Small Set Of Genes Research Articles

Abstract 2027: Spatial omics and multiplexed imaging to discover new biomarkers of response or resistance to immune checkpoint inhibitors (ICI) in advanced non-small cell lung cancer (NSCLC)

Abstract Introduction: Despite the impressive outcomes with ICI in NSCLC, only a minority of the patients show long-term benefit from ICI. While PD-L1 immunohistochemistry is an approved companion diagnostic test, it is neither sensitive nor specific. Here, we describe the use of spatial transcriptomics using the GeoMx Digital Spatial Profiler (DSP) as a discovery platform to find biomarkers for ICI response or resistance. Methods: Pre-treatment samples from 56 patients with NSCLC treated with ICI were collected and analyzed in a tissue microarray (TMA) format in two-fold redundancy. The human whole transcriptome, represented by 18000 genes assessed by oligonucleotide-tagged in situ hybridization, was sequenced on the NovaSeq platform to quantify the mRNAs present in each region of interest. Three molecularly defined tissue compartments, defined by fluorescence co-localization (tumor [panCK+], leukocytes [CD45+/CD68-], macrophages [CD68+]) were generated to assess mRNA. All statistical testing was performed using a two-sided significance level of α=0.05. Result: 54000 gene variables were generated per case, from them about 27000 were analyzed after removing targets with expression lower than a prespecified frequency. Expression distribution of EPCAM in tumor, PTPRC in leukocytes and CD68 in macrophages confirmed that DSP successfully profiled the molecular compartments. Differential gene expression (DEG) analysis was performed for 6-month clinical benefit across two distinct tumor TMA spots per patient. This resulted in a DEG list of about 250, 125 and 122 genes in CK, CD45 and CD68 compartment respectively. When we used the intersection of significantly associated with benefit genes the candidate list was reduced dramatically to 5, 10 and 10 distinct genes in CK (CYBA, ESD, SH3BGRL3, TOP2B, UFM1), CD45 (ATRN, DGAT1, DNAAF9, GMPS, IFT20, MPHOSPH6, PKP4, SOCS2, TFAP4, TGFB3) and CD68 (ANKS3, ARFRP1, CTSD, DPYD, GTF3C5, HS2ST1, KIAA0895L, PIGR, SFTPA2, YEATS4) compartment respectively. From them, using the Cox Proportional-Hazards Model we further identified CYBA, ESD and UFM1 in CK, DGAT1 and IFT20 in CD45 and ANKS3 and DPYD in CD68 to predict survival as well. Conclusions: Using DSP technology allows rapid, patient-specific assessment of the transcriptome in TMAs by in situ hybridization in spatially defined molecular compartments. Here we show a small set of candidate genes that are associated with outcome in this ICI-treated cohort. By intersecting two non-adjacent cores of the same patient tissue sample, we have begun to dissect tumor heterogeneity and pilot biomarker candidate information with unique molecularly defined compartments for tumor cells, lymphocytes, and macrophages. Results from this cohort may lead to a novel, spatially defined, transcriptomic approach for developing new biomarkers for immunotherapy. Citation Format: Myrto Moutafi, Sandra Martinez-Morilla, Rolando Garcia-Milian, Thazin Nwe Aung, Ioannis Vathiotis, Niki Gavrielatou, Vasiliki Xirou, Leonidas Salichos, David L. Rimm. Spatial omics and multiplexed imaging to discover new biomarkers of response or resistance to immune checkpoint inhibitors (ICI) in advanced non-small cell lung cancer (NSCLC) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2027.

Abstract 6385: Applying NeXT Liquid Biopsy™, an exome-scale platform, to monitor and discover somatic variants in a broad set of cancer types

