Post-translational modifications and their applications in eye research (Review)

Biology-inspired graph neural network encodes reactome and reveals biochemical reactions of disease

Functional heterogeneity of healthy human tissues complicates interpretation of molecular studies, impeding precision therapeutic target identification and treatment. Considering this, we generated a graph neural network with Reactome-based architecture and trained it using 9,115 samples from Genotype-Tissue Expression (GTEx). Our graph neural network (GNN) achieves adjusted Rand index (ARI) = 0.7909, while a Resnet18 control model achieves ARI = 0.7781, on 370 held-out healthy human tissue samples from The Cancer Genome Atlas (TCGA), despite the Resnet18 using over 600 times the parameters. Our GNN also succeeds in separating 83 healthy skin samples from 95 lesional psoriasis samples, revealing that upregulation of 26S- and NUB1-mediated degradation of NEDD8, UBD, and their conjugates is central to the largest perturbed reaction network component in psoriasis. We show that our results are not discoverable using traditional differential expression and hypergeometric pathway enrichment analyses yet are supported by separate human multi-omics and small-molecule mouse studies, suggesting future molecular disease studies may benefit from similar GNN analytical approaches.

Mass spectrometry-based retina proteomics

A subfield of neuroproteomics, retina proteomics has experienced a transformative growth since its inception due to methodological advances in enabling chemical, biochemical, and molecular biology techniques. This review focuses on mass spectrometry's contributions to facilitate mammalian and avian retina proteomics to catalog and quantify retinal protein expressions, determine their posttranslational modifications, as well as its applications to study the proteome of the retina in the context of biology, health and diseases, and therapy developments.

Applications of Genomics and Transcriptomics in Precision Medicine for Myopia Control or Prevention

Myopia is a globally emerging concern accompanied by multiple medical and socio-economic burdens with no well-established causal treatment to control thus far. The study of the genomics and transcriptomics of myopia treatment is crucial to delineate disease pathways and provide valuable insights for the design of precise and effective therapeutics. A strong understanding of altered biochemical pathways and underlying pathogenesis leading to myopia may facilitate early diagnosis and treatment of myopia, ultimately leading to the development of more effective preventive and therapeutic measures. In this review, we summarize current data about the genomics and transcriptomics of myopia in human and animal models. We also discuss the potential applicability of these findings to precision medicine for myopia treatment.

Lysine Malonylation and Its Links to Metabolism and Diseases

Malonylation is a recently identified post-translational modification with malonyl-coenzyme A as the donor. It conserved both in prokaryotes and eukaryotes. Recent advances in the identification and quantification of lysine malonylation by bioinformatic analysis have improved our understanding of its role in the regulation of protein activity, interaction, and localization and have elucidated its involvement in many biological processes. Malonylation has been linked to diverse physiological processes, including metabolic disorders, inflammation, and immune regulation. This review discusses malonylation in theory, describes the underlying mechanism, and summarizes the recent progress in malonylation research. The latest findings point to novel functions of malonylation and highlight the mechanisms by which malonylation regulates a variety of cellular processes. Our review also marks the association between lysine malonylation, the enzymes involved, and various diseases, and discusses promising diagnostic and therapeutic biomolecular targets for future clinical applications.

Text Mining and Machine Learning Protocol for Extracting Human-Related Protein Phosphorylation Information from PubMed

In the modern health care research, protein phosphorylation has gained an enormous attention from the researchers across the globe and requires automated approaches to process a huge volume of data on proteins and their modifications at the cellular level. The data generated at the cellular level is unique as well as arbitrary, and an accumulation of massive volume of information is inevitable. Biological research has revealed that a huge array of cellular communication aided by protein phosphorylation and other similar mechanisms imply different and diverse meanings. This led to a collection of huge volume of data to understand the biological functions of human evolution, especially for combating diseases in a better way. Text mining, an automated approach to mine the information from an unstructured data, finds its application in extracting protein phosphorylation information from the biomedical literature databases such as PubMed. This chapter outlines a recent text mining protocol that applies natural language parsing (NLP) for named entity recognition and text processing, and support vector machines (SVM), a machine learning algorithm for classifying the processed text related human protein phosphorylation. We discuss on evaluating the text mining system which is the outcome of the protocol on three corpora, namely, human Protein Phosphorylation (hPP) corpus, Integrated Protein Literature Information and Knowledge corpus (iProLink), and Phosphorylation Literature corpus (PLC). We also present a basic understanding on the chemistry and biology that drive the protein phosphorylation process in a human body. We believe that this basic understanding will be useful to advance the existing text mining systems for extracting protein phosphorylation information from PubMed.

