Integration of Meta-Multi-Omics Data Using Probabilistic Graphs and External Knowledge

Multi-omics has the promise to provide a detailed molecular picture of biological systems. Although obtaining multi-omics data is relatively easy, methods that analyze such data have been lagging. In this paper, we present an algorithm that uses probabilistic graph representations and external knowledge to perform optimal structure learning and deduce a multifarious interaction network for multi-omics data from a bacterial community. Kefir grain, a microbial community that ferments milk and creates kefir, represents a self-renewing, stable, natural microbial community. Kefir has been shown to have a wide range of health benefits. We obtained a controlled bacterial community using the two most abundant and well-studied species in kefir grains: Lentilactobacillus kefiri and Lactobacillus kefiranofaciens. We applied growth temperatures of 30 °C and 37 °C and obtained transcriptomic, metabolomic, and proteomic data for the same 20 samples (10 samples per temperature). We obtained a multi-omics interaction network, which generated insights that would not have been possible with single-omics analysis. We identified interactions among transcripts, proteins, and metabolites, suggesting active toxin/antitoxin systems. We also observed multifarious interactions that involved the shikimate pathway. These observations helped explain bacterial adaptation to different stress conditions, co-aggregation, and increased activation of L. kefiranofaciens at 37 °C.

KEGG2Net: Deducing gene interaction networks and acyclic graphs from KEGG pathways

The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database provides a manual curation of biological pathways that involve genes (or gene products), metabolites, chemical compounds, maps, and other entries. However, most applications and datasets involved in omics are gene or protein-centric requiring pathway representations that include direct and indirect interactions only between genes. Furthermore, special methodologies, such as Bayesian networks require acyclic representations of graphs. We developed KEGG2Net, a web resource that generates a network involving only the genes represented on a KEGG pathway with all of the direct and indirect gene-gene interactions deduced from the pathway. KEGG2Net offers four different methods to remove cycles from the resulting gene interaction network, converting them into directed acyclic graphs (DAGs). We generated synthetic gene expression data using the gene interaction networks deduced from the KEGG pathways and performed a comparative analysis of different cycle removal methods by testing the fitness of their DAGs to the data and by the number of edges they eliminate. Our results indicate that an ensemble method for cycle removal performs as the best approach to convert the gene interaction networks into DAGs. Resulting gene interaction networks and DAGs are represented in multiple user-friendly formats that can be used in other applications, and as images for quick and easy visualisation. The KEGG2Net web portal converts KEGG maps for any organism into gene-gene interaction networks and corresponding DAGS representing all of the direct and indirect interactions among the genes.

Comparative analysis of single-cell transcriptomics in human and Zebrafish oocytes

Background

Zebrafish is a popular model organism, which is widely used in developmental biology research. Despite its general use, the direct comparison of the zebrafish and human oocyte transcriptomes has not been well studied. It is significant to see if the similarity observed between the two organisms at the gene sequence level is also observed at the expression level in key cell types such as the oocyte.

Results

We performed single-cell RNA-seq of the zebrafish oocyte and compared it with two studies that have performed single-cell RNA-seq of the human oocyte. We carried out a comparative analysis of genes expressed in the oocyte and genes highly expressed in the oocyte across the three studies. Overall, we found high consistency between the human studies and high concordance in expression for the orthologous genes in the two organisms. According to the Ensembl database, about 60% of the human protein coding genes are orthologous to the zebrafish genes. Our results showed that a higher percentage of the genes that are highly expressed in both organisms show orthology compared to the lower expressed genes. Systems biology analysis of the genes highly expressed in the three studies showed significant overlap of the enriched pathways and GO terms. Moreover, orthologous genes that are commonly overexpressed in both organisms were involved in biological mechanisms that are functionally essential to the oocyte.

Conclusions

Orthologous genes are concurrently highly expressed in the oocytes of the two organisms and these genes belong to similar functional categories. Our results provide evidence that zebrafish could serve as a valid model organism to study the oocyte with direct implications in human.

Identification of Genes Differentially Expressed in Simvastatin-Induced Alveolar Bone Formation

Local delivery of simvastatin (SIM) has exhibited potential in preventing inflammation and limiting bone loss associated with experimental periodontitis. The primary aim of this study was to analyze transcriptome changes that may contribute to SIM's reduction of periodontal inflammation and bone loss. We evaluate the global genetic profile and signaling mechanisms induced by SIM on experimental periodontitis bone loss and inflammation. Twenty mature female Sprague Dawley rats were subjected to ligature-induced experimental periodontitis around maxillary second molars (M2) either unilaterally (one side untreated, n = 10) or bilaterally (n = 10). After the ligature removal at day 7, sites were injected with either carrier, pyrophosphate (PPi ×3), 1.5-mg SIM-dose equivalent SIM-pyrophosphate prodrug, or no injection. Three days after ligature removal, animals were euthanized; the M1-M2 interproximal was evaluated with μCT, histology, and protein expression. M2 palatal gingiva was harvested for RNA sequencing. Although ligature alone caused upregulation of proinflammatory and bone catabolic genes and proteins, seen in human periodontitis, SIM-PPi upregulated anti-inflammatory (IL-10, IL-1 receptor-like 1) and bone anabolic (insulin-like growth factor, osteocrin, fibroblast growth factor, and Wnt/ β-catenin) genes. The PPi carrier alone did not have these effects. Genetic profile and signaling mechanism data may help identify enhanced pharmacotherapeutic approaches to limit or regenerate periodontitis bone loss. © 2018 The Authors. JBMR Plus Published by Wiley Periodicals, Inc. on behalf of the American Society for Bone and Mineral Research.

Can mycobacterial genomics generate novel targets as speed-breakers against the race for drug resistance

The rise of multi-drug resistant and extensively drug resistant M. tuberculosis around the world poses a great threat to human health, and necessitates development of new, effective and inexpensive anti-tubercular agents. The availability of knowledge on molecular biology of M. tuberculosis infection coupled with whole genome sequences, transcriptomic, proteomic and metabolomic data sets have provided insights on the genes/proteins indispensable for initiation and maintenance of persistence, cross-talk with and/or sensing the host immune response, and finally the reactivation of persistent M. tuberculosis to a growing state. The review will focus on analysis of current state of M. tuberculosis genomic resources, host-pathogen interaction studies in the context of pathogen persistence, and the efforts made or required in the development and utilization of computational tools, models and metabolic network analyses to speed up the process of drug target discovery, particularly eradicating the dormant infections.

EpiCombFlu: exploring known influenza epitopes and their combination to design a universal influenza vaccine

MOTIVATION

Influenza is responsible for half a million deaths annually, and vaccination is the best preventive measure against this pervasive health problem. Influenza vaccines developed from surveillance data of each season are strain-specific, and therefore, are unable to provide protection against pandemic strains arising from antigenic shift and drift. Seasonal epidemics and occasional pandemics of influenza have created a need for a universal influenza vaccine (UIV). Researchers have shown that a combination of conserved epitopes has the potential to be used as a UIV.

RESULT

In the present work, available data on strains, proteins, epitopes and their associated information were used to develop a Web resource, 'EpiCombFlu', which can explore different influenza epitopes and their combinations for conservation among different strains, population coverage and immune response for vaccine design. Forward selection algorithm was implemented in EpiCombFlu to select optimum combination of epitopes that may be expressed and evaluated as potential UIV.

AVAILABILITY

The Web resource is freely available at http://117.211.115.67/influenza/home.html.

CONTACT

chittaranjan.rout@juit.ac.in

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.