Abstract Circulating tumor cell-free DNA (ctDNA) has become a biomarker for prognosis and disease monitoring. However, studies typically utilize assays limited to a small set of genes that may miss biologically important and clinically actionable mutations. To address this limitation, we have developed a whole-exome scale cfDNA platform, NeXT Liquid Biopsy (NeXT LB), that enables sensitive identification of mutations in plasma across ~20,000 genes following interventions such as surgery and treatment therapies. NeXT LB monitors tumor variants and discovers novel mutations in the plasma through analysis of tumor, normal, and plasma samples from the same patient. To enable sensitive detection across the exome in solid tumor and liquid biopsies, we developed an enhanced whole-exome assay and chemistry that augments challenging genomic regions to enable more uniform coverage across the exome. Additionally, we achieve a mean depth of coverage of ~2,000X across the exome, with boosted depth (~5,000X) for 247 clinically relevant oncogenic or tumor suppressor genes to further enhance sensitivity. Finally, we developed computational algorithms to sensitively monitor and discover somatic mutations in liquid biopsies without compromising specificity. In this work, we measure the sensitivity of NeXT LB using SeraCare reference samples with known variants at 0.5%, 1%, and 2% allele fraction (AF). We observe 100% sensitivity at 2% and 1% AF, and >95% sensitivity at 0.5% AF. Additionally, we measure >95% sensitivity for variants with AF >=2% using a proprietary cell-line media system. We generate low-pass Whole Genome Sequencing (lpWGS) data to estimate ctDNA fraction in conjunction with NeXT LB. Considering tumor heterogeneity, NeXT LB is capable of monitoring and discovering somatic variants when lpWGS-reported ctDNA fraction is >=3%, thereby highlighting the performance of the NeXT LB platform. We apply NeXT LB to sequence over 100 matched plasma and normal samples at 250 gigabases (G) and tumors at 50 G. This data demonstrate somatic variation in over 1,000 distinct genes across the cohort, thereby demonstrating the breadth and performance improvements provided by our exome-scale platform in contrast to existing targeted platforms. Additionally, we find that the plasma variants are enriched for higher AFs in solid tumors, thus allowing comprehensive coverage of driver genes and recapitulating hotspots identified in public datasets, including TCGA. We developed an exome-scale NeXT LB technology that enables sensitive monitoring and detection of somatic SNVs and indels from cfDNA. The NeXT LB platform covers a much broader landscape of tumor mutations from the plasma than existing targeted platforms, thereby enabling more comprehensive monitoring and discovery of mutations related to therapies, mechanisms of resistance, intra- and inter-tumor heterogeneity, among others. Citation Format: Fabio C p Navarro, Naveen Ramesh, Josette Northcott, Rui Chen, Lee D. McDaniel, Charles W. Abbott, Dan Norton, Robin Li, John Lyle, Jason Harris, Gabor Bartha, John West, Sean M. Boyle, Richard O. Chen. Applying NeXT Liquid Biopsy™, an exome-scale platform, to monitor and discover somatic variants in a broad set of cancer types [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6385.

Single-cell transcriptomic classification of rabies-infected cortical neurons

Cortical circuit tracing using modified rabies virus can identify input neurons making direct monosynaptic connections onto neurons of interest. However, challenges remain in our ability to establish the cell type identity of rabies-labeled input neurons. While transcriptomics may offer an avenue to characterize inputs, the extent of rabies-induced transcriptional changes in distinct neuronal cell types remains unclear, and whether these changes preclude characterization of rabies-infected neurons according to established transcriptomic cell types is unknown. We used single-nucleus RNA sequencing to survey the gene expression profiles of rabies-infected neurons and assessed their correspondence with established transcriptomic cell types. We demonstrated that when using transcriptome-wide RNA profiles, rabies-infected cortical neurons can be transcriptomically characterized despite global and cell-type-specific rabies-induced transcriptional changes. Notably, we found differential modulation of neuronal marker gene expression, suggesting that caution should be taken when attempting to characterize rabies-infected cells with single genes or small gene sets.

Genome-Wide Characterization of the Methyl CpG Binding Domain-Containing Proteins in Watermelon and Functional Analysis of Their Roles in Disease Resistance Through Ectopic Overexpression in Arabidopsis thaliana.

Methyl-CPG-Binding Domain (MBD) proteins play important roles in plant growth, development, and stress responses. The present study characterized the MBD families in watermelon and other cucurbit plants regarding the gene numbers and structures, phylogenetic and syntenic relationships, evolution events, and conserved domain organization of the MBD proteins. The watermelon ClMBD proteins were found to be localized in nucleus, and ClMBD2 and ClMBD3 interacted with ClIDM2 and ClIDM3. ClMBD2 bound to DNA harboring methylated CG sites but not to DNA with methylated CHG and CHH sites in vitro. The ClMBD genes exhibited distinct expression patterns in watermelon plants after SA and MeJA treatment and after infection by fungal pathogens Fusarium oxysporum f.sp. niveum and Didymella bryoniae. Overexpression of ClMBD2, ClMBD3, or ClMBD5 in Arabidopsis resulted in attenuated resistance against Botrytis cinerea, accompanied by down-regulated expression of AtPDF1.2 and increased accumulation of H2O2 upon B. cinerea infection. Overexpression of ClMBD1 and ClMBD2 led to down-regulated expression of AtPR1 and decreased resistance while overexpression of ClMBD5 resulted in up-regulated expression of AtPR1 and increased resistance against Pseudomonas syringae pv. tomato DC3000. Transcriptome analysis revealed that overexpression of ClMBD2 in Arabidopsis up-regulated the expression of a small set of genes that negatively regulate Arabidopsis immunity. These data suggest the importance of some ClMBD genes in plant immunity and provide the possibility to improve plant immunity through modification of specific ClMBD genes.

