Evolutionary transition from blood feeding to obligate nonbiting in a mosquito.

The spread of blood-borne pathogens by mosquitoes relies on their taking a blood meal; if there is no bite, there is no disease transmission. Although many species of mosquitoes never take a blood meal, identifying genes that distinguish blood feeding from obligate nonbiting is hampered by the fact that these different lifestyles occur in separate, genetically incompatible species. There is, however, one unique extant species with populations that share a common genetic background but blood feed in one region and are obligate nonbiters in the rest of their range: Wyeomyia smithii Contemporary blood-feeding and obligate nonbiting populations represent end points of divergence between fully interfertile southern and northern populations. This divergence has undoubtedly resulted in genetic changes that are unrelated to blood feeding, and the challenge is to winnow out the unrelated genetic factors to identify those related specifically to the evolutionary transition from blood feeding to obligate nonbiting. Herein, we determine differential gene expression resulting from directional selection on blood feeding within a polymorphic population to isolate genetic differences between blood feeding and obligate nonbiting. We show that the evolution of nonbiting has resulted in a greatly reduced metabolic investment compared with biting populations, a greater reliance on opportunistic metabolic pathways, and greater reliance on visual rather than olfactory sensory input. W. smithii provides a unique starting point to determine if there are universal nonbiting genes in mosquitoes that could be manipulated as a means to control vector-borne disease.

Evolutionary divergence of core and post-translational circadian clock genes in the pitcher-plant mosquito, Wyeomyia smithii.

BACKGROUND: Internal circadian (circa, about; dies, day) clocks enable organisms to maintain adaptive timing of their daily behavioral activities and physiological functions. Eukaryotic clocks consist of core transcription-translation feedback loops that generate a cycle and post-translational modifiers that maintain that cycle at about 24 h. We use the pitcher-plant mosquito, Wyeomyia smithii (subfamily Culicini, tribe Sabethini), to test whether evolutionary divergence of the circadian clock genes in this species, relative to other insects, has involved primarily genes in the core feedback loops or the post-translational modifiers. Heretofore, there is no reference transcriptome or genome sequence for any mosquito in the tribe Sabethini, which includes over 375 mainly circumtropical species. METHODS: We sequenced, assembled and annotated the transcriptome of W. smithii containing nearly 95 % of conserved single-copy orthologs in animal genomes. We used the translated contigs and singletons to determine the average rates of circadian clock-gene divergence in W. smithii relative to three other mosquito genera, to Drosophila, to the butterfly, Danaus, and to the wasp, Nasonia. RESULTS: Over 1.08 million cDNA sequence reads were obtained consisting of 432.5 million nucleotides. Their assembly produced 25,904 contigs and 54,418 singletons of which 62 % and 28 % are annotated as protein-coding genes, respectively, sharing homology with other animal proteomes. DISCUSSION: The W. smithii transcriptome includes all nine circadian transcription-translation feedback-loop genes and all eight post-translational modifier genes we sought to identify (Fig. 1). After aligning translated W. smithii contigs and singletons from this transcriptome with other insects, we determined that there was no significant difference in the average divergence of W. smithii from the six other taxa between the core feedback-loop genes and post-translational modifiers. CONCLUSIONS: The characterized transcriptome is sufficiently complete and of sufficient quality to have uncovered all of the insect circadian clock genes we sought to identify (Fig. 1). Relative divergence does not differ between core feedback-loop genes and post-translational modifiers of those genes in a Sabethine species (W. smithii) that has experienced a continual northward dispersal into temperate regions of progressively longer summer day lengths as compared with six other insect taxa. An associated microarray platform derived from this work will enable the investigation of functional genomics of circadian rhythmicity, photoperiodic time measurement, and diapause along a photic and seasonal geographic gradient.

Incremental value of three-dimensional echocardiography over the two-dimensional technique in the assessment of combined sinus of valsalva rupture into the right ventricle and adjacent perimembranous ventricular septal defect.

We describe the incremental value of live/real time three-dimensional (3D) echocardiography over the 2D technique in the assessment of ruptured right sinus of Valsalva into the right ventricle associated with an adjacent perimembranous ventricular septal defect in an elderly patient.

Biology-inspired graph neural network encodes reactome and reveals biochemical reactions of disease.

