No abstract

High-throughput sequencing produces an extraordinary amount of genomic data that is organized into a number of high-dimension datasets. Accordingly, visualization of genomic data has become essential for quality control, exploration, and data interpretation. The Variant Call Format (VCF) is a text file format generated during the variant calling process that contains genomic information and locations of variants in a group of sequenced samples. The current workflow for visualization of genomic variant data from VCF files requires use of a combination of existing tools. Here, we describe VIVA (VIsualization of VAriants), a command line utility and Jupyter Notebook based tool for evaluating and sharing genomic data for variant analysis and quality control of sequencing experiments from VCF files. VIVA combines the functionality of existing tools into a single command to interactively evaluate and share genomic data, as well as create publication quality graphics.

Candida parapsilosis has emerged as a frequent cause of invasive candidiasis with increasing evidence of unique biological features relative to C. albicans. As it adapts to conditions within a mammalian host, rapid changes in gene expression are necessary to facilitate colonization and persistence in this environment. Adhesion of the organism to biological surfaces is a key first step in this process and is the focus of this study. Building on previous observations showing the importance of a member of the ALS gene family in C. parapsilosis adhesion, three clinical isolates were cultured under two conditions that mimic the mammalian host and promote adhesion, incubation at 37°C in tissue culture medium 199 or in human plasma. Transcriptional profiles using RNA-seq were obtained in these adhesion-inducing conditions and compared to profiles following growth in yeast media that suppress adhesion to identify gene expression profiles associated with adhesion. Overall gene expression profiles among the three strains were similar in both adhesion-inducing conditions and distinct from adhesion-suppressing conditions. Pairwise analysis among the three growth conditions identified 133 genes that were differentially expressed at a cutoff of ±4-fold, with the most upregulated genes significantly enriched in iron acquisition and transmembrane transport, while the most downregulated genes were enriched in oxidation-reduction processes. Gene family enrichment analysis identified gene families with diverse functions that may have an important role in this important step for colonization and disease. IMPORTANCE Invasive Candida infections are frequent complications of the immunocompromised and are associated with substantive morbidity and mortality. Although C. albicans is the best-studied species, emerging infections by non-albicans Candida species have led to increased efforts to understand aspects of their pathogenesis that are unique from C. albicans. C. parapsilosis is a frequent cause of invasive infections, particularly among premature infants. Recent efforts have identified important virulence mechanisms that have features distinct from C. albicans. C. parapsilosis can exist outside a host environment and therefore requires rapid modifications when it encounters a mammalian host to prevent its clearance. An important first step in the process is adhesion to host surfaces. This work takes a global, nonbiased approach to investigate broad changes in gene expression that accompany efficient adhesion. As such, biological pathways and individual protein targets are identified that may be amenable to manipulation to reduce colonization and disease from this organism.

Preeclampsia is a hypertensive disorder of pregnancy, which complicates up to 15% of US deliveries. It is an idiopathic disorder associated with several different phenotypes. We sought to determine if the genetic architecture of preeclampsia can be described by clusters of patients with variants in genes in shared protein interaction networks. We performed a case-control study using whole exome sequencing on early onset preeclamptic mothers with severe clinical features and control mothers with uncomplicated pregnancies between 2016 and 2020. A total of 143 patients were enrolled, 61 women with early onset preeclampsia with severe features based on ACOG criteria, and 82 control women at term, matched for race and ethnicity. A network analysis and visualization tool, Proteinarium, was used to confirm there are clusters of patients with shared gene networks associated with severe preeclampsia. The majority of the sequenced patients appear in two significant clusters. We identified one case dominant and one control dominant cluster. Thirteen genes were unique to the case dominated cluster. Among these genes, LAMB2, PTK2, RAC1, QSOX1, FN1, and VCAM1 have known associations with the pathogenic mechanisms of preeclampsia. Using bioinformatic analysis, we were able to identify subsets of patients with shared protein interaction networks, thus confirming our hypothesis about the genetic architecture of preeclampsia.