A Bayesian network structure learning approach to identify genes associated with stress in spleens of chickens

Differences in the expression patterns of genes have been used to measure the effects of non-stress or stress conditions in poultry species. However, the list of genes identified can be extensive and they might be related to several biological systems. Therefore, the aim of this study was to identify a small set of genes closely associated with stress in a poultry animal model, the chicken (Gallus gallus), by reusing and combining data previously published together with bioinformatic analysis and Bayesian networks in a multi-step approach. Two datasets were collected from publicly available repositories and pre-processed. Bioinformatics analyses were performed to identify genes common to both datasets that showed differential expression patterns between non-stress and stress conditions. Bayesian networks were learnt using a Simulated Annealing algorithm implemented in the software Banjo. The structure of the Bayesian network consisted of 16 out of 19 genes together with the stress condition. Network structure showed CARD19 directly connected to the stress condition plus highlighted CYGB, BRAT1, and EPN3 as relevant, suggesting these genes could play a role in stress. The biological functionality of these genes is related to damage, apoptosis, and oxygen provision, and they could potentially be further explored as biomarkers of stress.

Ivermectin-induced gene expression changes in adult Parascaris univalens and Caenorhabditis elegans: a comparative approach to study anthelminthic metabolism and resistance in vitro.

BackgroundThe nematode Parascaris univalens is one of the most prevalent parasitic pathogens infecting horses but anthelmintic resistance undermines treatment approaches. The molecular mechanisms underlying drug activity and resistance remain poorly understood in this parasite since experimental in vitro models are lacking. The aim of this study was to evaluate the use of Caenorhabditis elegans as a model for P. univalens drug metabolism/resistance studies by a comparative gene expression approach after in vitro exposure to the anthelmintic drug ivermectin (IVM).MethodsTwelve adult P. univalens worms in groups of three were exposed to ivermectin (IVM, 10–13 M, 10–11 M, 10–9 M) or left unexposed for 24 h at 37 °C, and total RNA, extracted from the anterior end of the worms, was sequenced using Illumina NovaSeq. Differentially expressed genes (DEGs) involved in metabolism, transportation, or gene expression with annotated Caernorhabditis elegans orthologues were identified as candidate genes to be involved in IVM metabolism/resistance. Similarly, groups of 300 adult C. elegans worms were exposed to IVM (10–9 M, 10–8 M and 10–7 M) or left unexposed for 4 h at 20 °C. Quantitative RT-PCR of RNA extracted from the C. elegans worm pools was used to compare against the expression of selected P. univalens candidate genes after drug treatment.ResultsAfter IVM exposure, 1085 DEGs were found in adult P. univalens worms but the relative gene expression changes were small and large variabilities were found between different worms. Fifteen of the DEGs were chosen for further characterization in C. elegans after comparative bioinformatics analyses. Candidate genes, including the putative drug target lgc-37, responded to IVM in P. univalens, but marginal to no responses were observed in C. elegans despite dose-dependent behavioral effects observed in C. elegans after IVM exposure. Thus, the overlap in IVM-induced gene expression in this small set of genes was minor in adult worms of the two nematode species.ConclusionThis is the first time to our knowledge that a comparative gene expression approach has evaluated C. elegans as a model to understand IVM metabolism/resistance in P. univalens. Genes in P. univalens adults that responded to IVM treatment were identified. However, identifying conserved genes in P. univalens and C. elegans involved in IVM metabolism/resistance by comparing gene expression of candidate genes proved challenging. The approach appears promising but was limited by the number of genes studied (n = 15). Future studies comparing a larger number of genes between the two species may result in identification of additional candidate genes involved in drug metabolism and/or resistance.Graphical Supplementary InformationThe online version contains supplementary material available at 10.1186/s13071-022-05260-4.

Understanding Willow Transcriptional Response in the Context of Oil Sands Tailings Reclamation.

