The Reactome pathway knowledgebase

Identification of circ-FAM169A sponges miR-583 involved in the regulation of intervertebral disc degeneration

Objective

Low back pain (LBP) is the predominant cause of disc degeneration in patients, which brings serious social problems and economic burdens. Increasing evidence has indicated that intervertebral disc degeneration (IDD) is one of the most common causes triggering LBP. Accumulating evidence has shown that circRNAs are involved in the pathological process of IDD. Nevertheless, the circRNA-mediated IDD pathogenesis still remains unknown. This study explored the potential mechanism and functions of circ-FAM169A in NPCs.

Methods

Bioinformatics analysis was conducted to identify key circRNA, miRNA and mRNA and predict their potential role in IDD. Dual-luciferase reporter assay, western blot, qRT-PCR, and fluorescence in situ hybridisation (FISH) were used to demonstrate the interaction among circ-FAM169A, miR-583 and Sox9 in NPCs.

Results

Herein, we identified circ-FAM169A, which was dramatically up-regulated in degenerative nucleus pulposus (NP) tissues and negatively correlated with expression levels of miR-583. We constructed a circ-FAM169A-miR-583-mRNAs co-expression network and predicted circ-FAM169A-miR-583 pathway predominantly involved in extracellular matrix metabolism and cell apoptosis etc. FISH experiments confirmed circ-FAM169A and miR-583 co-existence in the cytoplasm of NPCs. Luciferase reporter assay illustrated that circ-FAM169A was directly bound to miR-583 and Sox9 was the directly target gene of miR-583. Additionally, miR-583 negatively regulated Sox9 mRNA and protein levels in NPCs.

Conclusion

Findings of this study indicated that circ-FAM169A-miR-583 pathway may play a significant role in the regulation of IDD, which will provide novel diagnostic biomarkers and develop effective treatment strategy of IDD diseases.

The translational potential of this article

This study suggested that circ-FAM169A-miR-583 pathway may regulate NPCs apoptosis and extracellular matrix synthesis and catabolism by targeting Sox9. It provides a novel therapeutic target and strategy for IVDD diseases.

eIF4E S209 phosphorylation licenses myc- and stress-driven oncogenesis

To better understand a role of eIF4E S209 in oncogenic translation, we generated EIF4E^S209A/+ heterozygous knockin (4EKI) HCT 116 human colorectal cancer (CRC) cells. 4EKI had little impact on total eIF4E levels, cap binding or global translation, but markedly reduced HCT 116 cell growth in spheroids and mice, and CRC organoid growth. 4EKI strongly inhibited Myc and ATF4 translation, the integrated stress response (ISR)-dependent glutamine metabolic signature, AKT activation and proliferation in vivo. 4EKI inhibited polyposis in Apc^Min/+ mice by suppressing Myc protein and AKT activation. Furthermore, p-eIF4E was highly elevated in CRC precursor lesions in mouse and human. p-eIF4E cooperated with mutant KRAS to promote Myc and ISR-dependent glutamine addiction in various CRC cell lines, characterized by increased cell death, transcriptomic heterogeneity and immune suppression upon deprivation. These findings demonstrate a critical role of eIF4E S209-dependent translation in Myc and stress-driven oncogenesis and as a potential therapeutic vulnerability.

Putative Epigenetic Biomarkers of Stress in Red Blood Cells of Chickens Reared Across Different Biomes

Production animals are constantly subjected to early adverse environmental conditions that influence the adult phenotype and produce epigenetic effects. CpG dinucleotide methylation in red blood cells (RBC) could be a useful epigenetic biomarker to identify animals subjected to chronic stress in the production environment. Here we compared a reduced fraction of the RBC methylome of chickens exposed to social isolation to non-exposed. These experiments were performed in two different locations: Brazil and Sweden. The aim was to identify stress-associated DNA methylation profiles in RBC across these populations, in spite of the variable conditions to which birds are exposed in each facility and their different lineages. Birds were increasingly exposed to a social isolation treatment, combined with food and water deprivation, at random periods of the day from weeks 1-4 after hatching. We then collected the RBC DNA from individuals and compared a reduced fraction of their methylome between the experimental groups using two bioinformatic approaches to identify differentially methylated regions (DMRs): one using fixed-size windows and another that preselected differential peaks with MACS2. Three levels of significance were used (P ≤ 0.05, P ≤ 0.005, and P ≤ 0.0005) to identify DMRs between experimental groups, which were then used for different analyses. With both of the approaches more DMRs reached the defined significance thresholds in BR individuals compared to SW. However, more DMRs had higher fold change values in SW compared to BR individuals. Interestingly, ChrZ was enriched above expectancy for the presence of DMRs. Additionally, when analyzing the locations of these DMRs in relation to the transcription starting site (TSS), we found three peaks with high DMR presence: 10 kb upstream, the TSS itself, and 20-40 kb downstream. Interestingly, these peaks had DMRs with a high presence (>50%) of specific transcription factor binding sites. Three overlapping DMRs were found between the BR and SW population using the most relaxed p-value (P ≤ 0.05). With the most stringent p-value (P ≤ 0.0005), we found 7 and 4 DMRs between treatments in the BR and SW populations, respectively. This study is the first approximation to identify epigenetic biomarkers of long-term exposure to stress in different lineages of production animals.

Network-based machine learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patients

Cancer patient classification using predictive biomarkers for anti-cancer drug responses is essential for improving therapeutic outcomes. However, current machine-learning-based predictions of drug response often fail to identify robust translational biomarkers from preclinical models. Here, we present a machine-learning framework to identify robust drug biomarkers by taking advantage of network-based analyses using pharmacogenomic data derived from three-dimensional organoid culture models. The biomarkers identified by our approach accurately predict the drug responses of 114 colorectal cancer patients treated with 5-fluorouracil and 77 bladder cancer patients treated with cisplatin. We further confirm our biomarkers using external transcriptomic datasets of drug-sensitive and -resistant isogenic cancer cell lines. Finally, concordance analysis between the transcriptomic biomarkers and independent somatic mutation-based biomarkers further validate our method. This work presents a method to predict cancer patient drug responses using pharmacogenomic data derived from organoid models by combining the application of gene modules and network-based approaches.

Intratumor Heterogeneity of MYO18A and FBXW7 Variants Impact the Clinical Outcome of Stage III Colorectal Cancer

Many studies failed to demonstrate benefit from the addition of targeted agents to current standard adjuvant FOLFOX chemotherapy in stage III colorectal cancer (CRC) patients. Intratumor heterogeneity may foster the resistant subclones and leads to cancer recurrence. Here, we built a cancer evolution model and applied machine learning analysis to identify potential therapeutic targets. Among 78 CRC cases, whole-genome (WGS) and deep targeted sequencing data generated from paired blood and primary tumor were used for phylogenetic tree reconstruction. Genetic alterations in the PI3K/AKT, and RTK oncogenic signaling pathways were commonly detected in founding clones. The dominant subclones frequently exhibited dysregulations in the TP53, FBXW7/NOTCH1 tumor suppression, and DNA repair pathways. Fourteen genetic mutations were simultaneously selected by random forest and LASSO methods. The logistic regression model had better accuracy (79%), precision (70%), and recall (65%) and area under the curve (AUC) (82%) for cancer recurrence prediction. Three genes, including MYO18A in the founding clone, FBXW7, and ATM in the dominant subclone, affected the prognosis were selected simultaneously by different feature sets. The in vitro studies, HCT-116 cells transfected with MYO18A siRNA demonstrated a significant reduction in cell migration activity by 20-40%. These results indicate that MYO18A plays a crucial role in the migration of human CRC cells. The cancer evolution model revealed the critical mutations in the founding and dominant subclones. They can be used to predict clinical outcomes and the development of novel therapeutic targets for stage III CRC.

