Pan-cancer molecular signatures connecting aspartate transaminase (AST) to cancer prognosis, metabolic and immune signatures.

Background: Serum aspartate transaminase (sAST) level is used routinely in conjunction with other clinical assays to assess liver health and disease. Increasing evidence suggests that sAST is associated with all-cause mortality and has prognostic value in several cancers, including gastrointestinal and urothelial cancers. Here, we undertake a systems approach to unravel molecular connections between AST and cancer prognosis, metabolism, and immune signatures at the transcriptomic and proteomic levels. Methods: We mined public gene expression data across multiple normal and cancerous tissues using the Genotype Tissue Expression (GTEX) resource and The Cancer Genome Atlas (TCGA) to assess the expression of genes encoding AST isoenzymes (GOT1 and GOT2) and their association with disease prognosis and immune infiltration signatures across multiple tumors. We examined the associations between AST and previously reported pan-cancer molecular subtypes characterized by distinct metabolic and immune signatures. We analyzed human protein-protein interaction networks for interactions between GOT1 and GOT2 with cancer-associated proteins. Using public databases and protein-protein interaction networks, we determined whether the subset of proteins that interact with AST (GOT1 and GOT2 interactomes) are enriched with proteins associated with specific diseases, miRNAs and transcription factors. Results: We show that AST transcript isoforms (GOT1 and GOT2) are expressed across a wide range of normal tissues. AST isoforms are upregulated in tumors of the breast, lung, uterus, and thymus relative to normal tissues but downregulated in tumors of the liver, colon, brain, kidney and skeletal sarcomas. At the proteomic level, we find that the expression of AST is associated with distinct pan-cancer molecular subtypes with an enrichment of specific metabolic and immune signatures. Based on human protein-protein interaction data, AST physically interacts with multiple proteins involved in tumor initiation, suppression, progression, and treatment. We find enrichments in the AST interactomes for proteins associated with liver and lung cancer and dermatologic diseases. At the regulatory level, the GOT1 interactome is enriched with the targets of cancer-associated miRNAs, specifically mir34a - a promising cancer therapeutic, while the GOT2 interactome is enriched with proteins that interact with cancer-associated transcription factors. Conclusions: Our findings suggest that perturbations in the levels of AST within specific tissues reflect pathophysiological changes beyond tissue damage and have implications for cancer metabolism, immune infiltration, prognosis, and treatment personalization.

Promoting data harmonization to evaluate vaccine hesitancy in LMICs: approach and applications.

BACKGROUND: Factors influencing the health of populations are subjects of interdisciplinary study. However, datasets relevant to public health often lack interdisciplinary breath. It is difficult to combine data on health outcomes with datasets on potentially important contextual factors, like political violence or development, due to incompatible levels of geographic support; differing data formats and structures; differences in sampling procedures and wording; and the stability of temporal trends. We present a computational package to combine spatially misaligned datasets, and provide an illustrative analysis of multi-dimensional factors in health outcomes. METHODS: We rely on a new software toolkit, Sub-National Geospatial Data Archive (SUNGEO), to combine data across disciplinary domains and demonstrate a use case on vaccine hesitancy in Low and Middle-Income Countries (LMICs). We use data from the World Bank's High Frequency Phone Surveys (HFPS) from Kenya, Indonesia, and Malawi. We curate and combine these surveys with data on political violence, elections, economic development, and other contextual factors, using SUNGEO. We then develop a stochastic model to analyze the integrated data and evaluate 1) the stability of vaccination preferences in all three countries over time, and 2) the association between local contextual factors and vaccination preferences. RESULTS: In all three countries, vaccine-acceptance is more persistent than vaccine-hesitancy from round to round: the long-run probability of staying vaccine-acceptant (hesitant) was 0.96 (0.65) in Indonesia, 0.89 (0.21) in Kenya, and 0.76 (0.40) in Malawi. However, vaccine acceptance was significantly less durable in areas exposed to political violence, with percentage point differences (ppd) in vaccine acceptance of -10 (Indonesia), -5 (Kenya), and -64 (Malawi). In Indonesia and Kenya, although not Malawi, vaccine acceptance was also significantly less durable in locations without competitive elections (-19 and -6 ppd, respectively) and in locations with more limited transportation infrastructure (-11 and -8 ppd). CONCLUSION: With SUNGEO, researchers can combine spatially misaligned and incompatible datasets. As an illustrative example, we find that vaccination hesitancy is correlated with political violence, electoral uncompetitiveness and limited access to public goods, consistent with past results that vaccination hesitancy is associated with government distrust.