One of the reclamation objectives for treated oil sands tailings (OST) is to establish boreal forest communities that can integrate with the surrounding area. Hence, selection of appropriate soil reclamation cover designs and plant species for revegetation are important aspects of tailings landform reclamation and closure. Research and monitoring of the long term and immediate impacts of capped OST on the growth and survival of native boreal plant species are currently underway. However, plant responses to OST-associated toxicity are not well known at the molecular level. Using RNA sequencing, we examined the effects of three types of OST on the willow transcriptome under different capping strategies. The transcriptomic data showed that some genes respond universally and others in a specific manner to different types of OST. Among the dominant and shared upregulated genes, we found some encoding protein detoxification (PD), Cytochrome P450 (CYPs), glutathione S-transferase regulatory process (GST), UDP-glycosyltransferase (UGT), and ABC transporter and regulatory process associated proteins. Moreover, genes encoding several stress-responsive transcription factors (bZIP, BHLH, ERF, MYB, NAC, WRKY) were upregulated with OST-exposure, while high numbers of transcripts related to photosynthetic activity and chloroplast structure and function were downregulated. Overall, the expression of 40 genes was found consistent across all tailings types and capping strategies. The qPCR analysis of a subset of these shared genes suggested that they could reliably distinguish plants exposed to different OST associated stress. Our results indicated that it is possible to develop OST stress exposure biosensors merely based on changes in the level of expression of a relatively small set of genes. The outcomes of this study will further guide optimization of OST capping and revegetation technology by using knowledge based plant stress adaptation strategies.

Nature-inspired metaheuristics model for gene selection and classification of biomedical microarray data.

Identifying a small subset of informative genes from a gene expression dataset is an important process for sample classification in the fields of bioinformatics and machine learning. In this process, there are two objectives: first, to minimize the number of selected genes, and second, to maximize the classification accuracy of the used classifier. In this paper, a hybrid machine learning framework based on a nature-inspired cuckoo search (CS) algorithm has been proposed to resolve this problem. The proposed framework is obtained by incorporating the cuckoo search (CS) algorithm with an artificial bee colony (ABC) in the exploitation and exploration of the genetic algorithm (GA). These strategies are used to maintain an appropriate balance between the exploitation and exploration phases of the ABC and GA algorithms in the search process. In preprocessing, the independent component analysis (ICA) method extracts the important genes from the dataset. Then, the proposed gene selection algorithms along with the Naive Bayes (NB) classifier and leave-one-out cross-validation (LOOCV) have been applied to find a small set of informative genes that maximize the classification accuracy. To conduct a comprehensive performance study, proposed algorithms have been applied on six benchmark datasets of gene expression. The experimental comparison shows that the proposed framework (ICA and CS-based hybrid algorithm with NB classifier) performs a deeper search in the iterative process, which can avoid premature convergence and produce better results compared to the previously published feature selection algorithm for the NB classifier.

Conservation of dynamic characteristics of transcriptional regulatory elements in periodic biological processes

BackgroundCell and circadian cycles control a large fraction of cell and organismal physiology by regulating large periodic transcriptional programs that encompass anywhere from 15 to 80% of the genome despite performing distinct functions. In each case, these large periodic transcriptional programs are controlled by gene regulatory networks (GRNs), and it has been shown through genetics and chromosome mapping approaches in model systems that at the core of these GRNs are small sets of genes that drive the transcript dynamics of the GRNs. However, it is unlikely that we have identified all of these core genes, even in model organisms. Moreover, large periodic transcriptional programs controlling a variety of processes certainly exist in important non-model organisms where genetic approaches to identifying networks are expensive, time-consuming, or intractable. Ideally, the core network components could be identified using data-driven approaches on the transcriptome dynamics data already available.ResultsThis study shows that a unified set of quantified dynamic features of high-throughput time series gene expression data are more prominent in the core transcriptional regulators of cell and circadian cycles than in their outputs, in multiple organism, even in the presence of external periodic stimuli. Additionally, we observe that the power to discriminate between core and non-core genes is largely insensitive to the particular choice of quantification of these features.ConclusionsThere are practical applications of the approach presented in this study for network inference, since the result is a ranking of genes that is enriched for core regulatory elements driving a periodic phenotype. In this way, the method provides a prioritization of follow-up genetic experiments. Furthermore, these findings reveal something unexpected—that there are shared dynamic features of the transcript abundance of core components of unrelated GRNs that control disparate periodic phenotypes.

Cytokine and Chemokine Receptor Patterns of Human Malignant Melanoma Cell Lines.

