Compositional shifts in root-associated bacterial and archaeal microbiota track the plant life cycle in field-grown rice.

Bacterial communities associated with roots impact the health and nutrition of the host plant. The dynamics of these microbial assemblies over the plant life cycle are, however, not well understood. Here, we use dense temporal sampling of 1,510 samples from root spatial compartments to characterize the bacterial and archaeal components of the root-associated microbiota of field grown rice (Oryza sativa) over the course of 3 consecutive growing seasons, as well as 2 sites in diverse geographic regions. The root microbiota was found to be highly dynamic during the vegetative phase of plant growth and then stabilized compositionally for the remainder of the life cycle. Bacterial and archaeal taxa conserved between field sites were defined as predictive features of rice plant age by modeling using a random forest approach. The age-prediction models revealed that drought-stressed plants have developmentally immature microbiota compared to unstressed plants. Further, by using genotypes with varying developmental rates, we show that shifts in the microbiome are correlated with rates of developmental transitions rather than age alone, such that different microbiota compositions reflect juvenile and adult life stages. These results suggest a model for successional dynamics of the root-associated microbiota over the plant life cycle.

Guidelines for cell-type heterogeneity quantification based on a comparative analysis of reference-free DNA methylation deconvolution software.

BACKGROUND: Cell-type heterogeneity of tumors is a key factor in tumor progression and response to chemotherapy. Tumor cell-type heterogeneity, defined as the proportion of the various cell-types in a tumor, can be inferred from DNA methylation of surgical specimens. However, confounding factors known to associate with methylation values, such as age and sex, complicate accurate inference of cell-type proportions. While reference-free algorithms have been developed to infer cell-type proportions from DNA methylation, a comparative evaluation of the performance of these methods is still lacking. RESULTS: Here we use simulations to evaluate several computational pipelines based on the software packages MeDeCom, EDec, and RefFreeEWAS. We identify that accounting for confounders, feature selection, and the choice of the number of estimated cell types are critical steps for inferring cell-type proportions. We find that removal of methylation probes which are correlated with confounder variables reduces the error of inference by 30-35%, and that selection of cell-type informative probes has similar effect. We show that Cattell's rule based on the scree plot is a powerful tool to determine the number of cell-types. Once the pre-processing steps are achieved, the three deconvolution methods provide comparable results. We observe that all the algorithms' performance improves when inter-sample variation of cell-type proportions is large or when the number of available samples is large. We find that under specific circumstances the methods are sensitive to the initialization method, suggesting that averaging different solutions or optimizing initialization is an avenue for future research. CONCLUSION: Based on the lessons learned, to facilitate pipeline validation and catalyze further pipeline improvement by the community, we develop a benchmark pipeline for inference of cell-type proportions and implement it in the R package medepir.

Structure, variation, and assembly of the root-associated microbiomes of rice.

Plants depend upon beneficial interactions between roots and microbes for nutrient availability, growth promotion, and disease suppression. High-throughput sequencing approaches have provided recent insights into root microbiomes, but our current understanding is still limited relative to animal microbiomes. Here we present a detailed characterization of the root-associated microbiomes of the crop plant rice by deep sequencing, using plants grown under controlled conditions as well as field cultivation at multiple sites. The spatial resolution of the study distinguished three root-associated compartments, the endosphere (root interior), rhizoplane (root surface), and rhizosphere (soil close to the root surface), each of which was found to harbor a distinct microbiome. Under controlled greenhouse conditions, microbiome composition varied with soil source and genotype. In field conditions, geographical location and cultivation practice, namely organic vs. conventional, were factors contributing to microbiome variation. Rice cultivation is a major source of global methane emissions, and methanogenic archaea could be detected in all spatial compartments of field-grown rice. The depth and scale of this study were used to build coabundance networks that revealed potential microbial consortia, some of which were involved in methane cycling. Dynamic changes observed during microbiome acquisition, as well as steady-state compositions of spatial compartments, support a multistep model for root microbiome assembly from soil wherein the rhizoplane plays a selective gating role. Similarities in the distribution of phyla in the root microbiomes of rice and other plants suggest that conclusions derived from this study might be generally applicable to land plants.

Tumor suppressor DEAR1 regulates mammary epithelial cell fate and predicts early onset and metastasis in triple negative breast cancer.

Triple negative breast cancer (TNBC) is a disease of poor prognosis, with the majority classified as the basal-like subtype associated with epithelial-mesenchymal transition and metastasis. Because basal breast cancers originate from proliferative luminal progenitor-like cells upon dysregulation of proper luminal differentiation, genes regulating luminal-basal transition are critical to elucidate novel therapeutic targets to improve TNBC outcomes. Herein we demonstrate that the tumor suppressor DEAR1/TRIM62 is a critical regulator of luminal cell fate. DEAR1 loss in human mammary epithelial cells results in significantly enhanced mammosphere formation that is accelerated in the presence of TGF-ß/SMAD3 signaling. Mammospheres formed following DEAR1 loss are enriched for ALDH1A1 and CK5 expression, EpCAM-/CD49f+ and CD44high/24low basal-like epithelial cells, indicating that DEAR1 regulates stem/progenitor cell properties and luminal-basal progenitor transition. We show that DEAR1 maintains luminal differentiation as a novel ubiquitin ligase for SNAI2/SLUG, a master regulator driving stemness and generation of basal-like progenitor populations. We also identify a significant inverse correlation between DEAR1 and SNAI2 expression in a 103 TNBC case cohort and show that low DEAR1 expression significantly correlates with young age of onset and shorter time to metastasis, suggesting DEAR1 could serve as a biomarker to stratify early onset TNBCs for targeted stem cell therapies.

