Identification of proliferative diabetic retinopathy-associated genes on the protein–protein interaction network by using heat diffusion algorithm

Serum α-Klotho and fibroblast growth factor 23 levels are not associated with non-proliferative diabetic retinopathy in type 1 diabetes mellitus

Diabetic retinopathy is a commonly observed cause of blindness and is a common problem in individuals with diabetes. Recent investigations have showed the capability of serum α-Klotho and FGF 23 in mitigating the effects of diabetic retinopathy. This study aimed to discover the correlation between FGF 23, α-Klotho, and diabetic retinopathy in type 1 diabetics. This case-control study included 63 diabetic patients and 66 healthy controls. Following an overnight duration of fasting, morning blood samples were taken from both the patient and the control groups. The serum concentrations of α-Klotho and FGF 23 were quantified. An experienced ophthalmologist inspected the retinopathy. All participants in this study have moderate non-proliferative retinopathy. A p value under 0.05 was considered statistically significant. The mean α-Klotho level for retinopathic diabetic patients was 501.7 ± 172.2 pg/mL and 579.6 ± 312.1 pg/mL for non-retinopathic diabetic patients. In comparison, α-Klotho level of the control group was 523.2 ± 265.4 pg/mL (p = 0.531). The mean of FGF 23 level did not demonstrate a significant difference (p = 0.259). The mean FGF 23 level were 75.7 ± 14.0 pg/mL, 74.0 ± 14.8 pg/mL and 79.3 ± 14.4 pg/mL in groups, respectively. In conclusion, there was no significant difference in FGF 23 and α-Klotho levels between type 1 diabetics with and without retinopathy when compared to the control group.

Identification of immune associated potential molecular targets in proliferative diabetic retinopathy

BACKGROUND

Diabetic retinopathy (DR) is one of the most common microvascular complications of diabetes and causes of blindness in developed countries. Our study was designed to identify immune-related genes involved in the progression of proliferative diabetic retinopathy (PDR).

METHODS

The "GSE102485" dataset of neovascular membrane samples (NVMs) from type 1 and 2 diabetes mellitus patients was downloaded from the Gene Expression Omnibus database. Functional enrichment analyses, protein-protein interaction network (PPI) construction, and module analysis of immune pathways in NVMs and controls were conducted via Gene Set Enrichment Analysis and Metascape.

RESULTS

The significantly upregulated hallmark gene sets in DR2 and DR1 groups were involved in five immune pathways. Only CCR4, CXCR6, C3AR1, LPAR1, C5AR1, and P2RY14 were not previously reported in the context of PDR molecular pathophysiology. Except for P2RY14, all of the above were upregulated in retinal samples from experimental diabetes mouse models and human retina microvascular endothelial cells (HRMECs) treated with high glucose (HG) by quantitative Real Time Polymerase Chain Reaction (qRT-PCR).

CONCLUSION

The genes identified herein provide insight into immune-related differential gene expression during DR progression.

Elucidation of endothelial progenitor cell dysfunction in diabetes by RNA sequencing and constructing lncRNA-miRNA-mRNA competing endogenous RNA network

With the rapid increase in the incidence of diabetes, non-healing diabetic wounds have posed a huge challenge to public health. Endothelial progenitor cell (EPC) has been widely reported to promote wound repairing, while its number and function were suppressed in diabetes. However, the specific mechanisms and competing endogenous RNA (ceRNA) network of EPCs in diabetes remain largely unknown. Thus, the transcriptome analyses were carried in the present study to clarify the mechanism underlying EPCs dysfunction in diabetes. EPCs were successfully isolated from rats. Compared to the control, diabetic rat-derived EPCs displayed impaired proliferation, migration, and tube formation ability. The differentially expressed (DE) RNAs were successfully identified by RNA sequencing in the control and diabetic groups. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses indicated that DE mRNAs were significantly enriched in terms and pathways involved in the functions of EPCs and wound healing. Protein-protein interaction networks revealed critical DE mRNAs in the above groups. Moreover, the whole lncRNA-miRNA-mRNA ceRNA network was constructed, in which 9 lncRNAs, 9 mRNAs, and 5 miRNAs were further validated by quantitative real-time polymerase chain reaction. Rno-miR-10b-5p and Tgfb2 were identified as key regulators of EPCs dysfunction in diabetes. The present research provided novel insight into the underlying mechanism of EPCs dysfunction in diabetes and prompted potential targets to restore the impaired functions, thus accelerating diabetic wound healing. KEY MESSAGES: • Compared to the control, diabetic rat-derived EPCs displayed impaired proliferation, migration, and tube formation ability. • The DE RNAs were successfully identified by RNA sequencing in the control and diabetic groups and analyzed by DE, GO, and KEGG analysis. • PPI and lncRNA-miRNA-mRNA ceRNA networks were constructed. • 9 lncRNAs, 9 mRNAs, and 5 miRNAs were further validated by qRT-PCR. • Rno-miR-10b-5p and Tgfb2 were identified as key regulators of EPCs dysfunction in diabetes.

