Exosomes, considered as cell debris or garbage bags, have been later characterized as nanometer-sized extracellular double-membrane lipid bilayer bio-vesicles secreted by the fusion of vesicular bodies with the plasma membrane. The constituents and the rate of exosomes formation differ in different pathophysiological conditions. Exosomes are also observed and studied in different parts of the eye, like the retina, cornea, aqueous, and vitreous humor. Tear fluid consists of exosomes that are shown to regulate various cellular processes. The role of exosomes in eye cancers, especially retinoblastoma (RB), is not well explored, although few studies point towards their presence. Retinoblastoma is an intraocular tumor that constitutes 3% of cases of cancer in children. Diagnosis of RB may require invasive procedures, which might lead to the spread of the disease to other parts. Due to this reason, better ways of diagnosis are being explored. Studies on the exosomes in RB tumors and serum might help designing better diagnostic approaches for RB. In this article, we reviewed studies on exosomes in the eye, with a special emphasis on RB. We also reviewed miRNAs expressed in RB tumor, serum, and cell lines and analyzed the targets of these miRNAs from the proteins identified in the RB tumor exosomes. hsa-miR-494 and hsa-miR-9, upregulated and downregulated, respectively in RB, have the maximum number of targets. Although oppositely regulated, they share the same targets in the proteins identified in RB tumor exosomes. Overall this review provides the up-to-date progress in the area of eye exosome research, with an emphasis on RB.

Transplant glomerulopathy develops through multiple mechanisms, including donor-specific antibodies, T cells and innate immunity. This study investigates circulating small RNA profiles in serum samples of kidney transplant recipients with biopsy-proven transplant glomerulopathy. Among total small RNA population, miRNAs were the most abundant species in the serum of kidney transplant patients. In addition, fragments arising from mature tRNA and rRNA were detected. Most of the tRNA fragments were generated from 5′ ends of mature tRNA and mainly from two parental tRNAs: tRNA-Gly and tRNA-Glu. Moreover, transplant patients with transplant glomerulopathy displayed a novel tRNA fragments signature. Gene expression analysis from allograft tissues demonstrated changes in canonical pathways related to immune activation such as iCos-iCosL signaling pathway in T helper cells, Th1 and Th2 activation pathway, and dendritic cell maturation. mRNA targets of down-regulated miRNAs such as miR-1224-5p, miR-4508, miR-320, miR-378a from serum were globally upregulated in tissue. Integration of serum miRNA profiles with tissue gene expression showed that changes in serum miRNAs support the role of T-cell mediated mechanisms in ongoing allograft injury.

Recent reports show that long non-coding RNAs (lncRNAs) have inefficient splicing and fewer alternative splice variants than mRNAs. Here, we have explored the efficiency of lncRNAs and mRNAs in producing various splice variants, given the number of exons in humans and mice. Intriguingly, lncRNAs produce more splice variants per exon, referred to as Transcript Complexity, than mRNAs. Most lncRNA splice variants are the product of the alternative last exon and exon skipping. LncRNAs and mRNAs with higher transcript complexity have shorter intron lengths. Longer exon length and GC/AG at 5’/3’ splice sites are associated with higher transcript complexity in lncRNAs. Lastly, our results indicate that inefficient splicing of lncRNAs may facilitate multiple introns splicing and, thus, more spliced products per exon.

3D chromatin structure plays an important role in regulating gene expression and alterations to this structure can result in developmental abnormalities and disease. While genomic approaches like Hi-C and Micro-C can provide valuable insights in 3D chromatin architecture, the resulting datasets are extremely large and difficult to manipulate. Here, we present mariner, a rapid and memory efficient tool to extract, aggregate, and plot data from Hi-C matrices within the R/Bioconductor environment. Mariner simplifies the process of querying and extracting contacts from multiple Hi-C files using a parallel and block-processing approach. Modular functions allow complete workflow customization for advanced users, yet all-in-one functions are available for running the most common types of analyses. Finally, tight integration with existing Bioconductor infrastructure enables complete analysis and visualization of Hi-C data in R. Available on GitHub at https://github.com/EricSDavis/mariner and on Bioconductor at https://www.bioconductor.org/packages/release/bioc/html/mariner.html.

Dendritic cells (DCs) are unique immune cells that can link innate and adaptive immune responses and Immunometabolism greatly impacts their phenotype. Rapamycin is a macrolide compound that has immunosuppressant functions and is used to prevent graft loss in kidney transplantation. The current study evaluated the therapeutic potential of ex-vivo rapamycin treated DCs to protect kidneys in a mouse model of acute kidney injury (AKI). For the rapamycin single (S) treatment (Rapa-S-DC), Veh-DCs were treated with rapamycin (10 ng/mL) for 1 h before LPS. In contrast, rapamycin multiple (M) treatment (Rapa-M-DC) were exposed to 3 treatments over 7 days. Only multiple ex-vivo rapamycin treatments of DCs induced a persistent reprogramming of mitochondrial metabolism. These DCs had 18-fold more mitochondria, had almost 4-fold higher oxygen consumption rates, and produced more ATP compared to Veh-DCs (Veh treated control DCs). Pathway analysis showed IL10 signaling as a major contributing pathway to the altered immunophenotype after Rapamycin treatment compared to vehicle with significantly lower cytokines Tnfa, Il1b, and Il6, while regulators of mitochondrial content Pgc1a, Tfam, and Ho1 remained elevated. Critically, adoptive transfer of rapamycin-treated DCs to WT recipients 24 h before bilateral kidney ischemia significantly protected the kidneys from injury with a significant 3-fold improvement in kidney function. Last, the infusion of DCs containing higher mitochondria numbers (treated ex-vivo with healthy isolated mitochondria (10 µg/mL) one day before) also partially protected the kidneys from IRI. These studies demonstrate that pre-emptive infusion of ex-vivo reprogrammed DCs that have higher mitochondria content has therapeutic capacity to induce an anti-inflammatory regulatory phenotype to protect kidneys from injury.

Synthetic lethality (SL) and synthetic viability (SV) are commonly studied genetic interactions in the targeted therapy approach in cancer. In SL, inhibiting either of the genes does not affect the cancer cell survival, but inhibiting both leads to a lethal phenotype. In SV, inhibiting the vulnerable gene makes the cancer cell sick; inhibiting the partner gene rescues and promotes cell viability. Many low and high-throughput experimental approaches have been employed to identify SLs and SVs, but they are time-consuming and expensive. The computational tools for SL prediction involve statistical and machine-learning approaches. Almost all machine learning tools are binary classifiers and involve only identifying SL pairs. Most importantly, there are limited properties known that best describe and discriminate SL from SV. We developed MAGICAL (Multi-class Approach for Genetic Interaction in Cancer via Algorithm Learning), a multi-class random forest based machine learning model for genetic interaction prediction. Network properties of protein derived from physical protein-protein interactions are used as features to classify SL and SV. The model results in an accuracy of ~80% for the training dataset (CGIdb, BioGRID, and SynLethDB) and performs well on DepMap and other experimentally derived reported datasets. Amongst all the network properties, the shortest path, average neighbor2, average betweenness, average triangle, and adhesion have significant discriminatory power. MAGICAL is the first multi-class model to identify discriminatory features of synthetic lethal and viable interactions. MAGICAL can predict SL and SV interactions with better accuracy and precision than any existing binary classifier.