Radiogenomics study to predict the nuclear grade of renal clear cell carcinoma

Purpose

To develop models based on radiomics and genomics for predicting the histopathologic nuclear grade with localized clear cell renal cell carcinoma (ccRCC) and to assess whether macro-radiomics models can predict the microscopic pathological changes.

Method

In this multi-institutional retrospective study, a computerized tomography (CT) radiomic model for nuclear grade prediction was developed. Utilizing a genomics analysis cohort, nuclear grade-associated gene modules were identified, and a gene model was constructed based on top 30 hub mRNA to predict the nuclear grade. Using a radiogenomic development cohort, biological pathways were enriched by hub genes and a radiogenomic map was created.

Results

The four-features-based SVM model predicted nuclear grade with an area under the curve (AUC) score of 0.94 in validation sets, while a five-gene-based model predicted nuclear grade with an AUC of 0.73 in the genomics analysis cohort. A total of five gene modules were identified to be associated with the nuclear grade. Radiomic features were only associated with 271 out of 603 genes in five gene modules and eight top 30 hub genes. Differences existed in the enrichment pathway between associated and un-associated with radiomic features, which were associated with two genes of five-gene signatures in the mRNA model.

Conclusion

The CT radiomics models exhibited higher predictive performance than mRNA models. The association between radiomic features and mRNA related to nuclear grade is not universal.

MAPKAPK2-centric transcriptome profiling reveals its major role in governing molecular crosstalk of IGFBP2, MUC4, and PRKAR2B during HNSCC pathogenesis

Transcriptome analysis of head and neck squamous cell carcinoma (HNSCC) has been pivotal to comprehending the convoluted biology of HNSCC tumors. MAPKAPK2 or MK2 is a critical modulator of the mRNA turnover of crucial genes involved in HNSCC progression. However, MK2-centric transcriptome profiles of tumors are not well known. This study delves into HNSCC progression with MK2 at the nexus to delineate the biological relevance and intricate crosstalk of MK2 in the tumor milieu. We performed next-generation sequencing-based transcriptome profiling of HNSCC cells and xenograft tumors to ascertain mRNA expression profiles in MK2-wild type and MK2-knockdown conditions. The findings were validated using gene expression assays, immunohistochemistry, and transcript turnover studies. Here, we identified a pool of crucial MK2-regulated candidate genes by annotation and differential gene expression analyses. Regulatory network and pathway enrichment revealed their significance and involvement in the HNSCC pathogenesis. Additionally, 3'-UTR-based filtering recognized important MK2-regulated downstream target genes and validated them by nCounter gene expression assays. Finally, immunohistochemistry and transcript stability studies revealed the putative role of MK2 in regulating the transcript turnover of IGFBP2, MUC4, and PRKAR2B in HNSCC. Conclusively, MK2-regulated candidate genes were identified in this study, and their plausible involvement in HNSCC pathogenesis was elucidated. These genes possess investigative values as targets for diagnosis and therapeutic interventions for HNSCC.

DenovoProfiling: A webserver for de novo generated molecule library profiling

Various deep learning-based architectures for molecular generation have been proposed for de novo drug design. The flourish of the de novo molecular generation methods and applications has created a great demand for the visualization and functional profiling for the de novo generated molecules. An increasing number of publicly available chemogenomic databases sets good foundations and creates good opportunities for comprehensive profiling of the de novo library. In this paper, we present DenovoProfiling, a webserver dedicated to de novo library visualization and functional profiling. Currently, DenovoProfiling contains six modules: (1) identification & visualization module for chemical structure visualization and identify the reported structures, (2) chemical space module for chemical space exploration using similarity maps, principal components analysis (PCA), drug-like properties distribution, and scaffold-based clustering, (3) ADMET prediction module for predicting the ADMET properties of the de novo molecules, (4) molecular alignment module for three dimensional molecular shape analysis, (5) drugs mapping module for identifying structural similar drugs, and (6) target & pathway module for identifying the reported targets and corresponding functional pathways. DenovoProfiling could provide structural identification, chemical space exploration, drug mapping, and target & pathway information. The comprehensive annotated information could give users a clear picture of their de novo library and could guide the further selection of candidates for chemical synthesis and biological confirmation. DenovoProfiling is freely available at http://denovoprofiling.xielab.net.

