Search for: All records

Rosalia funebris (RFUNE; Cerambycidae), the banded alder borer, is a longhorn beetle whose larvae feed on the wood of various economically and ecologically significant trees in western North America. Adults are short-lived and not known to consume plant material substantially. We sequenced, assembled, and annotated the RFUNE genome using HiFi and RNASeq data. We documented genome architecture and gene content, focusing on genes putatively involved in plant feeding (phytophagy). Comparisons were made to the well-studied genome of the Asian longhorned beetle (AGLAB; Anoplophora glabripennis) and other Cerambycidae. The 814 Mb RFUNE genome assembly was distributed across 42 contigs, with an N50 of 30.18 Mb. Repetitive sequences comprised 60.27% of the genome, and 99.0% of expected single-copy orthologous genes were fully assembled. We identified 12,657 genes, fewer than in the four other species studied, and 46.4% fewer than for Aromia moschata (same subfamily as RFUNE). Of the 7,258 orthogroups shared between RFUNE and AGLAB, 1,461 had more copies in AGLAB and 1,023 had more copies in RFUNE. We identified 240 genes in RFUNE that putatively arose via horizontal transfer events. The RFUNE genome encoded substantially fewer putative plant cell wall degrading enzymes than AGLAB, which may relate to the longer-lived plant-feeding adults of the latter species. The RFUNE genome provides new insights into cerambycid genome architecture and gene content and provides a new vantage point from which to study the evolution and genomic basis of phytophagy in beetles.

This work compares various existing rough-wall models on a large collection of rough surfaces with different characteristics and studies the potential of these models in accommodating new datasets. We consider three empirical roughness correlations, two physics-based models, and one data-driven machine-learning model on 68 rough surfaces inside and outside the Roughness Database1. Results show that correlation-type models and machine-learning models do not extrapolate outside the dataset against which they are calibrated or trained. In contrast, the physics-based sheltering model performs well in extrapolation. Recalibrating a roughness correlation against a large dataset proves unfruitful. However, retraining a machine learning model yields good results. We do not pursue further retraining and recalibrating of a physics-based model, as it requires new physical insights. Overall, our findings suggest that a universal rough-wall model is yet to be found. The capability of extrapolation will likely come from incorporating physics. Data, on the other hand, benefits machine learning models.

Discovering natural product biosynthetic pathways of medicinal plants is challenging and laborious. Capturing the coregulation patterns of pathway enzymes, particularly transcriptomic regulation, has proven an effective method to accelerate pathway identification. In this study, we developed a yeast‐based screening method to capture the protein‐protein interactions (PPI) between plant enzymes, which is another useful pattern to complement the prevalent approach. Combining this method with plant multiomics analysis, we discovered four enzyme complexes and their organized pathways from kratom, an alkaloid‐producing plant. The four pathway branches involved six enzymes, including a strictosidine synthase, a strictosidine β‐D‐glucosidase (MsSGD), and four medium‐chain dehydrogenase/reductases (MsMDRs). PPI screening selected six MsMDRs interacting with MsSGD from 20 candidates predicted by multiomics analysis. Four of the six MsMDRs were then characterized as functional, indicating the high selectivity of the PPI screening method. This study highlights the opportunity of leveraging post‐translational regulation features to discover novel plant natural product biosynthetic pathways.

Discovering natural product biosynthetic pathways of medicinal plants is challenging and laborious. Capturing the coregulation patterns of pathway enzymes, particularly transcriptomic regulation, has proven an effective method to accelerate pathway identification. In this study, we developed a yeast‐based screening method to capture the protein‐protein interactions (PPI) between plant enzymes, which is another useful pattern to complement the prevalent approach. Combining this method with plant multiomics analysis, we discovered four enzyme complexes and their organized pathways from kratom, an alkaloid‐producing plant. The four pathway branches involved six enzymes, including a strictosidine synthase, a strictosidine β‐D‐glucosidase (MsSGD), and four medium‐chain dehydrogenase/reductases (MsMDRs). PPI screening selected six MsMDRs interacting with MsSGD from 20 candidates predicted by multiomics analysis. Four of the six MsMDRs were then characterized as functional, indicating the high selectivity of the PPI screening method. This study highlights the opportunity of leveraging post‐translational regulation features to discover novel plant natural product biosynthetic pathways.

Quantifying the temperature sensitivity of methane (CH₄) production is crucial for predicting how wetland ecosystems will respond to climate warming. Typically, the temperature sensitivity (often quantified as a Q₁₀value) is derived from laboratory incubation studies and then used in biogeochemical models. However, studies report wide variation in incubation-inferred Q₁₀values, with a large portion of this variation remaining unexplained. Here we applied observations in a thawing permafrost peatland (Stordalen Mire) and a well-tested process-rich model (ecosys) to interpret incubation observations and investigate controls on inferred CH₄production temperature sensitivity. We developed a field-storage-incubation modeling approach to mimic the full incubation sequence, including field sampling at a particular time in the growing season, refrigerated storage, and laboratory incubation, followed by model evaluation. We found that CH₄production rates during incubation are regulated by substrate availability and active microbial biomass of key microbial functional groups, which are affected by soil storage duration and temperature. Seasonal variation in substrate availability and active microbial biomass of key microbial functional groups led to strong time-of-sampling impacts on CH₄production. CH₄production is higher with less perturbation post-sampling, i.e. shorter storage duration and lower storage temperature. We found a wide range of inferred Q₁₀values (1.2–3.5), which we attribute to incubation temperatures, incubation duration, storage duration, and sampling time. We also show that Q₁₀values of CH₄production are controlled by interacting biological, biochemical, and physical processes, which cause the inferred Q₁₀values to differ substantially from those of the component processes. Terrestrial ecosystem models that use a constant Q₁₀value to represent temperature responses may therefore predict biased soil carbon cycling under future climate scenarios.

