Patterns of transcription factor binding and epigenome at promoters allow interpretable predictability of multiple functions of non-coding and coding genes.

Understanding the biological roles of all genes only through experimental methods is challenging. A computational approach with reliable interpretability is needed to infer the function of genes, particularly for non-coding RNAs. We have analyzed genomic features that are present across both coding and non-coding genes like transcription factor (TF) and cofactor ChIP-seq (823), histone modifications ChIP-seq (n = 621), cap analysis gene expression (CAGE) tags (n = 255), and DNase hypersensitivity profiles (n = 255) to predict ontology-based functions of genes. Our approach for gene function prediction was reliable (>90% balanced accuracy) for 486 gene-sets. PubMed abstract mining and CRISPR screens supported the inferred association of genes with biological functions, for which our method had high accuracy. Further analysis revealed that TF-binding patterns at promoters have high predictive strength for multiple functions. TF-binding patterns at the promoter add an unexplored dimension of explainable regulatory aspects of genes and their functions. Therefore, we performed a comprehensive analysis for the functional-specificity of TF-binding patterns at promoters and used them for clustering functions to reveal many latent groups of gene-sets involved in common major cellular processes. We also showed how our approach could be used to infer the functions of non-coding genes using the CRISPR screens of coding genes, which were validated using a long non-coding RNA CRISPR screen. Thus our results demonstrated the generality of our approach by using gene-sets from CRISPR screens. Overall, our approach opens an avenue for predicting the involvement of non-coding genes in various functions.

Matching queried single-cell open-chromatin profiles to large pools of single-cell transcriptomes and epigenomes for reference supported analysis.

The true benefits of large single-cell transcriptome and epigenome data sets can be realized only with the development of new approaches and search tools for annotating individual cells. Matching a single-cell epigenome profile to a large pool of reference cells remains a major challenge. Here, we present scEpiSearch, which enables searching, comparison, and independent classification of single-cell open-chromatin profiles against a large reference of single-cell expression and open-chromatin data sets. Across performance benchmarks, scEpiSearch outperformed multiple methods in accuracy of search and low-dimensional coembedding of single-cell profiles, irrespective of platforms and species. Here we also demonstrate the unconventional utilities of scEpiSearch by applying it on single-cell epigenome profiles of K562 cells and samples from patients with acute leukaemia to reveal different aspects of their heterogeneity, multipotent behavior, and dedifferentiated states. Applying scEpiSearch on our single-cell open-chromatin profiles from embryonic stem cells (ESCs), we identified ESC subpopulations with more activity and poising for endoplasmic reticulum stress and unfolded protein response. Thus, scEpiSearch solves the nontrivial problem of amalgamating information from a large pool of single cells to identify and study the regulatory states of cells using their single-cell epigenomes.

Associating pathways with diseases using single-cell expression profiles and making inferences about potential drugs.

Finding direct dependencies between genetic pathways and diseases has been the target of multiple studies as it has many applications. However, due to cellular heterogeneity and limitations of the number of samples for bulk expression profiles, such studies have faced hurdles in the past. Here, we propose a method to perform single-cell expression-based inference of association between pathway, disease and cell-type (sci-PDC), which can help to understand their cause and effect and guide precision therapy. Our approach highlighted reliable relationships between a few diseases and pathways. Using the example of diabetes, we have demonstrated how sci-PDC helps in tracking variation of association between pathways and diseases with changes in age and species. The variation in pathways-disease associations in mice and humans revealed critical facts about the suitability of the mouse model for a few pathways in the context of diabetes. The coherence between results from our method and previous reports, including information about the drug target pathways, highlights its reliability for multidimensional utility.

Improving Chromatin-Interaction Prediction Using Single-Cell Open-Chromatin Profiles and Making Insight Into the Cis-Regulatory Landscape of the Human Brain.