Revealing Prognosis-Related Pathways at the Individual Level by a Comprehensive Analysis of Different Cancer Transcription Data

Identifying perturbed pathways at an individual level is important to discover the causes of cancer and develop individualized custom therapeutic strategies. Though prognostic gene lists have had success in prognosis prediction, using single genes that are related to the relevant system or specific network cannot fully reveal the process of tumorigenesis. We hypothesize that in individual samples, the disruption of transcription homeostasis can influence the occurrence, development, and metastasis of tumors and has implications for patient survival outcomes. Here, we introduced the individual-level pathway score, which can measure the correlation perturbation of the pathways in a single sample well. We applied this method to the expression data of 16 different cancer types from The Cancer Genome Atlas (TCGA) database. Our results indicate that different cancer types as well as their tumor-adjacent tissues can be clearly distinguished by the individual-level pathway score. Additionally, we found that there was strong heterogeneity among different cancer types and the percentage of perturbed pathways as well as the perturbation proportions of tumor samples in each pathway were significantly different. Finally, the prognosis-related pathways of different cancer types were obtained by survival analysis. We demonstrated that the individual-level pathway score (iPS) is capable of classifying cancer types and identifying some key prognosis-related pathways.

Impact of Zika Virus Infection on Human Neural Stem Cell MicroRNA Signatures

Zika virus (ZIKV) is a mosquito-borne virus, which can cause brain abnormalities in newborns, including microcephaly. MicroRNAs (miRNAs) are small non-coding RNAs, which post- transcriptionally regulate gene expression. They are involved in various processes including neurological development and host responses to viral infection, but their potential role in ZIKV pathogenesis remains poorly understood. MiRNAs can be incorporated into extracellular vesicles (EVs) and mediate cell-to-cell communication. While it is well known that in viral infections EVs carrying miRNAs can play a crucial role in disease pathogenesis, ZIKV effects on EV-delivered miRNAs and their contribution to ZIKV pathogenesis have not been elucidated. In the present study, we profiled intracellular and EV-derived miRNAs by next generation sequencing and analyzed the host mRNA transcriptome of neural stem cells during infection with ZIKV Uganda and French Polynesia strains. We identified numerous miRNAs, including miR-4792, which were dysregulated at the intracellular level and had altered levels in EVs during ZIKV infection. Integrated analyses of differentially expressed genes and miRNAs showed that ZIKV infection had an impact on processes associated with neurodevelopment and oxidative stress. Our results provide insights into the roles of intracellular and EV-associated host miRNAs in ZIKV pathogenesis.

Preclinical validation of therapeutic targets predicted by tensor factorization on heterogeneous graphs

Incorrect drug target identification is a major obstacle in drug discovery. Only 15% of drugs advance from Phase II to approval, with ineffective targets accounting for over 50% of these failures1-3. Advances in data fusion and computational modeling have independently progressed towards addressing this issue. Here, we capitalize on both these approaches with Rosalind, a comprehensive gene prioritization method that combines heterogeneous knowledge graph construction with relational inference via tensor factorization to accurately predict disease-gene links. Rosalind demonstrates an increase in performance of 18%-50% over five comparable state-of-the-art algorithms. On historical data, Rosalind prospectively identifies 1 in 4 therapeutic relationships eventually proven true. Beyond efficacy, Rosalind is able to accurately predict clinical trial successes (75% recall at rank 200) and distinguish likely failures (74% recall at rank 200). Lastly, Rosalind predictions were experimentally tested in a patient-derived in-vitro assay for Rheumatoid arthritis (RA), which yielded 5 promising genes, one of which is unexplored in RA.

Systems analysis of avascular necrosis of femoral head using integrative data analysis and literature mining delineates pathways associated with disease

Avascular necrosis of femoral head (AVNFH) is a debilitating disease, which affects the middle aged population. Though the disease is managed using bisphosphonate, it eventually leads to total hip replacement due to collapse of femoral head. Studies regarding the association of single nucleotide polymorphisms with AVNFH, transcriptomics, proteomics, metabolomics, biophysical, ultrastructural and histopathology have been carried out. Functional validation of SNPs was carried out using literature. An integrated systems analysis using the available datasets might help to gain further insights into the disease process. We have carried out an analysis of transcriptomic data from GEO-database, SNPs associated with AVNFH, proteomic and metabolomic data collected from literature. Based on deficiency of vitamins in AVNFH, an enzyme-cofactor network was generated. The datasets are analyzed using ClueGO and the genes are binned into pathways. Metabolomic datasets are analyzed using MetaboAnalyst. Centrality analysis using CytoNCA on the data sets showed cystathionine beta synthase and methylmalonyl-CoA-mutase to be common to 3 out of 4 datasets. Further, the genes common to at least two data sets were analyzed using DisGeNET, which showed their involvement with various diseases, most of which were risk factors associated with AVNFH. Our analysis shows elevated homocysteine, hypoxia, coagulation, Osteoclast differentiation and endochondral ossification as the major pathways associated with disease which correlated with histopathology, IHC, MRI, Micro-Raman spectroscopy etc. The analysis shows AVNFH to be a multi-systemic disease and provides molecular signatures that are characteristic to the disease process.

Genome-wide analysis of therapeutic response uncovers molecular pathways governing tamoxifen resistance in ER+ breast cancer

Background

Prioritization of breast cancer patients based on the risk of resistance to tamoxifen plays a significant role in personalized therapeutic planning and improving disease course and outcomes.

Methods

In this work, we demonstrate that a genome-wide pathway-centric computational framework elucidates molecular pathways as markers of tamoxifen resistance in ER+ breast cancer patients. In particular, we associated activity levels of molecular pathways with a wide spectrum of response to tamoxifen, which defined markers of tamoxifen resistance in patients with ER+ breast cancer.

Findings

We identified five biological pathways as markers of tamoxifen failure and demonstrated their ability to predict the risk of tamoxifen resistance in two independent patient cohorts (Test cohort1: log-rank p-value = 0.02, adjusted HR = 3.11; Test cohort2: log-rank p-value = 0.01, adjusted HR = 4.24). We have shown that these pathways are not markers of aggressiveness and outperform known markers of tamoxifen response. Furthermore, for adoption into clinic, we derived a list of pathway read-out genes and their associated scoring system, which assigns a risk of tamoxifen resistance for new incoming patients.

Interpretation

We propose that the identified pathways and their read-out genes can be utilized to prioritize patients who would benefit from tamoxifen treatment and patients at risk of tamoxifen resistance that should be offered alternative regimens.

Funding

This work was supported by the Rutgers SHP Dean's research grant, Rutgers start-up funds, Libyan Ministry of Higher Education and Scientific Research, and Katrina Kehlet Graduate Award from The NJ Chapter of the Healthcare Information Management Systems Society.