Cytokine and chemokine receptors can promote tumor progression, invasion, and metastasis development by inducing different intracellular signaling pathways. The aim of this study was to determine the cytokine and chemokine receptor gene expression patterns in human melanoma cell lines. We found a large set of cytokine and chemokine receptor genes that were significantly differentially expressed between melanoma cell lines that originated from different subtypes of primary melanomas as well as cell lines that originated from melanoma metastases. The relative expressions of two receptor genes (CCR2 and TNFRSF11B) were positively correlated with the invasive potential of the cell lines, whereas a negative correlation was observed for the TNFRSF14 gene expression. We also found a small set of receptor genes that exhibited a significantly decreased expression in association with a BRAFV600E mutation. Based on our results, we assume that the analyzed cytokine and chemokine receptor collection may provide potential to distinguish the different subtypes of melanomas, helping us to understand the biological behavior of BRAFV600E-mutated melanoma cells.

An estimate of the deepest branches of the tree of life from ancient vertically evolving genes.

Core gene phylogenies provide a window into early evolution, but different gene sets and analytical methods have yielded substantially different views of the tree of life. Trees inferred from a small set of universal core genes have typically supported a long branch separating the archaeal and bacterial domains. By contrast, recent analyses of a broader set of non-ribosomal genes have suggested that Archaea may be less divergent from Bacteria, and that estimates of inter-domain distance are inflated due to accelerated evolution of ribosomal proteins along the inter-domain branch. Resolving this debate is key to determining the diversity of the archaeal and bacterial domains, the shape of the tree of life, and our understanding of the early course of cellular evolution. Here, we investigate the evolutionary history of the marker genes key to the debate. We show that estimates of a reduced Archaea-Bacteria (AB) branch length result from inter-domain gene transfers and hidden paralogy in the expanded marker gene set. By contrast, analysis of a broad range of manually curated marker gene datasets from an evenly sampled set of 700 Archaea and Bacteria reveals that current methods likely underestimate the AB branch length due to substitutional saturation and poor model fit; that the best-performing phylogenetic markers tend to support longer inter-domain branch lengths; and that the AB branch lengths of ribosomal and non-ribosomal marker genes are statistically indistinguishable. Furthermore, our phylogeny inferred from the 27 highest-ranked marker genes recovers a clade of DPANN at the base of the Archaea and places the bacterial Candidate Phyla Radiation (CPR) within Bacteria as the sister group to the Chloroflexota.

Multi-Omic Investigation of Low-Nitrogen Conditional Resistance to Clubroot Reveals Brassica napus Genes Involved in Nitrate Assimilation.

Nitrogen fertilization has been reported to influence the development of clubroot, a root disease of Brassicaceae species, caused by the obligate protist Plasmodiophora brassicae. Our previous works highlighted that low-nitrogen fertilization induced a strong reduction of clubroot symptoms in some oilseed rape genotypes. To further understand the underlying mechanisms, the response to P. brassicae infection was investigated in two genotypes “Yudal” and HD018 harboring sharply contrasted nitrogen-driven modulation of resistance toward P. brassicae. Targeted hormone and metabolic profiling, as well as RNA-seq analysis, were performed in inoculated and non-inoculated roots at 14 and 27 days post-inoculation, under high and low-nitrogen conditions. Clubroot infection triggered a large increase of SA concentration and an induction of the SA gene markers expression whatever the genotype and nitrogen conditions. Overall, metabolic profiles suggested that N-driven induction of resistance was independent of SA signaling, soluble carbohydrate and amino acid concentrations. Low-nitrogen-driven resistance in “Yudal” was associated with the transcriptional regulation of a small set of genes, among which the induction of NRT2- and NR-encoding genes. Altogether, our results indicate a possible role of nitrate transporters and auxin signaling in the crosstalk between plant nutrition and partial resistance to pathogens.

Implementation of a prostate cancer-specific targeted sequencing panel for credentialing of patient-derived cell lines and genomic characterization of patient samples.

Primary and metastatic prostate cancers have low mutation rates and recurrent alterations in a small set of genes, enabling targeted sequencing of prostate cancer-associated genes as an efficient approach to characterizing patient samples (compared to whole-exome and whole-genome sequencing). For example, targeted sequencing provides a flexible, rapid, and cost-effective method for genomic assessment of patient-derived cell lines to evaluate fidelity to initial patient tumor samples. We developed a prostate cancer-specific targeted next-generation sequencing (NGS) panel to detect alterations in 62 prostate cancer-associated genes as well as recurring gene fusions with ETS family members, representing the majority of common alterations in prostate cancer. We tested this panel on primary prostate cancer tissues and blood biopsies from patients with metastatic prostate cancer. We generated patient-derived cell lines from primary prostate cancers using conditional reprogramming methods and applied targeted sequencing to evaluate the fidelity of these cell lines to the original patient tumors. The prostate cancer-specific panel identified biologically and clinically relevant alterations, including point mutations in driver oncogenes and ETS family fusion genes, in tumor tissues from 29 radical prostatectomy samples. The targeted panel also identified genomic alterations in cell-free DNA and circulating tumor cells (CTCs) from patients with metastatic prostate cancer, and in standard prostate cancer cell lines. We used the targeted panel to sequence our set of patient-derived cell lines; however, no prostate cancer-specific mutations were identified in the tumor-derived cell lines, suggesting preferential outgrowth of normal prostate epithelial cells. We evaluated a prostate cancer-specific targeted NGS panel to detect common and clinically relevant alterations (including ETS family gene fusions) in prostate cancer. The panel detected driver mutations in a diverse set of clinical samples of prostate cancer, including fresh-frozen tumors, cell-free DNA, CTCs, and cell lines. Targeted sequencing of patient-derived cell lines highlights the challenge of deriving cell lines from primary prostate cancers and the importance of genomic characterization to credential candidate cell lines. Our study supports that a prostate cancer-specific targeted sequencing panel provides an efficient, clinically feasible approach to identify genetic alterations across a spectrum of prostate cancer samples and cell lines.

