Smart nanotextiles for communication

Together with wireless technology, advances in nanotechnology and rapid and scalable synthesis of nanomaterials including the 2D graphene has transformed the realms of biomedical sciences. Recent research in the areas of drug delivery, cancer therapy, bio-sensing and bio-imaging have exploited the unique structural and physiological features of graphene and its different forms. Along with the Graphene, several other nanomaterials including carbon nanotubes (CNTs), make excellent candidates for applications associated with loading of drugs, cellular imaging, sensing other molecules and in-vivo cancer studies due to their biocompatibility and stability. Assimilating from the fundamentals of electromagnetic, wireless communication, medical and material science, a novel concept of nanonetworks was first introduced in 2008, which stems from the concept that a collection of nanodevices have the potential to harness the innate communication capabilities of the human body, thereby allowing them to cooperate and share information. It is anticipated that the advanced healthcare diagnosis can be realised if an efficient communication mechanism and data transfer are established between these nanodevices. The human body is a good example of a naturally existing communication network. For instance, the nervous system is composed of nerve cells, i.e. neurons that communicate the external stimulus to the brain and enable the communication between different systems by conveying information with a molecular impulse signal known as a spike. The human body needs communication amongst different cells to survive, the proposed intra- and inter-body nanonetworks ensure their stability without mechanically (or physically) disturbing the harmony of the in-built molecular structure of the body. Moreover, in several cases, the medicine technology fails to understand the root cause of the problem but once we have a monitoring network established in our body, we can extract various unknowns and treat them effectively. The vision of nanoscale networking attempts to achieve the functionality and performance of the internet with the exception that node size is measured in nanometres and channels are physically separated by up to hundreds or thousands of nanometres. In addition, nodes are assumed to be mobile and rapidly deployable. Nodes (or nanodevices) are expected to be either self-powered or spread in and around the specific location. In a visionary sense, an ultimate application of nanoscale networking would be an automated process, where the nano-nodes are in motion communicating in a complex dynamic environment of living organisms monitoring diseased or sensitive parts of the body

Chromosome-Level Genome Assembly for Acer pseudosieboldianum and Highlights to Mechanisms for Leaf Color and Shape Change

Acer pseudosieboldianum (Pax) Komarov is an ornamental plant with prominent potential and is naturally distributed in Northeast China. Here, we obtained a chromosome-scale genome assembly of A. pseudosieboldianum combining HiFi and Hi-C data, and the final assembled genome size was 690.24 Mb and consisted of 287 contigs, with a contig N50 value of 5.7 Mb and a BUSCO complete gene percentage of 98.4%. Genome evolution analysis showed that an ancient duplication occurred in A. pseudosieboldianum. Phylogenetic analyses revealed that Aceraceae family could be incorporated into Sapindaceae, consistent with the present Angiosperm Phylogeny Group system. We further construct a gene-to-metabolite correlation network and identified key genes and metabolites that might be involved in anthocyanin biosynthesis pathways during leaf color change. Additionally, we identified crucial teosinte branched1, cycloidea, and proliferating cell factors (TCP) transcription factors that might be involved in leaf morphology regulation of A. pseudosieboldianum, Acer yangbiense and Acer truncatum. Overall, this reference genome is a valuable resource for evolutionary history studies of A. pseudosieboldianum and lays a fundamental foundation for its molecular breeding

Bmi1 Promotes Erythroid Development Through Regulating Ribosome Biogenesis

While Polycomb group protein Bmi1 is important for stem cell maintenance, its role in lineage commitment is largely unknown. We have identified Bmi1 as a novel regulator of erythroid development. Bmi1 is highly expressed in mouse erythroid progenitor cells and its deficiency impairs erythroid differentiation. BMI1 is also important for human erythroid development. Furthermore, we discovered that loss of Bmi1 in erythroid progenitor cells results in decreased transcription of multiple ribosomal protein genes and impaired ribosome biogenesis. Bmi1 deficiency stabilizes p53 protein, leading to upregulation of p21 expression and subsequent G0/G1 cell cycle arrest. Genetic inhibition of p53 activity rescues the erythroid defects seen in the Bmi1 null mice, demonstrating that a p53-dependent mechanism underlies the pathophysiology of the anemia. Mechanistically, Bmi1 is associated with multiple ribosomal protein genes and may positively regulate their expression in erythroid progenitor cells. Thus, Bmi1 promotes erythroid development, at least in part through regulating ribosome biogenesis. Ribosomopathies are human disorders of ribosome dysfunction, including Diamond-Blackfan anemia (DBA) and 5q− syndrome, in which genetic abnormalities cause impaired ribosome biogenesis, resulting in specific clinical phenotypes. We observed that BMI1 expression in human hematopoietic stem and progenitor cells from patients with DBA is correlated with the expression of some ribosomal protein genes, suggesting that BMI1 deficiency may play a pathological role in DBA and other ribosomopathies

Integration of circulating tumor cell and neutrophil-lymphocyte ratio to identify high-risk metastatic castration-resistant prostate cancer patients.