Aberrantly Methylated and Expressed Genes as Prognostic Epigenetic Biomarkers for Colon Cancer

This study aimed to identify prognostic epigenetic biomarkers for colon cancer (CC). Methylation and mRNA expression in CC samples with clinical characteristics that corresponded to those in The Cancer Genome Atlas were analyzed. Differentially methylated genes (DMGs) and differentially expressed genes (DEGs) were screened between matched tumor and nontumor tissues. Among the 415 DEGs and DMGs that significantly correlated between cytosine-phosphate-guanine (CpG) methylation and gene expression, unc-5 netrin receptor C (UNC5C), solute carrier family 35 member F (SLC35F)1, Ly6/Neurotoxin (LYNX)1, stathmin (STMN)2, slit guidance ligand (SLIT)3, cell adhesion molecule L1 like (CHL1), CAP-Gly domain containing linker protein family member 4 (CLIP4), transmembrane protein (TMEM) 255A, granzyme B (GZMB), and brain expressed X-Linked (BEX)1 were promising epigenetic biomarkers. Prediction was more accurate when models were based on the expression and/or methylation of GZMB rather than clinical stage. Comparisons of tissues with high or low GZMB expression significantly associated the DEGs with natural killer-mediated cytotoxicity, cytokine-cytokine receptor interactions, and chemokine signaling pathways. From among the 10 epigenetic biomarkers, GZMB might serve as a tumor suppressor and function in several immune-related pathways in CC. Prognostic models based on GZMB expression and/or methylation would be significant for patients with CC.

A strategy to incorporate prior knowledge into correlation network cutoff selection

Correlation networks are frequently used to statistically extract biological interactions between omics markers. Network edge selection is typically based on the statistical significance of the correlation coefficients. This procedure, however, is not guaranteed to capture biological mechanisms. We here propose an alternative approach for network reconstruction: a cutoff selection algorithm that maximizes the overlap of the inferred network with available prior knowledge. We first evaluate the approach on IgG glycomics data, for which the biochemical pathway is known and well-characterized. Importantly, even in the case of incomplete or incorrect prior knowledge, the optimal network is close to the true optimum. We then demonstrate the generalizability of the approach with applications to untargeted metabolomics and transcriptomics data. For the transcriptomics case, we demonstrate that the optimized network is superior to statistical networks in systematically retrieving interactions that were not included in the biological reference used for optimization.

Metabolic profiling of Candida clinical isolates of different species and infection sources

Candida species are the most common cause of opportunistic fungal infections. Rapid identification and novel approaches for the characterization of these fungi are of great interest to improve the diagnosis and the knowledge about their pathogenic properties. This study aimed to characterize clinical isolates of Candida spp. by proteomics (MALDI-TOF MS) and metabolomics (1H-NMR), and to correlate their metabolic profiles with Candida species, source of infection and different virulence associated parameters. In particular, 49 Candida strains from different sources (blood, n = 15; vagina, n = 18; respiratory tract, n = 16), belonging mainly to C. albicans complex (61%), C. glabrata (20%) and C. parapsilosis (12%) species were used. Several extracellular and intracellular metabolites showed significantly different concentrations among isolates recovered from different sources of infection, as well as among different Candida species. These metabolites were mainly related to the glycolysis or gluconeogenesis, tricarboxylic acid cycle, nucleic acid synthesis and amino acid and lipid metabolism. Moreover, we found specific metabolic fingerprints associated with the ability to form biofilm, the antifungal resistance (i.e. caspofungin and fluconazole) and the production of secreted aspartyl proteinase. In conclusion, 1H-NMR-based metabolomics can be useful to deepen Candida spp. virulence and pathogenicity properties.

Generalizability of "GWAS Hits" in Clinical Populations: Lessons from Childhood Cancer Survivors

With mounting interest in translating genome-wide association study (GWAS) hits from large meta-analyses (meta-GWAS) in diverse clinical settings, evaluating their generalizability in target populations is crucial. Here, we consider long-term survivors of childhood cancers from the St. Jude Lifetime Cohort Study, and we show the limited generalizability of 1,376 robust SNP associations reported in the general population across 12 complex anthropometric and cardiometabolic phenotypes (n = 2,231; observed-to-expected replication ratio = 0.70, p = 6.2 × 10^-8). An examination of five comparable phenotypes in a second independent cohort of survivors from the Childhood Cancer Survivor Study corroborated the overall limited generalizability of meta-GWAS hits to survivors (n = 4,212; observed-to-expected replication ratio = 0.55, p = 5.6 × 10^-15). Finally, in direct comparisons of survivor samples against independent equivalently powered general population samples from the UK Biobank, we consistently observed lower meta-GWAS hit replication rates and poorer polygenic risk score predictive performance in survivor samples for multiple phenotypes. As a possible explanation, we found that meta-GWAS hits were less likely to be replicated in survivors who had been exposed to cancer therapies that are associated with phenotype risk. Examination of complementary DNA methylation data in a subset of survivors revealed that treatment-related methylation patterns at genomic sites linked to meta-GWAS hits may disrupt established genetic signals in survivors.

Differential regulation of the immune system in a brain-liver-fats organ network during short-term fasting

OBJECTIVE

Fasting regimens can promote health, mitigate chronic immunological disorders, and improve age-related pathophysiological parameters in animals and humans. Several ongoing clinical trials are using fasting as a potential therapy for various conditions. Fasting alters metabolism by acting as a reset for energy homeostasis, but the molecular mechanisms underlying the beneficial effects of short-term fasting (STF) are not well understood, particularly at the systems or multiorgan level.

METHODS

We performed RNA-sequencing in nine organs from mice fed ad libitum (0 h) or subjected to fasting five times (2-22 h). We applied a combination of multivariate analysis, differential expression analysis, gene ontology, and network analysis for an in-depth understanding of the multiorgan transcriptome. We used literature mining solutions, LitLab™ and Gene Retriever™, to identify the biological and biochemical terms significantly associated with our experimental gene set, which provided additional support and meaning to the experimentally derived gene and inferred protein data.

RESULTS

We cataloged the transcriptional dynamics within and between organs during STF and discovered differential temporal effects of STF among organs. Using gene ontology enrichment analysis, we identified an organ network sharing 37 common biological pathways perturbed by STF. This network incorporates the brain, liver, interscapular brown adipose tissue, and posterior-subcutaneous white adipose tissue; hence, we named it the brain-liver-fats organ network. Using Reactome pathways analysis, we identified the immune system, dominated by T cell regulation processes, as a central and prominent target of systemic modulations during STF in this organ network. The changes we identified in specific immune components point to the priming of adaptive immunity and parallel the fine-tuning of innate immune signaling.

CONCLUSIONS

Our study provides a comprehensive multiorgan transcriptomic profiling of mice subjected to multiple periods of STF and provides new insights into the molecular modulators involved in the systemic immunotranscriptomic changes that occur during short-term energy loss.