COVID-19 vaccination refusal trends in Kenya over 2021.

BACKGROUND: Vaccination refusal exacerbates global COVID-19 vaccination inequities. No studies in East Africa have examined temporal trends in vaccination refusal, precluding addressing refusal. We assessed vaccine refusal over time in Kenya, and characterized factors associated with changes in vaccination refusal. METHODS: We analyzed data from the Kenya Rapid Response Phone Survey (RRPS), a household cohort survey representative of the Kenyan population including refugees. Vaccination refusal (defined as the respondent stating they would not receive the vaccine if offered to them at no cost) was measured in February and October 2021. Proportions of vaccination refusal were plotted over time. We analyzed factors in vaccination refusal using a weighted multivariable logistic regression including interactions for time. FINDINGS: Among 11,569 households, vaccination refusal in Kenya decreased from 24 % in February 2021 to 9 % in October 2021. Vaccination refusal was associated with having education beyond the primary level (-4.1[-0.7,-8.9] percentage point difference (ppd)); living with somebody who had symptoms of COVID-19 in the past 14 days (-13.72[-8.9,-18.6]ppd); having symptoms of COVID-19 in the past 14 days (11.0[5.1,16.9]ppd); and distrusting the government in responding to COVID-19 (14.7[7.1,22.4]ppd). There were significant interactions with time and: refugee status and geography, living with somebody with symptoms of COVID-19, having symptoms of COVID-19, and believing in misinformation. INTERPRETATION: The temporal reduction in vaccination refusal in Kenya likely represents substantial strides by the Kenyan vaccination program and possible learnt lessons which require examination. Going forward, there are still several groups which need specific targeting to decrease vaccination refusal and improve vaccination equity, including those with lower levels of education, those with recent COVID-19 symptoms, those who do not practice personal COVID-19 mitigation measures, refugees in urban settings, and those who do not trust the government. Policy and program should focus on decreasing vaccination refusal in these populations, and research focus on understanding barriers and motivators for vaccination.

Cancer-malaria: hidden connections.

Cancer and malaria exemplify two maladies historically assigned to separated research spaces. Cancer, on the one hand, ranks among the top priorities in the research agenda of developed countries. Its rise is mostly explained by the ageing of these populations and linked to environment and lifestyle. Malaria, on the other hand, represents a major health burden for developing countries in the Southern Hemisphere. These two diseases also belong to separate fields of medicine: non-communicable diseases for cancer and communicable diseases for malaria.

Simultaneous genome-wide gene expression and transcript isoform profiling in the human malaria parasite.

Gene expression DNA microarrays have been vital for characterizing whole-genome transcriptional profiles. Nevertheless, their effectiveness relies heavily on the accuracy of genome sequences, the annotation of gene structures, and the sequence-dependent performance of individual probes. Currently available gene expression arrays for the malaria parasite Plasmodium falciparum rely on an average of 2 probes per gene, usually positioned near the 3' end of genes; consequently, existing designs are prone to measurement bias and cannot capture complexities such as the occurrence of transcript isoforms arising from alternative splicing or alternative start/ stop sites. Here, we describe two novel gene expression arrays with exon-focused probes designed with an average of 12 and 20 probes spanning each gene. This high probe density minimizes signal noise inherent in probe-to-probe sequence-dependent hybridization intensity. We demonstrate that these exon arrays accurately profile genome-wide expression, comparing favorably to currently available arrays and RNA-seq profiling, and can detect alternatively spliced transcript isoforms as well as non-coding RNAs (ncRNAs). Of the 964 candidate alternate splicing events from published RNA-seq studies, 162 are confirmed using the exon array. Furthermore, the exon array predicted 330 previously unidentified alternate splicing events. Gene expression microarrays continue to offer a cost-effective alternative to RNA-seq for the simultaneous monitoring of gene expression and alternative splicing events. Microarrays may even be preferred in some cases due to their affordability and the rapid turn-around of results when hundreds of samples are required for fine-scale systems biology investigations, including the monitoring of the networks of gene co-expression in the emergence of drug resistance.

