Cannabis sativa is widely cultivated for medicinal, food, industrial, and recreational use, but much remains unknown regarding its genetics, including the molecular determinants of cannabinoid content. Here, we describe a combined physical and genetic map derived from a cross between the drug-type strain Purple Kush and the hemp variety "Finola." The map reveals that cannabinoid biosynthesis genes are generally unlinked but that aromatic prenyltransferase (AP), which produces the substrate for THCA and CBDA synthases (THCAS and CBDAS), is tightly linked to a known marker for total cannabinoid content. We further identify the gene encoding CBCA synthase (CBCAS) and characterize its catalytic activity, providing insight into how cannabinoid diversity arises in cannabis. THCAS and CBDAS (which determine the drug vs. hemp chemotype) are contained within large (>250 kb) retrotransposon-rich regions that are highly nonhomologous between drug-and hemp-type alleles and are furthermore embedded within ∼40 Mb of minimally recombining repetitive DNA. The chromosome structures are similar to those in grains such as wheat, with recombination focused in gene-rich, repeat-depleted regions near chromosome ends. The physical and genetic map should facilitate further dissection of genetic and molecular mechanisms in this commercially and medically important plant.

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Modelling both primary sequence and secondary structure preferences for RNA binding proteins (RBPs) remains an ongoing challenge. Current models use varied RNA structure representations and can be difficult to interpret and evaluate. To address these issues, we present a universal RNA motif-finding/scanning strategy, termed PRIESSTESS (Predictive RBP-RNA InterpretablE Sequence-Structure moTif regrESSion), that can be applied to diverse RNA binding datasets. PRIESSTESS identifies dozens of enriched RNA sequence and/or structure motifs that are subsequently reduced to a set of core motifs by logistic regression with LASSO regularization. Importantly, these core motifs are easily visualized and interpreted, and provide a measure of RBP secondary structure specificity. We used PRIESSTESS to interrogate new HTR-SELEX data for 23 RBPs with diverse RNA binding modes and captured known primary sequence and secondary structure preferences for each. Moreover, when applying PRIESSTESS to 144 RBPs across 202 RNA binding datasets, 75% showed an RNA secondary structure preference but only 10% had a preference besides unpaired bases, suggesting that most RBPs simply recognize the accessibility of primary sequences.

Thousands of RNA-binding proteins (RBPs) crosslink to cellular mRNA. Among these are numerous unconventional RBPs (ucRBPs)—proteins that associate with RNA but lack known RNA-binding domains (RBDs). The vast majority of ucRBPs have uncharacterized RNA-binding specificities. We analyzed 492 human ucRBPs for intrinsic RNA-binding in vitro and identified 23 that bind specific RNA sequences. Most (17/23), including 8 ribosomal proteins, were previously associated with RNA-related function. We identified the RBDs responsible for sequence-specific RNA-binding for several of these 23 ucRBPs and surveyed whether corresponding domains from homologous proteins also display RNA sequence specificity. CCHC-zf domains from seven human proteins recognized specific RNA motifs, indicating that this is a major class of RBD. For Nudix, HABP4, TPR, RanBP2-zf, and L7Ae domains, however, only isolated members or closely related homologs yielded motifs, consistent with RNA-binding as a derived function. The lack of sequence specificity for most ucRBPs is striking, and we suggest that many may function analogously to chromatin factors, which often crosslink efficiently to cellular DNA, presumably via indirect recruitment. Finally, we show that ucRBPs tend to be highly abundant proteins and suggest their identification in RNA interactome capture studies could also result from weak nonspecific interactions with RNA.