Epithelial cadherin regulates transition between the naïve and primed pluripotent states in mouse embryonic stem cells

Inhibition of E-cad in mouse embryonic stem cells (mESCs) leads to a switch from LIF-BMP to Activin/Nodal-dependent pluripotency, consistent with transition from a naïve to primed pluripotent phenotype. We have used both genetic ablation and steric inhibition of E-cad function in mESCs to assess alterations to phenotype using quantitative mass spectrometry analysis, network models, and functional assays. Proteomic analyses revealed that one third of detected proteins were altered in E-cad null mESCs (Ecad^-/- mESCs) compared to wild type (624 proteins were downregulated and 705 were proteins upregulated). Network pathway analysis and subsequent cellular flux assays confirmed a metabolic shift from oxidative phosphorylation (OXPHOS) to aerobic glycolysis, specifically through mitochondrial complex III downregulation and hypoxia inducible factor 1a target upregulation. Central to this was the transcriptional coactivator EP300. E-cad is a well-known tumor suppressor, its downregulation during cancer initiation and metastasis can be linked to the metabolic switch known as Warburg effect. This study highlights a phenomena found in both primed pluripotent state and cancer stemness and links it to loss of E-cad. Data are available via ProteomeXchange with identifier PXD012679.

Toolbox Accelerating Glycomics (TAG): Glycan Annotation from MALDI-TOF MS Spectra and Mapping Expression Variation to Biosynthetic Pathways

Glycans present extraordinary structural diversity commensurate with their involvement in numerous fundamental cellular processes including growth, differentiation, and morphogenesis. Unlike linear DNA and protein sequences, glycans have heterogeneous structures that differ in composition, branching, linkage, and anomericity. These differences pose a challenge to developing useful software for glycomic analysis. To overcome this problem, we developed the novel Toolbox Accelerating Glycomics (TAG) program. TAG consists of three units: ‘TAG List’ creates a glycan list that is used for database searching in TAG Expression; ‘TAG Expression’ automatically annotates and quantifies glycan signals and draws graphs; and ‘TAG Pathway’ maps the obtained expression information to biosynthetic pathways. Herein, we discuss the concepts, outline the TAG process, and demonstrate its potential using glycomic expression profile data from Chinese hamster ovary (CHO) cells and mutants lacking a functional Npc1 gene (Npc1 knockout (KO) CHO cells). TAG not only drastically reduced the amount of time and labor needed for glycomic analysis but also detected and quantified more glycans than manual analysis. Although this study was limited to the analysis of N-glycans and free oligosaccharides, the glycomic platform will be expanded to facilitate the analysis of O-glycans and glycans of glycosphingolipids.

The mosaic genome of indigenous African cattle as a unique genetic resource for African pastoralism

Cattle pastoralism plays a central role in human livelihood in Africa. However, the genetic history of its success remains unknown. Here, through whole-genome sequence analysis of 172 indigenous African cattle from 16 breeds representative of the main cattle groups, we identify a major taurine × indicine cattle admixture event dated to circa 750-1,050 yr ago, which has shaped the genome of today's cattle in the Horn of Africa. We identify 16 loci linked to African environmental adaptations across crossbred animals showing an excess of taurine or indicine ancestry. These include immune-, heat-tolerance- and reproduction-related genes. Moreover, we identify one highly divergent locus in African taurine cattle, which is putatively linked to trypanotolerance and present in crossbred cattle living in trypanosomosis-infested areas. Our findings indicate that a combination of past taurine and recent indicine admixture-derived genetic resources is at the root of the present success of African pastoralism.

Transcriptome Analysis of Early Defenses in Rice against Fusarium fujikuroi

BACKGROUND

Bakanae is a seedborne disease caused by Fusarium fujikuroi. Rice seedlings emerging from infected seeds can show diverse symptoms such as elongated and slender stem and leaves, pale coloring, a large leaf angle, stunted growth and even death. Little is known about rice defense mechanisms at early stages of disease development.

RESULTS

This study focused on investigating early defenses against F. fujikuroi in a susceptible cultivar, Zerawchanica karatals (ZK), and a resistant cultivar, Tainung 67 (TNG67). Quantitative PCR revealed that F. fujikuroi colonizes the root and stem but not leaf tissues. Illumina sequencing was conducted to analyze the stem transcriptomes of F. fujikuroi-inoculated and mock-inoculated ZK and TNG67 plants collected at 7 days post inoculation (dpi). More differentially expressed genes (DEGs) were identified in ZK (n = 169) than TNG67 (n = 118), and gene ontology terms related to transcription factor activity and phosphorylation were specifically enriched in ZK DEGs. Among the complex phytohormone biosynthesis and signaling pathways, only DEGs involved in the jasmonic acid (JA) signaling pathway were identified. Fourteen DEGs encoding pattern-recognition receptors, transcription factors, and JA signaling pathway components were validated by performing quantitative reverse transcription PCR analysis of individual plants. Significant repression of jasmonate ZIM-domain (JAZ) genes (OsJAZ9, OsJAZ10, and OsJAZ13) at 3 dpi and 7 dpi in both cultivars, indicated the activation of JA signaling during early interactions between rice and F. fujikuroi. Differential expression was not detected for salicylic acid marker genes encoding phenylalanine ammonia-lyase 1 and non-expressor of pathogenesis-related genes 1. Moreover, while MeJA did not affect the viability of F. fujikuroi, MeJA treatment of rice seeds (prior to or after inoculation) alleviated and delayed bakanae disease development in susceptible ZK.

CONCLUSIONS

Different from previous transcriptome studies, which analyzed the leaves of infected plants, this study provides insights into defense-related gene expression patterns in F. fujikuroi-colonized rice stem tissues. Twelve out of the 14 selected DEGs were for the first time shown to be associated with disease resistance, and JA-mediated resistance was identified as a crucial component of rice defense against F. fujikuroi. Detailed mechanisms underlying the JA-mediated bakanae resistance and the novel defense-related DEGs are worthy of further investigation.

A High-Quality Reference Genome Assembly of the Saltwater Crocodile, Crocodylus porosus, Reveals Patterns of Selection in Crocodylidae

Crocodilians are an economically, culturally, and biologically important group. To improve researchers’ ability to study genome structure, evolution, and gene regulation in the clade, we generated a high-quality de novo genome assembly of the saltwater crocodile, Crocodylus porosus, from Illumina short read data from genomic libraries and in vitro proximity-ligation libraries. The assembled genome is 2,123.5 Mb, with N50 scaffold size of 17.7 Mb and N90 scaffold size of 3.8 Mb. We then annotated this new assembly, increasing the number of annotated genes by 74%. In total, 96% of 23,242 annotated genes were associated with a functional protein domain. Furthermore, multiple noncoding functional regions and mappable genetic markers were identified. Upon analysis and overlapping the results of branch length estimation and site selection tests for detecting potential selection, we found 16 putative genes under positive selection in crocodilians, 10 in C. porosus and 6 in Alligator mississippiensis. The annotated C. porosus genome will serve as an important platform for osmoregulatory, physiological, and sex determination studies, as well as an important reference in investigating the phylogenetic relationships of crocodilians, birds, and other tetrapods.

Radioprotectors.org: an open database of known and predicted radioprotectors

The search for radioprotectors is an ambitious goal with many practical applications. Particularly, the improvement of human radioresistance for space is an important task, which comes into view with the recent successes in the space industry. Currently, all radioprotective drugs can be divided into two large groups differing in their effectiveness depending on the type of exposure. The first of these is radioprotectors, highly effective for pulsed, and some types of relatively short exposure to irradiation. The second group consists of long-acting radioprotectors. These drugs are effective for prolonged and fractionated irradiation. They also protect against impulse exposure to ionizing radiation, but to a lesser extent than short-acting radioprotectors. Creating a database on radioprotectors is a necessity dictated by the modern development of science and technology. We have created an open database, Radioprotectors.org, containing an up-to-date list of substances with proven radioprotective properties. All radioprotectors are annotated with relevant chemical and biological information, including transcriptomic data, and can be filtered according to their properties. Additionally, the performed transcriptomics analysis has revealed specific transcriptomic profiles of radioprotectors, which should facilitate the search for potent radioprotectors.