Hyperpolarized Magnetic Resonance and Artificial Intelligence: Frontiers of Imaging in Pancreatic Cancer.

BACKGROUND: There is an unmet need for noninvasive imaging markers that can help identify the aggressive subtype(s) of pancreatic ductal adenocarcinoma (PDAC) at diagnosis and at an earlier time point, and evaluate the efficacy of therapy prior to tumor reduction. In the past few years, there have been two major developments with potential for a significant impact in establishing imaging biomarkers for PDAC and pancreatic cancer premalignancy: (1) hyperpolarized metabolic (HP)-magnetic resonance (MR), which increases the sensitivity of conventional MR by over 10,000-fold, enabling real-time metabolic measurements; and (2) applications of artificial intelligence (AI). OBJECTIVE: Our objective of this review was to discuss these two exciting but independent developments (HP-MR and AI) in the realm of PDAC imaging and detection from the available literature to date. METHODS: A systematic review following the PRISMA extension for Scoping Reviews (PRISMA-ScR) guidelines was performed. Studies addressing the utilization of HP-MR and/or AI for early detection, assessment of aggressiveness, and interrogating the early efficacy of therapy in patients with PDAC cited in recent clinical guidelines were extracted from the PubMed and Google Scholar databases. The studies were reviewed following predefined exclusion and inclusion criteria, and grouped based on the utilization of HP-MR and/or AI in PDAC diagnosis. RESULTS: Part of the goal of this review was to highlight the knowledge gap of early detection in pancreatic cancer by any imaging modality, and to emphasize how AI and HP-MR can address this critical gap. We reviewed every paper published on HP-MR applications in PDAC, including six preclinical studies and one clinical trial. We also reviewed several HP-MR-related articles describing new probes with many functional applications in PDAC. On the AI side, we reviewed all existing papers that met our inclusion criteria on AI applications for evaluating computed tomography (CT) and MR images in PDAC. With the emergence of AI and its unique capability to learn across multimodal data, along with sensitive metabolic imaging using HP-MR, this knowledge gap in PDAC can be adequately addressed. CT is an accessible and widespread imaging modality worldwide as it is affordable; because of this reason alone, most of the data discussed are based on CT imaging datasets. Although there were relatively few MR-related papers included in this review, we believe that with rapid adoption of MR imaging and HP-MR, more clinical data on pancreatic cancer imaging will be available in the near future. CONCLUSIONS: Integration of AI, HP-MR, and multimodal imaging information in pancreatic cancer may lead to the development of real-time biomarkers of early detection, assessing aggressiveness, and interrogating early efficacy of therapy in PDAC.

Histoepigenetic analysis of the mesothelin network within pancreatic ductal adenocarcinoma cells reveals regulation of retinoic acid receptor gamma and AKT by mesothelin.

To enable computational analysis of regulatory networks within the cancer cell in its natural tumor microenvironment, we develop a two-stage histoepigenetic analysis method. The first stage involves iterative computational deconvolution to estimate sample-specific cancer-cell intrinsic expression of a gene of interest. The second stage places the gene within a network module. We validate the method in simulation experiments, show improved performance relative to differential expression analysis from bulk samples, and apply it to illuminate the role of the mesothelin (MSLN) network in pancreatic ductal adenocarcinoma (PDAC). The network analysis and subsequent experimental validation in a panel of PDAC cell lines suggests AKT activation by MSLN through two known activators, retinoic acid receptor gamma (RARG) and tyrosine kinase non receptor 2 (TNK2). Taken together, these results demonstrate the potential of histoepigenetic analysis to reveal cancer-cell specific molecular interactions directly from patient tumor profiles.

Allele-specific epigenome maps reveal sequence-dependent stochastic switching at regulatory loci.

To assess the impact of genetic variation in regulatory loci on human health, we constructed a high-resolution map of allelic imbalances in DNA methylation, histone marks, and gene transcription in 71 epigenomes from 36 distinct cell and tissue types from 13 donors. Deep whole-genome bisulfite sequencing of 49 methylomes revealed sequence-dependent CpG methylation imbalances at thousands of heterozygous regulatory loci. Such loci are enriched for stochastic switching, which is defined as random transitions between fully methylated and unmethylated states of DNA. The methylation imbalances at thousands of loci are explainable by different relative frequencies of the methylated and unmethylated states for the two alleles. Further analyses provided a unifying model that links sequence-dependent allelic imbalances of the epigenome, stochastic switching at gene regulatory loci, and disease-associated genetic variation.