DICEP: An integrative approach to augmenting genomic island detection.

Mobilization of clusters of genes called genomic islands (GIs) across bacterial lineages facilitates dissemination of traits, such as, resistance against antibiotics, virulence or hypervirulence, and versatile metabolic capabilities. Robust delineation of GIs is critical to understanding bacterial evolution that has a vast impact on different life forms. Methods for identification of GIs exploit different evolutionary features or signals encoded within the genomes of bacteria, however, the current state-of-the-art in GI detection still leaves much to be desired. Here, we have taken a combinatorial approach that accounted for GI specific features such as compositional bias, aberrant phyletic pattern, and marker gene enrichment within an integrative framework to delineate GIs in bacterial genomes. Our GI prediction tool, DICEP, was assessed on simulated genomes and well-characterized bacterial genomes. DICEP compared favorably with current GI detection tools on real and synthetic datasets.

A gene network-driven approach to infer novel pathogenicity-associated genes: application to Pseudomonas aeruginosa PAO1.

IMPORTANCE: We present here a new systems-level approach to decipher genetic factors and biological pathways associated with virulence and/or antibiotic treatment of bacterial pathogens. The power of this approach was demonstrated by application to a well-studied pathogen Pseudomonas aeruginosa PAO1. Our gene co-expression network-based approach unraveled known and unknown genes and their networks associated with pathogenicity in P. aeruginosa PAO1. The systems-level investigation of P. aeruginosa PAO1 helped identify putative pathogenicity and resistance-associated genetic factors that could not otherwise be detected by conventional approaches of differential gene expression analysis. The network-based analysis uncovered modules that harbor genes not previously reported by several original studies on P. aeruginosa virulence and resistance. These could potentially act as molecular determinants of P. aeruginosa PAO1 pathogenicity and responses to antibiotics.

Molecular signatures in the progression of COVID-19 severity.

SARS-CoV-2 is the causative agent of COVID-19 that has infected over 642 million and killed over 6.6 million people around the globe. Underlying a wide range of clinical manifestations of this disease, from moderate to extremely severe systemic conditions, could be genes or pathways differentially expressing in the hosts. It is therefore important to gain insights into pathways involved in COVID-19 pathogenesis and host defense and thus understand the host response to this pathogen at the physiological and molecular level. To uncover genes and pathways involved in the differential clinical manifestations of this disease, we developed a novel gene co-expression network based pipeline that uses gene expression obtained from different SARS-CoV-2 infected human tissues. We leveraged the network to identify novel genes or pathways that likely differentially express and could be physiologically significant in the COVID-19 pathogenesis and progression but were deemed statistically non-significant and therefore not further investigated in the original studies. Our network-based approach aided in the identification of co-expression modules enriched in differentially expressing genes (DEGs) during different stages of COVID-19 and enabled discovery of novel genes involved in the COVID-19 pathogenesis, by virtue of their transcript abundance and association with genes expressing differentially in modules enriched in DEGs. We further prioritized by considering only those enriched gene modules that have most of their genes differentially expressed, inferred by the original studies or this study, and document here 7 novel genes potentially involved in moderate, 2 in severe, 48 in extremely severe COVID-19, and 96 novel genes involved in the progression of COVID-19 from severe to extremely severe conditions. Our study shines a new light on genes and their networks (modules) that drive the progression of COVID-19 from moderate to extremely severe condition. These findings could aid development of new therapeutics to combat COVID-19.

The Arabidopsis gene co-expression network.

Identifying genes that interact to confer a biological function to an organism is one of the main goals of functional genomics. High-throughput technologies for assessment and quantification of genome-wide gene expression patterns have enabled systems-level analyses to infer pathways or networks of genes involved in different functions under many different conditions. Here, we leveraged the publicly available, information-rich RNA-Seq datasets of the model plant Arabidopsis thaliana to construct a gene co-expression network, which was partitioned into clusters or modules that harbor genes correlated by expression. Gene ontology and pathway enrichment analyses were performed to assess functional terms and pathways that were enriched within the different gene modules. By interrogating the co-expression network for genes in different modules that associate with a gene of interest, diverse functional roles of the gene can be deciphered. By mapping genes differentially expressing under a certain condition in Arabidopsis onto the co-expression network, we demonstrate the ability of the network to uncover novel genes that are likely transcriptionally active but prone to be missed by standard statistical approaches due to their falling outside of the confidence zone of detection. To our knowledge, this is the first A. thaliana co-expression network constructed using the entire mRNA-Seq datasets (>20,000) available at the NCBI SRA database. The developed network can serve as a useful resource for the Arabidopsis research community to interrogate specific genes of interest within the network, retrieve the respective interactomes, decipher gene modules that are transcriptionally altered under certain condition or stage, and gain understanding of gene functions.

Analysis of transcribed sequences from young and mature zebrafish thrombocytes.

The zebrafish is an excellent model system to study thrombocyte function and development. Due to the difficulties in separating young and mature thrombocytes, comparative transcriptomics between these two cell types has not been performed. It is important to study these differences in order to understand the mechanism of thrombocyte maturation. Here, we performed single-cell RNA sequencing of the young and mature zebrafish thrombocytes and compared the two datasets for young and mature thrombocyte transcripts. We found a total of 9143 genes expressed cumulatively in both young and mature thrombocytes, and among these, 72% of zebrafish thrombocyte-expressed genes have human orthologs according to the Ensembl human genome annotation. We also found 397 uniquely expressed genes in young and 2153 uniquely expressed genes in mature thrombocytes. Of these 397 and 2153 genes, 272 and 1620 corresponded to human orthologous genes, respectively. Of all genes expressed in both young and mature thrombocytes, 4224 have been reported to be expressed in human megakaryocytes, and 1603 were found in platelets. Among these orthologs, 156 transcription factor transcripts in thrombocytes were found in megakaryocytes and 60 transcription factor transcripts were found in platelets including a few already known factors such as Nfe2 and Nfe212a (related to Nfe2) that are present in both megakaryocytes, and platelets. These results indicate that thrombocytes have more megakaryocyte features and since platelets are megakaryocyte fragments, platelets also appear to be thrombocyte equivalents. In conclusion, our study delineates the differential gene expression patterns of young and mature thrombocytes, highlighting the processes regulating thrombocyte maturation. Future knockdown studies of these young and mature thrombocyte-specific genes are feasible and will provide the basis for understanding megakaryocyte maturation.