Involvement of HB-EGF/Ascl1/Lin28a Genes in Dedifferentiation of Adult Mammalian Müller Glia

Previous studies from this lab have determined that dedifferentiation of Müller glia occurs after eye drop application of an α7 nicotinic acetylcholine receptor (nAChR) agonist, PNU-282987, to the adult rodent eye. PNU-282987 acts on α7 nAChRs on retinal pigment epithelial cells to stimulate production of Müller-derived progenitor cells (MDPCs) and ultimately lead to neurogenesis. This current study was designed to test the hypothesis that the activation of genes involved in the HB-EGF/Ascl1/Lin28a signaling pathway in Müller glia leads to the genesis of MDPCs. RNA-seq was performed on a Müller glial cell line (rMC-1) following contact with supernatant collected from a retinal pigment epithelial (RPE) cell line treated with PNU-282987. Differentially regulated genes were compared with published literature of Müller glia dedifferentiation that occurs in lower vertebrate regeneration and early mammalian development. HB-EGF was significantly up-regulated by 8 h and expression increased through 12 h. By 48 h, up-regulation of Ascl1 and Lin28a was observed, two genes known to be rapidly induced in dedifferentiating zebrafish Müller glia. Up-regulation of other genes known to be involved in mammalian development and zebrafish regeneration were also observed, as well as down-regulation of some factors necessary for Müller glia cell identity. RNA-seq results were verified using qRT-PCR. Using immunocytochemistry, the presence of markers associated with MDCP identity, Otx2, Nestin, and Vsx2, were found to be expressed in the 48 h treatment group cultures. This study is novel in its demonstration that Müller glia in adult rodents can be induced into regenerative activity by stimulating genes involved in the HB-EGF/Ascl1/Lin28a pathway that leads to MDPCs after introducing conditioned media from PNU-282987 treated RPE. This study furthers our understanding of the mechanism by which Müller glia dedifferentiate in response to PNU-282987 in the adult mammalian retina.

Lack of TLR4 modifies the miRNAs profile and attenuates inflammatory signaling pathways

TLR4 is a member of the toll-like receptors (TLR) immune family, which are activated by lipopolysaccharide, ethanol or damaged tissue, among others, by triggering proinflammatory cytokines release and inflammation. Lack of TLR4 protects against inflammatory processes and neuroinflammation linked with several neuropathologies. By considering that miRNAs are key post-transcriptional regulators of the proteins involved in distinct cellular processes, including inflammation, this study aimed to assess the impact of the miRNAs profile in mice cortices lacking the TLR4 response. Using mice cerebral cortices and next-generation sequencing (NGS), the findings showed that lack of TLR4 significantly reduced the quantity and diversity of the miRNAs expressed in WT mice cortices. The results also revealed a significant down-regulation of the miR-200 family, while cluster miR-99b/let-7e/miR-125a was up-regulated in TLR4-KO vs. WT. The bioinformatics and functional analyses demonstrated that TLR4-KO presented the systematic depletion of many pathways closely related to the immune system response, such as cytokine and interleukin signaling, MAPK and ion Channels routes, MyD88 pathways, NF-κβ and TLR7/8 pathways. Our results provide new insights into the molecular and biological processes associated with the protective effects of TLR-KO against inflammatory damage and neuroinflammation, and reveal the relevance of the TLR4 receptors response in many neuropathologies.

Integrative biomarker detection on high-dimensional gene expression data sets: a survey on prior knowledge approaches

Gene expression data provide the expression levels of tens of thousands of genes from several hundred samples. These data are analyzed to detect biomarkers that can be of prognostic or diagnostic use. Traditionally, biomarker detection for gene expression data is the task of gene selection. The vast number of genes is reduced to a few relevant ones that achieve the best performance for the respective use case. Traditional approaches select genes based on their statistical significance in the data set. This results in issues of robustness, redundancy and true biological relevance of the selected genes. Integrative analyses typically address these shortcomings by integrating multiple data artifacts from the same objects, e.g. gene expression and methylation data. When only gene expression data are available, integrative analyses instead use curated information on biological processes from public knowledge bases. With knowledge bases providing an ever-increasing amount of curated biological knowledge, such prior knowledge approaches become more powerful. This paper provides a thorough overview on the status quo of biomarker detection on gene expression data with prior biological knowledge. We discuss current shortcomings of traditional approaches, review recent external knowledge bases, provide a classification and qualitative comparison of existing prior knowledge approaches and discuss open challenges for this kind of gene selection.

COPD-dependent effects of genetic variation in key inflammation pathway genes on lung cancer risk

Genome-wide association studies (GWAS) have identified several loci contributing to lung cancer and COPD risk independently; however, inflammation-related pathways likely harbor additional lung cancer risk-associated variants in biologically relevant immune genes that differ dependent on COPD. We selected single nucleotide polymorphisms (SNPs) proximal to 2,069 genes within 48 immune pathways. We modeled the contribution of these variants to lung cancer risk in a discovery sample of 1,932 lung cancer cases and controls stratified by COPD status and validation sample of 953 cases and controls also stratified by COPD. There were 43 validated SNPs in those with COPD and 60 SNPs in those without COPD associated with lung cancer risk. Furthermore, 29 of 43 and 28 of 60 SNPs demonstrated a statistically significant interaction with COPD in the pooled sample. These variants demonstrated tissue-dependent effects on proximal gene expression, enhanced network connectivity and resided together in specific immune pathways. These results reveal that key inflammatory related genes and pathways, not found in prior GWAS, impact lung cancer risk in a COPD-dependent manner. Genetic variation identified in our study supplements prior lung cancer GWAS and serves as a foundation to further interrogate risk relationships in smoking and COPD populations.

Systems Biology Approaches to Understanding the Human Immune System

Systems biology is an approach to interrogate complex biological systems through large-scale quantification of numerous biomolecules. The immune system involves >1,500 genes/proteins in many interconnected pathways and processes, and a systems-level approach is critical in broadening our understanding of the immune response to vaccination. Changes in molecular pathways can be detected using high-throughput omics datasets (e.g., transcriptomics, proteomics, and metabolomics) by using methods such as pathway enrichment, network analysis, machine learning, etc. Importantly, integration of multiple omic datasets is becoming key to revealing novel biological insights. In this perspective article, we highlight the use of protein-protein interaction (PPI) networks as a multi-omics integration approach to unravel information flow and mechanisms during complex biological events, with a focus on the immune system. This involves a combination of tools, including: InnateDB, a database of curated interactions between genes and protein products involved in the innate immunity; NetworkAnalyst, a visualization and analysis platform for InnateDB interactions; and MetaBridge, a tool to integrate metabolite data into PPI networks. The application of these systems techniques is demonstrated for a variety of biological questions, including: the developmental trajectory of neonates during the first week of life, mechanisms in host-pathogen interaction, disease prognosis, biomarker discovery, and drug discovery and repurposing. Overall, systems biology analyses of omics data have been applied to a variety of immunology-related questions, and here we demonstrate the numerous ways in which PPI network analysis can be a powerful tool in contributing to our understanding of the immune system and the study of vaccines.