An integrative analysis of small molecule transcriptional responses in the human malaria parasite Plasmodium falciparum.

BACKGROUND: Transcriptional responses to small molecules can provide insights into drug mode of action (MOA). The capacity of the human malaria parasite, Plasmodium falciparum, to respond specifically to transcriptional perturbations has been unclear based on past approaches. Here, we present the most extensive profiling to date of the parasite's transcriptional responsiveness to thirty-one chemically and functionally diverse small molecules. METHODS: We exposed two laboratory strains of the human malaria parasite P. falciparum to brief treatments of thirty-one chemically and functionally diverse small molecules associated with biological effects across multiple pathways based on various levels of evidence. We investigated the impact of chemical composition and MOA on gene expression similarities that arise between perturbations by various compounds. To determine the target biological pathways for each small molecule, we developed a novel framework for encoding small molecule effects on a spectra of biological processes or GO functions that are enriched in the differentially expressed genes of a given small molecule perturbation. RESULTS: We find that small molecules associated with similar transcriptional responses contain similar chemical features, and/ or have a shared MOA. The approach also revealed complex relationships between drugs and biological pathways that are missed by most exisiting approaches. For example, the approach was able to partition small molecule responses into drug-specific effects versus non-specific effects. CONCLUSIONS: Our work provides a new framework for linking transcriptional responses to drug MOA in P. falciparum and can be generalized for the same purpose in other organisms.

Chemogenomic profiling of Plasmodium falciparum as a tool to aid antimalarial drug discovery.

The spread of Plasmodium falciparum multidrug resistance highlights the urgency to discover new targets and chemical scaffolds. Unfortunately, lack of experimentally validated functional information about most P. falciparum genes remains a strategic hurdle. Chemogenomic profiling is an established tool for classification of drugs with similar mechanisms of action by comparing drug fitness profiles in a collection of mutants. Inferences of drug mechanisms of action and targets can be obtained by associations between shifts in drug fitness and specific genetic changes in the mutants. In this screen, P. falciparum, piggyBac single insertion mutants were profiled for altered responses to antimalarial drugs and metabolic inhibitors to create chemogenomic profiles. Drugs targeting the same pathway shared similar response profiles and multiple pairwise correlations of the chemogenomic profiles revealed novel insights into drugs' mechanisms of action. A mutant of the artemisinin resistance candidate gene - "K13-propeller" gene (PF3D7_1343700) exhibited increased susceptibility to artemisinin drugs and identified a cluster of 7 mutants based on similar enhanced responses to the drugs tested. Our approach of chemogenomic profiling reveals artemisinin functional activity, linked by the unexpected drug-gene relationships of these mutants, to signal transduction and cell cycle regulation pathways.

The future of genomic medicine education in Africa.

There are many challenges and opportunities for Africans in the emerging area of genome medicine. In particular, there is a need for investment in local education using real-world African genetic data sets. Cloud-based computing platforms offer one solution for engaging the next generation of biomedical scientists in tackling disease in Africa, and by extension, the world.

Predicting functional and regulatory divergence of a drug resistance transporter gene in the human malaria parasite.

