Predicting transcription factor activity using prior biological information.

Dysregulation of normal transcription factor activity is a common driver of disease. Therefore, the detection of aberrant transcription factor activity is important to understand disease pathogenesis. We have developed Priori, a method to predict transcription factor activity from RNA sequencing data. Priori has two key advantages over existing methods. First, Priori utilizes literature-supported regulatory information to identify transcription factor-target gene relationships. It then applies linear models to determine the impact of transcription factor regulation on the expression of its target genes. Second, results from a third-party benchmarking pipeline reveals that Priori detects aberrant activity from 124 single-gene perturbation experiments with higher sensitivity and specificity than 11 other methods. We applied Priori and other top-performing methods to predict transcription factor activity from two large primary patient datasets. Our work demonstrates that Priori uniquely discovered significant determinants of survival in breast cancer and identified mediators of drug response in leukemia.

A framework for considering prior information in network-based approaches to omics data analysis.

For decades, molecular biologists have been uncovering the mechanics of biological systems. Efforts to bring their findings together have led to the development of multiple databases and information systems that capture and present pathway information in a computable network format. Concurrently, the advent of modern omics technologies has empowered researchers to systematically profile cellular processes across different modalities. Numerous algorithms, methodologies, and tools have been developed to use prior knowledge networks (PKNs) in the analysis of omics datasets. Interestingly, it has been repeatedly demonstrated that the source of prior knowledge can greatly impact the results of a given analysis. For these methods to be successful it is paramount that their selection of PKNs is amenable to the data type and the computational task they aim to accomplish. Here we present a five-level framework that broadly describes network models in terms of their scope, level of detail, and ability to inform causal predictions. To contextualize this framework, we review a handful of network-based omics analysis methods at each level, while also describing the computational tasks they aim to accomplish.

HMOX1 genetic polymorphisms and outcomes in infectious disease: A systematic review.

INTRODUCTION: Heme-oxygenase 1 (HMOX1) is a critical stress response gene that catalyzes the multistep oxidation of heme. A GT(n) repeat of variable length in the promoter in has been associated with a wide range of human diseases, including infections. This paper aims to summarise and systematically review associations between the length of the HMOX1 GT(n) promoter and infectious disease in humans. METHODS: A search using relevant terms was performed in PubMED and EMBASE through to 15/01/21 identifying all research that studied an association between the HMOX1 GT(n) repeat polymorphism and the incidence and/or outcome of any human infectious disease. Citations were screened for additional studies. Potential studies were screened for inclusion by two authors. Data was extracted on allele frequency, genotype, strength of association, mechanism of genotyping, and potential biases. A narrative review was performed across each type of infection. RESULTS: 1,533 studies were identified in the search, and one via citation screening. Sixteen studies were ultimately included, seven in malaria, three in HIV, three in sepsis, and one each in pneumonia, hepatitis C, and acute respiratory distress syndrome (ARDS). Sample sizes for nearly all studies were small (biggest study, n = 1,646). Allelic definition was different across all included studies. All studies were at some risk of bias. In malaria, three studies suggested that longer alleles were associated with reduced risk of severe malaria, particularly malaria-induced renal dysfunction, with four studies identifying a null association. In sepsis, two studies suggested an association with longer alleles and better outcomes. CONCLUSIONS: Despite the importance of HMOX1 in survival from infection, and the association between repeat length and gene expression, the clinical data supporting an association between repeat length and incidence and/or outcome of infection remain inconclusive.

An omic and multidimensional spatial atlas from serial biopsies of an evolving metastatic breast cancer.

