Summary: Rapidly enhancing oleoresin production in conifer stems through genomic selection and genetic engineering may increase resistance to bark beetles and terpenoid yield for liquid biofuels. We integrated association genetic and genomic prediction analyses of oleoresin flow (g 24 h⁻¹) using 4854 single nucleotide polymorphisms (SNPs) in expressed genes within a pedigreed population of loblolly pine (Pinus taeda) that was clonally replicated at three sites in thesoutheasternUnitedStates. Additive genetic variation in oleoresin flow (h²≈ 0.12–0.30) was strongly correlated between years in which precipitation varied (r_a≈ 0.95), while the genetic correlation between sites declined from 0.8 to 0.37 with increasing differences in soil and climate among sites. A total of 231SNPs were significantly associated with oleoresin flow, of which 81% were specific to individual sites.SNPs in sequences similar to ethylene signaling proteins,ABCtransporters, and diterpenoid hydroxylases were associated with oleoresin flow across sites. Despite this complex genetic architecture, we developed a genomic prediction model to accelerate breeding for enhanced oleoresin flow that is robust to environmental variation. Results imply that breeding could increase oleoresin flow 1.5‐ to 2.4‐fold in one generation.

A recent genome-wide screen identified ~300 essential or growth-supporting genes in the dental caries pathogen Streptococcus mutans. To be able to study these genes, we built a CRISPR interference tool around the Cas9 nuclease (Cas9_Smu) encoded in the S. mutans UA159 genome. Using a xylose-inducible dead Cas9_Smu with a constitutively active single-guide RNA (sgRNA), we observed titratable repression of GFP fluorescence that compared favorably to that of Streptococcus pyogenes dCas9 (Cas9_Spy). We then investigated sgRNA specificity and proto-spacer adjacent motif (PAM) requirements. Interference by sgRNAs did not occur with double or triple base-pair mutations, or if single base-pair mutations were in the 3’ end of the sgRNA. Bioinformatic analysis of >450 S. mutans genomes allied with in vivo assays revealed a similar PAM recognition sequence as the Cas9_Spy. Next, we created a comprehensive library of sgRNA plasmids that were directed at essential and growth-supporting genes. We discovered growth defects for 77% of the CRISPRi strains expressing sgRNAs. Phenotypes of CRISPRi strains, across several biological pathways, were assessed using fluorescence microscopy. A variety of cell structure anomalies were observed, including segregational instability of the chromosome, enlarged cells, and ovococci-to-rod shape transitions. CRISPRi was also employed to observe how silencing of cell wall glycopolysaccharide biosynthesis (rhamnose-glucose polysaccharide, RGP) affected both cell division and pathogenesis in a wax worm model. The CRISPRi tool and sgRNA library are valuable resources for characterizing essential genes in S. mutans, some of which could prove to be promising therapeutic targets. Streptococcus species cause diverse infections in humans including pneumonia and meningitis. They are common inhabitants of the mouth and certain species are associated with the incredibly prevalent oral disease dental caries (tooth decay). Streptococcus mutans is the most studied and most often linked organism to the development and progression of dental caries. Recent studies have shown that the genome of this organism encodes a number of essential genes that are required for the survival of the bacteria. Many of these genes are conserved in other pathogenic streptococci, and some are unique to S. mutans. To facilitate the study of these genes in a high-throughput manner we developed a CRISPR interference gene-silencing system. This tool, which modifies the CRISPR-Cas9 system encoded by S. mutans, allowed us to study virtually all the essential genes encoded by the organism. We found that these genes are required for optimal growth of the organism. We also found that the morphological defects of these strains are diverse but also shared among genes related to similar molecular pathways. We used the CRISPRi tool to comprehensively assess the contribution of L-rhamnose glycopolymers to S. mutans viability, pathogenicity and coordination of cell division. Overall, these findings increase our understanding of Streptococcus essential genes, and may help in the development of new antimicrobial therapies.