Global genomic analysis of microbial biotransformation of arsenic highlights the importance of arsenic methylation in environmental and human microbiomes

Arsenic is a ubiquitous toxic element, the global cycle of which is highly affected by microbial redox reactions and assimilation into organoarsenic compounds through sequential methylation reactions. While microbial biotransformation of arsenic has been studied for decades, the past years have seen the discovery of multiple new genes related to arsenic metabolism. Still, most studies focus on a small set of key genes or a small set of cultured microorganisms. Here, we leveraged the recently greatly expanded availability of microbial genomes of diverse organisms from lineages lacking cultivated representatives, including those reconstructed from metagenomes, to investigate genetic repertoires of taxonomic and environmental controls on arsenic metabolic capacities. Based on the collection of arsenic-related genes, we identified thirteen distinct metabolic guilds, four of which combine the aio and ars operons. We found that the best studied phyla have very different combinations of capacities than less well-studied phyla, including phyla lacking isolated representatives. We identified a distinct arsenic gene signature in the microbiomes of humans exposed or likely exposed to drinking water contaminated by arsenic and that arsenic methylation is important in soil and in human microbiomes. Thus, the microbiomes of humans exposed to arsenic have the potential to exacerbate arsenic toxicity. Finally, we show that machine learning can predict bacterial arsenic metabolism capacities based on their taxonomy and the environment from which they were sampled.

Complex Genetic Interactions between Piwi and HP1a in the Repression of Transposable Elements and Tissue-Specific Genes in the Ovarian Germline.

Insertions of transposable elements (TEs) in eukaryotic genomes are usually associated with repressive chromatin, which spreads to neighbouring genomic sequences. In ovaries of Drosophila melanogaster, the Piwi-piRNA pathway plays a key role in the transcriptional silencing of TEs considered to be exerted mostly through the establishment of H3K9me3 histone marks recruiting Heterochromatin Protein 1a (HP1a). Here, using RNA-seq, we investigated the expression of TEs and the adjacent genomic regions upon Piwi and HP1a germline knockdowns sharing a similar genetic background. We found that the depletion of Piwi and HP1a led to the derepression of only partially overlapping TE sets. Several TEs were silenced predominantly by HP1a, whereas the upregulation of some other TEs was more pronounced upon Piwi knockdown and, surprisingly, was diminished upon a Piwi/HP1a double-knockdown. We revealed that HP1a loss influenced the expression of thousands of protein-coding genes mostly not adjacent to TE insertions and, in particular, downregulated a putative transcriptional factor required for TE activation. Nevertheless, our results indicate that Piwi and HP1a cooperatively exert repressive effects on the transcription of euchromatic loci flanking the insertions of some Piwi-regulated TEs. We suggest that this mechanism controls the silencing of a small set of TE-adjacent tissue-specific genes, preventing their inappropriate expression in ovaries.

A Pathogen-Inducible Rice NAC Transcription Factor ONAC096 Contributes to Immunity Against Magnaprothe oryzae and Xanthomonas oryzae pv. oryzae by Direct Binding to the Promoters of OsRap2.6, OsWRKY62, and OsPAL1.