FunOMIC: Pipeline with built-in fungal taxonomic and functional databases for human mycobiome profiling

While analysis of the bacterial microbiome has become routine, that of the fungal microbiome is still hampered by the lack of robust databases and bioinformatic pipelines. Here, we present FunOMIC, a pipeline with built-in taxonomic (1.6 million marker genes) and functional (3.4 million non-redundant fungal proteins) databases for the identification of fungi. Applied to more than 2,600 human metagenomic samples, the tool revealed fungal species associated with geography, body sites, and diseases. Correlation network analysis provided new insights into inter-kingdom interactions. With this pipeline and two of the most comprehensive fungal databases, we foresee a fast-growing resource for mycobiome studies.

Analysis of the immune landscape in virus-induced cancers using a novel integrative mechanism discovery approach

Background

The mechanisms of carcinogenesis from viral infections are extraordinarily complex and not well understood. Traditional methods of analyzing RNA-sequencing data may not be sufficient for unraveling complicated interactions between viruses and host cells. Using RNA and DNA-sequencing data from The Cancer Genome Atlas (TCGA), we aim to explore whether virus-induced tumors exhibit similar immune-associated (IA) dysregulations using a new algorithm we developed that focuses on the most important biological mechanisms involved in virus-induced cancers. Differential expression, survival correlation, and clinical variable correlations were used to identify the most clinically relevant IA genes dysregulated in 5 virus-induced cancers (HPV-induced head and neck squamous cell carcinoma, HPV-induced cervical cancer, EBV-induced stomach cancer, HBV-induced liver cancer, and HCV-induced liver cancer) after which a mechanistic approach was adopted to identify pathways implicated in IA gene dysregulation.

Results

Our results revealed that IA dysregulations vary with the cancer type and the virus type, but cytokine signaling pathways are dysregulated in all virus-induced cancers. Furthermore, we also found that important similarities exist between all 5 virus-induced cancers in dysregulated clinically relevant oncogenic signatures and IA pathways. Finally, we also discovered potential mechanisms for genomic alterations to induce IA gene dysregulations using our algorithm.

Conclusions

Our study offers a new approach to mechanism identification through integrating functional annotations and large-scale sequencing data, which may be invaluable to the discovery of new immunotherapy targets for virus-induced cancers.

Characterising Alzheimer's disease through integrative NMR- and LC-MS-based metabolomics

Background

Alzheimer's Disease (AD) is a complex and multifactorial disease and novel approaches are needed to illuminate the underlying pathology. Metabolites comprise the end-product of genes, transcripts, and protein regulations and might reflect disease pathogenesis. Blood is a common biofluid used in metabolomics; however, since extracellular vesicles (EVs) hold cell-specific biological material and can cross the blood-brain barrier, their utilization as biological material warrants further investigation. We aimed to investigate blood- and EV-derived metabolites to add insigts to the pathological mechanisms of AD.

Methods

Blood samples were collected from 10 AD and 10 Mild Cognitive Impairment (MCI) patients, and 10 healthy controls. EVs were enriched from plasma using 100,000×g, 1 h, 4 °C with a wash. Metabolites from serum and EVs were measured using liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR) spectroscopy. Multivariate and univariate analyses were employed to identify altered metabolites in cognitively impaired individuals.

Results

While no significant EV-derived metabolites were found differentiating patients from healthy individuals, six serum metabolites were found important; valine (p = 0.001, fold change, FC = 0.8), histidine (p = 0.001, FC = 0.9), allopurinol riboside (p = 0.002, FC = 0.2), inosine (p = 0.002, FC = 0.3), 4-pyridoxic acid (p = 0.006, FC = 1.6), and guanosine (p = 0.004, FC = 0.3). Pathway analysis revealed branched-chain amino acids, purine and histidine metabolisms to be downregulated, and vitamin B6 metabolism upregulated in patients compared to controls.