Approaches for the identification of driver mutations in cancer: A tutorial from a computational perspective

Cancer is a complex disease caused by the accumulation of genetic alterations during the individual's life. Such alterations are called genetic mutations and can be divided into two groups: (1) Passenger mutations, which are not responsible for cancer and (2) Driver mutations, which are significant for cancer and responsible for its initiation and progression. Cancer cells undergo a large number of mutations, of which most are passengers, and few are drivers. The identification of driver mutations is a key point and one of the biggest challenges in Cancer Genomics. Many computational methods for such a purpose have been developed in Cancer Bioinformatics. Such computational methods are complex and are usually described in a high level of abstraction. This tutorial details some classical computational methods, from a computational perspective, with the transcription in an algorithmic format towards an easy access by researchers.

Exercise Induced Changes in Salivary and Serum Metabolome in Trained Standardbred, Assessed by 1H-NMR

In the present study, data related to the metabolomics of saliva and serum in trained standardbred horses are provided for the first time. Metabolomic analysis allows to analyze all the metabolites within selected biofluids, providing a better understanding of biochemistry modifications related to exercise. On the basis of the current advances observed in metabolomic research on human athletes, we aimed to investigate the metabolites’ profile of serum and saliva samples collected from healthy standardbred horses and the relationship with physical exercise. Twelve trained standardbred horses were sampled for blood and saliva before (T₀) and immediately after (T₁) standardized exercise. Metabolomic analysis of both samples was performed by ¹H-NMR spectroscopy. Forty-six metabolites in serum and 62 metabolites in saliva were detected, including alcohols, amino acids, organic acids, carbohydrates and purine derivatives. Twenty-six and 14 metabolites resulted to be significantly changed between T₀ and T₁ in serum and saliva, respectively. The findings of 2-hydroxyisobutyrate and 3-hydroxybutyrate in serum and GABA in equine saliva, as well as their modifications following exercise, provide new insights about the physiology of exercise in athletic horses. Glycerol might represent a novel biomarker for fitness evaluation in sport horses.

Exome Sequencing Reveals Signal Transduction Genes Involved in Impulse Control Disorders in Parkinson's Disease

Introduction: Impulse control disorders (ICDs) frequently complicate dopamine agonist (DA) therapy in Parkinson's disease (PD). There is growing evidence of a high heritability for ICDs in the general population and in PD. Variants on genes belonging to the reward pathway have been shown to account for part of this heritability. We aimed to identify new pathways associated with ICDs in PD.

Methods: Thirty-six Parkinsonian patients on DA therapy with (n = 18) and without ICDs (n = 18) matched on age at PD's onset, and gender was selected to represent the most extreme phenotypes of their category. Exome sequencing was performed, and variants with a strong functional impact in brain-expressed genes were selected. Allele frequencies and their distribution in genes and pathways were analyzed with single variant and SKAT-O tests. The 10 most associated variants, genes, and pathways were retained for replication in the Parkinson's progression markers initiative (PPMI) cohort.

Results: None of markers tested passed the significance threshold adjusted for multiple comparisons. However, the “Adenylate cyclase activating” pathway, one of the top associated pathways in the discovery data set (p = 1.6 × 10⁻³) was replicated in the PPMI cohort and was significantly associated with ICDs in a post hoc pooled analysis (combined p-value 3.3 × 10⁻⁵). Two of the 10 most associated variants belonged to genes implicated in cAMP and ERK signaling (rs34193571 in RasGRF2, p = 5 × 10⁻⁴; rs1877652 in PDE2A, p = 8 × 10⁻⁴) although non-significant after Bonferroni correction.

Conclusion: Our results suggest that genes implicated in the signaling pathways linked to G protein-coupled receptors participate to genetic susceptibility to ICDs in PD.

Exploring the functional impact of alternative splicing on human protein isoforms using available annotation sources

In recent years, the emphasis of scientific inquiry has shifted from whole-genome analyses to an understanding of cellular responses specific to tissue, developmental stage or environmental conditions. One of the central mechanisms underlying the diversity and adaptability of the contextual responses is alternative splicing (AS). It enables a single gene to encode multiple isoforms with distinct biological functions. However, to date, the functions of the vast majority of differentially spliced protein isoforms are not known. Integration of genomic, proteomic, functional, phenotypic and contextual information is essential for supporting isoform-based modeling and analysis. Such integrative proteogenomics approaches promise to provide insights into the functions of the alternatively spliced protein isoforms and provide high-confidence hypotheses to be validated experimentally. This manuscript provides a survey of the public databases supporting isoform-based biology. It also presents an overview of the potential global impact of AS on the human canonical gene functions, molecular interactions and cellular pathways.

Closing the gap between formats for storing layout information in systems biology

The understanding of complex biological networks often relies on both a dedicated layout and a topology. Currently, there are three major competing layout-aware systems biology formats, but there are no software tools or software libraries supporting all of them. This complicates the management of molecular network layouts and hinders their reuse and extension. In this paper, we present a high-level overview of the layout formats in systems biology, focusing on their commonalities and differences, review their support in existing software tools, libraries and repositories and finally introduce a new conversion module within the MINERVA platform. The module is available via a REST API and offers, besides the ability to convert between layout-aware systems biology formats, the possibility to export layouts into several graphical formats. The module enables conversion of very large networks with thousands of elements, such as disease maps or metabolic reconstructions, rendering it widely applicable in systems biology.

Proteogenomic Characterization Reveals Therapeutic Vulnerabilities in Lung Adenocarcinoma

To explore the biology of lung adenocarcinoma (LUAD) and identify new therapeutic opportunities, we performed comprehensive proteogenomic characterization of 110 tumors and 101 matched normal adjacent tissues (NATs) incorporating genomics, epigenomics, deep-scale proteomics, phosphoproteomics, and acetylproteomics. Multi-omics clustering revealed four subgroups defined by key driver mutations, country, and gender. Proteomic and phosphoproteomic data illuminated biology downstream of copy number aberrations, somatic mutations, and fusions and identified therapeutic vulnerabilities associated with driver events involving KRAS, EGFR, and ALK. Immune subtyping revealed a complex landscape, reinforced the association of STK11 with immune-cold behavior, and underscored a potential immunosuppressive role of neutrophil degranulation. Smoking-associated LUADs showed correlation with other environmental exposure signatures and a field effect in NATs. Matched NATs allowed identification of differentially expressed proteins with potential diagnostic and therapeutic utility. This proteogenomics dataset represents a unique public resource for researchers and clinicians seeking to better understand and treat lung adenocarcinomas.

Cell death and survival pathways in Alzheimer's disease: an integrative hypothesis testing approach utilizing -omic data sets

Whether a cell lives or dies is controlled by an array of intercepting and dynamic molecular pathways. Although there is evidence of neuronal loss in Alzheimer's disease (AD) and multiple programmed cell death (PCD) pathways have been implicated in this process, there has been no comprehensive evaluation of the dominant pathway responsible for cell death in AD. Likewise, the relative dominance of survival and PCD pathways in AD remains unclear. Here, we present the results of hypothesis-driven bioinformatic analysis of PCD and survival pathway activation in paired methylation and expression data from the middle temporal gyrus (MTG) as well as expression from laser-captured cells from the MTG and hippocampus. The results not only indicate activation of cell death pathways in AD-of which apoptosis is responsible for the largest fraction of upregulated genes-but also of cell survival pathways. These results are indicative of a complex balance between survival and death pathways in AD that future studies should work to delineate at a single cell level.

