Zinc accumulation in Atriplex lentiformis is driven by plant genes and the soil microbiome.

Successful phytoremediation of acidic metal-contaminated mine tailings requires amendments to condition tailings properties prior to plant establishment. This conditioning process is complex and includes multiple changes in tailings bio-physico-chemical properties. The objective of this project is to identify relationships between tailings properties, the soil microbiome, and plant stress response genes during growth of Atriplex lentiformis in compost-amended (10 %, 15 %, 20 % w/w) mine tailings. Analyses include RNA-Seq for plant root gene expression, 16S rRNA amplicon sequencing for bacterial/archaeal communities, metal concentrations in both tailings and plant organs, and phenotypic measures of plant stress. Zn accumulation in A. lentiformis leaves varied with compost levels and was the highest in the intermediate treatment (15 %, TC15). Microbial analysis identified Alicyclobacillus, Hydrotalea, and Pseudolabrys taxa with the highest relative abundance in TC15, and these taxa were strongly associated with Zn accumulation. Furthermore, we identified 190 root genes with significant gene expression changes. These root genes were associated with different pathways including, abscisic acid and auxin signaling, defense responses, ion channels, metal ion binding, oxidative stress, transcription regulation, and transmembrane transport. However, root gene expression changes were not driven by the increasing levels of compost. For example, there were 15 genes that were up-regulated in TC15, whereas 106 genes were down-regulated in TC15. The variables analyzed explained 86 % of the variance in Zn accumulation in A. lentiformis leaves. Importantly, Zn accumulation was driven by Zn shoot concentrations, leaf stress symptoms, plant root genes, and microbial taxa. Therefore, our results suggest there are strong plant-microbiome associations that drive Zn accumulation in A. lentiformis and different plant gene pathways are involved in alleviating varying levels of metal stress. Future work is needed to gain a mechanistic understanding of these plant-microbiome interactions to optimize phytoremediation strategies as they will govern the success or failure of the revegetation process.

The GPCR-Gαs-PKA signaling axis promotes T cell dysfunction and cancer immunotherapy failure.

Immune checkpoint blockade (ICB) targeting PD-1 and CTLA-4 has revolutionized cancer treatment. However, many cancers do not respond to ICB, prompting the search for additional strategies to achieve durable responses. G-protein-coupled receptors (GPCRs) are the most intensively studied drug targets but are underexplored in immuno-oncology. Here, we cross-integrated large singe-cell RNA-sequencing datasets from CD8+ T cells covering 19 distinct cancer types and identified an enrichment of Gαs-coupled GPCRs on exhausted CD8+ T cells. These include EP2, EP4, A2AR, ß1AR and ß2AR, all of which promote T cell dysfunction. We also developed transgenic mice expressing a chemogenetic CD8-restricted Gαs-DREADD to activate CD8-restricted Gαs signaling and show that a Gαs-PKA signaling axis promotes CD8+ T cell dysfunction and immunotherapy failure. These data indicate that Gαs-GPCRs are druggable immune checkpoints that might be targeted to enhance the response to ICB immunotherapies.

Digital Omicron detection using unscripted voice samples from social media.

The success of artificial intelligence in clinical environments relies upon the diversity and availability of training data. In some cases, social media data may be used to counterbalance the limited amount of accessible, well-curated clinical data, but this possibility remains largely unexplored. In this study, we mined YouTube to collect voice data from individuals with self-declared positive COVID-19 tests during time periods in which Omicron was the predominant variant1,2,3, while also sampling non-Omicron COVID-19 variants, other upper respiratory infections (URI), and healthy subjects. The resulting dataset was used to train a DenseNet model to detect the Omicron variant from voice changes. Our model achieved 0.85/0.80 specificity/sensitivity in separating Omicron samples from healthy samples and 0.76/0.70 specificity/sensitivity in separating Omicron samples from symptomatic non-COVID samples. In comparison with past studies, which used scripted voice samples, we showed that leveraging the intra-sample variance inherent to unscripted speech enhanced generalization. Our work introduced novel design paradigms for audio-based diagnostic tools and established the potential of social media data to train digital diagnostic models suitable for real-world deployment.

Adaptive laboratory evolution in S. cerevisiae highlights role of transcription factors in fungal xenobiotic resistance.

In vitro evolution and whole genome analysis were used to comprehensively identify the genetic determinants of chemical resistance in Saccharomyces cerevisiae. Sequence analysis identified many genes contributing to the resistance phenotype as well as numerous amino acids in potential targets that may play a role in compound binding. Our work shows that compound-target pairs can be conserved across multiple species. The set of 25 most frequently mutated genes was enriched for transcription factors, and for almost 25 percent of the compounds, resistance was mediated by one of 100 independently derived, gain-of-function SNVs found in a 170 amino acid domain in the two Zn2C6 transcription factors YRR1 and YRM1 (p < 1 × 10-100). This remarkable enrichment for transcription factors as drug resistance genes highlights their important role in the evolution of antifungal xenobiotic resistance and underscores the challenge to develop antifungal treatments that maintain potency.

SEPIA: simulation-based evaluation of prioritization algorithms.

BACKGROUND: The ability to prioritize people living with HIV (PLWH) by risk of future transmissions could aid public health officials in optimizing epidemiological intervention. While methods exist to perform such prioritization based on molecular data, their effectiveness and accuracy are poorly understood, and it is unclear how one can directly compare the accuracy of different methods. We introduce SEPIA (Simulation-based Evaluation of PrIoritization Algorithms), a novel simulation-based framework for determining the effectiveness of prioritization algorithms. SEPIA expands upon prior related work by defining novel metrics of effectiveness with which to compare prioritization techniques, as well as by creating a simulation-based tool with which to perform such effectiveness comparisons. Under several metrics of effectiveness that we propose, we compare two existing prioritization approaches: one phylogenetic (ProACT) and one distance-based (growth of HIV-TRACE transmission clusters). RESULTS: Using all proposed metrics, ProACT consistently slightly outperformed the transmission cluster growth approach. However, both methods consistently performed just marginally better than random, suggesting that there is significant room for improvement in prioritization tools. CONCLUSION: We hope that, by providing ways to quantify the effectiveness of prioritization methods in simulation, SEPIA will aid researchers in developing novel risk prioritization tools for PLWH.