While the archival digital memory industry approaches its physical limits, the demand is significantly increasing, therefore alternatives emerge. Recent efforts have demonstrated DNA’s enormous potential as a digital storage medium with superior information durability, capacity, and energy consumption. However, the majority of the proposed systems require on-demand de-novo DNA synthesis techniques that produce a large amount of toxic waste and therefore are not industrially scalable and environmentally friendly. Inspired by the architecture of semiconductor memory devices and recent developments in gene editing, we created a molecular digital data storage system called “DNA Mutational Overwriting Storage” (DMOS) that stores information by leveraging combinatorial, addressable, orthogonal, and independent in vitro CRISPR base-editing reactions to write data on a blank pool of greenly synthesized DNA tapes. As a proof of concept, this work illustrates writing and accurately reading of both a bitmap representation of our school’s logo and the title of this study on the DNA tapes.

Satellites bring opportunities to quantify precipitation amount and distribution over the globe, critical to understanding how the Earth system works. The amount and spatial distribution of oceanic precipitation from the latest versions (V07 and the previous version) of the Global Precipitation Measurement (GPM)Core Observatoryinstruments and selected members of the constellation of passive microwave sensors are quantified and compared with other products such as the Global Precipitation Climatology Project (GPCP V3.2); the MergedCloudSat, TRMM, and GPM (MCTG) climatology; and ERA5. Results show that GPM V07 products have a higher precipitation rate than the previous version, except for the radar-only product. Within ∼65°S–65°N, covered by all of the instruments, this increase ranges from about 9% for the combined radar–radiometer product to about 16% for radiometer-only products. While GPM precipitation products still show lower mean precipitation rate than MCTG (except over the tropics and Arctic Ocean), the V07 products (except radar-only) are generally more consistent with MCTG and GPCP V3.2 than V05. Over the tropics (25°S–25°N), passive microwave sounders show the highest precipitation rate among all of the precipitation products studied and the highest increase (∼19%) compared to their previous version. Precipitation products are least consistent in midlatitude oceans in the Southern Hemisphere, displaying the largest spread in mean precipitation rate and location of latitudinal peak precipitation. Precipitation products tend to show larger spread over regions with low and high values of sea surface temperature and total precipitable water. The analysis highlights major discrepancies among the products and areas for future research.

Insects perform feats of strength and endurance that belie their small stature. Insect-scale robots—although subject to the same scaling laws—demonstrate reduced performance because existing microactuator technologies are driven by low–energy density power sources and produce small forces and/or displacements. The use of high–energy density chemical fuels to power small, soft actuators represents a possible solution. We demonstrate a 325-milligram soft combustion microactuator that can achieve displacements of 140%, operate at frequencies >100 hertz, and generate forces >9.5 newtons. With these actuators, we powered an insect-scale quadrupedal robot, which demonstrated a variety of gait patterns, directional control, and a payload capacity 22 times its body weight. These features enabled locomotion through uneven terrain and over obstacles.

Inherited retinal degeneration is a term used to describe heritable disorders that result from the death of light sensing photoreceptor cells. Although we and others believe that it will be possible to use gene therapy to halt disease progression early in its course, photoreceptor cell replacement will likely be required for patients who have already lost their sight. While advances in autologous photoreceptor cell manufacturing have been encouraging, development of technologies capable of efficiently delivering genome editing reagents to stem cells using current good manufacturing practices (cGMP) are needed. Gene editing reagents were delivered to induced pluripotent stem cells (iPSCs) using a Zephyr microfluidic transfection platform (CellFE). CRISPR-mediated cutting was quantified using an endonuclease assay. CRISPR correction was confirmed via digital PCR and Sanger sequencing. The resulting corrected cells were also karyotyped and differentiated into retinal organoids. We describe use of a novel microfluidic transfection platform to correct, via CRISPR-mediated homology-dependent repair (HDR), a disease-causing NR2E3 mutation in patient-derived iPSCs using cGMP compatible reagents and approaches. We show that the resulting cell lines have a corrected genotype, exhibit no off-target cutting, retain pluripotency and a normal karyotype and can be differentiated into retinal tissue suitable for transplantation. The ability to codeliver CRISPR/Cas9 and HDR templates to patient-derived iPSCs without using proprietary transfection reagents will streamline manufacturing protocols, increase the safety of resulting cell therapies, and greatly reduce the regulatory burden of clinical trials.