide data for bioinformatics research, since it was first introduced in late 1990, particularly in life sciences and biotechnology research. The emergence and development of the Covid-19 disease further reinforces the need to understand bioinformatics and its technology. There are two of the most advance platforms in microarray technology, namely, are the Affymetrix GeneChip and Illumina BeadArray. This paper aims to give an overview about microarray technology on the two platforms and the advantage of using them on bioinformatics research.

Estimated seroprevalence of SARS-CoV-2 antibodies among adults in Orange County, California

Clinic-based estimates of SARS-CoV-2 may considerably underestimate the total number of infections. Access to testing in the US has been heterogeneous and symptoms vary widely in infected persons. Public health surveillance efforts and metrics are therefore hampered by underreporting. We set out to provide a minimally biased estimate of SARS-CoV-2 seroprevalence among adults for a large and diverse county (Orange County, CA, population 3.2 million). We implemented a surveillance study that minimizes response bias by recruiting adults to answer a survey without knowledge of later being offered SARS-CoV-2 test. Several methodologies were used to retrieve a population-representative sample. Participants (n = 2979) visited one of 11 drive-thru test sites from July 10th to August 16th, 2020 (or received an in-home visit) to provide a finger pin-prick sample. We applied a robust SARS-CoV-2 Antigen Microarray technology, which has superior measurement validity relative to FDA-approved tests. Participants include a broad age, gender, racial/ethnic, and income representation. Adjusted seroprevalence of SARS-CoV-2 infection was 11.5% (95% CI: 10.5–12.4%). Formal bias analyses produced similar results. Prevalence was elevated among Hispanics (vs. other non-Hispanic: prevalence ratio [PR] = 1.47, 95% CI 1.22–1.78) and household income < $50,000 (vs. > $100,000: PR = 1.42, 95% CI: 1.14 to 1.79). Results from a diverse population using a highly specific and sensitive microarray indicate a SARS-CoV-2 seroprevalence of ~ 12 percent. This population-based seroprevalence is seven-fold greater than that using official County statistics. In this region, SARS-CoV-2 also disproportionately affects Hispanic and low-income adults.