BACKGROUND: The neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), and circulating tumor cells (CTCs) have been associated with survival in castration-resistant prostate cancer (CRPC). However, no study has examined the prognostic value of NLR and PLR in the context of CTCs. METHODS: Baseline CTCs from mCRPC patients were enumerated using the CellSearch System. Baseline NLR and PLR values were calculated using the data from routine complete blood counts. The associations of CTC, NLR, and PLR values, individually and jointly, with progression-free survival (PFS) and overall survival (OS), were evaluated using Kaplan-Meier analysis, as well as univariate and multivariate Cox models. RESULTS: CTCs were detected in 37 (58.7%) of 63 mCRPC patients, and among them, 16 (25.4%) had ≥5 CTCs. The presence of CTCs was significantly associated with a 4.02-fold increased risk for progression and a 3.72-fold increased risk of death during a median follow-up of 17.6 months. OS was shorter among patients with high levels of NLR or PLR than those with low levels (log-rank P = 0.023 and 0.077). Neither NLR nor PLR was individually associated with PFS. Among the 37 patients with detectable CTCs, those with a high NLR had significantly shorter OS (log-rank P = 0.024); however, among the 26 patients without CTCs, the OS difference between high- and low-NLR groups was not statistically significant. Compared to the patients with CTCs and low NLR, those with CTCs and high levels of NLR had a 3.79-fold risk of death (P = 0.036). This association remained significant after adjusting for covariates (P = 0.031). Combination analyses of CTC and PLR did not yield significant results. CONCLUSION: Among patients with detectable CTCs, the use of NLR could further classify patients into different risk groups, suggesting a complementary role for NLR in CTC-based prognostic stratification in mCRPC

Improved Prognostic Stratification Using Circulating Tumor Cell Clusters in Patients with Metastatic Castration-Resistant Prostate Cancer.

Liquid biopsy-based biomarkers have advantages in monitoring the dynamics of metastatic castration-resistant prostate cancer (mCRPC), a bone-predominant metastatic disease. Previous studies have demonstrated associations between circulating tumor cells (CTCs) and clinical outcomes of mCRPC patients, but little is known about the prognostic value of CTC-clusters. In 227 longitudinally collected blood samples from 64 mCRPC patients, CTCs and CTC-clusters were enumerated using the CellSearch platform. The associations of CTC and CTC-cluster counts with progression-free survival (PFS) and overall survival (OS), individually and jointly, were evaluated by Cox models. CTCs and CTC-clusters were detected in 24 (37.5%) and 8 (12.5%) of 64 baseline samples, and in 119 (52.4%) and 27 (11.9%) of 227 longitudinal samples, respectively. CTC counts were associated with both PFS and OS, but CTC-clusters were only independently associated with an increased risk of death. Among patients with unfavorable CTCs (≥5), the presence of CTC-clusters signified a worse survival (log-rank p = 0.0185). mCRPC patients with both unfavorable CTCs and CTC-clusters had the highest risk for death (adjusted hazard ratio 19.84, p = 0.0072), as compared to those withconclusion, CTC-clusters provided additional prognostic information for further stratifying death risk among patients with unfavorable CTCs

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

Epistatic Module Detection for Case-Control Studies: A Bayesian Model with a Gibbs Sampling Strategy

The detection of epistatic interactive effects of multiple genetic variants on the susceptibility of human complex diseases is a great challenge in genome-wide association studies (GWAS). Although methods have been proposed to identify such interactions, the lack of an explicit definition of epistatic effects, together with computational difficulties, makes the development of new methods indispensable. In this paper, we introduce epistatic modules to describe epistatic interactive effects of multiple loci on diseases. On the basis of this notion, we put forward a Bayesian marker partition model to explain observed case-control data, and we develop a Gibbs sampling strategy to facilitate the detection of epistatic modules. Comparisons of the proposed approach with three existing methods on seven simulated disease models demonstrate the superior performance of our approach. When applied to a genome-wide case-control data set for Age-related Macular Degeneration (AMD), the proposed approach successfully identifies two known susceptible loci and suggests that a combination of two other loci—one in the gene SGCD and the other in SCAPER—is associated with the disease. Further functional analysis supports the speculation that the interaction of these two genetic variants may be responsible for the susceptibility of AMD. When applied to a genome-wide case-control data set for Parkinson's disease, the proposed method identifies seven suspicious loci that may contribute independently to the disease