Severe oligohydramnios (OH) due to prolonged loss of amniotic fluid can cause pulmonary hypoplasia. Animal model of pulmonary hypoplasia induced by amniotic fluid drainage is partly attributed to changes in mechanical compression of the lung.Although numerous studies on OH-model have demonstrated changes in several individual proteins, however, the underlying mechanisms for interrupting normal lung development in response to a decrease of amniotic fluid volume are not fully understood. In this study, we used a proteomic approach to explore differences in the expression of a wide range of proteins after induction of OH in a mouse model of pulmonary hypoplasia to find out the signaling/molecular pathways involved in fetal lung development. Liquid chromatography-massspectromery/mass spectrometry analysis found 474 proteins that were differentially expressed in OH-induced hypoplastic lungs in comparison to untouched (UnT) control. Among these proteins, we confirmed the downregulation of AKT1, SP-D, and CD200, and provided proofof-concept for the first time about the potential role that these proteins could play in fetal lung development.

Heme oxygenase-1 (HO-1) is a rate-limiting enzyme in degrading heme into biliverdin and iron. HO-1 can also enter the nucleus and regulate gene transcription independent of its enzymatic activity. Whether HO-1 can alter gene expression through direct binding to target DNA remains unclear. Here, we performed HO-1 CHIP-seq and then employed 3D structural modeling to reveal putative HO-1 DNA binding domains. We identified three probable DNA binding domains on HO-1. Using the Proteinarium, we identified several genes as the most highly connected nodes in the interactome among the HO-1 gene binding targets. We further demonstrated that HO-1 modulates the expression of these key genes using Hmox1 deficient cells. Finally, mutation of four conserved amino acids (E215, I211, E201, and Q27) within HO-1 DNA binding domain 1 significantly increased expression of Gtpbp3 and Eif1 genes that were identified within the top 10 binding hits normalized by gene length predicted to bind this domain. Based on these data, we conclude that HO-1 protein is a putative DNA binding protein, and regulates targeted gene expression. This provides the foundation for developing specific inhibitors or activators targeting HO-1 DNA binding domains to modulate targeted gene expression and corresponding cellular function.

The likely genetic architecture of complex diseases is that subgroups of patients share variants in genes in specific networks sufficient to express a shared phenotype. We combined high throughput sequencing with advanced bioinformatic approaches to identify such subgroups of patients with variants in shared networks. We performed targeted sequencing of patients with 2 or 3 generations of preterm birth on genes, gene sets and haplotype blocks that were highly associated with preterm birth. We analyzed the data using a multi-sample, protein–protein interaction (PPI) tool to identify significant clusters of patients associated with preterm birth. We identified shared protein interaction networks among preterm cases in two statistically significant clusters, p < 0.001. We also found two small control-dominated clusters. We replicated these data on an independent, large birth cohort. Separation testing showed significant similarity scores between the clusters from the two independent cohorts of patients. Canonical pathway analysis of the unique genes defining these clusters demonstrated enrichment in inflammatory signaling pathways, the glucocorticoid receptor, the insulin receptor, EGF and B-cell signaling, These results support a genetic architecture defined by subgroups of patients that share variants in genes in specific networks and pathways which are sufficient to give rise to the disease phenotype.

Rather than pathogenic variants in single genes, the likely genetic architecture of complex diseases is that subgroups of patients share variants in genes in specific networks and pathways sufficient to give rise to a shared phenotype. We combined high throughput sequencing with advanced bioinformatic approaches to identify subgroups of patients with shared networks and pathways associated with preterm birth (PTB). We previously identified genes, gene sets and haplotype blocks that were highly associated with preterm birth. We performed targeted sequencing on these genes and genomic regions on highly phenotyped patients with 2 or 3 generations of preterm birth, and term controls with no family history of preterm birth. We performed a genotype test for differential abundance of variants between cases and controls. We used the genotype association statistics for ranking purposes in order to analyze the data using a multi-sample, protein-protein interaction (PPI) tool to identify significant clusters of patients associated with preterm birth. We identified shared interaction networks of proteins among 45 preterm cases in two statistically significant clusters, p<0.001. We also found two small control-dominated clusters. For replication, we compared our data to an independent, large birth cohort. Sequence data on 60 cases and 321 controls identified 34 preterm cases with shared networks of proteins distributed in two significant clusters. Analysis of the layered PPI networks of these clusters showed significant similarity scores between the clusters from the two independent cohorts of patients. Canonical pathway analysis of the unique genes defining these clusters demonstrated enrichment in inflammatory signaling pathways, the glucocorticoid receptor, the insulin receptor, EGF and B-cell signaling, These results provide insights into the genetics of PTB and support a genetic architecture defined by subgroups of patients that share variants in genes in specific networks and pathways which are sufficient to give rise to the disease phenotype.