Functional heterogeneity of healthy human tissues complicates interpretation of molecular studies, impeding precision therapeutic target identification and treatment. Considering this, we generated a graph neural network with Reactome-based architecture and trained it using 9,115 samples from Genotype-Tissue Expression (GTEx). Our graph neural network (GNN) achieves adjusted Rand index (ARI) = 0.7909, while a Resnet18 control model achieves ARI = 0.7781, on 370 held-out healthy human tissue samples from The Cancer Genome Atlas (TCGA), despite the Resnet18 using over 600 times the parameters. Our GNN also succeeds in separating 83 healthy skin samples from 95 lesional psoriasis samples, revealing that upregulation of 26S- and NUB1-mediated degradation of NEDD8, UBD, and their conjugates is central to the largest perturbed reaction network component in psoriasis. We show that our results are not discoverable using traditional differential expression and hypergeometric pathway enrichment analyses yet are supported by separate human multi-omics and small-molecule mouse studies, suggesting future molecular disease studies may benefit from similar GNN analytical approaches.

Leveraging biochemical reactions to unravel functional impacts of cancer somatic variants affecting protein interaction interfaces.

Background: Considering protein mutations in their biological context is essential for understanding their functional impact, interpretation of high-dimensional datasets and development of effective targeted therapies in personalized medicine. Methods: We combined the curated knowledge of biochemical reactions from Reactome with the analysis of interaction-mediating 3D interfaces from Mechismo. In addition, we provided a software tool for users to explore and browse the analysis results in a multi-scale perspective starting from pathways and reactions to protein-protein interactions and protein 3D structures. Results: We analyzed somatic mutations from TCGA, revealing several significantly impacted reactions and pathways in specific cancer types. We found examples of genes not yet listed as oncodrivers, whose rare mutations were predicted to affect cancer processes similarly to known oncodrivers. Some identified processes lack any known oncodrivers, which suggests potentially new cancer-related processes (e.g. complement cascade reactions). Furthermore, we found that mutations perturbing certain processes are significantly associated with distinct phenotypes (i.e. survival time) in specific cancer types (e.g. PIK3CA centered pathways in LGG and UCEC cancer types), suggesting the translational potential of our approach for patient stratification. Our analysis also uncovered several druggable processes (e.g. GPCR signalling pathways) containing enriched reactions, providing support for new off-label therapeutic options. Conclusions: In summary, we have established a multi-scale approach to study genetic variants based on protein-protein interaction 3D structures. Our approach is different from previously published studies in its focus on biochemical reactions and can be applied to other data types (e.g. post-translational modifications) collected for many types of disease.

A crowdsourced analysis to identify ab initio molecular signatures predictive of susceptibility to viral infection.

The response to respiratory viruses varies substantially between individuals, and there are currently no known molecular predictors from the early stages of infection. Here we conduct a community-based analysis to determine whether pre- or early post-exposure molecular factors could predict physiologic responses to viral exposure. Using peripheral blood gene expression profiles collected from healthy subjects prior to exposure to one of four respiratory viruses (H1N1, H3N2, Rhinovirus, and RSV), as well as up to 24 h following exposure, we find that it is possible to construct models predictive of symptomatic response using profiles even prior to viral exposure. Analysis of predictive gene features reveal little overlap among models; however, in aggregate, these genes are enriched for common pathways. Heme metabolism, the most significantly enriched pathway, is associated with a higher risk of developing symptoms following viral exposure. This study demonstrates that pre-exposure molecular predictors can be identified and improves our understanding of the mechanisms of response to respiratory viruses.

Cell fusion potentiates tumor heterogeneity and reveals circulating hybrid cells that correlate with stage and survival.

High lethality rates associated with metastatic cancer highlight an urgent medical need for improved understanding of biologic mechanisms driving metastatic spread and identification of biomarkers predicting late-stage progression. Numerous neoplastic cell intrinsic and extrinsic mechanisms fuel tumor progression; however, mechanisms driving heterogeneity of neoplastic cells in solid tumors remain obscure. Increased mutational rates of neoplastic cells in stressed environments are implicated but cannot explain all aspects of tumor heterogeneity. We present evidence that fusion of neoplastic cells with leukocytes (for example, macrophages) contributes to tumor heterogeneity, resulting in cells exhibiting increased metastatic behavior. Fusion hybrids (cells harboring hematopoietic and epithelial properties) are readily detectible in cell culture and tumor-bearing mice. Further, hybrids enumerated in peripheral blood of human cancer patients correlate with disease stage and predict overall survival. This unique population of neoplastic cells provides a novel biomarker for tumor staging, as well as a potential therapeutic target for intervention.