BACKGROUND: The paradigm of resistance evolution to chemotherapeutic agents is that a key coding mutation in a specific gene drives resistance to a particular drug. In the case of resistance to the anti-malarial drug chloroquine (CQ), a specific mutation in the transporter pfcrt is associated with resistance. Here, we apply a series of analytical steps to gene expression data from our lab and leverage 3 independent datasets to identify pfcrt-interacting genes. Resulting networks provide insights into pfcrt's biological functions and regulation, as well as the divergent phenotypic effects of its allelic variants in different genetic backgrounds. RESULTS: To identify pfcrt-interacting genes, we analyze pfcrt co-expression networks in 2 phenotypic states - CQ-resistant (CQR) and CQ-sensitive (CQS) recombinant progeny clones - using a computational approach that prioritizes gene interactions into functional and regulatory relationships. For both phenotypic states, pfcrt co-expressed gene sets are associated with hemoglobin metabolism, consistent with CQ's expected mode of action. To predict the drivers of co-expression divergence, we integrate topological relationships in the co-expression networks with available high confidence protein-protein interaction data. This analysis identifies 3 transcriptional regulators from the ApiAP2 family and histone acetylation as potential mediators of these divergences. We validate the predicted divergences in DNA mismatch repair and histone acetylation by measuring the effects of small molecule inhibitors in recombinant progeny clones combined with quantitative trait locus (QTL) mapping. CONCLUSIONS: This work demonstrates the utility of differential co-expression viewed in a network framework to uncover functional and regulatory divergence in phenotypically distinct parasites. pfcrt-associated co-expression in the CQ resistant progeny highlights CQR-specific gene relationships and possible targeted intervention strategies. The approaches outlined here can be readily generalized to other parasite populations and drug resistances.

Networks' Characteristics Matter for Systems Biology.

A fundamental goal of systems biology is to create models that describe relationships between biological components. Networks are an increasingly popular approach to this problem. However, a scientist interested in modeling biological (e.g., gene expression) data as a network is quickly confounded by the fundamental problem: how to construct the network? It is fairly easy to construct a network, but is it the network for the problem being considered? This is an important problem with three fundamental issues: How to weight edges in the network in order to capture actual biological interactions? What is the effect of the type of biological experiment used to collect the data from which the network is constructed? How to prune the weighted edges (or what cut-off to apply)? Differences in the construction of networks could lead to different biological interpretations. Indeed, we find that there are statistically significant dissimilarities in the functional content and topology between gene co-expression networks constructed using different edge weighting methods, data types, and edge cut-offs. We show that different types of known interactions, such as those found through Affinity Capture-Luminescence or Synthetic Lethality experiments, appear in significantly varying amounts in networks constructed in different ways. Hence, we demonstrate that different biological questions may be answered by the different networks. Consequently, we posit that the approach taken to build a network can be matched to biological questions to get targeted answers. More study is required to understand the implications of different network inference approaches and to draw reliable conclusions from networks used in the field of systems biology.

Shared molecular strategies of the malaria parasite P. falciparum and the human virus HIV-1.

We augmented existing computationally predicted and experimentally determined interactions with evolutionarily conserved interactions between proteins of the malaria parasite, P. falciparum, and the human host. In a validation step, we found that conserved interacting host-parasite protein pairs were specifically expressed in host tissues where both the parasite and host proteins are known to be active. We compared host-parasite interactions with experimentally verified interactions between human host proteins and a very different pathogen, HIV-1. Both pathogens were found to use their protein repertoire in a combinatorial manner, providing a broad connection to host cellular processes. Specifically, the two biologically distinct pathogens predominately target central proteins to take control of a human host cell, effectively reaching into diversified cellular host cellular functions. Interacting signaling pathways and a small set of regulatory and signaling proteins were prime targets of both pathogens, suggesting remarkably similar patterns of host-pathogen interactions despite the vast biological differences of both pathogens. Such an identification of shared molecular strategies by the virus HIV-1 and the eukaryotic intracellular pathogen P. falciparum may allow us to illuminate new avenues of disease intervention.