Evaluating the clinical utility of measuring levels of factor H and the related proteins

After years of disappointing clinical results, the tide has finally changed and complement targeted-therapies have become a validated and accepted treatment option for several diseases. These accomplishments have revitalized the field and brought renewed attention to the prospects that complement therapeutics can offer. Streamlining diagnostics and therapeutics is imperative in this new era of clinical use of complement therapeutics. However, the incredible success in therapeutics has not been accompanied by the development of novel standardized tools for complement testing. Complement biomarkers can assist in the risk assessment and diagnosis of diseases as well as the prediction of disease progression and treatment response. Recently, a group of complement proteins has been suggested to be highly relevant in various complement-associated disorders, namely the human factor H (FH) protein family. This family of closely related proteins consists of FH, FH-like protein 1, and five factor H-related proteins, and they have been linked to eye, kidney, infectious, vascular, and autoimmune diseases as well as cancer. The goal of this review is to provide a comprehensive overview of the available data on circulating levels of FH and its related proteins in different pathologies. In addition, we examined the current literature to determine the clinical utility of measuring levels of the FH protein family in health and disease. Finally, we discuss future steps that are needed to make their clinical translation a reality.

Immune-Related Protein Interaction Network in Severe COVID-19 Patients toward the Identification of Key Proteins and Drug Repurposing

Coronavirus disease 2019 (COVID-19) is still an active global public health issue. Although vaccines and therapeutic options are available, some patients experience severe conditions and need critical care support. Hence, identifying key genes or proteins involved in immune-related severe COVID-19 is necessary to find or develop the targeted therapies. This study proposed a novel construction of an immune-related protein interaction network (IPIN) in severe cases with the use of a network diffusion technique on a human interactome network and transcriptomic data. Enrichment analysis revealed that the IPIN was mainly associated with antiviral, innate immune, apoptosis, cell division, and cell cycle regulation signaling pathways. Twenty-three proteins were identified as key proteins to find associated drugs. Finally, poly (I:C), mitomycin C, decitabine, gemcitabine, hydroxyurea, tamoxifen, and curcumin were the potential drugs interacting with the key proteins to heal severe COVID-19. In conclusion, IPIN can be a good representative network for the immune system that integrates the protein interaction network and transcriptomic data. Thus, the key proteins and target drugs in IPIN help to find a new treatment with the use of existing drugs to treat the disease apart from vaccination and conventional antiviral therapy.

Systems biology of angiogenesis signaling: Computational models and omics

Angiogenesis is a highly regulated multiscale process that involves a plethora of cells, their cellular signal transduction, activation, proliferation, differentiation, as well as their intercellular communication. The coordinated execution and integration of such complex signaling programs is critical for physiological angiogenesis to take place in normal growth, development, exercise, and wound healing, while its dysregulation is critically linked to many major human diseases such as cancer, cardiovascular diseases, and ocular disorders; it is also crucial in regenerative medicine. Although huge efforts have been devoted to drug development for these diseases by investigation of angiogenesis‐targeted therapies, only a few therapeutics and targets have proved effective in humans due to the innate multiscale complexity and nonlinearity in the process of angiogenic signaling. As a promising approach that can help better address this challenge, systems biology modeling allows the integration of knowledge across studies and scales and provides a powerful means to mechanistically elucidate and connect the individual molecular and cellular signaling components that function in concert to regulate angiogenesis. In this review, we summarize and discuss how systems biology modeling studies, at the pathway‐, cell‐, tissue‐, and whole body‐levels, have advanced our understanding of signaling in angiogenesis and thereby delivered new translational insights for human diseases.

