Forest ecosystems in the American west have long been influenced by timber harvests and fire suppression, and recently through treatments that reduce fuel for fire management. Precisely quantifying the structural changes to forests caused by fuel treatments is an essential step to evaluate their impacts. Satellite imagery-derived vegetation indices, such as the normalized difference vegetation index (NDVI), have been widely used to map forest dynamics. However, uncertainties in using these vegetation indices to quantify forest structural changes have not been thoroughly studied, mainly due to the lack of wall-to-wall validation data. In this study we generated forest structural changes in aboveground biomass (AGB) and canopy cover as a result of fuel treatments using bi-temporal airborne light detection and ranging (LiDAR) data and field measurements in a mixed coniferous forest of northern Sierra Nevada, California, USA. These LiDAR-derived forest structural measures were used to evaluate the uncertainties of using Landsat-derived vegetation indices to quantify treatments. Our results confirmed that vegetation indices can accurately map the extents of forest disturbance and canopy cover changes caused by fuel treatments, but the accuracy in quantifying AGB changes varied by the pre-treatment forest densities and treatment intensity. Changes in vegetation indices had relatively weaker correlations (coefficient of determination < 0.45) to biomass changes in forests with sparse (AGB < 100 Mg/ha) or dense biomass (AGB > 700 Mg/ha), than in forests with moderate-density (AGB between 100 Mg/ha and 700 Mg/ha) before the disturbances. Moreover, understory treatments (canopy height < 10 m) were poorly indicated by changes in satellite-derived vegetation indices. Our results suggest that when relating vegetation indices to AGB changes, researchers and managers should be cautious about their uncertainties in extremely dense or sparse forests, particularly when treatments mainly removed small trees or understory fuels.

Background

Optical mapping is an emerging technology that complements sequencing-based methods in genome analysis. It is widely used in improving genome assemblies and detecting structural variations by providing information over much longer (up to 1 Mb) reads. Current standards in optical mapping analysis involve assembling optical maps into contigs and aligning them to a reference, which is limited to pairwise comparison and becomes bias-prone when analyzing multiple samples.

Findings

We present a new method, OMMA, that extends optical mapping to the study of complex genomic features by simultaneously interrogating optical maps across many samples in a reference-independent manner. OMMA captures and characterizes complex genomic features, e.g., multiple haplotypes, copy number variations, and subtelomeric structures when applied to 154 human samples across the 26 populations sequenced in the 1000 Genomes Project. For small genomes such as pathogenic bacteria, OMMA accurately reconstructs the phylogenomic relationships and identifies functional elements across 21 Acinetobacter baumannii strains.

Conclusions

With the increasing data throughput of optical mapping system, the use of this technology in comparative genome analysis across many samples will become feasible. OMMA is a timely solution that can address such computational need. The OMMA software is available at https://github.com/TF-Chan-Lab/OMTools.

We present a new method, OMSV, for accurately and comprehensively identifying structural variations (SVs) from optical maps. OMSV detects both homozygous and heterozygous SVs, SVs of various types and sizes, and SVs with or without creating or destroying restriction sites. We show that OMSV has high sensitivity and specificity, with clear performance gains over the latest method. Applying OMSV to a human cell line, we identified hundreds of SVs >2 kbp, with 68 % of them missed by sequencing-based callers. Independent experimental validation confirmed the high accuracy of these SVs. The OMSV software is available at http://yiplab.cse.cuhk.edu.hk/omsv/ .

Large structural variants (SVs) in the human genome are difficult to detect and study by conventional sequencing technologies. With long-range genome analysis platforms, such as optical mapping, one can identify large SVs (>2 kb) across the genome in one experiment. Analyzing optical genome maps of 154 individuals from the 26 populations sequenced in the 1000 Genomes Project, we find that phylogenetic population patterns of large SVs are similar to those of single nucleotide variations in 86% of the human genome, while ~2% of the genome has high structural complexity. We are able to characterize SVs in many intractable regions of the genome, including segmental duplications and subtelomeric, pericentromeric, and acrocentric areas. In addition, we discover ~60 Mb of non-redundant genome content missing in the reference genome sequence assembly. Our results highlight the need for a comprehensive set of alternate haplotypes from different populations to represent SV patterns in the genome.

The microenvironment that surrounds pancreatic ductal adenocarcinoma (PDAC) is profoundly desmoplastic and immunosuppressive. Understanding triggers of immunosuppression during the process of pancreatic tumorigenesis would aid in establishing targets for effective prevention and therapy. Here, we interrogated differential molecular mechanisms dependent on cell of origin and subtype that promote immunosuppression during PDAC initiation and in established tumors. Transcriptomic analysis of cell-of-origin-dependent epithelial gene signatures revealed that Nt5e/CD73, a cell-surface enzyme required for extracellular adenosine generation, is one of the top 10% of genes overexpressed in murine tumors arising from the ductal pancreatic epithelium as opposed to those rising from acinar cells. These findings were confirmed by IHC and high-performance liquid chromatography. Analysis in human PDAC subtypes indicated that high Nt5e in murine ductal PDAC models overlaps with high NT5E in human PDAC squamous and basal subtypes, considered to have the highest immunosuppression and worst prognosis. Multiplex immunofluorescent analysis showed that activated CD8+ T cells in the PDAC tumor microenvironment express high levels of CD73, indicating an opportunity for immunotherapeutic targeting. Delivery of CD73 small-molecule inhibitors through various delivery routes reduced tumor development and growth in genetically engineered and syngeneic mouse models. In addition, the adenosine receptor Adora2b was a determinant of adenosine-mediated immunosuppression in PDAC. These findings highlight a molecular trigger of the immunosuppressive PDAC microenvironment elevated in the ductal cell of origin, linking biology with subtype classification, critical components for PDAC immunoprevention and personalized approaches for immunotherapeutic intervention.

Significance

Ductal-derived pancreatic tumors have elevated epithelial and CD8+GZM+ T-cell CD73 expression that confers sensitivity to small-molecule inhibition of CD73 or Adora2b to promote CD8+ T-cell-mediated tumor regression. See related commentary by DelGiorno, p. 977.