Single-cell open-chromatin profiles have the potential to reveal the pattern of chromatin-interaction in a cell type. However, currently available cis-regulatory network prediction methods using single-cell open-chromatin profiles focus more on local chromatin interactions despite the fact that long-range interactions among genomic sites play a significant role in gene regulation. Here, we propose a method that predicts both short and long-range interactions among genomic sites using single-cell open chromatin profiles. Our method, termed as single-cell epigenome based chromatin-interaction analysis (scEChIA) exploits signal imputation and refined L1 regularization. For a few single-cell open-chromatin profiles, scEChIA outperformed other tools even in terms of accuracy of prediction. Using scEChIA, we predicted almost 0.7 million interactions among genomic sites across seven cell types in the human brain. Further analysis revealed cell type for connection between genes and expression quantitative trait locus (eQTL) in the human brain and making insight about target genes of human-accelerated-elements and disease-associated mutations. Our analysis enabled by scEChIA also hints about the possible action of a few transcription factors (TFs), especially through long-range interaction in brain endothelial cells.

FITs: forest of imputation trees for recovering true signals in single-cell open chromatin profiles.

The advent of single-cell open-chromatin profiling technology has facilitated the analysis of heterogeneity of activity of regulatory regions at single-cell resolution. However, stochasticity and availability of low amount of relevant DNA, cause high drop-out rate and noise in single-cell open-chromatin profiles. We introduce here a robust method called as forest of imputation trees (FITs) to recover original signals from highly sparse and noisy single-cell open-chromatin profiles. FITs makes multiple imputation trees to avoid bias during the restoration of read-count matrices. It resolves the challenging issue of recovering open chromatin signals without blurring out information at genomic sites with cell-type-specific activity. Besides visualization and classification, FITs-based imputation also improved accuracy in the detection of enhancers, calculating pathway enrichment score and prediction of chromatin-interactions. FITs is generalized for wider applicability, especially for highly sparse read-count matrices. The superiority of FITs in recovering signals of minority cells also makes it highly useful for single-cell open-chromatin profile from in vivo samples. The software is freely available at https://reggenlab.github.io/FITs/.

Correction to: Transcriptome-wide identification of genes involved in Ascorbate-Glutathione cycle (Halliwell-Asada pathway) and related pathway for elucidating its role in antioxidative potential in finger millet (Eleusine coracana (L.)).

[This corrects the article DOI: 10.1007/s13205-018-1511-9.].

Transcriptome-wide identification of genes involved in Ascorbate-Glutathione cycle (Halliwell-Asada pathway) and related pathway for elucidating its role in antioxidative potential in finger millet (Eleusine coracana (L.)).

Finger millet is being recognized as a potential future crop due to their nutrient contents and antioxidative properties, which are much higher compared to the other minor millets for providing health benefits. The synthesis of these nutritional components is governed by the expression of several gene(s). Therefore, it is necessary to characterize these genes for understanding the molecular mechanisms behind de novo synthesis of nutrient components. Apart from this, these important compounds could also serve as candidate genes for imparting stress tolerance in other crop plants also. In the present study, effort has been made to identify genes involved in Ascorbate-Glutathione cycle (Halliwell-Asada Pathway) and related pathway genes for elucidating its role in antioxidative potential mechanism through transcriptome data analysis. APX, DHAR, MDHAR, GR, and SOD have been identified as the key genes of the pathway in two genotypes GP-1 (low Ca2+) and GP-45 (high Ca2+) of finger millet with reference to rice as a model system, besides, 30 putatively expressed genes/proteins were also investigated. Furthermore, the sequences of identified genes were analyzed systematically; gene ontology (GO) annotation and enrichment analysis of assembled unitranscripts were also performed using Blast2GO. As a result, 49 GO terms, 5 Enzyme Commission (EC) numbers, and 2 KEGG pathway maps were generated. GO results revealed that these genes are mainly involved in two biological processes (BP), viz., oxidation-reduction process (GO:0055114) and cellular oxidant detoxification (GO:0098869), and showed oxidoreductase activity (GO:0016491). KEGG analysis showed that APX, DHAR, MDHAR, and GR are directly connected to biosynthetic pathways of secondary metabolites, mainly polyphenolic compounds (flavonoid, tannin, and lignin) involved in glutathione metabolism (KEGG:00480) and ascorbate and aldarate metabolism (KEGG:00053). While SOD, is indirectly connected and also has significant medicinal attributes and antioxidant properties. Moreover, Fragments Per Kilobase of transcript per Million mapped reads (FPKM) values were also calculated for expression analysis and found that the FPKM values of genes present in GP-1 are higher than that of GP-45. Thus, GP-1 genotype was found to have higher stress regulated gene expression in comparison to GP-45. Taken together, the present transcriptome-based investigation unlocks new avenues for systematic functional analysis of novel ROS scavenging candidate genes that could be effectively applied for improvising human health and nutrition.