This article is categorized under:

Cardiovascular Diseases > Computational Models

Cancer > Computational Models

Amelioration of diabetic retinopathy in db/db mice by treatment with different proportional three active ingredients from Tibetan medicine Berberis dictyophylla F

ETHNOPHARMACOLOGICAL RELEVANCE

Berberis dictyophylla F., a famous Tibetan medicine, has been used to prevent and treat diabetic retinopathy (DR) for thousands of years in clinic. However, its underlying mechanisms remain unclear.

AIM OF THE STUDY

The present study was designed to probe the synergistic protection and involved mechanisms of berberine, magnoflorine and berbamine from Berberis dictyophylla F. on the spontaneous retinal damage of db/db mice.

MATERIALS AND METHODS

The 14-week spontaneous model of DR in db/db mice were randomly divided into eight groups: model group, calcium dobesilate (CaDob, 0.23 g/kg) group and groups 1-6 (different proportional three active ingredients from Berberis dictyophylla F.). All mice were intragastrically administrated for a continuous 12 weeks. Body weight and fasting blood glucose (FBG) were recorded and measured. Hematoxylin-eosin and periodic acid-Schiff (PAS) stainings were employed to evaluate the pathological changes and abnormal angiogenesis of the retina. ELISA was performed to assess the levels of IL-6, HIF-1α and VEGF in the serum. Immunofluorescent staining was applied to detect the protein levels of CD31, VEGF, p-p38, p-JNK, p-ERK and NF-κB in retina. In addition, mRNA expression levels of VEGF, Bax and Bcl-2 in the retina were monitored by qRT-PCR analysis.

RESULTS

Treatment with different proportional three active ingredients exerted no significant effect on the weight, but decreased the FBG, increased the number of retinal ganglionic cells and restored internal limiting membrane. The results of PAS staining demonstrated that the drug treatment decreased the ratio of endothelial cells to pericytes while thinned the basal membrane of retinal vessels. Moreover, these different proportional active ingredients can markedly downregulate the protein levels of retinal CD31 and VEGF, and serum HIF-1α and VEGF. The gene expression of retinal VEGF was also suppressed. The levels of retinal p-p38, p-JNK and p-ERK proteins were decreased by drug treatment. Finally, drug treatment reversed the proinflammatory factors of retinal NF-κB and serum IL-6, and proapoptotic Bax gene expression, while increased antiapoptotic Bcl-2 gene expression.

CONCLUSIONS

These results indicated that DR in db/db mice can be ameliorated by treatment with different proportional three active ingredients from Berberis dictyophylla F. The potential vascular protection mechanisms may be involved in inhibiting the phosphorylation of the MAPK signaling pathway, thus decreasing inflammatory and apoptotic events.

A heat diffusion multilayer network approach for the identification of functional biomarkers in rumen methane emissions

A better understanding of rumen microbial interactions is crucial for the study of rumen metabolism and methane emissions. Metagenomics-based methods can explore the relationship between microbial genes and metabolites to clarify the effect of microbial function on the host phenotype. This study investigated the rumen microbial mechanisms of methane metabolism in cattle by combining metagenomic data and network-based methods. Based on the relative abundance of 1461 rumen microbial genes and the main volatile fatty acids (VFAs), a multilayer heterogeneous network was constructed, and the functional modules associated with metabolite-microbial genes were obtained by heat diffusion algorithm. The PLS model by integrating data from VFAs and microbial genes explained 72.98% variation of methane emissions. Compared with single-layer networks, more previously reported biomarkers of methane prediction can be captured by the multilayer network. More biomarkers with the rank of top 20 topological centralities were from the PLS models of diffusion subsets. The heat diffusion algorithm is different from the strategy used by the microbial metabolic system to understand methane phenotype. It inferred 24 novel biomarkers that were preferentially affected by changes in specific VFAs. Results showed that the heat diffusion multilayer network approach improved the understanding of the microbial patterns of VFAs affecting methane emissions which represented by the functional microbial genes.