Mechanisms of therapeutic resistance and vulnerability evolve in metastatic cancers as tumor cells and extrinsic microenvironmental influences change during treatment. To support the development of methods for identifying these mechanisms in individual people, here we present an omic and multidimensional spatial (OMS) atlas generated from four serial biopsies of an individual with metastatic breast cancer during 3.5 years of therapy. This resource links detailed, longitudinal clinical metadata that includes treatment times and doses, anatomic imaging, and blood-based response measurements to clinical and exploratory analyses, which includes comprehensive DNA, RNA, and protein profiles; images of multiplexed immunostaining; and 2- and 3-dimensional scanning electron micrographs. These data report aspects of heterogeneity and evolution of the cancer genome, signaling pathways, immune microenvironment, cellular composition and organization, and ultrastructure. We present illustrative examples of how integrative analyses of these data reveal potential mechanisms of response and resistance and suggest novel therapeutic vulnerabilities.

COVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms.

We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective.

Erythrocytosis and thromboembolic events in transgender individuals receiving gender-affirming testosterone.

Erythrocytosis is a well-recognized consequence of exogenous testosterone, however its prevalence and contributions to thrombosis remain unknown in the context of gender-affirming hormonal therapy. We undertook a retrospective study of transgender and non-binary (TGNB) adults receiving exogenous testosterone. In the retrospective sample, 923 transgender individuals receiving testosterone were identified with 519 having documented pre- and post-testosterone hemoglobin and hematocrit (Hgb/Hct). The mean peak Hgb/Hct was 15.7 g/dL, and 47.0%. Mean time-to-peak Hgb/Hct was 31.2 months; 7.8% developed a hemoglobin >17.5 g/dL, whereas 20% developed a hematocrit of >50%. Testosterone dose reduction occurred in 42% of patients with erythrocytosis and 4.8% underwent phlebotomy. Thromboembolic events occurred in 0.9%, of which 80% had developed erythrocytosis by either Hgb or Hct, including two cases each of superficial and calf vein thrombosis as well as one ischemic stroke. We then performed an analysis of 14,294,784 hospitalizations from the 2016-17 US National Inpatient Sample (NIS), which identified 4141 admissions involving transgender individuals. Of those, seven had erythrocytosis with one concurrent venous thromboembolic event. Hematocrit >50% occurs in up to 20% of transgender individuals receiving testosterone. Despite the high incidence of erythrocytosis, thromboembolic events and hospitalizations involving erythrocytosis were uncommon.

Systematic interrogation of mutation groupings reveals divergent downstream expression programs within key cancer genes.

BACKGROUND: Genes implicated in tumorigenesis often exhibit diverse sets of genomic variants in the tumor cohorts within which they are frequently mutated. For many genes, neither the transcriptomic effects of these variants nor their relationship to one another in cancer processes have been well-characterized. We sought to identify the downstream expression effects of these mutations and to determine whether this heterogeneity at the genomic level is reflected in a corresponding heterogeneity at the transcriptomic level. RESULTS: By applying a novel hierarchical framework for organizing the mutations present in a cohort along with machine learning pipelines trained on samples' expression profiles we systematically interrogated the signatures associated with combinations of mutations recurrent in cancer. This allowed us to catalogue the mutations with discernible downstream expression effects across a number of tumor cohorts as well as to uncover and characterize over a hundred cases where subsets of a gene's mutations are clearly divergent in their function from the remaining mutations of the gene. These findings successfully replicated across a number of disease contexts and were found to have clear implications for the delineation of cancer processes and for clinical decisions. CONCLUSIONS: The results of cataloguing the downstream effects of mutation subgroupings across cancer cohorts underline the importance of incorporating the diversity present within oncogenes in models designed to capture the downstream effects of their mutations.

2D MXenes with antiviral and immunomodulatory properties: A pilot study against SARS-CoV-2.