The rice NAC transcriptional factor family harbors 151 members, and some of them play important roles in rice immunity. Here, we report the function and molecular mechanism of a pathogen-inducible NAC transcription factor, ONAC096, in rice immunity against Magnaprothe oryzae and Xanthomonas oryzae pv. oryzae. Expression of ONAC096 was induced by M. oryzae and by abscisic acid and methyl jasmonate. ONAC096 had the DNA binding ability to NAC recognition sequence and was found to be a nucleus-localized transcriptional activator whose activity depended on its C-terminal. CRISPR/Cas9-mediated knockout of ONAC096 attenuated rice immunity against M. oryzae and X. oryzae pv. oryzae as well as suppressed chitin- and flg22-induced reactive oxygen species burst and expression of PTI marker genes OsWRKY45 and OsPAL4; by contrast, overexpression of ONAC096 enhanced rice immunity against these two pathogens and strengthened chitin- or flg22-induced PTI. RNA-seq transcriptomic profiling and qRT-PCR analysis identified a small set of defense and signaling genes that are putatively regulated by ONAC096, and further biochemical analysis validated that ONAC096 could directly bind to the promoters of OsRap2.6, OsWRKY62, and OsPAL1, three known defense and signaling genes that regulate rice immunity. ONAC096 interacts with ONAC066, which is a positive regulator of rice immunity. These results demonstrate that ONAC096 positively contributes to rice immunity against M. oryzae and X. oryzae pv. oryzae through direct binding to the promoters of downstream target genes including OsRap2.6, OsWRKY62, and OsPAL1.

183 Small nucleolar RNA SNORD3A: a potential new biomarker and molecular player in heart failure

Abstract Aims Despite optimal therapy, heart failure (HF) remains a relentless and deadly disease. Given the relative inaccessibility of myocardial human tissues, identification of circulating biomarkers mirroring myocardial pathological signalling pathways, especially in peripheral blood mononuclear cells (PBMC) is expected to be extremely relevant. Small Nucleolar RNAs (snoRNAs) have been shown to play important roles in various cellular physiological processes. However, the connection between snoRNAs and pathological dysfunction in the heart or peripheral blood mononuclear cells (PBMC) is still poorly understood. To identify novel circulating PBMC biomarkers linked to myocardial dysfunction and HF. Methods Myocardial left ventricle (LV) samples and PBMC were obtained from patients affected by ischaemic HF (HF, n = 13) undergoing heart transplantation and control donors (CD, n = 7) and analysed by RNA sequencing analysis (RNASeq). SNORD3A expression levels in the different groups were evaluated by quantitative real-time PCR. HF was induced in 8-week-old wild type C57BL/6 mice by transverse aortic constriction (TAC). Sham-operated mice (sham) were used as controls. After 12-week-TAC (12w) or sham operation, mice were anesthetized, cardiac function was analysed by echocardiography, and cardiac/PBMC samples were collected after sacrifice. In order to test the role of SNORD3A in cardiomyocyte hypoxia, H9C2 cardiomyoblasts were transfected with SNORD3A-targeted antisense oligonucleotides (ASO) and cell survival was analysed. Results RNASeq analysis identified a small set of genes differentially expressed in the heart and PBMC from HF patients. Among these, SNORD3A was up-regulated in cardiac and PBMC samples from HF patients compared to CD (Figure 1A). Similarly, in murine HF induced by 12w TAC, SNORD3A levels were increased by rtPCR, both in the heart and PBMC (Figure 1B). SNORD3A expression levels were also significantly increased in H9C2 cells exposed to in vitro hypoxia (Figure 1C). Interestingly, H9C2 transfection with SNORD3A-specific ASO significantly reduced hypoxia-induced SNORD3A up-regulation and reduced hypoxia-induced cell death (Figure 1D). Conclusions In this study, we identify SNORD3A as a novel possible biomarker in human HF, similarly up-regulated in the heart and PBMC, induced by hypoxia in vitro and modulating cell survival.

Detecting prognostic biomarkers of breast cancer by regularized Cox proportional hazards models