Summary: Genetically improving constitutive resin canal development inPinusstems may enhance the capacity to synthesize terpenes for bark beetle resistance, chemical feedstocks, and biofuels. To discover genes that potentially regulate axial resin canal number (RCN), single nucleotide polymorphisms (SNPs) in 4027 genes were tested for association withRCNin two growth rings and three environments in a complex pedigree of 520Pinus taedaindividuals (CCLONES). The map locations of associated genes were compared withRCNquantitative trait loci (QTLs) in a (P. taeda × Pinus elliottii) × P. elliottiipseudo‐backcross of 345 full‐sibs (BC1). Resin canal number was heritable (h² ˜ 0.12–0.21) and positively genetically correlated with xylem growth (r_g ˜ 0.32–0.72) and oleoresin flow (r_g ˜ 0.15–0.51). Sixteen well‐supported candidate regulators ofRCNwere discovered inCCLONES, including genes associated across sites and ages, unidirectionally associated with oleoresin flow and xylem growth, and mapped toRCN QTLs inBC1. Breeding is predicted to increaseRCN11% in one generation and could be accelerated with genomic selection at accuracies of 0.45–0.52 across environments. There is significant genetic variation forRCNin loblolly pine, which can be exploited in breeding for elevated terpene content.

Metagenomic data of whole genome sequences (WGS) from samples across several cities around the globe may unravel city specific signatures of microbes. Illumina MiSeq sequencing data was provided from 12 cities in 7 different countries as part of the 2018 CAMDA “MetaSUB Forensic Challenge”, including also samples from three mystery sets. We used appropriate machine learning techniques on this massive dataset to effectively identify the geographical provenance of “mystery” samples. Additionally, we pursued compositional data analysis to develop accurate inferential techniques for such microbiome data. It is expected that this current data, which is of higher quality and higher sequence depth compared to the CAMDA 2017 MetaSUB challenge data, along with improved analytical techniques would yield many more interesting, robust and useful results that can be beneficial for forensic analysis. A preliminary quality screening of the data revealed a much better dataset in terms of Phred quality score (hereafter Phred score), and larger paired-end MiSeq reads, and a more balanced experimental design, though still not equal number of samples across cities. PCA (Principal Component Analysis) analysis showed interesting clusters of samples and a large amount of the variability in the data was explained by the first three components (~ 70%). The classification analysis proved to be consistent across both the testing mystery sets with a similar percentage of the samples correctly predicted (up to 90%). The analysis of the relative abundance of bacterial “species” showed that some “species” are specific to some regions and can play important roles for predictions. These results were also corroborated by the variable importance given to the “species” during the internal cross validation (CV) run with Random Forest (RF). The unsupervised analysis (PCA and two-way heatmaps) of the log2-cpm normalized data and relative abundance differential analysis seemed to suggest that the bacterial signature of common “species” was distinctive across the cities; which was also supported by the variable importance results. The prediction of the city for mystery sets 1 and 3 showed convincing results with high classification accuracy/consistency. The focus of this work on the current MetaSUB data and the analytical tools utilized here can be of great help in forensic, metagenomics, and other sciences to predict city of provenance of metagenomic samples, as well as in other related fields. Additionally, the pairwise analysis of relative abundance showed that the approach provided consistent and comparable “species” when compared with the classification importance variables. This article was reviewed by Manuela Oliveira, Dimitar Vassilev, and Patrick Lee.

Microbial communities can be location specific, and the abundance of species within locations can influence our ability to determine whether a sample belongs to one city or another. As part of the 2017 CAMDA MetaSUB Inter-City Challenge, next generation sequencing (NGS) data was generated from swipe samples collected from subway stations in Boston, New York City hereafter New York, and Sacramento. DNA was extracted and Illumina sequenced. Sequencing data was provided for all cities as part of 2017 CAMDA contest challenge dataset. Principal component analysis (PCA) showed clear clustering of the samples for the three cities, with a substantial proportion of the variance explained by the first three components. We ran two different classifiers and results were robust for error rate (< 6%) and accuracy (> 95%). The analysis of variance (ANOVA) demonstrated that overall, bacterial composition across the three cities is significantly different. A similar conclusion was reached using a novel bootstrap based test using diversity indices. Last but not least, a co-abundance association network analyses for the taxonomic levels “order”, “family”, and “genus” found different patterns of bacterial networks for the three cities. Bacterial fingerprint can be useful to predict sample provenance. In this work prediction of provenance reported with over 95% accuracy. Association based network analysis, emphasized similarities between the closest cities sharing common bacterial composition. ANOVA showed different patterns of bacterial amongst cities, and these findings strongly suggest that bacterial signature across multiple cities are different. This work advocates a data analysis pipeline which could be followed in order to get biological insight from this data. However, the biological conclusions from this analysis is just an early indication out of a pilot microbiome data provided to us through CAMDA 2017 challenge and will be subject to change as we get more complete data sets in the near future. This microbiome data can have potential applications in forensics, ecology, and other sciences. This article was reviewed by Klas Udekwu, Alexandra Graf, and Rafal Mostowy.