Conclusion

Using a combination of LC-MS and NMR methodologies we identified several altered mechanisms possibly related to AD pathology. EVs require additional optimization prior to their possible utilization as a biological material for AD-related metabolomics studies.

Disrupted hemispheric connectivity specialization in patients with major depressive disorder: Evidence from the REST-meta-MDD Project

BACKGROUND

Functional specialization is a feature of human brain for understanding the pathophysiology of major depressive disorder (MDD). The degree of human specialization refers to within and cross hemispheric interactions. However, most previous studies only focused on interhemispheric connectivity in MDD, and the results varied across studies. Hence, brain functional connectivity asymmetry in MDD should be further studied.

METHODS

Resting-state fMRI data of 753 patients with MDD and 451 healthy controls were provided by REST-meta-MDD Project. Twenty-five project contributors preprocessed their data locally with the Data Processing Assistant State fMRI software and shared final indices. The parameter of asymmetry (PAS), a novel voxel-based whole-brain quantitative measure that reflects inter- and intrahemispheric asymmetry, was reported. We also examined the effects of age, sex and clinical variables (including symptom severity, illness duration and three depressive phenotypes).

RESULTS

Compared with healthy controls, patients with MDD showed increased PAS scores (decreased hemispheric specialization) in most of the areas of default mode network, control network, attention network and some regions in the cerebellum and visual cortex. Demographic characteristics and clinical variables have significant effects on these abnormalities.

LIMITATIONS

Although a large sample size could improve statistical power, future independent efforts are needed to confirm our results.

CONCLUSIONS

Our results highlight the idea that many brain networks contribute to broad clinical pathophysiology of MDD, and indicate that a lateralized, efficient and economical brain information processing system is disrupted in MDD. These findings may help comprehensively clarify the pathophysiology of MDD in a new hemispheric specialization perspective.

Lingzhi and San-Miao-San with hyaluronic acid gel mitigate cartilage degeneration in anterior cruciate ligament transection induced osteoarthritis

Objective

To investigate the mitigate efficacy of Chinese medicine Lingzhi (LZ) and San-Miao-San (SMS) combined with hyaluronic acid (HA)-gel in attenuating cartilage degeneration in traumatic osteoarthritis (OA).

Methods

The standardized surgery of anterior cruciate ligament transection (ACLT) was made from the medial compartment of right hind limbs of 8-week-old female SD rats and resulted in a traumatic OA. Rats (n ​= ​5/group) were treated once intra-articular injection of 50 ​μl HA-gel, 50 ​μl HA-gel+50 ​μg LZ-SMS, 50 ​μl of saline+50 ​μg LZ-SMS and null (ACLT group) respectively, except sham group. Limbs were harvested for μCT scan and histopathological staining 3-month post-treatment. Inflammatory cytokines from plasma and synovial fluid were detected using Immunology Multiplex Assay kit. The putative targets of active compounds in LZ-SMS and known therapeutic targets for OA were combined to construct protein–protein interaction network. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was adopted to predict the potential targets and signaling pathway of LZ-SMS in OA through the tool of DAVID Bioinformatics.

Results

In vivo, HA-gel ​+ ​LZ-SMS treatment resulted in a higher volume ratio of hyaline cartilage (HC)/calcified cartilage (CC) and HC/Sum (total volume of cartilage), compared to ACLT and HA-gel groups. In addition, histological results showed the elevated cartilage matrix, chondrogenic and osteoblastic signals in HA-gel ​+ ​LZ-SMS treatment. Treatment also significantly altered subchondral bone (SCB) structure including an increase in BV/TV, Tb.Th, BMD, Conn.Dn, Tb.N, and DA, as well as a significant decrease in Tb.Sp and Po(tot), which implied a protective effect on maintaining the stabilization of tibial SCB microstructure. Furthermore, there was also a down-regulated inflammatory cytokines and upregulated anti-inflammatory cytokine IL-10 in HA+LZ-SMS group. Finally, 64 shared targets from 37 active compounds in LZ-SMS related to the core genes for the development of OA. LZ-SMS has a putative role in regulating inflammatory circumstance through influencing the MAPK signaling pathway.