BackgroundThe successful identification of breast cancer (BRCA) prognostic biomarkers is essential for the strategic interference of BRCA patients. Recently, various methods have been proposed for exploring a small prognostic gene set that can distinguish the high-risk group from the low-risk group.MethodsRegularized Cox proportional hazards (RCPH) models were proposed to discover prognostic biomarkers of BRCA from gene expression data. Firstly, the maximum connected network with 1142 genes by mapping 956 differentially expressed genes (DEGs) and 677 previously BRCA-related genes into the gene regulatory network (GRN) was constructed. Then, the 72 union genes of the four feature gene sets identified by Lasso-RCPH, Enet-RCPH, L_{0}-RCPH and SCAD-RCPH models were recognized as the robust prognostic biomarkers. These biomarkers were validated by literature checks, BRCA-specific GRN and functional enrichment analysis. Finally, an index of prognostic risk score (PRS) for BRCA was established based on univariate and multivariate Cox regression analysis. Survival analysis was performed to investigate the PRS on 1080 BRCA patients from the internal validation. Particularly, the nomogram was constructed to express the relationship between PRS and other clinical information on the discovery dataset. The PRS was also verified on 1848 BRCA patients of ten external validation datasets or collected cohorts.ResultsThe nomogram highlighted that the importance of PRS in guiding significance for the prognosis of BRCA patients. In addition, the PRS of 301 normal samples and 306 tumor samples from five independent datasets showed that it is significantly higher in tumors than in normal tissues (P<0.05). The protein expression profiles of the three genes, i.e., ADRB1, SAV1 and TSPAN14, involved in the PRS model demonstrated that the latter two genes are more strongly stained in tumor specimens. More importantly, external validation illustrated that the high-risk group has worse survival than the low-risk group (P<0.05) in both internal and external validations.ConclusionsThe proposed pipelines of detecting and validating prognostic biomarker genes for BRCA are effective and efficient. Moreover, the proposed PRS is very promising as an important indicator for judging the prognosis of BRCA patients.

PubMed Text Data Mining Automation for Biological Validation on Lists of Genes and Pathways

Abstract — A prognostic cancer marker is helpful in oncology to identify the abnormal cancer cells from the collected sample. This marker can be used as an indicator to determine a disease outcome, cancer treatment, and drug discovery. Identifying cancer markers is also beneficial to improve cancer patients’ survival rate in receiving the treatment decision-making. Cancer markers can be determined by testing every gene or pathway in the wet lab manually or using the text mining automation method. The use of text mining techniques effectively investigates hidden information and gathers new knowledge from many existing sources. Unfortunately, querying relevant text to excavate important information is a challenging task. PubMed text data mining is one of the applications that help explore potential cancer markers as the trend of scientific articles in PubMed is steadily increased. Besides, it can support biologists to concentrate on the identified small set of genes or pathways. PubMed identifiers (PMIDs) are then obtained as evidence to ascertain the relationship between diseases and genes (or pathways) used as biological validation. Thus, this technique can discover the biological relationship between disease and genes or pathways. Therefore, the PubMed text data mining automation is invented to link to the websites for saving time instead of manually.&#x0D; Keywords — PubMed, text data mining, biological validation, cancer markers, diseases, genes, pathways.

The genetic case for cardiorespiratory fitness as a clinical vital sign and the routine prescription of physical activity in healthcare

BackgroundCardiorespiratory fitness (CRF) and physical activity (PA) are well-established predictors of morbidity and all-cause mortality. However, CRF is not routinely measured and PA not routinely prescribed as part of standard healthcare. The American Heart Association (AHA) recently presented a scientific case for the inclusion of CRF as a clinical vital sign based on epidemiological and clinical observation. Here, we leverage genetic data in the UK Biobank (UKB) to strengthen the case for CRF as a vital sign and make a case for the prescription of PA.MethodsWe derived two CRF measures from the heart rate data collected during a submaximal cycle ramp test: CRF-vo2max, an estimate of the participants' maximum volume of oxygen uptake, per kilogram of body weight, per minute; and CRF-slope, an estimate of the rate of increase of heart rate during exercise. Average PA over a 7-day period was derived from a wrist-worn activity tracker. After quality control, 70,783 participants had data on the two derived CRF measures, and 89,683 had PA data. We performed genome-wide association study (GWAS) analyses by sex, and post-GWAS techniques to understand genetic architecture of the traits and prioritise functional genes for follow-up.ResultsWe found strong evidence that genetic variants associated with CRF and PA influenced genetic expression in a relatively small set of genes in the heart, artery, lung, skeletal muscle and adipose tissue. These functionally relevant genes were enriched among genes known to be associated with coronary artery disease (CAD), type 2 diabetes (T2D) and Alzheimer’s disease (three of the top 10 causes of death in high-income countries) as well as Parkinson’s disease, pulmonary fibrosis, and blood pressure, heart rate, and respiratory phenotypes. Genetic variation associated with lower CRF and PA was also correlated with several disease risk factors (including greater body mass index, body fat and multiple obesity phenotypes); a typical T2D profile (including higher insulin resistance, higher fasting glucose, impaired beta-cell function, hyperglycaemia, hypertriglyceridemia); increased risk for CAD and T2D; and a shorter lifespan.ConclusionsGenetics supports three decades of evidence for the inclusion of CRF as a clinical vital sign. Given the genetic, clinical and epidemiological evidence linking CRF and PA to increased morbidity and mortality, regular measurement of CRF as a marker of health and routine prescription of PA could be a prudent strategy to support public health.