Understanding the genetic regulatory code governing gene expression is an important challenge in molecular biology. However, how individual coding and non-coding regions of the gene regulatory structure interact and contribute to mRNA expression levels remains unclear. Here we apply deep learning on over 20,000 mRNA datasets to examine the genetic regulatory code controlling mRNA abundance in 7 model organisms ranging from bacteria to Human. In all organisms, we can predict mRNA abundance directly from DNA sequence, with up to 82% of the variation of transcript levels encoded in the gene regulatory structure. By searching for DNA regulatory motifs across the gene regulatory structure, we discover that motif interactions could explain the whole dynamic range of mRNA levels. Co-evolution across coding and non-coding regions suggests that it is not single motifs or regions, but the entire gene regulatory structure and specific combination of regulatory elements that define gene expression levels.

The lack of reference sequences from well-identified mycorrhizal fungi often poses a challenge to the inference of taxonomic affiliation of sequences from environmental samples, and many environmental sequences are thus left unidentified. Such unidentified sequences belonging to the widely distributed ectomycorrhizal fungal genus Inocybe (Basidiomycota) were retrieved from GenBank and divided into species that were identified in a phylogenetic context using a reference dataset from an ongoing study of the genus. The sequence metadata of the unidentified Inocybe sequences stored in GenBank, as well as data from the corresponding original papers, were compiled and used to explore the ecology and distribution of the genus. In addition, the relative occurrence of Inocybe was contrasted to that of other mycorrhizal genera.

Chloroplast-associated protein degradation: Protein degradation is vital for cellular functions, and it operates selectively with distinct mechanisms in different organelles. Some organellar proteins are targeted by the ubiquitin-proteasome system (UPS)—a major proteolytic network in the eukaryotic cytosol. In such cases, the organelle membrane presents a substantial barrier to protein degradation. Working in the model plant Arabidopsis , Ling et al. identified mechanisms underlying the UPS-mediated degradation of proteins in the outer membrane of chloroplasts (the organelles responsible for photosynthesis). They identified an Omp85-type β-barrel outer membrane channel and a cytosolic AAA ⁺ chaperone that fulfill conductance and motor functions in the retrotranslocation of target proteins from chloroplasts. This process thus enabled outer membrane protein processing by the cytosolic proteasome. Such chloroplast-associated protein degradation was initiated by ubiquitination of the targets by the chloroplast-localized E3 ubiquitin ligase SP1. Science , this issue p. eaav4467

Understanding the patterns and processes underlying the uneven distribution of biodiversity across space constitutes a major scientific challenge in systematic biology and biogeography, which largely relies on effectively mapping and making sense of rapidly increasing species occurrence data. There is thus an urgent need for making the process of coding species into spatial units faster, automated, transparent, and reproducible. Here we present SpeciesGeoCoder, an open-source software package written in Python and R, that allows for easy coding of species into user-defined operational units. These units may be of any size and be purely spatial (i.e., polygons) such as countries and states, conservation areas, biomes, islands, biodiversity hotspots, and areas of endemism, but may also include elevation ranges. This flexibility allows scoring species into complex categories, such as those encountered in topographically and ecologically heterogeneous landscapes. In addition, SpeciesGeoCoder can be used to facilitate sorting and cleaning of occurrence data obtained from online databases, and for testing the impact of incorrect identification of specimens on the spatial coding of species. The various outputs of SpeciesGeoCoder include quantitative biodiversity statistics, global and local distribution maps, and files that can be used directly in many phylogeny-based applications for ancestral range reconstruction, investigations of biome evolution, and other comparative methods. Our simulations indicate that even datasets containing hundreds of millions of records can be analyzed in relatively short time using a standard computer. We exemplify the use of SpeciesGeoCoder by inferring the historical dispersal of birds across the Isthmus of Panama, showing that lowland species crossed the Isthmus about twice as frequently as montane species with a marked increase in the number of dispersals during the last 10 million years.

Background: Triticum aestivum (wheat) is one of the world’s oldest crops and has been used for >8000 years as a food crop in North Africa, West Asia and Europe. Today, wheat is one of the most important sources of grain for humans, and is cultivated on greater areas of land than any other crop. As the human population increases and soil salinity becomes more prevalent, there is increased pressure on wheat breeders to develop salt-tolerant varieties in order to meet growing demands for yield and grain quality. Here we developed a mutant wheat population using the moderately salt-tolerant Bangladeshi variety BARI Gom-25, with the primary goal of further increasing salt tolerance. Results: After titrating the optimal ethyl methanesulfonate (EMS) concentration, ca 30,000 seeds were treated with 1% EMS, and 1676 lines, all originating from single seeds, survived through the first four generations. Most mutagenized lines showed a similar phenotype to BARI Gom-25, although visual differences such as dwarfing, giant plants, early and late flowering and altered leaf morphology were seen in some lines. By developing an assay for salt tolerance, and by screening the mutagenized population, we identified 70 lines exhibiting increased salt tolerance. The selected lines typically showed a 70% germination rate on filter paper soaked in 200 mM NaCl, compared to 0–30% for BARI Gom-25. From two of the salt-tolerant OlsAro lines (OA42 and OA70), genomic DNA was sequenced to 15x times coverage. A comparative analysis against the BARI Gom-25 genomic sequence identified a total of 683,201 (OA42), and 768,954 (OA70) SNPs distributed throughout the three sub-genomes (A, B and D). The mutation frequency was determined to be approximately one per 20,000 bp. All the 70 selected salt-tolerant lines were tested for root growth in the laboratory, and under saline field conditions in Bangladesh. The results showed that all the lines selected for tolerance showed a better salt tolerance phenotype than both BARI Gom-25 and other local wheat varieties tested. Conclusion: The mutant wheat population developed here will be a valuable resource in the development of novel salt-tolerant varieties for the benefit of saline farming.