Workflow for Rapidly Extracting Biological Insights from Complex, Multicondition Proteomics Experiments with WGCNA and PloGO2

We describe a useful workflow for characterizing proteomics experiments incorporating many conditions and abundance data using the popular weighted gene correlation network analysis (WGCNA) approach and functional annotation with the PloGO2 R package, the latter of which we have extended and made available to Bioconductor. The approach can use quantitative data from labeled or label-free experiments and was developed to handle multiple files stemming from data partition or multiple pairwise comparisons. The WGCNA approach can similarly produce a potentially large number of clusters of interest, which can also be functionally characterized using PloGO2. Enrichment analysis will identify clusters or subsets of proteins of interest, and the WGCNA network topology scores will produce a ranking of proteins within these clusters or subsets. This can naturally lead to prioritized proteins to be considered for further analysis or as candidates of interest for validation in the context of complex experiments. We demonstrate the use of the package on two published data sets using two different biological systems (plant and human plasma) and proteomics platforms (sequential window acquisition of all theoretical fragment-ion spectra (SWATH) and tandem mass tag (TMT)): an analysis of the effect of drought on rice over time generated using TMT and a pediatric plasma sample data set generated using SWATH. In both, the automated workflow recapitulates key insights or observations of the published papers and provides additional suggestions for further investigation. These findings indicate that the data set analysis using WGCNA combined with the updated PloGO2 package is a powerful method to gain biological insights from complex multifaceted proteomics experiments.

Gaussian Embedding for Large-scale Gene Set Analysis

Gene sets, including protein complexes and signaling pathways, have proliferated greatly, in large part as a result of high-throughput biological data. Leveraging gene sets to gain insight into biological discovery requires computational methods for converting them into a useful form for available machine learning models. Here, we study the problem of embedding gene sets as compact features that are compatible with available machine learning codes. We present Set2Gaussian, a novel network-based gene set embedding approach, which represents each gene set as a multivariate Gaussian distribution rather than a single point in the low-dimensional space, according to the proximity of these genes in a protein-protein interaction network. We demonstrate that Set2Gaussian improves gene set member identification, accurately stratifies tumors, and finds concise gene sets for gene set enrichment analysis. We further show how Set2Gaussian allows us to identify a previously unknown clinical prognostic and predictive subnetwork around NEFM in sarcoma, which we validate in independent cohorts.

Identification of DNA hydroxymethylation associated genes in osteoarthritis by combined analysis of hydroxymethylation and gene expression

BACKGROUND

Our study aimed to explore more mechanistic insights into the epigenetic regulation of osteoarthritis (OA).

METHODS

The expression profiles (accession number: GSE64393 and GSE64394) were downloaded from the Gene Expression Omnibus database. The differentially hydroxymethylated regions (DhMRs) and differentially expressed genes (DEGs) between OA and control groups were identified. The distribution of DhMRs in the whole genome and the correlation between DhMRs and DEGs were analyzed. Functional module mining for the DEGs and DhMRs was conducted, followed by protein-protein interaction (PPI) analysis. The transcriptional factor (TF) was predicted.

RESULTS

Total 52,282 DhMRs were obtained, among which 31,452 ones were annotated to 9726 genes. Additionally, 1806 DEGs were selected. Hydroxymethylation mainly occurred in gene body region. Correlation analysis revealed that more than 70% of DhMRs were uncorrelated with DEGs expression. Functional module mining for the DEGs and DhMRs identified 2 functional modules, which were involved in pathways of regulation of actin cytoskeleton, and TGF-β signaling pathway. A PPI network was constructed, and ITGB3 had the highest degree. Furthermore, 7 TFs were predicted, which regulated 12 candidate genes, such as HES1-PTEN.

CONCLUSIONS

The onset and progression of OA may be associated with the upregulated hydroxymethylation in gene body region of PTEN. HES1 may be important TF in the pathogenesis of OA. Additionally, pathways of regulation of actin cytoskeleton, and TGF-beta signaling pathway may also play important roles in OA progression.

Structured sparsity regularization for analyzing high-dimensional omics data

The development of new molecular and cell technologies is having a significant impact on the quantity of data generated nowadays. The growth of omics databases is creating a considerable potential for knowledge discovery and, concomitantly, is bringing new challenges to statistical learning and computational biology for health applications. Indeed, the high dimensionality of these data may hamper the use of traditional regression methods and parameter estimation algorithms due to the intrinsic non-identifiability of the inherent optimization problem. Regularized optimization has been rising as a promising and useful strategy to solve these ill-posed problems by imposing additional constraints in the solution parameter space. In particular, the field of statistical learning with sparsity has been significantly contributing to building accurate models that also bring interpretability to biological observations and phenomena. Beyond the now-classic elastic net, one of the best-known methods that combine lasso with ridge penalizations, we briefly overview recent literature on structured regularizers and penalty functions that have been applied in biomedical data to build parsimonious models in a variety of underlying contexts, from survival to generalized linear models. These methods include functions of $\ell _k$-norms and network-based penalties that take into account the inherent relationships between the features. The successful application to omics data illustrates the potential of sparse structured regularization for identifying disease's molecular signatures and for creating high-performance clinical decision support systems towards more personalized healthcare. Supplementary information: Supplementary data are available at Briefings in Bioinformatics online.

Enriching contextualized language model from knowledge graph for biomedical information extraction

Biomedical information extraction (BioIE) is an important task. The aim is to analyze biomedical texts and extract structured information such as named entities and semantic relations between them. In recent years, pre-trained language models have largely improved the performance of BioIE. However, they neglect to incorporate external structural knowledge, which can provide rich factual information to support the underlying understanding and reasoning for biomedical information extraction. In this paper, we first evaluate current extraction methods, including vanilla neural networks, general language models and pre-trained contextualized language models on biomedical information extraction tasks, including named entity recognition, relation extraction and event extraction. We then propose to enrich a contextualized language model by integrating a large scale of biomedical knowledge graphs (namely, BioKGLM). In order to effectively encode knowledge, we explore a three-stage training procedure and introduce different fusion strategies to facilitate knowledge injection. Experimental results on multiple tasks show that BioKGLM consistently outperforms state-of-the-art extraction models. A further analysis proves that BioKGLM can capture the underlying relations between biomedical knowledge concepts, which are crucial for BioIE.

Joint single cell DNA-seq and RNA-seq of gastric cancer cell lines reveals rules of in vitro evolution

Cancer cell lines are not homogeneous nor are they static in their genetic state and biological properties. Genetic, transcriptional and phenotypic diversity within cell lines contributes to the lack of experimental reproducibility frequently observed in tissue-culture-based studies. While cancer cell line heterogeneity has been generally recognized, there are no studies which quantify the number of clones that coexist within cell lines and their distinguishing characteristics. We used a single-cell DNA sequencing approach to characterize the cellular diversity within nine gastric cancer cell lines and integrated this information with single-cell RNA sequencing. Overall, we sequenced the genomes of 8824 cells, identifying between 2 and 12 clones per cell line. Using the transcriptomes of more than 28 000 single cells from the same cell lines, we independently corroborated 88% of the clonal structure determined from single cell DNA analysis. For one of these cell lines, we identified cell surface markers that distinguished two subpopulations and used flow cytometry to sort these two clones. We identified substantial proportions of replicating cells in each cell line, assigned these cells to subclones detected among the G0/G1 population and used the proportion of replicating cells per subclone as a surrogate of each subclone's growth rate.