Two-dimensional transition metal carbides/carbonitrides known as MXenes are rapidly growing as multimodal nanoplatforms in biomedicine. Here, taking SARS-CoV-2 as a model, we explored the antiviral properties and immune-profile of a large panel of four highly stable and well-characterized MXenes - Ti3C2Tx, Ta4C3T x , Mo2Ti2C3T x and Nb4C3T x . To start with antiviral assessment, we first selected and deeply analyzed four different SARS-CoV-2 genotypes, common in most countries and carrying the wild type or mutated spike protein. When inhibition of the viral infection was tested in vitro with four viral clades, Ti3C2T x in particular, was able to significantly reduce infection only in SARS-CoV-2/clade GR infected Vero E6 cells. This difference in the antiviral activity, among the four viral particles tested, highlights the importance of considering the viral genotypes and mutations while testing antiviral activity of potential drugs and nanomaterials. Among the other MXenes tested, Mo2Ti2C3T x also showed antiviral properties. Proteomic, functional annotation analysis and comparison to the already published SARS-CoV-2 protein interaction map revealed that MXene-treatment exerts specific inhibitory mechanisms. Envisaging future antiviral MXene-based drug nano-formulations and considering the central importance of the immune response to viral infections, the immune impact of MXenes was evaluated on human primary immune cells by flow cytometry and single-cell mass cytometry on 17 distinct immune subpopulations. Moreover, 40 secreted cytokines were analyzed by Luminex technology. MXene immune profiling revealed i) the excellent bio and immune compatibility of the material, as well as the ability of MXene ii) to inhibit monocytes and iii) to reduce the release of pro-inflammatory cytokines, suggesting an anti-inflammatory effect elicited by MXene. We here report a selection of MXenes and viral SARS-CoV-2 genotypes/mutations, a series of the computational, structural and molecular data depicting deeply the SARS-CoV-2 mechanism of inhibition, as well as high dimensional single-cell immune-MXene profiling. Taken together, our results provide a compendium of knowledge for new developments of MXene-based multi-functioning nanosystems as antivirals and immune-modulators.

Ribavirin shows antiviral activity against SARS-CoV-2 and downregulates the activity of TMPRSS2 and the expression of ACE2 in vitro.

Ribavirin is a guanosine analog with broad-spectrum antiviral activity against RNA viruses. Based on this, we aimed to show the anti-SARS-CoV-2 activity of this drug molecule via in vitro, in silico, and molecular techniques. Ribavirin showed antiviral activity in Vero E6 cells following SARS-CoV-2 infection, whereas the drug itself did not show any toxic effect over the concentration range tested. In silico analysis suggested that ribavirin has a broad-spectrum impact on SARS-CoV-2, acting at different viral proteins. According to the detailed molecular techniques, ribavirin was shown to decrease the expression of TMPRSS2 at both mRNA and protein levels 48 h after treatment. The suppressive effect of ribavirin in ACE2 protein expression was shown to be dependent on cell types. Finally, proteolytic activity assays showed that ribavirin also showed an inhibitory effect on the TMPRSS2 enzyme. Based on these results, we hypothesized that ribavirin may inhibit the expression of TMPRSS2 by modulating the formation of inhibitory G-quadruplex structures at the TMPRSS2 promoter. As a conclusion, ribavirin is a potential antiviral drug for the treatment against SARS-CoV-2, and it interferes with the effects of TMPRSS2 and ACE2 expression.

The association of mavenism and pleasure with food involvement in older adults.

BACKGROUND: Food involvement is concerned with the involvement people have in the preparation and consumption of food. Little is known about older people's food involvement or about the factors which may influence it. Therefore the main aim of this study was to examine food involvement and its associations among older Australians. METHODS: An Internet-based nationwide survey of 1,041 people aged 55 years and over (M = 66 years, SD 6.99) was conducted in 2012. Quota sampling was used to ensure that the age, gender and state of residence of the respondents were representative of the Australian population aged over 55 years. Bell and Marshall's Food Involvement Scale was administered, along with questions pertaining to socio-demographic, social and hedonic factors. RESULTS: Overall predictor variables explained 45% (p = <0.0001) of variance in food involvement. Food mavenism and pleasure motivation for food were the factors most strongly associated with food involvement (ß = .36; 95% CI .46, .61; p = < 0.0001 and ß = .31; 95% CI .78, 1.08; p = < 0.0001, respectively). The predictive ability of demographic factors was reasonably poor. CONCLUSIONS: Food mavenism and pleasure motivation are stronger predictors of Food Involvement than demographic factors. This suggests communication and health promotion opportunities among older people.