Conclusion

Our study elucidated a protective effect of HA-gel ​+ ​LZ-SMS in mitigating cartilage degradation and putative interaction with targets and signaling pathway for the development of traumatic OA.

The translational potential of this article

Our results provide a biological rationale for the use of LZ-SMS as a potential candidate for OA treatment.

Metabolomes and transcriptomes revealed the saponin distribution in root tissues of Panax quinquefolius and Panax notoginseng

Background

Panax quinquefolius and Panax notoginseng are widely used and well known for their pharmacological effects. As main pharmacological components, saponins have different distribution patterns in the root tissues of Panax plants.

Methods

In this study, the representative ginsenosides were detected and quantified by desorption electrospray ionization mass spectrometry and high-performance liquid chromatography analysis to demonstrate saponin distribution in the root tissues of P. quinquefolius and P. notoginseng, and saponin metabolite profiles were analyzed by metabolomes to obtain the biomarkers of different root tissues. Finally, the transcriptome analysis was performed to demonstrate the molecular mechanisms of saponin distribution by gene profiles.

Results

There was saponin distribution in the root tissues differed between P. quinquefolius and P. notoginseng. Eight-eight and 24 potential biomarkers were detected by metabolome analysis, and a total of 340 and 122 transcripts involved in saponin synthesis that were positively correlated with the saponin contents (R > 0.6, P < 0.05) in the root tissues of P. quinquefolius and P. notoginseng, respectively. Among them, GDPS1, CYP51, CYP64, and UGT11 were significantly correlated with the contents of Rg1, Re, Rc, Rb2, and Rd in P. quinquefolius. UGT255 was markedly related to the content of R1; CYP74, CYP89, CYP100, CYP103, CYP109, and UGT190 were markedly correlated with the Rd content in P. notoginseng.

Conclusions

These results provided the visual and quantitative profiles of and confirmed the pivotal transcripts of CYPs and UGTs regulating the saponin distribution in the root tissues of P. quinquefolius and P. notoginseng.

Adaptation of the targeted capture Methyl-Seq platform for the mouse genome identifies novel tissue-specific DNA methylation patterns of genes involved in neurodevelopment

Methyl-Seq was recently developed as a targeted approach to assess DNA methylation (DNAm) at a genome-wide level in human. We adapted it for mouse and sought to examine DNAm differences across liver and 2 brain regions: cortex and hippocampus. A custom hybridization array was designed to isolate 99 Mb of CpG islands, shores, shelves, and regulatory elements in the mouse genome. This was followed by bisulfite conversion and sequencing on the Illumina HiSeq2000. The majority of differentially methylated cytosines (DMCs) were present at greater than expected frequency in introns, intergenic regions, near CpG islands, and transcriptional enhancers. Liver-specific enhancers were observed to be methylated in cortex, while cortex specific enhancers were methylated in the liver. Interestingly, commonly shared enhancers were differentially methylated between the liver and cortex. Gene ontology and pathway analysis showed that genes that were hypomethylated in the cortex and hippocampus were enriched for neuronal components and neuronal function. In contrast, genes that were hypomethylated in the liver were enriched for cellular components important for liver function. Bisulfite-pyrosequencing validation of 75 DMCs from 19 different loci showed a correlation of r = 0.87 with Methyl-Seq data. We also identified genes involved in neurodevelopment that were not previously reported to be differentially methylated across brain regions. This platform constitutes a valuable tool for future genome-wide studies involving mouse models of disease.