Non-coding RNAs and related molecules associated with form-deprivation myopia in mice

The role of miRNAs and its regulatory mechanism in myopia are indeterminate. Our study aimed to investigate potential myopia‐associated non‐coding RNAs and related molecules by performing a comprehensive bioinformatic analysis of miRNA expression profile of mice with form‐deprivation myopia (FDM). Differentially expressed miRNAs in two raw microarray data sets (GSE58124 and GSE84220) from Gene Expression Omnibus (GEO) database were comprehensively analysed using GEO2R. Target genes were predicted using miRDB and enriched with Metascape online tool. Protein‐protein interaction (PPI) networks were constructed utilizing STRING and Cytoscape. Significant differentially expressed miRNAs were validated by real‐time polymerase chain reaction (qRT‐PCR) using RNA extracted from monocular FDM ocular tissues. As result, we identified three upregulated miRNAs (mmu‐miR‐1936, mmu‐miR‐338‐5p, and mmu‐miR‐673‐3p) significantly associated with myopia in the two microarray data sets (p < 0.05 and |Log (Fold Change) |>1). GO functional analysis suggested these three miRNAs were targeted in genes mostly enriched in morphogenesis and developmental growth of retinal tissues. Enrichment analysis revealed top eight transcription factors, including PAX6 and Smad3, related to myopia. Ten hub genes, including Rbx1, Fbxl3, Fbxo27, Fbxl7, Fbxo4, Cul3, Cul2, Klhl5, Fbxl16 and Klhl42, associated with ubiquitin conjugation were identified. qRT‐PCR confirmed the increased expression of mmu‐miR‐1936 and mmu‐miR‐338‐5p (p < 0.05), but no statistical difference was observed in mmu‐miR‐673‐3p expression in myopic retinas. Our findings indicated mmu‐miR‐1936, mmu‐miR‐338‐5p and mmu‐miR‐673‐3p upregulation may be associated with myopia development via post‐transcriptional gene regulation, and identified potential molecules that could be further explored in future studies of the mechanism in myopia.

Omics-Driven Biotechnology for Industrial Applications

Biomanufacturing is a key component of biotechnology that uses biological systems to produce bioproducts of commercial relevance, which are of great interest to the energy, material, pharmaceutical, food, and agriculture industries. Biotechnology-based approaches, such as synthetic biology and metabolic engineering are heavily reliant on "omics" driven systems biology to characterize and understand metabolic networks. Knowledge gained from systems biology experiments aid the development of synthetic biology tools and the advancement of metabolic engineering studies toward establishing robust industrial biomanufacturing platforms. In this review, we discuss recent advances in "omics" technologies, compare the pros and cons of the different "omics" technologies, and discuss the necessary requirements for carrying out multi-omics experiments. We highlight the influence of "omics" technologies on the production of biofuels and bioproducts by metabolic engineering. Finally, we discuss the application of "omics" technologies to agricultural and food biotechnology, and review the impact of "omics" on current COVID-19 research.

Mouse Models of Inherited Retinal Degeneration with Photoreceptor Cell Loss

Inherited retinal degeneration (RD) leads to the impairment or loss of vision in millions of individuals worldwide, most frequently due to the loss of photoreceptor (PR) cells. Animal models, particularly the laboratory mouse, have been used to understand the pathogenic mechanisms that underlie PR cell loss and to explore therapies that may prevent, delay, or reverse RD. Here, we reviewed entries in the Mouse Genome Informatics and PubMed databases to compile a comprehensive list of monogenic mouse models in which PR cell loss is demonstrated. The progression of PR cell loss with postnatal age was documented in mutant alleles of genes grouped by biological function. As anticipated, a wide range in the onset and rate of cell loss was observed among the reported models. The analysis underscored relationships between RD genes and ciliary function, transcription-coupled DNA damage repair, and cellular chloride homeostasis. Comparing the mouse gene list to human RD genes identified in the RetNet database revealed that mouse models are available for 40% of the known human diseases, suggesting opportunities for future research. This work may provide insight into the molecular players and pathways through which PR degenerative disease occurs and may be useful for planning translational studies.

Lysine malonylation and propionylation are prevalent in human lens proteins

Acylated lysine residues represent major chemical modifications in proteins. We investigated the malonylation and propionylation of lysine residues (MalK, PropK) in the proteins of aging human lenses. Western blot results showed that the two modifications are present in human lens proteins. Liquid chromatography-mass spectrometry (LC-MS/MS) results showed 4-18 and 4-32 pmol/mg protein of MalK and PropK, respectively, in human lens proteins with no apparent changes related to aging. Mass spectrometry results revealed that MalK- and PropK-modified lysine residues are present in all major crystallins, other cytosolic proteins, and membrane and cytoskeletal proteins of the lens. Several mitochondrial and cytosolic proteins in cultured human lens epithelial cells showed MalK and PropK modifications. Sirtuin 3 (SIRT3) and sirtuin 5 (SIRT5) were present in human lens epithelial and fiber cells. Moreover, lens epithelial cell lysate deacylated propionylated and malonylated lysozyme. The absence of SIRT3 and SIRT5 led to higher PropK and MalK levels in mouse lenses. Together, these data suggest that MalK and PropK are widespread modifications in lens and SIRT3 and SIRT5 could regulate their levels in lens epithelial cells.