Distinct mitochondrial defects trigger the integrated stress response depending on the metabolic state of the cell

Mitochondrial dysfunction is associated with activation of the integrated stress response (ISR) but the underlying triggers remain unclear. We systematically combined acute mitochondrial inhibitors with genetic tools for compartment-specific NADH oxidation to trace mechanisms linking different forms of mitochondrial dysfunction to the ISR in proliferating mouse myoblasts and in differentiated myotubes. In myoblasts, we find that impaired NADH oxidation upon electron transport chain (ETC) inhibition depletes asparagine, activating the ISR via the eIF2α kinase GCN2. In myotubes, however, impaired NADH oxidation following ETC inhibition neither depletes asparagine nor activates the ISR, reflecting an altered metabolic state. ATP synthase inhibition in myotubes triggers the ISR via a distinct mechanism related to mitochondrial inner-membrane hyperpolarization. Our work dispels the notion of a universal path linking mitochondrial dysfunction to the ISR, instead revealing multiple paths that depend both on the nature of the mitochondrial defect and on the metabolic state of the cell.

Upper airway gene expression differentiates COVID-19 from other acute respiratory illnesses and reveals suppression of innate immune responses by SARS-CoV-2

We studied the host transcriptional response to SARS-CoV-2 by performing metagenomic sequencing of upper airway samples in 238 patients with COVID-19, other viral or non-viral acute respiratory illnesses (ARIs). Compared to other viral ARIs, COVID-19 was characterized by a diminished innate immune response, with reduced expression of genes involved in toll-like receptor and interleukin signaling, chemokine binding, neutrophil degranulation and interactions with lymphoid cells. Patients with COVID-19 also exhibited significantly reduced proportions of neutrophils and macrophages, and increased proportions of goblet, dendritic and B-cells, compared to other viral ARIs. Using machine learning, we built 26-, 10- and 3-gene classifiers that differentiated COVID-19 from other acute respiratory illnesses with AUCs of 0.980, 0.950 and 0.871, respectively. Classifier performance was stable at low viral loads, suggesting utility in settings where direct detection of viral nucleic acid may be unsuccessful. Taken together, our results illuminate unique aspects of the host transcriptional response to SARS-CoV-2 in comparison to other respiratory viruses and demonstrate the feasibility of COVID-19 diagnostics based on patient gene expression.

Detecting qualitative changes in biological systems

Currently, most diseases are diagnosed only after significant disease-associated transformations have taken place. Here, we propose an approach able to identify when systemic qualitative changes in biological systems happen, thus opening the possibility for therapeutic interventions before the occurrence of symptoms. The proposed method exploits knowledge from biological networks and longitudinal data using a system impact analysis. The method is validated on eight biological phenomena, three synthetic datasets and five real datasets, for seven organisms. Most importantly, the method accurately detected the transition from the control stage (benign) to the early stage of hepatocellular carcinoma on an eight-stage disease dataset.

Antigen-stimulated PBMC transcriptional protective signatures for malaria immunization

Identifying immune correlates of protection and mechanisms of immunity accelerates and streamlines the development of vaccines. RTS,S/AS01E, the most clinically advanced malaria vaccine, has moderate efficacy in African children. In contrast, immunization with sporozoites under antimalarial chemoprophylaxis (CPS immunization) can provide 100% sterile protection in naïve adults. We used systems biology approaches to identifying correlates of vaccine-induced immunity based on transcriptomes of peripheral blood mononuclear cells from individuals immunized with RTS,S/AS01E or chemoattenuated sporozoites stimulated with parasite antigens in vitro. Specifically, we used samples of individuals from two age cohorts and three African countries participating in an RTS,S/AS01E pediatric phase 3 trial and malaria-naïve individuals participating in a CPS trial. We identified both preimmunization and postimmunization transcriptomic signatures correlating with protection. Signatures were validated in independent children and infants from the RTS,S/AS01E phase 3 trial and individuals from an independent CPS trial with high accuracies (>70%). Transcription modules revealed interferon, NF-κB, Toll-like receptor (TLR), and monocyte-related signatures associated with protection. Preimmunization signatures suggest that priming the immune system before vaccination could potentially improve vaccine immunogenicity and efficacy. Last, signatures of protection could be useful to determine efficacy in clinical trials, accelerating vaccine candidate testing. Nevertheless, signatures should be tested more extensively across multiple cohorts and trials to demonstrate their universal predictive capacity.

Circulating myeloid cells invade the central nervous system to mediate cachexia during pancreatic cancer

Weight loss and anorexia are common symptoms in cancer patients that occur prior to initiation of cancer therapy. Inflammation in the brain is a driver of these symptoms, yet cellular sources of neuroinflammation during malignancy are unknown. In a mouse model of pancreatic ductal adenocarcinoma (PDAC), we observed early and robust myeloid cell infiltration into the brain. Infiltrating immune cells were predominately neutrophils, which accumulated at a unique central nervous system entry portal called the velum interpositum, where they expressed CCR2. Pharmacologic CCR2 blockade and genetic deletion of Ccr2 both resulted in significantly decreased brain-infiltrating myeloid cells as well as attenuated cachexia during PDAC. Lastly, intracerebroventricular blockade of the purinergic receptor P2RX7 during PDAC abolished immune cell recruitment to the brain and attenuated anorexia. Our data demonstrate a novel function for the CCR2/CCL2 axis in recruiting neutrophils to the brain, which drives anorexia and muscle catabolism.

Transcriptomics in Toxicogenomics, Part II: Preprocessing and Differential Expression Analysis for High Quality Data

Preprocessing of transcriptomics data plays a pivotal role in the development of toxicogenomics-driven tools for chemical toxicity assessment. The generation and exploitation of large volumes of molecular profiles, following an appropriate experimental design, allows the employment of toxicogenomics (TGx) approaches for a thorough characterisation of the mechanism of action (MOA) of different compounds. To date, a plethora of data preprocessing methodologies have been suggested. However, in most cases, building the optimal analytical workflow is not straightforward. A careful selection of the right tools must be carried out, since it will affect the downstream analyses and modelling approaches. Transcriptomics data preprocessing spans across multiple steps such as quality check, filtering, normalization, batch effect detection and correction. Currently, there is a lack of standard guidelines for data preprocessing in the TGx field. Defining the optimal tools and procedures to be employed in the transcriptomics data preprocessing will lead to the generation of homogeneous and unbiased data, allowing the development of more reliable, robust and accurate predictive models. In this review, we outline methods for the preprocessing of three main transcriptomic technologies including microarray, bulk RNA-Sequencing (RNA-Seq), and single cell RNA-Sequencing (scRNA-Seq). Moreover, we discuss the most common methods for the identification of differentially expressed genes and to perform a functional enrichment analysis. This review is the second part of a three-article series on Transcriptomics in Toxicogenomics.