Modeling of the jasmonate signaling pathway in Arabidopsis thaliana with respect to pathophysiology of Alternaria blight in Brassica.

The productivity of Oilseed Brassica, one of the economically important crops of India, is seriously affected by the disease, Alternaria blight. The disease is mainly caused by two major necrotrophic fungi, Alternaria brassicae and Alternaria brassicicola which are responsible for significant yield losses. Till date, no resistant source is available against Alternaria blight, hence plant breeding methods can not be used to develop disease resistant varieties. Jasmonate mediated signalling pathway, which is known to play crucial role during defense response against necrotrophs, could be strengthened in Brassica plants to combat the disease. Since scanty information is available in Brassica-Alternaria pathosystems at molecular level therefore, in the present study efforts have been made to model jasmonic acid pathway in Arabidopsis thaliana to simulate the dynamic behaviour of molecular species in the model. Besides, the developed model was also analyzed topologically for investigation of the hubs node. COI1 is identified as one of the promising candidate genes in response to Alternaria and other linked components of plant defense mechanisms against the pathogens. The findings from present study are therefore informative for understanding the molecular basis of pathophysiology and rational management of Alternaria blight for securing food and nutritional security.

Domain analysis of 3 Keto Acyl-CoA synthase for structural variations in Vitis vinifera and Oryza brachyantha using comparative modelling.

The long chain fatty acids incorporated into plant lipids are derived from the iterative addition of C2 units which is provided by malonyl-CoA to an acyl-CoA after interactions with 3-ketoacyl-CoA synthase (KCS), found in several plants. This study provides functional characterization of three 3 ketoacyl CoA synthase like proteins in Vitis vinifera (one) and Oryza brachyantha (two proteins). Sequence analysis reveals that protein of Oryza brachyantha shows 96% similarity to a hypothetical protein in Sorghum bicolor; total 11 homologs were predicted in Sorghum bicolor. Conserved domain prediction confirm the presence of FAE1/Type III polyketide synthase-like protein, Thiolase-like, subgroup; Thiolase-like and 3-Oxoacyl-ACP synthase III, C-terminal and chalcone synthase like domain but very long chain 3-keto acyl CoA domain is absent. All three proteins were found to have Chalcone and stilbene synthases C terminal domain which is similar to domain of thiolase and ß keto acyl synthase. Its N terminal domain is absent in J3M9Z7 protein of Oryza brachyantha and F6HH63 protein of Vitis vinifera. Differences in N-terminal domain is responsible for distinguish activity. The J3MF16 protein of Oryza brachyantha contains N terminal domain and C terminal domain and characterized using annotation of these domains. Domains Gcs (streptomyces coelicolor) and Chalcone-stilbene synthases (KAS) in 2-pyrone synthase (Gerbera hybrid) and chalcone synthase 2 (Medicago sativa) were found to be present in three proteins. This similarity points toward anthocyanin biosynthetic process. Similarity to chalcone synthase 2 reveals its possible role in Naringenine and Chalcone synthase like activity. In 3 keto acyl CoA synthase of Oryza brachyantha. Active site residues C-240, H-407, N-447 are present in J3MF16 protein that are common in these three protein at different positions. Structural variations among dimer interface, product binding site, malonyl-CoA binding sites, were predicted in localized combination of conserved residues.